FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Jia, NN
   Cheng, X
   Su, S
   Ding, LY
AF Jia, Ningning
   Cheng, Xiang
   Su, Sen
   Ding, Liyuan
TI CoGCN: Combiningco-attentionwith graph convolutional network for entity
   linking with knowledge graphs
SO EXPERT SYSTEMS
LA English
DT Article
DE co-attention mechanism; entity linking; graph convolutional network;
   knowledge graphs
ID BASE
AB Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand-designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co-attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co-attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context-aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.
C1 [Jia, Ningning; Cheng, Xiang; Su, Sen; Ding, Liyuan] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China.
RP Cheng, X (通讯作者)，Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China.
EM chengxiang@bupt.edu.cn
FU Foundation for Innovative Research Groups of the National Natural
   Science Foundation of China [61921003]; National Natural Science
   Foundation of China [61872045]
FX Foundation for Innovative Research Groups of the National Natural
   Science Foundation of China, Grant/Award Number: 61921003; National
   Natural Science Foundation of China, Grant/Award Number: 61872045
CR [Anonymous], 2013, MSM
   [Anonymous], 2007, P 2007 JOINT C EMP M, DOI DOI 10.1145/2187836.2187900
   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
   Cetoli Alberto, 2019, Advances in Information Retrieval. 41st European Conference on IR Research, ECIR 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11438), P78, DOI 10.1007/978-3-030-15719-7_10
   Delpeuch A., 2019, ABS190409131 CORR
   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
   Hu SZ, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11040453
   Hulpus I, 2015, LECT NOTES COMPUT SC, V9366, P442, DOI 10.1007/978-3-319-25007-6_26
   Khamparia A, 2019, EXPERT SYST, V36, DOI 10.1111/exsy.12400
   Kinga D., 2015, INT C LEARN REPR ICL, V5
   Kipf T. N, 2017, 2017 INT C LEARN REP
   Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791
   Li HY, 2017, PROC INT C TOOLS ART, P285, DOI 10.1109/ICTAI.2017.00053
   Liu M, 2017, ACM T INFORM SYST, V36, DOI 10.1145/3086703
   Lu JH, 2016, PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING TECHNOLOGY (CSET2015), MEDICAL SCIENCE AND BIOLOGICAL ENGINEERING (MSBE2015), P289
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Milne D., 2008, P 17 ACM C INF KNOWL, P509, DOI DOI 10.1145/1458082.1458150
   Monti F., 2017, P ADV NEUR INF PROC, P3700
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Murty S, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P97, DOI 10.18653/v1/P18-1010
   Nguyen D. B., 2014, AIDA LIGHT HIGH THRO, P1184
   Nie F, 2018, AAAI CONF ARTIF INTE, P5908
   Palangi H, 2016, IEEE-ACM T AUDIO SPE, V24, P694, DOI 10.1109/TASLP.2016.2520371
   Ratinov L., 2011, COMPUT LINGUIST, V1, P1375
   Roder M, 2014, LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P3529
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Socher R., 2014, PROC C EMPIRICAL MET, P1532, DOI 10.3115/v1/D14-1162
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   Tay Y, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4425
   Usbeck R, 2014, LECT NOTES COMPUT SC, V8796, P457, DOI 10.1007/978-3-319-11964-9_29
   Xiong Caiming, 2016, ARXIV161101604
   Zhang S., 2015, MOL NEUROBIOL, V30, P1
   Zheng ZC, 2012, 2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2012), VOL 1, P82, DOI 10.1109/WI-IAT.2012.26
   Zhu GG, 2018, EXPERT SYST APPL, V101, P8, DOI 10.1016/j.eswa.2018.02.011
   Zuo Z., 2014, P 25 INT C COMP LING, P2075
   Zwicklbauer S, 2016, LECT NOTES COMPUT SC, V9678, P182, DOI 10.1007/978-3-319-34129-3_12
NR 40
TC 3
Z9 3
U1 3
U2 27
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 JAN
PY 2021
VL 38
IS 1
SI SI
AR e12606
DI 10.1111/exsy.12606
EA AUG 2020
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PI7FT
UT WOS:000558020100001
DA 2022-08-23
ER

PT J
AU Yang, ZX
   Dong, SB
AF Yang, Zuoxi
   Dong, Shoubin
TI HAGERec: Hierarchical Attention Graph Convolutional Network
   Incorporating Knowledge Graph for Explainable Recommendation
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Recommender system; Graph convolutional network; Hierarchical attention;
   Knowledge graph
AB Knowledge graph (KG) can provide auxiliary information for recommender system to alleviate the sparsity and cold start problems, while graph convolutional networks (GCN) has recently been established as the state-of-the-art representation learning method. The combination of them is a promising perspective to improve the performance of graph-structured recommendation. However, most of GCN-based recommendations focus on homogeneous graph or user/item-similarity graph, fail to fully make use of the complex and rich semantics between entities in heterogeneous knowledge graph. In this paper, we develop Hierarchical Attention Graph Convolutional Network Incorporating Knowledge Graph for Explainable Recommendation (HAGERec) to explore users' potential preferences from the high-order connectivity structure of heterogeneous knowledge graph. To exploit semantic information, HAGERec simultaneously learn the representations of users and items via a bi-directional information propagation strategy. Specifically, the entity's representation can be aggregated through messages passing from its local proximity structure, and a hierarchical attention mechanism is developed to adaptively characterize and adjust collaborative signals. With the help of the attention mechanism, an attentive entity sampling strategy is proposed to select relevant neighbor entities, and the explainability is endowed to the model by building knowledge-aware connectivity. Experiments conducted on four real-world public datasets demonstrate the state-of-the-art performance and the strong explainability of HAGERec. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Yang, Zuoxi; Dong, Shoubin] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Peoples R China.
RP Dong, SB (通讯作者)，South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Peoples R China.
EM csyangzuoxi@mail.scut.edu.cn; sbdong@scut.edu.cn
FU National Natural Science Foundation of China [61976239]; Guangdong
   Science and Technology Department [2018A030313309]
FX This work is supported by the National Natural Science Foundation of
   China (Grant No. 61976239) and Guangdong Science and Technology
   Department (Grant No. 2018A030313309).
CR Bellini V., 2017, P 2 WORKSH DEEP LEAR, P60, DOI [10.1145/3125486.3125496, DOI 10.1145/3125486.3125496]
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen LJ, 2018, PHYSICA A, V506, P679, DOI 10.1016/j.physa.2018.04.089
   Guan NN, 2019, KNOWL-BASED SYST, V164, P38, DOI 10.1016/j.knosys.2018.10.008
   Haidong Chen, 2016, 2016 IEEE International Workshop on Electromagnetics (iWEM): Applications and Student Innovation Competition, P1, DOI 10.1109/iWEM.2016.7504980
   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
   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
   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
   Kim D, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P233, DOI 10.1145/2959100.2959165
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Lee WP, 2016, KNOWL-BASED SYST, V106, P125, DOI 10.1016/j.knosys.2016.05.037
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu JX, 2020, KNOWL-BASED SYST, V191, DOI 10.1016/j.knosys.2019.105255
   Lu LY, 2012, PHYS REP, V519, P1, DOI 10.1016/j.physrep.2012.02.006
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Shi C, 2019, IEEE T KNOWL DATA EN, V31, P357, DOI 10.1109/TKDE.2018.2833443
   Socher R, 2013, ADV NEURAL INFORM PR, P926
   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, 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 W, 2016, DECIS SUPPORT SYST, V87, P80, DOI 10.1016/j.dss.2016.05.002
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J., 2013, P 2013 C EMP METH NA, P1366
   Wu YW, 2018, P IEEE RAP SYST PROT, P49, DOI 10.1109/RSP.2018.8631991
   Xu K, 2018, KNOWL-BASED SYST, V140, P120, DOI 10.1016/j.knosys.2017.10.031
   Yang C, 2020, KNOWL-BASED SYST, V187, DOI 10.1016/j.knosys.2019.07.010
   Yin RP, 2019, KNOWL-BASED SYST, V185, DOI 10.1016/j.knosys.2019.105020
   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 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 JN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4264
   Zhang Q, 2017, DECIS SUPPORT SYST, V104, P49, DOI 10.1016/j.dss.2017.10.002
   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 T, 2010, P NATL ACAD SCI USA, V107, P4511, DOI 10.1073/pnas.1000488107
NR 39
TC 23
Z9 24
U1 18
U2 116
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD SEP 27
PY 2020
VL 204
AR 106194
DI 10.1016/j.knosys.2020.106194
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MV7TB
UT WOS:000556554200016
DA 2022-08-23
ER

PT J
AU Liu, L
   Zeng, JH
   Zheng, XQ
AF Liu, Lu
   Zeng, Jiehang
   Zheng, Xiaoqing
TI Learning structured embeddings of knowledge graphs with generative
   adversarial framework
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Structured embedding learning; Knowledge graph; Generative adversarial
   network; Triple classification; Link prediction
ID WEB
AB Many large knowledge graphs are now available and ready to provide semantically structured information that is regarded as an important resource for question answering and decision support tasks. However, they are built on rigid symbolic frameworks which makes them hard to be used in other intelligent systems. Knowledge graph embedding approaches are gaining increasing attention, which embeds symbolic entities and relations into continuous vector spaces. Such graph embeddings are often learned by training a model to distinguish true triples from negative ones. Unfortunately, the negative triples created by replacing their heads or tails with randomly selected entities are easily identified by the model, which makes them insufficient to train useful models. To this end, we propose a method under a generative adversarial architecture to learn graph embeddings, in which a generative network is trained to provide continually improved "plausible"triples whereas a discriminative network learns to distinguish truth triples from the others by competing with the generator in a two-player minimax game. When arriving at a convergence, the generative network recovers the training data and can be used for knowledge graph completion, while the discriminative network is trained to be a good triple classifier. Extensive experiments demonstrate our method can improve multiple graph embedding models with a significant margin on both link prediction and triple classification tasks.
C1 [Liu, Lu; Zeng, Jiehang; Zheng, Xiaoqing] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China.
RP Liu, L (通讯作者)，Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China.
EM luliu19@fudan.edu.cn; jhzeng18@fudan.edu.cn; zhengxq@fudan.edu.cn
CR Arjovsky M., 2017, PREPRINT
   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
   Bordes A., 2011, P AAAI
   Bordes A., 2013, P 26 INT C NEUR INF
   Cai L, 2018, NAACL, P1470
   Chopra S, 2005, PROC CVPR IEEE, P539, DOI 10.1109/cvpr.2005.202
   Nguyen DQ, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3429
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Devlin J., 2019, LONG SHORT PAPERS, V1, DOI [10.18653/v1/N19-1423, 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
   Drumond L., 2012, P 27 ANN ACM S APL C, P326
   Ferrucci D, 2010, AI MAG, V31, P59, DOI 10.1609/aimag.v31i3.2303
   Goodfellow I., 2014, ADV NEURAL INFORM PR, Vvol 27
   Graupmann J., 2005, P 31 INT C VER LARG
   Guo S, 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, P84
   Hamilton WL, 2018, ADV NEUR IN, V31
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Huang P., 2019, PREPRINT
   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
   Kolda TG, 2009, SIAM REV, V51, P455, DOI 10.1137/07070111X
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   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
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu Y, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5070
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Ng V, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P104
   Nguyen D. Q., 2017, PREPRINT
   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
   Ren H., 2020, INT C LEARNING REPRE
   Ren H., 2020, P NEURAL INFORM PROC
   Rettinger A, 2012, DATA MIN KNOWL DISC, V24, P613, DOI 10.1007/s10618-012-0253-2
   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
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   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
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Wang B, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P1737, DOI 10.1145/3442381.3450043
   Wang PF, 2018, AAAI CONF ARTIF INTE, P2005
   Wang R, 2020, IEEE ACCESS, V8, P5212, DOI 10.1109/ACCESS.2019.2963367
   Wang W.Y., 2016, P 25 INT JOINT C ART, P2132
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   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
   Yang B., 2015, P INT C LEARNING REP
   Zhang XW, 2018, AAAI CONF ARTIF INTE, P5698
   Zhang YQ, 2019, PROC INT CONF DATA, P614, DOI 10.1109/ICDE.2019.00061
NR 51
TC 0
Z9 0
U1 5
U2 5
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 OCT 15
PY 2022
VL 204
AR 117361
DI 10.1016/j.eswa.2022.117361
PG 9
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 2P0IJ
UT WOS:000819434800004
DA 2022-08-23
ER

PT J
AU Zhu, J
   Zheng, ZT
   Yang, M
   Fung, GPC
   Tang, Y
AF Zhu, Jia
   Zheng, Zetao
   Yang, Min
   Fung, Gabriel Pui Cheong
   Tang, Yong
TI A semi-supervised model for knowledge graph embedding
SO DATA MINING AND KNOWLEDGE DISCOVERY
LA English
DT Article
DE Knowledge graph; Deep learning; Graph convolutional networks
AB Knowledge graphs have shown increasing importance in broad applications such as question answering, web search, and recommendation systems. The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces to perform various machine learning tasks. Most of the existing works only focused on the local structure of knowledge graphs when utilizing structural information of entities, which may not sincerely preserve the global structure of knowledge graphs.In this paper, we propose a semi-supervised model by adopting graph convolutional networks to utilize both local and global structural information of entities. Specifically, our model takes textual information of each entity into consideration as entity attributes in the process of learning. We show the effectiveness of our model by applying it to two traditional tasks for knowledge graph: entity classification and link prediction. Experimental results on two well-known corpora reveal the advantages of this model compared to state-of-the-art methods on both tasks. Moreover, the results show that even with only 1% labeled data to train, our model can still achieve good performance.
C1 [Zhu, Jia; Zheng, Zetao; Tang, Yong] S China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China.
   [Zhu, Jia] Guangzhou Key Lab Big Data, Intelligent Educ, Guangzhou, Peoples R China.
   [Yang, Min] Chinese Acad Sciences, Shenzhen Institutes Adv Technol, Beijing, Beijing, Peoples R China.
   [Fung, Gabriel Pui Cheong] Chinese Univ Hong Kong, Dept SEEM, Hong Kong, Peoples R China.
RP Zhu, J; Tang, Y (通讯作者)，S China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China.; Zhu, J (通讯作者)，Guangzhou Key Lab Big Data, Intelligent Educ, Guangzhou, Peoples R China.
EM jzhu@m.scnu.edu.cn; ytang@m.scnu.edu.cn
OI Zhu, Jia/0000-0002-5959-390X
CR Belkin M, 2003, NEURAL COMPUT, V15, P1373, DOI 10.1162/089976603321780317
   Belkin M, 2006, J MACH LEARN RES, V7, P2399
   Bishop M.C, 2006, PATTERN RECOGN
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   de Vries Gerben K. D., 2013, Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2013. Proceedings: LNCS 8188, P606, DOI 10.1007/978-3-642-40988-2_39
   Defferrard M., 2016, CONVOLUTIONAL NEURAL, V29, P1
   Huang CQ, 2018, INFORM SCIENCES, V432, P431, DOI 10.1016/j.ins.2017.12.024
   Ji GL, 2019, P 53 INT C ASS COMP, P687
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Kingma D. P., 2017, ARXIV
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Lafferty J, 2003, INT C MACH LEARN, P912, DOI DOI 10.1/1/14/4312
   Lai SW, 2015, AAAI CONF ARTIF INTE, P2267
   Lao N., 2012, P JOINT C EMP METH N, P1017
   Li Q., 2018, ARXIV180107606
   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
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Perozzi B., 2014, P 20 ACM SIGKDD INT, P701, DOI DOI 10.1145/2623330.2623732
   Shi BX, 2018, AAAI CONF ARTIF INTE, P1957
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Szumlanski Sean R., 2010, P 19 ACM INT C INF K, P19
   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 C EMP METH NAT LAN, P21
   Velickovic P., 2018, ICLR
   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 Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z., 2016, IJCAI, P1293
   Wang Z, 2014, P C EMP METH NAT LAN, P1591
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Welling M., 2016, 161107308 ARXIV
   Weston J., 2008, P 25 INT C MACH LEAR, P1168, DOI [10.1145/1390156.1390303, DOI 10.1145/1390156.1390303]
   Xiao H., 2016, P 25 INT JOINT C ART, P1315
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xu JC, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1318
   Yao L, 2019, AAAI CONF ARTIF INTE, P7370
   Zhong H., 2015, P 2015 C EMP METH NA, P267
   Zhou DY, 2004, ADV NEUR IN, V16, P321
   Zhu J, 2016, DATA MIN KNOWL DISC, V30, P550, DOI 10.1007/s10618-015-0428-8
   Zhu J, 2015, INFORM SCIENCES, V292, P1, DOI 10.1016/j.ins.2014.08.056
NR 41
TC 5
Z9 5
U1 8
U2 39
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1384-5810
EI 1573-756X
J9 DATA MIN KNOWL DISC
JI Data Min. Knowl. Discov.
PD JAN
PY 2020
VL 34
IS 1
BP 1
EP 20
DI 10.1007/s10618-019-00653-z
PG 20
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KI6WU
UT WOS:000511492100001
DA 2022-08-23
ER

PT J
AU Zins, C
AF Zins, Chaim
TI Conceptions of information science
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID DEFINITION
AB The field of information science is constantly changing. Therefore, information scientists are required to regularly review-and if necessary-redefine its fundamental building blocks. This article is one of four articles that documents the results of the Critical Delphi study conducted in 2003-2005. The study, "Knowledge Map of Information Science," was aimed at exploring the foundations of information science. The international panel was composed of 57 leading scholars from 16 countries who represent nearly all the major subfields and important aspects of the field. In this study, the author documents 50 definitions of information science, maps the major theoretical issues relevant to the formulation of a systematic conception, formulates six different conceptions of the field, and discusses their implications.
RP Zins, C (通讯作者)，26 Hahaganah St, IL-97852 Jerusalem, Israel.
EM chaimzins@gmail.com
CR BOYCE BR, 1985, ANNU REV INFORM SCI, V20, P153
   Buchanan Elizabeth A, 2001, READINGS CYBERETHICS, P523
   Buckland M, 1999, J AM SOC INFORM SCI, V50, P970, DOI 10.1002/(SICI)1097-4571(1999)50:11<970::AID-ASI3>3.0.CO;2-D
   Buckland M. K., 1991, INFORM INFORM SYSTEM
   Buckland MK, 1995, ANNU REV INFORM SCI, V30, P385
   Capurro R, 2003, ANNU REV INFORM SCI, V37, P343, DOI 10.1002/aris.1440370109
   CAPURRO R, 2000, HIERARCHIES COMMUNIC
   Cejpek J., 1998, INFORMACE KOMUNIKACE
   Debons A., 1988, INFORM SCI INTEGRATE
   DRAGULANESCU N, 2004, INFORM SCI TECHNOLOG
   ESS C, 2003, BLACKWELL GUIDE PHIL, P76
   Ess Charles, 2004, COMPANION DIGITAL HU, P132
   HARMON G, 1971, J AM SOC INFORM SCI, V22, P235, DOI 10.1002/asi.4630220402
   Hawkins DT, 2001, J AM SOC INF SCI TEC, V52, P44, DOI 10.1002/1532-2890(2000)52:1<44::AID-ASI1057>3.3.CO;2-Y
   Hersh WR, 2002, JAMA-J AM MED ASSOC, V288, P1955, DOI 10.1001/jama.288.16.1955
   Hjorland B, 1998, J DOC, V54, P606, DOI 10.1108/EUM0000000007183
   INGWERSEN P, 1995, ENCY LIB INFORMATION, V56, P137
   LECOADIC YF, 2004, COLLECTION QUE SAIS
   ROBERTS N, 1976, J DOC, V32, P249, DOI 10.1108/eb026627
   RUSH JE, 2006, UNPUB GUIDE INFORM S
   Saracevic T, 1999, J AM SOC INFORM SCI, V50, P1051, DOI 10.1002/(SICI)1097-4571(1999)50:12<1051::AID-ASI2>3.0.CO;2-Z
   TAVANI H, 2004, ETHICS TECHNOLOGY ET
   Taylor R. S., 1986, VALUE ADDED PROCESSE
   TRAVIS I, 2003, B AM SOC INFORM SCI, V30, P1
   ZINS C, IN PRESS J AM SOC IN
   Zins C, 2006, J DOC, V62, P447, DOI 10.1108/00220410610673846
NR 26
TC 68
Z9 75
U1 1
U2 28
PU JOHN WILEY & SONS INC
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN, NJ 07030 USA
SN 1532-2882
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD FEB 1
PY 2007
VL 58
IS 3
BP 335
EP 350
DI 10.1002/asi.20507
PG 16
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 166LD
UT WOS:000246379400003
DA 2022-08-23
ER

PT J
AU Yang, S
   Cai, XS
AF Yang, Shuang
   Cai, Xuesong
TI Bilateral knowledge graph enhanced online course recommendation
SO INFORMATION SYSTEMS
LA English
DT Article
DE Recommender system; Online course recommendation; Knowledge graph; Cold
   start; Personalized recommendation
ID PERSONALIZED RECOMMENDATION; MATRIX FACTORIZATION
AB Recommender system can provide users with items that meet their potential needs in mass information. Its development provides new ideas and supporting technologies for applications in online education scenarios. The previous recommendation methods usually only consider the enhancement of the item side, but ignore the importance of the user characteristics to the recommendation, and are not suitable for the online education scenario. To address this problem, we take knowledge graph as the auxiliary information source of collaborative filtering and propose an end-to-end framework using knowledge graph to enrich the semantics of the item representation. In particular, faced with the thorny problem of cold start, the framework makes use of the static features of users to personalize the modeling of new users. Experimenting with two public datasets and an industrial dataset, we demonstrate that the framework has significant performance improvements over the baseline and can maintain satisfactory performance with sparse user-item interactions. (c) 2022 Elsevier Ltd. All rights reserved.
C1 [Yang, Shuang] East China Normal Univ, Sch Software Engn, Shanghai, Peoples R China.
   [Cai, Xuesong] Shanghai Synergy Digital Technol Innovat Inst, Shanghai, Peoples R China.
RP Yang, S (通讯作者)，East China Normal Univ, Sch Software Engn, Shanghai, Peoples R China.
EM 52205902021@stu.ecnu.edu.cn; caixuesong@ssdtii.cn
CR Ahmed A., 2020, IEEE ACCESS, V8
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Chen L, 2020, AAAI CONF ARTIF INTE, V34, P27
   Chen Y, 2021, PATTERN RECOGN LETT, V151, P55, DOI 10.1016/j.patrec.2021.07.022
   Cheng H.T., 2016, P 1 WORKSHOP DEEP LE, P7, DOI DOI 10.1145/2988450.2988454
   Cheng ZY, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3291060
   Defferrard M., 2016, 30 INT C NEURAL INFO, P3844
   Derr T, 2018, IEEE DATA MINING, P929, DOI 10.1109/ICDM.2018.00113
   Fan WQ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P417, DOI 10.1145/3308558.3313488
   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
   Hongwei Wang, 2019, ACM Transactions on Information Systems, V37, DOI 10.1145/3312738
   Hu GN, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P667, DOI 10.1145/3269206.3271684
   Kipf T. N., 2016, ARXIV160902907
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Liu F, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P1526, DOI 10.1145/3343031.3350953
   Liu ZQ, 2019, AAAI CONF ARTIF INTE, P4424
   Luo L, 2019, EXPERT SYST APPL, V119, P311, DOI 10.1016/j.eswa.2018.11.003
   Qian SS, 2018, IEEE T MULTIMEDIA, V20, P2086, DOI 10.1109/TMM.2017.2785227
   Ren Wang, 2021, Journal of Physics: Conference Series, V1873, DOI 10.1088/1742-6596/1873/1/012091
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Runsheng Wang, 2020, Neural Information Processing. 27th International Conference, ICONIP 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12534), P455, DOI 10.1007/978-3-030-63836-8_38
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Shi C, 2021, IEEE T KNOWL DATA EN, V33, P1413, DOI 10.1109/TKDE.2019.2941938
   Song WP, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P555, DOI 10.1145/3289600.3290989
   Sourabh V, 2019, EXPERT SYST APPL, V120, P335, DOI 10.1016/j.eswa.2018.12.002
   Srivastava N., 2012, PROC INT C NEURAL IN, P2222, DOI DOI 10.1109/CVPR.2013.49
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Symeonidis P, 2019, EXPERT SYST APPL, V118, P261, DOI 10.1016/j.eswa.2018.09.053
   Tian F, 2014, AAAI CONF ARTIF INTE, P1293
   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, AAAI CONF ARTIF INTE, P2508
   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 Z., 2020, LECT NOTES COMPUTER, P479, DOI [10.1007/978-3-030-63836-8, DOI 10.1007/978-3-030-63836-8]
   Wu S, 2019, AAAI CONF ARTIF INTE, P346
   Xu K, 2019, PROC INT CONF PARAL, DOI 10.1145/3337821.3337923
   Xue HJ, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3203
   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
   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 LS, 2021, KNOWL-BASED SYST, V230, DOI 10.1016/j.knosys.2021.107399
   Zhang YX, 2019, AAAI CONF ARTIF INTE, P5829
   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
   Zheng E, 2018, EXPERT SYST APPL, V106, P244, DOI 10.1016/j.eswa.2018.04.014
   Zou F, 2021, NEUROCOMPUTING, V452, P716, DOI 10.1016/j.neucom.2020.08.080
NR 51
TC 1
Z9 1
U1 13
U2 13
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 JUL
PY 2022
VL 107
AR 102000
DI 10.1016/j.is.2022.102000
PG 11
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0N7WU
UT WOS:000783045100005
DA 2022-08-23
ER

PT J
AU Mantle, M
   Batsakis, S
   Antoniou, G
AF Mantle, Matthew
   Batsakis, Sotirios
   Antoniou, Grigoris
TI Large scale distributed spatio-temporal reasoning using real-world
   knowledge graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Qualitative reasoning; Distributed computing; Parallel computing;
   Knowledge graphs
AB Most of the existing work in the field of Qualitative Spatial Temporal Reasoning (QSTR) has focussed on comparatively small constraint networks that consist of hundreds or at most thousands of relations. Recently we have seen the emergence of much larger qualitative spatial knowledge graphs that feature hundreds of thousands and millions of relations. Traditional approaches to QSTR are unable to reason over networks of such size.
   In this article we describe ParQR, a parallel, distributed implementation of QSTR techniques that addresses the challenge of reasoning over large-scale qualitative spatial and temporal datasets. We have implemented ParQR using the Apache Spark framework, and evaluated our approach using both large scale synthetic datasets and real-world knowledge graphs. We show that our approach scales effectively, is able to handle constraint networks consisting of millions of relations, and outperforms current distributed implementations of QSTR. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Mantle, Matthew; Batsakis, Sotirios; Antoniou, Grigoris] Univ Huddersfield, Huddersfield, W Yorkshire, England.
   [Batsakis, Sotirios] Tech Univ Crete, Khania, Greece.
RP Mantle, M (通讯作者)，Univ Huddersfield, Huddersfield, W Yorkshire, England.
EM m.e.mantle@hud.ac.uk
CR Afrati F. N., 2012, 15 INT C EXTENDING D, P132
   ALLEN JF, 1983, COMMUN ACM, V26, P832, DOI 10.1145/182.358434
   Batsakis S, 2017, INT J ARTIF INTELL T, V26, DOI 10.1142/S0218213017600156
   Bliek C, 1999, IJCAI-99: PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 & 2, P456
   Cohn AG, 2001, FUND INFORM, V46, P1
   Focke R. W., 2011, P INT C INF FUS, P1
   Hogge J. C., 1987, TPLAN TEMPORAL INTER
   Huang JB, 2013, ARTIF INTELL, V195, P140, DOI 10.1016/j.artint.2012.09.009
   Ioannidis E., 1986, Proceedings of Very Large Data Bases. Twelfth International Conference on Very Large Data Bases, P403
   Iwasaki Y, 1997, IEEE EXPERT, V12, P16, DOI 10.1109/64.590068
   Kim J, 2018, LECT NOTES ELECTR EN, V474, P829, DOI 10.1007/978-981-10-7605-3_132
   Koubarakis M, 2011, IJCAI 2011 WORKSH BE, P33
   Ladkin PB, 1997, ANN MATH ARTIF INTEL, V19, P383, DOI 10.1023/A:1018968024833
   Li JJ, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P572
   Lutz C, 2007, J AUTOM REASONING, V38, P227, DOI 10.1007/s10817-006-9049-7
   Mantle M, 2017, LECT NOTES ARTIF INT, V10062, P29, DOI 10.1007/978-3-319-62428-0_3
   Nam S, 2017, INT CONF BIG DATA, P296, DOI 10.1109/BIGCOMP.2017.7881681
   Nebel B., 1996, ECAI 96. 12th European Conference on Artificial Intelligence, P38
   Nikolaou C, 2014, AAAI CONF ARTIF INTE, P2724
   Nikolaou C, 2016, ARTIF INTELL, V237, P138, DOI 10.1016/j.artint.2016.04.005
   Randell D. A., 1992, Principles of Knowledge Representation and Reasoning: Proceedings of the Third International Conference (KR '92), P165
   Renz J, 2001, J ARTIF INTELL RES, V15, P289, DOI 10.1613/jair.872
   Renz J, 2002, LNCS LNAI, V2293, DOI [10.1007/3-540-70736-0, DOI 10.1007/3-540-70736-0]
   Renz J, 2007, HANDBOOK OF SPATIAL LOGICS, P161, DOI 10.1007/978-1-4020-5587-4_4
   Sioutis M, 2016, KNOWL ENG REV, V32, P1, DOI 10.1017/S026988891600014X
   Sioutis M, 2014, PROC INT C TOOLS ART, P194, DOI 10.1109/ICTAI.2014.37
   Sioutis M, 2014, LECT NOTES ARTIF INT, V8445, P178, DOI 10.1007/978-3-319-07064-3_15
   Sioutis M, 2012, PROC INT C TOOLS ART, P436, DOI 10.1109/ICTAI.2012.66
   van Beek P., CSAI9601101 CORR
   Vilain M., 1986, Proceedings AAAI-86: Fifth National Conference on Artificial Intelligence, P377
   Westphal Matthias, 2012, KI 2012: Advances in Artificial Intelligence. Proceedings of the 35th Annual German Conference on AI, P180, DOI 10.1007/978-3-642-33347-7_16
   Westphal M., 2015, THESIS
   YANNAKAKIS M, 1981, SIAM J ALGEBRA DISCR, V2, P77, DOI 10.1137/0602010
   Zaharia Matei, 2010, HOTCLOUD, V10, P95, DOI DOI 10.HTTP://DL.ACM.0RG/CITATI0N.CFM?
NR 34
TC 5
Z9 7
U1 5
U2 39
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 JAN 1
PY 2019
VL 163
BP 214
EP 226
DI 10.1016/j.knosys.2018.08.035
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HF8CJ
UT WOS:000454468200018
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, XM
   Sun, XL
   Xie, CJ
   Lun, B
AF Zhang, Xiaoming
   Sun, Xiaoling
   Xie, Chunjie
   Lun, Bing
TI From Vision to Content: Construction of Domain-Specific Multi-Modal
   Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph; information fusion; multi-modal knowledge; metallic
   materials knowledge
ID METALLIC MATERIALS ONTOLOGY; DATABASE; DBPEDIA
AB Knowledge graphs are usually constructed to describe the various concepts that exist in real world as well as the relationships between them. There are many knowledge graphs in specific fields, but they usually pay more attention on text or structured data, ignoring the image vision information, and cannot play an adequate role in the emerging visualization applications. Aiming at this issue, we design a method that integrates image vision information and text information derived from Wikimedia Commons to construct a domain-specific multi-modal knowledge graph, taking the metallic materials domain as an example to illustrate the method. The text description of each image is regarded as its context semantic to acquire the image's context semantic labels based on the DBpedia resource. Furthermore, we adopt deep neural network model instead of simple visual descriptors to acquire the image's visual semantic labels using the concepts from WordNet. In order to fuse the visual semantic labels and context semantic labels, a path-based concept extension and fusion strategy is proposed based on the conceptual hierarchies of WordNet and DBpedia to obtain the effective extension concepts as well as the links between them, increasing the scale of the knowledge graph and enhancing the correlation between images. The experimental results show that the maximum extension level has a significant impact on the quality of the generated domain knowledge graph, and the best extension level number is respectively determined for both DBpedia and WordNet. In addition, the results of this paper are compared with IMGpedia to further show the effectiveness of the proposed method.
C1 [Zhang, Xiaoming; Sun, Xiaoling; Xie, Chunjie; Lun, Bing] Hebei Univ Sci & Technol, Sch Informat Sci & Engn, Shijiazhuang 050018, Hebei, Peoples R China.
RP Sun, XL (通讯作者)，Hebei Univ Sci & Technol, Sch Informat Sci & Engn, Shijiazhuang 050018, Hebei, Peoples R China.
EM 0211714022@stu.hebust.edu.cn
OI Sun, Xiaoling/0000-0001-6220-8382
FU Natural Science Foundation of Hebei Province [F2018208116]; Innovation
   Foundation for Postgraduate of Hebei Province [CXZZSS2018092]; National
   Natural Science Foundation of China [51271033]
FX This work was supported in part by the Natural Science Foundation of
   Hebei Province under Grant F2018208116, in part by the Innovation
   Foundation for Postgraduate of Hebei Province under Grant CXZZSS2018092,
   and in part by the National Natural Science Foundation of China under
   Grant 51271033.
CR Al-Zaidy RA, 2018, IEEE INT C SEMANT CO, P56, DOI 10.1109/ICSC.2018.00017
   Anderson P, 2018, PROC CVPR IEEE, P6077, DOI 10.1109/CVPR.2018.00636
   [Anonymous], 2013, P ICLR
   Ashino Toshihiro, 2010, Data Science Journal, V9, P54, DOI 10.2481/dsj.008-041
   Ben-younes H, 2017, IEEE I CONF COMP VIS, P2631, DOI 10.1109/ICCV.2017.285
   Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
   Blakesley Lindsay Elizabeth., 1999, ELECT RESOURCES REV, V3, P56, DOI [10.1108/err.1999.3.5.56.52, DOI 10.1108/ERR.1999.3.5.56.52]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Chen PH, 2018, IEEE ACCESS, V6, P31553, DOI 10.1109/ACCESS.2018.2839607
   Corbett P, 2018, J CHEMINFORMATICS, V10, DOI 10.1186/s13321-018-0313-8
   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
   Ferrada S, 2017, LECT NOTES COMPUT SC, V10588, P84, DOI 10.1007/978-3-319-68204-4_8
   Fukui A., 2016, P 2016 C EMPIRICAL M, P457
   Gabbard R, 2018, MACH TRANSL, V32, P31, DOI 10.1007/s10590-017-9208-0
   Gorinski P.J., 2019, ARXIV190303985
   Greenberg N., 2018, P 2018 C EMP METH NA, P2824
   Huang FR, 2019, KNOWL-BASED SYST, V171, P44, DOI 10.1016/j.knosys.2019.02.003
   Katiyar A., 2018, P NAACL HLT 18, P861, DOI DOI 10.18653/V1/N18-1079.HTTP://ACLWEB.0RG/ANTH0L0GY/N18-1079
   Kok Wei Chee, 2019, 10th International Conference on Robotics, Vision, Signal Processing and Power Applications. Enabling Research and Innovation Towards Sustainability. Lecture Notes in Electrical Engineering (LNEE 547), P591, DOI 10.1007/978-981-13-6447-1_75
   Kuhn M, 2016, NUCLEIC ACIDS RES, V44, pD1075, DOI 10.1093/nar/gkv1075
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lenz Marcel, 2018, J Biomed Opt, V23, P1, DOI 10.1117/1.JBO.23.7.071205
   Liu A., 2019, ARXIV190404458V2
   Lu CW, 2016, LECT NOTES COMPUT SC, V9905, P852, DOI 10.1007/978-3-319-46448-0_51
   Mehta A, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P705, DOI 10.1145/3308560.3317708
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Mousselly-SergiehH, 2018, P 7 JOINT C LEX COMP, P225
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Parchami M, 2017, IEEE IJCNN, P4625, DOI 10.1109/IJCNN.2017.7966443
   Pezeshkpour P., 2018, C EMP METH NAT LANG, P3208, DOI DOI 10.18653/V1/D18-1359
   Plummer BA, 2017, IEEE I CONF COMP VIS, P1946, DOI 10.1109/ICCV.2017.213
   Rau L. F., 2002, P 7 IEEE C ART INT A, V1, P29
   Silva V. S., 2018, ARXIV180607731
   Simonyan K., 2015, INT C LEARNING REPRE
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Swartz A, 2002, IEEE INTELL SYST, V17, P76, DOI 10.1109/5254.988466
   Szegedy C, 2016, PROC CVPR IEEE, P2818, DOI 10.1109/CVPR.2016.308
   Thoma S, 2017, ARXIV170406084
   Thoma S, 2017, LECT NOTES COMPUT SC, V10587, P694, DOI 10.1007/978-3-319-68288-4_41
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Wang Z, 2015, P 24 ACM INT C INF K, P653, DOI DOI 10.1145/2806416.2806533
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Xie RB, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3140
   Xin Y., 2019, U. S. Patent, Patent No. [692 392, 692392]
   Yu RC, 2017, IEEE I CONF COMP VIS, P1068, DOI 10.1109/ICCV.2017.121
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   Zhang KL, 2018, INT CONF CYBER DIST, P160, DOI 10.1109/CyberC.2018.00041
   Zhang X., 2014, INT J DATABASE THEOR, V7, P175, DOI DOI 10.14257/IJDTA.2014.7.6.16
   Zhang XM, 2018, COMPUT IND, V99, P140, DOI 10.1016/j.compind.2018.03.032
   Zhang XM, 2017, COMPUT PHYS COMMUN, V211, P98, DOI 10.1016/j.cpc.2016.07.005
   Zhang XM, 2016, ADV ENG INFORM, V30, P687, DOI 10.1016/j.aei.2016.09.002
   Zhao S, 2017, AIP ADV, V7, DOI 10.1063/1.4999209
   Zhao XJ, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), P56, DOI 10.1109/SANER.2017.7884609
NR 56
TC 0
Z9 0
U1 12
U2 41
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 108278
EP 108294
DI 10.1109/ACCESS.2019.2933370
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IS2JZ
UT WOS:000481980800043
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, ZY
   Yu, P
   Pai, N
   Chang, HCR
   Chen, SY
   Yin, MY
   Song, T
   Lau, SK
   Deng, C
AF Zhang, Zhenyu
   Yu, Ping
   Pai, Nagesh
   Chang, Hui Chen Rita
   Chen, Shiyan
   Yin, Mengyang
   Song, Ting
   Lau, Sim Kim
   Deng, Chao
TI Developing an Intuitive Graph Representation of Knowledge for
   Nonpharmacological Treatment of Psychotic Symptoms in Dementia
SO JOURNAL OF GERONTOLOGICAL NURSING
LA English
DT Article
ID ONTOLOGY; INTERVENTIONS; DISEASE; COHORT
AB Applying person-centered, nonpharmacological interventions to manage psychotic symptoms of dementia is promoted for health care professionals, particularly gerontological nurses, who are responsible for care of older adults in nursing homes. A knowledge graph is a graph consisting of a set of concepts that are linked together by their interrelationship and has been widely used as a formal representation of domain knowledge in health. However, there is lack of a knowledge graph for nonpharmacological treatment of psychotic symptoms in dementia. Therefore, we developed a comprehensive, human- and machine-understandable knowledge graph for this domain, named Dementia-Related Psychotic Symptom Nonpharmacological Treatment Ontology (DRPSNPTO). This graph was built by adopting the established NeOn methodology, a knowledge graph engineering method, to meet the quality standards for biomedical knowledge graphs. This intuitive graph representation of the domain knowledge sets a new direction for visualizing and computerizing gerontological knowledge to facilitate human comprehension and build intelligent aged care information systems. [Journal of Gerontological Nursing, 48(4), 49-55.]
C1 [Zhang, Zhenyu; Yu, Ping; Yin, Mengyang; Song, Ting; Lau, Sim Kim] Univ Wollongong, Fac Engn & Informat Sci, Sch Comp & Informat Technol, Ctr Digital Transformat, Wollongong, NSW, Australia.
   [Pai, Nagesh; Deng, Chao] Univ Wollongong, Fac Sci, Sch Med, Wollongong, NSW, Australia.
   [Chang, Hui Chen Rita] Univ Wollongong, Fac Sci, Sch Nursing, Wollongong, NSW, Australia.
   [Yu, Ping; Pai, Nagesh; Chang, Hui Chen Rita; Deng, Chao] Illawarra Hlth & Med Res Inst, Keiraville, NSW, Australia.
   [Chen, Shiyan] Fujian Med Univ, Affiliated Hosp 1, Neurol Dept, Fuzhou, Fujian, Peoples R China.
   [Yin, Mengyang] Catholic Healthcare Ltd, Syst & Reporting Residential Care, Macquarie Pk, NSW, Australia.
RP Yu, P (通讯作者)，Univ Wollongong, Sch Comp & Informat Technol, Ctr Digital Transformat, Northfield Ave, Wollongong, NSW 2522, Australia.
EM ping@uow.edu.au
RI ; Yu, Ping/B-1205-2008
OI Chang, Hui chen (Rita)/0000-0002-8305-0585; Zhang,
   Zhenyu/0000-0003-1853-4978; Yu, Ping/0000-0002-7910-9396
FU Australian Rotary Health; University of Wollongong; Woy Woy Rotary Club
FX Zhenyu Zhang is supported by a combined PhD scholarship provided by the
   Australian Rotary Health, Woy Woy Rotary Club, and University of
   Wollongong.
CR American Geriatrics Society, 2011, GUIDE MANAGEMENT PSY
   American Music Therapy Association, 2005, AMTA OFFICIAL DEFINI
   Amith M, 2018, J BIOMED INFORM, V80, P1, DOI 10.1016/j.jbi.2018.02.010
   Ausubel DP, 1978, ED PSYCHOL COGNITIVE
   Bona Jonathan P, 2019, Yearb Med Inform, V28, P140, DOI 10.1055/s-0039-1677912
   BRACHMAN RJ, 1983, COMPUTER, V16, P30
   Burns K, 2014, CLINICIANS FIELD GUI
   Chen RC, 2014, GERIATR GERONTOL INT, V14, P440, DOI 10.1111/ggi.12126
   Cohen-Mansfield J, 2003, J GERIATR PSYCH NEUR, V16, P219, DOI 10.1177/0891988703258665
   Cohen-Mansfield J, 2014, AM J ALZHEIMERS DIS, V29, P386, DOI 10.1177/1533317514522850
   Fick DM, 2019, J AM GERIATR SOC, V67, P674, DOI 10.1111/jgs.15767
   Hirdes JP, 2020, J AM MED DIR ASSOC, V21, P817, DOI 10.1016/j.jamda.2020.04.004
   Ienca M, 2018, FRONT MED-LAUSANNE, V5, DOI 10.3389/fmed.2018.00013
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Macfarlane S, 2021, AUST PRESCR, V44, P8, DOI 10.18773/austprescr.2020.078
   Malhotra A, 2014, ALZHEIMERS DEMENT, V10, P238, DOI 10.1016/j.jalz.2013.02.009
   Moher D, 2009, BMJ-BRIT MED J, V339, DOI [10.1136/bmj.i4086, 10.1136/bmj.b2535, 10.1016/j.ijsu.2010.02.007]
   Musen Mark A, 2015, AI Matters, V1, P4
   Nowrangi MA, 2015, ALZHEIMERS RES THER, V7, DOI 10.1186/s13195-015-0096-3
   Padala SP, 2020, PSYCHIAT RES, V288, DOI 10.1016/j.psychres.2020.113028
   Poveda-Villalon M, 2014, INT J SEMANT WEB INF, V10, P7, DOI 10.4018/ijswis.2014040102
   Prud'hommeaux E., 2008, SPARQL QUERY LANGUAG
   Remington R, 2010, AM J ALZHEIMERS DIS, V25, P367, DOI 10.1177/1533317510363470
   Savaskan Egemen, 2014, Praxis (Bern 1994), V103, P135, DOI 10.1024/1661-8157/a001547
   Shi LX, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/2858423
   Sirin E, 2007, J WEB SEMANT, V5, P51, DOI 10.1016/j.websem.2007.03.004
   Smith B, 2007, NAT BIOTECHNOL, V25, P1251, DOI 10.1038/nbt1346
   SNOMED International, 2020, 5 STEP BRIEFING
   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]
   Van Leeuwen E, 2018, COCHRANE DB SYST REV, DOI [10.1002/14651858.C.D007726.pub3, 10.1002/14651858.CD007726.pub3]
   Wallace T., 2012, REBOC REDUCING BEHAV
   Whetzel PL, 2011, NUCLEIC ACIDS RES, V39, pW541, DOI 10.1093/nar/gkr469
   World Health Organization, 2021, DEMENTIA KEY FACTS
   World Health Organization, 2019, ICD 11 INT CLASSIFIC
   World Health Organization, 2017, GLOB ACT PLAN HIV DR
   World Health Organization, 2020, STATE WORLDS NURSING
   Zhang Z, 2021, DEMENTIA RELATED PSY
   Zhang ZY, 2020, ALZH DEMENT-TRCI, V6, DOI 10.1002/trc2.12061
NR 38
TC 1
Z9 1
U1 3
U2 3
PU SLACK INC
PI THOROFARE
PA 6900 GROVE RD, THOROFARE, NJ 08086 USA
SN 0098-9134
EI 1938-243X
J9 J GERONTOL NURS
JI J. Gerontol. Nurs.
PD APR
PY 2022
VL 48
IS 4
BP 49
EP +
DI 10.3928/00989134-20220308-02
PG 17
WC Geriatrics & Gerontology; Gerontology; Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Geriatrics & Gerontology; Nursing
GA 0H2MB
UT WOS:000778570200007
PM 35343842
DA 2022-08-23
ER

PT J
AU Zhang, TT
   Liu, SN
AF Zhang, Tingting
   Liu, Shengnan
TI Hybrid Music Recommendation Algorithm Based on Music Gene and Improved
   Knowledge Graph
SO SECURITY AND COMMUNICATION NETWORKS
LA English
DT Article
AB Combining music as a specific recommendation object, a hybrid recommendation algorithm based on music genes and improved knowledge graph is proposed for the traditional single recommendation algorithm that cannot effectively solve the accuracy problem in music recommendation. The algorithm first gives the recommendation pattern of music genes and gets the relevant recommendation results through the genetic preference analysis. After that, the algorithm in this paper utilizes item and user label information and knowledge graphs from two different domains to enrich and mine the potential information of users and items. In addition, deep learning method is applied to extract low-dimensional, abstract deep semantic features of users and items, based on which, score prediction is performed. The mixed-mode based recommendation addresses the drawbacks of these two recommendations and can adopt different weighting strategies in different situations. The advantages of music gene and knowledge graph-based recommendation algorithms are combined via this method. The experimental results indicate that the algorithm in this paper outperforms other existing recommendation algorithms.
C1 [Zhang, Tingting; Liu, Shengnan] Shijiazhuang Univ, Coll Mus, Shijiazhuang 050035, Hebei, Peoples R China.
RP Zhang, TT (通讯作者)，Shijiazhuang Univ, Coll Mus, Shijiazhuang 050035, Hebei, Peoples R China.
EM 1101838@sjzc.edu.cn; 1101835@sjzc.edu.cn
FU Shijiazhuang University
FX THis work was supported by the Shijiazhuang University.
CR Alam M, 2021, PROG ARTIF INTELL, V10, P309, DOI 10.1007/s13748-021-00241-7
   Dan W, 2019, 2019 INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION, BIG DATA & SMART CITY (ICITBS), P430, DOI 10.1109/ICITBS.2019.00112
   Hou DB, 2021, IEEE ACCESS, V9, P15720, DOI 10.1109/ACCESS.2021.3052794
   Kollolu R., 2020, INT J MANAGEMENT TEC, V10, P255
   Linfei Wang, 2020, CIPAE 2020: Proceedings of the 2020 International Conference on Computers, Information Processing and Advanced Education, P121, DOI 10.1145/3419635.3419669
   Nitu Paromita, 2021, Big Data Mining and Analytics, V4, P139, DOI 10.26599/BDMA.2020.9020026
   Paul Dip, 2020, Emerging Technology in Modelling and Graphics. Proceedings of IEM Graph 2018. Advances in Intelligent Systems and Computing (AISC 937), P279, DOI 10.1007/978-981-13-7403-6_26
   Pulis M, 2021, 15TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS 2021), P719, DOI 10.1145/3460231.3478847
   Fathollahi MS, 2021, INT J MULTIMED INF R, V10, P43, DOI 10.1007/s13735-021-00206-5
   Wang J, 2020, J INTERNET TECHNOL, V21, P393, DOI 10.3966/160792642020032102008
   Wang WZ, 2019, 2019 INTERNATIONAL CONFERENCE ON SMART GRID AND ELECTRICAL AUTOMATION (ICSGEA), P280, DOI 10.1109/ICSGEA.2019.00071
   Wang Y, 2020, LECT NOTES ARTIF INT, V12084, P91, DOI 10.1007/978-3-030-47426-3_8
   Weite Feng, 2021, MultiMedia Modeling. 27th International Conference, MMM 2021. Proceedings. Lecture Notes in Computer Science (LNCS 12572), P328, DOI 10.1007/978-3-030-67832-6_27
   Wen XL, 2021, SOFT COMPUT, V25, P3087, DOI 10.1007/s00500-020-05364-y
   Xu DZ, 2020, PERS UBIQUIT COMPUT, V24, P247, DOI 10.1007/s00779-019-01343-9
   Xu YJ, 2020, ASIAPAC SIGN INFO PR, P332
NR 16
TC 0
Z9 0
U1 10
U2 10
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 APR 9
PY 2022
VL 2022
AR 5889724
DI 10.1155/2022/5889724
PG 11
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 0Z6LI
UT WOS:000791187200002
OA gold
DA 2022-08-23
ER

PT J
AU Zhao, L
   Deng, HH
   Qiu, LY
   Li, SM
   Hou, ZX
   Sun, H
   Chen, Y
AF Zhao, Ling
   Deng, Hanhan
   Qiu, Linyao
   Li, Sumin
   Hou, Zhixiang
   Sun, Hai
   Chen, Yun
TI Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph
   Embedding
SO SYMMETRY-BASEL
LA English
DT Article
DE multi-source spatio-temporal data; knowledge graph; embedded learning;
   data analysis
AB Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.
C1 [Zhao, Ling; Deng, Hanhan] Cent South Univ, Sch Geosci & Infophys, Changsha 410012, Peoples R China.
   [Qiu, Linyao] China Acad Elect & Informat Technol, Beijing 100086, Peoples R China.
   [Li, Sumin; Hou, Zhixiang] Changsha Univ Sci & Technol, Sch Architecture, Changsha 610059, Peoples R China.
   [Sun, Hai] China Telecom Shanghai Ideal Informat Ind Grp Ltd, Shanghai 200120, Peoples R China.
   [Chen, Yun] Shanghai Aerosp Control Technol Inst, Shanghai 201109, Peoples R China.
RP Chen, Y (通讯作者)，Shanghai Aerosp Control Technol Inst, Shanghai 201109, Peoples R China.
EM zhaoling@csu.edu.cn; 175012101@csu.edu.cn; qiulinyao@163.com;
   lisumin57@gmail.com; houzhixiang2008@163.com; sunhai@ideal.sh.cn;
   chenyun3305@sohu.com
OI Zhao, Ling/0000-0002-6103-1113
FU National Science Foundation of China [41871364, 41571397, 41871276,
   51678077]
FX This work was supported by the National Science Foundation of China
   (grant numbers 41871364, 41571397, 41871276, and 51678077).
CR AGRYZKOV T, 2019, SYMMETRY-BASEL, V11, DOI DOI 10.3390/sym11050704
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Borg J., 2018, P 2018 IEEE INT SMAR, P1
   Cao ZY, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11030392
   Chang S, 2015, P 21 ACM SIGKDD INT
   Chen YL, 2014, COMPUT GEOSCI-UK, V72, P76, DOI 10.1016/j.cageo.2014.06.008
   Chi Y, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10040995
   Du RH, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20020451
   Figueiredo D.R., 2017, P 23 ACM SIGKDD INT
   Grover Aditya, 2016, P ACM SIGKDD INT C K
   Gui H., 2017, P IEEE INT C DAT MIN
   Gui H, 2017, IEEE T KNOWL DATA EN, V29, P2428, DOI 10.1109/TKDE.2017.2733530
   Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950
   Hofmann T, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P289
   Huang Z., 2017, ARXIV170105291
   Jacob Y, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P373, DOI 10.1145/2556195.2556225
   Lin JY, 2019, INT J GEOGR INF SCI, V33, P1062, DOI 10.1080/13658816.2018.1563302
   Lin X., 2017, MATH PROBL ENG, V2017, P7
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu W., 2019, POWER SYST AUTOM, V43, P96
   Ma JT, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11091096
   Ma Z., 2017, P 2017 IEEE 18 INT S
   Mohamad S, 2017, IEEE INT SYMP CIRC S, P112
   Nguyen D. Q, 2016, ARXIV160608140
   Niu L., 2018, P INT C INT TRANSP X
   Perozzi B., 2014, P ACM SIGKDD INT C K
   Radha D., 2017, P 2017 2 INT C COMP
   Tang J., 2015, P 24 INT C WORLD WID
   Tang J, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1165, DOI 10.1145/2783258.2783307
   Trouillon T, 2016, ARXIV160606357
   Uselton S.P., 1998, P VIS 98 RES TRIANGL
   Visvizi A, 2018, J SCI TECHNOL POLICY, V9, P134, DOI 10.1108/JSTPM-02-2018-0020
   Wang D., 2016, P ACM SIGKDD INT C K
   Wang H., 2018, P 11 ACM INT C WEB S
   Wang PY, 2018, ACM T INTEL SYST TEC, V9, DOI 10.1145/3209686
   Wang Z., 2014, P 28 AAAI C ART INT
   Xueyi Yang, 2015, Applied Mechanics and Materials, V727-728, P955, DOI 10.4028/www.scientific.net/AMM.727-728.955
   Yang B., 2015, P INT C LEARN REPR
   Yang C., 2015, P INT C ART INT SAN
   Yao Y, 2017, INT J GEOGR INF SCI, V31, P825, DOI 10.1080/13658816.2016.1244608
NR 40
TC 0
Z9 0
U1 18
U2 32
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-8994
J9 SYMMETRY-BASEL
JI Symmetry-Basel
PD FEB
PY 2020
VL 12
IS 2
AR 199
DI 10.3390/sym12020199
PG 18
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA KW4PJ
UT WOS:000521147600078
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zhen, YY
   Zheng, LQ
   Chen, PH
AF Zhen, Yuanyi
   Zheng, Lanqin
   Chen, Penghe
TI Constructing Knowledge Graphs for Online Collaborative Programming
SO IEEE ACCESS
LA English
DT Article
DE Collaboration; Collaborative work; Text recognition; Programming
   profession; Text categorization; Knowledge engineering; Tagging;
   Knowledge graph; entity recognition; relation extraction; deep neural
   network model; online collaborative learning; collaborative programming
AB This study aimed to automatically construct knowledge graphs for online collaborative programming. We proposed several models and developed a system to construct knowledge graphs based on online discussion texts and the target knowledge graph for the C programming language. Our system included two main modules, namely, entity recognition and relation extraction. We proposed an innovative approach for recognizing knowledge entities, which included sequence tagging, text classification, and keyword matching. The extraction of relationships among knowledge entities was performed through queries of the target knowledge graph. The six kinds of knowledge graphs could be automatically generated through our method, including the activated and unactivated knowledge graphs of each student, each group, and each class. The accuracy of entity recognition reached 87.27%. The accuracies of relation extraction for students, groups, and the class reached 89.7%, 90.4%, and 90.2%, respectively. This study is very promising and significant for both teachers and practitioners to provide interventions and personalized learning services based on the constructed knowledge graphs.
C1 [Zhen, Yuanyi; Zheng, Lanqin] Beijing Normal Univ, Sch Educ Technol, Fac Educ, Beijing 10875, Peoples R China.
   [Chen, Penghe] Beijing Normal Univ, Adv Innovat Ctr Future Educ, Beijing 100875, Peoples R China.
RP Zheng, LQ (通讯作者)，Beijing Normal Univ, Sch Educ Technol, Fac Educ, Beijing 10875, Peoples R China.
EM bnuzhenglq@bnu.edu.cn
OI Zheng, Lanqin/0000-0001-9378-5027; Zhen, Yuanyi/0000-0003-2712-1198
FU National Natural Science Foundation of China [61907003, 61807003];
   International Joint Research Project of the Faculty of Education, Huiyan
   International College, Beijing Normal University [ICER202101]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61907003 and Grant 61807003, and in part
   by the International Joint Research Project of the Faculty of Education,
   Huiyan International College, Beijing Normal University, under Grant
   ICER202101.
CR Abu-Salih B, 2021, J NETW COMPUT APPL, V185, DOI 10.1016/j.jnca.2021.103076
   Abu-Salih B, 2021, DATA MIN KNOWL DISC, V35, P1497, DOI 10.1007/s10618-021-00760-w
   Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Beck L, 2013, ACM T COMPUT EDUC, V13, DOI 10.1145/2492686
   Chan C.K.K., 2013, INT HDB COLLABORATIV, P93
   Chaplot D. S., 2016, EDM, P318
   Chen PH, 2018, IEEE ACCESS, V6, P31553, DOI 10.1109/ACCESS.2018.2839607
   Chung J., 2014, CORR
   Denner J, 2014, J RES TECHNOL EDUC, V46, P277, DOI 10.1080/15391523.2014.888272
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Gupta S, 2014, CONLL, P98, DOI DOI 10.3115/V1/W14-1611
   Hadi W, 2018, APPL SOFT COMPUT, V69, P344, DOI 10.1016/j.asoc.2018.04.056
   Han Aaron L.-F, 2013, Language Processing and Intelligent Information Systems. 20th International Conference, IIS 2013. Proceedings. LNCS 7912, P57, DOI 10.1007/978-3-642-38634-3_8
   Harris J., 1999, LEARNING LEADING TEC, V27, P54
   Hmelo-Silver CE, 2003, COMPUT EDUC, V41, P397, DOI 10.1016/j.compedu.2003.07.001
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hogan A., 2020, ARXIV PREPRINT 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
   Huang Z, 2015, ARXIV150801991
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Lafferty J., 2001, P 18 INT C MACH LEAR
   Leaman R, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/1758-2946-7-S1-S3
   Ling Xiao, 2012, P AAAI C ART INT, V12, P94
   Liu HX, 2016, J ARTIF INTELL RES, V55, P1059, DOI 10.1613/jair.5002
   Liu QT, 2018, IEEE T LEARN TECHNOL, V11, P243, DOI 10.1109/TLT.2017.2708115
   Lu OHT, 2017, INTERACT LEARN ENVIR, V25, P220, DOI 10.1080/10494820.2016.1278391
   Lu Y, 2020, FRONT COMPUT SCI-CHI, V14, DOI 10.1007/s11704-019-9023-2
   Mladenovic S, 2016, INT J ENG EDUC, V32, P521
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   Nakashole  N., 2013, M ASS COMP LING
   Noroozi O, 2019, COMPUT HUM BEHAV, V100, P298, DOI 10.1016/j.chb.2018.12.019
   Nosek JT, 1998, COMMUN ACM, V41, P105, DOI 10.1145/272287.272333
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Redmond P., 2006, Internet and Higher Education, V9, P267, DOI 10.1016/j.iheduc.2006.08.003
   Reeves TC, 2004, ETR&D-EDUC TECH RES, V52, P53, DOI 10.1007/BF02504718
   Roller S, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P358
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Schneider E. W, 1972, ADIS
   Shaffer D. W., 2016, J LEARN ANAL, V3, P9, DOI [10.18608/jla.2016.33.3, DOI 10.18608/JLA.2016.33.3]
   Singhal Amit, 2012, OFFICIAL GOOGLE BLOG
   Sobocinski M, 2017, METACOGN LEARN, V12, P275, DOI 10.1007/s11409-016-9167-5
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Vaswani A, 2017, ADV NEUR IN, V30
   VITERBI AJ, 1967, IEEE T INFORM THEORY, V13, P260, DOI 10.1109/TIT.1967.1054010
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang XM, 2017, ETR&D-EDUC TECH RES, V65, P1655, DOI 10.1007/s11423-017-9551-0
   Wu B, 2019, J COMPUT ASSIST LEAR, V35, P421, DOI 10.1111/jcal.12348
   Wu JY, 2020, INTERACT LEARN ENVIR, V28, P65, DOI 10.1080/10494820.2018.1515085
   Xiang Y, 2020, EXP SYST APPL, V147, P1
   Xie K, 2018, INTERNET HIGH EDUC, V38, P9, DOI 10.1016/j.iheduc.2018.04.002
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Xu W., 2013, P 51 ANN M ASS COMP, P665
   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 K. C, 2016, GUIDELINES INSTRUCTI, P55
   Yogatama 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, P291
   Yu P. S., ARXIV200200388
   Zhang N, 2021, ASIA PAC J EDUC, DOI 10.1080/02188791.2020.1866493
   ZHENG L, IN PRESS, DOI DOI 10.1080/02602938.2021.1883549
NR 59
TC 0
Z9 0
U1 23
U2 35
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 117969
EP 117980
DI 10.1109/ACCESS.2021.3106324
PG 12
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 UK7VD
UT WOS:000692173200001
OA gold
DA 2022-08-23
ER

PT J
AU Li, QJ
   Li, F
   Li, SC
   Li, XY
   Liu, K
   Liu, Q
   Dong, PC
AF Li, Qijia
   Li, Feng
   Li, Shuchao
   Li, Xiaoyu
   Liu, Kang
   Liu, Qing
   Dong, Pengcheng
TI Improving Entity Linking by Introducing Knowledge Graph Structure
   Information
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE entity linking; knowledge graph; entity embedding; global model
AB Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most of the current methods are a combination of local and global models. The local model uses the local context information around the entity mention to independently resolve the ambiguity of each entity mention. The global model encourages thematic consistency across the target entities of all mentions in the document. However, the known global models calculate the correlation between entities from a semantic perspective, ignoring the correlation information between entities in nature. In this paper, we introduce knowledge graphs to enrich the correlation information between entities and propose an entity linking model that introduces the structural information of the knowledge graph (KGEL). The model can fully consider the relations between entities. To prove the importance of the knowledge graph structure, extensive experiments are conducted on multiple public datasets. Results illustrate that our model outperforms the baseline and achieves superior performance.
C1 [Li, Qijia; Li, Feng; Li, Shuchao; Li, Xiaoyu; Liu, Kang; Liu, Qing; Dong, Pengcheng] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China.
   [Li, Qijia; Li, Feng; Li, Shuchao; Li, Xiaoyu; Liu, Kang; Liu, Qing; Dong, Pengcheng] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Network Informat Syst Technol NIST, Beijing 100190, Peoples R China.
   [Li, Qijia] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100190, Peoples R China.
   [Li, Feng] Chinese Acad Sci, Aerosp Informat Res Inst, QILU Res Inst, Jinan 250000, Peoples R China.
RP Li, F (通讯作者)，Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China.; Li, F (通讯作者)，Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Network Informat Syst Technol NIST, Beijing 100190, Peoples R China.; Li, F (通讯作者)，Chinese Acad Sci, Aerosp Informat Res Inst, QILU Res Inst, Jinan 250000, Peoples R China.
EM liqijia19@mails.ucas.ac.cn; lifeng@mail.ie.ac.cn; lisc@aircas.ac.cn;
   lixy01@aircas.ac.cn; lkwnsh615@163.com; liuqing1@aircas.ac.cn;
   dongpc@aircas.ac.cn
OI Li, Qijia/0000-0003-1669-6172
FU Strategic Priority Research Program of the Chinese Academy of Sciences
   [Y835120378]
FX FundingThis research was funded by the Strategic Priority Research
   Program of the Chinese Academy of Sciences (grant number Y835120378).
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A, 2013, ADV NEURAL INF PROCE, V26, P9
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Cao ZS, 2021, AAAI CONF ARTIF INTE, V35, P6894
   Cetoli A., 2018, ARXIV181009164
   Chao L., 2020, ARXIV201103798
   Cheng X., 2013, P 2013 C EMP METH NA, P1787
   Chung J., 2014, CORR
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Eshel Y, 2017, P 21 C COMP NAT LANG, P58, DOI DOI 10.18653/V1/K17-1008
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   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
   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
   Ganea OE, 2017, P 2017 C EMP METH NA, P2619
   Guo ZC, 2018, SEMANT WEB, V9, P459, DOI 10.3233/SW-170273
   Gupta Nitish, 2017, P C EMP METH NAT LAN
   He Zhengyan, 2013, P 51 ANN M ASS COMP
   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
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Hou F., 2021, ARXIV210608495
   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
   Kolitsas Nikolaos, 2018, P 22 C COMP NAT LANG, P519
   Lafferty J., 2001, P 18 INT C MACH LEAR
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Ling J., 2020, ARXIV200103765
   Luo AG, 2018, PROCEDIA COMPUT SCI, V129, P110, DOI 10.1016/j.procs.2018.03.057
   Michelson M., 2010, P 4 WORKSH AN NOIS U, DOI DOI 10.1145/1871840.1871852
   Mulang I.O., 2019, ARXIV191206214
   Murphy K., 2013, ARXIV PREPRINT ARXIV
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Pershina M., 2015, P 2015 C N AM CHAPT, P238, DOI [DOI 10.3115/V1/N15-1026, 10.3115/v1/N15-1026]
   Le P, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1595
   Ratinov L., 2011, COMPUT LINGUIST, V1, P1375
   Suchanek FM, 2007, WWW
   Sun Y, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1333
   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
   Wu JS, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P1149, DOI 10.1145/3366423.3380192
   Yamada I., 2017, T ASS COMPUTATIONAL, V5, P397
   Yamada I., 2019, ARXIV190900426
   Yamada Ikuya, 2016, P 20 SIGNLL C COMP N, P250
   Yang X., 2019, ARXIV190902117
   Yih S.W.t., 2015, P JOINT C 53 ANN M A
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
   Zhao Y., 2021, P FIND ASS COMP LING, P1341
NR 51
TC 1
Z9 1
U1 7
U2 7
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 MAR
PY 2022
VL 12
IS 5
AR 2702
DI 10.3390/app12052702
PG 18
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA ZS2DJ
UT WOS:000768280400001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, YX
   Xu, XL
   Hong, QF
   Jin, JH
   Wu, TX
AF Wang, Yuxiang
   Xu, Xiaoliang
   Hong, Qifan
   Jin, Jiahui
   Wu, Tianxing
TI Top-k star queries on knowledge graphs through semantic-aware bounding
   match scores
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Top-k star query; Bounding match score; Semantic similarity
ID SIMILARITY; SEARCH; SCALE; BASE
AB Large-scale knowledge graphs containing millions of entities are very common nowadays. Querying knowledge graphs is essential for a wide range of emerging applications, e.g., question answering and semantic search. A star query aims to identify an entity by giving a set of related entities, which is an important query type on knowledge graphs. Answering star queries can be modeled as a graph query problem. Given a query graph Q, the graph query finds subgraphs in a knowledge graph G that match Q. We face two challenges on graph query: (1) existing graph query methods usually find subgraphs that are structurally similar to Q, which cannot measure whether a subgraph match satisfies the semantics of Q (i.e., real query intention), leading to an effectiveness issue, and (2) querying a largescale knowledge graph is usually time-consuming because of the large search space. In this paper, we propose a Top -k semantic-aware graph query method over knowledge graphs for star queries, which provides semantically similar matches for Q instead of structurally similar matches. The semantic similarity of a match to Q is measured by an online computed bounding match score. By using bounds, we can efficiently prune the unpromising matches with lower semantic similarities without evaluating all matches. Extensive experiments over three real-world knowledge graphs confirm the effectiveness and efficiency of our solution. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Wang, Yuxiang; Xu, Xiaoliang; Hong, Qifan] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou, Zhejiang, Peoples R China.
   [Jin, Jiahui; Wu, Tianxing] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China.
RP Xu, XL (通讯作者)，Room 401,Bldg 1, Hangzhou 310018, Zhejiang, Peoples R China.
EM lsswyx@hdu.edu.cn; xxl@hdu.edu.cn; qfhong@hdu.edu.cn; jjin@seu.edu.cn;
   wutianxing@seu.edu.cn
FU National key Research & Development Program [2017YFC0820503]; National
   Natural Science Foundation of China [62072149, 62072099]; Primary
   Research & Development Plan of Zhejiang Province [2021C03156]; Public
   Welfare Research Program of Zhejiang [LGG19F020017]; Natural Science
   Foundation of Zhejiang [LY19F030021]
FX This work was supported by the National key Research & Development
   Program under grants 2017YFC0820503, the National Natural Science
   Foundation of China under grants 62072149 and 62072099; the Primary
   Research & Development Plan of Zhejiang Province under grant 2021C03156;
   the Public Welfare Research Program of Zhejiang under grant
   LGG19F020017; and the Natural Science Foundation of Zhejiang under grant
   LY19F030021.
CR Arias M., ARXIV11035043
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Carbone P., 2015, IEEE DATA ENG B, V36, P28, DOI DOI 10.1109/IC2EW.2016.56
   Cheng JF, 2013, PROC INT CONF DATA, P1033, DOI 10.1109/ICDE.2013.6544895
   Cheng JF, 2008, PROC INT CONF DATA, P913, DOI 10.1109/ICDE.2008.4497500
   CHURCH KW, 1990, 27TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P76
   Cohen J., 1988, STAT POWER ANAL BEHA
   Fan WF, 2010, PROC VLDB ENDOW, V3, P1161, DOI 10.14778/1920841.1920986
   Gligorov R., 2007, P 16 INT C WORLD WID, P767, DOI [10.1145/1242572.1242676., DOI 10.1145/1242572.1242676]
   Guha R., 2003, P 12 INT C WORLD WID, P700, DOI DOI 10.1145/775152.775250
   Guo S, 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, P84
   Han S, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P227, DOI 10.1145/3132847.3132957
   He H., 2007, SIGMOD, P305, DOI DOI 10.1145/1247480.1247516
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Hu S., 2018, EMNLP, P2098
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   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
   Jiang J, 1997, INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, 1997 DIGEST OF TECHNICAL PAPERS, P94
   Jin JH, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P527, DOI 10.1145/2736277.2741131
   Jin JH, 2017, IEEE T PARALL DISTR, V28, P2342, DOI 10.1109/TPDS.2017.2665478
   Jin JH, 2014, INT C PAR DISTRIB SY, P9, DOI 10.1109/PADSW.2014.7097785
   Khan A., 2011, SIGMOD, P901
   Khan A, 2013, PROC VLDB ENDOW, V6, P181, DOI 10.14778/2535569.2448952
   Leacock Claudia, 1998, WORLDNET ELECT LEXIC, V24, P165, DOI 10.7551/mitpress/7287.003.0018
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li J, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1677, DOI 10.1145/3132847.3132992
   Lin D, 1998, ICML, V98
   Mikolov T., 2013, ADV NEURAL INFORM PR, V2, P3111, DOI DOI 10.1162/JMLR.2003.3.4-5.951
   Mottin D, 2016, VLDB J, V25, P741, DOI 10.1007/s00778-016-0429-2
   Nakashole  N., 2013, M ASS COMP LING
   Nakashole N., 2012, P 2012 JOINT C EMP M, P1135, DOI DOI 10.1080/17517575.2015.1080301
   Namaki MH, 2019, INT CONF MANAGE DATA, P1481, DOI 10.1145/3299869.3319890
   Namaki MH, 2018, INT CONF MANAGE DATA, P1729, DOI 10.1145/3183713.3193550
   Neumann T, 2008, PROC VLDB ENDOW, V1, P647, DOI 10.14778/1453856.1453927
   Peng P, 2019, PROC INT CONF DATA, P112, DOI 10.1109/ICDE.2019.00019
   Pennington J., 2014, EMNLP, P1532
   RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528
   Resnik P, 1999, J ARTIF INTELL RES, V11, P95, DOI 10.1613/jair.514
   Sadoghi M., 2018, EDBT, P433
   Shi Y., 2012, CIKM, P1223, DOI DOI 10.1145/2396761.2398423
   Song Q, 2019, PROC INT CONF DATA, P40, DOI 10.1109/ICDE.2019.00013
   Tong HH, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P737
   Unger C., 2014, CEUR WORKSHOP P, P1172
   Wang Z., 2014, AAAI, P1112
   Wu YH, 2013, PROC INT CONF DATA, P697, DOI 10.1109/ICDE.2013.6544867
   Yang S, 2012, SIGMOD
   Yang S, 2016, PROC INT CONF DATA, P990, DOI 10.1109/ICDE.2016.7498307
   Yang SQ, 2014, PROC VLDB ENDOW, V7, P565, DOI 10.14778/2732286.2732293
   Zaharia M., 2010, HOTCLOUD, V10, P95
   Zeng Z., 2009, PROC VLDB ENDOW, V2, P25, DOI [DOI 10.14778/1687627.1687631, 10.14778/1687627.1687631]
   Zhang XB, 2018, INT CONF MANAGE DATA, P1753, DOI 10.1145/3183713.3193555
   Zheng WG, 2018, PROC VLDB ENDOW, V11, P1373, DOI 10.14778/3236187.3236192
   Zheng WG, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P217, DOI 10.1145/3132847.3132977
   Zheng WG, 2016, PROC VLDB ENDOW, V9, P840, DOI 10.14778/2983200.2983201
   Zheng WG, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1595, DOI 10.1145/2505515.2505723
   Zhu GG, 2017, IEEE T KNOWL DATA EN, V29, P72, DOI 10.1109/TKDE.2016.2610428
   ZOU L, 2009, PROC VLDB ENDOW, V2, P886, DOI DOI 10.14778/1687627.1687727
   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 62
TC 1
Z9 1
U1 6
U2 25
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 2021
VL 213
AR 106655
DI 10.1016/j.knosys.2020.106655
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QC2CY
UT WOS:000614643400017
DA 2022-08-23
ER

PT J
AU Zins, C
AF Zins, Chaim
TI Classification schemes of information science: Twenty-eight scholars map
   the field
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
AB The field of Information Science is constantly changing. Therefore, information scientists are required to regularly review-and if necessary-redefine its fundamental building blocks. This article is one of a group of four articles, which resulted from a Critical Delphi study conducted in 2003-2005 (Zins, 2007a, 2007b, 2007c). The study, "Knowledge Map of Information Science," was aimed at exploring the foundations of information science. The international panel was composed of 57 leading scholars from 16 countries who represent nearly all the major subfields and important aspects of the field. This particular article documents 28 classification schemes of Information Science that were compiled by leading scholars in the academic community. This unique collection of 28 classification schemes portrays and documents the profile of contemporary Information Science at the beginning of the 21st century.
C1 Knowledge Mapping Res, IL-97852 Jerusalem, Israel.
RP Zins, C (通讯作者)，Knowledge Mapping Res, 26 Hahaganah St, IL-97852 Jerusalem, Israel.
EM chaim.zins@gmail.com
CR BLOOM B, 1956, COGNITIVE DOMANIN HD, V1
   BLOOM B, 1956, COGNITIVE DOMANIN HD, V2
   DEBONS A, 1988, INFORMATION SCI INTE
   DRAGULANESCU N, 1999, INFORMATION SCI TECN
   Drucker P. F., 2001, ESSENTIAL DRUCKER
   Engelbart DC, 1962, AFOSR3223
   MILLER JG, 1978, LIVING ORGANISM
   MILLER JG, 1995, LIVING SYSTEMS, pR13
   MILLER JG, 1978, LIVING ORG
   MILSTEAD JL, 1998, ASIS THESAURUS INFOR
   NEWELL S, 1972, HUMAN PROBLEM SOLVIN
   PENROSE R, 1994, SHADOWS MIND SEARCH
   Zins C, 2004, KNOWL ORGAN, V31, P49
   Zins C, 2007, J AM SOC INF SCI TEC, V58, P335, DOI 10.1002/asi.20507
   Zins C, 2007, J AM SOC INF SCI TEC, V58, P526, DOI 10.1002/asi.20505
   Zins C, 2007, J AM SOC INF SCI TEC, V58, P479, DOI 10.1002/asi.20508
   Zins C, 2006, J DOC, V62, P447, DOI 10.1108/00220410610673846
NR 17
TC 37
Z9 42
U1 0
U2 26
PU JOHN WILEY & SONS INC
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN, NJ 07030 USA
SN 1532-2882
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD MAR
PY 2007
VL 58
IS 5
BP 645
EP 672
DI 10.1002/asi.20506
PG 28
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 166LG
UT WOS:000246379700003
DA 2022-08-23
ER

PT J
AU Jiang, ST
   Liu, YL
   Zhang, YC
   Luo, P
   Cao, K
   Xiong, J
   Zhao, HT
   Wei, JB
AF Jiang, Shengteng
   Liu, Yueling
   Zhang, Yichi
   Luo, Peng
   Cao, Kuo
   Xiong, Jun
   Zhao, Haitao
   Wei, Jibo
TI Reliable Semantic Communication System Enabled by Knowledge Graph
SO ENTROPY
LA English
DT Article
DE semantic communication; knowledge graph; semantic extraction; semantic
   restoration
AB Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.
C1 [Jiang, Shengteng; Liu, Yueling; Zhang, Yichi; Luo, Peng; Cao, Kuo; Xiong, Jun; Zhao, Haitao; Wei, Jibo] Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Peoples R China.
RP Liu, YL; Cao, K (通讯作者)，Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Peoples R China.
EM jiangshengteng@nudt.edu.cn; liuyueling16@nudt.edu.cn;
   zhangyichi13@nudt.edu.cn; pengluo.eric@outlook.com;
   caokuo18@nudt.edu.cn; xj8765@nudt.edu.cn; haitaozhao@nudt.edu.cn;
   wjbhw@nudt.edu.cn
OI Liu, Yueling/0000-0003-4468-6860
FU National Natural Science Foundation of China [61931020, U19B2024,
   62001483]; science and technology innovation Program of Hunan Province
   [2021JJ40690]
FX This research was funded in part by the National Natural Science
   Foundation of China under grant nos. 61931020, U19B2024, and 62001483,
   and in part by the science and technology innovation Program of Hunan
   Province under grant no. 2021JJ40690.
CR Agirre Eneko, 2016, P 10 INT WORKSHOP SE, P497, DOI DOI 10.18653/V1/S16-1081
   Atanasov I., 2020, P 2020 11 NATL C INT, P1
   Mosa MA, 2021, APPL ARTIF INTELL, V35, P933, DOI 10.1080/08839514.2021.1966883
   Banerjee S., 2007, P 2 WORKSH STAT MACH, P228
   Bar-Hillel Y., 1954, J SYMBOLIC LOGIC, V19, P230
   Basu P., 2012, 2012 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), P58, DOI 10.1109/PerComW.2012.6197583
   Devlin J, 2018, ABS181004805 ARXIV
   Farsad N, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P2326, DOI 10.1109/ICASSP.2018.8461983
   Floridi L, 2004, MIND MACH, V14, P197, DOI 10.1023/B:MIND.0000021684.50925.c9
   GALLAGER RG, 1962, IRE T INFORM THEOR, V8, P21, DOI 10.1109/tit.1962.1057683
   Gardent C, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P179, DOI 10.18653/v1/P17-1017
   Greff K, 2017, IEEE T NEUR NET LEAR, V28, P2222, DOI 10.1109/TNNLS.2016.2582924
   Guler B, 2018, IEEE T COGN COMMUN, V4, P787, DOI 10.1109/TCCN.2018.2872596
   Hu Q., 2022, ARXIV
   HUFFMAN DA, 1952, P IRE, V40, P1098, DOI 10.1109/JRPROC.1952.273898
   International Telecommunication Union, 2020, REPORT IMPLEMENTATIO
   Jaradeh MY, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P243, DOI 10.1145/3360901.3364435
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Jie Bao, 2011, 2011 IEEE First International Network Science Workshop (NSW 2011), P110, DOI 10.1109/NSW.2011.6004632
   Khan LU, 2022, IEEE COMMUN MAG, V60, P74, DOI 10.1109/MCOM.001.21143
   Kilgarriff A, 2000, LANGUAGE, V76, P706, DOI 10.2307/417141
   Lan Q., 2021, ARXIV
   Liu YL, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11091358
   Lu K., 2021, ARXIV
   Mathur N., 2020, ARXIV
   Papineni K, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P311, DOI 10.3115/1073083.1073135
   Popovski P, 2020, J INDIAN I SCI, V100, P435, DOI 10.1007/s41745-020-00165-6
   Qi P, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020): SYSTEM DEMONSTRATIONS, P101
   Rao M, 2018, IEEE INT WORK SIGN P, P81
   Sah D.K., 2019, 5G ENABLED SECURE WI, P45
   SHANNON CE, 1948, BELL SYST TECH J, V27, P379, DOI 10.1002/j.1538-7305.1948.tb01338.x
   Shi G.M., 2021, ARXIV
   Shi GM, 2021, IEEE COMMUN MAG, V59, P44, DOI 10.1109/MCOM.001.2001239
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Weaver W, 1953, ET CETERA, V10, P261
   Weng Z.Z., 2021, P ICC 2021 IEEE INT
   Weng ZZ, 2021, IEEE J SEL AREA COMM, V39, P2434, DOI 10.1109/JSAC.2021.3087240
   Xie H.Q., 2021, ARXIV, DOI [10.1109/LWC.2021.3136045, DOI 10.1109/LWC.2021.3136045]
   Xie HQ, 2021, IEEE T SIGNAL PROCES, V69, P2663, DOI 10.1109/TSP.2021.3071210
   Xie HQ, 2021, IEEE J SEL AREA COMM, V39, P142, DOI 10.1109/JSAC.2020.3036968
   Zhang P, 2022, ENGINEERING-PRC, V8, P60, DOI 10.1016/j.eng.2021.11.003
   Zhang Y., 2021, SCI SIN INFORM, DOI [10.1360/SSI-2020-0379, DOI 10.1360/SSI-2020-0379]
   Zhou F.H., 2022, ARXIV
   Zhou QY, 2022, IEEE WIREL COMMUN LE, V11, P453, DOI 10.1109/LWC.2021.3132067
NR 45
TC 0
Z9 0
U1 12
U2 12
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD JUN
PY 2022
VL 24
IS 6
AR 846
DI 10.3390/e24060846
PG 18
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA 2K2FZ
UT WOS:000816159700001
PM 35741567
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhou, ZH
   Wang, C
   Feng, Y
   Chen, DF
AF Zhou, Zhehui
   Wang, Can
   Feng, Yan
   Chen, Defang
TI JointE: Jointly utilizing 1D and 2D convolution for knowledge graph
   embedding
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; Knowledge graph; Convolution networks
AB Knowledge graph embedding is a popular method to predict missing links for knowledge graphs by projecting entities and relations into continuous low-dimension embeddings. Some recent embedding models employ translation-based operations to learn the representations of entities and relations with shallow and linear structures, and others leverage neural networks, especially convolution neural networks, to embed the entities and relations with deep and non-linear structures. However, shallow and linear models limit the extraction capacity of the latent knowledge while deep and non-linear models lead to the overabundance of parameters and the loss of surface and explicit knowledge. In this paper, we propose JointE, which utilizes 1D and 2D convolution operations jointly to alleviate these issues effectively. More specifically, we utilize 2D convolution operations to facilitate the interactions between entities and relations, thereby capturing the latent knowledge sufficiently. To reduce the number of parameters significantly, we innovatively construct 2D convolution filters from internal embeddings rather than using external filters which costs plenty of redundant parameters. Furthermore, we appropriately employ 1D convolution filters over input embeddings to extract the surface and explicit knowledge and preserve it by element-wise addition. Experimental evaluation on five benchmark datasets demonstrates that our model outperforms all other state-of-the-art convolution-based models and simultaneously enhances the parameter efficiency. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Zhou, Zhehui; Wang, Can; Feng, Yan; Chen, Defang] Zhejiang Univ, Hangzhou 310027, Peoples R China.
RP Feng, Y (通讯作者)，Zhejiang Univ, Hangzhou 310027, Peoples R China.
EM fengyan@zju.edu.cn
RI Chen, Defang/ABD-8107-2021
OI Chen, Defang/0000-0003-0833-7401
FU National Key R&D Program of China [2019YFB1600700]; National Natural
   Science Foun-dation of China [U1866602]
FX Acknowledgments This work is supported by National Key R&D Program of
   China (Grant No: 2019YFB1600700) and National Natural Science
   Foun-dation of China (Grant No: U1866602) .
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Balazevic I, 2019, LECT NOTES COMPUT SC, V11731, P553, DOI 10.1007/978-3-030-30493-5_52
   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
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Carlson Andrew, 2010, P 24 C ART INT AAAI
   Dettmers T., 2018, CONVOLUTIONAL 2D KNO, P1811
   Ding BY, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P110
   Ioffe S., 2015, P INT C MACH LEARN L, VVolume 37, P448, DOI DOI 10.1007/S13398-014-0173-7.2
   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 D, 2021, KNOWL-BASED SYST, V227, DOI 10.1016/j.knosys.2021.107188
   Jiang X, 2019, P 2019 C N AM CHAPTE, P978
   Kingma D. P., 2017, ARXIV
   Li Z, 2021, KNOWL-BASED SYST, V212, DOI 10.1016/j.knosys.2020.106618
   Lin X.V., 2018, ARXIV180810568
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lv X, 2018, EMNLP, P1971
   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
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Ren F., 2020, 2020 28 INT C COMP L, P1532
   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 Zhiqing, 2019, ICLR POSTER
   Szegedy C, 2016, P IEEE C COMP VIS PA, P2818
   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, 2020, AAAI CONF ARTIF INTE, V34, P3009
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z., 2014, AAAI, P1112
   Xu WT, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3888
   Yang B., 2015, ICLR POSTER
   Zhang FX, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2987
   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 Q., KNOWL-BASED SYST, V212
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
NR 36
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 MAR 15
PY 2022
VL 240
AR 108100
DI 10.1016/j.knosys.2021.108100
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1A8JC
UT WOS:000791994900015
DA 2022-08-23
ER

PT J
AU Nayyeri, M
   Cil, GM
   Vahdati, S
   Osborne, F
   Kravchenko, A
   Angioni, S
   Salatino, A
   Recupero, DR
   Motta, E
   Lehmann, J
AF Nayyeri, Mojtaba
   Cil, Goekce Muege
   Vahdati, Sahar
   Osborne, Francesco
   Kravchenko, Andrey
   Angioni, Simone
   Salatino, Angelo
   Recupero, Diego Reforgiato
   Motta, Enrico
   Lehmann, Jens
TI Link Prediction of Weighted Triples for Knowledge Graph Completion
   Within the Scholarly Domain
SO IEEE ACCESS
LA English
DT Article
DE Computational modeling; Predictive models; Training; Semantics;
   Numerical models; Uncertainty; Industries; Scholarly data; knowledge
   graphs; knowledge graph embeddings; loss functions; link prediction;
   scholarly communication; science of science
ID NETWORKS
AB Knowledge graphs (KGs) are widely used for modeling scholarly communication, performing scientometric analyses, and supporting a variety of intelligent services to explore the literature and predict research dynamics. However, they often suffer from incompleteness (e.g., missing affiliations, references, research topics), leading to a reduced scope and quality of the resulting analyses. This issue is usually tackled by computing knowledge graph embeddings (KGEs) and applying link prediction techniques. However, only a few KGE models are capable of taking weights of facts in the knowledge graph into account. Such weights can have different meanings, e.g. describe the degree of association or the degree of truth of a certain triple. In this paper, we propose the Weighted Triple Loss, a new loss function for KGE models that takes full advantage of the additional numerical weights on facts and it is even tolerant to incorrect weights. We also extend the Rule Loss, a loss function that is able to exploit a set of logical rules, in order to work with weighted triples. The evaluation of our solutions on several knowledge graphs indicates significant performance improvements with respect to the state of the art. Our main use case is the large-scale AIDA knowledge graph, which describes 21 million research articles. Our approach enables to complete information about affiliation types, countries, and research topics, greatly improving the scope of the resulting scientometrics analyses and providing better support to systems for monitoring and predicting research dynamics.
C1 [Nayyeri, Mojtaba; Cil, Goekce Muege; Lehmann, Jens] Univ Bonn, SDA Res Grp, D-53115 Bonn, Germany.
   [Nayyeri, Mojtaba; Vahdati, Sahar] Inst Appl Informat InfAI, Nat Inspired Machine Intelligence, D-01069 Dresden, Germany.
   [Osborne, Francesco; Salatino, Angelo; Motta, Enrico] Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England.
   [Kravchenko, Andrey] Univ Oxford, Christ Church, Oxford OX1 1DP, England.
   [Angioni, Simone; Recupero, Diego Reforgiato] Univ Cagliari, Dept Math & Comp Sci, I-09124 Cagliari, Italy.
   [Lehmann, Jens] Fraunhofer IAIS, D-53757 Dresden, Germany.
RP Salatino, A (通讯作者)，Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England.
EM angelo.salatino@open.ac.uk
RI Salatino, Angelo A./AAD-7423-2022
OI Salatino, Angelo A./0000-0002-4763-3943; Angioni,
   Simone/0000-0002-6682-3419; Motta, Enrico/0000-0003-0015-1952; Lehmann,
   Jens/0000-0001-9108-4278; Reforgiato Recupero, Diego/0000-0001-8646-6183
FU project Speaker under Grant BMWi FKZ [01MK20011A]; project Joseph
   (Fraunhofer Zukunftsstiftung)-Cleopatra [GA 812997]; project Excellence
   Clusters ML2R [BmBF FKZ 01 15 18038 A/B/C]; project MLwin [01IS18050];
   ScaDS.AI [IS18026A-F]; project Tailor under Grant EU [952215]; project
   H2020-EU PLATOON [872592]
FX This work was supported in part by the project Speaker under Grant BMWi
   FKZ 01MK20011A, in part by the project Joseph (Fraunhofer
   Zukunftsstiftung)-Cleopatra under Grant GA 812997, in part by the
   project Excellence Clusters ML2R under Grant BmBF FKZ 01 15 18038 A/B/C,
   in part by the project MLwin under Grant 01IS18050, in part by ScaDS.AI
   under Grant IS18026A-F, in part by the project Tailor under Grant EU GA
   952215, and in part by the project H2020-EU PLATOON under Grant 872592.
CR Angioni S., 2020, P ADBIS EDA COMM WOR, P219
   Ankrah S, 2015, SCAND J MANAG, V31, P387, DOI 10.1016/j.scaman.2015.02.003
   Awad M., 2015, NEURAL INFORM PROCES, P67, DOI DOI 10.1007/978-1-4302-5990-9_4
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Brainard J., 2020, SCIENCE, V13, P1126, DOI [DOI 10.1126/SCIENCE.ABC7839, 10.1126/science.abc7839]
   Candan, 2011, P INT C WEB INT MIN, P1
   Cedeno, 2010, THESIS ARIZ STATE
   Chekol MW, 2017, AAAI CONF ARTIF INTE, P88
   Chen XL, 2019, AAAI CONF ARTIF INTE, P3363
   Dai YF, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9050750
   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
   Fortunato S, 2018, SCIENCE, V359, DOI 10.1126/science.aao0185
   Galarraga L, 2015, VLDB J, V24, P707, DOI 10.1007/s00778-015-0394-1
   Groth Paul, 2010, Information Services & Use, V30, P51, DOI 10.3233/ISU-2010-0613
   Gunawardana A, 2009, J MACH LEARN RES, V10, P2935
   Hogan A., 2020, ARXIV PREPRINT ARXIV
   Hogan A., 2018, P VOILA ISWC
   Jaradeh MY, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P243, DOI 10.1145/3360901.3364435
   Kim J, 2018, SCIENTOMETRICS, V116, P1867, DOI 10.1007/s11192-018-2824-5
   Kuhn T, 2016, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.78
   LACROIX T, 2018, P ICML, P2863
   Lehmann J., 2019, RECNLP WORKSH AAAI W
   Liu, ARXIV190410281
   Liu, ARXIV190900672
   Liu SL, 2012, PROC INT CONF DATA, P1313, DOI 10.1109/ICDE.2012.134
   Lu LY, 2011, PHYSICA A, V390, P1150, DOI 10.1016/j.physa.2010.11.027
   Lu LY, 2010, EPL-EUROPHYS LETT, V89, DOI 10.1209/0295-5075/89/18001
   Mannocci A., 2019, DATA SCI, V2, P181
   Mitchell T, 2018, COMMUN ACM, V61, P103, DOI 10.1145/3191513
   Mladenicent, 2016, SEMANT WEB, V9989, DOI [10.1007/978-3, DOI 10.1007/978-3]
   Moed HF, 2013, SCIENTOMETRICS, V94, P929, DOI 10.1007/s11192-012-0783-9
   Motta, 2020, P 22 INT C KNOWL ENG, P219
   Nayyeri Mojtaba, 2020, Semantic Web 17th International Conference, ESWC 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12123), P255, DOI 10.1007/978-3-030-49461-2_15
   Nayyeri M., 2019, ARXIV PREPRINT ARXIV
   Nayyeri M., 2019, ARXIV190807141
   Nayyeri M, 2021, NEUROCOMPUTING, V461, P530, DOI 10.1016/j.neucom.2021.02.100
   Nayyeri M, 2021, AAAI CONF ARTIF INTE, V35, P9064
   Nayyeri M, 2020, IEEE IJCNN
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Peroni S, 2020, QUANT SCI STUD, V1, P428, DOI 10.1162/qss_a_00023
   Peroni S, 2018, LECT NOTES COMPUT SC, V11137, P119, DOI 10.1007/978-3-030-00668-6_8
   Salama AH, 2020, DRUG DELIV TRANSL RE, V10, P1, DOI 10.1007/s13346-019-00648-9
   Salatino AA, 2019, LECT NOTES COMPUT SC, V11799, P296, DOI 10.1007/978-3-030-30760-8_26
   Salatino AA, 2018, LECT NOTES COMPUT SC, V11137, P187, DOI 10.1007/978-3-030-00668-6_12
   Schneider J., 2014, 4 INT C LINKED SCI, V1282, P60
   Shang KK, 2019, EPL-EUROPHYS LETT, V127, DOI 10.1209/0295-5075/127/48002
   Shang KK, 2017, PHYSICA A, V474, P49, DOI 10.1016/j.physa.2017.01.066
   Shang KK, 2017, EPL-EUROPHYS LETT, V117, DOI 10.1209/0295-5075/117/28002
   Shotton D, 2009, LEARN PUBL, V22, P85, DOI 10.1087/2009202
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Stanovsky G, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P142
   Sun Z., 2019, 7 INT C LEARN REPR
   Szklarczyk D, 2017, NUCLEIC ACIDS RES, V45, pD362, DOI 10.1093/nar/gkw937
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Udrea O, 2006, LECT NOTES COMPUT SC, V4011, P487
   Udrea O, 2010, ACM T COMPUT LOG, V11, DOI 10.1145/1656242.1656245
   VAHDATI S, 2016, KNOWLEDGE, V24, DOI DOI 10.1007/978-3-319-49004-5_50
   Vahdati S, 2018, LECT NOTES COMPUT SC, V11057, P103, DOI 10.1007/978-3-030-00066-0_9
   Wang YQ, 2020, ACM COMPUT SURV, V53, DOI 10.1145/3386252
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Xu C., 2020, P 28 INT C COMP LING
   Yang B., 2014, ABS14126575 CORR
   Yao L, 2017, AAAI CONF ARTIF INTE, P3119
   Yu P. S., ARXIV200200388
   Zhang X, 2017, NAT COMMUN, V8, P1, DOI [10.1038/ncomms14542, 10.1038/s41467-017-01968-5]
   Zhou J., ARXIV181208434
   Zhou XF, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1009, DOI 10.1145/3132847.3132939
NR 67
TC 3
Z9 3
U1 5
U2 5
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 116002
EP 116014
DI 10.1109/ACCESS.2021.3105183
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA UI2EW
UT WOS:000690428300001
OA Green Submitted, Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Li, ZF
   Zhao, Y
   Zhang, Y
   Zhang, ZL
AF Li, Zhifei
   Zhao, Yue
   Zhang, Yan
   Zhang, Zhaoli
TI Multi-relational graph attention networks for knowledge graph completion
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Multi-relational learning; Knowledge graph completion; Graph neural
   network; Attention mechanism
ID NEURAL-NETWORKS
AB Knowledge graphs are multi-relational data that contain massive entities and relations. As an effective graph representation technique based on deep learning, graph neural network has reported outstand-ing performance for modeling knowledge graphs in recent studies. However, previous graph neural network-based models have not fully considered the heterogeneity of knowledge graphs. Furthermore, the attention mechanism has demonstrated its great potential in many areas. In this paper, a novel heterogeneous graph neural network framework based on a hierarchical attention mechanism is proposed, including entity-level, relation-level, and self-level attentions. Thus, the proposed model can selectively aggregate informative features and weights them adequately. Then the learned embeddings of entities and relations can be utilized for the downstream tasks. Extensive experimental results on various heterogeneous graph tasks demonstrate the superior performance of the proposed model compared to several state-of-the-art methods. (C) 2022 Elsevier B.V. All rights reserved.
C1 [Li, Zhifei; Zhao, Yue; Zhang, Yan] Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Peoples R China.
   [Zhang, Zhaoli] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.
RP Li, ZF; Zhang, Y (通讯作者)，Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Peoples R China.
EM zhifei1993@hubu.edu.cn; zhangyan@hubu.edu.cn
FU National Natural Science Foundation of China [61977021, 62101179,
   62077020]; Major Project of Hubei Province [2019ACA144]; School Project
   of Hubei University [202111903000001, 202011903000002]; China University
   Collaborative Innovation Fund [2020ITA05050]
FX The authors sincerely thank anonymous reviewers for their constructive
   comments, which helped improve this paper. This work was supported in
   part by the National Natural Science Foun- dation of China under Grant
   61977021, 62101179, 62077020 and Major Project of Hubei Province under
   Grant 2019ACA144 and School Project of Hubei University under Grant
   202111903000001, 202011903000002 and China University Collaborative
   Innovation Fund under Grant 2020ITA05050.
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Borrego A, 2021, ENG APPL ARTIF INTEL, V103, DOI 10.1016/j.engappai.2021.104302
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Chen J., 2018, PROC 2 INT C LEARN R
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Defferrard M, 2016, ADV NEUR IN, V29
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dubey M, 2018, LECT NOTES COMPUT SC, V11136, P108, DOI 10.1007/978-3-030-00671-6_7
   Dzmitry B., 2015, INT C LEARN REPR ICL
   Ebisu T, 2020, IEEE T KNOWL DATA EN, V32, P941, DOI 10.1109/TKDE.2019.2893920
   Hamilton WL, 2017, ADV NEUR IN, V30
   Hao Y., 2018, P 27 INT C COMP LING, P3272
   He H, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1766, DOI 10.18653/v1/P17-1162
   Hongwei Wang, 2019, ACM Transactions on Information Systems, V37, DOI 10.1145/3312738
   Hu J, 2018, PROC CVPR IEEE, P7132, DOI [10.1109/TPAMI.2019.2913372, 10.1109/CVPR.2018.00745]
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Huang XQ, 2021, KNOWL-BASED SYST, V229, DOI 10.1016/j.knosys.2021.107310
   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
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Kingma D. P., 2015, ADAM METHOD STOCHAST, DOI DOI 10.48550/ARXIV.1412.6980
   Kipf T.N., 2017, SEMISUPERVISED CLASS
   Lin Y., 2015, P C EMP METH NAT LAN
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Madotto A, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1468
   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
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Niepert M, 2016, PR MACH LEARN RES, V48
   Paszke A., 2017, NIPS 2017 WORKSH AUT
   Paulheim H., 2012, P 2 INT C WEB INT MI, P1
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9981, P498, DOI 10.1007/978-3-319-46523-4_30
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   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
   Shen Y., 2019, P 2019 SIAM INT C DA, P639
   Shervashidze N, 2011, J MACH LEARN RES, V12, P2539
   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
   Sun Z., 2018, P ICLR
   Sun Z, 2020, P 58 ANN M ASS COMP, P5516
   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
   Vashishth S, 2020, 8 INT C LEARN REPR
   Vashishth S, 2020, AAAI CONF ARTIF INTE, V34, P3009
   Vashishth S, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3308
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   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 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, 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
   Wu F, 2019, PR MACH LEARN RES, V97
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Xu K., 2019, PROC 7 INT C LEARN R
   Xu K., 2020, PROC 8 INT C LEARN R
   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., 2015, P INT C LEARN REPR
   Zhang MH, 2018, AAAI CONF ARTIF INTE, P4438
   Zheng JX, 2021, KNOWL-BASED SYST, V230, DOI 10.1016/j.knosys.2021.107402
   Zhou H, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4623
NR 69
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 109262
DI 10.1016/j.knosys.2022.109262
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3E6ET
UT WOS:000830076700010
DA 2022-08-23
ER

PT J
AU Liu, XY
   Tan, HB
   Chen, QH
   Lin, GY
AF Liu, Xiyang
   Tan, Huobin
   Chen, Qinghong
   Lin, Guangyan
TI RAGAT: Relation Aware Graph Attention Network for Knowledge Graph
   Completion
SO IEEE ACCESS
LA English
DT Article
DE Knowledge engineering; Graph neural networks; Predictive models;
   Decoding; Task analysis; Solid modeling; Licenses; Knowledge graph
   completion; knowledge graph embedding; graph attention networks
AB Knowledge graph completion (KGC) is the task of predicting missing links based on known triples for knowledge graphs. Several recent works suggest that Graph Neural Networks (GNN) that exploit graph structures achieve promising performance on KGC. These models learn information called messages from neighboring entities and relations and then aggregate messages to update central entity representations. The drawback of existing GNN based models lies in that they tend to treat relations equally and learn fixed network parameters, overlooking the distinction of each relational information. In this work, we propose a Relation Aware Graph ATtention network (RAGAT) that constructs separate message functions for different relations, which aims at exploiting the heterogeneous characteristics of knowledge graphs. Specifically, we introduce relation specific parameters to augment the expressive capability of message functions, which enables the model to extract relational information in parameter space. To validate the effect of relation aware mechanism, RAGAT is implemented with a variety of relation aware message functions. Experiments show RAGAT outperforms state-of-the-art link prediction baselines on standard FB15k-237 and WN18RR datasets.
C1 [Liu, Xiyang; Tan, Huobin; Chen, Qinghong; Lin, Guangyan] Beihang Univ, Sch Software, Beijing 100191, Peoples R China.
RP Tan, HB (通讯作者)，Beihang Univ, Sch Software, Beijing 100191, Peoples R China.
EM thbin@buaa.edu.cn
OI Liu, Xiyang/0000-0003-1120-6584; Tan, Huobin/0000-0003-3113-6552; Chen,
   Qinghong/0000-0001-7471-483X
FU National Key Research and Development Program of China [2018YFB1402600]
FX This work was supported by the National Key Research and Development
   Program of China under Grant 2018YFB1402600.
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, 2014, P 2014 C EMP METH NA, P615, DOI DOI 10.3115/V1/D14-1067
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cai L, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P131, DOI 10.1145/3360901.3364441
   Che FH, 2020, AAAI CONF ARTIF INTE, V34, P2774
   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
   Jiang X, 2019, P 2019 C N AM CHAPTE, P978
   Kingma D. P., 2017, ARXIV
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Li WD, 2019, IEEE ACCESS, V7, P157960, DOI 10.1109/ACCESS.2019.2950230
   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 Quan, 2016, ARXIV160307704V2
   Ma Y, 2015, INT CONF ACOUST SPEE, P5346, DOI 10.1109/ICASSP.2015.7178992
   Marcheggiani D, 2017, P 2017 C EMP METH NA, DOI DOI 10.18653/V1/D17-1159
   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., 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
   Paszke A, 2019, ADV NEURAL INF PROCE, P8026
   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
   Singh A, 2012, CASE-BASED NEUROLOGY, P1
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Stoica G, 2020, AAAI CONF ARTIF INTE, V34, P3000
   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
   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, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Yang B., 2015, P INT C LEARN REPR I
   Yang Y, P 58 ANN M ASS COMP, V2020, P5516
   Ye R, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4135
   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 W, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P96, DOI 10.1145/3289600.3291014
NR 45
TC 5
Z9 5
U1 11
U2 33
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 20840
EP 20849
DI 10.1109/ACCESS.2021.3055529
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA QE6BF
UT WOS:000616290200001
OA gold
DA 2022-08-23
ER

PT J
AU Shen, Y
   Yuan, KQ
   Dai, JC
   Tang, BZ
   Yang, M
   Lei, K
AF Shen, Ying
   Yuan, Kaiqi
   Dai, Jingchao
   Tang, Buzhou
   Yang, Min
   Lei, Kai
TI KGDDS: A System for Drug-Drug Similarity Measure in Therapeutic
   Substitution based on Knowledge Graph Curation
SO JOURNAL OF MEDICAL SYSTEMS
LA English
DT Article
DE Drug-drug similarity; Knowledge graph; Therapeutic substitution; Medical
   knowledge curation; Visualization
ID VISUALIZATION
AB Measuring drug-drug similarity is important but challenging. Significant progresses have been made in drugs whose labeled training data is sufficient and available. However, handling data skewness and incompleteness with domain-specific knowledge graph, is still a relatively new territory and an under-explored prospect. In this paper, we present a system KGDDS for node-link-based bio-medical Knowledge Graph curation and visualization, aiding Drug-Drug Similarity measure. Specifically, we reuse existing knowledge bases to alleviate the difficulties in building a high-quality knowledge graph, ranging in size up to 7 million edges. Then we design a prediction model to explore the pharmacology features and knowledge graph features. Finally, we propose a user interaction model to allow the user to better understand the drug properties from a drug similarity perspective and gain insights that are not easily observable in individual drugs. Visual result demonstration and experimental results indicate that KGDDS can bridge the user/caregiver gap by facilitating antibiotics prescription knowledge, and has remarkable applicability, outperforming existing state-of-the-art drug similarity measures.
C1 [Shen, Ying; Yuan, Kaiqi; Dai, Jingchao; Lei, Kai] Peking Univ Shenzhen, Sch Elect & Comp Engn, Shenzhen 518055, Peoples R China.
   [Tang, Buzhou] Harbin Inst Technol Shenzhen, Sch Comp Sci & Technol, Shenzhen 518055, Peoples R China.
   [Yang, Min] Chinese Acad Sci, SIAT, Shenzhen 518055, Peoples R China.
RP Lei, K (通讯作者)，Peking Univ Shenzhen, Sch Elect & Comp Engn, Shenzhen 518055, Peoples R China.
EM shenying.sz@pku.edu.cn; kqyuan@pku.edu.cn; 1801213351@pku.edu.cn;
   tangbuzhou@hit.edu.cn; min.yang@siat.ac.cn; leik@pkusz.edu.cn
RI Lei, Kai/W-5518-2019
OI Lei, Kai/0000-0001-9197-895X; Tang, Buzhou/0000-0003-0271-8246; SHEN
   (chen ying), Ying/0000-0002-3220-904X
FU National Natural Science Foundation of China [61602013]; Shenzhen
   Fundamental Research Project [JCYJ20170818091546869,
   JCYJ20160330095313861]; CCF-Tencent Open Research Fund
FX This work was financially supported by the National Natural Science
   Foundation of China (No. 61602013), and the Shenzhen Fundamental
   Research Project (No. JCYJ20170818091546869, JCYJ20160330095313861). Min
   Yang was sponsored by CCF-Tencent Open Research Fund.
CR Abdelaziz I, 2017, J WEB SEMANT, V44, P104, DOI 10.1016/j.websem.2017.06.002
   Abello J, 2006, IEEE T VIS COMPUT GR, V12, P669, DOI 10.1109/TVCG.2006.120
   Al-Mubaid Hisham, 2006, Conf Proc IEEE Eng Med Biol Soc, V2006, P2713
   Auber D, 2004, MATH VIS, P105
   Baig MM, 2013, J MED SYST, V37, DOI 10.1007/s10916-012-9898-z
   Batet M, 2011, J BIOMED INFORM, V44, P118, DOI 10.1016/j.jbi.2010.09.002
   FU B, 2000, PROCEEDING OF THE IN, P117
   Ge T, 2016, PROCEEDINGS OF 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL-2016): SYSTEM DEMONSTRATIONS, P25
   Hliaoutakis A, 2006, INT J SEMANT WEB INF, V2, P55, DOI 10.4018/jswis.2006070104
   Hliaoutakis A, 2006, LECT NOTES COMPUT SC, V4172, P512
   Ho IW, 2015, JOURNAL OF BIOMEDICA, V27, P10
   Ho PL, 2012, REDUCING BACTERIAL R
   Holten D, 2006, IEEE T VIS COMPUT GR, V12, P741, DOI 10.1109/TVCG.2006.147
   Jovic A, 2007, ITI, P535
   Kai Lei, 2018, Knowledge Science, Engineering and Management. 11th International Conference, KSEM 2018. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11061), P479, DOI 10.1007/978-3-319-99365-2_43
   Katifori A, 2007, ACM COMPUT SURV, V39, DOI 10.1145/1287620.1287621
   LI Y, 2003, IEEE TRANS KNOWL DAT, V15, P4
   Luo H, 2014, NUCLEIC ACIDS RES, V42, pW46, DOI 10.1093/nar/gku433
   Monika L, 2010, J U COMPUT SCI, V16, P1036
   Nguyen HA, 2006, 2006 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, P623, DOI 10.1109/GRC.2006.1635880
   Pedersen T, 2007, J BIOMED INFORM, V40, P288, DOI 10.1016/j.jbi.2006.06.004
   Ping Zhang, 2013, Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2013. Proceedings: LNCS 8190, P579, DOI 10.1007/978-3-642-40994-3_37
   Resnik P., 2005, P 14 INT JOINT C ART, P448
   Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, P333, DOI 10.1561/1500000019
   Shiralkar P, 2017, IEEE DATA MINING, P859, DOI 10.1109/ICDM.2017.105
   Sintek M., 2003, ONTOVIZ TAB VISUALIZ
   Tang J., PROCEEDINGS OF THE 2, P1067
   Traverso Ignacio, 2016, P 12 INT C SEM SYST, P101
   Vilar S, 2014, NAT PROTOC, V9, P2147, DOI 10.1038/nprot.2014.151
   Vilar S, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0058321
   Zhang P, 2015, SCI REP-UK, V5, DOI 10.1038/srep12339
   Zhang Ping, 2014, AMIA Jt Summits Transl Sci Proc, V2014, P132
   Zhu GG, 2017, IEEE T KNOWL DATA EN, V29, P72, DOI 10.1109/TKDE.2016.2610428
NR 33
TC 12
Z9 12
U1 7
U2 51
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 APR
PY 2019
VL 43
IS 4
AR 92
DI 10.1007/s10916-019-1182-z
PG 9
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA HO0MC
UT WOS:000460596500001
PM 30834481
DA 2022-08-23
ER

PT J
AU Shin, E
   Yoo, S
   Ju, Y
   Shin, D
AF Shin, Eunji
   Yoo, Sangwoo
   Ju, Yongtaek
   Shin, Dongil
TI Knowledge graph embedding and reasoning for real-time analytics support
   of chemical diagnosis from exposure symptoms
SO PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
LA English
DT Article
DE Chemical Safety; Exposure Symptom Knowledge; Data Analytics; Knowledge
   Graph; Knowledge Graph Embedding; Knowledge Graph Reasoning
ID METHODOLOGY; ONTOLOGIES
AB Chemical exposure accidents pose a risk of serious injury and property damage if the diagnosis or response is not properly performed after the initial discovery. Due to lack of research on the dynamically changing environment and detection of chemical substances considering symptoms, real-time knowledge services are required, such as rapid diagnosis of chemicals exposed at the accident site and the following early response. In this study, we propose an AI-based analysis system, Symptom-based Expert for Advanced Response to Chemical Hazards (SEARCH), for chemical substance diagnosis from exposure symptoms ac-tively collected for real-time response and mitigation to hazardous material accidents. Knowledge is col-lected from chemical database such as WISER, PubChem etc., and integrated for the analytics of chemical exposure accidents and contact symptoms. We design and construct ontology and knowledge graph (KG) for 1001 major chemical substances. The built KG is verified using KG embedding models and the performance of each model is compared. The proposed system identifies the substance candidates through KG query and reasoning considering the exposure conditions. Using the symptom KG, the system SEARCH can provide the means to analyze real-time data from the field and transform it into insights and actions related to emergency response. (c) 2021 Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.
C1 [Shin, Eunji; Yoo, Sangwoo; Ju, Yongtaek; Shin, Dongil] Myongji Univ, Dept Disaster & Safety, Yongin 17058, Gyeonggido, South Korea.
   [Shin, Dongil] Myongji Univ, Dept Chem Engn, Yongin 17058, Gyeonggido, South Korea.
RP Shin, D (通讯作者)，Myongji Univ, Dept Disaster & Safety, Yongin 17058, Gyeonggido, South Korea.; Shin, D (通讯作者)，Myongji Univ, Dept Chem Engn, Dept Disaster & Safety, Yongin 17058, Gyeonggido, South Korea.
EM dongil@mju.ac.kr
FU Development of Knowledge Service for Chemical Contact Information and
   Detection System through Real-time Sensor Information Fusion (SEARCH) -
   Ministry of Trade, Industry and Energy, South Korea [20009265]
FX This study was supported by Development of Knowledge Service for
   Chemical Contact Information and Detection System through Real-time
   Sensor Information Fusion (SEARCH) funded by the Ministry of Trade,
   Industry and Energy, South Korea [Grant number 20009265] .
CR Aziz A, 2019, PROCESS SAF ENVIRON, V123, P87, DOI 10.1016/j.psep.2018.12.008
   Batres R, 2014, PROCESS SAF ENVIRON, V92, P119, DOI 10.1016/j.psep.2012.11.002
   Bhavnani Suresh K, 2007, AMIA Annu Symp Proc, P51
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   CHO SB, 1995, IEEE T NEURAL NETWOR, V6, P497, DOI 10.1109/72.363487
   Costabello L., 2019, AMPLIGRAPH LIB REPRE
   Durant JL, 2002, J CHEM INF COMP SCI, V42, P1273, DOI 10.1021/ci010132r
   Godo-Pla L, 2021, PROCESS SAF ENVIRON, V145, P63, DOI 10.1016/j.psep.2020.07.037
   Gundla NK, 2016, PROCEDIA COMPUT SCI, V91, P460, DOI 10.1016/j.procs.2016.07.120
   Hochstein Colette, 2008, Medical Reference Services Quarterly, V27, P1, DOI [10.1080/02763860802079962, 10.1300/J115v27n01_01]
   Huang Y. S., 1993, Proceedings. 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.93CH3309-2), P347, DOI 10.1109/CVPR.1993.1626170
   Hyun-Young Choi, 2018, Journal of KIISE, V45, P895, DOI 10.5626/JOK.2018.45.9.895
   Kidam K, 2013, PROCESS SAF ENVIRON, V91, P367, DOI 10.1016/j.psep.2012.08.002
   Kim J., 2018, ANN C HUM LANG TECHN, P465
   Kim S, 2016, NUCLEIC ACIDS RES, V44, pD1202, DOI 10.1093/nar/gkv951
   Krishna S., 1992, INTRO DATABASE KNOWL
   Nan C, 2008, PROCESS SAF ENVIRON, V86, P55, DOI 10.1016/j.psep.2007.10.014
   Nguyen D.Q., 2017, ARXIV170308098, V1703
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Pan J.Z., 2017, EXPLOIT LINKED DATA
   Singh A, 2008, INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, P650
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Venkatasubramanian V, 2003, COMPUT CHEM ENG, V27, P327, DOI 10.1016/S0098-1354(02)00162-X
   WEININGER D, 1988, J CHEM INF COMP SCI, V28, P31, DOI 10.1021/ci00057a005
   Wu YY, 2018, INT J MOL SCI, V19, DOI 10.3390/ijms19082358
   Zhou Q, 2019, J INTELL MANUF, V30, P1693, DOI 10.1007/s10845-017-1351-1
   김재헌, 2019, [Journal of Intelligence and Information Systems, 지능정보연구], V25, P43
NR 28
TC 0
Z9 0
U1 6
U2 10
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0957-5820
EI 1744-3598
J9 PROCESS SAF ENVIRON
JI Process Saf. Environ. Protect.
PD JAN
PY 2022
VL 157
BP 92
EP 105
DI 10.1016/j.psep.2021.11.002
EA NOV 2021
PG 14
WC Engineering, Environmental; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA XB7IE
UT WOS:000721498200003
OA Bronze
DA 2022-08-23
ER

PT J
AU Kang, YL
   Ou, RW
   Zhang, Y
   Li, HL
   Tian, SS
AF Kang, Yilin
   Ou, Renwei
   Zhang, Yi
   Li, Hongling
   Tian, Shasha
TI PG-CODE: Latent dirichlet allocation embedded policy knowledge graph for
   government department coordination
SO TSINGHUA SCIENCE AND TECHNOLOGY
LA English
DT Article
DE Entrepreneurship; Government; Technological innovation; Data models;
   Local government; Standards; Resource management; policy knowledge
   graph; department coordination; topic diffusion; latent dirichlet
   allocation; rural revitalization
AB Government policy-group integration and policy-chain inference are significant to the execution of strategies in current Chinese society. Specifically, the coordination of hierarchical policies implemented among government departments is one of the key challenges to rural revitalization. In recent years, various well-established quantitative methods have been proposed to evaluate policy coordination, but the majority of these relied on manual analysis, which can lead to subjective results. Thus, in this paper, a novel approach called "policy knowledge graph for the coordination among the government departments" (PG-CODE) is proposed, which incorporates topic modeling into policy knowledge graphs. Similar to a knowledge graph, a policy knowledge graph uses a graph-structured data model to integrate policy discourse. With latent Dirichlet allocation embedding, a policy knowledge graph could capture the underlying topics of the policies. Furthermore, coordination strength and topic diffusion among hierarchical departments could be inferred from the PG-CODE, as it can provide a better representation of coordination within the policy space. We implemented and evaluated the PG-CODE in the field of rural innovation and entrepreneurship policy, and the results effectively demonstrate improved coordination among departments.
C1 [Kang, Yilin; Ou, Renwei; Zhang, Yi; Tian, Shasha] South Cent Univ Nationalities, Sch Comp Sci, Wuhan 430074, Peoples R China.
   [Li, Hongling] South Cent Univ Nationalities, Sch Publ Management, Wuhan 430074, Peoples R China.
RP Li, HL (通讯作者)，South Cent Univ Nationalities, Sch Publ Management, Wuhan 430074, Peoples R China.
EM ylkang@mail.scuec.edu.cn; 1195821448@qq.com; 1031271461@qq.com;
   lihl723@163.com; shashatian77@mail.scuec.edu.cn
FU National Social Science Fund of China [20BGL231]; Natural Science
   Foundation of Hubei Province [2018CFB380]
FX This work was supported by the National Social Science Fund of China
   (No. 20BGL231), and the Natural Science Foundation of Hubei Province
   (No. 2018CFB380).
CR Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Chen B, 2010, AAAI CONF ARTIF INTE, P1007
   Cohen R., 2013, P INT AAAI C WEB SOC, V7
   Fleming D., 2010, J REGIONAL ANAL POLI, V40, P20, DOI DOI 10.22004/AG.ECON.132438
   Greene D., 2015, P ACM WEB SCI C, P2, DOI DOI 10.1145/2786451.2786464
   Han Q. X, 2016, J PARTY SCH CENTRAL, V20, P15
   Hughes CE, 2013, INT J DRUG POLICY, V24, P244, DOI 10.1016/j.drugpo.2012.08.004
   Jelodar H, 2019, MULTIMED TOOLS APPL, V78, P15169, DOI 10.1007/s11042-018-6894-4
   Jia S, 2018, IEEE ACCESS, V6, P64596, DOI 10.1109/ACCESS.2018.2877716
   Li A. G., 2017, REFORM, P149
   LIBECAP GD, 1978, J ECON HIST, V38, P338, DOI 10.1017/S0022050700105121
   Liu X. Y., 2020, FORUM SCI TECHNOLOGY, P13
   Murdoch J, 2000, J RURAL STUD, V16, P407, DOI 10.1016/S0743-0167(00)00022-X
   [彭纪生 Peng Jisheng], 2008, [管理世界, Management World], P25
   [曲佳彬 Qu Jiabin], 2018, [数据分析与知识发现, Data Analysis and Knowledge Discovery], V2, P64
   Shirota Y, 2015, 2015 IIAI 4TH INTERNATIONAL CONGRESS ON ADVANCED APPLIED INFORMATICS (IIAI-AAI), P8, DOI 10.1109/IIAI-AAI.2015.168
   Stathopoulou S., 2004, INT J ENTREPRENEURIA, V10, P404, DOI DOI 10.1108/13552550410564725
   Xie P., 2015, P 2015 C N AM CHAPT, P725
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yao L, 2017, AAAI CONF ARTIF INTE, P3119
   Yao L, 2015, LECT NOTES ARTIF INT, V9078, P586, DOI 10.1007/978-3-319-18032-8_46
   Zeng H. Y, 2015, MACROECONOMIC MANAGE, P21
   Zhang Z. S., 2016, J TIANJIN U SOCIAL S, V18, P385
   Zhao J., 2019, SCI TECHNOL ENG, V19, P225
NR 24
TC 1
Z9 1
U1 15
U2 30
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 680
EP 691
DI 10.26599/TST.2021.9010059
PG 12
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:000728972400005
OA gold
DA 2022-08-23
ER

PT J
AU Sergeyev, SV
AF Sergeyev, Sergey V.
TI Vector self-pulsing in erbium-doped fiber lasers
SO OPTICS LETTERS
LA English
DT Article
ID INSTABILITIES; MODULATION
AB Insight into instabilities of fiber laser regimes leading to complex self-pulsing operations is an opportunity to unlock the high power and dynamic operation tunability of lasers. Though many models have been suggested, there is no complete covering of self-pulsing complexity observed experimentally. Here, I further generalized our previous vector model of erbium-doped fiber laser and, for the first time, to the best of my knowledge, map tunability of complex vector self-pulsing on Poincare sphere (limit cycles and double scroll polarization attractors) for laser parameters, e.g., power, ellipticity of the pump wave, and in-cavity birefringence. Analysis validated by extensive numerical simulations demonstrates good correspondence to the experimental results on complex self-pulsing regimes obtained by many authors during the last 20 years. (C) 2016 Optical Society of America
C1 [Sergeyev, Sergey V.] Aston Univ, Aston Inst Photon Technol, Birmingham B4 7ET, W Midlands, England.
RP Sergeyev, SV (通讯作者)，Aston Univ, Aston Inst Photon Technol, Birmingham B4 7ET, W Midlands, England.
EM author_three@uni-jena.de
OI Sergeyev, Sergey/0000-0002-1515-5019
FU Leverhulme Trust [RPG-2014-304]; FP7-PEOPLE-IAPP [324391]
FX Leverhulme Trust (RPG-2014-304), FP7-PEOPLE-2012-IAPP (324391).
CR Arenas A, 2008, PHYS REP, V469, P93, DOI 10.1016/j.physrep.2008.09.002
   ARONSON DG, 1990, PHYSICA D, V41, P403, DOI 10.1016/0167-2789(90)90007-C
   Barmenkov YO, 2004, OPT EXPRESS, V12, P3171, DOI 10.1364/OPEX.12.003171
   Kir'yanov AV, 2004, LASER PHYS LETT, V1, P194, DOI 10.1002/lapl.200310048
   LEBOUDEC P, 1993, OPT LETT, V18, P1890, DOI 10.1364/OL.18.001890
   Lee H, 2010, IEEE J QUANTUM ELECT, V46, P1732, DOI 10.1109/JQE.2010.2063416
   LENERS R, 1995, QUANTUM SEMICL OPT, V7, P757, DOI 10.1088/1355-5111/7/5/002
   Mallek D, 2013, OPT COMMUN, V308, P130, DOI 10.1016/j.optcom.2013.06.062
   Nilsson J, 2011, SCIENCE, V332, P921, DOI 10.1126/science.1194863
   Peters PM, 1998, J NON-CRYST SOLIDS, V239, P162, DOI 10.1016/S0022-3093(98)00733-9
   Pikovsky A.S., 2001, SYNCHRONIZATION UNIV, DOI DOI 10.1007/978-94-010-0217-2
   Richardson DJ, 2010, J OPT SOC AM B, V27, pB63, DOI 10.1364/JOSAB.27.000B63
   SANCHEZ F, 1995, IEEE J QUANTUM ELECT, V31, P481, DOI 10.1109/3.364403
   Sanchez F, 1996, PHYS REV E, V53, P2110, DOI 10.1103/PhysRevE.53.2110
   Sergeyev S, 2010, OPT LETT, V35, P3736, DOI 10.1364/OL.35.003736
   Sergeyev S, 2007, PROC SPIE, V6610, DOI 10.1117/12.739954
   Sergeyev SV, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2014.0006
   Sergeyev SV, 2014, LIGHT-SCI APPL, V3, DOI 10.1038/lsa.2014.12
   Sergeyev SV, 1999, PHYS REV A, V59, P3909, DOI 10.1103/PhysRevA.59.3909
   TAMMELA S, 2003, OSA TECHNICAL DIGEST, V1, P376
   Thevenin J, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.104101
   Tigan G., 2008, SOLITONS FRACTALS, V36, P1315
   Toral-Acosta D, 2014, LASER PHYS, V24, DOI 10.1088/1054-660X/24/10/105107
   Tsatourian V, 2013, SCI REP-UK, V3, DOI 10.1038/srep03154
   Wise FW, 2008, LASER PHOTONICS REV, V2, P58, DOI 10.1002/lpor.200710041
   ZEGHLACHE H, 1995, PHYS REV A, V52, P4229, DOI 10.1103/PhysRevA.52.4229
NR 26
TC 9
Z9 9
U1 3
U2 16
PU OPTICAL SOC AMER
PI WASHINGTON
PA 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA
SN 0146-9592
EI 1539-4794
J9 OPT LETT
JI Opt. Lett.
PD OCT 15
PY 2016
VL 41
IS 20
BP 4700
EP 4703
DI 10.1364/OL.41.004700
PG 4
WC Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Optics
GA EA8AG
UT WOS:000386854500025
PM 28005871
DA 2022-08-23
ER

PT J
AU Dong, J
   Wang, J
   Chen, S
AF Dong, Jin
   Wang, Jian
   Chen, Sen
TI Knowledge graph construction based on knowledge enhanced word embedding
   model in manufacturing domain
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE BERT; knowledge graph construction; TransR; manufacturing; knowledge
   extraction
ID RELATION EXTRACTION; ENTITY
AB Manufacturing industry is the foundation of a country's economic development and prosperity. At present, the data in manufacturing enterprises have the problems of weak correlation and high redundancy, which can be solved effectively by knowledge graph. In this paper, a method of knowledge graph construction in manufacturing domain based on knowledge enhanced word embedding model is proposed. The main contributions are as follows: (1) At the algorithmic level, this paper proposes KEWE-BERT, an end-to-end model for joint entity and relation extraction, which superimposes the token embedding and knowledge embedding output by BERT and TransR so as to improve the effect of knowledge extraction; (2) At the application level, knowledge representation model ManuOnto and dataset ManuDT are constructed based on real manufacturing scenarios, and KEWE-BERT is used to construct knowledge graph from them. The knowledge graph constructed has rich semantic relations, which can be applied in actual production environment. Other than that, KEWE-BERT can extract effective knowledge and patterns from redundant texts in the enterprise, which providing a solution for enterprise data management.
C1 [Dong, Jin; Wang, Jian; Chen, Sen] Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China.
RP Dong, J (通讯作者)，Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China.
EM dongjin@tongji.edu.cn
FU National Science and Technology Innovation 2030 of China NextGeneration
   Artificial Intelligence Major Project, Data-Driven Tripartite
   Collaborative DecisionMaking and Optimization [2018AAA 0101801]
FX This work was supported by the National Science and Technology
   Innovation 2030 of China NextGeneration Artificial Intelligence Major
   Project, Data-Driven Tripartite Collaborative DecisionMaking and
   Optimization, under Grant 2018AAA 0101801.
CR Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Bordes A., 2013, TRANSLATING EMBEDDIN
   Cai R, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P756
   Chedid J.A., 2020, RAIRO-OPER RES, P55
   Collobert R., 2008, P 25 ICML, P160
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   E HH, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9183795
   Frber M., 2017, SEMANT WEB, P1
   Hu L, 2020, IEEE INTERNET THINGS, V7, P1939, DOI 10.1109/JIOT.2019.2962630
   Jain S., 2020, SCIREX CHALLENGE DAT
   Kamble SS, 2018, PROCESS SAF ENVIRON, V117, P408, DOI 10.1016/j.psep.2018.05.009
   Kazuma H., P 2013 C EMP METH NA, P1372
   Lee J, 2019, CORR ABS190108746, DOI DOI 10.1044/2019_AJSLP-CAC48-18-0220
   Li JQ, 2017, IEEE COMMUN SURV TUT, V19, P1504, DOI 10.1109/COMST.2017.2691349
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu WJ, 2020, AAAI CONF ARTIF INTE, V34, P2901
   Luo L., 2020, J BIOMED INFORM
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Miwa M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1105
   Mohamed AG, 2020, AUTOMAT CONSTR, V116, DOI 10.1016/j.autcon.2020.103209
   Moher D., 2009, PLOS MED, V6, DOI [10.1371/journal.pmed.1000097, DOI 10.1371/JOURNAL.PMED.1000097]
   Peters M.E., 2018, P 2018 C N AM CHAPT, P2227, DOI 10.18653/v1/N18-1202
   Roth B., P 2013 C EMP METH NA, P24
   Ruixue D., P 57 ANN M ASS COMP, P1462
   Ruiz L.G.B., 2020, EXPERT SYST APPL, P160
   Turian J, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P384
   Ulges A., 2019, ARXIV PREPRINT ARXIV
   Vashishth S., 2020, MEDTYPE IMPROVING ME
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang B.C., 2018, STRATEGIC STUDY CAE, V20, P29
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
   Zhao Y, 2020, SEMISUPERVISED JOINT
   Zheng SC, 2017, NEUROCOMPUTING, V257, P59, DOI 10.1016/j.neucom.2016.12.075
   Zhou J, 2019, ENGINEERING-PRC, V5, P624, DOI 10.1016/j.eng.2019.07.015
   Zhu GG, 2017, IEEE T KNOWL DATA EN, V29, P72, DOI 10.1109/TKDE.2016.2610428
NR 35
TC 1
Z9 1
U1 25
U2 54
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 3603
EP 3613
DI 10.3233/JIFS-210982
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA US3NM
UT WOS:000697340200072
DA 2022-08-23
ER

PT J
AU Sousa, RT
   Silva, S
   Pesquita, C
AF Sousa, Rita T.
   Silva, Sara
   Pesquita, Catia
TI Evolving knowledge graph similarity for supervised learning in complex
   biomedical domains
SO BMC BIOINFORMATICS
LA English
DT Article
DE Knowledge graph; Ontology; Semantic similarity; Machine learning;
   Genetic programming; Gene ontology; Protein-protein interaction
   prediction
ID PROTEIN-PROTEIN INTERACTIONS; GENE ONTOLOGY; SEMANTIC SIMILARITY;
   FUNCTIONAL SIMILARITY; TOOL; LOCALIZATION; PREDICTION; INFERENCE; WEB
AB Background In recent years, biomedical ontologies have become important for describing existing biological knowledge in the form of knowledge graphs. Data mining approaches that work with knowledge graphs have been proposed, but they are based on vector representations that do not capture the full underlying semantics. An alternative is to use machine learning approaches that explore semantic similarity. However, since ontologies can model multiple perspectives, semantic similarity computations for a given learning task need to be fine-tuned to account for this. Obtaining the best combination of semantic similarity aspects for each learning task is not trivial and typically depends on expert knowledge. Results We have developed a novel approach, evoKGsim, that applies Genetic Programming over a set of semantic similarity features, each based on a semantic aspect of the data, to obtain the best combination for a given supervised learning task. The approach was evaluated on several benchmark datasets for protein-protein interaction prediction using the Gene Ontology as the knowledge graph to support semantic similarity, and it outperformed competing strategies, including manually selected combinations of semantic aspects emulating expert knowledge. evoKGsim was also able to learn species-agnostic models with different combinations of species for training and testing, effectively addressing the limitations of predicting protein-protein interactions for species with fewer known interactions. Conclusions evoKGsim can overcome one of the limitations in knowledge graph-based semantic similarity applications: the need to expertly select which aspects should be taken into account for a given application. Applying this methodology to protein-protein interaction prediction proved successful, paving the way to broader applications.
C1 [Sousa, Rita T.; Silva, Sara; Pesquita, Catia] Univ Lisbon, Fac Ciencias, LASIGE, Lisbon, Portugal.
RP Sousa, RT (通讯作者)，Univ Lisbon, Fac Ciencias, LASIGE, Lisbon, Portugal.
EM risousa@ciencias.ulisboa.pt
RI Silva, Sara/A-7680-2010; Pesquita, Catia/O-8883-2015
OI Silva, Sara/0000-0001-8223-4799; Pesquita, Catia/0000-0002-1847-9393;
   Sousa, Rita/0000-0002-7241-8970
FU Fundacao para a Ciencia e a Tecnologia (FCT) through LASIGE Research
   Unit [UID/CEC/00408/2019]; SMILAX project [PTDC/EEI-ESS/4633/2014];
   PERSEIDS [PTDC/EMS-SIS/0642/2014]; BINDER [PTDC/CCI-INF/29168/2017];
   PREDICT [PTDC/CCI-CIF/29877/2017]; FCT [SFRH/BD/145377/2019]
FX CP, SS, RS are funded by the Fundacao para a Ciencia e a Tecnologia
   (FCT) through LASIGE Research Unit (UID/CEC/00408/2019). CP and RS are
   funded by the SMILAX project (PTDC/EEI-ESS/4633/2014). SS is funded by
   PERSEIDS (PTDC/EMS-SIS/0642/2014), BINDER (PTDC/CCI-INF/29168/2017) and
   PREDICT (PTDC/CCI-CIF/29877/2017). RS is funded by FCT
   (SFRH/BD/145377/2019). The funding had no rule in the design of the
   study and collection, analysis, and interpretation of data and in
   writing the manuscript.
CR [Anonymous], 2011, J MACH LEARN RES
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Bandyopadhyay S, 2017, IEEE ACM T COMPUT BI, V14, P762, DOI 10.1109/TCBB.2016.2555304
   Bannister CA, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0202685
   Ben-Hur A, 2005, BIOINFORMATICS, V21, pI38, DOI 10.1093/bioinformatics/bti1016
   Bloom JD, 2003, BMC EVOL BIOL, V3, DOI 10.1186/1471-2148-3-21
   Brameier M, 2007, BIOINFORMATICS, V23, P1159, DOI 10.1093/bioinformatics/btm066
   BRESLOW N, 1970, BIOMETRIKA, V57, P579, DOI 10.2307/2334776
   Cote RG, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-401
   Couto Francisco M, 2006, J Biomed Discov Collab, V1, P19, DOI 10.1186/1747-5333-1-19
   De Raedt L, 2008, COGN TECHNOL, P1
   De Vries GKD, 2013, P 2013 INT C DAT MIN, V1082
   Domingos P., 2015, MASTER ALGORITHM QUE
   Duan ZH, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-S4-S11
   Ehrlinger Lisa, 2016, JOINT P POST DEM TRA, V1695
   Eiben A. E., 2003, INTRO EVOLUTIONARY C, V53
   Ellson J, 2002, GRAPH DRAWING
   Espejo PG, 2010, IEEE T SYST MAN CY C, V40, P121, DOI 10.1109/TSMCC.2009.2033566
   Freudenberg J, 2002, BIOINFORMATICS, V18, pS110, DOI 10.1093/bioinformatics/18.suppl_2.S110
   Gandomi A. H., 2015, HDB GENETIC PROGRAMM
   Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081
   Guzzi PH, 2012, BRIEF BIOINFORM, V13, P569, DOI 10.1093/bib/bbr066
   Harispe S, 2014, J BIOMED INFORM, V48, P38, DOI 10.1016/j.jbi.2013.11.006
   Harispe S, 2014, BIOINFORMATICS, V30, P740, DOI 10.1093/bioinformatics/btt581
   Hoehndorf R, 2011, NUCLEIC ACIDS RES, V39, DOI 10.1093/nar/gkr538
   Huntley RP, 2015, NUCLEIC ACIDS RES, V43, pD1057, DOI 10.1093/nar/gku1113
   Jain S, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-562
   Jones E., 2001, SCIPY OPEN SOURCE SC
   Kohler S, 2009, AM J HUM GENET, V85, P457, DOI 10.1016/j.ajhg.2009.09.003
   Koza JR, 2010, GENET PROGRAM EVOL M, V11, P251, DOI 10.1007/s10710-010-9112-3
   Koza John R., 1992, GENETIC PROGRAMMING, V1
   Langdon W. B., 2013, FDN GENETIC PROGRAMM
   Lee PH, 2005, BIOINFORMATICS, V21, P2739, DOI 10.1093/bioinformatics/bti406
   Lei ZD, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-491
   Li M, 2014, SCI CHINA LIFE SCI, V57, P1064, DOI 10.1007/s11427-014-4747-6
   Lin N, 2004, BMC BIOINFORMATICS, V5, DOI 10.1186/1471-2105-5-154
   Liu WT, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-30455-0
   Maetschke SR, 2012, BIOINFORMATICS, V28, P69, DOI 10.1093/bioinformatics/btr610
   Meurer A, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.103
   Park Y, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-419
   Patil A, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-100
   Paulheim H., 2012, P 2 INT C WEB INT MI
   Perez-Iratxeta C, 2002, NAT GENET, V31, P316, DOI 10.1038/ng895
   Pesquita C., 2007, PROC 10 ANN BIOONTOL, V37, P38
   Pesquita C, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000443
   Poli Riccardo, 2008, FIELD GUIDE GENETIC
   Resnik P., 1995, P 14 INT JOINT C ART
   Ristoski P, 2016, SEMANTIC WEB ISWC 20
   Ristoski P, 2016, J WEB SEMANT, V36, P1, DOI 10.1016/j.websem.2016.01.001
   Ristoski P, 2015, J WEB SEMANT, V35, P142, DOI 10.1016/j.websem.2015.06.004
   Robinson PN, 2008, AM J HUM GENET, V83, P610, DOI 10.1016/j.ajhg.2008.09.017
   Saetrom P, 2005, NUCLEIC ACIDS RES, V33, P3263, DOI 10.1093/nar/gki644
   Schmachtenberg M, 2014, SEMANTIC WEB ISWC 20
   Seco N, 2004, P 16 EUR C ART INT E
   Silva S, 2018, SWARM EVOL COMPUT, V39, P323, DOI 10.1016/j.swevo.2017.11.003
   Silva S, 2012, GENET PROGRAM EVOL M, V13, P197, DOI 10.1007/s10710-011-9150-5
   Sipper M, 2018, BIODATA MIN, V11, DOI 10.1186/s13040-018-0164-x
   Smaili FZ, 2018, BIOINFORMATICS, V34, P52, DOI 10.1093/bioinformatics/bty259
   Turner FS, 2003, GENOME BIOL, V4, DOI 10.1186/gb-2003-4-11-r75
   Wu XM, 2006, NUCLEIC ACIDS RES, V34, P2137, DOI 10.1093/nar/gkl219
   Yu JT, 2010, BIOINFORMATICS, V26, P2610, DOI 10.1093/bioinformatics/btq483
   Zhang PS, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-135
   Zhang SB, 2016, J THEOR BIOL, V401, P30, DOI 10.1016/j.jtbi.2016.04.020
NR 63
TC 13
Z9 13
U1 2
U2 14
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD JAN 3
PY 2020
VL 21
IS 1
AR 6
DI 10.1186/s12859-019-3296-1
PG 19
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 KK4IS
UT WOS:000512708400002
PM 31900127
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Aasman, J
AF Aasman, Jans
TI Transmuting Information to Knowledge with an Enterprise Knowledge Graph
SO IT PROFESSIONAL
LA English
DT Article
AB The enterprise knowledge graph for entity 360-views has emerged as one of the most useful graph database technology applications when buttressed by W3C standard semantic technology, modern artificial intelligence, and visual discovery tools.
C1 [Aasman, Jans] Franz Inc, Oakland, CA 94612 USA.
RP Aasman, J (通讯作者)，Franz Inc, Oakland, CA 94612 USA.
EM ja@franz.com
CR Allen M., 2014, QUORA           0501
   [Anonymous], 2014, RESOURCE DESCRIPTION
   [Anonymous], 2017, LINK KNOWL GRAPH ENR
   Bibb R., 2001, DESTINATIONCRM   JUN
   Galkin M., 2016, RESEARCHGATE     MAY
   He Q., 2016, BUILDING LINKEDIN KN
   He Q., 2016, MACHINE LEARNING LIN
   Judah S., 2014, GARTNER BLOG    1017
   McCorry O., 2015, TECHCRUNCH      0311
   Ross G., 2005, TECHREPUBLIC    0131
   W3C, 2015, WHAT IS LINK DAT
NR 11
TC 2
Z9 3
U1 1
U2 44
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1520-9202
EI 1941-045X
J9 IT PROF
JI IT Prof.
PD NOV-DEC
PY 2017
VL 19
IS 6
BP 44
EP 51
PG 8
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA FP8WZ
UT WOS:000417928100008
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 Medical Knowledge Graph in Chinese Using Deep Semantic Mobile
   Computation Based on IoT and WoT
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB In this paper, a novel medical knowledge graph in Chinese approach applied in smart healthcare based on IoT and WoT is presented, using deep neural networks combined with self-attention to generate medical knowledge graph to make it more convenient for performing disease diagnosis and providing treatment advisement. Although great success has been made in the medical knowledge graph in recent studies, the issue of comprehensive medical knowledge graph in Chinese appropriate for telemedicine or mobile devices have been ignored. In our study, it is a working theory which is based on semantic mobile computing and deep learning. When several experiments have been carried out, it is demonstrated that it has better performance in generating various types of medical knowledge graph in Chinese, which is similar to that of the state-of-the-art. Also, it works well in the accuracy and comprehensive, which is much higher and highly consisted with the predictions of the theoretical model. It proves to be inspiring and encouraging that our work involving studies of medical knowledge graph in Chinese, which can stimulate the smart healthcare development.
C1 [Liu, Wanheng; Wang, Cong] Beijing Univ Posts & Telecommun, Beijing, Peoples R China.
   [Yin, Ling] Natl Populat Hlth Data Ctr, Beijing, Peoples R China.
   [Liu, Fulin; Ni, Zhiyu] Hebei Univ, Aftliated Hosp, Baoding, Hebei, Peoples R China.
RP Liu, WH (通讯作者)，Beijing Univ Posts & Telecommun, Beijing, Peoples R China.; Yin, L (通讯作者)，Natl Populat Hlth Data Ctr, Beijing, Peoples R China.
EM liuwanheng301@163.com; lwhlcf@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 Our work is supported by the 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 Alawad M., 2021, ARXIV210101337
   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
   Berners-Lee T, 2006, LINKED DATA
   Bollacker K, 2007, P 22 NAT C ART INT, P1962
   Chai XQ, 2020, IEEE ACCESS, V8, P149787, DOI 10.1109/ACCESS.2020.3016676
   Chen I.Y., 2020, 2020 PAC S BIOC
   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
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Lafferty John, 2001, CONDITIONAL RANDOM F
   Lei K., 2018, INT C KNOWL SCI ENG
   Lenat D.B., 1985, MAG AZINE, V6, P65
   Li X, 2020, HEALTH INF SCI SYST, V8, DOI 10.1007/s13755-020-0102-4
   Li XT, 2019, BMC NEUROL, V19, DOI 10.1186/s12883-019-1251-4
   Miller G. A., 1985, P 1 C UW CTR NEW OXF
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Shen Y, 2019, J MED SYST, V43, DOI 10.1007/s10916-019-1182-z
   Shi LX, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/2858423
   Singhal A, 2012, CASE BASED NEUROLOGY, V5, P16
   Song Y, 2019, DESTECH TRANS SOC
   Suchanek F.M., P 16 INT C WORLD WID, P697
   Sun X., 2018, P INT C HUM CTR COMP, P188
   Swick R. R., 1999, RESOURCE DESCRIPTION
   Wang H., 2018, DESIGN IMPLEMENTATIO
   Wang XL, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P1141, DOI 10.1145/3331184.3331350
   Wen D., 2017, P INT C SMART COMP C, P321
   Xiao J., 2019, JMIR MED INF, V8
   Xie Y., 2018, 2018 14 INT C NAT CO
   Zheng SJ, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa344
NR 29
TC 1
Z9 1
U1 10
U2 31
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 MAY 28
PY 2021
VL 2021
AR 5590754
DI 10.1155/2021/5590754
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA SR2TD
UT WOS:000660894800001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, JB
   Lu, GH
   Pan, ZN
   Yu, T
   Ding, MS
   Yang, HB
AF Chen, Junbin
   Lu, Guanhua
   Pan, Zhenning
   Yu, Tao
   Ding, Maosheng
   Yang, Huibiao
TI Research Review of the Knowledge Graph and its Application in Power
   System Dispatching and Operation
SO FRONTIERS IN ENERGY RESEARCH
LA English
DT Review
DE knowledge graph; knowledge graph construction; power systems;
   dispatching operation; application framework
ID FAULT-DIAGNOSIS; REPRESENTATION; INFORMATION; ALGORITHMS; WISDOM; DESIGN
AB With the construction of a new power system and the proposal of a double carbon goal, power system operation data are growing explosively, and the optimization of power system dispatching operation is becoming more and more complex. Relying on traditional pure manual dispatching is difficult to meet the dispatching needs. The emerging knowledge graph technology in the field of the artificial intelligence technology is one of the effective methods to solve this problem. Because the topological structure of the power system itself is consistent with the relational structure of graph theory, through the establishment of a relevant knowledge graph, the real operating state of the power system can be restored to the maximum extent by effectively preserving the correlation implicit in the data. Meanwhile, expressing the hidden knowledge in the power system dispatching operation in the form of a knowledge graph has become the focus of research at home and abroad. This study summarizes the development of the knowledge graph technology from the aspects of knowledge extraction, knowledge representation learning, knowledge mining, knowledge reasoning, knowledge fusion, and the application of knowledge graph and introduces the application and prospect of knowledge graph in the power system dispatching operation from the aspects of the auxiliary optimization decision, vertical risk control, operation mode analysis, optimization model improvement experience, and super regulation parameters.
C1 [Chen, Junbin; Lu, Guanhua; Pan, Zhenning; Yu, Tao] South China Univ Technol, Coll Elect Power, Guangzhou, Peoples R China.
   [Ding, Maosheng; Yang, Huibiao] State Grid Ningxia Elect Power Co, Yinchuan Ningxia Hui Autonomous Reg, Guyuan, Ningxia, Peoples R China.
RP Pan, ZN (通讯作者)，South China Univ Technol, Coll Elect Power, Guangzhou, Peoples R China.
EM scutpanzn@163.com
FU State Grid Corporation of Technology Project [5229NX21001Q]
FX The authors gratefully acknowledge the support of the State Grid
   Corporation of Technology Project (5229NX21001Q).
CR Ali SS, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9061030
   Bo Chai, 2019, 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), P683, DOI 10.1109/IAEAC47372.2019.8997608
   Chen F, 2015, INT J DISTRIB SENS N, DOI 10.1155/2015/431047
   Chen M., 2021, IOP C SERIES EARTH E
   Chen T, 2021, J SIGNAL PROCESS SYS, V93, P275, DOI 10.1007/s11265-020-01591-6
   CHEN ZX, 1992, COMPUT EDUC, V18, P267, DOI 10.1016/0360-1315(92)90098-P
   Chun S, 2020, J SUPERCOMPUT, V76, P8058, DOI 10.1007/s11227-018-2672-3
   Dapeng Yan, 2021, IEEE Transactions on Artificial Intelligence, V2, P384, DOI 10.1109/TAI.2021.3087116
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Etzioni O, 2008, COMMUN ACM, V51, P68, DOI 10.1145/1409360.1409378
   Fan SX, 2020, SCI PROGRAMMING-NETH, V2020, DOI 10.1155/2020/8842463
   Fang L, 2011, PROC VLDB ENDOW, V5, P241, DOI 10.14778/2078331.2078339
   Fu SY, 2020, J BIOMED INFORM, V109, DOI 10.1016/j.jbi.2020.103526
   Gai Xiaoping, 2021, E3S Web of Conferences, V256, DOI 10.1051/e3sconf/202125601039
   Gao ZP, 2020, J ENG-JOE, V2020, P1177, DOI 10.1049/joe.2019.1319
   GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108
   Guo XF, 2021, ENERGY REP, V7, P58, DOI 10.1016/j.egyr.2021.02.023
   Guo Y, 2021, IEEE T POWER SYST, V36, P3865, DOI 10.1109/TPWRS.2021.3055730
   Hatirnaz E, 2020, MULTIMED TOOLS APPL, V79, P17579, DOI 10.1007/s11042-020-08659-2
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Hossain M., 2012, PROC 18 ACM SIGKDD I, P1375, DOI [10.1145/2339530.2339742, DOI 10.1145/2339530.2339742]
   Hu Quangui, 2021, Electric Power Information and Communication Technology, V19, P73, DOI 10.16543/j.2095-641x.electric.power.ict.2021.01.010
   Hu XG, 2021, IEEE T INSTRUM MEAS, V70, DOI 10.1109/TIM.2020.3045843
   Hu XG, 2021, IEEE T AUTOM SCI ENG, DOI [10.1109/TNNLS.2021.3054867, 10.1109/TASE.2021.3069003]
   Jia YT, 2014, 2014 PROCEEDINGS OF THE IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2014), P657, DOI 10.1109/ASONAM.2014.6921655
   Jia YT, 2013, PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'13 COMPANION), P193
   Jiang Wei, 2021, Electric Power Information and Communication Technology, V19, P15, DOI 10.16543/j.2095-641x.electric.power.ict.2021.02.003
   Jiang ZX, 2021, IEEE ACCESS, V9, P21094, DOI 10.1109/ACCESS.2021.3055371
   Jiangyi Hu, 2021, IOP Conference Series: Earth and Environmental Science, V714, DOI 10.1088/1755-1315/714/4/042034
   Jianzhong Dou, 2020, IOP Conference Series: Materials Science and Engineering, V740, DOI 10.1088/1757-899X/740/1/012136
   Kallipolitis L, 2012, KNOWL-BASED SYST, V27, P38, DOI 10.1016/j.knosys.2011.12.007
   Kieffer Michel, 2021, arXiv
   Kumar AJ, 2017, SPEECH COMMUN, V92, P1, DOI 10.1016/j.specom.2017.05.001
   Kumar D, 2008, IEEE T KNOWL DATA EN, V20, P736, DOI 10.1109/TKDE.2008.32
   Lan Fei, 2010, Power System Technology, V34, P115
   Lei ZF, 2020, FUTURE GENER COMP SY, V102, P534, DOI 10.1016/j.future.2019.08.030
   Li P, 2021, IEEE T POWER SYST, V36, P1193, DOI 10.1109/TPWRS.2020.3024673
   Li RQ, 2019, IEEE IND ELEC, P2877, DOI 10.1109/IECON.2019.8927285
   [李新鹏 Li Xinpeng], 2019, [中国电力, Electric Power], V52, P70
   Liang K, 2022, ENERGIES, V15, DOI 10.3390/en15051810
   Lin Y., ARVIX1506
   Liu JX, 2021, INT J ELEC POWER, V133, DOI 10.1016/j.ijepes.2021.107196
   Liu Jin, 2020, Electric Power Information and Communication Technology, V18, P60, DOI 10.16543/j.2095-641x.electric.power.ict.2020.01.009
   Liu LQ, 2022, FRONT ENERGY RES, V10, DOI 10.3389/fenrg.2022.837553
   Liu Peng, 2021, Electric Power Information and Communication Technology, V19, P31, DOI 10.16543/j.2095-641x.electric.power.ict.2021.05.005
   Ma C, 2021, IEEE ACCESS, V9, P12750, DOI 10.1109/ACCESS.2021.3052054
   Madan S, 1997, ELECTR POW SYST RES, V41, P117, DOI 10.1016/S0378-7796(96)01188-1
   Mitchell TM, 2009, LECT NOTES COMPUT SC, V5823, P998, DOI 10.1007/978-3-642-04930-9_66
   Molzahn DK, 2017, IEEE T SMART GRID, V8, P2941, DOI 10.1109/TSG.2017.2720471
   Ong L, 2022, IND ENG CHEM RES, V61, P8390, DOI 10.1021/acs.iecr.1c03838
   Pan HL, 2021, SOFT COMPUT, DOI 10.1007/s00500-021-05735-z
   Peng BS, 2018, PROG NUCL ENERG, V108, P419, DOI 10.1016/j.pnucene.2018.06.003
   Do P, 2021, ACM T ASIAN LOW-RESO, V20, DOI 10.1145/3453651
   Benjamins VR, 2013, INT J HUM-COMPUT ST, V71, P166, DOI 10.1016/j.ijhcs.2012.10.005
   Rowley J, 2007, J INF SCI, V33, P163, DOI 10.1177/0165551506070706
   Sanin C, 2019, FUTURE GENER COMP SY, V92, P604, DOI 10.1016/j.future.2018.01.062
   Seferlis P, 2021, ENERGY, V224, DOI 10.1016/j.energy.2021.120158
   Sheng ZY, 2021, APPL INTELL, V51, P2485, DOI 10.1007/s10489-020-01932-9
   Sheth J, 2021, J BUS RES, V125, P780, DOI 10.1016/j.jbusres.2020.04.050
   Shi H, 2018, IEEE T SMART GRID, V9, P5271, DOI 10.1109/TSG.2017.2686012
   Shi ZZ, 2006, J COMPUT SCI TECH-CH, V21, P810, DOI 10.1007/s11390-006-0810-5
   Sowa J.F., 1991, MORGAN KAUFMANN SERI
   Sulaiman MH, 2015, APPL SOFT COMPUT, V32, P286, DOI 10.1016/j.asoc.2015.03.041
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Tang X, 2019, INFORM PROCESS MANAG, V56, P809, DOI 10.1016/j.ipm.2019.01.005
   Tang Y., 2019, IEEE PES ISGT ASIA 2
   Tuyet-Doan V., 2020, IEEE ACCESS, V8, P207377, DOI [10.1109/access.2020.3038386, DOI 10.1109/ACCESS.2020.3038386]
   Viloria A, 2019, PROCEDIA COMPUT SCI, V151, P1225, DOI 10.1016/j.procs.2019.04.176
   Wang CG, 2021, FRONT ENERGY RES, V8, DOI 10.3389/fenrg.2020.613331
   Wang Kai, 2021, Journal of Physics: Conference Series, V1846, DOI 10.1088/1742-6596/1846/1/012023
   Warren P, 2019, INT J HUM-COMPUT ST, V122, P145, DOI 10.1016/j.ijhcs.2018.08.009
   Wu TX, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093245
   Xi L, 2021, IEEE T NEUR NET LEAR, V32, P2483, DOI 10.1109/TNNLS.2020.3006080
   Xiang Wang, 2018, 2018 IEEE 2nd International Conference on Energy Internet (ICEI). Proceedings, P281, DOI 10.1109/ICEI.2018.00058
   Xiong WP, 2021, EVID-BASED COMPL ALT, V2021, DOI 10.1155/2021/9970063
   Yan JH, 2018, FRONT COMPUT SCI-CHI, V12, P55, DOI 10.1007/s11704-016-5228-9
   Yang ZJ, 2021, MOBILE NETW APPL, V26, P1884, DOI 10.1007/s11036-020-01726-w
   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 LF, 2021, APPL ENERG, V283, DOI 10.1016/j.apenergy.2020.116328
   Yu D, 2018, INT J ELEC POWER, V99, P577, DOI 10.1016/j.ijepes.2018.01.009
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   Zare S, 2021, ISA T, V108, P230, DOI 10.1016/j.isatra.2020.08.021
   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 ML, 2021, RENEW SUST ENERG REV, V135, DOI 10.1016/j.rser.2020.110098
   Zhang RH, 2021, J THERM SCI, V30, P840, DOI 10.1007/s11630-020-1356-5
   Zhang WJ, 2020, SCI CHINA TECHNOL SC, V63, P614, DOI 10.1007/s11431-019-9547-4
   Zhang XS, 2021, INT J ELEC POWER, V124, DOI 10.1016/j.ijepes.2020.106371
   Zhang ZW, 2020, IEEE ACCESS, V8, P7435, DOI 10.1109/ACCESS.2020.2963990
   Zhao X., 2020, J PHYS C SER, V1584, DOI [10.1088/1742-6596/1584/1/012018, DOI 10.1088/1742-6596/1584/1/012018]
   Zhixiang Ji, 2020, Global Energy Interconnection, V3, P186, DOI 10.1016/j.gloei.2020.05.010
   Zhou DY, 2014, COMPUT MATH METHOD M, V2014, DOI 10.1155/2014/298473
NR 92
TC 0
Z9 0
U1 11
U2 11
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-598X
J9 FRONT ENERGY RES
JI Front. Energy Res.
PD JUN 3
PY 2022
VL 10
AR 896836
DI 10.3389/fenrg.2022.896836
PG 11
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA 2F1TZ
UT WOS:000812699400001
OA gold
DA 2022-08-23
ER

PT J
AU Yang, Y
   Meng, GF
AF Yang, Yi
   Meng, Guanfei
TI A bibliometric analysis of comparative research on the evolution of
   international and Chinese ecological footprint research hotspots and
   frontiers since 2000
SO ECOLOGICAL INDICATORS
LA English
DT Article
DE Ecological footprint; Knowledge mapping; Research hotspots; Evolution of
   frontiers; Derek John de Solla Price curve; X index
ID CARBON FOOTPRINT; WATER FOOTPRINT; ENVIRONMENTAL DEGRADATION;
   ECONOMIC-DEVELOPMENT; POLICY SHOCKS; CONSUMPTION; IMPACT;
   SUSTAINABILITY; INDICATORS; PERSISTENCE
AB The ecological footprint (EF) methodology, which is a tool for evaluating the natural resource use level and regional carrying capacity, has been extensively used to measure the capacity to reach both an ecological supply and demand balance and a level of sustainable development. In this study, to systematically sort and analyse the distribution of EF research, its hot areas and the evolution of its fronts, papers with "ecological footprint" as the subject term in the Web of Science Core Collection Database and the China Knowledge Resource Integrated Database Core Journals since 2000 are visually analysed using CiteSpace. The characteristics of these papers, including the quantity trend, quality, author group, affiliated institution type and journal type, are summarized, and keyword co-appearance and paper co-citation knowledge maps are produced. The results show the following: (1) Chinese researchers have been closely following international research fronts and steadily improving their research results. (2) Hotspots such as carbon dioxide emissions, the carbon footprint (CF), water footprint (WF) and sustainable use have been receiving attention both in China and internationally. International researchers focus on hot topics such as public satisfaction, land and resource use, spatial equity and policy analysis. In comparison, Chinese researchers prefer to modify and improve relevant methods, such as the three-dimensional EF (3D EF) and footprint family methods. (3) The research frontier of EF is based on improving the EF accounting method, which is aimed at assessing and monitoring the relationship between ecology and the economic society on a multi-scale basis, establishing an index system that can evaluate the demand for natural resources and environmental pressure, and explore the issues of mechanisms of regional ecology resource allocation fairness, and market-led and government-led ecological governance efficiency.
C1 [Yang, Yi; Meng, Guanfei] Xian Univ Technol, Sch Econ & Management, Xian 710054, Shaanxi, Peoples R China.
RP Yang, Y (通讯作者)，Xian Univ Technol, Sch Econ & Management, Xian 710054, Shaanxi, Peoples R China.
EM yangyi_nwpu@163.com
FU National Social Science Foundation of China Western Project [15XJL009];
   Science and Technology Planning Project of Xi'an [2017109SF/RK003-(5)];
   Soft Science Research Program of Shaanxi-Joint Project [2018KRLY07]
FX This work was supported by the National Social Science Foundation of
   China Western Project (15XJL009), Science and Technology Planning
   Project of Xi'an (2017109SF/RK003-(5)),Soft Science Research Program of
   Shaanxi-Joint Project (2018KRLY07).
CR [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], 2017, DOCUMENTATION INFORM
   [Anonymous], 2008, PAS 2050 SPEC ASS LI
   [Anonymous], 2017, GUANG MING DAILY
   [Anonymous], 1992, UN FRAM CONV CLIM CH
   [Anonymous], 2017, PEOPLES DAILY
   [Anonymous], 2001, GUANG MING DAILY
   [Anonymous], 2015, ENH ACT CLIM CHANG C
   [Anonymous], [No title captured]
   Baabou W, 2017, ENVIRON SCI POLICY, V69, P94, DOI 10.1016/j.envsci.2016.12.013
   Beijing Water-affair Authority, 2012, BEIJ WAT RES B
   Bello MO, 2018, J ENVIRON MANAGE, V219, P218, DOI 10.1016/j.jenvman.2018.04.101
   Betts NL, 2008, GEOGRAPHY, V93, P60
   Butchart SHM, 2010, SCIENCE, V328, P1164, DOI 10.1126/science.1187512
   Chambers JQ, 2007, SCIENCE, V318, P1107, DOI 10.1126/science.1148913
   Charfeddine L, 2017, ENERG ECON, V65, P355, DOI 10.1016/j.eneco.2017.05.009
   Charfeddine L, 2017, RENEW SUST ENERG REV, V76, P138, DOI 10.1016/j.rser.2017.03.031
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   [陈孝慈 Chen Xiaoci], 2017, [中国安全科学学报, China Safety Science Journal（CSSJ）], V27, P13
   Chen Y., 2015, STUDIES SCI SCI, V33, P242
   Chu X, 2017, PHYS CHEM EARTH, V101, P43, DOI 10.1016/j.pce.2017.05.001
   Cui Y, 2018, ELECTRON COMMER RES, V18, P837, DOI 10.1007/s10660-018-9288-9
   [杜悦悦 Du Yueyue], 2016, [地理科学进展, Progress in Geography], V35, P1186
   Ewing BR, 2012, ECOL INDIC, V23, P1, DOI 10.1016/j.ecolind.2012.02.025
   Fang K, 2018, SCI TOTAL ENVIRON, V631-632, P476, DOI 10.1016/j.scitotenv.2018.02.299
   [方恺 Fang Kai], 2015, [生态学报, Acta Ecologica Sinica], V35, P7974
   [方恺 Fang Kai], 2015, [生态学报, Acta Ecologica Sinica], V35, P1647
   Fang K, 2014, ECOL INDIC, V36, P508, DOI 10.1016/j.ecolind.2013.08.017
   [方恺 Fang Kai], 2013, [生态学报, Acta Ecologica Sinica], V33, P267
   Galli A, 2014, BIOL CONSERV, V173, P121, DOI 10.1016/j.biocon.2013.10.019
   Galli A, 2012, ECOL INDIC, V16, P100, DOI 10.1016/j.ecolind.2011.06.017
   Gu X. W., 2005, J NE U, V26, P295
   [郭秀锐 Guo Xiurui], 2003, [地理研究, Geographical Research], V22, P654
   [韩增林 Han Zenglin], 2016, [地理科学, Scientia Geographica Sinica], V36, P643
   [何霄嘉 He Xiaojia], 2017, [地球科学进展, Advance in Earth Sciences], V32, P560
   Hoekstra AY, 2014, SCIENCE, V344, P1114, DOI 10.1126/science.1248365
   [胡秀芳 Hu Xiufang], 2015, [生态学报, Acta Ecologica Sinica], V35, P6934
   Huang L, 2016, LANDSCAPE URBAN PLAN, V145, P57, DOI 10.1016/j.landurbplan.2015.09.005
   Hubacek K, 2009, J CLEAN PROD, V17, P1241, DOI 10.1016/j.jclepro.2009.03.011
   Isman M, 2018, J CLEAN PROD, V174, P1032, DOI 10.1016/j.jclepro.2017.10.189
   Jorgenson AK, 2003, SOC PROBL, V50, P374, DOI 10.1525/sp.2003.50.3.374
   Kitzes J, 2009, ECOL ECON, V68, P1991, DOI 10.1016/j.ecolecon.2008.06.022
   Liu Hai-yan, 2017, Yingyong Shengtai Xuebao, V28, P4067, DOI 10.13287/j.1001-9332.201712.014
   [刘某承 Liu Moucheng], 2014, [生态学报, Acta Ecologica Sinica], V34, P97
   Lu Y, 2017, J CLEAN PROD, V163, P146, DOI 10.1016/j.jclepro.2016.03.034
   Lubchenco J, 1998, SCIENCE, V279, P491, DOI 10.1126/science.279.5350.491
   Mancini MS, 2017, ECOL INDIC, V77, P123, DOI 10.1016/j.ecolind.2017.01.033
   Mancini MS, 2016, ECOL INDIC, V61, P390, DOI 10.1016/j.ecolind.2015.09.040
   Martinez-Rocamora A, 2017, ENERG BUILDINGS, V155, P339, DOI 10.1016/j.enbuild.2017.09.038
   McDonald GW, 2004, ECOL ECON, V50, P49, DOI 10.1016/j.ecolecon.2004.02.008
   Mi ZF, 2016, APPL ENERG, V184, P1073, DOI 10.1016/j.apenergy.2016.06.094
   Ministry of Ecology and Environment of the People's Republic of China, 2017, NOT ISS BEIJ TIANJ H
   Monfreda C, 2004, LAND USE POLICY, V21, P231, DOI 10.1016/j.landusepol.2003.10.009
   Moran DD, 2008, ECOL ECON, V64, P470, DOI 10.1016/j.ecolecon.2007.08.017
   Morse S, 2014, SUSTAINABILITY-BASEL, V6, P4749, DOI 10.3390/su6084749
   Muniz I, 2016, EURE, V42, P209, DOI 10.4067/S0250-71612016000300009
   National Development and Reform Commission of the People's Republic of China, 2017, PROGR EST NAT CARB E
   Niccolucci V, 2011, ECOL MODEL, V222, P2939, DOI 10.1016/j.ecolmodel.2011.04.020
   Price D. J. de S, 1983, THIS WEEKS CITATION
   Rees W.E., 1992, ENVIRON URBAN, V4, P121, DOI [10.1177/095624789200400212, DOI 10.1177/095624789200400212]
   Solarin SA, 2018, ECOL INDIC, V89, P35, DOI 10.1016/j.ecolind.2018.01.064
   Song GB, 2015, SCI TOTAL ENVIRON, V529, P191, DOI 10.1016/j.scitotenv.2015.05.068
   Su Y, 2018, J CLEAN PROD, V195, P354, DOI 10.1016/j.jclepro.2018.05.184
   Sun Yan-zhi, 2015, Shengtaixue Zazhi, V34, P524
   Szigeti C, 2017, ECOL INDIC, V72, P111, DOI 10.1016/j.ecolind.2016.07.034
   Teixido-Figueras J, 2015, ECOL ECON, V118, P30, DOI 10.1016/j.ecolecon.2015.07.014
   The state forestry administration of the People's Republic of China, 2017, COMB DES SAND IND HI
   Tho SW, 2017, INT J SCI MATH EDUC, V15, P1217, DOI 10.1007/s10763-016-9740-z
   Tong Q. M, 2018, J HUAZHONG AGR U SOC, V1, P46, DOI [10.13300/j.cnki.hnwkxb.2018.01.006, DOI 10.13300/J.CNKI.HNWKXB.2018.01.006]
   Ulucak R, 2017, ECOL INDIC, V80, P337, DOI 10.1016/j.ecolind.2017.05.020
   United Nations Environment Programme, 1992, CONV BIOL DIV
   Verhofstadt E, 2016, ECOL ECON, V127, P80, DOI 10.1016/j.ecolecon.2016.03.021
   Wackernagel M, 1999, ECOL ECON, V29, P375, DOI 10.1016/S0921-8009(98)90063-5
   Wackernagel M., 1996, OUR ECOLOGICAL FOOTP
   Wackernagel M, 1994, ECOLOGICAL FOOTPRINT
   [王德录 Wang Delu], 2016, [水土保持研究, Research of Soil and Water Conservation], V23, P278
   [王占彪 Wang Zhanbiao], 2015, [中国农业科学, Scientia Agricultura Sinica], V48, P83
   Wiedmann T, 2007, ECOL ECON, V61, P15, DOI 10.1016/j.ecolecon.2006.12.003
   Wu DC, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8121345
   [吴普特 Wu Pute], 2017, [水利学报, Journal of Hydraulic Engineering], V48, P651
   [夏恩君 Xia Enjun], 2017, [科研管理, Science Research Management], V38, P1
   Xiao X.B., 2015, DOCUMENTATION INFORM, V164, P93, DOI [10.13366/j.dik.2015.02.093, DOI 10.13366/J.DIK.2015.02.093]
   [辛伟 Xin Wei], 2014, [心理科学进展, Advances in Psychological Science], V22, P334
   Xu Z., 2000, ACTA ECOL SIN, P607, DOI DOI 10.3321/J.ISSN:0375-5444.2000.05.010
   Xun FF, 2019, SCI TOTAL ENVIRON, V649, P582, DOI 10.1016/j.scitotenv.2018.08.116
   [杨屹 Yang Yi], 2017, [生态学报, Acta Ecologica Sinica], V37, P7061
   [杨屹 Yang Yi], 2017, [环境科学学报, Acta Scientiae Circumstantiae], V37, P2021
   Zhang F, 2009, FINANC RES LETT, V6, P219, DOI 10.1016/j.frl.2009.09.001
   [张惠 Zhang Hui], 2014, [地球科学进展, Advance in Earth Sciences], V29, P632
   Zhang JC, 2010, POWER AND ENERGY ENGINEERING CONFERENCE 2010, P562
   [张坤民 ZHANG Kunmin], 2008, [中国人口·资源与环境, China Population·Resources and Environment], V18, P1
   Zhang Z.Q, 2000, ECOL ECON, V10, P8
   [赵先贵 Zhao Xiangui], 2016, [地理研究, Geographical Research], V35, P2384
NR 94
TC 22
Z9 26
U1 27
U2 182
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1470-160X
EI 1872-7034
J9 ECOL INDIC
JI Ecol. Indic.
PD JUL
PY 2019
VL 102
BP 650
EP 665
DI 10.1016/j.ecolind.2019.03.031
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 IC4VG
UT WOS:000470964500065
DA 2022-08-23
ER

PT J
AU Popping, R
AF Popping, R
TI Representation of developments in labour market research
SO QUALITY & QUANTITY
LA English
DT Article
DE knowledge graphs; knowledge representation
AB A knowledge graph is a kind of semantic network representing some scientific theory. The paper describes the state of art in this field and addresses a number of problems that has not been solved yet. These are: implicit relations, strength of (causal) relations, and conditions. Concepts might be too broad or complex to be used properly, directions for solving this problem are explored. The solutions are applied to a knowledge graph in the field of labour markets.
C1 Univ Groningen, Dept Sociol, NL-9712 TG Groningen, Netherlands.
RP Popping, R (通讯作者)，Univ Groningen, Dept Sociol, NL-9712 TG Groningen, Netherlands.
EM r.popping@ppsw.rug.nl
RI Popping, Roel/E-3974-2013
OI Popping, Roel/0000-0003-4646-9472
CR Carley K.M., 1993, SOCIOL METHODOL, P75
   CARRE B, 1979, GRAPHS NETWORKS
   *ICS, 1995, ANN REP INT CTR SOC
   James P., 1992, Linguistic Instruments in Knowledge Engineering. Proceedings of the 1991 Workshop, P97
   Popping R, 2003, SOC SCI INFORM, V42, P91, DOI 10.1177/0539018403042001798
   POPPING R, 1997, SOC SCI INFORM, V35, P731
   Popping R, 2000, COMPUTER ASSISTED TE
   Stokman F.N., 1988, HUM-COMPUT INTERACT, P186, DOI [10.1007/978-3-642-73402-112, DOI 10.1016/J.STR.2008.10.011]
NR 8
TC 3
Z9 3
U1 0
U2 4
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0033-5177
J9 QUAL QUANT
JI Qual. Quant.
PD JUN
PY 2005
VL 39
IS 3
BP 241
EP 251
DI 10.1007/s11135-004-5008-8
PG 11
WC Social Sciences, Interdisciplinary; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Mathematics
GA 940AH
UT WOS:000230115600001
DA 2022-08-23
ER

PT J
AU Liu, H
   Zhou, SW
   Chen, CF
   Gao, TL
   Xu, JY
   Shu, ML
AF Liu, Hao
   Zhou, Shuwang
   Chen, Changfang
   Gao, Tianlei
   Xu, Jiyong
   Shu, Minglei
TI Dynamic knowledge graph reasoning based on deep reinforcement learning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph query answering; Dynamic reasoning hypothesis; Reward
   function; Dynamic reward; Judgment condition
AB Knowledge graph reasoning is a task of reasoning new knowledge or conclusions based on existing knowledge. Recently, reinforcement learning has become a new technical tool for knowledge graph reasoning. However, most previous work focuses on the short fixed-step multi-hop reasoning or the single-step reasoning. In this paper, a dynamic knowledge graph reasoning framework is proposed based on deep reinforcement learning, which learns to navigate the graph to find the promising target answer conditioned on the input query. A novel reward function is constructed based on the proposed dynamic reasoning hypothesis, and the dynamic reward is proposed to obtain the dynamic reasoning model. The judgment condition is established to achieve dynamic reasoning, and an embedding model is chosen as the pre-trained model for initialization. The experimental analysis on several typical datasets shows that our model is indeed effective, and can improve over existing path-based knowledge graph reasoning models, and can be further applied to longer reasoning paths with good results. (C) 2022 Published by Elsevier B.V.
C1 [Liu, Hao; Zhou, Shuwang; Chen, Changfang; Gao, Tianlei; Xu, Jiyong; Shu, Minglei] Qilu Univ Technol, Shandong Artificial Intelligence Inst, Shandong Acad Sci, Jinan, Peoples R China.
   [Liu, Hao; Zhou, Shuwang; Gao, Tianlei; Xu, Jiyong] Qilu Univ Technol, Shandong Comp Sci Ctr, Natl Supercomp Ctr Jinan, Shandong Acad Sci, Jinan, Peoples R China.
RP Shu, ML (通讯作者)，Qilu Univ Technol, Shandong Artificial Intelligence Inst, Shandong Acad Sci, Jinan, Peoples R China.
EM shuml@sdas.org
FU China National Key Research and Development Program [2019YFB1405300];
   Shan-dong Provincial Key Research and Development Program, China
   [2019TSLH0202, 2019TSLH0203]; Natural Sci-ence Foundation of Shandong
   Province, China [ZR2021MF084]
FX Acknowledgments The work was supported by the China National Key
   Research and Development Program (NO. 2019YFB1405300) ; the Shan-dong
   Provincial Key Research and Development Program, China (NO. 2019TSLH0202
   and NO. 2019TSLH0203) ; the Natural Sci-ence Foundation of Shandong
   Province, China under Grant (No. ZR2021MF084) .
CR Bast H, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P95, DOI 10.1145/2567948.2577016
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen J, 2021, IEEE T CYBERNETICS, DOI 10.1109/TCYB.2021.3064092
   Chen W., 2018, P 2018 C N AM CHAPT, V1, P1823, DOI [10.18653/v1/N18-1165, DOI 10.18653/V1/N18-1165]
   Das R., 2018, ICLR
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dietz L, 2018, ACM/SIGIR PROCEEDINGS 2018, P1387, DOI 10.1145/3209978.3210187
   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
   Guo S., 2016, P C EMP METH NAT LAN, P192, DOI 10.18653/v1/D16-1019
   Haarnoja T, 2018, PR MACH LEARN RES, V80
   Han X., 2018, P C EMP METH NAT LAN, P139
   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
   Jiang B, 2020, KNOWL-BASED SYST, V210, DOI 10.1016/j.knosys.2020.106479
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Li Jiwei, 2016, ARXIV160601541
   Lin X.V., 2018, P 2018 C EMP METH NA
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Qian JW, 2019, IEEE T DEPEND SECURE, V16, P679, DOI 10.1109/TDSC.2017.2697854
   Schoenmackers Stefan, 2010, P 2010 C EMP METH NA, P1088
   Shen YL, 2018, ADV NEUR IN, V31
   Song XY, 2021, INFORM SCIENCES, V580, P510, DOI 10.1016/j.ins.2021.08.100
   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
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang RJ, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1487, DOI 10.1145/3269206.3269252
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H, SSP SEMANTIC SPACE P, P7
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yang B., 2015, P INT C LEARN REPR I
   Zhong M., 2021, ARXIV210315447CS
NR 34
TC 1
Z9 1
U1 17
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 APR 6
PY 2022
VL 241
AR 108235
DI 10.1016/j.knosys.2022.108235
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V5CE
UT WOS:000788360300015
DA 2022-08-23
ER

PT J
AU Song, YC
   Li, AP
   Tu, HK
   Chen, K
   Li, CC
AF Song, Yichen
   Li, Aiping
   Tu, Hongkui
   Chen, Kai
   Li, Chenchen
TI A Novel Encoder-Decoder Knowledge Graph Completion Model for Robot Brain
SO FRONTIERS IN NEUROROBOTICS
LA English
DT Article
DE learning-based artificial intelligence; robot intelligence; human-robot
   interaction; knowledge graph reasoning and completion; knowledge graph
   embedding
AB With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. And studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Although the large-scale knowledge graphs contain a large amount of information, they are still incomplete compared with real-world knowledge. Most existing methods for knowledge graph completion focus on entity representation learning. However, the importance of relation representation learning is ignored, as well as the cross-interaction between entities and relations. In this paper, we propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show that our method achieves better link prediction performance than state-of-the-art embedding models on two benchmark datasets, WN18RR and FB15k-237.
C1 [Song, Yichen; Li, Aiping; Tu, Hongkui; Chen, Kai; Li, Chenchen] Natl Univ Def Technol, Coll Comp, Changsha, Peoples R China.
RP Li, AP (通讯作者)，Natl Univ Def Technol, Coll Comp, Changsha, Peoples R China.
EM 13017395458@163.com
FU National Key R&D Program of China [2016QY03D0603, 2017YFB0802204,
   2019QY1406, 2017YFB0803303]; Key R&D Program of Guangdong Province
   [2019B010136003]; National Natural Science Foundation of China
   [61732004, 61732022, 62072131, 61672020]
FX This work described in this paper was partially supported by the
   National Key R&D Program of China (Nos. 2016QY03D0603, 2017YFB0802204,
   2019QY1406, and 2017YFB0803303), the Key R&D Program of Guangdong
   Province (No. 2019B010136003), and the National Natural Science
   Foundation of China (Nos. 61732004, 61732022, 62072131, and 61672020).
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Gu ZQ, 2022, IEEE T NETW SCI ENG, V9, P333, DOI 10.1109/TNSE.2021.3058762
   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 Y., 2021, MDATA NEW KNOWLEDGE, DOI [10.1007/978-3-030-71590-8, DOI 10.1007/978-3-030-71590-8]
   Kipf Thomas N., 2017, INT C LEARN REPR ICL
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   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, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Shang C, 2019, AAAI CONF ARTIF INTE, P3060
   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, 34 AAAI C ART INT AA, P3009, DOI [10.1609/aaai.v34i03.5694, DOI 10.1609/AAAI.V34I03.5694]
   Vaswani A, 2017, ADV NEUR IN, V30
   Velickovic P., 2018, P ICLR
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H., 2015, ABS150905488 CORR
   Yang B., 2015, ICLR
   Zhang JN, 2018, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, P339
   Zhao XJ, 2020, WORLD WIDE WEB, V23, P2567, DOI 10.1007/s11280-020-00811-0
NR 23
TC 0
Z9 0
U1 6
U2 14
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 11
PY 2021
VL 15
AR 674428
DI 10.3389/fnbot.2021.674428
PG 8
WC Computer Science, Artificial Intelligence; Robotics; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Robotics; Neurosciences & Neurology
GA SG4WH
UT WOS:000653441500001
PM 34045950
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Sima, AC
   de Farias, TM
   Anisimova, M
   Dessimoz, C
   Robinson-Rechavi, M
   Zbinden, E
   Stockinger, K
AF Sima, Ana Claudia
   de Farias, Tarcisio Mendes
   Anisimova, Maria
   Dessimoz, Christophe
   Robinson-Rechavi, Marc
   Zbinden, Erich
   Stockinger, Kurt
TI Bio-SODA UX: enabling natural language question answering over knowledge
   graphs with user disambiguation
SO DISTRIBUTED AND PARALLEL DATABASES
LA English
DT Article; Early Access
DE Question answering; Knowledge graphs; Ranking
ID LINKED DATA
AB The problem of natural language processing over structured data has become a growing research field, both within the relational database and the Semantic Web community, with significant efforts involved in question answering over knowledge graphs (KGQA). However, many of these approaches are either specifically targeted at open-domain question answering using DBpedia, or require large training datasets to translate a natural language question to SPARQL in order to query the knowledge graph. Hence, these approaches often cannot be applied directly to complex scientific datasets where no prior training data is available. In this paper, we focus on the challenges of natural language processing over knowledge graphs of scientific datasets. In particular, we introduce Bio-SODA, a natural language processing engine that does not require training data in the form of question-answer pairs for generating SPARQL queries. Bio-SODA uses a generic graph-based approach for translating user questions to a ranked list of SPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm that includes node centrality as a measure of relevance for selecting the best SPARQL candidate query. Our experiments with real-world datasets across several scientific domains, including the official bioinformatics Question Answering over Linked Data (QALD) challenge, as well as the CORDIS dataset of European projects, show that Bio-SODA outperforms publicly available KGQA systems by an F1-score of least 20% and by an even higher factor on more complex bioinformatics datasets. Finally, we introduce Bio-SODA UX, a graphical user interface designed to assist users in the exploration of large knowledge graphs and in dynamically disambiguating natural language questions that target the data available in these graphs.
C1 [Sima, Ana Claudia; de Farias, Tarcisio Mendes; Anisimova, Maria; Dessimoz, Christophe; Robinson-Rechavi, Marc; Zbinden, Erich] SIB Swiss Inst Bioinformat, Lausanne, Switzerland.
   [de Farias, Tarcisio Mendes; Dessimoz, Christophe] Univ Lausanne, Dept Computat Biol, Lausanne, Switzerland.
   [de Farias, Tarcisio Mendes; Robinson-Rechavi, Marc] Univ Lausanne, Dept Ecol & Evolut, Lausanne, Switzerland.
   [Anisimova, Maria; Zbinden, Erich; Stockinger, Kurt] ZHAW Zurich Univ Appl Sci, Zurich, Switzerland.
   [Dessimoz, Christophe] UCL, Dept Genet Evolut & Environm, London, England.
   [Dessimoz, Christophe] UCL, Dept Comp Sci, London, England.
RP Sima, AC (通讯作者)，SIB Swiss Inst Bioinformat, Lausanne, Switzerland.
EM ana-claudia.sima@sib.swiss
RI Dessimoz, Christophe/D-3678-2011
OI Dessimoz, Christophe/0000-0002-2170-853X
FU Swiss National Science Foundation [NRP 75, 407540_167149]
FX We thank the Swiss National Science Foundation for funding (NRP 75,
   grant 407540_167149), Lukas Blunschi for the implementation of the SODA
   system for keyword search system over relational databases [47], on
   which our prototype is based, and Katrin Affolter for important
   contributions to the natural language processing pipeline in Bio-SODA.
   We thank Chiara Gabella and Severine Duvaud from the SIB User Experience
   Team for their advice on the design of Bio-SODA UX.
CR Affolter K, 2019, VLDB J, V28, P793, DOI 10.1007/s00778-019-00567-8
   Altenhoff AM, 2021, NUCLEIC ACIDS RES, V49, pD373, DOI 10.1093/nar/gkaa1007
   Amer-Yahia S, 2021, SIGMOD REC, V50, P23
   [Anonymous], 2013, PROC LBM, V2013, P39
   Bastian FB, 2021, NUCLEIC ACIDS RES, V49, pD831, DOI 10.1093/nar/gkaa793
   Blunschi L, 2012, PROC VLDB ENDOW, V5, P932, DOI 10.14778/2336664.2336667
   Bonifati A, 2017, PROC VLDB ENDOW, V11, P149, DOI 10.14778/3149193.3149196
   Brunner U, 2021, PROC INT CONF DATA, P2177, DOI 10.1109/ICDE51399.2021.00220
   Chakraborty N., 2019, ARXIV PREPRINT ARXIV
   Deutch D, 2020, VLDB J, V29, P485, DOI 10.1007/s00778-019-00584-7
   Diefenbach Dennis, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P145, DOI 10.1007/978-3-319-93417-4_10
   Diefenbach D., 2020, QANSWER KG
   Diefenbach D, 2020, SEMANT WEB, V11, P421, DOI 10.3233/SW-190343
   Diefenbach D, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3507, DOI 10.1145/3308558.3314124
   Dubey M, 2019, LECT NOTES COMPUT SC, V11779, P69, DOI 10.1007/978-3-030-30796-7_5
   Ferragina P., 2010, P 19 ACM INT C INF K, P1625, DOI DOI 10.1145/1871437.1871689
   Ferre S, 2017, SEMANT WEB, V8, P405, DOI 10.3233/SW-150208
   Gkirtzou K, 2015, LECT NOTES COMPUT SC, V9316, P111, DOI 10.1007/978-3-319-24592-8_9
   Hamon T., 2014, CLEF WORKING NOTES, V1212, P28
   Hamon T, 2017, SEMANT WEB, V8, P581, DOI 10.3233/SW-160244
   Hasnain A, 2017, J BIOMED SEMANT, V8, DOI 10.1186/s13326-017-0118-0
   Kellou-Menouer K, 2015, LECT NOTES COMPUT SC, V9381, P481, DOI 10.1007/978-3-319-25264-3_36
   Kokkalis A., 2012, P 2012 ACM SIGMOD IN, P673
   Li F, 2014, PROC VLDB ENDOW, V8, P73, DOI 10.14778/2735461.2735468
   Li F, 2016, SIGMOD REC, V45, P6, DOI 10.1145/2949741.2949744
   Lukovnikov D, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1211, DOI 10.1145/3038912.3052675
   Maheshwari G, 2019, LECT NOTES COMPUT SC, V11778, P487, DOI 10.1007/978-3-030-30793-6_28
   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
   Nadig S., 2020, INT C LANG RES EV LR
   Ngonga Ngomo A. C., 2013, P 22 INT C WORLD WID, P977, DOI [10.1145/2488388.2488473, DOI 10.1145/2488388.2488473]
   Olieman A., 2014, P 1 INT WORKSH ENT R, P13, DOI [10. 1145/2633211.2634353, DOI 10.1145/2633211.2634353]
   Page L., 1999, 199966 STANF INFOLAB
   Paulheim H, 2013, LECT NOTES COMPUT SC, V8218, P510, DOI 10.1007/978-3-642-41335-3_32
   Redaschi N., 2009, 3 INT BIOC C
   Saha D, 2016, PROC VLDB ENDOW, V9, P1209, DOI 10.14778/2994509.2994536
   Sakor A., 2019, ENTITY RELATION LINK
   Sima A.C., 2021, 33 INT C SCI STAT DA, P61
   Sima AC, 2019, DATABASE-OXFORD, DOI 10.1093/database/baz106
   Singh K.K., 2018, ARXIV PREPRINT ARXIV
   Song DZ, 2015, LECT NOTES COMPUT SC, V9367, P21, DOI 10.1007/978-3-319-25010-6_2
   Trivedi P, 2017, LECT NOTES COMPUT SC, V10588, P210, DOI 10.1007/978-3-319-68204-4_22
   Unger C., 2014, QUESTION ANSWERING L
   Vakulenko S, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1431, DOI 10.1145/3357384.3358026
   Yu Tao, 2018, ARXIV180908887, P3911, DOI 10 .18653/v1/D18-1425
   Zafar Hamid, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P714, DOI 10.1007/978-3-319-93417-4_46
   Zheng WG, 2018, PROC VLDB ENDOW, V11, P1373, DOI 10.14778/3236187.3236192
NR 47
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0926-8782
EI 1573-7578
J9 DISTRIB PARALLEL DAT
JI Distrib. Parallel Databases
DI 10.1007/s10619-022-07414-w
EA JUL 2022
PG 32
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Y8JV
UT WOS:000826136900001
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Jia, J
   Zhang, YZ
   Saad, M
AF Jia, Jia
   Zhang, Yingzhong
   Saad, Mohamed
TI An approach to capturing and reusing tacit design knowledge using
   relational learning for knowledge graphs
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Design knowledge; Tacit design knowledge; Knowledge graph; Relational
   learning; Tensor Factorization
ID REASONING APPROACH; ONTOLOGY; SYSTEM; REPRESENTATION; ACQUISITION;
   EVOLUTION
AB Tacit design knowledge plays an important role in the process of product design and is a valuable knowledge asset for enterprises. In terms of the characteristics of tacit rational design knowledge, this paper puts forward a scientific hypothesis and approach on capturing and reusing tacit rational design knowledge. The presented approach represents the observable design result facts of products using design knowledge graphs. A design issue-solving oriented knowledge graph model is presented, where directed relation edges represent design is-sues, and nodes stand for design solutions. When a new design solutions requirement needs to be searched, tacit design knowledge can be reused by relational learning for the constructed design knowledge graphs. In relational learning, the design knowledge graph is converted into a three-order tensor, where two modes are solution nodes, and the third mode holds the issue relations. Then, a tensor factorization approach is employed to calculate the latent features between design solutions for an issue relation. As a result, a score vector to represent the existence of issue-solution relations can be obtained. By sorting the scores in descending order, we may select the solution node with the highest score as the design solution to be searched. Finally, a stamping die design case study is provided. The case study shows that the proposed approach is feasible, and effective, and has better flexibility, scalability and efficiency than CBR methods.
C1 [Jia, Jia; Zhang, Yingzhong; Saad, Mohamed] Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China.
RP Zhang, YZ (通讯作者)，Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China.
EM zhangyz@dlut.edu.cn
FU National Natural Science Foundation of China [51775081]
FX Acknowledgments This work is supported by the National Natural Science
   Foundation of China (Grant No. 51775081) . The authors would like to
   thank all the editors and reviewers for their helpful suggestions on
   this study.
CR AAMODT A, 1994, AI COMMUN, V7, P39
   Armaghan N, 2012, INFORM SCIENCES, V210, P55, DOI 10.1016/j.ins.2012.04.033
   Azadeh A, 2010, EXPERT SYST APPL, V37, P627, DOI 10.1016/j.eswa.2009.06.018
   Bracewell R, 2009, COMPUT AIDED DESIGN, V41, P173, DOI 10.1016/j.cad.2008.10.005
   Brockmann EN, 2002, GROUP ORGAN MANAGE, V27, P436, DOI 10.1177/1059601102238356
   Castro-Schez JJ, 2013, KNOWL-BASED SYST, V44, P112, DOI 10.1016/j.knosys.2013.01.033
   Chandrasegaran SK, 2013, COMPUT AIDED DESIGN, V45, P204, DOI 10.1016/j.cad.2012.08.006
   Chen CH, 2008, KNOWL-BASED SYST, V21, P284, DOI 10.1016/j.knosys.2007.07.002
   Chen SL, 2016, ADV ENG INFORM, V30, P564, DOI 10.1016/j.aei.2016.06.005
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Chen YJ, 2010, CONCURRENT ENG-RES A, V18, P233, DOI 10.1177/1063293X10373824
   Efthymiou K, 2015, CIRP J MANUF SCI TEC, V8, P1, DOI 10.1016/j.cirpj.2014.10.006
   Farhan U, 2017, INT J ADV MANUF TECH, V92, P2689, DOI 10.1007/s00170-017-0274-5
   Gaines BR, 2013, INT J HUM-COMPUT ST, V71, P135, DOI 10.1016/j.ijhcs.2012.10.010
   Gao JJ, 2005, 2005 International Conference on Services Systems and Services Management, Vols 1 and 2, Proceedings, P1113
   Garcia-Crespo A, 2010, COMPUT IND, V61, P595, DOI 10.1016/j.compind.2010.01.004
   Guo Y, 2013, COMPUT IND, V64, P90, DOI 10.1016/j.compind.2012.10.006
   HARSHMAN RA, 1994, COMPUT STAT DATA AN, V18, P39, DOI 10.1016/0167-9473(94)90132-5
   Hashemi H, 2014, INT J ADV MANUF TECH, V74, P113, DOI 10.1007/s00170-014-5930-4
   He LL, 2019, IEEE ACCESS, V7, P101231, DOI 10.1109/ACCESS.2019.2931361
   Jintae Lee, 1991, Human-Computer Interaction, V6, P251
   Koskinen KU, 2002, INT J PROD ECON, V80, P57, DOI 10.1016/S0925-5273(02)00243-8
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   Lee KS, 2002, INT J ADV MANUF TECH, V20, P284, DOI 10.1007/s001700200154
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li XY, 2021, COMPUT IND, V129, DOI 10.1016/j.compind.2021.103449
   Li XY, 2017, ADV ENG INFORM, V34, P17, DOI 10.1016/j.aei.2017.08.001
   Liu LJ, 2014, INT J PROD RES, V52, P5997, DOI 10.1080/00207543.2014.895445
   Long XJN, 2021, ADV ENG INFORM, V50, DOI 10.1016/j.aei.2021.101409
   Mohamed SK, 2020, INFORM SCIENCES, V508, P343, DOI 10.1016/j.ins.2019.08.061
   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
   Noh JB, 2000, EXPERT SYST APPL, V19, P249, DOI 10.1016/S0957-4174(00)00037-3
   Polanyi Michael, 1967, TACIT DIMENSION
   Bejarano JCR, 2014, AI EDAM, V28, P49, DOI 10.1017/S0890060413000498
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Song B, 2016, ADV ENG INFORM, V30, P467, DOI 10.1016/j.aei.2016.06.002
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   TUCKER LR, 1966, PSYCHOMETRIKA, V31, P279, DOI 10.1007/BF02289464
   Wang FH, 2021, ADV ENG INFORM, V47, DOI 10.1016/j.aei.2020.101207
   Wang H, 2008, COMPUT AIDED DESIGN, V40, P1121, DOI 10.1016/j.cad.2008.11.001
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xie XL, 2013, COMPUT AIDED DESIGN, V45, P963, DOI 10.1016/j.cad.2013.02.004
   Xu LH, 2021, SOFT COMPUT, V25, P5683, DOI 10.1007/s00500-020-05564-6
   Zhang YZ, 2015, ADV ENG INFORM, V29, P985, DOI 10.1016/j.aei.2015.09.002
   Zhang YZ, 2013, ADV ENG INFORM, V27, P13, DOI 10.1016/j.aei.2012.10.005
NR 47
TC 0
Z9 0
U1 12
U2 12
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1474-0346
EI 1873-5320
J9 ADV ENG INFORM
JI Adv. Eng. Inform.
PD JAN
PY 2022
VL 51
AR 101505
DI 10.1016/j.aei.2021.101505
PG 17
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA 1K0FL
UT WOS:000798285200003
DA 2022-08-23
ER

PT J
AU Ji, KX
   Hui, B
   Luo, GC
AF Ji, Kexi
   Hui, Bei
   Luo, Guangchun
TI Graph Attention Networks With Local Structure Awareness for Knowledge
   Graph Completion
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph embedding; knowledge graph completion; graph attention
   networks
ID EMBEDDINGS
AB Graph neural networks have been proven to be very effective for representation learning of knowledge graphs. Recent methods such as SACN and CompGCN, have achieved the most advanced results in knowledge graph completion. However, previous efforts mostly rely on localized first-order approximations of spectral graph convolutions or first-order neighborhoods, ignoring the abundant local structures like cycles and stars. Therefore, the diverse semantic information beneath these structures is not well-captured, leaving opportunities for better knowledge representation which will finally help KGC. In this work, we propose LSA-GAT, a graph attention network with a novel neighborhood aggregation strategy for knowledge graph completion. The model can take special local structures into account, and derive a sophisticated representation covering both the semantic and structural information. Moreover, the LSA-GAT model is combined with a CNN-based decoder to form an encoder-decoder framework with a carefully designed training process. The experimental results show significant improvement of the proposed LSA-GAT compared to current state-of-the-art methods on FB15k-237 and WN18RR datasets.
C1 [Ji, Kexi; Hui, Bei; Luo, Guangchun] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Peoples R China.
   [Hui, Bei; Luo, Guangchun] Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 610054, Peoples R China.
RP Hui, B (通讯作者)，Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Peoples R China.; Hui, B (通讯作者)，Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 610054, Peoples R China.
EM bhui@uestc.edu.cn
FU National Key Research and Development Program of China [2018YFC0807500];
   National Natural Science Foundation of China [U19A2059]; Ministry of
   Science and Technology of Sichuan Province Program [2018GZDZX0048,
   20ZDYF0343]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018YFC0807500, in part by the
   National Natural Science Foundation of China under Grant U19A2059, and
   in part by the Ministry of Science and Technology of Sichuan Province
   Program under Grant 2018GZDZX0048 and Grant 20ZDYF0343.
CR 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, P5188
   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 J, 2017, LECT NOTES COMPUT SC, V10177, P19, DOI 10.1007/978-3-319-55753-3_2
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Etzioni O, 2008, COMMUN ACM, V51, P68, DOI 10.1145/1409360.1409378
   Jiang JJ, 2017, IEEE COMMUN SURV TUT, V19, P465, DOI 10.1109/COMST.2016.2615098
   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 KY, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P745, DOI 10.1145/3340531.3411970
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Nathani D., 2019, ARXIV190601195
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Shang C, 2019, AAAI CONF ARTIF INTE, P3060
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   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
   van der Merwe R, 2019, ARCH REC, V40, P239, DOI 10.1080/23257962.2017.1388224
   Vashishth S., 2020, P INT C LEARN REPR I
   Vashishth S, 2020, AAAI CONF ARTIF INTE, V34, P3009
   Velickovic P., 2018, P INT C LEARN REPR A
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Ward M, 2019, PEDIATR HEALTH MED T, V10, P1, DOI 10.2147/PHMT.S183900
   Xiong ZB, 2019, IEEE DATA MINING, P668, DOI 10.1109/ICDM.2019.00077
   Xu K., 2018, INT C LEARNING REPRE
   Yang B., 2015, ARXIV14126575
   Zhang K., P INT C LEARN REPR I
NR 28
TC 2
Z9 2
U1 14
U2 28
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 224860
EP 224870
DI 10.1109/ACCESS.2020.3044343
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PM3RC
UT WOS:000603719800001
OA gold
DA 2022-08-23
ER

PT J
AU Wu, TX
   Wang, HF
   Li, C
   Qi, GL
   Niu, X
   Wang, M
   Li, L
   Shi, CM
AF Wu, Tianxing
   Wang, Haofen
   Li, Cheng
   Qi, Guilin
   Niu, Xing
   Wang, Meng
   Li, Lin
   Shi, Chaomin
TI Knowledge graph construction from multiple online encyclopedias
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article; Proceedings Paper
CT 14th International Conference on Advanced Data Mining and Applications
   (ADMA)
CY 2018
CL Nanjing, PEOPLES R CHINA
DE Knowledge graph; Knowledge extraction; Knowledge linking; Semantic Web
ID DBPEDIA; WEB
AB In recent years, lots of knowledge graphs built from Wikipedia, the largest multilingual online encyclopedia, have been published on the Web to support various applications. However, since non-English data in Wikipedia are sparse, some projects work on knowledge graph construction from multiple non-English online encyclopedias, but many technical details are missing, so it is hard to reuse their frameworks or techniques. In this paper, we propose a new framework to solve knowledge graph construction from multiple online encyclopedias. The core modules are knowledge extraction and knowledge linking. Knowledge extraction consists of regular extraction, i.e., extracting targeted article contents in the whole online encyclopedias periodically, and live extraction, which only extracts the article contents of new and updated entities. Knowledge linking utilizes heuristic lightweight entity matching strategies and a semi-supervised learning method to find duplicated entities and properties from different online encyclopedias. Experimental results show that our approaches for knowledge extraction and linking outperform state-of-the-art baselines in different evaluation metrics, and our framework can generate a large-scale knowledge graph after inputting multiple online encyclopedias.
C1 [Wu, Tianxing; Li, Cheng; Qi, Guilin; Wang, Meng; Li, Lin; Shi, Chaomin] Southeast Univ, Nanjing, Peoples R China.
   [Wu, Tianxing] Nanyang Technol Univ, Singapore, Singapore.
   [Wang, Haofen] Tongji Univ, Intelligent Big Data Visualizat Lab, Shanghai, Peoples R China.
   [Niu, Xing] Univ Maryland, College Pk, MD 20742 USA.
RP Qi, GL (通讯作者)，Southeast Univ, Nanjing, Peoples R China.
EM carter.whfcarter@gmail.com; gqi@seu.edu.cn
CR Agrawal R., 1994, P 20 INT C VER LARG, V1215, P487
   Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Brown LD, 2001, STAT SCI, V16, P101, DOI 10.1214/ss/1009213286
   DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1, DOI 10.1111/j.2517-6161.1977.tb01600.x
   Euzenat J., 2007, ONTOLOGY MATCHING, DOI DOI 10.1007/978-3-540-49612-0
   Fellbaum Christiane, 1998, WORDNET ELECT LEXICA
   FLEISS JL, 1971, PSYCHOL BULL, V76, P378, DOI 10.1037/h0031619
   Guo M, 2007, IEEE INT SYMP CIRC S, P41, DOI 10.1109/ISCAS.2007.378177
   Hellmann S., 2009, DBPEDIA LIVE EXTRACT
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hu  W., 2011, P 20 INT C WORLD WID, P87
   Hu W, 2015, J WEB SEMANT, V34, P1, DOI 10.1016/j.websem.2015.07.003
   Jia HR, 2019, 2019 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL MATERIALS AND POWER EQUIPMENT (ICEMPE 2019), P703, DOI 10.1109/ICEMPE.2019.8727310
   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
   Liang JQ, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3749
   Mahdisoltani F., 2013, P CIDR
   Mikolov T, 2013, SPOK LANG TECHN WORK, P234
   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 A.C.N., 2011, P IJCAI, P2312, DOI [DOI 10.5591/978-1-57735-516-8/IJCAI11-385, DOI 10.5555/2283696.2283783]
   Nikolov A, 2007, K-CAP'07: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE, P185
   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
   Rico M, 2016, LECT NOTES COMPUT SC, V10024, P797, DOI 10.1007/978-3-319-49004-5_51
   Shafer G., 1976, MATH THEORY EVIDENCE, DOI 10.1515/9780691214696
   Sherif MA, 2017, LECT NOTES COMPUT SC, V10249, P103, DOI 10.1007/978-3-319-58068-5_7
   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
   Volker J, 2011, LECT NOTES COMPUT SC, V6643, P124, DOI 10.1007/978-3-642-21034-1_9
   Volz J, 2009, LECT NOTES COMPUT SC, V5823, P650, DOI 10.1007/978-3-642-04930-9_41
   Wang Z., 2013, P 12 INT SEM WEB C, V1035, P121
   WERBOS PJ, 1990, P IEEE, V78, P1550, DOI 10.1109/5.58337
   Wu TX, 2019, INT J SEMANT WEB INF, V15, P22, DOI 10.4018/IJSWIS.2019040102
   Wu TX, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093245
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   Yang Mohan, 2017, IJCAI, P1511
   Zhu H, 2017, IEEE IND ELEC, P6425, DOI 10.1109/IECON.2017.8217119
NR 39
TC 6
Z9 6
U1 8
U2 37
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 2020
VL 23
IS 5
SI SI
BP 2671
EP 2698
DI 10.1007/s11280-019-00719-4
PG 28
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA NM1XD
UT WOS:000567896200003
DA 2022-08-23
ER

PT J
AU Mohamed, SK
   Nounu, A
   Novacek, V
AF Mohamed, Sameh K.
   Nounu, Aayah
   Novacek, Vit
TI Biological applications of knowledge graph embedding models
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Review
DE biomedical knowledge graphs; knowledge graph embeddings; tensor
   factorization; link prediction; drug-target interactions; polypharmacy
   side effects
ID MULTICELLULAR FUNCTION; INTERACTION PREDICTION; DATABASE; NETWORKS;
   INTEGRATION; DRUGBANK; RESOURCE; DISEASE; DRUGS; GENE
AB Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and predictive tasks. Despite the high predictive accuracy of these approaches, they have limited scalability due to their dependency on time-consuming path exploratory procedures. In recent years, owing to the rapid advances of computational technologies, new approaches for modelling graphs and mining them with high accuracy and scalability have emerged. These approaches, i.e. knowledge graph embedding (KGE) models, operate by learning low-rank vector representations of graph nodes and edges that preserve the graph's inherent structure. These approaches were used to analyse knowledge graphs from different domains where they showed superior performance and accuracy compared to previous graph exploratory approaches. In this work, we study this class of models in the context of biological knowledge graphs and their different applications. We then show how KGE models can be a natural fit for representing complex biological knowledge modelled as graphs. We also discuss their predictive and analytical capabilities in different biology applications. In this regard, we present two example case studies that demonstrate the capabilities of KGE models: prediction of drug-target interactions and polypharmacy side effects. Finally, we analyse different practical considerations for KGEs, and we discuss possible opportunities and challenges related to adopting them for modelling biological systems.
C1 [Mohamed, Sameh K.] Natl Univ Ireland Galway, Insight Ctr, Comp Sci, Galway, Ireland.
   [Mohamed, Sameh K.] Data Sci Inst, Galway, Ireland.
   [Novacek, Vit] Natl Univ Ireland Galway, Data Sci Inst, Biomed Discovery Informat Unit, Galway, Ireland.
RP Mohamed, SK (通讯作者)，IDA Business Pk, Insight Ctr Data Analyt, Galway, Ireland.
EM sameh.kamal@insight-centre.org
FU CLARIFY project from European Union's Horizon 2020 research and
   innovation programme [875160]; Science Foundation Ireland (SFI)
   [12/RC/2289_2]; Insight research centre
FX The work presented in this paper was supported by the CLARIFY project
   that has received funding from the European Union's Horizon 2020
   research and innovation programme under grant agreement No 875160, and
   by Insight research centre supported by the Science Foundation Ireland
   (SFI) grant (12/RC/2289_2).
CR Abdelaziz I, 2017, J WEB SEMANT, V44, P104, DOI 10.1016/j.websem.2017.06.002
   Albert R, 2005, J CELL SCI, V118, P4947, DOI 10.1242/jcs.02714
   Alshahrani M, 2017, BIOINFORMATICS, V33, P2723, DOI 10.1093/bioinformatics/btx275
   Amrouch S., 2012, INF TECHN E SERV ICI, P1, DOI DOI 10.1109/ICITES.2012.6216651
   Aronson AR, 2004, STUD HEALTH TECHNOL, V107, P268
   Auer S, 2011, IJCAI 2011
   Bamshad MJ, 2011, NAT REV GENET, V12, P745, DOI 10.1038/nrg3031
   Barabasi AL, 2004, NAT REV GENET, V5, P101, DOI 10.1038/nrg1272
   Bateman A, 2000, NUCLEIC ACIDS RES, V28, P263, DOI 10.1093/nar/28.1.263
   Bateman A, 2015, NUCLEIC ACIDS RES, V43, pD204, DOI 10.1093/nar/gku989
   Bauer-Mehren A, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0020284
   Belleau F, 2008, J BIOMED INFORM, V41, P706, DOI 10.1016/j.jbi.2008.03.004
   Bizer C, 2006, 5 INT SEM WEB C, V175
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Bowes J, 2012, NAT REV DRUG DISCOV, V11, P909, DOI 10.1038/nrd3845
   Cai JJ, 2010, GENOME BIOL EVOL, V2, P393, DOI 10.1093/gbe/evq019
   Carbon S, 2019, NUCLEIC ACIDS RES, V47, pD330, DOI 10.1093/nar/gky1055
   Chen X, 2002, NUCLEIC ACIDS RES, V30, P412, DOI 10.1093/nar/30.1.412
   Cheng FX, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0041064
   Cheng FX, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002503
   CHEUNG TY, 1983, IEEE T SOFTWARE ENG, V9, P504
   COHEN JD, 1992, PSYCHOL REV, V99, P45, DOI 10.1037/0033-295X.99.1.45
   D'Agati VD, 2008, CURR OPIN NEPHROL HY, V17, P271, DOI 10.1097/MNH.0b013e3282f94a96
   Dettmers T., 2018, P 32 AAAI C ART INT
   Dumontier M, 2014, P ISWC 2014 POST DEM, V1272, P401
   Fabregat A, 2018, NUCLEIC ACIDS RES, V46, pD649, DOI [10.1093/nar/gkx1132, 10.1093/nar/gkz1031]
   Fagerberg L, 2014, MOL CELL PROTEOMICS, V13, P397, DOI 10.1074/mcp.M113.035600
   Farber M., 2017, SEMANT WEB, V9, P77, DOI DOI 10.3233/SW-170275
   Ferrucci D, 2010, AI MAG, V31, P59, DOI 10.1609/aimag.v31i3.2303
   Fraigniaud P, 2006, NETWORKS, V48, P166, DOI 10.1002/net.20127
   Garcia-Duran A, 2018, UAI
   Gardner M., 2015, P 2015 C EMP METH NA, P1488, DOI DOI 10.18653/V1/D15-1173
   Gaulton A, 2017, NUCLEIC ACIDS RES, V45, pD945, DOI 10.1093/nar/gkw1074
   Gibrat JF, 1996, CURR OPIN STRUC BIOL, V6, P377, DOI 10.1016/S0959-440X(96)80058-3
   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
   Guo S, 2016, EMNLP
   Gusmao AC, 2018, P WHI STOCKH SWED CO
   Hao M, 2017, SCI REP-UK, V7, DOI 10.1038/srep40376
   Hecker N, 2012, NUCLEIC ACIDS RES, V40, pD1113, DOI 10.1093/nar/gkr912
   Hewett M, 2002, NUCLEIC ACIDS RES, V30, P163, DOI 10.1093/nar/30.1.163
   Janjic V, 2012, BRIEF FUNCT GENOMICS, V11, P522, DOI 10.1093/bfgp/els037
   Kadlec R., 2017, P 2 WORKSH REPR LEAR, P69, DOI [10.18653/v1/W17-2609, DOI 10.18653/V1/W17-2609]
   Kanehisa M, 2017, NUCLEIC ACIDS RES, V45, pD353, DOI 10.1093/nar/gkw1092
   Kanehisa M, 2016, NUCLEIC ACIDS RES, V44, pD457, DOI 10.1093/nar/gkv1070
   Kantor ED, 2015, JAMA-J AM MED ASSOC, V314, P1818, DOI 10.1001/jama.2015.13766
   Krompass D, 2015, INT SEM WEB C BETHL
   Kuhn M, 2016, NUCLEIC ACIDS RES, V44, pD1075, DOI 10.1093/nar/gkv1075
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Landrum MJ, 2014, NUCLEIC ACIDS RES, V42, pD980, DOI 10.1093/nar/gkt1113
   Lao N, 2011, EMNLP
   Lerer A, 2019, 2 SYSML C PAL ALT CA
   Lim H, 2016, SCI REP-UK, V6, DOI 10.1038/srep38860
   Lipschitz WL, 1943, J PHARMACOL EXP THER, V79, P97
   Liu H, 2017, ICML
   Liu H, 2015, BIOINFORMATICS, V31, P221, DOI 10.1093/bioinformatics/btv256
   Malone B, 2018, DILS
   Mattingly CJ, 2003, ENVIRON HEALTH PERSP, V111, P793, DOI 10.1289/ehp.6028
   Mei JP, 2013, BIOINFORMATICS, V29, P238, DOI 10.1093/bioinformatics/bts670
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Minervini P, 2017, ECML PKDD
   Minervini P, 2017, LECT NOTES ARTIF INT, V10534, P668, DOI 10.1007/978-3-319-71249-9_40
   Minoda Y, 2001, ANESTHESIOLOGY, V95, P509, DOI 10.1097/00000542-200108000-00037
   Mitchell AL, 2019, NUCLEIC ACIDS RES, V47, pD351, DOI 10.1093/nar/gky1100
   Mitchell T, 2015, AAAI CONF ARTIF INTE, P2302
   Mohamed SK, 2020, INFORM SCIENCES, V508, P343, DOI 10.1016/j.ins.2019.08.061
   Mohamed SK, 2020, BIOINFORMATICS, V36, P603, DOI 10.1093/bioinformatics/btz600
   Mohamed SK, 2019, SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING, P11, DOI 10.1145/3297280.3297282
   Mohamed SK, 2019, LECT NOTES COMPUT SC, V11503, P240, DOI 10.1007/978-3-030-21348-0_16
   Mohamed SK, 2018, 33RD ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, P1992, DOI 10.1145/3167132.3167346
   Mohamed SK, 2019, DL4KGS ESWC CEUR WOR, V2106
   Mohamed SK, 2017, LDK
   Munoz E, 2016, AMIA 2016 AM MED INF
   Munoz E, 2019, SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING, P2243, DOI 10.1145/3297280.3297502
   Munoz E, 2019, BRIEF BIOINFORM, V20, P190, DOI 10.1093/bib/bbx099
   Nascimento ACA, 2016, BMC BIOINFORMATICS, V17, DOI 10.1186/s12859-016-0890-3
   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
   Olayan RS, 2018, BIOINFORMATICS, V34, P1164, DOI 10.1093/bioinformatics/btx731
   Orchard S, 2014, NUCLEIC ACIDS RES, V42, pD358, DOI 10.1093/nar/gkt1115
   Overington JP, 2006, NAT REV DRUG DISCOV, V5, P993, DOI 10.1038/nrd2199
   Papalexakis EE, 2017, ACM T INTEL SYST TEC, V8, DOI 10.1145/2915921
   Perozzi B., 2014, P 20 ACM SIGKDD INT, P701, DOI DOI 10.1145/2623330.2623732
   POHL JEF, 1972, CLIN SCI, V42, P145, DOI 10.1042/cs0420145
   Pujara J., 2017, EMNLP
   Qian R., 2013, BING SEARCH BLOG MAR
   Raman K., 2010, AUTOM EXP, V2, P12
   Rosdah AA, 2016, PHARMACOL RES PERSPE, V4, DOI 10.1002/prp2.235
   Rungruangsak-Torrissen K, 1999, FISH PHYSIOL BIOCHEM, V21, P223, DOI 10.1023/A:1007804823932
   Sleno L, 2008, CURR OPIN CHEM BIOL, V12, P46, DOI 10.1016/j.cbpa.2008.01.022
   Snoek J., 2012, NIPS
   SOLIS FJ, 1981, MATH OPER RES, V6, P19, DOI 10.1287/moor.6.1.19
   Stark C, 2011, NUCLEIC ACIDS RES, V39, pD698, DOI 10.1093/nar/gkq1116
   Su C, 2020, BRIEF BIOINFORM, V21, P182, DOI 10.1093/bib/bby117
   Szklarczyk D, 2017, NUCLEIC ACIDS RES, V45, pD362, DOI 10.1093/nar/gkw937
   Tang J., 2015, WWW
   Tatonetti NP, 2012, SCI TRANSL MED, V4, DOI 10.1126/scitranslmed.3003377
   Terstappen GC, 2007, NAT REV DRUG DISCOV, V6, P891, DOI 10.1038/nrd2410
   TheUniProtConsortium, 2017, Nucleic Acids Res, V45, pD158, DOI 10.1093/nar/gkw1099
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Tuncbag N, 2009, BRIEF BIOINFORM, V10, P217, DOI 10.1093/bib/bbp001
   Uhlen M, 2015, SCIENCE, V347, DOI 10.1126/science.1260419
   van der Maaten L, 2014, J MACH LEARN RES, V15, P3221
   Verster JC, 2004, CNS DRUG REV, V10, P45
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Warde-Farley D, 2010, NUCLEIC ACIDS RES, V38, pW214, DOI 10.1093/nar/gkq537
   Weber L, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P6151
   Wei F, 2016, ARXIVABS160902613
   Wishart DS, 2008, NUCLEIC ACIDS RES, V36, pD901, DOI 10.1093/nar/gkm958
   Wishart DS, 2006, NUCLEIC ACIDS RES, V34, pD668, DOI 10.1093/nar/gkj067
   Xu B, 2019, IEEE ACM T COMPUT BI, V16, P377, DOI 10.1109/TCBB.2017.2701824
   Yamanishi Y, 2008, BIOINFORMATICS, V24, pI232, DOI 10.1093/bioinformatics/btn162
   Yang B., 2015, ICLR
   Zeng XX, 2017, BMC MED GENOMICS, V10, DOI 10.1186/s12920-017-0313-y
   Zhang J, 2018, BRIEF BIOINFORM, V19, P821, DOI 10.1093/bib/bbx022
   Zhu YJ, 2019, BRIEF BIOINFORM, V20, P1308, DOI 10.1093/bib/bbx169
   Zitnik M, 2018, BIOINFORMATICS, V34, P457, DOI 10.1093/bioinformatics/bty294
   Zitnik M, 2017, BIOINFORMATICS, V33, pI190, DOI 10.1093/bioinformatics/btx252
   Zitnik M, 2016, BIOCOMPUT-PAC SYM, P81
NR 122
TC 10
Z9 10
U1 26
U2 75
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 MAR
PY 2021
VL 22
IS 2
SI SI
BP 1679
EP 1693
DI 10.1093/bib/bbaa012
PG 15
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA RQ3BS
UT WOS:000642298000084
PM 32065227
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Deagen, ME
   McCusker, JP
   Fateye, T
   Stouffer, S
   Brinson, LC
   McGuinness, DL
   Schadler, LS
AF Deagen, Michael E.
   McCusker, Jamie P.
   Fateye, Tolulomo
   Stouffer, Samuel
   Brinson, L. Cate
   McGuinness, Deborah L.
   Schadler, Linda S.
TI FAIR and Interactive Data Graphics from a Scientific Knowledge Graph
SO SCIENTIFIC DATA
LA English
DT Article
ID VISUALIZATION; VEGA
AB Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite-demonstrated here in the domain of polymer nanocomposite materials science-offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.
C1 [Deagen, Michael E.; Schadler, Linda S.] Univ Vermont, Dept Mech Engn, Burlington, VT 05405 USA.
   [McCusker, Jamie P.; Stouffer, Samuel; McGuinness, Deborah L.] Rensselaer Polytech Inst, Tetherless World Constellat, Troy, NY USA.
   [Fateye, Tolulomo; Brinson, L. Cate] Duke Univ, Dept Mech Engn & Mat Sci, Durham, NC 27706 USA.
RP Deagen, ME (通讯作者)，Univ Vermont, Dept Mech Engn, Burlington, VT 05405 USA.
EM mdeagen@mit.edu
OI Deagen, Michael/0000-0002-8034-0667
FU National Science Foundation through the NSF CSSI program [OAC-1835648,
   OAC-1835782]; NSF DIBBs program [ACI-1640840]; NSF DMREF program
   [CMMI-1818574, CMMI-1729743, CMMI-1729452]; NIST; CHiMaD at Northwestern
   University; Tetherless World Constellation at Rensselaer Polytechnic
   Institute
FX The authors gratefully acknowledge support from the National Science
   Foundation through the NSF CSSI program (OAC-1835648, OAC-1835782), NSF
   DIBBs program (ACI-1640840), and NSF DMREF program (CMMI-1818574,
   CMMI-1729743, CMMI-1729452) in addition to ongoing collaborations with
   NIST, CHiMaD at Northwestern University and funding from the Tetherless
   World Constellation at Rensselaer Polytechnic Institute. The authors
   also recognize the contributions from the Interactive Data Lab at the
   University of Washington and its alumni in developing and maintaining
   the open-source code and high-quality documentation of Vega-Lite; the
   teams behind Observable, DBpedia, and the SPARQL 1.1 query language for
   making possible the demonstration of interoperability; and Leland
   Wilkinson for an inspiring life and career that fundamentally reimagined
   data visualization through the grammar of graphics.
CR Alonen M, 2013, LECT NOTES COMPUT SC, V7955, P204, DOI 10.1007/978-3-642-41242-4_25
   [Anonymous], 2001, SCI AM
   [Anonymous], 2018, INT SEM WEB C
   Bandyopadhyay A, 2005, J MATER SCI, V40, P5233, DOI 10.1007/s10853-005-4417-y
   Borgman CL, 2012, J AM SOC INF SCI TEC, V63, P1059, DOI 10.1002/asi.22634
   Bostock M, 2011, IEEE T VIS COMPUT GR, V17, P2301, DOI 10.1109/TVCG.2011.185
   Bostock M, 2009, IEEE T VIS COMPUT GR, V15, P1121, DOI 10.1109/TVCG.2009.174
   Brinson LC, 2020, ACS MACRO LETT, V9, P1086, DOI 10.1021/acsmacrolett.0c00264
   De Donato Renato, 2020, Semantic Systems. In the Era of Knowledge Graphs. 16th International Conference on Semantic Systems, SEMANTiCS 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12378), P70, DOI 10.1007/978-3-030-59833-4_5
   Deagen ME., 2022, **DATA OBJECT**, DOI 10.6084/m9.figshare.19352258.v1
   Draxl C, 2018, MRS BULL, V43, P676, DOI 10.1557/mrs.2018.208
   Dumontier M, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-14
   Friendly M., 2008, HDB DATA VISUALIZATI, P15, DOI [DOI 10.1007/978-3-540-33037-0_2, DOI 10.1007/978-3-540-33037-02]
   Graves A., 2013, P 3 INT C WEB INT MI, P1
   Hanrahan Pat, 2006, P 2006 ACM SIGMOD IN, P721
   Heer J, 2012, COMMUN ACM, V55, P45, DOI 10.1145/2133806.2133821
   Himanen L, 2019, ADV SCI, V6, DOI 10.1002/advs.201900808
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Horton MK., 2021, NAT COMPUT SCI, V1, P3, DOI DOI 10.1038/S43588-020-00016-5
   Hu BY, 2021, J CHEMINFORMATICS, V13, DOI 10.1186/s13321-021-00502-6
   Krommyda Maria, 2019, 2019 First International Conference on Graph Computing (GC), P21, DOI 10.1109/GC46384.2019.00012
   Kuhn T, 2018, P IEEE INT C E-SCI, P83, DOI 10.1109/eScience.2018.00024
   Lebo T., 2013, INT SEM WEB C
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li HT, 2022, IEEE T VIS COMPUT GR, V28, P195, DOI 10.1109/TVCG.2021.3114863
   Masson D, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376559
   McCusker James P., 2020, The Semantic Web - ISWC 2020. 19th International Semantic Web Conference. Lecture Notes in Computer Science (LNCS 12507), P144, DOI 10.1007/978-3-030-62466-8_10
   Natarajan B, 2013, MACROMOLECULES, V46, P2833, DOI 10.1021/ma302281b
   Papadaki ME, 2021, ALGORITHMS, V14, DOI 10.3390/a14020034
   Polleres A, 2020, SEMANT WEB, V11, P101, DOI 10.3233/SW-190380
   Raji M, 2021, IEEE T VIS COMPUT GR, V27, P3656, DOI 10.1109/TVCG.2020.2984708
   Rietveld L, 2013, LECT NOTES COMPUT SC, V7955, P78, DOI 10.1007/978-3-642-41242-4_7
   Satyanarayan A, 2017, IEEE T VIS COMPUT GR, V23, P341, DOI 10.1109/TVCG.2016.2599030
   Satyanarayan A, 2016, IEEE T VIS COMPUT GR, V22, P659, DOI 10.1109/TVCG.2015.2467091
   Shneiderman B, 1996, IEEE SYMPOSIUM ON VISUAL LANGUAGES, PROCEEDINGS, P336, DOI 10.1109/VL.1996.545307
   Skjveland M. G., 2012, EXT SEM WEB C, P361
   Stolte C, 2002, IEEE T VIS COMPUT GR, V8, P52, DOI 10.1109/2945.981851
   Tang N, 2019, INT CONF MANAGE DATA, P2025, DOI 10.1145/3299869.3314029
   Thellmann K., 2015, INT SEMANTIC WEB C
   Walny J, 2020, IEEE T VIS COMPUT GR, V26, P12, DOI 10.1109/TVCG.2019.2934538
   Warren JA, 2018, JOM-US, V70, P1652, DOI 10.1007/s11837-018-2968-z
   Wickham H, 2011, WIRES COMPUT STAT, V3, P180, DOI 10.1002/wics.147
   Wilkinson L., 2012, HDB COMPUT STAT CONC, P375, DOI DOI 10.1007/978-3-642-21551-3_13
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Wongsuphasawat K, 2017, PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), P2648
   Wongsuphasawat K, 2016, IEEE T VIS COMPUT GR, V22, P649, DOI 10.1109/TVCG.2015.2467191
   Wu Aoyu, 2021, IEEE Trans Vis Comput Graph, VPP, DOI 10.1109/TVCG.2021.3099002
   Yi JS, 2007, IEEE T VIS COMPUT GR, V13, P1224, DOI 10.1109/TVCG.2007.70515
   Zhao H, 2018, APL MATER, V6, DOI 10.1063/1.5046839
NR 49
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 MAY 27
PY 2022
VL 9
IS 1
AR 239
DI 10.1038/s41597-022-01352-z
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 1P9SJ
UT WOS:000802340900007
PM 35624233
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yang, Y
   Zhu, Y
   Li, Y
AF Yang, Yang
   Zhu, Yi
   Li, Yun
TI Personalized recommendation with knowledge graph via dual-autoencoder
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Knowledge graph; Autoencoder; Recommendation systems
AB In the past decades, personalized recommendation systems have attracted a vast amount of attention and researches from multiple disciplines. Recently, for the powerful ability of feature representation learning, deep neural networks have achieved sound performance in the recommendation. However, most of the existing deep recommendation approaches require a large number of labeled data, which is often expensive and labor-some in applications. Meanwhile, the side information of users and items that can extend the feature space effectively is usually scarce. To address these problems, we propose a Personalized Recommendation method, which extends items' feature representations with Knowledge Graph via dual-autoencoder (short for PRKG). More specifically, we first extract items' side information from open knowledge graph like DBpedia as items' feature extension. Secondly, we learn the low-dimensional representations of additional features collected from DBpedia via the autoencoder module and then integrate the processed features into the original feature space. Finally, the reconstructed features is incorporated into the semi-autoencoder for personalized recommendations. Extensive experiments conducted on several real-world datasets validate the effectiveness of our proposed methods compared to several state-of-the-art models.
C1 [Yang, Yang; Zhu, Yi; Li, Yun] Yangzhou Univ, Sch Informat Engn, Yangzhou 225009, Jiangsu, Peoples R China.
   [Zhu, Yi] Hefei Univ Technol, Key Lab Knowledge Engn Big Data, Minist Educ, Hefei 230009, Peoples R China.
   [Zhu, Yi] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China.
RP Li, Y (通讯作者)，Yangzhou Univ, Sch Informat Engn, Yangzhou 225009, Jiangsu, Peoples R China.
EM liyun@yzu.edu.cn
FU National Natural Science Foundation of China [61906060]
FX This work is supported in part by the National Natural Science
   Foundation of China under grants 61906060.
CR Alashkar T, 2017, AAAI CONF ARTIF INTE, P941
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Dong BB, 2020, IEEE ACCESS, V8, P46030, DOI 10.1109/ACCESS.2020.2979255
   Dong MQ, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P688, DOI 10.1145/3394486.3403113
   Dubey M, 2018, LECT NOTES COMPUT SC, V11136, P108, DOI 10.1007/978-3-030-00671-6_7
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Gesese G. A., 2019, INT WORKSHOP DEEP LE, P31
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Hu YB, 2018, I C CONT AUTOMAT ROB, P1432, DOI 10.1109/ICARCV.2018.8581332
   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 H, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1073, DOI 10.1145/3292500.3330859
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li S, 2015, P 24 ACM INT C INF K, P811, DOI DOI 10.1145/2806416.2806527
   Lin YJ, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P981, DOI 10.1145/3397271.3401081
   Lu YF, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1563, DOI 10.1145/3394486.3403207
   Luo X, 2014, IEEE T IND INFORM, V10, P1273, DOI 10.1109/TII.2014.2308433
   Noda K, 2015, APPL INTELL, V42, P722, DOI 10.1007/s10489-014-0629-7
   Oramas S, 2017, ACM T INTEL SYST TEC, V8, DOI 10.1145/2926718
   Qi C, 2021, KNOWL-BASED SYST, V211, DOI 10.1016/j.knosys.2020.106561
   Qiang J., 2019, ARXIV PREPRINT ARXIV
   Salakhutdinov R, 2007, ADV NEURAL INFORM PR, P1257
   Sarwar B., 2000, TR00043 MINN U MINN
   Sedhain S, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P111, DOI 10.1145/2740908.2742726
   Strub F., 2016, P 1 WORKSH DEEP LEAR, P11
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Tee YK., 2018, INT C ROB INT TECHN
   Voulodimos A, 2018, COMPUT INTEL NEUROSC, V2018, DOI 10.1155/2018/7068349
   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 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
   Wei YW, 2020, MM '20: PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, P3541, DOI 10.1145/3394171.3413556
   Wu Y, 2016, PROCEEDINGS OF THE NINTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'16), P153, DOI 10.1145/2835776.2835837
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029
   Zhang S, 2017, LECT NOTES COMPUT SC, V10634, P185, DOI 10.1007/978-3-319-70087-8_20
   Zhou K, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1006, DOI 10.1145/3394486.3403143
   Zhu QN, 2020, AAAI CONF ARTIF INTE, V34, P6999
   Zhu Y, 2019, NEUROCOMPUTING, V369, P145, DOI 10.1016/j.neucom.2019.08.078
   Zhu Y, 2018, KNOWL-BASED SYST, V152, P100, DOI 10.1016/j.knosys.2018.04.010
NR 39
TC 4
Z9 4
U1 12
U2 24
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 APR
PY 2022
VL 52
IS 6
SI SI
BP 6196
EP 6207
DI 10.1007/s10489-021-02647-1
EA SEP 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0O7RV
UT WOS:000692729400002
DA 2022-08-23
ER

PT J
AU Li, X
   Zang, HY
   Yu, XY
   Wu, H
   Zhang, ZJ
   Liu, JM
   Wang, MZ
AF Li, Xin
   Zang, Hongyu
   Yu, Xiaoyun
   Wu, Hao
   Zhang, Zijian
   Liu, Jiamou
   Wang, Mingzhong
TI On improving knowledge graph facilitated simple question answering
   system
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Review
DE Knowledge graph; Question answering; Embedding; Neural networks
AB Leveraging knowledge graph will benefit question answering tasks, as KG contains well-structured informative data. However, training knowledge graph-based simple question answering systems is known computationally expensive due to the complex predicate extraction and candidate pool generation. Moreover, the existing methods based on convolutional neural network (CNN) or recurrent neural network (RNN) overestimate the importance of predicate features thus reduce performance. To address these challenges, we propose a time-efficient and resource-effective framework. We use leaky n-gram to balance recall and candidate pool size in candidate pool generation. For predicate extraction, we propose a soft-histogram and self-attention (SHSA) module which serves the role of preserving the global information of questions via feature matrices. And this leads to reduce the RNN module as the simple feedforward network in predicate representation. We also designed a Hamming lower-bound label encoding algorithm to encode the label representations in lower dimensions. Experiments on benchmark datasets show that our method outperforms the competitive work for end-tasks and achieves better recall with a significantly pruned candidate space.
C1 [Li, Xin; Zang, Hongyu; Wu, Hao; Zhang, Zijian] Beijing Inst Technol, Sch Comp Sci, Beijing, Peoples R China.
   [Yu, Xiaoyun] Beijing Bytedance Technol, Beijing, Peoples R China.
   [Zhang, Zijian; Liu, Jiamou] Univ Auckland, Sch Comp Sci, Auckland, New Zealand.
   [Wang, Mingzhong] Univ Sunshine Coast, Sch Sci Technol & Engn, Sippy Downs, Qld, Australia.
RP Li, X (通讯作者)，Beijing Inst Technol, Sch Comp Sci, Beijing, Peoples R China.
EM xinli@bit.edu.cn; zanghyu@bit.edu.cn; yuxiaoyun@bytedance.com;
   wuhao123@bit.edu.cn; zhangzijian@bit.edu.cn; jiamou.liu@auckland.ac.nz;
   mwang@usc.edu.au
FU National KeyR&D Program of China [2018YFC0830705]; NSFC [U19B2020,
   61772074]
FX We would like to thank the anonymous reviewers for their valuable
   comments. Dr. Hao Wu is the corresponding author.Our work is partially
   supported by the National KeyR&D Program of China under Grant
   (2018YFC0830705)and NSFC under Grant (U19B2020 and 61772074).
CR Bishop CM., 2016, J ELECTRON IMAGING, DOI DOI 10.1117/1.2819119
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2015, ABS150602075 CORR
   Cho K., 2014, ARXIV14061078, P1724
   Collobert R., 2008, P 25 ICML, P160
   Dai ZH, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P800
   Devlin J, 2018, P 2019 C N AM CHAPT
   Erling O., 2009, NETWORKED KNOWLEDGE, P7
   He X., 2016, 2016 C EMP METH NAT, P1598
   Kaiming He, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), P770, DOI 10.1109/CVPR.2016.90
   Kingma D. P., 2017, ARXIV
   Lukovnikov D, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1211, DOI 10.1145/3038912.3052675
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI 10.3115/v1/D14-1162
   Reddy Siva, 2014, TACL, P377, DOI DOI 10.1162/TACL_A_00190
   Ryan W., 2009, CHANNEL CODES CLASSI, DOI [10.1017/CBO9780511803253, DOI 10.1017/CBO9780511803253]
   Serban IV, 2016, ABS160306807 CORR
   Ture F, 2017, P 2017 C EMP METH NA, P2866, DOI [10.18653/v1/d17-1307, DOI 10.18653/V1/D17-1307]
   Uyar A, 2015, ONLINE INFORM REV, V39, P197, DOI 10.1108/OIR-10-2014-0257
   Vaswani A, 2017, ADV NEUR IN, V30
   Xu K, 2016, ACL
   Ye D, 2018, ABS180403317V2 CORR
   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
   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
NR 25
TC 2
Z9 2
U1 19
U2 49
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 AUG
PY 2021
VL 33
IS 16
BP 10587
EP 10596
DI 10.1007/s00521-021-05762-9
EA MAR 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TG5FX
UT WOS:000626423800001
DA 2022-08-23
ER

PT J
AU Zhong, SH
   Chen, R
   Song, F
   Xu, YM
AF Zhong, Shihu
   Chen, Rong
   Song, Fei
   Xu, Yanmin
TI Knowledge Mapping of Carbon Footprint Research in a LCA Perspective: A
   Visual Analysis Using CiteSpace
SO PROCESSES
LA English
DT Article
DE carbon footprint; life cycle assessment; CiteSpace; a visual analysis
ID LIFE-CYCLE ASSESSMENT; MILK-PRODUCTION; PRODUCTION SYSTEMS; HYBRID;
   WATER; DESIGN
AB Carbon emissions are inevitably linked to lifestyle and consumption behaviours, and the concept of "carbon footprinting" is now well-recognised beyond academia. Life cycle assessment (LCA) is one of the primary tools for assessing carbon footprints. The aim of this paper is to present a systematic review of literatures focusing on carbon footprint calculated with life cycle assessment. We used CiteSpace software to draw the knowledge map of related research to identify and trace the knowledge base and frontier terminology. It was found that the LCA application in respects of carbon footprint studies was completed mainly for the following aspect: beef production and dairy industry, seafood and fishery, nutrition, urban structure and energy use. The CiteSpace analysis showed the development path of the above aspects, for example, beef production and dairy industry has been a long-term topic in this kind of research, while the topic of nutrition appeared in recent years. There was also a cluster of literature discussing footprint evaluation tools, such as comparing LCA with input-output analysis. The CiteSpace analysis indicated that earlier methodological literature still plays an important role in recent research. Moreover, through the analysis of burst keywords, it was found that agriculture productions (dairy, meat, fish, crop) as well as global climate issues (greenhouse gases emission, global warming potential) have always been the areas of concern, which matches the result of co-citation analysis. Building materials (low-carbon building, natural buildings, sustainable buildings) and soil issues (soil carbon sequestration, soil organic carbon) are the topics of recent concern, which could arouse the attention of follower-up researchers.
C1 [Zhong, Shihu; Chen, Rong] Shanghai Univ Finance & Econ, Sch Publ Econ & Adm, Shanghai 200433, Peoples R China.
   [Song, Fei] Jiangsu Open Univ, Sch Informat Engn, Nanjing 210017, Jiangsu, Peoples R China.
   [Xu, Yanmin] Shanghai Univ Finance & Econ, Sch Humanities, Shanghai 200433, Peoples R China.
RP Zhong, SH; Chen, R (通讯作者)，Shanghai Univ Finance & Econ, Sch Publ Econ & Adm, Shanghai 200433, Peoples R China.; Song, F (通讯作者)，Jiangsu Open Univ, Sch Informat Engn, Nanjing 210017, Jiangsu, Peoples R China.
EM zhongshihu@163.sufe.edu.cn; 2017311013@live.sufe.edu.cn; songf@jsou.cn;
   2018310003@live.sufe.edu.cn
OI Zhong, Shihu/0000-0001-6100-1750
FU General Project of Philosophy and Social Science Research in Colleges
   and Universities in Jiangsu Province [2019SJA0678]; Fundamental Research
   Funds for the Central Universities [CXJJ-2018-361]
FX This research was funded by the General Project of Philosophy and Social
   Science Research in Colleges and Universities in Jiangsu Province, grant
   number No. 2019SJA0678 and the Fundamental Research Funds for the
   Central Universities, grant number CXJJ-2018-361.
CR Basbagill J, 2013, BUILD ENVIRON, V60, P81, DOI 10.1016/j.buildenv.2012.11.009
   Beloin-Saint-Pierre D, 2014, INT J LIFE CYCLE ASS, V19, P861, DOI 10.1007/s11367-014-0710-9
   Blengini GA, 2010, ENERG BUILDINGS, V42, P869, DOI 10.1016/j.enbuild.2009.12.009
   Bourque P. J, 1981, INT REGIONAL SCI REV, V6, P121
   Callon M., 1986, MAPPING DYNAMICS SCI
   Casolani N, 2016, LAND USE POLICY, V58, P394, DOI 10.1016/j.landusepol.2016.07.014
   Chai BC, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11154110
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   Chen CM, 2012, J AM SOC INF SCI TEC, V63, P431, DOI 10.1002/asi.21694
   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 GW, 2019, ENVIRON SCI TECHNOL, V53, P5545, DOI 10.1021/acs.est.8b07071
   de Vries M, 2010, LIVEST SCI, V128, P1, DOI 10.1016/j.livsci.2009.11.007
   Deng MJ, 2019, CARBON MANAG, V10, P387, DOI 10.1080/17583004.2019.1620036
   Finkbeiner M, 2009, INT J LIFE CYCLE ASS, V14, P91, DOI 10.1007/s11367-009-0064-x
   Finnveden G, 2009, J ENVIRON MANAGE, V91, P1, DOI 10.1016/j.jenvman.2009.06.018
   Flysjo A, 2011, AGR SYST, V104, P459, DOI 10.1016/j.agsy.2011.03.003
   Fry J, 2018, J CLEAN PROD, V176, P1254, DOI 10.1016/j.jclepro.2017.11.073
   Galli A, 2012, ECOL INDIC, V16, P100, DOI 10.1016/j.ecolind.2011.06.017
   Goedkoop M., 2009, RECIPE 2008 LIFE CYC, V1, P1
   Heinonen J., 2010, P C SUST BUILD HELS
   Hellweg S, 2014, SCIENCE, V344, P1109, DOI 10.1126/science.1248361
   Hertwich EG, 2009, ENVIRON SCI TECHNOL, V43, P6414, DOI 10.1021/es803496a
   IPCC, 2019, 2019 REFINEMENT 2006
   ISO, 2006, ENV MAN LIF CYCL ASS
   Knudsen MT, 2014, J CLEAN PROD, V64, P609, DOI 10.1016/j.jclepro.2013.07.009
   Kristensen T, 2011, LIVEST SCI, V140, P136, DOI 10.1016/j.livsci.2011.03.002
   Laurent A, 2012, ENVIRON SCI TECHNOL, V46, P4100, DOI 10.1021/es204163f
   Masson- Delmotte V, 2018, GLOBAL WARMING 1 5 C
   Mery Y, 2013, INT J LIFE CYCLE ASS, V18, P1062, DOI 10.1007/s11367-012-0541-5
   Nijdam D, 2012, FOOD POLICY, V37, P760, DOI 10.1016/j.foodpol.2012.08.002
   Onat NC, 2015, APPL ENERG, V150, P36, DOI 10.1016/j.apenergy.2015.04.001
   Pandey D, 2011, ENVIRON MONIT ASSESS, V178, P135, DOI 10.1007/s10661-010-1678-y
   Pelletier N, 2010, AGR SYST, V103, P380, DOI 10.1016/j.agsy.2010.03.009
   Pohl J, 2019, J CLEAN PROD, V219, P698, DOI 10.1016/j.jclepro.2019.02.018
   Ronga D, 2019, J CLEAN PROD, V220, P836, DOI 10.1016/j.jclepro.2019.02.111
   Rotz CA, 2010, J DAIRY SCI, V93, P1266, DOI 10.3168/jds.2009-2162
   Roy P, 2009, J FOOD ENG, V90, P1, DOI 10.1016/j.jfoodeng.2008.06.016
   Steinfeld H, 2006, REV SCI TECH OIE, V25, P505, DOI 10.20506/rst.25.2.1677
   Strohbach MW, 2012, LANDSCAPE URBAN PLAN, V104, P220, DOI 10.1016/j.landurbplan.2011.10.013
   Suh S, 2004, ENVIRON SCI TECHNOL, V38, P657, DOI 10.1021/es0263745
   Thoma G, 2013, INT DAIRY J, V31, pS3, DOI 10.1016/j.idairyj.2012.08.013
   Thomassen MA, 2008, AGR SYST, V96, P95, DOI 10.1016/j.agsy.2007.06.001
   Tilman D, 2014, NATURE, V515, P518, DOI 10.1038/nature13959
   Weidema BP, 2008, J IND ECOL, V12, P3, DOI 10.1111/j.1530-9290.2008.00005.x
   Wiedmann TO, 2011, ENVIRON SCI TECHNOL, V45, P5900, DOI 10.1021/es2007287
   Williams A.G., 2006, IS0505 CRANF U DEFR
   Zhong SH, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11030592
NR 48
TC 6
Z9 7
U1 59
U2 136
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-9717
J9 PROCESSES
JI Processes
PD NOV
PY 2019
VL 7
IS 11
AR 818
DI 10.3390/pr7110818
PG 22
WC Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering
GA JV3JY
UT WOS:000502263700044
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Jiang, JC
   Wang, T
   Wang, BR
   Ma, LJ
   Guan, Y
AF Jiang, Jingchi
   Wang, Tao
   Wang, Boran
   Ma, Linjiang
   Guan, Yi
TI Gated Tree-based Graph Attention Network (GTGAT) for medical knowledge
   graph reasoning
SO ARTIFICIAL INTELLIGENCE IN MEDICINE
LA English
DT Article
DE Medical knowledge graph; Graph attention network; Disease diagnosis
AB Knowledge graph (KG) is a multi-relational data that has proven valuable for many tasks including decision making and semantic search. In this paper, we present GTGAT (Gated Tree-based Graph Attention), a method for tackling the problems of transductive and inductive reasoning in generalized KGs. Based on recent advancement of graph attention network (GAT), we develop a gated tree-based method to distill valuable information in neighborhood via hierarchical-aware and semantic-aware attention mechanism. Our approach not only addresses several key challenges of GAT but is also capable of undertaking multiple downstream tasks. Experimental results have revealed that our proposed GTGAT has matched state-of-the-art approaches across transductive benchmarks on the Cora, Citeseer, and electronic medical record networks (EMRNet). Meanwhile, the inductive experiments on medical knowledge graphs show that GTGAT surpasses the best competing methods for personalized disease diagnosis.
C1 [Jiang, Jingchi; Wang, Tao; Ma, Linjiang; Guan, Yi] Harbin Inst Technol, Dept Comp Sci & Technol, Harbin, Peoples R China.
   [Wang, Boran] Harbin Inst Technol, Dept Comp Sci & Technol, Shenzhen, Peoples R China.
RP Wang, BR (通讯作者)，Harbin Inst Technol, Dept Comp Sci & Technol, Shenzhen, Peoples R China.
EM wangboran@hit.edu.cn
FU National Natural Science of China [62006063]; Heilongjiang Provincial
   Post-doctoral Science Foundation [LBH-Z20015]
FX This study was supported in part by a grant from National Natural
   Science of China [62006063] and the Heilongjiang Provincial
   Post-doctoral Science Foundation (CN) [LBH-Z20015] .
CR Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bordes A., 2013, ADV NEURAL INFORM PR, V26
   Bruna J., ARXIV PREPRINT ARXIV
   Callahan Alison, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P200
   Cho K., 2014, ARXIV14061078, P1724
   Defferrard M, 2016, ADV NEUR IN, V29
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Duvenaud David K, 2015, ADV NEURAL INFORM PR, P2224, DOI DOI 10.1021/ACS.JCIM.5B00572
   Getoor L, 2005, ADV INFO KNOW PROC, P189, DOI 10.1007/1-84628-284-5_7
   Hamilton WL, 2017, ADV NEUR IN, V30
   Henaff M., ARXIV PREPRINT ARXIV
   Hu ZN, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P2704, DOI 10.1145/3366423.3380027
   HUFFMAN DA, 1952, P IRE, V40, P1098, DOI 10.1109/JRPROC.1952.273898
   Jiang JC, 2020, ARTIF INTELL MED, V103, DOI 10.1016/j.artmed.2019.101772
   Jiang JC, 2017, KNOWL-BASED SYST, V138, P58, DOI 10.1016/j.knosys.2017.09.030
   Johnson R, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P562, DOI 10.18653/v1/P17-1052
   Kim Y., ARXIV PREPRINT ARXIV
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lai SW, 2015, AAAI CONF ARTIF INTE, P2267
   Li Y., ARXIV PREPRINT ARXIV
   Ma JQ, 2019, ADV NEUR IN, V32
   Meilicke C, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3137
   Mitchell J, 2010, COGNITIVE SCI, V34, P1388, DOI 10.1111/j.1551-6709.2010.01106.x
   Monti F, 2017, PROC CVPR IEEE, P5425, DOI 10.1109/CVPR.2017.576
   Ruffinelli D., 2019, INT C LEARNING REPRE
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Schlichtkrull M., MODELING RELATIONAL
   Sen P, 2008, AI MAG, V29, P93, DOI 10.1609/aimag.v29i3.2157
   Silva RRC, 2017, 21 INT DATABASE ENG
   Socher R., 2012, P 2012 JOINT C EMP M, P1201, DOI [10.5555/2390948.2391084, DOI 10.1162/153244303322533223]
   Socher R., 2013, EMNLP, P1631, DOI DOI 10.1371/JOURNAL.PONE.0073791
   Sun Z., ARXIV PREPRINT ARXIV
   Sun ZQ, 2020, AAAI CONF ARTIF INTE, V34, P222
   Tang XB, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3174
   Velickovic P., ARXIV PREPRINT ARXIV
   Wan GJ, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1926
   Xiong W., ARXIV PREPRINT ARXIV
   Xu K, 2020, AAAI CONF ARTIF INTE, V34, P9354
   Yang B., ARXIV PREPRINT ARXIV
   Yang ZR, 2016, PR MACH LEARN RES, V48
   Zambaldi V., ARXIV PREPRINT ARXIV
   Zhang CX, 2020, AAAI CONF ARTIF INTE, V34, P3041
   Zhang Y., EFFICIENT PROBABILIS
   Zhang Z, 2020, AAAI CONF ARTIF INTE, V34, P9612
NR 44
TC 0
Z9 0
U1 5
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0933-3657
EI 1873-2860
J9 ARTIF INTELL MED
JI Artif. Intell. Med.
PD AUG
PY 2022
VL 130
AR 102329
DI 10.1016/j.artmed.2022.102329
PG 8
WC Computer Science, Artificial Intelligence; Engineering, Biomedical;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Medical Informatics
GA 2U5WL
UT WOS:000823230600004
PM 35809972
DA 2022-08-23
ER

PT J
AU Yang, ZS
   Cheng, JY
AF Yang, Zhisheng
   Cheng, Jinyong
TI Recommendation Algorithm Based on Knowledge Graph to Propagate User
   Preference
SO INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
LA English
DT Article
DE Recommendation algorithm; Knowledge graph; Preference
AB In recommendation algorithms, data sparsity and cold start problems are inevitable. To solve such problems, researchers apply auxiliary information to recommendation algorithms, mine users' historical records to obtain more potential information, and then improve recommendation performance. In this paper, ST_RippleNet, a model that combines knowledge graphs with deep learning, is proposed. This model starts by building the required knowledge graph. Then, the potential interest of users is mined through the knowledge graph, which stimulates the spread of users' preferences on the set of knowledge entities. In preference propagation, we use a triple multi-layer attention mechanism to obtain triple information through the knowledge graph and use the user preference distribution for candidate items formed by users' historical click information to predict the final click probability. Using ST_RippleNet model can better obtain triple information in knowledge graph and mine more useful information. In the ST_RippleNet model, the music data set is added to the movie and book data set; additionally, an improved loss function is used in the model, which is optimized by the RMSProp optimizer. Finally, the tanh function is added to predict the click probability to improve recommendation performance. Compared with current mainstream recommendation methods, ST_RippleNet achieves very good performance in terms of the area under the curve (AUC) and accuracy (ACC) and substantially improves movie, book and music recommendations. (C) 2021 The Authors. Published by Atlantis Press B.V.
C1 [Yang, Zhisheng; Cheng, Jinyong] Qilu Univ Technol, Sch Comp Sci & Technol, Shandong Acad Sci, Jinan 250353, Peoples R China.
RP Cheng, JY (通讯作者)，Qilu Univ Technol, Sch Comp Sci & Technol, Shandong Acad Sci, Jinan 250353, Peoples R China.
EM cjy@qlu.edu.cn
FU National Key R&D Program, China [2019YFB2102600]; Key Research and
   Development Project of Shandong Province, China [2019JZZY020124]
FX This work was supported in part by the National Key R&D Program
   (2019YFB2102600), China, and the Key Research and Development Project of
   Shandong Province (2019JZZY020124), China.
CR Ali Z, 2020, KNOWL-BASED SYST, V210, DOI 10.1016/j.knosys.2020.106438
   Aljanabi M, 2020, COMPLEXITY, V2020, DOI 10.1155/2020/5287684
   Basawaraj, 2019, IEEE T NEUR NET LEAR, V30, P3505, DOI 10.1109/TNNLS.2019.2927106
   Bernabe-Moreno J, 2020, KNOWL-BASED SYST, V190, DOI 10.1016/j.knosys.2019.105236
   Bhattacharyya, 2019, ACM T ASIAN LOW-RESO, V19, P1
   Cai DS, 2019, NEUROCOMPUTING, V329, P237, DOI 10.1016/j.neucom.2018.10.055
   Canizo M, 2019, NEUROCOMPUTING, V363, P246, DOI 10.1016/j.neucom.2019.07.034
   Chen F, 2019, NEUROCOMPUTING, V368, P51, DOI 10.1016/j.neucom.2019.08.054
   Chen J., NEUROCOMPUTING, V420, P82
   Fu LY, 2020, IEEE ACM T NETWORK, V28, P360, DOI 10.1109/TNET.2019.2962731
   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
   Hu YT, 2020, NEUROCOMPUTING, V398, P485, DOI 10.1016/j.neucom.2019.03.098
   Huang LQ, 2020, NEUROCOMPUTING, V396, P324, DOI 10.1016/j.neucom.2018.12.083
   Kwon H, 2020, NEUROCOMPUTING, V417, P357, DOI 10.1016/j.neucom.2020.07.101
   Li S., NEUROCOMPUTING, V419, P273
   Li WD, 2020, NEUROCOMPUTING, V382, P174, DOI 10.1016/j.neucom.2019.11.079
   Lin X, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3352592
   Liu WQ, 2018, NEUROCOMPUTING, V320, P12, DOI 10.1016/j.neucom.2018.08.070
   Lu WP, 2021, IEEE INTELL SYST, V36, P6, DOI 10.1109/MIS.2020.3021188
   Lu WP, 2020, J VIS COMMUN IMAGE R, V71, DOI 10.1016/j.jvcir.2020.102794
   Ma QL, 2019, IEEE-ACM T AUDIO SPE, V27, P2127, DOI 10.1109/TASLP.2019.2942160
   Mahmoudi A, 2019, ACM T KNOWL DISCOV D, V13, DOI 10.1145/3314204
   Sunil K., 2020, IEEE ACM T COMPUT BI, P1
   Wang H., 2018, P 27 ACM INT C INF K
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Wang Q, 2020, KNOWL-BASED SYST, V209, DOI 10.1016/j.knosys.2020.106421
   Zhang WN, 2019, NEUROCOMPUTING, V334, P206, DOI 10.1016/j.neucom.2019.01.028
   Zhang XD, 2020, NEUROCOMPUTING, V395, P212, DOI 10.1016/j.neucom.2018.02.112
   [朱桂明 Zhu Guiming], 2019, [模式识别与人工智能, Pattern Recognition and Artificial Intelligence], V32, P661
NR 29
TC 1
Z9 1
U1 22
U2 42
PU ATLANTIS PRESS
PI PARIS
PA 29 AVENUE LAUMIERE, PARIS, 75019, FRANCE
SN 1875-6891
EI 1875-6883
J9 INT J COMPUT INT SYS
JI Int. J. Comput. Intell. Syst.
PY 2021
VL 14
IS 1
BP 1564
EP 1576
DI 10.2991/ijcis.d.210503.001
PG 13
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SP0HG
UT WOS:000659353200001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zhu, P
   Cheng, DW
   Luo, SQ
   Xu, RY
   Liang, YQ
   Luo, YF
AF Zhu, Peng
   Cheng, Dawei
   Luo, Siqiang
   Xu, Ruyao
   Liang, Yuqi
   Luo, Yifeng
TI Leveraging enterprise knowledge graph to infer web events? influences
   via self-supervised learning
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Information retrieval; Knowledge graph; Stock prediction;
   Self-supervised learning
ID MARKET PREDICTION; NEWS
AB Knowledge graph (KG) techniques have achieved successful results in many tasks, especially in semantic web and natural language processing domains. In recent years, representation learning on KG has been successfully applied to e-business applications, such as event-driven automatic investment strategies. However, there is still limited research about learning events' influence on KG for modern quantitative investment. In this paper, we propose a novel event influence learning framework to predict stock market trends, called ST-Trend, leveraging enterprise knowledge graph to represent company correlation relationships, for mining the deep background knowledge of web events, with three self-supervised learning tasks. In particular, we devise two jointly self-supervised tasks to identify the relations between web events and companies. The first task is for generating groundtruth event-company correlation labels based on the enterprise knowledge graph. The second task is used to train how to identify the correlated companies of an event based on the generated correlation labels, with the encoding of web events, company features, and technical sequential data. We then design the prediction network to infer an event's influence on stock price trends of the identified correlated companies based on the enterprise KG. Finally, we perform extensive experiments on a massive real-life dataset to validate the effectiveness of our proposed framework, and the experimental results demonstrate its superior performance in predicting stock market trends via considering events' influences with the enterprise knowledge graph.
C1 [Zhu, Peng; Xu, Ruyao; Luo, Yifeng] East China Normal Univ, Sch Data Sci & Engn, Shanghai, Peoples R China.
   [Zhu, Peng; Xu, Ruyao; Luo, Yifeng] Shanghai Engn Res Ctr Big Data Management, Shanghai, Peoples R China.
   [Cheng, Dawei] Tongji Univ, Dept Comp Sci & Technol, Shanghai, Peoples R China.
   [Luo, Siqiang] Nanyang Technol Univ, Nanyang, Singapore.
   [Liang, Yuqi] Seek Data Inc, Shanghai, Peoples R China.
RP Cheng, DW (通讯作者)，Tongji Univ, Dept Comp Sci & Technol, Shanghai, Peoples R China.
EM dcheng@tongji.edu.cn
FU National Key R&D Program of China [2018YFC0831904]; National Natural
   Science Foundation of China [61977026]; joint research program of Seek
   Data and ECNU
FX This work was supported by the National Key R&D Program of China
   (2018YFC0831904) , the National Natural Science Foundation of China
   (61977026) and the joint research program of Seek Data and ECNU.
CR Agibetov A, 2020, J WEB SEMANT, V64, DOI 10.1016/j.websem.2020.100590
   Ali M, 2021, J MACH LEARN RES, V22
   An Bo, 2018, NAACL, V1, P745
   Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
   Bolon-Canedo V, 2015, APPL SOFT COMPUT, V30, P136, DOI 10.1016/j.asoc.2015.01.035
   Bottou L, 2010, COMPSTAT'2010: 19TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STATISTICS, P177, DOI 10.1007/978-3-7908-2604-3_16
   Chan WS, 2003, J FINANC ECON, V70, P223, DOI 10.1016/S0304-405X(03)00146-6
   Chen YM, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1655, DOI 10.1145/3269206.3269269
   Cheng DW, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P2221, DOI 10.1145/3397271.3401427
   Cheng DW, 2022, PATTERN RECOGN, V121, DOI 10.1016/j.patcog.2021.108218
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ding QG, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4640
   Ding X., 2019, THESIS BEIJING FOREI
   Ding X, 2014, P 2014 C EMP METH NA, P1415, DOI DOI 10.3115/V1/D14-1148
   Ding X, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P2327
   Duan J., 2018, P 27 INT C COMP LING, P2823
   Esteban C, 2016, 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), P98
   Feng FL, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5843
   Gardner MW, 1998, ATMOS ENVIRON, V32, P2627, DOI 10.1016/S1352-2310(97)00447-0
   Gong JB, 2009, 2009 INTERNATIONAL CONFERENCE ON NEW TRENDS IN INFORMATION AND SERVICE SCIENCE (NISS 2009), VOLS 1 AND 2, P1366, DOI 10.1109/NISS.2009.267
   Granroth-Wilding M, 2016, AAAI CONF ARTIF INTE, P2727
   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 XL, 2022, J WEB SEMANT, V72, DOI 10.1016/j.websem.2021.100696
   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
   Hoseinzade E, 2019, EXPERT SYST APPL, V129, P273, DOI 10.1016/j.eswa.2019.03.029
   Hu ZN, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P261, DOI 10.1145/3159652.3159690
   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 XW, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2735, DOI 10.1145/3394486.3403324
   Kara Y, 2011, EXPERT SYST APPL, V38, P5311, DOI 10.1016/j.eswa.2010.10.027
   Kim Raehyun, 2019, ARXIV PREPRINT ARXIV
   King DB, 2015, ACS SYM SER, V1214, P1
   Kipf T. N., 2016, ARXIV160902907
   Li W, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4541
   Li ZY, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4201
   Liang X, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4490
   Liu QK, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1603, DOI 10.1145/3269206.3269286
   Moreno-Marcos PM, 2018, BEHAV INFORM TECHNOL, V37, P1021, DOI 10.1080/0144929X.2018.1458904
   Nassirtoussi AK, 2015, EXPERT SYST APPL, V42, P306, DOI 10.1016/j.eswa.2014.08.004
   Nayyeri M., 2021, P 2021 C EMPIRICAL M, P9529
   Nelson DMQ, 2017, IEEE IJCNN, P1419, DOI 10.1109/IJCNN.2017.7966019
   Oncharoen P, 2018, 2018 5TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATICS: CONCEPTS, THEORY AND APPLICATIONS (ICAICTA 2018), P19, DOI 10.1109/ICAICTA.2018.8541310
   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
   Raddant M, 2021, J INT MONEY FINANC, V110, DOI 10.1016/j.jimonfin.2020.102280
   Ringsquandl M., 2018, 17 INT SEM WEB C CEU
   Ringsquandl M, 2017, IEEE INT CONF BIG DA, P1676, DOI 10.1109/BigData.2017.8258105
   Sawhney R., 2020, P 2020 C EMP METH NA, P8415
   Si J., 2013, P 51 ANN M ASS COMP, V2, P24
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Vargas M.R., 2018, 2018 INT JOINT C NEU, P1
   Vaswani A., 2017, P 31TH ADV NEURAL IN
   Wallach Hanna M, 2006, P 23 INT C MACH LEAR, P977
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xu YM, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1970
   Zhang GP, 2003, NEUROCOMPUTING, V50, P159, DOI 10.1016/S0925-2312(01)00702-0
   Zhang X, 2018, KNOWL-BASED SYST, V143, P236, DOI 10.1016/j.knosys.2017.12.025
   Zhang Z., 2021, FINDINGS ASS COMPUTA, P1389
   Zhou ZK, 2016, LECT NOTES COMPUT SC, V10041, P328, DOI 10.1007/978-3-319-48740-3_24
NR 59
TC 0
Z9 0
U1 4
U2 4
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 OCT
PY 2022
VL 74
AR 100722
DI 10.1016/j.websem.2022.100722
PG 11
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1Y0GQ
UT WOS:000807825300001
DA 2022-08-23
ER

PT J
AU Wang, M
   Ma, XY
   Si, JW
   Tang, HJ
   Wang, HF
   Li, TL
   Ouyang, W
   Gong, LY
   Tang, YZ
   He, X
   Huang, W
   Liu, X
AF Wang, Meng
   Ma, Xinyu
   Si, Jingwen
   Tang, Hongjia
   Wang, Haofen
   Li, Tunliang
   Ouyang, Wen
   Gong, Liying
   Tang, Yongzhong
   He, Xi
   Huang, Wei
   Liu, Xing
TI Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph
SO FRONTIERS IN GENETICS
LA English
DT Article
DE adverse drug reaction; biomarker; knowledge graph; antitumor drugs;
   explainable model
ID DEFINITIONS; PREDICTION
AB Adverse drug reactions (ADRs) are a major public health concern, and early detection is crucial for drug development and patient safety. Together with the increasing availability of large-scale literature data, machine learning has the potential to predict unknown ADRs from current knowledge. By the machine learning methods, we constructed a Tumor-Biomarker Knowledge Graph (TBKG) which contains four types of node: Tumor, Biomarker, Drug, and ADR using biomedical literatures. Based on this knowledge graph, we not only discovered potential ADRs of antitumor drugs but also provided explanations. Experiments on real-world data show that our model can achieve 0.81 accuracy of three cross-validation and the ADRs discovery of Osimertinib was chosen for the clinical validation. Calculated ADRs of Osimertinib by our model consisted of the known ADRs which were in line with the official manual and some unreported rare ADRs in clinical cases. Results also showed that our model outperformed traditional co-occurrence methods. Moreover, each calculated ADRs were attached with the corresponding paths of "tumor-biomarker-drug" in the knowledge graph which could help to obtain in-depth insights into the underlying mechanisms. In conclusion, the tumor-biomarker knowledge-graph based approach is an explainable method for potential ADRs discovery based on biomarkers and might be valuable to the community working on the emerging field of biomedical literature mining and provide impetus for the mechanism research of ADRs.
C1 [Wang, Meng; Ma, Xinyu] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Si, Jingwen] Tsinghua Univ, Dept Pharmaceut Sci, Beijing, Peoples R China.
   [Tang, Hongjia; Li, Tunliang; Ouyang, Wen; Tang, Yongzhong; He, Xi; Liu, Xing] Cent South Univ, Third Xiangya Hosp, Dept Anesthesiol, Changsha, Peoples R China.
   [Wang, Haofen] Tongji Univ, Coll Design & Innovat, Shanghai, Peoples R China.
   [Gong, Liying] Cent South Univ, Third Xiangya Hosp, Dept Intens Care Unit, Changsha, Peoples R China.
   [Huang, Wei] Cent South Univ, Third Xiangya Hosp, Dept Cardiol, Changsha, Peoples R China.
RP Liu, X (通讯作者)，Cent South Univ, Third Xiangya Hosp, Dept Anesthesiol, Changsha, Peoples R China.
EM xingxingmail@csu.edu.cn
FU National Natural Science Foundation of China [81901842, 61906037]; China
   Primary Health Care Foundation [YLGX-WS-2020003]; CCFTencent Open Fund;
   CCF-Baidu [OF2020003]; Scientific research project of Hunan health and
   Family Planning Commission [20201802]; National Key R&D Program of China
   [2018YFC2001800]
FX The study was supported by the National Natural Science Foundation of
   China (81901842, 61906037), China Primary Health Care Foundation
   (YLGX-WS-2020003), CCFTencent Open Fund, CCF-Baidu (OF2020003),
   Scientific research project of Hunan health and Family Planning
   Commission (20201802) and the National Key R&D Program of China
   (2018YFC2001800).
CR Bean DM, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-16674-x
   Califf RM, 2018, EXP BIOL MED, V243, P213, DOI 10.1177/1535370217750088
   Callon M., 1986, MAPPING DYNAMICS SCI
   Carr DF, 2018, EXP BIOL MED, V243, P291, DOI 10.1177/1535370217733425
   Chinese Society of Lung Cancer Chinese Anti-Cancer Association, 2019, Zhongguo Fei Ai Za Zhi, V22, P57, DOI 10.3779/j.issn.1009-3419.2019.02.01
   Edwards IR, 2000, LANCET, V356, P1255, DOI 10.1016/S0140-6736(00)02799-9
   Fan B, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2019.102131
   Frid AA, 2010, REGUL TOXICOL PHARM, V56, P276, DOI 10.1016/j.yrtph.2009.11.005
   Guney E, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms10331
   Hristovski D, 2016, J MED SYST, V40, DOI 10.1007/s10916-016-0544-z
   Lee C. Y., 2020, BRIEF BIOINFORM, DOI 10.1093/bib/bbaa040
   Li XD, 2019, BMC MED INFORM DECIS, V19, DOI 10.1186/s12911-019-0938-1
   Malas TB, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-42806-6
   Murphy KP, 2012, MACHINE LEARNING: A PROBABILISTIC PERSPECTIVE, P1
   Odogwu L, 2018, ONCOLOGIST, V23, P353, DOI 10.1634/theoncologist.2017-0425
   Patton K, 2018, ANAESTHESIA, V73, P76, DOI 10.1111/anae.14143
   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
   Phillips KA, 2001, JAMA-J AM MED ASSOC, V286, P2270, DOI 10.1001/jama.286.18.2270
   Rastegar-Mojarad M, 2016, PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS, P289, DOI 10.1145/2975167.2975197
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Santiso S, 2019, IEEE J BIOMED HEALTH, V23, P2148, DOI 10.1109/JBHI.2018.2879744
   Shen Y, 2018, J BIOMED INFORM, V88, P1, DOI 10.1016/j.jbi.2018.10.007
   Shrestha S, 2017, INT J PHARMACOVIGILA, V2, P01
   Tan YX, 2016, METHODS, V110, P14, DOI 10.1016/j.ymeth.2016.07.023
   US Department of Health and Human Services, 2009, COMMON TERMINOLOGY C, P4
   Wang M., 2017, SAFE MED RECOMMENDAT
   Wang M, 2017, LECT NOTES COMPUT SC, V10588, P219, DOI 10.1007/978-3-319-68204-4_23
   Wang ZC, 2016, BIOINFORMATICS, V32, P2338, DOI 10.1093/bioinformatics/btw168
   Wu HZ, 2018, CURR DRUG METAB, V19, P559, DOI 10.2174/1389200218666171108154419
   Zhang TX, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3053-5
NR 31
TC 1
Z9 1
U1 4
U2 18
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 JAN 12
PY 2021
VL 11
AR 625659
DI 10.3389/fgene.2020.625659
PG 11
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA PX7ZO
UT WOS:000611573200001
PM 33584816
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Tilly, S
   Livan, G
AF Tilly, Sonja
   Livan, Giacomo
TI Macroeconomic forecasting with statistically validated knowledge graphs
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge graph; Time series forecasting; Natural language processing;
   Big data
ID BUSINESS CYCLES; MODELS
AB This study leverages narrative from global newspapers to construct theme-based knowledge graphs about world events, demonstrating that features extracted from such graphs improve forecasts of industrial production in three large economies compared to a number of benchmarks. Our analysis relies on a filtering methodology that extracts "backbones"of statistically significant edges from large graph data sets. We find that changes in the eigenvector centrality of nodes in such backbones capture shifts in relative importance between different themes significantly better than graph similarity measures. We supplement our results with an interpretability analysis, showing that the theme categories "disease"and "economic"have the strongest predictive power during the time period that we consider. Our work serves as a blueprint for the construction of parsimonious - yet informative - theme-based knowledge graphs to monitor in real time the evolution of relevant phenomena in socio-economic systems.
C1 [Tilly, Sonja; Livan, Giacomo] UCL, Comp Sci Dept, 66-72 Gower St, London WC1E 6EA, England.
   [Livan, Giacomo] London Sch Econ & Polit Sci, System Risk Ctr, London WC2A 2AE, England.
RP Tilly, S (通讯作者)，UCL, Comp Sci Dept, 66-72 Gower St, London WC1E 6EA, England.
EM sonja.tilly.19@ucl.ac.uk; g.livan@ucl.ac.uk
OI Tilly, Sonja/0000-0003-0398-0713
CR Adamic L, 2017, ECONOMET J, V20, pS126, DOI 10.1111/ectj.12090
   Bagrow JP, 2019, APPL NETW SCI, V4, DOI 10.1007/s41109-019-0156-x
   Baker S. R., 2020, Working Paper Series - National Bureau of Economic Research (Massachusetts)
   Baker SR, 2016, Q J ECON, V131, P1593, DOI 10.1093/qje/qjw024
   Benjamini Y, 2005, J AM STAT ASSOC, V100, P71, DOI 10.1198/016214504000001907
   Bildirici ME, 2015, PROCD SOC BEHV, V210, P416, DOI 10.1016/j.sbspro.2015.11.389
   Brooks C., 2010, REAL ESTATE MODELLIN, DOI [10.1017/CBO9780511814235, DOI 10.1017/CBO9780511814235]
   Brosch T, 2013, SWISS MED WKLY, V143, DOI 10.4414/smw.2013.13786
   Bruner J.S.., 1990, ACTS MEANING
   Buono D., 2018, EVALUATION NOWCASTIN
   Campi M, 2020, ENVIRON RES LETT, V15, DOI 10.1088/1748-9326/abc2f6
   Carvalho V.M, 2016, SUPPLY CHAIN DISRUPT
   Christiano LJ, 2005, J POLIT ECON, V113, P1, DOI 10.1086/426038
   Clore GL, 2009, COGN SYST RES, V10, P21, DOI 10.1016/j.cogsys.2008.03.002
   Colladon AF, 2019, DECIS SUPPORT SYST, V123, DOI 10.1016/j.dss.2019.113075
   Constantin A, 2018, J FINANC STABIL, V35, P226, DOI 10.1016/j.jfs.2016.10.011
   Coulombe P. G., 2020, ARXIV PREPRINT ARXIV
   Cubadda G, 2012, ECON MODEL, V29, P1099, DOI 10.1016/j.econmod.2012.03.027
   DEJONG S, 1993, CHEMOMETR INTELL LAB, V18, P251, DOI 10.1016/0169-7439(93)85002-X
   Elshendy M, 2018, J INF SCI, V44, P408, DOI 10.1177/0165551517698298
   Emmert-Streib F., 2018, FRONT APPL MATH STAT, V4, P37, DOI [10.3389/fams.2018.00037, DOI 10.3389/FAMS.2018.00037]
   Ghalmane Z, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-71876-0
   Giannone D, 2008, J MONETARY ECON, V55, P665, DOI 10.1016/j.jmoneco.2008.05.010
   Girardi A, 2016, J FORECASTING, V35, P542, DOI 10.1002/for.2393
   Gomez DM, 2017, APPL ECON, V49, P972, DOI 10.1080/00036846.2016.1210765
   GRANGER CWJ, 1969, ECONOMETRICA, V37, P424, DOI 10.2307/1912791
   Graves A, 2005, IEEE IJCNN, P2047
   Guo L, 2020, J BROADCAST ELECTRON, V64, P418, DOI 10.1080/08838151.2020.1796391
   Harvey D, 1997, INT J FORECASTING, V13, P281, DOI 10.1016/S0169-2070(96)00719-4
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Keynes, 2007, GEN THEORY EMPLOYMEN
   King G, 2017, SCIENCE, V358, P776, DOI 10.1126/science.aao1100
   Latora V., 2017, COMPLEX NETWORKS PRI
   Leamer E. E, 1985, ECON PHILOS, V1, P295, DOI [10.1017/S0266267100002546, DOI 10.1017/S0266267100002546]
   Leetaru K, 2012, D LIB MAGAZINE, DOI 10.1045/september2012-leetaru
   Leetaru K. H., 2014, D LIB MAGAZINE, V20, P8
   Marcaccioli R, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-08667-3
   McCracken MW, 2016, J BUS ECON STAT, V34, P574, DOI 10.1080/07350015.2015.1086655
   Nyman R., 2018, NEWS NARRATIVES FINA
   Piccardi C, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0208265
   Sallinger, 2020, ARXIV PREPRINT ARXIV
   Schuster M, 1997, IEEE T SIGNAL PROCES, V45, P2673, DOI 10.1109/78.650093
   Serrano MA, 2009, P NATL ACAD SCI USA, V106, P6483, DOI 10.1073/pnas.0808904106
   Shiller RJ, 2017, AM ECON REV, V107, P967, DOI 10.1257/aer.107.4.967
   Smets F, 2007, AM ECON REV, V97, P586, DOI 10.1257/aer.97.3.586
   Stern S., 2020, NETWORK PERSPECTIVE, DOI [10.1007/s41109-020-00272-4, DOI 10.1007/S41109-020-00272-4]
   Stock JH, 2001, J ECON PERSPECT, V15, P101, DOI 10.1257/jep.15.4.101
   Tantardini M, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-53708-y
   Temizsoy A., 2016, NETWORK CENTRALITY F, DOI [10.1016/j.jfs.2016.11.003, DOI 10.1016/J.JFS.2016.11.003]
   Thorsrud L. A, 2016, BIG DATA VERSUS BIG
   Tilly S, 2021, EXPERT SYST APPL, V175, DOI 10.1016/j.eswa.2021.114760
   Tobias R. D., 1995, P 20 ANN SAS US GROU, P1250
   Tuckett D., 2014, EC GROWTH E JOURNAL, DOI [10.2139/ssrn, DOI 10.2139/SSRN]
   Tumminello M, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0017994
   van Eyden R, 2019, APPL ENERG, V233, P612, DOI 10.1016/j.apenergy.2018.10.049
   Yang Yi, 2020, ARXIV PREPRINT ARXIV
NR 56
TC 0
Z9 0
U1 4
U2 16
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 115765
DI 10.1016/j.eswa.2021.115765
EA SEP 2021
PG 9
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 UZ0AD
UT WOS:000701874800014
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Chen, J
   Li, B
   Wang, J
   Zhao, YQ
   Yao, L
   Xiong, YM
AF Chen, Jian
   Li, Bing
   Wang, Jian
   Zhao, Yuqi
   Yao, Li
   Xiong, Yiming
TI Knowledge Graph Enhanced Third-Party Library Recommendation for Mobile
   Application Development
SO IEEE ACCESS
LA English
DT Article
DE Libraries; Semantics; Mobile applications; Licenses; Computational
   modeling; Collaboration; Three-dimensional displays; Third-party library
   recommendation; mobile app; knowledge graph; topic modeling; MKR
AB With the prevalence of smart terminal devices and the rapid development of the mobile Internet, mobile application markets become increasingly prosperous. Third-party libraries have played an essential role in mobile application development. These libraries can shorten development time, increase development efficiency, and improve development quality. Currently, a large number of third-party libraries have been published, which puts a heavy burden on developers in selecting appropriate libraries. Towards this issue, in this paper, we propose a novel third-party library recommendation approach by integrating topic modeling and knowledge graph techniques. In the topic modeling component, we extract topics from textual application descriptions and make recommendations based on libraries used by applications that share similar topics with the new application to develop. In the knowledge graph component, we leverage knowledge graph to incorporate structured information of third-party libraries and applications, as well as the interaction information of applications and libraries for the recommendation. Experiments conducted on a real-world dataset show that our proposed approach outperforms several state-of-the-art approaches in terms of recommendation performance.
C1 [Chen, Jian; Li, Bing; Wang, Jian; Zhao, Yuqi; Yao, Li; Xiong, Yiming] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
   [Li, Bing] Wuhan Univ, Res Ctr Complex Network, Wuhan 430072, Peoples R China.
RP Li, B (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.; Li, B (通讯作者)，Wuhan Univ, Res Ctr Complex Network, Wuhan 430072, Peoples R China.
EM bingli@whu.edu.cn
FU National Key Research and Development Program of China [2017YFB1400602];
   National Natural Science Foundation of China [61832014]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2017YFB1400602, and in part by
   the National Natural Science Foundation of China under Grant 61832014.
CR Agrawal R., 1994, P 20 INT C VER LARG, V1215, P487
   Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   [Anonymous], [No title captured]
   [Anonymous], 2018, KARS RECSYS
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Catherine R, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P325, DOI 10.1145/2959100.2959131
   Chen CY, 2016, IEEE INT CONF AUTOM, P834, DOI 10.1145/2970276.2970290
   Chen CY, 2016, 2016 IEEE 23RD INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), VOL 1, P338, DOI 10.1109/SANER.2016.21
   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
   Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   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
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Mandelin D, 2005, ACM SIGPLAN NOTICES, V40, P48, DOI 10.1145/1064978.1065018
   Oramas Sergio, 2017, ACM Transactions on Intelligent Systems and Technology, V8, DOI 10.1145/2926718
   Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711
   Roder M, 2015, WSDM'15: PROCEEDINGS OF THE EIGHTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P399, DOI 10.1145/2684822.2685324
   Thung F, 2013, WORK CONF REVERSE EN, P182, DOI 10.1109/WCRE.2013.6671293
   Tiddi I, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P1029, DOI 10.1145/2740908.2742019
   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, AAAI CONF ARTIF INTE, P2508
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang J, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2915
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yang B., 2019, ARXIV190209091
   Yu H.-G., 2017, P IEEE 26 C EL PERF, V1019808, P1, DOI DOI 10.23919/EETA.2017.7993206
   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 XQ, 2019, IEICE T INF SYST, VE102D, P522, DOI 10.1587/transinf.2018EDP7227
   Zhong H, 2009, LECT NOTES COMPUT SC, V5653, P318, DOI 10.1007/978-3-642-03013-0_15
NR 34
TC 2
Z9 2
U1 1
U2 18
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 42436
EP 42446
DI 10.1109/ACCESS.2020.2976884
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LB6DA
UT WOS:000524723300001
OA gold
DA 2022-08-23
ER

PT J
AU Liu, S
   Tan, NN
   Yang, H
   Lukac, N
AF Liu, Shuang
   Tan, Nannan
   Yang, Hui
   Lukac, Niko
TI An Intelligent Question Answering System of the Liao Dynasty Based on
   Knowledge Graph
SO INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
LA English
DT Article
DE Knowledge graph; Question answering; Siamese LSTM; MatchPyramid; Deep
   semantic matching
AB The Liao Dynasty was a minority regime established by the Khitan on the grasslands of northern China. To promote and spread the cultural knowledge of the Liao Dynasty, an intelligent question-and-answer system is constructed based on the knowledge graph in the historical and cultural field of the Liao Dynasty. In the traditional question answering system, the quality of answers was not high due to incomplete data and distinctive vocabulary. To solve this problem, a combination method of Liao Dynasty question-and-answer database and KB is proposed to realize knowledge graph question answering, and a joint model of Siamese LSTM and fusion MatchPyramid is proposed for semantic matching between questions in the question-and-answer database. With the joint model, it is easy to perform semantic matching by fusing sentence-level and word-level interactive features through LSTM and MatchPyramid. Furthermore, the question sentence with the same semantics as the user input question sentence is retrieved in the question-and-answer database, and the answer corresponding to the question sentence is returned as the result. The experimental results show that our proposed method has achieved relatively good performance in the historical domain of the Liao Dynasty and the open-domain knowledge graph, and improved the accuracy of question and answer.
C1 [Liu, Shuang; Tan, Nannan] Dalian Minzu Univ, Sch Comp Sci & Engn, Dalian 116600, Peoples R China.
   [Yang, Hui] Nanjing Inst Tourism & Hospitality, Sch Hotel Management, Nanjing 211100, Peoples R China.
   [Lukac, Niko] Univ Maribor, Fac Elect Engn & Comp Sci, Koroska Cesta 46, Maribor 2000, Slovenia.
RP Liu, S (通讯作者)，Dalian Minzu Univ, Sch Comp Sci & Engn, Dalian 116600, Peoples R China.
EM liushuang@dlnu.edu.cn; tannn761@gmail.com; yanghuimail2020@gmail.com;
   niko.lukac@um.si
FU Social Research Project on Economic Development in Liaoning Province
   [2021lslybkt-022]; Natural Science Foundation of Liaoning Province,
   China [20180550921, 2019-ZD-0175]; Scientific Research Fund Project of
   the Education Department of Liaoning Province [LJYT201906]
FX This research was funded by Social Research Project on Economic
   Development in Liaoning Province (Grant No. 2021lslybkt-022), Natural
   Science Foundation of Liaoning Province, China (Grant Nos. 20180550921
   and 2019-ZD-0175), and Scientific Research Fund Project of the Education
   Department of Liaoning Province (LJYT201906).
CR Abujabal A, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1191, DOI 10.1145/3038912.3052583
   Amit S., 2012, NOT STRINGS OFFICIAL
   Bahdanau D., 2014, ARXIV
   Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bromley J., 1993, International Journal of Pattern Recognition and Artificial Intelligence, V7, P669, DOI 10.1142/S0218001493000339
   Chen Q, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1657, DOI 10.18653/v1/P17-1152
   Chen Y., 2019, P 2019 C N AM CHAPTE, V1, P2913
   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
   Defferrard M., 2016, 30 INT C NEURAL INFO, P3844
   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
   Han J., 2020, FINDINGS ASS COMPUTA, P1475
   Hao ZF, 2019, NEURAL NETWORKS, V119, P57, DOI 10.1016/j.neunet.2019.07.014
   Huang PS, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P2333
   Huang ZH, 2015, COMPUT INTEL NEUROSC, V2015, DOI 10.1155/2015/685404
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Khodra M.L, 2017, 3 INT C SCI INF TECH, P136
   Kim Y., 2014, PROC C EMPIRICAL MET, P1746, DOI [10.3115/v1/d14-1181, 10.3115/v1/D14-1181]
   Lan YS, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P969
   Li D, 2018, P 57 ANN M ASS COMP, P731
   Li P., 2016, ARXIV160706275
   [罗达 Luo Da], 2019, [计算机科学, Computer Science], V46, P215
   Lv C, 2020, ACAI 2020 2020 3 INT, P1
   MrYener, OWNTHINK KNOWLEDGE G
   Niu X, 2011, LECT NOTES COMPUT SC, V7032, P205, DOI 10.1007/978-3-642-25093-4_14
   Pang L, 2016, AAAI CONF ARTIF INTE, P2793
   Pennington J., 2014, EMNLP, P1532
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   [王思宇 Wang Siyu], 2020, [中文信息学报, Journal of Chinese Information Processing], V34, P104
   Wang Z., 2013, INT SEM WEB C ISWC A, P121
   Wang ZG, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4144
   Weng Z. Q, COMPUT ENG
   Weston J., 2014, ARXIV14103916
   Wong Y. W., 2007, P 45 ANN M ASS COMP, P960
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Xiong WH, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4258
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   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
   Zaremba Wojciech, 2014, ARXIV14092329
   Zhang C., 2019, COMPUTER ENG, V46, P41
   Zhu SG, 2020, NEUROCOMPUTING, V372, P64, DOI 10.1016/j.neucom.2019.09.003
NR 42
TC 0
Z9 0
U1 5
U2 5
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 1875-6891
EI 1875-6883
J9 INT J COMPUT INT SYS
JI Int. J. Comput. Intell. Syst.
PD SEP 29
PY 2021
VL 14
IS 1
AR 170
DI 10.1007/s44196-021-00010-3
PG 12
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0G9ZI
UT WOS:000778400100001
OA gold
DA 2022-08-23
ER

PT J
AU Tang, XL
   Yuan, R
   Li, QY
   Wang, TY
   Yang, HZ
   Cai, YD
   Song, HJ
AF Tang, Xiaoli
   Yuan, Rui
   Li, Qianyu
   Wang, Tengyun
   Yang, Haizhi
   Cai, Yundong
   Song, Hengjie
TI Timespan-Aware Dynamic Knowledge Graph Embedding by Incorporating
   Temporal Evolution
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph embedding; gated recurrent units; time unbalance
AB Recently, Knowledge Graph Embedding (KGE) has attracted considerable research efforts, since it simplifies the manipulation while preserving the inherent structure of the KG. However to some extent, most existing KGE approaches ignore the historical changes of structural information involved in dynamic knowledge graphs (DKGs). To deal with this problem, this paper presents a Timespan-aware Dynamic knowledge Graph Embedding Evolution (TDG2E) method that considers temporal evolving process of DKGs. The major innovations of our paper are two-fold. Firstly, a Gated Recurrent Units (GRU) based model is utilized in TDG2E to deal with the dependency among sub-KGs that is inevitably involved in the learning process of the dynamic knowledge graph embedding. Furthermore, we incorporate an auxiliary loss to supervise the learning process of the next sub-KG by utilizing previous structural information (i.e., the hidden state of GRU). In contrast with existing approaches in the literature (e.g., HyTE and t-TransE), TDG2E preserves structural information of current sub-KG and the temporal evolving process of the DKG simultaneously. Secondly, to further deal with the time unbalance issue underlying the DKGs, a Timespan Gate is designed in GRU. It makes TDG2E possible to model the temporal evolving process of DKGs more effectively by incorporating the timespan between adjacent sub-KGs. Extensive experiments on two large temporal datasets (i.e., YAGO11k and Wikidata12k) extracted from real-world KGs validate that the proposed TDG2E significantly outperforms traditional KGE methods in terms of Mean Rank and Hit Rate.
C1 [Tang, Xiaoli; Li, Qianyu; Wang, Tengyun; Yang, Haizhi; Song, Hengjie] South China Univ Technol, Sch Software Engn, Guangzhou 510006, Peoples R China.
   [Yuan, Rui] Chinese Univ Hong Kong, Sch Journalism & Commun, Hong Kong, Peoples R China.
   [Cai, Yundong] Nanyang Technol Univ, Joint NTU UBC Res Ctr Excellence Act Living Elder, Singapore, Singapore.
RP Song, HJ (通讯作者)，South China Univ Technol, Sch Software Engn, Guangzhou 510006, Peoples R China.
EM sehjsong@scut.edu.cn
OI Tang, Xiaoli/0000-0002-1967-2953; Yang, Haizhi/0000-0002-0730-394X;
   Wang, Tengyun/0000-0001-8160-9392
FU Pre-Research Foundation of China [61400010205]; National Natural Science
   Foundation of China [71671069]; National Key Research and Development
   Program of China [2018YFC0830900]
FX This work was supported in part by the Pre-Research Foundation of China
   under Grant 61400010205, in part by the National Natural Science
   Foundation of China under Grant 71671069, and in part by the National
   Key Research and Development Program of China under Grant
   2018YFC0830900.
CR [Anonymous], 2018, P 32 AAAI C ART INT
   [Anonymous], [No title captured]
   [Anonymous], 2016, P 30 AAAI C ART INT
   [Anonymous], [No title captured]
   Balazevic I, 2019, LECT NOTES COMPUT SC, V11731, P553, DOI 10.1007/978-3-030-30493-5_52
   Battaglia P.W., 2018, ARXIV 180601261
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   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, ARXIV14063676
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Chung J., 2014, EMPIRICAL EVALUATION
   Daiber J., 2013, P 9 INT C SEM SYST, P121, DOI 10.1145/2506182.2506198
   Damljanovic D, 2012, LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P2023
   Dasgupta Shib Sankar, 2018, P 2018 C EMP METH NA, P2001
   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
   Erxleben F, 2014, LECT NOTES COMPUT SC, V8796, P50, DOI 10.1007/978-3-319-11964-9_4
   Feng J, 2016, PROC CVPR IEEE, P156, DOI 10.1109/CVPR.2016.24
   Garcia-Duran A, 2018, P 2018 C EMP METH NA
   Goyal P, 2018, KNOWL-BASED SYST, V151, P78, DOI 10.1016/j.knosys.2018.03.022
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   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
   Jiang K, 2016, C IND ELECT APPL, P2360, DOI 10.1109/ICIEA.2016.7603987
   Jin W., 2019, P ICLR RLGM, P1
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kazemi Seyed Mehran, 2019, ARXIV PREPRINT ARXIV
   Leblay J, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1771, DOI 10.1145/3184558.3191639
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li QY, 2019, IEEE ACCESS, V7, P115816, DOI 10.1109/ACCESS.2019.2932466
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu Y, 2019, LECT NOTES COMPUT SC, V11881, P583, DOI 10.1007/978-3-030-34223-4_37
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Palm RB, 2018, ADV NEUR IN, V31
   Pareja A, 2019, EVOLVEGCN EVOLVING G
   Sanchez-Gonzalez A., 2018, ARXIV180601242
   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
   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 Z., 2014, P 28 AAAI C ART INT
   Wang Z., 2016, IJCAI, P1293
   Yang B., 2015, P INT C LEARN REPR
   Yang J, 2015, KNOWL INF SYST, V42, P181, DOI 10.1007/s10115-013-0693-z
   Zheng ZC, 2012, 2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2012), VOL 1, P82, DOI 10.1109/WI-IAT.2012.26
   Zhu LH, 2016, IEEE T KNOWL DATA EN, V28, P2765, DOI 10.1109/TKDE.2016.2591009
   Zuo Y, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2857, DOI 10.1145/3219819.3220054
NR 51
TC 4
Z9 7
U1 6
U2 20
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 6849
EP 6860
DI 10.1109/ACCESS.2020.2964028
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LC6ED
UT WOS:000525422700009
OA gold
DA 2022-08-23
ER

PT J
AU Jin, JH
   Luo, JZ
   Khemmarat, S
   Dong, F
   Gao, LX
AF Jin, Jiahui
   Luo, Junzhou
   Khemmarat, Samamon
   Dong, Fang
   Gao, Lixin
TI GStar: an efficient framework for answering top-k star queries on
   billion-node knowledge graphs
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Graph pattern matching; Knowledge graphs; Billion-node graphs; Top-k
   query; Big data; Distributed system
ID ALGORITHM; SEARCH; ENGINE
AB Massive knowledge graphs, such as Linked Open Data or Freebase, contain billions of labeled entities and relationships. Star queries aim to identify an entity given a set of related entities, and they are common with massive knowledge graphs. It is important to find the best way to answer star queries, and we can do this by treating it as a graph pattern-matching problem. Because knowledge graphs are noisy and incomplete in nature, we must find answers that match the star pattern closely, and extract a precise match if possible. Thus, here we propose GStar, a framework to identify the top-k best answers for a star query. GStar effectively and efficiently answers top-k star queries on billion-node graphs through a novel query model, an index-free query algorithm, and a distributed query system. We evaluate GStar through experiments on real-world knowledge graphs. Experimental results show that our query model effectively answers real-life star-pattern queries; our query algorithm can answer top-k queries in a near-real-time manner without requiring expensive graph indices; and the distributed system scales well with both the graph size and number of machines used for computation.
C1 [Jin, Jiahui; Luo, Junzhou; Dong, Fang] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China.
   [Khemmarat, Samamon; Gao, Lixin] Univ Massachusetts, Dept Elect & Comp Engn, Amherst, MA 01003 USA.
RP Jin, JH (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China.
EM jjin@seu.edu.cn; jluo@seu.edu.cn; samamon@gmail.com; fdong@seu.edu.cn;
   lgao@engin.umass.edu
FU National Key R&D Program of China [2017YFB1003000]; National Natural
   Science Foundation of China [61702096, 61632008, 61320106007, 61572129,
   61602112, 61502097, 61370207, 61702097]; International S&T Cooperation
   Program of China [2015DFA10490]; Natural Science Foundation of Jiangsu
   Province [BK20170689, BK20160695]; Jiangsu Provincial Key Laboratory of
   Network and Information Security [BM2003201]; Key Laboratory of Computer
   Network and Information Integration of Ministry of Education of China
   [93K-9]; Fundamental Research Funds for the Central Universities;
   Collaborative Innovation Center of Novel Software Technology and
   Industrialization; U.S. NSF [CNS-1217284, CCF-1018114]; China
   Scholarship Council; Collaborative Innovation Center of Wireless
   Communications Technology
FX This work is supported by National Key R&D Program of China
   2017YFB1003000, National Natural Science Foundation of China under
   Grants No. 61702096, No. 61632008, No. 61320106007, No. 61572129, No.
   61602112, No. 61502097, No. 61370207 and No. 61702097; International S&T
   Cooperation Program of China No. 2015DFA10490; the Natural Science
   Foundation of Jiangsu Province under grant BK20170689; BK20160695 and
   Jiangsu Provincial Key Laboratory of Network and Information Security
   under Grants No. BM2003201; Key Laboratory of Computer Network and
   Information Integration of Ministry of Education of China under Grants
   No. 93K-9; the Fundamental Research Funds for the Central Universities;
   and partially supported by Collaborative Innovation Center of Novel
   Software Technology and Industrialization and Collaborative Innovation
   Center of Wireless Communications Technology. This work is also
   partially supported by U.S. NSF grants CNS-1217284 and CCF-1018114.
   Jiahui Jin was a visiting student at UMass Amherst, supported by China
   Scholarship Council, when this work was performed. Any opinions,
   findings, conclusions or recommendations expressed in this paper are
   those of the authors and do not necessarily reflect the views of the
   sponsor. Preliminary version [14] of this paper appeared in Proceedings
   of the 20th IEEE International Conference on Parallel and Distributed
   Systems (ICPADS' 14).
CR Akiba T., 2013, P 2013 ACM SIGMOD IN, P349, DOI DOI 10.1145/2463676.2465315
   Akiba T., 2012, EDBT, P144, DOI DOI 10.1145/2247596.2247614
   [Anonymous], 2010, P 9 USENIX C OP SYST
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Brandes U, 2001, J MATH SOCIOL, V25, P163, DOI 10.1080/0022250X.2001.9990249
   Chakrabarti D, 2004, SIAM PROC S, P442
   Checconi Fabio, 2014, 2014 IEEE International Parallel & Distributed Processing Symposium (IPDPS), P425, DOI 10.1109/IPDPS.2014.52
   Cheng JF, 2013, PROC INT CONF DATA, P1033, DOI 10.1109/ICDE.2013.6544895
   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, 2003, J COMPUT SYST SCI, V66, P614, DOI 10.1016/S0022-0000(03)00026-6
   Fan WF, 2010, PROC VLDB ENDOW, V3, P264, DOI 10.14778/1920841.1920878
   Han WS, 2010, PROC VLDB ENDOW, V3, P449, DOI 10.14778/1920841.1920901
   He H., 2007, SIGMOD, P305, DOI DOI 10.1145/1247480.1247516
   Ilyas IF, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1391729.1391730
   Jin J., 2017, SUPPLEMENTARY FILE G
   Jin JH, 2017, IEEE T PARALL DISTR, V28, P2342, DOI 10.1109/TPDS.2017.2665478
   Jin JH, 2014, INT C PAR DISTRIB SY, P9, DOI 10.1109/PADSW.2014.7097785
   Khan A., 2011, SIGMOD, P901
   Khan A, 2013, PROC VLDB ENDOW, V6, P181, DOI 10.14778/2535569.2448952
   Khemmarat S, 2014, PROC INT CONF DATA, P316, DOI 10.1109/ICDE.2014.6816661
   Lee J, 2012, PROC VLDB ENDOW, V6, P133, DOI 10.14778/2535568.2448946
   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]
   Neumann T, 2008, PROC VLDB ENDOW, V1, P647, DOI 10.14778/1453856.1453927
   Neumann T, 2009, DISTRIB PARALLEL DAT, V26, P3, DOI 10.1007/s10619-009-7041-z
   Qiu T, 2017, IEEE COMMUN MAG, V55, P132, DOI 10.1109/MCOM.2017.1700033
   Qiu T, 2017, IEEE ACM T NETWORK, V25, P2944, DOI 10.1109/TNET.2017.2713530
   Shang HC, 2008, PROC VLDB ENDOW, V1, P364, DOI 10.14778/1453856.1453899
   Stanton I, 2012, P 18 ACM SIGKDD INT, P1222, DOI [10.1145/2339530.2339722, DOI 10.1145/2339530.2339722]
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Z, 2012, PROC VLDB ENDOW, V5, P788, DOI 10.14778/2311906.2311907
   Tian YY, 2008, PROC INT CONF DATA, P963, DOI 10.1109/ICDE.2008.4497505
   Tong HH, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P737
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Yan D, 2016, PROC VLDB ENDOW, V9, P564, DOI 10.14778/2904483.2904488
   Zeng K, 2013, PROC VLDB ENDOW, V6, P265, DOI 10.14778/2535570.2488333
   Zhang YF, 2014, IEEE T PARALL DISTR, V25, P2091, DOI 10.1109/TPDS.2013.235
   ZOU L, 2009, PROC VLDB ENDOW, V2, P886, DOI DOI 10.14778/1687627.1687727
   Zou L, 2011, PROC VLDB ENDOW, V4, P482, DOI 10.14778/2002974.2002976
NR 39
TC 4
Z9 5
U1 0
U2 17
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD JUL
PY 2019
VL 22
IS 4
BP 1611
EP 1638
DI 10.1007/s11280-018-0611-0
PG 28
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IE9ED
UT WOS:000472676800010
DA 2022-08-23
ER

PT J
AU Zhu, GG
   Iglesias, CA
AF Zhu, Ganggao
   Iglesias, Carlos A.
TI Sematch: Semantic similarity framework for Knowledge Graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Semantic similarity; Taxonomy; Knowledge Graphs; Classification;
   Evaluation
AB Sematch is an integrated framework for the development, evaluation and application of semantic similarity for Knowledge Graphs. The framework provides a number of similarity tools and datasets, and allows users to compute semantic similarity scores of concepts, words, and entities, as well as to interact with Knowledge Graphs through SPARQL queries. Sematch focuses on knowledge-based semantic similarity that relies on structural knowledge in a given taxonomy (e.g. depth, path length, least common subsumer), and statistical information contents. Researchers can use Sematch to develop and evaluate semantic similarity metrics and exploit these metrics in applications. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Zhu, Ganggao; Iglesias, Carlos A.] Univ Politecn Madrid, Grp Sistemas Inteligentes, Madrid, Spain.
RP Zhu, GG (通讯作者)，Univ Politecn Madrid, Grp Sistemas Inteligentes, Madrid, Spain.
EM gzhu@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 R&D project SEMOLA
   [TEC2015-68284-R]; Regional Government of Madrid [P2013/ICE-3019]; EU
   Structural Fund FSE; EU Structural Fund FEDER; European Union [141111];
   Spanish Ministry of Economy and Competitiveness under R&D project
   EmoSpaces [RTC-2016-5053-7]
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 Araque O, 2016, INT CONF DAT MIN WOR, P900, DOI [10.1109/ICDMW.2016.0132, 10.1109/ICDMW.2016.71]
   Mihalcea R., 2004, TEXTRANK BRINGING OR
   Pedersen T, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P1024
   STEIGER JH, 1980, PSYCHOL BULL, V87, P245, DOI 10.1037/0033-2909.87.2.245
   ZHU G., 2015, SEMATCH SEMANTIC ENT
   Zhu GG, 2017, IEEE T KNOWL DATA EN, V29, P72, DOI 10.1109/TKDE.2016.2610428
NR 6
TC 25
Z9 25
U1 4
U2 64
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 AUG 15
PY 2017
VL 130
BP 30
EP 32
DI 10.1016/j.knosys.2017.05.021
PG 3
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EY7TV
UT WOS:000404196000003
DA 2022-08-23
ER

PT J
AU Zheng, WG
   Zhang, M
   Yang, DQ
   Zhang, ZY
   Han, WD
AF Zheng, Weiguo
   Zhang, Mei
   Yang, Deqing
   Zhang, Zeyang
   Han, Weidong
TI Towards combinational relation linking over knowledge graphs
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph; Combinational relation linking; Meta pattern; Relation
   assembly
AB Given a knowledge graph and a natural language phrase, relation linking aims to find relations (predicates or properties) from the underlying knowledge graph to match the phrase. It is very useful in many applications, such as natural language question answering, personalized recommendation and text summarization. However, the previous relation linking algorithms usually produce a single relation for the input phrase and pay little attention to the more general and challenging problem, i.e., combinational relation linking that extracts a subgraph pattern to match the compound phrase (e.g. father-in-law). In this paper, we focus on the task of combinational relation linking over knowledge graphs. To resolve the problem, we define several elementary meta patterns which can be used to build any combinational relation. Then we design a systematic method based on the data-driven relation assembly technique, which is performed under the guidance of meta patterns. To enhance the system's understanding ability, we introduce external knowledge during the linking process. Finally, extensive experiments over the real knowledge graph confirm the effectiveness of the proposed method.
C1 [Zheng, Weiguo; Yang, Deqing; Zhang, Zeyang; Han, Weidong] Fudan Univ, Sch Data Sci, 220 Handan Rd, Shanghai 200433, Peoples R China.
   [Zhang, Mei] Beijing Jindi Technol Co Ltd, 18 Suzhou St, Beijing 100080, Peoples R China.
RP Zheng, WG (通讯作者)，Fudan Univ, Sch Data Sci, 220 Handan Rd, Shanghai 200433, Peoples R China.
EM zhengweiguo@fudan.edu.cn; zhangmei@tianyancha.com;
   yangdeqing@fudan.edu.cn; zeyangzhang20@fudan.edu.cn;
   wdhan20@fudan.edu.cn
FU National Natural Science Foundation of China [61902074]; Shanghai
   Science and Technology Committee [19ZR1404900]; Shanghai Science and
   Technology Innovation Action Plan [21511100401]
FX This work was substantially supported by National Natural Science
   Foundation of China (Grant No. 61902074), Shanghai Science and
   Technology Committee (Grant No. 19ZR1404900). Shanghai Science and
   Technology Innovation Action Plan (No. 21511100401).
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bao J, 2016, COLING
   Beaumont R, 2015, WORKING NOTES CLEF
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Catherine R, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P288, DOI 10.1145/3109859.3109878
   Chen DD, 2014, J ANAL METHODS CHEM, V2014, DOI 10.1155/2014/575246
   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
   Devlin J., 2019, BERT PRETRAINING DEE, DOI [10.18653/v1/N19-1423, DOI 10.18653/V1/N19-1423]
   Dubey M, 2018, LECT NOTES COMPUT SC, V11136, P108, DOI 10.1007/978-3-030-00671-6_7
   Ebisu T, 2019, ARXIV190402856
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Fan WF, 2015, PROC VLDB ENDOW, V8, P1502
   Gerber D., 2011, 1 WORKSH WEB SCAL KN, V2011
   Guo S, 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, P84
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Kim Y., 2014, PROC C EMPIRICAL MET, P1746, DOI [10.3115/v1/d14-1181, 10.3115/v1/D14-1181]
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Levenshtein V.I., 1966, SOV PHYS DOKL, V10, P707
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Miwa M, 2014, P 2014 C EMP METH NA, P1858, DOI DOI 10.3115/V1/D14-1200
   Mulang I. O., 2017, P 13 INT C SEM SYST, P89, DOI DOI 10.1145/3132218.3132229
   Nakashole N., 2012, P 2012 JOINT C EMP M, P1135, DOI DOI 10.1080/17517575.2015.1080301
   Pan JZ, 2019, LECT NOTES COMPUT SC, V11778, P523, DOI 10.1007/978-3-030-30793-6_30
   Prudhommeaux E, 2008, W3C RECOMMENDATION
   Sakor A, 2019, ISWC, P265
   Sakor A, 2019, P 2019 C N AM CHAPT, P2336, DOI DOI 10.18653/V1/N19-1243
   Singh K, 2017, K-CAP 2017: PROCEEDINGS OF THE KNOWLEDGE CAPTURE CONFERENCE, DOI 10.1145/3148011.3148031
   Suchanek FM, 2007, WWW
   Wang Q, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1308
   Wang SL, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4461
   Wu P, 2018, 2018 INTERNATIONAL CONFERENCE ON AUDIO, LANGUAGE AND IMAGE PROCESSING (ICALIP), P79, DOI 10.1109/ICALIP.2018.8455241
   Xiao, 2015, ARXIV151204792
   Yang Z, 2016, P 2016 C N AM CHAPTE, P1480
   Yao, 2019, ARXIV190903193
   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
   Zheng W, 2019, COMBINATIONAL RELATI
NR 39
TC 0
Z9 0
U1 3
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
PD NOV
PY 2021
VL 24
IS 6
BP 1975
EP 1994
DI 10.1007/s11280-021-00951-x
EA SEP 2021
PG 20
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WR3AA
UT WOS:000698177200002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Huet, A
   Pinquie, R
   Veron, P
   Mallet, A
   Segonds, F
AF Huet, Armand
   Pinquie, Romain
   Veron, Philippe
   Mallet, Antoine
   Segonds, Frederic
TI CACDA: A knowledge graph for a context-aware cognitive design assistant
SO COMPUTERS IN INDUSTRY
LA English
DT Article
DE Design rule; Knowledge graph; Knowledge management; Context-awareness;
   Cognitive assistant
ID INFORMATION
AB The design of complex engineered systems highly relies on a laborious zigzagging between computeraided design (CAD) software and design rules prescribed by design manuals. Despite the emergence of knowledge management techniques (ontology, expert system, text mining, etc.), companies continue to store design rules in large and unstructured documents. To facilitate the integration of design rules and CAD software, we propose a knowledge graph that structures a large set of design rules in a computable format. The knowledge graph organises entities of design rules (nodes), relationships among design rules (edges), as well as contextual information. The categorisation of entities and relationships in four subcontexts: semantic, social, engineering, and IT - facilitates the development of the data model, especially the definition of the "design context" concept. The knowledge graph paves the way to a context-aware cognitive design assistant. Indeed, connected to or embedded in a CAD software, a context-aware cognitive design assistant will capture the design context in near real time and run reasoning operations on the knowledge graph to extend traditional CAD capabilities, such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Our validation experiment shows that the current version of the context-aware cognitive design assistant is more efficient than the traditional document-based design. On average, participants using an unstructured design rules document have a precision of 0.36 whereas participants using our demonstrator obtain a 0.61 precision score. Finally, designers supported by the design assistant spend more time designing than searching for applicable design rules compared to the traditional design approach. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Huet, Armand; Segonds, Frederic] HESAM Univ, Arts & Metiers Inst Technol, LCPI, F-75013 Paris, France.
   [Pinquie, Romain] Univ Grenoble Alpes, CNRS, Grenoble INP, G SCOP, Grenoble, France.
   [Veron, Philippe] HESAM Univ, Arts & Metiers Inst Technol, LISPEN, F-13617 Aix En Provence, France.
   [Mallet, Antoine] Capgemini DEMS, Toulouse, France.
RP Huet, A (通讯作者)，HESAM Univ, Arts & Metiers Inst Technol, LCPI, F-75013 Paris, France.
EM armand.huet@ensam.eu
OI HUET, Armand/0000-0002-4761-8866
CR Abowd GD, 1999, LECT NOTES COMPUT SC, V1707, P304
   Alami Merrouni Z, 2019, PROCEDIA COMPUT SCI, V148, P191, DOI [10.1016/j.proces.2019.01.036\, 10.1016/j.procs.2019.01.036]
   Alexopoulos K, 2016, INT J COMPUT INTEG M, V29, P839, DOI 10.1080/0951192X.2015.1130257
   Angles R., 2018, CEUR WORKSH P, V2100
   Baldauf M, 2007, INT J AD HOC UBIQ CO, V2, P263, DOI 10.1504/IJAHUC.2007.014070
   Ben-Yitzhak O., 2008, P INT C WEB SEARCH W, P33, DOI DOI 10.1145/1341531.1341539
   Camba JD, 2017, INT J INFORM MANAGE, V37, P611, DOI 10.1016/j.ijinfomgt.2017.06.002
   Cassens J., 2006, P 19 INT FLOR ART IN, P619
   Company P, 2015, COMPUT AIDED DESIGN, V63, P101, DOI 10.1016/j.cad.2014.10.001
   Dey AK, 2001, PERS UBIQUIT COMPUT, V5, P4, DOI 10.1007/s007790170019
   Dhuieb MA, 2016, PROC CIRP, V41, P484, DOI 10.1016/j.procir.2015.12.027
   Dhuieb MA, 2015, IFAC PAPERSONLINE, V48, P834, DOI 10.1016/j.ifacol.2015.06.187
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Faloutsos C, 1998, SURVEY INFORM RETRIE, V8958546, P1
   Fortineau Virginie, 2014, International Journal of Product Lifecycle Management, V7, P75, DOI 10.1504/IJPLM.2014.065458
   Fu KK, 2016, J MECH DESIGN, V138, DOI 10.1115/1.4034105
   Garcia L.E.R., ONTOLOGY BASED FEATU
   Gero J.S., 1999, COMPUT MODEL CREAT D, VIV, P175
   Gruber T.R, 1990, S KNOWL BAS HUM COMP, P1
   Gruszka A, 2017, EUR MANAG J, V35, P776, DOI 10.1016/j.emj.2017.07.001
   Gu T., 2004, P COMM NETW DISTR SY
   Hariya M, 2010, INT CONF COMP SCI, P296, DOI 10.1109/ICCSIT.2010.5565010
   Hasanov A, 2019, J AMB INTEL SMART EN, V11, P403, DOI 10.3233/AIS-190534
   Huang B, 2015, J COMPUT DES ENG, V2, P253, DOI 10.1016/j.jcde.2015.06.008
   Huet A., PROD LIF MAN INT C, P594, DOI [10.1007/ 978-3-030-62807-9 27, DOI 10.1007/978-3-030-62807-927]
   Ingwersen P., 2007, P 4 BIENN DISSANET C, P13
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Johansson J, 2018, ADV ENG INFORM, V38, P252, DOI 10.1016/j.aei.2018.07.005
   Li Z, 2008, J COMPUT INF SCI ENG, V8, DOI 10.1115/1.2830851
   Ma J, 2010, INT CONF COMP SCI, P555, DOI 10.1109/ICCSIT.2010.5564747
   Mackenzie C. A., 1987, International Journal for Artificial Intelligence in Engineering, V2, P2
   Madni Azad M., 2020, INSIGHT, V23, P31, DOI 10.1002/inst.12282
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Mattos TD, 2014, COMPUT IND, V65, P1193, DOI 10.1016/j.compind.2014.07.005
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Moitra A, 2016, AAAI CONF ARTIF INTE, P4028
   Pinquie R, 2018, PROC CIRP, V70, P410, DOI 10.1016/j.procir.2018.03.228
   Rouse William B., 2020, INSIGHT, V23, P52, DOI 10.1002/inst.12286
   Rowson H., 2018, PLM C TOR IT, P621
   Roy SB, 2009, PROC INT CONF DATA, P1463, DOI 10.1109/ICDE.2009.117
   Sanya IO, 2014, INT J PROD RES, V52, P6192, DOI 10.1080/00207543.2014.919422
   Shi F, 2017, J MECH DESIGN, V139, DOI 10.1115/1.4037649
   Shrestha P., 2011, ACTES 18E C TRAITEME, P1
   Skarka W, 2007, ENG APPL ARTIF INTEL, V20, P677, DOI 10.1016/j.engappai.2006.11.019
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Strobin L, 2014, LECT NOTES ARTIF INT, V8467, P793, DOI 10.1007/978-3-319-07173-2_68
   Tamine-Lechani L, 2010, KNOWL INF SYST, V24, P1, DOI 10.1007/s10115-009-0231-1
   Tecuci G, 2016, KNOWLEDGE ENG BUILDI
   Tessier S, 2013, ADV ENG INFORM, V27, P76, DOI 10.1016/j.aei.2012.11.008
   van Engelenburg S, 2019, J LOG ALGEBR METHODS, V103, P79, DOI 10.1016/j.jlamp.2018.11.003
   Voorhees E. M., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, P61
   Wang QM, 2014, COMPUT IND, V65, P1041, DOI 10.1016/j.compind.2014.04.004
   YOSHIKAWA H, 1993, ANN CIRP, V42, P131, DOI DOI 10.1016/S0007-8506(07)62409-3
NR 53
TC 7
Z9 7
U1 4
U2 14
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-3615
EI 1872-6194
J9 COMPUT IND
JI Comput. Ind.
PD FEB
PY 2021
VL 125
AR 103377
DI 10.1016/j.compind.2020.103377
PG 18
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QE1RE
UT WOS:000615986900004
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Li, CH
   Xian, XF
   Ai, XS
   Cui, ZM
AF Li, Chunhua
   Xian, Xuefeng
   Ai, Xusheng
   Cui, Zhiming
TI Representation Learning of Knowledge Graphs with Embedding Subspaces
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable labelled training data. The cost of obtaining high quality triples is high and the data sources are facing a serious problem of data sparsity, which may result in insufficient training of long-tail entities. However, unstructured text encoding entities and relational knowledge can be obtained anywhere in large quantities. Word vectors of entity names estimated from the unlabelled raw text using natural language model encode syntax and semantic properties of entities. Yet since these feature vectors are estimated through minimizing prediction error on unsupervised entity names, they may not be the best for knowledge graphs. We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and knowledge representation. Experiments on Freebase show that our method can rely less on the labelled data and outperforms the baselines when the labelled data is relatively less. Especially, it is applicable to zero-shot scenario.
C1 [Li, Chunhua; Ai, Xusheng] Suzhou Vocat Inst Ind Technol, Software & Serv Outsourcing Coll, Suzhou 215104, Peoples R China.
   [Xian, Xuefeng] Suzhou Vocat Univ, Sch Comp Engn, Suzhou 215104, Peoples R China.
   [Cui, Zhiming] Suzhou Univ Sci & Technol, Sch Elect & Informat Engn, Suzhou 215009, Peoples R China.
RP Cui, ZM (通讯作者)，Suzhou Univ Sci & Technol, Sch Elect & Informat Engn, Suzhou 215009, Peoples R China.
EM 00804@siit.edu.cn; xianxuefeng@jssvc.edu.cn; 00754@siit.edu.cn;
   zmcui@usts.edu.cn
RI Ai, Xusheng/ABD-8813-2020
OI Ai, Xusheng/0000-0001-5629-9134
FU National Natural Science Foundation of China [61876217]; Suzhou Science
   and Technology Plan Project [SYG201903]; Fundamental Computing Education
   Research Project of Association of Fundamental Computing Education in
   Chinese Universities [2018-AFCEC-328, 2019-AFCEC-288]; Research Funds of
   Suzhou Vocational Institute of Industrial Technology [2019kyqd001]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant no. 61876217, Suzhou Science and
   Technology Plan Project under Grant no. SYG201903, Fundamental Computing
   Education Research Project of Association of Fundamental Computing
   Education in Chinese Universities under Grant nos. 2018-AFCEC-328 and
   2019-AFCEC-288, and Research Funds of Suzhou Vocational Institute of
   Industrial Technology under Grant no. 2019kyqd001.
CR Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
   Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2011, P 25 AAAI C ART INT
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Chen D., 2013, LEARNING NEW FACTS K
   Collobert R, 2011, J MACH LEARN RES, V12, P2493
   Dettmers T., 2018, P 32 AAAI C ART INT
   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
   Garcia-Duran A, 2016, J ARTIF INTELL RES, V55, P715, DOI 10.1613/jair.5013
   Hussein R, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P437, DOI 10.1145/3269206.3271777
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   JI G, 2016, P 30 AAAI C ART INT
   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 Yongpo, 2016, P 30 AAAI C ART INT
   Kingma D. P., 2015, P ICLR
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   Lin Y, 2015, MODELING RELATION PA
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Luo Y., 2015, P 2015 C EMP METH NA, P1656, DOI DOI 10.18653/V1/D15-1191
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mikolov T., 2013, P INT C LEARN REPR M
   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
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9981, P498, DOI 10.1007/978-3-319-46523-4_30
   Shi B., 2017, P 31 AAAI C ART INT
   Singh A, 2008, INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, P650
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Wang YH, 2014, 2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), P5915, DOI 10.1109/WCICA.2014.7053732
   Wang Z., 2014, P 28 AAAI C ART INT
   Xiao H., 2016, SSP SEMANTIC SPACE P
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
NR 36
TC 2
Z9 2
U1 1
U2 6
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 AUG 25
PY 2020
VL 2020
AR 4741963
DI 10.1155/2020/4741963
PG 10
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NQ3MU
UT WOS:000570769400004
OA gold
DA 2022-08-23
ER

PT J
AU Lei, K
   Zhang, J
   Xie, YX
   Wen, DS
   Chen, DY
   Yang, M
   Shen, Y
AF Lei, Kai
   Zhang, Jin
   Xie, Yuexiang
   Wen, Desi
   Chen, Daoyuan
   Yang, Min
   Shen, Ying
TI Path-based reasoning with constrained type attention for knowledge graph
   completion
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Neural network; Knowledge graph completion; Multi-hop reasoning;
   Attention mechanism
AB Multi-hop reasoning over paths in knowledge graphs has attracted rising research interest in the field of knowledge graph completion. Entity types and relation types both contain various kinds of information content though only a subset of them are helpful in the specific triples. Although significant progress has been made by existing models, they have two major shortcomings. First, these models seldom learn an explicit representation of entities and relations with semantic information. Second, they reason without discriminating distinct role types that the same entity with multiple types plays in different triples. To address these issues, we develop a novel path-based reasoning with constrained type attention model, which tries to identify entity types by leveraging relation type constraints in the corresponding triples. Our experimental evaluation shows that the proposed model outperforms the state of the art on a real-world dataset. Further analyses also confirm that both word-level and triple-level attention mechanisms of our model are effective.
C1 [Lei, Kai; Zhang, Jin; Xie, Yuexiang; Wen, Desi; Chen, Daoyuan; Shen, Ying] Peking Univ, Sch Elect & Comp Engn SECE, Shenzhen Key Lab Informat Ctr Networking & Blockc, Shenzhen 518055, Peoples R China.
   [Yang, Min] Chinese Acad Sci, Shenzhen Inst Adv Technol SIAT, Shenzhen, Peoples R China.
   [Lei, Kai] Peng Cheng Lab, PCL Res Ctr Networks & Commun, Shenzhen 518055, Peoples R China.
RP Shen, Y (通讯作者)，Peking Univ, Sch Elect & Comp Engn SECE, Shenzhen Key Lab Informat Ctr Networking & Blockc, Shenzhen 518055, Peoples R China.
EM shenying@pkusz.edu.cn
OI SHEN (chen ying), Ying/0000-0002-3220-904X
FU National Natural Science Foundation of China [61602013]; Natural Science
   Foundation of Guangdong [2018A030313017]; Shenzhen Fundamental Research
   Project [JCYJ20170818091546869]
FX This work was financially supported by the National Natural Science
   Foundation of China (No. 61602013), Natural Science Foundation of
   Guangdong (No. 2018A030313017) and the Shenzhen Fundamental Research
   Project (No. JCYJ20170818091546869).
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   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, ARXIV14063676
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Catherine R, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P325, DOI 10.1145/2959100.2959131
   Chen W., 2018, P 2018 C N AM CHAPT, V1, P1823, DOI [10.18653/v1/N18-1165, DOI 10.18653/V1/N18-1165]
   Cho K., 2015, P INT C LEARN REPR I
   Gardner M., 2013, P 2013 C EMP METH NA, P833
   Gardner M., 2014, P 2014 C EMP METH NA, P397, DOI [10.3115/v1/D14-1044, DOI 10.3115/V1/D14-1044]
   Glorot X., 2010, J MACH LEARN RES, V9, P249
   Gormley MR, 2015, P 2015 C EMP METH NA, P1774, DOI DOI 10.18653/V1/D15-1205
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   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 X., 2017, P MACH LEARN RES, P590
   Kingma D. P., 2017, ARXIV
   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, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lin YK, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2124
   Manning C. D, 2013, P 17 C COMP NAT LANG, P133
   Neelakantan A., 2015, 2015 AAAI SPRING S S
   Neelakantan A, 2017, P 15 C EUR ASS COMP, P141
   Shen Y, 2018, ACM/SIGIR PROCEEDINGS 2018, P901, DOI 10.1145/3209978.3210081
   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
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499, DOI DOI 10.18653/V1/D15-1174
   Wen DS, 2018, LECT NOTES COMPUT SC, V10699, P321, DOI 10.1007/978-3-319-73830-7_32
   Yang Z, 2016, P 2016 C N AM CHAPTE, P1480
   Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205
   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 HJ, 2017, IEEE T IND INFORM, V13, P616, DOI 10.1109/TII.2016.2601521
   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 35
TC 2
Z9 2
U1 6
U2 31
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 JUN
PY 2020
VL 32
IS 11
SI SI
BP 6957
EP 6966
DI 10.1007/s00521-019-04181-1
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LS4RE
UT WOS:000536371900041
DA 2022-08-23
ER

PT J
AU Wang, L
AF Wang, Li
TI An Improved Knowledge Graph Question Answering System for English
   Teaching
SO MOBILE INFORMATION SYSTEMS
LA English
DT Article
AB Knowledge graph, as a structured semantic knowledge base, has become an essential foundation for artificial intelligence applications with its flexible composition structure and rich semantic representation capability. This paper combines the knowledge graph embedding scoring algorithm with the link scoring algorithm to effectively solve the problem of missing answers in the current knowledge graph embedding question and answer method. This method constructs a query link while searching for the best answer and gives the answer set through the query, which effectively alleviates the omission of answers in the existing methods. The experimental results show that the F1 score of the English teaching test system on the data set is 86.85%, where the answer selection method weakly relies on a priori information such as predicates in the test data and can be trained on a test pair data set without human intervention, with good generalization performance.
C1 [Wang, Li] Henan Finance Univ, Coll English Dept, Zhengzhou 450046, Henan, Peoples R China.
RP Wang, L (通讯作者)，Henan Finance Univ, Coll English Dept, Zhengzhou 450046, Henan, Peoples R China.
EM wangli1@hafu.edu.cn
CR Abu-Nasser B., 2017, INT J ENG INFORM SYS, V1, P218
   Acheampong FA, 2021, ARTIF INTELL REV, V54, P5789, DOI 10.1007/s10462-021-09958-2
   Cao ZY, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11030392
   Diefenbach D, 2018, KNOWL INF SYST, V55, P529, DOI 10.1007/s10115-017-1100-y
   Dimitrakis E, 2020, J INTELL INF SYST, V55, P233, DOI 10.1007/s10844-019-00584-7
   Fang YL, 2020, APPL INTELL, V50, P4281, DOI 10.1007/s10489-020-01761-w
   Gesese GA, 2021, SEMANT WEB, V12, P617, DOI 10.3233/SW-200404
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Kalyanathaya K.P., 2019, INT J RECENT TECHNOL, V7, P199
   Kotkar V.A, 2021, TURKISH J COMPUTER M, V12, P90
   Li J, 2022, IEEE T KNOWL DATA EN, V34, P50, DOI [10.1109/TKDE.2020.2981314, 10.1109/TKDE.2020.3038670]
   Li L, 2021, WORLD WIDE WEB, V24, P249, DOI 10.1007/s11280-020-00842-7
   Li XZ, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-2617-8
   Liu SS, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9183698
   Liu S, 2020, INFORMATION, V11, DOI 10.3390/info11040186
   Lu Q, 2020, INT J COMPUT INT SYS, V13, P538, DOI 10.2991/ijcis.d.200423.001
   Mohamed SK, 2021, BRIEF BIOINFORM, V22, P1679, DOI 10.1093/bib/bbaa012
   Momtazi S, 2018, INFORM PROCESS MANAG, V54, P380, DOI 10.1016/j.ipm.2018.01.001
   Nagamanjula R, 2020, SOC NETW ANAL MIN, V10, DOI 10.1007/s13278-020-00648-5
   Rinaldi AM, 2021, MULTIMED TOOLS APPL, V80, P3885, DOI 10.1007/s11042-020-09761-1
   Ruan T, 2019, BMC MED INFORM DECIS, V19, DOI 10.1186/s12911-019-0798-8
   Strobelt H, 2019, IEEE T VIS COMPUT GR, V25, P353, DOI 10.1109/TVCG.2018.2865044
   Thomas A, 2021, EXPERT SYST, V38, DOI 10.1111/exsy.12656
   Wang P, 2018, IEEE T PATTERN ANAL, V40, P2413, DOI 10.1109/TPAMI.2017.2754246
   Watanabe K, 2021, COMMUN METHODS MEAS, V15, P81, DOI 10.1080/19312458.2020.1832976
   Yuan JB, 2020, KNOWL INF SYST, V62, P317, DOI 10.1007/s10115-019-01351-4
   Zhang JM, 2021, J AMB INTEL HUM COMP, V12, P8427, DOI 10.1007/s12652-020-02572-0
   [郑庆华 Zheng Qinghua], 2019, [计算机研究与发展, Journal of Computer Research and Development], V56, P209
   Zheng R, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21010306
NR 29
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 1574-017X
EI 1875-905X
J9 MOB INF SYST
JI Mob. Inf. Syst.
PD JUN 29
PY 2022
VL 2022
AR 3401074
DI 10.1155/2022/3401074
PG 9
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 3A8LG
UT WOS:000827505300010
OA gold
DA 2022-08-23
ER

PT J
AU Lan, YY
   He, SZ
   Liu, K
   Zeng, XR
   Liu, SP
   Zhao, J
AF Lan, Yinyu
   He, Shizhu
   Liu, Kang
   Zeng, Xiangrong
   Liu, Shengping
   Zhao, Jun
TI Path-based knowledge reasoning with textual semantic information for
   medical knowledge graph completion
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Review
DE Medical knowledge graph completion; Path-based knowledge reasoning;
   Textual semantic representation; Pre-trained language model
AB Background Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness. Methods To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation. Results Experiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations. Conclusions In this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.
C1 [Lan, Yinyu; He, Shizhu; Liu, Kang; Zhao, Jun] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China.
   [Lan, Yinyu; He, Shizhu; Liu, Kang; Zhao, Jun] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China.
   [Zeng, Xiangrong; Liu, Shengping] Beijing Unisound Informat Technol Co Ltd, Beijing, Peoples R China.
RP He, SZ (通讯作者)，Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China.; He, SZ (通讯作者)，Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China.
EM shizhu.he@nlpr.ia.ac.cn
FU Natural Key R&D Program of China [2017YFB1002101]; National Natural
   Science Foundation of China [61922085, 61976211, 61702512]; Key Research
   Program of the Chinese Academy of Sciences [ZDBS-SSW-JSC006]; National
   Laboratory of Pattern Recognition; Youth Innovation Promotion
   Association CAS
FX Publication costs are funded by the Natural Key R&D Program of China
   (No. 2017YFB1002101), the National Natural Science Foundation of China
   (Nos. 61922085, 61976211, 61702512) and the Key Research Program of the
   Chinese Academy of Sciences (Grant No. ZDBS-SSW-JSC006). Publication
   costs are also funded by the independent research project of National
   Laboratory of Pattern Recognition and the Youth Innovation Promotion
   Association CAS. The funders did not play any role in the design of the
   study, the collection, analysis, and interpretation of data, or in
   writing of the manuscript.
CR Bello-Orgaz G, 2016, INFORM FUSION, V28, P45, DOI 10.1016/j.inffus.2015.08.005
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Brown T., 2020, ARXIV200514165, V33, P1877
   Chen DY, 2013, ICML WORKSH STRUCT L, P106
   Chisholm A, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P633
   Das R., 2016, ARXIV PREPRINT ARXIV, P132
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Hinton G., 2015, ARXIV PREPRINT ARXIV
   Jiang SP, 2012, IEEE DATA MINING, P912, DOI 10.1109/ICDM.2012.156
   Jiang X., 2017, P MACH LEARN RES, P590
   Kale Mihir, 2020, P 13 INT C NAT LANG, P97
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Liu HX, 2017, PR MACH LEARN RES, V70
   Liu Y., 2019, ARXIV190711692
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Murdoch TB, 2013, JAMA-J AM MED ASSOC, V309, P1351, DOI 10.1001/jama.2013.393
   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
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Peters M., 2018, IMPROVING LANGUAGE U
   Pujara J., 2017, P 2017 C EMP METH NA, P1751, DOI DOI 10.18653/V1/D17-1184
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Tylenda T, 2014, P 4 WORKSH AUT KNOWL, P1
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang H., 2018, ARXIV PREPRINT ARXIV
   Yang ZW, 2021, ANIM BIOTECHNOL, V32, P67, DOI 10.1080/10495398.2019.1653901
   Yao, 2019, ARXIV190903193
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
NR 29
TC 0
Z9 0
U1 23
U2 33
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 NOV 29
PY 2021
VL 21
IS SUPPL 9
SU 9
SI SI
AR 335
DI 10.1186/s12911-021-01622-7
PG 10
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA XE8PF
UT WOS:000723643400001
PM 34844576
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Liu, MF
   Li, XY
   Li, J
   Liu, YH
   Zhou, B
   Bao, JS
AF Liu, Mingfei
   Li, Xinyu
   Li, Jie
   Liu, Yahui
   Zhou, Bin
   Bao, Jinsong
TI A knowledge graph-based data representation approach for IIoT-enabled
   cognitive manufacturing
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Cognitive manufacturing; Industrial Internet of Things; Knowledge graph;
   Cyber-Physical Production System; Data fusion
ID DATA FUSION; SMART; ARCHITECTURE
AB The Industrial Internet of Things (IIoT) interconnects a large number of interconnected sensors, actuators, and edge computing devices in the manufacturing systems, where the massive data collected in the manufacturing process has the characteristics of multi-dimensional, heterogeneous, and time series. An effective data representation manner, which can fuse such complex information and enable cognitive manufacturing decision making from a global perspective, is necessary and challenging. To solve this issue, this paper proposes a knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing and applies it in a Cyber-Physical Production System (CPPS) scenario. Based on the digital thread of manufacturing process data, a multi-layer manufacturing knowledge graph is established, including device sensing data, production processing data, and business processing data. With the established knowledge graph, a cognition-driven approach is proposed with a perception-cognition dual system, which achieves perception analysis and cognition decision making in the resource allocation of the manufacturing process. Finally, responding to the orders of personalized products in a workshop is taken as an illustrative example. The performance of allocating resources of workshop devices under dynamic demand changes shows the advantages of the proposed approach. The proposed manner will lay the foundation for a human-like cognition for processing massive real-time industrial information in CPPS, thus paving a pathway towards the era of cognitive manufacturing.
C1 [Liu, Mingfei; Li, Xinyu; Li, Jie; Liu, Yahui; Zhou, Bin; Bao, Jinsong] Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China.
RP Li, XY; Bao, JS (通讯作者)，Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China.
EM lixinyu@dhu.edu.cn; bao@dhu.edu.cn
FU National Key R&D Program of China [2019YFB1706300]
FX Acknowledgment This work was funded by the National Key R&D Program of
   China (No.2019YFB1706300) .
CR Bannat A, 2011, IEEE T AUTOM SCI ENG, V8, P148, DOI 10.1109/TASE.2010.2053534
   Bengio Y, 2019, 33 C NEURAL INF PROC
   Bunte A, 2019, IEEE INT C EMERG, P729, DOI 10.1109/ETFA.2019.8869038
   Chen M, 2018, FUTURE GENER COMP SY, V86, P403, DOI 10.1016/j.future.2018.03.054
   Chen S, 2020, IEEE ACCESS, V8, P134233, DOI 10.1109/ACCESS.2020.3011254
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Civerchia F, 2017, J IND INF INTEGR, V7, P4, DOI 10.1016/j.jii.2017.02.003
   Salazar LAC, 2019, INT J ADV MANUF TECH, V105, P4005, DOI 10.1007/s00170-019-03800-4
   da Silva AF, 2016, IFAC PAPERSONLINE, V49, P108, DOI 10.1016/j.ifacol.2016.11.137
   Du XM, 2020, IEEE ACCESS, V8, P82581, DOI 10.1109/ACCESS.2020.2991463
   Dumitrache I, 2019, IFAC PAPERSONLINE, V52, P910, DOI 10.1016/j.ifacol.2019.11.311
   Francalanza E, 2017, COMPUT IND, V84, P39, DOI 10.1016/j.compind.2016.08.001
   Grossberg S, 2020, FRONT NEUROROBOTICS, V14, DOI 10.3389/fnbot.2020.00036
   Nguyen HL, 2020, INFORM FUSION, V61, P56, DOI 10.1016/j.inffus.2020.03.014
   Hu L, 2019, FUTURE GENER COMP SY, V90, P569, DOI 10.1016/j.future.2018.08.006
   Hu SJ, 2013, PROC CIRP, V7, P3, DOI 10.1016/j.procir.2013.05.002
   Iarovyi S, 2015, IEEE INTL CONF IND I, P1225, DOI 10.1109/INDIN.2015.7281910
   Jabbar S, 2018, IEEE ACCESS, V6, P24510, DOI 10.1109/ACCESS.2018.2820176
   Kim J, 2019, PROCEDIA COMPUT SCI, V155, P113, DOI 10.1016/j.procs.2019.08.019
   Kumar A, 2021, IEEE T IND INFORM, V17, P2938, DOI 10.1109/TII.2020.3005532
   Li JM, 2020, ADV ENG INFORM, V46, DOI 10.1016/j.aei.2020.101183
   Li X., 2021, ENGINEERING, DOI [10.1016/j.eng.2021.08.018, DOI 10.1016/J.ENG.2021.08.018]
   Li XY, 2021, COMPUT IND, V129, DOI 10.1016/j.compind.2021.103449
   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, 2020, J MECH DESIGN, V142, DOI 10.1115/1.4046807
   Li XY, 2018, INT J PROD RES, V56, P2897, DOI 10.1080/00207543.2017.1421785
   Li XY, 2017, ADV ENG INFORM, V34, P17, DOI 10.1016/j.aei.2017.08.001
   Liu BF, 2021, ROBOT CIM-INT MANUF, V70, DOI 10.1016/j.rcim.2021.102128
   Liu BF, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100984
   Liu C, 2020, ROBOT CIM-INT MANUF, V64, DOI 10.1016/j.rcim.2020.101956
   Lu YQ, 2020, J MANUF SYST, V56, P312, DOI 10.1016/j.jmsy.2020.06.010
   Lyu MT, 2022, ADV ENG INFORM, V51, DOI 10.1016/j.aei.2021.101494
   Maier P, 2010, ADV ENG INFORM, V24, P308, DOI 10.1016/j.aei.2010.05.015
   Pan J.Z., 2017, EXPLOIT LINKED DATA
   Qin ZJ, 2021, J MANUF SYST, V60, P35, DOI 10.1016/j.jmsy.2021.04.016
   Qu YY, 2021, IEEE T IND INFORM, V17, P2964, DOI 10.1109/TII.2020.3007817
   Sisinni E, 2018, IEEE T IND INFORM, V14, P4724, DOI 10.1109/TII.2018.2852491
   Velandia DMS, 2016, ROBOT CIM-INT MANUF, V41, P66, DOI 10.1016/j.rcim.2016.02.004
   Wang C, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, P2521, DOI 10.1109/ICMA.2015.7237883
   Wang G, 2021, J MANUF SYST, V59, P165, DOI 10.1016/j.jmsy.2021.02.008
   Wang JL, 2020, ROBOT CIM-INT MANUF, V61, DOI 10.1016/j.rcim.2019.101854
   Wang W, 2020, IEEE TETCI, V4, P32, DOI 10.1109/TETCI.2018.2876568
   Wang ZX, 2022, COMPUT IND ENG, V163, DOI 10.1016/j.cie.2021.107816
   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
   Xu LD, 2014, IEEE T IND INFORM, V10, P2233, DOI 10.1109/TII.2014.2300753
   Yoo S, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112965
   Yuanyuan Zhao, 2017, 2017 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII). Proceedings, P194, DOI 10.1109/ICIICII.2017.36
   Zhang G, 2021, INT J PROD RES, DOI 10.1080/00207543.2021.2013564
   Zhang G, 2021, ROBOT CIM-INT MANUF, V71, DOI 10.1016/j.rcim.2021.102161
   Zhang G, 2020, J CLEAN PROD, V273, DOI 10.1016/j.jclepro.2020.123144
   Zheng P, 2021, J MANUF SYST, V61, P16, DOI 10.1016/j.jmsy.2021.08.002
   Zhou B, 2022, INT J PROD RES, DOI 10.1080/00207543.2021.2022803
   Zhu KP, 2018, IEEE-ASME T MECH, V23, P2579, DOI 10.1109/TMECH.2018.2834622
NR 55
TC 2
Z9 2
U1 13
U2 13
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1474-0346
EI 1873-5320
J9 ADV ENG INFORM
JI Adv. Eng. Inform.
PD JAN
PY 2022
VL 51
AR 101515
DI 10.1016/j.aei.2021.101515
PG 12
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1K4JI
UT WOS:000798568700010
DA 2022-08-23
ER

PT J
AU Bai, FB
   Wen, Y
AF Bai, Fubao
   Wen, Yan
TI An Analysis of the Cognitive Ability of Knowledge Mapping Homeschooling
   Based on the Rasch Model
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID PARENTAL INVOLVEMENT; SCHOOL; PERFORMANCE; MOBILITY; CHILDREN; SCIENCE;
   SKILLS
AB Family cognition is a representation of the quality of family harmony, so family cognition score is an important exercise at the family level. The research based on the hybrid Rasch model is an important reference point. This study is based on the mixed Rasch model in item response theory. Firstly, descriptive statistical analysis is carried out on the family level, which is sorted according to the total score and specific proportion, and the difficulties and differences of family members in multiple choice questions at different levels are compared. Then, the number of potential categories estimated using the hybrid rush model is discussed in two cases. In one case, the number of categories is known and the categories are more detailed, so as to facilitate comparison and promote the harmony of family members.
C1 [Bai, Fubao; Wen, Yan] Longyan Univ, Sch Normal Educ, Longyan 364012, Peoples R China.
RP Bai, FB (通讯作者)，Longyan Univ, Sch Normal Educ, Longyan 364012, Peoples R China.
EM fubaobai@lyun.edu.cn; wenyan7756@163.com
CR Acosta S, 2014, EDUC STUD-UK, V40, P174, DOI 10.1080/03055698.2013.866889
   Alnandi AH, 2020, DISABIL REHABIL, V42, P2224, DOI 10.1080/09638288.2018.1555622
   An P, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2021.102844
   Antipkina I, 2020, J PSYCHOEDUC ASSESS, V38, P846, DOI 10.1177/0734282920903164
   Boyd RN, 2017, BMJ OPEN, V7, DOI 10.1136/bmjopen-2017-017204
   Bridges M, 2012, EARLY CHILD RES Q, V27, P555, DOI 10.1016/j.ecresq.2012.01.005
   Chien CW, 2012, AUST OCCUP THER J, V59, P108, DOI 10.1111/j.1440-1630.2012.00997.x
   Ehrlich SB, 2019, EARLY EDUC DEV, V30, P540, DOI 10.1080/10409289.2018.1556969
   Fiedler Sonja T, 2021, Discov Sustain, V2, P32, DOI 10.1007/s43621-021-00041-y
   Field DA, 2016, ARCH PHYS MED REHAB, V97, P462, DOI 10.1016/j.apmr.2015.08.428
   Heinzmann S., 2015, FRONTIERS INTERDISCI, VXXVI, P187, DOI DOI 10.36366/FRONTIERS.V26I1.366
   Ji BF, 2020, IEEE T VEH TECHNOL, V69, P7404, DOI 10.1109/TVT.2020.2989297
   Johnson GA, 2012, PROCEEDINGS OF THE ASME TURBO EXPO 2012, VOL 5, P1015
   Li JN, 2019, IEEE T COMPUT SOC SY, V6, P1395, DOI 10.1109/TCSS.2019.2917335
   Lin X, 2021, IEEE T EMERG TOP COM, V9, P1373, DOI 10.1109/TETC.2020.2971831
   Manz PH, 2014, J EARLY INTERVENTION, V36, P171, DOI 10.1177/1053815115573077
   Martin AJ, 2018, J EDUC PSYCHOL, V110, P465, DOI 10.1037/edu0000231
   Maul A, 2017, ETR&D-EDUC TECH RES, V65, P1, DOI 10.1007/s11423-016-9453-6
   McCarron RH, 2021, NEUROPSYCHOL REHABIL, V31, P1048, DOI 10.1080/09602011.2020.1760111
   McWayne C.M., 2015, INT J EDUC PSYCHOL, V3, P117, DOI [10.1080/21683603.2014.950439, DOI 10.1080/21683603.2014.950439]
   Oliveira IM, 2016, J CAREER ASSESSMENT, V24, P380, DOI 10.1177/1069072715580577
   Petridou A, 2018, J EDUC STUDENTS PLAC, V23, P359, DOI 10.1080/10824669.2018.1523733
   Price CA, 2019, APPL DEV SCI, V23, P239, DOI 10.1080/10888691.2017.1398090
   Robershaw K.L., 2022, J SCH CHOICE, V5, P1, DOI [10.1080/15582159.2021.2004493, DOI 10.1080/15582159.2021.2004493]
   Tatto MT, 2020, TEACH TEACH EDUC, V96, DOI 10.1016/j.tate.2020.103118
   Thiel H, 2013, RES ECON, V67, P189, DOI 10.1016/j.rie.2013.03.002
   Tieman BL, 2004, PHYS THER, V84, P419, DOI 10.1093/ptj/84.5.419
   Wahab NBA, 2015, PROCD SOC BEHV, V172, P398, DOI 10.1016/j.sbspro.2015.01.389
   Walker JMT, 2005, ELEM SCHOOL J, V106, P85, DOI 10.1086/499193
   Zhang Z.-W., 2021, COMPUTER SCI, V48, P296, DOI [10.11896/jsjkx.210400134, DOI 10.11896/JSJKX.210400134]
   [张争万 Zhang Zhengwan], 2020, [计算机应用, Journal of Computer Applications], V40, P3571
NR 31
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 MAY 5
PY 2022
VL 2022
AR 6245984
DI 10.1155/2022/6245984
PG 7
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 1M8MV
UT WOS:000800221600011
PM 35571682
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, YM
   Lin, LF
   Lin, YH
AF Li, Yung-Ming
   Lin, Lien-Fa
   Lin, Yu-Hui
TI A recommender mechanism for social knowledge navigation in an online
   encyclopedia
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Social media; Online encyclopedia; Knowledge-sharing; Knowledge
   ontology; Recommender systems
ID SYSTEM; MODEL; MAP
AB In today's world, knowledge is important for constructing core competitive advantages for individuals and organizations. Recently, Web 2.0 applications and social media have provided a convenient medium for people to share knowledge over the Internet. However, the huge amount of created knowledge can also leads to the problem of information overload. This research proposes a social knowledge navigation mechanism that utilizes the techniques of relevant knowledge network construction, knowledge importance analysis, and knowledge concept ontology construction to generate a visualized recommendation of a knowledge map of sub-concept and knowledge of an article reading sequence for supporting learning activities related to a free online encyclopedia. The results of experiments conducted on Wikipedia show that the proposed mechanism can effectively recommend useful articles and improve a knowledge seeker's learning effectiveness. (C) 2014 Elsevier Ltd. All rights reserved.
C1 [Li, Yung-Ming; Lin, Lien-Fa; Lin, Yu-Hui] Natl Chiao Tung Univ, Inst Informat Management, Hsinchu 300, Taiwan.
RP Li, YM (通讯作者)，Natl Chiao Tung Univ, Inst Informat Management, Hsinchu 300, Taiwan.
EM yml@mail.nctu.edu.tw; lienfa0704@gmail.com; clc0330@gmail.com
FU National Science Council of Taiwan (Republic of China) [NSC
   101-2410-H-009-009-MY3]
FX This research was supported by the National Science Council of Taiwan
   (Republic of China) under the Grant NSC 101-2410-H-009-009-MY3.
CR Agichtein E., 2008, P INT C WEB SEARCH W
   Ahn JW, 2011, LECT NOTES COMPUT SC, V6589, P309
   [Anonymous], WHAT IS US GEN CONT
   Berendt B, 2002, LECT NOTES COMPUT SC, V2342, P264
   Burkhard R. A., 2004, P IV 04, V8
   Chen H. W., 2008, J INFORM MANAGEMENT, V15, P157
   Cheong M, 2009, P CIKM 2009 COL WORK, P1
   Cole M., 2008, COMPUT EDUC, V52, P141
   Cui GY, 2009, 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 3, P287
   Erk K., 2007, P 45 ANN M ASS COMP, P216
   FOSS N. J., 2002, J INT MANAG, V8, P49, DOI DOI 10.1016/S1075-4253(01)00054-0
   Giacomo P., 2008, THESIS U MAASTRICHT
   Gollapudi S., 2010, P 3 ACM INT C WEB SE, P21, DOI DOI 10.1145/1718487.171849
   HAGE P, 1983, STRUCTURAL MODELS AN
   Haveliwala T., 2002, WWW2002
   He Q., 2010, P 19 INT C WORLD WID
   Kalakota R, 1997, ELECT COMMERCE MANAG
   Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003
   Kim MC, 1999, INFORM PROCESS MANAG, V35, P19, DOI 10.1016/S0306-4573(98)00040-5
   Kim S., 2003, KNOWLEDGE PROCESS MA, V10, P260, DOI DOI 10.1002/KPM.186
   Kwak H., 2010, INT CONF ADV COMMUN, P591
   Lai CH, 2009, J SYST SOFTWARE, V82, P2023, DOI 10.1016/j.jss.2009.06.044
   Leshed G., 2008, P 26 ANN SIGCHI C HU
   Libert Barry, 2007, WE ARE SMARTER ME UN
   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
   LINN RL, 1977, REV EDUC RES, V47, P121, DOI 10.3102/00346543047001121
   Liu DR, 2008, EXPERT SYST APPL, V34, P700, DOI 10.1016/j.eswa.2006.10.005
   Luo XF, 2010, NEW GENERAT COMPUT, V28, P113, DOI 10.1007/s00354-009-0078-8
   Ong TH, 2005, DECIS SUPPORT SYST, V39, P583, DOI 10.1016/j.dss.2004.03.008
   Rodrigues, 2009, P 4 INT C COMM TECHN
   SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0
   Seligmann A., 2008, HYPERTEXT, V2008, P19
   Shee DY, 2008, COMPUT EDUC, V50, P894, DOI 10.1016/j.compedu.2006.09.005
   Singh M. D., 2009, IEEE TENCON 2009 P, P1
   STEIN A, 1995, KNOWL-BASED SYST, V8, P82, DOI 10.1016/0950-7051(95)98370-L
   Steinbach M., 2000, P TEXT MIN WORKSH KD
   Strehl E, 2000, WORKSH ART INT WEB S, P58
   Su CY, 2010, COMPUT EDUC, V55, P1300, DOI 10.1016/j.compedu.2010.05.027
   Su C. Y., 2011, COMPUTERS ED
   Sun J., 2005, IEEE T SYSTEMS MAN B, V35
   Vail E. F., 1999, KNOWLEDGE MANAGEMENT, V8, P10
   Wu IC, 2011, J INF SCI, V37, P189, DOI 10.1177/0165551511400955
   Zhuge H, 2002, EXPERT SYST APPL, V23, P23, DOI 10.1016/S0957-4174(02)00024-6
NR 44
TC 8
Z9 8
U1 0
U2 57
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 2014
VL 50
IS 5
BP 634
EP 652
DI 10.1016/j.ipm.2014.04.006
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 AN0XJ
UT WOS:000340307000002
DA 2022-08-23
ER

PT J
AU Guttman, G
   Shpitalni, M
AF Guttman, G.
   Shpitalni, M.
TI Linear programming and genetic algorithms methods for creation of groups
   in networks of excellence
SO CIRP ANNALS-MANUFACTURING TECHNOLOGY
LA English
DT Article; Proceedings Paper
CT 56th General Assembly of CIRP
CY AUG 20-26, 2006
CL Kobe, JAPAN
DE knowledge management; linear programming; genetic algorithm
AB Creation of groups in Networks of Excellence (NoEs) based on knowledge mapping and expertise is a set covering problem known to be non-polynomial. Therefore it is usually approached by heuristic methods which yield good but not necessarily optimal coverage. Selecting teams to form a group within NoEs that are comprised of tens of teams can also be formulated and solved as an integer linear programming (ILP) problem whose solution is guaranteed to be optimal. This paper presents the ILP solution for team selection with typical objective functions. Several genetic algorithm-based methods are also compared to the optimal solution in terms of convergence (time and solution). The compared methods differ in selecting next-generation population schemes. The plain vanilla method is shown to be superior to both the roulette-based and the SUS methods.
C1 Technion Israel Inst Technol, Fac Mech Engn, Lab CAD & LCE, IL-32000 Haifa, Israel.
RP Guttman, G (通讯作者)，Technion Israel Inst Technol, Fac Mech Engn, Lab CAD & LCE, IL-32000 Haifa, Israel.
CR Baker JE, 1987, GENETIC ALGORITHMS T
   CHIARELLA C, 2005, RES PAPER SERIES U T, V150
   Dini G, 1999, CIRP ANNALS 1999 - MANUFACTURING TECHNOLOGY, P17, DOI 10.1016/S0007-8506(07)63122-9
   FORREST S, 1985, DOCUMENTATION PRISON
   Goldberg D.E, 1998, GENETIC ALGORITHMS S
   Holland J.H, 1992, ADAPTATION NATURAL A, P211
   Jin Y, 2005, CIRP ANN-MANUF TECHN, V54, P155, DOI 10.1016/S0007-8506(07)60072-9
   Kim JO, 2002, ELECTR POW SYST RES, V62, P201, DOI 10.1016/S0378-7796(02)00036-6
   Kimura F, 1998, CIRP ANNALS 1998 - MANUFACTURING TECHNOLOGY, VOL 47, NO 1, V47, P87
   MITCHELL M, 1996, INTRO GENETIC ALGORI, P166
   MITSUO G, 2000, GENETIC ALGORITHMS E, P53
   MOLCHO G, 2005, VRL KCIP KNOWL MAN W, P14
   SHPITALNI M, 2005, P GERM ISR S DES MAN, P3
   TANESE R, 1989, THESIS U MICHIGAN
   Ueda K, 1997, CIRP ANNALS 1997 MANUFACTURING TECHNOLOGY, VOLUME 46/1/1997, V46, P343, DOI 10.1016/S0007-8506(07)60839-7
   WIENDAHL HP, 1994, ANN CIRP, V43, P389
NR 16
TC 2
Z9 2
U1 0
U2 1
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 2006
VL 55
IS 1
BP 143
EP 146
DI 10.1016/S0007-8506(07)60385-0
PG 4
WC Engineering, Industrial; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA 078AS
UT WOS:000240073900033
DA 2022-08-23
ER

PT S
AU Vas, R
   Kovacs, B
   Gabor, A
AF Vas, R
   Kovacs, B
   Gabor, A
TI E-CRIME system: A knowledge management application in public
   administration
SO KNOWLEDGE MANAGEMENT IN ELECTRONIC GOVERNMENT, PROCEEDINGS
SE LECTURE NOTES IN COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 5th IFIP Interenational Working Conference on Knowledge Management in
   Electronic Government
CY MAY 17-19, 2004
CL Krems, AUSTRIA
SP IFIP WG 8 3, IFIP WG 8 5, IFIP WG 6 2, Univ Linz, Inst Informat Business & Govt, Danube Univ Krems
AB Legal security, which is an important factor of life quality, was the major subject of our research. We have concluded that legal security could be achieved by the perfection of jurisdiction, of which the three major elements are time, precision and efficiency [4]. The final goal of an appropriately working juridical system is to find equilibrium between time and precision and this way to reach maximum efficiency. This paper will discuss in details the problems of the Hungarian juridical system, particularly those, which can be solved or supported by IT tools. Our research project will be demonstrated, in which after the analysis of laws and legal studies the knowledge mapping of this domain has been realized. Based on this, an ontology has been made and an expert system has been developed. Our paper also describes how an advanced information system can improve the efficiency of the juridical processes.
C1 Budapest Univ Econ Sci & Publ Adm, Dept Informat Syst, Budapest, Hungary.
RP Vas, R (通讯作者)，Budapest Univ Econ Sci & Publ Adm, Dept Informat Syst, Budapest, Hungary.
EM rvas@informatika.bke.hu; bkovacs@informatika.bke.hu;
   gabor@informatika.bke.hu
CR Chandrasekaran B, 1999, IEEE INTELL SYST APP, V14, P20, DOI 10.1109/5254.747902
   *COMP ASS TECHN SU, 1998, GUID ALL PROD RUL RE
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Guarino N., 1995, VERY LARGE KNOWLEDGE, P25
   Klein MR, 1995, KNOWLEDGE BASED DECI
   KO A, 2001, 2 INT WORKSH KNOWL M
   MOLNAR B, 1999, MESTERSEGES INTELLIG
NR 7
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-22002-X
J9 LECT NOTES COMPUT SC
PY 2004
VL 3025
BP 221
EP 232
PG 12
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BAE35
UT WOS:000221801000023
DA 2022-08-23
ER

PT J
AU Tu, ZY
   Lv, MY
   Xu, XF
   Wang, ZJ
AF Tu, Zhiying
   Lv, Mengyao
   Xu, Xiaofei
   Wang, Zhongjie
TI Crowdsourcing service requirement oriented requirement pattern
   elicitation method
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Knowledge graph; Requirement pattern; Crowdsourcing service; Cognitive
   service
AB "Pattern" can always help machine to recognize the new encounters, so does "requirement pattern." Requirement pattern is one of the essences for the cognitive service to understand customer's intention. Since crowdsourcing service platform holds abundant user demands in the form of text information, the method proposed in this paper aims at eliciting valuable patterns from this "treasure." This method is based on a knowledge graph, which is constructed with the refined concepts of those text information of several different domains. Due to the irregularity and difference of user demand expressions, this paper will firstly explain the knowledge extraction method for heterogeneous text and the knowledge fusion-based knowledge graph construction method. Afterward, we will introduce the requirement pattern elicitation method based on this knowledge graph. The pattern could either be a frequent demand sequence or a domain-oriented rule or link. Finally, this paper will demonstrate a case study to show how those patterns can help to understand customers' intention effectively and accurately.
C1 [Tu, Zhiying; Lv, Mengyao; Xu, Xiaofei; Wang, Zhongjie] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China.
RP Tu, ZY (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China.
EM tzy_hit@hit.edu.cn; 1208211230@qq.com; xiaofei@hit.edu.cn;
   rainy@hit.edu.cn
FU National Key Research and Development Program of China [2018YFB1004804];
   National Science Foundation of China [61802089, 61772155, 61832004,
   61832014]
FX Research in this paper is partially supported by the National Key
   Research and Development Program of China (2018YFB1004804), the National
   Science Foundation of China (61802089, 61772155, 61832004, 61832014).
CR AbuDahab K, 2016, EXPERT SYST APPL, V51, P218, DOI 10.1016/j.eswa.2015.12.013
   [Anonymous], 2006, IEEE INT REQ ENG C
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen XH, 2016, INT J SOFTW ENG KNOW, V26, P15, DOI 10.1142/S0218194016500029
   Cohen W., 2003, P IJCAI 03 WORKSH IN, P73, DOI DOI 10.5555/3104278.3104293
   Cook DJ, 1994, J ARTIF INTELL RES, V1, P1442
   De Maio C, 2016, INFORM FUSION, V28, P60, DOI 10.1016/j.inffus.2015.06.004
   Deshpande M, 2005, IEEE T KNOWL DATA EN, V17, P1036, DOI 10.1109/TKDE.2005.127
   Dong XL, 2014, PROC VLDB ENDOW, V7, P881, DOI 10.14778/2732951.2732962
   Gao ZF, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), P25, DOI 10.1109/ICWS.2016.13
   Guy I., 2013, P 22 INT C WORLD WID, P515
   Han JW, 2000, SIGMOD RECORD, V29, P1
   Hao YC, 2016, COMM COM INF SC, V650, P3, DOI 10.1007/978-981-10-3168-7_1
   Jin XQ, 2014, LECT NOTES COMPUT SC, V8801, P46, DOI 10.1007/978-3-319-12277-9_5
   Khanna rJPT KGSVenkatesanDrV., 2015, INT J PHARM TECHNOLO, V3, P1556
   Li H, 2016, INT C WORLD WID WEB
   Li JW, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P165
   Lin J, 2011, P 17 ACM SIGKDD INT, P422
   Lin Y., 2015, COMPUTER SCI
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lu HC, 2018, GEOINFORMATICA, V5, P1
   Lu SY, 2013, INT CONF E BUS ENG, P419, DOI 10.1109/ICEBE.2013.64
   Mobasheri A, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17112498
   Nijssen S., 2004, P 10 ACM SIGKDD INT, P647, DOI DOI 10.1145/1014052.1014134
   Pei J, 2001, PROC INT CONF DATA, P215
   Resnik P, 2011, SEMANTIC AMBIGUITY U, P159
   Richardson M, 2006, MARKOV LOGIC NETWORK
   Rodriguez T, 2018, IEEE LAT AM T, V16, P639, DOI 10.1109/TLA.2018.8327424
   Sen P, 2012, P 21 INT C WORLD WID, P729
   Shi Xinyue, 2015, Journal of Frontiers of Computer Science and Technology, V9, P660, DOI 10.3778/j.issn.1673-9418.1410011
   Souag A, 2015, LECT NOTES COMPUT SC, V8978, P157, DOI 10.1007/978-3-319-15618-7_13
   Surdeanu M., 2012, P 2012 JOINT C EMP M, P455
   Wang HX, 2005, KNOWL INF SYST, V8, P82, DOI 10.1007/s10115-004-0170-9
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wei Qiang, 2014, Journal of Software, V25, P1640, DOI 10.13328/j.cnki.jos.004661
   Xiang J, 2007, P INT COMP SOFTW APP, P196
   Xu XZ, 2017, IEEE INT C BIOINFORM, P824, DOI 10.1109/BIBM.2017.8217761
   Yan XF, 2002, 2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P721, DOI 10.1109/ICDM.2002.1184038
   Yang F, 2017, DIFFERENTIABLE LEARN
   Zhao Z, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1406, DOI 10.1145/2736277.2741656
NR 40
TC 0
Z9 0
U1 7
U2 25
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 JUL
PY 2020
VL 32
IS 14
BP 10109
EP 10126
DI 10.1007/s00521-019-04542-w
EA OCT 2019
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MD6OP
UT WOS:000491552400001
DA 2022-08-23
ER

PT J
AU Wu, C
   Liu, SYY
   Zeng, ZY
   Chen, M
   Alhudhaif, A
   Tang, XY
   Alenezi, F
   Alnaim, N
   Peng, XC
AF Wu, Chao
   Liu, Sannyuya
   Zeng, Zeyu
   Chen, Mao
   Alhudhaif, Adi
   Tang, Xiangyang
   Alenezi, Fayadh
   Alnaim, Norah
   Peng, Xicheng
TI Knowledge graph-based multi-context-aware recommendation algorithm
SO INFORMATION SCIENCES
LA English
DT Article
DE Recommender system; Knowledge graph; Path representation; Neural
   networks
AB Because a knowledge graph's huge amount of item information can help recommender systems develop user/item representations, it has become the most important source of side information. Regardless of the numerous types of user/item representation approaches used in knowledge graph-based recommendation scenarios, they all have problems. In this paper, we propose a knowledge graph-based multi-context-aware recommendation algorithm for learning user/item representations that combines the advantages of path-based and propagation-based methods. A new concept (i.e., rule) is proposed first, which can be a useful way to characterize the user's preferences. Next, based on user-item interactions, an automatic rule discovery algorithm is proposed that can automatically select the most representative user preferences templates in a given recommendation scenario based on the knowledge graph and user behaviors. Then, the learning of high-order connectivity between long-distance user-item pairs is realized according to these templates. After that, a feature representation method of the local neighborhood characteristics of users and items is introduced to compensate for the defect that the path-based method can only catch the high-order connectivity. The experimental results demonstrate MANN's superiority over eight state-of-the-art baselines. (C) 2022 Elsevier Inc. All rights reserved.
C1 [Wu, Chao; Liu, Sannyuya; Zeng, Zeyu; Chen, Mao; Tang, Xiangyang; Peng, Xicheng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
   [Wu, Chao] Tencent Corp, Shenzhen 518000, Peoples R China.
   [Alhudhaif, Adi] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci Al Khad, Dept Comp Sci, POB 151, Al Kharj 11942, Saudi Arabia.
   [Tang, Xiangyang] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.
   [Alenezi, Fayadh] Jouf Univ, Coll Engn, Dept Elect Engn, Sakaka 72388, Saudi Arabia.
   [Alnaim, Norah] Imam Abdulrahman Bin Faisal Univ, Coll Sci & Humanities Jubail, Dept Comp Sci, Dammam 31441, Saudi Arabia.
RP Chen, M (通讯作者)，Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
EM chenmao@ccnu.edu.cn
RI Alhudhaif, Adi/AAN-6541-2021; Alenezi, Fayadh/ABB-4871-2021
OI Alhudhaif, Adi/0000-0002-7201-6963; 
FU Research Funds from the National Natural Science Foundation of China
   [62077019]
FX This work was financially supported by the Research Funds from the
   National Natural Science Foundation of China (Grant No. 62077019).
CR Chen JY, 2021, INFORM SCIENCES, V563, P326, DOI 10.1016/j.ins.2021.03.004
   Derrac J, 2011, SWARM EVOL COMPUT, V1, P3, DOI 10.1016/j.swevo.2011.02.002
   Geetha S, 2019, IEEE ACCESS, V7, P130283, DOI 10.1109/ACCESS.2019.2940540
   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
   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 YT, 2020, NEUROCOMPUTING, V398, P485, DOI 10.1016/j.neucom.2019.03.098
   Huang JS, 2022, NEUROCOMPUTING, V469, P119, DOI 10.1016/j.neucom.2021.10.049
   Huang X, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P732, DOI 10.1145/3292500.3330941
   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
   Kaadoud IC, 2022, KNOWL-BASED SYST, V235, DOI 10.1016/j.knosys.2021.107657
   Khalid A, 2022, EXPERT SYST APPL, V187, DOI 10.1016/j.eswa.2021.115926
   Khojamli H, 2021, EXPERT SYST APPL, V185, DOI 10.1016/j.eswa.2021.115482
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   Liao SH, 2021, TECHNOL SOC, V66, DOI 10.1016/j.techsoc.2021.101655
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HT, 2021, INFORM SCIENCES, V565, P370, DOI 10.1016/j.ins.2021.02.009
   Mikolov T., 2013, PROBLEM SOLVING ALGO, P3111
   Nair V., 2010, P 27 INT C MACH LEAR, DOI DOI 10.5555/3104322.3104425
   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, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Schedl M., 2019, FRONT APPL MATH STAT, V5, P44, DOI DOI 10.3389/FAMS.2019.00044
   Shi C, 2019, IEEE T KNOWL DATA EN, V31, P357, DOI 10.1109/TKDE.2018.2833443
   Sun Z, 2019, ELECTRON COMMER R A, V37, DOI 10.1016/j.elerap.2019.100879
   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, 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 SY, 2021, APPL SOFT COMPUT, V110, DOI 10.1016/j.asoc.2021.107753
   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
   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
   Yi D, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10031073
   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
NR 35
TC 0
Z9 0
U1 1
U2 1
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 MAY
PY 2022
VL 595
BP 179
EP 194
DI 10.1016/j.ins.2022.02.054
PG 16
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2H6XH
UT WOS:000814434700010
DA 2022-08-23
ER

PT J
AU Dong, B
   Yu, HZ
   Li, HS
AF Dong, Biao
   Yu, Haoze
   Li, Haisheng
TI A Knowledge Graph Construction Approach for Legal Domain
SO TEHNICKI VJESNIK-TECHNICAL GAZETTE
LA English
DT Article
DE China Judgments Online; Domain Knowledge Graph; Neo4j; Relationship
   Extraction
AB Considering that the existing domain knowledge graphs have difficulty in updating data in a timely manner and cannot make use of knowledge sufficiently in the construction process, this paper proposes a legal domain knowledge graph construction approach based on 'China Judgments Online' in order to manage the cases' knowledge contained in it. The construction process is divided into two steps. First, we extract the classification relationships of the cases from structured data. Then, we obtain attribute knowledge of cases from semi-structured data and unstructured data through a relationship extraction model based on an improved cross-entropy loss function. The triples describing knowledge of cases are stored through Neo4j. The accuracy of the proposed approach is verified through experiments and we construct a legal domain knowledge graph which contains more than 4K classification relationships and 12K attribute knowledge to prove its validity.
C1 [Dong, Biao] Beijing Technol & Business Univ, Dept Law, 11 Fucheng Rd, Beijing, Peoples R China.
   [Yu, Haoze; Li, Haisheng] Beijing Technol & Business Univ, Dept Comp, 11 Fucheng Rd, Beijing, Peoples R China.
RP Dong, B (通讯作者)，Beijing Technol & Business Univ, Dept Law, 11 Fucheng Rd, Beijing, Peoples R China.
EM dongb@btbu.edu.cn; 2628740743@qq.com; lihsh@btbu.edu.cn
CR Al-Zaidy RA, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2551, DOI 10.1145/3308558.3313642
   Amarilli A, 2014, LECT NOTES COMPUT SC, V8709, P1, DOI 10.1007/978-3-319-11116-2_1
   [Anonymous], 2016, IMDB OFF
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Chang, 2017, P 2017 C EMP METH NA, P1790, DOI DOI 10.18653/V1/D17-1189
   Court CJ, 2018, SCI DATA, V5, DOI 10.1038/sdata.2018.111
   Hasegawa Takaaki, 2004, P 42 ANN M ASS COMP, DOI DOI 10.3115/1218955.1219008
   Jing Wang, 2012, 2012 Seventh International Conference on Digital Information Management (ICDIM 2012), P16, DOI 10.1109/ICDIM.2012.6360156
   Liu T., 2018, P EMNLP, P2195
   Luo BF, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P430, DOI 10.18653/v1/P17-1040
   Necesal P, 2012, LECT NOTES ENG COMP, P271
   Qin LL, 2018, TEH VJESN, V25, P528, DOI 10.17559/TV-20171229024444
   Saric T, 2018, TEH VJESN, V25, P1770, DOI 10.17559/TV-20180419095119
   Wang J, 2020, TEH VJESN, V27, P67, DOI 10.17559/TV-20191004092659
   Wang Z, 2010, PROBASE UNIVERSAL KN
   Wang Z., 2013, P 12 INT SEM WEB C, V1035, P121
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yu HZ, 2021, J INF SCI, V47, P783, DOI 10.1177/0165551520932510
   Yu HZ, 2020, WORLD WIDE WEB, V23, P735, DOI 10.1007/s11280-019-00765-y
   Zhang H, 2020, INT J COMPUT COMMUN, V15, DOI 10.15837/ijccc.2020.1.3783
   Zhang H, 2020, SOFT COMPUT, V24, P6499, DOI 10.1007/s00500-020-04666-5
   Zhang M, 2005, LECT NOTES ARTIF INT, V3651, P378
   Zhang N., 2018, P EMNLP BRUSS BELG, P986, DOI DOI 10.18653/V1/D18-1120
   Zhu J, 2009, P 18 INT C WORLD WID, P101, DOI DOI 10.1145/1526709.1526724
NR 24
TC 3
Z9 3
U1 15
U2 34
PU UNIV OSIJEK, TECH FAC
PI SLAVONSKI BROD
PA TRG IVANE BRLIC-MAZURANIC 2, SLAVONSKI BROD, HR-35000, CROATIA
SN 1330-3651
EI 1848-6339
J9 TEH VJESN
JI Teh. Vjesn.
PD APR
PY 2021
VL 28
IS 2
BP 357
EP 362
DI 10.17559/TV-20201119084338
PG 6
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA RO7XC
UT WOS:000641254100002
DA 2022-08-23
ER

PT J
AU Xiao, H
   Chen, YD
   Shi, XD
AF Xiao, Han
   Chen, Yidong
   Shi, Xiaodong
TI Knowledge Graph Embedding Based on Multi-View Clustering Framework
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Knowledge graph; semantic analysis; knowledge representation; multi-view
   clustering
AB Knowledge representation is one of the critical problems in knowledge engineering and artificial intelligence, while knowledge embedding as a knowledge representation methodology indicates entities and relations in knowledge graph as low-dimensional, continuous vectors. In this way, knowledge graph is compatible with numerical machine learning models. Major knowledge embedding methods employ geometric translation to design score function, which is weak-semantic for natural language processing. To overcome this disadvantage, in this paper, we propose our model based on multi-view clustering framework, which could generate semantic representations of knowledge elements (i.e., entities/relations). With our semantic model, we also present an empowered solution to entity retrieval with entity description. Extensive experiments show that our model achieves substantial improvements against baselines on the task of knowledge graph completion, triple classification, entity classification, and entity retrieval.
C1 [Xiao, Han; Chen, Yidong; Shi, Xiaodong] Xiamen Univ, Sch Informat Sci & Engn, Dept Cognit Sci & Technol, Xiamen 361005, Fujian, Peoples R China.
RP Xiao, H (通讯作者)，Xiamen Univ, Sch Informat Sci & Engn, Dept Cognit Sci & Technol, Xiamen 361005, Fujian, Peoples R China.
EM bookman@xmu.edu.cn; ydchen@xmu.edu.cn; mandel@xmu.edu.cn
FU Fujian Provincial Key Laboratory of Information Processing and
   Intelligent Control (Minjiang University) [MJUKF-IPIC201804]; Xiamen
   University Principal Fund [20720190030]; Xiamen University Malaysia
   Research Fund [XMUMRF/2019-C4/IECE/0009]
FX This work is supported by Fujian Provincial Key Laboratory of
   Information Processing and Intelligent Control (Minjiang University) No.
   MJUKF-IPIC201804. This work is also supported by Xiamen University
   Principal Fund (No. 20720190030) and Xiamen University Malaysia Research
   Fund (No. XMUMRF/2019-C4/IECE/0009).
CR Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   Greff K, 2017, IEEE T NEUR NET LEAR, V28, P2222, DOI 10.1109/TNNLS.2016.2582924
   Guo S, 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, P84
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Huang ZH, 2015, COMPUT INTEL NEUROSC, V2015, DOI 10.1155/2015/685404
   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
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   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
   Lukovnikov D, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1211, DOI 10.1145/3038912.3052675
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Murphy K.P., 2012, MACHINE LEARNING PRO
   Neelakantan Arvind, 2015, P 2015 C N AM CHAPT, P515
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Shi BX, 2018, AAAI CONF ARTIF INTE, P1957
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Sowa J. F, 2000, PACIFIC GROVE CA BRO
   Sundermeyer M, 2012, 13TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2012 (INTERSPEECH 2012), VOLS 1-3, P194
   Wang Q, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1859
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Z, 2017, IEEE IJCNN, P1411, DOI 10.1109/IJCNN.2017.7966018
   Wen D., 2017, P INT C SMART COMP C, P321
   Xiao H., 2015, COMPUTER SCI
   Xiao H., 2016, P 25 INT JOINT C ART, P1315
   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, 2016, AAAI CONF ARTIF INTE, P2659
   Zhong H., 2015, P 2015 C EMP METH NA, P267
NR 36
TC 5
Z9 6
U1 11
U2 58
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 FEB 1
PY 2021
VL 33
IS 2
BP 585
EP 596
DI 10.1109/TKDE.2019.2931548
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA PS3DL
UT WOS:000607806200019
DA 2022-08-23
ER

PT J
AU Qiao, B
   Zou, ZY
   Huang, Y
   Fang, K
   Zhu, XH
   Chen, YM
AF Qiao, Bo
   Zou, Zhuoyang
   Huang, Yu
   Fang, Kui
   Zhu, Xinghui
   Chen, Yiming
TI A joint model for entity and relation extraction based on BERT
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Agricultural knowledge graph; Named entity recognition; Relation
   extraction; Joint extraction; BERT
AB In recent years, as the knowledge graph has attained significant achievements in many specific fields, which has become one of the core driving forces for the development of the internet and artificial intelligence. However, there is no mature knowledge graph in the field of agriculture, so it is a great significance study on the construction technology of agricultural knowledge graph. Named entity recognition and relation extraction are key steps in the construction of knowledge graph. In this paper, based on the joint extraction model LSTM-LSTM-Bias brought in BERT pre-training language model to proposed a agricultural entity relationship joint extraction model BERT-BILSTM-LSTM which is applied to the standard data set NYT and self-built agricultural data set AgriRelation. Experimental results showed that the model can effectively extracted the relationship between agricultural entities and entities.
C1 [Qiao, Bo; Zou, Zhuoyang; Fang, Kui; Zhu, Xinghui; Chen, Yiming] Hunan Agr Univ, Coll Informat & Intelligence, Changsha, Hunan, Peoples R China.
   [Huang, Yu] Hunan Agr Univ, Coll Biol Sci & Technol, Changsha, Hunan, Peoples R China.
RP Fang, K; Zhu, XH (通讯作者)，Hunan Agr Univ, Coll Informat & Intelligence, Changsha, Hunan, Peoples R China.
EM qiaobo@hunau.edu.cn; zzy@stu.hunau.edu.cn; huangyu@hunau.edu.cn;
   fk@hunau.edu.cn; zhuxh@hunau.edu.cn; chenym@hunau.edu.cn
FU Natural Science Foundation of Hunan Province of China [2019JJ50239];
   Scientific Research Fund of Hunan Provincial Education Department of
   China [20A249]; Key Research and Development Program of Hunan Province
   of China [2020NK2033]
FX Our works have been achieved significant help and supporting from
   Natural Science Foundation of Hunan Province of China (Grant No.
   2019JJ40133), Natural Science Foundation of Hunan Province of China
   (Grant No. 2019JJ50239), Scientific Research Fund of Hunan Provincial
   Education Department of China (Grant No. 20A249), as well as the Key
   Research and Development Program of Hunan Province of China (Grant No.
   2020NK2033).
CR Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Bikel DM, 1999, MACH LEARN, V34, P211, DOI 10.1023/A:1007558221122
   Chieu, 2002, COLING 2002
   Chiu J.P.C., 2015, ABS151108308 CORR
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   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
   Fang LM., 1994, AGR THESAURUS, P191
   Fu G., 2005, ACM SIGKDD EXPLOR NE, V7, P19
   Huang Z, 2015, ARXIV150801991
   Kazawa H, 2002, COLING 2002
   Levy Omer, 2014, WORD2VEC EXPLAINED D
   Li F, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1609-9
   Li Q, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P402, DOI 10.3115/v1/p14-1038
   Lin C, 2018, P 9 INT WORKSH HLTH, P165
   Ma XZ, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1064
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   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
   Nguyen TH, 2015, P 1 WORKSH VECT SPAC, P39, DOI DOI 10.3115/V1/W15-1506
   Shen, 2019, CCKS2019 SHARED RISK
   Shi P., 2019, ARXIV190405255
   Socher R., 2012, P 2012 JOINT C EMP M, P1201, DOI [10.5555/2390948.2391084, DOI 10.1162/153244303322533223]
   Uchimoto K, 2000, 38TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P326
   [武惠 Wu Hui], 2019, [小型微型计算机系统, Journal of Chinese Computer Systems], V40, P1142
   Yao L., 2010, P 2010 C EMP METH NA, P1013
   Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205
   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]
   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
   Zhou G., 2007, EMNLP CONLL, P728
   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
   Zhou ZW, 2017, PROC CVPR IEEE, P4761, DOI 10.1109/CVPR.2017.506
   Zhu H., 2019, ARXIV190200756
NR 34
TC 10
Z9 10
U1 51
U2 130
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 MAR
PY 2022
VL 34
IS 5
SI SI
BP 3471
EP 3481
DI 10.1007/s00521-021-05815-z
EA MAR 2021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZJ0US
UT WOS:000626423700001
DA 2022-08-23
ER

PT J
AU Sun, D
   Huang, Z
   Li, DS
   Guo, M
AF Sun, Ding
   Huang, Zhen
   Li, Dongsheng
   Guo, Min
TI Efficient Knowledge Graph Embedding Training Framework with Multiple
   GPUs
SO TSINGHUA SCIENCE AND TECHNOLOGY
LA English
DT Article
DE knowledge graph embedding; parallel algorithm; partitioning graph
   framework; graphics processing unit (GPU)
AB When training a large-scale knowledge graph embedding (KGE) model with multiple graphics processing units (GPUs), the partition-based method is necessary for parallel training. However, existing partition-based training methods suffer from low GPU utilization and high input/output (IO) overhead between the memory and disk. For a high IO overhead between the disk and memory problem, we optimized the twice partitioning with fine-grained GPU scheduling to reduce the IO overhead between the CPU memory and disk. For low GPU utilization caused by the GPU load imbalance problem, we proposed balanced partitioning and dynamic scheduling methods to accelerate the training speed in different cases. With the above methods, we proposed fine-grained partitioning KGE, an efficient KGE training framework with multiple GPUs. We conducted experiments on some benchmarks of the knowledge graph, and the results show that our method achieves speedup compared to existing framework on the training of KGE.
C1 [Sun, Ding; Huang, Zhen; Li, Dongsheng; Guo, Min] Natl Univ Def Technol, Coll Comp, Changsha 410073, Peoples R China.
RP Huang, Z (通讯作者)，Natl Univ Def Technol, Coll Comp, Changsha 410073, Peoples R China.
EM sunding@nudt.edu.cn; huangzhen@nudt.edu.cn; dsli@nudt.edu.cn;
   guomin720313@163.com
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Dai YF, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9050750
   Goyal P, 2018, KNOWL-BASED SYST, V151, P78, DOI 10.1016/j.knosys.2018.03.022
   Guo S, 2018, AAAI CONF ARTIF INTE, P4816
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   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
   Kazemi S. M., 2018, ARXIV
   Lerer A., 2019, ARXIV
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu Z. H., 2018, ARXIV
   Mahdisoltani F., 2015, 7 BIENN C INN DAT SY
   Mohoney J, 2021, PROCEEDINGS OF THE 15TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDI '21), P533
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Riedel S., 2013, P 2013 C N AM CHAPT, P74
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Trouillon T, 2016, PR MACH LEARN RES, V48
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   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 XZ, 2021, T ASSOC COMPUT LING, V9, P176, DOI 10.1162/tacl_a_00360
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Yang B, 2014, ARXIV
   Ying R, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P974, DOI 10.1145/3219819.3219890
   Zheng D, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P739, DOI 10.1145/3397271.3401172
NR 25
TC 0
Z9 0
U1 1
U2 1
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 FEB
PY 2023
VL 28
IS 1
BP 167
EP 175
DI 10.26599/TST.2021.9010067
PG 9
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 3E5HQ
UT WOS:000830013900017
OA Bronze
DA 2022-08-23
ER

PT J
AU Soylu, A
   Corcho, O
   Elvesaeter, B
   Badenes-Olmedo, C
   Blount, T
   Martinez, FY
   Kovacic, M
   Posinkovic, M
   Makgill, I
   Taggart, C
   Simperl, E
   Lech, TC
   Roman, D
AF Soylu, Ahmet
   Corcho, Oscar
   Elvesaeter, Brian
   Badenes-Olmedo, Carlos
   Blount, Tom
   Martinez, Francisco Yedro
   Kovacic, Matej
   Posinkovic, Matej
   Makgill, Ian
   Taggart, Chris
   Simperl, Elena
   Lech, Till C.
   Roman, Dumitru
TI TheyBuyForYou platform and knowledge graph: Expanding horizons in public
   procurement with open linked data
SO SEMANTIC WEB
LA English
DT Article
DE Public procurement; knowledge graph; linked datas; open data; ontology
ID BIG; ONTOLOGY
AB Public procurement is a large market affecting almost every organisation and individual; therefore, governments need to ensure its efficiency, transparency, and accountability, while creating healthy, competitive, and vibrant economies. In this context, open data initiatives and integration of data from multiple sources across national borders could transform the procurement market by such as lowering the barriers of entry for smaller suppliers and encouraging healthier competition, in particular by enabling cross-border bids. Increasingly more open data is published in the public sector; however, these are created and maintained in siloes and are not straightforward to reuse or maintain because of technical heterogeneity, lack of quality, insufficient metadata, or missing links to related domains. To this end, we developed an open linked data platform, called TheyBuyForYou, consisting of a set of modular APIs and ontologies to publish, curate, integrate, analyse, and visualise an EU-wide, cross-border, and cross-lingual procurement knowledge graph. We developed advanced tools and services on top of the knowledge graph for anomaly detection, cross-lingual document search, and data storytelling. This article describes the TheyBuyForYou platform and knowledge graph, reports their adoption by different stakeholders and challenges and experiences we went through while creating them, and demonstrates the usefulness of Semantic Web and Linked Data technologies for enhancing public procurement.
C1 [Soylu, Ahmet] OsloMet Oslo Metropolitan Univ, Oslo, Norway.
   [Corcho, Oscar; Badenes-Olmedo, Carlos; Martinez, Francisco Yedro] Univ Politecn Madrid, Madrid, Spain.
   [Elvesaeter, Brian; Lech, Till C.; Roman, Dumitru] SINTEF AS, Trondheim, Norway.
   [Blount, Tom] Univ Southampton, Southampton, Hants, England.
   [Kovacic, Matej; Posinkovic, Matej] Jozef Stefan Inst, Ljubljana, Slovenia.
   [Makgill, Ian] OpenOpps Ltd, London, England.
   [Taggart, Chris] OpenCorp Ltd, London, England.
   [Simperl, Elena] Kings Coll London, London, England.
RP Soylu, A (通讯作者)，OsloMet Oslo Metropolitan Univ, Oslo, Norway.
EM ahmet.soylu@oslomet.no; ocorcho@fi.upm.es; brian.elvesater@sintef.no;
   cbadenes@fi.upm.es; t.blount@soton.ac.uk; fyedro@fi.upm.es;
   matej.kovacic@ijs.si; matej.posinkovic@ijs.si; ian@spendnetwork.com;
   chris.taggart@opencorporates.com; elena.simperl@kcl.ac.uk;
   till.lech@sintef.no; dumitru.roman@sintef.no
OI Simperl, Elena/0000-0003-1722-947X; Elvesaeter,
   Brian/0000-0001-7304-4950; Soylu, Ahmet/0000-0001-6034-4137
FU EC [780247]
FX The work presented in this article was funded by the EC H2020 project
   TheyBuyForYou (grant 780247).
CR Ackerman JM, 2006, ADMIN LAW REV, V58, P85
   Akoglu L, 2015, DATA MIN KNOWL DISC, V29, P626, DOI 10.1007/s10618-014-0365-y
   [Anonymous], 2016, COST NONE AR ORG CRI
   Badenes-Olmedo C, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P147, DOI 10.1145/3360901.3364444
   Bansal SK, 2015, COMPUTER, V48, P42, DOI 10.1109/MC.2015.76
   Bennett M, 2013, J BANK REGUL, V14, P255, DOI 10.1057/jbr.2013.13
   Berk R., 2020, STAT LEARNING REGRES, DOI [10.1007/978-3-030-40189-4, DOI 10.1007/978-3-030-40189-4]
   Chandola V, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1541880.1541882
   Cheng XQ, 2014, IEEE T KNOWL DATA EN, V26, P2928, DOI 10.1109/TKDE.2014.2313872
   Chi EH, 2017, COMMUN ACM, V60, P92, DOI 10.1145/3068614
   Corcho O., 2006, ONTOLOGIES SOFTWARE, P1, DOI DOI 10.1007/3-540-34518-3_1
   Daga E., 2015, P SALAD 2015
   Distinto I, 2016, SEMANT WEB, V7, P267, DOI 10.3233/SW-140151
   Espinoza-Arias P, 2020, INFORMATION, V11, DOI 10.3390/info11030129
   Eurovoc,, 1995, THES EUROVOC
   Munoz-Soro JF, 2016, SEMANT WEB, V7, P295, DOI 10.3233/SW-150195
   Futia G, 2017, LECT NOTES COMPUT SC, V10249, P623, DOI 10.1007/978-3-319-58068-5_38
   Giese M, 2015, COMPUTER, V48, P60, DOI 10.1109/MC.2015.82
   Harrington P., 2012, MACHINE LEARNING ACT
   Hitzler P, 2021, COMMUN ACM, V64, P76, DOI 10.1145/3397512
   Janssen M, 2017, INFORM SYST FRONT, V19, P189, DOI 10.1007/s10796-017-9746-2
   Kivisto T, 2019, J PUBLIC PROCUR, V19, P108, DOI 10.1108/JOPP-06-2019-028
   Kosara R, 2017, P EUROVIS 2017, P31, DOI [10.2312/eurovisshort.20171129, DOI 10.2312/EUROVISSHORT.20171129]
   Alvarez JM, 2012, INT J SOFTW ENG KNOW, V22, P365, DOI 10.1142/S0218194012400086
   Alvarez-Rodriguez JM, 2014, COMPUT IND, V65, P797, DOI 10.1016/j.compind.2014.04.002
   Merono-Penuela A, 2016, LECT NOTES COMPUT SC, V9989, P342, DOI 10.1007/978-3-319-47602-5_48
   Minovic M, 2014, COMPUT IND, V65, P878, DOI 10.1016/j.compind.2014.02.003
   Nacasky M, 2014, COMPUT IND, V65, P862, DOI 10.1016/j.compind.2013.12.006
   Noy N.F., 2001, MCGUINNESS ONTOLOGY
   OECD principles for integrity in public procurement, 2009, OECD PRINC INT PUBL
   Roman D, 2020, P ISWC 2020 POST DEM
   Roman D, 2022, SEMANT WEB, V13, P41, DOI 10.3233/SW-210424
   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, 2018, P EKAW 2018 POST DEM
   Soylu A., 2019, P ISWC 2019 SAT TRAC
   Soylu A, 2020, P ISWC 2020 POST DEM
   Soylu A, 2019, LECT NOTES COMPUT SC, V11701, P230, DOI 10.1007/978-3-030-29374-1_19
   Soylu A, 2018, SEMANT WEB, V9, P627, DOI 10.3233/SW-180293
   Steinberger R, 2006, CS0609058 ARXIV
   TBFY, 2020, KG data dump, DOI 10.5281/zenodo.3712323
   Tong C, 2018, INFORMATION, V9, DOI 10.3390/info9030065
   Yan JH, 2018, FRONT COMPUT SCI-CHI, V12, P55, DOI 10.1007/s11704-016-5228-9
   Zhu SJ, 2020, VIS INFORM, V4, P24, DOI 10.1016/j.visinf.2020.07.002
NR 44
TC 4
Z9 4
U1 2
U2 2
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 265
EP 291
DI 10.3233/SW-210442
PG 27
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YV6TV
UT WOS:000752861100007
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Li, ML
   Wang, YZ
   Zhang, DH
   Jia, YT
   Cheng, XQ
AF Li, Manling
   Wang, Yuanzhuo
   Zhang, Denghui
   Jia, Yantao
   Cheng, Xueqi
TI Link Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach
SO IEEE TRANSACTIONS ON BIG DATA
LA English
DT Article
DE Link prediction; knowledge graph embedding; hierarchy
AB Link prediction over a knowledge graph aims to predict the missing head entities h or tail entities t and missing relations r for a triple (h, r, t). Recent years have witnessed great advance of knowledge graph embedding based link prediction methods, which represent entities and relations as elements of a continuous vector space. Most methods learn the embedding vectors by optimizing a margin-based loss function, where the margin is used to separate negative and positive triples in the loss function. The loss function utilizes the general structures of knowledge graphs, e.g., the vector of r is the translation of the vector of h and t, and the vector of t should be the nearest neighbor of the vector of h + r. However, there are many particular structures, and can be employed to promote the performance of link prediction. One typical structure in knowledge graphs is hierarchical structure, which existing methods have much unexplored. We argue that the hierarchical structures also contain rich inference patterns, and can further enhance the link prediction performance. In this paper, we propose a hierarchy-constrained link prediction method, called hTransM, on the basis of the translation-based knowledge graph embedding methods. It can adaptively determine the optimal margin by detecting the single-step and multi-step hierarchical structures. Moreover, we prove the effectiveness of hTransM theoretically, and experiments over three benchmark datasets and two sub-tasks of link prediction demonstrate the superiority of hTransM.
C1 [Li, Manling; Wang, Yuanzhuo; Zhang, Denghui; Jia, Yantao; Cheng, Xueqi] Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100864, Peoples R China.
RP Li, ML; Wang, YZ (通讯作者)，Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100864, Peoples R China.
EM limanlingcs@gmail.com; wangyuanzhuo@ict.ac.cn; zhangdenghui@ict.ac.cn;
   jiayantao@ict.ac.cn; cxq@ict.ac.cn
FU National Grand Fundamental Research 973 Program of China [2013CB329602,
   2014CB340401]; National Natural Science Foundation of China [61572469,
   61402442, 61572473, 61303244, 61402022, 61572467]; Beijing nova program;
   Beijing Natural Science Foundation [4154086]
FX This work is supported by National Grand Fundamental Research 973
   Program of China (No. 2013CB329602, 2014CB340401), National Natural
   Science Foundation of China (No.61572469, 61402442, 61572473, 61303244,
   61402022, 61572467), Beijing nova program (No.Z121101002512063), and
   Beijing Natural Science Foundation (No. 4154086).
CR Bengio S., 2010, ADV NEURAL INFORM PR, V23, P163, DOI DOI 10.5555/2997189.2997208
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ICML 2013 WORKSHOP S, P5
   Bordes A., 2012, P 15 INT C ART INT S, 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
   Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401
   Bousquet O, 2002, J MACH LEARN RES, V2, P499, DOI 10.1162/153244302760200704
   COHEN WW, 1995, NEW GENERAT COMPUT, V13, P369, DOI 10.1007/BF03037231
   Eades P., 1996, PROC INT S GRAPH DRA, P113
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   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
   Garcia-Duran A., 2015, P 2015 C EMP METH NA, P286, DOI DOI 10.18653/V1/D15-1034
   Healy P., 2013, HDB GRAPH DRAWING VI, P409
   Huang H, 2015, IEEE T KNOWL DATA EN, V27, P3374, DOI 10.1109/TKDE.2015.2453956
   Jacobson E, 1951, J SOC ISSUES, V7, P28
   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
   Jia YT, 2015, J COMPUT SCI TECH-CH, V30, P829, DOI 10.1007/s11390-015-1563-9
   Jia YT, 2016, AAAI CONF ARTIF INTE, P992
   Jia YT, 2014, 2014 PROCEEDINGS OF THE IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2014), P657, DOI 10.1109/ASONAM.2014.6921655
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lao N., 2012, PROC JOINT C EMPIRIC, P1017
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   Li ML, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16 COMPANION), P77, DOI 10.1145/2872518.2889387
   Li ML, 2016, AAAI CONF ARTIF INTE, P4228
   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 DW, 2014, AAAI CONF ARTIF INTE, P3120
   Liu YY, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0044459
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mitchell T. M, 2009, POPULATING SEMANTIC
   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, 2016, AAAI CONF ARTIF INTE, P1955
   Quinlan J., 1993, LECT NOTES ARTIF INT, P3, DOI DOI 10.1007/3-540-56602-3124
   Schoenmackers Stefan, 2010, P 2010 C EMP METH NA, P1088
   Sutskever I., 2009, NIPS
   Talukdar P. P., 2013, AMERICAS, V70, P319
   Toutanova K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1434
   Vapnik V., 2000, NATURE STAT LEARNING, V2
   Wang CM, 2010, PROCEEDINGS OF THE ASME 29TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING 2010, VOL 3, P203
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weinberger K., 2009, P NEUR INF PROC SYST, P1737
   Wu F, 2015, AAAI CONF ARTIF INTE, P1663
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Yan Koon-Kiu, 2010, Proc Natl Acad Sci U S A, V107, P9186, DOI 10.1073/pnas.0914771107
   Zhang J, 2015, IEEE T KNOWL DATA EN, V27, P2093, DOI 10.1109/TKDE.2015.2407351
   Zhao ZY, 2014, AAAI CONF ARTIF INTE, P3154
NR 50
TC 0
Z9 0
U1 1
U2 1
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 JUN 1
PY 2022
VL 8
IS 3
BP 630
EP 643
DI 10.1109/TBDATA.2018.2867583
PG 14
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1F3YX
UT WOS:000795107500005
DA 2022-08-23
ER

PT J
AU Wang, YS
   Zhang, HH
AF Wang, Yashen
   Zhang, Huanhuan
TI HARP: A Novel Hierarchical Attention Model for Relation Prediction
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE Relation prediction; hierarchical attention model; knowledge graph;
   concept information; representation learning
ID VALIDATION
AB Recent years have witnessed great advancement of representation learning (RL)-based models for the knowledge graph relation prediction task. However, they generally rely on structure information embedded in the encyclopedic knowledge graph, while the beneficial semantic information provided by lexical knowledge graph is ignored, leading the problem of shallow understanding and coarse-grained analysis for knowledge acquisition. Therefore, this article introduces concept information derived from the lexical knowledge graph (e.g., Probase), and proposes a novel Hierarchical Attention model for Relation Prediction, which consists of entity-level attention mechanism and concept-level attention mechanism, to throughly integrate multiple semantic signals. Experimental results demonstrate the efficiency of the proposed method on two benchmark datasets.
C1 [Wang, Yashen; Zhang, Huanhuan] China Acad Elect & Informat Technol CETC, Natl Engn Lab Risk Percept & Prevent RPP, Beijing 100041, Peoples R China.
RP Wang, YS (通讯作者)，China Acad Elect & Informat Technol CETC, Natl Engn Lab Risk Percept & Prevent RPP, Beijing 100041, Peoples R China.
EM yswang@bit.edu.cn; huanhuanz_bit@126.com
FU National Natural Science Foundation of China [U19B2026]; National Key
   Research and Development Project [2017YFC0820503]; China Postdoctoral
   Science Foundation [2018M641436]; NewGeneration of Artificial
   Intelligence Special Action Project [AI20191125008]; National Integrated
   Big Data Center Pilot Project [17111002, 20500908]; Joint Advanced
   Research Foundation of China Electronics Technology Group Corporation
   (CETC) [6141B08010102]
FX This work is funded by: (i) the National Natural Science Foundation of
   China (No. U19B2026); (ii) the National Key Research and Development
   Project (No. 2017YFC0820503); (iii) the China Postdoctoral Science
   Foundation (No. 2018M641436); (iv) the NewGeneration of Artificial
   Intelligence Special Action Project (No. AI20191125008); (v) the
   National Integrated Big Data Center Pilot Project (No. 17111002,
   20500908); and (vi) the Joint Advanced Research Foundation of China
   Electronics Technology Group Corporation (CETC) (No. 6141B08010102).
CR An B, 2018, ACCURATE TEXT ENHANC
   Bahdanau D., 2016, ARXIV14090473
   BALCI O, 1981, COMMUN ACM, V24, P190, DOI 10.1145/358598.358609
   Barbosa Denilson, 2019, ABS190310126 ARXIV
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Bordes Antoine, 2012, P 15 INT C ART INT S
   Chen Zhigang, 2015, P 2015 C EMP METH NA
   Cheng Lu Lin, 2019, P 19 SIAM INT C DAT
   Dai S. Z., 2018, P 2018 2 INT C ARTIF, P194
   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
   Eisenbarth Thomas, 2015, P 2015 INT WORKSH CO
   Er MJ, 2016, INFORM SCIENCES, V373, P388, DOI 10.1016/j.ins.2016.08.084
   Fan M, 2017, PATTERN RECOGN LETT, V93, P31, DOI 10.1016/j.patrec.2016.09.005
   Gao S, 2018, J AM MED INFORM ASSN, V25, P321, DOI 10.1093/jamia/ocx131
   GOLUB GH, 1970, NUMER MATH, V14, P403, DOI 10.1007/BF02163027
   Hain Thomas, 2019, ABS191007900 ARXIV
   Han X, 2018, AAAI CONF ARTIF INTE, P4832
   Han ZZ, 2019, IEEE T IMAGE PROCESS, V28, P3986, DOI 10.1109/TIP.2019.2904460
   He RD, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P388, DOI 10.18653/v1/P17-1036
   He Ruining, 2015, P 2015 AAAI C ART IN
   Henderson James, 2018, P 2018 C EMP METH NA
   Hermann KM, 2015, ADV NEUR IN, V28
   Hua W, 2015, PROC INT CONF DATA, P495, DOI 10.1109/ICDE.2015.7113309
   Huang HY, 2018, IEEE T KNOWL DATA EN, V30, P1282, DOI 10.1109/TKDE.2017.2787709
   Hui Siu Cheung, 2017, P 31 C ART INT
   Jankowski KRB, 2018, J HEALTH CARE CHAPL, V24, P30, DOI 10.1080/08854726.2017.1335050
   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
   Jun S, 2018, PREP BIOCHEM BIOTECH, P1
   Kabbur S, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P659
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Kumar Ankit, 2016, P INT C NANOCHANNEL
   Li PP, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1401, DOI 10.1145/2505515.2505567
   Li Zheng, 2018, P 2018 AAAI C ART IN
   Liang Yuan, 2018, COMPUT VIS MEDIA, V4, P1
   Lin Xinshi, 2018, P 2018 ACM SIGIR INT
   Lin Y., 2015, P C EMP METH NAT LAN
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lin YK, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2124
   Liu WX, 2019, IEEE SYMP COMP COMMU, P253
   Liu Y., 2015, P 29 AAAI C ART INT
   Luong T, 2015, P 2015 C EMP METH NA
   Ma Jing, 2019, P 2019 ANN M ASS COM
   Ma SH, 2017, LECT NOTES ARTIF INT, V10534, P717, DOI 10.1007/978-3-319-71249-9_43
   Moon C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2215, DOI 10.1145/3132847.3133095
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Pan WK, 2015, KNOWL-BASED SYST, V73, P173, DOI 10.1016/j.knosys.2014.09.013
   Park JW, 2016, AAAI CONF ARTIF INTE, P2638
   Precup Doina, 2016, ABS160505416 CORR
   Qi Yuan, 2019, P 2019 AAAI C ART IN
   Rana R., 2015, J PRACT CARDIOVASC S, V1, P69
   Rendle Steffen, 2009, P 2009 C UNC ART INT
   Rogers Ryan, 2018, ABS170907155 ARXIV
   Sargent RG, 2011, WINT SIMUL C PROC, P183, DOI 10.1109/WSC.2011.6147750
   Serafini Luciano, 2012, P 27 ANN ACM S APPL
   Shirakawa M., 2011, MSRTR2011125
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Song MN, 2018, PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS AND COMPUTER AIDED EDUCATION (ICISCAE 2018), P476, DOI 10.1109/ICISCAE.2018.8666926
   Song Y., 2011, P 22 INT JOINT C ART, P2330
   Song YQ, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P3820
   Sorokin D, 2017, P 2017 C EMP METH NA, P1784
   Szolovits Peter, 2017, ABS170401523 ARXIV
   Talukdar Partha, 2018, ABS181204361 ARXIV
   Tixier Antoine J.-P., 2018, ABS180809772 ARXIV
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang F., 2014, ACM INT C INF KNOWL, P1069
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Weixuan, 2019, P 2019 AAAI C ART IN
   Wang XH, 2019, IEEE-CAA J AUTOMATIC, V6, P540, DOI 10.1109/JAS.2017.7510664
   Wang YS, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P505
   Wang YS, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P559, DOI 10.1145/3038912.3052710
   Wang Yashen, 2019, P AS PAC WEB APWEB W
   Wang Z, 2014, P C EMP METH NAT LAN, P1591
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang ZY, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P3264
   Weninger Tim, 2016, P 2016 AAAI C ART IN
   Wu Jianxin, 2018, ABS181204239 ARXIV
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xiao Han, 2017, P 2017 AAAI C ART IN
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xing SN, 2019, NEUROCOMPUTING, V332, P417, DOI 10.1016/j.neucom.2018.12.027
   Xu K, 2015, PR MACH LEARN RES, V37, P2048
   Yan Ming, 2018, ABS181111934 ARXIV
   Yang CF, 2016, IEEE IC COMP COM NET
   Yang M.-C., 2014, P 2014 C EMP METH NA, P645, DOI DOI 10.3115/V1/D14-1071
   You QZ, 2016, PROC CVPR IEEE, P4651, DOI 10.1109/CVPR.2016.503
   Zhang Dongxu, 2015, P 3 WORKSH CONT VECT
   Zhuang F., 2018, P 27 INT JOINT C ART
NR 92
TC 3
Z9 3
U1 4
U2 12
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 APR
PY 2021
VL 15
IS 2
AR 17
DI 10.1145/3424673
PG 22
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RL5ZA
UT WOS:000639049700004
DA 2022-08-23
ER

PT J
AU Cudre-Mauroux, P
AF Cudre-Mauroux, Philippe
TI Leveraging Knowledge Graphs for Big Data Integration: the XI Pipeline
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge Graphs; Big Data Integration; crowdsourcing
AB This article gives an overview of recent efforts focusing on integrating heterogeneous data using Knowledge Graphs. I introduce a pipeline consisting of five steps to integrate semi-structured or unstructured content. I discuss some of the key applications of this pipeline through three use-cases, and present the lessons learnt along the way while designing and building data integration systems.
C1 [Cudre-Mauroux, Philippe] Univ Fribourg, eXascale Infolab, Fribourg, Switzerland.
RP Cudre-Mauroux, P (通讯作者)，Univ Fribourg, eXascale Infolab, Fribourg, Switzerland.
EM pcm@unifr.ch
OI Cudre-Mauroux, Philippe/0000-0003-2588-4212
CR Aberer K., 2011, 10 INT SEM WEB C ISW
   Decker S., 1999, Database Semantics. Semantic Issues in Multimedia Systems. IFIP TC2/WG2.6 Eighth Working Conference on Database Semantics (DS-8), P351
   Demartini G., 2012, WWW, P469
   Demartini G, 2013, VLDB J, V22, P665, DOI 10.1007/s00778-013-0324-z
   Difallah DE, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P238, DOI 10.1145/2736277.2741685
   Lutov A, 2018, IEEE INT CONF BIG DA, P2253, DOI 10.1109/BigData.2018.8622285
   Mavlyutov R., 2017, CIDR 2017
   Plu J., 2018, P 11 INT C LANG RES
   Poggi A., 2008, OWLED
   Pound Jeffrey, 2010, P 19 INT C WORLD WID, P771, DOI [10.1145/1772690.1772769, DOI 10.1145/1772690.1772769]
   Prokofyev R, 2015, LECT NOTES COMPUT SC, V9366, P458, DOI 10.1007/978-3-319-25007-6_27
   Prokofyev R, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P397, DOI 10.1145/2566486.2568013
   Sequeda JF, 2017, IEEE INTERNET COMPUT, V21, P92, DOI 10.1109/MIC.2017.46
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Smirnova A, 2019, 2019 6TH SWISS CONFERENCE ON DATA SCIENCE (SDS), P63, DOI 10.1109/SDS.2019.000-6
   Smirnova A, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3241741
   Tonon Alberto, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), P125, DOI 10.1145/2348283.2348304
   Tonon A., 2015, P WORKSH LINK DAT WE
   Tonon A, 2017, LECT NOTES COMPUT SC, V10250, P138, DOI 10.1007/978-3-319-58451-5_10
   Tonon A, 2016, J WEB SEMANT, V37-38, P170, DOI 10.1016/j.websem.2015.12.005
   Tonon A, 2013, LECT NOTES COMPUT SC, V8218, P640, DOI 10.1007/978-3-642-41335-3_40
NR 21
TC 9
Z9 9
U1 2
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 13
EP 17
DI 10.3233/SW-190371
PG 5
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:000512298900003
DA 2022-08-23
ER

PT J
AU Hao, XJ
   Ji, Z
   Li, XH
   Yin, LZY
   Liu, L
   Sun, MY
   Liu, Q
   Yang, RJ
AF Hao, Xuejie
   Ji, Zheng
   Li, Xiuhong
   Yin, Lizeyan
   Liu, Lu
   Sun, Meiying
   Liu, Qiang
   Yang, Rongjin
TI Construction and Application of a Knowledge Graph
SO REMOTE SENSING
LA English
DT Article
DE knowledge graph; surveying; remote sensing; knowledge visualization
AB With the development and improvement of modern surveying and remote-sensing technology, data in the fields of surveying and remote sensing have grown rapidly. Due to the characteristics of large-scale, heterogeneous and diverse surveys and the loose organization of surveying and remote-sensing data, effectively obtaining information and knowledge from data can be difficult. Therefore, this paper proposes a method of using ontology for heterogeneous data integration. Based on the heterogeneous, decentralized, and dynamic updates of large surveying and remote-sensing data, this paper constructs a knowledge graph for surveying and remote-sensing applications. First, data are extracted. Second, using the ontology editing tool Protege, a knowledge graph mode level is constructed. Then, using a relational database, data are stored, and a D2RQ tool maps the data from the mode level's ontology to the data layer. Then, using the D2RQ tool, a SPARQL protocol and resource description framework query language (SPARQL) endpoint service is used to describe functions such as query and reasoning of the knowledge graph. The graph database is then used to display the knowledge graph. Finally, the knowledge graph is used to describe the correlation between the fields of surveying and remote sensing.
C1 [Hao, Xuejie; Li, Xiuhong; Liu, Lu; Sun, Meiying; Liu, Qiang] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China.
   [Ji, Zheng] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.
   [Yin, Lizeyan] Clermont Auvergne Univ, Inst Comp Modeling & Their Applicat, F-63000 Clermont Ferrand, France.
   [Yang, Rongjin] Chinese Res Inst Environm Sci, 8 Da Yang Fang, Beijing 100012, Peoples R China.
RP Li, XH (通讯作者)，Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China.
EM 202031490007@mail.bnu.edu.cn; jz07@whu.edu.cn; lixh@bnu.edu.cn;
   Lizeyan.YIN@etu.uca.fr; 201821490033@mail.bnu.edu.cn;
   mysun@mail.bnu.edu.cn; toliuqiang@bnu.edu.cn; yangrj@craes.org.cn
OI Hao, Xuejie/0000-0003-1288-6155; Liu, Qiang/0000-0002-5302-9849
FU National Key Research and Development Project of China [2016YFC0502106];
   National Science and Technology Major Project of China [2018ZX07111002];
   National Natural Science Foundation of China [41476161]
FX This research was supported by the National Key Research and Development
   Project of China (No. 2016YFC0502106), National Science and Technology
   Major Project of China (No. 2018ZX07111002) and the National Natural
   Science Foundation of China(No. 41476161).
CR Abad A., 2015, APPL CLAY SCI, V115, P108, DOI [10.1111/1468-0335.t01-1-00269, DOI 10.1111/1468-0335.T01-1-00269]
   Arenas M, 2017, ACM T DATABASE SYST, V42, DOI 10.1145/3129247
   CAO Qian, 2015, INF STUD THEORY APPL, V12, P127, DOI DOI 10.16353/J.CNKI.10007490.2015.12.026
   Chen Bu-wei, 2010, Computer Engineering and Design, V31, P847
   Duan X., 2020, ELECTRON WORLD, P4, DOI [10.19353/j.cnki.dzsj.2020.04.054, DOI 10.19353/J.CNKI.DZSJ.2020.04.054]
   Gan J., 2005, J YUNNAN NORM U, V25, P9, DOI [10.3969/j.issn.1007-9793.2005.04.003, DOI 10.3969/J.ISSN.1007-9793.2005.04.003]
   Gennari JH, 2003, INT J HUM-COMPUT ST, V58, P89, DOI 10.1016/S1071-5819(02)00127-1
   He L., 2020, THESIS HARBIN U SCI THESIS HARBIN U SCI
   [蒋秉川 Jiang Bingchuan], 2018, [测绘学报, Acta Geodetica et Cartographica Sinica], V47, P1051
   Kong X.Y., 2005, FOUNDING GEODESY, V4th ed., P56
   Li Wei Xiao Yanghua Wang Wei, 2017, Computer Engineering, V43, P225, DOI 10.3969/j.issn.1000-3428.2017.03.038
   Liu J., 2019, THESIS ZHEJIANG U HA THESIS ZHEJIANG U HA
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   [陆锋 Lu Feng], 2017, [地球信息科学学报, Journal of Geo-Information Science], V19, P723
   Ma H.B., 2019, THESIS BEIJING U TE THESIS BEIJING U TE, P25
   Mallory EK, 2016, BIOINFORMATICS, V32, P106, DOI 10.1093/bioinformatics/btv476
   Sun J.B., 2009, PRINCIPLES APPL REMO, V3rd ed, P23
   Vyas Aditya, 2018, Procedia Computer Science, V132, P1602, DOI 10.1016/j.procs.2018.05.125
   Wang L., 2019, J CIVIL ENG MANAG, V36, P109, DOI [10.13579/j.cnki.2095-0985.2019.05.017, DOI 10.13579/J.CNKI.2095-0985.2019.05.017]
   Wang W.G., 2021, BIG DATA RES, V1, P1
   Wei T., 2020, IND TECHNOL INNOV, V37, P27, DOI [10.14103/j.issn.2095-8412.2020.02.005, DOI 10.14103/J.ISSN.2095-8412.2020.02.005]
   Xia GS, 2015, REMOTE SENS-BASEL, V7, P15014, DOI 10.3390/rs71115014
   Xie R., 2017, RADIO ENG, V47, P1
   Xu Zeng-lin, 2016, Journal of University of Electronic Science and Technology of China, V45, P589, DOI 10.3969/j.issn.1001-0548.2016.04.012
   Yang X., 2018, VALUE ENG, V8, P134, DOI [10.14018/j.cnki.cn13-1085/n.2018.08.054, DOI 10.14018/J.CNKI.CN13-1085/N.2018.08.054]
   Ye S., 2019, THESIS CHINA U MININ THESIS CHINA U MININ
   Zhang R., 2007, THESIS HUNAN AGR U THESIS HUNAN AGR U
   Zhang Z., 2018, THESIS HUAZHONG U SC THESIS HUAZHONG U SC
   Zhao K., 2019, WORLD CHIN MED, V14, P2636, DOI [10.3969/j.issn.1673-7202.2019.10.019, DOI 10.3969/J.ISSN.1673-7202.2019.10.019]
   Zhou W., 2019, THESIS E CHINA NORMA THESIS E CHINA NORMA
   Zhou Z., 2011, P 2011 FALL ACAD C C
   Zhu L.J., 2004, THESIS CHINA AGR U B THESIS CHINA AGR U B
NR 32
TC 5
Z9 5
U1 39
U2 67
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2072-4292
J9 REMOTE SENS-BASEL
JI Remote Sens.
PD JUL
PY 2021
VL 13
IS 13
AR 2511
DI 10.3390/rs13132511
PG 19
WC Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science & Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging
   Science & Photographic Technology
GA TG1PJ
UT WOS:000671182900001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Gazzotti, R
   Faron, C
   Gandon, F
   Lacroix-Hugues, V
   Darmon, D
AF Gazzotti, Raphael
   Faron, Catherine
   Gandon, Fabien
   Lacroix-Hugues, Virginie
   Darmon, David
TI Extending electronic medical records vector models with knowledge graphs
   to improve hospitalization prediction
SO JOURNAL OF BIOMEDICAL SEMANTICS
LA English
DT Article
DE Electronic medical records; Predictive model; Knowledge graph; Feature
   selection
ID SELECTION
AB Background Artificial intelligence methods applied to electronic medical records (EMRs) hold the potential to help physicians save time by sharpening their analysis and decisions, thereby improving the health of patients. On the one hand, machine learning algorithms have proven their effectiveness in extracting information and exploiting knowledge extracted from data. On the other hand, knowledge graphs capture human knowledge by relying on conceptual schemas and formalization and supporting reasoning. Leveraging knowledge graphs that are legion in the medical field, it is possible to pre-process and enrich data representation used by machine learning algorithms. Medical data standardization is an opportunity to jointly exploit the richness of knowledge graphs and the capabilities of machine learning algorithms. Methods We propose to address the problem of hospitalization prediction for patients with an approach that enriches vector representation of EMRs with information extracted from different knowledge graphs before learning and predicting. In addition, we performed an automatic selection of features resulting from knowledge graphs to distinguish noisy ones from those that can benefit the decision making. We report the results of our experiments on the PRIMEGE PACA database that contains more than 600,000 consultations carried out by 17 general practitioners (GPs). Results A statistical evaluation shows that our proposed approach improves hospitalization prediction. More precisely, injecting features extracted from cross-domain knowledge graphs in the vector representation of EMRs given as input to the prediction algorithm significantly increases the F1 score of the prediction. Conclusions By injecting knowledge from recognized reference sources into the representation of EMRs, it is possible to significantly improve the prediction of medical events. Future work would be to evaluate the impact of a feature selection step coupled with a combination of features extracted from several knowledge graphs. A possible avenue is to study more hierarchical levels and properties related to concepts, as well as to integrate more semantic annotators to exploit unstructured data.
C1 [Gazzotti, Raphael; Faron, Catherine; Gandon, Fabien] Univ Cote Azur, INRIA, CNRS, I3S, 2004,Route Lucioles, F-06902 Sophia Antipolis, France.
   [Lacroix-Hugues, Virginie; Darmon, David] Univ Cote Azur, RETINES, Dept Med Gen, 28,Ave Valombrose, F-06107 Nice, France.
RP Gazzotti, R (通讯作者)，Univ Cote Azur, INRIA, CNRS, I3S, 2004,Route Lucioles, F-06902 Sophia Antipolis, France.
EM gazzotti.raphael@gmail.com
OI Faron, Catherine/0000-0001-5959-5561; DARMON, David/0000-0002-4425-4163
FU French government labelled PIA program under its IDEX UCAJEDI project
   [ANR-15-IDEX-0001]; CovidOnTheWeb project - Inria; 3IA Cote d'Azur
   [19-P3IA-0002]
FX This work is partly funded by the French government labelled PIA program
   under its IDEX UCAJEDI project (ANR-15-IDEX-0001) and the 3IA Cote
   d'Azur (19-P3IA-0002) as well as the CovidOnTheWeb project funded by
   Inria.
CR [Anonymous], 2011, J MACH LEARN RES
   Artstein R, 2008, COMPUT LINGUIST, V34, P555, DOI 10.1162/coli.07-034-R2
   Bergstra J, 2012, J MACH LEARN RES, V13, P281
   Breiman L., 2001, RANDOM FORESTS, V45, P5, DOI 10.1023/A:1010933404324
   Cawley GC, 2010, J MACH LEARN RES, V11, P2079
   Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P27, DOI DOI 10.1145/1961189.1961199
   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
   Corby O, 2010, Proceedings 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT), P338, DOI 10.1109/WI-IAT.2010.144
   Cunningham H, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P168
   Daiber J., 2013, P 9 INT C SEM SYST, P121, DOI 10.1145/2506182.2506198
   Demsar J, 2006, J MACH LEARN RES, V7, P1
   Forman G., 2010, SIGKDD EXPLORATIONS, V12, P49, DOI 10.1145/1882471.1882479
   Frunza O, 2011, IEEE T KNOWL DATA EN, V23, P801, DOI 10.1109/TKDE.2010.152
   Gazzotti R, 2020, THESIS U COTE AZUR N
   Gazzotti R, 2020, PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), P2013, DOI 10.1145/3341105.3373932
   Gazzotti R, 2019, LECT NOTES COMPUT SC, V11503, P116, DOI 10.1007/978-3-030-21348-0_8
   Goldstein BA, 2017, J AM MED INFORM ASSN, V24, P198, DOI 10.1093/jamia/ocw042
   KRIPPEND.K, 1970, EDUC PSYCHOL MEAS, V30, P61, DOI 10.1177/001316447003000105
   Lacroix-Hugues V, 2017, STUD HEALTH TECHNOL, V245, P462, DOI 10.3233/978-1-61499-830-3-462
   Lafferty J., 2001, P 18 INT C MACH LEAR
   McCullagh Peter, 1989, GEN LINEAR MODELS, V37, DOI [10.1007/978-1-4899-3242-6, DOI 10.1007/978-1-4899-3242-6]
   Min H, 2017, J BIOMED SEMANT, V8, DOI 10.1186/s13326-017-0149-6
   Nadeau C, 2003, MACH LEARN, V52, P239, DOI 10.1023/A:1024068626366
   Peng X, ABS210709288 CORR, V2021
   Pennington J., 2014, EMNLP, P1532
   Sutton C, 2012, FOUND TRENDS MACH LE, V4, P267, DOI 10.1561/2200000013
   Tibshirani R, 2011, J R STAT SOC B, V73, P273, DOI 10.1111/j.1467-9868.2011.00771.x
   Wang Sheng-Min, 2018, Chonnam Med J, V54, P101, DOI 10.4068/cmj.2018.54.2.101
NR 28
TC 0
Z9 0
U1 6
U2 6
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 2041-1480
J9 J BIOMED SEMANT
JI J. Biomed. Semant.
PD FEB 22
PY 2022
VL 13
IS 1
AR 6
DI 10.1186/s13326-022-00261-9
PG 20
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematical & Computational Biology
GA ZF4UJ
UT WOS:000759564500001
PM 35193692
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Diao, LJ
   Yang, W
   Zhu, PH
   Cao, GF
   Song, SJ
   Kong, Y
AF Diao, Lijuan
   Yang, Wei
   Zhu, Penghua
   Cao, Gaofang
   Song, Shoujun
   Kong, Yang
TI The research of clinical temporal knowledge graph based on deep learning
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Temporal knowledge graph; knowledge graph link prediction; translation
   model TransR; long short term memory (LSTM) networks; incremental
   learning; deep learning
AB Temporal knowledge base exists on various fields. Take medical medicine field as example, diabetes is a typical chronic disease which evolves slowly. This paper starts from actual EMR data of hospitals by combination of experience and knowledge of clinical doctors. Link prediction on clinical knowledge base such as diabetic complication requires the analysis on temporal characteristic of temporal knowledge base, which is a great challenge for traditional link prediction models. This paper proposes temporal knowledge graph link prediction model based on deep learning. This model selects the TransR transformation model suitable for big data and makes entity projection in relation space containing different semantic meanings, so as to vector the entities and complex semantic relations in graph. Then it adopts LSTM recursive neural network and adds the top-bottom relational information of the graph for sequential learning. Finally it constantly carries out deep learning through incremental calculation and LSTM recursive network to improve the accuracy of prediction. The incremental LSTM model highlights the hidden semantic and clinical temporal information and effectively utilizes sequential learning to mining forward-backward dependent information. It compensates the deficiency of lower prediction accuracy on timely knowledge graph caused by the traditional link prediction models. Finally, it is proved that the new model has better performance over temporal knowledge graph link prediction.
C1 [Diao, Lijuan; Zhu, Penghua] North China Inst Aerosp Engn, Langfang, Hebei, Peoples R China.
   [Yang, Wei] Beijing Aerosp Automat Control Inst, Beijing, Peoples R China.
   [Cao, Gaofang; Kong, Yang] BinZhou Med Univ, Yantai, Peoples R China.
   [Song, Shoujun] Binzhou Med Univ, Yantai Affiliated Hosp, Yantai, Peoples R China.
RP Zhu, PH (通讯作者)，North China Inst Aerosp Engn, Langfang, Hebei, Peoples R China.; Cao, GF (通讯作者)，BinZhou Med Univ, Yantai, Peoples R China.
EM lijuan_diao@126.com
FU scientific research fund of Binzhou Medical University [BY2015KYQD07];
   Natural Science Foundation of Shandong province [ZR2013FM024]
FX The research presented in this paper was supported by the scientific
   research fund of Binzhou Medical University (Grant: No. BY2015KYQD07).
   The authors acknowledge the Natural Science Foundation of Shandong
   province (Grant: NO. ZR2013FM024).
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chang K., 2014, PROC C EMPIRICAL MET, P1568
   Chekol MW, 2017, AAAI CONF ARTIF INTE, P88
   Danleng X., 2017, APPL COMPUTATIONAL I
   Feng W., 2016, J DATABASE THEORY AP, V9, P163
   Jiang LY, 2012, OPTIK, V123, P1834, DOI 10.1016/j.ijleo.2012.02.045
   Jiang T., 2016, P 26 INT C COMP LING, P1715
   Li X, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1445
   Lijuan D., 2018, J EURASIP J WIRELESS, P1
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Luo Y, 2017, J CARDIOVASC TRANSL, V10, P305, DOI 10.1007/s12265-016-9727-8
   Morales C, 2017, LECT NOTES COMPUT SC, V10360, P246, DOI 10.1007/978-3-319-60131-1_14
   Ogishima S., 2015, P 15 WORLD C HLTH BI, P1057
   Qiao W., 2013, CHINESE J MED LIB IN, V22, P2
   Robert S., 2012, P 8 INT C LANG RES E, P3679
   Sahebi S., 2016, P 9 INT C ED DAT MIN, P502
   Sequeda J.F., 2017, PROC 13 INT SUMM SCH, P68
   Sulakhe D, 2014, NUCLEIC ACIDS RES, V42, pD1007, DOI 10.1093/nar/gkt1166
   Tong R., 2016, J MED IFORMATION, V37, P8
   Vang Katrine Juel, 2013, Journal of Information, Communication and Ethics in Society, V11, P245, DOI 10.1108/JICES-08-2013-0028
   Wang Z., 2014, AAAI, P1112
   Yun J., 2018, J NEURAL COMPUTING A, V29, P1
NR 23
TC 0
Z9 0
U1 5
U2 20
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 3
BP 4265
EP 4274
DI 10.3233/JIFS-189687
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WK4EC
UT WOS:000709679300006
DA 2022-08-23
ER

PT J
AU Steenwinckel, B
   Vandewiele, G
   Weyns, M
   Agozzino, T
   De Turck, F
   Ongenae, F
AF Steenwinckel, Bram
   Vandewiele, Gilles
   Weyns, Michael
   Agozzino, Terencio
   De Turck, Filip
   Ongenae, Femke
TI INK: knowledge graph embeddings for node classification
SO DATA MINING AND KNOWLEDGE DISCOVERY
LA English
DT Article
DE Knowledge graph representation; Knowledge graph embedding; Node
   classification; Semantic data mining
AB Deep learning techniques are increasingly being applied to solve various machine learning tasks that use Knowledge Graphs as input data. However, these techniques typically learn a latent representation for the entities of interest internally, which is then used to make decisions. This latent representation is often not comprehensible to humans, which is why deep learning techniques are often considered to be black boxes. In this paper, we present INK: Instance Neighbouring by using Knowledge, a novel technique to learn binary feature-based representations, which are comprehensible to humans, for nodes of interest in a knowledge graph. We demonstrate the predictive power of the node representations obtained through INK by feeding them to classical machine learning techniques and comparing their predictive performances for the node classification task to the current state of the art: Graph Convolutional Networks (R-GCN) and RDF2Vec. We perform this comparison both on benchmark datasets and using a real-world use case.
C1 [Steenwinckel, Bram; Vandewiele, Gilles; Weyns, Michael; Agozzino, Terencio; De Turck, Filip; Ongenae, Femke] IDLab, Technol Pk Zwijnaarde 126, B-9050 Ghent, Belgium.
RP Steenwinckel, B (通讯作者)，IDLab, Technol Pk Zwijnaarde 126, B-9050 Ghent, Belgium.
EM bram.steenwinckel@ugent.be; gilles.vandewiele@ugent.be;
   michael.weyns@ugent.be; terencio.agozzino@gmail.com;
   filip.deturck@ugent.be; femke.ongenae@ugent.be
OI Steenwinckel, Bram/0000-0002-3488-2334
FU Fund for Scientific Research Flanders (FWO) [1SA0219N, 1S31417N,
   1SD8821N]; imec.ICON project PROTEGO [HBC.2019.2812]; VLAIO; Televic;
   Amaron; Z-Plus; ML2Grow; imec
FX Bram Steenwinckel (1SA0219N), Gilles Vandewiele (1S31417N) and Michael
   Weyns (1SD8821N) are funded by a strategic base research Grant of the
   Fund for Scientific Research Flanders (FWO). This research is part of
   the imec.ICON project PROTEGO (HBC.2019.2812), co-funded by imec, VLAIO,
   Televic, Amaron, Z-Plus and ML2Grow.
CR Anelli VW, 2019, LECT NOTES COMPUT SC, V11778, P38, DOI 10.1007/978-3-030-30793-6_3
   [Anonymous], 2006, RDFLIB PYTH LIB WORK
   [Anonymous], 2011, J MACH LEARN RES
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Baldassarre Federico, 2019, ICML WORKSH LEARN RE
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   de Boer Victor, 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P733, DOI 10.1007/978-3-642-30284-8_56
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Gulisano V., 2017, ACM INT C DISTRIBUTE, P271
   Gunel, 2019, ROBUST RELATIONAL GR
   Hamilton W.L., 2017, ARXIV170905584
   Kazemi SM, 2018, ADV NEUR IN, V31
   Khalid S, 2014, 2014 SCIENCE AND INFORMATION CONFERENCE (SAI), P372, DOI 10.1109/SAI.2014.6918213
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lecue F, 2020, SEMANT WEB, V11, P41, DOI 10.3233/SW-190374
   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
   Losch Uta, 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P134, DOI 10.1007/978-3-642-30284-8_16
   Lundberg SM, 2017, ADV NEUR IN, V30
   Mikolov T., 2013, EFFICIENT ESTIMATION
   Mikolov T., 2013, PROBLEM SOLVING ALGO, P3111
   Miller E., 1998, D-Lib Magazine, DOI 10.1002/bult.105
   Moreau, 2020, ARXIV201010343
   Neil D, 2018, ARXIV181200279
   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, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Paulheim Heiko, 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P560, DOI 10.1007/978-3-642-30284-8_44
   Paulheim H, 2021, ARXIV210500674
   Portisch J, 2021, WEB CONFERENCE 2021: COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2021), P293, DOI 10.1145/3442442.3451382
   Ristoski P, 2020, J WEB SEMANT, V60, DOI 10.1016/j.websem.2019.100546
   Ristoski P, 2019, SEMANT WEB, V10, P721, DOI 10.3233/SW-180317
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9982, P186, DOI 10.1007/978-3-319-46547-0_20
   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
   Tan Z, 2018, IEEE ACCESS, V6, P8232, DOI 10.1109/ACCESS.2018.2797876
   Taniar D., 2006, WEB SEMANTICS ONTOLO
   Thanapalasingam T., 2021, ARXIV210710015
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Turck FD, IDLAB
   Union S., 2018, STARDOG
   van den Brink G, 2019, STUD REFORM THEOL, V38, P1, DOI 10.1163/9789004391741_002
   Vandewiele G, 2020, ARXIV200904404
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang M., 2019, ICLR WORKSH REPR LEA
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wilcke X., 2017, DATA SCI, V1, P39, DOI [10.3233/ds-170007, DOI 10.3233/DS-170007, 10.3233/DS-170007]
   Xiao H., 2015, COMPUTER SCI
   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, ABS14126575 CORR
   Zeman, 2015, KNOW LOD
   Zeman, 2016, KNOWLOD
   Zhang ZW, 2020, IEEE ACCESS, V8, P7435, DOI 10.1109/ACCESS.2020.2963990
   Zouaq A, 2020, PROCEEDINGS OF THE INTERNATIONAL WORKSHOP ON SEMANTIC BIG DATA (SBD 2020), DOI 10.1145/3391274.3393637
NR 55
TC 0
Z9 0
U1 6
U2 10
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1384-5810
EI 1573-756X
J9 DATA MIN KNOWL DISC
JI Data Min. Knowl. Discov.
PD MAR
PY 2022
VL 36
IS 2
BP 620
EP 667
DI 10.1007/s10618-021-00806-z
EA JAN 2022
PG 48
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0C0LD
UT WOS:000738439600009
DA 2022-08-23
ER

PT J
AU Shi, D
   Zhang, FF
   Wang, N
   Fang, W
AF Shi, Dan
   Zhang, Fangfei
   Wang, Nan
   Fang, Wei
TI Intelligent Electromagnetic Compatibility Management of Cell Phones by
   Using Knowledge Graphs
SO IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
LA English
DT Article
DE Electromagnetic compatibility management; entity; knowledge graph; long
   short-term memory; triple function
AB In this paper, a new electromagnetic compatibility (EMC) management method for cell phones is presented. The interference and sensitive characteristics of components in printed circuit board (PCB) of cell phones are analyzed, and the knowledge graph for these interference and sensitive units is established for the first time. In the knowledge graph, the interference and sensitive units are stored as entities and new triple functions are defined to associate these entities. The EMC management report is intelligently generated based on the knowledge graph from interactive sessions, in which long short-term memory and multilayer perceptron methods are used to extract the entities and key parameters of the triple function. The accuracy of the entity extraction model is 75.6%, and F1 measure is 70.9%. Two experiments, which validated the feasibility of the established model, were executed using the proposed method. The method is useful and practical for intelligent EMC management in the industry.
C1 [Shi, Dan; Zhang, Fangfei; Wang, Nan] Beijing Univ Post & Telecommun, Sch Elect Engn, Beijing 100088, Peoples R China.
   [Fang, Wei] Microsoft Res Asia, Beijing 100080, Peoples R China.
RP Shi, D (通讯作者)，Beijing Univ Post & Telecommun, Sch Elect Engn, Beijing 100088, Peoples R China.
EM shidan@buptemc.com; 77669674@qq.com; arthfa@microsoft.com
FU National Natural Science Foundation of China [61771069]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 61771069.
CR Ali BAA, 2017, GREEN ENERGY TECHNOL, P1, DOI 10.1007/978-3-319-46610-1_1
   Armstrong K, 2014, IEEE INT SYMP ELEC, P929, DOI 10.1109/ISEMC.2014.6899101
   Awad E. M., 1996, BUILDING EXPERT SYST
   Benajiba Y, 2009, IEEE T AUDIO SPEECH, V17, P926, DOI 10.1109/TASL.2009.2019927
   Brachman R. J., 2004, KNOWLEDGE REPRESENTA
   Bradley D. N., 2013, 6th IET Professional Development Course on Railway Electrification Infrastructure and Systems (REIS 2013), P172
   Brahimi R, 2011, IEEE T INSTRUM MEAS, V60, P539, DOI 10.1109/TIM.2010.2050373
   Chein M, 2009, ADV INFORM KNOWL PRO, P1
   CHOO V, 1994, 1994 IEEE INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY - COMPATIBILITY IN THE LOOP, P167, DOI 10.1109/ISEMC.1994.385621
   Chu MWC, 2002, 2002 IEEE INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY, VOLS 1 AND 2, SYMPOSIUM RECORD, P791, DOI 10.1109/ISEMC.2002.1032695
   Deb GK, 1999, PROCEEDING OF THE INTERNATIONAL CONFERENCE ON ELECTROMAGNETIC INTERFERENCE AND COMPATIBILITY '99, P17
   Demirkiran I, 2010, IEEE INT SYMP ELEC, P425, DOI 10.1109/ISEMC.2010.5711312
   Devabhaktuni V, 2013, IEEE T ELECTROMAGN C, V55, P385, DOI 10.1109/TEMC.2012.2214223
   Fagin R, 2003, REASONING KNOWLEDGE
   Hariri AO, 2016, IEEE T MAGN, V52, DOI 10.1109/TMAG.2015.2492922
   He L., IEEE T BIG DATA, DOI [10.1109/TB-DATA.2018.2805363, DOI 10.1109/TB-DATA.2018.2805363]
   He Y, 2017, IEEE T VEH TECHNOL, V66, P10433, DOI 10.1109/TVT.2017.2751641
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Huang HC, 2013, 2013 3RD INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, COMMUNICATIONS AND NETWORKS (CECNET), P533, DOI 10.1109/CECNet.2013.6703387
   Irawan H., 2015, P 4 INT C INT DIG ME, P1
   JAYARAM N, 2015, IEEE T KNOWL DATA EN, V27, P2797, DOI DOI 10.1109/TKDE.2015.2426696
   Jung S.-K., 2014, ADV ENERGY MATER, V4, P1
   Kirawanich Phumin, 2018, IEEE Electromagnetic Compatibility Magazine, V7, P54, DOI 10.1109/MEMC.2018.8410662
   Li DC, 2018, IEEE T NANOBIOSCI, V17, P165, DOI 10.1109/TNB.2018.2841053
   Medico R, 2019, IEEE T ELECTROMAGN C, V61, P352, DOI 10.1109/TEMC.2018.2821712
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Morgado C, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY (QRS 2018), P135, DOI 10.1109/QRS.2018.00027
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Perez R, 2002, 2002 IEEE INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY, VOLS 1 AND 2, SYMPOSIUM RECORD, P766, DOI 10.1109/ISEMC.2002.1032690
   Qi G. L., 2009, KNOWLEDGE GRAPH
   Qu Q, 2008, FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 5, PROCEEDINGS, P541, DOI 10.1109/FSKD.2008.7
   Rau L. F., 1991, Proceedings. Seventh IEEE Conference on Artificial Intelligence Applications (Cat. No.91CH2967-8), P29, DOI 10.1109/CAIA.1991.120841
   Simon H. A, 1959, IFIP C, P256
   Siu R, 2000, 2000 IEEE INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY, VOLS 1 AND 2, SYMPOSIUM RECORD, P285, DOI 10.1109/ISEMC.2000.875579
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Surekha M., 1990, 1990 International Symposium on Electromagnetic Compatibility Symposium Record (Cat. No. 90CH2903-3), P170, DOI 10.1109/ISEMC.1990.252752
   Uyar A, 2015, ONLINE INFORM REV, V39, P197, DOI 10.1108/OIR-10-2014-0257
   Wei W, 2016, IEEE T CYBERNETICS, V46, P930, DOI 10.1109/TCYB.2015.2418233
   Yu LC, 2018, IEEE-ACM T AUDIO SPE, V26, P671, DOI 10.1109/TASLP.2017.2788182
   Zhang WS, 2018, IEEE ACCESS, V6, P23551, DOI 10.1109/ACCESS.2018.2825538
   Zhang ZP, 2017, IT PROF, V19, P26, DOI 10.1109/MITP.2017.4241463
   Zhao WZ, 2016, IEEE T GEOSCI REMOTE, V54, P4544, DOI 10.1109/TGRS.2016.2543748
NR 42
TC 2
Z9 2
U1 3
U2 94
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0278-0046
EI 1557-9948
J9 IEEE T IND ELECTRON
JI IEEE Trans. Ind. Electron.
PD DEC
PY 2019
VL 66
IS 12
BP 9808
EP 9816
DI 10.1109/TIE.2019.2893839
PG 9
WC Automation & Control Systems; Engineering, Electrical & Electronic;
   Instruments & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering; Instruments & Instrumentation
GA IP8PX
UT WOS:000480309400070
DA 2022-08-23
ER

PT J
AU Wang, CG
   An, J
   Mu, G
AF Wang, Changgang
   An, Jun
   Mu, Gang
TI Power System Network Topology Identification Based on Knowledge Graph
   and Graph Neural Network
SO FRONTIERS IN ENERGY RESEARCH
LA English
DT Article
DE knowledge graph; power network; topology identification; graph neural
   network; knowledge inference
AB The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data-tolerant mechanism, and the accuracy of their performance in terms of identification is thus affected by the quality of data. Topology identification is related to the link prediction problem. The graph neural network can be used to predict the state of unlabeled nodes (lines) through training on features of labeled nodes (lines) with fault tolerance. Inspired by the characteristics of the graph neural network, we applied it to topology identification in this study. We propose a method to identify the topology of a power network based on a knowledge graph and the graph neural network. Traditional knowledge graphs can quickly mine possible connections between entities and generate graph structure data, but in the case of errors or informational conflicts in the data, they cannot accurately determine whether the relationships between the entities really exist. The graph neural network can use data mining to determine whether a connection obtained between entities is based on their eigenvalues, and has a fault tolerance mechanism to adapt to errors and informational conflicts in the graph data, but needs the graph data as database. The combination of the knowledge graph and the graph neural network can compensate for the deficiency of the single knowledge graph method. We tested the proposed method by using the IEEE 118-bus system and a provincial network system. The results showed that our approach is feasible and highly fault tolerant. It can accurately identify network topology even in the presence of conflicting and missing measurement-related information.
C1 [Wang, Changgang; An, Jun] Northeast Elect Power Univ, Minist Educ, Key Lab Modern Power Syst Simulat & Control & Ren, Jilin, Jilin, Peoples R China.
   [An, Jun; Mu, Gang] Northeast Elect Power Univ, Sch Elect Engn, Jilin, Jilin, Peoples R China.
RP An, J (通讯作者)，Northeast Elect Power Univ, Minist Educ, Key Lab Modern Power Syst Simulat & Control & Ren, Jilin, Jilin, Peoples R China.; An, J (通讯作者)，Northeast Elect Power Univ, Sch Elect Engn, Jilin, Jilin, Peoples R China.
EM anhuianjun@163.com
OI Wang, Changgang/0000-0003-0237-210X
FU National Natural Science Foundation of China [51877034]
FX This work was supported by the National Natural Science Foundation of
   China (No. 51877034).
CR Cao YX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1452
   Jing Y, 2020, PATTERN RECOGN, V108, DOI 10.1016/j.patcog.2020.107544
   Kipf T. N., 2017, 5 INT C LEARN REPR T
   [郎燕生 Lang Yansheng], 2017, [电力系统保护与控制, Power System Protection and Control], V45, P108
   [李樊 LI Fan], 2011, [电网技术, Power System Technology], V35, P134
   Li M, 2020, NEURAL NETWORKS, V128, P188, DOI 10.1016/j.neunet.2020.04.028
   Li Y, 2018, ENERGY, V162, P237, DOI 10.1016/j.energy.2018.07.200
   Li Y, 2019, IEEE T IND ELECTRON, V66, P1565, DOI 10.1109/TIE.2018.2840498
   [刘道伟 Liu Daowei], 2019, [中国电机工程学报, Proceedings of the Chinese Society of Electrical Engineering], V39, P7107
   [刘羽霄 Liu Yuxiao], 2018, [电力系统自动化, Automation of Electric Power Systems], V42, P157
   [刘梓权 Liu Ziquan], 2018, [电力系统自动化, Automation of Electric Power Systems], V42, P158
   Lourenco EM, 2015, IEEE T POWER SYST, V30, P3190, DOI 10.1109/TPWRS.2014.2379512
   Ma Jing, 2014, Automation of Electric Power Systems, V38, P74, DOI 10.7500/AEPS20131212003
   Mei Nian, 2008, Power System Technology, V32, P35
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Nikolentzos G, 2020, NEURAL NETWORKS, V130, P195, DOI 10.1016/j.neunet.2020.07.008
   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
   Song Shao-qun, 2005, Power System Technology, V29, P45
   Su CH, 2020, IEEE ACCESS, V8, P125447, DOI 10.1109/ACCESS.2020.3007835
   Wang Shouxiang, 2009, Automation of Electric Power Systems, V33, P82
   [王涛 Wang Tao], 2019, [中国电机工程学报, Proceedings of the Chinese Society of Electrical Engineering], V39, P5962
   Wu B, 2018, NEUROCOMPUTING, V321, P346, DOI 10.1016/j.neucom.2018.09.008
   Wu ZY, 2020, EXPERT SYST APPL, V158, DOI 10.1016/j.eswa.2020.113595
   Xie YX, 2020, KNOWL-BASED SYST, V194, DOI 10.1016/j.knosys.2020.105548
   Yu M, 2020, FUTURE GENER COMP SY, V113, P145, DOI 10.1016/j.future.2020.06.024
   Zhang CH, 2019, IEEE ACCESS, V7, P166246, DOI 10.1109/ACCESS.2019.2953888
   Zhu Wei, 2011, Proceedings of the CSEE, V31, P109
NR 27
TC 3
Z9 3
U1 23
U2 48
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-598X
J9 FRONT ENERGY RES
JI Front. Energy Res.
PD FEB 17
PY 2021
VL 8
AR 613331
DI 10.3389/fenrg.2020.613331
PG 12
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA QQ1ST
UT WOS:000624307500001
OA gold
DA 2022-08-23
ER

PT J
AU Kim, T
   Yun, Y
   Kim, N
AF Kim, Taejin
   Yun, Yeoil
   Kim, Namgyu
TI Deep Learning-Based Knowledge Graph Generation for COVID-19
SO SUSTAINABILITY
LA English
DT Article
DE deep learning; knowledge graph; text analytics; pre-trained language
   model; BERT
ID CHATBOT
AB Many attempts have been made to construct new domain-specific knowledge graphs using the existing knowledge base of various domains. However, traditional "dictionary-based" or "supervised" knowledge graph building methods rely on predefined human-annotated resources of entities and their relationships. The cost of creating human-annotated resources is high in terms of both time and effort. This means that relying on human-annotated resources will not allow rapid adaptability in describing new knowledge when domain-specific information is added or updated very frequently, such as with the recent coronavirus disease-19 (COVID-19) pandemic situation. Therefore, in this study, we propose an Open Information Extraction (OpenIE) system based on unsupervised learning without a pre-built dataset. The proposed method obtains knowledge from a vast amount of text documents about COVID-19 rather than a general knowledge base and add this to the existing knowledge graph. First, we constructed a COVID-19 entity dictionary, and then we scraped a large text dataset related to COVID-19. Next, we constructed a COVID-19 perspective language model by fine-tuning the bidirectional encoder representations from transformer (BERT) pre-trained language model. Finally, we defined a new COVID-19-specific knowledge base by extracting connecting words between COVID-19 entities using the BERT self-attention weight from COVID-19 sentences. Experimental results demonstrated that the proposed Co-BERT model outperforms the original BERT in terms of mask prediction accuracy and metric for evaluation of translation with explicit ordering (METEOR) score.
C1 [Kim, Taejin; Yun, Yeoil; Kim, Namgyu] Kookmin Univ, Grad Sch Business IT, Seoul 02707, South Korea.
RP Kim, N (通讯作者)，Kookmin Univ, Grad Sch Business IT, Seoul 02707, South Korea.
EM jeung722@kookmin.ac.kr; yunyi94@kookmin.ac.kr; ngkim@kookmin.ac.kr
OI Kim, Namgyu/0000-0002-7228-4562
FU BK21 FOUR (Fostering Outstanding Universities for Research) - Ministry
   of Education (MOE, Korea); National Research Foundation of Korea (NRF)
FX This research was supported by the BK21 FOUR (Fostering Outstanding
   Universities for Research) funded by the Ministry of Education (MOE,
   Korea) and National Research Foundation of Korea (NRF).
CR Adhikari A., 2019, ARXIV PREPRINT ARXIV
   Ait-Mlouk A, 2020, IEEE ACCESS, V8, P149220, DOI 10.1109/ACCESS.2020.3016142
   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
   Banko M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2670
   Bao QM, 2020, PROCEEDINGS OF THE AUSTRALASIAN COMPUTER SCIENCE WEEK MULTICONFERENCE (ACSW 2020)
   Belfin RV, 2019, INT CONF ADVAN COMPU, P717, DOI 10.1109/ICACCS.2019.8728499
   Bo L., 2019, P AI SOC GOOD WORKSH
   Bouranoui Z., 2019, ARXIV191112753
   Brown T., 2020, ARXIV200514165, V33, P1877
   Chen PH, 2018, IEEE ACCESS, V6, P31553, DOI 10.1109/ACCESS.2018.2839607
   Chen Y.C., 2019, ARXIV191103829
   Clark K, 2019, BLACKBOXNLP WORKSHOP ON ANALYZING AND INTERPRETING NEURAL NETWORKS FOR NLP AT ACL 2019, P276, DOI 10.18653/v1/w19-4828
   Condrobimo A., 2018, P 2018 INT C INF MAN, P65, DOI [10.1109/ICIMTech.2018.8528174, DOI 10.1109/ICIMTECH.2018.8528174]
   Csaky R., 2019, ARXIV190808835
   Cui B., 2019, P 2019 C EMPIRICAL M, P3548
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Divya S, 2018, J WEB DEV WEB DESIGN, V3, P7, DOI 10.1007/978-981-15-6707-0_57]
   Edward W.S., 1972, COURSE MODULARIZATIO
   English Wikipedia, OP INF EXTR
   English Wikipedia,, POT COR DIS 2019
   Etzioni O, 2011, NATURE, V476, P25, DOI 10.1038/476025a
   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
   GHAZVININEJAD M, 2019, EMNLP
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Jawahar G, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3651
   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
   Jo JY, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3404
   Kondylakis H, 2020, ALGORITHMS, V13, DOI 10.3390/a13090217
   Kovaleva Olga, 2019, P EMNLP IJCNLP, P4364, DOI [10.18653/v1/D19-1445, DOI 10.18653/V1/D19-1445]
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Lee J, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11060785
   Liu Q., 2019, P 33 C NEUR INF PROC
   Liu WJ, 2020, AAAI CONF ARTIF INTE, V34, P2901
   Malik KM, 2020, EXPERT SYST APPL, V145, DOI 10.1016/j.eswa.2019.113120
   Nickel Maximilian, 2015, ARXIV151004935
   Ostendorff M., 2019, ARXIV190908402V1
   Pascual D., 2020, ARXIV200405916
   Peters M., 2018, IMPROVING LANGUAGE U
   Rasmy L, 2021, NPJ DIGIT MED, V4, DOI 10.1038/s41746-021-00455-y
   Rogers A, 2020, T ASSOC COMPUT LING, V8, P842, DOI 10.1162/tacl_a_00349
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Shi P., 2019, ARXIV190405255
   Soderland S, 2010, AI MAG, V31, P93, DOI 10.1609/aimag.v31i3.2305
   Song KT, 2019, PR MACH LEARN RES, V97
   Song S., 2019, P 18 INT SEM WEB C A
   Soyeop Yoo, 2020, ICIC Express Letters, V14, P67, DOI 10.24507/icicel.14.01.67
   Stanovsky Gabriel, 2018, NAACL HLT, V1, P885, DOI DOI 10.18653/V1/N18-1081
   Sun ZQ, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4396
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Tchechmedjiev A, 2019, LECT NOTES COMPUT SC, V11779, P309, DOI 10.1007/978-3-030-30796-7_20
   Ulges A., 2019, ARXIV PREPRINT ARXIV
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Vig J, 2019, PROCEEDINGS OF THE 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, (ACL 2019), P37
   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 Q, 2020, ARXIV200700576
   Wang RJ, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1487, DOI 10.1145/3269206.3269252
   Wang X., 2019, ARXIV191106136
   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, P 2014 C EMP METH NA, P1591, DOI DOI 10.3115/V1/D14-1167
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   WEIZENBAUM J, 1966, COMMUN ACM, V9, P36, DOI 10.1145/365153.365168
   Wu X, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5271
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Yang W, 2019, ARXIV190406652
   Yang ZW, 2021, ANIM BIOTECHNOL, V32, P67, DOI 10.1080/10495398.2019.1653901
   Yao, 2019, ARXIV190903193
   Zhang H., 2019, ARXIV190209243
   Zhibin Lu, 2020, Advances in Information Retrieval, 42nd European Conference on IR Research, ECIR 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12035), P369, DOI 10.1007/978-3-030-45439-5_25
   Zhu J., 2020, ARXIV200206823
NR 73
TC 8
Z9 8
U1 12
U2 31
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB
PY 2021
VL 13
IS 4
AR 2276
DI 10.3390/su13042276
PG 19
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 QQ9EA
UT WOS:000624819300001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Jagvaral, B
   Lee, WK
   Roh, JS
   Kim, MS
   Park, YT
AF Jagvaral, Batselem
   Lee, Wan-Kon
   Roh, Jae-Seung
   Kim, Min-Sung
   Park, Young Tack
TI Path-based reasoning approach for knowledge graph completion using
   CNN-BiLSTM with attention mechanism
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge graph completion; Link prediction; Path-based reasoning;
   Low-dimensional embedding
AB Knowledge graphs are valuable resources for building intelligent systems such as question answering or recommendation systems. However, most knowledge graphs are impaired by missing relationships between entities. Embedding methods that translate entities and relations into a low-dimensional space achieve great results, but they only focus on the direct relations between entities and neglect the presence of path relations in graphs. On the contrary, path-based embedding methods consider a single path to make inferences. It also relies on simple recurrent neural networks while highly efficient neural network models are available for processing sequence data. We propose a new approach for knowledge graph completion that combines bidirectional long short-term memory (BiLSTM) and convolutional neural network modules with an attention mechanism. Given a candidate relation and two entities, we encode paths that connect the entities into a low-dimensional space using a convolutional operation followed by BiLSTM. Then, an attention layer is applied to capture the semantic correlation between a candidate relation and each path between two entities and attentively extract reasoning evidence from the representation of multiple paths to predict whether the entities should be connected by the candidate relation. We extend our model to perform multistep reasoning over path representations in an embedding space. A recurrent neural network is designed to repeatedly interact with an attention module to derive logical inference from the representation of multiple paths. We perform link prediction tasks on several knowledge graphs and show that our method achieves better performance compared with recent state-of-the-art path-reasoning methods. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Jagvaral, Batselem; Lee, Wan-Kon; Roh, Jae-Seung; Kim, Min-Sung; Park, Young Tack] Soongsil Univ, Sch Comp Sci & Engn, Seoul 06978, South Korea.
RP Park, YT (通讯作者)，Soongsil Univ, Sch Comp Sci & Engn, Seoul 06978, South Korea.
EM selmee006@gmail.com; leewangon@gmail.com; rjs951001@gmail.com;
   ms0510k@gmail.com; park@ssu.ac.kr
RI jagvaral, batselem/AAW-8594-2020
FU Institute for Information & communications Technology Promotion (IITP);
   Korea government (MSIT) [2019000067]
FX This work was supported by Institute for Information & communications
   Technology Promotion (IITP) and funded by the Korea government (MSIT)
   [grant number 2019000067] (Semantic Analysis Reasoning Methods for
   Automatic Completion of Large Scale Knowledge Graph).
CR Bandanau D., 2015, ARXIV14090473
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bouchard G., 2015, AAAI SPRING S
   Das R., 2018, INT C LEARN REPR ICL
   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
   Gan Zhe, 2019, ARXIV190200579
   Gardner M., 2013, P 2013 C EMP METH NA, P833
   Gardner M., 2015, P 2015 C EMP METH NA, P1488, DOI DOI 10.18653/V1/D15-1173
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Jiang X., 2017, P MACH LEARN RES, P590
   Kok S., 2007, P 24 INT C MACH LEAR, P433, DOI DOI 10.1145/1273496.1273551
   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
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Liu CX, 2017, DESTECH TRANS COMP, P566
   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
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sukhbaatar S., 2015, ADV NEURAL INFORM PR, P2440
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499, DOI DOI 10.18653/V1/D15-1174
   Trouillon T, 2017, J MACH LEARN RES, V18
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   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 Q, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1308
   Yang Bishan, 2015, ICRL, P12
   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 30
TC 16
Z9 17
U1 17
U2 259
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 MAR 15
PY 2020
VL 142
AR 112960
DI 10.1016/j.eswa.2019.112960
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 JQ2AZ
UT WOS:000498755200007
DA 2022-08-23
ER

PT J
AU Tempelmeier, N
   Demidova, E
AF Tempelmeier, Nicolas
   Demidova, Elena
TI Linking OpenStreetMap with knowledge graphs - Link discovery for
   schema-agnostic volunteered geographic information
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
ID BASE; WEB
AB Representations of geographic entities captured in popular knowledge graphs such as Wikidata and DBpedia are often incomplete. OpenStreetMap (OSM) is a rich source of openly available, volunteered geographic information that has a high potential to complement these representations. However, identity links between the knowledge graph entities and OSM nodes are still rare. The problem of link discovery in these settings is particularly challenging due to the lack of a strict schema and heterogeneity of the user-defined node representations in OSM. In this article, we propose OSM2KG a novel link discovery approach to predict identity links between OSM nodes and geographic entities in a knowledge graph. The core of the OSM2KG approach is a novel latent, compact representation of OSM nodes that captures semantic node similarity in an embedding. OSM2KG adopts this latent representation to train a supervised model for link prediction and utilises existing links between OSM and knowledge graphs for training. Our experiments conducted on several OSM datasets, as well as the Wikidata and DBpedia knowledge graphs, demonstrate that OSM2KG can reliably discover identity links. OSM2KG achieves an F1 score of 92.05% on Wikidata and of 94.17% on DBpedia on average, which corresponds to a 21.82 percentage points increase in F1 score on Wikidata compared to the best performing baselines. (c) 2020 Elsevier B.V. All rights reserved.
C1 [Tempelmeier, Nicolas; Demidova, Elena] Leibniz Univ Hannover, Res Ctr L3S, Hannover, Germany.
   [Demidova, Elena] Univ Bonn, Data Sci & Intelligent Syst Grp DSIS, Bonn, Germany.
RP Tempelmeier, N (通讯作者)，Leibniz Univ Hannover, Res Ctr L3S, Hannover, Germany.
EM tempelmeier@L3S.de; elena.demidova@cs.uni-bonn.de
RI Demidova, Elena/G-4760-2017
OI Demidova, Elena/0000-0002-5134-9072
FU DFG, German Research Foundation ("WorldKG'') [DE 2299/2-1, 424985896];
   Federal Ministry of Education and Research (BMBF), Germany [01IS18054,
   02K15A040]; Federal Ministry for Economic Affairs and Energy (BMWi),
   Germany [01ME19009B]
FX This work is partially funded by the DFG, German Research Foundation
   ("WorldKG'', DE 2299/2-1, 424985896), the Federal Ministry of Education
   and Research (BMBF), Germany ("SimpleML'', 01IS18054),
   ("Data4UrbanMobility'', 02K15A040), and the Federal Ministry for
   Economic Affairs and Energy (BMWi), Germany ("d-E-mand'', 01ME19009B).
CR Ahmed AF, 2018, PROCEDIA COMPUT SCI, V137, P139, DOI 10.1016/j.procs.2018.09.014
   Arsanjani JJ, 2015, LECT NOTES GEOINF CA, P1, DOI 10.1007/978-3-319-14280-7_1
   Bergstra J, 2012, J MACH LEARN RES, V13, P281
   Cardoso SD, 2016, FUTURE GENER COMP SY, V54, P389, DOI 10.1016/j.future.2015.05.006
   Chawla NV, 2002, J ARTIF INTELL RES, V16, P321, DOI 10.1613/jair.953
   Daiber J., 2013, P 9 INT C SEM SYST, P121, DOI 10.1145/2506182.2506198
   Dessi A, 2016, FUTURE GENER COMP SY, V54, P366, DOI 10.1016/j.future.2015.04.018
   Farber M, 2018, SEMANT WEB, V9
   Feng SS, 2017, AAAI CONF ARTIF INTE, P102
   Ganesh A., 2016, SCALING OPENSTREETMA
   Goodchild MF, 2007, GEOJOURNAL, V69, P211, DOI 10.1007/s10708-007-9111-y
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Gottschalk S, 2019, SEMANT WEB, V10, P1039, DOI 10.3233/SW-190355
   Gritta Milan, 2018, Lang Resour Eval, V52, P603, DOI 10.1007/s10579-017-9385-8
   Guttman A, 1984, P ACM SIGMOD INT C M, V14, P47, DOI DOI 10.1145/602259.602266
   Herzog D, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P96, DOI 10.1145/3308560.3317052
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Huang LW, 2020, FUTURE GENER COMP SY, V108, P1119, DOI 10.1016/j.future.2017.12.020
   Huber S, 2016, STATA J, V16, P416, DOI 10.1177/1536867X1601600209
   Karalis N, 2019, LECT NOTES COMPUT SC, V11779, P181, DOI 10.1007/978-3-030-30796-7_12
   Kejriwal M, 2017, LECT NOTES COMPUT SC, V10588, P139, DOI 10.1007/978-3-319-68204-4_14
   KIMMEL R, 1995, IEEE T PATTERN ANAL, V17, P635, DOI 10.1109/34.387512
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Liu W, 2018, FUTURE GENER COMP SY, V83, P183, DOI 10.1016/j.future.2017.12.014
   Louppe G., 2013, NIPS 13 P 26 INT C N, V26, P431
   Ma C, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P697, DOI 10.1145/3269206.3271733
   Manning C.D., 2008, INTRO INFORM RETRIEV
   Mikolov T., 2013, ADV NEURAL INFORM PR, V2, P3111, DOI DOI 10.1162/JMLR.2003.3.4-5.951
   Nentwig M, 2017, SEMANT WEB, V8, P419, DOI 10.3233/SW-150210
   Ngomo A.N., 2011, P 22 INT JOINT C ART, P2312, DOI DOI 10.5591/978-1-57735-516-8/IJCAI11-385
   Paulheim H, 2014, INT J SEMANT WEB INF, V10, P63, DOI 10.4018/ijswis.2014040104
   Punjani D, 2018, PROCEEDINGS OF THE 12TH WORKSHOP ON GEOGRAPHIC INFORMATION RETRIEVAL (GIR'18), DOI 10.1145/3281354.3281362
   Saveta T., 2017, JOINT P BLINK 2017 I, V1932
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Sherif MA, 2017, AAAI CONF ARTIF INTE, P175
   Sherif MA, 2017, LECT NOTES COMPUT SC, V10249, P103, DOI 10.1007/978-3-319-58068-5_7
   Smeros P., 2016, P WORKSH LINK DAT WE, V1593
   Stadler C, 2012, SEMANT WEB, V3, P333, DOI 10.3233/SW-2011-0052
   Touya G, 2015, LECT NOTES GEOINF CA, P81, DOI 10.1007/978-3-319-14280-7_5
   Volz J, 2009, CEUR WORKSHOP P, V538
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang HJ, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P237, DOI 10.1145/3132847.3133006
   Winkler W.E., 1999, TECH REP
   Xie M, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P15, DOI 10.1145/29833213.2983711
   Yang C, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3420, DOI 10.1145/3308558.3313456
   Yang DQ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2147, DOI 10.1145/3308558.3313635
NR 46
TC 7
Z9 7
U1 1
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 MAR
PY 2021
VL 116
BP 349
EP 364
DI 10.1016/j.future.2020.11.003
PG 16
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PG7WJ
UT WOS:000599941000005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Singh, K
   Lytra, I
   Radhakrishna, AS
   Shekarpour, S
   Vidal, ME
   Lehmann, J
AF Singh, Kuldeep
   Lytra, Ioanna
   Radhakrishna, Arun Sethupat
   Shekarpour, Saeedeh
   Vidal, Maria-Esther
   Lehmann, Jens
TI No one is perfect: Analysing the performance of question answering
   components over the DBpedia knowledge graph
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Question answering; Knowledge graph; Entity linking; Relation
   extraction; Relation linking; Experiment and analysis
ID LINKING; WEB
AB Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of Question Answering for user interaction. Existing QA systems have been extensively evaluated as black boxes and their performance has been characterised in terms of average results over all the questions of benchmarking datasets (i.e. macro evaluation). Albeit informative, macro evaluation studies do not provide evidence about QA components' strengths and concrete weaknesses. Therefore, the objective of this article is to analyse and micro evaluate available QA components in order to comprehend which question characteristics impact on their performance. For this, we measure at question level and with respect to different question features the accuracy of 29 components reused in QA frameworks for the DBpedia knowledge graph using state-of-the-art benchmarks. As a result, we provide a perspective on collective failure cases, study the similarities and synergies among QA components for different component types and suggest their characteristics preventing them from effectively solving the corresponding QA tasks. Finally, based on these extensive results, we present conclusive insights for future challenges and research directions in the field of Question Answering over knowledge graphs. (C) 2020 Published by Elsevier B.V.
C1 [Singh, Kuldeep; Lytra, Ioanna; Lehmann, Jens] Univ Bonn, Bonn, Germany.
   [Singh, Kuldeep; Lytra, Ioanna; Lehmann, Jens] Fraunhofer IAIS, St Augustin, Germany.
   [Radhakrishna, Arun Sethupat] Univ Minnesota, Minneapolis, MN 55455 USA.
   [Shekarpour, Saeedeh] Univ Dayton, Dayton, OH 45469 USA.
   [Vidal, Maria-Esther] TIB Leibniz Informat Ctr Sci & Technol, Hannover, Germany.
   [Vidal, Maria-Esther] L3S Res Ctr, Hannover, Germany.
RP Lytra, I (通讯作者)，Univ Bonn, Bonn, Germany.; Lytra, I (通讯作者)，Fraunhofer IAIS, St Augustin, Germany.
EM kuldeep.singh@iais.fraunhofer.de; ioanna.lytra@cs.uni-bonn.de;
   sethu021@umn.edu; sshekarpour1@udayton.edu; maria.vidal@tib.eu;
   jens.lehmann@cs.uni-bonn.de
FU EU [642795]
FX This work has received funding from the EU H2020 R&I programme for the
   Marie Sklodowska-Curie action WDAqua (GA No 642795).
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Ben Abacha A, 2015, INFORM PROCESS MANAG, V51, P570, DOI 10.1016/j.ipm.2015.04.006
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Choi KS, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1433, DOI 10.1145/3077136.3084372
   Cimiano P, 2013, LECT NOTES COMPUT SC, V8138, P321, DOI 10.1007/978-3-642-40802-1_30
   Cui WY, 2017, PROC VLDB ENDOW, V10, P565, DOI 10.14778/3055540.3055549
   Dai Zhiqiang, 2016, Electrical Measurement and Instrumentation, V53, P1
   Derczynski L, 2015, INFORM PROCESS MANAG, V51, P32, DOI 10.1016/j.ipm.2014.10.006
   Diefenbach D, 2018, KNOWL INF SYST, V55, P529, DOI 10.1007/s10115-017-1100-y
   Dojchinovski Milan, 2013, Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2013. Proceedings: LNCS 8190, P654, DOI 10.1007/978-3-642-40994-3_48
   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
   Echegoyen G, 2019, PROCES LENG NAT, P121, DOI 10.26342/2019-63-13
   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
   Ferrandez O, 2011, J WEB SEMANT, V9, P137, DOI 10.1016/j.websem.2011.01.002
   Finkel J.R., 2005, P 43 ANN M ASS COMP, P363, DOI DOI 10.3115/1219840.1219885
   Hazrina S, 2017, INFORM PROCESS MANAG, V53, P52, DOI 10.1016/j.ipm.2016.06.006
   Hoffart J., 2011, P EMNLP, DOI DOI 10.3115/V1/D11-1072
   Huang Z., 2009, P 2009 C EMP METH NA, P543
   Kim J., 2016, COLING 2016, P161
   Li F, 2014, PROC VLDB ENDOW, V8, P73, DOI 10.14778/2735461.2735468
   Lopez V, 2013, J WEB SEMANT, V21, P3, DOI 10.1016/j.websem.2013.05.006
   Martinez P, 2016, COMPUT IND, V78, P43, DOI 10.1016/j.compind.2015.10.006
   Marx E, 2014, P 10 INT C SEM SYST, P57, DOI DOI 10.1145/2660517.2660519
   Mazzeo G. M, 2016, EDBT, P608, DOI DOI 10.5441/002/EDBT.2016.60
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mohasseb A, 2018, INFORM PROCESS MANAG, V54, P1228, DOI 10.1016/j.ipm.2018.05.001
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Mulang I. O., 2017, P 13 INT C SEM SYST, P89, DOI DOI 10.1145/3132218.3132229
   Pan JZ, 2019, LECT NOTES COMPUT SC, V11778, P523, DOI 10.1007/978-3-030-30793-6_30
   Perera S., 2015, P 4 JOINT C LEX COMP, P228, DOI 10.18653/v1/S15-1028
   Perera S, 2016, LECT NOTES COMPUT SC, V9678, P118, DOI 10.1007/978-3-319-34129-3_8
   Reinanda R, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P771, DOI 10.1145/2983323.2983728
   Rizzo G, 2014, LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P4593
   ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7
   Sakor A, 2019, P 2019 C N AM CHAPT, P2336, DOI DOI 10.18653/V1/N19-1243
   Saleem M., 2017, CEUR WS
   Shekarpour S, 2017, AAAI CONF ARTIF INTE, P3936
   Shekarpour S, 2015, J WEB SEMANT, V30, P39, DOI 10.1016/j.websem.2014.06.002
   Shin S, 2019, INFORM PROCESS MANAG, V56, P445, DOI 10.1016/j.ipm.2018.12.003
   Simov K., 2016, P 8 GLOB WORDNET C, P391
   Singh Kuldeep, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P624, DOI 10.1007/978-3-319-93417-4_40
   Singh K, 2021, SYNTHESE, V198, P5477, DOI 10.1007/s11229-019-02416-1
   Singh K, 2019, LECT NOTES COMPUT SC, V11779, P277, DOI 10.1007/978-3-030-30796-7_18
   Singh K, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1247, DOI 10.1145/3178876.3186023
   Singh K, 2016, LECT NOTES COMPUT SC, V9989, P183, DOI 10.1007/978-3-319-47602-5_36
   Srivastava U.C., 2017, Asian Journal of Experimental Sciences, V31, P1
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Trivedi P, 2017, LECT NOTES COMPUT SC, V10588, P210, DOI 10.1007/978-3-319-68204-4_22
   Unger C., 2015, CEUR WS
   Usbeck R., 2017, SEMANT WEB J
   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
   Vakulenko S, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1431, DOI 10.1145/3357384.3358026
   Voorhees EM, 2000, INFORM PROCESS MANAG, V36, P3, DOI 10.1016/S0306-4573(99)00043-6
   Vrandecic D., 2012, P 21 INT C WORLD WID, P1063, DOI [DOI 10.1145/2187980.2188242, 10.1145/2187980.2188242]
   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
   Zou L, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P313, DOI 10.1145/2588555.2610525
NR 61
TC 6
Z9 6
U1 3
U2 10
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 DEC
PY 2020
VL 65
AR 100594
DI 10.1016/j.websem.2020.100594
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA PE4MD
UT WOS:000598338300004
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Korn, D
   Thieme, AJ
   Alves, VM
   Yeakey, M
   Borba, JVVB
   Capuzzi, SJ
   Fecho, K
   Bizon, C
   Edwards, SW
   Chirkova, R
   Colvis, CM
   Southall, NT
   Austin, CP
   Muratov, EN
   Tropsha, A
AF Korn, Daniel
   Thieme, Andrew J.
   Alves, Vinicius M.
   Yeakey, Michael
   Borba, Joyce V. V. B.
   Capuzzi, Stephen J.
   Fecho, Karamarie
   Bizon, Chris
   Edwards, Stephen W.
   Chirkova, Rada
   Colvis, Christine M.
   Southall, Noel T.
   Austin, Christopher P.
   Muratov, Eugene N.
   Tropsha, Alexander
TI Defining clinical outcome pathways
SO DRUG DISCOVERY TODAY
LA English
DT Review
DE Clinical outcome pathways; Key molecular events; Drug repurposing;
   Knowledge graphs
ID KIT INHIBITION; MAST-CELLS; C-KIT; IMATINIB; DRUG; METFORMIN; DISEASE
AB Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: molecular initiating event (MIE) intermediate event(s) clinical outcome. We illustrate the concept with COP examples both for primary and alternative (i.e., drug repurposing) therapeutic applications. We also describe the elucidation of COPs for several drugs of interest using the publicly accessible Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP) biomedical knowledge graph-mining tool. We propose that broader use of COP uncovered with the help of biomedical knowledge graph mining will likely accelerate drug discovery and repurposing efforts.
C1 [Korn, Daniel] Univ North Carolina Chapel Hill, Dept Comp Sci, Chapel Hill, NC USA.
   [Korn, Daniel; Thieme, Andrew J.; Alves, Vinicius M.; Borba, Joyce V. V. B.; Capuzzi, Stephen J.; Muratov, Eugene N.; Tropsha, Alexander] Univ North Carolina Chapel Hill, UNC Eshelman Sch Pharm, Chapel Hill, NC 27599 USA.
   [Yeakey, Michael] Massachusetts Coll Pharm & Hlth Sci, Boston, MA USA.
   [Fecho, Karamarie; Bizon, Chris] Univ North Carolina Chapel Hill, Renaissance Comp Inst, Chapel Hill, NC USA.
   [Edwards, Stephen W.] RTI Int, Res Triangle Pk, NC USA.
   [Chirkova, Rada; Southall, Noel T.; Austin, Christopher P.] North Carolina State Univ, Dept Comp Sci, Raleigh, NC USA.
   [Colvis, Christine M.] NIH, Natl Ctr Adv Translat Sci, Bethesda, MD USA.
RP Muratov, EN; Tropsha, A (通讯作者)，Univ North Carolina Chapel Hill, UNC Eshelman Sch Pharm, Chapel Hill, NC 27599 USA.
EM murik@email.unc.edu; alex_tropsha@unc.edu
RI Alves, Vinicius M./G-7955-2015; Southall, Noel/H-8991-2012
OI Alves, Vinicius M./0000-0002-6182-1748; Southall,
   Noel/0000-0003-4500-880X; Edwards, Stephen/0000-0002-7985-111X
FU National Institutes of Health [OT2TR002514]
FX Acknowledegements Authors from UNC-Chapel Hill were supported by a grant
   from the National Institutes of Health (Grant OT2TR002514) . All authors
   are grateful to their colleagues in the Biomedical Data Translator
   Program (https://ncats.nih.gov/translator) for multi-ple discussions
   that contributed to the development of the COP concept and the formalism
   described herein.
CR Alves V.M., 2020, APPROACHING COMPLEX, P77
   Alves VM, 2022, DRUG DISCOV TODAY, V27, P490, DOI 10.1016/j.drudis.2021.10.014
   Avram S, 2021, NUCLEIC ACIDS RES, V49, pD1160, DOI 10.1093/nar/gkaa997
   Bailey CJ, 2017, DIABETOLOGIA, V60, P1566, DOI 10.1007/s00125-017-4318-z
   Bizon C, 2019, J CHEM INF MODEL, V59, P4968, DOI 10.1021/acs.jcim.9b00683
   Blatt J, 2014, DRUG DISCOV TODAY, V19, P1696, DOI 10.1016/j.drudis.2014.08.003
   Bobrowski T, 2021, MOL THER, V29, P873, DOI 10.1016/j.ymthe.2020.12.016
   Buchdunger E, 2000, J PHARMACOL EXP THER, V295, P139
   Cahill KN, 2017, J ALLERGY CLIN IMMUN, V139, pAB169, DOI 10.1016/j.jaci.2016.12.553
   Cahill KN, 2017, NEW ENGL J MED, V376, P1911, DOI 10.1056/NEJMoa1613125
   Calcaterra NE, 2014, ACS CHEM NEUROSCI, V5, P253, DOI 10.1021/cn5000056
   Capdeville R, 2002, NAT REV DRUG DISCOV, V1, P493, DOI 10.1038/nrd839
   Capuzzi SJ, 2018, J CHEM INF MODEL, V58, P212, DOI 10.1021/acs.jcim.7b00589
   Dai J, 2014, CHEM-BIOL INTERACT, V216, P34, DOI 10.1016/j.cbi.2014.03.013
   Freedberg DE, 2020, GASTROENTEROLOGY, V159, P1129, DOI 10.1053/j.gastro.2020.05.053
   Frezza AM, 2019, CURR OPIN ONCOL, V31, P114, DOI 10.1097/CCO.0000000000000502
   Fuehrer NE, 2009, ARCH PATHOL LAB MED, V133, P1420, DOI 10.1043/1543-2165-133.9.1420
   Hutchinson Michael, 2007, Ther Clin Risk Manag, V3, P259, DOI 10.2147/tcrm.2007.3.2.259
   Kinney MA, 2019, NAT BIOTECHNOL, V37, P810, DOI 10.1038/s41587-019-0159-2
   Kinsman L, 2010, BMC MED, V8, DOI 10.1186/1741-7015-8-31
   Lee JL, 2006, DIGEST DIS SCI, V51, P1043, DOI 10.1007/s10620-006-8003-1
   Lipscomb CE, 2000, B MED LIBR ASSOC, V88, P265
   Longhi A, METFORMIN MAINTENANC
   Lopes LF, 2010, J CELL MOL MED, V14, P42, DOI 10.1111/j.1582-4934.2009.00983.x
   Lu ZY, 2011, DATABASE-OXFORD, DOI 10.1093/database/baq036
   Malone RW, 2021, FRONT PHARMACOL, V12, DOI 10.3389/fphar.2021.633680
   Marie PJ, 2015, BONE CANCER: PRIMARY BONE CANCER AND BONE METASTASES, 2ND EDITION, P39, DOI 10.1016/B978-0-12-416721-6.00004-2
   Mestres J, 2008, NAT BIOTECHNOL, V26, P983, DOI 10.1038/nbt0908-983
   Molenaar RJ, 2017, BMJ OPEN, V7, DOI 10.1136/bmjopen-2016-014961
   Morgan MM, 2016, PHARMACOL THERAPEUT, V165, P79, DOI 10.1016/j.pharmthera.2016.05.007
   Mungall CJ, 2017, NUCLEIC ACIDS RES, V45, pD712, DOI 10.1093/nar/gkw1128
   National Center for Advancing Translational Sciences, TRANSL REAS API
   National Center for Advancing Translational Sciences, 2017, BIOM DAT TRANSL TECH
   National Toxicology Program, 2021, ADV OUTC PATHW
   Nicholson DN, 2020, COMPUT STRUCT BIOTEC, V18, P1414, DOI 10.1016/j.csbj.2020.05.017
   Oprea TI, 2007, NAT CHEM BIOL, V3, P447, DOI 10.1038/nchembio0807-447
   Organisation for Economic Cooperation and Development, 2020, OECD SER ADV OUTC PA
   Pedersen MF, 2020, NEUROPHARMACOLOGY, V166, DOI [10.1016/j.neuropharrn.2019.107718, 10.1016/j.neuropharm.2019.107718]
   Pryor R, 2015, BIOCHEM J, V471, P307, DOI 10.1042/BJ20150497
   Rena G, 2017, DIABETOLOGIA, V60, P1577, DOI 10.1007/s00125-017-4342-z
   Richardson P, 2020, LANCET, V395, pE30, DOI 10.1016/S0140-6736(20)30304-4
   Roth BL, 2004, NAT REV DRUG DISCOV, V3, P353, DOI 10.1038/nrd1346
   SABESIN SM, 1991, ARCH INTERN MED, V151, P2394, DOI 10.1001/archinte.151.12.2394
   Schreurs AS, 2020, AM J PHYSIOL-CELL PH, V319, pC734, DOI 10.1152/ajpcell.00068.2020
   Society for Advancement of AOPs, US
   Tollefsen KE, 2014, REGUL TOXICOL PHARM, V70, P629, DOI 10.1016/j.yrtph.2014.09.009
NR 46
TC 0
Z9 0
U1 3
U2 3
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1359-6446
EI 1878-5832
J9 DRUG DISCOV TODAY
JI Drug Discov. Today
PD JUN
PY 2022
VL 27
IS 6
BP 1671
EP 1678
DI 10.1016/j.drudis.2022.02.008
PG 8
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA 2B2UQ
UT WOS:000810048400015
PM 35182735
DA 2022-08-23
ER

PT J
AU Xu, YW
   Zhang, HJ
   Cheng, K
   Liao, XL
   Zhang, ZX
   Li, YB
AF Xu, You-Wei
   Zhang, Hong-Jun
   Cheng, Kai
   Liao, Xiang-Lin
   Zhang, Zi-Xuan
   Li, Yun-Bo
TI Knowledge graph embedding with entity attributes using hypergraph neural
   networks
SO INTELLIGENT DATA ANALYSIS
LA English
DT Article
DE Attribute network; hypergraph neural network; knowledge graph embedding;
   long-tail entity
AB Knowledge graph embedding is aimed at capturing the semantic information of entities by modeling the structural information between entities. For long-tail entities which lack sufficient structural information, general knowledge graph embedding models often show relatively low performance in link prediction. In order to solve such problems, this paper proposes a general knowledge graph embedding framework to learn the structural information as well as the attribute information of the entities simultaneously. Under this framework, a H-AKRL (Hypergraph Neural Networks based Attribute-embodied Knowledge Representation Learning) model is put forward, where the hypergraph neural network is used to model the correlation between entities and attributes at a higher level. The complementary relationship between attribute information and structural information is taken full advantage of, enabling H-AKRL to finally achieve the goal of improving link prediction performance. Experiments on multiple real-world data sets show that the H-AKRL model has significantly improved the link prediction performance, especially in the embeddings of long tail entities.
C1 [Xu, You-Wei; Zhang, Hong-Jun; Cheng, Kai; Liao, Xiang-Lin; Zhang, Zi-Xuan; Li, Yun-Bo] Army Engn Univ PLA, Nanjing, Jiangsu, Peoples R China.
RP Zhang, HJ; Zhang, ZX (通讯作者)，Army Engn Univ PLA, Nanjing, Jiangsu, Peoples R China.
EM 786617160@qq.com; ariadneea@163.com
FU National Natural Science Foundation of China [61806221]
FX This work was supported by the National Natural Science Foundation of
   China (61806221).
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bai S, 2021, PATTERN RECOGN, V110, DOI 10.1016/j.patcog.2020.107637
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, NEURAL INFORM PROCES
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Chen MH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3998
   Nguyen DQ, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3429
   Das R., 2018, INT C LEARNING REPRE
   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
   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
   Feng YF, 2019, AAAI CONF ARTIF INTE, P3558
   Garcia-Duran A., 2017, ARXIV PREPRINT ARXIV
   Guan NN, 2019, KNOWL-BASED SYST, V164, P38, DOI 10.1016/j.knosys.2018.10.008
   Guo LB, 2019, PR MACH LEARN RES, V97
   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
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Hong R., 2019, IEEE T SYSTEMS MAN C
   Hou CB, 2020, NEUROCOMPUTING, V409, P231, DOI 10.1016/j.neucom.2020.05.080
   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 JW, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2635
   Kingma D. P., 2017, ARXIV
   Lin Y., 2016, P 25 INT JOINT C ART, P2866
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Liu Qiang, 2016, ADV NEURAL INFORM PR, P2370
   Lv X., 2018, P 2018 C EMPIRICAL M
   Mahdisoltani F., 2014, 7 BIENN C INN DAT SY
   Meilicke C, 2018, LECT NOTES COMPUT SC, V11136, P3, DOI 10.1007/978-3-030-00671-6_1
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mitchell T, 2018, COMMUN ACM, V61, P103, DOI 10.1145/3191513
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   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., 2018, 2018 C N AM CHAPTER, P327
   Nickel M, 2011, INT C INT C MACH LEA
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Pezeshkpour P., 2018, P 2018 C EMPIRICAL M
   Rastogi A, 2020, AAAI CONF ARTIF INTE, V34, P8689
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Schlichtkrull M., 2018, EUROPEAN SEMANTIC WE
   Shang C, 2019, AAAI CONF ARTIF INTE, P3060
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Socher R., 2013, NIPS
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Z., 2018, INT C LEARNING REPRE
   Tay Y, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1029, DOI 10.1145/3132847.3132937
   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
   Vashishth Shikhar, 2018, P 2018 C EMP METH NA, P1257
   Vashishth Shikhar, 2019, P ICLR
   Vu T, 2019, P 2019 C N AM CHAPTE
   Wang R, 2020, IEEE ACCESS, V8, P5212, DOI 10.1109/ACCESS.2019.2963367
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu L., 2021, KNOWL-BASED SYST
   Wu YR, 2018, REPRESENTATION LEARNING FOR NLP, P132
   Xiao H., 2016, P 25 INT JOINT C ART, P1315
   Xie RB, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3140
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yang B., 2015, ICLR
   Zhang QH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5429
   Zhang ZY, 2020, AAAI CONF ARTIF INTE, V34, P9620
   Zhang ZW, 2020, IEEE ACCESS, V8, P7435, DOI 10.1109/ACCESS.2020.2963990
   Zhou K, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1006, DOI 10.1145/3394486.3403143
NR 67
TC 0
Z9 0
U1 1
U2 1
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1088-467X
EI 1571-4128
J9 INTELL DATA ANAL
JI Intell. Data Anal.
PY 2022
VL 26
IS 4
BP 959
EP 975
DI 10.3233/IDA-216007
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Z0ME
UT WOS:000826278500009
DA 2022-08-23
ER

PT J
AU Feng, JZ
   Wei, QK
   Cui, JM
   Chen, J
AF Feng, Jianzhou
   Wei, Qikai
   Cui, Jinman
   Chen, Jing
TI Novel translation knowledge graph completion model based on 2D
   convolution
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Knowledge graph completion; Convolutional neural network; Translation
   model; Entity relation matrix
AB The knowledge graph completion task involves predicting missing entities and relations in a knowledge graph. Many models have achieved good results, but they have become increasingly complex. In this study, we propose a simple translation-based model that relies on the fact that the multiplication of subjects and relations is approximately equal to the object. First, we utilize embeddings to represent entities and relations. Second, we perform vector multiplication on subject embedding and relation embedding to generate a 2D matrix and achieve full fusion of embedding at the element level. Third, we adopt a convolutional neural network on the 2D matrix. Thereafter, we can generate feature maps, which are then spliced into a 1D feature vector. The feature vector is transformed into predicted object embedding through a fully connected operation. Finally, we use the scoring function to score the candidate triples. Experimental results strongly demonstrate that the translation knowledge graph completion model based on 2D convolution achieves state-of-the-art results compared with the baseline.
C1 [Feng, Jianzhou; Wei, Qikai; Cui, Jinman; Chen, Jing] Yanshan Univ, Sch Informat Sci & Engn, Qinhuangdao 066004, Hebei, Peoples R China.
   [Feng, Jianzhou; Wei, Qikai; Cui, Jinman; Chen, Jing] Yanshan Univ, Software Engn Key Lab Hebei Prov, Qinhuangdao 066004, Hebei, Peoples R China.
RP Wei, QK (通讯作者)，Yanshan Univ, Sch Informat Sci & Engn, Qinhuangdao 066004, Hebei, Peoples R China.; Wei, QK (通讯作者)，Yanshan Univ, Software Engn Key Lab Hebei Prov, Qinhuangdao 066004, Hebei, Peoples R China.
EM fjzwxh@ysu.edu.cn; mrweiqk@163.com; cuijinman@163.com;
   xychenjing@ysu.edu.cn
FU National Natural Science Foundation of China [61602401]; Scientific and
   technological research projects of colleges and universities in Hebei
   Province [ZD2019004]; Nature Scientist Fundation of Hebei Province
   [F2019203157]
FX This work is supported by the National Natural Science Foundation of
   China (61602401), Scientific and technological research projects of
   colleges and universities in Hebei Province (QN2018074), Scientific and
   technological research projects of colleges and universities in Hebei
   Province (ZD2019004) and the Nature Scientist Fundation of Hebei
   Province (F2019203157).
CR Balazevic I, 2019, LECT NOTES COMPUT SC, V11731, P553, DOI 10.1007/978-3-030-30493-5_52
   Bansal T, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4387
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Das R., 2017, ARXIV171105851
   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
   Ioffe S., 2015, P INT C MACH LEARN L, VVolume 37, P448, DOI DOI 10.1007/S13398-014-0173-7.2
   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
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kingma D. P., 2017, ARXIV
   Kok S., 2007, P 24 INT C MACH LEAR, P433, DOI DOI 10.1145/1273496.1273551
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Mouromtsev D, 2018, LECT NOTES COMPUT SC, V11155, P34, DOI 10.1007/978-3-319-98192-5_7
   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
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Sheng YX, 2019, LECT NOTES COMPUT SC, V11955, P340, DOI 10.1007/978-3-030-36718-3_29
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   Szegedy C, 2016, P IEEE C COMP VIS PA, P2818
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trouillon T, 2017, J MACH LEARN RES, V18
   Yang B., 2015, P INT C LEARN REPR I
   Zhang W, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P96, DOI 10.1145/3289600.3291014
NR 23
TC 0
Z9 0
U1 5
U2 13
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 FEB
PY 2022
VL 52
IS 3
BP 3266
EP 3275
DI 10.1007/s10489-021-02438-8
EA JUL 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YY8RB
UT WOS:000669164200001
DA 2022-08-23
ER

PT J
AU Zhang, X
   Yang, QQ
   Ding, JR
   Wang, ZY
AF Zhang, Xiang
   Yang, Qingqing
   Ding, Jinru
   Wang, Ziyue
TI Entity Profiling in Knowledge Graphs
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph; entity profiling; representation learning
AB Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs. In this work, we present a novel profiling approach to identify distinctive entity features. The distinctiveness of features is carefully measured by a HAS model, which is a scalable representation learning model to produce a multi-pattern entity embedding. We fully evaluate the quality of entity profiless generated from real KGs. The results show that our approach facilitates human understanding of entities in KGs.
C1 [Zhang, Xiang] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
   [Yang, Qingqing] Southeast Univ Monash Univ Joint Grad Sch, Suzhou 215123, Peoples R China.
   [Ding, Jinru] Southeast Univ, Sch Software Engn, Suzhou 215123, Peoples R China.
   [Zhang, Xiang; Wang, Ziyue] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 211189, Peoples R China.
RP Zhang, X (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.; Zhang, X (通讯作者)，Southeast Univ, Sch Cyber Sci & Engn, Nanjing 211189, Peoples R China.
EM x.zhang@seu.edu.cn
FU National Natural Science Foundation of China [U1736204]; National Key
   Research and Development Program of China [2017YFB1002801,
   2018YFC0830201]; Judicial Big Data Research Center, School of Law at
   Southeast University
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant U1736204, in part by the National Key
   Research and Development Program of China under Grant 2017YFB1002801 and
   Grant 2018YFC0830201, and in part by the Judicial Big Data Research
   Center, School of Law at Southeast University.
CR Al-Shamri MYH, 2016, KNOWL-BASED SYST, V100, P175, DOI 10.1016/j.knosys.2016.03.006
   [Anonymous], [No title captured]
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Azeroual O, 2018, INT J INFORM MANAGE, V41, P50, DOI 10.1016/j.ijinfomgt.2018.02.007
   Cheng G, 2011, LECT NOTES COMPUT SC, V7031, P114, DOI 10.1007/978-3-642-25073-6_8
   Dougherty J., 1995, Machine Learning. Proceedings of the Twelfth International Conference on Machine Learning, P194
   Eyharabide V, 2012, APPL INTELL, V36, P857, DOI 10.1007/s10489-011-0301-4
   Farseev A, 2015, ICMR'15: PROCEEDINGS OF THE 2015 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, P235, DOI 10.1145/2671188.2749381
   Goldberg Y., 2014, ABS14023722 CORR
   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
   Gunaratna K, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1060, DOI 10.24963/ijcai.2017/147
   Gunaratna K, 2015, AAAI CONF ARTIF INTE, P116
   Jeh G., 2002, P 8 ACM SIGKDD INT C, P538, DOI DOI 10.1145/775047.775126
   Katz L., 1953, PSYCHOMETRIKA, Vvol. 18, ppp. 39, DOI DOI 10.1007/BF02289026
   [李兴生 Li Xingsheng], 2003, [系统仿真学报, Journal of System Simulation], V15, P804
   Lu Z., 2016, P 25 INT JOINT C ART, P3804
   Nentwig M, 2016, INT CONF DAT MIN WOR, P194, DOI [10.1109/ICDMW.2016.47, 10.1109/ICDMW.2016.0035]
   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
   Rossi RA, 2015, IEEE T KNOWL DATA EN, V27, P1112, DOI 10.1109/TKDE.2014.2349913
   Rybak J, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1273, DOI 10.1145/2600428.2611190
   Sydow M, 2013, J INTELL INF SYST, V41, P109, DOI 10.1007/s10844-013-0239-6
   Thalhammer A, 2014, LECT NOTES COMPUT SC, V8798, P511, DOI 10.1007/978-3-319-11955-7_76
   Yang J, 2014, 2014 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE, ELECTRONICS AND ELECTRICAL ENGINEERING (ISEEE), VOLS 1-3, P1411
   Zhao P., 2009, P 18 ACM C INF KNOWL, P553, DOI DOI 10.1145/1645953.1646025
   Zheng L, 2018, KNOWL-BASED SYST, V141, P200, DOI 10.1016/j.knosys.2017.11.017
NR 25
TC 3
Z9 3
U1 1
U2 12
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 27257
EP 27266
DI 10.1109/ACCESS.2020.2971567
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LC6TQ
UT WOS:000525466900061
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Chen, PF
   Wang, YS
   Yu, XM
   Feng, RY
AF Chen, Panfeng
   Wang, Yisong
   Yu, Xiaomin
   Feng, Renyan
TI QLogicE: Quantum Logic Empowered Embedding for Knowledge Graph
   Completion
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Quantum logic; Knowledge graph; Knowledge graph completion; Link
   prediction; Combination model
AB Knowledge graph completion (KGC) is an important technique for implicitly identifying missing entities or relations in knowledge graphs (KGs) that are employed in various real-world applications such as question answering, information retrieval, and making recommendations. Knowledge graph embedding (KGE) is a typical KGC approach that embeds entity and relation vectors into a low-dimensional vector space for this purpose. In this paper, we present a novel KGE approach called QLogicE. It integrates translation and quantum embedding to capture features of elements within a fact, in the form of word embedding and the logical relationship among facts over KG, in the form of quantum logic. Extensive experimental results on challenging benchmark datasets confirm that the proposed approach QLogicE achieves impressive and (sometimes) surprising performance on 8 embedding dimensions, whereas state-of-the-art KGE approaches typically achieve their best performance at approximately 200 embedding dimensions. In addition, the proposed model achieves 94.84% Hits@1 on the challenging dataset FB15k237, which is almost twice as good as the best performance reported in this metric. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Chen, Panfeng; Wang, Yisong; Yu, Xiaomin; Feng, Renyan] Guizhou Univ, Dept Comp Sci & Technol, Guiyang, Guizhou, Peoples R China.
   [Chen, Panfeng; Wang, Yisong; Yu, Xiaomin; Feng, Renyan] Guizhou Univ, Inst Artificial Intelligence, Guiyang, Guizhou, Peoples R China.
   [Yu, Xiaomin] Guizhou Univ Finance & Econ, Guizhou Key Lab Econ Syst Simulat, Guiyang, Guizhou, Peoples R China.
RP Chen, PF; Wang, YS (通讯作者)，Guizhou Univ, Dept Comp Sci & Technol, Guiyang, Guizhou, Peoples R China.; Chen, PF; Wang, YS (通讯作者)，Guizhou Univ, Inst Artificial Intelligence, Guiyang, Guizhou, Peoples R China.
EM gzupanda@outlook.com; yswang@gzu.edu.cn; musexiaoyu521@outlook.com;
   renyan_feng@163.com
OI Chen, Panfeng/0000-0002-6155-7043
FU National Natural Science Foundation of China (NSFC) [61976065, U1836205]
FX Acknowledgement This work was supported by the National Natural Science
   Foundation of China (NSFC) under Grant 61976065 and U1836205.
CR Birkhoff G, 1936, ANN MATH, V37, P823, DOI 10.2307/1968621
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Che FH, 2020, AAAI CONF ARTIF INTE, V34, P2774
   Cheng K., 2020, P MACHINE LEARNING R, V119, P1
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Foulis D.J., 1996, ENCY APPL PHYS, V15, P229
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Garg D., 2019, ADV NEURAL INFORM PR, P5595
   Guo S., 2016, P C EMP METH NAT LAN, P192, DOI 10.18653/v1/D16-1019
   Guo S, 2018, AAAI CONF ARTIF INTE, P4816
   Hajek P., 1998, TRENDS LOGIC, V4, DOI [10.1007/978-94-011-5300-3, DOI 10.1007/978-94-011-5300-3]
   Islam MM, 2020, IEEE INT CONF BIG DA, P54, DOI 10.1109/BigData50022.2020.9378055
   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 SP, 2012, IEEE DATA MINING, P912, DOI 10.1109/ICDM.2012.156
   Kemp C., 2006, P 21 NAT C ART INT, P381, DOI DOI 10.1145/1837026.1837061
   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
   Mahdisoltani F., 2015, CIDR 2015
   McCray AT, 2003, COMP FUNCT GENOM, V4, P80, DOI 10.1002/cfg.255
   Meilicke C., 2020, ABS200404412 CORR
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Niu GL, 2020, AAAI CONF ARTIF INTE, V34, P2950
   Palumbo E, 2020, EXPERT SYST APPL, V151, DOI 10.1016/j.eswa.2020.113235
   Qiu YQ, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P474, DOI 10.1145/3336191.3371812
   Qu M, 2019, ADV NEUR IN, V32
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Rocktaschel Tim, 2014, P ACL 2014 WORKSH SE, P45, DOI DOI 10.3115/V1/W14-2409
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Stoica G, 2020, AAAI CONF ARTIF INTE, V34, P3000
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Vardhan LVH, 2020, WWW'20: COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2020, P669, DOI 10.1145/3366424.3391265
   Wan H, 2020, AAAI CONF ARTIF INTE, V34, P3017
   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, 2020, KNOWL-BASED SYST, V197, DOI 10.1016/j.knosys.2020.105910
   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
   Xu LY, 2018, ACM/SIGIR PROCEEDINGS 2018, P1289, DOI 10.1145/3209978.3210169
NR 37
TC 0
Z9 0
U1 5
U2 5
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 2022
VL 239
AR 107963
DI 10.1016/j.knosys.2021.107963
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V9BI
UT WOS:000788633300008
DA 2022-08-23
ER

PT J
AU Sun, K
   Yu, S
   Peng, CY
   Wang, YR
   Alfarraj, O
   Tolba, A
   Xia, F
AF Sun, Ke
   Yu, Shuo
   Peng, Ciyuan
   Wang, Yueru
   Alfarraj, Osama
   Tolba, Amr
   Xia, Feng
TI Relational Structure-Aware Knowledge Graph Representation in Complex
   Space
SO MATHEMATICS
LA English
DT Article
DE message passing; complex space; knowledge representation learning; link
   prediction; triple classification
ID NETWORKS
AB Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification.
C1 [Sun, Ke; Yu, Shuo] Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China.
   [Peng, Ciyuan; Xia, Feng] Federat Univ Australia, Inst Innovat Sci & Sustainabil, Ballarat, Vic 3353, Australia.
   [Wang, Yueru] Natl Tsing Hua Univ, Dept Math, Hsinchu 30013, Taiwan.
   [Alfarraj, Osama; Tolba, Amr] King Saud Univ, Community Coll, Comp Sci Dept, Riyadh 11437, Saudi Arabia.
RP Yu, S (通讯作者)，Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China.
EM sunke@mail.dlut.edu.cn; yushuo@dlut.edu.cn;
   ciyuanp@studentsiederation.edu.au; s108000198@m108.nthu.edu.tw;
   oalfarraj@ksu.edu.sa; atolba@ksu.edu.sa; f.xia@ieee.org
OI Sun, Ke/0000-0002-0231-5802; Peng, Ciyuan/0000-0002-3580-5402
FU King Saud University, Riyadh, Saudi Arabia [RSP-2021/102]; National
   Natural Science Foundation of China [62102060]
FX This work was funded by the Researchers Supporting Project No.
   RSP-2021/102 of King Saud University, Riyadh, Saudi Arabia. This work is
   partially supported by the National Natural Science Foundation of China
   under Grant No. 62102060.
CR Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Chen M, 2020, P INT C MACH LEARN, P1725
   Dai YF, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9050750
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dong L, 2019, ADV NEUR IN, V32
   Fang H, 2022, MATHEMATICS-BASEL, V10, DOI 10.3390/math10091402
   Feng Xia, 2021, IEEE Transactions on Artificial Intelligence, V2, P109, DOI 10.1109/TAI.2021.3076021
   Gilmer J, 2017, PR MACH LEARN RES, V70
   Guo QY, 2020, IEEE T KNOWL DATA EN, V34, P3549, DOI [10.1038/s41366-020-0625-4, 10.1109/TKDE.2020.3028705]
   He QZ, 2020, AAAI CONF ARTIF INTE, V34, P7919
   Hong DF, 2021, IEEE T GEOSCI REMOTE, V59, P5966, DOI 10.1109/TGRS.2020.3015157
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   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
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lin Q, 2022, MATHEMATICS-BASEL, V10, DOI 10.3390/math10081300
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   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, 2021, IEEE T KNOWL DATA EN, V33, P1763, DOI 10.1109/TKDE.2019.2946825
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Petrovska B, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20143906
   Ren J, 2021, IEEE TETCI, V5, P332, DOI 10.1109/TETCI.2021.3067655
   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
   Sun K, 2020, IEEE ACCESS, V8, P205600, DOI 10.1109/ACCESS.2020.3037118
   Sun Z., 2018, P ICLR
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vashishth S, 2020, AAAI CONF ARTIF INTE, V34, P3009
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang W, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3442199
   Wang W, 2021, ACM T WEB, V15, DOI 10.1145/3409736
   Wang WT, 2022, IEEE T SOFTWARE ENG, V48, P1665, DOI 10.1109/TSE.2020.3030745
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Yang B., 2015, P INT C LEARN REPR I
   Zhang CX, 2020, AAAI CONF ARTIF INTE, V34, P3041
   Zhang NY, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P1, DOI 10.1145/3366423.3380089
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
   Zhao F, 2021, IEEE INTERNET THINGS, V8, P16758, DOI 10.1109/JIOT.2020.3039750
NR 40
TC 0
Z9 0
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 JUN
PY 2022
VL 10
IS 11
AR 1930
DI 10.3390/math10111930
PG 16
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA 1Z4YU
UT WOS:000808832900001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Daruna, A
   Gupta, M
   Sridharan, M
   Chernova, S
AF Daruna, Angel
   Gupta, Mehul
   Sridharan, Mohan
   Chernova, Sonia
TI Continual Learning of Knowledge Graph Embeddings
SO IEEE ROBOTICS AND AUTOMATION LETTERS
LA English
DT Article
DE Robots; Knowledge engineering; Semantics; Task analysis; Learning
   systems; Neural networks; Training; Continual learning; representation
   learning
ID FRAMEWORK
AB In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications. However, while robots often observe previously unknown concepts, these representations typically assume that all concepts are known a priori, and incorporating new information requires all concepts to be learned afresh. Our work relaxes this limiting assumption of existing representations and tackles the incremental knowledge graph embedding problem by leveraging the principles of a range of continual learning methods. Through an experimental evaluation with several knowledge graphs and embedding representations, we provide insights about trade-offs for practitioners to match a semantics-driven robotics applications to a suitable continual knowledge graph embedding method.
C1 [Daruna, Angel; Gupta, Mehul; Chernova, Sonia] Georgia Inst Technol, Atlanta, GA 30332 USA.
   [Sridharan, Mohan] Univ Birmingham, Birmingham, W Midlands, England.
RP Daruna, A (通讯作者)，Georgia Inst Technol, Atlanta, GA 30332 USA.
EM adaruna3@gatech.edu; mgupta320@gatech.edu; m.sridharan@bham.ac.uk;
   chernova@cc.gatech.edu
RI Sridharan, Mohan/AAY-7586-2021
OI Sridharan, Mohan/0000-0001-9922-8969; Chernova,
   Sonia/0000-0001-6320-0825
FU US National Science Foundation [IIS 1564080, GRFP DGE-1650044]; US
   Office of Naval Research [N00014-17-1-2434]
FX This work was supported in part by the US National Science Foundation
   under Grants IIS 1564080 and GRFP DGE-1650044 (Daruna, Gupta, Chernova),
   and the US Office of Naval Research award N00014-17-1-2434 (Sridharan).
CR Arkin J, 2020, INT J ROBOT RES, V39, P1279, DOI 10.1177/0278364920917755
   Beetz M, 2018, IEEE INT CONF ROBOT, P512
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bowman S. R., 2016, CONLL, P10, DOI 10.18653/v1/k16-1002
   Cai L, 2018, NAACL, P1470
   Chernova S, 2020, SPR PROC ADV ROBOT, V10, P353, DOI 10.1007/978-3-030-28619-4_29
   Dai YF, 2020, KNOWL-BASED SYST, V190, DOI 10.1016/j.knosys.2019.105165
   Daruna A, 2019, IEEE INT CONF ROBOT, P9777, DOI 10.1109/ICRA.2019.8794070
   Das R., 2018, ICLR
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Diaz-Rodriguez N., 2018, ARXIV181013166
   Fulda N., 2017, P C ROB LEARN, P525
   Hsu Yen-Chang, 2018, ARXIV181012488
   Kazemi SM, 2020, J MACH LEARN RES, V21
   Kirkpatricka J, 2017, P NATL ACAD SCI USA, V114, P3521, DOI 10.1073/pnas.1611835114
   Lesort T, 2020, INFORM FUSION, V58, P52, DOI 10.1016/j.inffus.2019.12.004
   Liu HX, 2017, PR MACH LEARN RES, V70
   Lomonaco V., 2017, P 1 ANN C ROB LEARN, V78, P17
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Parisi GI, 2019, NEURAL NETWORKS, V113, P54, DOI 10.1016/j.neunet.2019.01.012
   Paulius D., P ROB SCI SYST, P2020, DOI [10.15607/RSS.2020.XVI.045, DOI 10.15607/RSS.2020.XVI.045]
   Rusu A.A., 2017, GOOGLE PATENTS
   Saxena A., 2014, ARXIV14120691
   Scalise R, 2019, IEEE INT C INT ROBOT, P4229, DOI 10.1109/IROS40897.2019.8968142
   Shin H, 2017, ADV NEUR IN, V30
   Song HJ, 2018, IEEE ACCESS, V6, P60489, DOI 10.1109/ACCESS.2018.2874656
   Thomason J, 2018, AAAI CONF ARTIF INTE, P5520
   van de Ven Gido M, 2018, ARXIV180910635
   Wang PF, 2018, AAAI CONF ARTIF INTE, P2005
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wu T., 2019, ABS191006708
   Zenke F, 2017, PR MACH LEARN RES, V70
   Zhu YK, 2014, LECT NOTES COMPUT SC, V8690, P408, DOI 10.1007/978-3-319-10605-2_27
NR 33
TC 4
Z9 4
U1 3
U2 34
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2377-3766
J9 IEEE ROBOT AUTOM LET
JI IEEE Robot. Autom. Lett.
PD APR
PY 2021
VL 6
IS 2
BP 1128
EP 1135
DI 10.1109/LRA.2021.3056071
PG 8
WC Robotics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Robotics
GA QJ0KZ
UT WOS:000619380200021
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Wang, ZK
   Li, LJ
   Zeng, D
   Wu, XF
AF Wang, Zikang
   Li, Linjing
   Zeng, Daniel
   Wu, Xiaofei
TI Incorporating prior knowledge from counterfactuals into knowledge graph
   reasoning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Multi-hop reasoning; Counterfactual
ID INFERENCE
AB Knowledge graph reasoning aims to find the missing links in knowledge graphs and is an important fundamental task. Existing methods mostly reason end-to-end and ignore the prior knowledge in the knowledge graph. In this paper, we attempt to mine prior knowledge from the knowledge graph based on counterfactuals and to use the prior knowledge to enhance the model. Specifically, we begin by constructing counterfactuals to assign a weight for each relation as prior knowledge and then perform reasoning based on both prior knowledge and reinforcement learning. This approach combines the advantages of prior knowledge and neural networks. Experiments on three large datasets show that the prior knowledge extracted from counterfactuals is effective in improving the multi-hop reasoning model. Prior knowledge also has the advantage of being path-length independent, which mitigates the performance degradation in multi-hop reasoning when the reasoning path is excessively long. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Wang, Zikang; Li, Linjing; Zeng, Daniel] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
   [Wang, Zikang; Li, Linjing; Zeng, Daniel] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China.
   [Li, Linjing; Zeng, Daniel] Shenzhen Artificial Intelligence & Data Sci Inst, Shenzhen, Peoples R China.
   [Wu, Xiaofei] Beijing Zhongke Wenge Sci & Technol Co Ltd, Beijing, Peoples R China.
RP Li, LJ (通讯作者)，Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
EM wangzikang2016@ia.ac.cn; linjing.li@ia.ac.cn; dajun.zeng@ia.ac.cn;
   xiaofei.wu@wenge.com
RI Wang, Zikang/ABD-3193-2021
OI Wang, Zikang/0000-0003-1406-1237
FU National Key Research and Development Program of China [2020AAA0103405];
   National Natural Science Foundation of China [71621002]; Strategic
   Priority Research Program of Chinese Academy of Sciences [XDA27030100]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2020AAA0103405, the National
   Natural Science Foundation of China under Grants 71621002, as well as
   the Strategic Priority Research Program of Chinese Academy of Sciences
   under Grant XDA27030100.
CR Albini E, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P451
   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
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Cohen W.W., 2016, ARXIV 160506523
   Das R., 2018, ICLR
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Goyal Y, 2019, PR MACH LEARN RES, V97
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Jiang SP, 2012, IEEE DATA MINING, P912, DOI 10.1109/ICDM.2012.156
   Johansson FD, 2016, PR MACH LEARN RES, V48
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lewis DK., 1973, COUNTERFACTUALS
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Logan RL, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5962
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mitchell T, 2015, AAAI CONF ARTIF INTE, P2302
   Pearl J, 2019, COMMUN ACM, V62, P54, DOI 10.1145/3241036
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Sutton RS, 2000, ADV NEUR IN, V12, P1057
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wan G., 2020, P 29 INT JOINT C ART, P1926
   Wang Heng, 2019, P 2019 C EMP METH NA, P2623
   Wang WY, 2015, MACH LEARN, V100, P101, DOI 10.1007/s10994-015-5488-x
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Xiong WH, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4258
   Yang A, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2346
   Yang B., 2015, 3 INT C LEARN REPR I, P7
   Yang F., 2017, ADV NEURAL INFORM PR, P2319
   Yang X., 2020, P 14 INT WORKSH SEM
NR 33
TC 2
Z9 2
U1 24
U2 53
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 JUL 8
PY 2021
VL 223
AR 107035
DI 10.1016/j.knosys.2021.107035
EA APR 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SD3JR
UT WOS:000651271700013
DA 2022-08-23
ER

PT J
AU Zhu, QN
   Zhou, XF
   Zhang, P
   Shi, Y
AF Zhu, Qiannan
   Zhou, Xiaofei
   Zhang, Peng
   Shi, Yong
TI A neural translating general hyperplane for knowledge graph embedding
SO JOURNAL OF COMPUTATIONAL SCIENCE
LA English
DT Article; Proceedings Paper
CT International Conference on Computational Science (ICCS)
CY JUN 11-13, 2018
CL Wuxi, PEOPLES R CHINA
DE Knowledge representation; Knowledge embedding; Knowledge graph
   completion; Link prediction; Neural network
AB For completing knowledge graph, many translation-based models, like that Trans(E and H) which embed a knowledge graph into a continuous vector space and encode relations as translation operations in that space, have achieved better performance. However, most of them have limitations in expressing complex relations for knowledge graph. In this paper, we propose a translation-neural based method NTransGH for knowledge graph completion. NTransGH combines translation mechanism for modeling relations as translation operations by generalized hyperplanes, and a neural network for capturing more complex interactions between entities and relations. We conduct experiment on two tasks link prediction and triplet classification with two datasets. Experimental results show that NTransGH has strong expression in mapping properties of complex relations, and achieves significant and consistent improvements over state-of-the-art embedding methods. This paper is an extension of our previous works [1]. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Zhu, Qiannan; Zhou, Xiaofei] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China.
   [Zhu, Qiannan; Zhou, Xiaofei] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China.
   [Zhang, Peng] Ant Financial, Serv Grp, Hangzhou, Zhejiang, Peoples R China.
   [Shi, Yong] CAS Res Ctr Fictitious Econ & Data Sci, Beijing, Peoples R China.
   [Shi, Yong] Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R China.
   [Shi, Yong] Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA.
RP Zhou, XF (通讯作者)，Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China.
EM zhouxiaofei@iie.ac.cn
FU National Key RD Program [2017YFB0803003]; National Natural Science
   Foundation of China [61202226]
FX This work is supported by National Key R&D Program No. 2017YFB0803003,
   and the National Natural Science Foundation of China (No. 61202226), We
   thank all anonymous reviewers for their constructive comments.
CR [Anonymous], 2016, P 2016 C EMP METH NA, DOI DOI 10.18653/V1/D16-1145
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   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., 2012, P 15 INT C ART INT S, P127
   Bordes A., 2011, INT C ART INT STAT
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Bottou L, 2010, COMPSTAT'2010: 19TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STATISTICS, P177, DOI 10.1007/978-3-7908-2604-3_16
   Brachman R. J., 2004, KNOWLEDGE REPRESENTA, DOI [10.1016/b978-1-55860-932-7.x5083-3, DOI 10.1016/B978-1-55860-932-7.X5083-3]
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Caroleo B., 2018, INT J INF TECHNOL DE, V17
   Drumond L., 2012, P 27 ANN ACM S APPL, P326, DOI DOI 10.1145/2245276.2245341
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   Franz T, 2009, LECT NOTES COMPUT SC, V5823, P213, DOI 10.1007/978-3-642-04930-9_14
   Garca-Durn A., COMPOSING RELATIONSH
   Harshman R. A., 1966, PSYCHOMETRIKA, V31, P279
   HARSHMAN RA, 1994, COMPUT STAT DATA AN, V18, P39, DOI 10.1016/0167-9473(94)90132-5
   He S., 2014, P C EMP METH NAT GAS, P1092
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Hordes A., 2011, AAAI C ART INT AAAI
   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
   Kolda T. G., 2005, HIGHER ORDER WEB LIN, P242, DOI [10.2172/974401, DOI 10.2172/974401]
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li J, 2017, PROCEDIA COMPUT SCI, V122, P174, DOI 10.1016/j.procs.2017.11.357
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin YK, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2124
   Lin YC, 2015, ADV SOC SCI EDUC HUM, V39, P2181
   Miettinen P., 2012, IEEE INT C DAT MIN, P447, DOI [10.1109/icdm201128, DOI 10.1109/ICDM201128]
   Mikolov T., 2013, ADV NEURAL INFORM PR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nguyen D., 2016, 24 AAAI C ART INT, P1306, DOI [10.18653/v1/n16-1054, DOI 10.18653/V1/N16-1054]
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Rendle S., 2010, P 3 ACM INT C WEB SE, P81, DOI DOI 10.1145/1718487.1718498
   Rendle S, 2013, PROC VLDB ENDOW, V6, P337, DOI 10.14778/2535573.2488340
   Riedel S., 2013, NAACL HLT, pxxi
   Scarpel R. A., 2015, ANN DATA SCI, V2, P1
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sutskever I., 2009, NIPS
   TUCKER LR, 1966, PSYCHOMETRIKA, V31, P279, DOI 10.1007/BF02289464
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J., 2013, P 2013 C EMP METH NA, P1366
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xiao H., 2016, LONG PAPERS, V1, DOI [10.18653/v1/p16-1219, DOI 10.18653/V1/P16-1219]
   Xie QZ, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P950, DOI 10.18653/v1/P17-1088
   Yahya M, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1107, DOI 10.1145/2505515.2505677
   Zhou XF, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1009, DOI 10.1145/3132847.3132939
   Zhu QN, 2018, LECT NOTES COMPUT SC, V10860, P624, DOI 10.1007/978-3-319-93698-7_48
NR 50
TC 10
Z9 13
U1 4
U2 22
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1877-7503
J9 J COMPUT SCI-NETH
JI J. Comput. Sci.
PD JAN
PY 2019
VL 30
BP 108
EP 117
DI 10.1016/j.jocs.2018.11.004
PG 10
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA HK4RQ
UT WOS:000457951200011
DA 2022-08-23
ER

PT J
AU Li, ZF
   Liu, H
   Zhang, ZL
   Liu, TT
   Shu, JB
AF Li, Zhifei
   Liu, Hai
   Zhang, Zhaoli
   Liu, Tingting
   Shu, Jiangbo
TI Recalibration convolutional networks for learning interaction knowledge
   graph embedding
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Representation learning; Link prediction;
   Convolutional networks
AB Knowledge graph embedding aims to learn the embedded representation of entities and relations in knowledge graphs which is very important for the subsequent link prediction task. However, two key issues are existed for learning knowledge graph embedding: 1) How to take full advantage of the deep learning algorithms to generate expressive embeddings? 2) How to solve the polysemy phenomenon caused by multi-relations knowledge graphs that entities and relations show different semantics after involving different predictions? In this article, to tackle the first problem, the multi-layer convolutional networks are adopted to generate features about entities and relations then used to predict candidate entity. Moreover, the representation power of the networks is strengthened by integrating an effective recalibration mechanism which can accentuate informative features selectively. To tackle the second problem, we propose to learn multiple specific interaction embeddings. Instead of directly learning one general embedding to preserve all information for each entity and relation, their interactions are captured to model the cross-semantic influence from relations to entities and from entities to relations. Compared to traditional embedding models, the proposed model can provide more generalization capabilities and effectively capture potential links between entities and relations. Experimental results have revealed that the proposed model achieves the state-of-the-art performance for general evaluation metrics on link prediction tasks. (c) 2020 Elsevier B.V. All rights reserved.
C1 [Li, Zhifei; Liu, Hai; Zhang, Zhaoli; Shu, Jiangbo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
   [Liu, Tingting] Hubei Univ, Sch Educ, 368 Youyi Rd, Wuhan 430062, Hubei, Peoples R China.
RP Liu, H (通讯作者)，Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
EM zhifei1993@mails.ccnu.edu.cn; hailiu0204@ccnu.edu.cn;
   zl.zhang@mail.ccnu.edu.cn
RI 于, 于增臣/AAH-4657-2021
FU National Natural Science Foundation of China [62011530436, 62077020,
   61875068, 61505064]; Fundamental Research Funds for the Central
   Universities [2020YBZZ006, CCNU20ZT017, CCNU2020ZN008]
FX The authors sincerely thank anonymous reviewers for their con structive
   comments, which helped improve this paper. This work was supported in
   part by the National Natural Science Foundation of China under Grant
   62011530436, Grant 62077020, Grant 61875068, and Grant 61505064, and 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
   Chen L, 2019, IEEE T CYBERNETICS, V49, P3307, DOI 10.1109/TCYB.2018.2841504
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ebisu T, 2020, IEEE T KNOWL DATA EN, V32, P941, DOI 10.1109/TKDE.2019.2893920
   Gao LL, 2020, NEUROCOMPUTING, V391, P227, DOI 10.1016/j.neucom.2018.11.102
   Hao P, 2019, IEEE T CYBERNETICS, V49, P83, DOI 10.1109/TCYB.2017.2764918
   Hao Y., 2018, P 27 INT C COMP LING, P3272
   He K., 2016, IEEE C COMP VIS PATT, DOI DOI 10.1109/CVPR.2016.90
   He Y, 2020, IEEE T CYBERNETICS, V50, P3594, DOI 10.1109/TCYB.2019.2933477
   Hongwei Wang, 2019, ACM Transactions on Information Systems, V37, DOI 10.1145/3312738
   Hu J, 2018, PROC CVPR IEEE, P7132, DOI [10.1109/TPAMI.2019.2913372, 10.1109/CVPR.2018.00745]
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Jaderberg M., 2015, ADV NEURAL INFORM PR, P2017, DOI [DOI 10.1038/NBT.3343, 10.5555/2969442.2969465]
   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., 2015, P 3 INT C LEARN REPR, P841
   Krizhevsky A, 2017, COMMUN ACM, V60, P84, DOI 10.1145/3065386
   Li FY, 2020, IEEE T CYBERNETICS, V50, P4508, DOI 10.1109/TCYB.2018.2887340
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu TT, 2020, IEEE T IND INFORM, V16, P544, DOI 10.1109/TII.2019.2934728
   Liu TT, 2019, IEEE-ASME T MECH, V24, P384, DOI 10.1109/TMECH.2018.2870056
   Liu TT, 2018, IEEE T IND INFORM, V14, P5268, DOI 10.1109/TII.2018.2794449
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nie D, 2019, IEEE T CYBERNETICS, V49, P1123, DOI 10.1109/TCYB.2018.2797905
   Paszke A., 2017, 31 C NEUR PROC SYST
   Roy AG, 2019, IEEE T MED IMAGING, V38, P540, DOI 10.1109/TMI.2018.2867261
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   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
   Szegedy C, 2016, P IEEE C COMP VIS PA, P2818
   Szegedy C, 2015, P IEEE C COMPUTER VI, P1
   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
   Yang X, 2015, PROCEEDINGS OF 2015 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2015), P1000, DOI 10.1109/ICCSNT.2015.7490905
   Zeng LH, 2020, IEEE T CYBERNETICS, V50, P452, DOI 10.1109/TCYB.2018.2873762
   Zhang W, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P96, DOI 10.1145/3289600.3291014
   Zhu ZG, 2020, NEUROCOMPUTING, V386, P325, DOI 10.1016/j.neucom.2019.12.077
NR 38
TC 16
Z9 17
U1 3
U2 18
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD FEB 28
PY 2021
VL 427
BP 118
EP 130
DI 10.1016/j.neucom.2020.07.137
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PX0PQ
UT WOS:000611067800011
DA 2022-08-23
ER

PT J
AU Wen, CH
   Liao, SH
   Huang, SF
AF Wen, Chih-Hao
   Liao, Shu-Hsien
   Huang, Shu-Fang
TI Mining Consumer Knowledge from Shopping Experience: TV Shopping Industry
SO INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY
LA English
DT Article
DE Consumer knowledge; data mining; TV shopping; association rules;
   clustering
ID SYSTEM
AB TV shopping becomes far much popular in recent years. TV nowadays is almost everywhere. People watch TV; meanwhile, they are more and more accustomed to buy goods via TV shopping channel. Even in recession, it is thriving and has become one of the most important consumption modes. This study uses cluster analysis to identify the profiles of TV shopping consumers. The rules between TV Shopping spokespersons and commodities from consumers are recognized by using association analysis. Depicting the marketing knowledge map of spokespersons, the best endorsement portfolio is found out to make recommendations. By the analysis of spokespersons, period, customer profiles and products, fourbusiness modes of TV shopping are proposed for consumers: new product, knowledge, low price and luxury product; the related recommendations are also provided for the industry reference.
C1 [Wen, Chih-Hao] Natl Def Univ, Dept Logist Management, Taoyuan, Taiwan.
   [Liao, Shu-Hsien; Huang, Shu-Fang] Tamkang Univ, Dept Management Sci & Decis Making, New Taipei, Taiwan.
RP Wen, CH (通讯作者)，Natl Def Univ, Dept Logist Management, Taoyuan, Taiwan.
RI Liao, Shu-hsien/AAW-1885-2020
OI Liao, Shu-hsien/0000-0002-3667-2867; Wen, Chih-Hao/0000-0001-5021-5243
CR Adderley R, 2007, KNOWL-BASED SYST, V20, P170, DOI 10.1016/j.knosys.2006.11.007
   Agrawal R, 1996, IEEE T KNOWL DATA EN, V8, P962, DOI 10.1109/69.553164
   Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170035.170072
   Bhattacharyya S, 2011, DECIS SUPPORT SYST, V50, P602, DOI 10.1016/j.dss.2010.08.008
   Daneshvary R, 2000, J CONSUM MARK, V17, P203, DOI 10.1108/07363760010328987
   Dudek D, 2009, INFORM SCIENCES, V179, P4123, DOI 10.1016/j.ins.2009.08.022
   GRANT AE, 1991, COMMUN RES, V18, P773, DOI 10.1177/009365091018006004
   Han J, 2012, MOR KAUF D, P1
   Hu Y, 2006, DECIS SUPPORT SYST, V1, P1
   Johnson KKP, 2006, INT J RETAIL DISTRIB, V34, P453, DOI 10.1108/09590550610667047
   Kalra A, 1998, J MARKETING RES, V35, P210, DOI 10.2307/3151849
   Kar B, 2014, P ADV TECHN EMB MULT, p1237 
   Kouris IN, 2005, DATA KNOWL ENG, V52, P353, DOI 10.1016/j.datak.2004.07.004
   Lennon S, 2003, CLOTH TEXT RES J, V21, P63, DOI DOI 10.1177/0887302X0302100202
   Li YM, 2012, DECIS SUPPORT SYST, V54, P9, DOI 10.1016/j.dss.2012.02.012
   Liao S, 2012, APPL CASE IBM SPSS M
   Liao SH, 2014, INT J DATA WAREHOUS, V10, P55, DOI 10.4018/ijdwm.2014040104
   Liao SH, 2009, EXPERT SYST APPL, V36, P4967, DOI 10.1016/j.eswa.2008.06.020
   Lim CM, 2011, PSYCHOL MARKET, V28, P763, DOI 10.1002/mar.20411
   Reece B.B., 2000, PROMOTIONAL STRATEGY
   Sharma A., 1990, J PERSONAL SELLING S, V10, P71, DOI DOI 10.1080/08853134.1990.10753850
   Stephens DL, 1996, J BUS RES, V37, P193, DOI 10.1016/S0148-2963(96)00069-0
   Vrahatis MN, 2002, J COMPLEXITY, V18, P375, DOI 10.1006/jcom.2001.0633
   Wang YF, 2004, EXPERT SYST APPL, V26, P427, DOI 10.1016/j.eswa.2003.10.001
   Wen CH, 2012, EXPERT SYST APPL, V39, P11257, DOI 10.1016/j.eswa.2012.03.072
   Yuvaraj D, 2016, INT ARAB J INF TECHN, V13, P203
NR 26
TC 1
Z9 1
U1 2
U2 10
PU ZARKA PRIVATE UNIV
PI ZARQA
PA COLL COMPUTING & INFORMATION SOC, PO BOX 132222, ZARQA, 13132, JORDAN
SN 1683-3198
J9 INT ARAB J INF TECHN
JI Int. Arab J. Inf. Technol.
PD NOV
PY 2018
VL 15
IS 6
BP 1043
EP 1051
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA GW5ZA
UT WOS:000447020800013
DA 2022-08-23
ER

PT J
AU Diaz, JRD
   Vilches-Blazquez, LM
AF Rondon Diaz, Juan D.
   Vilches-Blazquez, Luis M.
TI Characterizing water quality datasets through multi-dimensional
   knowledge graphs: a case study of the Bogota river basin
SO JOURNAL OF HYDROINFORMATICS
LA English
DT Article
DE Bogota river basin; knowledge graph; ontology; spatio-temporal
   characteristics; water quality
ID SEMANTIC WEB; INFORMATION; MANAGEMENT; SYSTEM
AB The world is transforming into a predominantly urban space, and it entails that cities have to be ready to provide services, for instance, to ensure availability and sustainable management of water and sanitation for all. In this scenario, the water quality evaluation has a crucial role and often needs multiple sources segregated. Our purpose is to build bridges between these data silos to provide an integrated and interoperable view, where different datasets can be provided and combined through knowledge graphs in order to characterize water quality. This work shows the quality of the Bogota river basin's water bodies by analyzing physicochemical and biological properties and using spatio-temporal and legal elements. So, our knowledge graphs allow us to discover what, when, and where infractions happened on water quality in a river basin of the most populated cities of Latin America during a critical period (2007-2013), highlighting the presence of high values of suspended solids and nitrites, lower amounts of dissolved oxygen, and the worst water quality during the driest periods (appearing until a maximum of 63 infractions in a year).
C1 [Rondon Diaz, Juan D.] Univ Nacl Colombia, Bogota, Colombia.
   [Vilches-Blazquez, Luis M.] Inst Politecn Nacl, Mexico City 07738, DF, Mexico.
RP Vilches-Blazquez, LM (通讯作者)，Inst Politecn Nacl, Mexico City 07738, DF, Mexico.
EM lmvilches.blazquez@gmail.com
OI Vilches-Blazquez, Luis M./0000-0001-5799-469X
CR Alilou H, 2019, J HYDROL, V573, P109, DOI 10.1016/j.jhydrol.2019.03.068
   Arribas-Bel D, 2014, APPL GEOGR, V49, P45, DOI 10.1016/j.apgeog.2013.09.012
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Baltaci F, 2008, DESALINATION, V226, P321, DOI 10.1016/j.desal.2007.02.114
   Basulto J., 2014, Rev Pediatr Aten Primaria, V16, P65, DOI 10.4321/s1139-76322014000100013
   Battle R, 2012, SEMANT WEB, V3, P355, DOI 10.3233/SW-2012-0065
   Behmel S, 2016, SCI TOTAL ENVIRON, V571, P1312, DOI 10.1016/j.scitotenv.2016.06.235
   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
   Camacho, 2020, REV INGENIERIA, DOI [10.16924/revinge.49.6, DOI 10.16924/REVINGE.49.6]
   Chambers J. M., 1991, STAT MODELS S
   CUAHSI-CUAHSI's Hydrologic Information System (CUAHSI-HIS), 2010, CUAHSI HIS
   Cudre-Mauroux P, 2020, SEMANT WEB, V11, P13, DOI 10.3233/SW-190371
   Curry E., 2014, 11 INT C HYDR
   DEFRA-Department for Environment Food and Rural Affairs, 2014, DEFR OP DAT STRAT
   Delpla I, 2020, J ENVIRON MANAGE, V255, DOI 10.1016/j.jenvman.2019.109869
   Diaz-Casallas DM, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11061668
   Ehrlinger L., 2016, DEFINITION KNOWLEDGE, P48
   Gasper R, 2011, CURR OPIN ENV SUST, V3, P150, DOI 10.1016/j.cosust.2010.12.009
   Giraldo E, 2002, WATER SCI TECHNOL, V45, P103, DOI 10.2166/wst.2002.0014
   Giri S, 2016, J ENVIRON MANAGE, V173, P41, DOI 10.1016/j.jenvman.2016.02.029
   Hakimi O, 2020, ADV FUNCT MATER, V30, DOI 10.1002/adfm.201909910
   Hunter J, 2011, INT J AGRIC ENVIRON, V2, P1, DOI 10.4018/jaeis.2011010101
   Jajaga Edmond, 2016, International Journal of Metadata, Semantics and Ontologies, V11, P207
   Jajaga E., 2013, SSN ISWC, P35
   Jajaga E, 2015, COMM COM INF SC, V544, P89, DOI 10.1007/978-3-319-24129-6_8
   Janowicz K, 2020, INT J GEOGR INF SCI, V34, P625, DOI 10.1080/13658816.2019.1684500
   K?mpgen B., 2014, SEPUBLICA
   Krotzsch M, 2016, LECT NOTES COMPUT SC, V9981, P376, DOI 10.1007/978-3-319-46523-4_23
   Liu Q, 2013, J HYDROINFORM, V15, P1169, DOI 10.2166/hydro.2012.152
   Masmoudi M, 2020, ENVIRON MODELL SOFTW, V124, DOI 10.1016/j.envsoft.2019.104581
   McDonald J.D., 2018, ENCY LIB INFORM SCI, V4th ed.
   Miranda D., 2008, COLOMBIAN J HORTICUL, V2, P180
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   Ouyang Y, 2005, WATER RES, V39, P2621, DOI 10.1016/j.watres.2005.04.024
   Pahl-Wostl C, 2020, INT J WATER RESOUR D, V36, P397, DOI 10.1080/07900627.2020.1721268
   Aguilar JAP, 2019, APPL GEOGR, V111, DOI 10.1016/j.apgeog.2019.102062
   Patroumpas K., 2014, EDBT ICDT WORKSH, P275
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Perry M., 2012, GEOSPARQL GEOGRAPHIC
   Radhapyari K., 2021, WATER CONSERVATION E, P229, DOI DOI 10.1016/B978-0-12-820200-5.00011-7
   Raymond CM, 2010, J ENVIRON MANAGE, V91, P1766, DOI 10.1016/j.jenvman.2010.03.023
   RESH VH, 1975, J WATER POLLUT CON F, V47, P9
   Forero AR, 2009, ARCH ENVIRON CON TOX, V57, P359, DOI 10.1007/s00244-008-9279-2
   Rodriguez-Jeangros N, 2018, J SUSTAIN WATER BUIL, V4, DOI 10.1061/JSWBAY.0000846
   Sankary N, 2018, J HYDROINFORM, V20, P1323, DOI 10.2166/hydro.2018.162
   Shi BQ, 2019, J ENVIRON MANAGE, V246, P203, DOI 10.1016/j.jenvman.2019.06.006
   Simon J.D., 2015, INT S ENV SOFTW SYST, P102, DOI DOI 10.1007/978-3-319-15994-2_9
   Suarez-Figueroa M. C., 2012, ONTOLOGY ENG NETWORK, P1, DOI [10.1007/978-3-642-24794-1, DOI 10.1007/978-3-642-24794-1]
   United Nations Department of Economic and Social Affairs Population Division, 2018, WORLD URB PROSP 2018
   Valipour M, 2021, CLIMATE, V9, DOI 10.3390/cli9050081
   van den Brink L, 2019, SEMANT WEB, V10, P95, DOI 10.3233/SW-180305
   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
   WARD RC, 1986, ENVIRON MANAGE, V10, P291, DOI 10.1007/BF01867251
   Weiss C, 2008, PROC VLDB ENDOW, V1, P1008, DOI 10.14778/1453856.1453965
   WHO-World Health Organization, 2017, GUID DRINK WAT QUAL
   Wilson RL, 1997, ANN OPER RES, V72, P105, DOI 10.1023/A:1018940103278
   Zhao PS, 2012, COMPUT GEOSCI-UK, V47, P3, DOI 10.1016/j.cageo.2012.04.021
NR 60
TC 0
Z9 0
U1 9
U2 9
PU IWA PUBLISHING
PI LONDON
PA REPUBLIC-EXPORT BLDG, UNITS 1 04 & 1 05, 1 CLOVE CRESCENT, LONDON,
   ENGLAND
SN 1464-7141
EI 1465-1734
J9 J HYDROINFORM
JI J. Hydroinform.
PD MAR
PY 2022
VL 24
IS 2
BP 295
EP 314
DI 10.2166/hydro.2022.070
EA FEB 2022
PG 20
WC Computer Science, Interdisciplinary Applications; Engineering, Civil;
   Environmental Sciences; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Environmental Sciences & Ecology; Water
   Resources
GA 0D9EP
UT WOS:000760209200001
OA gold
DA 2022-08-23
ER

PT J
AU Pan, ZQ
   Chen, HH
AF Pan, Zhiqiang
   Chen, Honghui
TI Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions
SO MATHEMATICS
LA English
DT Article
DE recommender systems; knowledge-enhanced recommendation; collaborative
   filtering; knowledge graph; self-supervised learning
AB Knowledge-enhanced recommendation (KER) aims to integrate the knowledge graph (KG) into collaborative filtering (CF) for alleviating the sparsity and cold start problems. The state-of-the-art graph neural network (GNN)-based methods mainly focus on exploiting the connectivity between entities in the knowledge graph, while neglecting the interaction relation between items reflected in the user-item interactions. Moreover, the widely adopted BPR loss for model optimization fails to provide sufficient supervisions for learning discriminative representation of users and items. To address these issues, we propose the collaborative knowledge-enhanced recommendation (CKER) method. Specifically, CKER proposes a collaborative graph convolution network (CGCN) to learn the user and item representations from the connection between items in the constructed interaction graph and the connectivity between entities in the knowledge graph. Moreover, we introduce the self-supervised learning to maximize the mutual information between the interaction- and knowledge-aware user preferences by deriving additional supervision signals. We conduct comprehensive experiments on two benchmark datasets, namely Amazon-Book and Last-FM, and the experimental results show that CKER can outperform the state-of-the-art baselines in terms of recall and NDCG on knowledge-enhanced recommendation.
C1 [Pan, Zhiqiang; Chen, Honghui] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
RP Chen, HH (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
EM panzhiqiang@nudt.edu.cn; chenhonghui@nudt.edu.cn
OI Pan, Zhiqiang/0000-0001-5484-1743
FU Postgraduate Scientific Research Innovation Project of Hunan Province
   [CX20200055]
FX This research was funded by the Postgraduate Scientific Research
   Innovation Project of Hunan Province under No. CX20200055.
CR Avd Oord, 2016, ADV NEURAL INFORM PR, P4790
   Bhaskaran S, 2021, MATHEMATICS-BASEL, V9, DOI 10.3390/math9020197
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bollacker K, 2007, P 22 NAT C ART INT, P1962
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Chen L., 2020, ARXIV201010783
   Chen T, 2020, PR MACH LEARN RES, V119
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Glorot X., 2010, J MACH LEARN RES, V9, P249
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   Gutmann M, 2010, P 13 INT C ART INT S, V9, P297, DOI DOI 10.1145/3292500.3330651
   He K., 2020, P IEEE CVF C COMP VI, DOI DOI 10.1109/CVPR42600.2020.00975
   He RN, 2016, AAAI CONF ARTIF INTE, P144
   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
   Hjelm, 2019, ICLR, P1
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Kingma D., 2014, ICLR, V27, P3581
   Kingma D. P., 2015, ADAM METHOD STOCHAST, DOI DOI 10.48550/ARXIV.1412.6980
   Liang DW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P689, DOI 10.1145/3178876.3186150
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Logeswaran L., 2018, INT C LEARN REPR ICL
   Ma JX, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P483, DOI 10.1145/3394486.3403091
   Ma XT, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8122132
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Rendle S., 2009, P 25 C UNC ART INT, P452
   RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   van den Oord Aaron, 2018, ARXIV180703748
   Wang BC, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8091607
   Wang F, 2017, PROCEEDINGS OF THE 2017 ACM MULTIMEDIA CONFERENCE (MM'17), P1041, DOI 10.1145/3123266.3123359
   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, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang P., 2018, ARXIV180306540
   Wang SJ, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P6332
   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 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
   XIA X, 2021, P AAAI C ARTIFICIAL, P4503
   Xu Y, 2020, IEEE T SERV COMPUT, V13, P289, DOI 10.1109/TSC.2019.2953033
   Xu Y, 2020, CONCURR COMP-PRACT E, V32, DOI 10.1002/cpe.5556
   Yao T, 2021, P 30 ACM INT C INF E, P4321
   Yu JL, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P413, DOI 10.1145/3442381.3449844
   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
   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 K, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P1893, DOI 10.1145/3340531.3411954
NR 51
TC 1
Z9 1
U1 8
U2 21
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD SEP
PY 2021
VL 9
IS 17
AR 2129
DI 10.3390/math9172129
PG 18
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA UN8SM
UT WOS:000694279000001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhou, XC
   Eibeck, A
   Lim, MQ
   Krdzavac, NB
   Kraft, M
AF Zhou, Xiaochi
   Eibeck, Andreas
   Lim, Mei Qi
   Krdzavac, Nenad B.
   Kraft, Markus
TI An agent composition framework for the J-Park Simulator - A knowledge
   graph for the process industry
SO COMPUTERS & CHEMICAL ENGINEERING
LA English
DT Article
DE Semantic web; Semantic web service composition; Agent; Cross-domain;
   Linked data; Knowledge graph
ID ONTOLOGY; NETWORK; OPTIMIZATION; METHODOLOGY; DESIGN; WASTE
AB Digital twins, Industry 4.0 and Industrial Internet of Things are becoming ever more important in the process industry. The Semantic Web, linked data, knowledge graphs and web services/agents are key technologies for implementing the above concepts. In this paper, we present a comprehensive semantic agent composition framework. It enables automatic agent discovery and composition to generate crossdomain applications. This framework is based on a light-weight agent ontology, OntoAgent, which is an adaptation of the Minimal Service Model (MSM) ontology. The MSM ontology was extended with grounding components to support the execution of an agent while keeping the compatibility with other existing web service description standards and extensibility. We illustrate how the comprehensive agent composition framework can be integrated into the J-Park Simulator (JPS) knowledge graph, for the automatic creation of a composite agent that simulates the dispersion of the emissions of a power plant within a selected spatial area. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Zhou, Xiaochi; Eibeck, Andreas; Lim, Mei Qi; Krdzavac, Nenad B.; Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore CARES, CREATE Tower,1 Create Way, Singapore 138602, Singapore.
   [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.
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
RI Kraft, Markus/D-7243-2016
OI Kraft, Markus/0000-0002-4293-8924
FU National Research Foundation (NRF), Prime Minister's Office, Singapore
   under its Campus for Research Excellence and Technological Enterprise
   (CREATE) programme; Alexander von Humboldt foundation
FX This project is supported by the National Research Foundation (NRF),
   Prime Minister's Office, Singapore under its Campus for Research
   Excellence and Technological Enterprise (CREATE) programme. Markus Kraft
   acknowledges the support of the Alexander von Humboldt foundation.
CR Afshari H, 2017, ASCE-ASME J RISK UNC, V3, DOI 10.1115/1.4035729
   Aiello M., 2008, 2008 10 IEEE C E COM, DOI [10.1109/cecandeee.2008.149, DOI 10.1109/CECANDEEE.2008.149]
   Cimren E, 2011, J IND ECOL, V15, P315, DOI 10.1111/j.1530-9290.2010.00310.x
   Eibeck A., 2019, J PARK SIMULAT UNPUB
   Farazi F., 2019, ONTOKIN ONTOLO UNPUB
   Fensel D, 2011, SEMANTIC WEB SERVICES, P107, DOI 10.1007/978-3-642-19193-0_7
   Fielding R. T., 2000, ARCHITECTURAL STYLES
   Fujii K, 2009, ACM T AUTON ADAP SYS, V4, DOI 10.1145/1516533.1516536
   Groger G, 2012, ISPRS J PHOTOGRAMM, V71, P12, DOI 10.1016/j.isprsjprs.2012.04.004
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Haslenda H, 2011, RESOUR CONSERV RECY, V55, P713, DOI 10.1016/j.resconrec.2011.02.004
   Hennig Patrick, 2010, 2010 IEEE International Conference on Web Services (ICWS), P123, DOI 10.1109/ICWS.2010.45
   KLUSCH M, 2005, P 1 INT AAAI FALL S, P55
   Kona Srividya, 2008, 2008 IEEE International Conference on Web Services (ICWS), P219, DOI 10.1109/ICWS.2008.118
   Kopecky Jacek, 2008, Wl 2008. 2008 IEEE/WIC/ACM International Conference on Web Intelligence. IAT 2008. 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Wl-IAT Workshop 2008 2008 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, P619, DOI 10.1109/WIIAT.2008.379
   Kopecky Jacek, 2008, 2008 Second IEEE International Conference on Semantic Computing (ICSC), P238, DOI 10.1109/ICSC.2008.54
   Kopecky J., 2007, SEMANTIC ANNOTATIONS
   Leong YT, 2017, J CLEAN PROD, V143, P1268, DOI 10.1016/j.jclepro.2016.11.147
   Liao ZW, 2007, IND ENG CHEM RES, V46, P4954, DOI 10.1021/ie061299i
   Martin D., 2004, OWL S SEMANTIC MARKU
   McIlraith S., 2002, P 8 INT C KNOWL REPR, V2, P482
   Morbach J, 2009, COMPUT CHEM ENG, V33, P1546, DOI 10.1016/j.compchemeng.2009.01.019
   Nair SK, 2016, COMPUT CHEM ENG, V93, P221, DOI 10.1016/j.compchemeng.2016.05.003
   Nam W., 2008, 2008 10 IEEE C E COM, DOI [10.1109/cecandeee.2008.108, DOI 10.1109/CECANDEEE.2008.108]
   Nau D, 2003, J ARTIF INTELL RES, V20, P379, DOI 10.1613/jair.1141
   Oh SC, 2007, INT J WEB SERV RES, V4, P1, DOI 10.4018/jwsr.2007010101
   Omer AM, 2009, PROCEEDINGS OF THE 2009 FIFTH INTERNATIONAL CONFERENCE ON NEXT GENERATION WEB SERVICES PRACTICES, NWESP 2009, P76, DOI 10.1109/NWeSP.2009.20
   Pan M, 2015, ENRGY PROCED, V75, P1536, DOI 10.1016/j.egypro.2015.07.313
   Pedrinaci C., 2010, ONT REP ED SEM WEB W, V596
   Raman K., 2008, 2008 10 IEEE C E COM, DOI [10.1109/cecandeee.2008.152, DOI 10.1109/CECANDEEE.2008.152]
   Rodriguez-Mier P, 2016, IEEE T SERV COMPUT, V9, P537, DOI 10.1109/TSC.2015.2402679
   Sheng QZ, 2014, INFORM SCIENCES, V280, P218, DOI 10.1016/j.ins.2014.04.054
   Shiaa MM, 2008, P IEEE I C SERV COMP, P397, DOI 10.1109/SCC.2008.141
   Sirin E., 2004, J WEB SEMANT, V1, P377, DOI DOI 10.1016/J.WEBSEM.2004.06.005
   Tan RR, 2012, COMPUT-AIDED CHEM EN, V31, P775
   Tiu BTC, 2017, RESOUR CONSERV RECY, V119, P89, DOI 10.1016/j.resconrec.2016.06.005
   Yan Y., 2008, 2008 10 IEEE C E COM, DOI [10.1109/cecandeee.2008.124, DOI 10.1109/CECANDEEE.2008.124]
   Zhang C, 2016, APPL ENERG, V184, P88, DOI 10.1016/j.apenergy.2016.10.016
   Zhou L, 2018, COMPUT CHEM ENG, V118, P49, DOI 10.1016/j.compchemeng.2018.07.010
NR 39
TC 16
Z9 17
U1 9
U2 43
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 NOV 2
PY 2019
VL 130
AR 106577
DI 10.1016/j.compchemeng.2019.106577
PG 18
WC Computer Science, Interdisciplinary Applications; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA JC5OQ
UT WOS:000489333200005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Mawkhiew, HB
   Sahoo, L
   Kharshiing, EV
AF Mawkhiew, Hame Bantei
   Sahoo, Lingaraj
   Kharshiing, Eros V.
TI Gene-to-trait knowledge graphs show association of plant photoreceptors
   with physiological and developmental processes that can confer agronomic
   benefits
SO GENETIC RESOURCES AND CROP EVOLUTION
LA English
DT Article
DE Crop physiology; Gene discovery; Knowledge graphs; Molecular breeding;
   Photoreceptors; Plant development
ID BLUE-LIGHT; NUCLEAR ARCHITECTURE; UV-B; PHYTOCHROME; GROWTH;
   CRYPTOCHROME-2; METABOLISM; RESPONSES; BIOMASS
AB Global population growth, climate change, altered precipitation rates and cropping patterns are increasingly challenging plant scientists to improve crop productivity for food and non-food applications. Hence, there is a pressing need for identifying candidate genes that can be targeted for breeding future crops having enhanced agronomic benefits. Network mapping utilises available data and creates knowledge graphs that aid in visualising association(s) between the individual data items. Here, we have generated gene-to-trait knowledge graphs of known plant photoreceptors using the KnetMiner gene discovery platform which generates biological networks from literature/data sets available in public databases. The resulting knowledge graphs indicate a close association of photoreceptors with various physiological and developmental processes such as shoot architecture, yield, disease resistance and water use efficiency among others, that can confer agronomically important benefits. Such information can be of assistance to plant biologists in the selection of potential gene targets for improving agronomically beneficial traits in plants. This report highlights the potential of machine learning and knowledge graphs as aids in more efficient knowledge discovery and novel decision-making processes which can also be employed for crop breeding or crop engineering.
C1 [Mawkhiew, Hame Bantei; Kharshiing, Eros V.] St Edmunds Coll, Dept Bot, Shillong 793003, Meghalaya, India.
   [Sahoo, Lingaraj] Inst Technol Guwahati, Dept Biosci & Bioengn, Gauhati 781039, Assam, India.
RP Kharshiing, EV (通讯作者)，St Edmunds Coll, Dept Bot, Shillong 793003, Meghalaya, India.
EM eros.kharshiing@gmail.com
OI Kharshiing, Eros/0000-0002-5570-8669
FU Eureka Project under North East Centre for Biological Sciences and
   Healthcare Engineering (NECBH) Twinning Outreach Programme - Department
   of Biotechnology (DBT), Ministry of Science and Technology, Govt. of
   India [NECBH/2019-20/139, BT/COE/34/SP28408/2018]; Science and
   Engineering Research Board, (Department of Science and Technology, New
   Delhi) [EEQ/2019/0000223]
FX This work is partly supported by Eureka Project with grant on
   NECBH/2019-20/139 under North East Centre for Biological Sciences and
   Healthcare Engineering (NECBH) Twinning Outreach Programme hosted by
   Indian Institute of Technology Guwahati (IITG), Guwahati, Assam funded
   by Department of Biotechnology (DBT), Ministry of Science and
   Technology, Govt. of India with number BT/COE/34/SP28408/2018 to EK and
   grant no. EEQ/2019/0000223 (Science and Engineering Research Board,
   (Department of Science and Technology, New Delhi) to EK. The funders had
   no role in the study design; in the collection, analyses and
   interpretation of data; in the writing of the article; and in the
   decision to submit the article for publication.
CR Adamski NM, 2020, ELIFE, V9, DOI [10.7554/elife.55646, 10.7554/eLife.55646]
   Alba R, 2000, PLANT PHYSIOL, V123, P363, DOI 10.1104/pp.123.1.363
   Ballare CL, 2014, ANNU REV PLANT BIOL, V65, P335, DOI 10.1146/annurev-arplant-050213-040145
   Bianchetti R, 2020, PLANT PHYSIOL, V183, P869, DOI 10.1104/pp.20.00019
   Botto JF, 2003, PLANT PHYSIOL, V133, P1547, DOI 10.1104/pp.103.029546
   Boyle EA, 2017, CELL, V169, P1177, DOI 10.1016/j.cell.2017.05.038
   Chen M, 2011, TRENDS CELL BIOL, V21, P664, DOI 10.1016/j.tcb.2011.07.002
   Christie JM, 2007, ANNU REV PLANT BIOL, V58, P21, DOI 10.1146/annurev.arplant.58.032806.103951
   Christie JM, 2015, PLANT CELL PHYSIOL, V56, P401, DOI 10.1093/pcp/pcu196
   D'Amico-Damiao V, 2018, FRONT PLANT SCI, V9, DOI 10.3389/fpls.2018.01897
   Fantini E, 2020, PHYSIOL PLANTARUM, V169, P336, DOI 10.1111/ppl.13088
   Fensel D., 2020, KNOWLEDGE GRAPHS, P1, DOI DOI 10.1007/978-3-030-37439-6
   Finlayson SA, 2010, PLANT PHYSIOL, V152, P1914, DOI 10.1104/pp.109.148833
   Gharibi M, 2020, FRONT BIG DATA, V3, DOI 10.3389/fdata.2020.00012
   Hallingback HR, 2016, GCB BIOENERGY, V8, P670, DOI 10.1111/gcbb.12280
   Hanley SJ, 2014, TREE PHYSIOL, V34, P1167, DOI 10.1093/treephys/tpt089
   Hart JE, 2019, P NATL ACAD SCI USA, V116, P12550, DOI 10.1073/pnas.1902915116
   Hassani-Pak K., 2020, KNETMINER COMPREHENS
   Ishii T, 2017, GM CROPS FOOD, V8, P44, DOI 10.1080/21645698.2016.1261787
   Ito S, 2012, MOL PLANT, V5, P573, DOI 10.1093/mp/sss013
   Jenkins GI, 2014, PLANT CELL, V26, P21, DOI 10.1105/tpc.113.119446
   Kaiserli E, 2018, CURR OPIN PLANT BIOL, V45, P103, DOI 10.1016/j.pbi.2018.05.018
   Kharshiing E., 2019, SENSORY BIOL PLANTS, P21
   Kharshiing E, 2016, J PHOTOCH PHOTOBIO B, V165, P157, DOI 10.1016/j.jphotobiol.2016.10.026
   Krahmer J, 2018, PLANT PHYSIOL, V176, P1039, DOI 10.1104/pp.17.01437
   Lassoued R, 2019, TRANSGENIC RES, V28, P247, DOI 10.1007/s11248-019-00118-5
   Legris M, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-13045-0
   Legris M, 2016, SCIENCE, V354, P897, DOI 10.1126/science.aaf5656
   Li L, 2012, GENE DEV, V26, P785, DOI 10.1101/gad.187849.112
   Mawphlang OIL, 2017, FRONT PLANT SCI, V8, DOI 10.3389/fpls.2017.01181
   Perrella G, 2016, NEW PHYTOL, V212, P908, DOI 10.1111/nph.14269
   Petrillo E, 2014, RNA BIOL, V11, P1215, DOI 10.4161/15476286.2014.972852
   Ray DK, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0217148
   Rizzini L, 2011, SCIENCE, V332, P103, DOI 10.1126/science.1200660
   Santer BD, 2003, SCIENCE, V301, P479, DOI 10.1126/science.1084123
   Santer BD, 1996, NATURE, V382, P39, DOI 10.1038/382039a0
   Shalitin D, 2002, NATURE, V417, P763, DOI 10.1038/nature00815
   Sharma S, 2014, PLANT PHYSIOL, V164, P2030, DOI 10.1104/pp.113.232306
   Shih PM, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms10382
   Trenberth KE, 2011, CLIM RES, V47, P123, DOI 10.3354/cr00953
   Gonzalez CV, 2015, PHYTOCHEMISTRY, V110, P46, DOI 10.1016/j.phytochem.2014.11.018
   Wargent JJ, 2013, NEW PHYTOL, V197, P1058, DOI 10.1111/nph.12132
   Wies G, 2019, ANN BOT-LONDON, V123, P1079, DOI 10.1093/aob/mcz015
   Yang DY, 2016, P NATL ACAD SCI USA, V113, P7667, DOI 10.1073/pnas.1601309113
   Yoon Byoung-Ha, 2017, Genomics & Informatics, V15, P19, DOI 10.5808/GI.2017.15.1.19
NR 45
TC 2
Z9 2
U1 1
U2 5
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0925-9864
EI 1573-5109
J9 GENET RESOUR CROP EV
JI Genet. Resour. Crop Evol.
PD OCT
PY 2021
VL 68
IS 7
BP 2727
EP 2735
DI 10.1007/s10722-021-01214-4
EA MAY 2021
PG 9
WC Agronomy; Plant Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture; Plant Sciences
GA UQ7QR
UT WOS:000651339500001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, F
   Sun, B
   Diao, XL
   Zhao, W
   Shu, T
AF Zhang, Fei
   Sun, Bo
   Diao, Xiaolin
   Zhao, Wei
   Shu, Ting
TI Prediction of adverse drug reactions based on knowledge graph embedding
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE Adverse Drug Reactions; Knowledge Graph Embedding; Word2Vec; DrugBank
ID INDUCED LIVER-INJURY
AB BackgroundAdverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data.MethodBased on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs.ResultFirst, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863.ConclusionIn this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.
C1 [Zhang, Fei; Sun, Bo; Diao, Xiaolin; Zhao, Wei] Chinese Acad Med Sci & Peking Union Med Coll, Natl Ctr Cardiovasc Dis, Fuwai Hosp, Dept Informat Ctr, 167 North Lishi Rd, Beijing 100037, Peoples R China.
   [Shu, Ting] Natl Hlth Commiss, Natl Inst Hosp Adm, Bldg 3,Yard 6,Shouti South Rd, Beijing 100044, Peoples R China.
RP Zhao, W (通讯作者)，Chinese Acad Med Sci & Peking Union Med Coll, Natl Ctr Cardiovasc Dis, Fuwai Hosp, Dept Informat Ctr, 167 North Lishi Rd, Beijing 100037, Peoples R China.; Shu, T (通讯作者)，Natl Hlth Commiss, Natl Inst Hosp Adm, Bldg 3,Yard 6,Shouti South Rd, Beijing 100044, Peoples R China.
EM zw@fuwai.com; nctingting@126.com
FU Information Center of the National Center for Cardiovascular Diseases
   and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union
   Medical College, Beijing, China
FX We appreciate the support from the Information Center of the National
   Center for Cardiovascular Diseases and Fuwai Hospital, Chinese Academy
   of Medical Sciences and Peking Union Medical College, Beijing, China.
CR Allison Biotechnology MJN, 2012, REINV CLIN TRIALS, V30, P562
   Bean DM, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-16674-x
   Concejo FB, 2008, REV ESP ENFERM DIG, V100, P377, DOI 10.4321/s1130-01082008000600017
   Bouvy JC, 2015, DRUG SAFETY, V38, P437, DOI 10.1007/s40264-015-0281-0
   Brehm TT, 2019, AM J THER, V26, pE528, DOI 10.1097/MJT.0000000000000829
   Cao DS, 2015, CPT-PHARMACOMET SYST, V4, P498, DOI 10.1002/psp4.12002
   Carretero-Gonzalez A, 2019, MEDICINE, V98, DOI 10.1097/MD.0000000000018098
   Chalasani N, 2015, GASTROENTEROLOGY, V148, P1340, DOI 10.1053/j.gastro.2015.03.006
   Chen MJ, 2016, DRUG DISCOV TODAY, V21, P648, DOI 10.1016/j.drudis.2016.02.015
   Dey S, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2544-0
   Edwards IR, 2000, LANCET, V356, P1255, DOI 10.1016/S0140-6736(00)02799-9
   Fremond L, 2006, GASTROEN CLIN BIOL, V30, P1224, DOI 10.1016/S0399-8320(06)73517-5
   Giacomini KM, 2007, NATURE, V446, P975, DOI 10.1038/446975a
   Gisi K, 2019, BMJ CASE REP, V12, DOI 10.1136/bcr-2018-226340
   Giustarini G, 2019, TOXICOL SCI, V167, P385, DOI 10.1093/toxsci/kfy244
   Hu BF, 2018, MOLECULES, V23, DOI 10.3390/molecules23123193
   Jamal S, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-00908-z
   Kawaguchi T, 2019, INTERNAL MED, V58, P2501, DOI 10.2169/internalmedicine.2740-19
   Kopecky J, 2019, Klin Onkol, V32, P133, DOI 10.14735/amko2019133
   Kuhn M, 2016, NUCLEIC ACIDS RES, V44, pD1075, DOI 10.1093/nar/gkv1075
   Kuniyoshi N, 2019, INTERNAL MED, V58, P2663, DOI 10.2169/internalmedicine.2708-19
   Kwan JM, 2019, AM J MED SCI, V357, P343, DOI 10.1016/j.amjms.2018.11.017
   LaBute MX, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0106298
   Lee CY, 2019, DRUG DISCOV TODAY, V24, P1332, DOI 10.1016/j.drudis.2019.03.003
   Levine RL, 2006, CHEST, V129, P1167, DOI 10.1378/chest.129.5.1167
   Li J, 2016, BRIEF BIOINFORM, V17, P2, DOI 10.1093/bib/bbv020
   Lounkine E, 2012, NATURE, V486, P361, DOI 10.1038/nature11159
   Luo H, 2018, COMB CHEM HIGH T SCR, V21, P314, DOI 10.2174/1386207321666180524110013
   Macedo G, 2017, GASTROENT HEPAT-BARC, V40, P298, DOI 10.1016/j.gastrohep.2016.02.010
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Moon Sang Yi, 2019, Korean J Gastroenterol, V73, P360, DOI 10.4166/kjg.2019.73.6.360
   Munoz E, 2019, BRIEF BIOINFORM, V20, P190, DOI 10.1093/bib/bbx099
   Ota T, 2019, INTERNAL MED, V58, P2651, DOI 10.2169/internalmedicine.2554-18
   Perez-Nueno VI, 2015, J CHEM INF MODEL, V55, P1804, DOI 10.1021/acs.jcim.5b00120
   Rajan S, 2019, BRIT J ANAESTH, V122, pE63, DOI 10.1016/j.bja.2019.01.003
   Vanga RR, 2013, CASE REP GASTROINTES, V2013, DOI 10.1155/2013/902892
   Watanabe JH, 2018, ANN PHARMACOTHER, V52, P829, DOI 10.1177/1060028018765159
   Whitebread S, 2005, DRUG DISCOV TODAY, V10, P1421, DOI 10.1016/S1359-6446(05)03632-9
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Zheng F., 2015, THESIS KUNMINGUNUNIV
NR 40
TC 6
Z9 7
U1 8
U2 16
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 FEB 4
PY 2021
VL 21
IS 1
AR 38
DI 10.1186/s12911-021-01402-3
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA QG0ZX
UT WOS:000617317400001
PM 33541342
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Issa, S
   Adekunle, O
   Hamdi, F
   Cherfi, SSS
   Dumontier, M
   Zaveri, A
AF Issa, Subhi
   Adekunle, Onaopepo
   Hamdi, Faycal
   Cherfi, Samira Si-Said
   Dumontier, Michel
   Zaveri, Amrapali
TI Knowledge Graph Completeness: A Systematic Literature Review
SO IEEE ACCESS
LA English
DT Article
DE Data integrity; Linked data; Systematics; Measurement; Bibliographies;
   Tools; Search problems; Assessment; completeness; data quality; KG;
   knowledge graph; linked data; LOD; metrics; survey; systematic
   literature review
AB The quality of a Knowledge Graph (also known as Linked Data) is an important aspect to indicate its fitness for use in an application. Several quality dimensions are identified, such as accuracy, completeness, timeliness, provenance, and accessibility, which are used to assess the quality. While many prior studies offer a landscape view of data quality dimensions, here we focus on presenting a systematic literature review for assessing the completeness of Knowledge Graph. We gather existing approaches from the literature and analyze them qualitatively and quantitatively. In particular, we unify and formalize commonly used terminologies across 56 articles related to the completeness dimension of data quality and provide a comprehensive list of methodologies and metrics used to evaluate the different types of completeness. We identify seven types of completeness, including three types that were not previously identified in previous surveys. We also analyze nine different tools capable of assessing Knowledge Graph completeness. The aim of this Systematic Literature Review is to provide researchers and data curators a comprehensive and deeper understanding of existing works on completeness and its properties, thereby encouraging further experimentation and development of new approaches focused on completeness as a data quality dimension of Knowledge Graph.
C1 [Issa, Subhi; Hamdi, Faycal; Cherfi, Samira Si-Said] Conservatoire Natl Arts & Metiers, Ctr Studies & Res Comp Sci & Commun CEDRIC, F-75003 Paris, France.
   [Adekunle, Onaopepo; Dumontier, Michel; Zaveri, Amrapali] Maastricht Univ, Inst Data Sci, NL-6229 GT Maastricht, Netherlands.
RP Issa, S (通讯作者)，Conservatoire Natl Arts & Metiers, Ctr Studies & Res Comp Sci & Commun CEDRIC, F-75003 Paris, France.
EM subhi.issa@cnam.fr
RI Hamdi, Fayçal/GMX-3396-2022
OI Hamdi, Fayçal/0000-0002-5314-1419; Cherfi, Samira/0000-0002-5989-5529
CR Acosta M, 2017, J WEB SEMANT, V45, P41, DOI 10.1016/j.websem.2017.07.001
   Albertoni R., 2015, P CEUR WORKSH, V1376, P1
   Albertoni R., 2013, JOINT EDBT ICDT 2013, P52
   Alec C, 2016, LECT NOTES ARTIF INT, V9717, P237, DOI 10.1007/978-3-319-40985-6_18
   Ali M., 2018, J THEOR APPL INF TEC, V96, P3924
   Assaf A, 2016, INT J SEMANT WEB INF, V12, P111, DOI 10.4018/IJSWIS.2016070104
   Assaf A, 2015, LECT NOTES COMPUT SC, V9341, P247, DOI 10.1007/978-3-319-25639-9_40
   Balaraman V., 2018, P INT C WORLD WID WE, P1787
   Basili R. Victor, 1994, GOAL QUESTION METRIC, VI
   Batini C, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1541880.1541883
   Behkamal Behshid, 2014, The Semantic Web: Trends and Challenges. 11th International Conference (ESWC 2014). Proceedings: LNCS 8465, P806, DOI 10.1007/978-3-319-07443-6_54
   Behkamal B., 2015, COMP SCI SOFTW ENG C, P1
   Behkamal B, 2014, J THEOR APPL EL COMM, V9, P64, DOI 10.4067/S0718-18762014000200006
   Ben Ellefi M, 2018, SEMANT WEB, V9, P677, DOI 10.3233/SW-180294
   Berners-Lee T, 2006, LINKED DATA
   Bingham, 1974, QUALITY CONTROL HDB
   Bronselaer A, 2018, IEEE T FUZZY SYST, V26, P627, DOI 10.1109/TFUZZ.2017.2686807
   Cappiello C, 2016, LECT NOTES COMPUT SC, V9671, P397, DOI 10.1007/978-3-319-38791-8_25
   Charles V., 2018, P INT C THEOR PRACT, V2038, P11
   Cherix D., 2014, P CEUR WORKSHOP, V1240, P7
   Christen P., 2007, QUALITY MEASURES DAT, P127, DOI DOI 10.1007/978-3-540-44918-8_6
   Darari F, 2020, SEMANT WEB, V11, P441, DOI 10.3233/SW-190344
   Darari F, 2018, 2018 INTERNATIONAL WORKSHOP ON BIG DATA AND INFORMATION SECURITY (IWBIS), P49, DOI 10.1109/IWBIS.2018.8471712
   Darari F, 2016, LECT NOTES COMPUT SC, V9671, P170, DOI 10.1007/978-3-319-38791-8_10
   Debattista J, 2018, SEMANT WEB, V9, P859, DOI 10.3233/SW-180306
   Drummond N., 2006, P ESI WORKSH CLOS WO, V15, P23
   Ell B, 2011, LECT NOTES COMPUT SC, V7031, P162, DOI 10.1007/978-3-642-25073-6_11
   Endris KM, 2018, LECT NOTES COMPUT SC, V11029, P69, DOI 10.1007/978-3-319-98809-2_5
   Faisal S, 2016, LECT NOTES COMPUT SC, V9671, P225, DOI 10.1007/978-3-319-38791-8_13
   Farber M., 2017, SEMANT WEB, V9, P77, DOI DOI 10.3233/SW-170275
   Feeney KC, 2014, INT J SEMANT WEB INF, V10, P35, DOI 10.4018/ijswis.2014040103
   Font L., 2017, OPEN J SEMANT WEB, V4, P1
   Furber C., 2011, P 1 INT WORKSH LINK, P1, DOI DOI 10.1145/1966901.1966903
   Furber Christian, 2011, ECIS, P76
   Galarraga L, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P375, DOI 10.1145/3018661.3018739
   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
   Gurdur D, 2019, J IND INF INTEGR, V15, P191, DOI 10.1016/j.jii.2018.11.002
   Hasan S., 2013, P 7 ACM INT C DISTR, P171
   Issa S., 2019, THESIS CNAM PARIS
   Issa S, 2019, LECT NOTES COMPUT SC, V11483, P312, DOI 10.1007/978-3-030-21290-2_20
   Issa S, 2017, LECT NOTES COMPUT SC, V10651, P238, DOI 10.1007/978-3-319-70625-2_22
   Jaffri A., 2008, P CEUR WORKSHOP, V369, P1
   Kitchenham B., 2007, 2007001 EBSE DURH U
   Knap T., 2012, Proceedings of the 2012 IEEE 36th IEEE Annual Computer Software and Applications Conference Workshops (COMPSACW), P106, DOI 10.1109/COMPSACW.2012.29
   Kontokostas D, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P747, DOI 10.1145/2566486.2568002
   Kubler S, 2018, GOV INFORM Q, V35, P13, DOI 10.1016/j.giq.2017.11.003
   Lajus J, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1115, DOI 10.1145/3178876.3186010
   Luggen M, 2019, LECT NOTES COMPUT SC, V11778, P453, DOI 10.1007/978-3-030-30793-6_26
   Margaritopoulos M, 2012, J AM SOC INF SCI TEC, V63, P724, DOI 10.1002/asi.21706
   Mc Gurk S., 2017, MEPDAW LDQ ESWC, P94
   Mendes P. N., 2012, P 2012 JOINT EDBT IC, P116, DOI [DOI 10.1145/2320765.2320803, 10.1145/2320765.2320803]
   Micic N, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA), P155, DOI 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.28
   Mihindukulasooriya N., 2016, P 1 INT WORKSH COMP, P1
   Motro A., 1998, Flexible Query Answering Systems. Third International Conference, FQAS'98. Proceedings, P298
   Mountantonakis M, 2017, LECT NOTES COMPUT SC, V10450, P155, DOI 10.1007/978-3-319-67008-9_13
   Mouromtsev D, 2015, LECT NOTES COMPUT SC, V9088, P637, DOI 10.1007/978-3-319-18818-8_39
   Nahari MK, 2017, 2017 3RD INTERNATIONAL CONFERENCE ON WEB RESEARCH (ICWR), P67, DOI 10.1109/ICWR.2017.7959307
   Naumann F., 2003, QUALITY DRIVEN QUERY, V2261
   Neumaier S, 2016, ACM J DATA INF QUAL, V8, DOI 10.1145/2964909
   Nguyen Khai, 2013, Semantic Technology. Second Joint International Conference, JIST 2012. Proceedings: LNCS 7774, P113
   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
   Pipino LL., 2002, COMMUN ACM, V45, P211, DOI [DOI 10.1145/505248.506010, 10.1145/505248.506010]
   Radulovic Filip, 2018, Semantic Web - Interoperability, Usability, Applicability, V9, P3, DOI 10.3233/SW-170267
   Rizzo G., 2017, P INT C KNOWL CAPT, P1
   Ruckhaus E, 2014, LECT NOTES COMPUT SC, V8798, P488, DOI 10.1007/978-3-319-11955-7_72
   Rula A., 2012, 2012 IEEE Sixth International Conference on Semantic Computing (ICSC 2012), P218, DOI 10.1109/ICSC.2012.17
   Rula A., 2014, 1 WORKSH LINK DAT QU, P1
   Scannapieco M., 2004, 9 INT C INF QUAL IQ, V4, P333
   Schultz A., 2012, P 21 INT WORLD WID W, P1
   Song DZ, 2011, LECT NOTES COMPUT SC, V7031, P649, DOI 10.1007/978-3-642-25073-6_41
   Soulet A, 2018, LECT NOTES COMPUT SC, V11136, P374, DOI 10.1007/978-3-030-00671-6_22
   Thakkar Harsh, 2016, P 6 INT C WEB INT MI, DOI [10.1145/2912845.2912857, DOI 10.1145/2912845.2912857]
   Thomas J, 2017, J CLIN EPIDEMIOL, V91, P31, DOI 10.1016/j.jclinepi.2017.08.011
   Wang R. Y., 1996, Journal of Management Information Systems, V12, P5
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Zaveri A., 2003, SEMANTICWEB ISWC 201, P244
   Zaveri A, 2016, SEMANT WEB, V7, P63, DOI 10.3233/SW-150175
   Zaveri A, 2014, INT J SEMANT WEB INF, V10, P1, DOI 10.4018/ijswis.2014040101
NR 79
TC 1
Z9 1
U1 3
U2 21
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 31322
EP 31339
DI 10.1109/ACCESS.2021.3056622
PG 18
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA QO8VU
UT WOS:000623416100001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Gao, H
   Shi, J
   Qi, GL
   Wang, M
AF Gao, Huan
   Shi, Jun
   Qi, Guilin
   Wang, Meng
TI Triple Context-Based Knowledge Graph Embedding
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph embedding; semantic Web; link prediction
AB Knowledge graph embedding aims to represent entities and relations of a knowledge graph in continuous vector spaces. It has increasingly drawn attention for its ability to encode semantics in low dimensional vectors as well as its outstanding performance on many applications, such as question answering systems and information retrieval tasks. Existing methods often handle each triple independently, without considering context information of a triple in the knowledge graph, such an information can be useful for inference of new knowledge. Moreover, the relations and paths between an entity pair also provide information for inference. In this paper, we define a novel context-dependent knowledge graph representation model named triple-context-based knowledge embedding, which is based on the notion of triple context used for embedding entities and relations. For each triple, the triple context is composed of two kinds of graph structured information: one is a set of neighboring entities along with their outgoing relations, the other is a set of relation paths which contain a pair of target entities. Our embedding method is designed to utilize the triple context of each triple while learning embeddings of entities and relations. The method is evaluated on multiple tasks in the paper. Experimental results reveal that our method achieves significant improvements over the state-of-the-art methods.
C1 [Gao, Huan; Shi, Jun; Qi, Guilin; Wang, Meng] Southeast Univ, Comp Sci & Engn Inst, Nanjing 211100, Jiangsu, Peoples R China.
RP Gao, H (通讯作者)，Southeast Univ, Comp Sci & Engn Inst, Nanjing 211100, Jiangsu, Peoples R China.
EM gh@seu.edu.cn
FU National Natural Science Foundation of China [61762063]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 61762063.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bordes A., 2011, P AAAI, 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
   Feng J., 2016, COLING, P641
   Guo S., 2016, P C EMP METH NAT LAN, P192, DOI 10.18653/v1/D16-1019
   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
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Lin Y., 2015, P AAAI, P1366
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mitchell T, 2015, AAAI CONF ARTIF INTE, P2302
   Nickel M., 2014, ADV NEURAL INFORM PR, P1179
   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
   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 J, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2299, DOI 10.1145/3132847.3133119
   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
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Toutanova K., 2016, P ACL, P1
   Toutanova K., 2015, P 2015 C EMP METH NA, p1499?1509
   Trouillon T, 2017, J MACH LEARN RES, V18
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Tu CC, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1722, DOI 10.18653/v1/P17-1158
   Wang Z, 2014, P C EMP METH NAT LAN, P1591
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J., 2013, P 2013 C EMP METH NA, P1366
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xu JC, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1318
   Yang B., 2014, EMBEDDING ENTITIES R
NR 33
TC 9
Z9 10
U1 4
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 2018
VL 6
BP 58978
EP 58989
DI 10.1109/ACCESS.2018.2875066
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA GZ7GL
UT WOS:000449646100001
OA gold
DA 2022-08-23
ER

PT J
AU Li, LQ
   Zhao, J
   Hou, L
   Zhai, YK
   Shi, JM
   Cui, FF
AF Li, Luqi
   Zhao, Jie
   Hou, Li
   Zhai, Yunkai
   Shi, Jinming
   Cui, Fangfang
TI An attention-based deep learning model for clinical named entity
   recognition of Chinese electronic medical records
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article; Proceedings Paper
CT 2nd International Workshop on Health Natural Language Processing
   (HealthNLP)
CY JUN 10, 2019
CL Xian, PEOPLES R CHINA
DE Named entity recognition; Attention mechanism; Chinese electronic
   medical records
AB Background: Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus.
   Methods: From the perspective of deep learning, we integrated the attention mechanism into neural network, and proposed an improved clinical named entity recognition method for Chinese electronic medical records called BiLSTM-Att-CRF, which could capture more useful information of the context and avoid the problem of missing information caused by long-distance factors. In addition, medical dictionaries and part-of-speech (POS) features were also introduced to improve the performance of the model.
   Results: Based on China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2018 Chinese EMRs corpus, our BiLSTM-Att-CRF model finally achieved better performance than other widely-used models without additional features(F1-measure of 85.4% in CCKS 2018, F1-measure of 90.29% in CCKS 2017), and achieved the best performance with POS and dictionary features (F1-measure of 86.11% in CCKS 2018, F1-measure of 90.48% in CCKS 2017). In particular, the BiLSTM-Att-CRF model had significant effect on the improvement of Recall.
   Conclusions: Our work preliminarily confirmed the validity of attention mechanism in discovering key information and mining text features, which might provide useful ideas for future research in clinical named entity recognition of Chinese electronic medical records. In the future, we will explore the deeper application of attention mechanism in neural network.
C1 [Li, Luqi; Hou, Li] Chinese Acad Med Sci, Peking Union Med Coll, Inst Med Informat, Beijing, Peoples R China.
   [Zhao, Jie; Zhai, Yunkai; Shi, Jinming; Cui, Fangfang] Zhengzhou Univ, Affiliated Hosp 1, Natl Engn Lab Internet Med Syst & Applicat, Zhengzhou, Peoples R China.
RP Hou, L (通讯作者)，Chinese Acad Med Sci, Peking Union Med Coll, Inst Med Informat, Beijing, Peoples R China.; Zhai, YK (通讯作者)，Zhengzhou Univ, Affiliated Hosp 1, Natl Engn Lab Internet Med Syst & Applicat, Zhengzhou, Peoples R China.
EM hou.li@imicams.ac.cn; zhaiyunkai@163.com
FU National Engineering Laboratory for Internet Medical Systems and
   Applications of China [NELIMSA2018P02]; National Social Science
   Foundation of China [14BTQ032]; National Key R&D Program of China
   [2017YFC0909900, 2016YFC0901901]; Key Laboratory of Knowledge Technology
   for Medical Integrative Publishing of China; program of China Knowledge
   Center for Engineering Sciences and Technology (Medical Knowledge
   Service System) [CKCEST-2019-1-10]
FX This research was supported by the National Engineering Laboratory for
   Internet Medical Systems and Applications of China (Gant NO.
   NELIMSA2018P02),the National Social Science Foundation of China (Gant
   No.14BTQ032), the National Key R&D Program of China (Gant NO.
   2017YFC0909900, Grant No.2016YFC0901901), the Key Laboratory of
   Knowledge Technology for Medical Integrative Publishing of China, the
   program of China Knowledge Center for Engineering Sciences and
   Technology (Medical Knowledge Service System) (Grant No.
   CKCEST-2019-1-10). The publication cost of this article was funded by
   the Gant NO. NELIMSA2018P02.
CR [Anonymous], 2014, P INT C LEARN REPR
   Cai X, 2019, BMC MED INFORM DECIS, V19, P102
   Cao, 2018, P 32 AAAI C ART INT
   CCKS, 2017, CHIN C KNOWL GRAPH S
   CCKS, 2018, CHIN C KNOWL GRAPH S
   Chalapathy R, 2016, P COL
   Devlin J., ARXIV181004805CSCL
   FRIEDMAN C, 1994, J AM MED INFORM ASSN, V1, P161, DOI 10.1136/jamia.1994.95236146
   i2b2, I2B2 INF INT BIOL BE
   Jensen PB, 2012, NAT REV GENET, V13, P395, DOI 10.1038/nrg3208
   Jozefowicz R, 2015, INT C INT C MACH LEA
   Lai SW, 2016, IEEE INTELL SYST, V31, P5, DOI 10.1109/MIS.2016.45
   Liu KX, 2017, 2017 14TH WEB INFORMATION SYSTEMS AND APPLICATIONS CONFERENCE (WISA 2017), P105, DOI 10.1109/WISA.2017.8
   Lossio-Ventura JA, 2016, IEEE INT C BIOINFORM, P1081, DOI 10.1109/BIBM.2016.7822672
   Luo L, 2018, STEM CELLS INT, V2018, P1, DOI DOI 10.HTTPS://D0I.0RG/10.1155/2018/1785892
   Luo L, 2018, BIOINFORMATICS, V34, P1381, DOI 10.1093/bioinformatics/btx761
   [马建红 Ma Jianhong], 2019, [计算机应用研究, Application Research of Computers], V36, P1385
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Pennington J, 2014, EMNLP
   Sui C., 2017, RES CHINESE NAMED EN
   Tan Z, 32 AAAI C ART INT
   Tang BZ, 2012, PROCEEDINGS OF THE ACM SIXTH INTERNATIONAL WORKSHOP ON DATA AND TEXT MINING IN BIOMEDICAL INFORMATICS, P13
   Tmtpost, CW2VEC METH AL HLTH
   Vaswani A, ARXIV170603762CSCV
   Wang Q, 2019, J BIOMED INFORM, V92, P103
   Wu YH, 2015, STUD HEALTH TECHNOL, V216, P624, DOI 10.3233/978-1-61499-564-7-624
   Zeng DH, 2017, ENTROPY-SWITZ, V19, DOI 10.3390/e19060283
   Zhang R, 2017, P NAT CCF C NAT LANG, V10619, P612, DOI DOI 10.1007/978-3-319-73618-1_51
NR 28
TC 14
Z9 15
U1 10
U2 40
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 5
PY 2019
VL 19
IS 1
SU 5
AR 235
DI 10.1186/s12911-019-0933-6
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Medical Informatics
GA KF2UE
UT WOS:000509102400004
PM 31801540
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Sorensen, PB
   Thomsen, M
   Assmuth, T
   Grieger, KB
   Baun, A
AF Sorensen, Peter B.
   Thomsen, Marianne
   Assmuth, Timo
   Grieger, Khara B.
   Baun, Anders
TI Conscious worst case definition for risk assessment, part I A knowledge
   mapping approach for defining most critical risk factors in integrative
   risk management of chemicals and nanomaterials
SO SCIENCE OF THE TOTAL ENVIRONMENT
LA English
DT Article
DE Risk management; Uncertainty; Worst case; Modelling; Protection;
   Chemicals; Nanomaterials
AB This paper helps bridge the gap between scientists and other stakeholders in the areas of human and environmental risk management of chemicals and engineered nanomaterials. This connection is needed due to the evolution of stakeholder awareness and scientific progress related to human and environmental health which involves complex methodological demands on risk management. At the same time, the available scientific knowledge is also becoming more scattered across multiple scientific disciplines. Hence, the understanding of potentially risky situations is increasingly multifaceted, which again challenges risk assessors in terms of giving the 'right' relative priority to the multitude of contributing risk factors. A critical issue is therefore to develop procedures that can identify and evaluate worst case risk conditions which may be input to risk level predictions. Therefore, this paper suggests a conceptual modelling procedure that is able to define appropriate worst case conditions in complex risk management. The result of the analysis is an assembly of system models, denoted the Worst Case Definition (WCD) model, to set up and evaluate the conditions of multi-dimensional risk identification and risk quantification. The model can help optimize risk assessment planning by initial screening level analyses and guiding quantitative assessment in relation to knowledge needs for better decision support concerning environmental and human health protection or risk reduction. The WCD model facilitates the evaluation of fundamental uncertainty using knowledge mapping principles and techniques in a way that can improve a complete uncertainty analysis. Ultimately, the WCD is applicable for describing risk contributing factors in relation to many different types of risk management problems since it transparently and effectively handles assumptions and definitions and allows the integration of different forms of knowledge, thereby supporting the inclusion of multifaceted risk components in cumulative risk management. (C) 2009 Elsevier B.V. All rights reserved.
C1 [Sorensen, Peter B.] Univ Aarhus, Natl Environm Res Inst, Dept Terr Ecol, DK-8600 Silkeborg, Denmark.
   [Thomsen, Marianne] Univ Aarhus, Natl Environm Res Inst, Dept Policy Anal, DK-4000 Roskilde, Denmark.
   [Assmuth, Timo] Finnish Environm Inst, FI-00251 Helsinki, Finland.
   [Grieger, Khara B.; Baun, Anders] Tech Univ Denmark, Dept Environm Engn, DK-2800 Lyngby, Denmark.
RP Sorensen, PB (通讯作者)，Univ Aarhus, Natl Environm Res Inst, Dept Terr Ecol, Vejlsovej 25,POB 314, DK-8600 Silkeborg, Denmark.
EM pbs@dmu.dk
RI Sørensen, Peter/J-4280-2013; , Peter/X-2881-2019; Sørensen, Peter
   B/B-7114-2019; Grieger, Khara/S-1214-2019; Baun, Anders/A-1330-2010
OI Sørensen, Peter/0000-0001-5557-5176; , Peter/0000-0003-1338-7198;
   Sørensen, Peter B/0000-0003-1338-7198; Baun, Anders/0000-0003-1396-408X;
   Thomsen, Marianne/0000-0003-2453-5141; Thomsen,
   Marianne/0000-0002-5254-606X
FU EU [003956]
FX This work has been part of and has been funded within the project Novel
   Methods for Integrated Risk Assessment of Cumulative Stressors in Europe
   (NoMiracle, contract No. 003956) under the EU's 6th Framework Program
   for R&D. Additional support from our employers is acknowledged.
CR [Anonymous], 2004, RIVM MNP GUIDANCE UN
   Apitz Sabine E., 2008, Integrated Environmental Assessment and Management, V4, P273, DOI 10.1897/1551-3793(2008)4[273:METIOI]2.0.CO;2
   Baun A, 2009, J ENVIRON MONITOR, V11, P1774, DOI 10.1039/b909730a
   Downs TJ, 2001, ECOSYST HEALTH, V7, P266, DOI 10.1046/j.1526-0992.2001.01038.x
   DUDEWICZ EJ, 1988, MODERN MATH STAT
   Grieger KD, 2009, NANOTOXICOLOGY, V3, P1, DOI 10.1080/17435390902944069
   HILDEN M, 1997, MONOGR BOREAL ENV RE, V5
   ISHIKAWA K., 1990, INTRO QUALITY CONTRO
   Kjaerulff UB, 2008, INFORM SCI STAT, P3
   LEPPANEN M, 2007, ABSTRACTION ONTOLOGY
   *RCEP, 2008, 27 ROYAL COMM ENV PO
   Scavarda A. J., 2004, 2 WORLD C POM 15 ANN
   SCENIHR Scientific Committee on Emerging and Newly Identified Health Risks, 2007, APPR RISK ASS METH A
   SORENSEN PB, 2009, AM J APPL SCI, V6, P1255
   van der Sluijs J, 2005, WATER SCI TECHNOL, V52, P133, DOI 10.2166/wst.2005.0161
   Walker W.E., 2003, INTEGR ASSESS, V4, P5, DOI DOI 10.1076/IAIJ.4.1.5.16466
   Wetzel R, 1975, LIMNOLOGY LAKE RIVER
   Wille R, 1982, ORDERED SETS, P445, DOI [DOI 10.1007/978-94-009-7798-3_15, 10.1007/978-94-009-7798-3_15]
NR 18
TC 9
Z9 9
U1 0
U2 21
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0048-9697
EI 1879-1026
J9 SCI TOTAL ENVIRON
JI Sci. Total Environ.
PD AUG 15
PY 2010
VL 408
IS 18
SI SI
BP 3852
EP 3859
DI 10.1016/j.scitotenv.2009.11.010
PG 8
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 639DA
UT WOS:000280949800015
PM 19945144
DA 2022-08-23
ER

PT J
AU Monka, S
   Halilaj, L
   Rettinger, A
AF Monka, Sebastian
   Halilaj, Lavdim
   Rettinger, Achim
TI A survey on visual transfer learning using knowledge graphs
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge graph; visual transfer learning; knowledge-based machine
   learning
ID CLASSIFICATION; LANGUAGE; DIMENSIONALITY; FEATURES
AB The information perceived via visual observations of real-world phenomena is unstructured and complex. Computer vision (CV) is the field of research that attempts to make use of that information. Recent approaches of CV utilize deep learning (DL) methods as they perform quite well if training and testing domains follow the same underlying data distribution. However, it has been shown that minor variations in the images that occur when these methods are used in the real world can lead to unpredictable and catastrophic errors. Transfer learning is the area of machine learning that tries to prevent these errors. Especially, approaches that augment image data using auxiliary knowledge encoded in language embeddings or knowledge graphs (KGs) have achieved promising results in recent years. This survey focuses on visual transfer learning approaches using KGs, as we believe that KGs are well suited to store and represent any kind of auxiliary knowledge. KGs can represent auxiliary knowledge either in an underlying graph-structured schema or in a vector-based knowledge graph embedding. Intending to enable the reader to solve visual transfer learning problems with the help of specific KG-DL configurations we start with a description of relevant modeling structures of a KG of various expressions, such as directed labeled graphs, hypergraphs, and hyper-relational graphs. We explain the notion of feature extractor, while specifically referring to visual and semantic features. We provide a broad overview of knowledge graph embedding methods and describe several joint training objectives suitable to combine them with high dimensional visual embeddings. The main section introduces four different categories on how a KG can be combined with a DL pipeline: 1) Knowledge Graph as a Reviewer; 2) Knowledge Graph as a Trainee; 3) Knowledge Graph as a Trainer; and 4) Knowledge Graph as a Peer. To help researchers find meaningful evaluation benchmarks, we provide an overview of generic KGs and a set of image processing datasets and benchmarks that include various types of auxiliary knowledge. Last, we summarize related surveys and give an outlook about challenges and open issues for future research.
C1 [Monka, Sebastian; Halilaj, Lavdim] Robert Bosch GmbH, Corp Res, Renningen, Germany.
   [Monka, Sebastian; Rettinger, Achim] Trier Univ, Comp Sci, Trier, Germany.
RP Monka, S (通讯作者)，Robert Bosch GmbH, Corp Res, Renningen, Germany.; Monka, S (通讯作者)，Trier Univ, Comp Sci, Trier, Germany.
EM sebastian.monka@de.bosch.com; lavdim.halilai@de.bosch.com;
   rettinger@uni-trier.de
FU project "KI Delta Learning" - Federal Ministry for Economic Affairs and
   Energy (BMWi) [19A19013D]
FX This publication was created as part of the research project "KI Delta
   Learning" (project number: 19A19013D) funded by the Federal Ministry for
   Economic Affairs and Energy (BMWi) on the basis of a decision by the
   German Bundestag.
CR Aditya S, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P6252
   Akata Z, 2016, IEEE T PATTERN ANAL, V38, DOI 10.1109/TPAMI.2015.2487986
   Angles R., 2018, GRAPH DATA MANAGEMEN, P1, DOI 10.1007/978-3-319-63962- 8_75-1.
   Angles R, 2020, IEEE ACCESS, V8, P86091, DOI 10.1109/ACCESS.2020.2993117
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Ba JL, 2015, IEEE I CONF COMP VIS, P4247, DOI 10.1109/ICCV.2015.483
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Bansal A, 2018, LECT NOTES COMPUT SC, V11205, P397, DOI 10.1007/978-3-030-01246-5_24
   Baytas IM, 2018, IEEE DATA MINING, P875, DOI 10.1109/ICDM.2018.00104
   Bertinetto L., 2019, INT C LEARN REPR
   Bishop M.C, 2006, PATTERN RECOGN
   Blanchard G., 2011, ADV NEURAL INFORM PR, V24, P2178
   Boudiaf M., 2020, P EUR C COMP VIS, P548
   Bridle JS., 1990, NEUROCOMPUTING, P227, DOI [10.1007/978-3-642-76153-9_28, DOI 10.1007/978-3-642-76153-9_28]
   Chami I., 2020, ABS200503675 CORR
   Changpinyo S, 2016, PROC CVPR IEEE, P5327, DOI 10.1109/CVPR.2016.575
   Chari S, 2020, STUD SEMANTIC WEB, V47, P245, DOI 10.3233/SSW200022
   Chen J., 2021, P 30 INT JOINT C ART, P4366, DOI [10.24963/ijcai.2021/597, DOI 10.24963/IJCAI.2021/597]
   Chen JY, 2018, SIXTEENTH INTERNATIONAL CONFERENCE ON PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P349
   Chen RQ, 2020, AAAI CONF ARTIF INTE, V34, P10575
   Chen T., 2020, NEURIPS
   Chen XL, 2018, PROC CVPR IEEE, P7239, DOI 10.1109/CVPR.2018.00756
   Chen ZM, 2019, PROC CVPR IEEE, P5172, DOI 10.1109/CVPR.2019.00532
   Dalal N, 2005, PROC CVPR IEEE, P886, DOI 10.1109/cvpr.2005.177
   DAmour A, 2020, ARXIV201103395
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Deng J, 2014, LECT NOTES COMPUT SC, V8689, P48, DOI 10.1007/978-3-319-10590-1_4
   Dessi D, 2021, FUTURE GENER COMP SY, V116, P253, DOI 10.1016/j.future.2020.10.026
   Dimou A., 2014, P WORKSH LINK DAT WE, V1184
   Ding ZY, 2019, PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON COMPUTER MODELING AND SIMULATION (ICCMS 2019) AND 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND APPLICATIONS (ICICA 2019), P142, DOI 10.1145/3307363.3307387
   Donahue J, 2014, PR MACH LEARN RES, V32
   Fatemi B, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2191
   Feng YF, 2019, AAAI CONF ARTIF INTE, P3558
   Frome Andrea, 2013, ADV NEURAL INFORM PR, P2121
   Fu YW, 2015, IEEE T PATTERN ANAL, V37, P2332, DOI 10.1109/TPAMI.2015.2408354
   Furber M., 2015, SEMANTIC WEB, V1, P1
   Futia G, 2020, INFORMATION, V11, DOI 10.3390/info11020122
   Galkin M., 2020, P 2020 C EMP METH NA, P7346, DOI 10.18653/v1/2020. emnlp-main.596
   Gao JY, 2019, AAAI CONF ARTIF INTE, P8303
   Gebru T, 2017, AAAI CONF ARTIF INTE, P4502
   Gebru T, 2017, IEEE I CONF COMP VIS, P1358, DOI 10.1109/ICCV.2017.151
   Geirhos Robert, 2020, Nature Machine Intelligence, V2, P665, DOI 10.1038/s42256-020-00257-z
   Geng YX, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P3325, DOI 10.1145/3442381.3450042
   Gong K, 2019, PROC CVPR IEEE, P7442, DOI 10.1109/CVPR.2019.00763
   Goodfellow I., 2015, P 3 INT C LEARNING R
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Gori M, 2005, IEEE IJCNN, P729
   Guan SP, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P583, DOI 10.1145/3308558.3313414
   Gui H, 2016, IEEE DATA MINING, P907, DOI [10.1109/ICDM.2016.0111, 10.1109/ICDM.2016.42]
   Hadsell Raia, 2006, CVPR
   Hendrycks D., 2020, ABS200616241 CORR
   Hendrycks D, 2021, PROC CVPR IEEE, P15257, DOI 10.1109/CVPR46437.2021.01501
   Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647
   Hogan A., 2020, ARXIV PREPRINT ARXIV
   Huang J, 2019, LECT NOTES COMPUT SC, V11448, P273, DOI 10.1007/978-3-030-18590-9_27
   Ilievski F., 2020, WIKIDATA WORKSHOP IS
   Jayathilaka M., 2021, CEUR WORKSHOP P, V2997
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Jia D, 2012, PROC CVPR IEEE, P3450, DOI 10.1109/CVPR.2012.6248086
   Jianfeng W., 2016, P 25 INT JOINT C ART, P1300
   Jiang CH, 2018, ADV NEUR IN, V31
   Jiang HJ, 2019, IEEE I CONF COMP VIS, P9764, DOI 10.1109/ICCV.2019.00986
   Joulin A, 2016, LECT NOTES COMPUT SC, V9911, P67, DOI 10.1007/978-3-319-46478-7_5
   Kaboli M., 2017, RES REPORT
   Kampffmeyer M, 2019, PROC CVPR IEEE, P11479, DOI 10.1109/CVPR.2019.01175
   Karpathy A, 2015, PROC CVPR IEEE, P3128, DOI 10.1109/CVPR.2015.7298932
   Kavukcuoglu K, 2009, PROC CVPR IEEE, P1605, DOI 10.1109/CVPRW.2009.5206545
   Kertkeidkachorn N, 2018, IEICE T INF SYST, VE101D, P90, DOI 10.1587/transinf.2017SWP0006
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Kodirov E, 2017, PROC CVPR IEEE, P4447, DOI 10.1109/CVPR.2017.473
   Kornblith S., 2020, ABS201016402 CORR
   Krishna R, 2017, INT J COMPUT VISION, V123, P32, DOI 10.1007/s11263-016-0981-7
   Krizhevsky A. e. a., 2009, LEARNING MULTIPLE LA
   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
   Larochelle Hugo, 2008, AAAI, P646
   Lee CW, 2018, PROC CVPR IEEE, P1576, DOI 10.1109/CVPR.2018.00170
   Li JJ, 2019, PROC CVPR IEEE, P7394, DOI 10.1109/CVPR.2019.00758
   Li X., 2020, PROC EUROPEAN C COMP, V12375, DOI 10.1007/978-3-030-58577-8_8
   Liang X., 2018, P ADV NEUR INF PROC, P1858
   Lin TY, 2014, LECT NOTES COMPUT SC, V8693, P740, DOI 10.1007/978-3-319-10602-1_48
   Liu Y, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P1104, DOI 10.1145/3366423.3380188
   Liu Z., 2019, ABS190804385 CORR
   Lonij V.P.A., 2017, ABS170808310 CORR
   Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94
   Luo RT, 2020, AAAI CONF ARTIF INTE, V34, P11709
   Marino K, 2017, PROC CVPR IEEE, P20, DOI 10.1109/CVPR.2017.10
   Mikolov T., 2013, PROBLEM SOLVING ALGO, P3111
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mitchell TM, 2008, SCIENCE, V320, P1191, DOI 10.1126/science.1152876
   Monka S, 2021, LECT NOTES COMPUT SC, V12922, P357, DOI 10.1007/978-3-030-88361-4_21
   Muandet Krikamol, 2013, INT C MACH LEARN, P10
   Naeem M.F., 2021, ABS210201987 CORR
   Naeem MF, 2021, PROC CVPR IEEE, P953, DOI 10.1109/CVPR46437.2021.00101
   Norouzi M., 2014, 2 INT C LEARN REPR I
   Ordonez V., 2011, ADV NEURAL INFORM PR, P1143
   Ordonez V, 2013, IEEE I CONF COMP VIS, P2768, DOI 10.1109/ICCV.2013.344
   Oreshkin BN, 2018, ADV NEUR IN, V31
   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
   Patterson G, 2012, PROC CVPR IEEE, P2751, DOI 10.1109/CVPR.2012.6247998
   Peng X., 2017, ARXIV PREPRINT ARXIV
   Peng ZM, 2019, IEEE I CONF COMP VIS, P441, DOI 10.1109/ICCV.2019.00053
   Pennington J., 2014, EMNLP, P1532
   Petersen K., 2008, WORKSHOPS COMPUTING
   Petersen K, 2015, INFORM SOFTWARE TECH, V64, P1, DOI 10.1016/j.infsof.2015.03.007
   Plexousakis D., 2020, CEUR WORKSHOP P
   Radford A., 2021, ARXIV210300020
   Recht B, 2019, PR MACH LEARN RES, V97
   Ren M., 2018, INT C LEARN REPR
   Ren SQ, 2015, ADV NEUR IN, V28, DOI 10.1109/TPAMI.2016.2577031
   Rochan M, 2015, PROC CVPR IEEE, P4315, DOI 10.1109/CVPR.2015.7299060
   Rohrbach  Marcus, 2013, NIPS
   Rosso P, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P1885, DOI 10.1145/3366423.3380257
   Roy A., 2020, ABS201206236 CORR
   Ruder S., 2017, EMNLP
   Russakovsky O, 2015, INT J COMPUT VISION, V115, P211, DOI 10.1007/s11263-015-0816-y
   Saenko K, 2010, LECT NOTES COMPUT SC, V6314, P213, DOI 10.1007/978-3-642-15561-1_16
   Salakhutdinov R, 2011, PROC CVPR IEEE, P1481, DOI 10.1109/CVPR.2011.5995720
   Seeliger A., 2019, LITERARY REV, V2465, P30
   Shahbaba B, 2007, BAYESIAN ANAL, V2, P221, DOI 10.1214/07-BA209
   Shankar V., 2021, P IEEE CVF INT C COM, P9661
   Sharma P, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2556
   Simonyan K., 2014, ARXIV
   Socher R., 2013, EMNLP, P1631, DOI DOI 10.1371/JOURNAL.PONE.0073791
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Sung F, 2018, PROC CVPR IEEE, P1199, DOI 10.1109/CVPR.2018.00131
   Tan CQ, 2018, LECT NOTES COMPUT SC, V11141, P270, DOI 10.1007/978-3-030-01424-7_27
   Tang YX, 2018, IEEE T PATTERN ANAL, V40, P3045, DOI 10.1109/TPAMI.2017.2771779
   Tsai YHH, 2017, IEEE I CONF COMP VIS, P3591, DOI 10.1109/ICCV.2017.386
   Tu K, 2018, AAAI CONF ARTIF INTE, P426
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Vaswani A, 2017, ADV NEUR IN, V30
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Venkateswara H, 2017, PROC CVPR IEEE, P5385, DOI 10.1109/CVPR.2017.572
   Vinyals O, 2016, ADV NEURAL INFORM PR, V29, P1
   von Ruden L., 2019, INFORM MACHINE LEARN
   Vrandecic D., 2012, P 21 INT C WORLD WID, P1063, DOI [DOI 10.1145/2187980.2188242, 10.1145/2187980.2188242]
   Vyas Maunil R., 2020, Computer Vision - ECCV 2020 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12375), P70, DOI 10.1007/978-3-030-58577-8_5
   Wang H, 2020, AAAI CONF ARTIF INTE, V34, P12160
   Wang J, 2014, PROC CVPR IEEE, P1386, DOI 10.1109/CVPR.2014.180
   Wang M, 2018, NEUROCOMPUTING, V312, P135, DOI 10.1016/j.neucom.2018.05.083
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang W, 2019, ACM T INTEL SYST TEC, V10, DOI 10.1145/3293318
   Wang XC, 2003, PATTERN RECOGN, V36, P2429, DOI 10.1016/S0031-3203(03)00044-X
   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
   Welinder P., 2010, CNSTR2010001 CAL I T
   Wohlin C., 2014, P 18 INT C EV ASS SO, P1, DOI [10.1145/2601248.2601268, DOI 10.1145/2601248.2601268]
   Xian YQ, 2019, IEEE T PATTERN ANAL, V41, P2251, DOI 10.1109/TPAMI.2018.2857768
   Yadati N, 2019, P ADV NEUR INF PROC, P1509
   Yang Y., 2015, P INT C LEARN REPR, P1
   Young P., 2014, P TACL, V2, P67, DOI DOI 10.1162/TACL_A_00166
   Yu CA, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1707, DOI 10.1145/3269206.3269274
   Yu Fei, 2020, ARXIV200616934
   Zhang B., 2020, P 2020 C EMP METH NA, P823, DOI [10.18653/v1/2020.emnlp-main.60, DOI 10.18653/V1/2020.EMNLP-MAIN.60]
   Zhang J, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3291124
   Zhang L, 2019, ABS190304687 CORR
   Zhang L, 2017, PROC CVPR IEEE, P3010, DOI 10.1109/CVPR.2017.321
   Zhang R., 2020, 8 INT C LEARN REPR I
   Zhang RC, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1185, DOI 10.1145/3178876.3186017
   Zhang YY, 2021, IEEE INTERNET THINGS, V8, P6372, DOI 10.1109/JIOT.2020.3033434
   Zhang ZM, 2015, IEEE I CONF COMP VIS, P4166, DOI 10.1109/ICCV.2015.474
   Zhao H, 2017, IEEE I CONF COMP VIS, P2021, DOI 10.1109/ICCV.2017.221
   Zhou BL, 2019, INT J COMPUT VISION, V127, P302, DOI 10.1007/s11263-018-1140-0
   Zhu YZ, 2018, PROC CVPR IEEE, P1004, DOI 10.1109/CVPR.2018.00111
NR 167
TC 2
Z9 2
U1 5
U2 5
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 477
EP 510
DI 10.3233/SW-212959
PG 34
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:000780041500009
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Li, N
   Yang, ZH
   Luo, L
   Wang, L
   Zhang, Y
   Lin, HF
   Wang, J
AF Li, Nan
   Yang, Zhihao
   Luo, Ling
   Wang, Lei
   Zhang, Yin
   Lin, Hongfei
   Wang, Jian
TI KGHC: a knowledge graph for hepatocellular carcinoma
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article; Proceedings Paper
CT 5th China Health Information Processing Conference
CY NOV 22-24, 2019
CL Guangzhou, PEOPLES R CHINA
DE Hepatocellular carcinoma; Information extraction; Knowledge graph
ID DATABASE
AB Background: Hepatocellular carcinoma is one of the most general malignant neoplasms in adults with high mortality. Mining relative medical knowledge from rapidly growing text data and integrating it with other existing biomedical resources will provide support to the research on the hepatocellular carcinoma. To this purpose, we constructed a knowledge graph for Hepatocellular Carcinoma (KGHC).
   Methods: We propose an approach to build a knowledge graph for hepatocellular carcinoma. Specifically, we first extracted knowledge from structured data and unstructured data. Since the extracted entities may contain some noise, we applied a biomedical information extraction system, named BioIE, to filter the data in KGHC. Then we introduced a fusion method which is used to fuse the extracted data. Finally, we stored the data into the Neo4j which can help researchers analyze the network of hepatocellular carcinoma.
   Results: KGHC contains 13,296 triples and provides the knowledge of hepatocellular carcinoma for healthcare professionals, making them free of digging into a large amount of biomedical literatures. This could hopefully improve the efficiency of researches on the hepatocellular carcinoma. KGHC is accessible free for academic research purpose at http://202.118.75.18:18895/browser/.
   Conclusions: In this paper, we present a knowledge graph associated with hepatocellular carcinoma, which is constructed with vast amounts of structured and unstructured data. The evaluation results show that the data in KGHC is of high quality.
C1 [Li, Nan; Yang, Zhihao; Luo, Ling; Lin, Hongfei; Wang, Jian] Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116024, Peoples R China.
   [Wang, Lei; Zhang, Yin] Beijing Inst Hlth Adm & Med Informat, Beijing 100850, Peoples R China.
RP Yang, ZH (通讯作者)，Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116024, Peoples R China.; Wang, L (通讯作者)，Beijing Inst Hlth Adm & Med Informat, Beijing 100850, Peoples R China.
EM yangzh@dlut.edu.cn; wangleibihami@gmail.com
RI Luo, Ling/AAA-3476-2019
OI Luo, Ling/0000-0002-5141-0259
FU National Key Research and Development Program of China [2016YFC0901902]
FX Publication costs are funded by the grant from the National Key Research
   and Development Program of China (No. 2016YFC0901902).
CR Amit S., 2012, INTRO KNOWLEDGE GRAP
   Balogh J, 2016, J HEPATOCELL CARCINO, V3, P41, DOI 10.2147/JHC.S61146
   Barrett T, 2012, NUCLEIC ACIDS RES, V40, pD57, DOI 10.1093/nar/gkr1163
   Benson DA, 2010, NUCLEIC ACIDS RES, V38, pD46, DOI [10.1093/nar/gkp1024, 10.1093/nar/gks1195, 10.1093/nar/gkn723, 10.1093/nar/gkt1030, 10.1093/nar/gkw1070, 10.1093/nar/gkq1079]
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Crissien Ana Maria, 2014, Gastroenterol Hepatol (N Y), V10, P153
   Danishuddin Mohd, 2012, Genomics Proteomics & Bioinformatics, V10, P360, DOI 10.1016/j.gpb.2012.04.002
   Dobson R., 2017, INT C DIG HLTH, P67
   Ernst P, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0549-5
   Forner A, 2012, LANCET, V379, P1245, DOI 10.1016/S0140-6736(11)61347-0
   Giorgi JM, 2018, BIOINFORMATICS, V34, P4087, DOI 10.1093/bioinformatics/bty449
   Ji B, 2019, BMC MED INFORM DECIS, V19, DOI 10.1186/s12911-019-0767-2
   Kamdar AMR, 2015, DATABASE, V2015, P1
   Kilicoglu H, 2012, BIOINFORMATICS, V28, P3158, DOI 10.1093/bioinformatics/bts591
   Kilicoglu HF-M, 2008, P 3 INT S SEM MIN BI
   Kringelum J, 2016, DATABASE-OXFORD, DOI 10.1093/database/bav123
   Kuhn M, 2016, NUCLEIC ACIDS RES, V44, pD1075, DOI 10.1093/nar/gkv1075
   Leaman R, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/1758-2946-7-S1-S3
   LEVANDOWSKY M, 1971, NATURE, V234, P34, DOI 10.1038/234034a0
   Lu YA, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/1758-2946-7-S1-S4
   Luo L, 2018, BIOINFORMATICS, V34, P1381, DOI 10.1093/bioinformatics/btx761
   National Center for Biotechnology Information (US), 2005, PUBMED HELP
   National Library of Medicine (US), 2005, MEDLINEPLUS
   Rindflesch Thomas C., 2011, Information Services & Use, V31, P15, DOI 10.3233/ISU-2011-0627
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Ruan T, 2016, BIOL ONTOLOGIES KNOW
   SANTISTEBAN J., 2015, CEUR WORKSHOP PROC, V1393, P23
   Shi LX, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/2858423
   Siu A, 2016, DISAMBIGUATION ENTIT, P72
   Taccioli C, 2011, DATABASE-OXFORD, DOI 10.1093/database/bar007
   Urbach D., 2011, BIODATA MIN, V4, P1
   Webber J., 2018, PROGRAMMATIC INTRO N
   Wheeler DL, 2007, NUCLEIC ACIDS RES, V35, pD5, DOI 10.1093/nar/gkl1031
   Xu W., 2015, ARXIV
   Yuan JB, 2020, KNOWL INF SYST, V62, P317, DOI 10.1007/s10115-019-01351-4
   Zhou ZQ, 2016, FRONT ARTIF INTEL AP, V285, P73, DOI 10.3233/978-1-61499-672-9-73
NR 37
TC 2
Z9 2
U1 4
U2 17
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 JUL 9
PY 2020
VL 20
SU 3
SI SI
AR 135
DI 10.1186/s12911-020-1112-5
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Medical Informatics
GA MP1ZO
UT WOS:000552009700017
PM 32646496
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Liu, JS
   Wang, CY
   Li, CX
   Li, NX
   Deng, J
   Pan, JZ
AF Liu, Jinshuo
   Wang, Chenyang
   Li, Chenxi
   Li, Ningxi
   Deng, Juan
   Pan, Jeff Z.
TI DTN: Deep triple network for topic specific fake news detection
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge graph; Knowledge graph embedding; Multi-channel; Deep
   learning; Fake news detection
AB Detection of fake news has spurred widespread interests in areas such as healthcare and Internet societies, in order to prevent propagating misleading information for commercial and political purposes. However, efforts to study a general framework for exploiting knowledge, for judging the trustworthiness of given news based on their content, have been limited. Indeed, the existing works rarely consider incorporating knowledge graphs (KGs), which could provide rich structured knowledge for better language understanding.
   In this work, we propose a deep triple network (DTN) that leverages knowledge graphs to facilitate fake news detection with triple-enhanced explanations. In the DTN, background knowledge graphs, such as open knowledge graphs and extracted graphs from news bases, are applied for both low-level and high-level feature extraction to classify the input news article and provide explanations for the classification.
   The performance of the proposed method is evaluated by demonstrating abundant convincing comparative experiments. Obtained results show that DTN outperforms conventional fake news detection methods from different aspects, including the provision of factual evidence supporting the decision of fake news detection. (C) 2021 Published by Elsevier B.V.
C1 [Liu, Jinshuo; Wang, Chenyang; Li, Chenxi; Li, Ningxi; Deng, Juan] Wuhan Univ, Wuhan, Peoples R China.
   [Pan, Jeff Z.] Univ Aberdeen, Aberdeen, Scotland.
   [Pan, Jeff Z.] Univ Edinburgh, Edinburgh, Midlothian, Scotland.
RP Pan, JZ (通讯作者)，Univ Edinburgh, Edinburgh, Midlothian, Scotland.
FU National Key RD Plan of China [2020YFA0607902]; National Natural Science
   Foundation of China [U1936107]; EU K-Drive project [286348];
   Aberdeen-Wuhan Joint Research Institute
FX This paper is supported by Aberdeen-Wuhan Joint Research Institute,
   National Key R&D Plan of China 2020YFA0607902, National Natural Science
   Foundation of China U1936107, and the EU K-Drive project (286348).
CR Allcott H, 2017, J ECON PERSPECT, V31, P211, DOI 10.1257/jep.31.2.211
   Annervaz K. M., 2018, PROC C N AM CHAPTER, P313
   Auer S, 2018, P INT C WEB INT MIN, DOI DOI 10.1186/1475-2859-8-19
   Bader Sebastian R., 2020, Semantic Web 17th International Conference, ESWC 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12123), P465, DOI 10.1007/978-3-030-49461-2_27
   Banksy P., 2019, P JOINT INT SEM TECH, P226
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Castillo Carlos, 2011, P 20 INT C WORLD WID, P675, DOI 10.1145/1963405.1963500
   Chen J., 2013, P 2 ACM SIGSPATIAL I
   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
   Ciampaglia GL, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0141938
   CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1007/BF00994018
   Deng SM, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P678, DOI 10.1145/3308560.3317701
   Dietz L, 2018, ACM/SIGIR PROCEEDINGS 2018, P1387, DOI 10.1145/3209978.3210187
   Edelstein E, 2020, NEW GENERAT COMPUT, V38, P397, DOI 10.1007/s00354-020-00087-y
   Fawei B., 2020, NEW GENERAT COMPUT, P453
   Fernandez-Reyes FC, 2018, LECT NOTES ARTIF INT, V11238, P206, DOI 10.1007/978-3-030-03928-8_17
   Finkel J.R., 2005, P 43 ANN M ASS COMP, P363, DOI DOI 10.3115/1219840.1219885
   Geng Y., 2021, P 30 WEB C WWW 2021
   Gilda S, 2017, IEEE ST CONF RES DEV, P110, DOI 10.1109/SCORED.2017.8305411
   Granik M, 2017, 2017 IEEE FIRST UKRAINE CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (UKRCON), P900, DOI 10.1109/UKRCON.2017.8100379
   He H, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1766, DOI 10.18653/v1/P17-1162
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Huang ZH, 2015, COMPUT INTEL NEUROSC, V2015, DOI 10.1155/2015/685404
   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
   Joulin Armand, 2016, ARXIV160701759
   Kim Y., 2014, PROC C EMPIRICAL MET, P1746, DOI [10.3115/v1/d14-1181, 10.3115/v1/D14-1181]
   Lai SW, 2015, AAAI CONF ARTIF INTE, P2267
   Lecue F, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1779
   Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791
   LeCun Y., 1990, P ADV NEUR INF PROC, VVolume 2
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu Y, 2019, IEEE ACCESS, V7, P154480, DOI 10.1109/ACCESS.2019.2946624
   Ma J., 2015, P 24 ACM INT C INF K, P1751, DOI DOI 10.1145/2806416.2806607
   McCallum A., 1998, P AAAI 98 WORKSH LEA, P41, DOI 10.1.1.46.1529
   Mikolov T., 2014, ARXIV14127753
   Pan J., 2017, REASONING WEB LOGICA
   Pan J.Z., 2014, SEMANTIC WEB ENABLED
   Pan J.Z., 2009, P 6 EUR SEM WEB C ES
   Pan J.Z., 2017, EXPLOITING LINKED DA
   Pan JZ, 2019, LECT NOTES COMPUT SC, V11778, P523, DOI 10.1007/978-3-030-30793-6_30
   Pan JZ, 2018, LECT NOTES COMPUT SC, V11136, P669, DOI 10.1007/978-3-030-00671-6_39
   Pan JZ, 2013, ONTOLOGY DRIVEN SOFT
   Peterson L.E., 2009, SCHOLARPEDIA, V4, P1883, DOI [10.4249/scholarpedia.1883, DOI 10.4249/SCHOLARPEDIA.1883]
   Popat K., 2018, ARXIV PREPRINT ARXIV
   Rajaram S., 2018, NEURAL ARCHITECTURES, DOI [10.13140/RG.2.2.36649.70245, DOI 10.13140/RG.2.2.36649.70245]
   Rajdev M, 2015, 2015 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT), VOL 1, P17, DOI 10.1109/WI-IAT.2015.102
   Samuel Hamman, 2018, Advances in Artificial Intelligence. 31st Canadian Conference on Artificial Intelligence, Canadian AI 2018. Proceedings: LNAI 10832, P108, DOI 10.1007/978-3-319-89656-4_9
   Shi BX, 2018, AAAI CONF ARTIF INTE, P1957
   Shi BX, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16 COMPANION), P101, DOI 10.1145/2872518.2889354
   Shiralkar P, 2017, IEEE DATA MINING, P859, DOI 10.1109/ICDM.2017.105
   Siegemund K., 2011, P INT WORKSH SEM WEB
   Singh K, 2019, LECT NOTES COMPUT SC, V11779, P277, DOI 10.1007/978-3-030-30796-7_18
   Sun Zhiqing, 2019, P ICLR
   Sutskever I., 2011, P 28 ANN INT C MACH, V28, P1017
   Taylor S., 2013, P 3 JOINT INT C SEM
   Tresp D.K.V, 2015, P 2 WORKSH LINK DAT
   Tripodi IJ, 2020, TOXICOL IN VITRO, V66, DOI 10.1016/j.tiv.2020.104877
   Vaswani A, 2017, ADV NEUR IN, V30
   Vo E.B. Duc-Thuan, 2016, ENCYCL SEMANT COMPUT, P1
   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 J., 2017, P IJCAI, P350
   Wang WS, 2017, P AMER CONTR CONF, P1133, DOI 10.23919/ACC.2017.7963105
   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
   Wiharja K, 2020, J WEB SEMANT, V65, DOI 10.1016/j.websem.2020.100616
   Wu HH, 2020, STUD HEALTH TECHNOL, V270, P1327, DOI 10.3233/SHTI200425
   Xu H, 2015, J KNOWL MANAG, V19, P60, DOI 10.1108/JKM-11-2014-0452
   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 Z, 2016, P 2016 C N AM CHAPTE, P1480
   Zhou T., 2019, P 12 ACM INT C WEB, P114, DOI DOI 10.1145/3289600.3290970
NR 73
TC 1
Z9 1
U1 5
U2 5
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 JUL
PY 2021
VL 70
AR 100646
DI 10.1016/j.websem.2021.100646
EA SEP 2021
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YZ0ME
UT WOS:000755176800001
DA 2022-08-23
ER

PT J
AU Yan, JZ
   Lv, TT
   Yu, YC
AF Yan, Jianzhuo
   Lv, Tiantian
   Yu, Yongchuan
TI Construction and Recommendation of a Water Affair Knowledge Graph
SO SUSTAINABILITY
LA English
DT Article
DE structured data; unstructured data; water affair; knowledge graph
   construction; information recommendation
ID ONTOLOGY; SEARCH
AB Water affair data mainly consists of structured data and unstructured data, and the storage methods of data are diverse and heterogeneous. To meet the needs of water affair information integration, a method of constructing a knowledge graph using a combination of water affair structured and unstructured data is proposed. To meet the needs of a water information search, an information recommendation system for constructing a water affair knowledge graph is proposed. In this paper, the edit distance algorithm and latent Dirichlet allocation (LDA) algorithm are used to construct a water affair structured data and unstructured data combination knowledge graph, and this graph is validated based on the semantic distance algorithm. Finally, this paper uses the recall rate, accuracy rate, and F comprehensive results to compare the algorithms. The evaluation results of the edit distance algorithm and the LDA algorithm exceed 90%, which is greater than the comparison algorithm, thus confirming the validity and accuracy of the construction of a water affair knowledge graph. Furthermore, a set of water affair verification sets is used to verify the recommendation method, which proves the effectiveness of the recommended method.
C1 [Yan, Jianzhuo; Lv, Tiantian; Yu, Yongchuan] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China.
RP Yan, JZ (通讯作者)，Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China.
EM yanjianzhuo@bjut.edu.cn; 1543368448@emails.bjut.edu.cn;
   yuyongchuan@bjut.edu.cn
OI Lv, Tiantian/0000-0002-1081-8629
FU Water Pollution Control and Treatment Science and Technology Major
   Project [2018ZX07111005]
FX This research was funded by [Water Pollution Control and Treatment
   Science and Technology Major Project] grant number [2018ZX07111005] and
   the APC was funded by [Water Pollution Control and Treatment Science and
   Technology Major Project].
CR Abu-Aisheh Z, 2018, EXPERT SYST APPL, V94, P41, DOI 10.1016/j.eswa.2017.10.043
   Barbu E, 2015, EXPERT SYST APPL, V42, P3501, DOI 10.1016/j.eswa.2014.11.070
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Castells P, 2005, LECT NOTES COMPUT SC, V3762, P977
   Chen YJ, 2013, INFORM MANAGE-AMSTER, V50, P285, DOI 10.1016/j.im.2013.05.001
   Cheung K, 2009, IEEE INTELL SYST, V24, P47, DOI 10.1109/MIS.2009.13
   Dahir S, 2018, PROCEDIA COMPUT SCI, V127, P499, DOI 10.1016/j.procs.2018.01.148
   Fernandez-Lopez M., 1997, P AAAI97 SPRING S SE, P33, DOI DOI 10.1109/AXMEDIS.2007.19
   GREGOROWICZ A., 2006, MINING LARGE SCALE T
   Hao W., 2017, RES CONSTRUCTION TCM
   Kethavarapu UPK, 2016, PROCEDIA COMPUT SCI, V85, P915, DOI 10.1016/j.procs.2016.05.282
   LeClair A, 2016, PROCEDIA COMPUT SCI, V83, P179, DOI 10.1016/j.procs.2016.04.114
   Li W, 2018, KNOWL-BASED SYST, V146, P203, DOI 10.1016/j.knosys.2018.02.004
   Li WJ, 2011, PROCEDIA ENGINEER, V15, DOI 10.1016/j.proeng.2011.08.721
   Liu Q., 2010, CHIN COMPUT LINGUIST, P2010, DOI [10.3969/j.issn.1001-3695.2010.09.034, DOI 10.3969/J.ISSN.1001-3695.2010.09.034]
   Noy N.F., 2001, KSL0105 STANDF U
   Ren R, 2015, PROCEDIA COMPUT SCI, V55, P843, DOI 10.1016/j.procs.2015.07.151
   Ruy FB, 2017, DATA KNOWL ENG, V109, P41, DOI 10.1016/j.datak.2017.03.004
   Sarkar A, 2013, PROC TECH, V10, P890, DOI 10.1016/j.protcy.2013.12.435
   Shinzato K, 2004, HLT-NAACL 2004: HUMAN LANGUAGE TECHNOLOGY CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE MAIN CONFERENCE, P73
   Sulthana AR, 2019, COMPUT ELECTR ENG, V74, P498, DOI 10.1016/j.compeleceng.2018.01.034
   Tham K.D., 1994, P 3 IEEE WORKSH EN T, P111
   Tu Shouzhong, 2016, Journal of China Universities of Posts and Telecommunications, V23, P40, DOI 10.1016/S1005-8885(16)60056-0
   Uschold M, 1996, KNOWL ENG REV, V11, P93, DOI 10.1017/S0269888900007797
   Vavliakis KN, 2013, J SYST SOFTWARE, V86, P89, DOI 10.1016/j.jss.2012.07.018
   Yates A., 2004, P 13 INT C WORLD WID, P100, DOI DOI 10.1145/988672.988687
   Zhang RQ, 2017, J SYST SOFTWARE, V126, P87, DOI 10.1016/j.jss.2016.03.061
NR 27
TC 5
Z9 5
U1 7
U2 43
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD OCT
PY 2018
VL 10
IS 10
AR 3429
DI 10.3390/su10103429
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 GY4UB
UT WOS:000448559400071
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ahn, YS
   Jeong, OR
AF Ahn, Yeon-Sun
   Jeong, Ok-Ran
TI Time-Aware PolarisX: Auto-Growing Knowledge Graph
SO CMC-COMPUTERS MATERIALS & CONTINUA
LA English
DT Article
DE Machine learning; natural language processing; knowledge graph;
   time-aware; information extraction
AB A knowledge graph is a structured graph in which data obtained from multiple sources are standardized to acquire and integrate human knowledge. Research is being actively conducted to cover a wide variety of knowledge, as it can be applied to applications that help humans. However, existing researches are constructing knowledge graphs without the time information that knowledge implies. Knowledge stored without time information becomes outdated over time, and in the future, the possibility of knowledge being false or meaningful changes is excluded. As a result, they can't reflect information that changes dynamically, and they can't accept information that has newly emerged. To solve this problem, this paper proposes Time-Aware PolarisX, an automatically extended knowledge graph including time information. Time Aware PolarisX constructed a BERT model with a relation extractor and an ensemble NER model including a time tag with an entity extractor to extract knowledge consisting of subject, relation, and object from unstructured text. Through two application experiments, it shows that the proposed system overcomes the limitations of existing systems that do not consider time information when applied to an application such as a chatbot. Also, we verify that the accuracy of the extraction model is improved through a comparative experiment with the existing model.
C1 [Ahn, Yeon-Sun; Jeong, Ok-Ran] Gachon Univ, Dept Software, Seongnam Si 13120, Gyeonggi Do, South Korea.
RP Jeong, OR (通讯作者)，Gachon Univ, Dept Software, Seongnam Si 13120, Gyeonggi Do, South Korea.
EM orjeong@gachon.ac.kr
FU Basic Science Research Program through the NRF (National Research
   Foundation of Korea); MSIT (Ministry of Science and ICT) , Korea, under
   the National Program for Excellence in SW; Gachon University
   [NRF2019R1A2C1008412, 2015-0-00932, GCU-2019-0773]
FX This research was supported by Basic Science Research Program through
   the NRF (National Research Foundation of Korea) , the MSIT (Ministry of
   Science and ICT) , Korea, under the National Program for Excellence in
   SW supervised by the IITP (Institute for Information & communications
   Technology Promotion) and the Gachon University research fund of 2019
   (Nos. NRF2019R1A2C1008412, 2015-0-00932, GCU-2019-0773) .
CR Aguilar G., 2019, ARXIV190604135
   [Anonymous], 2019, ARXIV PREPRINT ARXIV
   Auer S., 2007, SEMANTIC WEB, V4825
   Bos J., 2017, HDB LINGUISTIC ANNOT, V2, P463, DOI DOI 10.1007/978-94-024-0881-2_18
   Cohen A. D. N., 2020, ARXIV201004829
   De Meulder F., 2003, P 7 C NAT LANG LEARN
   Derczynski Leon, 2017, P 3 WORKSH NOIS US G, P140
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dietterich TG, 2000, LECT NOTES COMPUT SC, V1857, P1, DOI 10.1007/3-540-45014-9_1
   Esteban C, 2016, 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), P98
   Garcia-Duran A, 2018, P 2018 C EMP METH NA
   Havasi C., 2007, RECENT ADV NATURAL L, V309, P269, DOI [10.1075/cilt.309.22hav, DOI 10.1075/CILT.309.22HAV]
   Jansson P., 2017, P 3 WORKSH NOIS US G, P154
   Ji S., 2020, ARXIV200200388
   Jiang TY, 2016, P AMER CONTR CONF, P1717, DOI 10.1109/ACC.2016.7525164
   Karpf D., 2009, 2 LIFE PRODUCTION PR
   Leblay J., 2018, COMP WEB C 2018 WEB, P1771
   Lin B., 2017, SAF COAL MINES, V3, P160
   Loper E., 2002, CS0205028 ARXIV
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Mitchell T, 2018, COMMUN ACM, V61, P103, DOI 10.1145/3191513
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   PAL SK, 1992, IEEE T NEURAL NETWOR, V3, P683, DOI 10.1109/72.159058
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sutton C, 2012, FOUND TRENDS MACH LE, V4, P267, DOI 10.1561/2200000013
   Talukdar P. P., 2012, P 21 ACM INT C INF K, P992
   Trivedi R., 2017, ARXIV170505742
   von D_aniken P., 2017, P 3 WORKSH NOIS US G, P166
   Vrandecic D., 2012, P 21 INT C WORLD WID, P1063, DOI [DOI 10.1145/2187980.2188242, 10.1145/2187980.2188242]
   Wang X., 2020, ARXIV201005006
   Xu W., 2015, ARXIV
   Yamada I., 2020, ARXIV201001057
   Yoo S, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112965
   Yoo S, 2018, EXPERT SYST APPL, V105, P102, DOI 10.1016/j.eswa.2018.03.055
   Yu J., 2020, ARXIV200507150
   Zhang Y., 2007, P 2017 C EMP METH NA, P35
   Zhou W., 2020, ARXIV201011304
NR 37
TC 0
Z9 0
U1 4
U2 6
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 67
IS 3
BP 2695
EP 2708
DI 10.32604/cmc.2021.015636
PG 14
WC Computer Science, Information Systems; Materials Science,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Materials Science
GA QQ5NZ
UT WOS:000624571700001
OA gold
DA 2022-08-23
ER

PT J
AU Tang, H
   Liu, BS
   Qian, JB
AF Tang, Hao
   Liu, Baisong
   Qian, Jiangbo
TI Content-based and knowledge graph-based paper recommendation: Exploring
   user preferences with the knowledge graphs for scientific paper
   recommendation
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article
DE graph neural network; high&#8208; order associations; knowledge graph;
   scientific paper recommendation; self&#8208; attention mechanism
AB Researchers usually face difficulties in finding scientific papers relevant to their research interests due to increasing growth. Recommender systems emerge as a leading solution to filter valuable items intelligently. Recently, deep learning algorithms, such as convolutional neural network, improved traditional recommendation technologies, for example, the graph-based or content-based methods. However, existing graph-based methods ignore high-order association between users and items on graphs, and content-based methods ignore global features of texts for explicit user preferences. Therefore, this paper proposes a Content-based and knowledge Graph-based Paper Recommendation method (CGPRec), which uses a two-layer self-attention block to obtain global features of texts for more complete explicit user preferences, and proposes an improved graph convolutional network for modeling high-order associations on the knowledge graph to mine implicit user preferences. And the knowledge graph in this paper is constructed with concept nodes, user nodes, paper nodes, and other meta-data nodes. Experimental results on a public dataset, CiteULike-a, and a real application log dataset, AHData, show that our model outperforms compared with baseline methods.
C1 [Tang, Hao; Liu, Baisong; Qian, Jiangbo] Ningbo Univ, Fac Elect Engn & Comp Sci, Fenghua Rd, Ningbo, Zhejiang, Peoples R China.
RP Liu, BS (通讯作者)，Ningbo Univ, Fac Elect Engn & Comp Sci, Fenghua Rd, Ningbo, Zhejiang, Peoples R China.
EM lbs@nbu.edu.cn
RI Qian, Jiangbo/AAY-9587-2021
OI Qian, Jiangbo/0000-0003-4245-3246
FU ZhejiangNSF [LZ20F020001]; ChinaNSF [61472194]; K.C. Wong Magna Fund in
   Ningbo University
FX This work was supported in part by ZhejiangNSF GrantNo. LZ20F020001,
   ChinaNSF Grants No. 61472194 as well as programs sponsored by K.C. Wong
   Magna Fund in Ningbo University.
CR Ayala-Gomez F, 2018, J INTELL FUZZY SYST, V34, P3089, DOI 10.3233/JIFS-169493
   Bai XM, 2019, IEEE ACCESS, V7, P9324, DOI 10.1109/ACCESS.2018.2890388
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cai XY, 2019, IEEE ACCESS, V7, P457, DOI 10.1109/ACCESS.2018.2885507
   Cai XY, 2018, INT J COMPUT INT SYS, V11, P591, DOI 10.2991/ijcis.11.1.44
   Cai XJ, 2021, IEEE INTERNET THINGS, V8, P9645, DOI 10.1109/JIOT.2020.3040019
   Ebesu T, 2018, ACM/SIGIR PROCEEDINGS 2018, P515, DOI 10.1145/3209978.3209991
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Hongwei Wang, 2019, ACM Transactions on Information Systems, V37, DOI 10.1145/3312738
   Hsieh CK, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P193, DOI 10.1145/3038912.3052639
   Kanakia A, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2893, DOI 10.1145/3308558.3313700
   Kim Y., 2014, P 2014 C EMP METH NA, P1746
   Liu HW, 2020, COMPLEXITY, V2020, DOI 10.1155/2020/2085638
   Luo C, 2014, IEEE DATA MINING, P917, DOI 10.1109/ICDM.2014.64
   Ma W., 2019, P 11 INT C WIR COMM, P1
   Ma X, 2019, IEEE ACCESS, V7, P79887, DOI 10.1109/ACCESS.2019.2923293
   Sakib N, 2020, IEEE ACCESS, V8, P51246, DOI 10.1109/ACCESS.2020.2980589
   Shi, 2020, INFORM PROCESS MANAG, V57, P102
   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
   Tang H, 2021, CONCURR COMP-PRACT E, V33, DOI 10.1002/cpe.6227
   Ul Hassan M, 2020, IEEE T SERV COMPUT, V13, P263, DOI 10.1109/TSC.2019.2947471
   Ul Hassan M, 2019, FUTURE GENER COMP SY, V97, P512, DOI 10.1016/j.future.2019.02.060
   Waheed W, 2019, IEEE ACCESS, V7, P33145, DOI 10.1109/ACCESS.2019.2900520
   Wang C., 2011, P 17 ACM SIGKDD INT, P448, DOI DOI 10.1145/2020408.2020480
   Wang H., 2019, P 2019 INT JOINT C N, P1, DOI DOI 10.23919/APNOMS.2019.8893008
   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 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 Xiyu, 2018, Computer Engineering, V44, P226, DOI 10.3969/j.issn.1000-3428.2018.02.039
   Yin Liiing, 2017, Journal of the China Society for Scientific and Technical Information, V36, P715
   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, 2019, RELATIONAL COLLABORA
   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 J., 2019, ELECTRON COMMER R A
   Zhao WD, 2016, INFORM PROCESS MANAG, V52, P976, DOI 10.1016/j.ipm.2016.04.004
NR 36
TC 1
Z9 1
U1 16
U2 69
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 2021
VL 33
IS 13
AR e6227
DI 10.1002/cpe.6227
EA FEB 2021
PG 11
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SR8AO
UT WOS:000618042000001
DA 2022-08-23
ER

PT J
AU Gao, C
   Wang, R
   Zhang, L
   Yue, CW
AF Gao, Can
   Wang, Rui
   Zhang, Lin
   Yue, Changwu
TI Visualization Analysis of CRISPR Gene-editing Knowledge Map based on
   Citespace
SO BIOLOGY BULLETIN
LA English
DT Article
DE CRISPR; CiteSpace; Gene-editing; Visualization analysis
ID GENOME; TRENDS; CELLS
AB CRISPR is an adaptive immune defense system found in bacteria and archaea that is resistant to heterologous invasive genetic material. Later studies showed that the CRISPR system can be used for gene-editing. This study used the Web of Science database as a search object, then visually analyzed the literature related to CRISPR gene-editing technology with CiteSpace IV. The results show that publications had increased year by year. USA ranked first in terms of publications. China is second, but the centrality is very low. Doudna JA and Zhang F have made outstanding contributions. There are close connections between the internal institutions of the various states, but there are few links between the states. The hot spot and frontier are the application of CRISPR in animals, plants, detection, diagnosis, and clinical treatment.
C1 [Gao, Can; Yue, Changwu] Yanan Univ, Sch Basic Med, Yanan Key Lab Microbial Drug Innovat & Transforma, Yanan 716000, Shaanxi, Peoples R China.
   [Gao, Can; Wang, Rui] Chengdu Med Coll, Noncoding RNA & Drug Discovery Key Lab Sichuan Pr, Chengdu 610500, Sichuan, Peoples R China.
   [Zhang, Lin] Zhejiang Univ, Shaoxing Peoples Hosp, Shaoxing Hosp, Dept Pharm,Sch Med, Shaoxing 312000, Zhejiang, Peoples R China.
RP Zhang, L (通讯作者)，Zhejiang Univ, Shaoxing Peoples Hosp, Shaoxing Hosp, Dept Pharm,Sch Med, Shaoxing 312000, Zhejiang, Peoples R China.
EM zhanglinfudan@zju.edu.cn; changwuyue@126.com
FU National Nature Science Foundation of China [81860653, 82060654];
   Science and Technology Foundation of Shaanxi Province [2020JM-550,
   2020JM-545, 2021JM-416]; Open Project Foundation of Key Laboratory of
   noncoding RNA and drugs in Universities of Sichuan Province [FB19-01,
   FB20-02]; Shaoxing Medical Key Discipline Construction Plan [2019SZD06];
   Project of Clinical Research Fund Project of Zhejiang Medical
   Association [2020ZYC-A125]; Yangtze River Research Project for
   Sustainable Development of Hospitals in Zhejiang Province
   [2020ZHA-YZJ218]
FX This work was partly supported by The N43ational Nature Science
   Foundation of China (Grant No. 81860653,82060654); The Science and
   Technology Foundation of Shaanxi Province [2020JM-550, 2020JM-545 and
   2021JM-416]; Open Project Foundation of Key Laboratory of noncoding RNA
   and drugs in Universities of Sichuan Province [FB19-01 and FB20-02];
   Shaoxing Medical Key Discipline Construction Plan (NO. 2019SZD06);
   Project of Clinical Research Fund Project of Zhejiang Medical
   Association (2020ZYC-A125) Yangtze River Research Project for
   Sustainable Development of Hospitals in Zhejiang Province
   (2020ZHA-YZJ218).
CR Anguille S, 2015, PHARMACOL REV, V67, P731, DOI 10.1124/pr.114.009456
   Aubrey BJ, 2015, CELL REP, V10, P1422, DOI 10.1016/j.celrep.2015.02.002
   Carter J, 2015, CELL, V163, P260, DOI 10.1016/j.cell.2015.09.011
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Cong L, 2013, SCIENCE, V339, P819, DOI 10.1126/science.1231143
   Deng WL, 2015, P NATL ACAD SCI USA, V112, P11870, DOI 10.1073/pnas.1515692112
   Farzanegan R, 2017, ARCH IRAN MED, V20, P598, DOI 0172009/AIM.008
   Freije CA, 2019, MOL CELL, V76, P826, DOI 10.1016/j.molcel.2019.09.013
   Haapaniemi E, 2018, NAT MED, V24, P927, DOI 10.1038/s41591-018-0049-z
   Harrington LB, 2018, SCIENCE, V362, P839, DOI 10.1126/science.aav4294
   Heler R, 2014, MOL MICROBIOL, V93, P1, DOI 10.1111/mmi.12640
   Jinek M, 2012, SCIENCE, V337, P816, DOI 10.1126/science.1225829
   Joung J, 2020, NEW ENGL J MED, V383, P1492, DOI 10.1056/NEJMc2026172
   Kardkovacs Z.T., 2015, ACTA UNIVERSITATIS S
   Keeney S, 1997, CELL, V88, P375, DOI 10.1016/S0092-8674(00)81876-0
   Lander N, 2015, MBIO, V6, DOI 10.1128/mBio.01012-15
   Li Y, 2020, PROG CHEM, V32, P5, DOI 10.7536/PC191218
   Limsirichai P, 2016, MOL THER, V24, P499, DOI 10.1038/mt.2015.213
   Liu JJ, 2019, NATURE, V566, P218, DOI 10.1038/s41586-019-0908-x
   Maeder ML, 2019, NAT MED, V25, P229, DOI 10.1038/s41591-018-0327-9
   Makarova KS, 2020, NAT REV MICROBIOL, V18, P67, DOI 10.1038/s41579-019-0299-x
   Mohamed NV, 2019, J PARKINSON DIS, V9, P265, DOI 10.3233/JPD-181515
   Nagler A, 2020, PIGM CELL MELANOMA R, V33, P334, DOI 10.1111/pcmr.12825
   Narimani Manizheh, 2019, Iranian Biomedical Journal, V23, P369, DOI 10.29252/ibj.23.6.369
   Nelles DA, 2016, CELL, V165, P488, DOI 10.1016/j.cell.2016.02.054
   Russell TA, 2015, J VIROL METHODS, V213, P18, DOI 10.1016/j.jviromet.2014.11.009
   Sharma P, 2021, PROG MOL BIOL TRANSL, V180, P69, DOI 10.1016/bs.pmbts.2021.01.005
   Shen B, 2014, MBIO, V5, DOI 10.1128/mBio.01114-14
   Tanihara F, 2021, J REPROD DEVELOP, V67, P177, DOI 10.1262/jrd.2021-025
   Wu YX, 2013, CELL STEM CELL, V13, P659, DOI 10.1016/j.stem.2013.10.016
   Xu AT, 2015, BIOTECHNOL LETT, V37, P1265, DOI 10.1007/s10529-015-1796-2
   Yang HC, 2014, J HEPATOL, V60, pS124, DOI 10.1016/S0168-8278(14)60336-6
   Yang L, 2018, MOLECULES, V23, DOI 10.3390/molecules23040762
   Zhang HY, 2021, ENVIRON POLLUT, V285, DOI 10.1016/j.envpol.2021.117184
   Zhao X, 2018, CANCER LETT, V431, P171, DOI 10.1016/j.canlet.2018.05.042
NR 35
TC 1
Z9 1
U1 48
U2 61
PU PLEIADES PUBLISHING INC
PI NEW YORK
PA PLEIADES HOUSE, 7 W 54 ST, NEW YORK,  NY, UNITED STATES
SN 1062-3590
EI 1608-3059
J9 BIOL BULL+
JI Biol. Bull
PD NOV
PY 2021
VL 48
IS 6
BP 705
EP 720
DI 10.1134/S1062359021060108
PG 16
WC Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics
GA XQ0ZD
UT WOS:000731282400007
PM 34955625
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Bi, X
   Nie, HJ
   Zhang, XY
   Zhao, XG
   Yuan, Y
   Wang, GR
AF Bi, Xin
   Nie, Haojie
   Zhang, Xiyu
   Zhao, Xiangguo
   Yuan, Ye
   Wang, Guoren
TI Unrestricted multi-hop reasoning network for interpretable question
   answering over knowledge graph
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Question answering over knowledge graph; Unrestricted
   multi-hop reasoning
AB Knowledge graphs significantly boost the answer retrieval quality for natural language questions. The knowledge graph based question answering (KGQA) task returns accurate answer entities instead of keyword matches. For the more challenging task of multi-hop KGQA, existing methods either address fixed-length multi-hop reasoning, or perform a delayed detection of termination that requires an extra hop of reasoning. In addition, they suffer from two mapping problems between the question and relations: (1) one-to-many mapping when an individual question word corresponds to multiple hops of reasoning; (2) many-to-one mapping when a single hop of reasoning corresponds to multiple question words. Therefore, in this paper, we address these two issues of delayed determination and mapping problems by proposing an Unrestricted Multi-Hop Reasoning Network for Interpretable KGQA named UMRNet. Specifically, the proposed dynamic update strategy of question embeddings based on our attention redistribution mechanism is capable of handling the mapping problems. Furthermore, to avoid the need for an extra hop of reasoning, we propose a non-delayed termination detection mechanism that performs effective evaluation of the remaining reasoning information based on history attention. Extensive ablation studies and comparative experiments have been conducted on four KGQA benchmark datasets. The results demonstrate that the major modules of UMRNet are effective, and UMRNet outperforms the state-of-the-art methods regarding both accuracy and efficiency. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
C1 [Bi, Xin] Northeastern Univ, Key Lab, Minist Educ Safe Min Deep Met Mines, Shenyang 110819, Peoples R China.
   [Nie, Haojie; Zhang, Xiyu] Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110819, Peoples R China.
   [Zhao, Xiangguo] Northeastern Univ, Coll Software, Shenyang 110819, Peoples R China.
   [Yuan, Ye; Wang, Guoren] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.
RP Nie, HJ (通讯作者)，Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110819, Peoples R China.
EM qazxse2010@163.com
FU National Natural Sci-ence Foundation of China [62072087, 61932004,
   61732003, 61729201]; Fundamental Research Funds for the Central
   Universities, China [N181605012]
FX Acknowledgments This work is partially supported by the National Natural
   Sci-ence Foundation of China under Grant No. 62072087, 61932004,
   61732003 and 61729201, and the Fundamental Research Funds for the
   Central Universities, China under Grant No. N181605012.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Ba L.J., 2015, INT C LEARN REPR
   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
   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
   Cai B., 2022, ARXIV PREPRINT ARXIV
   Chen Q., 2020, P 28 INT C COMP LING, P2583, DOI DOI 10.18653/V1/2020.COLINGMAIN.232
   Chen Y., 2019, P 2019 C N AM CHAPTE, V1, P2913
   Chen Z.-Y., 2019, P 2019 C N AM CHAPT, P345, DOI DOI 10.18653/V1/N19-1031
   Costa TS, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P3157, DOI 10.1145/3340531.3412760
   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
   Han J., 2020, P 2020 C EMP METH NA, P1475
   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
   Hartig O, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1155, DOI 10.1145/3178876.3186014
   Lan YS, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P969
   Lan YS, 2019, IEEE DATA MINING, P359, DOI 10.1109/ICDM.2019.00046
   Li L, 2021, WORLD WIDE WEB, V24, P249, DOI 10.1007/s11280-020-00842-7
   Li Z, 2021, KNOWL-BASED SYST, V212, DOI 10.1016/j.knosys.2020.106618
   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
   Liang P., 2013, CORR ARXIV13094408
   Lv SW, 2020, AAAI CONF ARTIF INTE, V34, P8449
   Pennington J., 2014, EMNLP, P1532
   Perez J, 2006, LECT NOTES COMPUT SC, V4273, P30
   Qifan Zhang, 2020, Database Systems for Advanced Applications. 25th International Conference, DASFAA 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12113), P623, DOI 10.1007/978-3-030-59416-9_38
   Qin K., 2021, P 2021 C EMP METH NA, P4201
   Qiu YQ, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P1285, DOI 10.1145/3340531.3411888
   Qiu YQ, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P474, DOI 10.1145/3336191.3371812
   Saxena A, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P4498
   Song XY, 2021, INFORM SCIENCES, V580, P510, DOI 10.1016/j.ins.2021.08.100
   Su Y., 2016, P 2016 C EMP METH NA, P562
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang Q, 2020, KNOWL-BASED SYST, V197, DOI 10.1016/j.knosys.2020.105910
   Wang ZK, 2021, KNOWL-BASED SYST, V223, DOI 10.1016/j.knosys.2021.107035
   Wu P, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P6130
   Xu K., 2019, LONG SHORT PAPERS, V1, P2937
   Xue GT, 2022, NEUROCOMPUTING, V472, P212, DOI 10.1016/j.neucom.2021.03.138
   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 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
   Yuan Y, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P619, DOI 10.1145/3397271.3401157
   Yuan Y, 2019, PROC VLDB ENDOW, V12, P1058, DOI 10.14778/3339490.3339491
   Yuan Y, 2017, IEEE T KNOWL DATA EN, V29, P1212, DOI 10.1109/TKDE.2017.2656079
   Zhang H., 2018, CORR ABS180109893ARX
   Zhang L., 2016, CORR ARXIV161102266
   Zhang MW, 2022, KNOWL-BASED SYST, V238, DOI 10.1016/j.knosys.2021.107922
   Zhang YY, 2018, AAAI CONF ARTIF INTE, P6069
   Zhao C, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P1205, DOI 10.1145/3366423.3380197
   Zhou M., 2018, P ACL, P2010
   Zou L, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P313, DOI 10.1145/2588555.2610525
NR 49
TC 0
Z9 0
U1 7
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 MAY 11
PY 2022
VL 243
AR 108515
DI 10.1016/j.knosys.2022.108515
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0X7QX
UT WOS:000789897300002
DA 2022-08-23
ER

PT J
AU Cui, Y
   Sun, H
   Zhao, Y
   Yin, HZ
   Zheng, K
AF Cui, Yue
   Sun, Hao
   Zhao, Yan
   Yin, Hongzhi
   Zheng, Kai
TI Sequential-Knowledge-Aware Next POI Recommendation: A Meta-Learning
   Approach
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE POI recommendation; knowledge graphs; meta-learning
AB Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph-the sequential knowledge graph, which is sensitive to the check-in order of POIs-is built to model users' check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attentionmechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.
C1 [Cui, Yue; Sun, Hao; Zheng, Kai] Univ Elect Sci & Technol China, 2006 Xiyuan Ave, Chengdu 611731, Sichuan, Peoples R China.
   [Zhao, Yan] Aalborg Univ, Dept Comp Sci, Off 3-2-57,Selma Lagerlofs Vej 300, DK-9220 Aalborg, Denmark.
   [Yin, Hongzhi] Univ Queensland, St Lucia Campus, Brisbane, Qld 4072, Australia.
RP Zheng, K (通讯作者)，Univ Elect Sci & Technol China, 2006 Xiyuan Ave, Chengdu 611731, Sichuan, Peoples R China.
EM cuiyue@uestc.edu.cn; sunhao@std.uestc.edu.cn; yanz@cs.aau.dk;
   h.yin1@uq.edu.au; zhengkai@uestc.edu.cn
OI Yin, Hongzhi/0000-0003-1395-261X; Cui, Yue/0000-0002-1656-5407
FU NSFC [61972069, 61836007, 61832017]; Sichuan Science and Technology
   Program [2020JDTD0007]
FX This work was supported by NSFC (61972069, 61836007, 61832017) and
   Sichuan Science and Technology Program under grant 2020JDTD0007.
CR Bharadhwaj H., 2019, PROC INT JOINT C NEU, P1
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen Junkun, 2018, P AAAI 2018
   Cho Kyunghyun, 2014, ARXIV14061078, P1724, DOI DOI 10.3115/V1/D14-1179
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Du ZX, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2895, DOI 10.1145/3292500.3330726
   Feng J, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1459, DOI 10.1145/3178876.3186058
   Feng SS, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P1429, DOI 10.1145/3397271.3401049
   Finn C, 2017, PR MACH LEARN RES, V70
   Gao H., 2013, P 7 ACM C REC SYST, P93, DOI [10.1145/2507157.2507182, DOI 10.1145/2507157.2507182]
   He XN, 2018, IEEE T KNOWL DATA EN, V30, P2354, DOI 10.1109/TKDE.2018.2831682
   Higgins Irina, 2017, P ICLR 2017
   Kang W.-C., 2018, ARXIV180809781
   Kim J, 2019, PROC CVPR IEEE, P11, DOI 10.1109/CVPR.2019.00010
   Kipf Thomas N., 2017, P INT C LEARN REPR
   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
   Lim N, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P845, DOI 10.1145/3340531.3411876
   Liu Q, 2016, AAAI CONF ARTIF INTE, P194
   Liu TC, 2020, NEUROCOMPUTING, V400, P227, DOI 10.1016/j.neucom.2019.12.122
   Liu YC, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1015, DOI 10.1145/2939672.2939773
   Liu YD, 2017, PROC VLDB ENDOW, V10, P1010, DOI 10.14778/3115404.3115407
   Liu Yong, 2014, P CIKM 2014
   Luo Yadan, 2020, P AAAI 2020
   Ma WZ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1210, DOI 10.1145/3308558.3313607
   Nathani Deepak, 2019, P ACL 2019
   Nguyen D. Q., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Palumbo E, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P32, DOI 10.1145/3109859.3109889
   Pan ZY, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1720, DOI 10.1145/3292500.3330884
   Qian TY, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3295499
   Rusu Andrei A., 2019, P ICLR 2019
   Santoro A, 2016, PR MACH LEARN RES, V48
   Shi C, 2019, IEEE T KNOWL DATA EN, V31, P357, DOI 10.1109/TKDE.2018.2833443
   Snell Jake, 2017, P NIPS 2017
   Sun K, 2020, AAAI CONF ARTIF INTE, V34, P214
   Sun R, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P1405, DOI 10.1145/3340531.3411947
   Sung F, 2018, PROC CVPR IEEE, P1199, DOI 10.1109/CVPR.2018.00131
   Tang JX, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P565, DOI 10.1145/3159652.3159656
   TianxinWei ZiweiWu, 2020, P ICDM 2020
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Vinyals Oriol, 2017, P CVPR 2017
   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 Shu, 2018, P AAAI 2019
   Wu YX, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2301, DOI 10.1145/3357384.3358171
   Xie M, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P15, DOI 10.1145/29833213.2983711
   Xue F, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3314578
   Yao Huaxiu, 2019, P WWW 2019
   Yao ZJ, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P757, DOI 10.1145/3159652.3170459
   Ye M, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P325
   Ying Haochao, 2018, P IJCAI 2018
   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 J.-D., 2014, P 22 ACM SIGSPATIAL, P103
   Zhang ZQ, 2020, FRONT COMPUT SCI-CHI, V14, P314, DOI 10.1007/s11704-018-8011-2
   Zhao KZ, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3216
NR 53
TC 1
Z9 1
U1 20
U2 20
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 APR
PY 2022
VL 40
IS 2
AR 23
DI 10.1145/3460198
PG 22
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YV0FL
UT WOS:000752408800003
DA 2022-08-23
ER

PT J
AU Jiang, BC
   Tan, LH
   Ren, Y
   Li, F
AF Jiang, Bingchuan
   Tan, Liheng
   Ren, Yan
   Li, Feng
TI Intelligent Interaction with Virtual Geographical Environments Based on
   Geographic Knowledge Graph
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE virtual geographical environments (VGEs); knowledge graph (KG);
   geographic knowledge graph (GeoKG); semantic conversion model; question
   answering (QA)
ID CONCEPTUAL-FRAMEWORK
AB The core of intelligent virtual geographical environments (VGEs) is the formal expression of geographic knowledge. Its purpose is to transform the data, information, and scenes of a virtual geographic environment into "knowledge" that can be recognized by computer, so that the computer can understand the virtual geographic environment more easily. A geographic knowledge graph (GeoKG) is a large-scale semantic web that stores geographical knowledge in a structured form. Based on a geographic knowledge base and a geospatial database, intelligent interactions with virtual geographical environments can be realized by natural language question answering, entity links, and so on. In this paper, a knowledge-enhanced Virtual geographical environments service framework is proposed. We construct a multi-level semantic parsing model and an enhanced GeoKG for structured geographic information data, such as digital maps, 3D virtual scenes, and unstructured information data. Based on the GeoKG, we propose a bilateral LSTM-CRF (long short-term memory-conditional random field) model to achieve natural language question answering for VGEs and conduct experiments on the method. The results prove that the method of intelligent interaction based on the knowledge graph can bridge the distance between people and virtual environments.
C1 [Jiang, Bingchuan; Tan, Liheng; Ren, Yan; Li, Feng] Strateg Support Force Informat Engn Univ, Zhengzhou 450001, Henan, Peoples R China.
   [Jiang, Bingchuan] State Key Lab Geoinformat Engn, Xian 750054, Shaanxi, Peoples R China.
RP Tan, LH (通讯作者)，Strateg Support Force Informat Engn Univ, Zhengzhou 450001, Henan, Peoples R China.
EM jbc021@163.com; tanliheng_123@163.com; qingyuliuer@163.com;
   jueyun1020@126.com
FU National Natural Science Foundation of China [41801319]; Science and
   Technology Program of Henan in China [182102210147]; Foundation for
   State Key Laboratory of Geo-information Engineering of China
   [SKLGIE2017-M-4-3]
FX This research was funded by the National Natural Science Foundation of
   China under Grant 41801319, the Science and Technology Program of Henan
   in China under Grant 182102210147, and the Foundation for State Key
   Laboratory of Geo-information Engineering of China under Grant number
   SKLGIE2017-M-4-3.
CR Alhelbawy A., 2014, P 52 ANN M ASS COMP, V2
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Ballatore A, 2013, KNOWL INF SYST, V37, P61, DOI 10.1007/s10115-012-0571-0
   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, 2007, P 22 NAT C ART INT, P1962
   Bordes A, 2011, P AAAI C ART INT AAA
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Davies Clare, 2013, Spatial Information Theory. 11th International Conference, COSIT 2013. Proceedings: LNCS 8116, P320, DOI 10.1007/978-3-319-01790-7_18
   [段红伟 Duang Hongwei], 2014, [测绘学报, Acta Geodetica et Cartographica Sinica], V43, P193
   Florian A.T., 2008, P 2 INT WORKSH GEOGR, P43
   Gao Y, 2010, SCI CHINA TECHNOL SC, V53, P52, DOI 10.1007/s11431-010-3208-0
   Gur N, 2018, SEMANT WEB, V9, P557, DOI 10.3233/SW-170281
   Huang H., 2015, ARXIV150407678
   Hui Lin, 2015, Annals of GIS, V21, P261, DOI 10.1080/19475683.2015.1099568
   Jenatton R., 2012, P 25 INT C NEUR INF
   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 F.L., 2015, J REMOTE SENS, V19, P179, DOI [10.11834/jrs.20154013, DOI 10.11834/jrs.20154013]
   Jiang Bingchuan, 2013, Geomatics and Information Science of Wuhan University, V38, P875
   Kyzirakos Kostis, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P295, DOI 10.1007/978-3-642-35176-1_19
   Lan You, 2016, Annals of GIS, V22, P163, DOI 10.1080/19475683.2016.1199594
   Laurini R., 2017, P 2 INT C SMART DAT
   Laurini R, 2014, J VISUAL LANG COMPUT, V25, P2, DOI 10.1016/j.jvlc.2013.10.004
   [林珲 Lin Hui], 2016, [遥感学报, Journal of Remote Sensing], V20, P1290
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu Y., 2009, P 2009 17 INT C GEOI
   Loglisci C, 2012, INT CONF DAT MIN WOR, P789, DOI 10.1109/ICDMW.2012.20
   [陆锋 Lu Feng], 2017, [地球信息科学学报, Journal of Geo-Information Science], V19, P723
   Lu GN, 2011, SCI CHINA EARTH SCI, V54, P733, DOI 10.1007/s11430-011-4193-2
   Marrero M, 2013, COMPUT STAND INTER, V35, P482, DOI 10.1016/j.csi.2012.09.004
   Mekni M., 2011, P 3 INT C ADV COGN T
   Mekni M., 2013, J GEOGR INF SYST, V5, P548, DOI [10.4236/jgis.2013.56052, DOI 10.4236/JGIS.2013.56052]
   Mekni M., 2016, INT J COMPUT NETW CO, V4, P242
   Mekni M, 2018, INT J DIGIT EARTH, V11, P334, DOI 10.1080/17538947.2017.1381190
   Mekni M, 2011, WINT SIMUL C PROC, P381, DOI 10.1109/WSC.2011.6147765
   Niu X, 2011, LECT NOTES COMPUT SC, V7032, P205, DOI 10.1007/978-3-642-25093-4_14
   Qi G. L., 2017, TECHNOL INTELL ENG, V3, P4, DOI DOI 10.3772/J.ISSN.2095-915X.2017.01.002
   Qu Y., 2018, ARXIV180403317
   Schiff S., KNOW IT ALL
   Socher R., 2012, P 25 INT C NEUR INF
   Sowa J F, 1991, FRAME PROBLEM ARTIFI, P135
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wu TX, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093245
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   Yates A., 2007, P HUM LANG TECHN ANN, P25
   You L, 2016, INT ARCH PHOTOGRAMM, V41, P357, DOI 10.5194/isprsarchives-XLI-B2-357-2016
   [余丽 Yu Li], 2016, [测绘学报, Acta Geodetica et Cartographica Sinica], V45, P616
   Zhang F, 2018, ISPRS INT J GEO-INF, V7, DOI 10.3390/ijgi7010036
   Zhang W., 2011, IJCAI, V2011, P1909, DOI DOI 10.5591/978-1-57735-516-8/IJCAI11-319
   [周博通 Zhou Botong], 2018, [北京大学学报. 自然科学版, Acta Scientiarum Naturalium Universitatis Pekinensis], V54, P286
NR 52
TC 8
Z9 10
U1 8
U2 32
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. Geo-Inf.
PD OCT
PY 2019
VL 8
IS 10
AR 428
DI 10.3390/ijgi8100428
PG 19
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA JP6UM
UT WOS:000498398300003
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Kim, K
   Hur, Y
   Kim, G
   Lim, H
AF Kim, Kuekyeng
   Hur, Yuna
   Kim, Gyeongmin
   Lim, Heuiseok
TI GREG: A Global Level Relation Extraction with Knowledge Graph Embedding
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE relation extraction; knowledge graph; meta learning; text summarization;
   natural language processing; machine learning
AB In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations-usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level 'local' relation's to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model's modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently.
C1 [Kim, Kuekyeng; Hur, Yuna; Kim, Gyeongmin; Lim, Heuiseok] Korea Univ, Dept Comp Sci & Engn, Seoul 02841, South Korea.
RP Lim, H (通讯作者)，Korea Univ, Dept Comp Sci & Engn, Seoul 02841, South Korea.
EM overmind22@korea.ac.kr; yj72722@korea.ac.kr; totoro4007@korea.ac.kr;
   limhseok@korea.ac.kr
OI Kim, Gyeongmin/0000-0002-2851-0374; Kim, Kuekyeng/0000-0003-1446-3420
FU Ministry of Culture, Sport, and Tourism (MCST); Korea Creative Content
   Agency (KOCCA) in the Culture Technology (CT) Research and Development
   Program [R2017030045]
FX This research is supported by the Ministry of Culture, Sport, and
   Tourism (MCST) and the Korea Creative Content Agency (KOCCA) in the
   Culture Technology (CT) Research and Development Program 2017 (No.
   R2017030045).
CR Blumberg R, 2003, DM REV, V13, P62
   Cimiano P, 2006, ONTOLOGIES
   CsuYji BC., 2001, APPROXIMATION ARTIFI, V24, P7
   Gerber M, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P1583
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Kim H., 2017, DIGITAL HUMANITIES R
   Konstantinova N, 2014, COMM COM INF SC, V436, P15, DOI 10.1007/978-3-319-12580-0_2
   Lu D., 2018, P EMNLP, P4013
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Min Kim Gyeong, 2018, [Journal of the Korea Convergence Society, 한국융합학회논문지], V9, P47, DOI 10.15207/JKCS.2018.9.12.047
   Miwa M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1105
   Moschitti A., 2013, P 6 INT JOINT C NAT, P1330
   NGUYEN G, 2015, P 1 WORKSH VECT SPAC, P39
   Park E.L., 2014, P 26 ANN C HUM COGN, V6
   Peng N., 2017, T ASS COMPUTATIONAL, V5, P101, DOI [10.1162/tacl_a_00049, DOI 10.1162/TACL_A_00049]
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Quirk C, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P1171
   SANFELIU A, 1983, IEEE T SYST MAN CYB, V13, P353, DOI 10.1109/TSMC.1983.6313167
   Sorokin D., 2017, P 2017 C EMP METH NA, P1784
   Tishby N, 2015, 2015 IEEE INFORMATION THEORY WORKSHOP (ITW)
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z., 2014, P 28 AAAI C ART INT
   Yao Y., 2019, P ACL 2019 AUST TX U
   Young T, 2018, IEEE COMPUT INTELL M, V13, P55, DOI 10.1109/MCI.2018.2840738
NR 24
TC 7
Z9 7
U1 3
U2 16
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 1181
DI 10.3390/app10031181
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 LC4PI
UT WOS:000525305900450
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Ma, YP
   Tresp, V
   Zhao, LM
   Wang, YY
AF Ma, Yunpu
   Tresp, Volker
   Zhao, Liming
   Wang, Yuyi
TI Variational Quantum Circuit Model for Knowledge Graph Embedding
SO ADVANCED QUANTUM TECHNOLOGIES
LA English
DT Article
DE inference on relational database; knowledge graphs; quantum
   acceleration; representation learning; variational quantum circuit
AB In this work, the first quantum Ansatze for the statistical relational learning on knowledge graphs using parametric quantum circuits are proposed. Two types of variational quantum circuits for knowledge graph embedding are introduced. Inspired by the classical representation learning, latent features for entities are first considered as coefficients of quantum states, while predicates are characterized by parametric gates acting on the quantum states. For the first model, the quantum advantages disappear when it comes to the optimization of this model. Therefore, a second quantum circuit model is introduced where embeddings of entities are generated from parameterized quantum gates acting on the pure quantum state. The benefit of the second method is that the quantum embeddings can be trained efficiently meanwhile preserving the quantum advantages. It is shown that the proposed methods can achieve comparable results to the state-of-the-art classical models, for example, RESCAL, DISTMULT. Furthermore, after optimizing the models, the complexity of inductive inference on the knowledge graphs might be reduced with respect to the number of entities.
C1 [Ma, Yunpu; Tresp, Volker] Ludwig Maximilian Univ Munich, Geschwister Scholl Pl 1, D-80539 Munich, Germany.
   [Ma, Yunpu; Tresp, Volker] Siemens AG, Otto Hahn Ring 6, D-81739 Munich, Germany.
   [Zhao, Liming] Singapore Univ Technol & Design, Singapore, Singapore.
   [Wang, Yuyi] Swiss Fed Inst Technol, Gloriastr 35, CH-8092 Zurich, Switzerland.
RP Ma, YP (通讯作者)，Ludwig Maximilian Univ Munich, Geschwister Scholl Pl 1, D-80539 Munich, Germany.; Ma, YP (通讯作者)，Siemens AG, Otto Hahn Ring 6, D-81739 Munich, Germany.
EM yunpu.ma@siemens.com
FU BMBF; Siemens Corporate Technology
FX This research was supported by the BMBF funded project Machine Learning
   with Knowledge Graphs, and Siemens Corporate Technology.
CR Abadi M., 2015, TENSORFLOW LARGE SCA
   Asuncion A., 2007, UCI MACHINE LEARNING
   Bennett CH, 1997, SIAM J COMPUT, V26, P1510, DOI 10.1137/S0097539796300933
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Brassard G., 2002, CONT MATH, V305, P53
   Buhrman H, 2001, PHYS REV LETT, V87, DOI 10.1103/PhysRevLett.87.167902
   Garcia-Escartin JC, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.052330
   Chamorro-Posada P., 2017, ARXIV170606564
   Childs A.M., 2012, ARXIV12025822
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Garcia-Ripoll, 2018, ARXIV180100934
   Grover L. K., 1996, Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing, P212, DOI 10.1145/237814.237866
   Hochreiter S, 1997, Neural Comput, V9, P1735
   Huang G., 2016, EUR C COMP VIS, P646
   Kerenidis I., 2016, ARXIV160308675
   Leetaru K. H., 2013, ISA ANN CONV SAN FRA
   Ma YP, 2018, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, P403
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M., 2013, JOINT EUR C MACH LEA, P272
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Prakash A, 2014, THESIS
   Schuld M., 2018, ARXIV180400633
   Schuld M, 2014, QUANTUM INF PROCESS, V13, P2567, DOI 10.1007/s11128-014-0809-8
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trouillon T, 2016, PR MACH LEARN RES, V48
   TUCKER LR, 1966, PSYCHOMETRIKA, V31, P279, DOI 10.1007/BF02289464
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Yang B., 2014, ABS14126575 CORR
NR 28
TC 1
Z9 1
U1 1
U2 3
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
EI 2511-9044
J9 ADV QUANTUM TECHNOL
JI Adv. Quantum Technol.
PD AUG
PY 2019
VL 2
IS 7-8
SI SI
AR UNSP 1800078
DI 10.1002/qute.201800078
PG 13
WC Quantum Science & Technology; Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics; Optics
GA MJ4RK
UT WOS:000548079200005
DA 2022-08-23
ER

PT J
AU Shi, BX
   Weninger, T
AF Shi, Baoxu
   Weninger, Tim
TI Discriminative predicate path mining for fact checking in knowledge
   graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Fact checking; Path finding; Knowledge graph
AB Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false. We view this problem as a link-prediction task in a knowledge graph, and present a discriminative path-based method for fact checking in knowledge graphs that incorporates connectivity, type information, and predicate interactions. Given a statement S of the form (subject, predicate, object), for example, (Chicago, capitalOf, Illinois), our approach mines discriminative paths that alternatively define the generalized statement (U.S. city, predicate, U.S. state) and uses the mined rules to evaluate the veracity of statement S. We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using a public, million node knowledge graph extracted from Wikipedia and PubMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative predicate path model is easily interpretable and provides sensible reasons for the final determination. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Shi, Baoxu] Univ Notre Dame, Dept Comp Sci & Engn, 384E Nieuwland Sci Hall, Notre Dame, IN 46556 USA.
   [Weninger, Tim] Univ Notre Dame, Dept Comp Sci & Engn, 353 Fitzpatrick Hall, Notre Dame, IN 46556 USA.
RP Weninger, T (通讯作者)，Univ Notre Dame, Dept Comp Sci & Engn, 353 Fitzpatrick Hall, Notre Dame, IN 46556 USA.
EM bshi@nd.edu; tweninge@nd.edu
OI Weninger, Tim/0000-0003-3164-2615
FU AFOSR [FA9550-15-1-0003]; John Templeton Foundation [FP053369-M]
FX This work is sponsored by an AFOSR grant FA9550-15-1-0003, and a John
   Templeton Foundation grant FP053369-M.
CR Abedjan Ziawasch, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P140
   Adamic LA, 2003, SOC NETWORKS, V25, P211, DOI 10.1016/S0378-8733(03)00009-1
   Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509
   Ciampaglia Giovanni Luca, 2015, PLOS ONE, V10
   Cohen S, 2011, COMMUN ACM, V54, P66, DOI 10.1145/2001269.2001288
   Dong YX, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P199, DOI 10.1145/2783258.2783329
   Ennals R., 2010, P 19 INT C WORLD WID, P341
   Finn S., 2014, COMPUTATIONAL JOURNA, P1
   Fu XF, 2016, KNOWL-BASED SYST, V100, P1, DOI 10.1016/j.knosys.2016.01.039
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Graves L., 2012, FACT CHECKING UNIVER
   Guu K., 2015, ARXIV150601094
   Hassan N., 2015, P 24 ACM INT C INF K, P1835, DOI [DOI 10.1145/2806416.2806652, 10.1145/2806416.2806652]
   Hassan N, 2014, PROC VLDB ENDOW, V7, P1557, DOI 10.14778/2733004.2733029
   Haveliwala T.H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513
   Jeh G., 2002, P 8 ACM SIGKDD INT C, P538, DOI DOI 10.1145/775047.775126
   Katz L., 1953, PSYCHOMETRIKA, Vvol. 18, ppp. 39, DOI DOI 10.1007/BF02289026
   Kilicoglu H, 2012, BIOINFORMATICS, V28, P3158, DOI 10.1093/bioinformatics/bts591
   Kusumoto M, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P325, DOI 10.1145/2588555.2610526
   Kwok C, 2001, ACM T INFORM SYST, V19, P242, DOI 10.1145/502115.502117
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   LEVANDOWSKY M, 1971, NATURE, V234, P34, DOI 10.1038/234034a0
   Ley M, 2009, PROC VLDB ENDOW, V2, P1493, DOI 10.14778/1687553.1687577
   Li X, 2011, PROC INT CONF DATA, P63, DOI 10.1109/ICDE.2011.5767859
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Lichtenwalter R. N., 2012, P 21 INT C WORLD WID, P1019, DOI [10.1145/2187836.2187973, DOI 10.1145/2187836.2187973]
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Meng CP, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P754, DOI 10.1145/2736277.2741123
   Mikolov T., 2013, ADV NEURAL INFORM PR
   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
   Quinlan J.R., 1993, C4 5 PROGRAMS MACHIN, V16, P235
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   Ruiz PP, 2014, KNOWL-BASED SYST, V68, P4, DOI 10.1016/j.knosys.2014.02.002
   Shi C, 2014, IEEE T KNOWL DATA EN, V26, P2479, DOI 10.1109/TKDE.2013.2297920
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sun Y., 2012, KDD, P11
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Swift J., THE EXAMINER, V15
   Tunstall-Pedoe W, 2010, AI MAG, V31, P80, DOI 10.1609/aimag.v31i3.2298
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wu Y, 2014, PROC VLDB ENDOW, V7, P589, DOI 10.14778/2732286.2732295
   Xiao Jiang, 2011, KDD, P1280
   Yates A., 2004, P 13 INT C WORLD WID, P100, DOI DOI 10.1145/988672.988687
   You Wu, 2012, KDD, P1487
   Zhao MB, 2015, KNOWL-BASED SYST, V76, P148, DOI 10.1016/j.knosys.2014.12.014
NR 48
TC 52
Z9 52
U1 1
U2 32
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 JUL 15
PY 2016
VL 104
BP 123
EP 133
DI 10.1016/j.knosys.2016.04.015
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DO4EA
UT WOS:000377733400010
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Yuan, H
   Deng, WW
AF Yuan, Hui
   Deng, Weiwei
TI Doctor recommendation on healthcare consultation platforms: an
   integrated framework of knowledge graph and deep learning
SO INTERNET RESEARCH
LA English
DT Article
DE Online healthcare; Doctor recommendation; Knowledge graph; Deep
   learning; Interpretable recommendation
ID INFORMATION TRANSPARENCY; PATIENT INTERACTION; GENERAL-PRACTICE; ONLINE;
   QUALITY; MARKETS
AB Purpose Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps. Design/methodology/approach This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results. Findings The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations. Originality/value This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.
C1 [Yuan, Hui] Shanghai Int Studies Univ, Shanghai, Peoples R China.
   [Deng, Weiwei] South China Normal Univ, Guangzhou, Peoples R China.
RP Deng, WW (通讯作者)，South China Normal Univ, Guangzhou, Peoples R China.
EM weiweideng@m.scnu.edu.cn
OI Yuan, Hui/0000-0001-9267-8721
FU National Natural Science Foundation of China [72001144]; China
   Postdoctoral Science Foundation [2020M682757]; Innovative Research Team
   of Shanghai International Studies University [2020114044]; Guangzhou
   Science and Technology Plan Project [202002030384]; National Social
   Science Foundation of China [20BTQ075]
FX This research was supported by grants from the National Natural Science
   Foundation of China (No. 72001144), China Postdoctoral Science
   Foundation (No. 2020M682757), Innovative Research Team of Shanghai
   International Studies University (No. 2020114044), Guangzhou Science and
   Technology Plan Project (No. 202002030384), and National Social Science
   Foundation of China (No. 20BTQ075).
CR Agarwal R, 2010, INFORM SYST RES, V21, P796, DOI 10.1287/isre.1100.0327
   Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Atanasova S, 2018, COMPUT HUM BEHAV, V83, P106, DOI 10.1016/j.chb.2018.01.031
   Basile P, 2019, INFORM SYST, V86, P1, DOI 10.1016/j.is.2019.07.001
   Bertakis KD, 2009, PATIENT EDUC COUNS, V76, P356, DOI 10.1016/j.pec.2009.07.022
   Cao ZY, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11030392
   Chaudhari S, 2017, AI COMMUN, V30, P141, DOI 10.3233/AIC-170728
   Cramer H, 2008, USER MODEL USER-ADAP, V18, P455, DOI 10.1007/s11257-008-9051-3
   Dang Y., 2018, INT C INF SYST CAL 1
   Deng XY, 2019, IEEE ACCESS, V7, P55679, DOI 10.1109/ACCESS.2019.2913468
   Deveugele M, 2002, BRIT MED J, V325, P472, DOI 10.1136/bmj.325.7362.472
   Feng CL, 2019, IEEE ACCESS, V7, P29085, DOI 10.1109/ACCESS.2019.2899343
   Goh JM, 2016, MIS QUART, V40, P247, DOI 10.25300/MISQ/2016/40.1.11
   Granados N, 2010, INFORM SYST RES, V21, P207, DOI 10.1287/isre.1090.0249
   Guo L, 2016, J MED INTERNET RES, V18, DOI 10.2196/jmir.6015
   Guo QY, 2020, IEEE T KNOWL DATA EN, V34, P3549, DOI [10.1038/s41366-020-0625-4, 10.1109/TKDE.2020.3028705]
   Guo X., 2016, J MANAGE SCI ENG, P58, DOI [DOI 10.3724/SP.J.1383.101004, 10.3724/SP.J.1383.101004]
   He M, 2019, IEEE ACCESS, V7, P30276, DOI 10.1109/ACCESS.2019.2902398
   Hu JH, 2020, INT T OPER RES, V27, P1236, DOI 10.1111/itor.12569
   Jiang HX, 2014, 2014 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN HEALTHCARE AND E-HEALTH (CICARE), P154, DOI 10.1109/CICARE.2014.7007848
   Karidi DP, 2018, J AMB INTEL HUM COMP, V9, P2035, DOI 10.1007/s12652-017-0491-7
   Khedkar S., 2020, DEEP LEARNING TECHNI, P129
   Konstan J. A., 2000, PROC 2000 ACM C COMP, P241, DOI DOI 10.1145/358916.358995
   Li, 2020, FRONTIERS COMPUTER S, V15, P1
   Li YF, 2019, INT J MED INFORM, V130, DOI 10.1016/j.ijmedinf.2019.08.011
   Lin HC, 2018, INFORM MANAGE-AMSTER, V55, P771, DOI 10.1016/j.im.2018.03.006
   Ma WZ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1210, DOI 10.1145/3308558.3313607
   Mold F, 2019, JMIR MED INF, V7, P4, DOI 10.2196/13042
   Montavon G., 2019, EXPLAINABLE INTERPRE, P193, DOI DOI 10.1007/978-3-030-28954-6_10
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Nilashi M, 2016, ELECTRON COMMER R A, V19, P70, DOI 10.1016/j.elerap.2016.09.003
   Pan X, 2019, IEEE T AUTOM SCI ENG, V16, P115, DOI 10.1109/TASE.2018.2839651
   Pee LG, 2018, INT J INFORM MANAGE, V39, P228, DOI 10.1016/j.ijinfomgt.2017.12.010
   Ren JT, 2019, DECIS SUPPORT SYST, V125, DOI 10.1016/j.dss.2019.113115
   Schieber AC, 2014, FAM PRACT, V31, P706, DOI 10.1093/fampra/cmu057
   Schmidt-Thieme L., 2010, IEEE INT C DAT MIN S
   Sridevi M, 2019, LECT NOTE NETW SYST, V40, P709, DOI 10.1007/978-981-13-0586-3_69
   Swar B, 2017, COMPUT HUM BEHAV, V70, P416, DOI 10.1016/j.chb.2016.12.068
   Swearingen K., 2002, C HUM FACT COMP SYST
   The-Economist, 2020, TRACKING COVID 19 EX
   Volkovs M, 2017, ADV NEUR IN, V30
   Wang C, 2017, PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT 2016: THEORY AND APPLICATION OF INDUSTRIAL ENGINEERING, P233, DOI 10.2991/978-94-6239-255-7_42
   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, P5329
   Waqar M, 2019, BEHAV INFORM TECHNOL, V38, P959, DOI 10.1080/0144929X.2019.1625441
   Ward, 2019, ETHICAL DILEMMAS POS
   Wen HQ, 2020, IEEE T AUTOM SCI ENG, V17, P886, DOI 10.1109/TASE.2019.2950724
   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
   Xiao CJ, 2019, LECT NOTES ARTIF INT, V11670, P135, DOI 10.1007/978-3-030-29908-8_11
   Xu C, 2019, IEEE INTERNET THINGS, V6, P5665, DOI 10.1109/JIOT.2019.2904728
   Yan L, 2014, INFORM SYST RES, V25, P690, DOI 10.1287/isre.2014.0538
   Yang Y, 2020, COGN COMPUT, V12, P460, DOI 10.1007/s12559-018-9616-3
   Yang YF, 2019, INT J PROD ECON, V207, P70, DOI 10.1016/j.ijpe.2018.11.009
   Ye Y, 2019, INT J INFORM MANAGE, V47, P65, DOI 10.1016/j.ijinfomgt.2019.01.005
   Zejnilovic L., 2018, IEEE INT C DAT SCI A
   Zhang YL, 2020, J ASSOC INF SCI TECH, V71, P1179, DOI 10.1002/asi.24319
   Zhu K, 2004, MANAGE SCI, V50, P670, DOI 10.1287/mnsc.1040.0226
   Zhu P., 2019, HAW INT C SYST SCI H
NR 58
TC 2
Z9 2
U1 33
U2 92
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 454
EP 476
DI 10.1108/INTR-07-2020-0379
EA JUN 2021
PG 23
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:000661385500001
DA 2022-08-23
ER

PT J
AU Oliveira, D
   d'Aquin, M
AF Oliveira, Daniela
   d'Aquin, Mathieu
TI Extracting data models from background knowledge graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graphs; Ontologies; Data modelling
ID CURRENT STATE; LINKED DATA; TABLES
AB Knowledge Graphs have emerged as a core technology to aggregate and publish knowledge on the Web. However, integrating knowledge from different sources, not specifically designed to be interoperable, is not a trivial task. Finding the right ontologies to model a dataset is a challenge since several valid data models exist and there is no clear agreement between them. In this paper, we propose to facilitate the selection of a data model with the RICDaM (Recommending Interoperable and Consistent Data Models) framework. RICDaM generates and ranks candidates that match entity types and properties in an input dataset. These candidates are obtained by aggregating freely available domain RDF datasets in a knowledge graph and then enriching the relationships between the graph's entities. The entity type and object property candidates are obtained by exploiting the instances and structure of this knowledge graph to compute a score that considers both the accuracy and interoperability of the candidates. Datatype properties are predicted with a random forest model, trained on the knowledge graph properties and their values, so to make predictions on candidate properties and rank them according to different measures. We present experiments using multiple datasets from the library domain as a use case and show that our methodology can produce meaningful candidate data models, adaptable to specific scenarios and needs. (c) 2021 Elsevier B.V. All rights reserved.
C1 [d'Aquin, Mathieu] Univ Lorraine, LORIA, Nancy, France.
   [Oliveira, Daniela] Univ Lisbon, Fac Ciencias, Dept Informat, LASIGE, Lisbon, Portugal.
RP d'Aquin, M (通讯作者)，Univ Lorraine, LORIA, Nancy, France.
EM mathieu.daquin@loria.fr
RI ; Oliveira, Daniela/H-8808-2016
OI d'Aquin, Mathieu/0000-0001-7276-4702; Oliveira,
   Daniela/0000-0003-0559-8737
FU Science Foundation Ireland (SFI) [SFI/12/RC/2289, SFI/12/RC/2289_P2];
   European Regional Development Fund; Fundacao para a Ciencia e a
   Tecnologia, Portugal through the LASIGE Research Unit [UIDB/00408/2020,
   UIDP/00408/2020]
FX This research was supported by Science Foundation Ireland (SFI) under
   Grant Number SFI/12/RC/2289 and SFI/12/RC/2289_P2, co-funded by the
   European Regional Development Fund and the Fundacao para a Ciencia e a
   Tecnologia, Portugal through the LASIGE Research Unit, UIDB/00408/2020
   and UIDP/00408/2020.
CR Adelfio MD, 2013, PROC VLDB ENDOW, V6, P421, DOI 10.14778/2536336.2536343
   Algergawy A., 2019, CEUR WORKSHOP PROC, V2536, P40
   Andresen L., 2004, Library Hi Tech, V22, P40, DOI 10.1108/07378830410524486
   Chen ZY, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1515, DOI 10.1145/3184558.3191601
   d'Aquin M., 2013, P 5 ANN ACM WEB SCI, P43, DOI [10.1145/2464464.2464487, DOI 10.1145/2464464.2464487]
   Das Shagnik., 2016, R2RML RDB RDF MAPPIN
   de Medeiros LF, 2015, LECT NOTES COMPUT SC, V9114, P326, DOI 10.1007/978-3-319-19890-3_21
   Dimou A., 2014, LDOW
   Do B.-L., 2015, CEUR WORKSHOP PROC, V1551, P12
   Ehrlinger L., 2016, 12 INT C SEM SYST 1, V1695
   Faria D., 2019, P 14 INT WORKSH ONT, V2536, P6
   Faria D, 2013, LECT NOTES COMPUT SC, V8185, P527, DOI 10.1007/978-3-642-41030-7_38
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Haesendonck G, 2019, PROCEEDINGS OF THE INTERNATIONAL WORKSHOP ON SEMANTIC BIG DATA (SBD 2019), DOI 10.1145/3323878.3325802
   Hallo M, 2016, J INF SCI, V42, P117, DOI 10.1177/0165551515594729
   Hertling S, 2020, KNOWL INF SYST, V62, P2169, DOI 10.1007/s10115-019-01415-5
   Heyvaert P, 2018, LECT NOTES COMPUT SC, V11155, P213, DOI 10.1007/978-3-319-98192-5_40
   Hu W, 2015, LECT NOTES COMPUT SC, V9367, P446, DOI 10.1007/978-3-319-25010-6_29
   Krotzsch M, 2016, LECT NOTES COMPUT SC, V9981, P376, DOI 10.1007/978-3-319-46523-4_23
   Lefrancois M, 2017, LECT NOTES COMPUT SC, V10249, P35, DOI 10.1007/978-3-319-58068-5_3
   Lehmann J., 2007, P 1 C SOC SEM WEB CS, P11
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Limaye G, 2010, PROC VLDB ENDOW, V3, P1338, DOI 10.14778/1920841.1921005
   McKenna L, 2018, ACM-IEEE J CONF DIG, P7, DOI 10.1145/3197026.3197041
   Michel Franck, 2015, 11th International Conference on Web Information Systems and Technologies (WEBIST 2015). Proceedings, P443
   Neumaier S, 2016, LECT NOTES COMPUT SC, V9981, P428, DOI 10.1007/978-3-319-46523-4_26
   Oliveira D., 2019, CEUR WORKSHOP PROC, V2489, P24
   Oliveira D., 2021, THESIS NUI GALWAY
   Oliveira D., 2020, CEUR WORKSHOP PROC, V2717, P5
   Park H, 2019, CAT CLASSIF Q, V57, P261, DOI 10.1080/01639374.2019.1641171
   Paulheim H, 2013, LECT NOTES COMPUT SC, V8218, P510, DOI 10.1007/978-3-642-41335-3_32
   Pereira Nunes Bernardo, 2013, Database and Expert Systems Applications. 24th International Conference, DEXA 2013. Proceedings: LNCS 8055, P195, DOI 10.1007/978-3-642-40285-2_18
   Pereira Nunes Bernardo, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P548
   Priyatna F., 2019, ENHANCING MAINTAINAB, P11
   Saari D.G., 1994, GEOMETRY OF VOTING
   Sabou M, 2008, LECT NOTES COMPUT SC, V5021, P854
   Seo D, 2011, COMM COM INF SC, V264, P281
   Sleeman J, 2015, AI MAG, V36, P75, DOI 10.1609/aimag.v36i1.2569
   Smith-Yoshimura Karen, 2016, D-Lib Magazine, V22, DOI 10.1045/july2016-smith-yoshimura
   Smith-Yoshimura K., 2018, CODE4LIB J
   Syed Z., 2010, P 2 WEB SCI C RAL NC
   Tennant R., 2004, Library Hi Tech, V22, P175, DOI 10.1108/07378830410524602
   Tonon A, 2016, J WEB SEMANT, V37-38, P170, DOI 10.1016/j.websem.2015.12.005
   Ullah I, 2018, INFORM TECHNOL LIBR, V37, P47, DOI 10.6017/ital.v37i4.10432
   Venetis P, 2011, PROC VLDB ENDOW, V4, P528, DOI 10.14778/2002938.2002939
   Vennesland A, 2017, 2017 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2017), P154, DOI 10.1145/3106426.3106503
   Yi Y., 2018, CEUR WORKSHOP PROC, V2127, P6
   Zamazal O, 2017, J WEB SEMANT, V43, P46, DOI 10.1016/j.websem.2017.01.001
NR 48
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 FEB 15
PY 2022
VL 237
AR 107818
DI 10.1016/j.knosys.2021.107818
PG 22
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V5ZT
UT WOS:000788422500004
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Gottschalk, S
   Demidova, E
AF Gottschalk, Simon
   Demidova, Elena
TI EventKG - the hub of event knowledge on the web - and biographical
   timeline generation
SO SEMANTIC WEB
LA English
DT Article
DE Events; knowledge graph; biographical timelines
AB One of the key requirements to facilitate the semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events, entities and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. In this article we address this limitation, formalise the concept of a temporal knowledge graph and present its instantiation - EventKG. EventKG is a multilingual event-centric temporal knowledge graph that incorporates over 690 thousand events and over 2.3 million temporal relations obtained from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical RDF representation. Whereas popular entities often possess hundreds of relations within a temporal knowledge graph such as EventKG, generating a concise overview of the most important temporal relations for a given entity is a challenging task. In this article we demonstrate an application of EventKG to biographical timeline generation, where we adopt a distant supervision method to identify relations most relevant for an entity biography. Our evaluation results provide insights on the characteristics of EventKG and demonstrate the effectiveness of the proposed biographical timeline generation method.
C1 [Gottschalk, Simon; Demidova, Elena] Leibniz Univ Hannover, L3S Res Ctr, Hannover, Germany.
RP Gottschalk, S (通讯作者)，Leibniz Univ Hannover, L3S Res Ctr, Hannover, Germany.
EM gottschalk@L3S.de; demidova@L3S.de
FU EU Horizon 2020 under ERC [339233, 812997]; Federal Ministry of
   Education and Research (BMBF) [02K15A040, 01IS18054]
FX This work was partially funded by the EU Horizon 2020 under ERC grant
   "ALEXANDRIA" (339233) and MSCA-ITN-2018 "Cleopatra" (812997), the
   Federal Ministry of Education and Research (BMBF) under
   "Data4UrbanMobility" (02K15A040) and "Simple-ML" (01IS18054).
CR Al-Badrashiny M., 2017, TEXT AN C
   Allan J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P37, DOI 10.1145/290941.290954
   Alonso O, 2013, PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'13 COMPANION), P1037
   Althoff T, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P19, DOI 10.1145/2783258.2783325
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Boschee E., 2015, **DATA OBJECT**, DOI [10.7910/DVN/28075, DOI 10.7910/DVN/28075]
   Chekol MW, 2017, AAAI CONF ARTIF INTE, P88
   Chekol MW, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P697, DOI 10.1145/3132847.3133038
   Chisholm A, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P633
   Demidova E., 2012, P 21 WORLD WID WEB C, P325, DOI DOI 10.1145/2187980.2188040
   Demidova E, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P573
   Demidova E, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P579, DOI 10.1145/2505515.2505546
   Erxleben F, 2014, LECT NOTES COMPUT SC, V8796, P50, DOI 10.1007/978-3-319-11964-9_4
   Farber M, 2018, SEMANT WEB, V9
   Giang Tran, 2015, Advances in Information Retrieval. 37th European Conference on IR Research (ECIR 2015). Proceedings: LNCS 9022, P245, DOI 10.1007/978-3-319-16354-3_26
   Gottschalk Simon, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P272, DOI 10.1007/978-3-319-93417-4_18
   Gottschalk S., 2012, P 22 INT C THEOR PRA
   Gottschalk S, 2017, PROCEEDINGS OF THE 2017 ACM WEB SCIENCE CONFERENCE (WEBSCI '17), P387, DOI 10.1145/3091478.3098879
   Gottschalk S, 2018, LECT NOTES COMPUT SC, V11155, P164, DOI 10.1007/978-3-319-98192-5_31
   Gottschalk S, 2017, ACM T WEB, V11, DOI 10.1145/3004296
   Guha R., 2011, GOOGLE OFFICIAL BLOG
   Hienert D., 2012, P WORKSH DET REPR EX
   Hobbs J.R., 2006, TIME ONTOLOGY OWL W3
   Hoffner K, 2017, SEMANT WEB, V8, P895, DOI 10.3233/SW-160247
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Kuzey E., 2014, P 23 ACM INT C C INF, P1689, DOI DOI 10.1145/2661829.2661984
   Kuzey E, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P915, DOI 10.1145/2872427.2883055
   Lebret R., 2016, P C EMP METH NAT LAN, DOI [10.18653/v1/D16-18, DOI 10.18653/V1/D16-18]
   Leetaru K, 2013, INT STUD ASS M SAN F
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Library of Congress, EXT DAT TIM FORM EDT
   Mahdisoltani F., 2014, P 8 C INN DAT SYST R
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   McNemar Q, 1947, PSYCHOMETRIKA, V12, P153, DOI 10.1007/BF02295996
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Neumaier S., 2018, J WEB SEMANT, DOI [10.2139/ssrn.3304721, DOI 10.2139/SSRN.3304721]
   Prasojo RE, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P467, DOI 10.1145/3269206.3271812
   Rahim A, 2012, PROCEEDINGS OF THE ASME GAS TURBINE INDIA CONFERENCE 2012, P73
   Rogers R, 2013, DIGITAL METHODS, P1
   Rospocher M, 2016, J WEB SEMANT, V37-38, P132, DOI 10.1016/j.websem.2015.12.004
   Rula Anisa, 2014, The Semantic Web: Trends and Challenges. 11th International Conference (ESWC 2014). Proceedings: LNCS 8465, P488, DOI 10.1007/978-3-319-07443-6_33
   Schrodt PA, 2012, CAMEO CONFLICT MEDIA
   Setty V, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P231, DOI 10.1145/3018661.3018728
   Shaw R, 2009, LECT NOTES COMPUT SC, V5926, P153
   Singh J, 2016, PROCEEDINGS OF THE 2016 ACM CONFERENCE ON HUMAN INFORMATION INTERACTION AND RETRIEVAL (CHIIR'16), P183, DOI 10.1145/2854946.2854959
   Strotgen J., 2010, P 5 INT WORKSH SEM E, P321
   Surdeanu M., 2013, TEXT AN C
   Thalhammer A, 2016, LECT NOTES COMPUT SC, V9671, P244, DOI 10.1007/978-3-319-38791-8_14
   Tran GB, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P511, DOI 10.1145/2567948.2576942
   Tuan T.A., 2011, WWW, P269, DOI [10.1145/1963192.1963306, DOI 10.1145/1963192.1963306]
   van Hage WR, 2011, J WEB SEMANT, V9, P128, DOI 10.1016/j.websem.2011.03.003
   Voskarides N, 2017, LECT NOTES COMPUT SC, V10193, P317, DOI 10.1007/978-3-319-56608-5_25
   Wan R., 2000, SIGIR Forum, V34, P49
   Witten IH, 2011, MOR KAUF D, P1
   Yuan Q, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P587, DOI 10.1145/3269206.3271674
   Zhao J, 2012, PROCEEDINGS OF THE INTERNATIONAL WORKING CONFERENCE ON ADVANCED VISUAL INTERFACES, P433, DOI 10.1145/2254556.2254639
   Zheng WG, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P217, DOI 10.1145/3132847.3132977
NR 57
TC 23
Z9 24
U1 4
U2 20
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 1039
EP 1070
DI 10.3233/SW-190355
PG 32
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:000492839300005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Xu, J
   He, MQ
   Jiang, Y
AF Xu, Jin
   He, Mengqi
   Jiang, Ying
TI A novel framework of knowledge transfer system for construction projects
   based on knowledge graph and transfer learning
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Construction project; Knowledge transfer; Project similarity; Knowledge
   graph; Transfer learning
ID MANAGEMENT; ORGANIZATIONS; PREDICTION; ONTOLOGY
AB For construction enterprises, efficient knowledge sharing among projects not only effectively improves enterprise technology, level of management and competitiveness, but also promotes their sustainable development. Given the many benefits of knowledge management, enterprises have an urgent need for project knowledge sharing methods and tools. In this study, we build an automated and intelligent framework for a construction project knowledge transfer system based on knowledge graph and transfer learning. This framework aims to solve the problem of ineffective knowledge transfer that is encountered in the management of construction project knowledge sharing. First, to discover the relationship among knowledge and further obtain the relationships among projects, we design a domain knowledge graph ontology for construction projects and build an example. Then, based on this domain knowledge graph and combining construction data distance distribution with con-struction project knowledge background, we design a new construction project similarity measurement algo-rithm (PBG-MMD), which can guide the selection of the knowledge transfer source domain. Finally, a new transfer learning method is developed to automatically select the transfer source domain according to the domain context. The framework provides an effective answer for the problem of "what to transfer" in transfer learning and provides an effective solution to address the problem of "how to transfer" during knowledge transfer. Through the verification of practical case data, the proposed framework successfully realizes knowledge transfer among construction projects and provides an automated and intelligent knowledge sharing approach for con-struction enterprises.
C1 [Xu, Jin; He, Mengqi; Jiang, Ying] Southwest Jiaotong Univ, Sch Econ & Management, Dept Informat Syst & Operat Management, Chengdu 610031, Peoples R China.
   [Xu, Jin; He, Mengqi; Jiang, Ying] Sichuan Key Lab Serv Sci & Innovat, Chengdu 610031, Peoples R China.
RP Xu, J (通讯作者)，Southwest Jiaotong Univ, Sch Econ & Management, Dept Informat Syst & Operat Management, Chengdu 610031, Peoples R China.
EM xj_james@163.com
FU National Natural Science Foundation of China [72171197, 71942006,
   71472158]; National Engineering Laboratory of Integrated Transportation
   Big Data Application Technology; China Railway Engineering Services Co.,
   Ltd.
FX The authors would like to thank the editor and anonymous reviewers for
   helpful comments and suggestions. This study is supported by grants from
   National Natural Science Foundation of China (72171197, 71942006,
   71472158), National Engineering Laboratory of Integrated Transportation
   Big Data Application Technology, and China Railway Engineering Services
   Co., Ltd.
CR Abu-Salih B, 2021, J NETW COMPUT APPL, V185, DOI 10.1016/j.jnca.2021.103076
   Aerts G, 2017, INT J PROJ MANAG, V35, P224, DOI 10.1016/j.ijproman.2016.10.010
   Ajmal M, 2010, J KNOWL MANAG, V14, P156, DOI 10.1108/13673271011015633
   Almeida MV, 2014, INT J INFORM MANAGE, V34, P770, DOI 10.1016/j.ijinfomgt.2014.07.003
   Alzubi Y, 2018, WORLD J SCI TECHNOL, V15, P325, DOI 10.1108/WJSTSD-04-2018-0023
   Armbrecht FMR, 2001, RES TECHNOL MANAGE, V44, P28, DOI 10.1080/08956308.2001.11671438
   Bilal M, 2016, ADV ENG INFORM, V30, P500, DOI 10.1016/j.aei.2016.07.001
   Bilgin N, 2021, B ENG GEOL ENVIRON, V80, P3041, DOI 10.1007/s10064-021-02142-4
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bouayad D, 2017, TUNN UNDERGR SP TECH, V68, P142, DOI 10.1016/j.tust.2017.03.011
   Cheng M, 2009, IN C IND ENG ENG MAN, P2035, DOI 10.1109/ICIEEM.2009.5344268
   Ding Y, 2002, J INFORM SCI, V28, P123, DOI 10.1177/016555150202800204
   Dooley L, 2005, J MANUF TECHNOL MANA, V16, P466, DOI 10.1108/17410380510600464
   Du XT, 2020, SOFTWARE QUAL J, V28, P39, DOI 10.1007/s11219-019-09467-0
   Eriksson PE, 2013, INT J PROJ MANAG, V31, P333, DOI 10.1016/j.ijproman.2012.07.005
   Formentini M, 2011, INT J PROD ECON, V131, P545, DOI 10.1016/j.ijpe.2011.01.023
   Gao XJ, 2019, AUTOMAT CONSTR, V98, P225, DOI 10.1016/j.autcon.2018.11.013
   Goh S. C., 2002, Journal of Knowledge Management, V6, P23, DOI 10.1108/13673270210417664
   Nguyen HL, 2020, INFORM FUSION, V61, P56, DOI 10.1016/j.inffus.2020.03.014
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hu XY, 2021, INT J GEOMECH, V21, DOI 10.1061/(ASCE)GM.1943-5622.0002008
   Huang HW, 2018, TUNN UNDERGR SP TECH, V77, P166, DOI 10.1016/j.tust.2018.04.002
   Jin C, 2021, EXPERT SYST APPL, V171, DOI 10.1016/j.eswa.2021.114637
   Kamara J., 2002, CONSTRUCTION INNOVAT, V2, P53, DOI [10.1108/14714170210814685, DOI 10.1108/14714170210814685]
   Kivrak S, 2008, J MANAGE ENG, V24, P87, DOI 10.1061/(ASCE)0742-597X(2008)24:2(87)
   Lin GJ, 2018, IEEE T IND INFORM, V14, P3289, DOI 10.1109/TII.2018.2821768
   Lin JJ, 2021, NEURAL COMPUT APPL, V33, P681, DOI 10.1007/s00521-020-05057-5
   Liu C, 2019, INFORM SOFTWARE TECH, V107, P125, DOI 10.1016/j.infsof.2018.11.005
   Long MS, 2013, IEEE I CONF COMP VIS, P2200, DOI 10.1109/ICCV.2013.274
   Lu J, 2015, KNOWL-BASED SYST, V80, P14, DOI 10.1016/j.knosys.2015.01.010
   Miller Justin J, 2013, P SO ASS INF SYST C, V2324, P36
   Mueller J, 2015, PROJ MANAG J, V46, P53, DOI 10.1002/pmj.21471
   Nevo D., 2011, 44 HAWAII INT C SYST, P1, DOI [10.1109/HICSS.2011.421, DOI 10.1109/HICSS.2011.421]
   Newell S., 2009, MANAGING KNOWLEDGE W
   Okudan O, 2021, EXPERT SYST APPL, V173, DOI 10.1016/j.eswa.2021.114776
   Oufkir I, 2019, J KNOWL MANAG, V23, P1403, DOI 10.1108/JKM-08-2018-0497
   Pan SJ, 2011, IEEE T NEURAL NETWOR, V22, P199, DOI 10.1109/TNN.2010.2091281
   Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191
   Pan Sinno Jialin, 2008, AAAI, V8, P677
   Pemsel S, 2013, INT J PROJ MANAG, V31, P31, DOI 10.1016/j.ijproman.2012.03.004
   Prencipe A, 2001, RES POLICY, V30, P1373, DOI 10.1016/S0048-7333(01)00157-3
   Rachman A., 2018, IEEE INT C IND ENG E, P1077
   Rasmussen MH, 2019, AUTOMAT CONSTR, V108, DOI 10.1016/j.autcon.2019.102956
   Ren X, 2018, J KNOWL MANAG, V22, P1082, DOI 10.1108/JKM-05-2017-0184
   Rong XN, 2019, TUNN UNDERGR SP TECH, V93, DOI 10.1016/j.tust.2019.103043
   Salimi A, 2016, TUNN UNDERGR SP TECH, V58, P236, DOI 10.1016/j.tust.2016.05.009
   Shi G, 2021, KNOWL-BASED SYST, V228, DOI 10.1016/j.knosys.2021.107213
   Singhal A., 2012, OFFICIAL GOOGLE BLOG
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Sun J, 2019, J MANAGE ENG, V35, DOI 10.1061/(ASCE)ME.1943-5479.0000663
   Szulanski G, 1996, STRATEGIC MANAGE J, V17, P27, DOI 10.1002/smj.4250171105
   Tkachenko Elena, 2015, Electronic Journal of Knowledge Management, V13, P29
   Venkataraman D., 2017, 2017 4 INT C ADV COM
   Wang H, 2019, EXPERT SYST APPL, V121, P170, DOI 10.1016/j.eswa.2018.12.017
   Wen Q, 2016, AUTOMAT CONSTR, V72, P309, DOI 10.1016/j.autcon.2016.06.002
   Xu Z, 2019, J COMPUT SCI TECH-CH, V34, P1039, DOI 10.1007/s11390-019-1959-z
   Yosinski J, 2014, ADV NEUR IN, V27
   Zhang YA, 2020, EXPERT SYST APPL, V159, DOI 10.1016/j.eswa.2020.113609
   Zhou C, 2019, AUTOMAT CONSTR, V107, DOI 10.1016/j.autcon.2019.102924
   Zhuang FZ, 2021, P IEEE, V109, P43, DOI 10.1109/JPROC.2020.3004555
NR 60
TC 0
Z9 0
U1 6
U2 6
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 AUG 1
PY 2022
VL 199
AR 116964
DI 10.1016/j.eswa.2022.116964
PG 18
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 1C5HB
UT WOS:000793149000006
DA 2022-08-23
ER

PT J
AU Vlietstra, WJ
   Vos, R
   Sijbers, AM
   van Mulligen, EM
   Kors, JA
AF Vlietstra, Wytze J.
   Vos, Rein
   Sijbers, Anneke M.
   van Mulligen, Erik M.
   Kors, Jan A.
TI Using predicate and provenance information from a knowledge graph for
   drug efficacy screening
SO JOURNAL OF BIOMEDICAL SEMANTICS
LA English
DT Article
DE Predicate; Provenance; Drug efficacy screening; Machine learning;
   Knowledge graph; Drug repurposing; Systems pharmacology; Computational
   pharmacology
ID DISEASE; COMBINATION; GENES
AB Background: Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relationship between subject and object. A triple can also contain provenance information, which consists of references to the sources of the triple (e.g. scientific publications or database entries). Knowledge graphs have been used to classify drug-disease pairs for drug efficacy screening, but existing computational methods have often ignored predicate and provenance information. Using this information, we aimed to develop a supervised machine learning classifier and determine the added value of predicate and provenance information for drug efficacy screening. To ensure the biological plausibility of our method we performed our research on the protein level, where drugs are represented by their drug target proteins, and diseases by their disease proteins.
   Results: Using random forests with repeated 10-fold cross-validation, our method achieved an area under the ROC curve (AUC) of 78.1% and 743% for two reference sets. We benchmarked against a state-of-the-art knowledge-graph technique that does not use predicate and provenance information, obtaining AUCs of 65.6% and 64.6%, respectively. Classifiers that only used predicate information performed superior to classifiers that only used provenance information, but using both performed best.
   Conclusion: We conclude that both predicate and provenance information provide added value for drug efficacy screening.
C1 [Vlietstra, Wytze J.; Vos, Rein; van Mulligen, Erik M.; Kors, Jan A.] Erasmus Univ, Med Ctr, Dept Med Informat, NL-3015 GE Rotterdam, Netherlands.
   [Vos, Rein] Maastricht Univ, Dept Methodol & Stat, NL-6200 MD Maastricht, Netherlands.
   [Sijbers, Anneke M.] Radboudumc, Ctr Mol & Biomol Informat, NL-6525 GA Nijmegen, Netherlands.
RP Vlietstra, WJ (通讯作者)，Erasmus Univ, Med Ctr, Dept Med Informat, NL-3015 GE Rotterdam, Netherlands.
EM w.vlietstra@erasmusmc.nl
OI Vlietstra, Wytze/0000-0002-1096-8563
FU Netherlands Organization of Scientific Research [ODEX project]
   [21002-NWO-ODEX 650.002.002]
FX This work was supported by the Netherlands Organization of Scientific
   Research [ODEX project, 21002-NWO-ODEX 650.002.002].
CR Alshahrani M, 2017, BIOINFORMATICS, V33, P2723, DOI 10.1093/bioinformatics/btx275
   Auger JL, 2012, ARTHRITIS RES THER, V14, DOI 10.1186/ar4117
   Azzaoui K, 2013, DRUG DISCOV TODAY, V18, P843, DOI 10.1016/j.drudis.2013.05.008
   Banh HL, 2005, CLIN PHARMACOL THER, V77, P583, DOI 10.1016/j.clpt.2005.02.002
   BERMAN S, 1995, NEW ENGL J MED, V332, P1560, DOI 10.1056/NEJM199506083322307
   Bernier V, 2006, J AM SOC NEPHROL, V17, P232, DOI 10.1681/ASN.2005080854
   Bradley AP, 1997, PATTERN RECOGN, V30, P1145, DOI 10.1016/S0031-3203(96)00142-2
   BREIMAN L, 2001, MACH LEARN, V0045
   Chen HJ, 2009, BRIEF BIOINFORM, V10, P177, DOI 10.1093/bib/bbp002
   Croft D, 2011, NUCLEIC ACIDS RES, V39, pD691, DOI 10.1093/nar/gkq1018
   Dalleau K, 2017, J BIOMED SEMANT, V8, DOI 10.1186/s13326-017-0125-1
   Daminelli S, 2012, INTEGR BIOL-UK, V4, P778, DOI 10.1039/c2ib00154c
   Davis J., 2006, P 23 INT C MACH LEAR, P233
   Dysken M, 2002, AM J GERIAT PSYCHIAT, V10, P212, DOI 10.1176/appi.ajgp.10.2.212
   Ehrlinger L, 2016, CEUR WORKSH P, V1695
   Ekdahl KN, 2007, CLIN VACCINE IMMUNOL, V14, P549, DOI 10.1128/CVI.00486-06
   Fernandez-Delgado M, 2014, J MACH LEARN RES, V15, P3133
   Fry JM, 1998, NEUROLOGY, V50, pS43, DOI 10.1212/WNL.50.2_Suppl_1.S43
   Grau J, 2015, BIOINFORMATICS, V31, P2595, DOI 10.1093/bioinformatics/btv153
   Guney E, 2016, PACIFIC S BIOCOMPUT, V22, P132
   Guney E, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms10331
   Heinemann F, 2016, DRUG DISCOV TODAY, V21, P1740, DOI 10.1016/j.drudis.2016.07.008
   Hidalgo CA, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000353
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Hodos RA, 2016, WIRES SYST BIOL MED, V8, P186, DOI 10.1002/wsbm.1337
   Hofmann-Apitius M, 2015, INT J MOL SCI, V16, P29179, DOI 10.3390/ijms161226148
   Jensen AB, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5022
   Jordan F, 2012, BRIEF FUNCT GENOMICS, V11, P497, DOI 10.1093/bfgp/els035
   Kann MG, 2007, BRIEF BIOINFORM, V8, P333, DOI 10.1093/bib/bbm031
   Kelly TF, 2014, J AFFECT DISORDERS, V167, P333, DOI 10.1016/j.jad.2014.05.050
   Kilicoglu H, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-486
   Kuhn M, 2008, J STAT SOFTW, V28, P1, DOI 10.18637/jss.v028.i05
   Lee HS, 2012, BMC SYST BIOL, V6, DOI 10.1186/1752-0509-6-80
   Liu YB, 2010, BIOINFORMATICS, V26, P587, DOI 10.1093/bioinformatics/btp697
   Macheret F, 2012, J AM COLL CARDIOL, V60, P1558, DOI 10.1016/j.jacc.2012.05.049
   Manola F, W3C ORG TRIPLE SPECI
   Menche J, 2015, SCIENCE, V347, DOI 10.1126/science.1257601
   Mitsopoulos C, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004597
   Morgenthaler TI, 2007, SLEEP, V30, P1705, DOI 10.1093/sleep/30.12.1705
   Pinero J, 2017, NUCLEIC ACIDS RES, V45, pD833, DOI 10.1093/nar/gkw943
   Principi N, 2013, EUR J CLIN MICROBIOL, V32, P151, DOI 10.1007/s10096-012-1747-y
   R Development Core Team, 2016, R LANG ENV STAT COMP
   Robin X, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-77
   Santos R, 2017, NAT REV DRUG DISCOV, V16, P19, DOI 10.1038/nrd.2016.230
   Sartor MA, 2012, BIOINFORMATICS, V28, P1408, DOI 10.1093/bioinformatics/bts156
   Sirota M, 2011, SCI TRANSL MED, V3, DOI 10.1126/scitranslmed.3001318
   Vlietstra WJ, 2017, J BIOMED INFORM, V71, P178, DOI 10.1016/j.jbi.2017.05.018
   Wang WH, 2014, BIOINFORMATICS, V30, P2923, DOI 10.1093/bioinformatics/btu403
   Wei WQ, 2013, J AM MED INFORM ASSN, V20, P954, DOI 10.1136/amiajnl-2012-001431
   Weissenborn D, 2015, J BIOMED SEMANT, V6, DOI 10.1186/s13326-015-0021-5
   Wolfgram DF, 2013, UROLITHIASIS, V41, P315, DOI 10.1007/s00240-013-0568-5
   Xu JZ, 2006, BIOINFORMATICS, V22, P2800, DOI 10.1093/bioinformatics/btl467
NR 52
TC 6
Z9 6
U1 0
U2 20
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 2041-1480
J9 J BIOMED SEMANT
JI J. Biomed. Semant.
PD SEP 6
PY 2018
VL 9
AR 23
DI 10.1186/s13326-018-0189-6
PG 10
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA GS9BN
UT WOS:000444010400001
PM 30189889
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Yu, ZJ
   Zhang, AX
   Feng, HL
   Du, HM
   Wei, SP
   Zhao, Y
AF Yu, Zongjian
   Zhang, Anxiang
   Feng, Huali
   Du, Huaming
   Wei, Shaopeng
   Zhao, Yu
TI Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with
   Copy-Generation Networks
SO ENTROPY
LA English
DT Article
DE knowledge graphs; fine-grained entity typing; copy-generation networks;
   cross-entropy
AB Fine-grained entity typing (FET) aims to identify the semantic type of an entity in a plain text, which is a significant task for downstream natural language processing applications. However, most existing methods neglect rich known typing information about these entities in knowledge graphs. To address this issue, we take advantage of knowledge graphs to improve fine-grained entity typing through the use of a copy mechanism. Specifically, we propose a novel deep neural model called CopyFet for FET via a copy-generation mechanism. CopyFet can integrate two operations: (i) the regular way of making type inference from the whole type set in the generation model; (ii) the new copy mechanism which can identify the semantic type of a mention with reference to the type-copying vocabulary from a knowledge graph in the copy model. Despite its simplicity, this mechanism proves to be powerful since extensive experiments show that CopyFet outperforms state-of-the-art methods in FET on two benchmark datasets (FIGER (GOLD) and BBN). For example, CopyFet achieves the new state-of-the-art score of 76.4% and 83.6% on the accuracy metric in FIGER (GOLD) and BBN, respectively.
C1 [Yu, Zongjian; Feng, Huali; Du, Huaming; Wei, Shaopeng; Zhao, Yu] Southwestern Univ Finance & Econ, Financial Intelligence & Financial Engn Key Lab S, Chengdu 611130, Peoples R China.
   [Zhang, Anxiang] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA.
RP Zhao, Y (通讯作者)，Southwestern Univ Finance & Econ, Financial Intelligence & Financial Engn Key Lab S, Chengdu 611130, Peoples R China.
EM yuzj@swufe.edu.cn; adamzhang1679@gmail.com; fenghuali0723@163.com;
   dhmfcc@smail.swufe.edu.cn; 2191201z5003@smail.swufe.edu.cn;
   zhaoyu@swufe.edu.cn
OI huaming, du/0000-0001-8393-2732
FU National Natural Science Foundation of China [61906159]; Fundamental
   Research Funds for the Central Universities [kjcx20210102]
FX This work was supported by the National Natural Science Foundation of
   China under grant no. 61906159, and in part supported by the Fundamental
   Research Funds for the Central Universities under grant no.
   kjcx20210102.
CR Abhishek, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P797
   Ali MA, 2020, AAAI CONF ARTIF INTE, V34, P7391
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Chen B., 2019, P 2019 C N AM CHAPT, P2862
   Chen Tongfei., 2020, P P 58 ANN M ASS COM, P8465
   Choi E, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P87
   Collins M., 1999, JOINT SIGDAT C EMP M
   Corro L. D., 2015, P 2015 C EMPIRICAL M, P868
   Dai H., 2019, P 2019 C EMP METH NA, P6210
   Devlin J, 2018, P 2019 C N AM CHAPT
   Ding N, 2021, 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (ACL-IJCNLP 2021), VOL 1, P3198
   Dong L, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1243
   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
   Gillick D., 2014, ARXIV
   Gu JT, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1631
   Hou F, 2021, KNOWL INF SYST, V63, P845, DOI 10.1007/s10115-021-01549-5
   Jin Hailong, 2019, P 2019 C EMPIRICAL M, P4970
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li DTCA, 2021, NEUROCOMPUTING, V455, P283, DOI 10.1016/j.neucom.2021.03.122
   Lin Ying, 2019, P 2019 C EMP METH NA, P6198
   Ling X., 2012, P 26 AAAI C ARTI INT
   Liu H, 2022, IEEE T IND INFORM, V18, P4361, DOI 10.1109/TII.2021.3128240
   Liu Y., 2014, P INPROCEEDINGS COLI, P2107
   Meng R, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P582, DOI 10.18653/v1/P17-1054
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Moon C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2215, DOI 10.1145/3132847.3133095
   Murty S, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P97, DOI 10.18653/v1/P18-1010
   Obeidat Rasha, 2019, P 2019 C N AM CHAPT, P807
   Onoe Y., 2019, P 2019 C N AM CHAPT, P2407
   Onoe Y, 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), P2051
   Pan X., 2015, P 2015 C N AM CHAPT, P1130
   Pennington J., 2014, EMNLP, P1532
   Peters M.E., 2018, P 2018 C N AM CHAPT, P2227, DOI 10.18653/v1/N18-1202
   Ratinov L., 2009, P C NAT LANG LEARN, P147, DOI DOI 10.3115/1596374.1596399
   Ren X., 2016, P 2016 C EMP METH NA
   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
   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
   Shi H., 2020, P 20 9 INT C INT JOI
   Shimaoka S., 2016, P 5 WORKSH AKBC SAN, P69
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Toral A, 2005, LECT NOTES COMPUT SC, V3513, P181
   Vinyals O, 2015, ADV NEURAL INFORM PR, P2692
   Weischedel R., 2005, BBN PRONOUN COREFERE, P112
   Weischedel R., 2011, HDB NATURAL LANGUAGE
   Weischedel Ralph, 2013, ONTONOTES RELEASE 5
   Wu JS, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5264
   Xin J., 2018, P 2018 C EMPIRICAL M, P993
   Xin J, 2018, AAAI CONF ARTIF INTE, P5997
   Xiong Wenhan, 2019, P 2019 C N AM CHAPTE, P773
   Xu Peng, 2018, P 2018 C N AM CHAPT, V1, P16, DOI DOI 10.18653/V1/N18-1002
   Xu S, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P1355
   Yaghoobzadeh Y., 2020, P 58 ANN M ASS COMPU, P3060
   Yaghoobzadeh Y, 2018, J ARTIF INTELL RES, V61, P835, DOI 10.1613/jair.5601
   Yaghoobzadeh Yadollah, 2015, P 2015 C EMP METH NA, P715
   Yahya M, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1107, DOI 10.1145/2505515.2505677
   Yao L., 2013, P 2013 WORKSH AUT KN, P79
   Yogatama 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, P291
   Yosef M.A., 2012, P COLING 2012 POST M, P1361
   Yuan Z, 2018, AAAI CONF ARTIF INTE, P6037
   Zhang HY, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3808
   Zhang Sheng, 2018, P 7 JOINT C LEXICAL
   Zhao Y., 2020, P 58 ANN M ASS COMP, P6419
   Zhou Ben, 2018, P 2018 C EMP METH NA, P2065
   Zhou QY, 2018, AAAI CONF ARTIF INTE, P4987
   Zhu CC, 2021, AAAI CONF ARTIF INTE, V35, P4732
NR 65
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD JUL
PY 2022
VL 24
IS 7
AR 964
DI 10.3390/e24070964
PG 13
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA 3H3HS
UT WOS:000831930000001
PM 35885187
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Wang, SS
   Fu, K
   Sun, X
   Zhang, ZQ
   Li, SC
   Jin, L
AF Wang, Shensi
   Fu, Kun
   Sun, Xian
   Zhang, Zequn
   Li, Shuchao
   Jin, Li
TI Hierarchical-aware relation rotational knowledge graph embedding for
   link prediction
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Hierarchical-aware; Complex vectors;
   Transformation of modulus; Link prediction
AB Knowledge graph embedding, as the upstream task of link prediction which aims to predict new links between entities under the premise of known relations, its reliability greatly affects the performance of link prediction. However, previous distance-based models focus on modeling complicated relation patterns while ignoring the semantic hierarchy of knowledge graph, from TransE to RotatE. In this setting, all entities are regarded as the same type, and the fact that different entities belong to different levels is neglected. Therefore, we propose the general form of RotatE, the hierarchical-aware relation rotational knowledge graph embedding (HA-RotatE), to model the hierarchical-aware knowledge graph. HARotatE represents entities and relations as complex vectors and uses different moduli of entity embed dings to indicate the different hierarchical levels they belong to. The transformation of modulus and rotation from head entity to tail entity depends on different relations. Some relations are used to link entities of the same level, and others are used to link entities of different levels. We adopt the shared modulus transformation parameter method for avoiding overfitting. As the general form of RotatE, HA-RotatE also has the ability to model and infer various relation modes, i.e., symmetry/antisymmetric, inversion and composition. On benchmark datasets WN18RR and FB15k-237, the experiments on link prediction tasks show that: (1) HA-RotatE can effectively model the semantic hierarchy of the knowledge graph; (2) Compared with competitive benchmarks, our model substantially outperforms them in most metrics. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Wang, Shensi; Fu, Kun; Sun, Xian; Zhang, Zequn; Li, Shuchao; Jin, Li] Chinese Acad Sci, Aeroscope Informat Res Inst, Beijing 100190, Peoples R China.
   [Wang, Shensi; Fu, Kun; Sun, Xian; Zhang, Zequn; Li, Shuchao; Jin, Li] Chinese Acad Sci, Aeroscope Informat Res Inst, Key Lab Network Informat Syst Technol NIST, Beijing 100190, Peoples R China.
   [Wang, Shensi; Fu, Kun; Sun, Xian] Univ Chinese Acad Sci, Beijing 100190, Peoples R China.
   [Wang, Shensi] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100190, Peoples R China.
RP Fu, K (通讯作者)，Chinese Acad Sci, Aeroscope Informat Res Inst, Beijing 100190, Peoples R China.
EM kunfuiecas@gmail.com
OI jin, li/0000-0001-8833-4862
CR Balazevic I., 2019, ADV NEURAL INFORM PR, V32, P4463
   Bansal T, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4387
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   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
   Chang K., 2014, PROC C EMPIRICAL MET, P1568
   Daiber J., 2013, P 9 INT C SEM SYST, P121, DOI 10.1145/2506182.2506198
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Jiang X, 2019, P 2019 C N AM CHAPTE, P978
   Kingma D. P., 2017, ARXIV
   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
   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
   Recupero D.R., 2017, P ISWC 2017 POST DEM, V1963
   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, V26, P926, DOI DOI 10.5555/2999611.2999715
   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, OBSERVED VERSUS LATE, P57, DOI [10.18653/v1/w15-4007, DOI 10.18653/V1/W15-4007]
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vashishth S., 2020, 8 INT C LEARN REPR I
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang Q, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1859
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H., ABS150905490 CORR
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Yang B., 2015, P INT C LEARN REPR I
   Yu P.S., ABS200200388 CORR
   Zhang Z, 2018, EMNLP, P3198, DOI DOI 10.18653/V1/D18-1358
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
NR 37
TC 0
Z9 0
U1 1
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD OCT 11
PY 2021
VL 458
BP 259
EP 270
DI 10.1016/j.neucom.2021.05.093
EA JUN 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UJ8XB
UT WOS:000691561400004
DA 2022-08-23
ER

PT J
AU MILACIC, VR
AF MILACIC, VR
TI DESIGNER(R) - INTELLIGENT EXPERT-SYSTEM FOR INTELLIGENT MANUFACTURING
   SYSTEMS-DESIGN
SO ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
LA English
DT Article; Proceedings Paper
CT 2nd World Congress on Expert Systems
CY JAN 10-14, 1994
CL LISBON ESTORIL, PORTUGAL
SP Int Soc Intelligent Syst
AB This survey on expert systems activities and trends in Yugoslavia offers some results already obtained in the domain of manufacturing science and technology. In the scope of a long-term research project ''Intelligent Manufacturing Systems (IMS)-Theory and Application'' a Designer(R) Intelligent Expert System for manufacturing engineering has been proposed and partially developed. Designer(R) IES is based on new developed knowledge automata theory enhanced with cellular automats concept. Induction learning by analogy and Quasimorphism knowledge mapping from real world to model world is used to generate a reasoning structure. The Intelligent Expert System is divided into three main subsystems, with a very large knowledge base:
   Product Designer
   Process Designer, and
   Production Planning and Control Designer.
   All these segments were developed in pilot versions of expert systems for specific groups of activities inside each of these three domains.
RP MILACIC, VR (通讯作者)，UNIV PUERTO RICO,DEPT MECH ENGN,MAYAGUEZ CAMPUS,POB 5000,MAYAGUEZ,PR 00681, USA.
NR 0
TC 1
Z9 1
U1 0
U2 1
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB
SN 0736-5845
J9 ROBOT CIM-INT MANUF
JI Robot. Comput.-Integr. Manuf.
PD SEP
PY 1994
VL 11
IS 3
BP 121
EP 136
DI 10.1016/0736-5845(94)90029-9
PG 16
WC Computer Science, Interdisciplinary Applications; Engineering,
   Manufacturing; Robotics
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Robotics
GA RE707
UT WOS:A1994RE70700002
DA 2022-08-23
ER

PT J
AU Lan, W
   Dong, Y
   Chen, QF
   Zheng, RQ
   Liu, J
   Pan, Y
   Chen, YPP
AF Lan, Wei
   Dong, Yi
   Chen, Qingfeng
   Zheng, Ruiqing
   Liu, Jin
   Pan, Yi
   Chen, Yi-Ping Phoebe
TI KGANCDA: predicting circRNA-disease associations based on knowledge
   graph attention network
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article
DE knowledge graph; graph attention neural network; circRNA-disease
   associations
ID CIRCULAR RNA; CIRCHIPK3; INVASION; DATABASE
AB Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, many researches have shown that circRNA can be considered as the potential biomarker for clinical diagnosis and treatment of disease. Some computational methods have been proposed to predict circRNA-disease associations. However, the performance of these methods is limited as the sparsity of low-order interaction information. In this paper, we propose a new computational method (KGANCDA) to predict circRNA-disease associations based on knowledge graph attention network. The circRNA-disease knowledge graphs are constructed by collecting multiple relationship data among circRNA, disease, miRNA and lncRNA. Then, the knowledge graph attention network is designed to obtain embeddings of each entity by distinguishing the importance of information from neighbors. Besides the low-order neighbor information, it can also capture high-order neighbor information from multisource associations, which alleviates the problem of data sparsity. Finally, the multilayer perceptron is applied to predict the affinity score of circRNA-disease associations based on the embeddings of circRNA and disease. The experiment results show that KGANCDA outperforms than other state-of-the-art methods in 5-fold cross validation. Furthermore, the case study demonstrates that KGANCDA is an effective tool to predict potential circRNA-disease associations.
C1 [Lan, Wei] Guangxi Univ, Sch Comp Elect & Informat, Nanning 530004, Guangxi, Peoples R China.
   [Dong, Yi] Guangxi Univ, Comp Sci & Tecnol, Nanning, Peoples R China.
   [Chen, Qingfeng] Guangxi Univ, State Key Lab Conservat & Utilizat Subtrop Agrobi, Bioinformat Team, Nanning, Peoples R China.
   [Zheng, Ruiqing; Liu, Jin] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China.
   [Pan, Yi] Chinese Acad Sci, Shenzhen Inst Adv Technol, Sch Comp Sci & Control Engn, Shenzhen, Peoples R China.
   [Chen, Yi-Ping Phoebe] La Trobe Univ, Dept Comp Sci & Comp Engn, Bundoora, Vic 3086, Australia.
RP Chen, QF (通讯作者)，Guangxi Univ, Sch Comp Elect & Informat, Nanning 530004, Guangxi, Peoples R China.; Chen, QF (通讯作者)，Guangxi Univ, State Key Lab Conservat & Utilizat Subtrop Agrobi, Nanning 530004, Guangxi, Peoples R China.; Pan, Y (通讯作者)，Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China.
EM qingfeng@gxu.edu.cn; yi.pan@siat.ac.cn
RI Lan, Wei/T-7601-2017; Chen, Yi-Ping Phoebe/B-8844-2008
OI Lan, Wei/0000-0001-5839-7504; Chen, Yi-Ping Phoebe/0000-0002-4122-3767
FU National Natural Science Foundation of China [62072124, 61963004,
   61972185]; Natural Science Foundation of Guangxi [2021GXNSFAA075041,
   2018GXNSFBA281193]; Science and Technology Base and Talent Special
   Project of Guangxi [AD20159044]; Shenzhen Science and Technology Program
   [KQTD20200820113106007]; Hunan Provincial Science and Technology Program
   [2018WK4001]
FX National Natural Science Foundation of China (grant nos 62072124,
   61963004 and 61972185), the Natural Science Foundation of Guangxi (grant
   nos 2021GXNSFAA075041 and 2018GXNSFBA281193), the Science and Technology
   Base and Talent Special Project of Guangxi (grant no. AD20159044), the
   Shenzhen Science and Technology Program (grant no.
   KQTD20200820113106007), the Hunan Provincial Science and Technology
   Program (grant no. 2018WK4001).
CR Bian LJ, 2018, BIOCHEM BIOPH RES CO, V505, P346, DOI 10.1016/j.bbrc.2018.09.073
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen G, 2013, NUCLEIC ACIDS RES, V41, pD983, DOI 10.1093/nar/gks1099
   Chen QF, 2021, IEEE ACM T COMPUT BI, V18, P1106, DOI 10.1109/TCBB.2019.2936476
   Deepthi K, 2021, MOL DIAGN THER, V25, P87, DOI 10.1007/s40291-020-00499-y
   Deng L, 2019, SCI REP-UK, V9, P1
   Fan CY, 2020, FRONT GENET, V11, DOI 10.3389/fgene.2020.540751
   Fan CY, 2018, INT J BIOL SCI, V14, P1950, DOI 10.7150/ijbs.28260
   Fanale D, 2018, ADV EXP MED BIOL, V1087, P109, DOI 10.1007/978-981-13-1426-1_9
   Feng J, 2020, INT J MOL MED, V46, P1973, DOI 10.3892/ijmm.2020.4747
   Ge EX, 2020, GENOMICS, V112, P1335, DOI 10.1016/j.ygeno.2019.08.001
   Ge Z, 2018, EUR REV MED PHARMACO, V22, P8203, DOI 10.26355/eurrev_201812_16513
   Glorot X., 2010, JMLR WRKSHOP C P, P249
   Glorot X., 2011, J MACH LEARN RES, V15, P315, DOI DOI 10.1002/ECS2.1832
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Jeck WR, 2014, NAT BIOTECHNOL, V32, P453, DOI 10.1038/nbt.2890
   Ji WX, 2018, BIOCHEM BIOPH RES CO, V497, P122, DOI 10.1016/j.bbrc.2018.02.036
   Jin PC, 2018, BIOCHEM BIOPH RES CO, V503, P1570, DOI 10.1016/j.bbrc.2018.07.081
   Kingma D. P., 2015, ADAM METHOD STOCHAST, DOI DOI 10.48550/ARXIV.1412.6980
   Lan W, 2021, IEEE ACM TRANSAC COM, V99, P1
   Lan W, 2020, DATABASE-OXFORD, DOI 10.1093/database/baaa085
   Lan W, 2022, NEUROCOMPUTING, V469, P384, DOI 10.1016/j.neucom.2020.09.094
   Lan W, 2022, IEEE ACM T COMPUT BI, V19, P1715, DOI 10.1109/TCBB.2020.3034910
   Lan W, 2018, METHODS MOL BIOL, V1754, P205, DOI 10.1007/978-1-4939-7717-8_12
   Lan W, 2018, IEEE ACM T COMPUT BI, V15, P1774, DOI 10.1109/TCBB.2016.2586190
   Lan W, 2017, BIOINFORMATICS, V33, P458, DOI 10.1093/bioinformatics/btw639
   Lan W, 2016, NEUROCOMPUTING, V206, P50, DOI 10.1016/j.neucom.2016.03.080
   Lan W, 2015, TSINGHUA SCI TECHNOL, V20, P500, DOI 10.1109/TST.2015.7297749
   Lei XJ, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa350
   Li GH, 2020, J BIOMED INFORM, V112, DOI 10.1016/j.jbi.2020.103624
   Li Q, 2020, REPRODUCTION, V160, P511, DOI 10.1530/REP-19-0540
   Li YW, 2017, EMBO REP, V18, P1646, DOI 10.15252/embr.201643581
   Li YX, 2019, BIOCHEM BIOPH RES CO, V508, P991, DOI 10.1016/j.bbrc.2018.12.046
   Liang GH, 2020, MOL CANCER, V19, DOI 10.1186/s12943-020-01152-2
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu N, 2018, EUR REV MED PHARMACO, V22, P3713, DOI 10.26355/eurrev_201806_15250
   Lu CQ, 2020, BIOINFORMATICS, V36, P5656, DOI 10.1093/bioinformatics/btaa1077
   Lu CQ, 2021, IEEE J BIOMED HEALTH, V25, P891, DOI 10.1109/JBHI.2020.2999638
   Lu WY, 2017, CELL CYCLE, V16, P589, DOI 10.1080/15384101.2017.1278935
   Miao YR, 2018, NUCLEIC ACIDS RES, V46, pD276, DOI 10.1093/nar/gkx1004
   Panda AC, 2017, NUCLEIC ACIDS RES, V45, DOI 10.1093/nar/gkx297
   Patop IL, 2019, EMBO J, V38, DOI 10.15252/embj.2018100836
   Rophina M, 2020, DATABASE-OXFORD, DOI 10.1093/database/baaa019
   SANGER HL, 1976, P NATL ACAD SCI USA, V73, P3852, DOI 10.1073/pnas.73.11.3852
   Siegel RL, 2020, CA-CANCER J CLIN, V70, P145, DOI 10.3322/caac.21601
   Sun HY, 2020, J CLIN LAB ANAL, V34, DOI 10.1002/jcla.23235
   Vural H, 2019, PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019), P929, DOI 10.1145/3341161.3343514
   Wang L, 2021, IEEE T CYBERNETICS, V51, P5522, DOI 10.1109/TCYB.2020.3022852
   Wang L, 2020, BIOINFORMATICS, V36, P4038, DOI 10.1093/bioinformatics/btz825
   Wang L, 2020, PLOS COMPUT BIOL, V16, DOI 10.1371/journal.pcbi.1007568
   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
   Wei H, 2020, BRIEF BIOINFORM, V21, P1356, DOI 10.1093/bib/bbz057
   Xiao Q, 2021, MOL GENET GENOMICS, V296, P223, DOI 10.1007/s00438-020-01741-2
   Xiao Q, 2019, IEEE J BIOMED HEALTH, V23, P2661, DOI 10.1109/JBHI.2019.2891779
   Xie Y, 2021, Zhonghua Zhong Liu Za Zhi, V43, P449, DOI 10.3760/cma.j.cn112152-20200116-00040
   Xie YH, 2020, SIGNAL TRANSDUCT TAR, V5, DOI 10.1038/s41392-020-0116-z
   Xu S, 2018, PEERJ, V6, DOI 10.7717/peerj.5503
   Yan C, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2522-6
   Yang H, 2020, MOL CANCER, V19, DOI 10.1186/s12943-020-01165-x
   Yang R, 2019, MOL CANCER, V18, DOI 10.1186/s12943-018-0933-7
   Yuchen Zhang, 2020, Big Data Mining and Analytics, V3, P280, DOI 10.26599/BDMA.2020.9020025
   Zeng KX, 2018, CELL DEATH DIS, V9, DOI 10.1038/s41419-018-0454-8
   Zengqiang Fang, 2019, Big Data Mining and Analytics, V2, P261, DOI 10.26599/BDMA.2019.9020010
   Zhang LL, 2020, CELL DEATH DIS, V11, DOI 10.1038/s41419-019-2182-0
   Zhang W, 2019, IEEE ACCESS, V7, P83474, DOI 10.1109/ACCESS.2019.2920942
   Zhao Q, 2019, IEEE T NANOBIOSCI, V18, P578, DOI 10.1109/TNB.2019.2922214
   Zhao ZP, 2018, CASE STUD CONSTR MAT, V9, DOI 10.1016/j.cscm.2018.e00193
   Zheng XY, 2020, MOL CANCER, V19, DOI 10.1186/s12943-020-01183-9
   Zhong ZY, 2016, SCI REP-UK, V6, DOI 10.1038/srep30919
   Zhou GR, 2020, EUR REV MED PHARMACO, V24, P11587, DOI 10.26355/eurrev_202011_23801
NR 71
TC 2
Z9 2
U1 33
U2 45
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 bbab494
DI 10.1093/bib/bbab494
PG 13
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:000763000800165
PM 34864877
DA 2022-08-23
ER

PT J
AU Wang, MH
   Zhang, XY
   Feng, DE
   Wang, YP
   Tang, W
   Ye, P
AF Wang, Muhua
   Zhang, Xueying
   Feng, Deen
   Wang, Yipeng
   Tang, Wei
   Ye, Peng
TI Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A
   Focus on Meteorological Conditions
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE knowledge graph; alpine skiing event; risk assessment; RippleNet
ID MANAGEMENT
AB The alpine skiing event is particularly vulnerable to changes in meteorological conditions as a winter sport held outdoors. The commonly used risk assessment methods cannot be inflexible and cannot be dynamically adjusted to combine multiple risk factors and actual conditions. A knowledge graph can organize data resources in the risk domain as structured knowledge systems. This paper combines a knowledge graph and risk assessment to effectively assess the risk status. First of all, we introduce the relevant literature review of sports event risk assessment, combining the characteristics of alpine skiing events. Then, we summarize the risk types of alpine skiing events and related risk knowledge. Secondly, a model is proposed to introduce an event risk assessment model based on the RippleNet framework combined with the characteristics of large-scale sports events. Moreover, the validity of the model is verified. The results show that the RippleNet-based event risk assessment model can be used to assess the risk of alpine skiing events. In order to effectively deal with the large-scale sports events that occur with a variety of risks, the smooth implementation of large-scale sports events provides a strong guarantee.
C1 [Wang, Muhua; Feng, Deen; Tang, Wei] China Meteorol Adm, Publ Meteorol Serv Ctr, Beijing 100081, Peoples R China.
   [Zhang, Xueying; Wang, Yipeng] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China.
   [Zhang, Xueying; Wang, Yipeng] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.
   [Ye, Peng] Yangzhou Univ, Urban Planning & Dev Inst, Yangzhou 225127, Jiangsu, Peoples R China.
   [Ye, Peng] Yangzhou Univ, Coll Civil Sci & Engn, Yangzhou 225127, Jiangsu, Peoples R China.
RP Wang, MH (通讯作者)，China Meteorol Adm, Publ Meteorol Serv Ctr, Beijing 100081, Peoples R China.
EM wangmuhua@cma.gov.cn; zhangxueying@njnu.edu.cn; fengdn@12379.cn;
   wyp@njnu.edu.cn; tangw8@cma.gov.cn; 007839@yzu.edu.cn
OI Yipeng, Wang/0000-0002-0914-353X; YE, Peng/0000-0003-2149-8269
FU National Key Research and Development Program of China [2018YFF0300100,
   2018YFF0300105]; National Natural Science Foundation of China [41631177,
   41971337]
FX FundingThis work was funded in part by the National Key Research and
   Development Program of China under Grant 2018YFF0300100 and Grant
   2018YFF0300105, and in part by the National Natural Science Foundation
   of China under Grant 41631177 and Grant 41971337.
CR Alaka HA, 2018, EXPERT SYST APPL, V94, P164, DOI 10.1016/j.eswa.2017.10.040
   Aven T, 2011, SAFETY SCI, V49, P1080, DOI 10.1016/j.ssci.2011.04.017
   Berlonghi A.E., 1990, SPECIAL EVENT RISK M
   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 Qian, 2015, INFORM THEORY PRACTI, V38, P127
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Carriger JF, 2021, J ENVIRON MANAGE, V278, DOI 10.1016/j.jenvman.2020.111478
   Chang P. C., 1990, Tourism Management, V11, P45, DOI 10.1016/0261-5177(90)90007-V
   Cheng DW, 2022, PATTERN RECOGN, V121, DOI 10.1016/j.patcog.2021.108218
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Fuller C, 2004, SPORTS MED, V34, P349, DOI 10.2165/00007256-200434060-00001
   Guan WJ, 2020, EUR RESPIR J, V55, DOI 10.1183/13993003.00547-2020
   Guo L., 2018, SPORTS RES ED, V33, P35
   Hanstad D. V., 2012, Event Management, V16, P189, DOI 10.3727/152599512X13459279626683
   Ho K., 2000, ISRMIS20002006
   Huang K., 2019, THESIS BEIJING SPORT
   Huo D., 2014, J SHENYANG SPORT U, V33, P6
   Jiao Z., 2018, ARXIV180701882
   Leopkey B, 2009, EUR SPORT MANAG Q, V9, P187, DOI 10.1080/16184740802571443
   Liang H., 2018, STAT DECIS, V34, P85
   Liu PC, 2020, CHIN CONT DECIS CONF, P3612, DOI 10.1109/CCDC49329.2020.9164384
   [刘艳辉 Liu Yanhui], 2019, [工程地质学报, Journal of Engineering Geology], V27, P134
   Liu YX, 2022, CURR PSYCHOL, V41, P3651, DOI 10.1007/s12144-020-00884-0
   Luo WH, 2021, ADV CIV ENG, V2021, DOI 10.1155/2021/6624579
   Murav'ev AV, 2015, RUSS METEOROL HYDRO+, V40, P584, DOI 10.3103/S1068373915090034
   Niu X, 2011, LECT NOTES COMPUT SC, V7032, P205, DOI 10.1007/978-3-642-25093-4_14
   Rausand M, 2011, STAT PRACT, P1, DOI 10.1002/9781118281116
   Rebele T, 2016, LECT NOTES COMPUT SC, V9982, P177, DOI 10.1007/978-3-319-46547-0_19
   Robin A., 2005, SPORTS VENUE EVENT P
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Scott D, 2019, CURR ISSUES TOUR, V22, P1301, DOI 10.1080/13683500.2018.1436161
   Shi Yan, 2017, CHINA SPORT SCI, V37, P76, DOI DOI 10.16469/J.CSS.201702008
   Tao XH, 2020, WORLD WIDE WEB, V23, P2341, DOI 10.1007/s11280-020-00810-1
   Tarka MC, 2019, SPORTS HEALTH, V11, P265, DOI 10.1177/1941738119825842
   Tissot HC, 2021, J HEALTHC INFORM RES, V5, P359, DOI 10.1007/s41666-021-00096-6
   Wang H.F., 2019, KNOWLEDGE GRAPH METH
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   [吴凡 Wu Fan], 2012, [灾害学, Journal of Catastrophology], V27, P109
   Wu J., 2020, P INT C KNOWL SCI EN, V12274, P3
   Xiao Y., 2020, KNOWLEDGE GRAPH CONC
   Yan JH, 2018, FRONT COMPUT SCI-CHI, V12, P55, DOI 10.1007/s11704-016-5228-9
   Yang B., 2021, INF STUD THEORY APPL, V44, DOI [10.1007/s00521-021-06382-z, DOI 10.1007/S00521-021-06382-Z]
   Yang B., 2021, J MOD INF, V41, P110
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   Yuan F., 2019, THESIS BEIJING SPORT
   Zhang J., 2018, THESIS BEIJING JIAOT
   Zhang Q, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11102849
   Zhang W., 2019, P ADV E BUS ENG UB C, VVolume 41, P464
   Zhao Q, 2018, MATEC WEB CONF, V151, DOI 10.1051/matecconf/201815105003
   Zheng R, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21010306
NR 51
TC 1
Z9 1
U1 22
U2 22
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 DEC
PY 2021
VL 10
IS 12
AR 835
DI 10.3390/ijgi10120835
PG 15
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA YA4FS
UT WOS:000738291700001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Fang, YL
   Wang, H
   Zhao, LL
   Yu, FP
   Wang, CY
AF Fang, Youli
   Wang, Hong
   Zhao, Lili
   Yu, Fengping
   Wang, Caiyu
TI Dynamic knowledge graph based fake-review detection
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Fake review; Lstm; Knowledge graph; Data mining; Electronic commerce
ID SYSTEM
AB Online product reviews are an important driver of customers' purchasing behavior. Fake reviews seriously mislead consumers, challenging the fairness of the online shopping environment. Although the detection of fake reviews has progressed, several problems remain. First, fake comment recognition ignores the correlation between time and the semantics of the comment texts, which is always hidden in the context of the reviews. Second, the impact of multi-source information on fake comment recognition is not considered, as it constitutes a complex, high-dimensional, heterogeneous relationship between reviewers, reviews, stores and commodities. To overcome these problems, the present paper proposes a dynamic knowledge graph-based method for fake-review detection. Based on the characteristics of online product reviews, it first extracts four types of entities using a developed neural network model called sentence vector/twin-word embedding conditioned bidirectional long short-term memory. Time series related features are then added to the knowledge graph construction process, forming dynamic graph networks. To enhance the fake-review detection, four indicators are newly defined for determining the relationships among the four types of nodes. In experimental evaluations, our method surpassed the state-of-the-art results.
C1 [Fang, Youli; Wang, Hong; Zhao, Lili; Yu, Fengping; Wang, Caiyu] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China.
RP Wang, H (通讯作者)，Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China.
EM 1521055775@qq.com; wanghong@163.com
FU National Natural Science Foundation of China [61672329, 61373149,
   61472233, 61572300, 81273704]; Shandong Province Science and Technology
   Plan Supported Project [2014GGX101026]; Taishan Scholar Fund of Shandong
   Province [20110819, TSHW201502038]; NVIDIA Corporation
FX The work is partially supported by the National Natural Science
   Foundation of China (Nos.61672329, 61373149, 61472233, 61572300,
   81273704), Shandong Province Science and Technology Plan Supported
   Project (No.2014GGX101026) and Taishan Scholar Fund of Shandong Province
   (No.TSHW201502038, 20110819). We also gratefully acknowledge the support
   of NVIDIA Corporation with the donation of the TITAN X GPU used for this
   research.
CR Ahmed H, 2018, J GENERALIZED LIE TH, V1, P1
   Alonso S, 2010, INFORM SCIENCES, V180, P4477, DOI 10.1016/j.ins.2010.08.005
   Cao Q, 2011, DECIS SUPPORT SYST, V50, P511, DOI 10.1016/j.dss.2010.11.009
   Dematis I, 2018, LECT NOTES COMPUT SC, V10706, P581, DOI 10.1007/978-3-319-73117-9_41
   Dong YP, 2019, INTERPRET TRANSL TRA, V13, P408, DOI 10.1080/1750399X.2019.1617653
   Faris H, 2019, INFORM FUSION, V48, P67, DOI 10.1016/j.inffus.2018.08.002
   Ferrara E, 2019, COMMUN ACM, V62, P82, DOI 10.1145/3299768
   Gomez-Perez JM, 2017, SPRINGER INT PUBLISH, V16, P263
   Gomez-Perez JM, 2016, NAMED ENTITIES RECOG, V18
   Hagoort P, 2014, INTERPLAY SYNTAX SEM, V15
   Hubbard NA, 2017, MULT SCLER J, V23, P12
   KAHNEMAN D, 1973, PSYCHOL REV, V80, P237, DOI 10.1037/h0034747
   Li HY, 2014, COMPUT SIST, V18, P467, DOI 10.13053/CyS-18-3-2035
   Li Y., 2015, J INFORM COMPUTATION, V12, P1615, DOI [10.12733/jics20105452, DOI 10.12733/JICS20105452]
   Lim EP, 2010, P 19 ACM INT C INF K, P939, DOI [10.1145/1871437.1871557, DOI 10.1145/1871437.1871557]
   Liu R, 2018, INFORM SCIENCES, V450, P200, DOI 10.1016/j.ins.2018.03.031
   Le NT, 2020, INFORM SCIENCES, V514, P149, DOI 10.1016/j.ins.2019.12.010
   Ott M, 2011, P 49 ANN M ASS COMP, DOI DOI 10.1145/2567948.2577293
   Owens A, 2016, COMPUTER VISION ECCV
   Rayana S, 2015, ACM SIGKDD INT C KNO, V10, P24
   Rychtarikova R, 2018, ENTROPY, V18, P372
   Savage D, 2015, EXPERT SYST APPL, V42, P8650, DOI 10.1016/j.eswa.2015.07.019
   Scannapieco E, 2018, ASTROPHYS J LETT, V865, DOI 10.3847/2041-8213/aae1f9
   Serrat O., 2017, KNOWLEDGE SOLUTIONS
   Shivaprasad TK, 2017, PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT), P298, DOI 10.1109/ICICCT.2017.7975207
   Vinayakumar R, 2018, J INTELL FUZZY SYST, V34, P1277, DOI 10.3233/JIFS-169424
   Wang G, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2337542.2337546
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X, 2017, DETECTING DECEPTIVE
   Wang Z, 2018, APPL INTELL, V48, P3094, DOI 10.1007/s10489-018-1142-1
   Weihua Y, 2019, INF SCI, V510, P122
   Wu X, 2017, IEEE INT CONF BIG DA, P494, DOI 10.1109/BigData.2017.8257963
   Xingjian S. H. I., 2015, CONVOLUTIONAL LSTM N, P802
   Xu WX, 2013, 2013 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII), P282, DOI 10.1109/SII.2013.6776656
   Xue HY, 2015, 2015 2ND INTERNATIONAL CONFERENCE ON MATERIAL ENGINEERING AND APPLICATION (ICMEA 2015), P1, DOI 10.1109/PESGM.2015.7286000
   Yu C, 2016, ACTA SCI NAT U PEKIN, V16, P37
   Zhang Q, 2018, APPL RES COMPUTERS
NR 37
TC 14
Z9 14
U1 19
U2 63
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 DEC
PY 2020
VL 50
IS 12
BP 4281
EP 4295
DI 10.1007/s10489-020-01761-w
EA JUL 2020
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OU3JD
UT WOS:000548505400001
DA 2022-08-23
ER

PT J
AU Trouillon, T
   Dance, CR
   Gaussier, E
   Welbl, J
   Riedel, S
   Bouchard, G
AF Trouillon, Theo
   Dance, Christopher R.
   Gaussier, Eric
   Welbl, Johannes
   Riedel, Sebastian
   Bouchard, Guillaume
TI Knowledge Graph Completion via Complex Tensor Factorization
SO JOURNAL OF MACHINE LEARNING RESEARCH
LA English
DT Article
DE complex embeddings; tensor factorization; knowledge graph; matrix
   completion; statistical relational learning
ID MATRIX FACTORIZATION
AB In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs-labeled directed graphs|and predicting missing relationships-labeled edges. State-of-the-art embedding models propose different trade-offs between modeling expressiveness, and time and space complexity. We reconcile both expressiveness and complexity through the use of complex-valued embeddings and explore the link between such complex-valued embeddings and unitary diagonalization. We corroborate our approach theoretically and show that all real square matrices-thus all possible relation/adjacency matrices-are the real part of some unitarily diagonalizable matrix. This results opens the door to a lot of other applications of square matrices factorization. Our approach based on complex embeddings is arguably simple, as it only involves a Hermitian dot product, the complex counterpart of the standard dot product between real vectors, whereas other methods resort to more and more complicated composition functions to increase their expressiveness. The proposed complex embeddings are scalable to large data sets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.
C1 [Trouillon, Theo; Gaussier, Eric] Univ Grenoble Alpes, 700 Ave Centrale, St Martin Dheres 38401, France.
   [Dance, Christopher R.] NAVER LABS Europe, 6 Chem Maupertuis, Meylan 38240, France.
   [Welbl, Johannes; Riedel, Sebastian; Bouchard, Guillaume] UCL, Gower St, London WC1E 6BT, England.
   [Bouchard, Guillaume] Bloomsburg AI, 115 Hampstead Rd, London NW1 3EE, England.
RP Trouillon, T (通讯作者)，Univ Grenoble Alpes, 700 Ave Centrale, St Martin Dheres 38401, France.
EM THEO.TROUILLON@IMAG.FR; CHRIS.DANCE@NAVERLABS.COM;
   ERIC.GAUSSIER@IMAG.FR; J.WELBL@CS.UCL.AC.UK; S.RIEDEL@CS.UCL.AC.UK;
   G.BOUCHARD@CS.UCL.AC.UK
FU Association Nationale de la Recherche et de la Technologie through the
   CIFRE grant [2014/0121]; Paul Allen Foundation through an Allen
   Distinguished Investigator grant; Google Focused Research Award
FX This work was supported in part by the Association Nationale de la
   Recherche et de la Technologie through the CIFRE grant 2014/0121, in
   part by the Paul Allen Foundation through an Allen Distinguished
   Investigator grant, and in part by a Google Focused Research Award. We
   would like to thank Ariadna Quattoni, Stephane Clinchant, Jean-Marc
   Andeoli, Sofia Michel, Alejandro Blumentals, Leo Hubert and Pierre Comon
   for their helpful comments and feedback.
CR Alon N., 2016, COLT, P47
   Auer Soren, 2007, 6 INT SEM WEB C BUS, V7, P11
   Baruch Guy, 2014, ARXIV14107383
   Belouchrani A, 1997, IEEE T SIGNAL PROCES, V45, P434, DOI 10.1109/78.554307
   Beltrami E, 1873, GIORNALE MATH, V11, P98
   Bergstra J., 2010, PYTH SCI COMP C SCIP
   Bhojanapalli Srinadh, 2016, ARXIV160507221
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   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, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Bordes Antoine, 2013, ABS13047158 COMP RES
   Bordes Antoine, 2011, AAAI C ART INT
   Bouchard Guillaume, 2015, AAAI SPRING S KNOWL
   Cauchy A.-L., 1829, EXER MATH, V4, P174
   Chang K. W., 2014, C EMP METH NAT LANG
   CVETKOVIC D, 1997, EIGENSPACES GRAPHS
   Cyganiak R., 2014, W3C RECOMMENDATION
   Danihelka Ivo, 2016, ARXIV160203032
   De Lathauwer L, 2001, IEEE T SIGNAL PROCES, V49, P2262, DOI 10.1109/78.950782
   De Raedt L, 2016, SYNTHESIS LECT ARTIF, DOI DOI 10.2200/S00692ED1V01Y201601AIM032
   Denham W.W., 1973, THESIS U WASHINGTON
   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
   Duchi J, 2011, J MACH LEARN RES, V12, P2121
   Fellbaum C., 1998, WORDNET
   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, 2016, J ARTIF INTELL RES, V55, P715, DOI 10.1613/jair.5013
   Ge Rong, 2016, ARXIV160507272
   Hayashi K., 2017, ARXIV170205563
   Hitchcock F. L., 1927, J MATH PHYS, V6, P164, DOI 10.1002/sapm192761164
   Horn R. A., 2012, MATRIX ANAL, DOI 10.1017/CBO9781139020411
   Hu Q, 2016, INT CONF ACOUST SPEE, P5630, DOI 10.1109/ICASSP.2016.7472755
   Huang H, 2009, LECT NOTES COMPUT SC, V5802, P307
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Krompass Denis, 2014, INT SEM WEB C, P114
   Kruskal J B, 1989, MULTIWAY DATA ANAL, P7
   Kunegis J., 2012, P 4 ACM RECSYS WORKS, P37
   Linial N, 2007, COMBINATORICA, V27, P439, DOI 10.1007/s00493-007-2160-5
   Mao Y., 2004, P 4 ACM SIGCOMM C IN, V2004, P278, DOI [DOI 10.1145/1028788.1028827, 10.1145/1028788.1028827]
   McCray AT, 2003, COMP FUNCT GENOM, V4, P80, DOI 10.1002/cfg.255
   Nickel M., 2014, ADV NEURAL INFORM PR, P1179
   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
   Niepert M., 2016, NIPS, P3405
   PAATERO P, 1994, ENVIRONMETRICS, V5, P111, DOI 10.1002/env.3170050203
   Pirasteh P, 2015, KNOWL-BASED SYST, V83, P51, DOI 10.1016/j.knosys.2015.03.006
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Riedel S., 2013, P 2013 C N AM CHAPT, P74
   Rocktaschel T., 2015, P 2015 C N AM CHAPT, P1119
   Saad Y., 1992, NUMERICAL METHODS LA, V158
   Sahoo S. S., 2009, W3C RDB2RDF INCUBATO, P113
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Stoica P., 2005, SPECTRAL ANAL SIGNAL, V452
   Sutskever I., 2009, P ADV NEUR INF PROC, P1
   Taskar B., 2007, INTRO STAT RELATIONA
   Toutanova Kristina, 2015, C EMP METH NAT LANG
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Trouillon Theo, 2017, INT WORKSH STAT REL
   von Neumann J, 1930, MATH ANN, V102, P370
   Welbl Johannes, 2016, WORKSH AUT KNOWL BAS, P103
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xie F, 2015, KNOWL-BASED SYST, V81, P148, DOI 10.1016/j.knosys.2015.02.013
   Yang Bishan, 2015, INT C LEARN REPR
NR 65
TC 64
Z9 68
U1 2
U2 7
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 2017
VL 18
AR 130
PG 38
WC Automation & Control Systems; Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA FV4LS
UT WOS:000424545800001
DA 2022-08-23
ER

PT J
AU Portisch, J
   Heist, N
   Paulheim, H
AF Portisch, Jan
   Heist, Nicolas
   Paulheim, Heiko
TI Knowledge graph embedding for data mining vs. knowledge graph embedding
   for link prediction - two sides of the same coin?
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge graph embedding; link prediction; data mining; RDF2vec;
   KGlove; node2vec; DeepWalk; TransE; TransR; RotatE; RESCAL; DistMult;
   ComplEx
ID MODEL
AB Knowledge Graph Embeddings, i.e., projections of entities and relations to lower dimensional spaces, have been proposed for two purposes: (1) providing an encoding for data mining tasks, and (2) predicting links in a knowledge graph. Both lines of research have been pursued rather in isolation from each other so far, each with their own benchmarks and evaluation methodologies. In this paper, we argue that both tasks are actually related, and we show that the first family of approaches can also be used for the second task and vice versa. In two series of experiments, we provide a comparison of both families of approaches on both tasks, which, to the best of our knowledge, has not been done so far. Furthermore, we discuss the differences in the similarity functions evoked by the different embedding approaches.
C1 [Portisch, Jan; Heist, Nicolas; Paulheim, Heiko] Univ Mannheim, Data & Web Sci Grp, Mannheim, Germany.
   [Portisch, Jan] SAP SE, Walldorf, Germany.
RP Paulheim, H (通讯作者)，Univ Mannheim, Data & Web Sci Grp, Mannheim, Germany.
EM jan@informatik.uni-mannheim.de; nico@informatik.uni-mannheim.de;
   heiko@informatik.uni-mannheim.de
OI Heist, Nicolas/0000-0002-4354-9138; Paulheim, Heiko/0000-0003-4386-8195
FU Ministry of Science, Research and the Arts Baden-Wurttemberg; University
   of Mannheim
FX The publication of this article was funded by the Ministry of Science,
   Research and the Arts Baden-Wurttemberg and the University of Mannheim.
CR Ali M, 2021, J MACH LEARN RES, V22
   Basu S., 2020, P 1 WORKSH SCHOL DOC, P127, DOI [10.18653/v1/2020.sdp-1.15, DOI 10.18653/V1/2020.SDP-1.15]
   Bonte P, 2020, OWL2STREAMS BENCHMAR
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Celebi R, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3284-5
   Chen JY, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P1537, DOI 10.1145/3366423.3380226
   Cochez M, 2017, LECT NOTES COMPUT SC, V10587, P190, DOI 10.1007/978-3-319-68288-4_12
   Dai YF, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9050750
   Daza D, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P798, DOI 10.1145/3442381.3450141
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Gesese GA, 2021, SEMANT WEB, V12, P617, DOI 10.3233/SW-200404
   Han X., 2018, P C EMP METH NAT LAN, P139
   Hoffart J., 2012, P 21 ACM INT C INF K, P545, DOI DOI 10.1145/2396761.2396832
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   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
   Kejriwal M., 2017, P INT WORKSH SEM BIG, P1, DOI [10.1145/3066911.3066918, DOI 10.1145/3066911.3066918]
   Lavrac N, 2020, MACH LEARN, V109, P1465, DOI 10.1007/s10994-020-05890-8
   Lee M.D., 2005, P ANN M COGN SCI SOC, V27
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Ling Wang, 2015, P 2015 C N AM CHAPT, P1299, DOI DOI 10.3115/V1/N15-1142
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Moghaddam F. Bakhshandegan, 2021, FURTHER KNOWLEDGE GR, P74, DOI [10.3233/SSW210036, DOI 10.3233/SSW210036]
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Pellegrino Maria Angela, 2020, Semantic Web 17th International Conference, ESWC 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12123), P565, DOI 10.1007/978-3-030-49461-2_33
   Portisch J., 2021, INT SEM WEB C POST D
   Portisch J, 2020, PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020), P5641
   Ristoski P, 2014, LINKED DATA KNOWLEDG, V6
   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
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9982, P186, DOI 10.1007/978-3-319-46547-0_20
   Ristoski P, 2016, J WEB SEMANT, V36, P1, DOI 10.1016/j.websem.2016.01.001
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Shi BX, 2018, AAAI CONF ARTIF INTE, P1957
   Sofronova Radina, 2020, The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020 Satellite Events. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 12124), P203, DOI 10.1007/978-3-030-62327-2_35
   Steenwinckel Bram, 2020, The Semantic Web - ISWC 2020. 19th International Semantic Web Conference. Lecture Notes in Computer Science (LNCS 12507), P344, DOI 10.1007/978-3-030-62466-8_22
   Sun Z., 2018, P ICLR
   Tan P.-N., 2016, INTRO DATA MINING
   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
   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 YQ, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0248552
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yang B., 2015, P INT C LEARN REPR I
   Zheng D, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P739, DOI 10.1145/3397271.3401172
   Zouaq A, 2020, PROCEEDINGS OF THE INTERNATIONAL WORKSHOP ON SEMANTIC BIG DATA (SBD 2020), DOI 10.1145/3391274.3393637
NR 48
TC 1
Z9 1
U1 2
U2 2
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 399
EP 422
DI 10.3233/SW-212892
PG 24
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:000780041500006
OA hybrid
DA 2022-08-23
ER

PT J
AU Pestryakova, S
   Vollmers, D
   Sherif, MA
   Heindorf, S
   Saleem, M
   Moussallem, D
   Ngomo, ACN
AF Pestryakova, Svetlana
   Vollmers, Daniel
   Sherif, Mohamed Ahmed
   Heindorf, Stefan
   Saleem, Muhammad
   Moussallem, Diego
   Ngomo, Axel-Cyrille Ngonga
TI CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications
SO SCIENTIFIC DATA
LA English
DT Article; Data Paper
AB The rapid generation of large amounts of information about the coronavirus SARS-CoV-2 and the disease COVID-19 makes it increasingly difficult to gain a comprehensive overview of current insights related to the disease. With this work, we aim to support the rapid access to a comprehensive data source on COVID-19 targeted especially at researchers. Our knowledge graph, CovidPubGraph, an RDF knowledge graph of scientific publications, abides by the Linked Data and FAIR principles. The base dataset for the extraction is CORD-19, a dataset of COVID-19-related publications, which is updated regularly. Consequently, CovidPubGraph is updated biweekly. Our generation pipeline applies named entity recognition, entity linking and link discovery approaches to the original data. The current version of CovidPubGraph contains 268,108,670 triples and is linked to 9 other datasets by over 1 million links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications. CovidPubGraph is publicly available under the Creative Commons Attribution 4.0 International license.
C1 [Pestryakova, Svetlana; Vollmers, Daniel; Sherif, Mohamed Ahmed; Heindorf, Stefan; Saleem, Muhammad; Moussallem, Diego; Ngomo, Axel-Cyrille Ngonga] Paderborn Univ, Dept Comp Sci, DICE Res Grp, Paderborn, Germany.
RP Pestryakova, S; Sherif, MA (通讯作者)，Paderborn Univ, Dept Comp Sci, DICE Res Grp, Paderborn, Germany.
EM pestryak@mail.uni-paderborn.de; mohamed.sherif@upb.de
OI Sherif, Mohamed/0000-0002-9927-2203
FU German Federal Ministry of Economics and Climate Protection (BMWK)
   project RAKI [01MD19012D]; EU [860801]; BMVI project LIMBO [19F2029C];
   BMVI project OPAL [19F2028A]
FX This work has been supported by the German Federal Ministry of Economics
   and Climate Protection (BMWK) project RAKI (GA no. 01MD19012D), the EU
   H2020 project KnowGraphs (GA no. 860801) as well as the BMVI projects
   LIMBO (GA no. 19F2029C) and OPAL (GA no. 19F2028A).
CR [Anonymous], 2020, HUMAN CORONAVIRUS IN
   Buhmann L, 2016, J WEB SEMANT, V39, P15, DOI 10.1016/j.websem.2016.06.001
   Cai X, 2021, SCIENTOMETRICS, V126, P3683, DOI 10.1007/s11192-021-03873-7
   Demir C., 2021, ABS210615373 CORR
   Dong E., COVID 19 DATA REPOSI
   Dong ES, 2020, LANCET INFECT DIS, V20, P533, DOI 10.1016/S1473-3099(20)30120-1
   Farber M, 2019, LECT NOTES COMPUT SC, V11779, P113, DOI 10.1007/978-3-030-30796-7_8
   Groza T, 2007, LECT NOTES COMPUT SC, V4519, P518
   Han J., 2020, ARXIV200312218
   Heindorf S., 2022, WWW
   Horbach SPJM, 2021, RES EVALUAT, V30, P231, DOI 10.1093/reseval/rvaa037
   Janowicz K, COVID 19 STKO LAB UC
   Jiang G, CORD 19 ON FHIR SEMA
   Kanehisa M, 2019, NUCLEIC ACIDS RES, V47, pD590, DOI 10.1093/nar/gky962
   Kroll H., 2020, SEMANTICALLY ENRICHE
   Kuhn M, 2016, NUCLEIC ACIDS RES, V44, pD1075, DOI 10.1093/nar/gkv1075
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Moussallem D, 2017, K-CAP 2017: PROCEEDINGS OF THE KNOWLEDGE CAPTURE CONFERENCE, DOI 10.1145/3148011.3148024
   Neumann M, 2019, SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2019), P319
   Ngomo ACN, 2014, LECT NOTES COMPUT SC, V8714, P1, DOI 10.1007/978-3-319-10587-1_1
   Ngomo ACN, 2021, KUNSTL INTELL, V35, P413, DOI 10.1007/s13218-021-00713-x
   Pestryakova S., 2021, Zenodo, DOI 10.5281/zenodo.4650261
   Roder M., 2019, DATA SCI, P1
   Vandewiele G., COVID 19 LIT KNOWLED
   Wang L. L, 2020, ABS200410706 CORR
   Wang Q, KNOWLEDGE EXTRACTION
   Wang X., 2020, COMPREHENSIVE NAMED
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   wmflabs, WIKIDATA SCHOLIA TOP
   Zhou YD, 2020, CELL DISCOV, V6, DOI 10.1038/s41421-020-0153-3
NR 31
TC 0
Z9 0
U1 2
U2 2
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
EI 2052-4463
J9 SCI DATA
JI Sci. Data
PD JUL 8
PY 2022
VL 9
IS 1
AR 389
DI 10.1038/s41597-022-01298-2
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 2T3DF
UT WOS:000822355800004
PM 35803947
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Rasmusen, SC
   Penz, M
   Widauer, S
   Nako, P
   Kurteva, A
   Roa-Valverde, A
   Fensel, A
AF Rasmusen, Sven Carsten
   Penz, Manuel
   Widauer, Stephanie
   Nako, Petraq
   Kurteva, Anelia
   Roa-Valverde, Antonio
   Fensel, Anna
TI Raising Consent Awareness With Gamification and Knowledge Graphs: An
   Automotive Use Case
SO INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS
LA English
DT Article
DE Awareness; Gamification; Human-Computer Interaction; Incentives;
   Knowledge Graphs; Legal Comprehension; Semantics; User Interface
ID POLICIES
AB Consent is one of GDPR's lawful bases for data processing and specific requirements for it apply. Consent should be specific, unambiguous, and most of all informed. However, an informed consent request does not guarantee having individuals who are aware of what it means to consent and the implications that follow. Consent is often given blindly now, in particular because of information overload from long privacy policies written in legal language and complex interface designs that cause consent fatigue on the users' side. This paper presents a knowledge graph-based user interface for consent solicitation, which uses gamification to raise the legal awareness and ease individual's comprehension of consent. The knowledge graph models informed consent in a machine-readable format and provides a unified consent model to all entities involved in the data sharing process. The evaluation shows that with the help of gamification, the interface can raise individuals' average legal awareness to 92.86%.
C1 [Rasmusen, Sven Carsten; Kurteva, Anelia; Roa-Valverde, Antonio; Fensel, Anna] Univ Innsbruck, Semant Technol Inst, Innsbruck, Austria.
   [Penz, Manuel; Nako, Petraq] Univ Innsbruck, Comp Sci, Innsbruck, Austria.
   [Widauer, Stephanie] Univ Innsbruck, Comp Sci & Informat Syst, Innsbruck, Austria.
   [Fensel, Anna] Wageningen Univ & Res, Wageningen, Netherlands.
RP Rasmusen, SC (通讯作者)，Univ Innsbruck, Semant Technol Inst, Innsbruck, Austria.
FU CampaNeo project - FFG - The Austrian Research Promotion Agency
   [873839]; smashHit European Union project under Horizon 2020 [871477]
FX This research is supported by the CampaNeo project funded by FFG -The
   Austrian Research Promotion Agency (grant agreement number: 873839) as
   well as the smashHit European Union project funded under Horizon 2020
   (grant 871477). The authors would like to thank Timo Graen, Nils Henke,
   and Philip Matesanz from Volkswagen AG for their feedback regarding the
   evaluation.
CR Angulo J., 2015, P 33 ANN ACM C EXT A, P1803, DOI [10.1145/2702613.2732701, DOI 10.1145/2702613.2732701]
   Bechmann A, 2014, J MEDIA RES STUD, V11, P21, DOI 10.1080/16522354.2014.11073574
   Bless C., 2021, FURTHER KNOWLEDGE GR, V53, P44, DOI [10.3233/SSW210034, DOI 10.3233/SSW210034]
   Borgesius FJZ, 2016, COMPUT LAW SECUR REV, V32, P256, DOI 10.1016/j.clsr.2015.12.013
   Bretzke H, 2003, LECT NOTES ARTIF INT, V2702, P218
   Brookhaven National Laboratory, 2017, VIS SCI BIG DAT INF
   BYRNE DJ, 1988, BRIT MED J, V296, P839, DOI 10.1136/bmj.296.6625.839
   Cheong C., 2013, PAC AS C INF SYST
   Clark RC., 2016, E LEARNING SCI INSTR, DOI 10.1002/9781119239086
   Deterding S., 2011, P 15 INT AC MINDTREK, P9, DOI DOI 10.1145/2181037.2181040
   Deterding S., 2011, GAMIFICATION USING G, P2425, DOI DOI 10.1145/1979742.1979575
   Drozd O., 2020, ICT SYSTEMS SECURITY, P124, DOI [10.1007/978-3-030-58201-2_9, DOI 10.1007/978-3-030-58201-2_9]
   Drozd O, 2019, LECT NOTES COMPUT SC, V11711, P17, DOI 10.1007/978-3-030-27813-7_2
   Ebert N., 2021, P 2021 CHI C HUM FAC, P1, DOI [10.1145/3411764.3445516, DOI 10.1145/3411764.3445516]
   Eickhoff C, 2012, SIGIR 2012: PROCEEDINGS OF THE 35TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P871, DOI 10.1145/2348283.2348400
   Elbayoudi A., 2016, P 9 ACM INT C PERVAS, P1, DOI [10.1145/2910674.2935834, DOI 10.1145/2910674.2935834]
   Ericsson K.A., 1988, TEXT INTERDISCIPLINA, V8, P295
   Farzan R., 2008, S PERS TECHN
   FESTINGER L, 1962, SCI AM, V207, P93, DOI 10.1038/scientificamerican1062-93
   Fotopoulou E, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17092054
   Gross B. M, 1965, ANN AM ACAD POLIT SS, V360, P197
   Guarino N, 2009, HDB ONTOLOGIES, P1, DOI DOI 10.1007/978-3-540-92673-3_0
   Hamari J, 2014, P ANN HICSS, P3025, DOI 10.1109/HICSS.2014.377
   Herrera J. L., 2021, PERSONAL DATA GENTRI
   Hodgetts Darrin, 2010, SOCIAL PSYCHOL EVERY
   Hosseini C, 2020, ADV INTELL SYST, V1070, P552, DOI 10.1007/978-3-030-32523-7_39
   Irby C., 1977, METHODOLOGY USER INT
   Janowicz K, 2019, J WEB SEMANT, V56, P1, DOI 10.1016/j.websem.2018.06.003
   Jaworska J, 2008, LECT NOTES COMPUT SC, V5175, P62, DOI 10.1007/978-3-540-85481-4_7
   Joergensen RF, 2014, INTERNET POLICY REV, V3, DOI 10.14763/2014.4.330
   Kurteva A., 2021, LECT NOTES COMPUTER, V12706, DOI [10.1007/978-3-030-74296-6_1, DOI 10.1007/978-3-030-74296-6_1]
   Kurteva A., 2021, SEMANT WEB, P1
   Leary M.R., 2010, HDB SOCIAL PSYCHOL, P864, DOI [10.1002/9780470561119.socpsy002024, DOI 10.1002/9780470561119.SOCPSY002024]
   Lee MK, 2011, 29TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, P325
   Marczewski A., 2015, EVEN NINJA MONKEYS P, P165
   Martin B, 2019, LECT NOTES COMPUT SC, V11701, P316, DOI 10.1007/978-3-030-29374-1_26
   Marwick A, 2019, INFORM COMMUN SOC, V22, P1697, DOI 10.1080/1369118X.2018.1450432
   Matte C, 2020, P IEEE S SECUR PRIV, P791, DOI 10.1109/SP40000.2020.00076
   Mcdonald A.M, 2008, J LAW POLICY INFORM, V4, P543
   McStay A, 2013, NEW MEDIA SOC, V15, P596, DOI 10.1177/1461444812458434
   Miguel Carrascosa Juan, 2015, P 11 ACM C EM NETW E, P13
   NIELSEN J, 1992, COMPUTER, V25, P12, DOI 10.1109/2.121503
   Nouwens M, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376321
   Rodrigues L., 2021, ARXIV
   Rodriguez ND, 2014, ACM COMPUT SURV, V46, DOI 10.1145/2523819
   Seaborn K, 2015, INT J HUM-COMPUT ST, V74, P14, DOI 10.1016/j.ijhcs.2014.09.006
   Simsek U., 2016, P 12 INT C SEM SYST, P173, DOI [10.1145/2993318.2993346, DOI 10.1145/2993318.2993346]
   Staiano J, 2014, UBICOMP'14: PROCEEDINGS OF THE 2014 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING, P583, DOI 10.1145/2632048.2632074
   Streimikiene D, 2014, RENEW SUST ENERG REV, V35, P285, DOI 10.1016/j.rser.2014.04.012
   Taye Mohammad Mustafa, 2010, J COMPUTING, V2
   Tondello GF, 2016, CHI PLAY 2016: PROCEEDINGS OF THE 2016 ANNUAL SYMPOSIUM ON COMPUTER-HUMAN INTERACTION IN PLAY, P229, DOI 10.1145/2967934.2968082
   Uschold M, 1996, KNOWL ENG REV, V11, P93, DOI 10.1017/S0269888900007797
   Utz C, 2019, PROCEEDINGS OF THE 2019 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'19), P973, DOI 10.1145/3319535.3354212
   Vassileva J, 2012, USER MODEL USER-ADAP, V22, P177, DOI 10.1007/s11257-011-9109-5
   Wszalek Joseph, 2017, AJOB Neurosci, V8, P26, DOI 10.1080/21507740.2017.1285821
NR 55
TC 0
Z9 0
U1 2
U2 2
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.300820
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3G6ZO
UT WOS:000831500300026
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU An, B
   Han, XP
   Sun, L
AF An, Bo
   Han, Xianpei
   Sun, Le
TI Retrofitting Soft Rules for Knowledge Representation Learning
SO BIG DATA RESEARCH
LA English
DT Article
DE Knowledge representation; Soft rules; Link prediction
AB Recently, a significant number of studies have focused on knowledge graph completion using rule enhanced learning techniques, supported by the mined soft rules in addition to the hard logic rules. However, due to the difficulty in determining the confidence of the soft rules without the global semantics of knowledge graph such as the semantic relatedness between relations, the knowledge representation may not be optimal, leading to degraded effectiveness in its application to knowledge graph completion tasks. To address this challenge, this paper proposes a retrofit framework that iteratively enhances the knowledge representation and confidence of soft rules. Specifically, the soft rules guide the learning of knowledge representation, and the representation, in turn, provides global semantics of the knowledge graph to optimize the confidence of soft rules. Extensive evaluation shows that our method achieves state-of-the-art results on link prediction and triple classification tasks, brought by the fine-tuned soft rules. (C) 2020 Elsevier Inc. All rights reserved.
C1 [An, Bo; Han, Xianpei; Sun, Le] Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China.
RP Han, XP (通讯作者)，Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China.
EM xianpei@iscas.ac.cn
FU National Natural Science Foundation of China [61433015, 61572477,
   61772505]
FX This work is supported by the National Natural Science Foundation of
   China under Grants no. 61433015, 61572477 and 61772505.
CR An Bo, 2018, NAACL, V1, P745
   Bengio Y, 2011, AAAI C ART INT AAAI
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1, DOI 10.1111/j.2517-6161.1977.tb01600.x
   Dettmers T., 32 AAAI C ART INT
   Ding B., 2018, ARXIV PREPRINT ARXIV
   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
   Galarraga L, 2015, VLDB J, V24, P707, DOI 10.1007/s00778-015-0394-1
   Ganchev K, 2010, J MACH LEARN RES, V11, P2001
   Gu Y., 2020, IDENTIABILITY HIERAR
   Guo S., 2016, P C EMP METH NAT LAN, P192, DOI 10.18653/v1/D16-1019
   Guo S, 2018, AAAI CONF ARTIF INTE, P4816
   Guo S, 2016, COMM COM INF SC, V650, P219, DOI 10.1007/978-981-10-3168-7_22
   H?jek P., 1998, TR LOG STUD LOG LIB, DOI 10.1007/978-94-011-5300-3
   Han X., 2018, P C EMP METH NAT LAN
   Hinton G.E., 2015, ARXIV150302531
   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
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Minervini P, 2017, CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI2017)
   Mitchell T., 2015, AAAI C ART INT
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Riedel S., 2013, P 2013 C N AM CHAPT, P74
   Rocktaschel T., 2015, P 2015 C N AM CHAPT, P1119
   Sadeghian A., 2019, P 33 INT C NEUR INF, V32, P15347
   Socher R., 2013, INT C INT CONTR INF, P464
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun M., 2015, ARXIV PREPRINT ARXIV
   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
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang Q, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1859
   Wang W.Y., 2017, ARXIV PREPRINT ARXIV
   Wang Z., 2016, IJCAI, P1293
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xing E, 2016, ARXIV PREPRINT ARXIV
   Xu J, 2016, ARXIV PREPRINT ARXIV
   Yang B., 2014, ARXIV EPRINT
   Zhong H., 2015, P 2015 C EMP METH NA, P267
NR 42
TC 0
Z9 0
U1 1
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-5796
J9 BIG DATA RES
JI Big Data Res.
PD MAY 15
PY 2021
VL 24
AR 100156
DI 10.1016/j.bdr.2020.100156
EA APR 2021
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RQ5LE
UT WOS:000642459200010
DA 2022-08-23
ER

PT J
AU Qi, ZX
   Wang, HZ
   Shen, ZM
   Yang, DH
AF Qi, Zhixin
   Wang, Hongzhi
   Shen, Ziming
   Yang, Donghua
TI PreKar: A learned performance predictor for knowledge graph stores
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Performance prediction; Knowledge graph; Store generation; Store
   embedding
ID SELECTIVITY ESTIMATION; RANGE QUERIES; TIME
AB Effective knowledge graph storage management is identified as the basic premise to make full use of knowledge graphs. Due to the lack of performance evaluation for knowledge graph stores, it is difficult for users to decide which one is the best. However, none of existing studies of performance prediction focuses on storage structures. To fill this gap, we propose a learned performance predictor PreKar to estimate the time costs of processing the given workloads on the candidate stores. However, it is challenging to learn a well-trained model due to the low-diversity of historical workloads and the requirement of lightweight embedding strategies. To address this problem, we first develop a novel candidate stores generator, which not only discovers all possible candidate stores for model training, but also multiplies the umber of training instances. Based on the generated stores, we derive an effective and lightweight encoder to not only embed the main features of workloads and stores into the model, but also guarantee the high-efficiency of PreKar. Experimental results on real knowledge graphs demonstrate that PreKar achieves high accuracy on performance prediction and saves a huge amount of time to obtain performance for knowledge graph stores.
C1 [Qi, Zhixin; Wang, Hongzhi; Shen, Ziming; Yang, Donghua] Harbin Inst Technol, Sch Comp Sci & Technol, Xidazhi St, Harbin 150001, Heilongjiang, Peoples R China.
RP Wang, HZ (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Xidazhi St, Harbin 150001, Heilongjiang, Peoples R China.
EM qizhx@hit.edu.cn; wangzh@hit.edu.cn; yang.dh@hit.edu.cn
OI Wang, Hongzhi/0000-0002-7521-2871
FU National Nature Science Foundation of China [U1866602]
FX This study was funded by National Nature Science Foundation of China
   (Grant number U1866602).
CR Abadi DJ, 2009, VLDB J, V18, P385, DOI 10.1007/s00778-008-0125-y
   Acharya MS, 2019, 2019 SECOND INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN DATA SCIENCE (ICCIDS 2019)
   [Anonymous], WATDIV QUERY TEMPLAT
   Bruno N, 2001, SIGMOD RECORD, V30, P211
   Cai TT, 2022, IEEE T KNOWL DATA EN, V34, P1993, DOI 10.1109/TKDE.2020.3003047
   Cai YC, 2020, PR MACH LEARN RES, V119
   Chakkappen S, 2017, PROC VLDB ENDOW, V10, P1813, DOI 10.14778/3137765.3137785
   Chen J, 2021, IEEE T CYBERNETICS, DOI 10.1109/TCYB.2021.3064092
   Durand M, 2003, LECT NOTES COMPUT SC, V2832, P605
   Dutt A, 2019, PROC VLDB ENDOW, V12, P1044, DOI 10.14778/3329772.3329780
   Dutta Krittika, 2021, Proceedings of 5th International Conference on Computing Methodologies and Communication (ICCMC 2021), P1168, DOI 10.1109/ICCMC51019.2021.9418038
   GRAEFE G, 1989, SIGMOD REC, V18, P358, DOI 10.1145/67544.66960
   Gunopulos D, 2005, VLDB J, V14, P137, DOI 10.1007/s00778-003-0090-4
   Gunopulos D, 2000, SIGMOD REC, V29, P463
   Harbi R, 2016, VLDB J, V25, P355, DOI 10.1007/s00778-016-0420-y
   Harris S., 2004, P 1 INT WORKSH PRACT, P81
   Hasan S, 2020, SIGMOD'20: PROCEEDINGS OF THE 2020 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1035, DOI 10.1145/3318464.3389741
   Jagadish HV, 2001, SIGMOD REC, V30, P223
   K W., 2006, P 2 INT WORKSH SCAL, P35
   Ke GL, 2017, ADV NEUR IN, V30
   Kipf A., 2018, ARXIV180900677
   Leis V, 2015, PROC VLDB ENDOW, V9, P204
   Li Z, 2021, KNOWL-BASED SYST, V212, DOI 10.1016/j.knosys.2020.106618
   Liu FL, 2015, ACM SOCC'15: PROCEEDINGS OF THE SIXTH ACM SYMPOSIUM ON CLOUD COMPUTING, P153, DOI 10.1145/2806777.2806944
   Mackert L. F., 1986, SIGMOD Record, V15, P84
   Markl V., 2005, P 31 INT C VER LARG, P373
   Muller M, 2018, PROC VLDB ENDOW, V11, P1016, DOI 10.14778/3213880.3213882
   Neumann T, 2008, PROC VLDB ENDOW, V1, P647, DOI 10.14778/1453856.1453927
   Ozcaglar C, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3116, DOI 10.1145/3308558.3313672
   Pan Z., 2004, P 1 INT WORKSH PRACT, P109
   Qi Z., 2012, DUAL STORE STRUCTURE, V2020
   Srivastava U., 2006, ICDE, P39
   Sun J, 2019, PROC VLDB ENDOW, V13, P307, DOI 10.14778/3368289.3368296
   Sun W, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1887, DOI 10.1145/2723372.2723732
   To H, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1939, DOI 10.1145/2505515.2505756
   Tyree Stephen, 2011, P 20 INT C WORLD WID, P387
   Wang X., 2020, INT C WEB INF SYST E, P3
   Wang XY, 2021, PROC VLDB ENDOW, V14, P1640, DOI 10.14778/3461535.3461552
   Weiss C, 2008, PROC VLDB ENDOW, V1, P1008, DOI 10.14778/1453856.1453965
   WHANG KY, 1990, ACM T DATABASE SYST, V15, P208, DOI 10.1145/78922.78925
   Wu WT, 2016, SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1721, DOI 10.1145/2882903.2882914
   Wu WT, 2013, PROC INT CONF DATA, P1081, DOI 10.1109/ICDE.2013.6544899
   Wu YL, 2001, SIGMOD REC, V30, P449
   Xue GT, 2022, NEUROCOMPUTING, V472, P212, DOI 10.1016/j.neucom.2021.03.138
   Yang Y., 2021, IEEE T KNOWL DATA EN
   Zhang ZH, 2020, J CONTROL SCI ENG, V2020, DOI 10.1155/2020/4206919
   Zou L, 2014, VLDB J, V23, P565, DOI 10.1007/s00778-013-0337-7
NR 47
TC 0
Z9 0
U1 4
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
DI 10.1007/s11280-022-01033-2
EA MAR 2022
PG 21
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZY0DT
UT WOS:000772262200002
DA 2022-08-23
ER

PT J
AU Qu, GH
   Zhang, Y
   Tan, KC
   Han, JT
   Qu, WH
AF Qu, Guohua
   Zhang, Yue
   Tan, Kaichao
   Han, Jiangtao
   Qu, Weihua
TI Exploring Knowledge Domain and Emerging Trends in Climate Change and
   Environmental Audit: A Scientometric Review
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE climate change; environmental audit; knowledge map; public health
ID CHANGE PROGRESS REPORT; CARBON EMISSIONS; BIBLIOMETRIC ANALYSIS; CHANGE
   ADAPTATION; OZONE DEPLETION; IMPACTS; EFFICIENCY; CAPTURE; CITIES
AB Environmental audit is inevitably linked to climate change, one immediate target of the auditors is likely to be climate control, and the warming of the Earth and the consequent climatic changes affect us all. What is the link between environmental audit and climate change? What ties together some of these themes between environmental audit and climate change? The interaction between climate change and environmental audit has been one of the most challenging. In this paper, a scientometric analysis of 84 academic publications between 2013 and 2021 related to climate change and environmental audit is presented to characterize the knowledge domain by using the CiteSpace visualization software. First, we present the number of publications, the number of citations, research categories, and journals published through Web of Science database. Secondly, we analyze countries, authors, and journals with outstanding contributions through network analysis. Finally, we use keyword analysis and apply three types of knowledge mapping to our research, cluster view, timeline view, and time zone view, revealing the focus and future directions. We identify the most important topic in the field of climate change and environment audit as represented on the basis of existing literature data which include the Carbon Emissions, Social Capital, Energy Audit, Corporate Governance, Diffusion of Innovation Environmental Management System, and Audit Committee. The results show that climate change and environmental audit publications grew slowly, but the research are widely cited by scholars. Published journals are relatively scattered, but the cited journals are the world's top journals, and most research countries are developed countries. The most productive authors and institutions in this subject area are in UK, Australia, USA, Spain, and Netherlands. There are no leading figures, but the content of their research can be divided into six clusters. Future research content involving city, policy, dynamics, information, biodiversity, conservation and clustering social capital, diffusion of innovation environmental management, and audit committee are the directions for future research. It is worth noting that cities, policies, and adaptability are closely linked to public health.
C1 [Qu, Guohua; Zhang, Yue; Han, Jiangtao] Shanxi Univ Finance & Econ, Sch Management Sci & Engn, Taiyuan 030006, Peoples R China.
   [Tan, Kaichao] Harbin Normal Univ, Sch Comp Sci & Informat Engn, Harbin 150025, Peoples R China.
   [Qu, Weihua] Shanxi Univ, Sch Econ & Management, Taiyuan 030006, Peoples R China.
   [Qu, Weihua] Shanxi Univ, Inst Management & Decis, Taiyuan 030006, Peoples R China.
RP Qu, GH; Zhang, Y (通讯作者)，Shanxi Univ Finance & Econ, Sch Management Sci & Engn, Taiyuan 030006, Peoples R China.; Qu, WH (通讯作者)，Shanxi Univ, Sch Econ & Management, Taiyuan 030006, Peoples R China.; Qu, WH (通讯作者)，Shanxi Univ, Inst Management & Decis, Taiyuan 030006, Peoples R China.
EM qugh@sxufe.edu.cn; zhangyuesxufe@163.com; tankaichao424516@163.com;
   hanjt813@163.com; quweihua@sxu.edu.cn
FU national society and science fund committee: Formation Mechanism and
   Evolution Mechanism of the Third-Party International Environmental Audit
   Driven Corporate Environmental Behavior In Chinese context [21FGLB043]
FX This research was supported by the national society and science fund
   committee: Formation Mechanism and Evolution Mechanism of the
   Third-Party International Environmental Audit Driven Corporate
   Environmental Behavior In Chinese context (21FGLB043).
CR Al-Shaer H, 2018, BUS STRATEG ENVIRON, V27, P973, DOI 10.1002/bse.2046
   Alkaya E, 2015, WATER ENVIRON RES, V87, P14, DOI 10.2175/106143014X14062131178952
   Andersson E, 2018, RENEW SUST ENERG REV, V93, P165, DOI 10.1016/j.rser.2018.05.037
   Andrady A, 2017, PHOTOCH PHOTOBIO SCI, V16, P107, DOI 10.1039/c7pp90001e
   Andrady A, 2016, PHOTOCH PHOTOBIO SCI, V15, P141, DOI 10.1039/c6pp90004f
   Archambault E, 2009, J AM SOC INF SCI TEC, V60, P1320, DOI 10.1002/asi.21062
   Chapple L, 2013, ABACUS, V49, P1, DOI 10.1111/abac.12006
   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 Y., 2015, STUDIES SCI SCI, V33, P242
   Chu S, 2009, SCIENCE, V325, P1599, DOI 10.1126/science.1181637
   Chuai XW, 2012, J GEOGR SCI, V22, P630, DOI 10.1007/s11442-012-0952-z
   Rocha ABD, 2019, EUR J OPER RES, V279, P486, DOI 10.1016/j.ejor.2019.05.040
   Dai ZC, 2021, IEEE ACCESS, V9, P130127, DOI 10.1109/ACCESS.2021.3113805
   Donaldson T., 2013, CORP GOV-OXFORD, DOI [10.1002/9781444367072.wbiee458, DOI 10.1002/9781444367072.WBIEE458]
   Emery ART., 2004, MANAGERIAL AUDITING, V19, P916, DOI DOI 10.1108/02686900410549439
   Fang Y, 2018, J SUSTAIN TOUR, V26, P108, DOI 10.1080/09669582.2017.1329310
   Ferdous MI, 2019, ACCOUNT AUDIT ACCOUN, V32, P984, DOI 10.1108/AAAJ-09-2017-3145
   Fernando Y, 2017, RESOUR CONSERV RECY, V126, P62, DOI 10.1016/j.resconrec.2017.07.023
   Goodhew J, 2015, ENVIRON BEHAV, V47, P1059, DOI 10.1177/0013916514546218
   Gossling S, 2017, ENERG POLICY, V105, P418, DOI 10.1016/j.enpol.2017.03.019
   Gurran N, 2013, OCEAN COAST MANAGE, V86, P100, DOI 10.1016/j.ocecoaman.2012.10.014
   Haszeldine RS, 2009, SCIENCE, V325, P1647, DOI 10.1126/science.1172246
   Hebblewhite M, 2017, BIOL CONSERV, V206, P102, DOI 10.1016/j.biocon.2016.12.014
   Heidrich O, 2016, J ENVIRON MANAGE, V168, P36, DOI 10.1016/j.jenvman.2015.11.043
   Horisch J, 2015, ECOL ECON, V120, P241, DOI 10.1016/j.ecolecon.2015.11.002
   Huang L, 2019, J CLEAN PROD, V215, P1382, DOI 10.1016/j.jclepro.2019.01.058
   Indhumathi K, 2021, MATER TODAY-PROC, V37, P2648, DOI 10.1016/j.matpr.2020.08.517
   IPCC, 2014, 5 ASSESSMENT REPORT
   Jiang J.J., 2021, WUHAN FINANC, V1, P14
   Jiang YF, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9122224
   Karapidakis E.S., 2014, SCI B ELECT ENG FAC, V3, P78
   Le Quere C, 2009, NAT GEOSCI, V2, P831, DOI 10.1038/ngeo689
   Li W., 2014, ECON GEOGR, V34, P7
   Li Y, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13020584
   Lin BQ, 2020, SCI TOTAL ENVIRON, V698, DOI 10.1016/j.scitotenv.2019.134241
   Luo JM, 2021, FRONT ONCOL, V11, DOI 10.3389/fonc.2021.683232
   Mahath CS, 2019, RESOUR CONSERV RECY, V141, P40, DOI 10.1016/j.resconrec.2018.09.023
   Minghui L.I., 2011, J AUDIT EC, V26, P29
   Mwaura F, 2019, J S AFR I MIN METALL, V119, P485, DOI 10.17159/2411-9717/143/2019
   PETERS HPF, 1991, SCIENTOMETRICS, V20, P235, DOI 10.1007/BF02018157
   Qu G.H., 2015, CHIN J MANAG SCI, V23, P53
   [曲国华 Qu Guohua], 2020, [中国管理科学, Chinese Journal of Management Science], V28, P113
   Rawat KS, 2021, COMPUT APPL ENG EDUC, V29, P1324, DOI 10.1002/cae.22388
   Reckien D, 2018, J CLEAN PROD, V191, P207, DOI 10.1016/j.jclepro.2018.03.220
   Sharifi A, 2021, SUSTAIN SCI, V16, P1079, DOI 10.1007/s11625-020-00853-3
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Swiader M, 2020, FRONT ENV SCI-SWITZ, V8, DOI 10.3389/fenvs.2020.579838
   Tang S, 2018, BUS STRATEG ENVIRON, V27, P437, DOI 10.1002/bse.1985
   Taylor P.L., 2010, U BALT J ENV L, V18, P215
   Vasilyeva Elena, 2019, E3S Web of Conferences, V91, DOI 10.1051/e3sconf/20199108051
   Vicinanza D., 2015, P EUR WAV TID EN C L
   Wang ZH, 2018, J CLEAN PROD, V199, P1072, DOI 10.1016/j.jclepro.2018.06.183
   Widodo B, 2015, PROCEDIA ENVIRON SCI, V28, P519, DOI 10.1016/j.proenv.2015.07.062
   Yang Z.H, 2009, ACCOUNT NEWSL, V10, P25
NR 55
TC 1
Z9 1
U1 24
U2 24
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 APR
PY 2022
VL 19
IS 7
AR 4142
DI 10.3390/ijerph19074142
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 0M2KW
UT WOS:000781990600001
PM 35409825
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, CH
   Shiu, MF
   Chen, SH
AF Chen, Ching-Han
   Shiu, Ming-Fang
   Chen, Shu-Hui
TI Use Learnable Knowledge Graph in Dialogue System for Visually Impaired
   Macro Navigation
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE visually impaired assistance; navigation system; knowledge graph;
   dialogue system; NLP; reasoning
AB Featured Application Natural language dialogue macro-navigation for the visually impaired. The proposed technology can be applied to other professional fields, such as medical consultation or legal services. Dialogue in natural language is the most important communication method for the visually impaired. Therefore, the dialogue system is the main subsystem in the visually impaired navigation system. The purpose of the dialogue system is to understand the user's intention, gradually establish context through multiple conversations, and finally provide an accurate destination for the navigation system. We use the knowledge graph as the basis of reasoning in the dialogue system, and then update the knowledge graph so that the system gradually conforms to the user's background. Based on the experience of using the knowledge graph in the navigation system of the visually impaired, we expect that the same framework can be applied to more fields in order to improve the practicality of natural language dialogue in human-computer interaction.
C1 [Chen, Ching-Han; Shiu, Ming-Fang] Natl Cent Univ, Dept Comp Sci & Informat Engn, Taoyuan 320317, Taiwan.
   [Chen, Shu-Hui] Natl Cent Univ, Grad Inst Learning & Instruct, Taoyuan 320317, Taiwan.
   [Chen, Shu-Hui] Natl Taipei Univ, Ctr Gen Educ, New Taipei 23741, Taiwan.
RP Shiu, MF (通讯作者)，Natl Cent Univ, Dept Comp Sci & Informat Engn, Taoyuan 320317, Taiwan.
EM pierre@g.ncu.edu.tw; 108582003@cc.ncu.edu.tw; anita@ntpu.edu.tw
OI Chen, Ching-Han/0000-0003-4560-0889
FU MOST Taiwan [MOST-109-2221-E-008-055, MOST-1102634-F-008-005]
FX This research is supported by MOST Taiwan (MOST-109-2221-E-008-055 and
   MOST-1102634-F-008-005).
CR Antoine B., 2013, TRANSLATING EMBEDDIN, P2787
   Aprilia W, 2018, INDONES J ENGL LANG INDONES J ENGL LANG, V4, P69
   ARTHUR WB, 1994, AM ECON REV, V84, P406
   Bradley NA, 2005, PERS UBIQUIT COMPUT, V9, P395, DOI 10.1007/s00779-005-0350-y
   Chen CH, 2019, SENSOR MATER, V31, P1871, DOI 10.18494/SAM.2019.2326
   Feng N., 2012, INT J SEMANT WEB INF, V8, P1
   Francesco M.D., 1996, REASONING DESCRIPTIO, P191
   Fruchterman J., 1995, P C OR NAV SYST BLIN P C OR NAV SYST BLIN
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Golledge RG, 1998, INT J GEOGR INF SCI, V12, P727, DOI 10.1080/136588198241635
   Graham K., 2014, RDF 11 CONCEPTS ABST
   Hongshen C., 2017, SURVEY DIALOGUE SYST, P1
   Islam MM, 2019, IEEE SENS J, V19, P2814, DOI 10.1109/JSEN.2018.2890423
   KORNAI A, 1990, LANGUAGE, V66, P24, DOI 10.2307/415278
   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
   McTear M., 2016, CONVERSATIONAL INTER, P214
   Nicholas A.B., 2002, INVESTIGATING CONTEX
   Norgate SH, 2012, P I CIVIL ENG-MUNIC, V165, P231, DOI 10.1680/muen.12.00019
   Pearl J., 1985, GRAPHOIDS GRAPH BASE, P357
   Shekhar J., 2014, INT J COMPUT APPL, V103, P30
   Stina O., 2017, DESIGNING INTERFACES, P14
   Strothotte T., 1996, P 2 ANN ACM C ASS TE, P139, DOI DOI 10.1145/228347.228369
   Xiong W., 2017, ARXIV2017170706690 ARXIV2017170706690
   Zhao YJ, 2019, J PHYS CONF SER, V1267, DOI 10.1088/1742-6596/1267/1/012025
   Zhuosheng Z., 2018, MODELING MULTITURN C, P3740
NR 26
TC 1
Z9 1
U1 0
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 JUL
PY 2021
VL 11
IS 13
AR 6057
DI 10.3390/app11136057
PG 11
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA TH7WO
UT WOS:000672295800001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU He, M
   Du, XK
   Wang, B
AF He, Ming
   Du, Xiangkun
   Wang, Bo
TI Representation Learning of Knowledge Graphs via Fine-Grained Relation
   Description Combinations
SO IEEE ACCESS
LA English
DT Article
DE Knowledge representation; representation learning; knowledge graph
AB Knowledge representation learning attempts to represent entities and relations of knowledge graph in a continuous low-dimensional semantic space. However, most of the existing methods such as TransE, TransH, and TransR usually only utilize triples of knowledge graph. Other important information such as relation descriptions with relevant knowledge is still used ineffectively. To address these issues, in this paper, we propose a relation text-embodied knowledge representation learning method, in which relation descriptions are adopted as side information for representation learning. More specifically, we explore a convolutional neural model to build representations of fine-grained relation descriptions. Furthermore, knowledge representations of triples and representations of fine-grained relation descriptions are jointly embedding. Our model is evaluated on the tasks of both link prediction and triple classification. The experiment results show that our model exhibits a superior performance than other baselines, which demonstrates the availability of our method with fine-grained relation descriptions and knowledge graph jointly embedding.
C1 [He, Ming; Du, Xiangkun; Wang, Bo] Beijing Univ Technol, Faulty Informat Technol, Beijing 100124, Peoples R China.
RP He, M (通讯作者)，Beijing Univ Technol, Faulty Informat Technol, Beijing 100124, Peoples R China.
EM heming@bjut.edu.cn
OI Du, Xiangkun/0000-0002-6495-3893; wang, bo/0000-0001-8826-9968
FU Beijing Natural Science Foundation [4192008]; General Project of Beijing
   Municipal Education Commission [KM201710005023]
FX This work was supported in part by the Beijing Natural Science
   Foundation under Grant 4192008 and in part by the General Project of
   Beijing Municipal Education Commission under Grant KM201710005023.
CR Agarwal D, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P19
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes  A., 2013, IRREFLEXIVE HIERARCH
   Bordes A., 2012, MEAM446201206, P1
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Chang K W, 2013, P 2013 C EMP METH NA, P1602
   Collobert R, 2011, J MACH LEARN RES, V12, P2493
   Conneau A., 2016, VERY DEEP CONVOLUTIO
   Hinton G, 2012, IEEE SIGNAL PROC MAG, V29, P82, DOI 10.1109/MSP.2012.2205597
   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
   Kalchbrenner N, 2014, CONVOLUTIONAL NEURAL
   Kim Y., 2014, ARXIV14085882
   Krizhevsky A, 2017, COMMUN ACM, V60, P84, DOI 10.1145/3065386
   LeCun, 2015, ADV NEURAL INFORM PR, P649, DOI DOI 10.1063/1.4906785
   Lin YC, 2015, ADV SOC SCI EDUC HUM, V39, P2181
   Shen YL, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P373, DOI 10.1145/2567948.2577348
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Szumlanski Sean R., 2010, P 19 ACM INT C INF K, P19
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499, DOI DOI 10.18653/V1/D15-1174
   Turian J, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P384
   Wang HW, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P347, DOI 10.1145/3132847.3132889
   Wang Z, 2014, P C EMP METH NAT LAN, P1591
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston  J., 2013, P C EMP METH NAT LAN, P1134
   Xie R., 2016, P AAAI, P189
   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 D., 2015, PROC 3 WORKSHOP CONT, P32
NR 29
TC 3
Z9 7
U1 2
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 2019
VL 7
BP 26466
EP 26473
DI 10.1109/ACCESS.2019.2901544
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HP7KZ
UT WOS:000461868300001
OA gold
DA 2022-08-23
ER

PT J
AU Eibeck, A
   Lim, MQ
   Kraft, M
AF Eibeck, Andreas
   Lim, Mei Qi
   Kraft, Markus
TI J-Park Simulator: An ontology-based platform for cross-domain scenarios
   in process industry
SO COMPUTERS & CHEMICAL ENGINEERING
LA English
DT Article
DE Industry 4.0; Ontology; Linked data; Knowledge graph; Interoperability;
   Agent
ID DESIGN; TECHNOLOGIES
AB The J-Park Simulator (JPS) acts as a continuously growing platform for integrating real-time data, knowledge, models, and tools related to process industry. It aims at simulation and optimization in cross-domain and multi-level scenarios and relies heavily on ontologies and semantic technologies. In this paper, we demonstrate the interoperability between different applications in JPS, introduce new domain ontologies into the JPS, and integrate live data. For this, we utilize a knowledge graph to store and link semantically described data and models and create agents wrapping the applications and updating the data in the knowledge graph dynamically. We present a comprehensive industrial air pollution scenario, which has been implemented as part of the JPS, to show how knowledge graphs and modular domain ontologies support the interoperability between agents. We show that the architecture of JPS increases the interoperability and flexibility in cross-domain scenarios and conclude that the potential of ontologies outweighs additional wrapping efforts. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Eibeck, Andreas; Lim, Mei Qi; Kraft, Markus] CARES Cambridge Ctr Adv Res & Educ Singapore, 1 Create Way,CREATE Tower 05-05, Singapore 138602, Singapore.
   [Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, West Cambridge Site,Philippa Fawcett Dr, Cambridge CB3 0AS, England.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, 62 Nanyang Dr, Singapore 637459, Singapore.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, West Cambridge Site,Philippa Fawcett Dr, Cambridge CB3 0AS, England.
EM mk306@cam.ac.uk
RI Kraft, Markus/D-7243-2016
OI Kraft, Markus/0000-0002-4293-8924
FU National Research Foundation (NRF), Prime Minister's Office, Singapore
   under its Campus for Research Excellence and Technological Enterprise
   (CREATE) programme; Alexander von Humboldt foundation
FX This project is funded by the National Research Foundation (NRF), Prime
   Minister's Office, Singapore under its Campus for Research Excellence
   and Technological Enterprise (CREATE) programme. MK gratefully
   acknowledges the support of the Alexander von Humboldt foundation.
CR Anderl R., 2016, TECH REP
   Batres R, 2007, COMPUT CHEM ENG, V31, P519, DOI 10.1016/j.compchemeng.2006.07.004
   Batres R, 2017, CHEM-ING-TECH, V89, P1421, DOI 10.1002/cite.201700037
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Bizer C, 2011, SEMANTIC SERVICES, INTEROPERABILITY AND WEB APPLICATIONS: EMERGING CONCEPTS, P205, DOI 10.4018/978-1-60960-593-3.ch008
   Bramsiepe C, 2014, CHEM-ING-TECH, V86, P966, DOI 10.1002/cite.201400029
   Brownbridge G. P. E., 2011, SAE TECHNICAL PAPER, DOI [10.4271/2011-01-0237, DOI 10.4271/2011-01-0237]
   Chen D, 2008, COMPUT IND, V59, P647, DOI 10.1016/j.compind.2007.12.016
   Consortium W. W. W., GEN RDF TAB DAT WEB
   Consortium W. W. W., SEMANTIC WEB
   Farazi F., 2019, ONTOKIN ONTOLOGY CHE
   Fensel D, 2011, SEMANTIC WEB SERVICES, P1, DOI 10.1007/978-3-642-19193-0
   Fillinger S, 2019, CHEM-ING-TECH, V91, P256, DOI 10.1002/cite.201800122
   Fillinger S, 2017, CHEM-ING-TECH, V89, P1454, DOI 10.1002/cite.201700032
   Garud SS, 2018, COMPUT CHEM ENG, V119, P352, DOI 10.1016/j.compchemeng.2018.09.008
   Garud SS, 2017, COMPUT CHEM ENG, V106, P71, DOI 10.1016/j.compchemeng.2017.05.010
   Garud SS, 2017, COMPUT CHEM ENG, V96, P103, DOI 10.1016/j.compchemeng.2016.10.006
   Grangel-Gonzalez I, 2018, LECT NOTES COMPUT SC, V11029, P184, DOI 10.1007/978-3-319-98809-2_12
   Graube M, 2012, INT J DISTRIB SYST T, V3, P40, DOI 10.4018/jdst.2012070104
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Gyrard A, 2015, 2015 3RD INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD (FICLOUD) AND INTERNATIONAL CONFERENCE ON OPEN AND BIG (OBD), P9, DOI 10.1109/FiCloud.2015.10
   I. O. for Standardization, IND AUT SYST INT INT
   Kraft M, 2010, PHILOS T R SOC A, V368, P3633, DOI 10.1098/rsta.2010.0124
   Lu YY, 2017, J AM HEART ASSOC, V6, DOI 10.1161/JAHA.116.004168
   Morbach J, 2009, COMPUT CHEM ENG, V33, P1546, DOI 10.1016/j.compchemeng.2009.01.019
   Munoz E, 2010, COMPUT CHEM ENG, V34, P668, DOI 10.1016/j.compchemeng.2009.12.009
   Pan M, 2016, APPL ENERG, V175, P305, DOI 10.1016/j.apenergy.2016.05.019
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Rodriguez-Mier P, 2016, IEEE T SERV COMPUT, V9, P537, DOI 10.1109/TSC.2015.2402679
   Shvaiko P, 2013, IEEE T KNOWL DATA EN, V25, P158, DOI 10.1109/TKDE.2011.253
   Sikorski JJ, 2016, COMPUT CHEM ENG, V95, P108, DOI 10.1016/j.compchemeng.2016.06.019
   Wang H, 2015, COMBUST FLAME, V162, P2390, DOI 10.1016/j.combustflame.2015.02.005
   Wiesner A, 2011, COMPUT CHEM ENG, V35, P692, DOI 10.1016/j.compchemeng.2010.12.003
   Xiao GH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5511
   Zhang C, 2016, APPL ENERG, V184, P88, DOI 10.1016/j.apenergy.2016.10.016
   Zhou L, 2018, COMPUT CHEM ENG, V118, P49, DOI 10.1016/j.compchemeng.2018.07.010
   Zhou L, 2017, APPL ENERG, V204, P1284, DOI 10.1016/j.apenergy.2017.05.002
NR 37
TC 21
Z9 21
U1 6
U2 18
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 DEC 5
PY 2019
VL 131
AR 106586
DI 10.1016/j.compchemeng.2019.106586
PG 9
WC Computer Science, Interdisciplinary Applications; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA JP6TA
UT WOS:000498394500007
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Dou, JH
   Qin, JY
   Jin, ZX
   Li, Z
AF Dou, Jinhua
   Qin, Jingyan
   Jin, Zanxia
   Li, Zhuang
TI Knowledge graph based on domain ontology and natural language processing
   technology for Chinese intangible cultural heritage
SO JOURNAL OF VISUAL LANGUAGES AND COMPUTING
LA English
DT Article
DE Intangible cultural heritage; The 24 solar terms; Domain ontology;
   Knowledge graph; Natural language processing; Deep learning
AB Intangible cultural heritage (ICH) is a precious historical and cultural resource of a country. Protection and inheritance of ICH is important to the sustainable development of national culture. There are many different intangible cultural heritage items in China. With the development of information technology, ICH database resources were built by government departments or public cultural services institutions, but most databases were widely dispersed. Certain traditional database systems are disadvantageous to storage, management and analysis of massive data. At the same time, a large quantity of data has been produced, accompanied by digital intangible cultural heritage development. The public is unable to grasp key knowledge quickly because of the massive and fragmented nature of the data. To solve these problems, we proposed the intangible cultural heritage knowledge graph to assist knowledge management and provide a service to the public. ICH domain ontology was defined with the help of intangible cultural heritage experts and knowledge engineers to regulate the concept, attribute and relationship of ICH knowledge. In this study, massive ICH data were obtained, and domain knowledge was extracted from ICH text data using the Natural Language Processing (NLP) technology. A knowledge base based on domain ontology and instances for Chinese intangible cultural heritage was constructed, and the knowledge graph was developed. The pattern and characteristics behind the intangible cultural heritage were presented based on the ICH knowledge graph. The knowledge graph for ICH could foster support for organization, management and protection of the intangible cultural heritage knowledge. The public can also obtain the ICH knowledge quickly and discover the linked knowledge. The knowledge graph is helpful for the protection and inheritance of intangible cultural heritage.
C1 [Dou, Jinhua; Qin, Jingyan; Jin, Zanxia; Li, Zhuang] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, 30 Xueyuan Rd, Beijing, Peoples R China.
   [Dou, Jinhua; Qin, Jingyan] Univ Sci & Technol Beijingb, Sch Mech Engn, 30 Xueyuan Rd, Beijing, Peoples R China.
   [Dou, Jinhua] Tianjin Univ Technol, Sch Art & Design, 391 Binshui Xidao, Tianjin, Peoples R China.
RP Qin, JY (通讯作者)，Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Sch Mech Engn, 30 Xueyuan Rd, Beijing, Peoples R China.
EM doujinhua6971@163.com; Qinjingyanking@foxmail.com; Jinzanxia_go@163.com;
   lz_ustb@sina.com
FU Ministry of Education Humanities and Social Sciences Research Youth Fund
   Project [15YJCZH034]
FX This work was supported by the Ministry of Education Humanities and
   Social Sciences Research Youth Fund Project [Grant number 15YJCZH034].
CR [Anonymous], 2013, COMPUT SCI
   Arnaout H., 2017, J WEB SEMANT
   Blunsom Phil, 2014, P 52 ANN M ASS COMP
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Castiglione A, 2018, FUTURE GENER COMP SY, V86, P1134, DOI 10.1016/j.future.2017.04.006
   Cho K., 2014, COMPUT SCI
   Colace F., 2013, International Journal of Knowledge Society Research, V4, P18, DOI 10.4018/ijksr.2013100103
   Colace F, 2015, DATA CENTRIC SYST AP, P345, DOI 10.1007/978-3-319-20062-0_17
   Cowie J, 1996, COMMUN ACM, V39, P80, DOI 10.1145/234173.234209
   Crofts N., 2003, CULTIVATE INTERACT, V9, P1
   DOMEL P, 1995, COMPUT NETWORKS ISDN, V28, P85, DOI 10.1016/0169-7552(95)00103-6
   Fionda V, 2016, ARTIF INTELL, V239, P143, DOI 10.1016/j.artint.2016.07.003
   Gruber T, 2009, ENCY DATABASE SYSTEM, P1963, DOI DOI 10.1007/978-0-387-39940-9_1318
   Haubt RA, 2016, J ARCHAEOL SCI-REP, V10, P837, DOI 10.1016/j.jasrep.2016.10.013
   Headquarters U., 2003, CONV SAF INT CULT HE
   Jain A., 2010, P 23 INT C COMP LING, P510
   Kim Y., 1746, 2014 C EMP METH NAT, P1746
   Lee C, 2017, IEICE T INF SYST, VE100D, P882, DOI 10.1587/transinf.2016EDP7179
   Lin YK, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2124
   Liu X., 2011, P 49 ANN M ASS COMP, P359
   Messaoudi T., 2017, J CULT HERIT, P1
   Mikolov T., 2013, 1 INT C LEARNING REP
   Nguyen TH, 2015, P 1 WORKSH VECT SPAC, P39, DOI DOI 10.3115/V1/W15-1506
   Noy N., 2001, TECH REP SMI 2001 08, V32
   Pujara J., 2013, P WORKSH AUT KNOWL B, P19
   RAU LF, 1991, SEVENTH IEEE CONFERENCE ON ARTIFICIAL INTELLIGENCE APPLICATIONS, VOL 1, P29
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Strubell E, 2017, P 2017 C EMP METH NA, P2670
   Sun Y, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1333
   Surhone L. M., 2010, ARCHIVE2 OFFICIAL, V40, P212
   The State Council of the People's Republic of China, 2011, NOT STAT COUNC PUBL
   The State Council of the Peoples Republic of China, 2006, NOT STAT COUNC PUBL
   The State Council of the Peoples Republic of China, 2008, NOT STAT COUNC PUBL
   The State Council of the Peoples Republic of China, 2014, NOT STAT COUNC PUBL
   Unbehauen Jorg, 2012, Search Computing. Broadening Web Search: LNCS 7538, P34, DOI 10.1007/978-3-642-34213-4_3
   Wang P, 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, P352
   Yang Y, 2017, IEEE INT C SEMANT CO, P322, DOI 10.1109/ICSC.2017.37
   Yu F., 2016, P INT C LEARN REPR, P1, DOI [10.16373/j.cnki.ahr.150049, DOI 10.16373/J.CNKI.AHR.150049]
   Zhang GG, 2016, 2016 IEEE SECOND INTERNATIONAL CONFERENCE ON MULTIMEDIA BIG DATA (BIGMM), P398, DOI 10.1109/BigMM.2016.51
   Zhang GG, 2015, 2015 1ST IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA BIG DATA (BIGMM), P196, DOI 10.1109/BigMM.2015.37
   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 41
TC 23
Z9 29
U1 40
U2 199
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 1045-926X
EI 1095-8533
J9 J VISUAL LANG COMPUT
JI J. Vis. Lang. Comput.
PD OCT
PY 2018
VL 48
BP 19
EP 28
DI 10.1016/j.jvlc.2018.06.005
PG 10
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GY5NQ
UT WOS:000448625700004
DA 2022-08-23
ER

PT J
AU Zheng, X
   Wang, B
   Zhao, YM
   Mao, S
   Tang, Y
AF Zheng, Xue
   Wang, Bing
   Zhao, Yunmeng
   Mao, Shuai
   Tang, Yang
TI A knowledge graph method for hazardous chemical management: Ontology
   design and entity identification
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph; Ontology; Hazardous chemicals management; Named entity
   recognition
ID RECOGNITION
AB Hazardous chemicals are widely used in the production activities of the chemical industry. The risk management of hazardous chemicals is critical to the safety of life and property. Hence, the effective risk management of hazardous chemicals has always been important to the chemical industry. Since a large quantity of knowledge and information of hazardous chemicals is stored in isolated databases, it is challenging to manage hazardous chemicals in an information-rich manner. Herein, we prompt a knowledge graph to overcome the information gap between decentralized databases, which would improve the hazardous chemical management. In the implementation of the knowledge graph, we design an ontology schema of hazardous chemicals management. To facilitate enterprises to master the knowledge in the full lifecycle of hazardous chemicals, including production, transportation, storage, etc., we jointly use data from companies and open data from the public domain of hazardous chemicals to construct the knowledge graph. The named entity recognition task is one of the key tasks in the implementation of the knowledge graph, which is of great significance for extracting entity information from unstructured data, namely the hazardous chemical accidents records. To extract useful information from multi-source data, we adopt the pre-trained BERT-CRF model to conduct named entity recognition for incidents records. The model achieves good results, exhibiting the effectiveness in the task of named entity recognition in the chemical industry. (c) 2020 Elsevier B.V. All rights reserved.
C1 [Zheng, Xue; Wang, Bing; Zhao, Yunmeng; Mao, Shuai; Tang, Yang] East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China.
RP Tang, Y (通讯作者)，East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China.
EM tangtany@gmail.com
OI Tang, Yang/0000-0002-2750-8029; Mao, Shuai/0000-0002-1960-3456
FU National Key Research and Development Program of China [2018YFC0809302];
   National Natural Science Foundation of China [61988101, 61751305,
   61673176]; Programme of Introducing Talents of Discipline to
   Universities (the 111 Project) [B17017]
FX This work is supported in part by the National Key Research and
   Development Program of China under Grant 2018YFC0809302, the National
   Natural Science Foundation of China under Grants 61988101, 61751305,
   61673176 and the Programme of Introducing Talents of Discipline to
   Universities (the 111 Project) under Grant B17017.
CR [Anonymous], 2006, P 5 SIGHAN WORKSH CH
   Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dou JH, 2018, J VISUAL LANG COMPUT, V48, P19, DOI 10.1016/j.jvlc.2018.06.005
   Fan RY, 2020, ISPRS INT J GEO-INF, V9, DOI 10.3390/ijgi9010015
   Goyal A, 2018, COMPUT SCI REV, V29, P21, DOI 10.1016/j.cosrev.2018.06.001
   He X, 2018, IEEE INT C BIOINFORM, P1649, DOI 10.1109/BIBM.2018.8621340
   Jia Y, 2018, ENGINEERING-PRC, V4, P53, DOI 10.1016/j.eng.2018.01.004
   Li WY, 2019, P INT COMP SOFTW APP, P429, DOI 10.1109/COMPSAC.2019.10244
   Li WD, 2020, NEUROCOMPUTING, V382, P174, DOI 10.1016/j.neucom.2019.11.079
   Liu WQ, 2018, NEUROCOMPUTING, V320, P12, DOI 10.1016/j.neucom.2018.08.070
   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
   Mu XF, 2020, NEUROCOMPUTING, V375, P43, DOI 10.1016/j.neucom.2019.09.005
   Pietranik M, 2014, NEUROCOMPUTING, V146, P276, DOI 10.1016/j.neucom.2014.03.067
   Sobhana N. V., 2010, INT J COMPUTER APPL, V1, P143
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Wang B, 2018, SCI TOTAL ENVIRON, V643, P1, DOI 10.1016/j.scitotenv.2018.06.174
   Wang Q, 2020, NEUROCOMPUTING
   Weng H, 2017, LECT NOTES COMPUT SC, V10594, P170, DOI 10.1007/978-3-319-69182-4_18
   Al-Nabki MW, 2020, NEUROCOMPUTING, V382, P1, DOI 10.1016/j.neucom.2019.11.072
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   Yuan Liang, 2018, Computational Visual Media, V4, P123, DOI 10.1007/s41095-018-0110-3
   Zhang JC, 2019, NEUROCOMPUTING, V364, P245, DOI 10.1016/j.neucom.2019.06.087
   Zhang XT, 2013, NEUROCOMPUTING, V116, P382, DOI 10.1016/j.neucom.2011.12.057
   Zhao JS, 2009, COMPUT CHEM ENG, V33, P371, DOI 10.1016/j.compchemeng.2008.10.006
   Zheng SC, 2017, NEUROCOMPUTING, V257, P59, DOI 10.1016/j.neucom.2016.12.075
   Zhu P., 2019, INT J PERFORM ENG, V15
   Zhu SG, 2020, NEUROCOMPUTING, V372, P64, DOI 10.1016/j.neucom.2019.09.003
NR 29
TC 7
Z9 8
U1 23
U2 82
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD MAR 21
PY 2021
VL 430
BP 104
EP 111
DI 10.1016/j.neucom.2020.10.095
EA FEB 2021
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QG1QM
UT WOS:000617365300010
DA 2022-08-23
ER

PT J
AU Cox, S
   Ahalt, SC
   Balhoff, J
   Bizon, C
   Fecho, K
   Kebede, Y
   Morton, K
   Tropsha, A
   Wang, P
   Xu, H
AF Cox, Steven
   Ahalt, Stanley C.
   Balhoff, James
   Bizon, Chris
   Fecho, Karamarie
   Kebede, Yaphet
   Morton, Kenneth
   Tropsha, Alexander
   Wang, Patrick
   Xu, Hao
TI Visualization Environment for Federated Knowledge Graphs: Development of
   an Interactive Biomedical Query Language and Web Application Interface
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE knowledge graphs; clinical data; biomedical data; federation;
   ontologies; semantic harmonization; visualization; application
   programming interface; translational science; clinical practice
ID HYPERCALCEMIA
AB Background: Efforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results.
   Objective: We aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs.
   Methods: We developed a biomedical query language and web application interphase-termed as Translator Query Language (TranQL)-to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph.
   Results: We developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as "gene," "chemical substance," and "disease," that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated.
   Conclusions: TranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice.
C1 [Cox, Steven; Ahalt, Stanley C.; Balhoff, James; Bizon, Chris; Fecho, Karamarie; Kebede, Yaphet; Tropsha, Alexander; Xu, Hao] Univ N Carolina, Renaissance Comp Inst, 100 Europa Dr,Suite 540, Chapel Hill, NC 27517 USA.
   [Morton, Kenneth; Wang, Patrick] CoVar Appl Technol, Durham, NC USA.
   [Tropsha, Alexander] Univ N Carolina, UNC Eshelman Sch Pharm, Chapel Hill, NC 27517 USA.
RP Cox, S (通讯作者)，Univ N Carolina, Renaissance Comp Inst, 100 Europa Dr,Suite 540, Chapel Hill, NC 27517 USA.
EM scox@renci.org
OI Wang, Patrick/0000-0002-6693-6106; Tropsha,
   Alexander/0000-0003-3802-8896; Ahalt, Stanley/0000-0002-8395-1279; Xu,
   Hao/0000-0001-6659-6511; Kebede, Yaphet/0000-0002-5046-0246; Balhoff,
   James/0000-0002-8688-6599; Morton, Kenneth/0000-0002-9352-8669; Fecho,
   Karamarie/0000-0002-6704-9306; Cox, Steven/0000-0002-8561-4508
FU National Center for Advancing Translational Sciences, National
   Institutes of Health [OT3TR002020, OT2TR002514]
FX This work was supported by the National Center for Advancing
   Translational Sciences, National Institutes of Health (grant numbers
   OT3TR002020, OT2TR002514). The authors thank Matt Brush and Chris
   Mungall for creating and publishing the Biolink data model, and Eric
   Deutch and David Koslicki for contributing to the Translator KGS API
   specification. The authors also thank Marian Mersmann for proofreading
   the final version of the manuscript that was submitted for review.
CR [Anonymous], 2020, BIGGEST OPEN PROBLEM
   [Anonymous], 2019, CROUP SYMPT CAUS
   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 AM MED INFORM ASSN, V26, P1064, DOI 10.1093/jamia/ocz042
   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 H., 2011, INT J DIGIT CURAT, V6, P209
   Ma XG, 2018, CANCER BIOL MED, V15, P182, DOI 10.20892/j.issn.2095-3941.2018.0009
   Morton K, 2019, BIOINFORMATICS, V35, P5382, DOI 10.1093/bioinformatics/btz604
   NUSSBAUM SR, 1990, NEW ENGL J MED, V323, P1324, DOI 10.1056/NEJM199011083231907
   Rueda CA, 2019, SEMANTIC WEB CORE CO
   Sima AC, 2019, DATABASE-OXFORD, DOI 10.1093/database/baz106
   Smirnov A, 2019, INFORM FUSION, V52, P31, DOI 10.1016/j.inffus.2018.11.007
   United States Environmental Protection Agency, POL BYPHENYLS PBBS T
   Zhang XMA, 2019, NPJ DIGIT MED, V2, DOI 10.1038/s41746-019-0110-4
NR 16
TC 4
Z9 4
U1 5
U2 14
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 NOV
PY 2020
VL 8
IS 11
AR e17964
DI 10.2196/17964
PG 9
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA PA5GG
UT WOS:000595663400005
PM 33226347
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ge, XT
   Yang, Y
   Chen, JH
   Li, WC
   Huang, ZS
   Zhang, WY
   Peng, L
AF Ge, Xingtong
   Yang, Yi
   Chen, Jiahui
   Li, Weichao
   Huang, Zhisheng
   Zhang, Wenyue
   Peng, Ling
TI Disaster Prediction Knowledge Graph Based on Multi-Source
   Spatio-Temporal Information
SO REMOTE SENSING
LA English
DT Article
DE disaster prediction knowledge graph; spatio-temporal; disaster dynamic
   prediction; multi-source data fusion; forest fire risk prediction;
   geological landslide risk prediction
ID RISK
AB Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.
C1 [Ge, Xingtong; Chen, Jiahui; Li, Weichao; Zhang, Wenyue; Peng, Ling] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100049, Peoples R China.
   [Ge, Xingtong; Chen, Jiahui; Zhang, Wenyue] Univ Chinese Acad Sci, Coll Resources & Environm CRE, Beijing 100049, Peoples R China.
   [Yang, Yi] Chinese Acad Sci, Inst Automat, Beijing 100049, Peoples R China.
   [Huang, Zhisheng] Vrije Univ Amsterdam, Knowledge Representat & Reasoning KR&R Grp, NL-1081 HD Amsterdam, Netherlands.
   [Huang, Zhisheng] Wuhan Univ Sci & Technol, Sch Comp Sci & Engn, Wuhan 430081, Peoples R China.
   [Huang, Zhisheng] Ztone Int BV, NL-1448 VC Purmerend, Netherlands.
RP Peng, L (通讯作者)，Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100049, Peoples R China.
EM gexingtong21@mails.ucas.ac.cn; yangyi@ia.ac.cn;
   chenjiahui21@mails.ucas.ac.cn; liweichao@rsai.tech; z.huang@vu.nl;
   Zhangwenyue@rsai.tech; pengling@aircas.ac.cn
OI Ge, Xingtong/0000-0001-7603-2832
FU Beijing Municipal Science and Technology Project [Z191100001419002];
   Ningxia Key RD Program [2020BFG02013]
FX FundingThis work was supported by the Beijing Municipal Science and
   Technology Project (Z191100001419002), and Ningxia Key R&D Program
   (2020BFG02013).
CR Chen DY, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13061172
   Chen RN, 2022, IEEE GEOSCI REMOTE S, V19, DOI 10.1109/LGRS.2020.3028468
   Corominas J, 2005, LANDSLIDES, V2, P343, DOI 10.1007/s10346-005-0022-z
   [杜志强 Du Zhiqiang], 2020, [武汉大学学报. 信息科学版, Geomatics and Information Science of Wuhan University], V45, P1344
   Duan Weihong, 2013, KEY TECHNOLOGIES GEO
   [蒋秉川 Jiang Bingchuan], 2018, [测绘学报, Acta Geodetica et Cartographica Sinica], V47, P1051
   Li WL, 2020, NAT HAZARDS, V101, P409, DOI 10.1007/s11069-020-03879-z
   Liu J., 2020, GEOMAT INF SCI WUHAN, P1
   [陆锋 Lu Feng], 2017, [地球信息科学学报, Journal of Geo-Information Science], V19, P723
   Musen M. A, 2013, ENCY SYSTEMBIOL, P1763, DOI DOI 10.1007/978-1-4419-9863-7_1104
   O'Connor MJ, 2011, COMM COM INF SC, V127, P97
   Qiu LY, 2017, ENVIRON MODELL SOFTW, V91, P111, DOI 10.1016/j.envsoft.2017.01.025
   Scheuer S, 2013, COMPUT ENVIRON URBAN, V37, P82, DOI 10.1016/j.compenvurbsys.2012.07.007
   [尚慧 Shang Hui], 2012, [水土保持通报, Bulletin of Soil and Water Conservation], V32, P028
   SOWA JF, 1991, PRINCIPLES SEMANTIC
   Sun Y., 2020, J INF ENG U, V21, P464
   Tao Kunwang, 2020, Geomatics and Information Science of Wuhan University, V45, P1296, DOI 10.13203/j.whugis20200125
   [谢炎宏 Xie Yanhong], 2021, [测绘科学, Science of Surveying and Mapping], V46, P219
   Xu JH, 2014, INT J GEOGR INF SCI, V28, P185, DOI 10.1080/13658816.2013.845893
   Yang WC, 2018, ECOL INDIC, V89, P269, DOI 10.1016/j.ecolind.2018.02.015
   [张雪英 Zhang Xueying], 2020, [中国科学. 信息科学, Scientia Sinica Informationis], V50, P1019
NR 21
TC 1
Z9 1
U1 42
U2 42
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2072-4292
J9 REMOTE SENS-BASEL
JI Remote Sens.
PD MAR
PY 2022
VL 14
IS 5
AR 1214
DI 10.3390/rs14051214
PG 21
WC Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science & Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging
   Science & Photographic Technology
GA ZS8XG
UT WOS:000768742400001
OA gold
DA 2022-08-23
ER

PT J
AU Zhu, BR
   Zhang, XY
   Gu, M
   Deng, YD
AF Zhu, Biru
   Zhang, Xingyao
   Gu, Ming
   Deng, Yangdong
TI Knowledge Enhanced Fact Checking and Verification
SO IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
LA English
DT Article
DE Internet; Knowledge based systems; Encyclopedias; Online services;
   Semantics; Feature extraction; Tools; Automated fact checking; knowledge
   selection; knowledge enhanced fact checking
AB As the Internet and social media offer increasing opportunities for organizations and individuals to publicize online contents, it has become essential to develop effective means to identify misinformation like fake news. Recently, fact checking systems have been regarded as a promising tool to automatically deal with large amounts of information. How to effectively take advantage of existing unstructured document knowledge bases and structured knowledge graphs to build robust fact checking systems, however, remains to be a challenge. In this paper, we propose a knowledge enhanced fact checking system, which leverages the Wikidata5M knowledge graph and Wikipedia documents to incorporate external knowledge into the claim to be checked for more robust and accurate fact checking. First, we devise a contextualized knowledge graph selection method to identify the most relevant sub-graph with the checked claim from the large knowledge graph. We then construct a novel claim-evidence-knowledge graph and use a graph attention network to integrate natural language evidence with structured knowledge triplets by allowing them to propagate information among each other. By integrating the claim, retrieved evidence and selected knowledge triplets in a unified claim-evidence-knowledge graph, our method improves the label accuracy of predicted claims by more than 4% on the FEVER dataset over state-of-the-art fact checking models.
C1 [Zhu, Biru; Gu, Ming; Deng, Yangdong] Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China.
   [Zhang, Xingyao] STCA NLP Grp, Microsoft, Beijing 100080, Peoples R China.
RP Deng, YD (通讯作者)，Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China.
EM lili6138@outlook.com; xingyaozhang@microsoft.com;
   guming@tsinghua.edu.cn; dengyd@tsinghua.edu.cn
FU National Natural Science Foundation of China (NSFC) [61527812]
FX This work was supported in part by the National Natural Science
   Foundation of China (NSFC) under Grant 61527812.
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen Q, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1657, DOI 10.18653/v1/P17-1152
   Costa JO, 2018, 2018 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2018), P389, DOI 10.1109/WI.2018.00-63
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Ferragina P., 2010, P 19 ACM INT C INF K, P1625, DOI DOI 10.1145/1871437.1871689
   Gardner M, 2018, NLP OPEN SOURCE SOFTWARE (NLP-OSS), P1
   Gottschalk Simon, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P272, DOI 10.1007/978-3-319-93417-4_18
   Han J., 2020, P 2020 C EMP METH NA, P1475
   Hanselowski A., 2018, ARXIV PREPRINT ARXIV, P103
   Lee N, 2020, FACT EXTRACTION AND VERIFICATION (FEVER), P36
   Lin Bill Yuchen, 2019, P 2019 C EMP METH NA, P2829, DOI DOI 10.18653/V1/D19-1282
   Liu WJ, 2020, AAAI CONF ARTIF INTE, V34, P2901
   Liu Y., 2019, ARXIV190711692
   Liu Z., 2020, CANCER DISCOV, P7342
   Ma J, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2561
   Nie YX, 2019, AAAI CONF ARTIF INTE, P6859
   Petroni Fabio, 2019, P 2019 C EMP METH NA, P2463
   Qiu Delai, 2019, EMNLP IJCNLP, P5895
   Soleimani Amir, 2020, Advances in Information Retrieval. 42nd European Conference on IR Research, ECIR 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12036), P359, DOI 10.1007/978-3-030-45442-5_45
   Su Y., THE OPEN, V2, P2021
   Sun Tianxiang, 2020, P 28 INT C COMP LING, P3660
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Vlachos A., 2018, LONG PAPERS
   Wang M., 2019, DEEP GRAPH LIB GRAPH
   Wang S., 2019, P C EMP METH NAT LAN, P2553
   Wang XZ, 2021, T ASSOC COMPUT LING, V9, P176, DOI 10.1162/tacl_a_00360
   Wang Z., 2020, P 28 INT C COMP LING, P6498
   Yoneda T., 2018, FACT EXTR VERIFICATI, P97
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
   Zhong W., 2020, PROC 58 ANN M ASS CO, P6170
   Zhou J, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P892
NR 31
TC 0
Z9 0
U1 7
U2 8
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 3132
EP 3143
DI 10.1109/TASLP.2021.3120636
PG 12
WC Acoustics; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Acoustics; Engineering
GA WR0LO
UT WOS:000714201100001
DA 2022-08-23
ER

PT J
AU Yan, XY
   Jian, FH
   Sun, B
AF Yan, Xiaoyan
   Jian, Fanghong
   Sun, Bo
TI SAKG-BERT: Enabling Language Representation With Knowledge Graphs for
   Chinese Sentiment Analysis
SO IEEE ACCESS
LA English
DT Article
DE Analytical models; Sentiment analysis; Automobiles; Task analysis;
   Semantics; Blogs; Bit error rate; Sentiment analysis; pretraining model;
   knowledge graph; deep learning; car reviews
AB Sentiment analysis of online reviews is an important task in natural language processing. It has received much attention not only in academia but also in industry. Data have become an important source of competitive intelligence. Various pretraining models such as BERT and ERNIE have made great achievements in the task of natural language processing, but lack domain-specific knowledge. Knowledge graphs can enhance language representation. Furthermore, knowledge graphs have high entity / concept coverage and strong semantic expression ability. We propose a sentiment analysis knowledge graph (SAKG)-BERT model that combines sentiment analysis knowledge and the language representation model BERT. To improve the interpretability of the deep learning algorithm, we construct an SAKG in which triples are injected into sentences as domain knowledge. Our investigation reveals promising results in sentence completion and sentiment analysis tasks.
C1 [Yan, Xiaoyan] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
   [Jian, Fanghong; Sun, Bo] Cent China Normal Univ, Natl Engn Res Ctr ELearning, Wuhan 430079, Peoples R China.
RP Yan, XY (通讯作者)，Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
EM 55687640@qq.com
FU Industry-University-Research Innovation Fund, China Ministry of
   Education Science and Technology Development Center [2018A01023]
FX This work was supported by the Industry-University-Research Innovation
   Fund, China Ministry of Education Science and Technology Development
   Center, under Grant 2018A01023.
CR Araque O, 2019, KNOWL-BASED SYST, V165, P346, DOI 10.1016/j.knosys.2018.12.005
   Barbosa L., 2010, COLING POSTERS, P36
   Cui Y., ARXIV190608101S ARXIV190608101S
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Gu S., 2018, P 27 INT C COMP LING, P774
   Li L., 2018, P 22 C COMPUTATIONAL, P181
   Liu WJ, 2020, AAAI CONF ARTIF INTE, V34, P2901
   Liu Yang, 2007, P 30 ANN INT ACM SIG, P607, DOI [DOI 10.1145/1277741.1277845, 10.1145/1277741.1277845]
   [麻友 Ma You], 2018, [华东师范大学学报. 自然科学版, Journal of East China Normal University. Natural Science], P183
   Meskele D, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102211
   Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79, DOI 10.3115/1118693.1118704
   Pavlopoulos J., 2017, P 2017 C EMP METH NA, P1125, DOI DOI 10.18653/V1/D17-1117
   Rezaeinia SM, 2019, EXPERT SYST APPL, V117, P139, DOI 10.1016/j.eswa.2018.08.044
   Wan CX, 2020, INFORM SCIENCES, V519, P243, DOI 10.1016/j.ins.2020.01.036
   Wei W., 2019, P 20 WORKSH CHIN LEX P 20 WORKSH CHIN LEX, P309
   Yin P., 2013, SYSTEMS ENG, V30, P68
   Yong Zhang, 2013, Advances in Knowledge Discovery and Data Mining. 17th Pacific-Asia Conference, PAKDD 2013. Proceedings, P402, DOI 10.1007/978-3-642-37453-1_33
   Yu XH, 2012, IEEE T KNOWL DATA EN, V24, P720, DOI 10.1109/TKDE.2010.269
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
   Zhou J, 2019, IEEE ACCESS, V7, P78454, DOI 10.1109/ACCESS.2019.2920075
NR 20
TC 0
Z9 0
U1 13
U2 30
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 101695
EP 101701
DI 10.1109/ACCESS.2021.3098180
PG 7
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA TQ5EY
UT WOS:000678304600001
OA gold
DA 2022-08-23
ER

PT J
AU Tan, JY
   Qiu, QQ
   Guo, WW
   Li, TS
AF Tan, Jiyuan
   Qiu, Qianqian
   Guo, Weiwei
   Li, Tingshuai
TI Research on the Construction of a Knowledge Graph and Knowledge
   Reasoning Model in the Field of Urban Traffic
SO SUSTAINABILITY
LA English
DT Article
DE urban traffic; domain knowledge graph; ontology; knowledge reasoning
AB The integration of multi-source transportation data is complex and insufficient in most of the big cities, which made it difficult for researchers to conduct in-depth data mining to improve the policy or the management. In order to solve this problem, a top-down approach is used to construct a knowledge graph of urban traffic system in this paper. First, the model layer of the knowledge graph was used to realize the reuse and sharing of knowledge. Furthermore, the model layer then was stored in the graph database Neo4j. Second, the representation learning based knowledge reasoning model was adopted to implement knowledge completion and improve the knowledge graph. Finally, the proposed method was validated with an urban traffic data set and the results showed that the model could be used to mine the implicit relationship between traffic entities and discover traffic knowledge effectively.
C1 [Tan, Jiyuan; Qiu, Qianqian; Guo, Weiwei; Li, Tingshuai] North China Univ Technol, Beijing Key Lab Urban Intelligent Traff Control T, Beijing 100144, Peoples R China.
RP Guo, WW (通讯作者)，North China Univ Technol, Beijing Key Lab Urban Intelligent Traff Control T, Beijing 100144, Peoples R China.
EM tanjy@ncut.edu.cn; 2018309030129@mail.ncut.edu.cn;
   guoweiwei@ncut.edu.cn; tingshuai_li@mail.ncut.edu.cn
FU National Key R&D Program of China [2018YFB1601100]
FX This research was funded by the National Key R&D Program of China, grant
   number 2018YFB1601100.
CR Baohua Qi, 2020, 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), P188, DOI 10.1109/ICPICS50287.2020.9202380
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Gangemi Aldo, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P65, DOI 10.1007/978-3-642-35176-1_5
   Gong F, 2021, BIG DATA RES, V23, DOI 10.1016/j.bdr.2020.100174
   Han X., 2018, P C EMP METH NAT LAN, P139
   Hu L.W., 2019, CHINA J HIGHW TRANSP, V32, P149
   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
   [贾梦姝 Jia Mengshu], 2020, [地球信息科学学报, Journal of Geo-Information Science], V22, P2289
   JOUILI S, P 2013 INT C SOC COM, P708, DOI DOI 10.1109/SOCIALCOM.2013.106
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Kusmawan PY, 2014, I C COMP SYST APPLIC, P397, DOI 10.1109/AICCSA.2014.7073226
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lehmann J, 2009, J MACH LEARN RES, V10, P2639
   Li L, 2020, TRANSPORT RES C-EMER, V114, P225, DOI 10.1016/j.trc.2020.02.016
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   [刘知远 Liu Zhiyuan], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P247
   LU SY, P 2013 IEEE 10 INT C, P419, DOI DOI 10.1109/ICEBE.2013.64
   Ma DF, 2021, IEEE T INTELL TRANSP, V22, P2627, DOI 10.1109/TITS.2020.2973279
   Mikolov T., 2013, ARXIV PREPRINT ARXIV, P1
   Mohammed O., 2018, P IEEE INT SMART CIT, P1
   MUPPALLA R, 2017, IND KNOWL GRAPHS, V2017, P431, DOI DOI 10.1145/3091478.3162384
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Niu X, 2011, LECT NOTES COMPUT SC, V7032, P205, DOI 10.1007/978-3-642-25093-4_14
   Noy N. F., 2001, ONTOLOGY DEV
   [彭诗尧 Peng Shiyao], 2021, [地理学报, Acta Geographica Sinica], V76, P459
   Shen LO, 2019, IEEE T INTELL TRANSP, V20, P431, DOI 10.1109/TITS.2018.2815182
   Singhal A., INTRO KNOWLEDGE GRAP
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Suchanek FM, 2007, WWW
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao J, 2020, IEEE T NEUR NET LEAR, V31, P5285, DOI 10.1109/TNNLS.2020.2966031
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   Zhang NY, 2018, ISPRS INT J GEO-INF, V7, DOI 10.3390/ijgi7070264
   Zhang NY, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16091501
   Zhang Q, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11102849
   ZHENG Z, P 2020 IEEE INT C KN, P560, DOI DOI 10.1109/ICBK50248.2020.00084
   ZHOU G, P 2019 4 INT C EL CO, P105, DOI DOI 10.1109/ICECTT.2019.00031
NR 39
TC 3
Z9 3
U1 36
U2 69
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAR
PY 2021
VL 13
IS 6
AR 3191
DI 10.3390/su13063191
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 RV3CI
UT WOS:000645714900001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Lee, JY
   Kim, I
AF Lee, JaeYun
   Kim, Incheol
TI Vision-Language-Knowledge Co-Embedding for Visual Commonsense Reasoning
SO SENSORS
LA English
DT Article
DE visual commonsense reasoning; multimodal co-embedding; knowledge graph;
   graph convolutional network; pretrained multi-head self-attention
   network
AB Visual commonsense reasoning is an intelligent task performed to decide the most appropriate answer to a question while providing the rationale or reason for the answer when an image, a natural language question, and candidate responses are given. For effective visual commonsense reasoning, both the knowledge acquisition problem and the multimodal alignment problem need to be solved. Therefore, we propose a novel Vision-Language-Knowledge Co-embedding (ViLaKC) model that extracts knowledge graphs relevant to the question from an external knowledge base, ConceptNet, and uses them together with the input image to answer the question. The proposed model uses a pretrained vision-language-knowledge embedding module, which co-embeds multimodal data including images, natural language texts, and knowledge graphs into a single feature vector. To reflect the structural information of the knowledge graph, the proposed model uses the graph convolutional neural network layer to embed the knowledge graph first and then uses multi-head self-attention layers to co-embed it with the image and natural language question. The effectiveness and performance of the proposed model are experimentally validated using the VCR v1.0 benchmark dataset.
C1 [Lee, JaeYun; Kim, Incheol] Kyonggi Univ, Dept Comp Sci, Suwon 16227, South Korea.
RP Kim, I (通讯作者)，Kyonggi Univ, Dept Comp Sci, Suwon 16227, South Korea.
EM wodbs9522@kyonggi.ac.kr; kic@kyonggi.ac.kr
OI JAEYUN, LEE/0000-0003-4913-3691; Kim, Incheol/0000-0002-5754-133X
FU Institute for Information & Communications Technology Planning &
   Evaluation (IITP)/Development of ICT Technology Program/Robot task
   planning for single and multiple robots connected to a cloud system
   [2020-0-00096]
FX Institute for Information & Communications Technology Planning &
   Evaluation (IITP)/Development of ICT Technology Program/Robot task
   planning for single and multiple robots connected to a cloud
   system/2020-0-00096.
CR Abu-El-Haija S., 2017, ARXIV171009599
   Alberti C., 2019, ARXIV190805054
   Alexander G.-S., 2018, ARXIV181100538
   Antol S, 2015, IEEE I CONF COMP VIS, P2425, DOI 10.1109/ICCV.2015.279
   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
   Das A, 2017, PROC CVPR IEEE, P1080, DOI 10.1109/CVPR.2017.121
   Devlin J, 2018, P 2019 C N AM CHAPT
   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, 2020, IEEE T PATTERN ANAL, V42, P386, DOI 10.1109/TPAMI.2018.2844175
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Hong Yicong, P IEEE CVF C COMP VI, P1643
   Hong Yicong, ADV NEURAL INFORM PR, V33, P7685
   Kang C., 2019, ARXIV191103024
   Kipf T., 2017, P INT C LEARN REPR I
   Kovashka A., CASE STUDY SHORTCUT
   LeCun Y, 1989, NEURAL COMPUT, V1, P541, DOI 10.1162/neco.1989.1.4.541
   Li Gen, 2019, ARXIV190806066
   Li Liunian Harold, 2019, ARXIV190803557
   Lin J., 2019, ARXIV191014671
   Liu H, 2004, BT TECHNOL J, V22, P211, DOI 10.1023/B:BTTJ.0000047600.45421.6d
   Logan IV R.-L., 2019, ARXIV190904164
   Lu JS, 2019, ADV NEUR IN, V32
   Marino K, 2019, PROC CVPR IEEE, P3190, DOI 10.1109/CVPR.2019.00331
   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
   Qi Y., P IEEE CVF C COMP VI, P9982
   Qi Y., P EUR C COMP VIS ECC
   Radford A., IMPROVING LANGUAGE U
   Ren SQ, 2017, IEEE T PATTERN ANAL, V39, P1137, DOI 10.1109/TPAMI.2016.2577031
   RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
   Talukdar P.-P., P 33 ASS ADV ART INT, P8876
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang P, 2018, IEEE T PATTERN ANAL, V40, P2413, DOI 10.1109/TPAMI.2017.2754246
   Xiao N., 2019, ARXIV191011475
   Zellers R, 2019, PROC CVPR IEEE, P6713, DOI 10.1109/CVPR.2019.00688
NR 36
TC 0
Z9 0
U1 6
U2 14
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD MAY
PY 2021
VL 21
IS 9
AR 2911
DI 10.3390/s21092911
PG 19
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA SC6HC
UT WOS:000650768600001
PM 33919196
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Esfahani, AN
   Moghaddam, NB
   Maleki, A
   Nazemi, A
AF Esfahani, Alireza Nasr
   Moghaddam, Naser Bagheri
   Maleki, Abbas
   Nazemi, Amir
TI The knowledge map of energy security
SO ENERGY REPORTS
LA English
DT Article
DE Energy policy; Energy security; Knowledge map
AB Much efforts have been made in the area of energy security in recent years, but due to its wide scope, it is necessary to review relevant knowledge activities and to analyze the state of knowledge of the field of energy security measurement. The purpose of this article is to present a comprehensive map of knowledge in the field of energy security. For this reason, many documents and articles have been collected during the period 2002-2019 using 7 large and reputable scientific databases as well as 53 different journals, 90% white Q1 quality. There is employed meta-synthesis, scientometrics and network analysis. In the initial survey stage where 1290 articles were found, after analyzing the content using meta-synthesis, 240 scientific articles related to energy security were identified. Then, the following 14 key questions were addressed such as the main conceptual models, processes, and concept structures (concept map). The VOSviewer and Gephi software are also used to explain the conceptual structure (concept map), hot map, co-occurrence map between keywords and their categorization. After Energy Security (and its subsets), the issues of Renewable Energy Security and Energy Supply Security stand in top ranks. The most cited article in this area is the 2006 "Ensuring energy security'' article by Yergin on Energy Security. As many as 58 people have published at least two articles, Sovacool with fifteen articles is on the top author with the most published articles in this field. During the evaluation of the articles, 34 conceptual models and 104 quantitative and qualitative methods were found, which illustrate the wide scope of energy security. By examining the coherent vocabulary map in the field of energy security, it can be said that the most important dimensions of energy security include technology, trade, acceptability, productivity, diversity, equity, availability, governance and efficiency. (C) 2021 Published by Elsevier Ltd.
C1 [Esfahani, Alireza Nasr; Moghaddam, Naser Bagheri; Nazemi, Amir] Natl Res Inst Sci Policy NRISP, Tehran, Iran.
   [Maleki, Abbas] Sharif Univ Technol, Energy Engn Dept, Tehran, Iran.
RP Moghaddam, NB (通讯作者)，Natl Res Inst Sci Policy NRISP, Tehran, Iran.
EM Esfahani@nrisp.ac.ir; Bagheri@nrisp.ac.ir; maleki@sharif.edu;
   nazemi@nrisp.ac.ir
RI Maleki, Abbas/AAD-8842-2022; bagheri moghaddam, naser/AAA-4166-2022;
   Nasr Esfahani, AliReza/CAA-1205-2022
OI bagheri moghaddam, naser/0000-0003-3457-8479; Maleki,
   Abbas/0000-0002-4201-9549; Nasr Esfahani, Ali Reza/0000-0003-1327-8514
CR Ang BW, 2015, RENEW SUST ENERG REV, V42, P1077, DOI 10.1016/j.rser.2014.10.064
   [Anonymous], 2021, ENERGY SECURITY ENSU
   Balaid A, 2016, INT J INFORM MANAGE, V36, P451, DOI 10.1016/j.ijinfomgt.2016.02.005
   Board N.S, 2000, SCI ENG IND
   Bohi DR, 2012, EC ENERGY SECURITY
   Dai SL, 2020, J CLEAN PROD, V262, DOI 10.1016/j.jclepro.2020.121168
   Dike JC, 2013, ENERG POLICY, V60, P594, DOI 10.1016/j.enpol.2013.05.086
   Fennell D, 2008, J ENVIRON MANAGE, V88, P62, DOI 10.1016/j.jenvman.2007.01.020
   Gasser P, 2020, ENERG POLICY, V139, DOI 10.1016/j.enpol.2020.111339
   Kruyt B, 2009, ENERG POLICY, V37, P2166, DOI 10.1016/j.enpol.2009.02.006
   Lee PC, 2011, TECHNOL FORECAST SOC, V78, P132, DOI 10.1016/j.techfore.2010.06.002
   Lin X, 2020, INT J DISAST RISK RE, V43, DOI 10.1016/j.ijdrr.2019.101365
   Luo T, 2019, ENERG BUILDINGS, V194, P163, DOI 10.1016/j.enbuild.2019.03.050
   Mansson A, 2014, ENERGY, V73, P1, DOI 10.1016/j.energy.2014.06.073
   Martin BR, 2012, RES POLICY, V41, P1219, DOI 10.1016/j.respol.2012.03.012
   Naseri J.M., 2012, SCI MAPPING MANAGEME
   NOBLIT GW, 1988, METAETHNOGRAPHY SYNT
   Paul Shampa, 2007, International Information and Library Review, V39, P176, DOI 10.1016/j.iilr.2007.06.003
   Pelc K.I., 2002, KNOWL TECHNOL POLICY, V15, P36
   Reddick C., 2013, E PARTICIPATION LOCA
   Sandelowski M., 2006, HDB SYNTHESIZING QUA
   Song Y, 2019, ENERG POLICY, V132, P167, DOI 10.1016/j.enpol.2019.05.036
   Sovacool BK, 2011, ENERG POLICY, V39, P7472, DOI 10.1016/j.enpol.2010.10.008
   Sovacool BK, 2011, ENERGY, V36, P5846, DOI 10.1016/j.energy.2011.08.040
   Sovacool BK, 2011, ENERGY, V36, P5343, DOI 10.1016/j.energy.2011.06.043
   Vivoda V, 2010, ENERG POLICY, V38, P5258, DOI 10.1016/j.enpol.2010.05.028
   von Hippel D, 2011, ENERG POLICY, V39, P6719, DOI 10.1016/j.enpol.2009.07.001
   Yergin D, 2006, FOREIGN AFF, V85, P69, DOI 10.2307/20031912
   Zhou W, 2018, ENERGY REP, V4, P724, DOI 10.1016/j.egyr.2018.10.012
NR 29
TC 10
Z9 10
U1 6
U2 18
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-4847
J9 ENERGY REP
JI Energy Rep.
PD NOV
PY 2021
VL 7
BP 3570
EP 3589
DI 10.1016/j.egyr.2021.06.001
EA JUN 2021
PG 20
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Energy & Fuels
GA UY8JE
UT WOS:000701762900001
OA gold
DA 2022-08-23
ER

PT J
AU Sun, HX
   Xiao, J
   Zhu, W
   He, YL
   Zhang, S
   Xu, XW
   Hou, L
   Li, J
   Ni, Y
   Xie, GT
AF Sun, Haixia
   Xiao, Jin
   Zhu, Wei
   He, Yilong
   Zhang, Sheng
   Xu, Xiaowei
   Hou, Li
   Li, Jiao
   Ni, Yuan
   Xie, Guotong
TI Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on
   Claim Data: Model Development and Performance Evaluation
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE medical knowledge graph; FWA detection
ID INFORMATION EXTRACTION
AB Background: Fraud, Waste, and Abuse (FWA) detection is a significant yet challenging problem in the health insurance industry. An essential step in FWA detection is to check whether the medication is clinically reasonable with respect to the diagnosis. Currently, human experts with sufficient medical knowledge are required to perform this task. To reduce the cost, insurance inspectors tend to build an intelligent system to detect suspicious claims with inappropriate diagnoses/medications automatically.
   Objective: The aim of this study was to develop an automated method for making use of a medical knowledge graph to identify clinically suspected claims for FWA detection.
   Methods: First, we identified the medical knowledge that is required to assess the clinical rationality of the claims. We then searched for data sources that contain information to build such knowledge. In this study, we focused on Chinese medical knowledge. Second, we constructed a medical knowledge graph using unstructured knowledge. We used a deep learning-based method to extract the entities and relationships from the knowledge sources and developed a multilevel similarity matching approach to conduct the entity linking To guarantee the quality of the medical knowledge graph, we involved human experts to review the entity and relationships with lower confidence. These reviewed results could be used to further improve the machine-learning models. Finally, we developed the rules to identify the suspected claims by reasoning according to the medical knowledge graph.
   Results: We collected 185,796 drug labels from the China Food and Drug Administration, 3390 types of disease information from medical textbooks (eg, symptoms, diagnosis, treatment, and prognosis), and information from 5272 examinations as the knowledge sources. The final medical knowledge graph includes 1,616,549 nodes and 5,963,444 edges. We designed three knowledge graph reasoning rules to identify three kinds of inappropriate diagnosis/medications. The experimental results showed that the medical knowledge graph helps to detect 70% of the suspected claims.
   Conclusions: The medical knowledge graph-based method successfully identified suspected cases of FWA (such as fraud diagnosis, excess prescription, and irrational prescription) from the claim documents, which helped to improve the efficiency of claim processing.
C1 [Sun, Haixia; Xu, Xiaowei; Hou, Li; Li, Jiao] Chinese Acad Med Sci, Peking Union Med Coll, Inst Med Informat & Lib, Beijing, Peoples R China.
   [Xiao, Jin; Zhu, Wei; He, Yilong; Zhang, Sheng; Ni, Yuan; Xie, Guotong] PingAn Hlth Technol, Qianhai Complex A201,Qianwan Rd 1, Shenzhen, Peoples R China.
RP Xie, GT (通讯作者)，PingAn Hlth Technol, Qianhai Complex A201,Qianwan Rd 1, Shenzhen, Peoples R China.
EM xieguotong@pingan.com.cn
OI Li, Jiao/0000-0001-6391-8343; , Guotong/0000-0002-2772-8961; Zhang,
   Sheng/0000-0001-8465-7269
FU National Key RD Plan of China [2016YFC0901901]; Fundamental Research
   Funds for the Central Universities [2018PT33024, 2017PT63010]; PingAn
   Health Technology [PMS201836894755-1/1]
FX This work is supported by the National Key R&D Plan of China (grant no.
   2016YFC0901901), Fundamental Research Funds for the Central Universities
   (grant nos. 2018PT33024 and 2017PT63010), and PingAn Health Technology
   (grant no. PMS201836894755-1/1).
CR [Anonymous], ICD 10
   [Anonymous], 2014, P 2014 C EMP METH NA, P1556, DOI [DOI 10.3115/V1/D14-1164, 10.3115/v1/d14-1164]
   Ben Abacha Asma, 2011, J Biomed Semantics, V2 Suppl 5, pS4, DOI 10.1186/2041-1480-2-S5-S4
   Bilenko M, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P981
   Castano J, 2016, 2016 P 15 WORKSH BIO, DOI [10.18653/v1/w16-2901, DOI 10.18653/V1/W16-2901]
   Chen YK, 2015, J BIOMED INFORM, V58, P11, DOI 10.1016/j.jbi.2015.09.010
   Devlin J., 2019, BERT PRETRAINING DEE, DOI [10.18653/v1/N19-1423, DOI 10.18653/V1/N19-1423]
   Gong F, 2019, BMC MED INFORM DECIS, V19, DOI 10.1186/s12911-019-0771-6
   Guo ZH, 2017, APPL POWER ELECT CO, P1778, DOI 10.1109/APEC.2017.7930939
   Huang Z, 2018, 2018 P 32 PAC AS C L
   Jia S, 2018, INT GEOSCI REMOTE SE, P1, DOI 10.1109/IGARSS.2018.8518351
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Liu J, 2016, AI MAG, V37, P33, DOI 10.1609/aimag.v37i2.2630
   Liu Q, 2013, 2013 NOV 29 WORLD CO
   Liu S, 2020, INFORMATION, V11, DOI 10.3390/info11040186
   Long S, 2018, 2018 ICPCSEE 2018 CO, P161, DOI [10.1007/978-981-13-2206-8_15, DOI 10.1007/978-981-13-2206-8_15]
   Mei, 2010, P 16 ACM SIGKDD INT, P65, DOI DOI 10.1145/1835804.1835816
   Melnik S, 2002, 2002 P 18 INT C DAT, DOI [10.1109/icde.2002.994702], DOI 10.1109/ICDE.2002.994702]
   Patrick J, 2010, J AM MED INFORM ASSN, V17, P524, DOI 10.1136/jamia.2010.003939
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Shi LX, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/2858423
   Situ LY, 2018, INTERNETWARE'18: PROCEEDINGS OF THE TENTH ASIA-PACIFIC SYMPOSIUM ON INTERNETWARE, DOI 10.1145/3275219.3275225
   Sohn S, 2014, J AM MED INFORM ASSN, V21, P858, DOI 10.1136/amiajnl-2013-002190
   Stoilos G, 2005, LECT NOTES COMPUT SC, V3729, P624
   Umair A, 2012, J BASIC APPL SCI RES, P1373
   Uzuner O, 2010, J AM MED INFORM ASSN, V17, P514, DOI 10.1136/jamia.2010.003947
   Wang C, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P828
   Xia Y, 2014, NATURAL LANGUAGE PRO, P311, DOI DOI 10.1007/978-3-662-45924-9_28
   Xu H, 2010, J AM MED INFORM ASSN, V17, P19, DOI 10.1197/jamia.M3378
   Yin MW, 2019, J BIOMED INFORM, V98, DOI 10.1016/j.jbi.2019.103289
   Yuan K, 2018, APPL RES COMPUT
   Zeng D, 2015, P 2015 C EMP METH NA, P1753, DOI DOI 10.18653/V1/D15-1203
   Zhang Y N, 2017, J HEALTHC ENG, V2017, P2017, DOI DOI 10.1155/2017/4862861
   Zhang Y, 2018, JMIR MED INF, V6, P242, DOI 10.2196/medinform.9965
   2019, POUCH IL POUCH DIS, P1
NR 35
TC 4
Z9 4
U1 9
U2 26
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 JUL
PY 2020
VL 8
IS 7
AR e17653
DI 10.2196/17653
PG 18
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Medical Informatics
GA MX1AM
UT WOS:000557459300028
PM 32706714
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, P
   Wang, X
   Liang, H
   Zhang, SZ
   Zhang, YZ
   Jiang, YC
   Tang, Y
AF Li, Pu
   Wang, Xin
   Liang, Hui
   Zhang, Suzhi
   Zhang, Yazhou
   Jiang, Yuncheng
   Tang, Yong
TI A fuzzy semantic representation and reasoning model for multiple
   associative predicates in knowledge graph
SO INFORMATION SCIENCES
LA English
DT Article
DE Fuzzy annotating; Fuzzy reasoning; Fuzzy semantic extension; Knowledge
   graph
ID QUERY; WEB; RDF
AB As the latest achievement of the development in semiotics, knowledge graph has been recognized and widely used by more and more researchers for its rich semantic information and clear logical structure. How to discovery the deep relevant knowledge from the massive graph-structured data has become a hot spot of artificial intelligence. Considering that some predicates in knowledge graph express fuzzy relationships whose semantics are not certain, the basic schema of classical knowledge graph in the form of RDF triple cannot describe the fuzzy semantic information effectively. To counter above problems, in this paper, we present a new semantic representation and reasoning model for multiple associative predicates by introducing fuzzy theory. Concretely, the presented method defines a new fuzzy annotating strategy to represent the fuzzy semantics between associative predicates in different RDF triples. On this basis, some fuzzy reasoning rules are presented to realize fuzzy semantic extension for classical knowledge graph. Lastly, the experimental results show that our proposal can discover more implicit valid knowledge with fuzzy semantic and have a good consistency with the intuition of human judgments. Overall, the methods proposed in this paper constitute some effective ways of knowledge discovery of structured semantic data.(c) 2022 Elsevier Inc. All rights reserved.
C1 [Li, Pu; Wang, Xin; Liang, Hui; Zhang, Suzhi; Zhang, Yazhou] Zhengzhou Univ Light Ind, Software Engn Coll, Zhengzhou 450000, Peoples R China.
   [Jiang, Yuncheng; Tang, Yong] South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Peoples R China.
RP Li, P (通讯作者)，Zhengzhou Univ Light Ind, Software Engn Coll, Zhengzhou 450000, Peoples R China.; Jiang, YC; Tang, Y (通讯作者)，South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Peoples R China.
EM superlipu@163.com; ycjiang@scnu.edu.cn; ytang4@qq.com
FU National Natural Science Foundation of China [61802352, 61772211,
   61772210, 62072414, 62006212, 61975187]; Program for Young Key Teachers
   of Henan Province [2021GGJS095]; Project of collaborative innovation in
   Zhengzhou [2021ZDPY0208]; Research Foundation of Zhengzhou University of
   Light Industry [2017BSJJ052]; Project of Science and Technology in Henan
   Province [202102210178, 222102210030, 212102210418]
FX The works described in this paper are supported by The National Natural
   Science Foundation of China under Grant Nos. 61802352, 61772211,
   61772210, 62072414, 62006212, 61975187; The Program for Young Key
   Teachers of Henan Province under Grant No. 2021GGJS095; The Project of
   collaborative innovation in Zhengzhou under Grant No. 2021ZDPY0208; The
   Ph.D. Research Foundation of Zhengzhou University of Light Industry
   under Grant No. 2017BSJJ052; The Project of Science and Technology in
   Henan Province under Grant Nos. 202102210178, 222102210030,
   212102210418.
CR [Anonymous], 2001, SCI AM
   Bai LY, 2021, INFORM SCIENCES, V578, P166, DOI 10.1016/j.ins.2021.07.038
   Bellomarini L, 2022, INFORM SYST, V105, DOI 10.1016/j.is.2020.101528
   Bellomarini L, 2019, PROC INT CONF DATA, P26, DOI 10.1109/ICDE.2019.00011
   Boneva I, 2017, LECT NOTES COMPUT SC, V10587, P104, DOI 10.1007/978-3-319-68288-4_7
   Bonifati A, 2020, VLDB J, V29, P655, DOI 10.1007/s00778-019-00558-9
   Brickley D, 2014, RDF SCHEMA 11 W3C RE
   Buil-Aranda C, 2013, J WEB SEMANT, V18, P1, DOI 10.1016/j.websem.2012.10.001
   Cebiric S, 2019, VLDB J, V28, P295, DOI 10.1007/s00778-018-0528-3
   Chen J., INFORM SCIENCES, V563, P326
   Farber M, 2018, SEMANT WEB, V9
   Frankel F, 2008, NATURE, V455, P30, DOI 10.1038/455030a
   Frber M., 2019, P 18 INT SEM WEB C I, P184
   Guo S, 2018, AAAI CONF ARTIF INTE, P4816
   Harth A, 2014, EMER DIRECT DATAB, pXXI
   Hogan A., ACM
   Iana A., 2019, P 15 INT C SEM SYST, P177
   Janowicz K, 2014, SEMANT WEB, V5, P173, DOI 10.3233/SW-140135
   Li P, 2020, ENG APPL ARTIF INTEL, V94, DOI 10.1016/j.engappai.2020.103809
   Li P, 2018, INT J INTELL SYST, V33, P233, DOI 10.1002/int.21926
   Li P, 2017, INT J SEMANT WEB INF, V13, P109, DOI 10.4018/IJSWIS.2017100106
   Li P, 2017, ENG APPL ARTIF INTEL, V65, P268, DOI 10.1016/j.engappai.2017.07.027
   Lin Q., INFORM SCIENCES, V575, P66
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   Lu RQ, 2020, INFORM SCIENCES, V509, P87, DOI 10.1016/j.ins.2019.08.051
   Lv YH, 2008, IEEE INT CONF FUZZY, P1441
   Ma ZM, 2018, FUZZY SET SYST, V351, P41, DOI 10.1016/j.fss.2017.11.013
   Ma ZM, 2018, INT J INTELL SYST, V33, P1534, DOI 10.1002/int.21996
   Martinez-Rodriguez JL, 2020, SEMANT WEB, V11, P255, DOI 10.3233/SW-180333
   Motik B., 2012, OWL 2 WEB ONTOLOGY L
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   Oh B, 2022, INFORM SCIENCES, V586, P468, DOI 10.1016/j.ins.2021.11.085
   Schuetz C.G., 2020, SEMANT WEB, V2, P1
   Shin S, 2020, INFORM SCIENCES, V523, P234, DOI 10.1016/j.ins.2020.02.065
   Singhal Amit, 2012, OFFICIAL GOOGLE BLOG
   Stadler C, 2012, SEMANT WEB, V3, P333, DOI 10.3233/SW-2011-0052
   Suchanek FM, 2019, LECT NOTES COMPUT SC, V11810, P110, DOI 10.1007/978-3-030-31423-1_4
   Wan XL, 2019, INFORM SCIENCES, V505, P306, DOI 10.1016/j.ins.2019.07.087
   Wu Y., INFORM SCIENCES, V545, P132
   Yang J, 2020, IEEE T FUZZY SYST, V28, P706, DOI 10.1109/TFUZZ.2019.2914622
   ZADEH LA, 1975, INFORM SCIENCES, V9, P43, DOI 10.1016/0020-0255(75)90017-1
   ZADEH LA, 1975, INFORM SCIENCES, V8, P199, DOI 10.1016/0020-0255(75)90036-5
   ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X
   Zhang RC, 2020, INFORM SCIENCES, V521, P144, DOI 10.1016/j.ins.2020.02.016
NR 45
TC 0
Z9 0
U1 5
U2 5
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 2022
VL 599
BP 208
EP 230
DI 10.1016/j.ins.2022.03.079
PG 23
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1C3SG
UT WOS:000793042500003
DA 2022-08-23
ER

PT J
AU Sun, YH
   Li, GY
   Guan, MM
   Ning, B
AF Sun, Yunhao
   Li, Guanyu
   Guan, Mengmeng
   Ning, Bo
TI Subgraph-Indexed Sequential Subdivision for Continuous Subgraph Matching
   on Dynamic Knowledge Graph
SO COMPLEXITY
LA English
DT Article
AB Continuous subgraph matching problem on dynamic graph has become a popular research topic in the field of graph analysis, which has a wide range of applications including information retrieval and community detection. Specifically, given a query graph q, an initial graph G0, and a graph update stream oGi, the problem of continuous subgraph matching is to sequentially conduct all possible isomorphic subgraphs covering oGi of q on Gi (=G0 circle plus oGi). Since knowledge graph is a directed labeled multigraph having multiple edges between a pair of vertices, it brings new challenges for the problem focusing on dynamic knowledge graph. One challenge is that the multigraph characteristic of knowledge graph intensifies the complexity of candidate calculation, which is the combination of complex topological and attributed structures. Another challenge is that the isomorphic subgraphs covering a given region are conducted on a huge search space of seed candidates, which causes a lot of time consumption for searching the unpromising candidates. To address these challenges, a method of subgraph-indexed sequential subdivision is proposed to accelerating the continuous subgraph matching on dynamic knowledge graph. Firstly, a flow graph index is proposed to arrange the search space of seed candidates in topological knowledge graph and an adjacent index is designed to accelerate the identification of candidate activation states in attributed knowledge graph. Secondly, the sequential subdivision of flow graph index and the transition state model are employed to incrementally conduct subgraph matching and maintain the regional influence of changed candidates, respectively. Finally, extensive empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms.
C1 [Sun, Yunhao; Li, Guanyu; Guan, Mengmeng; Ning, Bo] Dalian Maritime Univ, Fac Informat Sci & Technol, Dalian 116026, Peoples R China.
RP Li, GY (通讯作者)，Dalian Maritime Univ, Fac Informat Sci & Technol, Dalian 116026, Peoples R China.
EM rabitlee@163.com
OI Ning, Bo/0000-0001-9512-7036; guan, mengmeng/0000-0002-7098-5509
FU National Natural Science Foundation of China [61976032]
FX This work was supported by the National Natural Science Foundation of
   China under Grant no. 61976032.
CR Abadi DJ, 2009, VLDB J, V18, P385, DOI 10.1007/s00778-008-0125-y
   Avnur R, 2000, SIGMOD REC, V29, P261
   Barbieri DF, 2010, INT J SEMANT COMPUT, V4, P3, DOI 10.1142/S1793351X10000936
   Bi F, 2016, SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1199, DOI 10.1145/2882903.2915236
   Broekstra J, 2002, LECT NOTES COMPUT SC, V2342, P54
   Cai TT, 2022, IEEE T KNOWL DATA EN, V34, P1993, DOI 10.1109/TKDE.2020.3003047
   Carroll J.J., P 13 INT C WORLD WID, P74
   Chen XY, 2020, GEOINFORMATICA, V24, P3, DOI 10.1007/s10707-019-00372-z
   CHONG EI, 2005, P 31 INT C VER LARG, P1216
   Choudhury S., 2015, P 18 INT C EXT DAT T, P157
   Cordella LP, 2004, IEEE T PATTERN ANAL, V26, P1367, DOI 10.1109/TPAMI.2004.75
   Erling O, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P619, DOI 10.1145/2723372.2742786
   Fan WF, 2013, ACM T DATABASE SYST, V38, DOI 10.1145/2489791
   Gao JR, 2019, J COMPUT SCI TECH-CH, V34, P1185, DOI 10.1007/s11390-019-1969-x
   Garey MR, 1979, COMPUTER INTRACTABIL
   Gillani S., 2016, P 28 INT C SCI STAT, P1
   Han W.-S., 2013, P 2013 ACM SIGMOD IN, P337, DOI DOI 10.1145/2463676.2465300
   Islam MS, 2015, IEEE T KNOWL DATA EN, V27, P2672, DOI 10.1109/TKDE.2015.2432798
   Kim J, 2015, PROC VLDB ENDOW, V8, P1238, DOI 10.14778/2809974.2809985
   Kim K, 2018, INT CONF MANAGE DATA, P411, DOI 10.1145/3183713.3196917
   Komazec S., 2012, P 6 ACM INT C DISTR, P58, DOI DOI 10.1145/2335484.2335491
   Levandoski JJ, 2009, 2009 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, VOLS 1 AND 2, P911, DOI 10.1109/ICWS.2009.49
   Li JX, 2016, IEEE T KNOWL DATA EN, V28, P1820, DOI 10.1109/TKDE.2016.2542804
   Ma YL, 2020, COGN COMPUT, V12, P296, DOI 10.1007/s12559-019-09680-w
   Maduko A., 2007, WWW, P1233
   Neumann T, 2008, PROC VLDB ENDOW, V1, P647, DOI 10.14778/1453856.1453927
   Pan JZ, 2009, HDB ONTOLOGIES, P71, DOI DOI 10.1007/978-3-540-92673-3_3
   Perez J, 2009, ACM T DATABASE SYST, V34, DOI 10.1145/1567274.1567278
   Phuoc D.L., 2010, CONTINUOUS QUERY OPT
   Qin D, 2020, IEEE T KNOWL DATA EN, V32, P453, DOI 10.1109/TKDE.2018.2879658
   Ramalingam G., 1993, Conference Record of the Twentieth Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, P502
   Ren XG, 2015, PROC VLDB ENDOW, V8, P617, DOI 10.14778/2735479.2735493
   Sidirourgos L, 2008, PROC VLDB ENDOW, V1, P1553
   Sintek M, 2006, LECT NOTES COMPUT SC, V4011, P363
   Song XY, 2021, IEEE INTELL SYST, V36, P46, DOI 10.1109/MIS.2020.3006961
   Sun GH, 2018, PROC INT CONF DATA, P281, DOI 10.1109/ICDE.2018.00034
   Sun YH, 2022, FRONT COMPUT SCI-CHI, V16, DOI 10.1007/s11704-020-0360-y
   ULLMANN JR, 1976, J ACM, V23, P31, DOI 10.1145/321921.321925
   Wang YS, 2019, PROC INT CONF DATA, P1478, DOI 10.1109/ICDE.2019.00133
   Wang YS, 2019, DATA SCI ENG, V4, P352, DOI 10.1007/s41019-019-00105-0
   Weiss C, 2008, PROC VLDB ENDOW, V1, P1008, DOI 10.14778/1453856.1453965
   Xu SJ, 2022, GEOINFORMATICA, V26, P379, DOI 10.1007/s10707-020-00422-x
   Yuan Y, 2019, PROC INT CONF DATA, P914, DOI 10.1109/ICDE.2019.00086
   Zhao PX, 2010, PROC VLDB ENDOW, V3, P340, DOI 10.14778/1920841.1920887
NR 44
TC 0
Z9 0
U1 6
U2 15
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 DEC 22
PY 2020
VL 2020
AR 8871756
DI 10.1155/2020/8871756
PG 18
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA PS4YZ
UT WOS:000607929900007
OA gold
DA 2022-08-23
ER

PT J
AU Jia, XF
   Li, CB
   Zhou, Y
AF Jia Xuefeng
   Li Cunbin
   Zhou Ying
TI An expert recommendation model to electric projects based on KG2E and
   collaborative filtering
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Expert recommendation; Electric projects; Knowledge graph; KG2E;
   Collaborative filtering
AB Science and technology projects are an essential starting point for the digital transformation of electric companies. Irrelevant projects' reviewers will cause unrealistic research results. The companies will also waste research funding. We use knowledge graphs and collaborative filtering to recommend experts for projects in the electric power field to solve this problem. First, we constructed an electric project knowledge graph through the project abstract and CNKI database. Then, we use semantic and collaborative similarity to find the experts most relevant to the project. Finally, we discussed the outperformance and conditions of the proposed model and compared the recommendation results with state-of-the-art methods. Research indicates: (1) The knowledge graph can effectively solve the cold-start problem of collaborative filtering. (2) The KG2E-CF model can improve the relevance between the results of the recommendation. (3) The proposed model should be combined with the theme words extraction algorithm to increase the relevance of the recommendation results. Therefore, the expert recommendation in the electric power field can adopt the model proposed in this paper.
C1 [Jia Xuefeng; Li Cunbin; Zhou Ying] North China Elect Power Univ, Sch Econ & Management, Beijing, Peoples R China.
   [Jia Xuefeng; Li Cunbin; Zhou Ying] North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R China.
RP Jia, XF (通讯作者)，North China Elect Power Univ, 2 Beinong Rd, Beijing 102206, Peoples R China.
EM jiaxuefeng0806@163.com; 844933566@qq.com; 850068063@qq.com
FU science and technology project of State Grid Corporation of China
   [1400-202057269A-0-0-00]
FX This study is supported by the science and technology project of State
   Grid Corporation of China, "Research and development of intelligent
   optimal selection technology for science and technology consulting
   experts based on knowledge graph" (1400-202057269A-0-0-00). The authors
   are grateful to the participants who help improve the paper with many
   pertinent comments and suggestions.
CR Ahn HJ, 2008, INFORM SCIENCES, V178, P37, DOI 10.1016/j.ins.2007.07.024
   Ayub M, 2020, SOFT COMPUT, V24, P11071, DOI 10.1007/s00500-019-04588-x
   Ayub M, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0220129
   Bailon-Elvira JC, 2019, PROCEDIA COMPUT SCI, V162, P207, DOI 10.1016/j.procs.2019.11.277
   Capelle M, 2015, 30TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, VOLS I AND II, P732, DOI 10.1145/2695664.2695700
   Changhao Liu, 2020, 2020 International Conference on Service Science (ICSS), P149, DOI 10.1109/ICSS50103.2020.00031
   Chartier JF, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.10.005
   de Campos LM, 2020, KNOWL-BASED SYST, V190, DOI 10.1016/j.knosys.2019.105337
   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
   Fang Y, 2020, P 2020 C EMP METH NA, P8823, DOI [10.18653/v1/2020.emnlp-main.710, DOI 10.18653/V1/P19-1222]
   Feng CJ, 2020, INFORM SCIENCES, V521, P365, DOI 10.1016/j.ins.2020.02.052
   Feng WL, 2019, CLUSTER COMPUT, V22, pS7401, DOI 10.1007/s10586-017-1576-y
   Gao SX, 2018, INT J COOP INF SYST, V27, DOI 10.1142/S0218843017410040
   Gardner M., 2014, P 2014 C EMP METH NA, P397, DOI [10.3115/v1/D14-1044, DOI 10.3115/V1/D14-1044]
   Geng ZQ, 2020, INFORM SCIENCES, V509, P183, DOI 10.1016/j.ins.2019.09.006
   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 S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Herlocker J L, 2017, ACM SIGIR FORUM, V51, P227, DOI DOI 10.1145/3130348.3130372
   Iftikhar A, 2020, IEEE ACCESS, V8, P123841, DOI 10.1109/ACCESS.2020.3005953
   Jagvaral B, 2020, EXPERT SYST APPL, V142, DOI 10.1016/j.eswa.2019.112960
   Ji S., 2020, ARXIV200200388 CS
   Katarya R, 2020, MULTIMED TOOLS APPL, V79, P35927, DOI 10.1007/s11042-020-09199-5
   Khasmakhi NN, 2019, ENG APPL ARTIF INTEL, V82, P126, DOI 10.1016/j.engappai.2019.03.020
   Klein T, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4831
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Li XY, 2020, J BIOMED INFORM, V107, DOI 10.1016/j.jbi.2020.103422
   Li ZN, 2020, J BIOMED INFORM, V108, DOI 10.1016/j.jbi.2020.103492
   [刘知远 Liu Zhiyuan], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P247
   Natarajan S, 2020, EXPERT SYST APPL, V149, DOI 10.1016/j.eswa.2020.113248
   Neshati M, 2017, INFORM PROCESS MANAG, V53, P1026, DOI 10.1016/j.ipm.2017.04.002
   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
   Saxena A, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P4498
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Tiwari P, 2021, NEURAL NETWORKS, V135, P1, DOI 10.1016/j.neunet.2020.11.012
   Walek B, 2020, EXPERT SYST APPL, V158, DOI 10.1016/j.eswa.2020.113452
   Wang JN, 2020, KNOWL-BASED SYST, V197, DOI 10.1016/j.knosys.2020.105842
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu D, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2020.102475
   Wu XD, 2011, PROC INT C TOOLS ART, P414, DOI 10.1109/ICTAI.2011.68
   Zhang G., 2021, SCI TECHNOLOGY MANAG, V41, P175
   Zhang R, 2021, J BIOMED INFORM, V115, DOI 10.1016/j.jbi.2021.103696
   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 43
TC 0
Z9 0
U1 12
U2 12
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 15
PY 2022
VL 198
AR 116758
DI 10.1016/j.eswa.2022.116758
PG 14
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 1B0TG
UT WOS:000792157000012
DA 2022-08-23
ER

PT J
AU Yang, YB
   Hu, QR
   Liu, Y
   Pan, XH
   Gao, S
   Hao, BX
AF Yang, Yinbin
   Hu, Qinran
   Liu, Yi
   Pan, Xiaohui
   Gao, Shang
   Hao, Baoxin
TI Power Grid Monitoring Event Recognition Method Integrating Knowledge
   Graph and Deep Learning
SO FRONTIERS IN ENERGY RESEARCH
LA English
DT Article
DE event recognition; knowledge graph; graph based neural network; GRU;
   recognition strategy
ID CLASSIFICATION; SYSTEM
AB "Eventization" of power grid monitoring is an effective way to deal with massive alarm information. The existing event recognition method adopts the method of text information mining, and the overall recognition accuracy is not high. Therefore, this paper proposes a power grid monitoring event recognition method integrating knowledge graph and deep learning. First, the method constructs the knowledge graph of monitoring equipment and uses the improved GraphSAGE (graph sample and aggregate) algorithm to perform representation learning on the graph, and integrate the structural characteristics of monitoring equipment into the generated alarm vectors. Then, the GRU (Gated Recurrent Unit) neural network trains the alarm vectors and related events. In addition, this paper combines the proposed method with the existing monitoring expert system, and puts forward a monitoring event recognition strategy. Finally, through the case analysis and comparison of the actual data of the power grid, the effectiveness of the proposed method and strategy is verified, which further improves the accuracy of monitoring event recognition.
C1 [Yang, Yinbin; Hu, Qinran] Southeast Univ, Nanjing, Peoples R China.
   [Liu, Yi; Pan, Xiaohui; Hao, Baoxin] State Grid Jiangsu Elect Power Co Ltd, Nanjing, Peoples R China.
   [Gao, Shang] NARI Grp Corp, Nanjing, Peoples R China.
RP Hu, QR (通讯作者)，Southeast Univ, Nanjing, Peoples R China.
EM qhu@seu.edu.cn
FU Science and Technology Program of State Grid Corporation of China
   [SGJS0000DKJS2000952]
FX Funding The authors gratefully acknowledge the support of the Science
   and Technology Program of State Grid Corporation of China
   (SGJS0000DKJS2000952).
CR Bai ZY, 2019, ENERGIES, V12, DOI 10.3390/en12173258
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Chu-Carroll J, 2012, IBM J RES DEV, V56, DOI [10.1147/JRD.2012.2185901, 10.1147/JRD.2012.2186519]
   Church KW, 2017, NAT LANG ENG, V23, P155, DOI 10.1017/S1351324916000334
   Day MY, 2016, PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, P1127, DOI 10.1109/ASONAM.2016.7752381
   Dong X.L., 2015, ARXIV
   ELAffendi MA, 2018, 2018 SIXTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION, NETWORKING, AND WIRELESS COMMUNICATIONS (DINWC), P70, DOI 10.1109/DINWC.2018.8356998
   Farhadi M, 2015, IEEE T SMART GRID, V6, P1658, DOI 10.1109/TSG.2015.2396995
   Huang L., 2021, 2021 INT C POWER SYS, P79, DOI [10.1109/powercon53785.2021.9697881, DOI 10.1109/POWERCON53785.2021.9697881]
   Kipf TN, 2016, ARXIV
   MINAKAWA T, 1995, IEEE T POWER SYST, V10, P932, DOI 10.1109/59.387936
   Mu JA, 2012, IEEE POW ENER SOC GE
   Oh J., 2019, ARXIV
   Pandey S, 2020, IEEE T POWER SYST, V35, P4421, DOI 10.1109/TPWRS.2020.2986019
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Shewalkar A, 2019, J ARTIF INTELL SOFT, V9, P235, DOI 10.2478/jaiscr-2019-0006
   Sun GQ, 2019, ENERGIES, V12, DOI 10.3390/en12142814
   Webber J., 2018, PROGRAMMATIC INTRO N
   Wei LH, 2011, IEEE T POWER DELIVER, V26, P1615, DOI 10.1109/TPWRD.2011.2117445
   Xia L, 2014, DISCRETE EVENT DYN S, V24, P103, DOI 10.1007/s10626-013-0170-6
NR 20
TC 0
Z9 0
U1 2
U2 2
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-598X
J9 FRONT ENERGY RES
JI Front. Energy Res.
PD JUL 7
PY 2022
VL 10
AR 950954
DI 10.3389/fenrg.2022.950954
PG 9
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA 3E0DT
UT WOS:000829663700001
OA gold
DA 2022-08-23
ER

PT J
AU Bao, F
   Tian, Y
   Gao, YD
AF Bao, Fu
   Tian, Yuan
   Gao, Yudou
TI Data switching method among heterogeneous power information system
   databases based on knowledge graph
SO INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES
   AND FIELDS
LA English
DT Article
DE data error correction; data switching; knowledge graph; power
   information system; triples
AB In order to solve the problems of long data switching time, low information utilization rate, and low switching accuracy in traditional data switching methods, a data switching method between heterogeneous power information system databases based on knowledge graphs is proposed. Based on the knowledge graph technology and its ternary structure, the data of the heterogeneous power information system is processed, and the N-Gram model is used to realize the knowledge extraction, and the data error correction is established through the model. Through ODBC technology to complete the link between heterogeneous power information system databases, and then select the data access interface and determine the data extraction task, and finally realize the data exchange between the heterogeneous power information system databases. Experimental results show that the average communication channel bandwidth during the data switching process is 1.8 Gb/s, the highest value of data switching accuracy reaches 82%, and the information utilization rate after data switching is relatively high, indicating that the method has certain effectiveness.
C1 [Bao, Fu; Tian, Yuan; Gao, Yudou] Yunnan Power Grid Co Ltd, Informat Ctr, Kunming 650200, Yunnan, Peoples R China.
RP Bao, F (通讯作者)，Yunnan Power Grid Co Ltd, Informat Ctr, Kunming 650200, Yunnan, Peoples R China.
EM kenguanjingu@163.com
CR Cui HJ, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11125385
   Fan SX, 2020, SCI PROGRAMMING-NETH, V2020, DOI 10.1155/2020/8842463
   Fares M, 2019, KNOWL-BASED SYST, V165, P432, DOI 10.1016/j.knosys.2018.12.017
   Huang DP., 2020, ACTA SCI NAT U PEKIN, V297, P100
   Huang H, 2020, MATH PROBL ENG, V2020, DOI 10.1155/2020/6402149
   Javan DS, 2021, INT J ELEC POWER, V129, DOI 10.1016/j.ijepes.2020.106760
   Kim T, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13042276
   Kristov L, 2019, IEEE POWER ENERGY M, V17, P42, DOI 10.1109/MPE.2018.2885204
   Meinecke S, 2020, ENERGIES, V13, DOI 10.3390/en13184826
   Sawant U, 2019, INFORM RETRIEVAL J, V22, P324, DOI 10.1007/s10791-018-9348-8
   Shi C, 2021, FRONT COMPUT SCI-CHI, V15, DOI 10.1007/s11704-020-9240-8
   Tan JY, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063191
   Tiwari P, 2021, NEURAL NETWORKS, V135, P1, DOI 10.1016/j.neunet.2020.11.012
   Wu, 2019, COMPUT SIMUL, V036, P380
   Yang P, 2020, IEEE T POWER SYST, V35, P2313, DOI 10.1109/TPWRS.2019.2951202
NR 15
TC 0
Z9 0
U1 0
U2 0
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0894-3370
EI 1099-1204
J9 INT J NUMER MODEL EL
JI Int. J. Numer. Model.-Electron. Netw. Device Fields
PD SEP
PY 2022
VL 35
IS 5
AR e3006
DI 10.1002/jnm.3006
EA APR 2022
PG 12
WC Engineering, Electrical & Electronic; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 3S2CM
UT WOS:000777464200001
DA 2022-08-23
ER

PT J
AU Do, P
   Phan, THV
   Gupta, BB
AF Phuc Do
   Phan, Truong H., V
   Gupta, Brij B.
TI Developing a Vietnamese Tourism Question Answering System Using
   Knowledge Graph and Deep Learning
SO ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION
   PROCESSING
LA English
DT Article
DE Question answering system; Knowledge graph; Natural language processing;
   Deep learning; Graph query; Vietnamese tourism
AB In recent years, Question Answering (QA) systems have increasingly become very popular in many sectors. This study aims to use a knowledge graph and deep learning to develop a QA system for tourism in Vietnam. First, the QA system replies to a user's question about a place in Vietnam. Then, the QA describes it in detail such as when the place was discovered, why the place's name was called like that, and so on. Finally, the system recommends some related tourist attractions to users. Meanwhile, deep learning is used to solve a simple natural language answer, and a knowledge graph is used to infer a natural language answering list related to entities in the question. The study experiments on a manual dataset collected from Vietnamese tourism websites. As a result, the QA system combining the two above approaches provides more information than other systems have done before. Besides that, the system gets 0.83 F1, 0.87 precision on the test set.
C1 [Phuc Do] Vietnam Natl Univ Ho Chi Minh, Univ Informat Technol, Ho Chi Minh City, Vietnam.
   [Phan, Truong H., V] Van Lang Univ, Ho Chi Minh City, Vietnam.
   [Gupta, Brij B.] Natl Inst Technol, Kurukshetra, Haryana, India.
   [Gupta, Brij B.] Asia Univ, Taichung, Taiwan.
RP Gupta, BB (通讯作者)，Natl Inst Technol, Kurukshetra, Haryana, India.; Gupta, BB (通讯作者)，Asia Univ, Taichung, Taiwan.
EM phucdo@uit.edu.vn; truong.phv@vlu.edu.vn; bbgupta@nitkkr.ac.in
RI Gupta, Brij B/E-9813-2011
OI Gupta, Brij B/0000-0003-4929-4698; H. V Phan, Truong/0000-0003-1773-6703
FU Vietnam National University Ho Chi Minh City (VNU-HCMC) [DS202026-01]
FX This research is funded by Vietnam National University Ho Chi Minh City
   (VNU-HCMC) under the grant number: DS202026-01.
CR Angles R, 2017, ACM COMPUT SURV, V50, DOI 10.1145/3104031
   Annervaz K. M., 2018, PROC C N AM CHAPTER, P313
   [Anonymous], 2016, ARXIV160908144CSCL
   Banerjee P., 2007, 16 TEXT RETR C TREC
   Burke R, 2011, AI MAG, V32, P13, DOI 10.1609/aimag.v32i3.2361
   Cano Erion, 2019, ARXIV190103888
   Castro Jamie, 2017, 11 A MEND INT WORKSH
   Chaudhary JR., 2018, INT J SCI RES SCI EN, V4, P1480
   Chen DQ, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1870, DOI 10.18653/v1/P17-1171
   Djallel Bouneffouf, 2013, COMPUT LANG
   Dubey M, 2018, LECT NOTES COMPUT SC, V11136, P108, DOI 10.1007/978-3-030-00671-6_7
   Francis Nadime, 2018, CYPHER EVOLVING QUER
   Garcia-Garcia A, 2018, APPL SOFT COMPUT, V70, P41, DOI 10.1016/j.asoc.2018.05.018
   Greenstein Eric, 2015, JAPANESE TO ENGLISH
   Guo Q, 2020, ARXIV PREPRINT ARXIV
   Harabagiu S. M., 2000, COLLING 2000, P292, DOI DOI 10.3115/990820.990863
   Hixon B., 2015, P 2015 C N AM CHAPT, P851
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Duong HT, 2014, LECT NOTES COMPUT SC, V8838, P186, DOI 10.1007/978-3-662-45237-0_19
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Kamilaris A, 2018, COMPUT ELECTRON AGR, V147, P70, DOI 10.1016/j.compag.2018.02.016
   Lakiotaki K, 2011, IEEE INTELL SYST, V26, P64, DOI 10.1109/MIS.2011.33
   Li Hongyu, 2017, 1 WORKSH KNOWL GRAPH
   Lopez V, 2016, LECT NOTES COMPUT SC, V9982, P363, DOI 10.1007/978-3-319-46547-0_32
   Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3
   Lu XL, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P105, DOI 10.1145/3331184.3331252
   Mack David, 2018, ANSWERING ENGLISH QU
   Maheshwari G, 2019, LECT NOTES COMPUT SC, V11778, P487, DOI 10.1007/978-3-030-30793-6_28
   Niboshi Keisuke, 2019, WRITE SEQUENCE SEQUE
   NILASHI M, 2013, RES J APPL SCI ENG T, V5, P4168
   Qi P., 2019, P 2019 C EMP METH NA, P2590, DOI [10.18653/v1/D19-1261, DOI 10.18653/V1/D19-1261]
   Ricci F., 2010, Information Technology and Tourism, V12, P205, DOI 10.3727/109830511X12978702284390
   Rohit G., 2018, ADV INTELLIGENT SYST, V816
   Sakre M. M., 2009, J ADV COMPUT SCI, V3, P5
   Shah H, 2018, ADV NEUR IN, V31
   Sharmaa Yashvardhan, 2018, INT C COMP INT DAT S
   Shawar Abu, 2009, INT C CORP LING
   Shibily Preena M. P., 2019, INT C SYST EN ENV IC
   Song Weiping, 2019, ARXIV190609506CSIR
   Sutskever Ilya, 2014, INT C ADV NEUR INF P
   Thaker Rukhsana, 2015, INT J ENG RES APPL, V5, P106
   Trung T, 2020, DATA BRIEF, V30, DOI 10.1016/j.dib.2020.105682
   Van Bruggen R., 2014, LEARNING NEO4J
   Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029
   Zheng WG, 2018, PROC VLDB ENDOW, V11, P1373, DOI 10.14778/3236187.3236192
NR 46
TC 5
Z9 5
U1 4
U2 6
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2375-4699
EI 2375-4702
J9 ACM T ASIAN LOW-RESO
JI ACM Trans. Asian Low-Resour. Lang. Inf. Process.
PD SEP
PY 2021
VL 20
IS 5
AR 81
DI 10.1145/3453651
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XB8PL
UT WOS:000721584900011
DA 2022-08-23
ER

PT J
AU Yan, W
   Ding, YH
AF Yan, Wei
   Ding, Yuhan
TI RDF knowledge graph keyword type search using frequent patterns
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE RDF knowledge graph; keyword; type graph; frequent search pattern; Bloom
   coding
ID QUERIES
AB With the rapid development of Semantic Web, the retrieval of RDF data has become a research hotspot. As the main method of data retrieval, keyword search has attracted much attention because of its simple operation. The existing RDF keyword search methods mainly search directly on RDF graph, which is no longer applicable to RDF knowledge graph. Firstly, we propose to transform RDF knowledge graph data into type graph to prune the search space. Then based on type graph, we extract frequent search patterns and establish a list from frequent search patterns to pattern instances. Finally, we propose a method of the Bloom coding, which can be used to quickly judge whether the information our need is in frequent search patterns. The experiments show that our approach outperforms the state-of-the-art methods on both accuracy and response time.
C1 [Yan, Wei; Ding, Yuhan] Liaoning Univ, Sch Informat, Shenyang, Peoples R China.
   [Yan, Wei] Liaoning Univ, Sch Innovat & Entrepreneurship, Shenyang, Peoples R China.
RP Yan, W (通讯作者)，Liaoning Univ, Sch Innovat & Entrepreneurship, Sch Informat, Shenyang 110036, Peoples R China.
EM yanwei@lnu.edu.cn
CR [Anonymous], 1999, RDF MODEL SYNTAX SPE
   Bhalotia G, 2002, PROC INT CONF DATA, P431, DOI 10.1109/ICDE.2002.994756
   Cai Z, 2020, VLDB J, V29, P1171, DOI 10.1007/s00778-020-00610-z
   Coffman J, 2014, IEEE T KNOWL DATA EN, V26, P30, DOI 10.1109/TKDE.2012.228
   De Virgilio R, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI), P66, DOI 10.1109/IRI.2011.6009522
   Ding BL, 2007, PROC INT CONF DATA, P811
   Dosso D, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P965, DOI 10.1145/3331184.3331284
   Elbassuoni S, 2012, EFFECTIVE SEARCHING
   Gkirtzou K., 2015, P 1 INT WORKSH NOV W, P3
   Guo YB, 2005, J WEB SEMANT, V3, P158, DOI 10.1016/j.websem.2005.06.005
   He H., 2007, SIGMOD, P305, DOI DOI 10.1145/1247480.1247516
   Kadilierakis Giorgos, 2020, Semantic Web 17th International Conference, ESWC 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12123), P121, DOI 10.1007/978-3-030-49461-2_8
   Kasneci G, 2009, PROC INT CONF DATA, P868, DOI 10.1109/ICDE.2009.64
   Ladwig G, 2010, LECT NOTES COMPUT SC, V6089, P288, DOI 10.1007/978-3-642-13489-0_20
   Le WC, 2014, IEEE T KNOWL DATA EN, V26, P2774, DOI 10.1109/TKDE.2014.2302294
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lian X, 2013, J WEB SEMANT, V22, P40, DOI 10.1016/j.websem.2013.08.001
   Lin XQ, 2018, J INF SCI ENG, V34, P489, DOI 10.6688/JISE.2018.34.2.11
   Menendez ES, 2019, LECT NOTES COMPUT SC, V11707, P143, DOI 10.1007/978-3-030-27618-8_11
   Mountantonakis M, 2020, ACM J DATA INF QUAL, V12, DOI 10.1145/3372750
   Peng P, 2019, IEEE T KNOWL DATA EN, V31, P670, DOI 10.1109/TKDE.2018.2841389
   Peng P, 2017, INFORM SYST, V67, P19, DOI 10.1016/j.is.2017.03.002
   Qing Wang, 2020, Database Systems for Advanced Applications. 25th International Conference, DASFAA 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12113), P613, DOI 10.1007/978-3-030-59416-9_37
   Shan Y, 2017, DATA KNOWL ENG, V110, P1, DOI 10.1016/j.datak.2017.02.001
   Tran T, 2009, PROC INT CONF DATA, P405, DOI 10.1109/ICDE.2009.119
   Wang X, 2020, WORLD WIDE WEB, V23, P1465, DOI 10.1007/s11280-019-00739-0
   Wu BW, 2015, PROC INT CONF DATA, P795, DOI 10.1109/ICDE.2015.7113334
   Zenz G, 2009, J WEB SEMANT, V7, P166, DOI 10.1016/j.websem.2009.07.005
   Zheng WG, 2016, PROC VLDB ENDOW, V9, P840, DOI 10.14778/2983200.2983201
NR 29
TC 0
Z9 0
U1 1
U2 4
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 1
BP 2239
EP 2253
DI 10.3233/JIFS-210950
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UB5PA
UT WOS:000685896700137
DA 2022-08-23
ER

PT J
AU Wang, Q
   Hao, YS
   Cao, J
AF Wang, Qi
   Hao, Yongsheng
   Cao, Jie
TI ADRL: An attention-based deep reinforcement learning framework for
   knowledge graph reasoning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Knowledge reasoning; Reinforcement learning; Deep
   learning; Attention
ID NEURAL-NETWORKS; GAME; GO
AB Knowledge graph reasoning is one of the key technologies for knowledge graph construction, which plays an important part in application scenarios such as vertical search and intelligent question answering. It is intended to infer the desired entity from the entities and relations that already exist in the knowledge graph. Most current methods for reasoning, such as embedding-based methods, globally embed all entities and relations, and then use the similarity of vectors to infer relations between entities or whether given triples are true. However, in real application scenarios, we require a clear and interpretable target entity as the output answer. In this paper, we propose a novel attention-based deep reinforcement learning framework (ADRL) for learning multi-hop relational paths, which improves the efficiency, generalization capacity, and interpretability of conventional approaches through the structured perception of deep learning and relational reasoning of reinforcement learning. We define the entire process of reasoning as a Markov decision process. First, we employ CNN to map the knowledge graph to a low-dimensional space, and a message-passing mechanism to sense neighbor entities at each level, and then employ LSTM to memorize and generate a sequence of historical trajectories to form a policy and value functions. We design a relational module that includes a self-attention mechanism that can infer and share the weights of neighborhood entity vectors and relation vectors. Finally, we employ the actor-critic algorithm to optimize the entire framework. Experiments confirm the effectiveness and efficiency of our method on several benchmark data sets. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Wang, Qi] Fudan Univ, Shanghai, Peoples R China.
   [Hao, Yongsheng] Nanjing Univ Informat Sci & Technol, Nanjing, Peoples R China.
   [Cao, Jie] Xuzhou Univ Technol, Xuzhou, Jiangsu, Peoples R China.
RP Wang, Q (通讯作者)，825 Zhangheng Rd, Shanghai, Peoples R China.
EM 17110240039@fudan.edu.cn; yongshenghao@yahoo.com; kindcj@nuist.edu.cn
OI Wang, Qi/0000-0003-3249-8459
FU National Social Science Foundation of China [16ZDA054]; National Science
   Foundation of China [U1736105]
FX The work was partly supported by the National Social Science Foundation
   of China (No. 16ZDA054), and the National Science Foundation of China
   (No. U1736105).
CR [Anonymous], 2018, ARXIV180601261
   [Anonymous], 2013, P ADV NEURAL INF PRO
   [Anonymous], 2017, ARXIV170407130
   [Anonymous], 2002, P ICML
   [Anonymous], 2017, ARXIV170606383
   [Anonymous], 24 AAA C ART INT
   [Anonymous], 2017, P EMNLP
   [Anonymous], KNOWLEDGE GRAPHS REP
   [Anonymous], 2018, P 2018 C EMP METH NA
   [Anonymous], ICML
   [Anonymous], INFORM SCI
   [Anonymous], 2015, ARXIV150600379, DOI DOI 10.18653/V1/D15-1082
   [Anonymous], 2018, ADV NEURAL INFORM PR
   [Anonymous], ON LINE Q LEARNING U
   [Anonymous], 2016, ARXIV160701426
   [Anonymous], 2015, P 53 ANN M ASS COMP
   [Anonymous], 2017, ADV NEURAL INFORM PR
   [Anonymous], P 54 ANN M ASS COMP
   [Anonymous], INFORM SCI
   [Anonymous], P 2010 C EMP METH NA
   [Anonymous], INT C IND LOG PROGR
   [Anonymous], P 20 INT C MACH LEAR
   Babaeizadeh M, 2016, ARXIV PREPRINT ARXIV
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Das R., 2017, ARXIV171105851
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ding SF, 2019, APPL INTELL, V49, P4303, DOI 10.1007/s10489-019-01501-9
   Dzeroski S, 2001, MACH LEARN, V43, P7, DOI 10.1023/A:1007694015589
   Espeholt L., 2018, ARXIV180201561
   Gardner M., 2013, P 2013 C EMPIRICAL M, P833
   Glorot X., 2010, P 13 INT C ART INT S, P249
   Hinton G, 2012, IEEE SIGNAL PROC MAG, V29, P82, DOI 10.1109/MSP.2012.2205597
   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]
   Mnih V., 2016, INT C MACH LEARN
   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
   Silver D, 2017, NATURE, V550, P354, DOI 10.1038/nature24270
   Silver D, 2016, NATURE, V529, P484, DOI 10.1038/nature16961
   Suchanek F., 2007, P 16 INT C WORLD WID
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Toutanova Kristina., 2015, P 2015 C EMPIRICAL M
   Vaswani A, 2017, ADV NEUR IN, V30
   Velickovic P., 2018, P INT C LEARN REPR A
   Wang H, 2019, KNOWL-BASED SYST, V163, P1009, DOI 10.1016/j.knosys.2018.10.022
   Wang HB, 2019, KNOWL-BASED SYST, V180, P75, DOI 10.1016/j.knosys.2019.05.020
   Wang YH, 2018, KNOWL-BASED SYST, V143, P248, DOI 10.1016/j.knosys.2017.11.005
   Xiao H., 2016, IEEE T IND ELECTRON, V1, P1, DOI DOI 10.1109/TIE.2016.2558138
   Xiao H., 2015, ARXIV150905490
   Yang B., 2015, P INT C LEARN REPR
   Yang Fan, 2017, ADV NEURAL INFORM PR
   Yuan YL, 2019, KNOWL-BASED SYST, V175, P107, DOI 10.1016/j.knosys.2019.03.018
   Zhang Chiyuan, 2018, ARXIV PREPRINT ARXIV
   Zhao XY, 2019, APPL INTELL, V49, P581, DOI 10.1007/s10489-018-1296-x
NR 62
TC 19
Z9 20
U1 22
U2 84
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 JUN 7
PY 2020
VL 197
AR 105910
DI 10.1016/j.knosys.2020.105910
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LH6FG
UT WOS:000528878400001
DA 2022-08-23
ER

PT J
AU Siirala, E
   Salantera, S
   Lundgren-Laine, H
   Peltonen, LM
   Engblom, J
   Junttila, K
AF Siirala, Eriikka
   Salantera, Sanna
   Lundgren-Laine, Helja
   Peltonen, Laura-Maria
   Engblom, Janne
   Junttila, Kristiina
TI Identifying nurse managers' essential information needs in daily unit
   operation in perioperative settings
SO NURSING OPEN
LA English
DT Article
DE information needs; nurse managers; nurses; nursing; perioperative
   settings
AB Aim To identify nurse managers' essential information needs in daily unit operation in perioperative settings.
   Design Qualitative and quantitative descriptive design.
   Methods The study consisted of (I) generation of an item pool of potential information needs, (II) assessment of the item pool by an expert panel and (III) confirming the essential information needs of nurse managers in daily unit operation with a survey (N = 288). Content validity index values were calculated for the assessments by expert panel and in the survey. Internal consistency of the final item pool was explored with Cronbach's alpha. The data were collected from 2011-2015.
   Results During the study process, the number of essential information needs decreased from 92-41. The final item pool consisted of 12 subthemes, and they were categorized into four main themes: patient's care process, surgical procedure, human resources and tangible resources. The findings can be used to create a knowledge map for information system purposes.
C1 [Siirala, Eriikka; Salantera, Sanna; Lundgren-Laine, Helja; Peltonen, Laura-Maria; Junttila, Kristiina] Univ Turku, Dept Nursing Sci, Turku, Finland.
   [Siirala, Eriikka; Salantera, Sanna] Turku Univ Hosp, Turku, Finland.
   [Lundgren-Laine, Helja] Cent Finland Hlth Care Dist, Jyvaskyla, Finland.
   [Engblom, Janne] Univ Turku, Dept Math & Stat, Turku, Finland.
   [Engblom, Janne] Univ Turku, Sch Econ, Turku, Finland.
   [Junttila, Kristiina] Univ Helsinki, Helsinki Univ Hosp, Nursing Res Ctr, Helsinki, Finland.
RP Siirala, E (通讯作者)，Univ Turku, Dept Nursing Sci, Turku, Finland.
EM eesiir@utu.fi
OI Peltonen, Laura-Maria/0000-0001-5740-6480; Junttila,
   Kristiina/0000-0002-3541-601X; Salantera, Sanna/0000-0003-2529-6699
FU Finnish Work Environment Fund [115484]
FX The Finnish Work Environment Fund, grant number: 115484.
CR Baker Susan, 2012, Nurs Manage, V43, P24, DOI 10.1097/01.NUMA.0000422890.99334.21
   Braun V., 2006, QUAL RES PSYCHOL, V3, P77, DOI [https://doi.org/10.1191/1478088706qp063oa, 10.1191/1478088706qp063oa]
   Cardoen B, 2010, EUR J OPER RES, V201, P921, DOI 10.1016/j.ejor.2009.04.011
   Cronbach L.J., 1957, PSYCHOL TESTS PERSON
   DeVellis R.F., 2012, SCALE DEV THEORY APP
   Dexter F, 2012, ANESTH ANALG, V115, P1188, DOI 10.1213/ANE.0b013e31826a5f9e
   Di Martinelly C, 2014, INT J PROD RES, V52, P7239, DOI 10.1080/00207543.2014.916827
   Finnish National Board on Research Integrity, 2012, RESP COND RES PROC H
   Gorsuch R.L., 1983, FACTOR ANAL CLASSIC, V2nd ed, DOI DOI 10.4324/9781315735740
   Grant JS, 1997, RES NURS HEALTH, V20, P269, DOI 10.1002/(SICI)1098-240X(199706)20:3<269::AID-NUR9>3.3.CO;2-3
   Gur S, 2018, J HEALTHC ENG, V2018, DOI 10.1155/2018/5341394
   Gutenstein M, 2019, HEALTH INFORM J, V25, P1563, DOI 10.1177/1460458218779099
   Kianto A, 2014, J INTELLECT CAP, V15, P362, DOI 10.1108/JIC-05-2014-0059
   Levine Wilton C, 2015, Anesthesiol Clin, V33, P697, DOI 10.1016/j.anclin.2015.07.006
   Lundgren-Laine H, 2013, BMC MED INFORM DECIS, V13, DOI 10.1186/1472-6947-13-15
   Markazi-Moghaddam N, 2016, IRAN J PUBLIC HEALTH, V45, P843
   McGeorge N, 2015, J COGN ENG DECIS MAK, V9, P329, DOI 10.1177/1555343415613723
   McPeake Joanne, 2014, Nurse Res, V21, P24, DOI 10.7748/nr2014.01.21.3.24.e1205
   Ministry of Social Affairs and Health (STM), 2020, PRESS REL MIN SOC AF
   Minnick AF, 2012, J HEALTHC QUAL, V34, P16, DOI 10.1111/j.1945-1474.2011.00142.x
   Peltokorpi A, 2011, HEALTH CARE MANAG SC, V14, P370, DOI 10.1007/s10729-011-9173-8
   Peltonen LM, 2019, J NURS MANAGE, V27, P233, DOI 10.1111/jonm.12700
   Peltonen LM, 2018, STUD HEALTH TECHNOL, V250, P203, DOI 10.3233/978-1-61499-872-3-203
   Polit DF, 2006, RES NURS HEALTH, V29, P489, DOI 10.1002/nur.20147
   Ranade-Kharkar P, 2017, J AM MED INFORM ASSN, V24, P933, DOI 10.1093/jamia/ocx023
   Rowley J, 2007, J INF SCI, V33, P163, DOI 10.1177/0165551506070706
   Sherman RO, 2014, NURS ECON, V32, P186
   Siirala E, 2016, J NURS MANAGE, V24, P806, DOI 10.1111/jonm.12385
   Tsang K. K., 2012, HONG KONG TEACH CEN, V11, P121
   Van Someren M.W., 1994, THINK ALOUD METHOD P, V1st ed.
   Watthananon J, 2012, PROCEDIA ENGINEER, V32, P1169, DOI 10.1016/j.proeng.2012.02.073
   Wiyartanti L, 2015, STUD HEALTH TECHNOL, V210, P384, DOI 10.3233/978-1-61499-512-8-384
NR 32
TC 3
Z9 3
U1 0
U2 3
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN, NJ 07030 USA
SN 2054-1058
J9 NURS OPEN
JI NURS. OPEN
PD MAY
PY 2020
VL 7
IS 3
BP 793
EP 803
DI 10.1002/nop2.454
EA FEB 2020
PG 11
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA KY6DX
UT WOS:000511704700001
PM 32257267
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Su, HN
AF Su, Hsin-Ning
TI Visualization of Global Science and Technology Policy Research Structure
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID CO-WORD ANALYSIS; BIBLIOMETRIC ANALYSIS; CARTOGRAPHY; NETWORK;
   INTELLIGENCE; DYNAMICS
AB This study proposes an approach for visualizing knowledge structures that creates a "research-focused parallelship network," "keyword co-occurrence network," and a knowledge map to visualize Sci-Tech policy research structure. A total of 1,125 Sci-Tech policy-related papers (873 journal papers [78%], 205 conference papers [18%], and 47 review papers [4%]) have been retrieved from the Web of Science database for quantitative analysis and mapping. Different network and contour maps based on these 1,125 papers can be constructed by choosing different information as the main actor, such as the paper title, the institute, the country, or the author keywords, to reflect Sci-Tech policy research structures in micro-, meso-, and macro-levels, respectively. The quantitative way of exploring Sci-Tech policy research papers is investigated to unveil important or emerging Sci-Tech policy implications as well as to demonstrate the dynamics and visualization of the evolution of Sci-Tech policy research.
C1 Natl Appl Res Labs, Sci & Technol Policy Res & Informat Ctr, Taipei 106, Taiwan.
RP Su, HN (通讯作者)，Natl Appl Res Labs, Sci & Technol Policy Res & Informat Ctr, 14 F,106,Sec 2,He Ping E Rd, Taipei 106, Taiwan.
EM ning@nchu.edu.tw
RI Su, Hsin-Ning/H-6209-2011
OI Su, Hsin-Ning/0000-0002-4325-0793
CR [Anonymous], 1992, NETWORKS ORG STRUCTU
   [Anonymous], 1988, SOCIAL STRUCTURES NE
   Baldwin C, 2003, INT J GERIATR PSYCH, V18, P41, DOI 10.1002/gps.770
   Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509
   Bhattacharya S, 1998, SCIENTOMETRICS, V43, P359, DOI 10.1007/BF02457404
   BRASS DJ, 1992, NETWORKS AND ORGANIZATIONS : STRUCTURE, FORM, AND ACTION, P191
   CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280
   CAMBROSIO A, 1993, SCIENTOMETRICS, V27, P119, DOI 10.1007/BF02016546
   Chiu WT, 2007, SCIENTOMETRICS, V73, P3, DOI 10.1007/s11192-005-1523-1
   Chiu WT, 2004, SCIENTOMETRICS, V61, P69, DOI 10.1023/B:SCIE.0000037363.49623.28
   Clarke A, 2007, EUR J PUBLIC HEALTH, V17, P24, DOI 10.1093/eurpub/ckm062
   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
   COURTIAL JP, 1994, SCIENTOMETRICS, V31, P251, DOI 10.1007/BF02016875
   DeLooze MA, 1997, SCIENTOMETRICS, V39, P267, DOI 10.1007/BF02458530
   Ding Y, 2001, INFORM PROCESS MANAG, V37, P817, DOI 10.1016/S0306-4573(00)00051-0
   DOSI G, 1982, RES POLICY, V11, P147, DOI 10.1016/0048-7333(82)90016-6
   FREEMAN LC, 1979, SOC NETWORKS, V1, P215, DOI 10.1016/0378-8733(78)90021-7
   GRANOVETTER MS, 1973, AM J SOCIOL, V78, P1360, DOI 10.1086/225469
   GRANOVETTER MS, 1970, THESIS HARVARD U BOS
   Gupta B. M., 2004, DESIDOC Bulletin of Information Technology, V24, P3
   HINZE S, 1994, SCIENTOMETRICS, V29, P353, DOI 10.1007/BF02033445
   Ho YS, 2007, J ENV PROT SCI, V1, P1
   Hori K, 2004, J UNIVERS COMPUT SCI, V10, P252
   Kostoff RN, 2005, ENERGY, V30, P709, DOI 10.1016/j.energy.2004.04.058
   Kostoff RN, 2002, J POWER SOURCES, V110, P163, DOI 10.1016/S0378-7753(02)00233-1
   Kostoff RN, 1998, INFORM PROCESS MANAG, V34, P69, DOI 10.1016/S0306-4573(97)00066-6
   Kostoff RN, 2008, TECHNOL FORECAST SOC, V75, P165, DOI 10.1016/j.techfore.2007.11.004
   Kostoff RN, 2007, TECHNOL FORECAST SOC, V74, P1519, DOI 10.1016/j.techfore.2007.02.004
   Kuhn T.S., 1962, STRUCTURE SCI REVOLU
   Lee P.-C., 2009, TECHNOLOGY MANAGEMEN, V14, P65
   Li T, 2008, NEUROSCI LETT, V441, P248, DOI 10.1016/j.neulet.2008.06.044
   MARSDEN PV, 1984, SOC FORCES, V63, P482, DOI 10.2307/2579058
   MOTTER AE, 1999, SCI PHYS REV E, V65
   NOYONS ECM, 1994, SCIENTOMETRICS, V30, P157, DOI 10.1007/BF02017220
   Noyons ECM, 1998, J AM SOC INFORM SCI, V49, P68, DOI 10.1002/(SICI)1097-4571(1998)49:1<68::AID-ASI9>3.0.CO;2-Z
   RIKKEN F, 1995, SCIENTOMETRICS, V33, P367, DOI 10.1007/BF02017337
   RIP A, 1984, SCIENTOMETRICS, V6, P381, DOI 10.1007/BF02025827
   Tian YG, 2008, J INFORMETR, V2, P65, DOI 10.1016/j.joi.2007.10.001
   Van Eck NJ, 2007, INT J UNCERTAIN FUZZ, V15, P625, DOI 10.1142/S0218488507004911
   VANRAAN AFJ, 1993, SCIENTOMETRICS, V26, P169, DOI 10.1007/BF02016799
   Wasserman S., 1994, ADV SOCIAL NETWORK A
   Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918
   Watts Duncan J., 2003, 6 DEGREES SCI CONNEC
   Yoon B, 2005, TECHNOL FORECAST SOC, V72, P145, DOI 10.1016/j.techfore.2004.08.011
   Yoon B. C., 2004, 2004 IEEE International Engineering Management Conference (IEEE Cat. No.04CH37574), P566
NR 45
TC 6
Z9 6
U1 1
U2 53
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-2882
EI 1532-2890
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD FEB
PY 2012
VL 63
IS 2
BP 242
EP 255
DI 10.1002/asi.21520
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 917FM
UT WOS:000302157900004
DA 2022-08-23
ER

PT J
AU Kwon, S
   Monnier, LV
   Barbau, R
   Bernstein, WZ
AF Kwon, Soonjo
   Monnier, Laetitia, V
   Barbau, Raphael
   Bernstein, William Z.
TI A New Implementation of ontostep: Flexible Generation of Ontology and
   Knowledge Graphs of express-Driven Data
SO JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
LA English
DT Article
DE OntoSTEP; STEP; express; ontology; knowledge graphs; OWL
ID PRODUCT; IFCOWL; REPRESENTATION; MODEL
AB ontostep is a method to translate the STandard for the Exchange of Product Model Data (STEP) schema and its instances to an ontology and knowledge graphs represented in the web ontology language (owl). ontostep models can be integrated with any owl models to enrich their semantics. However, the current implementation has several limitations, mainly in (1) supporting the latest International Organization for Standardization (ISO) 10303 schemas and (2) generating various representation types depending on the purpose of use. We present an improved implementation of ontostep to overcome these limitations. In this article, we demonstrate that the new implementation can successfully translate STEP schemas and instances in a faster and more flexible way, thus furthering the adoption of the full capabilities of ISO 10303. By encoding STEP entities in owl, we facilitate integration with other standards through knowledge graphs.
C1 [Kwon, Soonjo] Kumoh Natl Inst Technol, Dept Mech Syst Engn, Gumi 39177, Gyeongbuk, South Korea.
   [Monnier, Laetitia, V; Barbau, Raphael; Bernstein, William Z.] NIST, Engn Lab, Gaithersburg, MD 20899 USA.
   [Monnier, Laetitia, V] Univ Bourgogne, F-25000 Besancon, Bourgogne Franc, France.
   [Barbau, Raphael] Engisis LLC, Bethesda, MD 20817 USA.
RP Bernstein, WZ (通讯作者)，NIST, Engn Lab, Gaithersburg, MD 20899 USA.
EM soonjo.kwon@kumoh.ac.kr; laetitia.monnier@nist.gov;
   raphael.barbau@nist.gov; william.bernstein@afresearchlab.com
OI Kwon, Soonjo/0000-0003-1578-9262
CR Barbau R, 2012, COMPUT AIDED DESIGN, V44, P575, DOI 10.1016/j.cad.2012.01.008
   Beetz J, 2009, AI EDAM, V23, P89, DOI 10.1017/S0890060409000122
   Bonduel M., 6 LINK DAT ARCH CONS, V2159, P34
   Foufou S, 2005, Product Lifecycle Management: Emerging Solutions and Challenges for Global Networked Enterprise, P21
   Hanqing Gong, 2021, Procedia CIRP, V97, P514, DOI 10.1016/j.procir.2020.08.008
   Hedberg TD, 2020, J COMPUT INF SCI ENG, V20, DOI 10.1115/1.4044921
   ISO, 10303112004 ISO
   ISO, 1592614 ISOCD TR
   ISO, 10303212016 ISO
   ISO, 1592622003 ISO
   ISO, 10303211998 ISO
   Jiahui Qian, 2021, Procedia CIRP, V97, P502, DOI 10.1016/j.procir.2020.05.266
   Krima S., 2009, NISTIR, P7561, DOI [10.6028/NIST.IR.7561, DOI 10.6028/NIST.IR.7561]
   Kwon S, 2020, ADV ENG INFORM, V46, DOI 10.1016/j.aei.2020.101102
   Liebig T., 2015, INT SEM WEB C POST D
   Motik B., 2009, W3C RECOMMENDATION
   Pauwels P, 2016, EWORK AND EBUSINESS IN ARCHITECTURE, ENGINEERING AND CONSTRUCTION, P11
   Pauwels P, 2017, AUTOMAT CONSTR, V80, P77, DOI 10.1016/j.autcon.2017.03.001
   Pauwels P, 2016, AUTOMAT CONSTR, V63, P100, DOI 10.1016/j.autcon.2015.12.003
   Rachuri S, 2006, J COMPUT INF SCI ENG, V6, P11, DOI 10.1115/1.2164451
   Sarigecili MI, 2014, COMPUT AIDED DESIGN, V47, P72, DOI 10.1016/j.cad.2013.09.002
   Singh Gunjan, 2020, The Semantic Web - ISWC 2020. 19th International Semantic Web Conference. Lecture Notes in Computer Science (LNCS 12507), P81, DOI 10.1007/978-3-030-62466-8_6
   Terkaj W, 2015, AUTOMAT CONSTR, V57, P188, DOI 10.1016/j.autcon.2015.04.010
   Torma S., 2015, P 32 INT CIB W78 C E
   Zhao W, 2008, COMPUT IND, V59, P580, DOI 10.1016/j.compind.2008.02.002
NR 25
TC 0
Z9 0
U1 3
U2 3
PU ASME
PI NEW YORK
PA TWO PARK AVE, NEW YORK, NY 10016-5990 USA
SN 1530-9827
EI 1944-7078
J9 J COMPUT INF SCI ENG
JI J. Comput. Inf. Sci. Eng.
PD APR 1
PY 2022
VL 22
IS 2
AR 024502
DI 10.1115/1.4053079
PG 6
WC Computer Science, Interdisciplinary Applications; Engineering,
   Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA ZL6GY
UT WOS:000763774700003
DA 2022-08-23
ER

PT J
AU Gomez-Romero, J
   Molina-Solana, M
   Oehmichen, A
   Guo, YK
AF Gomez-Romero, Juan
   Molina-Solana, Miguel
   Oehmichen, Axel
   Guo, Yike
TI Visualizing large knowledge graphs: A performance analysis
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Graphs; Visualization; Big data; Linked data; Performance analysis
ID LARGE-SCALE; LINKED DATA; EXPLORATION; ONTOLOGIES
AB Knowledge graphs are an increasingly important source of data and context information in Data Science. A first step in data analysis is data exploration, in which visualization plays a key role. Currently, Semantic Web technologies are prevalent for modeling and querying knowledge graphs; however, most visualization approaches in this area tend to be overly simplified and targeted to small-sized representations. In this work, we describe and evaluate the performance of a Big Data architecture applied to large-scale knowledge graph visualization. To do so, we have implemented a graph processing pipeline in the Apache Spark framework and carried out several experiments with real-world and synthetic graphs. We show that distributed implementations of the graph building, metric calculation and layout stages can efficiently manage very large graphs, even without applying partitioning or incremental processing strategies. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Gomez-Romero, Juan] Univ Granada, Dept Comp Sci & Artificial Intelligence, C Periodista Daniel Saucedo Aranda S-N, E-18071 Granada, Spain.
   [Gomez-Romero, Juan; Molina-Solana, Miguel; Oehmichen, Axel; Guo, Yike] Imperial Coll London, Data Sci Inst, London, England.
RP Gomez-Romero, J (通讯作者)，Univ Granada, Dept Comp Sci & Artificial Intelligence, C Periodista Daniel Saucedo Aranda S-N, E-18071 Granada, Spain.
EM jgomez@decsai.ugr.es; m.molina-solana@imperial.ac.uk;
   axelfrancois.oehmichen11@imperial.ac.uk; y.guo@imperial.ac.uk
RI Molina-Solana, Miguel/F-4111-2012; Gomez Romero, Juan/F-7550-2011
OI Molina-Solana, Miguel/0000-0001-5688-2039; Oehmichen,
   Axel/0000-0003-0224-5709; Gomez Romero, Juan/0000-0003-0439-3692
FU Spanish Ministry of Economy and Business [TIN2015-64776-C3-1-R];
   University of Granada; Spanish Ministry of Education, Culture and Sport
   under the Jose Castillejo Research Stays Programme; European Union
   [743623]; Data Science Institute at Imperial College London, United
   Kingdom
FX This work was supported by the Spanish Ministry of Economy and Business
   under the TIN2015-64776-C3-1-R grant. Juan Gomez-Romero is supported by
   University of Granada under the Young Researchers Fellowship, and the
   Spanish Ministry of Education, Culture and Sport under the Jose
   Castillejo Research Stays Programme. Miguel Molina-Solana is supported
   by the European Union's Horizon 2020 research and innovation programme,
   United Kingdom under the Marie Sklodowska-Curie grant agreement No
   743623. The authors gratefully acknowledge technical and financial
   support from the Data Science Institute at Imperial College London,
   United Kingdom which provided the computational infrastructure used in
   this research work, and from the members of the Visualization group, who
   helped to polish the charts.
CR Albert R, 2002, REV MOD PHYS, V74, P47, DOI 10.1103/RevModPhys.74.47
   Arleo Alessio, 2018, Graph Drawing and Network Visualization. 25th International Symposium, GD 2017. Revised Selected Papers: LNCS 10692, P256, DOI 10.1007/978-3-319-73915-1_21
   Arleo A, 2017, INFORM SCIENCES, V381, P124, DOI 10.1016/j.ins.2016.11.012
   Atemezing G. A., 2014, P C CONS LINK DAT CO, V1264, P1
   Baader F., 2010, DESCRIPTION LOGIC HD, DOI 10.1017/CBO9780511711787
   Bach B, 2013, INT J SEMANT WEB INF, V9, P17, DOI 10.4018/ijswis.2013100102
   Banerjee DS, 2015, J PARALLEL DISTR COM, V76, P81, DOI 10.1016/j.jpdc.2014.11.006
   Bastian M., 2009, P 3 INT AAAI C WEB S, V3, P361
   Bikakis N., 2016, P EDBT ICDT 2016 JT
   Bikakis N, 2016, PROC INT CONF DATA, P1342, DOI 10.1109/ICDE.2016.7498340
   Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008
   Brunetti Josep Maria, 2013, P INT C INF INT WEB
   Callahan Alison, 2013, J Biomed Semantics, V4 Suppl 1, pS1, DOI 10.1186/2041-1480-4-S1-S1
   CRUZNEIRA C, 1992, COMMUN ACM, V35, P64, DOI 10.1145/129888.129892
   Dadzie AS, 2011, SEMANT WEB, V2, P89, DOI 10.3233/SW-2011-0037
   Dave A, 2016, FOURTH INTERNATIONAL WORKSHOP ON GRAPH DATA MANAGEMENT EXPERIENCES AND SYSTEMS (GRADES2016), DOI 10.1145/2960414.2960416
   Doerr KU, 2011, IEEE T VIS COMPUT GR, V17, P320, DOI 10.1109/TVCG.2010.59
   Dudas M, 2014, LECT NOTES ARTIF INT, V8876, P137, DOI 10.1007/978-3-319-13704-9_11
   Erdos P., 1959, PUBL MATH-DEBRECEN, V6, P290, DOI DOI 10.2307/1999405
   Falconer S., 2016, ONTOGRAF
   Fayyad U. M., 2002, INFORM VISUALIZATION
   Febretti A, 2013, PROC SPIE, V8649, DOI 10.1117/12.2005484
   Fu B, 2017, SEMANT WEB, V8, P23, DOI 10.3233/SW-140163
   Ghorbel F, 2017, PROCEDIA COMPUT SCI, V112, P854, DOI 10.1016/j.procs.2017.08.079
   Gomez-Romero J., 2017, THE GRAPHDL ONTOLOGY
   Gonzalez JE, 2014, P 11 USENIX C OP SYS, V14, P599
   Haag F, 2015, LECT NOTES ARTIF INT, V8982, P83, DOI 10.1007/978-3-319-17966-7_9
   Hitzler P., 2012, OWL 2 WEB ONTOLOGY L
   HOBBIT, 2016, PROJ BENCHM 4 VIS SE
   Holzschuher F, 2016, J COMPUT SYST SCI, V82, P45, DOI 10.1016/j.jcss.2015.06.006
   Horridge M., 2010, OWLVIZ
   Hussain A, 2014, APPL MATH INFORM SCI, V8, P355
   Jacomy M, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0098679
   Janowicz K, 2015, AI MAG, V36, P5, DOI 10.1609/aimag.v36i1.2560
   Kajdanowicz T, 2014, FUTURE GENER COMP SY, V32, P324, DOI 10.1016/j.future.2013.08.007
   Karau H., 2015, LEARNING SPARK
   Keim D, 2001, COMMUN ACM, V44, P38, DOI 10.1145/381641.381656
   Khan SA, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0179578
   Klimek J, 2013, LECT NOTES COMPUT SC, V7955, P147, DOI 10.1007/978-3-642-41242-4_14
   Kobourov S. G., 2016, HDB GRAPH DRAWING VI, P383
   Krivov S, 2007, J WEB SEMANT, V5, P54, DOI 10.1016/j.websem.2007.03.005
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Leigh J, 2013, P IEEE, V101, P115, DOI 10.1109/JPROC.2012.2191609
   Leskovec J, 2016, ACM T INTEL SYST TEC, V8, DOI 10.1145/2898361
   Liepins R., 2014, P 2014 INT C DEV, V1268, P37
   Lohmann S, 2016, SEMANT WEB, V7, P399, DOI 10.3233/SW-150200
   Malak M. S., 2016, E SPARK GRAPHX ACTIO
   Martin M, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P219, DOI 10.1145/2740908.2742848
   McCormick B. H., 1988, ACM BIOINFORMATICS C, V10, P15, DOI [10.1145/43965.43966, DOI 10.1145/43965.43966]
   McGinn D, 2016, BIG DATA-US, V4, P109, DOI 10.1089/big.2015.0056
   Molina-Solana M, 2017, APPL SOFT COMPUT, V53, P227, DOI 10.1016/j.asoc.2016.12.044
   Musen Mark A, 2015, AI Matters, V1, P4
   Newman D, 2010, J WEB SEMANT, V8, P169, DOI 10.1016/j.websem.2010.03.005
   Nikolaou C, 2015, J WEB SEMANT, V35, P35, DOI 10.1016/j.websem.2015.09.004
   O'Madadhain J., 2016, JUNG JAVA UNIVERSAL
   Page L., 1999, PAGERANK CITATION RA
   Pienta R, 2015, INT CONF BIG DATA, P271, DOI 10.1109/35021BIGCOMP.2015.7072812
   Ristoski P, 2016, J WEB SEMANT, V36, P1, DOI 10.1016/j.websem.2016.01.001
   Bolivar MPR, 2015, PUB ADMIN INF TECH, V8, P1, DOI 10.1007/978-3-319-03167-5_1
   Sana S., 2016, P 2 INT WORKSH VIS I, P17
   Schreiber G., 2014, RDF 1 1 PRIMER W3C W
   Shneiderman B, 1996, IEEE SYMPOSIUM ON VISUAL LANGUAGES, PROCEEDINGS, P336, DOI 10.1109/VL.1996.545307
   Shvachko Konstantin, 2010, SYMPOSIUM, P1, DOI DOI 10.1109/MSST.2010.5496972
   Sintek M., 2007, ONTOVIZ
   Smoot ME, 2011, BIOINFORMATICS, V27, P431, DOI 10.1093/bioinformatics/btq675
   Sowa JF, 2008, FOUND ARTIF INTELL, P213, DOI 10.1016/S1574-6526(07)03005-2
   STOREY MA, 2002, P 7 INT C INT US INT, P239, DOI DOI 10.1145/502716.502778
   TopQuadrant, 2018, TOPBRAID PLATF OV
   Udrescu L, 2016, SCI REP-UK, V6, DOI 10.1038/srep32745
   Vavilapalli V. K., 2013, P 4 ANN S CLOUD COMP, P1
   von Landesberger T, 2011, COMPUT GRAPH FORUM, V30, P1719, DOI 10.1111/j.1467-8659.2011.01898.x
   Wachsmann L., 2008, OWLPROPVIZ
   Wallace G, 2005, IEEE COMPUT GRAPH, V25, P24, DOI 10.1109/MCG.2005.89
   Wang L., 2012, GRAPHICS LIB SYSTEM
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Xin R. S., 2013, 1 INT WORKSH GRAPH D, P1, DOI DOI 10.1145/2484425.2484427
   Zaharia M., 2010, HOTCLOUD, V10, P95
NR 77
TC 10
Z9 12
U1 4
U2 62
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 DEC
PY 2018
VL 89
BP 224
EP 238
DI 10.1016/j.future.2018.06.015
PG 15
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GT2WH
UT WOS:000444360500020
OA hybrid, Green Submitted
DA 2022-08-23
ER

PT J
AU Huang, YF
   Zhao, F
   Gui, XY
   Jin, H
AF Huang, Yafan
   Zhao, Feng
   Gui, Xiangyu
   Jin, Hai
TI Path-enhanced explainable recommendation with knowledge graphs
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Recommender system; Knowledge graph; Metapath; Recurrent neural network
AB Recommender systems, which are used to predict user requirements precisely, play a vital role in the modern internet industry. As an effective tool with rich semantics, knowledge graphs have recently attracted growing research attention in enhancing recommendation results. By mining multihop relations (i.e., paths) between user-item interactions within a knowledge graph, implicit user preferences and other side information can be clearly revealed. Nevertheless, existing knowledge graph-based recommendation methods have two fundamental limitations. First, the indiscriminate utilization of user-item path sets conveys unclear information and negatively influences explainability. Moreover, obtaining reliable recommendation results with these methods requires large amounts of prior knowledge, which indicates that they show poor performance in terms of accuracy and handling cold-start issues. To address these issues, we propose a novel model called the Path-enhanced Recurrent Network (PeRN). Specifically, PeRN integrates a recurrent neural network encoder with a metapath-based entropy encoder to increase explainability and accuracy and reduce cold-start costs. The recurrent network encoder has a strong ability to represent sequential path semantics in a knowledge graph, while the entropy encoder, as an efficient statistical analysis tool, leverages metapath information to differentiate paths in a single user-item interaction. A path extraction algorithm with a bidirectional scheme is also proposed to make PeRN more feasible. The experimental results on two real-world datasets demonstrate our significant improvements with reasonable explanations, promising accuracy and a minimal amount of prior knowledge compared with several state-of-the-art baselines.
C1 [Huang, Yafan; Zhao, Feng; Gui, Xiangyu; Jin, Hai] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Natl Engn Res Ctr Big Data Technol & Syst, Serv Comp Technol & Syst Lab,Cluster & Grid Comp, Wuhan, Peoples R China.
RP Zhao, F (通讯作者)，Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Natl Engn Res Ctr Big Data Technol & Syst, Serv Comp Technol & Syst Lab,Cluster & Grid Comp, Wuhan, Peoples R China.
EM zhaof@hust.edu.cn
OI Huang, Yafan/0000-0001-7370-6766
FU National Key R&D Program of China [2018YFB1404302]; National Natural
   Science Foundation of China [62072203]
FX This work was supported in part by National Key R&D Program of China
   under Grants no. 2018YFB1404302, National Natural Science Foundation of
   China under Grants No.62072203.
CR 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, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Chen X, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P108, DOI 10.1145/3159652.3159668
   Fan SH, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2478, DOI 10.1145/3292500.3330673
   Haidong Chen, 2016, 2016 IEEE International Workshop on Electromagnetics (iWEM): Applications and Student Innovation Competition, P1, DOI 10.1109/iWEM.2016.7504980
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hu WP, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5010
   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
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Meng Y., 2017, ARXIV PREPRINT ARXIV
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Roy PC, 2019, PROC CVPR IEEE, P2581, DOI 10.1109/CVPR.2019.00269
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Wan WT, 2019, IEEE I CONF COMP VIS, P3404, DOI 10.1109/ICCV.2019.00350
   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, 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 X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao J., 2017, P 26 INT JOINT C ART
   Xu FL, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P529, DOI 10.1145/3357384.3357924
   Xu YA, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4620
   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
   Zheng Y, 2020, PROC INT CONF DATA, P133, DOI 10.1109/ICDE48307.2020.00019
NR 34
TC 3
Z9 3
U1 6
U2 18
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 1769
EP 1789
DI 10.1007/s11280-021-00912-4
EA JUL 2021
PG 21
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WJ2YV
UT WOS:000669197600001
DA 2022-08-23
ER

PT J
AU Deng, LZ
   Chen, LM
   Yang, T
   Liu, M
   Li, SC
   Jiang, TJ
AF Deng, Lizong
   Chen, Luming
   Yang, Tao
   Liu, Mi
   Li, Shicheng
   Jiang, Taijiao
TI Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious
   Diseases With a Fine-Grained Semantic Information Model: Development and
   Usability Study
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE knowledge graph; knowledge granularity; machine learning; high-fidelity
   phenotyping; phenotyping; phenotype; semantic
ID CLASSIFICATION; METAMAP; SYSTEM
AB Background: Phenotypes characterize the clinical manifestations of diseases and provide important information for diagnosis. Therefore, the construction of phenotype knowledge graphs for diseases is valuable to the development of artificial intelligence in medicine. However, phenotype knowledge graphs in current knowledge bases such as WikiData and DBpedia are coarse-grained knowledge graphs because they only consider the core concepts of phenotypes while neglecting the details (attributes) associated with these phenotypes.
   Objective: To characterize the details of disease phenotypes for clinical guidelines, we proposed a fine-grained semantic information model named PhenoSSU (semantic structured unit of phenotypes).
   Methods: PhenoSSU is an "entity-attribute-value" model by its very nature, and it aims to capture the full semantic information underlying phenotype descriptions with a series of attributes and values. A total of 193 clinical guidelines for infectious diseases from Wikipedia were selected as the study corpus, and 12 attributes from SNOMED-CT were introduced into the PhenoSSU model based on the co-occurrences of phenotype concepts and attribute values. The expressive power of the PhenoSSU model was evaluated by analyzing whether PhenoSSU instances could capture the full semantics underlying the descriptions of the corresponding phenotypes. To automatically construct fine-grained phenotype knowledge graphs, a hybrid strategy that first recognized phenotype concepts with the MetaMap tool and then predicted the attribute values of phenotypes with machine learning classifiers was developed.
   Results: Fine-grained phenotype knowledge graphs of 193 infectious diseases were manually constructed with the BRAT annotation tool. A total of 4020 PhenoSSU instances were annotated in these knowledge graphs, and 3757 of them (89.5%) were found to be able to capture the full semantics underlying the descriptions of the corresponding phenotypes listed in clinical guidelines. By comparison, other information models, such as the clinical element model and the HL7 fast health care interoperability resource model, could only capture the full semantics underlying 48.4% (2034/4020) and 21.8% (914/4020) of the descriptions of phenotypes listed in clinical guidelines, respectively. The hybrid strategy achieved an F1-score of 0.732 for the subtask of phenotype concept recognition and an average weighted accuracy of 0.776 for the subtask of attribute value prediction.
   Conclusions: PhenoSSU is an effective information model for the precise representation of phenotype knowledge for clinical guidelines, and machine learning can be used to improve the efficiency of constructing PhenoSSU-based knowledge graphs. Our work will potentially shift the focus of medical knowledge engineering from a coarse-grained level to a more fine-grained level.
C1 [Deng, Lizong; Chen, Luming; Yang, Tao; Li, Shicheng; Jiang, Taijiao] Suzhou Inst Syst Med, Chongwen Rd 100,Suzhou Ind Pk, Suzhou 215000, Peoples R China.
   [Liu, Mi] Soochow Univ, Jiangsu Inst Clin Immunol, Jiangsu Key Lab Clin Immunol, Affiliated Hosp 1, Suzhou, Peoples R China.
   [Jiang, Taijiao] Chinese Acad Med Sci, Ctr Syst Med, Inst Basic Med Sci, Beijing, Peoples R China.
   [Jiang, Taijiao] Peking Union Med Coll, Beijing, Peoples R China.
   [Jiang, Taijiao] Guangzhou Lab, Guangzhou, Peoples R China.
RP Jiang, TJ (通讯作者)，Suzhou Inst Syst Med, Chongwen Rd 100,Suzhou Ind Pk, Suzhou 215000, Peoples R China.
EM taijiao@ibms.pumc.edu.cn
OI yang, tao/0000-0002-7521-4295; Chen, Luming/0000-0003-2468-8631; Deng,
   Lizong/0000-0001-9314-262X
FU National Natural Science Foundation of China [32070678, 31671371];
   Emergency Key Program of Guangzhou Laboratory [EKPG21-12]; Chinese
   Academy of Medical Sciences Initiative for Innovative Medicine
   [2016-I2M-1-005, 2020-I2M-2-003]
FX This work was supported by grants 32070678 and 31671371 from the
   National Natural Science Foundation of China, and grant EKPG21-12 from
   Emergency Key Program of Guangzhou Laboratory,and grants 2016-I2M-1-005
   and 2020-I2M-2-003 from the Chinese Academy of Medical Sciences
   Initiative for Innovative Medicine. We sincerely thank colleagues in our
   lab and experts in the biomedical field for their thoughtful suggestions
   to improve this work.
CR [Anonymous], 2011, J MACH LEARN RES
   [Anonymous], Database of PhenoSSU
   [Anonymous], CLIN QUALITY LANGUAG
   [Anonymous], Wikipedia list of infectious diseases
   [Anonymous], Tuning the Hyper-Parameters of an Estimator-Scikit-Learn 0.24.1 Documentation
   Aronson AR, 2010, J AM MED INFORM ASSN, V17, P229, DOI 10.1136/jamia.2009.002733
   Bender D, 2013, COMP MED SY, P326, DOI 10.1109/CBMS.2013.6627810
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Brookes AJ, 2015, NAT REV GENET, V16, P702, DOI 10.1038/nrg3932
   Chawla NV, 2002, J ARTIF INTELL RES, V16, P321, DOI 10.1613/jair.953
   Chen YK, 2013, J AM MED INFORM ASSN, V20, pE253, DOI 10.1136/amiajnl-2013-001945
   Cirkovic A, 2020, J MED INTERNET RES, V22, DOI 10.2196/18097
   Delude CM, 2015, NATURE, V527, pS14, DOI 10.1038/527S14a
   Demner-Fushman D, 2017, J AM MED INFORM ASSN, V24, P841, DOI 10.1093/jamia/ocw177
   Deng L, NTR
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dhombres F, 2015, STUD HEALTH TECHNOL, V216, P795, DOI 10.3233/978-1-61499-564-7-795
   Donnelly K, 2006, STUD HEALTH TECHNOL, V121, P279
   Elhadad N, 2015, P 9 INT WORKSH SEM E, P303, DOI DOI 10.18653/V1/S15-2051
   Gaudet-Blavignac C, 2021, J MED INTERNET RES, V23, DOI 10.2196/24594
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Gulli A., 2017, DEEP LEARNING KERAS
   Haendel MA, 2018, NEW ENGL J MED, V379, P1452, DOI 10.1056/NEJMra1615014
   Harkema H, 2009, J BIOMED INFORM, V42, P839, DOI 10.1016/j.jbi.2009.05.002
   Hripcsak G, 2018, J AM MED INFORM ASSN, V25, P289, DOI 10.1093/jamia/ocx110
   Kocaman V, IMPROVING CLIN DOCUM
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lemaitre G, 2017, J MACH LEARN RES, V18
   Li R, 2020, J MED INTERNET RES, V22, DOI 10.2196/20645
   Liu H, J MED INTERNET RES, V23
   Luo Y, 2018, J AM MED INFORM ASSN, V25, P93, DOI 10.1093/jamia/ocx090
   McHugh ML, 2012, BIOCHEM MEDICA, V22, P276, DOI 10.11613/bm.2012.031
   Munsch N, 2020, J MED INTERNET RES, V22, DOI 10.2196/21299
   Oellrich A, 2016, BRIEF BIOINFORM, V17, P819, DOI 10.1093/bib/bbv083
   Oniki TA, 2016, J AM MED INFORM ASSN, V23, P248, DOI 10.1093/jamia/ocv134
   Rajagopalan MS, 2011, J ONCOL PRACT, V7, P319, DOI 10.1200/JOP.2010.000209
   Rappaport N, 2017, NUCLEIC ACIDS RES, V45, pD877, DOI 10.1093/nar/gkw1012
   Reavley NJ, 2012, PSYCHOL MED, V42, P1753, DOI 10.1017/S003329171100287X
   Robinson PN, 2012, HUM MUTAT, V33, P777, DOI 10.1002/humu.22080
   Singh V., REPLACE RETRIEVE KEY
   Stenetorp P., 2012, P DEM SESS EACL, P102
   Turki H, 2019, J BIOMED INFORM, V99, DOI 10.1016/j.jbi.2019.103292
   Uzuner O, 2011, J AM MED INFORM ASSN, V18, P552, DOI 10.1136/amiajnl-2011-000203
   Wang J, 2020, J MED INTERNET RES, V22, DOI 10.2196/16816
   Wang YS, 2018, J BIOMED INFORM, V77, P34, DOI 10.1016/j.jbi.2017.11.011
   Wu S, 2020, J AM MED INFORM ASSN, V27, P457, DOI 10.1093/jamia/ocz200
   Xiao H, NTR
   Zhang ZC, 2019, MATH BIOSCI ENG, V16, P1966, DOI 10.3934/mbe.2019096
   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 2
Z9 2
U1 11
U2 18
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 JUN 15
PY 2021
VL 23
IS 6
AR e26892
DI 10.2196/26892
PG 11
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA SU6IV
UT WOS:000663239500002
PM 34128811
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zheng, YY
   Shi, C
   Cao, XH
   Li, XL
   Wu, B
AF Zheng, Yuyan
   Shi, Chuan
   Cao, Xiaohuan
   Li, Xiaoli
   Wu, Bin
TI A Meta Path Based Method for Entity Set Expansion in Knowledge Graph
SO IEEE TRANSACTIONS ON BIG DATA
LA English
DT Article
DE Semantics; Task analysis; Resource description framework; Encyclopedias;
   Electronic publishing; Heterogeneous information network; entity set
   expansion; knowledge graph; meta path; PU learning
AB Entity Set Expansion (ESE) is the problem that expands a small set of seed entities into a more complete set, entities of which have common traits. As a popular data mining task, ESE has been widely used in many applications, such as dictionary construction, query suggestion and new brand identification. Existing ESE methods mainly utilize text and Web information. That is, the intrinsic relation among entities is inferred from their occurrences in text or Web. With the surge of knowledge graph in recent years, it is possible to extend entities according to their occurrences in knowledge graph. In this paper, we consider the knowledge graph as a heterogeneous information network (HIN) that contains different types of objects and links, and propose a novel method, called MP_ESE, to extend entities in the HIN. The MP_ESE employs meta paths, a relation sequence connecting entities, in HIN to capture the implicit common traits of seed entities. In addition, an automatic meta path generation method, called SMPG, has been designed to exploit the potential relations among entities. Heuristic learning and PU learning methods are employed to learn the weights of extracted meta paths. With these generated and weighted meta paths, the MP_ESE can effectively extend entities. Comprehensive experiments on real datasets show the effectiveness and efficiency of MP_ESE.
C1 [Zheng, Yuyan; Shi, Chuan; Cao, Xiaohuan; Wu, Bin] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China.
   [Li, Xiaoli] ASTAR, Inst Infocomm Res, Singapore 138632, Singapore.
RP Shi, C (通讯作者)，Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China.
EM zyy0716_source@163.com; devil_baba@126.com; shichuan@bupt.edu.cn;
   xlli@i2r.a-star.edu.sg; wubin@bupt.edu.cn
OI Li, Xiaoli/0000-0002-0762-6562
FU National Natural Science Foundation of China [61772082, 61375058,
   61320106006]; National Key Research and Development Program of China
   [2017YFB0803304]; National High-tech RD Program [2015AA050203]
FX This work is supported in part by the National Natural Science
   Foundation of China (No. 61772082, 61375058, 61320106006), the National
   Key Research and Development Program of China (2017YFB0803304), and the
   National High-tech R&D Program (863 Program 2015AA050203).
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bing L., 2013, P 6 ACM INT C WEB SE, P567
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Cafarella M.J., 2005, P C HUM LANG TECHN E, P563, DOI DOI 10.3115/1220575.1220646
   Cao BK, 2014, IEEE DATA MINING, P50, DOI 10.1109/ICDM.2014.25
   Cao H., 2008, P 14 ACM SIGKDD INT, P875, DOI DOI 10.1145/1401890.1401995
   Chen J, 2016, PROC INT CONF DATA, P1350, DOI 10.1109/ICDE.2016.7498342
   Cohen W. W., 2004, P 10 ACM SIGKDD INT, P89, DOI DOI 10.1145/1014052.1014065
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Elkan C., 2008, P 14 ACM SIGKDD INT, V14, P213, DOI DOI 10.1145/1401890.1401920
   Fetahu B, 2015, LECT NOTES COMPUT SC, V9366, P474, DOI 10.1007/978-3-319-25007-6_28
   He Y., 2011, P 20 INT C WORLD WID, P427
   Hu J., 2009, P 18 INT C WORLD WID, P471, DOI DOI 10.1145/1526709.1526773
   Jamali M., 2013, P 22 INT C WORLD WID, P643, DOI DOI 10.1145/2488388.2488445
   Jindal P., 2011, Proceedings of the 2011 IEEE 11th International Conference on Data Mining (ICDM 2011), P1110, DOI 10.1109/ICDM.2011.86
   Kahng M, 2012, SIGIR 2012: PROCEEDINGS OF THE 35TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1027, DOI 10.1145/2348283.2348452
   Katz J. J., 1985, PHILOS LINGUISTICS
   Kong XN, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P614
   Krishnan A, 2016, LECT NOTES COMPUT SC, V10041, P157, DOI 10.1007/978-3-319-48740-3_11
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   Li X.-L., 2010, P ACL C SHORT PAP, P359
   Li XL, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2802
   Lin D, 2002, P 8 ACM SIGKDD INT C, P613, DOI DOI 10.1145/775047.775138
   Metzger S, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1829, DOI 10.1145/2505515.2507873
   Metzger S, 2014, 2014 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, P60, DOI 10.1109/WI-IAT.2014.17
   Ng M. K.-P., 2011, P 17 ACM SIGKDD INT, P1217
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Pasca M., 2007, P 16 ACM C C INF KNO, P683, DOI DOI 10.1145/1321440.1321536
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Qi G.-J., 2012, P 5 ACM INT C WEB SE, P553
   [齐振宇 Qi Zhenyu], 2013, [中文信息学报, Journal of Chinese Information Processing], V27, P1
   Qi ZY, 2012, COMM COM INF SC, V321, P655
   Sadamitsu K., 2011, P 49 ANN M ASS COMP, P726
   Sarmento L, 2007, P 16 ACM C INF KNOWL, P959
   Shen JM, 2017, LECT NOTES ARTIF INT, V10534, P288, DOI 10.1007/978-3-319-71249-9_18
   Shi B., 2014, P INT C COMP LING, P2280
   Shi C, 2017, IEEE T KNOWL DATA EN, V29, P17, DOI 10.1109/TKDE.2016.2598561
   Shi C, 2014, IEEE T KNOWL DATA EN, V26, P2479, DOI 10.1109/TKDE.2013.2297920
   Shi Y, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P425, DOI 10.1145/3097983.3097990
   Singhal Amit, 2012, OFFICIAL GOOGLE BLOG
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Y, 2012, PROC VLDB ENDOW, V5, P394, DOI 10.14778/2140436.2140437
   Sun YZ, 2013, ACM T KNOWL DISCOV D, V7, DOI 10.1145/2500492
   Sun YZ, 2011, 2011 INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2011), P121, DOI 10.1109/ASONAM.2011.112
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Wang RC, 2007, IEEE DATA MINING, P342, DOI 10.1109/ICDM.2007.104
   Wang RC, 2008, IEEE DATA MINING, P1091, DOI 10.1109/ICDM.2008.145
   Yahya Mohamed, 2012, EMNLP
   Yu X., 2012, CIKM, P2025, DOI DOI 10.1145/2396761.2398565
   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., 2011, P ACM HYP HYP
   Zhang ZZ, 2016, AAAI CONF ARTIF INTE, P3101
   Zheng YY, 2017, LECT NOTES ARTIF INT, V10234, P317, DOI 10.1007/978-3-319-57454-7_25
   Zhou D, 2007, IEEE DATA MINING, P739, DOI 10.1109/ICDM.2007.57
   Zhou Y, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P1276, DOI 10.1145/2623330.2623737
   Zou L, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P313, DOI 10.1145/2588555.2610525
NR 57
TC 1
Z9 1
U1 1
U2 1
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 JUN 1
PY 2022
VL 8
IS 3
BP 616
EP 629
DI 10.1109/TBDATA.2018.2805366
PG 14
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1F3YX
UT WOS:000795107500004
DA 2022-08-23
ER

PT J
AU Koloski, B
   Perdih, TS
   Robnik-Sikonja, M
   Pollak, S
   Skrlj, B
AF Koloski, Boshko
   Perdih, Timen Stepisnik
   Robnik-Sikonja, Marko
   Pollak, Senja
   Skrlj, Blaz
TI Knowledge graph informed fake news classification via heterogeneous
   representation ensembles
SO NEUROCOMPUTING
LA English
DT Article
DE Fake news detection; Knowledge graphs; Text representation;
   Representation learning; Neuro-symbolic learning
AB Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance and robustness. An emerging problem in the modern era is fake news detection-many easily available pieces of information are not necessarily factually correct, and can lead to wrong conclusions or are used for manipulation. In this work we explore how different document representations, ranging from simple symbolic bag-of-words, to contextual, neural language model-based ones can be used for efficient fake news identification. One of the key contributions is a set of novel document representation learning methods based solely on knowledge graphs, i.e., extensive collections of (grounded) subject-predicate-object triplets. We demonstrate that knowledge graph-based representations already achieve competitive performance to conventionally accepted representation learners. Furthermore, when combined with existing, contextual representations, knowledge graph-based document representations can achieve state-of-the-art performance. To our knowledge this is the first larger-scale evaluation of how knowledge graph-based representations can be systematically incorporated into the process of fake news classification. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C1 [Koloski, Boshko; Skrlj, Blaz] Jozef Stefan Int Postgrad Sch, Ljubljana, Slovenia.
   [Koloski, Boshko; Perdih, Timen Stepisnik; Pollak, Senja; Skrlj, Blaz] Jozef Stefan Inst, Ljubljana 1000, Slovenia.
   [Robnik-Sikonja, Marko] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana 1000, Slovenia.
RP Skrlj, B (通讯作者)，Jozef Stefan Int Postgrad Sch, Ljubljana, Slovenia.; Skrlj, B (通讯作者)，Jozef Stefan Inst, Ljubljana 1000, Slovenia.
CR Alhindi T., 2018, P 1 WORKSH FACT EXTR, P85
   Allcott H, 2017, J ECON PERSPECT, V31, P211, DOI 10.1257/jep.31.2.211
   Amirkhani F.S.A.J.B.H., 2020, Fnid: Fake news inference dataset, DOI 10.21227/fbzd-sw81
   Bidgoly A, FAKE NEWS DETECTION
   Bordes A., 2013, ADV NEURAL INFORM PR, V26
   Buda J, 2020, CLEF
   Gilda S, 2017, IEEE ST CONF RES DEV, P110, DOI 10.1109/SCORED.2017.8305411
   Glazkova A., ARXIV PREPRINT ARXIV
   Halko N, 2011, SIAM REV, V53, P217, DOI 10.1137/090771806
   Kadam AB, 2020, J TRAVEL MED, V27, DOI 10.1093/jtm/taaa057
   Kai Shu, 2017, ACM SIGKDD Explorations Newsletter, V19, P22, DOI 10.1145/3137597.3137600
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kingma D. P., 2014, P 3 INT C LEARN REPR, P1, DOI DOI 10.1038/S41598-021-83424-5
   Koloski B, KNOWLEDGE GRAPH BASE
   Koloski B, 2020, CLEF
   Koloski B., ARXIV PREPRINT ARXIV
   Kraskov A, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.066138
   Lampiris G, 2020, ANN OPER RES, V294, P225, DOI 10.1007/s10479-019-03337-5
   Liu Y., CORR ABS190711692 AR
   Loper E., 2002, WORKSH EFF TOOLS MEH, V1, P63, DOI 10.3115/1118108.1118117
   Lu YJ, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P505
   Martinc M, 2018, CEUR WORKSHOP PROC, V2125
   McInnes L., ARXIV PREPRINT ARXIV
   Moller M, COVID TWITTER BERT A
   Ostendorff M., 2019, ARXIV190908402V1
   Patwa P., 2021, P 1 WORKSH COMB ONL
   Patwa P., ARXIV PREPRINT ARXIV
   Perdih TS, 2021, 2021 IEEE 19TH WORLD SYMPOSIUM ON APPLIED MACHINE INTELLIGENCE AND INFORMATICS (SAMI 2021), P105, DOI 10.1109/SAMI50585.2021.9378668
   Potthast M, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P231
   Princeton University, WORDNET
   Pulido CM, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17072430
   Rangel F., 2020, WORKING NOTES CLEF 2, P1
   Reimers N., 2019, PROC C EMPIRICAL MET, DOI DOI 10.18653/V1/D19-1410
   Sanh V., CORR ABS191001108 AR
   Schuster T, 2020, COMPUT LINGUIST, V46, P499, DOI 10.1162/coli_a_00380
   Shu K., 2020, ARXIV PREPRINT ARXIV, P1
   Sun Z., 2019, 7 INT C LEARNING REP
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Umer M, 2020, IEEE ACCESS, V8, P156695, DOI 10.1109/ACCESS.2020.3019735
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang WY, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2, P422, DOI 10.18653/v1/P17-2067
   Wang Z., 2014, KNOWLEDGE GRAPH TEXT, P1591, DOI DOI 10.3115/V1/D14-1167
   Yang B, 2015, ABS14126575 CORR
   Zhang JW, 2020, PROC INT CONF DATA, P1826, DOI 10.1109/ICDE48307.2020.00180
   Zhang S, 2019, ADV NEUR IN, V32
   Zhu ZC, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2494, DOI 10.1145/3308558.3313508
NR 47
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JUL 28
PY 2022
VL 496
BP 208
EP 226
DI 10.1016/j.neucom.2022.01.096
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2B3WG
UT WOS:000810121200005
OA Green Published, Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Cao, S
   Niu, SD
   Xiong, GH
   Qin, XL
   Liu, PF
AF Cao, Sheng
   Niu, Songdeng
   Xiong, Guanghao
   Qin, Xiaolin
   Liu, Pengfei
TI Student Model and Clustering Research on Personalized E-learning
SO JOURNAL OF INTERNET TECHNOLOGY
LA English
DT Article
DE E-learning; Mean shift; K-means; CWWCA; Student model
ID MEAN SHIFT; ENSEMBLE; SELECTION; FUSION
AB As the internet and data mining technologies are developing rapidly, how to provide various students with high-quality education services has become the hotspot in the internet environment. In order to promote the characteristics of online education and enhance the quality of personalized e-learning, in this paper, we propose a novel algorithm named MK-means by exploiting the cluster-wise weighing co-association matrix mechanism and improving the K-means algorithm based on the mean shift theory. The experimental results on the UCI's Iris and Wine test sets demonstrate its effectiveness and superiority, finding that the total Fmeasure of MK-means achieves better performance than the Hierarchical Clustering, FCM, K-means, SOM, and X-means algorithms. Finally, the new algorithm combined with the student model explains the clustering results in detail from perspectives of cognitive model and knowledge map respectively and can extend to support the personalized e-learning in a wide range.
C1 [Cao, Sheng; Niu, Songdeng; Liu, Pengfei] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Sichuan, Peoples R China.
   [Xiong, Guanghao] Univ Elect Sci & Technol China, Sch Aeronaut & Astronaut, Chengdu, Sichuan, Peoples R China.
   [Qin, Xiaolin] Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu, Sichuan, Peoples R China.
RP Cao, S (通讯作者)，Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Sichuan, Peoples R China.
EM caosheng@uestc.edu.cn; 842549301@qq.com; 714461296@qq.com;
   qinxl2001@126.com; liupengfei@uestc.edu.cn
OI /0000-0001-5087-8178
FU New Generation Artificial Intelligence Science and Technology Major
   Project of Sichuan Province [2018GZDZX0041]; Key R&D project of Sichuan
   Province [2020YFQ 0056, 2020YFG0011, 2020YFG0476, 2021YFG0132]
FX This research is supported by the New Generation Artificial Intelligence
   Science and Technology Major Project of Sichuan Province
   (2018GZDZX0041), and the Key R&D project of Sichuan Province (2020YFQ
   0056, 2020YFG0011, 2020YFG0476, 2021YFG0132).
CR Akbari E, 2015, ENG APPL ARTIF INTEL, V39, P146, DOI 10.1016/j.engappai.2014.12.005
   Alajmi Qasim, 2017, 2017 8th International Conference on Information Technology (ICIT). Proceedings, P12, DOI 10.1109/ICITECH.2017.8079909
   Alguliyev RM, 2020, EXPERT SYST APPL, V150, DOI 10.1016/j.eswa.2020.113294
   Alizadeh H, 2014, INTELL DATA ANAL, V18, P389, DOI 10.3233/IDA-140647
   Angeli C, 2017, COMPUT EDUC, V113, P226, DOI 10.1016/j.compedu.2017.05.021
   Chen WY, 2019, IEEE T LEARN TECHNOL, V12, P44, DOI 10.1109/TLT.2018.2793193
   CHENG YZ, 1995, IEEE T PATTERN ANAL, V17, P790, DOI 10.1109/34.400568
   Comaniciu D, 2002, IEEE T PATTERN ANAL, V24, P603, DOI 10.1109/34.1000236
   Cristofor D, 2002, J UNIVERS COMPUT SCI, V8, P153
   Dai YX, 2015, 2015 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC), P1747, DOI 10.1109/I2MTC.2015.7151544
   Elfaki AO, 2019, IEEE WORLD CONGR SER, P110, DOI 10.1109/SERVICES.2019.00038
   Franek L, 2014, PATTERN RECOGN, V47, P833, DOI 10.1016/j.patcog.2013.08.019
   Fred ALN, 2005, IEEE T PATTERN ANAL, V27, P835, DOI 10.1109/TPAMI.2005.113
   FUKUNAGA K, 1975, IEEE T INFORM THEORY, V21, P32, DOI 10.1109/TIT.1975.1055330
   Gagne R., 1985, CONDITIONS LEARNING, V4th
   Huang D, 2016, IEEE T KNOWL DATA EN, V28, P1312, DOI 10.1109/TKDE.2015.2503753
   Huang D, 2016, PATTERN RECOGN, V50, P131, DOI 10.1016/j.patcog.2015.08.015
   Ji YL, 2020, IEEE T IMAGE PROCESS, V29, P2742, DOI 10.1109/TIP.2019.2952088
   Leskovec J, 2014, MINING OF MASSIVE DATASETS, 2ND EDITION, P1
   Lethikim N, 2021, COMMUN STAT-SIMUL C, DOI 10.1080/03610918.2021.1900248
   Li M, 2016, J INTERNET TECHNOL, V17, P179, DOI 10.6138/JIT.2016.17.2.20140710
   Macqueen J., P 5 MATH STAT PROBAB, V5, P281
   Mojarad M, 2019, APPL INTELL, V49, P2567, DOI 10.1007/s10489-018-01397-x
   Nazari A, 2019, PATTERN ANAL APPL, V22, P133, DOI 10.1007/s10044-017-0676-x
   Pakhira MK, 2014, 2014 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS, P1047, DOI 10.1109/CICN.2014.220
   Parvin H, 2015, PATTERN ANAL APPL, V18, P87, DOI 10.1007/s10044-013-0364-4
   Parvin H, 2013, ADV DATA ANAL CLASSI, V7, P181, DOI 10.1007/s11634-013-0130-x
   Poluru RK, 2020, IET COMMUN, V14, P2189, DOI 10.1049/iet-com.2020.0236
   Rashidi F, 2019, ARTIF INTELL REV, V52, P1341, DOI 10.1007/s10462-019-09701-y
   Rodrigues H, 2019, COMPUT EDUC, V136, P87, DOI 10.1016/j.compedu.2019.03.007
   Setiawan F, 2020, INT J AD HOC UBIQ CO, V35, P162, DOI 10.1504/IJAHUC.2020.110824
   Wang RP, 2019, ADV INTELL SYST, V764, P208, DOI 10.1007/978-3-319-91189-2_21
   Wang SC, 2009, J INTERNET TECHNOL, V10, P505
   Wang X, 2009, PATTERN RECOGN, V42, P668, DOI 10.1016/j.patcog.2008.09.013
   Wang XD, 2018, INT SYMP COMP CONS, P453, DOI 10.1109/IS3C.2018.00120
   Weiszfeld E, 2009, ANN OPER RES, V167, P7, DOI 10.1007/s10479-008-0352-z
   Yarinezhad R, 2020, IET COMMUN, V14, P580, DOI 10.1049/iet-com.2019.0510
NR 37
TC 1
Z9 1
U1 8
U2 10
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 2021
VL 22
IS 4
BP 935
EP 947
DI 10.53106/160792642021072204020
PG 13
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA TW2CK
UT WOS:000682214900020
DA 2022-08-23
ER

PT J
AU Tang, HM
   Tang, WZ
   Li, RC
   Wang, YY
   Wang, S
   Wang, LH
AF Tang, Hongmei
   Tang, Wenzhong
   Li, Ruichen
   Wang, Yanyang
   Wang, Shuai
   Wang, Lihong
TI Analysis of Knowledge Graph Path Reasoning Based on Variational
   Reasoning
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph; knowledge graph reasoning; graph neural network
AB Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities. This paper proposes a new GraphDIVA model based on the variational reasoning divergent autoencoder (DIVA) model. The network structures and calculation processes of the models are analyzed. The GraphSAGE algorithm is introduced into the path reasoning module to solve the inability of the original model to perceive the features of the graph structure, which leads to a decline in the accuracy rate. Hence, GraphDIVA can achieve a higher accuracy rate with fewer learning iterations. The experiments show the efficiency and effectiveness of our model and proves that our method has a better effect on the accuracy rate and training difficulty than the baseline model on the FB15k-237 and NELL-995 benchmark datasets.
C1 [Tang, Hongmei; Tang, Wenzhong; Li, Ruichen] Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China.
   [Wang, Yanyang] Beihang Univ, Sch Aeronaut Sci & Engn, Beijing 100191, Peoples R China.
   [Wang, Yanyang] Beihang Univ, Jiangxi Res Inst, Nanchang 330096, Jiangxi, Peoples R China.
   [Wang, Shuai] Beihang Univ, Res Inst Frontier Sci, Beijing 100191, Peoples R China.
   [Wang, Lihong] Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China.
RP Wang, YY (通讯作者)，Beihang Univ, Sch Aeronaut Sci & Engn, Beijing 100191, Peoples R China.; Wang, YY (通讯作者)，Beihang Univ, Jiangxi Res Inst, Nanchang 330096, Jiangxi, Peoples R China.; Wang, LH (通讯作者)，Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China.
EM xjthm@buaa.edu.cn; tangwenzhong@buaa.edu.cn; presisco@buaa.edu.cn;
   wangyanyang@buaa.edu.cn; wangshuai@buaa.edu.cn; wlh@isc.org.cn
FU National Natural Science Foundation of China [61901016]
FX This work is supported by the National Natural Science Foundation of
   China, Grant No. 61901016.
CR Betteridge J., 2009, AAAI SPRING S LEARN, P1
   Caihua Y., 2012, RESOUR SCI, V10, P1959
   Chen W., 2018, ARXIV
   Das R., 2017, ARXIV
   Das R., 2016, ARXIV
   Duvenaud D., 2015, ARXIV
   Geng QC, 2018, SCI CHINA INFORM SCI, V61, DOI 10.1007/s11432-017-9189-6
   Hamilton WL, 2017, ADV NEUR IN, V30
   Hogan A., 2020, ARXIV
   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., 2013, ICLR 2014 INT C LEAR
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lian TongHui, 2013, Tourism Tribune, V28, P114
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu H, 2022, KNOWL-BASED SYST, V241, DOI 10.1016/j.knosys.2022.108235
   Liu SS, 2020, PHYSICA A, V537, DOI 10.1016/j.physa.2019.122717
   Neelakantan A., 2015, AAAI SPRING S 2015, P1
   Peng ZH, 2022, J INTELL INF SYST, V58, P513, DOI 10.1007/s10844-021-00671-8
   Polap D, 2018, FUTURE GENER COMP SY, V87, P16, DOI 10.1016/j.future.2018.04.050
   Tiwari P, 2021, NEURAL NETWORKS, V135, P1, DOI 10.1016/j.neunet.2020.11.012
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499, DOI DOI 10.18653/V1/D15-1174
   Wang HN, 2020, CANCER GENE THER, V27, P448, DOI 10.1038/s41417-019-0117-7
   Wang Q, 2021, NEUROCOMPUTING, V429, P101, DOI 10.1016/j.neucom.2020.12.040
   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
   Xiong W., 2017, ARXIV
   Xu Z, 2016, CONCURR COMP-PRACT E, V28, P4038, DOI 10.1002/cpe.3780
   Zhang Y., 2022, P ACM WEB C, P912
   Zhang YY, 2018, AAAI CONF ARTIF INTE, P6069
   Zhu AJ, 2022, KNOWL-BASED SYST, V248, DOI 10.1016/j.knosys.2022.108843
NR 33
TC 0
Z9 0
U1 5
U2 5
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 JUN
PY 2022
VL 12
IS 12
AR 6168
DI 10.3390/app12126168
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 2N1EN
UT WOS:000818131600001
OA gold
DA 2022-08-23
ER

PT J
AU Wu, Y
   Li, W
   Fan, XM
   Wang, BJ
AF Wu, Yong
   Li, Wei
   Fan, Xiaoming
   Wang, Binjun
TI Entity-Context and Relation-Context Combined Knowledge Graph Embeddings
SO ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
LA English
DT Article
DE Knowledge graphs; Hierarchical structures; Link prediction; Graph
   convolution network
AB Hierarchical structures are very common in knowledge graphs, and semantic hierarchy-preserved knowledge graph embeddings have achieved promising results in the knowledge graph link prediction task. However, handling one-to-many, many-to-one, and many-to-many relations that can provide hierarchical information is challenging and brings entity indistinguishability issues. To address this limitation, this paper proposes a novel knowledge graph embedding model, namely Entity-context and Relation-context combined Knowledge Graph Embeddings (ERKE), in which each relation is defined as a rotation with variable moduli from the source entity to the target entity in the polar coordinate system. It can be seen as a combination of two spaces-modulus space and phase space. In the modulus space, modulus information is used to model semantic hierarchies, and entity-context information is adopted to make node representations more expressive. Besides, based on the design of the propagation rule of Graph Convolution Network (GCN), a new GCN model suitable for processing semantic hierarchies in knowledge graphs is proposed. In the phase space, relation-context information is used to make entities easier to distinguish. Specifically, a rotation operation in the polar coordinate system is transformed to the addition operation in the rectangular coordinate system, and relations between entities are mapped into their entity-specific hyperplanes. The proposed method is verified by the experiments on three benchmark datasets, and experimental results demonstrate that the proposed method can learn the semantic hierarchies in knowledge graphs and improve the prediction accuracy of complex one-to-many, many-to-one, and many-to-many cases simultaneously.
C1 [Wu, Yong; Fan, Xiaoming; Wang, Binjun] Peoples Publ Secur Univ China, Sch Informat Technol & Cyber Secur, Beijing 100038, Peoples R China.
   [Li, Wei] Queensland Univ Technol, Sch Chem & Phys, Brisbane, Qld 4001, Australia.
RP Wang, BJ (通讯作者)，Peoples Publ Secur Univ China, Sch Informat Technol & Cyber Secur, Beijing 100038, Peoples R China.
EM wangbinjun@ppsuc.edu.cn
OI , Yong/0000-0003-4478-4663
FU National Social Science Foundation of China [20AZD114]; Key R&D and
   Promotion Projects in Henan Province of China [212102210165]; Special
   Funds for Fundamental Scientific Research Operation Fees of Central
   Universities [2020JKF310]
FX This paper is partly supported by the National Social Science Foundation
   of China (No. 20AZD114), the Key R&D and Promotion Projects in Henan
   Province of China (No. 212102210165), and the Special Funds for
   Fundamental Scientific Research Operation Fees of Central Universities
   (No. 2020JKF310).
CR Balazevic I, 2019, LECT NOTES COMPUT SC, V11731, P553, DOI 10.1007/978-3-030-30493-5_52
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cai L, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P131, DOI 10.1145/3360901.3364441
   Deng B, 2020, EMBEDDING ENTITIES R
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Diefenbach D, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1087, DOI 10.1145/3184558.3191541
   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
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   He H, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1766, DOI 10.18653/v1/P17-1162
   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
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   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
   Liu HX, 2017, PR MACH LEARN RES, V70
   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, AAAI CONF ARTIF INTE, P1955
   Rahul G., 2014, INT C WORLD WIDE WEB, P516
   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 BX, 2017, AAAI CONF ARTIF INTE, P1236
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Suchanek F, 2013, C INN DAT SYST RES C
   Sun Z., 2019, 7 INT C LEARN REPR
   Trouillon T, 2016, PR MACH LEARN RES, V48
   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
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xu CR, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P263
   Zhang S, 2019, ADV NEUR IN, V32
   Zhang Z, 2018, EMNLP, P3198, DOI DOI 10.18653/V1/D18-1358
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
   Zhou Y, 2020, ORTHOGONAL RELATION
NR 35
TC 0
Z9 0
U1 1
U2 6
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 2193-567X
EI 2191-4281
J9 ARAB J SCI ENG
JI Arab. J. Sci. Eng.
PD FEB
PY 2022
VL 47
IS 2
BP 1471
EP 1482
DI 10.1007/s13369-021-05977-x
EA JUL 2021
PG 12
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA ZC4ZZ
UT WOS:000673534300001
DA 2022-08-23
ER

PT J
AU Hu, X
   Duan, JL
   Dang, DP
AF Hu, Xin
   Duan, Jiangli
   Dang, Depeng
TI Scalable aggregate keyword query over knowledge graph
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Knowledge graph; Question answering; Keyword search; Aggregation
ID SEARCH
AB Existing keyword query systems over knowledge graphs are easy to use and can produce interesting results. However, they cannot address even simple aggregate queries (i.e., a query that needs statistics such as COUNT, SUM, AVG, MAX, MIN, >, < and =), and the sizes of existing schema graphs grow exponentially with the growth of the number of types or predicates in the knowledge graph, so that they have low scalability for building SPARQL statements. Therefore, we propose a framework called SAKQ (scalable aggregate keyword query over knowledge graph) that enables users to pose aggregate queries using simple keywords. First, we propose a scalable schema graph (i.e., type-predicate graph) that consists of the relationships between types and predicates, which has a small data size and contains all information needed for building SPARQL statements. Second, based on the type-predicate graph, we propose two algorithms to build query graphs with aggregation and transform the query graphs into SPARQL statements with aggregation. Finally, the experimental results over the benchmark datasets demonstrate that SAKQ can answer various general aggregate keyword queries. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Hu, Xin; Duan, Jiangli] Yangtze Normal Univ, Coll Big Data & Intelligent Engn, Chongqing, Peoples R China.
   [Duan, Jiangli] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing, Peoples R China.
   [Dang, Depeng] Beijing Normal Univ, Sch Artificial Intelligence, Beijing, Peoples R China.
RP Duan, JL (通讯作者)，Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing, Peoples R China.
EM d180201004@stu.cqupt.edu.cn
OI Duan, jiangli/0000-0002-0471-1147; hu, xin/0000-0002-4258-6746
FU youth project of science and technology research program of Chongqing
   Education Commission of China [KJQN201901414]; Startup Foundation for
   Introducing Talent of Yangtze Normal University, China [0107/011160052];
   major project of science and technology research program of Chongqing
   Education Commission of China [KJZD-M201901401]; Project of Chongqing
   Natural Science Foundation [cstc2019jcyj-msxmX0683]; National Natural
   Science Foundation of China [61672102]
FX This work was supported by the youth project of science and technology
   research program of Chongqing Education Commission of China (No.
   KJQN201901414), the Startup Foundation for Introducing Talent of Yangtze
   Normal University, China (No. 0107/011160052), the major project of
   science and technology research program of Chongqing Education
   Commission of China (No. KJZD-M201901401), the Project of Chongqing
   Natural Science Foundation (No. cstc2019jcyj-msxmX0683), and the
   National Natural Science Foundation of China (No. 61672102).
CR Navarro FA, 2018, PROCES LENG NAT, P39, DOI 10.26342/2018-61-4
   [Anonymous], [No title captured]
   Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Atre M, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1793, DOI 10.1145/2723372.2746483
   Bae M, 2014, NEUROCOMPUTING, V146, P264, DOI 10.1016/j.neucom.2014.04.062
   Bornea MA, 2013, SIGMOD, P121, DOI DOI 10.1145/2463676.2463718
   Cassavia N, 2017, ACM T INTERACT INTEL, V7, DOI 10.1145/2856059
   Chang JC, 2019, PROCEEDINGS OF IUI 2019, P498, DOI 10.1145/3301275.3302321
   Dass A, 2015, LECT NOTES COMPUT SC, V9114, P287, DOI 10.1007/978-3-319-19890-3_19
   Diefenbach D, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1087, DOI 10.1145/3184558.3191541
   Diefenbach D, 2017, IN SY AP IN WE HC, V10577, P81, DOI 10.1007/978-3-319-70407-4_16
   Dima C., 2013, WORKING NOTES CLEF 2
   Elbassuoni S., 2011, P 20 ACM INT C INF K, P237
   Elbassuoni S., 2010, IEEE DATA ENG B, V33, P16
   Ferre S., 2013, SQUALL2SPARQL TRANSL
   Ferre S, 2017, SEMANT WEB, V8, P405, DOI 10.3233/SW-150208
   Giannone C, 2013, P QUEST ANSW LINK DA, P1
   Gkirtzou K., 2015, P 1 INT WORKSH NOV W, P3
   Gkirtzou K, 2015, LECT NOTES COMPUT SC, V9316, P111, DOI 10.1007/978-3-319-24592-8_9
   Han S, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P227, DOI 10.1145/3132847.3132957
   Hoffner K, 2016, LECT NOTES COMPUT SC, V9981, P325, DOI 10.1007/978-3-319-46523-4_20
   Hoffner K., 2014, P 10 INT C SEM SYST, P61, DOI [10.1145/2660517.2660521, DOI 10.1145/2660517.2660521]
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Hu X, 2018, INFORM SCIENCES, V454, P363, DOI 10.1016/j.ins.2018.04.042
   Huang JJ, 2015, PIKM'15: PROCEEDINGS OF THE 8TH PH.D. WORKSHOP IN INFORMATION AND KNOWLEDGE MANAGEMENT, P3, DOI 10.1145/2809890.2809894
   Joris G, 2013, P WORK MULT QUEST AN, P1
   Le WC, 2014, IEEE T KNOWL DATA EN, V26, P2774, DOI 10.1109/TKDE.2014.2302294
   Li F, 2016, J MECH MED BIOL, V16, P1
   Lian X, 2015, IEEE T KNOWL DATA EN, V27, P1246, DOI 10.1109/TKDE.2014.2365791
   Lin XQ, 2018, J INF SCI ENG, V34, P489, DOI 10.6688/JISE.2018.34.2.11
   Meimaris Marios, 2015, Data 2015. 4th International Conference on Data Management Technologies and Applications, P232
   Nakashole N., 2012, P 2012 JOINT C EMP M, P1135, DOI DOI 10.1080/17517575.2015.1080301
   Nakashole N, 2012, PROC VLDB ENDOW, V5, P1982, DOI 10.14778/2367502.2367553
   Nakashole N, 2013, SIGMOD REC, V42, P29
   Park CS, 2018, INT J WEB INF SYST, V14, P299, DOI 10.1108/IJWIS-10-2017-0070
   Peng P, 2017, INFORM SYST, V67, P19, DOI 10.1016/j.is.2017.03.002
   Pound Jeffrey, 2012, P 21 ACM INT C INF K, P305, DOI DOI 10.1145/2396761.2396803
   Pradel Camille, 2012, Graph Structures for Knowledge Representation and Reasoning. Second International Workshop, GKR 2011. Revised Selected Papers, P172, DOI 10.1007/978-3-642-29449-5_7
   Rivero CR, 2016, KNOWL-BASED SYST, V93, P47, DOI 10.1016/j.knosys.2015.11.001
   Shan Y, 2017, DATA KNOWL ENG, V110, P1, DOI 10.1016/j.datak.2017.02.001
   Shi JM, 2016, SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1977, DOI 10.1145/2882903.2882941
   Sinha Souvik Brata, 2018, IDEAS, P12
   Stefanidis K, 2015, LECT NOTES COMPUT SC, V9341, P144, DOI 10.1007/978-3-319-25639-9_28
   Sun H, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1045, DOI 10.1145/2736277.2741651
   Le TN, 2014, IEEE INT SYMP INFO, P1101, DOI 10.1109/ISIT.2014.6875003
   Tran T, 2009, PROC INT CONF DATA, P405, DOI 10.1109/ICDE.2009.119
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Usbeck R., 2018, 9 CHALLENGE QUESTION, P1
   Wang C, 2017, 2017 INTERNATIONAL CONFERENCE ON FRONTIERS IN EDUCATIONAL TECHNOLOGIES AND MANAGEMENT SCIENCES (FETMS 2017), P541
   Wang H.W., 2013, THESIS
   Wu E, 2013, PROC VLDB ENDOW, V6, P553, DOI 10.14778/2536354.2536356
   Wu YH, 2013, PROC VLDB ENDOW, V6, P1774, DOI 10.14778/2556549.2556561
   Yang M, 2014, PROC VLDB ENDOW, V7, P1809, DOI 10.14778/2733085.2733088
   Zhai HT, 2014, INT J SOFTW INNOV, V2, P1, DOI 10.4018/ijsi.2014070101
   Zheng WG, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P217, DOI 10.1145/3132847.3132977
   Zhou Ming-Zhong, 2010, P IEEE WIR COMM NETW, P1, DOI DOI 10.1109/WCNC.2010.5506227
   Zou L, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P313, DOI 10.1145/2588555.2610525
NR 57
TC 5
Z9 5
U1 4
U2 19
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 JUN
PY 2020
VL 107
BP 588
EP 600
DI 10.1016/j.future.2020.02.011
PG 13
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LF3PD
UT WOS:000527331800043
DA 2022-08-23
ER

PT J
AU Cong, K
   Li, T
   Li, BB
   Gao, Z
   Xu, YB
   Gao, F
AF Cong, Kai
   Li, Tao
   Li, Beibei
   Gao, Zhan
   Xu, Yanbin
   Gao, Fei
TI KGDetector: Detecting Chinese Sensitive Information via Knowledge
   Graph-Enhanced BERT
SO SECURITY AND COMMUNICATION NETWORKS
LA English
DT Article
ID CENSORSHIP
AB The Bidirectional Encoder Representations from Transformers (BERT) technique has been widely used in detecting Chinese sensitive information. However, existing BERT-based frameworks usually fail to emphasize key entities in the texts that contribute significantly to knowledge inference. To meet this gap, we propose a BERT and knowledge graph-based novel framework to detect Chinese sensitive information (named KGDetector). Specifically, we first train a pretrained knowledge graph-based Chinese entity embedding model to characterize entities in the Chinese textual inputs. Finally, we propose an effective framework KGDetector to detect Chinese sensitive information, which employs the knowledge graph-based embedding model and the CNN classification model. Extensive experiments on our crafted Chinese sensitive information dataset demonstrate that KGDetector can effectively detect Chinese sensitive information, outperforming existing baseline frameworks.
C1 [Cong, Kai; Gao, Zhan; Xu, Yanbin] Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China.
   [Li, Tao; Li, Beibei; Gao, Fei] Sichuan Univ, Sch Cyber Sci & Engn, Chengdu 610065, Peoples R China.
RP Li, BB (通讯作者)，Sichuan Univ, Sch Cyber Sci & Engn, Chengdu 610065, Peoples R China.
EM congkai@stu.scu.edu.cn; taoliscu@outlook.com; libeibei@scu.edu.cn;
   m18980933897@163.com; 18981800986@189.cn; 2019326245013@stu.scu.edu.cn
FU National Key Research and Development Program of China [2020YFB1805400];
   National Natural Science Foundation of China [U19A2068]; Sichuan Science
   and Technology Program [2022YFG0193]
FX This study was supported in part by the National Key Research and
   Development Program of China (no. 2020YFB1805400), in part by the
   National Natural Science Foundation of China (no. U19A2068), and the
   Sichuan Science and Technology Program (no. 2022YFG0193).
CR Berardi G., P ACM INT C INF KNOW, P1711, DOI [10.1145/2806416.2806597, DOI 10.1145/2806416.2806597]
   Chen QW, 2019, 1ST INTERNATIONAL WORKSHOP ON DEEP LEARNING PRACTICE FOR HIGH-DIMENSIONAL SPARSE DATA WITH KDD (DLP-KDD 2019), DOI 10.1145/3326937.3341261
   Fu KW, 2013, IEEE INTERNET COMPUT, V17, P42, DOI 10.1109/MIC.2013.28
   Gan CQ, 2021, FUTURE GENER COMP SY, V118, P297, DOI 10.1016/j.future.2021.01.024
   Garcia-Pablos A, 2020, PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020), P4486
   Holowczak J, 2015, CCS'15: PROCEEDINGS OF THE 22ND ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P70, DOI 10.1145/2810103.2813696
   Hong Q., P INT C MEAS TECHN M, P832, DOI [10.1109/icmtma50254.2020.00181, DOI 10.1109/ICMTMA50254.2020.00181]
   Katic T., 2018, P INT S INTELLIGENT, DOI [10.1109/SISY.2018.8524814, DOI 10.1109/SISY.2018.8524814]
   Kou Y, 2017, LECT NOTES COMPUT SC, V10513, P377, DOI 10.1007/978-3-319-67744-6_25
   Lee SY, 2016, CHINA QUART, V228, P1061, DOI 10.1017/S0305741016001454
   Lerer Adam, 2019, P C MACH LEARN SYST
   Li Da., 2017, J HIGH ENERGY PHYS
   Lin Y, 2020, IEEE INT CONF TRUST, P1614, DOI 10.1109/TrustCom50675.2020.00223
   Matthes J, 2008, J COMMUN, V58, P258, DOI 10.1111/j.1460-2466.2008.00384.x
   Meng Ding, 2021, Journal of Physics: Conference Series, V1757, DOI 10.1088/1742-6596/1757/1/012088
   Mou Y, 2016, NEW MEDIA SOC, V18, P837, DOI 10.1177/1461444814548994
   Munikar M., 2019, 2019 ART INT TRANSF, DOI [10.1109/aitb48515.2019.8947435, DOI 10.1109/AITB48515.2019.8947435, 10.1109/AITB48515.2019.8947435]
   Neerbek Jan, 2018, Advances in Knowledge Discovery and Data Mining. 22nd Pacific-Asia Conference, PAKDD 2018. Proceedings: LNAI 10939, P373, DOI 10.1007/978-3-319-93040-4_30
   Neerbek J, 2017, PROC INT CONF DATA, P1399, DOI 10.1109/ICDE.2017.195
   Nobori D., 2014, 11 USENIX S NETW SYS, P229
   Ostendorff M., ARXIV
   Su YH, 2020, BIOMED RES INT, V2020, DOI 10.1155/2020/5491963
   Sun C, 2019, LECT NOTES ARTIF INT, V11856, P194, DOI 10.1007/978-3-030-32381-3_16
   Wang Y, 2020, ARTIFICIAL INTELLIGE, P269, DOI [10.1007/978-981-15-5577-0_20, DOI 10.1007/978-981-15-5577-0_20]
   Xu GX, 2018, IEEE ACCESS, V6, P55643, DOI 10.1109/ACCESS.2018.2870122
   Xu GS, 2019, INT CONF CYBER DIST, P474, DOI 10.1109/CyberC.2019.00087
   Yang YP, 2021, ENTROPY-SWITZ, V23, DOI 10.3390/e23111536
   Zhang XL, 2021, MATH PROBL ENG, V2021, DOI 10.1155/2021/6658520
NR 28
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 MAY 19
PY 2022
VL 2022
AR 4656837
DI 10.1155/2022/4656837
PG 9
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 1U1CN
UT WOS:000805157000011
OA gold
DA 2022-08-23
ER

PT J
AU SEIVER, A
AF SEIVER, A
TI A DECISION CLASS ANALYSIS OF CRITICAL CARE LIFE-SUPPORT DECISION-MAKING
SO INTERNATIONAL JOURNAL OF CLINICAL MONITORING AND COMPUTING
LA English
DT Article
DE DECISION ANALYSIS; DECISION-MAKING; CRITICAL CARE; INTENSIVE CARE;
   COMPUTER; ARTIFICIAL INTELLIGENCE
AB Decision analysis is a powerful methodology that can help clinicians make good decisions. Because it is not practical to place a decision analyst at the bedside in critical care units, the application of this methodology will require leveraging the analyst through computer-based systems. A decision class analysis is a collective analysis of a group of decisions that provides the high-level specification for such a computer system. This paper presents a decision class analysis of critical care life-support decisions. Key elements of this analysis are: the simplification of an otherwise extremely complex multistage sequential decision problem by using a sequence of two-stage models, and the use of six generic knowledge maps that capture the extremely complex relevant medical knowledge.
RP SEIVER, A (通讯作者)，STANFORD UNIV,SCH MED,VAMC PALO ALTO,DEPT SURG,SURG SERV 112,3801 MIRANDA AVE,PALO ALTO,CA 94304, USA.
NR 0
TC 1
Z9 1
U1 0
U2 0
PU KLUWER ACADEMIC PUBL
PI DORDRECHT
PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS
SN 0167-9945
J9 INT J CLIN MONIT COM
JI Int. J. Clin. Monit. Comput.
PD FEB
PY 1993
VL 10
IS 1
BP 31
EP 66
DI 10.1007/BF01133524
PG 36
WC Anesthesiology; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Anesthesiology; Medical Informatics
GA LE740
UT WOS:A1993LE74000005
PM 8326214
DA 2022-08-23
ER

PT J
AU Fan, RY
   Wang, LZ
   Yan, JN
   Song, WJ
   Zhu, YQ
   Chen, XD
AF Fan, Runyu
   Wang, Lizhe
   Yan, Jining
   Song, Weijing
   Zhu, Yingqian
   Chen, Xiaodao
TI Deep Learning-Based Named Entity Recognition and Knowledge Graph
   Construction for Geological Hazards
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE named entity recognition; knowledge graph; deep learning; geological
   hazards
ID NEURAL-NETWORKS
AB Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-based NER model; namely, the deep, multi-branch BiGRU-CRF model, which combines a multi-branch bidirectional gated recurrent unit (BiGRU) layer and a conditional random field (CRF) model. In an end-to-end and supervised process, the proposed model automatically learns and transforms features by a multi-branch bidirectional GRU layer and enhances the output with a CRF layer. Besides the deep, multi-branch BiGRU-CRF model, we also proposed a pattern-based corpus construction method to construct the corpus needed for the deep, multi-branch BiGRU-CRF model. Experimental results indicated the proposed deep, multi-branch BiGRU-CRF model outperformed state-of-the-art models. The proposed deep, multi-branch BiGRU-CRF model constructed a large-scale geological hazard literature knowledge graph containing 34,457 entities nodes and 84,561 relations.
C1 [Fan, Runyu; Wang, Lizhe; Yan, Jining; Song, Weijing; Zhu, Yingqian; Chen, Xiaodao] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China.
   [Fan, Runyu; Wang, Lizhe; Yan, Jining; Song, Weijing; Zhu, Yingqian; Chen, Xiaodao] China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430074, Peoples R China.
RP Wang, LZ (通讯作者)，China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China.; Wang, LZ (通讯作者)，China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430074, Peoples R China.
EM runyufan@cug.edu.cn; lzwang@cug.edu.cn; yanjn@cug.edu.cn;
   songwj@cug.edu.cn; yingqianzhu@cug.edu.cn; chenxd@cug.edu.cn
RI Yan, Jining/AAB-7885-2022
OI Yan, Jining/0000-0003-0680-5427; Fan, Runyu/0000-0002-5259-5670
FU National Natural Science Foundation of China [U1711266]
FX This work was supported by the National Natural Science Foundation of
   China (number U1711266).
CR Agichtein E, 2007, PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, P18, DOI 10.1109/WI.2007.130
   [Anonymous], [No title captured]
   Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Bauer F., 2011, LINKED OPEN DATA ESS
   BAUM LE, 1966, ANN MATH STAT, V37, P1554, DOI 10.1214/aoms/1177699147
   BENGIO Y, 1994, IEEE T NEURAL NETWOR, V5, P157, DOI 10.1109/72.279181
   Borthwick A., 1999, THESIS
   Chieu H.L., 2002, P 19 INT C COMP LING, V1, P1, DOI DOI 10.3115/1072228.1072253
   Chinchor N., 1998, P 7 MESS UND C MUC 7
   Chinchor N., 1997, P 7 C MESS UND FRASC, V29
   Chiu J.P., 2015, ARXIV151108308, DOI [10.1162/tacl_a_00104, DOI 10.1162/TACL_A_00104]
   Cho Kyunghyun, 2014, ARXIV14061078, P1724, DOI DOI 10.3115/V1/D14-1179
   Chowdhury GG, 2003, ANNU REV INFORM SCI, V37, P51, DOI 10.1002/aris.1440370103
   Christ Patrick Ferdinand, 2016, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. 19th International Conference. Proceedings: LNCS 9901, P415, DOI 10.1007/978-3-319-46723-8_48
   Chung J., 2014, EMPIRICAL EVALUATION
   Chung T, 2009, 2009 INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE SECURITY, P6
   Curran J.R., 2003, P 7 C NAT LANG LEARN, P164, DOI DOI 10.3115/1119176.1119200
   Dwibedi D., 2018, P IEEE C COMP VIS PA, P1111
   EKBAL A, 2010, INT J ELECT COMPUTER, V4, P155
   Friburger N, 2004, THEOR COMPUT SCI, V313, P93, DOI 10.1016/j.tcs.2003.10.007
   Gers FA, 1999, IEE CONF PUBL, P850, DOI 10.1049/cp:19991218
   Graves A, 2013, INT CONF ACOUST SPEE, P6645, DOI 10.1109/ICASSP.2013.6638947
   HAMMERTON J, 2003, P 7 C NAT LANG LEARN, V4, P172, DOI DOI 10.3115/1119176.1119202
   He K., 2016, IEEE C COMP VIS PATT, DOI DOI 10.1109/CVPR.2016.90
   Hearst MA, 1998, IEEE INTELL SYST APP, V13, P18, DOI 10.1109/5254.708428
   Hecht-Nielsen R., 1992, NEURAL NETWORKS PERC, P6593, DOI [DOI 10.1016/B978-0-12-741252-8.50010-8, 10.1016/0893-6080(88)90469-8]
   Hoberg T, 2015, IEEE T GEOSCI REMOTE, V53, P659, DOI 10.1109/TGRS.2014.2326886
   Isozaki H., 2002, P C COMPUTATIONAL NA, DOI DOI 10.3115/1071884.1071911
   Kazama J, 2002, P ACL 02 WORKSH NAT, V3, P1, DOI DOI 10.3115/1118149.1118150
   Kudo T., CRF YET ANOTHER CRF
   Lafferty J., 2001, P 18 INT C MACH LEAR
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Li D, 2008, P WORKSH CURR TRENDS, P94
   Li KL, 2015, IEEE T PARALL DISTR, V26, P3040, DOI 10.1109/TPDS.2014.2368568
   Li S, 2018, IEEE ACCESS, V6, P52286, DOI 10.1109/ACCESS.2018.2870203
   Ma XZ, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1064
   Marsh E., 1998, P 7 MESS UND C MUC 7
   McCallum A, 2003, P 7 C NAT LANG LEARN, V4, P188, DOI DOI 10.3115/1119176.1119206
   Mihalcea R., 2004, P 2004 C EMP METH NA, P404, DOI DOI 10.3115/1219044.1219064
   Mikolov T, 2010, 11TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2010 (INTERSPEECH 2010), VOLS 1-2, P1045
   Mikolov T, 2011, INT CONF ACOUST SPEE, P5528
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   Nguyen T. V. T., 2010, P 23 INT C COMP LING, P901
   Pascanu R., 2013, INT C MACH LEARN, P1310
   Powers D.M.W., 1998, PROC NEMLAP3 CONLL, P151, DOI DOI 10.3115/1603899.1603924
   Ramos J., 2003, P 1 INSTR C MACH LEA, DOI DOI 10.15804/TNER.2015.42.4.03
   Ratnaparkhi Adwait, 1996, C EMP METH NAT LANG
   Sak H, 2014, INTERSPEECH, P338
   Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
   Settles B., 2004, P INT JOINT WORKSH N, P104
   Sobhana N. V., 2010, INT J COMPUTER APPL, V1, P143
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   Sundermeyer M, 2012, 13TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2012 (INTERSPEECH 2012), VOLS 1-3, P194
   Sundheim B. M., 1995, P 6 MESS UND C MUC 6, P13, DOI DOI 10.1145/1072399.1072402
   Sutton C, 2012, FOUND TRENDS MACH LE, V4, P267, DOI 10.1561/2200000013
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   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
   Yates A., 2007, P HUM LANG TECHN ANN, P25
   Yu TS, 2018, PROC CVPR IEEE, P705, DOI 10.1109/CVPR.2018.00080
   Zhang J, 2004, J BIOMED INFORM, V37, P411, DOI 10.1016/j.jbi.2004.08.005
   Zhao S., 2004, P INT JOINT WORKSH N, P84, DOI DOI 10.3115/1567594.1567613
   Zheng S, 2015, IEEE I CONF COMP VIS, P1529, DOI 10.1109/ICCV.2015.179
   Zhou GD, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P473
   Zhu YQ, 2017, SCI PROGRAMMING-NETH, V2017, DOI 10.1155/2017/5072427
NR 65
TC 12
Z9 13
U1 22
U2 61
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 2020
VL 9
IS 1
AR 15
DI 10.3390/ijgi9010015
PG 22
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA KN1WJ
UT WOS:000514631100037
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Gong, F
   Wang, M
   Wang, HF
   Wang, S
   Liu, MY
AF Gong, Fan
   Wang, Meng
   Wang, Haofen
   Wang, Sen
   Liu, Mengyue
TI SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation
SO BIG DATA RESEARCH
LA English
DT Article
DE Knowledge graph; Embeddings; Recommendation system; Drug safety
AB Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Gong, Fan] Shanghai Univ Tradit Chinese Med, Shanghai Shuguang Hosp, Puan Rd, Shanghai, Peoples R China.
   [Wang, Meng] 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.
   [Wang, Haofen] Tongji Univ, Coll Design & Innovat, Shanghai, Peoples R China.
   [Wang, Sen] Univ Queensland, Brisbane, Qld, Australia.
   [Liu, Mengyue] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Peoples R China.
RP Wang, M (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
EM meng.wang@seu.edu.cn
OI Wang, Sen/0000-0002-5414-8276
FU National Science Foundation of China [61906037, U1736204]; Fundamental
   Research Funds for the Central Universities [4009009106, 22120200184];
   CCF-BAIDU [OF2020003]
FX This work was supported by National Science Foundation of China with
   Grant Nos. 61906037 and U1736204; the Fundamental Research Funds for the
   Central Universities with Grant Nos. 4009009106 and 22120200184; the
   CCF-Baidu Open Fund with Gran No. CCF-BAIDU OF2020003.
CR Almirall D, 2012, STAT MED, V31, P1887, DOI 10.1002/sim.4512
   Bennett CC, 2013, ARTIF INTELL MED, V57, P9, DOI 10.1016/j.artmed.2012.12.003
   Celebi R, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3284-5
   Chen B, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-255
   Chen Z, 2016, THEOR PRACT LOG PROG, V16, P604, DOI 10.1017/S1471068416000429
   Dumontier M., 2014, 12 SEM WEB C, P401
   Edwards IR, 2000, LANCET, V356, P1255, DOI 10.1016/S0140-6736(00)02799-9
   Ernst P, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0549-5
   Fernald GH, 2011, BIOINFORMATICS, V27, P1741, DOI 10.1093/bioinformatics/btr295
   Gunlicks-Stoessel M, 2016, J CLIN CHILD ADOLESC, V45, P480, DOI 10.1080/15374416.2015.1015133
   Hettige B, 2020, FRONT ARTIF INTEL AP, V325, P1810, DOI 10.3233/FAIA200296
   Johnson AEW, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.35
   Juurlink DN, 2003, JAMA-J AM MED ASSOC, V289, P1652, DOI 10.1001/jama.289.13.1652
   Law V, 2014, NUCLEIC ACIDS RES, V42, pD1091, DOI 10.1093/nar/gkt1068
   LEAPE LL, 1995, JAMA-J AM MED ASSOC, V274, P35, DOI 10.1001/jama.1995.03530010049034
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Niu F., 2011, P ADV NEUR INF PROC
   Panagioti M, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0135947
   Rosen-Zvi M, 2008, BIOINFORMATICS, V24, pI399, DOI 10.1093/bioinformatics/btn141
   Schriml LM, 2012, NUCLEIC ACIDS RES, V40, pD940, DOI 10.1093/nar/gkr972
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Wang M., 2017, ARXIV171005980
   Wang M, 2017, LECT NOTES COMPUT SC, V10588, P219, DOI 10.1007/978-3-319-68204-4_23
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wei WQ, 2013, J AM MED INFORM ASSN, V20, P954, DOI 10.1136/amiajnl-2012-001431
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Zhang Ping, 2014, AMIA Jt Summits Transl Sci Proc, V2014, P132
   Zhang YT, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1315, DOI 10.1145/3097983.3098109
NR 29
TC 8
Z9 9
U1 24
U2 60
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 100174
DI 10.1016/j.bdr.2020.100174
PG 8
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:000609166100009
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Cafarella, M
   Anderson, M
   Beltagy, I
   Cattan, A
   Chasins, S
   Dagan, I
   Downey, D
   Etzioni, O
   Feldman, S
   Gao, T
   Hope, T
   Huang, KX
   Johnson, S
   King, D
   Lo, K
   Lou, YZ
   Shapiro, M
   Shen, DH
   Subramanian, S
   Wang, LL
   Wang, YN
   Wang, YT
   Weld, DS
   Vo-Phamhi, J
   Zeng, AN
   Zou, JY
AF Cafarella, Michael
   Anderson, Michael
   Beltagy, Iz
   Cattan, Arie
   Chasins, Sarah
   Dagan, Ido
   Downey, Doug
   Etzioni, Oren
   Feldman, Sergey
   Gao, Tian
   Hope, Tom
   Huang, Kexin
   Johnson, Sophie
   King, Daniel
   Lo, Kyle
   Lou, Yuze
   Shapiro, Matthew
   Shen, Dinghao
   Subramanian, Shivashankar
   Wang, Lucy Lu
   Wang, Yuning
   Wang, Yitong
   Weld, Daniel S.
   Vo-Phamhi, Jenny
   Zeng, Anna
   Zou, Jiayun
TI Infrastructure for rapid open knowledge network development
SO AI MAGAZINE
LA English
DT Article
ID WEB
AB The past decade has witnessed a growth in the use of knowledge graph technologies for advanced data search, data integration, and query-answering applications. The leading example of a public, general-purpose open knowledge network (aka knowledge graph) is Wikidata, which has demonstrated remarkable advances in quality and coverage over this time. Proprietary knowledge graphs drive some of the leading applications of the day including, for example, Google Search, Alexa, Siri, and Cortana. Open Knowledge Networks are exciting: they promise the power of structured database-like queries with the potential for the wide coverage that is today only provided by the Web. With the current state of the art, building, using, and scaling large knowledge networks can still be frustratingly slow. This article describes a National Science Foundation Convergence Accelerator project to build a set of Knowledge Network Programming Infrastructure systems to address this issue.
C1 [Cafarella, Michael; Zeng, Anna] MIT, CSAIL, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
   [Anderson, Michael; Gao, Tian; Huang, Kexin; Lou, Yuze; Shapiro, Matthew; Shen, Dinghao; Wang, Yuning; Wang, Yitong; Vo-Phamhi, Jenny; Zou, Jiayun] Univ Michigan, Ann Arbor, MI 48109 USA.
   [Beltagy, Iz; Cattan, Arie; Downey, Doug; Etzioni, Oren; Feldman, Sergey; Hope, Tom; Johnson, Sophie; King, Daniel; Lo, Kyle; Subramanian, Shivashankar; Wang, Lucy Lu; Weld, Daniel S.] Allen Inst Artificial Intelligence, Seattle, WA USA.
   [Chasins, Sarah] Univ Calif Berkeley, Berkeley, CA 94720 USA.
   [Dagan, Ido] Bar Ilan Univ, Ramat Gan, Israel.
RP Cafarella, M (通讯作者)，MIT, CSAIL, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM michjc@csail.mit.edu
OI King, Daniel/0000-0002-6678-0586
FU National Science Foundation; Office of Naval Research
FX The authors would like to acknowledge the National Science Foundation
   and in particular the staff of the Convergence Accelerator program for
   their crucial support. This submission meets the author responsibilities
   listed in the AAAI "Publication Ethics and Malpractice Statement" of
   August 2017. This work was supported primarily by a grant from the
   National Science Foundation with additional support from the Office of
   Naval Research.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Barman S, 2016, ACM SIGPLAN NOTICES, V51, P748, DOI 10.1145/3022671.2984020
   Bateman A, 2006, NUCLEIC ACIDS RES, V34, pD1, DOI 10.1093/nar/gkj150
   Bizer C, 2009, IEEE INTELL SYST, V24, P87, DOI 10.1109/MIS.2009.102
   Cattan A., 2021, SCICO HIERARCHICAL C
   Cattan A., 2020, STREAMLINING CROSS D
   Chasins SE, 2018, UIST 2018: PROCEEDINGS OF THE 31ST ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, P963, DOI 10.1145/3242587.3242661
   Cohan A, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P2270
   Cybulska A, 2014, LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P4545
   Ferreira AA, 2012, SIGMOD REC, V41, P15, DOI 10.1145/2350036.2350040
   GeoNames, 2019, GEONAMES
   Lu Wang L., 2020, ARXIV200410706
   Neumann M., 2021, PAWLS PDF ANNOTATION
   Roberts K, 2020, J AM MED INFORM ASSN, V27, P1431, DOI 10.1093/jamia/ocaa091
   Shen Z., 2021, LAYOUTPARSER UNIFIED
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Subramanian S., 2021, S2AND BENCHMARK EVAL
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Xu YH, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1192, DOI 10.1145/3394486.3403172
   Yates A., 2004, P 13 INT C WORLD WID, P100, DOI DOI 10.1145/988672.988687
   Zeng A., 2021, UNPUB
   Zhang YT, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1002, DOI 10.1145/3219819.3219859
NR 23
TC 1
Z9 1
U1 1
U2 1
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 59
EP 68
DI 10.1002/aaai.12038
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0J8FN
UT WOS:000780336400008
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Lu, WJ
   Altenbek, G
AF Lu, Wenjie
   Altenbek, Gulila
TI A recommendation algorithm based on fine-grained feature analysis
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge graph; Recommender system; Fine-grained features;
   Collaborative learning
AB Most existing recommender methods have insufficient power to capture or recover fine-grained features of products and user preferences. Knowledge graphs contain considerable information about products and mutual relationships in the world. To analyze the fine-grained features during the process of recommendation, we propose a knowledge-aware collaborative learning framework (KACL), which is the first work to combine users' historical reviews with knowledge graphs. First, we use a named-entity recognition (NER) system to recognize the named entities that correspond to item features in the unstructured reviews. After that, we use an entity-linking (EL) system to map entities which identified in the first step to the corresponding entity in Wikipedia. Next, we constructed a sub-graph that depends on the extracted entity and the related ones and embedded the sub-graph into a low latent vector space as pretrain item embedding through the knowledge graph embedding model TransR. In the next step, we will fine-tune item embedding through deep learning built by 4 levels of relu and combine collaborative information with pretrain item embedding. And in accordance with the user review behavior, the item embedding will be averagely poled to express the user embedding, we will put the item embedding and the user embedding into the classification model. Finally, we use the KACL to integrate collaborative filtering with entity representation and make recommendations. Experiments are performed on realworld datasets to evaluate the effectiveness of our new method. The results show that the analysis of fine-grained features based on a knowledge graph helps our KACL improve recommender accuracy compared with the selected state-of-the-art models. (C) 2020 Published by Elsevier Ltd.
C1 [Lu, Wenjie; Altenbek, Gulila] Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Peoples R China.
   [Altenbek, Gulila] Base Kazakh & Kirghiz Language Natl Language Reso, Urumqi, Peoples R China.
   [Altenbek, Gulila] Xinjiang Lab Multilanguage Informat Technol, Urumqi, Peoples R China.
RP Altenbek, G (通讯作者)，Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Peoples R China.
EM 340440056@qq.com; gla@xju.edu.cn
CR Aciar S, 2006, 2006 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, (WI 2006 MAIN CONFERENCE PROCEEDINGS), P719, DOI 10.1109/WI.2006.144
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chambua J, 2018, EXPERT SYST APPL, V114, P629, DOI 10.1016/j.eswa.2018.07.059
   Chen N, 2014, 36TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2014), P767, DOI 10.1145/2568225.2568263
   Cheng H.T., 2016, P 1 WORKSH DEEP LEAR, P7
   Freitas A, 2011, LECT NOTES COMPUT SC, V6716, P40, DOI 10.1007/978-3-642-22327-3_5
   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
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Miller Alexander, 2016, EMNLP, DOI [10.18653/v1/D16-1147, DOI 10.18653/V1/D16-1147]
   Milne D., 2008, P 17 ACM C INF KNOWL, P509, DOI DOI 10.1145/1458082.1458150
   Mirtalaie MA, 2018, EXPERT SYST APPL, V114, P267, DOI 10.1016/j.eswa.2018.07.046
   Musat C. C., 2013, P 15 NAT C ART INT, P714
   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
   Palumbo E, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P32, DOI 10.1145/3109859.3109889
   Ricci F, 2006, INFORM COMMUNICATION, V2006, P296
   Shanmugavel G., 2016, HYBRID RECOMMENDER S, V2016
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Shi C., 2015, P 24 ACM INT C INF K, P453
   Sil A., 2013, P 22 ACM INT C INF K, P2369
   Tavakoli M, 2018, EXPERT SYST APPL, V113, P186, DOI 10.1016/j.eswa.2018.05.037
   Tiddi I, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P1029, DOI 10.1145/2740908.2742019
   Vagliano I., 2017, C REC SYST COM IT, P27
   Wang H. W., 2018, 27 ACM INT C INF KNO, P22
   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 Huai-Jun, 2018, Instrument Technique and Sensor, P23
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wietsma R., 2005, WORKSH PERV MOB INT WORKSH PERV MOB INT
   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 WS, 2013, ACM T INTEL SYST TEC, V4, DOI 10.1145/2414425.2414434
   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 ZL, 2018, LECT NOTES ARTIF INT, V10938, P222, DOI 10.1007/978-3-319-93037-4_18
NR 32
TC 3
Z9 3
U1 7
U2 56
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 JAN
PY 2021
VL 163
AR 113759
DI 10.1016/j.eswa.2020.113759
PG 14
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 OG8GL
UT WOS:000582115400008
DA 2022-08-23
ER

PT J
AU Jiang, XL
   Fu, Y
   Dong, CC
AF Jiang, Xiaolin
   Fu, Yu
   Dong, Changchun
TI Recommendation method for fusion of knowledge graph convolutional
   network
SO EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
LA English
DT Article
DE Knowledge graph; Recommended technology; Convolutional network
AB In the application of internet of vehicles system, it is particularly important to obtain real-time and effective vehicle information and provide personalized functional services for vehicle operation. This algorithm combines knowledge graph technology with convolutional network and presents a new algorithm model, that is, when calculating the representation of a given entity in the knowledge graph, the information of the neighboring entity is combined with the deviation. Through the integration of neighbor entity information, the local neighborhood structure can be better captured and stored in each entity, and the weight of different neighbor entities depends on the relationship between them and the specific user, which can better reflect the user's personalized interests, in order to fully demonstrate the characteristics of the entity. Compared with the traditional coordinated filtering technology SVD model, this model has improved accuracy and F1 value.
C1 [Jiang, Xiaolin; Dong, Changchun] Jinhua Adv Res Inst, Jinhua 321013, Zhejiang, Peoples R China.
   [Fu, Yu] Heilongjiang Univ Sci & Technol, Harbin 150000, Heilongjiang, Peoples R China.
RP Dong, CC (通讯作者)，Jinhua Adv Res Inst, Jinhua 321013, Zhejiang, Peoples R China.
EM 57102526@qq.com
FU Heilongjiang Province Natural Science Fund Project [F2020011];
   Heilongjiang Provincial Postdoctoral Science Foundation [LBH-Z16054]
FX This work was supported in part by the Heilongjiang Province Natural
   Science Fund Project (No. F2020011) and Heilongjiang Provincial
   Postdoctoral Science Foundation (No. LBH-Z16054).
CR Information Technology-Cloud Computing, 2020, COMPUT TECHNOL J
   Kojima R, 2020, J CHEMINFORMATICS, V12, DOI 10.1186/s13321-020-00435-6
   Liu X, 2019, IEEE INTERNET THINGS, V6, P5971, DOI 10.1109/JIOT.2018.2863267
   Liu X, 2018, PHYS COMMUN-AMST, V28, P130, DOI 10.1016/j.phycom.2018.03.015
   Ma C, 2018, IEEE T KNOWL DATA EN, V30, P2024, DOI 10.1109/TKDE.2018.2815544
   Phan T, 2021, NEURAL COMPUT APPL, V33, P14887, DOI 10.1007/s00521-021-06126-z
   Ren JT, 2019, DECIS SUPPORT SYST, V125, DOI 10.1016/j.dss.2019.113115
   Sang L, 2021, NEUROCOMPUTING, V454, P417, DOI 10.1016/j.neucom.2021.03.053
   Wang X, 2021, WORLD WIDE WEB, V24, P869, DOI 10.1007/s11280-021-00894-3
   Werneck H, 2021, INFORM SYST, V101, DOI 10.1016/j.is.2021.101789
   Xue YY, 2021, NEUROCOMPUTING, V456, P11, DOI 10.1016/j.neucom.2021.05.021
   Zeng P., 2021, COMPUT APPL RES, V38, P2919
NR 12
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 1687-6180
J9 EURASIP J ADV SIG PR
JI EURASIP J. Adv. Signal Process.
PD MAR 28
PY 2022
VL 2022
IS 1
AR 27
DI 10.1186/s13634-022-00854-7
PG 9
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 0A4MD
UT WOS:000773929500002
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Nie, BL
   Sun, SQ
AF Nie, Binling
   Sun, Shouqian
TI Knowledge graph embedding via reasoning over entities, relations, and
   text
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Knowledge graph embedding; Text-enhanced; Knowledge inference
AB Knowledge graph embedding has attracted significant research interest in the field of intelligent web, which aims to embed both entities and relations into a low-dimensional space. In particular, there are two fundamentally different kinds of models, latent feature models and graph feature models, to infer new predictions in the graph. Latent feature models are expert at using latent features of entities to explain triples and infer these features automatically from the data, while graph feature models are do well in extracting features from the observable graph patterns. Combining the strengths of these two fundamental models is a promising approach to increase the predictive performance of graph models. Thus, we propose a new combined model, named as Text-enhanced Knowledge Graph Embedding (TKGE), to perform inference over entities, relations, and text. The model is not only well-suited for modeling interactions of their latent features, but also well-suited for modeling paths between entities in the graph. Experimental results show that TKGE has significant improvements compared to baselines on two tasks: knowledge graph completion and triple classification. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Nie, Binling; Sun, Shouqian] Zhejiang Univ, Zheda Rd 38, Hangzhou, Zhejiang, Peoples R China.
RP Nie, BL (通讯作者)，Zhejiang Univ, Zheda Rd 38, Hangzhou, Zhejiang, Peoples R China.
EM nbl1221@zju.edu.cn
FU National Key Research and Development Program of China [2017YFD0700102]
FX The work is supported by National Key Research and Development Program
   of China (No. 2017YFD0700102).
CR 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
   Bordes A., 2011, P AAAI
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Das R., ARXIV160701426
   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
   Guu K., ARXIV150601094
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hoff PD, 2002, J AM STAT ASSOC, V97, P1090, DOI 10.1198/016214502388618906
   Jiang X, 2012, P INT WORKSH SEM TEC, P1
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   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
   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
   Luo WB, 2016, LECT NOTES ARTIF INT, V9773, P767, DOI 10.1007/978-3-319-42297-8_71
   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., 2014, ADV NEURAL INFORM PR, P1179
   Nickel M, 2013, THESIS
   Orr D., BILLION CLUES 800 MI
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Riedel S., RELATION EXTRACTION
   Shi B., PROJE EMBEDDING PROJ
   Singhal A., INTRO KNOWLEDGE GRAP
   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
   Sutskever Ilya, 2014, ADV NEURAL INFORM PR, V27, P3104
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Toutanova K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1434
   Vinyals O, 2015, ADV NEURAL INFORM PR, P2773, DOI DOI 10.4018/IJWLTT.2016100102
   Wang Hongwei, GRAPHGAN GRAPH REPRE
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Yoon H., 2016, P 2016 C N AM CHAPT, P907
NR 34
TC 19
Z9 23
U1 9
U2 107
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 2019
VL 91
BP 426
EP 433
DI 10.1016/j.future.2018.09.040
PG 8
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HC4RC
UT WOS:000451790900036
DA 2022-08-23
ER

PT J
AU Song, DD
   Zhang, F
   Lu, MY
   Yang, SC
   Huang, HY
AF Song, Dandan
   Zhang, Feng
   Lu, Meiyan
   Yang, Sicheng
   Huang, Heyan
TI DTransE: Distributed Translating Embedding for Knowledge Graph
SO IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
LA English
DT Article
DE Computational modeling; Training; Partitioning algorithms; Mathematical
   model; Parallel processing; Memory management; Prediction algorithms;
   TransE; distributed computing; workload partitioning; knowledge graph
   and representation
AB Knowledge graphs play an important role in many applications, such as link prediction and question answering. Translating embedding for knowledge graphs is done with the aim of encoding structured information on entities and their rich relations in a low-dimensional embedding space. TransE is one of the most important methods in translation-based models, and uses translation invariance to implement translating embedding for knowledge graphs. In this line of work, translating embedding models represent the relation as a translation from the head entity to the tail entity and have achieved impressive results. Currently, the TransE model is only developed on single-node machines. Unfortunately, the computing and storage capacities of a single machine can easily reach their limits as knowledge graphs become larger and more complex, which limits the application scope of TransE. In order to solve this problem, we propose a distributed TransE method, known as DTransE, which can utilize distributed computing resources to calculate knowledge graph embeddings. However, building a distributed TransE is complicated and involves challenges of knowledge graph partitioning and computation. To solve these challenges, we provide a high-quality edge partitioning algorithm for the power-law graph by considering the high-degree and low-degree vertices with adaptive weights, which can balance the workload. By using the unactivated Gather-Apply-Scatter model on TransE, the processes periodically exchange messages in a loop. The irregular data distribution among the processes is also optimized to further accelerate communication. As far as we know, this is the first work on a distributed TransE method. We use link prediction to evaluate the DTransE in a distributed environment. Experiments show that, compared to the original TransE method, our proposed DTransE is, on average, 24.5 times faster with a minimum loss of accuracy; compared to the state-of-the-art parallel TransE implementation, DTransE is two times faster on average.
C1 [Song, Dandan; Lu, Meiyan; Yang, Sicheng; Huang, Heyan] Beijing Inst Technol, Sch Comp Sci & Technol, Engn Res Ctr High Volume Language Informat Proc &, Beijing 100081, Peoples R China.
   [Zhang, Feng] Renmin Univ China, Key Lab Data Engn & Knowledge Engn MOE, Beijing 100872, Peoples R China.
   [Zhang, Feng] Renmin Univ China, Sch Informat, Beijing 100872, Peoples R China.
RP Song, DD (通讯作者)，Beijing Inst Technol, Sch Comp Sci & Technol, Engn Res Ctr High Volume Language Informat Proc &, Beijing 100081, Peoples R China.
EM sdd@bit.edu.cn; fengzhang@ruc.edu.cn; lmy9559@126.com;
   yangsicheng@bit.edu.cn; hhy63@bit.edu.cn
RI Zhang, Feng/X-6906-2019
OI Zhang, Feng/0000-0003-1983-7321; Song, Dandan/0000-0002-7239-6900
FU National Key Research and Development Program of China [2020YFC0832606];
   National Natural Science Foundation of China [61976021, 61802412,
   U1811262]; Beijing Natural Science Foundation [L192027]; State Key
   Laboratory of Computer Architecture (ICT,CAS) [CARCHA202007]
FX This work was supported by the National Key Research and Development
   Program of China Grant No. 2020YFC0832606 and the National Natural
   Science Foundation of China under Grants 61976021, 61802412, and
   U1811262, Beijing Natural Science Foundation under Grant L192027, and
   State Key Laboratory of Computer Architecture (ICT,CAS) under Grant No.
   CARCHA202007.
CR [Anonymous], 2017, 2017 P 19 WORKSH ALG
   Benlic U, 2011, COMPUT OPER RES, V38, P1066, DOI 10.1016/j.cor.2010.10.007
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bourse F, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P1456, DOI 10.1145/2623330.2623660
   BUI T, 1989, ACM IEEE D, P775
   Chen R, 2018, ACM TRANS PARALLEL C, V5, DOI 10.1145/3298989
   Chevalier C, 2008, PARALLEL COMPUT, V34, P318, DOI 10.1016/j.parco.2007.12.001
   Fiduccia C. M., 1982, P 19 IEEE DES AUT C, V19, P175, DOI DOI 10.1109/DAC.1982.1585498
   Gonzalez J.E., 2012, 10 USENIX S OP SYST, V12, P17
   Gonzalez JE, 2014, P 11 USENIX C OP SYS, V14, P599
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Hendrickson B, 1995, SUPERCOMP PROC, P626
   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
   Kernighan B. W., 1970, Bell System Technical Journal, V49, P291
   Li DS, 2019, PROC VLDB ENDOW, V12, P891, DOI 10.14778/3324301.3324306
   Li L., 2017, PROC ACM MEAS ANAL C, V1
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Low Y, 2012, PROC VLDB ENDOW, V5, P716, DOI 10.14778/2212351.2212354
   Mahdisoltani F., 2015, CIDR
   Martella C., 2015, PRACTICAL GRAPH ANAL, V1
   Mayer C, 2018, INT CON DISTR COMP S, P685, DOI 10.1109/ICDCS.2018.00072
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Niu F., 2011, P ADV NEUR INF PROC
   Petroni F., 2015, P 24 ACM INT C INF K, P243, DOI DOI 10.1145/2806416.2806424
   Qian W, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4286
   Sajjad HP, 2016, IEEE INT CONGR BIG, P1, DOI 10.1109/BigDataCongress.2016.10
   Sanders Peter, 2013, Experimental Algorithms 12th International Symposium, SEA 2013. Proceedings, P164, DOI 10.1007/978-3-642-38527-8_16
   Schlag S., 2019, P 21 WORKSH ALG ENG, P211
   Schloegel K., 2003, SOURCEBOOK PARALLEL, P491
   Tang J., 2016, ACM T INFORM SYST, V34
   Tay Y, 2017, AAAI CONF ARTIF INTE, P1243
   VANDRIESSCHE R, 1995, PARALLEL COMPUT, V21, P29, DOI 10.1016/0167-8191(94)00059-J
   Walshaw C., 2007, MESH PARTITIONING TE, P27, DOI [10.4203/csets.17.2, DOI 10.4203/CSETS.17.2]
   Wang Z., 2014, AAAI, P1112
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Xie C., 2014, P ADV NEURAL INFORM, P1673
   Yao XC, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P956
   Zhang DH, 2017, 2017 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2017), P460, DOI 10.1145/3106426.3106447
   Zhang F, 2021, IEEE T KNOWL DATA EN, V33, P867, DOI 10.1109/TKDE.2019.2940184
NR 42
TC 3
Z9 3
U1 7
U2 57
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1045-9219
EI 1558-2183
J9 IEEE T PARALL DISTR
JI IEEE Trans. Parallel Distrib. Syst.
PD OCT 1
PY 2021
VL 32
IS 10
BP 2509
EP 2523
DI 10.1109/TPDS.2021.3066442
PG 15
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA RP8EC
UT WOS:000641955400003
DA 2022-08-23
ER

PT J
AU Xie, C
   Pan, ZW
   Shu, C
AF Xie, Cheng
   Pan, Ziwen
   Shu, Chao
TI Microstructure Representation Knowledge Graph to Explore the Twinning
   Formation
SO CRYSTALS
LA English
DT Article
DE deformation twinning; knowledge graph; autoencoder; material genome;
   representation learning
ID CONVOLUTIONAL NEURAL-NETWORK; STRUCTURE-PROPERTY LINKAGES;
   HIGH-STRENGTH; ALLOY; PREDICTION; DEFORMATION; RECOGNITION; MECHANISM;
   EVOLUTION; BEHAVIOR
AB Deformation twinning is an important mechanism of the plastic deformation of materials. The density of twins also affects the properties of the material. At present, the research methods of deformation twinning mainly depend on in situ EBSD, numerically investigated analysis and the finite element method. The application of machine learning methods to material microstructure research can shorten the time taken for material analysis. Machine learning methods are faced with the problem of the effective representation of the microstructure. We present a deformation twinning research method based on the representation of grain morphology features in a knowledge graph. We construct an autoencoder to extract grain morphology characteristics for building a grain knowledge graph. Then, a graph convolutional network (GCN) and fully connected network are developed to extract grain knowledge graph features and predict the twin density of materials subjected to specific tensile deformation. We use Mg-2Zn-3Li alloy as an experimental example to predict the twin density on three indexes of average grain size, twin boundaries density and average grain surface. The R-2 score of the prediction result on the twin boundaries density is up to 0.510, and the R-2 score of the average grain size and average grain surface is over 0.750. Therefore, the proposed method for deformation twinning research is effective and feasible.
C1 [Xie, Cheng; Pan, Ziwen; Shu, Chao] Yunnan Univ, Sch Software, Kunming 650000, Yunnan, Peoples R China.
RP Xie, C (通讯作者)，Yunnan Univ, Sch Software, Kunming 650000, Yunnan, Peoples R China.
EM xiecheng@ynu.edu.cn; panzw@mail.ynu.edu.cn;
   shuchao_shanty@mail.ynu.edu.cn
OI shu, chao/0000-0002-4239-5990; Pan, Ziwen/0000-0003-3451-9241; Xie,
   Cheng/0000-0002-4484-7428
FU National Natural Science Foundation of China; Major Project Fund of the
   Department of Science and Technology of Yunnan Province, China; National
   Natural Science Foundation of China [62162064]; National Natural Science
   Foundation of China Youth Project, Zero Learning Research for New Visual
   Feature Synthesis and Related Knowledge Generation [62106216]; Yunnan
   Province Rare and Precious Metal Materials Genetic Engineering (Phase I
   2021), R&D and construction of special database for copper materials and
   engineering demonstration (CN) [202102AB080019-1]; Yunnan Province
   Science and Technology Major Project, construction of a special database
   for liquid metal (CN) [202002AB080001-5]
FX This research was funded by the National Natural Science Foundation of
   China and the Major Project Fund of the Department of Science and
   Technology of Yunnan Province, China. The specific fund names and
   numbers are (1) National Natural Science Foundation of China regional
   project, research on the discovery method of non-intelligent IoT devices
   driven by data and knowledge, 62162064; (2) National Natural Science
   Foundation of China Youth Project, Zero Learning Research for New Visual
   Feature Synthesis and Related Knowledge Generation, 62106216; (3) Yunnan
   Province Rare and Precious Metal Materials Genetic Engineering (Phase I
   2021), R&D and construction of special database for copper materials and
   engineering demonstration (CN), 202102AB080019-1; and (4) Yunnan
   Province Science and Technology Major Project, construction of a special
   database for liquid metal (CN), 202002AB080001-5.
CR Albawi S, 2017, I C ENG TECHNOL
   Beyerlein IJ, 2010, PHILOS MAG, V90, P2161, DOI 10.1080/14786431003630835
   Cang RJ, 2018, COMP MATER SCI, V150, P212, DOI 10.1016/j.commatsci.2018.03.074
   Chan H, 2020, NPJ COMPUT MATER, V6, DOI 10.1038/s41524-019-0267-z
   Chen SF, 2019, J ALLOY COMPD, V805, P138, DOI 10.1016/j.jallcom.2019.07.015
   Choi WM, 2018, NPJ COMPUT MATER, V4, DOI 10.1038/s41524-017-0060-9
   Dai MY, 2021, NPJ COMPUT MATER, V7, DOI 10.1038/s41524-021-00574-w
   Dong J, 2019, IEEE ACCESS, V7, P138162, DOI 10.1109/ACCESS.2019.2931958
   Hart GLW, 2021, NAT REV MATER, V6, P730, DOI 10.1038/s41578-021-00340-w
   Herriott C, 2020, COMP MATER SCI, V175, DOI 10.1016/j.commatsci.2020.109599
   Hongjia Zhang, 2019, Key Engineering Materials, V793, P17, DOI 10.4028/www.scientific.net/KEM.793.17
   Khosravani A, 2017, ACTA MATER, V123, P55, DOI 10.1016/j.actamat.2016.10.033
   Kim Y, 2021, MATER DESIGN, V202, DOI 10.1016/j.matdes.2021.109544
   Kondo R, 2017, ACTA MATER, V141, P29, DOI 10.1016/j.actamat.2017.09.004
   Latypov MI, 2018, MATER CHARACT, V145, P671, DOI 10.1016/j.matchar.2018.09.020
   Lei CS, 2018, MAT SCI ENG A-STRUCT, V709, P72, DOI 10.1016/j.msea.2017.10.043
   Liu FY, 2019, J MAGNES ALLOY, V7, P258, DOI 10.1016/j.jma.2019.02.004
   Liu JC, 2019, FRONT MATER, V6, DOI 10.3389/fmats.2019.00209
   Liu ZY, 2020, ACTA MATER, V193, P191, DOI 10.1016/j.actamat.2020.04.041
   Luo HW, 2018, INT J WAVELETS MULTI, V16, DOI 10.1142/S0219691318500169
   Mahajan S., 1973, International Metallurgical Reviews, V18, P43
   Oganov AR, 2019, NAT REV MATER, V4, P331, DOI 10.1038/s41578-019-0101-8
   Pan FS, 2016, PROG NAT SCI-MATER, V26, P630, DOI 10.1016/j.pnsc.2016.11.016
   Praveen S, 2018, MAT SCI ENG A-STRUCT, V735, P394, DOI 10.1016/j.msea.2018.08.079
   Qayyum F, 2020, INT J MATER RES, V111, P392, DOI 10.3139/146.111900
   Qiao H, 2017, J ALLOY COMPD, V725, P96, DOI 10.1016/j.jallcom.2017.07.133
   Rekha S, 2017, IOP CONF SER-MAT SCI, V197, DOI 10.1088/1757-899X/197/1/012010
   Rong QY, 2019, COMPOS SCI TECHNOL, V184, DOI 10.1016/j.compscitech.2019.107861
   Rundo F, 2018, IET COMPUT VIS, V12, P957, DOI 10.1049/iet-cvi.2018.5195
   Sainath TN, 2015, INT CONF ACOUST SPEE, P4580, DOI 10.1109/ICASSP.2015.7178838
   Shi Z., 2021, PROCEDIA MANUF, V53, P16, DOI [10.1016/j.promfg.2021.06.005, DOI 10.1016/J.PROMFG.2021.06.005]
   Shu C, 2022, CRYSTALS, V12, DOI 10.3390/cryst12020280
   Song LH, 2018, MAT SCI ENG A-STRUCT, V710, P57, DOI 10.1016/j.msea.2017.10.055
   Tiamiyu AA, 2018, INT J PLASTICITY, V107, P79, DOI 10.1016/j.ijplas.2018.03.014
   Wang YF, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-26396-3
   Warmuzek M, 2021, COMP MATER SCI, V199, DOI 10.1016/j.commatsci.2021.110722
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Yabansu YC, 2015, ACTA MATER, V94, P26, DOI 10.1016/j.actamat.2015.04.049
   Yabansu YC, 2014, ACTA MATER, V81, P151, DOI 10.1016/j.actamat.2014.08.022
   Yang C, 2020, MATER DESIGN, V189, DOI 10.1016/j.matdes.2020.108509
   Yang ZJ, 2018, COMP MATER SCI, V151, P278, DOI 10.1016/j.commatsci.2018.05.014
   Zhang AY, 2019, MAT SCI ENG A-STRUCT, V754, P269, DOI 10.1016/j.msea.2019.03.095
   Zhang F, 2020, MAT SCI ENG A-STRUCT, V771, DOI 10.1016/j.msea.2019.138571
   Zhou CC, 2022, ASTRON J, V163, DOI 10.3847/1538-3881/ac4245
   Zhou J, 2018, ARTIF CELL NANOMED B, V46, pS1016, DOI 10.1080/21691401.2018.1442841
NR 45
TC 0
Z9 0
U1 5
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4352
J9 CRYSTALS
JI Crystals
PD APR
PY 2022
VL 12
IS 4
AR 466
DI 10.3390/cryst12040466
PG 13
WC Crystallography; Materials Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Crystallography; Materials Science
GA 0T1TB
UT WOS:000786755300001
OA gold
DA 2022-08-23
ER

PT J
AU Zhao, Y
   Zhang, AX
   Feng, HL
   Li, Q
   Gallinari, P
   Ren, FJ
AF Zhao, Yu
   Zhang, Anxiang
   Feng, Huali
   Li, Qing
   Gallinari, Patrick
   Ren, Fuji
TI Knowledge graph entity typing via learning connecting embeddings
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Entity typing; KG completion; Embedding model
ID WORD EMBEDDINGS
AB Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model via connecting them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Zhao, Yu; Feng, Huali; Li, Qing] Southwestern Univ Finance & Econ, Sch Comp Sci, Chengdu, Peoples R China.
   [Zhang, Anxiang] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA.
   [Gallinari, Patrick] Univ Paris 06, LIP6, Paris, France.
   [Ren, Fuji] Univ Tokushima, Dept Informat Sci & Intelligent Syst, Tokushima, Japan.
RP Li, Q (通讯作者)，Southwestern Univ Finance & Econ, Sch Comp Sci, Chengdu, Peoples R China.
EM lig_t@swufe.edu.cn
FU National Natural Science Foundation of China [61906159]; Sichuan Science
   and Technology Program, China [2018JY0607]; Fundamental Research Funds
   for the Central Universities, China [JBK2003008]; Financial Intelligence
   and Financial Engineering Key Laboratory of Sichuan Province, China
FX This work was supported by 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;
   the Financial Intelligence and Financial Engineering Key Laboratory of
   Sichuan Province, China.
CR Abhishek, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P797
   Barbosa D, 2018, P NAACL
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen J, 2013, INT CONF RENEW ENERG, P79, DOI 10.1109/ICRERA.2013.6749730
   Choi E., 2018, P ACL
   Deng TQ, 2020, INFORM SCIENCES, V508, P1, DOI 10.1016/j.ins.2019.08.060
   Dettmers T., 2017, AAAI, P1811
   Ding BY, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P110
   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
   Esposito M, 2020, INFORM SCIENCES, V514, P88, DOI 10.1016/j.ins.2019.12.002
   Gupta M, 2017, PROC IEEE INT CONF S, P681, DOI 10.1109/ICSME.2017.39
   Hajishirzi Hannaneh, 2013, P 2013 C EMPIRICAL M, P289
   Hao J., 2019, P KDD 2019
   Hayashi T, 2019, ACTA POLYTECH HUNG, V16, P7, DOI 10.12700/APH.16.7.2019.7.1
   Jain P., 2018, P ACL
   Ji G., 2016, P AAAI
   Jin Hailong, 2019, P 2019 C EMPIRICAL M, P4970
   Kingma D. P., 2017, ARXIV
   Li Q, 2019, KNOWL-BASED SYST, V176, P122, DOI 10.1016/j.knosys.2019.03.025
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lv X, 2018, EMNLP, P1971
   Moniruzzaman A.B.M., 2019, P WWW 2019
   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, ARXIV150406658
   Le NT, 2020, INFORM SCIENCES, V514, P149, DOI 10.1016/j.ins.2019.12.010
   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
   Pantel Patrick, 2012, ACL ASS P COMP LING, P563
   Pota M, 2019, KNOWL-BASED SYST, V164, P309, DOI 10.1016/j.knosys.2018.11.003
   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
   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
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xie R., 2018, P AAAI
   Yogatama 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, P291
   Yuan Z., 2018, P AAAI
   Zeng XH, 2014, PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON TEACHING AND COMPUTATIONAL SCIENCE, P23
   Zhang R., 2018, P AAAI
   Zhou Ben, 2018, P 2018 C EMP METH NA, P2065
NR 47
TC 0
Z9 0
U1 0
U2 20
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 MAY 21
PY 2020
VL 196
AR 105808
DI 10.1016/j.knosys.2020.105808
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LF3DO
UT WOS:000527301700030
DA 2022-08-23
ER

PT J
AU Lin, XX
   Liang, YC
   Giunchiglia, F
   Feng, XY
   Guan, RC
AF Lin, Xixun
   Liang, Yanchun
   Giunchiglia, Fausto
   Feng, Xiaoyue
   Guan, Renchu
TI Relation path embedding in knowledge graphs
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Knowledge graph completion; Relation paths; Path projection; Type
   constraints; Knowledge representation learning
AB Large-scale knowledge graphs have currently reached impressive sizes; however, they are still far from complete. In addition, most existing methods for knowledge graph completion only consider the direct links between entities, ignoring the vital impact of the semantics of relation paths. In this paper, we study the problem of how to better embed entities and relations of knowledge graphs into different low-dimensional spaces by taking full advantage of the additional semantics of relation paths and propose a novel relation path embedding model named as RPE. Specifically, with the corresponding relation and path projections, RPE can simultaneously embed each entity into two types of latent spaces. Moreover, type constraints are extended from traditional relation-specific type constraints to the proposed path-specific type constraints and both of the two type constraints can be seamlessly incorporated into RPE. The proposed model is evaluated on the benchmark tasks of link prediction and triple classification. The results of experiments demonstrate our method outperforms all baselines on both tasks. They indicate that our model is capable of catching the semantics of relation paths, which is significant for knowledge representation learning.
C1 [Lin, Xixun; Liang, Yanchun; Feng, Xiaoyue; Guan, Renchu] Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Natl Educ Minist, Changchun 130012, Jilin, Peoples R China.
   [Liang, Yanchun; Guan, Renchu] Jilin Univ, Zhuhai Coll, Zhuhai Lab Key Lab Symbol Computat & Knowledge En, Minist Educ, Zhuhai 519041, Peoples R China.
   [Giunchiglia, Fausto] Univ Trento, DISI, Trento, Italy.
RP Guan, RC (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Natl Educ Minist, Changchun 130012, Jilin, Peoples R China.; Guan, RC (通讯作者)，Jilin Univ, Zhuhai Coll, Zhuhai Lab Key Lab Symbol Computat & Knowledge En, Minist Educ, Zhuhai 519041, Peoples R China.
EM guanrenchu@jlu.edu.cn
RI Guan, Renchu/K-5223-2015; Guan, Renchu/S-5503-2019
OI Guan, Renchu/0000-0002-7162-7826; Guan, Renchu/0000-0002-7162-7826
FU National Natural Science Foundation of China [61572228, 61472158,
   61300147, 61602207]; Science Technology Development Project from Jilin
   Province [20160101247JC]; Zhuhai Premier Discipline Enhancement Scheme;
   Guangdong Premier Key-Discipline Enhancement Scheme; European Commission
   [600854]
FX The authors are grateful for the support of the National Natural Science
   Foundation of China (Nos. 61572228, 61472158, 61300147, and 61602207),
   the Science Technology Development Project from Jilin Province (No.
   20160101247JC), the Zhuhai Premier Discipline Enhancement Scheme, the
   Guangdong Premier Key-Discipline Enhancement Scheme, and the Smart
   Society Collaborative Project funded by the European Commission's 7th
   Framework ICT Programme for Research and Technological Development under
   Grant Agreement No. 600854.
CR Ahn S., 2016, ARXIV160800318
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   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 K., 2014, P 2014 C EMP METH NA, P1568
   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
   Garcia-Duran A., 2015, P 2015 C EMP METH NA, P286, DOI DOI 10.18653/V1/D15-1034
   Gardner M., 2015, P 2015 C EMP METH NA, P1488, DOI DOI 10.18653/V1/D15-1173
   Guu K., 2015, ARXIV150601094
   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
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lao 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, P666
   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
   Mikolov T., 2013, ADV NEURAL INFORM PR
   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
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Riedel S., 2013, P 2013 C N AM CHAPT, P74
   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
   TOUTANOVA K, 2016, P 54 ANN M ASS COMP, V1, P1434
   Wang Q, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1859
   Wang Q, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1308
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   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
   Zhang HJ, 2017, IEEE T IND INFORM, V13, P616, DOI 10.1109/TII.2016.2601521
NR 35
TC 12
Z9 12
U1 5
U2 15
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 2019
VL 31
IS 9
SI SI
BP 5629
EP 5639
DI 10.1007/s00521-018-3384-6
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JB5ZZ
UT WOS:000488645700076
OA hybrid, Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Wang, X
   Wu, H
   Hsu, CH
AF Wang, Xin
   Wu, Hao
   Hsu, Ching-Hsien
TI Mashup-Oriented API Recommendation via Random Walk on Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Mashup development; API recommendation; random walks with restart;
   knowledge graph
AB With the growing prosperity of the Web API economy, mashup-oriented API recommendation has become an important requirement. Various methods based on different principles of technology have been used to deal with this issue. In recent years, the Web API ecosystem has accumulated a wealth of knowledge that can be used to enhance the recommendation models, and however, current concerns in this regard still remain. To cope with this issue, we present a graph-based algorithmic framework for the task of mashup-oriented API recommendation. Especially, we design a concise schema of the knowledge graph to encode the mashup-specific contexts and model the mashup requirement with graphic entities. We then exploit random walks with restart to assess the potential relevance between the mashup requirement and the Web APIs according to the knowledge graph. In addition, we propose the query-specific weighting strategies to enhance the knowledge graph construction. The experimental results demonstrate that our proposed method is much superior to some state-of-the-art methods, also achieves robust effects on reducing computational overhead, and suppresses the negative Matthew effect in APIs' recommendation.
C1 [Wang, Xin; Wu, Hao] Yunnan Univ, Sch Informat Sci & Engn, Kunming 650500, Yunnan, Peoples R China.
   [Hsu, Ching-Hsien] Natl Chung Cheng Univ, Dept Comp Sci & Informat Engn, Chiayi 62102, Taiwan.
RP Wu, H (通讯作者)，Yunnan Univ, Sch Informat Sci & Engn, Kunming 650500, Yunnan, Peoples R China.
EM haowu@ynu.edu.cn
RI Wu, Hao/T-5893-2019; Hsu, Ching-Hsien/AAE-6917-2020
OI Wu, Hao/0000-0002-3696-9281; 
FU National Natural Science Foundation of China [61562090, 61472345,
   61562092]; China Postdoctoral Science Foundation [2016M592721]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61562090, Grant 61472345, and Grant
   61562092, and in part by the China Postdoctoral Science Foundation under
   Grant 2016M592721.
CR Bao QH, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017), P220, DOI 10.1109/ICWS.2017.36
   Bianchini D, 2017, ACM T WEB, V11, DOI 10.1145/3061710
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bouguettaya A, 2017, COMMUN ACM, V60, P64, DOI 10.1145/2983528
   Cao BQ, 2014, INT J GRID DISTRIB, V7, P163, DOI 10.14257/ijgdc.2014.7.2.15
   Cappiello C, 2010, IEEE INTERNET COMPUT, V14, P14, DOI 10.1109/MIC.2010.60
   Dojchinovski M, 2018, SEMANT WEB, V9, P381, DOI 10.3233/SW-170259
   Gao W, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017), P65, DOI 10.1109/ICWS.2017.17
   Gu Q, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), P452, DOI 10.1109/ICWS.2016.65
   Guo LT, 2017, IEEE ACCESS, V5, P12714, DOI 10.1109/ACCESS.2017.2721934
   Haveliwala TH, 2003, IEEE T KNOWL DATA EN, V15, P784, DOI 10.1109/TKDE.2003.1208999
   Keman Huang, 2012, Proceedings of the 2012 IEEE 19th International Conference on Web Services (ICWS), P552, DOI 10.1109/ICWS.2012.32
   Konstas I, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P195, DOI 10.1145/1571941.1571977
   Li HC, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC), P84, DOI 10.1109/SCC.2017.19
   Liang TT, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), P436, DOI 10.1109/ICWS.2016.63
   Liu XZ, 2014, SCI CHINA INFORM SCI, V57, DOI 10.1007/s11432-013-4782-0
   Rahman MM, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC), P225, DOI 10.1109/SCC.2017.36
   Rosen-Zvi Michal, 2004, P 20 C UNC ART INT B, P487, DOI 10.1016/j.nima.2010.11.062
   Tan W, 2016, IEEE INTERNET COMPUT, V20, P64, DOI 10.1109/MIC.2016.74
   Wang H, 2018, 2018 IEEE 3RD INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA), P78, DOI 10.1109/ICCCBDA.2018.8386490
   Wenxing Xu, 2013, 2013 IEEE 20th International Conference on Web Services (ICWS), P107, DOI 10.1109/ICWS.2013.24
   Wu H, 2018, FUTURE GENER COMP SY, V82, P669, DOI 10.1016/j.future.2017.06.020
   Wu H, 2015, INT J DISTRIB SENS N, DOI 10.1155/2015/613612
   Wu H, 2014, PERS UBIQUIT COMPUT, V18, P1855, DOI 10.1007/s00779-014-0785-0
   Xia BF, 2015, IEEE T SERV COMPUT, V8, P674, DOI 10.1109/TSC.2014.2379251
   Xue QH, 2017, 2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), P119, DOI 10.1109/ICMLA.2017.0-169
   Yao LN, 2021, IEEE T SERV COMPUT, V14, P502, DOI 10.1109/TSC.2018.2803171
   Yu ZW, 2017, IEEE T SERV COMPUT, V10, P914, DOI 10.1109/TSC.2015.2485988
   Zhong Y, 2018, IEEE T AUTOM SCI ENG, V15, P468, DOI 10.1109/TASE.2016.2624310
   Zhong Y, 2014, 2014 IEEE 21ST INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2014), P25, DOI 10.1109/ICWS.2014.17
   [No title captured]
NR 31
TC 14
Z9 15
U1 1
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 7651
EP 7662
DI 10.1109/ACCESS.2018.2890156
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HJ1HN
UT WOS:000456913100001
OA gold
DA 2022-08-23
ER

PT J
AU Li, MD
   Sun, ZY
   Zhang, WS
   Liu, W
AF Li, Mingda
   Sun, Zhengya
   Zhang, Wensheng
   Liu, Wei
TI Leveraging semantic property for temporal knowledge graph completion
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Temporal knowledge graphs; Knowledge graph embedding; Semantic property;
   Link prediction
AB Temporal knowledge graphs (TKGs) have become an effective tool for numerous intelligent applications. Due to their incompleteness, TKG embedding methods have been proposed to infer the missing temporal facts, and work by learning latent representations for entities, relations and timestamps. However, these methods primarily focus on measuring the plausibility of the whole temporal fact, and ignore the semantic property that there exists a bias between any relation and its involved entities at various time steps. In this paper, we present a novel temporal knowledge graph completion framework, which imposes relational constraints to preserve the semantic property implied in TKGs. Specifically, we borrow ideas from two well-known transformation functions, i.e., tensor decomposition and hyperplane projection, and design relational constraints associated with timestamps. We then adopt suitable regularization schemes to accommodate specific relational constraints, which combat overfitting and enforce temporal smoothness. Experimental studies indicate the superiority of our proposal compared to existing baselines on the task of temporal knowledge graph completion.
C1 [Li, Mingda; Sun, Zhengya; Zhang, Wensheng] Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China.
   [Li, Mingda; Sun, Zhengya; Zhang, Wensheng] Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China.
   [Liu, Wei] Beijing Informat Sci & Technol Univ, Dept Automat, Beijing 100192, Peoples R China.
RP Sun, ZY (通讯作者)，Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China.; Sun, ZY (通讯作者)，Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China.
EM limingda2018@ia.ac.cn; zhengya.sun@ia.ac.cn;
   zhangwenshengia@hotmail.com; willie@bistu.edu.cn
FU National Key Research and Development Program of China [2021YFF0704000];
   National Natural Science Foundation of China [61876183, 61961160707,
   61976212]
FX The authors are thankful for the financial support from the National Key
   Research and Development Program of China (No. 2021YFF0704000) and the
   National Natural Science Foundation of China (Nos. 61876183,
   61961160707, 61976212).
CR Bai LY, 2021, APPL SOFT COMPUT, V103, DOI 10.1016/j.asoc.2021.107144
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Boschee Elizabeth, 2015, HARVARD DATAVERSE, V12
   Dasgupta SS, 2018, P 2018 C EMP METH NA
   Deng S, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1585, DOI 10.1145/3394486.3403209
   Garcia-Duran Alberto, 2018, P 2018 C EMP METH NA, P4816
   Goel R, 2020, AAAI CONF ARTIF INTE, V34, P3988
   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
   Jin Woojeong, 2020, P EMNLP, P6669
   Kazemi SM, 2020, J MACH LEARN RES, V21
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Lacroix T, 2020, P INT C LEARN REPR
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Leblay J, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1771, DOI 10.1145/3184558.3191639
   Leetaru K, 2013, ISA ANN CONV, V2, P1
   Li MD, 2021, NEUROCOMPUTING, V429, P77, DOI 10.1016/j.neucom.2020.12.012
   Li ZF, 2021, IEEE T NEUR NET LEAR, DOI 10.1109/TNNLS.2021.3055147
   Li ZX, 2021, SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P408, DOI 10.1145/3404835.3462963
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Ma YP, 2019, J WEB SEMANT, V59, DOI 10.1016/j.websem.2018.12.008
   Mezni H, IEEE T SERV COMPUT, DOI [10.1109/TSC.2021.3075053, DOI 10.1109/TSC.2021.3075053]
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Sadeghian A, 2021, AAAI CONF ARTIF INTE, V35, P6471
   Shao PP, 2022, KNOWL-BASED SYST, V238, DOI 10.1016/j.knosys.2021.107841
   Trivedi R, 2017, PR MACH LEARN RES, V70
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang HW, 2021, KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1697, DOI 10.1145/3447548.3467247
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu J, 2020, P EMNLP
   Wu J, 2020, P 2020 C EMPIRICAL M
   Xu Y, 2020, LAPLACIAN DIGITAL SI
   Xu Y, 2022, 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
   Yang B., 2015, P INT C LEARN REPR I
   Zhang Z, 2020, P 34 C NEUR INF PROC
   Zhang ZL, 2022, IEEE T KNOWL DATA EN, V34, P2335, DOI 10.1109/TKDE.2020.3005952
   Zhu CC, 2021, AAAI CONF ARTIF INTE, V35, P4732
NR 40
TC 0
Z9 0
U1 0
U2 0
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/s10489-022-03981-8
EA AUG 2022
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3P4XK
UT WOS:000837543300008
DA 2022-08-23
ER

PT J
AU Lee, JW
   Park, J
AF Lee, Jae-won
   Park, Jaehui
TI An Approach to Constructing a Knowledge Graph Based on Korean
   Open-Government Data
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE open-government data; metadata; knowledge graph; linked data
ID WEB
AB A data platform collecting the whole metadata held by government agencies and a knowledge graph showing the relationship between the collected open-government data are proposed in this paper. By practically applying the data platform and the knowledge graph to the public sector in Korea, three improvements were expected: (1) enhancing user accessibility across open-government data; (2) allowing users to acquire relevant data as well as desired data with a single query; and (3) enabling data-driven decision-making. In particular, the barriers for citizens to acquire the necessary data have been greatly reduced by using the proposed knowledge graph, which is considered to be important for data-driven decision-making. The reliability and feasibility of constructing a metadata-based open-data platform and a knowledge graph are estimated to be considerably high as the proposed approach is applied to a real service of the public sector in Korea.
C1 [Lee, Jae-won] Natl Informat Soc Agcy, Dept Open Data, Daegu 41068, South Korea.
   [Park, Jaehui] Incheon Natl Univ, Dept Comp Sci & Engn, Incheon 22012, South Korea.
RP Park, J (通讯作者)，Incheon Natl Univ, Dept Comp Sci & Engn, Incheon 22012, South Korea.
EM jaewonlee@nia.or.kr; jaehui@inu.ac.kr
FU Basic Science Research Program through the National Research Foundation
   of Korea(NRF) - Ministry of Education [NRF-2018R1D1A1B07044633]
FX This research was supported by Basic Science Research Program through
   the National Research Foundation of Korea(NRF) funded by the Ministry of
   Education(NRF-2018R1D1A1B07044633).
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   BAEZEYATES R, 1999, MODERN INFORM RETRIE
   Balmin A., 2004, P 30 INT C VER LARG, P564
   Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X
   Dawes SS, 2016, GOV INFORM Q, V33, P15, DOI 10.1016/j.giq.2016.01.003
   Fikes R., 2004, WEB SEMANTICS SCI SE, V2, P19, DOI DOI 10.1016/J.WEBSEM.2004.07.002
   Gasco-Hernandez M, 2018, GOV INFORM Q, V35, P233, DOI 10.1016/j.giq.2018.01.003
   Haveliwala T.H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513
   Janssen M, 2012, INFORM SYST MANAGE, V29, P258, DOI 10.1080/10580530.2012.716740
   Jeh G., 2002, P 8 ACM SIGKDD INT C, P538, DOI DOI 10.1145/775047.775126
   Kalampokis E, 2013, LECT NOTES COMPUT SC, V8074, P99, DOI 10.1007/978-3-642-40358-3_9
   Kim HH, 2005, J AM SOC INF SCI TEC, V56, P1167, DOI 10.1002/asi.20220
   Kumar VK, 2019, WEB SERVICES CONCEPT, P1068
   Lee JW, 2013, J INF SCI, V39, P608, DOI 10.1177/0165551513480100
   Lee T, 2006, LECT NOTES COMPUT SC, V4244, P242
   Lenat D. B., 1989, BUILDING LARGE KNOWL
   Lourenco RP, 2015, GOV INFORM Q, V32, P323, DOI 10.1016/j.giq.2015.05.006
   Meymandpour R, 2016, KNOWL-BASED SYST, V109, P276, DOI 10.1016/j.knosys.2016.07.012
   OECD, 2015, DAT DRIV INN BIG DAT
   ROCHA C., 2004, P 13 INT C WORLD WID, P374, DOI DOI 10.1145/988672.988723
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Tammisto Y, 2012, LECT NOTES BUS INF P, V114, P297
   Vetro A, 2016, GOV INFORM Q, V33, P325, DOI 10.1016/j.giq.2016.02.001
   Xu S., 2008, P 31 ANN INT ACM SIG, P155, DOI DOI 10.1145/1390334.1390363
NR 24
TC 1
Z9 1
U1 4
U2 28
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 OCT
PY 2019
VL 9
IS 19
AR 4095
DI 10.3390/app9194095
PG 12
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 JM5MM
UT WOS:000496258100153
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Wang, R
   Li, BC
   Hu, SW
   Du, WQ
   Zhang, M
AF Wang, Rui
   Li, Bicheng
   Hu, Shengwei
   Du, Wenqian
   Zhang, Min
TI Knowledge Graph Embedding via Graph Attenuated Attention Networks
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph embedding; attenuated attention mechanism; neighbor
   nodes; translational model; graph attention networks
AB Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, state-of-the-art models are based on graph convolutional neural networks. These models automatically extract features, in combination with the features of the graph model, to generate feature embeddings with a strong expressive ability. However, these methods assign the same weights on the relation path in the knowledge graph and ignore the rich information presented in neighbor nodes, which result in incomplete mining of triple features. To this end, we propose Graph Attenuated Attention networks(GAATs), a novel representation method, which integrates an attenuated attention mechanism to assign different weight in different relation path and acquire the information from the neighborhoods. As a result, entities and relations can be learned in any neighbors. Our empirical research provides insight into the effectiveness of the attenuated attention-based models, and we show significant improvement compared to the state-of-the-art methods on two benchmark datasets WN18RR and FB15k-237.
C1 [Wang, Rui; Li, Bicheng; Hu, Shengwei; Du, Wenqian; Zhang, Min] Huaqiao Univ, Coll Comp Sci & Technol, Xiamen 361021, Fujian, Peoples R China.
RP Li, BC (通讯作者)，Huaqiao Univ, Coll Comp Sci & Technol, Xiamen 361021, Fujian, Peoples R China.
EM lbclm@163.com
FU Fujian Social Science Fund [FJ2017B073]; Scientific Research Funds of
   Huaqiao University
FX This work was supported in part by the Fujian Social Science Fund
   Projectunder Grant FJ2017B073, and in part by the Scientific Research
   Funds of Huaqiao University.
CR Abu-El-Haija S, 2018, ADV NEUR IN, V31
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], 2019, P C EMP METH NAT LAN
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], 2017, P 2017 C N AM CHAPT
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Dettmers T., 2018, P 32 AAAI C ART INT
   Efstathiou I., 2017, P 15 C EUR CHAPT ASS, V2, P480
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   Fu XY, 2018, 2018 9TH IEEE INTERNATIONAL CONFERENCE ON BIG KNOWLEDGE (ICBK), P25, DOI 10.1109/ICBK.2018.00012
   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
   Lee JB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1666, DOI 10.1145/3219819.3219980
   Lin XX, 2019, NEURAL COMPUT APPL, V31, P5629, DOI 10.1007/s00521-018-3384-6
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Ma F, 2019, REMOTE SENS-BASEL, V11, DOI 10.3390/rs11212586
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Nguyen D.Q., 2017, ARXIV170308098, V1703
   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
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Sarwar M, 2018, J INTELL FUZZY SYST, V34, P547, DOI 10.3233/JIFS-17758
   Sarwar M, 2017, NEW MATH NAT COMPUT, V13, P261, DOI 10.1142/S1793005717400105
   Shen Y., 2019, P 2019 SIAM INT C DA, P639
   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
   van der Merwe R, 2019, ARCH REC, V40, P239, DOI 10.1080/23257962.2017.1388224
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   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
   Xiao H., 2015, ARXIV150905490
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Xu C., 2019, ARXIV190600687
   Zhang MY, 2018, LECT NOTES COMPUT SC, V10772, P276, DOI 10.1007/978-3-319-76941-7_21
   Zhou H, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4623
NR 41
TC 17
Z9 19
U1 14
U2 33
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 5212
EP 5224
DI 10.1109/ACCESS.2019.2963367
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LB5LP
UT WOS:000524677500003
OA gold
DA 2022-08-23
ER

PT J
AU Huang, XF
   Zhang, JX
   Xu, ZS
   Ou, L
   Tong, JB
AF Huang, Xiaofeng
   Zhang, Jixin
   Xu, Zisang
   Ou, Lu
   Tong, Jianbin
TI A knowledge graph based question answering method for medical domain
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Knowledge graph; Medical domain; Question answering; Weighted path
   ranking
AB Question answering (QA) is a hot field of research in Natural Language Processing. A big challenge in this field is to answer questions from knowledge-dependable domain. Since traditional QA hardly satisfies some knowledge-dependable situations, such as disease diagnosis, drug recommendation, etc. In recent years, researches focus on knowledge-based question answering (KBQA). However, there still exist some problems in KBQA, traditional KBQA is limited by a range of historical cases and takes too much human labor. To address the problems, in this paper, we propose an approach of knowledge graph based question answering (KGQA) method for medical domain, which firstly constructs a medical knowledge graph by extracting named entities and relations between the entities from medical documents. Then, in order to understand a question, it extracts the key information in the question according to the named entities, and meanwhile, it recognizes the questions' intentions by adopting information gain. The next an inference method based on weighted path ranking on the knowledge graph is proposed to score the related entities according to the key information and intention of a given question. Finally, it extracts the inferred candidate entities to construct answers. Our approach can understand questions, connect the questions to the knowledge graph and inference the answers on the knowledge graph. Theoretical analysis and real-life experimental results show the efficiency of our approach.
C1 [Huang, Xiaofeng; Zhang, Jixin] Hubei Univ Technol, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
   [Xu, Zisang] Changsha Univ Sci & Technol, Comp & Commun Engineer Inst, Changsha, Hunan, Peoples R China.
   [Ou, Lu] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha, Hunan, Peoples R China.
   [Tong, Jianbin] Cent South Univ, Xiangya Hosp 3, Hunan Prov Key Lab Brain Homeostasis, Changsha, Hunan, Peoples R China.
RP Zhang, JX (通讯作者)，Hubei Univ Technol, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
EM zhangjx@hbut.edu.cn
FU Research Foundation of Education Commission of Hubei Province
   [Q20201408]; China Postdoctoral Science Foundation [2020M672488];
   Research Foundation of Education Commission of Hunan Province [20C0080];
   Science and Technology Key Projects of Hunan Province [2018TP1009]
FX This work is supported by the Research Foundation of Education
   Commission of Hubei Province No. Q20201408, the China Postdoctoral
   Science Foundation No. 2020M672488, the Research Foundation of Education
   Commission of Hunan Province No. 20C0080 and the Science and Technology
   Key Projects of Hunan Province under Grant 2018TP1009. The funders had
   no role in study design, data collection and analysis, decision to
   publish, or preparation of the manuscript.
CR Athenikos S. J., 2009, P 2009 ACM S APPL CO, P847, DOI DOI 10.1145/1529282.1529462
   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
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Debattista J, 2018, SEMANT WEB, V9, P859, DOI 10.3233/SW-180306
   Duan H., 2008, ACL, P156
   Farber M, 2018, SEMANT WEB, V9
   Goodwin TR, 2018, ACM T INTEL SYST TEC, V9, DOI 10.1145/3106745
   Goodwin TR, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P297, DOI 10.1145/2983323.2983819
   Guofei Feng, 2018, Journal of Shanghai Jiaotong University (Science), V23, P678, DOI 10.1007/s12204-018-1982-1
   Jieba, 2020, JIEBA
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lao N, 2010, P ANN C NEUR INF PRO, P1234
   Li H, 2017, P INT WORLD WID WEB
   Lukovnikov D, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1211, DOI 10.1145/3038912.3052675
   Mihalcea R., 2004, P 2004 C EMP METH NA, P404
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Qiu YQ, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P35, DOI 10.1145/3184558.3186916
   Roberts K, 2016, INFORM RETRIEVAL J, V19, P113, DOI 10.1007/s10791-015-9259-x
   Salem ABM, 2007, PROC WRLD ACAD SCI E, V25, P9
   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
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Vembandasamy K., 2015, INT J INNOVATIVE SCI, V2
   Wang P, 2016, P ACM INT C INF KNOW
   Xian L, 2015, VLDB ENDOWMENT, V6, P97
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yan G, 2018, P INT C BIG DAT COMP
   Yin P, 2015, P 24 ACM INT C INF K
   Yue B, 2017, P INT WORLD WID WEB
   Zaveri A, 2016, SEMANT WEB, V7, P63, DOI 10.3233/SW-150175
NR 31
TC 1
Z9 1
U1 32
U2 54
PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
EI 2376-5992
J9 PEERJ COMPUT SCI
JI PeerJ Comput. Sci.
PD SEP 1
PY 2021
VL 7
AR e667
DI 10.7717/peerj-cs.667
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA US0AD
UT WOS:000697099400001
PM 34604514
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Jiang, C
   Ngo, V
   Chapman, R
   Yu, Y
   Liu, HF
   Jiang, GQ
   Zong, NS
AF Jiang, Chao
   Ngo, Victoria
   Chapman, Richard
   Yu, Yue
   Liu, Hongfang
   Jiang, Guoqian
   Zong, Nansu
TI Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19
   Literature, LitCovid, and Pubtator: Framework Development and Validation
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE adversarial generative network; knowledge graph; deep denoising; machine
   learning; COVID-19; biomedical; neural network; network model; training
   data
AB Background: Multiple types of biomedical associations of knowledge graphs, including COVID-19-related ones, are constructed based on co-occurring biomedical entities retrieved from recent literature. However, the applications derived from these raw graphs (eg, association predictions among genes, drugs, and diseases) have a high probability of false-positive predictions as co-occurrences in the literature do not always mean there is a true biomedical association between two entities.
   Objective: Data quality plays an important role in training deep neural network models; however, most of the current work in this area has been focused on improving a model's performance with the assumption that the preprocessed data are clean. Here, we studied how to remove noise from raw knowledge graphs with limited labeled information.
   Methods: The proposed framework used generative-based deep neural networks to generate a graph that can distinguish the unknown associations in the raw training graph. Two generative adversarial network models, NetGAN and Cross-Entropy Low-rank Logits (CELL), were adopted for the edge classification (ie, link prediction), leveraging unlabeled link information based on a real knowledge graph built from LitCovid and Pubtator.
   Results: The performance of link prediction, especially in the extreme case of training data versus test data at a ratio of 1:9, demonstrated that the proposed method still achieved favorable results (area under the receiver operating characteristic curve >0.8 for the synthetic data set and 0.7 for the real data set), despite the limited amount of testing data available.
   Conclusions: Our preliminary findings showed the proposed framework achieved promising results for removing noise during data preprocessing of the biomedical knowledge graph, potentially improving the performance of downstream applications by providing cleaner data.
C1 [Jiang, Chao; Chapman, Richard] Auburn Univ, Dept Comp Sci & Software Engn, Auburn, AL USA.
   [Ngo, Victoria] VA Palo Alto Hlth Care Syst, Ctr Innovat Implementat, Sacramento, CA USA.
   [Ngo, Victoria] Stanford Univ, Stanford Sch Med, Stanford Hlth Policy, Stanford, CA USA.
   [Ngo, Victoria] Stanford Univ, Freeman Spogli Inst Int Studies, Stanford, CA USA.
   [Yu, Yue; Liu, Hongfang; Jiang, Guoqian; Zong, Nansu] Mayo Clin, Dept Artificial Intelligence & Informat Res, Rochester, MN USA.
   [Zong, Nansu] Mayo Clin, Dept Artificial Intelligence & Informat Res, 200 First St SW, Rochester, MN 55905 USA.
RP Zong, NS (通讯作者)，Mayo Clin, Dept Artificial Intelligence & Informat Res, 200 First St SW, Rochester, MN 55905 USA.
EM Zong.Nansu@mayo.edu
OI Ngo, Victoria/0000-0001-9973-8379; jiang, chao/0000-0002-0467-6177
FU National Institutes of Health (NIH) National Institute of General
   Medical Sciences (NIGMS) [K99GM135488]
FX Acknowledgments This work was supported by funding from the National
   Institutes of Health (NIH) National Institute of General Medical
   Sciences (NIGMS) (K99GM135488) .
CR Algan G, 2021, KNOWL-BASED SYST, V215, DOI 10.1016/j.knosys.2021.106771
   [Anonymous], DEEP DENOISING RAW B
   [Anonymous], CORD 19 ON FHIR
   [Anonymous], 2020, REPOSITORY ICML 2020
   [Anonymous], IMPLEMENTATION PAPER
   [Anonymous], CORONAVIRUS DIS COVI
   [Anonymous], SCIKIT LEARN MACHINE
   Arjovsky M, 2017, PR MACH LEARN RES, V70
   Bojchevski A, 2018, PR MACH LEARN RES, V80
   Chen QY, 2021, NUCLEIC ACIDS RES, V49, pD1534, DOI 10.1093/nar/gkaa952
   Chen QY, 2020, NATURE, V579, P193, DOI 10.1038/d41586-020-00694-1
   Choi Edward, 2017, MACH LEARN HEALTHC 2
   Dong HW, PREPRINT
   El Mouden Zakariyaa Ait, 2020, Procedia Comput Sci, V177, P204, DOI 10.1016/j.procs.2020.10.029
   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
   Groza A., PREPRINT
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Jin WG, 2018, PR MACH LEARN RES, V80
   Li W.-W., PREPRINT
   Long YH, 2022, BIOINFORMATICS, V38, P2254, DOI 10.1093/bioinformatics/btac100
   McCallum AK, 2000, INFORM RETRIEVAL, V3, P127, DOI 10.1023/A:1009953814988
   Oniani D, 2020, J AM MED INFORM ASSN, V27, P1259, DOI 10.1093/jamia/ocaa117
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Rendsburg L, 2020, 2020 37 INT C MACH L
   Rossanez A, 2020, BMC MED INFORM DECIS, V20, DOI 10.1186/s12911-020-01341-5
   Shannon P, 2003, GENOME RES, V13, P2498, DOI 10.1101/gr.1239303
   Wang YQ, 2020, ACM COMPUT SURV, V53, DOI 10.1145/3386252
   Wei CH, 2013, NUCLEIC ACIDS RES, V41, pW518, DOI 10.1093/nar/gkt441
   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
   Yang QS, 2018, IEEE T MED IMAGING, V37, P1348, DOI 10.1109/TMI.2018.2827462
   Zhou Y, 2019, IEEE INT CONF BIG DA, P1018, DOI 10.1109/BigData47090.2019.9006299
   Zhou Y, 2018, IEEE INT CONF BIG DA, P1162, DOI 10.1109/BigData.2018.8621992
   Zhou Y, 2018, IEEE DATA MINING, P1464, DOI 10.1109/ICDM.2018.00203
   Zhou ZX, 2020, IEEE T BIO-MED ENG, V67, P298, DOI 10.1109/TBME.2019.2912986
   Zong NS, 2021, BRIEF BIOINFORM, V22, P568, DOI 10.1093/bib/bbz147
NR 36
TC 0
Z9 0
U1 0
U2 0
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 e38584
DI 10.2196/38584
PG 11
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:000828200900011
PM 35658098
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Do, P
   Phan, T
   Le, H
   Gupta, BB
AF Do, Phuc
   Phan, Trung
   Le, Hung
   Gupta, Brij B.
TI Building a knowledge graph by using cross-lingual transfer method and
   distributed MinIE algorithm on apache spark
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Knowledge graph; Cross-lingual transfer method; Distributed MinIE;
   Natural language processing; Triples extraction
AB The simplest and effective way to store human knowledge through centuries was using text. Along with the advancement of technology nowadays, the volume of text has grown to be larger and larger. To extract useful information from this amount of text becomes an exceptionally complex task. As an effort to solve that problem, in this paper, we present a pipeline to extract core knowledge from large quantity text using distributed computing. The components of our pipeline are systems that were known to yield good results. The outputs of our proposed system are stored in a knowledge graph. A knowledge graph is a graph for storing knowledge in the form of triples (head, relation, tail). Some of the existing knowledge graphs in the world are Google knowledge graph, YAGO, DBLP, or DBpedia. These knowledge graphs have one thing in common-they are in English. The English language is studied by many researchers in the world and it had become a rich-resource language (with many natural language processing tools and data set). Vietnamese, on the other hand, is a low-resource language. Therefore, we use cross-lingual transfer method to build a Vietnamese knowledge graph. Firstly, we collect data in form of text about Vietnam tourism, which was written mostly in Vietnamese, using Google search and Wikipedia. In the next step, we translate them into English with Google Translate and use English Natural Language Processing tools like Stanford Parser, Co-referencing, ClausIE, MinIE to extract useful triples from this text. Lastly, the triples are translated back to Vietnamese to build a Vietnam tourism knowledge graph. Since we are working with massive text, we develop a distributed algorithm to extract triples from sentences of massive text. This is a distributed version of MinIE, which was originally developed for a single machine model. In Apache Spark framework, we divide massive text into many smaller parts and move them to the worker nodes with distributed MinIE function. Spark distributed MinIE will extract the triples of sentences in the local text of this worker node in parallel. Finally, the result of worker nodes will be sent back to the master node for building the knowledge graph. We conduct experiments with the distributed MinIE on spark cluster to prove the outperformance of our proposed algorithm.
C1 [Do, Phuc; Phan, Trung; Le, Hung] Vietnam Natl Univ, Univ Informat Technol, Ho Chi Minh City, Vietnam.
   [Gupta, Brij B.] Natl Inst Technol, Kurukshetra, Haryana, India.
   [Gupta, Brij B.] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan.
   [Gupta, Brij B.] Staffordshire Univ, Stoke On Trent ST4 2DE, Staffs, England.
RP Gupta, BB (通讯作者)，Natl Inst Technol, Kurukshetra, Haryana, India.; Gupta, BB (通讯作者)，Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan.; Gupta, BB (通讯作者)，Staffordshire Univ, Stoke On Trent ST4 2DE, Staffs, England.
EM phucdo@uit.edu.vn; trungphansg@gmail.com; hungle1abc@gmail.com;
   bbgupta@nitkkr.ac.in
RI Gupta, Brij B/E-9813-2011
OI Gupta, Brij B/0000-0003-4929-4698
FU Vietnam National University Ho Chi Minh City (VNU-HCMC) [DS2020-26-01]
FX This research is funded by Vietnam National University Ho Chi Minh City
   (VNU-HCMC) under the Grant Number DS2020-26-01
CR Al-Ayyoub M, 2018, J COMPUT SCI-NETH, V26, P522, DOI 10.1016/j.jocs.2017.11.011
   Al-Qerem A, 2020, SOFT COMPUT, V24, P5695, DOI 10.1007/s00500-019-04220-y
   Al-Smadi M, 2018, J COMPUT SCI-NETH, V27, P386, DOI 10.1016/j.jocs.2017.11.006
   [Anonymous], 2020, KNOWLEDGE GRAPH COOK
   [Anonymous], 2019, SPARK NLP
   Bhushan K, 2019, J AMB INTEL HUM COMP, V10, P1985, DOI 10.1007/s12652-018-0800-9
   Caroro RA, 2020, INT J SOFTW SCI COMP, V12, P34, DOI 10.4018/IJSSCI.2020040103
   Do P., 2019, HDB RES CLOUD COMPUT, P271
   Dorgham O, 2018, INT J CLOUD APPL COM, V8, P154, DOI 10.4018/IJCAC.2018010108
   Drabas D. L. Tomasz, 2017, LEARNING PYSPARK
   Ehrlinger L., 2016, 12 INT C SEM SYST
   Gavrilov Andrei Dmitri, 2018, International Journal of Software Science and Computational Intelligence, V10, P19, DOI 10.4018/IJSSCI.2018100102
   Hossain K, 2019, INT J CLOUD APPL COM, V9, P43, DOI 10.4018/IJCAC.2019040103
   Hossain MS, 2018, FUTURE GENER COMP SY, V83, P596, DOI 10.1016/j.future.2017.03.029
   Jadad HA, 2019, INT J CLOUD APPL COM, V9, P58, DOI 10.4018/IJCAC.2019070104
   Kejriwal M., 2019, DOMAIN SPECIFIC KNOW
   Lazib Lydia, 2019, International Journal of High Performance Computing and Networking, V13, P211
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Luciano DC., 2013, CLAUSIE CLAUSE BASED, P355, DOI DOI 10.1145/2488388.2488420
   RGLdC Kiril Gashteovski, 2017, P 2017 C EMP METH NA
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   T. S. N. L. P. Group The Stanford Parser, STANFORD PARSER STAT
   Talb D, 2017, INTRO NATURAL LANGUA
   Tanon TP, 2020, SEMANTIC WEB
   Wolf T., 2019, ARXIV
   Yadav R, 2015, SPARK COOKBOOK
NR 26
TC 3
Z9 3
U1 0
U2 9
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 JUN
PY 2022
VL 34
IS 11
SI SI
BP 8393
EP 8409
DI 10.1007/s00521-020-05495-1
EA NOV 2020
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1O7GM
UT WOS:000592168200008
DA 2022-08-23
ER

PT J
AU Wang, JH
   Shi, YL
   Li, D
   Zhang, K
   Chen, ZY
   Li, H
AF Wang, Jihu
   Shi, Yuliang
   Li, Dong
   Zhang, Kun
   Chen, Zhiyong
   Li, Hui
TI McHa: a multistage clustering-based hierarchical attention model for
   knowledge graph-aware recommendation
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Recommender systems; Knowledge graph; Attention mechanism; Multistage
   clustering
AB Knowledge graph-aware recommendation has become an important research topic in recent years. The user preference representation, which preserves the user's taste towards items (e.g., movies, books.), is obtained through aggregating the information of entities or attributes in knowledge graphs directly. However, the fine-grained heterogeneity information, which can be derived from the groups of items or entities, remains barely exploited in the process of encoding the user interaction intention for the items. To fill up this gap, we propose a MultistageClustering-basedHierarchicalAttention (McHa) model to capture the user preference representation. In our work, we first group the items and their neighboring entities in the knowledge graph into item clusters and entity clusters (jointly referred to as multistage clusters), respectively. Then, the user preference representation is obtained by hierarchically aggregating the heterogeneity information derived from the multistage clusters with the weights generated by the hierarchical attention layers. We conduct extensive experimental comparisons with baselines and the variants. The experimental results indicate that McHa has achieved state-of-the-art performance on three benchmark datasets in two scenarios.
C1 [Wang, Jihu; Shi, Yuliang; Chen, Zhiyong; Li, Hui] Shandong Univ, Sch Software, Jinan, Peoples R China.
   [Shi, Yuliang] Dareway Software Co Ltd, Jinan, Peoples R China.
   [Li, Dong] State Grid Shandong Elect Power Co, Informat & Telecommun Co, Jinan, Peoples R China.
   [Zhang, Kun] Univ Jinan, Sch Informat Sci & Engn, Jinan, Peoples R China.
RP Shi, YL (通讯作者)，Shandong Univ, Sch Software, Jinan, Peoples R China.; Shi, YL (通讯作者)，Dareway Software Co Ltd, Jinan, Peoples R China.
EM jihu_wang@mail.sdu.edu.cn; lidong@sd.sgcc.com.cn; kunzhangcs@126.com;
   chenzy@sdu.edu.cn; lih@sdu.edu.cn
FU National Key Research and Development Plan of China [2018YFB1003804];
   Project of State Grid Shandong Electric Power Company [2020A-135]
FX This work was supported by the National Key Research and Development
   Plan of China (No.2018YFB1003804) and the Project of State Grid Shandong
   Electric Power Company (2020A-135).
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   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
   Chen C, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1583, DOI 10.1145/3178876.3186070
   Chen H.-C., 2010, P 4 CHIN SUMM WORKSH
   Chen JY, 2021, INFORM SCIENCES, V563, P326, DOI 10.1016/j.ins.2021.03.004
   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
   Chen X, 2019, AAAI CONF ARTIF INTE, P53
   Chicaiza J, 2021, INFORMATION, V12, DOI 10.3390/info12060232
   Eissa AHB, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1235, DOI 10.1145/3184558.3191562
   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
   Gao JY, 2019, AAAI CONF ARTIF INTE, P3622
   Gao RY, 2019, PROCEEDINGS OF THE 2019 ACM SIGIR INTERNATIONAL CONFERENCE ON THEORY OF INFORMATION RETRIEVAL (ICTIR'19), P228, DOI 10.1145/3341981.3344215
   Geyik SC, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2221, DOI 10.1145/3292500.3330691
   Guo Q, 2020, ARXIV PREPRINT ARXIV
   Hou YF, 2019, WORLD WIDE WEB, V22, P221, DOI 10.1007/s11280-018-0558-1
   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 XJ, 2021, NEUROCOMPUTING, V428, P376, DOI 10.1016/j.neucom.2020.01.118
   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
   Huang YF, 2021, WORLD WIDE WEB, V24, P1769, DOI 10.1007/s11280-021-00912-4
   Ji S., IEEE T NEURAL NETW L
   Jiangzhou Liu, 2021, 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), P2450, DOI 10.1109/IAEAC50856.2021.9390863
   Kingma D. P., 2017, ARXIV
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   Li QY, 2019, IEEE ACCESS, V7, P115816, DOI 10.1109/ACCESS.2019.2932466
   Lin HL, 2017, PROCEDIA COMPUT SCI, V108, P345, DOI 10.1016/j.procs.2017.05.045
   Liu FZ, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4981
   Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3
   Lu L., 2019, ARXIV191003193
   Ma WZ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1210, DOI 10.1145/3308558.3313607
   Ma X., 2021, IEEE TKDE
   Mansoury Masoud, 2020, UMAP'20. Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, P154, DOI 10.1145/3340631.3394860
   Paleti L, 2021, EVOL INTELL, V14, P835, DOI 10.1007/s12065-020-00464-y
   Palumbo E, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P32, DOI 10.1145/3109859.3109889
   Qian R., 2013, BING SEARCH BLOG
   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
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Satuluri V, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P3183, DOI 10.1145/3394486.3403370
   Seo S, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P297, DOI 10.1145/3109859.3109890
   Shafqat W, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11041366
   Su Xing, ARXIV210512584
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Tai CY, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P99, DOI 10.1145/3397271.3401126
   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
   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 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, 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
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu L, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P2198, DOI 10.1145/3442381.3450015
   Xian YK, 2021, 15TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS 2021), P484, DOI 10.1145/3460231.3474240
   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
   Xie LJ, 2021, COMPLEX INTELL SYST, V7, P1241, DOI 10.1007/s40747-021-00315-y
   Yang Z, 2016, P 2016 C N AM CHAPTE, P1480
   Yingqiang Ge, 2021, WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, P445, DOI 10.1145/3437963.3441824
   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
   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
   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 J, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2347, DOI 10.1145/3292500.3330686
NR 66
TC 0
Z9 0
U1 6
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 MAY
PY 2022
VL 25
IS 3
SI SI
BP 1103
EP 1127
DI 10.1007/s11280-022-01022-5
EA FEB 2022
PG 25
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1D5UO
UT WOS:000762160000002
DA 2022-08-23
ER

PT J
AU Jayaram, N
   Khan, A
   Li, CK
   Yan, XF
   Elmasri, R
AF Jayaram, Nandish
   Khan, Arijit
   Li, Chengkai
   Yan, Xifeng
   Elmasri, Ramez
TI Querying Knowledge Graphs by Example Entity Tuples
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Knowledge graphs; entity graphs; query by example; graph query
   processing
AB We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content-rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing structured queries over "simple" tables is difficult, complex graphs are only harder to query. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to form complex graph queries. Our system, Graph Query By Example (GQBE), automatically discovers a weighted hidden maximum query graph based on input query tuples, to capture a user's query intent. It then efficiently finds and ranks the top approximate matching answer graphs and answer tuples. We conducted experiments and user studies on the large Freebase and DBpedia datasets and observed appealing accuracy and efficiency. Our system provides a complementary approach to the existing keyword-based methods, facilitating user-friendly graph querying. To the best of our knowledge, there was no such proposal in the past in the context of graphs.
C1 [Jayaram, Nandish; Li, Chengkai; Elmasri, Ramez] Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76010 USA.
   [Khan, Arijit] Swiss Fed Inst Technol, Syst Grp, Zurich, Switzerland.
   [Yan, Xifeng] Univ Calif Santa Barbara, Dept Comp Sci, Santa Barbara, CA 93106 USA.
RP Jayaram, N (通讯作者)，Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76010 USA.
EM nandish.jayaram@mavs.uta.edu; arijit.khan@inf.ethz.ch; cli@uta.edu;
   xyan@cs.ucsb.edu; elmasri@uta.edu
OI Khan, Arijit/0000-0002-7312-6312; Li, Chengkai/0000-0002-1724-8278
FU US National Science Foundation (NSF) [IIS-1018865, IIS-14-08928,
   CCF-1117369]; Army Research Laboratory [W911NF-09-2-0053]; HP Labs
   Innovation Research Awards; National Natural Science Foundation of China
   [61370019]; Div Of Information & Intelligent Systems [1018865] Funding
   Source: National Science Foundation
FX This work is partially supported by the US National Science Foundation
   (NSF) IIS-1018865, IIS-14-08928, CCF-1117369, the Army Research
   Laboratory under cooperative agreement W911NF-09-2-0053 (NSCTA), 2011
   and 2012 HP Labs Innovation Research Awards, and the National Natural
   Science Foundation of China Grant 61370019. The authors also thank Ning
   Yan for his useful contributions during our discussions. The work was
   done while Arijit Khan was at UCSB.
CR Abadi DJ, 2007, P 33 INT C VER LARG, P411
   Amer- Yahia S., 2005, P 31 INT C VER LARG, P361
   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
   Chang L., 2011, P 20 ACM INT C INF K, P1465
   Cohen J., 1988, STAT POWER ANAL BEHA
   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
   GABOW HN, 1978, SIAM J COMPUT, V7, P280, DOI 10.1137/0207024
   Jagadish H. V., 2007, SIGMOD, P13, DOI [10.1145/1247480.1247483, DOI 10.1145/1247480.1247483]
   Jayaram N., 2014, P WORKSH GRAPH DAT M, P1
   Jayaram N, 2014, PROC INT CONF DATA, P1250, DOI 10.1109/ICDE.2014.6816753
   Kasneci G., 2009, P 18 ACM C INF KNOWL, P1653, DOI DOI 10.1145/1645953.1646196
   Khan A., 2011, SIGMOD, P901
   Li ZP, 2009, ACTA MATH APPL SIN-E, V25, P697, DOI 10.1007/s10255-008-8829-8
   Lim Lipyeow, 2013, P 16 INT C EXT DAT T, P347, DOI [10.1145/2452376.2452417, DOI 10.1145/2452376.2452417]
   Manning C.D., 2008, INTRO INFORM RETRIEV
   Mottin D, 2014, PROC VLDB ENDOW, V7, P365, DOI 10.14778/2732269.2732273
   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
   Tian YY, 2008, PROC INT CONF DATA, P963, DOI 10.1109/ICDE.2008.4497505
   Tong HH, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P737
   Wang RC, 2007, IEEE DATA MINING, P342, DOI 10.1109/ICDM.2007.104
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Yu X., 2012, P 18 ACM SIGKDD INT, P1500
   Zloof M. M., 1975, P MAY 19 22 1975 NAT, P431, DOI DOI 10.1147/SJ.164.0324
NR 26
TC 30
Z9 31
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 OCT 1
PY 2015
VL 27
IS 10
BP 2797
EP 2811
DI 10.1109/TKDE.2015.2426696
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 CR3PL
UT WOS:000361245300016
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Shi, HY
   Zhang, YL
   Xu, ZY
   Xu, XL
   Qi, LY
AF Shi, Haoyang
   Zhang, Yulan
   Xu, Zhanyang
   Xu, Xiaolong
   Qi, Lianyong
TI Multi-source temporal knowledge graph embedding for edge computing
   enabled internet of vehicles
SO NEUROCOMPUTING
LA English
DT Article
DE Edge computing; Knowledge graph; Internet of vehicles
ID TWITTER
AB Edge computing furnishes communicational and computational mediators such as edge nodes (ENs) between vehicles and cloud platforms, accelerating interactive services such as route arrangement and congestion warning for the Internet of Vehicles (IoV). Implemented with the knowledge graph (KG), EN is further enabled to represent the structural relations between multi-source entities (e.g., vehicles and roads) for information reasoning tasks such as traffic flow prediction. However, due to the spatial precision dissimilarity, discontinuity, and relevance of the multi-source data, plus underestimating temporal consecutiveness in traditional rules, establishing KGs for ENs remains a challenge. Given this challenge, a temporal knowledge graph empowered reasoning model named TKGERM is developed. Firstly, confluent data for each EN are initiated by logical smoothing and filtering with multi-source data. Then, the temporal KGs are constructed by extending KG triples into temporal quadruples using timestamp information in the complex vector representation space. Furthermore, with the relative temporal information of past events, logical rules are explored by the temporal KG for traffic information reasoning. Finally, the time performance and effectiveness of TKGERM are evaluated by implementing a series of experiments on real-world data collected from the Bureau of Transportation in Guangdong, China. (c) 2021 Published by Elsevier B.V.
C1 [Shi, Haoyang; Zhang, Yulan; Xu, Zhanyang; Xu, Xiaolong] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China.
   [Shi, Haoyang; Zhang, Yulan] WeiFang Univ Sci & Technol, Weifang Key Lab Blockchain Agr Vegetables, Shouguang 262700, Peoples R China.
   [Xu, Zhanyang; Xu, Xiaolong] Nanjing Univ Informat Sci & Technol, Jiangsu Engn Ctr Network Monitoring, Nanjing 210044, Peoples R China.
   [Xu, Xiaolong] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China.
   [Qi, Lianyong] Qufu Normal Univ, Sch Informat Sci & Engn, Rizhao 276826, Peoples R China.
   [Qi, Lianyong] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210023, Peoples R China.
RP Zhang, YL; Xu, XL (通讯作者)，Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China.
EM zhangyulan@wfust.edu.cn; zhanyang_xu@nuist.edu.cn; njuxlxu@gmail.com
CR Abedin SF, 2019, IEEE T COMMUN, V67, P489, DOI 10.1109/TCOMM.2018.2870888
   Akujuobi U, 2022, IEEE T KNOWL DATA EN, V34, P2988, DOI 10.1109/TKDE.2020.3017687
   Cai ZP, 2019, IEEE T IND INFORM, V15, P6492, DOI 10.1109/TII.2019.2911697
   Cai ZP, 2020, IEEE T NETW SCI ENG, V7, P766, DOI 10.1109/TNSE.2018.2830307
   Cai ZP, 2018, IEEE T DEPEND SECURE, V15, P577, DOI 10.1109/TDSC.2016.2613521
   Cao A.., 2019, 2019 IEEE 21 INT C H, DOI [10.1109/hpcc/smartcity/dss.2019.00378, DOI 10.1109/HPCC/SMARTCITY/DSS.2019.00378]
   D'Andre E, 2017, EXPERT SYST APPL, V73, P43, DOI 10.1016/j.eswa.2016.12.018
   Du BW, 2020, IEEE T INTELL TRANSP, V21, P972, DOI 10.1109/TITS.2019.2900481
   Goel R, 2020, AAAI CONF ARTIF INTE, V34, P3988
   Gu YM, 2016, TRANSPORT RES C-EMER, V67, P321, DOI 10.1016/j.trc.2016.02.011
   Hu X., NEUROCOMPUTING, V428, P376
   Iqbal Z, 2018, INT J DISTRIB SENS N, V14, DOI 10.1177/1550147718815845
   Lin L, 2018, IEEE T KNOWL DATA EN, V30, P1310, DOI 10.1109/TKDE.2017.2718525
   Liu Y, 2021, WORLD WIDE WEB, V24, P135, DOI 10.1007/s11280-020-00836-5
   Manzo M, 2019, MACH LEARN KNOW EXTR, V1, DOI 10.3390/make1030055
   Ren L, 2020, IEEE T NEUR NET LEAR, V31, P3721, DOI 10.1109/TNNLS.2020.3001602
   Sanchez-Macian A, 2017, IEEE T DEVICE MAT RE, V17, P698, DOI 10.1109/TDMR.2017.2750718
   Wang D, 2017, ACM T INTERNET TECHN, V18, DOI 10.1145/3122982
   Wang XK, 2021, IEEE T IND INFORM, V17, P2231, DOI [10.1109/TII.2020.2999901, 10.1109/TPAMI.2020.3015894]
   Wang Y, 2016, PROC C EMPIRICAL MET, P606, DOI [10.18653/v1/D16-1058, DOI 10.18653/V1/D16-1058]
   Wang YY, 2019, PERS UBIQUIT COMPUT, V23, P233, DOI 10.1007/s00779-019-01204-5
   Wu HB, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100936
   Xiang CC, 2020, IEEE T NETW SCI ENG, V7, P2205, DOI 10.1109/TNSE.2020.2984658
   Xu XL, 2021, IEEE T INTELL TRANSP, V22, P5213, DOI 10.1109/TITS.2020.2982186
   Xu XL, 2020, IEEE INTERNET THINGS, V7, P7919, DOI [10.1109/TITS.2020.2995622, 10.1109/JIOT.2020.3000871]
   Yang SR, 2019, IEEE T VEH TECHNOL, V68, P3140, DOI 10.1109/TVT.2019.2899125
   Yu L, 2021, NEUROCOMPUTING, V423, P135, DOI 10.1016/j.neucom.2020.09.043
   Zhang ZH, 2018, TRANSPORT RES C-EMER, V86, P580, DOI 10.1016/j.trc.2017.11.027
   Zhao L, 2020, IEEE T INTELL TRANSP, V21, P3848, DOI 10.1109/TITS.2019.2935152
   Zhuang WH, 2020, P IEEE, V108, P274, DOI 10.1109/JPROC.2019.2951169
NR 30
TC 1
Z9 1
U1 9
U2 9
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JUN 28
PY 2022
VL 491
BP 597
EP 606
DI 10.1016/j.neucom.2021.12.0360925-2312/0
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1G7LY
UT WOS:000796026700001
DA 2022-08-23
ER

PT J
AU Yao, JF
   Sun, W
   Jian, ZQ
   Wu, QQ
   Wang, XL
AF Yao, Junfeng
   Sun, Wen
   Jian, Zhongquan
   Wu, Qingqiang
   Wang, Xiaoli
TI Effective knowledge graph embeddings based on multidirectional semantics
   relations for polypharmacy side effects prediction
SO BIOINFORMATICS
LA English
DT Article
ID LARGE-SCALE PREDICTION; DRUGS
AB Motivation: Polypharmacy is the combined use of drugs for the treatment of diseases. However, it often shows a high risk of side effects. Due to unnecessary interactions of combined drugs, the side effects of polypharmacy increase the risk of disease and even lead to death. Thus, obtaining abundant and comprehensive information on the side effects of polypharmacy is a vital task in the healthcare industry. Early traditional methods used machine learning techniques to predict side effects. However, they often make costly efforts to extract features of drugs for prediction. Later, several methods based on knowledge graphs are proposed. They are reported to outperform traditional methods. However, they still show limited performance by failing to model complex relations of side effects among drugs.
   Results: To resolve the above problems, we propose a novel model by further incorporating complex relations of side effects into knowledge graph embeddings. Our model can translate and transmit multidirectional semantics with fewer parameters, leading to better scalability in large-scale knowledge graphs. Experimental evaluation shows that our model outperforms state-of-the-art models in terms of the average area under the ROC and precision-recall curves.
   Availability and implementation: Code and data are available at: .
C1 [Yao, Junfeng; Sun, Wen; Jian, Zhongquan; Wu, Qingqiang; Wang, Xiaoli] Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China.
   [Yao, Junfeng; Jian, Zhongquan; Wu, Qingqiang] Xiamen Univ, Inst Artificial Intelligence, Xiamen 361005, Fujian, Peoples R China.
   [Yao, Junfeng; Wu, Qingqiang] Xiamen Univ, Key Lab Digital Protect & Intelligent Proc Intang, Xiamen 361005, Fujian, Peoples R China.
RP Wu, QQ; Wang, XL (通讯作者)，Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China.; Wu, QQ (通讯作者)，Xiamen Univ, Inst Artificial Intelligence, Xiamen 361005, Fujian, Peoples R China.; Wu, QQ (通讯作者)，Xiamen Univ, Key Lab Digital Protect & Intelligent Proc Intang, Xiamen 361005, Fujian, Peoples R China.
EM wuqq@xmu.edu.cn; xlwang@xmu.edu.cn
OI Jian, Zhongquan/0000-0003-1205-0411
FU Natural Science Foundation of China [61702432, 62072388]; Specific
   Collaborative Fund for Fuzhou-Xiamen-Quanzhou Innovative Technologies
   and Projects [3502ZCQXT202001]; Natural Science Foundation of Fujian
   Province of China [2021J01003]
FX This work was supported by the Natural Science Foundation of China
   [62072388]; and Specific Collaborative Fund for Fuzhou-Xiamen-Quanzhou
   Innovative Technologies and Projects [3502ZCQXT202001]. X.W. was
   supported by the Natural Science Foundation of China [61702432] and
   Natural Science Foundation of Fujian Province of China [2021J01003].
CR Abdelaziz I, 2017, J WEB SEMANT, V44, P104, DOI 10.1016/j.websem.2017.06.002
   Bang S, 2021, BIOINFORMATICS, V37, P2955, DOI 10.1093/bioinformatics/btab174
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bresso E, 2013, BMC BIOINFORMATICS, V14, DOI 10.1186/1471-2105-14-207
   Carletti Vincenzo, 2021, Structural, Syntactic, and Statistical Pattern Recognition. Joint IAPR International Workshops, S+SSPR 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12644), P119, DOI 10.1007/978-3-030-73973-7_12
   Cheng FX, 2014, J AM MED INFORM ASSN, V21, pE278, DOI 10.1136/amiajnl-2013-002512
   Dai YF, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa256
   Duke JD, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002614
   Garcia-Seco D, 2015, BMC GENOMICS, V16, DOI 10.1186/s12864-014-1198-1
   Giacomini KM, 2007, NATURE, V446, P975, DOI 10.1038/446975a
   Glorot X., 2010, JMLR WRKSHOP C P, P249
   Gottlieb A, 2012, MOL SYST BIOL, V8, DOI 10.1038/msb.2012.26
   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
   Huang KX, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-77766-9
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   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
   Lin X, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2739
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Liu M, 2012, J AM MED INFORM ASSN, V19, pE28, DOI 10.1136/amiajnl-2011-000699
   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
   Novacek Vit, 2020, AMIA Jt Summits Transl Sci Proc, V2020, P449
   Percha B, 2013, TRENDS PHARMACOL SCI, V34, P178, DOI 10.1016/j.tips.2013.01.006
   Ryu JY, 2018, P NATL ACAD SCI USA, V115, pE4304, DOI 10.1073/pnas.1803294115
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Sun, 2019, INT C LEARN REPR 201
   Tatonetti NP, 2012, SCI TRANSL MED, V4, DOI 10.1126/scitranslmed.3003377
   Teru K.K., 2020, INDUCTIVE RELATION P
   Trouillon T, 2016, PR MACH LEARN RES, V48
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Vilar S, 2014, NAT PROTOC, V9, P2147, DOI 10.1038/nprot.2014.151
   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 Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang R., 2020, INT C APPL INF, P89
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wishart DS, 2008, NUCLEIC ACIDS RES, V36, pD901, DOI 10.1093/nar/gkm958
   Yang B., 2015, P INT C LEARN REPR I
   Yang SH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1935
   Yu Y, 2021, BIOINFORMATICS, V37, P2988, DOI 10.1093/bioinformatics/btab207
   Zhang P, 2015, SCI REP-UK, V5, DOI 10.1038/srep12339
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
   Zitnik M, 2018, BIOINFORMATICS, V34, P457, DOI 10.1093/bioinformatics/bty294
NR 44
TC 0
Z9 0
U1 12
U2 12
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 2315
EP 2322
DI 10.1093/bioinformatics/btac094
EA FEB 2022
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 0L4DY
UT WOS:000759662700001
PM 35176135
DA 2022-08-23
ER

PT J
AU Mao, NY
   Huang, WT
   Zhong, H
AF Mao, Ningyi
   Huang, Wenti
   Zhong, Hai
TI KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly
   Supervised Relation Extraction
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE relation extraction; knowledge graph embedding; graph convolutional
   network; attention mechanism
AB Distantly supervised relation extraction is the most popular technique for identifying semantic relation between two entities. Most prior models only focus on the supervision information present in training sentences. In addition to training sentences, external lexical resource and knowledge graphs often contain other relevant prior knowledge. However, relation extraction models usually ignore such readily available information. Moreover, previous works only utilize a selective attention mechanism over sentences to alleviate the impact of noise, they lack the consideration of the implicit interaction between sentences with relation facts. In this paper, (1) a knowledge-guided graph convolutional network is proposed based on the word-level attention mechanism to encode the sentences. It can capture the key words and cue phrases to generate expressive sentence-level features by attending to the relation indicators obtained from the external lexical resource. (2) A knowledge-guided sentence selector is proposed, which explores the semantic and structural information of triples from knowledge graph as sentence-level knowledge attention to distinguish the importance of each individual sentence. Experimental results on two widely used datasets, NYT-FB and GDS, show that our approach is able to efficiently use the prior knowledge from the external lexical resource and knowledge graph to enhance the performance of distantly supervised relation extraction.
C1 [Mao, Ningyi] Cent South Univ, Sch Business, Changsha 410083, Peoples R China.
   [Huang, Wenti; Zhong, Hai] Cent South Univ, Sch Comp Sci & Engn, 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 191611116@csu.edu.cn; huangwenti@csu.edu.cn; zhonghai@csu.edu.cn
FU Science and Technology Plan of Hunan [2016TP1003]; Key Technology R&D
   Program of Hunan Province [2018GK2052]
FX This research was funded by the Science and Technology Plan of Hunan
   grant number 2016TP1003 and the Key Technology R&D Program of Hunan
   Province grant number 2018GK2052.
CR Alicante, 2011, P 8 REC ADV NAT LANG, P509
   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
   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
   Cui WY, 2017, PROC VLDB ENDOW, V10, P565, DOI 10.14778/3055540.3055549
   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
   Guo ZJ, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P241
   Han J., 2018, ARXIV PREPRINT ARXIV, P2216, DOI DOI 10.18653/V1/D18-1245
   Han X, 2018, AAAI CONF ARTIF INTE, P4832
   Harabagiu S., 2010, P 5 INT WORKSH SEM E, P256
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Jat S., 2018, ARXIV201818040698
   Kambhatla Nanda, 2004, P ACL 2004 INT POST, DOI 10.3115/ 1219044.1219066
   Kaur J, 2021, CMC-COMPUT MATER CON, V69, P2617, DOI 10.32604/cmc.2021.017470
   Khan AW, 2021, IEEE ACCESS, V9, P107309, DOI 10.1109/ACCESS.2021.3100287
   Li Q, 2019, P 2019 C EMP METH NA, P229
   Lin YK, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2124
   Liu T., 2018, P EMNLP, P2195
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Palmer M, 2005, COMPUT LINGUIST, V31, P71, DOI 10.1162/0891201053630264
   Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI 10.3115/v1/D14-1162
   Prayaga S.S, 2018, P 2018 C EMP METH NA, P1257
   Qian L., 2009, P 2009 C EMP METH NA, P1437
   Quirk C, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P1171
   Ren F, 2018, P 27 INT C COMP LING, P1167
   Riedel S, 2010, LECT NOTES ARTIF INT, V6323, P148, DOI 10.1007/978-3-642-15939-8_10
   Ruppenhofer J., FRAMENET 2 EXTENDED
   Schuler K. K., 2005, VERBNET BROAD COVERA
   Socher R., 2012, P 2012 JOINT C EMP M, P1201, DOI [10.5555/2390948.2391084, DOI 10.1162/153244303322533223]
   Suchanek FM, 2006, P 12 ACM SIGKDD INT, P712, DOI DOI 10.1145/1150402.1150492
   Vaswani A., 2017, P ADV NEURAL INFORM, P5998
   Xiao Y., 2020, ARXIV PREPRINT ARXIV
   Young T., 2017, ARXIV PREPRINT ARXIV
   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., 2016, ARXIV160907479
   Zhang D., 2015, ARXIV2015150801006
   Zhang M, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P825
   Zhang Z., 2004, P 13 ACM INT C INF K, P581
   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 40
TC 1
Z9 1
U1 13
U2 24
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 2021
VL 11
IS 16
AR 7734
DI 10.3390/app11167734
PG 18
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA UF4ZM
UT WOS:000688584100001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Liu, HJ
   Bai, LY
   Ma, XN
   Yu, WT
   Xu, CM
AF Liu, Huajing
   Bai, Luyi
   Ma, Xiangnan
   Yu, Wenting
   Xu, Changming
TI ProjFE: Prediction of fuzzy entity and relation for knowledge graph
   completion
SO APPLIED SOFT COMPUTING
LA English
DT Article
DE Fuzzy entities and relations; Combination operator; Knowledge graph;
   ReLU activation function; Sigmoid activation function
AB A growing number of tasks about knowledge graph completion have been studied and improved recently, but most of them use translation matrices or reflect known entity to other space, always focusing on improving the method of translating known entities and relations. Differing from current works, our paper employs a combination operator instead of the translation matrix to avoid massive calculations, and takes fuzzy membership degree into consideration in the predicting process to enhance accuracy of projection. Hence, we propose a method called ProjFE to predict the missing parts of triplets for knowledge graph completion. This model uses fuzzy combination operators to combine the fuzzy known entities and relations. Score function is employed to access to a descending order of the correct candidates after combination, where the target entity is the top one. What is more, we use sigmoid and ReLU activation functions for evaluations, which could alleviate some undesirable gradient problems in the training process. It is worth noting that our method ProjFE tends to have a relatively smaller parameter size than some existing models. Besides, our model is proved to perform better in terms of Mean Rank. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Liu, Huajing; Bai, Luyi; Ma, Xiangnan; Yu, Wenting; Xu, Changming] Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China.
RP Bai, LY (通讯作者)，Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China.
EM baily@neuq.edu.cn
OI Bai, Luyi/0000-0001-9546-3208
FU National Natural Science Foundation of China [61402087]; Natural Science
   Foundation of Hebei Province, China [F2019501030]; Fundamental Research
   Funds for the Central Universities, China [N172304026]; Scientific
   Research Fund of Hebei Education Department, China [QN2014339]
FX This work was supported by the National Natural Science Foundation of
   China (61402087), the Natural Science Foundation of Hebei Province,
   China (F2019501030), the Fundamental Research Funds for the Central
   Universities, China (N172304026), and the Scientific Research Fund of
   Hebei Education Department, China (QN2014339).
CR Bordes A., 2012, P 15 INT C ART INT S, P127
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Dong XL, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2869, DOI 10.1145/3219819.3219938
   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
   Guillaumin M, 2009, IEEE I CONF COMP VIS, P309, DOI 10.1109/ICCV.2009.5459266
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Larose D. T., 2014, J AM STAT ASSOC, P1465
   Lin HT, 2003, NEURAL COMPUT, V3, P1
   Lin YC, 2015, ADV SOC SCI EDUC HUM, V39, P2181
   Liu Z. Y., 2016, COMPUT RES DEV, V53, P12
   [刘知远 Liu Zhiyuan], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P247
   Lopez-Quintero JF, 2018, SOFT COMPUT, V22, P1845, DOI 10.1007/s00500-016-2437-y
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Pareek H. H., 2014, ADV NEURAL INFORM PR, P361
   Piraquive F.N.D., 2015, INT C KNOWL MAN ORG, P235
   Piraquive FN., 2014, LECT NOTES BUSINESS, DOI [10.1007/978-3-319-08618-7_29, DOI 10.1007/978-3-319-08618-7_29]
   Radford A., 2015, COMPUT SCI
   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
   Salinas E, 1996, P NATL ACAD SCI USA, V93, P11956, DOI 10.1073/pnas.93.21.11956
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Song Q, 2018, IEEE T KNOWL DATA EN, V30, P1887, DOI 10.1109/TKDE.2018.2807442
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wenger E., 2014, J RES COMPUT ED, P142
   Xie J., 2006, FUZZY MATH METHODS I
   Zhao Y. P., 2012, J INTELL, P28
   Zhou H., 2018, IJCAI ECAI
NR 29
TC 3
Z9 3
U1 2
U2 24
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1568-4946
EI 1872-9681
J9 APPL SOFT COMPUT
JI Appl. Soft. Comput.
PD AUG
PY 2019
VL 81
AR 105525
DI 10.1016/j.asoc.2019.105525
PG 9
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IK3EH
UT WOS:000476471800038
DA 2022-08-23
ER

PT J
AU Cai, XJ
   Xie, LJ
   Tian, R
   Cui, ZH
AF Cai, Xingjuan
   Xie, Lijie
   Tian, Rui
   Cui, Zhihua
TI Explicable recommendation based on knowledge graph
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Explainable recommendation; Knowledge graph; Unified method;
   Recommendation system; Many-objective evolutionary algorithm
ID ALGORITHM
AB Most of the existing researches on recommendation system assemble in how to enhance precision of recommendation, ignoring acceptance and recognition of users. To work out the problem, a model of explainable recommendation on account of knowledge graph as well as many-objective evolutionary algorithm is proposed, which combines recommendation and explanation. In this work, embedding vectors obtained by embedding based method are used to quantify the explainability, so as to obtain the explainability of paths between users and items. Candidate recommendation list of users is gained from constructed knowledge graph. Many-objective evolutionary algorithm is used to optimize the list of candidate recommendation so as to seek a set of tradeoff solutions to the four objective functions of accuracy, diversity, novelty and explainability. Then, the best path among object user and recommended items is chosen in knowledge graph as the explanation. Finally, the conclusion that can be drawn from various experiments is that the presented model can boost explainability without reducing the precision, diversity as well as novelty.
C1 [Cai, Xingjuan; Xie, Lijie; Tian, Rui; Cui, Zhihua] Taiyuan Univ Sci & Technol, Complex Syst & Computat Intelligent Lab, Taiyuan, Peoples R China.
RP Xie, LJ; Cui, ZH (通讯作者)，Taiyuan Univ Sci & Technol, Complex Syst & Computat Intelligent Lab, Taiyuan, Peoples R China.
EM xingjuancai@163.com; lijiexie11@163.com; ruitianwz@163.com;
   cuizhihua@tyust.edu.cn
FU National Natural Science Foundation of China [61806138]; Key R&D program
   of Shanxi Province (International Cooperation) [201903D421048]; Key R&D
   program of Shanxi Province (High Technology) [201903D121119]; Key R&D
   program (international science and technology cooperation project) of
   Shanxi Province, China [201903D421003]; Postgraduate Innovation Project
   of Shanxi Province [2021Y697]
FX This work is supported by the National Natural Science Foundation of
   China under Grant No.61806138; Key R & D program of Shanxi Province
   (International Cooperation) under Grant No. 201903D421048; Key R & D
   program of Shanxi Province (High Technology) under Grant No.
   201903D121119; Key R & D program (international science and technology
   cooperation project) of Shanxi Province, China under Grant
   No.201903D421003, the Postgraduate Innovation Project of Shanxi Province
   under Grant No.2021Y697.
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Ariff MIM, 2021, 2021 IEEE INTERNATIONAL IOT, ELECTRONICS AND MECHATRONICS CONFERENCE (IEMTRONICS), P235, DOI 10.1109/IEMTRONICS52119.2021.9422550
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Cheng R, 2016, IEEE T EVOLUT COMPUT, V20, P773, DOI 10.1109/TEVC.2016.2519378
   Cui LZ, 2017, J PARALLEL DISTR COM, V103, P53, DOI 10.1016/j.jpdc.2016.10.014
   Cui ZH, 2021, IEEE INTERNET THINGS, V8, P12540, DOI 10.1109/JIOT.2021.3056578
   Cui ZH, 2021, INFORM SCIENCES, V579, P1, DOI 10.1016/j.ins.2021.07.077
   Gao L, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3378
   Guo QY, 2020, IEEE T KNOWL DATA EN, V34, P3549, DOI [10.1038/s41366-020-0625-4, 10.1109/TKDE.2020.3028705]
   He RN, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P161, DOI 10.1145/3109859.3109882
   Jain A, 2020, EXPERT SYST APPL, V139, DOI 10.1016/j.eswa.2019.112857
   Jain H, 2014, IEEE T EVOLUT COMPUT, V18, P602, DOI 10.1109/TEVC.2013.2281534
   Jin JC, 2021, IEEE T INTELL TRANSP, DOI 10.1109/TITS.2021.3068874
   Jin JC, 2021, IEEE T INTELL TRANSP, V22, P1616, DOI 10.1109/TITS.2020.2973736
   Lin YJ, 2020, IEEE T KNOWL DATA EN, V32, P1502, DOI 10.1109/TKDE.2019.2906190
   Ozsoy MG, 2020, IEEE ACCESS, V8, P181835, DOI 10.1109/ACCESS.2020.3028587
   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
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Tintarev N, 2011, RECOMMENDER SYSTEMS HANDBOOK, P479, DOI 10.1007/978-0-387-85820-3_15
   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 HW, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P592, DOI 10.1145/3159652.3159666
   Wang SF, 2016, KNOWL-BASED SYST, V104, P145, DOI 10.1016/j.knosys.2016.04.018
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu L, 2021, IEEE T SYST MAN CY-S, V51, P464, DOI 10.1109/TSMC.2018.2872842
   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
   Yang SX, 2013, IEEE T EVOLUT COMPUT, V17, P721, DOI 10.1109/TEVC.2012.2227145
   Yang XW, 2014, COMPUT COMMUN, V41, P1, DOI 10.1016/j.comcom.2013.06.009
   Yu X., 2013, RECSYS, P347
   Zarzour H., 2020, 2020 7 INT C SOCIAL
   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
   Zhou X., ARXIV210307461
   Zhu F., 2021, IEEE T KNOWL DATA EN, DOI [10.1109/TKDE.2021.3104873, DOI 10.1109/TKDE.2021.3104873]
NR 37
TC 1
Z9 1
U1 13
U2 13
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 AUG 15
PY 2022
VL 200
AR 117035
DI 10.1016/j.eswa.2022.117035
PG 11
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 1K5IB
UT WOS:000798633100001
DA 2022-08-23
ER

PT J
AU Dai, YF
   Wang, SP
   Xiong, NN
   Guo, WZ
AF Dai, Yuanfei
   Wang, Shiping
   Xiong, Neal N.
   Guo, Wenzhong
TI A Survey on Knowledge Graph Embedding: Approaches, Applications and
   Benchmarks
SO ELECTRONICS
LA English
DT Article
DE knowledge graph embedding; knowledge representation; deep learning;
   statistical relational learning
AB A knowledge graph (KG), also known as a knowledge base, is a particular kind of network structure in which the node indicates entity and the edge represent relation. However, with the explosion of network volume, the problem of data sparsity that causes large-scale KG systems to calculate and manage difficultly has become more significant. For alleviating the issue, knowledge graph embedding is proposed to embed entities and relations in a KG to a low-, dense and continuous feature space, and endow the yield model with abilities of knowledge inference and fusion. In recent years, many researchers have poured much attention in this approach, and we will systematically introduce the existing state-of-the-art approaches and a variety of applications that benefit from these methods in this paper. In addition, we discuss future prospects for the development of techniques and application trends. Specifically, we first introduce the embedding models that only leverage the information of observed triplets in the KG. We illustrate the overall framework and specific idea and compare the advantages and disadvantages of such approaches. Next, we introduce the advanced models that utilize additional semantic information to improve the performance of the original methods. We divide the additional information into two categories, including textual descriptions and relation paths. The extension approaches in each category are described, following the same classification criteria as those defined for the triplet fact-based models. We then describe two experiments for comparing the performance of listed methods and mention some broader domain tasks such as question answering, recommender systems, and so forth. Finally, we collect several hurdles that need to be overcome and provide a few future research directions for knowledge graph embedding.
C1 [Dai, Yuanfei; Wang, Shiping; Xiong, Neal N.; Guo, Wenzhong] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China.
   [Wang, Shiping; Guo, Wenzhong] Fuzhou Univ, Key Lab Network Comp & Intelligent Informat Proc, Fuzhou 350108, Peoples R China.
   [Xiong, Neal N.] Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, OK USA.
RP Guo, WZ (通讯作者)，Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China.; Guo, WZ (通讯作者)，Fuzhou Univ, Key Lab Network Comp & Intelligent Informat Proc, Fuzhou 350108, Peoples R China.
EM daiyuanfly@gmail.com; shipingwangphd@163.com; xiong31@nsuok.edu;
   guowenzhong@fzu.edu.cn
RI xiong, naixue/M-4277-2019
OI xiong, naixue/0000-0002-0394-4635; Dai, Yuanfei/0000-0002-1703-1538;
   Wang, Shiping/0000-0001-5195-9682
FU Guiding Project of Fujian Province [2018H0017]
FX This work is supported by the Guiding Project of Fujian Province under
   Grant No. 2018H0017.
CR Abdelaziz I, 2017, J WEB SEMANT, V44, P104, DOI 10.1016/j.websem.2017.06.002
   An Bo, 2018, NAACL, V1, P745
   [Anonymous], PROBABILISTIC REASON
   [Anonymous], 2015, P 2015 INT C LEARN R
   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
   Bordes A, 2014, P 2014 C EMP METH NA, P615, DOI DOI 10.3115/V1/D14-1067
   BORDES A, 2014, JOINT EUR C MACH LEA, P165
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Brigham E.O, 1988, FAST FOURIER TRANSFO, V448
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Cai L, 2018, NAACL, P1470
   Dai YF, 2020, KNOWL-BASED SYST, V190, DOI 10.1016/j.knosys.2019.105165
   Damljanovic D, 2012, LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P2023
   Defferrard M., 2016, 30 INT C NEURAL INFO, P3844
   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
   Fu CB, 2017, 2017 14TH INTERNATIONAL WORKSHOP ON COMPLEX SYSTEMS AND NETWORKS (IWCSN), P149, DOI 10.1109/IWCSN.2017.8276520
   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
   Guo L, 2014, AUTOM CONTROL ENG SE, P1
   Guo S, 2017, IEEE T KNOWL DATA EN, V29, P884, DOI 10.1109/TKDE.2016.2638425
   Guo S, 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, P84
   Guu K., 2015, ARXIV150601094
   Han Y, 2016, PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING TECHNOLOGY (CSET2015), MEDICAL SCIENCE AND BIOLOGICAL ENGINEERING (MSBE2015), P548
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Jebara T, 2004, J MACH LEARN RES, V5, P819
   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
   Jia YT, 2016, AAAI CONF ARTIF INTE, P992
   Jiang T., 2016, P 2016 C EMP METH NA, P2350, DOI DOI 10.18653/V1/D16-1260
   Kazemi SM, 2018, ADV NEUR IN, V31
   Ke X, 2019, IEEE T MULTIMEDIA, V21, P2093, DOI 10.1109/TMM.2019.2895511
   Kingma D. P., 2017, ARXIV
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Kullback Solomon, 1997, INFORM THEORY STAT
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Li FL, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2495, DOI 10.1145/3132847.3133169
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lin Y, 2014, SIGNALS COMMUN TECHN, P3, DOI 10.1007/978-3-642-39655-7_1
   Liu ZM, 2017, AAAI CONF ARTIF INTE, P154
   Lu J, 2015, KNOWL-BASED SYST, V80, P14, DOI 10.1016/j.knosys.2015.01.010
   Mao YC, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION, P268, DOI 10.1109/ICInfA.2015.7279297
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Mikolov T., 2013, ADV NEURAL INFORM PR, V2, P3111, DOI DOI 10.1162/JMLR.2003.3.4-5.951
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nair V., 2010, P 27 INT C MACH LEAR, DOI DOI 10.5555/3104322.3104425
   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., 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
   Nikolentzos G, 2017, AAAI CONF ARTIF INTE, P2429
   Ouyang X, 2017, LECT NOTES ARTIF INT, V10412, P534, DOI 10.1007/978-3-319-63558-3_45
   Pease A., 2002, WORK NOT AAAI 2002 W
   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
   PLATE TA, 1995, IEEE T NEURAL NETWOR, V6, P623, DOI 10.1109/72.377968
   Riedel S., 2013, P 2013 C N AM CHAPT, P74
   Sang ST, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2167-5
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
   Shen  W., 2012, P 21 INT C WORLD WID, P449, DOI DOI 10.1145/2187836.2187898
   Shen W, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P68
   Siegmund D., 1985, ANN MATH STAT, P102, DOI 10.1007/978-1-4612-5110-1_9
   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
   Sun Z., 2019, P INT C LEARN REPR N
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   Toutanova K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1434
   Trivedi R, 2017, PR MACH LEARN RES, V70
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Vonk TM, 2014, IEEE INT ULTRA SYM, P9, DOI 10.1109/ULTSYM.2014.0003
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang F, 2015, DATA MIN KNOWL DISC, V29, P534, DOI 10.1007/s10618-014-0356-z
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang SP, 2017, KNOWL-BASED SYST, V138, P176, DOI 10.1016/j.knosys.2017.09.033
   Wang SP, 2017, IEEE T MULTIMEDIA, V19, P1454, DOI 10.1109/TMM.2017.2663324
   Wang Z., 2016, IJCAI, P1293
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J., 2013, ARXIV PREPRINT ARXIV, P1366
   Wu F, 2015, AAAI CONF ARTIF INTE, P1663
   Wu F, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P118
   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 R., 2016, P 25 INT JOINT C ART, P2965
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xu D, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P672, DOI 10.1145/3336191.3371778
   Xu FL, 2016, UBICOMP'16: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING, P1064, DOI 10.1145/2971648.2971673
   Xu K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2326
   Xu Z, 2016, CONCURR COMP-PRACT E, V28, P4038, DOI 10.1002/cpe.3780
   Yosef MA, 2011, PROC VLDB ENDOW, V4, P1450
   Zhang C, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P361, DOI 10.1145/3038912.3052601
   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 MY, 2018, LECT NOTES COMPUT SC, V10772, P276, DOI 10.1007/978-3-319-76941-7_21
   Zhang S., 2019, ADV NEURAL INFORM PR, P2731
   Zhao Y, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1412
   Zheng ZC, 2012, 2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2012), VOL 1, P82, DOI 10.1109/WI-IAT.2012.26
   Zhou T, 2007, PHYS REV E, V76, DOI 10.1103/PhysRevE.76.046115
   Zhu Xiaoyan, 2015, TRANSA ADAPTIVE APPR
NR 100
TC 35
Z9 36
U1 11
U2 34
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD MAY
PY 2020
VL 9
IS 5
AR 750
DI 10.3390/electronics9050750
PG 29
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA MM0MO
UT WOS:000549854600049
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zeng, XX
   Tu, XQ
   Liu, YS
   Fu, XZ
   Su, YS
AF Zeng, Xiangxiang
   Tu, Xinqi
   Liu, Yuansheng
   Fu, Xiangzheng
   Su, Yansen
TI Toward better drug discovery with knowledge graph
SO CURRENT OPINION IN STRUCTURAL BIOLOGY
LA English
DT Article
ID DISEASE RELATIONSHIPS
AB Drug discovery is the process of new drug identification. This process is driven by the increasing data from existing chemical libraries and data banks. The knowledge graph is introduced to the domain of drug discovery for imposing an explicit structure to integrate heterogeneous biomedical data. The graph can provide structured relations among multiple entities and unstructured semantic relations associated with entities. In this review, we summarize knowledge graph-based works that implement drug repurposing and adverse drug reaction prediction for drug discovery. As knowledge representation learning is a common way to explore knowledge graphs for prediction problems, we introduce several representative embedding models to provide a comprehensive understanding of knowledge representation learning.
C1 [Zeng, Xiangxiang; Tu, Xinqi; Liu, Yuansheng; Fu, Xiangzheng] Hunan Univ, Coll Informat Sci & Engn, Changsha 410086, Peoples R China.
   [Su, Yansen] Anhui Univ, Sch Comp Sci & Technol, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230601, Peoples R China.
RP Liu, YS (通讯作者)，Hunan Univ, Coll Informat Sci & Engn, Changsha 410086, Peoples R China.
EM yuanshengliu@hnu.edu.cn
CR Abdelaziz I, 2017, J WEB SEMANT, V44, P104, DOI 10.1016/j.websem.2017.06.002
   Alshahrani M., DRUG REPURPOSING JOI
   Alshahrani M, 2017, BIOINFORMATICS, V33, P2723, DOI 10.1093/bioinformatics/btx275
   Balazevic I., ARXIV PREPRINT ARXIV
   Bateman A, 2006, NUCLEIC ACIDS RES, V34, pD1, DOI 10.1093/nar/gkj150
   Berdigaliyev N, 2020, FUTURE MED CHEM, V12, P939, DOI 10.4155/fmc-2019-0307
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Carbon S, 2019, NUCLEIC ACIDS RES, V47, pD330, DOI 10.1093/nar/gky1055
   Chao L., 2021, 59 ANN M ASS COMP LI
   Che FH, 2020, AAAI CONF ARTIF INTE, V34, P2774
   Chen X, 2002, NUCLEIC ACIDS RES, V30, P412, DOI 10.1093/nar/30.1.412
   Chen Y., BIOINFORMATICS
   Chiang AP, 2009, CLIN PHARMACOL THER, V86, P507, DOI 10.1038/clpt.2009.103
   Chu Y., 2021, BRIEF BIOINFORM, V22, DOI DOI 10.1093/BIB/BBAA205
   Croft D, 2011, NUCLEIC ACIDS RES, V39, pD691, DOI 10.1093/nar/gkq1018
   Dai YF, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa256
   Dai YF, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9050750
   dAmato C, 2021, LECT NOTES COMPUT SC, P441, DOI DOI 10.1007/978-3-030-77385-4_26
   Deng YF, 2020, BIOINFORMATICS, V36, P4316, DOI 10.1093/bioinformatics/btaa501
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ding YJ, 2019, NEUROCOMPUTING, V325, P211, DOI 10.1016/j.neucom.2018.10.028
   Dudley JT, 2011, BRIEF BIOINFORM, V12, P303, DOI 10.1093/bib/bbr013
   Feeney A., ARXIV PREPRINT ARXIV
   Flockhart D., PREVENTABLE ADVERSE, P452
   Gaulton A, 2012, NUCLEIC ACIDS RES, V40, pD1100, DOI 10.1093/nar/gkr777
   Gilmer J, 2017, PR MACH LEARN RES, V70
   Hecker N, 2012, NUCLEIC ACIDS RES, V40, pD1113, DOI 10.1093/nar/gkr912
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Hogan A., ARXIV PREPRINT ARXIV
   Honig LS, 2018, NEW ENGL J MED, V378, P321, DOI 10.1056/NEJMoa1705971
   Irtaza RS, DRUG DISCOVERY KNOWL
   Jeon W, 2019, BIOINFORMATICS, V35, P4979, DOI 10.1093/bioinformatics/btz307
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Jiaheng Dou, 2021, Database Systems for Advanced Applications. 26th International Conference, DASFAA 2021. Proceedings. Lecture Notes in Computer Science (LNCS 12681), P240, DOI 10.1007/978-3-030-73194-6_17
   Kanatsoulis C. I., ARXIV PREPRINT ARXIV
   Kanehisa M, 2000, NUCLEIC ACIDS RES, V28, P27, DOI 10.1093/nar/28.1.27
   Kang B., ARXIV PREPRINT ARXIV
   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
   Kazemi S. M., ARXIV PREPRINT ARXIV
   Kim S, 2016, NUCLEIC ACIDS RES, V44, pD1202, DOI 10.1093/nar/gkv951
   Kishimoto Koki, 2019, Advances in Information Retrieval. 41st European Conference on IR Research, ECIR 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11437), P181, DOI 10.1007/978-3-030-15712-8_12
   Kohler S, 2017, NUCLEIC ACIDS RES, V45, pD865, DOI 10.1093/nar/gkw1039
   Kuhn M, 2016, NUCLEIC ACIDS RES, V44, pD1075, DOI 10.1093/nar/gkv1075
   Li MZ, 2021, AAAI CONF ARTIF INTE, V35, P4189
   Li W, ARXIV PREPRINT ARXIV
   Lin X, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2739
   Liu M, 2012, J AM MED INFORM ASSN, V19, pE28, DOI 10.1136/amiajnl-2011-000699
   Luo T, 2020, FRONT ARTIF INTEL AP, V325, P817, DOI 10.3233/FAIA200171
   Ma T., ARXIV PREPRINT ARXIV
   Madhukar NS, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-12928-6
   McCusker JP, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.106
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Mohamed SK, 2021, BRIEF BIOINFORM, V22, P1679, DOI 10.1093/bib/bbaa012
   Mohamed SK, 2020, BIOINFORMATICS, V36, P603, DOI 10.1093/bioinformatics/btz600
   Mohs Richard C, 2017, Alzheimers Dement (N Y), V3, P651, DOI 10.1016/j.trci.2017.10.005
   Munoz E, 2019, BRIEF BIOINFORM, V20, P190, DOI 10.1093/bib/bbx099
   Nicholson DN, 2020, COMPUT STRUCT BIOTEC, V18, P1414, DOI 10.1016/j.csbj.2020.05.017
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Percha B, 2018, BIOINFORMATICS, V34, P2614, DOI 10.1093/bioinformatics/bty114
   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
   Pirmohamed M, 1998, BRIT MED J, V316, P1295, DOI 10.1136/bmj.316.7140.1295
   Prasad V, 2017, JAMA INTERN MED, V177, P1569, DOI 10.1001/jamainternmed.2017.3601
   Prykhodko O, 2019, J CHEMINFORMATICS, V11, DOI 10.1186/s13321-019-0397-9
   Pushpakom S, 2019, NAT REV DRUG DISCOV, V18, P41, DOI 10.1038/nrd.2018.168
   Ryu JY, 2018, P NATL ACAD SCI USA, V115, pE4304, DOI 10.1073/pnas.1803294115
   Sang ST, 2019, IEEE ACCESS, V7, P8404, DOI 10.1109/ACCESS.2018.2886311
   Shi X., ARXIV PREPRINT ARXIV
   Song B., BRIEF BIOINFORM
   Sosa DN, 2020, PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, P463
   Su C., CBKH CORNELL BIOMEDI
   Su C, 2020, BRIEF BIOINFORM, V21, P182, DOI 10.1093/bib/bby117
   Sun Z., ARXIV PREPRINT ARXIV
   Tatonetti NP, 2012, SCI TRANSL MED, V4, DOI 10.1126/scitranslmed.3003377
   Thafar MA, 2020, J CHEMINFORMATICS, V12, DOI 10.1186/s13321-020-00447-2
   Thorn Caroline F, 2013, Methods Mol Biol, V1015, P311, DOI 10.1007/978-1-62703-435-7_20
   Tran H. N., ARXIV PREPRINT ARXIV
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang Q., ARXIV PREPRINT ARXIV
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wishart DS, 2008, NUCLEIC ACIDS RES, V36, pD901, DOI 10.1093/nar/gkm958
   Yang B., ARXIV PREPRINT ARXIV
   Yu DH, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P1619, DOI 10.1145/3442381.3449925
   Yu JX, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P3442, DOI 10.1145/3442381.3449879
   Yu Y., ARXIV PREPRINT ARXIV
   Zeng XX, 2020, J PROTEOME RES, V19, P4624, DOI 10.1021/acs.jproteome.0c00316
   Zhang W, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P96, DOI 10.1145/3289600.3291014
   Zhang W, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0774-y
   Zhao K, 2019, IEEE J BIOMED HEALTH, V23, P1304, DOI 10.1109/JBHI.2018.2856535
   Zhao S., ARXIV PREPRINT ARXIV
   Zhu YJ, 2020, HEALTH INFORM J, V26, P2737, DOI 10.1177/1460458220937101
   Zhu YJ, 2019, BRIEF BIOINFORM, V20, P1308, DOI 10.1093/bib/bbx169
NR 91
TC 11
Z9 11
U1 33
U2 48
PU CURRENT BIOLOGY LTD
PI LONDON
PA 84 THEOBALDS RD, LONDON WC1X 8RR, ENGLAND
SN 0959-440X
EI 1879-033X
J9 CURR OPIN STRUC BIOL
JI Curr. Opin. Struct. Biol.
PD FEB
PY 2022
VL 72
BP 114
EP 126
DI 10.1016/j.sbi.2021.09.003
PG 13
WC Biochemistry & Molecular Biology; Cell Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Cell Biology
GA ZS8SS
UT WOS:000768730600014
PM 34649044
OA Bronze
DA 2022-08-23
ER

PT J
AU Bhatt, S
   Sheth, A
   Shalin, V
   Zhao, JJ
AF Bhatt, Shreyansh
   Sheth, Amit
   Shalin, Valerie
   Zhao, Jinjin
TI Knowledge Graph Semantic Enhancement of Input Data for Improving AI
SO IEEE INTERNET COMPUTING
LA English
DT Article
DE Training data; Machine learning algorithms; Task analysis; Computer
   architecture; Semantics; Machine learning; Biological neural networks
AB Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real-world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance the input data for two applications that use machine learning-recommendation and community detection. The KG improves both accuracy and explainability.
C1 [Bhatt, Shreyansh; Zhao, Jinjin] Amazon, Seattle, WA 98109 USA.
   [Sheth, Amit] Univ South Carolina, Artificial Intelligence Inst, Columbia, SC 29208 USA.
   [Shalin, Valerie] Wright State Univ, Psychol, Dayton, OH 45435 USA.
RP Bhatt, S (通讯作者)，Amazon, Seattle, WA 98109 USA.
EM bhattshr@amazon.com; amit@sc.edu; valerie.shalin@wright.edu;
   jinjzhao@amazon.com
RI Sheth, Amit/ABC-4600-2020
OI Sheth, Amit/0000-0002-0021-5293
FU NSF [1513721]; Context-Aware Harassment Detection on Social Media
FX The authors would like to thank Manas Gaur and Ruwan Wickramarachchi for
   their review and suggestions. Dr. Sheth and Dr. Shalin's work was funded
   in part by NSF under award #1513721 titled "TWC SBE: Medium:
   Context-Aware Harassment Detection on Social Media." Any opinions,
   findings, and conclusions or recommendations expressed in this article
   are those of the authors and do not necessarily reflect the views of the
   National Science Foundation.
CR Bhatt S, 2019, P INT AAAI C WEB SOC, V13, P3113
   Bhatt S, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P51, DOI 10.1145/3289600.3291031
   Fang Yuan, 2017, OBJECT DETECTION MEE
   Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081
   Halevy A, 2009, IEEE INTELL SYST, V24, P8, DOI 10.1109/MIS.2009.36
   Kurohashi S., 2018, P OF THE 2018 C N AM, V2, P253, DOI [DOI 10.18653/V1/N18-2041, 10.18653/v1/N18-2041]
   Lalithsena S., 2018, DOMAIN SPECIFIC KNOW
   Nalamothu A., 2019, ABUSIVE HATE SPEECH
   Saif Hassan, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P508, DOI 10.1007/978-3-642-35176-1_32
   SHETH A, 2001, Patent No. 6311194
   Sheth A, 2019, IEEE INTERNET COMPUT, V23, P54, DOI 10.1109/MIC.2019.2960071
   Sheth A, 2017, 2017 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2017), P1, DOI 10.1145/3106426.3109448
   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
   Yang B, 2019, R RES LANDSCAPE ENV, P143
   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
NR 18
TC 4
Z9 4
U1 3
U2 15
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 MAR-APR
PY 2020
VL 24
IS 2
SI SI
BP 66
EP 72
DI 10.1109/MIC.2020.2979620
PG 7
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LR4QG
UT WOS:000535680900008
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Pino-Diaz, J
   Jimenez-Contreras, E
   Ruiz-Banos, R
   Bailon-Moreno, R
AF Pino-Diaz, Jose
   Jimenez-Contreras, Evaristo
   Ruiz-Banos, Rosario
   Bailon-Moreno, Rafael
TI Strategic Knowledge Maps of the Techno-Scientific Network (SK Maps)
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
AB Knowledge engineering and information mapping are two recent scientific disciplines in constant development where mathematics, linguistics, computer science, and information visualization converge. Their main focus is to discover and display new knowledge in large document databases. They have broad and innovative fields of application for strategic scouting in science and technology, knowledge management, business intelligence, and scientific and technological evaluation. This article presents a new method for mapping the strategic research network and illustrates its application to the strategic analysis of the knowledge domain "Spanish Research in Protected Areas for the Period 1981-2005." This strategic knowledge is displayed through a set of two-dimensional cartographic maps and three-dimensional images of two networks: the international network WoS_KWAJ (1981-2005) and the national network IEDCYT_KWAJ (1981-2005). These maps can be very useful in decision-making processes for science and technology policy.
C1 [Pino-Diaz, Jose] Univ Malaga, Dept Filol Griega Estudios Arabes Linguist Gen &, E-29071 Malaga, Spain.
   [Jimenez-Contreras, Evaristo; Ruiz-Banos, Rosario] Univ Granada, Dept Bibliotecon & Documentac, E-18071 Granada, Spain.
   [Bailon-Moreno, Rafael] Univ Granada, Dept Ingn Quim, E-18071 Granada, Spain.
RP Pino-Diaz, J (通讯作者)，Univ Malaga, Dept Filol Griega Estudios Arabes Linguist Gen &, Campus Teatinos, E-29071 Malaga, Spain.
EM jpinod@uma.es; evaristo@ugr.es; rruizb@ugr.es; bailonm@ugr.es
RI White, Howard D/A-7034-2009; Pino-Díaz, José J./K-8656-2014
OI Pino-Díaz, José J./0000-0002-9865-8008
CR Aiello A., 2004, THESIS U BUENOS AIRE
   Bailon-Moreno R, 2003, THESIS U GRANADA SPA
   Bailon-Moreno R., 2006, J AM SOC INFORM SCI, V57, P847
   Batagelj V., 2010, NETWORKS PAJEK
   Callon M., 1986, MAPPING DYNAMICS SCI
   Callon Michel, 1991, GESTION RECHERCHE NO, P207
   Callon Michel, 1989, SCI SES RESEAUX GENE
   Chaomei Chen, 1999, 1999 IEEE International Conference on Information Visualization (Cat. No. PR00210), P18, DOI 10.1109/IV.1999.781529
   Conchillo Jimenez A., 1993, B FACTORES HUMANOS, V2, P12
   Courtial J.P, 1999, PAPERS SOCIAL REPRES, V8, P31
   COURTIAL JP, 1990, SCIENTOMETRICS, V19, P127, DOI 10.1007/BF02130469
   Davidson R, 1996, ACM T GRAPHIC, V15, P301, DOI 10.1145/234535.234538
   De la Rosa Troyano F., 2005, REDES REV HISPANA AN, V8, P1, DOI DOI 10.5565/REV/REDES.65
   Eades P., 1984, C NUMERANTIUM, V42, P149, DOI DOI 10.1007/3-540-63938-1_
   Fan W., 2006, J ACM BLACKSBURG, V49, P76
   Frick A., 1995, INT S GRAPH DRAW, P388, DOI DOI 10.1007/3-540-58950-3
   FRUCHTERMAN TMJ, 1991, SOFTWARE PRACT EXPER, V21, P1129, DOI 10.1002/spe.4380211102
   Guerrero Casas F.M., 2002, 10 JORN ASEPUMA AS E, P1
   Han J. W., 2001, DATA MINING CONCEPTS, P550
   He Q, 1999, LIBR TRENDS, V48, P133
   Jurado-Alameda E., 2002, ING QUIM-URUGUAY, P177
   Jurado-Alameda E., 2002, ING QUIM-URUGUAY, P141
   KAMADA T, 1989, INFORM PROCESS LETT, V31, P7, DOI 10.1016/0020-0190(89)90102-6
   Kohonen T., 2001, SELF ORG MAPS, DOI [10.1007/978-3-642-56927-2, DOI 10.1007/978-3-642-56927-2]
   Latour B., 1983, SCI OBSERVED PERSPEC
   LAW J, 1992, SCIENTOMETRICS, V23, P417, DOI 10.1007/BF02029807
   LAW J, 1988, SCIENTOMETRICS, V14, P251, DOI 10.1007/BF02020078
   Linares G., 2001, REV INVESTIGACION OP, V22, P173
   Lopez-Herrera AG, 2009, INFORM RES, V14
   Meulman J.J., 2005, SPSS CATEGORIES 14 0
   Michelet B, 1988, THESIS U PARIS 7 FRA
   Old L.J, 2002, GIS 2002 C IND IN
   Old LJ, 2001, SCIENTOMETRICS, V51, P563, DOI 10.1023/A:1019603321216
   Polanco X, 2008, ENCONTROS BIBLI REV
   Polanco X, 1997, UNIVERSO MEDICION PE, P335
   Prieto Vinuela J.J, 2004, 9 JORN EC CRIT
   SHNEIDERMAN B, 1992, ACM T GRAPHIC, V11, P92, DOI 10.1145/102377.115768
   Solis Arias V, 2007, 2 JORN ESP AN INP OU
   SUGIYAMA K, 1995, J VISUAL LANG COMPUT, V6, P217, DOI 10.1006/jvlc.1995.1013
   SWANSON DR, 1986, LIBR QUART, V56, P103, DOI 10.1086/601720
   SWANSON DR, 1987, J AM SOC INFORM SCI, V38, P228, DOI 10.1002/(SICI)1097-4571(198707)38:4<228::AID-ASI2>3.0.CO;2-G
   Torres-Salinas D, 2009, REV ESP DOC CIENT, V32, P34, DOI 10.3989/redc.2009.2.686
   Vargas-Quesada B, 2005, THESIS U GRANADA SPA
   Vargas-Quesada B., 2006, P 1 INT C MULT INF S, P539
   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
NR 45
TC 4
Z9 4
U1 1
U2 52
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-2882
EI 1532-2890
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD APR
PY 2012
VL 63
IS 4
BP 796
EP 804
DI 10.1002/asi.21712
PG 9
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 917EW
UT WOS:000302156300012
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Ahmad, J
   Rehman, A
   Rauf, HT
   Javed, K
   Alkhayyal, MA
   Alnuaim, AA
AF Ahmad, Jawad
   Rehman, Abdur
   Rauf, Hafiz Tayyab
   Javed, Kashif
   Alkhayyal, Maram Abdullah
   Alnuaim, Abeer Ali
TI Service Recommendations Using a Hybrid Approach in Knowledge Graph with
   Keyword Acceptance Criteria
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph; recommendation systems; keyword acceptance
ID SYSTEM; MODEL
AB Businesses are overgrowing worldwide; people struggle for their businesses and startups in almost every field of life, whether industrial or academic. The businesses or services have multiple income streams with which they generate revenue. Most companies use different marketing and advertisement strategies to engage their customers and spread their services worldwide. Service recommendation systems are gaining popularity to recommend the best services and products to customers. In recent years, the development of service-oriented computing has had a significant impact on the growth of businesses. Knowledge graphs are commonly used data structures to describe the relations among data entities in recommendation systems. Domain-oriented user and service interaction knowledge graph (DUSKG) is a framework for keyword extraction in recommendation systems. This paper proposes a novel method of chunking-based keyword extractions for hybrid recommendations to extract domain-specific keywords in DUSKG. We further show that the performance of the hybrid approach is better than other techniques. The proposed chunking method for keyword extraction outperforms the existing value feature entity extraction (VF2E) by extracting fewer keywords.
C1 [Ahmad, Jawad; Rehman, Abdur] Univ Gujrat, Dept Comp Sci, Gujrat 50700, Pakistan.
   [Rauf, Hafiz Tayyab] Univ Bradford, Fac Engn & Informat, Dept Comp Sci, Bradford BD7 1DP, W Yorkshire, England.
   [Javed, Kashif] Univ Engn & Technol, Dept Elect Engn, Lahore 54890, Pakistan.
   [Alkhayyal, Maram Abdullah] King Saud Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 51178, Riyadh 11543, Saudi Arabia.
   [Alnuaim, Abeer Ali] King Saud Univ, Dept Comp Sci & Engn, Collage Appl Studies & Community Serv, POB 22459, Riyadh 11495, Saudi Arabia.
RP Rauf, HT (通讯作者)，Univ Bradford, Fac Engn & Informat, Dept Comp Sci, Bradford BD7 1DP, W Yorkshire, England.
EM jawad.ahmad@uog.edu.pk; a.rehman@uog.edu.pk;
   hafiztayyabrauf093@gmail.com; kashif.javed@uet.edu.pk;
   442204299@student.ksu.edu.sa; abalnuaim@ksu.edu.sa
RI Alnuaim, Abeer/E-1524-2017; Rauf, Hafiz Tayyab/AAA-7762-2021
OI Alnuaim, Abeer/0000-0002-2537-2439; Rauf, Hafiz
   Tayyab/0000-0002-1515-3187
FU King Saud University, Riyadh, Saudi Arabia [RSP-2021/314]
FX The authors extend their appreciation to the Researchers Supporting
   Program (Project number RSP-2021/314), King Saud University, Riyadh,
   Saudi Arabia.
CR [Anonymous], 2015, P 1 INT WORKSH MACH
   Cao ZY, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11030392
   Catherine R, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P325, DOI 10.1145/2959100.2959131
   Chu WT, 2017, WORLD WIDE WEB, V20, P1313, DOI 10.1007/s11280-017-0437-1
   Ebadi A., 2016, INT J COMPUT INF ENG, V10, P1377
   Gao W, 2016, LECT NOTES COMPUT SC, V9936, P646, DOI 10.1007/978-3-319-46295-0_44
   Hu SH, 2019, P IEEE I C SERV COMP, P197, DOI 10.1109/SCC.2019.00041
   Jain A, 2015, LECT NOTES COMPUT SC, V9435, P188, DOI 10.1007/978-3-662-48616-0_12
   Jiang ZS, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P288, DOI 10.1145/3159652.3159715
   Kaushik A., 2018, INT J ADV RES IDEAS, V4, P425
   Li DTCA, 2021, NEUROCOMPUTING, V455, P283, DOI 10.1016/j.neucom.2021.03.122
   Liu H., 2022, IEEE T IND INF, V18, P7107, DOI 10.1109/TII.2022.3143605
   Liu H, 2022, IEEE T IND INFORM, V18, P4361, DOI 10.1109/TII.2021.3128240
   Liu HX, 2022, INT J RADIAT BIOL, DOI [10.1080/09553002.2022.2063430, 10.1109/TMM.2021.3081873]
   Liu JX, 2016, IEEE T SERV COMPUT, V9, P686, DOI 10.1109/TSC.2015.2433251
   Liu XM, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), P185, DOI 10.1109/ICWS.2015.34
   Lusompa A, 2020, THESIS U CALIFORNIA
   Mashal I, 2016, J AMB INTEL HUM COMP, V7, P889, DOI 10.1007/s12652-016-0357-4
   Nguyen T., 2018, EAI ENDORSED T IND N, V5, pe4, DOI [10.4108/eai.19-12-2018.156079, DOI 10.4108/EAI.19-12-2018.156079]
   Nilashi M, 2015, ELECTRON COMMER R A, V14, P542, DOI 10.1016/j.elerap.2015.08.004
   Qian X., 2021, 210206343 ARXIV
   Rodriguez-Mier P, 2016, IEEE T SERV COMPUT, V9, P537, DOI 10.1109/TSC.2015.2402679
   Shao BL, 2021, EXPERT SYST APPL, V165, DOI 10.1016/j.eswa.2020.113764
   Sobecki Andrzej, 2015, TASK Quarterly, V19, P445
   Tian G, 2017, INFORM SYST FRONT, V19, P75, DOI 10.1007/s10796-015-9590-1
   Wang HF, 2019, FUTURE GENER COMP SY, V100, P600, DOI 10.1016/j.future.2019.05.045
   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 WL, 2015, WORLD WIDE WEB, V18, P1351, DOI 10.1007/s11280-014-0307-z
   Yin YY, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17092059
   Zhang HY, 2017, WIREL COMMUN MOB COM, DOI 10.1155/2017/6783240
   Zhang L, 2015, ACM T KNOWL DISCOV D, V10, DOI 10.1145/2753765
   Zhang S, 2017, INT J COMPUT COMMUN, V12, P728, DOI 10.15837/ijccc.2017.5.2930
   Zhang ZL, 2022, IEEE T KNOWL DATA EN, V34, P2335, DOI 10.1109/TKDE.2020.3005952
   Zhao WX, 2016, ACM T KNOWL DISCOV D, V10, DOI 10.1145/2842629
   Zheng Y, 2019, WIREL COMMUN MOB COM, DOI 10.1155/2019/6082047
NR 36
TC 0
Z9 0
U1 2
U2 2
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 APR
PY 2022
VL 12
IS 7
AR 3544
DI 10.3390/app12073544
PG 21
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 0L8VK
UT WOS:000781745200001
OA gold
DA 2022-08-23
ER

PT J
AU Sun, PP
   Gu, LG
AF Sun, Pingping
   Gu, Lingang
TI Fuzzy knowledge graph system for artificial intelligence-based smart
   education
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE AI-assisted smart education; fuzzy knowledge graph; AI technology;
   system construction
ID PATH
AB Fuzzy knowledge graph system is a semantic network that reveals the relationships between entities, and a tool or methodology that can formally describe things in the real world and their relationships. Smart education is an educational concept or model that uses advanced information technology to build a smart environment, integrates theory and practice to build an educational framework for information age, and provides paths to practice it. Artificial intelligence (AI) is a comprehensive discipline developed by the interpenetration of computer science, cybernetics, information theory, linguistics, neurophysiology and other disciplines, which is a direction for the development of information technology in the future. On the basis of summarizing and analyzing of previous research works, this paper expounded the research status and significance of AI technology, elaborated the development background, current status and future challenges of the construction and application of fuzzy knowledge graph system for smart education, introduced the methods and principles of data acquisition methods and digitalized apprenticeship, realized the process design, information extraction, entity recognition and relationship mining of smart education, constructed a systematic framework for fuzzy knowledge graph, and analyzed the high-quality resources sharing and personalized service of AI-assisted smart education, discussed automatic knowledge acquisition and fusion of fuzzy knowledge graph, performed co-occurrence relationship analysis, and finally conducted application case analysis. The results show that the smart education knowledge graph for AI-assisted smart education can integrate teaching experience and domain knowledge of discipline experts, enhance explainable and robust machine intelligence for AI-assisted smart education, and provide data-driven and knowledge-driven information processing methods; it can also discover the analysis hotspots and main content of research objects through clustering of high-frequency topic words, reveal the corresponding research structure in depth, and then systematically explore its research dimensions, subject background and theoretical basis.
C1 [Sun, Pingping] Hangzhou Vocat & Tech Coll, Business & Tourism Inst, Hangzhou, Zhejiang, Peoples R China.
   [Gu, Lingang] Hangzhou Vocat & Tech Coll, Special Equipment Inst, Hangzhou 310018, Zhejiang, Peoples R China.
RP Gu, LG (通讯作者)，Hangzhou Vocat & Tech Coll, Special Equipment Inst, Hangzhou 310018, Zhejiang, Peoples R China.
EM david202005@126.com
CR Agirre E, 2014, COMPUT LINGUIST, V40, P57, DOI 10.1162/COLI_a_00164
   Ali M, 2019, BIOINFORMATICS, V35, P3538, DOI 10.1093/bioinformatics/btz117
   Chen PH, 2018, IEEE ACCESS, V6, P31553, DOI 10.1109/ACCESS.2018.2839607
   Dragoni M, 2015, COGN COMPUT, V7, P186, DOI 10.1007/s12559-014-9308-6
   Ernst P, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0549-5
   Fan M, 2016, COGN COMPUT, V8, P1087, DOI 10.1007/s12559-016-9425-5
   Fang YX, 2019, IEEE T KNOWL DATA EN, V31, P2093, DOI 10.1109/TKDE.2018.2872982
   Franco-Salvador M, 2016, INFORM PROCESS MANAG, V52, P550, DOI 10.1016/j.ipm.2015.12.004
   Heyvaert P, 2019, SEMANT WEB, V10, P1071, DOI 10.3233/SW-190358
   Jia Y, 2018, ENGINEERING-PRC, V4, P53, DOI 10.1016/j.eng.2018.01.004
   Jin JH, 2017, IEEE T PARALL DISTR, V28, P2342, DOI 10.1109/TPDS.2017.2665478
   Karidi DP, 2018, J AMB INTEL HUM COMP, V9, P2035, DOI 10.1007/s12652-017-0491-7
   Kejriwal M, 2019, SEMANT WEB, V10, P961, DOI 10.3233/SW-190370
   Lin XX, 2019, NEURAL COMPUT APPL, V31, P5629, DOI 10.1007/s00521-018-3384-6
   Lully V, 2018, PROCEDIA COMPUT SCI, V137, P211, DOI 10.1016/j.procs.2018.09.020
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Ping PP, 2017, CIRC RES, V120, P1078, DOI 10.1161/CIRCRESAHA.116.310024
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Segler MHS, 2017, CHEM-EUR J, V23, P6118, DOI 10.1002/chem.201604556
   Song Q, 2018, IEEE T KNOWL DATA EN, V30, P1887, DOI 10.1109/TKDE.2018.2807442
   Uyar A, 2015, ONLINE INFORM REV, V39, P197, DOI 10.1108/OIR-10-2014-0257
   Zhou SR, 2020, APPL SOFT COMPUT, V86, DOI 10.1016/j.asoc.2019.105778
NR 22
TC 2
Z9 2
U1 23
U2 59
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 40
IS 2
BP 2929
EP 2940
DI 10.3233/JIFS-189332
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QH1ZP
UT WOS:000618076700103
DA 2022-08-23
ER

PT J
AU Suo, XH
   Guo, B
   Shen, Y
   Wang, W
   Chen, YS
   Zhang, Z
AF Suo, Xinhua
   Guo, Bing
   Shen, Yan
   Wang, Wei
   Chen, Yaosen
   Zhang, Zhen
TI Embodying the Number of an Entity's Relations for Knowledge
   Representation Learning
SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
LA English
DT Article
DE The number of entity's relations; attention mechanism; knowledge
   representation learning; knowledge graph
AB Knowledge representation learning (knowledge graph embedding) plays a critical role in the application of knowledge graph construction. The multi-source information knowledge representation learning, which is one class of the most promising knowledge representation learning at present, mainly focuses on learning a large number of useful additional information of entities and relations in the knowledge graph into their embeddings, such as the text description information, entity type information, visual information, graph structure information, etc. However, there is a kind of simple but very common information - the number of an entity's relations which means the number of an entity's semantic types has been ignored. This work proposes a multi-source knowledge representation learning model KRL-NER, which embodies information of the number of an entity's relations between entities into the entities' embeddings through the attention mechanism. Specifically, first of all, we design and construct a submodel of the KRL-NER LearnNER which learns an embedding including the information on the number of an entity's relations; then, we obtain a new embedding by exerting attention onto the embedding learned by the models such as TransE with this embedding; finally, we translate based onto the new embedding. Experiments, such as related tasks on knowledge graph: entity prediction, entity prediction under different relation types, and triple classification, are carried out to verify our model. The results show that our model is effective on the large-scale knowledge graphs, e.g. FB15K.
C1 [Suo, Xinhua; Guo, Bing; Wang, Wei; Chen, Yaosen; Zhang, Zhen] SiChuan Univ, Sch Comp Sci & Engn, 24,South Sect 1,Yihuan Rd, Chengdu 610065, Sichuan, Peoples R China.
   [Shen, Yan] Chengdu Univ Informat Technol, Sch Comp Sci, Southwest Airport Econ Dev Zone, 24,Sect 1,XueFu Rd, Chengdu 610225, Sichuan, Peoples R China.
RP Guo, B (通讯作者)，SiChuan Univ, Sch Comp Sci & Engn, 24,South Sect 1,Yihuan Rd, Chengdu 610065, Sichuan, Peoples R China.
EM 648681767@qq.com; guobing@scu.edu.cn; sheny@cuit.edu.cn;
   wong.wei@163.com; 380003859@qq.com; zhangzhen@stu.scu.edu.cn
RI Zhang, Zhen/GPT-4307-2022
FU National Natural Science Foundation of China [61772352]; National Key
   Research and Development Project [2020YFB1711800, 2020YFB1707900];
   Science and Technology Project of Sichuan Province [2019YFG0400,
   2021YFG0152, 2020YFG0479, 2020YFG0322, 2020GFW035]; R&D Project of
   Chengdu City [2019-YF05-01790-GX]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant No. 61772352; National Key Research and
   Development Project under Grant No. 2020YFB1711800 and 2020YFB1707900;
   the Science and Technology Project of Sichuan Province under Grant No.
   2019YFG0400, 2021YFG0152, 2020YFG0479, 2020YFG0322, 2020GFW035, and the
   R&D Project of Chengdu City under Grant No. 2019-YF05-01790-GX.
CR [Anonymous], ARXIV160307704
   Bahdanau D., 2014, ARXIV
   Balazevic I., ARXIV190109590
   Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chaudhari S., ARXIV190402874
   Chen K, 2021, INT J SOFTW ENG KNOW, V31, P55, DOI 10.1142/S0218194021400039
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Chenchen Li, 2020, 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). Proceedings, P312, DOI 10.1109/DSC50466.2020.00055
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   Guan NN, 2019, KNOWL-BASED SYST, V164, P38, DOI 10.1016/j.knosys.2018.10.008
   Guo LB, 2019, PR MACH LEARN RES, V97
   Han X., 2018, P C EMP METH NAT LAN, P139
   Nguyen HL, 2020, INFORM FUSION, V61, P56, DOI 10.1016/j.inffus.2020.03.014
   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
   Li C., 2021, MDATA NEW KNOWLEDGE, P165
   Li ZF, 2021, IEEE T NEUR NET LEAR, DOI 10.1109/TNNLS.2021.3055147
   Lin Y., ARXIV181210901
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Mikolov T., 2013, ARXIV PREPRINT ARXIV, P1
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Min S. Y., ARXIV200700271
   Nguyen D.Q., 2019, P NAACL HLT 2019, P2180, DOI DOI 10.18653/V1/N19-1226
   Niu G., ARXIV200912030
   Qian W, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4286
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Sadeghian A, 2021, AAAI CONF ARTIF INTE, V35, P6471
   Singhal Amit, 2012, OFFICIAL GOOGLE BLOG
   Sun Z., 2019, 7 INT C LEARN REPR
   Turian J, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P384
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang B, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P1737, DOI 10.1145/3442381.3450043
   Wang K, 2020, COMPUTING, V102, P2587, DOI 10.1007/s00607-020-00842-5
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z., 2016, IJCAI, P1293
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H, 2017, AAAI CONF ARTIF INTE, P3104
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xie RB, 2020, IEEE T COGN DEV SYST, V12, P169, DOI 10.1109/TCDS.2019.2906685
   Xie RB, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3140
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yang B., ARXIV14126575
   Zhu J, 2020, DATA MIN KNOWL DISC, V34, P1, DOI 10.1007/s10618-019-00653-z
   Zhu Y., 2020, C EMP METH NAT LANG, P2662
NR 48
TC 0
Z9 0
U1 6
U2 9
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-1940
EI 1793-6403
J9 INT J SOFTW ENG KNOW
JI Int. J. Softw. Eng. Knowl. Eng.
PD OCT
PY 2021
VL 31
IS 10
BP 1495
EP 1515
DI 10.1142/S0218194021500509
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA WX0ZH
UT WOS:000718333000006
DA 2022-08-23
ER

PT J
AU Jiao, SL
   Zhu, ZF
   Wu, WQ
   Zuo, ZC
   Qi, JT
   Wang, WL
   Zhang, GY
   Liu, PY
AF Jiao, Songlin
   Zhu, Zhenfang
   Wu, Wenqing
   Zuo, Zicheng
   Qi, Jiangtao
   Wang, Wenling
   Zhang, Guangyuan
   Liu, Peiyu
TI An improving reasoning network for complex question answering over
   temporal knowledge graphs
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Complex questions; Temporal knowledge graphs; Question answering;
   Temporal reasoning
AB Question Answering Over Temporal Knowledge Graphs is an important topic in question answering, which aims to find an entity or timestamp to answer temporal reasoning questions from temporal knowledge graphs. Answering complex questions remains a major challenge for question answering over temporal knowledge graphs because it is associated with complex temporal reasoning. The performance of the existing state-of-the-art model falls short when the question contains constraints (e.g., 'before/after', 'first/lase and 'during') that require complex temporal reasoning based on multiple relevant facts. In this paper, we propose an improving reasoning method called the Complex Temporal Reasoning Network, which improves the complex temporal reasoning for temporal reasoning questions. For each question, we capture implicit temporal features and relation representation and then integrate them to generate implicit temporal relation representation. The experimental results on the CRONQUESTIONS dataset demonstrate that our method significantly outperforms all baselines. In particular, we demonstrate the effectiveness of our method on complex questions. The source code of CTRN will be available at https://github.com/2399240664/CTRN.
C1 [Jiao, Songlin; Zhu, Zhenfang; Wu, Wenqing; Zuo, Zicheng; Qi, Jiangtao; Zhang, Guangyuan] Shandong Jiao Tong Univ, Sch Informat Sci & Elect Engn, Jinan 250357, Peoples R China.
   [Wang, Wenling] Lu Dong Univ, Chinese Lexicog Res Ctr, Yantai 264025, Peoples R China.
   [Liu, Peiyu] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China.
RP Zhu, ZF (通讯作者)，Shandong Jiao Tong Univ, Sch Informat Sci & Elect Engn, Jinan 250357, Peoples R China.
EM nihaoqingchen@gmail.com; zhuzf_sdjtu@126.com; winchywwq@163.com;
   zuozicheng@yeah.net; qijiangtaohappy@hotmail.com; 362607793@qq.com;
   xdzhanggy@163.com; liupy@sdnu.edu.cn
FU National Social Science Foundation [19BYY076]; Key R & D project of
   Shandong Province [2019 JZZY010129]; Shandong Natural Science Foundation
   [ZR2021MF064, ZR2021QG041]; Shandong Provincial Social Science Planning
   Project [19BJCJ51]
FX This work was supported in part by the National Social Science
   Foundation under Award 19BYY076, in part Key R & D project of Shandong
   Province 2019 JZZY010129, in part Shandong Natural Science Foundation
   under Award ZR2021MF064 and Award ZR2021QG041, and in part by the
   Shandong Provincial Social Science Planning Project under Award
   19BJCJ51.
CR Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Cohen W W, 2020, 8 INT C LEARNING REP
   Costa TS, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P3157, DOI 10.1145/3340531.3412760
   Dasgupta SS, 2018, P 2018 C EMPIRICAL M, P2001
   Dhingra Bhuwan, 2022, Transactions of the Association for Computational Linguistics, V10, P257, DOI 10.1162/tacl_a_00459
   Esposito M, 2020, INFORM SCIENCES, V514, P88, DOI 10.1016/j.ins.2019.12.002
   Fevry Thibault, 2020, P 2020 C EMP METH NA, P4937
   Garcia-Duran Alberto, 2018, P 2018 C EMP METH NA, P4816
   Goel R, 2020, AAAI CONF ARTIF INTE, V34, P3988
   He GL, 2021, WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P535, DOI 10.1145/3437963.3441753
   Jain P, 2020, P 2020 C EMP METH NA, P3733
   Jia Z., 2021, P 30 ACM INT C INF K, P792, DOI [10.1145/3459637.3482416, DOI 10.1145/3459637.3482416]
   Jia Z, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1057, DOI 10.1145/3184558.3191536
   Jia Z, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1807, DOI 10.1145/3269206.3269247
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kingma D. P., 2015, ADAM METHOD STOCHAST, DOI DOI 10.48550/ARXIV.1412.6980
   Kipf T., 2016, 5 INT C LEARN REPR, P1
   Lacroix T, 2020, P INT C LEARN REPR
   Mavromatis C, 2021, ARXIV
   Miller Alexander, 2016, EMNLP, DOI [10.18653/v1/D16-1147, DOI 10.18653/V1/D16-1147]
   Mitchell T, 2018, COMMUN ACM, V61, P103, DOI 10.1145/3191513
   Pang S, 2021, FINDINGS ASS COMPUTA, P2627
   Pota M, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10144710
   Sanh V, 2019, ARXIV
   Saxena A, P 59 ANN M ASS COMPU, V1, P6663
   Sun H., 2018, P 2018 C EMP METH NA, P4231, DOI DOI 10.18653/V1/D18-1455
   Sun H., 2019, P 2019 C EMP METH NA, P2380
   Sun Z., 2018, P ICLR
   Trivedi R, 2017, PR MACH LEARN RES, V70
   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 WQ, 2020, FUTURE INTERNET, V12, DOI 10.3390/fi12030045
NR 34
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/s10489-022-03913-6
EA JUL 2022
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3E0PY
UT WOS:000829695500004
DA 2022-08-23
ER

PT J
AU Lin, Y
   Xu, B
   Feng, JJ
   Lin, HF
   Xu, K
AF Lin, Yuan
   Xu, Bo
   Feng, Jiaojiao
   Lin, Hongfei
   Xu, Kan
TI Knowledge-enhanced recommendation using item embedding and path
   attention
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Item embedding; Preference propagation; Recommendation
AB Recommender systems have attracted widespread attention in various online applications. To effectively recommend the needed items of users, knowledge graphs have been introduced to provide rich and complementary information to infer user preferences in recommender systems. Existing efforts have explored user preferences through specific paths and item embedding in knowledge graphs. However, user preferences have hardly been fully captured because users and items are always separately modeled. To address this problem, we propose a model to represent items from a user's perspective that provides effective supplementary information. User preferences encoded in historically clicked items are propagated along links in the knowledge graph. We propose a gated attention unit to capture user preferences from specific types of paths. Based on the captured preference information through the knowledge graph and supplementary item information, we generate effective reasoning paths to infer the underlying rationale of user-item interactions using the sequential model. Through extensive experiments on real-world datasets, we demonstrate that the proposed model achieves significant improvements over the state-of-the-art solutions. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Lin, Yuan; Xu, Bo; Feng, Jiaojiao; Lin, Hongfei; Xu, Kan] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian, Peoples R China.
RP Xu, B (通讯作者)，Dalian Univ Technol, Sch Comp Sci & Technol, Dalian, Peoples R China.
EM zhlin@dlut.edu.cn; xubo@dlut.edu.cn; jiaojiaofeng@mail.dlut.edu.cn;
   hflin@dlut.edu.cn; xukan@dlut.edu.cn
OI Xu, Bo/0000-0001-5453-978X
CR Ahmadian S., 2019, KNOWL-BASED SYST
   Alhamid MF, 2016, IEEE T HUM-MACH SYST, V46, P615, DOI 10.1109/THMS.2015.2509965
   Bahdanau D., 2014, ARXIV
   Barkan O., 2016, 2016 IEEE 26 INT WOR, P1, DOI DOI 10.1109/MLSP.2016.7738886
   Caselles-Dupre H, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P352, DOI 10.1145/3240323.3240377
   Chen JW, 2018, ACM/SIGIR PROCEEDINGS 2018, P909, DOI 10.1145/3209978.3210083
   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
   Guo H., 2017, DEEPFM FACTORIZATION
   Haidong Chen, 2016, 2016 IEEE International Workshop on Electromagnetics (iWEM): Applications and Student Innovation Competition, P1, DOI 10.1109/iWEM.2016.7504980
   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
   Hu YF, 2008, IEEE DATA MINING, P263, DOI 10.1109/ICDM.2008.22
   Huang J, 2018, ACM/SIGIR PROCEEDINGS 2018, P505, DOI 10.1145/3209978.3210017
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Levy O, 2014, NEURIPS, DOI DOI 10.5555/2969033.2969070
   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
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Oliver NM, 2000, IEEE T PATTERN ANAL, V22, P831, DOI 10.1109/34.868684
   Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI 10.3115/v1/D14-1162
   Rawashdeh M, 2013, J INTELL INF SYST, V40, P307, DOI 10.1007/s10844-012-0227-2
   Rendle Steffen, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P995, DOI 10.1109/ICDM.2010.127
   Shan Y, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P255, DOI 10.1145/2939672.2939704
   Shi DQ, 2020, KNOWL-BASED SYST, V195, DOI 10.1016/j.knosys.2020.105618
   Shi Y, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2190, DOI 10.1145/3219819.3220006
   Shi Yu, 2018, Proc SIAM Int Conf Data Min, V2018, P144, DOI 10.1137/1.9781611975321.16
   Tran T, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P687, DOI 10.1145/3269206.3271730
   Volkovs MN, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P313, DOI 10.1145/2766462.2767716
   Wang DJ, 2017, IEEE IJCNN, P596, DOI 10.1109/IJCNN.2017.7965907
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang JZ, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P839, DOI 10.1145/3219819.3219869
   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, 2014, AAAI CONF ARTIF INTE, P1112
   Wu ZY, 2020, KNOWL-BASED SYST, V210, DOI 10.1016/j.knosys.2020.106481
   Xiao J., 2017, P 26 INT JOINT C ART
   Xie F., 2020, KNOWL-BASED SYST, P211
   Yang ZX, 2020, KNOWL-BASED SYST, V204, DOI 10.1016/j.knosys.2020.106194
   Yu JL, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P357, DOI 10.1145/3269206.3271725
   Yu X, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P283, DOI 10.1145/2556195.2556259
   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 HP, 2018, KNOWL-BASED SYST, V143, P102, DOI 10.1016/j.knosys.2017.12.011
NR 43
TC 2
Z9 2
U1 12
U2 24
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 107484
DI 10.1016/j.knosys.2021.107484
EA SEP 2021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WK7QW
UT WOS:000709919000022
DA 2022-08-23
ER

PT J
AU Zhang, Y
   Cao, WH
   Liu, JT
   Rao, ZY
AF Zhang, Yi
   Cao, Wanhua
   Liu, Juntao
   Rao, Ziyun
TI A Novel Negative Sampling Based on Frequency of Relational Association
   Entities for Knowledge Graph Embedding
SO JOURNAL OF WEB ENGINEERING
LA English
DT Article
DE Knowledge embeddings; negative sampling; link prediction; knowledge
   graph
AB Knowledge graph embedding improves the performance of relation extraction and knowledge reasoning by encoding entities and relationships in low-dimensional semantic space. During training, negative samples are usually constructed by replacing the head/tail entity. And the different replacing relationships lead to different accuracy of the prediction results. This paper develops a negative triplets construction framework according to the frequency of relational association entities. The proposed construction framework can fully consider the quantitative of relations and entities in the dataset to assign the proportion of relation and entity replacement and the frequency of the entities associated with each relationship to set reasonable proportions for different relations. To verify the validity of the proposed construction framework, it is integrated into the state-of-the-art knowledge graph embedding models, such as TransE, TransH, DistMult, ComplEx, and Analogy. And both the evaluation criteria of relation prediction and entity prediction are used to evaluate the performance of link prediction more comprehensively. The experimental results on two commonly used datasets, WN18 and FB15K, show that the proposed method improves entity link and triplet classification accuracy, especially the accuracy of relational link prediction.
C1 [Zhang, Yi; Cao, Wanhua] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150001, Peoples R China.
   [Zhang, Yi; Cao, Wanhua; Liu, Juntao; Rao, Ziyun] Wuhan Digital Engn Res Inst, Wuhan 430205, Peoples R China.
RP Cao, WH (通讯作者)，Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150001, Peoples R China.; Cao, WH (通讯作者)，Wuhan Digital Engn Res Inst, Wuhan 430205, Peoples R China.
EM yzhang85@hrbeu.edu.cn
CR 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
   Dai YF, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9050750
   Guo S, 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, P84
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   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
   Li X, 2021, NEURAL COMPUT APPL, V33, P10587, DOI 10.1007/s00521-021-05762-9
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Liu JQ, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102522
   Shao B., 2020, EXPERT SYST APPL
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xiao H., 2015, COMPUTER SCI
   Yang B., 2015, P INT C LEARN REPR I
NR 19
TC 0
Z9 0
U1 2
U2 3
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 6
BP 1867
EP 1884
DI 10.13052/jwe1540-9589.2068
PG 18
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XC4TD
UT WOS:000722006400008
OA Bronze
DA 2022-08-23
ER

PT J
AU You, YW
   Wang, DZ
   Liu, JX
   Chen, YQ
   Ma, XD
   Li, WK
AF You, Yanwei
   Wang, Dizhi
   Liu, Jianxiu
   Chen, Yuquan
   Ma, Xindong
   Li, Wenkai
TI Physical Exercise in the Context of Air Pollution: An Emerging Research
   Topic
SO FRONTIERS IN PHYSIOLOGY
LA English
DT Review
DE knowledge map; visualized analysis; air pollution; physical exercise;
   physiological mechanisms
ID DIESEL EXHAUST EXPOSURE; HEART-RATE-VARIABILITY; PARTICULATE MATTER;
   NEUROTROPHIC FACTOR; NITROGEN-DIOXIDE; AMBIENT PM2.5; CARDIOVASCULAR
   MORTALITY; PARKINSONS-DISEASE; COGNITIVE FUNCTION; HEALTH-BENEFITS
AB Physical exercise (PE) brings physiological benefits to human health; paradoxically, exposure to air pollution (AP) is harmful. Hence, the combined effects of AP and PE are interesting issues worth exploring. The objective of this study is to review literature involved in AP-PE fields to perform a knowledge-map analysis and explore the collaborations, current hotspots, physiological applications, and future perspectives. Herein, cluster, co-citation, and co-occurrence analysis were applied using CiteSpace and VOSviewer software. The results demonstrated that AP-PE domains have been springing up and in rapid growth since the 21st century. Subsequently, active countries and institutions were identified, and the productive institutions were mainly located in USA, China, UK, Spain, and Canada. Developed countries seemed to be the major promoters. Additionally, subject analysis found that environmental science, public health, and sports medicine were the core subjects, and multidimensional communications were forming. Thereafter, a holistic presentation of reference co-citation clusters was conducted to discover the research topics and trace the development focuses. Youth, elite athletes, and rural population were regarded as the noteworthy subjects. Commuter exposure and moderate aerobic exercise represented the common research context and exercise strategy, respectively. Simultaneously, the research hotspots and application fields were elaborated by keyword co-occurrence distribution. It was noted that physiological adaptations including respiratory, cardiovascular, metabolic, and mental health were the major themes; oxidative stress and inflammatory response were the mostly referred mechanisms. Finally, several challenges were proposed, which are beneficial to promote the development of the research field. Molecular mechanisms and specific pathways are still unknown and the equilibrium points and dose-effect relationships remain to be further explored. We are highly confident that this study provides a unique perspective to systematically and comprehensively review the pieces of AP-PE research and its related physiological mechanisms for future investigations.
C1 [You, Yanwei; Wang, Dizhi; Liu, Jianxiu; Ma, Xindong] Tsinghua Univ, Div Sports Sci & Phys Educ, Beijing, Peoples R China.
   [Chen, Yuquan] Chinese Acad Med Sci & Peking Union Med Coll, Inst Med Informat Lib, Beijing, Peoples R China.
   [Li, Wenkai] Shanghai Univ Sport, China Table Tennis Coll, Shanghai, Peoples R China.
RP Ma, XD (通讯作者)，Tsinghua Univ, Div Sports Sci & Phys Educ, Beijing, Peoples R China.; Li, WK (通讯作者)，Shanghai Univ Sport, China Table Tennis Coll, Shanghai, Peoples R China.
EM maxd@mail.tsinghua.edu.cn; liwenkai@sus.edu.cn
CR Afshin A, 2017, NEW ENGL J MED, V377, P13, DOI 10.1056/NEJMoa1614362
   Aguiar AS, 2014, NEUROSCIENCE, V271, P56, DOI 10.1016/j.neuroscience.2014.04.027
   Ampofo AG, 2020, DIABETES RES CLIN PR, V167, DOI 10.1016/j.diabres.2020.108362
   Anderson JO, 2012, J MED TOXICOL, V8, P166, DOI 10.1007/s13181-011-0203-1
   Angelucci F, 2005, MOL PSYCHIATR, V10, P345, DOI 10.1038/sj.mp.4001637
   ARIS RM, 1993, AM REV RESPIR DIS, V148, P1363, DOI 10.1164/ajrccm/148.5.1363
   Berend N, 2016, RESPIROLOGY, V21, P237, DOI 10.1111/resp.12644
   Bonavita S, 2017, PHYSICAL ACTIVITY AND THE AGING BRAIN: EFFECTS OF EXERCISE ON NEUROLOGICAL FUNCTION, P121, DOI 10.1016/B978-0-12-805094-1.00012-5
   Booth FW, 2012, COMPR PHYSIOL, V2, P1143, DOI 10.1002/cphy.c110025
   Bornmann L, 2014, EMBO REP, V15, P1228, DOI 10.15252/embr.201439608
   Bos I, 2012, NEUROSCIENCE, V223, P131, DOI 10.1016/j.neuroscience.2012.07.057
   Bos I, 2014, SPORTS MED, V44, P1505, DOI 10.1007/s40279-014-0222-6
   Bos I, 2012, INHAL TOXICOL, V24, P676, DOI 10.3109/08958378.2012.714004
   Bostrom P, 2012, NATURE, V481, P463, DOI 10.1038/nature10777
   Bougault V, 2012, BRIT J SPORT MED, V46, P402, DOI 10.1136/bjsports-2011-090821
   Bowatte G, 2017, J ALLERGY CLIN IMMUN, V139, P122, DOI 10.1016/j.jaci.2016.05.008
   Bowe B, 2018, LANCET PLANET HEALTH, V2, pE301, DOI [10.1016/S2542-5196(18)30140-2, 10.1016/s2542-5196(18)30140-2]
   Brunekreef B, 2009, ENVIRON HEALTH PERSP, V117, P1791, DOI 10.1289/ehp.0800467
   Burnett R, 2018, P NATL ACAD SCI USA, V115, P9592, DOI 10.1073/pnas.1803222115
   Calderon-Garciduenas L, 2004, TOXICOL PATHOL, V32, P650, DOI 10.1080/01926230490520232
   Calderon-Garciduenas L, 2015, PRIM HEALTH CARE RES, V16, P329, DOI 10.1017/S146342361400036X
   Cao SZ, 2021, SCI TOTAL ENVIRON, V778, DOI 10.1016/j.scitotenv.2021.145812
   Carlisle AJ, 2001, BRIT J SPORT MED, V35, P214, DOI 10.1136/bjsm.35.4.214
   Chadegani A.A., 2013, ASIAN SOC SCI, V9, P18, DOI 10.5539/ass.v9n5p18
   Chen CM, 2014, EXPERT OPIN BIOL TH, V14, P1295, DOI 10.1517/14712598.2014.920813
   Chen CH, 2015, ENVIRON RES, V137, P40, DOI 10.1016/j.envres.2014.11.021
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen GB, 2021, ENVIRON RES LETT, V16, DOI 10.1088/1748-9326/abde5c
   Chen H, 2017, LANCET, V389, P718, DOI 10.1016/S0140-6736(16)32399-6
   Chen SR, 2005, REV FR ALLERGOL, V45, P442, DOI 10.1016/j.allerg.2005.08.002
   Cohen AJ, 2017, LANCET, V389, P1907, DOI [10.1016/S0140-6736(17)30505-6, 10.1016/s0140-6736(17)30505-6]
   Colberg SR, 2010, MED SCI SPORT EXER, V42, P2282, DOI 10.1249/MSS.0b013e3181eeb61c
   Connor B, 1997, MOL BRAIN RES, V49, P71, DOI 10.1016/S0169-328X(97)00125-3
   Cramer SC, 2008, CURR OPIN NEUROL, V21, P76, DOI 10.1097/WCO.0b013e3282f36cb6
   Cutrufello PT, 2011, INHAL TOXICOL, V23, P658, DOI 10.3109/08958378.2011.604106
   Daigle CC, 2003, INHAL TOXICOL, V15, P539, DOI 10.1080/08958370304468
   Dales RE, 2012, ENVIRON INT, V46, P1, DOI 10.1016/j.envint.2012.05.002
   de Hartog JJ, 2010, ENVIRON HEALTH PERSP, V118, P1109, DOI 10.1289/ehp.0901747
   de Nazelle A, 2011, ENVIRON INT, V37, P766, DOI 10.1016/j.envint.2011.02.003
   Dhital S, 2019, ENVIRON SCI POLLUT R, V26, P13103, DOI [10.1007/s11356-019-05792-w., 10.1007/s11356-019-04482-x]
   Di Q, 2017, JAMA-J AM MED ASSOC, V318, P2446, DOI 10.1001/jama.2017.17923
   Di Q, 2017, NEW ENGL J MED, V376, P2513, DOI 10.1056/NEJMoa1702747
   Dishman RK, 2006, OBESITY, V14, P345, DOI 10.1038/oby.2006.46
   Donnelly AA, 2016, FRONT PSYCHOL, V7, DOI 10.3389/fpsyg.2016.01167
   DUFAUX B, 1986, METABOLISM, V35, P105, DOI 10.1016/0026-0495(86)90108-3
   Fagard RH, 2011, PROG CARDIOVASC DIS, V53, P404, DOI 10.1016/j.pcad.2011.03.006
   Normando VMF, 2013, RESP PHYSIOL NEUROBI, V186, P53, DOI 10.1016/j.resp.2012.12.010
   Fitch K, 2016, J SPORT MED PHYS FIT, V56, P922
   Fleisch AF, 2014, ENVIRON HEALTH PERSP, V122, P378, DOI 10.1289/ehp.1307065
   Florida-James G, 2004, J SPORT SCI, V22, P967, DOI 10.1080/02640410400000272
   Foreman KJ, 2018, LANCET, V392, P2052, DOI [10.1016/S0140-6736(18)31694-5, 10.1016/s0140-6736(18)31694-5]
   Frampton MW, 2002, AM J PHYSIOL-LUNG C, V282, pL155, DOI 10.1152/ajplung.2002.282.1.L155
   Gan ZJ, 2018, CELL RES, V28, P969, DOI 10.1038/s41422-018-0078-7
   Gauderman WJ, 2004, NEW ENGL J MED, V351, P1057, DOI 10.1056/NEJMoa040610
   Ghozikali MG, 2018, ENVIRON SCI POLLUT R, V25, P21822, DOI 10.1007/s11356-018-2264-z
   Giles LV, 2014, MED SCI SPORT EXER, V46, P1999, DOI 10.1249/MSS.0000000000000309
   Giles LV, 2012, INHAL TOXICOL, V24, P783, DOI 10.3109/08958378.2012.717649
   Gleeson M, 2000, IMMUNOL CELL BIOL, V78, P536, DOI 10.1046/j.1440-1711.2000.00956.x
   Golbidi S, 2011, CARDIOL RES PRACT, V2011, DOI 10.4061/2011/972807
   Gomez-Pinilla F, 2008, NAT REV NEUROSCI, V9, P568, DOI 10.1038/nrn2421
   Gondalia R, 2019, ENVIRON INT, V132, DOI 10.1016/j.envint.2019.03.071
   Gowers AM, 2012, RESPIROLOGY, V17, P887, DOI 10.1111/j.1440-1843.2012.02195.x
   Green DJ, 2008, J APPL PHYSIOL, V105, P766, DOI 10.1152/japplphysiol.01028.2007
   Grunewald ZI, 2020, HYPERTENSION, V76, P1319, DOI 10.1161/HYPERTENSIONAHA.120.15262
   Guarnieri M, 2014, LANCET, V383, P1581, DOI 10.1016/S0140-6736(14)60617-6
   Guo C, 2020, CIRCULATION, V142, P645, DOI 10.1161/CIRCULATIONAHA.120.045915
   Gusdon AM, 2017, EXP GERONTOL, V90, P1, DOI 10.1016/j.exger.2017.01.013
   Hamanaka RB, 2018, FRONT ENDOCRINOL, V9, DOI 10.3389/fendo.2018.00680
   Han KT, 2021, J LEISURE RES, V52, P619, DOI 10.1080/00222216.2020.1788474
   Hansel NN, 2016, COPD, V13, P372, DOI 10.3109/15412555.2015.1089846
   Haskell WL, 2007, MED SCI SPORT EXER, V39, P1423, DOI [10.1249/mss.0b013e3180616b27, 10.1161/CIRCULATIONAHA.107.185649]
   Hawley JA, 2008, ACTA PHYSIOL, V192, P127, DOI 10.1111/j.1748-1716.2007.01783.x
   Heyn P, 2004, ARCH PHYS MED REHAB, V85, P1694, DOI 10.1016/j.apmr.2004.03.019
   HOFFMANGOETZ L, 1994, IMMUNOL TODAY, V15, P382, DOI 10.1016/0167-5699(94)90177-5
   Iaccarino G, 2021, J CARDIOVASC TRANSL, V14, P256, DOI 10.1007/s12265-020-10057-w
   Idavain J, 2019, SCI TOTAL ENVIRON, V650, P65, DOI 10.1016/j.scitotenv.2018.08.391
   Jo S, 2021, JAMA NEUROL, V78, P800, DOI 10.1001/jamaneurol.2021.1335
   Johnston FH, 2013, BMJ-BRIT MED J, V346, DOI 10.1136/bmj.e8446
   Kasapis C, 2005, J AM COLL CARDIOL, V45, P1563, DOI 10.1016/j.jacc.2004.12.077
   Kettunen J, 2007, STROKE, V38, P918, DOI 10.1161/01.STR.0000257999.49706.3b
   Kim KN, 2016, ENVIRON HEALTH PERSP, V124, P1547, DOI 10.1289/EHP192
   Kim SR, 2021, EUR HEART J, V42, P2487, DOI 10.1093/eurheartj/ehab139
   Kim SR, 2020, ENVIRON INT, V145, DOI 10.1016/j.envint.2020.106161
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Kocot K, 2021, ENVIRON RES, V195, DOI 10.1016/j.envres.2021.110746
   Kramer U, 2010, ENVIRON HEALTH PERSP, V118, P1273, DOI 10.1289/ehp.0901689
   Ku TT, 2017, TOXICOL RES-UK, V6, P7, DOI 10.1039/c6tx00314a
   Kvam S, 2016, J AFFECT DISORDERS, V202, P67, DOI 10.1016/j.jad.2016.03.063
   Lampron A, 2013, NEURON, V78, P214, DOI 10.1016/j.neuron.2013.04.005
   Landrigan PJ, 2018, LANCET, V391, P462, DOI 10.1016/S0140-6736(17)32345-0
   Lautenschlager NT, 2008, JAMA-J AM MED ASSOC, V300, P1027, DOI 10.1001/jama.300.9.1027
   Lavie CJ, 2019, CIRC RES, V124, P799, DOI 10.1161/CIRCRESAHA.118.312669
   Lavie CJ, 2009, MAYO CLIN PROC, V84, P373, DOI [10.4065/84.4.373, 10.1016/S0025-6196(11)60548-X]
   Lee DC, 2014, J AM COLL CARDIOL, V64, P472, DOI 10.1016/j.jacc.2014.04.058
   Li CS, 2021, ENVIRON POLLUT, V283, DOI 10.1016/j.envpol.2021.117350
   Li J., 2010, J ELECT RESOURCES ME, V7, P196, DOI DOI 10.1080/15424065.2010.505518
   Li RY, 2021, SCI TOTAL ENVIRON, V759, DOI 10.1016/j.scitotenv.2020.143491
   Liang J, 2021, REV NEUROSCIENCE, V32, P615, DOI 10.1515/revneuro-2020-0099
   Lindsay G, 2011, AUST NZ J PUBL HEAL, V35, P54, DOI 10.1111/j.1753-6405.2010.00621.x
   Lippi G, 2008, INT J SPORTS MED, V29, P696, DOI 10.1055/s-2008-1038684
   Lopez AD, 2006, GLOBAL BURDEN OF DISEASE AND RISK FACTORS, P1, DOI 10.1596/978-0-8213-6262-4
   Lovinsky-Desir S, 2021, ANN AM THORAC SOC, V18, P84, DOI 10.1513/AnnalsATS.201910-792OC
   Lu F, 2015, ENVIRON RES, V136, P196, DOI 10.1016/j.envres.2014.06.029
   Lu H., 2021, SCI TOTAL ENVIRON, V775, P145839, DOI [10.1016/j.scitotenv.2021.145839, DOI 10.1016/J.SCITOTENV.2021.145839]
   Lu JJ, 2015, INT J ENV RES PUB HE, V12, P14887, DOI 10.3390/ijerph121114887
   Lopez-Campos JL, 2016, RESPIROLOGY, V21, P14, DOI 10.1111/resp.12660
   Malig BJ, 2013, AM J EPIDEMIOL, V178, P58, DOI 10.1093/aje/kws451
   Mao GZ, 2018, SCI TOTAL ENVIRON, V635, P1081, DOI 10.1016/j.scitotenv.2018.04.173
   Marmett B, 2021, ENVIRON RES, V194, DOI 10.1016/j.envres.2020.110528
   McGee SL, 2020, NAT REV ENDOCRINOL, V16, P495, DOI 10.1038/s41574-020-0377-1
   Meho LI, 2007, J AM SOC INF SCI TEC, V58, P2105, DOI 10.1002/asi.20677
   Meng X, 2016, INT J ENV RES PUB HE, V13, DOI 10.3390/ijerph13080748
   Merigo JM, 2019, TOURISM GEOGR, V21, P881, DOI 10.1080/14616688.2019.1666913
   Molina-Sotomayor E, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17176203
   Molina-Sotomayor E, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16020245
   Mueller N, 2015, PREV MED, V76, P103, DOI 10.1016/j.ypmed.2015.04.010
   Mustafic H, 2012, JAMA-J AM MED ASSOC, V307, P713, DOI 10.1001/jama.2012.126
   Mutlu EA, 2018, ENVIRON POLLUT, V240, P817, DOI 10.1016/j.envpol.2018.04.130
   Mutlu EA, 2011, PART FIBRE TOXICOL, V8, DOI 10.1186/1743-8977-8-19
   Navarro A, 2004, AM J PHYSIOL-REG I, V286, pR505, DOI 10.1152/ajpregu.00208.2003
   Nieman DC, 1997, INT J SPORTS MED, V18, pS91, DOI 10.1055/s-2007-972705
   Nocon M, 2008, EUR J CARDIOV PREV R, V15, P239, DOI 10.1097/HJR.0b013e3282f55e09
   Nyhan M, 2014, SCI TOTAL ENVIRON, V468, P821, DOI 10.1016/j.scitotenv.2013.08.096
   O'Donovan G, 2010, J SPORT SCI, V28, P573, DOI 10.1080/02640411003671212
   Oberdorster G, 2004, INHAL TOXICOL, V16, P437, DOI 10.1080/08958370490439597
   Oelrich B, 2007, EUR UROL, V52, P1238, DOI 10.1016/j.eururo.2007.06.050
   Olivo CR, 2022, ENVIRON RES, V203, DOI 10.1016/j.envres.2021.111768
   Pasqua LA, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15071502
   Pedersen BK, 2000, PHYSIOL REV, V80, P1055, DOI 10.1152/physrev.2000.80.3.1055
   Martinho VJPD, 2019, SCI TOTAL ENVIRON, V688, P346, DOI 10.1016/j.scitotenv.2019.06.199
   Perez L, 2013, EUR RESPIR J, V42, P594, DOI 10.1183/09031936.00031112
   Petzinger GM, 2013, LANCET NEUROL, V12, P716, DOI 10.1016/S1474-4422(13)70123-6
   Polichetti G, 2009, TOXICOLOGY, V261, P1, DOI 10.1016/j.tox.2009.04.035
   Pope CA, 2016, CIRC RES, V119, P1204, DOI 10.1161/CIRCRESAHA.116.309279
   Pope CA, 2002, JAMA-J AM MED ASSOC, V287, P1132, DOI 10.1001/jama.287.9.1132
   Poynter ME, 2006, AM J PHYSIOL-LUNG C, V290, pL144, DOI 10.1152/ajplung.00131.2005
   Rao XQ, 2018, ANTIOXID REDOX SIGN, V28, P797, DOI 10.1089/ars.2017.7394
   Raza W, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18052631
   Reche C, 2020, SCI TOTAL ENVIRON, V717, DOI 10.1016/j.scitotenv.2020.137161
   Rhew SH, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0253253
   Robinson CL, 2011, THORAX, V66, P1051, DOI 10.1136/thx.2011.158956
   Rodriguez K, 1996, SCIENTOMETRICS, V35, P59, DOI 10.1007/BF02018233
   Rundell KW, 2007, INHAL TOXICOL, V19, P133, DOI 10.1080/08958370601051727
   Rundell KW, 2013, IMMUNOL ALLERGY CLIN, V33, P409, DOI 10.1016/j.iac.2013.02.009
   Saeedi P, 2019, DIABETES RES CLIN PR, V157, DOI 10.1016/j.diabres.2019.107843
   Saotome M, 2019, INT J MOL SCI, V20, DOI 10.3390/ijms20143552
   Schultz ES, 2017, CURR ALLERGY ASTHM R, V17, DOI 10.1007/s11882-017-0709-y
   SELTZER J, 1986, J APPL PHYSIOL, V60, P1321, DOI 10.1152/jappl.1986.60.4.1321
   Shih P, 2021, ENVIRON RES LETT, V16, DOI 10.1088/1748-9326/ac1224
   Shimizu E, 2003, BIOL PSYCHIAT, V54, P70, DOI 10.1016/S0006-3223(03)00181-1
   Shou YK, 2019, ECOTOX ENVIRON SAFE, V174, P344, DOI 10.1016/j.ecoenv.2019.02.086
   Skute I, 2019, J TECHNOL TRANSFER, V44, P916, DOI 10.1007/s10961-017-9637-1
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Soke F, 2021, EXP GERONTOL, V150, DOI 10.1016/j.exger.2021.111384
   Solimini AG, 2015, BMC PUBLIC HEALTH, V15, DOI 10.1186/s12889-015-2018-5
   Solomon C, 2000, EUR RESPIR J, V15, P922, DOI 10.1034/j.1399-3003.2000.15e19.x
   Soya H, 2007, BIOCHEM BIOPH RES CO, V358, P961, DOI 10.1016/j.bbrc.2007.04.173
   Suh HH, 2000, ENVIRON HEALTH PERSP, V108, P625, DOI 10.2307/3454398
   Sun QH, 2005, JAMA-J AM MED ASSOC, V294, P3003, DOI 10.1001/jama.294.23.3003
   Sweileh WM, 2018, MULTIDISCIP RESP MED, V13, DOI 10.1186/s40248-018-0128-5
   Tainio M, 2016, PREV MED, V87, P233, DOI 10.1016/j.ypmed.2016.02.002
   Thijssen DHJ, 2012, CLIN SCI, V122, P311, DOI 10.1042/CS20110469
   Thyfault JP, 2020, DIABETOLOGIA, V63, P1464, DOI 10.1007/s00125-020-05177-6
   Tian YH, 2017, ENVIRON POLLUT, V230, P227, DOI 10.1016/j.envpol.2017.06.029
   Trnjar K, 2017, ACTA CLIN CROAT, V56, P210, DOI 10.20471/acc.2017.56.02.03
   Tsai SS, 2003, STROKE, V34, P2612, DOI 10.1161/01.STR.0000095564.33543.64
   Underwood E, 2017, SCIENCE, V355, P342, DOI 10.1126/science.355.6323.342
   Venkataraman C, 1999, INHAL TOXICOL, V11, P151, DOI 10.1080/089583799197221
   Verma V, 2015, ENVIRON SCI TECHNOL, V49, P4646, DOI 10.1021/es505577w
   Volek JS, 1997, J APPL PHYSIOL, V82, P49, DOI 10.1063/1.365847
   Voss MW, 2013, HUM BRAIN MAPP, V34, P2972, DOI 10.1002/hbm.22119
   Wagner DR, 2018, J EXERC SCI FIT, V16, P12, DOI 10.1016/j.jesf.2018.01.002
   Wang CP, 2022, J HAZARD MATER, V421, DOI 10.1016/j.jhazmat.2021.126750
   Wang FF, 2016, BMC PUBLIC HEALTH, V16, DOI 10.1186/s12889-016-3015-z
   Ward T, 2010, ENVIRON POLLUT, V158, P723, DOI 10.1016/j.envpol.2009.10.016
   Wegner M, 2014, CNS NEUROL DISORD-DR, V13, P1002, DOI 10.2174/1871527313666140612102841
   Woodcock J, 2009, LANCET, V374, P1930, DOI 10.1016/S0140-6736(09)61714-1
   Xia T, 2016, NATL SCI REV, V3, P416, DOI 10.1093/nsr/nww064
   Yan WW, 2020, NEUROIMAGE, V220, DOI 10.1016/j.neuroimage.2020.117121
   Yang BY, 2018, LANCET PLANET HEALTH, V2, pE64, DOI 10.1016/S2542-5196(18)30001-9
   You YW, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.697633
   You YW, 2021, FRONT PSYCHIATRY, V12, DOI 10.3389/fpsyt.2021.661108
   You YW, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18116150
   Younan D, 2021, NEUROLOGY, V96, pE1190, DOI 10.1212/WNL.0000000000011149
   Yu HJ, 2017, INT J BEHAV NUTR PHY, V14, DOI 10.1186/s12966-017-0592-x
   Yu ZB, 2021, ENVIRON RES, V197, DOI 10.1016/j.envres.2021.111170
   Zijlema WL, 2016, INT J HYG ENVIR HEAL, V219, P212, DOI 10.1016/j.ijheh.2015.11.006
NR 187
TC 3
Z9 3
U1 31
U2 31
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1664-042X
J9 FRONT PHYSIOL
JI Front. Physiol.
PD FEB 28
PY 2022
VL 13
AR 784705
DI 10.3389/fphys.2022.784705
PG 21
WC Physiology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Physiology
GA ZV5II
UT WOS:000770563400001
PM 35295574
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ma, XT
   Dong, LY
   Wang, YQ
   Li, YL
   Zhang, H
AF Ma, Xintao
   Dong, Liyan
   Wang, Yuequn
   Li, Yongli
   Zhang, Hao
TI MNI: An enhanced multi-task neighborhood interaction model for
   recommendation on knowledge graph
SO PLOS ONE
LA English
DT Article
AB To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.
C1 [Ma, Xintao; Dong, Liyan; Wang, Yuequn; Zhang, Hao] Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China.
   [Ma, Xintao; Dong, Liyan; Wang, Yuequn; Zhang, Hao] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun, Peoples R China.
   [Li, Yongli] Northeast Normal Univ, Sch Informat Sci & Technol, Changchun, Peoples R China.
RP Zhang, H (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China.; Zhang, H (通讯作者)，Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun, Peoples R China.
EM zhangh@jlu.edu.cn
OI Ma, Xintao/0000-0002-0214-1881
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   [Anonymous], 2004, BOOK CROSSING DATASE
   [Anonymous], 2002, LASTFM ONLINE MUSIC
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cheng H.T., 2016, P 1 WORKSHOP DEEP LE, P7, DOI DOI 10.1145/2988450.2988454
   GroupLens, 1997, MOVIELENS DATASET
   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 HF, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1725
   Guo QY, 2020, IEEE T KNOWL DATA EN, V34, P3549, DOI [10.1038/s41366-020-0625-4, 10.1109/TKDE.2020.3028705]
   Hamilton Will, 2017, NEURIPS
   Hou YF, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P65, DOI 10.1145/3292500.3330948
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Jamali M, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P397
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Juan YC, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P43, DOI 10.1145/2959100.2959134
   Kipf T.N., 2017, SEMISUPERVISED CLASS
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Nie BL, 2019, FUTURE GENER COMP SY, V91, P426, DOI 10.1016/j.future.2018.09.040
   Qu Y., 2019, ARXIV PREPRINT ARXIV
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Rendle S, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P635
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   van der Merwe R, 2019, ARCH REC, V40, P239, DOI 10.1080/23257962.2017.1388224
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang CC, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbab286
   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, AAAI CONF ARTIF INTE, P2508
   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
   Wang YQ, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0251162
   Xu YB, 2018, IEEE DATA MINING, P1356, DOI 10.1109/ICDM.2018.00185
   Ying R, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P974, DOI 10.1145/3219819.3219890
   Yosinski J, 2014, ADV NEUR IN, V27
   Yousuf MI, 2020, DATA MIN KNOWL DISC, V34, P905, DOI 10.1007/s10618-020-00683-y
   Yu X., 2013, RECSYS, P347
   Yu X., 2013, IJCAI, 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 L, 2021, INTERDISCIP SCI, V13, P535, DOI 10.1007/s12539-021-00458-z
   Zhang L, 2021, GENOMICS, V113, P874, DOI 10.1016/j.ygeno.2021.02.002
   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 ZY, 2020, KNOWL-BASED SYST, V204, DOI 10.1016/j.knosys.2020.106218
NR 48
TC 0
Z9 0
U1 5
U2 10
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 OCT 28
PY 2021
VL 16
IS 10
AR e0258410
DI 10.1371/journal.pone.0258410
PG 18
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA YZ6CS
UT WOS:000755563200019
PM 34710122
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ballatore, A
   Bertolotto, M
   Wilson, DC
AF Ballatore, Andrea
   Bertolotto, Michela
   Wilson, David C.
TI A Structural-Lexical Measure of Semantic Similarity for Geo-Knowledge
   Graphs
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE geographic concepts; semantic similarity; geo-knowledge graphs;
   network-lexical similarity measure (NLS); lexical similarity; network
   similarity
ID CONTEXT
AB Graphs have become ubiquitous structures to encode geographic knowledge online. The Semantic Web's linked open data, folksonomies, wiki websites and open gazetteers can be seen as geo-knowledge graphs, that is labeled graphs whose vertices represent geographic concepts and whose edges encode the relations between concepts. To compute the semantic similarity of concepts in such structures, this article defines the network-lexical similarity measure (NLS). This measure estimates similarity by combining two complementary sources of information: the network similarity of vertices and the semantic similarity of the lexical definitions. NLS is evaluated on the OpenStreetMap Semantic Network, a crowdsourced geo-knowledge graph that describes geographic concepts. The hybrid approach outperforms both network and lexical measures, obtaining very strong correlation with the similarity judgments of human subjects.
C1 [Ballatore, Andrea] Univ Calif Santa Barbara, Ctr Spatial Studies, Santa Barbara, CA 93106 USA.
   [Bertolotto, Michela] Natl Univ Ireland Univ Coll Dublin, Sch Comp Sci & Informat, Dublin 4, Ireland.
   [Wilson, David C.] Univ N Carolina, Dept Software & Informat Syst, Charlotte, NC 28223 USA.
RP Ballatore, A (通讯作者)，Univ Calif Santa Barbara, Ctr Spatial Studies, Santa Barbara, CA 93106 USA.
EM aballatore@spatial.ucsb.edu; michela.bertolotto@ucd.ie; davils@uncc.edu
RI Ballatore, Andrea/F-3775-2018
OI Ballatore, Andrea/0000-0003-3477-7654; Bertolotto,
   Michela/0000-0003-0122-7656
FU Strategic Research Cluster Grant by Science Foundation Ireland under the
   National Development Plan [07/SRC/I1168]
FX The research presented in this article was funded by a Strategic
   Research Cluster Grant (07/SRC/I1168) by Science Foundation Ireland
   under the National Development Plan. The authors gratefully acknowledge
   this support.
CR Amsler R., 1972, 14 LING RES CTR
   Ballatore A., 2013, INTELLIGENT SYSTEMS, V50, P93, DOI [10.1007/978-3-642-37688-7, DOI 10.1007/978-3-642-37688-7]
   Ballatore A, 2013, J SPAT INT SCI, P27, DOI 10.5311/JOSIS.2013.7.128
   Ballatore A, 2014, GEOINFORMATICA, V18, P747, DOI 10.1007/s10707-013-0197-8
   Ballatore A, 2013, INT J GEOGR INF SCI, V27, P2099, DOI 10.1080/13658816.2013.790548
   Ballatore A, 2013, KNOWL INF SYST, V37, P61, DOI 10.1007/s10115-012-0571-0
   Banerjee S., 2002, COMPUT LINGUIST, P117, DOI DOI 10.1007/3-540-45715-1_11
   Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13
   Budanitsky Alexander, 2001, WORKSH WORDNET OTH L, V2, P2
   Chein M, 2009, ADV INFORM KNOWL PRO, P1
   Corley C., 2005, P ACL WORKSH EMP MOD, P13, DOI DOI 10.3115/1631862.1631865
   Euzenat Jerome, 2011, Journal on Data Semantics XV: LNCS 6720, P158, DOI 10.1007/978-3-642-22630-4_6
   Fellbaum C, 2010, THEORY AND APPLICATIONS OF ONTOLOGY: COMPUTER APPLICATIONS, P231, DOI 10.1007/978-90-481-8847-5_10
   Fernando S., 2008, P COMP LING UK CLUK, P1
   Ferrara Felice, 2013, Computational Linguistics and Intelligent Text Processing. 14th International Conference, CICLing 2013. Proceedings, P447, DOI 10.1007/978-3-642-37247-6_36
   Goodchild MF, 2007, GEOJOURNAL, V69, P211, DOI 10.1007/s10708-007-9111-y
   Heath T., 2011, SYNTHESIS LECT SEMAN, DOI [DOI 10.2200/S00334ED1V01Y201102WBE001, 10.2200/s00334ed1v01y201102wbe001]
   Hirst G, 1998, LANG SPEECH & COMMUN, P305
   Janowicz K, 2007, LECT NOTES COMPUT SC, V4853, P128
   Jeh G., 2002, P 8 ACM SIGKDD INT C, P538, DOI DOI 10.1145/775047.775126
   Jiang J, 1997, INT C RES COMP LING
   Kessler C, 2007, LECT NOTES ARTIF INT, V4635, P277
   KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103
   Landauer TK, 2007, HDB LATENT SEMANTIC
   Leacock C, 1998, LANG SPEECH & COMMUN, P265
   Li Peng, 2010, Chinese Birds, V1, P1
   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
   Oliva J, 2011, DATA KNOWL ENG, V70, P390, DOI 10.1016/j.datak.2011.01.002
   Patwardhan S., 2006, P EACL 2006 WORKSH M, P1
   Purves R., 2011, SIGSPATIAL SPECIAL, V3, P2, DOI DOI 10.1145/2047296.2047297
   RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528
   Resnik P, 1995, INT JOINT CONF ARTIF, P448
   Rodriguez MA, 2004, INT J GEOGR INF SCI, V18, P229, DOI 10.1080/13658810310001629592
   Schwering A., 2008, T GIS, V12, P5, DOI DOI 10.1111/J.1467-9671.2008.01084.X
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Turney P.D., 2001, P 12 EUR C MACH LEAR, V2167, P491, DOI DOI 10.1007/3-540-44795-4_42
   Turney PD, 2006, COMPUT LINGUIST, V32, P379, DOI 10.1162/coli.2006.32.3.379
   WU ZB, 1994, 32ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P133
   Yu WR, 2010, LECT NOTES COMPUT SC, V6184, P280
   Zhao P., 2009, P 18 ACM C INF KNOWL, P553, DOI DOI 10.1145/1645953.1646025
NR 41
TC 12
Z9 14
U1 6
U2 31
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. Geo-Inf.
PD JUN
PY 2015
VL 4
IS 2
BP 471
EP 492
DI 10.3390/ijgi4020471
PG 22
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA CO2LL
UT WOS:000358987600003
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Nickel, M
   Murphy, K
   Tresp, V
   Gabrilovich, E
AF Nickel, Maximilian
   Murphy, Kevin
   Tresp, Volker
   Gabrilovich, Evgeniy
TI A Review of Relational Machine Learning for Knowledge Graphs
SO PROCEEDINGS OF THE IEEE
LA English
DT Review
DE Graph-based models; knowledge extraction; knowledge graphs; latent
   feature models; statistical relational learning
ID LINK-PREDICTION; SEMANTIC WEB; NETWORKS; BASE
AB Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained'' on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive data sets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's knowledge vault project as an example of such combination.
C1 [Nickel, Maximilian] MIT, Lab Computat & Stat Learning, Cambridge, MA 02139 USA.
   [Nickel, Maximilian] Ist Italiano Tecnol, I-16163 Genoa, Italy.
   [Murphy, Kevin; Gabrilovich, Evgeniy] Google Inc, Mountain View, CA 94043 USA.
   [Tresp, Volker] Siemens AG, Corp Technol, D-81739 Munich, Germany.
   [Tresp, Volker] Univ Munich, D-80539 Munich, Germany.
RP Nickel, M (通讯作者)，MIT, Lab Computat & Stat Learning, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM mnick@mit.edu
OI Gabrilovich, Evgeniy/0000-0001-7933-1926
FU Center for Brains, Minds and Machines (CBMM) under NSF STC award
   [CCF-1231216]; German Federal Ministry for Economic Affairs and Energy
   under "Smart Data'' technology program [01MT14001]
FX The work of M. Nickel was supported by the Center for Brains, Minds and
   Machines (CBMM) under NSF STC award CCF-1231216. The work of V. Tresp
   was supported by the German Federal Ministry for Economic Affairs and
   Energy under the "Smart Data'' technology program (Grant 01MT14001).
CR Adamic LA, 2003, SOC NETWORKS, V25, P211, DOI 10.1016/S0378-8733(03)00009-1
   Amir G., 2007, PROC INT C NEURAL IN, P553
   ANDERSON CJ, 1992, SOC NETWORKS, V14, P137, DOI 10.1016/0378-8733(92)90017-2
   Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bach S. H., 2015, ARXIV150504406 CSLG
   Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509
   Belleau F, 2008, J BIOMED INFORM, V41, P706, DOI 10.1016/j.jbi.2008.03.004
   Berners-Lee T, 2006, LINKED DATA
   BERNERSLEE T, 2001, SEMANTIC WEB
   Bhattacharya I., 2007, ACM T KNOWL DISCOV D, V1, P1, DOI [10.1145/1217299.1217304, DOI 10.1145/1217299.1217304]
   Biega J., 2013, P 22 INT C WORLD WID, P325
   Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901
   Bleiholder J, 2008, ACM COMPUT SURV, V41, DOI 10.1145/1456650.1456651
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2011, P 25 AAAI C ART INT
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bottou L, 2010, COMPSTAT'2010: 19TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STATISTICS, P177, DOI 10.1007/978-3-7908-2604-3_16
   Brachman R. J., 2004, KNOWLEDGE REPRESENTA
   Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Chang Kai-Wei, 2014, P 2014 C EMP METH NA
   Culotta A, 2005, P 14 ACM INT C INF K, P257, DOI DOI 10.1145/1099554
   Cyganiak R., 2014, RESOURCE DESCRIPTION
   d'Amato C., 2006, P SEM WEB APPL PERSP
   Davis J., 2006, P 23 INT C MACH LEAR, DOI [10.1145/1143844.1143874, DOI 10.1145/1143844.1143874]
   DAVIS R, 1993, AI MAG, V14, P17
   De Raedt L, 2008, COGN TECHNOL, P1
   Dong XL, 2014, PROC VLDB ENDOW, V7, P881, DOI 10.14778/2732951.2732962
   Dong XL, 2015, PROC VLDB ENDOW, V8, P938, DOI 10.14778/2777598.2777603
   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
   Drumond L., 2012, P 27 ANN ACM S APPL, P326, DOI DOI 10.1145/2245276.2245341
   DZEROSKI S, 2001, RELATIONAL DATA MINI
   Erdos D, 2013, P 22 ACM INT C INF K, P1569
   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
   Fan J., 2012, AKBC WEKEX 2012 KNOW
   Ferrucci D, 2010, AI MAG, V31, P59, DOI 10.1609/aimag.v31i3.2303
   Fortunato S, 2010, PHYS REP, V486, P75, DOI 10.1016/j.physrep.2009.11.002
   Franz T, 2009, LECT NOTES COMPUT SC, V5823, P213, DOI 10.1007/978-3-642-04930-9_14
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Getoor L., 2005, ACM SIGKDD EXPLORATI, V7, P3, DOI DOI 10.1145/1117454.1117456
   Halford GS, 1998, BEHAV BRAIN SCI, V21, P803, DOI 10.1017/S0140525X98001769
   Halpin H, 2010, LECT NOTES COMPUT SC, V6496, P305, DOI 10.1007/978-3-642-17746-0_20
   Hoff P.D., 2008, ADV NEURAL INFORM PR
   Hoff PD, 2002, J AM STAT ASSOC, V97, P1090, DOI 10.1198/016214502388618906
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Hogan  Aidan, 2010, P 3 INT WORKSH LINK
   HOLLAND PW, 1983, SOC NETWORKS, V5, P109, DOI 10.1016/0378-8733(83)90021-7
   Hong Li, 2015, 7th International Conference on Agents and Artificial Intelligence (ICAART 2015). Proceedings, P317
   Huang Y., 2013, SEMANT WEB J
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Jensen D., 2002, P 19 INT C MACH LEAR, P259
   Ji H., 2013, KNOWL INF SYST, P1
   Jiang X, 2012, P INT WORKSH SEM TEC, P1
   Katz L., 1953, PSYCHOMETRIKA, Vvol. 18, ppp. 39, DOI DOI 10.1007/BF02289026
   Kemp C., 2006, AAAI, V3, P5
   Kimmig A., 2012, P NIPS WORKSH PROB P
   Klyne G., 2004, RESOURCE DESCRIPTION
   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
   Kolda TG, 2005, FIFTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P242, DOI 10.1109/ICDM.2005.77
   Koller D, 2009, PROBABILISTIC GRAPHI
   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
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   Krompass D., 2014, P INT C DAT SCI ADV
   Krompass D., 2014, P 1 WORKSH LINK DAT
   Krompass D, 2014, LECT NOTES COMPUT SC, V8797, P114, DOI 10.1007/978-3-319-11915-1_8
   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, 2009, J MACH LEARN RES, V10, P2639
   Leicht EA, 2006, PHYS REV E, V73, DOI 10.1103/PhysRevE.73.026120
   LENAT DB, 1995, COMMUN ACM, V38, P33, DOI 10.1145/219717.219745
   LENAT DB, 1991, ARTIF INTELL, V47, P185, DOI 10.1016/0004-3702(91)90055-O
   Li X, 2012, PROC VLDB ENDOW, V6, P97, DOI 10.14778/2535568.2448943
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Lisi FA, 2010, THEOR PRACT LOG PROG, V10, P331, DOI 10.1017/S1471068410000116
   Liu WP, 2010, EPL-EUROPHYS LETT, V89, DOI 10.1209/0295-5075/89/58007
   Losch Uta, 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P134, DOI 10.1007/978-3-642-30284-8_16
   Lu LY, 2011, PHYSICA A, V390, P1150, DOI 10.1016/j.physa.2010.11.027
   Manning C. D, 2013, P 17 C COMP NAT LANG, P133
   McGuinness D.L., 2004, W3C RECOMM
   Miettinen P., 2011, Proceedings of the 2011 IEEE 11th International Conference on Data Mining (ICDM 2011), P447, DOI 10.1109/ICDM.2011.28
   Mikolov T, 2013, P 1 INT C LEARN REPR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Minervini P, 2014, P 10 INT WORKSH UNC, V1259, P13
   Minsky M., 1974, MIT AI LAB MEMO, V306
   Momtchev V, 2009, P INT SEM WEB CHALL
   MUGGLETON S, 1995, NEW GENERAT COMPUT, V13, P245, DOI 10.1007/BF03037227
   Muggleton S., 1991, New Generation Computing, V8, P295
   Murphy K.P., 2012, MACHINE LEARNING PRO
   Nakashole N., 2012, P 2012 JOINT C EMP M, P1135, DOI DOI 10.1080/17517575.2015.1080301
   Ndapandula N., 2011, P 4 ACM INT C WEB SE, P227
   Neville J, 2007, J MACH LEARN RES, V8, P653
   NEWCOMBE HB, 1959, SCIENCE, V130, P954, DOI 10.1126/science.130.3381.954
   Newman MEJ, 2001, P NATL ACAD SCI USA, V98, P404, DOI 10.1073/pnas.021544898
   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., 2013, P STRUCT LEARN INF G
   Nickel M., 2014, ADV NEURAL INFORM PR, P1179
   Nickel M., 2011, P LEARN SEM WORKSH G
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2013, THESIS
   Niu F, 2012, INT J SEMANT WEB INF, V8, P42, DOI 10.4018/jswis.2012070103
   Orbanz P., 2015, IEEE T PATTERN ANAL
   Plate, 1997, CONNECTIONIST SYSTEM, P15
   Platt JC, 2000, ADV NEUR IN, P61
   Poon H., 2006, P AAAI
   Pujara J., 2015, AI MAG
   Qian R., 2013, BING SEARCH BLOG MAR
   QUINLAN JR, 1990, MACH LEARN, V5, P239, DOI 10.1007/BF00117105
   Rahm E, 2001, VLDB J, V10, P334, DOI 10.1007/s007780100057
   Rendle S., 2010, P 3 ACM INT C WEB SE, P81, DOI DOI 10.1145/1718487.1718498
   Rendle S, 2013, PROC VLDB ENDOW, V6, P337, DOI 10.14778/2535573.2488340
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Rettinger A, 2012, DATA MIN KNOWL DISC, V24, P613, DOI 10.1007/s10618-012-0253-2
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Riedel S., 2013, JOINT HUM LANG TECHN
   Ruttenberg A, 2009, BRIEF BIOINFORM, V10, P193, DOI 10.1093/bib/bbp004
   Schmitz M., 2012, P 2012 JOINT C EMP M, P523
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Singla P, 2006, IEEE DATA MINING, P572
   SMOLENSKY P, 1990, ARTIF INTELL, V46, P159, DOI 10.1016/0004-3702(90)90007-M
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sowa J., 2000, KNOWLEDGE REPRESENTA
   Sowa J. F., 2006, ENCY COGN SCI
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Suciu D., 2011, SYNTHESIS LECT DATA
   Suh B., 2009, P 5 INT S WIK OP COL
   Sun Y., 2012, SYNTH LECT DATA MIN, V3, P1, DOI DOI 10.2200/S00433ED1V01Y201207DMK005
   Sutskever I., 2009, NIPS
   Taskar B., 2007, INTRO STAT RELATIONA
   Taskar B., 2004, ADV NEURAL INF PROCE, V16
   Teflioudi C., 2015, VLDB J, P1
   Tejada S, 2001, INFORM SYST, V26, P607, DOI 10.1016/S0306-4379(01)00042-4
   Toutanova Kristina, 2015, P 3 WORKSH CONT VECT
   Tresp V., 2009, P IRMLES, V2009
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang DZ, 2008, PROC VLDB ENDOW, V1, P340, DOI 10.14778/1453856.1453896
   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, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P515, DOI 10.1145/2566486.2568032
   Whang SE, 2012, PROC INT CONF DATA, P294, DOI 10.1109/ICDE.2012.119
   WOLPERT DH, 1992, NEURAL NETWORKS, V5, P241, DOI 10.1016/S0893-6080(05)80023-1
   XU Z., 2006, P 22 C ANN C UNC ART, P544
   Yang B., 2014, CORR
   Zhang C., 2013, P 2013 ACM SIGMOD IN, P397, DOI [10.1145/2463676.2463702, DOI 10.1145/2463676.2463702]
NR 147
TC 550
Z9 583
U1 112
U2 647
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9219
EI 1558-2256
J9 P IEEE
JI Proc. IEEE
PD JAN
PY 2016
VL 104
IS 1
SI SI
BP 11
EP 33
DI 10.1109/JPROC.2015.2483592
PG 23
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA CZ6ZS
UT WOS:000367250300003
OA Green Submitted, hybrid
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Niu, HR
   He, HT
   Feng, JZ
   Nie, JL
   Zhang, YS
   Ren, JD
AF Niu, Haoran
   He, Haitao
   Feng, Jianzhou
   Nie, Junlan
   Zhang, Yangsen
   Ren, Jiadong
TI Knowledge Graph Completion Based on GCN of Multi-Information Fusion and
   High-Dimensional Structure Analysis Weight
SO CHINESE JOURNAL OF ELECTRONICS
LA English
DT Article
DE Knowledge graph completion; Graph convolutional network; Channel
   attention convolution; High-dimensional structure analysis weight
AB Knowledge graph completion (KGC) can solve the problem of data sparsity in the knowledge graph. A large number of models for the KGC task have been proposed in recent years. However, the underutilisation of the structure information around nodes is one of the main problems of the previous KGC model, which leads to relatively single encoding information. To this end, a new KGC model that encodes and decodes the feature information is proposed. First, we adopt the subgraph sampling method to extract node structure. Moreover, the graph convolutional network (GCN) introduced the channel attention convolution encode node structure features and represent them in matrix form to fully mine the node feature information. Eventually, the high-dimensional structure analysis weight decodes the encoded matrix embeddings and then constructs the scoring function. The experimental results show that the model performs well on the datasets used.
C1 [Niu, Haoran; He, Haitao; Feng, Jianzhou; Nie, Junlan; Ren, Jiadong] Yanshan Univ, Qinhuangdao 066004, Hebei, Peoples R China.
   [Zhang, Yangsen] Beijing Informat Sci & Technol Univ, Beijing 100096, Peoples R China.
RP He, HT (通讯作者)，Yanshan Univ, Qinhuangdao 066004, Hebei, Peoples R China.
EM niuhaoran822@163.com; haitao@ysu.edu.cn; fjzwxh@ysu.edu.cn;
   niejll3@163.com; zhangyangsen@163.com; jdren@ysu.edu.cn
FU National Natural Science Foundation of China [61602401,61772449];
   Scientific and Technological Research Projects of Colleges and
   Universities in Hebei Province [QN2018074]; Nature Scientist Fund of
   Hebei Province [F2019203157]
FX This work was supported by the National Natural Science Foundation of
   China (61602401,61772449), Scientific and Technological Research
   Projects of Colleges and Universities in Hebei Province (QN2018074), and
   Nature Scientist Fund of Hebei Province (F2019203157).
CR Anqi Tian, 2020, ICMLC 2020: Proceedings of the 2020 12th International Conference on Machine Learning and Computing, P580, DOI 10.1145/3383972.3384067
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Balazevic I, 2019, LECT NOTES COMPUT SC, V11731, P553, DOI 10.1007/978-3-030-30493-5_52
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Cai L, 2018, NAACL, P1470
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Guillaumin M, 2009, IEEE I CONF COMP VIS, P309, DOI 10.1109/ICCV.2009.5459266
   He W., 2015, JOINT INT SEM TECHN, P141
   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
   Jia YT, 2016, AAAI CONF ARTIF INTE, P992
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   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
   Pujara J., 2009, P 3 WORKSH AUT KNOWL, P309
   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, 2018, AAAI CONF ARTIF INTE, P1957
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   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., 2020, INT C LEARN REPR
   Wang JH, 2020, CHINESE J ELECTRON, V29, P1134, DOI 10.1049/cje.2020.09.015
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yang H, 2014, C IND ELECT APPL, P1, DOI [10.1016/j.ceramint.2014.05.109, 10.1109/ICIEA.2014.6931121]
   Yao L., 2018, P 33 AAAI C ART INT, P601
   Ye R, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4135
NR 30
TC 0
Z9 0
U1 5
U2 5
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1022-4653
EI 2075-5597
J9 CHINESE J ELECTRON
JI Chin. J. Electron.
PD MAR
PY 2022
VL 31
IS 2
BP 387
EP 396
DI 10.1049/cje.2021.00.080
PG 10
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA ZR6MW
UT WOS:000767895700018
OA hybrid
DA 2022-08-23
ER

PT S
AU Jalabert, F
   Ranwez, S
   Derozier, V
   Crampes, M
AF Jalabert, Fabien
   Ranwez, Sylvie
   Derozier, Vincent
   Crampes, Michel
BE Staab, S
   Svatek, V
TI (IDEE)-D-2: An integrated and interactive data exploration environment
   used for ontology design
SO MANAGING KNOWLEDGE IN A WORLD OF NETWORKS, PROCEEDINGS
SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
LA English
DT Article; Proceedings Paper
CT 15th International Conference on Managing Knowledge in an World of
   Networks
CY OCT 02-06, 2006
CL Podebrady, CZECH REPUBLIC
SP K-Space, Univ Econ, NeOn, X-Media, Univ Koblenz Landau, ISWeb
ID LANGUAGES
AB Many communities need to organize and structure data to improve their utilization and sharing. Much research has been focused on this problem. Many solutions are based on a Terminological and Ontological Resource (TOR) which represents the domain knowledge for a given application. However TORs are often designed without taking into account heterogeneous data from specific resources. For example, in the biomedical domain, these sources may be medical reports, bibliographical resources or biological data extracted from GOA, Gene Ontology or KEGG. This paper presents an integrated visual environment for knowledge engineering. It integrates heterogeneous data from domain databases. Relevant concepts and relations are thus extracted from data resources, using several analysis and treatment processes. The resulting ontology embryo is visualized through a user friendly adaptive interface displaying a knowledge map. The experiments and evaluations dealt with in this paper concern biological data.
C1 LGI2P Res Ctr, EMA Site EERIE, F-30035 Nimes 1, France.
RP Jalabert, F (通讯作者)，LGI2P Res Ctr, EMA Site EERIE, Parc Sci G Besse, F-30035 Nimes 1, France.
EM Fabien.Jalabert@ema.fr; Sylvie.Ranwez@ema.fr; Vincent.Derozier@ema.fr;
   Michel.Crampes@ema.fr
RI Ranwez, Sylvie/F-5975-2013
OI Ranwez, Sylvie/0000-0002-8707-0313; Derozier,
   Vincent/0000-0002-8301-068X
CR Borodin A., 2005, ACM Transactions on Internet Technology, V5, P231, DOI 10.1145/1052934.1052942
   BOURIGAULT D, 1996, 7 EURALEX INT C LE 2, P771
   BOUTIN F, 2004, ECDL 04 EUR C DIG LI, P366
   CHOUEKA Y, 1988, C US OR CONT BAS TEX
   Corcho O, 2003, DATA KNOWL ENG, V46, P41, DOI 10.1016/S0169-023X(02)00195-7
   CRAMPES M, 2006, MMCN2006 13 ANN MULT
   CRAMPES M, 2006, INFORM VISUALIZA SEP
   Daille B., 2003, P ACL 2003 WORKSH MU, P9, DOI DOI 10.3115/1119282.1119284
   DIAS G, 2000, 5 EAMT WORKSH HARV E
   Eades P., 1984, C NUMERANTIUM, V42, P149, DOI DOI 10.1007/3-540-63938-1_
   GAUME B, J EXPT THEORITICAL A, V14, P61
   HABERT B, 1996, P 16 COLING COP, V490
   Harris Z. S., 1968, MATH STRUCTURES LANG
   HEER J, 2004, PREFUSE SOFTWARE FRA
   Hindle D., 1993, Computational Linguistics, V19, P103
   JACQUEMIN C, 2003, HDB COMPUTATIONAL LI, P599
   JACQUEMIN C, 1994, JOURNEES IA 94 PARIS, P155
   LAURLSTON A, 1994, TERMINOLOGY, V1, P147
   MALAISE V, 2005, THESIS U TECHNOLOGIQ
   Manning C., 1999, FDN STAT NATURAL LAN
   MIZOGUCHI R, 2004, HDB ONTOLOGIES, P175
   Page Lawrence, 1998, PAGERANK CITATION CI
   Ploux S, 2003, COMPUT LINGUIST, V29, P155, DOI 10.1162/089120103322145298
   Salton G., 1983, INTRO MODERN INFORM
   SUEN CY, 1979, IEEE T PATTERN ANAL, V1, P164, DOI 10.1109/TPAMI.1979.4766902
   SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7
   Veronis J, 2004, COMPUT SPEECH LANG, V18, P223, DOI 10.1016/j.csl.2004.05.002
   [No title captured]
NR 28
TC 0
Z9 0
U1 0
U2 7
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-46363-1
J9 LECT NOTES ARTIF INT
PY 2006
VL 4248
BP 256
EP 271
PG 16
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BFG20
UT WOS:000241683600022
DA 2022-08-23
ER

PT J
AU Gao, YR
   Lin, RH
   Lu, YH
AF Gao, Yarui
   Lin, Runhui
   Lu, Yanhong
TI A Visualized Analysis of the Research Current Hotspots and Trends on
   Innovation Chain Based on the Knowledge Map
SO SUSTAINABILITY
LA English
DT Article
DE innovation chain; hotspots; visualization tools; knowledge mapping
AB The continued increase in global trade protectionism, refinement of labor division, high innovation cost, and development of information technology have led to many enterprises actively being engaged in innovation to improve their national economic competitiveness. Although significant research has been carried out on this by numerous academic institutions, little is known about innovation trends in Chinese enterprises. In the existing methods of literature research, the scientific knowledge map, which, based on bibliometrics, is an effective tool for management knowledge, can visually describe the knowledge resources and their carriers under the state of time series and provides a new way for literature analysis. In this paper, the CiteSpace tool was used to map knowledge domains. A total of 459 and 5645 studies published between 2010 and 2020 were downloaded from the CNKI and Web of Science databases. By analyzing the keywords "co-occurrence matrix", "author cooperation networks", and "high-frequency cited literatures", we found the differences of the research current, hotspots, and trends both in China and the world, but we were not limited to these. The research results are as follows: In China: (1) There were 759 nodes in the map of key authors, which shows that innovation chain research in China is still in the early stages. In addition, the layout of author nodes was relatively scattered while density was low; therefore, it was hard to form clusters. There is a need to strengthen academic cooperation to improve research on innovation chains. (2) From the keyword network analysis map of the innovation chain, we found that the Chinese research hotspots were: innovation chain, industrial chain, collaborative innovation, scientific and technological innovation, innovation-driven, technological innovation, strategic emerging industries, innovation ecosystem, and integration of industry and education, among other fields. In the world: (1) Most academic studies on the innovation chain have been published in different fields; these journals are about production, operation, management science, and economy, among others. These findings show that the innovation chain has received attention from multiple disciplines, and, therefore, it belongs to an interdisciplinary research field. Studies from different fields have analyzed the innovation chain from their own research perspectives. Therefore, current research outcomes on the innovation chain are difficult to unify. (2) The most important authors and key studies were analyzed. According to the co-citation map, studies on the "innovation chain" with high co-citation frequencies were not studies on the innovation chain but had the innovation chain as a theme or a concept without in-depth research on the innovation chain. (3) Through co-citation and cluster analysis of keywords, we found that international studies on the "innovation chain" are more focused on the global value chain, blockchain technology, strategic analysis, sustainable development, and absorptive capacity among other fields. Research frontier themes were mainly communication technology, continuous operation management, technological change, ecological innovation, supply chain integration, Industry 4.0, logistics innovation, nanotechnology, circular economy, and supply chain innovation, among other fields.
   Therefore, international scholars focus more on: technological issues related to innovation, using advanced communication technology, blockchain technology, and nanotechnology to improve innovation abilities. Moreover, they insist on sustainable development in the process of innovation, advocating for green innovation and ecological innovation. Finally, results of the visualization show that current research is mainly focused on innovation, not the innovation chain. Therefore, experts in this field should pay more attention to the study of structural stability and knowledge mobility of the innovation chain.
C1 [Gao, Yarui] Sias Univ, Business Sch, Zhengzhou 451150, Peoples R China.
   [Gao, Yarui; Lin, Runhui; Lu, Yanhong] Nankai Univ, Business Sch, Tianjin 300071, Peoples R China.
   [Lin, Runhui; Lu, Yanhong] China Acad Corp Governance, Tianjin 300071, Peoples R China.
RP Gao, YR (通讯作者)，Sias Univ, Business Sch, Zhengzhou 451150, Peoples R China.; Gao, YR (通讯作者)，Nankai Univ, Business Sch, Tianjin 300071, Peoples R China.
EM 11573@sias.edu.cn; linrh@nankai.edu.cn; luyanhong@mail.nankai.edu.cn
FU Laboratory for Economic Behaviors and Policy Simulation [71772096];
   National Natural Science Foundation of China
FX This research was funded by [Laboratory for Economic Behaviors and
   Policy Simulation] grant number [71772096]. And The APC was funded by
   [the National Natural Science Foundation of China].
CR Abdullah M, 2016, REV MANAG SCI, V10, P683, DOI 10.1007/s11846-015-0173-9
   Bocken NMP, 2014, J CLEAN PROD, V65, P42, DOI 10.1016/j.jclepro.2013.11.039
   Cai J, 2009, J TECH EC MANAG RES, V6, P53
   Chang A, 2011, SCI MANAG RES, V2, P30
   Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309
   Chen Y, 2014, PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL CONFERENCE ON ELECTRONIC COMMERCE (ICEC 2014), P17, DOI 10.1145/2617848.2617864
   Ding X., 2020, SCI TECHNOL MANAG RE, V40, P10
   Geissdoerfer M, 2017, J CLEAN PROD, V143, P757, DOI 10.1016/j.jclepro.2016.12.048
   Ghisellini P, 2016, J CLEAN PROD, V114, P11, DOI 10.1016/j.jclepro.2015.09.007
   Hu Z, 2010, REFORM STRATEGY, V26, P38
   Huang Y., 2013, SCI MANAG RES, V33, P170
   Li J., 2016, CITESPACE SCI TEXT M, V2
   Li Y., 2018, SCI MANAG RES, V36, P25
   Li Y, 2020, SCI TECHNOL MANAG RE, V40, P204
   Ling Y., 2020, ECONOMIST, V6, P26
   Liu D., 2021, J INT BUS EC, V3, P17
   Liu Z, 2015, ACAD MON, V47, P5
   Liu Z, 2012, IND EC RES, V3, P9
   Liu Z., 2020, ECONOMIST, V4, P51
   Liu ZJ, 2021, ENVIRON PROG SUSTAIN, V40, DOI 10.1002/ep.13479
   [史璐璐 Shi Lulu], 2020, [科研管理, Science Research Management], V41, P56
   Su D., 2016, SCI TECHNOL PROG COU, V33, P148
   [谭劲松 Tan Justin], 2019, [管理科学学报, Journal of Management Sciences in China], V22, P1
   Wang C., 2018, INF THEORY PRACT, V41, P87
   Wang C., 2017, MOD INF, V37, P16
   Wen X., 2014, SCI TECHNOL PROG COU, V31, P157
   Wu S., 2014, SCI TECHNOL PROG COU, V31, P50
   Yang Z, 2019, J NANJING UNIV NAT S, V56, P62
   Yu Y., 2020, NANKAI MANAG REV, V23, P4
   Zhan R., 2020, J SCI TECHNOL MANAG, V5, P179
   Zhang J., 2017, EC THEORY EC MANAG, V6, P5
   Zhang J, 2020, J NANJING UNIV NAT S, V57, P38
   Zhang J., 2020, EXPLOR FREE VIEWS, V37, P198
   Zhang J, 2020, J NANTONG U SOC SCI, V36, P108
   Zhang Q., 2021, J NATL I ED ADM, V4, P48
NR 35
TC 1
Z9 1
U1 53
U2 53
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 1708
DI 10.3390/su14031708
PG 14
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 ZC1GS
UT WOS:000757277900001
OA gold
DA 2022-08-23
ER

PT J
AU Lourenco, V
   Paes, A
AF Lourenco, Vitor
   Paes, Aline
TI Learning attention-based representations from multiple patterns for
   relation prediction in knowledge graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graphs; Representation learning; Embeddings; Attention
   mechanism
AB Knowledge bases, and their representations in the form of knowledge graphs (KGs), are naturally incomplete. Since scientific and industrial applications have extensively adopted them, there is a high demand for solutions that complete their information. Several recent works tackle this challenge by learning embeddings for entities and relations, then employing them to predict new relations among the entities. Despite their aggrandizement, most of those methods focus only on the local neighbors of a relation to learn the embeddings. As a result, they may fail to capture the KGs' context information by neglecting long-term dependencies and the propagation of entities' semantics. In this manuscript, we propose /EMP (Attention-based Embeddings from Multiple Patterns), a novel model for learning contextualized representations by: (i) acquiring entities context information through an attention-enhanced message-passing scheme, which captures the entities local semantics while focusing on different aspects of their neighborhood; and (ii) capturing the semantic context, by leveraging the paths and their relationships between entities. Our empirical findings draw insights into how attention mechanisms can improve entities' context representation and how combining entities and semantic path contexts improves the general representation of entities and the relation predictions. Experimental results on several large and small knowledge graph benchmarks show that /EMP either outperforms or competes with state-of-the-art relation prediction methods. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Lourenco, Vitor; Paes, Aline] Univ Fed Fluminense, Inst Comp, Ave Gal Milton Tavares Souza,S-N,Boa Viagem, Niteroi, RJ, Brazil.
RP Lourenco, V (通讯作者)，Univ Fed Fluminense, Inst Comp, Ave Gal Milton Tavares Souza,S-N,Boa Viagem, Niteroi, RJ, Brazil.
EM vitorlourenco@id.uff.br; alinepaes@ic.uff.br
OI Lourenco, Vitor/0000-0003-0711-1339
CR [Anonymous], 2016, PR MACH LEARN RES
   [Anonymous], 2019, PR MACH LEARN RES
   Bansal T, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4387
   Bollacker K., 2008, SIGMOD C, p1247?1250
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cui P., 2021, P 35 AAAI C ARTIFICI, V35, P2021
   Das R, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P132
   Denham W.W., 1973, THESIS U WASHINGTON
   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
   Dumancic S, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P6088
   Gal?rraga L., 2015, VLDB J
   Guruganesh G., 2020, ADV NEURAL INFORM PR, V33
   Guu K., 2015, ARXIV150601094
   Hamilton W., 2017, ADV NEUR IN, P1024
   Kazemi Seyed Mehran, 2018, ADV NEUR IN
   Kingma D. P., 2015, ADAM METHOD STOCHAST, DOI DOI 10.48550/ARXIV.1412.6980
   Kipf T., 2016, 5 INT C LEARN REPR, P1
   Kok S., 2007, P 24 INT C MACH LEAR, P433, DOI DOI 10.1145/1273496.1273551
   Levesque HJ., 1984, CONCEPTUAL MODELLING, P165
   Lin Bill Yuchen, 2019, P 2019 C EMP METH NA, P2829, DOI DOI 10.18653/V1/D19-1282
   Lin XX, 2019, NEURAL COMPUT APPL, V31, P5629, DOI 10.1007/s00521-018-3384-6
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Liu ZH, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2395
   Luo Y., 2015, P 2015 C EMP METH NA, P1656, DOI DOI 10.18653/V1/D15-1191
   Massa F., 2019, PYTORCH IMPERATIVE S
   McCray AT, 2003, COMP FUNCT GENOM, V4, P80, DOI 10.1002/cfg.255
   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
   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, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Oh B, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P257, DOI 10.1145/3269206.3271769
   Palumbo E, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P32, DOI 10.1145/3109859.3109889
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Shang Chao, 2019, Proc Conf AAAI Artif Intell, V33, P3060, DOI 10.1609/aaai.v33i01.33013060
   Solomon S, 2007, AR4 CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P1
   Sun Z., 2019, 7 INT C LEARN REPR
   Teru K., 2020, P 37 INT C MACH LEAR, P9448
   Toutanova K., 2015, P 2015 C EMP METH NA, p1499?1509
   Trouillon T, 2016, PR MACH LEARN RES, V48
   van der Merwe R, 2019, ARCH REC, V40, P239, DOI 10.1080/23257962.2017.1388224
   Velickovic P., 2018, P ICLR
   Wang HW, 2021, KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1697, DOI 10.1145/3447548.3467247
   Wang Q, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1308
   Wang YS, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424673
   Xu CR, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P263
   Xu X, 2019, INT C LEARNING REPRE
   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
   Zhang SQ, 2019, ADV NEUR IN, V32
   Zhang Z, 2020, AAAI CONF ARTIF INTE, V34, P9612
   Zhang ZH, 2022, KNOWL-BASED SYST, V239, DOI 10.1016/j.knosys.2021.108038
NR 51
TC 0
Z9 0
U1 1
U2 1
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
SI SI
AR 109232
DI 10.1016/j.knosys.2022.109232
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Z9PH
UT WOS:000826899900004
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhu, Q
   Nguyen, DT
   Grishagin, I
   Southall, N
   Sid, E
   Pariser, A
AF Zhu, Qian
   Nguyen, Dac-Trung
   Grishagin, Ivan
   Southall, Noel
   Sid, Eric
   Pariser, Anne
TI An integrative knowledge graph for rare diseases, derived from the
   Genetic and Rare Diseases Information Center (GARD)
SO JOURNAL OF BIOMEDICAL SEMANTICS
LA English
DT Article
DE GARD; Rare diseases; Ontology; Data integration; Knowledge graph
ID IRON
AB Background The Genetic and Rare Diseases (GARD) Information Center was established by the National Institutes of Health (NIH) to provide freely accessible consumer health information on over 6500 genetic and rare diseases. As the cumulative scientific understanding and underlying evidence for these diseases have expanded over time, existing practices to generate knowledge from these publications and resources have not been able to keep pace. Through determining the applicability of computational approaches to enhance or replace manual curation tasks, we aim to both improve the sustainability and relevance of consumer health information, but also to develop a foundational database, from which translational science researchers may start to unravel disease characteristics that are vital to the research process. Results We developed a meta-ontology based integrative knowledge graph for rare diseases in Neo4j. This integrative knowledge graph includes a total of 3,819,623 nodes and 84,223,681 relations from 34 different biomedical data resources, including curated drug and rare disease associations. Semi-automatic mappings were generated for 2154 unique FDA orphan designations to 776 unique GARD diseases, and 3322 unique FDA designated drugs to UNII, as well as 180,363 associations between drug and indication from Inxight Drugs, which were integrated into the knowledge graph. We conducted four case studies to demonstrate the capabilities of this integrative knowledge graph in accelerating the curation of scientific understanding on rare diseases through the generation of disease mappings/profiles and pathogenesis associations. Conclusions By integrating well-established database resources, we developed an integrative knowledge graph containing a large volume of biomedical and research data. Demonstration of several immediate use cases and limitations of this process reveal both the potential feasibility and barriers of utilizing graph-based resources and approaches to support their use by providers of consumer health information, such as GARD, that may struggle with the needs of maintaining knowledge reliant on an evolving and growing evidence-base. Finally, the successful integration of these datasets into a freely accessible knowledge graph highlights an opportunity to take a translational science view on the field of rare diseases by enabling researchers to identify disease characteristics, which may play a role in the translation of discover across different research domains.
C1 [Zhu, Qian; Nguyen, Dac-Trung; Grishagin, Ivan; Southall, Noel] NIH, Natl Ctr Adv Translat Sci NCATS, Div Preclin Innovat, Rockville, MD 20850 USA.
   [Sid, Eric; Pariser, Anne] NIH, Natl Ctr Adv Translat Sci NCATS, Off Rare Dis Res, Bethesda, MD 20892 USA.
RP Zhu, Q (通讯作者)，NIH, Natl Ctr Adv Translat Sci NCATS, Div Preclin Innovat, Rockville, MD 20850 USA.
EM qian.zhu@nih.gov
RI Southall, Noel/H-8991-2012
OI Southall, Noel/0000-0003-4500-880X; Sid, Eric/0000-0001-7697-3026; Zhu,
   Qian/0000-0002-4858-6333
FU Intramural/Extramural research program of the NCATS, NIH
FX This research was supported in part by the Intramural/Extramural
   research program of the NCATS, NIH.
CR [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], **NON-TRADITIONAL**
   Astakhov V, 2005, LECT NOTES COMPUT SC, V3615, P317
   BEAMISH MR, 1972, BRIT J CANCER, V26, P444, DOI 10.1038/bjc.1972.61
   Demner-Fushman D, 2017, J AM MED INFORM ASSN, V24, P841, DOI 10.1093/jamia/ocw177
   Engel P, 2013, J RARE DISORD, V1, P1
   Fogh RH, 2005, BIOINFORMATICS, V21, P1678, DOI 10.1093/bioinformatics/bti234
   Genes SaG, 2013, RAR DIS IMP REP INS
   Gratzl S, 2015, IEEE C INF VIS INFOV
   Hamosh A, 2005, NUCLEIC ACIDS RES, V33, pD514
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Karczewski KJ, 2018, NAT REV GENET, V19, P299, DOI 10.1038/nrg.2018.4
   Kohler S, 2014, NUCLEIC ACIDS RES, V42, pD966, DOI 10.1093/nar/gkt1026
   Lopes P, 2013, J BIOMED INFORM, V46, P1108, DOI 10.1016/j.jbi.2013.08.006
   Mungall CJ, 2017, NUCLEIC ACIDS RES, V45, pD712, DOI 10.1093/nar/gkw1128
   Reid DW, 2002, CHEST, V121, P48, DOI 10.1378/chest.121.1.48
   Simonart T, 1998, INT J CANCER, V78, P720, DOI 10.1002/(SICI)1097-0215(19981209)78:6<720::AID-IJC9>3.3.CO;2-6
   Thompson R, 2014, J GEN INTERN MED, V29, pS780, DOI 10.1007/s11606-014-2908-8
   Weinreich S S, 2008, Ned Tijdschr Geneeskd, V152, P518
NR 29
TC 3
Z9 3
U1 4
U2 14
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 2041-1480
J9 J BIOMED SEMANT
JI J. Biomed. Semant.
PD NOV 12
PY 2020
VL 11
IS 1
AR 13
DI 10.1186/s13326-020-00232-y
PG 13
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA OQ9JU
UT WOS:000589092900001
PM 33183351
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Xie, ZW
   Zhu, RJ
   Liu, J
   Zhou, GY
   Huang, JX
   Cui, XH
AF Xie, Zhiwen
   Zhu, Runjie
   Liu, Jin
   Zhou, Guangyou
   Huang, Jimmy Xiangji
   Cui, Xiaohui
TI GFCNet: Utilizing graph feature collection networks for coronavirus
   knowledge graph embeddings
SO INFORMATION SCIENCES
LA English
DT Article
DE Natural Language Processing; Text Mining; Knowledge Graph; COVID-19
ID REPRESENTATION
AB In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic rela-tions which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with dif-ferent scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and effi-ciency of our proposed model. In addition, we also explain the future directions of deepen-ing the study on COVID-19 KGE task.(c) 2022 Elsevier Inc. All rights reserved.
C1 [Xie, Zhiwen; Liu, Jin] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
   [Zhu, Runjie] York Univ, Lassonde Sch Engn, Toronto, ON, Canada.
   [Zhou, Guangyou] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
   [Huang, Jimmy Xiangji] York Univ, Sch Informat Technol, Toronto, ON, Canada.
   [Cui, Xiaohui] Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China.
RP Liu, J (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.; Huang, JX (通讯作者)，York Univ, Sch Informat Technol, Toronto, ON, Canada.
EM xiezhiwen@whu.edu.cn; sherryzh@cse.yorku.ca; jinliu@whu.edu.cn;
   gyzhou@mail.ccnu.edu.cn; jhuang@yorku.ca; xcui@whu.edu.cn
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]; Funamental Research
   Funds for the Central Universities [CCNU22QN015]; Natural Sciences and
   Engineering Research Council (NSERC) of Canada; NSERC CREATE award in
   ADERSIM 12; York Research Chairs (YRC) program; ORF-RE (Ontario Research
   Fund -Research Excellence) award in BRAIN Alliance 13
FX This work was supported by the National Natural Science Foundation of
   China under Grants 61972290 and 61972173, the National Key R & D Program
   of China under Grant 2018YFC1604000, the Funamental Research Funds for
   the Central Universities (No. CCNU22QN015) . This work was also
   supported by the Natural Sciences and Engineering Research Council
   (NSERC) of Canada, an NSERC CREATE award in ADERSIM 12 , the York
   Research Chairs (YRC) program and an ORF-RE (Ontario Research Fund
   -Research Excellence) award in BRAIN Alliance 13 . The authors thank the
   anonymous reviewers and associate editor for their insightful comments.
CR Abbas Asmaa, 2021, Applied Intelligence. The International Journal of Research on Intelligent Systems for Real Life Complex Problems, V51, P854, DOI 10.1007/s10489-020-01829-7
   Abbas K, 2021, BMC BIOINFORMATICS, V22, DOI 10.1186/s12859-021-04082-y
   Aggarwal J., 2020, P 1 WORKSH NLP COVID
   Ba J.L., 2016, ARXIV
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Bansal T, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4387
   Bellomarini L., 2020, ARXIV
   Bonner S., 2021, ARXIV
   Cai L, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P131, DOI 10.1145/3360901.3364441
   Das D., 2020, P 1 WORKSH NLP COVID
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Feng MH, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P470, DOI 10.1145/3308558.3313715
   Gozes O, 2020, ARXIV
   Guo LB, 2019, PR MACH LEARN RES, V97
   Han YM, 2020, INFORM SCIENCES, V531, P1, DOI 10.1016/j.ins.2020.04.036
   Hatamian M, 2021, EMPIR SOFTW ENG, V26, DOI 10.1007/s10664-020-09934-4
   He KM, 2015, IEEE I CONF COMP VIS, P1026, DOI 10.1109/ICCV.2015.123
   Huang XJ, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P307, DOI 10.1145/1571941.1571995
   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
   Kadlec R., 2017, P 2 WORKSH REPR LEAR, P69, DOI [10.18653/v1/W17-2609, DOI 10.18653/V1/W17-2609]
   Kaiming He, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), P770, DOI 10.1109/CVPR.2016.90
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kingma D., 2014, ARXIV14126980
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Lin Y., 2016, P 25 INT JOINT C ART, P2866
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lu Wang L., 2020, ARXIV200410706
   Mohamed S.K., 2019, P WORKSH DEEP LEARN, P1
   Narin A, 2021, PATTERN ANAL APPL, V24, P1207, DOI 10.1007/s10044-021-00984-y
   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, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Roberts K, 2020, J AM MED INFORM ASSN, V27, P1431, DOI 10.1093/jamia/ocaa091
   Ruffinelli D., 2020, 8 INT C LEARNING REP
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Shen I, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P3519, DOI 10.1145/3394486.3406711
   Soni S., 2020, ARXIV
   Sun Z., 2019, 7 INT C LEARNING REP
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vashishth S, 2020, AAAI CONF ARTIF INTE, V34, P3009
   Velickovic P., 2018, ARXIV
   Voorhees E.M., 2005, P 14 TEXT RETRIEVAL
   Wang H., 2020, ARXIV
   Wang L.L., 2020, ARXIV
   Wang Q., 2020, ARXIV
   Wang S., 2020, MEDRXIV, DOI [10.1101/2020.02.14.20023028, DOI 10.1101/2020.02.14.20023028]
   Wang X., 2020, ARXIV
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wise C., 2020, ARXIV
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Xiao H., 2016, P 25 INT JOINT C ART, P1315
   Xiao YP, 2021, INFORM SCIENCES, V548, P1, DOI 10.1016/j.ins.2020.09.039
   Xie XZ, 2021, MED IMAGE ANAL, V69, DOI 10.1016/j.media.2021.101985
   Xie Z., 2020, P 58 ANN M ASS COMP, P5929
   Xie Z., IEEE ACM T AUDIO SPE, V29, P1762
   Yang B., 2015, P INT C LEARN REPR I
   Yin XS, 2013, IEEE T KNOWL DATA EN, V25, P1201, DOI 10.1109/TKDE.2012.24
   Yu P., ARXIV
   Yu S, 2017, IEEE COMMUN SURV TUT, V19, P531, DOI 10.1109/COMST.2016.2610963
   Yu S, 2015, IEEE T KNOWL DATA EN, V27, P170, DOI 10.1109/TKDE.2014.2320725
   Zeng XX, 2020, J PROTEOME RES, V19, P4624, DOI 10.1021/acs.jproteome.0c00316
   Zhang R, 2021, J BIOMED INFORM, V115, DOI 10.1016/j.jbi.2021.103696
   Zhang S, 2019, ADV NEUR IN, V32
   Zhao S., 2020, ARXIV
NR 66
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 608
BP 1557
EP 1571
DI 10.1016/j.ins.2022.07.031
PG 15
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3L2NM
UT WOS:000834603000004
PM 35855405
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Yue, SW
   Xiao, L
   Li, JX
   Wang, N
AF Yue, Siwen
   Xiao, Le
   Li, Jiaxin
   Wang, Nan
TI Research on application of knowledge graph for aircraft maintenance
SO ADVANCES IN MECHANICAL ENGINEERING
LA English
DT Article
DE Knowledge graph; aircraft maintenance; knowledge reasoning; maintenance
   decision-making; few-shot
ID SYSTEM
AB Aircraft maintenance technology is related to flight safety. It is not only a significant guarantee for the safety of passengers' lives and property but also an important means for airlines to reduce costs and improve profitability. This paper focuses on aircraft maintenance decision-making, analyzes the current situation and defects of maintenance decision-making, applies knowledge graph technology to this field, and constructs aircraft maintenance knowledge graph; through the character analysis of sample data and experimental comparison, the key factors affecting the accuracy of aircraft maintenance decisions are found; few-shot knowledge reasoning technology is introduced to improve the maintenance strategy reasoning algorithm. In the 737NG aircraft maintenance decision-making system of an airline, the feasibility and effectiveness of the method are verified, which provides strong support for flight safety.
C1 [Yue, Siwen; Xiao, Le; Li, Jiaxin; Wang, Nan] Henan Univ Technol, Coll Informat Sci & Engn, 100 Lianhua St, Zhengzhou, Henan, Peoples R China.
RP Xiao, L (通讯作者)，Henan Univ Technol, Coll Informat Sci & Engn, 100 Lianhua St, Zhengzhou, Henan, Peoples R China.
EM xiaole@haut.edu.cn
FU Natural Science Foundation of He'nan Province [152102210068]; Foundation
   of He'nan Educational Committee [18A520026]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: Natural
   Science Foundation of He'nan Province (No.152102210068); Foundation of
   He'nan Educational Committee (No.18A520026).
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bu XW, 2022, IEEE T FUZZY SYST, V30, P2529, DOI 10.1109/TFUZZ.2021.3089031
   Bu XW, 2022, IEEE T FUZZY SYST, V30, P270, DOI 10.1109/TFUZZ.2020.3036706
   Cai YN., 2019, QUAL RELIAB, P56
   Cao M., 2021, ACTA AERONAUT ASTRON
   Chen X, 2018, J AEROSP INFORM SYST, V15, P414, DOI 10.2514/1.I010524
   Deng QC, 2021, DECIS SUPPORT SYST, V146, DOI 10.1016/j.dss.2021.113545
   Dinis D, 2019, COMPUT IND ENG, V128, P920, DOI 10.1016/j.cie.2018.10.015
   Dong HF., 2020, AERONAUT STAND QUAL, V302, P54
   Eickemeyer SC, 2014, INT J PROD RES, V52, P7007, DOI 10.1080/00207543.2014.923122
   Eickemeyer SC, 2013, PROD ENG-RES DEV, V7, P131, DOI 10.1007/s11740-013-0444-8
   Ferreiro S, 2012, EXPERT SYST APPL, V39, P6402, DOI 10.1016/j.eswa.2011.12.027
   Koornneef H, 2020, DECIS SUPPORT SYST, V135, DOI 10.1016/j.dss.2020.113338
   Li LL., 2019, INT COMBUST ENGINE, V40, P147
   Li R, 2020, AEROSP SCI TECHNOL, V102, DOI 10.1016/j.ast.2020.105877
   Nie TP., 2021, ACTA AERONAUT ASTRON
   Pan MQ, 2002, AVIAT MAINT ENG, P34
   Qiang Fu, 2019, 2019 IEEE 5th International Conference on Computer and Communications (ICCC), P225, DOI 10.1109/ICCC47050.2019.9064184
   Qiu H., 2019, 2019 IEEE 1 INT C CI, P125
   Sheng J., 2020, EMNLP PUNTA CANA DOM, P1681
   Shi XY., 2013, J 11 AERONAUT U, V2013, P3
   Wang J., 2020, CHINA SCI TECHNOL IN, V626, P44
   [王仲生 Wang Zhongsheng], 2002, [测控技术, Measurement & Control Technology], V21, P41
   Yan H., 2020, 2020 AS PAC INT S AD, P1
   Yang Y., 2010, MACH, V2010, P59
   Yuan L., 2021, J PHYS C SER, V1738
   Zhao Z., 2012, AVIAT MAINT ENG, V2012, P59
NR 27
TC 0
Z9 0
U1 1
U2 1
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1687-8132
EI 1687-8140
J9 ADV MECH ENG
JI Adv. Mech. Eng.
PD JUL
PY 2022
VL 14
IS 7
AR 16878132221107429
DI 10.1177/16878132221107429
PG 13
WC Thermodynamics; Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Thermodynamics; Engineering
GA 3P3PA
UT WOS:000837449500001
OA gold
DA 2022-08-23
ER

PT J
AU Jiao, Y
   Wang, ZY
   Liu, JH
   Li, X
   Chen, R
   Chen, WJ
AF Jiao, Yu
   Wang, Ziyi
   Liu, Jiahao
   Li, Xian
   Chen, Ran
   Chen, Weijiong
TI Backtracking and prospect on LNG supply chain safety
SO JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
LA English
DT Article
DE Liquefied natural gas; Supply chain; Safety; FLNG; CiteSpace; VOS viewer
ID LIQUEFIED NATURAL-GAS; COMPUTATIONAL FLUID-DYNAMICS; QUANTITATIVE RISK
   ANALYSIS; MIXED REFRIGERANT; REGASIFICATION UNITS; LIQUEFACTION PROCESS;
   ENERGY-EFFICIENT; FLOATING STORAGE; EMERGING TRENDS; OPTIMIZATION
AB The safety issues of Liquefied Natural Gas (LNG) in production, storage, loading/unloading, transportation/ shipping, and re-gasification have became a major concern, since an accident in the LNG industry would be very costly. Understanding the threat of LNG not only contributes to the process safety and reliability in the research and development (R&D) system, but improves the efficiency of loss prevention, fire protection and emergency responses. As of April 2019, in order to obtain the present status and trend of LNG safety research, basing 1122 documents of the Web of Science database about safety research of LNG as a data source, CiteSpace and VOS viewer were used for network knowledge map analysis. A comprehensive knowledge map of LNG safety field was obtained from several research aspects including scientific research power, research hot spots and trends, research knowledge base and frontier. According to the study results, the development of LNG safety research was divided into four stages from 1970s to 2019, China and South Korea made a lot of contributions, and the United States is the most influential. Among them, the research from 2005 to 2019 was the most representative. Current research results indicate that a combination of Formal Safety Assessment (FSA) methodology and Dynamic Procedure for Atypical Scenarios Identification (DyPASI) will fully identify risks; The PHAST and TerEx programs quickly define safety zones. Computational Fluid Dynamics (CFD) software package can provide accurate quantitative data for the study of LNG safety. Research on quantitative risk assessment (QRA) and LNG evaporated gas (BOG) has been a hot topic and trend in this field. The application of expansion foam in LNG accident mitigation covers most of the research content in this field, and the optimization of LNG liquefaction process has a great influence on this industry. As the international demand for LNG energy output increases, floating liquefied natural gas (FLNG) will have considerable development, and increasingly researchers attach vital importance to the safety of LNG offshore production integrated unit.
C1 [Jiao, Yu; Wang, Ziyi; Liu, Jiahao; Li, Xian; Chen, Ran; Chen, Weijiong] Shanghai Maritime Univ, Coll Ocean Sci & Engn, Shanghai 201306, Peoples R China.
RP Jiao, Y (通讯作者)，Shanghai Maritime Univ, Coll Ocean Sci & Engn, Shanghai 201306, Peoples R China.
EM yujiao@shmtu.edu.cn
OI Xian, Li/0000-0001-8800-0256; Jiao, Yu/0000-0002-9146-3873; ziyi,
   wang/0000-0002-8840-8961
FU Natural Science Foundation of China [51109127]
FX This work is financially supported by Natural Science Foundation of
   China (Grant No. 51109127). The authors deeply appreciate the supports.
CR Ali W, 2019, APPL THERM ENG, V150, P19, DOI 10.1016/j.applthermaleng.2018.12.165
   Aneziris ON, 2014, J LOSS PREVENT PROC, V28, P23, DOI 10.1016/j.jlp.2013.07.014
   Aneziris O, 2020, SAFETY SCI, V124, DOI 10.1016/j.ssci.2019.104595
   [Anonymous], 1990, FALCON SERIES DATA R
   [Anonymous], 2019, 59A NFPA
   Aronsson E., 2012, FLNG COMP LNG CARRIE
   Aspelund A, 2010, COMPUT CHEM ENG, V34, P1606, DOI 10.1016/j.compchemeng.2009.10.018
   Austbo B, 2014, COMPUT CHEM ENG, V71, P391, DOI 10.1016/j.compchemeng.2014.09.010
   Basha O, 2014, J LOSS PREVENT PROC, V30, P307, DOI 10.1016/j.jlp.2014.04.012
   Bernatik A, 2011, J LOSS PREVENT PROC, V24, P19, DOI 10.1016/j.jlp.2010.08.003
   Bittante A, 2018, ENERGY, V148, P79, DOI 10.1016/j.energy.2018.01.120
   Blanchat T.K., 2013, LNG SHIP INSULATION
   Chen C, 2005, P 10 INT C INT US IN, P98, DOI DOI 10.1145/1040830.1040859
   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
   Cormier BR, 2009, J LOSS PREVENT PROC, V22, P332, DOI 10.1016/j.jlp.2008.12.004
   Cozzani V, 2006, J HAZARD MATER, V129, P1, DOI 10.1016/j.jhazmat.2005.08.012
   Cozzani V, 2004, J HAZARD MATER, V107, P67, DOI 10.1016/j.jhazmat.2003.09.013
   D'alessandro AA, 2016, J LOSS PREVENT PROC, V43, P255, DOI 10.1016/j.jlp.2016.05.012
   Dan S, 2014, PROCESS SAF ENVIRON, V92, P430, DOI 10.1016/j.psep.2014.04.011
   Dincer I, 2007, EXERGY: ENERGY, ENVIRONMENT AND SUSTAINABLE DEVELOPMENT, P36, DOI 10.1016/B978-008044529-8.50006-9
   Elsayed T, 2009, APPL OCEAN RES, V31, P179, DOI 10.1016/j.apor.2009.08.004
   Fay JA, 2003, J HAZARD MATER, V96, P171, DOI 10.1016/S0304-3894(02)00197-8
   Gavelli F, 2008, J HAZARD MATER, V159, P158, DOI 10.1016/j.jhazmat.2008.02.037
   Harada T., 2018, J MECANIQUE
   HAVENS J, 1992, J LOSS PREVENT PROC, V5, P28, DOI 10.1016/0950-4230(92)80062-D
   Jensen J, 2007, OUTLOOK GLOBAL TRADE
   Khan MS, 2017, J NAT GAS SCI ENG, V45, P165, DOI 10.1016/j.jngse.2017.04.035
   Khan MS, 2015, ENERGY, V89, P757, DOI 10.1016/j.energy.2015.06.021
   Khan MS, 2013, APPL ENERG, V111, P1018, DOI 10.1016/j.apenergy.2013.06.010
   Kim JH, 2015, MATER DESIGN, V65, P914, DOI 10.1016/j.matdes.2014.09.085
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Kohout A, 2019, J LOSS PREVENT PROC, V57, P304, DOI 10.1016/j.jlp.2018.07.018
   Kulitsa M, 2018, J LOSS PREVENT PROC, V54, P352, DOI 10.1016/j.jlp.2018.04.007
   Kurle YM, 2018, IND ENG CHEM RES, V57, P5903, DOI 10.1021/acs.iecr.7b03633
   Lagarrigue V., 2018, OFFSHORE TECHNOLOGY
   Lee DH, 2014, INT J NAV ARCH OCEAN, V6, P307, DOI 10.2478/IJNAOE-2013-0181
   Lee J., 2006, WORLD MAR TECHN C LO
   Lee Y, 2019, J LOSS PREVENT PROC, V58, P90, DOI 10.1016/j.jlp.2019.01.006
   Li XD, 2019, OCEAN ENG, V171, P332, DOI 10.1016/j.oceaneng.2018.10.047
   Liang Y., 2015, NATURAL RESOURCES DE, P51
   Lin WS, 2010, ENERGY, V35, P4383, DOI 10.1016/j.energy.2009.04.036
   Luketa-Hanlin A., 2004, SANDIA NATL LABS ALB
   Luketa-Hanlin A, 2007, J HAZARD MATER, V140, P504, DOI 10.1016/j.jhazmat.2006.10.023
   Luketa-Hanlin A, 2006, J HAZARD MATER, V132, P119, DOI 10.1016/j.jhazmat.2005.10.008
   Maksym Kulitsa, 2018, Natural Gas Industry B, V5, P391, DOI 10.1016/j.ngib.2018.05.001
   Mobil E, 2013, OUTL EN VIEW 2040
   Nakamura K, 2010, J PHYS G NUCL PARTIC, V37, P1, DOI 10.1088/0954-3899/37/7A/075021
   Nwaoha TC, 2013, J MAR ENG TECHNOL, V12, P57
   Nwaoha TC, 2013, OCEAN ENG, V72, P31, DOI 10.1016/j.oceaneng.2013.06.010
   Paltrinieri N, 2015, RELIAB ENG SYST SAFE, V137, P18, DOI 10.1016/j.ress.2014.12.006
   Park C, 2012, ENERGY, V44, P69, DOI 10.1016/j.energy.2012.02.053
   Ping Q, 2017, J ASSOC INF SCI TECH, V68, P1234, DOI 10.1002/asi.23770
   Qi RF, 2010, J HAZARD MATER, V183, P51, DOI 10.1016/j.jhazmat.2010.06.090
   Qyyum MA, 2018, ENERGY, V157, P483, DOI 10.1016/j.energy.2018.05.173
   Raj PK, 2007, J HAZARD MATER, V140, P444, DOI 10.1016/j.jhazmat.2006.10.029
   Gomez MR, 2014, RENEW SUST ENERG REV, V38, P781, DOI 10.1016/j.rser.2014.07.029
   ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7
   Sieminski A., 2014, INT EN OUTL, V18
   Siu N, 2008, NUCL ENG TECHNOL, V40, P349, DOI 10.5516/NET.2008.40.5.349
   Stankovic G., 2018, STROJ VESTN-J MECH E, P64
   Stavrou DI, 2017, INT J OFFSHORE POLAR, V27, P266, DOI 10.17736/ijope.2017.mk52
   Steuer C., 2019, OUTLOOK COMPETITIVE, DOI [10.26889/9781784671310, DOI 10.26889/9781784671310]
   Sun BA, 2014, J LOSS PREVENT PROC, V29, P92, DOI 10.1016/j.jlp.2014.02.003
   Sun B, 2013, J LOSS PREVENT PROC, V26, P1246, DOI 10.1016/j.jlp.2013.06.005
   SUTTON SB, 1986, BOUND-LAY METEOROL, V35, P125, DOI 10.1007/BF00117305
   University Engineers I, 1974, EXP STUD MIT FLAMM V
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vanem E, 2008, RELIAB ENG SYST SAFE, V93, P1328, DOI 10.1016/j.ress.2007.07.007
   Wang K, 2019, J HAZARD MATER, V366, P282, DOI 10.1016/j.jhazmat.2018.10.087
   Wang LQ, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15061172
   Wang MY, 2014, ENERG CONVERS MANAGE, V88, P947, DOI 10.1016/j.enconman.2014.09.007
   Wicaksono D.S., 2007, P 17 EUR S COMP AID
   Wilks D.S, 2011, STAT METHODS ATMOSPH
   Won W, 2014, KOREAN J CHEM ENG, V31, P732, DOI 10.1007/s11814-014-0047-x
   Wood DA, 2018, INT J ENERG RES, V42, P1391, DOI 10.1002/er.3883
   Woodward J.L, 2010, RISK ANAL RISK REDUC
   Xin PW, 2016, J OFFSHORE MECH ARCT, V138, DOI 10.1115/1.4033076
   Yoon IK, 2019, J CHEM ENG JPN, V52, P349, DOI 10.1252/jcej.17we050
   You W, 2019, PROCESS SAF PROG, V38, P87, DOI 10.1002/prs.11994
   Yun G, 2009, J LOSS PREVENT PROC, V22, P91, DOI 10.1016/j.jlp.2008.10.001
   Zhang B, 2014, J HAZARD MATER, V280, P380, DOI 10.1016/j.jhazmat.2014.07.078
   Zhou TQ, 2017, SAFETY SCI, V96, P183, DOI 10.1016/j.ssci.2017.03.015
NR 83
TC 2
Z9 2
U1 13
U2 34
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0950-4230
EI 1873-3352
J9 J LOSS PREVENT PROC
JI J. Loss Prev. Process Ind.
PD JUL
PY 2021
VL 71
AR 104433
DI 10.1016/j.jlp.2021.104433
EA APR 2021
PG 17
WC Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA SU9EX
UT WOS:000663434100008
DA 2022-08-23
ER

PT J
AU Noy, N
   Gao, YQ
   Jain, A
   Narayanan, A
   Patterson, A
   Taylor, J
AF Noy, Natasha
   Gao, Yuqing
   Jain, Anshu
   Narayanan, Anant
   Patterson, Alan
   Taylor, Jamie
TI Industry-Scale Knowledge Graphs: Lessons and Challenges
SO COMMUNICATIONS OF THE ACM
LA English
DT Article
C1 [Noy, Natasha] Google, Mountain View, CA 94043 USA.
   [Noy, Natasha] Stanford Univ, Ontol Engn & Semant Web, Stanford, CA 94305 USA.
   [Gao, Yuqing] Microsoft, Artificial Intelligence Knowledge Graph Org, Redmond, WA USA.
   [Jain, Anshu] IBM Watson, San Jose, CA USA.
   [Narayanan, Anant] Facebook, Menlo Pk, CA USA.
   [Narayanan, Anant] Ozlo, Dev Large Scale Data Pipelines, Palo Alto, CA USA.
   [Patterson, Alan] eBay, San Jose, CA USA.
   [Patterson, Alan] True Knowledge, Cambridge, England.
   [Taylor, Jamie] Google, Knowledge Graph, Schema Team, Mountain View, CA USA.
   [Taylor, Jamie] Metaweb Technol, San Francisco, CA USA.
RP Noy, N (通讯作者)，Google, Mountain View, CA 94043 USA.
OI Noy, Natasha/0000-0002-7437-0624
CR Hoffner K, 2017, SEMANT WEB, V8, P895, DOI 10.3233/SW-160247
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   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
NR 4
TC 76
Z9 82
U1 4
U2 22
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 0001-0782
EI 1557-7317
J9 COMMUN ACM
JI Commun. ACM
PD AUG
PY 2019
VL 62
IS 8
BP 36
EP 43
DI 10.1145/3331166
PG 8
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IM3XG
UT WOS:000477928700011
OA Bronze
DA 2022-08-23
ER

PT J
AU Huang, WT
   Mao, YY
   Yang, Z
   Zhu, L
   Long, J
AF Huang, Wenti
   Mao, Yiyu
   Yang, Zhan
   Zhu, Lei
   Long, Jun
TI Relation classification via knowledge graph enhanced transformer encoder
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Relation classification; Knowledge graph embedding; Transformer
AB Relation classification is an important task in natural language processing fields. The goal is to predict predefined relations for the marked nominal pairs in given sentences. State-of-the-art works usually focus on using deep neural networks as classifier to conduct the relation prediction. The rich semantic information of relationships in the triples of existing knowledge graph (KG) can be used as additional supervision for relation classification. However, these relationships were simply used as labels to specify the class of sentences in previous works, and their semantic information was completely ignored. In this paper, a novel approach is proposed for relation classification, which jointly uses information from textual sentences and knowledge graphs. To this end, we introduce a Transformer encoder to measure the semantic similarity between sentences and relation types. Besides, we connect the semantic information of marked nominals in sentences with that of the corresponding entities in knowledge graph to generate the semantic matching information between textual relations and KG relations. The matching information can provide additional supervision for relation classification. Since the words and entities are used interactively with each other in our work, we propose an embedding translating strategy to handle the semantic gap problem between word embeddings and entity embeddings. Experimental results on two widely used datasets, SemEval-2010 Task 8 and TACRED, show that our approach is able to efficiently use the semantic information from the knowledge graph to enhance the performance of the Transformer encoder for relation classification. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Huang, Wenti; Mao, Yiyu; Yang, Zhan; Zhu, Lei; Long, Jun] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
   [Huang, Wenti; Yang, Zhan; Zhu, Lei; Long, Jun] Cent South Univ, Network Resources Management & Trust Evaluat Key, Changsha 410083, Peoples R China.
RP Long, J (通讯作者)，Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
EM huangwenti@csu.edu.cn; yiyumao@csu.edu.cn; zyang22@csu.edu.cn;
   leizhu@csu.edu.cn; junlong@csu.edu.cn
FU Science and Technology Plan of Hunan [2016TP1003]; Key Technology R&D
   Program of Hunan Province [2018GK2052]
FX This work was supported in part by the Science and Technology Plan of
   Hunan under Grant No. 2016TP1003, and in part by the Key Technology R&D
   Program of Hunan Province under Grant No. 2018GK2052.
CR Alicante, 2011, P 8 REC ADV NAT LANG, P509
   [Anonymous], 2016, STAT-US
   [Anonymous], 2018, ARXIV180703052
   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
   Cai R, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P756
   Chen H, 2018, NEUROCOMPUTING, V294, P12, DOI 10.1016/j.neucom.2017.11.064
   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
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   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
   Graves A, 2013, INT CONF ACOUST SPEE, P6645, DOI 10.1109/ICASSP.2013.6638947
   Harabagiu S., 2010, P 5 INT WORKSH SEM E, P256
   Hendrickx I, 2009, P WORKSH SEM EV REC, P94, DOI DOI 10.3115/1621969.1621986
   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
   Kaiming He, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), P770, DOI 10.1109/CVPR.2016.90
   Kambhatla Nanda, 2004, P ACL 2004 INT POST, DOI 10.3115/ 1219044.1219066
   Krizhevsky A., 2012, ADV NEURAL INF PROCE, V25, P1097, DOI [10.1145/3065386, DOI 10.1145/3065386]
   Ma M., 2019, P 2019 2 INT C ALG C, P427
   Manning CD, 2017, EMNLP, P35
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Miwa M., 2016, ARXIV160100770
   Qian L., 2009, P 2009 C EMP METH NA, P1437
   Ren F, 2018, P 27 INT C COMP LING, P1167
   Socher R., 2012, P 2012 JOINT C EMP M, P1201, DOI [10.5555/2390948.2391084, DOI 10.1162/153244303322533223]
   Suchanek FM, 2006, P 12 ACM SIGKDD INT, P712, DOI DOI 10.1145/1150402.1150492
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Verga Patrick, 2018, P NAACL HLT, V1, P872
   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
   Xiao Y., 2020, ARXIV200311518
   Xu Kun, 2015, P 2015 C EMP METH NA, P536
   Xu Y., 2016, P 26 INT C COMP LING, P1461
   Xu Y, 2015, P 2015 C EMP METH NA, P1785, DOI DOI 10.18653/V1/D15-1206
   Zeng D., 2014, P COLING, P2335
   Zhang D., 2015, ARXIV150801006
   Zhang M, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P825
   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
   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 39
TC 6
Z9 6
U1 9
U2 55
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 28
PY 2020
VL 206
AR 106321
DI 10.1016/j.knosys.2020.106321
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NT3ND
UT WOS:000572851100004
DA 2022-08-23
ER

PT J
AU Liu, T
   Pan, XL
   Wang, X
   Feenstra, KA
   Heringa, J
   Huang, ZS
AF Liu, Ting
   Pan, Xueli
   Wang, Xu
   Feenstra, K. Anton
   Heringa, Jaap
   Huang, Zhisheng
TI Predicting the relationships between gut microbiota and mental disorders
   with knowledge graphs
SO HEALTH INFORMATION SCIENCE AND SYSTEMS
LA English
DT Article
DE Knowledge graph; Mental disorders; Neurotransmitters;
   Microbiota-gut&#8211; brain axis; Biomedical ontology; Gut microbiota
ID ACETYLCHOLINE; HISTAMINE; DOPAMINE; NERVOSA; SEARCH
AB Gut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as 'matchmaker' between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways. We therefore gather the dispersed results that in the existing studies into a structured knowledge base for identifying and predicting the potential relationships between gut microbiota and mental disorders. In this study, we propose to construct a gut microbiota knowledge graph for mental disorder, which named as MiKG4MD. It is extendable by linking to future ontologies by just adding new relationships between existing information and new entities. This extendibility is emphasized for the integration with existing popular ontologies/terminologies, e.g. UMLS, MeSH, and KEGG. We demonstrate the performance of MiKG4MD with three SPARQL query test cases. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationships between gut microbiota and mental disorders.
C1 [Liu, Ting; Pan, Xueli; Wang, Xu; Huang, Zhisheng] Vrije Univ Amsterdam, Knowledge Representat & Reasoning KR&R Grp, Amsterdam, Netherlands.
   [Liu, Ting; Feenstra, K. Anton; Heringa, Jaap] Vrije Univ Amsterdam, Ctr Integrat Bioinformat VU IBIVU, Amsterdam, Netherlands.
   [Huang, Zhisheng] Capital Med Univ, Brain Protect Innovat Ctr, Beijing, Peoples R China.
RP Huang, ZS (通讯作者)，Capital Med Univ, Brain Protect Innovat Ctr, Beijing, Peoples R China.
EM z.huang@vu.nl
RI Feenstra, Anton/H-9195-2019; Wang, Xu/C-1588-2018
OI Feenstra, Anton/0000-0001-6755-9667; Pan, Xueli/0000-0002-3736-7047;
   Wang, Xu/0000-0002-7585-759X; Liu, Ting/0000-0002-8208-3430
FU China Scholarship Council
FX This work was partially supported by the China Scholarship Council.
CR Achich Nassira, 2017, INT C INT SYST DES A, P880, DOI DOI 10.1007/978-3-319-76348-4_84
   American Psychiatric Association, 2013, DIAGNOSTIC STAT MANU, V51, DOI 10.1176/appi.pn.2016.7a7
   Avena NM, 2012, NEUROPHARMACOLOGY, V63, P87, DOI 10.1016/j.neuropharm.2011.11.010
   Biswas S., 2019, IEEE ACM T COMPUT BI, V99, P1
   Blandina P, 2004, LEARN MEMORY, V11, P1, DOI 10.1101/lm.68004
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Brachman R.J., 1983, P AAAI 83 WASH DC, P31
   Brisch R, 2014, FRONT PSYCHIATRY, V5, DOI 10.3389/fpsyt.2014.00047
   Brown RE, 2012, PHYSIOL REV, V92, P1087, DOI 10.1152/physrev.00032.2011
   BROWN RP, 1985, HOSP COMMUNITY PSYCH, V36, P141
   Coletti MH, 2001, J AM MED INFORM ASSN, V8, P317, DOI 10.1136/jamia.2001.0080317
   Cudre-Mauroux P, 2020, SEMANT WEB, V11, P13, DOI 10.3233/SW-190371
   Diaz M, 2015, INT J FOOD MICROBIOL, V215, P117, DOI 10.1016/j.ijfoodmicro.2015.08.026
   Elnagar S, 2019, LECT NOTES BUS INF P, V359, P3, DOI 10.1007/978-3-030-29608-7_1
   Frank JE, 2008, AM FAM PHYSICIAN, V77, P635
   Gurvits IG, 2000, PSYCHIAT CLIN N AM, V23, P27, DOI 10.1016/S0193-953X(05)70141-6
   Huang ZS, 2017, LECT NOTES COMPUT SC, V10594, P149, DOI 10.1007/978-3-319-69182-4_16
   Janssens Y, 2018, BMC MICROBIOL, V18, DOI 10.1186/s12866-018-1197-5
   Jarbrink-Sehgal E, 2020, CURR OPIN NEUROBIOL, V62, P102, DOI 10.1016/j.conb.2020.01.016
   Kanehisa M, 2000, NUCLEIC ACIDS RES, V28, P27, DOI 10.1093/nar/28.1.27
   Kleiman SC, 2015, PSYCHOSOM MED, V77, P969, DOI 10.1097/PSY.0000000000000247
   Koivu A, 2020, HEALTH INF SCI SYST, V8, DOI 10.1007/s13755-020-00105-9
   Kupka RW, 2007, BIPOLAR DISORD, V9, P531, DOI 10.1111/j.1399-5618.2007.00467.x
   Lan G., 2020, ARXIV200504166
   Lan GJ, 2019, PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION), P123, DOI 10.1145/3319619.3322033
   Lan GJ, 2019, 2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), P2571, DOI 10.1109/SSCI44817.2019.9002863
   Lan GJ, 2019, 2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), P1974, DOI 10.1109/SSCI44817.2019.9002696
   Lan GJ, 2018, I C CONT AUTOMAT ROB, P1959, DOI 10.1109/ICARCV.2018.8581288
   Lan Gongjin, 2020, ARXIV200107804CSNE
   Lane CA, 2020, JAMA NEUROL, V77, P175, DOI 10.1001/jamaneurol.2019.3774
   Li X, 2020, HEALTH INF SCI SYST, V8, DOI 10.1007/s13755-020-0102-4
   Liu T., 2020, J ARTIF INTELL MED S, DOI [10.2991/jaims.d.200420.001, DOI 10.2991/JAIMS.D.200420.001]
   Liu WH, 2016, BEHAV BRAIN RES, V298, P202, DOI 10.1016/j.bbr.2015.10.046
   Lyte M, 2011, BIOESSAYS, V33, P574, DOI 10.1002/bies.201100024
   Montoya A, 2016, NEUROPSYCH DIS TREAT, V12, P541, DOI 10.2147/NDT.S91311
   Moschitti Alessandro, 2017, EXPLOITING LINKED DA, P181, DOI DOI 10.1007/978-3-319-45654-6_7
   Paval D, 2017, DEV NEUROSCI-BASEL, V39, P355, DOI 10.1159/000478725
   Pham T, 2020, HEALTH INF SCI SYST, V8, DOI 10.1007/s13755-020-0100-6
   Picciotto MR, 2012, NEURON, V76, P116, DOI 10.1016/j.neuron.2012.08.036
   Rieder R, 2017, BRAIN BEHAV IMMUN, V66, P9, DOI 10.1016/j.bbi.2017.01.016
   Sang ST, 2019, IEEE ACCESS, V7, P8404, DOI 10.1109/ACCESS.2018.2886311
   Shi JJ, 2008, AM J MED GENET B, V147B, P1270, DOI 10.1002/ajmg.b.30769
   Shi LX, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/2858423
   Smith RP, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0222394
   Strandwitz P, 2018, BRAIN RES, V1693, P128, DOI 10.1016/j.brainres.2018.03.015
   Thomas MSC., 2020, ED NEUROSCIENCE DEV, DOI [10.4324/9781003016830, DOI 10.4324/9781003016830]
   Thursby E, 2017, BIOCHEM J, V474, P1823, DOI 10.1042/BCJ20160510
   Valles-Colomer M, 2019, NAT MICROBIOL, V4, P623, DOI 10.1038/s41564-018-0337-x
   Zheng P, 2016, MOL PSYCHIATR, V21, P786, DOI 10.1038/mp.2016.44
   Zheng WG, 2016, PROC VLDB ENDOW, V9, P840, DOI 10.14778/2983200.2983201
NR 50
TC 1
Z9 1
U1 6
U2 112
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2047-2501
J9 HEALTH INF SCI SYST
JI Health Inf. Sci. Syst.
PD JAN 24
PY 2021
VL 9
IS 1
AR 3
DI 10.1007/s13755-020-00128-2
PG 9
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA PA9GM
UT WOS:000595935700001
PM 33262885
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Zheng, L
   Liu, S
   Song, ZF
   Dou, FT
AF Zheng, Liang
   Liu, Shuo
   Song, Zhuofei
   Dou, Fangtong
TI Diversity-Aware Entity Exploration on Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph; diversified entity exploration; random walk model
ID WEB
AB Knowledge graphs are graph-structured knowledge bases containing abundant entities and relations among the entities. Entity exploration can help users understand the overall structure of knowledge graphs, as well as find the entities of interest in an exploratory manner. Being different from typical entity-centric search whose goal is to retrieve the most related entities for a user's specific need, the related entities that present diverse aspects are preferred for user's ambiguous needs in entity exploration. In this paper, we propose a novel diversity-aware entity exploration approach based on random walk model, which naturally mimics human conceptual exploration by surfing a class association graph. This model leverages the diversity, representativeness and relatedness of entity classes to rank these classes in a unified way. Furthermore, for each top ranked class, the associated entities are ranked by combining their diversity and popularity. We compare our algorithm with four baseline algorithms and the experimental results indicate that it outperforms baselines. Furthermore, we conduct a task-based user study to evaluate our approach and the experimental results show that our work provides effective support for entity exploration.
C1 [Zheng, Liang; Liu, Shuo; Song, Zhuofei; Dou, Fangtong] Shanghai Lixin Univ Accounting & Finance, Sch Informat Management, Shanghai 201620, Peoples R China.
RP Zheng, L (通讯作者)，Shanghai Lixin Univ Accounting & Finance, Sch Informat Management, Shanghai 201620, Peoples R China.
EM liangzheng@lixin.edu.cn
FU Shanghai Young College Teachers Training Foundation of China [ZZLX21044]
FX This work was supported by Shanghai Young College Teachers Training
   Foundation of China under Grant ZZLX21044.
CR Al-Tawil M, 2020, SEMANT WEB, V11, P205, DOI 10.3233/SW-190347
   Angel A., 2011, P ACM SIGMOD INT C M, P781
   Arnaout H, 2018, J WEB SEMANT, V48, P66, DOI 10.1016/j.websem.2017.12.001
   Balog Krisztian, 2011, TREC
   Behrens F, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P199, DOI 10.1145/3184558.3186978
   Bi B, 2015, WSDM'15: PROCEEDINGS OF THE EIGHTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P139, DOI 10.1145/2684822.2685304
   Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Blanco R, 2013, LECT NOTES COMPUT SC, V8219, P33, DOI 10.1007/978-3-642-41338-4_3
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bron M., 2010, CIKM, P1079
   Chatzopoulos S., 2020, P VLDB ENDOWMENT, P2913
   Chen J., 2017, P 22 INT C INT US IN, P137
   Cheng G, 2011, LECT NOTES COMPUT SC, V7031, P114, DOI 10.1007/978-3-642-25073-6_8
   Dadzie AS, 2017, SEMANT WEB, V8, P1, DOI 10.3233/SW-160249
   Diligenti M, 2004, IEEE T KNOWL DATA EN, V16, P4, DOI 10.1109/TKDE.2004.1264818
   Drosou M, 2010, SIGMOD REC, V39, P41, DOI 10.1145/1860702.1860709
   Fang Y, 2015, WORLD WIDE WEB, V18, P521, DOI 10.1007/s11280-013-0267-8
   Gunaratna K, 2015, AAAI CONF ARTIF INTE, P116
   Han XR, 2019, PROC VLDB ENDOW, V12, P1966, DOI 10.14778/3352063.3352111
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Kennedy L, 2008, P 17 INT C WORLD WID, P297, DOI [10.1145/1367497.1367539, DOI 10.1145/1367497.1367539]
   Kumar R., 2010, P 19 INT C WORLD WID, P561, DOI DOI 10.1145/1772690.1772748
   Lee S., 2011, P 5 ACM C REC SYST, P93, DOI DOI 10.1145/2043932.2043952
   Marchionini G, 2006, COMMUN ACM, V49, P41, DOI 10.1145/1121949.1121979
   Mei Q Z, 2010, P 16 ACM SIGKDD INT, P1009, DOI DOI 10.1145/1835804.1835931
   Ni CC, 2020, WWW'20: COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2020, P811, DOI 10.1145/3366424.3383570
   Qu Y., 2009, P WEBSCI, P18
   Rafiei Davood, 2010, P 19 INT C WORLD WID, P781, DOI DOI 10.1145/1772690.1772770
   The World Wide Web Consortium (W3C), RESOURCE DESCRIPTION
   Troullinou G, 2017, SEMANT WEB, V8, P797, DOI 10.3233/SW-170264
   Vakkari P., 2010, P 4 WORKSH HUM COMP, P24
   White RW., 2009, SYNTHESIS LECT INFOR, V1, P1, DOI DOI 10.2200/S00174ED1V01Y200901ICR003
   Zhou JH, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P2345, DOI 10.1145/3340531.3412163
   Ziegler Cai-Nicolas, 2005, P 14 INT C WORLD WID, P22, DOI DOI 10.1145/1060745.1060754
NR 35
TC 1
Z9 1
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 2021
VL 9
BP 118782
EP 118793
DI 10.1109/ACCESS.2021.3107732
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA WL5QU
UT WOS:000710460700001
OA gold
DA 2022-08-23
ER

PT J
AU Nayyeri, M
   Alam, MM
   Lehmann, J
   Vahdati, S
AF Nayyeri, Mojtaba
   Alam, Mirza Mohtashim
   Lehmann, Jens
   Vahdati, Sahar
TI 3D Learning and Reasoning in Link Prediction Over Knowledge Graphs
SO IEEE ACCESS
LA English
DT Article
DE Computational modeling; Three-dimensional displays; Quaternions; Solid
   modeling; Cognition; Predictive models; Task analysis; Link prediction;
   knowledge graph embedding; 3D rotation; learning and reasoning Rodrigues
   formula
AB Knowledge Graph Embeddings (KGE) are used for representation learning in Knowledge Graphs (KGs) by measuring the likelihood of a relation between nodes. Rotation-based approaches, specially axis-angle representations, were shown to improve the performance of many Machine Learning (ML)-based models in different tasks including link prediction. There is a perceived disconnect between the topics of KGE models and axis-angle rotation-based approaches. This is particuarly visible when considering the ability of KGEs to learn relational patterns such as symmetry, inversion, implication, equivalence, composition, and reflexivity considering axis-angle rotation-based approaches. In this article, we propose RodE, a new KGE model which employs an axis-angle representation for rotations based on Rodrigues' formula. RodE inherits the main advantages of 3-dimensional rotation from angle, orientation and distance preservation in the embedding space. Thus, the model efficiently captures the similarity between the nodes in a graph in the vector space. Our experiments show that RodE outperforms state-of-the-art models on standard datasets.
C1 [Nayyeri, Mojtaba; Alam, Mirza Mohtashim; Lehmann, Jens] Univ Bonn, Smart Data Analyt Grp SDA, D-53115 Bonn, Germany.
   [Nayyeri, Mojtaba; Alam, Mirza Mohtashim; Vahdati, Sahar] Inst Appl Informat InfAI, Nat Inspired Machine Intelligence, D-01069 Dresden, Germany.
   [Lehmann, Jens] Fraunhofer IAIS, D-01069 Dresden, Germany.
RP Nayyeri, M (通讯作者)，Univ Bonn, Smart Data Analyt Grp SDA, D-53115 Bonn, Germany.; Nayyeri, M (通讯作者)，Inst Appl Informat InfAI, Nat Inspired Machine Intelligence, D-01069 Dresden, Germany.
EM anayyeri@cs.uni-bonn.de
OI nayyeri, mojtaba/0000-0002-9177-0312; Lehmann, Jens/0000-0001-9108-4278
FU Speech Recognition Project (SPEAKER) under Grant BMWi FKZ [01MK20011A];
   Speech Recognition Project (JOSEPH) through the Fraunhofer
   Zukunftsstiftung; H2020-EU Learning, Applying, Multiplying Big Data
   Analytics (LAMBDA) Project under Grant EU [809965]; Competence Center
   Machine Learning Rhine-Ruhr (ML2R) Project under Grant BmBF FKZ [01 15
   18038 A/B/C]; Maschinelles Lernen mit Wissensgraphen (MLwin) Project
   under Grant BmBF [01IS18050 D/F]; Scalable Data Analytics and Artificial
   Intelligence (ScaDS.AI) Project under Grant BmBF [01/IS18026A-F];
   Trustworthy AI integrating Learning, Optimization and Reasoning (TAILOR)
   Project under Grant EU [952215]; H2020-EU Digital Platform and Analytic
   Tools for Energy (PLATOON) Project [872592]
FX This work was supported in part by the Speech Recognition Project
   (SPEAKER) under Grant BMWi FKZ 01MK20011A, in part by the Speech
   Recognition Project (JOSEPH) through the Fraunhofer Zukunftsstiftung, in
   part by the H2020-EU Learning, Applying, Multiplying Big Data Analytics
   (LAMBDA) Project under Grant EU 809965, in part by the Competence Center
   Machine Learning Rhine-Ruhr (ML2R) Project under Grant BmBF FKZ 01 15
   18038 A/B/C, in part by the Maschinelles Lernen mit Wissensgraphen
   (MLwin) Project under Grant BmBF 01IS18050 D/F, in part by the Scalable
   Data Analytics and Artificial Intelligence (ScaDS.AI) Project under
   Grant BmBF 01/IS18026A-F, in part by the Trustworthy AI integrating
   Learning, Optimization and Reasoning (TAILOR) Project under Grant EU
   952215, and in part by the H2020-EU Digital Platform and Analytic Tools
   for Energy (PLATOON) Project under Grant 872592.
CR [Anonymous], 2005, ROTATIONS QUATERNION
   Balazevic Ivana, 2019, P EMNLP
   Baym G., 2018, LECT QUANTUM MECH
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bonev I., 2002, 2002 PAR KIN MACH IN, P171
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Choromanski K, 2019, PR MACH LEARN RES, V89, P236
   Dai JS, 2015, MECH MACH THEORY, V92, P144, DOI 10.1016/j.mechmachtheory.2015.03.004
   Dettmers T., 2018, P 32 AAAI C ART INT, P1
   Franciscus M., 2011, RODRIGUES ROTATION F
   Gallego G, 2015, J MATH IMAGING VIS, V51, P378, DOI 10.1007/s10851-014-0528-x
   Gibbs J.W., 1922, VECTOR ANAL TXB USE
   Girard, 2007, QUATERNIONS CLIFFORD
   Hemingway EG, 2018, MULTIBODY SYST DYN, V44, P31, DOI 10.1007/s11044-018-9620-0
   Huang H., 2013, 27 AAAI C ART INT CI, P431
   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
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Murray R. M., 1994, MATH INTRO ROBOTIC M
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Rivest LP, 2006, CAN J STAT, V34, P187, DOI 10.1002/cjs.5550340201
   Rodriguez JJ, 2006, IEEE T PATTERN ANAL, V28, P1619, DOI 10.1109/TPAMI.2006.211
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Sun Z., 2019, 7 INT C LEARN REPR
   Sun Z., 2019, ARXIV191103903
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trouillon T, 2016, PR MACH LEARN RES, V48
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang RY, 2007, ACM T GRAPHIC, V26, DOI [10.1145/1276377.1276468, 10.1145/1239451.1239524]
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yang B., 2015, P INT C LEARN REPR
   Yang T., 2020, ARXIV200510956
   Zhang S., 2019, ARXIV190410281
   Zhang S, 2019, ADV NEUR IN, V32
NR 37
TC 0
Z9 0
U1 2
U2 5
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 196459
EP 196471
DI 10.1109/ACCESS.2020.3034183
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA OR9JN
UT WOS:000589782100001
OA gold
DA 2022-08-23
ER

PT J
AU Hoffmann, C
   Linden, P
   Vidal, ME
AF Hoffmann, Claus
   Linden, Pascal
   Vidal, Maria-Esther
TI Creating and Capturing Artificial Emotions in Autonomous Robots and
   Software Agents
SO JOURNAL OF WEB ENGINEERING
LA English
DT Article
DE Autonomous agents; artificial emotions; agent knowledge graphs
ID ARCHITECTURE; FRAMEWORK; APPRAISAL; COGNITION; BEHAVIOR
AB This paper presents ARTEMIS, a control system for autonomous robots or software agents. ARTEMIS can create human-like artificial emotions during interactions with their environment. We describe the underlying mechanisms for this. The control system also captures its past artificial emotions. A specific interpretation of a knowledge graph, called an Agent Knowledge Graph, stores these artificial emotions. ARTEMIS then utilizes current and stored emotions to adapt decision making and planning processes. As proof of concept, we realize a concrete software agent based on the ARTEMIS control system. This software agent acts as a user assistant and executes their orders and instructions. The environment of this user assistant consists of several other autonomous agents that offer their services. The execution of a user's orders requires interactions of the user assistant with these autonomous service agents. These interactions lead to the creation of artificial emotions within the user assistant. The first experiments show that it is possible to realize an autonomous user assistant with plausible artificial emotions with ARTEMIS and record these artificial emotions in its Agent Knowledge Graph. The results also show that captured emotions support successful planning and decision making in complex dynamic environments. The user assistant with emotions surpasses an emotionless version of the user assistant.
C1 [Hoffmann, Claus] Res Grp Robots & Software Agents Emot, St Augustin, Germany.
   [Linden, Pascal] Univ Bonn, Masters Program, Bonn, Germany.
   [Vidal, Maria-Esther] TIB Leibnitz Informat Ctr Sci & Technol, Hannover, Germany.
RP Hoffmann, C (通讯作者)，Res Grp Robots & Software Agents Emot, St Augustin, Germany.
EM hoffmann.claus@web.de; s6palind@uni-bonn.de; Maria.Vidal@tib.eu
CR Anderson J. R., 1998, ATOMIC COMPONENTS TH
   Andrea E., 2000, Affective Interactions. Towards a New Generation of Computer Interfaces (Lecture Notes in Artificial Intelligence Vol.1814), P150
   Salichs MA, 2012, IEEE T AFFECT COMPUT, V3, P56, DOI 10.1109/T-AFFC.2011.32
   Antos D., 2011, P IJCAI
   Becker C, 2004, LECT NOTES COMPUT SC, V3068, P154
   Breazeal C., 2002, DESIGNING SOCIABLE R
   Canamero D., 1997, Proceedings of the First International Conference on Autonomous Agents, P148, DOI 10.1145/267658.267688
   Detje F., 1999, HANDELN ERKLAREN, DOI [10.1007/978-3-663-08224-8, DOI 10.1007/978-3-663-08224-8]
   Do rner D., 2001, SOZIONIK AKTUELL, V2
   Dorner D, 2013, REV GEN PSYCHOL, V17, P297, DOI 10.1037/a0032947
   Domingue J, 2009, TOWARDS THE FUTURE INTERNET: A EUROPEAN RESEARCH PERSPECTIVE, P203, DOI 10.3233/978-1-60750-007-0-203
   Dorner D., 2003, LOGIC COGNITIVE SYST
   DORNER D, 2002, MECH SEELENWAGENS NE
   Dorner<spacing D., 1990, PSYCHOL EMOTION
   Ekman P, 2011, EMOT REV, V3, P364, DOI 10.1177/1754073911410740
   Gadanho SC, 2004, J MACH LEARN RES, V4, P385, DOI 10.1162/153244304773633870
   Gardenfors P, 2014, GEOMETRY OF MEANING: SEMANTICS BASED ON CONCEPTUAL SPACES, P1
   Graupner S, 2009, 2009 13TH ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE WORKSHOPS (EDOCW 2009), P25, DOI 10.1109/EDOCW.2009.5332021
   Hakak NM, 2017, 2017 INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATIONS AND ELECTRONICS (COMPTELIX), P397, DOI 10.1109/COMPTELIX.2017.8004002
   Hoffmann C, 2020, LECT NOTES COMPUT SC, V12128, P277, DOI 10.1007/978-3-030-50578-3_19
   Hudlicka E., 2008, AAAI SPRING S EM PER, P8
   Jiang H., 2007, P 6 INT JOINT C AUT
   KAHNEMAN D, 1979, ECONOMETRICA, V47, P263, DOI 10.2307/1914185
   Kowalczuk Z, 2016, FRONT ROBOT AI, V3, DOI 10.3389/frobt.2016.00021
   LAIRD JE, 1987, ARTIF INTELL, V33, P1, DOI 10.1016/0004-3702(87)90050-6
   Lin J, 2012, ADV COGNITIVE SYSTEM
   Marcella S.C, 2009, COGN SYST RES, P10
   Marinier RP, 2009, COGN SYST RES, V10, P48, DOI 10.1016/j.cogsys.2008.03.004
   Marsella S., 2010, BLUEPRINT AFFECTIVE, P21
   MEHRABIAN A, 1995, GENET SOC GEN PSYCH, V121, P339
   Mehrabian A, 1996, CURR PSYCHOL, V14, P261, DOI 10.1007/BF02686918
   Moors A, 2013, EMOT REV, V5, P119, DOI 10.1177/1754073912468165
   Newell A.., 1994, UNIFIED THEORIES COG
   Ortony A., 1988, COGNITIVE STRUCTURE
   Pervin, 1990, HDB PERSONALITY THEO, P609, DOI [DOI 10.1016/0191-8869(91)90043-B., 10.2307/2075902]
   RAO AS, 1991, PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P473
   Russell S., 2009, ARTIF INTELL, V3rd
   Scherer K. R., 1984, APPROACHES EMOTION, P293
   Scherer K.R., 2010, BLUEPRINT AFFECTIVE, P47
   SMITH CA, 1985, J PERS SOC PSYCHOL, V48, P813, DOI 10.1037/0022-3514.48.4.813
   Velasquez JD, 1998, FIFTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-98) AND TENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICAL INTELLIGENCE (IAAI-98) - PROCEEDINGS, P70
   Verein Berliner Kaufleute und Industrieller, LEITS EHRBAR KAUFL
   Wikipedia, HONOURABLE MERCHANT
   Wooldridge M., 2009, INTRO MULTIAGENT SYS
NR 44
TC 0
Z9 0
U1 2
U2 3
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 4
BP 933
EP +
DI 10.13052/jwe1540-9589.2043
PG 37
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XA5AU
UT WOS:000720660300002
OA Bronze
DA 2022-08-23
ER

PT J
AU Jackson, P
AF Jackson, Paul
TI Transactive directories of organizational memory: Towards a working data
   model
SO INFORMATION & MANAGEMENT
LA English
DT Article
DE Transactive memory systems; Organizational memory; Organizational
   learning; Knowledge mapping; Knowledge sharing
ID KNOWLEDGE; SYSTEMS; EXPERTISE; TEAMS
AB Transactive memory system is a term from group psychology that describes a system that helps small groups maintain and use personal directories to allocate and retrieve knowledge. Such systems have been observed at the level of whole organizations, suggesting that they provide a means for conceptualizing the exploitation of organizational memory. In this paper, I describe a longitudinal investigation of a global engineering consulting firm in which! used inductive analysis of interview data to map and then develop a conceptual entity-relationship model of organizational memory. This model formed the basis for a transactive directory to facilitate knowledge retrieval and allocation in the firm. (C) 2012 Elsevier B.V. All rights reserved.
C1 Edith Cowan Univ, Sch Management, Joondalup, WA 6026, Australia.
RP Jackson, P (通讯作者)，Edith Cowan Univ, Sch Management, 100 Joondalup Dr, Joondalup, WA 6026, Australia.
EM p.jackson@ecu.edu.au
CR Ackerman M. S., 2004, Computer Supported Cooperative Work: The Journal of Collaborative Computing, V13, P155, DOI 10.1023/B:COSU.0000045805.77534.2a
   Akgun AE, 2005, INFORM MANAGE-AMSTER, V42, P1105, DOI 10.1016/j.im.2005.01.001
   Argote L, 2011, ORGAN SCI, V22, P1123, DOI 10.1287/orsc.1100.0621
   Austin JR, 2003, J APPL PSYCHOL, V88, P866, DOI 10.1037/0021-9010.88.5.866
   Brandon DP, 2004, ORGAN SCI, V15, P633, DOI 10.1287/orsc.1040.0069
   Cha H. S., 2008, MIS Q, V32
   Chang DR, 2008, J BUS RES, V61, P13, DOI 10.1016/j.jbusres.2006.05.005
   Earl M, 2001, J MANAGE INFORM SYST, V18, P215
   Henry RM, 2007, INFORM MANAGE-AMSTER, V44, P598, DOI 10.1016/j.im.2007.06.002
   Jackson P, 2008, DECIS SUPPORT SYST, V44, P409, DOI 10.1016/j.dss.2007.05.001
   Lewis K, 2003, J APPL PSYCHOL, V88, P587, DOI 10.1037/0021-9010.88.4.587
   Lewis K, 2004, MANAGE SCI, V50, P1519, DOI 10.1287/mnsc.1040.0257
   Lu L, 2011, J AM SOC INF SCI TEC, V62, P133, DOI 10.1002/asi.21449
   Markus ML, 2001, J MANAGE INFORM SYST, V18, P57
   Nevo D, 2005, DECIS SUPPORT SYST, V39, P549, DOI 10.1016/j.dss.2004.03.002
   Oshri I, 2008, INFORM SYST J, V18, P593, DOI 10.1111/j.1365-2575.2007.00243.x
   Randall D, 2001, EUR J INFORM SYST, V10, P113, DOI 10.1057/palgrave.ejis.3000396
   Ren YQ, 2006, MANAGE SCI, V52, P671, DOI 10.1287/mnsc.1050.0496
   Weinberger H, 2008, J AM SOC INF SCI TEC, V59, P1454, DOI 10.1002/asi.20859
   Yuan YC, 2010, COMMUN RES, V37, P20, DOI 10.1177/0093650209351469
   Zhang ZX, 2007, J APPL PSYCHOL, V92, P1722, DOI 10.1037/0021-9010.92.6.1722
NR 21
TC 9
Z9 10
U1 1
U2 53
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 MAR
PY 2012
VL 49
IS 2
BP 118
EP 125
DI 10.1016/j.im.2012.01.002
PG 8
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 928KF
UT WOS:000302980900006
DA 2022-08-23
ER

PT J
AU Ulitzur, N
   Humbert, M
   Pfeffer, SR
AF Ulitzur, N
   Humbert, M
   Pfeffer, SR
TI Mapmodulin: A possible modulator of the interaction of
   microtubule-associated proteins with microtubules
SO PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF
   AMERICA
LA English
DT Article
ID CYTOPLASMIC DYNEIN; PURIFICATION; TUBULIN; MAP2; TRANSPORT; KINESIN;
   BINDING; LOCALIZATION; DYNAMICS; MOTILITY
AB We have purified and characterized a 31-kDa protein named mapmodulin that binds to the microtubule-associated proteins (MAPs) MAP2, MAP4, and tau. Mapmodulin binds free MAPs in strong preference to microtubule-associated MAPs, and appears to do so via the MAP's tubulin-binding domain, Mapmodulin inhibits the initial rate of MAP2 binding to microtubules, a property that may allow mapmodulin to displace MAPs from the path of organelles translocating along microtubules. In support of this possibility, mapmodulin stimulates the microtubule- and dynein-dependent localization of Golgi complexes in semi intact CHO cells, To our knowledge, mapmodulin represents the first example of a protein that can bind and potentially regulate multiple MAP proteins.
C1 STANFORD UNIV, SCH MED, DEPT BIOCHEM, STANFORD, CA 94305 USA.
FU NIGMS NIH HHS [GM49843] Funding Source: Medline; NATIONAL INSTITUTE OF
   GENERAL MEDICAL SCIENCES [R01GM049843] Funding Source: NIH RePORTER
CR AIZAWA H, 1991, J BIOL CHEM, V266, P9841
   BALCH WE, 1984, CELL, V39, P405, DOI 10.1016/0092-8674(84)90019-9
   BARLOW S, 1994, J CELL BIOL, V126, P1017, DOI 10.1083/jcb.126.4.1017
   BULINSKI JC, 1980, J BIOL CHEM, V255, P1570
   CHAPIN SJ, 1994, CELL MOTIL CYTOSKEL, V27, P133, DOI 10.1002/cm.970270205
   CHAPIN SJ, 1992, CELL MOTIL CYTOSKEL, V23, P236, DOI 10.1002/cm.970230403
   CLARY DO, 1990, J BIOL CHEM, V265, P10109
   CORTHESYTHEULAZ I, 1992, J CELL BIOL, V118, P1333, DOI 10.1083/jcb.118.6.1333
   FINK TM, 1995, GENOMICS, V29, P309, DOI 10.1006/geno.1995.1257
   GILL SR, 1991, J CELL BIOL, V115, P1639, DOI 10.1083/jcb.115.6.1639
   GOTTLIEB RA, 1985, J CELL BIOL, V101, P1782, DOI 10.1083/jcb.101.5.1782
   HAGIWARA H, 1994, J BIOL CHEM, V269, P3581
   HIROKAWA N, 1994, CURR OPIN CELL BIOL, V6, P74, DOI 10.1016/0955-0674(94)90119-8
   KAUR KJ, 1991, GLYCOCONJUGATE J, V8, P90, DOI 10.1007/BF00731017
   KIRSCHNER M, 1986, CELL, V45, P329, DOI 10.1016/0092-8674(86)90318-1
   Li M, 1996, BIOCHEMISTRY-US, V35, P6998, DOI 10.1021/bi960581y
   LITTAUER UZ, 1986, P NATL ACAD SCI USA, V83, P7162, DOI 10.1073/pnas.83.19.7162
   LOPEZ LA, 1993, CELL MOTIL CYTOSKEL, V24, P1, DOI 10.1002/cm.970240102
   LOPEZ LA, 1995, J BIOL CHEM, V270, P12511, DOI 10.1074/jbc.270.21.12511
   MACCIONI RB, 1995, PHYSIOL REV, V75, P835, DOI 10.1152/physrev.1995.75.4.835
   MATSUOKA K, 1994, P NATL ACAD SCI USA, V91, P9670, DOI 10.1073/pnas.91.21.9670
   OLMSTED JB, 1989, J CELL BIOL, V109, P211, DOI 10.1083/jcb.109.1.211
   PASCHAL BM, 1989, NATURE, V342, P569, DOI 10.1038/342569a0
   PEDROTTI B, 1994, BIOCHEMISTRY-US, V33, P8798, DOI 10.1021/bi00195a023
   SCHROER TA, 1991, J CELL BIOL, V115, P1309, DOI 10.1083/jcb.115.5.1309
   TABAS I, 1979, J BIOL CHEM, V254, P1655
   Thaler C D, 1996, Int Rev Cytol, V164, P269
   VAESEN M, 1994, BIOL CHEM H-S, V375, P113, DOI 10.1515/bchm3.1994.375.2.113
   VALLEE RB, 1986, METHOD ENZYMOL, V134, P89
   VONMASSOW A, 1989, CELL MOTIL CYTOSKEL, V14, P562, DOI 10.1002/cm.970140413
   VOTER WA, 1982, J ULTRA MOL STRUCT R, V80, P374, DOI 10.1016/S0022-5320(82)80051-8
   Wang XM, 1996, J CELL BIOL, V132, P345, DOI 10.1083/jcb.132.3.345
NR 32
TC 47
Z9 49
U1 0
U2 2
PU NATL ACAD SCIENCES
PI WASHINGTON
PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
SN 0027-8424
J9 P NATL ACAD SCI USA
JI Proc. Natl. Acad. Sci. U. S. A.
PD MAY 13
PY 1997
VL 94
IS 10
BP 5084
EP 5089
DI 10.1073/pnas.94.10.5084
PG 6
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA WZ257
UT WOS:A1997WZ25700049
PM 9144194
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Sha, X
   Sun, Z
   Zhang, J
AF Sha, Xiao
   Sun, Zhu
   Zhang, Jie
TI Hierarchical attentive knowledge graph embedding for personalized
   recommendation
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Knowledge graphs; Graph neural network; Attention mechanism;
   Collaborative filtering; Recommender systems
AB Knowledge graphs (KGs) have proven to be effective for high-quality recommendation, where the connectivities between users and items provide rich and complementary information to user-item interactions. Most existing methods, however, are insufficient to exploit the KGs for capturing user preferences, as they either represent the user-item connectivities via paths with limited expressiveness or implicitly model them by propagating information over the entire KG with inevitable noise. In this paper, we design a novel hierarchical attentive knowledge graph embedding (HAKG) framework to exploit the KGs for effective recommendation. Specifically, HAKG first extracts the expressive subgraphs that link user-item pairs to characterize their connectivities, which accommodate both the semantics and topology of KGs. The subgraphs are then encoded via a hierarchical attentive subgraph encoding to generate effective subgraph embeddings for enhanced user preference prediction. Extensive experiments show the superiority of HAKG against state-of-the-art recommendation methods, as well as its potential in alleviating the data sparsity issue.
C1 [Sha, Xiao; Zhang, Jie] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore.
   [Sun, Zhu] Macquarie Univ, Dept Comp, N Ryde, NSW, Australia.
RP Sun, Z (通讯作者)，Macquarie Univ, Dept Comp, N Ryde, NSW, Australia.
EM SHAX0001@e.ntu.edu.sg; z.sun@mq.cu.au; ZhangJ@ntu.edu.sg
OI SUN, ZHU/0000-0002-3350-7022
CR Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Chen C., 2020, P 43 INT ACM SIGIR C, P189
   Chen L, 2020, AAAI CONF ARTIF INTE, V34, P27
   Chen XC, 2020, IEEE IJCNN
   Deqing Yang, 2020, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, P1909, DOI 10.1145/3397271.3401313
   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]
   Hamilton Will, 2017, NEURIPS
   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 Z., 2017, ARXIV170105291
   Jin JR, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P75, DOI 10.1145/3394486.3403050
   Kangzhi Zhao, 2020, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, P239, DOI 10.1145/3397271.3401171
   Kingma D. P., 2017, ARXIV
   Kunaver M, 2017, KNOWL-BASED SYST, V123, P154, DOI 10.1016/j.knosys.2017.02.009
   Liang DW, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P689, DOI 10.1145/3178876.3186150
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lu YF, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1563, DOI 10.1145/3394486.3403207
   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., 2012, ARXIV PREPRINT ARXIV
   Rendle S, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P635
   Shi C, 2019, IEEE T KNOWL DATA EN, V31, P357, DOI 10.1109/TKDE.2018.2833443
   Shi C, 2016, KNOWL INF SYST, V49, P835, DOI 10.1007/s10115-016-0925-0
   Sijin Zhou, 2020, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, P179, DOI 10.1145/3397271.3401174
   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
   Tarjan R., 1972, SIAM Journal on Computing, V1, P146, DOI 10.1137/0201010
   Teru K., 2020, P 37 INT C MACH LEAR, P9448
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Velickovic P., 2018, ICLR
   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, 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, 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 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
   Wenjing Meng, 2020, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, P1091, DOI 10.1145/3397271.3401098
   Wu L., IEEE T KNOWL DATA EN, DOI [10.1109/TKDE.2020.3048414, DOI 10.1109/TKDE.2020.3048414]
   Wu Q., 2019, ARXIV190506589
   Wu YJ, 2020, AAAI CONF ARTIF INTE, V34, P1054
   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
   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
   Yang B., 2014, ABS14126575 CORR
   Ye Bi, 2020, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, P2211, DOI 10.1145/3397271.3401426
   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 YH, 2020, PATTERN RECOGN LETT, V140, P348, DOI 10.1016/j.patrec.2020.11.010
   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 K, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1006, DOI 10.1145/3394486.3403143
   Zhou R, 2006, ARTIF INTELL, V170, P385, DOI 10.1016/j.artint.2005.12.002
   Zhu QN, 2020, AAAI CONF ARTIF INTE, V34, P6999
   Zuohui Fu, 2020, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, P69, DOI 10.1145/3397271.3401051
NR 55
TC 0
Z9 0
U1 10
U2 23
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 JUL-AUG
PY 2021
VL 48
AR 101071
DI 10.1016/j.elerap.2021.101071
EA JUL 2021
PG 14
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 TZ2DL
UT WOS:000684285700001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhong, XY
   Wang, WQ
AF Zhong, Xiaoyu
   Wang, Weiqun
TI Consistency of Zhejiang Provincial College Entrance Examination
   Questions and Curriculum Standards Based on Knowledge Map Construction
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID STUDENTS; COURSES; ONLINE; EDUCATION; PRESSURE; TEACHER; PROGRAM
AB Students' scores on the advanced placement exercise test are an important guide to the development of personalized instructional tutoring. Traditional exercise test systems only counts the total value of students' scores in each question and cannot analyze students' different levels of mastery of each aspect of knowledge and intelligence, which limits the accuracy of teaching and tutoring. By introducing the cognitive diagnosis theory, we designed and implemented a personalized online college entrance examination exercise test system, which realizes the functions of test management, course management, group paper management, and learning reports. Based on the college entrance examination score record and knowledge point correlation matrix, we can analyze the students' knowledge mastery status comprehensively.
C1 [Zhong, Xiaoyu; Wang, Weiqun] Scoohw Univ, Coll Chem Chem Engn & Mat Sci, Suzhou 215000, Peoples R China.
RP Zhong, XY (通讯作者)，Scoohw Univ, Coll Chem Chem Engn & Mat Sci, Suzhou 215000, Peoples R China.
EM 20205409002@stu.suda.edu.cn
CR AbdEllatif Mahmoud, 2018, Future Computing and Informatics Journal, V3, P7, DOI 10.1016/j.fcij.2017.10.006
   Bylieva D, 2019, EDUC SCI, V9, DOI 10.3390/educsci9030167
   Cai XQ, 2019, REV ECON STAT, V101, P249, DOI 10.1162/rest_a_00749
   Cao CH, 2021, SECUR COMMUN NETW, V2021, DOI 10.1155/2021/8527068
   Childre AL, 2014, RURAL SPEC EDUC Q, V33, P23
   Cooper KM, 2019, ADV PHYSIOL EDUC, V43, P221, DOI 10.1152/advan.00028.2019
   Craig L, 2018, P SOC INFORM TECHNOL, P128
   Fisher JB, 2010, J TEACH EDUC, V61, P302, DOI 10.1177/0022487110369556
   Holmlund TD, 2018, INT J STEM EDUC, V5, DOI 10.1186/s40594-018-0127-2
   Huang J, 2020, SCI INS EDUC FRONT, V7, P879, DOI [10.15354/sief.20.or054, DOI 10.15354/SIEF.20.OR054]
   Kim JE, 2019, INT J IND ERGONOM, V70, P116, DOI 10.1016/j.ergon.2018.12.006
   Li H., 2016, P INT C BIOINSP COMP, P513
   Lin QY, 2008, INTERNET HIGH EDUC, V11, P53, DOI 10.1016/j.iheduc.2007.12.005
   Lopez A, 2019, IMPACT TEACHER PREPA
   Mahanal S, 2019, INT J INSTR, V12, P417, DOI 10.29333/iji.2019.12227a
   Mohammed TF, 2021, CBE-LIFE SCI EDUC, V20, DOI 10.1187/cbe.21-05-0132
   Monereo C, 2020, QUAD PSICOL, V22, DOI 10.5565/rev/qpsicologia.1572
   Orhan F, 2008, TURK ONLINE J DISTAN, V9, P54
   Peterson C. L., 2004, Journal of Research on Technology in Education, V36, P345
   Ranjan P.A, 2018, THESIS INDIA PATNA U
   Suwantarathip O, 2019, TURK ONLINE J DISTAN, V20, P115
   Tallent-Runnels MK, 2006, REV EDUC RES, V76, P93, DOI 10.3102/00346543076001093
   Tao J, 2018, ENGL SPECIF PURP, V49, P1, DOI 10.1016/j.esp.2017.09.003
   Wang K, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0163-0
   Wu TT, 2018, J COMPUT ASSIST LEAR, V34, P315, DOI 10.1111/jcal.12244
   Xie T, 2021, J ELECTRON INF TECHN, V43, P956, DOI 10.11999/JEIT200061
   Xu D, 2011, EDUC EVAL POLICY AN, V33, P360, DOI 10.3102/0162373711413814
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhu HP, 2018, KNOWL-BASED SYST, V143, P102, DOI 10.1016/j.knosys.2017.12.011
   Zivin JG, 2020, J ENVIRON ECON MANAG, V104, DOI 10.1016/j.jeem.2020.102365
NR 30
TC 0
Z9 0
U1 0
U2 0
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 25
PY 2022
VL 2022
AR 3959688
DI 10.1155/2022/3959688
PG 9
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 1X3QZ
UT WOS:000807373700001
OA gold
DA 2022-08-23
ER

PT J
AU Monaco, D
   Pellegrino, MA
   Scarano, V
   Vicidomini, L
AF Monaco, Daniele
   Pellegrino, Maria Angela
   Scarano, Vittorio
   Vicidomini, Luca
TI Linked open data in authoring virtual exhibitions
SO JOURNAL OF CULTURAL HERITAGE
LA English
DT Article
DE Virtual exhibitions; Linked open data; Knowledge graph; Virtual reality;
   Query builder; Natural language interface
ID REALITY
AB In the last years, virtual exhibitions have been widely adopted to enhance traditional museums and enable active interaction with culture without posing any physical constraints. Nevertheless, people interested in cultural heritage still behave as visitors. To fully engage them, we propose to let cultural heritage lovers play the role of exhibition curators. In authoring virtual exhibitions, users have to perform a data selection phase that poses several challenges, including finding data sources and extracting data of interest. We aim to take advantage of data published as Knowledge Graphs in the Linked Open Data format. Users can query geographically distributed artworks thanks to their linking nature, manipulate heterogeneous data, and easily customise their exhibitions by exploiting the wide range of available cultural heritage knowledge graphs. However, the complexity of linked open data query languages (such as SPARQL) threatens their exploitation. Consequently, we need to mask SPARQL technical challenges and guide users in naturally posing questions to unlock the potentialities of linked open data to a broader audience. We propose a virtual exhibition authoring tool that guides users from knowledge graphs querying to the automatic generation of virtual experiences. The Knowledge Graph query phase relies on ELODIE, a natural language interface to scaffold users in retrieving data of interest without asking for technical skills in query languages. We introduce our prototype by describing its operating mechanism and by detailing its components. We present a Van Gogh's experience as a use case by collecting all the artist's artworks published on DBpedia (a well-known and general purpose knowledge graph) and organise them in a virtual reality-based virtual exhibition. Finally, we conclude by overviewing advantages and technical challenges posed by linked open data in designing and developing knowledge graph exploitation tools. (c) 2021 Elsevier Masson SAS. All rights reserved.
C1 [Monaco, Daniele; Pellegrino, Maria Angela; Scarano, Vittorio; Vicidomini, Luca] Univ Salerno, Dipartimento Informat, Via Giovanni Paolo II 132, I-84084 Fisciano, SA, Italy.
RP Pellegrino, MA (通讯作者)，Univ Salerno, Dipartimento Informat, Via Giovanni Paolo II 132, I-84084 Fisciano, SA, Italy.
EM mapellegrino@unisa.it; vitsca@unisa.it; lvicidomini@unisa.it
FU Regione Campania in Italy [PON03PE_00164 RIPA-PAUN]
FX The research leading to the results presented in this paper has been
   partially conducted in the project entitled "Reti intelligenti dei
   Parchi Archeologici (RIPA-PAUN)" within the DATABENC consortium under
   grant agreement no. PON03PE_00164 RIPA-PAUN funded by the Regione
   Campania in Italy.
CR AAT editors, 2017, ART ARCH THES AAT
   Agostino D, 2020, MUS MANAGE CURATOR, V35, P362, DOI 10.1080/09647775.2020.1790029
   Andreoli R, 2019, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL CONFERENCE ON DIGITAL GOVERNMENT RESEARCH (DGO2019): GOVERNANCE IN THE AGE OF ARTIFICIAL INTELLIGENCE, P297, DOI 10.1145/3325112.3325258
   Barsanti SG, 2015, INT ARCH PHOTOGRAMM, V40-5, P165, DOI 10.5194/isprsarchives-XL-5-W7-165-2015
   Bekele MK, 2018, ACM J COMPUT CULT HE, V11, DOI 10.1145/3145534
   Berners-Lee T., 2006, LINKED DATA
   Blanco-Pons S, 2019, J CULT HERIT, V39, P177, DOI 10.1016/j.culher.2019.03.014
   Bonis B, 2013, DESIDOC J LIB INF TE, V33, P183, DOI 10.14429/djlit.33.4604
   Borissova V, 2018, J CULT HERIT, V34, P145, DOI 10.1016/j.culher.2018.04.023
   Brooks J, 2019, XRDS, V25, P46, DOI [10. 1145/3301483, DOI 10.1145/3301483]
   Brownlow R, 2015, ACM J COMPUT CULT HE, V8, DOI 10.1145/2700427
   Bruno F, 2010, J CULT HERIT, V11, P42, DOI 10.1016/j.culher.2009.02.006
   Christou C., P ACM S VIRT REAL SO, P133, DOI [10.1145/1180495.1180523, DOI 10.1145/1180495.1180523]
   CIDOC CRM Special Interest Group, 2006, CIDOC CRM CONC REF M
   Ciurea C, 2019, STUD INFORM CONTROL, V28, P55, DOI 10.24846/v28i1y201906
   Cordasco G, 2017, DG.O 2017: THE PROCEEDINGS OF THE 18TH ANNUAL INTERNATIONAL CONFERENCE ON DIGITAL GOVERNMENT RESEARCH: INNOVATIONS AND TRANSFORMATIONS IN GOVERNMENT, P242, DOI 10.1145/3085228.3085302
   Damljanovic D, 2010, LECT NOTES COMPUT SC, V6088, P106, DOI 10.1007/978-3-642-13486-9_8
   Daquino M, 2017, ACM J COMPUT CULT HE, V10, DOI 10.1145/3051487
   de Boer Victor, 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P733, DOI 10.1007/978-3-642-30284-8_56
   De Donato R, 2019, INT C COMP SUPP COOP, P117, DOI 10.1109/CSCWD.2019.8791867
   Deliot C., 2014, CATALOGUE INDEX, V174, P13
   Di Stefano C., 2017, 3DTV C TRUE VIS CAPT, P1
   Dijkshoorn C, 2018, SEMANT WEB, V9, P221, DOI 10.3233/SW-170257
   Doerr M., 2009, HDB ONTOLOGIES, DOI [10.1007/978-3-540-92673-3_21, DOI 10.1007/978-3-540-92673-3_21]
   Donato Renato, 2021, Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference. Workshops. Advances in Intelligent Systems and Computing (AISC 1236), P272, DOI 10.1007/978-3-030-52287-2_28
   Donato R.D, 2020, SEMANTICS
   Dragoni M, 2017, ACM J COMPUT CULT HE, V10, DOI 10.1145/3012289
   Dumitrescu G, 2014, INF EC, V18
   Eyharabide Victoria, 2019, Semantic Systems. The Power of AI and Knowledge Graphs. 15th International Conference, SEMANTiCS 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11702), P57, DOI 10.1007/978-3-030-33220-4_5
   Ferre S, 2014, DATA KNOWL ENG, V94, P163, DOI 10.1016/j.datak.2014.07.010
   Foo S, 2008, DESIDOC J LIB INF TE, V28, P22, DOI 10.14429/djlit.28.4.194
   Giangreco I, 2019, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES: COMPANION (IUI 2019), P119, DOI 10.1145/3308557.3308706
   Haslhofer B, 2011, INT C DUBL COR MET A, P94
   Haslhofer B, 2019, ENCY BIG DATA TECHNO
   Hauttekeete L, 2011, J CULT HERIT, V12, P459, DOI 10.1016/j.culher.2011.04.004
   Hayashi M, 2016, 2016 INTERNATIONAL CONFERENCE ON CYBERWORLDS (CW), P219, DOI 10.1109/CW.2016.44
   Haydar M, 2011, VIRTUAL REAL-LONDON, V15, P311, DOI 10.1007/s10055-010-0176-4
   Hernandez Luis A., 2007, International Journal of Architectural Computing, V5, P487, DOI 10.1260/147807707782581783
   Hou X., 2019, P ASS INF SCI TECHN, V56, P409
   Hsieh C.-K, 2014, ACM J COMPUT CULT HE, V7
   Hyvonen E, 2005, J WEB SEMANT, V3, P224, DOI 10.1016/j.websem.2005.05.008
   Hyvonen E., 2012, SYNTHESIS LECT SEMAN, V2, P1, DOI 10.2200/ S00452ED1V01Y201210WBE003
   Janssen M, 2012, INFORM SYST MANAGE, V29, P258, DOI 10.1080/10580530.2012.716740
   Katsouri I, 2015, ACM J COMPUT CULT HE, V8, DOI 10.1145/2665072
   Kiourt C, 2016, J CULT HERIT, V22, P984, DOI 10.1016/j.culher.2016.06.007
   Knoblock C.A, 2017, INT SEM WEB C
   Kostagiolas P, 2011, LIBR REV, V60, P486, DOI 10.1108/00242531111147206
   Kuzelewska E, 2022, INT J SEMIOTIC LAW, V35, P1067, DOI 10.1007/s11196-020-09712-x
   Lei YG, 2007, K-CAP'07: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE, P135
   Li YL, 2019, INFORM PROCESS MANAG, V56, P704, DOI 10.1016/j.ipm.2018.10.012
   Carvajal DAL, 2020, J CULT HERIT, V45, P234, DOI 10.1016/j.culher.2020.04.013
   Machidon OM, 2020, J CULT HERIT, V41, P152, DOI 10.1016/j.culher.2019.07.010
   Mallia M, 2019, COMM COM INF SC, V904, P135, DOI 10.1007/978-3-030-05819-7_11
   Matsumura F., 2012, 3 INT C CONS LINK DA, P51
   McCrae J.P, 2007, LOD CLOUD
   Millais P, 2018, CHI 2018: EXTENDED ABSTRACTS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3170427.3188537
   Minelli SH, 2014, PROCEDIA COMPUT SCI, V38, P28, DOI 10.1016/j.procs.2014.10.006
   Open Knowledge International, 2006, FREE DAT MAN PLATF P
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Peek R., 2012, INFORM TODAY, V29, P24
   Reski N, 2020, VIRTUAL REAL-LONDON, V24, P1, DOI 10.1007/s10055-019-00378-w
   Sullivan A. M., 2016, J MARSHALL REV INTEL, V604
   UNESCO, 1977, UNESCO THES
   W3Consortium, 2015, CORS EN
   Walczak K, 2006, COMPUTER, V39, P93, DOI 10.1109/MC.2006.108
   White M, 2004, COMPUTER GRAPHICS INTERNATIONAL, PROCEEDINGS, P622, DOI 10.1109/CGI.2004.1309277
   WOJCIECHOWSKI R, 2004, P 9 INT C 3D WEB TEC, P135, DOI [DOI 10.1145/985040.985060, 10.1145/985040.985060]
NR 67
TC 2
Z9 2
U1 18
U2 18
PU ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
PI ISSY-LES-MOULINEAUX
PA 65 RUE CAMILLE DESMOULINS, CS50083, 92442 ISSY-LES-MOULINEAUX, FRANCE
SN 1296-2074
EI 1778-3674
J9 J CULT HERIT
JI J. Cult. Herit.
PD JAN-FEB
PY 2022
VL 53
BP 127
EP 142
DI 10.1016/j.culher.2021.11.002
PG 16
WC Archaeology; Art; Chemistry, Analytical; Geosciences, Multidisciplinary;
   Materials Science, Multidisciplinary; Spectroscopy
WE Science Citation Index Expanded (SCI-EXPANDED); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Archaeology; Art; Chemistry; Geology; Materials Science; Spectroscopy
GA ZE0SU
UT WOS:000758602900011
OA Bronze
DA 2022-08-23
ER

PT J
AU Trappey, AJC
   Trappey, CV
   Chang, AC
AF Trappey, Amy J. C.
   Trappey, Charles, V
   Chang, Ai-Che
TI Intelligent Extraction of a Knowledge Ontology From Global Patents: The
   Case of Smart Retailing Technology Mining
SO INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS
LA English
DT Article
DE Clustering; Intellectual Property (IP); Latent Dirichlet Allocation
   (LDA); Ontology Schema; Patent Mining
ID CLUSTER-ANALYSIS
AB The growth of global patents increased over the last decade as enterprises and inventors sought greater protection of their intellectual property (IP) rights. Global patents represent state-of-the-art knowledge for given domains. This research develops a hierarchical Latent Dirichlet Allocation (LDA)-based approach as a computational intelligent method to discover topics and form a top-down ontology, a semantic schema, representing the collective patent knowledge. To validate the knowledge extraction, 1,546 smart retailing patents collected from the Derwent Innovation platform from 2011 and 2016 are used to build the domain ontology schema. The patent set focuses on in-use, globally established, and non-disputed IP covering payment, user experience, and information integration for smart retailing. The clustering and LDA-based ontology system automatically build the knowledge map, which identifies the technology trends and the technology gaps enabling the development of competitive R&D and management strategies.
C1 [Trappey, Amy J. C.] Natl Tsing Hua Univ, Hsinchu, Taiwan.
   [Trappey, Charles, V] Natl Chiao Tung Univ, Hsinchu, Taiwan.
   [Chang, Ai-Che] Shih Hsin Univ, Taipei, Taiwan.
RP Trappey, AJC (通讯作者)，Natl Tsing Hua Univ, Hsinchu, Taiwan.
RI Trappey, Charles/AAM-7310-2021
OI Trappey, Charles/0000-0002-3069-6702
FU Ministry of Science and Technology (MOST), Taiwan; MOST
   [MOST-108-2221-E-007-075-MY3, MOST-108-2410-H-009-025-MY2]
FX This research is partially supported by the Ministry of Science and
   Technology (MOST), Taiwan. The authors extend their gratitude to MOST
   for the research grant (MOST-108-2221-E-007-075-MY3 and
   MOST-108-2410-H-009-025-MY2) and also to Andy C.H. Zou, our MS graduate
   student, who has assisted us during our research on smart retailing case
   search, data management, and illustrations.
CR Abbas O. A., 2008, INT ARAB J INF TECHN, V5, P321
   Abul Bashar M, 2017, COMPUT INTELL-US, V33, P948, DOI 10.1111/coin.12133
   Adriaens F, 2019, DATA MIN KNOWL DISC, V33, P1088, DOI 10.1007/s10618-019-00627-1
   [Anonymous], 2011, J MACH LEARN RES
   Beltz H, 2017, IEEE IJCNN, P1388, DOI 10.1109/IJCNN.2017.7966015
   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
   Chandrasekaran B, 1999, IEEE INTELL SYST APP, V14, P20, DOI 10.1109/5254.747902
   Choukri D, 2014, PROCEDIA COMPUT SCI, V37, P48, DOI 10.1016/j.procs.2014.08.011
   D'Agostini G, 2003, REP PROG PHYS, V66, P1383, DOI 10.1088/0034-4885/66/9/201
   Daud A, 2010, FRONT COMPUT SCI CHI, V4, P280, DOI 10.1007/s11704-009-0062-y
   Gao Y, 2013, IEEE T IMAGE PROCESS, V22, P363, DOI 10.1109/TIP.2012.2202676
   Girolami MA, 2003, P 26 ANN INT ACM SIG, P433, DOI DOI 10.1145/860435.860537
   Gregory J., 2015, TECHNICAL REPORT ACC
   Guarino N, 1997, INT J HUM-COMPUT ST, V46, P293, DOI 10.1006/ijhc.1996.0091
   Hendler J, 2001, IEEE INTELL SYST APP, V16, P30, DOI 10.1109/5254.920597
   Hosseinpour F., 2014, INT J DIGITAL CONTEN, V8, P1
   Hsu FC, 2006, INT J TECHNOL MANAGE, V36, P336, DOI 10.1504/IJTM.2006.010271
   Isa D, 2009, EXPERT SYST APPL, V36, P9584, DOI 10.1016/j.eswa.2008.07.082
   Kim M, 2016, COMPUT IND ENG, V98, P289, DOI 10.1016/j.cie.2016.06.006
   Magali R.G.M., 2017, ISYS REV BRASILEIRA, V10, P6
   Maskeri Girish, 2008, 1st India Software Engineering Conference. ISEC 2008, P113
   Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0
   Moody C.E., 2016, MIXING DIRICHLET TOP
   Pavlik J. V., 2018, CONVERGING MEDIA
   PUNJ G, 1983, J MARKETING RES, V20, P134, DOI 10.2307/3151680
   Rokach L, 2005, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, P321, DOI 10.1007/0-387-25465-X_15
   Rosen-Zvi Michal, 2004, P 20 C UNC ART INT B, P487, DOI 10.1016/j.nima.2010.11.062
   ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7
   Smart Card Alliance, 2011, SMART CARD ALLIANCE, P1
   Steyvers Mark, 2007, HDB LATENT SEMANTIC, V427, P424, DOI DOI 10.1109/MSP.2010.938079
   Tang YK, 2018, DATA MIN KNOWL DISC, V32, P885, DOI 10.1007/s10618-018-0555-0
   Thomson Reuters, 2012, THOMSON REUTERS
   Timmermans H., 2014, PROCEDIA ENV SCI, V22, P101, DOI DOI 10.1016/J.PROENV.2014.11.010
   Trappey AJC, 2019, INT J SEMANT WEB INF, V15, P68, DOI 10.4018/IJSWIS.2019040104
   Trappey CV, 2010, J SYST SCI SYST ENG, V19, P162, DOI 10.1007/s11518-010-5134-x
   Uriarte RB, 2015, IEEE ACM INT SYMP, P515, DOI 10.1109/CCGrid.2015.41
   Wang X., 2020, KEYWORD BASED TOPIC
   Wei W, 2010, IEEE T KNOWL DATA EN, V22, P1028, DOI 10.1109/TKDE.2009.122
   World Intellectual Property Organization, 2018, WORLD INTELLECTUAL P
   Yoon J, 2017, COMPUT IND ENG, V107, P376, DOI 10.1016/j.cie.2016.04.009
NR 41
TC 3
Z9 4
U1 11
U2 38
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.
PD OCT-DEC
PY 2020
VL 16
IS 4
BP 61
EP 80
DI 10.4018/IJSWIS.2020100104
PG 20
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 OE5SW
UT WOS:000580590900004
DA 2022-08-23
ER

PT J
AU Wallis, SE
AF Wallis, Steven E.
TI The missing piece of the integrative studies puzzle
SO INTERDISCIPLINARY SCIENCE REVIEWS
LA English
DT Article
DE Interdisciplinary studies; theory; Metatheory; Integrative Propositional
   analysis; causal knowledge mapping
ID CONCEPTUAL CHANGE; THEORY CONSTRUCTION; SYSTEMS THINKING; REFLECTIONS;
   CONTEXT; TOOL
AB Within Interdisciplinary Studies (IS), the effectiveness of Integrative Studies has been hampered by the difficulty of effectively integrating/synthesizing theories among multiple disciplines. Without effective integration, we cannot generate theories that will enable highly effective action. This paper presents Integrative Propositional Analysis (IPA) - a tool based on an emerging stream of research on the structure of theories. Using a few simple rules, IPA provides a concrete visual-mapping approach to integrating theories. And, of equal or greater importance, IPA provides an effective method for measuring 'how integrated' or 'how coherent' theories are as a new way to predict how useful the theory will be for representing our understanding of complex situations and improving the ability of individuals, organizations, and coalitions to take effective action based on those theories. This is expected to be particularly useful for students and interdisciplinary teams for integrating theoretical perspectives in an initial assessment of problems and situations.
C1 [Wallis, Steven E.] Fdn Adv Social Theory, Petaluma, CA 94954 USA.
   [Wallis, Steven E.] Capella Univ, Harold Abel Sch Social & Behav Sci, Minneapolis, MN 55402 USA.
RP Wallis, SE (通讯作者)，Fdn Adv Social Theory, Petaluma, CA 94954 USA.; Wallis, SE (通讯作者)，Capella Univ, Harold Abel Sch Social & Behav Sci, Minneapolis, MN 55402 USA.
EM swallis@projectfast.org
CR Ackermann F., 2004, SYSTEMS MODELLING TH, P127
   Appelbaum R.P., 1970, THEORIES SOCIAL CHAN
   Axelrod R., 1976, STRUCTURE DECISION C
   Axelrod R. M., 2000, HARNESSING COMPLEXIT
   Bakker J. I., 2011, SOCIOL FOCUS, V44, P167
   Bammer G, 2013, DISCIPLINING INTERDISCIPLINARITY: INTEGRATION AND IMPLEMENTATION SCIENCES FOR RESEARCHING COMPLEX REAL-WORLD PROBLEMS, P1
   Bateson G., 1985, STEPS ECOLOGY MIND
   Beaulieu P, 2017, J MIL STRATEG STUD, V17, P1
   BROWN S, 1998, COMPETING EDGE STRAT
   Bryson JM, 2004, VISIBLE THINKING UNL
   Burrell G., 1997, PANDEMONIUM RETRO OR
   Calas MB, 1999, ACAD MANAGE REV, V24, P649, DOI 10.2307/259347
   Canas A. J, 2003, SUMMARY LIT PERTAINI
   Checkland P, 2012, SYST RES BEHAV SCI, V29, P465, DOI 10.1002/sres.2158
   Clark SG, 2011, ENVIRON MANAGE, V47, P701, DOI 10.1007/s00267-011-9619-2
   Craik K.J.W., 1943, NATURE EXPLANATION
   DANEKE GA, 1999, SYSTEMIC CHOICES NON
   Dennard L., 2008, COMPLEXITY POLICY AN
   Dent E.B., 1998, CYBERNET SYST, P513
   diSessa A, 2002, RECONSIDERING CONCEP, P28, DOI [10.1007/0-306-47637-1_2, DOI 10.1007/0-306-47637-1_2]
   diSessa AA, 2006, CAMB HANDB PSYCHOL, P265
   Dubin R., 1978, THEORY BUILDING
   Edwards MG, 2010, ORG TRANSFORMATION S
   Fink G, 2017, ADV HUM RESOURCE MAN, P41, DOI 10.4018/978-1-5225-1983-6.ch003
   Fiske DW, 1986, METATHEORY SOCIAL SC
   FLOWER L, 1989, COLL COMPOS COMMUN, V40, P282, DOI 10.2307/357775
   FORRESTER JW, 1994, SYST DYNAM REV, V10, P245, DOI 10.1002/sdr.4260100211
   GENTNER D, 1986, COGNITIVE SCI, V10, P277, DOI 10.1207/s15516709cog1003_2
   George -Walker L, 2014, ED RES INT, P1
   Goltz S. M., 2017, MANAG TEACH REV, V2, P211
   Goodier C, 2010, FUTURES, V42, P219, DOI 10.1016/j.futures.2009.11.007
   Hammond D., 2003, SCI SYNTHESIS EXPLOR
   Herubel JPVM, 2000, SERIALS LIBR, V37, P51, DOI 10.1300/J123v37n04_04
   Hill T, 1997, LONG RANGE PLANN, V30, P46, DOI 10.1016/S0024-6301(96)00095-7
   Hoffmann M.H.G., 2011, FRAMING MATTERS PERS, P136
   Holbrook JB, 2013, SYNTHESE, V190, P1865, DOI 10.1007/s11229-012-0179-7
   HOOD WW, 2002, ANAL FUZZY SET LIT U
   Houston D., 2017, J MULTIDISCIPLINARY, V30, P29
   Johnson L., 2016, AGENT BASED MODEL BA
   JOHNSONLAIRD PN, 1980, COGNITIVE SCI, V4, P71, DOI 10.1207/s15516709cog0401_4
   Kaplan A., 1964, CONDUCT INQUIRY METH
   Kleiner A., 1994, 5 DISCIPLINE FIELDBO
   Knorr-Cetina, 1981, MANUFACTURE KNOWLEDG
   Krathwohl DR, 2002, THEOR PRACT, V41, P212, DOI 10.1207/s15430421tip4104_2
   Lasorsa D.L., 2004, BUILD SOCIAL SCI THE
   Ledoux L, 2012, PHILOS MANAG, V11, P11, DOI 10.5840/pom201211317
   Lewis MW, 2002, HUM RELAT, V55, P251, DOI 10.1177/0018726702055002185
   Light PC, 2016, PS-POLIT SCI POLIT, V49, P5, DOI 10.1017/S1049096515001110
   Mansilla V.B., 2010, OXFORD HDB INTERDISC, P288
   Maturana Humberto, 1998, TREE KNOWLEDGE BIOL
   Meadows D, 1999, LEVERAGE POINTS PLAC
   Meehl PE, 2002, PSYCHOL REP, V91, P339, DOI 10.2466/PR0.91.6.339-404
   Menken S., 2016, INTRO INTERDISCIPLIN
   MINATI G, 2008, NEW APPROACHES MODEL
   Muller KH, 2012, DRUS ISTRAZ, V21, P315, DOI 10.5559/di.21.2.02
   Muller K.H., 2011, NEW SCI CYBERNETICS, V2
   Newell W., 2007, HDB DECISION MAKING, P245
   Newell W.H., 1997, ADV INTERDISCIPLINAR
   Newell WH, 2001, ISSUES INTEGRATIVE S, V19, P1
   Novak J. D., 2010, J E LEARNING KNOWLED, V6, P21
   Novak JD, 2002, SCI EDUC, V86, P548, DOI 10.1002/sce.10032
   Ozdemir G., 2007, EURASIA J MATH SCI T, V3, P351, DOI DOI 10.12973/ejmste/75414
   Panetti E, 2018, TECHNOL ANAL STRATEG, V30, P993, DOI 10.1080/09537325.2018.1433295
   Pearl J., 2009, CAUSALITY
   Popper K, 2002, LOGIC SCI DISCOVERY, V2
   Porter T. M, 1996, TRUST NUMBERS PURSUI
   Quine W, 1969, ONTOLOGICAL RELATIVI
   Quine W. V., 1980, LOGICAL POINT VIEW
   Reese D. D., 2009, NEW FRONT AN RES P 2
   Repko A. F., 2017, INTERDISCIPLINARY RE
   RuizPrimo MA, 1996, J RES SCI TEACH, V33, P569, DOI 10.1002/(SICI)1098-2736(199608)33:6<569::AID-TEA1>3.0.CO;2-M
   Senge P.M., 2006, 5 DISCIPLINE ART PRA
   Seselja D, 2014, SYNTHESE, V191, P3111, DOI 10.1007/s11229-014-0476-4
   Shackelford C., 2014, THESIS
   Shaw DR, 2012, SYST RES BEHAV SCI, V29, P484, DOI 10.1002/sres.2157
   Sosa E., 2003, EPISTEMIC JUSTIFICAT, V7, P240
   Stacey RD, 2000, COMPLEXITY MANAGEMEN
   Stinchcombe A. L., 1987, CONSTRUCTING SOCIAL
   SUEDFELD P, 1976, J PERS SOC PSYCHOL, V34, P169, DOI 10.1037/0022-3514.34.2.169
   Suedfeld P., 1992, HDB THEMATIC CONTENT, P393
   Szostak R., 2002, ISSUE INTEGRAT STUD, V20, P103
   Szostak R., 2007, ISSUES INTEGRATIVE S, V25, P32
   Szostak R, 2007, J RES PRACT, V3
   Trochim W, 2005, EMERGENCE-COMPLEX OR, V7
   Umpleby S.A., 2010, AUSTR SOC CYB STUD
   Umpleby SA, 1997, CYBERNET SYST, V28, P635, DOI 10.1080/019697297125886
   Van de Ven AndrewH., 2007, ENGAGED SCHOLARSHIP
   von Bertalanffy L, 1972, ACAD MANAGE J, V15, P407, DOI [10.2307/255139, DOI 10.2307/255139]
   WALLACE JD, 1990, J RES SCI TEACH, V27, P1033, DOI 10.1002/tea.3660271010
   Wallis, 2010, INTEGRAL REV, V6, P153
   Wallis S, 2010, INTEGRAL REV TRANSDI, V6, P3
   Wallis Steven E, 2009, International Journal of Collaborative Enterprise, V1, P180, DOI 10.1504/IJCENT.2009.029288
   Wallis S. E., 2016, USING INTEGRATIVE PR
   Wallis S.E., 2015, ASS BUS SIM EXP LEAR
   Wallis S. E., 2014, ORG TRANSFORMATION S, V11, P3, DOI [http://dx.doi.org/10.1179/1477963313Z.00000000023, DOI 10.1179/1477963313Z]
   Wallis S.E., 2016, SCI LAWS J, V2, P10
   Wallis S.E., 2011, AVOIDING POLICY FAIL
   Wallis S. E., 2013, SYSTEMS APP IN PRESS, V15, P94
   Wallis S. E., 2006, STUDY COMPLEX ADAPTI
   Wallis S. E., 2008, INTEGRAL REV, V4, P71
   Wallis S. E., 2008, INTELLIGENT COMPLEX, P1, DOI DOI 10.4018/978-1-59904-717-1.CH001
   Wallis S.E., 2015, EMERGENCE COMPLEXITY, V17, P1
   Wallis SE., 2010, CYBERNET SYST, P151, DOI [DOI 10.4018/978-1-61520-668-1.CH009, 10.4018/978-1-61520-668-1.ch009]
   WALLIS SE, 2018, J POLICY COMPLEX SYS, V4, P169
   Wallis SE, 2016, SYSTEMIC PRACTICE AC, V30, P103
   WALLIS SE, 2016, FOUND SCI, V20, P213
   Wallis S, 2014, MODES OF EXPLANATION: AFFORDANCES FOR ACTION AND PREDICTION, P197
   Wallis SE, 2017, FOUND SCI, V22, P733, DOI 10.1007/s10699-016-9496-5
   Wallis SE, 2016, KYBERNETES, V45, P1437, DOI 10.1108/K-04-2016-0072
   Wallis SE, 2016, FOUND SCI, V21, P579, DOI 10.1007/s10699-015-9425-z
   Wallis SE, 2012, PHILOS MANAG, V11, P67, DOI 10.5840/pom201211320
   Wallis SE, 2015, REV GEN PSYCHOL, V19, P365, DOI 10.1037/gpr0000048
   Wallis SE, 2014, FOUND SCI, V19, P353, DOI 10.1007/s10699-014-9344-4
   Wallis SE, 2014, SYST RES BEHAV SCI, V31, P23, DOI 10.1002/sres.2159
   Wallis SE, 2010, SOC RESPONSIB J, V6, P536, DOI 10.1108/17471111011083428
   WEICK KE, 1989, ACAD MANAGE REV, V14, P516, DOI 10.2307/258556
   Wexler M., 2001, J KNOWLEDGE MANAGEME, V5, P249, DOI [10.1108/EUM0000000005868, DOI 10.1108/EUM0000000005868]
   Wong EM, 2011, ACAD MANAGE J, V54, P1207, DOI 10.5465/amj.2008.0762
   Wright B., 2019, PRACTICAL MAPPING AP
NR 119
TC 3
Z9 3
U1 0
U2 5
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0308-0188
EI 1743-2790
J9 INTERDISCIPL SCI REV
JI Interdiscip. Sci. Rev.
PD OCT 2
PY 2019
VL 44
IS 3-4
SI SI
BP 402
EP 429
DI 10.1080/03080188.2019.1680145
EA OCT 2019
PG 28
WC Multidisciplinary Sciences; Social Sciences, Interdisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Social Sciences - Other Topics
GA JQ2HN
UT WOS:000497548900001
DA 2022-08-23
ER

PT J
AU Jiang, WB
   Sun, YR
AF Jiang, Wenbo
   Sun, Yanrui
TI Social-RippleNet: Jointly modeling of ripple net and social information
   for recommendation
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Recommend algorithms; Social relation; Knowledge graph; Ripple net;
   Neural networks
ID NETWORKS
AB To alleviate the data sparseness and cold-start problem of recommender systems and improve the accuracy, researchers tend to use side information to improve recommendation performance. Previous studies have investigated social recommendation and knowledge graph recommendation separately, rarely integrated them into one model for the recommendation. We propose a model (Social-RippleNet) which jointly combines knowledge graphs and social information for recommendations to alleviate data sparsity and cold-start issues in recommender systems. Social-RippleNet consists of user modeling, item modeling, and rating prediction. In user modelling, we combine such features as of interaction, additional information, and its own to obtain user latent features. And so does in item modelling to obtain item latent features. Besides, we improve Ripple Net based on the idea of multiple "ripples" superposition to extract item knowledge graph features. In rating prediction, we combine user and item latent features to predict the user rating of items. We randomly select 35% and 50% samples from the MovieLens-1M dataset, and compare our model (Social-RippleNet) with several state-of-the-art baselines for Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Experimental results show that the proposed Social-RippleNet model is effective. We also perform a model analysis that comparing the Social-RippleNet model with its variants. The comparison results show that introducing user-own features, social features, and item knowledge graph features into the model can improve the prediction effect.
C1 [Jiang, Wenbo; Sun, Yanrui] Northeastern Univ, Coll Sci, Wenhua Rd, Shenyang 110819, Liaoning, Peoples R China.
RP Sun, YR (通讯作者)，Northeastern Univ, Coll Sci, Wenhua Rd, Shenyang 110819, Liaoning, Peoples R China.
EM wenboj@126.com; yanruisun@126.com
FU National Key Research and Development Program of China [2021YFF0306401]
FX This work is supported by National Key Research and Development Program
   of China (2021YFF0306401)
CR Amit S., 2012, INTRO KNOWLEDGE GRAP
   Fan WQ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P417, DOI 10.1145/3308558.3313488
   Feng SS, 2019, APPL INTELL, V49, P2046, DOI 10.1007/s10489-018-1375-z
   [冯永 Feng Yong], 2019, [自动化学报, Acta Automatica Sinica], V45, P518
   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 QY, 2020, IEEE T KNOWL DATA EN, V34, P3549, DOI [10.1038/s41366-020-0625-4, 10.1109/TKDE.2020.3028705]
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Holzinger A, 2021, INFORM FUSION, V71, P28, DOI 10.1016/j.inffus.2021.01.008
   Jamali M., 2010, P 4 ACM C REC SYST, P135, DOI DOI 10.1145/1864708.1864736
   Jiang M, 2014, IEEE T KNOWL DATA EN, V26, P2789, DOI 10.1109/TKDE.2014.2300487
   Kautz H, 1997, COMMUN ACM, V40, P63, DOI 10.1145/245108.245123
   King I, 2008, P 17 ACM C INF KNOWL, P931
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   [刘华锋 Liu Huafeng], 2018, [软件学报, Journal of Software], V29, P340
   Ma Hao, 2011, P 4 ACM INT C WEB SE, P287
   Mnih A., 2007, ADV NEURAL INFORM PR, V20
   Palumbo E, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P32, DOI 10.1145/3109859.3109889
   [饶子昀 Rao Ziyun], 2021, [自动化学报, Acta Automatica Sinica], V47, P2061
   Rendle S, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P635
   Sarwar B., 2001, P ACM WORLD WID WEB
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Tang JL, 2013, SOC NETW ANAL MIN, V3, P1113, DOI 10.1007/s13278-013-0141-9
   Tieleman T., 2012, COURSERA NEURAL NETW, V4, P26, DOI DOI 10.1007/S12654-012-0173-1
   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 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
   Wang XH, 2019, IEEE ACCESS, V7, P82826, DOI 10.1109/ACCESS.2019.2924443
   Wei DF, 2020, COMPUTER ENG APPL, P110
   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
   [朱文强 Zhu Wenqiang], 2020, [小型微型计算机系统, Journal of Chinese Computer Systems], V41, P2036
NR 34
TC 0
Z9 0
U1 7
U2 7
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/s10489-022-03620-2
EA MAY 2022
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1S1AT
UT WOS:000803791000002
DA 2022-08-23
ER

PT J
AU Yang, P
   Ai, CM
   Yao, Y
   Li, B
AF Yang, Peng
   Ai, Chengming
   Yao, Yu
   Li, Bing
TI EKPN: enhanced knowledge-aware path network for recommendation
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Recommendation; Knowledge graph; Implicit features; Collaborative
   filtering
AB Incorporating a knowledge graph (KG) into recommender systems has been widely studied by researchers. Existing methods mostly extract the paths connecting user-item pairs and model these paths or iteratively propagate the user preference over the KG. These methods can capture the semantics of entities and relations well and help to comprehend users' interests. However, these methods ignore the implicit features between the KG's external items and thus cannot fully capture the users' preferences. To solve this problem, we propose a novel model named E nhanced K nowledge-aware P ath N etwork (EKPN) to exploit the KG and capture implicit features between items outside the KG for recommendation. EKPN consists of two neural network modules: one module captures explicit features between items in the knowledge graph by automatically generating paths from users to candidate items for recommendation; the other module explores implicit features between items outside the knowledge graph by utilizing users' historical interactions. To better capture the implicit features between items outside the knowledge graph, we propose the activation gate mechanism. Finally, we use a fusion mechanism to combine the two modules to enhance each other and achieve higher performance. Extensive validation on two real-world datasets shows the superiority of EKPN over baselines.
C1 [Yang, Peng; Ai, Chengming; Yao, Yu; Li, Bing] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Yang, Peng; Ai, Chengming; Yao, Yu; Li, Bing] Southeast Univ, Sch Cyber Sci & Engn, Nanjing, Peoples R China.
   [Yang, Peng; Ai, Chengming; Yao, Yu; Li, Bing] Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing, Peoples R China.
RP Yang, P (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.; Yang, P (通讯作者)，Southeast Univ, Sch Cyber Sci & Engn, Nanjing, Peoples R China.; Yang, P (通讯作者)，Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing, Peoples R China.
EM pengyang@seu.edu.cn
FU Consulting Project of Chinese Academy of Engineering [2020XY-5,
   2018-XY-07]; Fundamental Research Funds for the Central Universities
   [2242021S30009]; Collaborative Innovation Center of Novel Software
   Technology and Industrialization
FX This work was supported in part by the Consulting Project of Chinese
   Academy of Engineering under Grant 2020XY-5, 2018-XY-07, and in part by
   the Fundamental Research Funds for the Central Universities under Grant
   2242021S30009, the Collaborative Innovation Center of Novel Software
   Technology and Industrialization.
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Bayer I, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1341, DOI 10.1145/3038912.3052694
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Catherine R, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P325, DOI 10.1145/2959100.2959131
   Chaudhari S, 2017, AI COMMUN, V30, P141, DOI 10.3233/AIC-170728
   Cheng H.T., 2016, P 1 WORKSHOP DEEP LE, P7, DOI DOI 10.1145/2988450.2988454
   Erhan D, 2010, J MACH LEARN RES, V11, P625
   Glorot X., 2010, JMLR WRKSHOP C P, P249
   Guo HF, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1725
   Hamilton WL, 2017, ADV NEUR IN, V30
   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
   Jrvelin Kalervo, 2017, ACM SIGIR Forum, V51, P243, DOI 10.1145/3130348.3130374
   Kingma D. P., 2017, ARXIV
   Lian JX, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1754, DOI 10.1145/3219819.3220023
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Morales G. D. F., 2012, P 5 ACM INT C WEB SE, P153, DOI DOI 10.1145/2124295.2124315
   Rendle S, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P635
   Shi C., 2015, P 24 ACM INT C INF K, P453
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   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 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, 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
   Xue HJ, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3203
   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 LM, 2018, HT'18: PROCEEDINGS OF THE 29TH ACM CONFERENCE ON HYPERTEXT AND SOCIAL MEDIA, P201, DOI 10.1145/3209542.3209557
NR 32
TC 0
Z9 0
U1 8
U2 11
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 JUN
PY 2022
VL 52
IS 8
BP 9308
EP 9319
DI 10.1007/s10489-021-02758-9
EA JAN 2022
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1U7XE
UT WOS:000737750700002
DA 2022-08-23
ER

PT J
AU Tosi, MDL
   dos Reis, JC
AF Tosi, Mauro Dalle Lucca
   dos Reis, Julio Cesar
TI SciKGraph: A knowledge graph approach to structure a scientific field
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Knowledge graphs; Knowledge representation; Overlap clustering; Semantic
   annotation; Document classification
ID SOFTWARE; SCIENCE; TOOL
AB Understanding the structure of a scientific domain and extracting specific information from it is laborious. The high amount of manual effort required to this end indicates that the way knowledge has been structured and visualized until the present day should be improved in software tools. Nowadays, scientific domains are organized based on citation networks or bag-of-words techniques, disregarding the intrinsic semantics of concepts presented in literature documents. We propose a novel approach to structure scientific fields, which uses semantic analysis from natural language texts to construct knowledge graphs. Then, our approach clusters knowledge graphs in their main topics and automatically extracts information such as the most relevant concepts in topics and overlapping concepts between topics. We evaluate the proposed model in two datasets from distinct areas. The results achieve up to 84% of accuracy in the task of document classification without using annotated data to segment topics from a set of input documents. Our solution identifies coherent keyphrases and key concepts considering the dataset used. The SciKGraph framework contributes by structuring knowledge that might aid researchers in the study of their areas, reducing the effort and amount of time devoted to groundwork. ? 2020 Elsevier Ltd. All rights reserved.
C1 [Tosi, Mauro Dalle Lucca] Univ Luxembourg, Fac Sci Technol & Commun, Belval, Luxembourg.
   [dos Reis, Julio Cesar] Univ Estadual Campinas, Inst Comp, Campinas, SP, Brazil.
RP Tosi, MDL (通讯作者)，Univ Luxembourg, Fac Sci Technol & Commun, Belval, Luxembourg.
EM mauro.dalleluccatosi@uni.lu; jreis@ic.unicamp.br
RI Tosi, Mauro Dalle Lucca/AAE-9511-2022; Dalle Lucca Tosi,
   Mauro/ADO-8638-2022
OI Tosi, Mauro Dalle Lucca/0000-0002-0218-2413; Dalle Lucca Tosi,
   Mauro/0000-0002-0218-2413; Dos Reis, Julio Cesar/0000-0002-9545-2098
FU Sao Paulo Research Foundation (FAPESP) [2017/02325-5, 2013/08293-7];
   Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil
   (CAPES) [001]
FX This work was financially supported by the Sao Paulo Research Foundation
   (FAPESP) (grants #2017/02325-5 and#2013/08293-7) and the Coordenacao de
   Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES) -Finance
   Code 001.
CR ALTMAN NS, 1992, AM STAT, V46, P175, DOI 10.2307/2685209
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Babelnet, LARGEST MULTILINGUAL
   Bornmann L, 2015, J ASSOC INF SCI TECH, V66, P2215, DOI 10.1002/asi.23329
   Boudin F., 2013, INT JOINT C NAT LANG, P834
   Cambria E, 2014, IEEE COMPUT INTELL M, V9, P48, DOI 10.1109/MCI.2014.2307227
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen MM, 2015, SOC NETW ANAL MIN, V5, DOI 10.1007/s13278-015-0279-8
   CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1007/BF00994018
   Dogrusoz U, 2009, INFORM SCIENCES, V179, P980, DOI 10.1016/j.ins.2008.11.017
   Ehrlinger L., 2016, DEFINITION KNOWLEDGE, P48
   Feng Xia, 2016, IEEE Transactions on Big Data, V2, P101, DOI 10.1109/TBDATA.2016.2555318
   Garfield E, 2009, J INFORMETR, V3, P173, DOI 10.1016/j.joi.2009.03.009
   Goldberg Y., 2014, ABS14023722 CORR
   Gupta S, 2017, WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P1267, DOI 10.1145/3041021.3053062
   Hagberg A. A., 2008, P 7 PYTH SCI C PAS, P11
   Jung S, 2014, KNOWL-BASED SYST, V69, P34, DOI 10.1016/j.knosys.2014.04.036
   Kowsari K, 2019, INFORMATION, V10, DOI 10.3390/info10040150
   Kowsari K, 2017, 2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), P364, DOI 10.1109/ICMLA.2017.0-134
   Lewis DM, 2017, SERIALS REV, V43, P239, DOI 10.1080/00987913.2017.1368057
   MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084
   McLevey J, 2017, J INFORMETR, V11, P176, DOI 10.1016/j.joi.2016.12.005
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   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
   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
   Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI 10.3115/v1/D14-1162
   Perez-Suarez A, 2013, NEUROCOMPUTING, V121, P234, DOI 10.1016/j.neucom.2013.04.025
   Rocchio J.J, 1971, SMART RETRIEVAL SYST
   Rous B, 2012, COMMUN ACM, V55, P12, DOI 10.1145/2366316.2366320
   Ruiz-Rosero J, 2019, SCIENTOMETRICS, V121, P1165, DOI 10.1007/s11192-019-03213-w
   SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0
   Shannon P, 2003, GENOME RES, V13, P2498, DOI 10.1101/gr.1239303
   Silva FN, 2016, J INFORMETR, V10, P487, DOI 10.1016/j.joi.2016.03.008
   Tosi M. D. L., 2019, RES C MET SEM RES, P236
   van Eck N.J., 2011, TEXT MINING VISUALIZ, V7, P50
   van Eck NJ, 2014, J INFORMETR, V8, P802, DOI 10.1016/j.joi.2014.07.006
   Yau CK, 2014, SCIENTOMETRICS, V100, P767, DOI 10.1007/s11192-014-1321-8
   Zhang J, 2011, INT CONF CLOUD COMPU, P103, DOI 10.1109/CCIS.2011.6045041
NR 41
TC 4
Z9 5
U1 14
U2 38
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD FEB
PY 2021
VL 15
IS 1
AR 101109
DI 10.1016/j.joi.2020.101109
PG 22
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 RC2DK
UT WOS:000632614000007
DA 2022-08-23
ER

PT J
AU Zhang, RC
   Mao, YY
   Zhao, WH
AF Zhang, Richong
   Mao, Yongyi
   Zhao, Weihua
TI Knowledge graphs completion via probabilistic reasoning
SO INFORMATION SCIENCES
LA English
DT Article
DE Knowledge graph completion; knowledge graph reasoning; Rule-based
   inference
AB Constructing large-scale knowledge base has encountered a bottleneck because of the limitation of natural language processing. Many approaches have been put forward to infer new facts based on existing knowledge. Graph feature models mine rule-like patterns from a knowledge base and use them to predict missing edges. These models take account of the graph structure information and they can explain the existence of a fact reasonably. Existing models only describe local interaction between entities, but how to model co-relationships among facts globally is a tough problem. In this paper, we develop an efficient model which uses association rules to make inferences. First, we use a rule mining model to detect simple association rules and use them to produce large amounts of evidence. Second, based on all the produced evidence and the connections among them, we construct a factor graph which represents the inference space. Then, we develop an EM inference model, wherein the E-step we use Belief Propagation to calculate the marginal distribution of candidate edges and, in the M-step we propose a Generalized Iterative Proportional Fitting algorithm to re-learn the confidence of soft rules. Experiments show that our approach outperforms state-of-the-art approaches in knowledge base completion (KBC) tasks. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Zhang, Richong; Zhao, Weihua] Beihang Univ, SKLSDE, Sch Comp Sci & Engn, 37 Xueyuan Rd, Beijing 100191, Peoples R China.
   [Zhang, Richong] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, 37 Xueyuan Rd, Beijing 100191, Peoples R China.
   [Mao, Yongyi] Univ Ottawa, Sch Elect Engn & Comp Sci, 800 King Edward Ave, Ottawa, ON K1N 6N5, Canada.
RP Zhang, RC (通讯作者)，Beihang Univ, SKLSDE, Sch Comp Sci & Engn, 37 Xueyuan Rd, Beijing 100191, Peoples R China.; Zhang, RC (通讯作者)，Beihang Univ, Sch Comp Sci & Engn, BDBC, 37 Xueyuan Rd, Beijing 100191, Peoples R China.
EM zhangrc@act.buaa.edu.cn; yymao@eecs.uottawa.ca; zhaowh@act.buaa.edu.cn
FU National Natural Science Foundation of China [61772059, 61421003];
   Beijing Advanced Innovation Center for Big Data and Brain Computing
   (BDBC); State Key Laboratory of Software Development Environment;
   Beijing ST Committee [Z19110 0 0086190 07]
FX This work is supported partly by the National Natural Science Foundation
   of China (Nos. 61772059 and 61421003), by the Beijing Advanced
   Innovation Center for Big Data and Brain Computing (BDBC), by State Key
   Laboratory of Software Development Environment and by the Beijing S&T
   Committee (No. Z19110 0 0086190 07).
CR Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170035.170072
   [Anonymous], P C ART INT
   [Anonymous], P AAAI WORKSH STAT R
   [Anonymous], 2013, P WORKSH AUT KNOWL B
   [Anonymous], 1997, WILEY SERIES PROBABI
   [Anonymous], 2012, BROWSING LINKED OPEN
   [Anonymous], 2019, AAAI
   [Anonymous], 2016, ACM S APPL COMP ACM
   Appice A., 2003, Intelligent Data Analysis, V7, P541
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Chen Y, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P649, DOI 10.1145/2588555.2610516
   Collobert R., 2008, P 25 ICML, P160
   Domingos P., 2010, P 27 INT C INT C MAC, P551
   FIENBERG SE, 1970, ANN MATH STAT, V41, P907, DOI 10.1214/aoms/1177696968
   Frey BJ, 1998, ADV NEUR IN, V10, P479
   Galarraga L.A., 2013, P 22 INT C WORLD WID, P413, DOI DOI 10.1145/2488388.2488425
   Huynh T. N., 2008, P 25 INT C MACH LEAR, P416, DOI DOI 10.1145/1390156.1390209
   Jordan MI, 1999, MACH LEARN, V37, P183, DOI 10.1023/A:1007665907178
   Kass RE, 1998, AM STAT, V52, P93, DOI 10.2307/2685466
   Khot T., 2011, Proceedings of the 2011 IEEE 11th International Conference on Data Mining (ICDM 2011), P320, DOI 10.1109/ICDM.2011.87
   Kindermann R., 1980, AM MATH SOC
   Kschischang FR, 2001, IEEE T INFORM THEORY, V47, P498, DOI 10.1109/18.910572
   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
   Lin Y., 2015, P AAAI
   McCray AT, 2003, COMP FUNCT GENOM, V4, P80, DOI 10.1002/cfg.255
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   MUGGLETON S, 1995, NEW GENERAT COMPUT, V13, P245, DOI 10.1007/BF03037227
   MUGGLETON S, 1990, NEW GENERAT COMPUT, V8, P295
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Schoenmackers S., 2008, P C EMP METH NAT LAN, P79
   Schoenmackers Stefan, 2010, P 2010 C EMP METH NA, P1088
   Shapiro E. Y., 1991, COMPUTATIONAL LOGIC, P199
   Suchanek FM, 2011, PROC VLDB ENDOW, V5, P157, DOI 10.14778/2078331.2078332
   Volker J, 2011, LECT NOTES COMPUT SC, V6643, P124, DOI 10.1007/978-3-642-21034-1_9
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wei Z., 2015, P 24 ACM INT C INF K, P1331, DOI DOI 10.1145/2806416.2806513
   West R, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P515, DOI 10.1145/2566486.2568032
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xiao H, 2017, AAAI CONF ARTIF INTE, P3104
   Xiao X, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms11296
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yan X, 2003, P 9 ACM SIGKDD INT C, P286, DOI DOI 10.1145/956750.956784
NR 44
TC 7
Z9 8
U1 5
U2 45
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 521
BP 144
EP 159
DI 10.1016/j.ins.2020.02.016
PG 16
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LE8YX
UT WOS:000527015900009
DA 2022-08-23
ER

PT J
AU Liao, JZ
   Zhao, X
   Tang, JY
   Zeng, WX
   Tan, Z
AF Liao, Jinzhi
   Zhao, Xiang
   Tang, Jiuyang
   Zeng, Weixin
   Tan, Zhen
TI To hop or not, that is the question: Towards effective multi-hop
   reasoning over knowledge graphs
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph reasoning; Reinforcement learning; Reasoning path
ID DBPEDIA
AB With the proliferation of large-scale knowledge graphs (KGs), multi-hop knowledge graph reasoning has been a capstone that enables machines to be able to handle intelligent tasks, especially where some explicit reasoning path is appreciated for decision making. To train a KG reasoner, supervised learning-based methods suffer from false-negative issues, i.e., unseen paths during training are not to be found in prediction; in contrast, reinforcement learning (RL)-based methods do not require labeled paths, and can explore to cover many appropriate reasoning paths. In this connection, efforts have been dedicated to investigating several RL formulations for multi-hop KG reasoning. Particularly, current RL-based methods generate rewards at the very end of the reasoning process, due to which short paths of hops less than a given threshold are likely to be overlooked, and the overall performance is impaired. To address the problem, we propose RL-MHR, a revised RL formulation of multi-hop KG reasoning that is characterized by two novel designs-the stop signal and the worth-trying signal. The stop signal instructs the agent of RL to stay at the entity after finding the answer, preventing from hopping further even if the threshold is not reached; meanwhile, the worth-trying signal encourages the agent to try to learn some partial patterns from the paths that fail to lead to the answer. To validate the design of our model RL-MHR, comprehensive experiments are carried out on three benchmark knowledge graphs, and the results and analysis suggest the superiority of RL-MHR over state-of-the-art methods.
C1 [Liao, Jinzhi; Zhao, Xiang; Tang, Jiuyang; Zeng, Weixin; Tan, Zhen] Natl Univ Def Technol, 109 Deya Rd, Changsha, Hunan, Peoples R China.
   [Liao, Jinzhi; Zhao, Xiang; Tang, Jiuyang; Zeng, Weixin; Tan, Zhen] Sci & Technol Informat Syst Engn Lab, 109 Deya Rd, Changsha, Peoples R China.
RP Zhao, X (通讯作者)，Natl Univ Def Technol, 109 Deya Rd, Changsha, Hunan, Peoples R China.; Zhao, X (通讯作者)，Sci & Technol Informat Syst Engn Lab, 109 Deya Rd, Changsha, Peoples R China.
EM xiangzhao@nudt.edu.cn
RI Zeng, Weixin/AAX-2993-2021
OI Liao, Jinzhi/0000-0002-2898-6559; Zeng, Weixin/0000-0002-2690-3190
FU NSFC [61872446, 61902417, 71971212]; NSF of Hunan Province [2019JJ20024]
FX This work was partially supported by NSFC under grants Nos. 61872446,
   61902417 and 71971212, NSF of Hunan Province under grant No.
   2019JJ20024.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Balazevic I, 2019, LECT NOTES COMPUT SC, V11731, P553, DOI 10.1007/978-3-030-30493-5_52
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Cohen W. W., 2016, ARXIV160506523
   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
   Gardner M., 2014, P 2014 C EMP METH NA, P397, DOI [10.3115/v1/D14-1044, DOI 10.3115/V1/D14-1044]
   Glorot X., 2010, JMLR WRKSHOP C P, P249
   Godin F., 2019, P 2019 C N AM CHAPT, V2, P122
   Gunning D, 2019, SCI ROBOT, V4, DOI 10.1126/scirobotics.aay7120
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Kingma D. P., 2017, ARXIV
   Kok S., 2007, P 24 INT C MACH LEAR, P433, DOI DOI 10.1145/1273496.1273551
   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, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lin X.V., 2018, ARXIV180810568
   Meilicke C, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3137
   Montavon G., 2017, P 8 WORKSH COMP APPR, V17, P52, DOI [10.18653/v1/W17-5221, DOI 10.18653/V1/W17-5221]
   MUGGLETON S, 1995, NEW GENERAT COMPUT, V13, P245, DOI 10.1007/BF03037227
   MUGGLETON S, 1992, FIFTH GENERATION COMPUTER SYSTEMS 1992, VOLS 1 AND 2, P1071
   Murdoch W. J., 2017, ICLR
   Qu M, 2019, ADV NEUR IN, V32
   QUINLAN JR, 1990, MACH LEARN, V5, P239, DOI 10.1007/BF00117105
   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
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Rieger L., 2019, ARXIV190913584
   Rocktaschel T., 2017, ADV NEURAL INFORM PR, V30, P3788
   Shen Y., 2018, NEURIPS, V31, P6786
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sukhbaatar S, 2015, ADV NEUR IN, V28
   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
   Wang WY, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P2129, DOI 10.1145/2505515.2505573
   Welbl J., 2018, IEE T ASS COMPUT LIN, V6, P287, DOI 10.1162/tacl_a_00021
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Xu K, 2015, PR MACH LEARN RES, V37, P2048
   Yang B., 2015, P INT C LEARN REPR I
   Yang F, 2017, ADV NEUR IN, V30
   Zhang S, 2019, ADV NEUR IN, V32
NR 45
TC 1
Z9 1
U1 5
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 SEP
PY 2021
VL 24
IS 5
SI SI
BP 1837
EP 1856
DI 10.1007/s11280-021-00911-5
EA JUL 2021
PG 20
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WJ2YV
UT WOS:000678407900001
OA hybrid
DA 2022-08-23
ER

PT J
AU Ge, XT
   Yang, Y
   Peng, L
   Chen, LJ
   Li, WC
   Zhang, WY
   Chen, JH
AF Ge, Xingtong
   Yang, Yi
   Peng, Ling
   Chen, Luanjie
   Li, Weichao
   Zhang, Wenyue
   Chen, Jiahui
TI Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi
   Source Heterogeneous Data
SO REMOTE SENSING
LA English
DT Article
DE forest fire prediction; knowledge graph; spatio-temporal data; machine
   learning; multi-source heterogeneous data fusion
ID VARIABILITY
AB Forest fires have frequently occurred and caused great harm to people's lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the features from large-scale, multi-source, heterogeneous data. There is a lack of a method that can effectively extract features required by machine learning-based forest fire predictions from multi-source spatio-temporal data. This paper proposes a forest fire prediction method that integrates spatio-temporal knowledge graphs and machine learning models. This method can fuse multi-source heterogeneous spatio-temporal forest fire data by constructing a forest fire semantic ontology and a knowledge graph-based spatio-temporal framework. This paper defines the domain expertise of forest fire analysis as the semantic rules of the knowledge graph. This paper proposes a rule-based reasoning method to obtain the corresponding data for the specific machine learning-based forest fire prediction methods, which are dedicated to tackling the problem with real-time prediction scenarios. This paper performs experiments regarding forest fire predictions based on real-world data in the experimental areas Xichang and Yanyuan in Sichuan province. The results show that the proposed method is beneficial for the fusion of multi-source spatio-temporal data and highly improves the prediction performance in real forest fire prediction scenarios.
C1 [Ge, Xingtong; Peng, Ling; Chen, Luanjie; Li, Weichao; Zhang, Wenyue; Chen, Jiahui] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China.
   [Ge, Xingtong; Peng, Ling; Chen, Luanjie; Zhang, Wenyue; Chen, Jiahui] Univ Chinese Acad Sci, Coll Resources & Environm CRE, Beijing 100190, Peoples R China.
   [Yang, Yi] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China.
RP Peng, L (通讯作者)，Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China.; Peng, L (通讯作者)，Univ Chinese Acad Sci, Coll Resources & Environm CRE, Beijing 100190, Peoples R China.
EM gexingtong21@mails.ucas.ac.cn; yangyi@ia.ac.cn; pengling@aircas.ac.cn;
   chenluanjie20@mails.ucas.ac.cn; liweichao@rsai.tech;
   zhangwenyue@rsai.tech; chenjiahui21@mails.ucas.ac.cn
OI Ge, Xingtong/0000-0001-7603-2832
FU Ningxia Key RD Program [2020BFG02013]; Beijing Municipal Science and
   Technology Project [Z191100001419002]
FX This work was supported by Ningxia Key R&D Program (2020BFG02013), and
   the Beijing Municipal Science and Technology Project (Z191100001419002).
CR Ali AA, 2009, GLOBAL CHANGE BIOL, V15, P1230, DOI 10.1111/j.1365-2486.2009.01842.x
   [Anonymous], ERA5 LAND HOURLY DAT
   [Anonymous], LP DAAC HOMEPAGE
   [Anonymous], FIRE MAP NASA
   [Anonymous], NATL FOREST RESOURCE
   [Anonymous], DATA CTR RESOURCES E
   [Anonymous], WHAT IS ARCPY HELP A
   Pham BT, 2020, SYMMETRY-BASEL, V12, DOI 10.3390/sym12061022
   Chen JH, 2021, IEEE I C SQRS-C, P440, DOI 10.1109/QRS-C55045.2021.00071
   Chen JJ, 2022, FORESTS, V13, DOI 10.3390/f13030480
   Cui LL, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14030712
   Fang K.N., 2011, STAT INF FORUM, V26, P32
   Ge XT, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14051214
   Hering AS, 2009, ENVIRON ECOL STAT, V16, P225, DOI 10.1007/s10651-007-0080-6
   Jaafari A, 2017, ECOL INFORM, V39, P32, DOI 10.1016/j.ecoinf.2017.03.003
   Jiahui Chen, 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, P3920, DOI 10.1109/IGARSS47720.2021.9553867
   Kontschieder P, 2015, IEEE I CONF COMP VIS, P1467, DOI 10.1109/ICCV.2015.172
   Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791
   LeCun Y., 1990, P ADV NEUR INF PROC, VVolume 2
   Li Xin-Hai, 2013, Chinese Journal of Applied Entomology, V50, P1190, DOI 10.7679/j.issn.2095-1353.2013.163
   [马文苑 Ma Wenyuan], 2020, [中南林业科技大学学报, Journal of Central South University of Forestry & Technology], V40, P57
   O'Connor MJ, 2011, COMM COM INF SC, V127, P97
   Podur J, 2003, ECOL MODEL, V164, P1, DOI 10.1016/S0304-3800(02)00386-1
   Prapas I., 2021, ARXIV
   Sakr G E, 2010, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2010), P1311, DOI 10.1109/AIM.2010.5695809
   Salavati G, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14073881
   Schulte LA, 2005, ECOLOGY, V86, P431, DOI 10.1890/03-4065
   Singh M, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14020992
   Piralilou ST, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14030672
   [张雷 Zhang Lei], 2014, [生态学报, Acta Ecologica Sinica], V34, P650
   Zhen XT, 2016, MED IMAGE ANAL, V30, P120, DOI 10.1016/j.media.2015.07.003
NR 31
TC 0
Z9 0
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2072-4292
J9 REMOTE SENS-BASEL
JI Remote Sens.
PD JUL
PY 2022
VL 14
IS 14
AR 3496
DI 10.3390/rs14143496
PG 21
WC Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science & Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging
   Science & Photographic Technology
GA 3I1MJ
UT WOS:000832488400001
OA gold
DA 2022-08-23
ER

PT J
AU Meng, LY
AF Meng, LingYan
TI Information Extraction and Knowledge Graph Construction for Enterprises
   in China's Free Trade Zone
SO SECURITY AND COMMUNICATION NETWORKS
LA English
DT Article
AB With the establishment and rapid development of the China (Shanghai) Pilot Free Trade Zone (FTZ), the scale of enterprises in the zone has grown rapidly. This paper takes the actual needs of the Shanghai FTZ as the background, extracts key information such as entities from the Big Data related to Internet enterprises, further constructs the enterprise knowledge graph, and applies it to the supervision and service of the FTZ. The enterprise knowledge graph is constructed using the Neo4j graph database. To verify the effectiveness of the named entity identification and relationship extraction methods proposed in this paper, experiments were conducted to validate them, and both achieved good results.
C1 [Meng, LingYan] Univ Pendidikan Sultan Idris UPSI, Fac Management & Econ, Tanjong Malim 35900, Perak Darul Rid, Malaysia.
   [Meng, LingYan] Yangdong Beijing Educ & Technol CO LTD, Beijing 100083, Peoples R China.
RP Meng, LY (通讯作者)，Univ Pendidikan Sultan Idris UPSI, Fac Management & Econ, Tanjong Malim 35900, Perak Darul Rid, Malaysia.; Meng, LY (通讯作者)，Yangdong Beijing Educ & Technol CO LTD, Beijing 100083, Peoples R China.
EM menglingyan@yangdong-edu.com
CR AHLSTROM D, 2003, SAM ADV MANAGE J, V68, P4
   Bechtel B, 2015, ISPRS INT J GEO-INF, V4, P199, DOI 10.3390/ijgi4010199
   Blazevic J., 2005, D LIB MAGAZINES, V24, P120
   Cao CH, 2021, SECUR COMMUN NETW, V2021, DOI 10.1155/2021/8527068
   Chen X.M., 2021, CHIN GLOB GOV, V2021, P10
   Curtis G. E., 2002, TRENDS ORGAN CRIME, V7, P19, DOI [10.1007/s12117-002-1011-4, DOI 10.1007/S12117-002-1011-4]
   Ditmore MH, 2011, HEALTH EDUC RES, V26, P466, DOI 10.1093/her/cyq087
   Elwert T., 2015, RECYCLING, V1, P25, DOI [10.3390/recycling1010025, DOI 10.3390/RECYCLING1010025]
   Fagiolo G., 2012, REV OFCE, V124, P67, DOI DOI 10.3917/RE0F.124.0067
   Gueorguiev D, 2012, J ASIAN ECON, V23, P111, DOI 10.1016/j.asieco.2011.11.006
   Hao S.U., 2016, CONT WORLD, V2017, P44
   Hebblewhite M, 2014, BIOL CONSERV, V178, P50, DOI 10.1016/j.biocon.2014.07.013
   Hengsdijk H, 2003, EUR J AGRON, V19, P549, DOI 10.1016/S1161-0301(03)00002-9
   Ho K.G., 2010, AGU FALL M ABSTRACTS, V12, P93
   Jing L.I., 2003, COMPUTER SCI, V9, P823
   Kil S.K., 2003, OCEAN POLICY RES, V18, P1
   Li H., 2016, P INT C BIOINSP COMP, P513
   Liu X., 2018, CHINAS FOREIGN TRADE, V565, P37
   Mutalib SNSA, 2013, ENVIRON SCI-PROC IMP, V15, P1717, DOI 10.1039/c3em00161j
   Nick C., 2002, J AM HIST, V2002, P512
   Nkem J, 2010, ENVIRON SCI POLICY, V13, P498, DOI 10.1016/j.envsci.2010.06.004
   Su H.Y., 2016, PROCEDIA SOCIAL BEHA, V2021, P42
   Su Hai, 2005, Journal of Shanghai Jiaotong University, V39, P2034
   Su H, 2011, CLIN CANCER RES, V17, P2955, DOI 10.1158/1078-0432.CCR-10-2724
   Subacchi P., 2017, CHINA, V2017, P64
   Ward P., 2011, CHINAS FOREIGN TRADE, V2011, P94
   Yi O, 2008, NANZAN REV AM STUDIE, V30, P175
NR 27
TC 0
Z9 0
U1 0
U2 0
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 JUN 10
PY 2022
VL 2022
AR 2962545
DI 10.1155/2022/2962545
PG 7
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 2G9PQ
UT WOS:000813933400007
DA 2022-08-23
ER

PT J
AU Santos, A
   Colaco, AR
   Nielsen, AB
   Niu, LL
   Strauss, M
   Geyer, PE
   Coscia, F
   Albrechtsen, NJW
   Mundt, F
   Jensen, LJ
   Mann, M
AF Santos, Alberto
   Colaco, Ana R.
   Nielsen, Annelaura B.
   Niu, Lili
   Strauss, Maximilian
   Geyer, Philipp E.
   Coscia, Fabian
   Albrechtsen, Nicolai J. Wewer
   Mundt, Filip
   Jensen, Lars Juhl
   Mann, Matthias
TI A knowledge graph to interpret clinical proteomics data
SO NATURE BIOTECHNOLOGY
LA English
DT Article
ID PEPTIDE IDENTIFICATION; GENE; INHIBITION; EXPRESSION; SIGNATURES;
   MEDICINE; SCALE
AB A knowledge graph platform integrates proteomics with other omics data and biomedical databases.
   Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making.
C1 [Santos, Alberto; Colaco, Ana R.; Nielsen, Annelaura B.; Niu, Lili; Strauss, Maximilian; Geyer, Philipp E.; Coscia, Fabian; Albrechtsen, Nicolai J. Wewer; Mundt, Filip; Jensen, Lars Juhl; Mann, Matthias] Univ Copenhagen, Fac Hlth Sci, NNF Ctr Prot Res, Copenhagen, Denmark.
   [Santos, Alberto] Univ Oxford, Li Ka Shing Big Data Inst, Oxford, England.
   [Santos, Alberto] Univ Copenhagen, Fac Hlth Sci, Ctr Hlth Data Sci, Copenhagen, Denmark.
   [Strauss, Maximilian; Geyer, Philipp E.] OmicEra Diagnost GmbH, Planegg, Germany.
   [Geyer, Philipp E.; Coscia, Fabian; Mann, Matthias] Max Planck Inst Biochem, Dept Prote & Signal Transduct, Martinsried, Germany.
   [Albrechtsen, Nicolai J. Wewer] Univ Copenhagen, Fac Hlth & Med Sci, Dept Biomed Sci, Copenhagen, Denmark.
   [Albrechtsen, Nicolai J. Wewer] Univ Copenhagen, Rigshosp, Dept Clin Biochem, Copenhagen, Denmark.
RP Santos, A; Mann, M (通讯作者)，Univ Copenhagen, Fac Hlth Sci, NNF Ctr Prot Res, Copenhagen, Denmark.; Santos, A (通讯作者)，Univ Oxford, Li Ka Shing Big Data Inst, Oxford, England.; Santos, A (通讯作者)，Univ Copenhagen, Fac Hlth Sci, Ctr Hlth Data Sci, Copenhagen, Denmark.; Mann, M (通讯作者)，Max Planck Inst Biochem, Dept Prote & Signal Transduct, Martinsried, Germany.
EM alberto.santos@sund.ku.dk; mmann@biochem.mpg.de
OI Coscia, Fabian/0000-0002-2244-5081; Jensen, Lars
   Juhl/0000-0001-7885-715X; Santos, Alberto/0000-0002-9163-7730; Wewer
   Albrechtsen, Nicolai/0000-0003-4230-5753
FU Max Planck Society
FX Open access funding provided by Max Planck Society.
CR Aebersold R, 2016, NATURE, V537, P347, DOI 10.1038/nature19949
   Ahrens M, 2013, CELL METAB, V18, P296, DOI 10.1016/j.cmet.2013.07.004
   Albrechtsen NJW, 2018, CELL SYST, V7, P601, DOI 10.1016/j.cels.2018.10.012
   [Anonymous], 2016, NATURE, V533, P437, DOI 10.1038/533437a
   Apweiler R, 2004, NUCLEIC ACIDS RES, V32, pD115, DOI 10.1093/nar/gkh131
   Archer TC, 2018, CANCER CELL, V34, P396, DOI 10.1016/j.ccell.2018.08.004
   Baker M, 2016, NATURE, V533, P452, DOI 10.1038/533452a
   Balaur I, 2017, BIOINFORMATICS, V33, P1096, DOI 10.1093/bioinformatics/btw731
   Barabasi AL, 2004, NAT REV GENET, V5, P101, DOI 10.1038/nrg1272
   Barabasi AL, 2011, NAT REV GENET, V12, P56, DOI 10.1038/nrg2918
   Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008
   Bonawitz K., 2019, ARXIV190201046
   Brisimi TS, 2018, INT J MED INFORM, V112, P59, DOI 10.1016/j.ijmedinf.2018.01.007
   Bruderer R, 2015, MOL CELL PROTEOMICS, V14, P1400, DOI 10.1074/mcp.M114.044305
   Chami Ines, 2020, ARXIV200503675
   Chen FX, 2020, APSIPA TRANS SIGNAL, V9, DOI 10.1017/ATSIP.2020.13
   Cheng FX, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-05116-5
   Choi M, 2014, BIOINFORMATICS, V30, P2524, DOI 10.1093/bioinformatics/btu305
   Cook HV, 2019, METHODS MOL BIOL, V1939, P73, DOI 10.1007/978-1-4939-9089-4_5
   Corsello SM, 2020, NAT CANCER, V1, P235, DOI 10.1038/s43018-019-0018-6
   Coscia F, 2018, CELL, V175, P159, DOI 10.1016/j.cell.2018.08.065
   Cox J, 2008, NAT BIOTECHNOL, V26, P1367, DOI 10.1038/nbt.1511
   Demichev V, 2020, NAT METHODS, V17, P41, DOI 10.1038/s41592-019-0638-x
   Deshmukh AS, 2019, CELL METAB, V30, P963, DOI 10.1016/j.cmet.2019.10.001
   Doll S, 2019, PROTEOM CLIN APPL, V13, DOI 10.1002/prca.201800113
   Doll S, 2018, MOL ONCOL, V12, P1296, DOI 10.1002/1878-0261.12326
   Fabregat A, 2018, PLOS COMPUT BIOL, V14, DOI 10.1371/journal.pcbi.1005968
   Filbin MR, 2021, CELL REP MED, V2, DOI 10.1016/j.xcrm.2021.100287
   Geyer PE, 2019, EMBO MOL MED, V11, DOI 10.15252/emmm.201910427
   Geyer PE, 2017, MOL SYST BIOL, V13, DOI 10.15252/msb.20156297
   Gingras AC, 2005, MOL CELL PROTEOMICS, V4, P1725, DOI 10.1074/mcp.M500231-MCP200
   Giurgiu M, 2019, NUCLEIC ACIDS RES, V47, pD559, DOI 10.1093/nar/gky973
   Griss J, 2014, MOL CELL PROTEOMICS, V13, P2765, DOI 10.1074/mcp.O113.036681
   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
   Hagberg A. A., 2008, P 7 PYTH SCI C PAS, P11
   Halu A, 2019, NPJ SYST BIOL APPL, V5, DOI 10.1038/s41540-019-0092-5
   Han JF, 2015, AM J CANCER RES, V5, P945
   Have CT, 2013, BIOINFORMATICS, V29, P3107, DOI 10.1093/bioinformatics/btt549
   Hein MY, 2015, CELL, V163, P712, DOI 10.1016/j.cell.2015.09.053
   Henke E, 2020, FRONT MOL BIOSCI, V6, DOI 10.3389/fmolb.2019.00160
   Himmelstein DS, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004259
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Hornbeck PV, 2015, NUCLEIC ACIDS RES, V43, pD512, DOI 10.1093/nar/gku1267
   Kalin JH, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-017-02242-4
   Kong AT, 2017, NAT METHODS, V14, P513, DOI [10.1038/NMETH.4256, 10.1038/nmeth.4256]
   Lamb J, 2006, SCIENCE, V313, P1929, DOI 10.1126/science.1132939
   Langfelder P, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-559
   Lazar C, 2016, J PROTEOME RES, V15, P1116, DOI 10.1021/acs.jproteome.5b00981
   Lee JSH, 2018, CELL, V174, P1045, DOI 10.1016/j.cell.2018.08.012
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Leopold JA, 2018, CIRC RES, V122, P1302, DOI 10.1161/CIRCRESAHA.117.310782
   Liben-Nowell D., 2004, LINK PREDICTION PROB
   Mann M, 2021, CELL SYST, V12, P759, DOI 10.1016/j.cels.2021.06.006
   Mendez KM, 2019, METABOLOMICS, V15, DOI 10.1007/s11306-019-1588-0
   Mimasu S, 2010, BIOCHEMISTRY-US, V49, P6494, DOI 10.1021/bi100299r
   Muller JB, 2020, NATURE, V582, P592, DOI 10.1038/s41586-020-2402-x
   Mughal S, 2017, BIOINFORMATICS, V33, P3317, DOI 10.1093/bioinformatics/btx397
   Nahnsen S, 2013, MOL CELL PROTEOMICS, V12, P549, DOI 10.1074/mcp.R112.025163
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Niu L, 2019, MOL SYST BIOL, V15, DOI 10.15252/msb.20188793
   Nowak-Sliwinska P, 2019, BBA-REV CANCER, V1871, P434, DOI 10.1016/j.bbcan.2019.04.005
   Pafilis E, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0065390
   Pan J, 2020, NATURE, V578, P559, DOI 10.1038/s41586-020-2016-3
   Pei G, 2017, METHOD ENZYMOL, V585, P135, DOI 10.1016/bs.mie.2016.09.016
   Perez-Riverol Y, 2020, J PROTEOME RES, V19, P3906, DOI 10.1021/acs.jproteome.0c00376
   Perfetto L, 2016, NUCLEIC ACIDS RES, V44, pD548, DOI 10.1093/nar/gkv1048
   Perkel JM, 2018, NATURE, V563, P145, DOI 10.1038/d41586-018-07196-1
   Pinero J, 2020, NUCLEIC ACIDS RES, V48, pD845, DOI 10.1093/nar/gkz1021
   Pletscher-Frankild S, 2015, METHODS, V74, P83, DOI 10.1016/j.ymeth.2014.11.020
   Pushpakom S, 2019, NAT REV DRUG DISCOV, V18, P41, DOI 10.1038/nrd.2018.168
   Rafehi Haloom, 2016, Non-Coding RNA, V2, P4, DOI 10.3390/ncrna2020004
   Rule A, 2019, PLOS COMPUT BIOL, V15, DOI 10.1371/journal.pcbi.1007007
   Sarvari J, 2013, HEPAT MON, V13, DOI 10.5812/hepatmon.8351
   Shannon P, 2003, GENOME RES, V13, P2498, DOI 10.1101/gr.1239303
   Shirakami Y, 2012, BBA-MOL CELL BIOL L, V1821, P124, DOI 10.1016/j.bbalip.2011.06.023
   Strogatz SH, 2001, NATURE, V410, P268, DOI 10.1038/35065725
   Tusher VG, 2001, P NATL ACAD SCI USA, V98, P5116, DOI 10.1073/pnas.091062498
   Tyanova S, 2016, NAT METHODS, V13, P731, DOI [10.1038/nmeth.3901, 10.1038/NMETH.3901]
   Vashishta A, 2014, NEUROMOL MED, V16, P727, DOI 10.1007/s12017-014-8322-x
   Veselkov K, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-45349-y
   Virtanen P, 2020, NAT METHODS, V17, P261, DOI 10.1038/s41592-019-0686-2
   Wang XQ, 2013, MOL CARCINOGEN, V52, P218, DOI 10.1002/mc.21846
   Wilkinson Mark D, 2016, Sci Data, V3, P160018, DOI 10.1038/sdata.2016.18
   Wittenbecher C, 2019, DIABETES, V68, P188, DOI 10.2337/db18-0620
   Yoon Byoung-Ha, 2017, Genomics & Informatics, V15, P19, DOI 10.5808/GI.2017.15.1.19
NR 85
TC 5
Z9 5
U1 38
U2 41
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 1087-0156
EI 1546-1696
J9 NAT BIOTECHNOL
JI Nat. Biotechnol.
PD MAY
PY 2022
VL 40
IS 5
BP 692
EP +
DI 10.1038/s41587-021-01145-6
EA JAN 2022
PG 14
WC Biotechnology & Applied Microbiology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biotechnology & Applied Microbiology
GA 1L7FB
UT WOS:000750630900002
PM 35102292
OA Green Published, hybrid, Green Accepted
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Novak, J
   Wurst, M
AF Novak, J
   Wurst, M
TI Supporting knowledge creation and sharing in communities based on
   mapping implicit knowledge
SO JOURNAL OF UNIVERSAL COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 3rd International Conference on Knowledge Management (I-Know 03)
CY JUL 02-04, 2003
CL Graz, AUSTRIA
SP Know Ctr
DE knowledge management; communities of practice; knowledge discovery;
   knowledge visualisation; semantic web
AB This paper discusses some implications of knowledge creation processes in informal social networks for the development of technologies to support them. The principal point of departure are social theories of learning and the theories of organisational knowledge creation. The focus is on models for the exchange and sharing of implicit knowledge. A model of personalised learning knowledge maps is presented as one possible way of addressing the problem of capturing, visualising and sharing implicit knowledge of a community of users. In particular, we discuss how this model resolves one critical shortcoming of the existing socialisation and externalisation approaches: the creation of a semantic representation of a shared understanding of the community which reflects implicit knowledge and incorporates personal views of individual users. Finally, we outline the application to a real-world interdisciplinary Internet platform netzspannung.org.
C1 Fraunhofer Inst Media Commun, MARS Exploratory Media Lab, Schloss Birlinghoven, Germany.
   Univ Dortmund, Dept Artificial Intelligence, Dortmund, Germany.
RP Novak, J (通讯作者)，Fraunhofer Inst Media Commun, MARS Exploratory Media Lab, Schloss Birlinghoven, Germany.
EM j.novak@imk.frauenhofer.de; wurst@ls8.cs.uni-dortmund.de
CR AHA DW, 1991, MACH LEARN, V6, P37, DOI 10.1023/A:1022689900470
   BECKS A, 2000, P AVI 2000 PAL
   BROWN LA, 1991, CYTOPATHOLOGY, V2, P1, DOI 10.1111/j.1365-2303.1991.tb00377.x
   BRUCKMANN A, 1993, P 3CYBERC 3 INT C CY
   Bruner J.S.., 1990, ACTS MEANING
   Castells M., 1996, RISE NETWORK SOC
   CHALMERS M, 2001, DELOS WORKSH PERS RE
   Davenport TH., 1998, WORKING KNOWLEDGE OR
   ERICKSON T, 2001, KNOWLEDGE MANAGEMENT
   FLEISCHMANN M, 2001, P CAST01
   GRATHER W, 2001, P INT ACM SIGGROUP C
   HERLOCKER K, 2000, P CSCW 2000 DEC
   HONELKA T, 1997, THESIS HELSINKI U TE
   JOACHIMS T, 1997, P IJCAI
   Kelly George., 2020, PSYCHOL PERSONAL CON
   Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729
   Lave J., 1991, SITUATED LEARNING LE
   LIN X, 1991, P 14 ACM SIGIR C
   Luckmann, 1966, SOCIAL CONSTRUCTION
   Nonaka I., 1995, KNOWLEDGE CREATING C
   NOVAK J, 2003, P 3 INT C KNOWL MAN
   NOVAK J, 2003, SPRINGER LECT NOTES
   NOVAK J, 2002, P IEEE 4 INT WORKSH
   Orr Julian, 1996, TALKING MACHINES ETH
   Papert S., 1990, INTRO CONSTRUCTIONIS
   Papert S., 1980, MINDSTORMS CHILDREN
   POLANYI M, 1973, TACIT DIMENSION
   Polanyi M, 1958, PERSONAL KNOWLEDGE
   Prusak L, 2001, GOOD CO SOCIAL CAPIT
   RESNICK P, 1909, P ACM 1994 C CSCW CH
   RHEINGOLD H, 1993, VIRTUAL COMMUNITIES
   SACK W, 2000, J MANAGEMENT INF WIN
   SHARDANAND S, 1995, P CHI 95
   SHAW ML, 1995, P CSCL
   Turkle Sherry, 1995, LIFE SCREEN IDENTITY
   Vygotsky L. S., 1962, THOUGHT AND LANGUAGE
   Wenger E, 1998, COMMUNITIES PRACTICE
   ZIEGLER J, 2002, P INT C COMP HUM INT
NR 38
TC 14
Z9 14
U1 0
U2 4
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 2004
VL 10
IS 3
BP 235
EP 251
PG 17
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA 958GJ
UT WOS:000231432400007
DA 2022-08-23
ER

PT J
AU Pan, HL
   Yang, XH
AF Pan, Hailan
   Yang, Xiaohuan
TI Intelligent recommendation method integrating knowledge graph and
   Bayesian network
SO SOFT COMPUTING
LA English
DT Article; Early Access
DE Knowledge graph; Bayesian network; Intelligent recommendation algorithm;
   Artificial intelligence
AB In recent years, the amount of Internet information data has exploded, and the problem of "information overload" has become a huge challenge for Internet development. Through the prior knowledge of the known data distribution, combined with sample training data to estimate the mathematical model of the overall data. Intelligent recommendation methods based on knowledge graphs and Bayesian networks are a hot spot in the current Internet research, and they are of great significance to search engines and e-commerce. This paper studies the fusion of knowledge graph and Bayesian belief network and its application in personalized recommendation and designs a Bayesian network that describes the purchase behavior of users based on the purchase records of customers across the network. User recommendation is the process of fusing knowledge graphs and Bayesian network reasoning. For the design of the recommendation network, a random reasoning algorithm with approximate reasoning is selected in consideration of calculation scale and accuracy. This paper uses the content filtering technology of KNN algorithm to supplement the sparse matrix, which can improve the quality of collaborative filtering recommendation, thereby improving the quality of final recommendation. In this paper, based on the knowledge graph, the EPCA algorithm and the robust recommendation algorithm are merged to form a robust collaborative recommendation model based on Bayesian probability matrix decomposition. The robust recommendation algorithm further improves the robustness of the recommendation algorithm by introducing the suspected users and target items detected by the EPCA algorithm. Research shows that the PS value of the VarSelect SVD algorithm used in this paper is approximately between 0.36 and 1.04. Compared with the first two algorithms, the PS value of VarSelect SVD has been significantly reduced, and the algorithm robustness has been significantly improved.
C1 [Pan, Hailan] Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China.
   [Pan, Hailan] Shanghai Polytech Univ, Res Ctr Resource Recycling Sci & Engn, Shanghai, Peoples R China.
   [Pan, Hailan] Shanghai Polytech Univ, Sch Econ & Management, Shanghai, Peoples R China.
   [Yang, Xiaohuan] Cosco Shipping Technol CO Ltd, Shanghai, Peoples R China.
RP Pan, HL (通讯作者)，Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China.; Pan, HL (通讯作者)，Shanghai Polytech Univ, Res Ctr Resource Recycling Sci & Engn, Shanghai, Peoples R China.; Pan, HL (通讯作者)，Shanghai Polytech Univ, Sch Econ & Management, Shanghai, Peoples R China.
EM panhailan@sspu.edu.cn; yang.xiaohuan@coscoshipping.com
FU Gaoyuan Discipline of Shanghai-Environmental Science and Engineering
   (Resource Recycling Science and Engineering), Discipline of Management
   Science and Engineering of Shanghai Polytechnic University [XXKPY1606]
FX The work was supported in part by Gaoyuan Discipline of
   Shanghai-Environmental Science and Engineering (Resource Recycling
   Science and Engineering), Discipline of Management Science and
   Engineering of Shanghai Polytechnic University (Grant No. XXKPY1606).
CR Amirkhani H, 2017, IEEE T PATTERN ANAL, V39, P2154, DOI 10.1109/TPAMI.2016.2636828
   Bothorel C, 2015, NETW SCI, V3, P408, DOI 10.1017/nws.2015.9
   Caimo A, 2015, J MANAGE, V41, P665, DOI 10.1177/0149206314552192
   Dai HP, 2018, IEEE ACM T NETWORK, V26, P520, DOI 10.1109/TNET.2018.2793949
   Fotiadis DI, 2016, IEEE J BIOMED HEALTH, P1
   Gong J, 2011, ADV ENG INFORM, V25, P771, DOI 10.1016/j.aei.2011.06.002
   Gutierrez BT, 2015, J GEOPHYS RES-EARTH, V120, P2452, DOI 10.1002/2015JF003671
   He CC, 2020, CHINESE J ELECTRON, V29, P147, DOI 10.1049/cje.2019.11.004
   He LH, 2016, IEEE T SYST MAN CY-S, V46, P843, DOI 10.1109/TSMC.2015.2450680
   Jing-Xing G., 2015, J SYST MANAG, V108, P289
   Jones E, 2017, INT J UNCERTAIN FUZZ, V25, P349, DOI 10.1142/S0218488517500143
   Kourou K, 2017, IEEE J BIOMED HEALTH, V21, P320, DOI 10.1109/JBHI.2016.2636448
   Lessan J, 2019, COMPUT IND ENG, V127, P1214, DOI 10.1016/j.cie.2018.03.017
   Li H, 2019, INTELL DATA ANAL, V23, P41, DOI 10.3233/IDA-183877
   Ma YF, 2014, J BRIDGE ENG, V19, DOI 10.1061/(ASCE)BE.1943-5592.0000611
   Maiti S, 2020, NEURAL COMPUT APPL, V32, P3183, DOI 10.1007/s00521-019-04276-9
   McNally RJ, 2017, PSYCHOL MED, V47, P1204, DOI 10.1017/S0033291716003287
   O'Gorman B, 2015, EUR PHYS J-SPEC TOP, V224, P163, DOI 10.1140/epjst/e2015-02349-9
   Schirle M, 2016, DRUG DISCOV TODAY, V21, P82, DOI 10.1016/j.drudis.2015.08.001
   Tjarnberg A., 2015, WIRELESS NETW, V21, P1934, DOI [10.1007/s11276-014-0884-x, DOI 10.1007/S11276-014-0884-X]
   Tschiatschek S, 2015, IEEE T PATTERN ANAL, V37, P774, DOI 10.1109/TPAMI.2014.2353620
   Wang Z, 2015, IEEE T MED IMAGING, V34, P1549, DOI 10.1109/TMI.2015.2405341
   Xie XF, 2016, AM J KIDNEY DIS, V67, P728, DOI 10.1053/j.ajkd.2015.10.011
   Xu Z., 2020, J SURVEILLANCE SECUR, V1, P16, DOI DOI 10.20517/JSSS.2020.04
   Yang YL, 2019, NEURAL COMPUT APPL, V31, P4819, DOI 10.1007/s00521-017-3323-y
NR 25
TC 2
Z9 2
U1 11
U2 32
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/s00500-021-05735-z
EA MAR 2021
PG 10
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RC3WI
UT WOS:000632734400001
DA 2022-08-23
ER

PT J
AU Sang, L
   Xu, M
   Qian, SS
   Wu, XD
AF Sang, Lei
   Xu, Min
   Qian, Shengsheng
   Wu, Xindong
TI Knowledge Graph enhanced Neural Collaborative Filtering with Residual
   Recurrent Network
SO NEUROCOMPUTING
LA English
DT Article
DE Recommendation system; Knowledge Graph; Relational Path Embedding;
   Neural Collaborative Filtering; Residual Recurrent Network
ID RECOMMENDATION
AB Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities' long-term relational dependencies for the recommendation. Specially, entity embedding learning is not properly designed to combine user item interaction information with KG context information. In this paper, a two-channel neural interaction method named Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark data sets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation. CO 2021 Published by Elsevier B.V.
C1 [Sang, Lei] Anhui Univ, Sch Comp Sci & Technol, Hefei 230000, Anhui, Peoples R China.
   [Sang, Lei; Xu, Min] Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia.
   [Sang, Lei; Wu, Xindong] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China.
   [Qian, Shengsheng] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China.
   [Wu, Xindong] Hefei Univ Technol, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei 230009, Peoples R China.
RP Xu, M (通讯作者)，Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia.
EM Min.Xu@uts.edu.au
RI Wu, Xindong/AAB-6713-2022
OI Wu, Xindong/0000-0003-2396-1704
FU programs for Innovative Research Team in University of the Ministry of
   Education [IRT_17R32]; National Key Research and Development Program of
   China [2016YFB1000901]; National Natural Science Foundation of China
   [61673152, 91746209, 428]
FX This work was supported in part by the programs for Innovative Research
   Team in University of the Ministry of Education under Grant IRT_17R32,
   National Key Research and Development Program of China, under grant
   2016YFB1000901, and in part by the National Natural Science Foundation
   of China under Grant 61673152 and Grant 91746209. 428
CR Bai T, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1979, DOI 10.1145/3132847.3133083
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao Y., 2019, ARXIV PREPRINT ARXIV
   Chen SL, 2018, KNOWL-BASED SYST, V158, P109, DOI 10.1016/j.knosys.2018.05.040
   Cheng H.T., 2016, P 1 WORKSH DEEP LEAR, P7
   Chua T.S., 2018, ARXIV PREPRINT ARXIV
   Deng Z.H., 2019, ARXIV PREPRINT ARXIV
   Funk S, 2006, NETFLIX UPDATE TRY T
   Guo GB, 2017, KNOWL-BASED SYST, V138, P202, DOI 10.1016/j.knosys.2017.10.005
   Guo LB, 2019, PR MACH LEARN RES, V97
   Gutmann M, 2010, P 13 INT C ART INT S, V9, P297, DOI DOI 10.1145/3292500.3330651
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   He X., 2018, ARXIV PREPRINT ARXIV
   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
   Hu GN, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P667, DOI 10.1145/3269206.3271684
   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
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   Li J, 2017, KNOWL-BASED SYST, V127, P58, DOI 10.1016/j.knosys.2017.02.032
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HT, 2020, NEUROCOMPUTING, V374, P77, DOI 10.1016/j.neucom.2019.09.052
   Ma H, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P73
   Musto C, 2017, KNOWL-BASED SYST, V136, P1, DOI 10.1016/j.knosys.2017.08.015
   Pan WK, 2016, NEUROCOMPUTING, V177, P447, DOI 10.1016/j.neucom.2015.11.059
   Rendle S., P 25 C UNC ART INT A P 25 C UNC ART INT A
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Salakhutdinov R., 2008, P INT C MACH LEARN, P880, DOI [10.1145/1390156., DOI 10.1145/1390156.1390267]
   Sang L., 2020, EXPERT SYST APPL
   Sang L., 2020, IEEE T MULTIMEDIA
   Sang L, 2019, NEUROCOMPUTING, V334, P44, DOI 10.1016/j.neucom.2018.12.067
   Sedhain S, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P111, DOI 10.1145/2740908.2742726
   Shen TC, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1611
   Shi C., 2019, AAAI
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Tay Y., 2018, P 27 INT C WORLD WID
   Vaswani A, 2017, ADV NEUR IN, V30
   Velic P., 2017, ARXIV PREPRINT ARXIV
   Wang C., 2018, IEEE T MULTIMEDIA, V10, P1, DOI DOI 10.3390/rs11010063
   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, 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, 2017, SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P185, DOI 10.1145/3077136.3080771
   Wu L, 2019, IEEE T MULTIMEDIA, V21, P1412, DOI 10.1109/TMM.2018.2877886
   Xue Hong-Jian, 2017, P 26 INT JOINT C ART
   Yang Z, 2018, NEUROCOMPUTING, V278, P126, DOI 10.1016/j.neucom.2017.04.080
   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 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
NR 54
TC 2
Z9 2
U1 3
U2 16
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD SEP 24
PY 2021
VL 454
BP 417
EP 429
DI 10.1016/j.neucom.2021.03.053
EA MAY 2021
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TI0KA
UT WOS:000672468800015
DA 2022-08-23
ER

PT J
AU Yahya, M
   Breslin, JG
   Ali, MI
AF Yahya, Muhammad
   Breslin, John G.
   Ali, Muhammad Intizar
TI Semantic Web and Knowledge Graphs for Industry 4.0
SO APPLIED SCIENCES-BASEL
LA English
DT Review
DE smart manufacturing; industry 4; 0 knowledge graph; industry 4; 0
   semantic Modelling
ID ONTOLOGY-BASED APPROACH; FUZZY-LOGIC INFERENCE; PREDICTIVE MAINTENANCE;
   ELECTRIC VEHICLES; INTEROPERABILITY; MANAGEMENT; PRODUCT; QUALITY;
   POWER; OPTIMIZATION
AB In recent years, due to technological advancements, the concept of Industry 4.0 (I4.0) is gaining popularity, while presenting several technical challenges being tackled by both the industrial and academic research communities. Semantic Web including Knowledge Graphs is a promising technology that can play a significant role in realizing I4.0 implementations. This paper surveys the use of the Semantic Web and Knowledge Graphs for I4.0 from different perspectives such as managing information related to equipment maintenance, resource optimization, and the provision of on-time and on-demand production and services. Moreover, to solve the challenges of limited depth and expressiveness in the current ontologies, we have proposed an enhanced reference generalized ontological model (RGOM) based on Reference Architecture Model for I4.0 (RAMI 4.0). RGOM can facilitate a range of I4.0 concepts including improved asset monitoring, production enhancement, reconfiguration of resources, process optimizations, product orders and deliveries, and the life cycle of products. Our proposed RGOM can be used to generate a knowledge graph capable of providing answers in response to any real-time query.
C1 [Yahya, Muhammad; Breslin, John G.] Natl Univ Ireland, Data Sci Inst, Galway, Ireland.
   [Ali, Muhammad Intizar] Dublin City Univ, Dublin, Ireland.
RP Yahya, M (通讯作者)，Natl Univ Ireland, Data Sci Inst, Galway, Ireland.
EM m.yahya1@nuigalway.ie; john.breslin@nuigalway.ie;
   ali.intizar@insight-centre.org
RI Yahya, Muhammad/GLR-3672-2022; Breslin, John/B-7025-2015
OI Breslin, John/0000-0001-5790-050X; yahya, muhammad/0000-0002-5766-5172;
   yahya, muhammad/0000-0002-8656-1938; Ali, Muhammad
   Intizar/0000-0002-0674-2131
FU Science Foundation Ireland (SFI) [SFI/16/RC/3918]; Science Foundation
   Ireland (SFI) - European Regional Development Fund
FX This publication has emanated from research supported in part by a
   research grant from Science Foundation Ireland (SFI) under GrantNumber
   SFI/16/RC/3918 (Confirm), and also by a research grant from Science
   Foundation Ireland (SFI) under Grant NumberSFI/12/RC/2289_P2 (Insight),
   with both grants co-funded by the European Regional Development Fund.
CR Ahmad S, 2018, WIREL COMMUN MOB COM, V2018, DOI 10.1155/2018/6951318
   Ahmed Z., 2010, ARXIV10081723
   Aljumaili M, 2015, J QUAL MAINT ENG, V21, P358, DOI 10.1108/JQME-09-2014-0048
   [Anonymous], 2016, INT C EM TECHN FACT
   Bader Sebastian R., 2019, Semantic Systems. The Power of AI and Knowledge Graphs. 15th International Conference, SEMANTiCS 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11702), P159, DOI 10.1007/978-3-030-33220-4_12
   Borgo S., 2019, ONTOLOGIES, P751, DOI [DOI 10.1007/978-0-387-37022-4_27, 10.1007/978-0-387-37022-4_27]
   Broy M., 2012, AGENDACPS INTEGRATED
   Buchgeher G, 2021, IEEE ACCESS, V9, P55537, DOI 10.1109/ACCESS.2021.3070395
   Cao QS, 2020, SEMANT WEB, V11, P927, DOI 10.3233/SW-200406
   Cao QS, 2019, PROCEDIA COMPUT SCI, V159, P630, DOI 10.1016/j.procs.2019.09.218
   Chen D, 2008, COMPUT IND, V59, P647, DOI 10.1016/j.compind.2007.12.016
   Cheng HB, 2017, IEEE INTL CONF IND I, P351, DOI 10.1109/INDIN.2017.8104797
   Cheng HB, 2016, PROCEEDINGS 2016 THIRD INTERNATIONAL CONFERENCE ON TRUSTWORTHY SYSTEMS AND THEIR APPLICATIONS (TSA), P42, DOI 10.1109/TSA.2016.17
   Chien CF, 2005, ISSM 2005: IEEE International Symposium on Semiconductor Manufacturing, Conference Proceedings, P327, DOI 10.1109/ISSM.2005.1513369
   Chungoora N, 2013, INT J PROD RES, V51, P327, DOI 10.1080/00207543.2011.627885
   Compton M, 2012, J WEB SEMANT, V17, P25, DOI 10.1016/j.websem.2012.05.003
   Corcho O., 2004, ADV INF KNOWL PROCES
   Darmois E., 2016, IOT STANDARDS STATE, P237
   der Mauer M.A., 2019, DIGITALIZATION CASES, P79
   Du J, 2020, INFORM SYST FRONT, V22, P1467, DOI 10.1007/s10796-019-09941-x
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Erboz G., 2017, P 7 INT C MAN ICOM 2, P1
   Ferrer BR, 2017, IEEE IND ELEC, P3466, DOI 10.1109/IECON.2017.8216587
   Ferrer BR, 2016, AT-AUTOM, V64, P231, DOI 10.1515/auto-2015-0073
   Gimenez DA, 2008, COMPUT IND, V59, P231, DOI 10.1016/j.compind.2007.06.019
   Giustozzi F, 2018, PROCEDIA COMPUT SCI, V126, P675, DOI 10.1016/j.procs.2018.08.001
   Grangel-Gonzalez Irlan, 2016, 2016 IEEE Tenth International Conference on Semantic Computing (ICSC). Proceedings, P230, DOI 10.1109/ICSC.2016.58
   Grangel-Gonzalez I., 2017, P 22 IEEE INT C EM T, P1
   Grangel-Gonzalez I, 2020, IEEE INT C EMERG, P93, DOI 10.1109/ETFA46521.2020.9212156
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Gruninger M., 2009, HDB ONTOLOGIES, P423, DOI DOI 10.1007/978-3-540-92673-3_19
   Hatefi SM, 2015, INT J COMPUT INTEG M, V28, P664, DOI 10.1080/0951192X.2014.900863
   Hendi H., 2014, P 4 INT C COMPL SYST
   Herzog R, 2016, AT-AUTOM, V64, P742, DOI 10.1515/auto-2016-0067
   Hobbs J.R., 2006, TIME ONTOLOGY OWL W3
   Hodges J, 2017, COMPUTER, V50, P26, DOI 10.1109/MC.2017.4041353
   Hoppe T., 2017, P IEEE INT SYST ENG, P1
   Hussain S, 2020, ENERGIES, V13, DOI 10.3390/en13184634
   Hussain S, 2020, ENERGIES, V13, DOI 10.3390/en13123119
   Hussain S, 2019, IEEE ACCESS, V7, P65467, DOI 10.1109/ACCESS.2019.2917297
   Jarvenpaa E, 2019, J INTELL MANUF, V30, P959, DOI 10.1007/s10845-018-1427-6
   Jarvenpaa E, 2016, 2016 IEEE INTERNATIONAL SYMPOSIUM ON ASSEMBLY AND MANUFACTURING (ISAM), P120, DOI 10.1109/ISAM.2016.7750724
   Ramirez-Duran VJ, 2020, SEMANT WEB, V11, P887, DOI 10.3233/SW-200376
   Kaar C, 2018, P 10 INT C SUBJ OR B, P1
   Kagermann H, 2013, UMSETZUNGSEMPFEHLUNG
   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
   Karray MH, 2012, APPL ONTOL, V7, P269, DOI 10.3233/AO-2012-0112
   Kovalenko O., 2018, IOS PRESS J
   Kumar N., 2019, J HUMAN TECHNOLOGY, V15, P55, DOI [10.17011/ht/urn.201902201608, DOI 10.17011/HT/URN.201902201608]
   Kumar VRS, 2019, KNOWL ENG REV, V34, DOI 10.1017/S0269888919000109
   Lasi H, 2014, BUS INFORM SYST ENG+, V6, P239, DOI 10.1007/s12599-014-0334-4
   Lemaignan S, 2006, DIS 2006: IEEE WORKSHOP ON DISTRIBUTED INTELLIGENT SYSTEMS: COLLECTIVE INTELLIGENCE AND ITS APPLICATIONS, PROCEEDINGS, P195
   Ling J, 2017, INTEGR MATER MANUF I, V6, P207, DOI 10.1007/s40192-017-0098-z
   Lu Y, 2015, IEEE INT CON AUTO SC, P998, DOI 10.1109/CoASE.2015.7294229
   Lusa A, 2008, EUR J IND ENG, V2, P50, DOI 10.1504/EJIE.2008.016329
   Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602
   Mazzola L, 2016, KEOD: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 2, P136, DOI 10.5220/0006056301360143
   Mourtzis D, 2014, PROC CIRP, V17, P499, DOI 10.1016/j.procir.2014.01.087
   Nishioka Y., 2015, IND VALUE CHAIN INIT
   Olszewska JI, 2017, IEEE ROMAN, P189, DOI 10.1109/ROMAN.2017.8172300
   Pal K., 2019, P BIG DAT KNOWL SHAR, P71
   Pal K, 2018, PROCEDIA COMPUT SCI, V130, P985, DOI 10.1016/j.procs.2018.04.101
   Pal K, 2017, PROCEDIA COMPUT SCI, V109, P999, DOI 10.1016/j.procs.2017.05.442
   Panetto H, 2012, ADV ENG INFORM, V26, P334, DOI 10.1016/j.aei.2011.12.002
   Park J., 2015, KOREA SMART FACTORY
   Petersen Niklas, 2016, Semantic Technology. 6th Joint International Conference, JIST 2016. Revised Selected Papers: LNCS 10055, P315, DOI 10.1007/978-3-319-50112-3_24
   Prestes E, 2013, ROBOT AUTON SYST, V61, P1193, DOI 10.1016/j.robot.2013.04.005
   Pullmann J., 2017, ONTOLOGY BASED INFOR, P1
   Ray SR, 2006, J INTELL MANUF, V17, P681, DOI 10.1007/s10845-006-0037-x
   Rivas A, 2020, LECT NOTES COMPUT SC, V12392, P179, DOI 10.1007/978-3-030-59051-2_12
   Rut J., 2016, AUTOBUSY TECH EKSPLO, V17, P1841
   Sabilla Shoffi Izza, 2018, 2018 International Conference on Information and Communications Technology (ICOIACT), P244, DOI 10.1109/ICOIACT.2018.8350727
   Sadati N, 2018, EXPERT SYST APPL, V93, P456, DOI 10.1016/j.eswa.2017.10.028
   Saeidlou S, 2017, PROD MANUF RES, V5, DOI 10.1080/21693277.2017.1374887
   Schmidt B, 2017, PROC CIRP, V62, P583, DOI 10.1016/j.procir.2016.06.047
   Seyedamir A, 2018, IEEE INTL CONF IND I, P374, DOI 10.1109/INDIN.2018.8471929
   Shekhar C, 2017, ARAB J SCI ENG, V42, P4833, DOI 10.1007/s13369-017-2636-6
   Singh S, 2019, INT J COMPUT INTEG M, V32, P642, DOI 10.1080/0951192X.2019.1599443
   Sjodin DR, 2018, RES TECHNOL MANAGE, V61, P22, DOI 10.1080/08956308.2018.1471277
   Teslya N., 2018, P MATEC WEB C EDP SC, VVolume 161, P03027
   The Great Soviet Encyclopedia, PROD LIN MAN, V3rd
   Tranfield D, 2003, BRIT J MANAGE, V14, P207, DOI 10.1111/1467-8551.00375
   Uddin MK, 2012, ASSEMBLY AUTOM, V32, P163, DOI 10.1108/01445151211212316
   Ustundag A, 2018, SPRINGER SER ADV MAN, P1, DOI 10.1007/978-3-319-57870-5
   Vegetti M, 2011, ENG APPL ARTIF INTEL, V24, P1305, DOI 10.1016/j.engappai.2011.02.014
   Wan JF, 2018, IEEE-ASME T MECH, V23, P2537, DOI 10.1109/TMECH.2018.2814784
   Wan JF, 2019, IEEE T IND INFORM, V15, P507, DOI 10.1109/TII.2018.2843811
   Wan JF, 2018, IEEE COMMUN MAG, V56, P52, DOI 10.1109/MCOM.2018.1700629
   Wang SS, 2019, J INF SCI, V45, P283, DOI 10.1177/0165551518787693
   Wang SY, 2016, INT J DISTRIB SENS N, DOI 10.1155/2016/3159805
   Wang SY, 2016, COMPUT NETW, V101, P158, DOI 10.1016/j.comnet.2015.12.017
   Wegener P.D., 2018, GERMAN STANDARDIZATI
   Williams D., 2020, SOFTW QUAL PROF, V22, P26
   Xavier A, 2016, INDEP J MANAG PROD, V7, P606, DOI 10.14807/ijmp.v7i5.453
   Zeid A, 2019, MACHINES, V7, DOI 10.3390/machines7020021
   Zhou KL, 2015, 2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), P2147, DOI 10.1109/FSKD.2015.7382284
NR 96
TC 7
Z9 7
U1 16
U2 42
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 JUN
PY 2021
VL 11
IS 11
AR 5110
DI 10.3390/app11115110
PG 23
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA SP3PI
UT WOS:000659583800001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, J
   Chen, YG
   Zhang, XL
   Du, XY
   Wang, K
   Wen, JR
AF Chen, Jun
   Chen, Yueguo
   Zhang, Xiangling
   Du, Xiaoyong
   Wang, Ke
   Wen, Ji-Rong
TI Entity set expansion with semantic features of knowledge graphs
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge graph; Semantic feature; Entity set expansion; Semantic
   search; Ranking model
ID WEB
AB A large-scale knowledge graph contains a huge number of path-based semantic features, which provides a flexible mechanism to assign and expand semantics/attributes to entities. A particular set of these semantic features can be exploited on the fly, to support particular entity-oriented semantic search tasks. In this paper, we use entity set expansion as an example to show how these path-based semantic features can be effectively utilized in a semantic search application. The entity set expansion problem is to expand a small set of seed entities to a more complete set of similar entities. Traditionally, people solve this problem by exploiting the statistical co-occurrence of entities in the web pages, where semantic correlation among the seed entities is not well exploited. We propose to address the entity set expansion problem using the path-based semantic features of knowledge graphs. Our method first discovers relevant semantic features of the seed entities, which can be treated as the common aspects of these seed entities, and then retrieves relevant entities based on the discovered semantic features. Probabilistic models are proposed to rank entities, as well as semantic features, by handling the incompleteness of knowledge graphs. Extensive experiments on a public knowledge graph (i.e., DBpedia V3.9) and three public test collections (i.e., CLEF-QALD 2-4, SemSearch-LS 2011, and INEX-XER 2009) show that our method significantly outperforms the state-of-the-art techniques. Crown Copyright (C) 2018 Published by Elsevier B.V. All rights reserved.
C1 [Chen, Jun; Chen, Yueguo; Zhang, Xiangling; Du, Xiaoyong; Wen, Ji-Rong] Renmin Univ China, Sch Informat, Beijing, Peoples R China.
   [Chen, Jun; Chen, Yueguo; Zhang, Xiangling; Du, Xiaoyong] MOE, Key Lab Data Engn & Knowledge Engn, Beijing, Peoples R China.
   [Wang, Ke] Simon Fraser Univ, Sch Comp Sci, Burnaby, BC, Canada.
RP Chen, YG (通讯作者)，Renmin Univ China, Sch Informat, Beijing, Peoples R China.
EM chenyueguo@ruc.edu.cn
FU National Science Foundation of China [61472426, U1711261, 61432006]
FX This work is supported by the National Science Foundation of China under
   the grant (No. 61472426, U1711261, and 61432006).
CR Abedjan Ziawasch, 2013, Datenbank-Spektrum, V13, P111, DOI 10.1007/s13222-013-0126-x
   Agrawal R., 1994, P 20 INT C VER LARG, P487
   [Anonymous], 2008, P 12 C EMP METH NAT
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Balog K, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P737
   Bing L., 2013, P 6 ACM INT C WEB SE, P567
   Bizer C, 2010, J WEB SEMANT, V8, P341, DOI 10.1016/j.websem.2010.08.005
   Blanco R., 2011, P 1 INT WORKSH ENT O, P65
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bron Marc, 2013, Advances in Information Retrieval. 35th European Conference on IR Research, ECIR 2013. Proceedings, P392, DOI 10.1007/978-3-642-36973-5_33
   Cao H., 2008, P 14 ACM SIGKDD INT, P875, DOI DOI 10.1145/1401890.1401995
   Cao XH, 2018, LECT NOTES ARTIF INT, V10938, P288, DOI 10.1007/978-3-319-93037-4_23
   Carterette B., 2007, P 16 ACM C INF KNOWL, P623
   Chen J., 2017, P 22 INT C INT US IN, P137
   Chen J, 2016, PROC INT CONF DATA, P1350, DOI 10.1109/ICDE.2016.7498342
   Cohen W., 2009, P JOINT C 47 ANN M A, P441, DOI [10.3115/1687878.1687941, DOI 10.3115/1687878.1687941]
   Cohen W. W., 2004, P 10 ACM SIGKDD INT, P89, DOI DOI 10.1145/1014052.1014065
   Cucchiarelli A, 2001, COMPUT LINGUIST, V27, P123, DOI 10.1162/089120101300346822
   Demartini G, 2010, LECT NOTES COMPUT SC, V6203, P254, DOI 10.1007/978-3-642-14556-8_26
   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, 2005, ARTIF INTELL, V165, P91, DOI 10.1016/j.artint.2005.03.001
   He Y., 2011, P 20 INT C WORLD WID, P427
   Hu J., 2009, P 18 INT C WORLD WID, P471, DOI DOI 10.1145/1526709.1526773
   Ji M, 2015, DATA MIN KNOWL DISC, V29, P976, DOI 10.1007/s10618-014-0396-4
   Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126
   Kozareva Z, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P1482
   Li X.-L., 2010, P ACL C SHORT PAP, P359
   Lim Lipyeow, 2013, P 16 INT C EXT DAT T, P347, DOI [10.1145/2452376.2452417, DOI 10.1145/2452376.2452417]
   Lopez V, 2013, J WEB SEMANT, V21, P3, DOI 10.1016/j.websem.2013.05.006
   McIntosh T., 2009, P JOINT C 47 ANN M A, V1, P396
   McIntosh T., 2008, P AUSTR LANG TECHN A, P97
   Meng CP, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P754, DOI 10.1145/2736277.2741123
   Metzger S, 2014, 2014 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, P60, DOI 10.1109/WI-IAT.2014.17
   Pantel P., 2006, ACL 2006
   Pasca M., 2007, P 16 ACM C C INF KNO, P683, DOI DOI 10.1145/1321440.1321536
   Passant A, 2010, LECT NOTES COMPUT SC, V6497, P209, DOI 10.1007/978-3-642-17749-1_14
   Pennacchiotti M., 2011, P 15 C COMP NAT LANG, P163
   Quinlan J.R., 1993, C4 5 PROGRAMS MACHIN, V16, P235
   Sadamitsu K., 2011, P 49 ANN M ASS COMP, P726
   Schutze H., 2008, INTRO INFORM RETRIEV, V39
   SHANNON CE, 1948, BELL SYST TECH J, V27, P623, DOI 10.1002/j.1538-7305.1948.tb00917.x
   Shi B., 2014, P INT C COMP LING, P2280
   Shi C., 2012, P 15 INT C EXT DAT T, P180, DOI DOI 10.1145/2247596.2247618
   Shi C, 2017, IEEE T KNOWL DATA EN, V29, P17, DOI 10.1109/TKDE.2016.2598561
   Shi C, 2014, IEEE T KNOWL DATA EN, V26, P2479, DOI 10.1109/TKDE.2013.2297920
   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
   Tong HH, 2006, IEEE DATA MINING, P613
   Yates A., 2004, P 13 INT C WORLD WID, P100, DOI DOI 10.1145/988672.988687
   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
   Zhang ZZ, 2016, AAAI CONF ARTIF INTE, P3101
   Zheng YY, 2017, LECT NOTES ARTIF INT, V10234, P317, DOI 10.1007/978-3-319-57454-7_25
   Zhu MZ, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P667, DOI 10.1145/2556195.2556206
NR 53
TC 6
Z9 7
U1 3
U2 30
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 OCT
PY 2018
VL 52-53
BP 33
EP 44
DI 10.1016/j.websem.2018.09.001
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HB5BM
UT WOS:000451072400003
DA 2022-08-23
ER

PT J
AU Munir, S
   Malick, RAS
   Jami, SI
   Ahmed, G
   Khan, S
   Rodrigues, JJPC
AF Munir, Siraj
   Malick, Rauf Ahmed Shams
   Jami, Syed Imran
   Ahmed, Ghufran
   Khan, Suleman
   Rodrigues, Joel J. P. C.
TI An integrated approach: using knowledge graph and network analysis for
   harnessing digital advertisement
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article; Early Access
DE Knowledge graph; Network analysis; Community detection; Semantics
ID SOCIAL NETWORK; CENTRALITY
AB Complex network analysis helps in finding hidden patterns within a graph network. This concept is extended for knowledge graphs to identify hidden concepts using state-of-the-art network analysis techniques. In this paper, a profiling knowledge graph is analyzed to identify hidden concepts which result in the identification of implicit communities within a campus network. The proposed work is verified with the interesting results achieved by applying different metrics using a state-of-the-art network analysis algorithm. The results of the proposed work are mapped in the domain of digital advertisement to answer intelligent semantic queries. Various factors of centrality measures identify the prospective influencers within a campus network. Moreover, bridge analysis determines amplifier nodes in the knowledge graph that will help in the digital advertisement. The proposed work concludes with a discussion on link prediction. It shows the future interactions to design digital advertising campaigns through billboards.
C1 [Munir, Siraj; Jami, Syed Imran] Mohammad Ali Jinnah Univ, Dept Comp Sci, Karachi, Pakistan.
   [Malick, Rauf Ahmed Shams; Ahmed, Ghufran] Natl Univ Comp & Emerging Sci, Dept Comp Sci, Karachi, Pakistan.
   [Khan, Suleman] Northumbria Univ, Dept Comp & Informat Sci, Newcastle Upon Tyne, Tyne & Wear, England.
   [Rodrigues, Joel J. P. C.] Fed Univ Piaui UFPI, Teresina, Brazil.
   [Rodrigues, Joel J. P. C.] Inst Telecommunicocoes, Aveiro, Portugal.
RP Munir, S (通讯作者)，Mohammad Ali Jinnah Univ, Dept Comp Sci, Karachi, Pakistan.
EM sirajmunir93@gmail.com; rauf.malick@nu.edu.pk; imran.jami@jinnah.edu;
   ghufran.ahmed@nu.edu.pk; suleman.khan@northumbria.ac.uk; joeljr@ieee.org
CR Abebe R, 2018, MITIGATING OVEREXPOS
   Ahmed AA, 2021, ENG REP, V3, DOI 10.1002/eng2.12416
   Alamsyah A, 2021, ARXIVABS210208888
   Benitez-Andrades JA, 2020, INFORM SCIENCES, V540, P390, DOI 10.1016/j.ins.2020.06.008
   Bashbaghi S., 2019, DEEP LEARNING OBJECT, P133
   Bauer C., 2016, MANAGEMENT REV Q, V66, P159, DOI [10.1007/s11301-015-0118-z, DOI 10.1007/S11301-015-0118-Z]
   Bhatt S, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P51, DOI 10.1145/3289600.3291031
   Bollacker K, 2019, STUD SYST DECIS CONT, V176, P203, DOI 10.1007/978-3-030-00317-3_9
   BONACICH P, 1987, AM J SOCIOL, V92, P1170, DOI 10.1086/228631
   Brandes U, 2008, SOC NETWORKS, V30, P136, DOI 10.1016/j.socnet.2007.11.001
   Buccafurri F, 2013, INFORM SCIENCES, V224, P1, DOI 10.1016/j.ins.2012.10.021
   Chartier JF, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.10.005
   Demsar U., 2018, T GIS, VVol. 12, P61, DOI DOI 10.1111/j.1467-9671.2008.01086.x
   Garcia-Sanchez F, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2019.102153
   Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799
   Golbeck J., 2013, ANAL SOCIAL WEB, P25
   Guney E, 2019, INFORM SCIENCES, V503, P589, DOI 10.1016/j.ins.2019.07.043
   Hussain N, 2020, INT J SOFTW ENG KNOW, V30, P859, DOI 10.1142/S0218194020400100
   Karczmarczyk A, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0209372
   Karidi DP, 2018, J AMB INTEL HUM COMP, V9, P2035, DOI 10.1007/s12652-017-0491-7
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Lienhart R, 2002, IEEE IMAGE PROC, P900
   Lou KH, 2021, IEEE T INTELL TRANSP, V22, P4635, DOI 10.1109/TITS.2020.2991029
   Milovanovic S, 2019, FUTURE GENER COMP SY, V93, P121, DOI 10.1016/j.future.2018.10.028
   More JS, 2019, SOC NETW ANAL MIN, V9, DOI 10.1007/s13278-018-0548-4
   Munir S, 2021, OPEN COMPUT SCI, V11, P294, DOI 10.1515/comp-2020-0209
   Munir S, 2019, INT J COMPUT SCI NET, V19, P193
   Newman MEJ, 2003, SIAM REV, V45, P167, DOI 10.1137/S003614450342480
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Vahdati S, 2018, UNVEILING SCHOLARLY
   Viola P, 2004, INT J COMPUT VISION, V57, P137, DOI 10.1023/B:VISI.0000013087.49260.fb
   Wakil K, 2021, ISPRS INT J GEO-INF, V10, DOI 10.3390/ijgi10020060
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Wasserman SaF K., 1994, SOCIAL NETWORK ANAL, P201
   Zhang L, 2020, ELECTRON COMMER RES, V20, P3, DOI 10.1007/s10660-018-9316-9
   Zhong C, 2014, INT J GEOGR INF SCI, V28, P2178, DOI 10.1080/13658816.2014.914521
NR 36
TC 0
Z9 0
U1 8
U2 8
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.
DI 10.1007/s11042-021-11856-2
EA JAN 2022
PG 16
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 YK9XC
UT WOS:000745555600002
DA 2022-08-23
ER

PT J
AU Wu, Y
   Zhu, XY
   Zhu, YA
AF Wu, Yang
   Zhu, Xiyong
   Zhu, Yinan
TI An Improved Approach to the Construction of Chinese Medical Knowledge
   Graph Based on CTD-BLSTM Model
SO IEEE ACCESS
LA English
DT Article
DE Unified modeling language; Knowledge engineering; Data mining; Training;
   Task analysis; Feature extraction; Drugs; Bi-LSTM; semi-supervised;
   entity recognition; entity-relationship extraction; knowledge graph
AB In the process of constructing the knowledge graph, entity recognition and relationship extraction are not only the most fundamental but also the most important tasks, and the effect of their model directly affects the final result of the graph. To establish a more refined knowledge graph, this paper proposes a model of extending Bi-LSTM structural units with Double-word vector and combining Semi-supervised Co-training method. The improved model is used in Chinese named entity recognition and entity-relationship extraction in the Chinese medical field, named Co-Training Double Word embedding conditioned BLSTM (CTD-BLSTM). Experiments show that the CTD-BLSTM model obtains higher accuracy and recall rate than BLSTM in the Chinese medical named entity recognition and entity-relationship extraction. It performs better recognition and adaptability to support the construction of the knowledge graph.
C1 [Wu, Yang; Zhu, Xiyong] Zhejiang Sci Tech Univ, Sch Econ & Management, Hangzhou 310018, Peoples R China.
   [Zhu, Yinan] Tsinghua Univ, Sch Sofaware, Beijing 100084, Peoples R China.
RP Zhu, XY (通讯作者)，Zhejiang Sci Tech Univ, Sch Econ & Management, Hangzhou 310018, Peoples R China.
EM zxywolf@163.com
FU National Natural Science Foundation of China (NSFC) [71501172]; National
   Natural Science Foundation (NSF) of Zhejiang Province [LY18G020017]
FX This work was supported in part by the National Natural Science
   Foundation of China (NSFC) under Grant 71501172, and in part by National
   Natural Science Foundation (NSF) of Zhejiang Province under Grant
   LY18G020017.
CR Blum A., 1998, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, P92, DOI 10.1145/279943.279962
   Chen ES, 2008, J AM MED INFORM ASSN, V15, P87, DOI 10.1197/jamia.M2401
   Dingare S, 2005, COMP FUNCT GENOM, V6, P77, DOI 10.1002/cfg.457
   [冯艳红 Feng Yanhong], 2018, [计算机科学, Computer Science], V45, P261
   Frunza O., 2010, P 2010 WORKSH BIOM N, P91
   Haofen W., 2016, J MED INFORM, V37, P8
   Khoo CSG, 2000, 38TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P336
   Li JQ, 2020, J SUPERCOMPUT, V76, P1450, DOI 10.1007/s11227-017-2229-x
   Li LS, 2020, IEEE ACM T COMPUT BI, V17, P841, DOI 10.1109/TCBB.2018.2868346
   Li-Shuang L, 2018, J CHIN INF PROCESS, V2018, P116
   Liu MF, 2017, MULTIMED TOOLS APPL, V76, P10555, DOI 10.1007/s11042-015-3093-4
   Minqiang L, 2018, RES IMPLEMENTATION S
   Murugesan G, 2017, EURASIP J BIOINFORM, DOI 10.1186/s13637-017-0060-6
   Pei Y, 2018, J COMPUT RES DEV, V55, P1548
   Peters M., 2018, IMPROVING LANGUAGE U
   Peters ME, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1756, DOI 10.18653/v1/P17-1161
   Tang Nan, 2011, Computer Engineering, V37, P184, DOI 10.3969/j.issn.1000-3428.2011.10.063
   Tong Y., 2015, CHINA DIGITAL MED, V10, P80
   Xia Y, 2017, CEUR WORKSHOP P, P43
   Yukun Z, 2019, COMPUT ENG SCI, V41, P1110
   Zhang W, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2366, DOI 10.1145/3308558.3313612
   Zheng S., 2017, ARXIV170605075
   Zheng SC, 2017, NEUROCOMPUTING, V257, P59, DOI 10.1016/j.neucom.2016.12.075
   Zhihua Z, 2007, MACHINE LEARNING ITS, P259
NR 24
TC 1
Z9 1
U1 15
U2 32
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 74969
EP 74976
DI 10.1109/ACCESS.2021.3079962
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA TJ6SE
UT WOS:000673608100001
OA gold
DA 2022-08-23
ER

PT J
AU Xue, YL
   Xu, LY
   Qiu, BC
   Wang, L
   Zhang, GW
AF Xue, Yunlan
   Xu, Lingyu
   Qiu, Bingcheng
   Wang, Lei
   Zhang, Gaowei
TI Relationship discovery in public opinion and actual behavior for social
   media stock data space
SO EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
LA English
DT Article
DE Big data; Relationship; Sliding window; Matrix clustering; Similarity
ID CROSS-CORRELATION
AB With the rise of social data media, the cyber world nearly parallels to the real world. The trajectory of a hot event is reflected in social media by Public Opinion Data Space (OS) and Actual Behavior Data Space (BS). However, the relationships with a variety of mechanisms in each space or between them are often unknown. To solve the above issues, the traditional methods for inferring relationship are by performing a statistical similarity analysis of time sequence from dynamic elements. In specially, the research of clustering nonlinear correlation data object is rare, so we propose Matrix Similarity Clustering Algorithm (MSCA) based on random matrix theory and combined with sliding window technology to cluster the similarity of multidimensional time sequences. This method is effective to detect the trend relationship of time sequences with multiple dynamic elements. In addition, we construct a knowledge map to analyse the relationships in OS and BS.
C1 [Xue, Yunlan; Qiu, Bingcheng] Guangdong Polytech Inst, Dept Comp Sci, Guangzhou, Guangdong, Peoples R China.
   [Xu, Lingyu; Wang, Lei; Zhang, Gaowei] Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China.
RP Xu, LY (通讯作者)，Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China.
EM xly@shu.edu.cn
FU Ministry of Science and Technology [201105033]; National Natural Science
   Foundation of China [40976108]
FX This work is supported by The Ocean Public Welfare Project of the
   Ministry of Science and Technology (No. 201105033) and The National
   Natural Science Foundation of China (No. 40976108).
CR Aste T, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/8/085009
   Batista Gustavo EAPA, 2011, P SIAM INT C DAT MIN, P699, DOI DOI 10.1137/1.9781611972818.60
   Bouchaud J., 2003, THEORY FINANCIAL RIS
   Boucheham B, 2010, PATTERN RECOGN LETT, V31, P629, DOI 10.1016/j.patrec.2009.11.019
   Cohen Ira, 2007, P 15 ACM INT C MULT, V3, P301, DOI DOI 10.1145/1291233.1291297
   Ding H, 2008, PROC VLDB ENDOW, V1, P1542
   Esling P, 2012, ACM COMPUT SURV, V45, DOI 10.1145/2379776.2379788
   FALOUTSOS C, 1994, P ACM SIGMOD INT C M, P419
   Holland J, 1997, EMERGENCE CHAOS ORDE
   Jiang XF, 2012, EPL-EUROPHYS LETT, V97, DOI 10.1209/0295-5075/97/48006
   Jolliffe I., 2011, PRINCIPAL COMPONENT
   Keogh E, 2003, DATA MIN KNOWL DISC, V7, P349, DOI 10.1023/A:1024988512476
   Levnajic Z, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.034101
   Li R, 2012, PROC INT CONF DATA, P1273, DOI 10.1109/ICDE.2012.125
   MANTEGNA RN, 2000, INTRO ECONOPHYSICS C
   Meng H, 2014, SCI REP-UK, V4, DOI 10.1038/srep03655
   Oh G, 2011, EUR PHYS J B, V79, P55, DOI 10.1140/epjb/e2010-90492-x
   Pan RK, 2007, EPL-EUROPHYS LETT, V77, DOI 10.1209/0295-5075/77/58004
   Plerou V, 2002, PHYS REV E, V65, DOI 10.1103/PhysRevE.65.066126
   Podobnik B, 2010, EPL-EUROPHYS LETT, V90, DOI 10.1209/0295-5075/90/68001
   Rajkumar R, 2010, STRASBOURG, V14, P731
   Ren F, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0097711
   Shen J, 2009, EPL-EUROPHYS LETT, V86, DOI 10.1209/0295-5075/86/48005
   Subbian K, 2012, SDM, P624
   Timme M, 2014, J PHYS A-MATH THEOR, V47, DOI 10.1088/1751-8113/47/34/343001
   Utsugi A, 2004, PHYS REV E, V70, DOI 10.1103/PhysRevE.70.026110
   Xue YL, 2015, 2015 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION, AND KNOWLEDGE IN THE INTERNET OF THINGS (IIKI), P39, DOI 10.1109/IIKI.2015.15
   Yetomi H, 2011, PHYS REV E, V83
   YI B-K, 2000, P 26 INT C VER LARG, P385
   Yoon H, 2005, IEEE T KNOWL DATA EN, V17, P1186, DOI 10.1109/TKDE.2005.144
NR 30
TC 2
Z9 2
U1 0
U2 26
PU SPRINGEROPEN
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 1687-1472
EI 1687-1499
J9 EURASIP J WIREL COMM
JI EURASIP J. Wirel. Commun. Netw.
PD SEP 9
PY 2016
AR 216
DI 10.1186/s13638-016-0684-3
PG 13
WC Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Telecommunications
GA DV6EA
UT WOS:000383022900001
OA gold
DA 2022-08-23
ER

PT J
AU Fan, M
   Zhou, Q
   Zheng, TF
   Grishman, R
AF Fan, Miao
   Zhou, Qiang
   Zheng, Thomas Fang
   Grishman, Ralph
TI Distributed representation learning for knowledge graphs with entity
   descriptions
SO PATTERN RECOGNITION LETTERS
LA English
DT Article
DE Knowledge graph; Representation learning; Entity description; Knowledge
   graph completion; Entity type classification
AB Recent studies of knowledge representation attempt to project both entities and relations, which originally compose a high-dimensional and sparse knowledge graph, into a continuous low-dimensional space. One canonical approach TransE [2] which represents entities and relations with vectors (embeddings), achieves leading performances solely with triplets, i.e. (head_entity, relation, tail_entity), in a knowledge base. The cutting-edge method DKRL [23] extends TransE via enhancing the embeddings with entity descriptions by means of deep neural network models. However, DKRL requires extra space to store parameters of inner layers, and relies on more hyperparameters to be tuned. Therefore, we create a single layer model which requests much fewer parameters. The model measures the probability of each triplet along with corresponding entity descriptions, and learns contextual embeddings of entities, relations and words in descriptions simultaneously, via maximizing the loglikelihood of the observed knowledge. We evaluate our model in the tasks of knowledge graph completion and entity type classification with two benchmark datasets: FB500K and EN15K, respectively. Experimental results demonstrate that the proposed model outperforms both TransE and DKRL, indicating that it is both efficient and effective in learning better distributed representations for knowledge bases. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Fan, Miao; Zhou, Qiang; Zheng, Thomas Fang] Tsinghua Univ, Div Tech Innovat & Dev, Tsinghua Natl Lab Informat Sci & Technol, CSLT, Beijing 100084, Peoples R China.
   [Grishman, Ralph] NYU, Courant Inst Math Sci, Dept Comp Sci, 251 Mercer St, New York, NY 10003 USA.
RP Fan, M (通讯作者)，Tsinghua Univ, Div Tech Innovat & Dev, Tsinghua Natl Lab Informat Sci & Technol, CSLT, Beijing 100084, Peoples R China.
EM fanmiao.cslt.thu@gmail.com
FU China Scholarship Council; National Program on Key Basic Research
   Project (973 Program) [2013CB329304]; National Science Foundation of
   China (NSFC) [61433018, 61373075]; Proteus Project of NYU
FX This work is supported by China Scholarship Council, National Program on
   Key Basic Research Project (973 Program) under Grant 2013CB329304,
   National Science Foundation of China (NSFC) under Grant nos. 61433018
   and 61373075, Proteus Project of NYU. The first author conducted this
   research while he was a joint-supervision Ph.D. student of Tsinghua
   University and New York University.
CR [Anonymous], 2011, J MACH LEARN RES
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Carlson A., 2010, P 24 C ART INT AAAI
   Fan M., 2015, P INT C REC ADV NAT, P186
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   Fan M. Q, 2016, P 2016 IEEE WIC ACM
   Fan M, 2016, COGN COMPUT, V8, P1087, DOI 10.1007/s12559-016-9425-5
   Fan M, 2015, 2015 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT), VOL 1, P53, DOI 10.1109/WI-IAT.2015.125
   Garcia-Duran A., 2015, P 2015 C EMP METH NA, P286, DOI DOI 10.18653/V1/D15-1034
   Garcia-Duran A, 2016, J ARTIF INTELL RES, V55, P715, DOI 10.1613/jair.5013
   Goldberg Y., 2014, CORR
   LIN Y, 2015, P 20 9 AAAI C ART, P2181
   Manning C. D., 2008, INTRO INFORM RETRIEV, V1
   Mikolov T., 2013, CORR
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Neelakantan Arvind, 2015, P 2015 C N AM CHAPT, P515
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Shekarpour S., 2016, ARXIV160103541 ARXIV
   Wang Z. M., 2014, J NONLINEAR FUNCT AN, DOI DOI 10.1007/978-3-319-01796-9_
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J., 2013, P 2013 C EMP METH NA, P1366
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xie R., 2016, P 30 AAAI C ART INT
NR 23
TC 19
Z9 24
U1 0
U2 41
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 JUL 1
PY 2017
VL 93
SI SI
BP 31
EP 37
DI 10.1016/j.patrec.2016.09.005
PG 7
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EY7WY
UT WOS:000404204100005
DA 2022-08-23
ER

PT J
AU Qi, DL
   Chen, SD
   Sun, X
   Luan, RP
   Tong, D
AF Qi, Donglin
   Chen, Shudong
   Sun, Xiao
   Luan, Ruipeng
   Tong, Da
TI A multiscale convolutional gragh network using only structural
   information for entity alignment
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Entity alignment; Knowledge graph; Graph neural network; Semi-supervised
   learning
AB As an essential method of knowledge graph fusion, entity alignment aims to find entities that refer to the same real-world object in different knowledge graphs. Current entity alignment methods usually adopt extra information such as entity names, attribute triples except for structural information of the knowledge graph. However, due to the difficult availability and possibly low effectiveness of extra information, it is necessary to improve the performance of entity alignment when using only structural information. In this paper, a novel entity alignment method based on the multiscale convolutional graph network (MCEA) is proposed, which utilizes only structural information of the graph for entity alignment. Firstly, the convolution region of long-tail entities is extended to enhance the ability of information capture of the graph network. Secondly, intermediate results from semi-supervised learning are introduced to negative sampling in order to improve sampling quality. Thirdly, the stable marriage algorithm is chosen as the alignment strategy to obtain final alignment results. The experimental results show that this method has achieved better performance on Hits@K and MRR than the state-of-the-art methods, especially in the case of less labeled data. Moreover, we also find that the impact of the alignment strategy has become limited when the model generates sufficient accurate entity embeddings.
C1 [Qi, Donglin; Chen, Shudong; Tong, Da] Chinese Acad Sci, Inst Microelect, Beijing, Peoples R China.
   [Qi, Donglin; Chen, Shudong; Tong, Da] Univ Chinese Acad Sci, Beijing, Peoples R China.
   [Sun, Xiao] Inst Syst Engn, Beijing, Peoples R China.
   [Luan, Ruipeng] 91550 Troops, Dalian, Peoples R China.
RP Sun, X (通讯作者)，Inst Syst Engn, Beijing, Peoples R China.
EM qidonglin@ime.ac.cn; chenshudong@ime.ac.cn; psunxiao@163.com
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 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
   Conneau A, 2018, INT C LEARN REPR
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Fey M, 2020, INT C LEARNING REPRE
   He FZ, 2019, LECT NOTES COMPUT SC, V11446, P367, DOI 10.1007/978-3-030-18576-3_22
   He KM, 2015, IEEE I CONF COMP VIS, P1026, DOI 10.1109/ICCV.2015.123
   Joulin A., 2017, P 15 C EUROPEAN CHAP, P427
   Kipf T., 2016, 5 INT C LEARN REPR, P1
   Lin L, 2020, APPL INTELL, V50, P2867, DOI 10.1007/s10489-020-01673-9
   Liu FY, 2021, AAAI CONF ARTIF INTE, V35, P4257
   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
   MCVITIE DG, 1971, COMMUN ACM, V14, P486, DOI 10.1145/362619.362631
   Pennington J., 2014, EMNLP, P1532
   Sun ZQ, 2020, AAAI CONF ARTIF INTE, V34, P222
   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
   Velickovi P., 2017, ARXIV171010903
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Weixin Zeng, 2021, Database Systems for Advanced Applications. 26th International Conference, DASFAA 2021. Proceedings. Lecture Notes in Computer Science (LNCS 12681), P272, DOI 10.1007/978-3-030-73194-6_19
   Zeng WX, 2020, PROC INT CONF DATA, P1870, DOI 10.1109/ICDE48307.2020.00191
   Zhu RB, 2021, LECT NOTES ARTIF INT, V12712, P501, DOI 10.1007/978-3-030-75762-5_40
NR 26
TC 0
Z9 0
U1 4
U2 4
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/s10489-022-03916-3
EA JUL 2022
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3E0PY
UT WOS:000829695500003
DA 2022-08-23
ER

PT J
AU Zhang, ZH
   Huang, JB
   Tan, QL
AF Zhang, Zhenghao
   Huang, Jianbin
   Tan, Qinglin
TI Association Rules Enhanced Knowledge Graph Attention Network
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge Graphs; Graph attention network; Association rules; Knowledge
   inference; High-order neighborhood; Embedding propagation
AB Embedding knowledge graphs into continuous vector spaces has recently attracted increasing interest in knowledge base completion. However, in most existing embedding methods, only fact triplets are utilized, and logical rules have not been thoroughly studied for the knowledge base completion task. To overcome the problem, we propose an association rules enhanced knowledge graph attention network (AR-KGAN). In this paper, triplets and logical rules are jointly modeled in the proposed unified framework to achieve more predictive entity and relation embeddings. Association rules and corresponding correlation degrees between them can be automatically obtained according to our designed mining algorithm. The major component of AR-KGAN is an encoder of an effective neighborhood aggregator, which addresses the problems by aggregating neighbors with both association rules based and graph based attention weights. The decoder enables AR-KGAN to be translational between entities and relations while keeping the superior link prediction performance. Then, the global loss is minimized over both atomic and complex formulas to achieve the embedding task. In this manner, we learn embeddings compatible with triplets and association rules, which are certainly more predictive for knowledge acquisition and inference. The results show that the proposed AR-KGAN model achieves significant and consistent improvements over state-of-the-art methods on three benchmark datasets.(C) 2021 Published by Elsevier B.V.
C1 [Zhang, Zhenghao; Huang, Jianbin; Tan, Qinglin] Xidian Univ, Sch Comp Sci & Technol, Xian, Shaanxi, Peoples R China.
RP Huang, JB (通讯作者)，Xidian Univ, Sch Comp Sci & Technol, Xian, Shaanxi, Peoples R China.
EM zhangzhenghao1108@126.com; jbhuang@xidian.edu.cn;
   qltan@stu.xidian.edu.cn
FU National Natural Science Foundation of China [61876138]
FX Acknowledgments The work was supported by the National Natural Science
   Foundation of China [grant numbers: 61876138] . Any opinions, findings
   and conclusions expressed here are those of the authors and do not
   necessarily reflect the views of the funding agencies.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Beltagy I., 2014, WORKSH 28 AAAI C ART
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Guo S., 2016, P C EMP METH NAT LAN, P192, DOI 10.18653/v1/D16-1019
   Hamaguchi T, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1802
   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
   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 X, 2019, P 2019 C N AM CHAPTE, P978
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Lin X.V., 2018, ARXIV180810568
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Luo Y., 2015, P 2015 C EMP METH NA, P1656, DOI DOI 10.18653/V1/D15-1191
   Neelakantan A., 2015, ARXIV PREPRINT ARXIV
   Nguyen D, 2017, ARXIV PREPRINT ARXIV, P8
   Nguyen T., 2017, ADV NEURAL INFORM PR, P2670
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Rocktaschel T., 2015, P 2015 C N AM CHAPT, P1119
   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 C, 2017, IEEE T KNOWL DATA EN, V29, P17, DOI 10.1109/TKDE.2016.2598561
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Tang J, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1165, DOI 10.1145/2783258.2783307
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499, DOI DOI 10.18653/V1/D15-1174
   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, 2019, ARXIV PREPRINT ARXIV
   Wang P., 2019, ARXIV PREPRINT ARXIV
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wei Z., 2015, P 24 ACM INT C INF K, P1331, DOI DOI 10.1145/2806416.2806513
   West R, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P515, DOI 10.1145/2566486.2568032
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yang B., 2014, ABS14126575 CORR
   Zhang Y, 2016, P NAACL HLT
   Zhong H., 2015, P 2015 C EMP METH NA, P267
NR 37
TC 2
Z9 2
U1 3
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 MAR 5
PY 2022
VL 239
AR 108038
DI 10.1016/j.knosys.2021.108038
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0W0BT
UT WOS:000788704200017
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Lin, L
   Liu, J
   Lv, YC
   Guo, F
AF Lin, Lin
   Liu, Jie
   Lv, Yancheng
   Guo, Feng
TI A similarity model based on reinforcement local maximum connected same
   destination structure oriented to disordered fusion of knowledge graphs
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Maximum common subgraph; Same destination paths; Similarity model;
   Fusion modeling; Fragmentation knowledge graph
ID ALGORITHM; CONSTRUCTION; HEURISTICS
AB The alignment and fusion of knowledge graphs have been at entity alignment and fusion which match and align knowledge graphs (KGs) by measuring similarity of the entities in KGs. Nevertheless, false fusion of completely different KGs can be easily caused if only considering entity similarity but ignoring entity relationship similarity. This paper focuses on entity relationship similarity model and KGs fusion method to achieve automatic construction of KGs. First, Same Destination Paths is developed based on Maximum Common Subgraph, which is used to build the Local Maximum Connected Same Destination Structure (LMCSDS) model to measure the entity relationship similarity of KGs. Then, a fusion method for similar fragmentation KGs (FKGs) is developed by analyzing the types of FKGs. Third, a Reinforcement Local Maximum Connected Same Destination Structure (RLMCSDS) similarity model is developed to ensure that the similarity between FKGs can still be measured correctly after fusion of FKGs. Meanwhile, the fusion results obtained by the developed RLMCSDS model and fusion method are theoretically proved to be independent with the fusion order of FKGs. Finally, experimental results on several datasets demonstrate the outstanding performance of the RLMCSDS model in comparison with some existing methods. Moreover, a complete KG can be built by the RLMCSDS model and the fusion method.
C1 [Lin, Lin; Liu, Jie; Lv, Yancheng; Guo, Feng] Harbin Inst Technol, Harbin 150001, Peoples R China.
RP Lin, L (通讯作者)，Harbin Inst Technol, Harbin 150001, Peoples R China.
EM waiwaiyl@163.com; 624003414@qq.com; xgzlyc@163.com; guof6570969@163.com
FU national key research and development project [2018YFB1700902]; national
   natural science foundation of China [51775132]
FX This work was supported by national key research and development project
   (grant numbers 2018YFB1700902) and the national natural science
   foundation of China (grant numbers 51775132).
CR BABEL L, 1994, COMPUTING, V52, P31, DOI 10.1007/BF02243394
   Batet M, 2013, APPL INTELL, V38, P29, DOI 10.1007/s10489-012-0355-y
   Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014
   Ben Aouicha M, 2016, APPL INTELL, V45, P475, DOI 10.1007/s10489-015-0755-x
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   BRON C, 1973, COMMUN ACM, V16, P575, DOI 10.1145/362342.362367
   Depolli M, 2013, J CHEM INF MODEL, V53, P2217, DOI [10.1021/ci4002525, 10.1021/ci400332e]
   Douze M, 2009, INT C IM VID RETR JU, P19
   Duesbury E, 2017, MATCH-COMMUN MATH CO, V77, P213
   Durand PJ, 1999, INTERNET J CHEM, V2
   Gan WS, 2018, IEEE INT CONF BIG DA, P2617, DOI 10.1109/BigData.2018.8622405
   Grosso A, 2008, J HEURISTICS, V14, P587, DOI 10.1007/s10732-007-9055-x
   Kawabata T, 2011, J CHEM INF MODEL, V51, P1775, DOI 10.1021/ci2001023
   Kim JB, 2017, IET IMAGE PROCESS, V11, P633, DOI 10.1049/iet-ipr.2016.0935
   Koch I, 2001, THEOR COMPUT SCI, V250, P1, DOI 10.1016/S0304-3975(00)00286-3
   Lever J, 2018, BIOINFORMATICS, V34, P652, DOI 10.1093/bioinformatics/btx613
   Li Q, 2006, J SYST SIMUL, P6
   Lowe D. G., 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision, P1150, DOI 10.1109/ICCV.1999.790410
   Mahalanobis A, 1998, P SOC PHOTO-OPT INS, V3370, P460, DOI 10.1117/12.321849
   MCGREGOR JJ, 1982, SOFTWARE PRACT EXPER, V12, P23, DOI 10.1002/spe.4380120103
   O'Shea K, 2014, APPL INTELL, V40, P199, DOI 10.1007/s10489-013-0488-7
   Qazi N, 2017, EUR INTELL SECUR INF, P143, DOI 10.1109/EISIC.2017.29
   Raval Mihir, 2012, BMC Res Notes, V5, P594, DOI 10.1186/1756-0500-5-594
   Raymond JW, 2002, J CHEM INF COMP SCI, V42, P305, DOI 10.1021/ci010381f
   ROSEN KH, DISCRETE MATH ITS AP
   SWAIN MJ, 1991, INT J COMPUT VISION, V7, P11, DOI 10.1007/BF00130487
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Wang Z., 2014, 28 AAAI C ART INT
   Xiao H, 2015, ONE POINT MANIFOLD K
   Xiao H, 2017, AAAI CONF ARTIF INTE, P3104
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   Zhang CH, 2017, TSINGHUA SCI TECHNOL, V22, P185
   Zhang C, 2012, ADV MATER RES-SWITZ, V429, P287, DOI 10.4028/www.scientific.net/AMR.429.287
   Zhang XM, 2017, COMPUT PHYS COMMUN, V211, P98, DOI 10.1016/j.cpc.2016.07.005
   Zhu YY, 2013, VLDB J, V22, P345, DOI 10.1007/s00778-012-0292-8
NR 35
TC 7
Z9 7
U1 4
U2 23
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 SEP
PY 2020
VL 50
IS 9
BP 2867
EP 2886
DI 10.1007/s10489-020-01673-9
EA APR 2020
PG 20
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MT2DY
UT WOS:000523072100001
DA 2022-08-23
ER

PT J
AU Page, R
AF Page, Roderic
TI Liberating links between datasets using lightweight data publishing: an
   example using plant names and the taxonomic literature
SO BIODIVERSITY DATA JOURNAL
LA English
DT Article
DE datasette; linked data; data publishing; biodiversity knowledge graph;
   taxonomic names
AB Constructing a biodiversity knowledge graph will require making millions of cross links between diversity entities in different datasets. Researchers trying to bootstrap the growth of the biodiversity knowledge graph by constructing databases of links between these entities lack obvious ways to publish these sets of links. One appealing and lightweight approach is to create a "datasette", a database that is wrapped together with a simple web server that enables users to query the data. Datasettes can be packaged into Docker containers and hosted online with minimal effort. This approach is illustrated using a dataset of links between globally unique identifiers for plant taxonomic names and identifiers for the taxonomic articles that published those names.
C1 [Page, Roderic] Univ Glasgow, Glasgow, Lanark, Scotland.
RP Page, R (通讯作者)，Univ Glasgow, Glasgow, Lanark, Scotland.
EM rdmpage@gmail.com
RI Page, Roderic/A-1375-2009
OI Page, Roderic/0000-0002-7101-9767
CR Agosti Donat, 2009, BMC Res Notes, V2, P53, DOI 10.1186/1756-0500-2-53
   Bateman A, 2017, NUCLEIC ACIDS RES, V45, pD158, DOI 10.1093/nar/gkw1099
   BURGSTALLERMUEHLBA, 2016, DATABASE OXFORD 0317, DOI DOI 10.1093/DATABASE/BAW015
   Cui H, 2012, J AM SOC INF SCI TEC, V63, P738, DOI 10.1002/asi.22618
   Federhen S, 2012, NUCLEIC ACIDS RES, V40, pD136, DOI 10.1093/nar/gkr1178
   Gloor D, 2017, WORLD SPIDER CATALOG, DOI [10.24436/2, DOI 10.24436/2]
   Groth Paul, 2010, Information Services & Use, V30, P51, DOI 10.3233/ISU-2010-0613
   King D, 2011, ZOOKEYS, P151, DOI 10.3897/zookeys.150.2167
   Kuhn T, 2016, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.78
   Michel E, 2016, ZOOKEYS, P1, DOI 10.3897/zookeys.550.7460
   Michel F, 2017, S4BIODIV 2017 2 INT, P1
   Miller Holly, 2009, BMC Res Notes, V2, P101, DOI 10.1186/1756-0500-2-101
   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, 2016, ZOOKEYS, P247, DOI 10.3897/zookeys.550.9293
   Page RDM, 2013, PEERJ, V1, DOI 10.7717/peerj.190
   Page RDM, 2011, PLOS CURR-TREE LIFE, DOI 10.1371/currents.RRN1228
   Page RDM, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S14-S5
   Queralt-Rosinach N, 2016, SEMANT WEB, V7, P519, DOI 10.3233/SW-150189
   Senderov V, 2018, J BIOMED SEMANT, V9, DOI 10.1186/s13326-017-0174-5
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wieczorek J, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0029715
   Willison S, 2017, DATASETTE INSTANTLY
NR 22
TC 2
Z9 2
U1 0
U2 1
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 JUL 23
PY 2018
VL 6
AR e27539
DI 10.3897/BDJ.6.e27539
PG 8
WC Biodiversity Conservation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biodiversity & Conservation
GA HA1SQ
UT WOS:000450003000001
PM 30065607
OA gold, Green Published, Green Submitted, Green Accepted
DA 2022-08-23
ER

PT J
AU Fuji, M
   Nakazawa, K
   Yoshida, H
AF Fuji, Masaru
   Nakazawa, Katsuhito
   Yoshida, Hiroaki
TI "Trustworthy and Explainable Al" Achieved Through Knowledge Graphs and
   Social Implementation
SO FUJITSU SCIENTIFIC & TECHNICAL JOURNAL
LA English
DT Article
ID DEEP TENSOR
AB While Al performance continues to improve, when experts use it in practical applications, its presented inferences may not provide sufficient information for decision-making. This failure to ensure sufficient reliability has encouraged R&D on explainable Al to be conducted on a global scale in recent years. In response to this problem, Fujitsu has achieved the world's first "trustworthy and explainable Al" that makes use of knowledge graphs capable of representing expertise in a systematic manner to explain the reason and basis. This provides users with sufficient information to make decisions and improves Al reliability. This paper outlines knowledge graphs as a basis for Fujitsu's trustworthy and explainable Al. It also introduces application examples in the fields of finance and chemistry.
C1 [Fuji, Masaru; Nakazawa, Katsuhito; Yoshida, Hiroaki] Fujitsu Labs Ltd, Kawasaki, Kanagawa, Japan.
RP Fuji, M (通讯作者)，Fujitsu Labs Ltd, Kawasaki, Kanagawa, Japan.
CR Fuji M, 2019, FUJITSU SCI TECH J, V55, P58
   Maruhashi K, 2017, FUJITSU SCI TECH J, V53, P26
   Okamoto S., 2019, BIOCLINICA, V34, P48
   Tanaka K., 2018, JAPIO YB
   Tani S., 2019, FUJITSU, V70, P41
NR 5
TC 1
Z9 1
U1 2
U2 2
PU FUJITSU LTD
PI KAWASAKI
PA 1015 KAMIKODANAKU NAKAHARA-KU, KAWASAKI, 211, JAPAN
SN 0016-2523
J9 FUJITSU SCI TECH J
JI Fujitsu Sci. Tech. J.
PY 2020
VL 56
IS 1
BP 39
EP 45
PG 7
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA OY1FU
UT WOS:000593999600008
DA 2022-08-23
ER

PT J
AU Zhang, L
   Li, X
   Li, WH
   Zhou, HL
   Bai, Q
AF Zhang, Lan
   Li, Xiang
   Li, Weihua
   Zhou, Huali
   Bai, Quan
TI Context-Aware Recommendation System using Graph-based Behaviours
   Analysis
SO JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING
LA English
DT Article
DE Contextual information extraction; knowledge graph; context-awareness;
   recommendation system; user behaviour analysis
AB Recommendation systems have been extensively studied over the last decade in various domains. It has been considered a powerful tool for assisting business owners in promoting sales and helping users with decision-making when given numerous choices. In this paper, we propose a novel Graph-based Context-Aware Recommendation Systems with Knowledge Graph to analyse and predict users' behaviours, i.e., making recommendations based on historical events and their implicit associations. The model incorporates contextual information extracted from both users' historical behaviours and events relations, where the contexts have been modelled as knowledge graphs. By leveraging the advantages offered from the knowledge graph, events dependencies and their subtle relations can be established and have been introduced in the recommendation process. Experimental results indicate that the proposed approach can outperform the state-of-the-art algorithms and achieve more accurate recommendations.
C1 [Zhang, Lan; Zhou, Huali] Auckland Univ Technol, Sch Engn Comp & Math Sci, Auckland, New Zealand.
   [Li, Weihua] Auckland Univ Technol, Auckland, New Zealand.
   [Li, Xiang] Univ Tasmania, Informat Technol & Syst, Hobart, Tas, Australia.
   [Bai, Quan] Univ Tasmania, Sch ICT, Hobart, Tas, Australia.
RP Zhang, L (通讯作者)，Auckland Univ Technol, Sch Engn Comp & Math Sci, Auckland, New Zealand.
EM xth6271@autuni.ac.nz; xli68@utas.edu.au; weihua.li@aut.ac.nz;
   hualizhouster@gmail.com; quan.bai@utas.edu.au
OI Li, Xiang/0000-0001-9251-2852; Li, Weihua/0000-0001-9215-4979
CR Barbero LG, 2010, IEEE ICC
   Chaudhari S, 2017, AI COMMUN, V30, P141, DOI 10.3233/AIC-170728
   Cui NN, 2020, WORLD WIDE WEB, V23, P491, DOI 10.1007/s11280-019-00726-5
   Guo Q., 2020, IEEE T KNOWL DATA EN
   Han JY, 2020, IEEE T NEUR NET LEAR, V31, P737, DOI 10.1109/TNNLS.2019.2909432
   Haruna K, 2017, APPL SCI-BASEL, V7, DOI 10.3390/app7121211
   Hu BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1531, DOI 10.1145/3219819.3219965
   Kadie C., 1998, 14 C UNC ART INT SAN, P43
   Kempe David, 2003, P ACM C KNOWL DISC D, P137
   Li WH, 2019, IEEE T BIG DATA, V5, P223, DOI 10.1109/TBDATA.2018.2824826
   Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3
   Lu J, 2015, DECIS SUPPORT SYST, V74, P12, DOI 10.1016/j.dss.2015.03.008
   Lu S., 2018, MATH PROBL ENG, V2018, P1
   Ma W., 2019, WORLD WID WEB C
   Manotumruksa J., 2018, 41 INT ACM SIGIR C R
   Mooney C, 1997, MONTE CARLO SIMULATI
   Namahoot CS., 2015, RECENT ADV INFORM CO
   Oku K., 2006, 7 INT C MOB DAT MAN
   Panzarasa P, 2009, J AM SOC INF SCI TEC, V60, P911, DOI 10.1002/asi.21015
   Patel H., 2018, INT J COMPUT SCI ENG, V6, P74, DOI [10.26438/ijcse/v6i10.7478, DOI 10.26438/IJCSE/V6I10.7478]
   Pazzani MJ, 1999, ARTIF INTELL REV, V13, P393, DOI 10.1023/A:1006544522159
   Sarwar B, 2001, P 10 INT C WORLD WID, P285
   Tewari G, 2003, DECIS SUPPORT SYST, V34, P127, DOI 10.1016/S0167-9236(02)00076-3
   Tung H.-W., 2004, IEEE INT C E TECHN E
   Van Setten M., 2004, INT C AD HYP AD WEB
   Wang C., 2020, P AAAI C ART INT
   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
   Xiang Liang, 2010, P KDD
   Xiao C., 2020, AS PAC WEB APWEB WEB
   Xiao C., 2019, PAC RIM INT C ART IN
   Xu Y., 2018, 2018 IEEE INT C DAT
   Yuan ST, 2003, EXPERT SYST APPL, V24, P399, DOI 10.1016/S0957-4174(02)00189-6
   Zhang J., 2007, P 2007 ACM C REC SYS
   Zhang WX, 2018, PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE2018), DOI 10.1145/3207677.3277928
   Zhuang FZ, 2017, NEURAL NETWORKS, V90, P83, DOI 10.1016/j.neunet.2017.03.009
NR 37
TC 2
Z9 2
U1 7
U2 23
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1004-3756
EI 1861-9576
J9 J SYST SCI SYST ENG
JI J. Syst. Sci. Syst. Eng.
PD AUG
PY 2021
VL 30
IS 4
BP 482
EP 494
DI 10.1007/s11518-021-5499-z
EA MAY 2021
PG 13
WC Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Operations Research & Management Science
GA TZ1BL
UT WOS:000653624800001
DA 2022-08-23
ER

PT J
AU Wang, SY
   Qiu, JT
AF Wang, Siyu
   Qiu, Jiangtao
TI A deep neural network model for fashion collocation recommendation using
   side information in e-commerce
SO APPLIED SOFT COMPUTING
LA English
DT Article
DE Collocation recommendation; Knowledge graph; Deep learning
AB A fashion collocation recommendation model is a useful tool to enhance customer experiences and increase cross-selling in e-commerce. Prior research efforts focus on using titles and images to build the model. However, accessible side information in e-commerce, such as textual descriptions, purchase data and category information of items, generally bear valuable information regarding this task. This study seeks to develop a fashion collocation recommendation model that leverages accessible side information in e-commerce. First, a knowledge graph is built to model the purchase data and category information of items and an approach is subsequently proposed to learn the knowledge embeddings in the knowledge graph. To cope with the challenge of Chinese titles, a model is developed to encode the Chinese titles into vectors. Finally, a fashion collocation recommendation model combines the knowledge embeddings of the knowledge graph and the encoded titles to compute the probability of fashion collocation between items. The experiments are conducted on two datasets. Compared with baseline models, our model obtains nearly 6% and 1% AUC improvement in TIANCHI and POG dataset, respectively. The experimental results demonstrate that our efforts to leverage side information to improve fashion collocation recommendations are feasible and effective. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Wang, Siyu; Qiu, Jiangtao] Southwestern Univ Finance & Econ, Chengdu, Peoples R China.
RP Qiu, JT (通讯作者)，Southwestern Univ Finance & Econ, Chengdu, Peoples R China.
EM siyu_wang@smail.swufe.edu.cn; qiujt_t@swufe.edu.cn
FU National Natural Science Foundation of China [71571145]
FX This work was supported by the National Natural Science Foundation of
   China (No. 71571145).
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen W, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2662, DOI 10.1145/3292500.3330652
   Chung HC, 2009, COMPUT STAT DATA AN, V53, P4358, DOI 10.1016/j.csda.2009.06.009
   Grauman, 2017, P IEEE INT C COMP VI, P22
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Huang J, 2005, IEEE T KNOWL DATA EN, V17, P299, DOI 10.1109/TKDE.2005.50
   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
   Kalchbrenner N, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P655, DOI 10.3115/v1/p14-1062
   Kang WC, 2017, IEEE DATA MINING, P207, DOI 10.1109/ICDM.2017.30
   Kim Y., 2014, P 2014 C EMP METH NA, P1746
   Lai YY, 2019, AAAI CONF ARTIF INTE, P4130
   Li YC, 2017, IEEE T MULTIMEDIA, V19, P1946, DOI 10.1109/TMM.2017.2690144
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu XP, 2020, AGR FOREST METEOROL, V282, DOI 10.1016/j.agrformet.2019.107863
   McAuley J, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2783258.2783381
   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
   Paszke A, 2019, ADV NEUR IN, V32
   Qiu JT, 2015, ELECTRON COMMER RES, V15, P427, DOI 10.1007/s10660-015-9191-6
   Le Q, 2014, PR MACH LEARN RES, V32, P1188
   Santos Cicero Nogueira dos, 2017, ICLR, P2
   Sawada, 22 INT JOINT C ART I
   Shen DH, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P440
   Vasileva MI, 2018, LECT NOTES COMPUT SC, V11220, P405, DOI 10.1007/978-3-030-01270-0_24
   Vaswani A, 2017, ADV NEUR IN, V30
   Veit A, 2015, IEEE I CONF COMP VIS, P4642, DOI 10.1109/ICCV.2015.527
   Wang, 2017, ARXIV ARTIF INTELL
   Yang XJ, 2020, APPL SOFT COMPUT, V96, DOI 10.1016/j.asoc.2020.106636
   YAO L, 2019, PROCEEDINGS OF AAAI, V33, P7370, DOI DOI 10.1609/AAAI.V33I01.33017370
   Yuksel A. E., 2019, P INT C REC ADV NAT, P1380
   Zhang HJ, 2020, IEEE T IND INFORM, V16, P6750, DOI 10.1109/TII.2019.2924725
   Zhang HJ, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (IEEE ICWS 2018), P179, DOI 10.1109/ICWS.2018.00030
   Zhang Y, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P77, DOI 10.1145/3240323.3240368
NR 33
TC 2
Z9 2
U1 10
U2 13
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1568-4946
EI 1872-9681
J9 APPL SOFT COMPUT
JI Appl. Soft. Comput.
PD OCT
PY 2021
VL 110
AR 107753
DI 10.1016/j.asoc.2021.107753
EA JUL 2021
PG 10
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XN6PP
UT WOS:000729624800007
DA 2022-08-23
ER

PT J
AU Zhang, Q
   Wen, YQ
   Zhou, CH
   Long, H
   Han, D
   Zhang, F
   Xiao, CS
AF Zhang, Qi
   Wen, Yuanqiao
   Zhou, Chunhui
   Long, Hai
   Han, Dong
   Zhang, Fan
   Xiao, Changshi
TI Construction of Knowledge Graphs for Maritime Dangerous Goods
SO SUSTAINABILITY
LA English
DT Article
DE knowledge graph; maritime dangerous goods; ontology; knowledge
   representation; knowledge management
ID RISK-MANAGEMENT; ONTOLOGY; SYSTEM; TRANSPORTATION; FRAMEWORK
AB Dangerous goods occupy an important proportion in international shipping, and government and enterprises pay a lot of attention to transport safety. There are a wide variety of dangerous goods, and the knowledge involved is extensive and complex. Organizing and managing this knowledge plays an important role in the safe transportation of dangerous goods. The knowledge graph is a mass of brand-new knowledge management technologies that provide powerful technical support for integrating domain knowledge and solving the problem of the knowledge island. This paper first introduces the knowledge of maritime dangerous goods (MDG); constructs a three-layer knowledge structure of MDG, dividing this knowledge into two categories; uses ontology to express the concepts, entities, and relations of MDG; and puts forward the representation methods of the conceptual layer and entity layer and designs them in detail. Finally, the knowledge graph of maritime dangerous goods (KGMDG) is constructed. Furthermore, we demonstrate the knowledge visualization, retrieval, and automatic judgment of segregation requirement based on KGMDG. It is proved that KGMDG does not only help to simplify the retrieval process of professional knowledge and to promote intelligent transportation but is also conducive to the sharing, dissemination, and utilization of MDG knowledge.
C1 [Zhang, Qi; Zhou, Chunhui; Han, Dong; Zhang, Fan; Xiao, Changshi] Wuhan Univ Technol, Sch Nav, Wuhan 430063, Hubei, Peoples R China.
   [Wen, Yuanqiao] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Hubei, Peoples R China.
   [Wen, Yuanqiao; Zhou, Chunhui; Zhang, Fan; Xiao, Changshi] Natl Engn Res Ctr Water Transport Safety, Wuhan 430063, Hubei, Peoples R China.
   [Wen, Yuanqiao; Zhou, Chunhui; Zhang, Fan; Xiao, Changshi] Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Hubei, Peoples R China.
   [Long, Hai] Univ Leipzig, IMISE, D-04107 Leipzig, Germany.
RP Zhou, CH (通讯作者)，Wuhan Univ Technol, Sch Nav, Wuhan 430063, Hubei, Peoples R China.; Wen, YQ (通讯作者)，Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Hubei, Peoples R China.; Wen, YQ; Zhou, CH (通讯作者)，Natl Engn Res Ctr Water Transport Safety, Wuhan 430063, Hubei, Peoples R China.; Wen, YQ; Zhou, CH (通讯作者)，Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Hubei, Peoples R China.
EM zhangqiwhut@163.com; wenyqwhut@foxmail.com; chunhui@whut.edu.cn;
   longhail2008@yahoo.de; handong@whut.edu.cn; michael_zf@whut.edu.cn;
   cs_xiao@hotmail.com
OI Zhang, Qi/0000-0001-9990-4269
FU National Science Foundation of China (NSFC) [51709218]; National Key R&D
   Program of China [2018YFC0213900]
FX This research was funded by the National Science Foundation of China
   (NSFC) (Project Number: 51679180), the National Key R&D Program of China
   (Project Number: 2018YFC1407405), the National Key R&D Program of China
   (Project Number: 2018YFC0213900), the National Science Foundation of
   China (NSFC) (Project Number: 51709218).
CR Antezana E, 2009, BRIEF BIOINFORM, V10, P392, DOI 10.1093/bib/bbp024
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Braffort A, 2016, UNIVERSAL ACCESS INF, V15, P541, DOI 10.1007/s10209-015-0415-2
   Chau KW, 2007, ADV ENG SOFTW, V38, P172, DOI 10.1016/j.advengsoft.2006.07.003
   Chen PH, 2018, IEEE ACCESS, V6, P31553, DOI 10.1109/ACCESS.2018.2839607
   Cresswell M.J., 2017, LOGICS LANGUAGES
   Davis E., 2015, INT ENCY SOCIAL BEHA
   Ding LY, 2016, SAFETY SCI, V87, P202, DOI 10.1016/j.ssci.2016.04.008
   Ellis J, 2011, SAFETY SCI, V49, P1231, DOI 10.1016/j.ssci.2011.04.004
   Ellis J, 2010, WMU J MARIT AFF, V9, P5, DOI 10.1007/BF03195163
   Eriksson O, 2018, J INF TECHNOL-UK, V33, P105, DOI 10.1057/s41265-018-0053-2
   Ernst P, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0549-5
   Gruber T, 2009, ENCY DATABASE SYSTEM, P1963, DOI [DOI 10.1007/978-0-387-39940-9_1318, 10.1007/978-0-387-39940-9_1318]
   Horrocks I., 2004, W3C MEMBER SUBMISSIO, V21, P79
   IMO IMDG Code, 2014, IMDG COD INT MAR DAN
   Lam J., 2012, P INT FORUM SHIPP PO
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lieggio M, 2012, TRANSPORT PLAN TECHN, V35, P677, DOI 10.1080/03081060.2012.718155
   Lirong J., 2015, J MED INFORM, V36, P51
   Liu H, 2017, J ARTIF INTELL SOFT, V7, P111, DOI 10.1515/jaiscr-2017-0008
   Liu P., 2018, P INT C SMART HLTH W, P78
   Liu XY, 2017, J LOSS PREVENT PROC, V49, P983, DOI 10.1016/j.jlp.2017.02.004
   Miao F., 2018, P 2018 IEEE 3 ADV IN
   Mullai A., 2007, RISK ANAL FRAMEWORK, VII
   Nardi D., 2010, DESCRIPTION LOGIC HD
   Nazaruks V, 2017, ENASE: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, P346, DOI 10.5220/0006388303460354
   Niu X., 2011, LECT NOTES COMPUTER
   Qi G. L., 2017, TECHNOL INTELL ENG, V3, P4, DOI DOI 10.3772/J.ISSN.2095-915X.2017.01.002
   Rebele T., 2016, P 15 INT SEM WEB C K
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Singhal A., INTRO KNOWLEDGE GRAP
   van der Vlies V, 2015, J RISK RES, V18, P947, DOI 10.1080/13669877.2014.940595
   Wang RJ, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1487, DOI 10.1145/3269206.3269252
   Xu B., 2017, LECT NOTES COMPUTER
   Xu Z, 2015, J SYST SOFTWARE, V102, P217, DOI 10.1016/j.jss.2014.07.024
   Yan JZ, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103429
   Yang Q, 2018, J LOSS PREVENT PROC, V52, P81, DOI 10.1016/j.jlp.2018.02.001
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   Zhang C, 2017, APPL ENERG, V204, P1412, DOI 10.1016/j.apenergy.2017.03.130
   Zhang YZ, 2015, ADV ENG INFORM, V29, P985, DOI 10.1016/j.aei.2015.09.002
NR 41
TC 11
Z9 11
U1 19
U2 48
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAY 2
PY 2019
VL 11
IS 10
AR 2849
DI 10.3390/su11102849
PG 16
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 IC5LV
UT WOS:000471010300132
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Wang, P
   Zhou, J
AF Wang, Peng
   Zhou, Jing
TI JECI++: A Modified Joint Knowledge Graph Embedding Model for Concepts
   and Instances
SO BIG DATA RESEARCH
LA English
DT Article
DE Knowledge graph; Embedding; Hierarchical tree; Neighbor context;
   Circular convolution
AB Concepts and instances are important parts in knowledge graphs, but most knowledge graph embedding models treat them as entities equally, that leads to inaccurate embeddings of concepts and instances. Aiming to solve this problem, we propose a novel knowledge graph embedding model called JECI++ to jointly embed concepts and instances. First, JECI++ simplifies hierarchical concepts based on sub Class Of relation and instance Of relation, then re-links instances to the simplified concepts as new instanceOf triples. Consequently, an instance can be obtained by its neighbor instances and its belonging simplified concepts. Second, circular convolution is utilized to locate an instance in the embedding space, based on neighbor instances and simplified concepts. Finally, simplified concepts and instances are jointly embedded by the embeddings learner with CBOW (Continuous Bag-of-Words) or Skip-Gram strategies. Especially, JECI++ can alleviate the problem of complex relations by incorporating neighbor information of instances. JECI++ is evaluated by link prediction and triple classification on real world datasets. Experimental results demonstrate that it outperforms state-of-the-art models in most cases. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Wang, Peng; Zhou, Jing] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Wang, Peng] Southeast Univ, Sch Cyber Sci & Engn, Nanjing, Peoples R China.
RP Wang, P (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
EM pwang@seu.edu.cn
OI Wang, Peng/0000-0001-8782-857X
FU National Key R&D Program of China [2018YFD1100302, 2018YFB1003901];
   National Natural Science Foundation of China [61872177, 61972082,
   61472077]; "13th Five-Year" All-Army Common Information System Equipment
   PreResearch Project [31511110310, 31510040201]
FX The authors would like to thank the anonymous reviewers for their
   helpful and constructive comments and suggestions that greatly
   contributed to improving the paper. The work is supported by National
   Key R&D Program of China (2018YFD1100302, 2018YFB1003901), the National
   Natural Science Foundation of China (No. 61872177, No. 61972082, No.
   61472077), and "13th Five-Year" All-Army Common Information System
   Equipment PreResearch Project (No. 31511110310, No. 31510040201).
CR Asprino L, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3962
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bollacker K., 2008, P 27 ACM SIGMOD INT
   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
   Chen Z, 2016, ADVANCED MATERIAL ENGINEERING (AME 2015), P192
   Davis PJ, 2013, CIRCULANT MATRICES
   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
   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
   Guo S, 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, P84
   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
   Hogan A., ARXIV200302320
   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
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   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
   Lv X, 2018, EMNLP, P1971
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Turian J, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P384
   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
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Yang B., 2015, P 3 INT C LEARN REPR
   Zhong H., 2015, P 2015 C EMP METH NA, P267
   Zhou J, 2019, J COASTAL RES, P82, DOI 10.2112/SI97-010.1
NR 31
TC 0
Z9 0
U1 1
U2 10
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-5796
J9 BIG DATA RES
JI Big Data Res.
PD MAY 15
PY 2021
VL 24
AR 100160
DI 10.1016/j.bdr.2020.100160
EA APR 2021
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 RQ5LE
UT WOS:000642459200007
DA 2022-08-23
ER

PT J
AU Zhu, AJ
   Ouyang, DQ
   Liang, S
   Shao, J
AF Zhu, Anjie
   Ouyang, Deqiang
   Liang, Shuang
   Shao, Jie
TI Step by step: A hierarchical framework for multi-hop knowledge graph
   reasoning with reinforcement learning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Multi-hop reasoning; Hierarchical reinforcement
   learning; Dynamic prospect
AB Recently, knowledge graph reasoning has sparked great interest in research community, which aims at inferring missing information in triples and provides critical support to various tasks (e.g., question answering and recommendation). To date, multi-hop reasoning is a dominant approach which infers the target answer by walking along the path connecting entities and relations, ensuring both accuracy and interpretability. However, in most knowledge graphs, there are multiple relations related to an identical entity, and multiple tail entities for an identical pair of head entity and relation. Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning. In order to deal with such an issue, this work presents a novel paradigm for knowledge graph reasoning by decomposing it to a two-level hierarchical decision process. We apply the hierarchical reinforcement learning framework which dismantles the task into a high-level process for relation detector and a low-level process for entity reasoning, respectively. In this way, the action space is effectively controlled where the policies can be optimized. The interactions between entity and relation decision enhance the rationality of reasoning. Moreover, we introduce a dynamic prospect mechanism for low-level policy where the information can guide us to a refined and improved action space, assisted by embedding based method. Our proposed model is evaluated on four benchmark datasets and the results validate its superiority over state-of-the-art baselines, showing the interpretability of reasoning process simultaneously. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
C1 [Zhu, Anjie; Liang, Shuang; Shao, Jie] Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China.
   [Ouyang, Deqiang] Chongqing Univ, Chongqing 400044, Peoples R China.
   [Shao, Jie] UESTC, Shenzhen Inst Adv Study, Shenzhen 518110, Peoples R China.
RP Ouyang, DQ (通讯作者)，Chongqing Univ, Chongqing 400044, Peoples R China.
EM deqiangouyang@cqu.edu.cn
OI Ouyang, Deqiang/0000-0003-2259-886X
FU National Natural Science Foundation of China [61832001]; Shenzhen
   Science and Technology Program, China [JCYJ20210324121213037]; Zhejiang
   Lab's International Talent Fund for Young Professionals, China;
   CAAI-Huawei MindSpore Open Fund, China
FX This work is supported by the National Natural Science Foundation of
   China (No. 61832001) , Shenzhen Science and Technology Program, China
   (No. JCYJ20210324121213037) , the Zhejiang Lab's International Talent
   Fund for Young Professionals, China and CAAI-Huawei MindSpore Open Fund,
   China.
CR Amin S., 2020, P ICML, P257
   Bacon PL, 2017, AAAI CONF ARTIF INTE, P1726
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Chen W., 2018, P 2018 C N AM CHAPT, V1, P1823, DOI [10.18653/v1/N18-1165, DOI 10.18653/V1/N18-1165]
   Das R., 2018, 6 INT C LEARNING REP
   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
   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
   Fu Cong, 2019, P 2019 C EMP METH NA, P2672
   He GL, 2021, WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P535, DOI 10.1145/3437963.3441753
   Jiasheng Zhang, 2021, Database Systems for Advanced Applications. 26th International Conference, DASFAA 2021. Proceedings. Lecture Notes in Computer Science (LNCS 12681), P207, DOI 10.1007/978-3-030-73194-6_15
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kingma D. P., 2017, ARXIV
   Kok S., 2007, P 24 INT C MACH LEAR, P433, DOI DOI 10.1145/1273496.1273551
   Lan YS, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P969
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   Li Z, 2021, KNOWL-BASED SYST, V212, DOI 10.1016/j.knosys.2020.106618
   Liang S, 2021, IEEE T KNOWL DATA EN, DOI [10.1109/TKDE.2021.3110898, DOI 10.1109/TKDE.2021.3110898]
   Lin X.V., 2018, ARXIV180810568
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Liu ZH, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2395
   Lv X., 2020, P 2020 C EMPIRICAL M, P5694
   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., 2018, P 2018 C N AM CHAPT, P327, DOI DOI 10.18653/V1/N18-2053
   Qu M, 2019, ADV NEUR IN, V32
   Rocktaschel T., 2017, ADV NEURAL INFORM PR, V30, P3788
   Sanyal S., 2020, ICLR
   Shen Y., 2018, WORKSHOP TRACK P
   Song XY, 2021, INFORM SCIENCES, V580, P510, DOI 10.1016/j.ins.2021.08.100
   Sun Z., 2019, 7 INT C LEARNING REP
   Sutton RS, 1999, ARTIF INTELL, V112, P181, DOI 10.1016/S0004-3702(99)00052-1
   Sutton RS, 2000, ADV NEUR IN, V12, P1057
   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
   Wan GJ, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1926
   Wang Q, 2020, KNOWL-BASED SYST, V197, DOI 10.1016/j.knosys.2020.105910
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang ZK, 2021, KNOWL-BASED SYST, V223, DOI 10.1016/j.knosys.2021.107035
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yang B., 2015, ICLR
   Yang F, 2017, ADV NEUR IN, V30
   Yang Y., 2021, ARXIV PREPRINT ARXIV, P2021, DOI 10.1109/TKDE.2021.3101356
   Zhu AJ, 2021, IEEE T NEUR NET LEAR, DOI 10.1109/TNNLS.2021.3128666
NR 48
TC 1
Z9 1
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 JUL 19
PY 2022
VL 248
AR 108843
DI 10.1016/j.knosys.2022.108843
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1M3CM
UT WOS:000799850400006
DA 2022-08-23
ER

PT J
AU Shi, Y
   Xiao, Y
   Quan, P
   Lei, ML
   Niu, LF
AF Shi, Yong
   Xiao, Yang
   Quan, Pei
   Lei, MingLong
   Niu, Lingfeng
TI Distant Supervision Relation Extraction via adaptive dependency-path and
   additional knowledge graph supervision
SO NEURAL NETWORKS
LA English
DT Article
DE Distant Supervision Relation Extraction; Dependency-path; Adaptive
   method; Additional knowledge graph supervision
ID WEB
AB Relation Extraction systems train an extractor by aligning relation instances in Knowledge Base with a large amount of labeled corpora. Since the labeled datasets are very expensive, Distant Supervision Relation Extraction (DSRE) utilizes rough corpus annotated with Knowledge Graph to reduce the cost of acquisition. Nevertheless, the data noise problem limits the performance of the DSRE. Dependency trees can be used to filter the wrong-labeled instances in the distant supervision bag. However, existing dependency tree relation extraction strategies are all based on manually-set paths between the subject and object entities, and suffer from the problem of pruning the trees too aggressively or too insufficiently. To circumvent the shortcomings, in this paper, we propose a novel DSRE framework A2DSRE, based on the Adaptive dependency-path and Additional KG supervision. To obtain the dependency paths related to entity relations adaptively, we introduce an advanced graph neural network-GeniePath into DSRE, which assigns higher weights to those direct neighbor words that contribute more to relation prediction through breadth exploration, and conducts depth exploration to determine the correlation between relations and high-order neighbors. In this way, the irrelevant nodes are pruned while the relevant nodes are kept, our method can obtain more appropriate paths associated with relations. At the same time, to further reduce the noises in the data, we incorporate additional supervision information from the knowledge graph by retracting the margin between the representation of the bag and the pre-training knowledge graph embedding. Extensive numerical experiments validate the effectiveness of our new method. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Xiao, Yang; Quan, Pei] Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China.
   [Shi, Yong; Niu, Lingfeng] Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China.
   [Shi, Yong; Xiao, Yang; Quan, Pei; Niu, Lingfeng] Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China.
   [Shi, Yong; Xiao, Yang; Quan, Pei; Niu, Lingfeng] Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China.
   [Shi, Yong] Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA.
   [Lei, MingLong] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China.
RP Niu, LF (通讯作者)，Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China.
EM yshi@ucas.ac.cn; ynynny@sina.com; quanpei17@mails.ucas.ac.cn;
   leiml@bjut.edu.cn; niulf@ucas.ac.cn
RI Xiao, Yang/GOP-0787-2022; Lei, Minglong/AAH-2944-2020
OI Xiao, Yang/0000-0003-1058-9536; Lei, Minglong/0000-0003-4406-8747; niu,
   ling feng/0000-0002-5827-8449; Quan, Pei/0000-0001-6005-5714
FU National Natural Science Foundation of China [11671379, 11991021,
   91546201, 71331005, 71932008]; UCAS, China [Y55202LY00]
FX We would like to thank the anonymous reviewers for their useful comments
   that improved the quality of this work. We would also like to express
   our gratitude to the handling editors for their valuable comments and
   suggestions. This work was supported by the National Natural Science
   Foundation of China [No. 11671379, No. 11991021, No. 91546201, No.
   71331005, No. 71932008], UCAS, China Grant [No. Y55202LY00].
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
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   BENGIO Y, 1994, IEEE T NEURAL NETWOR, V5, P157, DOI 10.1109/72.279181
   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
   Bunescu R., 2006, ADV NEURAL INFORM PR, V18, P171
   Cui WY, 2017, PROC VLDB ENDOW, V10, P565, DOI 10.14778/3055540.3055549
   Defferrard M., 2016, NIPS, P3844
   Fazzinga B, 2011, J WEB SEMANT, V9, P453, DOI 10.1016/j.websem.2011.08.003
   Finkel J. R., 2005, P 43 ANN M ASS COMP, P363, DOI DOI 10.3115/1219840.1219885
   Graves A, 2005, NEURAL NETWORKS, V18, P602, DOI 10.1016/j.neunet.2005.06.042
   GuoDong Z., 2005, P 43 ANN M ASS COMP, P427, DOI DOI 10.3115/1219840.1219893
   Han X, 2018, AAAI CONF ARTIF INTE, P4832
   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
   Jat, 2018, ARXIV180406987
   Ji GL, 2017, AAAI CONF ARTIF INTE, P3060
   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
   Klambauer G., 2017, P 31 INT C NEUR INF, P972
   Lee K., 2017, P 2017 C EMP METH NA, P188, DOI [10.18653/v1/D17-1018, DOI 10.18653/V1/D17-1018]
   Liao J, 2017, KNOWL-BASED SYST, V135, P9, DOI 10.1016/j.knosys.2017.07.015
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lin YK, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2124
   Ling Xiao, 2012, P AAAI C ART INT, V12, P94
   Liu Y., 2014, P COLING 2014 25 INT, P2107
   Liu ZQ, 2019, AAAI CONF ARTIF INTE, P4424
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Marcheggiani D, 2017, P 2017 C EMP METH NA, DOI DOI 10.18653/V1/D17-1159
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   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
   Qu JF, 2018, NEURAL NETWORKS, V100, P59, DOI 10.1016/j.neunet.2018.01.006
   RAVAL M, 2012, OFFICIAL GOOGLE BLOG, V5, DOI DOI 10.1186/1756-0500-5-594
   Riedel S, 2010, LECT NOTES ARTIF INT, V6323, P148, DOI 10.1007/978-3-642-15939-8_10
   Santoro A., 2017, ADV NEURAL INFORM PR, P4967
   Schmitz M., 2012, P 2012 JOINT C EMP M, P523
   Socher R., 2014, PROC C EMPIRICAL MET, P1532, DOI 10.3115/v1/D14-1162
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Surdeanu M., 2012, P 2012 JOINT C EMP M, P455
   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
   Nguyen TH, 2018, AAAI CONF ARTIF INTE, P5900
   Vashishth S, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1605
   Vashishth Shikhar, 2018, P 2018 C EMP METH NA, P1257
   Velickovic P., 2018, P 6 INT C LEARNING R
   Wang G., 2018, P 2018 C EMPIRICAL M, P2246, DOI DOI 10.18653/V1/D18-1248
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yaghoobzadeh Y, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P1183
   Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205
   Zeng D., 2014, P COLING, P2335
   Zeng D., 2015, P C EMP METH NAT LAN, P1753, DOI DOI 10.18653/V1/D15-1203
   Zhang CY, 2015, KNOWL-BASED SYST, V83, P128, DOI 10.1016/j.knosys.2015.03.017
   Zhang HZ, 2019, KNOWL-BASED SYST, V183, DOI 10.1016/j.knosys.2019.104870
   Zhang Ningyu, 2019, LONG SHORT PAPERS, P3016, DOI [10.18653/v1/n19-1306, DOI 10.18653/V1/N19-1306]
   Zhang Y., 2018, P 2018 C EMP METH NA, DOI [10.18653/v1/D18-1244, DOI 10.18653/V1/D18-1244]
   Zhou P, 2018, NEURAL NETWORKS, V108, P240, DOI 10.1016/j.neunet.2018.08.016
NR 58
TC 7
Z9 7
U1 6
U2 32
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0893-6080
EI 1879-2782
J9 NEURAL NETWORKS
JI Neural Netw.
PD FEB
PY 2021
VL 134
BP 42
EP 53
DI 10.1016/j.neunet.2020.10.012
PG 12
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA PL7KV
UT WOS:000603296800004
PM 33285426
DA 2022-08-23
ER

PT J
AU Peng, ZH
   Yu, H
   Jia, XY
AF Peng, Zhihan
   Yu, Hong
   Jia, Xiuyi
TI Path-based reasoning with K-nearest neighbor and position embedding for
   knowledge graph completion
SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph completion; Path reasoning; Position embedding; Relation
   similarity; K-nearest neighbor
AB Knowledge graph completion aims to perform link prediction between entities. Reasoning over paths in incomplete knowledge graphs has become a hot research topic. However, most of the existing path reasoning methods ignore both the overlapping phenomenon of paths between similar relations and the order information of relations in paths, and they only consider the obvious paths between entities. To address the problems of knowledge graph reasoning, a new path-based reasoning method with K-Nearest Neighbor and position embedding is proposed in this paper. The method first projects entities and relations to continuous vector space, and then utilizes the idea of the K-Nearest Neighbor algorithm to find the K nearest neighbors of each relation. After that, the paths of similar relations are merged. Then, paths are modeled through the combination operations on relations. The position information of the relations is considered during the combination, that is, the position embedding is added to the relation vector in the path. A series of experiments are conducted on real datasets to prove the effectiveness of the proposed method. The experimental results show that the proposed method significantly outperforms all baselines on entity prediction and relation prediction tasks.
C1 [Peng, Zhihan; Yu, Hong] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing 400065, Peoples R China.
   [Jia, Xiuyi] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China.
RP Yu, H (通讯作者)，Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing 400065, Peoples R China.
EM yuhong@cqupt.edu.cn
FU National Natural Science Foundation of China [61876027, 61751312,
   61533020]; Natural Science Foundation of Chongqing
   [cstc2019jcyj-cxttX0002]
FX This work was jointly supported by the National Natural Science
   Foundation of China (61876027, 61751312, 61533020), and the Natural
   Science Foundation of Chongqing (cstc2019jcyj-cxttX0002).
CR 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
   Chang K., 2014, PROC C EMPIRICAL MET, P1568
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Guan R, 2016, ARXIV161107232
   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
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Kazemi SM, 2018, ADV NEUR IN, V31
   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, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lei K., 2019, NEURAL COMPUT APPL, P1
   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
   Mikolov T., 2013, PROBLEM SOLVING ALGO, P3111
   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
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Ningning Jia, 2020, Advances in Information Retrieval, 42nd European Conference on IR Research, ECIR 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12035), P17, DOI 10.1007/978-3-030-45439-5_2
   Niu GL, 2020, AAAI CONF ARTIF INTE, V34, P2950
   Pierleoni A, 2019, ARXIV191100492
   Sacenti JAP, 2022, J INTELL INF SYST, V58, P43, DOI 10.1007/s10844-021-00650-z
   Shen Y., 2019, P 2019 SIAM INT C DA, P639
   Singhal Amit, 2012, OFFICIAL GOOGLE BLOG
   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
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang H., 2020, ARXIV PREPRINT ARXIV
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yang B., 2015, P INT C LEARN REPR I
   Yang M, 2020, IEEE T KNOWL DATA EN
   Zhang S, 2019, ADV NEUR IN, V32
   Zhang SH, 2020, J INTELL INF SYST, V55, P445, DOI 10.1007/s10844-019-00592-7
   Zhang T., 2019, P EMNLP IJCNLP, P2662
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
NR 41
TC 1
Z9 1
U1 8
U2 10
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0925-9902
EI 1573-7675
J9 J INTELL INF SYST
JI J. Intell. Inf. Syst.
PD JUN
PY 2022
VL 58
IS 3
BP 513
EP 533
DI 10.1007/s10844-021-00671-8
EA SEP 2021
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1G7YV
UT WOS:000702237700001
DA 2022-08-23
ER

PT J
AU Yoo, S
   Jeong, O
AF Yoo, SoYeop
   Jeong, OkRan
TI EP-Bot: Empathetic Chatbot Using Auto-Growing Knowledge Graph
SO CMC-COMPUTERS MATERIALS & CONTINUA
LA English
DT Article
DE Emotional chatbot; conversational AI; knowledge graph; emotion
AB People occasionally interact with each other through conversation. In particular, we communicate through dialogue and exchange emotions and information from it. Emotions are essential characteristics of natural language. Conversational artificial intelligence is an integral part of all the technologies that allow computers to communicate like humans. For a computer to interact like a human being, it must understand the emotions inherent in the conversation and generate the appropriate responses. However, existing dialogue systems focus only on improving the quality of understanding natural language or generating natural language, excluding emotions. We propose a chatbot based on emotion, which is an essential element in conversation. EP-Bot (an Empathetic PolarisX-based chatbot) is an empathetic chatbot that can better understand a person's utterance by utilizing PolarisX, an auto growing knowledge graph. PolarisX extracts new relationship information and expands the knowledge graph automatically. It is helpful for computers to understand a person's common sense. The proposed EP-Bot extracts knowledge graph embedding using PolarisX and detects emotion and dialog act from the utterance. Then it generates the next utterance using the embeddings. EP-Bot could understand and create a conversation, including the person's common sense, emotion, and intention. We verify the novelty and accuracy of EP-Bot through the experiments.
C1 [Yoo, SoYeop; Jeong, OkRan] Gachon Univ, Seongnam Si 13120, South Korea.
RP Jeong, O (通讯作者)，Gachon Univ, Seongnam Si 13120, South Korea.
EM orjeong@gachon.ac.kr
FU Basic Science Research Program through the NRF (National Research
   Foundation of Korea); MSIT (Ministry of Science and ICT) , Korea, under
   the National Program for Excellence in SW; Gachon University
   [NRF2019R1A2C1008412, 2015-0-00932, GCU-2019-0773]
FX Funding Statement: This research was supported by Basic Science Research
   Program through the NRF (National Research Foundation of Korea) , the
   MSIT (Ministry of Science and ICT) , Korea, under the National Program
   for Excellence in SW supervised by the IITP (Institute for Information &
   communications Technology Promotion) and the Gachon University research
   fund of 2019 (Nos. NRF2019R1A2C1008412, 2015-0-00932, GCU-2019-0773) .
CR Almeida F., 2019, ARXIV E PRINTS
   [Anonymous], 2018, ARXIV181208989
   Bell E., 2020, ARXIV200302958, P276
   Bogaerts B., 2019, ELECT P THEORETICAL, V306, P396
   Chavarin A., 2019, ARXIV190402743, P1
   Chien-Hao Kao, 2019, 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), DOI 10.1109/TAAI48200.2019.8959855
   Chowdhary KR, 2020, FUNDAMENTALS ARTIFIC, P603
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   GAO JF, 2019, INFORM RETRIEVAL, V0013, P00127, DOI DOI 10.1561/1500000074
   Havasi C., 2009, RECENT ADV NATURAL L, V2007, P269
   Hirschberg J, 2015, SCIENCE, V349, P261, DOI 10.1126/science.aaa8685
   Jing K., 2019, ARXIV190603591
   Karna Mounika, 2020, 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI). Proceedings, P988, DOI 10.1109/ICOEI48184.2020.9142879
   Lan Z., 2019, ARXIV190911942
   Lee MC, 2020, MULTIMED TOOLS APPL, V79, P19629, DOI 10.1007/s11042-020-08841-6
   LI Y, 2017, P 8 INT JOINT C NAT, V1, P986
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Otter DW, 2021, IEEE T NEUR NET LEAR, V32, P604, DOI 10.1109/TNNLS.2020.2979670
   Pamungkas E. W., 2019, ARXIV190609774
   Peters M., 2018, P C N AM CHAPT ASS C, V1, P2227
   Peters M., 2018, IMPROVING LANGUAGE U
   Poria S, 2019, IEEE ACCESS, V7, P100943, DOI 10.1109/ACCESS.2019.2929050
   Ram A., 2018, ARXIV180103604, P1
   Smith BG, 2018, PUBLIC RELAT REV, V44, P562, DOI 10.1016/j.pubrev.2018.07.005
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sutskever Ilya, 2014, ADV NEURAL INFORM PR, V27, P3104
   Vaswani A, 2017, ADV NEURAL INFORM PR, P5999
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Yan R., 2018, IJCAI INT JOINT C AR, V2018, P5520
   Yoo S, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112965
   Zaib M, 2020, PROCEEDINGS OF THE AUSTRALASIAN COMPUTER SCIENCE WEEK MULTICONFERENCE (ACSW 2020), DOI 10.1145/3373017.3373028
NR 32
TC 3
Z9 3
U1 9
U2 20
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 67
IS 3
BP 2807
EP 2817
DI 10.32604/cmc.2021.015634
PG 11
WC Computer Science, Information Systems; Materials Science,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Materials Science
GA QQ5NZ
UT WOS:000624571700007
OA gold
DA 2022-08-23
ER

PT J
AU Wang, LF
   Lu, XY
   Jiang, ZJ
   Zhang, ZK
   Li, RH
   Zhao, M
   Chen, DQ
AF Wang, Lifang
   Lu, Xinyu
   Jiang, Zejun
   Zhang, Zhikai
   Li, Ronghan
   Zhao, Meng
   Chen, Daqing
TI FRS: A simple knowledge graph embedding model for entity prediction
SO MATHEMATICAL BIOSCIENCES AND ENGINEERING
LA English
DT Article
DE knowledge graphs; entity prediction; knowledge graphs embedding; FRS
AB Entity prediction is the task of predicting a missing entity that has a specific relationship with another given entity. Researchers usually use knowledge graphs embedding(KGE) methods to embed triples into continuous vectors for computation and perform the tasks of entity prediction. However, KGE models tend to use simple operations to refactor entities and relationships, resulting in insufficient interaction of components of knowledge graphs (KGs), thus limiting the performance of the entity prediction model. In this paper, we propose a new entity prediction model called FRS(Feature Refactoring Scoring) to alleviate the problem of insufficient interaction and solve information incompleteness problems in the KGs. Different from the traditional KGE methods of directly using simple operations, the FRS model innovatively provides the procedure of feature processing in the entity prediction tasks, realizing the alignment of entities and relationships in the same feature space and improving the performance of entity prediction model. Although FRS is a simple three-layer network, we find that our own model outperforms state-of-the-art KGC methods in FB15K and WN18. Through extensive experiments on FRS, we discover several insights. For example, the effect of embedding size and negative candidate sampling probability on experimental results is in reverse.
C1 [Wang, Lifang; Lu, Xinyu; Jiang, Zejun; Zhang, Zhikai; Li, Ronghan; Zhao, Meng] Northwestern Polytech Univ, Sch Comp Sci & Engn, Xian 710072, Shaanxi, Peoples R China.
   [Chen, Daqing] London South Bank Univ, Sch Engn, Div Comp Sci & Informat, London SE1 0AA, England.
RP Jiang, ZJ (通讯作者)，Northwestern Polytech Univ, Sch Comp Sci & Engn, Xian 710072, Shaanxi, Peoples R China.
EM claud@nwpu.edu.cn
RI Chen, Daqing/AAD-8630-2019
OI Chen, Daqing/0000-0003-0030-1199
FU National Natural Science Foundation of China [61373120]; Electronic
   Service Technology and Engineering Lab, Northwestern Polytechnical
   University
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61373120. The work of this paper was
   supported by the Electronic Service Technology and Engineering Lab,
   Northwestern Polytechnical University, through a Ph.D. Scholarship.
CR [Anonymous], [No title captured]
   Bengio Y, 2011, 25 AAAI C ART INT
   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, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Chang L, 2017, IEEE ACCESS, V5, P20898, DOI 10.1109/ACCESS.2017.2759139
   Pham DH, 2018, DATA KNOWL ENG, V114, P26, DOI 10.1016/j.datak.2017.06.001
   Gad-Elrab MH, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P87, DOI 10.1145/3289600.3290996
   Guan SP, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P247, DOI 10.1145/3269206.3271704
   Hadley EB, 2019, READ RES QUART, V54, P41, DOI 10.1002/rrq.225
   He KM, 2016, LECT NOTES COMPUT SC, V9908, P630, DOI 10.1007/978-3-319-46493-0_38
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Ji G., 2016, 30 AAAI C ART INT
   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
   Lee CW, 2018, PROC CVPR IEEE, P1576, DOI 10.1109/CVPR.2018.00170
   Lin Y., KNOWLEDGE REPRESENTA
   Lin Y., 2015, 29 AAAI C ART INT
   Meilicke C, 2018, LECT NOTES COMPUT SC, V11136, P3, DOI 10.1007/978-3-030-00671-6_1
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Nguyen D. Q., ARXIV170308098
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M., 2016, 30 AAAI C ART INT
   Shi B., 2017, 31 AAAI C ART INT
   Shi B., 2018, 32 AAAI C ART INT
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Trouillon T, 2016, PR MACH LEARN RES, V48
   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 Z., 2014, 28 AAAI C ART INT
   Wang Z., 2016, IJCAI, P1293
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xiao H., 2017, 31 AAAI C ART INT
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie R., 2016, 30 AAAI C ART INT
   Xu K., ARXIV180603536
   Yoon H., 2016, P 2016 C N AM CHAPT, P907
   Young T, 2018, IEEE COMPUT INTELL M, V13, P55, DOI 10.1109/MCI.2018.2840738
   Zhen T., 2018, SCI PROGRAMMING, V2018, P1
NR 38
TC 1
Z9 1
U1 1
U2 11
PU AMER INST MATHEMATICAL SCIENCES-AIMS
PI SPRINGFIELD
PA PO BOX 2604, SPRINGFIELD, MO 65801-2604 USA
SN 1547-1063
EI 1551-0018
J9 MATH BIOSCI ENG
JI Math. Biosci. Eng.
PY 2019
VL 16
IS 6
BP 7789
EP 7807
DI 10.3934/mbe.2019391
PG 19
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA IZ8CY
UT WOS:000487331700083
PM 31698640
OA Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Wolff, V
   Lefebvre, A
   Renaud, J
AF Wolff, Valery
   Lefebvre, Arnaud
   Renaud, Jean
TI Maps of dispersions for machining processes
SO CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS
LA English
DT Article
DE knowledge capitalization; know-how maps; machining dispersions;
   tolerancing and dimensioning
ID TACIT KNOWLEDGE
AB During product design, the design office defines dimensional and geometrical parameters according to the use criteria and product functionality. The manufacturing department must integrate the manufacturing and the workpiece position dispersions during the choice of tools and the machine operating modes and parameter values to respect the functional constraints. In this article, the authors suggest a model for the turning dispersions taking into account not only geometrical specifications of position or orientation, but also the experience of method actors. A representation using the principle of know-how maps in two or three dimensions is privileged. The most interesting aspect is that these maps include tacit and explicit knowledge. An experimental study realized on a machine tool (HES 300) allows one to elaborate knowledge maps especially for the turning process.
C1 Univ Lyon 1, IUT B, Dept Mech Engn, F-69627 Villeurbanne, France.
   PRISMa Lab Lyon 1, F-69622 Villeurbanne, France.
   RGIP, F-54360 Nancy, France.
RP Wolff, V (通讯作者)，Univ Lyon 1, IUT B, Dept Mech Engn, 17 Rue France, F-69627 Villeurbanne, France.
EM valery.wolff@iutb.univ-lyon1.fr
RI WOLFF, VALERY/H-5906-2013
CR ALENGRY P, 1988, REV PSYCHOL FRANCAIS, V33, P171
   ANSELMETTI B, 1992, CIRP ANN-MANUF TECHN, V21, P167
   ANSELMETTI B, 2003, COTATION FABRICATION, V3
   AUSSENACGILLES N, 1992, RIA, V6, P7
   BROWN SM, 1992, J MANAGE STUD, V29, P287, DOI 10.1111/j.1467-6486.1992.tb00666.x
   CORBEL JC, 1997, METHODOLOGIE RETOUR
   COSSETTE P, 1992, J MANAGE STUD, V29, P325, DOI 10.1111/j.1467-6486.1992.tb00668.x
   COSSETTE P, 1994, CARTES COGNITIVES OR
   DESSEIN G, 1998, 2 C INT CONC FABR CO, P759
   DETERSSAC G, 1991, MODELES ANAL TRAVAIL, P295
   Drucker P., 1993, POSTCAPITALISM SOC
   EDEN C, 1988, EUR J OPER RES, V36, P1613
   EDEN C, 2003, EUROPEAN J OPERATION
   ERMINE JL, 1996, COLLECTION HERMES
   Goupy, 1988, METHODE PLANS EXPERI
   HEIDER F, 1971, J PSYCHOL, V21
   Herschel RT., 2001, J KNOWL MANAG, V5, P107, DOI [DOI 10.1108/13673270110384455, 10.1108/13673270110384455]
   HOUDOY H, 1999, PILOTAGE EVALUATION, P69
   Johannessen JA, 2001, INT J INFORM MANAGE, V21, P3, DOI 10.1016/S0268-4012(00)00047-5
   Kelly George., 2020, PSYCHOL PERSONAL CON
   LANGFIELD-SMITH K, 1992, J MANAGE STUD, V29, P349, DOI 10.1111/j.1467-6486.1992.tb00669.x
   LEFEBVRE A, 2001, THESIS U BLAISE PASC
   LEFEBVRE A, 2001, 4 C INT GEN IND GI A, V1, P141
   LEWKOWICZ M, 2000, DESIGNING COOPERATIV
   MONY C, 1994, DFM ENJEUX TENDANCES
   PACHULSKI A, 2000, ECKM 2000 BLED SLOV
   PILLET M, 1996, APPL MAITRISE STAT P
   PILLET M, 1995, PLANS EXPERIENCES OU
   Polanyi M., 1983, TACIT DIMENSION
   RENAUD J, 2002, THESIS
   SCHEIDER F, 1999, TECHNOLOGIE FORMATIO, P56
   Shanteau J, 2002, EUR J OPER RES, V136, P253, DOI 10.1016/S0377-2217(01)00113-8
   STEELS L, 1993, P ISMICK 93 COMP FRA, P9
   STEELS L, 1987, AICOM, V1, P9
   VERSTRAETE T, 1996, CAHIER RECHERCHE CLA
   VIGIER MG, 1988, PRATIQUE PLANS EXPER
   VOIRPIN JM, 1998, INCOM 98 NANC
   Vranes S, 1999, ENG APPL ARTIF INTEL, V12, P221, DOI 10.1016/S0952-1976(98)00058-X
   WOLFF V, 2000, THESIS INSA LYON
   Woo JH, 2004, AUTOMAT CONSTR, V13, P203, DOI 10.1016/j.autcon.2003.09.003
   [No title captured]
NR 41
TC 2
Z9 2
U1 2
U2 9
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1063-293X
EI 1531-2003
J9 CONCURRENT ENG-RES A
JI Concurrent Eng.-Res. Appl.
PD JUN
PY 2006
VL 14
IS 2
BP 129
EP 139
DI 10.1177/1063293X06066196
PG 11
WC Computer Science, Interdisciplinary Applications; Engineering,
   Manufacturing; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 084VX
UT WOS:000240563600005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Hao, J
   Zhao, L
   Milisavljevic-Syed, J
   Ming, ZJ
AF Hao, Jia
   Zhao, Lei
   Milisavljevic-Syed, Jelena
   Ming, Zhenjun
TI Integrating and navigating engineering design decision-related knowledge
   using decision knowledge graph
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Design; Decision support; Knowledge graph; Searching; Navigation
ID SUPPLY CHAIN NETWORK; MODEL
AB Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge.
C1 [Hao, Jia; Zhao, Lei; Ming, Zhenjun] Beijing Inst Technol, Sch Mech Engn, 5 Zhongguancun South St, Beijing 100081, Peoples R China.
   [Milisavljevic-Syed, Jelena] Univ Liverpool, Sch Engn, Div Ind Design, Syst Realizat Lab, Liverpool, Merseyside, England.
RP Ming, ZJ (通讯作者)，Beijing Inst Technol, Sch Mech Engn, 5 Zhongguancun South St, Beijing 100081, Peoples R China.
EM haojia632@bit.edu.cn; zhao_lei@bit.edu.cn; zhenjun.ming@bit.edu.cn
OI Milisavljevic-Syed, Jelena/0000-0003-2957-8933
FU National Ministries Foundation [JCKY20192019204B021,
   2019-JCJQ-ZD-049-02]; National Natural Science Foundation of China
   [51805033]; Beijing Institute of Technology Research Fund Program for
   Young Scholars [3030011182037]
FX Jia Hao acknowledges the support from National Ministries Foundation
   (JCKY20192019204B021 and 2019-JCJQ-ZD-049-02). Zhenjun Ming acknowledges
   the support from National Natural Science Foundation of China (51805033)
   and Beijing Institute of Technology Research Fund Program for Young
   Scholars (3030011182037).
CR Amin SH, 2013, APPL MATH MODEL, V37, P4165, DOI 10.1016/j.apm.2012.09.039
   [Anonymous], 1989, ENG OPTIM
   Arendt JL, 2012, J MECH DESIGN, V134, DOI 10.1115/1.4007396
   Bascaran E., 1987, ENG OPTIMIZ, V12, P175, DOI [10.1080/03052158708941093, DOI 10.1080/03052158708941093]
   Bordes A, 2014, P 2014 C EMP METH NA, P615, DOI DOI 10.3115/V1/D14-1067
   Dolsak B, 2011, ADV ENG INFORM, V25, P330, DOI 10.1016/j.aei.2010.11.001
   Dong XL, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2869, DOI 10.1145/3219819.3219938
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Fleischmann M, 2001, PROD OPER MANAG, V10, P156
   Grangel-Gonzalez I., 2019, KNOWLEDGE GRAPH BASE
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Hachey B, 2013, ARTIF INTELL, V194, P130, DOI 10.1016/j.artint.2012.04.005
   Hao J, 2014, DECIS SUPPORT SYST, V61, P106, DOI 10.1016/j.dss.2014.02.001
   He L, 2022, IEEE T BIG DATA, V8, P607, DOI 10.1109/TBDATA.2018.2805363
   He T., 2018, J MODERN INFORM, V38, P55
   Jiang HM, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100969
   Jing LT, 2021, ADV ENG INFORM, V47, DOI 10.1016/j.aei.2020.101204
   Kharlamov E, 2017, J WEB SEMANT, V44, P54, DOI 10.1016/j.websem.2017.02.001
   Kroetsch M., 2016, J WEB SEMANT
   Lamothe J, 2006, EUR J OPER RES, V169, P1030, DOI 10.1016/j.ejor.2005.02.007
   Lamparter S., P INT SEM WEB C PD I
   Li XL, 2018, INT J ADV MANUF TECH, V96, P4173, DOI 10.1007/s00170-018-1862-8
   Li Z, 2008, J COMPUT INF SCI ENG, V8, DOI 10.1115/1.2830851
   Lockard C, 2018, PROC VLDB ENDOW, V11, P1084, DOI 10.14778/3231751.3231758
   McMahon C, 2004, J COMPUT INF SCI ENG, V4, P329, DOI 10.1115/1.1812557
   Mehr AF, 2006, J MECH DESIGN, V128, P1014, DOI 10.1115/1.2205868
   Ming ZJ, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100985
   Ming ZJ, 2016, AI EDAM, V30, P390, DOI 10.1017/S0890060416000378
   Mistree F., 1990, T SOC NAV ARCHIT MAR, V98, P565
   Mistree F., 1993, PROG ASTRONAUT AERON, V150, P251, DOI DOI 10.2514/5.9781600866234.0251.0290
   Mistree F., 1988, DESIGN AIRCRAFT USIN
   Mistree F., 1988, 4134 NASA
   Nellippallil AB, 2018, J MECH DESIGN, V140, DOI 10.1115/1.4041050
   Panchal J.H., 2004, AIAA ISSMO MULT AN O
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Perez J, 2009, ACM T DATABASE SYST, V34, DOI 10.1145/1567274.1567278
   Rezapour S, 2017, EUR J OPER RES, V259, P1017, DOI 10.1016/j.ejor.2016.11.041
   Ringsquandl M., P 17 INT SEM WEB C C
   Ringsquandl M, 2015, LECT NOTES COMPUT SC, V9367, P225, DOI 10.1007/978-3-319-25010-6_13
   Sarica S., P ASME 2019 INT DES
   Seepersad CC, 2006, J MECH DESIGN, V128, P1285, DOI 10.1115/1.2338575
   Shahtaheri Y, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100939
   SMITH WF, 1987, MAR TECHNOL SNAME N, V24, P131
   Wang PL, 2019, DATA INTELLIGENCE, V1, P333, DOI 10.1162/dint_a_00019
   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
   Yuan Liang, 2018, Computational Visual Media, V4, P123, DOI 10.1007/s41095-018-0110-3
   Zhang C, 2017, INT J PROD RES, V55, P7187, DOI 10.1080/00207543.2017.1351643
   Zhao MX, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9132720
   Zhao Z., 2018, INT J PURE APPL MATH, V118, P1869
NR 49
TC 2
Z9 2
U1 23
U2 36
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1474-0346
EI 1873-5320
J9 ADV ENG INFORM
JI Adv. Eng. Inform.
PD OCT
PY 2021
VL 50
AR 101366
DI 10.1016/j.aei.2021.101366
EA JUL 2021
PG 14
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA WA4ON
UT WOS:000702866100003
DA 2022-08-23
ER

PT J
AU Qian, JW
   Li, XY
   Zhang, CH
   Chen, LL
   Jung, T
   Han, JZ
AF Qian, Jianwei
   Li, Xiang-Yang
   Zhang, Chunhong
   Chen, Linlin
   Jung, Taeho
   Han, Junze
TI Social Network De-Anonymization and Privacy Inference with Knowledge
   Graph Model
SO IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
LA English
DT Article
DE Social network data publishing; attack and privacy preservation;
   knowledge graph
AB Social network data is widely shared, transferred and published for research purposes and business interests, but it has raised much concern on users' privacy. Even though users' identity information is always removed, attackers can still de-anonymize users with the help of auxiliary information. To protect against de-anonymization attack, various privacy protection techniques for social networks have been proposed. However, most existing approaches assume specific and restrict network structure as background knowledge and ignore semantic level prior belief of attackers, which are not always realistic in practice and do not apply to arbitrary privacy scenarios. Moreover, the privacy inference attack in the presence of semantic background knowledge is barely investigated. To address these shortcomings, in this work, we introduce knowledge graphs to explicitly express arbitrary prior belief of the attacker for any individual user. The processes of de-anonymization and privacy inference are accordingly formulated based on knowledge graphs. Our experiment on data of real social networks shows that knowledge graphs can power de-anonymization and inference attacks, and thus increase the risk of privacy disclosure. This suggests the validity of knowledge graphs as a general effective model of attackers' background knowledge for social network attack and privacy preservation.
C1 [Qian, Jianwei; Chen, Linlin; Jung, Taeho; Han, Junze] Illinois Inst Technol, Dept Comp Sci, Chicago, IL 60616 USA.
   [Li, Xiang-Yang] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230000, Anhui, Peoples R China.
   [Zhang, Chunhong] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100088, Peoples R China.
RP Qian, JW (通讯作者)，Illinois Inst Technol, Dept Comp Sci, Chicago, IL 60616 USA.
EM jqian15@hawk.iit.edu; xiangyang.li@gmail.com; zhangch@bupt.edu.cn;
   lchen96@hawk.iit.edu; tjung@hawk.iit.edu; jhan20@hawk.iit.edu
RI Jung, Taeho/J-4697-2019
OI Jung, Taeho/0000-0001-5873-0629
FU China National Funds for Distinguished Young Scientists [61625205]; Key
   Research Program of Frontier Sciences, CAS [QYZDY-SSW-JSC002]; NSFC
   [61520106007]; NSF [CNS 1526638, CMMI 1436786, ECCS-1247944]
FX The research of Li is partially supported by China National Funds for
   Distinguished Young Scientists with No. 61625205, Key Research Program
   of Frontier Sciences, CAS, No. QYZDY-SSW-JSC002, NSFC with No.
   61520106007, NSF ECCS-1247944, NSF CMMI 1436786, and NSF CNS 1526638.
   Xiang-Yang Li is the contact author.
CR Acquisti A, 2005, P ACM WORKSH PRIV EL, P71, DOI DOI 10.1145/1102199.1102214
   Ahuja R. K., 1988, DTIC DOCUMENT
   AHUJA RK, 1994, SIAM J COMPUT, V23, P906, DOI 10.1137/S0097539791199334
   [Anonymous], 2013, ERROR CORRECTION COD
   Backstrom L., 2007, P 16 INT C WORLD WID, P181, DOI DOI 10.1145/1242572.1242598
   Bhagat S., 2009, PROC VLDB ENDOW, V2, P766, DOI DOI 10.14778/1687627.1687714
   Broder AZ, 1998, COMPRESSION AND COMPLEXITY OF SEQUENCES 1997 - PROCEEDINGS, P21, DOI 10.1109/SEQUEN.1997.666900
   Charikar Moses S, 2002, STOC
   Chester S, 2013, SOC NETW ANAL MIN, V3, P151, DOI 10.1007/s13278-012-0059-7
   Chiasserini C., 2015, DEANONYMIZING SCALE
   Datar M., 2004, P SOCG BROOKL NY US, P253
   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
   HAGENAUER J, 1987, IEEE J SEL AREA COMM, V5, P215, DOI 10.1109/JSAC.1987.1146523
   Hay M., 2007, COMPUTER SCI DEP FAC, V180
   Hay M, 2008, PROC VLDB ENDOW, V1, P102, DOI 10.14778/1453856.1453873
   Indyk P., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, P604, DOI 10.1145/276698.276876
   James P., 1992, ORDER, V501, P6439
   Ji S., 2015, P NDSS S
   Ji SL, 2015, PROCEEDINGS OF THE 24TH USENIX SECURITY SYMPOSIUM, P303
   Ji SL, 2014, CCS'14: PROCEEDINGS OF THE 21ST ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P1040, DOI 10.1145/2660267.2660278
   Jung T., 2017, P IEEE INFOCOM
   Kohlmayer F, 2012, PROCEEDINGS OF 2012 ASE/IEEE INTERNATIONAL CONFERENCE ON PRIVACY, SECURITY, RISK AND TRUST AND 2012 ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM/PASSAT 2012), P708, DOI 10.1109/SocialCom-PASSAT.2012.52
   Korula N., 2013, EFFICIENT RECONCILIA
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Leskovec J, 2014, MINING OF MASSIVE DATASETS, 2ND EDITION, P1
   Li TC, 2009, PROC INT CONF DATA, P6, DOI 10.1109/ICDE.2009.86
   Li XX, 2016, CELL POLYM, V35, P1
   Liu KQ, 2008, CONF REC ASILOMAR C, P93, DOI 10.1109/ACSSC.2008.5074369
   Machanavajjhala Ashwin, 2007, ACM T KNOWL DISCOV D, V1, P3, DOI DOI 10.1145/1217299.1217302
   Mittal P., 2013, P NDSS S
   Narayanan A, 2009, P IEEE S SECUR PRIV, P173, DOI 10.1109/SP.2009.22
   Nilizadeh S, 2014, CCS'14: PROCEEDINGS OF THE 21ST ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P537, DOI 10.1145/2660267.2660324
   Pedarsani P, 2013, ANN ALLERTON CONF, P1598, DOI 10.1109/Allerton.2013.6736720
   Peng W, 2014, IEEE T COMPUT, V63, P290, DOI 10.1109/TC.2012.202
   Preis R, 1999, LECT NOTES COMPUT SC, V1563, P259
   Proserpio D., 2014, CALIBRATING DATA SEN
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Qian J., 2016, P MSN M
   Qian JW, 2016, IEEE INFOCOM SER
   Qian JW, 2017, IEEE T COMPUT, V66, P326, DOI 10.1109/TC.2016.2595562
   Qianyin J., 2015, 2015 IEEE 1 INT SMAR, P1
   Sala A., 2011, P 2011 ACM SIGCOMM C, P81
   Shah C, 2014, COMPUTER, V47, P22, DOI 10.1109/MC.2014.54
   Shouling Ji, 2014, Information Security. 17th International Conference, ISC 2014. Proceedings: LNCS 8783, P237, DOI 10.1007/978-3-319-13257-0_14
   Srivatsa M., 2012, P ACM C COMP COMM SE, P628
   Sweeney L, 2002, INT J UNCERTAIN FUZZ, V10, P557, DOI 10.1142/S0218488502001648
   Tai C H, 2011, STRUCTURAL DIVERSITY, P35
   Tai C H, 2011, P 17 ACM SIGKDD INT, P1262
   Thompson B, 2009, IEEE RAD FREQ INTEGR, P215
   Wang GJ, 2013, IEEE INFOCOM SER, P2886
   Wang SQ, 2010, BIOTECH AGR IND MED, P459
   Xiao Q, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P911, DOI 10.1145/2623330.2623642
   Xu Z, 2014, COMPUTER, V47, P26, DOI 10.1109/MC.2014.61
   Yartseva L., 2013, P 1 ACM C ONL SOC NE, P119
   Ying X., 2008, P 2008 SIAM INT C DA, P739, DOI DOI 10.1137/1.9781611972788.67
   Zhang J, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1423
   Zhou B., 2008, ACM SIGKDD EXPLOR NE, V10, P12, DOI DOI 10.1145/1540276.1540279
   Zhou B, 2008, PROC INT CONF DATA, P506, DOI 10.1109/ICDE.2008.4497459
   Zou L., 2009, PROC VLDB ENDOW, V2, P946, DOI [DOI 10.14778/1687627.1687734, 10.14778/1687627.1687734]
NR 59
TC 16
Z9 19
U1 2
U2 33
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1545-5971
EI 1941-0018
J9 IEEE T DEPEND SECURE
JI IEEE Trans. Dependable Secur. Comput.
PD JUL-AUG
PY 2019
VL 16
IS 4
BP 679
EP 692
DI 10.1109/TDSC.2017.2697854
PG 14
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA II7CN
UT WOS:000475350000010
DA 2022-08-23
ER

PT J
AU Hasan, SMS
   Rivera, D
   Wu, XC
   Durbin, EB
   Christian, JB
   Tourassi, G
AF Hasan, S. M. Shamimul
   Rivera, Donna
   Wu, Xiao-Cheng
   Durbin, Eric B.
   Christian, J. Blair
   Tourassi, Georgia
TI Knowledge Graph-Enabled Cancer Data Analytics
SO IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
LA English
DT Article
DE Cancer; Resource description framework; Tumors; Data models; Libraries;
   Data analysis; Data visualization; Knowledge graph; cancer registry;
   treatment
ID BREAST-CANCER; SECONDARY USE; ASSOCIATION; WEB
AB Cancer registries collect unstructured and structured cancer data for surveillance purposes which provide important insights regarding cancer characteristics, treatments, and outcomes. Cancer registry data typically (1) categorize each reportable cancer case or tumor at the time of diagnosis, (2) contain demographic information about the patient such as age, gender, and location at time of diagnosis, (3) include planned and completed primary treatment information, and (4) may contain survival outcomes. As structured data is being extracted from various unstructured sources, such as pathology reports, radiology reports, medical records, and stored for reporting and other needs, the associated information representing a reportable cancer is constantly expanding and evolving. While some popular analytic approaches including SEER*Stat and SAS exist, we provide a knowledge graph approach to organizing cancer registry data. Our approach offers unique advantages for timely data analysis and presentation and visualization of valuable information. This knowledge graph approach semantically enriches the data, and easily enables linking with third-party data which can help explain variation in cancer incidence patterns, disparities, and outcomes. We developed a prototype knowledge graph based on the Louisiana Tumor Registry dataset. We present the advantages of the knowledge graph approach by examining: i) scenario-specific queries, ii) links with openly available external datasets, iii) schema evolution for iterative analysis, and iv) data visualization. Our results demonstrate that this graph based solution can perform complex queries, improve query run-time performance by up to 76%, and more easily conduct iterative analyses to enhance researchers' understanding of cancer registry data.
C1 [Hasan, S. M. Shamimul; Christian, J. Blair; Tourassi, Georgia] Oak Ridge Natl Lab, POB 2009, Oak Ridge, TN 37830 USA.
   [Rivera, Donna] NCI, Bethesda, MD 20892 USA.
   [Wu, Xiao-Cheng] Louisiana Tumor Registry, New Orleans, LA 70112 USA.
   [Durbin, Eric B.] Univ Kentucky, Kentucky Canc Registry, Lexington, KY 40506 USA.
RP Hasan, SMS (通讯作者)，Oak Ridge Natl Lab, POB 2009, Oak Ridge, TN 37830 USA.
EM hasans@ornl.gov; donna.rivera@nih.gov; xwu@lsuhsc.edu;
   ericd@cri.uky.edu; christianjb@ornl.gov; tourassig@ornl.gov
OI Hasan, S.M.Shamimul/0000-0002-0610-1668; Tourassi,
   Georgia/0000-0002-9418-9638
FU U.S. Department of Energy [DE-AC05-00OR22725]
FX This manuscript has been authored by UT-Battelle, LLC under Contract No.
   DE-AC05-00OR22725 with the U.S. Department of Energy. The United States
   Government retains and the publisher, by accepting the article for
   publication, acknowledges that the United States Government retains a
   non-exclusive, paid-up, irrevocable, world-wide license to publish or
   reproduce the published form of this manuscript, or allow others to do
   so, for United States Government purposes. The Department of Energy will
   provide public access to these results of federally sponsored research
   in accordance with the DOE Public Access Plan
   (http://energy.gov/downloads/doe-public-access-plan).
CR Angles R, 2008, LECT NOTES COMPUT SC, V5318, P114, DOI 10.1007/978-3-540-88564-1_8
   [Anonymous], 2018, WHAT IS DIFFERENCE K
   [Anonymous], 2020, ONTOLOGY MAPPING OWL
   [Anonymous], 2012, STAT LINKED DATASPAC
   [Anonymous], 2018, SITE RECODE ICD 3 WH
   [Anonymous], 2006, RDFLIB PYTH LIB WORK
   [Anonymous], 2018, SURV EP RES PROGR CA
   [Anonymous], 2018, CTR FORDISEASE CONTR
   [Anonymous], 2018, NATL CANC INSTITUTES
   [Anonymous], 2018, ICD 3 SITE CODES
   [Anonymous], 2020, CLIMATE JSON
   [Anonymous], 2020, DATA CLEANSING
   [Anonymous], 2004, SEMANTIC WEB PRIMER
   [Anonymous], 2018, GEPHI SEMANTIC WEB I
   [Anonymous], 2019, TRIPLE NEGATIVE BREA
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bastian M., 2009, P INT AAAI C WEB SOC, P361, DOI 10.13140/2.1.1341.1520
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bray F, 2018, CA-CANCER J CLIN, V68, P394, DOI 10.3322/caac.21492
   Chu WW, 2008, ACAD PR SER BIOM ENG, P307, DOI 10.1016/B978-012373583-6.50018-9
   Dent R, 2007, CLIN CANCER RES, V13, P4429, DOI 10.1158/1078-0432.CCR-06-3045
   Esteban-Gil A, 2017, J BIOMED SEMANT, V8, DOI 10.1186/s13326-017-0154-9
   Force LM, 2019, LANCET ONCOL, V20, P1211, DOI 10.1016/S1470-2045(19)30339-0
   Fox E.A., 2012, SYNTH LECT INFORM CO, V4, P1
   Hasan S., 2019, P 2019 IEEE 90 VEH T, P1, DOI DOI 10.1109/VTCFALL.2019.8891254
   Hasan SMS, 2017, J HEALTHC INFORM RES, V1, P260, DOI 10.1007/s41666-017-0010-9
   Hersh W.R., 2007, AM J MANAG CARE, V81, P126, DOI DOI 10.1038/sj.clpt.6100029
   Hossain F, 2019, FRONT PUBLIC HEALTH, V7, DOI 10.3389/fpubh.2019.00018
   Hu Y., 2005, MATH J, V10, P37
   Lund MJ, 2009, BREAST CANCER RES TR, V113, P357, DOI 10.1007/s10549-008-9926-3
   McGuinness D.L., 2004, W3C RECOMM
   Milicchio F., 2007, POSTGRESQL DATABASE, P275
   Miller E., 1998, D-Lib Magazine, DOI 10.1002/bult.105
   Mishra RB, 2011, ARTIF INTELL REV, V35, P339, DOI 10.1007/s10462-010-9197-3
   Murdoch TB, 2013, JAMA-J AM MED ASSOC, V309, P1351, DOI 10.1001/jama.2013.393
   Pan Z., 2004, DESCRIPTION LOGICS R
   Piwowar HA, 2008, PLOS MED, V5, P1315, DOI 10.1371/journal.pmed.0050183
   Plasilova ML, 2016, MEDICINE, V95, DOI 10.1097/MD.0000000000004614
   Pourshams A, 2019, LANCET GASTROENTEROL, V4, P934, DOI 10.1016/S2468-1253(19)30347-4
   Rakha EA, 2007, CANCER-AM CANCER SOC, V109, P25, DOI 10.1002/cncr.22381
   Rea S, 2012, J BIOMED INFORM, V45, P763, DOI 10.1016/j.jbi.2012.01.009
   Safran C, 2007, J AM MED INFORM ASSN, V14, P1, DOI 10.1197/jamia.M2273
   Sineshaw HM, 2014, BREAST CANCER RES TR, V145, P753, DOI 10.1007/s10549-014-2976-9
   Smith B, 2007, NAT BIOTECHNOL, V25, P1251, DOI 10.1038/nbt1346
   Tao C, 2013, J AM MED INFORM ASSN, V20, P554, DOI 10.1136/amiajnl-2012-001326
   Taylor J., 2019, QUEUE, V17, P48, DOI 10.1145/3329781.3332266
   Vafajoo A, 2018, BIOMED PHYS ENG EXPR, V4, DOI 10.1088/2057-1976/aae6c1
   Vallega KA, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0157741
   White D., 2013, TECH REP
   Yan JH, 2018, FRONT COMPUT SCI-CHI, V12, P55, DOI 10.1007/s11704-016-5228-9
   Zhu N.Q., 2013, DATA VISUALIZATION D
NR 51
TC 6
Z9 6
U1 15
U2 35
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2168-2194
EI 2168-2208
J9 IEEE J BIOMED HEALTH
JI IEEE J. Biomed. Health Inform.
PD JUL
PY 2020
VL 24
IS 7
BP 1952
EP 1967
DI 10.1109/JBHI.2020.2990797
PG 16
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Mathematical & Computational Biology;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Mathematical & Computational Biology; Medical
   Informatics
GA MF6DB
UT WOS:000545429400013
PM 32386166
OA hybrid, Green Accepted
DA 2022-08-23
ER

PT J
AU Principe, RAA
   Maurino, A
   Palmonari, M
   Ciavotta, M
   Spahiu, B
AF Alva Principe, Renzo Arturo
   Maurino, Andrea
   Palmonari, Matteo
   Ciavotta, Michele
   Spahiu, Blerina
TI ABSTAT-HD: a scalable tool for profiling very large knowledge graphs
SO VLDB JOURNAL
LA English
DT Article; Early Access
DE Knowledge graph; Data management; Data quality; Data profiling;
   Distributed processing engine
ID RDF
AB Processing large-scale and highly interconnected Knowledge Graphs (KG) is becoming crucial for many applications such as recommender systems, question answering, etc. Profiling approaches have been proposed to summarize large KGs with the aim to produce concise and meaningful representation so that they can be easily managed. However, constructing profiles and calculating several statistics such as cardinality descriptors or inferences are resource expensive. In this paper, we present ABSTAT-HD, a highly distributed profiling tool that supports users in profiling and understanding big and complex knowledge graphs. We demonstrate the impact of the new architecture of ABSTAT-HD by presenting a set of experiments that show its scalability with respect to three dimensions of the data to be processed: size, complexity and workload. The experimentation shows that our profiling framework provides informative and concise profiles, and can process and manage very large KGs.
C1 [Alva Principe, Renzo Arturo; Maurino, Andrea; Palmonari, Matteo; Ciavotta, Michele; Spahiu, Blerina] Univ Milano Bicocca, Dept Informat Syst & Commun, Viale Sarca 336, Milan, Italy.
RP Spahiu, B (通讯作者)，Univ Milano Bicocca, Dept Informat Syst & Commun, Viale Sarca 336, Milan, Italy.
EM renzo.alvaprincipe@unimib.it; andrea.maurino@unimib.it;
   matteo.palmnonari@unimib.it; michele.ciavotta@unimib.it;
   blerina.spahiu@unimib.it
CR Abedjan Z, 2014, PROC INT CONF DATA, P1198, DOI 10.1109/ICDE.2014.6816740
   Ali W., 2020, ARXIV200910331
   [Anonymous], 2017, P S APPL COMP SAC 20
   [Anonymous], 2013, LDOW
   Armbrust M, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1383, DOI 10.1145/2723372.2742797
   Auer Soren, 2012, Knowledge Engineering and Knowledge Management. 18th International Conference, EKAW 2012. Proceedings, P353, DOI 10.1007/978-3-642-33876-2_31
   Baldacci L, 2019, IEEE T KNOWL DATA EN, V31, P819, DOI 10.1109/TKDE.2018.2850339
   Bohm C, 2010, I C DATA ENGIN WORKS, P175, DOI 10.1109/ICDEW.2010.5452762
   Campinas S, 2012, INT WORKSHOP DATABAS, P261, DOI 10.1109/DEXA.2012.38
   Cebiric S, 2019, VLDB J, V28, P295, DOI 10.1007/s00778-018-0528-3
   Cebiric S, 2015, PROC VLDB ENDOW, V8, P2013
   Chen X, 2015, 2015 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT), VOL 1, P292, DOI 10.1109/WI-IAT.2015.186
   Christmann P, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P729, DOI 10.1145/3357384.3358016
   Consens M.P., 2015, PROC VLDB ENDOW, V8, P2028, DOI [10.14778/2824032.2824128, DOI 10.14778/2824032.2824128]
   Cossu M., 2018, ARXIV180205898
   Diao Y., 2019, SPADE MODULAR FRAMEW
   Diao YL, 2021, INT CONF MANAGE DATA, P392, DOI 10.1145/3448016.3457307
   Dudas M, 2015, LECT NOTES COMPUT SC, V9341, P36, DOI 10.1007/978-3-319-25639-9_7
   Forchhammer B., 2014, PROFILES ESWC
   Goasdoue F, 2020, VLDB J, V29, P1191, DOI 10.1007/s00778-020-00611-y
   Guo Q, 2020, ARXIV PREPRINT ARXIV
   Guo XT, 2019, LECT NOTES COMPUT SC, V11446, P742, DOI 10.1007/978-3-030-18576-3_44
   Gurajada S, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P289, DOI 10.1145/2588555.2610511
   Jabeen H., 2020, KNOWLEDGE GRAPHS BIG, P105
   Langegger A, 2009, PROCEEDINGS OF THE 20TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATION, P79, DOI 10.1109/DEXA.2009.25
   Mihindukulasooriya N., 2015, INT SEM WEB C
   Mohamed SK, 2020, BIOINFORMATICS, V36, P603, DOI 10.1093/bioinformatics/btz600
   Myklebust EB, 2019, LECT NOTES COMPUT SC, V11779, P490, DOI 10.1007/978-3-030-30796-7_30
   Noia T., 2018, SEMANTIC WEB ESWC 20
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   Ozsu MT, 2016, FRONT COMPUT SCI-CHI, V10, P418, DOI 10.1007/s11704-016-5554-y
   Pan ZY, 2018, J AMB INTEL HUM COMP, V9, P1693, DOI 10.1007/s12652-018-0876-2
   Pavlo A, 2009, ACM SIGMOD/PODS 2009 CONFERENCE, P165
   Polleres A., 2020, KNOWLEDGE GRAPHS
   Principe RAA, 2018, LECT NOTES COMPUT SC, V11155, P170, DOI 10.1007/978-3-319-98192-5_32
   Reza T., 2019, ARXIV191208453
   Riondato M, 2017, DATA MIN KNOWL DISC, V31, P314, DOI 10.1007/s10618-016-0468-8
   Sahu S, 2020, VLDB J, V29, P595, DOI 10.1007/s00778-019-00548-x
   Schatzle A, 2016, PROC VLDB ENDOW, V9, P804, DOI 10.14778/2977797.2977806
   Schaible J, 2016, LECT NOTES COMPUT SC, V9678, P101, DOI 10.1007/978-3-319-34129-3_7
   Schatzle A., 2013, P 5 WORKSH SEM WEB I, P1
   Sejdiu G, 2018, LECT NOTES COMPUT SC, V11137, P206, DOI 10.1007/978-3-030-00668-6_13
   Song Q, 2018, IEEE T KNOWL DATA EN, V30, P1887, DOI 10.1109/TKDE.2018.2807442
   Spahiu B., 2018, C 9 WORKSH ONT DES P, P103
   Spahiu B, 2016, LECT NOTES COMPUT SC, V9989, P381, DOI 10.1007/978-3-319-47602-5_51
   Studer Rudi, 2010, HDB ONTOLOGIES
   Trotter W.T., 1995, HDB COMBINATORICS, P433
   Wu Buwen, 2014, P 23 ACM INT C INF K, P509, DOI 10.1145/2661829.2661876
   Wylot M, 2018, ACM COMPUT SURV, V51, DOI 10.1145/3177850
   Xin Lian, 2018, MATEC Web of Conferences, V173, DOI 10.1051/matecconf/201817301015
   Zeng K, 2013, PROC VLDB ENDOW, V6, P265, DOI 10.14778/2535570.2488333
   Zhang H, 2014, CEUR WORKSHOP P, P233
   Zhang XF, 2013, PROC INT CONF DATA, P565, DOI 10.1109/ICDE.2013.6544856
   Zneika M, 2019, SEMANT WEB, V10, P555, DOI 10.3233/SW-190346
NR 54
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1066-8888
EI 0949-877X
J9 VLDB J
JI VLDB J.
DI 10.1007/s00778-021-00704-2
EA SEP 2021
PG 26
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UY2TW
UT WOS:000701382700001
OA hybrid
DA 2022-08-23
ER

PT J
AU Bai, LY
   Yu, WT
   Chen, MZ
   Ma, XN
AF Bai, Luyi
   Yu, Wenting
   Chen, Mingzhuo
   Ma, Xiangnan
TI Multi-hop reasoning over paths in temporal knowledge graphs using
   reinforcement learning
SO APPLIED SOFT COMPUTING
LA English
DT Article
DE Temporal knowledge graph; Multi-hop reasoning; Reinforcement learning;
   Long Short-Term Memory Networks
AB Knowledge graphs (KGs) are usually incomplete-many new facts can be inferred from KGs with existing information. In some traditional reasoning methods, temporal information is not taken into consideration, meaning that only triplets (head, relation, tail) are trained. In current dynamic knowledge graphs, it is a challenge to consider the temporal aspects of facts. Recent temporal reasoning methods embed temporal information into low-dimensional spaces. These methods mainly support implicitly reasoning, which means they cannot get the specific reasoning paths. These methods limit the accuracy of reasoning paths and ignore multiple explainable reasoning paths in temporal knowledge graphs (TKGs). To overcome this limitation, we propose a multi-hop reasoning model TPath in this paper. It is a reinforcement learning (RL) framework which can learn multi-hop reasoning paths and continuously adjust the reasoning paths in TKGs. More importantly, we add time vectors in reasoning paths, which further improve the accuracy of reasoning paths. Meanwhile, considering the diversity of temporal reasoning paths, we propose a new reward function. In TPath, the agent employs the Long Short-Term Memory networks (LSTM) to capture current observations from the environment, and it outputs action vectors (relation vectors and time vectors) to the environment through activation functions. Experimentally, our model outperforms other state-of-the-art reasoning methods in several aspects over two public temporal knowledge graph datasets. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Bai, Luyi; Yu, Wenting; Chen, Mingzhuo; Ma, Xiangnan] Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China.
RP Bai, LY (通讯作者)，Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China.
EM baily@neuq.edu.cn
OI Bai, Luyi/0000-0001-9546-3208
FU National Natural Science Foundation of China [61402087]; Natural Science
   Foundation of Hebei Province, China [F2019501030]; Natural Science
   Foundation of Liaoning Province, China [2019MS130]; Key Project of
   Scientific Research Funds in Colleges and Universities of Hebei
   Education Department, China [ZD2020402]; Fundamental Research Funds for
   the Central Universities, China [N2023019]; Program for 333 Talents in
   Hebei Province, China [A202001066]
FX The work was supported by the National Natural Science Foundation of
   China (61402087) , the Natural Science Foundation of Hebei Province,
   China (F2019501030) , the Natural Science Foundation of Liaoning
   Province, China (2019MS130) , the Key Project of Scientific Research
   Funds in Colleges and Universities of Hebei Education Department, China
   (ZD2020402) , the Fundamental Research Funds for the Central
   Universities, China (N2023019) , and in part by the Program for 333
   Talents in Hebei Province, China (A202001066) .
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Das R., 2018, ARXIV PREPRINT ARXIV
   Dasgupta Shib Sankar, 2018, P 2018 C EMP METH NA, P2001
   Garcia-Duran Alberto, 2018, P 2018 C EMP METH NA, P4816
   Gutierrez C, 2007, IEEE T KNOWL DATA EN, V19, P207, DOI 10.1109/TKDE.2007.34
   Han Z., 2020, IEEE T MULTIMEDIA, DOI DOI 10.1109/TMM.2020.2966887
   Heck L.P., 2013, P 14 ANN C INT SPEEC, P1594
   Hildebrandt M, 2020, AAAI CONF ARTIF INTE, V34, P4123
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Jiang T., 2016, P 26 INT C COMP LING, P1715
   Jin W., 2019, ARXIV180204364
   King DB, 2015, ACS SYM SER, V1214, P1
   Leblay J, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1771, DOI 10.1145/3184558.3191639
   Leetaru K, 2013, GDELT GOBAL DATA EVE
   Lin X.V., 2018, ARXIV180810568
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Ma YP, 2019, J WEB SEMANT, V59, DOI 10.1016/j.websem.2018.12.008
   Nguyen GH, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P969, DOI 10.1145/3184558.3191526
   Pugliese A., 2008, P 17 INT C WORLD WID, P605
   Shellman S., 2015, ICEWS CODED EVENT DA
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Trivedi R, 2017, PR MACH LEARN RES, V70
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang Heng, 2019, P 2019 C EMP METH NA, P2623
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Xiong CM, 2016, PR MACH LEARN RES, V48
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yang B., 2014, P 3 INT C LEARN REPR
   Yang F, 2017, ADV NEUR IN, V30
   Zimmermann A, 2012, J WEB SEMANT, V11, P72, DOI 10.1016/j.websem.2011.08.006
NR 32
TC 3
Z9 3
U1 22
U2 54
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1568-4946
EI 1872-9681
J9 APPL SOFT COMPUT
JI Appl. Soft. Comput.
PD MAY
PY 2021
VL 103
AR 107144
DI 10.1016/j.asoc.2021.107144
EA FEB 2021
PG 9
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RE5DD
UT WOS:000634173700004
DA 2022-08-23
ER

PT J
AU Wang, J
   Cui, SS
   Zhao, GS
   Zhao, ZN
AF Wang, Jian
   Cui, Shanshan
   Zhao, Guosheng
   Zhao, Zhongnan
TI Diffusion Analysis and Incentive Method for Mobile Crowdsensing User
   Based on Knowledge Graph Reasoning
SO SECURITY AND COMMUNICATION NETWORKS
LA English
DT Article
ID MECHANISM
AB Aiming at the problem that the mobile crowdsensing (MCS) system relies on a specific platform with a large user group presupposed, this paper proposes a sensing user diffusion analysis and incentive method based on knowledge graph reasoning. We consider motivating users to participate under the constraint of limited budget so that the platform and users can get the most benefits. In this paper, we focus on socially aware users represented by self-organizing social networks, combine the knowledge graph to establish a knowledge graph for the crowdsensing system, use rules to derive user influence, and optimize user contributions. With the goal of maximizing social welfare, we propose a social awareness reverse auction (SARA) mechanism, in which the total contribution of users is the key to select winners, and the winners are paid based on critical prices. Through experimental simulations, we verify that SARA is close to the optimal social welfare under budget constraints.
C1 [Wang, Jian; Cui, Shanshan; Zhao, Guosheng; Zhao, Zhongnan] Harbin Univ Sci & Technol, Sch Comp Sci & Technol, Harbin 150080, Peoples R China.
RP Wang, J (通讯作者)，Harbin Univ Sci & Technol, Sch Comp Sci & Technol, Harbin 150080, Peoples R China.
EM wangjianlydia@163.com; 1972356261@qq.com; zgswj@163.com;
   1736819170@qq.com
FU National Natural Science Foundation of China [61403109, 61202458];
   Specialized Research Fund for the Doctoral Program of Higher Education
   of China [20112303120007]; Heilongjiang Natural Science Foundation
   [LH2020F034]; China Postdoctoral Science Foundation [2019M651263];
   Scientific Research Fund of Heilongjiang Provincial Education Department
   [12541169]
FX This present research work was supported by the National Natural Science
   Foundation of China (61403109 and 61202458), Specialized Research Fund
   for the Doctoral Program of Higher Education of China (20112303120007),
   Heilongjiang Natural Science Foundation (LH2020F034), China Postdoctoral
   Science Foundation (2019M651263), and Scientific Research Fund of
   Heilongjiang Provincial Education Department (12541169).
CR Cheng Y., 2014, P 12 ACM C EMB NETW, P251, DOI [10.1145/2668332.2668346, DOI 10.1145/2668332.2668346]
   Duan LJ, 2014, IEEE T MOBILE COMPUT, V13, P2320, DOI 10.1109/TMC.2014.2307327
   Ganti RK, 2010, P 8 INT C MOB SYST A, P151, DOI DOI 10.1145/1814433.1814450
   Han Zhong-Ming, 2017, Journal of Software, V28, P84, DOI 10.13328/j.cnki.jos.005115
   Hu SH, 2015, ACM T SENSOR NETWORK, V11, DOI 10.1145/2770876
   Jiang C., 2019, CHINESE J INTERNET T, V3, P27
   Jin H., 2015, INT S MOB AD HOC NET, P167, DOI DOI 10.1145/2746285.2746310
   Jin HM, 2019, IEEE T MOBILE COMPUT, V18, P1951, DOI 10.1109/TMC.2018.2868106
   Long H., 2019, J COMMUNICATIONS, V40, P43
   Nie JT, 2019, IEEE T WIREL COMMUN, V18, P724, DOI 10.1109/TWC.2018.2885747
   Osborne M. J., 1994, COURSE GAME THEORY
   Peng D, 2018, IEEE T MOBILE COMPUT, V17, P307, DOI 10.1109/TMC.2017.2714668
   Quan Y., 2018, J COMMUNICATIONS, V39, P1
   Song GJ, 2015, IEEE T PARALL DISTR, V26, P1379, DOI 10.1109/TPDS.2014.2320515
   Sun J, 2016, IEEE GLOB COMM CONF
   Wang HW, 2018, IEEE T PARALL DISTR, V29, P901, DOI 10.1109/TPDS.2017.2775232
   Wang JT, 2019, IEEE T MOBILE COMPUT, V18, P1661, DOI 10.1109/TMC.2018.2865355
   Wang Y, 2010, P 16 ACM SIGKDD INT, P1039, DOI DOI 10.1145/1835804.1835935
   Wu Yao, 2016, Journal of Software, V27, P2025, DOI 10.13328/j.cnki.jos.005049
   Xu J, 2020, IEEE T MOBILE COMPUT, V19, P1618, DOI 10.1109/TMC.2019.2911512
   Yadav A, 2017, AAMAS'17: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, P150
   Zhao YM, 2019, IEEE T SERV COMPUT, V12, P489, DOI 10.1109/TSC.2016.2599160
   Zheng ZZ, 2017, IEEE T MOBILE COMPUT, V16, P2392, DOI 10.1109/TMC.2016.2632721
   Zhou RT, 2018, IEEE T MOBILE COMPUT, V17, P1737, DOI 10.1109/TMC.2017.2777481
NR 24
TC 0
Z9 1
U1 6
U2 12
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 MAY 8
PY 2021
VL 2021
AR 6697862
DI 10.1155/2021/6697862
PG 15
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA SX1NE
UT WOS:000664977800005
OA gold
DA 2022-08-23
ER

PT J
AU Kim, HJ
   Baek, JW
   Chung, K
AF Kim, Hyun-Jin
   Baek, Ji-Won
   Chung, Kyungyong
TI Associative Knowledge Graph Using Fuzzy Clustering and Min-Max
   Normalization in Video Contents
SO IEEE ACCESS
LA English
DT Article
DE Streaming media; Data mining; Clustering algorithms; Object detection;
   Classification algorithms; Real-time systems; Machine learning
   algorithms; Associative knowledge; fuzzy clustering; min-max
   normalization; fuzzy theory; knowledge graph
ID ALGORITHM; CLASSIFICATION; SYSTEM
AB Video content data have a variety of objects that could be associated with each other. Although content data contains similar objects or themes, their associations can become ambiguous. Accordingly, if associations between video content data are found in general association rules, their accuracy and confidence are low. Therefore, this study proposes the associative knowledge graph using fuzzy clustering and min-max normalization in video contents. With the use of the objects of video content, the proposed method finds clear and accurate associations between video content data and generates a knowledge graph. In the first step, the streaming video content data massively generated are collected, and objects in each image video are classified by an object detection algorithm. In the second step, normalization is executed in consideration of the different length of each video content and generate transaction data based on object frequency. In this way, it is possible to consider all the collected video content in the same condition. Additionally, it is possible to find unnecessary objects and significant objects in video content. Lastly, the degree of ambiguity is analyzed through fuzzy clustering using a probability that each object is involved in a group. Associations between fuzzy-clustered objects are extracted and an association knowledge graph is created. In this way, the accuracy and confidence of associations between different video content data are improved. As for performance, the association knowledge graph generated in the proposed method was better than a conventional association rule method in terms of the count of generated rules, support, and confidence.
C1 [Kim, Hyun-Jin] Sungkyunkwan Univ, Dept Artificial Intelligence, Suwon 16419, South Korea.
   [Baek, Ji-Won] Kyonggi Univ, Dept Comp Sci, Suwon 16227, South Korea.
   [Chung, Kyungyong] Kyonggi Univ, Div AI Comp Sci & Engn, Suwon 16227, South Korea.
RP Chung, K (通讯作者)，Kyonggi Univ, Div AI Comp Sci & Engn, Suwon 16227, South Korea.
EM dragonhci@gmail.com
OI Baek, jiwon/0000-0001-9332-2815; Chung, Kyungyong/0000-0002-6439-9992;
   Kim, Hyun Jin/0000-0002-6390-0765
CR Arora J, 2020, PROCEDIA COMPUT SCI, V167, P646, DOI 10.1016/j.procs.2020.03.331
   Baek JW, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10196662
   Dhalmahapatra K, 2019, COMPUT IND ENG, V128, P277, DOI 10.1016/j.cie.2018.12.044
   Gunarathne P, 2018, MIS QUART, V42, P489, DOI 10.25300/MISQ/2018/14290
   Kang C, 2021, IEEE T FUZZY SYST, V29, P34, DOI 10.1109/TFUZZ.2020.2966163
   Kim HJ, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10134590
   Lei T, 2018, IEEE T FUZZY SYST, V26, P3027, DOI 10.1109/TFUZZ.2018.2796074
   Li HL, 2020, KNOWL-BASED SYST, V197, DOI 10.1016/j.knosys.2020.105907
   Ma WZ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1210, DOI 10.1145/3308558.3313607
   Maggiori E, 2017, INT GEOSCI REMOTE SE, P5157, DOI 10.1109/IGARSS.2017.8128163
   Miao JQ, 2020, APPL SOFT COMPUT, V91, DOI 10.1016/j.asoc.2020.106200
   Pereira RM, 2020, COMPUT METH PROG BIO, V194, DOI 10.1016/j.cmpb.2020.105532
   Redmon J, 2017, PROC CVPR IEEE, P6517, DOI 10.1109/CVPR.2017.690
   Ren TB, 2019, APPL SOFT COMPUT, V81, DOI 10.1016/j.asoc.2019.105503
   Santurkar S, 2018, ADV NEUR IN, V31
   Seresht NG, 2020, APPL SOFT COMPUT, V96, DOI 10.1016/j.asoc.2020.106679
   Sharif M, 2020, IEEE ACCESS, V8, P167448, DOI 10.1109/ACCESS.2020.3021660
   SHIN DH, 2020, IEEE ACCESS, V8
   Singh B., 2018, ARXIV180509300, P9333, DOI 10.5555/3327546.3327604
   Singh D, 2020, APPL SOFT COMPUT, V97, DOI 10.1016/j.asoc.2019.105524
   Syakur MA, 2018, IOP CONF SER-MAT SCI, V336, DOI 10.1088/1757-899X/336/1/012017
   Tang YM, 2019, NEUROCOMPUTING, V329, P407, DOI 10.1016/j.neucom.2018.11.007
   Ting CK, 2018, MEMET COMPUT, V10, P15, DOI 10.1007/s12293-016-0220-3
   Ul Haq E, 2020, MULTIMED TOOLS APPL, V79, P30685, DOI 10.1007/s11042-020-09579-x
   Wu YH, 2021, IEEE T IMAGE PROCESS, V30, P3113, DOI 10.1109/TIP.2021.3058783
   Yoo H, 2017, WIRELESS PERS COMMUN, V93, P161, DOI 10.1007/s11277-016-3715-9
   Yu Jun, 2022, IEEE Trans Pattern Anal Mach Intell, V44, P563, DOI 10.1109/TPAMI.2019.2932058
   Zhang Y, 2020, IEEE ACCESS, V8, P56607, DOI 10.1109/ACCESS.2020.2982456
   Zhao LQ, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9030537
   Zhong Y., 2020, P IEEE CVF WINT C AP, P1286
   Zhou SB, 2018, APPL SOFT COMPUT, V71, P78, DOI 10.1016/j.asoc.2018.06.033
   Zhu XF, 2020, PATTERN RECOGN, V105, DOI 10.1016/j.patcog.2019.107175
NR 32
TC 3
Z9 3
U1 4
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 2021
VL 9
BP 74802
EP 74816
DI 10.1109/ACCESS.2021.3080180
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA TJ5RN
UT WOS:000673538600001
OA gold
DA 2022-08-23
ER

PT J
AU Nie, WZ
   Wang, Y
   Song, D
   Li, WH
AF Nie, Weizhi
   Wang, Ya
   Song, Dan
   Li, Wenhui
TI 3D Model Retrieval Based on a 3D Shape Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Three-dimensional displays; Solid modeling; Shape; Task analysis; Data
   models; Machine learning; Biological system modeling; 3D model
   retrieval; knowledge graph; graph embedding; multi-view representation
AB A development of 3D construction technology has led to 3D models being applied in many fields, and the number of 3D models has exploded in recent years. Thus, 3D model retrieval has become a popular topic with many proposed approaches. However, all of the methods focus on the 3D model's global structural descriptor design based on various deep learning networks and ignore the local structural information of the 3D model and the correlation of the local structures. In this paper, we propose a novel 3D model retrieval method based on a 3D shape knowledge graph. We first introduce the concept of a geometric word that can be utilized to assemble other 3D model. Second, we construct a 3D shape knowledge graph based on the geometric words, models and their relations. Additionally, we propose a novel graph embedding method to generate embeddings of nodes. Finally, an effective multiple entities' retrieval method is used to handle the 3D model retrieval problem. More specifically, the 3D shape knowledge graph retains the basic structural information and saves these as a set of triples. Any 3D model can find its geometric words in a rich enough knowledge graph. It is reasonable that our approach can solve the cross-domain model retrieval problem. Our approach focuses on the structural information of 3D model and is not restricted by the database. We evaluate the proposed method on the ModelNet40 dataset for the 3D model retrieval task. Meanwhile, we also utilize the ShapeNet dataset to evaluate the performance of cross-domain retrieval task. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework can achieve superior performance.
C1 [Nie, Weizhi; Wang, Ya; Song, Dan; Li, Wenhui] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China.
RP Song, D; Li, WH (通讯作者)，Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China.
EM dan.song@tju.edu.cn; liwenhui@tju.edu.cn
RI Nie, Weizhi/ABF-5316-2021
OI wang, ya/0000-0001-8966-9859
FU National Natural Science Foundation of China [61872267, 61772359,
   61702471, 61902277]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61872267, Grant 61772359, Grant
   61702471, and Grant 61902277.
CR [Anonymous], 2016, SIGNAL PROCESS, DOI DOI 10.1016/J.SIGPRO.2015.10.037
   Bai S, 2017, IEEE T MULTIMEDIA, V19, P1257, DOI 10.1109/TMM.2017.2652071
   Berndt D.J., 1994, KDD WORKSH, V10, P359
   Hua BS, 2018, PROC CVPR IEEE, P984, DOI 10.1109/CVPR.2018.00109
   Chang A. X., 2015, ARXIV151203012
   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 DY, 2003, COMPUT GRAPH FORUM, V22, P223, DOI 10.1111/1467-8659.00669
   Chen XZ, 2017, PROC CVPR IEEE, P6526, DOI 10.1109/CVPR.2017.691
   Dai A, 2017, PROC CVPR IEEE, P6545, DOI 10.1109/CVPR.2017.693
   Dietz L, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P215, DOI 10.1145/3331184.3331257
   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 Y, 2012, IEEE T IMAGE PROCESS, V21, P4290, DOI 10.1109/TIP.2012.2199502
   Garigliotti D, 2017, ICTIR'17: PROCEEDINGS OF THE 2017 ACM SIGIR INTERNATIONAL CONFERENCE THEORY OF INFORMATION RETRIEVAL, P27, DOI 10.1145/3121050.3121054
   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 YY, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3295822
   Hasibi F., 2015, P INT C THEOR INF RE, P171
   He XW, 2018, PROC CVPR IEEE, P1945, DOI 10.1109/CVPR.2018.00208
   Hegde V., 2016, P NIPS, P1
   Ji RR, 2020, IEEE T PATTERN ANAL, V42, P2410, DOI 10.1109/TPAMI.2019.2936024
   Jiang JW, 2019, AAAI CONF ARTIF INTE, P8513
   Kazhdan M., 2003, P S GEOM PROC JUN, P1
   Kipf T. N., 2017, P INT C LEARN REPR, P1
   Klokov R, 2017, IEEE I CONF COMP VIS, P863, DOI 10.1109/ICCV.2017.99
   Lin SH, 2019, IEEE T PATTERN ANAL, V41, P2889, DOI 10.1109/TPAMI.2018.2873305
   Liu AA, 2018, IEEE T CYBERNETICS, V48, P916, DOI 10.1109/TCYB.2017.2664503
   Liu AA, 2016, IEEE T IMAGE PROCESS, V25, P2103, DOI 10.1109/TIP.2016.2540802
   Liu X., 2014, TECH REP
   Liu XT, 2015, INFORM RETRIEVAL J, V18, P473, DOI 10.1007/s10791-015-9267-x
   Long MS, 2014, PROC CVPR IEEE, P1410, DOI 10.1109/CVPR.2014.183
   Long MS, 2013, IEEE I CONF COMP VIS, P2200, DOI 10.1109/ICCV.2013.274
   Lu X, 2020, IEEE T MULTIMEDIA, V22, P2048, DOI 10.1109/TMM.2019.2947358
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Perozzi B., 2017, P IEEE ACM INT C ADV, P258
   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
   Qi CR, 2017, ADV NEUR IN, V30
   Qi CR, 2016, PROC CVPR IEEE, P5648, DOI 10.1109/CVPR.2016.609
   Ribeiro LFR, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P385, DOI 10.1145/3097983.3098061
   Sfikas K, 2018, COMPUT GRAPH-UK, V71, P208, DOI 10.1016/j.cag.2017.12.001
   Shi BG, 2015, IEEE SIGNAL PROC LET, V22, P2339, DOI 10.1109/LSP.2015.2480802
   Su H, 2015, IEEE I CONF COMP VIS, P945, DOI 10.1109/ICCV.2015.114
   Wang Y, 2019, ACM T GRAPHIC, V38, DOI 10.1145/3326362
   Wu ZR, 2015, PROC CVPR IEEE, P1912, DOI 10.1109/CVPR.2015.7298801
   Xie J, 2017, IEEE T MULTIMEDIA, V19, P2463, DOI 10.1109/TMM.2017.2698200
   Xiong C., 2015, P INT C THEOR INF RE, P111
   You HX, 2019, AAAI CONF ARTIF INTE, P9119
   You HX, 2018, PROCEEDINGS OF THE 2018 ACM MULTIMEDIA CONFERENCE (MM'18), P1310, DOI 10.1145/3240508.3240702
   Yu K, 2002, NEURAL PROCESS LETT, V15, P147, DOI 10.1023/A:1015244902967
   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, 2017, PROC CVPR IEEE, P5150, DOI 10.1109/CVPR.2017.547
   Zhang ZZ, 2018, IEEE T IMAGE PROCESS, V27, P5957, DOI 10.1109/TIP.2018.2862625
   Zhou HY, 2020, IEEE T MULTIMEDIA, V22, P1496, DOI 10.1109/TMM.2019.2943740
   Zhu L, 2018, IEEE T NEUR NET LEAR, V29, P5264, DOI 10.1109/TNNLS.2018.2797248
NR 52
TC 1
Z9 1
U1 1
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 142632
EP 142641
DI 10.1109/ACCESS.2020.3013595
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NC0WA
UT WOS:000560931700001
OA gold
DA 2022-08-23
ER

PT J
AU Jia, YT
   Wang, YZ
   Jin, XL
   Cheng, XQ
AF Jia, Yantao
   Wang, Yuanzhuo
   Jin, Xiaolong
   Cheng, Xueqi
TI Path-specific knowledge graph embedding
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Path-specific; Knowledge graph embedding; Relation path
AB Knowledge graph embedding aims to represent entities, relations and multi-step relation paths of a knowledge graph as vectors in low-dimensional vector spaces, and supports many applications, such as entity prediction, relation prediction, etc. Existing embedding methods learn the representations of entities, relations, and multi-step relation paths by minimizing a general margin-based loss function shared by all relation paths. This setting fails to consider the differences among different relation paths.
   In this paper, we propose an embedding method by minimizing a path-specific margin-based loss function for knowledge graph embedding, called PaSKoGE. For each path, it adaptively determines its margin-based loss function by encoding the correlation between relations and multi-step relation paths for any given pair of entities. PaSKoGE outperforms the-state-of-the-art methods. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Jia, Yantao; Wang, Yuanzhuo; Jin, Xiaolong; Cheng, Xueqi] Chinese Acad Sci, Key Lab Network Data Sci & Technol, Inst Comp Technol, Beijing, Peoples R China.
RP Jia, YT (通讯作者)，Chinese Acad Sci, Key Lab Network Data Sci & Technol, Inst Comp Technol, Beijing, Peoples R China.
EM jiayantao@ict.ac.cn
FU National Key Research and Development Program of China [2016YFB1000902];
   National Grand Fundamental Research 973 Program of China [2012CB316303,
   2014CB340401]; National Natural Science Foundation of China [61402442,
   61572469, 91646120, 61572473, 61303244, 61402124]
FX This work is supported by National Key Research and Development Program
   of China under grant 2016YFB1000902, National Grand Fundamental Research
   973 Program of China (Nos. 2012CB316303, 2014CB340401), National Natural
   Science Foundation of China (Nos. 61402442, 61572469, 91646120,
   61572473, 61303244, 61402124).
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2011, P AAAI
   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
   Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401
   Garcia-Duran A., 2015, P 2015 C EMP METH NA, P286, DOI DOI 10.18653/V1/D15-1034
   Guu K., 2015, ARXIV150601094
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Jia Y., 2016, 30 AAAI C ART INT
   Jia YT, 2018, ACM T WEB, V12, DOI 10.1145/3132733
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   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
   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
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sutskever I., 2009, NIPS
   Toutanova K., 2016, P ANN M ASS COMP LIN
   Vapnik V., 1999, NATURE STAT LEARNING
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weinberger K.Q., 2008, P 25 INT C MACH LEAR, P1160, DOI DOI 10.1145/1390156.1390302
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wu F., 2015, 29 AAAI C ART INT
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Zhao Z., 2014, 28 AAAI C ART INT
NR 27
TC 16
Z9 22
U1 3
U2 37
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 JUL 1
PY 2018
VL 151
BP 37
EP 44
DI 10.1016/j.knosys.2018.03.020
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GH7TF
UT WOS:000433655600004
DA 2022-08-23
ER

PT J
AU Zhang, BY
   Yang, C
   Zhang, HK
   Wang, ZG
   Sun, JQ
   Wang, LH
   Zhao, YH
   Wang, YG
AF Zhang, Boyao
   Yang, Chao
   Zhang, Haikuo
   Wang, Zongguo
   Sun, Jingqi
   Wang, Lihua
   Zhao, Yonghua
   Wang, Yangang
TI Graph Representation Learning for Similarity Stocks Analysis
SO JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO
   TECHNOLOGY
LA English
DT Article; Early Access
DE Industrial chain knowledge graph; Graph representation learning;
   Similarity stock analysis; Momentum spillover effects
ID KNOWLEDGE GRAPH; EXPERIENCE; LINKS
AB Listed companies with similar or related fundamentals usually influence each other, and these influences are usually reflected in stock prices. For example, the momentum spillover effect in the behavioral finance theory describes the formation of lead-lag effects between the stock prices of related companies. The relationship between listed companies consists of many types, such as relationships in the industry chain, industry information, transaction information, patent sharing degree, equity, etc. We construct a set of industry chain knowledge graph of listed companies to describe the production and supply relationship between the upstream and downstream of listed companies. Then, graph representation learning method is used to study the relevance between listed company entities in the knowledge graph. It includes dimensions such as industry and transaction information of listed companies as weights to optimize the graph representation learning process, and finally calculates the similarity index between listed companies. To evaluate the effectiveness of the method, we conduct a link prediction experiment and construct a stock quantitative investment portfolio based on the similarity index. The result of the quantitative backtest experiment based on China's stock market data in the last 10 years shows that the graph representation learning method we proposed can be used to study the momentum spillover effect and obtain investment returns.
C1 [Zhang, Boyao; Wang, Zongguo; Zhao, Yonghua; Wang, Yangang] Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China.
   [Zhang, Boyao] Univ Chinese Acad Sci, Beijing, Peoples R China.
   [Yang, Chao; Sun, Jingqi; Wang, Lihua] Beijing Univ Aeronaut & Astronaut, Coll Software, Beijing, Peoples R China.
   [Zhang, Haikuo] China Internet Network Informat Ctr, Beijing, Peoples R China.
RP Zhang, HK (通讯作者)，China Internet Network Informat Ctr, Beijing, Peoples R China.
EM zhangby@sccas.cn; zf1921410@buaa.edu.cn; zhanghaikuo@cnnic.cn;
   wangzg@cnic.cn; sunjingqi@buaa.edu.cn; wanglihua@buaa.edu.cn;
   yhzhao@sccas.cn; wangyg@sccas.cn
OI Zhang, Haikuo/0000-0001-6875-7155
FU Technology and Innovation Major Project of the Ministry of Science and
   Technology of China [2020AAA0108400, 2020AAA0108403]; Beijing Natural
   Science Foundation [Z210002]
FX This work was partially supported by the Technology and Innovation Major
   Project of the Ministry of Science and Technology of China under Grant
   2020AAA0108400 and 2020AAA0108403, and partially supported by the
   Beijing Natural Science Foundation (Z210002). Portions of this work were
   presented at the 2021 IEEE 6th International Conference on Smart Cloud
   (SmartCloud) in 2021, Similarity Analysis of Knowledge Graph-based
   Company Embedding for Stocks Portfolio.
CR Ali U, 2020, J FINANC ECON, V136, P649, DOI 10.1016/j.jfineco.2019.10.007
   Bordes A., 2013, ADV NEURAL INFORM PR, V26
   Cohen L, 2008, J FINANC, V63, P1977, DOI 10.1111/j.1540-6261.2008.01379.x
   Deng W. T, 2019, APPL QUANTITATIVE IN
   Feng FL, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3309547
   Gai KK, 2015, IEEE I C EMBED SOFTW, P1332, DOI 10.1109/HPCC-CSS-ICESS.2015.250
   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 Qiu, 2018, Smart Blockchain. First International Conference, SmartBlock 2018. Proceedings: Lecture Notes in Computer Science (LNCS 121373), P159, DOI 10.1007/978-3-030-05764-0_17
   Hertwig R, 2004, PSYCHOL SCI, V15, P534, DOI 10.1111/j.0956-7976.2004.00715.x
   Huang X, 2019, J FINANC ECON, V131, P404, DOI 10.1016/j.jfineco.2018.08.003
   Kim H, 2017, KNOWL MAN RES PRACT, V15, P305, DOI 10.1057/s41275-017-0056-8
   Kipf T. N., 2016, ARXIV160902907
   Kumar A, 2006, J FINANC, V61, P2451, DOI 10.1111/j.1540-6261.2006.01063.x
   Lee CMC, 2019, J FINANC ECON, V132, P76, DOI 10.1016/j.jfineco.2018.11.008
   Lu RQ, 2019, IEEE T KNOWL DATA EN, V31, P1630, DOI 10.1109/TKDE.2018.2866863
   Makarov I, 2021, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.357
   Mccallum A., 2003, CONLL 03 P 7 C NAT L, V4, P188, DOI 10.3115/1119176.1119206
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Oak S, 2008, INT J HOSP MANAG, V27, P3, DOI 10.1016/j.ijhm.2007.06.003
   Parsons CA, 2020, REV FINANC STUD, V33, P4721, DOI 10.1093/rfs/hhz145
   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
   Qiu MK, 2015, IEEE T EMERG TOP COM, V3, P544, DOI 10.1109/TETC.2015.2398824
   Qiu MK, 2013, PHILOS T R SOC A, V371, DOI 10.1098/rsta.2012.0310
   Qiu MK, 2013, J SYST ARCHITECT, V59, P260, DOI 10.1016/j.sysarc.2012.12.003
   Qiu MK, 2013, J COMPUT SYST SCI, V79, P518, DOI 10.1016/j.jcss.2012.11.002
   Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
   Sun J., 2002, COLING 2002, DOI [10.3115/1072228.1072240, DOI 10.3115/1072228.1072240]
   Tang XY, 2012, J PARALLEL DISTR COM, V72, P525, DOI 10.1016/j.jpdc.2011.12.004
   Tay FEH, 2001, OMEGA-INT J MANAGE S, V29, P309, DOI 10.1016/S0305-0483(01)00026-3
   Thakur K, 2015, 2015 IEEE 2ND INTERNATIONAL CONFERENCE ON CYBER SECURITY AND CLOUD COMPUTING (CSCLOUD), P307, DOI 10.1109/CSCloud.2015.71
   Wang JH, 2017, J SYST ARCHITECT, V72, P69, DOI 10.1016/j.sysarc.2016.05.003
   Weber M, 1993, SOCIOL RELIG
   White H., 1988, IEEE International Conference on Neural Networks (IEEE Cat. No.88CH2632-8), P451, DOI 10.1109/ICNN.1988.23959
   Wu G, 2013, J PARALLEL DISTR COM, V73, P330, DOI 10.1016/j.jpdc.2012.09.007
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205
   Zeng D., 2014, P COLING, P2335
   Zhang H.P., 2003, COMPUT LINGUIST, V8, P29
   Zhao H, 2016, FUTURE GENER COMP SY, V56, P766, DOI 10.1016/j.future.2015.05.005
NR 39
TC 0
Z9 0
U1 8
U2 8
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1939-8018
EI 1939-8115
J9 J SIGNAL PROCESS SYS
JI J. Signal Process. Syst. Signal Image Video Technol.
DI 10.1007/s11265-022-01755-6
EA APR 2022
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 0I1XL
UT WOS:000779219100001
DA 2022-08-23
ER

PT J
AU Yang, ZX
   Dong, SB
AF Yang, Zuoxi
   Dong, Shoubin
TI HSRec: Hierarchical self-attention incorporating knowledge graph for
   sequential recommendation
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Sequential recommendation; knowledge graph; hierarchical self-attention;
   fine-grained preferences; dynamic preference evolution
AB Modeling user's fine-grained preferences and dynamic preference evolution from their chronological behaviors are challenging and crucial for sequential recommendation. In this paper, we develop a Hierarchical Self-Attention Incorporating Knowledge Graph for Sequential Recommendation (HSRec). HSRec models not only the user's intrinsic preferences but also the user's external potential interests to capture the user's fine-grained preferences. Specifically, the intrinsic interest module and potential interest module are designed to capture these two preferences respectively. In the intrinsic interest module, user's sequential patterns are characterized from their behaviors via the self-attention mechanism. As for the potential interest module, high-order paths can be generated with the help of the knowledge graph. Therefore, a hierarchical self-attention mechanism is designed to aggregate the semantic information of user interaction from these paths. Specifically, an entity-level self-attention mechanism is applied to capture the sequential patterns contained in the high-order paths while an interaction-level self-attention mechanism is designed to further capture the semantic information from user interactions. Moreover, according to the high-order semantic relevance, HSRec can explore the user's dynamic preferences at each time, thus describing the user's dynamic preference evolution. Finally, experiments conducted on three real world datasets demonstrate the state-of-the-art performance of the HSRec.
C1 [Yang, Zuoxi; Dong, Shoubin] South China Univ Technol, Guangdong Key Lab Comp Network, Sch Comp Sci & Engn, Guangzhou, Peoples R China.
   [Dong, Shoubin] South China Univ Technol, Zhongshan Inst Modern Ind Technol, Zhongshan, Peoples R China.
RP Dong, SB (通讯作者)，South China Univ Technol, Guangdong Key Lab Comp Network, Sch Comp Sci & Engn, Guangzhou, Peoples R China.; Dong, SB (通讯作者)，South China Univ Technol, Zhongshan Inst Modern Ind Technol, Zhongshan, Peoples R China.
EM sbdong@scut.edu.cn
CR Ba Jimmy Lei, 2016, ARXIV160706450, P5
   Demeester T, 2020, AAAI CONF ARTIF INTE, V34, P3757
   Guo C, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P1705, DOI 10.1145/3397271.3401217
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   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, 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
   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
   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
   Ma C, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P825, DOI 10.1145/3292500.3330984
   Oner SC, 2020, J INTELL FUZZY SYST, V38, P1027, DOI 10.3233/JIFS-179466
   Poongodi M, 2019, J INTELL FUZZY SYST, V36, P4057, DOI 10.3233/JIFS-169966
   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]
   Rosewelt L. Antony, 2020, J INTELL FUZZY SYST, P1
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   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 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, 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 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
   Yan A, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2173, DOI 10.1145/3357384.3358113
   Zhang M., 2021, PAKDD, P411
   Zhao WX, 2019, DATA INTELLIGENCE, V1, P121, DOI 10.1162/dint_a_00008
NR 31
TC 0
Z9 0
U1 2
U2 2
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 2022
VL 42
IS 4
BP 3749
EP 3760
DI 10.3233/JIFS-211953
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZW4PK
UT WOS:000771196600060
DA 2022-08-23
ER

PT J
AU Yang, QY
   Hou, CP
   Huang, MY
   Wang, ZP
AF Yang, Qingyuan
   Hou, Chunping
   Huang, Meiyan
   Wang, Zhipeng
TI Instance interactive association graph convolutional network for domain
   adaptive person re-identification
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Domain characteristic; Knowledge graph; Graph convolutional network;
   Domain adaptive person re-identification
AB Domain adaptive person re-identification (re-ID) is a challenging task due to the large domain divergency between different datasets and the complicated variations of the target domain. Style-transferring based methods mainly narrow the domain divergency with respect to a specific imaging factor, e.g., illumination. However, in consideration of the complex practical scenarios, domain adaptive re-ID demands comprehensive domain characteristic knowledge, i.e., seasons, illuminations, camera views, etc., to alleviate the domain divergency and intra-domain variations. This paper proposes a data-driven Instance Interactive Association Graph Convolutional Network (IIAGCN) to tackle the problems. Specifically, our IIAGCN method first constructs a cross-domain knowledge graph with the inter-domain nodes (target-source image pairs) and the intra-domain nodes (target-target image pairs). Then an Information Interactive Graph Convolutional (IIGC) layer is designed to extract the instance-level domain characteristic knowledge from the knowledge graph. With the learned knowledge, we can learn domain characteristic-aware and discriminative image representations for better domain adaptation. In addition, we introduce the memory bank component to store image features of the whole dataset, which enlarges the node diversity of the knowledge graph. Experiments on large-scale person re-ID datasets demonstrate the superiority of our method under the unsupervised re-ID setting.
C1 [Yang, Qingyuan; Hou, Chunping; Huang, Meiyan; Wang, Zhipeng] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China.
RP Huang, MY (通讯作者)，Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China.
EM yangqingyuan@tju.edu.cn; hcp@tju.edu.cn; meiyanhuang7295@tju.edu.cn;
   zpwang@tju.edu.cn
FU National Natural Science Foundation of China [61520106002, 61731003]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant Nos. 61520106002, Grant 61731003.
CR Cheng D, 2016, PROC CVPR IEEE, P1335, DOI 10.1109/CVPR.2016.149
   Dai CQ, 2020, MULTIMED TOOLS APPL, V79, P12597, DOI 10.1007/s11042-019-08604-y
   Deng C, 2020, IEEE T MULTIMEDIA, V22, P885, DOI 10.1109/TMM.2019.2934833
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Deng WJ, 2018, PROC CVPR IEEE, P994, DOI 10.1109/CVPR.2018.00110
   Fan HH, 2018, ACM T MULTIM COMPUT, V14, DOI 10.1145/3243316
   Fu Y, 2019, AAAI CONF ARTIF INTE, P8295
   Gao, 2020, IEEE T MULTIMEDIA
   Gray D, 2008, LECT NOTES COMPUT SC, V5302, P262, DOI 10.1007/978-3-540-88682-2_21
   He K., 2016, IEEE C COMP VIS PATT, DOI DOI 10.1109/CVPR.2016.90
   Jin X, 2020, AAAI CONF ARTIF INTE, V34, P11173
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kodirov E., 2015, BMVC, P1
   Li W, 2018, PROC CVPR IEEE, P2285, DOI 10.1109/CVPR.2018.00243
   Li YJ, 2019, IEEE I CONF COMP VIS, P7918, DOI 10.1109/ICCV.2019.00801
   Lian, 2019, IEEE T MULTIMEDIA
   Liao SC, 2015, PROC CVPR IEEE, P2197, DOI 10.1109/CVPR.2015.7298832
   Lin S., 2018, P 10 INT C MOD ID CO, P1
   Lin YT, 2019, AAAI CONF ARTIF INTE, P8738
   Liu J, 2019, APPL INTELL, V49, P3436, DOI 10.1007/s10489-019-01459-8
   Luo CC, 2019, IEEE I CONF COMP VIS, P4975, DOI 10.1109/ICCV.2019.00508
   Peng PX, 2016, PROC CVPR IEEE, P1306, DOI 10.1109/CVPR.2016.146
   Qi L, 2019, IEEE I CONF COMP VIS, P8079, DOI 10.1109/ICCV.2019.00817
   Ren CX, 2020, IEEE T INF FOREN SEC, V15, P1290, DOI 10.1109/TIFS.2019.2939750
   Shen YT, 2018, LECT NOTES COMPUT SC, V11219, P508, DOI 10.1007/978-3-030-01267-0_30
   Tang YZ, 2020, IEEE T IMAGE PROCESS, V29, P5641, DOI 10.1109/TIP.2020.2985545
   Wang GC, 2019, AAAI CONF ARTIF INTE, P8933
   Wang JY, 2018, PROC CVPR IEEE, P2275, DOI 10.1109/CVPR.2018.00242
   Wang XL, 2018, PROC CVPR IEEE, P7794, DOI 10.1109/CVPR.2018.00813
   Wang ZD, 2019, PROC CVPR IEEE, P1117, DOI 10.1109/CVPR.2019.00121
   Wei LH, 2018, PROC CVPR IEEE, P79, DOI 10.1109/CVPR.2018.00016
   Wu AC, 2019, IEEE I CONF COMP VIS, P6921, DOI 10.1109/ICCV.2019.00702
   Wu Y, 2019, IEEE T IMAGE PROCESS, V28, P2872, DOI 10.1109/TIP.2019.2891895
   Wu ZR, 2018, PROC CVPR IEEE, P3733, DOI 10.1109/CVPR.2018.00393
   Xiao T, 2017, PROC CVPR IEEE, P3376, DOI 10.1109/CVPR.2017.360
   Yan CX, 2018, MULTIMED TOOLS APPL, V77, P3553, DOI 10.1007/s11042-017-5202-z
   Yan HL, 2017, PROC CVPR IEEE, P945, DOI 10.1109/CVPR.2017.107
   Yan SJ, 2018, AAAI CONF ARTIF INTE, P7444
   Yang X, 2022, IEEE T PATTERN ANAL, V44, P1992, DOI 10.1109/TPAMI.2020.3026079
   Yin JY, 2020, APPL INTELL, V50, P3607, DOI 10.1007/s10489-020-01752-x
   Yu HX, 2017, IEEE I CONF COMP VIS, P994, DOI 10.1109/ICCV.2017.113
   Zheng L, 2015, IEEE I CONF COMP VIS, P1116, DOI 10.1109/ICCV.2015.133
   Zheng ZD, 2017, IEEE I CONF COMP VIS, P3774, DOI 10.1109/ICCV.2017.405
   Zhong Z, 2020, AAAI CONF ARTIF INTE, V34, P13001
   Zhong Z, 2018, LECT NOTES COMPUT SC, V11217, P176, DOI 10.1007/978-3-030-01261-8_11
   Zhong Z, 2019, PROC CVPR IEEE, P598, DOI 10.1109/CVPR.2019.00069
   Zhong Z, 2018, PROC CVPR IEEE, pCP99, DOI 10.1109/CVPR.2018.00541
NR 47
TC 0
Z9 0
U1 4
U2 9
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 MAY
PY 2022
VL 52
IS 7
BP 7747
EP 7760
DI 10.1007/s10489-021-02806-4
EA OCT 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1A0YQ
UT WOS:000705820600001
DA 2022-08-23
ER

PT J
AU Liu, YSY
   Gu, F
   Gu, XJ
   Wu, YJ
   Guo, JF
   Zhang, J
AF Liu, Yangshengyan
   Gu, Fu
   Gu, Xinjian
   Wu, Yijie
   Guo, Jianfeng
   Zhang, Jin
TI Resource Recommendation Based on Industrial Knowledge Graph in
   Low-Resource Conditions
SO INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
LA English
DT Article
DE Resource recommendation; Industrial knowledge graph; Few-shot relational
   learning; Schema-based reasoning
AB Resource recommendation is extremely challenging under low-resource conditions because representation learning models require sufficient triplets for their training, and the presence of massive long-tail resources leads to data sparsity and cold-start problems. In this paper, an industrial knowledge graph is developed to integrate resources for manufacturing enterprises, and we further formulate long-tail recommendations as a few-shot relational learning problem of learning-to-recommend resources with few interactions under low-resource conditions. First, an industrial knowledge graph is constructed based on the predesigned resource schema. Second, we conduct schema-based reasoning on the schema to heuristically complete the knowledge graph. At last, we propose a multi-head attention-based meta relational learning model with schema-based reasoning to recommend long-tail resources under low-resource conditions. With the IN-Train setting, 5-shot experimental results on the NELL-One and Wiki-One datasets achieve average improvements of 28.8 and 13.3% respectively, compared with MetaR. Empirically, the attention mechanism with relation space translation learns the most important relations for fast convergence. The proposed graph-based platform specifies how to recommend resources using the industrial knowledge graph under low-resource conditions.
C1 [Liu, Yangshengyan; Gu, Fu; Gu, Xinjian; Wu, Yijie; Zhang, Jin] Zhejiang Univ, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China.
   [Liu, Yangshengyan; Gu, Fu; Gu, Xinjian; Zhang, Jin] Zhejiang Univ, Dept Ind & Syst Engn, Hangzhou 310027, Peoples R China.
   [Gu, Fu; Gu, Xinjian; Zhang, Jin] Zhejiang Univ, Key Lab Adv Mfg Technol Zhejiang Prov, Hangzhou 310027, Peoples R China.
   [Gu, Fu] Zhejiang Univ, Ctr Engn Management, Polytech Inst, Hangzhou 310015, Peoples R China.
   [Guo, Jianfeng] Univ Chinese Acad Sci, Sch Publ Policy & Management, Beijing 100049, Peoples R China.
   [Guo, Jianfeng] Chinese Acad Sci, Inst Sci, Beijing 100190, Peoples R China.
   [Guo, Jianfeng] Chinese Acad Sci, Inst Dev, Beijing 100190, Peoples R China.
RP Gu, F (通讯作者)，Zhejiang Univ, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China.; Gu, F (通讯作者)，Zhejiang Univ, Dept Ind & Syst Engn, Hangzhou 310027, Peoples R China.; Gu, F (通讯作者)，Zhejiang Univ, Key Lab Adv Mfg Technol Zhejiang Prov, Hangzhou 310027, Peoples R China.; Gu, F (通讯作者)，Zhejiang Univ, Ctr Engn Management, Polytech Inst, Hangzhou 310015, Peoples R China.
EM gufu@zju.edu.cn
FU National Natural Science Foundation of China [71901194, 51775493];
   Natural Science Foundation of Zhejiang Province [LZ15E050003]; Ministry
   of Industry and Information Technology of the People's Republic China's
   2016 Green Manufacturing System Integration Project "Construction of
   Green Supply Chain System for Home Appliance Industry"; Eyas Program
   Incubation Project of Zhejiang Provincial Administration for Market
   Regulation [CY2022221]; Ningbo Science and Technology Innovation 2025
   Major Project [2018B10069]
FX This study is financially supported by the National Natural Science
   Foundation of China (No. 71901194 & No. 51775493), Natural Science
   Foundation of Zhejiang Province (No. LZ15E050003), Ministry of Industry
   and Information Technology of the People's Republic China's 2016 Green
   Manufacturing System Integration Project "Construction of Green Supply
   Chain System for Home Appliance Industry", Eyas Program Incubation
   Project of Zhejiang Provincial Administration for Market Regulation
   (CY2022221), and Ningbo Science and Technology Innovation 2025 Major
   Project (No. 2018B10069).
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Arista R, 2019, PRODUCT LIFECYCLE MA
   Bader SR, 2020, EUR SEM WEB C
   Bao J, 2016, COLING
   Baskar S, 2021, SOFT COMPUT, V25, P12275, DOI 10.1007/s00500-021-05910-2
   Berrada L, 2018, ARXIV
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bowen Hao, 2020, Web and Big Data. 4th International Joint Conference, APWeb-WAIM 2020. Lecture Notes in Computer Science (LNCS 12317), P363, DOI 10.1007/978-3-030-60259-8_27
   Chen AX, 2009, PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6, P578, DOI 10.1109/ICMLC.2009.5212454
   Chen H, 2022, DYNAMIC DUAL QUATERN
   Chen M, 2019, ARXIV COMPUTATION LA
   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
   Concilio G, 2019, SPRINGERBR APPL SCI, P61, DOI 10.1007/978-3-030-00123-0_4
   Craswell N, 2009, ENCY DATABASE SYSTEM, P1703
   Cui L, 2019, ARXIV
   Dai W, 2020, IEEE T COMPUT SOC SY, V7, P780, DOI 10.1109/TCSS.2020.2986836
   Dan L, 2018, INT SYMP COMP CONS, P1, DOI 10.1109/IS3C.2018.00010
   Dong S, 2021, COMPUT SCI REV, V40, DOI 10.1016/j.cosrev.2021.100379
   Dou JH, 2018, J VISUAL LANG COMPUT, V48, P19, DOI 10.1016/j.jvlc.2018.06.005
   Drakopoulos G, 2020, EVOL SYST-GER, V11, P397, DOI 10.1007/s12530-018-9244-x
   Du Z, 2019, ABS190605489 ARXIV
   Feng S, 2019, WORLD WIDE WEB, V22, P59, DOI 10.1007/s11280-018-0529-6
   Gan JD, 2018, IEEE I C NETW INFRAS, P11, DOI 10.1109/ICNIDC.2018.8525690
   Gu J-y, 2010, 2010 INT C ARTIFICIA, P29
   Guo QY, 2020, IEEE T KNOWL DATA EN, V34, P3549, DOI [10.1038/s41366-020-0625-4, 10.1109/TKDE.2020.3028705]
   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
   Hu BL, 2014, EUR MANAG J, V32, P587, DOI 10.1016/j.emj.2013.10.009
   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
   Jian Li, 2020, Web Information Systems and Applications. 17th International Conference, WISA 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12432), P59, DOI 10.1007/978-3-030-60029-7_6
   Kampffmeyer M, 2019, PROC CVPR IEEE, P11479, DOI 10.1109/CVPR.2019.01175
   King DB, 2015, ACS SYM SER, V1214, P1
   Kolbusz J, 2017, LECT NOTES ARTIF INT, V10245, P97, DOI 10.1007/978-3-319-59063-9_9
   Kosmopoulos D, 2018, PERVASIVE MOB COMPUT, V47, P54, DOI 10.1016/j.pmcj.2018.05.002
   Kulkarni VN, 2020, HDB SMART MAT TECHNO, P1
   LeCun Y., 2015, NATURE, V521, P436, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]
   Li Chuan, 2015, ScientificWorldJournal, V2015, P495309, DOI 10.1155/2015/495309
   Li XY, 2021, COMPUT IND, V129, DOI 10.1016/j.compind.2021.103449
   Lin X, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3352592
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lu HL, 2017, 2017 16TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS 2017), P675
   Lv X, 2019, ADAPTING METAKNOWLED
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Nayyeri M, 2019, ARXIV
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nie K, 2020, CHIN AUTOM CONGR, P4013, DOI 10.1109/CAC51589.2020.9327031
   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
   Noy N, 2019, QUEUE, V17, P20
   Qianxiu Hao, 2021, MM '21: Proceedings of the 29th ACM International Conference on Multimedia, P3909, DOI 10.1145/3474085.3475407
   Qiao B, 2017, CLUSTER COMPUT, V20, P81, DOI 10.1007/s10586-016-0725-z
   Reichheld FF, 2003, HARVARD BUS REV, V81, P46
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Sankar A, 2021, 15TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS 2021), P166, DOI 10.1145/3460231.3474268
   Sauro J, 2016, QUANTIFYING THE USER EXPERIENCE: PRACTICAL STATISTICS FOR USER RESEARCH, 2ND EDITION, P39, DOI 10.1016/B978-0-12-802308-2.00004-7
   Seel NM., 2012, ENCY SCI LEARNING, P2951, DOI [10.1007/978-1-4419-1428-6_364, DOI 10.1007/978-1-4419-1428-6_364]
   Shi HB, 2021, ENTERP INF SYST-UK, V15, P1128, DOI 10.1080/17517575.2019.1709663
   Sohail SS, 2018, ARAB J SCI ENG, V43, P8029, DOI 10.1007/s13369-018-3282-3
   Steele J, 2001, J MANUF SYST, V19, P407, DOI 10.1016/S0278-6125(01)80012-1
   Sun R, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P1405, DOI 10.1145/3340531.3411947
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Uyangoda Lasitha, 2018, 2018 Thirteenth International Conference on Digital Information Management (ICDIM), P24, DOI 10.1109/ICDIM.2018.8847002
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang C, 2019, ABS190606532 ARXIV
   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 T, 2020, NEURAL COMPUT APPL, V32, P235, DOI 10.1007/s00521-018-3806-5
   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
   Wang ZK, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI), P211, DOI 10.1109/ISI.2018.8587330
   Wu Y, 2020, IEEE I C VI COM I PR, P116
   Xiao J, 2020, 2020 12 INT C INTELL, P22
   Xiong W., 2018, ARXIV
   Xu B, 2017, CN DBPEDIA NEVER END
   Xu J, 2020, IEEE SYMP COMP COMMU, P703
   Yang B, 2015, ABS14126575 CORR
   Yang N, 2020, WORLD WIDE WEB, V23, P241, DOI 10.1007/s11280-019-00683-z
   Yang ZS, 2021, INT J COMPUT INT SYS, V14, P1564, DOI 10.2991/ijcis.d.210503.001
   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 H, 2019, WEB INFORM SYSTEMS A
   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 N, 2019, P WEB C
   Zhang WN, 2019, NEUROCOMPUTING, V334, P206, DOI 10.1016/j.neucom.2019.01.028
   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
   Zhen L, 2018, 2018 2 INT C DATA SC, P21
   Zheng P, 2018, FRONT MECH ENG-PRC, V13, P137, DOI 10.1007/s11465-018-0499-5
   Zou Y, 2020, 2020 IEEE 4 INFORM T, P12
NR 88
TC 0
Z9 0
U1 3
U2 3
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 1875-6891
EI 1875-6883
J9 INT J COMPUT INT SYS
JI Int. J. Comput. Intell. Syst.
PD JUL 3
PY 2022
VL 15
IS 1
AR 42
DI 10.1007/s44196-022-00097-2
PG 21
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Q1ZE
UT WOS:000820226900001
OA gold
DA 2022-08-23
ER

PT J
AU Nayyeri, M
   Cil, GM
   Vahdati, S
   Osborne, F
   Rahman, M
   Angioni, S
   Salatino, A
   Recupero, DR
   Vassilyeva, N
   Motta, E
   Lehmann, J
AF Nayyeri, Mojtaba
   Cil, Gokce Muge
   Vahdati, Sahar
   Osborne, Francesco
   Rahman, Mahfuzur
   Angioni, Simone
   Salatino, Angelo
   Recupero, Diego Reforgiato
   Vassilyeva, Nadezhda
   Motta, Enrico
   Lehmann, Jens
TI Trans4E: Link prediction on scholarly knowledge graphs
SO NEUROCOMPUTING
LA English
DT Article
DE Scholarly knowledge graph; Knowledge graph embedding; Scholarly
   communication; Science graph; Metaresearch queries; Link prediction;
   Research of research
AB The incompleteness of Knowledge Graphs (KGs) is a crucial issue affecting the quality of AI-based services. In the scholarly domain, KGs describing research publications typically lack important information, hindering our ability to analyse and predict research dynamics. In recent years, link prediction approaches based on Knowledge Graph Embedding models became the first aid for this issue. In this work, we present Trans4E, a novel embedding model that is particularly fit for KGs which include N to M relations with N >> M. This is typical for KGs that categorize a large number of entities (e.g., research articles, patents, persons) according to a relatively small set of categories. Trans4E was applied on two large-scale knowledge graphs, the Academia/Industry DynAmics (AIDA) and Microsoft Academic Graph (MAG), for completing the information about Fields of Study (e.g., 'neural networks', 'machine learning', 'artificial intelligence'), and affiliation types (e.g., 'education', 'company', 'government'), improving the scope and accuracy of the resulting data. We evaluated our approach against alternative solutions on AIDA, MAG, and four other benchmarks (FB15k, FB15k-237, WN18, and WN18RR). Trans4E outperforms the other models when using low embedding dimensions and obtains competitive results in high dimensions. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Nayyeri, Mojtaba; Cil, Gokce Muge; Rahman, Mahfuzur; Vassilyeva, Nadezhda; Lehmann, Jens] Univ Bonn, SDA Res Grp, Bonn, Germany.
   [Vahdati, Sahar] Inst Appl Informat InfAI, Leipzig, Germany.
   [Lehmann, Jens] Fraunhofer IAIS, Dresden, Germany.
   [Osborne, Francesco; Salatino, Angelo; Recupero, Diego Reforgiato] Open Univ, Knowledge Media Inst, Milton Keynes, Bucks, England.
   [Angioni, Simone] Univ Cagliari, Dept Math & Comp Sci, Cagliari, Italy.
RP Salatino, A (通讯作者)，Open Univ, Knowledge Media Inst, Milton Keynes, Bucks, England.
EM nayyeri@cs.uni-bonn.de; s6gocill@uni-bonn.de; vahdati@infai.org;
   francesco.osborne@open.ac.uk; s6mrrahm@uni-bonn.de;
   simone.angioni@unica.it; angelo.salatino@open.ac.uk;
   diego.reforgiato@unica.it; vassilyeva@cs.uni-bonn.de;
   enrico.motta@open.ac.uk; jens.lehmann@cs.uni-bonn.de
RI Salatino, Angelo A./AAD-7423-2022
OI Salatino, Angelo A./0000-0002-4763-3943; Rahman,
   Mahfuzur/0000-0003-0273-3112; Osborne, Francesco/0000-0001-6557-3131;
   Motta, Enrico/0000-0003-0015-1952
FU NVIDIA Corporation; EC Horizon 2020 grant LAMBDA [809965]; CLEOPATRA
   project [812997]
FX We gratefully acknowledge the support of NVIDIA Corporation with the
   donation of the Titan X GPU used for this research. This work is
   supported by the EC Horizon 2020 grant LAMBDA (GA No. 809965) , and the
   CLEOPATRA project (GA No. 812997).
CR Ammar W., 2018, P 2018 C N AM CHAPT, P84, DOI DOI 10.18653/V1/N18-3011
   Angioni S., 2020, P ADBIS EDA COMM WOR, P219
   Arora Siddhant, 2020, ARXIV PREPRINT ARXIV
   Belleau F, 2008, J BIOMED INFORM, V41, P706, DOI 10.1016/j.jbi.2008.03.004
   Bendre N., 2020, ARXIV PREPRINT ARXIV
   Bonatti P.A., 2019, DAGSTUHL REPORTS
   Bordes A., 2013, ADVANCES
   Bose A, 2019, CONF REC ASILOMAR C, P1534, DOI 10.1109/IEEECONF44664.2019.9048709
   Cavallari S, 2019, IEEE COMPUT INTELL M, V14, P39, DOI 10.1109/MCI.2019.2919396
   Chen M., 2019, INT JOINT C NAT LANG, P4208
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Dessi D., AI KG AUTOMATICALLY
   Farber M, 2019, LECT NOTES COMPUT SC, V11779, P113, DOI 10.1007/978-3-030-30796-7_8
   Gong K, 2019, PROC CVPR IEEE, P7442, DOI 10.1109/CVPR.2019.00763
   Goyal P, 2018, KNOWL-BASED SYST, V151, P78, DOI 10.1016/j.knosys.2018.03.022
   Groth Paul, 2010, Information Services & Use, V30, P51, DOI 10.3233/ISU-2010-0613
   Henk V., 2019, RECNLP WORKSH AAAI C
   Tran HN, 2019, LECT NOTES COMPUT SC, V11799, P154, DOI 10.1007/978-3-030-30760-8_14
   Jaradeh MY, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P243, DOI 10.1145/3360901.3364435
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Knoth P., 2011, PROC CERN WORKSHOP I
   Knoth P., 2012, D LIB MAGAZINE, V18
   Kuhn T, 2016, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.78
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Lee J, 2017, AAAI CONF ARTIF INTE, P2154
   Li LJ, 2019, IEEE I CONF COMP VIS, P10312, DOI 10.1109/ICCV.2019.01041
   Mannocci A., 2019, DATA SCI, V2, P181
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Nayyeri Mojtaba, 2020, Semantic Web 17th International Conference, ESWC 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12123), P255, DOI 10.1007/978-3-030-49461-2_15
   Nayyeri M, 2020, LECT NOTES COMPUT SC, V12506, P438, DOI 10.1007/978-3-030-62419-4_25
   Nguyen D.Q., 2019, P NAACL HLT 2019, P2180, DOI DOI 10.18653/V1/N19-1226
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Nuzzolese AG, 2016, LECT NOTES COMPUT SC, V9989, P84, DOI [10.1007/978-3-319-47602-5-18, 10.1007/978-3-319-47602-5_18]
   Osborne F, 2017, K-CAP 2017: PROCEEDINGS OF THE KNOWLEDGE CAPTURE CONFERENCE, DOI 10.1145/3148011.3148080
   Paliwal A., 2019, ARXIV190510006
   Peroni S, 2020, QUANT SCI STUD, V1, P428, DOI 10.1162/qss_a_00023
   Peroni S, 2018, LECT NOTES COMPUT SC, V11137, P119, DOI 10.1007/978-3-030-00668-6_8
   Salatino A., 2020, KNOWLEDGE ENG KNOWLE
   Salatino AA, 2020, DATA INTELLIGENCE, V2, P379, DOI 10.1162/dint_a_00055
   Salatino AA, 2018, LECT NOTES COMPUT SC, V11137, P187, DOI 10.1007/978-3-030-00668-6_12
   Shotton D, 2009, LEARN PUBL, V22, P85, DOI 10.1087/2009202
   Stanovsky G, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P142
   Sun FY, 2019, ADV NEUR IN, V32
   Sun Z., 2019, 7 INT C LEARN REPR
   Sun Z., ARXIV PREPRINT ARXIV
   Trouillon T, 2016, PR MACH LEARN RES, V48
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   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 KS, 2020, QUANT SCI STUD, V1, P396, DOI 10.1162/qss_a_00021
   Wang Q., 2017, IEEE TKDE, V29
   Wang W, 2020, ALGORITHM MOL BIOL, V15, DOI 10.1186/s13015-020-00167-0
   Wang YQ, 2020, ACM COMPUT SURV, V53, DOI 10.1145/3386252
   Wang Z., IJCAI, P5240
   Wolstencroft K, 2013, NUCLEIC ACIDS RES, V41, pW557, DOI 10.1093/nar/gkt328
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Yao L, 2017, AAAI CONF ARTIF INTE, P3119
   Yin W, 2020, ARXIV PREPRINT ARXIV
   Zhang CX, 2020, AAAI CONF ARTIF INTE, V34, P3041
   ZHANG S, 2019, ARXIV PREPRINT ARXIV
   Zhang YT, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1002, DOI 10.1145/3219819.3219859
   Zhou J., 2020, ARXIV PREPRINT ARXIV, V1, P57, DOI 10.1016/j.aiopen.2021.01.001
   Zhuang FZ, 2021, P IEEE, V109, P43, DOI 10.1109/JPROC.2020.3004555
NR 62
TC 5
Z9 5
U1 10
U2 20
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD OCT 21
PY 2021
VL 461
BP 530
EP 542
DI 10.1016/j.neucom.2021.02.100
EA SEP 2021
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WC2IT
UT WOS:000704086300004
OA Green Submitted, Green Accepted
DA 2022-08-23
ER

PT J
AU Zheng, XY
   Kong, Y
   Chang, TT
   Liao, X
   Ma, YW
   Du, Y
AF Zheng, Xiaoyu
   Kong, Yi
   Chang, Tingting
   Liao, Xin
   Ma, Yiwu
   Du, Yong
TI High-Throughput Computing Assisted by Knowledge Graph to Study the
   Correlation between Microstructure and Mechanical Properties of 6XXX
   Aluminum Alloy
SO MATERIALS
LA English
DT Article
DE knowledge graph; high-throughput computing; microstructure design;
   crystal plasticity; Al alloys
ID FATIGUE-CRACK INITIATION; HARDENING MODEL; YIELD STRENGTH; DEFORMATION;
   PREDICTION
AB It is of great academic and engineering application to study the evolution of microstructure and properties of age-strengthened aluminum alloys during heat treatment and to establish quantitative prediction models that can be applied to industrial production. The main factors affecting the peak aging state strength of age-strengthened aluminum alloys are the precipitates, solid solution elements, grain size effects, and textures formed during the material processing. In this work, these multi-scale factors are integrated into the framework of the knowledge graph to assist the following crystal plasticity finite elements simulations. The constructed knowledge graph is divided into two parts: static data and dynamic data. Static data contains the basic properties of the material and the most basic property parameters. Dynamic data is designed to improve awareness of static data. High-throughput computing is performed to further obtain clear microstructure-property relationships by varying the parameters of materials properties and the characteristics of the structure models. The constructed knowledge graph can be used to guide material design for 6XXX Al-Mg-Si based alloys. The past experimental values are used to calibrate the phenomenological parameters and test the reliability of the analysis process.
C1 [Zheng, Xiaoyu; Kong, Yi; Du, Yong] Cent South Univ, State Key Lab Powder Met, Changsha 410083, Peoples R China.
   [Chang, Tingting; Liao, Xin] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Hunan, Peoples R China.
   [Ma, Yiwu] Hunan Univ, Natl Supercomp Ctr Changsha, Changsha 410082, Hunan, Peoples R China.
RP Kong, Y (通讯作者)，Cent South Univ, State Key Lab Powder Met, Changsha 410083, Peoples R China.
EM xiaoyu_zheng@csu.edu.cn; yikong@csu.edu.cn; tingtingchang@hnu.edu.cn;
   xinliao@hnu.edu.cn; myw@hnu.edu.cn; yong-du@csu.edu.cn
FU National Key Research and Development Program of China [2018YFB0704003];
   National Natural Science Foundation of China [51771234, 51820105001]
FX This research was funded by the National Key Research and Development
   Program of China (grant nos. 2018YFB0704003), and the National Natural
   Science Foundation of China (grant No. 51771234 and 51820105001).
CR ASARO RJ, 1983, ADV APPL MECH, V23, P1, DOI 10.1016/S0065-2156(08)70242-4
   Borukhovich E, 2016, MATERIALS, V9, DOI 10.3390/ma9080673
   Cheong KS, 2007, ACTA MATER, V55, P1757, DOI 10.1016/j.actamat.2006.10.038
   Esmaeili S, 2003, ACTA MATER, V51, P2243, DOI 10.1016/S1359-6454(03)00028-4
   Groeber MA, 2014, INTEGR MATER MANUF I, V3, DOI 10.1186/2193-9772-3-5
   Hirsch J, 2014, T NONFERR METAL SOC, V24, P1995, DOI 10.1016/S1003-6326(14)63305-7
   Hirsch J, 2011, MATER TRANS, V52, P818, DOI 10.2320/matertrans.L-MZ201132
   Holmedal B, 2015, PHIL MAG LETT, V95, P594, DOI 10.1080/09500839.2015.1125029
   Kim Y, 2021, MAT SCI ENG A-STRUCT, V820, DOI 10.1016/j.msea.2021.141566
   Li YL, 2020, INT J PLASTICITY, V132, DOI 10.1016/j.ijplas.2020.102759
   Luo CT, 2011, INT J FATIGUE, V33, P403, DOI 10.1016/j.ijfatigue.2010.09.014
   Luo CT, 2009, AIAA J, V47, P2567, DOI 10.2514/1.39559
   Manonukul A, 2004, P ROY SOC A-MATH PHY, V460, P1881, DOI 10.1098/rspa.2003.1258
   Miller WS, 2000, MAT SCI ENG A-STRUCT, V280, P37, DOI 10.1016/S0921-5093(99)00653-X
   Mrdjenovich D, 2020, MATTER-US, V2, P464, DOI 10.1016/j.matt.2019.11.013
   Myhr OR, 2015, METALL MATER TRANS A, V46A, P6018, DOI 10.1007/s11661-015-3175-y
   Myhr OR, 2010, METALL MATER TRANS A, V41A, P2276, DOI 10.1007/s11661-010-0258-7
   Myhr OR, 2001, ACTA MATER, V49, P65, DOI 10.1016/S1359-6454(00)00301-3
   PEIRCE D, 1982, ACTA METALL MATER, V30, P1087, DOI 10.1016/0001-6160(82)90005-0
   Prasad M., 2019, J OPEN SOURCE SOFTW, V4, P1732, DOI [10.21105/joss.01732, DOI 10.21105/JOSS.01732]
   Quey R, 2011, COMPUT METHOD APPL M, V200, P1729, DOI 10.1016/j.cma.2011.01.002
   Quey R, 2018, J APPL CRYSTALLOGR, V51, P1162, DOI 10.1107/S1600576718009019
   Quey R, 2018, COMPUT METHOD APPL M, V330, P308, DOI 10.1016/j.cma.2017.10.029
   Sachs G, 1928, T FARADAY SOC, V24, P0084, DOI 10.1039/tf9282400084
   Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62
   SKELTON RP, 1993, MATER SCI TECH SER, V9, P1001, DOI 10.1179/mst.1993.9.11.1001
   Taylor GI, 1938, J I MET, V62, P307
   Wang WY, 2019, COMP MATER SCI, V158, P42, DOI 10.1016/j.commatsci.2018.11.001
   Yang MJ, 2020, MAT SCI ENG A-STRUCT, V774, DOI 10.1016/j.msea.2019.138776
   Zhang K, 2019, INT J PLASTICITY, V114, P144, DOI 10.1016/j.ijplas.2018.10.015
   Zhao Q, 2021, MATER DESIGN, V206, DOI 10.1016/j.matdes.2021.109794
NR 31
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1944
J9 MATERIALS
JI Materials
PD AUG
PY 2022
VL 15
IS 15
AR 5296
DI 10.3390/ma15155296
PG 16
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 3R4BB
UT WOS:000838859300001
PM 35955239
OA Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Hu, BJ
   Ye, YQ
   Zhong, YQ
   Pan, J
   Hu, MS
AF Hu, Bojing
   Ye, Yaqin
   Zhong, Yingqiang
   Pan, Jiao
   Hu, Maosheng
TI TransMKR: Translation-based knowledge graph enhanced multi-task
   point-of-interest recommendation
SO NEUROCOMPUTING
LA English
DT Article
DE Point-of-interest recommendation; TransMKR; Knowledge graph; Knowledge
   representation
AB Point-of-interest (POI) recommendation aims to predict favorite POIs that have never visited before for users given their historical check-ins records. The introduction of knowledge graph can solve the hetero-geneity of auxiliary information. However, the existing knowledge graph methods about POI recommen-dation only make use of the relationship between users and POIs or the context information (such as time series) for recommendation, but they neglect the deep relation consider the attribute connection of POIs themselves. In this paper, we propose a translation-based knowledge graph enhanced multi-task learning framework (TransMKR) for the POI recommendation. We improve the KGE module of MKR with TransR to quantify the relation between POIs and their attributes. As the POIs vector and the entity vector are actu-ally two descriptions of the same item, the cross-sharing of information between them makes them obtain additional information from each other, and this enhances the expressive ability of POI data, thus it can alleviate the problem of data sparsity. An exquisitely designed structure is devised to capture the deep associated attributes of POIs under different relations. Empirically, this approach achieves the state -of-the-art model for POI recommendation based on geographical location on two public real-world datasets. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Hu, Bojing] China Univ Geosci, Natl Engn Res Ctr Geog Informat Syst, Wuhan, Peoples R China.
   [Ye, Yaqin; Zhong, Yingqiang; Pan, Jiao; Hu, Maosheng] China Univ Geosci, Sch Geog & Informat Engn, Wuhan, Peoples R China.
RP Ye, YQ (通讯作者)，China Univ Geosci, Sch Geog & Informat Engn, Wuhan, Peoples R China.
EM yeyaqin@cug.edu.cn
FU National Natural Science Foundation of China [41801378, 61972365];
   Research on the interactive evolution of industrial structure and
   population migration in Hubei [KH216676]; National Key Research and
   Development Program of China [2016YFB0502304, 2017YFB050380504]
FX This research was funded by the National Natural Science Foundation of
   China, Grant No. 41801378, 61972365, Research on the interactive
   evolution of industrial structure and population migration in Hubei,
   project number is KH216676, and National Key Research and Development
   Program of China, project number are 2016YFB0502304 and
   2017YFB050380504.
CR Chang B., 2018, IJCAI, V2018, p27th
   Chen Y.-C., IEEE T CYBERN
   Chengwen Zhang, 2020, 2020 Proceedings of IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), P847, DOI 10.1109/ICAICA50127.2020.9182385
   Guo Q, 2019, LECT NOTES COMPUT SC, V11496, P72, DOI 10.1007/978-3-030-19274-7_6
   Han P., 2020, INT JOINT C ART INT
   He RN, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P161, DOI 10.1145/3109859.3109882
   Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
   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
   Lian DF, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2009, DOI 10.1145/3394486.3403252
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
   Liu B., ARXIV PREPRINT ARXIV
   Liu W, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1807
   Liu YD, 2017, PROC VLDB ENDOW, V10, P1010, DOI 10.14778/3115404.3115407
   Pasricha R, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P63, DOI 10.1145/3240323.3240356
   Qian TY, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3295499
   Ren XY, 2017, NEUROCOMPUTING, V241, P38, DOI 10.1016/j.neucom.2017.02.005
   Wang H, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3877
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Wang SH, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P391, DOI 10.1145/3038912.3052638
   Wang XY, 2015, IEEE T MULTIMEDIA, V17, P409, DOI 10.1109/TMM.2014.2385473
   Wang XJ, 2020, LECT NOTES ARTIF INT, V12084, P53, DOI 10.1007/978-3-030-47426-3_5
   Xiong X, 2020, ENG APPL ARTIF INTEL, V88, DOI 10.1016/j.engappai.2019.103374
   Ye M, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P325
   Yu FQ, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P1264, DOI 10.1145/3366423.3380202
   Zhang J.-D., 2014, P 22 ACM SIGSPATIAL, P103
   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
NR 27
TC 2
Z9 2
U1 5
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD FEB 14
PY 2022
VL 474
BP 107
EP 114
DI 10.1016/j.neucom.2021.11.049
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZI5XM
UT WOS:000761694000009
DA 2022-08-23
ER

PT J
AU Nguyen, HL
   Jung, JJ
AF Hoang Long Nguyen
   Jung, Jason J.
TI Social event decomposition for constructing knowledge graph
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Social event decomposition; Event-driven knowledge graph; SocioScope
   framework; Independent component analysis
ID INDEPENDENT COMPONENT ANALYSIS; FEATURE-EXTRACTION; ICA; IMPLEMENTATION;
   RECOGNITION; SEPARATION; FRAMEWORK; REMOVAL; TRUST; POINT
AB Given the large amount of data collected from social media, it is very difficult for users to identify social events and understand their societies. In this paper, we propose a novel method for i) decomposing and discovering social events and ii) representing social events and their relationships as a knowledge graph. In particular, the proposed method is based on Independent Component Analysis (ICA) and the SocioScope Knowledge Graph (SKG) model. To demonstrate the actual performance, the proposed method has been evaluated with the support of the SocioScope framework (Nguyen and Jung, 2018). Then, it was verified that the system can efficiently provide people with a high understandability and traceability of social events. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Hoang Long Nguyen; Jung, Jason J.] Chung Ang Univ, Dept Comp Engn, 84 Heukseok Ro, Seoul, South Korea.
RP Jung, JJ (通讯作者)，Chung Ang Univ, Dept Comp Engn, 84 Heukseok Ro, Seoul, South Korea.
EM longnh238@gmail.com; j3ung@cau.ac.kr
RI Nguyen, Hoang Long/K-6449-2019; Jung, Jason J./B-9622-2012
OI Nguyen, Hoang Long/0000-0002-8031-6341; Jung, Jason
   J./0000-0003-0050-7445
FU National Research Foundation of Korea (NRF) - Korean government (MSIP)
   [NRF-2017R1A2B4010774]
FX This work was supported by the National Research Foundation of Korea
   (NRF) grant funded by the Korean government (MSIP)
   (NRF-2017R1A2B4010774).
CR Abel F., 2012, P INT C WORLD WID WE, P305, DOI DOI 10.1145/2187980.2188035
   Adam N, 2012, COLLABORATECOM, P181, DOI 10.4108/icst.collaboratecom.2012.250713
   Bartlett MS, 2002, IEEE T NEURAL NETWOR, V13, P1450, DOI 10.1109/TNN.2002.804287
   Bello-Orgaz G, 2016, INFORM FUSION, V28, P45, DOI 10.1016/j.inffus.2015.08.005
   Benson E., 2011, P 49 ANN M ASS COMP, V1, P389
   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
   Chagnaa A, 2007, J INF PROCESS SYST, V3, P33, DOI 10.3745/JIPS.2007.3.1.033
   Chien JT, 2006, IEEE T AUDIO SPEECH, V14, P1245, DOI 10.1109/TSA.2005.858061
   Cui A., 2012, P 21 ACM INT C INF K
   Ding W, 2011, PROCEDIA ENGINEER, V24, P111, DOI 10.1016/j.proeng.2011.11.2611
   Nguyen HL, 2018, FUTURE GENER COMP SY, V83, P358, DOI 10.1016/j.future.2018.01.064
   Nguyen HL, 2017, INT CONF INTEL ENVIR, P108, DOI 10.1109/IE.2017.15
   Nguyen HL, 2017, IEEE ACCESS, V5, P4664, DOI 10.1109/ACCESS.2017.2677917
   Honkela T, 2010, NAT LANG ENG, V16, P277, DOI 10.1017/S1351324910000057
   Hyvarinen A, 2000, NEURAL NETWORKS, V13, P411, DOI 10.1016/S0893-6080(00)00026-5
   Kim CM, 2003, IEEE T NEURAL NETWOR, V14, P1038, DOI 10.1109/TNN.2003.818381
   Kosala R, 2012, PROCEDIA ENGINEER, V50, P1, DOI 10.1016/j.proeng.2012.10.001
   Kuzey E, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P103, DOI 10.1145/2567948.2577009
   Lambert RH, 1997, INT CONF ACOUST SPEE, P423, DOI 10.1109/ICASSP.1997.599665
   Lee JH, 2000, INT CONF ACOUST SPEE, P1631, DOI 10.1109/ICASSP.2000.862023
   Lehmann J, 2012, P 21 INT C WORLD WID, P251, DOI DOI 10.1145/2187836.2187871
   LENAT DB, 1995, COMMUN ACM, V38, P33, DOI 10.1145/219717.219745
   Leung TW, 2004, INT C PATT RECOG, P367, DOI 10.1109/ICPR.2004.1334222
   Li YD, 2006, PHYSIOL MEAS, V27, P425, DOI 10.1088/0967-3334/27/4/008
   Long NH, 2015, CYBERNET SYST, V46, P69, DOI 10.1080/01969722.2015.1007737
   Noh SI, 2005, IEICE T INF SYST, VE88D, P2573, DOI 10.1093/ietisy/e88-d.11.2573
   O'Connor B., 2010, 4 INT AAAI C WEBL SO
   Patil D, 2011, MEAS SCI REV, V11, P118, DOI 10.2478/v10048-011-0022-1
   Petrovic S., P HUM LANG TECHN 201, P181
   Phuvipadawat S, 2010, Proceedings of the 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology - Workshops (WI-IAT 2010), P120, DOI 10.1109/WI-IAT.2010.205
   Potamitis L, 2000, ELECTRON LETT, V36, P1977, DOI 10.1049/el:20001365
   Rospocher M, 2016, J WEB SEMANT, V37-38, P132, DOI 10.1016/j.websem.2015.12.004
   Sakaki T., 2010, P 19 INT C WORLD WID, P851
   Sankaranarayanan J., 2009, P 17 ACM SIGSPATIAL, P42, DOI DOI 10.1145/1653771.1653781
   Shamma D., 2011, P ACM 2011 C COMP SU, P355
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   van Hage WR, 2011, J WEB SEMANT, V9, P128, DOI 10.1016/j.websem.2011.03.003
   Zeng K, 2013, IEEE INTELL SYST, V28, P72, DOI 10.1109/MIS.2013.23
NR 39
TC 13
Z9 13
U1 6
U2 41
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 10
EP 18
DI 10.1016/j.future.2019.05.016
PG 9
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JX6FD
UT WOS:000503827500002
DA 2022-08-23
ER

PT J
AU Zhang, M
   Teng, HB
   Jiang, M
   Wen, T
   Tang, JF
AF Zhang Min
   Teng Haibin
   Jiang Ming
   Wen Tao
   Tang Jingfan
TI A Longest Matching Resource Mapping Algorithm with State Compression
   Dynamic programming Optimization
SO INTELLIGENT AUTOMATION AND SOFT COMPUTING
LA English
DT Article
DE Knowledge graphs; NLP application; Phrase detection; question answering
   systems; Resource mapping; State Compression Dynamic Programming
ID QUESTION ANSWERING SYSTEM; DESIGN
AB Mapping from sentence phrases to knowledge graph resources is an important step for applications such as search engines, automatic question answering systems based on acknowledge base and knowledge graphs. The existing solution maps a simple phrase to a knowledge graph resource strictly or approximately from the text. However, it is difficult to detect phrases and map the composite semantic resource. This paper proposes a longest matching resource mapping scheme to solve this problem, namely, to find the longest substring in a sentence that can match the knowledge base resource. Based on this scheme, we propose an optimization algorithm based on state compression dynamic programming. Furthermore, we improve the operating efficiency by removing invalid states. Experimental results show that our proposed optimization algorithm considerably improves the efficiency of the benchmark algorithm in terms of running time.
C1 [Zhang Min; Teng Haibin; Jiang Ming; Wen Tao; Tang Jingfan] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China.
RP Zhang, M (通讯作者)，Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China.
EM jiangming@hdu.edu.cn
CR Amaral C, 2005, ACCESSING MULTILINGU, P364
   Blooma MJ, 2007, LECT NOTES COMPUT SC, V4822, P497
   Breck E, 2000, EVALUATE YOUR QUESTI, P1495
   Buchholz S, 2002, NAT LANG ENG, V7, P301
   Cao YG, 2011, J BIOMED INFORM, V44, P277, DOI 10.1016/j.jbi.2011.01.004
   Cimiano P, 2013, LECT NOTES COMPUT SC, V8138, P321, DOI 10.1007/978-3-642-40802-1_30
   Collaborative QoS Prediction for Mobile Service with Data Filtering and SlopeOne Model, 2017, MOB INF SYST
   Dima C., 2013, P QUESTION ANSWERING
   Guler FM, 2010, LECT NOTES ARTIF INT, V6208, P97, DOI 10.1007/978-3-642-14197-3_12
   Hartrumpf Sven, 2005, EXTENDING KNOWLEDGE
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   He SY, 2014, IEEE ENER CONV, P1092, DOI 10.1109/ECCE.2014.6953521
   Heck L, 2015, SIGNAL INFORM PROCES, P597
   Hu HQ, 2006, IEICE T INF SYST, VE89D, P1848, DOI 10.1093/ietisy/e89-d.6.1848
   Jia Keliang, 2008, Wuhan University Journal of Natural Sciences, V13, P121, DOI 10.1007/s11859-008-0425-5
   Jo Y, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P370
   Katz B, 2001, HUMAN LANGUAGE TECHN
   Li Hao, 2012, CHIN OPT LETT, V10, P67
   Li HD, 2017, WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P79, DOI 10.1145/3041021.3054173
   Liu PQ, 2010, 2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL V, P132
   Liu PQ, 2011, COMM COM INF SC, V228, P205
   Lukovnikov D, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1211, DOI 10.1145/3038912.3052675
   Molaei MR, 2007, INTELL AUTOM SOFT CO, V13, P413
   Ning Q, 2007, J ZHENGZHOU U
   Park S., 2015, P 2015 C N AM CHAPT, P111
   Pechsiri C., 2016, Information Processing in Agriculture, V3, P36, DOI 10.1016/j.inpa.2016.01.002
   Pujara J., 2013, P WORKSH AUT KNOWL B, P19
   Purwarianti A, 2007, PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS, P537
   Sadeghi F, 2015, PROC CVPR IEEE, P1456, DOI 10.1109/CVPR.2015.7298752
   Selvi P., 2010, COMPUTER SCI
   Sherkat E, 2014, 2014 4TH INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE), P279, DOI 10.1109/ICCKE.2014.6993377
   Suchanek Fabian, 2013, P 2013 ACM SIGMOD IN, P933
   Sun JP, 2003, 2003 INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING, PROCEEDINGS, P263
   Taieb MAH, 2013, KNOWL-BASED SYST, V50, P260, DOI 10.1016/j.knosys.2013.06.015
   Vang K.J., 2014, J INF COMMUN ETHICS, V11, P245
   Verner IM, 2007, INTELL AUTOM SOFT CO, V13, P57, DOI 10.1080/10798587.2007.10642950
   Wang YH, 2014, 2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), P5915, DOI 10.1109/WCICA.2014.7053732
   Wongsoontorn S, 2008, INTELL AUTOM SOFT CO, V14, P13, DOI 10.1080/10798587.2008.10642979
   Xia QL, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P2069, DOI 10.18653/v1/P17-1189
   Xu Kun, 2016, QUESTION ANSWERING F, P2326
   Yahya M, 2012, P 21 INT C WORLD WID, P445, DOI DOI 10.1145/2187980.2188070
   Yahya Mohamed, 2012, EMNLP
   Yamamoto Y, 2011, LECT NOTES COMPUT SC, V6588, P83, DOI 10.1007/978-3-642-20152-3_7
   Yin YY, 2012, CHIN CONT DECIS CONF, P603, DOI 10.1109/CCDC.2012.6244093
   Yin YY, 2017, ENTROPY-SWITZ, V19, DOI 10.3390/e19070358
   Yin YY, 2016, INT J SOFTW ENG KNOW, V26, P611, DOI 10.1142/S0218194016400040
   Yin YY, 2008, 2008 IEEE ASIA-PACIFIC SERVICES COMPUTING CONFERENCE, VOLS 1-3, PROCEEDINGS, P1308, DOI 10.1109/APSCC.2008.135
   Zhang YZ, 2016, AAAI CONF ARTIF INTE, P3094
NR 48
TC 2
Z9 2
U1 1
U2 10
PU TECH SCIENCE PRESS
PI HENDERSON
PA 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA
SN 1079-8587
EI 2326-005X
J9 INTELL AUTOM SOFT CO
JI Intell. Autom. Soft Comput.
PD SEP
PY 2019
VL 25
IS 3
BP 625
EP 635
DI 10.31209/2019.100000117
PG 11
WC Automation & Control Systems; Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA IV2BK
UT WOS:000484082400018
DA 2022-08-23
ER

PT J
AU Ott, S
   Barbosa-Silva, A
   Samwald, M
AF Ott, Simon
   Barbosa-Silva, Adriano
   Samwald, Matthias
TI LinkExplorer: predicting, explaining and exploring links in large
   biomedical knowledge graphs
SO BIOINFORMATICS
LA English
DT Article
AB Machine learning algorithms for link prediction can be valuable tools for hypothesis generation. However, many current algorithms are black boxes or lack good user interfaces that could facilitate insight into why predictions are made. We present LinkExplorer, a software suite for predicting, explaining and exploring links in large biomedical knowledge graphs. LinkExplorer integrates our novel, rule-based link prediction engine SAFRAN, which was recently shown to outcompete other explainable algorithms and established black-box algorithms. Here, we demonstrate highly competitive evaluation results of our algorithm on multiple large biomedical knowledge graphs, and release a web interface that allows for interactive and intuitive exploration of predicted links and their explanations.
C1 [Ott, Simon; Barbosa-Silva, Adriano; Samwald, Matthias] Med Univ Vienna, Inst Artificial Intelligence, A-1090 Vienna, Austria.
RP Samwald, M (通讯作者)，Med Univ Vienna, Inst Artificial Intelligence, A-1090 Vienna, Austria.
EM matthias.samwald@meduniwien.ac.at
OI Samwald, Matthias/0000-0002-4855-2571
FU  [5171]
FX This work was supported by netidee [Grant No. 5171] (`OpenBioLink').
CR Abbas K, 2021, BMC BIOINFORMATICS, V22, DOI 10.1186/s12859-021-04082-y
   Adadi A, 2018, IEEE ACCESS, V6, P52138, DOI 10.1109/ACCESS.2018.2870052
   Bordes A., 2013, TRANSLATING EMBEDDIN
   Breit A, 2020, BIOINFORMATICS, V36, P4097, DOI 10.1093/bioinformatics/btaa274
   Broscheit S., 2020, P 2020 C EMP METH NA, P165
   Callahan T.J., 2020, FRAMEWORK AUTOMATED
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Hartig O., 2019, FDN ALTERNATIVE APPR
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Hu Weihua, 2020, ARXIV200500687
   Meilicke C, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3137
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Ott S., 2021, 3 C AUT KNOWL BAS CO
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Ruffinelli Daniel, 2020, INT C LEARN REPR
   Sun Z., 2019, INT C LEARN REPR NEW
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Yang B., 2015, P INT C LEARNR REPR
NR 18
TC 0
Z9 0
U1 9
U2 9
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 2371
EP 2373
DI 10.1093/bioinformatics/btac068
EA FEB 2022
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 0L4DY
UT WOS:000764155100001
PM 35139158
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Liang, ZW
   Yang, JN
   Liu, H
   Huang, KJ
   Cui, L
   Qu, LZ
   Li, X
AF Liang, Zongwei
   Yang, Junan
   Liu, Hui
   Huang, Keju
   Cui, Lin
   Qu, Lingzhi
   Li, Xiang
TI HRER: A New Bottom-Up Rule Learning for Knowledge Graph Completion
SO ELECTRONICS
LA English
DT Article
DE NLP; knowledge graphs; link prediction; reasoning; uneven distribution;
   reliability; horn rule; entity rule
ID EMBEDDINGS
AB Knowledge graphs (KGs) are collections of structured facts, which have recently attracted growing attention. Although there are billions of triples in KGs, they are still incomplete. These incomplete knowledge bases will bring limitations to practical applications. Predicting new facts from the given knowledge graphs is an increasingly important area. We investigate the models based on logic rules in this paper. This paper proposes HRER, a new bottom-up rule learning for knowledge graph completion. First of all, inspired by the observation that the known information of KGs is incomplete and unbalanced, HRER modifies the indicators for screening based on the existing relation rule mining methods. The new metric HRR is more effective than traditional confidences in filtering Horn rules. Besides, motivated by the differences between the embedding-based methods and the methods based on logic rules, HRER proposes entity rules. The entity rules make up for the limited expression of Horn rules to some extent. HRER needs a few parameters to control the number of rules and can provide the explanation for prediction. Experiments show that HRER achieves the state-of-the-art across the standard link prediction datasets.
C1 [Liang, Zongwei; Yang, Junan; Liu, Hui; Huang, Keju; Cui, Lin; Qu, Lingzhi; Li, Xiang] Natl Univ Def Technol, Coll Elect Engn, Hefei 230037, Peoples R China.
RP Liang, ZW (通讯作者)，Natl Univ Def Technol, Coll Elect Engn, Hefei 230037, Peoples R China.
EM zwliang17@nudt.edu.cn; yangjunan@ustc.edu; christ592604@163.com;
   huangkeju@163.com; cuilin17@nudt.edu.cn; qulingzhi@nudt.edu.cn;
   lix20@nudt.edu.cn
OI zongwei, liang/0000-0001-7942-0937
FU Anhui Provincial Natural Science Foundation [1908085MF202]; Independent
   Scientific Research Program of National University of Defense Science
   and Technology [ZK18-03-14]; Anhui Provincial Natural Science Foundation
   of FUNDER [1908085MF202]; Independent Scientific Research Program of
   National University of Defense Science and Technology of FUNDER
   [ZK18-03-14]
FX This work was partially supported by the Anhui Provincial Natural
   Science Foundation (No. 1908085MF202) and Independent Scientific
   Research Program of National University of Defense Science and
   Technology (No. ZK18-03-14). This research was funded by Anhui
   Provincial Natural Science Foundation of FUNDER grant number No.
   1908085MF202 and and Independent Scientific Research Program of National
   University of Defense Science and Technology of FUNDER grant number No.
   ZK18-03-14.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Balaevic I., 2019, ARXIV190109590
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   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
   Gao LM, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11125572
   Goethals B., 2002, Pattern Detection and Discovery. ESF Exploratory Workshop Proceedings (Lecture Notes in Artificial Intelligence Vol. 2447), P125
   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
   Kazemi SM, 2018, ADV NEUR IN, V31
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Mahdy AMS, 2020, RESULTS PHYS, V18, DOI 10.1016/j.rinp.2020.103174
   Mahdy AMS, 2021, NUMER METH PART D E, V37, P1120, DOI 10.1002/num.22570
   Meilicke C., 2020, ARXIV200404412
   Meilicke C, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3137
   Meilicke C, 2018, LECT NOTES COMPUT SC, V11136, P3, DOI 10.1007/978-3-030-00671-6_1
   MUGGLETON S, 1995, NEW GENERAT COMPUT, V13, P245, DOI 10.1007/BF03037227
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Nguyen D.Q., 2018, ARXIV171202121
   Nguyen D.Q., 2019, ARXIV180804122
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Schoenmackers Stefan, 2010, P 2010 C EMP METH NA, P1088
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Sun Z., 2019, ARXIV
   Tan P.-N., 2002, P 8 ACM SIGKDD INT C, P32, DOI DOI 10.1145/775047.775053
   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, ARXIV191103082
   Wang MH, 2021, SYMMETRY-BASEL, V13, DOI 10.3390/sym13030485
   Wang P, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10121407
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yang B., 2014, ARXIV
   Zhang YQ, 2020, PROC INT CONF DATA, P433, DOI 10.1109/ICDE48307.2020.00044
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
NR 37
TC 1
Z9 1
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD MAR
PY 2022
VL 11
IS 6
AR 908
DI 10.3390/electronics11060908
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA 0C7LN
UT WOS:000775490600001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Mosbach, S
   Menon, A
   Farazi, F
   Krdzavac, N
   Zhou, XC
   Akroyd, J
   Kraft, M
AF Mosbach, Sebastian
   Menon, Angiras
   Farazi, Feroz
   Krdzavac, Nenad
   Zhou, Xiaochi
   Akroyd, Jethro
   Kraft, Markus
TI Multiscale Cross-Domain Thermochemical Knowledge-Graph
SO JOURNAL OF CHEMICAL INFORMATION AND MODELING
LA English
DT Article
ID DATABASE; ENTHALPIES; MANAGEMENT; MECHANISM; SCALE
AB In this paper, we develop a set of software agents which improve a knowledge-graph containing thermodynamic data of chemical species by means of quantum chemical calculations and error-canceling balanced reactions. The knowledge-graph represents species-associated information by making use of the principles of linked data, as employed in the Semantic Web, where concepts correspond to vertices and relationships between the concepts correspond to edges of the graph. We implement this representation by means of ontologies, which formalize the definition of concepts and their relationships, as a critical step to achieve interoperability between heterogeneous data formats and software. The agents, which conduct quantum chemical calculations and derive the estimates of standard enthalpies of formation, update the knowledge-graph with newly obtained results, improving data values, and adding nodes and connections between them. A key distinguishing feature of our approach is that it extends an existing, general-purpose knowledge-graph, called J-Park Simulator (http://theworldavatar.com), and its ecosystem of autonomous agents, thus enabling seamless cross-domain applications in wider contexts. To this end, we demonstrate how quantum calculations can directly affect the atmospheric dispersion of pollutants in an industrial emission use-case.
C1 [Mosbach, Sebastian; Menon, Angiras; Farazi, Feroz; Zhou, Xiaochi; Akroyd, Jethro; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.
   [Mosbach, Sebastian; Krdzavac, Nenad; Akroyd, Jethro; Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore CARES, Singapore 138602, Singapore.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, 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.
EM mk306@cam.ac.uk
RI ; Kraft, Markus/D-7243-2016; Akroyd, Jethro/A-8698-2010
OI Farazi, Feroz/0000-0002-6786-7309; Kraft, Markus/0000-0002-4293-8924;
   Akroyd, Jethro/0000-0002-2143-8656; Mosbach,
   Sebastian/0000-0001-7018-9433
FU National Research Foundation (NRF), Prime Minister's Office, Singapore,
   under its Campus for Research Excellence and Technological Enterprise
   (CREATE) programme; Alexander von Humboldt Foundation; EPSRC
   [EP/R029369/1]; ARCHER
FX This work was partly funded by the National Research Foundation (NRF),
   Prime Minister's Office, Singapore, under its Campus for Research
   Excellence and Technological Enterprise (CREATE) programme. M.K.
   gratefully acknowledges the support of the Alexander von Humboldt
   Foundation. The authors are grateful to EPSRC (grant number:
   EP/R029369/1) and ARCHER for financial and computational support as a
   part of their funding to the UK Consortium on Turbulent Reacting Flows
   (http://www.ukctrf.com/www.ukctrf.com).
CR American Chemical Society, 2019, CAS REG
   Angele K., 2020, KNOWLEDGE GRAPHS MET
   [Anonymous], 2020, LINK OP DAT CLOUD
   Barone V., 2016, GAUSSIAN 16 REVISION
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Berners-Lee T, 2006, LINKED DATA
   Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901
   Buerger P, 2018, COMBUST FLAME, V187, P105, DOI 10.1016/j.combustflame.2017.08.013
   Cambridge CARES, 2020, ATM POLL DISP
   Cambridge CARES, 2020, ONTOKIN WEB INT
   Cambridge CARES, 2020, J PARK SIM
   Cambridge CARES, 2020, KNOWL GRAPH STAT
   Cambridge CARES, 2020, ONTOCOMPCHEM WEB INT
   CMCL Innovations, 2020, SRM ENG SUIT VERS 11
   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
   Eibeck A., 2020, DATA-CENTRIC ENG, V1
   Eibeck A, 2019, COMPUT CHEM ENG, V131, DOI 10.1016/j.compchemeng.2019.106586
   Farazi F, 2020, ACS OMEGA, V5, P18342, DOI 10.1021/acsomega.0c02055
   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
   Frenklach M, 2007, P COMBUST INST, V31, P125, DOI 10.1016/j.proci.2006.08.121
   Fu G, 2015, J CHEMINFORMATICS, V7, DOI 10.1186/s13321-015-0084-4
   Gao CW, 2016, COMPUT PHYS COMMUN, V203, P212, DOI 10.1016/j.cpc.2016.02.013
   Gao YG, 2019, PHYS CHEM CHEM PHYS, V21, P1928, DOI 10.1039/c8cp07308b
   Goldsmith CF, 2012, J PHYS CHEM A, V116, P9033, DOI 10.1021/jp303819e
   Goodman J, 2009, J CHEM INF MODEL, V49, P2897, DOI 10.1021/ci900437n
   HOHENBERG P, 1964, PHYS REV B, V136, pB864, DOI 10.1103/PhysRev.136.B864
   Johnson III, 2020, NIST STANDARD REFERE
   Keceli M, 2019, P COMBUST INST, V37, P363, DOI 10.1016/j.proci.2018.07.113
   Kim S, 2016, NUCLEIC ACIDS RES, V44, pD1202, DOI 10.1093/nar/gkv951
   Krdzavac N, 2019, J CHEM INF MODEL, V59, P3154, DOI 10.1021/acs.jcim.9b00227
   Lambert V. R., 2015, 9 US NAT COMB M
   Lasi H, 2014, BUS INFORM SYST ENG+, V6, P239, DOI 10.1007/s12599-014-0334-4
   Leal B, 2010, INTERNET OF THINGS-BOOK, P3, DOI 10.1007/978-1-4419-1674-7_1
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li YP, 2019, J PHYS CHEM A, V123, P2142, DOI 10.1021/acs.jpca.8b10789
   MARQUARDT W, 2010, RWTHEDITION, P1
   Menon A, 2019, CURR OPIN CHEM ENG, V26, P33, DOI 10.1016/j.coche.2019.08.004
   Mosbach S, 2009, COMBUST FLAME, V156, P1156, DOI 10.1016/j.combustflame.2009.01.003
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   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
   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
   Simon J. D., 1999, MOL THERMODYNAMICS
   Taylor KR, 2006, J CHEM INF MODEL, V46, P939, DOI 10.1021/ci050378m
   Wang H, 2013, ENERG FUEL, V27, P7843, DOI 10.1021/ef401992e
   Weber R, 2015, COMPUT THEOR CHEM, V1072, P58, DOI 10.1016/j.comptc.2015.08.015
   WEININGER D, 1988, J CHEM INF COMP SCI, V28, P31, DOI 10.1021/ci00057a005
   Zhang C, 2017, APPL ENERG, V204, P1412, DOI 10.1016/j.apenergy.2017.03.130
   Zhou L, 2018, COMPUT CHEM ENG, V118, P49, DOI 10.1016/j.compchemeng.2018.07.010
   Zhou L, 2017, APPL ENERG, V204, P1284, DOI 10.1016/j.apenergy.2017.05.002
   Zhou XC, 2020, COMPUT CHEM ENG, V139, DOI 10.1016/j.compchemeng.2020.106896
   Zhou XC, 2019, COMPUT CHEM ENG, V130, DOI 10.1016/j.compchemeng.2019.106577
   ZIEGLER T, 1991, CHEM REV, V91, P651, DOI 10.1021/cr00005a001
NR 58
TC 5
Z9 5
U1 4
U2 10
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 1549-9596
EI 1549-960X
J9 J CHEM INF MODEL
JI J. Chem Inf. Model.
PD DEC 28
PY 2020
VL 60
IS 12
BP 6155
EP 6166
DI 10.1021/acs.jcim.0c01145
PG 12
WC Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science,
   Information Systems; Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy; Chemistry; Computer Science
GA PT8QA
UT WOS:000608875100055
PM 33242243
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhu, YJ
   Che, C
   Jin, B
   Zhang, NR
   Su, C
   Wang, F
AF Zhu, Yongjun
   Che, Chao
   Jin, Bo
   Zhang, Ningrui
   Su, Chang
   Wang, Fei
TI Knowledge-driven drug repurposing using a comprehensive drug knowledge
   graph
SO HEALTH INFORMATICS JOURNAL
LA English
DT Article
DE drug repurposing; graph embedding; knowledge graph; machine learning;
   meta path
ID CELL LUNG-CANCER; SIMILARITY; METFORMIN; DATABASE
AB Due to the huge costs associated with new drug discovery and development, drug repurposing has become an important complement to the traditional de novo approach. With the increasing number of public databases and the rapid development of analytical methodologies, computational approaches have gained great momentum in the field of drug repurposing. In this study, we introduce an approach to knowledge-driven drug repurposing based on a comprehensive drug knowledge graph. We design and develop a drug knowledge graph by systematically integrating multiple drug knowledge bases. We describe path- and embedding-based data representation methods of transforming information in the drug knowledge graph into valuable inputs to allow machine learning models to predict drug repurposing candidates. The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only using treatment information on other diseases. In addition, this approach supports exploratory investigation through the review of meta paths that connect drugs with diseases. This knowledge-driven approach is an effective drug repurposing strategy supporting large-scale prediction and the investigation of case studies.
C1 [Zhu, Yongjun] Sungkyunkwan Univ, Seoul, South Korea.
   [Che, Chao] Dalian Univ, Dalian, Peoples R China.
   [Jin, Bo] Dalian Univ Technol, Dalian, Peoples R China.
   [Zhang, Ningrui] IQVIA, Shanghai, Peoples R China.
   [Su, Chang; Wang, Fei] Cornell Univ, Ithaca, NY 10065 USA.
RP Wang, F (通讯作者)，Cornell Univ, Dept Populat Hlth Sci, Weill Cornell Med, Ithaca, NY 10065 USA.
EM few2001@med.cornell.edu
RI Zhu, Yongjun/K-2486-2015
OI Zhu, Yongjun/0000-0003-4787-5122
FU Ministry of Education of the Republic of Korea; National Research
   Foundation of Korea [NRF-2019S1A5A8033338]
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 by the Ministry of Education of the Republic of Korea and
   the National Research Foundation of Korea (Grant No.
   NRF-2019S1A5A8033338).
CR Aldea M, 2014, TUMOR BIOL, V35, P5101, DOI 10.1007/s13277-014-1676-8
   Andronis C, 2011, BRIEF BIOINFORM, V12, P357, DOI 10.1093/bib/bbr005
   Ashburn TT, 2004, NAT REV DRUG DISCOV, V3, P673, DOI 10.1038/nrd1468
   Bertolini F, 2015, NAT REV CLIN ONCOL, V12, P732, DOI 10.1038/nrclinonc.2015.169
   Bisgin H, 2014, BMC BIOINFORMATICS, V15, DOI 10.1186/1471-2105-15-267
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   CHEN H, 2015, COMPUT MATH METHOD M, V2015, P1, DOI DOI 10.1155/2015/130620
   Cheng FX, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002503
   Donner Y, 2018, MOL PHARMACEUT, V15, P4314, DOI 10.1021/acs.molpharmaceut.8b00284
   Elkan C., 2008, P 14 ACM SIGKDD INT, V14, P213, DOI DOI 10.1145/1401890.1401920
   Gottlieb A, 2011, MOL SYST BIOL, V7, DOI 10.1038/msb.2011.26
   Hartenfeller M, 2011, METHODS MOL BIOL, V672, P299, DOI 10.1007/978-1-60761-839-3_12
   Ishida J, 2016, J TRANSL MED, V14, DOI 10.1186/s12967-016-1031-5
   Jahchan NS, 2013, CANCER DISCOV, V3, P1364, DOI 10.1158/2159-8290.CD-13-0183
   Kanehisa M, 2008, NUCLEIC ACIDS RES, V36, pD480, DOI 10.1093/nar/gkm882
   Kuhn M, 2016, NUCLEIC ACIDS RES, V44, pD1075, DOI 10.1093/nar/gkv1075
   Langedijk J, 2015, DRUG DISCOV TODAY, V20, P1027, DOI 10.1016/j.drudis.2015.05.001
   Law V, 2014, NUCLEIC ACIDS RES, V42, pD1091, DOI 10.1093/nar/gkt1068
   Letouzey F, 2000, LECT NOTES ARTIF INT, V1968, P71
   Li J, 2013, BMC BIOINFORMATICS, V14, DOI 10.1186/1471-2105-14-S16-S3
   Li Jiao, 2012, Proceedings (IEEE Int Conf Bioinformatics Biomed), V2012, P1
   Li YY, 2012, GENOME MED, V4, DOI 10.1186/gm326
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu ZC, 2014, GENOME MED, V6, DOI 10.1186/s13073-014-0094-2
   Mei SY, 2019, PHARMACEUTICS, V11, DOI 10.3390/pharmaceutics11090466
   Menden MP, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0061318
   Moridi M, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-3165-y
   Munoz-Mari J, 2010, IEEE T GEOSCI REMOTE, V48, P3188, DOI 10.1109/TGRS.2010.2045764
   Napolitano F, 2013, J CHEMINFORMATICS, V5, DOI 10.1186/1758-2946-5-30
   Nidhi, 2006, J CHEM INF MODEL, V46, P1124, DOI 10.1021/ci060003g
   Novac N, 2013, TRENDS PHARMACOL SCI, V34, P267, DOI 10.1016/j.tips.2013.03.004
   Sharp ME, 2017, J BIOMED SEMANT, V8, DOI 10.1186/s13326-016-0110-0
   Shim JS, 2014, INT J BIOL SCI, V10, P654, DOI 10.7150/ijbs.9224
   Sleire L, 2017, PHARMACOL RES, V124, P74, DOI 10.1016/j.phrs.2017.07.013
   Su C, 2020, BRIEF BIOINFORM, V21, P182, DOI 10.1093/bib/bby117
   Sun YZ, 2011, 2011 INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2011), P121, DOI 10.1109/ASONAM.2011.112
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Tan BX, 2011, CANCER-AM CANCER SOC, V117, P5103, DOI 10.1002/cncr.26151
   Tan FJ, 2014, MOL BIOSYST, V10, P1126, DOI 10.1039/c3mb70554d
   Tari LB, 2014, METHODS MOL BIOL, V1159, P253, DOI 10.1007/978-1-4939-0709-0_14
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Whirl-Carrillo M, 2012, CLIN PHARMACOL THER, V92, P414, DOI 10.1038/clpt.2012.96
   Wu C, 2013, BMC SYST BIOL, V7, DOI 10.1186/1752-0509-7-S5-S6
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xuan P, 2019, BIOINFORMATICS, V35, P4108, DOI 10.1093/bioinformatics/btz182
   Yang H, 2016, NUCLEIC ACIDS RES, V44, pD1069, DOI 10.1093/nar/gkv1230
   Yang L, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0028025
   Zhu YJ, 2019, BRIEF BIOINFORM, V20, P1308, DOI 10.1093/bib/bbx169
NR 49
TC 12
Z9 12
U1 10
U2 26
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1460-4582
EI 1741-2811
J9 HEALTH INFORM J
JI Health Inform. J.
PD DEC
PY 2020
VL 26
IS 4
BP 2737
EP 2750
AR 1460458220937101
DI 10.1177/1460458220937101
EA JUL 2020
PG 14
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA PF7PY
UT WOS:000549974800001
PM 32674665
OA gold
DA 2022-08-23
ER

PT J
AU Leng, YL
   Wang, HM
   Lu, FY
AF Leng, Yonglin
   Wang, Hongmin
   Lu, Fuyu
TI Artificial Intelligence Knowledge Graph for Dynamic Networks: An
   Incremental Partition Algorithm
SO IEEE ACCESS
LA English
DT Article
DE Artificial intelligence; knowledge graph; RDF; incremental partition;
   dynamic adjustment
ID FRAMEWORK; MANAGEMENT; VEHICLES; INTERNET
AB The quick and intelligent requests and answers in artificial intelligence (AI) are inseparable from intelligent data. Knowledge graph makes data more intelligent by establishing association among data, which provides convenience for intelligent search, reasoning and analysis of data. Resource Description Framework (RDF) is an effective data representation model of knowledge graph. This paper takes RDF as the research object and proposes an incremental partition method of intelligent data (IPID) to realize the distributed storage of large-scale AI data. First, IPID gives a mixed object function integrating edge cut and load balancing. Second, IPID devises the initial and incremental partitioning algorithms of RDF. The initial partition divides the original RDF graph into kernel vertices, boundary vertices and free vertices. The boundary and freedom nodes select the kernel vertex with the maximum gain of object function to form a sub-partition. And the incremental partition is in charge of the selection of sub-partition of new and deleted data by the object function. Meanwhile, the incremental partition algorithm would also execute a dynamic adjustment strategy at a certain time interval according to the balance and tightness of sub-partition to satisfy the partitioning object. Finally, IPID is tested on the knowledge graph datasets. The experimental results show that the object function guarantees the quality of knowledge graph partition in edge cut and load balancing, and effectively realizes the incremental partition.
C1 [Leng, Yonglin; Wang, Hongmin; Lu, Fuyu] Bohai Univ, Coll Informat Sci & Technol, Jinzhou, Peoples R China.
RP Wang, HM (通讯作者)，Bohai Univ, Coll Informat Sci & Technol, Jinzhou, Peoples R China.
EM wanghongmin0704@163.com
FU Science and Technology Project of the Liaoning Provincial Education
   Department [LQ2017004]; Social Science Planning Foundation of Liaoning
   Province [L14AGL002, L19BGL016]; National Key Research and Development
   Program of China [2019YFD0901605]; Science and Technology Foundation of
   Liaoning Province [2019-ZD-0503, 20170540005]
FX This work was supported in part by the Science and Technology Project of
   the Liaoning Provincial Education Department under Grant LQ2017004, in
   part by the Social Science Planning Foundation of Liaoning Province
   under Grant L14AGL002 and Grant L19BGL016, in part by the National Key
   Research and Development Program of China under Grant 2019YFD0901605,
   and in part by the Science and Technology Foundation of Liaoning
   Province under Grant 2019-ZD-0503 and Grant 20170540005.
CR AlFarraj O, 2019, COMPUT NETW, V151, P224, DOI 10.1016/j.comnet.2019.01.020
   [Anonymous], [No title captured]
   Huang JW, 2011, PROC VLDB ENDOW, V4, P1123
   Khosravi-Farsani H, 2013, J INF SCI, V39, P198, DOI 10.1177/0165551512463994
   Lee KS, 2013, IEEE INT CONF CLOUD, P327, DOI 10.1109/CLOUD.2013.63
   Lee Kuang-Chih, 2013, P 7 INT WORKSH DAT M, P1
   Lv XQ, 2019, FRONT COMPUT SCI-CHI, V13, P539, DOI 10.1007/s11704-018-7443-z
   Martella C, 2017, PROC INT CONF DATA, P1083, DOI 10.1109/ICDE.2017.153
   Xu N, 2014, PROC VLDB ENDOW, V7, P1917, DOI 10.14778/2733085.2733097
   Ning ZL, 2020, IEEE T IND INFORM, V16, P1352, DOI 10.1109/TII.2019.2937079
   Ning ZL, 2021, IEEE T INTELL TRANSP, V22, P2212, DOI 10.1109/TITS.2020.2997832
   Ning ZL, 2019, IEEE T IND INFORM, V15, P5737, DOI 10.1109/TII.2019.2929740
   Ning ZL, 2019, IEEE NETWORK, V33, P198, DOI 10.1109/MNET.2019.1800309
   Ning ZL, 2019, ACM T INTEL SYST TEC, V10, DOI 10.1145/3317572
   Predari M, 2016, EUROMICRO WORKSHOP P, P280, DOI 10.1109/PDP.2016.109
   Pujol JM, 2012, IEEE ACM T NETWORK, V20, P1162, DOI 10.1109/TNET.2012.2188815
   Tolba A, 2019, COMPUT NETW, V152, P78, DOI 10.1016/j.comnet.2019.01.038
   Tolba A, 2019, CLUSTER COMPUT, V22, pS3183, DOI 10.1007/s10586-018-2014-5
   Tsourakakis CE, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P333, DOI 10.1145/2556195.2556213
   Wang L, 2014, PROC INT CONF DATA, P568, DOI 10.1109/ICDE.2014.6816682
   Wang XJ, 2019, IEEE WIREL COMMUN, V26, P87, DOI 10.1109/MWC.001.1900009
   Wu BW, 2015, PROC INT CONF DATA, P795, DOI 10.1109/ICDE.2015.7113334
   Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029
   Zheng WG, 2016, PROC VLDB ENDOW, V9, P840, DOI 10.14778/2983200.2983201
   [朱牧 Zhu Mu], 2013, [计算机研究与发展, Journal of Computer Research and Development], V50, P2520
NR 25
TC 2
Z9 2
U1 1
U2 8
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 63434
EP 63442
DI 10.1109/ACCESS.2020.2982652
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LK4LK
UT WOS:000530832200068
OA gold
DA 2022-08-23
ER

PT J
AU Kanakaris, N
   Giarelis, N
   Siachos, I
   Karacapilidis, N
AF Kanakaris, Nikos
   Giarelis, Nikolaos
   Siachos, Ilias
   Karacapilidis, Nikos
TI Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting
   Future Research Collaborations
SO ENTROPY
LA English
DT Article
DE knowledge graph; link prediction; natural language processing; document
   representation; future research collaborations; graph kernels; word
   embeddings
ID LINK PREDICTION; NEIGHBORS
AB We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.
C1 [Kanakaris, Nikos; Giarelis, Nikolaos; Siachos, Ilias; Karacapilidis, Nikos] Univ Patras, Ind Management & Informat Syst Lab, MEAD, Rion 26504, Greece.
RP Kanakaris, N (通讯作者)，Univ Patras, Ind Management & Informat Syst Lab, MEAD, Rion 26504, Greece.
EM nkanakaris@upnet.gr; giarelis@ceid.upatras.gr; ilias.siachos@upnet.gr;
   karacap@upatras.gr
OI Kanakaris, Nikos/0000-0001-9352-5807; Karacapilidis,
   Nikos/0000-0002-6581-6831; Giarelis, Nikolaos/0000-0003-2611-3129;
   Siachos, Ilias/0000-0001-5489-3500
FU European Union; Greek national funds through the Operational Program
   Competitiveness, Entrepreneurship and Innovation, under the call
   RESEARCHCREATE-INNOVATE [T2EDK-04389]
FX The work presented in this paper is supported by the inPOINT project
   (https://inpointproject.eu/), which is co-financed by the European Union
   and Greek national funds through the Operational Program
   Competitiveness, Entrepreneurship and Innovation, under the call
   RESEARCHCREATE-INNOVATE (Project id: T2EDK-04389).
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
   Adamic LA, 2003, SOC NETWORKS, V25, P211, DOI 10.1016/S0378-8733(03)00009-1
   Aggarwal C., 2018, MACHINE LEARNING TEX
   Albert R, 2002, REV MOD PHYS, V74, P47, DOI 10.1103/RevModPhys.74.47
   Almeida F., 2019, ARXIV E PRINTS
   Andreas J, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P822
   [Anonymous], 2018, CSIROS DATA61 STELLA
   Borgwardt KM, 2005, Fifth IEEE International Conference on Data Mining, Proceedings, P74, DOI 10.1109/ICDM.2005.132
   Fey M., 2019, ARXIV190302428V3
   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
   Gartner T, 2003, LECT NOTES ARTIF INT, V2777, P129, DOI 10.1007/978-3-540-45167-9_11
   Giarelis Nikolaos, 2020, Discovery Science. 23rd International Conference, DS 2020. Proceedings. Lecture Notes in Artificial Intelligence. Subseries of Lecture Notes in Computer Science (LNAI 12323), P437, DOI 10.1007/978-3-030-61527-7_29
   Giarelis N., 2020, IFIP INT C ART INT A, P96
   Giarelis N., 2020, P 12 KES INT C INTEL
   Guns R, 2014, SCIENTOMETRICS, V101, P1461, DOI 10.1007/s11192-013-1228-9
   Hamilton W., 2017, P 31 INT C NEUR INF, P1025
   Huang J., 2008, P 2008 INT C WEB SEA, P107, DOI [10.1145/1341531.1341548, DOI 10.1145/1341531.1341548]
   Jaccard P., 1901, B SOC VAUDOISE SCI N, V37, P547
   Joulin A., 2016, P INT C LANG RES EV
   Julian K., 2016, APPL MACH LEARN LINK
   Kholghi M., 2016, P AUSTR LANG TECHN A
   Kusner M.J., 2015, P 32 INT C INT C MAC
   Le Q., 2014, P 31 INT C MACHINE L
   Li SB, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-35423-2
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Linardatos P, 2021, ENTROPY-SWITZ, V23, DOI 10.3390/e23010018
   Lundberg SM., 2017, ADV NEURAL INFORM PR, V30, P4765
   Mikolov T., 2013, P 26 INT C NEUR INF
   Molokwu BC, 2019, COMM COM INF SC, V1143, P573, DOI 10.1007/978-3-030-36802-9_61
   Nathani D., 2019, P 57 ANN M ASS COMP
   Neumann M, 2016, MACH LEARN, V102, P209, DOI 10.1007/s10994-015-5517-9
   Nikolentzos G, 2019, ARXIV PREPRINT ARXIV
   Nikolentzos G, 2017, AAAI CONF ARTIF INTE, P2429
   Panagopoulos G, 2017, J INFORMETR, V11, P198, DOI 10.1016/j.joi.2016.11.003
   Pedregosa F., 2011, J. Mach. Learn. Res., V11, P198
   Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI 10.3115/v1/D14-1162
   Tran PV, 2018, PR INT CONF DATA SC, P237, DOI 10.1109/DSAA.2018.00034
   Pho P, 2020, J BIG DATA-GER, V7, DOI 10.1186/s40537-020-00366-x
   Ponomariov B, 2016, SCIENTOMETRICS, V109, P1939, DOI 10.1007/s11192-016-2127-7
   Ramon J., 2003, 1 INT WORKSH MIN GRA
   Rousseau F, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P59, DOI 10.1145/2505515.2505671
   Rousseau F, 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, P1702
   Siglidis G., 2018, J. Mach. Learn. Res., V21, P1
   Sun YZ, 2011, 2011 INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2011), P121, DOI 10.1109/ASONAM.2011.112
   Vahdati S, 2018, LECT NOTES COMPUT SC, V11057, P103, DOI 10.1007/978-3-030-00066-0_9
   Van Der Heijden N., 2020, P AAAI C ART INT NEW
   Vathy-Fogarassy A., 2013, GRAPH BASED CLUSTERI
   Veira N., 2019, P 28 INT JOINT C ART
   Vishwanathan SVN, 2010, J MACH LEARN RES, V11, P1201
   Wang L.L., 2020, ARXIV200410706V4
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang ZY, 2018, LECT NOTES COMPUT SC, V10882, P745, DOI 10.1007/978-3-319-93000-8_84
   West D. B., 2001, INTRO GRAPH THEORY, V2, P139
   Ye X, 2016, PROC INT CONF SOFTW, P404, DOI 10.1145/2884781.2884862
   Ying R., 2019, ARXIV 190303894V4
   Yu Q, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0101214
NR 56
TC 2
Z9 2
U1 5
U2 10
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD JUN
PY 2021
VL 23
IS 6
AR 664
DI 10.3390/e23060664
PG 18
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA SX7BG
UT WOS:000665355000001
PM 34070422
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Shakiba, A
AF Shakiba, Ali
TI Distributed Decision Making with S-approximation Spaces
SO INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED
   SYSTEMS
LA English
DT Article
DE S-approximation spaces; distributed uncertainty; information fusion;
   group-decision making; decider significant set
ID ROUGH SET; AHP
AB The combination of identical S-approximation spaces, except with different decider mappings, is studied in this paper by considering the construction of more complex S-approximation spaces from simpler ones that use different decision criteria, e.g., due to levels of expertise. It can be used to model group decision making problems where each decider makes an independent decision based on a shared knowledge map, e.g., several doctors with the same knowledge and different, independent decision criteria decide on a possible disease(s) for a patient, based on the same set of observations. These results can formalize the management of distributed uncertainty and can be used to invent novel distributed uncertain data processing algorithms. Also, we introduce the decider significance concept to minimize the number of combinations to obtain the same effect as the original combination. We show that finding a minimum set of significant deciders is NP-hard. and give an illustrative example in a medical expert system.
C1 [Shakiba, Ali] Vali E Asr Univ Rafsanjan, Dept Comp Sci, Rafsanjan, Iran.
RP Shakiba, A (通讯作者)，Vali E Asr Univ Rafsanjan, Dept Comp Sci, Rafsanjan, Iran.
EM ali.shakiba@vru.ac.ir
RI Shakiba, Ali/J-6420-2016
OI Shakiba, Ali/0000-0002-2253-1166
CR An L., 2020, SOFT COMPUT, P1
   Davvaz B, 2008, INFORM SCIENCES, V178, P3247, DOI 10.1016/j.ins.2008.03.014
   DEMPSTER AP, 1967, ANN MATH STAT, V38, P325, DOI 10.1214/aoms/1177698950
   Garey MR, 1979, COMPUTER INTRACTABIL
   Gass SI, 1998, DECIS SUPPORT SYST, V22, P59, DOI 10.1016/S0167-9236(96)00061-9
   Hooshmandasl M.R., 2014, J DISCRETE MATH, V2014, P1
   Pagliani P, 2004, FUND INFORM, V59, P221
   PAWLAK Z, 1982, INT J COMPUT INF SCI, V11, P341, DOI 10.1007/BF01001956
   Polikar R., 2006, IEEE Circuits and Systems Magazine, V6, P21, DOI 10.1109/MCAS.2006.1688199
   Rademaker M, 2014, INFORM FUSION, V17, P74, DOI 10.1016/j.inffus.2012.01.003
   RAMANATHAN R, 1994, EUR J OPER RES, V79, P249, DOI 10.1016/0377-2217(94)90356-5
   Ruta D., 2005, Information Fusion, V6, P63, DOI 10.1016/j.inffus.2004.04.008
   Sang BB, 2017, INT J MACH LEARN CYB, P1
   Shafer G., 1976, MATH THEORY EVIDENCE, DOI 10.1515/9780691214696
   Shakiba A, 2017, IRAN J FUZZY SYST, V14, P127
   Shakiba A, 2020, IRAN J MATH SCI INFO, V15, P117, DOI 10.29252/ijmsi.15.2.117
   Shakiba A, 2016, KNOWL INF SYST, V49, P749, DOI 10.1007/s10115-015-0913-9
   Shakiba A, 2016, J INTELL FUZZY SYST, V30, P3385, DOI 10.3233/IFS-152086
   Shakiba A, 2015, FUND INFORM, V139, P307, DOI 10.3233/FI-2015-1236
   Van den Honert RC, 2001, GROUP DECIS NEGOT, V10, P275, DOI 10.1023/A:1011201501379
   Wozniak M, 2014, INFORM FUSION, V16, P3, DOI 10.1016/j.inffus.2013.04.006
   Xie G, 2008, INT J APPROX REASON, V49, P331, DOI 10.1016/j.ijar.2007.04.005
   Yao Y.Y., 1998, ROUGH SETS KNOWLEDGE, P286, DOI DOI 10.1007/978-3-540-72458-2_1
   Yao YY, 2014, INFORM SCIENCES, V267, P306, DOI 10.1016/j.ins.2014.01.039
   Yao YY, 1998, INFORM SCIENCES, V111, P239, DOI 10.1016/S0020-0255(98)10006-3
   Yiyu Yao, 2012, Rough Sets and Current Trends in Computing. Proceedings 8th International Conference, RSCTC 2012, P1, DOI 10.1007/978-3-642-32115-3_1
   ZADEH LA, 1983, FUZZY SET SYST, V11, P199, DOI 10.1016/S0165-0114(83)80081-5
   ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X
   Zadeh LA., 1986, AI MAG, V7, P85, DOI [DOI 10.1609/AIMAG.V7I2.542, 10.1142/9789814261302_0033, DOI 10.1142/9789814261302_0033]
   Zhan JM, 2019, ARTIF INTELL REV, V52, P2381, DOI 10.1007/s10462-018-9617-3
NR 30
TC 0
Z9 0
U1 0
U2 1
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-4885
EI 1793-6411
J9 INT J UNCERTAIN FUZZ
JI Int. J. Uncertainty Fuzziness Knowl.-Based Syst.
PD APR
PY 2021
VL 29
IS 02
BP 281
EP 311
DI 10.1142/S021848852150013X
PG 31
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RM0QV
UT WOS:000639367900005
DA 2022-08-23
ER

PT J
AU Xie, T
   Wu, B
   Jia, BJ
   Wang, B
AF Xie, Tao
   Wu, Bin
   Jia, Bingjing
   Wang, Bai
TI Graph-ranking collective Chinese entity linking algorithm
SO FRONTIERS OF COMPUTER SCIENCE
LA English
DT Article
DE collective entity linking; knowledge mapping; word embedding; entity
   correlation graph; PageRank
AB Entity linking (EL) systems aim to link entity mentions in the document to their corresponding entity records in a reference knowledge base. Existing EL approaches usually ignore the semantic correlation between the mentions in the text, and are limited to the scale of the local knowledge base. In this paper, we propose a novel graphranking collective Chinese entity linking (GRCCEL) algorithm, which can take advantage of both the structured relationship between entities in the local knowledge base and the additional background information offered by external knowledge sources. By improved weighted word2vec textual similarity and improved PageRank algorithm, more semantic information and structural information can be captured in the document. With an incremental evidence mining process, more powerful discrimination capability for similar entities can be obtained. We evaluate the performance of our algorithm on some open domain corpus. Experimental results show the effectiveness of our method in Chinese entity linking task and demonstrate the superiority of our method over state-of-the-art methods.
C1 [Xie, Tao; Wu, Bin; Jia, Bingjing; Wang, Bai] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China.
RP Xie, T (通讯作者)，Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China.
EM 13618323206@163.com
FU National Basic Research (973) Program of China [2013CB329606]; Natural
   Science Research Program of Anhui Science and Technology University
   [ZRC2016494]
FX This work was supported in part by the National Basic Research (973)
   Program of China (2013CB329606) and the Natural Science Research Program
   of Anhui Science and Technology University (ZRC2016494).
CR Alhelbawy A, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P75
   [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
   Dredze M., 2010, P 23 INT C COMP LING, P277
   Guo M, 2007, IEEE INT SYMP CIRC S, P41, DOI 10.1109/ISCAS.2007.378177
   Guo Z., 2014, P 23 ACM INT C C INF, P499, DOI DOI 10.1145/2661829.2661887
   Hachey B, 2011, LECT NOTES COMPUT SC, V6997, P213, DOI 10.1007/978-3-642-24434-6_16
   Han XP, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P765
   Hoffart J., 2011, P EMNLP, DOI DOI 10.3115/V1/D11-1072
   Huai B X, 2014, J SOFTWARE, V21, P1235
   Ji H., 2011, P 49 ANN M ASS COMP, V1, P1148
   Li X, 2013, P INT C LANG RES EV, P3098
   Li Y, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P1070
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Nguyen HT, 2010, LECT NOTES ARTIF INT, V5991, P11
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Wu F, 2013, P 48 ANN M ASS COMP, P118
   Xiao L, 2012, P AAAI C ART INT, P1189
   Xu J, 2016, LECT NOTES COMPUT SC, V10065, P183, DOI 10.1007/978-3-319-49178-3_14
   [杨陟卓 Yang Zhizhuo], 2013, [计算机研究与发展, Journal of Computer Research and Development], V50, P437
   Zeng Y, 2013, COMM COM INF SC, V400, P266
   Zhang T, 2015, J CHINESE INFORM PRO, V22, P58
   Zuo Z, 2014, P INT C COMP LING, P266
NR 25
TC 3
Z9 3
U1 3
U2 43
PU HIGHER EDUCATION PRESS
PI BEIJING
PA CHAOYANG DIST, 4, HUIXINDONGJIE, FUSHENG BLDG, BEIJING 100029, PEOPLES R
   CHINA
SN 2095-2228
EI 2095-2236
J9 FRONT COMPUT SCI-CHI
JI Front.. Comput. Sci.
PD APR
PY 2020
VL 14
IS 2
BP 291
EP 303
DI 10.1007/s11704-018-7175-0
PG 13
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JT5FO
UT WOS:000501015800004
DA 2022-08-23
ER

PT J
AU Li, XM
   Alazab, M
   Li, Q
   Yu, KP
   Yin, QJ
AF Li, Xinmeng
   Alazab, Mamoun
   Li, Qian
   Yu, Keping
   Yin, Quanjun
TI Question-aware memory network for multi-hop question answering in
   human-robot interaction
SO COMPLEX & INTELLIGENT SYSTEMS
LA English
DT Article
DE Human-robot interaction; Cognitive computing; Question answering;
   Knowledge graph; Memory network; Knowledge reasoning
AB Knowledge graph question answering is an important technology in intelligent human-robot interaction, which aims at automatically giving answer to human natural language question with the given knowledge graph. For the multi-relation question with higher variety and complexity, the tokens of the question have different priority for the triples selection in the reasoning steps. Most existing models take the question as a whole and ignore the priority information in it. To solve this problem, we propose question-aware memory network for multi-hop question answering, named QA2MN, to update the attention on question timely in the reasoning process. In addition, we incorporate graph context information into knowledge graph embedding model to increase the ability to represent entities and relations. We use it to initialize the QA2MN model and fine-tune it in the training process. We evaluate QA2MN on PathQuestion and WorldCup2014, two representative datasets for complex multi-hop question answering. The result demonstrates that QA2MN achieves state-of-the-art Hits@1 accuracy on the two datasets, which validates the effectiveness of our model.
C1 [Li, Xinmeng; Li, Qian; Yin, Quanjun] Natl Univ Def Technol, Coll Syst Engn, Changsha 410000, Peoples R China.
   [Alazab, Mamoun] Charles Darwin Univ, Coll Engn IT & Environm, Darwin, NT, Australia.
   [Yu, Keping] Waseda Univ, Global Informat & Telecommun Inst, Tokyo 1690072, Japan.
RP Yin, QJ (通讯作者)，Natl Univ Def Technol, Coll Syst Engn, Changsha 410000, Peoples R China.
EM xml.nudt@gmail.com; mamoun.alazab@cdu.edu.au; liqian9510@outlook.com;
   keping.yu@aoni.waseda.jp; yinquanjun@nudt.edu.cn
FU National Natural Science Foundation of China [61473300]
FX This work was partially funded by the National Natural Science
   Foundation of China (Grand number 61473300).
CR 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
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2015, ARXIV150602075
   Chen Y., 2019, P 2019 C N AM CHAPTE, V1, P2913
   Chung J., 2014, EMPIRICAL EVALUATION
   Cochez, 2019, ARXIV190806917
   Fischer, 2019, ARXIV190709361
   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
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Jain Sarthak, 2016, P NAACL STUD RES WOR, P109, DOI DOI 10.18653/V1/N16-2016
   Kingma D. P., 2017, ARXIV
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Liang P., 2013, ARXIV PREPRINT ARXIV
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Miller Alexander, 2016, EMNLP, DOI [10.18653/v1/D16-1147, DOI 10.18653/V1/D16-1147]
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Qiu YQ, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P474, DOI 10.1145/3336191.3371812
   Saxena A, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P4498
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Sukhbaatar S, 2015, ADV NEUR IN, V28
   Sutskever I., 2014, ADV NEURAL INF PROCE
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Weston J., 2013, ARXIV PREPRINT ARXIV, P1366
   Wu ZY, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P663, DOI 10.1145/3336191.3371782
   Xu K., 2019, LONG SHORT PAPERS, V1, P2937
   Yih, 2020, ARXIV201214610
   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
   Zhang L., 2016, ARXIV161102266
   Zhang YY, 2018, AAAI CONF ARTIF INTE, P6069
   Zhou M., 2018, P ACL, P2010
NR 33
TC 10
Z9 10
U1 3
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 APR
PY 2022
VL 8
IS 2
SI SI
BP 851
EP 861
DI 10.1007/s40747-021-00448-0
EA JUL 2021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0W4UK
UT WOS:000679333300001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Chen, H
   Li, GY
   Sun, YH
   Jiang, W
AF Chen, Heng
   Li, Guanyu
   Sun, Yunhao
   Jiang, Wei
TI A quaternion-group knowledge graph embedding model
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; quaternion group; link prediction
AB Capturing the composite embedding representation of a multi-hop relation path is an extremely vital task in knowledge graph completion. Recently, rotation-based relation embedding models have been widely studied to embed composite relations into complex vector space. However, these models make some over-simplified assumptions on the composite relations, resulting the relations to be commutative. To tackle this problem, this paper proposes a novel knowledge graph embedding model, named QuatGE, which can provide sufficient modeling capabilities for complex composite relations. In particular, our method models each relation as a rotation operator in quaternion group-based space. The advantages of our model are twofold: (1) Since the quaternion group is a non-commutative group (i.e., non-Abelian group), the corresponding rotation matrices of composite relations can be non-commutative; (2) The model has a more expressive setting with stronger modeling capabilities, which is flexible to model and infer the complete relation patterns, including: symmetry/anti-symmetry, inversion and commutative/non-commutative composition. Experimental results on four benchmark datasets show that the proposed method outperforms the existing state-of-the-art models for link prediction, especially on composite relations.
C1 [Chen, Heng; Li, Guanyu; Sun, Yunhao; Jiang, Wei] Dalian Maritime Univ, Fac Informat Sci & Technol, Dalian, Peoples R China.
   [Chen, Heng] Dalian Univ Foreign Languages, Res Ctr Language Intelligence, Dalian, Peoples R China.
RP Li, GY (通讯作者)，Dalian Maritime Univ, Fac Informat Sci & Technol, Dalian, Peoples R China.
EM rabitlee@163.com
FU National Natural Science Foundation of China [61976032, 61806038];
   Scientific Research Funding Project of Education Department of Liaoning
   Province [2020JYT03]; Innovative Talents in Colleges and Universities of
   Liaoning Province
FX This work was supported by the National Natural Science Foundation of
   China (No.61976032 and No.61806038), the Scientific Research Funding
   Project of Education Department of Liaoning Province (No.2020JYT03) and
   the Innovative Talents in Colleges and Universities of Liaoning Province
   (No.WR2019005).
CR Bansal T, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4387
   Chen Cai, 2019, ARXIV PREPRINT ARXIV
   Chen H, 2020, IEEE ACCESS, V8, P100890, DOI 10.1109/ACCESS.2020.2997177
   Dai JS, 2015, MECH MACH THEORY, V92, P144, DOI 10.1016/j.mechmachtheory.2015.03.004
   Dettmers T, 2017, ARXIV170701476
   Dietz L, 2018, ACM/SIGIR PROCEEDINGS 2018, P1387, DOI 10.1145/3209978.3210187
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Hong P., 2020, ARXIV PREPRINT 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
   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
   Jozashoori S, 2019, LECT NOTES COMPUT SC, V11877, P58, DOI 10.1007/978-3-030-33246-4_4
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Moon C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2215, DOI 10.1145/3132847.3133095
   Nguyen D.Q., 2019, P NAACL HLT 2019, P2180, DOI DOI 10.18653/V1/N19-1226
   Nguyen D. Q, 2017, ARXIV171202121
   Nickel Maximilian, 2015, ARXIV151004935
   Sun Z., 2019, 7 INT C LEARN REPR
   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 Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Yao L., 2019, ADV NEURAL INFORM PR, P2735
   Zhou K, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1006, DOI 10.1145/3394486.3403143
NR 23
TC 0
Z9 0
U1 5
U2 16
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 1
BP 2459
EP 2468
DI 10.3233/JIFS-202546
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UB5PA
UT WOS:000685896700153
DA 2022-08-23
ER

PT J
AU Liu, CG
   Yu, YL
   Li, XX
   Wang, P
AF Liu, Chenguang
   Yu, Yongli
   Li, Xingxin
   Wang, Peng
TI Application of Entity Relation Extraction Method Under CRF and Syntax
   Analysis Tree in the Construction of Military Equipment Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE CRF; military equipment; knowledge graph; entity relation extraction;
   confidence model
AB In the field of military research, manufacturing and management of weapons and equipment are very important. Due to the continuous advancement of science and technology, many military equipment databases have a loose structure, which makes them difficult to be utilized efficiently, resulting in low efficiency, chaotic management, and other issues. In order to solve these problems, an entity-relation extraction method based on CRF and syntactic analysis tree is proposed according to the latest text extraction algorithm. Finally, a military knowledge graph construction method is optimized via massive data training, model comparison and improvement. The ternary data extraction method is significantly better than the single algorithm extraction method, and the accuracy of the extracted training model can reach 72%. Compared with the traditional entity-relation extraction method, the accuracy of the entity-relation extraction method based on the fusion of CRF and syntax analysis tree is improved by 12.6% when the confidence model is added, and the comprehensive evaluation accuracy can reach 78.11%. This result has significant practical value for the construction of knowledge graphs in the field of military equipment.
C1 [Liu, Chenguang; Yu, Yongli; Li, Xingxin; Wang, Peng] Army Engn Univ Peoples Liberat Army, Shijiazhuang 050051, Hebei, Peoples R China.
RP Li, XX (通讯作者)，Army Engn Univ Peoples Liberat Army, Shijiazhuang 050051, Hebei, Peoples R China.
EM lxx_1226@sina.com
OI Liu, Chenguang/0000-0002-0058-8073
CR [Anonymous], 2017, P EMNLP
   [Anonymous], 2020, LIGHT SCI APPL, DOI DOI 10.1038/S41377-020-0243-X
   Calkins H, 2017, J ARRYTHM, V33, P369, DOI 10.1016/j.joa.2017.08.001
   Chen Zhiyu, 2020, Journal of Shanghai Jiaotong University (Science), V25, P578, DOI 10.1007/s12204-020-2179-y
   Fan M, 2017, PATTERN RECOGN LETT, V93, P31, DOI 10.1016/j.patrec.2016.09.005
   He L., 2018, ARXIV180809602
   Hu LQ, 2019, J VIS COMMUN IMAGE R, V58, P37, DOI 10.1016/j.jvcir.2018.10.024
   Huang J, 2019, IEEE T CIRC SYST VID, V29, P2822, DOI 10.1109/TCSVT.2018.2870740
   Ji L, 2019, DATA INTELLIGENCE, V1, P238, DOI 10.1162/dint_a_00013
   Liu HL, 2016, NEURAL COMPUT APPL, V27, P271, DOI 10.1007/s00521-014-1776-9
   Minyong Shi, 2019, 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS). Proceedings, P266
   Mujovic H, 2017, LEXONOMICA, V9, P161, DOI 10.18690/18557147.9.2.161-178(2017)
   Peake JM, 2018, FRONT PHYSIOL, V9, DOI 10.3389/fphys.2018.00743
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Wang J, 2018, INT CONF ADV CLOUD B, P89, DOI 10.1109/CBD.2018.00025
   Wang S, 2019, IEEE ACCESS, V7, P136951, DOI 10.1109/ACCESS.2019.2942338
   Xia Q, 2017, PROTEOM CLIN APPL, V11, DOI 10.1002/prca.201600099
   Xu DL, 2016, IEEE INT C BIOINFORM, P1669, DOI 10.1109/BIBM.2016.7822769
   Yang Z., 2020, ARXIV200608339
   Zhao Z., 2018, INT J PURE APPL MATH, V118, P1869
NR 21
TC 2
Z9 2
U1 6
U2 29
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 200581
EP 200588
DI 10.1109/ACCESS.2020.3034894
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA OR8WQ
UT WOS:000589748000001
OA gold
DA 2022-08-23
ER

PT J
AU Gu, LC
   Xia, YC
   Yuan, XH
   Wang, C
   Jiao, J
AF Gu, Lichuan
   Xia, Yingchun
   Yuan, Xiaohui
   Wang, Chao
   Jiao, Jun
TI Research on the Model for Tobacco Disease Prevention and Control Based
   on Case-based Reasoning and Knowledge Graph
SO FILOMAT
LA English
DT Article; Proceedings Paper
CT 3rd International Conference on Fuzzy Systems and Data Mining (FSDM)
CY NOV 24-27, 2017
CL Natl Dong Hwa Univ, Hualien, TAIWAN
HO Natl Dong Hwa Univ
DE Knowledge graph; case-based reasoning; tobacco disease; prediction;
   prevention and control
AB Tobacco is one of the most important economic crops in China. The yield and quality of tobacco reduce severely because of long-time disease invasion. Currently, the main focus of researches on tobacco disease prevention and control is the diagnosis of disease that has occurred, which ignores to predict disease before it outbreaks. Therefore, in this paper, we follow the idea that prediction is used before disease prevention and control and study the model for tobacco disease prevention and control by using knowledge graph and case-based reasoning (CBR). In order to implement the model, we choose tobacco mosaic virus (TMV) as research object and follow the following methods to prevent occurrence of that. At first, a method to predicting environmental factors by using principal component analysis (PCA) and support vector machine (SVM) is proposed. According to the prediction result, knowledge graph and CBR are used to retrieve the most similarity case and finally determine the best solution. Experimental results demonstrate that our model can achieve high accuracy and give the most appropriate scheme for disease prevention and control.
C1 [Gu, Lichuan; Xia, Yingchun; Wang, Chao; Jiao, Jun] Anhui Agr Univ, Sch Informat & Comp, Hefei, Anhui, Peoples R China.
   [Yuan, Xiaohui] Univ North Texas, Dept Comp Sci & Engn, Denton, TX 76203 USA.
RP Gu, LC (通讯作者)，Anhui Agr Univ, Sch Informat & Comp, Hefei, Anhui, Peoples R China.
EM glc@ahau.edu.cn; yingchun.xia@ahau.edu.cn; xiaohui.yuan@unt.edu;
   31727122@qq.com; jiaojun2000@sina.com
RI Yuan, Xiaohui/AAQ-1172-2020
OI Yuan, Xiaohui/0000-0001-6897-4563
FU National Natural Science Foundation of China [31771679, 31371533];
   Special Fund for Key Program of Science and Technology of Anhui
   Province, China [15czz03131, 16030701092]; Natural Science Foundation of
   the Anhui Higher Education Institutions of China [kJ2016A836]
FX Research supported by the National Natural Science Foundation of China
   (Grant No.31771679, No. 31371533), the Special Fund for Key Program of
   Science and Technology of Anhui Province, China (Grant No.15czz03131,
   16030701092), the Natural Science Foundation of the Anhui Higher
   Education Institutions of China(Grant No.kJ2016A836)
CR Abdi H, 2010, WIRES COMPUT STAT, V2, P433, DOI 10.1002/wics.101
   Cho B., 2017, CLUSTER COMPUT, V20, P1
   Cindy M., 2010, COMPUTATIONAL INTELL, V25, P165
   Jingyuan F., 2014, DISTRIBUTION IDENTIF
   Mordelet F, 2014, PATTERN RECOGN LETT, V37, P201, DOI 10.1016/j.patrec.2013.06.010
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Niu XiaoYi, 2013, Journal of Northwest A & F University - Natural Science Edition, V41, P103
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Uyar A, 2015, ONLINE INFORM REV, V39, P197, DOI 10.1108/OIR-10-2014-0257
   Zhang Y. L, 2015, RES AUTOMATIC IDENTI
NR 10
TC 0
Z9 0
U1 5
U2 21
PU UNIV NIS, FAC SCI MATH
PI NIS
PA PO BOX 224, VISEGRADSKA 33, NIS, 18000, SERBIA MONTENEG
SN 0354-5180
J9 FILOMAT
JI Filomat
PY 2018
VL 32
IS 5
BP 1947
EP 1952
DI 10.2298/FIL1805947G
PG 6
WC Mathematics, Applied; Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Mathematics
GA HA4HS
UT WOS:000450221000047
OA Bronze
DA 2022-08-23
ER

PT J
AU Gao, J
   Huang, XP
   Zhang, LL
AF Gao, Jie
   Huang, Xinping
   Zhang, Lili
TI Comparative Analysis between International Research Hotspots and
   National-Level Policy Keywords on Artificial Intelligence in China from
   2009 to 2018
SO SUSTAINABILITY
LA English
DT Article
DE artificial intelligence; international research; knowledge map
   visualization; policy documents quantification; research hotspot; policy
   keyword
ID HUMAN ACTIVITY RECOGNITION; ANALYTICS RESEARCH; SPECIAL-ISSUE;
   GENERATION; CLASSIFICATION; FEATURES; SYSTEMS; MODELS; CITIES; CARE
AB In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China's international AI research and its national-level policy keywords is the basis for further clarification and reference in academics and political circles. There has been very little research on the interaction between academic research and policy making. Understanding the relationship between the content of academic research and the content emphasized by actual operational policy will help scholars to better apply research to practice, and help decision-makers to manage effectively. Based on 3577 English publications about AI published by Chinese scholars in 2009-2018, and 262 Chinese national-level policy documents published during this period, this study carried out scientometric analysis and quantitative analysis of policy documents through the knowledge maps of AI international research hotspots in China and the co-occurrence maps of Chinese policy keywords, and conducted a comparative analysis that divided China's AI development into three stages: the initial exploration stage, the steady rising stage, and the rapid development stage. The studies showed that in the initial exploration stage (2009-2012), research hotspots and policy keywords had a certain alienation relationship; in the steady rising stage (2013-2015), research hotspots focused more on cutting-edge technologies and policy keywords focused more on macro-guidance, and the relationship began to become close; and in the rapid development stage (2016-2018), the research hotspots and policy keywords became closely integrated, and they were mutually infiltrated and complementary, thus realizing organic integration and close connection. Through comparative analysis between international research hotspots and national-level policy keywords on AI in China from 2009 to 2018, the development of AI in China was revealed to some extent, along with the interaction between academics and politics in the past ten years, which is of great significance for the sustainable development and effective governance of China's artificial intelligence.
C1 [Gao, Jie; Huang, Xinping; Zhang, Lili] Tsinghua Univ, Sch Publ Policy & Management, Beijing 100084, Peoples R China.
   [Gao, Jie; Huang, Xinping; Zhang, Lili] Tsinghua Univ, Ctr Sci Technol & Educ Policy, Beijing 100084, Peoples R China.
RP Huang, XP (通讯作者)，Tsinghua Univ, Sch Publ Policy & Management, Beijing 100084, Peoples R China.; Huang, XP (通讯作者)，Tsinghua Univ, Ctr Sci Technol & Educ Policy, Beijing 100084, Peoples R China.
EM littlehope@mail.tsinghua.edu.cn; huangxinping@mail.tsinghua.edu.cn;
   zhangll18@mail.tsinghua.edu.cn
RI Huang, XinPing/Q-6409-2018
FU National Natural Science Foundation of China [71904101, 71801169]; China
   Postdoctoral Science Foundation [2018M640150, 2019M650754]; National
   Social Science Foundation of China [18ZDA075, 18CTQ040]
FX This research was funded by [The National Natural Science Foundation of
   China] grant number [71904101, 71801169], [China Postdoctoral Science
   Foundation] grant number [2018M640150, 2019M650754], and [The National
   Social Science Foundation of China] grant number [18ZDA075, 18CTQ040].
CR Ahmed A., 2019, P INT C APPL ENG MAT
   [Anonymous], 2018, P 2018 INT C APPL EN
   Bakli MS, 2018, GEO-SPAT INF SCI, V21, P102, DOI 10.1080/10095020.2017.1413798
   Batool M., 2019, P INT C APPL ENG MAT
   Boden MA, 1998, ARTIF INTELL, V103, P347, DOI 10.1016/S0004-3702(98)00055-1
   Cai Z., 2016, CHINA SCI TECHNOL RE, V34, P12
   Center for Science Technology & Education Policy at Tsinghua University, 2018, DOM FOR AI POL AN RE, P1
   Chen CM, 2017, J DATA INFO SCI, V2, P1, DOI 10.1515/jdis-2017-0006
   Chen M., 2018, LIBRARY, V12, P8
   Chen XL, 2018, BMC MED INFORM DECIS, V18, DOI 10.1186/s12911-018-0692-9
   Chen Y., 2015, STUDIES SCI SCI, V33, P242
   China Institute for Science and Technology Policy at Tsinghua University, 2018, CHINA AI DEV REP 201
   Chui KT, 2018, ENERGIES, V11, DOI 10.3390/en11112869
   Conitzer V, 2011, ARTIF INTELL, V175, P1251, DOI 10.1016/j.artint.2010.11.007
   Cui TN, 2018, SCIENTOMETRICS, V116, P1013, DOI 10.1007/s11192-018-2782-y
   Damiani E, 2010, WORLD WIDE WEB, V13, P1, DOI 10.1007/s11280-009-0082-4
   Davis E, 2016, ARTIF INTELL, V233, P60, DOI 10.1016/j.artint.2015.12.003
   Gao J., 2018, SCI TECHNOL PROG POL, V35, P9
   Gao J., 2019, SCI TECHNOL MANAG RE, V39, P206
   [高山行 Gao Shanxing], 2018, [科学学研究, Studies in Science of Science], V36, P2004
   Gil Y, 2014, SCIENCE, V346, P171, DOI 10.1126/science.1259439
   Hassan SU, 2019, BEHAV INFORM TECHNOL, V38, P924, DOI 10.1080/0144929X.2019.1583282
   Hovy E, 2013, ARTIF INTELL, V194, P2, DOI 10.1016/j.artint.2012.10.002
   Hsu F.S., 2013, ASIAPAC SIGN INFO PR, P1
   Hu ZW, 2019, SCIENTOMETRICS, V118, P1, DOI 10.1007/s11192-018-2954-9
   Huang C., 2015, J PUBLIC MANAGEMENT, V12, P129, DOI 10.16149/j.cnki.23-1523.2015.02.012.
   Huang C., 2016, QUANTITATIVE RES POL, P3
   Huang C, 2020, HIGH EDUC POLICY, V33, P323, DOI 10.1057/s41307-018-0096-6
   Huang C, 2017, J CHIN GOV, V2, P235, DOI 10.1080/23812346.2017.1342381
   Irrera N, 2017, FRONT PHARMACOL, V8, DOI 10.3389/fphar.2017.00459
   Jala A, 2019, J ELECTR ENG TECHNOL, V14, P1733, DOI 10.1007/s42835-019-00187-w
   Jalal A, 2007, 2007 CIT: 7TH IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY, PROCEEDINGS, P369
   Jalal A, 2005, HEALTHCOM 2005: 7TH INTERNATIONAL WORKSHOP ON ENTERPRISE NETWORKING AND COMPUTING IN HEALTHCARE INDUSTRY, PROCEEDINGS, P295, DOI 10.1109/HEALTH.2005.1500466
   JALAL A, 2011, PROC INT C SMART, V6719, P25
   Jalal A., 2019, P IEEE INT C INF TEC
   Jalal A., 2015, P 2015 INT C INF EL
   Jalal A., 2018, P 2018 INT C FRONT I
   Jalal A, 2019, EDUC INF TECHNOL, V24, P2797, DOI 10.1007/s10639-019-09892-5
   Jalal A, 2019, INT J INTERACT MULTI, V5, P71, DOI [10.9781/ijimai.2017.07.003, 10.9781/ijimai.2018.07.003]
   Jalal A, 2018, INT CONF FRONT INFO, P105, DOI 10.1109/FIT.2018.00026
   Jalal A, 2019, J ELECTR ENG TECHNOL, V14, P455, DOI 10.1007/s42835-018-00012-w
   Jalal A, 2017, J ELECTR ENG TECHNOL, V12, P1657, DOI 10.5370/JEET.2017.12.4.1657
   Jalal A, 2017, INT J INTERACT MULTI, V4, P54, DOI 10.9781/ijimai.2017.447
   Jalal A, 2017, PATTERN RECOGN, V61, P295, DOI 10.1016/j.patcog.2016.08.003
   Jalal A, 2015, INT CONF UBIQ ROBOT, P450, DOI 10.1109/URAI.2015.7358903
   Jalal A, 2015, 2015 IEEE 29TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS WORKSHOPS WAINA 2015, P445, DOI 10.1109/WAINA.2015.38
   Jalal A, 2014, 2014 11TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE (AVSS), P74, DOI 10.1109/AVSS.2014.6918647
   Jalal A, 2014, SENSORS-BASEL, V14, P11735, DOI 10.3390/s140711735
   Jia K., 2018, E GOVT, V9, P78
   Jia K., 2017, CHINA ADM, V10, P40
   Kamal S., 2016, J ELECTR ENG TECHNOL, V11, P1921, DOI DOI 10.5370/JEET.2016.11.6.1857
   Kang Y, 2012, PR IEEE COMP DESIGN, P5, DOI [10.1109/APPEEC.2012.6307592, 10.1109/ICCD.2012.6378608]
   Koller D., 1997, VRST'97. ACM Symposium on Virtual Reality Software and Technology 1997, P87
   Li K., 2017, ARTIF INTELL, P24
   Li L, 2016, UBICOMP'16 ADJUNCT: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING, P149, DOI 10.1145/2968219.2971405
   [李梅芳 Li Meifang], 2018, [科学学研究, Studies in Science of Science], V36, P1565
   Li T., 2017, P INT C CYB EN DISTR, P182
   Liu H., 2018, E GOVT, V11, P47
   Liu JY, 2018, IEEE ACCESS, V6, P34403, DOI 10.1109/ACCESS.2018.2819688
   Luo XM, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16060822
   Lytras MD, 2019, LIBR HI TECH, V37, P2, DOI 10.1108/LHT-03-2019-277
   Lytras MD, 2018, BEHAV INFORM TECHNOL, V37, P937, DOI 10.1080/0144929X.2018.1512940
   Lytras MD, 2017, INT J SEMANT WEB INF, V13, P1, DOI 10.4018/IJSWIS.2017010101
   Lytras MD, 2016, MOB INF SYST, V2016, DOI 10.1155/2016/2439389
   Lytras MD, 2015, J UNIVERS COMPUT SCI, V21, P1379
   Lytras MD, 2015, COMPUT HUM BEHAV, V51, P557, DOI 10.1016/j.chb.2015.06.004
   Lytras MD, 2010, INFORM SYST MANAGE, V27, P196, DOI 10.1080/10580530.2010.493804
   McCarthy J, 2014, RESONANCE, V19, P283
   Miller T, 2019, ARTIF INTELL, V267, P1, DOI 10.1016/j.artint.2018.07.007
   Mu KD, 2018, ARTIF INTELL, V259, P52, DOI 10.1016/j.artint.2018.02.003
   Nguyen TN, 2017, P 8 INT S INF COMM T, P370
   Ni QQ, 2017, APPL COMPOS MATER, V24, P23, DOI 10.1007/s10443-016-9512-5
   de Pablos PO, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10062073
   Ou Y., 2017, LIB INF, V6, P104
   Pablos P.O.D., 2019, HUM FACTOR ERGON MAN, V29, P3
   Pan YH, 2016, ENGINEERING, V2, P409, DOI 10.1016/J.ENG.2016.04.018
   Parkes DC, 2015, SCIENCE, V349, P267, DOI 10.1126/science.aaa8403
   Piyathilaka L, 2013, C IND ELECT APPL, P567
   Rajan K, 2017, ARTIF INTELL, V247, P1, DOI 10.1016/j.artint.2017.03.003
   Russell S. J., 2010, APPL MECH MAT, V263
   Safdari A, 2019, THEOR APPL MECH LETT, V9, P1, DOI 10.1016/j.taml.2019.01.008
   Singh D, 2017, PATTERN RECOGN, V65, P265, DOI 10.1016/j.patcog.2017.01.001
   Song W., 2019, SCI TECHNOL MANAG RE, V39, P192
   Sony A., 2011, P 2011 INT C SIGN PR
   Sun B, 2018, WIRELESS PERS COMMUN, V102, P1879, DOI 10.1007/s11277-018-5243-2
   Tang H., 2018, CHINA RADIO, V5, P45
   Visvizi A, 2018, J SCI TECHNOL POLICY, V9, P134, DOI 10.1108/JSTPM-02-2018-0020
   Visvizi A, 2018, J SCI TECHNOL POLICY, V9, P126, DOI 10.1108/JSTPM-07-2018-079
   WALDROP MM, 1987, SCIENCE, V237, P484, DOI 10.1126/science.237.4814.484
   Wang K, 2019, ARTIF INTELL, V275, P540, DOI 10.1016/j.artint.2019.07.003
   Wang Y, 2018, CHINA Q INT STRATEG, V4, P241, DOI 10.1142/S2377740018500148
   Wu HT, 2014, 2014 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA), P219, DOI 10.1109/ICInfA.2014.6932656
   Yang TT, 2017, WATER RESOUR RES, V53, P2786, DOI 10.1002/2017WR020482
   Yoshimoto H, 2003, PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS, P247, DOI 10.1109/MFI-2003.2003.1232665
   Yuan Y., 2018, J CHINA ACAD ELECT I, V13, P663
   Zhang X, 2017, BEHAV INFORM TECHNOL, V36, P548, DOI 10.1080/0144929X.2016.1268647
   Zhang Y, 2013, J ZHEJIANG UNIV-SC B, V14, P1, DOI 10.1631/jzus.B1200292
   ZHOU P, 2018, CHINA SOC SCI TECH I, V37, P2325, DOI DOI 10.1002/ADV.21908
   Zhu J, 2017, SCIENTOMETRICS, V110, P893, DOI 10.1007/s11192-016-2187-8
   Zhuhadar L, 2016, COMPUT HUM BEHAV, V61, P198, DOI 10.1016/j.chb.2016.02.073
NR 100
TC 5
Z9 5
U1 7
U2 51
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 6574
DI 10.3390/su11236574
PG 18
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:000508186400046
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, G
   Peng, JB
   Xu, TX
   Xiao, L
AF Chen, Guo
   Peng, Jiabin
   Xu, Tianxiang
   Xiao, Lu
TI Extracting entity relations for "problem-solving" knowledge graph of
   scientific domains using word analogy
SO ASLIB JOURNAL OF INFORMATION MANAGEMENT
LA English
DT Article; Early Access
DE Entity relation extraction; Problem-solving; Word analogy; Knowledge
   graph; Artificial intelligence; Research trend analysis
AB Purpose Problem-solving" is the most crucial key insight of scientific research. This study focuses on constructing the "problem-solving" knowledge graph of scientific domains by extracting four entity relation types: problem-solving, problem hierarchy, solution hierarchy and association. Design/methodology/approach This paper presents a low-cost method for identifying these relationships in scientific papers based on word analogy. The problem-solving and hierarchical relations are represented as offset vectors of the head and tail entities and then classified by referencing a small set of predefined entity relations. Findings This paper presents an experiment with artificial intelligence papers from the Web of Science and achieved good performance. The F1 scores of entity relation types problem hierarchy, problem-solving and solution hierarchy, which were 0.823, 0.815 and 0.748, respectively. This paper used computer vision as an example to demonstrate the application of the extracted relations in constructing domain knowledge graphs and revealing historical research trends. Originality/value This paper uses an approach that is highly efficient and has a good generalization ability. Instead of relying on a large-scale manually annotated corpus, it only requires a small set of entity relations that can be easily extracted from external knowledge resources.
C1 [Chen, Guo; Peng, Jiabin; Xu, Tianxiang] Naming Univ Sci & Technol, Nanjing, Peoples R China.
   [Xiao, Lu] Nanjing Univ Finance & Econ, Sch Journalism, Nanjing, Peoples R China.
RP Xiao, L (通讯作者)，Nanjing Univ Finance & Econ, Sch Journalism, Nanjing, Peoples R China.
EM ahjk_xiaolu@163.com
FU MOE (Ministry of Education in China) Project of Humanities and Social
   Sciences [21YJC870003]; Jiangsu Provincial Social Science Foundation of
   China [21TQC002]
FX This study is supported by the MOE (Ministry of Education in China)
   Project of Humanities and Social Sciences (No. 21YJC870003); and the
   Jiangsu Provincial Social Science Foundation of China (No. 21TQC002).
CR Agarwal S, 2017, IEEE INT MEM WORKSH, P52
   Ammar W, 2017, P 11 INT WORKSH SEM, P592, DOI DOI 10.18653/V1/S17-2097HTTP://WWW.ACLWEB.ORG/ANTHOLOGY/S17-2097
   Augenstein I, 2017, P 11 INT WORKSH SEM, P546, DOI DOI 10.18653/V1/S17-2091
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Brin S, 1999, LECT NOTES COMPUT SC, V1590, P172
   Chao L., 2021, 59 ANN M ASS COMP LI
   Chen L, 2020, SCIENTOMETRICS, V125, P289, DOI 10.1007/s11192-020-03634-y
   Chiang H.-Y., 2020, P 24 C COMP NAT LANG, P119
   Chu HT, 2017, LIBR INFORM SCI RES, V39, P284, DOI 10.1016/j.lisr.2017.11.001
   De Lacalle O.L., 2013, P 2013 C EMP METH NA, P415
   Denaux R, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P29, DOI 10.1145/3360901.3364445
   Deng S., 2022, ARXIV PREPRINT ARXIV
   Drozd A., 2016, P COLING 16 26 INT C, P3519
   Vo DT, 2019, INFORM PROCESS MANAG, V56, P424, DOI 10.1016/j.ipm.2018.10.011
   Fu RJ, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1199
   Gabor K, 2016, LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P3694
   Gao TY, 2019, AAAI CONF ARTIF INTE, P6407
   Gladkova A, 2016, NAACL, DOI DOI 10.18653/V1/N16-2002
   Goldberg Y, 2016, J ARTIF INTELL RES, V57, P345, DOI 10.1613/jair.4992
   Gupta S., 2011, P 5 INT JOINT C NAT, P1, DOI DOI 10.1145/2064942.2064944
   Heffernan K, 2018, SCIENTOMETRICS, V116, P1367, DOI 10.1007/s11192-018-2718-6
   Hoey Michael., 2001, TEXTUAL INTERACTION
   Jiang M., 2019, 13 WORKSH GRAPH BAS, P186
   Levy O., 2014, P 18 C COMP NAT LANG, P171, DOI DOI 10.3115/V1/W14-1618
   Liu K, 2020, SCI CHINA TECHNOL SC, V63, P1971, DOI 10.1007/s11431-020-1673-6
   Luan Y., 2017, P EMNLP ASS COMP LIN, DOI DOI 10.18653/V1/D17-1279
   Luan Y., 2018, P 2018 C EMPIRICAL M, P3219, DOI 10.18653/v1/D18-1360
   Luan Y., 2019, NAACL HLT, P3036, DOI 10.18653/v1/N19-1308
   Mikolov T, 2013, P NAACL HLT 2013, P746
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Nasar Z, 2018, SCIENTOMETRICS, V117, P1931, DOI 10.1007/s11192-018-2921-5
   Qu M, 2020, INT C MACHINE LEARNI, V119, P7867
   Rotsztejn J., 2018, P 12 INT WORKSH SEM, P689, DOI 10.18653/v1/S18-1112
   Tshitoyan V, 2019, NATURE, V571, P95, DOI 10.1038/s41586-019-1335-8
   Vylomova E, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1671
   Wadden D., 2019, P 2019 C EMP METH NA, P5788, DOI 10.18653/v1/D19-1585
   Wang YZ, 2020, J INFORMETR, V14, DOI [10.1016/j.joi.2020.101091, 10.1016/j.joi.101091]
   Wen W, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102596
   Winter E. O., 1968, SENTENCE CLAUSE SCI
   Xiaojin Zhu, 2002, CMUCALD02107
   Yao L., 2011, P C EMP METH NAT LAN, P1456
   Ye H, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2019.102096
   Zhang Z., 2004, P 13 ACM INT C INF K, P581
NR 43
TC 0
Z9 0
U1 8
U2 8
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2050-3806
EI 1758-3748
J9 ASLIB J INFORM MANAG
JI Aslib J. Inf. Manag.
DI 10.1108/AJIM-03-2022-0129
EA JUN 2022
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 1X3DH
UT WOS:000807338100001
DA 2022-08-23
ER

PT J
AU Nair, S
   Varadi, M
   Nadzirin, N
   Pravda, L
   Anyango, S
   Mir, S
   Berrisford, J
   Armstrong, D
   Gutmanas, A
   Velankar, S
AF Nair, Sreenath
   Varadi, Mihaly
   Nadzirin, Nurul
   Pravda, Lukas
   Anyango, Stephen
   Mir, Saqib
   Berrisford, John
   Armstrong, David
   Gutmanas, Aleksandras
   Velankar, Sameer
TI PDBe aggregated API: programmatic access to an integrative knowledge
   graph of molecular structure data
SO BIOINFORMATICS
LA English
DT Article
AB The PDBe aggregated API is an open-access and open-source RESTful API that provides programmatic access to a wealth of macromolecular structural data and their functional and biophysical annotations through 80+ API endpoints. The API is powered by the PDBe graph database (https://pdbe.org/graph-schema), an open-access integrative knowledge graph that can be used as a discovery tool to answer complex biological questions.
C1 [Nair, Sreenath; Varadi, Mihaly; Nadzirin, Nurul; Pravda, Lukas; Anyango, Stephen; Mir, Saqib; Berrisford, John; Armstrong, David; Gutmanas, Aleksandras; Velankar, Sameer] European Bioinformat Inst EMBL EBI, European Mol Biol Lab, Prot Data Bank Europe, Wellcome Genome Campus, Hinxton CB10 1SA, England.
RP Varadi, M (通讯作者)，European Bioinformat Inst EMBL EBI, European Mol Biol Lab, Prot Data Bank Europe, Wellcome Genome Campus, Hinxton CB10 1SA, England.
EM mvaradi@ebi.ac.uk
RI ; Pravda, Lukas/I-7241-2013
OI Gutmanas, Aleksandras/0000-0001-6311-0176; Varadi,
   Mihaly/0000-0002-3687-0839; Armstrong, David/0000-0002-4986-1229; Nair,
   Sreenath/0000-0002-6861-7627; Pravda, Lukas/0000-0002-6435-4719;
   Anyango, Stephen/0000-0003-4838-443X; Velankar,
   Sameer/0000-0002-8439-5964
FU Biotechnology and Biological Sciences Research Council [BB/T01959X/1,
   BB/P024351/1]; Wellcome Trust [104948]; European Molecular Biology
   Laboratory-European Bioinformatics Institute
FX This work was supported by Biotechnology and Biological Sciences
   Research Council via the 3D-Gateway [BB/T01959X/1] and FunPDBe
   [BB/P024351/1] grants, the Wellcome Trust [104948] and the European
   Molecular Biology Laboratory-European Bioinformatics Institute.
CR Armstrong DR, 2020, NUCLEIC ACIDS RES, V48, pD335, DOI 10.1093/nar/gkz990
   Batool M, 2019, INT J MOL SCI, V20, DOI 10.3390/ijms20112783
   Berman H, 2003, NAT STRUCT BIOL, V10, P980, DOI 10.1038/nsb1203-980
   Burley SK, 2019, NUCLEIC ACIDS RES, V47, pD520, DOI 10.1093/nar/gky949
   Coker EA, 2019, NUCLEIC ACIDS RES, V47, pD917, DOI 10.1093/nar/gky1129
   Dana JM, 2019, NUCLEIC ACIDS RES, V47, pD482, DOI 10.1093/nar/gky1114
   Gerstein M, 2000, NAT STRUCT BIOL, V7, P960, DOI 10.1038/80739
   Krissinel E, 2007, J MOL BIOL, V372, P774, DOI 10.1016/j.jmb.2007.05.022
   Krivak R, 2018, J CHEMINFORMATICS, V10, DOI 10.1186/s13321-018-0285-8
   Metz A, 2012, CURR PHARM DESIGN, V18, P4630, DOI 10.2174/138161212802651553
   Varadi M, 2020, NUCLEIC ACIDS RES, V48, pD344, DOI 10.1093/nar/gkz853
   Waman VP, 2021, BRIEF BIOINFORM, V22, P742, DOI 10.1093/bib/bbaa362
NR 12
TC 1
Z9 1
U1 0
U2 1
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 3950
EP 3952
DI 10.1093/bioinformatics/btab424
EA JUN 2021
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 XT8LN
UT WOS:000733832700043
PM 34081107
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Wang, JB
   Zhang, W
   Chen, XY
   Lei, J
   Lai, XL
AF Wang, Jingbin
   Zhang, Wang
   Chen, Xinyuan
   Lei, Jing
   Lai, Xiaolian
TI 3DRTE: 3D Rotation Embedding in Temporal Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Quaternions; Three-dimensional displays; Solid modeling; Protocols;
   Training; Adaptation models; Fuses; Temporal knowledge graph; 3D
   rotation embedding; self-attention; quaternion
AB Temporal knowledge graph (TKG) embedding has received increasing attention in the academia. However, most existing methods are extensions of traditional translation models. Due to their intrinsic limitations, it is often difficult for such methods to effectively model essential characteristics of TKG, namely three basic relation patterns including symmetry/antisymmetry, inversion, and composition. In this paper, a new 3-Dimensional Rotation Temporal Embedding (3DRTE) method is proposed. Firstly, we selectively fuse temporal and relational features of fact triples by taking advantages of self-attention mechanism in processing sequential information. Then, entities are modelled as points in three-dimensional space, and the relations are interpreted as two isoclinic rotations between entities with Quaternion. Experimental results on several public datasets show that our method obtains state-of-the-art results.
C1 [Wang, Jingbin; Zhang, Wang; Lei, Jing; Lai, Xiaolian] Fuzhou Univ, Coll Software, Coll Math & Comp Sci, Fuzhou 350116, Peoples R China.
   [Chen, Xinyuan] Fuzhou Melbourne Polytech, Dept Informat Engn, Fuzhou 350121, Peoples R China.
RP Zhang, W (通讯作者)，Fuzhou Univ, Coll Software, Coll Math & Comp Sci, Fuzhou 350116, Peoples R China.
EM fzu_zw@qq.com
FU National Natural Science Foundation of China [61672159];
   Industry-University-Research Collaboration in Fujian Province of China
   [2017H6008, 2018H6010]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61672159, and in part by the
   Industry-University-Research Collaboration in Fujian Province of China
   under Grant 2017H6008 and Grant 2018H6010.
CR [Anonymous], **DATA OBJECT**, DOI DOI 10.7910/DVN/28075
   Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Dasgupta Shib Sankar, 2018, P 2018 C EMP METH NA, P2001
   Dettmers T, 2017, ARXIV170701476
   Ebisu A, 2018, SIGGRAPH'18: ACM SIGGRAPH 2018 STUDIO, DOI 10.1145/3214822.3214824
   Garcia-Duran A, 2018, P 2018 C EMP METH NA
   Glorot X., 2010, J MACH LEARN RES, V9, P249
   Goel R, 2020, AAAI CONF ARTIF INTE, V34, P3988
   Hamilton W.R, 1844, PHILOS MAG, V25, P489, DOI DOI 10.1080/14786444408645047
   He KM, 2015, IEEE I CONF COMP VIS, P1026, DOI 10.1109/ICCV.2015.123
   Jiang T., 2016, P 2016 C EMP METH NA, P2350, DOI DOI 10.18653/V1/D16-1260
   Kazemi S. M., 2018, THESIS, DOI [10.14288/1.0375812, DOI 10.14288/1.0375812]
   King DB, 2015, ACS SYM SER, V1214, P1
   Leetaru K., 2017, P 34 INT C MACH LEAR, P1
   Lin HL, 2017, PROCEDIA COMPUT SCI, V108, P345, DOI 10.1016/j.procs.2017.05.045
   Parcollet T., 2019, P ICLR, P1
   Sun Z., 2019, 7 INT C LEARN REPR
   Trivedi R, 2017, PR MACH LEARN RES, V70
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yang B., 2015, P INT C LEARN REPR
   Zhang S, 2019, ADV NEUR IN, V32
NR 23
TC 0
Z9 0
U1 2
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 2020
VL 8
BP 207515
EP 207523
DI 10.1109/ACCESS.2020.3036897
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA OY8GA
UT WOS:000594478400001
OA gold
DA 2022-08-23
ER

PT J
AU Dorpinghaus, J
   Stefan, A
   Schultz, B
   Jacobs, M
AF Doerpinghaus, Jens
   Stefan, Andreas
   Schultz, Bruce
   Jacobs, Marc
TI Context mining and graph queries on giant biomedical knowledge graphs
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Current research information systems; Knowledge graphs; Graph
   embeddings; Semantic search; Complexity; NLP; Graph theory
ID ONTOLOGY; SYSTEMS
AB Contextual information is widely considered for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such context data, but also to store this data for further query and discovery approaches. Classical approaches use RDF triple stores, which have serious limitations. Here, we propose a multiple step knowledge graph approach using labeled property graphs based on polyglot persistence systems to utilize context data for context mining, graph queries, knowledge discovery and extraction. We introduce the graph-theoretic foundation for a general context concept within semantic networks and show a proof of concept based on biomedical literature and text mining. Our test system contains a knowledge graph derived from the entirety of PubMed and SCAIView data and is enriched with text mining data and domain-specific language data using Biological Expression Language. Here, context is a more general concept than annotations. This dense graph has more than 71M nodes and 850M relationships. We discuss the impact of this novel approach with 27 real-world use cases represented by graph queries. Storing and querying a giant knowledge graph as a labeled property graph is still a technological challenge. Here, we demonstrate how our data model is able to support the understanding and interpretation of biomedical data. We present several real-world use cases that utilize our massive, generated knowledge graph derived from PubMed data and enriched with additional contextual data. Finally, we show a working example in context of biologically relevant information using SCAIView.
C1 [Doerpinghaus, Jens] Fed Inst Vocat Educ & Training BIBB, Bonn, Germany.
   [Stefan, Andreas; Schultz, Bruce; Jacobs, Marc] Fraunhofer Inst Algorithms & Sci Comp SCA1, St Augustin, Germany.
   [Stefan, Andreas] Bonn Rhein Sieg Univ Appl Sci, Dept Comp Sci, St Augustin, Germany.
   [Doerpinghaus, Jens] German Ctr Neurodegenerat Dis DZNE, Bonn, Germany.
RP Dorpinghaus, J (通讯作者)，Fed Inst Vocat Educ & Training BIBB, Bonn, Germany.; Dorpinghaus, J (通讯作者)，German Ctr Neurodegenerat Dis DZNE, Bonn, Germany.
EM jens.doerpinghaus@bibb.de
FU Fraunhofer Society under the MAVO Project; EU/EFPIA Innovative Medicines
   Initiative Joint Undertaking under AETIONOMY [115568]; European Union;
   EFPIA; Human Brain Pharmacome
FX This study was funded by 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); and EFPIA.
CR Ackoff R.L., 1989, J APPL SYSTEMS ANAL, V16, P3, DOI DOI 10.1002/9781444303179.CH3
   Aggarwal C. C., 2012, MINING TEXT DATA, DOI [10.1007/978-1-4614-3223-4, DOI 10.1007/978-1-4614-3223-4_1, 10.1007/978-1-4614-3223-4_1]
   Angles R, 2017, ACM COMPUT SURV, V50, DOI 10.1145/3104031
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Cai DS, 2019, NEUROCOMPUTING, V329, P237, DOI 10.1016/j.neucom.2018.10.055
   Callahan Alison, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P200
   Cook S.A., 1971, P 3 ANN ACM S THEOR, P151, DOI [10.1145/800157.805047, DOI 10.1145/800157.805047]
   Cyganiak R., 2014, W3C RECOMMENDATION
   Desai M., 2018, INT J COMPUT INTELL, V1
   Dorpinghaus J, 2019, FED CONF COMPUT SCI, P265, DOI 10.15439/2019F3
   Dorpinghaus J, 2019, 15 INT C SEM SYST PO, P46
   Dorpinghaus J, 2018, 14 INT C SEM SYST SE
   Dorpinghaus J, 2018, 2018 FED C COMP SCI
   Dumontier M., 2014, 12 SEM WEB C, P401
   Fluck J, 2013, P 2013 WORKSH BIOM N, P80
   Franzoni V, 2019, ARXIV PREPRINT ARXIV
   Guidelines for the construction, 2005, FORM MAN MON CONTR V
   Hanisch D, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-S1-S14
   Hey J., 2004, INTERGOVERNMENTAL OC, V26, P1
   Hitzler P., 2012, OWL 2 WEB ONTOLOGY L
   Iyappan A, 2017, J ALZHEIMERS DIS, V59, P1153, DOI 10.3233/JAD-161148
   Khan K, 2019, ECOTOX ENVIRON SAFE, V168, P287, DOI 10.1016/j.ecoenv.2018.10.060
   Kim J, 2019, SCIENTOMETRICS, V118, P383, DOI 10.1007/s11192-018-2960-y
   Li S, 2014, RES LIB SCI, P21
   Madan S, 2018, SEMANTIC DATA INTEGR, DOI [10.6084/m9.figshare.7339244.v1, DOI 10.6084/M9.FIGSHARE.7339244.V1]
   Madan S, 2017, P BIOCREATIVE 6 WORK
   Madan S, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw136
   Malhotra A, 2014, ALZHEIMERS DEMENT, V10, P238, DOI 10.1016/j.jalz.2013.02.009
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   Natsiavas P, 2015, SEMANTIC WEB APPL TO, P224
   Osswald A, 2015, LIBER Q J ASS EUR RE, V26, P43
   Prajapati Parinda, 2019, Emerging Technologies in Data Mining and Information Security. Proceedings of IEMIS 2018. Advances in Intelligent Systems and Computing (AISC 813), P259, DOI 10.1007/978-981-13-1498-8_23
   ROGERS FB, 1963, B MED LIBR ASSOC, V51, P114
   Rowley J, 2007, J INF SCI, V33, P163, DOI 10.1177/0165551506070706
   Summers E, 2009, SKOS SIMPLE KNOWLEDG
   Vo Jakob, 2016, NKOS TPDL, V1676, P15
   Volanakis A, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-24571-0
   Vrandecic D, 2018, CEUR WORKSHOP P
   Webber J, 2015, GRAPH DATABASES
   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
   Wood PT, 2012, SIGMOD REC, V41, P50, DOI 10.1145/2206869.2206879
   Yang H, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15061113
   ZELENY M, 1987, HUM SYST MANAGE, V7, P59
   Zeng M., 2007, P ASS INF SCI TECHNO, V44, P1, DOI [10.1002/pra2.2015.14505201003, DOI 10.1002/PRA2.2015.14505201003]
   Zeng ML, 2008, KNOWL ORGAN, V35, P160, DOI 10.5771/0943-7444-2008-2-3-160
NR 46
TC 0
Z9 0
U1 11
U2 11
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 MAY
PY 2022
VL 64
IS 5
BP 1239
EP 1262
DI 10.1007/s10115-022-01668-7
EA MAR 2022
PG 24
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1F4PH
UT WOS:000774877600001
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Shen, Y
   Ding, N
   Zheng, HT
   Li, YL
   Yang, M
AF Shen, Ying
   Ding, Ning
   Zheng, Hai-Tao
   Li, Yaliang
   Yang, Min
TI Modeling Relation Paths for Knowledge Graph Completion
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Cognition; Semantics; Noise measurement; Data models; Data mining;
   Neural networks; Encyclopedias; Big data applications; semantic web;
   data mining; knowledge discovery
ID WEB
AB Knowledge graphs (KG) often encounter knowledge incompleteness. The path reasoning that predicts the unknown path relation between pairwise entities based on existing facts is one of the most promising approaches to the knowledge graph completion. However, most conventional path reasoning methods exclusively consider the entity description included in fact triples, ignoring both the type information of entities and the interaction between different semantic representations. In this study, we propose a novel method, Type-aware Attentive Path Reasoning (TAPR), to complete the knowledge graph by simultaneously considering KG structural information, textual information, and type information. More specifically, we first leverage types to enrich the representational learning of entities and relationships. Next, we describe a type-level attention to select the most relevant type of given entity in a specific triple without any predefined rules or patterns to reduce the impact of noisy types. After learning the distributed representation of all paths, path-level attention assigns different weights to paths, from which relations among entity pairs are calculated. We conduct a series of experiments on a real-world dataset to demonstrate the effectiveness of TAPR. Experimental results show that our method significantly outperforms all baselines on link prediction and entity prediction tasks.
C1 [Shen, Ying] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Guangdong, Peoples R China.
   [Ding, Ning; Zheng, Hai-Tao] Tsinghua Univ, Grad Sch Shenzhen, Shenzhen 510100, Peoples R China.
   [Li, Yaliang] Alibaba Grp, Bellevue, WA 98004 USA.
   [Yang, Min] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 510100, Peoples R China.
RP Yang, M (通讯作者)，Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 510100, Peoples R China.
EM shenying@pkusz.edu.cn; dingn18@mails.tsinghua.edu.cn;
   zheng.haitao@sz.tsinghua.edu.cn; yaliang.li@alibaba-inc.com;
   min.yang@siat.ac.cn
OI Zheng, Hai-Tao/0000-0001-5128-5649; SHEN (chen ying),
   Ying/0000-0002-3220-904X
FU National Natural Science Foundation of China [61602013, 61773229];
   Shenzhen Fundamental Research Project [JCYJ20170818091546869]; Basic
   Scientific Research Program of Shenzhen City [JCYJ20160331184440545];
   CCF-Tencent Open Research Fund
FX This work was financially supported by the National Natural Science
   Foundation of China (No.61602013 and No. 61773229), the Shenzhen
   Fundamental Research Project (Grant No. JCYJ20170818091546869), and
   Basic Scientific Research Program of Shenzhen City (Grant No.
   JCYJ20160331184440545). The corresponding author Min Yang was sponsored
   by the CCF-Tencent Open Research Fund. Ying Shen realized the
   development methodology and the creation of models. Ning Ding and
   Yaliang Li conducted an investigation process, and implemented
   algorithms and programming. Hai-Tao Zheng analyzed the experimental
   results. Min Yang was responsible for the management and coordination
   responsibility for the research activity planning and execution.
CR Annervaz K. M., 2018, PROC C N AM CHAPTER, P313
   [Anonymous], 2001, SCI AM
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Biega J., 2013, P 22 INT C WORLD WID, P325
   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
   Cannaviccio M., 2016, P 19 INT WORKSH WEB
   Cao Shaosheng, 2015, P 24 ACM INT C INF K, P891, DOI [10.1145/, DOI 10.1145/2806416.2806512]
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22
   Chen W., 2018, P 2018 C N AM CHAPT, V1, P1823, DOI [10.18653/v1/N18-1165, DOI 10.18653/V1/N18-1165]
   Cho K., 2014, ARXIV14061078, P1724
   Clark M, 2012, INFORM PROCESS MANAG, V48, P552, DOI 10.1016/j.ipm.2011.07.002
   Das R., 2016, ARXIV PREPRINT ARXIV, P132
   Das R., 2018, ICLR
   Deaton C., 2005, P INT C INT AN, P1
   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
   He L., 2018, COGN COMPUT, P1
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Jiang X., 2017, P MACH LEARN RES, P590
   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
   Lei K., 2019, NEURAL COMPUT APPL, V218, P1
   LENAT DB, 1991, ARTIF INTELL, V47, P185, DOI 10.1016/0004-3702(91)90055-O
   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
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Neelakantan A., 2017, THESIS U MASSACHUSET, P1
   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 PT, 2015, LECT NOTES COMPUT SC, V9366, P605, DOI 10.1007/978-3-319-25007-6_35
   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., 2014, ADV NEURAL INFORM PR, P1179
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Shen Y, 2018, J BIOMED INFORM, V88, P1, DOI 10.1016/j.jbi.2018.10.007
   Shen Y, 2018, IEEE ACCESS, V6, P52095, DOI 10.1109/ACCESS.2018.2862885
   Socher R., 2012, P 2012 JOINT C EMP M, P1201, DOI [10.5555/2390948.2391084, DOI 10.1162/153244303322533223]
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Suh Bongwon, 2009, P 5 INT S WIK OP COL, DOI DOI 10.1145/1641309.1641322
   Sutskever I., 2009, NIPS
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499, DOI DOI 10.18653/V1/D15-1174
   Trouillon T, 2017, J MACH LEARN RES, V18
   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 Q, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1308
   Wang Z., 2016, IJCAI, P1293
   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
   Wen D., 2017, P INT C SMART COMP C, P321
   West R, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P515, DOI 10.1145/2566486.2568032
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yang B., 2014, ABS14126575 CORR
   Yao XC, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P956
   Zeng W, 2017, P 2017 C EMP METH NA, P1768, DOI DOI 10.18653/V1/D17-1186
NR 55
TC 1
Z9 1
U1 5
U2 9
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 NOV 1
PY 2021
VL 33
IS 11
BP 3607
EP 3617
DI 10.1109/TKDE.2020.2970044
PG 11
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA WC2RV
UT WOS:000704109900010
DA 2022-08-23
ER

PT J
AU Franco-Salvador, M
   Gupta, P
   Rosso, P
   Banchs, RE
AF Franco-Salvador, Marc
   Gupta, Parth
   Rosso, Paolo
   Banchs, Rafael E.
TI Cross-language plagiarism detection over continuous-space- and knowledge
   graph-based representations of language
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Cross-language; Plagiarism detection; Continuous representations;
   Knowledge graphs; Multilingual semantic network
AB Cross-language (CL) plagiarism detection aims at detecting plagiarised fragments of text among documents in different languages. The main research question of this work is on whether knowledge graph representations and continuous space representations can complement to each other and improve the state-of-the-art performance in CL plagiarism detection methods. In this sense, we propose and evaluate hybrid models to assess the semantic similarity of two segments of text in different languages. The proposed hybrid models combine knowledge graph representations with continuous space representations aiming at exploiting their complementarity in capturing different aspects of cross -lingual similarity. We also present the continuous word alignment-based similarity analysis, a new model to estimate similarity between text fragments. We compare the aforementioned approaches with several state-of-the-art models in the task of CL plagiarism detection and study their performance in detecting different length and obfuscation types of plagiarism cases. We conduct experiments over Spanish-English and German English datasets. Experimental results show that continuous representations allow the continuous word alignment-based similarity analysis model to obtain competitive results and the knowledge-based document similarity model to outperform the state-of-the-art in CL plagiarism detection. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Franco-Salvador, Marc; Gupta, Parth; Rosso, Paolo] Univ Politecn Valencia, PRHLT, Res Ctr, Camino Vera S-N, E-46022 Valencia, Spain.
   [Banchs, Rafael E.] Inst Infocomm Res, Singapore 138632, Singapore.
RP Franco-Salvador, M (通讯作者)，Univ Politecn Valencia, PRHLT, Res Ctr, Camino Vera S-N, E-46022 Valencia, Spain.
EM mfranco@prhlt.upv.es; pgupta@dsic.upv.es
FU FPI-UPV pre-doctoral grant [de registro - 3505]; 
   [TIN2012-38603-C02-01];  [TIN2015-71147-C2-1-P]
FX This research has been carried out in framework of the FPI-UPV
   pre-doctoral grant (No de registro - 3505) awarded to Parth Gupta and in
   the framework of the national projects DIANA-APPLICATIONS - Finding
   Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01), and
   SomEMBED: SOcial Media language understanding - EMBEDing contexts
   (TIN2015-71147-C2-1-P). We would like to thank Martin Potthast, Daniel
   Ortiz-Martinez, and Luis A. Leiva for their support and comments during
   this research.
CR Banerjee S., 2003, IJCAI, V3, P805
   Barron Cedeno L.A., 2012, THESIS U POLITECNICA
   Barron-Cedeno A., 2008, PAN 08
   Barron-Cedeno A, 2013, KNOWL-BASED SYST, V50, P211, DOI 10.1016/j.knosys.2013.06.018
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bromley J., 1993, International Journal of Pattern Recognition and Artificial Intelligence, V7, P669, DOI 10.1142/S0218001493000339
   CLOUGH P, 2003, OLD NEW CHALLENGES A
   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
   DUMAIS ST, 1997, AAAI 97 SPRING S CRO, P18
   Efron B., 1994, INTRO BOOTSTRAP
   Fellbaum Christiane, 1998, WORDNET ELECT LEXICA
   Franco-Salvador M., 2014, P 14 C EUR CHAPT ASS, P414
   Franco-Salvador M., 2013, LNCS, V7814, P710
   Franco-Salvador M, 2016, INFORM PROCESS MANAG, V52, P550, DOI 10.1016/j.ipm.2015.12.004
   Gabrilovich E, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1606
   Gupta P., 2014, 37 INT ACM SIGIR C R, P677
   Gupta P., 2012, LNCS, V7488, P67
   Gupta P, 2015, TECHNICAL REPORT
   Hassan S, 2011, P 25 AAAI C ART INT, P884
   Hermann KM, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P58
   Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647
   HUANG PS, 2013, P CIKM, P2333
   JACKSON DA, 1989, AM NAT, V133, P436, DOI 10.1086/284927
   Lauly S., 2014, ADV NEURAL INFORM PR
   Lauly Stanislas, 2014, ABS14011803 CORR
   Maurer H, 2006, J UNIVERS COMPUT SCI, V12, P1050
   McNamee P, 2004, INFORM RETRIEVAL, V7, P73, DOI 10.1023/B:INRT.0000009441.78971.be
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Mikolov T., 2013, P 1 INT C LEARN REPR, P1
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Och F. J., 2003, Computational Linguistics, V29, P19, DOI 10.1162/089120103321337421
   Pereira RC, 2010, LECT NOTES COMPUT SC, V6360, P15, DOI 10.1007/978-3-642-15998-5_4
   Pinto D, 2009, J ALGORITHMS, V64, P51, DOI 10.1016/j.jalgor.2009.02.005
   Platt J. C., 2010, EMNLP, P251
   Potthast M., 2010, 2 INT COMP PLAG DET
   Potthast M., 2011, 3 INT COMP PLAG DET
   Potthast M., 2010, P 23 INT C COMP LING
   Potthast M., 2014, 6 INT COMP PLAG DET, P845
   Potthast M, 2008, LECT NOTES COMPUT SC, V4956, P522
   Potthast M, 2011, LANG RESOUR EVAL, V45, P45, DOI 10.1007/s10579-009-9114-z
   Salakhutdinov R, 2009, INT J APPROX REASON, V50, P969, DOI 10.1016/j.ijar.2008.11.006
   Shen YL, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P373, DOI 10.1145/2567948.2577348
   Steinberger Ralf., 2006, P 5 INT C LANGUAGE R
   Yih W.-t., 2011, P P 15 C COMP NAT LA, P247
NR 44
TC 35
Z9 38
U1 1
U2 18
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 NOV 1
PY 2016
VL 111
BP 87
EP 99
DI 10.1016/j.knosys.2016.08.004
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DY7TH
UT WOS:000385331700008
OA Green Published
DA 2022-08-23
ER

PT J
AU Shang, Y
   Tian, Y
   Zhou, M
   Zhou, TS
   Lyu, KW
   Wang, ZX
   Xin, R
   Liang, TB
   Zhu, SQ
   Li, JS
AF Shang, Yong
   Tian, Yu
   Zhou, Min
   Zhou, Tianshu
   Lyu, Kewei
   Wang, Zhixiao
   Xin, Ran
   Liang, Tingbo
   Zhu, Shiqiang
   Li, Jingsong
TI EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of
   Non-Used Information Buried in Routine Clinical Practice
SO IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
LA English
DT Article
DE Bioinformatics; Licenses; 6G mobile communication; Knowledge graph;
   electronic health record; ontology; non-used information
ID CHRONIC KIDNEY-DISEASE; MANAGEMENT
AB Non-used clinical information has negative implications on healthcare quality. Clinicians pay priority attention to clinical information relevant to their specialties during routine clinical practices but may be insensitive or less concerned about information showing disease risks beyond their specialties, resulting in delayed and missed diagnoses or improper management. In this study, we introduced an electronic health record (EHR)-oriented knowledge graph system to efficiently utilize non-used information buried in EHRs. EHR data were transformed into a semantic patient-centralized information model under the ontology structure of a knowledge graph. The knowledge graph then creates an EHR data trajectory and performs reasoning through semantic rules to identify important clinical findings within EHR data. A graphical reasoning pathway illustrates the reasoning footage and explains the clinical significance for clinicians to better understand the neglected information. An application study was performed to evaluate unconsidered chronic kidney disease (CKD) reminding for non-nephrology clinicians to identify important neglected information. The study covered 71,679 patients in non-nephrology departments. The system identified 2,774 patients meeting CKD diagnosis criteria and 10,377 patients requiring high attention. A follow-up study of 5,439 patients showed that 82.1% of patients who met the diagnosis criteria and 61.4% of patients requiring high attention were confirmed to be CKD positive during follow-up research. The application demonstrated that the proposed approach is feasible and effective in clinical information utilization. Additionally, it's valuable as an explainable artificial intelligence to provide interpretable recommendations for specialist physicians to understand the importance of non-used data and make comprehensive decisions.
C1 [Shang, Yong; Tian, Yu; Zhou, Tianshu; Lyu, Kewei; Wang, Zhixiao; Li, Jingsong] Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Engn Res Ctr EMR & Intelligent Expert Syst, Minist Educ, Hangzhou 310027, Peoples R China.
   [Zhou, Min; Liang, Tingbo] Zhejiang Univ, Coll Med, Affiliated Hosp 1, Hangzhou 310027, Peoples R China.
   [Xin, Ran; Zhu, Shiqiang; Li, Jingsong] Zhejiang Lab, Hangzhou 310027, Peoples R China.
RP Li, JS (通讯作者)，Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Engn Res Ctr EMR & Intelligent Expert Syst, Minist Educ, Hangzhou 310027, Peoples R China.; Liang, TB (通讯作者)，Zhejiang Univ, Coll Med, Affiliated Hosp 1, Hangzhou 310027, Peoples R China.; Zhu, SQ (通讯作者)，Zhejiang Lab, Hangzhou 310027, Peoples R China.
EM yshang6618@126.com; ty.1987823@163.com; zyzm1993@163.com;
   alexzhou@163.com; kwlyu@zju.edu.cn; zxwang6890@gmail.com;
   xinran@zhejianglab.com; liangtingbo@zju.edu.cn; zhusq@zhejianglab.com;
   ljs@zju.edu.cn
OI Zhang, Qi/0000-0002-6096-0690
FU Major Scientific Project of Zhejiang Lab [2020ND8AD01]; National Natural
   Science Foundation of China [81771936, 81801796]; National Key Research
   and Development Program of China [2018YFC0116901]
FX This work was supported in part by the Major Scientific Project of
   Zhejiang Lab under Grant 2020ND8AD01, in part by the National Natural
   Science Foundation of China under Grants 81771936 and 81801796, in part
   by the National Key Research and Development Program of China under
   Grant 2018YFC0116901.
CR Allen AS, 2011, J GEN INTERN MED, V26, P386, DOI 10.1007/s11606-010-1523-6
   [Anonymous], 2019, BOOK OHDSI
   Audetat MC, 2017, MED TEACH, V39, P797, DOI 10.1080/0142159X.2017.1331034
   Barbour SJ, 2019, JAMA INTERN MED, V179, P942, DOI 10.1001/jamainternmed.2019.0600
   Boulware LE, 2006, AM J KIDNEY DIS, V48, P192, DOI 10.1053/j.ajkd.2006.04.073
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Choi Edward, 2016, JMLR Workshop Conf Proc, V56, P301
   Cole NI, 2018, BMC NEPHROL, V19, DOI 10.1186/s12882-018-0882-9
   Devarakonda MV, 2017, INT J MED INFORM, V105, DOI 10.1016/j.ijmedinf.2017.05.015
   Eckardt KU, 2012, KIDNEY INT SUPPL, V2, P7, DOI 10.1038/kisup.2012.8
   El Fadly AbdenNaji, 2011, J Biomed Inform, V44 Suppl 1, pS94, DOI 10.1016/j.jbi.2011.07.007
   Fensel D, 2020, KNOWLEDGE GRAPHS MET, P2
   Fox CH, 2006, J AM BOARD FAM MED, V19, P54, DOI 10.3122/jabfm.19.1.54
   Gardner RL, 2019, J AM MED INFORM ASSN, V26, P106, DOI 10.1093/jamia/ocy145
   Glimm B, 2014, J AUTOM REASONING, V53, P245, DOI 10.1007/s10817-014-9305-1
   Goebel R, 2018, LECT NOTES COMPUT SC, V11015, P295, DOI 10.1007/978-3-319-99740-7_21
   Goetz GD, 2012, AM J MANAG CARE, V18, P48
   Gyrard A., 2018, PERSONALIZED HLTH KN
   Hagras H, 2018, COMPUTER, V51, P28, DOI 10.1109/MC.2018.3620965
   Haynes RB, 1997, CAN MED ASSOC J, V157, P788
   Israni RK, 2009, AM J KIDNEY DIS, V54, P238, DOI 10.1053/j.ajkd.2009.01.258
   Johnson DW, 2013, NEPHROLOGY, V18, P340, DOI 10.1111/nep.12052
   Jones KH, 2017, INT J MED INFORM, V97, P43, DOI 10.1016/j.ijmedinf.2016.09.010
   Karnick M, 2008, IEEE IJCNN, P3455, DOI 10.1109/IJCNN.2008.4634290
   Klann JG, 2018, J AM MED INFORM ASSN, V25, P1331, DOI 10.1093/jamia/ocy093
   Koren RR, 2017, ISR J HEALTH POLICY, V6, DOI 10.1186/s13584-017-0173-0
   Levin A, 2013, KIDNEY INT SUPPL, V3, P5, DOI 10.1038/kisup.2012.77
   Makino M, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-48263-5
   Pacaci A, 2018, FRONT PHARMACOL, V9, DOI 10.3389/fphar.2018.00435
   Perotte A, 2015, J AM MED INFORM ASSN, V22, P872, DOI 10.1093/jamia/ocv024
   Ramnarayan Padmanabhan, 2006, BMC Med Inform Decis Mak, V6, P37, DOI 10.1186/1472-6947-6-37
   Romagnani P, 2017, NAT REV DIS PRIMERS, V3, DOI 10.1038/nrdp.2017.88
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Rutten FH, 2006, EUR J HEART FAIL, V8, P706, DOI 10.1016/j.ejheart.2006.01.010
   Seneviratne O, 2018, LECT NOTES COMPUT SC, V11137, P223, DOI 10.1007/978-3-030-00668-6_14
   Shaw T, 2019, MED J AUSTRALIA, V210, pS17, DOI 10.5694/mja2.50022
   Shen Y, 2019, J MED SYST, V43, DOI 10.1007/s10916-019-1182-z
   Sheth A., 2014, P WORKSH PERS DAT AN P WORKSH PERS DAT AN
   Tang B, 2005, SURGERY, V138, P14, DOI 10.1016/j.surg.2005.02.014
   Tao C, 2013, J AM MED INFORM ASSN, V20, P554, DOI 10.1136/amiajnl-2012-001326
   Vassalotti JA, 2016, AM J MED, V129, P153, DOI 10.1016/j.amjmed.2015.08.025
   Wang F, 2019, JAMA INTERN MED, V179, P293, DOI 10.1001/jamainternmed.2018.7117
   Wang M., 2017, ARXIV171005980
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Zhang LX, 2012, LANCET, V379, P815, DOI 10.1016/S0140-6736(12)60033-6
   Zhang R, 2014, J BIOMED INFORM, V49, P134, DOI 10.1016/j.jbi.2014.01.004
NR 46
TC 0
Z9 0
U1 12
U2 28
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2168-2194
EI 2168-2208
J9 IEEE J BIOMED HEALTH
JI IEEE J. Biomed. Health Inform.
PD JUL
PY 2021
VL 25
IS 7
BP 2463
EP 2475
DI 10.1109/JBHI.2021.3085003
PG 13
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Mathematical & Computational Biology;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Mathematical & Computational Biology; Medical
   Informatics
GA TQ5SZ
UT WOS:000678341200012
PM 34057901
OA hybrid
DA 2022-08-23
ER

PT J
AU Fang, BF
   Mei, GF
   Yuan, XH
   Wang, L
   Wang, ZJ
   Wang, JY
AF Fang, Baofu
   Mei, Gaofei
   Yuan, Xiaohui
   Wang, Le
   Wang, Zaijun
   Wang, Junyang
TI Visual SLAM for robot navigation in healthcare facility
SO PATTERN RECOGNITION
LA English
DT Article
DE COVID-19 pandemic; Visual SLAM; Dynamic scenes; Semantic descriptors;
   Knowledge graph
AB The COVID-19 pandemic has affected many countries, posing a threat to human health and safety, and putting tremendous pressure on the medical system. This paper proposes a novel SLAM technology us ing RGB and depth images to improve hospital operation efficiency, reduce the risk of doctor-patient cross-infection, and curb the spread of the COVID-19. Most current visual SLAM researches assume that the environment is stationary, which makes handling real-world scenarios such as hospitals a challenge. This paper proposes a method that effectively deals with SLAM problems for scenarios with dynamic objects, e.g., people and movable objects, based on the semantic descriptor extracted from images with help of a knowledge graph. Specifically, our method leverages a knowledge graph to construct a priori movement relationship between entities and establishes high-level semantic information. Built upon this knowledge graph, a semantic descriptor is constructed to describe the semantic information around key points, which is rotation-invariant and robust to illumination. The seamless integration of the knowledge graph and semantic descriptor helps eliminate the dynamic objects and improves the accuracy of track ing and positioning of robots in dynamic environments. Experiments are conducted using data acquired from healthcare facilities, and semantic maps are established to meet the needs of robots for delivering medical services. In addition, to compare with the state-of-the-art methods, a publicly available dataset is used in our evaluation. Compared with the state-of-the-art methods, our proposed method demonstrated great improvement with respect to both accuracy and robustness in dynamic environments. The computational efficiency is also competitive. (c) 2021 Elsevier Ltd. All rights reserved.
C1 [Fang, Baofu; Mei, Gaofei; Wang, Le; Wang, Junyang] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China.
   [Fang, Baofu] Hefei Univ Technol, Intelligent Interconnected Syst Lab Anhui Prov, Hefei 230009, Peoples R China.
   [Fang, Baofu] Hefei Univ Technol, Anhui Prov Key Lab Affect Comp & Adv Intelligent, Hefei 230009, Peoples R China.
   [Wang, Zaijun] Civil Aviat Flight Univ China, Key Lab Flight Tech & Flight Safety, Guanghan 618307, Peoples R China.
   [Yuan, Xiaohui] Univ North Texas, Dept Comp Sci & Engn, Denton, TX 76207 USA.
RP Yuan, XH (通讯作者)，Univ North Texas, Dept Comp Sci & Engn, Denton, TX 76207 USA.
EM xiaohui.yuan@unt.edu
OI Yuan, Xiaohui/0000-0001-6897-4563
FU special fund for Basic Scientific Research in Central Colleges and
   Universities [ACAIM190102]; Project of Collaborative Innovation in Anhui
   Colleges and Universities [GXXT-2019-003]; Key Laboratory of Flight
   Techniques and Flight Safety [2018KF06]; Scientific Research Project of
   Civil Aviation Flight University of China [J2020-125]; Key Laboratory of
   Flight Techniques and Flight Safety, CAAC [FZ2020KF02]
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 by the special fund for Basic Scientific Research in
   Central Colleges and Universities (Grant No. ACAIM190102), the Project
   of Collaborative Innovation in Anhui Colleges and Universities (Grant
   No. GXXT-2019-003), the Open Fund of Key Laboratory of Flight Techniques
   and Flight Safety (Grant No.2018KF06), Scientific Research Project of
   Civil Aviation Flight University of China (Grant No.J2020-125) and Open
   Fund of Key Laboratory of Flight Techniques and Flight Safety, CAAC
   (Grant No. FZ2020KF02). We would like to express our gratitude to all
   those who helped us during the writing of this paper. We acknowledge the
   help of Professor H. Wang, who has offered suggestions in academic
   studies. We awe a special debt of gratitude to the Hospital of the Hefei
   University of Technology, from where we can get the data of hospital
   environment data. We thank doctor Gao, who is vice director of the
   Hospital of the Hefei University of Technology, and chief nurse, Ms.
   Zhang for their reasonable and wise advice in the healthcare field.
   Finally, thanks to all the partners of the lab for their company and
   support.
CR Alonso I, 2019, IEEE INT CONF ROBOT, P4717, DOI 10.1109/ICRA.2019.8793923
   Badrinarayanan V, 2017, IEEE T PATTERN ANAL, V39, P2481, DOI 10.1109/TPAMI.2016.2644615
   Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014
   Bescos B, 2018, IEEE ROBOT AUTOM LET, V3, P4076, DOI 10.1109/LRA.2018.2860039
   Calonder M, 2010, LECT NOTES COMPUT SC, V6314, P778, DOI 10.1007/978-3-642-15561-1_56
   Chen LC, 2018, IEEE T PATTERN ANAL, V40, P834, DOI 10.1109/TPAMI.2017.2699184
   Ding YL, 2017, IEEE SYS MAN CYBERN, P3457, DOI 10.1109/SMC.2017.8123165
   Engel J, 2014, LECT NOTES COMPUT SC, V8690, P834, DOI 10.1007/978-3-319-10605-2_54
   Fang BF, 2019, IEEE ACCESS, V7, P46104, DOI 10.1109/ACCESS.2019.2909307
   Gu JX, 2018, PATTERN RECOGN, V77, P354, DOI 10.1016/j.patcog.2017.10.013
   He KM, 2017, IEEE I CONF COMP VIS, P2980, DOI 10.1109/ICCV.2017.322
   Hu ZL, 2020, MECH SYST SIGNAL PR, V145, DOI 10.1016/j.ymssp.2020.106922
   Hu ZL, 2018, PATTERN RECOGN, V83, P134, DOI 10.1016/j.patcog.2018.05.014
   Kaneko M, 2018, IEEE COMPUT SOC CONF, P371, DOI 10.1109/CVPRW.2018.00063
   Kim DH, 2016, IEEE T ROBOT, V32, P1565, DOI 10.1109/TRO.2016.2609395
   Klappstein J, 2009, LECT NOTES COMPUT SC, V5414, P611
   Klein George, 2007, P1
   Li SL, 2017, IEEE ROBOT AUTOM LET, V2, P2263, DOI 10.1109/LRA.2017.2724759
   Lin TY, 2014, LECT NOTES COMPUT SC, V8693, P740, DOI 10.1007/978-3-319-10602-1_48
   Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94
   Matterport, MASK R CNN OBJECT DE
   McCormac John, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA), P4628, DOI 10.1109/ICRA.2017.7989538
   Montiel J.M.M., 2017, 2017 EUR C MOB ROB E, P1
   Munoz-Salinas R, 2020, PATTERN RECOGN, V101, DOI 10.1016/j.patcog.2019.107193
   Munoz-Salinas R, 2019, PATTERN RECOGN, V86, P156, DOI 10.1016/j.patcog.2018.09.003
   Mur-Artal R, 2017, IEEE T ROBOT, V33, P1255, DOI 10.1109/TRO.2017.2705103
   Reddy ND, 2015, IEEE INT C INT ROBOT, P1897, DOI 10.1109/IROS.2015.7353626
   Rosin PL, 1999, COMPUT VIS IMAGE UND, V73, P291, DOI 10.1006/cviu.1998.0719
   Rosten E, 2006, LECT NOTES COMPUT SC, V3951, P430, DOI 10.1007/11744023_34
   Rublee E, 2011, IEEE I CONF COMP VIS, P2564, DOI 10.1109/ICCV.2011.6126544
   Salas-Moreno RF, 2013, PROC CVPR IEEE, P1352, DOI 10.1109/CVPR.2013.178
   Sturm J, 2012, IEEE INT C INT ROBOT, P573, DOI 10.1109/IROS.2012.6385773
   Sun YX, 2017, ROBOT AUTON SYST, V89, P110, DOI 10.1016/j.robot.2016.11.012
   Tan W, 2013, INT SYM MIX AUGMENT, P209, DOI 10.1109/ISMAR.2013.6671781
   Wang K, 2019, IEEE INT CONF ROBOT, P5224, DOI 10.1109/ICRA.2019.8793499
   Wang RZ, 2019, REMOTE SENS-BASEL, V11, DOI 10.3390/rs11101143
   Whelan T, 2016, INT J ROBOT RES, V35, P1697, DOI 10.1177/0278364916669237
   Yang G, 2020, CHIN J MECH ENG-EN, V33, DOI 10.1186/s10033-020-00464-0
   Ye RZ, 2021, CHEST, V159, P270, DOI 10.1016/j.chest.2020.06.068
   Yu C, 2018, IEEE INT C INT ROBOT, P1168, DOI 10.1109/IROS.2018.8593691
   Yuan XH, 2017, IEEE-CAA J AUTOMATIC, V4, P677, DOI 10.1109/JAS.2017.7510625
   Zhao C, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093099
   Zhuang C., 2020, INT C URB INT APPL T, P172
NR 43
TC 11
Z9 12
U1 10
U2 70
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 MAY
PY 2021
VL 113
DI 10.1016/j.patcog.2021.107822
EA JAN 2021
PG 12
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QT0FP
UT WOS:000626268600010
PM 33495660
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Wang, F
   Zhang, LL
   Chen, XC
   Wang, ZM
   Xu, X
AF Wang, Feng
   Zhang, Lingling
   Chen, Xingchen
   Wang, Ziming
   Xu, Xin
TI A personalized self-learning system based on knowledge graph and
   differential evolution algorithm
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article
DE differential evolution algorithm; knowledge graph; learning path;
   self-learning system
AB Discovering the most adaptive learning path and content is an urgent issue for nowadays e-learning environment, for achieving learning goals efficiently and effectively. The main challenge of building this system is to provide appropriate educational guide and resource for different learners with respective interests and knowledge base. In order to reduce people's cognitive overload and fulfill their self-learning requirements, this article proposes a framework for a self-learning system. The system is design to be closed and updated automatically, in which learning path is discovered based on differential evolution (DE) algorithm and knowledge graph. The output of the system includes: (1) the personalized learning path adapted to learner's specific needs; (2) learning resource recommendation matching the learning path; (3) test results of learners' learning effect after following the learning path and resources recommendation; (4) revised learning path and resources recommendation according to learner's evaluation. Experimental results show that the system based on DE algorithm and disciplinary knowledge graph is feasible in optimal learning path discovery and further learning resources recommendation.
C1 [Wang, Feng] Kunming Univ Sci & Technol, Sch Management & Econ, Kunming, Yunnan, Peoples R China.
   [Zhang, Lingling] Univ Chinese Acad Sci, Sch Econ & Management, 19A Yuquan Rd, Beijing 100049, Peoples R China.
   [Chen, Xingchen] Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China.
   [Wang, Ziming] Chinese Acad Sci, Inst Zool, Beijing, Peoples R China.
   [Xu, Xin] Hong Kong Polytech Univ, Dept Management & Mkt, Hong Kong, Peoples R China.
RP Zhang, LL (通讯作者)，Univ Chinese Acad Sci, Sch Econ & Management, 19A Yuquan Rd, Beijing 100049, Peoples R China.
EM zll933@163.com
RI Xu, Xin/AFK-1224-2022; Xu, Xin/AAF-8379-2022
OI Xu, Xin/0000-0001-6143-6471; Xu, Xin/0000-0001-6143-6471
FU Key Laboratory of Big Data Mining and Knowledge Management at the
   University of Chinese Academy of Sciences; National Natural Science
   Foundation of China [71471169, 91546201]
FX The authors of this work would like to thank Professor Zhang Lingling
   and Key Laboratory of Big Data Mining and Knowledge Management at the
   University of Chinese Academy of Sciences for their valuable
   contributions. This work is supported by the National Natural Science
   Foundation of China (Grant No. 71471169 and No. 91546201).
CR Ahmad Kardan, 2013, 4th International Conference on e-Learning and e-Teaching (ICELET 2013), P20, DOI 10.1109/ICELET.2013.6681639
   Alkhattabi M, 2011, COMPUT HUM BEHAV, V27, P862, DOI 10.1016/j.chb.2010.11.011
   Cai XJ, 2020, CONCURR COMP-PRACT E, V32, DOI 10.1002/cpe.5478
   Cavus N, 2010, ADV ENG SOFTW, V41, P248, DOI 10.1016/j.advengsoft.2009.07.009
   Chen ZX, 2006, INT J INF TECH DECIS, V5, P703, DOI 10.1142/S0219622006002271
   Chun-Hsiung Lee, 2008, WSEAS Transactions on Information Science and Applications, V5, P320
   Cui ZH, 2019, SCI CHINA INFORM SCI, V62, DOI 10.1007/s11432-018-9729-5
   Cui ZH, 2018, IEEE T IND INFORM, V14, P3187, DOI 10.1109/TII.2018.2822680
   Cui ZH, 2017, J PARALLEL DISTR COM, V103, P42, DOI 10.1016/j.jpdc.2016.10.011
   Gaeta A, 2009, PERS UBIQUIT COMPUT, V13, P207, DOI 10.1007/s00779-007-0183-y
   Hassan MU, 2020, IEEE COMMUN SURV TUT, V22, P746, DOI 10.1109/COMST.2019.2944748
   Hsieh TC, 2010, EXPERT SYST APPL, V37, P4156, DOI 10.1016/j.eswa.2009.11.007
   [刘萌 Liu Meng], 2016, [计算机仿真, Computer Simulation], V33, P180
   Liu Y., 2018, J HENAN I SCI TECHNO, V46, P40
   Ma L., 2017, INFORM SCIENTIST, V35, P56
   Storn R, 1997, J GLOBAL OPTIM, V11, P341, DOI 10.1023/A:1008202821328
   [孙文平 Sun Wenping], 2019, [计算机科学, Computer Science], V46, P56
   Ul Hassan M, 2020, IEEE T SERV COMPUT, V13, P263, DOI 10.1109/TSC.2019.2947471
   Wang GG, 2020, IEEE T EMERG TOP COM, V8, P20, DOI 10.1109/TETC.2017.2703784
   Wang PH, 2020, CONCURR COMP-PRACT E, V32, DOI 10.1002/cpe.5464
   Wang PH, 2019, MATHEMATICS-BASEL, V7, DOI 10.3390/math7020184
   Wang TI, 2008, EXPERT SYST APPL, V34, P2449, DOI 10.1016/j.eswa.2007.04.014
   Wang YC, 2019, MATHEMATICS-BASEL, V7, DOI 10.3390/math7020135
   Wu L., 2016, SYSTEM SCI MATH, V36, P2272
   Yaghmaie M, 2011, EXPERT SYST APPL, V38, P3280, DOI 10.1016/j.eswa.2010.08.113
   Zafra A, 2011, EXPERT SYST APPL, V38, P15020, DOI 10.1016/j.eswa.2011.05.044
   Zhang MQ, 2018, MEMET COMPUT, V10, P199, DOI 10.1007/s12293-017-0237-2
NR 27
TC 0
Z9 0
U1 20
U2 42
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 APR 10
PY 2022
VL 34
IS 8
SI SI
AR e6190
DI 10.1002/cpe.6190
EA JUL 2021
PG 12
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZS1FJ
UT WOS:000669428500001
DA 2022-08-23
ER

PT J
AU Fang, A
   Lou, P
   Hu, JH
   Zhao, WQ
   Feng, M
   Ren, HL
   Chen, XL
AF Fang, An
   Lou, Pei
   Hu, Jiahui
   Zhao, Wanqing
   Feng, Ming
   Ren, Huiling
   Chen, Xianlai
TI Head and Tail Entity Fusion Model in Medical Knowledge Graph
   Construction: Case Study for Pituitary Adenoma
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE knowledge graph; pituitary adenoma; entity fusion; similarity
   calculation
AB Background: Pituitary adenoma is one of the most common central nervous system tumors. The diagnosis and treatment of pituitary adenoma remain very difficult. Misdiagnosis and recurrence often occur, and experienced neurosurgeons are in serious shortage. A knowledge graph can help interns quickly understand the medical knowledge related to pituitary tumor.
   Objective: The aim of this study was to develop a data fusion method suitable for medical data using data of pituitary adenomas integrated from different sources. The overall goal was to construct a knowledge graph for pituitary adenoma (KGPA) to be used for knowledge discovery.
   Methods: A complete framework suitable for the construction of a medical knowledge graph was developed, which was used to build the KGPA. The schema of the KGPA was manually constructed. Information of pituitary adenoma was automatically extracted from Chinese electronic medical records (CEMRs) and medical websites through a conditional random field model and newly designed web wrappers. An entity fusion method is proposed based on the head-and-tail entity fusion model to fuse the data from heterogeneous sources.
   Results: Data were extracted from 300 CEMRs of pituitary adenoma and 4 health portals. Entity fusion was carried out using the proposed data fusion model. The F1 scores of the head and tail entity fusions were 97.32% and 98.57%, respectively. Triples from the constructed KGPA were selected for evaluation, demonstrating 95.4% accuracy.
   Conclusions: This paper introduces an approach to fuse triples extracted from heterogeneous data sources, which can be used to build a knowledge graph. The evaluation results showed that the data in the KGPA are of high quality. The constructed KGPA can help physicians in clinical practice.
C1 [Fang, An] Cent South Univ, Life Sci Coll, Changsha, Peoples R China.
   [Fang, An; Lou, Pei; Hu, Jiahui; Zhao, Wanqing; Ren, Huiling] Chinese Acad Med Sci, Inst Med Informat, Beijing, Peoples R China.
   [Feng, Ming] Chinese Acad Med Sci, Peking Union Med Coll Hosp, Peking Union Med Coll, Beijing, Peoples R China.
   [Chen, Xianlai] Cent South Univ, Big Data Inst, 932 South Lushan Rd, Changsha 410083, Peoples R China.
   [Chen, Xianlai] Cent South Univ, Natl Engn Lab Med Big Data Applicat Technol, Changsha, Peoples R China.
RP Chen, XL (通讯作者)，Cent South Univ, Big Data Inst, 932 South Lushan Rd, Changsha 410083, Peoples R China.
EM chenxianlai@csu.edu.cn
OI Chen, Xianlai/0000-0002-4338-015X; Pei, Lou/0000-0003-1426-670X; Hu,
   Jiahui/0000-0003-4352-3250
FU Science and Technology Innovation 2030-Major Project [2020AAA0104902];
   Chinese Academy of Medical Sciences Initiative for Innovative Medicine
   [2017-I2M-3-014]; Chinese Academy of Medical Sciences and Peking Union
   Medical College Fundamental Scientific Research Funds Project of the
   Central Public Welfare Research Institution [2018PT33005]; Hunan
   Provincial Key Research and Development Program [2020SK2089]
FX This research has been funded by the Science and Technology Innovation
   2030-Major Project (2020AAA0104902), the Chinese Academy of Medical
   Sciences Initiative for Innovative Medicine (2017-I2M-3-014), the
   Chinese Academy of Medical Sciences and Peking Union Medical College
   Fundamental Scientific Research Funds Project of the Central Public
   Welfare Research Institution (2018PT33005), and the Hunan Provincial Key
   Research and Development Program (2020SK2089).
CR [Anonymous], Unified Medical Language System (UMLS)
   Balaur I, 2017, BIOINFORMATICS, V33, P1096, DOI 10.1093/bioinformatics/btw731
   [奥德玛 Byambasuren Odmaa], 2019, [中文信息学报, Journal of Chinese Information Processing], V33, P1
   Devlin J, 2018, BERT PRETRAIN ING DE BERT PRETRAIN ING DE
   Ernst P, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0549-5
   Fu Yang, 2020, Journal of Wuhan University (Natural Science Edition), V66, P261, DOI 10.14188/j.1671-8836.2018.0217
   Gong F, 2019, BMC MED INFORM DECIS, V19, DOI 10.1186/s12911-019-0771-6
   [胡佳慧 Hu Jiahui], 2019, [数据分析与知识发现, Data Analysis and Knowledge Discovery], V3, P123
   Kaushik C, 2010, J COMPUT ASSIST TOMO, V34, P612, DOI 10.1097/RCT.0b013e3181dbe5d1
   Kim JP, 2008, J KOREAN NEUROSURG S, V43, P246, DOI 10.3340/jkns.2008.43.5.246
   Kinoshita Y, 2014, CLIN ENDOCRINOL, V80, P720, DOI 10.1111/cen.12349
   Li LF, 2020, ARTIF INTELL MED, V103, DOI 10.1016/j.artmed.2020.101817
   Li N, 2020, BMC MED INFORM DECIS, V20, DOI 10.1186/s12911-020-1112-5
   Li WD, 2019, IEEE ACCESS, V7, P157960, DOI 10.1109/ACCESS.2019.2950230
   Lin HL, 2017, PROCEDIA COMPUT SCI, V108, P345, DOI 10.1016/j.procs.2017.05.045
   [刘旭利 Liu Xuli], 2020, [中文信息学报, Journal of Chinese Information Processing], V34, P37
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Osamura RY, 2004, FRONT HORM RES, V32, P20
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Ruan T, 2017, J BIOMED SEMANT, V8, DOI 10.1186/s13326-017-0145-x
   Su X, 2019, IEEE T IND INFORM, V15, P5765, DOI 10.1109/TII.2019.2912175
   Sun Q, 2019, KNOWLEDGE EXTRACTION
   Uzuner O, 2011, J AM MED INFORM ASSN, V18, P552, DOI 10.1136/amiajnl-2011-000203
   Wang CY, 2020, J BIOMED INFORM, V111, DOI 10.1016/j.jbi.2020.103583
   Yang C, 2018, J MED INFORM, V39, P48
   Yang Jin-Feng, 2016, Journal of Software, V27, P2725, DOI 10.13328/j.cnki.jos.004880
   Zhang QG, 2020, IEEE ACCESS, V8, P165010, DOI 10.1109/ACCESS.2020.3023332
NR 27
TC 0
Z9 0
U1 12
U2 24
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 JUL
PY 2021
VL 9
IS 7
AR e28218
DI 10.2196/28218
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA TS9BJ
UT WOS:000679941500013
PM 34057414
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Bellomarini, L
   Benedetto, D
   Gottlob, G
   Sallinger, E
AF Bellomarini, Luigi
   Benedetto, Davide
   Gottlob, Georg
   Sallinger, Emanuel
TI Vadalog: A modern architecture for automated reasoning with large
   knowledge graphs
SO INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graphs; Reasoning; Query answering; Datalog; Vadalog
AB The introduction of novel Datalog +/- fragments with good theoretical properties, together with the growing use of enterprise knowledge graphs motivated the development of Vadalog, a knowledge graph management system developed at the University of Oxford. It adopts Warded Datalog +/- as the core of its language for knowledge representation and reasoning, which exhibits a very good tradeoff between computational complexity of reasoning and expressive power, capturing PTIME data complexity while allowing ontological reasoning and full recursion. In this paper, we provide a detailed illustration of the Vadalog system, presenting: the essentials of the first implementation of Warded Datalog +/-; a comprehensive overview of the architecture with specific focus on runtime execution model, memory management, graph traversal strategies and join algorithms; and a detailed experimental evaluation.
   This paper is a substantially expanded version of the AMW 2019 paper "Datalog-based reasoning for Knowledge Graphs". To stand apart from previous works on the topic, our focus in this work shall be a comprehensive presentation of the architecture of the Vadalog system and showing how our techniques work together to provide a full-fledged KGMS. In particular, roughly half of this paper is new material created particularly for this comprehensive architectural view. This includes a new series of experiments designed to shed light on architectural choices and alternatives. (C) 2020 Published by Elsevier Ltd.
C1 [Bellomarini, Luigi] Banca Italia, Rome, Italy.
   [Benedetto, Davide] Univ Roma Tre, Rome, Italy.
   [Gottlob, Georg; Sallinger, Emanuel] TU Wien, Vienna, Austria.
   [Gottlob, Georg; Sallinger, Emanuel] Univ Oxford, Oxford, England.
RP Sallinger, E (通讯作者)，TU Wien, Fac Informat, Knowledge Graph Lab, Database & Artificial Intelligence Grp,Inst Log &, A-1040 Vienna, Austria.
EM sallinger@dbai.tuwien.ac.at
FU EPSRC, UK [EP/M025268/1]; EU [809965]; Vienna Science and Technology
   Fund (WWTF), Austria [VRG18-013]
FX The work on this paper was supported by EPSRC, UK programme grant
   EP/M025268/1 VADA, the EU H2020 grant 809965, and the Vienna Science and
   Technology Fund (WWTF), Austria grant VRG18-013.
CR Abiteboul S., 1995, FDN DATABASES LOGICA
   Aref M, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1371, DOI 10.1145/2723372.2742796
   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
   Arocena PC, 2015, PROC VLDB ENDOW, V9, P108
   Baget J., 2011, P 22 INT JOINT C ART, P712, DOI DOI 10.5591/978-1-57735-516-8/IJCAI11-126
   Baget J.-F., 2010, P KR, P466
   Baget JF, 2015, LECT NOTES COMPUT SC, V9202, P328, DOI 10.1007/978-3-319-21542-6_21
   Barcelo P., 2012, LECT NOTES COMPUTER, V7494
   Bellomarini L., 2019, AMW
   Bellomarini L, 2019, PROC INT CONF DATA, P26, DOI 10.1109/ICDE.2019.00011
   Bellomarini L, 2018, PROC VLDB ENDOW, V11, P975, DOI 10.14778/3213880.3213888
   Bellomarini L, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2
   Benedikt M, 2017, PODS'17: PROCEEDINGS OF THE 36TH ACM SIGMOD-SIGACT-SIGAI SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, P37, DOI 10.1145/3034786.3034796
   Benedikt M, 2015, PROC VLDB ENDOW, V8, P690, DOI 10.14778/2735703.2735708
   Berger G, 2019, PROCEEDINGS OF THE 38TH ACM SIGMOD-SIGACT-SIGAI SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS (PODS '19), P270, DOI 10.1145/3294052.3319688
   Blumauer A., 2016, TAXONOMIES ONTOLOGIE
   Bonifati A, 2016, 28TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT (SSDBM) 2016), DOI 10.1145/2949689.2949698
   BUSCHMANN F, 2007, PATTERN ORIENTED SOF
   Cali A, 2013, J ARTIF INTELL RES, V48, P115, DOI 10.1613/jair.3873
   Cali A, 2012, J WEB SEMANT, V14, P57, DOI 10.1016/j.websem.2012.03.001
   Cali A, 2012, ARTIF INTELL, V193, P87, DOI 10.1016/j.artint.2012.08.002
   Cali A, 2010, IEEE S LOG, P228, DOI 10.1109/LICS.2010.27
   Calvanese D, 2017, SEMANT WEB, V8, P471, DOI 10.3233/SW-160217
   Dessloch S, 2008, PROC INT CONF DATA, P1307, DOI 10.1109/ICDE.2008.4497540
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Fayzrakhmanov RR, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1095, DOI 10.1145/3178876.3186008
   Furche Tim, 2016, AMW
   Garcia-Molina H., 2008, DATABASE SYSTEMS COM
   GARLASCHELLI D, 2004, SCALE FREE TOPOLOGY
   Geerts F, 2014, PROC VLDB ENDOW, V7, P1565, DOI 10.14778/2733004.2733031
   Gottlob G, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P2999
   Graefe G., 1993, Proceedings. Ninth International Conference on Data Engineering (Cat. No.92CH3258-1), P209, DOI 10.1109/ICDE.1993.344061
   Hidalgo C.A., 2008, SCHOLARPEDIA, V3, P1716
   Kolaitis PG, 2014, PODS'14: PROCEEDINGS OF THE 33RD ACM SIGMOD-SIGACT-SIGART SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, P176, DOI 10.1145/2594538.2594544
   Kolaitis PG, 2018, THEOR COMPUT SYST, V62, P899, DOI 10.1007/s00224-017-9812-7
   Konstantinou N, 2017, SIGMOD'17: PROCEEDINGS OF THE 2017 ACM INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1599, DOI 10.1145/3035918.3058730
   Krotzsch M, 2016, J WEB SEMANT, V37-38, P53, DOI 10.1016/j.websem.2016.04.002
   Leone N., 2012, EFFICIENTLY COMPUTAB
   Leone N, 2019, ACM T COMPUT LOG, V20, DOI 10.1145/3308448
   Marx M., 2017, IJCAI, P1188
   Meier M, 2014, VLDB J, V23, P495, DOI 10.1007/s00778-013-0333-y
   Michels C, 2017, ACM-IEEE J CONF DIG, P319
   Motik B, 2014, AAAI CONF ARTIF INTE, P129
   Moustafa WE, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), P56, DOI 10.1109/BigData.2016.7840589
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Pichler R, 2009, PROC VLDB ENDOW, V2, P1606, DOI 10.14778/1687553.1687603
   Pichler R, 2013, THEOR COMPUT SYST, V52, P483, DOI 10.1007/s00224-012-9397-0
   Sallinger E., 2013, DATA EXCHANGE INFORM, V5, P97
   Schulz Stephan, 2013, Logic for Programming, Artificial Intelligence, and Reasoning. 19th International Conference, LPAR-19, Proceedings: LNCS 8312, P735, DOI 10.1007/978-3-642-45221-5_49
   Subotic P, 2018, PROC VLDB ENDOW, V12, P141, DOI 10.14778/3282495.3282500
   Theodorou V, 2017, DATA KNOWL ENG, V112, P1, DOI 10.1016/j.datak.2017.08.004
   Urbani J, 2016, AAAI CONF ARTIF INTE, P258
   VADA, 2016, PROJECT
NR 53
TC 1
Z9 1
U1 11
U2 15
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 MAR
PY 2022
VL 105
AR 101528
DI 10.1016/j.is.2020.101528
EA DEC 2021
PG 15
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YD3XN
UT WOS:000740349400007
DA 2022-08-23
ER

PT J
AU Elgendi, I
   Hossain, MF
   Jamalipour, A
   Munasinghe, KS
AF Elgendi, Ibrahim
   Hossain, Md Farhad
   Jamalipour, Abbas
   Munasinghe, Kumudu S.
TI Protecting Cyber Physical Systems Using a Learned MAPE-K Model
SO IEEE ACCESS
LA English
DT Article
DE Legitimate; malicious attacker; monitor; analysis; planning; execution;
   and knowledge base model; machine learning
ID INTRUSION DETECTION
AB Industry 4.0 leverages on cyber-physical systems (CPSs) that enable different physical sensors, actuators, and controllers to be interconnected via switches and cloud computing servers, forming complex online systems. Protecting these against advanced cyber threats is a primary concern for future application. Cyberattackers can impair such systems by producing different types of cyber threats, ranging from network attacks to CPS controller attacks, which could impose catastrophic damage to CPS infrastructure, companies, governments, and even the general public. This paper proposes a learned monitor, analyze, plan, execute, and knowledge (MAPE-K) base model as a method for supporting self-adaptation for the CPSs, ensuring reliability, flexibility, and protection against cyber threats. The model aims to gauge normal behavior in an industry environment and generate alarms to alert users to any abnormalities or threats. In turn, our evaluation shows 99.55% accuracy in detecting cyber threats.
C1 [Elgendi, Ibrahim; Munasinghe, Kumudu S.] Univ Canberra, Fac Educ Sci Technol & Math, Canberra, ACT 2601, Australia.
   [Hossain, Md Farhad] Bangladesh Univ Engn & Technol, Dept Elect & Elect Engn, Dhaka 1216, Bangladesh.
   [Jamalipour, Abbas] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia.
RP Elgendi, I (通讯作者)，Univ Canberra, Fac Educ Sci Technol & Math, Canberra, ACT 2601, Australia.
EM Ibrahim.Elgendi@canberra.edu.au
OI Munasinghe, Kumudu/0000-0002-0141-8973; Elgendi,
   Ibrahim/0000-0002-0849-3677
CR Afek Y, 2017, IEEE INFOCOM SER
   Arcaini P, 2015, 2015 IEEE/ACM 10TH INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS, P13, DOI 10.1109/SEAMS.2015.10
   Bellovin S., 2000, ICMP TRACEBACK MESSA
   Bianchi G, 2014, ACM SIGCOMM COMP COM, V44, P45
   Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199
   Chen YQ, 2018, P IEEE S SECUR PRIV, P648, DOI 10.1109/SP.2018.00016
   Clarke EM, 2011, LECT NOTES COMPUT SC, V6996, P1, DOI 10.1007/978-3-642-24372-1_1
   Dietterich T. G., 2002, Structural, Syntactic, and Statistical Pattern Recognition. Joint IAPR International Workshops SSPR 2002 and SPR 2002 (Lecture Notes in Computer Science Vol. 2396), P15
   Eddy W., 2007, 4987 RFC
   Fayaz SK, 2015, PROCEEDINGS OF THE 24TH USENIX SECURITY SYMPOSIUM, P817
   Ferguson AD, 2013, ACM SIGCOMM COMP COM, V43, P327, DOI 10.1145/2534169.2486003
   Ferguson P., 2000, RFC, DOI [10.17487/rfc2827, DOI 10.17487/RFC2827]
   Garcia-Teodoro P, 2009, COMPUT SECUR, V28, P18, DOI 10.1016/j.cose.2008.08.003
   Giotis Kostas, 2014, 2014 Third European Workshop on Software Defined Networks (EWSDN), P85, DOI 10.1109/EWSDN.2014.24
   Golait D, 2017, IEEE T INF FOREN SEC, V12, P730, DOI 10.1109/TIFS.2016.2632071
   Goldstein M, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0152173
   Han S, 2014, IEEE SYST J, V8, P1049, DOI 10.1109/JSYST.2013.2257594
   Hankel M., 2015, REFERENCE ARCHITECTU, P1
   i-SCOOP, IND 4 0 4 IND REV GU
   Industrial Internet Consortium, 2016, IND INT THINGS G
   Ioannidis J., 2002, P NETW DISTR SYST SE, P1
   Jazdi N, 2014, IEEE INT CONF AUTO
   Kephart JO, 2003, COMPUTER, V36, P41, DOI 10.1109/MC.2003.1160055
   Liao HJ, 2013, J NETW COMPUT APPL, V36, P16, DOI 10.1016/j.jnca.2012.09.004
   Lu N, 2014, CHINA COMMUN, V11, P136, DOI 10.1109/CC.2014.6969802
   Ma ZD, 2017, 2017 2ND IEEE EUROPEAN SYMPOSIUM ON SECURITY AND PRIVACY WORKSHOPS (EUROS&PW), P153, DOI 10.1109/EuroSPW.2017.65
   Mahajan R, 2002, ACM SIGCOMM COMP COM, V32, P62, DOI 10.1145/571697.571724
   Mazu Networks, 2003, SELF OPT NETW TRAFF
   Muccini H, 2016, PROCEEDINGS OF 2016 IEEE/ACM 11TH INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS), P75, DOI [10.1145/2897053.2897069, 10.1109/SEAMS.2016.016]
   Musil Angelika, 2017, PATTERNS SELF ADAPTA, P331, DOI [10.1007/978-3-319-56345-9_13, DOI 10.1007/978-3-319-56345-9_13]
   Priescu I, 2009, IACSIT-SC 2009: INTERNATIONAL ASSOCIATION OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY - SPRING CONFERENCE, P117, DOI 10.1109/IACSIT-SC.2009.64
   Sapankevych NL, 2009, IEEE COMPUT INTELL M, V4, P24, DOI 10.1109/MCI.2009.932254
   Savage S, 2000, ACM SIGCOMM COMP COM, V30, P295, DOI 10.1145/347057.347560
   Sen K, 2004, LECT NOTES COMPUT SC, V3114, P202
   Settanni Giuseppe, 2018, 2018 IEEE Industrial Cyber-Physical Systems (ICPS). Proceedings, P173, DOI 10.1109/ICPHYS.2018.8387655
   Shin S. W., 2013, P 2 ACM SIGCOMM WORK, P165, DOI [10.1145/2491185.2491220, DOI 10.1145/2491185.2491220]
   Snoeren AC, 2001, ACM SIGCOMM COMP COM, V31, P3, DOI 10.1145/964723.383060
   Soldo F, 2009, IEEE INFOCOM SER, P2446, DOI 10.1109/INFCOM.2009.5062172
   Stouffer K., 2011, SPECIAL PUBLICATION, V800-82
   Tauber M, 2010, 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshop (SASOW), P9, DOI 10.1109/SASOW.2010.40
   Ussath Martin, 2016, 2016 Annual Conference on Information Science and Systems (CISS), P181, DOI 10.1109/CISS.2016.7460498
   Wang W, 2013, 2013 22ND WIRELESS AND OPTICAL COMMUNICATIONS CONFERENCE (WOCC 2013), P410, DOI 10.1109/WOCC.2013.6676402
   Whitman M. E., 2012, PRINCIPLES INFORM SE
NR 43
TC 3
Z9 3
U1 1
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 90954
EP 90963
DI 10.1109/ACCESS.2019.2927037
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IM3AP
UT WOS:000477864400014
OA gold
DA 2022-08-23
ER

PT J
AU Qin, YF
   Ren, SH
   Shao, B
   Qin, H
   Wang, HD
   Li, GM
   Zhu, YL
   Sun, CL
   Li, C
   Zhang, JY
   Wang, H
AF Qin, Ya-Fei
   Ren, Shao-Hua
   Shao, Bo
   Qin, Hong
   Wang, Hong-da
   Li, Guang-Ming
   Zhu, Yang-Lin
   Sun, Cheng-Lu
   Li, Chuan
   Zhang, Jing-Yi
   Wang, Hao
TI The intellectual base and research fronts of IL-37: A bibliometric
   review of the literature from WoSCC
SO FRONTIERS IN IMMUNOLOGY
LA English
DT Review
DE IL-37; bibliometric analysis; visualization; CiteSpace; immunity
ID NF-KAPPA-B; INNATE INFLAMMATION; INTERLEUKIN-37 SUPPRESSES;
   SCIENTOMETRIC ANALYSIS; RHEUMATOID-ARTHRITIS; MULTIPLE-SCLEROSIS;
   ELEVATED SERUM; IN-VITRO; EXPRESSION; CYTOKINE
AB BackgroundIL-37 is a recently identified cytokine with potent immunosuppressive functions. The research fronts of IL-37 are worth investigating, and there is no bibliometric analysis in this field. The purpose of this study is to construct the intellectual base and predict research hotspots of IL-37 research both quantitatively and qualitatively according to bibliometric analysis. MethodsThe articles were downloaded from the Web of Science Core Collection (WoSCC) database from the inception of the database to 1 April 2022. CiteSpace 5.8.R3 (64-bit, Drexel University, Philadelphia, PA, USA) and Online Analysis Platform of Literature Metrology (https://bibliometric.com/) were used to perform bibliometric and knowledge-map analyses. ResultsA total of 534 papers were included in 200 academic journals by 2,783 authors in 279 institutions from 50 countries/regions. The journal Cytokine published the most papers on IL-37, while Nature Immunology was the most co-cited journal. The publications belonged mainly to two categories of Immunology and Cell Biology. USA and China were the most productive countries. Meanwhile, the University of Colorado Denver in USA produced the highest number of publications followed by Radboud University Nijmegen in the Netherlands and Monash University in Australia. Charles A. Dinarello published the most papers, while Marcel F. Nold had the most co-citations. Top 10 co-citations on reviews, mechanisms, and diseases were regarded as the knowledge base. The keyword co-occurrence and co-citations of references revealed that the mechanisms and immune-related disorders were the main aspects of IL-37 research. Notably, the involvement of IL-37 in various disorders and the additional immunomodulatory mechanisms were two emerging hotspots in IL-37 research. ConclusionsThe research on IL-37 was thoroughly reviewed using bibliometrics and knowledge-map analyses. The present study is a benefit for academics to master the dynamic evolution of IL-37 and point out the direction for future research.
C1 [Qin, Ya-Fei; Ren, Shao-Hua; Shao, Bo; Qin, Hong; Wang, Hong-da; Li, Guang-Ming; Zhu, Yang-Lin; Sun, Cheng-Lu; Li, Chuan; Zhang, Jing-Yi; Wang, Hao] Tianjin Med Univ Gen Hosp, Dept Gen Surg, Tianjin, Peoples R China.
   [Qin, Ya-Fei; Ren, Shao-Hua; Shao, Bo; Qin, Hong; Wang, Hong-da; Li, Guang-Ming; Zhu, Yang-Lin; Sun, Cheng-Lu; Li, Chuan; Zhang, Jing-Yi; Wang, Hao] Tianjin Med Univ Gen Hosp, Tianjin Gen Surg Inst, Tianjin, Peoples R China.
RP Wang, H (通讯作者)，Tianjin Med Univ Gen Hosp, Dept Gen Surg, Tianjin, Peoples R China.; Wang, H (通讯作者)，Tianjin Med Univ Gen Hosp, Tianjin Gen Surg Inst, Tianjin, Peoples R China.
EM hwangca272@hotmail.com
FU National Natural Science Foundation of China [82071802]; Science and
   Technology Project of Tianjin Health Commission [TJWJ2021MS004]; Tianjin
   Key Medical Discipline (Specialty) Construction Project; Natural Science
   Foundation of Tianjin [19JCYBJC27100]
FX This work was supported by grants to HW from the National Natural
   Science Foundation of China (No. 82071802), Science and Technology
   Project of Tianjin Health Commission (No. TJWJ2021MS004), Tianjin Key
   Medical Discipline (Specialty) Construction Project, and Natural Science
   Foundation of Tianjin (No. 19JCYBJC27100).
CR Ballak DB, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5711
   Bertran K, 2020, POULTRY SCI, V99, P6503, DOI 10.1016/j.psj.2020.09.017
   Boraschi D, 2011, EUR CYTOKINE NETW, V22, P127, DOI 10.1684/ecn.2011.0288
   Bufler P, 2002, P NATL ACAD SCI USA, V99, P13723, DOI 10.1073/pnas.212519099
   Bulau AM, 2014, P NATL ACAD SCI USA, V111, P2650, DOI 10.1073/pnas.1324140111
   Cavalli E, 2020, MOLECULES, V25, DOI 10.3390/molecules25010020
   Cavalli G, 2018, IMMUNOL REV, V281, P179, DOI 10.1111/imr.12605
   Cavalli G, 2016, RHEUMATOLOGY, V55, P2220, DOI 10.1093/rheumatology/kew325
   Chai M, 2017, J GERIATR CARDIOL, V14, P285, DOI 10.11909/j.issn.1671-5411.2017.05.013
   Chai M, 2015, J INTERF CYTOK RES, V35, P530, DOI 10.1089/jir.2014.0212
   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, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Chen SQ, 2019, FRONT PHARMACOL, V10, DOI 10.3389/fphar.2019.00246
   Chen YL, 2018, CHIN J INTEGR MED, V24, P163, DOI 10.1007/s11655-017-2795-2
   Cheng P, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph182413065
   Cho SX, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-19400-w
   Dinarello CA, 2018, IMMUNOL REV, V281, P8, DOI 10.1111/imr.12621
   Dinarello CA, 2016, EUR J IMMUNOL, V46, P1067, DOI 10.1002/eji.201545828
   Ding Y, 2022, OXID MED CELL LONGEV, V2022, DOI 10.1155/2022/1806513
   Farrokhi M, 2015, ACTA NEUROL BELG, V115, P609, DOI 10.1007/s13760-015-0491-3
   Gao WT, 2003, J IMMUNOL, V170, P107, DOI 10.4049/jimmunol.170.1.107
   Gao Y, 2019, INT IMMUNOPHARMACOL, V72, P374, DOI 10.1016/j.intimp.2019.03.045
   Gazzaz ZJ, 2020, J DIABETES RES, V2020, DOI 10.1155/2020/1514282
   Genovese AV, 2019, RADIOLOGY, V293, P424, DOI 10.1148/radiol.2019190306
   Godsell J, 2016, SCI REP-UK, V6, DOI 10.1038/srep34604
   Grimsby S, 2004, FEBS LETT, V577, P93, DOI 10.1016/j.febslet.2004.09.069
   Guo HS, 2020, GENE, V737, DOI 10.1016/j.gene.2020.144445
   Huang NN, 2018, INT IMMUNOPHARMACOL, V55, P198, DOI 10.1016/j.intimp.2017.12.010
   Ji QW, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-02987-4
   Jia C, 2021, CELL DEATH DIS, V12, DOI 10.1038/s41419-021-03852-z
   Jing L, 2019, J DENT RES, V98, P813, DOI 10.1177/0022034519847443
   Kacem O, 2018, CLIN IMMUNOL, V192, P14, DOI 10.1016/j.clim.2018.03.016
   Kapelouzou A, 2019, CYTOKINE, V116, P150, DOI 10.1016/j.cyto.2019.01.009
   Kaplanski G, 2018, IMMUNOL REV, V281, P138, DOI 10.1111/imr.12616
   Kim MS, 2019, J IMMUNOL, V203, P2265, DOI 10.4049/jimmunol.1801515
   Kodonas K, 2021, INT ENDOD J, V54, P220, DOI 10.1111/iej.13422
   Kouchaki E, 2017, IRAN J ALLERGY ASTHM, V16, P329
   Kumar S, 2000, J BIOL CHEM, V275, P10308, DOI 10.1074/jbc.275.14.10308
   Kumar S, 2002, CYTOKINE, V18, P61, DOI 10.1006/cyto.2002.0873
   Li HM, 2020, MOL MED REP, V21, P151, DOI 10.3892/mmr.2019.10805
   Li SZ, 2015, P NATL ACAD SCI USA, V112, P2497, DOI 10.1073/pnas.1424626112
   Li TY, 2019, MOL IMMUNOL, V112, P322, DOI 10.1016/j.molimm.2019.06.008
   Li YM, 2014, MEDIAT INFLAMM, V2014, DOI 10.1155/2014/136329
   Liang YD, 2017, J PAIN RES, V10, P951, DOI 10.2147/JPR.S132808
   Liu GY, 2014, NEURAL REGEN RES, V9, P1716, DOI 10.4103/1673-5374.141810
   Liu HP, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0169922
   Liu K, 2017, CLIN CHIM ACTA, V468, P140, DOI 10.1016/j.cca.2017.02.017
   Liu TX, 2019, BBA-MOL CELL BIOL L, P1338, DOI 10.1016/j.bbalip.2019.05.009
   Lotfy H, 2020, VASCULAR, V28, P629, DOI 10.1177/1708538120921735
   Lu CC, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.00294
   Luo P, 2020, RHEUMATOLOGY, V59, P3070, DOI 10.1093/rheumatology/keaa192
   Luo P, 2019, CELL PROLIFERAT, V52, DOI 10.1111/cpr.12692
   Luo YC, 2014, P NATL ACAD SCI USA, V111, P15178, DOI 10.1073/pnas.1416714111
   Lv J, 2018, ALLERGY, V73, P1642, DOI 10.1111/all.13395
   Ma CQ, 2021, FRONT ONCOL, V10, DOI 10.3389/fonc.2020.627891
   Mantovani A, 2019, IMMUNITY, V50, P778, DOI 10.1016/j.immuni.2019.03.012
   Mao XB, 2019, MEDIAT INFLAMM, V2019, DOI 10.1155/2019/9515346
   McCurdy S, 2021, QJM-INT J MED, DOI 10.1093/qjmed/hcab011
   McCurdy S, 2017, J IMMUNOL, V199, P3604, DOI 10.4049/jimmunol.1601907
   McNamee EN, 2011, P NATL ACAD SCI USA, V108, P16711, DOI 10.1073/pnas.1111982108
   Meng P, 2019, IMMUNOL CELL BIOL, V97, P403, DOI 10.1111/imcb.12223
   Moed HF, 2009, ARCH IMMUNOL THER EX, V57, P13, DOI 10.1007/s00005-009-0001-5
   Mountford S, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.603649
   Mu XF, 2021, FRONT ONCOL, V10, DOI 10.3389/fonc.2020.526866
   Nold MF, 2010, NAT IMMUNOL, V11, P1014, DOI 10.1038/ni.1944
   Nold-Petry CA, 2015, NAT IMMUNOL, V16, P354, DOI 10.1038/ni.3103
   Ozguclu S, 2019, CLIN RHEUMATOL, V38, P495, DOI 10.1007/s10067-018-4288-7
   Qin Y, 2022, FRONT MED-LAUSANNE, V8, DOI 10.3389/fmed.2021.775495
   Qin Y, 2020, BRAIN RES BULL, V156, P15, DOI 10.1016/j.brainresbull.2019.12.004
   Quirk S, 2014, EXPERT REV CLIN IMMU, V10, P1703, DOI 10.1586/1744666X.2014.971014
   Racca V, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-65581-1
   Ragaba D, 2019, CYTOKINE, V113, P305, DOI 10.1016/j.cyto.2018.07.027
   Rosewell A, 2013, EMERG INFECT DIS, V19, P1811, DOI 10.3201/eid1911.121843
   Sanchez-Fernandez A, 2021, THERANOSTICS, V11, P1, DOI 10.7150/thno.47435
   Sharaf N, 2014, CYTOKINE, V68, P16, DOI 10.1016/j.cyto.2014.03.002
   Sharma S, 2008, J IMMUNOL, V180, P5477, DOI 10.4049/jimmunol.180.8.5477
   Shou XL, 2017, DIS MARKERS, V2017, DOI 10.1155/2017/9134079
   Song LJ, 2018, MED SCI MONITOR, V24, P5660, DOI 10.12659/MSM.909254
   Song LJ, 2013, J CLIN IMMUNOL, V33, P111, DOI 10.1007/s10875-012-9791-z
   Su ZC, 2021, FRONT IMMUNOL, V12, DOI 10.3389/fimmu.2021.696605
   Tawfik MG, 2017, INT J RHEUM DIS, V20, P996, DOI 10.1111/1756-185X.13122
   Teng X, 2014, J IMMUNOL, V192, P1815, DOI 10.4049/jimmunol.1300047
   Tomasoni R, 2017, ELIFE, V6, DOI 10.7554/eLife.21735
   Wang MX, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16234654
   Wang PP, 2019, J CELL PHYSIOL, V234, P7141, DOI 10.1002/jcp.27468
   Wang WQ, 2015, ACTA PHARMACOL SIN, V36, P1377, DOI 10.1038/aps.2015.51
   Xia LP, 2015, CYTOKINE, V76, P553, DOI 10.1016/j.cyto.2015.06.005
   Xia T, 2015, DIS MARKERS, V2015, P1, DOI 10.1155/2015/795043
   Xiao FJ, 2017, FRONT CHEM, V5, DOI 10.3389/fchem.2017.00067
   Xie YD, 2016, INT IMMUNOPHARMACOL, V38, P26, DOI 10.1016/j.intimp.2016.05.003
   Xu JY, 2020, J AM SOC NEPHROL, V31, P54, DOI 10.1681/ASN.2019050545
   Yang DW, 2019, QUANT IMAG MED SURG, V9, P465, DOI 10.21037/qims.2019.02.10
   Yang LB, 2015, APMIS, V123, P1025, DOI 10.1111/apm.12467
   Yang XJ, 2020, BMC MED INFORM DECIS, V20, DOI 10.1186/s12911-020-01278-9
   Yang Y, 2020, J DIABETES RES, V2020, DOI 10.1155/2020/5767582
   Ye J, 2021, EXP THER MED, V21, DOI 10.3892/etm.2021.9990
   Ye L, 2015, J IMMUNOL, V194, P5110, DOI 10.4049/jimmunol.1401810
   Ye L, 2014, J TRANSL MED, V12, DOI 10.1186/1479-5876-12-69
   Zeng QC, 2017, P NATL ACAD SCI USA, V114, P1631, DOI 10.1073/pnas.1619667114
   Zhang J, 2021, FRONT ONCOL, V11, DOI 10.3389/fonc.2021.686726
   Zhang XL, 2020, FOODS, V9, DOI 10.3390/foods9020166
   Zhang ZZ, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.2009217118
   Zhao JJ, 2014, SCI REP-UK, V4, DOI 10.1038/srep05177
   Zhou PT, 2020, FRONT CELL DEV BIOL, V8, DOI 10.3389/fcell.2020.00056
   Zhu XQ, 2021, FRONT PHARMACOL, V12, DOI 10.3389/fphar.2021.687757
   Zhu YL, 2022, CYTOKINE, V150, DOI 10.1016/j.cyto.2021.155760
NR 107
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 931783
DI 10.3389/fimmu.2022.931783
PG 20
WC Immunology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Immunology
GA 3O9WI
UT WOS:000837186400001
PM 35935954
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Tao, SH
   Qiu, RH
   Ping, Y
   Xu, WP
   Ma, H
AF Tao, Shaohua
   Qiu, Runhe
   Ping, Yuan
   Xu, Woping
   Ma, Hui
TI Making Explainable Friend Recommendations Based on Concept Similarity
   Measurements via a Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Internet; Encyclopedias; Electronic publishing; Cognition;
   Knowledge engineering; Knowledge graph; similarity measurement; semantic
   similarity; the shortest path; Euclidean-shortest path; imbalance
   structure
ID NETWORKS
AB Studying the similarities between the concepts in a knowledge graph can be useful in making friend recommendations on various microblogging platforms. Most existing approaches that only focus on accurate friend recommendation and can not give a reasoning explaining. In addition, existing similarity measurements are too costly and ineffective to be used in practical applications. To solve these problems, we purposed the shortest path-guide reasoning path, we perform explicit reasoning with knowledge for decision making so that the friend recommendation are supported by an interpretable causal inference. Then we designed a novel Weighted Euclidean-Shortest Path (WESP) method for measuring concept similarity in a knowledge graph and applied it to friend recommendations on a microblogging platform. First we took the shortest path as an example to measure concepts similarity. Although it was easy to use the shortest path to measure the similarity between concept pairs, the results of the measurements via the shortest path were affected by local structural imbalance in the knowledge graph. The imbalance had a significant impact on measuring concepts similarity; the more balanced the local structure, the greater similarity between the concept pairs. Then, we applied the WESP method to friend recommendations on the microblogging platform. We use the optimization similarity measurement (OSM) model that calculated the similarity between corresponding concept pairs. Our experimental results showed that the OSM method achieved better performance than the baseline methods in making friend recommendations.
C1 [Tao, Shaohua; Qiu, Runhe] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China.
   [Tao, Shaohua; Ping, Yuan; Ma, Hui] Xuchang Univ, Sch Informat Engn, Xuchang 420001, Henan, Peoples R China.
   [Tao, Shaohua; Qiu, Runhe] Donghua Univ, Engn Res Ctr Digitized Text & Apparel Technol, Minist Educ, Shanghai 201620, Peoples R China.
   [Xu, Woping] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China.
RP Qiu, RH (通讯作者)，Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China.; Qiu, RH (通讯作者)，Donghua Univ, Engn Res Ctr Digitized Text & Apparel Technol, Minist Educ, Shanghai 201620, Peoples R China.
EM qiurh@dhu.edu.cn
OI xu, woping/0000-0001-8503-6165
FU National Key Research and Development Program of China [2017YFB0802000];
   National Natural Science Foundation of China [U1736111]; Plan For
   Scientific Innovation Talent of Henan Province [184100510012]; Program
   for Science and Technology Innovation Talents in Universities of He'nan
   Province [18HASTIT022]; Innovation Scientists and Technicians Troop
   Construction Projects of He'nan Province; Key Technologies Research and
   Development Program of Henan Province [192102210295]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2017YFB0802000, in part by the
   National Natural Science Foundation of China under Grant U1736111, in
   part by the Plan For Scientific Innovation Talent of Henan Province
   under Grand 184100510012, in part by the Program for Science and
   Technology Innovation Talents in Universities of He'nan Province under
   Grant 18HASTIT022, in part by the Innovation Scientists and Technicians
   Troop Construction Projects of He'nan Province, and in part by the Key
   Technologies Research and Development Program of Henan Province under
   Grant 192102210295.
CR [Anonymous], 2015, 2015 IEEE OPT INT C
   Bagci H., 2016, P 25 INT C COMP WORL, P241
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Borders A., 2014, P C EMP METH NAT LAN, P615
   Bordes A., 2014, P JOINT EUR C MACH L, P165
   Catherine R, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P325, DOI 10.1145/2959100.2959131
   Chen YG, 2014, AAAI CONF ARTIF INTE, P16
   Cheng W.-L., 2016, P BIG DAT BENCHM PER, V9495, P37
   Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110
   Guo S., 2017, ARXIV171111231
   Hannon J., 2010, P 4 ACM C REC SYST R, P199
   Kalmijn M, 2007, SOC NETWORKS, V29, P25, DOI 10.1016/j.socnet.2005.11.008
   Kaptein R., 2013, P 27 AAAI C ART INT, P111
   Lake BM, 2017, BEHAV BRAIN SCI, V40, DOI 10.1017/S0140525X16001837
   Li YY, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1657, DOI 10.1145/3132847.3132855
   Ma D., 2018, P WORLD WID WEB C WO, P1632
   Milne D., 2008, P 17 ACM C INF KNOWL, P509
   Moon C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2215, DOI 10.1145/3132847.3133095
   Qiu Y.-Q., 2018, EXPERT SYST APPL, V42, P9086
   Rafailidis D, 2018, 2018 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), P421, DOI 10.1109/ASONAM.2018.8508362
   Ren MF, 2019, SYST SCI CONTROL ENG, V7, P158, DOI 10.1080/21642583.2019.1588804
   Sadilek A., 2012, P 5 ACM INT C WEB SE, P723
   Sanderson M, 2010, NAT LANG ENG, V16, P100, DOI 10.1017/S1351324909005129
   Sil A, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P2369, DOI 10.1145/2505515.2505601
   Singer P, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1591, DOI 10.1145/3038912.3052716
   Singer Y., 2012, P 5 ACM INT C WEB SE, P733
   Wan SX, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P1045
   Wang C, 2007, IEEE DATA MINING, P322, DOI 10.1109/ICDM.2007.108
   West R, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P515, DOI 10.1145/2566486.2568032
   Wohlfarth T, 2008, LECT NOTES ARTIF INT, V5345, P50, DOI 10.1007/978-3-540-89447-6_7
   Yin X, 2020, IEEE T AUTOMAT CONTR, V65, P376, DOI 10.1109/TAC.2019.2914257
   Yu F, 2017, LECT NOTES ARTIF INT, V10235, P91, DOI 10.1007/978-3-319-57529-2_8
   Zhang B., 2018, ARXIV180500631
   Zhang LB, 2018, IEEE ACCESS, V6, P9454, DOI 10.1109/ACCESS.2018.2789866
   Zhang Q., 2016, INT J ADV MECHATRONI, V7, P61
   Zhang QC, 2017, IEEE T AUTOMAT CONTR, V62, P2936, DOI 10.1109/TAC.2016.2604683
   Zhang Y., 2018, ESSENTIALS ENG THERM
   Zhou YY, 2018, IEEE T AUTOMAT CONTR, V63, P1155, DOI 10.1109/TAC.2017.2742661
NR 38
TC 1
Z9 1
U1 5
U2 20
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 146027
EP 146038
DI 10.1109/ACCESS.2020.3014670
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ND7PZ
UT WOS:000562096200001
OA gold
DA 2022-08-23
ER

PT J
AU Bizon, C
   Cox, S
   Balhoff, J
   Kebede, Y
   Wang, P
   Morton, K
   Fecho, K
   Tropsha, A
AF Bizon, Chris
   Cox, Steven
   Balhoff, James
   Kebede, Yaphet
   Wang, Patrick
   Morton, Kenneth
   Fecho, Karamarie
   Tropsha, Alexander
TI ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources
SO JOURNAL OF CHEMICAL INFORMATION AND MODELING
LA English
DT Article
ID GENE ONTOLOGY; TOOL
AB A proliferation of data sources has led to the notional existence of an implicit Knowledge Graph (KG) that contains vast amounts of biological knowledge contributed by distributed Application Programming Interfaces (APIs). However, challenges arise when integrating data across multiple APIs due to incompatible semantic types, identifier schemes, and data formats. We present ROBOKOP KG (http://robokopkg.renci.org), which is a KG that was initially built to support the open biomedical question-answering application, ROBOKOP (Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways) (http://robokop.renci.org). Additionally, we present the ROBOKOP Knowledge Graph Builder (KGB), which constructs the KG and provides an extensible framework to handle graph query over and integration of federated data sources.
C1 [Bizon, Chris; Cox, Steven; Balhoff, James; Kebede, Yaphet; Fecho, Karamarie] Univ N Carolina, Renaissance Comp Inst, Chapel Hill, NC 27517 USA.
   [Wang, Patrick; Morton, Kenneth] CoVar Appl Technol, Durham, NC 27701 USA.
   [Tropsha, Alexander] Univ N Carolina, Sch Pharm, Chapel Hill, NC 27599 USA.
RP Bizon, C (通讯作者)，Univ N Carolina, Renaissance Comp Inst, Chapel Hill, NC 27517 USA.
EM bizon@renci.org
RI Tropsha, Alexander/AAB-8324-2020
OI Tropsha, Alexander/0000-0003-3802-8896; Fecho,
   Karamarie/0000-0002-6704-9306; Morton, Kenneth/0000-0002-9352-8669
FU National Center for Advancing Translational Sciences, National
   Institutes of Health [OT2TR002514]
FX This work was supported by the National Center for Advancing
   Translational Sciences, National Institutes of Health (Grant OT2TR002514
   to A.T.). The authors thank Matt Brush and Chris Mungall for creation of
   the BioLink data model, Eric Deutch and David Koslicki for contributions
   to the Translator KG API specification, Richard Bruskiewich for advice
   on services and useful discussions, and Matt Might and Will Byrd for
   feedback on the ROBOKOP KG and the isopropyl alcohol use case.
CR Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Banda JM, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.26
   Bateman A, 2019, NUCLEIC ACIDS RES, V47, pD506, DOI 10.1093/nar/gky1049
   Binns D, 2009, BIOINFORMATICS, V25, P3045, DOI 10.1093/bioinformatics/btp536
   Buniello A, 2019, NUCLEIC ACIDS RES, V47, pD1005, DOI 10.1093/nar/gky1120
   Callahan Alison, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P200
   Carbon S, 2019, NUCLEIC ACIDS RES, V47, pD330, DOI 10.1093/nar/gky1055
   Carbon S, 2009, BIOINFORMATICS, V25, P288, DOI 10.1093/bioinformatics/btn615
   Chambers J, 2013, J CHEMINFORMATICS, V5, DOI 10.1186/1758-2946-5-3
   Chen B, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-255
   Collarana D, 2017, IEEE INT C SEMANT CO, P109, DOI 10.1109/ICSC.2017.85
   Nguyen DT, 2017, NUCLEIC ACIDS RES, V45, pD995, DOI 10.1093/nar/gkw1072
   Davis AP, 2019, NUCLEIC ACIDS RES, V47, pD948, DOI 10.1093/nar/gky868
   Emonet V., 2018, FIGSHARE, DOI [10.6084/m9.figshare.7345868.v1, DOI 10.6084/M9.FIGSHARE.7345868.V1]
   Fecho K, 2019, CTS-CLIN TRANSL SCI, V12, P86, DOI 10.1111/cts.12591
   Gaulton A, 2017, NUCLEIC ACIDS RES, V45, pD945, DOI 10.1093/nar/gkw1074
   Hastings J, 2016, NUCLEIC ACIDS RES, V44, pD1214, DOI 10.1093/nar/gkv1031
   Himmelstein DS, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004259
   Kanehisa M, 2000, NUCLEIC ACIDS RES, V28, P27, DOI 10.1093/nar/28.1.27
   Kim S, 2019, NUCLEIC ACIDS RES, V47, pD1102, DOI 10.1093/nar/gky1033
   Kohler S, 2019, NUCLEIC ACIDS RES, V47, pD1018, DOI 10.1093/nar/gky1105
   Landrum MJ, 2018, NUCLEIC ACIDS RES, V46, pD1062, DOI 10.1093/nar/gkx1153
   Mi HY, 2019, NUCLEIC ACIDS RES, V47, pD419, DOI 10.1093/nar/gky1038
   Morton K., 2019, BIOINFORMATICS, DOI [10.1093/bioinfor-matics/btz604, DOI 10.1093/BIOINFOR-MATICS/BTZ604]
   Pawliczek P, 2018, HUM MUTAT, V39, P1690, DOI 10.1002/humu.23637
   Pellegrini Joseph, 2009, AANA J, V77, P293
   Povey S, 2001, HUM GENET, V109, P678, DOI 10.1007/s00439-001-0615-0
   Singhal A., INTRO KNOWLEDGE GRAP
   Ursu O, 2017, NUCLEIC ACIDS RES, V45, pD932, DOI 10.1093/nar/gkw993
   Wang SM, 1999, J CLIN ANESTH, V11, P231, DOI 10.1016/S0952-8180(99)00035-5
   Wilcke X., 2017, DATA SCI, V1, P39, DOI [10.3233/ds-170007, DOI 10.3233/DS-170007, 10.3233/DS-170007]
   Wishart DS, 2007, NUCLEIC ACIDS RES, V35, pD521, DOI 10.1093/nar/gkl923
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Womack F., 2019, LEVERAGING DISTRIBUT, DOI [10.1101/765305, DOI 10.1101/765305]
   Xin JW, 2016, GENOME BIOL, V17, DOI 10.1186/s13059-016-0953-9
   Zaveri A, 2017, LECT NOTES COMPUT SC, V10250, P154, DOI 10.1007/978-3-319-58451-5_11
   Zerbino DR, 2018, NUCLEIC ACIDS RES, V46, pD754, DOI 10.1093/nar/gkx1098
NR 37
TC 12
Z9 12
U1 1
U2 10
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 1549-9596
EI 1549-960X
J9 J CHEM INF MODEL
JI J. Chem Inf. Model.
PD DEC
PY 2019
VL 59
IS 12
BP 4968
EP 4973
DI 10.1021/acs.jcim.9b00683
PG 6
WC Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science,
   Information Systems; Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy; Chemistry; Computer Science
GA JZ2SG
UT WOS:000504953400002
PM 31769676
DA 2022-08-23
ER

PT J
AU DIENG, R
AF DIENG, R
TI CONFLICT-MANAGEMENT IN KNOWLEDGE ACQUISITION
SO AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND
   MANUFACTURING
LA English
DT Article
DE CONFLICT MANAGEMENT; KNOWLEDGE ACQUISITION FROM MULTIPLE EXPERTS;
   KNOWLEDGE GRAPHS; PROBLEM CLASSES
ID SYSTEMS
AB This article presents our approach for conflict management in our knowledge acquisition tool KATEMES, aimed at tackling multiple experts. We offer a method for helping the knowledge engineer to detect expertise conflicts in the framework of the KADS knowledge acquisition method. We also propose techniques for conflict management through comparison of knowledge graphs.
C1 INRIA,ACACIA PROJECT,F-06902 SOPHIA ANTIPOLIS,FRANCE.
CR Boose J. H., 1986, Future Computing Systems, V1, P191
   BOOSE JH, 1987, INT J MAN MACH STUD, V26, P3, DOI 10.1016/S0020-7373(87)80032-9
   BOOSE JH, 1988, 2ND P EUR WORKSH KNO
   BREUKER J, 1994, REUSABLE PROBLEM SOL
   BREUKER J, 1989, TOPICS EXPERT SYSTEM
   BREUKER J, 1987, MODEL DERIVEN KNOWLE
   DIENG R, 1992, LECT NOTES ARTIF INT, V599, P298
   DIENG R, 1993, LNAI, V723, P407
   DIENG R, 1994, LECT NOTES ARTIF INT, V867, P63
   DIENG R, 1994, 8TH P BANFF KA KBS W
   Easterbrook S., 1991, Knowledge Acquisition, V3, P255, DOI 10.1016/1042-8143(91)90007-A
   EASTERBROOK S, 1989, P 3 EUR KNOWL ACQ WO, P530
   EASTERBROOK SM, 1992, CSRP227 U SUSS TECH
   EGGEN J, 1990, P EKAW 90, P123
   GAINES BR, 1989, 11 INT JOINT C ART I, P633
   GARNER BJ, 1992, LNAI, V754, P158
   JAGANNATHAN V, 1985, OCT P ACM NE REG C B, P103
   KAYAALP MM, 1994, P KAW 94 BANFF
   Klein M., 1992, Artificial Intelligence in Engineering, V7, P93, DOI 10.1016/0954-1810(92)90008-P
   Klein M., 1989, Artificial Intelligence in Engineering, V4, P168, DOI 10.1016/0954-1810(89)90013-7
   Liou Y. I., 1990, Knowledge Acquisition, V2, P129, DOI 10.1016/S1042-8143(05)80008-1
   MCGRAW KL, 1988, ARTIF INTELL REV, V2, P31, DOI 10.1007/BF00139624
   MINEAU GW, 1995, P KAW 95
   MITTAL ML, 1984, IEEE P WORKSH PRINC, P75
   MURRAY KS, 1990, INT J MAN MACH STUD, V33, P373, DOI 10.1016/S0020-7373(05)80037-9
   NEWELL A, 1982, ARTIF INTELL, V18, P87, DOI 10.1016/0004-3702(82)90012-1
   SCHREIBER G, 1991, P EKAW 91 CRIEFF
   Shaw M. L. G., 1989, Knowledge Acquisition, V1, P341, DOI 10.1016/S1042-8143(89)80010-X
   SHAW MLG, 1988, INT J MAN MACH STUD, V29, P329, DOI 10.1016/S0020-7373(88)80026-9
   SHAW MLG, 1987, INT J MAN MACH STUD, V27, P251, DOI 10.1016/S0020-7373(87)80055-X
   SHAW MLG, 1989, SIGART NEWSLETTER KN, V108, P168
   SHW MLG, 1988, P EKAW 88
   SKUCE D, 1995, P KAW 95
   SOWA JF, 1984, CONCEPTUAL STRUCTURE
   Wielinga B. J., 1992, Knowledge Acquisition, V4, P5, DOI 10.1016/1042-8143(92)90013-Q
   WOLF WA, 1989, SIGART NEWSLETTER KN, V108, P138
NR 36
TC 1
Z9 1
U1 1
U2 1
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 40 WEST 20TH STREET, NEW YORK, NY 10011-4211
SN 0890-0604
J9 AI EDAM
JI AI EDAM-Artif. Intell. Eng. Des. Anal. Manuf.
PD SEP
PY 1995
VL 9
IS 4
BP 337
EP 351
DI 10.1017/S0890060400002870
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 RW923
UT WOS:A1995RW92300009
DA 2022-08-23
ER

PT J
AU Tiwari, P
   Zhu, HY
   Pandey, HM
AF Tiwari, Prayag
   Zhu, Hongyin
   Pandey, Hari Mohan
TI DAPath: Distance-aware knowledge graph reasoning based on deep
   reinforcement learning
SO NEURAL NETWORKS
LA English
DT Article
DE Knowledge graph reasoning; Reinforcement learning; Graph self-attention;
   GRU
AB Knowledge graph reasoning aims to find reasoning paths for relations over incomplete knowledge graphs (KG). Prior works may not take into account that the rewards for each position (vertex in the graph) may be different. We propose the distance-aware reward in the reinforcement learning framework to assign different rewards for different positions. We observe that KG embeddings are learned from independent triples and therefore cannot fully cover the information described in the local neighborhood. To this effect, we integrate a graph self-attention (GSA) mechanism to capture more comprehensive entity information from the neighboring entities and relations. To let the model remember the path, we incorporate the GSA mechanism with GRU to consider the memory of relations in the path. Our approach can train the agent in one-pass, thus eliminating the pre-training or finetuning process, which significantly reduces the problem complexity. Experimental results demonstrate the effectiveness of our method. We found that our model can mine more balanced paths for each relation. (c) 2020 Elsevier Ltd. All rights reserved.
C1 [Tiwari, Prayag] Univ Padua, Dept Informat Engn, Padua, Italy.
   [Zhu, Hongyin] Chinese Acad Sci, Inst Automat, Beijing, Peoples R China.
   [Pandey, Hari Mohan] Edge Hill Univ, Dept Comp Sci, Ormskirk L39 4QP, England.
RP Tiwari, P (通讯作者)，Univ Padua, Dept Informat Engn, Padua, Italy.; Pandey, HM (通讯作者)，Edge Hill Univ, Dept Comp Sci, Ormskirk L39 4QP, England.
EM prayag.tiwari@dei.unipd.it; zhuhongyin14@mails.ucas.ac.cn;
   pandeyh@edgehill.ac.uk
RI Tiwari, Prayag/N-6261-2017; Pandey, Hari Mohan/M-9658-2015
OI Tiwari, Prayag/0000-0002-2851-4260; Pandey, Hari
   Mohan/0000-0002-9128-068X; zhu, hong yin/0000-0002-4223-5209
FU European Union's Horizon 2020 research and innovation program under the
   Marie Sklodowska-Curie Grant [721321]
FX This project has received funding from the European Union's Horizon 2020
   research and innovation program under the Marie Sklodowska-Curie Grant
   Agreement No. 721321.
CR [Anonymous], ARXIV160902907
   Balazevic I., 2019, P EMNLP IJCNLP
   Bansal T, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4387
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Carlson A., 2010, P AAAI
   Chung J., CORR
   Das R, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P132
   Das Rajarshi, 2018, ICLR 2018
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Elfwing S, 2018, NEURAL NETWORKS, V107, P3, DOI 10.1016/j.neunet.2017.12.012
   Graves A, 2005, NEURAL NETWORKS, V18, P602, DOI 10.1016/j.neunet.2005.06.042
   GULLAPALLI V, 1990, NEURAL NETWORKS, V3, P671, DOI 10.1016/0893-6080(90)90056-Q
   Guo LB, 2019, PR MACH LEARN RES, V97
   Haruno M, 2006, NEURAL NETWORKS, V19, P1242, DOI 10.1016/j.neunet.2006.06.007
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Iwashita AS, 2012, INT C PATT RECOG, P1233
   Jagvaral B, 2020, EXPERT SYST APPL, V142, DOI 10.1016/j.eswa.2019.112960
   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 ZN, 2019, IEEE INT SYMP POWER, P978, DOI 10.1109/PEDG.2019.8807755
   Johnson A, 2005, NEURAL NETWORKS, V18, P1163, DOI 10.1016/j.neunet.2005.08.009
   Karevan Z, 2020, NEURAL NETWORKS, V125, P1, DOI 10.1016/j.neunet.2019.12.030
   Kazemi S. M., 2018, NEURAL INFORM PROCES, P4289
   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
   Laurent PA, 2008, NEURAL NETWORKS, V21, P1493, DOI 10.1016/j.neunet.2008.09.004
   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, ARXIV180810568
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu CX, 2017, DESTECH TRANS COMP, P566
   Liu HX, 2017, PR MACH LEARN RES, V70
   Luo B, 2015, NEURAL NETWORKS, V71, P150, DOI 10.1016/j.neunet.2015.08.007
   Lv X., 2019, P EMNLP IJCNLP, P3374
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   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., 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
   Rossi A., ARXIV200200819
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Schweighofer N, 2003, NEURAL NETWORKS, V16, P5, DOI 10.1016/S0893-6080(02)00228-9
   Shen Y., 2018, NEURIPS, V31, P6786
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Sutton RS, 2000, ADV NEUR IN, V12, P1057
   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
   Velickovic P., INT C LEARN REPR
   Wang D., APPL SOFT COMPUTING, V86
   Wang H., 2019, P EMNLP, P2623, DOI [10.18653/v1/D19-1264, DOI 10.18653/V1/D19-1264]
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang R, 2020, IEEE ACCESS, V8, P5212, DOI 10.1109/ACCESS.2019.2963367
   Wang W.Y., 2016, P 25 INT JOINT C ART, P2132
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Xiao H., 2016, P ACL
   Xiao H., CORR
   Yang B, 2015, P ICLR 2015
   Yang F, 2017, PROCEEDINGS OF 2017 IEEE INTERNATIONAL CONFERENCE ON UNMANNED SYSTEMS (ICUS), P231, DOI 10.1109/ICUS.2017.8278346
   Zhang T., 2019, P EMNLP IJCNLP, P2662
   Zhang YQ, 2020, PROC INT CONF DATA, P433, DOI 10.1109/ICDE48307.2020.00044
   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 61
TC 10
Z9 12
U1 7
U2 41
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0893-6080
EI 1879-2782
J9 NEURAL NETWORKS
JI Neural Netw.
PD MAR
PY 2021
VL 135
BP 1
EP 12
DI 10.1016/j.neunet.2020.11.012
PG 12
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA PW9LL
UT WOS:000610987500001
PM 33310193
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Edelstein, E
   Pan, JZ
   Soares, R
   Wyner, A
AF Edelstein, Elspeth
   Pan, Jeff Z.
   Soares, Ricardo
   Wyner, Adam
TI Knowledge-Driven Intelligent Survey Systems Towards Open Science
SO NEW GENERATION COMPUTING
LA English
DT Article
DE Knowledge Graph; Intelligent survey system; Grammaticality judgments
AB In this paper, we propose Knowledge Graph (KG), an articulated underlying semantic structure, as a semantic bridge between humans, systems, and scientific knowledge. To illustrate our proposal, we focus on KG-based intelligent survey systems. In state-of-the-art systems, information is hard-coded or implicit, making it hard for researchers to reuse, customise, link, or transmit structured knowledge. Furthermore, such systems do not facilitate dynamic interaction based on semantic structure. We design and implement a knowledge-driven intelligent survey system which is based on knowledge graph, a widely used technology that facilitates sharing and querying hypotheses, survey content, results, and analyses. The approach is developed, implemented, and tested in the field of Linguistics. Syntacticians and morphologists develop theories of grammar of natural languages. To evaluate theories, they seek intuitive grammaticality (well-formedness) judgments from native speakers, which either support hypotheses or provide counter-evidence. Our preliminary experiments show that a knowledge graph-based linguistic survey can provide more nuanced results than the traditional document-based grammaticality judgment surveys by allowing for tagging and manipulation of specific linguistic variables.
C1 [Edelstein, Elspeth] Univ Aberdeen, Sch Language Literature Mus & Visual Culture, Aberdeen, Scotland.
   [Pan, Jeff Z.; Soares, Ricardo] Univ Aberdeen, Dept Comp Sci, Aberdeen, Scotland.
   [Wyner, Adam] Swansea Univ, Sch Law, Swansea, W Glam, Wales.
   [Wyner, Adam] Swansea Univ, Dept Comp Sci, Swansea, W Glam, Wales.
RP Pan, JZ (通讯作者)，Univ Aberdeen, Dept Comp Sci, Aberdeen, Scotland.
EM jeff.z.pan@abdn.ac.uk
FU EU K-Drive project [286348]
FX We are grateful to all of our survey participants, and to Anne
   Eschenbruecher, Sally Lamond, and Evelyn Williams for their assistance
   in participant recruitment. We are also grateful to Patrik Bansky for
   his work on refinement of the survey system. Funding was partially
   provided by the EU K-Drive project (Grant no. 286348).
CR Baader F., 2010, DESCRIPTION LOGIC HD, DOI 10.1017/CBO9780511711787
   Bechhofer S, 2013, FUTURE GENER COMP SY, V29, P599, DOI 10.1016/j.future.2011.08.004
   Chiang C., 2013, P THE WORLD ELECT VE, P1
   Edelstein Elspeth., 2014, MICROSYNTACTIC VARIA, P242
   Erlewine MY, 2016, NAT LANG LINGUIST TH, V34, P481, DOI 10.1007/s11049-015-9305-9
   Fecher B, 2014, OPEN SCI ONE TERM FI
   Gibson E, 2011, LANG LINGUIST COMPAS, V5, P509, DOI 10.1111/j.1749-818x.2011.00295.x
   Giunchiglia F, 2009, SCI KNOWLEDGE OBJECT
   Keller F, 2009, BEHAV RES METHODS, V41, P1, DOI 10.3758/BRM.41.1.12
   Mons B, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-142
   Murray TE, 2002, AM SPEECH, V77, P32, DOI 10.1215/00031283-77-1-32
   Myers J, 2006, P 18 C COMP LING SPE, P271
   Nosek BA, 2015, SCIENCE, V348, P1422, DOI 10.1126/science.aab2374
   Pan J., 2016, EXPLOITING LINKED DA
   Pan J., 2017, REASONING WEB LOGICA
   Pan J.Z., 2009, P 6 EUR SEM WEB C ES
   Pan JZ, 2009, J DATA SEMANT, V4090/2006, P28
   Pan JZ, 2013, ONTOLOGY DRIVEN SOFT
   Poveda-Villalon M, 2014, INT J SEMANT WEB INF, V10, P7, DOI 10.4018/ijswis.2014040102
   Qi G., 2007, P 2007 INT WORKSH DE
   Schlereth C., 2012, QUANTITATIVE MARKETI, P225, DOI 10.1007/978-3-8349-3722-3%5F11
NR 21
TC 2
Z9 2
U1 2
U2 6
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 JUL
PY 2020
VL 38
IS 3
BP 397
EP 421
DI 10.1007/s00354-020-00087-y
EA MAR 2020
PG 25
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MF1JN
UT WOS:000523098300001
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Kang, SZ
   Ji, LX
   Li, ZL
   Hao, XD
   Ding, YH
AF Kang, Shize
   Ji, Lixin
   Li, Zhenglian
   Hao, Xindi
   Ding, Yuehang
TI Iterative Cross-Lingual Entity Alignment Based on TransC
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article
DE cross-lingual entity alignment; ontology; knowledge embeddings;
   iterative alignment
AB The goal of cross-lingual entity alignment is to match entities from knowledge graph of different languages that represent the same object in the real world. Knowledge graphs of different languages can share the same ontology which we guess may be useful for entity alignment. To verify this idea, we propose a novel embedding model based on TransC. This model first adopts TransC and parameter sharing model to map all the entities and relations in knowledge graphs to a shared low-dimensional semantic space based on a set of aligned entities. Then, the model iteratively uses reinitialization and soft alignment strategy to perform entity alignment. The experimental results show that, compared with the benchmark algorithms, the proposed model can effectively fuse ontology information and achieve relatively better results.
C1 [Kang, Shize; Ji, Lixin; Ding, Yuehang] Informat Engn Univ, Zhengzhou 450001, Peoples R China.
   [Li, Zhenglian; Hao, Xindi] Beijing Informat & Commun Res Ctr, Beijing 100000, Peoples R China.
RP Kang, SZ (通讯作者)，Informat Engn Univ, Zhengzhou 450001, Peoples R China.
EM xiaozebixia@163.com
FU National Natural Science Foundation of China [61521003]
FX This work is supported by the National Natural Science Foundation of
   China (61521003).
CR Chen XM, 2017, CHIN AUTOM CONGR, P2115, DOI 10.1109/CAC.2017.8243121
   Lv X, 2018, EMNLP, P1971
   Mukkamala MC, 2017, PR MACH LEARN RES, V70
   Sun ZQ, 2017, LECT NOTES COMPUT SC, V10587, P628, DOI 10.1007/978-3-319-68288-4_37
   Zhu H, 2017, IEEE IND ELEC, P6425, DOI 10.1109/IECON.2017.8217119
NR 5
TC 2
Z9 2
U1 2
U2 4
PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
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 MAY
PY 2020
VL E103D
IS 5
BP 1002
EP 1005
DI 10.1587/transinf.2019DAL0001
PG 4
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LK2DK
UT WOS:000530668200010
OA gold
DA 2022-08-23
ER

PT J
AU Lecue, F
AF Lecue, Freddy
TI On the role of knowledge graphs in explainable AI
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge graph; explainable AI; machine learning; artificial
   intelligence
ID SEMANTIC WEB
AB The current hype of Artificial Intelligence (AI) mostly refers to the success of machine learning and its sub-domain of deep learning. However, AI is also about other areas, such as Knowledge Representation and Reasoning, or Distributed AI, i.e., areas that need to be combined to reach the level of intelligence initially envisioned in the 1950s. Explainable AI (XAI) now refers to the core backup for industry to apply AI in products at scale, particularly for industries operating with critical systems. This paper reviews XAI not only from a Machine Learning perspective, but also from the other AI research areas, such as AI Planning or Constraint Satisfaction and Search. We expose the XAI challenges of AI fields, their existing approaches, limitations and opportunities for Knowledge Graphs and their underlying technologies.
C1 [Lecue, Freddy] Thales, CortAIx, Montreal, PQ, Canada.
   [Lecue, Freddy] INRIA, WIMMICS, Sophia Antipolis, France.
RP Lecue, F (通讯作者)，Thales, CortAIx, Montreal, PQ, Canada.
EM freddy.lecue@inria.fr
CR AAMODT A, 1994, AI COMMUN, V7, P39
   Adebayo Julius, 2018, ADV NEURAL INFORM PR, P9505, DOI DOI 10.5555/3327546.3327621
   Alirezaie M., 2019, ARXIV190413196 CORR
   Amir D, 2018, PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18), P1168
   Amir O, 2018, PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18), P1203
   [Anonymous], 2017, P 31 AAAI C ART INT
   Antol S, 2015, IEEE I CONF COMP VIS, P2425, DOI 10.1109/ICCV.2015.279
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bau D, 2017, PROC CVPR IEEE, P3319, DOI 10.1109/CVPR.2017.354
   Belle V, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5116
   Bengio Y., 2019, ARXIV190110912 CORR
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bottou L, 2013, J MACH LEARN RES, V14, P3207
   Brooks D.J., 2010, 2010 AAAI FALL S ARL
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Chakraborti T, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P156
   Chen J., 2018, ARXIV180802610 CORR
   Chen J., 2019, ARXIV190204187 CORR
   Chen Y, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P649, DOI 10.1145/2588555.2610516
   Chocron P, 2018, PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18), P1150
   Cohen P.R., 2018, ARXIV181201144 CORR
   Cohen PR, 2003, LECT NOTES ARTIF INT, V2650, P1
   Craven MW, 1996, ADV NEUR IN, V8, P24
   Cudre-Mauroux P, 2020, SEMANT WEB, V11, P13, DOI 10.3233/SW-190371
   d'Amato C, 2020, SEMANT WEB, V11, P195, DOI 10.3233/SW-200388
   Donadello I, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1596
   Forjan M, 2012, 11TH IFAC/IEEE INTERNATIONAL CONFERENCE ON PROGRAMMABLE DEVICES AND EMBEDDED SYSTEMS (PDES 2012)
   Fox M., 2017, ARXIV170910256 CORR
   Fox M, 2016, FRONT ARTIF INTEL AP, V285, pV
   Ghallab M., 2004, AUTOMATED PLANNING T
   Goebel R, 2018, LECT NOTES COMPUT SC, V11015, P295, DOI 10.1007/978-3-319-99740-7_21
   Gromann D, 2020, SEMANT WEB, V11, P29, DOI 10.3233/SW-190373
   GuoDong Z., 2005, P 43 ANN M ASS COMP, P427, DOI DOI 10.3115/1219840.1219893
   Hamilton WL, 2018, ADV NEUR IN, V31
   Han XP, 2017, AAAI CONF ARTIF INTE, P3443
   High R., 2012, ERA COGNITIVE SYSTEM
   Hitzler P, 2020, SEMANT WEB, V11, P3, DOI 10.3233/SW-190368
   Ignatiev A, 2019, AAAI CONF ARTIF INTE, P1511
   Jiang J, 2008, PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P713, DOI 10.1109/ICMLC.2008.4620497
   JOHNSON WL, 1994, PROCEEDINGS OF THE TWELFTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, P1257
   Junker U, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P167
   Kambhatla Nanda, 2004, P ACL 2004 INT POST, DOI 10.3115/ 1219044.1219066
   Kersting K., 2007, INTRO STAT RELATIONA
   Kim B., 2016, NIPS
   Kitano H., 1991, P 29 ANN M, P25, DOI [10.3115/981344.981348, DOI 10.3115/981344.981348]
   Koh PW, 2017, PR MACH LEARN RES, V70
   Koller D, 2009, PROBABILISTIC GRAPHI
   Kwok C, 2001, ACM T INFORM SYST, V19, P242, DOI 10.1145/502115.502117
   Labreuche C, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P331
   Lang J, 2018, P 27 INT JOINT C ART
   LISBOA, 2012, P 20 INT S ART NEUR, V12, P163
   Lowe R., 2015, P 16 ANN M SPEC INT, P285, DOI DOI 10.18653/V1/W15-4640
   Lundberg S.M., 2018, ARXIV180203888 CORR
   Makni B., 2018, THESIS
   Manhaeve R., 2018, ADV NEURAL INFORM PR, P3753
   Mittelstadt B, 2019, FAT*'19: PROCEEDINGS OF THE 2019 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, P279, DOI 10.1145/3287560.3287574
   NASH J, 1951, ANN MATH, V54, P286, DOI 10.2307/1969529
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   O'Sullivan BP, 2007, PEDIATR PULM, P323
   Patki S., 2019, ARXIV190309243 CORR
   Pomarlan M., 2018, P WORKSH NLG HUM ROB, P17, DOI DOI 10.18653/V1/W18-6904
   Ramanishka V, 2017, PROC CVPR IEEE, P3135, DOI 10.1109/CVPR.2017.334
   Rattenbury Tye, 2007, 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P103, DOI 10.1145/1277741.1277762
   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
   Rosenthal S., 2016, P INT JOINT C ART IN, V16, P862
   Russell S., 2010, ARTIFICIAL INTELLIGE
   Russell S.J., 2022, ARTIF INTELL, P430
   Sabour S, 2018, INT C LEARN REPR, P1, DOI DOI 10.2514/6.2003
   Shapley L. S., 1972, International Journal of Game Theory, V1, P111
   Shapley LS., 1953, CONTRIBUTIONS THEORY, V2, P307
   Sheh R.K., 2017, WORKSH 31 AAAI C ART
   Shi J, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2299, DOI 10.1145/3132847.3133119
   Shrikumar A, 2017, PMLR, V70, P3145
   Silver D, 2017, NATURE, V550, P354, DOI 10.1038/nature24270
   Socher R, 2011, P 28 INT C MACH LEAR, P129, DOI DOI 10.1007/978-3-540-87479-9
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sundararajan M, 2017, PR MACH LEARN RES, V70
   Sycara K, 2003, AUTON AGENT MULTI-AG, V7, P29, DOI 10.1023/A:1024172719965
   Zeldam S., 2018, THESIS
NR 82
TC 29
Z9 29
U1 15
U2 61
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 41
EP 51
DI 10.3233/SW-190374
PG 11
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:000512298900006
DA 2022-08-23
ER

PT J
AU Long, CK
   Hai, PV
   Tuan, TM
   Lan, LTH
   Chuan, PM
   Son, LH
AF Long, Cu Kim
   Pham Van Hai
   Tran Manh Tuan
   Luong Thi Hong Lan
   Pham Minh Chuan
   Le Hoang Son
TI A novel fuzzy knowledge graph pairs approach in decision making
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE M-CFIS-FKG; Knowledge graph; Fuzzy knowledge graph; FKG-pairs;
   Approximate reasoning; Decision making
ID INFERENCE SYSTEM; DESIGN
AB Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records' label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k*) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems
C1 [Long, Cu Kim; Pham Van Hai] Hanoi Univ Sci & Technol SoICT HUST, Sch Informat Commun Technol, Hanoi, Vietnam.
   [Long, Cu Kim] Minist Sci & Technol MOST, Informat Technol Ctr, Hanoi, Vietnam.
   [Tran Manh Tuan; Luong Thi Hong Lan] Thuyloi Univ, Fac Comp Sci & Engn, 175 Tay Son, Hanoi, Vietnam.
   [Pham Minh Chuan] UTEHY, Dept Comp Sci, Fac Informat Technol, Khoai Chau, Hung Yen, Vietnam.
   [Le Hoang Son] Vietnam Natl Univ, VNU Informat Technol Inst, Hanoi, Vietnam.
   [Le Hoang Son] Vietnam Natl Univ, VNU Univ Sci, Hanoi, Vietnam.
RP Tuan, TM (通讯作者)，Thuyloi Univ, Fac Comp Sci & Engn, 175 Tay Son, Hanoi, Vietnam.
EM longck.2006@gmail.com; haipv@soict.hust.edu.vn; tmtuan@tlu.edu.vn;
   lanlhbk@tlu.edu.vn; chuanpm@gmail.com; sonlh@vnu.edu.vn
OI Tuan, Tran/0000-0002-1117-7253
FU Vietnam National Foundation for Science and Technology Development
   (NAFOSTED) [102.05-2019.316]
FX This research is funded by Vietnam National Foundation for Science and
   Technology Development (NAFOSTED) under grant number 102.05-2019.316.
CR Abdel-Basset M, 2020, MULTIMED TOOLS APPL, V79, P9977, DOI 10.1007/s11042-019-07742-7
   Alves MA, 2021, COMPUT BIOL MED, V132, DOI 10.1016/j.compbiomed.2021.104335
   [Anonymous], 2021, DAT SOURC COD THIS P
   ATANASSOV KT, 1986, FUZZY SET SYST, V20, P87, DOI 10.1016/S0165-0114(86)80034-3
   Bai W., 2020, INT J COGN COMPUT EN, V1, P18, DOI DOI 10.1016/J.IJCCE.2020.09.002
   Bakhshipour A, 2020, J FOOD MEAS CHARACT, V14, P1402, DOI 10.1007/s11694-020-00390-8
   Banerjee S, 2021, MULTIMED TOOLS APPL, V80, P8377, DOI 10.1007/s11042-020-09794-6
   Cai Y., 2020, 2020 IEEE 9 JOINT IN, V9, DOI [10.1109/ITAIC49862.2020.9338752, DOI 10.1109/ITAIC49862.2020.9338752]
   Chang FT, 2021, J CLEAN PROD, V282, DOI 10.1016/j.jclepro.2020.124541
   Chen JY, 2021, INFORM SCIENCES, V563, P326, DOI 10.1016/j.ins.2021.03.004
   Cuong B. C., 2014, J COMPUT SCI CYBERN, V30, P409, DOI [10.1109/WICT.2013.7113099, DOI 10.1109/WICT.2013.7113099]
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Figalist I, 2021, INFORM SOFTWARE TECH, V132, DOI 10.1016/j.infsof.2020.106458
   Nguyen HL, 2020, INFORM FUSION, V61, P56, DOI 10.1016/j.inffus.2020.03.014
   Souza MLH, 2020, J MANUF SYST, V56, P133, DOI 10.1016/j.jmsy.2020.05.016
   Hogan A., 2019, DAGSTUHL REPORTS, V2019, P74
   Lan LTH, 2020, IEEE ACCESS, V8, P164899, DOI 10.1109/ACCESS.2020.3021097
   Horta VAC, 2021, FUTURE GENER COMP SY, V120, P109, DOI 10.1016/j.future.2021.02.009
   Phan HT, 2021, INFORM SCIENCES, V561, P243, DOI 10.1016/j.ins.2021.01.008
   Jack H., 2022, ENG DESIGN PLANNING, P211, DOI [10.1016/B978-0-12-821055-0.00006-2, DOI 10.1016/B978-0-12-821055-0.00006-2]
   Jacyntho M. D. A., 2021, WEB SEMANTICS CUTTIN, P195
   Johann G, 2021, SOFTW IMPACTS, V10, DOI 10.1016/j.simpa.2021.100145
   Kapadia B., 2020, STUD INDIAN PLACE NA, V40, P104
   Karaboga D, 2019, ARTIF INTELL REV, V52, P2263, DOI 10.1007/s10462-017-9610-2
   Ketipi MK., 2020, P MANUF, V51, P1305, DOI [10.1016/j.promfg.2020.10.182, DOI 10.1016/J.PROMFG.2020.10.182]
   Khokhlov I, 2020, 2020 IEEE 6TH WORLD FORUM ON INTERNET OF THINGS (WF-IOT)
   Klement EP, 2010, IEEE T FUZZY SYST, V18, P178, DOI 10.1109/TFUZZ.2009.2039367
   Komsiyah S, 2021, PROCEDIA COMPUT SCI, V179, P268, DOI 10.1016/j.procs.2021.01.006
   Krotzsch M, 2017, DESCRIPTION LOGICS
   Lampropoulos G, 2020, VIS INFORM, V4, P32, DOI 10.1016/j.visinf.2020.01.001
   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 LF, 2020, ARTIF INTELL MED, V103, DOI 10.1016/j.artmed.2020.101817
   Li XY, 2021, COMPUT IND, V129, DOI 10.1016/j.compind.2021.103449
   Liu JT, 2021, RELIAB ENG SYST SAFE, V207, DOI 10.1016/j.ress.2020.107352
   Liu YJ, 2021, APPL SOFT COMPUT, V101, DOI 10.1016/j.asoc.2020.107005
   Long JW, 2020, APPL SOFT COMPUT, V91, DOI 10.1016/j.asoc.2020.106205
   Lourdusamy R., 2021, WEB SEMANTICS CUTTIN, P69, DOI [10.1016/B978-0-12-822468-7.00012-2, DOI 10.1016/B978-0-12-822468-7.00012-2]
   Man JY, 2007, NAFIPS 2007 - 2007 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY, P415, DOI 10.1109/NAFIPS.2007.383875
   Manzoor N, 2021, INT J PSYCHOPHYSIOL, V162, P1, DOI 10.1016/j.ijpsycho.2021.01.002
   MohamedIsmayil A, 2019, AM INT J RES SCI TEC, P205
   Mosleh M, 2021, MULTIMED TOOLS APPL, V80, P20423, DOI 10.1007/s11042-021-10686-6
   Moussa S, 2017, PROCEDIA COMPUT SCI, V112, P800, DOI 10.1016/j.procs.2017.08.048
   Muruganantham A, 2020, MULTIMED TOOLS APPL, V79, P3913, DOI 10.1007/s11042-019-7470-2
   Ortega LC, 2019, IEEE ACCESS, V7, P174368, DOI 10.1109/ACCESS.2019.2956918
   Pan ZL, 2021, AUTOMAT CONSTR, V125, DOI 10.1016/j.autcon.2021.103617
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   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
   Qiao C, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102309
   Saini J, 2021, EXPERT SYST APPL, V183, DOI 10.1016/j.eswa.2021.115376
   Selvachandran G, 2021, IEEE T FUZZY SYST, V29, P716, DOI 10.1109/TFUZZ.2019.2961350
   Sharma A, 2021, MULTIMED TOOLS APPL, V80, P35081, DOI 10.1007/s11042-020-09366-8
   Singhal Amit, 2012, OFFICIAL GOOGLE BLOG
   Son LH, 2015, EXPERT SYST APPL, V42, P51, DOI 10.1016/j.eswa.2014.07.026
   Son TT, 1999, TAP CHI TIN HOC VA D, V15, P6
   Song KH, 2021, KNOWL-BASED SYST, V221, DOI 10.1016/j.knosys.2021.106835
   Sun Y, 2021, KNOWL-BASED SYST, V215, DOI 10.1016/j.knosys.2020.106594
   Tang M, 2021, OMEGA-INT J MANAGE S, V100, DOI 10.1016/j.omega.2019.102141
   Tao SH, 2020, IEEE ACCESS, V8, P146027, DOI 10.1109/ACCESS.2020.3014670
   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
   Tran Thi Ngan, 2020, Frontiers in Intelligent Computing: Theory and Applications. Proceedings of the 7th International Conference on FICTA (2018). Advances in Intelligent Systems and Computing (AISC 1013), P11, DOI 10.1007/978-981-32-9186-7_2
   Ngan TT, 2018, ROM J INF SCI TECH, V21, P344
   Triantaphyllou E, 2020, OMEGA-INT J MANAGE S, V94, DOI 10.1016/j.omega.2020.102208
   Troussas C, 2019, EXPERT SYST APPL, V127, P85, DOI 10.1016/j.eswa.2019.03.003
   Tu CH, 2018, LECT NOTES ARTIF INT, V10751, P243, DOI 10.1007/978-3-319-75417-8_23
   Tuan TM, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8050707
   Verborgh R, 2016, J WEB SEMANT, V37-38, P184, DOI 10.1016/j.websem.2016.03.003
   Wang R, 2021, ADV ENG INFORM, V48, DOI 10.1016/j.aei.2021.101257
   Wu Q, 2021, APPL SOFT COMPUT, V98, DOI 10.1016/j.asoc.2020.106757
   Yazdanbakhsh O, 2019, INT J APPROX REASON, V105, P417, DOI 10.1016/j.ijar.2018.10.018
   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
   ZADEH LA, 1979, MACH INTELL, V9, P149
   Zhang Y, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102324
   Zheng X, 2021, NEUROCOMPUTING, V430, P104, DOI 10.1016/j.neucom.2020.10.095
   Zhou B, 2021, ROBOT CIM-INT MANUF, V71, DOI 10.1016/j.rcim.2021.102160
   Zuo C, 2019, MATHEMATICS-BASEL, V7, DOI 10.3390/math7050470
NR 80
TC 0
Z9 0
U1 1
U2 1
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 JUL
PY 2022
VL 81
IS 18
BP 26505
EP 26534
DI 10.1007/s11042-022-13067-9
EA APR 2022
PG 30
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 2P6GW
UT WOS:000788451300003
DA 2022-08-23
ER

PT J
AU Pilkington, G
   Knighting, K
   Bray, L
   Downing, J
   Jack, BA
   Maden, M
   Mateus, C
   Noyes, J
   O'Brien, MR
   Roe, B
   Tsang, A
   Spencer, S
AF Pilkington, Gerlinde
   Knighting, Katherine
   Bray, Lucy
   Downing, Julia
   Jack, Barbara A.
   Maden, Michelle
   Mateus, Ceu
   Noyes, Jane
   O'Brien, Mary R.
   Roe, Brenda
   Tsang, Anthony
   Spencer, Sally
TI The specification, acceptability and effectiveness of respite care and
   short breaks for young adults with complex healthcare needs: protocol
   for a mixed- methods systematic review
SO BMJ OPEN
LA English
DT Review
ID LIFE-LIMITING CONDITIONS; INTERVENTION EFFECTIVENESS; CHILDREN; PEOPLE;
   IMPLEMENTATION; GUIDANCE; SERVICES; PREVALENCE; GUIDELINES; TRANSITION
AB Introduction The number of young adults with complex healthcare needs due to life-limiting conditions/complex physical disability has risen significantly as children with complex conditions survive into adulthood. Respite care and short breaks are an essential service, however, needs often go unmet after the transition to adult services, leading to a significant impact on the life expectancy and quality of life for this population. We aim to identify, appraise and synthesise relevant evidence to explore respite care and short breaks provision for this population, and to develop a conceptual framework for understanding service models.
   Methods and analysis A mixed-methods systematic review conducted in two stages: (1) knowledge map and (2) evidence review. We will comprehensively search multiple electronic databases; use the Citations, Lead authors, Unpublished materials, Google Scholar, Theories, Early examples, and Related projects (CLUSTER) approach, search relevant websites and circulate a 'call for evidence'. Using the setting, perspective, intervention/phenomenon of interest, comparison and evaluation framework, two reviewers will independently select evidence for inclusion into a knowledge map and subsequent evidence review, extract data relating to study and population characteristics, methods and outcomes; and assess the quality of evidence. A third reviewer will arbitrate where necessary. Evidence will be synthesised using the following approaches: quantitative (narratively/conducting meta-analyses where appropriate); qualitative (framework approach); policy and guidelines (documentary analysis informed approach). An overall, integrated synthesis will be created using a modified framework approach. We will use Grading of Recommendations Assessment, Development and Evaluation (GRADE)/GRADE-Confidence in the Evidence from Reviews of Qualitative Research to assess the strength and confidence of the synthesised evidence. Throughout, we will develop a conceptual framework to articulate how service models work in relation to context and setting.
   Ethics and dissemination Ethical approval is not required as this is a systematic review. We will present our work in academic journals, at appropriate conferences; we will disseminate findings across networks using a range of media. Steering and advisory groups were established to ensure findings are shared widely and in accessible formats.
C1 [Pilkington, Gerlinde; Spencer, Sally] Edge Hill Univ, Postgrad Med Inst PGMI, Fac Hlth & Social Care, Ormskirk, England.
   [Knighting, Katherine; Bray, Lucy; Jack, Barbara A.; O'Brien, Mary R.; Roe, Brenda; Tsang, Anthony] Edge Hill Univ, Fac Hlth & Social Care, Res & Innovat Team, Ormskirk, England.
   [Downing, Julia] Int Childrens Palliat Care Network, Assagay, South Africa.
   [Downing, Julia] Edge Hill Univ, Ormskirk, South Africa.
   [Maden, Michelle] Univ Liverpool, Liverpool Reviews & Implementat Grp, Liverpool, Merseyside, England.
   [Mateus, Ceu] Univ Lancaster, Fac Hlth & Med, Div Hlth Res, Lancaster, England.
   [Noyes, Jane] Bangor Univ, Sch Hlth Sci, Bangor, Gwynedd, Wales.
RP Pilkington, G (通讯作者)，Edge Hill Univ, Postgrad Med Inst PGMI, Fac Hlth & Social Care, Ormskirk, England.
EM pilkingg@edgehill.ac.uk
RI Mateus, Ceu/AAC-8146-2021; Tsang, Anthony/AAZ-5809-2020
OI Mateus, Ceu/0000-0001-6219-219X; Tsang, Anthony/0000-0002-2735-7515;
   Downing, Julia/0000-0002-3450-785X; O'Brien, Mary/0000-0002-8509-3650;
   Roe, Brenda/0000-0001-8227-0116; Pilkington,
   Gerlinde/0000-0003-0028-0746; Knighting, Katherine/0000-0002-6292-823X;
   Bray, Lucy/0000-0001-8414-3233; Spencer, Sally/0000-0001-6330-7200;
   Jack, Barbara/0000-0001-6318-516X
FU National Institute for Health Services Research (NIHR), Health Services
   & Delivery Research (HSDR) programme [16/115/17]; Edge Hill University
FX The review is funded by the National Institute for Health Services
   Research (NIHR), Health Services & Delivery Research (HS&DR) programme
   (Study ID 16/115/17). The study is sponsored by Edge Hill University.
CR Anderson LM, 2011, RES SYNTH METHODS, V2, P33, DOI 10.1002/jrsm.32
   [Anonymous], 2016, CRITICAL APPRAISAL C
   [Anonymous], STUD QUAL ASS TOOLS
   [Anonymous], 2015, COCHRANE METHODS
   [Anonymous], 2015, CAR BREAKS RESP CAR
   Booth A, 2006, LIBR HI TECH, V24, P355, DOI 10.1108/07378830610692127
   Booth A, 2013, BMC MED RES METHODOL, V13, DOI 10.1186/1471-2288-13-118
   Bowen GA, 2009, QUAL RES J, V9, P27, DOI 10.3316/QRJ0902027
   Bray N, 2014, BMC HEALTH SERV RES, V14, P1
   Brouwers MC, 2010, CAN MED ASSOC J, V182, pE839, DOI 10.1503/cmaj.090449
   Burns KH, 2010, PEDIATRICS, V126, P638, DOI 10.1542/peds.2009-1658
   Care Quality Commission, 2014, POND SEA CHILDR TRAN
   Carter B, 2015, J CHILD HEALTH CARE, V19, P304, DOI 10.1177/1367493513516391
   CASP, 2014, 10 QUEST HELP YOU MA
   Center for Evidence-Based Management (CEBMa), 2016, CRIT APPR CROSS SECT
   Cochrane Effective Practice and Organisation of Care (EPOC), 2016, SUGG RISK BIAS CRIT, P1
   Critical Appraisal Skills Programme (CASP), 2018, 11 QUESTIONS HELP YO
   Doug M, 2011, ARCH DIS CHILD, V96, P78, DOI 10.1136/adc.2009.163931
   Drummond MF, 1996, BRIT MED J, V313, P275, DOI 10.1136/bmj.313.7052.275
   Flemming K, 2018, J CLIN EPIDEMIOL, V97, P79, DOI 10.1016/j.jclinepi.2017.10.022
   Foundation for people with learning disabilities, 2014, LEARN DIS STAT EFF B
   Fraser LK, 2011, EUR J PAEDIATR NEURO, V15, P326, DOI 10.1016/j.ejpn.2011.02.003
   Fraser LF, PREVALENCE LIFE LIMI
   Fraser LK, 2012, PEDIATRICS, V129, pE923, DOI 10.1542/peds.2011-2846
   Harden A, 2018, J CLIN EPIDEMIOL, V97, P70, DOI 10.1016/j.jclinepi.2017.11.029
   Harris J, 2016, J EPIDEMIOL COMMUN H, V70, P207, DOI 10.1136/jech-2015-205701
   Higgins J. P., 2008, COCHRANE HDB SYSTEMA
   Hoffmann TC, 2014, BMJ-BRIT MED J, V348, DOI [10.1136/bmj.g1687, 10.1055/s-0041-111066]
   Jamal F, 2015, HEALTH EXPECT, V18, P3225, DOI 10.1111/hex.12312
   Kirk S, 2014, PALLIATIVE MED, V28, P342, DOI 10.1177/0269216313507626
   Kneale D, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0142187
   Lewin S, 2015, PLOS MED, V12, DOI 10.1371/journal.pmed.1001895
   Ling J, 2012, INT J PALLIAT NURS, V18, P129, DOI 10.12968/ijpn.2012.18.3.129
   Marsh S, 2011, YOUNG PEOPLE LIFE LI
   Mitchell TK, 2016, INT J PALLIAT NURS, V22, P57, DOI 10.12968/ijpn.2016.22.2.57
   Moher D, 2009, PLOS MED, V6, DOI 10.1371/journal.pmed.1000097
   Moher David, 2009, BMJ, V339, pb2535, DOI 10.1136/bmj.b2535
   Mooney A, DISABLED CHILDREN NU
   Noyes J, 2014, BRIDGING GAP TRANSIT
   Noyes J, 2016, J CLIN EPIDEMIOL, V75, P78, DOI 10.1016/j.jclinepi.2015.12.009
   O'Leary Z., 2014, ESSENTIAL GUIDE DOIN, V2nd ed.
   Oakley, 2005, EVALUATION, V11, P428, DOI DOI 10.1177/1356389005059383
   Oliver SR, 2008, HEALTH EXPECT, V11, P72, DOI 10.1111/j.1369-7625.2007.00476.x
   Pluye P, 2014, ANNU REV PUBL HEALTH, V35, P29, DOI 10.1146/annurev-publhealth-032013-182440
   Remedios C, 2015, PALLIATIVE MED, V29, P223, DOI 10.1177/0269216314560008
   Robertson J, 2011, HEALTH SOC CARE COMM, V19, P337, DOI 10.1111/j.1365-2524.2010.00977.x
   Thomas J, 2003, CHILDREN HLTH EATING
   Tripp D., 1998, BEING REFLEXIVE CRIT, P36
   Welch V, 2012, PLOS MED, V9, DOI 10.1371/journal.pmed.1001333
   Whitaker R, 2016, HEALTH TECHNOL ASSES, V20, P1, DOI [10.1186/s12916-017-0904-7, 10.3310/hta20160]
   Whiting Mark, 2014, Nurs Child Young People, V26, P24, DOI 10.7748/ncyp2014.05.26.4.24.e389
NR 51
TC 2
Z9 2
U1 0
U2 0
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 JUN
PY 2019
VL 9
IS 6
AR e030470
DI 10.1136/bmjopen-2019-030470
PG 10
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC General & Internal Medicine
GA ND7XT
UT WOS:000562117100229
PM 31213455
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Du, JX
   Wang, SH
   Ye, XY
   Sinton, DS
   Kemp, K
AF Du, Jiaxin
   Wang, Shaohua
   Ye, Xinyue
   Sinton, Diana S.
   Kemp, Karen
TI GIS-KG: building a large-scale hierarchical knowledge graph for
   geographic information science
SO INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
LA English
DT Article
DE Geographic information science (GIS); ontology; knowledge graph;
   information retrieval; natural language processing
ID QUEST; MARK; WEB
AB An organized knowledge base can facilitate the exploration of existing knowledge and the detection of emerging topics in a domain. Knowledge about and around Geographic Information Science and its associated system technologies (GIS) is complex, extensive and emerging rapidly. Taking the challenge, we built a GIS knowledge graph (GIS-KG) by (1) merging existing GIS bodies of knowledge to create a hierarchical ontology and then (2) applying deep-learning methods to map GIS publications to the ontology. We conducted several experiments on information retrieval to evaluate the novelty and effectiveness of the GIS-KG. Results showed the robust support of GIS-KG for knowledge search of existing GIS topics and potential to explore emerging research themes.
C1 [Du, Jiaxin; Ye, Xinyue] Texas A&M Univ, Dept Landscape Architecture & Urban Planning, College Stn, TX 77843 USA.
   [Wang, Shaohua] New Jersey Inst Technol, Ying Wu Coll Comp, Newark, NJ 07102 USA.
   [Sinton, Diana S.] Univ Consortium Geog Informat Sci, Ithaca, NY 14851 USA.
   [Sinton, Diana S.] Cornell Univ, Coll Agr & Life Sci, Ithaca, NY 14853 USA.
   [Kemp, Karen] Univ Southern Calif, Dornsife Coll Letters Arts & Sci, Los Angeles, CA USA.
RP Ye, XY (通讯作者)，Texas A&M Univ, Dept Landscape Architecture & Urban Planning, College Stn, TX 77843 USA.
EM xinyue.ye@tamu.edu
OI Sinton, Diana/0000-0001-5828-9001
FU NSF [1937908, 2122054]; Texas A&M University Harold Adams
   Interdisciplinary Professorship Research Fund; Texas A&M University
   College of Architecture Faculty Startup Fund
FX This work was supported by the NSF [1937908, 2122054]; Texas A&M
   University Harold Adams Interdisciplinary Professorship Research Fund;
   Texas A&M University College of Architecture Faculty Startup Fund
CR Adhikari A., 2019, ARXIV PREPRINT ARXIV
   Ahearn SC, 2013, INT J GEOGR INF SCI, V27, P2227, DOI 10.1080/13658816.2013.802324
   ALTAHA KK, 1994, INT J GEOGR INF SYST, V8, P95, DOI 10.1080/02693799408901988
   Babinski G., 2012, WASH GIS C, V9
   Biljecki F, 2016, INT J GEOGR INF SCI, V30, P1302, DOI 10.1080/13658816.2015.1130831
   Bird S., 2009, NATURAL LANGUAGE PRO
   Bojanowski P., 2017, T ASSOC COMPUT LING, V5, P135, DOI [DOI 10.1162/TACL_A_00051, 10.1162/tacl_a_00051]
   Bostock M, 2011, IEEE T VIS COMPUT GR, V17, P2301, DOI 10.1109/TVCG.2011.185
   Bourque P., 2014, P 10 INT WORKSH SOFT, P8
   Brown T., 2020, ARXIV200514165, V33, P1877
   Cochrane L, 2017, CARTOGR GEOGR INF SC, V44, P507, DOI 10.1080/15230406.2016.1212673
   Dai ZY, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P985, DOI 10.1145/3331184.3331303
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   DiBiase David, 2010, URISA Journal, V22, P55
   DiBiase D., 2007, CARTOGR GEOGR INF SC, V34, P113, DOI DOI 10.1559/152304007781002253
   Fan WQ, 2019, RECSYS 2019: 13TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, P305, DOI 10.1145/3298689.3347011
   Fricke S, 2018, J MED LIBR ASSOC, V106, P145, DOI 10.5195/jmla.2018.280
   Ghorbanzadeh Omid, 2020, Journal of Spatial Science, V65, P401, DOI 10.1080/14498596.2018.1505564
   Guo JF, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P701, DOI 10.1145/2983323.2983768
   Gusenbauer M, 2019, SCIENTOMETRICS, V118, P177, DOI 10.1007/s11192-018-2958-5
   Haklay M, 2008, GEOGR COMPASS, V2, P2011, DOI 10.1111/j.1749-8198.2008.00167.x
   Harzing AW, 2017, SCIENTOMETRICS, V112, P1887, DOI 10.1007/s11192-017-2454-3
   Herzig J, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P4320
   Hofer B, 2020, T GIS, V24, P587, DOI 10.1111/tgis.12628
   Holden E., 2012, P 13 ANN C INF TECHN, P195
   Janowicz K., 2021, ARCUSER, P16
   Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418
   Johnson A, 2019, KEY CHAL GEOGR, P271, DOI 10.1007/978-3-030-04750-4_14
   Johnson John, 2010, URISA Journal, V22, P31
   Kusner MJ, 2015, PR MACH LEARN RES, V37, P957
   Li WW, 2019, ISPRS INT GEO-INF, V8, DOI 10.3390/ijgi8110496
   MacAvaney S, 2018, ACM/SIGIR PROCEEDINGS 2018, P1205, DOI 10.1145/3209978.3210135
   Mai G., 2020, INT C LEARN REPR VIR
   Mai GC, 2020, T GIS, V24, P623, DOI 10.1111/tgis.12629
   Malczewski J, 2020, INT J GEOGR INF SCI, V34, P1257, DOI 10.1080/13658816.2020.1712403
   Manning C.D., 2008, INTRO INFORM RETRIEV
   Marble, 2000, BIBLIO APPL GISCIENC
   Marble D, 2001, ESRI US C DIEGO CAL, V1
   Martin-Martin A, 2018, J INFORMETR, V12, P1160, DOI 10.1016/j.joi.2018.09.002
   Miller George, 1998, WORDNET ELECT LEXICA
   Nyerges T, 2014, INT J GEOGR INF SCI, V28, P1165, DOI 10.1080/13658816.2013.853304
   Pele O, 2008, LECT NOTES COMPUT SC, V5304, P495, DOI 10.1007/978-3-540-88690-7_37
   Pele O, 2009, IEEE I CONF COMP VIS, P460, DOI 10.1109/ICCV.2009.5459199
   Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, P333, DOI 10.1561/1500000019
   Robertson SE, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P603
   Siabato W, 2014, T GIS, V18, P799, DOI 10.1111/tgis.12080
   Sinha A, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P243, DOI 10.1145/2740908.2742839
   Skupin A, 2014, INT J GEOGR INF SCI, V28, P1209, DOI 10.1080/13658816.2014.906040
   Tang J., 2008, P 14 ACM SIGKDD INT, P990, DOI DOI 10.1145/1401890.1402008
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   Vancauwenberghe G., 2016, INT SCI J MICRO MACR, V6, P7
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Wang, 2020, MANUAL DIGITAL EARTH, P755
   Wang KS, 2019, FRONT BIG DATA, V2, DOI 10.3389/fdata.2019.00045
   Wang WB, 2021, T GIS, V25, P2675, DOI 10.1111/tgis.12798
   Wikle T, 2020, T GIS, V24, P1681, DOI 10.1111/tgis.12677
   Yang CW, 2011, INT J DIGIT EARTH, V4, P305, DOI 10.1080/17538947.2011.587547
   Ye XY, 2021, J GEOVIS SPAT ANAL, V5, DOI 10.1007/s41651-021-00073-y
   Zaharia M, 2016, COMMUN ACM, V59, P56, DOI 10.1145/2934664
   Zalta E.N., 1995, STANFORD ENCY PHILOS
   Zhan FB, 2014, INT J GEOGR INF SCI, V28, P1233, DOI 10.1080/13658816.2013.847186
   [钟赛香 Zhong Saixiang], 2015, [地理学报, Acta Geographica Sinica], V70, P678
NR 62
TC 0
Z9 0
U1 24
U2 41
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 MAY 4
PY 2022
VL 36
IS 5
BP 873
EP 897
DI 10.1080/13658816.2021.2005795
EA NOV 2021
PG 25
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 0T6SA
UT WOS:000722837200001
DA 2022-08-23
ER

PT J
AU Mohamed, A
   Abuoda, G
   Ghanem, A
   Kaoudi, Z
   Aboulnaga, A
AF Mohamed, Aisha
   Abuoda, Ghadeer
   Ghanem, Abdurrahman
   Kaoudi, Zoi
   Aboulnaga, Ashraf
TI RDFFrames: Knowledge Graph Access for Machine Learning Tools
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB Knowledge graphs represented in RDF are becoming increasingly popular and are essential to many machine learning applications. A rich ecosystem of RDF data management systems and tools has evolved over the years, most notably RDF database management systems that support the SPARQL query language. Surprisingly, machine learning tools for knowledge graphs typically do not use SPARQL despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of expected data model and interface style. Machine learning tools work on data in tabular format and process it using imperative relational API calls, while SPARQL matches graph patterns to RDF triples. To access knowledge graphs for machine learning, we observe that it is more natural to use a navigational paradigm based on graph traversal rather than the SPARQL paradigm based on triple patterns. We demonstrate RDFFrames, a framework that bridges the gap between machine learning tools and RDF database systems by offering the usability and flexibility of machine learning tools together with the performance of a database system. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPARQL query, executes it on the database system, and returns the results in a standard tabular format.
C1 [Mohamed, Aisha; Aboulnaga, Ashraf] HBKU, Qatar Comp Res Inst, Ar Rayyan, Qatar.
   [Abuoda, Ghadeer] HBKU, Coll Sci & Engn, Ar Rayyan, Qatar.
   [Ghanem, Abdurrahman] Bluescape, San Carlos, CA USA.
   [Kaoudi, Zoi] Tech Univ Berlin, Berlin, Germany.
RP Mohamed, A (通讯作者)，HBKU, Qatar Comp Res Inst, Ar Rayyan, Qatar.
OI Kaoudi, Zoi/0000-0003-4520-5360
CR Hasan W., 1992, SIGMOD
   Mohamed A., 2020, ARXIV200203614
   Wang Y., 2018, AAAI
   Wickham H, 2011, J STAT SOFTW, V40, P1, DOI 10.18637/jss.v040.i01
NR 4
TC 1
Z9 1
U1 1
U2 3
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 2020
VL 13
IS 12
BP 2889
EP 2892
DI 10.14778/3415478.3415501
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PC9FL
UT WOS:000597303100024
DA 2022-08-23
ER

PT J
AU Li, Q
   Wang, DL
   Feng, S
   Niu, C
   Zhang, YF
AF Li, Qian
   Wang, Daling
   Feng, Shi
   Niu, Cheng
   Zhang, Yifei
TI Global Graph Attention Embedding Network for Relation Prediction in
   Knowledge Graphs
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article; Early Access
DE Spread spectrum communication; Semantics; Predictive models; Feature
   extraction; Prediction algorithms; Task analysis; Learning systems;
   Graph attention network; knowledge graphs; path embedding; relation
   prediction
AB The incompleteness of knowledge graphs triggers considerable research interest in relation prediction. As the key to predicting relations among entities, many efforts have been devoted to learning the embeddings of entities and relations by incorporating a variety of neighbors' information which includes not only the information from direct outgoing and incoming neighbors but also the ones from the indirect neighbors on the multihop paths. However, previous models usually consider entity paths of limited length or ignore sequential information of the paths. Either simplification will make the model lack a global understanding of knowledge graphs and may result in the loss of important and indispensable information. In this article, we propose a novel global graph attention embedding network (GGAE) for relation prediction by combining global information from both direct neighbors and multihop neighbors. Concretely, given a knowledge graph, we first introduce the path construction algorithms to obtain meaningful paths, then design path modeling methods to capture the potential long-distance sequential information in the multihop paths, final propose an entity graph attention and a relation graph attention mechanisms to obtain entity embeddings and relation embeddings. Moreover, an entity graph attention mechanism is proposed to calculate the entity embeddings by aggregating direct incoming and outgoing neighbors from: 1) an original knowledge graph with the original entity and relation embeddings and 2) a new knowledge graph constructed by the paths whose embeddings are updated by path modeling methods. for each relation, we construct a new graph with related entities and present a relation graph attention to learn the features. Therefore, our model can encapsulate the information from different distance neighbors, and enable the embeddings of entities and relations to better capture all-sided semantic information. The experimental results on benchmark datasets verify the superiority of our model over the state-of-the-art ones.
C1 [Li, Qian; Wang, Daling; Feng, Shi; Zhang, Yifei] Northeastern Univ, Coll Comp Sci & Engn, Shenyang 110169, Peoples R China.
   [Niu, Cheng] Tencent Co, Beijing 518000, Peoples R China.
RP Wang, DL (通讯作者)，Northeastern Univ, Coll Comp Sci & Engn, Shenyang 110169, Peoples R China.
EM wangdaling@cse.neu.edu.cn
OI Li, Qian/0000-0002-9589-251X; Feng, Shi/0000-0002-2846-7652
FU National Key Research and Development Program of China [2018YFB1004700];
   National Natural Science Foundation of China [61772122, 61872074];
   National Defense Basic Research Program [JCKY2018205C012]; Fundamental
   Research Funds for the Central Universities of China [N2016008]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018YFB1004700, in part by the
   National Natural Science Foundation of China under Grant 61772122 and
   Grant 61872074, in part by the National Defense Basic Research Program
   under Grant JCKY2018205C012, and in part by the Fundamental Research
   Funds for the Central Universities of China under Grant N2016008.
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao QX, 2022, IEEE T NEUR NET LEAR, V33, P2758, DOI 10.1109/TNNLS.2020.3045034
   Chen W., 2018, P 2018 C N AM CHAPT, V1, P1823, DOI [10.18653/v1/N18-1165, DOI 10.18653/V1/N18-1165]
   Chen YZ, 2016, ROY SOC OPEN SCI, V3, DOI 10.1098/rsos.160064
   Cho Kyunghyun, 2014, ARXIV14061078, P1724, DOI DOI 10.3115/V1/D14-1179
   Das R., 2018, ICLR
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Diefenbach D, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1087, DOI 10.1145/3184558.3191541
   Garcia-Duran A., 2015, P 2015 C EMP METH NA, P286, DOI DOI 10.18653/V1/D15-1034
   Gelbukh A., 2019, P C EMP METH NAT LAN, P154, DOI 10.18653/v1/D19-1015
   Guo LB, 2019, PR MACH LEARN RES, V97
   Guu K., 2015, ARXIV150601094
   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
   He H, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1766, DOI 10.18653/v1/P17-1162
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hopcroft J. E., 1973, SIAM Journal on Computing, V2, P225, DOI 10.1137/0202019
   Huang H, 2022, IEEE T NEUR NET LEAR, V33, P983, DOI 10.1109/TNNLS.2020.3036825
   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
   Lin X.V., 2018, ARXIV180810568
   Lin XX, 2019, NEURAL COMPUT APPL, V31, P5629, DOI 10.1007/s00521-018-3384-6
   Lin Y., 2015, P 2015 C EMP METH NA, P705, DOI DOI 10.18653/V1/D15-1082
   Liu YY, 2011, NATURE, V473, P167, DOI 10.1038/nature10011
   Moon C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2215, DOI 10.1145/3132847.3133095
   Murota Kazuo, 2009, MATRICES MATROIDS SY, V20
   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
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Schuhmacher M, 2014, WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P543, DOI 10.1145/2556195.2556250
   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
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   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
   West R, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P515, DOI 10.1145/2566486.2568032
   Xiong Chenyan, 2015, P 24 ACM INT C INF K, P951
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Xu CF, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3940
   Xu YA, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4620
   Yang B., 2014, ABS14126575 CORR
   Zeiner Herwig, 2019, Decision Support Systems IX: Main Developments and Future Trends. 5th International Conference on Decision Support System Technology (EmC-ICDSST 2019). Proceedings. Lecture Notes in Business Information Processing (LNBIP 348), P57, DOI 10.1007/978-3-030-18819-1_5
   Zhang T., 2019, P EMNLP IJCNLP, P2662
NR 42
TC 1
Z9 1
U1 9
U2 24
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.2021.3083259
EA JUN 2021
PG 14
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 XR5LC
UT WOS:000732269300001
PM 34115594
DA 2022-08-23
ER

PT J
AU Du, Y
   Ranwez, S
   Sutton-Charani, N
   Ranwez, V
AF Du, Yu
   Ranwez, Sylvie
   Sutton-Charani, Nicolas
   Ranwez, Vincent
TI Post-hoc recommendation explanations through an efficient exploitation
   of the DBpedia category hierarchy
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Linked Open Data (LOD); Knowledge graph; Recommender system;
   Recommendation explanation; DBpedia; Ontology
ID SYSTEMS
AB Leveraging knowledge graphs for post-hoc recommendation explanations has been investigated in recent years. Existing approaches rely mainly on the overlap properties (encoded by knowledge graphs) that characterize both user liked items and the recommended ones. These approaches, however, do not fully leverage the property hierarchy of knowledge graphs which may lead to flawed explanations. In this paper we introduce an approach that takes the whole property hierarchy into account. This is done with a limited computation time overhead thanks to efficient algorithmic optimizations relying on sub-ontology extraction. The hierarchical relationships among properties are also considered to avoid redundant properties for explanation. We carried out a user study of 155 participants in the movie recommendation domain and used both offline and online metrics to assess the proposed approach. Significant improvements, in terms of informativeness (by 39%), persuasiveness (by 22%), engagement (by 29%) and user trust (by 26%), are suggested by the obtained results, as compared to the state-of-the-art property-based explanation model. Our findings indicate the superiority of accounting for the whole property hierarchy when dealing with post-hoc recommendation explanations. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
C1 [Du, Yu; Ranwez, Sylvie; Sutton-Charani, Nicolas] Univ Montpellier, EuroMov Digital Hlth Mot, IMT Mines Ales, Ales, France.
   [Ranwez, Vincent] Univ Montpellier, UMR AGAP Inst, Inst Agro, CIRAD, F-34398 Montpellier, France.
RP Ranwez, S (通讯作者)，Univ Montpellier, EuroMov Digital Hlth Mot, IMT Mines Ales, Ales, France.
EM yu.du@mines-ales.fr; sylvie.ranwez@mines-ales.fr;
   nicolas.sutton-charani@mines-ales.fr; vincent.ranwez@supagro.fr
RI ranwez, vincent/I-5490-2017; Ranwez, Sylvie/F-5975-2013
OI ranwez, vincent/0000-0002-9308-7541; Ranwez, Sylvie/0000-0002-8707-0313
CR Abdollahi B, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P79, DOI 10.1145/3109859.3109913
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bizer C., 2008, P 17 INT C WORLD WID, P1265, DOI DOI 10.1145/1367497.1367760
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Dacrema MF, 2019, RECSYS 2019: 13TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, P101, DOI 10.1145/3298689.3347058
   Di Noia T., 2012, P 8 INT C SEM SYST, P1, DOI DOI 10.1145/2362499.2362501
   Dooms S., 2013, WORKSH CROWDS HUM CO, P43
   Du Y, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102721
   European organization for nuclear research, 2013, OPENAIRE, DOI [10.25495/7GXK-RD71, DOI 10.25495/7GXK-RD71]
   Farber M, 2018, SEMANT WEB, V9
   Gedikli F, 2014, INT J HUM-COMPUT ST, V72, P367, DOI 10.1016/j.ijhcs.2013.12.007
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Herlocker J. L., 2000, CSCW 2000. ACM 2000 Conference on Computer Supported Cooperative Work, P241
   Lin W., 2000, DATA MIN KNOWL DISC, V6, P83, DOI DOI 10.1023/A:1013284820704
   Lops P, 2019, USER MODEL USER-ADAP, V29, P239, DOI 10.1007/s11257-019-09231-w
   Love MI, 2014, GENOME BIOL, V15, DOI 10.1186/s13059-014-0550-8
   Lully V, 2018, PROCEDIA COMPUT SCI, V137, P211, DOI 10.1016/j.procs.2018.09.020
   Musto C, 2021, USER MODEL USER-ADAP, V31, P629, DOI 10.1007/s11257-020-09270-8
   Musto C, 2019, INT J HUM-COMPUT ST, V121, P93, DOI 10.1016/j.ijhcs.2018.03.003
   Musto C, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P151, DOI 10.1145/2959100.2959173
   Nanou T, 2010, MULTIMEDIA SYST, V16, P219, DOI 10.1007/s00530-010-0190-0
   Nunes I, 2017, USER MODEL USER-ADAP, V27, P393, DOI 10.1007/s11257-017-9195-0
   Peake G, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2060, DOI 10.1145/3219819.3220072
   Ranwez V, 2012, IEEE T KNOWL DATA EN, V24, P2288, DOI 10.1109/TKDE.2011.173
   Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3
   Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1
   Sanchez D, 2011, KNOWL-BASED SYST, V24, P297, DOI 10.1016/j.knosys.2010.10.001
   Tintarev N, 2012, USER MODEL USER-ADAP, V22, P399, DOI 10.1007/s11257-011-9117-5
   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 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
NR 31
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 JUN 7
PY 2022
VL 245
AR 108560
DI 10.1016/j.knosys.2022.108560
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1Q9CM
UT WOS:000802978200013
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Lu, DL
   Zhu, DJ
   Du, HW
   Sun, YD
   Wang, YS
   Li, XF
   Qu, RN
   Cao, N
   Higgs, R
AF Lu, Donglei
   Zhu, Dongjie
   Du, Haiwen
   Sun, Yundong
   Wang, Yansong
   Li, Xiaofang
   Qu, Rongning
   Cao, Ning
   Higgs, Russell
TI Fusion Recommendation System Based on Collaborative Filtering and
   Knowledge Graph
SO COMPUTER SYSTEMS SCIENCE AND ENGINEERING
LA English
DT Article
DE Fusion recommendation system; knowledge graph; graph embedding
ID NETWORK; MODEL
AB The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to the user based on the known historical interaction data of the target user. Furthermore, the combination of the recommended algorithm based on collaborative filtration and other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model (CoFM) is one representative research. CoFM, a fusion recommendation model combining the collaborative filtering model FM and the graph embedding model TransE, introduces the information of many entities and their relations in the knowledge graph into the recommendation system as effective auxiliary information. It can effectively improve the accuracy of recommendations and alleviate the problem of sparse user historical interaction data. Unfortunately, the graph-embedded model TransE used in the CoFM model cannot solve the 1-N, N-1, and N-N problems well. To tackle this problem, a novel fusion recommendation model Joint Factorization Machines and TransH Model (JFMH) is proposed, which improves CoFM by replacing the TransE model with TransH model. A large number of experiments on two widely used benchmark data sets show that compared with CoFM, JFMH has improved performance in terms of item recommendation and knowledge graph completion, and is more competitive than multiple baseline methods.
C1 [Lu, Donglei; Cao, Ning] Wuxi Vocat Coll Sci & Technol, Sch Artificial Intelligence, Wuxi 214028, Jiangsu, Peoples R China.
   [Zhu, Dongjie; Wang, Yansong] Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China.
   [Du, Haiwen; Sun, Yundong] Harbin Inst Technol, Sch Astronaut, Harbin 150001, Peoples R China.
   [Li, Xiaofang; Qu, Rongning] Harbin Inst Technol, Dept Math, Weihai 264209, Peoples R China.
   [Higgs, Russell] Univ Coll Dublin, Sch Math Sci, Dublin 4, Ireland.
RP Zhu, DJ (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China.
EM zhudongjie@hit.edu.cn
OI Higgs, Russell/0000-0002-0896-6788
FU State Grid Shandong Electric Power Company Science and Technology
   Project Funding [520613200001, 520613180002, 62061318C002]; Weihai
   Scientific Research and Innovation Fund
FX This work is funded by State Grid Shandong Electric Power Company
   Science and Technology Project Funding under Grant no. 520613200001,
   520613180002, 62061318C002, and Weihai Scientific Research and
   Innovation Fund (2020).
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
   Bai HT, 2020, CMC-COMPUT MATER CON, V65, P1591, DOI 10.32604/cmc.2020.09981
   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
   Di Noia T, 2016, ACM T INTEL SYST TEC, V8, DOI 10.1145/2899005
   Guo Q, 2020, ARXIV PREPRINT ARXIV
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   Huang J, 2018, ACM/SIGIR PROCEEDINGS 2018, P505, DOI 10.1145/3209978.3210017
   Jiang WJ, 2019, CMC-COMPUT MATER CON, V61, P849, DOI 10.32604/cmc.2019.05932
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   Piao G., 2018, P ESWC 2018 HER CREL
   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
   Schafer J.B., 1999, P 1 ACM C EL COMM
   Serrano D, 2008, SYM REL DIST SYST, P127, DOI 10.1109/SRDS.2008.22
   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 Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wulam A, 2019, CMC-COMPUT MATER CON, V60, P1003, DOI 10.32604/cmc.2019.07704
   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 Y, 2018, ARXIV PREPRINT ARXIV
   Zhu DJ, 2020, CMC-COMPUT MATER CON, V63, P1343, DOI 10.32604/cmc.2020.010008
   Zhu DJ, 2020, CMC-COMPUT MATER CON, V63, P979, DOI 10.32604/cmc.2020.06478
NR 24
TC 0
Z9 0
U1 18
U2 18
PU TECH SCIENCE PRESS
PI HENDERSON
PA 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA
SN 0267-6192
J9 COMPUT SYST SCI ENG
JI Comput. Syst. Sci. Eng.
PY 2022
VL 42
IS 3
BP 1133
EP 1146
DI 10.32604/csse.2022.021525
PG 14
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZF4MM
UT WOS:000759543900002
OA hybrid
DA 2022-08-23
ER

PT J
AU Amador-Dominguez, E
   Serrano, E
   Manrique, D
   Hohenecker, P
   Lukasiewicz, T
AF Amador-Dominguez, Elvira
   Serrano, Emilio
   Manrique, Daniel
   Hohenecker, Patrick
   Lukasiewicz, Thomas
TI An ontology-based deep learning approach for triple classification with
   out-of-knowledge-base entities
SO INFORMATION SCIENCES
LA English
DT Article
DE Knowledge graph embeddings; Entity initialization; Knowledge graph
   completion; Word embeddings; Ontological information
ID EMBEDDINGS
AB Knowledge graphs (KGs) are one of the most common frameworks for knowledge representation. However, they suffer from a severe scalability problem that hinders their usage. KG embedding aims to provide a solution to this issue. Nonetheless, general approaches are incapable of representing and reasoning about information not previously contained in the graph. This paper proposes to leverage semantic and ontological information for a significant benefit of knowledge graph completion, focusing on triple classification. The goal of this task is to determine whether a given fact holds. Furthermore, this paper also considers the classification of facts that include entities that have not been seen during training, denoted out-of-knowledge-base or OOKB entities. An incremental method is presented, composed of six stages. Although the proposal can be applied to any KG embedding model, this work focuses on its application for semantic matching models, such as ComplEx and DistMult. Compared to other approaches, our proposal is model-agnostic, computationally inexpensive, and does not require retraining. The results show that triple classification accuracy scales up to 15% with the proposed approach, as well as accelerating the convergence of the model to its optimal solution. Furthermore, facts containing OOKB entities can be classified with a reasonable accuracy.
   (c) 2021 Elsevier Inc. All rights reserved.
C1 [Amador-Dominguez, Elvira; Serrano, Emilio] Univ Politecn Madrid, Dept Artificial Intelligence, ETSI Informat, Ontol Engn Grp, Madrid 28660, Spain.
   [Manrique, Daniel] Univ Politecn Madrid, Dept Artificial Intelligence, Artificial Intelligence Lab, ETSI Informat, Madrid 28660, Spain.
   [Hohenecker, Patrick; Lukasiewicz, Thomas] Univ Oxford, Dept Comp Sci, Oxford OX1 3QD, England.
RP Serrano, E (通讯作者)，Univ Politecn Madrid, Dept Artificial Intelligence, ETSI Informat, Ontol Engn Grp, Madrid 28660, Spain.
EM emilioserra@fi.upm.es
RI Manrique, Daniel/F-9625-2013
OI Manrique, Daniel/0000-0002-0792-4156; Amador-Dominguez,
   Elvira/0000-0001-6838-1266
FU Spanish Ministry of Science, Innovation and Universities under the
   program "Estancias de movilidad en el extranjero Jose Castillejo para
   jovenes doctores" [CAS18/00229]; Autonomous Region of Madrid through the
   program CABAHLA-CM [P2018/TCS4423]; "Universidad Politecnica de Madrid"
   under the program "Ayudas al Personal Docente e Investigador para
   Estancias Breves en el Extranjero"; "Universidad Politecnica de Madrid"
   under the program "Ayudas para Contratos Predoctorales para la
   Realizacion del Doctorado"; UK EPSRC [EP/R013667/1]; Alan Turing
   Institute under the EPSRC [EP/N510129/1]; EPSRC [OUCL/2016/PH];
   Oxford-DeepMind Graduate Scholarship [GAF1617_OGSMFDMCS_1036172]
FX This research work is supported by the Spanish Ministry of Science,
   Innovation and Universities under the program "Estancias de movilidad en
   el extranjero Jose Castillejo para jovenes doctores" (CAS18/00229), by
   the Autonomous Region of Madrid through the program CABAHLA-CM (GA No.
   P2018/TCS4423), and by the "Universidad Politecnica de Madrid" under the
   programs "Ayudas al Personal Docente e Investigador para Estancias
   Breves en el Extranjero", and "Ayudas para Contratos Predoctorales para
   la Realizacion del Doctorado". Furthermore, this work was supported by
   the UK EPSRC grant EP/R013667/1 and by the Alan Turing Institute under
   the EPSRC grant EP/N510129/1. Patrick is also supported by the EPSRC
   grant OUCL/2016/PH and the OxfordDeepMind Graduate Scholarship grant
   GAF1617_OGSMFDMCS_1036172. The authors also thank the reviewers and
   editors for their valuable comments and suggestions, which have improved
   this paper.
CR [Anonymous], 2018, P INT C LANG RES EV
   Arora S., 2020, ABS200712374 CORR
   Ba J, 2015, 3 INT C LEARN REPR C
   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
   Devlin J, 2018, P 2019 C N AM CHAPT
   Duchi J, 2011, J MACH LEARN RES, V12, P2121
   Guo S., 2016, P C EMP METH NAT LAN, P192, DOI 10.18653/v1/D16-1019
   Hamaguchi T, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1802
   Hohenecker P, 2020, J ARTIF INTELL RES, V68, P503
   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
   Kazemi S.M., 2018, ADV NEURAL INFORM PR, V31, P4295
   Li C., 2019, ABS191006708 CORR
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Mikolov Tomas, 2013, 1 INT C LEARN REPR W
   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
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Noy N.F., 2001, TECHNICAL REPORT STA
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Peters M.E., 2018, P N AM CHAPT ASS COM
   Ramamurthy A., 2020, ABS200111850 CORR
   Raunak V, 2019, 4TH WORKSHOP ON REPRESENTATION LEARNING FOR NLP (REPL4NLP-2019), P235
   Reiter R., 1978, Logic and data bases, P55
   Shah H, 2019, AAAI CONF ARTIF INTE, P3044
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sun Zhiqing, 2019, 7 INT C LEARN REPR
   Tang X, 2019, INFORM PROCESS MANAG, V56, P809, DOI 10.1016/j.ipm.2019.01.005
   Tay Y, 2017, AAAI CONF ARTIF INTE, P1243
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang P., 2019, ABS190303772 CORR
   Wang Q, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1859
   Yang B., 2015, P INT C LEARN REPR
   Zhang RC, 2020, INFORM SCIENCES, V521, P144, DOI 10.1016/j.ins.2020.02.016
   Zhang W, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P96, DOI 10.1145/3289600.3291014
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
   Zhou J, 2018, ARTIF CELL NANOMED B, V46, pS1016, DOI 10.1080/21691401.2018.1442841
NR 39
TC 2
Z9 2
U1 4
U2 13
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 564
BP 85
EP 102
DI 10.1016/j.ins.2021.02.018
EA MAR 2021
PG 18
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RY1BF
UT WOS:000647651100005
DA 2022-08-23
ER

PT J
AU Wang, YJ
   Zhu, L
   Ma, JT
   Hu, GW
   Liu, JC
   Qiao, YQ
AF Wang, Yanjun
   Zhu, Liang
   Ma, Jiangtao
   Hu, Guangwu
   Liu, Jiangchuan
   Qiao, Yaqiong
TI Knowledge Graph-Based Spatial-Aware User Community Preference Query
   Algorithm for LBSNs
SO BIG DATA RESEARCH
LA English
DT Article
DE LBSNs; User community; Spatial preference query; Pruning strategy;
   Knowledge graph
ID ROUTE RECOMMENDATION; PATTERN
AB User community preference in Location-Based Social Networks (LBSNs) can meet the diversified location demands of group LBSN users. Although individual's location-based service recommendation or personal spatial preference query problem has been well addressed by many studies, user group or user community preference query is still under way and most only consider the spatial distance factor, which causes accuracy cannot satisfy user demands. To solve the user community spatial preference problem and improve its performance, a knowledge graph-based spatial-aware user community preference query algorithm, Type R-tree (tR-tree) Query Algorithm (TRQA) is proposed to effectively discover user's community preference from LBSNs considering both location semantic information and preference weight of users' Points of Interest (POIs). To achieve this goal, this paper first leverages the tR-tree spatial index to improve query efficiency. Then a community satisfaction degree model based on knowledge graphs is introduced to comprehensively evaluate whether the POI can best meet the preference requirements of a user community. The experimental results show that TRQA has outperformed Perceptual Quality Adaptation Algorithm (PQA) in terms of pruning efficiency and query time. The query time of our proposed algorithm is 80% shorter than PQA as the number of users in the user community changes. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Wang, Yanjun; Qiao, Yaqiong] State Key Lab Math Engn & Adv Comp, Zhengzhou 450001, Peoples R China.
   [Wang, Yanjun; Zhu, Liang; Ma, Jiangtao] Zhengzhou Univ Light Ind, Zhengzhou 450001, Peoples R China.
   [Hu, Guangwu] Shenzhen Inst Informat Technol, Sch Comp Sci, Shenzhen 518172, Peoples R China.
   [Liu, Jiangchuan] Simon Fraser Univ, Sch Comp Sci, Burnaby, BC, Canada.
RP Hu, GW (通讯作者)，Shenzhen Inst Informat Technol, Sch Comp Sci, Shenzhen 518172, Peoples R China.
EM hugw@sziit.edu
OI HU, Guangwu/0000-0003-3947-9998; , Jiangtao/0000-0001-5181-4045
FU National Natural Science Foundation of China [61902361]; Henan
   Provincial Science and Technology Department [202102310237,
   202102310295]; Doctoral Research Fund of Zhengzhou University of Light
   Industry [2018BSJJ039]; Fundamental Research Project of Shenzhen
   Municipality [JCYJ20170817115335418]
FX This work is supported by National Natural Science Foundation of China
   (No. 61902361), Henan Provincial Science and Technology Department
   (202102310237, 202102310295), the Doctoral Research Fund of Zhengzhou
   University of Light Industry (2018BSJJ039), and the Fundamental Research
   Project of Shenzhen Municipality (JCYJ20170817115335418).
CR Aridhi S, 2016, BIG DATA RES, V6, P1, DOI 10.1016/j.bdr.2016.07.002
   Asban P, 2019, WORLD ENVIRONMENTAL AND WATER RESOURCES CONGRESS 2019: GROUNDWATER, SUSTAINABILITY, HYDRO-CLIMATE/CLIMATE CHANGE, AND ENVIRONMENTAL ENGINEERING, P336
   Cai TT, 2022, IEEE T KNOWL DATA EN, V34, P1993, DOI 10.1109/TKDE.2020.3003047
   Cao XL, 2011, IEEE INT CONF TRUST, P373, DOI 10.1109/TrustCom.2011.48
   Chen LS, 2013, PROC VLDB ENDOW, V6, P217, DOI 10.14778/2535569.2448955
   Ding YC, 2019, IEEE T BIG DATA, V5, P550, DOI 10.1109/TBDATA.2018.2830780
   Dong Sicong, 2020, 25 INT C DAT SYST AD
   Duan XR, 2017, P INT COMP SOFTW APP, P602, DOI 10.1109/COMPSAC.2017.68
   Geng M, 2012, 2012 THIRD INTERNATIONAL CONFERENCE ON NETWORKING AND COMPUTING (ICNC 2012), P137, DOI 10.1109/ICNC.2012.28
   Ghosh B, 2018, PROC VLDB ENDOW, V12, P99, DOI 10.14778/3282495.3282497
   Haldar NA, 2019, VLDB J, V28, P623, DOI 10.1007/s00778-019-00553-0
   He Tao, 2020, 34 AAAI C ART INT
   Hu L, 2013, IEEE T KNOWL DATA EN, V25, P863, DOI 10.1109/TKDE.2011.267
   Jin X, 2019, IEEE T KNOWL DATA EN, V31, P2051, DOI 10.1109/TKDE.2018.2873376
   Jung J, 2019, INT CONF BIG DATA, P277
   Kumar A, 2014, IEEE T KNOWL DATA EN, V26, P2410, DOI 10.1109/TKDE.2014.2298015
   Li FF, 2011, IEEE T KNOWL DATA EN, V23, P1526, DOI 10.1109/TKDE.2010.181
   Li JX, 2020, INFORM SYST, V92, DOI 10.1016/j.is.2020.101522
   Li JX, 2016, IEEE T KNOWL DATA EN, V28, P1820, DOI 10.1109/TKDE.2016.2542804
   Li WG, 2017, IEEE T KNOWL DATA EN, V29, P2182, DOI 10.1109/TKDE.2017.2721408
   Li YF, 2017, IEEE T SERV COMPUT, V10, P646, DOI 10.1109/TSC.2015.2508440
   Li YF, 2015, IEEE T KNOWL DATA EN, V27, P2729, DOI 10.1109/TKDE.2015.2419663
   Lu ZM, 2010, PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON ASIAN AND PACIFIC COASTS, VOL 3, P1
   Mouratidis K, 2010, PROC INT CONF DATA, P533, DOI 10.1109/ICDE.2010.5447851
   Rocha JB, 2010, PROC VLDB ENDOW, V4, P93, DOI 10.14778/1921071.1921076
   Sharma R, 2017, BIG DATA RES, V10, P44, DOI 10.1016/j.bdr.2017.10.003
   Shen CY, 2016, IEEE T KNOWL DATA EN, V28, P196, DOI 10.1109/TKDE.2015.2468726
   Udomlamlert K, 2017, WORLD WIDE WEB, V20, P237, DOI 10.1007/s11280-016-0385-1
   Wang B, 2019, IEEE ACCESS, V7, P10627, DOI 10.1109/ACCESS.2019.2891570
   Wang Y, 2018, IEEE ACCESS, V6, P51401, DOI 10.1109/ACCESS.2018.2868749
   Wen YT, 2017, IEEE T KNOWL DATA EN, V29, P1639, DOI 10.1109/TKDE.2017.2690421
   Xu CQ, 2021, IEEE T SERV COMPUT, V14, P1068, DOI 10.1109/TSC.2018.2855740
   Xu CF, 2013, IEEE T KNOWL DATA EN, V25, P2796, DOI 10.1109/TKDE.2012.160
   Yang SY, 2015, COMPUT J, V58, P2928, DOI 10.1093/comjnl/bxv016
   Yiu ML, 2011, IEEE T KNOWL DATA EN, V23, P433, DOI 10.1109/TKDE.2010.119
   Yiu ML, 2007, PROC INT CONF DATA, P1051
   Yiu ML, 2005, IEEE T KNOWL DATA EN, V17, P820, DOI 10.1109/TKDE.2005.87
   Yu A., 2012, SIGMOD, P397
   Zhang DQ, 2015, IEEE T INTELL TRANSP, V16, P123, DOI 10.1109/TITS.2014.2328231
   Zhang FX, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2987
   Zhao HK, 2016, IEEE DATA MINING, P679, DOI [10.1109/ICDM.2016.64, 10.1109/ICDM.2016.0079]
   Zheng K, 2015, PROC INT CONF DATA, P423, DOI 10.1109/ICDE.2015.7113303
   Zhu L, 2019, J INF SCI ENG, V35, P959, DOI 10.6688/JISE.201909_35(5).0002
   Zhu L, 2018, J INF SCI ENG, V34, P803, DOI [10.6688/JISE,201807_34(4).0001, 10.6688/JISE.201807_34(4).0001]
   Zhu L, 2017, IEEE CONF COMPUT, P820, DOI 10.1109/INFCOMW.2017.8116482
   Zhu L, 2017, J NETW COMPUT APPL, V82, P35, DOI 10.1016/j.jnca.2016.12.033
   Zhu L, 2016, IEEE INT CONF COMM, P553, DOI 10.1109/ICCW.2016.7503845
NR 47
TC 2
Z9 2
U1 3
U2 18
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 100169
DI 10.1016/j.bdr.2020.100169
PG 11
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:000609166100002
DA 2022-08-23
ER

PT J
AU Sang, ST
   Yang, ZH
   Wang, L
   Liu, XX
   Lin, HF
   Wang, J
AF Sang, Shengtian
   Yang, Zhihao
   Wang, Lei
   Liu, Xiaoxia
   Lin, Hongfei
   Wang, Jian
TI SemaTyP: a knowledge graph based literature mining method for drug
   discovery
SO BMC BIOINFORMATICS
LA English
DT Article
DE Literature-based discovery; Knowledge graph; Drug discovery; Literature
   mining
ID BIOMEDICAL TEXT; SELECTION; NETWORK
AB Background: Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries.
   Methods: Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases.
   Results: The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs.
   Conclusions: In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.
C1 [Sang, Shengtian; Yang, Zhihao; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian] Dalian Univ Technol, Coll Comp Sci & Technol, Hongling Rd, Dalian 116023, Peoples R China.
   [Wang, Lei] Beijing Inst Hlth Adm & Med Informat, Beijing 100850, Peoples R China.
RP Yang, ZH (通讯作者)，Dalian Univ Technol, Coll Comp Sci & Technol, Hongling Rd, Dalian 116023, Peoples R China.
EM yangzh@dlut.edu.cn
OI Sang, Shengtian/0000-0003-3824-9824
FU national key Research and Development Program of China [2016YFC0901902];
   Natural Science Foundation of China [61272373, 61572102, 61572098];
   Trans-Century Training Program Foundation for the Talents by the
   Ministry of Education of China [NCET-13-0084]
FX This work was supported by the grants from the national key Research and
   Development Program of China (No. 2016YFC0901902), Natural Science
   Foundation of China (No. 61272373, 61572102 and 61572098), and
   Trans-Century Training Program Foundation for the Talents by the
   Ministry of Education of China (NCET-13-0084). The funding bodies did
   not play any role in the design of the study, data collection and
   analysis, or preparation of the manuscript.
CR Ahlers CB, 2007, PACIFIC SYMPOSIUM ON BIOCOMPUTING 2007, P209
   Anson BD, 2009, GENET ENG BIOTECHN N, V29, P34
   Aronson AR, 2010, J AM MED INFORM ASSN, V17, P229, DOI 10.1136/jamia.2009.002733
   Aronson AR, 2001, J AM MED INFORM ASSN, P17
   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
   Cameron D. H., 2014, THESIS WRIGHT STATE
   Cameron D, 2013, J BIOMED INFORM, V46, P238, DOI 10.1016/j.jbi.2012.09.004
   Carlson A., 2010, AAAI C ART INT
   Chen X, 2012, MOL BIOSYST, V8, P1970, DOI 10.1039/c2mb00002d
   Hoffart J., 2011, P 20 INT C COMP WORL, P229, DOI DOI 10.1145/1963192.1963296
   Hristovski Dimitar, 2006, AMIA Annu Symp Proc, P349
   Kore P., 2012, OPEN J MED CHEM, V2, P139, DOI [10.4236/ojmc.2012.24017, DOI 10.4236/OJMC.2012.24017]
   Korhonen A., 2014, PROC INT M COMPUT IN, P89
   Krompass D, 2015, LECT NOTES COMPUT SC, V9366, P640, DOI 10.1007/978-3-319-25007-6_37
   Liu H, 2016, BMC BIOINFORMATICS, V17, DOI 10.1186/s12859-016-1336-7
   Liu Ying, 2012, AMIA Annu Symp Proc, V2012, P587
   Lovasz L, 1993, COMBINATORICS P ERDO, V2, P1
   Marsi E, 2014, P 4 WORKSH BUILD EV
   Moffat JG, 2017, CELL CHEM BIOL, V24, P545, DOI 10.1016/j.chembiol.2017.05.005
   Morgan S, 2011, HEALTH POLICY, V100, P4, DOI 10.1016/j.healthpol.2010.12.002
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   Sang S, 2015, BIOMED RES INT, V2015, P12
   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
   SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7
   Tibshirani R, 1996, J ROY STAT SOC B MET, V58, P267, DOI 10.1111/j.2517-6161.1996.tb02080.x
   Weiss G. H., 1994, ASPECTS APPL RANDOM
   Zhu T, 2013, J MED CHEM, V56, P6560, DOI 10.1021/jm301916b
   Zou H, 2005, J R STAT SOC B, V67, P301, DOI 10.1111/j.1467-9868.2005.00503.x
NR 30
TC 36
Z9 38
U1 10
U2 43
PU BIOMED CENTRAL LTD
PI LONDON
PA 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD MAY 30
PY 2018
VL 19
AR 193
DI 10.1186/s12859-018-2167-5
PG 11
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 GI0GU
UT WOS:000434048200005
PM 29843590
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Tian, F
   Gao, B
   Chen, EH
   Liu, TY
AF Tian, Fei
   Gao, Bin
   Chen, En-Hong
   Liu, Tie-Yan
TI Learning Better Word Embedding by Asymmetric Low-Rank Projection of
   Knowledge Graph
SO JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
LA English
DT Article; Proceedings Paper
CT 4th International Conference on Computational Visual Media (CVM)
CY APR 06-08, 2016
CL Cardiff Univ, Cardiff, WALES
HO Cardiff Univ
DE natural language processing; word embedding; neural network; knowledge
   graph
AB Word embedding, which refers to low-dimensional dense vector representations of natural words, has demonstrated its power in many natural language processing tasks. However, it may suffer from the inaccurate and incomplete information contained in the free text corpus as training data. To tackle this challenge, there have been quite a few studies that leverage knowledge graphs as an additional information source to improve the quality of word embedding. Although these studies have achieved certain success, they have neglected some important facts about knowledge graphs: 1) many relationships in knowledge graphs are many-to-one, one-to-many or even many-to-many, rather than simply one-to-one; 2) most head entities and tail entities in knowledge graphs come from very different semantic spaces. To address these issues, in this paper, we propose a new algorithm named ProjectNet. ProjectNet models the relationships between head and tail entities after transforming them with different low-rank projection matrices. The low-rank projection can allow non one-to-one relationships between entities, while different projection matrices for head and tail entities allow them to originate in different semantic spaces. The experimental results demonstrate that ProjectNet yields more accurate word embedding than previous studies, and thus leads to clear improvements in various natural language processing tasks.
C1 [Tian, Fei; Chen, En-Hong] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Peoples R China.
   [Gao, Bin] Microsoft Res Asia, Machine Learning Grp, Beijing 100080, Peoples R China.
   [Liu, Tie-Yan] Microsoft Res Asia, Beijing 100080, Peoples R China.
RP Tian, F; Chen, EH (通讯作者)，Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Peoples R China.; Gao, B (通讯作者)，Microsoft Res Asia, Machine Learning Grp, Beijing 100080, Peoples R China.; Liu, TY (通讯作者)，Microsoft Res Asia, Beijing 100080, Peoples R China.
EM tianfei@mail.ustc.edu.cn; bingao@microsoft.com; cheneh@ustc.edu.cn;
   tyliu@microsoft.com
CR Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes Antoine, 2011, AAAI
   Botha JA, 2014, PR MACH LEARN RES, V32, P1899
   Collobert R., 2008, P 25 ICML, P160
   Cui Q, 2015, ACM T INFORM SYST, V34, DOI 10.1145/2797137
   Finkelstein L, 2001, WWW, DOI DOI 10.1145/371920.372094
   Horn R. A., 1990, MATRIX ANAL
   Huang E. H., 2012, P 50 ANN M ASS COMP, V1
   Jenatton R., 2012, ADV NEURAL INFORM PR, P3167
   Jiang Bian, 2014, Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2014. Proceedings: LNCS 8724, P132, DOI 10.1007/978-3-662-44848-9_9
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Luong T, 2013, P 17 C COMP NAT LANG, P104, DOI DOI 10.1007/BF02579642
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mikolov T., 2016, ARXIV13013781
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Tian F., 2014, COLING, P151
   Turian J, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P384
   Wang Z, 2014, P C EMP METH NAT LAN, P1591
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xu C., 2014, P 23 ACM INT C C INF, P1219
   Yu M, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P545, DOI 10.3115/v1/p14-2089
   Zheng ZC, 2012, 2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2012), VOL 1, P82, DOI 10.1109/WI-IAT.2012.26
NR 26
TC 3
Z9 5
U1 0
U2 12
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1000-9000
EI 1860-4749
J9 J COMPUT SCI TECH-CH
JI J. Comput. Sci. Technol.
PD MAY
PY 2016
VL 31
IS 3
BP 624
EP 634
DI 10.1007/s11390-016-1651-5
PG 11
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA DL8ZY
UT WOS:000375932200015
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Sang, ST
   Yang, ZH
   Liu, XX
   Wang, L
   Lin, HF
   Wang, J
   Dumontier, M
AF Sang, Shengtian
   Yang, Zhihao
   Liu, Xiaoxia
   Wang, Lei
   Lin, Hongfei
   Wang, Jian
   Dumontier, Michel
TI GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From
   Biomedical Literatures
SO IEEE ACCESS
LA English
DT Article
DE Drug discovery; biomedical knowledge graph; recurrent neural network;
   deep learning
ID FISH-OIL; NEURAL-NETWORKS; PREDICTION; RAYNAUDS; SYSTEMS
AB Drug discovery is the process by which new candidate medications are discovered. Developing a new drug is a lengthy, complex, and expensive process. Here, in this paper, we propose a biomedical knowledge graph embedding-based recurrent neural network method called GrEDeL, which discovers potential drugs for diseases by mining published biomedical literature. GrEDeL first builds a biomedical knowledge graph by exploiting the relations extracted from biomedical abstracts. Then, the graph data are converted into a low dimensional space by leveraging the knowledge graph embedding methods. After that, a recurrent neural network model is trained by the known drug therapies which are represented by graph embeddings. Finally, it uses the learned model to discover candidate drugs for diseases of interest from biomedical literature. The experimental results show that our method could not only effectively discover new drugs by mining literature, but also could provide the corresponding mechanism of actions for the candidate drugs. It could be a supplementary method for the current traditional drug discovery methods.
C1 [Sang, Shengtian; Yang, Zhihao; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian] Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116023, Peoples R China.
   [Wang, Lei] Beijing Inst Hlth Adm & Med Informat, Beijing 100191, Peoples R China.
   [Dumontier, Michel] Maastricht Univ, Inst Data Sci, NL-6229 ER Maastricht, Netherlands.
RP Yang, ZH (通讯作者)，Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116023, Peoples R China.; Wang, L (通讯作者)，Beijing Inst Hlth Adm & Med Informat, Beijing 100191, Peoples R China.
EM yangzh@dlut.edu.cn; wangleibihami@gmail.com
OI Sang, Shengtian/0000-0003-3824-9824
FU National Key Research and Development Program of China [2016YFC0901902];
   Natural Science Foundation of China [61272373, 61572102, 61572098];
   Trans-Century Training Program Foundation for the Talents by the
   Ministry of Education of China [NCET-13-0084]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2016YFC0901902, in part by the
   Natural Science Foundation of China under Grant 61272373, Grant
   61572102, and Grant 61572098, and in part by the Trans-Century Training
   Program Foundation for the Talents by the Ministry of Education of China
   under Grant NCET-13-0084.
CR Ahlers Caroline B, 2007, AMIA Annu Symp Proc, P6
   Ba D. P. K., 2015, P INT C LEARN REPR, P13
   Bakal G, 2018, J BIOMED INFORM, V82, P189, DOI 10.1016/j.jbi.2018.05.003
   Baskin II, 2016, EXPERT OPIN DRUG DIS, V11, P785, DOI 10.1080/17460441.2016.1201262
   BENGIO Y, 1994, IEEE T NEURAL NETWOR, V5, P157, DOI 10.1109/72.279181
   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
   Byvatov E, 2003, J CHEM INF COMP SCI, V43, P1882, DOI 10.1021/ci0341161
   Cai H., 2017, COMPREHENSIVE SURVEY
   Cameron D, 2015, J BIOMED INFORM, V54, P141, DOI 10.1016/j.jbi.2015.01.014
   Cao DS, 2014, MOL INFORM, V33, P669, DOI 10.1002/minf.201400009
   Chen HM, 2018, DRUG DISCOV TODAY, V23, P1241, DOI 10.1016/j.drudis.2018.01.039
   Chen X, 2002, NUCLEIC ACIDS RES, V30, P412, DOI 10.1093/nar/30.1.412
   Chen X, 2012, MOL BIOSYST, V8, P1970, DOI 10.1039/c2mb00002d
   DIGIACOMO RA, 1989, AM J MED, V86, P158, DOI 10.1016/0002-9343(89)90261-1
   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
   Gori M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2766
   Goulding R., 2009, OHI DEL, V1
   Gubiani D., 2015, TECH REP
   Henry S, 2017, J BIOMED INFORM, V74, P20, DOI 10.1016/j.jbi.2017.08.011
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hoffart J., 2011, P 20 INT C COMP WORL, P229, DOI DOI 10.1145/1963192.1963296
   Hristovski Dimitar, 2006, AMIA Annu Symp Proc, P349
   Hristovski Dimitar, 2013, Cardiovascular & Hematological Agents in Medicinal Chemistry, V11, P14
   Hu PW, 2016, IEEE IJCNN, P1236, DOI 10.1109/IJCNN.2016.7727339
   Husken M, 2003, NEUROCOMPUTING, V50, P223, DOI 10.1016/S0925-2312(01)00706-8
   Korhonen A., 2014, PROC INT M COMPUT IN, P89
   LINDBERG DAB, 1993, METHOD INFORM MED, V32, P281, DOI 10.1055/s-0038-1634945
   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
   Liu H, 2016, BMC BIOINFORMATICS, V17, DOI 10.1186/s12859-016-1336-7
   Malas T. B., TECH REP
   McCray A. T., 1989, Proceedings: The Thirteenth Annual Symposium on Computer Applications in Medical Care (Cat. No.89TH0286-5), P503
   Morgan S, 2011, HEALTH POLICY, V100, P4, DOI 10.1016/j.healthpol.2010.12.002
   Rastegar-Mojarad M, 2015, IEEE INT C BIOINFORM, P669, DOI 10.1109/BIBM.2015.7359766
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   Sang ST, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2167-5
   Sang ST, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/698527
   SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7
   Weeber M, 2001, J AM SOC INF SCI TEC, V52, P548, DOI 10.1002/asi.1104.abs
   Wen M, 2017, J PROTEOME RES, V16, P1401, DOI 10.1021/acs.jproteome.6b00618
   WERBOS PJ, 1990, P IEEE, V78, P1550, DOI 10.1109/5.58337
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wren JD, 2004, BIOINFORMATICS, V20, P389, DOI 10.1093/bioinformatics/btg421
   Zaremba W., 2014, ARXIV
NR 44
TC 19
Z9 20
U1 5
U2 23
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 8404
EP 8415
DI 10.1109/ACCESS.2018.2886311
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HJ1HU
UT WOS:000456913800001
OA gold
DA 2022-08-23
ER

PT J
AU Chang, L
   Zhu, ML
   Gu, TL
   Bin, CZ
   Qian, JY
   Zhang, J
AF Chang, Liang
   Zhu, Manli
   Gu, Tianlong
   Bin, Chenzhong
   Qian, Junyan
   Zhang, Ji
TI Knowledge Graph Embedding by Dynamic Translation
SO IEEE ACCESS
LA English
DT Article
DE Dynamic translation; embeddings; knowledge graph; translation-based
   models
AB Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued vectors. It can efficiently measure semantic correlations of entities and relations in knowledge graphs, and improve the performance of knowledge acquisition, fusion and inference. Among various embedding models appeared in recent years, the translation-based models such as TransE, TransH, TransR and TranSparse achieve state-of-the-art performance. However, the translation principle applied in these models is too strict and can not deal with complex entities and relations very well. In this paper, by introducing parameter vectors into the translation principle which treats each relation as a translation from the head entity to the tail entity, we propose a novel dynamic translation principle which supports flexible translation between the embeddings of entities and relations. We use this principle to improve the TransE, TransR and TranSparse models respectively and build new models named TransE-DT, TransR-DT and TranSparse-DT correspondingly. Experimental results show that our dynamic translation principle achieves great improvement in both the link prediction task and the triple classification task.
C1 [Chang, Liang; Zhu, Manli; Qian, Junyan] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China.
   [Gu, Tianlong; Bin, Chenzhong] Guilin Univ Elect Technol, Guangxi Expt Ctr Informat Sci, Guilin 541004, Peoples R China.
   [Zhang, Ji] Univ Southern Queensland, Fac Hlth Engn & Sci, Toowoomba, Qld 4350, Australia.
RP Chang, L (通讯作者)，Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China.
EM changl@guet.edu.cn
OI Qian, Junyan/0000-0002-1325-6975
FU Natural Science Foundation of China [61363030, 61572146, U1501252];
   Natural Science Foundation of Guangxi Province [2015GXNSFAA139285,
   2016GXNSFDA380006]; Innovation Project of GUET Graduate Education
   [2016YJCX13]; High Level of Innovation Team of Colleges and Universities
   in Guangxi and Outstanding Scholars Program
FX This work was supported in part by the Natural Science Foundation of
   China under Grant 61363030, Grant 61572146, and Grant U1501252, in part
   by the Natural Science Foundation of Guangxi Province under Grant
   2015GXNSFAA139285 and Grant 2016GXNSFDA380006, in part by the Innovation
   Project of GUET Graduate Education under Grant 2016YJCX13, and in part
   by the High Level of Innovation Team of Colleges and Universities in
   Guangxi and Outstanding Scholars Program.
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   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., 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
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Duchi J, 2011, J MACH LEARN RES, V12, P2121
   Feng J, 2016, FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P557
   Gui H, 2017, IEEE T KNOWL DATA EN, V29, P2428, DOI 10.1109/TKDE.2017.2733530
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   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
   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
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   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
   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
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Riedel S., 2013, P 2013 C N AM CHAPT, P74
   Rocktaschel T., 2015, P 2015 C N AM CHAPT, P1119
   Shen W, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P68
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   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
   Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
   Sutskever I., 2009, NIPS
   Szumlanski Sean R., 2010, P 19 ACM INT C INF K, P19
   Wang WY, 2015, MACH LEARN, V100, P101, DOI 10.1007/s10994-015-5488-x
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J., 2013, P 2013 C EMP METH NA, P1366
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
NR 35
TC 14
Z9 19
U1 3
U2 23
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 2017
VL 5
BP 20898
EP 20907
DI 10.1109/ACCESS.2017.2759139
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA FL0ZL
UT WOS:000413942100024
OA gold, Green Accepted
DA 2022-08-23
ER

PT J
AU Guo, QM
   Wang, X
   Zhu, ZF
   Liu, PY
   Xu, LC
AF Guo, Qimeng
   Wang, Xue
   Zhu, Zhenfang
   Liu, Peiyu
   Xu, Liancheng
TI A knowledge inference model for question answering on an incomplete
   knowledge graph
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Knowledge graph; Multi-hop question; Neural networks; Attention
   mechanisms
AB Question answering over knowledge graph (KGQA) is a task to solve natural language questions on knowledge graphs (KGs). Multi-hop KGQA requires multi-steps reasoning on the KG to find the correct answers to complex questions. However, it is difficult to find the triple required by the question directly when solving complex multi-hop questions for KGs with missing links. To mitigate this challenge, we propose an effective reasoning model that fuses neighbor interaction and a relation recognition module for multi-hop QA. Specifically, we adopt neighbor interaction networks to learn a better entity representation. The model identifies the relations contained in the questions through neural networks to further precisely determine the range of answers. Our method selectively captures the complex hidden information within the KG and overcomes the limitation of the answer range. It can perform well without the help of additional text corpora. The experimental results on two datasets show that our model can effectively capture richer semantic information for reasoning and achieve better results than all baseline models.
C1 [Guo, Qimeng; Wang, Xue; Liu, Peiyu; Xu, Liancheng] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250357, Peoples R China.
   [Zhu, Zhenfang] Shandong Jiaotong Univ, Sch Informat Sci & Elect Engn, Jinan 250357, Peoples R China.
RP Liu, PY; Xu, LC (通讯作者)，Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250357, Peoples R China.
EM liupy@sdnu.edu.cn; lcxu@sdnu.edu.cn
FU National Social Science Foundation [19BYY076]; Key R & D project of
   Shandong Province [2019JZZY010129]; Shandong Provincial Social Science
   Planning Project [19BJCJ51, 18CXWJ01, 18BJYJ04]
FX This work was supported in part by the National Social Science
   Foundation under Award 19BYY076, in part Key R & D project of Shandong
   Province 2019JZZY010129, and in part by the Shandong Provincial Social
   Science Planning Project under Award 19BJCJ51, Award 18CXWJ01, and Award
   18BJYJ04.
CR Abdelaziz I, 2021, AAAI CONF ARTIF INTE, V35, P15985
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao Y, 2019, LONG SHORT PAPERS, V1, P357
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Devlin J, 2018, P 2019 C N AM CHAPT
   Donglai Ge, 2019, Natural Language Processing and Chinese Computing. 8th CCF International Conference, NLPCC 2019. Proceedings. Lecture Notes in Artificial Intelligence, Subseries of Lecture Notes in Computer Science (LNAI 11839), P772, DOI 10.1007/978-3-030-32236-6_70
   Esposito M, 2020, INFORM SCIENCES, V514, P88, DOI 10.1016/j.ins.2019.12.002
   Huang Y, 2021, P 2021 C N AM CHAPT, P5810
   Kaiser M, 2021, 44 INT ACM SIGIR C R
   Kipf T., 2016, 5 INT C LEARN REPR, P1
   Li Z, 2021, BIOINFORMATICS
   Liang Y, 2019, ARXIV
   Liu Y., 2019, ARXIV
   Lv SW, 2020, AAAI CONF ARTIF INTE, V34, P8449
   Ma T, 2021, NEURAL NETWORKS
   Miller Alexander, 2016, EMNLP, DOI [10.18653/v1/D16-1147, DOI 10.18653/V1/D16-1147]
   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, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Pota M, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10144710
   Qilin Sun, 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 12431), P82, DOI 10.1007/978-3-030-60457-8_7
   Saxena A, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P4498
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Shen T, 2019, EMNLP IJCNLP 2019, P2442
   Sun H., 2018, P 2018 C EMP METH NA, P4231, DOI DOI 10.18653/V1/D18-1455
   Sun H., 2019, P 2019 C EMP METH NA, P2380
   Sun Z., 2018, P ICLR
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Undorf M, 2021, METACOGN LEARN, V16, P431, DOI 10.1007/s11409-021-09259-7
   Vakulenko Svitlana, 2021, WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, P355, DOI 10.1145/3437963.3441748
   Velickovic P., 2018, ARXIV
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xu FY, 2022, APPL INTELL, V52, P4744, DOI 10.1007/s10489-021-02660-4
   Yang MC, 2015, EXPERT SYST APPL, V42, P9086, DOI 10.1016/j.eswa.2015.07.009
   Yasunaga Michihiro, P 2021 C N AM CHAPT, P521
   Yih WT, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, P201
   Zeiler MD, 2014, LECT NOTES COMPUT SC, V8689, P818, DOI 10.1007/978-3-319-10590-1_53
   Zhang M, 2021, NAACL HLT 2021, P18
   Zhang YY, 2018, AAAI CONF ARTIF INTE, P6069
NR 41
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/s10489-022-03927-0
EA JUL 2022
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3F9GT
UT WOS:000830969200003
DA 2022-08-23
ER

PT J
AU Zhang, M
   Geng, GH
   Zeng, S
   Jia, HP
AF Zhang, Min
   Geng, Guohua
   Zeng, Sheng
   Jia, Huaping
TI Knowledge Graph Completion for the Chinese Text of Cultural Relics Based
   on Bidirectional Encoder Representations from Transformers with
   Entity-Type Information
SO ENTROPY
LA English
DT Article
DE knowledge graph completion; cultural relics; link prediction;
   Bidirectional Encoder Representations from Transformers (BERT); entity
   type
AB Knowledge graph completion can make knowledge graphs more complete, which is a meaningful research topic. However, the existing methods do not make full use of entity semantic information. Another challenge is that a deep model requires large-scale manually labelled data, which greatly increases manual labour. In order to alleviate the scarcity of labelled data in the field of cultural relics and capture the rich semantic information of entities, this paper proposes a model based on the Bidirectional Encoder Representations from Transformers (BERT) with entity-type information for the knowledge graph completion of the Chinese texts of cultural relics. In this work, the knowledge graph completion task is treated as a classification task, while the entities, relations and entity-type information are integrated as a textual sequence, and the Chinese characters are used as a token unit in which input representation is constructed by summing token, segment and position embeddings. A small number of labelled data are used to pre-train the model, and then, a large number of unlabelled data are used to fine-tune the pre-training model. The experiment results show that the BERT-KGC model with entity-type information can enrich the semantics information of the entities to reduce the degree of ambiguity of the entities and relations to some degree and achieve more effective performance than the baselines in triple classification, link prediction and relation prediction tasks using 35% of the labelled data of cultural relics.
C1 [Zhang, Min; Geng, Guohua; Zeng, Sheng] Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China.
   [Zhang, Min; Jia, Huaping] Weinan Normal Univ, Coll Comp, Weinan 714099, Peoples R China.
RP Geng, GH (通讯作者)，Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China.
EM zhangmin@stumail.nwu.edu.cn; ghgeng@nwu.edu.cn;
   shengzeng@stumail.nwu.edu.cn; jiahuaping_1979@126.com
FU National Natural Science Foundation of China [61731015, 61673319,
   61802311]; Key Research and Development Program in Shaanxi Province
   [2019ZDLSF07-02]; National Key Research and Development Program of China
   [2017YFB1402103]; Weinan Key RD project [ZDYF-JCYJ-19_zsg]; andWeinan
   Normal University scientific research project [18YKS16, 18YKS13]
FX This work was sponsored by the National Natural Science Foundation of
   China, grant numbers 61731015, 61673319 and 61802311; Key Research and
   Development Program in Shaanxi Province, grant number 2019ZDLSF07-02;
   National Key Research and Development Program of China, grant number
   2017YFB1402103; Weinan Key R&D project, grant number ZDYF-JCYJ-19_zsg;
   andWeinan Normal University scientific research project, grant numbers
   18YKS16 and 18YKS13.
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Dai Andrew M., 2015, ADV NEURAL INFORM PR, P3079
   Dettmers T., 2018, P 32 AAAI C ART INT
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Feldman J, 2019, P 2019 C EMP METH NA
   Gardner M., 2014, P 2014 C EMP METH NA, P397, DOI [10.3115/v1/D14-1044, DOI 10.3115/V1/D14-1044]
   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
   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
   Minkov E, 2017, J ASSOC INF SCI TECH, V68, P1911, DOI 10.1002/asi.23837
   Nguyen D.Q., 2017, ARXIV170308098, V1703
   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, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Peters M., 2018, IMPROVING LANGUAGE U
   Radford A., 2018, IMPROVING LANGUAGE U
   Sancheti A, 2018, LECT NOTES ARTIF INT, V10938, P312, DOI 10.1007/978-3-319-93037-4_25
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Wang H., 2018, ARXIV PREPRINT ARXIV
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang YH, 2014, 2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), P5915, DOI 10.1109/WCICA.2014.7053732
   Wang Z., 2016, IJCAI, P1293
   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 MY, 2019, PROCEEDINGS OF THE 2019 IEEE EURASIA CONFERENCE ON IOT, COMMUNICATION AND ENGINEERING (ECICE), P17
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xu J, 2015, STATISTIC APPLICATION IN MODERN SOCIETY, P100
   Yang B, 2015, P INT C LEARN REPR I
   Yao L., 2019, ARXIV19090319V2
   Zhang M, 2020, IEEE ACCESS, V8, P94236, DOI 10.1109/ACCESS.2020.2995447
   Zhang M, 2020, ENTROPY-SWITZ, V22, DOI 10.3390/e22020252
NR 34
TC 3
Z9 4
U1 6
U2 22
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD OCT
PY 2020
VL 22
IS 10
AR 1168
DI 10.3390/e22101168
PG 15
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA OH8SB
UT WOS:000582860400001
PM 33286937
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ryu, PM
   Jang, MG
   Kim, HK
   Park, SY
AF Ryu, Pum Mo
   Jang, Myung-Gil
   Kim, Hyun-Ki
   Park, So-Young
TI Graph-Based Knowledge Consolidation in Ontology Population
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article
DE knowledge graph; knowledge consolidation; entity consolidation; relation
   consolidation
AB We propose a novel method for knowledge consolidation based on a knowledge graph as a next step in relation extraction from text. The knowledge consolidation method consists of entity consolidation and relation consolidation. During the entity consolidation process, identical entities are found and merged using both name similarity and relation similarity measures. In the relation consolidation process, incorrect relations are removed using cardinality properties, temporal information and relation weight in given graph structure. In our experiment, we could generate compact and clean knowledge graphs where number of entities and relations are reduced by 6.1% and by 17.4% respectively with increasing relation accuracy from 77.0% to 85.5%.
C1 [Ryu, Pum Mo; Jang, Myung-Gil; Kim, Hyun-Ki] ETRI, Speech Language Informat Res Ctr, Taejon, South Korea.
   [Park, So-Young] Sangmyung Univ, Div Digital Media Technol, Seoul, South Korea.
RP Park, SY (通讯作者)，Sangmyung Univ, Div Digital Media Technol, 7 Hongji Dong, Seoul, South Korea.
EM ssoya@smu.ac.kr
CR Alani H., 2003, P K CAP 03
   [Anonymous], OWL WEB ONTOLOGY LAN
   Hogan A., 2007, P 1 I3 WORKSH WWW200
   Hoser B, 2006, LECT NOTES COMPUT SC, V4011, P514
   Lee C., P SIGIR 2007, P799
   Ristad ES, 1998, IEEE T PATTERN ANAL, V20, P522, DOI 10.1109/34.682181
NR 6
TC 1
Z9 1
U1 0
U2 24
PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
PI TOKYO
PA KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011,
   JAPAN
SN 0916-8532
EI 1745-1361
J9 IEICE T INF SYST
JI IEICE Trans. Inf. Syst.
PD SEP
PY 2013
VL E96D
IS 9
BP 2139
EP 2142
DI 10.1587/transinf.E96.D.2139
PG 4
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 244SA
UT WOS:000326409300031
OA gold
DA 2022-08-23
ER

PT J
AU Omran, PG
   Taylor, K
   Mendez, SR
   Haller, A
AF Omran, Pouya Ghiasnezhad
   Taylor, Kerry
   Mendez, Sergio Rodriguez
   Haller, Armin
TI Active knowledge graph completion
SO INFORMATION SCIENCES
LA English
DT Article
DE Existential rule learning; Knowledge graph completion; Rule learning;
   Knowledge graph
AB Enterprise and public Knowledge Graphs (KGs) are known to be incomplete. Methods for automatic completion, sometimes by rule learning, scale well. While previous rule-based methods learn closed (non-existential) rules, we introduce Open Path (OP) rules that are constrained existential rules. We present a novel algorithm, OPRL, for learning OP rules. Closed rules complete a KG by answering queries of unclear origin, usually derived from a holdback test set in experimental settings. However, OP rules can generate relevant queries for KG completion. OPRL generates queries even when there is no closed rule to answer the query, or when the correct answer is a missing entity that is not present in the KG. For OPRL to scale well, we propose a novel embedding-based fitness function to effi-ciently estimate rule quality. Additionally, we introduce a novel, efficient vector computa-tion to formally assess rule quality. We evaluate OPRL using adaptations of Freebase, YAGO2, Wikidata, anda synthetic Poker KG. We find that OPRL mines hundreds of accurate rules from massive KGs with up to 8 M facts. The OP rules generate queries with precision as high as 98% and recall of 62% on a complete KG, demonstrating the first solution for active knowledge graph completion. Crown Copyright (c) 2022 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C1 [Omran, Pouya Ghiasnezhad; Taylor, Kerry; Mendez, Sergio Rodriguez; Haller, Armin] Australian Natl Univ, Canberra, Australia.
RP Omran, PG (通讯作者)，Australian Natl Univ, Canberra, Australia.
EM P.G.Omran@anu.edu.au; Kerry.Taylor@anu.edu.au;
   Sergio.RodriguezMendez@anu.edu.au; Armin.Haller@anu.edu.au
OI Ghiasnezhad Omran, Pouya/0000-0002-4473-3877
FU Australian Government Department of Finance; Australian National
   University
FX The authors acknowledge the support of the Australian Government
   Department of Finance and the Australian National University for this
   work.
CR Agrawal R., 1994, P 20 INT C VER LARG, V1215, P487
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185
   Bellomarini L, 2018, PROC VLDB ENDOW, V11, P975, DOI 10.14778/3213880.3213888
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Borrego A., 2019, GENERATING RULES FIL
   Cattral Robert, 2002, RECENT ADV COMPUTERS, V1, P296
   Chen Y, 2016, VLDB J, V25, P893, DOI 10.1007/s00778-016-0444-3
   Dua D., 2017, UCI MACHINE LEARNING
   Galarraga L, 2015, VLDB J, V24, P707, DOI 10.1007/s00778-015-0394-1
   Ghiasnezhad Omran P., 2020, ISWC SATELLITE TRACK, P89
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Meilicke C, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3137
   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
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   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
   Sun Z., 2019, 7 INT C LEARN REPR
   Taylor K, 1999, J LOGIC PROGRAM, V40, P127, DOI 10.1016/S0743-1066(99)00016-3
   Ho VT, 2018, LECT NOTES COMPUT SC, V11136, P72, DOI 10.1007/978-3-030-00671-6_5
   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
   Wu LL, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P6478
   Yang B, 2015, ABS14126575 CORR
   Yang F, 2017, ADV NEUR IN, V30
   Zhang W, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2366, DOI 10.1145/3308558.3313612
NR 30
TC 0
Z9 0
U1 6
U2 6
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 604
BP 267
EP 279
DI 10.1016/j.ins.2022.05.027
PG 13
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1X1SE
UT WOS:000807241000004
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Zhang, Y
   Xu, HP
   Zhang, X
   Wu, XX
   Yang, ZL
AF Zhang, Yao
   Xu, Hengpeng
   Zhang, Xu
   Wu, Xingxing
   Yang, Zhenglu
TI TRFR: A ternary relation link prediction framework on Knowledge graphs
SO AD HOC NETWORKS
LA English
DT Article
DE Knowledge graph; Artificial intelligence; Wireless network
AB Artificial intelligence has been widely used in daily wireless networks for its flexibility and adaptability in solving extremely complex problems in real-time. These wireless network applications using artificial intelligence will generate a lot of valuable data. Knowledge graph is able to mine and store useful information from these large amounts of data and make data-driven wireless network applications more intelligent. Existing knowledge graph represents knowledge facts in triples whose examinations are restricted within two entities and binary relations. This nature induces the weakness of expanding to complex learning scenarios with multi-entity relations, which motivates the research insights into the n-ary relation. However, existing n-ary relation link prediction all embedding-based which are limited by their interpretability. We investigate the ternary relation link prediction task and propose a novel unified framework TRFR which is the first path-based model on n-ary relational data. TRFR incorporates the hierarchical attention mechanism and reinforcement learning technique. In addition, we release a newly constructed dataset NELL-995-3 to fill the shortage of learning resources for n-ary link prediction. Extensive experiments demonstrate the superiority of the proposed framework compared with a wide variety of state-of-the-art knowledge graph-based approaches in ternary relation link prediction task on one benchmark dataset and one newly constructed dataset.
C1 [Zhang, Yao; Zhang, Xu; Wu, Xingxing; Yang, Zhenglu] Nankai Univ, CS, TKLNDST, Tianjin 300350, Peoples R China.
   [Xu, Hengpeng] Tianjin Normal Univ, Tianjin Key Lab Wireless Mobile Commun & Power Tr, Coll Elect & Commun Engn, Tianjin 300387, Peoples R China.
RP Wu, XX; Yang, ZL (通讯作者)，Nankai Univ, CS, TKLNDST, Tianjin 300350, Peoples R China.; Xu, HP (通讯作者)，Tianjin Normal Univ, Tianjin Key Lab Wireless Mobile Commun & Power Tr, Coll Elect & Commun Engn, Tianjin 300387, Peoples R China.
EM yaozhang@mail.nankai.edu.cn; xuhengpeng@mail.nankai.edu.cn;
   xuzhang@mail.nankai.edu.cn; wuxx@nankai.edu.cn; yangzl@nankai.edu.cn
OI Zhang, Xu/0000-0002-0766-5326; Yang, Zhenglu/0000-0001-9528-965X
FU Ministry of Education of Humanities and Social Science, China
   [16YJC790123]
FX This work was partially supported by the Ministry of Education of
   Humanities and Social Science, China project under Grant 16YJC790123.
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen W., 2018, P 2018 C N AM CHAPT, V1, P1823, DOI [10.18653/v1/N18-1165, DOI 10.18653/V1/N18-1165]
   Christmann P, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P729, DOI 10.1145/3357384.3358016
   Das Rajarshi, 2018, ICLR 2018
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Guan SP, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P583, DOI 10.1145/3308558.3313414
   Jianfeng W., 2016, P 25 INT JOINT C ART, P1300
   Kingma D., 2015, P ICLR
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Liu CX, 2017, DESTECH TRANS COMP, P566
   Moon S, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P845
   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
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Yang B., 2015, P ICLR
   Zhang RC, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1185, DOI 10.1145/3178876.3186017
NR 22
TC 4
Z9 4
U1 5
U2 19
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8705
EI 1570-8713
J9 AD HOC NETW
JI Ad Hoc Netw.
PD MAR 15
PY 2021
VL 113
AR 102402
DI 10.1016/j.adhoc.2020.102402
EA JAN 2021
PG 7
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA PY8VF
UT WOS:000612318400004
DA 2022-08-23
ER

PT J
AU Chen, K
   Shen, GH
   Huang, ZQ
   Wang, HJ
AF Chen, Kai
   Shen, Guohua
   Huang, Zhiqiu
   Wang, Haijuan
TI Improved Entity Linking for Simple Question Answering Over Knowledge
   Graph
SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
LA English
DT Article
DE Knowledge graph; Simple Question Answering; entity linking; neural
   network
AB Question Answering systems over Knowledge Graphs (KG) answer natural language questions using facts contained in a knowledge graph, and Simple Question Answering over Knowledge Graphs (KG-SimpleQA) means that the question can be answered by a single fact. Entity linking, which is a core component of KG-SimpleQA, detects the entities mentioned in questions, and links them to the actual entity in KG. However, traditional methods ignore some information of entities, especially entity types, which leads to the emergence of entity ambiguity problem. Besides, entity linking suffers from out-of-vocabulary (OOV) problem due to the limitation of pre-trained word embeddings. To address these problems, we encode questions in a novel way and encode the features contained in the entities in a multilevel way. To evaluate the enhancement of the whole KG-SimpleQA brought by our improved entity linking, we utilize a relatively simple approach for relation prediction. Besides, to reduce the impact of losing the feature during the encoding procedure, we utilize a ranking algorithm to re-rank (entity, relation) pairs. According to the experimental results, our method for entity linking achieves an accuracy of 81.8% that beats the state-of-the-art methods, and our improved entity linking brings a boost of 5.6% for the whole KG-SimpleQA.
C1 [Chen, Kai; Wang, Haijuan] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China.
   [Shen, Guohua; Huang, Zhiqiu] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Collaborat Innovat Ctr Novel Software Technol & I, Nanjing 211106, Peoples R China.
   [Shen, Guohua; Huang, Zhiqiu] Minist Ind & Informat Technol, Key Lab Safety Crit Software, Nanjing 211106, Peoples R China.
RP Shen, GH (通讯作者)，Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Collaborat Innovat Ctr Novel Software Technol & I, Nanjing 211106, Peoples R China.; Shen, GH (通讯作者)，Minist Ind & Informat Technol, Key Lab Safety Crit Software, Nanjing 211106, Peoples R China.
EM ck18652995232@163.com; ghshen@nuaa.edu.cn; zqhuang@nuaa.edu.cn;
   18895681787@163.com
FU National Key Research and Development Program [2016YFB1000802,
   2018YFB1003902]; National Natural Science Foundation of China [61772270,
   1015-XCA1816403]
FX Supported by National Key Research and Development Program (Nos.
   2016YFB1000802 and 2018YFB1003902), the National Natural Science
   Foundation of China (No. 61772270), and the funding of the Key
   Laboratory of Safety-Critical Software (No. 1015-XCA1816403).
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   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., CORR
   Cai Qingqing, 2013, P 51 ANN M ASS COMP, V1, P423
   Chen JZ, 2016, PROCEEDINGS OF 2016 12TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS), P551, DOI [10.1109/CIS.2016.0134, 10.1109/CIS.2016.133]
   Dai ZH, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P800
   Feng Y., 2016, P COLING 2016 26 INT, P2397
   He X., 2016, 2016 C EMP METH NAT, P1598
   Joachims T., 2006, P 12 ACM SIGKDD INT, P217, DOI DOI 10.1145/1150402.1150429
   Kwiatkowski Tom, 2013, P 2013 C EMP METH NA
   Lukovnikov D, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1211, DOI 10.1145/3038912.3052675
   Mohammed S., 2018, P 2018 C N AM CHAPT, V2, P291, DOI [10.18653/v1/N18-2047, DOI 10.18653/V1/N18-2047]
   Qu Y., CORR
   Sahlgren M, 2008, ITAL J LINGUIST, V20, P33
   Socher R., 2014, PROC C EMPIRICAL MET, P1532, DOI 10.3115/v1/D14-1162
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Ture F, 2017, P 2017 C EMP METH NA, P2866, DOI [10.18653/v1/d17-1307, DOI 10.18653/V1/D17-1307]
   Wang RZ, 2019, IEEE ACCESS, V7, P46773, DOI 10.1109/ACCESS.2019.2909826
   Yang M.-C., 2014, P 2014 C EMP METH NA, P645, DOI DOI 10.3115/V1/D14-1071
   Yao XC, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P956
   Yin W, 2016, ARXIV160603391
   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
   Zettlemoyer L. S., CORR
   Zettlemoyer L. S., 2009, P JOINT C 47 ANN M A, V2, P976
   Zhang HZ, 2018, IEEE ACCESS, V6, P75429, DOI 10.1109/ACCESS.2018.2883304
NR 27
TC 1
Z9 1
U1 4
U2 22
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-1940
EI 1793-6403
J9 INT J SOFTW ENG KNOW
JI Int. J. Softw. Eng. Knowl. Eng.
PD JAN
PY 2021
VL 31
IS 01
BP 55
EP 80
DI 10.1142/S0218194021400039
PG 26
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QD4ID
UT WOS:000615483100005
DA 2022-08-23
ER

PT J
AU Dessi, D
   Osborne, F
   Recupero, DR
   Buscaldi, D
   Motta, E
AF Dessi, Danilo Z.
   Osborne, Francesco
   Recupero, Diego Reforgiato
   Buscaldi, Davide
   Motta, Enrico
TI Generating knowledge graphs by employing Natural Language Processing and
   Machine Learning techniques within the scholarly domain
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
ID WEB
AB The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, (iii) show the advantage of such an hybrid system over alternative approaches, and (vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Dessi, Danilo Z.; Recupero, Diego Reforgiato] Univ Cagliari, Dept Math & Comp Sci, Cagliari, Italy.
   [Dessi, Danilo Z.] FIZ Karlsruhe Leibniz Inst Informat Infrastruct, Eggenstein Leopoldshafen, Germany.
   [Dessi, Danilo Z.] Inst AIFB, Karlsruhe Inst Technol, Karlsruhe, Germany.
   [Osborne, Francesco; Motta, Enrico] Open Univ, Knowledge Media Inst, Milton Keynes, Bucks, England.
   [Buscaldi, Davide] Univ Paris 13, CNRS UMR 7030, LIPN, Villetaneuse, France.
RP Dessi, D (通讯作者)，Univ Cagliari, Dept Math & Comp Sci, Cagliari, Italy.
EM danilo_dessi@unica.it
OI Motta, Enrico/0000-0003-0015-1952; Osborne,
   Francesco/0000-0001-6557-3131
FU Sardinia Regional Government, Italy; French National Research Agency
   (ANR), France as part of the program "Investissements d'Avenir"
   [ANR-10-LABX-0083]; IdEx Universite de Paris [ANR-18-IDEX-0001]; NVIDIA,
   USA Corporation
FX Danilo Dessi acknowledges Sardinia Regional Government, Italy for the
   financial support of his PhD scholarship (P.O.R. Sardegna F.S.E.
   Operational Programme of the Autonomous Region of Sardinia, European
   Social Fund 2014-2020). This work has also been partially supported by a
   public grant overseen by the French National Research Agency (ANR),
   France as part of the program ``Investissements d'Avenir'' (reference:
   ANR-10-LABX-0083). It contributes to the IdEx Universite de Paris
   ANR-18-IDEX-0001. We gratefully acknowledge the support of NVIDIA, USA
   Corporation with the donation of the Titan X Pascal GPU used for this
   research.
CR Al-Zaidy RA, 2018, IEEE INT C SEMANT CO, P56, DOI 10.1109/ICSC.2018.00017
   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
   Anginoni S., 2019, P 18 INT SEM WEB C I, V2456, P85
   Angioni S., 2020, P ADBIS EDA COMM WOR, P219
   Auer S, 2018, P INT C WEB INT MIN, DOI DOI 10.1186/1475-2859-8-19
   Augenstein I, 2017, P 11 INT WORKSH SEM, P546, DOI DOI 10.18653/V1/S17-2091
   Belleau F, 2008, J BIOMED INFORM, V41, P706, DOI 10.1016/j.jbi.2008.03.004
   Buscaldi D., 2019, PROC CEUR WORKSHOP, V2377, P21
   Buscaldi D, 2019, LECT NOTES COMPUT SC, V11762, P8, DOI 10.1007/978-3-030-32327-1_2
   Carriero V.A., 2019, 1 INT WORKSH OP DAT, P97
   Curran J. R., 2007, P 45 ANN M ASS COMP, P33
   Dessi D, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2350-8
   Ehrlinger L., 2016, SEMANTICS POSTERS DE, V48
   Fathalla S, 2020, PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), P2057, DOI 10.1145/3341105.3374132
   Gabor Kata, 2018, P 12 INT WORKSH SEM, P679, DOI DOI 10.18653/V1/S18-1111
   Gangemi A, 2017, SEMANT WEB, V8, P873, DOI 10.3233/SW-160240
   Groth Paul, 2010, Information Services & Use, V30, P51, DOI 10.3233/ISU-2010-0613
   Jaradeh M.Y., 2019, ARXIV190110816
   Kitano H, 2016, AI MAG, V37, P39, DOI 10.1609/aimag.v37i1.2642
   Knoth P., 2012, D LIB MAGAZINE, V18
   Kuhn T, 2016, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.78
   Labropoulou P., 2018, P 11 INT C LANG RES
   Lan C., 2016, ARXIV160704747
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li AR, 2019, LECT NOTES COMPUT SC, V11809, P52, DOI 10.1007/978-3-030-33982-1_5
   Luan Y., 2018, P 2018 C EMPIRICAL M, P3219, DOI 10.18653/v1/D18-1360
   Martinez-Rodriguez JL, 2018, EXPERT SYST APPL, V113, P339, DOI 10.1016/j.eswa.2018.07.017
   Mesbah S, 2018, LECT NOTES COMPUT SC, V11136, P127, DOI 10.1007/978-3-030-00671-6_8
   Mikolov T., 2013, PROBLEM SOLVING ALGO, P3111
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Moro A., 2014, TACL, V2, P231, DOI DOI 10.1162/TACL_A_00179
   Motta, 2019, ISWC SATELLITES, V2456, P93
   Nuzzolese AG, 2016, LECT NOTES COMPUT SC, V9989, P84, DOI [10.1007/978-3-319-47602-5-18, 10.1007/978-3-319-47602-5_18]
   Nuzzolese AG, 2016, LECT NOTES COMPUT SC, V9982, P150, DOI 10.1007/978-3-319-46547-0_16
   O'Donoghue P, 2015, ROUT STUD SPORT PERF, P220
   Peroni S, 2020, QUANT SCI STUD, V1, P428, DOI 10.1162/qss_a_00023
   Peroni S, 2018, LECT NOTES COMPUT SC, V11137, P119, DOI 10.1007/978-3-030-00668-6_8
   Peronil S, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.132
   Ronzano F, 2015, LECT NOTES ARTIF INT, V9356, P209, DOI 10.1007/978-3-319-24282-8_18
   Salatino A., 2020, KNOWLEDGE ENG KNOWLE
   Salatino A.A., 2019, CSO CLASSIFIER ONTOL
   Salatino AA, 2018, ACM-IEEE J CONF DIG, P303, DOI 10.1145/3197026.3197052
   Salatino AA, 2018, LECT NOTES COMPUT SC, V11137, P187, DOI 10.1007/978-3-030-00668-6_12
   Schneider J., 2014, USING MICROPUBLICATI
   Shotton D, 2009, LEARN PUBL, V22, P85, DOI 10.1087/2009202
   Tennant JP, 2019, PUBLICATIONS, V7, DOI 10.3390/publications7020034
   Wang KS, 2020, QUANT SCI STUD, V1, P396, DOI 10.1162/qss_a_00021
   Wolstencroft K, 2013, NUCLEIC ACIDS RES, V41, pW557, DOI 10.1093/nar/gkt328
   Zhang YT, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1002, DOI 10.1145/3219819.3219859
NR 49
TC 14
Z9 14
U1 9
U2 102
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 MAR
PY 2021
VL 116
BP 253
EP 264
DI 10.1016/j.future.2020.10.026
PG 12
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PG6MB
UT WOS:000599846600020
OA Green Submitted, Green Accepted
DA 2022-08-23
ER

PT J
AU Chen, H
   Zhang, CW
   Li, J
   Yu, PS
   Jing, N
AF Chen, Hao
   Zhang, Chenwei
   Li, Jun
   Yu, Philip S.
   Jing, Ning
TI KGGen: A Generative Approach for Incipient Knowledge Graph Population
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Bit error rate; Sociology; Statistics; Decoding; Task analysis;
   Generators; Training; Knowledge graph population; generative adversarial
   learning; variational autoencoder; deep bidirectional transformers
AB Knowledge graph is becoming an indispensable resource that offers structured information for numerous AI applications. However, the knowledge graph often suffers from its incompleteness. Building a complete, high-quality knowledge graph is time-consuming and requires significant human annotation efforts. In this paper, we study the Knowledge Graph Population task, which aims at extending the scale of structured knowledge, with a special focus on reducing data preparation and annotation efforts. Previous works mainly based on discriminative methods build classifiers and verify candidate triplets that are extracted from texts, which heavily rely on the quality of data collection and co-occurrance of entities in the text. However, such methods fail to generalize on entity pairs that are not highly co-occurred, and fail to discover entity pairs that are not co-occurred at all in the given text corpus. We introduce a generative perspective to approach this task and define each relationship by learning the data distribution that embodies the core common properties for relational reasoning. A generative model KGGen is proposed, which samples from the learned data distribution for each relation and can generate triplets regardless of entity pair co-occurrence in the text corpus. To further improve the generation quality while alleviate human annotation efforts, adversarial learning is adopted to not only encourage generating high quality triplets, but also give model the ability to automatically assess the generation quality. Quantitative and qualitative experimental results conducted on two real-world generic knowledge graphs show that the proposed model KGGen generates novel and meaningful triplets with improved efficiency and less human annotation comparing with the state-of-the-art approaches.
C1 [Chen, Hao; Li, Jun; Jing, Ning] Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Peoples R China.
   [Zhang, Chenwei] Amazon, Seattle, WA 98109 USA.
   [Yu, Philip S.] Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA.
RP Li, J (通讯作者)，Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Peoples R China.
EM hchen@nudt.edu.cn; cwzhang@amazon.com; junli@nudt.edu.cn; psyu@uic.edu;
   ningjing@nudt.edu.cn
OI Chen, Hao/0000-0002-7880-3394
FU National NSF of China [41871284, 61806211, U19A2058]; NSF [III-1526499,
   III-1763325, III1909323, SaTC-1930941]
FX The authors would also like to thank the anonymous referees for their
   valuable comments and helpful suggestions. This work was supported in
   part by the National NSF of China under grants No.41871284, No.61806211
   and No.U19A2058, and in part by NSF under grants III-1526499,
   III-1763325, III1909323, and SaTC-1930941.
CR Agichtein E., 2000, ACM 2000. Digital Libraries. Proceedings of the Fifth ACM Conference on Digital Libraries, P85
   Arjovsky M, 2017, PR MACH LEARN RES, V70
   Baeza-Yates R, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P76
   Bao JM, 2017, IEEE I CONF COMP VIS, P2764, DOI 10.1109/ICCV.2017.299
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bosselut A, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4762
   Chang K., 2014, PROC C EMPIRICAL MET, P1568
   Chen X, 2016, ADV NEUR IN, V29
   Culotta Aron, 2006, P MAIN C HUM LANG TE, P296, DOI [DOI 10.3115/1220835.1220873, 10.3115/1220835.1220873]
   Devlin J, 2018, P 2019 C N AM CHAPT
   Dosovitskiy A., 2016, ADV NEURAL INFORM PR, DOI DOI 10.5555/3157096.3157170
   Dosovitskiy A, 2017, IEEE T PATTERN ANAL, V39, P692, DOI 10.1109/TPAMI.2016.2567384
   Gardner M., 2015, P 2015 C EMP METH NA, P1488, DOI DOI 10.18653/V1/D15-1173
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   He LR, 2018, COGN COMPUT, V10, P1075, DOI 10.1007/s12559-018-9565-x
   Hendrycks D., 2016, ARXIV160608415
   Jiang M, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P877, DOI 10.1145/3097983.3098105
   Kingma D. P., 2017, ARXIV
   Kingma Diederik P, 2013, AUTOENCODING VARIATI
   Komninos A, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2, P324, DOI 10.18653/v1/P17-2051
   Larsen ABL, 2016, PR MACH LEARN RES, V48
   Li X, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1445
   Li YL, 2020, IEEE T BIG DATA, V6, P309, DOI 10.1109/TBDATA.2016.2612236
   Liu Liyuan, 2017, P EMNLP 2017 ASS COM, P46
   Mao XD, 2017, IEEE I CONF COMP VIS, P2813, DOI 10.1109/ICCV.2017.304
   Mescheder L, 2017, PR MACH LEARN RES, V70
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Peters M., 2018, IMPROVING LANGUAGE U
   Rosca Mihaela, 2017, ARXIV170604987
   Sahay S., 2008, ACM T KNOWL DISCOV D, V2, P1
   Sap M, 2019, AAAI CONF ARTIF INTE, P3027
   Scott DW, 2015, WILEY SER PROBAB ST, P1, DOI 10.1002/9781118575574
   Serban IV, 2017, AAAI CONF ARTIF INTE, P3295
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Sohn K., 2015, ADV NEURAL INFORM PR, P3483
   Suchanek FM, 2007, WWW
   Syed Z., 2010, P 7 INT C LANG RES E, P3301
   Trouillon T, 2017, J MACH LEARN RES, V18
   Tsoumakas G, 2010, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, SECOND EDITION, P667, DOI 10.1007/978-0-387-09823-4_34
   Vaswani A, 2017, ADV NEUR IN, V30
   Verga P, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P613
   Wang CG, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P3882
   Wang Q, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1859
   wordvec G. C., 2013, PRETRAINED ENTITY VE
   Yao L., 2011, P C EMP METH NAT LAN, P1456
   Young T, 2018, IEEE COMPUT INTELL M, V13, P55, DOI 10.1109/MCI.2018.2840738
   Yu LT, 2017, AAAI CONF ARTIF INTE, P2852
   Zeng D., 2014, P COLING, P2335
   Zhang CW, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2720, DOI 10.1145/3219819.3220010
   Zhang H, 2017, IEEE I CONF COMP VIS, P5908, DOI 10.1109/ICCV.2017.629
   Zhang X, 2017, NAT COMMUN, V8, P1, DOI [10.1038/ncomms14542, 10.1038/s41467-017-01968-5]
   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 JY, 2017, IEEE I CONF COMP VIS, P2242, DOI 10.1109/ICCV.2017.244
NR 56
TC 0
Z9 0
U1 3
U2 3
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 MAY 1
PY 2022
VL 34
IS 5
BP 2254
EP 2267
DI 10.1109/TKDE.2020.3014166
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 0F4LD
UT WOS:000777332000018
DA 2022-08-23
ER

PT J
AU Dastidar, PG
AF Dastidar, Prabir G.
TI National and institutional productivity and collaboration in Antarctic
   science: an analysis of 25 years of journal publications (1980-2004)
SO POLAR RESEARCH
LA English
DT Article
DE Antarctic science; journal articles; knowledge mapping; network
   analysis; Science Citation Index; scientometrics
AB Journal publications on Antarctic science were analysed for a period of 25 years (1980-2004) through a set of scientometrics and network analysis techniques. The study is based on 10 942 records (research articles, review articles, letters, etc.) with the word fragment "antarc*" in the title published in 961 international, peer-reviewed journals and retrieved from Thomson Scientific's Science Citation index database. During the period under investigation, productivity increased threefold and there was a 13-fold increase in journal publications co-written by authors from different countries. The five nations with the highest output were the USA (with 26.7% of the total output), the UK (13.8%), Australia (9.7%), Germany (8.8%) and Italy (6.0%). The top five institutions in terms of journal publications were the British Antarctic Survey (972 publications), the Alfred Wegener Institute of Polar and Marine Research, Germany (475), the Australian Antarctic Division (312), the University of Tasmania, Australia (305), and the National Aeronautics and Space Administration, USA (293).
C1 Minist Earth Sci, New Delhi 110003, India.
RP Dastidar, PG (通讯作者)，Minist Earth Sci, Block No 9 &12,CGO Complex,Lodi Rd, New Delhi 110003, India.
EM prabirgd11@rediffmail.com
RI Dastidar, Prabir G/A-4803-2014
OI Dastidar, Prabir G/0000-0001-5871-6261
CR COHEN HK, 2002, HDB ANTARTIC TREATY
   Dastidar PG, 2005, CURR SCI INDIA, V89, P1552
   Dastidar PG, 2005, SCIENTOMETRICS, V65, P199, DOI 10.1007/s11192-005-0268-1
   Dastidar PG, 2004, SCIENTOMETRICS, V59, P15
   OCONNOR M, 1978, WRITING SCI PAPERS E
   PERSSON O, 2007, BIBEXCEL DOWNLOADED
   Yank V, 1999, ANN INTERN MED, V130, P661, DOI 10.7326/0003-4819-130-8-199904200-00013
NR 7
TC 14
Z9 17
U1 0
U2 17
PU BLACKWELL PUBLISHING
PI OXFORD
PA 9600 GARSINGTON RD, OXFORD OX4 2DQ, OXON, ENGLAND
SN 0800-0395
J9 POLAR RES
JI Polar Res.
PY 2007
VL 26
IS 2
BP 175
EP 180
DI 10.1111/j.1751-8369.2007.00017.x
PG 6
WC Ecology; Geosciences, Multidisciplinary; Oceanography
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Geology; Oceanography
GA 209NU
UT WOS:000249396100013
DA 2022-08-23
ER

PT S
AU Hoede, C
   Zhang, L
AF Hoede, C
   Zhang, L
BE Delugach, HS
   Stumme, G
TI Word graphs: The third set
SO CONCEPTUAL STRUCTURES: BROADENING THE BASE, PROCEEDINGS
SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
LA English
DT Article; Proceedings Paper
CT 9th International Conference on Conceptual Structures (ICCS)
CY JUL 30-AUG 03, 2001
CL STANFORD, CALIFORNIA
DE knowledge graphs; word graphs; logic word; classification; ontology
AB This is the third paper in a series of natural language processing in term of knowledge graphs. A word is a basic unit in natural language processing. This is why we study word graphs. Word graphs were already built for prepositions and adwords (including adjectives, adverbs and Chinese quantity words) in two other papers [2], [3]. In this paper, we propose the concept of the logic word and classify logic words into groups in terms of semantics and the way they are used in describing reasoning processes.
C1 Univ Twente, Fac Math Sci, NL-7500 AE Enschede, Netherlands.
RP Hoede, C (通讯作者)，Univ Twente, Fac Math Sci, POB 217, NL-7500 AE Enschede, Netherlands.
CR HOEDE C, 1996, ICCS 96, P81
   HOEDE C, 1998, SPRINGER LECT NOTES, V1453, P375
   HOFLAND K, 1982, WORD FREQUENCIES BRI, P43
   PEIRCE C.S., 1885, AM J MATH, V7, P180, DOI DOI 10.2307/2369451
   SOWA JF, 1994, KNOWLEDGE REPRESENTA, P99
   VANDENBERG H, 1993, KNOWLEDGE GRAPHS LOG
NR 6
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-42344-3
J9 LECT NOTES ARTIF INT
PY 2001
VL 2120
BP 15
EP 28
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BU10A
UT WOS:000175011100002
DA 2022-08-23
ER

PT J
AU Zhang, QJ
   Wang, RG
   Yang, J
   Xue, LX
AF Zhang, Qianjin
   Wang, Ronggui
   Yang, Juan
   Xue, Lixia
TI Knowledge graph embedding by reflection transformation
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; Reflection transformation; Link prediction
AB Due to the incompleteness of knowledge graph, knowledge graph embedding(KGE) has become a key technique for automatically predict missing facts in knowledge graph. KGE aims to learn low dimensional representations for both relations and entities. Despite existing KGE models achieved state-of-the-art(SOTA) performances, modeling and inferring relation connectivity(such as symmetry/antisymmetry, inversion, and composition), as well as complex relations prediction(such as M-to-1, 1-to-M and M-to-M) still have great challenges. In this paper, we propose a new KGE model called ReflectE, which regards each relation as a normal vector of a relation-specific reflection hyperplane. Specifically, ReflectE regards the tail entity(or head entity) in a triple as the reflection of the head entity(or tail entity) on a relation-specific hyperplane. Therefore it can model symmetric and inverse relations by reflection transformation naturally. Furthermore, ReflectE models complex relations by learning a relation-specific dynamic reflection hyperplane. In order to evaluate the effectiveness of our proposed model ReflectE, we choose previous SOTA models as baselines and conduct link prediction task on three popular datasets. Experimental results show that, compared with conventional distance-based KGE models, ReflectE achieves SOTA results for link prediction.(c) 2021 Elsevier B.V. All rights reserved.
C1 [Zhang, Qianjin; Wang, Ronggui; Yang, Juan; Xue, Lixia] Hefei Univ Technol, Sch Comp & Informat, Hefei 230601, Peoples R China.
RP Yang, J (通讯作者)，Hefei Univ Technol, Sch Comp & Informat, Hefei 230601, Peoples R China.
EM yangjuan_a1002@126.com
OI Zhang, Qianjin/0000-0002-1321-4329
FU National Key R&D Program of China [2020YFC1512601]
FX Acknowledgment This work is supported by the National Key R&D Program of
   China under Grant No. 2020YFC1512601.
CR Allen C., 2019, P EMNLP
   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
   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]
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Guo DY, 2018, ADV NEUR IN, V31
   HOUSEHOLDER AS, 1958, J ACM, V5, P339, DOI 10.1145/320941.320947
   Jia YT, 2018, KNOWL-BASED SYST, V151, P37, DOI 10.1016/j.knosys.2018.03.020
   Jiang X, 2019, P 2019 C N AM CHAPTE, P978
   Kazemi S.M., 2018, P NIPS
   Kingma D. P., 2017, 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
   Mahdisoltani F., 2013, P CIDR
   Malaviya C, 2020, AAAI CONF ARTIF INTE, V34, P2925
   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, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Niu GL, 2020, AAAI CONF ARTIF INTE, V34, P2950
   Shang C, 2019, AAAI CONF ARTIF INTE, P3060
   Sun Z., 2019, 7 INT C LEARN REPR
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trouillon T, 2016, PR MACH LEARN RES, V48
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Vashishth S., 2020, INT C LEARN REPR
   Vashishth S, 2020, AAAI CONF ARTIF INTE, V34, P3009
   Wang P, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10121407
   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 XL, 2018, PROC CVPR IEEE, P6857, DOI 10.1109/CVPR.2018.00717
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   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 B., 2015, P 3 ICLR
   Zhang QJ, 2021, KNOWL-BASED SYST, V212, DOI 10.1016/j.knosys.2020.106564
   Zhang Z, 2020, AAAI CONF ARTIF INTE, V34, P9612
NR 37
TC 0
Z9 0
U1 5
U2 5
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 28
PY 2022
VL 238
AR 107861
DI 10.1016/j.knosys.2021.107861
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0I1IU
UT WOS:000779180700020
DA 2022-08-23
ER

PT J
AU Nguyen, HL
   Vu, DT
   Jung, JJ
AF Hoang Long Nguyen
   Dang Thinh Vu
   Jung, Jason J.
TI Knowledge graph fusion for smart systems: A Survey
SO INFORMATION FUSION
LA English
DT Article
ID SEMANTIC WEB; LARGE-SCALE; FRAMEWORK; BASE; DBPEDIA; COMMON
C1 [Hoang Long Nguyen; Dang Thinh Vu; Jung, Jason J.] Chung Ang Univ, Dept Comp Engn, 84 Heukseok Ro, Seoul, South Korea.
RP Jung, JJ (通讯作者)，Chung Ang Univ, Dept Comp Engn, 84 Heukseok Ro, Seoul, South Korea.
EM longnh238@gmail.com; vudangthinh@gmail.com; j2jung@gmail.com
RI Jung, Jason J./B-9622-2012; Nguyen, Hoang Long/K-6449-2019
OI Jung, Jason J./0000-0003-0050-7445; Nguyen, Hoang
   Long/0000-0002-8031-6341
FU National Research Foundation of Korea (NRF) - Korean government (MSIP)
   [NRF-2020R1A2B5B01002207]
FX This work was supported by the National Research Foundation of Korea
   (NRF) grant funded by the Korean government (MSIP)
   (NRF-2020R1A2B5B01002207).
CR Abdelaziz I, 2017, J WEB SEMANT, V44, P104, DOI 10.1016/j.websem.2017.06.002
   Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Alizadeh AM, 2018, PATHOG GLOB HEALTH, V112, P306, DOI 10.1080/20477724.2018.1514137
   Althebyan Q, 2007, 2007 IEEE INFORMATION ASSURANCE WORKSHOP, P239, DOI 10.1109/IAW.2007.381939
   [Anonymous], 2001, SCI AM
   [Anonymous], 2016, P 3 WORKSH NEW TREND
   [Anonymous], P 6 INT C EMP METH N
   [Anonymous], GEOSPECIES ONTOLOGY
   [Anonymous], 2003, KDD 2003 WORKSH CIT
   [Anonymous], 25 YEARS DEV KNOWLED
   [Anonymous], 2009, COMMON SENSE GUIDE P
   [Anonymous], 2015, P 1 INT WORKSH MACH
   [Anonymous], P NATO IST 153 RWS 2
   [Anonymous], 2013, ARXIV13094408
   [Anonymous], 2019, P SEKE
   [Anonymous], 2017, P S APPL COMP SAC 20
   [Anonymous], P 21 INT C EXT DAT T
   [Anonymous], 2018, 2018 INT C CYBER SIT
   [Anonymous], P 50 HAW INT C SYST
   [Anonymous], 2014, ARXIV
   [Anonymous], 2014, INTERSPEECH
   [Anonymous], REFERENCE REV
   [Anonymous], 2011, P 4 ACM INT C WEB SE, DOI DOI 10.1145/1935826.1935903
   Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Auer S, 2007, LECT NOTES COMPUT SC, V4519, P503
   Bellekens Pieter, 2008, 2008 8th International Conference on Web Engineering (ICWE), P362, DOI 10.1109/ICWE.2008.20
   Bellini V., 2017, P 2 WORKSH DEEP LEAR, P60, DOI [10.1145/3125486.3125496, DOI 10.1145/3125486.3125496]
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bilenko M, 2003, IEEE INTELL SYST, V18, P16, DOI 10.1109/MIS.2003.1234765
   Bilenko M., 2003, P 9 ACM SIGKDD INT C, P39
   Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901
   Blanco R, 2013, LECT NOTES COMPUT SC, V8219, P33, DOI 10.1007/978-3-642-41338-4_3
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bleiholder J, 2008, ACM COMPUT SURV, V41, DOI 10.1145/1456650.1456651
   Bollacker K, 2007, INT WORKSH INF INT W, P22
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bordes Antoine, 2011, AAAI
   Bordes Antoine, 2015, ARXIV150602075
   Brocheler M., 2010, P 26 C UNC ART INT U, P73
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Carlson Andrew, 2010, P 3 ACM INT C WEB SE, P101, DOI 10.1145/ 1718487.1718501
   CARROLL JD, 1970, PSYCHOMETRIKA, V35, P283, DOI 10.1007/BF02310791
   Catherine R, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P325, DOI 10.1145/2959100.2959131
   Chaudhari S, 2017, AI COMMUN, V30, P141, DOI 10.3233/AIC-170728
   Chaudhuri S., 2003, P 2003 ACM SIGMOD IN, P313, DOI [10.4022/jafib.1481, DOI 10.4022/JAFIB.1481, DOI 10.1145/872757.872796]
   Chi Y, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10040995
   Ciampaglia GL, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0128193
   Cochinwala M, 2001, INFORM SCIENCES, V137, P1, DOI 10.1016/S0020-0255(00)00070-0
   Cohen W.W., 2002, P 8 ACM SIGKDD INT C, P475, DOI DOI 10.1145/775047.775116
   Cui Y, 2011, IEEE INFOCOM SER, P1395, DOI 10.1109/INFCOM.2011.5934925
   Dai Z, 2016, ARXIV160601994
   de Gemmis M, 2015, INFORM PROCESS MANAG, V51, P695, DOI 10.1016/j.ipm.2015.06.008
   Deng YL, 2019, PROCEEDINGS OF THE ACM CONFERENCE ON GLOBAL COMPUTING EDUCATION (COMPED '19), P194, DOI 10.1145/3300115.3309531
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Di Noia T, 2016, ACM T INTEL SYST TEC, V8, DOI 10.1145/2899005
   DIJKSTRA E, 1990, PREDICATE CALCULUS P
   Dong XL, 2014, PROC VLDB ENDOW, V7, P881, DOI 10.14778/2732951.2732962
   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
   Drumond L., 2012, P 27 ANN ACM S APPL, P326, DOI DOI 10.1145/2245276.2245341
   Duan HM, 2018, J INTERDISCIP MATH, V21, P213, DOI 10.1080/09720502.2017.1369679
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Ernst P, 2014, PROC INT CONF DATA, P1254, DOI 10.1109/ICDE.2014.6816754
   Farber M, 2018, SEMANT WEB, V9
   Feilmayr C, 2016, DATA KNOWL ENG, V101, P1, DOI 10.1016/j.datak.2015.11.003
   FELLEGI IP, 1969, J AM STAT ASSOC, V64, P1183, DOI 10.2307/2286061
   Franco-Salvador M, 2016, INFORM PROCESS MANAG, V52, P550, DOI 10.1016/j.ipm.2015.12.004
   Franz T, 2009, LECT NOTES COMPUT SC, V5823, P213, DOI 10.1007/978-3-642-04930-9_14
   Gegick M., 2005, ACM SIGSOFT SOFTWARE, V30, P1, DOI DOI 10.1145/1082983.1083211
   Golub D, 2016, ARXIV160400727
   Gravano L., 2001, Proceedings of the 27th International Conference on Very Large Data Bases, P491
   HARSHMAN RA, 1994, COMPUT STAT DATA AN, V18, P39, DOI 10.1016/0167-9473(94)90132-5
   Hassan N, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1803, DOI 10.1145/3097983.3098131
   Hernandez MA, 1998, DATA MIN KNOWL DISC, V2, P9, DOI 10.1023/A:1009761603038
   Hoang HH, 2014, COMPUT INFORM, V33, P553
   Nguyen HL, 2019, FUTURE GENER COMP SY, V100, P10, DOI 10.1016/j.future.2019.05.016
   Nguyen HL, 2018, FUTURE GENER COMP SY, V83, P358, DOI 10.1016/j.future.2018.01.064
   Nguyen HL, 2017, INT CONF INTEL ENVIR, P108, DOI 10.1109/IE.2017.15
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Iannacone M., 2015, P 10 ANN CYBER INFOR, P1
   JARO MA, 1989, J AM STAT ASSOC, V84, P414, DOI 10.2307/2289924
   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
   Jia Y, 2018, ENGINEERING-PRC, V4, P53, DOI 10.1016/j.eng.2018.01.004
   Jiang SP, 2012, IEEE DATA MINING, P912, DOI 10.1109/ICDM.2012.156
   Jiang Yun-fei, 2002, Journal of Software, V13, P686
   Jing XR, 2006, I C WIREL COMM NETW, P547
   Jojic O, 2016, ARXIV160605029
   Jung JJ, 2010, EXPERT SYST APPL, V37, P8401, DOI 10.1016/j.eswa.2010.05.041
   Jung JJ, 2010, INFORM SCIENCES, V180, P3248, DOI 10.1016/j.ins.2010.04.018
   Jurafsky D., 2000, SPEECH LANGUAGE PROC
   Karidi DP, 2018, J AMB INTEL HUM COMP, V9, P2035, DOI 10.1007/s12652-017-0491-7
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kejriwal M., 2014, P 9 INT WORKSH ONT M, P49
   Kejriwal M, 2013, IEEE DATA MINING, P340, DOI 10.1109/ICDM.2013.60
   Kolda TG, 2009, SIAM REV, V51, P455, DOI 10.1137/07070111X
   Krathwohl DR, 2002, THEOR PRACT, V41, P212, DOI 10.1207/s15430421tip4104_2
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   LEVENSHT.VI, 1965, DOKL AKAD NAUK SSSR+, V163, P845
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu JB, 2019, POULTRY SCI, V98, P4829, DOI 10.3382/ps/pez217
   Lu C, 2016, LECT NOTES COMPUT SC, V10024, P416, DOI 10.1007/978-3-319-49004-5_27
   Mahdisoltani F., 2015, CIDR 2015
   McCallum A., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, P169, DOI 10.1145/347090.347123
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Ming Du, 2019, Knowledge Science, Engineering and Management. 12th International Conference, KSEM 2019. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11775), P47, DOI 10.1007/978-3-030-29551-6_5
   Modi A, 2016, 2016 IEEE 2ND INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING (IEEE CIC), P408, DOI [10.1109/CIC.2016.060, 10.1109/CIC.2016.58]
   Mohammed S, 2017, ARXIV171201969
   Long NH, 2015, CYBERNET SYST, V46, P69, DOI 10.1080/01969722.2015.1007737
   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, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Ostuni VC, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P85, DOI 10.1145/2740908.2742775
   Palaskar N, 2016, IEEE HI ASS SYS ENGR, P9, DOI 10.1109/HASE.2016.39
   Palumbo E, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P32, DOI 10.1145/3109859.3109889
   Pan JZ, 2018, LECT NOTES COMPUT SC, V11136, P669, DOI 10.1007/978-3-030-00671-6_39
   Papadakis G, 2012, P 5 ACM INT C WEB SE, P53, DOI DOI 10.1145/2124295.2124305
   Papadakis G, 2013, IEEE T KNOWL DATA EN, V25, P2665, DOI 10.1109/TKDE.2012.150
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Perez J, 2009, ACM T DATABASE SYST, V34, DOI 10.1145/1567274.1567278
   Pingitore A, 2019, ADOLESCENT HEALTH AND WELLBEING: CURRENT STRATEGIES AND FUTURE TRENDS, P1
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Ramadan B, 2015, LECT NOTES ARTIF INT, V9078, P574, DOI 10.1007/978-3-319-18032-8_45
   Rebele T, 2016, LECT NOTES COMPUT SC, V9982, P177, DOI 10.1007/978-3-319-46547-0_19
   Rendle S., 2010, P 3 ACM INT C WEB SE, P81, DOI DOI 10.1145/1718487.1718498
   Ruan T, 2016, LECT NOTES COMPUT SC, V9982, P418, DOI 10.1007/978-3-319-46547-0_35
   Rudnik C, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P1232, DOI 10.1145/3308560.3316761
   Sicilia MA, 2004, IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS, P900, DOI 10.1109/ICALT.2004.1357711
   Sikos LF, 2018, PROCEDIA COMPUT SCI, V126, P29, DOI 10.1016/j.procs.2018.07.206
   SMITH TF, 1981, J MOL BIOL, V147, P195, DOI 10.1016/0022-2836(81)90087-5
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Suchanek FM, 2011, PROC VLDB ENDOW, V5, P157, DOI 10.14778/2078331.2078332
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Tay Y, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P751, DOI 10.1145/3018661.3018695
   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
   TUCKER LR, 1966, PSYCHOMETRIKA, V31, P279, DOI 10.1007/BF02289464
   Vatant B., 2012, GEONAMES ONTOLOGY
   Vrandecic D., 2012, P 21 INT C WORLD WID, P1063, DOI [DOI 10.1145/2187980.2188242, 10.1145/2187980.2188242]
   Wang D S, 2010, Proceedings of the 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2010), P151, DOI 10.1109/SNPD.2010.31
   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 JZ, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P839, DOI 10.1145/3219819.3219869
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weikum G., 2009, P 18 ACM C INF KNOWL, P513, DOI DOI 10.1145/1645953.1646020
   Wilcke X., 2017, DATA SCI, V1, P39, DOI [10.3233/ds-170007, DOI 10.3233/DS-170007, 10.3233/DS-170007]
   Wu LL, 2017, ADV MATH PHYS, V2017, DOI 10.1155/2017/6128102
   Xin BG, 2019, ADV DIFFER EQU-NY, DOI 10.1186/s13662-019-2074-8
   Xiong CY, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1271, DOI 10.1145/3038912.3052558
   Yang B., 2015, P INT C LEARN REPR
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   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 158
TC 23
Z9 25
U1 6
U2 70
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1566-2535
EI 1872-6305
J9 INFORM FUSION
JI Inf. Fusion
PD SEP
PY 2020
VL 61
BP 56
EP 70
DI 10.1016/j.inffus.2020.03.014
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA LX3HP
UT WOS:000539726400006
DA 2022-08-23
ER

PT J
AU Huang, HC
   Hong, ZZY
   Zhou, HM
   Wu, JX
   Jin, N
AF Huang, Haichao
   Hong, Zhouzhenyan
   Zhou, Huiming
   Wu, Jiaxian
   Jin, Ning
TI Knowledge Graph Construction and Application of Power Grid Equipment
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID DATA-MANAGEMENT; INTERNET; EDGE; PRESERVATION
AB Recent development of artificial intelligence (AI) technology enquires the traditional power grid system involving additional information and connectivity of all devices for the smooth transit to the next generation of smart grid system. In an AI-enhanced power grid system, each device has its unique name, function, property, location, and many more. A large number of power grid devices can form a complex power grid knowledge graph through serial and parallel connection relationships. The scale of power grid equipment is usually extremely large, with thousands and millions of power devices. Finding the proper way of understanding and operating these devices is difficult. Furthermore, the collection, analysis, and management of power grid equipment become major problems in power grid management. With the development of AI technology, the combination of labeling technology and knowledge graph technology provides a new solution understanding the internal structure of a power grid. As a result, this study focuses on knowledge graph construction techniques for large scale power grid located in China. A semiautomatic knowledge graph construction technology is proposed and applied to the power grid equipment system. Through a series of experimental simulations, we show that the efficiency of daily operations, maintenance, and management of the power grid can be largely improved.
C1 [Huang, Haichao] State Grid Zhejiang Elect Power Co Ltd, Informat & Commun Branch, 219 Shimin St, Hangzhou 310016, Peoples R China.
   [Hong, Zhouzhenyan] Zhejiang Univ, Int Campus, Hangzhou 314400, Peoples R China.
   [Zhou, Huiming] Zhejiang Huayun Informat Technol Co Ltd, Hangzhou 310012, Peoples R China.
   [Wu, Jiaxian; Jin, Ning] China Jiliang Univ, Key Lab Elect Wave Informat Technol & Metrol Zhei, Coll Informat Engn, Hangzhou 310018, Peoples R China.
RP Jin, N (通讯作者)，China Jiliang Univ, Key Lab Elect Wave Informat Technol & Metrol Zhei, Coll Informat Engn, Hangzhou 310018, Peoples R China.
EM jdhhc@163.com; zhouzhenyan.17@intl.zju.edu.cn; zhouhuiming@hyit.com.cn;
   wujiaxianleo@163.com; jinning1117@cjlu.edu.cn
FU Zhejiang Provincial Natural Science Foundation of China [LY19F020016]
FX This work was supported by Zhejiang Provincial Natural Science
   Foundation of China under Grant no. LY19F020016.
CR Al Asbahi AAMH, 2019, ENERGY REP, V5, P704, DOI 10.1016/j.egyr.2019.06.009
   Al-Momani AM, 2018, J ORGAN END USER COM, V30, P51, DOI 10.4018/JOEUC.2018100104
   Amoore L, 2015, ECON SOC, V44, P341, DOI 10.1080/03085147.2015.1043793
   Bostock M, 2011, IEEE T VIS COMPUT GR, V17, P2301, DOI 10.1109/TVCG.2011.185
   Chen WJ, 2019, J CLEAN PROD, V231, P161, DOI 10.1016/j.jclepro.2019.05.207
   Chen Y, 2021, IEEE T CLOUD COMPUT, V9, P1050, DOI 10.1109/TCC.2019.2898657
   Eckman BA, 2006, IBM J RES DEV, V50, P545, DOI 10.1147/rd.506.0545
   Ercan G, 2019, LECT NOTES COMPUT SC, V11503, P52, DOI 10.1007/978-3-030-21348-0_4
   Farber F, 2011, SIGMOD REC, V40, P45, DOI 10.1145/2094114.2094126
   Grubljesic T, 2019, J ORGAN END USER COM, V31, P37, DOI 10.4018/JOEUC.2019040103
   Hao YX, 2019, IEEE NETWORK, V33, P58, DOI 10.1109/MNET.2019.1800235
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Li JX, 2020, INFORM SYST, V92, DOI 10.1016/j.is.2020.101522
   Lu RQ, 2020, INFORM SCIENCES, V509, P87, DOI 10.1016/j.ins.2019.08.051
   Ma WZ, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1210, DOI 10.1145/3308558.3313607
   Nie BL, 2019, FUTURE GENER COMP SY, V91, P426, DOI 10.1016/j.future.2018.09.040
   Pittman SJ, 2019, MAR POLICY, V103, P160, DOI 10.1016/j.marpol.2019.02.012
   Qi LY, 2021, IEEE T NETW SCI ENG, V8, P1145, DOI 10.1109/TNSE.2020.2969489
   Skrobot VL, 2007, ENERG FUEL, V21, P3394, DOI 10.1021/ef0701337
   Song Q, 2018, IEEE T KNOWL DATA EN, V30, P1887, DOI 10.1109/TKDE.2018.2807442
   Wang HF, 2019, FUTURE GENER COMP SY, V100, P600, DOI 10.1016/j.future.2019.05.045
   Wang WF, 2020, WORLD WIDE WEB, V23, P1177, DOI 10.1007/s11280-019-00705-w
   Webber J., 2018, PROGRAMMATIC INTRO N
   Xia T, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI), P194, DOI 10.1109/ISI.2019.8823450
   Xu XL, 2021, IEEE T INTELL TRANSP, V22, P5213, DOI 10.1109/TITS.2020.2982186
   Yan JY, 2019, NAT ENERGY, V4, P709, DOI 10.1038/s41560-019-0441-z
   Yan K, 2017, J HEALTHC ENG, V2017, DOI 10.1155/2017/4818604
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   Zheng YY, 2022, IEEE T BIG DATA, V8, P616, DOI 10.1109/TBDATA.2018.2805366
   Zhong CW, 2019, ENERGIES, V12, DOI 10.3390/en12030527
   Zhong WY, 2020, COMPUT COMMUN, V157, P116, DOI 10.1016/j.comcom.2020.04.018
   Zhou XH, 2017, RES SER CHIN DREAM, P1, DOI 10.1007/978-981-10-4986-6_1
   Zhou XK, 2021, IEEE T COMPUT SOC SY, V8, P171, DOI 10.1109/TCSS.2020.2987846
   Zhou XK, 2021, IEEE ACM T COMPUT BI, V18, P912, DOI 10.1109/TCBB.2020.2994780
   Zhou XK, 2020, IEEE INTERNET THINGS, V7, P6429, DOI 10.1109/JIOT.2020.2985082
   Zhou XK, 2021, IEEE T EMERG TOP COM, V9, P246, DOI 10.1109/TETC.2018.2860051
   Zhu XY, 2019, IEEE ACCESS, V7, P53262, DOI 10.1109/ACCESS.2019.2912402
   Zhu ZZ, 2019, COMPUT IND ENG, V130, P671, DOI 10.1016/j.cie.2019.03.022
NR 38
TC 0
Z9 1
U1 12
U2 20
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 OCT 14
PY 2020
VL 2020
AR 8269082
DI 10.1155/2020/8269082
PG 10
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA ON0TU
UT WOS:000586425700015
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, HP
   Chao, B
   Huang, ZJ
   Li, TY
AF Zhang, Haiping
   Chao, Bo
   Huang, Zhijing
   Li, Tingyu
TI Construction and Research on Chinese Semantic Mapping Based on
   Linguistic Features and Sparse Self-Learning Neural Networks
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID RECOGNITION
AB In this paper, we adopt the algorithms of linguistic feature Rong and sparse self-learning neural network to conduct an in-depth study and analysis of Chinese semantic mapping, which complements the emotion semantic representation ability of traditional word embedding and fully explores the emotion semantic information contained in the text in the task preprocessing stage. We incorporate various semantic features such as lexical information and location information to make the model have richer emotion semantic expression, and the model also uses an attention mechanism to allow various features to interact and abstract deeper contextual internal semantic associations to improve the model's sentiment classification performance. Finally, experiments are conducted on two publicly available English sentiment classification corpora, and the results prove that the model outperforms other comparison models and effectively improves the sentiment classification performance. The model uses deep memory networks and capsule networks to construct a transfer learning framework and effectively leverages the transfer learning properties of capsule networks to transfer knowledge embedded in large-scale labeled data from similar domains to the target domain, improving the classification performance on small data sets. The use of multidimensional combined features compensates for the lack of a one-dimensional feature attention mechanism, while multiple domain category-based attention computation layers are superimposed to obtain deeper domain-specific sentiment feature information. Based on the segmented convolutional neural network, the model first introduces the dependent subtree of relational attributes to obtain the position weights of each word in the sentence, then introduces domain ontology knowledge in the output layer to constrain the extraction results, and conducts experimental comparison through the data set to verify the validity of the model, which ensures the accuracy of the network term entity and relational attribute recognition extraction and makes the knowledge map constructed in this paper. It ensures the accuracy of the extraction rate of the web term entities and relationship attributes and makes the knowledge map constructed in this paper more factual.
C1 [Zhang, Haiping; Chao, Bo; Huang, Zhijing] Jiangxi Univ Chinese Med, Int Educ Coll, Nanchang 330006, Peoples R China.
   [Li, Tingyu] Jiangxi Univ Chinese Med, Sch Humanity, Nanchang 330004, Peoples R China.
RP Li, TY (通讯作者)，Jiangxi Univ Chinese Med, Sch Humanity, Nanchang 330004, Peoples R China.
EM 20081032@jxutcm.edu.cn; 30749526@qq.com; 30822877@qq.com;
   20131038@jxutcm.edu.cn
CR Amiri SA, 2018, MULTIMED TOOLS APPL, V77, P8677, DOI 10.1007/s11042-017-4763-1
   Bao YQ, 2020, STRUCT HEALTH MONIT, V19, P1963, DOI 10.1177/1475921720909440
   Cao HL, 2019, INTEL SERV ROBOT, V12, P45, DOI 10.1007/s11370-018-0266-9
   Cheng JY, 2020, ROBOTICA, V38, P256, DOI 10.1017/S0263574719000584
   Crossley S, 2020, J WRIT RES, V11, P415, DOI 10.17239/jowr-2020.11.03.01
   Du C, 2019, INT J COMPUT COMMUN, V14, P7, DOI 10.15837/ijccc.2019.1.3374
   Hellwig O, 2019, INDOGER FORSCH, V124, P1, DOI 10.1515/if-2019-0001
   Huurne M.T., 2021, INT J ELECT BUSINESS, V16, P391, DOI [10.1504/ijeb.2021.118501, DOI 10.1504/IJEB.2021.118501]
   Iosifidis V, 2020, KNOWL INF SYST, V62, P1393, DOI 10.1007/s10115-019-01392-9
   Jiang PP, 2021, ACM T ASIAN LOW-RESO, V20, DOI 10.1145/3406204
   Jiang XW, 2019, J MED IMAG HEALTH IN, V9, P2031, DOI 10.1166/jmihi.2019.2804
   Li DM, 2018, IEEE T IND INFORM, V14, P3253, DOI 10.1109/TII.2018.2810291
   Li QC, 2021, IEEE T IND INFORM, V17, P3069, DOI 10.1109/TII.2020.3030186
   Li YB, 2022, J SUPERCOMPUT, V78, P6296, DOI 10.1007/s11227-021-04130-7
   Lou G, 2020, CHINA COMMUN, V17, P117, DOI 10.23919/JCC.2020.02.010
   MacArthur CA, 2019, READ WRIT, V32, P1553, DOI 10.1007/s11145-018-9853-6
   Saragih E., 2019, STUDIES ENGLISH LANG, V6, P333, DOI [10.24815/siele.v6i2.14786, DOI 10.24815/SIELE.V6I2.14786]
   Vajjala S, 2018, INT J ARTIF INTELL E, V28, P79, DOI 10.1007/s40593-017-0142-3
   Yang Y, 2018, INT J AUTOM COMPUT, V15, P194, DOI 10.1007/s11633-018-1118-y
   Zhang K, 2021, IEEE INTERNET THINGS, V8, P11935, DOI 10.1109/JIOT.2021.3063497
   Zou GF, 2019, MULTIMED TOOLS APPL, V78, P6969, DOI 10.1007/s11042-018-6449-8
NR 21
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 20
PY 2022
VL 2022
AR 2315802
DI 10.1155/2022/2315802
PG 12
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 3K6AB
UT WOS:000834155800017
PM 35769283
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ren, JB
   Liu, Y
   Wang, FL
   He, GW
   Deng, XG
   Wei, ZQ
   Yao, HQ
AF Ren, Jiangbo
   Liu, Yan
   Wang, Fenlian
   He, Gaowen
   Deng, Xiguang
   Wei, Zhenquan
   Yao, Huiqiang
TI Mechanism and Influencing Factors of REY Enrichment in Deep-Sea
   Sediments
SO MINERALS
LA English
DT Article
DE rare earth elements and yttrium; REY-rich phosphate; enrichment
   mechanism; deep-sea sediments; knowledge map; linear regression
AB Deep-sea sediments with high contents of rare-earth elements and yttrium (REY) are expected to serve as a potential resource for REY, which have recently been proved to be mainly contributed by phosphate component. Studies have shown that the carriers of REY in deep-sea sediments include aluminosilicate, Fe-Mn oxyhydroxides, and phosphate components. The n-ary sumation REY of the phosphate component is 1-2 orders of magnitude higher than those of the other two carriers, expressed as n-ary sumation REY = 0.001 x [Al2O3] - 0.002 x [MnO] + 0.056 x [P2O5] - 32. The sediment P2O5 content of 1.5% explains 89.1% of the total variance of the sediment n-ary sumation REY content. According to global data, P has a stronger positive correlation with n-ary sumation REY compared with Mn, Fe, Al, etc.; 45.5% of samples have a P2O5 content of less than 0.25%, and n-ary sumation REY of not higher than 400 ppm. The n-ary sumation REY of the phosphate component reaches n x 10(4) ppm, much higher than that of marine phosphorites and lower than that of REY-phosphate minerals, which are called REY-rich phosphates in this study. The results of microscopic observation and separation by grain size indicate that the REY-rich phosphate component is mainly composed of bioapatite. When n-ary sumation REY > 2000 ppm, the average CaO/P2O5 ratio of the samples is 1.55, indicating that the phosphate composition is between carbonate fluoroapatite and hydroxyfluorapatite. According to a knowledge map of sediment elements, the phosphate component is mainly composed of P, Ca, Sr, REY, Sc, U, and Th, and its chemical composition is relatively stable. The phosphate component has a negative Ce anomaly and positive Y anomaly, and a REY pattern similar to that of marine phosphorites and seawater. After the early diagenesis process (biogeochemistry, adsorption, desorption, transformation, and migration), the REY enrichment in the phosphate component is completed near the seawater/sediment interface. In the process of REY enrichment, the precipitation and enrichment of P is critical. According to current research progress, the REY enrichment is the result of comprehensive factors, including low sedimentation rate, high n-ary sumation REY of the bottom seawater, a non-carbonate depositional environment, oxidation conditions, and certain bottom current conditions.
C1 [Ren, Jiangbo; Liu, Yan; Wang, Fenlian; He, Gaowen; Deng, Xiguang; Wei, Zhenquan; Yao, Huiqiang] Southern Marine Sci & Engn Guangdong Lab Guangzho, Guangzhou 511458, Peoples R China.
   [Ren, Jiangbo; Wang, Fenlian; He, Gaowen; Deng, Xiguang; Wei, Zhenquan; Yao, Huiqiang] Guangzhou Marine Geol Survey, Minist Nat Resources, Key Lab Marine Mineral Resources, Guangzhou 510075, Peoples R China.
   [Liu, Yan] Chinese Acad Geol Sci, Inst Geol, Minist Nat Resources, Key Lab Deep Earth Dynam, Beijing 100037, Peoples R China.
RP Liu, Y; He, GW (通讯作者)，Southern Marine Sci & Engn Guangdong Lab Guangzho, Guangzhou 511458, Peoples R China.; He, GW (通讯作者)，Guangzhou Marine Geol Survey, Minist Nat Resources, Key Lab Marine Mineral Resources, Guangzhou 510075, Peoples R China.; Liu, Y (通讯作者)，Chinese Acad Geol Sci, Inst Geol, Minist Nat Resources, Key Lab Deep Earth Dynam, Beijing 100037, Peoples R China.
EM dourjb222@163.com; ly@cags.ac.cn; fenlian0523@163.com; hegaowen@163.com;
   dengxg68@163.com; wei_zhenquan@163.com; hqyao@163.com
OI Ren, Jiangbo/0000-0003-3936-0516
FU National Natural Science Foundation of China [42002085, 41702096,
   41922014]; China Ocean Mineral Resources R&D Association Research
   Program [DY135-R2-1-05]; Key Special Project for Introduced Talents Team
   of Southern Marine Science and Engineering Guangdong Laboratory
   (Guangzhou) [GML2019ZD0106]; Geological Survey of China [DD20191009];
   fundamental Research Funds for the Institute of Geology, Chinese Academy
   of Geological Sciences [J2004]
FX This research was funded by the National Natural Science Foundation of
   China (42002085, 41702096 and 41922014), China Ocean Mineral Resources
   R&D Association Research Program (DY135-R2-1-05), Key Special Project
   for Introduced Talents Team of Southern Marine Science and Engineering
   Guangdong Laboratory (Guangzhou) (GML2019ZD0106), Project of the
   Geological Survey of China (DD20191009), and fundamental Research Funds
   for the Institute of Geology, Chinese Academy of Geological Sciences
   (Grant J2004).
CR Alibo DS, 1999, GEOCHIM COSMOCHIM AC, V63, P363, DOI 10.1016/S0016-7037(98)00279-8
   Bau M, 2014, CHEM GEOL, V381, P1, DOI 10.1016/j.chemgeo.2014.05.004
   BERNAT M, 1970, GEOCHIM COSMOCHIM AC, V34, P1053, DOI 10.1016/0016-7037(70)90162-6
   Borst AM, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-17801-5
   D'Hondt S, 2015, NAT GEOSCI, V8, P299, DOI 10.1038/NGEO2387
   Deng YN, 2018, ACTA PETROL SIN, V34, P733
   Deng YN, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-16379-1
   Dutkiewicz A, 2020, GEOLOGY, V48, P293, DOI 10.1130/G46836.1
   ELDERFIELD H, 1987, EARTH PLANET SC LETT, V82, P280, DOI 10.1016/0012-821X(87)90202-0
   ELDERFIELD H, 1986, SCI TOTAL ENVIRON, V49, P175, DOI 10.1016/0048-9697(86)90239-1
   Follmi KB, 1996, EARTH-SCI REV, V40, P55, DOI 10.1016/0012-8252(95)00049-6
   GROMET LP, 1984, GEOCHIM COSMOCHIM AC, V48, P2469, DOI 10.1016/0016-7037(84)90298-9
   Hein JR, 2016, MINERALS-BASEL, V6, DOI 10.3390/min6030088
   Horikawa K, 2011, EARTH PLANET SC LETT, V310, P119, DOI 10.1016/j.epsl.2011.07.018
   Iijima K, 2016, GEOCHEM J, V50, P557, DOI 10.2343/geochemj.2.0431
   Kashiwabara T, 2018, GEOCHIM COSMOCHIM AC, V240, P274, DOI 10.1016/j.gca.2018.08.013
   Kato Y, 2011, NAT GEOSCI, V4, P535, DOI [10.1038/NGEO1185, 10.1038/ngeo1185]
   Kon Y, 2014, RESOUR GEOL, V64, P47, DOI 10.1111/rge.12026
   Lecuyer C, 2004, CHEM GEOL, V204, P63, DOI 10.1016/j.chemgeo.2003.11.003
   Liao JL, 2019, CHEM GEOL, V512, P58, DOI 10.1016/j.chemgeo.2019.02.039
   [刘志强 Liu Zhiqiang], 2015, [中国稀土学报, Journal of the Chinese Society of Rare Earths], V33, P506
   Martin EE, 2004, EARTH PLANET SC LETT, V220, P25, DOI 10.1016/S0012-821X(04)00030-5
   Martin EE, 2010, CHEM GEOL, V269, P414, DOI 10.1016/j.chemgeo.2009.10.016
   Menendez A, 2017, ORE GEOL REV, V87, P100, DOI 10.1016/j.oregeorev.2016.09.036
   Mimura K, 2019, J ASIAN EARTH SCI, V186, DOI 10.1016/j.jseaes.2019.104059
   Muller RD, 2008, GEOCHEM GEOPHY GEOSY, V9, DOI 10.1029/2007GC001743
   Ohta J, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-66835-8
   Ohta J, 2016, GEOCHEM J, V50, P591, DOI 10.2343/geochemj.2.0435
   PAGEL M, 1993, CAN J EARTH SCI, V30, P731, DOI 10.1139/e93-059
   [潘家华 Pan Jiahua], 2004, [地球学报, Acta Geoscientia Sinica], V25, P453
   Pattan JN, 2011, J ASIAN EARTH SCI, V40, P569, DOI 10.1016/j.jseaes.2010.10.010
   Paul SAL, 2019, GEOCHIM COSMOCHIM AC, V251, P56, DOI 10.1016/j.gca.2019.02.019
   Paytan A, 2007, CHEM REV, V107, P563, DOI 10.1021/cr0503613
   POITRASSON F, 1995, J PETROL, V36, P1251, DOI 10.1093/petrology/36.5.1251
   Ren J.B., 2021, EARTH SCI FRONT, V28, P412, DOI [10.13745/j.esf.sf.2020.6.37, DOI 10.13745/J.ESF.SF.2020.6.37]
   [任江波 Ren Jiangbo], 2019, [地球科学, Earth Science], V44, P3529
   [任江波 Ren Jiangbo], 2017, [地质学报, Acta Geologica Sinica], V91, P1312
   [任江波 Ren Jiangbo], 2015, [地学前缘, Earth Science Frontiers], V22, P200
   Stichel T, 2012, EARTH PLANET SC LETT, V317, P282, DOI 10.1016/j.epsl.2011.11.025
   STONECIPHER SA, 1976, CHEM GEOL, V17, P307, DOI 10.1016/0009-2541(76)90044-9
   Takaya Y, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-23948-5
   Takebe M, 2005, J GEOL, V113, P201, DOI 10.1086/427669
   Tanaka E, 2020, ORE GEOL REV, V119, DOI 10.1016/j.oregeorev.2020.103392
   TOYODA K, 1990, NATURE, V345, P607, DOI 10.1038/345607a0
   TOYODA K, 1990, GEOCHIM COSMOCHIM AC, V54, P1093, DOI 10.1016/0016-7037(90)90441-M
   Trotter JA, 2006, CHEM GEOL, V233, P196, DOI 10.1016/j.chemgeo.2006.03.004
   Trotter JA, 2016, PALAEOGEOGR PALAEOCL, V462, P92, DOI 10.1016/j.palaeo.2016.09.007
   Wang FL, 2016, ACTA PETROL SIN, V32, P2057
   Yasukawa K, 2019, GEOCHEM GEOPHY GEOSY, V20, P3402, DOI 10.1029/2019GC008214
   Yasukawa K, 2016, SCI REP-UK, V6, DOI 10.1038/srep29603
   Yasukawa K, 2015, GEOCHEM J, V49, P621, DOI 10.2343/geochemj.2.0361
   Yasukawa K, 2014, J ASIAN EARTH SCI, V93, P25, DOI 10.1016/j.jseaes.2014.07.005
   [张霄宇 Zhang Xiaoyu], 2019, [中国稀土学报, Journal of the Chinese Society of Rare Earths], V37, P517
   Zhang XY, 2017, J RARE EARTH, V35, P1047, DOI 10.1016/S1002-0721(17)61012-3
   Zheng XY, 2016, GEOCHIM COSMOCHIM AC, V177, P217, DOI 10.1016/j.gca.2016.01.018
   [朱克超 Zhu Kechao], 2016, [地球学报, Acta Geoscientia Sinica], V37, P287
   Zhu Kechao, 2015, Earth Science - Journal of China University of Geosciences, V40, P1052, DOI 10.3799/dqkx.2015.087
   Zimmermann B, 2009, GEOCHIM COSMOCHIM AC, V73, P91, DOI 10.1016/j.gca.2008.09.033
NR 58
TC 5
Z9 5
U1 15
U2 31
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-163X
J9 MINERALS-BASEL
JI Minerals
PD FEB
PY 2021
VL 11
IS 2
AR 196
DI 10.3390/min11020196
PG 16
WC Geochemistry & Geophysics; Mineralogy; Mining & Mineral Processing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geochemistry & Geophysics; Mineralogy; Mining & Mineral Processing
GA QO0QT
UT WOS:000622852600001
OA gold
DA 2022-08-23
ER

PT J
AU Piplai, A
   Mittal, S
   Joshi, A
   Finin, T
   Holt, J
   Zak, R
AF Piplai, Aritran
   Mittal, Sudip
   Joshi, Anupam
   Finin, Tim
   Holt, James
   Zak, Richard
TI Creating Cybersecurity Knowledge Graphs From Malware After Action
   Reports
SO IEEE ACCESS
LA English
DT Article
DE Computer security; Security; Malware; Data mining; Blogs; Pipelines;
   Companies; Artificial intelligence; computer security; cyber threat
   intelligence; after action reports; knowledge graphs; semantic web
AB After Action Reports (AARs) provide incisive analysis of cyber-incidents. Extracting cyber-knowledge from these sources would provide security analysts with credible information, which they can use to detect or find patterns indicative of a cyber-attack. In this paper, we describe a system to extract information from AARs, aggregate the extracted information by fusing similar entities together, and represent that extracted information in a Cybersecurity Knowledge Graph (CKG). We extract entities by building a customized named entity recognizer called 'Malware Entity Extractor' (MEE). We then build a neural network to predict how pairs of 'malware entities' are related to each other. When we have predicted entity pairs and the relationship between them, we assert the 'entity-relationship set' in a CKG. Our next step in the process is to fuse similar entities, to improve our CKG. This fusion helps represent intelligence extracted from multiple documents and reports. The fused CKG has knowledge from multiple AARs, with relationships between entities extracted from separate reports. As a result of this fusion, a security analyst can execute queries and retrieve better answers on the fused CKG, than a knowledge graph with no fusion. We also showcase various reasoning capabilities that can be leveraged by a security analyst using our fused CKG.
C1 [Piplai, Aritran; Joshi, Anupam; Finin, Tim] Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Baltimore, MD 21201 USA.
   [Mittal, Sudip] Univ North Carolina Wilmington, Dept Comp Sci, Wilmington, NC 28403 USA.
   [Holt, James; Zak, Richard] Lab Phys Sci, College Pk, MD 20740 USA.
   [Zak, Richard] Booz Allen Hamilton, Mclean, VA 22102 USA.
RP Piplai, A (通讯作者)，Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Baltimore, MD 21201 USA.
EM apiplai1@umbc.edu
OI Finin, Tim/0000-0002-6593-1792; Mittal, Sudip/0000-0001-9151-8347; Holt,
   James/0000-0002-6368-8696; Joshi, Anupam/0000-0002-8641-3193
FU United States Department of Defense grant; National Science Foundation
   (NSF) grant [2025685]
FX This work was supported in part by a United States Department of Defense
   grant, in part by a gift from IBM research, and in part by a National
   Science Foundation (NSF) grant, award number 2025685.
CR Adobe, 2020, ADOBE SECURITY B
   Anderson B, 2011, J COMPUT VIROL HACKI, V7, P247, DOI 10.1007/s11416-011-0152-x
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chiu Jason PC, 2016, T ASS COMPUT LINGUIS, V4, P357, DOI 10.1162/tacl_a_00104
   Cyberscoop, 2018, NOTPETYA MALW ATTR
   Ekbal A., 2010, INT J ELECT COMPUT E, P589
   Finkel J.R., 2005, P 43 ANN M ASS COMP, P363, DOI DOI 10.3115/1219840.1219885
   FireEye, 2020, FIREEYE REPORTS
   Gao JY, 2019, PROC VLDB ENDOW, V12, P1679, DOI 10.14778/3342263.3342642
   Gasmi H, 2018, THIRTEENTH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING ADVANCES (ICSEA 2018), P1
   Homeland Security, 2020, ACTION REPORT DEFINT
   Intergovernmental Panel Climate Change Working Grp III, 2014, CLIMATE CHANGE 2014: MITIGATION OF CLIMATE CHANGE, P1
   Ji GC, 2016, BIOTECHNOL BIOTEC EQ, V30, P834, DOI 10.1080/13102818.2016.1208060
   Jiang XX, 2007, CCS'07: PROCEEDINGS OF THE 14TH ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P128
   Jones C.L., 2015, P 10 ANN CYB INF SEC, P1
   Joshi K, 2016, INT CONF CONTEMP, P79
   Joshi M, 2017, 2017 IEEE 1ST INTERNATIONAL CONFERENCE ON EDGE COMPUTING (IEEE EDGE), P142, DOI 10.1109/IEEE.EDGE.2017.27
   Kaspersky, 2020, KASPERSKY REPORTS
   Khurana N, 2019, IEEE INT WORKS MACH
   Liu J, 2016, 2016 IEEE FIRST INTERNATIONAL CONFERENCE ON DATA SCIENCE IN CYBERSPACE (DSC 2016), P161, DOI 10.1109/DSC.2016.12
   Liu SY, 2017, LECT NOTES ARTIF INT, V10318, P343, DOI 10.1007/978-3-319-59888-8_29
   Ma C., 2018, P 12 INT WORKSH SEM, P707
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   McDole A., 2020, MALWARE ANAL USING A
   McDole A., 2020, P 2020 IEEE INT S CI, P1
   Microsoft, 2020, MICROSOFT SECURITY B
   Mikolov T., 2013, ADV NEURAL INFORM PR
   MITRE, 2020, CWE MITRE
   MITRE, 2020, CVELIST PROJECT
   Mittal S, 2016, PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, P860, DOI 10.1109/ASONAM.2016.7752338
   Mittal Sudip, 2019, ARXIV190502895
   Narayanan S, 2018, 2018 4TH IEEE INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING (CIC 2018), P354, DOI 10.1109/CIC.2018.00054
   Neil L, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI), P7, DOI 10.1109/ISI.2018.8587375
   Oasis Group, 2013, STIX 2 0 DOCUMENTATI
   Oasis Group, 2019, TAXII
   Park Y, 2010, ELECTRON MATER LETT, V6, P1, DOI 10.3365/eml.2010.03.001
   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
   Piplai A., 2020, P IEEE INT C INT SEC
   R Manikandan, 2018, P 12 INT WORKSH SEM, P868
   Ranade P, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI), P238, DOI 10.1109/ISI.2018.8587374
   Simonite Tom, 2018, WIRED
   Syed S, 2016, RECOVERY OF GOLD FROM SECONDARY SOURCES, P1
   Vadapalli S. R., 2018, P INT C SEC MAN SAM, P220
   W3, 2020, SPARQL QUERY LANGUAG
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
NR 45
TC 10
Z9 10
U1 2
U2 15
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 211691
EP 211703
DI 10.1109/ACCESS.2020.3039234
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PC7OP
UT WOS:000597186300001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Cai, WS
   Wang, YZ
   Mao, S
   Zhan, JY
   Jiang, YC
AF Cai, Weishan
   Wang, Yizhao
   Mao, Shun
   Zhan, Jieyu
   Jiang, Yuncheng
TI Multi-heterogeneous neighborhood-aware for Knowledge Graphs alignment
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Entity alignment; Knowledge Graphs; Attribute structure; Attention
   mechanism; Heterogeneous graph attention
AB Entity alignment is an important task for the Knowledge Graph (KG) completion, which aims to identify the same entities in different KGs. Most of previous works only utilize the relation structures of KGs, but ignore the heterogeneity of relations and attributes of KGs. However, these information can provide more feature information and improve the accuracy of entity alignment. In this paper, we propose a novel Multi-Heterogeneous Neighborhood-Aware model (MHNA) for KGs alignment. MHNA aggregates multi-heterogeneous information of aligned entities, including the entity name, relations, attributes and attribute values. An important contribution is to design a variant attention mechanism, which adds the feature information of relations and attributes to the calculation of attention coefficients. Extensive experiments on three well-known benchmark datasets show that MHNA significantly outperforms 12 stateof-the-art approaches, demonstrating that our approach has good scalability and superiority in both cross-language and monolingual KGs. An ablation study further supports the effectiveness of our variant attention mechanism.
C1 [Cai, Weishan; Wang, Yizhao; Mao, Shun; Zhan, Jieyu; Jiang, Yuncheng] South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Peoples R China.
   [Cai, Weishan] Hanshan Normal Univ, Sch Comp & Informat Engn, Chaozhou 521041, Peoples R China.
RP Jiang, YC (通讯作者)，South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Peoples R China.
EM caiws@m.scnu.edu.cn; wyz0714@m.scnu.edu.cn; shunm@m.scnu.edu.cn;
   zhanjieyu@scnu.edu.cn; ycjiang@scnu.edu.cn
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, 202007040006]
FX The authors would like to thank the anonymous referees for their
   valuable comments as well as helpful suggestions from Professors Jim
   Jansen and Paolo Rosso which greatly improved the exposition of the
   paper. The work described in this paper is funded by the National
   Natural Science Foundation of China under Grant Nos. 61772210 and
   U1911201, Guangdong Province Universities Pearl River Scholar Funded
   Scheme (2018) and the Project of Science and Technology in Guangzhou in
   China under Grant Nos. 201807010043 and 202007040006.
CR 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 B, 2020, LECT NOTES ARTIF INT, V12084, P845, DOI 10.1007/978-3-030-47426-3_65
   Chen L, 2019, INFORM PROCESS MANAG, V56, P1, DOI 10.1016/j.ipm.2018.08.006
   Chen MH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3998
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Fan SH, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2478, DOI 10.1145/3292500.3330673
   Hu Linmei, 2019, P 2019 C EMP METH NA, P4820, DOI DOI 10.18653/V1/D19-1488
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li A, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2703, DOI 10.1145/3357384.3357820
   Li Chengjiang, 2019, P 2019 C EMPIRICAL M, P2723
   Liu Zhiyuan, 2020, P 2020 C EMP METH NA, P6355
   Liu ZQ, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2077, DOI 10.1145/3269206.3272010
   Lu WM, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102271
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Pei SC, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3130, DOI 10.1145/3308558.3313646
   Qiao C, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102309
   Rahimi A, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2009
   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 C, 2019, IEEE T KNOWL DATA EN, V31, P357, DOI 10.1109/TKDE.2018.2833443
   Shin S, 2019, INFORM PROCESS MANAG, V56, P445, DOI 10.1016/j.ipm.2018.12.003
   Suchanek FM, 2007, WWW
   Sun ZQ, 2020, AAAI CONF ARTIF INTE, V34, P222
   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
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   Velickovic Petar, 2018, INT C LEARN REPR JOL
   Wang X, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2022, DOI 10.1145/3308558.3313562
   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
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   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
   Yang Mohan, 2017, IJCAI, P1511
   Ye R, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4135
   Yun S, 2019, ADV NEUR IN, V32
   Zeng K., 2021, OPEN, V2, P1, DOI [10.1016/j.aiopen.2021.02.002, DOI 10.1016/J.AIOPEN.2021.02.002]
   Zeng WX, 2020, PROC INT CONF DATA, P1870, DOI 10.1109/ICDE48307.2020.00191
   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 QH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5429
   Zhu H., 2017, IJCAI, P4258, DOI DOI 10.24963/IJCAI.2017/595
   Zhu Q, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P2241, DOI 10.1145/3366423.3380289
   Zhu QN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1943
   Zong, 2020, P 29 INT JOINT C ART, P4039
NR 50
TC 0
Z9 0
U1 18
U2 25
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 JAN
PY 2022
VL 59
IS 1
AR 102790
DI 10.1016/j.ipm.2021.102790
EA OCT 2021
PG 13
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 WW6TV
UT WOS:000718047300009
OA Bronze
DA 2022-08-23
ER

PT J
AU Zhou, WA
   Wang, SR
   Jiang, C
AF Zhou, Wen'an
   Wang, Shirui
   Jiang, Chao
TI Knowledge Graph Embedding With Interactive Guidance From Entity
   Descriptions
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Task analysis; Knowledge acquisition; Licenses; Convolutional
   neural networks; Sparse matrices; Knowledge graph embedding; entity
   descriptions; interactive guidance
AB Knowledge Graph (KG) embedding aims to represent both entities and relations into a continuous low-dimensional vector space. Most previous attempts perform the embedding task using only knowledge triples to indicate relations between entities. Entity descriptions, although containing rich background information, have not been well utilized in these methods. In this paper, we propose Entity Descriptions-Guided Embedding (EDGE), a novel method for learning the knowledge graph representations with semantic guidance from entity descriptions. EDGE enables an embedding model to learn simultaneously from 1) knowledge triples that have been directly observed in a given KG, and 2) entity descriptions which have rich semantic information about these entities. In the learning process, EDGE encodes the semantics of entity descriptions to enhance the learning of knowledge graph embedding, and integrates such learned KG embedding to constraint their corresponding word embeddings in entity descriptions. Through this interactive procedure, semantics of entity descriptions may be better transferred into the learned KG embedding. We evaluate EDGE in link prediction and entity classification on Freebase and WordNet. Experimental results show that: 1) with entity descriptions injected, EDGE achieves significant and consistent improvements over state-of-the-art baselines; and 2) compared to those one-time injection schemes studied before, the interactive guidance strategy maximizes the utility of entity descriptions for KG embedding, and indeed achieves substantially better performance.
C1 [Zhou, Wen'an; Wang, Shirui; Jiang, Chao] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China.
RP Wang, SR (通讯作者)，Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China.
EM wangshirui@bupt.edu.cn
RI zhou, wenan/AAI-7453-2020
OI Wang, Shirui/0000-0001-5598-8333
FU Fundamental Research Funds for the Central Universities [2019XD-A20]
FX This work was supported by the Fundamental Research Funds for the
   Central Universities under Grant 2019XD-A20.
CR [Anonymous], 2014, P ADV NEUR INF PROC
   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, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Conneau Alexis, 2017, ARXIV170502364
   Corrado G. S, 2013, NIPS 13, V3111, P3119
   Goodwin TR, 2018, ACM T INTEL SYST TEC, V9, DOI 10.1145/3106745
   Guan NN, 2019, KNOWL-BASED SYST, V164, P38, DOI 10.1016/j.knosys.2018.10.008
   Guo S, 2018, AAAI CONF ARTIF INTE, P4816
   Guo WZ, 2018, INT SYMP PARAL ARCH, P227, DOI 10.1109/PAAP.2018.00045
   Hu ZT, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2410, DOI 10.18653/v1/p16-1228
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Jahr K, 2018, ADV ENG INFORM, V38, P488, DOI 10.1016/j.aei.2018.08.015
   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
   Jia Z, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1807, DOI 10.1145/3269206.3269247
   Jiang J., 2018, ARXIV180908422
   Jin H, 2019, IEEE ACCESS, V7, P75235, DOI 10.1109/ACCESS.2019.2918675
   Li Y, 2018, LECT NOTES ARTIF INT, V11108, P3, DOI 10.1007/978-3-319-99495-6_1
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Neelakantan Arvind, 2015, P 2015 C N AM CHAPT, P515
   Nguyen D.Q., 2016, P 2016 C N AM CHAPT, P460, DOI DOI 10.18653/V1/N16-1054
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Peters M.E., 2018, P 2018 C N AM CHAPT, P2227, DOI 10.18653/v1/N18-1202
   Ringsquandl Martin, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P541, DOI 10.1007/978-3-319-93417-4_35
   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
   Sudholt S, 2017, PROC INT CONF DOC, P493, DOI 10.1109/ICDAR.2017.87
   Sutskever I., 2014, ADV NEURAL INF PROCE, V2, P3104, DOI DOI 10.1021/acs.analchem.7b05329
   Talman A, 2018, ARXIV180808762
   Tarus JK, 2018, ARTIF INTELL REV, V50, P21, DOI 10.1007/s10462-017-9539-5
   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 Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wei Z., 2015, P 24 ACM INT C INF K, P1331, DOI DOI 10.1145/2806416.2806513
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xiao H., 2017, P 31 AAAI C ART INT
   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
   Yan C., 2019, IEEE ACCESS, V7
   Yang B., 2015, P ICLR
   Zhang D., 2015, PROC 3 WORKSHOP CONT, P32
   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 45
TC 3
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 2019
VL 7
BP 156686
EP 156693
DI 10.1109/ACCESS.2019.2950015
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA JN8TY
UT WOS:000497165400109
OA gold
DA 2022-08-23
ER

PT J
AU Zhao, AP
   Yu, Y
AF Zhao, Anping
   Yu, Yu
TI Knowledge-enabled BERT for aspect-based sentiment analysis
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Sentiment knowledge graph; BERT; Aspect-sentiment analysis
AB To provide explainable and accurate aspect terms and the corresponding aspect-sentiment detection, it is often useful to take external domain-specific knowledge into consideration. In this work, we propose a knowledge-enabled language representation model BERT for aspect-based sentiment analysis. Specifically, our proposal leverages the additional information from a sentiment knowledge graph by injecting sentiment domain knowledge into the language representation model, which obtains the embedding vectors of entities in the sentiment knowledge graph and words in the text in a consistent vector space. In addition, the model is capable of achieving better performance with a small amount of training data by incorporating external domain knowledge into the language representation model to compensate for the limited training data. As a result, our model is able to provide explainable and detailed results for aspect-based sentiment analysis. Experimental results demonstrate the effectiveness of the proposed method, showing that the knowledge-enabled BERT is an excellent choice for solving aspect-based sentiment analysis problems. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Zhao, Anping] Wenzhou Univ, Coll Comp & Artificial Intelligence, Wenzhou, Zhejiang, Peoples R China.
   [Yu, Yu] Wenzhou Univ, Coll Educ, Wenzhou, Zhejiang, Peoples R China.
RP Zhao, AP (通讯作者)，Wenzhou Univ, Coll Comp & Artificial Intelligence, Wenzhou, Zhejiang, Peoples R China.
EM apzhao@wzu.edu.cn
FU Basic Scientific Research Projects of Wenzhou, China [G2020024]
FX This work was supported in part by the Basic Scientific Research
   Projects of Wenzhou, China (grant no. G2020024).
CR Akhtar MS, 2020, IEEE COMPUT INTELL M, V15, P64, DOI 10.1109/MCI.2019.2954667
   Basiri ME, 2021, FUTURE GENER COMP SY, V115, P279, DOI 10.1016/j.future.2020.08.005
   Cambria E, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P105, DOI 10.1145/3340531.3412003
   Cambria E, 2019, IEEE COMPUT INTELL M, V14, P16, DOI 10.1109/MCI.2019.2901082
   Cambria E, 2016, IEEE INTELL SYST, V31, P102, DOI 10.1109/MIS.2016.31
   Carley K., 2019, ARXIV190902606, P5469, DOI DOI 10.48550/ARXIV.1909.02606
   Cavallari S, 2019, IEEE COMPUT INTELL M, V14, P39, DOI 10.1109/MCI.2019.2919396
   Chen F, 2019, NEUROCOMPUTING, V368, P51, DOI 10.1016/j.neucom.2019.08.054
   Das Dipankar, 2017, PRACTICAL GUIDE SENT
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Fares M, 2019, KNOWL-BASED SYST, V165, P432, DOI 10.1016/j.knosys.2018.12.017
   Ghosal D, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3198
   He RD, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P579
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Li X., 2019, P 5 WORKSH NOIS US G, P34
   Lin PQ, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5088
   Liu F, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P278
   Liu Q, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1023, DOI 10.1145/3178876.3186001
   Liu W., 2019, P AAAI C ART INT P AAAI C ART INT, V34, P2901
   Ma YK, 2018, AAAI CONF ARTIF INTE, P5876
   Majumder N., 2018, P 2018 C EMP METH NA, P3402
   Peters M., 2018, IMPROVING LANGUAGE U
   Pontiki M., 2016, SEMEVAL 2016
   Song Y., 2019, THESIS CHENGDU U CHI
   Sun C, 2019, 2019 C N AM CHAPTER
   Sun K., 2019, P 2019 C EMP METH NA P 2019 C EMP METH NA, P5683
   Tay Y, 2018, P 2018 C EMP METH NA, P3443
   Teng Z., 2016, P 2016 C EMP METH NA, P1629
   Vaswani A, 2017, ADV NEURAL INFORM PR, P5999
   Wang K., 2020, P 58 ANN M ASS COMP P 58 ANN M ASS COMP
   Wu Z, 2020, ASS ADVANCEMENT ARTI
   Xu Han, 2019, ARXIV PREPRINT ARXIV, DOI DOI 10.18653/V1/N19-1242
   Yang M, 2019, NEURAL NETWORKS, V117, P240, DOI 10.1016/j.neunet.2019.05.021
   Yu JF, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5408
   Zhang Chen, 2019, P 2019 C EMP METH NA, P4560
NR 35
TC 11
Z9 11
U1 32
U2 66
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 107220
DI 10.1016/j.knosys.2021.107220
EA JUN 2021
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TS0WX
UT WOS:000679379400019
DA 2022-08-23
ER

PT J
AU Gupta, A
   Bhatia, R
AF Gupta, Amit
   Bhatia, Rajesh
TI Knowledge Based Deep Inception Model for Web Page Classification
SO JOURNAL OF WEB ENGINEERING
LA English
DT Article
DE Web page classification; transfer learning; knowledge graph embedding;
   pre-trained model
AB Web Page Classification is decisive for information retrieval and management task and plays an imperative role for natural language processing (NLP) problems in web engineering. Traditional machine learning algorithms excerpt covet features from web pages whereas deep leaning algorithms crave features as the network goes deeper. Pre-trained models such as BERT attains remarkable achievement for text classification and continue to show state-of-the-art results. Knowledge Graphs can provide rich structured factual information for better language modelling and representation. In this study, we proposed an ensemble Knowledge Based Deep Inception (KBDI) approach for web page classification by learning bidirectional contextual representation using pre-trained BERT incorporating Knowledge Graph embeddings and fine-tune the target task by applying Deep Inception network utilizing parallel multi-scale semantics. Proposed ensemble evaluates the efficacy of fusing domain specific knowledge embeddings with the pre-trained BERT model. Experimental interpretation exhibit that the proposed BERT fused KBDI model outperforms benchmark baselines and achieve better performance in contrast to other conventional approaches evaluated on web page classification datasets.
C1 [Gupta, Amit; Bhatia, Rajesh] Punjab Engn Coll Deemed Univ, Dept Comp Sci & Engn, Chandigarh, India.
RP Gupta, A (通讯作者)，Punjab Engn Coll Deemed Univ, Dept Comp Sci & Engn, Chandigarh, India.
EM amitgupta.phdcse15@pec.edu.in; rbhatia@pec.edu.in
CR Altingovde IS, 2001, LECT NOTES COMPUT SC, V2113, P699
   [Anonymous], 2004, P 37 INT C MACH LEAR
   [Anonymous], 2017, 2017 IEEE INT C MULT, DOI [10.1109/ICME.2017.8019447, DOI 10.1109/ICME.2017.8019447]
   Biega J, 2013, PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'13 COMPANION), P325
   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
   Brin S, 2012, COMPUT NETW, V56, P3825, DOI 10.1016/j.comnet.2012.10.007
   Chen RC, 2006, EXPERT SYST APPL, V31, P427, DOI 10.1016/j.eswa.2005.09.079
   Cho, 2016, ARXIV PREPRINT ARXIV
   Chung J., 2014, CORR
   Conneau A, 2017, 15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS, P1107
   DEBRA PME, 1994, COMPUT NETWORKS ISDN, V27, P183, DOI 10.1016/0169-7552(94)90132-5
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Erxleben F, 2014, LECT NOTES COMPUT SC, V8796, P50, DOI 10.1007/978-3-319-11964-9_4
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Holden N, 2004, LECT NOTES COMPUT SC, V3242, P1092
   Howard J, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P328
   Huang ML, 2017, ACM T INFORM SYST, V35, DOI 10.1145/3052770
   Jiang, 2017, 2017 4 IAPR AS C PAT
   Kalchbrenner N, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P655, DOI 10.3115/v1/p14-1062
   Kim Y., 2014, ARXIV14085882, DOI DOI 10.3115/V1/D14-1181
   Kwon O.W., 2020, P 5 INT WORKSH INF R, P9
   Lewis DD, 2004, J MACH LEARN RES, V5, P361
   Li SH, 2021, MATEC WEB CONF, V336, DOI 10.1051/matecconf/202133606022
   Luo XY, 2021, ALEX ENG J, V60, P3401, DOI 10.1016/j.aej.2021.02.009
   Melanie, 2005, DUPLICATE DETECTION
   Menczer F., 2004, ACM T INTERNET TECHN, V4, P378, DOI [DOI 10.1145/1031114.1031117, 10.1145/1031114.1031117]
   Meshkizadeh Sara, 2010, JCS S, V8
   Minaee S, 2022, ACM COMPUT SURV, V54, DOI 10.1145/3439726
   Mitchell T, 2018, COMMUN ACM, V61, P103, DOI 10.1145/3191513
   Ozel Selma Ayse, 2011, 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA 2011), P282, DOI 10.1109/INISTA.2011.5946076
   Ozel SA, 2011, EXPERT SYST APPL, V38, P3407, DOI 10.1016/j.eswa.2010.08.126
   Peters M., 2018, IMPROVING LANGUAGE U
   Qi XG, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459357
   Ribeiro A, 2003, LECT NOTES ARTIF INT, V2663, P103
   Selamat A, 2004, INFORM SCIENCES, V158, P69, DOI 10.1016/j.ins.2003.03.003
   Szegedy C, 2017, AAAI CONF ARTIF INTE, P4278
   Szegedy C, 2015, PROC CVPR IEEE, P1, DOI 10.1109/CVPR.2015.7298594
   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
   Wang BX, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2311
   Yang B., 2015, P INT C LEARN REPR I
   Yang Z, 2016, P 2016 C N AM CHAPTE, P1480
   Zhang X., 2015, ADV NEURAL INFORM PR, V28, P649, DOI DOI 10.5555/2969239.2969312
   Zhou C., 2015, ABS151108630 CORR
NR 44
TC 0
Z9 0
U1 3
U2 3
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 7
SI SI
BP 2131
EP 2167
DI 10.13052/jwe1540-9589.2075
PG 37
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XC4TF
UT WOS:000722006600006
DA 2022-08-23
ER

PT J
AU Yang, K
   Kong, XY
   Wang, YF
   Zhang, J
   De Melo, G
AF Yang, Kai
   Kong, Xinyu
   Wang, Yafang
   Zhang, Jie
   De Melo, Gerard
TI Reinforcement Learning Over Knowledge Graphs for Explainable Dialogue
   Intent Mining
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph; dialogue intent mining; reinforcement learning
AB In light of the millions of households that have adopted intelligent assistant powered devices, multi-turn dialogue has become an important field of inquiry. Most current methods identify the underlying intent in the dialogue using opaque classification techniques that fail to provide any interpretable basis for the classification. To address this, we propose a scheme to interpret the intent in multi-turn dialogue based on specific characteristics of the dialogue text. We rely on policy-guided reinforcement learning to identify paths in a graph to confirm concrete paths of inference that serve as interpretable explanations. The graph is induced based on the multi-turn dialogue user utterances, the intents, i.e., standard queries of the dialogues, and the sub-intents associated with the dialogues. Our reinforcement learning method then discerns the characteristics of the dialogue in chronological order as the basis for multi-turn dialogue path selection. Finally, we consider a wide range of recently proposed knowledge graph-based recommender systems as baselines, mostly based on deep reinforcement learning and our method performs best.
C1 [Yang, Kai; Kong, Xinyu; Wang, Yafang; Zhang, Jie] Ant Financial Serv Grp, Hangzhou 310099, Peoples R China.
   [De Melo, Gerard] Rutgers State Univ, Dept Comp Sci, Piscataway, NJ 08854 USA.
RP Wang, YF (通讯作者)，Ant Financial Serv Grp, Hangzhou 310099, Peoples R China.
EM yafang.wyf@antfin.com
FU National Natural Science Foundation of China [61503217]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 61503217.
CR [Anonymous], [No title captured]
   Cao Y., 2019, UNIFYING KNOWLEDGE G
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Das R., 2017, ARXIV171105851
   Deng L, 2012, IEEE W SP LANG TECH, P210, DOI 10.1109/SLT.2012.6424224
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Di Noia T, 2016, ACM T INTEL SYST TEC, V8, DOI 10.1145/2899005
   Fu C., 2019, ARXIV190900230
   Godin F., 2019, ARXIV190210236
   Hashemi H. B., 2016, P INT C WEB SEARCH D, P1
   Hongshen Chen, 2017, ACM SIGKDD Explorations Newsletter, V19, P25, DOI 10.1145/3166054.3166058
   Huang PS, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P2333
   Kadlec R., 2015, IMPROVED DEEP LEARNI, Vabs/1510.03753
   Lin XY, 2018, 2018 IEEE/ACM 13TH INTERNATIONAL WORKSHOP ON SOFTWARE ENGINEERING FOR SCIENCE (SE4SCIENCE), P1, DOI 10.1145/3194747.3194750
   Liu Z, 2019, PATTERN ANAL APPL, V22, P1527, DOI 10.1007/s10044-019-00792-5
   Lowe R., 2015, P 16 ANN M SPEC INT, P285
   Lowe Ryan Thomas, 2017, DIALOGUE DISCOURSE, V8, P31
   Moon C., 2015, PROC ACM C INF KNOWL, P2181
   Ostuni V. C, 2013, P 7 ACM C REC SYST, P85, DOI DOI 10.1145/2507157.2507172
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Serban IV, 2016, AAAI CONF ARTIF INTE, P3776
   Shang LF, 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, P1577
   Shen YL, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P373, DOI 10.1145/2567948.2577348
   Shridhar K., 2019, 2019 INT JOINT C NEU, P1
   Song W., 2019, ARXIV190609506
   Tortay A, 2018, IEEE SYS MAN CYBERN, P3069, DOI 10.1109/SMC.2018.00521
   Tur G, 2012, INT CONF ACOUST SPEE, P5045, DOI 10.1109/ICASSP.2012.6289054
   van Rossum B, 2019, LECT NOTES COMPUT SC, V11496, P19, DOI 10.1007/978-3-030-19274-7_2
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang X., 2018, P AAAI C ART INT, V33, P5329
   Wever T., 2017, P 32 ACM SIGAPP S AP, P656
   Wu T., 2019, ARXIV191006708
   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
   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 W., 2017, P 2017 IEEE INT C IM, P1
   Yang Hongxia, 2019, ARXIV190605489
   Yann D., 2014, P INT C LEARN REPR, P28
   Zhang TY, 2018, AAAI CONF ARTIF INTE, P6053
   Zhang Y., 2019, ARXIV191202206
   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
   Zhao Wayne Xin, 2016, ACM T KNOWL DISCOV D, V10, P1
   Zho XY, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1118
NR 44
TC 3
Z9 3
U1 3
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 2020
VL 8
BP 85348
EP 85358
DI 10.1109/ACCESS.2020.2991257
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MM0LN
UT WOS:000549851900005
OA gold
DA 2022-08-23
ER

PT J
AU Ma, FC
   Lyu, PH
   Yao, Q
   Yao, L
   Zhang, SJ
AF Ma, Fei-Cheng
   Lyu, Peng-Hui
   Yao, Qiang
   Yao, Lan
   Zhang, Shi-Jing
TI Publication trends and knowledge maps of global translational medicine
   research
SO SCIENTOMETRICS
LA English
DT Article
DE Translational medical research; Scientometric; Research trend; Web of
   science; PubMed; Knowledge mapping
ID SCIENCE-CITATION-INDEX; CLINICAL-APPLICATIONS; BREAST-CANCER;
   STEM-CELLS; CARE; QUALITY; MANAGEMENT; GRAPHENE; DISEASE
AB Translational medical research literatures have increased rapidly in last decades and there have been fewer attempts or efforts to map global research context of translational medical related research. The main purpose of this study is to evaluate the global progress and to assess the current quantitatively trends on translational medical research by using a scientometric approach to survey translational medicine related literatures in Science Citation Index Expanded (SCI-E), Social Science Citation Index and PubMed database from 1992 to 2012. The scientometric methods and knowledge visualization technologies were employed in this paper. The document types, languages, publication patterns, subject categories, journals, geographic and institutional distributions, top cited papers, and the distribution of keywords as well as MeSH terms were thoroughly examined. Translational medicine research has increased rapidly over past 20 years, most notably in the last 4 years. In total, there are currently 3,627 research articles in 1,062 journals listed in 91 SCI-E subject categories. The top 20 productive countries and institutes were analyzed herein, where 11 key papers in translational medical research and research foci were identified. Research outputs descriptors have suggested that the presence of a solid development in translational medical research, where research in this field has mainly focused on experimental medicine, general internal medicine, and medical laboratory technologies. All these outcomes have been concentrated in several journals such as Translational Research, Translational Oncology, Translational Stroke Research, and Translational Neuroscience. G7 countries make up the leading nations for translational medical research, where the center is located in USA. American institutions have made great advances in paper productions, citations, and cooperation, with overall great strengths and good development prospects. Moreover, the evolution pathway of translational medical research has been summarized as bellows: problems emerged, causes analyzed, challenges faced and solutions proposed, translational medical research programs been formally established, theoretical and applied research, all of which was in full swing. During this process, neoplasms and genomics, interdisciplinary communication between academic medical centers/institutes, drug design and development, cardiovascular and brain diseases, and even biomedical research have been identified as mainstream topics in translational medical research fields.
C1 [Ma, Fei-Cheng; Lyu, Peng-Hui] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.
   [Yao, Qiang; Yao, Lan; Zhang, Shi-Jing] Huazhong Univ Sci & Technol, Sch Med & Hlth Management, Tongji Med Coll, Wuhan 430030, Peoples R China.
RP Lyu, PH (通讯作者)，Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.
EM fchma@whu.educn; ph@nimte.ac.cn; yaoqiang@scine.cn; zhangsj@scinet.cn
RI LYU, Penghui/A-6864-2013
OI LYU, Penghui/0000-0002-7542-4591
FU Natural Science Foundation of China [71173249]
FX This work was supported by the Natural Science Foundation of China
   (Grant No. 71173249: Research on Formation Mechanism and Evolution Laws
   of Knowledge Networks). The authors are grateful to Hong Cui, Hong-xun
   Song, Bin Zhang and Nathan D. Bobai for their helpful discussion and
   suggestions. The authors would also like to thank anonymous reviewers
   for their valuable comments.
CR Arunachalam S, 2000, CURR SCI INDIA, V79, P621
   Boyack KW, 2005, SCIENTOMETRICS, V64, P351, DOI 10.1007/s11192-005-0255-6
   Brander C, 2006, J TRANSL MED, V4, DOI 10.1186/1479-5876-4-19
   Chen KH, 2011, J INFORMETR, V5, P233, DOI 10.1016/j.joi.2010.10.007
   Chen W, 2007, J AM CHEM SOC, V129, P10418, DOI 10.1021/ja071658g
   Chow LWC, 2010, EXPERT OPIN INV DRUG, V19, pS13, DOI 10.1517/13543781003718858
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Contopoulos-Ioannidis DG, 2003, AM J MED, V114, P477, DOI 10.1016/S0002-9343(03)00013-5
   CTSAs Advice, 2012, CTSAS
   Damani SB, 2011, WIRES SYST BIOL MED, V3, P206, DOI 10.1002/wsbm.113
   Debucquoy A, 2010, CLIN CANCER RES, V16, P2709, DOI 10.1158/1078-0432.CCR-09-1622
   Dillon DA, 1998, CRIT REV ONCOGENESIS, V9, P125, DOI 10.1615/CritRevOncog.v9.i2.30
   Dougherty D, 2008, JAMA-J AM MED ASSOC, V299, P2319, DOI 10.1001/jama.299.19.2319
   Drolet BC, 2011, TRANSL RES, V157, P1, DOI 10.1016/j.trsl.2010.10.002
   Editor, 1968, NEW ENGL J MED, V278, P1014
   Enachescu C, 2003, SCIENTOMETRICS, V56, P43, DOI 10.1023/A:1021946506987
   Faba-Perez C, 2003, SCIENTOMETRICS, V58, P623, DOI 10.1023/B:SCIE.0000006884.08036.73
   Feuerstein GZ, 2009, STROKE, V40, pS121, DOI 10.1161/STROKEAHA.108.535104
   FitzGerald GA, 2010, MT SINAI J MED, V77, P327, DOI 10.1002/msj.20198
   Freemantle N, 1999, BMJ-BRIT MED J, V318, P1730, DOI 10.1136/bmj.318.7200.1730
   Garcia D, 2012, J AGROMEDICINE, V17, P5, DOI 10.1080/1059924X.2012.632746
   GARFIELD E, 1990, CURR CONTENTS, V32, P5
   Geraghty J, 1996, LANCET, V348, P422, DOI 10.1016/S0140-6736(05)64535-7
   Goetsch L, 2010, BIOMARK MED, V4, P149, DOI 10.2217/BMM.09.67
   Gotlib J, 2004, BLOOD, V103, P2879, DOI 10.1182/blood-2003-06-1824
   Hoos A, 2001, LAB INVEST, V81, P1331, DOI 10.1038/labinvest.3780347
   Lakhani SR, 2001, J CLIN PATHOL-MOL PA, V54, P281, DOI 10.1136/mp.54.5.281
   Li LL, 2009, SCIENTOMETRICS, V80, P39, DOI 10.1007/s11192-008-1939-5
   Lou YC, 2012, SCIENTOMETRICS, V90, P807, DOI 10.1007/s11192-011-0549-9
   Lu TY, 2011, STEM CELL RES THER, V2, DOI 10.1186/scrt85
   Lucio-Arias D, 2007, VALIDATION STUDY HIS, VI, P886
   Lucio-Arias D, 2007, VALIDATION STUDY HIS, VII, P886
   Marincola Francesco M, 2003, J Transl Med, V1, P1, DOI 10.1186/1479-5876-1-1
   McBride P, 1998, ARCH INTERN MED, V158, P1238, DOI 10.1001/archinte.158.11.1238
   Mielke MM, 2010, NEUROMOL MED, V12, P331, DOI 10.1007/s12017-010-8121-y
   Campanario JM, 2011, SCIENTOMETRICS, V87, P75, DOI 10.1007/s11192-010-0333-2
   National Center for Biotechnology Information (US), 2005, PUBMED HELP
   Norman CD, 2010, J EVAL CLIN PRACT, V16, P1016, DOI 10.1111/j.1365-2753.2010.01534.x
   Porter A, 2012, TRANSL RES, V159, P4, DOI 10.1016/j.trsl.2011.09.004
   Pouris A, 2011, SCIENTOMETRICS, V86, P541, DOI 10.1007/s11192-010-0277-6
   Psyrri A, 2010, ANN ONCOL, V21, P1952, DOI 10.1093/annonc/mdq060
   Rigby MR, 2010, CURR OPIN ENDOCRINOL, V17, P131, DOI 10.1097/MED.0b013e32833759d2
   Rost K, 2000, GEN HOSP PSYCHIAT, V22, P66, DOI 10.1016/S0163-8343(00)00059-1
   Rubio DM, 2010, ACAD MED, V85, P470, DOI 10.1097/ACM.0b013e3181ccd618
   Runyan CM, 2010, FACIAL PLAST SURG, V26, P385, DOI 10.1055/s-0030-1265017
   Suk FM, 2011, EUR J GASTROEN HEPAT, V23, P295, DOI 10.1097/MEG.0b013e3283457af7
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wang CD, 1998, SCIENTOMETRICS, V42, P89
   Wang L, 2010, NEW CARBON MATER, V25, P401
   Westfall JM, 2007, JAMA-J AM MED ASSOC, V297, P403, DOI 10.1001/jama.297.4.403
   Woolf SH, 2008, JAMA-J AM MED ASSOC, V299, P211, DOI 10.1001/jama.2007.26
   Zerhouni EA, 2005, NEW ENGL J MED, V353, P1621, DOI 10.1056/NEJMsb053723
NR 52
TC 11
Z9 12
U1 5
U2 163
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2014
VL 98
IS 1
BP 221
EP 246
DI 10.1007/s11192-013-1003-y
PG 26
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 284LQ
UT WOS:000329319200016
DA 2022-08-23
ER

PT J
AU Zhou, B
   Bao, JS
   Chen, ZY
   Liu, YH
AF Zhou, Bin
   Bao, Jinsong
   Chen, Zhiyu
   Liu, Yahui
TI KGAssembly: Knowledge graph-driven assembly process generation and
   evaluation for complex components
SO INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
LA English
DT Article; Early Access
DE Knowledge graph; graph embedding; assembly sequence; assembly
   evaluation; complex components
AB The semantic information of process documents for the assembly of complex components plays an important role in the guidance of assembly operations and the feasibility evaluation of process plans. There are many types of semantic elements contained in assembly process documents. The semantic relationship between assembly elements is complex. Additionally, there is a lack of effective modeling method to deal with the implicit associative semantic knowledge that exists among existing assembly process cases. In this case, it is a great challenge to use the professional knowledge in assembly process documents to guide the intelligent decision-making of assembly process planning and the intelligent analysis of assembly process enforceability evaluation. In this paper, a knowledge graph-driven assembly process generation and evaluation method for complex components is proposed. An APKG (assembly process knowledge graph) model is built. A distributed graph embedding-based model SKGCN (sequence knowledge graph convolutional network) is designed to generate assembly process planning. Furthermore, model and knowledge dual-driven evaluation method for assembly sequences is presented. It provides assembly expert knowledge support for the evaluation method of interference detection of assembly sequence based on point cloud assembly feature recognition. Finally, the approach is evaluated by assembling an aero-engine compressor rotor.
C1 [Zhou, Bin; Bao, Jinsong; Chen, Zhiyu; Liu, Yahui] Donghua Univ, Coll Mech Engn, Shanghai, Peoples R China.
RP Bao, JS (通讯作者)，Donghua Univ, Coll Mech Engn, Shanghai, Peoples R China.
EM bao@dhu.edu.cn
OI Zhou, Bing/0000-0002-0838-7858; Ye, Fan/0000-0002-0131-6424; BAO,
   Jinsong/0000-0003-1999-1003
FU National Key Research and Development Program of China [2019YFB1706300]
FX This work was supported by the National Key Research and Development
   Program of China [Grant No. 2019YFB1706300)].
CR Bao JS, 2014, KEY ENG MATER, V579-580, P711, DOI 10.4028/www.scientific.net/KEM.579-580.711
   Bordes A., 2013, ADV NEURAL INFORM PR
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Dai D, 2019, AAAI CONF ARTIF INTE, P6300
   Gruhier E, 2015, ADV ENG INFORM, V29, P495, DOI 10.1016/j.aei.2015.04.004
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kumar SL, 2015, INT J COMPUT INTEG M, V28, P1046, DOI 10.1080/0951192X.2014.953586
   Li H, 2014, INT J ADV MANUF TECH, V72, P89, DOI 10.1007/s00170-014-5630-0
   Li X, 2017, INT J ADV MANUF TECH, V90, P1307, DOI 10.1007/s00170-016-9449-8
   Li XL, 2018, INT J ADV MANUF TECH, V96, P4173, DOI 10.1007/s00170-018-1862-8
   Liu Jianhua, 2018, Journal of Mechanical Engineering, V54, P2, DOI 10.3901/JME.2018.11.002
   Ma Hongzhan, 2015, China Mechanical Engineering, V26, P652, DOI 10.3969/j.issn.1004-132X.2015.05.015
   Nguyen D.Q., 2019, P NAACL HLT 2019, P2180, DOI DOI 10.18653/V1/N19-1226
   Ni J, 2018, P I MECH ENG B-J ENG, V232, P2605, DOI 10.1177/0954405417699012
   Qiang Lihong, 2015, Journal of Mechanical Engineering, V51, P202, DOI 10.3901/JME.2015.22.202
   Sanyal S., 2020, INT C LEARN REPR ADD
   Sayed MS, 2014, INT J ADV MANUF TECH, V74, P1033, DOI 10.1007/s00170-014-5918-0
   Soshkin M, 2016, BUS EC, V51, P166, DOI DOI 10.1057/S11369-016-0008-Y
   Thomas U, 2015, IEEE INT CON AUTO SC, P586, DOI 10.1109/CoASE.2015.7294142
   Tian Fu-jun, 2012, Computer Integrated Manufacturing Systems, V18, P913
   Wang Cheng-en, 2010, Computer Integrated Manufacturing Systems, V16, P942
   Wang H., 2019, P EMNLP, P2623, DOI [10.18653/v1/D19-1264, DOI 10.18653/V1/D19-1264]
   Wang Hao, 2017, Journal of Mechanical Engineering, V53, P137, DOI 10.3901/JME.2017.23.137
   Wang Qing, 2015, Journal of Zhejiang University. Engineering Science, V49, P1224, DOI 10.3785/j.issn.1008-973X.2015.07.004
   Wang Y, 2019, ACM T GRAPHIC, V38, DOI 10.1145/3326362
   Wang ZK, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI), P211, DOI 10.1109/ISI.2018.8587330
   WHO, 2012, WORLD MALARIA REPORT 2012, P1
   Xu LD, 2014, IEEE T AUTOM SCI ENG, V11, P492, DOI 10.1109/TASE.2012.2232652
   Yang XQ, 2014, ADV INTELL SYST, V250, P205, DOI 10.1007/978-81-322-1695-7_24
   Yuan Bao-zun, 2014, Computer Integrated Manufacturing Systems, V20, P807, DOI 10.13196/j.cims.2014.04.yuanbaoxun.0807.10.20140411
   Zhang Y.N., 2014, MACHINERY DESIGN MAN, V2014, P205, DOI [10.3969/j..1671-833X.2014.08.013, DOI 10.3969/J..1671-833X.2014.08.013]
   Zhong Yanru, 2018, Computer Integrated Manufacturing Systems, V24, P1345, DOI 10.13196/j.cims.2018.06.003
   [周思杭 Zhou Sihang], 2011, [机械工程学报, Chinese Journal of Mechanical Engineering], V47, P1
   Zhou ZD, 2019, P I MECH ENG B-J ENG, V233, P1450, DOI 10.1177/0954405418789975
NR 34
TC 14
Z9 16
U1 24
U2 67
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0951-192X
EI 1362-3052
J9 INT J COMPUT INTEG M
JI Int. J. Comput. Integr. Manuf.
DI 10.1080/0951192X.2021.1891572
EA MAR 2021
PG 21
WC Computer Science, Interdisciplinary Applications; Engineering,
   Manufacturing; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA QX6VG
UT WOS:000629483200001
DA 2022-08-23
ER

PT J
AU Kim, HJ
   Baek, JW
   Chung, KY
AF Kim, Hyun-Jin
   Baek, Ji-Won
   Chung, Kyungyong
TI Optimization of Associative Knowledge Graph using TF-IDF based Ranking
   Score
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE TF-IDF; association rule; apriori; FP-tree; associative knowledge graph
ID A-PRIORI; EXTRACTION; MAPREDUCE
AB This study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-IDF based ranking, optimized transactions are generated. News data are first collected through crawling and then are converted into a corpus through preprocessing. Unnecessary data are removed through preprocessing including lowercase conversion, removal of punctuation marks and stop words. In the document term matrix, words are extracted and then transactions are generated. In the data cleaning process, the Apriori algorithm is applied to generate association rules and make a knowledge graph. To optimize the generated knowledge graph, the proposed method utilizes TF-IDF based ranking scores to remove terms with low scores and recreate transactions. Based on the result, the association rule algorithm is applied to create an optimized knowledge model. The performance is evaluated in rule generation speed and usefulness of association rules. The association rule generation speed of the proposed method is about 22 seconds faster. And the lift value of the proposed method for usefulness is about 0.43 to 2.51 higher than that of each one of conventional association rule algorithms.
C1 [Kim, Hyun-Jin; Chung, Kyungyong] Kyonggi Univ, Div Comp Sci & Engn, Suwon 16227, Gyeonggi, South Korea.
   [Baek, Ji-Won] Kyonggi Univ, Dept Comp Sci, Suwon 16227, Gyeonggi, South Korea.
RP Chung, KY (通讯作者)，Kyonggi Univ, Div Comp Sci & Engn, Suwon 16227, Gyeonggi, South Korea.
EM khyunjin1993@gmail.com; hy1233hh@naver.com; kychung@kgu.ac.kr
OI Chung, Kyungyong/0000-0002-6439-9992; Kim, Hyun Jin/0000-0002-6390-0765;
   Baek, jiwon/0000-0001-9332-2815
FU Korea Agency for Infrastructure Technology Advancement(KAIA) - Ministry
   of Land, Infrastructure and Transport [20CTAP-C157011-01]
FX This work is supported by the Korea Agency for Infrastructure Technology
   Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure
   and Transport (Grant 20CTAP-C157011-01).
CR [Anonymous], MBC NEWS MUNHWA BROA
   Back JW, 2019, TECHNOL HEALTH CARE, V27, P459, DOI 10.3233/THC-191730
   Choi SY, 2020, PERS UBIQUIT COMPUT, V24, P571, DOI 10.1007/s00779-019-01230-3
   Chung K, 2020, PEER PEER NETW APPL, V13, P610, DOI 10.1007/s12083-019-00791-7
   Chung KY, 2020, INFORM TECHNOL MANAG, V21, P41, DOI 10.1007/s10799-019-00304-1
   D'Haen J, 2016, DECIS SUPPORT SYST, V82, P69, DOI 10.1016/j.dss.2015.12.002
   Duan YC, 2017, 2017 IEEE/ACIS 15TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING RESEARCH, MANAGEMENT AND APPLICATIONS (SERA), P327, DOI 10.1109/SERA.2017.7965747
   Emebo O., 2016, CSREA PRESS INT C DA, P146
   Jung H, 2021, J AMB INTEL HUM COMP, V12, P589, DOI 10.1007/s12652-020-02042-7
   Jung H, 2016, CLUSTER COMPUT, V19, P2261, DOI 10.1007/s10586-016-0672-8
   Kang JS, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9204284
   Kang J, 2019, ANAL CHEM, V91, P10001, DOI 10.1021/acs.analchem.9b01827
   Kaur J., 2016, PERFORM EVALUATION, V2, P126
   Kim D, 2019, INFORM SCIENCES, V477, P15, DOI 10.1016/j.ins.2018.10.006
   Kim JC, 2020, PERS UBIQUIT COMPUT, V24, P583, DOI 10.1007/s00779-019-01231-2
   Kim JC, 2019, CLUSTER COMPUT, V22, P1039, DOI 10.1007/s10586-018-2852-1
   Kim JC, 2019, WIRELESS PERS COMMUN, V105, P691, DOI 10.1007/s11277-018-5722-5
   Kim S, 2018, ANN OCCUP ENVIRON ME, V30, DOI 10.1186/s40557-018-0239-7
   Ko JW, 2015, MULTIMED TOOLS APPL, V74, P8907, DOI 10.1007/s11042-013-1581-y
   Lee Jong Hwa, 2019, [The Journal of Information Systems, 정보시스템연구], V28, P105
   Liu L, 2018, INT J COMMUN SYST, V31, DOI 10.1002/dac.3453
   Luna JM, 2018, IEEE T CYBERNETICS, V48, P2851, DOI 10.1109/TCYB.2017.2751081
   MOHSEN A, 2016, INT J COMPUTER ELECT, V10, P252
   Nair JJ, 2017, J INTELL FUZZY SYST, V32, P3209, DOI 10.3233/JIFS-169264
   Oweis NE, 2016, INT J ADV COMPUT SC, V7, P151
   Paik JH, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P343
   Park S.-S., 2022, PERS UBIQUIT COMPUT, V26, P355, DOI DOI 10.1007/S00779-019-01261-W
   Prajapati Dinesh J., 2017, Future Computing and Informatics Journal, V2, P19, DOI 10.1016/j.fcij.2017.04.003
   Qaiser Shahzad, 2018, INT J COMPUT APPL, V181, DOI 10.5120/ijca2018917395
   Regar E., 2018, INT J COMPUT SCI SOF, V7, P212
   Rozeva A, 2017, AIP CONF PROC, V1910, DOI 10.1063/1.5014006
   Sheng GH, 2018, IEEE T SMART GRID, V9, P695, DOI 10.1109/TSG.2016.2562123
   Shouzhong T, 2016, J CHINA U POSTS TELE, V23, P40, DOI DOI 10.1016/S1005-8885(16)60056-0
   Silva J, 2019, PROCEDIA COMPUT SCI, V151, P1207, DOI 10.1016/j.procs.2019.04.173
   Song GY, 2014, PERS UBIQUIT COMPUT, V18, P1387, DOI 10.1007/s00779-013-0740-5
   Tandon N, 2017, SIGMOD REC, V46, P49, DOI 10.1145/3186549.3186562
   Widiartha K.K., 2019, INT J APPL COMPUT SC, V1, P53
   Wu-Yan E, 2020, J NONLINEAR SCI, V30, P2195, DOI 10.1007/s00332-018-9448-z
   Xue Y, 2016, INT J COMPUT SCI ENG, V13, P66, DOI 10.1504/IJCSE.2016.10000013
   Yoon B, 2014, TECHNOL FORECAST SOC, V86, P287, DOI 10.1016/j.techfore.2013.10.013
   Zhang Yun-tao, 2005, Journal of Zhejiang University (Science), V6A, P49, DOI 10.1631/jzus.2005.A0049
   Zhao F, 2020, COMPUT COMMUN, V159, P271, DOI 10.1016/j.comcom.2020.05.017
NR 42
TC 7
Z9 7
U1 3
U2 10
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 2020
VL 10
IS 13
AR 4590
DI 10.3390/app10134590
PG 21
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA MM7JT
UT WOS:000550330400001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, WJ
   Zhao, S
   Zhang, X
AF Chen, Wenjie
   Zhao, Shuang
   Zhang, Xin
TI Enhancing knowledge graph embedding with type-constraint features
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Knowledge graph embedding; Translation-based model; Relational
   type-constraints; Entity-specific embedding; Constraint-specific
   embedding
AB Knowledge graph (KG) embedding represents entities and relations with latent vectors, which has been widely adopted in relation extraction and KG completion. Among existing works, translation-based models treat each relation as the translation from head entitiy to tail entitiy and have attracted much attention. However, these models only utilize fact triples but ignore prior knowledge on relational type-constraints. This paper presents a generic framework to enhance knowledge graph embedding with type-constraint features (ETF). In ETF, the embedding of entity is comprised of two parts-entity-specific embedding and constraint-specific embedding. The former expresses translation features of entities, and the latter represents semantic constraints influence by linked relations. Besides, the adaptive margin-based loss is designed to learn embeddings, which effectively separates the negative and positive triples. Finally, the results on four public datasets demonstrate that ETF makes significant improvements over the baselines.
C1 [Chen, Wenjie; Zhao, Shuang; Zhang, Xin] Chinese Acad, Chengdu Documentat & Informat Ctr, 16,South Sect 2,1st Ring Rd, Chengdu, Sichuan, Peoples R China.
RP Chen, WJ (通讯作者)，Chinese Acad, Chengdu Documentat & Informat Ctr, 16,South Sect 2,1st Ring Rd, Chengdu, Sichuan, Peoples R China.
EM chenwj@clas.ac.cn
OI wenjie, chen/0000-0001-7515-3624
CR Anuar FM, 2016, IEEE T SYST MAN CY-S, V46, P220, DOI 10.1109/TSMC.2015.2421878
   Bordes A., 2012, P 15 INT C ART INT S, P127
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cheng LJ, 2021, ADV ATMOS SCI, V38, P523, DOI 10.1007/s00376-021-0447-x
   Ebisu T, 2020, IEEE T KNOWL DATA EN, V32, P941, DOI 10.1109/TKDE.2019.2893920
   Fang YL, 2020, APPL INTELL, V50, P4281, DOI 10.1007/s10489-020-01761-w
   Feng J, 2021, APPL INTELL
   Feng J., 2014, KNOWLEDGE GRAPH EMBE
   Han X., 2018, P C EMP METH NAT LAN, P139
   He S, 2015, LEARNING REPRESENT K
   Hong Y, 2020, 11TH IEEE INTERNATIONAL CONFERENCE ON KNOWLEDGE GRAPH (ICKG 2020), P544, DOI 10.1109/ICBK50248.2020.00082
   Hui B, 2022, APPL INTELL, V52, P954, DOI 10.1007/s10489-021-02363-w
   Ji G, 2016, KNOWLEDGE GRAPH COMP
   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
   Krompass D, 2014, 2014 INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA), P18, DOI 10.1109/DSAA.2014.7058046
   Li M, 2021, ENHANCING KNOWLEDGE
   Lin Y, 2015, MODELING RELATION PA
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Nguyen DQ, 2020, RELATIONAL MEMORY BA
   Nickel M., 2015, HOLOGRAPHIC EMBEDDIN
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Do P, 2022, APPL INTELL, V52, P636, DOI 10.1007/s10489-021-02460-w
   Ren F, 2020, TRANSP NEW KNOWLEDGE
   Tan Z, 2018, IEEE ACCESS, V6, P8232, DOI 10.1109/ACCESS.2018.2797876
   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 S., 2021, APPL INTELL, V4
   Xiao H, IEEE T KNOWL DATA EN, P1
   Xiao H., 2015, COMPUTER SCI
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yan ZH, 2019, IEEE IJCNN
   Yang B., 2014, LEARNING MULTIRELATI
   Zhang YG, 2020, I S BIOMED IMAGING, P99, DOI 10.1109/ISBI45749.2020.9098407
   Zhang Z, 2018, P 2018 C EMP METH NA
   Zhou X, 2021, IEEE T KNOWL DATA EN, P99
NR 36
TC 0
Z9 0
U1 6
U2 6
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/s10489-022-03518-z
EA APR 2022
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0R9PO
UT WOS:000785917300003
DA 2022-08-23
ER

PT J
AU Gao, LM
   Zhu, HL
   Zhuo, HH
   Xu, J
AF Gao, Liming
   Zhu, Huiling
   Zhuo, Hankz Hankui
   Xu, Jin
TI Dual Quaternion Embeddings for Link Prediction
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph embedding; link prediction; artificial intelligence
AB The applications of knowledge graph have received much attention in the field of artificial intelligence. The quality of knowledge graphs is, however, often influenced by missing facts. To predict the missing facts, various solid transformation based models have been proposed by mapping knowledge graphs into low dimensional spaces. However, most of the existing transformation based approaches ignore that there are multiple relations between two entities, which is common in the real world. In order to address this challenge, we propose a novel approach called DualQuatE that maps entities and relations into a dual quaternion space. Specifically, entities are represented by pure quaternions and relations are modeled based on the combination of rotation and translation from head to tail entities. After that we utilize interactions of different translations and rotations to distinguish various relations between head and tail entities. Experimental results exhibit that the performance of DualQuatE is competitive compared to the existing state-of-the-art models.
C1 [Gao, Liming; Zhuo, Hankz Hankui] Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510000, Peoples R China.
   [Zhu, Huiling] Jinan Univ, Coll Informat Sci & Technol, Guangzhou 510000, Peoples R China.
   [Xu, Jin] Tencent Inc, WeChat, Data Qual Team, Guangzhou 510000, Peoples R China.
RP Zhuo, HH (通讯作者)，Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510000, Peoples R China.
EM gaolm3@mail2.sysu.edu.cn; zhuhl02@gmail.com; zhuohank@mail.sysu.edu.cn;
   jinxxu@tencent.com
FU National Natural Science Foundation of China [62076263]; National
   Natural Science Foundation of China for Young Scientists of China
   [11701592]; Joint Funds of the National Natural Science Foundation of
   China [U1811263]; Guangdong Natural Science Funds for Distinguished
   Young Scholar [2017A030306028]; Guangdong special branch plans young
   talent with scientific and technological innovation [2017TQ04X866];
   Pearl River Science and Technology New Star of Guangzhou; Guangdong
   Province Key Laboratory of Big Data Analysis and Processing
FX This research was funded by the National Natural Science Foundation of
   China (Grant No. 62076263), the National Natural Science Foundation of
   China for Young Scientists of China (Grant No. 11701592), the Joint
   Funds of the National Natural Science Foundation of China (Grant No.
   U1811263), Guangdong Natural Science Funds for Distinguished Young
   Scholar (Grant No. 2017A030306028), Guangdong special branch plans young
   talent with scientific and technological innovation (Grant No.
   2017TQ04X866), Pearl River Science and Technology New Star of Guangzhou
   and Guangdong Province Key Laboratory of Big Data Analysis and
   Processing.
CR Agrawal N., 2020, INTERACTE IMPROVING, P3009
   Balazevic I., 2019, ADV NEURAL INFORM PR, V32, P4463
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chami I., P 58 ANN M ASS COMP, P6901
   Chen Z., 2019, COATTENTIVE MULTITAS, P2137
   Deng Zhi-Hong, 2019, ICLR
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Feng W, 2018, P 27 INT JOINT C ART, P4064, DOI DOI 10.24963/IJCAI.2018/565
   Hamilton WR., 1844, PHILOS MAG, V25, P489, DOI 10.1080/14786444408644923
   Kanakaris N, 2021, ENTROPY-SWITZ, V23, DOI 10.3390/e23060664
   Kazemi S.M., 2018, SIMPLE EMBEDDING LIN, P4289
   Kotelnikov A.P., 1895, ANN IMP U KAZAN, V24
   Kumar V, 2019, LECT NOTES COMPUT SC, V11778, P382, DOI 10.1007/978-3-030-30793-6_22
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   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 F.M., 2015, P 7 BIENN C INN DAT
   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 Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Xu CR, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P263
   Yang Bishan., 2015, EMBEDDING ENTITIES R
   Zhang S., 2019, NEURIPS, P2731
   Zhang Z., 2020, LEARNING HIERARCHY A, P3065
   Zhuo HHK, 2020, ACM T INTEL SYST TEC, V11, DOI 10.1145/3368270
   Zhuo HH, 2017, AAAI CONF ARTIF INTE, P3686
   Zhuo HH, 2019, ACM T INTEL SYST TEC, V10, DOI 10.1145/3319403
   Zhuo HH, 2017, ARTIF INTELL, V246, P1, DOI 10.1016/j.artint.2017.01.004
   Zhuo HH, 2014, ARTIF INTELL, V212, P134, DOI 10.1016/j.artint.2014.04.003
   Zhuo HH, 2014, ARTIF INTELL, V212, P80, DOI 10.1016/j.artint.2014.03.004
NR 31
TC 3
Z9 3
U1 2
U2 7
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 JUN
PY 2021
VL 11
IS 12
AR 5572
DI 10.3390/app11125572
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 SZ7PZ
UT WOS:000666753600001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Chekol, MW
   Pirro, G
   Schoenfisch, J
   Stuckenschmidt, H
AF Chekol, Melisachew W.
   Pirro, Giuseppe
   Schoenfisch, Joerg
   Stuckenschmidt, Heiner
TI TECORE : Temporal Conflict Resolution in Knowledge Graphs
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs (KGs), maintaining both numerical and non-numerical facts supported by an underlying schema, are a prominent example. Knowledge Graph management is challenging because: (i) most of existing KGs focus on static data, thus impeding the availability of timewise knowledge; (ii) facts in KGs are usually accompanied by a confidence score, which witnesses how likely it is for them to hold. We demonstrate TECORE, a system for temporal inference and conflict resolution in uncertain temporal knowledge graphs (UTKGs). At the heart of TECORE are two state-of-the-art probabilistic reasoners that are able to deal with temporal constraints efficiently. While one is scalable, the other can cope with more expressive constraints. The demonstration will focus on enabling users and applications to find inconsistencies in UTKGs. TECORE provides an interface allowing to select UTKGs and editing constraints; shows the maximal consistent subset of the UTKG, and displays statistics (e.g., number of noisy facts removed) about the debugging process.
C1 [Chekol, Melisachew W.; Schoenfisch, Joerg; Stuckenschmidt, Heiner] Univ Mannheim, DWS Grp, Mannheim, Germany.
   [Pirro, Giuseppe] ICAR CNR, Arcavacata Di Rende, CS, Italy.
RP Chekol, MW (通讯作者)，Univ Mannheim, DWS Grp, Mannheim, Germany.
EM mel@informatik.uni-mannheim.de; pirro@icar.cnr.it; joerg@dwslab.de;
   heiner@informatik.uni-mannheim.de
RI Pirrò, Giuseppe/AAF-1993-2019; Pirrò, Giuseppe/AAZ-4500-2020
OI Pirrò, Giuseppe/0000-0002-7499-5798; Stuckenschmidt,
   Heiner/0000-0002-0209-3859
CR Bach S., 2015, CORR
   Cafarella MJ, 2011, COMMUN ACM, V54, P72, DOI 10.1145/1897816.1897839
   Chekol M. W., 2017, P AAAI 2017
   Chekol MW, 2016, FRONT ARTIF INTEL AP, V285, P1017, DOI 10.3233/978-1-61499-672-9-1017
   Dylla M, 2013, PROC VLDB ENDOW, V6, P1810, DOI 10.14778/2556549.2556564
   Ling X, 2010, AAAI CONF ARTIF INTE, P1385
   Papaioannou K, 2016, PROC INT CONF DATA, P1382, DOI 10.1109/ICDE.2016.7498350
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Schlobach S, 2007, J AUTOM REASONING, V39, P317, DOI 10.1007/s10817-007-9076-z
   Zhou XF, 2016, PROC VLDB ENDOW, V9, P1461, DOI 10.14778/3007263.3007284
NR 10
TC 3
Z9 3
U1 2
U2 4
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 1929
EP 1932
DI 10.14778/3137765.3137811
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:000416494000044
DA 2022-08-23
ER

PT J
AU Gordon, SN
   Murphy, PJ
   Gallo, JA
   Huber, P
   Hollander, A
   Edwards, A
   Jankowski, P
AF Gordon, Sean N.
   Murphy, Philip J.
   Gallo, John A.
   Huber, Patrick
   Hollander, Allan
   Edwards, Ann
   Jankowski, Piotr
TI People, Projects, Organizations, and Products: Designing a Knowledge
   Graph to Support Multi-Stakeholder Environmental Planning and Design
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE decision support software; open systems; human-environment systems;
   environmental planning; information needs assessment; ontology;
   knowledge graph
ID CONNECTING PEOPLE; SEMANTIC WEB; INFORMATION; NEEDS; FRAMEWORK;
   MANAGERS; SCIENCE; POLICY
AB As the need for more broad-scale solutions to environmental problems is increasingly recognized, traditional hierarchical, government-led models of coordination are being supplemented by or transformed into more collaborative inter-organizational networks (i.e., collaboratives, coalitions, partnerships). As diffuse networks, such regional environmental planning and design (REPD) efforts often face challenges in sharing and using spatial and other types of information. Recent advances in semantic knowledge management technologies, such as knowledge graphs, have the potential to address these challenges. In this paper, we first describe the information needs of three multi-stakeholder REPD initiatives in the western USA using a list of 80 need-to-know questions and concerns. The top needs expressed were for help in tracking the participants, institutions, and information products relevant to the REDP's focus. To address these needs, we developed a prototype knowledge graph based on RDF and GeoSPARQL standards. This semantic approach provided a more flexible data structure than traditional relational databases and also functionality to query information across different providers; however, the lack of semantic data expertise, the complexity of existing software solutions, and limited online hosting options are significant barriers to adoption. These same barriers are more acute for geospatial data, which also faces the added challenge of maintaining and synchronizing both semantic and traditional geospatial datastores.
C1 [Gordon, Sean N.] Oregon State Univ, Inst Nat Resources, Portland, OR 97207 USA.
   [Murphy, Philip J.] InfoHarvest Inc, Seattle, WA 98165 USA.
   [Gallo, John A.] Conservat Biol Inst, Corvallis, OR 97333 USA.
   [Huber, Patrick; Hollander, Allan] Univ Calif Davis, Dept Environm Sci & Policy, Davis, CA 95616 USA.
   [Edwards, Ann] Resilient Earth, Vashon, WA 98070 USA.
   [Jankowski, Piotr] San Diego State Univ, Dept Geog, San Diego, CA 92182 USA.
   [Jankowski, Piotr] Adam Mickiewicz Univ, Inst Geoecol & Geoinformat, PL-61680 Poznan, Poland.
RP Gordon, SN (通讯作者)，Oregon State Univ, Inst Nat Resources, Portland, OR 97207 USA.
EM sean.gordon@oregonstate.edu; philip.murphy@infoharvest.com;
   john.gallo@consbio.org; prhuber@ucdavis.edu; adhollander@ucdavis.edu;
   ann@resilient-earth.com; pjankows@sdsu.edu
RI ; Jankowski, Piotr/B-8481-2015
OI Gallo, John/0000-0002-5602-4277; Huber, Patrick/0000-0002-8251-5731;
   Gordon, Sean/0000-0003-2399-3220; Jankowski, Piotr/0000-0002-6303-6217
CR Abrams J, 2019, FOREST POLICY ECON, V106, DOI 10.1016/j.forpol.2019.101977
   Ahearn SC, 2013, INT J GEOGR INF SCI, V27, P2227, DOI 10.1080/13658816.2013.802324
   Alatrish E., 2013, MANAGEMENT INFORM SY, V8, P18
   [Anonymous], W3C OWL 2 WEB ONTOLO, VSecond
   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
   Bixler RP, 2016, FRONT ECOL ENVIRON, V14, P115, DOI 10.1002/fee.1255
   Campellone RM, 2018, LANDSCAPE URBAN PLAN, V176, P64, DOI 10.1016/j.landurbplan.2018.04.008
   CCG, 2020, PUG SOUND PARTN 2018
   Chaudhri V.K., STANFORD CS 520 KNOW
   Clunis J.S., 2019, ICONFERENCE 2019 P I, DOI [10.21900/iconf.2019.103300, DOI 10.21900/ICONF.2019.103300]
   Davis MLES, 2014, ENVIRON MANAGE, V53, P690, DOI 10.1007/s00267-014-0229-7
   Fonseca AM, 2015, ADV INTELL SYST, V353, P33, DOI 10.1007/978-3-319-16486-1_4
   Forman, 1995, LAND MOSAICS ECOLOGY
   Gallo JA, 2020, LAND-BASEL, V9, DOI 10.3390/land9110444
   Global Forum on Agricultural Research, AGRIVIVO ONT
   Global Water Partnership, WHAT IS IWRM
   Gober P, 2013, SOC NATUR RESOUR, V26, P356, DOI 10.1080/08941920.2012.713448
   Graves M, 2007, CAT CLASSIF Q, V43, P191, DOI [10.1300/J104v43n03_11, 10.1300/J104v43n03_10]
   Guston DH, 2001, SCI TECHNOL HUM VAL, V26, P399, DOI 10.1177/016224390102600401
   Hitzler P., 2019, CEUR WORKSHOP P
   Hitzler P, 2021, COMMUN ACM, V64, P76, DOI 10.1145/3397512
   Hollander A., 2021, PPODTOTTL
   Hollander A.D., 2018, P CEUR WORKSH P SUN
   Hollander A. D., 2019, PPOD ONTOLOGY
   Hollander AD, 2020, ANN AM ASSOC GEOGR, V110, P535, DOI 10.1080/24694452.2019.1662764
   Huber Patrick, 2015, Ecosystem Health and Sustainability, V1, P7, DOI 10.1890/EHS14-0008.1
   Integratedmodelling, RES SEM MOD COMM
   Johnson K.N., 1999, BIOREGIONAL ASSESSME
   Kalabokidis K, 2011, NAT HAZARD EARTH SYS, V11, P3157, DOI 10.5194/nhess-11-3157-2011
   Koubarakis M, 2010, LECT NOTES COMPUT SC, V6088, P425, DOI 10.1007/978-3-642-13486-9_29
   Lemos MC, 2006, ANNU REV ENV RESOUR, V31, P297, DOI 10.1146/annurev.energy.31.042605.135621
   Li NC, 2012, T GIS, V16, P313, DOI 10.1111/j.1467-9671.2012.01325.x
   Li WW, 2019, ISPRS INT GEO-INF, V8, DOI 10.3390/ijgi8110496
   Mai GC, 2019, T GIS, V23, P579, DOI 10.1111/tgis.12538
   Miewald T., 2020, CASCADES COAST COLLA
   Mitchell S., 2018, VIVO ONTOLOGY RES DI
   Mungall C., RELATION ONTOLOGY
   Musen Mark A, 2015, AI Matters, V1, P4
   NLC, 2018, PATHW FORW PROGR PRI
   NLC, 2018, ASS STAT LANDSC CONS
   Noy NF, 2004, SIGMOD REC, V33, P65
   NWIFC, 2020, 2020 STAT OUR WAT
   OGC, 2012, GEOSPARQL A GEOGR QU
   PNCLC, 2019, WORK LANDS CONS COMM
   PNCLC, 2018, WORK LANDS CONS COMM
   PSEMP, 2019, PSEMP SPAT DAT WORK
   PSP Science Panel, 2020, PRIOR SCI SUPP REC P
   Puget Sound Partnership, 2018, 2018 2022 ACT AG PUG
   Rayner S, 2005, CLIMATIC CHANGE, V69, P197, DOI 10.1007/s10584-005-3148-z
   Ryan CM, 2011, WEST J APPL FOR, V26, P126, DOI 10.1093/wjaf/26.3.126
   Sack H., 2020, KNOWLEDGE GRAPHS
   Scarlett L, 2016, FRONT ECOL ENVIRON, V14, P116, DOI 10.1002/fee.1247
   SCHNEIDER A, 1990, J POLIT, V52, P510, DOI 10.2307/2131904
   Schreur P.E., 2019, METADATA SEMANTIC RE, P425, DOI 10.1007/978-3-030-36599-8_39
   Senderov V, 2018, J BIOMED SEMANT, V9, DOI 10.1186/s13326-017-0174-5
   Sheehan T, 2016, ECOL INFORM, V34, P92, DOI 10.1016/j.ecoinf.2016.05.001
   Shotton D., 2017, FUNDING RES ADM PROJ
   Smythe KD, 1996, ENVIRON MANAGE, V20, P865, DOI 10.1007/BF01205966
   State of Washington, 2007, PUG SOUND PARTN CREA, VVolume 90.71.210
   Thalhath N., 2021, METADATA SEMANTIC RE, P336, DOI 10.1007/978-3-030-71903-6_31
   Thomsen JM, 2017, J ENVIRON PLANN MAN, V60, P1153, DOI 10.1080/09640568.2016.1202814
   Tudorache T, 2013, SEMANT WEB, V4, P89, DOI 10.3233/SW-2012-0057
   USDOI USDA USDOC CEQ, 2021, CONSERVING RESTORING
   Villa F, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0091001
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang W, 2015, COMPUT ENVIRON URBAN, V50, P30, DOI 10.1016/j.compenvurbsys.2014.11.001
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Wondolleck J.M., 2000, MAKING COLLABORATION
   Yan JZ, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103429
   Zhang C., 2019, GEOGRAPHIC INFORM SC, DOI [10.22224/gistbok/2019.4.9, DOI 10.22224/GISTBOK/2019.4.9]
NR 71
TC 1
Z9 1
U1 1
U2 1
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 DEC
PY 2021
VL 10
IS 12
AR 823
DI 10.3390/ijgi10120823
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 XY8QT
UT WOS:000737230800001
OA gold
DA 2022-08-23
ER

PT J
AU Guo, X
   Qian, HZ
   Wu, F
   Liu, JN
AF Guo, Xuan
   Qian, Haizhong
   Wu, Fang
   Liu, Junnan
TI A Method for Constructing Geographical Knowledge Graph from Multisource
   Data
SO SUSTAINABILITY
LA English
DT Article
DE knowledge graph; geographical knowledge graph; knowledge extraction;
   geographic dataset; internet encyclopedias
ID LINKED DATA; REPRESENTATIONS
AB Global problems all occur at a particular location on or near the Earth's surface. Sitting at the junction of artificial intelligence (AI) and big data, knowledge graphs (KGs) organize, interlink, and create semantic knowledge, thus attracting much attention worldwide. Although the existing KGs are constructed from internet encyclopedias and contain abundant knowledge, they lack exact coordinates and geographical relationships. In light of this, a geographical knowledge graph (GeoKG) construction method based on multisource data is proposed, consisting of a modeling schema layer and a filling data layer. This method has two advantages: (1) the knowledge can be extracted from geographic datasets; (2) the knowledge on multisource data can be represented and integrated. Firstly, the schema layer is designed to represent geographical knowledge. Then, the methods of extraction and integration from multisource data are designed to fill the data layer, and a storage method is developed to associate semantics with geospatial knowledge. Finally, the GeoKG is verified through linkage rate, semantic relationship rate, and application cases. The experiments indicate that the method could automatically extract and integrate knowledge from multisource data. Additionally, our GeoKG has a higher success rate of linking web pages with geographic datasets, and its exact coordinates have increased to 100%. This paper could bridge the distance between a Geographic Information System and a KG, thus facilitating more geospatial applications.</p>
C1 [Guo, Xuan; Qian, Haizhong] Informat Engn Univ, Inst Geospatial Informat, Zhengzhou 450001, Peoples R China.
   [Wu, Fang; Liu, Junnan] Zhengzhou Univ, Sch Earth Sci & Technol, Zhengzhou 450001, Peoples R China.
RP Liu, JN (通讯作者)，Zhengzhou Univ, Sch Earth Sci & Technol, Zhengzhou 450001, Peoples R China.
EM gx930707@163.com; haizhongqian@163.com; dtis123@163.com;
   lpjunnan.love@163.com
OI Liu, Junnan/0000-0002-3211-5342; Guo, Xuan/0000-0001-6678-4168
FU National Natural Science Foundation of China [41571442]; Excellent Youth
   Foundation of Henan Scientific Committee [212300410014]
FX This work was supported by the National Natural Science Foundation of
   China, grant number 41571442, and the Excellent Youth Foundation of
   Henan Scientific Committee, grant number 212300410014.
CR Almeida PD, 2016, LECT NOTES COMPUT SC, V9789, P1, DOI 10.1007/978-3-319-42089-9_1
   Auer S, 2009, LECT NOTES COMPUT SC, V5823, P731, DOI 10.1007/978-3-642-04930-9_46
   Ballatore A, 2013, KNOWL INF SYST, V37, P61, DOI 10.1007/s10115-012-0571-0
   Codescu M., 2014, STATE MAP EUR SOTM E, V2011, P23
   Deng S, 2017, P CHINA C KNOWLEDGE, DOI [10.1007/978-981-10-7359-5, DOI 10.1007/978-981-10-7359-5]
   Elkin P.L., 2012, TERMINOLOGY TERMINOL, P11
   Gould N, 2016, CARTOGR GEOGR INF SC, V43, P208, DOI 10.1080/15230406.2015.1072737
   Hofer B, 2017, INT J GEOGR INF SCI, V31, P694, DOI 10.1080/13658816.2016.1227441
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Hook PA, 2007, IEEE INT CONF INF VI, P442
   Huang WM, 2020, IEEE ACCESS, V8, P85473, DOI 10.1109/ACCESS.2020.2992023
   Huang WM, 2018, INT J GEOGR INF SCI, V32, P1117, DOI 10.1080/13658816.2018.1441416
   Janowicz K, 2010, T GIS, V14, P111, DOI 10.1111/j.1467-9671.2010.01186.x
   Jiang BC, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8100428
   Kacprzyk J, 2021, INT J COMPUT INT SYS, V14, P1388, DOI 10.2991/ijcis.d.210420.001
   Krisnadhi AA, 2015, CEUR WORKSHOP P, V1486
   Laurini R., 2017, P 2 INT C SMART DAT
   Li WL, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8030104
   Li WW, 2020, J SPAT INT SCI, P71, DOI 10.5311/JOSIS.2020.20.658
   Liu JN, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13042005
   [刘瑜 LIU Yu], 2007, [地理与地理信息科学, Geography and Geo-information Science], V23, P1
   Mai GC, 2019, T GIS, V23, P579, DOI 10.1111/tgis.12538
   Mekni M., 2013, J GEOGR INF SYST, V5, P548, DOI [10.4236/jgis.2013.56052, DOI 10.4236/JGIS.2013.56052]
   Palacio MP, 2003, PROCEEDINGS OF THE FOURTH MEXICAN INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE (ENC 2003), P117, DOI 10.1109/ENC.2003.1232884
   Schade S, 2012, INT GEOSCI REMOTE SE, P2894, DOI 10.1109/IGARSS.2012.6350721
   Varanka Dalia E., 2018, International Journal of Cartography, V4, P201, DOI 10.1080/23729333.2017.1421004
   Wu TX, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093245
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   Yingjie Hu, 2013, Spatial Information Theory. 11th International Conference, COSIT 2013. Proceedings: LNCS 8116, P438, DOI 10.1007/978-3-319-01790-7_24
   Zeyua L., 2014, STUD PHILOS SCI TECH, V31, P82
   Zheng K, 2022, INT J GEOGR INF SCI, V36, P674, DOI 10.1080/13658816.2021.1962527
   Zhu YQ, 2017, SCI PROGRAMMING-NETH, V2017, DOI 10.1155/2017/5072427
NR 32
TC 0
Z9 0
U1 15
U2 25
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD OCT
PY 2021
VL 13
IS 19
AR 10602
DI 10.3390/su131910602
PG 17
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 WL3TX
UT WOS:000710332800001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Guo, CC
   Zhang, CH
   Han, X
   Ji, Y
AF Guo, Chenchen
   Zhang, Chunhong
   Han, Xiao
   Ji, Yang
TI AWML: adaptive weighted margin learning for knowledge graph embedding
SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph; Knowledge representation learning; Adaptive margin;
   Adaptive importance weight
AB Knowledge representation learning (KRL), exploited by various applications such as question answering and information retrieval, aims to embed the entities and relations contained by the knowledge graph into points of a vector space such that the semantic and structure information of the graph is well preserved in the representing space. However, the previous works mainly learned the embedding representations by treating each entity and relation equally which tends to ignore the inherent imbalance and heterogeneous properties existing in knowledge graph. By visualizing the representation results obtained from classic algorithm TransE in detail, we reveal the disadvantages caused by this homogeneous learning strategy and gain insight of designing policy for the homogeneous representation learning. In this paper, we propose a novel margin-based pairwise representation learning framework to be incorporated into many KRL approaches, with the method of introducing adaptivity according to the degree of knowledge heterogeneity. More specially, an adaptive margin appropriate to separate the real samples from fake samples in the embedding space is first proposed based on the sample's distribution density, and then an adaptive weight is suggested to explicitly address the trade-off between the different contributions coming from the real and fake samples respectively. The experiments show that our Adaptive Weighted Margin Learning (AWML) framework can help the previous work achieve a better performance on real-world Knowledge Graphs Freebase and WordNet in the tasks of both link prediction and triplet classification.
C1 [Guo, Chenchen; Zhang, Chunhong; Han, Xiao; Ji, Yang] Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Univ Wireless Commun, Beijing, Peoples R China.
RP Zhang, CH (通讯作者)，Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Univ Wireless Commun, Beijing, Peoples R China.
EM orangegcc@bupt.edu.cn; zhangch@bupt.edu.cn; hanxiao1007@bupt.edu.cn;
   jiyang@bupt.edu.cn
OI Guo, Chenchen/0000-0002-6876-0765
FU National Natural Science Foundation of China [61602048, 61601046,
   61520106007]
FX This research is supported by the National Natural Science Foundation of
   China under Grant No.61602048, No.61601046 and No.61520106007.
CR 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, 2009, P 25 AAAI C ART INT, P301, DOI 10.1016/j.procs.2017.05.045
   Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401
   Duchi J, 2011, J MACH LEARN RES, V12, P2121
   Ferrandez A, 2016, J INTELL INF SYST, V46, P61, DOI 10.1007/s10844-014-0351-2
   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, 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
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   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 ZNA, 2018, J HAZARD TOXIC RADIO, V22, DOI 10.1061/(ASCE)HZ.2153-5515.0000421
   Metzger S, 2017, J INTELL INF SYST, V49, P333, DOI 10.1007/s10844-017-0443-x
   Mikolov T., 2013, ADV NEURAL INFORM PR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Minervini P, 2016, J INTELL INF SYST, V47, P91, DOI 10.1007/s10844-016-0414-7
   Miyamoto Y., 2016, GATED WORD CHARACTER
   Nickel M., 2015, 13 AAAI C ART INT
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Shi B, 2017, AAAI
   Socher R., 2013, ADV NEURAL INFORM PR, P926
   Sutskever I., 2009, P ADV NEUR INF PROC, P1
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Wang R., 2017, MAGAN MARGIN ADAPTAT
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J., 1999, 7th European Symposium on Artificial Neural Networks. ESANN'99. Proceedings, P219
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xiao H., 2015, ARXIV15090
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yang Zhilin, 2016, WORDS CHARACTERS FIN
   Zhang CH, 2017, TSINGHUA SCI TECHNOL, V22, P185
   Zhao F., 2017, ARXIV17100
   Zhou M, 2016, LECT NOTES COMPUT SC, V9784, P236, DOI 10.1007/978-3-319-42553-5_20
NR 36
TC 1
Z9 1
U1 2
U2 26
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0925-9902
EI 1573-7675
J9 J INTELL INF SYST
JI J. Intell. Inf. Syst.
PD AUG
PY 2019
VL 53
IS 1
BP 167
EP 197
DI 10.1007/s10844-018-0535-2
PG 31
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IK7ZJ
UT WOS:000476812100008
DA 2022-08-23
ER

PT J
AU Heyvaert, P
   De Meester, B
   Dimou, A
   Verborgh, R
AF Heyvaert, Pieter
   De Meester, Ben
   Dimou, Anastasia
   Verborgh, Ruben
TI Rule-driven inconsistency resolution for knowledge graph generation
   rules
SO SEMANTIC WEB
LA English
DT Article
DE Inconsistency; knowledge graph; methodology; resolution; rule-driven
AB Knowledge graphs, which contain annotated descriptions of entities and their interrelations, are often generated using rules that apply semantic annotations to certain data sources. (Re)using ontology terms without adhering to the axioms defined by their ontologies results in inconsistencies in these graphs, affecting their quality. Methods and tools were proposed to detect and resolve inconsistencies, the root causes of which include rules and ontologies. However, these either require access to the complete knowledge graph, which is not always available in a time-constrained situation, or assume that only generation rules can be refined but not ontologies. In the past, we proposed a rule-driven method for detecting and resolving inconsistencies without complete knowledge graph access, but it requires a predefined set of refinements to the rules and does not guide users with respect to the order the rules should be inspected. We extend our previous work with a rule-driven method, called Resglass, that considers refinements for generation rules as well as ontologies. In this article, we describe Resglass, which includes a ranking to determine the order with which rules and ontology elements should be inspected, and its implementation. The ranking is evaluated by comparing the manual ranking of experts to our automatic ranking. The evaluation shows that our automatic ranking achieves an overlap of 80% with experts ranking, reducing this way the effort required during the resolution of inconsistencies in both rules and ontologies.
C1 [Heyvaert, Pieter; De Meester, Ben; Dimou, Anastasia; Verborgh, Ruben] Univ Ghent, IMEC, Dept Elect & Informat Syst, IDLab, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium.
RP Heyvaert, P (通讯作者)，Univ Ghent, IMEC, Dept Elect & Informat Syst, IDLab, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium.
EM pheyvaer.heyvaert@ugent.be; ben.demeester@ugent.be;
   anastasia.dimou@ugent.be; ruben.verborgh@ugent.be
RI Dimou, Anastasia/AAD-7338-2020; De Meester, Ben/AAR-4511-2020
OI Dimou, Anastasia/0000-0003-2138-7972; De Meester,
   Ben/0000-0003-0248-0987
FU Ghent University, imec, Flanders Innovation & Entrepreneurship (AIO);
   Research Foundation - Flanders (FWO); European Union
FX The described research activities were funded by Ghent University, imec,
   Flanders Innovation & Entrepreneurship (AIO), the Research Foundation -
   Flanders (FWO), and the European Union. Special thanks to Tom Seymoens
   and Io Taxidou for their valuable advice.
CR Acosta M, 2013, LECT NOTES COMPUT SC, V8219, P260, DOI 10.1007/978-3-642-41338-4_17
   Allemang D., 2011, SEM WEB WORK ONT
   Arenas M., 2012, DIRECT MAPPING RELAT
   Arndt D, 2017, LECT NOTES COMPUT SC, V10364, P22, DOI 10.1007/978-3-319-61252-2_3
   Bizer C, 2011, SEMANTIC SERVICES, INTEROPERABILITY AND WEB APPLICATIONS: EMERGING CONCEPTS, P205, DOI 10.4018/978-1-60960-593-3.ch008
   Bosch T., 2015, RDF VALIDATION REQUI
   Das Shagnik., 2016, R2RML RDB RDF MAPPIN
   De Meester B, 2017, LECT NOTES COMPUT SC, V10250, P33, DOI 10.1007/978-3-319-58451-5_3
   De Meester B, 2016, LECT NOTES COMPUT SC, V9989, P46, DOI 10.1007/978-3-319-47602-5_10
   Dimou A., 2016, P 15 INT SEM WEB C P, V1690
   Dimou A, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.105
   Dimou A, 2015, LECT NOTES COMPUT SC, V9367, P133, DOI 10.1007/978-3-319-25010-6_8
   Farber M, 2018, SEMANT WEB, V9
   Farid M, 2016, SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P2089, DOI 10.1145/2882903.2899391
   GUARINO N, 1995, VERY LARGE KNOWLEDGE
   Hearst MA, 2006, COMMUN ACM, V49, P59, DOI 10.1145/1121949.1121983
   Hua-Jun Zeng, 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P210
   Kaki M, 2005, INTERACT COMPUT, V17, P187, DOI 10.1016/j.intcom.2005.01.001
   Kendall M.G., 1975, RANK CORRELATION MET, DOI [10.1111/j.2044-8317.1956.tb00172.x, DOI 10.1111/J.2044-8317.1956.TB00172.X]
   Knublauch H, 2017, SHAPES CONSTRAINT LA
   Kontokostas D, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P747, DOI 10.1145/2566486.2568002
   Lefrancois M, 2017, LECT NOTES COMPUT SC, V10249, P35, DOI 10.1007/978-3-319-58068-5_3
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Mendes P. N., 2012, P 2012 JOINT EDBT IC, P116, DOI [DOI 10.1145/2320765.2320803, 10.1145/2320765.2320803]
   Motik Boris, 2012, TECHNICAL REPORT
   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
   Prud'hommeaux E., TECHNICAL REPORT
   Rashid MRA, 2019, J WEB SEMANT, V54, P48, DOI 10.1016/j.websem.2018.11.004
   Van de Walle R., 2014, RML GENERIC LANGUAGE
   W. W. W. Consortium, 2014, TECHNICAL REPORT
   Webber W, 2010, ACM T INFORM SYST, V28, DOI 10.1145/1852102.1852106
NR 32
TC 5
Z9 5
U1 0
U2 4
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 1071
EP 1086
DI 10.3233/SW-190358
PG 16
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:000492839300006
DA 2022-08-23
ER

PT J
AU Twietmeyer, GA
   Lyth, DM
   Mallak, LA
   Aller, BM
AF Twietmeyer, Geoffry A.
   Lyth, David M.
   Mallak, Larry A.
   Aller, Betsy M.
TI Evaluating a new knowledge management tool
SO EMJ-ENGINEERING MANAGEMENT JOURNAL
LA English
DT Article
DE culture; measurement; knowledge management; application; knowledge map
AB Common solutions for knowledge management generally depend on databases or text-based searches. This article presents a new approach to knowledge management-treemaps. This approach allows employees to search for expertise in specific areas and gives the searcher multiple employees to contact for that knowledge, ranked by a self-assessment of their knowledge. Additionally the same knowledge measurement tool provides managers and human resources an easy summary that allows them to view organizational knowledge gaps and training opportunities.
   This article walks the reader through an actual application of developing treemaps to Support decision making concerning the location of organizational knowledge/expertise. We set the context for the study, develop and test the tree maps, a rid evaluate the value of those treemaps in an actual decision making task. We offer recommendations for engineering managers to use treemaps to support their decision-making tasks.
C1 [Twietmeyer, Geoffry A.] GHSP, Grand Haven, MI 49417 USA.
   [Lyth, David M.; Mallak, Larry A.; Aller, Betsy M.] Western Michigan Univ, Kalamazoo, MI 49008 USA.
RP Twietmeyer, GA (通讯作者)，GHSP, 1250 South Beechtree, Grand Haven, MI 49417 USA.
EM twietmeg@ghsp.com
CR Arora R., 2002, Journal of Knowledge Management, V6, P240, DOI 10.1108/13673270210434340
   CABLE J, 2004, P PROJ MAN I C LOND
   CARSON E, 2004, J INTELLECTUAL CAPIT, V10, P443
   DAVENPORT TH, 2000, WORKING KNOWLEDGE OR
   ERICKSON GS, 2002, J GLOBAL COMPETITIVE, V10, P423
   MALLAK LA, 2007, P PORTL INT C MAN EN
NR 6
TC 3
Z9 3
U1 0
U2 5
PU AMER SOC ENGINEERING MANAGEMENT
PI ROLLA
PA PO BOX 820, ROLLA, MO 65402 USA
SN 1042-9247
J9 EMJ-ENG MANAG J
JI EMJ-Eng. Manag. J.
PD MAR
PY 2008
VL 20
IS 1
BP 10
EP 18
PG 9
WC Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics
GA 340RT
UT WOS:000258663400004
DA 2022-08-23
ER

PT J
AU Wang, JB
   Nie, K
   Chen, XY
   Lei, J
AF Wang, Jingbin
   Nie, Kuan
   Chen, Xinyuan
   Lei, Jing
TI SUKE: Embedding Model for Prediction in Uncertain Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Task analysis; Generators; Training; Predictive models; Probabilistic
   logic; Licenses; Uncertainty; Artificial intelligence; knowledge
   representation; uncertain knowledge graph
AB Graph embedding models are widely used in knowledge graph completion (KGC) task. However, most models are based on the assumption that knowledge is completely certain, and this is inconsistent with real-world situations. Although there are multiple studies on uncertain knowledge embedding tasks, they often use knowledge confidence to learn embedding and cannot make full use the structural and uncertain information of knowledge. This paper presents a new embedding model named Structural and Uncertain Knowledge Embedding (SUKE), which comprises two components: an evaluator and a confidence generator. For unknown triples, the evaluator learns the structural and uncertain information to evaluate its rationality and obtain a candidate set. The confidence generator then determines the confidence of the candidate set to achieve KGC. To verify the effectiveness of the proposed model, confidence prediction, triple evaluation, and fact classification tasks are performed on three data sets. Experimental results show that SUKE performs better than mainstream embedding methods. The model proposed in this paper can help advance the research on the embedding of uncertain knowledge graphs.
C1 [Wang, Jingbin; Nie, Kuan; Lei, Jing] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Peoples R China.
   [Wang, Jingbin; Nie, Kuan; Lei, Jing] Fuzhou Univ, Coll Software, Fuzhou 350116, Peoples R China.
   [Chen, Xinyuan] Fuzhou Melbourne Polytech, Dept Informat Engn, Fuzhou 350121, Peoples R China.
RP Nie, K (通讯作者)，Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Peoples R China.; Nie, K (通讯作者)，Fuzhou Univ, Coll Software, Fuzhou 350116, Peoples R China.
EM sxurjxynk@163.com
OI Nie, Kuan/0000-0002-8499-4158
FU National Natural Science Foundation of China [61672159];
   Industry-University-Research Collaboration in Fujian Province of China
   [2017H6008, 2018H6010]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61672159, and in part by the
   Industry-University-Research Collaboration in Fujian Province of China
   under Grant 2017H6008 and Grant 2018H6010.
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   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., 2013, ADV NEURAL INFORM PR, P2787
   Bouchard, 2016, P INT C MACH LEARN I, P1
   Chen XL, 2019, AAAI CONF ARTIF INTE, P3363
   Dellal I, 2019, KNOWL INF SYST, V61, P1633, DOI 10.1007/s10115-019-01332-7
   Deng Zhi-Hong, 2019, ICLR
   Dettmers T., 2018, P 32 AAAI C ART INT, P1
   Fan M, 2016, 2016 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2016), P42, DOI [10.1109/WI.2016.17, 10.1109/WI.2016.0017]
   Hoffmann R, 2011, P 49 ANN M ASS COMP, P541, DOI DOI 10.5555/2002472
   Hu JF, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P157, DOI 10.1145/3132847.3132885
   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
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu Z., 2017, ARXIV170503202
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mitchell T, 2018, COMMUN ACM, V61, P103, DOI 10.1145/3191513
   Nguyen D.Q., 2019, P NAACL HLT 2019, P2180, DOI DOI 10.18653/V1/N19-1226
   Nguyen D. Q, 2017, ARXIV171202121
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Shan YC, 2018, 2018 9TH IEEE INTERNATIONAL CONFERENCE ON BIG KNOWLEDGE (ICBK), P33, DOI 10.1109/ICBK.2018.00013
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Szklarczyk D, 2017, NUCLEIC ACIDS RES, V45, pD362, DOI 10.1093/nar/gkw937
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
   Yang B., 2015, P INT C LEARN REPR
NR 25
TC 1
Z9 2
U1 7
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 2021
VL 9
BP 3871
EP 3879
DI 10.1109/ACCESS.2020.3047086
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PQ5JK
UT WOS:000606581600001
OA gold
DA 2022-08-23
ER

PT J
AU Xie, LD
   Huang, HM
   Du, Q
AF Xie, Luodi
   Huang, Huimin
   Du, Qing
TI A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph; embedding; variational auto-encoder
AB Knowledge graph (KG) embedding has been widely studied to obtain low-dimensional representations for entities and relations. It serves as the basis for downstream tasks, such as KG completion and relation extraction. Traditional KG embedding techniques usually represent entities/relations as vectors or tensors, mapping them in different semantic spaces and ignoring the uncertainties. The affinities between entities and relations are ambiguous when they are not embedded in the same latent spaces. In this paper, we incorporate a co-embedding model for KG embedding, which learns low-dimensional representations of both entities and relations in the same semantic space. To address the issue of neglecting uncertainty for KG components, we propose a variational auto-encoder that represents KG components as Gaussian distributions. In addition, compared with previous methods, our method has the advantages of high quality and interpretability. Our experimental results on several benchmark datasets demonstrate our model's superiority over the state-of-the-art baselines.
C1 [Xie, Luodi] Sun Yat Sen Univ, Sch Comp Sci, Guangzhou 510000, Peoples R China.
   [Huang, Huimin] Wenzhou Univ Technol, Sch Data Sci & Artificial Intelligence, Wenzhou 325000, Peoples R China.
   [Du, Qing] South China Univ Technol, Sch Software, Guangzhou 510000, Peoples R China.
RP Huang, HM (通讯作者)，Wenzhou Univ Technol, Sch Data Sci & Artificial Intelligence, Wenzhou 325000, Peoples R China.
EM xield@mail2.sysu.edu.cn; huanghm45@gmail.com; duqing@scut.edu.cn
FU Wenzhou Science and Technology Planning Project [2021R0082]
FX FundingThis research was funded by Wenzhou Science and Technology
   Planning Project #2021R0082.
CR 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
   Bordes A., 2014, CSCL14044326 ARXIV
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dosovitskiy A., 2016, CSLG160202644 ARXIV
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Heck L., 2013, P INT SPEECH COMM AS
   Jiang Z., 2016, CSCV161105148 ARXIV
   Kingma D.P., 2013, STATML13126114 ARXIV
   Kingma DP, 2014, ADV NEUR IN, V27
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Makhzani A., 2015, CSLG151105644 ARXIV
   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
   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
   Schlichtkrull M., 2017, STATML170306103 ARXI
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vilnis L., 2014, CSCL14126626 ARXIV
   Wang WY, 2015, MACH LEARN, V100, P101, DOI 10.1007/s10994-015-5488-x
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Yang B., 2014, CSCL14126575 ARXIV
NR 25
TC 1
Z9 1
U1 2
U2 2
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 JAN
PY 2022
VL 12
IS 2
AR 715
DI 10.3390/app12020715
PG 11
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA ZC1UQ
UT WOS:000757314200001
OA gold
DA 2022-08-23
ER

PT J
AU Zhou, XC
   Lim, MQ
   Kraft, M
AF Zhou, Xiaochi
   Lim, Mei Qi
   Kraft, Markus
TI A Smart Contract-based agent marketplace for the J-Park Simulator - a
   knowledge graph for the process industry
SO COMPUTERS & CHEMICAL ENGINEERING
LA English
DT Article
DE Blockchain; Smart Contract; Agent; Agent composition; Knowledge Graph
ID BLOCKCHAIN TECHNOLOGY; REPUTATION SYSTEMS; ONTOLOGY; ATTACKS
AB The chemical industry is increasingly relying on agents for data acquisition, optimization, and simulation. In order to enable efficient management of agents, Knowledge Graphs (KG) together with agent compo-sition frameworks are therefore applied. However, a method to assess the reliability of agents for such systems is absent. Therefore, this paper proposes a Smart Contract-based agent marketplace for com-position frameworks to estimate the reliability of agents. In this agent marketplace, we improved the feedback-based reputation system by leveraging Smart Contracts to eliminate fraudulent ratings and to enable automation. The marketplace incorporates a rating-dependent payment mechanism as well, to further enhance trust. The paper also illustrates how this marketplace is integrated into the J-Park Simulator (JPS) agent composition framework for the automated agent selection and transaction.(C) 2020 Elsevier Ltd. All rights reserved.
C1 [Zhou, Xiaochi; Lim, Mei Qi; Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore CARES, CREATE Tower,1 Create Way, Singapore 138602, Singapore.
   [Zhou, Xiaochi; 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.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn, Cambridge CB3 0AS, England.
EM mk306@cam.ac.uk
FU CMCL Innovations; National Research Foundation (NRF), Prime Minister's
   Office, Singapore under its Campus for Research Excellence and
   Technological Enterprise (CREATE) programme; Alexander von Humboldt
   foundation
FX This project is supported by CMCL Innovations and the National Research
   Foundation (NRF), Prime Minister's Office, Singapore under its Campus
   for Research Excellence and Technological Enterprise (CREATE) programme.
   Markus Kraft acknowledges the support of the Alexander von Humboldt
   foundation.
CR [Anonymous], 2019, SOLIDITY DOCUMENTATI
   Buerger P, 2018, COMBUST FLAME, V187, P105, DOI 10.1016/j.combustflame.2017.08.013
   Buterin V., 2013, ETHEREUM NEXT GENERA
   Calvaresi D, 2018, 2018 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2018), P56, DOI 10.1109/WI.2018.0-107
   Carboni Davide, 2015, ABS150201504 CORR
   Cuccuru P, 2017, INT J LAW INFORM TEC, V25, P179, DOI 10.1093/ijlit/eax003
   Dennis R, 2015, INT CONF INTERNET, P131, DOI 10.1109/ICITST.2015.7412073
   Devanand A, 2019, COMPUT CHEM ENG, V125, P339, DOI 10.1016/j.compchemeng.2019.03.024
   Eibeck A, 2019, COMPUT CHEM ENG, V131, DOI 10.1016/j.compchemeng.2019.106586
   Farazi F, 2020, J CHEM INF MODEL, V60, P108, DOI 10.1021/acs.jcim.9b00960
   Fraga D., 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), P41, DOI 10.1109/TrustCom.2012.58
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Johnson III R.D., 2018, 101 NIST
   Josang A., 2007, FDN SECURITY ANAL DE, P209
   Klems M., 2017, P INT C SERV OR COMP, P731
   Koutrouli E, 2012, COMPUT SCI REV, V6, P47, DOI 10.1016/j.cosrev.2012.01.002
   Liu S., 2013, P 2013 INT C AUT AG, P821
   Macrinici D, 2018, TELEMAT INFORM, V35, P2337, DOI 10.1016/j.tele.2018.10.004
   Mol Instincts, 2019, MOL INSTINCTS
   Morbach J, 2009, COMPUT CHEM ENG, V33, P1546, DOI 10.1016/j.compchemeng.2009.01.019
   Nakamoto S., 2008, DECENTRALIZED BUS RE, P21260
   Resnick P, 2000, COMMUN ACM, V43, P45, DOI 10.1145/355112.355122
   Sikorski JJ, 2017, APPL ENERG, V195, P234, DOI 10.1016/j.apenergy.2017.03.039
   Sun Y, 2012, IEEE SIGNAL PROC MAG, V29, P87, DOI 10.1109/MSP.2011.942344
   Whitby A., 2004, FILTERING OUT UNFAIR
   Wood G., 2014, ETHEREUM SECURE DECE, V151
   Yang Y., 2009, P ACM SAC 2009, P1308, DOI DOI 10.1145/1529282.1529575
   Zhang Y, 2013, ELECTRON COMMER R A, V12, P299, DOI 10.1016/j.elerap.2012.11.005
   Zhou L, 2018, COMPUT CHEM ENG, V118, P49, DOI 10.1016/j.compchemeng.2018.07.010
   Zhou XC, 2019, COMPUT CHEM ENG, V130, DOI 10.1016/j.compchemeng.2019.106577
NR 30
TC 6
Z9 6
U1 7
U2 18
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 AUG 4
PY 2020
VL 139
AR 106896
DI 10.1016/j.compchemeng.2020.106896
PG 16
WC Computer Science, Interdisciplinary Applications; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA MU2ZR
UT WOS:000555543100011
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Lissandrini, M
   Mottin, D
   Velegrakis, Y
   Palpanas, T
AF Lissandrini, Matteo
   Mottin, Davide
   Velegrakis, Yannis
   Palpanas, Themis
TI X(2)Q: Your Personal Example-based Graph Explorer
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB Exploring knowledge graphs can be a daunting task for any user, expert or novice. This is due to the complexity of the schema or because they are unfamiliar with the contents of the data, or even because they do not know precisely what they are looking for. For the same reason there is a significant demand for exploratory methods for this kind of data. We propose X(2)Q, a system that facilitates the exploration of knowledge graphs with a hands-on approach. X(2)Q embodies the flexible multi-exemplar query paradigm, in which easy to express examples serve as the basis for formulating sophisticated, and hard to express queries. Our system helps building examples in an interactive fashion, by showing results of the partial exemplar query as well as suggestions for improving the current examples. Then, the user feedback is incorporated in our scores to filter the irrelevant suggestions upfront. X(2)Q returns answers in real-time on Freebase, one of the largest available knowledge graphs.
C1 [Lissandrini, Matteo; Velegrakis, Yannis] Univ Trento, Trento, Italy.
   [Mottin, Davide] Hasso Plattner Inst, Potsdam, Germany.
   [Palpanas, Themis] Paris Descartes Univ, Paris, France.
RP Lissandrini, M (通讯作者)，Univ Trento, Trento, Italy.
EM ml@disi.unitn.eu; davide.mottin@hpi.de; velgias@disi.unitn.eu;
   themis@mi.parisdescartes.fr
RI Lissandrini, Matteo/ABD-9472-2021
OI Lissandrini, Matteo/0000-0001-7922-5998; Mottin,
   Davide/0000-0001-8256-2258
CR Cao G., 2008, P 31 ANN INT ACM SIG, P243, DOI DOI 10.1145/1390334.1390377
   El-Roby A, 2016, PROC VLDB ENDOW, V9, P1481, DOI 10.14778/3007263.3007289
   Fionda V, 2017, PROC VLDB ENDOW, V10, P1913, DOI 10.14778/3137765.3137807
   Huang K, 2017, PROC VLDB ENDOW, V10, P1861, DOI 10.14778/3137765.3137794
   Jayaram N., 2015, TKDE, V27
   Jayaram N, 2015, PROC VLDB ENDOW, V8, P1941
   Lafferty John, 2017, ACM SIGIR Forum, V51, P251, DOI 10.1145/3130348.3130375
   Lissandrini Matteo, 2018, ICDE
   Mottin D, 2017, SIGMOD'17: PROCEEDINGS OF THE 2017 ACM INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1737, DOI 10.1145/3035918.3054778
   Mottin D, 2017, PROC VLDB ENDOW, V10, P1977, DOI 10.14778/3137765.3137824
   Mottin D, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P901, DOI 10.1145/2588555.2594529
   Mottin Davide, 2016, VLDB J, V2016, P1
   Pienta R, 2017, SIGMOD'17: PROCEEDINGS OF THE 2017 ACM INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1587, DOI 10.1145/3035918.3056418
NR 13
TC 3
Z9 3
U1 0
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 2018
VL 11
IS 12
BP 2026
EP 2029
DI 10.14778/3229863.3236251
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HD5CA
UT WOS:000452544100044
DA 2022-08-23
ER

PT J
AU Speck, R
   Ngomo, ACN
AF Speck, Rene
   Ngomo, Axel-Cyrille Ngonga
TI Leopard - A baseline approach to attribute prediction and validation for
   knowledge graph population
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Attribute prediction; Attribute validation; Knowledge graph population
AB In this paper, we report on the participation of Leopard to the Semantic Web Challenge at the 16th International Semantic Web Conference. Leopard is a baseline approach to predict and validate attributes for knowledge graph population. The approach was designed as a baseline for the challenge. It combines diverse text extraction methods with a simple precision ranking and utilizes sources from the multilingual Document Web as well as from the multilingual Data Web. Despite being designed to be a baseline, Leopard achieved the second-best score in both challenge tasks (53.42% F1-Score and 53.09% AUC) behind IBM's system Socrates (55.40% F1-Score and 68.01% AUC). Our approach is open source and can be found at https://github.com/dice-group/Leopard. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Speck, Rene] Univ Leipzig, DICE Grp, Leipzig, Germany.
   [Ngomo, Axel-Cyrille Ngonga] Paderborn Univ, DICE Grp, Paderborn, Germany.
RP Speck, R (通讯作者)，Univ Leipzig, DICE Grp, Leipzig, Germany.
EM speck@informatik.uni-leipzig.de; axel.ngonga@upb.de
FU H2020 project HOBBIT [688227]; BMWI, Germany projects GEISER
   [01MD16014E]; OPAL, Germany [19F2028A]; EuroStars projects DIESEL
   [01QE1512C]; QAMEL, Germany [01QE1549C]
FX This work has been supported by the H2020 project HOBBIT (no. 688227),
   the BMWI, Germany projects GEISER (no. 01MD16014E) and OPAL, Germany
   (no. 19F2028A), the EuroStars projects DIESEL (no. 01QE1512C) and QAMEL,
   Germany (no. 01QE1549C).
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Baron C., 2015, 24 WWW C
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Daiber J., 2013, P 9 INT C SEM SYST, P121, DOI 10.1145/2506182.2506198
   Furche T, 2013, VLDB J, V22, P47, DOI 10.1007/s00778-012-0286-6
   Gerber D., DEFACTO TEMPORAL MUL
   Gerber D., 2011, 1 WORKSH WEB SCAL KN
   Glass Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P256, DOI 10.1007/978-3-319-93417-4_17
   Glass M, 2018, LECT NOTES COMPUT SC, V11136, P38, DOI 10.1007/978-3-030-00671-6_3
   Jurafsky, 2009, P JOINT C 47 ANN M A, P1003
   Martinez-Rodriguez J., INFORM EXTRACTION ME
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Roder M., SEMANTIC WEB J
   Roth B., 2015, P 2015 TEXT AN C TAC, P16
   Shin J, 2015, PROC VLDB ENDOW, V8, P1310, DOI 10.14778/2809974.2809991
   Speck R., 2015, 14 INT SEM WEB C ISW
   Speck R., 2014, INT SEM WEB C 2014 I
   Speck R., 2017, K CAP 2017 KNOWL CAP, P4
   Speck R, 2014, LECT NOTES COMPUT SC, V8796, P519, DOI 10.1007/978-3-319-11964-9_33
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Wilkinson Mark D, 2016, Sci Data, V3, P160018, DOI 10.1038/sdata.2016.18
   Zaveri A., QUALITY ASSESSMENT L
NR 22
TC 0
Z9 0
U1 0
U2 8
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 MAR
PY 2019
VL 55
BP 102
EP 107
DI 10.1016/j.websem.2018.12.006
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HQ1OB
UT WOS:000462166300007
DA 2022-08-23
ER

PT J
AU Xu, Q
   Wang, X
   Li, JX
   Zhang, QP
   Chai, LL
AF Xu, Qiang
   Wang, Xin
   Li, Jianxin
   Zhang, Qingpeng
   Chai, Lele
TI Distributed Subgraph Matching on Big Knowledge Graphs Using Pregel
SO IEEE ACCESS
LA English
DT Article
DE Subgraph matching; distributed; knowledge graphs; RDF; Pregel
ID SPARQL
AB With RDF becoming the de facto standard for representing knowledge graphs, it is indispensable to develop scalable subgraph matching algorithms over big RDF graphs stored in distributed clusters. In this paper, we propose a novel distributed subgraph matching method SP-Tree, using the Pregel model, to answer subgraph matching queries on big RDF graphs. In our method, the query graph is transformed to a variant spanning tree based on the shortest paths. Two optimization techniques are proposed to improve the efficiency of our algorithms. One employs RDF shapes to filter out local computations and messages passed, the other postpones the Cartesian product operations in the matching process to reduce intermediate results. The extensive experiments on both synthetic and real-world datasets show that our SP-Tree subgraph matching method outperforms the state-of-the-art methods by an order of magnitude.
C1 [Xu, Qiang; Wang, Xin; Chai, Lele] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China.
   [Wang, Xin] Tianjin Key Lab Cognit Comp & Applicat, Tianjin 300350, Peoples R China.
   [Li, Jianxin] Deakin Univ, Sch Informat Technol, Burwood, Vic 3125, 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 300350, Peoples R China.; Wang, X (通讯作者)，Tianjin Key Lab Cognit Comp & Applicat, Tianjin 300350, Peoples R China.
EM wangx@tju.edu.cn
RI Zhang, Qingpeng/D-4682-2011
OI Zhang, Qingpeng/0000-0002-6819-0686; Li, Jianxin/0000-0002-9059-330X
FU National Natural Science Foundation of China [61572353]; National
   High-Tech Research and Development Program of China (863 Program)
   [2013AA013204]; Natural Science Foundation of Tianjin [17JCYBJC15400]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61572353, in part by the National
   High-Tech Research and Development Program of China (863 Program) under
   Grant 2013AA013204, and in part by the Natural Science Foundation of
   Tianjin under Grant 17JCYBJC15400.
CR Abdelaziz I, 2017, PROC VLDB ENDOW, V10, P2049, DOI 10.14778/3151106.3151109
   Afrati FN, 2013, PROC INT CONF DATA, P62, DOI 10.1109/ICDE.2013.6544814
   Boneva I, 2017, LECT NOTES COMPUT SC, V10587, P104, DOI 10.1007/978-3-319-68288-4_7
   Chandra A.K., 1977, P 9 ANN ACM S THEOR, P77
   Fan WF, 2017, PROC VLDB ENDOW, V10, P1889, DOI 10.14778/3137765.3137801
   Gonzalez J.E., 2012, 10 USENIX S OP SYST, V12, P17
   Gurajada S, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P289, DOI 10.1145/2588555.2610511
   Harbi R, 2016, VLDB J, V25, P355, DOI 10.1007/s00778-016-0420-y
   Husain MF, 2011, IEEE T KNOWL DATA EN, V23, P1312, DOI 10.1109/TKDE.2011.103
   Jose RS, 2014, SPRINGER PR COMPLEX, P599, DOI 10.1007/978-3-319-04379-1__99
   Knublauch H., 2017, W3C REC, V20
   Lai LB, 2016, PROC VLDB ENDOW, V10, P217
   Lai LB, 2015, PROC VLDB ENDOW, V8, P974, DOI 10.14778/2794367.2794368
   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]
   McCune RR, 2015, ACM COMPUT SURV, V48, DOI 10.1145/2818185
   Papailiou Nikolaos, 2013, 2013 IEEE International Conference on Big Data, P255, DOI 10.1109/BigData.2013.6691582
   Peng P, 2016, VLDB J, V25, P243, DOI 10.1007/s00778-015-0415-0
   Plantenga T, 2013, J PARALLEL DISTR COM, V73, P164, DOI 10.1016/j.jpdc.2012.10.005
   Prud'hommeaux E, 2014, P 10 INT C SEM SYST, P32, DOI DOI 10.1145/2660517.2660523
   Rohloff K, 2010, PROGRAMMING SUPPORT, P4
   Salihoglu S., 2013, P 25 INT C SCI STAT
   Sch_atzle A., 2015, VLDB WORKSH BIG GRAP, P155
   Schatzle A, 2016, PROC VLDB ENDOW, V9, P804, DOI 10.14778/2977797.2977806
   Shao B., 2013, P ACM SIGMOD INT C M, DOI DOI 10.1145/2463676.2467799
   Shao YX, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P625, DOI 10.1145/2588555.2588557
   Staworko S., 2015, P 18 INT C DAT THEOR, P195
   Sun Z, 2012, PROC VLDB ENDOW, V5, P788, DOI 10.14778/2311906.2311907
   VALIANT LG, 1990, COMMUN ACM, V33, P103, DOI 10.1145/79173.79181
NR 29
TC 5
Z9 5
U1 1
U2 6
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 116453
EP 116464
DI 10.1109/ACCESS.2019.2936465
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IV4HD
UT WOS:000484233300004
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, XM
   Zhang, X
   Luo, X
   Guo, HT
   Zhang, LQ
   Guo, JW
AF Zhang, Xuan-ming
   Zhang, Xuan
   Luo, Xu
   Guo, Hai-tao
   Zhang, Li-qun
   Guo, Ji-wei
TI Knowledge mapping visualization analysis of the military health and
   medicine papers published in the web of science over the past 10 years
SO MILITARY MEDICAL RESEARCH
LA English
DT Article
DE Military health; Military medicine; Knowledge mapping; CiteSpace
ID PREVALENCE; MANAGEMENT; EVOLUTION; COMBAT
AB Background: Military medicine is a research field that seeks to solve the medical problems that occur in modern war conditions based on public medicine theory.
   Methods: We explore the main research topics of military health and medical research in the web of science (TM) core collection (WoSCC) from 2007 to 2016, and the goal of this work is to serve as a reference for orientation and development in military health and medicine. Based on CiteSpace III, a reference co-citation analysis is performed for 7921 papers published in the WoSCC from 2007 to 2016. In addition, a cluster analysis of research topics is performed with a comprehensive analysis of high-yield authors, outstanding research institutions and their cooperative networks.
   Results: Currently, the research topics in military health and medicine mainly focus on the following seven aspects: mental health diagnoses and interventions, an army study to assess risk and resilience in service members (STARRS), large-scale military action, brain science, veterans, soldier parents and children of wartime, and wound infection. We also observed that the annual publication rate increased with time. Wessely S, Greenberg N, Fear NT, Smith TC, Smith B, Jones N, Ryan MAK, Boyko EJ, Hull L, and Rona RJ were the top 10 authors in military health and medicine research. The top 10 institutes were the Uniformed Services University of the Health Sciences, the United States Army, the United States Navy, Kings College London, Walter Reed National Military Medical Center, Boston University, Walter Reed Army Institute of Research, Walter Reed Army Medical Center, Naval Health Research Center, and the VA Boston Healthcare System.
   Conclusions: We are able to perform a comprehensive analysis of studies in military health and medicine research and summarize the current research climate and the developmental trends in the WoSCC. However, further studies and collaborations are needed worldwide. Overall, our findings provide valuable information and new perspectives and shape future research directions for further research in the area of military health and medicine.
C1 [Zhang, Li-qun] Third Mil Med Univ, Xinqiao Hosp, Dept Clin Lab, Chongqing 400037, Peoples R China.
   [Zhang, Xuan-ming; Zhang, Xuan; Luo, Xu; Guo, Hai-tao; Guo, Ji-wei] Third Mil Med Univ, Southwest Hosp, Chongqing 400038, Peoples R China.
RP Zhang, LQ (通讯作者)，Third Mil Med Univ, Xinqiao Hosp, Dept Clin Lab, Chongqing 400037, Peoples R China.; Guo, JW (通讯作者)，Third Mil Med Univ, Southwest Hosp, Chongqing 400038, Peoples R China.
EM 1434103777@qq.com; jiwei_guotmmu@163.com
FU PLA [CWS11L050]; China Academy of Engineering Physics [wss-2014-03]
FX This study was supported financially by the project of the "12th
   Five-Year" for medical scientific and technology (CWS11L050) of the PLA
   and the science and technology development fund of China Academy of
   Engineering Physics (wss-2014-03).
CR Balazs GC, 2014, MIL MED, V179, P1228, DOI 10.7205/MILMED-D-14-00156
   Cameron KL, 2014, CLIN SPORT MED, V33, P573, DOI 10.1016/j.csm.2014.06.004
   Cohen GH, 2015, EPIDEMIOL REV, V37, P7, DOI 10.1093/epirev/mxu007
   Emmons S, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0159161
   Fan W, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0147187
   Fink DS, 2016, J AFFECT DISORDERS, V202, P171, DOI 10.1016/j.jad.2016.05.025
   Fitzpatrick KF, 2010, MIL MED, V175, P13, DOI 10.7205/MILMED-D-10-00159
   Fu XB, 2014, MILITARY MED RES, V1, DOI 10.1186/2054-9369-1-2
   Garcia D, 2015, PEERJ, V3, DOI 10.7717/peerj.1380
   Hibberd JM, 2011, J OCCUP ENVIRON MED, V53, P352, DOI 10.1097/JOM.0b013e3182135973
   Hoggatt KJ, 2015, TRAFFIC INJ PREV, V16, P17, DOI 10.1080/15389588.2014.896994
   Imray CHE, 2015, LANCET, V386, P2520, DOI 10.1016/S0140-6736(15)01165-4
   Kelley ML, 2015, DRUG ALCOHOL DEPEN, V152, P201, DOI 10.1016/j.drugalcdep.2015.03.038
   Lancichinetti A, 2011, PHYS REV E, V84, DOI 10.1103/PhysRevE.84.066122
   Levy BS, 2009, ANNU REV PUBL HEALTH, V30, P123, DOI 10.1146/annurev.publhealth.031308.100147
   Linde AS, 2016, STUD HEALTH TECHNOL, V220, P209, DOI 10.3233/978-1-61499-625-5-209
   Mao Z, 2016, MILITARY MED RES, V3, DOI 10.1186/s40779-016-0101-6
   Nasioudis D, 2015, MILITARY MED RES, V2, DOI 10.1186/s40779-015-0058-x
   Painter K, 2014, INTEGR ENVIRON ASSES, V10, P37, DOI 10.1002/ieam.1486
   Rivera JC, 2014, MIL MED, V179, P133, DOI 10.7205/MILMED-D-13-00425
   Sheng Y, 2015, PHYS MED BIOL, V60, P7277, DOI 10.1088/0031-9155/60/18/7277
   Stadler JG, 2016, BIG DATA, V4, P129, DOI 10.1089/big.2015.0059
   Xiao X, 2016, EPIDEMICS-NETH, V17, P1, DOI 10.1016/j.epidem.2016.08.001
NR 23
TC 28
Z9 28
U1 4
U2 54
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 2095-7467
EI 2054-9369
J9 MILITARY MED RES
JI MILITARY MED. RES.
PD JUL 12
PY 2017
VL 4
BP 1
EP 10
AR 23
DI 10.1186/s40779-017-0131-8
PG 10
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA FB1RU
UT WOS:000405921900001
PM 28717517
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Deng, JF
   Wang, T
   Wang, ZW
   Zhou, JL
   Cheng, LL
AF Deng, Jianfeng
   Wang, Tao
   Wang, Zhuowei
   Zhou, Jiale
   Cheng, Lianglun
TI Research on Event Logic Knowledge Graph Construction Method of Robot
   Transmission System Fault Diagnosis
SO IEEE ACCESS
LA English
DT Article
DE Fault diagnosis; Robots; Ontologies; Feature extraction; Knowledge
   engineering; Service robots; Semantics; Event logic knowledge graph;
   fault diagnosis ontology; event argument knowledge extraction; stacked
   BiLSTM; self-attention; BiGCN
ID MACHINE-TOOLS; ONTOLOGY; MANAGEMENT; EXTRACTION; FRAMEWORK; BASE
AB Knowledge graph technology has important guiding significance for efficient and orderly fault diagnosis of robot transmission system. Taking the historical robot maintenance logs of robot transmission system as the research object, a top-down fault diagnosis event logic knowledge graph construction method is proposed. Firstly, we define event arguments of fault phenomenon and fault cause events, define event argument classes and relation between classes, and construct an event logic knowledge ontology model. According to the event logic knowledge ontology, the fault diagnosis event argument entity and relation in the corpus are labeled, and an event logic knowledge extraction dataset is formed. Secondly, an event argument entity and relation joint extraction model is proposed. Using stacked bidirectional long short-term memory(BiLSTM) to obtain deep context features of text. As a supplement to stacked BiLSTM, self-attention mechanism extracts character dependency features from multiple subspaces, and uses conditional random field(CRF) to realize entity recognition. The character dependency features are mapped to the entity label weight embedding, and spliced with deep context features to extract relations. Bidirectional graph convolutional network(BiGCN) is introduced for relation inference, graph convolution features are used to update deep context features to perform joint extraction in the second phase. Experimental results show that this method can improve the effect of event argument entity and relation joint extraction and is better than other methods. Finally, an event logic knowledge graph of robot transmission system fault diagnosis is constructed, which provides decision support for autonomous fault diagnosis of robot transmission system.
C1 [Deng, Jianfeng; Wang, Tao; Zhou, Jiale] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China.
   [Wang, Zhuowei; Cheng, Lianglun] Guangdong Univ Technol, Sch Comp, Guangzhou 510006, Peoples R China.
RP Wang, T (通讯作者)，Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China.
EM wangtao_cps@gdut.edu.cn
FU Key Program of NSFC-Guangdong [U1801263, U2001201, U1701262]; Natural
   Science Foundation of Guangdong Province [2020A1515010890,
   2020B1515120010]; Projects of Science and Technology Plan of Guangdong
   Province [2017B090901019, 2016B010127005]; Science and Technology Plan
   of Foshan City [1920001001367]; Guangdong Provincial Key Laboratory of
   Cyber-Physical System [2020B1212060069]
FX This work was supported in part by the Key Program of NSFC-Guangdong
   Joint under Grant U1801263, Grant U2001201, and Grant U1701262; in part
   by the Natural Science Foundation of Guangdong Province under Grant
   2020A1515010890 and Grant 2020B1515120010; in part by the Projects of
   Science and Technology Plan of Guangdong Province under Grant
   2017B090901019 and Grant 2016B010127005; in part by the Science and
   Technology Plan of Foshan City under Grant 1920001001367; and in part by
   the Guangdong Provincial Key Laboratory of Cyber-Physical System under
   Grant 2020B1212060069.
CR Abu-Salih B, 2021, J NETW COMPUT APPL, V185, DOI 10.1016/j.jnca.2021.103076
   Bekoulis G, 2018, EXPERT SYST APPL, V114, P34, DOI 10.1016/j.eswa.2018.07.032
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   [曹明宇 Cao Mingyu], 2019, [计算机研究与发展, Journal of Computer Research and Development], V56, P1432
   Castellano-Quero M, 2020, ADV INTELL SYST, V1093, P159, DOI 10.1007/978-3-030-36150-1_14
   Chen Lerui, 2019, Journal of Xi'an Jiaotong University, V53, P93, DOI 10.7652/xjtuxb201904014
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Cui Z., 2018, ARXIV180102143
   Develder C, 2018, P 2018 C EMP METH NA, P2830
   Ebisu T, 2020, IEEE T KNOWL DATA EN, V32, P941, DOI 10.1109/TKDE.2019.2893920
   Fang WT, 2015, I C INTELL COMPUT TE, P756, DOI 10.1109/ICICTA.2015.192
   Feng J, 2021, IEEE T IND ELECTRON, V68, P3454, DOI 10.1109/TIE.2020.2982085
   Fu TJ, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1409
   Gao Y, 2020, IEEE T KNOWL DATA EN, V32, P688, DOI 10.1109/TKDE.2019.2892430
   Geng DQ, 2020, IEEE INT CONF ELECTR, P220
   Guo KH, 2020, IEEE INT CONF BIG DA, P1322, DOI 10.1109/BigData50022.2020.9378471
   [郭榕 Guo Rong], 2021, [电网技术, Power System Technology], V45, P2092
   Guo S, 2017, IEEE T KNOWL DATA EN, V29, P884, DOI 10.1109/TKDE.2016.2638425
   Nguyen HL, 2019, FUTURE GENER COMP SY, V100, P10, DOI 10.1016/j.future.2019.05.016
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Hong Y, 2020, PROGNOST SYST HEALT, P176, DOI 10.1109/PHM-Besancon49106.2020.00036
   Hsu HK, 2021, MECHANIKA, V27, P70, DOI 10.5755/j02.mech.24401
   Hua Guan, 2017, 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), P1, DOI 10.1109/HealthCom.2017.8210821
   Huang Z., 2016, MECH FAULT DIAGNOSIS
   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
   Lai S, 2016, ARXIV161105962
   Li C., 2018, J ZHEJIANG U, V52, P6
   Li Z., 2018, P 27 INT JOINT C ART
   Liu H., 2020, COMPUT INTEGR MANUF, V26, P49
   Luo ZL, 2017, IEEE T KNOWL DATA EN, V29, P2125, DOI 10.1109/TKDE.2017.2720734
   Malik KM, 2020, EXPERT SYST APPL, V145, DOI 10.1016/j.eswa.2019.113120
   Martinez-Garcia JR, 2020, IET SOFTW, V14, P234, DOI 10.1049/iet-sen.2019.0272
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Miwa M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1105
   Nunez DL, 2018, ADV ENG INFORM, V38, P746, DOI 10.1016/j.aei.2018.10.006
   Qiang Z, 2017, ADV ENG INFORM, V32, P92, DOI 10.1016/j.aei.2017.01.002
   Ringsquandl M, 2017, IEEE INT CONF BIG DA, P1676, DOI 10.1109/BigData.2017.8258105
   Rospocher M, 2016, J WEB SEMANT, V37-38, P132, DOI 10.1016/j.websem.2015.12.004
   Ruan T, 2016, LECT NOTES COMPUT SC, V9982, P418, DOI 10.1007/978-3-319-46547-0_35
   Shi DQ, 2020, KNOWL-BASED SYST, V195, DOI 10.1016/j.knosys.2020.105618
   Shi X., 2013, MECH FAULT DIAGNOSIS
   [田佳来 Tian Jialai], 2021, [北京大学学报. 自然科学版, Acta Scientiarum Naturalium Universitatis Pekinensis], V57, P53
   Tsalapati E, 2021, EXPERT SYST APPL, V163, DOI 10.1016/j.eswa.2020.113550
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang JQ, 2020, KNOWL-BASED SYST, V203, DOI 10.1016/j.knosys.2020.106119
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2019, 2019 8TH INTERNATIONAL CONFERENCE ON SOFTWARE AND COMPUTER APPLICATIONS (ICSCA 2019), P419, DOI 10.1145/3316615.3316659
   Weipeng Huang, 2019, Natural Language Processing and Chinese Computing. 8th CCF International Conference, NLPCC 2019. Proceedings. Lecture Notes in Artificial Intelligence, Subseries of Lecture Notes in Computer Science (LNAI 11839), P713, DOI 10.1007/978-3-030-32236-6_65
   [吴世锴 Wu Shikai], 2020, [暨南大学学报. 自然科学与医学版, Journal of Jinan University. Natural Science & Medicine Edition], V41, P1
   Xu FX, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18030729
   Zeng XR, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P506
   [张栋豪 Zhang Donghao], 2021, [机械工程学报, Journal of Mechanical Engineering], V57, P90
   Zhang W., 2020, SCI SINICA INFORM, V50, P988
   Zhang Y., 2019, MACH TOOL HYDRAUL, V47, P181
   Zhang ZW, 2020, J DIGIT IMAGING, V33, P826, DOI 10.1007/s10278-020-00322-4
   [赵威 Zhao Wei], 2020, [机器人, Robot], V42, P460
   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
   Zhong WM, 2019, ENGINEERING, V5, P1041, DOI 10.1016/j.eng.2019.09.004
   Zhou JH, 2019, IEEE ACCESS, V7, P38856, DOI 10.1109/ACCESS.2019.2905048
   Zhou L, 2018, COMPUT CHEM ENG, V118, P49, DOI 10.1016/j.compchemeng.2018.07.010
   Zhou Q, 2019, J INTELL MANUF, V30, P1693, DOI 10.1007/s10845-017-1351-1
   Zhou Q, 2018, INT J ADV MANUF TECH, V95, P937, DOI 10.1007/s00170-017-1268-z
   Zhou Y, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P729, DOI 10.1145/3308560.3317704
NR 63
TC 0
Z9 0
U1 37
U2 37
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 17656
EP 17673
DI 10.1109/ACCESS.2022.3150409
PG 18
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ZC9GS
UT WOS:000757819500001
OA gold
DA 2022-08-23
ER

PT J
AU Verborgh, R
   Vander Sande, M
   Hartig, O
   Van Herwegen, J
   De Vocht, L
   De Meester, B
   Haesendonck, G
   Colpaert, P
AF Verborgh, Ruben
   Vander Sande, Miel
   Hartig, Olaf
   Van Herwegen, Joachim
   De Vocht, Laurens
   De Meester, Ben
   Haesendonck, Gerald
   Colpaert, Pieter
TI Triple Pattern Fragments: A low-cost knowledge graph interface for the
   Web
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Linked Data; Linked Data Fragments; Querying; SPARQL
ID ARCHITECTURE
AB Billions of Linked Data triples exist in thousands of RDF knowledge graphs on the Web, but few of those graphs can be queried live from Web applications. Only a limited number of knowledge graphs are available in a queryable interface, and existing interfaces can be expensive to host at high availability. To mitigate this shortage of live queryable Linked Data, we designed a low-cost Triple Pattern Fragments interface for servers, and a client-side algorithm that evaluates SPARQL queries against this interface. This article describes the Linked Data Fragments framework to analyze Web interfaces to Linked Data and uses this framework as a basis to define Triple Pattern Fragments. We describe client-side querying for single knowledge graphs and federations thereof. Our evaluation verifies that this technique reduces server load and increases caching effectiveness, which leads to lower costs to maintain high server availability. These benefits come at the expense of increased bandwidth and slower, but more stable query execution times. These results substantiate the claim that lightweight interfaces can lower the cost for knowledge publishers compared to more expressive endpoints, while enabling applications to query the publishers' data with the necessary reliability. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Verborgh, Ruben; Vander Sande, Miel; Van Herwegen, Joachim; De Vocht, Laurens; De Meester, Ben; Haesendonck, Gerald; Colpaert, Pieter] Univ Ghent, iMinds, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium.
   [Hartig, Olaf] Univ Potsdam, Hasso Plattner Inst, Prof Dr Helmert Str 2-3, D-14482 Potsdam, Germany.
RP Verborgh, R (通讯作者)，Univ Ghent, iMinds, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium.
EM ruben.verborgh@ugent.be
RI De Meester, Ben/AAR-4511-2020; Verborgh, Ruben/P-6571-2014
OI De Meester, Ben/0000-0003-0248-0987; Verborgh,
   Ruben/0000-0002-8596-222X; Hartig, Olaf/0000-0002-1741-2090; Colpaert,
   Pieter/0000-0001-6917-2167
FU Ghent University, iMinds; Institute for the Promotion of Innovation by
   Science and Technology in Flanders (IWT); European Union
FX The research activities in this article were funded by Ghent University,
   iMinds, the Institute for the Promotion of Innovation by Science and
   Technology in Flanders (IWT), and the European Union. Ruben Verborgh is
   a postdoctoral fellow of the Research Foundation Flanders.
CR Acosta M, 2015, LECT NOTES COMPUT SC, V9366, P111, DOI 10.1007/978-3-319-25007-6_7
   Acosta M, 2011, LECT NOTES COMPUT SC, V7031, P18, DOI 10.1007/978-3-642-25073-6_2
   Aluc G, 2014, LECT NOTES COMPUT SC, V8796, P197, DOI 10.1007/978-3-319-11964-9_13
   Amundsen M, 2011, REST: FROM RESEARCH TO PRACTICE, P93, DOI 10.1007/978-1-4419-8303-9_4
   Angles R, 2008, LECT NOTES COMPUT SC, V5318, P114, DOI 10.1007/978-3-540-88564-1_8
   Barcelo P., 2013, P 32 S PRINC DAT SYS
   Beckett D., 2014, RDF 1 1 N TRIPLES RE
   Beckett D., 2014, RDF 1 1 TURTLE RECOM
   Beek W, 2014, LECT NOTES COMPUT SC, V8796, P213, DOI 10.1007/978-3-319-11964-9_14
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Bizer C., 2014, RDF 1 1 TRIG RECOMME
   Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901
   Broekstra J., 2011, BINARY RDF IN SESAME
   Buil-Aranda C., 2013, P 12 INT SEM WEB C
   Card S. K., 1991, Human Factors in Computing Systems. Reaching Through Technology. CHI '91. Conference Proceedings, P181
   Carothers G., 2014, RDF 1 1 N QUADS RECO
   Castillo S., 2015, FED DSATUR DECOMPOSI
   Cyganiak R., 2014, W3C RECOMMENDATION
   Erling O., 2014, SEMANTICS 2014 3
   Erling O, 2010, SEMANTIC WEB INFORMATION MANAGEMENT, P501, DOI 10.1007/978-3-642-04329-1_21
   Ermilov I, 2013, COMM COM INF SC, V394, P242
   Feigenbaum L., 2013, SPARQL 1 1 PROTOCOL
   Fernandez JD, 2014, LECT NOTES COMPUT SC, V8797, P244, DOI 10.1007/978-3-319-11915-1_16
   Fernandez JD, 2013, J WEB SEMANT, V19, P22, DOI 10.1016/j.websem.2013.01.002
   Fielding R.T., 2008, REST APIS MUST BE HY
   Fielding R.T., 2000, THESIS U CALIFORNIA, V7
   Gorlitz O., 2011, P 2 INT WORKSH CONS
   GRAEFE G, 1993, COMPUT SURV, V25, P73, DOI 10.1145/152610.152611
   Grobe M., 2009, P 37 ANN ACM SIGUCCS
   Harris S., 2013, SPARQL 1 1 QUERY LAN
   Hartig Olaf, 2013, Datenbank-Spektrum, V13, P89, DOI 10.1007/s13222-013-0122-1
   Hartig O., 2011, P 4 WORKSH LINK DAT
   Hartig O., 2010, P 3 INT PROV ANN WOR
   Hartig O., 2012, P 9 EXT SEM WEB C
   Hartig O, 2009, LECT NOTES COMPUT SC, V5823, P293, DOI 10.1007/978-3-642-04930-9_19
   Hellerstein JM, 2007, FOUND TRENDS DATABAS, V1, P141, DOI 10.1561/1900000002
   Hose K., 2012, P 4 INT WORKSH SEM W, P2
   Lanthaler M., 2013, LDOW, V996
   Matteis L., 2014, P ISWC DEV WORKSH
   Matteis L., 2013, RESTPARK MINIMAL RES
   Miller R. B., 1968, AFIPS 68 1, P267, DOI [10.1145/1476589.1476628, DOI 10.1145/1476589.1476628]
   Montoya Gabriela, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P313, DOI 10.1007/978-3-642-35173-0_21
   Montoya G., 2012, P 3 INT WORKSH CONS
   Morsey M., 2011, P 9 INT SEM WEB C
   Ogbuji C., 2013, SPARQL 1 1 GRAPH STO
   Ozsu MT, 2011, PRINCIPLES OF DISTRIBUTED DATABASE SYSTEMS, THIRD EDITION, P1, DOI 10.1007/978-1-4419-8834-8_1
   Perez J, 2009, ACM T DATABASE SYST, V34, DOI 10.1145/1567274.1567278
   Rietveld L., 2015, P 12 EXT SEM WEB C
   Saleem M., 2014, LNCS, P176, DOI DOI 10.1007/978-3-319-07443-6
   Saleem M., 2016, SEMANT WEB IN PRESS
   Schmachtenberg M., 2014, LECT NOTES COMPUTER, V8796, P180
   Schmidt M, 2011, LECT NOTES COMPUT SC, V7031, P585, DOI 10.1007/978-3-642-25073-6_37
   Schmidt Michael, 2010, P 13 INT C DAT THEOR, P4, DOI DOI 10.1145/1804669.1804675
   Schwarte A, 2011, LECT NOTES COMPUT SC, V7031, P601, DOI 10.1007/978-3-642-25073-6_38
   Seaborne A., RDF BINARY USING APA
   SHETH AP, 1990, COMPUT SURV, V22, P183
   SMITH JM, 1975, COMMUN ACM, V18, P568, DOI 10.1145/361020.361025
   Speicher S., 2015, LINKED DATA PLATFORM
   Sporny M., 2014, JSON LD 1 0 RECOMMEN
   Umbrich J, 2011, WORLD WIDE WEB, V14, P495, DOI 10.1007/s11280-010-0107-z
   Van Herwegen J., 2015, P 14 INT SEM WEB C
   Van Herwegen J., 2015, P 12 EXT SEM WEB C
   Vander Sande M., 2015, P 11 INT C SEM SYST
   Vander Sande M., 2015, P 14 INT SEM WEB C
   Verborgh R., 2015, P 12 EXT SEM WEB C S
   Verborgh R., 2015, P 6 INT WORKSH MOD S
   Verborgh R., 2014, P 7 WORKSH LINK DAT
   Verborgh R., 2014, THESIS GHENT U GHENT
   Verborgh R., LINKED DATA FRAGMENT
   Verborgh R, 2014, LECT NOTES COMPUT SC, V8796, P180, DOI 10.1007/978-3-319-11964-9_12
   WIEDERHOLD G, 1992, COMPUTER, V25, P38, DOI 10.1109/2.121508
   Wilde E., 2009, P 4 WORKSH EM WEB SE, P39
   Yao S. B., 1979, ACM Transactions on Database Systems, V4, P133, DOI 10.1145/320071.320072
NR 73
TC 95
Z9 95
U1 1
U2 53
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 MAR
PY 2016
VL 37-38
BP 184
EP 206
DI 10.1016/j.websem.2016.03.003
PG 23
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DM6JK
UT WOS:000376457600012
OA Green Published
DA 2022-08-23
ER

PT J
AU Song, YJ
   Ma, P
   Gao, Y
   Xiao, PG
   Xu, LJ
   Liu, HB
AF Song, Yanjun
   Ma, Pei
   Gao, Yu
   Xiao, Peigen
   Xu, Lijia
   Liu, Haibo
TI A Bibliometrics Analysis of Metformin Development From 1980 to 2019
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Review
DE metformin; bibliometrics analysis; development process; research
   frontier; Web of Science
ID ACTIVATED PROTEIN-KINASE; TYPE-2; CANCER; ROSIGLITAZONE; MECHANISMS;
   INHIBITOR; TRIAL; RISK
AB Metformin, the first-line oral blood glucose-lowering agent to manage type 2 diabetes, has gained growing popularity on both clinical application and basic research since early 1980s. A thorough and systematic knowledge map of metformin is pertinent to evaluate the research frontier and determine knowledge gaps. To this end, 20, 526 publications were analyzed by bibliometrics and data visualization to demonstrate the current global research status, potential hotspots, and perspectives on future research directions. In addition, the metformin development along the historical line was illustrated over the last 40 years. In sum, this study provides a comprehensive analysis that delineates the evolution of the historical milestones of metformin development, and we discuss the future research directions based on objective data analysis from a wide spectrum of metformin research areas.
C1 [Song, Yanjun; Ma, Pei; Gao, Yu; Xiao, Peigen; Xu, Lijia; Liu, Haibo] Chinese Acad Med Sci, Peking Union Med Coll, Inst Med Plant Dev, Beijing, Peoples R China.
RP Xu, LJ; Liu, HB (通讯作者)，Chinese Acad Med Sci, Peking Union Med Coll, Inst Med Plant Dev, Beijing, Peoples R China.
EM ljxu@implad.ac.cn; hbliu@implad.ac.cn
OI Song, Yanjun/0000-0001-6678-7721
FU National Natural Science Foundation of China [81573576]; CAMS Innovation
   Fund for Medical Sciences [CIFMS 2016-I2M-1-012]; Fundamental Research
   Funds for the Central Universities [3332020102]
FX This research was funded by the National Natural Science Foundation of
   China (Grant 81573576), the CAMS Innovation Fund for Medical Sciences
   (CIFMS 2016-I2M-1-012), and the Fundamental Research Funds for the
   Central Universities (Grant 3332020102).
CR Aljofan M, 2019, FUTUR SCI OA, V5, DOI 10.2144/fsoa-2019-0053
   [Anonymous], 2014, DIABETES RES CLIN PR, V104, P1, DOI 10.1016/j.diabres.2012.10.001
   Bailey CJ, 2017, DIABETOLOGIA, V60, P1566, DOI 10.1007/s00125-017-4318-z
   Bonnet F, 2017, DIABETES OBES METAB, V19, P473, DOI 10.1111/dom.12854
   Bouchoucha M, 2011, DIABETES METAB, V37, P90, DOI 10.1016/j.diabet.2010.11.002
   Buse JB, 2009, LANCET, V374, P39, DOI 10.1016/S0140-6736(09)60659-0
   Castillo-Quan JI, 2016, CELL, V167, P1670, DOI 10.1016/j.cell.2016.11.058
   Charbonnel B, 2006, DIABETES CARE, V29, P2638, DOI 10.2337/dc06-0706
   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 CM, 2003, INFOVIS 2002: IEEE SYMPOSIUM ON INFORMATION VISUALIZATION 2003, PROCEEDINGS, P67, DOI 10.1109/INFVIS.2003.1249010
   Coll AP, 2020, NATURE, V578, P444, DOI 10.1038/s41586-019-1911-y
   DeCensi A, 2010, CANCER PREV RES, V3, P1451, DOI 10.1158/1940-6207.CAPR-10-0157
   DeFronzo RA, 2005, DIABETES CARE, V28, P1092, DOI 10.2337/diacare.28.5.1092
   Dowling RJO, 2007, CANCER RES, V67, P10804, DOI 10.1158/0008-5472.CAN-07-2310
   Drucker DJ, 2006, LANCET, V368, P1696, DOI 10.1016/S0140-6736(06)69705-5
   Foretz M, 2019, NAT REV ENDOCRINOL, V15, P569, DOI 10.1038/s41574-019-0242-2
   Foretz M, 2014, CELL METAB, V20, P953, DOI 10.1016/j.cmet.2014.09.018
   Forslund K, 2015, NATURE, V528, P262, DOI 10.1038/nature15766
   Fryer LGD, 2002, J BIOL CHEM, V277, P25226, DOI 10.1074/jbc.M202489200
   Garber A, 2009, LANCET, V373, P473, DOI 10.1016/S0140-6736(08)61246-5
   Garfield E, 1999, CAN MED ASSOC J, V161, P979
   GARFIELD E, 1987, SCIENTIST, V1, P9
   Graham GG, 2011, CLIN PHARMACOKINET, V50, P81, DOI 10.2165/11534750-000000000-00000
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Holman RR, 2008, NEW ENGL J MED, V359, P1577, DOI 10.1056/NEJMoa0806470
   Kahn SE, 2006, NEW ENGL J MED, V355, P2427, DOI 10.1056/NEJMoa066224
   Kalender A, 2010, CELL METAB, V11, P390, DOI 10.1016/j.cmet.2010.03.014
   Knowler WC, 2002, NEW ENGL J MED, V346, P393, DOI 10.1056/NEJMoa012512
   Kulkarni AS, 2020, CELL METAB, V32, P15, DOI 10.1016/j.cmet.2020.04.001
   Lin HL, 2019, ENVIRON SCI POLLUT R, V26, P17666, DOI 10.1007/s11356-019-05149-3
   Madiraju AK, 2014, NATURE, V510, P542, DOI 10.1038/nature13270
   McCreight LJ, 2016, DIABETOLOGIA, V59, P426, DOI 10.1007/s00125-015-3844-9
   Moghetti P, 2000, J CLIN ENDOCR METAB, V85, P139, DOI 10.1210/jc.85.1.139
   Musi N, 2002, DIABETES, V51, P2074, DOI 10.2337/diabetes.51.7.2074
   NATTRASS M, 1978, DIABETOLOGIA, V14, P71, DOI 10.1007/BF01263443
   Nissen SE, 2007, NEW ENGL J MED, V356, P2457, DOI 10.1056/NEJMoa072761
   PRITCHARD A, 1969, J DOC, V25, P348
   Pryor R, 2019, CELL, V178, P1299, DOI 10.1016/j.cell.2019.08.003
   Rena G, 2017, DIABETOLOGIA, V60, P1577, DOI 10.1007/s00125-017-4342-z
   Sanchez-Rangel E, 2017, DIABETOLOGIA, V60, P1586, DOI 10.1007/s00125-017-4336-x
   Scheen AJ, 2020, DIABETES METAB, V46, P423, DOI 10.1016/j.diabet.2020.07.006
   Shaw RJ, 2005, SCIENCE, V310, P1642, DOI 10.1126/science.1120781
   Shu Y, 2007, J CLIN INVEST, V117, P1422, DOI 10.1172/JCI30558
   Sivalingam Vanitha, 2015, Lancet, V385 Suppl 1, pS90, DOI 10.1016/S0140-6736(15)60405-6
   Thompson DF, 2015, PHARMACOTHERAPY, V35, P551, DOI 10.1002/phar.1586
   Turner RC, 1998, LANCET, V352, P854, DOI 10.1016/s0140-6736(98)07037-8
   World Health Organization, 2019, WHO MODEL LIST ESSEN, P60
   Zakikhani M, 2006, CANCER RES, V66, P10269, DOI 10.1158/0008-5472.CAN-06-1500
   Zilov AV, 2019, DIABETES-METAB RES, V35, DOI 10.1002/dmrr.3173
   Zinman B, 2015, NEW ENGL J MED, V373, P2117, DOI 10.1056/NEJMoa1504720
NR 51
TC 2
Z9 2
U1 25
U2 44
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 APR 28
PY 2021
VL 12
AR 645810
DI 10.3389/fphar.2021.645810
PG 14
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA SA5HX
UT WOS:000649334700001
PM 33995056
OA Green Published, gold
DA 2022-08-23
ER

PT S
AU Novak, J
   Wurst, M
AF Novak, J
   Wurst, M
BE Tergan, SO
   Keller, T
TI Collaborative knowledge visualization for cross-community learning
SO KNOWLEDGE AND INFORMATION VISUALIZATION: SEARCHING FOR SYNERGIES
SE Lecture Notes in Computer Science
LA English
DT Article; Proceedings Paper
CT International Workshop on Visual Artefacts for the Organization of
   Information and Knowledge, Searching for Synergies
CY MAY, 2004
CL Knowledge Media Res Ctr, Tubingen, GERMANY
HO Knowledge Media Res Ctr
AB Knowledge exchange between heterogeneous communities of practice has been recognized as the critical source of innovation and creation of new knowledge. This paper considers the problem of enabling such cross community knowledge exchange through knowledge visualization. We discuss the social nature of knowledge construction and describe main requirements for practical solutions to the given problem, as well as existing approaches. Based on this analysis, we propose a model for collaborative elicitation and visualization of community knowledge perspectives based on the construction of personalised learning knowledge maps and shared concept networks that incorporate implicit knowledge and personal views of individual users. We show how this model supports explicit and implicit exchange of knowledge between the members of different communities and present its prototypical realization in the Knowledge Explorer, an interactive tool for collaborative visualization and cross-community sharing of knowledge. Concrete application scenarios and evaluation experiences are discussed on the example of the Internet platform netzspannung.org.
C1 Fraunhofer IMK MARS, Schloss Birlinghoven, D-53754 St Augustin, Germany.
   Univ Dortmund, FB 4, D-44221 Dortmund, Germany.
RP Novak, J (通讯作者)，Fraunhofer IMK MARS, Schloss Birlinghoven, D-53754 St Augustin, Germany.
EM j.novak@imk.fraunhofer.de; worst@kimo.cs.uni-dortmund.de
CR Baldonado MQ Wang, 2000, P WORK C ADV VIS INT, P110, DOI DOI 10.1145/345513.345271
   BOLAND RJ, 1995, ORGAN SCI, V6, P350, DOI 10.1287/orsc.6.4.350
   BROWN LA, 1991, CYTOPATHOLOGY, V2, P1, DOI 10.1111/j.1365-2303.1991.tb00377.x
   Card S., 1995, P SIGCHI C HUM FACT, P51, DOI DOI 10.1145/223904.223911
   Chen CM, 1999, INFORM PROCESS MANAG, V35, P401, DOI 10.1016/S0306-4573(98)00068-5
   DOUGHERTY D, 1992, ORGAN SCI, V3, P179, DOI 10.1287/orsc.3.2.179
   FAYYAD UM, 1996, ADV KNOWLEDGE DISCOV, P1, DOI DOI 10.1609/AIMAG.V17I3.1230
   Papert S., 1991, CONSTRUCTIONISM, P1, DOI DOI 10.1111/1467-9752.00269
   Resnick P., 1994, P 1994 ACM C COMP SU
   SACK W, 2001, J MANAGE INFORM SYST, V17, P73
   Shardanand U, 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931
   Shaw MLG, 1995, PROCEEDINGS OF CSCL '95 - THE FIRST INTERNATIONAL CONFERENCE ON COMPUTER SUPPORT FOR COLLABORATIVE LEARNING, P300
   [No title captured]
   [No title captured]
   [No title captured]
   [No title captured]
   [No title captured]
NR 18
TC 11
Z9 12
U1 0
U2 6
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 3-540-26921-5
J9 LECT NOTES COMPUT SC
PY 2005
VL 3426
BP 95
EP 116
PG 22
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BCR01
UT WOS:000230860800006
DA 2022-08-23
ER

PT J
AU Yani, M
   Krisnadhi, AA
   Budi, I
AF Yani, Mohammad
   Krisnadhi, Adila Alfa
   Budi, Indra
TI A better entity detection of question for knowledge graph question
   answering through extracting position-based patterns
SO JOURNAL OF BIG DATA
LA English
DT Article
DE Entity detection; Entity recognition; Knowledge graph question
   answering; Complex question; Question pattern
ID CHALLENGES
AB Entity detection task on knowledge graph question answering systems has been studied well on simple questions. However, the task is still challenging on complex questions. It is due to a complex question is composed of more than one fact or triple. This paper proposes a method to detect entities and their position on triples mentioned in a question. Unlike existing approaches that only focus on detecting the entity name, our method can determine in which triple an entity is located. Furthermore, our approach can also define if an entity is a head or a tail of a triple mentioned in a question. We tested our approach to SimpleQuestions, LC-QuAD 2.0, and QALD series benchmarks. The experiment result demonstrates that our model outperforms the previous works on SimpleQuestions and QALD series datasets. 99.15% accuracy and 96.15% accuracy on average, respectively. Our model can also improve entity detection performance on LC-QuAD 2.0 with a merged dataset, namely, 97.4% accuracy. This paper also presents Wikidata QALD series version that is helpful for researchers to assess the knowledge graph question answering system they develop.
C1 [Yani, Mohammad; Krisnadhi, Adila Alfa; Budi, Indra] Univ Indonesia, Fac Comp Sci, Depok, Indonesia.
   [Yani, Mohammad] Politekn Negeri Indramayu, Informat Dept, Indramayu, Indonesia.
RP Krisnadhi, AA (通讯作者)，Univ Indonesia, Fac Comp Sci, Depok, Indonesia.
EM adila@cs.ui.ac.id
FU Faculty of Computer Science of Universitas Indonesia
FX Supported by Faculty of Computer Science of Universitas Indonesia.
CR Azmy M, 2018, P 27 INT C COMP LING, P2093
   Bakhshi M, 2022, KNOWL-BASED SYST, V235, DOI 10.1016/j.knosys.2021.107626
   Bakhshi M, 2020, EXPERT SYST APPL, V146, DOI 10.1016/j.eswa.2020.113205
   Bordes Antoine, 2015, ARXIV150602075
   Cabrio E, 2013, CEUR WORKSHOP PROC, V1179
   Chao ZH, 2018, 2018 5TH INTERNATIONAL CONFERENCE ON BEHAVIORAL, ECONOMIC, AND SOCIO-CULTURAL COMPUTING (BESC), P109, DOI [10.1109/BESC.2018.00030, 10.1109/BESC.2018.8697305]
   Cui H, 2021, KNOWL INF SYST, V63, P2741, DOI 10.1007/s10115-021-01609-w
   Cyganiak R., 2014, W3C RECOMMENDATION
   Dai ZH, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P800
   Deng D, 2015, VLDB J, V24, P143, DOI 10.1007/s00778-014-0367-9
   Diefenbach D, 2017, CEUR WORKSHOP PROC, V1963
   Dubey M, 2019, LECT NOTES COMPUT SC, V11779, P69, DOI 10.1007/978-3-030-30796-7_5
   He X., 2016, 2016 C EMP METH NAT, P1598
   Hoffner K, 2017, SEMANT WEB, V8, P895, DOI 10.3233/SW-160247
   Hu S., 2018, CHIN CONTR DEC C IEE
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Jurafsky D., 2009, SPEECH LANGUAGE PROC, DOI [10.1162/089120100750105975, DOI 10.1162/089120100750105975]
   Kim J, 2018, JOINT P 4 WORKSHOP S, V2241, P51
   Kuo CY., 2021, BILSTM CRF ENTITY TY, P161, DOI [10.1109/IAICT52856.2021.9532562, DOI 10.1109/IAICT52856.2021.9532562]
   Lukovnikov D, 2019, LECT NOTES COMPUT SC, V11778, P470, DOI 10.1007/978-3-030-30793-6_27
   Marivate Vukosi, 2020, Machine Learning and Knowledge Extraction. 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9. International Cross-Domain Conference, CD-MAKE 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12279), P385, DOI 10.1007/978-3-030-57321-8_21
   Sakor A, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P3141, DOI 10.1145/3340531.3412777
   Song SL, 2019, CLUSTER COMPUT, V22, P847, DOI 10.1007/s10586-017-1332-3
   Steinmetz N, 2021, J DATA SEMANT, V10, P241, DOI 10.1007/s13740-021-00128-9
   Ture F, 2017, P 2017 C EMP METH NA, P2866, DOI [10.18653/v1/d17-1307, DOI 10.18653/V1/D17-1307]
   Unger C., 2011, QUESTION ANSWERING L, P1
   Unger C., 2016, SEMANTIC WEB EVALUAT, V641, P171, DOI [10.1007/978-3-319-46565-4_13, DOI 10.1007/978-3-319-46565-4_13]
   Unger C, 2012, QALD 2 OPEN CHALLENG
   Unger C., 2015, CEUR WORKSHOP PROC
   Unger C., 2014, CEUR WORKSHOP P, P1172
   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, 2018, JOINT P 4 WORKSHOP S, V2241, P58
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Wolf T., 2020, P 2020 C EMP METH NA, P38
   Yani M, 2021, INFORMATION, V12, DOI 10.3390/info12070271
   Yu Arkhipov M, 2020, INT C COMP LING INT
   Zou L, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P313, DOI 10.1145/2588555.2610525
NR 38
TC 0
Z9 0
U1 1
U2 1
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 JUN 17
PY 2022
VL 9
IS 1
AR 80
DI 10.1186/s40537-022-00631-1
PG 26
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2E9CV
UT WOS:000812519500001
OA gold
DA 2022-08-23
ER

PT J
AU Kim, H
   Choi, J
   Park, S
   Jung, Y
AF Kim, Hyuntae
   Choi, Jongyun
   Park, Soyoung
   Jung, Yuchul
TI Layout Aware Semantic Element Extraction for Sustainable Science &
   Technology Decision Support
SO SUSTAINABILITY
LA English
DT Article
DE multi-modal; document layout analysis; metadata; document structure;
   document object; semantic elements; knowledge graph; transformer;
   decision support
AB New scientific and technological (S&T) knowledge is being introduced rapidly, and hence, analysis efforts to understand and analyze new published S&T documents are increasing daily. Automated text mining and vision recognition techniques alleviate the burden somewhat, but the various document layout formats and knowledge content granularities across the S&T field make it challenging. Therefore, this paper proposes LA-SEE (LAME and Vi-SEE), a knowledge graph construction framework that simultaneously extracts meta-information and useful image objects from S&T documents in various layout formats. We adopt Layout-aware Metadata Extraction (LAME), which can accurately extract metadata from various layout formats, and implement a transformer-based instance segmentation (i.e., Vision based Semantic Elements Extraction (Vi-SEE)) to maximize the vision-based semantic element recognition. Moreover, to constructing a scientific knowledge graph consisting of multiple S&T documents, we newly defined an extensible Semantic Elements Knowledge Graph (SEKG) structure. For now, we succeeded in extracting about 6 million semantic elements from 49,649 PDFs. In addition, to illustrate the potential power of our SEKG, we provide two promising application scenarios, such as a scientific knowledge guide across multiple S&T documents and questions and answering over scientific tables.
C1 [Kim, Hyuntae; Choi, Jongyun; Park, Soyoung; Jung, Yuchul] Kumoh Natl Inst Technol, Dept Comp Engn, Gumi 39177, South Korea.
RP Jung, Y (通讯作者)，Kumoh Natl Inst Technol, Dept Comp Engn, Gumi 39177, South Korea.
EM jyc@kumoh.ac.kr; jyc@kumoh.ac.kr; jyc@kumoh.ac.kr; jyc@kumoh.ac.kr
OI Jung, Yuchul/0000-0002-8871-1979
FU Korea Institute of Science and Technology Information (KISTI) through
   Construction on Science & Technology Content Curation Program
   [K20-L01-C01]; National Research Foundation of Korea (NRF) - Korean
   Government (MSIT) [NRF-2018R1C1B5031408]; Ministry of Science and ICT;
   NIPA
FX This work was funded by the Korea Institute of Science and Technology
   Information (KISTI) through Construction on Science & Technology Content
   Curation Program (K20-L01-C01) and the National Research Foundation of
   Korea (NRF) under a grant funded by the Korean Government (MSIT) (No.
   NRF-2018R1C1B5031408). In addition, this research is the result of a
   study on the HPC Support project supported by the Ministry of Science
   and ICT and NIPA.
CR Adhikari A., 2019, ARXIV190408398
   Ahmed MW, 2020, IEEE ACCESS, V8, P99458, DOI 10.1109/ACCESS.2020.2997907
   [Anonymous], KOELECTRA PRETRAINED
   [Anonymous], KNOWLEDGE GRAPH
   [Anonymous], DETR GITHUB
   [Anonymous], PDFMINER PYTHON PDF
   [Anonymous], CO CODATASET DETECTI
   Augenstein I, 2017, P 11 INT WORKSH SEM, P546, DOI DOI 10.18653/V1/S17-2091
   Beltagy Iz, 2019, ARXIV190310676, DOI 10.48550/ARXIV.1903.10676
   Carion Nicolas, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12346), P213, DOI 10.1007/978-3-030-58452-8_13
   Chen W., 2021, P INT C LEARN REPR V
   Choi J., 2021, ARXIV211212353
   Constantin A., 2013, P 2013 ACM S DOC ENG, P177, DOI [10.1145/2494266.2494271, 10. 1145/2494266. 2494271., DOI 10.1145/2494266.2494271]
   Devlin J., 2019, BERT PRETRAINING DEE, DOI [10.18653/v1/N19-1423, DOI 10.18653/V1/N19-1423]
   Duan N., 2020, P 58 ANN M ASS COMP, P6708, DOI DOI 10.18653/V1/2020.ACL-MAIN.599
   Gabor Kata, 2018, P 12 INT WORKSH SEM, P679, DOI DOI 10.18653/V1/S18-1111
   Garncarek L, 2021, LECT NOTES COMPUT SC, V12821, P532, DOI 10.1007/978-3-030-86549-8_34
   Geva M, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P946
   Gu X., 2021, ARXIV201201775
   Han H, 2003, ACM-IEEE J CONF DIG, P37
   Hasan KS, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1262
   He KM, 2017, IEEE I CONF COMP VIS, P2980, DOI 10.1109/ICCV.2017.322
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Hou YF, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5203
   Hu J., 2021, ARXIV210500637
   Jain S., 2020, P 58 ANN M ASS COMPU, P7506, DOI 10.18653/v1/2020.acl-main.670
   Jurgens D., 2018, T ASS COMPUTATIONAL, P391, DOI [10.1162/tacl_a_00028, DOI 10.1162/TACL_A_00028]
   Kaplan F., 2021, J DATA MIN DIGIT HUM, DOI [10.46298/jdmdh.6107, DOI 10.46298/JDMDH.6107]
   Kim SN, 2013, LANG RESOUR EVAL, V47, P723, DOI 10.1007/s10579-012-9210-3
   Kim Y., 2014, PROC C EMPIRICAL MET, P1746, DOI [10.3115/v1/d14-1181, 10.3115/v1/D14-1181]
   Lan Z, 2019, INT C LEARN REPR
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Li M., P 28 INT C COMP LING, P949, DOI [10.18653/v1/2020.coling-main.82, DOI 10.18653/V1/2020.COLING-MAIN.82]
   Li MH, 2020, PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020), P1918
   Lin TY, 2014, LECT NOTES COMPUT SC, V8693, P740, DOI 10.1007/978-3-319-10602-1_48
   Liu SK, 2021, IEEE ACCESS, V9, P50050, DOI 10.1109/ACCESS.2021.3068728
   Minh-Thang Luong, 2010, International Journal of Digital Library Systems, V1, P1, DOI 10.4018/jdls.2010100101
   Mondal I., 2021, FINDINGS ASS COMPUTA, P1885, DOI [10.18653/v1/2021.findings-acl.165, DOI 10.18653/V1/2021.FINDINGS-ACL.165]
   Ren MY, 2017, PROC CVPR IEEE, P293, DOI 10.1109/CVPR.2017.39
   Ren SQ, 2017, IEEE T PATTERN ANAL, V39, P1137, DOI 10.1109/TPAMI.2016.2577031
   Ronzano F, 2016, LECT NOTES COMPUT SC, V9792, P11, DOI 10.1007/978-3-319-53637-8_2
   Senn-Wu Kim, 2019, Journal of KIISE, V46, P644, DOI 10.5626/JOK.2019.46.7.644
   Shen YH, 2019, PROC CVPR IEEE, P697, DOI 10.1109/CVPR.2019.00079
   Sun PZ, 2021, PROC CVPR IEEE, P14449, DOI 10.1109/CVPR46437.2021.01422
   Teufel S., 2006, P 2006 EMP METH NAT, P103
   Tsai CT, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1733, DOI 10.1145/2505515.2505613
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang JF, 2021, PROC CVPR IEEE, P15844, DOI 10.1109/CVPR46437.2021.01559
   Xu J, 2020, SCI DATA, V7, DOI 10.1038/s41597-020-0543-2
   Xu Y., 2021, C NEUR INF PROC SYST
   Xu Y, 2021, 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (ACL-IJCNLP 2021), VOL 1, P2579
   Xu Zhong, 2019, 2019 International Conference on Document Analysis and Recognition (ICDAR). Proceedings, P1015, DOI 10.1109/ICDAR.2019.00166
   Yang C, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P1629, DOI 10.1145/3397271.3401312
   Yu SS, 2019, IEEE ACCESS, V7, P176600, DOI 10.1109/ACCESS.2019.2953990
NR 54
TC 0
Z9 0
U1 3
U2 3
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 2802
DI 10.3390/su14052802
PG 18
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 ZW7CM
UT WOS:000771366700001
OA gold
DA 2022-08-23
ER

PT J
AU Zheng, K
   Xie, MH
   Zhang, JBA
   Xie, J
   Xia, SH
AF Zheng, Kun
   Xie, Ming Hui
   Zhang, Jin Biao
   Xie, Juan
   Xia, Shu Hao
TI A knowledge representation model based on the geographic spatiotemporal
   process
SO INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
LA English
DT Article
DE Knowledge graph; geographic spatiotemporal process; time; geo-events;
   geo-entities' evolution
ID INFORMATION
AB Knowledge graphs (KGs) represent entities and relations as computable networks, which is of great value for discovering hidden knowledge and patterns. Geographic KGs mainly describe static facts and have difficulty representing changes, greatly limiting their application in geographic spatiotemporal processes. By analyzing the spatiotemporal features and evolution of geographic elements, this study presents the geographic evolutionary knowledge graph (GEKG). Its representation model has five core elements: time, geographic event (geo-event), geographic entity (geo-entity), activity and property, and defines six relations: logical, semantic, evolutionary and temporal relation, participation and inclusion. It establishes a hierarchical cubical model structure and each temporal layer extends vertically and horizontally starting with the earliest geo-event. Vertical expansion refers to the connection between different kinds of element, such as the participation relation between geo-entities and geo-events. Horizontal expansion indicates the association between the same kinds of element, such as the semantic relation between geo-entities. For different layers, the spatiotemporal differences of elements produce the evolutionary relation. Finally, the comparison of GEKG with Yet Another Great Ontology (YAGO) and Geographic Knowledge Graph (GeoKG) shows that GEKG has more advantages in representing geographic evolutionary knowledge, revealing the evolution mechanism of geographic elements and the evolutionary reasons.
C1 [Zheng, Kun; Xie, Ming Hui; Xie, Juan; Xia, Shu Hao] China Univ Geosci, Sch Geog & Informat Engn, Wuhan, Peoples R China.
   [Zhang, Jin Biao] Guangdong Meteorol Observat Data Ctr, Guangzhou, Guangdong, Peoples R China.
RP Xie, MH (通讯作者)，China Univ Geosci, Sch Geog & Informat Engn, Wuhan, Peoples R China.
EM taria@cug.edu.cn
FU National Natural Science Foundation of China [61501412, 61672474];
   National Key R&D Program of China [2018YFB1004600]; Science and
   Technology Planning Project of Guangdong Province, China
   [2018B020207012]; National Science and Technology Major Project of China
   [2017ZX05036-001-010]
FX This work was supported by the National Natural Science Foundation of
   China [No. 61501412,No. 61672474]; the National Key R&D Program of China
   [No. 2018YFB1004600]; Science and Technology Planning Project of
   Guangdong Province, China [No. 2018B020207012]; Science and National
   Science and Technology Major Project of China [No. 2017ZX05036-001-010].
CR Cao YW, 2018, OPEN GEOSCI, V10, P782, DOI 10.1515/geo-2018-0061
   Chen JY, 2017, COMM COM INF SC, V784, P165, DOI 10.1007/978-981-10-7359-5_17
   [邓敏 Deng Min], 2020, [地球信息科学学报, Journal of Geo-Information Science], V22, P41
   Deng M, 2018, T GIS, V22, P183, DOI 10.1111/tgis.12302
   Ding XH, 2019, PR MACH LEARN RES, V97
   Fan RY, 2020, ISPRS INT J GEO-INF, V9, DOI 10.3390/ijgi9010015
   [龚健雅 Gong Jianya], 2014, [武汉大学学报. 信息科学版, Geomatics and Information Science of Wuhan University], V39, P883
   Huang Y, 2019, ISPRS INT GEO-INF, V8, DOI 10.3390/ijgi8120566
   [蒋秉川 Jiang Bingchuan], 2018, [测绘学报, Acta Geodetica et Cartographica Sinica], V47, P1051
   Jin W, 2020, P 2020 C EMP METH NA, P6669
   Leung Y, 2019, INT J GEOGR INF SCI, V33, P2218, DOI 10.1080/13658816.2019.1633468
   Li J, 2017, IEEE INT C COMPUT, P361, DOI 10.1109/CSE-EUC.2017.72
   Li X, 2012, INT J GEOGR INF SCI, V26, P1667, DOI 10.1080/13658816.2011.643803
   Li ZY, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4201
   Lin H, 2018, GEOMATICS INFORM SCI, V43, P452
   Liu, 2020, METEOROLOGICAL DISAS, V27, P19
   Liu JQ, 2019, IEEE INFOCOM SER, P2260, DOI 10.1109/INFOCOM.2019.8737547
   Liu T, 2010, COLING 2010 23 INT C, V8, P23
   Liu XD, 2019, SCI CHINA EARTH SCI, V62, P1053, DOI 10.1007/s11430-018-9337-8
   Liu X, 2018, INT CONF DAT MIN WOR, P905, DOI 10.1109/ICDMW.2018.00132
   Lopez-Pellicer FJ, 2012, INT J GEOGR INF SCI, V26, P579, DOI 10.1080/13658816.2011.599811
   [罗文 Luo Wen], 2013, [应用基础与工程科学学报, Journal of Basic Science and Engineering], V21, P276
   Patel H, 2018, 2018 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), P617, DOI 10.1109/ASONAM.2018.8508674
   Shi, 2019, GEOMATICS WORLD, V26, P52
   Tasnim M, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P961, DOI 10.1145/3308560.3316521
   Trivedi R, 2017, PR MACH LEARN RES, V70
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Wang S, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8040184
   Wang W., 2020, COMPUTER TECHNOLOGY, V30, P1
   Xu, 2020, P 29 INT JOINT C ART, P3941, DOI DOI 10.24963/IJCAI.2020/545
   Xue CJ, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8020100
   Zheng Maohui, 2013, Geomatics and Information Science of Wuhan University, V38, P935
   Zhu YQ, 2017, SCI PROGRAMMING-NETH, V2017, DOI 10.1155/2017/5072427
   Zou B, 2016, INT J GEOGR INF SCI, V30, P727, DOI 10.1080/13658816.2015.1095921
NR 34
TC 3
Z9 3
U1 33
U2 47
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 2022
VL 36
IS 4
BP 674
EP 691
DI 10.1080/13658816.2021.1962527
EA AUG 2021
PG 18
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 0G1UE
UT WOS:000681505000001
DA 2022-08-23
ER

PT J
AU Debruyne, C
   Munnelly, G
   Kilgallon, L
   O'Sullivan, D
   Crooks, P
AF Debruyne, Christophe
   Munnelly, Gary
   Kilgallon, Lynn
   O'Sullivan, Declan
   Crooks, Peter
TI Creating a Knowledge Graph for Ireland's Lost History: Knowledge
   Engineering and Curation in the Beyond 2022 Project
SO ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE
LA English
DT Article
DE Knowledge graph creation; knowledge graph management; digital humanities
ID LINKED DATA; WEB
AB The Beyond 2022 project aims to create a virtual archive by digitally reconstructing and digitizing historical records lost in a catastrophic fire which consumed items in the Public Record Office of Ireland in 1922. The project is developing a knowledge graph (KG) to facilitate information retrieval and discovery over the reconstructed items. The project decided to adopt Semantic Web technologies to support its distributed KG and reasoning. In this article, we present our approach to KG generation and management. We elaborate on how we help historians contribute to the KG (via a suite of spreadsheets) and its ontology. We furthermore demonstrate how we use named graphs to store different versions of factoids and their provenance information and how these are serviced in two different endpoints. Modeling data in this manner allows us to acknowledge that history is, to some extent, subjective and different perspectives can exist in parallel. The construction of the KG is driven by competency questions elicited from subject matter experts within the consortium. We avail of CIDOC-CRM as our KG's foundation, though we needed to extend this ontology with various qualifiers (types) and relations to support the competency questions. We illustrate how one can explore the KG to gain insights and answer questions. We conclude that CIDOC-CRM provides an adequate, albeit complex, foundation for the KG and that named graphs and Linked Data principles are a suitable mechanism to manage sets of factoids and their provenance.
C1 [Debruyne, Christophe; Munnelly, Gary; O'Sullivan, Declan] Trinity Coll Dublin, Coll Green, ADAPT Ctr, Dublin 2, Ireland.
   [Kilgallon, Lynn; Crooks, Peter] Trinity Coll Dublin, Coll Green, Dept Hist, Dublin 2, Ireland.
   [Debruyne, Christophe] Univ Liege, Montefiore Inst, Quartier Polytech 1,Allee Decouverte 10, B-4000 Liege, Belgium.
RP Debruyne, C (通讯作者)，Trinity Coll Dublin, Coll Green, ADAPT Ctr, Dublin 2, Ireland.
EM debruync@tcd.ie; munnellg@tcd.ie; kilgalll@tcd.ie;
   declan.osullivan@tcd.ie; perooks@tcd.ie
RI Debruyne, Christophe/D-9888-2011
OI Debruyne, Christophe/0000-0003-4734-3847
FU Government of Ireland, through the Department of Tourism, Culture, Arts,
   Gaeltacht, Sport and Media, under the Project Ireland 2040 framework;
   ADAPT Centre for Digital Content Technology under the SFI Research
   Centres Programme [13/RC/2106_P2]
FX Beyond 2022 is funded by the Government of Ireland, through the
   Department of Tourism, Culture, Arts, Gaeltacht, Sport and Media, under
   the Project Ireland 2040 framework. The project is also partially
   supported by the ADAPT Centre for Digital Content Technology under the
   SFI Research Centres Programme (Grant 13/RC/2106_P2).
CR Gomez JA, 2018, ENVIRON SCI POLLUT R, V25, P977, DOI 10.1007/s11356-016-8339-9
   Colutto Sebastian, 2019, 2019 15th International Conference on eScience (eScience). Proceedings, P463, DOI 10.1109/eScience.2019.00060
   Connolly Philomena, 1998, IRISH EXCHEQUE UNPUB
   Damova M, 2011, ADV INTEL SOFT COMPU, V101, P17
   Das Souripriya, 2012, R2RML RDB RDF MAPPIN
   Debruyne C, 2016, INT J DIGIT LIBRARIE, V17, P159, DOI 10.1007/s00799-016-0180-8
   Debruyne Christophe, 2020, Beyond 2022 Knowledge Graph Sample Data, DOI 10.5281/zenodo.4276353
   Debruyne Christophe, 2016, P WORKSHOP LINKED DA
   Feeney KC, 2014, INT J SEMANT WEB INF, V10, P35, DOI 10.4018/ijswis.2014040103
   Fewer Michael, 2019, HIST IRELAND, V27, P44
   Garijo D, 2017, LECT NOTES COMPUT SC, V10588, P94, DOI 10.1007/978-3-319-68204-4_9
   Goerz Gunther, 2008, P 2008 ANN C CIDOC T
   Gruber T, 2009, ENCY DATABASE SYSTEM, P1963, DOI [DOI 10.1007/978-0-387-39940-9_1318, 10.1007/978-0-387-39940-9_1318]
   Gruninger  M., 1995, BENCHMARKING THEORY, P22, DOI DOI 10.1007/978-0-387-34847-6_3
   Hartig O, 2010, LECT NOTES COMPUT SC, V6378, P78, DOI 10.1007/978-3-642-17819-1_10
   Hepp M, 2008, SEMAT WEB BEYOND-COM, V7, P3
   Hoekstra R, 2015, LECT NOTES COMPUT SC, V8628, P215, DOI 10.1007/978-3-319-16462-5_18
   Hyvonen Eero, 2014, P ISWC 2014 POST DEM, V1272, P1
   Isaac A., 2009, SKOS SIMPLE KNOWLEDG
   Jarrar M, 2008, LECT NOTES COMPUT SC, V4891, P7
   Knublauch Holger, 2017, SHAPES CONSTRAINT LA
   Koho M., 2016, JOINT P 4 INT WORKSH, V1615
   Lebo T., 2013, PROV O PROV ONTOLOGY
   Lin CH, 2008, INT J DIGIT LIBRARIE, V8, P115, DOI 10.1007/s00799-008-0034-0
   Makela E, 2017, INT J DIGIT LIBRARIE, V18, P333, DOI 10.1007/s00799-016-0186-2
   McKenna L, 2018, ACM-IEEE J CONF DIG, P7, DOI 10.1145/3197026.3197041
   Moody T. W., 2011, NEW HIST IRELAND VOL
   Mouromtsev Dmitry, 2015, P ISWC 2015 POSTERS, V1486
   Raimond Yves, 2014, RDF 1 1 PRIMER
   Reid Zoe, 2018, UNWRAPPING CONSERVIN
   Ryan C, 2015, INT J DIGIT LIBRARIE, V15, P73, DOI 10.1007/s00799-014-0129-8
   Seaborne A, 2013, SPARQL 1 1 QUERY LAN
   Shimizu C, 2020, J WEB SEMANT, V63, DOI 10.1016/j.websem.2020.100567
   Spyns P, 2002, SIGMOD REC, V31, P12
   Tennison Jeni, 2016, CSV WEB PRIMER
   Valtolina S, 2007, CC2007-CREATIVITY AND COGNITION 2007 SEEDING CREATIVITY: TOOLS, MEDIA, AND ENVIRONMENTS, P107
   Webster G, 2015, PROCEEDINGS OF THE 2015 ACM INTERNATIONAL CONFERENCE ON COMPUTER-SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW'15), P639, DOI 10.1145/2675133.2675247
   Zhao Jun, 2012, P THEWWW2012 WORKSHO
NR 38
TC 0
Z9 0
U1 1
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1556-4673
EI 1556-4711
J9 ACM J COMPUT CULT HE
JI ACM J. Comput. Cult. Herit.
PD JUN
PY 2022
VL 15
IS 2
AR 25
DI 10.1145/3474829
PG 25
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 2U2IY
UT WOS:000822985500006
OA Bronze
DA 2022-08-23
ER

PT J
AU Xiao, ZW
   Zhang, CX
AF Xiao, Ziwei
   Zhang, Chunxiao
TI Construction of Meteorological Simulation Knowledge Graph Based on Deep
   Learning Method
SO SUSTAINABILITY
LA English
DT Article
DE meteorological simulation; knowledge graph; knowledge extraction; deep
   learning; bilateral long short-term memory (BiLSTM); conditional random
   field (CRF)
ID NETWORK
AB With the maturity of meteorological simulation technology, the research literature in this field is undergoing a rapid increase. The published literature can provide useful guidance for current research to get scientific results; however, it tends to be rather time consuming to obtain exact knowledge from massive literature, and it is necessary to transform the literature into structured knowledge to meet the efficient management, sharing, and reuse of meteorological simulation knowledge. In this paper, methods of meteorological simulation knowledge extraction and knowledge graph construction are proposed. A deep learning model based on bilateral long short-term memory-conditional random field (BiLSTM-CRF) is used to extract the meteorological simulation knowledge from the massive literature. Then, the Neo4j graph database is used to construct the meteorological simulation knowledge graph. Based on the meteorological simulation knowledge graph, it can realize the structured storage and integration of meteorological simulation knowledge, which can bridge the gap in the transformation of massive literature to sharable and reusable knowledge. Furthermore, the meteorological simulation knowledge graph can be used as an expert resource and contribute to sustainable guidance and optimization for meteorological simulation research.
C1 [Xiao, Ziwei; Zhang, Chunxiao] China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China.
RP Zhang, CX (通讯作者)，China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China.
EM cugbxzw@163.com; zcx@cugb.edu.cn
FU Fundamental Research Funds for the Central Universities [2652018082]
FX This research was funded by the Fundamental Research Funds for the
   Central Universities, grant number 2652018082.
CR Alizadeh R, 2020, ENERG ECON, V91, DOI 10.1016/j.eneco.2020.104894
   Alizadeh R, 2020, RENEW SUST ENERG REV, V134, DOI 10.1016/j.rser.2020.110326
   [Anonymous], 2018, DEEP LEARNING NATURA
   Armah Frederick A, 2010, Sustainability, V2, P252, DOI 10.3390/su2010252
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bei NF, 2020, SCI TOTAL ENVIRON, V704, DOI 10.1016/j.scitotenv.2019.135210
   Bollacker K, 2007, P 22 NAT C ART INT, P1962
   Cabaneros SM, 2019, ENVIRON MODELL SOFTW, V119, P285, DOI 10.1016/j.envsoft.2019.06.014
   Chen Jun, 2019, Geomatics and Information Science of Wuhan University, V44, P38, DOI 10.13203/j.whugis20180441
   Chen R, 2020, ATMOSPHERE-BASEL, V11, DOI 10.3390/atmos11070676
   Chen T.Y., 2010, THESIS FUDAN UNIV
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   [程宸 Cheng Chen], 2014, [高原气象, Plateau Meteorology], V33, P1045
   Cui Bin, 2019, Journal of Software, V30, P164, DOI 10.13328/j.cnki.jos.005646
   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 L, 2020, ENVIRON INT, V134, DOI 10.1016/j.envint.2019.105209
   [龚健雅 Gong Jianya], 2014, [武汉大学学报. 信息科学版, Geomatics and Information Science of Wuhan University], V39, P883
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Guo CF, 2019, IOP C SER EARTH ENV, V252, DOI 10.1088/1755-1315/252/5/052161
   Heck L, 2014, 2014 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP), P597, DOI 10.1109/GlobalSIP.2014.7032187
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Holzschuher F., 2013, P JOINT EDBT ICDT 20, P195, DOI DOI 10.1145/2457317.2457351
   Huang ZH, 2015, COMPUT INTEL NEUROSC, V2015, DOI 10.1155/2015/685404
   Ko H, 2021, ADDIT MANUF, V37, DOI 10.1016/j.addma.2020.101620
   Li JY, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-59593-0
   Li X., P 13 INT C NAT COMP
   Lin H, 2013, EARTH-SCI REV, V126, P74, DOI 10.1016/j.earscirev.2013.08.001
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   [陆锋 Lu Feng], 2017, [地球信息科学学报, Journal of Geo-Information Science], V19, P723
   Luo L, 2018, BIOINFORMATICS, V34, P1381, DOI 10.1093/bioinformatics/btx761
   Majidpour M., 2014, P 13 INT C CLEAN ENE
   Mario M., 2018, MULTIMED TOOLS APPL, V77, P18657
   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
   Rong X., 2014, ARXIV 1411 2738
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Sahoo B, 2016, INT J CLIMATOL, V36, P95, DOI 10.1002/joc.4331
   Sak H, 2014, INTERSPEECH, P338
   Shi KZ, 2020, FUTURE GENER COMP SY, V102, P323, DOI 10.1016/j.future.2019.08.013
   Sowa J.F., 1991, PRINCIPLES SEMANTIC, P135
   Tao HR, 2020, J CLEAN PROD, V243, DOI 10.1016/j.jclepro.2019.118574
   Thompson JL, 2010, SOC NATUR RESOUR, V23, P742, DOI 10.1080/08941920802102032
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   [王姬卜 Wang Jibu], 2018, [地球信息科学学报, Journal of Geo-Information Science], V20, P871
   Wei J., 2019, ARXIV 1901 11196
   Xie T., 2020, COMPUT SYST APPL, V29, P48
   Xu BL, 2011, INFORM SCIENCES, V181, P2231, DOI 10.1016/j.ins.2011.01.017
   Xu Zeng-lin, 2016, Journal of University of Electronic Science and Technology of China, V45, P589, DOI 10.3969/j.issn.1001-0548.2016.04.012
   Yan C, 2020, SCI CHINA EARTH SCI, V63, P875, DOI 10.1007/s11430-019-9525-6
   Yan JZ, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103429
   Yuan Guo-ming, 2011, COMPUTING TECHNOLOGY, V30, P138, DOI DOI 10.3969/J.ISSN.1003-6199.2011.01.033
   Zhang CX, 2016, ECOL MODEL, V319, P147, DOI 10.1016/j.ecolmodel.2015.04.023
   Zhang CX, 2015, APPL GEOGR, V63, P326, DOI 10.1016/j.apgeog.2015.07.011
   Zhang CX, 2014, ARAB J GEOSCI, V7, P2215, DOI 10.1007/s12517-014-1273-6
   [张杰 Zhang Jie], 2020, [地理学报, Acta Geographica Sinica], V75, P113
   Zhang L, 2018, INFORM RES, V250, P81
   Zhang YH, 2020, INT J DIGIT EARTH, V13, P1637, DOI 10.1080/17538947.2020.1773950
   [朱建章 Zhu Jianzhang], 2016, [中国图象图形学报, Journal of Image and Graphics], V21, P1425
NR 58
TC 3
Z9 4
U1 14
U2 42
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB
PY 2021
VL 13
IS 3
AR 1311
DI 10.3390/su13031311
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 QD7HA
UT WOS:000615683100001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, J
   Yang, T
   Huang, ZQ
   Wang, KJ
   Liu, MC
   Lyu, CY
AF Chen, Jing
   Yang, Tao
   Huang, Ziqiang
   Wang, Kejun
   Liu, Meichen
   Lyu, Chunyan
TI Incorporating structured emotion commonsense knowledge and interpersonal
   relation into context-aware emotion recognition
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Structured emotion commonsense knowledge; Emotion knowledge graph;
   Interpersonal relation; Contextual information; Multi-modal emotion
   recognition
AB Traditional emotion recognition technology often focuses on recognizing human biometrics such as facial expressions or body postures. However, psychological research shows that the context (contextual information) also plays an important role in perceiving the emotions of others. Existing research methods, that are based on contextual information, have heavily relied on the semantic features of images. They do not take into account the interrelationships between objects and fail to use external knowledge. Meanwhile, external knowledge is likely to be very helpful in perceiving emotion. In this paper, by incorporating external structured emotion commonsense knowledge, two methods are proposed for constructing emotion knowledge graphs based on the objective text of images, and a multi-modal emotion recognition model is designed. The model has three branches, one of which focuses on human biometrics, and another two branches employ emotion knowledge graphs to perceive emotion from contextual information. Before constructing the emotion knowledge graphs, we convert the visual content into the text information to obtain the prime and ample contextual information from the object, scene, and the relationship between the objects. This approach can reduce redundant and invalid information. After that, the structured emotion commonsense knowledge is integrated into the objective text in word sharing. A large-scale emotion knowledge graph based on all valid words (LEKG) and a small-scale emotion knowledge graph based on the document itself (TEKG) are constructed, respectively. We propose two fusion modules, one of which is attention-based, and the other is a deep reasoning module that incorporates interpersonal relation. We conduct extensive experiments on the benchmark dataset EMOTIC. The experimental results prove that our method is superior to the most advanced methods, and it has obvious advantages in global context-aware tasks.
C1 [Chen, Jing; Yang, Tao; Huang, Ziqiang; Wang, Kejun; Liu, Meichen; Lyu, Chunyan] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin, Peoples R China.
   [Wang, Kejun] Beijing Inst Technol, Sch Informat Technol, Zhuhai, Peoples R China.
RP Wang, KJ (通讯作者)，Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin, Peoples R China.; Wang, KJ (通讯作者)，Beijing Inst Technol, Sch Informat Technol, Zhuhai, Peoples R China.
EM wangkejun@hrbeu.edu.cn
FU National Natural Science Foundation of China [61573114]; Science and
   Technology on Underwater Test and Control Laboratory [YS24071804]
FX This study is supported by the National Natural Science Foundation of
   China (No. 61573114), and the Science and Technology on Underwater Test
   and Control Laboratory under Grant (YS24071804)
CR [Anonymous], 2019, P 2019 C EMP METH NA
   Bao LX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019:): STUDENT RESEARCH WORKSHOP, P253
   Barrett L.F., 2017, EMOTIONS ARE MADE SE
   Barrett LF, 2011, CURR DIR PSYCHOL SCI, V20, P286, DOI 10.1177/0963721411422522
   Bhattacharya U, 2020, AAAI CONF ARTIF INTE, V34, P1342
   Bi B, 2019, P 2019 C EMP METH NA
   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
   Cao Z, 2021, IEEE T PATTERN ANAL, V43, P172, DOI 10.1109/TPAMI.2019.2929257
   Chen Zhaomin, 2021, IEEE Trans Pattern Anal Mach Intell, VPP, DOI 10.1109/TPAMI.2021.3063496
   Cornia Marcella, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P10575, DOI 10.1109/CVPR42600.2020.01059
   Ghosal D, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3198
   HALL ET, 1963, AM ANTHROPOL, V65, P1003, DOI 10.1525/aa.1963.65.5.02a00020
   Hamilton WL, 2017, ADV NEUR IN, V30
   He K, 2015, PROC CVPR IEEE, DOI [10.1109/CVPR.2016.90, DOI 10.1109/CVPR.2016.90]
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Karpathy A, 2017, IEEE T PATTERN ANAL, V39, P664, DOI 10.1109/TPAMI.2016.2598339
   Kendon A., 1990, CONDUCTING INTERACTI
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kosti R, 2020, IEEE T PATTERN ANAL, V42, P2755, DOI 10.1109/TPAMI.2019.2916866
   Kosti R, 2017, PROC CVPR IEEE, P1960, DOI 10.1109/CVPR.2017.212
   Lei ZY, 2018, ACM/SIGIR PROCEEDINGS 2018, P1197, DOI 10.1145/3209978.3210128
   Ma YK, 2018, AAAI CONF ARTIF INTE, P5876
   Margatina K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3944
   Mittal Trisha, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P14222, DOI 10.1109/CVPR42600.2020.01424
   Niu Z.Y., 2019, P 2019 C EMP METH NA, P1782, DOI 10.18653/v1/D19-1187
   Pennington, 2014, P 2014 C EMP METH, DOI DOI 10.3115/V1/D14-1162
   Qi F, 2021, IEEE T MULTIMEDIA, V23, P3999, DOI 10.1109/TMM.2020.3035285
   Ren SQ, 2017, IEEE T PATTERN ANAL, V39, P1137, DOI 10.1109/TPAMI.2016.2577031
   SOMMER R, 1959, SOCIOMETRY, V22, P247, DOI 10.2307/2785668
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Susanto Y, 2020, IEEE INTELL SYST, V35, P96, DOI 10.1109/MIS.2020.2992799
   Velickovic Petar, 2018, INT C LEARN REPR ICL
   Wang M, 2019, ARXIV 190901315
   Wang ZX, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1021
   Yang PC, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2002
   Yujia L, 2016, ARXIV 151105493
   Zhang BW, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3188
   Zhang MH, 2019, IEEE INT CON MULTI, P151, DOI 10.1109/ICME.2019.00034
   Zhang YF, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P334
   Zhou BL, 2018, IEEE T PATTERN ANAL, V40, P1452, DOI 10.1109/TPAMI.2017.2723009
NR 41
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/s10489-022-03729-4
EA JUN 2022
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1W3PC
UT WOS:000806686600007
DA 2022-08-23
ER

PT J
AU Zhang, R
   Hristovski, D
   Schutte, D
   Kastrin, A
   Fiszman, M
   Kilicoglu, H
AF Zhang, Rui
   Hristovski, Dimitar
   Schutte, Dalton
   Kastrin, Andrej
   Fiszman, Marcelo
   Kilicoglu, Halil
TI Drug repurposing for COVID-19 via knowledge graph completion
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE COVID-19; Drug repurposing; Knowledge graph completion; Literature-based
   discovery; Text mining
ID LITERATURE-BASED DISCOVERY; FISH OIL; REPOSITORY
AB Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.
   Methods: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from PubMed and other COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative and accurate subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant. We used this subset to construct a knowledge graph, and applied five state-of-the-art, neural knowledge graph completion algorithms (i.e., TransE, RotatE, DistMult, ComplEx, and STELP) to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach.
   Results: Accuracy classifier based on PubMedBERT achieved the best performance (F1 = 0.854) in identifying accurate semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1 = 0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as others that have not yet been studied. Discovery patterns enabled identification of additional candidate drugs and generation of plausible hypotheses regarding the links between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (i.e., paclitaxel, SB 203580, alpha 2-antiplasmin, metoclopramide, and oxymatrine) and the mechanistic explanations for their potential use are further discussed.
   Conclusion: We showed that a LBD approach can be feasible not only for discovering drug candidates for COVID19, but also for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions. Source code and data are available at https://github.com/kilicogluh/lbd-covid.
C1 [Zhang, Rui; Schutte, Dalton] Univ Minnesota, Inst Hlth Informat, Minneapolis, MN 55455 USA.
   [Zhang, Rui; Schutte, Dalton] Univ Minnesota, Dept Pharmaceut Care & Hlth Syst, Minneapolis, MN 55455 USA.
   [Hristovski, Dimitar; Kastrin, Andrej] Univ Ljubljana, Fac Med, Inst Biostat & Med Informat, Ljubljana, Slovenia.
   [Fiszman, Marcelo] Pontifical Catholic Univ Rio Janeiro, NITES Nacleo Inovacao & Tecnol Saude, Rio De Janeiro, Brazil.
   [Kilicoglu, Halil] Univ Illinois, Sch Informat Sci, Champaign, IL USA.
RP Zhang, R (通讯作者)，Univ Minnesota, Inst Hlth Informat, Minneapolis, MN 55455 USA.; Zhang, R (通讯作者)，Univ Minnesota, Dept Pharmaceut Care & Hlth Syst, Minneapolis, MN 55455 USA.
EM zhan1386@umn.edu
RI Kastrin, Andrej/AAH-9234-2021
OI Kastrin, Andrej/0000-0002-3495-0165
FU U.S. National Institutes of Health's National Center for Complementary
   and Integrative Health [R01AT009457]; Slovenian Research Agency
   [P3-0154, Z5-9352, J5-2552, J5-1780]
FX RZ and DS were supported by the U.S. National Institutes of Health's
   National Center for Complementary and Integrative Health (Grant No.
   R01AT009457). DH was supported by Slovenian Research Agency (Grant No.
   J5-1780, J5-2552, and P3-0154). AK was supported by the Slovenian
   Research Agency (Grant No. P3-0154, Z5-9352, and J5-2552).
CR Ahlers Caroline B, 2007, AMIA Annu Symp Proc, P6
   Al-Khafaji K, 2021, J BIOMOL STRUCT DYN, V39, P3387, DOI 10.1080/07391102.2020.1764392
   Alsentzer E., 2019, P 2 CLIN NAT LANG PR
   Altay O, 2020, ISCIENCE, V23, DOI 10.1016/j.isci.2020.101303
   [Anonymous], 2020, FFDA TAKES ADDITIONA
   [Anonymous], 2020, DAILYMED PACLITAXEL
   Aronson AR, 2010, J AM MED INFORM ASSN, V17, P229, DOI 10.1136/jamia.2009.002733
   Barabasi AL, 2011, NAT REV GENET, V12, P56, DOI 10.1038/nrg2918
   Boccaletti S, 2006, PHYS REP, V424, P175, DOI 10.1016/j.physrep.2005.10.009
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Brandao S.C.S., 2020, CYTOKINE GROWTH FACT
   Cairelli Michael J, 2013, AMIA Annu Symp Proc, V2013, P164
   Cameron D, 2015, J BIOMED INFORM, V54, P141, DOI 10.1016/j.jbi.2015.01.014
   Cava C, 2020, VIRUSES-BASEL, V12, DOI 10.3390/v12040404
   Chen GC, 2014, PLOS COMPUT BIOL, V10, DOI 10.1371/journal.pcbi.1003666
   Chen QY, 2020, NATURE, V579, P193, DOI 10.1038/d41586-020-00694-1
   Chen X, 2002, NUCLEIC ACIDS RES, V30, P412, DOI 10.1093/nar/30.1.412
   Chi YW, 2020, J MICROENCAPSUL, V37, P134, DOI 10.1080/02652048.2019.1705409
   Choudhury A, 2020, J MED VIROL, V92, P2105, DOI 10.1002/jmv.25987
   Cohen T, 2014, CPT-PHARMACOMET SYST, V3, DOI 10.1038/psp.2014.37
   Cohen T, 2017, J BIOMED INFORM, V68, P150, DOI 10.1016/j.jbi.2017.03.003
   Cohen T, 2011, LECT NOTES COMPUT SC, V7052, P48, DOI 10.1007/978-3-642-24971-6_6
   Cohen T, 2010, J BIOMED INFORM, V43, P240, DOI 10.1016/j.jbi.2009.09.003
   Constantin C, 2020, EXP THER MED, V20, P151, DOI 10.3892/etm.2020.8704
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dodd LE, 2020, NEW ENGL J MED
   Elfiky AA, 2020, LIFE SCI, V253, DOI 10.1016/j.lfs.2020.117592
   Elfiky AA, 2020, LIFE SCI, V248, DOI 10.1016/j.lfs.2020.117477
   FDA, 2020, FDA TAK KEY ACT FIGH
   FLEISS JL, 1971, PSYCHOL BULL, V76, P378, DOI 10.1037/h0031619
   Gaestel M, 2013, BIOL CHEM, V394, P1301, DOI 10.1515/hsz-2013-0197
   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]
   Gordon DE, 2020, NATURE, V583, P459, DOI 10.1038/s41586-020-2286-9
   Gu Y., 2020, ARXIV200715779
   Gysi Deisy Morselli, 2020, ArXiv
   Henry S, 2017, J BIOMED INFORM, V74, P20, DOI 10.1016/j.jbi.2017.08.011
   Horby P, 2020, NEW ENGL J MED, DOI 10.1101/2020.06.22.20137273
   Hristovski Dimitar, 2006, AMIA Annu Symp Proc, P349
   Hristovski Dimitar, 2013, Cardiovascular & Hematological Agents in Medicinal Chemistry, V11, P14
   Hristovski D, 2010, LECT N BIOINFORMAT, V6004, P53, DOI 10.1007/978-3-642-13131-8_7
   Huang M, 2012, J ETHNOPHARMACOL, V143, P228, DOI 10.1016/j.jep.2012.06.028
   Ji HL, 2020, PHYSIOL REV, V100, P1065, DOI 10.1152/physrev.00013.2020
   Kandeel M, 2020, LIFE SCI, V251, DOI 10.1016/j.lfs.2020.117627
   Kastrin A, 2014, LECT NOTES ARTIF INT, V8777, P135, DOI 10.1007/978-3-319-11812-3_12
   Kilicoglu H, 2020, BMC BIOINFORMATICS, V21, DOI 10.1186/s12859-020-3517-7
   Kilicoglu H, 2012, BIOINFORMATICS, V28, P3158, DOI 10.1093/bioinformatics/bts591
   Kingma D. P., 2017, ARXIV
   Lan X, 2020, PHARMACOL RES, V151, DOI 10.1016/j.phrs.2019.104541
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Lee SC, 1997, PREP BIOCHEM BIOTECH, V27, P227, DOI 10.1080/10826069708001281
   LINDBERG DAB, 1993, METHOD INFORM MED, V32, P281, DOI 10.1055/s-0038-1634945
   McCray AT, 2001, STUD HEALTH TECHNOL, V84, P216
   McInnes B.T., 2004, THESIS
   Miesbach W, 2020, CLIN APPL THROMB-HEM, V26, DOI 10.1177/1076029620938149
   Nguyen D., 2018, NAACL ASS P COMP LIN
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Peng YF, 2019, SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2019), P58
   Pero RW, 1999, MOL CELL BIOCHEM, V193, P119, DOI 10.1023/A:1006932714982
   Preiss J, 2015, J AM MED INFORM ASSN, V22, P987, DOI 10.1093/jamia/ocv002
   Pushpakom S, 2019, NAT REV DRUG DISCOV, V18, P41, DOI 10.1038/nrd.2018.168
   R.C. Group, 2020, N ENGL J MED
   Ran S, 2015, CANCER RES, V75, P2405, DOI 10.1158/0008-5472.CAN-14-3525
   Rastegar-Mojarad M, 2015, IEEE INT C BIOINFORM, P669, DOI 10.1109/BIBM.2015.7359766
   Ray S., 2020, ARXIV200702338
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   Rindflesch TC, 2018, J BIOMED SEMANT, V9, DOI 10.1186/s13326-018-0192-y
   Riva LR, 2020, DIXIT, P1, DOI 10.22235/d.vi32.2036
   Sanders JM, 2020, JAMA-J AM MED ASSOC, V323, P1824, DOI 10.1001/jama.2020.6019
   Sang ST, 2019, IEEE ACCESS, V7, P8404, DOI 10.1109/ACCESS.2018.2886311
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Sebastian Y, 2017, KNOWL ENG REV, V32, P1, DOI 10.1017/S0269888917000042
   Singh TU, 2020, PHARMACOL REP, V72, P1479, DOI 10.1007/s43440-020-00155-6
   Sosa DN, 2020, PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, P463
   Stark C, 2006, NUCLEIC ACIDS RES, V34, pD535, DOI 10.1093/nar/gkj109
   Sukumar S.R., 2016, REASONING HYPOTHESIS
   Sun Z., 2019, ABS190210197 ARXIV
   SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7
   Swanson DR, 1997, ARTIF INTELL, V91, P183, DOI 10.1016/S0004-3702(97)00008-8
   Sybrandt J, 2018, IEEE INT CONF BIG DA, P1504, DOI 10.1109/BigData.2018.8621974
   Takeuchi Yuko, 1993, Environmental Medicine (Nagoya), V37, P95
   Tay MZ, 2020, NAT REV IMMUNOL, V20, P363, DOI 10.1038/s41577-020-0311-8
   Tizabi Y, 2020, FEBS J, V287, P3656, DOI 10.1111/febs.15521
   Trouillon T, 2016, PR MACH LEARN RES, V48
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Vasilakes J, 2018, JAMIA OPEN, V1, P275, DOI 10.1093/jamiaopen/ooy021
   Wang B., 2020, ARXIV200414781
   Wang JM, 2020, J CHEM INF MODEL, V60, P3277, DOI 10.1021/acs.jcim.0c00179
   Wang L., 2020, MATH PROBL ENG, V2020, P1, DOI DOI 10.1155/2020/9746538
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X., 2020, MED RES REV
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weaver BA, 2014, MOL BIOL CELL, V25, P2677, DOI 10.1091/mbc.E14-04-0916
   Weeber M, 2001, J AM SOC INF SCI TEC, V52, P548, DOI 10.1002/asi.1104.abs
   Wiersinga WJ, 2020, JAMA-J AM MED ASSOC, V324, P782, DOI 10.1001/jama.2020.12839
   Wilkowski Bartlomiej, 2011, AMIA Annu Symp Proc, V2011, P1514
   Wishart DS, 2008, NUCLEIC ACIDS RES, V36, pD901, DOI 10.1093/nar/gkm958
   Yang B., 2014, ARXIV14126575 ARXIV
   Yang HT, 2017, BRIEF BIOINFORM, V18, P488, DOI 10.1093/bib/bbw030
   Yao, 2019, ARXIV190903193
   Zeng XX, 2020, J PROTEOME RES, V19, P4624, DOI 10.1021/acs.jproteome.0c00316
   Zhang F., BIORXIV
   Zhang R., 2014, CANC INFORM, V13
   Zhang Rui, 2015, AMIA Jt Summits Transl Sci Proc, V2015, P69
   Zhang Y., 2020, J CHROMATOGR SCI, V58, P562, DOI [10.1093/chromsci/bmz125, DOI 10.1093/CHROMSCI/BMZ125]
   Zheng D, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P739, DOI 10.1145/3397271.3401172
   Zhou YD, 2020, CELL DISCOV, V6, DOI 10.1038/s41421-020-0153-3
   Zhou YD, 2020, PLOS BIOL, V18, DOI 10.1371/journal.pbio.3000970
   Zhou Y, 2020, LANCET DIGIT HEALTH, V2, pE417, DOI 10.1016/S2589-7500(20)30165-5
   Zitnik M, 2018, BIOINFORMATICS, V34, P457, DOI 10.1093/bioinformatics/bty294
NR 113
TC 23
Z9 23
U1 8
U2 25
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 103696
DI 10.1016/j.jbi.2021.103696
EA FEB 2021
PG 15
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA RN6WD
UT WOS:000640491300001
PM 33571675
OA Green Submitted, Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Rozanec, JM
   Fortuna, B
   Mladenic, D
AF Rozanec, Joze M.
   Fortuna, Blaz
   Mladenic, Dunja
TI Knowledge graph-based rich and confidentiality preserving Explainable
   Artificial Intelligence (XAI)
SO INFORMATION FUSION
LA English
DT Article
DE Explainable Artificial Intelligence; Knowledge Graph; Demand
   forecasting; Smart manufacturing; Confidentiality; Privacy
ID DEMAND; FORECASTS; ONTOLOGY
AB The paper proposes a novel architecture for explainable artificial intelligence based on semantic technologies and artificial intelligence. We tailor the architecture for the domain of demand forecasting and validate it on a real-world case study. The explanations provided result from knowledge fusion regarding concepts describing features relevant to a particular forecast, related media events, and metadata regarding external datasets of interest. The Knowledge Graph enhances the quality of explanations by informing concepts at a higher abstraction level rather than specific features. By doing so, explanations avoid exposing sensitive details regarding the demand forecasting models, thus preserving confidentiality. In addition, the Knowledge Graph enables linking domain knowledge, forecasted values, and forecast explanations while also providing insights into actionable aspects on which users can take action. The ontology and dataset we developed for this use case are publicly available for further research.
C1 [Rozanec, Joze M.; Fortuna, Blaz; Mladenic, Dunja] Jozef Stefan Inst, Jamova 39, Ljubljana 1000, Slovenia.
   [Rozanec, Joze M.; Fortuna, Blaz] Qlector Doo, Rovsnikova 7, Ljubljana 1000, Slovenia.
   [Rozanec, Joze M.] Jozef Stefan Int Postgrad Sch, Jamova 39, Ljubljana 1000, Slovenia.
RP Rozanec, JM (通讯作者)，Jozef Stefan Inst, Jamova 39, Ljubljana 1000, Slovenia.
EM joze.rozanec@ijs.si; blaz.fortuna@ijs.si; dunja.mladenic@ijs.si
RI ; Fortuna, Blaz/K-5427-2014
OI Mladenic, Dunja/0000-0002-0360-6505; Fortuna, Blaz/0000-0002-8585-9388
FU Slovenian Research Agency, Slovenia; European Union [H2020-869951,
   H2020-956573]
FX This work was supported by the Slovenian Research Agency, Slovenia and
   the European Union's Horizon 2020 program project FACTLOG under grant
   agreement H2020-869951, and European Union's Horizon 2020 program
   project STAR under grant agreement number H2020-956573.
CR Adadi A, 2018, IEEE ACCESS, V6, P52138, DOI 10.1109/ACCESS.2018.2870052
   Ameri F., 2012, P WORK ONT SEM WEB M, P1
   Ameri Farhad, 2006, P INT DES ENG TECHN, P651, DOI DOI 10.1115/DETC2006-99600
   Angelov PP, 2021, WIRES DATA MIN KNOWL, V11, DOI 10.1002/widm.1424
   [Anonymous], 1996, P 16 ANN C BRIT COMP
   Armstrong JS, 2000, INT J FORECASTING, V16, P383, DOI 10.1016/S0169-2070(00)00058-3
   Assi A, 2019, LECT NOTES ARTIF INT, V11606, P422, DOI [10.1007/978-3-030-22999-3_37, 10.1016/j.knosys.2019.104925]
   Babai MZ, 2020, IMA J MANAG MATH, V31, P281, DOI 10.1093/imaman/dpaa003
   BARNES JH, 1984, STRATEGIC MANAGE J, V5, P129, DOI 10.1002/smj.4250050204
   Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
   Benbarrad T, 2021, J SENS ACTUAT NETW, V10, DOI 10.3390/jsan10010007
   Bergman JJ, 2017, ROBOT CIM-INT MANUF, V47, P17, DOI 10.1016/j.rcim.2016.12.010
   Biran O., 2017, ICJAI 17 WORKSH EXPL, P8
   Biran O, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1461
   Borgo S., 2019, ONTOLOGIES, P751, DOI [DOI 10.1007/978-0-387-37022-4_27, 10.1007/978-0-387-37022-4_27]
   Brank J., P SIKDD
   Bruhl Bernhard, 2009, Advances in Data Mining, Applications and Theoretical Aspects. Proceedings 9th Industrial Confer, ICDM 2009, P146, DOI 10.1007/978-3-642-03067-3_13
   Cannataro M., 2003, P 1 INT WORKSH SEM P, P113
   Chandriah KK, 2021, MULTIMED TOOLS APPL, V80, P26145, DOI 10.1007/s11042-021-10913-0
   Chang XM, 2010, J MECH DESIGN, V132, DOI 10.1115/1.4000697
   Chen L.-W, 2017, EXTENDING WIKIFICATI
   Chromik M., 2021, HUMAN CENTRIC EXPLAN
   Corredor P, 2014, INT BUS REV, V23, P824, DOI 10.1016/j.ibusrev.2014.01.001
   Courtot M, 2011, APPL ONTOL, V6, P23, DOI 10.3233/AO-2011-0087
   Coviello E, 2011, IEEE T AUDIO SPEECH, V19, P1343, DOI 10.1109/TASL.2010.2090148
   Das A., 2020, OPPORTUNITIES CHALLE
   de Souza FDM, 2016, INT C PATT RECOG, P2097, DOI 10.1109/ICPR.2016.7899945
   Diamantini C., 2009, 3 GENERATION DATA MI, P13
   Diaz-Rodriguez N, 2021, ARXIV PREPRINT ARXIV
   Dong XL, 2018, INT CONF MANAGE DATA, P1645, DOI 10.1145/3183713.3197387
   Dong YP, 2017, PROC CVPR IEEE, P975, DOI 10.1109/CVPR.2017.110
   Doran D, 2017, ARXIV171000794
   Drucker H, 1997, ADV NEUR IN, V9, P155
   Dwivedi A., 2013, INT J COMPUT APPL, V74, P1, DOI [10.5120/12911-9383, DOI 10.5120/12911-9383]
   El-Assady M., P ACM WORKSH HUM CTR
   Ermolayev Vadim, 2008, 2008 IEEE 32nd International Computer Software and Applications Conference (COMPSAC), P539, DOI 10.1109/COMPSAC.2008.141
   Farahani D.S., 2016, DATA ANAL, P69
   Fernandez RC, 2018, PROC INT CONF DATA, P989, DOI 10.1109/ICDE.2018.00093
   Fernandez-Lopez M., 1997, METHONTOLOGY ONTOLOG
   Ferreira J.J., 2021, ARXIV PREPRINT ARXIV
   Fildes R, 2021, INT J FORECASTING, V37, P1031, DOI 10.1016/j.ijforecast.2020.11.004
   Gao JJ, 2018, ADV MECH ENG, V10, DOI 10.1177/1687814017749325
   Gaur M., 2020, ARXIV201008660
   Google, 2015, WORD2VEC EFF IMPL CO
   Gottschalk Simon, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P272, DOI 10.1007/978-3-319-93417-4_18
   Grieves M.W, 2019, VIRTUALLY INTELLIGEN
   Gudi A., 2015, ARXIV151200743
   Guidotti R., 2018, ARXIV PREPRINT ARXIV
   Hall P., 2017, H2O
   HOGARTH RM, 1981, MANAGE SCI, V27, P115, DOI 10.1287/mnsc.27.2.115
   Huang X, 2019, PROCEDIA COMPUT SCI, V159, P437, DOI 10.1016/j.procs.2019.09.198
   Hyndman R.J., 2006, FORESIGHT INT J APPL, V4, P43
   Islama S.R., 2021, IMPLICATIONS COMBINI
   Johnston FR, 1996, J OPER RES SOC, V47, P113, DOI 10.1057/jors.1996.10
   Kashiparekh K., 2019, 2019 INT JOINT C NEU, P1
   Keane M.T., 2021, ARXIV PREPRINT ARXIV
   Keane MT, 2020, LECT NOTES ARTIF INT, V12311, P163, DOI 10.1007/978-3-030-58342-2_11
   Khoudja MA, 2018, PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON APPLIED SMART SYSTEMS (ICASS)
   Kim H. M., 1995, Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE '95 (Cat. No.95TB8030), P105, DOI 10.1109/ENABL.1995.484554
   Kourtis G, 2019, IEEE T IND INFORM, V15, P4888, DOI 10.1109/TII.2019.2916622
   Kusner MJ, 2015, PR MACH LEARN RES, V37, P957
   Leban G, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P107, DOI 10.1145/2567948.2577024
   Leitao P, 2006, COMPUT IND, V57, P121, DOI 10.1016/j.compind.2005.05.005
   Lemaignan S, 2006, DIS 2006: IEEE WORKSHOP ON DISTRIBUTED INTELLIGENT SYSTEMS: COLLECTIVE INTELLIGENCE AND ITS APPLICATIONS, PROCEEDINGS, P195
   Lengu D, 2014, EUR J OPER RES, V235, P624, DOI 10.1016/j.ejor.2013.12.043
   Liao QV, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376590
   Lim B, 2021, INT J FORECASTING, V37, P1748, DOI 10.1016/j.ijforecast.2021.03.012
   Lolli F, 2017, INT J PROD ECON, V183, P116, DOI 10.1016/j.ijpe.2016.10.021
   Loyola-Gonzalez O, 2019, IEEE ACCESS, V7, P154096, DOI 10.1109/ACCESS.2019.2949286
   Malhi A, 2020, LECT NOTES ARTIF INT, V12175, P129, DOI 10.1007/978-3-030-51924-7_8
   McCrae JP, 2018, CYBERN INF TECHNOL, V18, P109, DOI 10.2478/cait-2018-0010
   Mercier D., 2020, INT C NEUR INF PROC, P318
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Miller RJ, 2018, PROC VLDB ENDOW, V11, P2130, DOI 10.14778/3229863.3240491
   Mizoguchi R., 2010, ADV ONTOLOGIES, P1
   Morwitz VG, 2007, INT J FORECASTING, V23, P347, DOI 10.1016/j.ijforecast.2007.05.015
   Nentwig M, 2017, SEMANT WEB, V8, P419, DOI 10.3233/SW-150210
   Neo4j, 2020, NEO4J GRAPH DAT PLAT
   Noy Natalya F, 2003, AMIA Annu Symp Proc, P953
   Ozyegen O., 2020, ARXIV PREPRINT ARXIV
   P.O. of the European Union, 2020, EU OP DAT PORT OFF P
   Panigutti C, 2020, FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, P629, DOI 10.1145/3351095.3372855
   Panov Pance, 2008, 2008 IEEE International Conference on Data Mining Workshops, P752, DOI 10.1109/ICDMW.2008.62
   Panov P, 2014, DATA MIN KNOWL DISC, V28, P1222, DOI 10.1007/s10618-014-0363-0
   Pedreschi D., 2018, ARXIV PREPRINT ARXIV
   Rajkumar R, 2010, DES AUT CON, P731
   Raut Rakesh D., 2020, IEEE Engineering Management Review, V48, P83, DOI 10.1109/EMR.2020.2987884
   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
   Ro zanec J.z.M., 2020, Ontology for contextualized demand forecasting explanations, DOI 10.7910/DVN/GIREAY
   Rozanec J., 2021, EXPLAINING BAD FOREC
   Rozanec JM, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11156787
   Salinas D, 2020, INT J FORECASTING, V36, P1181, DOI 10.1016/j.ijforecast.2019.07.001
   Saluja R., 2021, ARXIV PREPRINT ARXIV
   Samek W., 2019, LNCS, P5, DOI [DOI 10.1007/978-3-030-28954-6_1, 10.1007/978-3-030-28954-6]
   Schmidt D., 2018, EM TOP SEM TECHN BES, V36, P27, DOI DOI 10.3233/978-1-61499-894-5-27
   Sharma R., 2012, INT J COMPUT APPL, V53, P25, DOI [10.5120/8474-2403, DOI 10.5120/8474-2403]
   Siddiqui SA, 2019, IEEE ACCESS, V7, P67027, DOI 10.1109/ACCESS.2019.2912823
   Sokol K., 2020, ARXIV PREPRINT ARXIV
   Srinivasan R, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4812
   STONE M, 1974, J R STAT SOC B, V36, P111, DOI 10.1111/j.2517-6161.1974.tb00994.x
   Syntetos AA, 2012, INT J PROD RES, V50, P2101, DOI 10.1080/00207543.2011.562561
   Syntetos AA, 2005, J OPER RES SOC, V56, P495, DOI 10.1057/palgrave.jors.2601841
   Tao F, 2018, J MANUF SYST, V48, P157, DOI 10.1016/j.jmsy.2018.01.006
   Thieblin E, 2020, SEMANT WEB, V11, P689, DOI 10.3233/SW-190366
   Tonekaboni S., 2019, EXPLAINING TIME SERI
   Tubaro P, 2019, J IND BUS ECON, V46, P333, DOI 10.1007/s40812-019-00121-1
   Ubaidillah NZ, 2020, INT J BUS SOC, V21, P997
   USCHOLD M, 1995, WORKSH BAS ONT ISS K
   Vahabi A., 2016, MANAGEMENT, V1, P1, DOI [10.14569/IJACSA.2016.071104, DOI 10.14569/IJACSA.2016.071104]
   van der Waa J., 2018, ARXIV180607470
   Verma S., 2020, ARXIV
   Wang FK, 2011, EXPERT SYST APPL, V38, P10587, DOI 10.1016/j.eswa.2011.02.100
   Wang XR, 2021, IUI '21 - 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P318, DOI 10.1145/3397481.3450650
   Wang XY, 2020, TECHNOL FORECAST SOC, V155, DOI 10.1016/j.techfore.2020.120032
   Weber R., 2021, ARXIV PREPRINT ARXIV
   WILLIAMS TM, 1984, J OPER RES SOC, V35, P939, DOI 10.1057/jors.1984.185
   Zafar M.R., 2019, P ACM SIGKDD WORKSH
   zanec J.z.M. Ro., 2020, Data for contextualized demand forecasting explanations, DOI 10.7910/DVN/FPXYCM
NR 119
TC 1
Z9 1
U1 17
U2 31
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1566-2535
EI 1872-6305
J9 INFORM FUSION
JI Inf. Fusion
PD MAY
PY 2022
VL 81
BP 91
EP 102
DI 10.1016/j.inffus.2021.11.015
EA DEC 2021
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA XW0AW
UT WOS:000735294100006
OA hybrid
DA 2022-08-23
ER

PT J
AU Li, Q
   Cao, ZH
   Tanveer, M
   Pandey, HM
   Wang, C
AF Li, Qi
   Cao, Zehong
   Tanveer, M.
   Pandey, Hari Mohan
   Wang, Chen
TI A Semantic Collaboration Method Based on Uniform Knowledge Graph
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Internet of Things (IoT); knowledge graph; semantic collaboration;
   Semantic IoT (SIoT)
ID INTERNET; THINGS; FRAMEWORK; WEB
AB The Semantic Internet of Things (SIoT) is the extension of the Internet of Things (IoT) and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the IoT. However, the SIoT has the characteristics of both the IoT and the Semantic Web environment, and the corresponding semantic data present many new data features. In this article, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph (UKG), allowing us to be applied to the environment of the SIoT better. Here, we design a semantic collaboration method based on a UKG. It can take the UKG as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing the semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the SIoT. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes.
C1 [Li, Qi] Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China.
   [Cao, Zehong] Univ Technol Sydney, Sch Comp Sci, Sydney, NSW 2007, Australia.
   [Cao, Zehong] Univ Tasmania, Dept Discipline ICT, Hobart, Tas 7001, Australia.
   [Tanveer, M.] Indian Inst Technol Indore, Discipline Math, Indore 452020, India.
   [Pandey, Hari Mohan] Edge Hill Univ, Dept Comp Sci, Ormskirk L39 4QP, England.
   [Wang, Chen] Chongqing Univ, Coll Comp Sci, Chongqing 400030, Peoples R China.
RP Cao, ZH (通讯作者)，Univ Technol Sydney, Sch Comp Sci, Sydney, NSW 2007, Australia.; Cao, ZH (通讯作者)，Univ Tasmania, Dept Discipline ICT, Hobart, Tas 7001, Australia.
EM zhcaonctu@gmail.com
RI Pandey, Hari Mohan/M-9658-2015; Cao, Zehong/O-7351-2016
OI Pandey, Hari Mohan/0000-0002-9128-068X; Cao, Zehong/0000-0003-3656-0328;
   Wang, Chen/0000-0001-9780-0984
FU National Key Research and Development Program of China [2017YFB1402401]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2017YFB1402401.
CR Aitken S., 2008, COBRA COBRA CT ONTOL
   Alexander N, 2008, STAT MED, V27, P3442, DOI 10.1002/sim.3199
   Arias-de-Reyna E, 2018, IEEE SIGNAL PROC MAG, V35, P130, DOI 10.1109/MSP.2018.2840156
   Atzori L, 2010, COMPUT NETW, V54, P2787, DOI 10.1016/j.comnet.2010.05.010
   Bakal G, 2018, J BIOMED INFORM, V82, P189, DOI 10.1016/j.jbi.2018.05.003
   Barnaghi P, 2012, INT J SEMANT WEB INF, V8, P1, DOI [10.4018/jswis.2012010101, 10.4018/jswis.201201010149]
   Berners-Lee T, 2001, NATURE, V410, P1023, DOI 10.1038/35074206
   Buhmann L, 2015, LECT NOTES COMPUT SC, V9367, P76, DOI 10.1007/978-3-319-25010-6_5
   Chen B, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-255
   Choi K. S., 2007, NAT LANG PROC KNOWL, P14
   Nogueira JRD, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI 2015), P455, DOI 10.1109/ICHI.2015.72
   Dillon TS, 2009, LECT NOTES COMPUT SC, V5585, P2, DOI 10.1007/978-3-642-02830-4_2
   Doerr M, 2003, AI MAG, V24, P75
   Jeon P. B., 2011, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, P337, DOI 10.1109/WI-IAT.2011.88
   Kotis K., 2012, 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), P630, DOI 10.1109/CISIS.2012.200
   Kotis K., 2012, ALIGNING SMART CONTR, P39
   Lin HL, 2017, PROCEDIA COMPUT SCI, V108, P345, DOI 10.1016/j.procs.2017.05.045
   Liu Y, 2019, PERS UBIQUIT COMPUT, V23, P413, DOI 10.1007/s00779-019-01202-7
   Loukil F, 2018, P INT COMP SOFTW APP, P701, DOI 10.1109/COMPSAC.2018.10322
   Mayer S, 2017, IEEE INTELL SYST, V32, P96, DOI 10.1109/MIS.2017.9
   Ohlsson A, 2012, JIMD REP, V2, P113, DOI 10.1007/8904_2011_59
   Palavalli A, 2016, IEEE INT C SEMANT CO, P91, DOI 10.1109/ICSC.2016.35
   Perera C, 2014, IEEE COMMUN SURV TUT, V16, P414, DOI 10.1109/SURV.2013.042313.00197
   Pomp A, 2017, IEEE INT C SEMANT CO, P262, DOI 10.1109/ICSC.2017.45
   Raskin V., 1998, Machine Translation, V13, P135, DOI 10.1023/A:1008039100776
   Reka SS, 2018, RENEW SUST ENERG REV, V91, P90, DOI 10.1016/j.rser.2018.03.089
   Sheth A, 2016, IEEE INTELL SYST, V31, P108, DOI 10.1109/MIS.2016.34
   Terzija V., 2011, P IEEE PES GEN M, P1, DOI DOI 10.1109/PES.2011.6039929.
   Terziyan V. Y., 2003, LECT NOTES COMPUTER, P279
   Tsai CW, 2014, WIREL NETW, V20, P2201, DOI 10.1007/s11276-014-0731-0
   Vega-Barbas M., 2012, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS 2012), P725, DOI 10.1109/IMIS.2012.99
   Wang W, 2013, AUTOMATIKA, V54, P388, DOI 10.7305/automatika.54-4.414
   Yang H, 2019, IISE TRANS, V51, P1190, DOI 10.1080/24725854.2018.1555383
   Yorio Zachary, 2018, 2018 Systems and Information Engineering Design Symposium (SIEDS), P24, DOI 10.1109/SIEDS.2018.8374744
   Zarko I. P., 2015, LNCS, V9001
   Zhao XB, 2018, IEEE COMMUN LETT, V22, P1462, DOI 10.1109/LCOMM.2018.2831669
   Zhou BL, 2019, INT J COMPUT VISION, V127, P302, DOI 10.1007/s11263-018-1140-0
NR 37
TC 2
Z9 3
U1 1
U2 15
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2327-4662
J9 IEEE INTERNET THINGS
JI IEEE Internet Things J.
PD MAY
PY 2020
VL 7
IS 5
BP 4473
EP 4484
DI 10.1109/JIOT.2019.2960150
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LS0EE
UT WOS:000536066300069
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Huang, CL
   Huang, CC
AF Huang, Chung-Lin
   Huang, Chung-Chi
TI Study on customized knowledge graph of student pilot learning in FITS
   training
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Customized knowledge graph; FAA-industry training standards; machine
   learning
AB Knowledge graphs are useful sources for various AI applications, however the basic paradigm to support pilot training is still unclear. In the paper, It is proposed to generate the customized knowledge graph of flight trainings using machine learning method for the flight training program. In order to provide the successful key to the further understanding of the learning problems between the students and the instructors. In this research, we collected data from an aeronautical academic in Taiwan that students were trained for Recreation Pilot License Program. We performed a test on 24 students at the first of each training course, 16 data of collected been used on building the module, 8 of them used to exam the module. There are 12 courses in the training program, and 30 hours total time were suggested by academic. The score which we applied on test were based on LCG method which is the sum of Maneuver and SRM Grades. For the indicators of course component in Learner Centered Grading, namely (a) CCS1: Operation & Effect of Controls; (b) CCS2: Straight & Level; (c) CCS3: Climbing & Descending; (d) CCS4: Turning; (e) CCS5: Stalling; (f) CCS6: Revision; (g) CCS7: Circuits; (h) CCS8: Cross-Wind Training; (i) CCS9: Circuit Emergency; (j) CCS10: Solo Circuit; (k) CCS11: Forced Landing; and (l) CCS12: Precautionary & Searching Landing. Through the method of Knowledge Graph, we deduct and predict the number of hours that need to be added for each student's learning. Using the dynamic knowledge graph to display the key issues of the course learning continuously, and make follow-up decisions for the students, instructors and airliners.
C1 [Huang, Chung-Lin] Lunghwa Univ Sci & Technol, Dept Appl Foreign Language, Taoyuan 333, Taiwan.
   [Huang, Chung-Chi] Far East Univ, Dept Automat & Control Engn, Tainan, Taiwan.
RP Huang, CL (通讯作者)，Lunghwa Univ Sci & Technol, Dept Appl Foreign Language, Taoyuan 333, Taiwan.
EM alston@mail.lhu.edu.tw
CR [Anonymous], 2016, SEMANTICS
   Ayers F.H, 2006, J AVIATION AEROSPACE, V3, P13
   Beckman W.S., 2008, COLLEGIATE AVIATION, V26, P1
   Boeing Commercial Airplanes, PILOT OUTLOOK 2018 2
   Chen Z, P 28 AAAI C ART INT
   Craig P.A., 2005, COLLEGIATE AVIATION, V23, P23
   Craig P.A, 2009, INT J APPL AVIATION, V9
   Dornan W.A., 2006, COLLEGIATE AVIATION, V24, P58
   Federal Aviation Administration, FITS CURRICULUM ACCE
   Federal Aviation Administration, FAA IND TRAINING STA
   Federal Aviation Administration, FITS GENERIC PRIVATE
   Federal Aviation Administration, CFI ASE FUNDAMENTALS
   Federal Aviation Administration, 2016, STAT REPORT, P1
   Glista T, 2003, FAA AVIATION NEWS, P1
   Huang SB, 2011, PROC FRONT EDUC CONF
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Zhao Q, 2018, MATEC WEB CONF, V151, DOI 10.1051/matecconf/201815105003
NR 17
TC 0
Z9 0
U1 0
U2 6
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 40
IS 4
BP 7969
EP 7979
DI 10.3233/JIFS-189619
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RN7PP
UT WOS:000640545600019
DA 2022-08-23
ER

PT J
AU Bakhshi, M
   Nematbakhsh, M
   Mohsenzadeh, M
   Rahmani, AM
AF Bakhshi, Mahdi
   Nematbakhsh, Mohammadali
   Mohsenzadeh, Mehran
   Rahmani, Amir Masoud
TI Data-driven construction of SPARQL queries by approximate question graph
   alignment in question answering over knowledge graphs
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge graph; Answering natural language questions; Disambiguation of
   interpretations; Pairwise graph alignment
ID SEMANTIC WEB
AB As increasingly more semantic real-world data is stored in knowledge graphs, providing intuitive and effective query methods for end-users is a fundamental and challenging task. Since there is a gap between the plain natural language question (NLQ) and structured data, most RDF question/answering (QJA) systems construct SPARQL queries from NLQs and obtain precise answers from knowledge graphs. A major challenge is how to disambiguate the mapping of phrases and relations in a question to the dataset items, especially in complex questions. In this paper, we propose a novel data-driven graph similarity framework for RDF Q/A to extract the query graph patterns directly from the knowledge graph instead of constructing them with semantically mapped items. An uncertain question graph is presented to model the interpretations of an NLQ based on which our problem is reduced to a graph alignment problem. In formulating the alignment, both the lexical and structural similarity of graphs are considered, hence, the target RDF subgraph is used as a query graph pattern to construct the final query. We create a pruned entity graph dynamically based on the complexity of an input question to reduce the search space on the knowledge graph. Moreover, to reduce the calculating cost of the graph similarity, we compute the similarity scores only for same-distance graph elements and equip the process with an edge association aware surface form extraction method. Empirical studies over real datasets indicate that our proposed approach is flexible and effective as it outperforms state-of-the-art methods significantly. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Bakhshi, Mahdi; Mohsenzadeh, Mehran; Rahmani, Amir Masoud] Islamic Azad Univ, Dept Comp Engn, Sci & Res Branch, Tehran, Iran.
   [Nematbakhsh, Mohammadali] Univ Isfahan, Dept Comp Engn, Esfahan, Iran.
RP Nematbakhsh, M (通讯作者)，Univ Isfahan, Dept Comp Engn, Esfahan, Iran.
EM mbakhshi@srbiau.ac.ir; nematbakhsh@eng.ui.ac.ir;
   mohsenzadeh@srbiau.ac.ir; rahmani@srbiau.ac.ir
RI Bakhshi/ABA-6044-2020; Mohsenzadeh, Mehran/AAA-4884-2022; Rahmani, Amir
   Masoud/K-2702-2013
OI Bakhshi/0000-0001-9110-0557; Mohsenzadeh, Mehran/0000-0001-6835-409X;
   Rahmani, Amir Masoud/0000-0001-8641-6119
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
   Aggarwal N., 2012, P INT LINK DAT ILD 2, V913, P96
   Bast H., 2015, P 24 ACM INT C INF K, P1431, DOI [10.1145/2806416.2806472, DOI 10.1145/2806416.2806472]
   Beaumont R., 2015, SEMGRAPHQA QALD5 HMS
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Chenhao Zhu, 2016, Semantic Technology. 5th Joint International Conference, JIST 2015. Revised Selected Papers: LNCS 9544, P219, DOI 10.1007/978-3-319-31676-5_16
   Clark C, 2014, BIOINFORMATICS, V30, P2351, DOI 10.1093/bioinformatics/btu307
   Cui WY, 2017, PROC VLDB ENDOW, V10, P565, DOI 10.14778/3055540.3055549
   Daiber J., 2013, P 9 INT C SEM SYST, P121, DOI 10.1145/2506182.2506198
   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
   Deng D, 2015, VLDB J, V24, P143, DOI 10.1007/s00778-014-0367-9
   Diefenbach D, 2018, KNOWL INF SYST, V55, P529, DOI 10.1007/s10115-017-1100-y
   El-Kebir M, 2011, LECT N BIOINFORMAT, V7036, P225, DOI 10.1007/978-3-642-24855-9_20
   Elmsallati A, 2016, IEEE ACM T COMPUT BI, V13, P689, DOI 10.1109/TCBB.2015.2474391
   Ferre S, 2017, SEMANT WEB, V8, P405, DOI 10.3233/SW-150208
   Forascu C., 2015, WORK NOT CLEF 2015 C
   Freitas A, 2015, COMM COM INF SC, V548, P191, DOI 10.1007/978-3-319-25518-7_16
   Freitas A, 2013, DATA KNOWL ENG, V88, P126, DOI 10.1016/j.datak.2013.08.003
   Freitas A, 2012, IEEE INTERNET COMPUT, V16, P24, DOI 10.1109/MIC.2011.141
   Freitas Andre, 2014, NAT LANG INT WEB DAT, P1
   Giannone C., 2013, CEUR WORKSHOP P, P1179
   Guyonvarch J., 2013, CEUR WORKSHOP P, V1179
   Hakimov S, 2015, LECT NOTES COMPUT SC, V9103, P103, DOI 10.1007/978-3-319-19581-0_8
   Han L., 2013, 2 JOINT C LEX COMP S
   Han L., 2012, P 21 ACM INT C INF K, P2090
   He S., 2014, CLEF, P1249
   Hoffner K., 2016, SEMANT WEB, V9, P1, DOI DOI 10.3233/SW-160249
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Hung HH, 2014, VLDB J, V23, P401, DOI 10.1007/s00778-013-0322-1
   Kaufmann E, 2010, J WEB SEMANT, V8, P377, DOI 10.1016/j.websem.2010.06.001
   Klau GW, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S1-S59
   Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211
   Liu K, 2015, IEEE INTELL SYST, V30, P26, DOI 10.1109/MIS.2015.70
   Liu SS, 2017, 2017 INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS, ELECTRONICS AND CONTROL (ICCSEC), P875, DOI 10.1109/ICCSEC.2017.8446958
   Lopez V, 2013, J WEB SEMANT, V21, P3, DOI 10.1016/j.websem.2013.05.006
   Lopez V, 2011, SEMANT WEB, V2, P125, DOI 10.3233/SW-2011-0041
   Mazzeo G. M, 2016, EDBT, P608, DOI DOI 10.5441/002/EDBT.2016.60
   Mendes PN, 2012, LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P1813
   Milano M, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1635-7
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nakashole N., 2012, P 2012 JOINT C EMP M, P1135, DOI DOI 10.1080/17517575.2015.1080301
   Park S., 2014, CEUR WORKSHOP P, P1236
   Reddy S, 2016, T ASSOC COMPUT LING, V4, P127, DOI DOI 10.1162/TACL_A_00088
   Reddy Siva, 2014, TACL, P377, DOI DOI 10.1162/TACL_A_00190
   Ruseti S., 2015, CEUR WORKSHOP P, V1391
   Soylu A., 2015, VOILA ISWC, P91
   Syed Z, 2015, COMM COM INF SC, V548, P199, DOI 10.1007/978-3-319-25518-7_17
   Unger C., 2016, SEMANTIC WEB EVALUAT, V641, P171, DOI [10.1007/978-3-319-46565-4_13, DOI 10.1007/978-3-319-46565-4_13]
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Unger C, 2014, LECT NOTES COMPUT SC, V8714, P100, DOI 10.1007/978-3-319-10587-1_2
   Veyseh A. P. B., 2016, P TEXTGRAPHS 10 WORK, P15, DOI DOI 10.18653/V1/W16-1403
   Vogelstein JT, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0121002
   Xu K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2326
   Yahya M, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1107, DOI 10.1145/2505515.2505677
   Yahya Mohamed, 2012, EMNLP
   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
   Zheng WG, 2019, INFORM SCIENCES, V481, P141, DOI 10.1016/j.ins.2018.12.032
   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
   Zou L, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P313, DOI 10.1145/2588555.2610525
NR 63
TC 12
Z9 12
U1 3
U2 47
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 MAY 15
PY 2020
VL 146
AR 113205
DI 10.1016/j.eswa.2020.113205
PG 19
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 KU4AX
UT WOS:000519653400029
DA 2022-08-23
ER

PT J
AU Zhu, X
   Gu, YM
   Xiao, ZF
AF Zhu, Xian
   Gu, Yueming
   Xiao, Zhifeng
TI HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a
   Two-Stage Framework Based on Deep Transfer Learning
SO FRONTIERS IN GENETICS
LA English
DT Article
DE biobert; knowledge graph; herb; chemical; disease; gene; BERT; ontology
ID ALTERNATIVE MEDICINE; REMEDIES; HEALTH
AB Recent advances have witnessed a growth of herbalism studies adopting a modern scientific approach in molecular medicine, offering valuable domain knowledge that can potentially boost the development of herbalism with evidence-supported efficacy and safety. However, these domain-specific scientific findings have not been systematically organized, affecting the efficiency of knowledge discovery and usage. Existing knowledge graphs in herbalism mainly focus on diagnosis and treatment with an absence of knowledge connection with molecular medicine. To fill this gap, we present HerbKG, a knowledge graph that bridges herbal and molecular medicine. The core bio-entities of HerbKG include herbs, chemicals extracted from the herbs, genes that are affected by the chemicals, and diseases treated by herbs due to the functions of genes. We have developed a learning framework to automate the process of HerbKG construction. The resulting HerbKG, after analyzing over 500K PubMed abstracts, is populated with 53K relations, providing extensive herbal-molecular domain knowledge in support of downstream applications. The code and an interactive tool are available at https://github.com/FeiYee/HerbKG.
C1 [Zhu, Xian] Nanjing Univ, Sch Informat Management, Nanjing, Peoples R China.
   [Zhu, Xian] Nanjing Univ Chinese Med, Sch Hlth Econ & Management, Nanjing, Peoples R China.
   [Gu, Yueming] Univ Melbourne, Fac Engn & Informat Technol, Sch Comp & Informat Syst, Parkville, Vic, Australia.
   [Xiao, Zhifeng] Penn State Erie, Behrend Coll, Sch Engn, Erie, PA USA.
RP Xiao, ZF (通讯作者)，Penn State Erie, Behrend Coll, Sch Engn, Erie, PA USA.
EM zux2@psu.edu
CR Al-Saleem J, 2021, J CHEM INF MODEL, V61, P4058, DOI 10.1021/acs.jcim.1c00642
   [Anonymous], 2004, HERB MED WIK FREE EN
   [Anonymous], 2004, DIS WIK FREE ENC
   [Anonymous], 2004, GEN WIK FREE ENC
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Babu PS, 2007, PHYTOMEDICINE, V14, P15, DOI 10.1016/j.phymed.2006.11.005
   Bairoch Amos, 2018, J Biomol Tech, V29, P25, DOI 10.7171/jbt.18-2902-002
   Beltagy I., 2019, P 2019 C EMPIRICAL M, P3615
   Benesty J, 2009, SPRINGER TOP SIGN PR, V2, P1, DOI 10.1007/978-3-642-00296-0
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Boudin M, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P3225, DOI 10.1145/3340531.3418510
   Brackman G, 2008, BMC MICROBIOL, V8, DOI 10.1186/1471-2180-8-149
   Burstein HJ, 1999, NEW ENGL J MED, V340, P1733, DOI 10.1056/NEJM199906033402206
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Egede LE, 2002, DIABETES CARE, V25, P324, DOI 10.2337/diacare.25.2.324
   Ernst P, 2014, PROC INT CONF DATA, P1254, DOI 10.1109/ICDE.2014.6816754
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Guarino N, 2009, HDB ONTOLOGIES, P1, DOI DOI 10.1007/978-3-540-92673-3_0
   Kerdjoudj F., 2015, ARXIV
   Leaman R, 2016, BIOINFORMATICS, V32, P2839, DOI 10.1093/bioinformatics/btw343
   Lee CW, 2007, J PHARMACOL SCI, V104, P19, DOI 10.1254/jphs.FP0061204
   Lee J.-S., 2019, ARXIV
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Li JP, 2016, ONCOL REP, V35, P1501, DOI 10.3892/or.2015.4493
   Liu Z, 2019, CSEDU: PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED EDUCATION - VOL 1, P15, DOI [10.5220/0006647600150021, 10.1142/S2010194518600157]
   Mahdisoltani F., 2014, 7 BIENNIAL C INNOVAT
   Miao F., 2018 IEEE 3 ADV INFO, P1194, DOI [10.1109/iaeac.2018.8577236, DOI 10.1109/IAEAC.2018.8577236]
   Mosbach Marius, 2020, STABILITY FINE TUNIN
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Pechenick EA, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0137041
   Roozbeh J, 2013, IRAN J KIDNEY DIS, V7, P492
   Saha A., 2018, 32 AAAI C ARTIFICIAL
   Sanh V., 2019, ARXIV191001108
   Shen Y, 2019, J MED SYST, V43, DOI 10.1007/s10916-019-1182-z
   Sohn S, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-402
   Some BMJ, 2019, STUD HEALTH TECHNOL, V264, P1548, DOI 10.3233/SHTI190528
   Sosa D.N., 2019, PAC S BIOCOMPUT, V25, P463, DOI [10.1142/9789811215636_0041, DOI 10.1142/9789811215636_0041]
   Su C., 2021, CBKH CORNELL BIOMEDI, DOI [10.1101/2021.03.12.21253461, DOI 10.1101/2021.03.12.21253461]
   Szelenyi I, 2002, DRUG TODAY, V38, P265, DOI 10.1358/dot.2002.38.4.668337
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang XY, 2019, LECT NOTES COMPUT SC, V11446, P709, DOI 10.1007/978-3-030-18576-3_42
   Webber J., 2018, PROGRAMMATIC INTRO N
   Wei CH, 2019, NUCLEIC ACIDS RES, V47, pW587, DOI 10.1093/nar/gkz389
   Wei CH, 2018, BIOINFORMATICS, V34, P80, DOI 10.1093/bioinformatics/btx541
   Wei CH, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/918710
   Wei CH, 2015, IEEE J BIOMED HEALTH, V19, P1385, DOI 10.1109/JBHI.2015.2422651
   Wei CH, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0038460
   Wise C., 2020, ARXIV
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   Zheng SJ, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbaa344
   Zheng ZQ, 2020, 11TH IEEE INTERNATIONAL CONFERENCE ON KNOWLEDGE GRAPH (ICKG 2020), P560, DOI 10.1109/ICBK50248.2020.00084
   Zhu X, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11167318
   Zhu YJ, 2020, HEALTH INFORM J, V26, P2737, DOI 10.1177/1460458220937101
NR 55
TC 0
Z9 0
U1 3
U2 3
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 APR 27
PY 2022
VL 13
AR 799349
DI 10.3389/fgene.2022.799349
PG 17
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA 1G3VL
UT WOS:000795778500001
PM 35571049
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Khan, N
   Ma, ZM
   Yan, L
   Ullah, A
AF Khan, Nasrullah
   Ma, Zongmin
   Yan, Li
   Ullah, Aman
TI Hashing-based semantic relevance attributed knowledge graph embedding
   enhancement for deep probabilistic recommendation
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Knowledge graph; KGEE; Recommendation; Information relevance; Hashing;
   DNN
AB Knowledge graph embedding (KGE) is effectively exploited in providing precise and accurate recommendations from many perspectives in different application scenarios. However, such methods that utilize entire embedded Knowledge Graph (KG) without applying information-relevance regulatory constraints fail to stop the noise penetration into the underlying information. Moreover, higher computational time complexity is a CPU overhead in KG-enhanced systems and applications. The occurrence of these limitations significantly degrade the recommendation performance. Therefore, to cope with these challenges we proposed novel KGEE (Knowledge Graph Embedding Enhancement) approach of Hashing-based Semantic-relevance Attributed Graph-embedding Enhancement (H-SAGE) to model semantically-relevant higher-order entities and relations into the unique Meta-paths. For this purpose, we introduced Node Relevance-based Guided-walk (NRG) modeling technique. Further, to deal with the computational time-complexity, we converted the relevant information to the Hash-codes and proposed Deep-Probabilistic (dProb) technique to place hash-codes in the relevant hash-buckets. Again, we used dProb to generate guided function-calls to maximize the possibility of Hash-Hits in the hash-buckets. In case of Hash-Miss, we applied Locality Sensitive (LS) hashing to retrieve the required information. We performed experiments on three benchmark datasets and compared the empirical as well as the computational performance of H-SAGE with the baseline approaches. The achieved results and comparisons demonstrate that the proposed approach has outperformed the-state-of-the-art methods in the mentioned facets of evaluation.
C1 [Khan, Nasrullah; Ma, Zongmin; Yan, Li] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 210016, Peoples R China.
   [Khan, Nasrullah] COMSATS Univ Islamabad, Dept Comp Sci, Vehari 61100, Pakistan.
   [Ma, Zongmin] Collaborat Innovat Ctr Novel Software Technol & I, Nanjing 210000, Peoples R China.
   [Ullah, Aman] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
RP Ma, ZM (通讯作者)，Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 210016, Peoples R China.; Ma, ZM (通讯作者)，Collaborat Innovat Ctr Novel Software Technol & I, Nanjing 210000, Peoples R China.
EM nasrullah@nuaa.edu.cn; zongminma@nuaa.edu.cn; yanli@nuaa.edu.cn;
   dr.aman@csu.edu.cn
RI Khan, Nasrullah/AFW-7106-2022
OI Khan, Nasrullah/0000-0002-4942-9583
FU Basic Research Program of Jiangsu Province [BK20191274]; National
   Natural Science Foundation of China [62176121, 61772269]
FX This research work was supported in part by the Basic Research Program
   of Jiangsu Province (BK20191274) and the National Natural Science
   Foundation of China (62176121, 61772269).
CR Ahmadian S, 2018, APPL INTELL, V48, P4448, DOI 10.1007/s10489-018-1219-x
   Bobadilla J, 2021, NEURAL COMPUT APPL, V33, P7291, DOI 10.1007/s00521-020-05494-2
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cantador I., 2011, P 5 ACM C REC SYST, P387, DOI DOI 10.1145/2043932.2044016
   Cao Z, 2017, P IEEE INT C COMPUTE
   Chen G, 2009, INFORM PROCESS MANAG, V45, P368, DOI 10.1016/j.ipm.2008.12.004
   Dalal N, 2005, PROC CVPR IEEE, P886, DOI 10.1109/cvpr.2005.177
   Dolker EM, 2018, NDT&E INT, V99, P141, DOI 10.1016/j.ndteint.2018.07.002
   Guo XB, 2020, INFORM SCIENCES, V540, P263, DOI 10.1016/j.ins.2020.06.041
   Hansen C, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P971, DOI 10.1145/3397271.3401060
   He R, 2016, P 25 INT C WORLD WID
   He X, 2019, WORLD WIDE WEB C
   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 J, 2018, ACM/SIGIR PROCEEDINGS 2018, P505, DOI 10.1145/3209978.3210017
   Huang WJ, 2020, WIREL NETW, DOI 10.1007/s11276-020-02387-z
   Hui B, 2022, APPL INTELL, V52, P954, DOI 10.1007/s10489-021-02363-w
   Ji G, 2015, P 53 ANN M ASS COMPU, V1
   Kingma D. P., 2017, ARXIV
   Lei Sang, 2021, Expert Systems with Applications, V164, DOI 10.1016/j.eswa.2020.113992
   Liu X., 2020, IEEE T CIRC SYST VID
   Ma M, 2021, APPL INTELL, V117
   Nisha CC, 2019, APPL INTELL, V49, P1937, DOI 10.1007/s10489-018-1359-z
   Qin T., 2013, ADV NEURAL INFORM PR, P3120
   Ramachandran P., 2017, SEARCHING ACTIVATION
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Salakhutdinov R, 2009, INT J APPROX REASON, V50, P969, DOI 10.1016/j.ijar.2008.11.006
   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
   Singh PK, 2020, APPL INTELL, V50, P4708, DOI 10.1007/s10489-020-01775-4
   Sun Z, 2018, P 12 ACM C RECOMMEND
   Sun Z, 2019, ELECTRON COMMER R A, V37, DOI 10.1016/j.elerap.2019.100879
   Tan Q, 2020, P WEB C 2020
   Tran DH, 2021, NEURAL COMPUT APPL, DOI 10.1007/s00521-020-05667-z
   Wang H, 2019, WORLD WIDE WEB C
   Wang H., 2018, P 2018 WORLD WIDE WE
   Wang HM, 2021, APPL SOFT COMPUT, V104, DOI 10.1016/j.asoc.2021.107232
   Wang M, 2020, FRONT ARTIF INTEL AP, V325, P921, DOI 10.3233/FAIA200184
   Wang S, 2021, APPL INTELL, V112
   Wang X, 2019, P 25 ACM SIGKDD INT
   Wang X, 2019, AAAI CONF ARTIF INTE, P5329
   Xin X, 2019, P 42 INT ACM SIGIR C
   Yu X, 2014, P 7 ACM INT C WEB SE
   Zhang DH, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10113818
   Zhang F, 2016, P 22 ACM SIGKDD INT
   Zhang J., 2019, ELECTRON COMMER R A
   Zhang YJ, 2011, J BIOMED INFORM, V44, P1086, DOI 10.1016/j.jbi.2011.08.011
   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 WD, 2016, INFORM PROCESS MANAG, V52, P976, DOI 10.1016/j.ipm.2016.04.004
   ZHOU Y, 2020, ARXIV PREPRINT ARXIV
NR 50
TC 1
Z9 1
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/s10489-022-03235-7
EA MAY 2022
PG 26
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1A3KW
UT WOS:000791661100001
PM 35572050
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Hussey, P
   Das, S
   Farrell, S
   Ledger, L
   Spencer, A
AF Hussey, Pamela
   Das, Subhashis
   Farrell, Sharon
   Ledger, Lorraine
   Spencer, Anne
TI Title A Knowledge Graph to Understand Nursing Big Data: Case Example for
   Guidance
SO JOURNAL OF NURSING SCHOLARSHIP
LA English
DT Article
DE Intellectual disability; Knowledge graph; Ontology
AB Purpose To provide a summary of research on ontology development in the Centre of eIntegrated Care at Dublin City University, Ireland.
   Design Design science methods using Open Innovation 2.0.
   Methods This was a co-participatory study focusing on adoption of health informatics standards and translation of nursing knowledge to advance nursing theory through a nursing knowledge graph (NKG). In this article we outline groundwork research conducted through a focused analysis to advance structural interoperability and to inform integrated care in Ireland. We provide illustrated details on a simple example of initial research available through open access.
   Findings For this phase of development, the initial completed research is presented and discussed.
   Conclusions We conclude by promoting the use of knowledge graphs for visualization of diverse knowledge translation, which can be used as a primer to gain valuable insights into nursing interventions to inform big data science in the future.
   Clinical Relevance In line with stated global policy, the uptake and use of health informatics standards in design science within the profession of nursing is a priority. Nursing leaders should initially focus on health informatics standards relating to structural interoperability to inform development of NKGs. This will provide a robust foundation to gain valuable insights into articulating the nursing contribution in relation to the design of digital health and progress the nursing contribution to targeted data sources for the advancement of United Nations Sustainable Development Goal Three.
C1 [Hussey, Pamela] Dublin City Univ, Sch Nursing Psychotherapy & Community Hlth, Hlth Informat & Nursing, Ctr eIntegrated Care, Dublin, Ireland.
   [Das, Subhashis] Dublin City Univ, Ctr eIntegrated Care, Adapt Res Ctr, Dublin, Ireland.
   [Farrell, Sharon] Dublin City Univ, Ctr eIntegrated Care, Sch Nursing Psychotherapy & Community Hlth, Dublin, Ireland.
   [Ledger, Lorraine; Spencer, Anne] St Michaels House,Ballymun Rd, Dublin, Ireland.
RP Hussey, P (通讯作者)，Dublin City Univ, Fac Sci & Hlth, Ctr eIntegrated Care, Sch Nursing Psychotherapy & Community Hlth, Dublin 9, Ireland.
EM pamela.hussey@dcu.ie
RI Das, Subhashis/O-5210-2019
OI Das, Subhashis/0000-0001-9663-9009; Hussey, Pamela/0000-0003-2840-9361
FU European Union's Horizon 2020 research and innovation program under the
   ELITE-S Marie Sklodowska-Curie grant [801522]; European Regional
   Development Fund through the ADAPT Centre for Digital Content Technology
   [13/RC/2106]; DAVRA Networks; Science Foundation Ireland
FX This research has received funding from the European Union's Horizon
   2020 research and innovation program under the ELITE-S Marie
   Sklodowska-Curie grant agreement No. 801522, by Science Foundation
   Ireland and co-funded by the European Regional Development Fund through
   the ADAPT Centre for Digital Content Technology grant number 13/RC/2106
   and DAVRA Networks. We also wish to thank St. Michael's House, Ballymun,
   Dublin.
CR 9000 Store, 2020, 9001 ISO
   ADAPT Research Team, 2020, AD CTR
   Benson T., 2016, PRINCIPLES HLTH INTE
   Blobel B., 2019, PHEALTH 2019 P 16 IN
   Blobel B., 2018, EUROPEAN J BIOMEDICA, V14, P10, DOI [10.24105/ejbi.2018.14.2.3, DOI 10.24105/EJBI.2018.14.2.3]
   Blobel B., 2016, EUROPEAN J BIOMEDICA, V12
   Blobel B., PHEALTH 2020 P 17 IN
   Blobel B, 2019, FRONT MED-LAUSANNE, V6, DOI 10.3389/fmed.2019.00083
   Centre for eIntegrated Care, 2020, P HUSS
   ContSys, 2015, 139402015 ISO CONTSY
   Curley M, 2018, INNOV TECH KNOWL MAN, P1, DOI 10.1007/978-3-319-62878-3_1
   DIGITALEUROPE, 2020, VOIC DIG TRANSF IND
   eHealth Ireland, 2015, HOM
   ELITE Standards, 2020, MSCA FELL PROGR
   European Committee for Standardization, 2020, 251 CENTC
   Gruber T, 2009, ENCY DATABASE SYSTEM
   He YQ, 2020, SCI DATA, V7, DOI 10.1038/s41597-020-0523-6
   International Council of Nurses, 2020, ICNP
   International Organization for Standardization, 2019, 22272 ISODTR
   International Organization for Standardization, 2020, 215 ISOTC
   International Organization for Standardization, 2014, 181042014 ISO
   International Organization for Standardization, 2020, 215JWG7 ISOTC
   Matney S, 2011, ADV NURS SCI, V34, P6, DOI 10.1097/ANS.0b013e3182071813
   Meyer I, 2014, ACHIEVING EFFECTIVE INTEGRATED E-CARE BEYOND THE SILOS, P1, DOI 10.4018/978-1-4666-6138-7
   National Standards Authority of Ireland, 2020, STANDARDS INNOVATION
   Nursing Now, 2020, HOM
   Oemig F, 2020, STUD HEALTH TECHNOL, V273, P75, DOI 10.3233/SHTI200617
   Trotter L.J., 2020, MORE MED NURSE PRACT
   WHO, 2020, COORD GLOB RES ROADM
   World Health Organisation, 2016, SHANGH DECL PROM HLT
   World Health Organization, 2020, INT STAT CLASS DIS R, V11th
   World Health Organization (WHO), 2020, STAT WORLDS NURS 202
   World Health Organization [WHO], 2016, WHO FRAM INT PEOPL C
   Zhang Y, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102324
NR 34
TC 1
Z9 1
U1 4
U2 15
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 MAY
PY 2021
VL 53
IS 3
BP 323
EP 332
DI 10.1111/jnu.12650
EA APR 2021
PG 10
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA SA8HI
UT WOS:000636306800001
PM 33811733
OA Green Accepted, hybrid
DA 2022-08-23
ER

PT J
AU Guo, L
   Yan, F
   Lu, YQ
   Zhou, M
   Yang, T
AF Guo, Liang
   Yan, Fu
   Lu, Yuqian
   Zhou, Ming
   Yang, Tao
TI An automatic machining process decision-making system based on knowledge
   graph
SO INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
LA English
DT Article
DE Knowledge graph; process reasoning; automatic decision-making; knowledge
   base
ID DESIGN RATIONALE; ONTOLOGY; MODEL; METHODOLOGY; FRAMEWORK;
   REPRESENTATION; INFORMATION; SELECTION; BEHAVIOR; TOOLS
AB Automatic process decision-making is a key module in intelligent process design(IPD), which determines the intelligence degree of IPD and affects the quality of product design. The traditional process decision-making method fails to solve the problem of knowledge expression, especially the integration of enterprise manufacturing resources and process knowledge. What's more, heterogeneous knowledge also leads to the application of traditional knowledge mainly in keyword retrieval. So the process reasoning is mainly applied to the feature level, but the reasoning ability for the part level is weak. To overcome the above problems, the Knowledge Graph(KG) is introduced into the automatic machining process decision-making system. Firstly, a three-level information model is built to reorganize part information, process knowledge, and equipment resources based on KG. Secondly, the process reasoning framework based on KG is established, which is composed of process knowledge graph(PKG) information and process reasoning algorithm. Thirdly, to integrate process reasoning based on PKG, a hybrid reasoning algorithm based on semantic analysis(SA) and attributes weighting(AW) is built, which solved the problem of heterogeneity among process knowledge when making decisions. Finally, a prototype system was developed, and the aero-engine cone gear axis was tested to verify the effectiveness of the proposed system.
C1 [Guo, Liang; Yan, Fu; Zhou, Ming; Yang, Tao] Southwest Petr Univ, Sch Mechatron Engn, Chengdu, Peoples R China.
   [Lu, Yuqian] Univ Auckland, Dept Mech Engn, Auckland, New Zealand.
RP Guo, L (通讯作者)，Southwest Petr Univ, Sch Mechatron Engn, Chengdu, Peoples R China.
EM gl@swpu.edu.cn
RI Lu, Yuqian/U-6820-2019
OI Lu, Yuqian/0000-0001-5954-0421
FU Youth Science Foundation of National Natural Science Foundation
   [51705438]; Independent Innovation Special Fund Project [ZZCX-2017039];
   Sichuan science and technology project [2018JY0366]; Chengdu
   International Science and Technology Cooperation Project
   [:2020-GH02-00040-HZ]; Young Science and Technology Innovation Team of
   SWPU [2019CXTD02]
FX This work was supported by the Youth Science Foundation of National
   Natural Science Foundation [No.51705438]; Independent Innovation Special
   Fund Project [No.ZZCX-2017039]; Sichuan science and technology project
   [No.2018JY0366]; Chengdu International Science and Technology
   Cooperation Project [No:2020-GH02-00040-HZ]; Young Science and
   Technology Innovation Team of SWPU [No.2019CXTD02].
CR Amadori K, 2012, ADV ENG INFORM, V26, P180, DOI 10.1016/j.aei.2012.01.004
   Barbau R, 2012, COMPUT AIDED DESIGN, V44, P575, DOI 10.1016/j.cad.2012.01.008
   Baxter D, 2007, RES ENG DES, V18, P37, DOI 10.1007/s00163-007-0028-8
   Burge JE, 2008, AI EDAM, V22, P311, DOI 10.1017/S0890060408000218
   Eum K, 2013, INT J PRECIS ENG MAN, V14, P1719, DOI 10.1007/s12541-013-0231-7
   Gero JS, 2007, AI EDAM, V21, P379, DOI 10.1017/S0890060407000340
   Goel AK, 2009, AI EDAM, V23, P23, DOI 10.1017/S0890060409000080
   Grabowik C, 2017, MATEC WEB CONF, V112, DOI 10.1051/matecconf/201711206019
   Guerra-Zubiaga DA, 2016, PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2015, VOL 2B
   Guo L, 2021, INT J COMPUT INTEG M, V34, P257, DOI 10.1080/0951192X.2020.1858507
   Hardwick M, 2013, INT J ADV MANUF TECH, V68, P1023, DOI 10.1007/s00170-013-4894-0
   Helgoson M, 2012, PROC CIRP, V3, P573, DOI 10.1016/j.procir.2012.07.098
   Hicks BJ, 2002, INT J INFORM MANAGE, V22, P263, DOI 10.1016/S0268-4012(02)00012-9
   Iheukwumere-Esotu LO, 2020, ENERGIES, V13, DOI 10.3390/en13071721
   Kertkeidkachorn N, 2018, IEICE T INF SYST, VE101D, P90, DOI 10.1587/transinf.2017SWP0006
   Kestel P, 2019, ADV ENG INFORM, V39, P292, DOI 10.1016/j.aei.2019.02.001
   La Rocca G, 2012, ADV ENG INFORM, V26, P157, DOI 10.1016/j.aei.2012.02.008
   Lee HC, 2007, COMPUT IND ENG, V53, P241, DOI 10.1016/j.cie.2007.06.015
   [李秀玲 Li Xiuling], 2019, [西北工业大学学报, Journal of Northwestern Polytechnical University], V37, P1174
   [李秀玲 Li Xiuling], 2018, [计算机辅助设计与图形学学报, Journal of Computer-Aided Design & Computer Graphics], V30, P1342
   Liang JS, 2018, ROBOT CIM-INT MANUF, V53, P28, DOI 10.1016/j.rcim.2018.03.003
   Lu YQ, 2019, J INTELL MANUF, V30, P317, DOI 10.1007/s10845-016-1250-x
   Mawussi KB, 2011, COMPUT IND ENG, V61, P84, DOI 10.1016/j.cie.2011.02.016
   Mcharek M, 2019, COMPUT IND, V105, P213, DOI 10.1016/j.compind.2018.12.008
   Mu ZD, 2011, APPL MECH MATER, V66-68, P273, DOI 10.4028/www.scientific.net/AMM.66-68.273
   Novak P, 2015, ADV ENG INFORM, V29, P575, DOI 10.1016/j.aei.2015.05.001
   Peng GZ, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100956
   Poorkiany M, 2016, ADV ENG INFORM, V30, P522, DOI 10.1016/j.aei.2016.06.004
   Qin FW, 2016, ADV ENG INFORM, V30, P751, DOI 10.1016/j.aei.2016.10.001
   Radu C., 2020, 2020 9 INT C IND TEC
   Sadeghian R, 2016, INT J ADV MANUF TECH, V82, P1795, DOI 10.1007/s00170-015-7440-4
   Sedighi M, 2010, J INTELL MANUF, V21, P657, DOI 10.1007/s10845-009-0243-4
   Shan W, 2019, ROBOT CIM-INT MANUF, V58, P80, DOI 10.1016/j.rcim.2019.01.012
   Verhagen WJC, 2012, ADV ENG INFORM, V26, P5, DOI 10.1016/j.aei.2011.06.004
   Wang YL, 2011, ADV MATER RES-SWITZ, V308-310, P816, DOI 10.4028/www.scientific.net/AMR.308-310.816
   Wang ZX, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100983
   Xiong Y, 2020, ADV ENG INFORM, V45, DOI 10.1016/j.aei.2020.101135
   [Zhang Baoping 张保平], 2015, [Journal of Measurement Science and Instrumentation, 测试科学与仪器], V6, P384
   Zhang XZ, 2013, INT J PROD RES, V51, P7405, DOI 10.1080/00207543.2012.757669
   Zhang XM, 2017, COMPUT PHYS COMMUN, V211, P98, DOI 10.1016/j.cpc.2016.07.005
   Zhang YZ, 2015, ADV ENG INFORM, V29, P985, DOI 10.1016/j.aei.2015.09.002
   Zhang YZ, 2014, INT J ADV MANUF TECH, V73, P1011, DOI 10.1007/s00170-014-5864-x
   Zhao W, 2008, COMPUT IND, V59, P580, DOI 10.1016/j.compind.2008.02.002
   Zhu GG, 2017, IEEE T KNOWL DATA EN, V29, P72, DOI 10.1109/TKDE.2016.2610428
NR 44
TC 1
Z9 1
U1 35
U2 52
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0951-192X
EI 1362-3052
J9 INT J COMPUT INTEG M
JI Int. J. Comput. Integr. Manuf.
PD DEC 2
PY 2021
VL 34
IS 12
BP 1348
EP 1369
DI 10.1080/0951192X.2021.1972461
EA SEP 2021
PG 22
WC Computer Science, Interdisciplinary Applications; Engineering,
   Manufacturing; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA XE5JG
UT WOS:000695451700001
DA 2022-08-23
ER

PT J
AU Sakurai, K
   Togo, R
   Ogawa, T
   Haseyama, M
AF Sakurai, Keigo
   Togo, Ren
   Ogawa, Takahiro
   Haseyama, Miki
TI Controllable Music Playlist Generation Based on Knowledge Graph and
   Reinforcement Learning
SO SENSORS
LA English
DT Article
DE music playlist generation; knowledge graph; reinforcement learning;
   multimedia techniques; music recommendation; preference sensing
AB In this study, we propose a novel music playlist generation method based on a knowledge graph and reinforcement learning. The development of music streaming platforms has transformed the social dynamics of music consumption and paved a new way of accessing and listening to music. The playlist generation is one of the most important multimedia techniques, which aims to recommend music tracks by sensing the vast amount of musical data and the users' listening histories from music streaming services. Conventional playlist generation methods have difficulty capturing the target users' long-term preferences. To overcome the difficulty, we use a reinforcement learning algorithm that can consider the target users' long-term preferences. Furthermore, we introduce the following two new ideas: using the informative knowledge graph data to promote efficient optimization through reinforcement learning, and setting the flexible reward function that target users can design the parameters of itself to guide target users to new types of music tracks. We confirm the effectiveness of the proposed method by verifying the prediction performance based on listening history and the guidance performance to music tracks that can satisfy the target user's unique preference.
C1 [Sakurai, Keigo] Hokkaido Univ, Grad Sch Informat Sci & Technol, Kita Ku, N-14,W-9, Sapporo, Hokkaido 0600814, Japan.
   [Togo, Ren; Ogawa, Takahiro; Haseyama, Miki] Hokkaido Univ, Fac Informat Sci & Technol, Kita Ku, N-14,W-9, Sapporo, Hokkaido 0600814, Japan.
RP Sakurai, K (通讯作者)，Hokkaido Univ, Grad Sch Informat Sci & Technol, Kita Ku, N-14,W-9, Sapporo, Hokkaido 0600814, Japan.
EM sakurai@lmd.ist.hokudai.ac.jp; togo@lmd.ist.hokudai.ac.jp;
   ogawa@lmd.ist.hokudai.ac.jp; miki@ist.hokudai.ac.jp
OI Ogawa, Takahiro/0000-0001-5332-8112; Togo, Ren/0000-0002-4474-3995
FU JSPS KAKENHI [JP21H03456]
FX This work was partially supported by JSPS KAKENHI, grant number
   JP21H03456.
CR Andric A, 2006, MULTIMED TOOLS APPL, V29, P127, DOI 10.1007/s11042-006-0003-9
   [Anonymous], MUSIC BIZ CONSUMER I
   Ben-Elazar S, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P445, DOI 10.1145/3018661.3018718
   Bonnin G, 2015, ACM COMPUT SURV, V47, DOI 10.1145/2652481
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen CW, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P527, DOI 10.1145/3240323.3240342
   Cho K., 2014, 8 WORKSH SYNT SEM ST, DOI [DOI 10.3115/V1/W14-4012, 10.3115/v1/w14-4012]
   Choi K., 2016, ARXIV
   Dias R, 2017, MULTIMED TOOLS APPL, V76, P14375, DOI 10.1007/s11042-016-3836-x
   Eriksson M, 2020, J CULT ECON-UK, V13, P415, DOI 10.1080/17530350.2019.1708780
   Gatzioura A, 2021, IEEE T KNOWL DATA EN, V33, P1819, DOI 10.1109/TKDE.2019.2952099
   Ge YQ, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P2261, DOI 10.1145/3397271.3401431
   Guo QY, 2020, IEEE T KNOWL DATA EN, V34, P3549, DOI [10.1038/s41366-020-0625-4, 10.1109/TKDE.2020.3028705]
   He RN, 2016, IEEE DATA MINING, P191, DOI [10.1109/ICDM.2016.88, 10.1109/ICDM.2016.0030]
   Huang LW, 2021, KNOWL-BASED SYST, V213, DOI 10.1016/j.knosys.2020.106706
   Irene RT, 2019, EUR SIGNAL PR CONF
   Jannach D., 2015, P 9 ACM C REC SYST, P187
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Kaelbling LP, 1996, J ARTIF INTELL RES, V4, P237, DOI 10.1613/jair.301
   Kaya M., 2018, ACM RECOMMENDER SYST, P1
   Kim J, 2020, NEUROCOMPUTING, V376, P214, DOI 10.1016/j.neucom.2019.10.033
   Korver J, 2019, THESIS FLORIDA SO CO
   Lee J.H., 2011, P ISMIR, P109
   Liebman E., 2014, ARXIV
   Liebman E, 2019, MIS QUART, V43, P765, DOI 10.25300/MISQ/2019/14750
   Matt C, 2015, BUS INFORM SYST ENG+, V57, P339, DOI 10.1007/s12599-015-0401-5
   Nabizadeh A. H., 2016, P 9 INT C C COMP SCI, P61, DOI [10.1145/2948992.2949010, DOI 10.1145/2948992.2949010]
   Nguyen TT, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P677, DOI 10.1145/2566486.2568012
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Oliver N., P 7 INT SOC MUS INF
   Paul Dip, 2020, Emerging Technology in Modelling and Graphics. Proceedings of IEM Graph 2018. Advances in Intelligent Systems and Computing (AISC 937), P279, DOI 10.1007/978-981-13-7403-6_26
   Pauws S., P 2 INT SOC MUS INF, P222
   Pichl M, 2016, IEEE INT SYM MULTIM, P475, DOI [10.1109/ISM.2016.0107, 10.1109/ISM.2016.139]
   Sakurai Keigo, 2020, 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), P942, DOI 10.1109/GCCE50665.2020.9291748
   Sakurai K, 2022, ITE TRANS MEDIA TECH, V10, P8, DOI 10.3169/mta.10.8
   Sakurai K, 2021, 2021 IEEE 3RD GLOBAL CONFERENCE ON LIFE SCIENCES AND TECHNOLOGIES (IEEE LIFETECH 2021), P53, DOI [10.1109/LifeTech52111.2021.9391870, 10.1109/LIFETECH52111.2021.9391870]
   Schedl M., 2017, P LATE BREAKINGDEMOS
   Shih S.Y., 2018, ARXIV
   Song W., 2019, ARXIV
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Swanson K., 2013, MEIEA J, V13, P207, DOI [10.25101/13.10, DOI 10.25101/13.10]
   Troncy R., 2018, ACM RECOMMENDER SYST, P1
   TURNBULL BW, 1976, J R STAT SOC B, V38, P290
   Vall A., 2018, ARXIV
   Vall A., P 9 ACM C REC SYST V, P387
   Volkovs M, 2018, RECSYS CHALLENGE'18: PROCEEDINGS OF THE ACM RECOMMENDER SYSTEMS CHALLENGE 2018, DOI 10.1145/3267471.3267480
   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 Z, 2014, AAAI CONF ARTIF INTE, P1112
   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
   Yang HP, 2019, LECT NOTES COMPUT SC, V11447, P416, DOI 10.1007/978-3-030-18579-4_25
   Yang YH, 2008, IEEE T AUDIO SPEECH, V16, P448, DOI 10.1109/TASL.2007.911513
   Zhang ZW, 2022, IEEE T KNOWL DATA EN, V34, P249, DOI 10.1109/TKDE.2020.2981333
   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 56
TC 0
Z9 0
U1 3
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD MAY
PY 2022
VL 22
IS 10
AR 3722
DI 10.3390/s22103722
PG 17
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA 1Q0BR
UT WOS:000802365100001
PM 35632130
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Mu, RH
   Zeng, XQ
AF Mu, Ruihui
   Zeng, Xiaoqin
TI 被撤回的出版物: Collaborative Filtering Recommendation Algorithm Based on
   Knowledge Graph (Retracted article. See vol. 2019, 2019)
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article; Retracted Publication
AB To solve the problem that collaborative filtering algorithm only uses the user-item rating matrix and does not consider semantic information, we proposed a novel collaborative filtering recommendation algorithm based on knowledge graph. Using the knowledge graph representation learning method, this method embeds the existing semantic data into a low-dimensional vector space. It integrates the semantic information of items into the collaborative filtering recommendation by calculating the semantic similarity between items. The shortcoming of collaborative filtering algorithm which does not consider the semantic information of items is overcome, and therefore the effect of collaborative filtering recommendation is improved on the semantic level. Experimental results show that the proposed algorithm can get higher values on precision, recall, and F-measure for collaborative filtering recommendation.
C1 [Mu, Ruihui; Zeng, Xiaoqin] Hohai Univ, Coll Comp & Informat, Nanjing 210098, Jiangsu, Peoples R China.
   [Mu, Ruihui] Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang 453003, Peoples R China.
RP Mu, RH (通讯作者)，Hohai Univ, Coll Comp & Informat, Nanjing 210098, Jiangsu, Peoples R China.; Mu, RH (通讯作者)，Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang 453003, Peoples R China.
EM muruihui@126.com
FU National Key Research and Development Plan Key Projects of China
   [2017YFC0405800]; National Natural Science Foundation of China
   [60971088, 60571048, 61432008, 61375121]; Natural Science Foundation of
   the Colleges and Universities in Jiangsu Province of China [17KJB520022]
FX This research was partially supported by the National Key Research and
   Development Plan Key Projects of China under Grant no. 2017YFC0405800,
   the National Natural Science Foundation of China Grant (Grant nos.
   60971088, 60571048, 61432008, and 61375121), and the Natural Science
   Foundation of the Colleges and Universities in Jiangsu Province of
   China, no. 17KJB520022.
CR Abel F, 2010, NEW REV HYPERMEDIA M, V16, P33, DOI 10.1080/13614568.2010.497193
   Al-khalifa H., 2006, P C INN INF TECHN, P89
   Anand D, 2014, EXPERT SYST APPL, V41, P2424, DOI 10.1016/j.eswa.2013.09.041
   Antoine B., 2013, P NIPS, P926
   Bell RM, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P95
   Biancalana Claudio, 2009, 2009 International Conference on Computational Science and Engineering (CSE), P1060, DOI 10.1109/CSE.2009.492
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cai Y., 2010, P INT C WEB INF SYST, P266
   Cai Y, 2014, NEURAL NETWORKS, V58, P98, DOI 10.1016/j.neunet.2014.05.017
   Choi SM, 2012, EXPERT SYST APPL, V39, P8079, DOI 10.1016/j.eswa.2012.01.132
   Choy S., 2006, P 2006 IEEE WIC ACM, P262
   Du Q, 2016, NEUROCOMPUTING, V204, P142, DOI 10.1016/j.neucom.2015.10.135
   Fernandez-Tobias I, 2014, LECT NOTES BUS INF P, V188, P125
   Font F, 2013, INT J SEMANT WEB INF, V9, P1, DOI 10.4018/jswis.2013040101
   Fremal S., 2017, EXPERT SYSTEMS APPL, V77, P222
   Garcin F., 2012, P INT JOINT C WEB IN, P164
   Godoy D, 2016, INT J INTELL SYST, V31, P314, DOI 10.1002/int.21753
   Hsieh W.-T., 2009, EXPERT SYSTEMS APPL, V36, P8363
   Jin S., 2009, P 2009 IEEE INT C GR, P223
   Kim HN, 2012, INFORM SYST, V37, P61, DOI 10.1016/j.is.2011.07.002
   Kumar H., 2014, INFORM SCI, V281, P299
   Ma J, 2013, PROCEEDINGS OF THE ASME/ISCIE INTERNATIONAL SYMPOSIUM ON FLEXIBLE AUTOMATION, ISFA 2012, P549
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Morrison P. J., 2008, INFORM PROCESSING MA, V44, P1352
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Noll M. G., 2007, P 6 INT SEM WEB 2 AS, P257
   Riedl J, 2001, P INT C WORLD WID WE, P285, DOI 10.1145/371920.372071
   Robertson S. E., 1994, P 17 ANN INT ACM SIG, P320
   Smyth B., 2007, COMPUT J, V40, P130
   Song B, 2014, LECT NOTES COMPUT SC, V8795, P188, DOI 10.1007/978-3-319-11897-0_22
   Tan B., 2006, P 12 ACM SIGKDD INT, P888
   Vallet D., 2010, P 32 EUR C ADV INF R, P531
   VizSearch Y.-J., 2005, COMPUT EDUC, V45, P503
   Wang LZ, 2013, FUTURE GENER COMP SY, V29, P739, DOI 10.1016/j.future.2012.09.001
   Xie H., 2012, J COMPUTER SCI TECHN, V27, P611
   Xu S., 2008, P 31 ANN INT ACM SIG, P265
   Yang Y., 2015, P 14 ICWL C WEB BAS, P252
   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
   Zheng Q., 2016, P 14 ICWL C WEB BAS, P272
   Zhou D., 2005, ADV NEURAL INFORM PR, V15, P78
   Zhu ZQ, 2015, PATTERN RECOGN, V48, P2592, DOI 10.1016/j.patcog.2015.01.001
   Zijian Zhang, 2013, Advances in Brain Inspired Cognitive Systems. 6th International Conference, BICS 2013. Proceedings: LNCS 7888, P172, DOI 10.1007/978-3-642-38786-9_20
NR 43
TC 9
Z9 9
U1 1
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.
PY 2018
VL 2018
AR 9617410
DI 10.1155/2018/9617410
PG 11
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA GQ3EO
UT WOS:000441544000001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Alves, VM
   Korn, D
   Pervitsky, V
   Thieme, A
   Capuzzi, SJ
   Baker, N
   Chirkova, R
   Ekins, S
   Muratov, EN
   Hickey, A
   Tropsha, A
AF Alves, Vinicius M.
   Korn, Daniel
   Pervitsky, Vera
   Thieme, Andrew
   Capuzzi, Stephen J.
   Baker, Nancy
   Chirkova, Rada
   Ekins, Sean
   Muratov, Eugene N.
   Hickey, Anthony
   Tropsha, Alexander
TI Knowledge-based approaches to drug discovery for rare diseases
SO DRUG DISCOVERY TODAY
LA English
DT Review
DE Informatics; Rare diseases; Drug discovery; Data mining; Knowledge
   graphs
ID METFORMIN; CHALLENGES; PHENOTYPES; TRICKS; FUTURE
AB The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.
C1 [Alves, Vinicius M.; Korn, Daniel; Pervitsky, Vera; Thieme, Andrew; Capuzzi, Stephen J.; Muratov, Eugene N.; Tropsha, Alexander] Univ N Carolina, Lab Mol Modeling, Div Chem Biol & Med Chem, UNC Eshelman Sch Pharm, Chapel Hill, NC 27599 USA.
   [Alves, Vinicius M.; Hickey, Anthony] Univ N Carolina, UNC Eshelman Sch Pharm, UNC Catalyst Rare Dis, Chapel Hill, NC 27599 USA.
   [Baker, Nancy] ParlezChem, 123 W Union St, Hillsborough, NC 27278 USA.
   [Chirkova, Rada] North Carolina State Univ, Dept Comp Sci, Raleigh, NC 27695 USA.
   [Ekins, Sean] Collaborat Pharmaceut Inc, 840 Main Campus Dr,Lab 3510, Raleigh, NC 27606 USA.
   [Muratov, Eugene N.] Univ Fed Paraiba, Dept Pharmaceut Sci, Joao Pessoa, PB, Brazil.
RP Tropsha, A (通讯作者)，Univ N Carolina, Lab Mol Modeling, Div Chem Biol & Med Chem, UNC Eshelman Sch Pharm, Chapel Hill, NC 27599 USA.; Hickey, A (通讯作者)，Univ N Carolina, UNC Eshelman Sch Pharm, UNC Catalyst Rare Dis, Chapel Hill, NC 27599 USA.
EM ahickey@unc.edu; alex_tropsha@unc.edu
RI Alves, Vinicius M./G-7955-2015
OI Alves, Vinicius M./0000-0002-6182-1748
FU National Center for Advancing Translational Sciences [OT2TR003441];
   National Institutes of Health [1U01CA207160]; University of North
   Carolina Research Opportunity Initiative grant; National Institute of
   General Medical Sciences [GM122196-02A1]; National Institute of
   Neurological Disorders and Stroke [1R43NS107079-01, 3R43NS107079-01S1];
   FY2018 UNC Research Opportunities Initiative Award
FX A.T. appreciates the financial support for studies in his group provided
   by the National Center for Advancing Translational Sciences
   (OT2TR003441) , the National Institutes of Health (1U01CA207160) , and
   the University of North Carolina Research Opportunity Initiative grant
   to support the Infohub for Rare Dis-eases project. S.E. acknowledges
   funding to Collaborations Phar-maceuticals Inc. from the National
   Institute of General Medical Sciences (GM122196-02A1) , National
   Institute of Neurological Disorders and Stroke (1R43NS107079-01 and
   3R43NS107079-01S1) , and the FY2018 UNC Research Opportunities
   Initiative Award.
CR Al-Ali H, 2016, MEDCHEMCOMM, V7, P788, DOI 10.1039/c6md00129g
   Alves V.M., 2020, APPROACHING COMPLEX, P77
   Amberger JS, 2015, NUCLEIC ACIDS RES, V43, pD789, DOI 10.1093/nar/gku1205
   Anderson E, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-70026-w
   Anighoro A, 2014, J MED CHEM, V57, P7874, DOI 10.1021/jm5006463
   [Anonymous], AVEXIS REC FDA APPR
   Ashburn TT, 2004, NAT REV DRUG DISCOV, V3, P673, DOI 10.1038/nrd1468
   Austin CP, 2018, CTS-CLIN TRANSL SCI, V11, P21, DOI 10.1111/cts.12500
   Bailey CJ, 2017, DIABETOLOGIA, V60, P1566, DOI 10.1007/s00125-017-4318-z
   Baker NC, 2018, DRUG DISCOV TODAY, V23, P661, DOI 10.1016/j.drudis.2018.01.018
   Baker NC, 2010, J BIOMED INFORM, V43, P510, DOI 10.1016/j.jbi.2010.03.008
   Baxter K, 2013, SCI TRANSL MED, V5, DOI 10.1126/scitranslmed.3003505
   Baynam GS, 2020, NAT GENET, V52, P21, DOI 10.1038/s41588-019-0552-2
   Bellgard MI, 2014, HEALTH POLICY TECHN, V3, P325, DOI 10.1016/j.hlpt.2014.08.007
   Bernstam EV, 2010, J BIOMED INFORM, V43, P104, DOI 10.1016/j.jbi.2009.08.006
   Bi YL, 2017, BIOORG MED CHEM LETT, V27, P2962, DOI 10.1016/j.bmcl.2017.05.010
   BioMarin Pharmaceutical, SINGL ARM STUD EV EF
   Bizon C, 2019, J CHEM INF MODEL, V59, P4968, DOI 10.1021/acs.jcim.9b00683
   Bloss S, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0172532
   Capuzzi SJ, 2018, J MED CHEM, V61, P3582, DOI [10.1021/acsdmedchem.8b00035, 10.1021/acs.jmedchem.8b00035]
   Capuzzi SJ, 2018, J CHEM INF MODEL, V58, P212, DOI 10.1021/acs.jcim.7b00589
   CBO, RES DEV PHARM IND
   Cherkasov A, 2014, J MED CHEM, V57, P4977, DOI 10.1021/jm4004285
   Children's Hospital Medical Center Cincinnati., TEST DRUG EFF CYST F
   Choi BK, 2018, P NATL ACAD SCI USA, V115, P10666, DOI 10.1073/pnas.1806643115
   Coiera Enrico, 2015, GUIDE HLTH INFORM, V3rd, DOI DOI 10.1201/B13617
   Colbaugh Rich, 2018, AMIA Annu Symp Proc, V2018, P340
   Condren Michelle E, 2013, J Pediatr Pharmacol Ther, V18, P8, DOI 10.5863/1551-6776-18.1.8
   Crooke ST, 2021, NAT BIOTECHNOL, V39, P671, DOI 10.1038/s41587-021-00945-0
   da Silva FS, 2017, J BIOMED SEMANT, V8, DOI 10.1186/s13326-017-0127-z
   Nguyen DT, 2017, NUCLEIC ACIDS RES, V45, pD995, DOI 10.1093/nar/gkw1072
   Dai J, 2014, CHEM-BIOL INTERACT, V216, P34, DOI 10.1016/j.cbi.2014.03.013
   Dwan K, 2014, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD005088.pub3
   Ehrhart F, 2021, SCI DATA, V8, DOI 10.1038/s41597-021-00905-y
   Ekins S, 2019, NAT MATER, V18, P435, DOI 10.1038/s41563-019-0338-z
   Ekins S, 2018, PHARM RES-DORDR, V35, DOI 10.1007/s11095-018-2481-7
   Ekins S, 2017, NAT BIOTECHNOL, V35, P117, DOI 10.1038/nbt.3787
   Ekins S, 2016, PHARM RES-DORDR, V33, P2594, DOI 10.1007/s11095-016-2029-7
   Ekins S, 2014, J COMPUT AID MOL DES, V28, P997, DOI 10.1007/s10822-014-9762-y
   Ekins S, 2011, DRUG DISCOV TODAY, V16, P298, DOI 10.1016/j.drudis.2011.02.016
   FDA, FDA APPR OR TREATM S
   FDA, FDA APPR OC RAR CHRO
   FDA, FDA APPR 1 DRUG SPIN
   FDA, DEV PROD RAR DIS CON
   Floris M, 2018, TRENDS GENET, V34, P558, DOI 10.1016/j.tig.2018.04.004
   Fourches D, 2015, NAT CHEM BIOL, V11, P535, DOI 10.1038/nchembio.1881
   Franks ME, 2004, LANCET, V363, P1802, DOI 10.1016/S0140-6736(04)16308-3
   Frezza AM, 2019, CURR OPIN ONCOL, V31, P114, DOI 10.1097/CCO.0000000000000502
   Gasteiger J, 2020, CHEMPHYSCHEM, V21, P2233, DOI 10.1002/cphc.202000518
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   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
   Gurovich Y, 2019, NAT MED, V25, P60, DOI 10.1038/s41591-018-0279-0
   Henry S, 2017, J BIOMED INFORM, V74, P20, DOI 10.1016/j.jbi.2017.08.011
   Hoban MD, 2016, BLOOD, V127, P839, DOI 10.1182/blood-2015-09-618587
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Hsieh TC, 2019, GENET MED, V21, P2807, DOI 10.1038/s41436-019-0566-2
   Hughes JP, 2011, BRIT J PHARMACOL, V162, P1239, DOI 10.1111/j.1476-5381.2010.01127.x
   Hunter L.E., 2017, DATA SCI, V1, P1
   Institute of Medicine (US) Committee on Accelerating Rare Diseases Research and Orphan Product Development, 2010, RAR DIS ORPH PROD AC
   Jayasundara K, 2019, ORPHANET J RARE DIS, V14, DOI 10.1186/s13023-018-0990-4
   Jia JM, 2018, FRONT GENET, V9, DOI 10.3389/fgene.2018.00587
   Kagashe I, 2017, J MED INTERNET RES, V19, DOI 10.2196/jmir.7393
   Kakkis ED, 2015, ORPHANET J RARE DIS, V10, DOI 10.1186/s13023-014-0195-4
   Kaufmann P, 2018, ORPHANET J RARE DIS, V13, DOI 10.1186/s13023-018-0936-x
   Kempf L, 2018, AM J MED GENET A, V176, P773, DOI 10.1002/ajmg.a.38413
   Kern Steven E, 2009, Expert Rev Clin Pharmacol, V2, P609, DOI 10.1586/ecp.09.40
   Kiernan MC, 2011, LANCET, V377, P942, DOI 10.1016/S0140-6736(10)61156-7
   Kinch MS, 2015, DRUG DISCOV TODAY, V20, P393, DOI 10.1016/j.drudis.2014.09.003
   Kostoff RN, 2008, TECHNOL FORECAST SOC, V75, P226, DOI 10.1016/j.techfore.2007.11.007
   Kouznetsova J, 2014, EMERG MICROBES INFEC, V3, DOI 10.1038/emi.2014.88
   Kuroda M, 2015, J VIROL, V89, P6481, DOI 10.1128/JVI.03156-14
   Landrum MJ, 2018, NUCLEIC ACIDS RES, V46, pD1062, DOI 10.1093/nar/gkx1153
   Lane TR, 2019, ANTIMICROB AGENTS CH, V63, DOI 10.1128/AAC.01142-19
   Langtry HD, 1999, DRUGS, V57, P967, DOI 10.2165/00003495-199957060-00015
   Lee HM, 2016, SCHIZOPHR RES TREAT, V2016, DOI 10.1155/2016/6378137
   Litterman Nadia K, 2014, F1000Res, V3, P261, DOI 10.12688/f1000research.5564.1
   Longhi A, METFORMIN MAINTENANC
   Lopez-Lopez E, 2021, J CHEM INF MODEL, V61, P26, DOI 10.1021/acs.jcim.0c01301
   Madrid PB, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0060579
   Makurvet F.D., MED DRUG DISCOV, V9
   Marie PJ, 2015, BONE CANCER: PRIMARY BONE CANCER AND BONE METASTASES, 2ND EDITION, P39, DOI 10.1016/B978-0-12-416721-6.00004-2
   Melnykov V, 2020, J CLASSIF, V37, P97, DOI 10.1007/s00357-019-09314-8
   Middleton PG, 2019, NEW ENGL J MED, V381, P1809, DOI 10.1056/NEJMoa1908639
   Miller RG, 2012, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD001447.pub3
   Milne CP, 2017, CLIN THER, V39, P2173, DOI 10.1016/j.clinthera.2017.08.016
   Miotto R, 2018, BRIEF BIOINFORM, V19, P1236, DOI 10.1093/bib/bbx044
   Molenaar RJ, 2017, BMJ OPEN, V7, DOI 10.1136/bmjopen-2016-014961
   Moser HW, 2005, ARCH NEUROL-CHICAGO, V62, P1073, DOI 10.1001/archneur.62.7.1073
   National Center for Biotechnology Information, 1998, GENES DIS
   NewsWise, DIAGN 2 127 2 SOLV Y
   NGUYEN TA, 1987, AI MAG, V8, P69
   NIH, FAQS RAR DIS GEN RAR
   Nosengo N, 2016, NATURE, V534, P314, DOI 10.1038/534314a
   Novartis, FDA APPR NOV KES OF
   Nurputra DK, 2013, ANN HUM GENET, V77, P435, DOI 10.1111/ahg.12031
   Patton M, 25 ACM SIGPLAN INT C
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   PhrMA, DEC INN RAR DIS 2005
   PhRMA, BIOP RES IND PROF
   Pierzynowska K, 2020, METAB BRAIN DIS, V35, P1237, DOI 10.1007/s11011-020-00617-z
   Polamreddy P, 2019, DRUG DISCOV TODAY, V24, P789, DOI 10.1016/j.drudis.2018.11.022
   Proschak E, 2019, J MED CHEM, V62, P420, DOI 10.1021/acs.jmedchem.8b00760
   Pryor R, 2015, BIOCHEM J, V471, P307, DOI 10.1042/BJ20150497
   Przybyla P, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw145
   Pushpakom S, 2019, NAT REV DRUG DISCOV, V18, P41, DOI 10.1038/nrd.2018.168
   Rappaport N, 2017, NUCLEIC ACIDS RES, V45, pD877, DOI 10.1093/nar/gkw1012
   Ravi D, 2017, IEEE J BIOMED HEALTH, V21, P4, DOI 10.1109/JBHI.2016.2636665
   Robinson I., 2013, GRAPH DATABASES
   Roos M, 2017, ADV EXP MED BIOL, V1031, P165, DOI 10.1007/978-3-319-67144-4_9
   Sang ST, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2167-5
   Schattenberg JM, 2021, J HEPATOL, V74, P1344, DOI 10.1016/j.jhep.2021.01.013
   Schork NJ, 1997, AM J RESP CRIT CARE, V156, pS103, DOI 10.1164/ajrccm.156.4.12-tac-5
   Schreurs AS, 2020, AM J PHYSIOL-CELL PH, V319, pC734, DOI 10.1152/ajpcell.00068.2020
   Scott LJ, 2018, DRUGS, V78, P269, DOI 10.1007/s40265-018-0861-2
   Shen FC, 2018, JMIR MED INF, V6, P46, DOI 10.2196/11301
   Soua R, 2018, GLOB INFORM INFRAS
   Sun W, 2017, AM J MED GENET A, V173, P2307, DOI 10.1002/ajmg.a.38326
   SWANSON DR, 1990, B MED LIBR ASSOC, V78, P29
   Tindera M., FORBES
   Tricco AC, 2018, BMC MED INFORM DECIS, V18, DOI 10.1186/s12911-018-0621-y
   Uddin M, 2019, NPJ DIGIT MED, V2, DOI 10.1038/s41746-019-0191-0
   Vertex Pharmaceuticals, TIM VERT COMM ADV CY
   Wakap SN, 2020, EUR J HUM GENET, V28, P165, DOI 10.1038/s41431-019-0508-0
   Weber M., NEUROLOGY, V96, P4437
   WHO-World Health Organization, NEGLECTED TROPICAL D
   Wishart DS, 2008, NUCLEIC ACIDS RES, V36, pD901, DOI 10.1093/nar/gkm958
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Zhao MG, 2020, NAR GENOM BIOINFORM, V2, DOI 10.1093/nargab/lqaa032
   Zhao MZ, 2018, INTERDISCIP SCI, V10, P195, DOI 10.1007/s12539-017-0270-3
NR 130
TC 1
Z9 1
U1 23
U2 27
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1359-6446
EI 1878-5832
J9 DRUG DISCOV TODAY
JI Drug Discov. Today
PD FEB
PY 2022
VL 27
IS 2
BP 490
EP 502
DI 10.1016/j.drudis.2021.10.014
PG 13
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Pharmacology & Pharmacy
GA YR5OG
UT WOS:000750040900011
PM 34718207
DA 2022-08-23
ER

PT J
AU Zhang, GZ
   Cao, XG
   Zhang, MY
AF Zhang, Guozhen
   Cao, Xiangang
   Zhang, Mengyuan
TI A Knowledge Graph System for the Maintenance of Coal Mine Equipment
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID SEMANTIC WEB
AB With the rapid development of coal mine intelligent technology, the complexity of coal mine equipment has been continuously improved and the equipment maintenance resources have been continuously enriched. The traditional coal mine equipment maintenance knowledge management technology can no longer meet the current needs of equipment maintenance knowledge management, and the problems of low utilization rate, poor interoperability, and serious loss of knowledge have gradually emerged. It is urgent to study new knowledge system construction and knowledge management application technology for large-scale coal mine equipment maintenance resources. Knowledge graph is a technical method to describe the relationship between things in the objective world by using a graph model. It can effectively solve the problem of knowledge dynamic mining and management under large-scale data. Therefore, this paper focuses on the establishment of a coal mine equipment maintenance knowledge graph system by using knowledge graph technology. The main research contents are as follows: Firstly, based on the current situation that there is no unified basic knowledge system in the field of coal mine equipment maintenance, this paper establishes the coal mine equipment maintenance ontology (CMEMO) to effectively solve the problem that there are no unified representation, integration, and sharing of coal mine equipment maintenance knowledge in this field and provide support for the construction of coal mine equipment maintenance knowledge graph. Then, aiming at the problem that the traditional named-entity recognition method has a poor recognition effect and relies too much on artificial feature design, this paper proposes a named-entity recognition model for coal mine equipment maintenance based on neural network (BERT-BiLSTM-CRF) and applies the model to the coal mine equipment maintenance data set for verification. The experimental results show that, under the same data set, the entity recognition effect of this model is more leading than that of other models. Finally, through demand analysis and architecture design, combined with the constructed ontology model of coal mine equipment maintenance field, the entity identification of coal mine equipment maintenance is completed based on the BERT-BiLSTM-CRF model and the Django application framework is used to build the coal mine equipment maintenance knowledge graph system to realize the functions of each module of the knowledge graph system.
C1 [Zhang, Guozhen; Cao, Xiangang; Zhang, Mengyuan] Xian Univ Sci & Technol, Sch Mech Engn, Xian 710054, Shaanxi, Peoples R China.
   [Zhang, Guozhen; Cao, Xiangang; Zhang, Mengyuan] Key Lab Intelligent Testing Mine Mech & Elect Equ, Xian 710054, Shaanxi, Peoples R China.
RP Zhang, GZ (通讯作者)，Xian Univ Sci & Technol, Sch Mech Engn, Xian 710054, Shaanxi, Peoples R China.; Zhang, GZ (通讯作者)，Key Lab Intelligent Testing Mine Mech & Elect Equ, Xian 710054, Shaanxi, Peoples R China.
EM 1318649644@qq.com
RI Zhang, Guozhen/AEG-8878-2022
CR Amit S., 2012, INTRO KNOWLEDGE GRAP
   Berners-Lee T., 2001, SCI AM, V284, P34
   Brzychczy E, 2020, RESOURCES-BASEL, V9, DOI 10.3390/resources9020017
   Carlson Andrew, 2010, P 24 C ART INT AAAI
   Deng S, 2019, DESIGN IMPLEMENTATIO
   Ding Enjie, 2020, Industry and Mine Automation, V46, P5, DOI 10.13272/j.issn.1671-251x.17624
   Duque-Ramos A, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0104463
   Etzioni O, 2008, COMMUN ACM, V51, P68, DOI 10.1145/1409360.1409378
   Gallagher S, 2012, GOOGLE MICROSOFT TAU
   Gu R, 2014, IEEE INT CONF BIG DA, P561, DOI 10.1109/BigData.2014.7004274
   Han X, 2016, RES KEY ISSUES INTEL
   Nguyen HL, 2020, INFORM FUSION, V61, P56, DOI 10.1016/j.inffus.2020.03.014
   Li J., 2009, RES CONSTRUCTION MET
   Li M, 2017, COAL SCI TECHNOL, V45, P121
   Liu YW, 2020, J ADV TRANSPORT, V2020, DOI 10.1155/2020/7985719
   Loow J, 2019, MINING METALL EXPLOR, V36, P701, DOI 10.1007/s42461-019-00104-9
   Pasca M., 2006, P 21 NAT C ART INT, V6, P1400
   Ren H., 2019, ACTA COALAE SINICA, V44, P2923
   Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62
   Speer R, 2012, LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P3679
   Stecula K, 2018, ADV INTELL SYST, V637, P74, DOI 10.1007/978-3-319-64465-3_8
   Tutak M, 2017, IOP C SER EARTH ENV, V95, DOI 10.1088/1755-1315/95/4/042026
   [王国法 Wang Guofa], 2020, [煤炭科学技术, Coal Science and Technology], V48, P1
   [王国法 Wang Guofa], 2019, [煤炭学报, Journal of China Coal Society], V44, P349
NR 24
TC 0
Z9 0
U1 19
U2 19
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 NOV 10
PY 2021
VL 2021
AR 2866751
DI 10.1155/2021/2866751
PG 13
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA YQ9GW
UT WOS:000749612300004
OA gold
DA 2022-08-23
ER

PT J
AU Abate, MA
   Meyer-Stout, PJ
   Stamatakis, MK
   Gannett, PM
   Dunsworth, TS
   Nardi, AH
AF Abate, MA
   Meyer-Stout, PJ
   Stamatakis, MK
   Gannett, PM
   Dunsworth, TS
   Nardi, AH
TI Development and evaluation of computerized problem-based learning cases
   emphasizing basic sciences concepts
SO AMERICAN JOURNAL OF PHARMACEUTICAL EDUCATION
LA English
DT Article
ID KNOWLEDGE MAPS; EDUCATION; STUDENTS
AB Computerized problem-based learning (PBL) cases were developed that replicate the critical thinking process undertaken by students during traditional facilitator-led PBL sessions. Eight computerized PBL cases emphasizing medicinal chemistry and pharmaceutics concepts were developed initially from among those used previously during facilitator-led sessions. Two case versions were developed: one incorporating concept maps emphasizing key ideas and another lacking the maps. Quiz scores and attitudes were measured and compared among students using the computerized PBL cases (with and without maps) and the previous year's class who attended only facilitator-led sessions. Scores on the 12 quizzes administered did not differ significantly among the different student groups. Students consistently felt that the PBL sessions improved their problem-solving skills, use of information resources, and communication/interaction skills. Computerized PBL cases can be a useful alternative to facilitator-led sessions to reduce faculty time demands in this process while retaining the benefits.
C1 W Virginia Univ, Sch Pharm, Morgantown, WV 26506 USA.
   W Virginia Univ, Coll Human Resources & Educ, Morgantown, WV 26506 USA.
RP Abate, MA (通讯作者)，W Virginia Univ, Sch Pharm, Morgantown, WV 26506 USA.
RI Gannett, Peter M/J-3347-2015; Gannett, Peter/AAL-1932-2020
OI Gannett, Peter M/0000-0002-7859-5468; Gannett, Peter/0000-0002-7859-5468
CR ALBANESE MA, 1993, ACAD MED, V68, P52, DOI 10.1097/00001888-199301000-00012
   [Anonymous], 1993, AM J PHARM EDUC, V57, P377
   Catney CM, 1999, AM J PHARM EDUC, V63, P97
   CHOLOWSKI KM, 1995, J NURS EDUC, V34, P148
   DEES SM, 1992, ADDICT BEHAV, V17, P307, DOI 10.1016/0306-4603(92)90037-V
   DONNER RS, 1993, B MED LIBR ASSOC, V81, P294
   DUNCANHEWITT WC, 1992, AM J PHARM EDUC, V56, P242
   EDMONDSON KM, 1994, ACAD MED, V69, P108, DOI 10.1097/00001888-199402000-00004
   GALINSKY RE, 1991, DICP ANN PHARMAC, V25, P431, DOI 10.1177/106002809102500420
   Gardner SF, 1996, AM J PHARM EDUC, V60, P56
   Haworth IS, 1998, AM J PHARM EDUC, V62, P398
   HIRUMI A, 1991, J EDUC RES, V84, P273, DOI 10.1080/00220671.1991.10886027
   IRVINE LMC, 1995, J ADV NURS, V21, P1175, DOI 10.1046/j.1365-2648.1995.21061175.x
   JANG R, 1990, AM J PHARM EDUC, V54, P161
   Maudsley G, 1999, ACAD MED, V74, P178, DOI 10.1097/00001888-199902000-00016
   MAYLATH NS, 1989, J SCHOOL HEALTH, V59, P269
   MOORE GT, 1994, ACAD MED, V69, P983, DOI 10.1097/00001888-199412000-00017
   NICHOL MB, 1991, AM J PHARM EDUC, V55, P369
   NORMAN GR, 1988, MED EDUC, V22, P279, DOI 10.1111/j.1365-2923.1988.tb00754.x
   ONEIL EH, 1992, JAMA-J AM MED ASSOC, V268, P1146, DOI 10.1001/jama.268.9.1146
   PRAWAT RS, 1992, AM J EDUC, V100, P354, DOI 10.1086/444021
   SCHOR NF, 1995, ACAD MED, V70, P814, DOI 10.1097/00001888-199509000-00017
   SMALL PA, 1998, J MED EDUC, V63, P848
   Smith B E, 1992, J Nurs Educ, V31, P16
   WATSON GR, 1989, MED TEACH, V11, P265, DOI 10.3109/01421598909146411
   WILKERSON LA, 1994, ACAD MED, V69, P646, DOI 10.1097/00001888-199408000-00009
   1992, MOSAIC, V23, P37
NR 27
TC 28
Z9 29
U1 2
U2 9
PU AMER ASSOC COLL PHARMACY
PI ALEXANDRIA
PA 1426 PRINCE STREET, ALEXANDRIA, VA 22314-2815 USA
SN 0002-9459
EI 1553-6467
J9 AM J PHARM EDUC
JI Am. J. Pharm. Educ.
PD SPR
PY 2000
VL 64
IS 1
BP 74
EP 82
PG 9
WC Education, Scientific Disciplines; Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Pharmacology & Pharmacy
GA 305EJ
UT WOS:000086526200014
DA 2022-08-23
ER

PT J
AU Ait-Mlouk, A
   Vu, XS
   Jiang, LL
AF Ait-Mlouk, Addi
   Vu, Xuan-Son
   Jiang, Lili
TI WINFRA: A Web-Based Platform for Semantic Data Retrieval and Data
   Analytics
SO MATHEMATICS
LA English
DT Article
DE heterogeneous data federation; RDF; knowledge graph; data mining;
   natural language processing; association rules
ID ASSOCIATION RULES
AB Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and knowledge graph representation. The processing step integrates data from multiple sources virtually by creating virtual databases. Afterwards, the developed RDF Generator is built to generate RDF files for different data sources, together with SPARQL queries, to support semantic data search and knowledge graph representation. Furthermore, some application cases are provided to demonstrate how it facilitates advanced data analytics over semantic data and showcase our proposed approach toward semantic association rules.
C1 [Ait-Mlouk, Addi; Vu, Xuan-Son; 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 addi.ait-mlouk@cs.umu.se; sonvx@cs.umu.se; lili.jiang@cs.umu.se
OI Addi, Ait-Mlouk/0000-0003-0385-9390; Jiang, Lili/0000-0002-7788-3986
FU Federated Database project [570066000]; Umea University, Sweden
FX This work is supported by the Federated Database project (570066000)
   funded by Umea University, Sweden.
CR Afolabi Ibukun, 2017, International Journal of Metadata, Semantics and Ontologies, V12, P28
   Agrawal R., 1994, P 20 INT C VER LARG, P487
   Alonen M, 2013, LECT NOTES COMPUT SC, V7955, P204, DOI 10.1007/978-3-642-41242-4_25
   Barati M, 2017, KNOWL-BASED SYST, V133, P183, DOI 10.1016/j.knosys.2017.07.009
   Brunetti J. M., 2012, P 2012 INT C POST DE, V914, P5
   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
   Djokic-Petrovic M, 2017, J BIOMED SEMANT, V8, DOI 10.1186/s13326-017-0151-z
   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
   Goethals B., 2002, PATTERN DETECT DISCO, P145
   Hahsler M., 2017, J BUSINESS EC, V87, P317, DOI [10.1007/s11573-016-0822-8, DOI 10.1007/S11573-016-0822-8]
   Honnibal M., 2015, P C EMP METH NAT LAN, P1373
   Hu KY, 1999, LECT NOTES ARTIF INT, V1711, P443
   Marinica C, 2010, IEEE T KNOWL DATA EN, V22, P784, DOI 10.1109/TKDE.2010.29
   Martinez-Rodriguez JL, 2020, SEMANT WEB, V11, P255, DOI 10.3233/SW-180333
   Meester B.D., 2019, MAPPING LANGUAGES AN
   MUGGLETON S, 1995, NEW GENERAT COMPUT, V13, P245, DOI 10.1007/BF03037227
   MUGGLETON S, 1994, J LOGIC PROGRAM, V20, P629, DOI 10.1016/0743-1066(94)90035-3
   Nebot V, 2012, KNOWL-BASED SYST, V25, P51, DOI 10.1016/j.knosys.2011.05.009
   Sanchez D, 2008, DATA MIN KNOWL DISC, V16, P313, DOI 10.1007/s10618-008-0092-3
   Settanni M, 2015, FRONT PSYCHOL, V6, DOI 10.3389/fpsyg.2015.00898
   Skjaeveland Martin G., 2015, The Semantic Web: ESWC 2012 Satellite Events. Revised Selected Papers: LNCS 7540, P361, DOI 10.1007/978-3-662-46641-4_27
   Stuhr M., 2011, CEUR WORKSHOP P, P73
   Svatek V, 2006, LECT NOTES COMPUT SC, V4289, P163
   Vu X.S., 2019, ARXIV190508635
   Vu X.S., 2018, P 9 GLOB WORDNET C, P173
   Vu XS, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3595, DOI 10.1145/3308558.3314138
   Zhaohui Huang, 2009, Proceedings of the 2009 Fifth International Joint Conference on INC, IMS and IDC, P338, DOI 10.1109/NCM.2009.131
NR 28
TC 1
Z9 1
U1 1
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD NOV
PY 2020
VL 8
IS 11
AR 2090
DI 10.3390/math8112090
PG 15
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA OY1SH
UT WOS:000594032200001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Vandewiele, G
   Steenwinckel, B
   De Turck, F
   Ongenae, F
AF Vandewiele, Gilles
   Steenwinckel, Bram
   De Turck, Filip
   Ongenae, Femke
TI MINDWALC: mining interpretable, discriminative walks for classification
   of nodes in a knowledge graph
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; Data mining; Explainable AI; Decision tree; Random
   forest; Feature extraction
AB Background Leveraging graphs for machine learning tasks can result in more expressive power as extra information is added to the data by explicitly encoding relations between entities. Knowledge graphs are multi-relational, directed graph representations of domain knowledge. Recently, deep learning-based techniques have been gaining a lot of popularity. They can directly process these type of graphs or learn a low-dimensional numerical representation. While it has been shown empirically that these techniques achieve excellent predictive performances, they lack interpretability. This is of vital importance in applications situated in critical domains, such as health care. Methods We present a technique that mines interpretable walks from knowledge graphs that are very informative for a certain classification problem. The walks themselves are of a specific format to allow for the creation of data structures that result in very efficient mining. We combine this mining algorithm with three different approaches in order to classify nodes within a graph. Each of these approaches excels on different dimensions such as explainability, predictive performance and computational runtime. Results We compare our techniques to well-known state-of-the-art black-box alternatives on four benchmark knowledge graph data sets. Results show that our three presented approaches in combination with the proposed mining algorithm are at least competitive to the black-box alternatives, even often outperforming them, while being interpretable. Conclusions The mining of walks is an interesting alternative for node classification in knowledge graphs. Opposed to the current state-of-the-art that uses deep learning techniques, it results in inherently interpretable or transparent models without a sacrifice in terms of predictive performance.
C1 [Vandewiele, Gilles; Steenwinckel, Bram; De Turck, Filip; Ongenae, Femke] Univ Ghent, IMEC, IDLab, Technol Pk Zwijnaarde 126, B-9000 Ghent, Belgium.
RP Vandewiele, G (通讯作者)，Univ Ghent, IMEC, IDLab, Technol Pk Zwijnaarde 126, B-9000 Ghent, Belgium.
EM gilles.vandewiele@ugent.be
RI Steenwinckel, Bram/AAZ-1142-2021
OI Vandewiele, Gilles/0000-0001-9531-0623; De Turck,
   Filip/0000-0003-4824-1199
FU Fonds Wetenschappelijk Onderzoek (FWO) [1S31417N, 1SA0219N]; FWO
FX GV (1S31417N) and BS (1SA0219N) are both funded by a strategic base
   research grant of Fonds Wetenschappelijk Onderzoek (FWO). Publication
   costs are funded by FWO.
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Backstrom L, 2011, P 4 ACM INT C WEB SE, DOI [10.1145/1935826.1935914.1011.4071, DOI 10.1145/1935826.1935914.1011.4071]
   Bhagat S, 2011, SOC NETW DATA ANAL, DOI [10.1007/978-1-4419-8462-351101.3291, DOI 10.1007/978-1-4419-8462-351101.3291]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   BREIMAN L, 2001, MACH LEARN, V0045
   Breiman L., 1984, BIOMETRICS, VVolume 37, P237, DOI DOI 10.2307/2530946
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Cochez M, 2017, INT SEM WEB C
   de Vries GKD, 2015, J WEB SEMANT, V35, P71, DOI 10.1016/j.websem.2015.08.002
   de Vries GK, 2013, JOINT EUR C MACH LEA
   Defferrard M., 2016, 30 INT C NEURAL INFO, P3844
   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
   Fanizzi N, 2008, INT SEM WEB C
   FLINT RF, 1960, GEOL SOC AM BULL, V71, P1809, DOI 10.1130/0016-7606(1960)71[1809:DASTFS]2.0.CO;2
   Goyal P, 2018, KNOWL-BASED SYST, V151, P78, DOI 10.1016/j.knosys.2018.03.022
   Hamilton Will, 2017, NEURIPS
   Hamilton WL, 2017, IEEE DATA ENG B, DOI [10.1038/nature23018.1709.05584, DOI 10.1038/NATURE23018.1709.05584]
   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
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kuhn M, 2004, C50 DECISION TREES R
   Latouche P., 2015, EUR S ART NEUR NETW, V22, P207
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Losch U, 2012, SEM WEB C
   Lundberg S., 2017, ARXIV PREPRINT ARXIV
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Quinlan J. R., 1986, Machine Learning, V1, P81, DOI 10.1007/BF00116251
   Ribeiro M. T., 2016, SIGKDD
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9982, P186, DOI 10.1007/978-3-319-46547-0_20
   Rudin C, 2019, NAT MACH INTELL, V1, P206, DOI 10.1038/s42256-019-0048-x
   Saeed MR, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI), P378, DOI 10.1109/IRI.2018.00063
   Schaffer C, 1991, EUR WORK SESS LEARN
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Slack D, 2019, ARXIV191102508
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Vishwanathan SVN, 2010, J MACH LEARN RES, V11, P1201
   Wilcke X., 2017, DATA SCI, V1, P39, DOI [10.3233/ds-170007, DOI 10.3233/DS-170007, 10.3233/DS-170007]
NR 38
TC 3
Z9 3
U1 3
U2 12
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 191
DI 10.1186/s12911-020-01134-w
PG 15
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Medical Informatics
GA PG5QX
UT WOS:000599790500008
PM 33317504
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Yang, Y
   Meng, GF
AF Yang, Yi
   Meng, Guanfei
TI The evolution and research framework of carbon footprint: Based on the
   perspective of knowledge mapping
SO ECOLOGICAL INDICATORS
LA English
DT Article
DE Bibliometric analysis; Research hotspots; Knowledge bases; Research
   frontiers
ID TIME-SERIES; EMISSIONS; CONSUMPTION; INDEX; COCITATION; GROWTH; WATER;
   LAND
AB Carbon footprint can monitor the degree of human stress on the ecological environment and has been widely used to measure the level of climate change and sustainable development. To systematically sort out and analyse the development history of carbon footprint research, a keyword co-appearance and literature co-citation knowledge mapping has been drawn, the carbon footprint research hotspots, knowledge bases, research frontiers, and research features have been analysed, from Web of Science core database since 2008 as a data source. From the review, by establishing a carbon footprint research framework from the perspectives of both government and market, the problems of regional environmental governance efficiency, fairness mechanisms, and carbon emission rights are analysed under the government-led and market-led approaches and reviewed the development of carbon footprint research methods. In addition, the boundary and the applicability of the carbon footprint accounting methods has been discussed. The results show: (1) China is increasingly becoming an ideal for carbon footprint research due to its varied and complicated problem of climate and environment (2) Future research hotspots will focus more on carbon sinks, land use changes, energy consumption, industrial ecology, buildings, livestock, and international trade. (3) Half-life, burst, and centrality indicate that the knowledge bases of carbon footprint are the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios, ISO14040 (2006) and Livestock's long shadow. (4) The latest international fronts of carbon footprint research focus on the carbon footprint generated in international trade, innovations of methods and discrimination and definition of the theory concept, for example, the expression of carbon footprint means the use of either ecological land area units or physical weight units. (4) The research features are mainly concerned with the carbon footprint application of the multi-region input-output model in international trade, multi-scale assessment of human impact on the environment and sustainable development, and resource-environment effects associated with food loss and waste.
C1 [Yang, Yi; Meng, Guanfei] Xian Univ Technol, Sch Econ & Management, Xian 710054, Peoples R China.
RP Yang, Y (通讯作者)，Xian Univ Technol, Sch Econ & Management, Xian 710054, Peoples R China.
EM yangyi_nwpu@xaut.edu.cn
FU National Social Science Foundation of China Western Project [15XJL009];
   Science and Technology Planning Project of Xi'an [2017109SF/RK003-(5)];
   Soft Science Research Program of Shaanxi-Joint Project [2018KRLY07]
FX This work was supported by the National Social Science Foundation of
   China Western Project (15XJL009), Science and Technology Planning
   Project of Xi'an (2017109SF/RK003-(5)), Soft Science Research Program of
   Shaanxi-Joint Project (2018KRLY07).
CR Ali Y, 2017, RENEW SUST ENERG REV, V67, P921, DOI 10.1016/j.rser.2016.09.022
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], 2017, DOCUMENTATION INFORM
   [Anonymous], 2008, PAS 2050 SPEC ASS LI
   [Anonymous], 2006, ENV MAN LIF CYCL ASS
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], 2004, GHG PROT
   [Anonymous], 2012, 14067 ISO
   [Anonymous], [No title captured]
   [Anonymous], 2015, ENH ACT CLIM CHANG C
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [白伟荣 Bai Weirong], 2014, [生态学报, Acta Ecologica Sinica], V34, P7486
   Braun T, 2006, SCIENTOMETRICS, V69, P169, DOI 10.1007/s11192-006-0147-4
   Burke M, 2018, NATURE, V557, P549, DOI 10.1038/s41586-018-0071-9
   Cadarso MA, 2016, J CLEAN PROD, V111, P529, DOI 10.1016/j.jclepro.2014.09.019
   Cagiao J, 2011, ECOL INDIC, V11, P1526, DOI 10.1016/j.ecolind.2011.02.013
   Chambers JQ, 2007, SCIENCE, V318, P1107, DOI 10.1126/science.1148913
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   [陈孝慈 Chen Xiaoci], 2017, [中国安全科学学报, China Safety Science Journal（CSSJ）], V27, P13
   Chen Y., 2015, STUDIES SCI SCI, V33, P242
   Cohen MA, 2012, ENERG ECON, V34, pS53, DOI 10.1016/j.eneco.2012.08.032
   Davis SJ, 2010, P NATL ACAD SCI USA, V107, P5687, DOI 10.1073/pnas.0906974107
   Dhital S, 2019, ENVIRON SCI POLLUT R, V26, P13103, DOI [10.1007/s11356-019-05792-w., 10.1007/s11356-019-04482-x]
   Finnveden G, 2009, J ENVIRON MANAGE, V91, P1, DOI 10.1016/j.jenvman.2009.06.018
   Fort J, 2018, J CLEAN PROD, V177, P795, DOI 10.1016/j.jclepro.2018.01.002
   [高杨 Gao Yang], 2014, [系统工程, Systems Engineering], V32, P119
   [耿涌 Geng Yong], 2010, [中国人口·资源与环境, China Population·Resources and Environment], V20, P6
   Gill B, 2018, ECOL ECON, V145, P160, DOI 10.1016/j.ecolecon.2017.09.004
   Goodland R., 2009, WORLD WATCH, P10
   [韩增林 Han Zenglin], 2016, [地理科学, Scientia Geographica Sinica], V36, P643
   Hawkins J, 2016, J CLEAN PROD, V139, P460, DOI 10.1016/j.jclepro.2016.08.060
   Hertwich EG, 2009, ENVIRON SCI TECHNOL, V43, P6414, DOI 10.1021/es803496a
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Hoekstra AY, 2014, SCIENCE, V344, P1114, DOI 10.1126/science.1248365
   [侯彩霞 Hou Caixia], 2014, [自然资源学报, Journal of Natural Resources], V29, P587
   IPCC, 2006, 2019 REFINEMENT 2006
   *IPCC, 2000, INT PAN CLIM CHANG S
   ISO, 2006, 14040 ISO
   Johnson E, 2009, ENVIRON IMPACT ASSES, V29, P165, DOI 10.1016/j.eiar.2008.11.002
   Kramer KJ, 1999, ENERG POLICY, V27, P203, DOI 10.1016/S0301-4215(99)00014-2
   Laine J, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9101801
   Laurent A, 2012, ENVIRON SCI TECHNOL, V46, P4100, DOI 10.1021/es204163f
   [李彬彬 Li Binbin], 2017, [自然资源学报, Journal of Natural Resources], V32, P1983
   Li JL, 2019, CHINA ECON REV, V56, DOI 10.1016/j.chieco.2019.101304
   Li JL, 2019, RESOUR CONSERV RECY, V145, P349, DOI 10.1016/j.resconrec.2019.01.036
   Liu X, 2017, J CLEAN PROD, V146, P20, DOI 10.1016/j.jclepro.2016.03.129
   Luyssaert S, 2008, NATURE, V455, P213, DOI 10.1038/nature07276
   Malakahmad A, 2017, WASTE MANAGE, V70, P282, DOI 10.1016/j.wasman.2017.08.044
   Mancini MS, 2016, ECOL INDIC, V61, P390, DOI 10.1016/j.ecolind.2015.09.040
   Mohan RR, 2018, ATMOS ENVIRON, V178, P73, DOI 10.1016/j.atmosenv.2018.01.029
   Munoz P, 2010, ECOL ECON, V69, P2003, DOI 10.1016/j.ecolecon.2010.05.017
   Onat NC, 2014, BUILD ENVIRON, V72, P53, DOI 10.1016/j.buildenv.2013.10.009
   Patra AK, 2017, J CLEAN PROD, V162, P678, DOI 10.1016/j.jclepro.2017.06.096
   Peters GP, 2010, CURR OPIN ENV SUST, V2, P245, DOI 10.1016/j.cosust.2010.05.004
   Pilkington A, 2009, J OPER MANAG, V27, P185, DOI 10.1016/j.jom.2008.08.001
   [师帅 Shi Shuai], 2017, [中国人口·资源与环境, China Population Resources and Environment], V27, P36
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Song GB, 2016, ECOL INDIC, V60, P275, DOI 10.1016/j.ecolind.2015.06.044
   Spash CL, 2007, ENVIRON VALUE, V16, P532
   Steen-Olsen K, 2012, ENVIRON SCI TECHNOL, V46, P10883, DOI 10.1021/es301949t
   Sun CW, 2019, ENERGY, V169, P637, DOI 10.1016/j.energy.2018.12.032
   [唐洪松 Tang Hongsong], 2017, [干旱区地理, Arid Land Geography], V40, P1338
   Vida E, 2017, SCI TOTAL ENVIRON, V609, P1286, DOI 10.1016/j.scitotenv.2017.07.271
   [王长波 Wang Changbo], 2015, [自然资源学报, Journal of Natural Resources], V30, P1232
   [王倩 Wang Qian], 2016, [中国人口·资源与环境, China Population Resources and Environment], V26, P53
   Wang SJ, 2018, ECOL INDIC, V90, P460, DOI 10.1016/j.ecolind.2018.03.033
   [王微 Wang Wei], 2010, [环境科学与技术, Enuivonmental Science and Technology], V33, P71
   WHITE HD, 1981, J AM SOC INFORM SCI, V32, P163, DOI 10.1002/asi.4630320302
   Wiedmann T., 2007, DEFINITION CARBON FO
   Wiedmann T, 2010, ECON SYST RES, V22, P19, DOI 10.1080/09535311003612591
   [项国鹏 Xiang Guopeng], 2016, [生态学报, Acta Ecologica Sinica], V36, P7168
   [辛伟 Xin Wei], 2014, [心理科学进展, Advances in Psychological Science], V22, P334
   Zaki NAM, 2017, GEOCARTO INT, V32, P701, DOI 10.1080/10106049.2016.1178814
   [张坤民 ZHANG Kunmin], 2008, [中国人口·资源与环境, China Population·Resources and Environment], V18, P1
   [张琦峰 Zhang Qifeng], 2018, [自然资源学报, Journal of Natural Resources], V33, P696
   [赵先贵 Zhao Xiangui], 2014, [干旱区资源与环境, Journal of Arid Land Resources and Environment], V28, P21
NR 82
TC 12
Z9 12
U1 18
U2 102
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1470-160X
EI 1872-7034
J9 ECOL INDIC
JI Ecol. Indic.
PD MAY
PY 2020
VL 112
AR 106125
DI 10.1016/j.ecolind.2020.106125
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 KS5ZB
UT WOS:000518385800086
OA hybrid
DA 2022-08-23
ER

PT J
AU Kejriwal, M
   Szekely, P
AF Kejriwal, Mayank
   Szekely, Pedro
TI Knowledge Graphs for Social Good: An Entity-Centric Search Engine for
   the Human Trafficking Domain
SO IEEE TRANSACTIONS ON BIG DATA
LA English
DT Article
DE Prototypes; Big Data; Knowledge engineering; Search problems;
   Robustness; Search engines; Knowledge graphs; query-centric knowledge
   graph construction; information retrieval for social good;
   entity-centric search; investigative questions; knowledge discovery;
   human trafficking; semantic search
ID INFORMATION EXTRACTION
AB Web advertising related to Human Trafficking (HT) activity has been on the rise in recent years. Answering entity-centric questions over crawled HT Web corpora to assist investigators in the real world is an important social problem, involving many technical challenges. This paper describes a recent entity-centric knowledge graph effort that resulted in a semantic search engine to assist analysts and investigative experts in the HT domain. The overall approach takes as input a large corpus of advertisements crawled from the Web, structures it into an indexed knowledge graph, and enables investigators to satisfy their information needs by posing investigative search queries to a special-purpose semantic execution engine. We evaluated the search engine on real-world data collected from over 90,000 webpages, a significant fraction of which correlates with HT activity. Performance on four relevant categories of questions on a mean average precision metric were found to be promising, outperforming a learning-to-rank approach on three of the four categories. The prototype uses open-source components and scales to terabyte-scale corpora. Principles of the prototype have also been independently replicated, with similarly successful results.
C1 [Kejriwal, Mayank; Szekely, Pedro] USC Viterbi Sch Engn, Informat Sci Inst, 4676 Admiralty Way,Ste 1001, Los Angeles, CA 90292 USA.
RP Kejriwal, M (通讯作者)，USC Viterbi Sch Engn, Informat Sci Inst, 4676 Admiralty Way,Ste 1001, Los Angeles, CA 90292 USA.
EM kejriwal@isi.edu; pszekely@isi.edu
OI Kejriwal, Mayank/0000-0001-5988-8305
FU Defense Advanced Research Projects Agency (DARPA); Air Force Research
   Laboratory (AFRL) [FA8750-14-C-0240]
FX We gratefully acknowledge our collaborators and all (former and current)
   members of our team who contributed their efforts and expertise to DIG,
   particularly during the dry and final evaluation runs: Amandeep Singh,
   Linhong Zhu, Lingzhe Teng, Nimesh Jain, Rahul Kapoor, Muthu Rajendran R.
   Gurumoorthy, Sanjay Singh, Majid Ghasemi Gol, Brian Amanatullah, Craig
   Knoblock and Steve Minton. This research is supported by the Defense
   Advanced Research Projects Agency (DARPA) and the Air Force Research
   Laboratory (AFRL) under contract number FA8750-14-C-0240. The views and
   conclusions contained herein are those of the authors and should not be
   interpreted as necessarily representing the official policies or
   endorsements, either expressed or implied, of DARPA, AFRL, or the U.S.
   Government.
CR Alvari H, 2016, IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS: CYBERSECURITY AND BIG DATA, P133, DOI 10.1109/ISI.2016.7745456
   [Anonymous], Inferlink r&d capabilities
   [Anonymous], READABILITY TEXT EXT
   [Anonymous], ISI EXTRACTION TOOLK
   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
   Brambilla M, 2013, VLDB J, V22, P587, DOI 10.1007/s00778-013-0327-9
   Chang CH, 2006, IEEE T KNOWL DATA EN, V18, P1411, DOI 10.1109/TKDE.2006.152
   Chen H, 2012, INTEGR SER INFORM SY, V30, P1, DOI 10.1007/978-1-4614-1557-2
   Dalvi B. B., 2012, P 5 ACM INT C WEB SE, P243, DOI DOI 10.1145/2124295.2124327
   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
   Doan AnHai, 2012, PRINCIPLEDATA INTE
   Doan AnHai, 2006, SIGMOD 06 P 2006 ACM, P799, DOI DOI 10.1145/1142473.1142595
   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 XL, 2013, PROC INT CONF DATA, P1245, DOI 10.1109/ICDE.2013.6544914
   Duckham M, 2005, LECT NOTES COMPUT SC, V3468, P152
   El-Masri R., 1979, P ACM SIGMOD INT C M, P191
   Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581
   Etzioni O, 2008, COMMUN ACM, V51, P68, DOI 10.1145/1409360.1409378
   Ferraram A., 2013, SEMANTIC WEB ONTOLOG, P169
   Frank J. R., 2012, DTIC DOCUMENT
   Freitas A, 2012, IEEE INTERNET COMPUT, V16, P24, DOI 10.1109/MIC.2011.141
   Gabrilovich E, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1606
   Gomez-Cabrero D, 2014, BMC SYST BIOL, V8, DOI 10.1186/1752-0509-8-S2-I1
   Greiman V, 2013, PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON INFORMATION WARFARE AND SECURITY (ICIW-2013), P90
   Gupta N, 2013, PROC INT CONF DATA, P5, DOI 10.1109/ICDE.2013.6544808
   Harrendorf S., 2010, INT STAT CRIME JUSTI
   Harris S., 2016, WORLD WIDE WEB CONSO, V19
   Hirschman L., 2001, Natural Language Engineering, P275, DOI 10.1017/S1351324901002807
   Hogan Aidan, 2007, WWW 07, P1301
   Hultgren M, 2016, P ANN HICSS, P4344, DOI 10.1109/HICSS.2016.539
   Jain A, 2007, PROC INT CONF DATA, P1230
   Jing Han, 2011, Proceedings 2011 6th International Conference on Pervasive Computing and Applications (ICPCA 2011), P363, DOI 10.1109/ICPCA.2011.6106531
   Kapoor R., 2017, P 4 INT ACM WORKSH M
   Kejriwal M, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P997, DOI 10.1145/3038912.3052642
   Krishnamurthy R, 2008, SIGMOD RECORD, V37, P7
   Kushmerick N., THESIS U WASHINGTON
   Lenzerini M., 2002, P 21 ACM SIGMOD SIGA, V21, P233, DOI DOI 10.1145/543613.543644
   Lerman K, 2003, J ARTIF INTELL RES, V18, P149, DOI 10.1613/jair.1145
   Li Yunyao, 2008, P C EMP METH NAT LAN, P21
   Lin Thomas, 2012, WWW, P589, DOI DOI 10.1145/2187836.2187916
   Loper E., 2002, P ACL WORKSH EFF TOO
   Loshin D, 2013, BIG DATA ANAL STRATE
   Marchionini G, 2006, COMMUN ACM, V49, P41, DOI 10.1145/1121949.1121979
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Moser A, 2007, TWENTY-THIRD ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE, PROCEEDINGS, P421, DOI 10.1109/ACSAC.2007.21
   Muslea I., 1998, P AAAI 98 WORKSH AI, P74
   Niu Feng, 2012, VLDS, V12, P25
   Pantel P., 2009, P 2009 C EMP METH NA, P938
   Peng FC, 2006, INFORM PROCESS MANAG, V42, P963, DOI 10.1016/j.ipm.2005.09.002
   Pujara J., 2013, PROC 12 INT SEMANTIC, P542
   Saleiro Pedro, 2016, Advances in Information Retrieval. 38th European Conference on IR Research, ECIR 2016. Proceedings; LNCS 9626, P845, DOI 10.1007/978-3-319-30671-1_78
   Sarawagi  S., 2004, ADV NEURAL INFORM PR, V17, P1185
   Savona EU, 2007, MEASURING HUMAN TRAFFICKING: COMPLEXITIES AND PITFALLS, P1, DOI 10.1007/0-387-68044-6
   Singhal A., 2017, OFFICIAL GOOGLE BLOG
   Stevenson M., 2003, OXFORD HDB COMPUTATI, P249
   Szekely P, 2015, LECT NOTES COMPUT SC, V9367, P205, DOI 10.1007/978-3-319-25010-6_12
   Tonon A, 2012, SIGIR 2012: PROCEEDINGS OF THE 35TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P125
   Wick M., 2012, GEONAMES GEOGRAPHICA
   Zaharia M., 2010, HOTCLOUD, V10, P95
NR 61
TC 4
Z9 4
U1 3
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 JUN 1
PY 2022
VL 8
IS 3
BP 592
EP 606
DI 10.1109/TBDATA.2017.2763164
PG 15
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1F3YX
UT WOS:000795107500002
DA 2022-08-23
ER

PT J
AU Bin, CZ
   Qin, SG
   Rao, GJ
   Gu, TL
   Chang, L
AF Bin, Chenzhong
   Qin, Saige
   Rao, Guanjun
   Gu, Tianlong
   Chang, Liang
TI Multiview Translation Learning for Knowledge Graph Embedding
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB Recently, knowledge graph embedding methods have attracted numerous researchers' interest due to their outstanding effectiveness and robustness in knowledge representation. However, there are still some limitations in the existing methods. On the one hand, translation-based representation models focus on conceiving translation principles to represent knowledge from a global perspective, while they fail to learn various types of relational facts discriminatively. It is prone to make the entity congestion of complex relational facts in the embedding space reducing the precision of representation vectors associating with entities. On the other hand, parallel subgraphs extracted from the original graph are used to learn local relational facts discriminatively. However, it probably causes the relational fact damage of the original knowledge graph to some degree during the subgraph extraction. Thus, previous methods are unable to learn local and global knowledge representation uniformly. To that end, we propose a multiview translation learning model, named MvTransE, which learns relational facts from global-view and local-view perspectives, respectively. Specifically, we first construct multiple parallel subgraphs from an original knowledge graph by considering entity semantic and structural features simultaneously. Then, we embed the original graph and construct subgraphs into the corresponding global and local feature spaces. Finally, we propose a multiview fusion strategy to integrate multiview representations of relational facts. Extensive experiments on four public datasets demonstrate the superiority of our model in knowledge graph representation tasks compared to state-of-the-art methods.
C1 [Bin, Chenzhong] Guilin Univ Elect Technol, Sch Comp Sci & Informat Secur, Guilin 541004, Peoples R China.
   [Bin, Chenzhong; Qin, Saige; Rao, Guanjun; Gu, Tianlong; Chang, Liang] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China.
RP Bin, CZ (通讯作者)，Guilin Univ Elect Technol, Sch Comp Sci & Informat Secur, Guilin 541004, Peoples R China.; Bin, CZ; Gu, TL (通讯作者)，Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China.
EM binchenzhong@163.com; 1132272313@qq.com; rao-rgj@foxmail.com;
   cctlgu@guet.edu.cn; changl@guet.edu.cn
OI Bin, Chenzhong/0000-0002-7200-0929
FU National Natural Science Foundation of China [U1811264, 61966009,
   U1711263]; Natural Science Foundation of Guangxi Province
   [2020GXNSFAA159055, 2019GXNSFBA245059, 2019GXNSFBA245049]; Guangxi
   Innovation-Driven Development Project [AA17202024]; Beihai Science and
   Technology Project Contract [202082001]
FX This work was partially supported by the National Natural Science
   Foundation of China (Nos. U1811264, 61966009, and U1711263), the Natural
   Science Foundation of Guangxi Province (Nos. 2020GXNSFAA159055,
   2019GXNSFBA245059, and 2019GXNSFBA245049), the Guangxi Innovation-Driven
   Development Project (No. AA17202024), and the Beihai Science and
   Technology Project Contract (No. 202082001).
CR [Anonymous], 2011, P 28 INT C INT C MAC
   [Anonymous], 1987, KNOWLEDGE GRAPHS REP
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2012, P 15 INT C ART INT S, 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
   Chang L, 2017, IEEE ACCESS, V5, P20898, DOI 10.1109/ACCESS.2017.2759139
   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
   Duchi J, 2011, J MACH LEARN RES, V12, P2121
   Feng J, 2016, FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P557
   Jenatton R., 2012, NIPS, P3176
   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
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Ng A.Y, 2013, P 26 INT C NEUR INF
   Tay Y, 2017, AAAI CONF ARTIF INTE, P1243
   Toutanova K., 2015, WORKSH CONT VECT SPA, P57
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang PF, 2018, AAAI CONF ARTIF INTE, P2005
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xia TA, 2010, IEEE T SYST MAN CY B, V40, P1438, DOI 10.1109/TSMCB.2009.2039566
   Yang X, 2015, PROCEEDINGS OF 2015 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2015), P1000, DOI 10.1109/ICCSNT.2015.7490905
   Zhai DM, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168767
NR 26
TC 0
Z9 0
U1 0
U2 5
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 AUG 25
PY 2020
VL 2020
AR 7084958
DI 10.1155/2020/7084958
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NQ3MU
UT WOS:000570769400001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, J
   Liu, J
   Zhao, GS
AF Wang, Jian
   Liu, Jia
   Zhao, Guosheng
TI Dynamic link prediction method of task and user in Mobile Crowd Sensing
SO COMPUTER COMMUNICATIONS
LA English
DT Article
DE Mobile Crowd Sensing; Task allocation; Knowledge graph; Link prediction
ID INCENTIVE MECHANISM; RECRUITMENT; NETWORKS
AB To address the problem that the existing Mobile Crowd Sensing task allocation methods only considering the perspective of sensing users, ignoring the potential relationship between tasks and users, this paper proposes a task allocation method based on link prediction. First, we analyze the characteristics of sensing users and sensing tasks in Mobile Crowd Sensing, especially the common attribute characteristics of both, and establish the Mobile Crowd Sensing knowledge graph. Then the deep link relationship between sensing users and sensing tasks is mined by the link prediction method based on knowledge graph reasoning. Finally, according to the predicted fit between users and tasks, high-quality sensing users that meet the requirements of the task are selected, to improve the perceptual quality. The experiment results on different datasets show that the method proposed in this paper is high-quality to other comparison methods in sensing data quality and coverage.
C1 [Wang, Jian; Liu, Jia] Harbin Univ Sci & Technol, Sch Comp Sci & Technol, Harbin 150080, Peoples R China.
   [Zhao, Guosheng] Harbin Normal Univ, Coll Comp Sci & Informat Engn, Harbin 150025, Peoples R China.
RP Wang, J (通讯作者)，Harbin Univ Sci & Technol, Sch Comp Sci & Technol, Harbin 150080, Peoples R China.
EM wangjianlydia@163.com
FU National Natu-ral Science Foundation of China [61403109, 61202458];
   Special-ized Research Fund for the Doctoral Program of Higher Education
   of China [20112303120007]; Natural Science Foundation of Heilongjiang
   Province [LH2020F034]
FX Acknowledgments This present research work was supported by the National
   Natu-ral Science Foundation of China (61403109, 61202458) , the
   Special-ized Research Fund for the Doctoral Program of Higher Education
   of China (20112303120007) and the Natural Science Foundation of
   Heilongjiang Province (LH2020F034) .
CR Azmy S.B., 2018, QUALITY COVERAGE NOV, P1
   Azzam R, 2016, J NETW COMPUT APPL, V72, P38, DOI 10.1016/j.jnca.2016.06.015
   Bhattacharjee S, 2020, IEEE T MOBILE COMPUT, V19, P200, DOI 10.1109/TMC.2018.2889458
   Boubiche DE, 2019, COMPUT HUM BEHAV, V101, P352, DOI 10.1016/j.chb.2018.10.028
   Cheng Y., 2014, P 12 ACM C EMB NETW, P251, DOI [10.1145/2668332.2668346, DOI 10.1145/2668332.2668346]
   Galinina O, 2018, IEEE WIREL COMMUN, V25, P140, DOI 10.1109/MWC.2018.1600468
   Gao H., 2017, 2017 IEEE WIRELESS C, P1
   Gao H, 2020, IEEE INTERNET THINGS, V7, P5302, DOI 10.1109/JIOT.2020.2976778
   Gao X., 2020, IEEE T KNOWL DATA EN, V99, P1
   Guo B, 2015, ACM COMPUT SURV, V48, DOI 10.1145/2794400
   Guo WZ, 2019, IEEE ACCESS, V7, P78406, DOI 10.1109/ACCESS.2019.2896226
   Jin HM, 2018, IEEE ACM T NETWORK, V26, P2019, DOI 10.1109/TNET.2018.2840098
   Kadadha M, 2020, FUTURE GENER COMP SY, V105, P650, DOI 10.1016/j.future.2019.12.007
   Kuang R, 2016, LECT NOTES COMPUT SC, V9713, P341, DOI 10.1007/978-3-319-41009-8_37
   Lu LY, 2011, PHYSICA A, V390, P1150, DOI 10.1016/j.physa.2010.11.027
   Truong NB, 2019, IEEE T INF FOREN SEC, V14, P2705, DOI 10.1109/TIFS.2019.2903659
   Ray A, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON ADVANCED NETWORKS AND TELECOMMUNICATIONS SYSTEMS (ANTS)
   Song SW, 2020, IEEE INTERNET THINGS, V7, P7407, DOI 10.1109/JIOT.2020.2984826
   Song Z, 2014, IEEE T VEH TECHNOL, V63, P4618, DOI 10.1109/TVT.2014.2317701
   Srilakshmi B., 2014, IEEE T KNOWL DATA EN, V26, P1384
   Sun P, 2022, IEEE T MOBILE COMPUT, V21, P352, DOI 10.1109/TMC.2020.3003673
   Wang JT, 2019, IEEE T MOBILE COMPUT, V18, P1661, DOI 10.1109/TMC.2018.2865355
   Wang JT, 2018, IEEE INTERNET THINGS, V5, P3747, DOI 10.1109/JIOT.2018.2864341
   Wang YA, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19102371
   Xiong HY, 2016, IEEE T MOBILE COMPUT, V15, P2010, DOI 10.1109/TMC.2015.2483505
   Yang J, 2020, IEEE ACCESS, V8, P10628, DOI 10.1109/ACCESS.2020.2965734
   Yao L, 2016, PROCEDIA COMPUT SCI, V83, P82, DOI 10.1016/j.procs.2016.04.102
   Zhao YN, 2021, IEEE T INTELL TRANSP, V22, P2314, DOI 10.1109/TITS.2020.3014263
NR 28
TC 0
Z9 0
U1 5
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0140-3664
EI 1873-703X
J9 COMPUT COMMUN
JI Comput. Commun.
PD MAY 1
PY 2022
VL 189
BP 110
EP 119
DI 10.1016/j.comcom.2022.03.014
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1W2ZC
UT WOS:000806644700010
DA 2022-08-23
ER

PT J
AU Ali, W
   Saleem, M
   Yao, B
   Hogan, A
   Ngomo, ACN
AF Ali, Waqas
   Saleem, Muhammad
   Yao, Bin
   Hogan, Aidan
   Ngomo, Axel-Cyrille Ngonga
TI A survey of RDF stores & SPARQL engines for querying knowledge graphs
SO VLDB JOURNAL
LA English
DT Article
DE Knowledge graph; Storage; Indexing; Query processing; SPARQL
ID ANT COLONY OPTIMIZATION; SEMANTIC WEB; QUERIES; PERFORMANCE; REPOSITORY;
   CLOUDS
AB RDF has seen increased adoption in recent years, prompting the standardization of the SPARQL query language for RDF, and the development of local and distributed engines for processing SPARQL queries. This survey paper provides a comprehensive review of techniques and systems for querying RDF knowledge graphs. While other reviews on this topic tend to focus on the distributed setting, the main focus of the work is on providing a comprehensive survey of state-of-the-art storage, indexing and query processing techniques for efficiently evaluating SPARQL queries in a local setting (on one machine). To keep the survey self-contained, we also provide a short discussion on graph partitioning techniques used in the distributed setting. We conclude by discussing contemporary research challenges for further improving SPARQL query engines. An extended version also provides a survey of over one hundred SPARQL query engines and the techniques they use, along with twelve benchmarks and their features.
C1 [Ali, Waqas; Yao, Bin] Shanghai Jiao Tong Univ, SEIEE, Shanghai, Peoples R China.
   [Saleem, Muhammad] Univ Leipzig, AKSW, Leipzig, Germany.
   [Yao, Bin] Hangzhou Inst Adv Technol, Hangzhou, Peoples R China.
   [Hogan, Aidan] Univ Chile, DCC, Santiago, Chile.
   [Hogan, Aidan] IMFD, Santiago, Chile.
   [Ngomo, Axel-Cyrille Ngonga] Paderborn Univ, DICE, Paderborn, Germany.
RP Yao, B (通讯作者)，Shanghai Jiao Tong Univ, SEIEE, Shanghai, Peoples R China.; Yao, B (通讯作者)，Hangzhou Inst Adv Technol, Hangzhou, Peoples R China.
EM waqasali@sjtu.edu.cn; saleem@informatik.uni-leipzig.de;
   yaobin@cs.sjtu.edu.cn; ahogan@dcc.uchile.cl; axel.ngonga@upb.de
OI ali, waqas/0000-0001-5691-3093
FU European Union's Horizon 2020 research and innovation programme under
   the Marie Sklodowska-Curie Grant [860801]; Fondecyt [1181896];
   ANID-Millennium Science Initiative Program [ICN17_002]; NSFC [61922054,
   61872235, 61832017, 61729202, 61832013]; National Key Research and
   Development Program of China [2020YFB1710202, 2018YFC1504504]; Science
   and Technology Commission of Shanghai Municipality (STCSM) AI
   [19511120300]; German Federal Ministry of Education and Research (BMBF)
   within the EuroStars project 3DFed [E1114681, 01QE2114]; project
   KnowGraphs [860801]
FX This work was partially funded by a grant from the European Union's
   Horizon 2020 research and innovation programme under the Marie
   Sklodowska-Curie Grant Agreement No. 860801. Hogan was supported by
   Fondecyt Grant No. 1181896 and ANID-Millennium Science Initiative
   Program-Code ICN17_002. Bin Yao was supported by the NSFC (61922054,
   61872235, 61832017, 61729202, 61832013), the National Key Research and
   Development Program of China (2020YFB1710202, 2018YFC1504504), the
   Science and Technology Commission of Shanghai Municipality (STCSM) AI
   under Project 19511120300. This work was also supported by the German
   Federal Ministry of Education and Research (BMBF) within the EuroStars
   project E1114681 3DFed under the Grant No. 01QE2114 and project
   KnowGraphs (No. 860801).
CR Abadi DJ, 2009, VLDB J, V18, P385, DOI 10.1007/s00778-008-0125-y
   Abdelaziz I, 2017, PROC VLDB ENDOW, V10, P2049, DOI 10.14778/3151106.3151109
   Abdelaziz I, 2017, IEEE T PARALL DISTR, V28, P3374, DOI 10.1109/TPDS.2017.2720174
   Abul-Basher Z, 2017, PROC INT CONF DATA, P1426, DOI 10.1109/ICDE.2017.205
   Adams T., 2005, XTECH C, P1
   Akhter Adnan, 2018, Knowledge Engineering and Knowledge Management. 21st International Conference, EKAW 2018. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11313), P3, DOI 10.1007/978-3-030-03667-6_1
   Alaoui K, 2019, 4TH INTERNATIONAL CONFERENCE ON SMART CITY APPLICATIONS (SCA' 19), DOI 10.1145/3368756.3369047
   Angles R, 2017, ACM COMPUT SURV, V50, DOI 10.1145/3104031
   Arroyuelo D, 2021, INT CONF MANAGE DATA, P102, DOI 10.1145/3448016.3457256
   Atre M., 2009, P 5 INT WORKSH SCAL, P33
   Atserias A, 2013, SIAM J COMPUT, V42, P1737, DOI 10.1137/110859440
   Auer S., 2017, INT C SEM SYST SEMAN, P104
   Baier J, 2017, PROCEEDINGS OF THE 28TH ACM CONFERENCE ON HYPERTEXT AND SOCIAL MEDIA (HT'17), P165, DOI 10.1145/3078714.3078731
   Banane, 2019, INT J ADV TRENDS COM, V8, P734
   Barton S., 2004, INT WORKSH DATABASES, P7
   BEERI C, 1991, J LOGIC PROGRAM, V10, P255, DOI 10.1016/0743-1066(91)90038-Q
   Blin G., 2012, REV AFRICAINE RECHER, P25
   Bornea MA, 2013, SIGMOD, P121, DOI DOI 10.1145/2463676.2463718
   Brickley D, 2014, RDF SCHEMA 11 W3C RE
   Brisaboa NR, 2020, IEEE DATA COMPR CONF, P123, DOI 10.1109/DCC47342.2020.00020
   Brisaboa NR, 2015, LECT NOTES COMPUT SC, V9309, P103, DOI 10.1007/978-3-319-23826-5_11
   Brocheler M, 2009, LECT NOTES COMPUT SC, V5823, P97, DOI 10.1007/978-3-642-04930-9_7
   Buil-Aranda C, 2013, LECT NOTES COMPUT SC, V8219, P277, DOI 10.1007/978-3-642-41338-4_18
   Buluc A, 2016, LECT NOTES COMPUT SC, V9220, P117, DOI 10.1007/978-3-319-49487-6_4
   Calvez P, 2021, MOTIVATIONS ANALYTIC
   Cappellari P, 2012, WORLD WIDE WEB, V15, P631, DOI 10.1007/s11280-011-0153-1
   Cebiric S, 2019, VLDB J, V28, P295, DOI 10.1007/s00778-018-0528-3
   Chantrapornchai C, 2018, IEEE T PARALL DISTR, V29, P2121, DOI 10.1109/TPDS.2018.2814567
   Chawla T, 2020, COMPUT SCI REV, V38, DOI 10.1016/j.cosrev.2020.100309
   Chin G., 2020, ARXIV200907410 CORR
   CHONG EI, 2005, P 31 INT C VER LARG, P1216
   Corby O, 2017, LECT NOTES COMPUT SC, V10587, P208, DOI 10.1007/978-3-319-68288-4_13
   Dey S.C., 2013, P JOINT EDBT ICDT 20, P214
   Elzein NM, 2018, SUSTAIN CITIES SOC, V39, P375, DOI 10.1016/j.scs.2018.02.019
   Erling O, 2010, SEMANTIC WEB INFORMATION MANAGEMENT, P501, DOI 10.1007/978-3-642-04329-1_21
   Fionda V, 2019, SEMANT WEB, V10, P1127, DOI 10.3233/SW-190365
   Galarraga L, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P267, DOI 10.1145/2567948.2577302
   Groppe S, 2007, ICEIS 2007: PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, P13
   Gryz J, 2013, A MEND INT WORKSH FD
   Gubichev A., 2014, EDBT, P439, DOI [10.5441/002/edbt.2014.40, DOI 10.5441/002/EDBT.2014]
   Haase P, 2004, LECT NOTES COMPUT SC, V3298, P502
   Hammoud M, 2015, PROC VLDB ENDOW, V8, P654, DOI 10.14778/2735703.2735705
   Harbi R, 2016, VLDB J, V25, P355, DOI 10.1007/s00778-016-0420-y
   Harris S, 2013, W3C RECOMMENDATION
   Harris S., 2009, 5 INT WORKSH SCAL SE, P94
   Harth A, 2005, Third Latin American Web Congress, Proceedings, P71, DOI 10.1109/LAWEB.2005.25
   Harth A, 2011, CUMULUSRDF LINKED DA
   Harth A, 2007, LECT NOTES COMPUT SC, V4825, P211
   Hitzler P., 2012, OWL 2 WEB ONTOLOGY L
   Hogan A, 2020, LECT NOTES COMPUT SC, V12506, P511, DOI 10.1007/978-3-030-62419-4_29
   Hogan A, 2019, LECT NOTES COMPUT SC, V11778, P258, DOI 10.1007/978-3-030-30793-6_15
   Hogenboom A, 2013, EXPERT SYST APPL, V40, P1555, DOI 10.1016/j.eswa.2012.08.074
   Hogenboom A, 2009, LECT NOTES COMPUT SC, V5692, P181, DOI 10.1007/978-3-642-03964-5_18
   Hose K, 2013, I C DATA ENGIN WORKS, P1, DOI 10.1109/ICDEW.2013.6547414
   Huang JW, 2011, PROC VLDB ENDOW, V4, P1123
   Ioannidis Y. E., 1987, SIGMOD Record, V16, P9
   Jachiet L, 2020, SIGMOD'20: PROCEEDINGS OF THE 2020 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P681, DOI 10.1145/3318464.3380567
   Jamour F, 2019, PROCEEDINGS OF THE FOURTEENTH EUROSYS CONFERENCE 2019 (EUROSYS '19), DOI 10.1145/3302424.3303962
   Janke Daniel, 2018, Reasoning Web. Learning, Uncertainty, Streaming, and Scalability. 14th International Summer School 2018. Tutorial Lectures: Lecture Notes in Computer Science (LNCS 11078), P173, DOI 10.1007/978-3-030-00338-8_7
   Kalayci EG, 2015, INFORM SYST, V50, P51, DOI 10.1016/j.is.2015.01.013
   Kaoudi Z, 2015, VLDB J, V24, P67, DOI 10.1007/s00778-014-0364-z
   Karvounarakis G, 2003, COMPUT NETW, V42, P617, DOI 10.1016/S1389-1286(03)00227-5
   Karypis G, 1998, SIAM J SCI COMPUT, V20, P359, DOI 10.1137/S1064827595287997
   Katib A, 2016, J WEB SEMANT, V37-38, P90, DOI 10.1016/j.websem.2016.03.005
   Khan L.R, 2009, UTDCS0809
   Kimelfeld B., 2018, ARXIV181110955 CORR
   Kitagawa H, 2019, INT WORKSH ACC AN DA, P47
   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
   Kyzirakos Kostis, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P295, DOI 10.1007/978-3-642-35176-1_19
   Lampo T, 2011, LECT NOTES COMPUT SC, V7045, P716, DOI 10.1007/978-3-642-25106-1_22
   Le WC, 2012, PROC INT CONF DATA, P666, DOI 10.1109/ICDE.2012.37
   Letelier A, 2013, ACM T DATABASE SYST, V38, DOI 10.1145/2500130
   Li K., 2021, GSMART EFFICIENT SPA
   Liu BL, 2010, INT J PARALLEL EMERG, V25, P123, DOI 10.1080/17445760802431839
   Lorey J, 2013, LECT NOTES COMPUT SC, V7955, P46, DOI 10.1007/978-3-642-41242-4_5
   Luo Y., 2012, SEMANTIC SEARCH WEB, P31
   Ma ZM, 2016, KNOWL ENG REV, V31, P391, DOI 10.1017/S0269888916000217
   Madkour A, 2018, LECT NOTES COMPUT SC, V11136, P583, DOI 10.1007/978-3-030-00671-6_34
   Maharjan R, 2009, PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE OF COMPUTATIONAL SCIENCES AND ITS APPLICATIONS, P56, DOI 10.1109/ICCSA.2009.27
   Malyshev S, 2018, LECT NOTES COMPUT SC, V11137, P376, DOI 10.1007/978-3-030-00668-6_23
   Martin M, 2010, LECT NOTES COMPUT SC, V6089, P304, DOI 10.1007/978-3-642-13489-0_21
   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
   Miettinen P., 2015, ARXIV150300301 CORR
   Pham MD, 2016, LECT NOTES COMPUT SC, V9981, P463, DOI 10.1007/978-3-319-46523-4_28
   Minier T, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1268, DOI 10.1145/3308558.3313652
   Neumann T, 2011, INT WORKSH WEB DAT W
   Neumann T, 2009, ACM SIGMOD/PODS 2009 CONFERENCE, P627
   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
   Ngo HQ, 2018, J ACM, V65, DOI 10.1145/3180143
   Ngomo A.N., 2020, ARXIV210213027 CORR
   Nguyen VQ, 2017, IEICE T INF SYST, VE100D, P2648, DOI 10.1587/transinf.2017EDL8060
   Ozsu MT, 2016, FRONT COMPUT SCI-CHI, V10, P418, DOI 10.1007/s11704-016-5554-y
   Pan ZY, 2018, J AMB INTEL HUM COMP, V9, P1693, DOI 10.1007/s12652-018-0876-2
   Papadaki ME, 2021, ALGORITHMS, V14, DOI 10.3390/a14020034
   Papailiou Nikolaos, 2013, 2013 IEEE International Conference on Big Data, P255, DOI 10.1109/BigData.2013.6691582
   Papailiou N, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1777, DOI 10.1145/2723372.2723714
   Peng P, 2021, IEEE T KNOWL DATA EN, V33, P1692, DOI 10.1109/TKDE.2019.2947050
   Perego R, 2021, IEEE T KNOWL DATA EN, V33, P3187, DOI 10.1109/TKDE.2020.2966609
   Perez J, 2006, LECT NOTES COMPUT SC, V4273, P30
   Picalausa Francois, 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P406, DOI 10.1007/978-3-642-30284-8_34
   Poncelet P., 2018, QUERYING RDF DATA MU, P135
   Poulovassilis, 2016, EXTENDING DATABASE T, P636
   Primpel A, 2020, WEB DATA COMMONS MIC
   Ravindra P, 2011, LECT NOTES COMPUT SC, V6644, P46, DOI 10.1007/978-3-642-21064-8_4
   Reutter JL, 2015, LECT NOTES COMPUT SC, V9366, P19, DOI 10.1007/978-3-319-25007-6_2
   Rohloff K, 2010, PROGRAMMING SUPPORT, P4
   Rojas-Ledesma J, 2020, INT C DAT THEOR ICDT
   Sakr S, 2009, SIGMOD REC, V38, P23, DOI 10.1145/1815948.1815953
   Saleem M, 2015, LECT NOTES COMPUT SC, V9367, P261, DOI 10.1007/978-3-319-25010-6_15
   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
   Schatzle Alexander, 2011, P INT WORKSH SEM WEB, V4
   Schmidt Michael, 2010, P 13 INT C DAT THEOR, P4, DOI DOI 10.1145/1804669.1804675
   Schreiber G., 2014, RDF 1 1 PRIMER W3C W
   Selinger P. G., 1979, P 1979 ACM SIGMOD IN, P23, DOI DOI 10.1145/582095.582099
   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
   Shi JX, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P317
   Sintek M, 2006, LECT NOTES COMPUT SC, V4011, P363
   Specht G., 2011, GRUNDL DAT GI WORKSH, P91
   Stocker M, 2008, P INT C WORLD WID WE, P595, DOI [DOI 10.1145/1367497.1367578, 10.1145/1367497.1367578]
   Stuckenschmidt H., 2005, International Journal of Web Engineering and Technology, V2, P207, DOI 10.1504/IJWET.2005.008484
   Stuckenschmidt H, 2004, LECT NOTES ARTIF INT, V3055, P295
   Suignard M., 3987 RFC
   Svoboda M, 2011, LECT NOTES COMPUT SC, V7046, P474
   Thakkar H, 2020, IEEE INT C SEMANT CO, P408, DOI 10.1109/ICSC.2020.00080
   Tran T, 2013, IEEE T KNOWL DATA EN, V25, P2076, DOI 10.1109/TKDE.2012.134
   Thimm M, 2017, WORKSH BENCHM LINK D
   Thimm M, 2017, INT WORKSH SEM BIG D, P1
   Thompson B, 2014, EMER DIRECT DATAB, P193
   Trautner T, 2018, INT C DAT THEOR ICDT
   Tsialiamanis P., 2012, P 15 INT C EXT DAT T, P324
   Udrea Octavian, 2007, AAAI, P1465
   Veldhuizen T. L., 2014, P 17 INT C DAT THEOR, P96, DOI 10.5441/002/icdt.2014.13
   Vidal ME, 2010, LECT NOTES COMPUT SC, V6088, P228, DOI 10.1007/978-3-642-13486-9_16
   Vlachou A, 2019, SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING, P439, DOI 10.1145/3297280.3299732
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wadhwa S, 2019, INT CONF MANAGE DATA, P1463, DOI 10.1145/3299869.3319882
   Wang, 2015, WEB INFORM SYSTEMS E, P108
   Wang SY, 2018, PROCEEDINGS OF THE 2018 USENIX ANNUAL TECHNICAL CONFERENCE, P651
   Weiss C, 2008, PROC VLDB ENDOW, V1, P1008, DOI 10.14778/1453856.1453965
   Wilkinson K., 2003, P 1 INT WORKSH SEM W, V3, P131
   Williams GT, 2011, LECT NOTES COMPUT SC, V7031, P762, DOI 10.1007/978-3-642-25073-6_48
   Wu G, 2012, J ZHEJIANG U-SCI C, V13, P281, DOI 10.1631/jzus.C1101009
   Wu G, 2009, J COMPUT SCI TECH-CH, V24, P652, DOI 10.1007/s11390-009-9265-9
   Wylot M, 2018, ACM COMPUT SURV, V51, DOI 10.1145/3177850
   Yakovets N, 2016, SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1875, DOI 10.1145/2882903.2882944
   Yasin Muhammad Qasim, 2018, Database Systems for Advanced Applications. DASFAA 2018 International Workshops: BDMS, BDQM, GDMA, and SeCoP. Proceedings: LNCS 10829, P156, DOI 10.1007/978-3-319-91455-8_15
   Santana LHZ, 2020, PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), P386, DOI 10.1145/3341105.3375753
   Zeng K, 2013, PROC VLDB ENDOW, V6, P265, DOI 10.14778/2535570.2488333
   Zervakis L., 2020, P13, DOI 10.5441/002/edbt.2020.03
   Zhang WE, 2015, LECT NOTES COMPUT SC, V9050, P259, DOI 10.1007/978-3-319-18123-3_16
   Zhang XF, 2013, PROC INT CONF DATA, P565, DOI 10.1109/ICDE.2013.6544856
   Zou L, 2011, PROC VLDB ENDOW, V4, P482, DOI 10.14778/2002974.2002976
NR 157
TC 1
Z9 1
U1 8
U2 15
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1066-8888
EI 0949-877X
J9 VLDB J
JI VLDB J.
PD MAY
PY 2022
VL 31
IS 3
BP 603
EP 628
DI 10.1007/s00778-021-00711-3
EA NOV 2021
PG 26
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0U6QE
UT WOS:000718071500001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Fang, WL
   Ma, L
   Love, PED
   Luo, HB
   Ding, LY
   Zhou, AO
AF Fang, Weili
   Ma, Ling
   Love, Peter E. D.
   Luo, Hanbin
   Ding, Lieyun
   Zhou, Ao
TI Knowledge graph for identifying hazards on construction sites:
   Integrating computer vision with ontology
SO AUTOMATION IN CONSTRUCTION
LA English
DT Article
DE Hazards; Ontology; Computer vision; Safety; Knowledge graph database
ID MANAGEMENT; SAFETY; MODEL; BIM; CLASSIFICATION; RECOGNITION; FEATURES;
   WORKERS; DESIGN; ERRORS
AB Hazards potentially affect the safety of people on construction sites include falls from heights (FFH), trench and scaffold collapse, electric shock and arc flash/arc blast, and failure to use proper personal protective equipment. Such hazards are significant contributors to accidents and fatalities. Computer vision has been used to auto-matically detect safety hazards to assist with the mitigation of accidents and fatalities. However, as safety regulations are subject to change and become more stringent prevailing computer vision approaches will become obsolete as they are unable to accommodate the adjustments that are made to practice. This paper integrates computer vision algorithms with ontology models to develop a knowledge graph that can automatically and accurately recognise hazards while adhering to safety regulations, even when they are subjected to change. Our developed knowledge graph consists of: (1) an ontological model for hazards: (2) knowledge extraction; and (3) knowledge inference for hazard identification. We focus on the detection of hazards associated with FFH as an example to illustrate our proposed approach. We also demonstrate that our approach can successfully detect FFH hazards in varying contexts from images.
C1 [Fang, Weili; Luo, Hanbin; Ding, Lieyun; Zhou, Ao] Huazhong Univ Sci & Technol, Sch Civil Engn & Mech, Wuhan 430074, Hubei, Peoples R China.
   [Fang, Weili; Luo, Hanbin; Ding, Lieyun; Zhou, Ao] Hubei Engn Res Ctr Virtual Safe & Automated Const, Wuhan 430074, Hubei, Peoples R China.
   Columbia Univ, Dept Civil Engn & Engn Mech, New York, NY 10027 USA.
   [Fang, Weili; Love, Peter E. D.] Curtin Univ, Dept Civil & Mech Engn, Perth, WA 6845, Australia.
   [Ma, Ling] UCL, Bartlett Sch Construct & Project Management, London WC1E 6BT, England.
RP Ma, L (通讯作者)，UCL, Bartlett Sch Construct & Project Management, London WC1E 6BT, England.
EM weili.fang@curtin.edu.au; l.ma@ucl.ac.uk; p.love@curtin.edu.au;
   luohbcem@hust.edu.cn; dly@hust.edu.cn
RI Ma, Ling/F-3413-2019; Fang, Weili/O-5424-2018; Ma, Ling/F-6641-2010
OI Ma, Ling/0000-0002-9187-471X; Fang, Weili/0000-0001-8974-2534; Ma,
   Ling/0000-0002-9187-471X; Ding, Lieyun/0000-0002-9873-3776
FU National Natural Science Foundation of China [71732001, 51978302,
   51678265, 51878311]; China Scholarship Council (CSC)
FX The authors would like to acknowledge the financial support provided by
   the National Natural Science Foundation of China (Grant No. 71732001,
   No.51978302, No. 51678265, No.51878311) and the China Scholarship
   Council (CSC). The authors would also like to thank the three anonymous
   reviewers for their constructive and insightful comments that have
   helped improve this manuscript.
CR Anumba Chimay J, 2008, Construction Innovation, V8, P218, DOI 10.1108/14714170810888976
   Azar ER, 2012, J COMPUT CIVIL ENG, V26, P769, DOI 10.1061/(ASCE)CP.1943-5487.0000179
   Azar ER, 2012, AUTOMAT CONSTR, V24, P194, DOI 10.1016/j.autcon.2012.03.003
   Chi NW, 2014, ADV ENG INFORM, V28, P381, DOI 10.1016/j.aei.2014.05.001
   Chi S, 2011, COMPUT-AIDED CIV INF, V26, P368, DOI 10.1111/j.1467-8667.2010.00690.x
   Corry E, 2015, AUTOMAT CONSTR, V57, P249, DOI 10.1016/j.autcon.2015.05.002
   Dalal N, 2005, 2005 IEEE COMP SOC C, DOI DOI 10.1109/CVPR.2005.177
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Ding LY, 2016, SAFETY SCI, V87, P202, DOI 10.1016/j.ssci.2016.04.008
   Ding LY, 2018, AUTOMAT CONSTR, V86, P118, DOI 10.1016/j.autcon.2017.11.002
   Edwards H., 2017, FATAL INJURIES ARISI
   El-Diraby TE, 2014, J INF TECHNOL CONSTR, V19, P474
   Elghamrawy T, 2009, CONSTRUCTION RES C, P457, DOI DOI 10.1061/41020(339)47
   Fang WL, 2020, AUTOMAT CONSTR, V110, DOI 10.1016/j.autcon.2019.103013
   Fang WL, 2020, ADV ENG INFORM, V43, DOI 10.1016/j.aei.2019.100980
   Fang WL, 2019, ADV ENG INFORM, V39, P170, DOI 10.1016/j.aei.2018.12.005
   Fang WL, 2018, ADV ENG INFORM, V37, P139, DOI 10.1016/j.aei.2018.05.003
   Fang WL, 2018, AUTOMAT CONSTR, V91, P53, DOI 10.1016/j.autcon.2018.02.018
   Goh YM, 2010, J CONSTR ENG M ASCE, V136, P170, DOI 10.1061/(ASCE)CO.1943-7862.0000116
   Gong J, 2010, J COMPUT CIVIL ENG, V24, P252, DOI 10.1061/(ASCE)CP.1943-5487.0000027
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Guia J, 2017, ICEIS: PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS - VOL 1, P351, DOI 10.5220/0006356003510356
   Guo BHW, 2017, AUTOMAT CONSTR, V82, P138, DOI 10.1016/j.autcon.2017.02.009
   Hadikusumo BHW, 2002, AUTOMAT CONSTR, V11, P501, DOI 10.1016/S0926-5805(01)00061-9
   Han KK, 2015, AUTOMAT CONSTR, V53, P44, DOI 10.1016/j.autcon.2015.02.007
   He KM, 2017, IEEE I CONF COMP VIS, P2980, DOI 10.1109/ICCV.2017.322
   Hippolyte JL, 2018, AUTOMAT CONSTR, V86, P210, DOI 10.1016/j.autcon.2017.10.004
   Ji SW, 2013, IEEE T PATTERN ANAL, V35, P221, DOI 10.1109/TPAMI.2012.59
   Johnpaul C.I., 2017, 4 INT C ADV COMP COM, DOI [10.1109/ICACCS.2017.8014558, DOI 10.1109/ICACCS.2017.8014558]
   Kim D, 2019, AUTOMAT CONSTR, V99, P168, DOI 10.1016/j.autcon.2018.12.014
   Kim H, 2018, AUTOMAT CONSTR, V88, P23, DOI 10.1016/j.autcon.2017.12.034
   Kim H, 2016, J COMPUT CIVIL ENG, V30, DOI 10.1061/(ASCE)CP.1943-5487.0000562
   Kim K, 2017, AUTOMAT CONSTR, V83, P390, DOI 10.1016/j.autcon.2017.06.014
   LeCun Y., 2015, NATURE, V521, P436, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]
   Lee SK, 2014, AUTOMAT CONSTR, V41, P96, DOI 10.1016/j.autcon.2013.10.020
   Lingard H, 2013, CONSTR MANAG ECON, V31, P505, DOI 10.1080/01446193.2013.816435
   Love PED, 2019, ERGONOMICS, V62, P1273, DOI 10.1080/00140139.2019.1644379
   Love PED, 2018, APPL ERGON, V69, P104, DOI 10.1016/j.apergo.2018.01.007
   Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94
   Martinez-Aires MD, 2018, SAFETY SCI, V101, P11, DOI 10.1016/j.ssci.2017.08.015
   Nadhim EA, 2016, INT J ENV RES PUB HE, V13, DOI 10.3390/ijerph13070638
   Nanni L, 2017, PATTERN RECOGN, V71, P158, DOI 10.1016/j.patcog.2017.05.025
   Niu J, 2015, ADV ENG INFORM, V29, P472, DOI 10.1016/j.aei.2015.03.009
   OSHA, 2015, COMM US STAT
   Park MW, 2012, AUTOMAT CONSTR, V28, P15, DOI 10.1016/j.autcon.2012.06.001
   Simonyan K., 2014, PART ADV NEURAL INFO
   Wang HH, 2011, J COMPUT CIVIL ENG, V25, P442, DOI 10.1061/(ASCE)CP.1943-5487.0000125
   Wang HH, 2011, J COMPUT CIVIL ENG, V25, P331, DOI 10.1061/(ASCE)CP.1943-5487.0000094
   Wang H, 2013, IEEE I CONF COMP VIS, P3551, DOI 10.1109/ICCV.2013.441
   Xiao TT, 2018, LECT NOTES COMPUT SC, V11209, P432, DOI 10.1007/978-3-030-01228-1_26
   Yu YT, 2017, AUTOMAT CONSTR, V82, P193, DOI 10.1016/j.autcon.2017.05.002
   Zhang L, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17081814
   Zhong BT, 2020, J CONSTR ENG M, V146, DOI 10.1061/(ASCE)CO.1943-7862.0001767
   Zhou KH, 2016, AER ADV ENG RES, V30, P6
NR 54
TC 23
Z9 24
U1 48
U2 131
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0926-5805
EI 1872-7891
J9 AUTOMAT CONSTR
JI Autom. Constr.
PD NOV
PY 2020
VL 119
AR 103310
DI 10.1016/j.autcon.2020.103310
PG 10
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA OC3GD
UT WOS:000579045500002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Xie, YH
   Hu, LY
   Chen, XX
   Feng, J
   Zhang, DZ
AF Xie, Yonghong
   Hu, Liangyuan
   Chen, Xingxing
   Feng, Jim
   Zhang, Dezheng
TI Auxiliary Diagnosis Based on the Knowledge Graph of TCM Syndrome
SO CMC-COMPUTERS MATERIALS & CONTINUA
LA English
DT Article
DE Knowledge graph; reinforcement learning; auxiliary diagnosis; inference
   path
AB As one of the most valuable assets in China, traditional medicine has a long history and contains pieces of knowledge. The diagnosis and treatment of Traditional Chinese Medicine (TCM) has benefited from the natural language processing technology. This paper proposes a knowledge-based syndrome reasoning method in computer-assisted diagnosis. This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path. According to this reasoning path, we could infer the path from the symptoms to the syndrome and get all possibilities via the relationship between symptoms and causes. Moreover, this study applies the Term Frequency-Inverse Document Frequency (TF-IDF) idea to the computer-assisted diagnosis of TCM for the score of syndrome calculation. Finally, combined with symptoms, syndrome, and causes, the disease could be confirmed comprehensively by voting, and the experiment shows that the system can help doctors and families to disease diagnosis effectively.
C1 [Xie, Yonghong; Hu, Liangyuan; Zhang, Dezheng] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.
   [Chen, Xingxing] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China.
   [Xie, Yonghong; Hu, Liangyuan; Chen, Xingxing; Zhang, Dezheng] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China.
   [Feng, Jim] Amphenol Global Interconnect Syst, San Jose, CA 95131 USA.
RP Zhang, DZ (通讯作者)，Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.; Zhang, DZ (通讯作者)，Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China.
EM zdzchina@126.com
FU National Key Research and Development Program of China [2017YFB1002304];
   National Natural Science Foundation of China [61672178]
FX Supported by the National Key Research and Development Program of China
   under Grant 2017YFB1002304 and the National Natural Science Foundation
   of China (No. 61672178). The author who received the grant is Azguri,
   and the official website of the funder is http://www.most.gov.cn/.
CR Al-Talib G., 2013, INT J COMPUTER NETWO, V1, P189
   [Anonymous], 2017, J NANJING U TECHNOL
   Arulkumaran K, 2017, IEEE SIGNAL PROC MAG, V34, P26, DOI 10.1109/MSP.2017.2743240
   Caicedo JC, 2015, IEEE I CONF COMP VIS, P2488, DOI 10.1109/ICCV.2015.286
   Dimitrovski I, 2015, COMPUT MED IMAG GRAP, V39, P14, DOI 10.1016/j.compmedimag.2014.06.005
   Feng W. W., 2015, J MODERN INFORM, V35, P90
   Francois-Lavet V, 2018, FOUND TRENDS MACH LE, V11, P219, DOI 10.1561/2200000071
   Hua R, 2019, COMPUT SYST SCI ENG, V34, P215
   Ji M., 2012, J SO I METALLURGY, P1298
   Lin P., 2016, J COMPUTER APPL, V36, P992
   Lin Zheng, 2014, COMPUTER MODERNIZATI, V229, P6
   Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
   Summers J. E., 2018, J ACOUST SOC AM, V143, P1716
   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., 2013, P 12 INT SEM WEB C, V1035, P121
   Zhang Li-li, 2013, Computer Engineering, V39, P34, DOI 10.3969/j.issn.1000-3428.2013.06.006
NR 16
TC 4
Z9 5
U1 5
U2 17
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 2020
VL 65
IS 1
BP 481
EP 494
DI 10.32604/cmc.2020.010297
PG 14
WC Computer Science, Information Systems; Materials Science,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Materials Science
GA MP5PC
UT WOS:000552255300029
OA gold
DA 2022-08-23
ER

PT J
AU Mao, JY
   Yao, Y
   Heinrich, S
   Hinz, T
   Weber, C
   Wermter, S
   Liu, ZY
   Sun, MS
AF Mao, Jiayuan
   Yao, Yuan
   Heinrich, Stefan
   Hinz, Tobias
   Weber, Cornelius
   Wermter, Stefan
   Liu, Zhiyuan
   Sun, Maosong
TI Bootstrapping Knowledge Graphs From Images and Text
SO FRONTIERS IN NEUROROBOTICS
LA English
DT Article
DE relation learning; relation prediction; information extraction;
   knowledge graphs; inductive logic programming
ID SYSTEMATICITY; CONNECTIONISM
AB The problem of generating structured Knowledge Graphs (KGs) is difficult and open but relevant to a range of tasks related to decision making and information augmentation. A promising approach is to study generating KGs as a relational representation of inputs (e.g., textual paragraphs or natural images), where nodes represent the entities and edges represent the relations. This procedure is naturally a mixture of two phases: extracting primary relations from input, and completing the KG with reasoning. In this paper, we propose a hybrid KG builder that combines these two phases in a unified framework and generates KGs from scratch. Specifically, we employ a neural relation extractor resolving primary relations from input and a differentiable inductive logic programming (ILP) model that iteratively completes the KG. We evaluate our framework in both textual and visual domains and achieve comparable performance on relation extraction datasets based on Wikidata and the Visual Genome. The framework surpasses neural baselines by a noticeable gap in reasoning out dense KGs and overall performs particularly well for rare relations.
C1 [Mao, Jiayuan; Yao, Yuan; Liu, Zhiyuan; Sun, Maosong] Tsinghua Univ, Dept Comp Sci & Technol, Nat Language Proc Lab, Beijing, Peoples R China.
   [Heinrich, Stefan; Hinz, Tobias; Weber, Cornelius; Wermter, Stefan] Univ Hamburg, Dept Informat, Knowledge Technol Grp, Hamburg, Germany.
RP Liu, ZY (通讯作者)，Tsinghua Univ, Dept Comp Sci & Technol, Nat Language Proc Lab, Beijing, Peoples R China.
EM liuzy@tsinghua.edu.cn
OI Wermter, Stefan/0000-0003-1343-4775; Heinrich,
   Stefan/0000-0001-9913-3206; Liu, Zhiyuan/0000-0002-7709-2543
FU German Research Foundation (DFG); National Science Foundation of China
   (NSFC) [TRR-169]; European Union under the SOCRATES project [721619]
FX The authors gratefully acknowledge support from the German Research
   Foundation (DFG) and the National Science Foundation of China (NSFC)
   under the project Crossmodal Learning (TRR-169), as well as from the
   European Union under the SOCRATES project (No. 721619).
CR Andreas J, 2016, PROC CVPR IEEE, P39, DOI 10.1109/CVPR.2016.12
   [Anonymous], [No title captured]
   Bahdanau D., 2019, P INT C LEARN REPR I
   Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bowman S, 2015, P 2015 C EMP METH NA, P632, DOI DOI 10.18653/V1/D15-1075
   Chao YW, 2015, IEEE I CONF COMP VIS, P1017, DOI 10.1109/ICCV.2015.122
   Chen T., 2019, P IEEE C COMP VIS PA
   Chen XL, 2018, PROC CVPR IEEE, P7239, DOI 10.1109/CVPR.2018.00756
   Christopoulou F., 2019, ARXIV190207023
   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
   Elliott D, 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, P42
   Feng J, 2018, AAAI CONF ARTIF INTE, P5779
   FODOR J, 1990, COGNITION, V35, P183, DOI 10.1016/0010-0277(90)90014-B
   FODOR JA, 1988, COGNITION, V28, P3, DOI 10.1016/0010-0277(88)90031-5
   Garee MJ, 2018, WINT SIMUL C PROC, P941, DOI 10.1109/WSC.2018.8632423
   Girshick R., 2015, ADV NEUR IN
   Girshick R, 2015, IEEE I CONF COMP VIS, P1440, DOI 10.1109/ICCV.2015.169
   Goutte C, 2005, LECT NOTES COMPUT SC, V3408, P345
   Graves A, 2005, NEURAL NETWORKS, V18, P602, DOI 10.1016/j.neunet.2005.06.042
   Greff K, 2017, IEEE T NEUR NET LEAR, V28, P2222, DOI 10.1109/TNNLS.2016.2582924
   Gu J., 2019, P IEEE C COMP VIS PA
   HADLEY RF, 1994, MIND LANG, V9, P431, DOI 10.1111/j.1468-0017.1994.tb00225.x
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Jansen PA, 2012, CONNECT SCI, V24, P25, DOI 10.1080/09540091.2012.664121
   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
   Johnson J, 2015, PROC CVPR IEEE, P3668, DOI 10.1109/CVPR.2015.7298990
   Kersting K., 2000, P AAAI 2000 WORKSH L, P29
   Kim J, 2018, INTERFACE FOCUS, V8, DOI 10.1098/rsfs.2018.0011
   Kimmig A., 2012, P NIPS WORKSH PROB P, P1
   Kingma D. P., 2017, ARXIV
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Krishna R, 2017, INT J COMPUT VISION, V123, P32, DOI 10.1007/s11263-016-0981-7
   Krizhevsky A., 2012, ADV NEURAL INF PROCE, V25, P1097, DOI [10.1145/3065386, DOI 10.1145/3065386]
   LeCun Y., 2015, NATURE, V521, P436, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]
   Li Y., 2018, P ADV NEUR INF PROC, P9225
   Li YB, 2017, INT CON DISTR COMP S, P1261, DOI 10.1109/ICDCS.2017.54
   Liang XD, 2017, PROC CVPR IEEE, P4408, DOI 10.1109/CVPR.2017.469
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   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
   Lu CW, 2016, LECT NOTES COMPUT SC, V9905, P852, DOI 10.1007/978-3-319-46448-0_51
   Mintz M., 2009, P JOINT C NAT LANG P, P1003
   Muggleton S., 1991, New Generation Computing, V8, P295
   Nguyen G, 2015, IEEE INT CONF INTELL, P39, DOI 10.1109/INES.2015.7329735
   Okamoto J., 2007, P 10 C PAC ASS COMP, P94
   Paszke A., 2017, AUT WORKSH FUT GRAD
   Qi M., 2019, P IEEE C COMP VIS PA
   Qin P., 2018, ARXIV180509929
   Ramanathan V, 2015, PROC CVPR IEEE, P1100, DOI 10.1109/CVPR.2015.7298713
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Riedel S, 2010, LECT NOTES ARTIF INT, V6323, P148, DOI 10.1007/978-3-642-15939-8_10
   S Woo, 2018, ADV NEURAL INFORM PR, P560
   SIEGELMANN HT, 1995, J COMPUT SYST SCI, V50, P132, DOI 10.1006/jcss.1995.1013
   Socher R., 2014, PROC C EMPIRICAL MET, P1532, DOI 10.3115/v1/D14-1162
   Sorokin D., 2017, P 2017 C EMP METH NA, P1784
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   Vlachos A., 2014, P ACL 2014 WORKSH LA, P18, DOI [DOI 10.3115/V1/W14-2508, 10.3115/v1/W14-2508]
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xu DJ, 2017, IEEE CONF COMPUT, P541, DOI 10.1109/INFCOMW.2017.8116435
   Xu K, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2326
   Yang F., 2017, ARXIV170208367
   Yang J, 2018, PROC IEEE MICR ELECT, P67, DOI 10.1109/MEMSYS.2018.8346484
   Zellers R, 2018, PROC CVPR IEEE, P5831, DOI 10.1109/CVPR.2018.00611
   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
   Zhang H., 2017, P IEEE C COMP VIS PA, V2, P4
   Zhu H., 2019, ARXIV190200756
NR 69
TC 1
Z9 1
U1 1
U2 10
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 NOV 12
PY 2019
VL 13
AR 93
DI 10.3389/fnbot.2019.00093
PG 12
WC Computer Science, Artificial Intelligence; Robotics; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Robotics; Neurosciences & Neurology
GA JR2TM
UT WOS:000499483900001
PM 31798437
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhang, YH
   Zhu, J
   Zhu, Q
   Xie, YK
   Li, WL
   Fu, L
   Zhang, JX
   Tan, JM
AF Zhang, Yunhao
   Zhu, Jun
   Zhu, Qing
   Xie, Yakun
   Li, Weilian
   Fu, Lin
   Zhang, Junxiao
   Tan, Jianmei
TI The construction of personalized virtual landslide disaster environments
   based on knowledge graphs and deep neural networks
SO INTERNATIONAL JOURNAL OF DIGITAL EARTH
LA English
DT Article
DE Landslide disaster scene; virtual disaster environment; knowledge graph;
   deep neural network; personalized recommendation
ID GEOGRAPHIC-SCENARIO; 3D WEBGIS; VISUALIZATION; RISK; VGES;
   COMMUNICATION; OPTIMIZATION; MANAGEMENT; SIMULATION
AB Virtual Landslide Disaster environments are important for multilevel simulation, analysis and decision-making about Landslide Disasters. However, in the existing related studies, complex disaster scene objects and relationships are not deeply analyzed, and the scene contents are fixed, which is not conducive to meeting multilevel visualization task requirements for diverse users. To resolve the above issues, a construction method for Personalized Virtual Landslide Disaster Environments Based on Knowledge Graphs and Deep Neural networks is proposed in this paper. The characteristics of relationships among users, scenes and data were first discussed in detail; then, a knowledge graph of virtual Landslide Disaster environments was established to clarify the complex relationships among disaster scene objects, and a Deep Neural network was introduced to mine the user history information and the relationships among object entities in the knowledge graph. Therefore, a personalized Landslide Disaster scene data recommendation mechanism was proposed. Finally, a prototype system was developed, and an experimental analysis was conducted. The experimental results show that the method can be used to recommend intelligently appropriate disaster information and scene data to diverse users. The recommendation accuracy stabilizes above 80% - a level able to effectively support The Construction of Personalized Virtual Landslide Disaster environments.
C1 [Zhang, Yunhao; Zhu, Jun; Zhu, Qing; Xie, Yakun; Li, Weilian; Fu, Lin; Zhang, Junxiao] Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu, Sichuan, Peoples R China.
   [Tan, Jianmei] China Mobile Commun Sichuan Grp Co Ltd, Chengdu, Sichuan, Peoples R China.
RP Zhu, J (通讯作者)，Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu, Sichuan, Peoples R China.
EM zhujun@swjtu.edu.cn
FU National Key Research and Development Program of China [2016YFC0803105];
   National Natural Science Foundation of China [41801297, 41801301,
   41941019]
FX This paper was supported by the National Key Research and Development
   Program of China [grant number 2016YFC0803105], and the National Natural
   Science Foundation of China [grant numbers 41801297, 41801301 and
   41941019].
CR Ahmad A, 2019, INT J ADV COMPUT SC, V10, P77
   Bainbridge WS, 2007, SCIENCE, V317, P472, DOI 10.1126/science.1146930
   Bian MS, 2015, PROCEEDINGS OF THE 36TH IAHR WORLD CONGRESS, P4869
   Chang A. X., 2014, 2014 C EMP METH NAT, P2028
   [常亮 Chang Liang], 2019, [智能系统学报, CAAI Transactions on Intelligent Systems], V14, P207
   Chen M, 2018, INT J DIGIT EARTH, V11, P329, DOI 10.1080/17538947.2017.1419452
   Chen M, 2015, ENVIRON EARTH SCI, V74, P6975, DOI 10.1007/s12665-015-4761-4
   Chen M, 2013, ENVIRON PLANN B, V40, P1103, DOI 10.1068/b38160
   Chen WJ, 2016, INT J DISAST RISK RE, V20, P26, DOI 10.1016/j.ijdrr.2016.10.003
   Chen XS, 2018, ADV ENG INFORM, V37, P42, DOI 10.1016/j.aei.2018.04.015
   Covington P, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P191, DOI 10.1145/2959100.2959190
   Coyne B., 2012, P COLING 2012, P679
   [崔鹏 Cui Peng], 2014, [地理科学进展, Progress in Geography], V33, P145
   Denolle MA, 2014, SCIENCE, V343, P399, DOI 10.1126/science.1245678
   Ding Y, 2014, GEO-SPAT INF SCI, V17, P190, DOI 10.1080/10095020.2014.988199
   Dransch D, 2010, INT J DIGIT EARTH, V3, P292, DOI 10.1080/17538941003774668
   Evans SY, 2014, P I CIVIL ENG-CIV EN, V167, P48, DOI 10.1680/cien.13.00020
   [范维澄 Fan Weicheng], 2015, [中国科学基金, Bulletin of National Natural Science Foundation of China], V29, P436
   Fan XT, 2009, J APPL REMOTE SENS, V3, DOI 10.1117/1.3154425
   [范一大 Fan Yida], 2016, [遥感学报, Journal of Remote Sensing], V20, P1170
   Gariano SL, 2016, EARTH-SCI REV, V162, P227, DOI 10.1016/j.earscirev.2016.08.011
   Grolinger K, 2013, IEEE ENABL TECHNOL, P313, DOI 10.1109/WETICE.2013.48
   Hou J, 2018, COMMUN STAT-SIMUL C, V47, P510, DOI 10.1080/03610918.2017.1283704
   Hu Y, 2018, ISPRS INT GEO-INF, V7, DOI 10.3390/ijgi7060215
   Imran M, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P159, DOI 10.1145/2567948.2577034
   [贾中浩 Jia Zhonghao], 2019, [智能系统学报, CAAI Transactions on Intelligent Systems], V14, P430
   Li BR, 2015, EARTHQ SCI, V28, P223, DOI 10.1007/s11589-015-0124-1
   Li JW, 2018, INT J PATTERN RECOGN, V32, DOI 10.1142/S021800141850026X
   Li WL, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8030104
   Li Y, 2013, INT J GEOGR INF SCI, V27, P2043, DOI 10.1080/13658816.2013.786081
   [林珲 Lin Hui], 2016, [遥感学报, Journal of Remote Sensing], V20, P1290
   Lin H, 2015, T GIS, V19, P493, DOI 10.1111/tgis.12167
   Lin H, 2013, EARTH-SCI REV, V126, P74, DOI 10.1016/j.earscirev.2013.08.001
   Lin H, 2013, ANN ASSOC AM GEOGR, V103, P465, DOI 10.1080/00045608.2012.689234
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu MW, 2017, INT J GEOGR INF SCI, V31, P1891, DOI 10.1080/13658816.2017.1334897
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   [陆锋 Lu Feng], 2017, [地球信息科学学报, Journal of Geo-Information Science], V19, P723
   Lu GN, 2018, INT J DIGIT EARTH, V11, P356, DOI 10.1080/17538947.2017.1374477
   [马雷雷 Ma Leilei], 2016, [地理与地理信息科学, Geography and Geo-information Science], V32, P12
   Peng GQ, 2018, NAT HAZARDS, V90, P217, DOI 10.1007/s11069-017-3045-6
   Purohit H, 2019, IEEE INT C SEMANT CO, P474, DOI [10.1109/ICOSC.2019.8665638, 10.1109/ICSC.2019.00091]
   Segoni S, 2018, LANDSLIDES, V15, P1483, DOI 10.1007/s10346-018-0966-4
   Sheridan MF, 2002, MANAG INFORMAT SYST, V5, P691
   Steichen B., 2014, 22 INT C US MOD AD P, P183
   [王保魁 Wang Baokui], 2019, [系统仿真学报, Journal of System Simulation], V31, P2228
   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
   [许珺 Xu Jun], 2010, 地球信息科学学报, V12, P496
   Yin LZ, 2015, ENVIRON EARTH SCI, V74, P7133, DOI 10.1007/s12665-015-4418-3
   Yuan Liang, 2018, Computational Visual Media, V4, P123, DOI 10.1007/s41095-018-0110-3
   Zhang YH, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8050209
   Zhu J, 2015, IEEE J-STARS, V8, P105, DOI 10.1109/JSTARS.2014.2340399
NR 54
TC 12
Z9 13
U1 12
U2 76
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1753-8947
EI 1753-8955
J9 INT J DIGIT EARTH
JI Int. J. Digit. Earth
PD DEC 1
PY 2020
VL 13
IS 12
BP 1637
EP 1655
DI 10.1080/17538947.2020.1773950
EA JUN 2020
PG 19
WC Geography, Physical; Remote Sensing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Physical Geography; Remote Sensing
GA PA4YF
UT WOS:000542894300001
DA 2022-08-23
ER

PT J
AU Siciliani, L
   Basile, P
   Lops, P
   Semeraro, G
AF Siciliani, Lucia
   Basile, Pierpaolo
   Lops, Pasquale
   Semeraro, Giovanni
TI MQALD: Evaluating the impact of modifiers in question answering over
   knowledge graphs
SO SEMANTIC WEB
LA English
DT Article
DE Question answering; knowledge graphs; dataset; benchmark; SPARQL
   modifiers
ID WEB
AB Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users' questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user's question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art.
C1 [Siciliani, Lucia; Basile, Pierpaolo; Lops, Pasquale; Semeraro, Giovanni] Univ Bari Aldo Moro, Dept Comp Sci, Via E Orabona 4, I-70125 Bari, Italy.
RP Siciliani, L (通讯作者)，Univ Bari Aldo Moro, Dept Comp Sci, Via E Orabona 4, I-70125 Bari, Italy.
EM lucia.siciliani@uniba.it; pierpaolo.basile@uniba.it;
   pasquale.lops@uniba.it; giovanni.semeraro@uniba.it
RI Semeraro, Giovanni/AAC-2156-2020
OI Semeraro, Giovanni/0000-0001-6883-1853
FU PRIN 2009 project: "Modelli per la personalizzazione di percorsi
   formativi in un sistema di gestione dell'apprendimento"
FX This work was supported by the PRIN 2009 project: "Modelli per la
   personalizzazione di percorsi formativi in un sistema di gestione
   dell'apprendimento".
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes Antoine, 2015, ARXIV150602075
   Chakraborty N., 2019, ABS190709361 CORR
   Costa Tarcisio Souza, 2020, CIKM '20: Proceedings of the 29th International Conference on Information & Knowledge Management, P3157, DOI 10.1145/3340531.3412760
   Diefenbach D, 2017, CCIS, DOI DOI 10.1007/978-3-319-69146-6_8
   Diefenbach D, 2020, SEMANT WEB, V11, P421, DOI 10.3233/SW-190343
   Diefenbach D, 2018, KNOWL INF SYST, V55, P529, DOI 10.1007/s10115-017-1100-y
   Dubey M, 2019, LECT NOTES COMPUT SC, V11779, P69, DOI 10.1007/978-3-030-30796-7_5
   Dubey M, 2016, LECT NOTES COMPUT SC, V9678, P300, DOI 10.1007/978-3-319-34129-3_19
   Hoffner K, 2017, SEMANT WEB, V8, P895, DOI 10.3233/SW-160247
   Hu S., 2018, EMNLP, P2098
   Jia Z, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1057, DOI 10.1145/3184558.3191536
   Kaufmann E, 2007, LECT NOTES COMPUT SC, V4825, P281
   Mishra A, 2016, J KING SAUD UNIV-COM, V28, P345, DOI 10.1016/j.jksuci.2014.10.007
   Ozcan F, 2020, SIGMOD'20: PROCEEDINGS OF THE 2020 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P2629, DOI 10.1145/3318464.3383128
   Saleem M., 2017, CEUR WORKSHOP PROC, V1932
   Siciliani Lucia, 2019, AI*IA 2019 - Advances in Artificial Intelligence. XVIIIth International Conference of the Italian Association for Artificial Intelligence. Proceedings. Lecture Notes in Artificial Intelligence (LNAI 11946), P210, DOI 10.1007/978-3-030-35166-3_15
   Talmor A., 2018, P 2018 C N AM CHAPT, V1, P641
   Thanisch P., 1995, J NATURAL LANGUAGE E, V1, P29, DOI DOI 10.1017/S135132490000005X
   Trivedi P, 2017, LECT NOTES COMPUT SC, V10588, P210, DOI 10.1007/978-3-319-68204-4_22
   Usbeck R., 2018, 8 CHALLENGE QUESTION, V2241, P51
   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, 2019, SEMANT WEB, V10, P293, DOI 10.3233/SW-180312
   Usbeck R, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1133, DOI 10.1145/2736277.2741626
   Vollmers D., ARXIV210306752, P2021
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
NR 29
TC 1
Z9 1
U1 5
U2 5
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 215
EP 231
DI 10.3233/SW-210440
PG 17
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YV6TV
UT WOS:000752861100005
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Li, Q
   Li, LL
   Zhong, J
   Huang, LF
AF Li, Qing
   Li, Lili
   Zhong, Jiang
   Huang, L. Frank
TI Real-time sepsis severity prediction on knowledge graph deep learning
   networks for the intensive care unit
SO JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
LA English
DT Article
DE Deep neural networks; Sepsis; Intensive care units; Clinical
   informatics; Illness severity prediction; Knowledge graph
ID CHRONIC HEALTH EVALUATION; ACUTE PHYSIOLOGY; SEPTIC SHOCK; MORTALITY;
   CLASSIFICATION; APACHE; SYSTEM; SCORE; MODEL
AB Sepsis is the third-highest mortality disease in intensive care units (ICUs). In this paper, we proposed a deep learning model for predicting the severity of sepsis patients. Most existing models based on attention mechanisms do not fully utilize knowledge graph based information for different organ systems, such that might constitute crucial features for predicting the severity of sepsis patients. Therefore, we have employed a medical knowledge graph as a reliable and robust source of side information. End-to-end neural networks that incorporate analyses of various organ systems simultaneously and intuitively were developed in the proposed model to reflect upon the condition of patients in a timely fashion. We have developed a pre-training technique in the proposed model to combine it with labeled data by multi-task learning. Experimental results on realworld clinical datasets, MIMIC-III and eIR, demonstrate that our model outperforms state-of-the-art models in predicting the severity of sepsis patients.
C1 [Li, Qing; Huang, L. Frank] Cincinnati Childrens Hosp Med Ctr, Div Expt Hematol & Canc Biol, Brain Tumor Ctr, Cincinnati, OH 45229 USA.
   [Li, Lili] Chongqing Univ, Sch Civil Engn, Chongqing, Peoples R China.
   [Zhong, Jiang] Chongqing Univ, Coll Comp Sci, Chongqing, Peoples R China.
RP Huang, LF (通讯作者)，Cincinnati Childrens Hosp Med Ctr, Div Expt Hematol & Canc Biol, Brain Tumor Ctr, Cincinnati, OH 45229 USA.
EM Frank.Huang@cchmc.org
CR [Anonymous], 2017, ARXIV170106675
   Byrne L, 2017, ANN INTENSIVE CARE, V7, DOI 10.1186/s13613-016-0231-8
   Caruana R, 1996, ADV NEUR IN, V8, P959
   Che ZP, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-24271-9
   Chen WT, 2018, IEEE DATA MINING, P917, DOI 10.1109/ICDM.2018.00111
   Cho K., 2014, LEARNING PHRASE REPR, V10, P1
   Collins FS, 2015, NEW ENGL J MED, V372, P793, DOI 10.1056/NEJMp1500523
   Cooper GF, 1997, ARTIF INTELL MED, V9, P107, DOI 10.1016/S0933-3657(96)00367-3
   Dabek F, 2015, LECT NOTES ARTIF INT, V9250, P252, DOI 10.1007/978-3-319-23344-4_25
   Ghassemi M, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P75, DOI 10.1145/2623330.2623742
   Gotts JE, 2016, BMJ-BRIT MED J, V353, DOI 10.1136/bmj.i1585
   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
   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
   Hammerla NY, 2015, AAAI CONF ARTIF INTE, P1742
   Harutyunyan H., 2017, ARXIV170307771
   Henry J., 2016, ONC DATA BRIEF
   Huang L, 2014, BIOINFORMATICS, V30, P228, DOI 10.1093/bioinformatics/btu278
   Johnson A. E., 2017, PROC 2 MACHINE LEARN, P361
   Johnson AEW, 2018, CRIT CARE MED, V46, P494, DOI 10.1097/CCM.0000000000002965
   Johnson AEW, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.35
   Johnson AEW, 2013, CRIT CARE MED, V41, P1711, DOI 10.1097/CCM.0b013e31828a24fe
   KNAUS WA, 1981, CRIT CARE MED, V9, P591, DOI 10.1097/00003246-198108000-00008
   KNAUS WA, 1991, CHEST, V100, P1619, DOI 10.1378/chest.100.6.1619
   KNAUS WA, 1992, CHEST, V102, P1920, DOI 10.1016/S0012-3692(16)40910-4
   Komorowski M, 2018, NAT MED, V24, P1716, DOI 10.1038/s41591-018-0213-5
   LeGall JR, 1996, JAMA-J AM MED ASSOC, V276, P802, DOI 10.1001/jama.276.10.802
   LEGALL JR, 1993, JAMA-J AM MED ASSOC, V270, P2957, DOI 10.1001/jama.270.24.2957
   Lipton Z.C., 2016, MACH LEARN HEALTHC
   Liu Q, 2019, GENES-BASEL, V10, DOI 10.3390/genes10080602
   Liu V, 2014, JAMA-J AM MED ASSOC, V312, P90, DOI 10.1001/jama.2014.5804
   Liu X., 2019, ARXIV190111504
   Marik PE, 2015, ACTA ANAESTH SCAND, V59, P561, DOI 10.1111/aas.12479
   McCullagh P, 2019, LONDON GEN LINEAR MO
   Nemati S, 2018, CRIT CARE MED, V46, P547, DOI [10.1097/CCM.0000000000002936, 10.1097/ccm.0000000000002936]
   Oellrich A, 2016, BRIEF BIOINFORM, V17, P819, DOI 10.1093/bib/bbv083
   Pirracchio R., 2016, SECONDARY ANAL ELECT, P295, DOI DOI 10.1007/978-3-319-43742-2_20
   Purushotham S., 2016, VARIATIONAL RECURREN
   Purushotham S, 2017, ARXIV PREPRINT ARXIV
   Saeed M, 2011, CRIT CARE MED, V39, P952, DOI 10.1097/CCM.0b013e31820a92c6
   Shann F, 1997, INTENS CARE MED, V23, P201, DOI 10.1007/s001340050317
   Singer M, 2016, JAMA-J AM MED ASSOC, V315, P801, DOI 10.1001/jama.2016.0287
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Vincent JL, 2006, CRIT CARE MED, V34, P344, DOI 10.1097/01.CCM.0000194725.48928.3A
   Vincent JL, 1996, INTENS CARE MED, V22, P707, DOI 10.1007/s001340050156
   Waechter J, 2014, CRIT CARE MED, V42, P2158, DOI 10.1097/CCM.0000000000000520
   Wang S, 2017, ACM T KNOWL DISCOV D, V11, DOI 10.1145/3003729
   Wang S, 2016, IEEE T KNOWL DATA EN, V28, P3191, DOI 10.1109/TKDE.2016.2605687
   Weng W.-H., 2017, REPRESENTATION REINF
   Yan CG, 2020, IEEE T MULTIMEDIA, V22, P229, DOI 10.1109/TMM.2019.2924576
   Yan CG, 2019, IEEE T MULTIMEDIA, V21, P2675, DOI 10.1109/TMM.2019.2903448
   Yan CG, 2018, IEEE T MULTIMEDIA, V20, P3389, DOI 10.1109/TMM.2018.2838320
NR 52
TC 4
Z9 4
U1 4
U2 22
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1047-3203
EI 1095-9076
J9 J VIS COMMUN IMAGE R
JI J. Vis. Commun. Image Represent.
PD OCT
PY 2020
VL 72
AR 102901
DI 10.1016/j.jvcir.2020.102901
PG 9
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OD3DV
UT WOS:000579732400014
DA 2022-08-23
ER

PT J
AU Wang, ZK
   Li, LJ
   Zeng, DD
AF Wang, Zikang
   Li, Linjing
   Zeng, Daniel Dajun
TI Hierarchical Multihop Reasoning on Knowledge Graphs
SO IEEE INTELLIGENT SYSTEMS
LA English
DT Article
AB Multihop knowledge reasoning aims to find missing entities for incomplete triples by finding paths on knowledge graphs. It is a fundamental and important task. In this article, we devise a hierarchical reinforcement learning algorithm to model the reasoning process more effectively. Unlike existing methods directly reason on entities and relations, we adopt a high-level reasoning layer to deal with abstract concepts, which guides the reasoning process conducted at the low level for concrete entities and relations. Our approach yields competitive results on link prediction on both NELL-995 and FB15k-237 datasets. The comparison to baselines also demonstrates the effectiveness of the hierarchical structure.
C1 [Wang, Zikang; Li, Linjing; Zeng, Daniel Dajun] Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China.
   [Wang, Zikang; Li, Linjing; Zeng, Daniel Dajun] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Li, Linjing; Zeng, Daniel Dajun] 4Data Sci Inst Longhua, Shenzhen 518129, Peoples R China.
RP Wang, ZK (通讯作者)，Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China.
EM wangzikang2016@ia.ac.cn; linjing.li@ia.ac.cn; dajun.zeng@ia.ac.cn
OI Wang, Zikang/0000-0003-1406-1237
FU National Key Research and Development Program of China [2020AAA0103405];
   National Natural Science Foundation of China [71621002]; Strategic
   Priority Research Program of Chinese Academy of Sciences [XDA27030100]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2020AAA0103405, in part by the
   National Natural Science Foundation of China under Grant 71621002, and
   in part by the Strategic Priority Research Program of Chinese Academy of
   Sciences under Grant XDA27030100.
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   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
   Dietterich TG, 2000, J ARTIF INTELL RES, V13, P227, DOI 10.1613/jair.639
   Lao N, 2011, P C EMP METH NAT LAN, P529, DOI DOI 10.5555/2145432.2145494
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   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
   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
   Wang Heng, 2019, P 2019 C EMP METH NA, P2623
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1023/A:1022672621406
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Yang B., 2015, P INT C LEARN REPR U
NR 15
TC 0
Z9 0
U1 3
U2 3
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1541-1672
EI 1941-1294
J9 IEEE INTELL SYST
JI IEEE Intell. Syst.
PD JAN 1
PY 2022
VL 37
IS 1
BP 71
EP 78
DI 10.1109/MIS.2021.3095055
PG 8
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1C1VZ
UT WOS:000792916700009
DA 2022-08-23
ER

PT J
AU Lin, RH
   Fang, QY
   Wu, BD
AF Lin, Rongheng
   Fang, Qiyu
   Wu, Budan
TI Hydro-graph: A knowledge graph for hydrogen research trends and
   relations
SO INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
LA English
DT Article; Proceedings Paper
CT 7th International Conference on Energy, Engineering and Environmental
   Engineering (ICEEEE)
CY JUL 18-19, 2019
CL Beijing Jiaotong Univ, Beijing, PEOPLES R CHINA
HO Beijing Jiaotong Univ
DE Hydrogen research; Knowledge graph; Literature analysis; Visualization
ID STORAGE; ENERGY; CHALLENGES
AB The field of hydrogen research is a huge community, generating a large amount of literature every year. Researchers need to do a lot of review to get a general understanding of the field, and to realize the research trends and relations. Since the reviews are done manually by the researchers, it is not only inefficient but the timing of the reviews might not be real time. This paper presents a tool called Hydro-graph, which aims to provide researchers in the field of hydrogen research with a knowledge graph of relevant fields. It can help researchers quickly understand the development status, research hotspots and research trends of a certain field. Furthermore, it can find representative authors in the field, and analyse their research direction and development process. Experimental results show that Hydro-graph can effectively help researchers in the hydrogen research community to analyse relevant fields. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
C1 [Lin, Rongheng; Fang, Qiyu; Wu, Budan] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China.
RP Lin, RH; Wu, BD (通讯作者)，Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China.
EM rhlin@bupt.edu.cn; wubudan@bupt.edu.cn
OI lin, rongheng/0000-0001-9562-7356
FU State Grid Corporation of China [520940180016]
FX This work was supported by the State Grid Corporation of China under the
   project title:"The Improved Core Analysis Algorithms and Utilities for
   Smart Grid Big Data " (520940180016).
CR Aaldto-Saksa PT, 2018, J POWER SOURCES, V396, P803, DOI 10.1016/j.jpowsour.2018.04.011
   Abdalla AM, 2018, ENERG CONVERS MANAGE, V165, P602, DOI 10.1016/j.enconman.2018.03.088
   Antoine Bordes, 2014, P 20 ACM SIGKDD INT
   dos Santos KG, 2017, RENEW SUST ENERG REV, V68, P563, DOI 10.1016/j.rser.2016.09.128
   Emrouznejad A, 2014, INT J INTELL SYST, V29, P994, DOI 10.1002/int.21673
   Fonseca JD, 2019, INT J HYDROGEN ENERG, V44, P9486, DOI 10.1016/j.ijhydene.2018.09.177
   Glazer N, 2011, STUD SCI EDUC, V47, P183, DOI 10.1080/03057267.2011.605307
   Karchiyappan T, 2019, ENERG SOURCE PART A, V41, P902, DOI 10.1080/15567036.2018.1520368
   Khetkorn W, 2017, BIORESOURCE TECHNOL, V243, P1194, DOI 10.1016/j.biortech.2017.07.085
   Kumar S, 2017, RENEW SUST ENERG REV, V72, P791, DOI 10.1016/j.rser.2017.01.063
   Lianos P, 2017, APPL CATAL B-ENVIRON, V210, P235, DOI 10.1016/j.apcatb.2017.03.067
   Ong BC, 2017, INT J HYDROGEN ENERG, V42, P10142, DOI 10.1016/j.ijhydene.2017.01.117
   Ouyang LZ, 2017, J ALLOY COMPD, V691, P422, DOI 10.1016/j.jallcom.2016.08.179
   Pasquale Minervini, 2015, MACH LEARN APPL ICML
   Patrick Ernst, 2014, 2014 IEEE 30 INT C D
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Ren JW, 2017, INT J HYDROGEN ENERG, V42, P289, DOI 10.1016/j.ijhydene.2016.11.195
   Roderic Page, 2016, RES IDEAS OUTCOMES, P2
   Sinha A, 2015, WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P243, DOI 10.1145/2740908.2742839
   Sun ZY, 2018, INT J ENERG RES, V42, P2845, DOI 10.1002/er.4048
   Sun ZY, 2017, KOREAN J CHEM ENG, V34, P1846, DOI 10.1007/s11814-017-0084-3
   Sun ZY, 2016, ENERGY, V116, P116, DOI 10.1016/j.energy.2016.09.103
   Thelwall M, 2018, J INFORMETR, V12, P1, DOI 10.1016/j.joi.2017.11.001
   Waleed Ammar, 2018, ARXIV180502262
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Xin Dong, 2014, P 20 ACM SIGKDD INT
NR 26
TC 2
Z9 3
U1 3
U2 14
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0360-3199
EI 1879-3487
J9 INT J HYDROGEN ENERG
JI Int. J. Hydrog. Energy
PD AUG 7
PY 2020
VL 45
IS 39
SI SI
BP 20408
EP 20418
DI 10.1016/j.ijhydene.2019.12.036
PG 11
WC Chemistry, Physical; Electrochemistry; Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Chemistry; Electrochemistry; Energy & Fuels
GA MV4YU
UT WOS:000556366300028
DA 2022-08-23
ER

PT J
AU Fecho, K
   Bizon, C
   Miller, F
   Schurman, S
   Schmitt, C
   Xue, W
   Morton, K
   Wang, P
   Tropsha, A
AF Fecho, Karamarie
   Bizon, Chris
   Miller, Frederick
   Schurman, Shepherd
   Schmitt, Charles
   Xue, William
   Morton, Kenneth
   Wang, Patrick
   Tropsha, Alexander
TI A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses
   for Real-World Environmental Health Observations: Cohort Study and
   Informatics Application
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE knowledge graph; knowledge representation; data exploration;
   generalizability; discovery; open science; immune-mediated disease
ID CARBON-MONOXIDE; CELLS
AB Background: Knowledge graphs are a common form of knowledge representation in biomedicine and many other fields. We developed an open biomedical knowledge graph-based system termed Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ROBOKOP consists of both a front-end user interface and a back-end knowledge graph. The ROBOKOP user interface allows users to posit questions and explore answer subgraphs. Users can also posit questions through direct Cypher query of the underlying knowledge graph, which currently contains roughly 6 million nodes or biomedical entities and 140 million edges or predicates describing the relationship between nodes, drawn from over 30 curated data sources.
   Objective: We aimed to apply ROBOKOP to survey data on workplace exposures and immune-mediated diseases from the Environmental Polymorphisms Registry (EPR) within the National Institute of Environmental Health Sciences.
   Methods: We analyzed EPR survey data and identified 45 associations between workplace chemical exposures and immune-mediated diseases, as self-reported by study participants (n= 4574), with 20 associations significant at P<.05 after false discovery rate correction. We then used ROBOKOP to (1) validate the associations by determining whether plausible connections exist within the ROBOKOP knowledge graph and (2) propose biological mechanisms that might explain them and serve as hypotheses for subsequent testing. We highlight the following three exemplar associations: carbon monoxide-multiple sclerosis, ammonia-asthma, and isopropanol-allergic disease.
   Results: ROBOKOP successfully returned answer sets for three queries that were posed in the context of the driving examples. The answer sets included potential intermediary genes, as well as supporting evidence that might explain the observed associations.
   Conclusions: We demonstrate real-world application of ROBOKOP to generate mechanistic hypotheses for associations between workplace chemical exposures and immune-mediated diseases. We expect that ROBOKOP will find broad application across many biomedical fields and other scientific disciplines due to its generalizability, speed to discovery and generation of mechanistic hypotheses, and open nature.
C1 [Fecho, Karamarie; Bizon, Chris; Tropsha, Alexander] Univ N Carolina, Renaissance Comp Inst, 100 Europa Dr,Suite 540, Chapel Hill, NC 27517 USA.
   [Fecho, Karamarie] Copperline Profess Solut, Pittsboro, NC USA.
   [Miller, Frederick; Schurman, Shepherd; Schmitt, Charles; Xue, William] NIEHS, Durham, NC USA.
   [Morton, Kenneth; Wang, Patrick] CoVar Appl Technol, Durham, NC USA.
   [Tropsha, Alexander] Univ N Carolina, Eshelman Sch Pharm, Chapel Hill, NC 27517 USA.
RP Fecho, K (通讯作者)，Univ N Carolina, Renaissance Comp Inst, 100 Europa Dr,Suite 540, Chapel Hill, NC 27517 USA.
EM kfecho@copperlineprofessionalsolutions.com
RI Schurman, Shepherd/ABF-8125-2021
OI Schmitt, Charles/0000-0002-3148-2263; Tropsha,
   Alexander/0000-0003-3802-8896; Xue, William/0000-0003-2130-3097; Fecho,
   Karamarie/0000-0002-6704-9306; Wang, Patrick/0000-0002-6693-6106;
   Schurman, Shepherd/0000-0002-9133-7906; Morton,
   Kenneth/0000-0002-9352-8669
FU National Center for Advancing Translational Sciences, National
   Institutes of Health [OT2TR002514]; Clinical Research Branch, Intramural
   Research Program of the National Institutes of Health, National
   Institute of Environmental Health Sciences [ZID ES103354-01]; Biomedical
   Data Translator Consortium
FX The authors wish to thank the Biomedical Data Translator Consortium for
   their support and intellectual input. We also acknowledge and appreciate
   the contributions of the broader Reasoning Over Biomedical Objects
   linked in Knowledge Oriented Pathways ROBOKOP) team, including Vinicius
   Alves, Jim Balhoff, Steven Cox, Yaphet Kabede, Daniel Korn, Eugene
   Muratov, and Max Wang. In addition, we thank the members of the
   Environmental Polymorphisms Registry (EPR) Executive Leadership
   Committee and others in the EPR IMD Study Group, including Farida
   Akhtari, Montserrat Ayala-Ramirez, Perry Blackshear, Askia Dunnon, David
   Fargo, Michael Fessler, Stavros Garantziotis, Janet E Hall, Nathaniel
   MacNell, John McGrath, Alison Motsinger-Reif, and Christine Parks for
   useful discussions, and Jeremy Erickson, Andy Rooney, and Vickie Walker
   for helpful comments on the manuscript. This work was supported by the
   National Center for Advancing Translational Sciences, National
   Institutes of Health (OT2TR002514) and by the Clinical Research Branch,
   Intramural Research Program of the National Institutes of Health,
   National Institute of Environmental Health Sciences (ZID ES103354-01).
CR [Anonymous], EXPLORING YOUR GENES
   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
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Brouard S, 2002, J BIOL CHEM, V277, P17950, DOI 10.1074/jbc.M108317200
   Burton MD, 2011, J NEUROINFLAMM, V8, DOI 10.1186/1742-2094-8-54
   Chen B, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002574
   Dantzer R, 2009, IMMUNOL ALLERGY CLIN, V29, P247, DOI 10.1016/j.iac.2009.02.002
   Davis AP, 2019, NUCLEIC ACIDS RES, V47, pD948, DOI 10.1093/nar/gky868
   Fagone P, 2012, CLIN EXP IMMUNOL, V167, P179, DOI 10.1111/j.1365-2249.2011.04491.x
   Fagone P, 2013, J NEUROIMMUNOL, V261, P82, DOI 10.1016/j.jneuroim.2013.04.013
   Fang Y, 2016, PROC INT CONF DATA, P277, DOI 10.1109/ICDE.2016.7498247
   Fecho K, 2020, AMIA 2020 VIRT ANN S AMIA 2020 VIRT ANN S
   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
   Flinn AM, 2018, ORPHANET J RARE DIS, V13, DOI 10.1186/s13023-018-0807-5
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Holst G, 2018, ENV EPIDEMIOLOGY, V2, pe019, DOI [10.1097/ee9., DOI 10.1097/EE9.0000000000000019]
   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
   Kasneci G, 2008, PROC INT CONF DATA, P953, DOI 10.1109/ICDE.2008.4497504
   Kim SH, 2009, RESP MED, V103, P356, DOI 10.1016/j.rmed.2008.10.008
   KLEIN DJ, 1993, J MATH CHEM, V12, P81, DOI 10.1007/BF01164627
   Kohler S, 2019, NUCLEIC ACIDS RES, V47, pD1018, DOI 10.1093/nar/gky1105
   Morton K, 2019, BIOINFORMATICS, V35, P5382, DOI 10.1093/bioinformatics/btz604
   NG WH, 1990, BRIT J CLIN PHARMACO, V30, pS89, DOI 10.1111/j.1365-2125.1990.tb05474.x
   Nicholson DN, 2020, COMPUT STRUCT BIOTEC, V18, P1414, DOI 10.1016/j.csbj.2020.05.017
   Schipper HM, 2019, PROG NEUROBIOL, V172, P40, DOI 10.1016/j.pneurobio.2018.06.008
   Trikojat K, 2017, BRAIN BEHAV IMMUN, V65, P202, DOI 10.1016/j.bbi.2017.05.005
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Yang SS, 2018, BEHAV NEUROL, V2018, DOI 10.1155/2018/8750464
   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 31
TC 1
Z9 1
U1 5
U2 8
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 JUL
PY 2021
VL 9
IS 7
AR e26714
DI 10.2196/26714
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA TS9BJ
UT WOS:000679941500020
PM 34283031
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhang, LZ
   Li, M
   Yan, K
   Wang, RZ
   Hui, B
AF Zhang, Lizong
   Li, Meng
   Yan, Ke
   Wang, Ruozhou
   Hui, Bei
TI Hierarchical Knowledge-Based Graph Embedding Model for Image-Text
   Matching in IoTs
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Internet of Things; Feature extraction; Snow; Noise measurement;
   Knowledge engineering; Data mining; Data models; Graph embedding;
   hierarchical knowledge graph; image-text matching; Internet of Things
   systems (IoTs)
ID INDUSTRIAL INTERNET; HEALTH-CARE; THINGS; FUSION
AB The development of Internet of Things systems (IoTs) and 5G technology has allowed image and text information to be collected and spread at an unprecedentedly high speed. To improve the data processing capabilities of IoTs, the semantic relations between images and text should be extracted efficiently and accurately. Therefore, to reduce the enormous semantic differences between images and text, existing methods introduce consensus knowledge graphs into image-text matching tasks. However, these methods result in noisy edges during the graph construction stage and overlook detailed knowledge extraction, leading to reduced performance in semantic matching. In this article, a two-layer heterogeneous knowledge graph network is proposed to solve the above problems. The proposed model incorporates category knowledge and local knowledge for improved data representation. Specifically, a category-based hierarchical knowledge graph is constructed to learn representations of knowledge concepts through a hierarchical correlation graph embedding (HCGE) module. Then, a globally guided local attention (GLA) module is used to extract fine-grained local knowledge. Finally, the similarity between the input image and text is calculated based on knowledge-fused features to complete the matching process. Extensive experiments show that the proposed model can learn more effective knowledge features to improve the efficacy of image-text matching in.
C1 [Zhang, Lizong; Li, Meng; Yan, Ke; Wang, Ruozhou] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China.
   [Zhang, Lizong; Li, Meng; Yan, Ke; Wang, Ruozhou] Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 610000, Peoples R China.
   [Hui, Bei] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 611731, Peoples R China.
RP Yan, K (通讯作者)，Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China.
EM kyan@uestc.edu.cn
OI Zhang, Lizong/0000-0002-0719-9556; Yan, Ke/0000-0002-4834-9549
FU National Natural Science Foundation of China [U19A2059]; Sichuan Science
   and Technology Program [2019YFG0507, 2020YFG0328]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant U19A2059, and in part by the Sichuan
   Science and Technology Program under Grant 2019YFG0507 and Grant
   2020YFG0328.
CR Abuhasel KA, 2020, IEEE ACCESS, V8, P117354, DOI 10.1109/ACCESS.2020.3004711
   Aggarwal M., MACHINE LEARNING SOC, P2021, DOI [10.1007/978-981-33-4022-0, DOI 10.1007/978-981-33-4022-0]
   Anderson P, 2018, PROC CVPR IEEE, P3674, DOI 10.1109/CVPR.2018.00387
   Battisti ALE, 2021, IEEE ACCESS, V9, P59255, DOI 10.1109/ACCESS.2021.3073240
   Cai ZP, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3459992
   Cai ZP, 2019, INT CON DISTR COMP S, P144, DOI 10.1109/ICDCS.2019.00023
   Cai ZP, 2020, IEEE T NETW SCI ENG, V7, P766, DOI 10.1109/TNSE.2018.2830307
   Chakraborty P, 2021, IEEE INTERNET THINGS, V8, P7303, DOI 10.1109/JIOT.2020.3040729
   Chen C, 2021, COMPUT NETW, V186, DOI 10.1016/j.comnet.2020.107744
   Choi J, 2021, IEEE INTERNET THINGS, V8, P541, DOI 10.1109/JIOT.2020.3004774
   Dey AK, 2021, INTERNET THINGS-NETH, V14, DOI 10.1016/j.iot.2021.100380
   Diao H., 2021, SIMILARITY REASONING
   Faghri Fartash, 2017, ARXIV170705612
   Frome Andrea, 2013, ADV NEURAL INFORM PR, P2121
   Gao Z, 2021, COMPUT COMMUN, V166, P57, DOI 10.1016/j.comcom.2020.11.013
   Georgousis S, 2021, IEEE ACCESS, V9, P22106, DOI 10.1109/ACCESS.2021.3055280
   Gu JX, 2019, PROC CVPR IEEE, P1969, DOI 10.1109/CVPR.2019.00207
   Haoran Wang, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12369), P18, DOI 10.1007/978-3-030-58586-0_2
   Hasan MK, 2021, IEEE ACCESS, V9, P47731, DOI 10.1109/ACCESS.2021.3061710
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hou JY, 2019, IEEE I CONF COMP VIS, P8917, DOI 10.1109/ICCV.2019.00901
   Hu ZB, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P789
   Huang Y, 2018, PROC CVPR IEEE, P6163, DOI 10.1109/CVPR.2018.00645
   Hui Chen, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P12652, DOI 10.1109/CVPR42600.2020.01267
   Jan MA, 2021, FUTURE GENER COMP SY, V122, P40, DOI 10.1016/j.future.2021.03.020
   Jeon G., 2019, CONCURRENCY COMPUT P, V33
   Ji Z, 2019, IEEE I CONF COMP VIS, P5753, DOI 10.1109/ICCV.2019.00585
   Karpathy Andrej, 2014, ADV NEURAL INFORM PR, P1889
   Klaib AF, 2021, EXPERT SYST APPL, V166, DOI 10.1016/j.eswa.2020.114037
   Lee KH, 2018, LECT NOTES COMPUT SC, V11208, P212, DOI 10.1007/978-3-030-01225-0_13
   Li KY, 2021, IEEE INTERNET THINGS, V8, P6904, DOI 10.1109/JIOT.2020.3036583
   Li KY, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P745, DOI 10.1145/3340531.3411970
   Li KP, 2019, IEEE I CONF COMP VIS, P4653, DOI 10.1109/ICCV.2019.00475
   Li XQ, 2021, FUTURE GENER COMP SY, V118, P282, DOI 10.1016/j.future.2021.01.016
   Ma L, 2015, ARXIV150600333
   Ma L, 2020, IEEE T CIRC SYST VID, V30, P2250, DOI 10.1109/TCSVT.2019.2916167
   Mao J, 2014, CELL DEATH DIS, V5, DOI 10.1038/cddis.2013.515
   Mao KM, 2021, COMPUT COMMUN, V173, P150, DOI 10.1016/j.comcom.2021.04.002
   Mogadala A., 2017, ARXIV171006303
   Ren SQ, 2017, IEEE T PATTERN ANAL, V39, P1137, DOI 10.1109/TPAMI.2016.2577031
   Schuster M, 1997, IEEE T SIGNAL PROCES, V45, P2673, DOI 10.1109/78.650093
   Shafique K, 2020, IEEE ACCESS, V8, P23022, DOI 10.1109/ACCESS.2020.2970118
   Song YX, 2021, IEEE INTERNET THINGS, V8, P4250, DOI 10.1109/JIOT.2020.3034385
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang MD, 2021, IEEE INTERNET THINGS, V8, P1662, DOI 10.1109/JIOT.2020.3015237
   Wang Q, 2021, MICROPROCESS MICROSY, V80, DOI 10.1016/j.micpro.2020.103558
   Wang T, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P12, DOI 10.1145/3343031.3350875
   Wu Q, 2016, PROC CVPR IEEE, P4622, DOI 10.1109/CVPR.2016.500
   Wu XJ, 2021, T EMERG TELECOMMUN T, V32, DOI 10.1002/ett.4182
   Yi YX, 2021, IEEE INTERNET THINGS, V8, P2177, DOI 10.1109/JIOT.2020.3026995
   Zhang J, 2021, IEEE ACCESS, V9, P15170, DOI 10.1109/ACCESS.2021.3052469
   Zheng X, 2020, IEEE J SEL AREA COMM, V38, P968, DOI 10.1109/JSAC.2020.2980802
NR 53
TC 0
Z9 0
U1 2
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2327-4662
J9 IEEE INTERNET THINGS
JI IEEE Internet Things J.
PD JUN 15
PY 2022
VL 9
IS 12
BP 9399
EP 9409
DI 10.1109/JIOT.2021.3098897
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1Y4FB
UT WOS:000808096100032
DA 2022-08-23
ER

PT J
AU Guo, QY
   Zhuang, FZ
   Qin, C
   Zhu, HS
   Xie, X
   Xiong, H
   He, Q
AF Guo, Qingyu
   Zhuang, Fuzhen
   Qin, Chuan
   Zhu, Hengshu
   Xie, Xing
   Xiong, Hui
   He, Qing
TI A Survey on Knowledge Graph-Based Recommender Systems
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Recommender systems; Motion pictures; Feature extraction; Avatars;
   Machine learning; Electronic mail; Blood; Knowledge graph; recommender
   system; explainable recommendation
AB To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users' preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.
C1 [Guo, Qingyu; Zhuang, Fuzhen; He, Qing] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China.
   [Guo, Qingyu] Hong Kong Univ Sci & Technol, Kowloon, Clearwater Bay, Hong Kong, Peoples R China.
   [Qin, Chuan] Univ Sci & Technol China, Hefei 230052, Anhui, Peoples R China.
   [Qin, Chuan; Zhu, Hengshu] Baidu Inc, Baidu Talent Intelligence Ctr, Beijing 100085, Peoples R China.
   [Xie, Xing] Microsoft Res Asia, Beijing 100080, Peoples R China.
   [Xiong, Hui] Rutgers State Univ, New Brunswick, NJ 08901 USA.
RP Zhuang, FZ (通讯作者)，Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China.
EM qguoag@connect.ust.hk; zhuangfuzhen@ict.ac.cn; chuanqin0426@gmail.com;
   zhuhengshu@gmail.com; xingx@microsoft.com; hxiong@rutgers.edu;
   heqing@ict.ac.cn
OI Guo, Qian/0000-0003-3343-2849; Jia, Chunrong/0000-0001-6541-6478; Xiong,
   Hui/0000-0001-6016-6465; Zhuang, Fuzhen/0000-0001-9170-7009
FU National Key Research and Development Program of China [2018YFB1004300];
   National Natural Science Foundation of China [U1836206, U1811461,
   61773361, 61836013, 71531001]; Project of Youth Innovation Promotion
   Association CAS [2017146]
FX The research work was supported by the National Key Research and
   Development Program of China under Grant No. 2018YFB1004300, the
   National Natural Science Foundation of China under Grant No. U1836206,
   U1811461, 61773361, 61836013, 71531001, the Project of Youth Innovation
   Promotion Association CAS under Grant No. 2017146.
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
   Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
   Gomez JA, 2018, ENVIRON SCI POLLUT R, V25, P977, DOI 10.1007/s11356-016-8339-9
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   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 YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Catherine R, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P325, DOI 10.1145/2959100.2959131
   Chan Liu, 2019, 2019 IEEE International Conference on Computer Science and Educational Informatization (CSEI), P168
   Chen JD, 2019, AAAI CONF ARTIF INTE, P6252
   Cho Kyunghyun, 2014, ARXIV14061078, P1724, DOI DOI 10.3115/V1/D14-1179
   Dadoun A, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P896, DOI 10.1145/3308560.3316535
   DBLP, 2013, DBLP DATASET
   Ding C, 2010, IEEE T PATTERN ANAL, V32, P45, DOI 10.1109/TPAMI.2008.277
   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
   Douban, 2005, DOUBAN MOVIE
   Ehrlinger L., 2016, PROC 12 INT C SEMANT
   F_arber M., 2018, ARXIV 180911099
   Fang Y, 2016, PROC INT CONF DATA, P277, DOI 10.1109/ICDE.2016.7498247
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   GroupLens, 1997, MOVIELENS DATASET
   GroupLens, 2011, LAST FM A DATASET
   Hamilton WL, 2017, ADV NEUR IN, V30
   Han JY, 2020, IEEE T NEUR NET LEAR, V31, P737, DOI 10.1109/TNNLS.2019.2909432
   Hogan A, 2020, ARXIV 200302320
   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
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Huang XW, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P548, DOI 10.1145/3343031.3350893
   Huang ZY, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1261, DOI 10.1145/3357384.3357995
   Jamali M, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P397
   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
   Kim Y., 2014, PROC C EMPIRICAL MET, P1746, DOI [10.3115/v1/d14-1181, 10.3115/v1/D14-1181]
   KKBox, 2018, KKBOX DAT
   Last. fm, 2002, LAST FM ONL MUS SYST
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li Da., 2017, J HIGH ENERGY PHYS
   Li QY, 2019, IEEE ACCESS, V7, P115816, DOI 10.1109/ACCESS.2019.2932466
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu H, 2022, IEEE T KNOWL DATA EN, V34, P723, DOI 10.1109/TKDE.2020.2985954
   Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3
   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
   Massa P, 2007, RECSYS 07: PROCEEDINGS OF THE 2007 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P17
   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
   Meetup, 2002, MEETUP
   Meituan-Dianping, 2009, DIANP
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   MovieLens, 1997, MOVIELENS WEBSITE
   Palumbo E., 2018, PROC 1 WORKSHOP DEEP, P14
   Palumbo E, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P32, DOI 10.1145/3109859.3109889
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Qian R., 2013, BING SEARCH BLOG MAR
   Qin C, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2165, DOI 10.1145/3292500.3330706
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Schedl M, 2016, ICMR'16: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, P103, DOI 10.1145/2911996.2912004
   Sen S., 2009, P 18 INT C WORLD WID, P671
   Sha X., 2019, ARXIV 191008288
   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
   Shi C, 2017, IEEE T KNOWL DATA EN, V29, P17, DOI 10.1109/TKDE.2016.2598561
   Shi Y, 2014, ACM COMPUT SURV, V47, DOI 10.1145/2556270
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Song W., 2019, ARXIV 190609506
   Song WP, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P555, DOI 10.1145/3289600.3290989
   Su Xiaoyuan, 2009, ADV ARTIFICIAL INTEL, DOI DOI 10.1155/2009/421425
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   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
   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
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang C, 2020, AAAI CONF ARTIF INTE, V34, P6127
   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 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 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
   Xi DB, 2019, AAAI CONF ARTIF INTE, P5458
   Xian Y., 2019, ARXIV 190605237
   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
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   Xu YB, 2018, IEEE DATA MINING, P1356, DOI 10.1109/ICDM.2018.00185
   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
   Yu X., 2013, PROC 1 INT JOINT C A
   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
   Zhang Q, 2019, IEEE T NEUR NET LEAR, V30, P1998, DOI 10.1109/TNNLS.2018.2875144
   Zhang S, 2006, SIAM PROC S, P549
   Zhang Y., 2018, ARXIV 180306540
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
   Zhao C, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2165, DOI 10.1145/3357384.3358166
   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 J, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2347, DOI 10.1145/3292500.3330686
   Zhao PP, 2019, AAAI CONF ARTIF INTE, P5877
   Zhen Y., 2009, P 3 ACM C REC SYST R, P69, DOI [10.1145/1639714.1639727, DOI 10.1145/1639714.1639727]
   Zheng L, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P425, DOI 10.1145/3018661.3018665
   Zhuang FZ, 2017, NEURAL NETWORKS, V90, P83, DOI 10.1016/j.neunet.2017.03.009
   Ziegler Cai-Nicolas, 2005, P 14 INT C WORLD WID, P22, DOI DOI 10.1145/1060745.1060754
NR 109
TC 41
Z9 41
U1 0
U2 13
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 OCT 7
PY 2020
VL 34
IS 8
BP 3549
EP 3568
DI 10.1109/TKDE.2020.3028705
PG 20
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA VL2UK
UT WOS:000822377700001
OA Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Jiang, ZX
   Chi, CY
   Zhan, YY
AF Jiang, Zhixue
   Chi, Chengying
   Zhan, Yunyun
TI Let Knowledge Make Recommendations for You
SO IEEE ACCESS
LA English
DT Article
DE Feature extraction; Semantics; Deep learning; Computational modeling;
   Data models; Data mining; Knowledge representation; Knowledge graph;
   recommendation system; knowledge feature representation
AB The knowledge graph can make more accurate personalized recommendations for the recommendation system, but it is also interpretative and has traces to follow. The purpose of the recommendation system is to recommend a series of unobserved items for users. At present, recommendation systems based on knowledge graphs are mainly implemented in two ways: Embedding-based and path-based. Embedding methods usually directly use information from the knowledge graph to enrich the representation of an item or user. Still, it failed to introduce multi-hop relations, and it is challenging to use semantic network information. A path-based recommendation algorithm utilizes the knowledge graph to gain multi-hop knowledge and compare the similarity between users or items to improve the recommendation effect. This paper (1) Aiming at the problem of how the recommendation algorithm effectively utilizes the semantically related information of knowledge, a self-attention-based knowledge representation learning model is designed to learn the semantic information of the entity-relationship by using the overall triplet of the entity-relationship to achieve high-quality knowledge features, Which brings more and more helpful information to the recommendation. (2) Constructing a content recommendation model with unified, embedded behavior and knowledge features, using historical user preferences combined with knowledge graphs to dynamically learn knowledge features to bring users more accurate and diverse recommendations. (3) Aiming at the problem of knowledge feature representation learning, a self-attention-based knowledge representation learning model is proposed. Focusing on the difference in the importance of triples for determining entity semantics, the self-attention mechanism is used to learn semantics from triples to improve knowledge features. The quality of the representation provides high-quality auxiliary information for the recommendation system. The model's performance is demonstrated through link prediction and triple classification experiments to prove the feasibility of the method proposed in this article.
C1 [Jiang, Zhixue; Chi, Chengying] Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan 114031, Peoples R China.
   [Zhan, Yunyun] Technol Univ Dublin, Coll Sci & Hlth, Dublin D08 X622 2, Ireland.
RP Chi, CY (通讯作者)，Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan 114031, Peoples R China.
EM chichengying@ustl.edu.cn
FU National Natural Science Foundation of China (NSFC) [61672138]
FX This work was supported by the National Natural Science Foundation of
   China (NSFC) under Grant 61672138.
CR Bag S, 2019, INFORM SCIENCES, V483, P53, DOI 10.1016/j.ins.2019.01.023
   Berant Jonathan, 2013, P 2013 C EMP METH NA
   Bergmanis T., 2018, P 2018 C N AM CHAPTE, V1, p1391 1400
   Bordes A, 2014, P 2014 C EMP METH NA, P615, DOI DOI 10.3115/V1/D14-1067
   Church KW, 2017, NAT LANG ENG, V23, P155, DOI 10.1017/S1351324916000334
   Haofen W., 2016, J MED INFORM, V37, P8
   Hu X, 2018, DOCTOR ONLINE, V8, P44
   Kaiqi Y., 2018, COMPUT APPL RES, V35, P1929
   lee tae seok, 2016, [KIPS Transactions on Software and Data Engineering, 정보처리학회논문지. 소프트웨어 및 데이터 공학], V5, P419, DOI 10.3745/KTSDE.2016.5.9.419
   LESK Michael, 1986, P 5 ANN INT C SYST D, V5, P24, DOI [10.1145/318723.318728, DOI 10.1145/318723.318728]
   Liu B., 2016, INTELL J, V25, P92
   Liu WJ, 2020, AAAI CONF ARTIF INTE, V34, P2901
   Liu Y., P AAAI, V2021, P1
   Mengwei H., 2018, COMPUT RES DEV, V55, P2587
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Niwattanakul Suphakit, 2013, IMECS 2013 Proceedings of International Multiconference of Engineers and Computer Scientists, P380
   Wang Q, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1308
   Wang X, 2018, IEEE T VLSI SYST, V26, P143, DOI 10.1109/TVLSI.2017.2753843
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xinxin Y., 2012, P 7 NAT C INF SYST T, P17
   Yao XC, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P956
   Yin X., P DASFFA, V2021, P1
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
NR 23
TC 1
Z9 1
U1 10
U2 21
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 118194
EP 118204
DI 10.1109/ACCESS.2021.3106914
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA UK7WI
UT WOS:000692176300001
OA gold
DA 2022-08-23
ER

PT J
AU Rastogi, P
   Poliak, A
   Lyzinski, V
   Van Durme, B
AF Rastogi, Pushpendre
   Poliak, Adam
   Lyzinski, Vince
   Van Durme, Benjamin
TI Neural variational entity set expansion for automatically populated
   knowledge graphs
SO INFORMATION RETRIEVAL JOURNAL
LA English
DT Article; Proceedings Paper
CT 2nd Workshop on Knowledge Graphs and Semantics for Text Retrieval,
   Analysis, and Understanding (KG4IR) / International Workshop on Data
   Search (DATA SEARCH) Colocated with SIGIR Conference
CY JUL 12, 2018
CL Ann Arbor, MI
SP SIGIR
DE Set expansion; Cold start recommendation; Content based recommendation;
   Variational autoencoder; Product of experts (POE); Unsupervised learning
AB We propose Neural variational set expansion to extract actionable information from a noisy knowledge graph (KG) and propose a general approach for increasing the interpretability of recommendation systems. We demonstrate the usefulness of applying a variational autoencoder to the Entity set expansion task based on a realistic automatically generated KG.
C1 [Rastogi, Pushpendre; Poliak, Adam; Lyzinski, Vince; Van Durme, Benjamin] Johns Hopkins Univ, Baltimore, MD 21218 USA.
RP Rastogi, P (通讯作者)，Johns Hopkins Univ, Baltimore, MD 21218 USA.
EM pushpendre@jhu.edu
FU Defense Advanced Research Projects Agency [FA8750-13-2-001]
FX Funding was provided by Defense Advanced Research Projects Agency (Grant
   No. FA8750-13-2-001).
CR Al-Badrashiny M., 2017, TEXT AN C TAC
   Altszyler E., 2016, COMP STUDY LSA VS WO
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Balog K, 2009, P TREC2009
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bouchacourt D., 2017, MULTILEVEL VARIATION
   Cohen W., 2009, P JOINT C 47 ANN M A, P441, DOI [10.3115/1687878.1687941, DOI 10.3115/1687878.1687941]
   Demartini G, 2010, LECT NOTES COMPUT SC, V6203, P254, DOI 10.1007/978-3-642-14556-8_26
   Ghahramani Z., 2005, P ADV NEURAL INFORM, P435
   Gottipati S, 2011, P C EMP METH NAT LAN, P804
   He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
   He Y., 2015, P NAACL DENV COL US, P31
   He Y., 2011, P 20 INT C WORLD WID, P427
   Hinton GE, 1999, IEE CONF PUBL, P1, DOI 10.1049/cp:19991075
   Jebara T, 2004, J MACH LEARN RES, V5, P819
   Kingma Diederik P., 2013, AUTOENCODING VARIATI
   Kipf T., 2017, P ICLR
   Lang J., 2013, P 2013 C N AM CHAPT, P772
   Lee W., 2017, ACM C INF KNOWL MAN
   Levy O, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P302, DOI 10.3115/v1/p14-2050
   Lin D., 1998, P 36 ANN M ASS COMP, V2, P768, DOI DOI 10.3115/980432.980696
   Martin M P, 2017, C EUROPEAN ASS COMPU, V2, P503
   Metzger S, 2014, 2014 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, P60, DOI 10.1109/WI-IAT.2014.17
   Miao YS, 2016, PR MACH LEARN RES, V48
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mitra B., 2017, ARXIV E PRINTS
   Paca M., 2007, IJCAI
   Pantel P., 2009, P 2009 C EMP METH NA, P938
   Pasca M., 2008, ACL, P19
   Pujara J., 2017, P 2017 C EMP METH NA, P1751, DOI DOI 10.18653/V1/D17-1184
   Rastogi P., 2017, VERTEX NOMINATION CO
   Rehurek Radim, 2010, P LREC 2010WORKSH NE, P45
   Rezende D.J, 2014, P INT C MACH LEARN B
   Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, P333, DOI 10.1561/1500000019
   Sadamitsu K., 2011, P 49 ANN M ASS COMP, P726
   Schutze H., 2008, INTRO INFORM RETRIEV, V39
   Shen JM, 2017, LECT NOTES ARTIF INT, V10534, P288, DOI 10.1007/978-3-319-71249-9_18
   Sonderby C. K., 2016, ADV NEURAL INFORM PR, P3738
   Tong S., 2008, Patent No. [7,350,187, 7350187]
   Vartak M., 2017, ADV NEURAL INFORM PR, P6907
   Wang R., 2008, P C EMP METH NAT LAN, P947
   Wang RC, 2007, IEEE DATA MINING, P342, DOI 10.1109/ICDM.2007.104
   Wang RC, 2008, IEEE DATA MINING, P1091, DOI 10.1109/ICDM.2008.145
   Zaheer Manzil, 2017, ADV NEURAL INFORM PR, P3391
   Zaidan O., 2007, COMPUT LINGUIST, P260
   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
   Zheng YY, 2017, LECT NOTES ARTIF INT, V10234, P317, DOI 10.1007/978-3-319-57454-7_25
NR 48
TC 3
Z9 3
U1 1
U2 41
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1386-4564
EI 1573-7659
J9 INFORM RETRIEVAL J
JI Inf. Retr. J.
PD AUG
PY 2019
VL 22
IS 3-4
SI SI
BP 232
EP 255
DI 10.1007/s10791-018-9342-1
PG 24
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA HZ5UO
UT WOS:000468918400002
DA 2022-08-23
ER

PT J
AU Wang, YQ
   Chen, Y
   Zhang, Z
   Wang, T
AF Wang, Yinquan
   Chen, Yao
   Zhang, Zhe
   Wang, Tian
TI A probabilistic ensemble approach for knowledge graph embedding
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Ensemble learning
AB Knowledge graph embedding (KGE) is a technique for embedding entities and relations of knowledge graphs (KGs) into continuous vector spaces while maintaining the inherent structure of KGs, in this way link prediction can be facilitated by scoring candidate triples. Existing KGE models only capture specific features of a KG, however, link prediction capacity heavily relies on comprehensive features of a KG. In this work, we propose an ensemble approach for KGE by incorporating the features captured by different KGE models. As a key step, we propose a probabilistic scoring index for characterizing the link prediction performance of a given KGE model. Based on this we established a theoretical framework to calculate the optimal parameters for the ensemble model and predict its corresponding link prediction rate. In particular, we proved that our ensemble approach can always improve link prediction performance under some assumptions. The Upper Confidence Bound Algorithm is then used to adjust the parameters. Experimental results on two widely used KGs show that the proposed ensemble approach achieves the state-of-the-art link prediction rate.
C1 [Wang, Yinquan] Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing, Peoples R China.
   [Wang, Yinquan] Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China.
   [Chen, Yao; Zhang, Zhe] Southwestern Univ Finance & Econ, Dept Comp Sci, Chengdu, Peoples R China.
   [Wang, Tian] Beihang Univ, Inst Artificial Intelligence, Beijing, Peoples R China.
RP Wang, T (通讯作者)，Beihang Univ, Inst Artificial Intelligence, Beijing, Peoples R China.
EM wangtian@buaa.edu.cn
FU Natural Science Foundation of Beijing Municipality [L191007]; National
   Natural Science Foundation of China [62141604, 61972016, 62032016];
   Funda-mental Research Funds for the Central Universities [YWF-22-L-610]
FX Acknowledgements This work is partially supported by Natural Science
   Foundation of Beijing Municipality (L191007) , the National Natural
   Science Foundation of China (62141604, 61972016, 62032016) , the
   Funda-mental Research Funds for the Central Universities (YWF-22-L-610)
   .
CR Auer P, 2002, MACH LEARN, V47, P235, DOI 10.1023/A:1013689704352
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   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., 2013, ADV NEURAL INFORM PR, P2787
   Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
   Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
   Butun E, 2020, IEEE T CYBERNETICS, V50, P4518, DOI 10.1109/TCYB.2019.2900495
   Chen XL, 2021, NEUROCOMPUTING, V461, P497, DOI 10.1016/j.neucom.2021.02.098
   Chen X, 2021, AUTOPHAGY, V17, P2054, DOI 10.1080/15548627.2020.1810918
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Freund Y., 1996, Machine Learning. Proceedings of the Thirteenth International Conference (ICML '96), P148
   Goyal P, 2019, IEEE I CONF COMP VIS, P6400, DOI 10.1109/ICCV.2019.00649
   Han X., 2018, P C EMP METH NAT LAN, P139
   Huang HY, 2021, NEUROCOMPUTING, V420, P162, DOI 10.1016/j.neucom.2020.09.025
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Kingma D. P., 2017, ARXIV
   Li C, 2021, NEUROCOMPUTING, V461, P516, DOI 10.1016/j.neucom.2021.01.139
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   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 LC, 2022, IEEE T CYBERNETICS, V52, P265, DOI 10.1109/TCYB.2020.2979320
   Liu T., 2018, ARXIV PREPRINT ARXIV
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Pan SR, 2020, IEEE T CYBERNETICS, V50, P2475, DOI 10.1109/TCYB.2019.2932096
   ROSENBLATT M, 1956, P NATL ACAD SCI USA, V42, P43, DOI 10.1073/pnas.42.1.43
   Sun Z., 2019, 7 INT C LEARN REPR
   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 Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Willard S., 2012, GEN TOPOLOGY
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xu C., 2021, 2021 INT JOINT C NEU, P1
   Xu JM, 2020, IEEE T CYBERNETICS, V50, P3640, DOI 10.1109/TCYB.2019.2894591
   Yang B., 2014, ABS14126575 CORR
   Yang KX, 2022, IEEE T SYST MAN CY-S, V52, P2464, DOI 10.1109/TSMC.2021.3051138
   Yang Xu, 2021, IEEE Transactions on Network Science and Engineering, V8, P2946, DOI 10.1109/TNSE.2021.3055762
   Zhang QJ, 2022, NEUROCOMPUTING, V470, P109, DOI 10.1016/j.neucom.2021.10.088
   Zhang QJ, 2021, KNOWL-BASED SYST, V212, DOI 10.1016/j.knosys.2020.106564
   ZHANG S, 2019, ARXIV PREPRINT ARXIV
   Zheng W, 2021, NEUROCOMPUTING, V459, P315, DOI 10.1016/j.neucom.2021.06.071
   Zheng WF, 2021, PATTERN RECOGN, V120, DOI 10.1016/j.patcog.2021.108153
   Zhou Z-H., 2012, ENSEMBLE METHODS FDN, DOI DOI 10.1201/B12207
NR 44
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD AUG 21
PY 2022
VL 500
BP 1041
EP 1051
DI 10.1016/j.neucom.2022.06.032
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2T8JJ
UT WOS:000822714000005
DA 2022-08-23
ER

PT J
AU Wolffe, TAM
   Vidler, J
   Halsall, C
   Hunt, N
   Whaley, P
AF Wolffe, Taylor A. M.
   Vidler, John
   Halsall, Crispin
   Hunt, Neil
   Whaley, Paul
TI A Survey of Systematic Evidence Mapping Practice and the Case for
   Knowledge Graphs in Environmental Health and Toxicology
SO TOXICOLOGICAL SCIENCES
LA English
DT Article
DE systematic evidence map; knowledge graph; evidence synthesis
ID ONTOLOGY; DATABASE; REVIEWS; CHEMICALS; EXPOSURE; SUPPORT; SCHEMA;
   POLICY; TOOL
AB Systematic evidence mapping offers a robust and transparent methodology for facilitating evidence-based approaches to decision-making in chemicals policy and wider environmental health (EH). Interest in the methodology is growing; however, its application in EH is still novel. To facilitate the production of effective systematic evidence maps for EH use cases, we survey the successful application of evidence mapping in other fields where the methodology is more established. Focusing on issues of "data storage technology," "data integrity," "data accessibility," and "transparency," we characterize current evidence mapping practice and critically review its potential value for EH contexts. We note that rigid, flat data tables and schema-first approaches dominate current mapping methods and highlight how this practice is ill-suited to the highly connected, heterogeneous, and complex nature of EH data. We propose this challenge is overcome by storing and structuring data as "knowledge graphs." Knowledge graphs offer a flexible, schemaless, and scalable model for systematically mapping the EH literature. Associated technologies, such as ontologies, are well-suited to the long-term goals of systematic mapping methodology in promoting resource-efficient access to the wider EH evidence base. Several graph storage implementations are readily available, with a variety of proven use cases in other fields. Thus, developing and adapting systematic evidence mapping for EH should utilize these graph-based resources to ensure the production of scalable, interoperable, and robust maps to aid decision-making processes in chemicals policy and wider EH.
C1 [Wolffe, Taylor A. M.; Halsall, Crispin; Whaley, Paul] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England.
   [Wolffe, Taylor A. M.; Hunt, Neil] Univ Lancaster, Yordas Grp, Lancaster, England.
   [Vidler, John] Univ Lancaster, Sch Comp & Commun, Lancaster LA1 4WA, England.
   [Whaley, Paul] Johns Hopkins Bloomberg Sch Publ Hlth, Evidence Based Toxicol Collaborat, Baltimore, MD 21205 USA.
RP Wolffe, TAM (通讯作者)，Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England.; Wolffe, TAM (通讯作者)，Univ Lancaster, Yordas Grp, Lancaster, England.
EM t.wolffe@lancaster.ac.uk
RI Halsall, Crispin/D-8135-2014; Whaley, Paul/I-7160-2019
OI Halsall, Crispin/0000-0001-8007-9756; Whaley, Paul/0000-0003-4021-0785;
   Wolffe, Taylor/0000-0003-1251-4132
FU Centre for Global Eco-innovation - European Regional Development Fund;
   Yordas Group, a global consultancy in the area of chemical safety,
   regulations, and sustainability; Evidence-Based Toxicology Collaboration
   at Johns Hopkins Bloomberg School of Public Health
FX T.A.M.W.'s PhD is financially supported by the Centre for Global
   Eco-innovation (a programe funded by the European Regional Development
   Fund) and Yordas Group, a global consultancy in the area of chemical
   safety, regulations, and sustainability. P.W.'s contribution to the
   manuscript was funded by the Evidence-Based Toxicology Collaboration at
   Johns Hopkins Bloomberg School of Public Health.
CR Aggarwal CC, 2010, ADV DATABASE SYST, V40, P275, DOI 10.1007/978-1-4419-6045-0_9
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Bernes C., 2015, Environmental Evidence, V4
   Bernes C, 2017, ENVIRON EVID, V6, P1
   Beverly B., 2019, ABSTRACT 3267 POTENT
   BLAHA MR, 1988, COMMUN ACM, V31, P414, DOI 10.1145/42404.42407
   Caracciolo AB, 2013, HUM ECOL RISK ASSESS, V19, P1682, DOI 10.1080/10807039.2012.708271
   Castelltort Arnaud, 2016, Journal of Innovation in Digital Ecosystems, V3, P70, DOI 10.1016/j.jides.2016.10.004
   Cheng SH, 2019, ENVIRON EVID, V8, DOI 10.1186/s13750-019-0148-4
   Clapton J., 2009, SCIE SYSTEMATIC MAPP
   Collaboration for Environmental Evidence, 2019, ENV EV SYST MAP SUBM
   Collaboration for Environmental Evidence, 2019, CEE M
   Collaboration for Environmental Evidence, 2019, COMPL REV
   Commission of the European Communities, 2001, WHIT PAP STRAT FUT C, V13, DOI [10.1007/BF03038641, DOI 10.1007/BF03038641]
   Cresswell CJ, 2018, ENVIRON EVID, V7, DOI 10.1186/s13750-018-0120-8
   Ding W, 2014, INFORM SOFTWARE TECH, V56, P545, DOI 10.1016/j.infsof.2014.01.008
   [ECHA] European Chemicals Agency, 2016, ECHA 16 B 25
   Edwards SW, 2016, J PHARMACOL EXP THER, V356, P170, DOI 10.1124/jpet.115.228239
   EFSA, 2009, EFSA J, V7, DOI 10.2903/j.efsa.2009.337r
   Elmasri R., 2013, FUNDAMENTALS DATABAS
   EPA, 2018, APPL SYST REV TSCA R, P1
   Ghrab A., 2016, GRAD GRAPH DATABASE
   Gupta P., 2013, P 22 INT C WORLD WID, P505, DOI DOI 10.1145/2488388.2488433
   Haddaway N. R., 2018, STAKEHOLDER ENGAGEME
   Haddaway NR, 2018, ENVIRON EVID, V7, DOI [10.1186/s13750-018-0140-4, 10.1186/s13750-018-0121-7]
   Haddaway NR, 2015, ENVIRON EVID, V4, DOI 10.1186/s13750-015-0049-0
   Haddaway NR, 2016, AMBIO, V45, P613, DOI 10.1007/s13280-016-0773-x
   Haddaway NR, 2014, ENVIRON EVID, V3, DOI 10.1186/2047-2382-3-17
   Hardy B, 2012, ALTEX-ALTERN ANIM EX, V29, P139, DOI 10.14573/altex.2012.2.139
   Have CT, 2013, BIOINFORMATICS, V29, P3107, DOI 10.1093/bioinformatics/btt549
   Hoffmann S, 2006, HUM EXP TOXICOL, V25, P497, DOI 10.1191/0960327106het648oa
   Hoffmann S, 2017, ARCH TOXICOL, V91, P2551, DOI 10.1007/s00204-017-1980-3
   Ingre-Khans E, 2016, ENVIRON SCI-PROC IMP, V18, P1508, DOI 10.1039/c6em00389c
   James KL, 2016, ENVIRON EVID, V5, DOI 10.1186/s13750-016-0059-6
   Jankovic S, 2018, ENTERP INF SYST-UK, V12, P1180, DOI 10.1080/17517575.2018.1462404
   Johnson V., 2011, WHAT IS EVIDENCE SCA
   Karp PD, 1996, TRENDS BIOTECHNOL, V14, P273, DOI 10.1016/0167-7799(96)10044-5
   Kleppmann M, 2017, DESIGNING DATA INTEN, V1st
   Leisher C, 2016, ENVIRON EVID, V5, DOI 10.1186/s13750-016-0057-8
   Lewis KA, 2016, HUM ECOL RISK ASSESS, V22, P1050, DOI 10.1080/10807039.2015.1133242
   Liu Z. H., 2015, MANAGEMENT FLEXIBLE
   LYNDON ML, 1989, MICH LAW REV, V87, P1795, DOI 10.2307/1289204
   Macura B, 2015, ENVIRON EVID, V4, DOI 10.1186/s13750-015-0051-6
   Mandrioli D, 2018, ENVIRON INT, V119, P174, DOI 10.1016/j.envint.2018.06.005
   Martin O.V., 2018, PROTOCOL SYSTEMATIC, DOI [10.5281/zenodo.2525277, DOI 10.5281/ZENODO.2525277]
   Martin P, 2018, ENVIRON HEALTH PERSP, V126, DOI 10.1289/EHP3067
   McIntosh EJ, 2018, ENVIRON EVID, V7, DOI 10.1186/s13750-018-0134-2
   McKinnon MC, 2016, ENVIRON EVID, V5, DOI 10.1186/s13750-016-0058-7
   Mostafa F, 2008, WIT TRANS INFO COMM, P71
   Neaves LE, 2015, ENVIRON EVID, V4, DOI 10.1186/s13750-015-0031-x
   Noy NF, 2004, KNOWL INF SYST, V6, P428, DOI 10.1007/s10115-003-0137-2
   NTP-OHAT, 2019, OFF HLTH ASS TRANSL
   Oliver S, 2016, EVID POLICY, V12, P235, DOI 10.1332/174426415X14399963605641
   Ontotext, 2019, WHAT IS KNOWL GRAPH
   Ontotext, 2019, WHAT IS INF
   Pavlopoulos GA, 2011, BIODATA MIN, V4, DOI 10.1186/1756-0381-4-10
   Pelch KE, 2019, ENVIRON INT, V130, DOI 10.1016/j.envint.2019.05.045
   Pelch KE, 2019, ENVIRON HEALTH PERSP, V127, DOI 10.1289/EHP4386
   Pool R., 2014, IDENTIFYING REDUCING
   Randall NP, 2015, ENVIRON EVID, V4, DOI 10.1186/s13750-015-0044-5
   Randall NP, 2012, ENVIRON EVID, V1, DOI 10.1186/2047-2382-1-4
   Rhomberg LR, 2013, CRIT REV TOXICOL, V43, P753, DOI 10.3109/10408444.2013.832727
   Robinson I., 2015, GRAPH DATABASES NEW, DOI [10.1016/b978-0-12-407192-6.00003-0, DOI 10.1016/B978-0-12-407192-6.00003-0]
   Rus I, 2002, IEEE SOFTWARE, V19, P26, DOI 10.1109/MS.2002.1003450
   Sadowski G., 2015, FRAUD DETECTION DISC
   Schultz TW, 2003, J MOL STRUC-THEOCHEM, V622, P1, DOI 10.1016/S0166-1280(02)00614-0
   Segaran T., 2009, PROGRAMMING SEMANTIC
   Sexton K, 2007, ENVIRON HEALTH PERSP, V115, P825, DOI 10.1289/ehp.9333
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Sola P., 2017, ENV EVIDENCE, V6, P1
   Thorn JPR, 2016, ENVIRON EVID, V5, DOI 10.1186/s13750-016-0064-9
   US EPA, 2016, FR LAUT CHEM SAF 21
   Vandenberg LN, 2016, ENV HLTH, V15, P1
   Vinken M, 2014, ARCH TOXICOL, V88, P1, DOI 10.1007/s00204-013-1093-6
   Walker VR, 2018, ENVIRON INT, V115, P48, DOI 10.1016/j.envint.2017.12.032
   Weaver J, 2013, SEMANT WEB, V4, P245, DOI 10.3233/SW-2012-0078
   Webber J., 2018, POWERING REAL TIME R
   Wolffe TAM, 2019, ENVIRON INT, V130, DOI 10.1016/j.envint.2019.05.065
   World Health Organization, 2019, FRAM US SYST REV MET
   Zynda M., 2013, INTERACTIONS, V20, P68
NR 80
TC 9
Z9 9
U1 2
U2 6
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1096-6080
EI 1096-0929
J9 TOXICOL SCI
JI Toxicol. Sci.
PD MAY
PY 2020
VL 175
IS 1
BP 35
EP 49
DI 10.1093/toxsci/kfaa025
PG 15
WC Toxicology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Toxicology
GA LR4UZ
UT WOS:000535693200007
PM 32096866
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Zhao, JZ
   Zhang, X
   Gao, C
   Li, ZD
   Wang, BL
AF Zhao, Jing-zhuan
   Zhang, Xuan
   Gao, Chen
   Li, Zhu-dong
   Wang, Bao-lei
TI KG2Lib: knowledge-graph-based convolutional network for third-party
   library recommendation
SO JOURNAL OF SUPERCOMPUTING
LA English
DT Article; Early Access
DE Software development; Library recommendation; Knowledge graph; Graph
   convolutional network
AB In the process of software system evolution, software users constantly put forward a large number of expectations. For these expectations, software developers usually use the existing third-party libraries and other software resources to accelerate their development processes. At present, tons of third-party libraries are available. Therefore, appropriate recommendation methods are very important for developers to find suitable libraries for their development projects. In this paper, we present KG2Lib, a recommendation method to assist software developers in selecting suitable software libraries for their current projects. KG2Lib exploits a knowledge-graph-based convolutional network to recommend software libraries by relying on a set of libraries which were already called by current projects. The interaction matrix, weight matrix and knowledge graph are the inputs of KG2Lib. What's more, KG2Lib recommends libraries to developers from project level and library level, which can better capture the fine-grained information to achieve better recommend performance. The performance of KG2Lib was evaluated on three datasets with four existing baseline models. The experimental results show that KG2Lib achieves better performance and helps software developers accurately select the appropriate third-party libraries.
C1 [Zhao, Jing-zhuan; Zhang, Xuan; Li, Zhu-dong; Wang, Bao-lei] Yunnan Univ, Sch Software, Kunming 650000, Yunnan, Peoples R China.
   [Zhang, Xuan] Key Lab Software Engn Yunnan Prov, Kunming 650000, Yunnan, Peoples R China.
   [Zhang, Xuan] Engn Res Ctr Cyberspace, Kunming 650000, Yunnan, Peoples R China.
   [Gao, Chen] Yunnan Univ, Sch Informat Sci Engn, Kunming 650000, Yunnan, Peoples R China.
RP Zhang, X (通讯作者)，Yunnan Univ, Sch Software, Kunming 650000, Yunnan, Peoples R China.; Zhang, X (通讯作者)，Key Lab Software Engn Yunnan Prov, Kunming 650000, Yunnan, Peoples R China.; Zhang, X (通讯作者)，Engn Res Ctr Cyberspace, Kunming 650000, Yunnan, Peoples R China.
EM zhxuan@ynu.edu.cn
FU National Natural Science Foundation of China [61862063, 61502413,
   61262025]; National Social Science Foundation of China [18BJL104];
   Science Foundation of Young and Middle-aged Academic and Technical
   Leaders of Yunnan [202205AC160040]; Science Foundation of Yunnan Jinzhi
   Expert Workstation [202205AF150006]; Natural Science Foundation of Key
   Laboratory of Software Engineering of Yunnan Province [2020SE301]
FX This work is supported by the National Natural Science Foundation of
   China under Grant Nos. 61862063, 61502413, 61262025; the National Social
   Science Foundation of China under Grant No. 18BJL104; the Science
   Foundation of Young and Middle-aged Academic and Technical Leaders of
   Yunnan under Grant No. 202205AC160040; the Science Foundation of Yunnan
   Jinzhi Expert Workstation under Grant No. 202205AF150006; the Natural
   Science Foundation of Key Laboratory of Software Engineering of Yunnan
   Province under Grant No. 2020SE301; the Science Foundation of
   "Knowledge-driven intelligent software engineering innovation team."
CR Alnusair A, 2016, PROCEEDINGS OF 2016 IEEE 17TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IEEE IRI), P531, DOI 10.1109/IRI.2016.79
   Bauer V, 2012, 2012 28TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE (ICSM), P483, DOI 10.1109/ICSM.2012.6405311
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Chen J, 2020, IEEE ACCESS, V8, P42436, DOI 10.1109/ACCESS.2020.2976884
   Chen JY, 2021, INFORM SCIENCES, V563, P326, DOI 10.1016/j.ins.2021.03.004
   D'Souza AR, 2016, IEEE INT WORK C SO, P51, DOI 10.1109/SCAM.2016.22
   Dang DP, 2021, J SUPERCOMPUT, V77, P14280, DOI 10.1007/s11227-021-03832-2
   Deshpande Niranjana, 2022, Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Proceedings. Lecture Notes in Computer Science (13224), P173, DOI 10.1007/978-3-031-02462-7_12
   Di Noia T, 2015, LECT NOTES COMPUT SC, V9203, P88, DOI 10.1007/978-3-319-21768-0_4
   Dong BB, 2021, NEUROCOMPUTING, V442, P307, DOI 10.1016/j.neucom.2021.01.129
   Guan ZD, 2018, LECT NOTES COMPUT SC, V10942, P286, DOI 10.1007/978-3-319-93818-9_27
   Hell F, 2020, INFORMATION, V11, DOI 10.3390/info11110525
   Jiang B, 2021, IEEE ACCESS, V9, P50880, DOI 10.1109/ACCESS.2021.3068570
   Jiang YB, 2021, NEUROCOMPUTING, V449, P71, DOI 10.1016/j.neucom.2021.03.076
   Katsuragawa D, 2018, INT WORK SOFTW, P2, DOI 10.1145/3194124.3194129
   Lei R, 2010, SIGN PROC MULT INT C
   Li HZ, 2021, FRONT COMPUT SCI-CHI, V15, DOI 10.1007/s11704-020-0028-7
   Liu AL, 2015, ACSR ADV COMPUT, V31, P451
   Mei DH, 2021, IEEE ACCESS, V9, P34433, DOI 10.1109/ACCESS.2021.3061915
   Nagarnaik P, 2015, 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION SYSTEMS (ICECS), P1603, DOI 10.1109/ECS.2015.7124857
   Nguyen PT, 2020, J SYST SOFTWARE, V161, DOI 10.1016/j.jss.2019.110460
   Ohtomo K, 2021, ITE TRANS MEDIA TECH, V9, P54, DOI 10.3169/mta.9.54
   Ouni A, 2017, INFORM SOFTWARE TECH, V83, P55, DOI 10.1016/j.infsof.2016.11.007
   Pan HL, 2021, SOFT COMPUT, DOI 10.1007/s00500-021-05735-z
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Robillard MP, 2010, IEEE SOFTWARE, V27, P80, DOI 10.1109/MS.2009.161
   Saied MA, 2018, J SYST SOFTWARE, V145, P164, DOI 10.1016/j.jss.2018.08.032
   Sun XB, 2019, IEEE ACCESS, V7, P49872, DOI 10.1109/ACCESS.2019.2910732
   Sun ZS, 2020, PROCEEDINGS OF THE 2020 IEEE 27TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER '20), P542, DOI 10.1109/SANER48275.2020.9054865
   Thung F, 2013, WORK CONF REVERSE EN, P182, DOI 10.1109/WCRE.2013.6671293
   Vargas S, 2014, PROCEEDINGS OF THE 8TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'14), P145, DOI 10.1145/2645710.2645744
   Wang HM, 2007, J WUHAN UNIV TECHNOL
   Wang X, 2021, WORLD WIDE WEB, V24, P869, DOI 10.1007/s11280-021-00894-3
   Xu CY, 2018, J SYST SOFTWARE, V142, P195, DOI 10.1016/j.jss.2018.04.060
   Yang Fuqing, 1999, Acta Electronica Sinica, V27, P68
   Yang ZX, 2020, KNOWL-BASED SYST, V204, DOI 10.1016/j.knosys.2020.106194
   Yin CY, 2020, J SUPERCOMPUT, V76, P5161, DOI 10.1007/s11227-019-02751-7
   Yu B, 2020, IEEE ACCESS, V8, P115717, DOI 10.1109/ACCESS.2020.3004250
   Yun W, 2021, INT J INTELL SYST, V36, P1686, DOI 10.1002/int.22357
   Zhang L, 2021, J SYST SCI SYST ENG, V30, P482, DOI 10.1007/s11518-021-5499-z
   Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029
   Zhang YZ, 2020, IEEE ACCESS, V8, P90652, DOI 10.1109/ACCESS.2020.2994147
   Zhang YH, 2020, IEEE ACCESS, V8, P134553, DOI 10.1109/ACCESS.2020.3011105
   Zhang Z, 2021, NEURAL NETWORKS, V140, P13, DOI 10.1016/j.neunet.2021.02.016
   Zhao XQ, 2019, IEICE T INF SYST, VE102D, P522, DOI 10.1587/transinf.2018EDP7227
   Zheng Y, 2020, PROC INT CONF DATA, P133, DOI 10.1109/ICDE48307.2020.00019
   Zhong T, 2020, WORLD WIDE WEB, V23, P3125, DOI 10.1007/s11280-020-00824-9
NR 47
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-8542
EI 1573-0484
J9 J SUPERCOMPUT
JI J. Supercomput.
DI 10.1007/s11227-022-04603-3
EA JUN 2022
PG 26
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 2O8RQ
UT WOS:000819320000001
DA 2022-08-23
ER

PT J
AU Zhang, R
   Trisedya, BD
   Li, M
   Jiang, Y
   Qi, JZ
AF Zhang, Rui
   Trisedya, Bayu Distiawan
   Li, Miao
   Jiang, Yong
   Qi, Jianzhong
TI A benchmark and comprehensive survey on knowledge graph entity alignment
   via representation learning
SO VLDB JOURNAL
LA English
DT Article; Early Access
DE Knowledge graph; Entity alignment; Knowledge graph alignment; Knowledge
   base; Representation learning; Deep learning; Embedding; Graph neural
   networks; Graph convolutional networks
ID WEB
AB In the last few years, the interest in knowledge bases has grown exponentially in both the research community and the industry due to their essential role in AI applications. Entity alignment is an important task for enriching knowledge bases. This paper provides a comprehensive tutorial-type survey on representative entity alignment techniques that use the new approach of representation learning. We present a framework for capturing the key characteristics of these techniques, propose a benchmark addressing the limitation of existing benchmark datasets, and conduct extensive experiments using our benchmark. The framework gives a clear picture of how various techniques work. The experiments yield important results about the empirical performance of the techniques and how various factors affect the performance. One important observation not stressed by previous work is that techniques making good use of attribute triples and relation predicates as features stand out as winners. We are also the first to investigate the question of how to perform entity alignments on large-scale knowledge graphs such as the full Wikidata and Freebase (in Experiment 5).
C1 [Zhang, Rui; Jiang, Yong] Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen, Peoples R China.
   [Trisedya, Bayu Distiawan; Li, Miao; Qi, Jianzhong] Univ Melbourne, Parkville, Vic, Australia.
RP Zhang, R (通讯作者)，Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen, Peoples R China.
EM rayteam@yeah.net; bayu.trisedya@unimelb.edu.au;
   miao4@student.unimelb.edu.au; jiangy@mail.sz.tsinghua.edu.cn;
   jianzhong.qi@unimelb.edu.au
RI QI, JIANZHONG/P-7112-2015
OI QI, JIANZHONG/0000-0001-6501-9050
CR Auer S, 2011, IJCAI 2011
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bhattacharya I., 2006, MINING GRAPH DATA, P311, DOI [10.1002/9780470073049.ch13, DOI 10.1002/9780470073049.CH13]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   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 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
   CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1007/BF00994018
   Devlin J., 2019, BERT PRETRAINING DEE
   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
   Du L., 2015, ALTA 2015
   Farber M, 2019, LECT NOTES COMPUT SC, V11779, P113, DOI 10.1007/978-3-030-30796-7_8
   FELLEGI IP, 1969, J AM STAT ASSOC, V64, P1183, DOI 10.2307/2286061
   Galarraga L, 2015, VLDB J, V24, P707, DOI 10.1007/s00778-015-0394-1
   Gilmer J, 2017, PR MACH LEARN RES, V70
   Guo LB, 2019, PR MACH LEARN RES, V97
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   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, IEEE T NEUR NET LEAR
   Kathuria Mamta, 2016, International Journal of Information Technology and Computer Science, V8, P47, DOI 10.5815/ijitcs.2016.12.06
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Kuhn HW, 2005, NAV RES LOG, V52, P7, DOI 10.1002/nav.20053
   Kulkarni S, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P457
   Li Chengjiang, 2019, P 2019 C EMPIRICAL M, P2723
   Lin XX, 2019, IEEE DATA MINING, P429, DOI 10.1109/ICDM.2019.00053
   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
   Mao X, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P420, DOI 10.1145/3336191.3371804
   Mikolov T., 2013, WORKSH ICLR, DOI 10.1109/TCBB.2021.3077905
   Nie H, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3658
   Ott M., 2019, P NAACL HLT 2019 DEM
   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
   Qin KK, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P1255, DOI 10.1145/3340531.3411924
   Rahimi A, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2009
   RAIMOND Y, 2008, P 1 WORKSH LINK DAT
   Roth AE, 2008, INT J GAME THEORY, V36, P537, DOI 10.1007/s00182-008-0117-6
   Scharffe F., 2009, P IJCAI 2009 WORKSH
   Shi Xiaofei, 2019, EMNLP IJCNLP, P813
   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
   Tejada S, 2001, INFORM SYST, V26, P607, DOI 10.1016/S0306-4379(01)00042-4
   Trisedya BD, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P229
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   Vaswani A., 2017, P 31 ADV NEUR INF PR, P6000
   Velickovic P., 2018, P 6 INT C LEARNING R
   Verykios VS, 2000, INFORM SCIENCES, V126, P83, DOI 10.1016/S0020-0255(00)00013-X
   Volz J, 2009, LECT NOTES COMPUT SC, V5823, P650, DOI 10.1007/978-3-642-04930-9_41
   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, 2020, P 2020 C EMP METH NA, P1672, DOI DOI 10.18653/V1/2020.EMNLP-MAIN.130
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wu Q, 2018, IEEE T PATTERN ANAL, V40, P1367, DOI 10.1109/TPAMI.2017.2708709
   Wu YT, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5278
   Wu Yuting, 2019, EMNLP IJCNLP, P240
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Xiao H., 2016, ACL 2016
   Xiao H., 2016, P 25 INT JOINT C ART, P1315
   Xie QZ, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P950, DOI 10.18653/v1/P17-1088
   Xu K, 2020, AAAI CONF ARTIF INTE, V34, P9354
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Xu L, 2019, AAAI CONF ARTIF INTE, P7346
   Yang H., 2019, EMNLP 2019
   Yang K, 2020, AAAI CONF ARTIF INTE, V34, P3025
   Yang S., 2020, EMNLP 2020
   Ye R, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4135
   Yuan Y, 2019, AAAI CONF ARTIF INTE, P9176
   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 Z., 2020, COLING 2020
   Zhao X, 2022, IEEE T KNOWL DATA EN, V34, P2610, DOI 10.1109/TKDE.2020.3018741
   Zhou XF, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1009, DOI 10.1145/3132847.3132939
   Zhu H, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4258
   Zhu Q, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P2241, DOI 10.1145/3366423.3380289
   Zhu QN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1943
   Zhuang CY, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P499, DOI 10.1145/3178876.3186116
NR 85
TC 1
Z9 1
U1 2
U2 2
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1066-8888
EI 0949-877X
J9 VLDB J
JI VLDB J.
DI 10.1007/s00778-022-00747-z
EA MAY 2022
PG 26
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1M1DB
UT WOS:000799715500001
DA 2022-08-23
ER

PT J
AU Huang, Y
   Sun, HL
   Xu, K
   Lu, SF
   Wang, TY
   Zhang, XF
AF Huang, Yan
   Sun, Haili
   Xu, Ke
   Lu, Songfeng
   Wang, Tongyang
   Zhang, Xinfang
TI CoRelatE: Learning the correlation in multi-fold relations for knowledge
   graph embedding
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; Representation learning; Multi-fold relation;
   Correlation learning
AB Existing approaches for knowledge graph embedding usually represent knowledge as triples of binary relations between entities and embed them into continuous vector space, which are not practical for the complex facts in the form of multi-fold relations in real life. In this work, we address the problem of multi-fold relation embedding in knowledge graph and propose a new framework CoRelatE that learns correlations between entities, facts and relations from the instances. We first model the entity-relation correlation directly via combinational operator, then utilize a graph convolutional network to model the correlation between entities and their related facts. Finally, the facts are forced to embed close to their corresponding relations to learn the fact-relation correlation. We formulate the objective as a joint optimization problem and introduce an efficient algorithm to solve it. We compare the proposed CoRelatE model with several state-of-the-art models including HypE, NaLP and RAE, m-TransH. Experimental results on four datasets of multi-fold relations and two datasets of binary relations for link prediction and instance classification tasks validate the effectiveness and merits of our model. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Huang, Yan; Zhang, Xinfang] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China.
   [Sun, Haili; Lu, Songfeng; Wang, Tongyang] Huazhong Univ Sci & Technol, Hubei Engn Res Ctr Big Data Secur, Sch Cyber Sci & Engn, Wuhan 430074, Peoples R China.
   [Huang, Yan; Xu, Ke; Lu, Songfeng] Shenzhen Huazhong Univ Sci & Technol, Res Inst, Shenzhen 518063, Peoples R China.
   [Xu, Ke] South Cent Univ Nationalities, Coll Comp Sci, Wuhan 430074, Peoples R China.
RP Lu, SF (通讯作者)，Huazhong Univ Sci & Technol, Hubei Engn Res Ctr Big Data Secur, Sch Cyber Sci & Engn, Wuhan 430074, Peoples R China.; Xu, K; Lu, SF (通讯作者)，Shenzhen Huazhong Univ Sci & Technol, Res Inst, Shenzhen 518063, Peoples R China.; Xu, K (通讯作者)，South Cent Univ Nationalities, Coll Comp Sci, Wuhan 430074, Peoples R China.
EM xuke@scuec.edu.cn; platanus@hust.edu.cn
OI Huang, Yan/0000-0002-7925-4841
FU Hubei Provincinal Science and Technology Major Project of China
   [2020AEA011]; Key Research and Development Plan of Hubei Province of
   China [2020BAB100]; Science and Technology Program of Shenzhen of China
   [JCYJ20180306124612893]
FX This work is supported by the Hubei Provincinal Science and Technology
   Major Project of China under Grant No. 2020AEA011, the Key Research and
   Development Plan of Hubei Province of China under Grant No. 2020BAB100,
   and the Science and Technology Program of Shenzhen of China under Grant
   No. JCYJ20180306124612893.
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cui WY, 2017, PROC VLDB ENDOW, V10, P565, DOI 10.14778/3055540.3055549
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Fatemi B, 2019, AAAI CONF ARTIF INTE, P3526
   Gomez-Bombarelli R, 2018, ACS CENTRAL SCI, V4, P268, DOI 10.1021/acscentsci.7b00572
   Guan SP, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P583, DOI 10.1145/3308558.3313414
   Jianfeng W., 2016, P 25 INT JOINT C ART, P1300
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Peng Y, 2019, IEEE T CIRC SYST VID, P1
   Ren X, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P1015, DOI 10.1145/3038912.3052708
   Rouces J, 2015, LECT NOTES COMPUT SC, V9088, P505, DOI 10.1007/978-3-319-18818-8_31
   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
   Sun Z., 2019, 7 INT C LEARN REPR
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vazquez D., INT JOINT C ART INT, V2020, P2191
   Nguyen V, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P759, DOI 10.1145/2566486.2567973
   Wan SX, 2016, AAAI CONF ARTIF INTE, P2835
   Wang Z, 2014, P C EMP METH NAT LAN, P1591
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yang B., 2015, P INT C LEARN REPR I
   Zhang JC, 2019, PROC CVPR IEEE, P8319, DOI 10.1109/CVPR.2019.00852
   Zhang RC, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1185, DOI 10.1145/3178876.3186017
NR 29
TC 1
Z9 2
U1 5
U2 14
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 2021
VL 213
AR 106601
DI 10.1016/j.knosys.2020.106601
EA JAN 2021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QC2DF
UT WOS:000614644100007
DA 2022-08-23
ER

PT J
AU Cardoso, C
   Sousa, RT
   Kohler, S
   Pesquita, C
AF Cardoso, Carlota
   Sousa, Rita T.
   Koehler, Sebastian
   Pesquita, Catia
TI A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity
   in the Biomedical Domain
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Article
ID SEMANTIC SIMILARITY; GENE ONTOLOGY; INFORMATION-CONTENT
AB The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein-protein interactions, associations between diseases and genes, cellular localization of proteins, among others.
   In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is nontrivial. We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by exploiting proxies for biomedical entity similarity. These data sets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, and explore proxy similarities calculated based on protein sequence similarity, protein family similarity, protein-protein interactions and phenotype-based gene similarity. Data sets have varying sizes and cover four different species at different levels of annotation completion. For each data set, we also provide semantic similarity computations with state-of-the-art representative measures.
C1 [Cardoso, Carlota; Sousa, Rita T.; Pesquita, Catia] Univ Lisbon, Dept Informat, Fac Ciencias, LASIGE, P-1749016 Lisbon, Portugal.
   [Koehler, Sebastian] Ada Hlth GmbH, Karl Liebknecht Str 1, D-10178 Berlin, Germany.
RP Cardoso, C (通讯作者)，Univ Lisbon, Dept Informat, Fac Ciencias, LASIGE, P-1749016 Lisbon, Portugal.
EM cmacardoso@fc.ul.pt
RI Pesquita, Catia/O-8883-2015
OI Pesquita, Catia/0000-0002-1847-9393
FU Portuguese FCT through the LASIGE Research Unit [UIDB/00408/2020,
   UIDP/00408/2020, PTDC/EEI-ESS/4633/2014, SFRH/BD/145377/2019]
FX This work was funded by the Portuguese FCT through the LASIGE Research
   Unit [UIDB/00408/2020 and UIDP/00408/2020], the SMILAX project
   [PTDC/EEI-ESS/4633/2014] and grant [SFRH/BD/145377/2019] to RTS.
CR Al Mubaid Hisham, 2008, 2008 IEEE Symposium on Computers and Communications (ISCC), P531, DOI 10.1109/ISCC.2008.4625763
   Amberger JS, 2015, NUCLEIC ACIDS RES, V43, pD789, DOI 10.1093/nar/gku1205
   Ashburner M, 2000, NAT GENET, V25, P25, DOI 10.1038/75556
   Bairoch A, 2000, NUCLEIC ACIDS RES, V28, P304, DOI 10.1093/nar/28.1.304
   Ben-Hur A, 2005, BIOINFORMATICS, V21, pI38, DOI 10.1093/bioinformatics/bti1016
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bodenreider O, 2006, BRIEF BIOINFORM, V7, P256, DOI 10.1093/bib/bbl027
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Camon E, 2003, GENOME RES, V13, P662, DOI 10.1101/gr.461403
   Cheatham M, 2014, LECT NOTES COMPUT SC, V8797, P33, DOI 10.1007/978-3-319-11915-1_3
   Ehsani R, 2016, BMC BIOINFORMATICS, V17, DOI 10.1186/s12859-016-1160-0
   El-Gebali S, 2019, NUCLEIC ACIDS RES, V47, pD427, DOI 10.1093/nar/gky995
   Friedberg I, 2006, BRIEF BIOINFORM, V7, P225, DOI 10.1093/bib/bbl004
   Gong XF, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2064-y
   Guzzi PH, 2012, BRIEF BIOINFORM, V13, P569, DOI 10.1093/bib/bbr066
   Harispe S., 2015, SYNTHESIS LECT HUMAN, VVolume 8, P1
   Hoehndorf R, 2011, NUCLEIC ACIDS RES, V39, DOI 10.1093/nar/gkr538
   Ikram N, 2018, IEEE ACM T COMPUT BI, V15, P905, DOI 10.1109/TCBB.2017.2695542
   Jain S, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-562
   Kohler S, 2019, NUCLEIC ACIDS RES, V47, pD1018, DOI 10.1093/nar/gky1105
   Kohler S, 2009, AM J HUM GENET, V85, P457, DOI 10.1016/j.ajhg.2009.09.003
   Kustra R, 2006, COMP MED SY, P555, DOI 10.1109/CBMS.2006.100
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Liu WT, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-30455-0
   Liu ZP, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-475
   Maetschke SR, 2012, BIOINFORMATICS, V28, P69, DOI 10.1093/bioinformatics/btr610
   Mahdavi MA, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-262
   Makrodimitris S, 2019, BIOINFORMATICS, V35, P1116, DOI 10.1093/bioinformatics/bty751
   Masino AJ, 2014, BMC BIOINFORMATICS, V15, DOI 10.1186/1471-2105-15-248
   Morales C, 2017, LECT NOTES COMPUT SC, V10360, P246, DOI 10.1007/978-3-319-60131-1_14
   Mortensen Jonathan M, 2013, AMIA Annu Symp Proc, V2013, P1020
   Nair PS, 2013, HUM MUTAT, V34, P42, DOI 10.1002/humu.22204
   Palma G., 2014, DATABASE, V2014
   Palma G, 2014, LECT NOTES COMPUT SC, V8796, P131, DOI 10.1007/978-3-319-11964-9_9
   Paul M., 2018, NEW FAMILY SIMILARIT, DOI DOI 10.1101/459107
   Pedersen T, 2007, J BIOMED INFORM, V40, P288, DOI 10.1016/j.jbi.2006.06.004
   Pesquita C., 2009, CHALLENGES BIOINF JB, V157, P190
   Pesquita C, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-S5-S4
   Pesquita C, 2017, METHODS MOL BIOL, V1446, P161, DOI 10.1007/978-1-4939-3743-1_12
   Pesquita C, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000443
   Resnik P, 1995, INT JOINT CONF ARTIF, P448
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9982, P186, DOI 10.1007/978-3-319-46547-0_20
   Seco N, 2004, FRONT ARTIF INTEL AP, V110, P1089
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Sousa RT, 2020, BMC BIOINFORMATICS, V21, DOI 10.1186/s12859-019-3296-1
   Wang HY, 2005, COMP MED SY, P389
   Xu YG, 2013, GENOMICS, V101, P368, DOI 10.1016/j.ygeno.2013.04.010
   Xue HS, 2019, BMC SYST BIOL, V13, DOI 10.1186/s12918-019-0697-8
   Yu GX, 2016, BMC SYST BIOL, V10, DOI 10.1186/s12918-016-0361-5
   Yu JT, 2010, BIOINFORMATICS, V26, P2610, DOI 10.1093/bioinformatics/btq483
   Zhang JM, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2152-z
   Zhong XS, 2019, BMC GENOMICS, V20, DOI 10.1186/s12864-019-6272-2
   Zitnik, 2020, OPEN GRAPH BENCHMARK
NR 53
TC 4
Z9 4
U1 2
U2 2
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD NOV 11
PY 2020
AR baaa078
DI 10.1093/database/baaa078
PG 16
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA PC8DL
UT WOS:000597224900001
PM 33181823
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Church, KW
AF Church, Kenneth Ward
TI Benchmarks and goals
SO NATURAL LANGUAGE ENGINEERING
LA English
DT Article
DE Benchmarks; Knowledge graph completion; Bilingual lexicon induction;
   MUSE; WordNet
AB Benchmarks can be a useful step toward the goals of the field (when the benchmark is on the critical path), as demonstrated by the GLUE benchmark, and deep nets such as BERT and ERNIE. The case for other benchmarks such as MUSE and WN18RR is less well established. Hopefully, these benchmarks are on a critical path toward progress on bilingual lexicon induction (BLI) and knowledge graph completion (KGC). Many KGC algorithms have been proposed such as Trans[DEHRM], but it remains to be seen how this work improves WordNet coverage. Given how much work is based on these benchmarks, the literature should have more to say than it does about the connection between benchmarks and goals. Is optimizing P@10 on WN18RR likely to produce more complete knowledge graphs? Is MUSE likely to improve Machine Translation?
C1 [Church, Kenneth Ward] Baidu, Sunnyvale, CA 94089 USA.
RP Church, KW (通讯作者)，Baidu, Sunnyvale, CA 94089 USA.
EM kenneth.ward.church@gmail.com
RI Church, Kenneth/AAV-9667-2021
OI Church, Kenneth/0000-0001-8378-6069
CR [Anonymous], 1995, GENESIS, V22, P1
   Artetxe M., 2018, ACL, DOI [10.18653/v1/P18-1073, DOI 10.18653/V1/P18-1073]
   Bar-Hillel Y, 1960, ADV COMPUT, V1, P91, DOI DOI 10.1016/S0065-2458(08)60607-5
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Brown P. F., 1993, Computational Linguistics, V19, P263
   Church K.W., 1993, COMPUT LINGUIST, V19, P1
   Church KW, 2020, NAT LANG ENG, V26, P245, DOI 10.1017/S1351324920000030
   Conneau A, 2017, ARXIV171004087
   Dagan I, 1994, P 4 C APPL NAT LANG, P34, DOI DOI 10.3115/974358.974367
   Deng Zhi-Hong, 2019, ICLR
   Dettmers T., 2018, AAAI
   Devlin J, 2018, P 2019 C N AM CHAPT, V1, P4171, DOI [10.18653/v1/N19-1423, DOI 10.18653/V1/N19-1423]
   Dong Z., 2010, HOWNET ITS COMPUTATI, P53
   Europarl P., 2005, P MT SUMM, P79
   Fung P, 1998, LECT NOTES ARTIF INT, V1529, P1
   GALE WA, 1992, COMPUT HUMANITIES, V26, P415, DOI 10.1007/BF00136984
   Goldratt E. M., 1984, GOAL PROCESS ONGOING
   Hamp B., 1997, AUT INF EXTR BUILD L
   Huang JJ, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4072
   Irvine A., 2013, NAACL
   Irvine A, 2017, COMPUT LINGUIST, V43, P273, DOI 10.1162/COLI_a_00284
   Kementchedjhieva Y., 2019, EMNLP, P3327
   Kilgarriff A., 2004, P 11 EURALEX INT C, P105
   Koehn P, 2007, P 45 ANN M ACL INT P, P177
   Le Q.V., 2016, GOOGLES NEURAL MACHI
   LIN Y, 2015, AAAI
   Mikolov T., 2013, COMPUTER SCI
   Mikolov T., 2017, ABS171209405 CORR
   Miller George, 1998, WORDNET ELECT LEXICA
   Nguyen D.Q., 2017, ARXIV170308098, V1703
   Nguyen D. Q, 2017, ARXIV171202121
   Nickel M., 2019, AAAI
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Rapp Reinhard, 1995, CMPLG9505037 ARXIV
   Ruder Sebastian, 2017, ARXIV170604902
   Smadja F, 1996, COMPUT LINGUIST, V22, P1
   Smith S. L., 2017, ARXIV170203859
   Sun Yu, 2020, AAAI
   Trouillon T., 2016, INT C MACH LEARN ICM
   Wang A., 2018, ICLR
   Wang Z., 2014, AAAI
   Yang B., 2015, ICLR
   Yu S.Y., 2019, ARXIV190604239
NR 43
TC 2
Z9 2
U1 2
U2 3
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
SN 1351-3249
EI 1469-8110
J9 NAT LANG ENG
JI Nat. Lang. Eng.
PD SEP
PY 2020
VL 26
IS 5
BP 579
EP 592
AR PII S1351324920000418
DI 10.1017/S1351324920000418
PG 14
WC Computer Science, Artificial Intelligence; Linguistics; Language &
   Linguistics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Computer Science; Linguistics
GA MX6OR
UT WOS:000557841300005
OA hybrid
DA 2022-08-23
ER

PT J
AU Li, CZ
   Hu, MC
   Xiao, B
   Chen, Z
   Tam, VWY
   Zhao, YY
AF Li, Clyde Zhengdao
   Hu, Mingcong
   Xiao, Bing
   Chen, Zhe
   Tam, Vivian W. Y.
   Zhao, Yiyu
TI Mapping the Knowledge Domains of Emerging Advanced Technologies in the
   Management of Prefabricated Construction
SO SUSTAINABILITY
LA English
DT Review
DE emerging advanced technologies; prefabricated construction; knowledge
   map; literature review
ID INFORMATION MODELING BIM; GREENHOUSE-GAS EMISSIONS; HOUSING PRODUCTION;
   COST-ANALYSIS; ON-SITE; INTERNET; THINGS; COORDINATION; PLATFORM; DESIGN
AB Emerging advanced technologies (EAT) have been regarded as significant technological innovations which can greatly improve the transforming construction industry. Given that research on EAT related to the management of prefabricated construction (MPC) has not yet been conducted, various researchers require a state-of-the-art summary of EAT research and implementation in the MPC field. The purpose of this paper is to provide a systematic literature review by analysing the selected 526 related publications in peer-reviewed leading journals during 2009-2020. Through a thorough review of selected papers from the state-of-the-art academic journals in the construction industry, EAT is recognised as the key area affecting the development of the MPC discipline. This study has value in offering original insights to summarise the advanced status quo of this field, helping subsequent researchers gain an in-depth understanding of the underlying structure of this field and allowing them to continue future research directions.
C1 [Li, Clyde Zhengdao; Hu, Mingcong; Chen, Zhe; Zhao, Yiyu] Shenzhen Univ, Coll Civil & Transportat Engn, Sino Australia Joint Res Ctr BIM & Smart Construc, Shenzhen 518000, Peoples R China.
   [Xiao, Bing; Tam, Vivian W. Y.] Univ Western Sydney, Sch Built Environm, Locked Bag 1797, Penrith, NSW 2751, Australia.
RP Xiao, B (通讯作者)，Univ Western Sydney, Sch Built Environm, Locked Bag 1797, Penrith, NSW 2751, Australia.
EM clyde.zhengdao.li@szu.edu.cn; humingcong0120@163.com;
   aprilshaw097@163.com; czsdjzu@163.com; V.Tam@westernsydney.edu.au;
   zhaoycolin609@163.com
OI Tam, Vivian/0000-0002-1074-8018
FU Humanities and Social Sciences Foundation of the Ministry of Education
   of China [18YJCZH090]; National Natural Science Foundation of China
   [52078302, 71801159]; Natural Science Foundation of Guangdong Province
   of China [2021A1515012204]; Shenzhen Science and Technology Innovation
   Commission [JCYJ20190808174409266, GJHZ20200731095806017,
   SGDX20201103093600002]
FX This research was supported by the Humanities and Social Sciences
   Foundation of the Ministry of Education of China (Grant No.18YJCZH090),
   National Natural Science Foundation of China (Grant No. 52078302 and No.
   71801159), the Natural Science Foundation of Guangdong Province of China
   (Grant No. 2021A1515012204) and Shenzhen Science and Technology
   Innovation Commission (Grant No. JCYJ20190808174409266, No.
   GJHZ20200731095806017 and No. SGDX20201103093600002).
CR Akinosho TD, 2020, J BUILD ENG, V32, DOI 10.1016/j.jobe.2020.101827
   Aldosari M, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12020306
   Arashpour M, 2016, INT J PROJ MANAG, V34, P1393, DOI 10.1016/j.ijproman.2016.02.004
   Aye L, 2012, ENERG BUILDINGS, V47, P159, DOI 10.1016/j.enbuild.2011.11.049
   Balaid A, 2016, INT J INFORM MANAGE, V36, P451, DOI 10.1016/j.ijinfomgt.2016.02.005
   Banihashemi S, 2018, AUTOMAT CONSTR, V88, P1, DOI 10.1016/j.autcon.2017.12.026
   Bortolini R, 2019, AUTOMAT CONSTR, V98, P248, DOI 10.1016/j.autcon.2018.11.031
   Bosche F, 2015, AUTOMAT CONSTR, V49, P201, DOI 10.1016/j.autcon.2014.05.014
   Braun A, 2019, AUTOMAT CONSTR, V106, DOI 10.1016/j.autcon.2019.102879
   Briones-Llorente R, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11113117
   Bygballe LE, 2010, J PURCH SUPPLY MANAG, V16, P239, DOI 10.1016/j.pursup.2010.08.002
   Chang CG, 2015, AER ADV ENG RES, V21, P804
   Chen K, 2018, INT J COMPUT INTEG M, V31, P349, DOI 10.1080/0951192X.2017.1379095
   Chen XR, 2018, LECT NOTES COMPUT SC, V10955, P771, DOI 10.1007/978-3-319-95933-7_85
   Chen Y, 2019, STRUCT DES TALL SPEC, V28, DOI 10.1002/tal.1582
   Dallasega P, 2018, COMPUT IND, V99, P205, DOI 10.1016/j.compind.2018.03.039
   Darko A, 2020, COMPUT IND, V123, DOI 10.1016/j.compind.2020.103327
   Du J, 2017, IEEE ACCESS, V5, P4132, DOI 10.1109/ACCESS.2017.2665778
   Feng CG, 2015, FRONT ENG MANAG, V2, P178, DOI 10.15302/J-FEM-2015038
   Generalova EM, 2016, PROCEDIA ENGINEER, V153, P167, DOI 10.1016/j.proeng.2016.08.098
   Getuli V, 2016, PROCEDIA ENGINEER, V164, P542, DOI 10.1016/j.proeng.2016.11.656
   Goh M, 2019, AUTOMAT CONSTR, V101, P227, DOI 10.1016/j.autcon.2018.12.017
   Guo ZL, 2017, IOP C SER EARTH ENV, V81, DOI 10.1088/1755-1315/81/1/012139
   Han S, 2017, J CONSTR ENG M, V143, DOI 10.1061/(ASCE)CO.1943-7862.0001360
   Han ZX, 2017, ICCREM 2017: PREFABRICATED BUILDINGS, INDUSTRIALIZED CONSTRUCTION, AND PUBLIC-PRIVATE PARTNERSHIPS, P71
   He QH, 2017, INT J PROJ MANAG, V35, P670, DOI 10.1016/j.ijproman.2016.08.001
   Hong JK, 2018, J CLEAN PROD, V172, P649, DOI 10.1016/j.jclepro.2017.10.171
   Hosseini MR, 2018, AUTOMAT CONSTR, V87, P235, DOI 10.1016/j.autcon.2017.12.002
   Hsu PY, 2018, AUTOMAT CONSTR, V94, P47, DOI 10.1016/j.autcon.2018.05.029
   Hwang BG, 2018, AUTOMAT CONSTR, V94, P168, DOI 10.1016/j.autcon.2018.06.016
   Innella F, 2019, J CONSTR ENG M, V145, DOI 10.1061/(ASCE)CO.1943-7862.0001712
   Ji YB, 2020, ENG CONSTR ARCHIT MA, V27, P1703, DOI 10.1108/ECAM-01-2020-0017
   Ji YB, 2019, ADV CIV ENG, V2019, DOI 10.1155/2019/3720191
   Ji YB, 2017, APPL SCI-BASEL, V7, DOI 10.3390/app7020180
   Jiang L, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10072516
   Jin RY, 2020, ENERG BUILDINGS, V207, DOI 10.1016/j.enbuild.2019.109567
   Jin RY, 2018, J CLEAN PROD, V202, P1202, DOI 10.1016/j.jclepro.2018.08.195
   Kim MK, 2016, AUTOMAT CONSTR, V72, P102, DOI 10.1016/j.autcon.2016.08.035
   Kim YW, 2016, CAN J CIVIL ENG, V43, P287, DOI 10.1139/cjce-2015-0010
   Lee JH, 2013, ADV ENG INFORM, V27, P108, DOI 10.1016/j.aei.2012.11.004
   Lei Z, 2016, WINT SIMUL C PROC, P3281, DOI 10.1109/WSC.2016.7822359
   Li CZ, 2018, AUTOMAT CONSTR, V89, P146, DOI 10.1016/j.autcon.2018.01.001
   Li CZ, 2018, J CLEAN PROD, V185, P366, DOI 10.1016/j.jclepro.2018.02.308
   Li CZ, 2018, J CLEAN PROD, V195, P1533, DOI 10.1016/j.jclepro.2017.09.066
   Li CZD, 2017, J CLEAN PROD, V165, P1048, DOI 10.1016/j.jclepro.2017.07.156
   Li CZ, 2016, HABITAT INT, V57, P74, DOI 10.1016/j.habitatint.2016.07.002
   Li H, 2020, J CONSTR ENG M, V146, DOI 10.1061/(ASCE)CO.1943-7862.0001931
   Li J, 2019, AUTOMAT CONSTR, V102, P288, DOI 10.1016/j.autcon.2019.02.005
   Li L., 2015, P 1 INT C SUST BUILD, P161, DOI [10.1201/b19239-28, DOI 10.1201/B19239-28]
   Li X, 2019, AUTOMAT CONSTR, V100, P46, DOI 10.1016/j.autcon.2018.12.024
   Li X, 2018, J MANAGE ENG, V34, DOI 10.1061/(ASCE)ME.1943-5479.0000577
   Li X, 2017, AUTOMAT CONSTR, V84, P195, DOI 10.1016/j.autcon.2017.09.011
   Li ZD, 2014, HABITAT INT, V43, P240, DOI 10.1016/j.habitatint.2014.04.001
   Liu HX, 2019, J CLEAN PROD, V235, P1189, DOI 10.1016/j.jclepro.2019.07.055
   Liu HZ, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0235980
   Liu JK, 2018, WIRELESS PERS COMMUN, V102, P3021, DOI 10.1007/s11277-018-5323-3
   Liu KN, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093046
   Lu WS, 2011, AUTOMAT CONSTR, V20, P101, DOI 10.1016/j.autcon.2010.09.007
   Luo LZ, 2020, J CLEAN PROD, V276, DOI 10.1016/j.jclepro.2020.124325
   Luo LZ, 2020, J MANAGE ENG, V36, DOI 10.1061/(ASCE)ME.1943-5479.0000739
   Luo LZ, 2019, J MANAGE ENG, V35, DOI 10.1061/(ASCE)ME.1943-5479.0000675
   Ma G., 2019, ADV CIV ENG, V2019, P1, DOI DOI 10.1155/2019/6870507
   Mao C, 2016, HABITAT INT, V57, P215, DOI 10.1016/j.habitatint.2016.08.002
   Mao L, 2018, ARCH CIV ENG, V64, P21, DOI 10.2478/ace-2018-0014
   Marti JV, 2010, ADV ENG SOFTW, V41, P916, DOI 10.1016/j.advengsoft.2010.05.003
   Martinez J, 2012, AUTOMAT CONSTR, V22, P298, DOI 10.1016/j.autcon.2011.09.005
   Monleon-Getino A, 2015, HIST COMUN SOC, V20, P427, DOI 10.5209/rev_HICS.2015.v20.n2.51392
   Montaser A, 2014, AUTOMAT CONSTR, V39, P167, DOI 10.1016/j.autcon.2013.06.012
   Moon D, 2019, AUTOMAT CONSTR, V98, P322, DOI 10.1016/j.autcon.2018.07.020
   Moon S, 2015, J CONSTR ENG M, V141, DOI 10.1061/(ASCE)CO.1943-7862.0000970
   Orlowski K, 2018, BUILDINGS-BASEL, V8, DOI 10.3390/buildings8090120
   Panda B, 2018, MATERIALWISS WERKST, V49, P666, DOI 10.1002/mawe.201700279
   Park HK, 2016, KSCE J CIV ENG, V20, P1201, DOI 10.1007/s12205-015-0198-2
   Qi Y, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10114293
   Razkenari MA, 2018, CONSTRUCTION RESEARCH CONGRESS 2018: INFRASTRUCTURE AND FACILITY MANAGEMENT, P101
   Rebolj D, 2017, AUTOMAT CONSTR, V84, P323, DOI 10.1016/j.autcon.2017.09.021
   Riveiro B, 2016, AUTOMAT CONSTR, V72, P258, DOI 10.1016/j.autcon.2016.02.009
   Roca D, 2013, AUTOMAT CONSTR, V36, P128, DOI 10.1016/j.autcon.2013.08.020
   Said HM, 2018, J CONSTR ENG M, V144, DOI 10.1061/(ASCE)CO.1943-7862.0001417
   Sardroud JM, 2012, SCI IRAN, V19, P381, DOI 10.1016/j.scient.2012.02.023
   Shahtaheri Y, 2017, AUTOMAT CONSTR, V83, P303, DOI 10.1016/j.autcon.2017.03.011
   Shi QQ, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10041260
   Shirowzhan S, 2019, AUTOMAT CONSTR, V105, DOI 10.1016/j.autcon.2019.102841
   Sui X, 2018, ACSR ADV COMPUT, V77, P256
   Tan K, 2018, MATEC WEB CONF, V206, DOI 10.1051/matecconf/201820601008
   Tang PB, 2010, AUTOMAT CONSTR, V19, P829, DOI 10.1016/j.autcon.2010.06.007
   Tang S, 2019, AUTOMAT CONSTR, V101, P127, DOI 10.1016/j.autcon.2019.01.020
   Tetik M, 2019, AUTOMAT CONSTR, V107, DOI 10.1016/j.autcon.2019.102910
   Turk Z, 2017, PROCEDIA ENGINEER, V196, P638, DOI 10.1016/j.proeng.2017.08.052
   Wang HQ, 2018, INDOOR BUILT ENVIRON, V27, P1017, DOI 10.1177/1420326X18786662
   Wang MD, 2020, BUILDINGS-BASEL, V10, DOI 10.3390/buildings10110204
   Woodhead R, 2018, AUTOMAT CONSTR, V93, P35, DOI 10.1016/j.autcon.2018.05.004
   Wu P, 2016, AUTOMAT CONSTR, V68, P21, DOI 10.1016/j.autcon.2016.04.005
   Xiahou XE, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15030442
   Xu GY, 2019, ENTERP INF SYST-UK, V13, P87, DOI 10.1080/17517575.2018.1455109
   Xu GY, 2018, AUTOMAT CONSTR, V93, P123, DOI 10.1016/j.autcon.2018.05.012
   Xu Z, 2020, PROCESSES, V8, DOI 10.3390/pr8121593
   Xu Z, 2019, ADV CIV ENG, V2019, DOI 10.1155/2019/1609523
   Yang F, 2016, I S WORKL CHAR PROC, P95
   Yang HX, 2018, MATH PROBL ENG, V2018, DOI 10.1155/2018/4062871
   Yang Y, 2017, AGRO FOOD IND HI TEC, V28, P542
   Yin XF, 2019, AUTOMAT CONSTR, V101, P72, DOI 10.1016/j.autcon.2019.01.010
   Yu ZH, 2017, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON FUTURE NETWORKS AND DISTRIBUTED SYSTEMS (ICFNDS '17), DOI 10.1145/3102304.3102340
   Yuan ZM, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12197862
   Zhai Y, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100997
   Zhai Y, 2018, INT J PROD ECON, V200, P192, DOI 10.1016/j.ijpe.2018.03.014
   Zhai Y, 2017, INT J PROD RES, V55, P3984, DOI 10.1080/00207543.2016.1231432
   Zhang CB, 2018, J CLEAN PROD, V201, P481, DOI 10.1016/j.jclepro.2018.07.238
   Zhang JY, 2016, PROCEDIA ENGINEER, V145, P1456, DOI 10.1016/j.proeng.2016.04.183
   Zhong RY, 2017, AUTOMAT CONSTR, V76, P59, DOI 10.1016/j.autcon.2017.01.006
   Zhong RY, 2015, IFAC PAPERSONLINE, V48, P1079, DOI 10.1016/j.ifacol.2015.06.227
NR 111
TC 1
Z9 1
U1 18
U2 41
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD AUG
PY 2021
VL 13
IS 16
AR 8800
DI 10.3390/su13168800
PG 31
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 UH7SI
UT WOS:000690125200001
OA gold
DA 2022-08-23
ER

PT J
AU Gray, PH
AF Gray, PH
TI The effects of knowledge management systems on emergent teams: towards a
   research model
SO JOURNAL OF STRATEGIC INFORMATION SYSTEMS
LA English
DT Article
DE knowledge management systems; requisite variety; knowledge maps
ID PERSPECTIVE
AB This article describes how knowledge management systems can enhance the effectiveness of teams that analyze complex, non-recurring problems by improving the way that team composition evolves. Knowledge management systems reduce the costs of searching for specialized knowledge resources, making it more likely that teams will incorporate a diversity of knowledge. Drawing on the concept of requisite variety, this article argues that increases in team knowledge variety lead to improvements in the effectiveness of the solutions generated by a team, which in turn enhance their organization's adaptive ability. This process also reinforces the existing distribution of knowledge within the organization, increasing employee specialization. The author develops a series of propositions and combines them into a research model from which he draws implications for researchers and managers. (C) 2000 Elsevier Science B.V. All rights reserved.
C1 Queens Univ, Sch Business, Kingston, ON K7L 4S8, Canada.
EM pgray@business.queensu.ca
CR Aadne J.H., 1996, MANAGING KNOWLEDGE, P9
   Alavi M., 1999, COMMUNICATION ASS IN, V1, P7, DOI DOI 10.17705/1CAIS.00107
   ANCONA DG, 1992, ORGAN SCI, V3, P321, DOI 10.1287/orsc.3.3.321
   [Anonymous], 1977, ADMIN SCI QUART
   ARGYRIS C, 1980, INFORMATION SYSTEMS, P15
   ASHBY WR, 1968, MODERN SYSTEMS RES B, P129
   AULIN A, 1982, CYBERNETIC LAWS SOCI
   Beer S., 1974, DESIGNING FREEDOM
   Benbasat I., 1989, INFORMATION SYSTEM R, P97
   BLACKBURN TR, 1971, SCIENCE, V172, P1003, DOI 10.1126/science.172.3987.1003
   BLAU PM, 1970, AM SOCIOL REV, V35, P201, DOI 10.2307/2093199
   Brooks F. P, 1982, MYTHICAL MAN MONTH E
   CHOO CW, 1995, INFORMATION MANAGEME
   COHEN WM, 1990, ADMIN SCI QUART, V35, P128, DOI 10.2307/2393553
   Cook T. D., 1979, QUASIEXPERIMENTATION
   COOK TD, 1994, ANNU REV PSYCHOL, V45, P545, DOI 10.1146/annurev.psych.45.1.545
   COOPER RG, 1994, J PROD INNOVAT MANAG, V11, P3, DOI 10.1111/1540-5885.1110003
   CROWE M, 1997, HARVARD MANAGEME AUG, P3
   Davenport TH., 1998, WORKING KNOWLEDGE OR
   Drucker P., 1994, ATLANTIC MONTHLY, V273, P53, DOI DOI 10.1037/0003-066X.44.3.546
   DRUCKER PF, 1988, HARVARD BUS REV, V66, P45
   Galbraith J.R., 1973, ADDISON WESLEY SERIE
   Galunic D.C., 1994, RES ORG BEHAV, V215
   Grant RM, 1996, STRATEGIC MANAGE J, V17, P109, DOI 10.1002/smj.4250171110
   KIESLER S, 1982, ADMIN SCI QUART, V27, P548, DOI 10.2307/2392530
   LAWLER EE, 1995, FORTUNE 1000 CO
   MANKIN D, 1998, TOMORROWS ORG CRAFTI
   MINTZBERG H, 1976, ADMIN SCI QUART, V21, P246, DOI 10.2307/2392045
   Nahapiet J, 1998, ACAD MANAGE REV, V23, P242, DOI 10.2307/259373
   PAYNE JW, 1982, PSYCHOL BULL, V92, P382, DOI 10.1037/0033-2909.92.2.382
   Ruggles R, 1998, CALIF MANAGE REV, V40, P80, DOI 10.2307/41165944
   Simon H.A., 1945, ADM BEHAV
   Simon HA, 1991, ORGAN SCI, V2, P125, DOI 10.1287/orsc.2.1.125
   Steiner I., 1972, GROUP PROCESS PRODUC
   TOFFLER A, 1995, FUTURIST         MAR, P14
   TRICKER RI, 1992, INFORMATION SYSTEMS, P14
   Tsoukas H, 1996, STRATEGIC MANAGE J, V17, P11, DOI 10.1002/smj.4250171104
   TUSHMAN ML, 1978, MANAGERIAL BEHAV, P157
   Van Valen L, 1973, EVOL THEORY, V1, P1, DOI DOI 10.1038/344864A0
   VANDEVEN AH, 1976, AM SOCIOL REV, V41, P322
   VINHOLT T, 1990, INTELLECTUAL TEAMWOR, P291
   Yin R., 1994, CASE STUDY RES DESIG
NR 42
TC 66
Z9 66
U1 0
U2 16
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0963-8687
EI 1873-1198
J9 J STRATEGIC INF SYST
JI J. Strateg. Inf. Syst.
PD SEP
PY 2000
VL 9
IS 2-3
BP 175
EP 191
DI 10.1016/S0963-8687(00)00040-8
PG 17
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 383CY
UT WOS:000165866600005
DA 2022-08-23
ER

PT J
AU Wang, J
   Li, XC
   Wang, P
   Liu, QL
   Deng, ZW
   Wang, JZ
AF Wang, Jing
   Li, Xinchun
   Wang, Peng
   Liu, Quanlong
   Deng, Zhiwen
   Wang, Jingzhi
TI Research Trend of the Unified Theory of Acceptance and Use of Technology
   Theory: A Bibliometric Analysis
SO SUSTAINABILITY
LA English
DT Article
DE bibliometrix; CiteSpace; the unified theory of acceptance and use of
   technology (UTAUT); bibliometrics; knowledge map
ID INFORMATION-TECHNOLOGY; BANKING ADOPTION; UTAUT; MODEL; INNOVATION;
   CONTEXT; TTF
AB Information technology-acceptance research has always been a research hotspot. In 2003, Venkatesh established the unified theory of acceptance and use of technology (UTAUT), which pushed information technology-acceptance research to a new climax. This study uses bibliometrics, Bibliometrix, and CiteSpace software to conduct data mining and quantitative analysis on 1694 research papers in the UTAUT in the Web of Science core collection database from 2003 to 2021 (the data update time is 13 August 2021). Combined with a visual bibliometric analysis, this paper makes an in-depth discussion on the UTAUT model from the aspects of research trends, research fields, main research journals, authors/institutions, national or regional cooperation networks, etc. This study comprehensively and systematically shows the evolution track and characteristics of the UTAUT. On this basis, the future development trend of the UTAUT is put forward.
C1 [Wang, Jing; Li, Xinchun; Liu, Quanlong; Deng, Zhiwen; Wang, Jingzhi] China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Peoples R China.
   [Wang, Jing; Wang, Peng; Deng, Zhiwen] China Univ Min & Technol, Lib China Univ Min & Technol, Xuzhou 221116, Peoples R China.
RP Li, XC (通讯作者)，China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Peoples R China.
EM wangjinglib@cumt.edu.cn; tbh090@cumt.edu.cn; pwang@cumt.edu.cn;
   qll2016@cumt.edu.cn; dengzw@cumt.edu.cn; TS21070069A31TM@cumt.edu.cn
OI wang, jing/0000-0001-5460-2195; Liu, Quanlong/0000-0002-8789-7660
FU Fundamental Research Funds for the Central Universities [2020ZDPYSK02]
FX FundingThis work was supported by the Fundamental Research Funds for the
   Central Universities, grant number 2020ZDPYSK02.
CR Aldossari MQ, 2020, J COMPUT INFORM SYST, V60, P507, DOI 10.1080/08874417.2018.1543000
   Amrouni K.I., 2019, P FGIC 2 C GOV INT K
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Brown SA, 2010, J MANAGE INFORM SYST, V27, P9, DOI 10.2753/MIS0742-1222270201
   Chang IC, 2007, EXPERT SYST APPL, V33, P296, DOI 10.1016/j.eswa.2006.05.001
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Criollo-C S, 2021, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.550
   Czerwinska M, 2020, J COMPUT SCI I, V17, P417, DOI [10.35784/jcsi.2412, DOI 10.35784/JCSI.2412]
   DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
   Dwivedi Y.K., 2010, P 16 AM C INF SYST A
   Dwivedi YK, 2019, INFORM SYST FRONT, V21, P719, DOI 10.1007/s10796-017-9774-y
   Eckhardt A, 2009, J INF TECHNOL-UK, V24, P11, DOI 10.1057/jit.2008.31
   Fishbein M.A., 1977, PHILOS RHETOR, V41, P842
   Gansser OA, 2021, TECHNOL SOC, V65, DOI 10.1016/j.techsoc.2021.101535
   Giovanis A, 2019, INT J RETAIL DISTRIB, V47, P894, DOI 10.1108/IJRDM-05-2018-0089
   Hew JJ, 2015, IND MANAGE DATA SYST, V115, P1269, DOI 10.1108/IMDS-01-2015-0028
   IJzerman RVH, 2019, J MED INTERNET RES, V21, DOI 10.2196/13432
   Jang W, 2020, INT J SPORT MARK SPO, V21, P1, DOI 10.1108/IJSMS-01-2019-0013
   Kang MS, 2017, J GLOB INF MANAG, V25, P81, DOI 10.4018/JGIM.2017070105
   Kuciapski M, 2017, J KNOWL MANAG, V21, P1053, DOI 10.1108/JKM-03-2016-0136
   Lee SW, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11113141
   Maity M, 2019, INFORM TECHNOL PEOPL, V32, P94, DOI 10.1108/ITP-11-2017-0384
   Martins C, 2014, INT J INFORM MANAGE, V34, P1, DOI 10.1016/j.ijinfomgt.2013.06.002
   Mensah IK, 2019, SAGE OPEN, V9, DOI 10.1177/2158244019855823
   Merhi M, 2019, TECHNOL SOC, V59, DOI 10.1016/j.techsoc.2019.101151
   Mou J, 2021, TECHNOL FORECAST SOC, V167, DOI 10.1016/j.techfore.2021.120734
   Nisha N, 2019, J GLOB INF MANAG, V27, P19, DOI 10.4018/JGIM.2019010102
   Nistor N, 2014, BRIT J EDUC TECHNOL, V45, P36, DOI 10.1111/j.1467-8535.2012.01383.x
   Nordhoff S, 2021, TRANSPORT RES F-TRAF, V78, P58, DOI 10.1016/j.trf.2021.01.001
   Obienu AC, 2021, EDUC INF TECHNOL, V26, P2091, DOI 10.1007/s10639-020-10341-x
   Oliveira T, 2014, INT J INFORM MANAGE, V34, P689, DOI 10.1016/j.ijinfomgt.2014.06.004
   Palau-Saumell R, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11041210
   Palos-Sanchez PR, 2019, INT J MOB COMMUN, V17, P579
   Rabu SNA, 2019, EDUC INF TECHNOL, V24, P359, DOI 10.1007/s10639-018-9779-2
   Rahi S, 2019, J RES INTERACT MARK, V13, P411, DOI 10.1108/JRIM-02-2018-0032
   Raigoso D, 2021, BRAIN SCI, V11, DOI 10.3390/brainsci11060738
   Rana NP, 2016, COMPUT HUM BEHAV, V59, P265, DOI 10.1016/j.chb.2016.02.019
   Sagnier C, 2019, TRAV HUMAIN, V82, P183, DOI 10.3917/th.823.0183
   Shachak A, 2019, J BIOMED INFORM, V100, DOI 10.1016/j.jbi.2019.103315
   Sohn K, 2020, TELEMAT INFORM, V47, DOI 10.1016/j.tele.2019.101324
   Tai YM, 2013, J ELECTRON COMMER RE, V14, P67
   Tamilmani K, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102269
   Tamilmani K, 2021, INFORM SYST FRONT, V23, P987, DOI 10.1007/s10796-020-10007-6
   Taneja B, 2022, FORESIGHT, V24, P210, DOI 10.1108/FS-08-2020-0079
   Thong JYL, 2011, IEEE T ENG MANAGE, V58, P613, DOI 10.1109/TEM.2010.2058851
   Tomczyk S, 2021, J MED INTERNET RES, V23, DOI 10.2196/25447
   van Raaij EM, 2008, COMPUT EDUC, V50, P838, DOI 10.1016/j.compedu.2006.09.001
   Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 2022, ANN OPER RES, V308, P641, DOI 10.1007/s10479-020-03918-9
   Venkatesh V, 2016, J ASSOC INF SYST, V17, P328, DOI 10.17705/1jais.00428
   Venkatesh V, 2012, MIS QUART, V36, P157
   Wang YS, 2009, BRIT J EDUC TECHNOL, V40, P92, DOI 10.1111/j.1467-8535.2007.00809.x
   Williams MD, 2015, J ENTERP INF MANAG, V28, P443, DOI 10.1108/JEIM-09-2014-0088
   Williams MD, 2010, INTEGR SER INFORM SY, V28, P37, DOI 10.1007/978-1-4419-6108-2_3
   Xu X, 2017, J MANAGE INFORM SYST, V34, P102, DOI 10.1080/07421222.2017.1297172
   Xu ZS, 2021, TECHNOL FORECAST SOC, V170, DOI 10.1016/j.techfore.2021.120896
   Zhou T, 2010, COMPUT HUM BEHAV, V26, P760, DOI 10.1016/j.chb.2010.01.013
NR 58
TC 2
Z9 2
U1 53
U2 69
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 10
DI 10.3390/su14010010
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 YF4EB
UT WOS:000741760800001
OA gold
DA 2022-08-23
ER

PT J
AU Allen, B
AF Allen, B
TI Individual differences and the conundrums of user-centered design: Two
   experiments
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
LA English
DT Article
ID HIERARCHICAL FILE SYSTEM; KNOWLEDGE MAPS; INFORMATION; TEXT; MEMORY
AB Individual differences between users of information systems can influence search performance. In user-centered design it is important to match users with system configurations that will optimize their performance. Two matching strategies were explored in the first experiment: the capitalization match, and the compensatory match. Findings suggest that a compensatory match is likely to be encountered more frequently in designing information systems. Having determined an optimal match between users and system configurations, it is necessary to find ways to ensure that users are guided to the appropriate configuration. The second experiment examined user selection of system configurations, and concluded that users do not act to optimize system configuration when they select features. This result suggests that information systems must have mechanisms such as user models to direct users to optimal configurations. These experiments suggest some of the complexities and problems encountered in applying individual differences research to user-centered design of information systems.
C1 Univ Missouri, Sch Informat Sci & Learning Technol, Columbia, MO 65211 USA.
RP Allen, B (通讯作者)，Univ Missouri, Sch Informat Sci & Learning Technol, 20 Rothwell, Columbia, MO 65211 USA.
CR ALLEN B, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P298
   ALLEN B, 1994, INFORM PROCESS MANAG, V30, P177, DOI 10.1016/0306-4573(94)90063-9
   ALLEN BL, 1991, ANNU REV INFORM SCI, V26, P3
   ANDREWS K, 1995, INFORMATION VISUALIZ, P97
   BORGMAN CL, 1989, INFORM PROCESS MANAG, V25, P237, DOI 10.1016/0306-4573(89)90042-3
   Branting LK, 1997, INT J HUM-COMPUT ST, V46, P55, DOI 10.1006/ijhc.1996.0083
   DUMAIS ST, 1991, PROCEEDINGS OF THE HUMAN FACTORS SOCIETY 35TH ANNUAL MEETING, VOL 1, P398
   Ekstrom R.B., 1976, MANUAL KIT FACTOR RE
   GREENE SL, 1990, INT J MAN MACH STUD, V32, P303, DOI 10.1016/S0020-7373(08)80005-3
   Haynes T., 1997, Proceedings of the First International Conference on Autonomous Agents, P308, DOI 10.1145/267658.267733
   Hearst M. A., 1995, Human Factors in Computing Systems. CHI'95 Conference Proceedings, P59
   HETZLER B, 1997, SIGIR FORUM, V31, P28
   HUBER GP, 1983, MANAGE SCI, V29, P567, DOI 10.1287/mnsc.29.5.567
   Jackson S., 1994, RANDOM FACTORS ANOVA
   Korfhage RR, 1995, P SOC PHOTO-OPT INS, V2606, P380, DOI 10.1117/12.227260
   KULHAVY RW, 1993, AM J PSYCHOL, V106, P483, DOI 10.2307/1422965
   KULHAVY RW, 1993, ETR&D-EDUC TECH RES, V41, P47, DOI 10.1007/BF02297511
   Messick S., 1976, INDIVIDUALITY LEARNI
   MILLER N, 1997, NEED METRICS VISUAL
   ODONNELL A, 1993, CONTEMP EDUC PSYCHOL, V18, P222, DOI 10.1006/ceps.1993.1018
   Odubiyi J. B., 1997, Proceedings of the First International Conference on Autonomous Agents, P292, DOI 10.1145/267658.267731
   Paivio A., 1990, MENTAL REPRESENTATIO
   REWEY KL, 1991, CONTEMP EDUC PSYCHOL, V16, P203, DOI 10.1016/0361-476X(91)90021-C
   RITTSCHOF KA, 1994, CONTEMP EDUC PSYCHOL, V19, P129, DOI 10.1006/ceps.1994.1012
   Shapira B, 1997, INFORM PROCESS MANAG, V33, P273, DOI 10.1016/S0306-4573(97)00003-4
   STANNEY KM, 1995, ERGONOMICS, V38, P1184, DOI 10.1080/00140139508925181
   VICENTE KJ, 1988, INT J MAN MACH STUD, V29, P647, DOI 10.1016/S0020-7373(88)80072-5
   VICENTE KJ, 1987, HUM FACTORS, V29, P349, DOI 10.1177/001872088702900308
   WADDILL PJ, 1992, MEM COGNITION, V20, P472, DOI 10.3758/BF03199580
   WISE JA, 1995, INFORMATION VISUALIZ, P51
NR 30
TC 37
Z9 37
U1 1
U2 5
PU JOHN WILEY & SONS INC
PI NEW YORK
PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA
SN 0002-8231
J9 J AM SOC INFORM SCI
JI J. Am. Soc. Inf. Sci.
PD APR
PY 2000
VL 51
IS 6
BP 508
EP 520
DI 10.1002/(SICI)1097-4571(2000)51:6<508::AID-ASI3>3.0.CO;2-Q
PG 13
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 295WD
UT WOS:000085990100003
DA 2022-08-23
ER

PT J
AU Nie, ZW
   Liu, YJ
   Yang, LY
   Li, SN
   Pan, F
AF Nie, Zhiwei
   Liu, Yuanji
   Yang, Luyi
   Li, Shunning
   Pan, Feng
TI Construction and Application of Materials Knowledge Graph Based on
   Author Disambiguation: Revisiting the Evolution of LiFePO4
SO ADVANCED ENERGY MATERIALS
LA English
DT Article
DE lithium iron phosphate; machine learning; matching dependencies;
   materials knowledge graphs
ID PHOSPHO-OLIVINES; DENSITY; IRON
AB Due to the recent innovations in computer technology, the emerging field of materials informatics has now become a catalyst for a revolution of the research paradigm in materials science. Knowledge graphs, which provide support for knowledge management, are able to collectively capture the scientific knowledge from the vast collection of research articles and accomplish the automatic recognition of the relationships between entities. In this work, a materials knowledge graph, named MatKG, is constructed, which establishes a unique correspondence between subjects and objects in the materials science area. An emphasis is placed on the disambiguation of authors, addressed by a deduplication model based on machine learning and matching dependencies algorithms. Specifically, MatKG is applied to perform tracking on research trends in the study of LiFePO4 and to automatically chronicle the milestones achieved so far. It is believed that MatKG can serve as a versatile research platform for amalgamating and refining the scientific knowledge of materials in a variety of subfields and intersectional domains.
C1 [Nie, Zhiwei; Liu, Yuanji; Yang, Luyi; Li, Shunning; Pan, Feng] Peking Univ, Shenzhen Grad Sch, Sch Adv Mat, Shenzhen 518055, Peoples R China.
RP Li, SN; Pan, F (通讯作者)，Peking Univ, Shenzhen Grad Sch, Sch Adv Mat, Shenzhen 518055, Peoples R China.
EM lisn@pku.edu.cn; panfeng@pkusz.edu.cn
RI Li, Shunning/ABB-4187-2021
OI Li, Shunning/0000-0002-5381-6025; Nie, Zhiwei/0000-0002-2781-5248
FU National Key R&D Program of China [2016YFB0700600]; Chemistry and
   Chemical Engineering Guangdong Laboratory [1922018]; Shenzhen Science
   and Technology Research Grant [JCYJ20200109140416788]
FX This work was financially supported by National Key R&D Program of China
   (2016YFB0700600), the Chemistry and Chemical Engineering Guangdong
   Laboratory (Grant No.1922018), and the Shenzhen Science and Technology
   Research Grant (No. JCYJ20200109140416788).
CR Bahmani Z, 2017, INT J APPROX REASON, V83, P118, DOI 10.1016/j.ijar.2017.01.003
   Chen ZH, 2002, J ELECTROCHEM SOC, V149, pA1184, DOI 10.1149/1.1498255
   Chung SY, 2002, NAT MATER, V1, P123, DOI 10.1038/nmat732
   Cui Z, 2017, J MATER CHEM A, V5, P1595, DOI 10.1039/c6ta09853c
   Fan WF, 2011, IEEE T KNOWL DATA EN, V23, P683, DOI 10.1109/TKDE.2010.154
   Herle PS, 2004, NAT MATER, V3, P147, DOI 10.1038/nmat1063
   Huang H, 2001, ELECTROCHEM SOLID ST, V4, pA170, DOI 10.1149/1.1396695
   Huhtala Y, 1999, COMPUT J, V42, P100, DOI 10.1093/comjnl/42.2.100
   Islam MS, 2005, CHEM MATER, V17, P5085, DOI 10.1021/cm050999v
   Jain A, 2011, COMP MATER SCI, V50, P2295, DOI 10.1016/j.commatsci.2011.02.023
   McCallum AK, 2000, INFORM RETRIEVAL, V3, P127, DOI 10.1023/A:1009953814988
   MERCIER M, 1968, CR ACAD SCI B PHYS, V267, P1345
   Padhi AK, 1997, J ELECTROCHEM SOC, V144, P1609, DOI 10.1149/1.1837649
   Padhi AK, 1997, J ELECTROCHEM SOC, V144, P1188, DOI 10.1149/1.1837571
   PAQUESLE.MT, 1974, SPECTROCHIM ACTA A, VA 30, P673, DOI 10.1016/0584-8539(74)80190-X
   Raccuglia P, 2016, NATURE, V533, P73, DOI 10.1038/nature17439
   SANTORO RP, 1967, ACTA CRYSTALLOGR, V22, P344, DOI 10.1107/S0365110X67000672
   Schneider K.M, 2005, INT C INT TEXT PTOC
   Song S, 2009, P 18 ACM C INF KNOWL
   STRELTSOV VA, 1993, ACTA CRYSTALLOGR B, V49, P147, DOI 10.1107/S0108768192004701
   Yakubovich O. V., 1977, Soviet Physics - Doklady, V22, P347
   Zhai C, 2012, SURVEY TEXT CLASSIFI
NR 22
TC 3
Z9 4
U1 17
U2 43
PU WILEY-V C H VERLAG GMBH
PI WEINHEIM
PA POSTFACH 101161, 69451 WEINHEIM, GERMANY
SN 1614-6832
EI 1614-6840
J9 ADV ENERGY MATER
JI Adv. Energy Mater.
PD APR
PY 2021
VL 11
IS 16
AR 2003580
DI 10.1002/aenm.202003580
EA MAR 2021
PG 5
WC Chemistry, Physical; Energy & Fuels; Materials Science,
   Multidisciplinary; Physics, Applied; Physics, Condensed Matter
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Energy & Fuels; Materials Science; Physics
GA RT5JR
UT WOS:000627504400001
DA 2022-08-23
ER

PT J
AU Chumachenko, A
   Kreminskyi, B
   Mosenkis, I
   Yakimenko, A
AF Chumachenko, Artem
   Kreminskyi, Boris
   Mosenkis, Iurii
   Yakimenko, Alexander
TI Dynamical entropic analysis of scientific concepts
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Concept classification; scientific ontology; Shannon entropy;
   time-series warping distance
ID TOPICS
AB In the present era of information, the problem of effective knowledge retrieval from a collection of scientific documents becomes especially important for continuous scientific progress. The information available in scientific publications traditionally consists of bibliometric metadata and its semantic component such as title, abstract and text. While the former having a machine-readable format usually used for knowledge mapping and pattern recognition, the latter designed for human interpretation and analysis. Only a few studies use full-text analysis, based on carefully selected scientific ontology, to map the actual structure of the scientific knowledge or uncover similarities between documents. Unfortunately, the presence of common (basic) concepts across semantically unrelated documents creates spurious connections between different topics. We revise the known method based on the entropic information-theoretic measure used for selecting basic concepts and propose to analyse the dynamics of Shannon entropy for more rigorous sorting of concepts by their generality.
C1 [Chumachenko, Artem; Yakimenko, Alexander] Taras Shevchenko Natl Univ Kyiv, Dept Phys, 64-13 Volodymyrska St, UA-01601 Kiev, Ukraine.
   [Kreminskyi, Boris] Inst Educ Content Modernizat, State Sci Inst, Kiev, Ukraine.
   [Mosenkis, Iurii] Taras Shevchenko Natl Univ Kyiv, Inst Philol, Kiev, Ukraine.
RP Chumachenko, A (通讯作者)，Taras Shevchenko Natl Univ Kyiv, Dept Phys, 64-13 Volodymyrska St, UA-01601 Kiev, Ukraine.
EM chumachenko.a@gmail.com
CR Ahlgren P, 2008, SCIENTOMETRICS, V76, P273, DOI 10.1007/s11192-007-1935-1
   Ahlgren P, 2009, J INFORMETR, V3, P49, DOI 10.1016/j.joi.2008.11.003
   Blei David M, 2006, P 23 INT C MACH LEAR, P113, DOI DOI 10.1145/1143844.1143859
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bornmann L, 2015, J ASSOC INF SCI TECH, V66, P2215, DOI 10.1002/asi.23329
   Boyack KW, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0018029
   Boyack KW, 2005, SCIENTOMETRICS, V64, P351, DOI 10.1007/s11192-005-0255-6
   Chumachenko AV, 2020, SCIENTOMETRICS, V125, P739, DOI 10.1007/s11192-020-03610-6
   Clifford J., USING DYNAMIC TIME W
   Gemmetto V., 2016, ARXIV PREPRINT ARXIV
   Ginsparg P, 2011, NATURE, V476, P145, DOI 10.1038/476145a
   Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101
   Ha-Thuc V., 2008, P 2 PHD WORKSH INF K, P25, DOI [10.1145/1458550.1458556, DOI 10.1145/1458550.1458556]
   Knegjens, PAPERSCAPE
   Lancichinetti A, 2015, PHYS REV X, V5, DOI 10.1103/PhysRevX.5.011007
   Leydesdorff L, 2009, J AM SOC INF SCI TEC, V60, P348, DOI 10.1002/asi.20967
   Mane KK, 2004, P NATL ACAD SCI USA, V101, P5287, DOI 10.1073/pnas.0307626100
   Manning C.D., 2008, INTRO INFORM RETRIEV
   Martini, 2018, UNLEASHING POWER SEM
   Muller M., 2007, INFORM RETRIEVAL MUS, P69, DOI DOI 10.1007/978-3-540-74048-3_4
   Rios PDL., 2017, ARXIV PREPRINT ARXIV
   Schreiber T, 2000, PHYS REV LETT, V85, P461, DOI 10.1103/PhysRevLett.85.461
   Shi F, 2017, J MECH DESIGN, V139, DOI 10.1115/1.4037649
   Small H, 2014, RES POLICY, V43, P1450, DOI 10.1016/j.respol.2014.02.005
   Steyvers Mark, 2004, P 10 ACM SIGKDD INT, P306, DOI [10.1145/1014052.1014087, DOI 10.1145/1014052.1014087]
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Xiaoyong Liu, 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P186
NR 27
TC 0
Z9 0
U1 0
U2 6
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.
PD AUG
PY 2022
VL 48
IS 4
BP 561
EP 569
AR 0165551520972034
DI 10.1177/0165551520972034
EA NOV 2020
PG 9
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 3A1MC
UT WOS:000630799000001
DA 2022-08-23
ER

PT J
AU Xu, XY
   Wu, JZ
   Yang, MT
   Luo, TY
   Meng, QR
   Li, WH
   Wu, YJ
AF Xu, Xiaoyun
   Wu, Jingzheng
   Yang, Mutian
   Luo, Tianyue
   Meng, Qianru
   Li, Weiheng
   Wu, Yanjun
TI AI-CTO: Knowledge graph for automated and dependable software stack
   solution
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Knowledge graph; graph embedding; software architecture
AB As the scale of software systems continues expanding, software architecture is receiving more and more attention as the blueprint for the complex software system. An outstanding architecture requires a lot of professional experience and expertise. In current practice, architects try to find solutions manually, which is time-consuming and error-prone because of the knowledge barrier between newcomers and experienced architects. The problem can be solved by easing the process of apply experience from prominent architects. To this end, this paper proposes a novel graph-embedding-based method, AI-CTO, to automatically suggest software stack solutions according to the knowledge and experience of prominent architects. Firstly, AI-CTO converts existing industry experience to knowledge, i.e., knowledge graph. Secondly, the knowledge graph is embedded in a low-dimensional vector space. Then, the entity vectors are used to predict valuable software stack solutions by an SVM model. We evaluate AI-CTO with two case studies and compare its solutions with the software stacks of large companies. The experiment results show that AI-CTO can find effective and correct stack solutions and it outperforms other baseline methods.
C1 [Xu, Xiaoyun; Wu, Jingzheng; Yang, Mutian; Luo, Tianyue; Li, Weiheng; Wu, Yanjun] Chinese Acad Sci, Inst Software, Beijing, Peoples R China.
   [Wu, Jingzheng; Wu, Yanjun] Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China.
   [Yang, Mutian; Luo, Tianyue] Beijing ZhongKeWeiLan Technol Co Ltd, Beijing, Peoples R China.
   [Meng, Qianru] Beijing Baidu Inc, Beijing, Peoples R China.
RP Wu, JZ (通讯作者)，Chinese Acad Sci, Inst Software, Beijing, Peoples R China.; Wu, JZ (通讯作者)，Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China.
EM jingzheng08@iscas.ac.cn
FU National Key R&D Program of China [2018YFB0803600]; National Natural
   Science Foundation of China [61772507]
FX This work is supported by National Key R&D Program of China (No.
   2018YFB0803600) and National Natural Science Foundation of China (No.
   61772507).
CR [Anonymous], 2017, 31 AAAI C ART INT
   Anthony F., 2011, P 2011 C EMPIRICAL M, P1535, DOI DOI 10.1234/12345678
   Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
   Black D.C., 2009, SYSTEM C GROUND, V71
   Booch G., 2005, UNIFIED MODELING LAN
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Capilla R, 2016, J SYST SOFTWARE, V116, P191, DOI 10.1016/j.jss.2015.08.054
   Carlson A., 2010, 24 AAAI C ART INT
   DELUGACH HS, 1992, J SYST SOFTWARE, V19, P207, DOI 10.1016/0164-1212(92)90052-L
   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
   Garlan D., 1993, Advances in software engineering and knowledge engineering, P1
   Garlan D., 2000, P C FUT SOFTW ENG, P91
   Garlan D., 1997, P 1997 C CTR ADV STU, P7
   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
   Hoover CL, 1996, NINTH CONFERENCE ON SOFTWARE ENGINEERING EDUCATION, PROCEEDINGS, P100, DOI 10.1109/CSEE.1996.491366
   Huang Hong, 2020, P 29 INT JOINT C ART, P1237
   Ji G., 2016, 30 AAAI C ART INT
   Khajehnejad M., 2020, P 29 INT JOINT C ART, V7, P4306
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lin ZQ, 2017, J COMPUT SCI TECH-CH, V32, P242, DOI 10.1007/s11390-017-1718-y
   Mikolov T., 2013, ARXIV PREPRINT ARXIV
   Nguyen D. Q, 2016, ARXIV160608140
   Nie F., 2017, 31 AAAI C ART INT
   Ozkaya M., 2017, INT C MOD DRIV ENG S
   Ozkaya M, 2020, J COMPUT LANG, V58, DOI 10.1016/j.cola.2020.100963
   Ozkaya M, 2020, INFORM SOFTWARE TECH, V121, DOI 10.1016/j.infsof.2020.106275
   Ozkaya M, 2019, IET SOFTW, V13, P338, DOI 10.1049/iet-sen.2018.5409
   Padhye R, 2014, 36TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE COMPANION 2014), P516, DOI 10.1145/2591062.2591115
   Pearson K, 1901, PHILOS MAG, V2, P559, DOI 10.1080/14786440109462720
   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
   Perry D. E., 1992, SIGSOFT Software Engineering Notes, V17, P40
   Ponzanelli L., 2014, P 11 WORK C MIN SOFT, P102, DOI DOI 10.1145/2597073.2597077
   Ponzanelli L, 2017, PROC INT CONF SOFTW, P94, DOI 10.1109/ICSE.2017.17
   Qian W, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4286
   Satish N, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P979, DOI 10.1145/2588555.2610518
   Schmitz M., 2012, P 2012 JOINT C EMP M, P523
   Seedorf S., 2006, P WORKSH SEM WEB EN, P5
   Singhal A., 2012, INTRO KNOWLEDGE GRAP
   Tassey G., 2002, 7007011 RIT NAT I ST, P429
   Trisedya BD, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P229
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   van Vliet H, 2016, J SYST SOFTWARE, V117, P638, DOI 10.1016/j.jss.2016.01.017
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3307, DOI 10.1145/3308558.3313417
   Wang M, 2016, IEEE ANTENNAS PROP, P1381, DOI 10.1109/APS.2016.7696397
   Wang Z., 2016, IJCAI, P1293
   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
   Wu F, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P118
   Xie R., 2016, 30 AAAI C ART INT
   Yang H, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO), P1123, DOI 10.1109/ROBIO.2016.7866476
   Zhao XJ, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), P56, DOI 10.1109/SANER.2017.7884609
   Zhong Q, 2009, ACM SIGMOD/PODS 2009 CONFERENCE, P669
NR 54
TC 0
Z9 0
U1 6
U2 10
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 40
IS 1
BP 799
EP 812
DI 10.3233/JIFS-200899
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PQ8QE
UT WOS:000606807200054
DA 2022-08-23
ER

PT J
AU Mao, S
   Zhao, YM
   Chen, JH
   Wang, B
   Tang, Y
AF Mao, Shuai
   Zhao, Yunmeng
   Chen, Jinhe
   Wang, Bing
   Tang, Yang
TI Development of process safety knowledge graph: A Case study on delayed
   coking process
SO COMPUTERS & CHEMICAL ENGINEERING
LA English
DT Article
DE Process safety; Knowledge graph; Delayed coking process
AB Process safety is one of the essential preconditions for the achievement of green manufacturing. The improvement of process safety management requires a comprehensive risk analysis based on the collection of almost all safety related information, which are usually unstructured knowledge and experience. To handle the information and support the risk analysis, a process safety knowledge graph is prompted and the development of domain ontology on delayed coking process is elaborated. The combined top-down and bottom-up approaches are used in defining the process safety schema on ontology level. Several multi-structured data sources are introduced in establishing the process safety knowledge, in which the hazard and operability analysis (HAZOP) reports and process diagrams are most important. The ontology design and data extraction are demonstrated in the manuscript while various related applications are discussed. This process safety knowledge graph might empower the knowledge-based analysis abilities in discovering the hidden relationships between possible risk causes and consequences in an emergency situation, and could provide a foundation for more application related to process safety. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Mao, Shuai; Zhao, Yunmeng; Wang, Bing; Tang, Yang] East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China.
   [Chen, Jinhe] Minist Emergency Management, Natl Registrat Ctr Chem, Beijing, Peoples R China.
RP Wang, B (通讯作者)，East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China.
EM mshecust@163.com; yunmeng.zhao@ecust.edu.cn; cjh53271@163.com;
   wangb07@ecust.edu.cn; tangtany@gmail.com
OI Mao, Shuai/0000-0002-1960-3456; Tang, Yang/0000-0002-2750-8029
FU National Key Research and Development Program of China [2018YFC0809302];
   National Natural Science Foundation of China [61988101, 61751305,
   6167317]; (111 Project) [B17017]
FX This work is supported in part by National Key Research and Development
   Program of China under Grant 2018YFC0809302, the National Natural
   Science Foundation of China under Grants 61988101, 61751305, 6167317 and
   theProgramme of Introducing Talents of Discipline to Universities (the
   111 Project) under Grant B17017.
CR Bean DM, 2017, SCI REP-UK, V7, P1
   Cernansky Rachel, 2015, Nature, V519, P379
   Chen X, 2010, INT J CHEM REACT ENG, V8
   Crowl DA., 2001, CHEM PROCESS SAFETY
   Cui L, 2010, PROCESS SAF ENVIRON, V88, P327, DOI 10.1016/j.psep.2010.04.002
   Forjan M, 2012, 11TH IFAC/IEEE INTERNATIONAL CONFERENCE ON PROGRAMMABLE DEVICES AND EMBEDDED SYSTEMS (PDES 2012)
   Hollnagel E, 2012, FRAM: THE FUNCTIONAL RESONANCE ANALYSIS METHOD: MODELLING COMPLEX SOCIO-TECHNICAL SYSTEMS, P1
   Huang P, 2015, PROCESS SAF PROG, V34, P313, DOI 10.1002/prs.11789
   Jain P, 2019, J LOSS PREVENT PROC, V62, DOI 10.1016/j.jlp.2019.103957
   Jain P, 2019, COMPUT CHEM ENG, V121, P27, DOI 10.1016/j.compchemeng.2018.10.019
   Jia Y, 2018, ENGINEERING-PRC, V4, P53, DOI 10.1016/j.eng.2018.01.004
   Khan FI, 1997, J LOSS PREVENT PROC, V10, P191, DOI 10.1016/S0950-4230(97)00002-8
   Kotek L, 2012, PROCEDIA ENGINEER, V42, P808, DOI 10.1016/j.proeng.2012.07.473
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   Meilicke C, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3137
   Morbach J, 2007, ENG APPL ARTIF INTEL, V20, P147, DOI 10.1016/j.engappai.2006.06.010
   Morbach J, 2009, COMPUT CHEM ENG, V33, P1546, DOI 10.1016/j.compchemeng.2009.01.019
   Qian F, 2017, ENGINEERING-PRC, V3, P154, DOI 10.1016/J.ENG.2017.02.011
   Rahman S, 2009, J LOSS PREVENT PROC, V22, P373, DOI 10.1016/j.jlp.2009.01.008
   Shirali G, 2013, IRAN OCCUPATIONAL HL, V10
   Sohoglu E, 2012, J NEUROSCI, V32, P8443, DOI 10.1523/JNEUROSCI.5069-11.2012
   [孙晨 Sun Chen], 2018, [华东师范大学学报. 自然科学版, Journal of East China Normal University. Natural Science], P55
   Tauseef SM, 2011, J LOSS PREVENT PROC, V24, P426, DOI 10.1016/j.jlp.2011.03.005
   Tian LD, 2012, IND ENG CHEM RES, V51, P3923, DOI 10.1021/ie2025272
   Wang B, 2018, SCI TOTAL ENVIRON, V643, P1, DOI 10.1016/j.scitotenv.2018.06.174
   Wang J.H., 2008, Patent No. [7,428,487, 7428487]
   Wang Z, 2014, P C EMP METH NAT LAN, P1591
   Yuan ZH, 2017, ENGINEERING-PRC, V3, P179, DOI 10.1016/J.ENG.2017.02.012
   Zahedi G, 2009, INT J CHEM REACT ENG, V7
   Zhou J, 2018, ENGINEERING-PRC, V4, P11, DOI 10.1016/j.eng.2018.01.002
NR 30
TC 5
Z9 6
U1 7
U2 27
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 DEC 5
PY 2020
VL 143
AR 107094
DI 10.1016/j.compchemeng.2020.107094
PG 8
WC Computer Science, Interdisciplinary Applications; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA PE2AL
UT WOS:000598171700014
DA 2022-08-23
ER

PT J
AU Oramas, S
   Ostuni, VC
   Di Noia, T
   Serra, X
   Di Sciascio, E
AF Oramas, Sergio
   Ostuni, Vito Claudio
   Di Noia, Tommaso
   Serra, Xavier
   Di Sciascio, Eugenio
TI Sound and Music Recommendation with Knowledge Graphs
SO ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
LA English
DT Article
DE Knowledge graphs; recommender systems; music; entity linking; novelty;
   diversity
ID OF-THE-ART
AB The Web has moved, slowly but steadily, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as well as a tool to reason on them in order to extract new and implicit information. Knowledge graphs are currently used, for example, to explain search results, to explore knowledge spaces, to semantically enrich textual documents, or to feed knowledge-intensive applications such as recommender systems. In this work, we describe how to create and exploit a knowledge graph to supply a hybrid recommendation engine with information that builds on top of a collections of documents describing musical and sound items. Tags and textual descriptions are exploited to extract and link entities to external graphs such as WordNet and DBpedia, which are in turn used to semantically enrich the initial data. By means of the knowledge graph we build, recommendations are computed using a feature combination hybrid approach. Two explicit graph feature mappings are formulated to obtain meaningful item feature representations able to catch the knowledge embedded in the graph. Those content features are further combined with additional collaborative information deriving from implicit user feedback. An extensive evaluation on historical data is performed over two different datasets: a dataset of sounds composed of tags, textual descriptions, and user's download information gathered from Freesound. org and a dataset of songs that mixes song textual descriptions with tags and user's listening habits extracted from Songfacts.com and Last.fm, respectively. Results show significant improvements with respect to state-of-the-art collaborative algorithms in both datasets. In addition, we show how the semantic expansion of the initial descriptions helps in achieving much better recommendation quality in terms of aggregated diversity and novelty.
C1 [Oramas, Sergio; Serra, Xavier] Univ Pompeu Fabra, Mus Technol Grp, Roc Boronat 138, Barcelona 08018, Spain.
   [Ostuni, Vito Claudio] Pandora Media Inc, 2100 Franklyn St, Oakland, CA 94612 USA.
   [Di Noia, Tommaso; Di Sciascio, Eugenio] Polytech Univ Bari, SisInf Lab, Via Orabona 4, I-70125 Bari, Italy.
RP Oramas, S (通讯作者)，Univ Pompeu Fabra, Mus Technol Grp, Roc Boronat 138, Barcelona 08018, Spain.
EM sergio.oramas@upf.edu; vostuni@pandora.com; tommaso.dinoia@poliba.it;
   xavier.serra@upf.edu; eugenio.disciascio@poliba.it
RI Serra, Xavier/C-9299-2014
OI Serra, Xavier/0000-0003-1395-2345
CR Adomavicius G, 2012, IEEE T KNOWL DATA EN, V24, P896, DOI 10.1109/TKDE.2011.15
   Aghdam M. Hosseinzadeh, 2015, P 9 ACM C REC SYST R, P241, DOI DOI 10.1145/2792838.2799684
   Anand SS, 2007, ACM T INTERNET TECHN, V7, DOI 10.1145/1278366.1278371
   Beliakov Gleb, 2015, RECOMMENDER SYSTEMS, P777
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bogdanov D., 2013, MM 13, P855, DOI 10.1145/2502081.2502229
   Bonnin G, 2015, ACM COMPUT SURV, V47, DOI 10.1145/2652481
   Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
   Cantador I, 2008, AI COMMUN, V21, P203, DOI 10.3233/AIC-2008-0437
   Castells, 2010, P 1 INT WORKSH INF H, P1, DOI [10.1145/1869446.1869447, DOI 10.1145/1869446.1869447]
   Di Noia T., 2012, P 8 INT C SEM SYST, P1, DOI DOI 10.1145/2362499.2362501
   Fernandez-Tobias I., 2011, P 2 INT WORKSH INF H, V9, P25, DOI DOI 10.1145/2039320.2039324
   Font Frederic, 2014, P INT SEM WEB C ISWC, P1
   Gantner Z, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P176, DOI 10.1109/ICDM.2010.129
   Garcia-Silva A, 2012, KNOWL ENG REV, V27, P57, DOI 10.1017/S026988891100018X
   Hariri N, 2012, P 6 ACM C REC SYST, P131, DOI [DOI 10.1145/2365952.2365979, 10.1145/2365952.2365979]
   Hayes Conor, 2010, AAAI SPRING S LINK D
   Ho CH, 2012, J MACH LEARN RES, V13, P3323
   Hu YF, 2008, IEEE DATA MINING, P263, DOI 10.1109/ICDM.2008.22
   Jannach D., 2015, P 9 ACM C REC SYST, P187
   Kaminskas M, 2012, COMPUT SCI REV, V6, P89, DOI 10.1016/j.cosrev.2012.04.002
   Khrouf H., 2013, P 7 ACM C REC SYST, P185, DOI DOI 10.1145/2507157.2507171
   Knees P, 2013, ACM T MULTIM COMPUT, V10, DOI 10.1145/2542205.2542206
   McNee S.M., 2006, P CHI 06 HUM FACT CO, P1097, DOI DOI 10.1145/1125451.1125659
   Middleton S., 2009, HDB ONTOLOGIES, P779, DOI DOI 10.1007/978-3-540-92673-3_35
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mobasher B, 2004, LECT NOTES COMPUT SC, V3209, P57, DOI 10.1007/978-3-540-30123-3_4
   Moro Andrea, 2014, T ASS COMPUT LING TA
   Musto C, 2014, LECT NOTES COMPUT SC, V8538, P381
   Ning X., 2012, P 6 ACM C REC SYST, P155, DOI DOI 10.1145/2365952.2365983
   Ostuni V. C, 2013, P 7 ACM C REC SYST, P85, DOI DOI 10.1145/2507157.2507172
   Ostuni Vito Claudio, 2014, LECT NOTES BUSINESS
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Roma G., 2012, P 2012 3 INT WORKSH, P1
   Romito Davide, 2012, P 6 ACM C REC SYST R, P253, DOI DOI 10.1145/2365952.2366007
   Rowe Matthew, 2014, 2014 IEEE WIC ACM IN
   Semeraro G., 2009, P 3 ACM C REC SYST R, P301, DOI DOI 10.1145/1639714.1639773
   Serra X., 2016, P 10 INT C LANG RES
   Shawe-Taylor J., 2004, KERNEL METHODS PATTE, DOI 10.1017/CBO9780511809682
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Sordo M, 2015, LECT NOTES COMPUT SC, V9103, P369, DOI 10.1007/978-3-319-19581-0_33
   Steck Harald, 2013, P 7 ACM C REC SYST, P213
   Vigliensoni Gabriel, 2014, P 1 INT WORKSH SOC M, P27
   Ziegler C.-N., 2004, P 13 ACM INT C INF K, P406, DOI DOI 10.1145/1031171.1031252
NR 45
TC 13
Z9 18
U1 4
U2 4
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 2157-6904
EI 2157-6912
J9 ACM T INTEL SYST TEC
JI ACM Trans. Intell. Syst. Technol.
PD JAN
PY 2017
VL 8
IS 2
SI SI
AR 21
DI 10.1145/2926718
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EM7YQ
UT WOS:000395528700006
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Zhang, NY
   Deng, SM
   Chen, HJ
   Chen, X
   Chen, JY
   Li, XQ
   Zhang, YY
AF Zhang, Ningyu
   Deng, Shumin
   Chen, Huajun
   Chen, Xi
   Chen, Jiaoyan
   Li, Xiaoqian
   Zhang, Yiyi
TI Structured Knowledge Base as Prior Knowledge to Improve Urban Data
   Analysis
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE internet of things; smart city; energy management; traffic pattern
ID KERNEL DENSITY-ESTIMATION
AB Urban computing at present often relies on a large number of manually extracted features. This may require a considerable amount of feature engineering, and the procedure may miss certain hidden features and relationships among data items. In this paper, we propose a method to use structured prior knowledge in the form of knowledge graphs to improve the precision and interpretability in applications such as optimal store placement and traffic accident inference. Specifically, we integrate sub-graph feature extraction, sub-knowledge graph gated neural networks, and kernel-based knowledge graph convolutional neural networks as ways of incorporating large urban knowledge graphs into a fully end-to-end learning system. Experiments using data from several large cities showed that our method outperforms the baseline methods.
C1 [Zhang, Ningyu; Li, Xiaoqian; Zhang, Yiyi] Artificial Intelligence & Future Network Technol, Zhejiang Lab, Hangzhou 311121, Zhejiang, Peoples R China.
   [Deng, Shumin; Chen, Huajun; Chen, Xi] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310058, Zhejiang, Peoples R China.
   [Deng, Shumin; Chen, Huajun; Chen, Xi] Alibaba Zhejiang Univ Joint Inst Frontier Technol, AIBABA, Hangzhou 310058, Zhejiang, Peoples R China.
   [Chen, Jiaoyan] Univ Oxford, Dept Comp Sci, Oxford OX1 3QR, England.
RP Chen, HJ (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310058, Zhejiang, Peoples R China.
EM zhangningyu@zju.edu.cn; 231sm@zju.edu.cn; huajunsir@zju.edu.cn;
   xichen@zju.edu.cn; jiaoyanchen@zju.edu.cn; lixiaoqian15@nudt.edu.cn;
   yiyizhang@zju.edu.cn
RI Zhang, Ningyu/AAQ-7391-2021; Deng, Shumin/AAP-7003-2021
OI Zhang, Ningyu/0000-0002-1970-0678; 
FU NSFC [61473260/61673338]; Alibaba-Zhejiang University Joint Institute of
   Frontier Technologies
FX This work is funded by NSFC 61473260/61673338, and Supported by
   Alibaba-Zhejiang University Joint Institute of Frontier Technologies.
CR Abadi M., 2016, ARXIV160508695V2
   Gomez JA, 2018, ENVIRON SCI POLLUT R, V25, P977, DOI 10.1007/s11356-016-8339-9
   [Anonymous], ARXIV180401182
   [Anonymous], [No title captured]
   Bengio Y, 2009, FOUND TRENDS MACH LE, V2, P1, DOI 10.1561/2200000006
   Bennison D, 2000, INT J RETAIL DISTRIB, V28, P357, DOI DOI 10.1108/09590550010337391
   Bil M, 2013, ACCIDENT ANAL PREV, V55, P265, DOI 10.1016/j.aap.2013.03.003
   Chen T., 2016, KDD16 P 22 ACM, P785, DOI DOI 10.1145/2939672.2939785
   Chen T. Y, 2017, MINING LOCATION BASE, P68
   Chen XL, 2013, IEEE I CONF COMP VIS, P1409, DOI 10.1109/ICCV.2013.178
   Ching W, 2017, P 2017 WORLD TRANSP
   Chollet F, KERAS
   Gardner M., 2015, P 2015 C EMP METH NA, P1488, DOI DOI 10.18653/V1/D15-1173
   Graells-Garrido E, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16071098
   Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418
   Karamshuk D, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P793
   Krizhevsky A, 2017, COMMUN ACM, V60, P84, DOI 10.1145/3065386
   Kubis A, 2007, REV REG RES, V27, P43, DOI 10.1007/s10037-006-0010-3
   Lao N, 2015, LEARNING RELATIONAL, P666
   Lau BPL, 2018, IEEE INTERNET THINGS, V5, P473, DOI 10.1109/JIOT.2017.2748987
   Li J, 2009, PODS'09: PROCEEDINGS OF THE TWENTY-EIGHTH ACM SIGMOD-SIGACT-SIGART SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, P259, DOI 10.1145/1559795.1559835
   Li Y., 2015, P 4 INT C LEARN REPR
   Li Y., 2015, P 23 SIGSPATIAL INT, P85
   Liu H, 2004, BT TECHNOL J, V22, P211, DOI 10.1023/B:BTTJ.0000047600.45421.6d
   Malisiewicz T., 2009, ADV NEURAL INFORM PR, P1222
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mitchell T., 2014, P 2014 IEEE INT C BI
   Niepert M., 2017, P 33 ANN INT C MACH
   Pan J.Z., 2017, EXPLOITING LINKED DA
   Rogers D, 1997, NEW PERSPECTIVES, V5, P14
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Xiao XK, 2011, PROC INT CONF DATA, P804, DOI 10.1109/ICDE.2011.5767845
   Xie ZX, 2013, J TRANSP GEOGR, V31, P64, DOI 10.1016/j.jtrangeo.2013.05.009
   Zhang N, 2016, ELM MEETS URBAN COMP, P51
   Zhang N., 2014, P CHINESE SEMANTIC W
   Zhang N., 2016, COMPUT INTELL NEUROS, V2016
   Zhang NY, 2016, ISPRS INT J GEO-INF, V5, DOI 10.3390/ijgi5100180
   Zhang NY, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16091501
   Zhu Y, 2014, REASONING OBJECT AFF, P408
NR 39
TC 6
Z9 6
U1 7
U2 20
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 JUL
PY 2018
VL 7
IS 7
AR 264
DI 10.3390/ijgi7070264
PG 16
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA GU3CE
UT WOS:000445150900031
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Zeb, A
   Saif, S
   Chen, JD
   Ul Haq, A
   Gong, ZG
   Zhang, DF
AF Zeb, Adnan
   Saif, Summaya
   Chen, Junde
   Ul Haq, Anwar
   Gong, Zhiguo
   Zhang, Defu
TI Complex graph convolutional network for link prediction in knowledge
   graphs
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge graph; Link prediction; Graph convolutional network; Complex
   embeddings; Tensor decomposition
AB Knowledge graph (KG) embedding models map nodes and edges to fixed-length vectors and obtain the similarity of nodes as the output of a scoring function to predict missing links between nodes. KG embedding methods based on graph convolutional networks (GCNs) have recently gained significant attention due to their ability to add information of neighboring nodes into the nodes' embeddings. However, existing GCNs are primarily based on real-valued embeddings, which have high distortion, particularly when modeling graphs with varying geometric structures. In this paper, we propose complex graph convolutional network (ComplexGCN), a novel extension of the standard GCNs in complex space to combine the expressiveness of complex geometry with GCNs for improving the representation quality of KG components. The proposed ComplexGCN comprises a set of complex graph convolutional layers and a complex scoring function based on PARATUCK2 decomposition: the former includes information of neighboring nodes into the nodes' embeddings, while the latter leverages these embeddings to predict new links between nodes. The proposed model demonstrates enhanced performance compared to existing methods on the two recent standard link prediction datasets.
C1 [Zeb, Adnan; Chen, Junde; Ul Haq, Anwar; Zhang, Defu] Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China.
   [Saif, Summaya] COMSATS Univ, Dept Math, Islamabad 44000, Pakistan.
   [Ul Haq, Anwar] Univ Malakand, Dept Comp Sci & IT, Chakdara 18800, Pakistan.
   [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; sumaiaali723@gmail.com; chen2wo@126.com;
   anwar@uom.edu.pk; fstzgg@um.edu.mo; dfzhang@xmu.edu.cn
OI Haq, Anwar Ul/0000-0003-2319-5624; , xtu502/0000-0003-1748-4374
FU National Natural Science Foundation of China [61672439]
FX Acknowledgment This work is partially supported by the National Natural
   Science Foundation of China (Grant no. 61672439) .
CR Arjovsky M, 2016, PR MACH LEARN RES, V48
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   BENGIO Y, 1994, IEEE T NEURAL NETWOR, V5, P157, DOI 10.1109/72.279181
   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
   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]
   Chami I, 2019, ADV NEUR IN, V32
   Danihelka I, 2016, PR MACH LEARN RES, V48
   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
   Guberman N, 2016, ARXIV1602 09046
   Han X., 2018, P C EMP METH NAT LAN, P139
   Harshman R. A., 1978, 1 JOINT M PSYCH SOC
   Harshman RA, 1996, PSYCHOMETRIKA, V61, P133, DOI 10.1007/BF02296963
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   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
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Lei Sang, 2021, Expert Systems with Applications, V164, DOI 10.1016/j.eswa.2020.113992
   Lei ZF, 2021, EXPERT SYST APPL, V186, DOI 10.1016/j.eswa.2021.115708
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Liu Q, 2019, ADV NEUR IN, V32
   Malik KM, 2020, EXPERT SYST APPL, V145, DOI 10.1016/j.eswa.2019.113120
   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., 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, 2017, ADV NEUR IN, V30
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   Paszke A, 2017, AUTOMATIC DIFFERENTI
   Pittman R. J., 2017, EBAY BLOG
   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
   Sheikh N., 2021, ARXIV210207200
   Shen Y., 2018, ADV NEURAL INFORM PR, P6786
   Sun Z., 2018, P ICLR
   Sun Z, 2020, P 58 ANN M ASS COMP, P5516
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   Trabelsi C, 2018, INT C LEARN REPR VAN
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Velickovic P., 2018, P ICLR
   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
   Wisdom S., 2016, P ADV NEUR INF PROC, P4887
   Yang B., 2015, P INT C LEARN REPR I
   Yang F, 2017, ADV NEUR IN, V30
   Ye R, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4135
   Zhao XJ, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), P56, DOI 10.1109/SANER.2017.7884609
NR 55
TC 0
Z9 0
U1 10
U2 10
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 AUG 15
PY 2022
VL 200
AR 116796
DI 10.1016/j.eswa.2022.116796
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 1Q4OY
UT WOS:000802670000001
DA 2022-08-23
ER

PT J
AU Shu, C
   He, JJ
   Xue, GJ
   Xie, C
AF Shu, Chao
   He, Junjie
   Xue, Guangjie
   Xie, Cheng
TI Grain Knowledge Graph Representation Learning: A New Paradigm for
   Microstructure-Property Prediction
SO CRYSTALS
LA English
DT Article
DE materials genome; polycrystalline; graph neural network; graph
   representation learning; microstructure property
ID MECHANICAL-PROPERTIES; TEXTURE; SIZE
AB The mesoscopic structure significantly affects the properties of polycrystalline materials. Current artificial-based microstructure-performance analyses are expensive and require rich expert knowledge. Recently, some machine learning models have been used to predict the properties of polycrystalline materials. However, they cannot capture the complex interactive relationship between the grains in the microstructure, which is a crucial factor affecting the material's macroscopic properties. Here, we propose a grain knowledge graph representation learning method. First, based on the polycrystalline structure, an advanced digital representation of the knowledge graph is constructed, embedding ingenious knowledge while completely restoring the polycrystalline structure. Then, a heterogeneous grain graph attention model (HGGAT) is proposed to realize the effective high-order feature embedding of the microstructure and to mine the relationship between the structure and the material properties. Through benchmarking with other machine learning methods on magnesium alloy datasets, HGGAT consistently demonstrates superior accuracy on different performance labels. The experiment shows the rationality and validity of the grain knowledge graph representation and the feasibility of this work to predict the material's structural characteristics.
C1 [Shu, Chao; Xie, Cheng] Yunnan Univ, Sch Software, Kunming 650000, Yunnan, Peoples R China.
   [He, Junjie; Xue, Guangjie] Yunnan Univ, Sch Mat & Energy, Kunming 650000, Yunnan, Peoples R China.
RP Xie, C (通讯作者)，Yunnan Univ, Sch Software, Kunming 650000, Yunnan, Peoples R China.
EM shuchao_shanty@mail.ynu.edu.cn; 0210015@ynu.edu.cn;
   12019101472@mail.ynu.edu.cn; xiecheng@ynu.edu.cn
OI Xie, Cheng/0000-0002-4484-7428; shu, chao/0000-0002-4239-5990
FU National Natural Science Foundation of China; Major Project Fund of the
   Department of Science and Technology of Yunnan Province, China; National
   Natural Science Foundation of China regional project [62162064];
   National Natural Science Foundation of China Youth Project [62106216];
   Yunnan Province Rare and Precious Metal Materials Genetic Engineering
   [202102AB080019-1]; Yunnan Province Science and Technology Major Project
   [202002AB080001-5]
FX This research was funded by the National Natural Science Foundation of
   China and the Major Project Fund of the Department of Science and
   Technology of Yunnan Province, China. The specific fund name and number
   are (1) National Natural Science Foundation of China regional project,
   research on the discovery method of non-intelligent IoT devices driven
   by data and knowledge, 62162064; (2) National Natural Science Foundation
   of China Youth Project, Zero Learning Research for New Visual Feature
   Synthesis and Related Knowledge Generation, 62106216; (3) Yunnan
   Province Rare and Precious Metal Materials Genetic Engineering (Phase I
   2021), R & D and construction of special database for copper materials
   and engineering demonstration (CN), 202102AB080019-1; and (4) Yunnan
   Province Science and Technology Major Project, construction of a special
   database for liquid metal (CN), 202002AB080001-5.
CR Ahmad R, 2019, ACTA MATER, V172, P161, DOI 10.1016/j.actamat.2019.04.019
   Albawi S, 2017, I C ENG TECHNOL
   Alian AR, 2018, INT J MECH MATER DES, V14, P551, DOI 10.1007/s10999-017-9389-y
   Beausir B., 2017, ANAL TOOLS ELECT XRA
   Bialobrzeska B, 2017, METALS-BASEL, V7, DOI 10.3390/met7010026
   Cang RJ, 2018, COMP MATER SCI, V150, P212, DOI 10.1016/j.commatsci.2018.03.074
   Carneiro I, 2020, METALS-BASEL, V10, DOI 10.3390/met10081097
   Chai YF, 2018, MAT SCI ENG A-STRUCT, V730, P303, DOI 10.1016/j.msea.2018.06.011
   Chan H, 2020, NPJ COMPUT MATER, V6, DOI 10.1038/s41524-019-0267-z
   Dai MY, 2021, NPJ COMPUT MATER, V7, DOI 10.1038/s41524-021-00574-w
   Gurney K., 2018, INTRO NEURAL NETWORK
   Hamilton W.L., 2020, SYNTH LECT ARTIF INT, V14, P1, DOI 10.2200/S01045ED1V01Y202009AIM046
   Hart GLW, 2021, NAT REV MATER, V6, P730, DOI 10.1038/s41578-021-00340-w
   He JJ, 2019, J ALLOY COMPD, V797, P443, DOI 10.1016/j.jallcom.2019.05.087
   Herriott C, 2020, COMP MATER SCI, V175, DOI 10.1016/j.commatsci.2020.109599
   Jain A, 2013, APL MATER, V1, DOI 10.1063/1.4812323
   Jeh G., 2002, P 8 ACM SIGKDD INT C, P538, DOI DOI 10.1145/775047.775126
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Joost WJ, 2017, SCRIPTA MATER, V128, P107, DOI 10.1016/j.scriptamat.2016.07.035
   Kang LM, 2019, ADV ENG MATER, V21, DOI 10.1002/adem.201801359
   Khosravani A, 2017, ACTA MATER, V123, P55, DOI 10.1016/j.actamat.2016.10.033
   Knezevic M, 2018, ADV ENG MATER, V20, DOI 10.1002/adem.201700956
   Kondo R, 2017, ACTA MATER, V141, P29, DOI 10.1016/j.actamat.2017.09.004
   Latypov MI, 2018, MATER CHARACT, V145, P671, DOI 10.1016/j.matchar.2018.09.020
   Li XY, 2020, NAT REV MATER, V5, P706, DOI 10.1038/s41578-020-0212-2
   Liu H, 2002, DATA MIN KNOWL DISC, V6, P393, DOI 10.1023/A:1016304305535
   Liu R, 2016, SCI REP-UK, V6, DOI 10.1038/srep27433
   Oganov AR, 2019, NAT REV MATER, V4, P331, DOI 10.1038/s41578-019-0101-8
   Pokuri BSS, 2019, NPJ COMPUT MATER, V5, DOI 10.1038/s41524-019-0231-y
   Rekha S, 2017, IOP CONF SER-MAT SCI, V197, DOI 10.1088/1757-899X/197/1/012010
   Rong QY, 2019, COMPOS SCI TECHNOL, V184, DOI 10.1016/j.compscitech.2019.107861
   Sahoo SK, 2021, MAT SCI ENG A-STRUCT, V823, DOI 10.1016/j.msea.2021.141634
   Sainath TN, 2015, INT CONF ACOUST SPEE, P4580, DOI 10.1109/ICASSP.2015.7178838
   Sateesan A, 2021, NEURAL PROCESS LETT, V53, P2331, DOI 10.1007/s11063-021-10458-1
   Shao CW, 2017, ACTA MATER, V134, P128, DOI 10.1016/j.actamat.2017.05.004
   Sharma BB, 2020, CRIT REV SOLID STATE, V45, P134, DOI 10.1080/10408436.2019.1582003
   Si Zhang, 2018, Computational Data and Social Networks. 7th International Conference, CSoNet 2018. Proceedings: Lecture Notes in Computer Science (LNCS 11280), P79, DOI 10.1007/978-3-030-04648-4_7
   Smith BD, 2018, INT J PLASTICITY, V101, P1, DOI 10.1016/j.ijplas.2013.10.004
   Trang TTT, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-04981-4
   Velickovi P., 2017, ARXIV171010903
   WANG N, 1995, ACTA METALL MATER, V43, P519, DOI 10.1016/0956-7151(94)00253-E
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2022, DOI 10.1145/3308558.3313562
   Wang XF, 2019, MAT SCI ENG A-STRUCT, V753, P122, DOI 10.1016/j.msea.2019.03.034
   Wang ZF, 2019, J MANUF PROCESS, V38, P187, DOI 10.1016/j.jmapro.2019.01.007
   Wang ZL, 2019, MAT SCI ENG A-STRUCT, V744, P661, DOI 10.1016/j.msea.2018.12.049
   Wu ZH, 2021, IEEE T NEUR NET LEAR, V32, P4, DOI 10.1109/TNNLS.2020.2978386
   Yabansu YC, 2015, ACTA MATER, V94, P26, DOI 10.1016/j.actamat.2015.04.049
   Yabansu YC, 2014, ACTA MATER, V81, P151, DOI 10.1016/j.actamat.2014.08.022
   Yang C, 2020, MATER DESIGN, V189, DOI 10.1016/j.matdes.2020.108509
   Yu WR, 2019, VLDB J, V28, P401, DOI 10.1007/s00778-018-0536-3
   Yuan B, 2016, 2016 29TH IEEE INTERNATIONAL SYSTEM-ON-CHIP CONFERENCE (SOCC), P323, DOI 10.1109/SOCC.2016.7905501
   Zhang JG, 2002, PATTERN RECOGN, V35, P735, DOI 10.1016/S0031-3203(01)00074-7
   Zhang S., 2019, COMPUT SOC NETW, V6, P11, DOI [10.1186/s40649-019-0069-y, DOI 10.1186/S40649-019-0069-Y]
   Zhou J, 2018, ARTIF CELL NANOMED B, V46, pS1016, DOI 10.1080/21691401.2018.1442841
   Zhou QH, 2020, J PHYS CHEM LETT, V11, P3920, DOI 10.1021/acs.jpclett.0c00665
NR 56
TC 1
Z9 1
U1 9
U2 9
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4352
J9 CRYSTALS
JI Crystals
PD FEB
PY 2022
VL 12
IS 2
AR 280
DI 10.3390/cryst12020280
PG 15
WC Crystallography; Materials Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Crystallography; Materials Science
GA ZM3RX
UT WOS:000764280100001
OA gold
DA 2022-08-23
ER

PT J
AU Ennajari, H
   Bouguila, N
   Bentahar, J
AF Ennajari, Hafsa
   Bouguila, Nizar
   Bentahar, Jamal
TI Combining Knowledge Graph and Word Embeddings for Spherical Topic
   Modeling
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article; Early Access
DE Analytical models; Data models; Semantics; Probabilistic logic; Task
   analysis; Mathematical models; Integrated circuit modeling; Knowledge
   graph (KG) embedding; representation learning; topic modeling; von
   Mises-Fisher (vMF) distribution; word embedding
AB Probabilistic topic models are considered as an effective framework for text analysis that uncovers the main topics in an unlabeled set of documents. However, the inferred topics by traditional topic models are often unclear and not easy to interpret because they do not account for semantic structures in language. Recently, a number of topic modeling approaches tend to leverage domain knowledge to enhance the quality of the learned topics, but they still assume a multinomial or Gaussian document likelihood in the Euclidean space, which often results in information loss and poor performance. In this article, we propose a Bayesian embedded spherical topic model (ESTM) that combines both knowledge graph and word embeddings in a non-Euclidean curved space, the hypersphere, for better topic interpretability and discriminative text representations. Extensive experimental results show that our proposed model successfully uncovers interpretable topics and learns high-quality text representations useful for common natural language processing (NLP) tasks across multiple benchmark datasets.
C1 [Ennajari, Hafsa; Bouguila, Nizar; Bentahar, Jamal] Concordia Univ, Concordia Inst Informat Syst Engn CIISE, Montreal, PQ H3G 1M8, Canada.
RP Ennajari, H (通讯作者)，Concordia Univ, Concordia Inst Informat Syst Engn CIISE, Montreal, PQ H3G 1M8, Canada.
EM h_ennaja@encs.concordia.ca; nizar.bouguila@concordia.ca;
   bentahar@ciise.concordia.ca
OI Bentahar, Jamal/0000-0002-3136-4849
FU Natural Sciences and Engineering Research Council of Canada (NSERC)
FX This work was supported by the Natural Sciences and Engineering Research
   Council of Canada (NSERC).
CR Baldwin T., 2010, HUMAN LANGUAGE TECHN, P100
   Banerjee A, 2005, J MACH LEARN RES, V6, P1345
   Blei D.M, 2003, P 26 ANN INT ACM SIG, P127, DOI [10.1145/860435.860460, DOI 10.1145/860435.860460]
   Blei DM, 2017, J AM STAT ASSOC, V112, P859, DOI 10.1080/01621459.2017.1285773
   Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826
   Blei David M, 2006, P 23 INT C MACH LEAR, P113, DOI DOI 10.1145/1143844.1143859
   Blei DM, 2004, ADV NEUR IN, V16, P17
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   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
   Boyd-Graber J., 2009, P 25 C UNC ART INT M, P75
   Chen JY, 2021, IEEE T NEUR NET LEAR, V32, P2772, DOI 10.1109/TNNLS.2020.3009247
   Chen ZY, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P209, DOI 10.1145/2505515.2505519
   Das R, 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, P795
   Dhillon I.S., 2003, TR0306 U TEX AUST DE
   Dieng AB, 2020, T ASSOC COMPUT LING, V8, P439, DOI 10.1162/tacl_a_00325
   Edouard G., 2016, ARXIV161203651
   FISHER R, 1953, PROC R SOC LON SER-A, V217, P295, DOI 10.1098/rspa.1953.0064
   Guo S, 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, P84
   Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50, DOI 10.1145/312624.312649
   Johnson, 2015, T ASS COMPUTATIONAL, V3, P299, DOI DOI 10.1162/tacl_a_00140
   Jordan MI, 1999, MACH LEARN, V37, P183, DOI 10.1023/A:1007665907178
   JUPP PE, 1989, INT STAT REV, V57, P261, DOI 10.2307/1403799
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Levy O., 2014, P 18 C COMP NAT LANG, P171, DOI DOI 10.3115/V1/W14-1618
   Li D, 2019, P 2019 C N AM ASS CO, V1, P940
   Mardia K. V., 2009, DIRECTIONAL STAT, V494
   Meng Y, 2019, ADV NEUR IN, V32
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mimno D., 2011, P 2011 C EMP METH NA, P262, DOI DOI 10.5555/2145432.2145462
   Moon TK, 1996, IEEE SIGNAL PROC MAG, V13, P47, DOI 10.1109/79.543975
   Newman D., 2009, P ADCS, P11
   Newman D., 2006, P 12 ACM SIGKDD INT, P680, DOI DOI 10.1145/1150402.1150487
   Pang B., 2004, ANN M ASS COMP LING, V42, P271, DOI DOI 10.3115/1218955.1218990
   Pang B., 2005, P 43 ANN M ASS COMP, P115, DOI DOI 10.3115/1219840.1219855
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Reisinger Joseph, 2010, P 27 INT C MACH LEAR, P903, DOI 10.1007/s10955-009-9892-0
   Shi Y, 2014, J THORAC ONCOL, V9, P154, DOI 10.1097/JTO.0000000000000033
   SraS, 2018, APPL DIRECTIONAL STA, P225
   Stevens K., 2012, P 2012 JOINT C EMP M, V2012, P952, DOI DOI 10.5555/2390948.2391052
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Trouillon T, 2016, PR MACH LEARN RES, V48
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Varadarajan J, 2013, INT J COMPUT VISION, V103, P100, DOI 10.1007/s11263-012-0596-6
   Vulic I, 2015, INFORM PROCESS MANAG, V51, P111, DOI 10.1016/j.ipm.2014.08.003
   Wang C., 2008, P 24 C UNC ART INT, P579
   Wang HQ, 2018, IEEE T NEUR NET LEAR, V29, P718, DOI 10.1109/TNNLS.2016.2626379
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang XY, 2019, LECT NOTES COMPUT SC, V11446, P709, DOI 10.1007/978-3-030-18576-3_42
   Yao L, 2017, AAAI CONF ARTIF INTE, P3119
   Zhang P, 2020, IEEE T KNOWL DATA EN, V32, P2322, DOI 10.1109/TKDE.2019.2922179
   Zhang X, 2015, ADV NEUR IN, V28
   Zhong S, 2005, KNOWL INF SYST, V8, P374, DOI 10.1007/s10115-004-0194-1
NR 56
TC 1
Z9 1
U1 5
U2 7
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.2021.3112045
EA SEP 2021
PG 15
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 XT3FS
UT WOS:000733478400001
PM 34559665
DA 2022-08-23
ER

PT J
AU Melluso, N
   Grangel-Gonzalez, I
   Fantoni, G
AF Melluso, Nicola
   Grangel-Gonzalez, Irlan
   Fantoni, Gualtiero
TI Enhancing Industry 4.0 standards interoperability via knowledge graphs
   with natural language processing
SO COMPUTERS IN INDUSTRY
LA English
DT Article
DE Standards; Interoperability; Natural language processing; Knowledge
   graphs; Industry 4; 0
AB Industry 4.0 (I4.0) has brought several challenges related to the need to acquire and integrate large amounts of data from multiple sources in order to integrate these elements into an automated manufacturing system. Establishing interoperability is crucial to meet these challenges, and standards development and adoptions play a key role in achieving this. Therefore, academics and industrial stakeholders must join their forces in order to develop methods to enhance interoperability and to mitigate possible conflicts between standards. The aim of this paper is to propose an approach that enhances interoperability between standards through the combined use of Natural Language Processing (NLP) and Knowledge Graphs (KG). In particular, the proposed method is based on the measurement of semantic similarity among the textual content of standards documents belonging to different standardization frameworks. The present study contributes to the research and practice in three ways. First, it fills research gaps concerning the synergy of NLP, KGs and I4.0. Second, it provides an automatic method that improves the process of creating, curating and enriching a KG. Third, it provides qualitative and quantitative evidence of Semantic Interoperability Conflicts (SICs). The experimental results of the application of the proposed method to the I4.0 Standards Knowledge Graph (I40KG) show that different standards are still struggling to use a shared language and that there exists a strong different in the view of I4.0 proposed by the two main standardization frameworks (RAMI and IIRA). By automatically enriching the I40KG with a solid experimental approach, we are paving the way for actionable knowledge which has been extracted from the PDFs and made available in the I40KG.(c) 2022 Elsevier B.V. All rights reserved.
C1 [Melluso, Nicola] Univ Pisa, Dept Energy Syst Terr & Construct Engn, Pisa, Italy.
   [Grangel-Gonzalez, Irlan] Bosch Corp Res, Renningen, Germany.
   [Fantoni, Gualtiero] Univ Pisa, Dept Civil & Ind Engn, Pisa, Italy.
   [Melluso, Nicola; Fantoni, Gualtiero] Univ Pisa, Business Engn Data Sci Lab B4DS, I-56120 Pisa, Italy.
RP Melluso, N (通讯作者)，Univ Pisa, Dept Energy Syst Terr & Construct Engn, Pisa, Italy.
EM nicola.melluso@phd.unipi.it
OI Melluso, Nicola/0000-0003-4273-1509
CR [Anonymous], ISO/IEC Directives
   Bader Sebastian R., 2020, Semantic Web 17th International Conference, ESWC 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12123), P465, DOI 10.1007/978-3-030-49461-2_27
   Bader SR, 2019, FUTURE INTERNET, V11, DOI 10.3390/fi11070151
   Bastian M., 2009, P 3 INT AAAI C WEB S, V3, P361
   Blind K, 2016, EU-SPRI FORUM SCI TE, P423
   Blind K, 2009, J TECHNOL TRANSFER, V34, P320, DOI 10.1007/s10961-008-9089-8
   Burns T., 2019, PROCEDIA MANUF, V38, P646, DOI DOI 10.1016/J.PROMFG.2020.01.083
   Casillo M, 2020, PR IEEE INT CONF TEA, P371, DOI 10.1109/TALE48869.2020.9368339
   Chen D, 2008, COMPUT IND, V59, P647, DOI 10.1016/j.compind.2007.12.016
   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, 2018, COMPUT IND, V100, P244, DOI 10.1016/j.compind.2018.04.006
   Dessi D, 2021, FUTURE GENER COMP SY, V116, P253, DOI 10.1016/j.future.2020.10.026
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Fantoni G, 2021, COMPUT IND, V124, DOI 10.1016/j.compind.2020.103357
   Fareri S, 2020, COMPUT IND, V118, DOI 10.1016/j.compind.2020.103222
   Fareri S., 2021, EXPERT SYST APPL, V184
   Giordano V., 2021, IEEE T ENG MANAGE, DOI [10.1109/TEM.2021.3078231, DOI 10.1109/TEM.2021.3078231]
   Grangel-Gonzalez I, 2020, IEEE INT C EMERG, P93, DOI 10.1109/ETFA46521.2020.9212156
   Grangel-Gonzalez I, 2021, WEB CONFERENCE 2021: COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2021), P16, DOI 10.1145/3442442.3453542
   Grangel-Gonzalez I, 2017, IEEE INT C EMERG
   Grangel-Gonzlez I., 2019, THESIS U LANDESBIBLI
   Ho JY, 2017, TECHNOL FORECAST SOC, V115, P301, DOI 10.1016/j.techfore.2016.04.014
   Honnibal M, 2017, SPACY 2 NATURAL LANG, P1
   Hoppe T., 2017, P IEEE INT SYST ENG, P1
   Jacomy M, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0098679
   Jarvenpaa E, 2019, J INTELL MANUF, V30, P959, DOI 10.1007/s10845-018-1427-6
   Jirkovsky V, 2017, IEEE T IND INFORM, V13, P660, DOI 10.1109/TII.2016.2596101
   Koch C, 2021, IEEE T ENG MANAGE, V68, P59, DOI 10.1109/TEM.2020.2979697
   Lampathaki F, 2009, COMPUT STAND INTER, V31, P1045, DOI 10.1016/j.csi.2008.12.006
   Li XY, 2021, COMPUT IND, V129, DOI 10.1016/j.compind.2021.103449
   Lin S.-W., 2017, REFERENCE ARCHITECTU
   Melluso N, PROC SOC, V1, P1173
   Mihalcea R., 2006, 775780 AAAI
   Mikolov T., 2013, EFFICIENT ESTIMATION
   Nathan P., 2020, Derwen, DOI 10.5281/zenodo.4717287
   Orgun B, 2008, EXPERT SYST, V25, P179, DOI 10.1111/j.1468-0394.2008.00461.x
   Perez-Lara M, 2020, WIREL NETW, V26, P4767, DOI 10.1007/s11276-018-1873-2
   Shinyama Y., 2015, PDFMINER PYTHON PDF
   Silva RL, 2022, J IND PROD ENG, V39, P167, DOI 10.1080/21681015.2021.1965665
   Song K, 2017, TECHNOVATION, V60-61, P1, DOI 10.1016/j.technovation.2017.03.001
   Tassey G, 2000, RES POLICY, V29, P587, DOI 10.1016/S0048-7333(99)00091-8
   Thoben K. D., 2017, INT J AUTO TECH-JPN, V11, P4, DOI [DOI 10.20965/IJAT.2017.P0004, 10.20965/ijat.2017.p0004]
   Wang SY, 2016, COMPUT NETW, V101, P158, DOI 10.1016/j.comnet.2015.12.017
   Wang X., 2021, FUNDAM RES, V1, P607
   Wang Z., 2014, P 2014 C EMP METH NA, P1591, DOI DOI 10.3115/V1/D14-1167
   Wiegmann PM, 2022, RES POLICY, V51, DOI 10.1016/j.respol.2021.104427
   Yahya M, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11115110
NR 48
TC 0
Z9 0
U1 10
U2 10
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-3615
EI 1872-6194
J9 COMPUT IND
JI Comput. Ind.
PD SEP
PY 2022
VL 140
AR 103676
DI 10.1016/j.compind.2022.103676
PG 13
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 1J6LE
UT WOS:000798028200001
DA 2022-08-23
ER

PT J
AU Gu, TL
   Liang, HH
   Bin, CZ
   Chang, L
AF Gu, Tianlong
   Liang, Haohong
   Bin, Chenzhong
   Chang, Liang
TI Combining user-end and item-end knowledge graph learning for
   personalized recommendation
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Personalized recommendation; property knowledge graph; graph convolution
   network
AB How to accurately model user preferences based on historical user behaviour and auxiliary information is of great importance in personalized recommendation tasks. Among all types of auxiliary information, knowledge graphs (KGs) are an emerging type of auxiliary information with nodes and edges that contain rich structural information and semantic information. Many studies prove that incorporating KG into personalized recommendation tasks can effectively improve the performance, rationality and interpretability of recommendations. However, existing methods either explore the independent meta-paths for user-item pairs in KGs or use a graph convolution network on all KGs to obtain embeddings for users and items separately. Although both types of methods have respective effects, the former cannot fully capture the structural information of user-item pairs in KGs, while the latter ignores the mutual effect between the target user and item during the embedding learning process. To alleviate the shortcomings of these methods, we design a graph convolution-based recommendation model called Combining User-end and Item-end Knowledge Graph Learning (CUIKG), which aims to capture the relevance between users' personalized preferences and items by jointly mining the associated attribute information in their respective KG. Specifically, we describe user embedding from a user KG and then introduce user embedding, which contains the user profile into the item KG, to describe item embedding with the method of Graph Convolution Network. Finally, we predict user preference probability for a given item via multilayer perception. CUIKG describes the connection between user-end KG and item-end KG, and mines the structural and semantic information present in KG. Experimental results with two real-world datasets demonstrate the superiority of the proposed method over existing methods.
C1 [Gu, Tianlong; Liang, Haohong; Bin, Chenzhong; Chang, Liang] Guilin Univ Elect Technol, Sch Comp Sci & Informat Secur, Guilin, Peoples R China.
   [Gu, Tianlong] Jinan Univ, Coll Informat Sci & Technol, Coll Cyber Secur, Guangzhou, Peoples R China.
RP Bin, CZ (通讯作者)，Guilin Univ Elect Technol, Guilin, Peoples R China.
EM binchenzhong@163.com
FU National Natural Science Foundation of China
   [62066010,61862016,61966009]; Natural Science Foundation of Guangxi
   Province [2020GXNS-FAA159055]; Guangxi Innovation-Driven Development
   Grand Project [AA17202024]
FX This work was partially supported by the National Natural Science
   Foundation of China (Nos. 62066010,61862016,61966009), the Natural
   Science Foundation of Guangxi Province (No. 2020GXNS-FAA159055), and the
   Guangxi Innovation-Driven Development Grand Project (No. AA17202024).
CR Bengio Y, 2017, ABS171010903 CORR
   Bobadilla J, 2013, KNOWL-BASED SYST, V46, P109, DOI 10.1016/j.knosys.2013.03.012
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bresson X., 201736973707 NIPS
   Bruna J., 2014, ICLR
   Chami I, 2019, ADV NEUR IN, V32
   Chen X, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P108, DOI 10.1145/3159652.3159668
   Defferrard M., 2016, 30 INT C NEURAL INFO, P3844
   Gao HJ, 2015, AAAI CONF ARTIF INTE, P1721
   Ge Y., 2019, ABS190601829 CORR
   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 S, 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, P84
   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, 2018, ACM/SIGIR PROCEEDINGS 2018, P355, DOI 10.1145/3209978.3209981
   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
   Huang J, 2018, ACM/SIGIR PROCEEDINGS 2018, P505, DOI 10.1145/3209978.3210017
   Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944
   Lazaridou Angeliki, 2015, P 2015 C N AM CHAPT, P153, DOI DOI 10.3115/V1/N15-1016
   Lian DF, 2013, PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'13 COMPANION), P231
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lu J, 2015, DECIS SUPPORT SYST, V74, P12, DOI 10.1016/j.dss.2015.03.008
   Le Q, 2014, PR MACH LEARN RES, V32, P1188
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Rendle S, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P635
   Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
   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
   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
   Ye ZL, 2019, LECT NOTES ARTIF INT, V11838, P265, DOI 10.1007/978-3-030-32233-5_21
   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 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 JN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4264
   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 J, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2347, DOI 10.1145/3292500.3330686
NR 40
TC 0
Z9 1
U1 5
U2 19
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 40
IS 5
BP 9213
EP 9225
DI 10.3233/JIFS-201635
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RT4UM
UT WOS:000644456300039
DA 2022-08-23
ER

PT J
AU Mailis, T
   Kotidis, Y
   Christoforidis, S
   Kharlamov, E
   Ioannidis, Y
AF Mailis, Theofilos
   Kotidis, Yannis
   Christoforidis, Stamatis
   Kharlamov, Evgeny
   Ioannidis, Yannis
TI View Selection over Knowledge Graphs in Triple Stores
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
ID RDF; WEB; EFFICIENT; MATERIALIZE
AB Knowledge Graphs (KGs) are collections of interconnected and annotated entities that have become powerful assets for data integration, search enhancement, and other industrial applications. Knowledge Graphs such as DBPEDIA may contain billion of triple relations and are intensively queried with millions of queries per day. A prominent approach to enhance query answering on Knowledge Graph databases is View Materialization, ie., the materialization of an appropriate set of computations that will improve query performance.
   We study the problem of view materialization and propose a view selection methodology for processing query workloads with more than a million queries. Our approach heavily relies on subgraph pattern mining techniques that allow to create efficient summarizations of massive query workloads while also identifying the candidate views for materialization. In the core of our work is the correspondence between the view selection problem to that of Maximizing a Nondecreasing Submodular Set Function Subject to a Knapsack Constraint. The latter leads to a tractable view-selection process for native triple stores that allows a (1 - e(-1))-approximation of the optimal selection of views. Our experimental evaluation shows that all the steps of the view-selection process are completed in a few minutes, while the corresponding rewritings accelerate 67.68% of the queries in the DBPEDIA query workload. Those queries are executed in 2.19% of their initial time on average.
C1 [Mailis, Theofilos; Ioannidis, Yannis] Natl & Kapodistrian Univ Athens, Athena Res Ctr, Athens, Greece.
   [Kotidis, Yannis] Athens Univ Econ & Business, Athens, Greece.
   [Christoforidis, Stamatis] Natl & Kapodistrian Univ Athens, Athens, Greece.
   [Kharlamov, Evgeny] Bosch Ctr AI, Berlin, Germany.
   [Kharlamov, Evgeny] Univ Oslo, Oslo, Norway.
RP Mailis, T (通讯作者)，Natl & Kapodistrian Univ Athens, Athena Res Ctr, Athens, Greece.
EM tmailis@di.uoa.gr; kotidis@aueb.gr; stachris@di.uoa.gr;
   evgeny.kharlamov@de.bosch.com; yannis@di.uoa.gr
FU European Union (European Social Fund-ESF) through the Operational
   Programme "Human Resources Development, Education and Lifelong Learning"
   [MIS-5033021]; European Union's Horizon 2020 Framework Programme for
   Research and Innovation [945539, 777413]
FX This research is co-financed by Greece and the European Union (European
   Social Fund-ESF) through the Operational Programme "Human Resources
   Development, Education and Lifelong Learning" in the context of the
   project "Reinforcement of Postdoctoral Researchers - 2nd Cycle"
   (MIS-5033021), implemented by the State Scholarships Foundation (IKY).;
   This research has received funding from the European Union's Horizon
   2020 Framework Programme for Research and Innovation under the Specific
   Grant Agreements No. 945539 (Human Brain Project SGA3) and No. 777413
   (Delivering Agile Research Excellence on European e-Infrastructures).
CR Abadi DJ, 2009, VLDB J, V18, P385, DOI 10.1007/s00778-008-0125-y
   Agarawal S., 2000, P 26 INT C VLDB, P191
   Agrawal Sanjay, 2000, 26 INT C VER LARG DA
   Apache Jena, 2007, SEM WEB FAM JAV
   Atre M, 2010, P 19 INT C WORLD WID, P41, DOI [10.1145/1772690.1772696, DOI 10.1145/1772690.1772696]
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bayir MA, 2007, IEEE T SYST MAN CY C, V37, P147, DOI 10.1109/TSMCC.2006.876060
   Bleco D, 2019, INFORM SYST, V81, P49, DOI 10.1016/j.is.2018.11.007
   Bonifati A, 2017, PROC VLDB ENDOW, V11, P149, DOI 10.14778/3149193.3149196
   Broekstra J, 2002, LECT NOTES COMPUT SC, V2342, P54
   Buron M, 2020, PROC VLDB ENDOW, V13, P2933, DOI 10.14778/3415478.3415512
   Chandra Ashok K., 1977, STOC, P77
   Chaves Leonardo Weiss F, 2009, ADV DATABASE, P1088
   Chirkova R, 2002, VLDB J, V11, P216, DOI 10.1007/s00778-002-0070-0
   Dritsou V, 2011, LECT NOTES COMPUT SC, V6644, P77, DOI 10.1007/978-3-642-21064-8_6
   Farber M, 2018, SEMANT WEB, V9
   Fensel D., 2020, KNOWLEDGE GRAPHS, P95, DOI DOI 10.1007/978-3-030-37439-6_4
   Ferrucci D, 2010, AI MAG, V31, P59, DOI 10.1609/aimag.v31i3.2303
   Fournier-Viger P, 2020, WIRES DATA MIN KNOWL, V10, DOI 10.1002/widm.1372
   Fournier-Viger P, 2019, LECT NOTES COMPUT SC, V11932, P209, DOI 10.1007/978-3-030-37188-3_13
   Fournier-Viger Philippe, 2019, HUE SPA
   Goasdoue F, 2011, PROC VLDB ENDOW, V5, P97, DOI 10.14778/2078324.2078326
   Gupta H, 1997, LECT NOTES COMPUT SC, V1186, P98
   Gurajada S, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P289, DOI 10.1145/2588555.2610511
   Harinarayan V., 1996, SIGMOD Record, V25, P205
   Huang JW, 2011, PROC VLDB ENDOW, V4, P1123
   Jindal A, 2018, INT CONF MANAGE DATA, P191, DOI 10.1145/3183713.3190656
   Jindal A, 2018, PROC VLDB ENDOW, V11, P800, DOI 10.14778/3192965.3192971
   Kathuria T, 2017, PODS'17: PROCEEDINGS OF THE 36TH ACM SIGMOD-SIGACT-SIGAI SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, P53, DOI 10.1145/3034786.3034792
   Kharlamov E, 2017, J WEB SEMANT, V44, P54, DOI 10.1016/j.websem.2017.02.001
   Kharlamov E, 2016, LECT NOTES COMPUT SC, V9982, P344, DOI 10.1007/978-3-319-46547-0_31
   Kharlamov Evgeny, 2019, J WEB SEMANT
   Kotidis Y, 2001, ACM T DATABASE SYST, V26, P388, DOI 10.1145/503099.503100
   Kougioumtzi Eleni, 2021, DEBS '21: Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems, P188, DOI 10.1145/3465480.3467841
   Levy A. Y., 1995, Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1995, P95, DOI 10.1145/212433.220198
   Mailis T, 2019, INT CONF MANAGE DATA, P1499, DOI 10.1145/3299869.3319864
   Malyshev S, 2018, LECT NOTES COMPUT SC, V11137, P376, DOI 10.1007/978-3-030-00668-6_23
   Mami I, 2012, SIGMOD REC, V41, P20, DOI 10.1145/2206869.2206874
   McBride Brian, 2001, ISWC, P23
   Meimaris M, 2017, PROC INT CONF DATA, P497, DOI 10.1109/ICDE.2017.106
   Messanakis Kostas, 2021, LECT NOTES COMPUTER, V12925, P216
   Mistry H, 2001, SIGMOD REC, V30, P307
   Morfonios K, 2007, ACM COMPUT SURV, V39, DOI 10.1145/1287620.1287623
   Nenov Y, 2015, LECT NOTES COMPUT SC, V9367, P3, DOI 10.1007/978-3-319-25010-6_1
   Neumann T, 2010, PROC VLDB ENDOW, V3, P256, DOI 10.14778/1920841.1920877
   Papailiou N, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1777, DOI 10.1145/2723372.2723714
   Picalausa Francois, 2011, P INT WORKSH SEM WEB, P7
   Qian R., 2013, BING SEARCH BLOG MAR
   Roy P, 2000, SIGMOD REC, V29, P249
   SELLIS TK, 1988, ACM T DATABASE SYST, V13, P23, DOI 10.1145/42201.42203
   Shi LX, 2017, BIOMED RES INT, V2017, DOI 10.1155/2017/2858423
   Sidirourgos L, 2008, PROC VLDB ENDOW, V1, P1553
   Singhal A., 2012, OFFICIAL GOOGLE BLOG
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Svingos C, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), P410, DOI 10.1109/BigData.2016.7840631
   Sviridenko M, 2004, OPER RES LETT, V32, P41, DOI 10.1016/S0167-6377(03)00062-2
   Theodoratos D, 1997, PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL CONFERENCE ON VERY LARGE DATABASES, P126
   Ting D, 2019, INT CONF MANAGE DATA, P69, DOI 10.1145/3299869.3319897
   Wang J., 2017, EDBT, V2017, P13
   Wang J., 2016, EDBT, P41, DOI 10.5441/002/edbt.2016.07
   Weiss C, 2008, PROC VLDB ENDOW, V1, P1008, DOI 10.14778/1453856.1453965
   Yan XF, 2002, 2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P721, DOI 10.1109/ICDM.2002.1184038
   Zhang XF, 2013, PROC INT CONF DATA, P565, DOI 10.1109/ICDE.2013.6544856
   Zhou Jingren, 2007, SIGMOD, P533
NR 64
TC 0
Z9 0
U1 0
U2 0
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 SEP
PY 2021
VL 14
IS 13
BP 3281
EP 3294
DI 10.14778/3484224.3484227
PG 14
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YH1OU
UT WOS:000742944600004
DA 2022-08-23
ER

PT J
AU Rossi, A
   Barbosa, D
   Firmani, D
   Matinata, A
   Merialdo, P
AF Rossi, Andrea
   Barbosa, Denilson
   Firmani, Donatella
   Matinata, Antonio
   Merialdo, Paolo
TI Knowledge Graph Embedding for Link Prediction: A Comparative Analysis
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE Knowledge graphs; link prediction; knowledge graph embeddings;
   comparative analysis
AB Knowledge Graphs (KGs) have found many applications in industrial and in academic settings, which in turn, have motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even the largest KGs suffer from incompleteness; Link Prediction (LP) techniques address this issue by identifying missing facts among entities already in the KG. Among the recent LP techniques, those based on KG embeddings have achieved very promising performance in some benchmarks. Despite the fast-growing literature on the subject, insufficient attention has been paid to the effect of the design choices in those methods. Moreover, the standard practice in this area is to report accuracy by aggregating over a large number of test facts in which some entities are vastly more represented than others; this allows LP methods to exhibit good results by just attending to structural properties that include such entities, while ignoring the remaining majority of the KG. This analysis provides a comprehensive comparison of embedding-based LP methods, extending the dimensions of analysis beyond what is commonly available in the literature. We experimentally compare the effectiveness and efficiency of 18 state-of-the-art methods, consider a rule-based baseline, and report detailed analysis over the most popular benchmarks in the literature.
C1 [Rossi, Andrea; Firmani, Donatella; Matinata, Antonio; Merialdo, Paolo] Roma Tre Univ, Dept Engn, Via Vasca Navale 79, I-00146 Rome, Italy.
   [Barbosa, Denilson] Univ Alberta, Dept Comp Sci, 2-21 Athabasca Hall, Edmonton, AB T6G 2E8, Canada.
RP Rossi, A (通讯作者)，Roma Tre Univ, Dept Engn, Via Vasca Navale 79, I-00146 Rome, Italy.
EM andrea.rossi3@uniroma3.it; denilson@ualberta.ca;
   donatella.firmani@uniroma3.it; ant.matinata@stud.uniroma3.it;
   paolo.merialdo@uniroma3.it
RI Barbosa, Denilson/AAM-8499-2020
CR Agrawal Nilesh, 2020, P 2020 AAAI C ART IN
   Ahmadi Naser, 2020, J DATA INFORM QUALIT, V12
   An B, 2018, ACCURATE TEXT ENHANC
   [Anonymous], 2015, P 2015 INT C LEARN R
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bahare Fatemi, FASTER SIMPLE IMPLEM
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes Antoine, 2013, P 26 INT C NEUR INF
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Cohen William W., 2011, P 2011 C EMP METH NA
   Dean J., 2013, P 2013 INT C LEARN R
   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
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Efron B., 1993, INTRO BOOTSTRAP
   Facebook Research, KNOWLEDGE BASE COMPL
   Galarraga L, 2015, VLDB J, V24, P707, DOI 10.1007/s00778-015-0394-1
   Guo S, 2018, AAAI CONF ARTIF INTE, P4816
   Hitchcock F. L., 1927, J MATH PHYS, V6, P164, DOI 10.1002/sapm192761164
   HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554
   Hovy E, 2013, ARTIF INTELL, V194, P2, DOI 10.1016/j.artint.2012.10.002
   Hu Wei., 2019, P 2019 INT C MACH LE
   Johnson Mark, 2016, P 2016 C N AM CHAPT
   Kadlec Rudolph, 2017, P 2 WORKSH REPR LEAR
   Kolda TG, 2009, SIAM REV, V51, P455, DOI 10.1137/07070111X
   Kostadinov Simeon, 2018, RECURRENT NEURAL NET
   Kriegel Hans-Peter, 2011, P 2011 INT C MACH LE
   Lao N, 2010, MACH LEARN, V81, P53, DOI 10.1007/s10994-010-5205-8
   Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791
   Li Chengkai, 2018, P 27 ACM INT C INF
   Lin Y., 2015, P C EMP METH NAT LAN
   Liu Hanxiao, 2017, P 2017 INT C MACH LE
   Meilicke C., 2020, ARXIV PREPRINT ARXIV
   Meilicke Christian, 2018, P 17 INT SEM WEB C
   Munoz Emir., 2019, P 2019 WORKSH DEEP L
   Nguyen D.Q., 2017, ARXIV170308098, V1703
   Nguyen D.Q., 2019, 17 ANN C N AM CHAPTE, P2180
   Nguyen Dai Quoc, 2018, P 2018 C N AM CHAPT
   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
   Poole David., 2018, P 2018 INT C NEUR IN
   Poole David, 2019, ABS190600137
   Rossi A., 2020, EDBT ICDT WORKSH
   Sabour Sara., 2017, P 2017 INT C NEUR IN
   Sack Harald, 2019, P 2019 WORKSH DEEP L
   Schockaert Steven., 2018, P 2018 INT C PRINC K
   Schutze H., 2008, INTRO INFORM RETRIEV, V39
   Shimbo Masashi., 2017, P 2017 ANN M ASS COM
   Singh Sameer., 2018, P 2018 C EMP METH NA
   Singh Sameer, 2019, P 2019 C N AM CHAPT
   Stuckenschmidt Heiner., 2019, P 28 INT JOINT C ART
   Suchanek F.M., 2015, P 7 BIENN C INN DAT
   Suchanek Fabian M., 2007, P 2007 INT C WORLD W
   Tabacof Pedro, 2019, **DATA OBJECT**, DOI [10.5281/zenodo.2595043, DOI 10.5281/ZENODO.2595043]
   Takasu Atsuhiro., 2019, P EDBT ICDT 2019 JOI
   Talukdar Partha P, 2018, P 56 ANN M ASS COMP
   Tang Jian., 2019, P INT C LEARN REPR
   Teflioudi Christina, 2013, P 22 INT C WORLD WID
   Toutanova Kristina, 2015, P 3 WORKSH CONT VECT
   Trivedi R, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P252, DOI 10.18653/v1/P18-1024
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Trouillon Theo, 2017, ABS170701475
   University Mannheim Data and Web Science Group, ANYBURL
   Usunier Nicolas., 2018, P 2018 INT C MACH LE
   Huynh VP, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P689, DOI 10.1145/3357384.3358036
   Voorhees Ellen M, 1999, P TEXT RETRIEVAL C
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang Bin, 2019, P 2019 C N AM CHAPT
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Yanjie, 2019, P 4 WORKSH REPR LEAR
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhigang., 2016, P 2016 INT JOINT C A
   West R, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P515, DOI 10.1145/2566486.2568032
   Zhang Richong, 2016, P 2016 INT JOINT C A
   Zhang Rui., 2019, P 2019 AAAI C ART IN
   Zhang W, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P96, DOI 10.1145/3289600.3291014
   Zhang Yongdong, 2020, P 2020 AAAI C ART IN
   Zhang Yongqi, 2020, P 2020 IEEE 36 INT C
NR 79
TC 48
Z9 49
U1 16
U2 27
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 APR
PY 2021
VL 15
IS 2
AR 14
DI 10.1145/3424672
PG 49
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RL5ZA
UT WOS:000639049700001
OA Green Submitted
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Choi, B
   Jang, D
   Ko, Y
AF Choi, Bonggeun
   Jang, Daesik
   Ko, Youngjoong
TI MEM-KGC: Masked Entity Model for Knowledge Graph Completion With
   Pre-Trained Language Model
SO IEEE ACCESS
LA English
DT Article
DE Task analysis; Predictive models; Training; Bit error rate; Semantics;
   Micromechanical devices; Licenses; Knowledge graph completion; link
   prediction; masked language model; pre-trained language model
AB The knowledge graph completion (KGC) task aims to predict missing links in knowledge graphs. Recently, several KGC models based on translational distance or semantic matching methods have been proposed and have achieved meaningful results. However, existing models have a significant shortcoming-they cannot train entity embedding when an entity does not appear in the training phase. As a result, such models use randomly initialized embeddings for entities that are unseen in the training phase and cause a critical decrease in performance during the test phase. To solve this problem, we propose a new approach that performs KGC task by utilizing the masked language model (MLM) that is used for a pre-trained language model. Given a triple (head entity, relation, tail entity), we mask the tail entity and consider the head entity and the relation as a context for the tail entity. The model then predicts the masked entity from among all entities. Then, the task is conducted by the same process as an MLM, which predicts a masked token with a given context of tokens. Our experimental results show that the proposed model achieves significantly improved performances when unseen entities appear during the test phase and achieves state-of-the-art performance on the WN18RR dataset.
C1 [Choi, Bonggeun] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon 16419, Gyeonggi Do, South Korea.
   [Jang, Daesik; Ko, Youngjoong] Sungkyunkwan Univ, Dept Comp Sci & Engn, Suwon 16419, Gyeonggi Do, South Korea.
RP Ko, Y (通讯作者)，Sungkyunkwan Univ, Dept Comp Sci & Engn, Suwon 16419, Gyeonggi Do, South Korea.
EM youngjoong.ko@gmail.com
FU Institute for Information & Communications Technology Planning &
   Evaluation (IITP) through Korea Government [Ministry of Science and ICT
   (MSIT)] [2013-2-00131]
FX This work was supported by the Institute for Information &
   Communications Technology Planning & Evaluation (IITP) through Korea
   Government [Ministry of Science and ICT (MSIT)], Development of
   Knowledge Evolutionary WiseQA Platform Technology for Human Knowledge
   Augmented Services under Grant 2013-2-00131.
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Cao ZS, 2021, AAAI CONF ARTIF INTE, V35, P6894
   De Cao N., 2019, P C N, V1, P2306
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Huang H., 2020, P 28 INT C COMP LING, P556
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Isabelle, 2018, P 27 INT C COMP LING, P3306
   Kim B., 2020, P 28 INT C COMP LING, P1737, DOI DOI 10.18653/V1/2020.COLING-MAIN.153
   Kingma D. P., 2015, P ICLR
   Liu XD, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4487
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Moon S, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P845
   Sun Z., 2019, 7 INT C LEARN REPR
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   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
   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
   Yang B., 2014, ABS14126575 CORR
   Yang B., 2015, P INT C LEARN REPR I
   Yao, 2019, ARXIV190903193
   Zhang Z., 2020, FINDINGS ASS COMPUTA, P259
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
NR 25
TC 0
Z9 0
U1 1
U2 2
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 132025
EP 132032
DI 10.1109/ACCESS.2021.3113329
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA UZ9UF
UT WOS:000702542900001
OA gold
DA 2022-08-23
ER

PT J
AU Tan, Z
   Zhao, X
   Fang, Y
   Xiao, WD
AF Tan, Zhen
   Zhao, Xiang
   Fang, Yang
   Xiao, Weidong
TI GTrans: Generic Knowledge Graph Embedding via Multi-State Entities and
   Dynamic Relation Spaces
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph embedding; multi-state entities; dynamic relation
   spaces; triplets classification; link prediction
AB Knowledge graph embedding aims to construct a low-dimensional and continuous space, which is able to describe the semantics of high-dimensional and sparse knowledge graphs. Among existing solutions, translation models have drawn much attention lately, which use a relation vector to translate the head entity vector, the result of which is close to the tail entity vector. Compared with classical embedding methods, translation models achieve the state-of-the-art performance; nonetheless, the rationale and mechanism behind them still aspire after understanding and investigation. In this connection, we quest into the essence of translation models, and present a generic model, namely, GTrans, to entail all the existing translation models. In GTrans, each entity is interpreted by a combination of two states-eigenstate and mimesis. Eigenstate represents the features that an entity intrinsically owns, and mimesis expresses the features that are affected by associated relations. The weighting of the two states can be tuned, and hence, dynamic and static weighting strategies are put forward to best describe entities in the problem domain. Besides, GTrans incorporates a dynamic relation space for each relation, which not only enables the flexibility of our model but also reduces the noise from other relation spaces. In experiments, we evaluate our proposed model with two benchmark tasks triplets classification and link prediction. Experiment results witness significant and consistent performance gain that is offered by GTrans over existing alternatives.
C1 [Tan, Zhen; Zhao, Xiang; Fang, Yang] Natl Univ Def Technol, Changsha 410073, Hunan, Peoples R China.
   [Xiao, Weidong] Natl Univ Def Technol, Dept Informat Syst Engn, Changsha 410073, Hunan, Peoples R China.
RP Zhao, X (通讯作者)，Natl Univ Def Technol, Changsha 410073, Hunan, Peoples R China.
EM xiangzhao@nudt.edu.cn
RI Zhao, Xiang/B-1303-2010
OI Zhao, Xiang/0000-0001-6339-0219
FU NSFC [71690233, 71331008]
FX This work was supported by NSFC under Grant 71690233 and Grant 71331008.
CR Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
   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
   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
   Golub GH., 2012, MATRIX COMPUTATIONS
   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
   Jia YT, 2016, AAAI CONF ARTIF INTE, P992
   Lin YC, 2015, ADV SOC SCI EDUC HUM, V39, P2181
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   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
   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 Z, 2014, AAAI CONF ARTIF INTE, P1112
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Yang B., 2014, EMBEDDING ENTITIES R
   Zeiler MD., 2012, ADADELTA ADAPTIVE LE
NR 24
TC 13
Z9 16
U1 7
U2 12
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 8232
EP 8244
DI 10.1109/ACCESS.2018.2797876
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA FZ3EK
UT WOS:000427469000011
OA gold
DA 2022-08-23
ER

PT J
AU Mohamed, SK
   Novacek, V
   Nounu, A
AF Mohamed, Sameh K.
   Novacek, Vit
   Nounu, Aayah
TI Discovering protein drug targets using knowledge graph embeddings
SO BIOINFORMATICS
LA English
DT Article
ID INTERACTION PREDICTION; PERSPECTIVES
AB Motivation: Computational approaches for predicting drug-target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges. Many can only process a limited number of drugs and/or have poor proteome coverage. The current approaches also often suffer from high false positive prediction rates.
   Results: We propose a novel computational approach for predicting drug target proteins. The approach is based on formulating the problem as a link prediction in knowledge graphs (robust, machine-readable representations of networked knowledge). We use biomedical knowledge bases to create a knowledge graph of entities connected to both drugs and their potential targets. We propose a specific knowledge graph embedding model, TriModel, to learn vector representations (i.e. embeddings) for all drugs and targets in the created knowledge graph. These representations are consequently used to infer candidate drug target interactions based on their scores computed by the trained TriModel model. We have experimentally evaluated our method using computer simulations and compared it to five existing models. This has shown that our approach outperforms all previous ones in terms of both area under ROC and precision-recall curves in standard benchmark tests.
C1 [Mohamed, Sameh K.; Novacek, Vit] NUI Galway, Coll Engn & Informat, Data Sci Inst, Galway, Ireland.
   [Mohamed, Sameh K.; Novacek, Vit] NUI Galway, Insight Ctr Data Analyt, Galway, Ireland.
   [Nounu, Aayah] Univ Bristol, Bristol Med Sch, Integrat Epidemiol Unit, MRC, Bristol, Avon, England.
RP Mohamed, SK (通讯作者)，NUI Galway, Coll Engn & Informat, Data Sci Inst, Galway, Ireland.; Mohamed, SK (通讯作者)，NUI Galway, Insight Ctr Data Analyt, Galway, Ireland.
EM sameh.kamal@insight-centre.org
RI Novacek, Vit/ABD-3387-2020
FU Science Foundation Ireland (SFI) [SFI/12/RC/2289]; European Regional
   Development Fund
FX This publication has emanated from research supported in part by a
   research grant from Science Foundation Ireland (SFI) under Grant Number
   SFI/12/RC/2289, co-funded by the European Regional Development Fund.
CR Bateman A, 2017, NUCLEIC ACIDS RES, V45, pD158, DOI 10.1093/nar/gkw1099
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bowes J, 2012, NAT REV DRUG DISCOV, V11, P909, DOI 10.1038/nrd3845
   Cheng F., 2012, PLOS ONE, V7, P1
   Cheng FX, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002503
   Corbett A, 2012, NAT REV DRUG DISCOV, V11, P833, DOI 10.1038/nrd3869
   Davis J., 2006, P 23 INT C MACH LEAR, DOI [10.1145/1143844.1143874, DOI 10.1145/1143844.1143874]
   Drews J, 2000, SCIENCE, V287, P1960, DOI 10.1126/science.287.5460.1960
   Dumontier M, 2014, P ISWC 2014 POST DEM, V1272, P401
   Glorot X., 2010, J MACH LEARN RES, V9, P249
   Gunther S, 2008, NUCLEIC ACIDS RES, V36, pD919, DOI 10.1093/nar/gkm862
   Hao M, 2017, SCI REP-UK, V7, DOI 10.1038/srep40376
   Hecker N, 2012, NUCLEIC ACIDS RES, V40, pD1113, DOI 10.1093/nar/gkr912
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Kanehisa M, 2017, NUCLEIC ACIDS RES, V45, pD353, DOI 10.1093/nar/gkw1092
   Kanehisa M, 2006, NUCLEIC ACIDS RES, V34, pD354, DOI 10.1093/nar/gkj102
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li LX, 2017, LANCET, V390, P490, DOI 10.1016/S0140-6736(17)30770-5
   Lim H., 2016, SCI REP, V6, P4
   Liu H, 2015, BIOINFORMATICS, V31, P221, DOI 10.1093/bioinformatics/btv256
   Liu T. - Y., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016]
   Mei JP, 2013, BIOINFORMATICS, V29, P238, DOI 10.1093/bioinformatics/bts670
   Mitchell AL, 2019, NUCLEIC ACIDS RES, V47, pD351, DOI 10.1093/nar/gky1100
   Munoz E, 2019, BRIEF BIOINFORM, V20, P190, DOI 10.1093/bib/bbx099
   Nascimento ACA, 2016, BMC BIOINFORMATICS, V17, DOI 10.1186/s12859-016-0890-3
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Olayan RS, 2018, BIOINFORMATICS, V34, P1164, DOI 10.1093/bioinformatics/btx731
   Placzek S, 2017, NUCLEIC ACIDS RES, V45, pD380, DOI 10.1093/nar/gkw952
   Reddi Sashank J., 2018, ICLR
   Rosdah AA, 2016, PHARMACOL RES PERSPE, V4, DOI 10.1002/prp2.235
   Rothwell PM, 2010, LANCET, V376, P1741, DOI 10.1016/S0140-6736(10)61543-7
   Schomburg I, 2004, NUCLEIC ACIDS RES, V32, pD431, DOI 10.1093/nar/gkh081
   Sleno L, 2008, CURR OPIN CHEM BIOL, V12, P46, DOI 10.1016/j.cbpa.2008.01.022
   Sneader W, 2005, DRUG DISCOVERY: A HISTORY, P1
   Terstappen GC, 2007, NAT REV DRUG DISCOV, V6, P891, DOI 10.1038/nrd2410
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wan FP, 2019, BIOINFORMATICS, V35, P104, DOI 10.1093/bioinformatics/bty543
   Wishart DS, 2008, NUCLEIC ACIDS RES, V36, pD901, DOI 10.1093/nar/gkm958
   Wishart DS, 2006, NUCLEIC ACIDS RES, V34, pD668, DOI 10.1093/nar/gkj067
   Xie L, 2012, ANNU REV PHARMACOL, V52, P361, DOI 10.1146/annurev-pharmtox-010611-134630
   Yamanishi Y, 2008, BIOINFORMATICS, V24, pI232, DOI 10.1093/bioinformatics/btn162
   Yang B., 2015, ICLR
NR 43
TC 50
Z9 52
U1 13
U2 55
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 JAN 15
PY 2020
VL 36
IS 2
BP 603
EP 610
DI 10.1093/bioinformatics/btz600
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 LE3ZX
UT WOS:000526660300034
PM 31368482
OA Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Nie, Q
   Sheng, X
   Zhang, N
   Wang, JJ
   Shang, YN
   Hu, FH
AF Nie, Qing
   Sheng, Xing
   Zhang, Nan
   Wang, Jianjiang
   Shang, Yingnan
   Hu, Fanghuai
TI Construction of A Nautical Knowledge Graph Based on Multiple Data
   Sources
SO JOURNAL OF COASTAL RESEARCH
LA English
DT Article
DE Domain knowledge graph; nautical; multiple data sources
ID WRAPPER INDUCTION; INFORMATION
AB To automatically recognize the nautical chart symbol is a difficult task; the first recognize step works well by the image recognition methods based on deep neural networks, however, these symbols usually have their own domain meanings which are different from their common meanings; a feasible method to improve the task is using domain knowledge to describe the domain meanings of these symbols. This paper proposes an automatic nautical knowledge graph construction method based on multiple data sources. The data sources adopted include structured data like data in relational databases, semi-structured data like data from web pages, and unstructured data like text. The author uses different methods to extract knowledge from these three types of data sources, including the rule-based method, wrapper method and machine learning method, etc. Then the author tests the proposed method by using it to establish a knowledge graph for the nautical industry. The established knowledge graph contains about 1,500 concepts and over 250 million hyponymy relations, about 80 million entities and about 30 million relations among the entities; and the average precision of these knowledge is above 0.9. The experimental result proves that the proposed method is precise and efficient.
C1 [Nie, Qing] YingDa Media Investment Grp CO LTD, Beijing 100005, Peoples R China.
   [Sheng, Xing] State Grid Corp China, Beijing 100031, Peoples R China.
   [Zhang, Nan] NARI Technol Co Ltd, Nanjing 211106, Jiangsu, Peoples R China.
   [Wang, Jianjiang; Hu, Fanghuai] State Grid Yantai Power Supply Co, Yantai 264000, Peoples R China.
   [Shang, Yingnan] Shanghai Haiyizhi Informat Technol Co LTD, Shanghai 200082, Peoples R China.
RP Zhang, N (通讯作者)，NARI Technol Co Ltd, Nanjing 211106, Jiangsu, Peoples R China.
EM zhangnan2@sgepri.sgcc.com.cn
FU Science & Technology Project of State Gird Research on key technology of
   state scientific knowledge system research and development
   [9900/2017-66001A]
FX This work is supported by Science & Technology Project of State Gird
   (9900/2017-66001A) Research on key technology of state scientific
   knowledge system research and development.
CR Chen CF, 2018, IEEE T GEOSCI REMOTE, V56, P1059, DOI 10.1109/TGRS.2017.2758795
   Guo JY, 2014, MAR GEOPHYS RES, V35, P141, DOI 10.1007/s11001-014-9216-x
   Harris G, 2003, MODSIM 2003: INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION, VOLS 1-4, P1
   Hu FH, 2014, SCI WORLD J, DOI 10.1155/2014/848631
   Hu Q, 2018, REMOTE SENS LETT, V9, P448, DOI [10.1080/2150704X.2018.1437289, 10.1080/2150704x.2018.1437289]
   Kong QL, 2017, ACTA GEOPHYS, V65, P1, DOI 10.1007/s11600-016-0001-x
   Kushmerick N, 1997, INT JOINT CONF ARTIF, P729
   Li WQ, 2018, CHINESE J GEOPHYS-CH, V61, P2237, DOI 10.6038/cjg2018L0445
   Muslea I, 2001, AUTON AGENT MULTI-AG, V4, P93, DOI 10.1023/A:1010022931168
   Pirro G, 2015, LECT NOTES COMPUT SC, V9366, P622, DOI 10.1007/978-3-319-25007-6_36
   Ruan T, 2014, P ISWC, P1
   Ruan T, 2016, LECT NOTES COMPUT SC, V9982, P418, DOI 10.1007/978-3-319-46547-0_35
   Signori P, 2015, INT J PHYS DISTR LOG, V45, P536, DOI 10.1108/IJPDLM-07-2014-0160
   Singhal A., 2012, OFFICIAL GOOGLE BLOG
   Szekely P, 2015, LECT NOTES COMPUT SC, V9367, P205, DOI 10.1007/978-3-319-25010-6_12
   Wang JB, 2018, MAR GEOD, V41, P24, DOI 10.1080/01490419.2017.1380091
   Xin R, 2018, ISPRS INT GEO-INF, V7, DOI 10.3390/ijgi7060225
   Xu B, 2017, LECT NOTES ARTIF INT, V10351, P428, DOI 10.1007/978-3-319-60045-1_44
   Yang FL, 2011, MAR GEOD, V34, P138, DOI 10.1080/01490419.2010.518501
   Zhang K, 2016, MAR GEOD, V39, P98, DOI 10.1080/01490419.2015.1082521
   Zhao X, 2017, INT J REMOTE SENS, V38, P1404, DOI 10.1080/01431161.2016.1278284
NR 21
TC 0
Z9 0
U1 2
U2 21
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 SUM
PY 2019
SI 94
BP 223
EP 226
DI 10.2112/SI94-047.1
PG 4
WC Environmental Sciences; Geography, Physical; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Physical Geography; Geology
GA IX5GB
UT WOS:000485711600048
DA 2022-08-23
ER

PT J
AU Kim, B
   Lee, D
   Kim, D
   Kim, H
   Kim, S
   Kwon, O
   Kim, H
AF Kim, Boeun
   Lee, Dohaeng
   Kim, Damrin
   Kim, Hongjin
   Kim, Sihyung
   Kwon, Ohwoog
   Kim, Harksoo
TI Generative Model Using Knowledge Graph for Document-Grounded
   Conversations
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE document-grounded conversation; generative model; knowledge graph; Levi
   graph; open information extraction
AB Featured Application Core technology for document-grounded conversation. Document-grounded conversation (DGC) is a natural language generation task to generate fluent and informative responses by leveraging dialogue history and document(s). Recently, DGCs have focused on fine-tuning using pretrained language models. However, these approaches have a problem in that they must leverage the background knowledge under capacity constraints. For example, the maximum length of the input is limited to 512 or 1024 tokens. This problem is fatal in DGC because most documents are longer than the maximum input length. To address this problem, we propose a document-grounded generative model using a knowledge graph. The proposed model converts knowledge sentences extracted from the given document(s) into knowledge graphs and fine-tunes the pretrained model using the graph. We validated the effectiveness of the proposed model using a comparative experiment on the well-known Wizard-of-Wikipedia dataset. The proposed model outperformed the previous state-of-the-art model in our experiments on the Doc2dial dataset.
C1 [Kim, Boeun; Lee, Dohaeng; Kim, Damrin; Kim, Hongjin] Konkuk Univ, Artificial Intelligence, Seoul 05029, South Korea.
   [Kim, Sihyung] NAVER Corp, Seongnam 13561, South Korea.
   [Kwon, Ohwoog] Elect & Telecommun Res Inst, Language Intelligence Res Lab, Daejeon 34129, South Korea.
   [Kim, Harksoo] Konkuk Univ, Comp Sci & Engn, Seoul 05029, South Korea.
RP Kim, S (通讯作者)，NAVER Corp, Seongnam 13561, South Korea.; Kim, H (通讯作者)，Konkuk Univ, Comp Sci & Engn, Seoul 05029, South Korea.
EM boeun@konkuk.ac.kr; dsdhlee@konkuk.ac.kr; ekafls33@konkuk.ac.kr;
   jin3430@konkuk.ac.kr; sihyung.kim@navercorp.com; ohwoog@etri.re.kr;
   nlpdrkim@konkuk.ac.kr
OI Kim, Harksoo/0000-0002-8286-7198; Oh-Woog, Kwon/0000-0002-5904-2358;
   Kim, Sihyung/0000-0002-2690-5727
FU Konkuk University; Institute of Information & Communications Technology
   Planning & Evaluation(IITP) - Korea government(MSIT) [2019-0-00004];
   National Research Foundation of Korea(NRF) - Korea government(MSIT)
   [2020R1F1A1069737]
FX This paper was supported by Konkuk University Researcher Fund in 2021.
   This work was also supported by Institute of Information &
   Communications Technology Planning & Evaluation(IITP) grant funded by
   the Korea government(MSIT) (2019-0-00004, Development of semi-supervised
   learning language intelligence technology and Korean tutoring service
   for foreigners) and the National Research Foundation of Korea(NRF) grant
   funded by the Korea government(MSIT) (No. 2020R1F1A1069737).Y
CR Bao SQ, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P85
   Chen B, 2014, P 9 WORKSH STAT MACH, P362, DOI DOI 10.3115/V1/W14-3346
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dinan E., 2018, INT C LEARN REPR
   Feng S., 2020, ARXIV201106623
   Ghazvininejad M, 2018, AAAI CONF ARTIF INTE, P5110
   He H., 2021, ARXIV210202096
   Kim B., 2020, ARXIV200207510
   Kim B, 2021, 1ST WORKSHOP ON DOCUMENT-GROUNDED DIALOGUE AND CONVERSATIONAL QUESTION ANSWERING (DIALDOC 2021), P98
   Kim J, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9183908
   Kim S, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10093335
   Levi Friedrich Wilhelm, 1942, FIN GEOM SYST 6 PUBL
   Lewis M., 2019, P 58 ANN M ASS COMP, DOI 10.18653/v1/2020.acl
   Papineni K, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P311, DOI 10.3115/1073083.1073135
   Post Matt, 2018, WMT, DOI DOI 10.18653/V1/W18-6319
   Radford A., 2019, OPENAI BLOG, V1, P9
   Saha S., 2018, P 27 INT C COMP LING, P2288
   Srivatsan R, 2015, SEVA, SAVIOUR AND STATE: CASTE POLITICS, TRIBAL WELFARE AND CAPITALIST DEVELOPMENT, P1
   Sukhbaatar S., 2015, ADV NEUR IN, VVolume 28
   Sutskever I, 2014, ADV NEUR IN, V27
   Vaswani A, 2017, ADV NEUR IN, V30
   Velickovi P., 2017, ARXIV171010903
   Yang S., 2020, EMNLP 2020
   Zhao X., 2020, ARXIV201008824
NR 24
TC 1
Z9 1
U1 5
U2 5
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 APR
PY 2022
VL 12
IS 7
AR 3367
DI 10.3390/app12073367
PG 10
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 0L0FY
UT WOS:000781161800001
OA gold
DA 2022-08-23
ER

PT J
AU Duan, YJ
   Liu, X
   Jatowt, A
   Zhuang, CY
   Yu, HT
   Lynden, S
   Kim, KS
   Matono, A
AF Duan, Yijun
   Liu, Xin
   Jatowt, Adam
   Zhuang, Chenyi
   Yu, Hai-Tao
   Lynden, Steven
   Kim, Kyoung-Sook
   Matono, Akiyoshi
TI Multiview Actionable Knowledge Graph Generation From Wikipedia
   Biographies
SO IEEE ACCESS
LA English
DT Article
DE Online services; Internet; Encyclopedias; Biographies; Task analysis;
   Electronic mail; Licenses; Actionable knowledge graph; neural network
   matrix factorization; text mining; web mining
AB Actionable knowledge graph (AKG), a specialized version of knowledge graph, was proposed recently to represent, analyze, and predict human action, thus facilitating deeper understanding of human action by robots. However, the automatic construction of AKGs from action-related corpora is still an unexplored problem. In this study, we first propose three unsupervised matrix factorization-based frameworks for AKG generation from three different perspectives: subject, context and functionality of action, respectively. Further, we propose a hybrid model based on neural network matrix factorization (NNMF) that considers multi-source signals simultaneously. It not only learns the latent action representations, but also learns the optimal learning objective rather than assuming it to be fixed. To quantitatively verify the utility of the constructed AKGs, we introduce a novel application, that is, predicting the most likely missing action records in Wikipedia biographies. Experimental results on a large-scale Wikipedia biography dataset show that the proposed model brings significant improvement over the baselines, which demonstrates the strong expressiveness of our generated AKGs.
C1 [Duan, Yijun; Liu, Xin; Lynden, Steven; Kim, Kyoung-Sook; Matono, Akiyoshi] Natl Inst Adv Ind Sci & Technol Tokyo Waterfront, Koto City, Tokyo 1350064, Japan.
   [Jatowt, Adam] Univ Innsbruck, Dept Comp Sci, A-6020 Innsbruck, Austria.
   [Zhuang, Chenyi] Ant Grp, Hangzhou 311121, Peoples R China.
   [Yu, Hai-Tao] Univ Tsukuba, Fac Lib Informat & Media Sci, Tsukuba, Ibaraki 3058577, Japan.
RP Duan, YJ (通讯作者)，Natl Inst Adv Ind Sci & Technol Tokyo Waterfront, Koto City, Tokyo 1350064, Japan.
EM yijun.duan@aist.go.jp
FU New Energy and Industrial Technology Development Organization (NEDO)
   [JPNP20006]
FX This work was supported by the New Energy and Industrial Technology
   Development Organization (NEDO) under Project JPNP20006.
CR Bamman D., 2014, T ASS COMPUT LINGUIS, V2, P363
   Blanco R, 2017, PROC NTCIR C, P1
   Broder A., 2002, SIGIR Forum, V36, P3, DOI 10.1145/792550.792552
   Bunescu R., 2006, PROC 11 C EUR CHAPTE
   Conway M, 2010, LIT LINGUIST COMPUT, V25, P23, DOI 10.1093/llc/fqp035
   Cucerzan S, 2007, P 2007 JOINT C EMP M, P708
   Dai YF, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9050750
   Dettmers T., 2017, ARXIV
   Devlin J., 2018, ARXIV
   Duan YJ, 2017, PROCEEDINGS OF THE 28TH ACM CONFERENCE ON HYPERTEXT AND SOCIAL MEDIA (HT'17), P105, DOI 10.1145/3078714.3078725
   Guo S., 2016, P C EMP METH NAT LAN, P192, DOI 10.18653/v1/D16-1019
   Hitchcock F. L., 1927, J MATH PHYS, V6, P164, DOI 10.1002/sapm192761164
   Hogan A., 2020, ARXIV
   HOGAN DP, 1986, ANNU REV SOCIOL, V12, P109, DOI 10.1146/annurev.so.12.080186.000545
   Jatowt A, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1909, DOI 10.1145/2983323.2983871
   Kang X., 2017, 2 NTCIR
   Lee DD, 1999, NATURE, V401, P788, DOI 10.1038/44565
   Lee DD, 2001, ADV NEUR IN, V13, P556
   LILLARD LA, 1993, DEMOGRAPHY, V30, P653, DOI 10.2307/2061812
   Lin MQ, 2017, IEEE CONF COMPUT, P1, DOI 10.1109/INFCOMW.2017.8116343
   Lu FY, 2020, IEEE ACCESS, V8, P92072, DOI 10.1109/ACCESS.2020.2995074
   Page L., 1999, PAGERANK CITATION RA
   Pascual-Montano A, 2006, IEEE T PATTERN ANAL, V28, P403, DOI 10.1109/TPAMI.2006.60
   Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
   Peters M. E., 2018, ARXIV
   Rahman M, 2018, P I MECH ENG B-J ENG, V232, P2499, DOI 10.1177/0954405417699013
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9981, P498, DOI 10.1007/978-3-319-46523-4_30
   Salakhutdinov R, 2007, ADV NEURAL INFORM PR, P1257
   Takahashi Y., 2011, P 22 ACM C HYP HYP, P83
   Van de Cruys T., 2014, P C EMP METH NAT LAN, P26
   Wagner C., 2015, ARXIV
   Wang R, 2020, IEEE ACCESS, V8, P5212, DOI 10.1109/ACCESS.2019.2963367
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wolf T., 2019, ARXIV
   Wu Y, 2016, PROCEEDINGS OF THE NINTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'16), P73, DOI 10.1145/2835776.2835836
   Yao ZJ, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P673, DOI 10.1145/3159652.3159703
   Yijun Duan, 2019, Web and Big Data. Third International Joint Conference, APWeb-WAIM 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11642), P349, DOI 10.1007/978-3-030-26075-0_27
   Zhuang CY, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3616
NR 38
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 73879
EP 73892
DI 10.1109/ACCESS.2022.3189769
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3Q9DU
UT WOS:000838524200001
OA gold
DA 2022-08-23
ER

PT J
AU Yang, ZJ
   Ye, JB
   Wang, LL
   Lin, X
   He, L
AF Yang, Zijing
   Ye, Jiabo
   Wang, Linlin
   Lin, Xin
   He, Liang
TI Inferring substitutable and complementary products with Knowledge-Aware
   Path Reasoning based on dynamic policy network
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Recommender system; Knowledge graph
AB Inferring the substitutable and complementary products for a given product is an essential and fundamental concern for the recommender system. To achieve this, existing approaches take advantage of the knowledge graphs to learn more evidences for inference, whereas they often suffer from invalid reasoning for lack of elegant decision making strategies. Therefore, we propose a novel Knowledge-Aware Path Reasoning (KAPR) model which leverages the dynamic policy network to make explicit reasoning over knowledge graphs, for inferring the substitutable and complementary relationships. Our contributions can be highlighted as three aspects. Firstly, we model this inference scenario as a Markov Decision Process in order to accomplish a knowledge-aware path reasoning over knowledge graphs. Secondly, we integrate both structured and unstructured knowledge to provide adequate evidences for making accurate decision-making. Thirdly, we evaluate our model on a series of real-world datasets, achieving competitive performance compared with state-of-the-art approaches. Our code is released on https://gitee.com/yangzijing_flower/kapr/tree/master. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Yang, Zijing; Ye, Jiabo; Wang, Linlin; Lin, Xin; He, Liang] East China Normal Univ, Dept Comp Sci & Technol, Shanghai 200241, Peoples R China.
RP Wang, LL (通讯作者)，East China Normal Univ, Dept Comp Sci & Technol, Shanghai 200241, Peoples R China.
EM 51194506043@stu.ecnu.edu.cn; 2194506006@stu.ecnu.edu.cn;
   llwang@cs.ecnu.edu.cn; xlin@cs.ecnu.edu.cn; lhe@cs.ecnu.edu.cn
RI He, Liang/CAF-0477-2022
FU National Innovation 2030 Major S&T Project of China [2020AAA0104200,
   2020AAA0104205]; Fundamental Research Funds for the Central
   Universities; National Natural Science Foundation of China [62006077];
   Shanghai Sailing Program [20YF1411800]; Science and Technology
   Commission of Shanghai Municipality [19511120601]
FX This work was supported by the National Innovation 2030 Major S&T
   Project of China (2020AAA0104200, 2020AAA0104205), the Fundamental
   Research Funds for the Central Universities, National Natural Science
   Foundation of China (Grant No. 62006077), Shanghai Sailing Program
   (Grant No. 20YF1411800), and the Science and Technology Commission of
   Shanghai Municipality (Grant No. 19511120601).
CR Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen XS, 2019, PR MACH LEARN RES, V97
   Gui T, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P535, DOI 10.1145/3331184.3331237
   He RN, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P507, DOI 10.1145/2872427.2883037
   Huang J, 2018, ACM/SIGIR PROCEEDINGS 2018, P505, DOI 10.1145/3209978.3210017
   Le Q.V., 2014, COMPUT RES REPOS COR, P1, DOI [DOI 10.1145/2740908.2742760, 10.1145/2740908.2742760]
   Liu YD, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P2621, DOI 10.1145/3340531.3412695
   Lixin Zou, 2020, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, P749, DOI 10.1145/3397271.3401181
   Tran LV, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P255, DOI 10.1145/3331184.3331251
   McAuley J, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2783258.2783381
   Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI 10.3115/v1/D14-1162
   Rakesh V, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P438, DOI 10.1145/3289600.3290963
   Shani G, 2005, J MACH LEARN RES, V6, P1265
   Tintarev N, 2007, I C DATA ENGIN WORKS, P801, DOI 10.1109/ICDEW.2007.4401070
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang XT, 2018, IEEE DATA MINING, P587, DOI 10.1109/ICDM.2018.00074
   Wang ZH, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P619, DOI 10.1145/3159652.3159710
   Wu C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2379, DOI 10.1145/3132847.3133163
   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 W., 2017, P 2017 C EMPIRICAL M, P564
   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 J, 2019, AAAI CONF ARTIF INTE, P435
   Zhang SJ, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4306
   Zhang YF, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1449, DOI 10.1145/3132847.3132892
   Zheng GJ, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P167, DOI 10.1145/3178876.3185994
NR 26
TC 0
Z9 0
U1 8
U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 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 107579
DI 10.1016/j.knosys.2021.107579
EA NOV 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WW8BM
UT WOS:000718134800021
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Huang, ZC
   Li, XT
   Ye, YM
   Zhang, BQ
   Xu, GN
   Gan, WS
AF Huang, Zhichao
   Li, Xutao
   Ye, Yunming
   Zhang, Baoquan
   Xu, Guangning
   Gan, Wensheng
TI Multi-view knowledge graph fusion via knowledge-aware attentional graph
   neural network
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Multi-view knowledge graph fusion; Entity alignment; Knowledge-aware
   attention; Multi-view GNN
AB Knowledge graphs (KGs) play a vital role in natural language processing (NLP), which can serve several downstream tasks. Because different views of KGs are usually constructed independently, the multi-view knowledge graph fusion (MVKGF) becomes a hotspot. Although multi-view learning studied very well in past decades, MVKGF is still not well tackled because of the heterogeneous relations and the multi-view KGs. To overcome MVKGF, entity alignment is the most studied. Existing entity alignment methods are dominated by embedding based methods, such as TransE and Graph Neural Networks (GNNs), where the alignment is achieved by measuring the similarities between entity embeddings. However, most previous approaches suffer from the issues of the diverse knowledge facts and the complex neighboring structures. In this paper, we propose a novel K nowledge-aware A ttentional G raph N eural N etwork (KAGNN) model to carefully incorporate both knowledge facts and neighboring structures. In particular, a knowledge-aware attention mechanism is designed to preserve the original semantics and determine the importance of each knowledge fact. Furthermore, a three-layered GCN with highway gates is adopted to learn better entity representations from the neighboring structure information. Thus, our model can be regarded as a multi-view extension of GNN. We validate our model on three cross-lingual datasets and the results show our model beats the state-of-the-art baselines by a large margin.
C1 [Huang, Zhichao; Li, Xutao; Ye, Yunming; Zhang, Baoquan; Xu, Guangning] Harbin Inst Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China.
   [Gan, Wensheng] Jinan Univ, Coll Cyber Secur, Guangzhou 510632, Peoples R China.
RP Li, XT; Ye, YM (通讯作者)，Harbin Inst Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China.
EM iceshzc@stu.hit.edu.cn; lixutao@hit.edu.cn; yeyumning@hit.edu.cn;
   zhangbaoquan@stu.hitedu.cn; 20B951010@stu.hitedu.cn; wsgan001@gmail.com
OI Huang, Zhichao/0000-0002-7662-5184
FU National Key R&D Program of China [2018YFB2101100, 2018YFB2101101]; NSFC
   [61972111, U1836107]
FX This research was supported in part by the National Key R&D Program of
   China, 2018YFB2101100, 2018YFB2101101 and NSFC under Grant No. 61972111
   and U1836107.
CR Belhadi H, 2020, APPL INTELL, V50, P1204, DOI 10.1007/s10489-019-01593-3
   Bruna J., 2014, 2 INT C LEARN REPR I
   Cao YX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1452
   Chao GQ, 2019, INFORM SCIENCES, V494, P278, DOI 10.1016/j.ins.2019.04.039
   Chao GQ, 2019, INFORM FUSION, V45, P296, DOI 10.1016/j.inffus.2018.03.002
   Chao GQ, 2016, IEEE T NEUR NET LEAR, V27, P1445, DOI 10.1109/TNNLS.2015.2442256
   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
   Cui WY, 2017, PROC VLDB ENDOW, V10, P565, DOI 10.14778/3055540.3055549
   Defferrard M, 2016, ADV NEUR IN, V29
   Friedman M, 1937, J AM STAT ASSOC, V32, P675, DOI 10.2307/2279372
   Hao YC, 2016, COMM COM INF SC, V650, P3, DOI 10.1007/978-981-10-3168-7_1
   Huang ZC, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1258
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li Chengjiang, 2019, P 2019 C EMPIRICAL M, P2723
   Li CS, 2021, INT J SOFTW ENG KNOW, V31, P533, DOI 10.1142/S0218194021400064
   Li CS, 2020, NEURAL COMPUT APPL, V32, P6383, DOI 10.1007/s00521-019-04145-5
   Nemenyi P.B., 1963, THESIS
   Pei SC, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3130, DOI 10.1145/3308558.3313646
   Rebele T, 2016, LECT NOTES COMPUT SC, V9982, P177, DOI 10.1007/978-3-319-46547-0_19
   Srivastava RK, 2015, ARXIV 150500387
   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
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   Velickovi P., 2017, ARXIV171010903
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Wei YW, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P1437, DOI 10.1145/3343031.3351034
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wu F, 2019, PR MACH LEARN RES, V97
   Wu YT, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5278
   Wu Yuting, 2019, EMNLP IJCNLP, P240
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Yan ZF, 2020, LECT NOTES ELECTR EN, V589, P19, DOI 10.1007/978-981-32-9441-7_3
   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
   Zhu QN, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1943
NR 38
TC 0
Z9 0
U1 4
U2 4
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/s10489-022-03667-1
EA JUN 2022
PG 20
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1T9ST
UT WOS:000805064000003
DA 2022-08-23
ER

PT J
AU Jiang, B
   Yang, JC
   Qin, YB
   Wang, T
   Wang, MC
   Pan, WF
AF Jiang, Bo
   Yang, Junchen
   Qin, Yanbin
   Wang, Tian
   Wang, Muchou
   Pan, Weifeng
TI A Service Recommendation Algorithm Based on Knowledge Graph and
   Collaborative Filtering
SO IEEE ACCESS
LA English
DT Article
DE Mashups; Collaborative filtering; Feature extraction; Filtering;
   Collaboration; Licenses; Computational modeling; Web API recommendation;
   knowledge graph; representation learning; collaborative filtering
ID MASHUPS; USER
AB With the rapid development of the Internet, the number of Web APIs is increasing. How to recommend accurate and appropriate Web APIs to mashups has become a focus and difficulty in the field of service computing. The existing methods are mainly based on collaborative filtering technology, but these methods have problems such as the data sparsity and cold start, which leads to poor recommendation effects. This paper proposes a service recommendation model based on knowledge graph and collaborative filtering. In this model, the knowledge graph connects the APIs and mashups related information to mine the potential relations between mashups and APIs, hence reducing the impact of data sparsity. All the API entities in the service knowledge graph are embedded into the low-dimensional space through the representation learning algorithm, then the distances between the API vectors are calculated to recommend the related APIs. In addition, in order to solve the cold-start problem of recommending APIs to target mashups that have no APIs usage, the similarities of functional sets extracted from mashups are calculated to recommend APIs for target mashups. At the same time, the model obtains the Mashup-API usage record, using the technology of collaborative filtering to recommend appropriate APIs to target mashups. Finally, the similarities of the above recommended APIs are normalized and sorted to form the final recommendation result. The experimental results show that our proposed model significantly improves the accuracy of service recommendation.
C1 [Jiang, Bo; Yang, Junchen; Qin, Yanbin; Wang, Tian; Pan, Weifeng] Zhejiang Gongshang Univ, Sch Comp Sci & Informat Engn, Hangzhou 310018, Peoples R China.
   [Wang, Muchou] Wenzhou Univ, Wenzhou Univ Lib, Wenzhou 325000, Peoples R China.
RP Jiang, B; Yang, JC (通讯作者)，Zhejiang Gongshang Univ, Sch Comp Sci & Informat Engn, Hangzhou 310018, Peoples R China.; Wang, MC (通讯作者)，Wenzhou Univ, Wenzhou Univ Lib, Wenzhou 325000, Peoples R China.
EM nancybjiang@zjgsu.edu.cn; 19020100041@pop.zjgsu.edu.cn; wzuwmc@163.com
FU Natural Science Foundation of Zhejiang Province [LY21F020002]; Key
   Research and Development Program Project of Zhejiang Province
   [2019C01004]
FX This work was supported in part by the Natural Science Foundation of
   Zhejiang Province under Grant LY21F020002, and in part by the Key
   Research and Development Program Project of Zhejiang Province under
   Grant 2019C01004.
CR Benslimane D, 2008, IEEE INTERNET COMPUT, V12, P13, DOI 10.1109/MIC.2008.110
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cao BQ, 2014, INT J GRID DISTRIB, V7, P163, DOI 10.14257/ijgdc.2014.7.2.15
   Cao ZY, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11030392
   Craswell N., ENCY DATABASE SYST
   Espinha T, 2014, 2014 SOFTWARE EVOLUTION WEEK - IEEE CONFERENCE ON SOFTWARE MAINTENANCE, REENGINEERING, AND REVERSE ENGINEERING (CSMR-WCRE), P84, DOI 10.1109/CSMR-WCRE.2014.6747228
   Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
   Hoyer V, 2008, LECT NOTES COMPUT SC, V5364, P708, DOI 10.1007/978-3-540-89652-4_62
   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
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li H, 2021, IEEE ACCESS, V9, P28076, DOI 10.1109/ACCESS.2021.3058450
   Li QY, 2019, IEEE ACCESS, V7, P115816, DOI 10.1109/ACCESS.2019.2932466
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mozgovoy M, 2013, HUM-CENT COMPUT INFO, V3, DOI 10.1186/2192-1962-3-5
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Pan WF, 2021, INT J WEB SERV RES, V18, P34, DOI 10.4018/IJWSR.2021010103
   Pan WF, 2021, IEEE T SOFTWARE ENG, V47, P2272, DOI 10.1109/TSE.2019.2946357
   Pan WF, 2018, FUTURE GENER COMP SY, V87, P267, DOI 10.1016/j.future.2018.04.052
   Pan WF, 2018, FUTURE GENER COMP SY, V83, P431, DOI 10.1016/j.future.2017.09.039
   Paradarami TK, 2017, EXPERT SYST APPL, V83, P300, DOI 10.1016/j.eswa.2017.04.046
   Partner J., 2014, NEO4J ACTION PEARSON, P304
   Rahman MM, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC), P225, DOI 10.1109/SCC.2017.36
   Re C., 2014, ARXIV14076439
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Suchanek FM, 2007, WWW
   Tian G, 2017, INFORM SYST FRONT, V19, P75, DOI 10.1007/s10796-015-9590-1
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang X, 2019, IEEE ACCESS, V7, P7651, DOI 10.1109/ACCESS.2018.2890156
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xiao H., 2015, COMPUTER SCI
   Xiong RB, 2018, EXPERT SYST APPL, V110, P191, DOI 10.1016/j.eswa.2018.05.039
   Yu X, 2019, PATTERN RECOGN, V94, P96, DOI 10.1016/j.patcog.2019.05.030
   Yu X, 2018, KNOWL-BASED SYST, V141, P80, DOI 10.1016/j.knosys.2017.11.010
   Yu X, 2014, FRONT COMPUT SCI-CHI, V8, P793, DOI 10.1007/s11704-014-3161-3
   Zhong Y, 2018, IEEE T AUTOM SCI ENG, V15, P468, DOI 10.1109/TASE.2016.2624310
NR 38
TC 3
Z9 3
U1 8
U2 27
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 50880
EP 50892
DI 10.1109/ACCESS.2021.3068570
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA RK6HO
UT WOS:000638395100001
OA gold
DA 2022-08-23
ER

PT J
AU Brown, JA
AF Brown, Jay A.
TI Using a Relational Database to Index Infectious Disease Information
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article; Proceedings Paper
CT 6th International Symposium on Recent Advances in Environmental Health
   Research
CY SEP 13-16, 2009
CL Jackson, MS
SP Jackson State Univ
DE decision support; relational database; infectious diseases; public
   health informatics; early detection; differential diagnosis; indexing
   information; knowledge mapping
ID PUBLIC-HEALTH MANAGEMENT; BIOLOGICAL WEAPON; COMPUTER-PROGRAM;
   BIOTERRORISM; DIAGNOSIS; FEVER; PATIENT; ANTHRAX; GIDEON
AB Mapping medical knowledge into a relational database became possible with the availability of personal computers and user-friendly database software in the early 1990s. To create a database of medical knowledge, the domain expert works like a mapmaker to first outline the domain and then add the details, starting with the most prominent features. The resulting "intelligent database" can support the decisions of healthcare professionals. The intelligent database described in this article contains profiles of 275 infectious diseases. Users can query the database for all diseases matching one or more specific criteria (symptom, endemic region of the world, or epidemiological factor). Epidemiological factors include sources (patients, water, soil, or animals), routes of entry, and insect vectors. Medical and public health professionals could use such a database as a decision-support software tool.
C1 Natl Lib Med, Specialized Informat Serv, Bethesda, MD 20892 USA.
RP Brown, JA (通讯作者)，Natl Lib Med, Specialized Informat Serv, 6707 Democracy Blvd,Suite 510, Bethesda, MD 20892 USA.
EM brownjay@haz-map.com
CR [Anonymous], PRACTICE GUIDELINES
   Arnon SS, 2001, JAMA-J AM MED ASSOC, V285, P1059, DOI 10.1001/jama.285.8.1059
   BARTLETT JG, ABX GUIDE
   Beers MH, 2006, MERCK MANUAL
   Berger Stephen A, 2005, Int J Health Geogr, V4, P10, DOI 10.1186/1476-072X-4-10
   Berner ES, 2005, J AM MED INFORM ASSN, V12, P614, DOI 10.1197/jamia.M1873
   Borio L, 2002, JAMA-J AM MED ASSOC, V287, P2391, DOI 10.1001/jama.287.18.2391
   Bottieau E, 2008, MED DECIS MAKING, V28, P435, DOI 10.1177/0272989X07312715
   Bravata DM, 2004, EMERG INFECT DIS, V10, P100, DOI 10.3201/eid1001.030243
   Brown JA, 2008, AM J IND MED, V51, P428, DOI 10.1002/ajim.20578
   BROWN JA, OUTBREAKID BIBLIO
   Brunette GW, 2009, CDC HLTH INFORM INT
   Calain P, 2007, HEALTH POLICY PLANN, V22, P13, DOI 10.1093/heapol/czl035
   *CDCP, BIOS MICR BIOM LA BM
   *CDCP, MMWR, V53
   Centers for Disease Control and Prevention, GUID IS PREC PREV TR
   *CTR CONTR PREV CD, GUID INF CONTR HLTH
   D'Acremont V, 2003, J TRAVEL MED, V10, pS25
   Dennis DT, 2001, JAMA-J AM MED ASSOC, V285, P2763, DOI 10.1001/jama.285.21.2763
   DOMINO FJ, 5 MINUTE CLIN CONSUL
   Edward M., 2010, PRINCIPLES PRACTICE
   Eitzen EM, 1999, ANN EMERG MED, V34, P221, DOI 10.1016/S0196-0644(99)70233-7
   FAUCI AS, HARRISONS PRACTICE A
   *GEN HOSP CORP DXP, DIAGN DEC SUPP
   GOLDMAN L, 2008, CECIL MED, P2103
   Gorbach SL, 2004, INFECT DIS
   Guerrant R, 2011, TROPICAL INFECT DIS, V3rd, P323
   Henderson DA, 1999, JAMA-J AM MED ASSOC, V281, P2127, DOI 10.1001/jama.281.22.2127
   Hennekens C.H., 1987, EPIDEMIOLOGY MED, V1, P101
   Henretig FM, 2002, EMERG MED CLIN N AM, V20, P351, DOI 10.1016/S0733-8627(01)00005-0
   Heymann D.L., 2008, CONTROL COMMUNICABLE
   Hubalek Z, 2003, EMERG INFECT DIS, V9, P403, DOI 10.3201/eid0903.020208
   Inglesby TV, 2002, JAMA-J AM MED ASSOC, V287, P2236, DOI 10.1001/jama.287.17.2236
   Inglesby TV, 2000, JAMA-J AM MED ASSOC, V283, P2281, DOI 10.1001/jama.283.17.2281
   ISADA CM, 2003, LEXICOMPS INFECT DIS
   Kimura Mikio, 2005, Travel Med Infect Dis, V3, P157, DOI 10.1016/j.tmaid.2004.08.003
   *NAT LIB MED HAZ M, OCC EXP HAZ AG
   NIERENGARTEN MB, SYNDROME BASED SURVE
   O'Brien KK, 2003, AM FAM PHYSICIAN, V67, P1927
   OCARROLL PW, 2003, PUBLIC HLTH INFORM S, P18
   PARSAYE K, 1993, INTELLIGENT DATABASE, P4
   Reingold AL, 1998, EMERG INFECT DIS, V4, P21, DOI 10.3201/eid0401.980104
   Rosen P, 2000, BMJ-BRIT MED J, V320, P71, DOI 10.1136/bmj.320.7227.71
   Ross JJ, 1998, CLIN INFECT DIS, V26, P766, DOI 10.1086/517123
   SCHANK RC, 1995, TELL ME STORY NARRAT, P11
   SMOLINSKI MS, 2003, MICROBIAL THREATS HL, pR17
   Temte JL, 2004, ANN FAM MED, V2, P438, DOI 10.1370/afm.125
   *USA, USAMRIID MED MAN BIO
   Wall G, 2008, TRANSPORT POLICY, V15, P55, DOI 10.1016/j.tranpol.2007.10.010
   Wallach J, 2007, INTERPRETATION DIAGN, V8th
   Weinstein RS., 2003, BIOL CHEM TERRORISM
   CONTROL COMMUNICABLE
NR 52
TC 1
Z9 1
U1 0
U2 6
PU MOLECULAR DIVERSITY PRESERVATION INTERNATIONAL-MDPI
PI BASEL
PA KANDERERSTRASSE 25, CH-4057 BASEL, SWITZERLAND
SN 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD MAY
PY 2010
VL 7
IS 5
BP 2177
EP 2190
DI 10.3390/ijerph7052177
PG 14
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA 601YW
UT WOS:000278105200022
PM 20623018
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Alobaid, A
   Corcho, O
AF Alobaid, Ahmad
   Corcho, Oscar
TI Balancing coverage and specificity for semantic labelling of subject
   columns
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Semantic labelling; Semantic annotation; Knowledge graph
ID TABLES; EFFICIENT
AB Many data are published on the Web using tabular data formats (e.g., spreadsheets). One of the main challenges for their effective (re)use is their generalized lack of semantics (e.g., column names are not usually standardized, and their meaning and content are not always clear). There is a common understanding that the reuse of tabular data may be improved by annotating them with the types used in knowledge graphs. In this paper, we present a novel approach to automatically type entity columns in tabular data with ontology classes. In contrast with existing proposals in the state-of-the-art, our approach does not require external linguistic resources, lookup services, model training, building a model of the knowledge graph beforehand, or having a human in the loop.(c) 2022 Published by Elsevier B.V.
C1 [Alobaid, Ahmad; Corcho, Oscar] Univ Politecn Madrid, Boadilla Del Monte, Madrid, Spain.
RP Alobaid, A (通讯作者)，Univ Politecn Madrid, Boadilla Del Monte, Madrid, Spain.
EM aalobaid@fi.upm.es; ocorcho@fi.upm.es
FU Spanish project Knowledge Spaces: Tecnicas y herramientas para la
   gestion de grafos de conocimientos para dar soporte a espacios de datos,
   Spain [PID2020-118274RB-I00]
FX This work has been supported the Spanish project Knowledge Spaces:
   Tecnicas y herramientas para la gestion de grafos de conocimientos para
   dar soporte a espacios de datos, Spain (PID2020-118274RB-I00).
CR Alobaid Ahmad, 2018, Knowledge Engineering and Knowledge Management. 21st International Conference, EKAW 2018. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11313), P19, DOI 10.1007/978-3-030-03667-6_2
   Alobaid A., 2020, THESIS U POLITECNICA
   Alobaid A., Balancing alpha for tada-entity, P2021, DOI 10.5281/zenodo.5579320
   Alobaid A., 2018, TADA-Entity, DOI 10.5281/ zenodo.1462135
   Alobaid A., 2020, SEMANT WEB
   Andrews P, 2012, SEMANT WEB, V3, P223, DOI 10.3233/SW-2011-0056
   Ayala D, 2019, KNOWL-BASED SYST, V163, P931, DOI 10.1016/j.knosys.2018.10.017
   Cafarella MJ, 2008, PROC VLDB ENDOW, V1, P538, DOI 10.14778/1453856.1453916
   Campinas S., 2011, 1 INT WORKSH ENT OR, P26
   Chabot Y., 2019, DAGOBAH END TO END C
   Chaves-Fraga D., 2020, INT J SOFTW ENG KNOW, P1
   Cremaschi M., 2019, SEMTAB ISWC 2019, P15
   Demartini G., 2012, WWW, P469
   Deng D, 2013, PROC VLDB ENDOW, V6, P1606, DOI 10.14778/2536258.2536271
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Ermilov I, 2016, LECT NOTES COMPUT SC, V10024, P163, DOI 10.1007/978-3-319-49004-5_11
   Goel A., 2012, P 8 IND C COMP VIS G
   Gutierrez C., 2019, BRIEF HIST KNOWLEDGE
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Jimenez-Ruiz Ernesto, 2020, Semantic Web 17th International Conference, ESWC 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12123), P514, DOI 10.1007/978-3-030-49461-2_30
   Kacprzak Emilia, 2018, Knowledge Engineering and Knowledge Management. 21st International Conference, EKAW 2018. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 11313), P163, DOI 10.1007/978-3-030-03667-6_11
   Kalyanpur A, 2011, LECT NOTES COMPUT SC, V7032, P144, DOI 10.1007/978-3-642-25093-4_10
   Limaye G, 2010, PROC VLDB ENDOW, V3, P1338, DOI 10.14778/1920841.1921005
   Martinez-Rodriguez JL, 2020, SEMANT WEB, V11, P255, DOI 10.3233/SW-180333
   Pham M, 2016, LECT NOTES COMPUT SC, V9981, P446, DOI 10.1007/978-3-319-46523-4_27
   Morikawa H., 2019, SEMTAB ISWC 2019, P49
   Naseer A., 2014, SEMANT WEB CHALL
   Neumaier S, 2016, LECT NOTES COMPUT SC, V9981, P428, DOI 10.1007/978-3-319-46523-4_26
   Nguyen P.-M., 2019, ARXIV PREPRINT ARXIV
   Nuzzolese AG, 2013, LECT NOTES COMPUT SC, V7955, P253, DOI 10.1007/978-3-642-41242-4_34
   Okazaki N., 2010, P 23 INT C COMP LING, P851
   Oliveira D., 2019, SEMTAB ISWC
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Presutti Valentina, 2012, Knowledge Engineering and Knowledge Management. 18th International Conference, EKAW 2012. Proceedings, P114, DOI 10.1007/978-3-642-33876-2_12
   Priyatna F., 2019, SEKE, P291
   Quercini G., 2013, P 16 INT C EXT DAT T, P693
   Ramnandan SK, 2015, LECT NOTES COMPUT SC, V9088, P403, DOI 10.1007/978-3-319-18818-8_25
   Ritze D., 2015, P 5 INT C WEB INT MI, P10
   Ritze D., 2018, T2DV2 GOLD STANDARD
   Ritze D., 2018, T2D GOLD STANDARD MA
   Ritze D., 2017, P 20 INT C EXT DAT T, V42, P19, DOI [DOI 10.5441/002/EDBT.2017.20, 10.5441/002/edbt.2017.20]
   Ritze D, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P251, DOI 10.1145/2872427.2883017
   Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, P333, DOI 10.1561/1500000019
   Shanmugasundaram K., 2004, P 11 ACM C COMP COMM, P31
   Songyun Duan, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P49, DOI 10.1007/978-3-642-35176-1_4
   Srinivas Kavitha, 2015, P 10 INT WORKSH ONT, P25
   Steenwinckel B., 2019, CSV2KG TRANSFORMING
   Suchanek FM, 2011, PROC VLDB ENDOW, V5, P157, DOI 10.14778/2078331.2078332
   Syed Z., P 2 WEB SCI C, V5
   Taheriyan M, 2016, J WEB SEMANT, V37-38, P152, DOI 10.1016/j.websem.2015.12.003
   Thawani A., 2019, SEMTAB ISWC 2019, P25
   Tonon A, 2012, SIGIR 2012: PROCEEDINGS OF THE 35TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P125
   Tonon A, 2013, LECT NOTES COMPUT SC, V8218, P640, DOI 10.1007/978-3-642-41335-3_40
   Usbeck R, 2014, LECT NOTES COMPUT SC, V8796, P457, DOI 10.1007/978-3-319-11964-9_29
   Venetis P, 2011, PROC VLDB ENDOW, V4, P528, DOI 10.14778/2002938.2002939
   Vrandecic D., 2012, P 21 INT C WORLD WID, P1063, DOI [DOI 10.1145/2187980.2188242, 10.1145/2187980.2188242]
   W3C, 2008, RDF SEMANTICS
   Zhang M., 2013, P 2013 ACM SIGMOD IN, P145, DOI DOI 10.1145/2463676.2465276
   Zhang ZQ, 2017, SEMANT WEB, V8, P921, DOI 10.3233/SW-160242
   Zhang ZQ, 2014, LECT NOTES COMPUT SC, V8796, P487, DOI 10.1007/978-3-319-11964-9_31
NR 60
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 MAR 15
PY 2022
VL 240
AR 108092
DI 10.1016/j.knosys.2021.108092
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V6AQ
UT WOS:000788424800004
OA hybrid
DA 2022-08-23
ER

PT J
AU Li, JD
   Liu, SY
   Liu, A
   Huang, RH
AF Li, Jiandong
   Liu, Shaoying
   Liu, Ai
   Huang, Runhe
TI Knowledge Graph Construction for SOFL Formal Specifications
SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
LA English
DT Article
DE SOFL formal specification; knowledge graph; SOFL ontology
AB Formal specifications can provide a solid foundation for software development and support for techniques of software quality assurance, such as specification-based inspection and testing. To ensure that these techniques can be applied effectively in practice, efficiently and accurately understanding specifications becomes extremely important. While this may be relatively easy for well-trained developers in formal methods, it can be rather difficult for computer since computer does not easily understand specifications. This difficulty poses a challenge for realizing automatic specification-based verification techniques that are in high demand for reducing development cost and improving software reliability. In this paper, we address this problem by discussing how the formal specification can be transformed into a knowledge graph to provide comprehensible, well-organized details of the specification for developers and computers. The transformation is done by extracting and storing information about attributes of each component and by establishing relationships between components in a formal specification. We elaborate on a top-down approach of constructing a knowledge graph from a specification, including creating an ontology, designing the Entity-Relationship (ER) diagram of the relational database based on the created ontology, extracting and storing attribute and relationship information in the relational database, mapping ontology to its instances and relational data to RDF triples, and displaying knowledge graph. Further, we present a case study to show how our approach works on the formal specification of an ATM system. Finally, we describe three experiments to evaluate its performance in improving specification readability, effectively guiding inspectors to establish traceability links between specifications and programs, and detecting defects through program inspection, respectively.
C1 [Li, Jiandong; Liu, Shaoying; Liu, Ai] Hiroshima Univ, Grad Sch Adv Sci & Engn, Hiroshima 7398527, Japan.
   [Huang, Runhe] Hosei Univ, Fac Comp & Informat Sci, Tokyo 1848584, Japan.
RP Liu, SY (通讯作者)，Hiroshima Univ, Grad Sch Adv Sci & Engn, Hiroshima 7398527, Japan.
EM jiandong-li@hiroshima-u.ac.jp; sliu@hiroshima-u.ac.jp;
   liuai@hiroshima-u.ac.jp; rhuang@hosei.ac.jp
OI Liu, Shaoying/0000-0002-6748-5052; LI, JIANDONG/0000-0002-2896-2282;
   Liu, Ai/0000-0001-8222-2157
FU ROIS NII Open Collaborative Research 2021 [21FS02]; China Scholarship
   Council (CSC) [202108050145]
FX The research was supported by ROIS NII Open Collaborative Research
   2021(21FS02) and China Scholarship Council (CSC No. 202108050145).
   Besides, the authors would like to thank all of the four doctoral
   students in our laboratory, who have joined the experiments on this
   work.
CR Abrial J., 1996, B BOOK ASSIGNING PRO
   Abu-Salih B, 2021, J NETW COMPUT APPL, V185, DOI 10.1016/j.jnca.2021.103076
   Al-Moslmi T, 2020, IEEE ACCESS, V8, P32862, DOI 10.1109/ACCESS.2020.2973928
   [Anonymous], 2004, RESOURCE DESCRIPTION
   apache, APACHE JENA TDB
   Barrasa J., 2020, NSMNTXNEO4J RDF SEMA
   Cleland-Huang J., 2012, SOFTWARE SYSTEMS TRA
   Craigen D., 1995, IND APPL FORMAL METH
   Dick J., 1993, FME'93: Industrial-Strength Formal Methods. First International Symposium of Formal Methods Europe Proceedings, P268
   Farber M, 2018, SEMANT WEB, V9
   GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Li RQ, 2019, IEEE IND ELEC, P2877, DOI 10.1109/IECON.2019.8927285
   Lin ZQ, 2017, J COMPUT SCI TECH-CH, V32, P242, DOI 10.1007/s11390-017-1718-y
   Liu S, 1998, IEEE T SOFTWARE ENG, V24, P24, DOI 10.1109/32.663996
   Liu S., 2013, CASE STUDY MODELING
   Liu S., 2013, FORMAL ENG IND SOFTW
   Liu S.., 2018, PROC INT WORKSHOP ST, P3
   Liu SY, 2022, IEEE T SOFTWARE ENG, V48, P691, DOI 10.1109/TSE.2020.2999884
   Liu SY, 2010, FRONT ARTIF INTEL AP, V217, P383, DOI 10.3233/978-1-60750-629-4-383
   Liu SY, 2012, IEEE T SOFTWARE ENG, V38, P1100, DOI 10.1109/TSE.2011.102
   Liu SY, 1998, IEEE INT C ENG COMP, P100, DOI 10.1109/ICECCS.1998.706660
   Lu Zhang, 2020, Signal and Information Processing, Networking and Computers. Proceedings of the 6th International Conference on Signal and Information Processing, Networking and Computers (ICSINC). Lecture Notes in Electrical Engineering (LNEE 628), P470, DOI 10.1007/978-981-15-4163-6_56
   Martin J.D., 2001, PROC 19 INT UNICODE
   Moran K, 2020, PROC INT CONF SOFTW, P873, DOI 10.1145/3377811.3380418
   Musen Mark A, 2015, AI Matters, V1, P4
   Nayak A, 2020, ACM INT CONF PR SER, P289, DOI 10.1145/3371158.3371202
   Noy N.F., 2001, KNOWLEDGE SYSTEMS LA, P32
   Pan J.Z., 2017, EXPLOIT LINKED DATA
   PARNAS DL, 1994, IEEE T SOFTWARE ENG, V20, P948, DOI 10.1109/32.368133
   Shaoying Liu, 2016, 2016 IEEE International Conference on Software Quality, Reliability and Security (QRS). Proceedings, P448, DOI 10.1109/QRS.2016.56
   Singhal A., 2012, OFFICIAL GOOGLE  MAY
   Smirnova A, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3241741
   Smith M., OWL 2 WEB ONTOLOGY L, V2
   Wang R, 2021, IEEE T RELIAB, V70, P1053, DOI 10.1109/TR.2021.3078714
   wiwiss, D2RQ PLATFORM
   Woodcock J., 1996, USING Z SPECIFICATIO
   Xie R, 2019, 2019 IEEE 26TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING (SANER), P434, DOI 10.1109/SANER.2019.8667969
   Zhao Z., 2018, INT J PURE APPL MATH, V118, P1869
NR 40
TC 0
Z9 0
U1 2
U2 2
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-1940
EI 1793-6403
J9 INT J SOFTW ENG KNOW
JI Int. J. Softw. Eng. Knowl. Eng.
PD APR
PY 2022
VL 32
IS 04
BP 605
EP 644
DI 10.1142/S0218194022500279
PG 40
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1M7WU
UT WOS:000800178600007
DA 2022-08-23
ER

PT J
AU Chen, Y
AF Chen, Ying
TI Constructing Bug Knowledge Graph as a Service for Bug Search
SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
LA English
DT Article
DE Bug search; bug knowledge base; bug knowledge graph; topic model
ID RECOMMENDATION
AB When encountering bug issues, developers tend to search the bug repository and commit repository for references. However, the links between bug reports and commits in version control systems are often missed, and the information in bug repository and commit repository can provide is simple. When developers search a bug issue, they can only get the information of bug reports or commits, which are loose and difficult for developers to refer. What's more, many searching results are not accurate. To deal with these problems, this paper proposes an approach to deal with the bug and commit information with the topic model, and construct bug knowledge graph as a service to assist in bug search. In addition, as the amount of bug related information continuously increase, it is time-consuming to update the data. We can automatically update the bug knowledge graph with the LTM topic model (a lifelong topic model). Finally, the experiment with the bug reports from Bugzilla@Mozilla and the corresponding commits from Github was conducted. The experiment results show that our approach is effective and efficient to help developers search relevant bugs for reference by constructing the bug knowledge as a service.
C1 [Chen, Ying] Yangzhou Univ, Sch Informat Engn, Yangzhou, Jiangsu, Peoples R China.
RP Chen, Y (通讯作者)，Yangzhou Univ, Sch Informat Engn, Yangzhou, Jiangsu, Peoples R China.
EM chenying@yzu.edu.cn
CR Aggarwal K, 2017, J SOFTW-EVOL PROC, V29, DOI 10.1002/smr.1821
   Aggarwal K, 2015, 2015 22ND INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), P211, DOI 10.1109/SANER.2015.7081831
   Alipour A, 2013, IEEE WORK CONF MIN S, P183, DOI 10.1109/MSR.2013.6624026
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Bugayenko Y, 2018, COMMUN ACM, V61, P12, DOI 10.1145/3237196
   Chaparro O, 2019, EMPIR SOFTW ENG, V24, P2947, DOI 10.1007/s10664-018-9672-z
   Chen ZW, 2014, PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON ADVANCED ICT, (ICAICTE 2014), P307
   Deshmukh J, 2017, PROC IEEE INT CONF S, P115, DOI 10.1109/ICSME.2017.69
   Duan W., 2016, P 5 ACM SIGSPATIAL I, P7, DOI 10.1145/3006386.3006388
   Ebrahimi N, 2019, INFORM SOFTWARE TECH, V113, P98, DOI 10.1016/j.infsof.2019.05.007
   Falcao F, 2020, PROCEEDINGS OF THE 2020 IEEE 27TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER '20), P378, DOI 10.1109/SANER48275.2020.9054824
   Hasibi F., 2018, THESIS
   Hindle A, 2016, EMPIR SOFTW ENG, V21, P368, DOI 10.1007/s10664-015-9387-3
   Hofmann Thomas, 2017, ACM SIGIR Forum, V51, P211, DOI 10.1145/3130348.3130370
   Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50, DOI 10.1145/312624.312649
   Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950
   Hu DY, 2018, ASIA PAC SOFWR ENG, P725, DOI 10.1109/APSEC.2018.00108
   Hu S, 2018, IEEE T KNOWL DATA EN, V30, P824, DOI 10.1109/TKDE.2017.2766634
   Khatiwada S, 2018, INFORM SOFTWARE TECH, V93, P45, DOI 10.1016/j.infsof.2017.08.012
   Klein N., 2014, MSR 14, P324
   Lazar A, 2014, P 11 WORK C MIN SOFT, P308, DOI DOI 10.1145/2597073.2597088
   Le TDB, 2015, INT C PROGRAM COMPRE, P36, DOI 10.1109/ICPC.2015.13
   Liu Y., 2015, SEMEVAL, P80, DOI [10.18653/v1/s15-2014, DOI 10.18653/V1/S15-2014]
   Poshyvanyk D, 2006, INT C PROGRAM COMPRE, P137, DOI 10.1109/ICPC.2006.17
   Rocha H., 2015, J SOFTW ENG RES DEV, V3, P3, DOI DOI 10.1186/s40411-015-0018-x
   Rocha H, 2016, 2016 IEEE 23RD INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), VOL 1, P46, DOI 10.1109/SANER.2016.87
   Rodriguez-Perez G, 2020, EMPIR SOFTW ENG, V25, P1294, DOI 10.1007/s10664-019-09781-y
   Roy M, 2020, J COMPOS SCI, V4, DOI 10.3390/jcs4010001
   Sun X., 2016, SCI PROGRAM, V2016
   Sun XB, 2019, IEEE ACCESS, V7, P18811, DOI 10.1109/ACCESS.2019.2894976
   Sun XB, 2019, SCI CHINA INFORM SCI, V62, DOI 10.1007/s11432-017-9459-5
   Sun XB, 2018, SCI CHINA INFORM SCI, V61, DOI 10.1007/s11432-017-9419-x
   Sun XB, 2017, ASIA PAC SOFWR ENG, P348, DOI 10.1109/APSEC.2017.41
   Sun XB, 2018, FRONT COMPUT SCI-CHI, V12, P528, DOI 10.1007/s11704-016-6023-3
   Sun XB, 2017, J SYST SOFTWARE, V134, P355, DOI 10.1016/j.jss.2017.09.021
   Sun XB, 2016, 2016 17TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD), P357, DOI 10.1109/SNPD.2016.7515925
   Sun XB, 2015, INFORM SOFTWARE TECH, V66, P1, DOI 10.1016/j.infsof.2015.05.003
   Sun Y, 2017, IEEE INT CONF AUTOM, P147, DOI 10.1109/ASE.2017.8115627
   Sun Y, 2017, INFORM SOFTWARE TECH, V84, P33, DOI 10.1016/j.infsof.2016.11.010
   Timperley CS, 2018, PROC IEEE ACM INT C, P446, DOI 10.1145/3183440.3195050
   Wang L, 2017, PROC IEEE ACM INT C, P189, DOI 10.1109/ICSE-C.2017.102
   Wu H, 2019, IEEE ACCESS, V7, P169548, DOI 10.1109/ACCESS.2019.2953173
   Xu CY, 2018, J SYST SOFTWARE, V142, P195, DOI 10.1016/j.jss.2018.04.060
   Yan JH, 2018, FRONT COMPUT SCI-CHI, V12, P55, DOI 10.1007/s11704-016-5228-9
   Yang XL, 2016, PROC INT SYMP SOFTW, P127, DOI 10.1109/ISSRE.2016.33
   Zhang T, 2019, INT J SOFTW ENG KNOW, V29, P457, DOI 10.1142/S0218194019500207
   Zhang W.E., 2018, MANAGING DATA KNOWLE
   Zhang W, 2019, INFORM SOFTWARE TECH, V110, P121, DOI 10.1016/j.infsof.2019.03.001
   Zhang Y, 2018, INT C PROGRAM COMPRE, P120, DOI 10.1145/3196321.3196348
   Zhang YY, 2018, AAAI CONF ARTIF INTE, P6069
   Zhou C, 2020, J SYST SOFTWARE, V165, DOI 10.1016/j.jss.2020.110572
   Zhou C, 2018, INT C PROGRAM COMPRE, P108, DOI 10.1145/3196321.3196335
   Zhou C, 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, P944, DOI 10.1145/3236024.3275428
NR 53
TC 0
Z9 0
U1 1
U2 14
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-1940
EI 1793-6403
J9 INT J SOFTW ENG KNOW
JI Int. J. Softw. Eng. Knowl. Eng.
PD JAN
PY 2021
VL 31
IS 01
BP 81
EP 98
DI 10.1142/S0218194021400040
PG 18
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QD4ID
UT WOS:000615483100006
DA 2022-08-23
ER

PT J
AU Song, DZ
   Schilder, F
   Hertz, S
   Saltini, G
   Smiley, C
   Nivarthi, P
   Hazai, O
   Landau, D
   Zaharkin, M
   Zielund, T
   Molina-Salgado, H
   Brew, C
   Bennett, D
AF Song, Dezhao
   Schilder, Frank
   Hertz, Shai
   Saltini, Giuseppe
   Smiley, Charese
   Nivarthi, Phani
   Hazai, Oren
   Landau, Dudi
   Zaharkin, Mike
   Zielund, Tom
   Molina-Salgado, Hugo
   Brew, Chris
   Bennett, Dan
TI Building and Querying an Enterprise Knowledge Graph
SO IEEE TRANSACTIONS ON SERVICES COMPUTING
LA English
DT Article
DE Knowledge graph; data acquisition; data transformation; data modeling;
   data interlinking; natural language interface
ID ENTITY LINKING
AB Information providers are faced with a critical challenge to process, retrieve and present information to their users in order to satisfy their complex information needs, because data has been increasing in an unprecedented manner, coming from diverse sources, and covering a variety of domains in heterogeneous formats. In this paper, we present Thomson Reuters' effort in developing a family of services for building and querying an enterprise knowledge graph in order to address this challenge. We first acquire data from various sources via different approaches. Furthermore, we mine useful information from the data by adopting a variety of techniques, including Named Entity Recognition and Relation Extraction; such mined information is further integrated with existing structured data (e.g., via Entity Linking techniques) in order to obtain relatively comprehensive descriptions of the entities. By modeling the data as an RDF graph model, we enable easy data management and the embedding of rich semantics in our data. Finally, in order to facilitate the querying of this mined and integrated data, i.e., the knowledge graph, we propose TR Discover, a natural language interface that allows users to ask questions of our knowledge graph in their own words; such natural language questions are then translated into executable queries for answer retrieval. We evaluate our services, i.e., named entity recognition, relation extraction, entity linking and natural language interface, on real-world datasets, and demonstrate and discuss their practicability and limitations.
C1 [Hertz, Shai; Saltini, Giuseppe; Nivarthi, Phani; Landau, Dudi; Zaharkin, Mike; Bennett, Dan] Thomson Reuters, Cent Technol Platform Grp, Eagan, MN USA.
   [Song, Dezhao; Schilder, Frank; Smiley, Charese; Zielund, Tom; Molina-Salgado, Hugo] Thomson Reuters, Res & Dev Grp, Eagan, MN 55123 USA.
RP Song, DZ (通讯作者)，Thomson Reuters, Res & Dev Grp, Eagan, MN 55123 USA.
EM dezhao.song@thomsonreuters.com; frank.schilder@thomsonreuters.com;
   shai.hertz@homsonreuters.com; Giuseppe.Saltini@homsonreuters.com;
   charese.smiley@thomsonreuters.com; phani.nivarthi@homsonreuters.com;
   oren.hazai@gmail.com; dudi.landau@homsonreuters.com;
   mike.zaharkin@homsonreuters.com; thomas.zielund@thomsonreuters.com;
   hugo.salgado@thomsonreuters.com; christopher.brew@gmail.com;
   dan.bennett@homsonreuters.com
CR [Anonymous], 2014, ORACLE SPATIAL GRAPH
   Bishop B, 2011, SEMANT WEB, V2, P33, DOI 10.3233/SW-2011-0026
   Bobed C, 2016, INFORM SCIENCES, V329, P412, DOI 10.1016/j.ins.2015.09.013
   Bovet J, 2008, SOFTWARE PRACT EXPER, V38, P1305, DOI 10.1002/spe.872
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Carroll J.J., 2005, P 14 INT C WORLD WID, P613, DOI [10.1145/1060745.1060835, DOI 10.1145/1060745.1060835]
   Chieu H.L., 2002, P 19 INT C COMP LING, V1, P1, DOI DOI 10.3115/1072228.1072253
   Christen P, 2012, IEEE T KNOWL DATA EN, V24, P1537, DOI 10.1109/TKDE.2011.127
   Cimiano P, 2008, DATA KNOWL ENG, V65, P325, DOI 10.1016/j.datak.2007.10.007
   Collobert R, 2011, J MACH LEARN RES, V12, P2493
   Cornea R. C., 2014, uS Patent, Patent No. [8,645,825, 8645825]
   d'Aquin M, 2011, SEMANT WEB, V2, P55, DOI 10.3233/SW-2011-0031
   Demartini G., 2013, P 6 BIENN C INN DAT
   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
   Dozier C., 2010, P ENT WORKSH LREC
   Etzioni O., 2011, IJCAI, V11, P3, DOI DOI 10.5591/978-1-57735-516-8/IJCAI11-012
   Fan RE, 2008, J MACH LEARN RES, V9, P1871
   Feldman R., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management, P586
   Ferragina P, 2012, IEEE SOFTWARE, V29, P70, DOI 10.1109/MS.2011.122
   GuoDong Z., 2005, P 43 ANN M ASS COMP, P427, DOI DOI 10.3115/1219840.1219893
   Hachey B, 2013, ARTIF INTELL, V194, P130, DOI 10.1016/j.artint.2012.04.005
   Harth A, 2005, Third Latin American Web Congress, Proceedings, P71, DOI 10.1109/LAWEB.2005.25
   Hearst MA, 1992, P 14 C COMP LING, P539, DOI DOI 10.3115/992133.992154
   Lample G., 2016, P NAACL HLT, P260
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Li F, 2014, PROC VLDB ENDOW, V8, P73, DOI 10.14778/2735461.2735468
   Li Q, 2014, INTERNATIONAL CONFERENCE ON HUMANITY AND SOCIAL SCIENCE (ICHSS 2014), P405
   Matteis L., 2015, P WORKSH LINK DAT WE
   McCallum A, 2003, P 7 C NAT LANG LEARN, V4, P188, DOI DOI 10.3115/1119176.1119206
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Miller S., 2000, ANLP 2000, P226
   Miwa M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1105
   Nadeau D, 2007, LINGUIST INVESTIG, V30, P3
   Navigli R, 2012, ARTIF INTELL, V193, P217, DOI 10.1016/j.artint.2012.07.001
   Parr T, 2014, ACM SIGPLAN NOTICES, V49, P579, DOI [10.1145/2660193.2660202, 10.1145/2714064.2660202]
   Pujara J, 2013, LECT NOTES COMPUT SC, V8218, P542, DOI 10.1007/978-3-642-41335-3_34
   Ratinov L., 2009, P C NAT LANG LEARN, P147, DOI DOI 10.3115/1596374.1596399
   Shen W, 2015, IEEE T KNOWL DATA EN, V27, P443, DOI 10.1109/TKDE.2014.2327028
   Sil Avirup, 2012, EMNLP CONLL, P116
   Socher R., 2012, P 2012 JOINT C EMP M, P1201, DOI [10.5555/2390948.2391084, DOI 10.1162/153244303322533223]
   Song DZ, 2015, LECT NOTES COMPUT SC, V9367, P21, DOI 10.1007/978-3-319-25010-6_2
   Unger C., 2012, P 21 INT C WORLD WID, P639, DOI DOI 10.1145/2187836.2187923
   Usbeck R, 2015, LECT NOTES COMPUT SC, V9088, P353, DOI 10.1007/978-3-319-18818-8_22
   Veeramachaneni S., 2009, P NAACL HLT WORKSH S, P10
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang CY, 2015, 2015 13TH IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW), P59, DOI 10.1109/ICDEW.2015.7129545
   Winkler W. E., 1999, STATE RECORD LINKAGE
   Yahya M, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1107, DOI 10.1145/2505515.2505677
   Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205
   Zhou GD, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P473
NR 51
TC 10
Z9 13
U1 6
U2 65
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1939-1374
J9 IEEE T SERV COMPUT
JI IEEE Trans. Serv. Comput.
PD MAY-JUN
PY 2019
VL 12
IS 3
BP 356
EP 369
DI 10.1109/TSC.2017.2711600
PG 14
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IC6TX
UT WOS:000471106200003
DA 2022-08-23
ER

PT J
AU Ding, LF
   Xiao, GH
   Pano, A
   Stadler, C
   Calvanese, D
AF Ding, Linfang
   Xiao, Guohui
   Pano, Albulen
   Stadler, Claus
   Calvanese, Diego
TI Towards the next generation of the LinkedGeoData project using virtual
   knowledge graphs
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE LinkedGeoData; Virtual knowledge graph; GeoSPARQL; Ontop; OpenStreetMap
ID ONTOLOGY-BASED DATA; LINK DISCOVERY; ACCESS; WEB
AB With the advancement of Semantic Technologies, large geospatial data sources have been increasingly published as Linked data on the Web. The LinkedGeoData project is one of the most prominent such projects to create a large knowledge graph from OpenStreetMap (OSM) with global coverage and interlinking of other data sources. In this paper, we report on the ongoing effort of exposing the relational database in LinkedGeoData as a SPARQL endpoint using Virtual Knowledge Graph (VKG) technology. Specifically, we present two realizations of VKGs, using the two systems Sparqlify and Ontop. In order to improve compliance with the OGC GeoSPARQL standard, we have implemented GeoSPARQL support in Ontop v4. Moreover, we have evaluated the VKG-powered LinkedGeoData in the test areas of Italy and Germany. Our experiments demonstrate that such system supports complex GeoSPARQL queries, which confirms that query answering in the VKG approach is efficient. (C) 2021 The Author(s). Published by Elsevier B.V.
C1 [Ding, Linfang; Xiao, Guohui; Pano, Albulen; Calvanese, Diego] Free Univ Bozen Bolzano, KRDB Res Ctr, Piazza Domenicani 3, I-39100 Bolzano, Italy.
   [Xiao, Guohui; Pano, Albulen; Calvanese, Diego] Ontop Srl, Via A Volta 13-A, I-39100 Bolzano, Italy.
   [Stadler, Claus] Univ Leipzig, Inst Appl Informat, D-04109 Leipzig, Germany.
   [Calvanese, Diego] Umea Univ, Dept Comp Sci, S-90187 Umea, Sweden.
RP Xiao, GH (通讯作者)，Free Univ Bozen Bolzano, KRDB Res Ctr, Piazza Domenicani 3, I-39100 Bolzano, Italy.
EM ding@inf.unibz.it; xiao@inf.unibz.it; albulen.pano@ontopic.biz;
   cstadler@informatik.uni-leipzig.de; calvanese@inf.unibz.it
RI ; Calvanese, Diego/L-4126-2015
OI Xiao, Guohui/0000-0002-5115-4769; Calvanese, Diego/0000-0001-5174-9693
FU EU [863410]; Italian PRIN project HOPE [2017MMJJRE]; European Regional
   Development Fund (ERDF) [FESR1133, FESR1151]; Free University of
   Bozen-Bolzano; Wallenberg AI, Autonomous Systems and Software Program
   (WASP) - Knut and Alice Wallenberg Foundation
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 projects IDEE (FESR1133) and GEOBIMM
   (FESR1151), by the Free University of Bozen-Bolzano through the projects
   KGID (RTD 2019 Computer Science project), GeoVKG (CRC 2019 project), and
   STyLoLa (ID 2017 project) and through the Open Access Publishing Fund,
   and by the Wallenberg AI, Autonomous Systems and Software Program (WASP)
   funded by the Knut and Alice Wallenberg Foundation.
CR Auer S, 2009, LECT NOTES COMPUT SC, V5823, P731, DOI 10.1007/978-3-642-04930-9_46
   Bereta K, 2019, J WEB SEMANT, V58, DOI 10.1016/j.websem.2019.100514
   Bereta K, 2018, INT GEOSCI REMOTE SE, P407, DOI 10.1109/IGARSS.2018.8518255
   Bereta K, 2016, LECT NOTES COMPUT SC, V9981, P37, DOI 10.1007/978-3-319-46523-4_3
   Bereta Konstantina, 2019, P501, DOI [10.5441/002/edbt.2019.46, 10.5441/002/EDBT.2019.46]
   Bizer C., 2007, P W3C WORKSH RDF ACC, P35
   Bruggemann S, 2016, LECT NOTES COMPUT SC, V9678, P741, DOI 10.1007/978-3-319-34129-3_45
   Calvanese D, 2017, SEMANT WEB, V8, P471, DOI 10.3233/SW-160217
   Ding LF, 2020, ISPRS INT J GEO-INF, V9, DOI 10.3390/ijgi9080474
   Ding LF, 2021, GEOINFORMATICA, V25, P733, DOI 10.1007/s10707-019-00384-9
   Dressler Kevin, 2017, Semantic Web - Interoperability, Usability, Applicability, V8, P185, DOI 10.3233/SW-150209
   Ermilov I, 2013, COMM COM INF SC, V394, P242
   Georgala K, 2018, COMM COM INF SC, V927, P3, DOI 10.1007/978-3-030-00072-1_1
   Guohui Xiao, 2020, The Semantic Web - ISWC 2020. 19th International Semantic Web Conference. Lecture Notes in Computer Science (LNCS 12507), P259, DOI 10.1007/978-3-030-62466-8_17
   Herring John, 2010, 06104R4 OGC
   Kharlamov E, 2017, J WEB SEMANT, V44, P3, DOI 10.1016/j.websem.2017.05.005
   Kontokostas D, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P747, DOI 10.1145/2566486.2568002
   Nentwig M, 2017, SEMANT WEB, V8, P419, DOI 10.3233/SW-150210
   Perry M., 2012, 11052R4 OGC
   Priyatna F, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P479, DOI 10.1145/2566486.2567981
   Saleem M, 2015, LECT NOTES COMPUT SC, V9367, P261, DOI 10.1007/978-3-319-25010-6_15
   Sherif MA, 2017, AAAI CONF ARTIF INTE, P175
   Stadler C, 2020, J WEB SEMANT, V65, DOI 10.1016/j.websem.2020.100614
   Stadler C, 2019, LECT NOTES COMPUT SC, V11779, P293, DOI 10.1007/978-3-030-30796-7_19
   Stadler C, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P175, DOI 10.1145/2567948.2577022
   Stadler C, 2012, SEMANT WEB, V3, P333, DOI 10.3233/SW-2011-0052
   Stadler Claus, 2015, P 8 WORKSH LINK DAT, V1409
   Stolze Knut, 2003, LNI, VP-26, P247
   Tandy J., 2017, SPATIAL DATA WEB BES
   Tempelmeier N, 2021, FUTURE GENER COMP SY, V116, P349, DOI 10.1016/j.future.2020.11.003
   Trisedya BD, 2019, AAAI CONF ARTIF INTE, P297
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Xiao GH, 2019, DATA INTELLIGENCE, V1, P201, DOI 10.1162/dint_a_00011
   Xiao GH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5511
NR 34
TC 1
Z9 1
U1 1
U2 4
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 NOV
PY 2021
VL 71
AR 100662
DI 10.1016/j.websem.2021.100662
EA OCT 2021
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 XK3OO
UT WOS:000727379400007
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Pan, ZL
   Su, C
   Deng, YC
   Cheng, J
AF Pan, Zaolin
   Su, Cheng
   Deng, Yichuan
   Cheng, Jack
TI Image2Triplets: A computer vision-based explicit relationship extraction
   framework for updating construction activity knowledge graphs
SO COMPUTERS IN INDUSTRY
LA English
DT Article
DE Zero-shot learning; Human-object interaction detection; Computer vision;
   Explicit relationship extraction; Knowledge graph
AB Knowledge graph (KG) is an effective tool for knowledge management, particularly in the architecture, engineering and construction (AEC) industry, where knowledge is fragmented and complicated. However, research on KG updates in the industry is scarce, with most current research focusing on text-based KG updates. Considering the superiority of visual data over textual data in terms of accuracy and timeliness, the potential of computer vision technology for explicit relationship extraction in KG updates is yet to be explored. This paper combines zero-shot human-object interaction detection techniques with general KGs to propose a novel framework called Image2Triplets that can extract explicit visual relationships from images to update the construction activity KG. Comprehensive experiments on the images of architectural decoration processes have been performed to validate the proposed framework. The results and insights will contribute new knowledge and evidence to human-object interaction detection, KG update and construction informatics from the theoretical perspective. (C) 2022 Elsevier B.V. All rights reserved.
C1 [Pan, Zaolin; Su, Cheng; Deng, Yichuan] South China Univ Technol, Sch Civil Engn & Transportat, Guangzhou, Peoples R China.
   [Pan, Zaolin] Sino Singapore Int Joint Res Inst, Guangzhou, Peoples R China.
   [Su, Cheng; Deng, Yichuan] South China Univ Technol, State Key Lab Subtrop Bldg Sci, Guangzhou, Peoples R China.
   [Cheng, Jack] Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Hong Kong, Peoples R China.
RP Deng, YC (通讯作者)，South China Univ Technol, Sch Civil Engn & Transportat, Guangzhou, Peoples R China.
EM ctycdeng@scut.edu.cn
FU Guangdong Science Foundation [2018A030310363]; Science and Technology
   Program of Guangzhou [201804020069]; China-Singapore International Joint
   Research Institute [205-D119001]
FX The authors would like to acknowledge the support of the Guangdong
   Science Foundation, Grant No. 2018A030310363; the support of the Science
   and Technology Program of Guangzhou, Grant No. 201804020069; and the
   support of the China-Singapore International Joint Research Institute,
   Project No. 205-D119001.
CR Ahmeti A., 2020, UPDATES CONTEXT ONTO
   [Anonymous], 2021, NEO4J NEO4J
   Anumba P., 2008, CHARLES CARRILLO KNO
   Bansal A, 2020, AAAI CONF ARTIF INTE, V34, P10460
   Berners-Lee T., 2001, SCI AM, V284, P34
   Brin S, 1999, LECT NOTES COMPUT SC, V1590, P172
   Chao YW, 2018, IEEE WINT CONF APPL, P381, DOI 10.1109/WACV.2018.00048
   Chen WW, 2020, CONSTRUCTION RESEARCH CONGRESS 2020: COMPUTER APPLICATIONS, P1353, DOI 10.1061/9780784482865.143
   Cheng J., 2002, INT C CONC ENG CONST, V3
   Cui Y., 2020, ABS20041 CORR
   Dork M, 2011, PROC SPIE, V7868, DOI 10.1117/12.872578
   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
   Drucker P., 2018, EFFECTIVE EXECUTIVE, DOI [10.4324/9780080549354, DOI 10.4324/9780080549354]
   Fang WL, 2020, AUTOMAT CONSTR, V119, DOI 10.1016/j.autcon.2020.103310
   Fazio P., 1991, COMPUTING CIVIL ENG, P719
   Flynn P, 2007, LECT NOTES COMPUT SC, V4822, P327
   Gao Chen, 2018, BMVC
   Gao R, 2020, IEEE T IMAGE PROCESS, V29, P3665, DOI 10.1109/TIP.2020.2964429
   Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081
   Gupta S., 2015, ARXIV PREPRINT ARXIV
   He KM, 2020, IEEE T PATTERN ANAL, V42, P386, DOI 10.1109/TPAMI.2018.2844175
   Ji GL, 2017, AAAI CONF ARTIF INTE, P3060
   Kamara J., 2002, CONSTRUCTION INNOVAT, V2, P53, DOI [10.1108/14714170210814685, DOI 10.1108/14714170210814685]
   Kambhatla Nanda, 2004, P ACL 2004 INT POST, DOI 10.3115/ 1219044.1219066
   Kampffmeyer M, 2019, PROC CVPR IEEE, P11479, DOI 10.1109/CVPR.2019.01175
   Kamsu-Foguem B, 2015, COMPUT IND, V70, P79, DOI 10.1016/j.compind.2015.02.004
   Kato K, 2018, LECT NOTES COMPUT SC, V11218, P247, DOI 10.1007/978-3-030-01264-9_15
   Lampert CH, 2014, IEEE T PATTERN ANAL, V36, P453, DOI 10.1109/TPAMI.2013.140
   Leng S., 2019, P 36RD CIB W78 C
   Li JH, 2021, J SIGNAL PROCESS SYS, V93, P239, DOI 10.1007/s11265-020-01592-5
   Li R, 2021, IEEE T IND INFORM, V17, P321, DOI 10.1109/TII.2020.2967561
   Li XY, 2021, COMPUT IND, V129, DOI 10.1016/j.compind.2021.103449
   Lin TY, 2014, LECT NOTES COMPUT SC, V8693, P740, DOI 10.1007/978-3-319-10602-1_48
   Lin TY, 2017, PROC CVPR IEEE, P936, DOI 10.1109/CVPR.2017.106
   Lu CW, 2016, LECT NOTES COMPUT SC, V9905, P852, DOI 10.1007/978-3-319-46448-0_51
   Ma ZL, 2016, COMPUT-AIDED CIV INF, V31, P4, DOI 10.1111/mice.12175
   Martinez P, 2019, AUTOMAT CONSTR, V107, DOI 10.1016/j.autcon.2019.102947
   Miwa M, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1105
   MOHAN S, 1990, J CONSTR ENG M ASCE, V116, P87, DOI 10.1061/(ASCE)0733-9364(1990)116:1(87)
   Moreo A, 2013, KNOWL-BASED SYST, V53, P108, DOI 10.1016/j.knosys.2013.08.018
   NECHES R, 1991, AI MAG, V12, P36
   Niu J., 2012, Proceedings of the 2012 ASCE International Conference on Computing in Civil Engineering, P9, DOI 10.1061/9780784412343.0002
   Norouzi M., 2013, ARXIV13125650
   Ovchinnikova E., 2006, P 2 AUSTR WORKSH ADV, V72, P51
   Pan ZL, 2021, AUTOMAT CONSTR, V125, DOI 10.1016/j.autcon.2021.103617
   Park M, 2013, KSCE J CIV ENG, V17, P1654, DOI 10.1007/s12205-013-1155-6
   Prechelt L, 1998, LECT NOTES COMPUT SC, V1524, P55
   Rahman S, 2020, AAAI CONF ARTIF INTE, V34, P11932
   Rahman S, 2019, LECT NOTES COMPUT SC, V11361, P547, DOI 10.1007/978-3-030-20887-5_34
   Rasmussen MH, 2019, AUTOMAT CONSTR, V108, DOI 10.1016/j.autcon.2019.102956
   Romera-Paredes B, 2015, PR MACH LEARN RES, V37, P2152
   Smith ML, 2021, COMPUT IND, V130, DOI 10.1016/j.compind.2021.103472
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Suchen Wang, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P11649, DOI 10.1109/CVPR42600.2020.01167
   Sveiby K.E., 1986, KUNSKAPSFORETAGET SE
   Ulutan Oytun, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P13614, DOI 10.1109/CVPR42600.2020.01363
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Wang HW, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P417, DOI 10.1145/3269206.3271739
   Wang J, 2020, CONSTRUCTION RESEARCH CONGRESS 2020: COMPUTER APPLICATIONS, P812
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xu BJ, 2019, PROC CVPR IEEE, P2019, DOI 10.1109/CVPR.2019.00212
   Xue F, 2020, AUTOMAT CONSTR, V118, DOI 10.1016/j.autcon.2020.103270
   Yan CX, 2020, IEEE T IMAGE PROCESS, V29, P8163, DOI 10.1109/TIP.2020.3011807
   Yang Liu, 2019, Foundations and Trends in Smart Learning. Proceedings of 2019 International Conference on Smart Learning Environments. Lecture Notes in Educational Technology (LNET), P67, DOI 10.1007/978-981-13-6908-7_9
   Ye Liu, 2020, MM '20: Proceedings of the 28th ACM International Conference on Multimedia, P4235, DOI 10.1145/3394171.3413600
   Zhang CX, 2020, 2020 SECOND INTERNATIONAL CONFERENCE ON TRANSDISCIPLINARY AI (TRANSAI 2020), P1, DOI 10.1109/TransAI49837.2020.00007
   Zhong BT, 2020, J CONSTR ENG M, V146, DOI 10.1061/(ASCE)CO.1943-7862.0001767
   Zhu P., 2020, P IEEE CVF C COMP V, P11690, DOI [10.1109/cvpr42600.2020.01171, DOI 10.1109/CVPR42600.2020.01171]
   Zhu PK, 2020, IEEE T CIRC SYST VID, V30, P998, DOI 10.1109/TCSVT.2019.2899569
   Zhu YQ, 2017, SCI PROGRAMMING-NETH, V2017, DOI 10.1155/2017/5072427
NR 71
TC 0
Z9 0
U1 28
U2 28
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-3615
EI 1872-6194
J9 COMPUT IND
JI Comput. Ind.
PD MAY
PY 2022
VL 137
AR 103610
DI 10.1016/j.compind.2022.103610
PG 17
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YX6BR
UT WOS:000754186600004
DA 2022-08-23
ER

PT J
AU Li, ZR
   Zhong, Q
   Yang, J
   Duan, YJ
   Wang, WJ
   Wu, CK
   He, KL
AF Li, Zongren
   Zhong, Qin
   Yang, Jing
   Duan, Yongjie
   Wang, Wenjun
   Wu, Chengkun
   He, Kunlun
TI DeepKG: an end-to-end deep learning-based workflow for biomedical
   knowledge graph extraction, optimization and applications
SO BIOINFORMATICS
LA English
DT Article
ID EMBEDDINGS; ENTITY
AB DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research. To improve the performance of DeepKG, a cascaded hybrid information extraction framework is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144900 COVID-19 scholarly full-text literature, DeepKG generates a high-quality knowledge graph with 7980 entities and 43760 3-tuples, a candidate drug list, and relevant animal experimental studies are being carried out. To accelerate more studies, we make DeepKG publicly available and provide an online tool including the data of 3-tuples, potential drug list, question answering system, visualization platform.
C1 [Li, Zongren; He, Kunlun] Chinese Peoples Liberat Army Gen Hosp, Med Big Data Res Ctr, Beijing 100039, Peoples R China.
   [Li, Zongren] Chinese Peoples Liberat Army Gen Hosp, Med Artificial Intelligence Res Ctr, Beijing 100853, Peoples R China.
   [Zhong, Qin; Yang, Jing; Duan, Yongjie] Chinese Peoples Liberat Army Gen Hosp, Med Sch Chinese PLA, Beijing 100039, Peoples R China.
   [Wang, Wenjun] Chinese Peoples Liberat Army Gen Hosp, Bioengn Res Ctr, Beijing 100039, Peoples R China.
   [Wu, Chengkun] Natl Univ Def Technol, Sch Comp Sci, State Key Lab High Performance Comp, Changsha 410073, Hunan, Peoples R China.
RP He, KL (通讯作者)，Chinese Peoples Liberat Army Gen Hosp, Med Big Data Res Ctr, Beijing 100039, Peoples R China.; Wu, CK (通讯作者)，Natl Univ Def Technol, Sch Comp Sci, State Key Lab High Performance Comp, Changsha 410073, Hunan, Peoples R China.
EM chengkun_wu@nudt.edu.cn; kunlunhe@plagh.org
OI li, zongren/0000-0002-0246-7048; Yang, Jing/0000-0003-1780-1659
FU technology project `Public service platform for artificial intelligence
   aided diagnosis in medical and health industry' of Ministry of Industry
   and Information Technology of the Peoples Republic of China
   [2020-0103-3-1]
FX This work was supported by the technology project `Public service
   platform for artificial intelligence aided diagnosis in medical and
   health industry' of Ministry of Industry and Information Technology of
   the Peoples Republic of China [2020-0103-3-1].
CR Abbas K, 2021, BMC BIOINFORMATICS, V22, DOI 10.1186/s12859-021-04082-y
   Berber B, 2021, BRIEF BIOINFORM, V22, P1023, DOI 10.1093/bib/bbaa379
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Crichton G, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1776-8
   Habibi M, 2017, BIOINFORMATICS, V33, pI37, DOI 10.1093/bioinformatics/btx228
   Lin HL, 2017, PROCEDIA COMPUT SCI, V108, P345, DOI 10.1016/j.procs.2017.05.045
   Lu Wang L., 2020, ARXIV200410706
   Rotmensch M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-05778-z
   Sang ST, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2167-5
   Shen L, 2021, J AM MED INFORM ASSN, V28, P95, DOI 10.1093/jamia/ocaa250
   Wei ZP, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P1476
   Xu J, 2020, SCI DATA, V7, DOI 10.1038/s41597-020-0543-2
   Zeng XX, 2020, J PROTEOME RES, V19, P4624, DOI 10.1021/acs.jproteome.0c00316
NR 13
TC 0
Z9 0
U1 9
U2 12
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 7
PY 2022
VL 38
IS 5
BP 1477
EP 1479
DI 10.1093/bioinformatics/btab767
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 0D9AS
UT WOS:000776280200047
PM 34788369
OA Green Published
DA 2022-08-23
ER

PT J
AU Zhu, JZ
   Jia, YT
   Xu, J
   Qiao, JZ
   Cheng, XQ
AF Zhu, Ji-Zhao
   Jia, Yan-Tao
   Xu, Jun
   Qiao, Jian-Zhong
   Cheng, Xue-Qi
TI Modeling the Correlations of Relations for Knowledge Graph Embedding
SO JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
LA English
DT Article
DE knowledge graph embedding; low-rank; matrix decomposition
AB Knowledge graph embedding, which maps the entities and relations into low-dimensional vector spaces, has demonstrated its effectiveness in many tasks such as link prediction and relation extraction. Typical methods include TransE, TransH, and TransR. All these methods map different relations into the vector space separately and the intrinsic correlations of these relations are ignored. It is obvious that there exist some correlations among relations because different relations may connect to a common entity. For example, the triples (Steve Jobs, PlaceOfBrith, California) and (Apple Inc., Location, California) share the same entity California as their tail entity. We analyze the embedded relation matrices learned by TransE/TransH/TransR, and find that the correlations of relations do exist and they are showed as low-rank structure over the embedded relation matrix. It is natural to ask whether we can leverage these correlations to learn better embeddings for the entities and relations in a knowledge graph. In this paper, we propose to learn the embedded relation matrix by decomposing it as a product of two low-dimensional matrices, for characterizing the low-rank structure. The proposed method, called TransCoRe (Translation-Based Method via Modeling the Correlations of Relations), learns the embeddings of entities and relations with translation-based framework. Experimental results based on the benchmark datasets of WordNet and Freebase demonstrate that our method outperforms the typical baselines on link prediction and triple classification tasks.
C1 [Zhu, Ji-Zhao; Qiao, Jian-Zhong] Northeastern Univ, Coll Comp Sci & Engn, Shenyang 110169, Liaoning, Peoples R China.
   [Zhu, Ji-Zhao; Jia, Yan-Tao; Xu, Jun; Cheng, Xue-Qi] Chinese Acad Sci, Inst Comp Technol, Key Lab Network Data Sci & Technol, Beijing 110190, Peoples R China.
RP Qiao, JZ (通讯作者)，Northeastern Univ, Coll Comp Sci & Engn, Shenyang 110169, Liaoning, Peoples R China.; Xu, J (通讯作者)，Chinese Acad Sci, Inst Comp Technol, Key Lab Network Data Sci & Technol, Beijing 110190, Peoples R China.
EM zhujz.neu@gmail.com; jiayantao@ict.ac.cn; junxu@ict.ac.cn;
   qiaojianzhong@mail.neu.edu.cn; cxq@ict.ac.cn
FU National Basic Research 973 Program of China [2014CB340405]; National
   Key Research and Development Program of China [2016YFB1000902]; National
   Natural Science Foundation of China [61402442, 61272177, 61173008,
   61232010, 61303244, 61572469, 91646120, 61572473]
FX This work was supported by the National Basic Research 973 Program of
   China under Grant No. 2014CB340405, the National Key Research and
   Development Program of China under Grant No. 2016YFB1000902, and the
   National Natural Science Foundation of China under Grant Nos. 61402442,
   61272177, 61173008, 61232010, 61303244, 61572469, 91646120 and 61572473.
CR Alter O, 2000, P NATL ACAD SCI USA, V97, P10101, DOI 10.1073/pnas.97.18.10101
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bollacker K, 2007, P 22 NAT C ART INT, P1962
   Bordes A., 2012, P 15 INT C ART INT S, 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
   Chang K., 2014, PROC C EMPIRICAL MET, P1568
   Chang K W, 2013, P 2013 C EMP METH NA, P1602
   De Lathauwer L, 2000, SIAM J MATRIX ANAL A, V21, P1253, DOI 10.1137/S0895479896305696
   Franz T, 2009, LECT NOTES COMPUT SC, V5823, P213, DOI 10.1007/978-3-642-04930-9_14
   Jayaram N, 2015, IEEE T KNOWL DATA EN, V27, P2797, DOI 10.1109/TKDE.2015.2426696
   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
   Jia YT, 2016, AAAI CONF ARTIF INTE, P992
   Kiers HAL, 2000, J CHEMOMETR, V14, P105, DOI 10.1002/1099-128X(200005/06)14:3<105::AID-CEM582>3.0.CO;2-I
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   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
   Mikolov T., 2013, ADV NEURAL INFORM PR, V2, P3111, DOI DOI 10.1162/JMLR.2003.3.4-5.951
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Pearson K, 1985, P R SOC LOND, V58, P240, DOI [10.1098/rspl.1895.0041, DOI 10.1098/RSPL.1895.0041]
   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
   Tang J, 2016, ACM T INFORM SYST, V34, DOI 10.1145/2746230
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu W, 2012, ACM SIGMOD, DOI DOI 10.1145/2213836.2213891
NR 27
TC 11
Z9 13
U1 0
U2 21
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1000-9000
EI 1860-4749
J9 J COMPUT SCI TECH-CH
JI J. Comput. Sci. Technol.
PD MAR
PY 2018
VL 33
IS 2
BP 323
EP 334
DI 10.1007/s11390-018-1821-8
PG 12
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GA5MV
UT WOS:000428379000007
DA 2022-08-23
ER

PT J
AU Ge, X
   Wang, YC
   Wang, B
   Kuo, CCJ
AF Ge, Xiou
   Wang, Yun-Cheng
   Wang, Bin
   Kuo, C. C. Jay
TI CORE: A knowledge graph entity type prediction method via complex space
   regression and emb e dding
SO PATTERN RECOGNITION LETTERS
LA English
DT Article
DE Knowledge graph; Complex space embedding; Entity type prediction;
   Self-adversarial negative samplin; Multi-label classification
AB Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE ( CO mplex space R egression and E mbedding), is proposed in this work. The proposed CORE method leverages the expressive power of two complex space embedding models; namely, RotatE and ComplEx models. It embeds entities and types in two different complex spaces using either RotatE or ComplEx. Then, we derive a complex regression model to link these two spaces. Finally, a mechanism to optimize embedding and regression parameters jointly is introduced. Experiments show that CORE outperforms benchmarking methods on representative KG entity type inference datasets. Strengths and weaknesses of various entity type prediction methods are analyzed. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Ge, Xiou; Wang, Yun-Cheng; Kuo, C. C. Jay] Univ Southern Calif, Los Angeles, CA 90007 USA.
   [Wang, Bin] Natl Univ Singapore, Singapore, Singapore.
RP Ge, X (通讯作者)，Univ Southern Calif, Los Angeles, CA 90007 USA.
EM xiouge@usc.edu
CR Auer S., 2007, SEMANT WEB
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Chen T, 2016, INT C KNOWLEDGE DISC, P785
   Cui Z., 2021, AAAI
   Culotta A., 2004, DEPENDENCY TREE KERN
   Fan M., 2017, PATTERN RECOGNIT LET
   Gangemi A., 2012, ISWC
   Gautam B., 2020, PATTERN RECOGNIT LET
   Gupta N., 2017, EMNLP
   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
   Jain N., 2021, ESWC
   Jain P., 2018, ACL
   Ji S., 2021, IEEE TNNLS
   Jin H., 2018, COLING
   Ma SH, 2017, LECT NOTES ARTIF INT, V10534, P717, DOI 10.1007/978-3-319-71249-9_43
   Moon C, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P2215, DOI 10.1145/3132847.3133095
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel Maximilian, 2016, HOLOGRAPHIC EMBEDDIN
   Niu G., 2020, EMNLP FINDINGS
   Paulheim H, 2013, LECT NOTES COMPUT SC, V8218, P510, DOI 10.1007/978-3-642-41335-3_32
   Sofronova R., 2020, ESWC
   Suchanek F. M., 2007, YAGO CORE SEMANTIC K
   Sun Z., 2019, 7 INT C LEARN REPR
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang YY, 2021, KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1748, DOI 10.1145/3447548.3467326
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xin J, 2018, AAAI CONF ARTIF INTE, P5997
   Yaghoobzadeh Y., 2015, EMNLP
   Yang B., 2014, ICLR
   Zhao Y., 2020, ACL
   Zhou G., 2005, EXPLORING VARIOUS KN
NR 32
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 97
EP 103
DI 10.1016/j.patrec.2022.03.024
PG 7
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1C2LQ
UT WOS:000792957500001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Al-Saleem, J
   Granet, R
   Ramakrishnan, S
   Ciancetta, NA
   Saveson, C
   Gessner, C
   Zhou, QQ
AF Al-Saleem, Jacob
   Granet, Roger
   Ramakrishnan, Srinivasan
   Ciancetta, Natalie A.
   Saveson, Catherine
   Gessner, Chris
   Zhou, Qiongqiong
TI Knowledge Graph-Based Approaches to Drug Repurposing for COVID-19
SO JOURNAL OF CHEMICAL INFORMATION AND MODELING
LA English
DT Article
ID INHIBITORS
AB The COVID-19 pandemic has motivated researchers all over the world in trying to find effective drugs and therapeutics for treating this disease. To save time, much effort has focused on repurposing drugs known for treating other diseases than COVID-19. To support these drug repurposing efforts, we built the CAS Biomedical Knowledge Graph and identified 1350 small molecules as potentially repurposable drugs that target host proteins and disease processes involved in COVID-19. A computer algorithm-driven drug-ranking method was developed to prioritize those identified small molecules. The top 50 molecules were analyzed according to their molecular functions and included 11 drugs in clinical trials for treating COVID-19 and new candidates that may be of interest for clinical investigation. The CAS Biomedical Knowledge Graph provides researchers an opportunity to accelerate innovation and streamline the investigative process not just for COVID-19 but also in many other diseases.
C1 [Al-Saleem, Jacob; Granet, Roger; Ramakrishnan, Srinivasan; Ciancetta, Natalie A.; Saveson, Catherine; Gessner, Chris; Zhou, Qiongqiong] CAS, Div Amer Chem Soc A, Columbus, OH 43202 USA.
RP Al-Saleem, J (通讯作者)，CAS, Div Amer Chem Soc A, Columbus, OH 43202 USA.
EM JAl-Saleem@cas.org
OI zhou, Qiongqiong/0000-0001-6711-369X
CR Amarelle L, 2018, INT J MOL SCI, V19, DOI 10.3390/ijms19082154
   Bekerman E, 2017, J CLIN INVEST, V127, P1338, DOI 10.1172/JCI89857
   Blanco-Melo D, 2020, CELL, V181, P1036, DOI 10.1016/j.cell.2020.04.026
   Chang HM, 2004, P NATL ACAD SCI USA, V101, P9578, DOI 10.1073/pnas.0400567101
   Cho J, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-72879-7
   Cohen P, 2010, CELL, V143, P686, DOI 10.1016/j.cell.2010.11.016
   Delorey TM, 2021, NATURE, V595, P107, DOI 10.1038/s41586-021-03570-8
   Domingo-Fernandez D, 2021, BIOINFORMATICS, V37, P1332, DOI 10.1093/bioinformatics/btaa834
   Gatla HR, 2019, INT J MOL SCI, V20, DOI 10.3390/ijms20051110
   Gordon DE, 2020, NATURE, V583, P459, DOI 10.1038/s41586-020-2286-9
   Haqshenas G, 2019, SCI SIGNAL, V12, DOI 10.1126/scisignal.aau9894
   Hogan A., ARXIV200302320
   Hsieh K., 2020, ARXIV PREPRINT ARXIV
   Kircheis R., 2020, ARXIV200810404
   Larsson P, 2016, J THROMB HAEMOST, V14, P2496, DOI 10.1111/jth.13527
   Lenze EJ, 2020, JAMA-J AM MED ASSOC, V324, P2292, DOI 10.1001/jama.2020.22760
   Limanaqi F, 2020, ANTIOXIDANTS-BASEL, V9, DOI 10.3390/antiox9111105
   Liu K, 2020, ACS PHARMACOL TRANSL, V3, P1361, DOI 10.1021/acsptsci.0c00163
   Liu S., CCS CHEM, V2021, P559
   Liu X., 2021, TARGETING CORONAVIRU, DOI [10.1101/2021.02.17.21251933, DOI 10.1101/2021.02.17.21251933]
   Longhitano L, 2020, INT J MOL SCI, V21, DOI 10.3390/ijms21103622
   Vela JM, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.582310
   Ng Y. L., PHARMACOL THERAPEUT, V228
   Pillaiyar T, 2022, J MED CHEM, V65, P955, DOI 10.1021/acs.jmedchem.1c00335
   Plante KS, 2021, BIOMED PHARMACOTHER, V138, DOI 10.1016/j.biopha.2021.111457
   Roskoski R, 2020, PHARMACOL RES, V152, DOI 10.1016/j.phrs.2019.104609
   Takahashi Y, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-82970-2
   Treon SP, 2020, BLOOD, V135, P1912, DOI 10.1182/blood.2020006288
   Wen ZY, 2020, J MOL CELL BIOL, V12, P968, DOI 10.1093/jmcb/mjaa042
   Xie ZJ, 2020, ANTIOXIDANTS-BASEL, V9, DOI 10.3390/antiox9111129
   Yao XH, 2021, CELL RES, V31, P836, DOI 10.1038/s41422-021-00523-8
   Zhang R, 2021, J BIOMED INFORM, V115, DOI 10.1016/j.jbi.2021.103696
   Zhou QA, 2020, ACS PHARMACOL TRANSL, V3, P813, DOI 10.1021/acsptsci.0c00074
NR 33
TC 4
Z9 4
U1 11
U2 27
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 1549-9596
EI 1549-960X
J9 J CHEM INF MODEL
JI J. Chem Inf. Model.
PD AUG 23
PY 2021
VL 61
IS 8
BP 4058
EP 4067
DI 10.1021/acs.jcim.1c00642
EA JUL 2021
PG 10
WC Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science,
   Information Systems; Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy; Chemistry; Computer Science
GA UE9ZU
UT WOS:000688241800030
PM 34297570
OA Green Published
DA 2022-08-23
ER

PT J
AU Wu, YZ
   Liu, Q
   Chen, RR
   Li, CY
   Peng, ZR
AF Wu, Yuezhong
   Liu, Qiang
   Chen, Rongrong
   Li, Changyun
   Peng, Ziran
TI A Group Recommendation System of Network Document Resource Based on
   Knowledge Graph and LSTM in Edge Computing
SO SECURITY AND COMMUNICATION NETWORKS
LA English
DT Article
ID INFORMATION
AB The Internet has become one of the important channels for users to obtain information and knowledge. It is crucial to work out how to acquire personalized requirement of users accurately and effectively from huge amount of network document resources. Group recommendation is an information system for group participation in common activities that meets the common interests of all members in the group. This paper proposes a group recommendation system for network document resource exploration using the knowledge graph and LSTM in edge computing, which can solve the problem of information overload and resource trek effectively. An extensive system test has been carried out in the field of big data application in packaging industry. The experimental results show that the proposed system recommends network document resource more accurately and further improves recommendation quality using the knowledge graph and LSTM in edge computing. Therefore, it can meet the user's personalized resource need more effectively.
C1 [Wu, Yuezhong; Liu, Qiang; Li, Changyun; Peng, Ziran] Hunan Univ Technol, Coll Comp Sci, Zhuzhou 412007, Peoples R China.
   [Wu, Yuezhong; Liu, Qiang; Li, Changyun] Intelligent Informat Percept & Proc Technol Hunan, Zhuzhou 412007, Peoples R China.
   [Chen, Rongrong] Hunan Univ Technol, Coll Business, Zhuzhou 412007, 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 412007, Peoples R China.
EM wuyuezhong@hut.edu.cn; liuqiang@hut.edu.cn; chenrongrong@hut.edu.cn;
   lichangyun@hut.edu.cn; pengziran@hut.edu.cn
OI Wu, Yuezhong/0000-0002-3618-6068; Liu, Qiang/0000-0001-8701-8737
FU National Key R&D Program Funded Project of China [2018YFB1700200]; Hunan
   Provincial Key Research and Development Project of China [2019GK2133];
   Scientific Research Project of Hunan Provincial Department of Education
   [19B147, 18K077, 17C0479]; Project of China Packaging Federation
   [17ZBLWT001KT010]; Intelligent Information Perception and Processing
   Technology Hunan Province Key Laboratory [2017KF07]
FX This work was supported in part by the National Key R&D Program Funded
   Project of China under grant no. 2018YFB1700200, in part by the Hunan
   Provincial Key Research and Development Project of China under grant no.
   2019GK2133, in part by the Scientific Research Project of Hunan
   Provincial Department of Education under grant nos. 19B147, 18K077, and
   17C0479, in part by the Project of China Packaging Federation under
   funding support no. 17ZBLWT001KT010, and in part by the Intelligent
   Information Perception and Processing Technology Hunan Province Key
   Laboratory under grant no. 2017KF07.
CR Adomavicius G, 2005, ACM T INFORM SYST, V23, P103, DOI 10.1145/1055709.1055714
   Amit S., 2012, INTRO KNOWLEDGE GRAP
   Burke R., 2000, ENCY LIB INFORM SCI, V69, P175, DOI DOI 10.1007/S10115-015-0897-5
   Cao KY, 2020, IEEE ACCESS, V8, P85714, DOI 10.1109/ACCESS.2020.2991734
   [常亮 Chang Liang], 2019, [智能系统学报, CAAI Transactions on Intelligent Systems], V14, P207
   [常亮 Chang Liang], 2017, [计算机科学, Computer Science], V44, P1
   De Pessemier T, 2017, MULTIMED TOOLS APPL, V76, P2787, DOI 10.1007/s11042-016-3265-x
   Garcia I, 2012, INFORM SCIENCES, V189, P155, DOI 10.1016/j.ins.2011.11.037
   Gelsinger P., 2012, BIG BETS BIG DATA
   Guezouli L., 2012, P INF TECHN E SERV I, P1
   Gunawardana A, 2009, J MACH LEARN RES, V10, P2935
   He K., 2016, PROCEEDINGSOFTHEIEEE, P770
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   Hu Chuan, 2018, Journal of Software, V29, P3164, DOI 10.13328/j.cnki.jos.005288
   Hu F. H., 2014, CHINESE KNOWLEDGE GR
   Hu F. Y., 2018, RES IMPLEMENTATION H
   Huang CL, 2020, ACM T INTERNET TECHN, V20, DOI 10.1145/3414841
   Jiang JL, 2020, SECUR COMMUN NETW, V2020, DOI 10.1155/2020/8849181
   Jiang WJ, 2016, ACM COMPUT SURV, V49, DOI 10.1145/2906151
   Ju B, 2016, FRONT INFORM TECH EL, V17, P489, DOI 10.1631/FITEE.1500313
   Khan WZ, 2019, FUTURE GENER COMP SY, V97, P219, DOI 10.1016/j.future.2019.02.050
   Lai CH, 2015, J ASSOC INF SCI TECH, V66, P545, DOI 10.1002/asi.23185
   Li CY, 2017, APPL MATH SER B, V32, P14, DOI 10.1007/s11766-017-3451-2
   Lin X. Y., 2017, TELECOMMUNICATIONS S, V33, P94
   [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
   Manouselis N, 2008, INT J COMPUT APPL T, V33, P176, DOI 10.1504/IJCAT.2008.021940
   McAfee A, 2012, HARVARD BUS REV, V90, P60
   Meng Xiang-Wu, 2015, Journal of Software, V26, P1356, DOI 10.13328/j.cnki.jos.004831
   Meng Xiang-Wu, 2013, Journal of Software, V24, P91, DOI 10.3724/SP.J.1001.2013.04292
   Mokri I., 2005, ACTA ELECTROTECHNICA, V5, P3
   Park HS, 2006, LECT NOTES COMPUT SC, V4223, P970
   Pazzani MJ, 1999, ARTIF INTELL REV, V13, P393, DOI 10.1023/A:1006544522159
   Roy SB, 2010, VLDB J, V19, P877, DOI 10.1007/s00778-010-0209-3
   Saito R, 2015, INT J COMPUT SCI ENG, V11, P249, DOI 10.1504/IJCSE.2015.072648
   Satyanarayanan M, 2017, COMPUTER, V50, P30, DOI 10.1109/MC.2017.9
   Wang T, 2021, IEEE T INTELL TRANSP, V22, P1797, DOI 10.1109/TITS.2020.2997377
   Wang T, 2020, IEEE INTERNET THINGS, V7, P4218, DOI 10.1109/JIOT.2020.2966870
   Wang T, 2020, IEEE T IND INFORM, V16, P6663, DOI 10.1109/TII.2019.2962844
   Wu Y., 2019, COMMUNICATIONS COMPU, V1137, P94, DOI [10.1007/978-981-15-1922-2_6, DOI 10.1007/978-981-15-1922-2_6]
   Wu YK, 2019, J PARALLEL DISTR COM, V131, P189, DOI 10.1016/j.jpdc.2019.04.007
   Wu YZ, 2017, IEEE INT SYMP PARAL, P715, DOI 10.1109/ISPA/IUCC.2017.00111
   Xie T, 2016, KNOWL-BASED SYST, V111, P36, DOI 10.1016/j.knosys.2016.08.001
   Xu K. Y., 2011, LIB INFORM SERVICE, V55, P59
   Xu XL, 2021, IEEE T IND INFORM, V17, P2910, DOI 10.1109/TII.2020.2987994
   Xu ZY, 2016, PHOTONICS RES, V4, P45, DOI 10.1364/PRJ.4.000045
   [尹祎 Yin Yi], 2017, [计算机学报, Chinese Journal of Computers], V40, P2797
   Yuan Jingbo, 2012, LNCS, V7529, P306
   Zhou XM, 2017, VLDB J, V26, P637, DOI 10.1007/s00778-017-0469-2
NR 48
TC 7
Z9 8
U1 4
U2 15
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 DEC 2
PY 2020
VL 2020
AR 8843803
DI 10.1155/2020/8843803
PG 11
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA PG7HL
UT WOS:000599902200001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Fu, SC
   Ji, XH
   Lu, CX
   Fan, SC
AF Fu, Shaochuan
   Ji, Xuehua
   Lu, Changxiang
   Fan, Shucheng
TI A Visual Analysis of Frontiers and Dynamic Tendencies for Humanitarian
   Logistics
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Review
ID CRITICAL SUCCESS FACTORS; SUPPLY CHAIN MANAGEMENT; LOCATION-ROUTING
   MODEL; FACILITY LOCATION; INTELLECTUAL STRUCTURE; EMERGENCY; EVOLUTION;
   NETWORK
AB Under the background of regular epidemic prevention and control, the frontiers and prospects of humanitarian logistics (HL) have substantial value in enhancing emergency capacity. Taking the relevant articles collected in Web of Science and Scopus databases as the research object, this study discusses the basic characteristics, hot topics, and the potential directions of HL, using the bibliometric analysis and statistical analysis methods. Results show that the number of articles presents an overall increase with fluctuation. The major driving force of HL research is from the USA and China. Although a certain cooperation size among authors has been formed, close contact among research teams from different institutions in different countries is lacking. In addition, the four main topics of HL are highlighted through the "co-occurrence" analysis of keywords by VOSviewer. Further research directions and suggestions can be identified under the analysis of each topic and the knowledge map of CiteSpace. It is expected that results will provide a theoretical reference to scholars when identifying innovative ideas in this field.
C1 [Fu, Shaochuan; Ji, Xuehua; Lu, Changxiang; Fan, Shucheng] Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100000, Peoples R China.
RP Ji, XH (通讯作者)，Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100000, Peoples R China.
EM 20113037@bjtu.edu.cn
CR Abounacer R, 2014, COMPUT OPER RES, V41, P83, DOI 10.1016/j.cor.2013.08.001
   Afshar A., 2012, SOCIO-ECON PLAN SCI, V46, P327, DOI [10.1016/j.seps.2011.12.003, DOI 10.1016/J.SEPS.2011.12.003]
   Akgun I, 2015, OMEGA-INT J MANAGE S, V52, P168, DOI 10.1016/j.omega.2014.04.003
   Altay N, 2006, EUR J OPER RES, V175, P475, DOI 10.1016/j.ejor.2005.05.016
   Archambault E, 2009, J AM SOC INF SCI TEC, V60, P1320, DOI 10.1002/asi.21062
   Bayram V, 2015, TRANSPORT RES B-METH, V72, P146, DOI 10.1016/j.trb.2014.11.010
   Behl A, 2019, IFAC PAPERSONLINE, V52, P159, DOI 10.1016/j.ifacol.2019.11.169
   Boonmee C, 2017, INT J DISAST RISK RE, V24, P485, DOI 10.1016/j.ijdrr.2017.01.017
   Bozorgi-Amiri A, 2016, INT J ADV MANUF TECH, V85, P1633, DOI 10.1007/s00170-015-7923-3
   Bozorgi-Amiri A, 2013, OR SPECTRUM, V35, P905, DOI 10.1007/s00291-011-0268-x
   Cai S., 2018, J RISK ANAL CRISIS R, V8, P101, DOI [10.2991/jrarc.2018.8.2.6, DOI 10.2991/JRARC.2018.8.2.6]
   Caunhye A.M., 2012, SOCIOECONOMIC PLANNI, V46, P4, DOI [10.1016/j.seps.2011.04.004, DOI 10.1016/J.SEPS.2011.04.004]
   Chen CM, 2012, EXPERT OPIN BIOL TH, V12, P593, DOI 10.1517/14712598.2012.674507
   [陈伟珂 Chen Weike], 2015, [灾害学, Journal of Catastrophology], V30, P152
   Chen Y., 2015, STUDIES SCI SCI, V33, P242
   Choudhary, 2020, PROD PLAN CONTROL, V23, P183
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Cui Y, 2018, ELECTRON COMMER RES, V18, P837, DOI 10.1007/s10660-018-9288-9
   Dasaklis T.K., 2017, INT, V4, P27, DOI [10.1080/23302674.2015.1126379, DOI 10.1080/23302674.2015.1126379]
   Davis LB, 2013, INT J PROD ECON, V141, P561, DOI 10.1016/j.ijpe.2012.09.012
   Dubey Rameshwar, 2014, International Journal of Services and Operations Management, V17, P367, DOI 10.1504/IJSOM.2014.059999
   Dubey R, 2020, INT J PROD RES, V58, P3381, DOI 10.1080/00207543.2020.1722860
   Dubey R, 2019, ANN OPER RES, V283, P159, DOI 10.1007/s10479-017-2676-z
   Duran S, 2011, INTERFACES, V41, P223, DOI 10.1287/inte.1100.0526
   Ellegaard O, 2015, SCIENTOMETRICS, V105, P1809, DOI 10.1007/s11192-015-1645-z
   Garcia-Lillo F, 2016, INT J HOSP MANAG, V52, P121, DOI 10.1016/j.ijhm.2015.10.007
   Holguin-Veras J, 2012, J OPER MANAG, V30, P494, DOI 10.1016/j.jom.2012.08.003
   Hong JD, 2012, J IND ENG MANAG-JIEM, V5, P4, DOI 10.3926/jiem.415
   Hu FY, 2014, INT J GEOGR INF SCI, V28, P1482, DOI 10.1080/13658816.2014.894638
   Kinay OB, 2018, EUR J OPER RES, V270, P132, DOI 10.1016/j.ejor.2018.03.006
   Li H.Q., 2016, J MODERN INFORM, V1, P160
   Li QP, 2018, ISPRS INT J GEO-INF, V7, DOI 10.3390/ijgi7020077
   Li YZ, 2021, J ENERGY CHEM, V53, P1, DOI 10.1016/j.jechem.2020.05.012
   Mohammadi S, 2020, COMPUT IND ENG, V148, DOI 10.1016/j.cie.2020.106734
   Peng M, 2014, COMPUT OPER RES, V42, P14, DOI 10.1016/j.cor.2013.03.003
   Pilkington A, 2009, J OPER MANAG, V27, P185, DOI 10.1016/j.jom.2008.08.001
   PRITCHARD A, 1969, J DOC, V25, P348
   Rennemo SJ, 2014, TRANSPORT RES E-LOG, V62, P116, DOI 10.1016/j.tre.2013.12.006
   Rezaei-Malek M, 2016, COMPUT IND ENG, V94, P201, DOI 10.1016/j.cie.2016.01.014
   Salman FS, 2015, COMPUT OPER RES, V62, P266, DOI 10.1016/j.cor.2014.07.015
   Seuring S, 2012, SUPPLY CHAIN MANAG, V17, P544, DOI 10.1108/13598541211258609
   Sheu JB, 2007, TRANSPORT RES E-LOG, V43, P655, DOI 10.1016/j.tre.2007.01.001
   Singh RK, 2018, INT J PROD RES, V56, P6809, DOI 10.1080/00207543.2018.1424373
   Tofighi S, 2016, EUR J OPER RES, V250, P239, DOI 10.1016/j.ejor.2015.08.059
   Uater, 2017, TRANSPORT SCI, V51, P60
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Van Wassenhove LN, 2006, J OPER RES SOC, V57, P475, DOI 10.1057/palgrave.jors.2602125
   Verma A, 2015, COMPUT OPER RES, V62, P197, DOI 10.1016/j.cor.2014.10.006
   Wang HJ, 2014, TRANSPORT RES E-LOG, V69, P160, DOI 10.1016/j.tre.2014.06.006
   Yadav DK, 2018, BENCHMARKING, V25, P400, DOI 10.1108/BIJ-10-2016-0154
   Yadav DK, 2015, INT J DISAST RISK RE, V12, P213, DOI 10.1016/j.ijdrr.2015.01.008
   Yang X, 2008, INTRO MATH OPTIMIZAT
   Ye F., 2015, ELECT NOTES DISCRET, V47, P61, DOI [10.1016/j.endm.2014.11.009, DOI 10.1016/J.ENDM.2014.11.009]
   Zhao, 2014, INT J MATH OPERATION, V10, P1
   Zhou Q, 2011, SAFETY SCI, V49, P243, DOI 10.1016/j.ssci.2010.08.005
   Zhu JL, 2019, LEADERSHIP QUART, V30, P215, DOI 10.1016/j.leaqua.2018.06.003
NR 56
TC 0
Z9 0
U1 12
U2 25
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 AUG 20
PY 2021
VL 2021
AR 5555058
DI 10.1155/2021/5555058
PG 16
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Mathematics
GA UJ2KG
UT WOS:000691120300006
OA gold
DA 2022-08-23
ER

PT S
AU Agarwal, P
AF Agarwal, P
BE Egenhofer, MJ
   Freksa, C
   Miller, HJ
TI Contested nature of place: Lnowledge mapping for resolving ontological
   distinctions between geographical concepts
SO GEOGRAPHIC INFORMATION SCIENCE, PROCEEDINGS
SE Lecture Notes in Computer Science
LA English
DT Article; Proceedings Paper
CT 3rd International Conference on Geographic Information Science
CY OCT 20-23, 2004
CL Adelphi, MD
ID CATEGORIES; SENSE
AB In the theoretical literature and geographical models, place is defined with reference to other spatial concepts, such as region, neighbourhood and space. The boundaries and distinctions between these concepts and place are not clear and the overlap in the semantic fields of these concepts is representative of the vagueness that exists in geographical concepts. This vagueness is a major issue in achieving interoperability in the geographical domain and for the development of a comprehensive geo-ontology. In this paper, the principles of conceptual structures are applied for mapping semantic correspondences between place and overlapping geographical concepts. Knowledge mapping is carried out for meaning negotiation from lexical analysis of syntactic proximities between concepts. Experiments with human subjects were performed to elicit the cognitive semantics inherent in conceptual schema of individuals. Distance and proximity measures resolve the cognitive semantics of the concepts. The results provide an indication of the overlap and distinctions between the semantic fields of nearby spatial concepts, and a foundation for defining the relationships and classifications to specify the ontological distinctions between geographic concepts.
C1 Univ Nottingham, Sch Geog, GIS Res Grp, Nottingham NG7 2RD, England.
RP Agarwal, P (通讯作者)，Univ Nottingham, Sch Geog, GIS Res Grp, Nottingham NG7 2RD, England.
EM lgxpa@nottingham.ac.uk
RI Smith, Barry/A-9525-2011
OI Smith, Barry/0000-0003-1384-116X
CR AGARWAL P, IN PRESS INT J GEOGR
   Barsalou Lawrence W., 1993, CHICAGO LINGUISTICS, P23, DOI DOI 10.1037/A0037668
   Battig W. F., 1968, J EXPT PSYCHOL MON 2, V80, P1
   BISHR Y, 1997, ITC PUBLICATION, V56
   BOYER C, 1996, CYCBER CITIES
   Canter D, 1997, ADV ENV BEH, V4, P109
   Christaller W., 1972, MAN SPACE ETLIRORURC, P601
   Duncan J. S., 1993, PLACE CULTURE REPRES, DOI [10.4324/9780203714034, DOI 10.4324/9780203714034]
   FELBAUM C, 1998, WORDNET ELECT LEXICA
   Gardenfors P., 2004, CONCEPTUAL SPACES GE
   Gardenfors P, 1996, PHILOS COGNITIVE SCI, P159
   GOH CH, 1997, THESIS MIT
   GOLLEDGE RG, 1992, GEOFORUM, V23, P199, DOI 10.1016/0016-7185(92)90017-X
   HAHN U, 1997, KNOWLEDGE CONCEPTS C, P43
   Hardt Daniel., 1994, P 9 AMSTERDAM C, P333
   Harrison Steve R, 1996, CSCW, V96, P67, DOI DOI 10.1145/240080.240193
   Jordan T., 1998, P 8 INT S SPAT DAT H, P98
   KOHENEN T, 1997, SELF ORGANISING MAPS
   LAKOFF G, 1988, MEANING MENTAL REPRE, P119
   Lloyd R, 1996, PROF GEOGR, V48, P181, DOI 10.1111/j.0033-0124.1996.00181.x
   MARK D, 2001, SPATIAL INFORMATION, P488
   Mark DM, 1999, LECT NOTES COMPUT SC, V1661, P283
   MARK DM, IN PRESS RES CHALLEN
   Massey Doreen, 1994, SPACE PLACE GENDER
   MCCULLOUGH D, 1982, HIST PRESERVATION, V34, P24
   MEDIN DL, 1989, AM PSYCHOL, V44, P1469, DOI 10.1037/0003-066X.44.12.1469
   MILLER GA, INTRO WORDNET ONLINE
   PATWARDHAN S, 2003, WORDNET SIMILARITY
   Peacocke C, 2004, MIND LANG, V19, P85, DOI 10.1111/j.1468-0017.2004.00249.x
   Putnam H., 1975, MIND LANGUAGE REALIT, P215, DOI DOI 10.1017/CB09780511625251
   Relph E., 1976, PLACE PLACELESSNESS
   Schvaneveldt R. W., 1973, ATTENTION PERFORM, VIV, P395
   SCHVANEVELDT RW, 1981, J EXP PSYCHOL HUMAN, V7, P673, DOI 10.1037/0096-1523.7.3.673
   Smith B, 2001, INT J GEOGR INF SCI, V15, P591, DOI 10.1080/13658810110061199
   STARRS PF, 1994, SPECTRUM-J STATE GOV, V67, P5
   TUAN YF, 1990, SPACE PLACE PERSPECT
   TVERSKY A, 1982, PSYCHOL REV, V89, P123, DOI 10.1037/0033-295X.89.2.123
   TwiggerRoss CL, 1996, J ENVIRON PSYCHOL, V16, P205, DOI 10.1006/jevp.1996.0017
   *UCGIS, 1998, COGN GEOGR INF
   Vogele T, 2003, LECT NOTES COMPUT SC, V2825, P239
   *VT, VIS THES ONL
   Worboys, 2003, FDN GEOGRAPHIC INFOR, P173, DOI DOI 10.1201/9780203009543.CH9
NR 42
TC 8
Z9 9
U1 0
U2 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 3-540-23558-2
J9 LECT NOTES COMPUT SC
PY 2004
VL 3234
BP 1
EP 21
DI 10.1007/978-3-540-30231-5_1
PG 21
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BBD28
UT WOS:000224989100001
DA 2022-08-23
ER

PT J
AU Hernandez, D
   Galarraga, L
   Hose, K
AF Hernandez, Daniel
   Galarraga, Luis
   Hose, Katja
TI Computing How-Provenance for SPARQL Queries via Query Rewriting
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
ID KNOWLEDGE; SCALE; TRUST
AB Over the past few years, we have witnessed the emergence of large knowledge graphs built by extracting and combining information from multiple sources. This has propelled many advances in query processing over knowledge graphs, however the aspect of providing provenance explanations for query results has so far been mostly neglected. We therefore propose a novel method, SPARQLprov, based on query rewriting, to compute how-provenance polynomials for SPARQL queries over knowledge graphs. Contrary to existing works, SPARQLprov is system-agnostic and can be applied to standard and already deployed SPARQL engines without the need of customized extensions. We rely on spm-semirings to compute polynomial annotations that respect the property of commutation with homomorphisms on monotonic and non-monotonic SPARQL queries without aggregate functions. Our evaluation on real and synthetic data shows that SPARQLprov over standard engines incurs an acceptable runtime overhead w.r.t. the original query, competing with state-of-the-art solutions for how-provenance computation.
C1 [Hernandez, Daniel; Hose, Katja] Aalborg Univ, Aalborg, Denmark.
   [Galarraga, Luis] INRIA, Rocquencourt, France.
RP Hernandez, D (通讯作者)，Aalborg Univ, Aalborg, Denmark.
EM danielh@cs.aau.dk; luis.galarraga@inria.fr; khose@cs.aau.dk
RI Hernández, Daniel/ABC-4444-2021
OI Hernández, Daniel/0000-0002-7896-0875; Hose, Katja/0000-0001-7025-8099
FU Danish Council for Independent Research (DFF) [DFF-804800051B]; Poul Due
   Jensen Foundation
FX This research was partially funded by the Danish Council for Independent
   Research (DFF) under grant agreement no. DFF-804800051B and the Poul Due
   Jensen Foundation.
CR Aluc G, 2014, LECT NOTES COMPUT SC, V8796, P197, DOI 10.1007/978-3-319-11964-9_13
   Amsterdamer Yael, 2011, P 30 ACM SIGMOD SIGA, P153
   Angles R, 2016, LECT NOTES COMPUT SC, V9981, P20, DOI 10.1007/978-3-319-46523-4_2
   Arab Bahareh Sadat, 2018, DATA ENG B, V41, P51
   Avgoustaki A, 2016, LECT NOTES COMPUT SC, V9678, P575, DOI 10.1007/978-3-319-34129-3_35
   Beckett D., 2004, RDF XML SYNTAX SPECI
   BONATTI PA, 2019, DAGSTUHL REPORTS, V8, P29, DOI DOI 10.4230/DAGREP.8.9.29
   Buil-Aranda C, 2011, LECT NOTES COMPUT SC, V6644, P1, DOI 10.1007/978-3-642-21064-8_1
   Chang S., 2018, SCALING KNOWLEDGE AC
   Cheney J, 2007, FOUND TRENDS DATABAS, V1, P379, DOI 10.1561/1900000006
   Cui YW, 2000, ACM T DATABASE SYST, V25, P179, DOI 10.1145/357775.357777
   Devarajan Deepika, 2017, HAPPY BIRTHDAY WATSO
   Dividino R, 2009, J WEB SEMANT, V7, P204, DOI 10.1016/j.websem.2009.07.004
   Dong XL, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2724, DOI 10.1145/3394486.3403323
   Frey J, 2019, SEMANT WEB, V10, P205, DOI 10.3233/SW-180307
   Galarraga L, 2018, LECT NOTES COMPUT SC, V11136, P547, DOI 10.1007/978-3-030-00671-6_32
   Gaur G, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P405, DOI 10.1145/3340531.3411958
   Geerts F, 2016, J ACM, V63, DOI 10.1145/2810037
   Geerts F, 2010, J APPL LOGIC, V8, P173, DOI 10.1016/j.jal.2009.09.001
   Glavic B, 2009, PROC INT CONF DATA, P174, DOI 10.1109/ICDE.2009.15
   Green Todd J., 2007, PODS, DOI DOI 10.1145/1265530.1265535
   Halpin H, 2014, LECT NOTES COMPUT SC, V8796, P425, DOI 10.1007/978-3-319-11964-9_27
   Harris Steve, 2013, SPARQL 1 1 QUERY LA
   Hartig O, 2009, LECT NOTES COMPUT SC, V5554, P5, DOI 10.1007/978-3-642-02121-3_5
   Heist N, 2019, LECT NOTES COMPUT SC, V11778, P219, DOI 10.1007/978-3-030-30793-6_13
   Hernandez D, 2016, LECT NOTES COMPUT SC, V9982, P88, DOI 10.1007/978-3-319-46547-0_10
   Hoffart J., 2011, P 20 INT C COMP WORL, P229, DOI DOI 10.1145/1963192.1963296
   Hu W, 2017, DATABASE-OXFORD, P1, DOI 10.1093/database/bax059
   Ibragimov D., 2015, ENABLING REAL TIME B, P114, DOI [10.1007/978-3-662-46839- 5_8, DOI 10.1007/978-3-662-46839-5_8]
   Ibragimov D, 2016, LECT NOTES COMPUT SC, V9981, P341, DOI 10.1007/978-3-319-46523-4_21
   Kaminski M, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P227, DOI 10.1145/2872427.2883022
   Kontchakov R, 2016, FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P369
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   Perez J, 2009, ACM T DATABASE SYST, V34, DOI 10.1145/1567274.1567278
   Polleres Axel, 2007, WWW, P787
   Senellart P, 2018, PROC VLDB ENDOW, V11, P2034, DOI 10.14778/3229863.3236253
   Sequeda J. F., 2012, P 21 INT C WORLD WID, P649, DOI [DOI 10.1145/2187836.2187924, 10.1145/2187836.2187924]
   Shrivastava S., 2017, BRING RICH KNOWLEDGE
   Singhal A., 2012, OFFICIAL GOOGLE BLOG
   Viegas Damasio Carlos, 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P625, DOI 10.1007/978-3-642-35176-1_39
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wylot M, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P455, DOI 10.1145/2566486.2568014
NR 43
TC 0
Z9 0
U1 0
U2 0
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 SEP
PY 2021
VL 14
IS 13
BP 3389
EP 3401
DI 10.14778/3484224.3484235
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YH1OU
UT WOS:000742944600012
DA 2022-08-23
ER

PT J
AU Bai, LY
   Ma, XN
   Zhang, MC
   Yu, WT
AF Bai, Luyi
   Ma, Xiangnan
   Zhang, Mingcheng
   Yu, Wenting
TI TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph
   Completion
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE Temporal knowledge graph; completion; tendency strategy
ID NETWORKS
AB Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the Goodness of relations and the Closeness of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.
C1 [Bai, Luyi; Ma, Xiangnan; Zhang, Mingcheng; Yu, Wenting] Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, 143 Taishan Rd, Qinhuangdao 066004, Hebei, Peoples R China.
RP Bai, LY (通讯作者)，Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, 143 Taishan Rd, Qinhuangdao 066004, Hebei, Peoples R China.
EM baily@neuq.edu.cn; 11121157908@qq.com; 739437934@qq.com;
   806308301@qq.com
OI Bai, Luyi/0000-0001-9546-3208
FU National Natural Science Foundation of China [61402087]; Natural Science
   Foundation of Hebei Province [F2019501030]; Natural Science Foundation
   of Liaoning Province [2019-MS-130]; Key Project of Scientific Research
   Funds in Colleges and Universities of Hebei Education Department
   [ZD2020402]; Fundamental Research Funds for the Central Universities
   [N2023019]; Program for 333 Talents in Hebei Province [A202001066]
FX The work was supported in part by the National Natural Science
   Foundation of China (61402087), in part by the Natural Science
   Foundation of Hebei Province (F2019501030), in part by the Natural
   Science Foundation of Liaoning Province (2019-MS-130), in part by the
   Key Project of Scientific Research Funds in Colleges and Universities of
   Hebei Education Department (ZD2020402), in part by the Fundamental
   Research Funds for the Central Universities (N2023019), and in part by
   the Program for 333 Talents in Hebei Province (A202001066).
CR Balazevic I, 2019, LECT NOTES COMPUT SC, V11731, P553, DOI 10.1007/978-3-030-30493-5_52
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cho Kyunghyun, 2014, P 2014 C EMP METH N, P5998
   Dasgupta Shib Sankar, 2018, P 2018 C EMP METH NA, P2001
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Erxleben F, 2014, LECT NOTES COMPUT SC, V8796, P50, DOI 10.1007/978-3-319-11964-9_4
   Federico Bianchi, 2020, ARXIV200414843
   Garcia-Duran Alberto, 2018, P 2018 C EMP METH NA, P4816
   Gemulla Rainer, 2019, P INT C LEARN REPR
   Goel R, 2020, AAAI CONF ARTIF INTE, V34, P3988
   Hospedales Timothy M., 2019, P INT JOINT C NAT LA
   Ji S., 2020, ARXIV200200388
   Jiang T., 2016, P 2016 C EMP METH NA, P2350, DOI DOI 10.18653/V1/D16-1260
   Jin Woojeong, 2019, P 7 INT C LEARN REPR
   Kazemi SM, 2018, ADV NEUR IN, V31
   Kazemi Seyed Mehran, 2020, ARXIV190511485
   King DB, 2015, ACS SYM SER, V1214, P1
   Leblay J, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1771, DOI 10.1145/3184558.3191639
   Leetaru Kalev, 2013, P INT STUD ASS ANN C
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lio Pietro, 2017, P INT C LEARN REPR
   Ma Yunpu, 2018, J WEB SEMANT
   Mahdisoltani Farzaneh, 2013, P 7 BIENN C INN DAT
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nguyen GH, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P969, DOI 10.1145/3184558.3191526
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Sankar A, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P519, DOI 10.1145/3336191.3371845
   Schrodt PA, 2012, CAMEO CONFLICT MEDIA
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   Starz James, 2015, **DATA OBJECT**, P12, DOI [10.7910/DVN/28075, DOI 10.7910/DVN/28075]
   Tang Jian., 2019, P INT C LEARN REPR
   Trivedi R, 2017, PR MACH LEARN RES, V70
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Usunier Nicolas, 2020, P INT C LEARN REPR
   Vaswani A, 2017, ADV NEUR IN, V30
   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 RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xiong C., 2017, P INT C LEARN REPR
   Xiong CM, 2016, PR MACH LEARN RES, V48
   Yang B., 2015, P INT C LEARN REPR
   Zha Hongyuan, 2019, P INT C LEARN REPR
NR 45
TC 2
Z9 2
U1 4
U2 12
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 APR
PY 2021
VL 15
IS 3
AR 41
DI 10.1145/3443687
PG 17
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RX5ZB
UT WOS:000647300600008
DA 2022-08-23
ER

PT J
AU Zhu, JW
   Han, X
   Deng, HH
   Tao, C
   Zhao, L
   Wang, P
   Lin, T
   Li, HF
AF Zhu, Jiawei
   Han, Xing
   Deng, Hanhan
   Tao, Chao
   Zhao, Ling
   Wang, Pu
   Lin, Tao
   Li, Haifeng
TI KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network
   for Traffic Forecasting
SO IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
LA English
DT Article; Early Access
DE Roads; Forecasting; Predictive models; Data models; Semantics;
   Correlation; Mathematical models; Traffic flow forecasting; multisource
   spatial-temporal data; knowledge representation; spatial-temporal graph
   convolutional networks
ID PREDICTION; FLOW
AB While considering the spatial and temporal features of traffic, capturing the impacts of various external factors on travel is an essential step towards achieving accurate traffic forecasting. However, existing studies seldom consider external factors or neglect the effect of the complex correlations among external factors on traffic. Intuitively, knowledge graphs can naturally describe these correlations. Since knowledge graphs and traffic networks are essentially heterogeneous networks, it is challenging to integrate the information in both networks. On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks. We first construct a knowledge graph for traffic forecasting and derive knowledge representations by a knowledge representation learning method named KR-EAR. Then, we propose the Knowledge Fusion Cell (KF-Cell) to combine the knowledge and traffic features as the input of a spatial-temporal graph convolutional backbone network. Experimental results on the real-world dataset show that our strategy enhances the forecasting performances of backbones at various prediction horizons. The ablation and perturbation analysis further verify the effectiveness and robustness of the proposed method. To the best of our knowledge, this is the first study that constructs and utilizes a knowledge graph to facilitate traffic forecasting; it also offers a promising direction to integrate external information and spatial-temporal information for traffic forecasting. The source code is available at https://github.com/lehaifeng/T-GCN/tree/master/KST-GCN.
C1 [Zhu, Jiawei; Han, Xing; Tao, Chao; Zhao, Ling; Li, Haifeng] Cent South Univ, Sch Geosci & Infophys, Changsha 410083, Peoples R China.
   [Deng, Hanhan] Hisense TransTech Co Ltd, Qingdao 266071, Peoples R China.
   [Wang, Pu] Cent South Univ, Sch Traff & Transportat Engn, Changsha 410083, Peoples R China.
   [Lin, Tao] Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310027, Peoples R China.
RP Li, HF (通讯作者)，Cent South Univ, Sch Geosci & Infophys, Changsha 410083, Peoples R China.
EM lihaifeng@csu.edu.cn
RI Lin, Tao/AAB-3023-2020
OI Zhu, Jiawei/0000-0002-2961-1302; Li, Haifeng/0000-0003-1173-6593
FU National Natural Science Foundation of China [42171458, 61973047];
   Fundamental Research Funds for the Central Universities of Central South
   University [2019zzts881]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 42171458 and Grant 61973047, and in part
   by the Fundamental Research Funds for the Central Universities of
   Central South University under Grant 2019zzts881. The Associate Editor
   for this article was J. Sanchez-Medina. (Corresponding author: Haifeng
   Li.)
CR Ahmed M. S., 1997, TRANSP RES REC, V722, P1
   Barros J, 2015, 2015 INTERNATIONAL CONFERENCE ON MODELS AND TECHNOLOGIES FOR INTELLIGENT TRANSPORTATION SYSTEMS (MT-ITS), P132, DOI 10.1109/MTITS.2015.7223248
   Chen XJ, 2020, EXPERT SYST APPL, V141, DOI 10.1016/j.eswa.2019.112948
   Fu R, 2016, 2016 31ST YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC), P324, DOI 10.1109/YAC.2016.7804912
   Gui H, 2017, IEEE T KNOWL DATA EN, V29, P2428, DOI 10.1109/TKDE.2017.2733530
   Gui H, 2016, IEEE DATA MINING, P907, DOI [10.1109/ICDM.2016.0111, 10.1109/ICDM.2016.42]
   HAMED MM, 1995, J TRANSP ENG-ASCE, V121, P249, DOI 10.1061/(ASCE)0733-947X(1995)121:3(249)
   Huang WH, 2014, IEEE T INTELL TRANSP, V15, P2191, DOI 10.1109/TITS.2014.2311123
   Jun M, 2008, 2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL II, PROCEEDINGS, P104, DOI 10.1109/IITA.2008.207
   Kasneci G, 2008, PROC INT CONF DATA, P953, DOI 10.1109/ICDE.2008.4497504
   [况爱武 Kuang Aiwu], 2004, [系统工程, Systems Engineering], V22, P63
   Lana I, 2018, IEEE INTEL TRANSP SY, V10, P93, DOI 10.1109/MITS.2018.2806634
   Liao BB, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P537, DOI 10.1145/3219819.3219895
   Lin Y., 2016, ETHNICITY, V1, P41
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lv YS, 2015, IEEE T INTELL TRANSP, V16, P865, DOI 10.1109/TITS.2014.2345663
   Ma XL, 2015, TRANSPORT RES C-EMER, V54, P187, DOI 10.1016/j.trc.2015.03.014
   Muppalla R, 2017, PROCEEDINGS OF THE 2017 ACM WEB SCIENCE CONFERENCE (WEBSCI '17), P431, DOI 10.1145/3091478.3162384
   Nagy AM, 2018, PERVASIVE MOB COMPUT, V50, P148, DOI 10.1016/j.pmcj.2018.07.004
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   Park D., 1999, Computer-Aided Civil and Infrastructure Engineering, V14, P357, DOI 10.1111/0885-9507.00154
   Sha X, 2019, ARXIV191008288
   Shan SB, 2017, IET SOFTW, V11, P126, DOI 10.1049/iet-sen.2016.0189
   Tang J, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1165, DOI 10.1145/2783258.2783307
   Tian YX, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON SMART CITY/SOCIALCOM/SUSTAINCOM (SMARTCITY), P153, DOI 10.1109/SmartCity.2015.63
   van Lint JWC, 2002, TRANSPORT RES REC, P30, DOI 10.3141/1811-04
   Wang HW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2000, DOI 10.1145/3308558.3313411
   Xu Z, 2016, CONCURR COMP-PRACT E, V28, P4038, DOI 10.1002/cpe.3780
   Yu B, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3634
   Yu C, 2017, INT CONF E BUS ENG, P1, DOI 10.1109/ICEBE.2017.11
   Yu HY, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17071501
   Zhang D, 2018, IET INTELL TRANSP SY, V12, P578, DOI 10.1049/iet-its.2017.0313
   Zhang JB, 2017, AAAI CONF ARTIF INTE, P1655
   Zhang JP, 2011, IEEE T INTELL TRANSP, V12, P1624, DOI 10.1109/TITS.2011.2158001
   Zhao L, 2020, IEEE T INTELL TRANSP, V21, P3848, DOI 10.1109/TITS.2019.2935152
   Zhou J, 2018, ARTIF CELL NANOMED B, V46, pS1016, DOI 10.1080/21691401.2018.1442841
   Zhu J., 2020, ARXIV200206823
   Zou ZN, 2018, 2018 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI), P1305, DOI 10.1109/SmartWorld.2018.00227
NR 38
TC 3
Z9 4
U1 42
U2 45
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1524-9050
EI 1558-0016
J9 IEEE T INTELL TRANSP
JI IEEE Trans. Intell. Transp. Syst.
DI 10.1109/TITS.2021.3136287
EA JAN 2022
PG 11
WC Engineering, Civil; Engineering, Electrical & Electronic; Transportation
   Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Transportation
GA YK8MX
UT WOS:000745461300001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Duan, HJ
   Liu, PY
   Ding, Q
AF Duan, Huajuan
   Liu, Peiyu
   Ding, Qi
TI RFAN: Relation-fused multi-head attention network for knowledge graph
   enhanced recommendation
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Knowledge graph; Recommendation systems; Multi-head attention network;
   Entity embedding
AB Knowledge graphs (KGs) provide rich external structures and semantic information for recommendation systems and have attracted extensive attention recently. Some existing KG-based recommendation methods process triplets independently through knowledge graph embedding, and they fail to fully capture the complex and implicit relation information in the neighbourhood around a given entity. To solve this problem, we borrow Graph Attention Network (GAT) and propose a relation-fused multi-head attention network (RFAN) that integrates relations into an attention network in a KG to learn the representation of entities through different contributions of neighbours and then combine them with user-item interactions to capture user preferences. In addition, we use a non-sampling strategy to learn parameters from the whole training data to improve training efficiency. We apply the proposed model on three public benchmarks, and the experimental results show that the RFAN outperforms several state-of-the-art methods.
C1 [Duan, Huajuan; Liu, Peiyu; Ding, Qi] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Shandong, Peoples R China.
RP Liu, PY (通讯作者)，Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Shandong, Peoples R China.
EM duanhuaj@163.com; liupy@sdnu.com.cn; 2632548757@qq.com
FU Key R & D project of Shandong Province [2019JZZY010129]; Shandong
   Provincial Social Science Planning Project [19BJCJ51, 18CXWJ01,
   18BJYJ04]
FX This work was supported in part Key R & D project of Shandong Province
   2019JZZY010129, and in part by the Shandong Provincial Social Science
   Planning Project under Award 19BJCJ51, Award 18CXWJ01, and Award
   18BJYJ04.
CR Bi JW, 2020, INT J MACH LEARN CYB, V11, P763, DOI 10.1007/s13042-019-00981-y
   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, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P189, DOI 10.1145/3397271.3401040
   Chen C, 2020, AAAI CONF ARTIF INTE, V34, P19
   Chen C, 2020, ACM T INFORM SYST, V38, DOI 10.1145/3373807
   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 JR, 2021, INT J MACH LEARN CYB, V12, P1097, DOI 10.1007/s13042-020-01223-2
   Da'u A, 2020, ARTIF INTELL REV, V53, P2709, DOI 10.1007/s10462-019-09744-1
   Fu TY, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1797, DOI 10.1145/3132847.3132953
   Han JW, 2021, IEEE T PATTERN ANAL, V43, P1423, DOI 10.1109/TPAMI.2019.2949562
   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
   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
   Hui B, 2022, APPL INTELL, V52, P954, DOI 10.1007/s10489-021-02363-w
   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
   Lian JX, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P1754, DOI 10.1145/3219819.3220023
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Nathani D, 2019, LEARNING ATTENTION B
   Rendle S, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2168752.2168771
   Sha X, 2019, ATTENTIVE KNOWLEDGE
   Shi C, 2017, DATA ANALYTIC, P1, DOI 10.1007/978-3-319-56212-4
   Sun Z, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P297, DOI 10.1145/3240323.3240361
   Tewari AS, 2020, PROCEDIA COMPUT SCI, V167, P1934, DOI 10.1016/j.procs.2020.03.215
   Velikovi P., 2017, GRAPH ATTENTION NETW
   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 H, 2018, RIPPLENET PROPAGATIN
   Wang H, 2019, ACM T INFORM SYSTEMS
   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, 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 X, 2018, EXPLAINABLE REASONIN
   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
   Wu L, 2021, SURVEY ACCURACY ORIE
   Yang Y, 2022, APPL INTELL, V52, P6196, DOI 10.1007/s10489-021-02647-1
   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
   Zhang HR, 2018, INT J MACH LEARN CYB
   Zhang J., 2014, KNOWLEDGE GRAPH EMBE
NR 40
TC 0
Z9 0
U1 6
U2 6
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/s10489-022-03521-4
EA APR 2022
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0T7YX
UT WOS:000787181700001
DA 2022-08-23
ER

PT J
AU Han, XL
   Dell'Aglio, D
   Grubenmann, T
   Cheng, R
   Bernstein, A
AF Han, Xiaolin
   Dell'Aglio, Daniele
   Grubenmann, Tobias
   Cheng, Reynold
   Bernstein, Abraham
TI A framework for differentially-private knowledge graph embeddings
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Differential privacy; Knowledge graph embeddings
ID WEB
AB Knowledge graph (KG) embedding methods are at the basis of many KG-based data mining tasks, such as link prediction and node clustering. However, graphs may contain confidential information about people or organizations, which may be leaked via embeddings. Research recently studied how to apply differential privacy to a number of graphs (and KG) analyses, but embedding methods have not been considered so far. This study moves a step toward filling such a gap, by proposing the Differential Private Knowledge Graph Embedding (DPKGE) framework. DPKGE extends existing KG embedding methods (e.g., TransE, TransM, RESCAL, and DistMult) and processes KGs containing both confidential and unrestricted statements. The resulting embeddings protect the presence of any of the former statements in the embedding space using differential privacy. Our experiments identify the cases where DPKGE produces useful embeddings, by analyzing the training process and tasks executed on top of the resulting embeddings. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Han, Xiaolin; Cheng, Reynold] Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China.
   [Dell'Aglio, Daniele] Aalborg Univ, Dept Comp Sci, Aalborg, Denmark.
   [Dell'Aglio, Daniele; Bernstein, Abraham] Univ Zurich, Dept Informat, Zurich, Switzerland.
   [Grubenmann, Tobias] Univ Bonn, Dept Comp Sci, Bonn, Germany.
RP Han, XL (通讯作者)，Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China.
EM xlhan@cs.hku.hk; dade@cs.aau.dk; grubenmann@cs.uni-bonn.de;
   ckcheng@cs.hku.hk; bernstein@ifi.uzh.ch
RI Dell'Aglio, Daniele/AAH-4635-2019
OI Dell'Aglio, Daniele/0000-0003-4904-2511; Grubenmann,
   Tobias/0000-0003-0391-584X; Han, Xiaolin/0000-0002-4347-1692
FU Research Grants Council of Hong Kong [HKU 17229116, 17205015];
   University of Hong Kong [104005858, 104005994]; HKU-TCL Joint Research
   Center for Artificial Intelligence [200009430]; Guangdong-Hong
   Kong-Macau Joint Laboratory Program [2020B1212030009]; Swiss National
   Science Foundation [407550_167177]; Federal Ministry of Education and
   Research (BMBF), Germany [01IS18054]; European Commission [872592,
   812997]
FX Reynold Cheng and Xiaolin Han were supported by the Research Grants
   Council of Hong Kong (RGC Projects HKU 17229116, 17205015), University
   of Hong Kong (Projects 104005858, 104005994), HKU-TCL Joint Research
   Center for Artificial Intelligence (Project 200009430), and the
   Guangdong-Hong Kong-Macau Joint Laboratory Program 2020 (Project
   2020B1212030009). Daniele Dell'Aglio was partially supported by the
   Swiss National Science Foundation (contract #407550_167177). Tobias
   Grubenmann was supported by the Federal Ministry of Education and
   Research (BMBF), Germany, under Simple-ML (01IS18054), and the European
   Commission under PLATOON (872592) and Cleopatra (812997). We also thank
   the anonymous reviewers for their insightful feedback.
CR Abadi M, 2016, CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P308, DOI 10.1145/2976749.2978318
   Balazvic I., 2019, ARXIV PREPRINT ARXIV
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Blocki J, 2012, ANN IEEE SYMP FOUND, P410, DOI 10.1109/FOCS.2012.67
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Calvanese D, 2017, SEMANT WEB, V8, P471, DOI 10.3233/SW-160217
   Cao L., PROC VLDB ENDOW, V14, P2755
   Chen S., 2013, P 2013 ACM SIGMOD IN, P653, DOI DOI 10.1145/2463676.2465304
   Cunningham T., 2021, PROC VLDB ENDOW, V14, P2283
   DALENIUS T, 1982, J STAT PLAN INFER, V6, P73, DOI 10.1016/0378-3758(82)90058-1
   Dell'Aglio D, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P1977, DOI 10.1145/3366423.3380265
   Do K, 2018, INT C PATT RECOG, P332, DOI 10.1109/ICPR.2018.8545027
   Dwork C, 2013, FOUND TRENDS THEOR C, V9, P211, DOI 10.1561/0400000042
   Fan M, 2014, P 28 PAC AS C LANG I, V28, P328, DOI DOI 10.3115/V1/P14-1048.URL
   Fredrikson M, 2015, CCS'15: PROCEEDINGS OF THE 22ND ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P1322, DOI 10.1145/2810103.2813677
   Hay M, 2009, IEEE DATA MINING, P169, DOI 10.1109/ICDM.2009.11
   Hsu J, 2014, P IEEE CSFW, P398, DOI 10.1109/CSF.2014.35
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   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, KNOWL-BASED SYST, V151, P37, DOI 10.1016/j.knosys.2018.03.020
   Johnson AEW, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.35
   Kasiviswanathan SP, 2013, LECT NOTES COMPUT SC, V7785, P457, DOI 10.1007/978-3-642-36594-2_26
   Kirrane S, 2018, SEMANT WEB, V9, P153, DOI 10.3233/SW-180289
   Kotsogiannis I, 2020, PROC INT CONF DATA, P493, DOI 10.1109/ICDE48307.2020.00049
   Kotsogiannis I, 2019, PROC VLDB ENDOW, V12, P1371, DOI 10.14778/3342263.3342274
   Lee J, 2011, LECT NOTES COMPUT SC, V7001, P325, DOI 10.1007/978-3-642-24861-0_22
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   McSherry F, 2009, ACM SIGMOD/PODS 2009 CONFERENCE, P19
   Narayanan A, 2008, P IEEE S SECUR PRIV, P111, DOI 10.1109/SP.2008.33
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Nissim K, 2007, ACM S THEORY COMPUT, P75, DOI 10.1145/1250790.1250803
   Noy N, 2019, COMMUN ACM, V62, P36, DOI 10.1145/3331166
   Pollard TJ, 2018, SCI DATA, V5, DOI 10.1038/sdata.2018.178
   Reuben J., 2018, SICHERHEIT, P273
   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
   Silva R., 2017, IDEAS, P74
   Sweeney L, 1997, J LAW MED ETHICS, V25, P98, DOI 10.1111/j.1748-720X.1997.tb01885.x
   Sweeney L, 2002, INT J UNCERTAIN FUZZ, V10, P557, DOI 10.1142/S0218488502001648
   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
   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
   Yang B., 2014, ABS14126575 CORR
   Yu S.Y., 2019, ARXIV190604239
   Zhao Y., 2015, JIST, P271
   Zhu JZ, 2018, J COMPUT SCI TECH-CH, V33, P323, DOI 10.1007/s11390-018-1821-8
NR 49
TC 1
Z9 1
U1 10
U2 10
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 2022
VL 72
AR 100696
DI 10.1016/j.websem.2021.100696
PG 17
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZI0GA
UT WOS:000761304400004
DA 2022-08-23
ER

PT J
AU Domingo-Fernandez, D
   Baksi, S
   Schultz, B
   Gadiya, Y
   Karki, R
   Raschka, T
   Ebeling, C
   Hofmann-Apitius, M
   Kodamullil, AT
AF Domingo-Fernandez, Daniel
   Baksi, Shounak
   Schultz, Bruce
   Gadiya, Yojana
   Karki, Reagon
   Raschka, Tamara
   Ebeling, Christian
   Hofmann-Apitius, Martin
   Kodamullil, Alpha Tom
TI COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect
   knowledge model of COVID-19 pathophysiology
SO BIOINFORMATICS
LA English
DT Article
AB The COVID-19 crisis has elicited a global response by the scientific community that has led to a burst of publications on the pathophysiology of the virus. However, without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats.
C1 [Domingo-Fernandez, Daniel; Schultz, Bruce; Gadiya, Yojana; Karki, Reagon; Raschka, Tamara; Ebeling, Christian; Hofmann-Apitius, Martin; Kodamullil, Alpha Tom] Fraunhofer Inst Algorithms & Sci Comp SCAI, Dept Bioinformat, D-53754 St Augustin, Germany.
   [Domingo-Fernandez, Daniel; Gadiya, Yojana; Karki, Reagon; Raschka, Tamara; Hofmann-Apitius, Martin; Kodamullil, Alpha Tom] Univ Bonn, Bonn Aachen Int Ctr Informat Technol B IT, D-53113 Bonn, Germany.
   [Baksi, Shounak] KINFRA Hi Tech Pk, Causal Biomodels, Cochin 683503, Kerala, India.
RP Domingo-Fernandez, D; Schultz, B; Kodamullil, AT (通讯作者)，Fraunhofer Inst Algorithms & Sci Comp SCAI, Dept Bioinformat, D-53754 St Augustin, Germany.; Domingo-Fernandez, D; Kodamullil, AT (通讯作者)，Univ Bonn, Bonn Aachen Int Ctr Informat Technol B IT, D-53113 Bonn, Germany.; Baksi, S (通讯作者)，KINFRA Hi Tech Pk, Causal Biomodels, Cochin 683503, Kerala, India.
EM daniel.domingo.fernandez@scai.fraunhofer.de;
   shounak.baksi@causalitybiomodels.com; bruce.schultz@scai.fraunhofer.de
RI Domingo-Fernández, Daniel/AGH-3497-2022; Hofmann-Apitius,
   Martin/E-6123-2018
OI Domingo-Fernández, Daniel/0000-0002-2046-6145; Schultz,
   Bruce/0000-0003-4492-9463; Gadiya, Yojana/0000-0002-7683-0452;
   Hofmann-Apitius, Martin/0000-0001-9012-6720; Ebeling,
   Christian/0000-0002-7560-6846; Baksi, Shounak/0000-0001-7225-5377; Tom
   Kodamullil, Alpha/0000-0001-9896-3531
FU MAVO program of the Fraunhofer Society; ICON program of the Fraunhofer
   Society
FX This work was supported by the MAVO and ICON programs of the Fraunhofer
   Society.
CR Blanco-Melo D, 2020, CELL, V181, P1036, DOI 10.1016/j.cell.2020.04.026
   Chahrour M, 2020, CUREUS J MED SCIENCE, V12, DOI 10.7759/cureus.7357
   Cowen L, 2017, NAT REV GENET, V18, P551, DOI 10.1038/nrg.2017.38
   Domingo-Fernandez D, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-2863-9
   Hamilton W.L., 2017, ARXIV170905584
   Humayun F, 2020, FRONT BIOENG BIOTECH, V8, DOI 10.3389/fbioe.2020.00074
   Nelson W, 2019, FRONT GENET, V10, DOI 10.3389/fgene.2019.00381
   Ostaszewski M, 2020, SCI DATA, V7, DOI 10.1038/s41597-020-0477-8
   Wang Q., 2020, COVID 19 LIT KNOWLED
NR 9
TC 21
Z9 21
U1 12
U2 82
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 MAY 1
PY 2021
VL 37
IS 9
BP 1332
EP 1334
DI 10.1093/bioinformatics/btaa834
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 TF5XZ
UT WOS:000670795400025
PM 32976572
OA hybrid, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Lai, YJ
   Chen, C
   Zheng, ZB
   Zhang, YQ
AF Lai, Yujing
   Chen, Chuan
   Zheng, Zibin
   Zhang, Yangqing
TI Block term decomposition with distinct time granularities for temporal
   knowledge graph completion
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge graph completion; Temporal knowledge graph; Tensor completion
ID TENSOR
AB To solve the incompleteness problem in temporal knowledge graphs (TKGs) and to discover the new knowledge, TKG completion remains an essential task always solved by graph embedding technology. Existing TKG completion methods encode time at only single granularity, which is insufficient in exploiting the rich information of distinct time granularities. Furthermore, most of them lack a comprehensive consideration of the characteristic of both time points and time periods, resulting in the inability to handle the two types of facts with different time forms, namely the discrete facts and continuous facts, simultaneously. In this paper, we propose a novel TKG embedding model which introduces the block term tensor decomposition and utilizes the core tensor and factor matrices to capture information presented by facts under distinct time granularities. By focusing on moments included in the time period and treating the discrete fact as a special case of the continuous fact, the model manages the processing of different types of facts in a unified manner. Besides, we explicitly design the static properties of entities and relations as well as their interactions to conform to reality. Experiments on 3 real datasets of different types verify the effectiveness of our proposed method compared with most state-of-the-art methods.
C1 [Lai, Yujing; Chen, Chuan; Zheng, Zibin] Sun Yat sen Univ, Sch Comp Sci & Engn, Guangzhou 510275, Peoples R China.
   [Chen, Chuan; Zheng, Zibin] Sun Yat sen Univ, Natl Engn Res Ctr Digital Life, Guangzhou 510275, Peoples R China.
   [Zhang, Yangqing] Merchants Union Consumer Finance Co, Ltd, Kexing Sci Pk, Shenzhen 518057, Peoples R China.
RP Chen, C (通讯作者)，Sun Yat sen Univ, Sch Comp Sci & Engn, Guangzhou 510275, Peoples R China.; Chen, C (通讯作者)，Sun Yat sen Univ, Natl Engn Res Ctr Digital Life, Guangzhou 510275, Peoples R China.
EM laiyj23@mail2.sysu.edu.cn; chenchuan@mail.sysu.edu.cn;
   zhzibin@mail.sysu.edu.cn; zhangyq@mucfc.com
FU Key-Area Research and Devel-opment Program of Guangdong Province
   [2020B010165003]; Na-tional Natural Science Foundation of China
   [62176269, U1911202]; Guangdong Basic and Applied Basic Research
   Foundation, China [2019A1515011043]
FX Acknowledgments The research is supported by the Key-Area Research and
   Devel-opment Program of Guangdong Province (2020B010165003) , the
   Na-tional Natural Science Foundation of China (62176269, U1911202) , the
   Guangdong Basic and Applied Basic Research Foundation, China
   (2019A1515011043) .
CR Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Dasgupta SS, 2018, P 2018 C EMPIRICAL M, P2001
   De Lathauwer L, 2008, SIAM J MATRIX ANAL A, V30, P1033, DOI 10.1137/070690729
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ding Z., 2021, ARXIV210105151
   Garcia-Duran Alberto, 2018, P 2018 C EMP METH NA, P4816
   Goel R, 2020, AAAI CONF ARTIF INTE, V34, P3988
   He S., 2015, P ACM INT C INF KNOW, P623, DOI [DOI 10.1145/2806416.2806502, 10.1145/2806416.2806502]
   Kazemi SM, 2018, ADV NEUR IN, V31
   Lacroix T, 2020, P INT C LEARN REPR
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Lautenschlager Jennifer, 2015, HarvardDataverse, V3, DOI 10.7910/DVN/28117
   Leblay J, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1771, DOI 10.1145/3184558.3191639
   Lee D, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P695, DOI 10.1145/3340531.3411932
   Lehmann J, 2020, COLING, P1583
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Luo T, 2020, FRONT ARTIF INTEL AP, V325, P817, DOI 10.3233/FAIA200171
   Ma XD, 2019, ADV NEUR IN, V32
   Molokwu Bonaventure C., 2020, Computational Science - ICCS 2020. 20th International Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12142), P183, DOI 10.1007/978-3-030-50433-5_15
   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
   Tran HN, 2020, FRONT ARTIF INTEL AP, V325, P833, DOI 10.3233/FAIA200173
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Vashishth S, 2020, 8 INT C LEARN REPR
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang ZH, 2021, LECT NOTES ARTIF INT, V12714, P141, DOI 10.1007/978-3-030-75768-7_12
   Xu C, 2019, IEEE WCNC
   Yang B., 2015, 3 INT C LEARN REPR
   Ye JM, 2018, PROC CVPR IEEE, P9378, DOI 10.1109/CVPR.2018.00977
   Yu DH, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P1619, DOI 10.1145/3442381.3449925
   Zhu CC, 2021, AAAI CONF ARTIF INTE, V35, P4732
NR 34
TC 0
Z9 0
U1 7
U2 7
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 117036
DI 10.1016/j.eswa.2022.117036
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 1L1CN
UT WOS:000799030000001
DA 2022-08-23
ER

PT J
AU Chen, JY
   Lecue, F
   Pan, JZ
   Deng, SM
   Chen, HJ
AF Chen, Jiaoyan
   Lecue, Freddy
   Pan, Jeff Z.
   Deng, Shumin
   Chen, Huajun
TI Knowledge graph embeddings for dealing with concept drift in machine
   learning
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Ontology; Knowledge graph embedding; Stream learning; Concept drift
AB Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. As data is evolving on a temporal basis, its underlying knowledge is subject to many challenges. Concept drift,1 as one of core challenge from the stream learning community, is described as changes of statistical properties of the data over time, causing most of machine learning models to be less accurate as changes over time are in unforeseen ways. This is particularly problematic as the evolution of data could derive to dramatic change in knowledge. We address this problem by studying the semantic representation of data streams in the Semantic Web, i.e., ontology streams. Such streams are ordered sequences of data annotated with ontological vocabulary. In particular we exploit three levels of knowledge encoded in ontology streams to deal with concept drifts: i) existence of novel knowledge gained from stream dynamics, ii) significance of knowledge change and evolution, and iii) (in)consistency of knowledge evolution. Such knowledge is encoded as knowledge graph embeddings through a combination of novel representations: entailment vectors, entailment weights, and a consistency vector. We illustrate our approach on classification tasks of supervised learning. Key contributions of the study include: (i) an effective knowledge graph embedding approach for stream ontologies, and (ii) a generic consistent prediction framework with integrated knowledge graph embeddings for dealing with concept drifts. The experiments have shown that our approach provides accurate predictions towards air quality in Beijing and bus delay in Dublin with real world ontology streams. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Chen, Jiaoyan] Univ Oxford, Dept Comp Sci, Oxford, England.
   [Lecue, Freddy] INRIA, Le Chesnay Rocquencourt, France.
   [Lecue, Freddy] Thales, CortAIx, Montreal, PQ, Canada.
   [Pan, Jeff Z.] Univ Aberdeen, Dept Comp Sci, Aberdeen, Scotland.
   [Pan, Jeff Z.] Huawei, Knowledge Graph Lab, Reading, Berks, England.
   [Deng, Shumin; Chen, Huajun] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China.
   [Deng, Shumin; Chen, Huajun] ZJU Alibaba Joint Lab Knowledge Engine, Hangzhou, Peoples R China.
RP Lecue, F (通讯作者)，INRIA, Le Chesnay Rocquencourt, France.; Lecue, F (通讯作者)，Thales, CortAIx, Montreal, PQ, Canada.
EM jiaoyan.chen@cs.ox.ac.uk; freddy.lecue@inria.fr; jeff.z.pan@abdn.ac.uk;
   231sm@zju.edu.cn; huajunsir@zju.edu.cn
RI Deng, Shumin/AAP-7003-2021
FU SIRIUS Centre for Scalable Data Access (Research Council of Norway)
   [237889]; Royal Society, UK; EPSRC project DBOnto; EPSRC project MaSI3;
   EPSRC project ED3; EU Marie Curie, UK IAPP K-Drive project [286348]; 
   [NSFC91846204]
FX This work is partially funded by the SIRIUS Centre for Scalable Data
   Access (Research Council of Norway, project 237889), the Royal Society,
   UK, EPSRC projects DBOnto, MaSI3, ED3, NSFC91846204, and the EU Marie
   Curie, UK IAPP K-Drive project (286348).
CR Baader F., 2017, INTRO DESCRIPTION LO
   Baader F, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P364
   Barbieri DF, 2009, P 18 INT C WORLD WID, P1061, DOI [10.1145/1526709.1526856, DOI 10.1145/1526709.1526856]
   Bechhofer S., 2004, W3C RECOMMENDATION, V10
   Beck H., 2016, P 25 INT JOINT C ART, P929
   Bengio Y., 2012, P MACHINE LEARNING R, P17
   Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
   Bifet A., 2007, P SIAM INT C DAT MIN, V7, P2007, DOI DOI 10.1137/1.9781611972771.42
   Bifet A, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P59, DOI 10.1145/2783258.2783372
   Bifet A, 2010, LECT NOTES ARTIF INT, V6321, P135, DOI 10.1007/978-3-642-15880-3_15
   Bifet A, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P139
   Bottou Leon, 2012, Neural Networks: Tricks of the Trade. Second Edition: LNCS 7700, P421, DOI 10.1007/978-3-642-35289-8_25
   Cai HY, 2018, IEEE T KNOWL DATA EN, V30, P1616, DOI 10.1109/TKDE.2018.2807452
   Cao LJ, 2003, IEEE T NEURAL NETWOR, V14, P1506, DOI 10.1109/TNN.2003.820556
   Chen JY, 2015, PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, P991, DOI 10.1109/BigData.2015.7363850
   Cheung DW, 1996, PROC INT CONF DATA, P106, DOI 10.1109/ICDE.1996.492094
   Chu W., 2011, P 17 ACM SIGKDD INT, P195, DOI DOI 10.1145/2020408.2020444
   Coble J., 2000, FLAIRS-2000. Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference, P192
   Le-Phuoc D, 2011, LECT NOTES COMPUT SC, V7031, P370, DOI 10.1007/978-3-642-25073-6_24
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Diaz G.I., 2018, P 21 INT C EXT DAT T
   Domingos P., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, P71, DOI 10.1145/347090.347107
   Du JF, 2019, AAAI CONF ARTIF INTE, P2784
   Gama J, 2014, ACM COMPUT SURV, V46, DOI 10.1145/2523813
   Gao J, 2007, IEEE DATA MINING, P143
   Gao J, 2007, PROCEEDINGS OF THE SEVENTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING, P3
   Gomes HM, 2017, ACM COMPUT SURV, V50, DOI 10.1145/3054925
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
   HUANG Z, 2005, INT SEM WEB C, P398
   Kharlamov E, 2016, LECT NOTES COMPUT SC, V9982, P344, DOI 10.1007/978-3-319-46547-0_31
   Kingma D. P., 2017, ARXIV
   Klarman Szymon, 2013, Logic for Programming, Artificial Intelligence, and Reasoning. 19th International Conference, LPAR-19, Proceedings: LNCS 8312, P536, DOI 10.1007/978-3-642-45221-5_36
   Kolter JZ, 2007, J MACH LEARN RES, V8, P2755
   Krizhevsky A., 2012, ADV NEURAL INF PROCE, V25, P1097, DOI [10.1145/3065386, DOI 10.1145/3065386]
   Lecue F., 2013, IJCAI, P2662
   Lecue F, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1457
   Lecue F, 2015, AAAI CONF ARTIF INTE, P189
   Lipton Zachary C, 2014, Mach Learn Knowl Discov Databases, V8725, P225, DOI 10.1007/978-3-662-44851-9_15
   Margara A, 2014, J WEB SEMANT, V25, P24, DOI 10.1016/j.websem.2014.02.001
   Mehdi G, 2017, LECT NOTES COMPUT SC, V10588, P314, DOI 10.1007/978-3-319-68204-4_29
   Minervini P, 2020, STUD SEMANTIC WEB, V47, P125, DOI 10.3233/SSW200015
   Nenov Y, 2015, LECT NOTES COMPUT SC, V9367, P3, DOI 10.1007/978-3-319-25010-6_1
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Pan, 2011, P ACM C INF KNOWL MA
   PAULHEIM H, 2016, EUR SEM WEB C, P135
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Ren Y., 2011, ACM INT C INF KNOWL, P831
   Ren Y, 2016, LECT NOTES COMPUT SC, V9898, P167, DOI 10.1007/978-3-319-45276-0_13
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9981, P498, DOI 10.1007/978-3-319-46523-4_30
   SABOIA JLM, 1977, J AM STAT ASSOC, V72, P264, DOI 10.2307/2286787
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Van Asch V, 2013, MACRO AND MICROAVERE, V49
   Vaswani A., 2017, 31, P5998, DOI DOI 10.5555/3295222.3295349
   Ho VT, 2018, LECT NOTES COMPUT SC, V11136, P72, DOI 10.1007/978-3-030-00671-6_5
   Wen YD, 2016, LECT NOTES COMPUT SC, V9911, P499, DOI 10.1007/978-3-319-46478-7_31
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Wiharja K., 2018, LECT NOTES COMPUTER
   Young T, 2018, IEEE COMPUT INTELL M, V13, P55, DOI 10.1109/MCI.2018.2840738
   Zhang T., 2004, 21 INT C MACH LEARN, P116, DOI [10.1145/ 1015330.1015332, DOI 10.1145/1015330.1015332]
NR 59
TC 1
Z9 1
U1 4
U2 9
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 FEB
PY 2021
VL 67
AR 100625
DI 10.1016/j.websem.2020.100625
EA JAN 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 QW9TA
UT WOS:000628989400001
OA Bronze
DA 2022-08-23
ER

PT J
AU Zheng, SJ
   Rao, JH
   Song, Y
   Zhang, JX
   Xiao, XL
   Fang, EF
   Yang, YD
   Niu, ZM
AF Zheng, Shuangjia
   Rao, Jiahua
   Song, Ying
   Zhang, Jixian
   Xiao, Xianglu
   Fang, Evandro Fei
   Yang, Yuedong
   Niu, Zhangming
TI PharmKG: a dedicated knowledge graph benchmark for bomedical data mining
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article
DE knowledge graph; knowledge graph embedding; computational prediction
   model; drug repositioning; Alzheimer's disease
ID TUMOR-NECROSIS-FACTOR; PARKINSONS-DISEASE; ATOMOXETINE; METHYLPHENIDATE;
   DYSFUNCTION; DEPRESSION; ENALAPRIL; CHILDREN; BEHAVIOR; BIOLOGY
AB Biomedical knowledge graphs (KGs), which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods and non-uniform evaluation metrics. In this work, we established a comprehensive KG system for the biomedical field in an attempt to bridge the gap. Here, we introduced PharmKG, a multi-relational, attributed biomedical KG, composed of more than 500 000 individual interconnections between genes, drugs and diseases, with 29 relation types over a vocabulary of similar to 8000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure and disease word embedding, while preserving the semantic and biomedical features. For baselines, we offered nine state-of-the-art KG embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a KG in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical KG construction, embedding and application.
C1 [Zheng, Shuangjia; Rao, Jiahua; Yang, Yuedong] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510000, Peoples R China.
   [Zhang, Jixian; Xiao, Xianglu; Niu, Zhangming] Aladdin Healthcare Technol Ltd, 25 City Rd, London EC1Y 1AA, England.
   [Song, Ying] Sun Yat Sen Univ, Sch Syst Sci & Engn, Guangzhou, Peoples R China.
   [Fang, Evandro Fei] Univ Oslo, Dept Clin Mol Biol, Lorenskog, Norway.
   [Fang, Evandro Fei] Univ Oslo, Akershus Univ Hosp, Lorenskog, Norway.
   [Yang, Yuedong] Sun Yat Sen Univ, Natl Super Comp Ctr Guangzhou, Guangzhou, Peoples R China.
RP Yang, YD (通讯作者)，Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510000, Peoples R China.; Niu, ZM (通讯作者)，Aladdin Healthcare Technol Ltd, 25 City Rd, London EC1Y 1AA, England.
EM yangyd25@mail.sysu.edu.cn; zhangming@aladdinid.com
OI Zheng, Shuangjia/0000-0001-9747-4285; Fang, Evandro
   Fei/0000-0003-0355-7202
FU Innovative Medicines Initiative Program IMI2-RIA [101005122]; National
   Natural Science Foundation of China [62041209, 61772566, 81971327];
   Helse Sor-Ost [2017056]; Research Council of Norway [262175, 277813];
   Akershus University Hospital Strategic grant [269901]; Norwegian Cancer
   Society [207819]
FX Innovative Medicines Initiative Program - IMI2-RIA (#101005122);
   National Natural Science Foundation of China (#62041209, #61772566,
   #81971327); Helse Sor-Ost (#2017056 to E.F.F.); Research Council of
   Norway (#262175, #277813 to E.F.F.); Akershus University Hospital
   Strategic grant (#269901 to E.F.F.); Rosa Sloyfe grant (#207819 to
   E.F.F.) from the Norwegian Cancer Society.
CR Abdelaziz I, 2017, J WEB SEMANT, V44, P104, DOI 10.1016/j.websem.2017.06.002
   Ali M, 2019, BIOINFORMATICS, V35, P3538, DOI 10.1093/bioinformatics/btz117
   Alshahrani M, 2017, BIOINFORMATICS, V33, P2723, DOI 10.1093/bioinformatics/btx275
   Aman Y, 2020, MECH AGEING DEV, V185, DOI 10.1016/j.mad.2019.111194
   Aslam M, 2017, GENES ENVIRON, V39, P1
   Auriel E, 2009, CLIN NEUROPHARMACOL, V32, P75, DOI 10.1097/WNF.0b013e318170576c
   Bang M, 2019, BIOMOL THER, V27, P530, DOI 10.4062/biomolther.2019.151
   Barabasi AL, 2004, NAT REV GENET, V5, P101, DOI 10.1038/nrg1272
   Basaran R, 2013, MOL NEURODEGENER, V8, pP9
   Belleau F, 2008, J BIOMED INFORM, V41, P706, DOI 10.1016/j.jbi.2008.03.004
   BOKA G, 1994, NEUROSCI LETT, V172, P151, DOI 10.1016/0304-3940(94)90684-X
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Breit A, 2020, BIOINFORMATICS, V36, P4097, DOI 10.1093/bioinformatics/btaa274
   Cai Y, 2015, CLIN INTERV AGING, V10
   Cao Shaosheng, 2015, P 24 ACM INT C INF K, P891, DOI [10.1145/, DOI 10.1145/2806416.2806512]
   Carbon S, 2019, NUCLEIC ACIDS RES, V47, pD330, DOI 10.1093/nar/gky1055
   Chatterjee A, 2002, J NEUROPSYCH CLIN N, V14, P461, DOI 10.1176/appi.neuropsych.14.4.461
   Chen X, 2002, NUCLEIC ACIDS RES, V30, P412, DOI 10.1093/nar/30.1.412
   COHEN JD, 1992, PSYCHOL REV, V99, P45, DOI 10.1037/0033-295X.99.1.45
   COWBURN RF, 1993, NEUROSCI LETT, V155, P163, DOI 10.1016/0304-3940(93)90698-K
   Davis AP, 2019, NUCLEIC ACIDS RES, V47, pD948, DOI 10.1093/nar/gky868
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Druker BJ, 2002, TRENDS MOL MED, V8, pS14, DOI 10.1016/S1471-4914(02)02305-5
   Eisele YS, 2007, MOL BIOL CELL, V18, P3591, DOI 10.1091/mbc.e07-01-0035
   Fang EF, 2019, NAT NEUROSCI, V22, P401, DOI 10.1038/s41593-018-0332-9
   Garnock-Jones KP, 2009, PEDIATR DRUGS, V11, P203
   Gavin AC, 2002, NATURE, V415, P141, DOI 10.1038/415141a
   Gilmour BC, 2020, MECH AGEING DEV, V186, DOI 10.1016/j.mad.2020.111208
   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
   Hamosh A, 2000, HUM MUTAT, V15, P57, DOI 10.1002/(SICI)1098-1004(200001)15:1<57::AID-HUMU12>3.0.CO;2-G
   He K, 2016, P 22 ACM SIGKDD INT, P855
   Hewett M, 2002, NUCLEIC ACIDS RES, V30, P163, DOI 10.1093/nar/30.1.163
   Himmelstein DS, 2017, ELIFE, V6, DOI 10.7554/eLife.26726
   Hwang S, 2019, NUCLEIC ACIDS RES, V47, pD573, DOI 10.1093/nar/gky1126
   Ibanez L, 2018, FRONT NEUROSCI-SWITZ, V12, DOI 10.3389/fnins.2018.00230
   Janjic V, 2012, BRIEF FUNCT GENOMICS, V11, P522, DOI 10.1093/bfgp/els037
   Jankovic J, 2009, J NEUROL SCI, V284, P177, DOI 10.1016/j.jns.2009.03.022
   KALARIA RN, 1993, AM J PATHOL, V143, P886
   Kipf T. N., 2018, EUR SEM WEB C
   Kuhn M, 2016, NUCLEIC ACIDS RES, V44, pD1075, DOI 10.1093/nar/gkv1075
   Lamb J, 2006, SCIENCE, V313, P1929, DOI 10.1126/science.1132939
   Landrum G., 2006, RDKIT OPEN SOURCE CH
   Lautrup S, 2019, CELL METAB, V30, P630, DOI 10.1016/j.cmet.2019.09.001
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Li J, 2016, BRIEF BIOINFORM, V17, P2, DOI 10.1093/bib/bbv020
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lipscomb CE, 2000, B MED LIBR ASSOC, V88, P265
   Liu YM, 2020, FRONT AGING NEUROSCI, V11, DOI 10.3389/fnagi.2019.00372
   Lu Y, 2014, J NEUROL SCI, V336, P161, DOI 10.1016/j.jns.2013.10.030
   Luo YA, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-00680-8
   Marsh L, 2009, MOVEMENT DISORD, V24, P277, DOI 10.1002/mds.22307
   Meamar R, 2013, J RES MED SCI, V18, pS1
   Mendonca DA, 2007, MOVEMENT DISORD, V22, P2070, DOI 10.1002/mds.21656
   Michelson D, 2007, J AM ACAD CHILD PSY, V46, P242, DOI 10.1097/01.chi.0000246056.83791.b6
   Mogi M, 2000, J NEURAL TRANSM, V107, P335, DOI 10.1007/s007020050028
   Mohamed SK, 2020, BIOINFORMATICS, V36, P603, DOI 10.1093/bioinformatics/btz600
   Nathani D, 2019, ARXIV PREPRINT ARXIV
   Nguyen D. T., 2017, ARXIV PREPRINT ARXIV
   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
   OHM TG, 1991, BRAIN RES, V540, P229, DOI 10.1016/0006-8993(91)90512-T
   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
   Outeiro TF, 2007, SCIENCE, V317, P516, DOI 10.1126/science.1143780
   Percha B, 2018, BIOINFORMATICS, V34, P2614, DOI 10.1093/bioinformatics/bty114
   Perozzi Bryan, 2014, P 20 ACM SIGKDD INT
   Rogers D, 2010, J CHEM INF MODEL, V50, P742, DOI 10.1021/ci100050t
   Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323
   Sosa DN, LIT BASED KNOWLEDGE
   Su C, 2020, BRIEF BIOINFORM, V21, P182, DOI 10.1093/bib/bby117
   Su JH, 2003, NEUROBIOL DIS, V12, P182, DOI 10.1016/S0969-9961(02)00019-0
   Tansey MG, 2007, EXP NEUROL, V208, P1, DOI 10.1016/j.expneurol.2007.07.004
   TODD PA, 1986, DRUGS, V31, P198, DOI 10.2165/00003495-198631030-00002
   Trouillon Theo, 2016, PR MACH LEARN RES
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Velickovi P., 2017, ARXIV171010903
   Wang DD, 2016, J INTEGR NEUROSCI, V15, P247, DOI 10.1142/S021963521650014X
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z., 2014, AAAI, P1112
   Weintraub D, 2010, NEUROLOGY, V75, P448, DOI 10.1212/WNL.0b013e3181ebdd79
   Wishart DS, 2018, NUCLEIC ACIDS RES, V46, pD1074, DOI 10.1093/nar/gkx1037
   Wold S., 1987, CHEMOM INTELL LAB SY, V2, P37
   Wu C, 2009, GENOME BIOL, V10, DOI 10.1186/gb-2009-10-11-r130
   Xiao C, 2017, P 31 AAAI C ART INT, P3
   Yang B., 2014, P 3 INT C LEARN REPR
   Zeng XX, 2019, BIOINFORMATICS, V35, P5191, DOI 10.1093/bioinformatics/btz418
   Zhou Y., 2020, ARXIV PREPRINT ARXIV
   Zhou YD, 2020, CELL DISCOV, V6, DOI 10.1038/s41421-020-0153-3
   Zhu Y, 2020, HLTH INFORM J
   Zhu YJ, 2019, BRIEF BIOINFORM, V20, P1308, DOI 10.1093/bib/bbx169
   Zitnik M, 2018, BIOINFORMATICS, V34, P457, DOI 10.1093/bioinformatics/bty294
NR 90
TC 8
Z9 8
U1 12
U2 33
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
PY 2021
VL 22
IS 4
AR bbaa344
DI 10.1093/bib/bbaa344
PG 15
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA WK1AV
UT WOS:000709466800102
PM 33341877
DA 2022-08-23
ER

PT J
AU Liang, ZW
   Yang, JN
   Liu, H
   Huang, KJ
   Qu, LZ
   Cui, L
   Li, X
AF Liang, Zongwei
   Yang, Junan
   Liu, Hui
   Huang, Keju
   Qu, Lingzhi
   Cui, Lin
   Li, Xiang
TI SeAttE: An Embedding Model Based on Separating Attribute Space for
   Knowledge Graph Completion
SO ELECTRONICS
LA English
DT Article
DE NLP; knowledge graphs; knowledge representation; link prediction;
   attribute space; separation
AB Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is the task of inferring missing facts based on existing ones. Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. The tensor decomposition models are an embedding family with good performance in link prediction. The previous tensor decomposition models do not consider the problem of attribute separation. These models mainly explore particular regularization to improve performance. No matter how sophisticated the design of tensor decomposition models is, the performance is theoretically under the basic tensor decomposition model. Moreover, the unnoticed task of attribute separation in the traditional models is just handed over to the training. However, the amount of parameters for this task is tremendous, and the model is prone to overfitting. We investigate the design approaching the theoretical performance of tensor decomposition models in this paper. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Therefore, the comparison of actual triples needs first to separate specific attribute dimensions, which is ignored by existing models. Inspired by this observation, we design a novel tensor ecomposition model based on Separating Attribute space for knowledge graph completion (SeAttE). The major novelty of this paper is that SeAttE is the first model among the tensor decomposition family to consider the attribute space separation task. Furthermore, SeAttE transforms the learning of too many parameters for the attribute space separation task into the structure's design. This operation allows the model to focus on learning the semantic equivalence between relations, causing the performance to approach the theoretical limit. We also prove that RESCAL, DisMult and ComplEx are special cases of SeAttE in this paper. Furthermore, we classify existing tensor decomposition models for subsequent researchers. Experiments on the benchmark datasets show that SeAttE has achieved state-of-the-art among tensor decomposition models.
C1 [Liang, Zongwei; Yang, Junan; Liu, Hui; Huang, Keju; Qu, Lingzhi; Cui, Lin; Li, Xiang] Natl Univ Def Technol, Coll Elect Engn, Hefei 230037, Peoples R China.
RP Liang, ZW (通讯作者)，Natl Univ Def Technol, Coll Elect Engn, Hefei 230037, Peoples R China.
EM zwliang17@nudt.edu.cn; yangjunan@ustc.edu.cn; christ592604@163.com;
   huangkeju@163.com; qulingzhi@nudt.edu.cn; cuilin17@nudt.edu.cn;
   lix20@nudt.edu.cn
FU Anhui Provincial Natural Science Foundation [1908085MF202]; Independent
   Scientific Research Program of National University of Defense Science
   and Technology [ZK18-03-14]; Anhui Provincial Natural Science Foundation
   OF FUNDER [1908085MF202]; Independent Scientific Research Program of
   National University of Defense Science and Technology OF FUNDER
   [ZK18-03-14]
FX This work was partially supported by the Anhui Provincial Natural
   Science Foundation (No. 1908085MF202) and Independent Scientific
   Research Program of National University of Defense Science and
   Technology (No. ZK18-03-14).; This research was funded by Anhui
   Provincial Natural Science Foundation OF FUNDER grant number No.
   1908085MF202 and and Independent Scientific Research Program of National
   University of Defense Science and Technology OF FUNDER grant number No.
   ZK18-03-14.
CR Akrami F, 2020, SIGMOD'20: PROCEEDINGS OF THE 2020 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1995, DOI 10.1145/3318464.3380599
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Balazevic Ivana, 2019, P EMNLP
   Biswas R., 2021, MADLINK ATTENTIVE MU
   Bordes A., 2011, P 25 AAAI C ARTIFICI
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Gao H., 2021, ARXIV210509002
   Gao LM, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11125572
   Guo LB, 2019, PR MACH LEARN RES, V97
   Hildebrandt M, 2020, AAAI CONF ARTIF INTE, V34, P4123
   Hitchcock F. L., 1927, J MATH PHYS, V6, P164, DOI 10.1002/sapm192761164
   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
   Kazemi SM, 2018, ADV NEUR IN, V31
   Lacroix T, 2018, PR MACH LEARN RES, V80
   Lei D., 2020, P 2020 C EMP METH NA, P8541
   Liang Z., 2021, P 2021 C EMPIRICAL M, P7920
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   Lu HN, 2022, NEUROCOMPUTING, V476, P115, DOI 10.1016/j.neucom.2021.12.079
   Mahdisoltani F., 2015, CIDR 2015
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Nguyen D.Q., 2018, ARXIV171202121
   Nguyen D.Q., 2019, ARXIV180804122
   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 G., 2020, ARXIV200912030
   Qu M., 2021, P 9 INT C LEARNING R
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Sun Z., 2019, 7 INT C LEARN REPR
   Talukdar P., 2020, ARXIV191103082
   Tang Y, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P2713
   Toutanova K., 2015, P 3 WORKSHOP CONTINU
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wan GJ, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1926
   Wang MH, 2021, SYMMETRY-BASEL, V13, DOI 10.3390/sym13030485
   Wang P, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10121407
   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
   Yang B., 2011, P 28 INT C MACHINE L
   Zhang YQ, 2020, PROC INT CONF DATA, P433, DOI 10.1109/ICDE48307.2020.00044
   Zhang Z, 2020, P 34 C NEUR INF PROC
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
NR 45
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD APR
PY 2022
VL 11
IS 7
AR 1058
DI 10.3390/electronics11071058
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA 0M2SK
UT WOS:000782010200001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, HC
   Wang, YN
   Li, JF
   Luo, T
AF Wang, Hanchen
   Wang, Yining
   Li, Jianfeng
   Luo, Tao
TI Degree aware based adversarial graph convolutional networks for entity
   alignment in heterogeneous knowledge graph
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph; Entity alignment; Knowledge graph embedding; Graph
   convolutional network; Generative adversarial network
ID SCALE
AB Entity alignment, as the vital technique for knowledge graph construction and integration, aims to match entities that refer to the same real-world identity in different knowledge graphs (KGs). Recently, much effort has been devoted to embedding-based methods for entity alignment. For most of such methods, the entity with a high degree is hard to be aligned with its equivalent counterpart with a low degree. This degree difference between equivalent entities poses a great challenge for entity alignment. To solve this problem, a novel entity alignment framework that integrates a graph convolutional network (GCN) based embedding initializer and a degree aware generative adversarial network is proposed. In particular, the embedding initializer utilizes a GCN with highway gates to generate the preliminary embedding of entities based on their topological characteristics in the KGs. By alleviating the relevance between embeddings and degree features, the degree aware GAN mitigates the impact of degree difference and generates the final degree level irrelevant alignment result. To quantify the heterogeneity between the KGs, an evaluation metric called heterogeneity entropy of degree (HED) that represents the degree difference is defined in this paper. Based on HED, the impact of KG heterogeneity on the performance of the proposed DAGCN model is investigated based on WK3l-15k and DBP15k datasets. The experimental results show that the proposed degree aware adversarial graph convolutional network (DAGCN) outperforms other state-of-the-art methods over all metrics, especially when the HED is large. (C) 2022 Elsevier B.V. All rights reserved.
C1 [Wang, Hanchen; Wang, Yining; Li, Jianfeng; Luo, Tao] Beijing Univ Posts & Telecommun, Beijing Lab Adv Informat Networks, Beijing Key Lab Network Syst Architecture & Conve, Beijing, Peoples R China.
RP Luo, T (通讯作者)，Beijing Univ Posts & Telecommun, Beijing Lab Adv Informat Networks, Beijing Key Lab Network Syst Architecture & Conve, Beijing, Peoples R China.
EM hanchenwang@bupt.edu.cn; wyy0206@bupt.edu.cn; lijf@bupt.edu.cn;
   tluo@bupt.edu.cn
FU National Key Research and Development Program of China [2019YFC1709202,
   2019YFC1709200]
FX This work is supported in part by the National Key Research and
   Development Program of China No. 2019YFC1709202 and 2019YFC1709200.
CR Barabasi AL, 2003, SCI AM, V288, P60, DOI 10.1038/scientificamerican0503-60
   Berrendorf Max, 2020, Advances in Information Retrieval. 42nd European Conference on IR Research, ECIR 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12036), P3, DOI 10.1007/978-3-030-45442-5_1
   Bessadok A., 2021, ARXIV PREPRINT ARXIV
   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
   Fey M., 2019, INT C LEARN REPR
   Goodfellow I., 2020, P 27 INT C NEUR INF, P2672
   Gross JL, 2005, GRAPH THEORY ITS APP
   Guha R., 2003, P 12 INT C WORLD WID, P700, DOI DOI 10.1145/775152.775250
   Hao YC, 2016, COMM COM INF SC, V650, P3, DOI 10.1007/978-981-10-3168-7_1
   Heimann M, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P117, DOI 10.1145/3269206.3271788
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Mahdisoltani F, 2013, CIDR
   Mhiri Islem, 2021, Information Processing in Medical Imaging. 27th International Conference, IPMI 2021. Proceedings. Lecture Notes in Computer Science (LNCS 12729), P203, DOI 10.1007/978-3-030-78191-0_16
   Monti F., 2018, ARXIV180600770
   Pei SC, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3130, DOI 10.1145/3308558.3313646
   Pennington J., 2014, P 2014 C EMP METH NA, P1532
   Rahimi A, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2009
   Srivastava R.K., 2015, ADV NEURAL INFORM PR
   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
   Nguyen T, 2011, PROC VLDB ENDOW, V5, P133, DOI 10.14778/2078324.2078329
   Wang Z., 2013, INT SEM WEB C POST D, V1035, P121
   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
   Xu K, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3156
   Ye R, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4135
   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
   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 YY, 2018, AAAI CONF ARTIF INTE, P6069
   Zhu H, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4258
NR 36
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD MAY 28
PY 2022
VL 487
BP 99
EP 109
DI 10.1016/j.neucom.2022.02.002
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Q5TN
UT WOS:000820484600009
DA 2022-08-23
ER

PT J
AU Zhang, Y
   Shi, XY
   Mi, SY
   Yang, X
AF Zhang, Yu
   Shi, Xinyu
   Mi, Siya
   Yang, Xu
TI Image captioning with transformer and knowledge graph
SO PATTERN RECOGNITION LETTERS
LA English
DT Article
DE Image captioning; Transformer; Knowledge graph
AB The Transformer model has achieved very good results in machine translation tasks. In this paper, we adopt the Transformer model for the image captioning task. To promote the performance of image captioning, we improve the Transformer model from two aspects. First, we augment the maximum likelihood estimation (MLE) with an extra Kullback-Leibler (KL) divergence term to distinguish the difference between incorrect predictions. Second, we introduce a method to help the Transformer model generate captions by leveraging the knowledge graph. Experiments on benchmark datasets demonstrate the effectiveness of our method. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Zhang, Yu; Shi, Xinyu] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
   [Zhang, Yu; Shi, Xinyu] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing 211189, Peoples R China.
   [Mi, Siya] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 211189, Peoples R China.
   [Yang, Xu] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore.
RP Zhang, Y (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.; Zhang, Y (通讯作者)，Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing 211189, Peoples R China.
EM zhang_yu@seu.edu
OI Shi, Xinyu/0000-0002-5906-088X; yang, xu/0000-0002-8276-2679
FU National Key R&D Program of China [2018AAA0100100]; National Natural
   Science Foundation of China [61702095]; Natural Science Foundation of
   Jiangsu Province [BK20190341]
FX This work is supported by National Key R&D Program of China (No.
   2018AAA0100100), National Natural Science Foundation of China, 61702095
   and Natural Science Foundation of Jiangsu Province, BK20190341.
CR Anderson P., 2018, BOTTOM UP  TOP DOWN, P6077
   Anderson P, 2016, LECT NOTES COMPUT SC, V9909, P382, DOI 10.1007/978-3-319-46454-1_24
   Bengio S, 2015, SCHEDULED SAMPLING S, P1171
   Chen L, 2017, PROC CVPR IEEE, P6298, DOI 10.1109/CVPR.2017.667
   Chen XH, 2020, PATTERN RECOGN LETT, V132, P132, DOI 10.1016/j.patrec.2018.12.018
   Denkowski M., 2014, P 9 WORKSH STAT MACH, P376, DOI DOI 10.3115/V1/W14-3348
   Ding ST, 2019, PATTERN RECOGN LETT, V123, P89, DOI 10.1016/j.patrec.2019.03.021
   Farhadi A, 2010, LECT NOTES COMPUT SC, V6314, P15, DOI 10.1007/978-3-642-15561-1_2
   He K, 2015, PROC CVPR IEEE, DOI [10.1109/CVPR.2016.90, DOI 10.1109/CVPR.2016.90]
   He XW, 2019, PATTERN RECOGN LETT, V119, P229, DOI 10.1016/j.patrec.2017.10.018
   Hinton G., 2015, ARXIV PREPRINT ARXIV
   Hossain MZ, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3295748
   Kinghorn P, 2019, PATTERN RECOGN LETT, V119, P77, DOI 10.1016/j.patrec.2017.09.013
   Klein G, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017): SYSTEM DEMONSTRATIONS, P67, DOI 10.18653/v1/P17-4012
   Li Z., 2019, DATA DEPENDENT GAUSS
   Lin C.Y., 2004, TEXT SUMMARIZATION B, P74, DOI DOI 10.2307/3105454
   Lin TY, 2014, LECT NOTES COMPUT SC, V8693, P740, DOI 10.1007/978-3-319-10602-1_48
   Lu J., 2017, P IEEE C COMPUTER VI, P375
   Papineni K, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P311, DOI 10.3115/1073083.1073135
   Ranzato M., 2015, INT C LEARN REPR
   Ren Z, 2017, PROC CVPR IEEE, P1151, DOI 10.1109/CVPR.2017.128
   Sun C., 2015, AUTOMATIC CONCEPT DI, P2596
   Tian CN, 2019, PATTERN RECOGN LETT, V125, P639, DOI 10.1016/j.patrec.2019.07.002
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Vedantam R, 2015, PROC CVPR IEEE, P4566, DOI 10.1109/CVPR.2015.7299087
   Vinyals O., 2015, SHOW TELL NEURAL IMA, P3156, DOI DOI 10.1109/CVPR.2015.7298935
   Xu K, 2015, PR MACH LEARN RES, V37, P2048
   Yao T, 2017, IEEE I CONF COMP VIS, P4904, DOI 10.1109/ICCV.2017.524
   You QZ, 2016, PROC CVPR IEEE, P4651, DOI 10.1109/CVPR.2016.503
   Young P., 2014, P TACL, V2, P67, DOI DOI 10.1162/TACL_A_00166
NR 30
TC 12
Z9 12
U1 15
U2 44
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 MAR
PY 2021
VL 143
BP 43
EP 49
DI 10.1016/j.patrec.2020.12.020
EA JAN 2021
PG 7
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QD8TY
UT WOS:000615785700007
DA 2022-08-23
ER

PT J
AU Destandau, M
   Fekete, JD
AF Destandau, Marie
   Fekete, Jean-Daniel
TI The missing path: Analysing incompleteness in knowledge graphs
SO INFORMATION VISUALIZATION
LA English
DT Article
DE Knowledge graphs; incompleteness; coordinated views; exploratory
   analysis; Wikidata
AB Knowledge Graphs (KG) allow to merge and connect heterogeneous data despite their differences; they are incomplete by design. Yet, KG data producers need to ensure the best level of completeness, as far as possible. The difficulty is that they have no means to distinguish cases where incomplete entities could and should be fixed. We present a new visualization tool: The Missing Path, to support them in identifying coherent subsets of entities that can be repaired. It relies on a map, grouping entities according to their incomplete profile. The map is coordinated with histograms and stacked charts to support interactive exploration and analysis; the summary of a subset can be compared with the one of the full collection to reveal its distinctive features. We conduct an iterative design process and evaluation with nine Wikidata contributors. Participants gain insights and find various strategies to identify coherent subsets to be fixed.
C1 [Destandau, Marie; Fekete, Jean-Daniel] Univ Paris Saclay, LISN, INRIA, CNRS, Gif Sur Yvette, France.
RP Destandau, M (通讯作者)，Univ Paris Saclay, LISN, INRIA, CNRS, Gif Sur Yvette, France.
EM marie.destandau@gmail.com
RI Fekete, Jean-Daniel/N-9175-2018
OI Fekete, Jean-Daniel/0000-0003-3770-8726; Destandau,
   Marie/0000-0001-6911-2543
CR Aidan H., 2010, P WORKSH NEW FORMS R, P44
   Auer Soren, 2012, Knowledge Engineering and Knowledge Management. 18th International Conference, EKAW 2012. Proceedings, P353, DOI 10.1007/978-3-642-33876-2_31
   Ben Ellefi M, 2018, SEMANT WEB, V9, P677, DOI 10.3233/SW-180294
   Bizer C, 2009, J WEB SEMANT, V7, P1, DOI 10.1016/j.websem.2008.02.005
   Commonists, 2019, STOR INT MY QUEST MA
   Destandau M., 2020, ABS200209949 ARXIV
   Destandau M, 2021, SEMANT WEB, V12, P99, DOI 10.3233/SW-200383
   Gleicher M, 2011, INFORM VISUAL, V10, P289, DOI 10.1177/1473871611416549
   Harth A., 2012, P AAAI C ART INT, P613
   Issa S, 2019, LECT NOTES COMPUT SC, V11483, P312, DOI 10.1007/978-3-030-21290-2_20
   McInnes L., 2018, ABS180203426 ARXIV
   Mendes P. N., 2012, P 2012 JOINT EDBT IC, P116, DOI [DOI 10.1145/2320765.2320803, 10.1145/2320765.2320803]
   Mullins C, 2015, SIGDOC2015: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL CONFERENCE ON THE DESIGN OF COMMUNICATION, DOI 10.1145/2775441.2775478
   Nonato LG, 2019, IEEE T VIS COMPUT GR, V25, P2650, DOI 10.1109/TVCG.2018.2846735
   Perer Adam, 2008, 13th International Conference on Intelligent User Interfaces. IUI 2008, P109, DOI 10.1145/1378773.1378788
   Perer A., P SIGCHI C HUM FACT, P265
   Plaisant C., 2006, P 2006 AVI WORKSH TI, P1, DOI [DOI 10.1145/1168149.1168158, 10.1145/1168149.1168158]
   Radulovic F, 2018, SEMANT WEB, V9
   Troullinou G, 2018, LECT NOTES COMPUT SC, V11136, P268, DOI 10.1007/978-3-030-00671-6_16
   Weise M., 2016, 2 INT WORKSH VIS INT, P120
   Wisesa A, 2019, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE (K-CAP '19), P123, DOI 10.1145/3360901.3364425
   Zaveri A, 2016, SEMANT WEB, V7, P63, DOI 10.3233/SW-150175
   Zaveri A, 2013, LECT NOTES COMPUT SC, V8219, P244, DOI 10.1007/978-3-642-41338-4_16
NR 23
TC 0
Z9 0
U1 0
U2 4
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1473-8716
EI 1473-8724
J9 INFORM VISUAL
JI Inf. Vis.
PD JAN
PY 2021
VL 20
IS 1
BP 66
EP 82
DI 10.1177/1473871621991539
PG 17
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QK8ZM
UT WOS:000620668200005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhao, Q
   Li, JQ
   Xu, C
   Yang, JJ
   Zhao, L
AF Zhao, Qing
   Li, Jianqiang
   Xu, Chun
   Yang, Jijiang
   Zhao, Liang
TI Knowledge-Enhanced Relation Extraction for Chinese EMRs
SO IT PROFESSIONAL
LA English
DT Article
AB The growing demand for the meaningful use of electronic medical records has led to great interest in medical entities and relation extraction technologies. Most existing methods perform relation extraction based on manually labeled documents and rarely consider incorporating knowledge graphs that include rich, valuable structured knowledge, which will cause semantic ambiguities. To address this problem, we propose a knowledge-enhanced relation extraction (KERE) model. First, we extract knowledge information from the knowledge graph to generate a knowledge-guided word embedding. Then, the lexical features are considered supplementary information for semantic understanding. Experiments on real-world datasets show that the KERE model achieves important improvements in a biomedical relation extraction task.
C1 [Zhao, Qing; Li, Jianqiang; Yang, Jijiang; Zhao, Liang] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China.
   [Xu, Chun] Xinjiang Univ Finance & Econ, Res Big Data Anal, Urumqi, Peoples R China.
   [Yang, Jijiang] Tsinghua Univ, Tsinghua Natl Labs Informat Sci & Technol, Beijing, Peoples R China.
RP Yang, JJ (通讯作者)，Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China.
EM zhaoqing1025@emails.bjut.edu.cn; lijianqiang@bjut.edu.cn;
   xuchun@mails.ucas.edu.cn; yangjijiang@tsinghua.edu.cn;
   zhaoliang123@emails.bjut.edu.cn
RI Yang, Ji-Jiang/AFU-9826-2022
OI Yang, Ji-Jiang/0000-0002-4124-6408
FU Xinjiang Nature Science Foundation of China [2019D01A23]; China
   Scholarships Council [201906540057]
FX This work was supported in part by the program of the Xinjiang Nature
   Science Foundation of China (2019D01A23) and in part by the China
   Scholarships Council (201906540057).
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Nguyen DQ, 2018, SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2018), P129
   Gu JH, 2017, DATABASE-OXFORD, DOI 10.1093/database/bax024
   Hinton Geoffrey E., 2012, ABS12070580 CORR
   Kambhatla N., 2004, P ACL, P21
   Li JQ, 2020, J SUPERCOMPUT, V76, P1450, DOI 10.1007/s11227-017-2229-x
   Powers DMW, 2007, SEI07001 FLIND U S A
   Rink B, 2011, J AM MED INFORM ASSN, V18, P594, DOI 10.1136/amiajnl-2011-000153
   Wang CL, 2012, INT C MULTIMED INFO, P99, DOI 10.1109/MINES.2012.147
   Wu H., 2018, J AMBIENT INTELL HUM, P1
   Yang JJ, 2015, COMPUT IND, V69, P3, DOI 10.1016/j.compind.2015.01.012
NR 11
TC 4
Z9 4
U1 9
U2 33
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1520-9202
EI 1941-045X
J9 IT PROF
JI IT Prof.
PD JUL-AUG
PY 2020
VL 22
IS 4
BP 57
EP 62
DI 10.1109/MITP.2020.2984598
PG 6
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA MN1TB
UT WOS:000550629700010
DA 2022-08-23
ER

PT J
AU Opdahl, AL
   Tessem, B
AF Opdahl, Andreas L.
   Tessem, Bjornar
TI Ontologies for finding journalistic angles
SO SOFTWARE AND SYSTEMS MODELING
LA English
DT Article
DE Computational journalism; Data journalism; Journalistic knowledge
   platforms; News angles; Knowledge graphs; Ontology
ID KNOWLEDGE GRAPHS; DESIGN SCIENCE; SEMANTIC WEB; MACHINE
AB Journalism relies more and more on information and communication technology (ICT). ICT-basedjournalistic knowledge platformscontinuously harvest potentially news-relevant information from the Internet and make it useful for journalists. Because information about the same event is available from different sources and formats vary widely,knowledge graphsare emerging as a preferred technology for integrating, enriching, and preparing information for journalistic use. The paper explores how journalistic knowledge graphs can be augmented with support fornews angles, which can help journalists to detect newsworthy events and make them interesting for the intended audience. We argue that finding newsworthy angles on news-related information is an important example of a topical problem in information science: that of detecting interesting events and situations in big data sets and presenting those events and situations in interesting ways.
C1 [Opdahl, Andreas L.; Tessem, Bjornar] Univ Bergen, Dept Informat Sci & Media Studies, N-5020 Bergen, Norway.
RP Opdahl, AL (通讯作者)，Univ Bergen, Dept Informat Sci & Media Studies, N-5020 Bergen, Norway.
EM Andreas.Opdahl@uib.no; Bjornar.Tessem@uib.no
FU University of Bergen - Norwegian Research Council [275872]
FX Open Access funding provided by University of Bergen. The News Angler
   project is funded by the Norwegian Research Council's IKTPLUSS programme
   as Project 275872.
CR Al-Moslmi T., 2019, P 3 EUROPEAN DATA CO, P3
   Al-Moslmi T, 2020, IEEE ACCESS, V8, P32862, DOI 10.1109/ACCESS.2020.2973928
   Allemang D, 2011, SEMANTIC WEB FOR THE WORKING ONTOLOGIST: EFFECTIVE MODELING IN RDFS AND OWL, 2ND EDITION, P1
   Berven A., 2018, NOKOBIT, P1
   Bizer C, 2011, SEMANTIC SERVICES, INTEROPERABILITY AND WEB APPLICATIONS: EMERGING CONCEPTS, P205, DOI 10.4018/978-1-60960-593-3.ch008
   Breslin JG, 2005, LECT NOTES COMPUT SC, V3532, P500
   Brown A, 2011, TPM-PHILOS MAG, P87, DOI 10.5840/tpm201155112
   Corcoglioniti F, 2016, IEEE T KNOWL DATA EN, V28, P3261, DOI 10.1109/TKDE.2016.2602206
   Gallofre Ocana M., 2018, P 4 NORW BIG DAT S N, P17
   Gangemi A, 2002, LECT NOTES ARTIF INT, V2473, P166
   Gangemi A, 2009, RDF OWL ONTOLOGY
   Gangemi A, 2017, SEMANT WEB, V8, P873, DOI 10.3233/SW-160240
   Germann U, 2018, NLP OPEN SOURCE SOFTWARE (NLP-OSS), P47
   Gervas P, 2009, AI MAG, V30, P49, DOI 10.1609/aimag.v30i3.2250
   Gregor S, 2013, MIS QUART, V37, P337, DOI 10.25300/MISQ/2013/37.2.01
   Hak Lae Kim, 2008, 2008 Second IEEE International Conference on Semantic Computing (ICSC), P315, DOI 10.1109/ICSC.2008.79
   Harcup T, 2017, JOURNALISM STUD, V18, P1470, DOI 10.1080/1461670X.2016.1150193
   Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625
   Hevner AR, 2007, SCANDINAVIAN J INFOR, V19, P4
   Kim HL, 2010, J INF SCI, V36, P57, DOI 10.1177/0165551509346785
   Kobilarov G, 2009, LECT NOTES COMPUT SC, V5554, P723, DOI 10.1007/978-3-642-02121-3_53
   Kolitsas N., 2018, ARXIV180807699
   Latar N.L., 2015, NEW WORLD TRANSITION, P65
   Laublet Philippe, 2008, LDOW
   Leban G, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P107, DOI 10.1145/2567948.2577024
   Liu XM, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P207, DOI 10.1145/2983323.2983363
   Lohmann S., 2011, P 7 INT C SEM SYST I, P95, DOI DOI 10.1145/2063518.2063531
   Lohmann S, 2015, LECT NOTES ARTIF INT, V8982, P154, DOI 10.1007/978-3-319-17966-7_21
   Machill M, 2009, JOURNALISM STUD, V10, P178, DOI 10.1080/14616700802337768
   Mendes P. N., 2011, P 7 INT C SEM SYST, P1, DOI DOI 10.1145/2063518.2063519
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Miroshnichenko A, 2018, INFORMATION, V9, DOI 10.3390/info9070183
   Newman Richard, 2005, TAG ONTOLOGY
   Opdahl A.L., 2016, NOKOBIT, V24, P1
   Opdahl AL, 2019, LECT NOTES BUS INF P, V352, P279, DOI 10.1007/978-3-030-20618-5_19
   Peroni S., 2011, P 7 INT C SEM SYST, P111, DOI [10.1145/2063518.2063533, DOI 10.1145/2063518.2063533]
   Raimond Y., 2007, THE EVENT ONTOLOGY
   Raval Mihir, 2012, BMC Res Notes, V5, P594, DOI 10.1186/1756-0500-5-594
   Rospocher M, 2016, J WEB SEMANT, V37-38, P132, DOI 10.1016/j.websem.2015.12.004
   Scherp A, 2009, K-CAP'09: PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE, P137
   Segers R, 2015, P MAPLEX
   Segers R., 2017, P EV STOR NEWS WORKS, P37
   Shaw R, 2009, LECT NOTES COMPUT SC, V5926, P153
   Shekarpour S, 2019, IEEE INT C SEMANT CO, P385, DOI [10.1109/ICSC.2019.00076, 10.1109/ICOSC.2019.8665605]
   Shoemaker P.J., 1995, MEDIATING MESSAGE TH
   Tessem B., 2019, 13 INT C RES CHALL I, P1, DOI 10.1109/RCIS.2019.8877058
   Thurman N, 2019, HDB JOURNALISM STUDI
   Troncy R, 2008, LECT NOTES COMPUT SC, V5318, P483, DOI 10.1007/978-3-540-88564-1_31
   Vaishnavi V., 2004, DESIGN SCI RES INFOR
   van Hage WR, 2011, J WEB SEMANT, V9, P128, DOI 10.1016/j.websem.2011.03.003
   Vossen P, 2016, KNOWL-BASED SYST, V110, P60, DOI 10.1016/j.knosys.2016.07.013
NR 51
TC 4
Z9 4
U1 1
U2 3
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.
PD FEB
PY 2021
VL 20
IS 1
BP 71
EP 87
DI 10.1007/s10270-020-00801-w
EA JUN 2020
PG 17
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QM0VS
UT WOS:000539961500001
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Zou, XY
   Wang, XZ
   Cen, S
   Dai, GQ
   Liu, C
AF Zou, Xiaoying
   Wang, Xizhao
   Cen, Si
   Dai, Guoquan
   Liu, Chao
TI Knowledge graph embedding with self adaptive double-limited loss?
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Embedding; Loss function; Negative sampling; Hard pair
AB Many well-performing embedding models for knowledge graphs employ a negative sampling framework to complete the representation learning in which the loss function is a critical component in distinguishing between positive and negative triplets. One of the most recently proposed loss functions is the double-limited scoring loss, which sets fixed upper and lower bounds respectively for positive and negative triplets. We find that, for all positive and negative triplets, fixed upper and lower bounds are not appropriate since triplets that are difficult to be distinguished usually have changing bounds. In this paper, we propose a self adaptive double-limited loss (ADL) that dynamically adjusts the upper limit of positive triplet scores and the lower limit of negative triplet scores through evaluating the score proportion between positive and negative triplets. Furthermore, based upon ADL, we build several knowledge graph embedding models, including TransE-ADL, TransH-ADL, TransD-ADL, TorusE-ADL, and ComplEx-ADL, in which the gradient descent technique is used to train their parameters. Dynamically adjusted bounds lead to a reasonable partition of positive and negative triplets within embedding space, improving prediction accuracy significantly. The experimental results of link prediction confirm this improvement compared to the state-of-the-art baselines.
C1 [Zou, Xiaoying; Wang, Xizhao; Cen, Si; Dai, Guoquan; Liu, Chao] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China.
   [Wang, Xizhao] Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China.
RP Wang, XZ (通讯作者)，Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China.; Wang, XZ (通讯作者)，Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China.
EM xiaoyingzou12@gmail.com; xizhaowang@ieee.org; szucensi@163.com;
   egbertdai@foxmail.com; zzulchao@163.com
FU Postgraduate Innovation Development Fund Project of Shenzhen University,
   China [0000470814]; National Natural Science Foundation of China
   [61976141, 61732011, 62106148]; China Postdoctoral Science Foundation
   [2021M702259]
FX This work was supported in part by the Postgraduate Innovation
   Development Fund Project of Shenzhen University, China (Grants
   0000470814), in part by the National Natural Science Foundation of China
   (Grants 61976141, 61732011 and 62106148) and the Project funded by China
   Postdoctoral Science Foundation under Grant no. 2021M702259.
CR Akrami F, 2020, SIGMOD'20: PROCEEDINGS OF THE 2020 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1995, DOI 10.1145/3318464.3380599
   Balazevic I., 2019, P 2019 C EMP METH NA, P5185, DOI [10.18653/v1/D19-1522, DOI 10.18653/V1/D19-1522]
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2011, P AAAI
   Bordes A., 2012, P 15 INT C ART INT S, P127
   Bordes A., 2013, ADV NEURAL INFORM PR, V26
   Bottou L, 2010, COMPSTAT'2010: 19TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STATISTICS, P177, DOI 10.1007/978-3-7908-2604-3_16
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Glorot X., 2010, JMLR WRKSHOP C P, P249
   He S., 2014, P C EMP METH NAT GAS, P1092
   Hu B., 2021, NEUROCOMPUTING
   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
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu HX, 2017, PR MACH LEARN RES, V70
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Nayyeri M., 2019, ARXIV PREPRINT ARXIV
   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
   Nissen ME, 1999, DECIS SUPPORT SYST, V27, P47, DOI 10.1016/S0167-9236(99)00036-6
   Rossi A, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3424672
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Shi BX, 2017, AAAI CONF ARTIF INTE, P1236
   Socher R, 2013, ADV NEURAL INFORM PR, V26, P926, DOI DOI 10.5555/2999611.2999715
   Socher R., 2013, NIPS
   Sun Z., 2019, 7 INT C LEARN REPR
   Szumlanski Sean R., 2010, P 19 ACM INT C INF K, P19
   Toutanova K., 2015, P 2015 C EMP METH NA, P1499
   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
   Yang B., 2014, ABS14126575 CORR
   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 W, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P96, DOI 10.1145/3289600.3291014
   Zhang ZQ, 2020, AAAI CONF ARTIF INTE, V34, P3065
   Zhou X., ARXIV210307461
   Zhou XF, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1009, DOI 10.1145/3132847.3132939
   Zhu SG, 2020, NEUROCOMPUTING, V372, P64, DOI 10.1016/j.neucom.2019.09.003
NR 43
TC 0
Z9 0
U1 1
U2 1
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 27
PY 2022
VL 252
AR 109310
DI 10.1016/j.knosys.2022.109310
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3A5BH
UT WOS:000827274700004
DA 2022-08-23
ER

PT J
AU Futia, G
   Vetro, A
   De Martin, JC
AF Futia, Giuseppe
   Vetro, Antonio
   De Martin, Juan Carlos
TI SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph
   neural networks
SO SOFTWAREX
LA English
DT Article
DE Knowledge Graphs; Semantic Modeling; Graph neural networks
ID STRUCTURED DATA
AB SeMi (SEmantic Modeling machIne) is a tool to semi-automatically build large-scale Knowledge Graphs from structured sources such as CSV, JSON, and XML files. To achieve such a goal, SeMi builds the semantic models of the data sources, in terms of concepts and relations within a domain ontology. Most of the research contributions on automatic semantic modeling is focused on the detection of semantic types of source attributes. However, the inference of the correct semantic relations between these attributes is critical to reconstruct the precise meaning of the data. SeMi covers the entire process of semantic modeling: (i) it provides a semi-automatic step to detect semantic types; (ii) it exploits a novel approach to inference semantic relations, based on a graph neural network trained on background linked data. At the best of our knowledge, this is the first technique that exploits a graph neural network to support the semantic modeling process. Furthermore, the pipeline implemented in SeMi is modular and each component can be replaced to tailor the process to very specific domains or requirements. This contribution can be considered as a step ahead towards automatic and scalable approaches for building Knowledge Graphs. (C) 2020 The Authors. Published by Elsevier B.V.
C1 [Futia, Giuseppe; Vetro, Antonio; De Martin, Juan Carlos] Politecn Torino, Nexa Ctr Internet & Soc DAUIN, Corso Duca Abruzzi 24, Turin, Italy.
RP Futia, G (通讯作者)，Politecn Torino, Nexa Ctr Internet & Soc DAUIN, Corso Duca Abruzzi 24, Turin, Italy.
EM giuseppe.futia@polito.it
RI Vetro, Antonio/P-7939-2018
OI Vetro, Antonio/0000-0003-2027-3308
CR Athreya RG, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P143, DOI 10.1145/3184558.3186964
   Vu B, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P1944, DOI 10.1145/3308558.3313711
   Bizer C, 2011, SEMANTIC SERVICES, INTEROPERABILITY AND WEB APPLICATIONS: EMERGING CONCEPTS, P205, DOI 10.4018/978-1-60960-593-3.ch008
   Craswell N, 2009, ENCY DATABASE SYSTEM, V1703
   Cyganiak R., 2015, TECHNICAL REPORT
   Das Shagnik., 2016, R2RML RDB RDF MAPPIN
   de Medeiros LF, 2015, LECT NOTES COMPUT SC, V9114, P326, DOI 10.1007/978-3-319-19890-3_21
   Dimou A, 2014, RML GENERIC LANGUAGE, V1184
   DuCharme Bob, 2013, LEARNING SPARQL QUER
   Erling O., 2009, NETWORKED KNOWLEDGE, P7
   Futia G, 2015, AI APPROACHES COMPLE, P380
   Futia G, 2017, LECT NOTES COMPUT SC, V10249, P623, DOI 10.1007/978-3-319-58068-5_38
   Gormley C., 2015, ELASTICSEARCH DEFINI
   Guha RV, 2016, COMMUN ACM, V59, P44, DOI 10.1145/2844544
   Hasnain A, 2014, LECT NOTES COMPUT SC, V8796, P114, DOI 10.1007/978-3-319-11964-9_8
   Hooi B, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P895, DOI 10.1145/2939672.2939747
   HWANG FK, 1992, NETWORKS, V22, P55, DOI 10.1002/net.3230220105
   Knoblock Craig A., 2012, The Semantic Web: Research and Applications. Proceedings 9th Extended Semantic Web Conference (ESWC 2012), P375, DOI 10.1007/978-3-642-30284-8_32
   KOU L, 1981, ACTA INFORM, V15, P141, DOI 10.1007/BF00288961
   Lassila O., 1998, RESOURCE DESCRIPTION
   Mihalcea R., 2006, 21 NAT C ART INT 18, V1, P775
   Pomp A, 2019, ENHANCING KNOWLEDGE
   Pomp A, 2019, IEEE INT C SEMANT CO, P98, DOI 10.1109/ICOSC.2019.8665620
   Ramnandan SK, 2015, LECT NOTES COMPUT SC, V9088, P403, DOI 10.1007/978-3-319-18818-8_25
   Rizzo G, 2017, DIGIT SCHOLARSH HUM, V32, P195, DOI 10.1093/llc/fqv031
   Ruemmele N, 2018, ARXIV180109788
   Schiavone L, 2018, COMM COM INF SC, V806, P15, DOI 10.1007/978-3-319-73165-0_2
   Taheriyan M, 2016, LECT NOTES COMPUT SC, V9981, P549, DOI 10.1007/978-3-319-46523-4_33
   Taheriyan M, 2016, J WEB SEMANT, V37-38, P152, DOI 10.1016/j.websem.2015.12.003
   Taheriyan M, 2013, LECT NOTES COMPUT SC, V8218, P607, DOI 10.1007/978-3-642-41335-3_38
   Tomassetti Federico, 2011, 15th Annual Conference on Evaluation & Assessment in Software Engineering (EASE 2011), P31
   Toutanova K., 2015, P 3 WORKSH CONT VECT, P57, DOI DOI 10.18653/V1/W15-4007
   van der Merwe R, 2019, ARCH REC, V40, P239, DOI 10.1080/23257962.2017.1388224
   Wang M, 2018, DEEP GRAPH LIB
   Yang B., 2015, P INT C LEARN REPR
NR 35
TC 4
Z9 4
U1 1
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-7110
J9 SOFTWAREX
JI SoftwareX
PD JUL-DEC
PY 2020
VL 12
AR 100516
DI 10.1016/j.softx.2020.100516
PG 10
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PH8TA
UT WOS:000600676600005
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhu, GC
   Hao, MH
   Zheng, CL
   Wang, LL
AF Zhu, Guicun
   Hao, Meihui
   Zheng, Changlong
   Wang, Linlin
TI Design of Knowledge Graph Retrieval System for Legal and Regulatory
   Framework of Multilevel Latent Semantic Indexing
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID CONSTRUCTION; INTELLIGENCE; PATTERNS; NETWORK
AB Latent semantic analysis (LSA) is a natural language statistical model, which is considered as a method to acquire, generalize, and represent knowledge. Compared with other retrieval models based on concept dictionaries or concept networks, the retrieval model based on LSA has the advantages of strong computability and less human participation. LSA establishes a latent semantic space through truncated singular value decomposition. Words and documents in the latent semantic space are projected onto the dimension representing the latent concept, and then the semantic relationship between words can be extracted to present the semantic structure in natural language. This paper designs the system architecture of the public prosecutorial knowledge graph. Combining the graph data storage technology and the characteristics of the public domain ontology, a knowledge graph storage method is designed. By building a prototype system, the functions of knowledge management, knowledge query, and knowledge push are realized. A named entity recognition method based on bidirectional long-short-term memory (bi-LSTM) combined with conditional random field (CRF) is proposed. Bi-LSTM-CRF performs named entity recognition based on character-level features. CRF can use the transition matrix to further obtain the relationship between each position label, so that bi-LSTM-CRF not only retains the context information but also considers the influence between the current position and the previous position. The experimental results show that the LSTM-entity-context method proposed in this paper improves the representation ability of text semantics compared with other algorithms. However, this method only introduces relevant entity information to supplement the semantic representation of the text. The order in the case is often ignored, especially when it comes to the time series of the case characteristics, and the "order problem" may eventually affect the final prediction result. The knowledge graph of legal documents of theft cases based on ontology can be updated and maintained in real time. The knowledge graph can conceptualize, share, and perpetuate knowledge related to procuratorial organs and can also reasonably utilize and mine many useful experiences and knowledge to assist in decision-making.
C1 [Zhu, Guicun] Liaocheng Univ, Dept Econ Management, Dongchang Coll, Liaocheng 252000, Shandong, Peoples R China.
   [Hao, Meihui; Wang, Linlin] Dongchang Middle Sch Liaocheng Econ & Technol Dev, Liaocheng 252000, Shandong, Peoples R China.
   [Zheng, Changlong] Liaocheng Yucai Sch, Liaocheng 252000, Shandong, Peoples R China.
RP Zhu, GC (通讯作者)，Liaocheng Univ, Dept Econ Management, Dongchang Coll, Liaocheng 252000, Shandong, Peoples R China.
EM zhuguicun@lcudcc.edu.cn; haomeihui66@163.com; longlong19850807@163.com;
   wang1991762022@163.com
CR Aljarah I, 2021, J INF SCI, V47, P483, DOI 10.1177/0165551520917651
   An J, 2018, J INFORMETR, V12, P217, DOI 10.1016/j.joi.2018.01.001
   Baath R, 2019, J PSYCHOLINGUIST RES, V48, P683, DOI 10.1007/s10936-018-09625-8
   Chen PH, 2018, IEEE ACCESS, V6, P31553, DOI 10.1109/ACCESS.2018.2839607
   Chung K, 2019, WIRELESS PERS COMMUN, V105, P583, DOI 10.1007/s11277-018-5979-8
   Ebisu T, 2020, IEEE T KNOWL DATA EN, V32, P941, DOI 10.1109/TKDE.2019.2893920
   Feng JZ, 2019, CLUSTER COMPUT, V22, pS5839, DOI [10.7633/j.issn.1003-6202.2018.08.001, 10.7633/j.issn.1003-6202.2018.06.001, 10.1007/s10586-017-1626-5]
   Fernandez-Beltran R, 2018, IEEE J-STARS, V11, P4982, DOI 10.1109/JSTARS.2018.2881342
   Fernandez-Beltran R, 2018, MULTIMED TOOLS APPL, V77, P16771, DOI 10.1007/s11042-017-5247-z
   Gu YH, 2018, WORLD WIDE WEB, V21, P1705, DOI 10.1007/s11280-018-0542-9
   Haase P, 2019, SEMANT WEB, V10, P1109, DOI 10.3233/SW-190360
   Hasibuan M. S., 2019, J TECHNOLOGY SCI ED, V9, P85, DOI [DOI 10.3926/jotse.540, DOI 10.3926/JOTSE.540]
   He LL, 2019, IEEE ACCESS, V7, P101231, DOI 10.1109/ACCESS.2019.2931361
   Li X, 2021, IEEE ROBOT AUTOM LET, V6, P3459, DOI 10.1109/LRA.2021.3062807
   Liao PC, 2018, J CLEAN PROD, V203, P353, DOI 10.1016/j.jclepro.2018.08.218
   Liu XX, 2019, IEEE ACCESS, V7, P62251, DOI 10.1109/ACCESS.2019.2915987
   Menon A, 2019, CURR OPIN CHEM ENG, V26, P33, DOI 10.1016/j.coche.2019.08.004
   Noor U, 2019, FUTURE GENER COMP SY, V95, P467, DOI 10.1016/j.future.2019.01.022
   Roque C, 2019, ACCIDENT ANAL PREV, V131, P336, DOI 10.1016/j.aap.2019.07.021
   Selvalakshmi B, 2019, CLUSTER COMPUT, V22, P12871, DOI 10.1007/s10586-018-1789-8
   Sheth A, 2019, IEEE INTERNET COMPUT, V23, P67, DOI 10.1109/MIC.2019.2928449
   Valdez D, 2018, SOC SCI QUART, V99, P1665, DOI 10.1111/ssqu.12528
   Wang CB, 2018, COMPUT GEOSCI-UK, V112, P112, DOI 10.1016/j.cageo.2017.12.007
   Wang S, 2019, IEEE ACCESS, V7, P136951, DOI 10.1109/ACCESS.2019.2942338
   Xu XH, 2019, KNOWL-BASED SYST, V163, P495, DOI 10.1016/j.knosys.2018.09.010
NR 25
TC 0
Z9 0
U1 0
U2 0
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 JUL 19
PY 2022
VL 2022
AR 6781043
DI 10.1155/2022/6781043
PG 11
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 3M0ZT
UT WOS:000835188300003
PM 35909848
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Diaz-Rodriguez, N
   Lamas, A
   Sanchez, J
   Franchi, G
   Donadello, I
   Tabik, S
   Filliat, D
   Cruz, P
   Montes, R
   Herrera, F
AF Diaz-Rodriguez, Natalia
   Lamas, Alberto
   Sanchez, Jules
   Franchi, Gianni
   Donadello, Ivan
   Tabik, Siham
   Filliat, David
   Cruz, Policarpo
   Montes, Rosana
   Herrera, Francisco
TI EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep
   learning representations with expert knowledge graphs: The MonuMAI
   cultural heritage use case
SO INFORMATION FUSION
LA English
DT Article
DE Explainable artificial intelligence; Deep learning; Neural-symbolic
   learning; Expert knowledge graphs; Compositionality; Part-based object
   detection and classification
ID CLASSIFICATION; MODELS
AB The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are black-box methods hard to debug, interpret, and certify. DL alone cannot provide explanations that can be validated by a non technical audience such as end-users or domain experts. In contrast, symbolic AI systems that convert concepts into rules or symbols - such as knowledge graphs - are easier to explain. However, they present lower generalization and scaling capabilities. A very important challenge is to fuse DL representations with expert knowledge. One way to address this challenge, as well as the performance-explainability trade-off is by leveraging the best of both streams without obviating domain expert knowledge. In this paper, we tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph. We present the eXplainable Neural-symbolic learning (X-NeSyL) methodology, designed to learn both symbolic and deep representations, together with an explainability metric to assess the level of alignment of machine and human expert explanations. The ultimate objective is to fuse DL representations with expert domain knowledge during the learning process so it serves as a sound basis for explainability. In particular, X-NeSyL methodology involves the concrete use of two notions of explanation, both at inference and training time respectively: (1) EXPLANet: Expert-aligned eXplainable Part-based cLAssifier NETwork Architecture, a compositional convolutional neural network that makes use of symbolic representations, and (2) SHAP-Backprop, an explainable AI-informed training procedure that corrects and guides the DL process to align with such symbolic representations in form of knowledge graphs. We showcase X-NeSyL methodology using MonuMAI dataset for monument facade image classification, and demonstrate that with our approach, it is possible to improve explainability at the same time as performance.
C1 [Diaz-Rodriguez, Natalia; Sanchez, Jules; Franchi, Gianni; Filliat, David] Inst Polytech Paris, U2IS, ENSTA, F-91762 Palaiseau, France.
   [Diaz-Rodriguez, Natalia; Sanchez, Jules; Franchi, Gianni] Inria Flowers, F-91762 Palaiseau, France.
   [Donadello, Ivan] Free Univ Bozen Bolzano, I-39100 Bolzano, Italy.
   [Diaz-Rodriguez, Natalia; Tabik, Siham; Herrera, Francisco] Univ Granada, DaSCI Andalusian Inst Data Sci & Computat Intelli, Granada 18071, Spain.
   [Herrera, Francisco] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia.
   [Cruz, Policarpo] Univ Granada, Dept Art Hist, Granada 18071, Spain.
   [Diaz-Rodriguez, Natalia; Herrera, Francisco] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada 18071, Spain.
   [Montes, Rosana] Univ Granada, Dept Software Engn, Granada 18071, Spain.
RP Diaz-Rodriguez, N (通讯作者)，Inst Polytech Paris, U2IS, ENSTA, F-91762 Palaiseau, France.; Diaz-Rodriguez, N (通讯作者)，Inria Flowers, F-91762 Palaiseau, France.; Diaz-Rodriguez, N (通讯作者)，Univ Granada, DaSCI Andalusian Inst Data Sci & Computat Intelli, Granada 18071, Spain.; Diaz-Rodriguez, N (通讯作者)，Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada 18071, Spain.
EM nataliadiaz@ugr.es
RI Donadello, Ivan/AAX-9887-2021; Díaz-Rodríguez, Natalia/B-1843-2019;
   Montes Soldado, Rosana/C-2434-2012; Filliat, David/E-7993-2018
OI Donadello, Ivan/0000-0002-0701-5729; Díaz-Rodríguez,
   Natalia/0000-0003-3362-9326; Montes Soldado, Rosana/0000-0002-0183-044X;
   Filliat, David/0000-0002-5739-1618; SANCHEZ, Jules/0000-0002-8104-0930
FU French ANRT (Association Nationale Recherche Technologie - ANRT); SEGULA
   Technologies; Andalusian Excellence project [P18FR-4961]; Spanish
   National Project [PID2020-119478GB-I00]; Ramon y Cajal Programme
   [RYC-201518136]; Spanish Government Juan de la Cierva Incorporacion
   contract [IJC2019-039152-I]
FX This research was funded by the French ANRT (Association Nationale
   Recherche Technologie - ANRT) industrial Cifre PhD contract with SEGULA
   Technologies, the Andalusian Excellence project P18FR-4961 and the
   Spanish National Project PID2020-119478GB-I00. S. Tabik was supported by
   the Ramon y Cajal Programme (RYC-201518136). N. Diaz-Rodriguez is
   currently supported by the Spanish Government Juan de la Cierva
   Incorporacion contract (IJC2019-039152-I).
CR Adebayo J, 2018, ADV NEUR IN, V31
   Andreas Jacob, 2019, ARXIV PREPRINT ARXIV
   [Anonymous], 2007, DESCRIPTION LOGIC HD, Vsecond
   Artur S, 2009, COGNITIVE TECHNOLOGI
   Bach S, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0130140
   Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
   Bennetot A., 2019, NESY WORKSH IJCAI, P71
   Bernstein EJ, 2005, PROC CVPR IEEE, P734
   Besold TR, 2017, NEURO SYMBOLIC ARTIF
   Bianchi F, 2019, LECT NOTES COMPUT SC, V11784, P161, DOI 10.1007/978-3-030-31095-0_11
   Binder Alexander, 2016, International Conference on Information Science and Applications (ICISA) 2016. LNEE 376, P913, DOI 10.1007/978-981-10-0557-2_87
   Bollacker K, 2019, STUD SYST DECIS CONT, V176, P203, DOI 10.1007/978-3-030-00317-3_9
   Buhrmester V., 2019, ARXIV PREPRINT ARXIV, P1
   Carriero VA, 2019, LECT NOTES COMPUT SC, V11779, P36, DOI 10.1007/978-3-030-30796-7_3
   Chattopadhay A, 2018, IEEE WINT CONF APPL, P839, DOI 10.1109/WACV.2018.00097
   Chen XJ, 2014, PROC CVPR IEEE, P1979, DOI 10.1109/CVPR.2014.254
   Confalonieri R, 2021, ARTIF INTELL, V296, DOI 10.1016/j.artint.2021.103471
   Daniele Alessandro, 2020, ABS200906087 CORR
   De Kok R., 1999, INT ARCH PHOTOGRAMM, V32, P4
   De Raedt L, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2468
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Diaz-Rodriguez Natalia, 2020, UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, P317, DOI 10.1145/3386392.3399276
   Diaz-Rodriguez Natalia, 2017, 6 WORKSH AUT KNOWL B
   Diligenti Michelangelo, 2016, OINT EUR C MACH LEAR, V9852, P33
   Donadello I, 2019, IEEE IJCNN
   Donadello I, 2016, INTELL ARTIF, V10, P33, DOI 10.3233/IA-160093
   Donadello Ivan, P 26 INT JOINT C ART, P1596
   Dong H., 2019, ICLR, P1, DOI DOI 10.1145/3340531.3411949
   Ebrahimi M, 2021, APPL INTELL, V51, P6326, DOI 10.1007/s10489-020-02165-6
   Everingham M, 2010, INT J COMPUT VISION, V88, P303, DOI 10.1007/s11263-009-0275-4
   Felzenszwalb PF, 2010, IEEE T PATTERN ANAL, V32, P1627, DOI 10.1109/TPAMI.2009.167
   Fodor J., 2002, COMPOSITIONALITY PAP
   Fong RC, 2017, IEEE I CONF COMP VIS, P3449, DOI 10.1109/ICCV.2017.371
   Garcez AD, 2019, J APPL LOG-IFCOLOG, V6, P611
   Garcez ASA, 1999, APPL INTELL, V11, P59, DOI 10.1023/A:1008328630915
   Ge WF, 2019, PROC CVPR IEEE, P3029, DOI 10.1109/CVPR.2019.00315
   Getoor L., 2005, ACM SIGKDD EXPLORATI, V7, P3, DOI DOI 10.1145/1117454.1117456
   Girshick R., 2015, ADV NEUR IN
   Guidotti R, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3236009
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   He X., 2020, SAMPLE EFFICIENT DEE, DOI 10.1101/2020.04.13.20063941
   Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647
   Holzinger A, 2019, APPL INTELL, V49, P2401, DOI 10.1007/s10489-018-1361-5
   Huber D, 2004, PROC CVPR IEEE, P82
   Huitzil I, 2018, COMM COM INF SC, V854, P100, DOI 10.1007/978-3-319-91476-3_9
   Hupkes Dieuwke, 2019, ARXIV PREPRINT ARXIV
   Jain S., 2019, P 2019 C N AM CHAPT, V1, P3543
   Karpathy A, 2014, PROC CVPR IEEE, P1725, DOI 10.1109/CVPR.2014.223
   Kindermans P., 2018, ICLR
   Kindermans P.-J., 2019, EXPLAINABLE AL INTER, DOI [10.1007/978-3-030-28954-6.14, DOI 10.1007/978-3-030-28954-6_14]
   Kingma D. P., 2017, ARXIV
   Lake BM, 2015, SCIENCE, V350, P1332, DOI 10.1126/science.aab3050
   Lamas A, 2021, NEUROCOMPUTING, V420, P266, DOI 10.1016/j.neucom.2020.09.041
   Lecue F, 2020, SEMANT WEB, V11, P41, DOI 10.3233/SW-190374
   LeCun Y., 1989, PROC ADV NEURAL INF, P396, DOI DOI 10.1111/DSU.12130
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Lesort T, 2020, INFORM FUSION, V58, P52, DOI 10.1016/j.inffus.2019.12.004
   Lin Tsung-Yi, 2020, IEEE Trans Pattern Anal Mach Intell, V42, P318, DOI [10.1109/TPAMI.2018.2858826, 10.1109/ICCV.2017.324]
   Lin TY, 2014, LECT NOTES COMPUT SC, V8693, P740, DOI 10.1007/978-3-319-10602-1_48
   Lowe Sindy, 2020, ARXIV PREPRINT ARXIV
   Lundberg SM, 2017, ADV NEUR IN, V30
   Manhaeve R, 2018, ADV NEUR IN, V31
   Mao J., 2019, P INT C LEARN REPR
   Marra Giuseppe, 2020, FRONT ARTIF INTEL AP, V325, P1340, DOI DOI 10.3233/FAIA200237
   Maruhashi K, 2018, AAAI CONF ARTIF INTE, P3770
   Minervini Pasquale, 2018, P 22 C COMP NAT LANG, P65
   Molnar C., 2020, INTERPRETABLE MACHIN
   Montavon G, 2017, PATTERN RECOGN, V65, P211, DOI 10.1016/j.patcog.2016.11.008
   Nanni L., 2020, ARXIV200412823
   Olah Chris, 2017, DISTILL, V2, pe7
   Pisoni G, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11020870
   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
   Rocktaschel Tim, 2016, P 5 WORKSH AUT KNOWL, P45
   Sacca C, 2014, BMC BIOINFORMATICS, V15, DOI 10.1186/1471-2105-15-103
   SANFELIU A, 1983, IEEE T SYST MAN CYB, V13, P353, DOI 10.1109/TSMC.1983.6313167
   Sarker MK, 2020, COMM COM INF SC, V1232, P72, DOI 10.1007/978-3-030-65384-2_6
   Sarker MK, 2019, AAAI CONF ARTIF INTE, P3036
   Selvaraju RR, 2017, IEEE I CONF COMP VIS, P618, DOI 10.1109/ICCV.2017.74
   Serafini L, 2016, LECT NOTES COMPUT SC, V10037, P334, DOI 10.1007/978-3-319-49130-1_25
   Shrikumar A, 2017, PR MACH LEARN RES, V70
   Springenberg JT, 2015, P INT C LEARN REPR I
   Stone A, 2017, PROC CVPR IEEE, P732, DOI 10.1109/CVPR.2017.85
   Suchanek FM, 2019, LECT NOTES COMPUT SC, V11810, P110, DOI 10.1007/978-3-030-31423-1_4
   Sundararajan M, 2017, PR MACH LEARN RES, V70
   Tiddi I, 2020, STUD SEMANTIC WEB, V47, P1, DOI 10.3233/SSW47
   TOWELL GG, 1994, ARTIF INTELL, V70, P119, DOI 10.1016/0004-3702(94)90105-8
   Townsend J, 2020, IEEE T NEUR NET LEAR, V31, P3456, DOI 10.1109/TNNLS.2019.2944672
   Van Krieken E, 2019, J APPL LOG-IFCOLOG, V6, P633
   Vaswani A, 2017, ADV NEUR IN, V30
   Viviano Joseph D, 2021, SALIENCY IS POSSIBLE
   Wang HB, 2020, IEEE MULTIMEDIA, V27, P112, DOI 10.1109/MMUL.2020.2999464
   Wiegreffe S, 2019, P 2019 C EMP METH NA, P11, DOI 10.18653/v1/D19-1002
   Xu JY, 2018, PR MACH LEARN RES, V80
   Xu K, 2015, PR MACH LEARN RES, V37, P2048
   Yang Yingzhen, 2020, ICLR
   Zeiler MD, 2014, LECT NOTES COMPUT SC, V8689, P818, DOI 10.1007/978-3-319-10590-1_53
   Zeiler MD, 2011, IEEE I CONF COMP VIS, P2018, DOI 10.1109/ICCV.2011.6126474
   Zhao X, 2022, IEEE T KNOWL DATA EN, V34, P2610, DOI 10.1109/TKDE.2020.3018741
   Zhou B, 2016, PROC CVPR IEEE, P2921, DOI 10.1109/CVPR.2016.319
NR 100
TC 2
Z9 2
U1 10
U2 35
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1566-2535
EI 1872-6305
J9 INFORM FUSION
JI Inf. Fusion
PD MAR
PY 2022
VL 79
BP 58
EP 83
DI 10.1016/j.inffus.2021.09.022
EA OCT 2021
PG 26
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WU7WT
UT WOS:000716752300002
OA Green Submitted, Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Gesese, GA
   Biswas, R
   Alam, M
   Sack, H
AF Gesese, Genet Asefa
   Biswas, Russa
   Alam, Mehwish
   Sack, Harald
TI A survey on knowledge graph embeddings with literals: Which model links
   better literal-ly?
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge graphs; knowledge graph embeddings; knowledge graph embeddings
   with literals; link prediction; survey
AB Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations. In addition to the structured information KGs help in facilitating interconnectivity and interoperability between different resources represented in the Linked Data Cloud. KGs have been used in a variety of applications such as entity linking, question answering, recommender systems, etc. However, KG applications suffer from high computational and storage costs. Hence, there arises the necessity for a representation able to map the high dimensional KGs into low dimensional spaces, i.e., embedding space, preserving structural as well as relational information. This paper conducts a survey of KG embedding models which not only consider the structured information contained in the form of entities and relations in a KG but also its unstructured information represented as literals such as text, numerical values, images, etc. Along with a theoretical analysis and comparison of the methods proposed so far for generating KG embeddings with literals, an empirical evaluation of the different methods under identical settings has been performed for the general task of link prediction.
C1 [Gesese, Genet Asefa] Karlsruhe Inst Technol, FIZ Karlsruhe Leibniz Inst Informat Infrastruct, Karlsruhe, Germany.
   Karlsruhe Inst Technol, Inst Appl Informat & Formal Descript Syst AIFB, Karlsruhe, Germany.
RP Gesese, GA (通讯作者)，Karlsruhe Inst Technol, FIZ Karlsruhe Leibniz Inst Informat Infrastruct, Karlsruhe, Germany.
EM genet-asefa.gesese@fiz-karlsruhe.de; russa.biswas@fiz-karlsruhe.de;
   mehwish.alam@fiz-karlsruhe.de; harald.sack@fiz-karlsruhe.de
OI Alam, Mehwish/0000-0002-7867-6612
CR [Anonymous], 1990, COMPUTERS INTRACTABI
   Berthelot D., 2017, BEGAN BOUNDARY EQUIL
   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
   Bordes A., 2013, P 26 INT C NEUR INF
   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
   Chang K., 2014, PROC C EMPIRICAL MET, P1568
   Chen MH, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3998
   Cochez M., 2018, JOINT P ISWC 2018 WO, P103
   Cochez M, 2017, LECT NOTES COMPUT SC, V10587, P190, DOI 10.1007/978-3-319-68288-4_12
   Costa GD, 2018, 2018 IEEE FIRST INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND KNOWLEDGE ENGINEERING (AIKE), P110, DOI 10.1109/AIKE.2018.00024
   Costa GD, 2016, INT CON ADV INFO NET, P1138, DOI 10.1109/AINA.2016.23
   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
   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
   Esteban C, 2016, 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), P98
   Feng J., 2016, COLING, P641
   Francis-Landau M., 2016, P 2016 C N AM CHAPT, P1256
   Galarraga L, 2015, VLDB J, V24, P707, DOI 10.1007/s00778-015-0394-1
   Garcia-Duran A., 2018, P C UNC ART INT UAI
   Garcia-Duran A., 2015, P 2015 C EMP METH NA, P286, DOI DOI 10.18653/V1/D15-1034
   Gesese G. A., 2019, INT WORKSHOP DEEP LE, P31
   Goyal P, 2018, KNOWL-BASED SYST, V151, P78, DOI 10.1016/j.knosys.2018.03.022
   Guo S., 2016, P C EMP METH NAT LAN, P192, DOI 10.18653/v1/D16-1019
   Guo S, 2017, IEEE T KNOWL DATA EN, V29, P884, DOI 10.1109/TKDE.2016.2638425
   Guu K., 2015, ARXIV150601094
   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
   Isola P, 2017, PROC CVPR IEEE, P5967, DOI 10.1109/CVPR.2017.632
   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
   Jia YT, 2016, AAAI CONF ARTIF INTE, P992
   Jiang T., 2016, P 2016 C EMP METH NA, P2350, DOI DOI 10.18653/V1/D16-1260
   Jiang X, 2012, P INT WORKSH SEM TEC, P1
   Kotnis B., 2017, ARXIV PREPRINT ARXIV
   Kriegel H.-P., 2012, P 21 INT C WORLD WID, P271, DOI DOI 10.1145/2187836.2187874
   Kristiadi A, 2019, LECT NOTES COMPUT SC, V11778, P347, DOI 10.1007/978-3-030-30793-6_20
   Krizhevsky A, 2017, COMMUN ACM, V60, P84, DOI 10.1145/3065386
   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
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   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
   Lu LY, 2011, PHYSICA A, V390, P1150, DOI 10.1016/j.physa.2010.11.027
   Luo Y., 2015, P 2015 C EMP METH NA, P1656, DOI DOI 10.18653/V1/D15-1191
   Mahdisoltani F, 2013, CIDR
   MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
   Mousselly-SergiehH, 2018, P 7 JOINT C LEX COMP, P225
   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, AAAI CONF ARTIF INTE, P1955
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI 10.3115/v1/D14-1162
   Pezeshkpour P., 2018, C EMP METH NAT LANG, P3208, DOI DOI 10.18653/V1/D18-1359
   Rocktaschel T., 2015, P 2015 C N AM CHAPT, P1119
   Ruffinelli Daniel, 2020, INT C LEARN REPR INT C LEARN REPR
   Simonyan K., 2014, ARXIV
   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 Z., 2019, CORR ABS191107963
   Tay Y., 2017, P 2017 ACM C INF KNO, DOI [10.1145/31328473132937, DOI 10.1145/31328473132937]
   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
   Trsedya B.D., 2019, P AAAI C ART INT AAA, DOI [10.1609/aaai.v33i01.3301297, DOI 10.1609/AAAI.V33I01.3301297]
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   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, P C EMP METH NAT LAN, P1591
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wei Z., 2015, P 24 ACM INT C INF K, P1331, DOI DOI 10.1145/2806416.2806513
   Weston J., 2013, P 2013 C EMP METH NA, P1366
   Wu YR, 2018, REPRESENTATION LEARNING FOR NLP, P132
   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
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xu JC, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1318
   Yang B., 2015, P 3 INT C LEARN REPR
   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, 2018, P INT C MACH LEARN S, P5902
   Zhong H., 2015, P 2015 C EMP METH NA, P267
NR 84
TC 6
Z9 6
U1 1
U2 10
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 4
BP 617
EP 647
DI 10.3233/SW-200404
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 SW1WL
UT WOS:000664311200005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Sang, ST
   Liu, XX
   Chen, XY
   Zhao, D
AF Sang, Shengtian
   Liu, Xiaoxia
   Chen, Xiaoyu
   Zhao, Di
TI A Scalable Embedding Based Neural Network Method for Discovering
   Knowledge From Biomedical Literature
SO IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
LA English
DT Article; Proceedings Paper
CT 18th Asia Pacific Bioinformatics Conference (APBC)
CY AUG 18-20, 2020
CL ELECTR NETWORK
DE Unified modeling language; Biological system modeling; Diseases; Drugs;
   Semantics; Deep learning; Task analysis; Literature-based discovery;
   knowledge graph; bidirectional recurrent neural network; drug discovery
ID FISH-OIL; PREDICTION; TEXT
AB Nowadays, the amount of biomedical literatures is growing at an explosive speed, and much useful knowledge is yet undiscovered in the literature. Classical information retrieval techniques allow to access explicit information from a given collection of information, but are not able to recognize implicit connections. Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting literature. It could significantly support scientific research by identifying new connections between biomedical entities. However, most of the existing approaches to LBD are not scalable and may not be sufficient to detect complex associations in non-directly-connected literature. In this article, we present a model which incorporates biomedical knowledge graph, graph embedding, and deep learning methods for literature-based discovery. First, the relations between biomedical entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Second, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Third, a bidirectional Long Short-Term Memory (BLSTM) network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.
C1 [Sang, Shengtian] Stanford Univ, Dept Med, Stanford, CA 94305 USA.
   [Liu, Xiaoxia] Dalian Maritime Univ, Informat Sci & Technol Coll, Dalian 116026, Peoples R China.
   [Chen, Xiaoyu; Zhao, Di] Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116026, Peoples R China.
RP Liu, XX (通讯作者)，Dalian Maritime Univ, Informat Sci & Technol Coll, Dalian 116026, Peoples R China.; Zhao, D (通讯作者)，Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116026, Peoples R China.
EM sangst@stanford.edu; xiaoxia_liu@dlmu.edu.nc;
   xiaoyu1127@mail.dlut.edu.cn; zhao_di@mail.dlut.edu.cn
OI Sang, Shengtian/0000-0003-0851-9520
FU Fundamental Research Funds for the Central Universities [3132019202]
FX This work was supported by the Grants from the Fundamental Research
   Funds for the Central Universities No. 3132019202.
CR Ahlers CB, 2007, PACIFIC SYMPOSIUM ON BIOCOMPUTING 2007, P209
   Ahlers Caroline B, 2007, AMIA Annu Symp Proc, P6
   Aronson AR, 2010, J AM MED INFORM ASSN, V17, P229, DOI 10.1136/jamia.2009.002733
   Aronson AR, 2001, J AM MED INFORM ASSN, P17
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Byvatov E, 2003, J CHEM INF COMP SCI, V43, P1882, DOI 10.1021/ci0341161
   Cameron D. H., 2014, THESIS WRIGHT STATE
   Cameron D, 2013, J BIOMED INFORM, V46, P238, DOI 10.1016/j.jbi.2012.09.004
   Cao DS, 2014, MOL INFORM, V33, P669, DOI 10.1002/minf.201400009
   Chen X, 2002, NUCLEIC ACIDS RES, V30, P412, DOI 10.1093/nar/30.1.412
   Chen X, 2012, MOL BIOSYST, V8, P1970, DOI 10.1039/c2mb00002d
   Cherdioui S, 2013, 2013 11TH INTERNATIONAL SYMPOSIUM ON PROGRAMMING AND SYSTEMS (ISPS), P137, DOI 10.1109/ISPS.2013.6581479
   Cohen T., 2012, Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), P218, DOI 10.1109/BIBMW.2012.6470307
   DIGIACOMO RA, 1989, AM J MED, V86, P158, DOI 10.1016/0002-9343(89)90261-1
   Habiba U, 2015, 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL INFORMATION AND COMMUNICATION TECHNOLOGY (EICT), P1, DOI 10.1109/EICT.2015.7391912
   Henry S, 2017, J BIOMED INFORM, V74, P20, DOI 10.1016/j.jbi.2017.08.011
   Hristovski Dimitar, 2013, Cardiovascular & Hematological Agents in Medicinal Chemistry, V11, P14
   Husken M, 2003, NEUROCOMPUTING, V50, P223, DOI 10.1016/S0925-2312(01)00706-8
   Kingma D. P., 2017, ARXIV
   Korhonen A., 2014, PROC INT M COMPUT IN, P89
   Liu H, 2016, BMC BIOINFORMATICS, V17, DOI 10.1186/s12859-016-1336-7
   McCray A. T., 1989, Proceedings: The Thirteenth Annual Symposium on Computer Applications in Medical Care (Cat. No.89TH0286-5), P503
   Preiss J., 2014, PROC WORKSHOP BIOMED, P112
   Rindflesch Thomas C., 2011, Information Services & Use, V31, P15, DOI 10.3233/ISU-2011-0627
   Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003
   Sang ST, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2167-5
   Sang ST, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/698527
   Smalheiser NR, 2012, J AM SOC INF SCI TEC, V63, P218, DOI 10.1002/asi.21599
   Srinivasan P, 2004, J AM SOC INF SCI TEC, V55, P396, DOI 10.1002/asi.10389
   SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7
   Weeber M, 2001, J AM SOC INF SCI TEC, V52, P548, DOI 10.1002/asi.1104.abs
   Wen M, 2017, J PROTEOME RES, V16, P1401, DOI 10.1021/acs.jproteome.6b00618
   WERBOS PJ, 1990, P IEEE, V78, P1550, DOI 10.1109/5.58337
   Wren JD, 2004, BIOINFORMATICS, V20, P389, DOI 10.1093/bioinformatics/btg421
   Yu Z, 2015, 2015 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING (ASRU), P338, DOI 10.1109/ASRU.2015.7404814
NR 35
TC 1
Z9 1
U1 1
U2 2
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1545-5963
EI 1557-9964
J9 IEEE ACM T COMPUT BI
JI IEEE-ACM Trans. Comput. Biol. Bioinform.
PD MAY-JUN
PY 2022
VL 19
IS 3
BP 1294
EP 1301
DI 10.1109/TCBB.2020.3003947
PG 8
WC Biochemical Research Methods; Computer Science, Interdisciplinary
   Applications; Mathematics, Interdisciplinary Applications; Statistics &
   Probability
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Biochemistry & Molecular Biology; Computer Science; Mathematics
GA 1V0RE
UT WOS:000805807200006
PM 32750871
DA 2022-08-23
ER

PT J
AU Shi, LX
   Li, SJ
   Yang, XR
   Qi, JH
   Pan, G
   Zhou, BB
AF Shi, Longxiang
   Li, Shijian
   Yang, Xiaoran
   Qi, Jiaheng
   Pan, Gang
   Zhou, Binbin
TI Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous
   Medical Knowledge and Services
SO BIOMED RESEARCH INTERNATIONAL
LA English
DT Article
ID MANAGEMENT; SYSTEM
AB With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.
C1 [Shi, Longxiang; Li, Shijian; Yang, Xiaoran; Qi, Jiaheng; Pan, Gang; Zhou, Binbin] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Zhejiang, Peoples R China.
RP Li, SJ (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Zhejiang, Peoples R China.
EM shijianli@zju.edu.cn
FU National Key Research and Development Plan [2016YFB1001203]
FX The authors thank Yaoping He from Health and Family Planning Commission
   of Wenzhou, China, for providing the domain knowledge and expert
   advices. The work of the authors is supported by National Key Research
   and Development Plan under Grant no. 2016YFB1001203.
CR A-Hospital, 2016, MERCK DIAGN MAN CHIN
   A-Hospital, 2016, SELF DIAGN COMM DIS
   Adnan M., 2009, HLTH CARE INFORMATIC, V13
   Aisenbrey A, 2012, WORLD ACAD SCI ENG T, V64, P557
   Alther M., 2015, HEALTHCARE DATA ANAL
   [Anonymous], 2012, CLASS COD VAL DOM HL
   Nguyen BV, 2015, INFORM SYST FRONT, V17, P493, DOI 10.1007/s10796-014-9507-4
   Begum S, 2011, IEEE T SYST MAN CY C, V41, P421, DOI 10.1109/TSMCC.2010.2071862
   Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061
   Bos L., 2006, MED CARE COMPUNETICS, V3, P121
   Cases M, 2013, J INTERN MED, V274, P321, DOI 10.1111/joim.12105
   Chein M, 2009, ADV INFORM KNOWL PRO, P1
   Chen P. P., 2002, SOFTWARE PIONEERS, P296
   Cios KJ, 2002, ARTIF INTELL MED, V26, P1, DOI 10.1016/S0933-3657(02)00049-0
   Cook DA, 2015, ACAD MED, V90, P33, DOI 10.1097/ACM.0000000000000551
   Dolin RH, 2001, J AM MED INFORM ASSN, V8, P552, DOI 10.1136/jamia.2001.0080552
   Doyle-Lindrud S, 2015, CLIN J ONCOL NURS, V19, P31, DOI 10.1188/15.CJON.31-32
   Ernst P, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0549-5
   Ernst P, 2014, PROC INT CONF DATA, P1254, DOI 10.1109/ICDE.2014.6816754
   Hripcsak G, 2013, J AM MED INFORM ASSN, V20, P117, DOI 10.1136/amiajnl-2012-001145
   Liu X., 2008, DAIGNOSIS MANUAL GEN
   Musen MA, 2014, BIOMEDICAL INFORM, P643, DOI [10.1007/978-1-4471-4474-8_22., 10.1007/978-1-4471-4474-8_22]
   National Health and Family Planning Commission of the People's Republic of China, 2012, CLASS COD VAL DOM HL
   Ndapandula N., 2011, P 4 ACM INT C WEB SE, P227
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Ogbuji C., 2011, P ICBO INT C BIOM ON, P217
   Ontotext A. D., 2014, LINKED LIFE DATA
   Qian Q., 2013, J MED INFORM, V34, P42
   SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220
   Scheuermann Richard H, 2009, Summit Transl Bioinform, V2009, P116
   Serbanati LD, 2011, J BIOMED INFORM, V44, P621, DOI 10.1016/j.jbi.2011.02.011
   Smith B, 2007, NAT BIOTECHNOL, V25, P1251, DOI 10.1038/nbt1346
   Stanford Center for Biomedical Informatics Research, 2016, STANF CTR BIOM INF R
   Stewart S. M., 2013, THESIS
   Szalma S, 2010, J TRANSL MED, V8, DOI 10.1186/1479-5876-8-68
   Tang PC, 2006, J AM MED INFORM ASSN, V13, P121, DOI 10.1197/jamia.M2025
   Tao C., 2011, P 1 INT WORKSH MAN I, P75, DOI [DOI 10.1145/2064747.2064765, 10.1145/2064747.2064765]
   The Apache Software Foundation, 2016, AP JEN FREE OP SOURC
   The Semantic Health Net Project, 2016, SEMANTIC HEALTHNET P
   W3C, 2011, W3C SEM WEB ACT
   W3C OWLWorking Group, 2013, WEB ONT LANG OWL
   W3C RDF Working Group, 2014, RESOURCE DESCRIPTION
   Wang HQ, 2013, ARTIF INTELL MED, V58, P81, DOI 10.1016/j.artmed.2013.02.005
   Wright A, 2009, J BIOMED INFORM, V42, P334, DOI 10.1016/j.jbi.2008.09.003
   Zeng-Treitler Qing, 2007, AMIA Annu Symp Proc, P846
   Zillner S, 2008, COMP MED SY, P296, DOI 10.1109/CBMS.2008.11
NR 46
TC 37
Z9 38
U1 3
U2 14
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.
PY 2017
VL 2017
AR 2858423
DI 10.1155/2017/2858423
PG 12
WC Biotechnology & Applied Microbiology; Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biotechnology & Applied Microbiology; Research & Experimental Medicine
GA EM2OW
UT WOS:000395156400001
PM 28299322
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Wei, F
   Chen, SY
   Jin, J
   Zhang, S
   Zhou, HW
   Wu, YB
AF Wei, Feng
   Chen, Shuyu
   Jin, Jie
   Zhang, Shuai
   Zhou, Hongwei
   Wu, Yingbo
TI Adaptive Alleviation for Popularity Bias in Recommender Systems with
   Knowledge Graph
SO SECURITY AND COMMUNICATION NETWORKS
LA English
DT Article
AB Recommender systems are known to suffer from the popularity bias problem: popular items are recommended frequently, and nonpopular ones rarely, if at all. Prior studies focused on tackling this issue by increasing the number of recommended nonpopular (long-tail) items. However, these methods ignore the users' personal popularity preferences and increase the exposure rate of the nonpopular items indiscriminately, which may hurt the user experience because different users have diverse interests in popularity. In this work, we propose a novel debias framework with knowledge graph (AWING), which adaptively alleviates popularity bias from the users' perspective. Concretely, we explore fine-grained preferences (including popularity preference) behind a user-item interaction by using the heterogeneous graph transformer over the knowledge graph embedded with popularity nodes and endow the preferences with explicit semantics. Based on this idea, we can manipulate how much popularity preference affects recommendation results and improves the exposure rate of nonpopular items while considering the popularity preferences of different users. Experiments on public datasets show that the proposed method AWING can effectively alleviate popularity bias and ensure the user experience at the same time. The case study further demonstrates the feasibility of AWING on the explainable recommendation task.
C1 [Wei, Feng; Chen, Shuyu; Zhang, Shuai; Zhou, Hongwei; Wu, Yingbo] Chongqing Univ, Sch Big Data & Software Engn, Chongqing 400044, Peoples R China.
   [Jin, Jie] China Telecom, Chongqing Branch, Sci & Technol Innovat Dept, Chongqing, Peoples R China.
RP Chen, SY (通讯作者)，Chongqing Univ, Sch Big Data & Software Engn, Chongqing 400044, Peoples R China.
EM fengwei@cqu.edu.cn; sychen@cqu.edu.cn; jinjie.cq@chinatelecom.cn;
   zhangshuai@cqu.edu.cn; 1033096371@qq.com; wyb@cqu.edu.cn
FU National Key Research and Development Project [2019YFB1706101]; National
   Natural Science Foundation [72161005]; Chongqing Technology Innovation
   and Application Development [CSTC2019jscx-zdztzxX0031]
FX This work was supported in part by the National Key Research and
   Development Project under grant 2019YFB1706101, National Natural Science
   Foundation under grant 72161005, and research program of Chongqing
   Technology Innovation and Application Development under grant
   CSTC2019jscx-zdztzxX0031.
CR Abdollahpouri H., 2019, 32 INT FLAIRS C
   Abdollahpouri H., UNFAIRNESS POPULARIT
   Abdollahpouri H, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P42, DOI 10.1145/3109859.3109912
   Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
   Adomavicius G, 2012, IEEE T KNOWL DATA EN, V24, P896, DOI 10.1109/TKDE.2011.15
   Bastings J., 2020, ELEPHANT INTERPRETAB
   Brynjolfsson E, 2006, MIT SLOAN MANAGE REV, V47, P67
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Chen ZH, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P579, DOI 10.1145/3397271.3401043
   Ehrlinger L., 2016, SEMANTICS, V1695, P1
   Gomez-Perez J.M., 2017, EXPLOITING LINKED DA, P1, DOI [10.1007/978-3-319-45654-6_1, DOI 10.1007/978-3-319-45654-6_1]
   Gruson A, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P420, DOI 10.1145/3289600.3291027
   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
   HetRec2011, 2011, US
   Hu ZN, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P2704, DOI 10.1145/3366423.3380027
   Huang R., P 30 ACM INT C INF K, P3098, DOI [10.1145/3459637.3482098, DOI 10.1145/3459637.3482098]
   Jamali M., 2010, P 4 ACM C REC SYST, P135, DOI DOI 10.1145/1864708.1864736
   Joachims T, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P781, DOI 10.1145/3018661.3018699
   Kamishima T., 2014, P 8 ACM C REC SYST O
   Loshchilov I., 2019, P 7 INT C LEARN REPR
   Park YJ, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P11
   Rendle S., 2009, P 25 C UNC ART INT, P452
   Shani G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P257, DOI 10.1007/978-0-387-85820-3_8
   Su Xiaoyuan, 2009, ADV ARTIFICIAL INTEL, DOI DOI 10.1155/2009/421425
   Tal O, 2019, COMPLEXITY, DOI 10.1155/2019/2926749
   Tu K., P 30 ACM INT C INF K, P1834, DOI [10.1145/3459637.3482331, DOI 10.1145/3459637.3482331]
   Velikovi P., 2017, ARXIV171010903
   Wang WC, 2018, SECUR COMMUN NETW, DOI 10.1155/2018/1265352
   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
   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 X, 2018, ENVIRON RES LETT, V13, DOI 10.1088/1748-9326/aaba49
NR 31
TC 0
Z9 0
U1 6
U2 6
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 APR 7
PY 2022
VL 2022
AR 4264489
DI 10.1155/2022/4264489
PG 9
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 0Z6LX
UT WOS:000791188700006
OA gold
DA 2022-08-23
ER

PT J
AU Fei, H
   Ren, YF
   Zhang, Y
   Ji, DH
   Liang, XH
AF Fei, Hao
   Ren, Yafeng
   Zhang, Yue
   Ji, Donghong
   Liang, Xiaohui
TI Enriching contextualized language model from knowledge graph for
   biomedical information extraction
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Review
DE biomedical information extraction; language model; neural network;
   knowledge graph
AB Biomedical information extraction (BioIE) is an important task. The aim is to analyze biomedical texts and extract structured information such as named entities and semantic relations between them. In recent years, pre-trained language models have largely improved the performance of BioIE. However, they neglect to incorporate external structural knowledge, which can provide rich factual information to support the underlying understanding and reasoning for biomedical information extraction. In this paper, we first evaluate current extraction methods, including vanilla neural networks, general language models and pre-trained contextualized language models on biomedical information extraction tasks, including named entity recognition, relation extraction and event extraction. We then propose to enrich a contextualized language model by integrating a large scale of biomedical knowledge graphs (namely, BioKGLM). In order to effectively encode knowledge, we explore a three-stage training procedure and introduce different fusion strategies to facilitate knowledge injection. Experimental results on multiple tasks show that BioKGLM consistently outperforms state-of-the-art extraction models. A further analysis proves that BioKGLM can capture the underlying relations between biomedical knowledge concepts, which are crucial for BioIE.
C1 [Fei, Hao] Xidian Univ, Xian, Peoples R China.
   [Fei, Hao; Ren, Yafeng; Ji, Donghong] Wuhan Univ, Wuhan, Peoples R China.
   [Fei, Hao] Wuhan Univ, Inst Big Data Anal & Artificial, Sch Cyber Sci & Engn, Wuhan, Peoples R China.
   [Ren, Yafeng; Zhang, Yue] Singapore Univ Technol & Design, Singapore, Singapore.
   [Ren, Yafeng] Guangdong Univ Foreign Studies, Guangzhou, Guangdong, Peoples R China.
   [Zhang, Yue] Tsinghua Univ, Comp Sci, Beijing, Peoples R China.
   [Zhang, Yue] Univ Oxford, Oxford, England.
   [Zhang, Yue] Univ Cambridge, Cambridge, England.
   Westlake Univ, Hangzhou, Peoples R China.
   [Ji, Donghong] Wuhan Univ, Comp Sch, Wuhan, Peoples R China.
   [Ji, Donghong] Tsinghua Univ, Beijing, Peoples R China.
   [Ji, Donghong] Inst Infocomm Res, Singapore, Singapore.
   [Liang, Xiaohui] Johns Hopkins Univ, Baltimore, MD 21218 USA.
   [Liang, Xiaohui] Wuhan Univ, Sch Hlth Sci, Wuhan, Peoples R China.
RP Ren, YF (通讯作者)，Guangdong Univ Foreign Studies, Lab Language & Artificial Intelligence, Guangzhou, Peoples R China.
EM renyafeng@whu.edu.cn
FU National Natural Science Foundation of China [61702121, 61772378];
   Research Foundation of Ministry of Education of China [18JZD015]; Key
   Project of State Language Commission of China [ZDI135-112]; Guangdong
   Basic and Applied Basic Research Foundation [2020A151501705]
FX National Natural Science Foundation of China (61702121 and 61772378);
   Research Foundation of Ministry of Education of China (18JZD015); Key
   Project of State Language Commission of China (ZDI135-112); Guangdong
   Basic and Applied Basic Research Foundation (2020A151501705).
CR Ahmed M, 2019, IEEE INT C SEMANT CO, P224, DOI [10.1109/ICSC.2019.00050, 10.1109/ICOSC.2019.8665584]
   Alsentzer E., 2019, P 2 CLIN NAT LANG PR
   Bateman A, 2015, NUCLEIC ACIDS RES, V43, pD204, DOI 10.1093/nar/gku989
   Bedmar I Segura, 2013, P 7 INT WORKSH SEM E, P341
   Bhasuran B, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0200699
   Bjorne J, 2011, P BIONLP SHAR TASK 2, P183
   Bjorne J, 2018, SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2018), P98
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bravo A, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0472-9
   Cao Y., 2018, P 2018 C EMPIRICAL M, P227
   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
   Chen Q, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2406
   Chen ZW, 2018, BMC MED INFORM DECIS, V18, DOI 10.1186/s12911-018-0655-1
   Cho K., 2014, ARXIV14061078, P1724
   Conneau A, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P2126
   Croft D, 2014, NUCLEIC ACIDS RES, V42, pD472, DOI 10.1093/nar/gkt1102
   Deleger L., 2016, BIONLP SHAR TASK WOR, P12, DOI DOI 10.18653/V1/W16-3002
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Dewi IN, 2017, IEEE INT C BIOINFORM, P1795, DOI 10.1109/BIBM.2017.8217933
   Fei H, 2020, INFORM SCIENCES, V512, P175, DOI 10.1016/j.ins.2019.09.075
   Giorgi JM, 2018, BIOINFORMATICS, V34, P4087, DOI 10.1093/bioinformatics/bty449
   Goldberg Y., 2019, CORR
   Gray KA, 2015, NUCLEIC ACIDS RES, V43, pD1079, DOI 10.1093/nar/gku1071
   Gurulingappa H, 2012, J BIOMED INFORM, V45, P885, DOI 10.1016/j.jbi.2012.04.008
   Han X., 2016, ABS161104125
   Jawahar G, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3651
   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, ABS190402181
   Kilicoglu H, 2012, BIOINFORMATICS, V28, P3158, DOI 10.1093/bioinformatics/bts591
   Kim J-D, 2004, P INT JOINT WORKSH N, P70, DOI DOI 10.3115/1567594.1567610
   Kotitsas S, 2019, SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2019), P298
   Lee J., 2019, ABS190108746
   Lever J., 2016, P 4 BIONLP SHARED TA, P42
   Li D, 2019, P 2019 C N AM CHAPTE, P1421, DOI [10.18653/v1/N19-1145, DOI 10.18653/V1/N19-1145]
   Li F, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1609-9
   Li J, 2016, DATABASE-OXFORD, DOI 10.1093/database/baw068
   Li LS, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2275-2
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Ling Y, 2017, IEEE IJCNN, P968, DOI 10.1109/IJCNN.2017.7965957
   Madotto A, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1468
   Messina A, 2018, ADV INTELL SYST, V611, P299, DOI 10.1007/978-3-319-61566-0_28
   Mihaylov T, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P821
   Mikolov T., 2013, WORKSH ICLR, V26, DOI 10.48550/arXiv.1301.3781
   Mohamed SK, 2019, LECT NOTES COMPUT SC, V11503, P240, DOI 10.1007/978-3-030-21348-0_16
   Peng Y., 2015, P BIONLP, V15, P21
   Pennington J., 2014, EMNLP, P1532
   Peters M.E., 2018, P 2018 C N AM CHAPT, P2227, DOI 10.18653/v1/N18-1202
   Pyysalo S, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-50
   Radford A., 2018, IMPROVING LANGUAGE U
   Ren YF, 2019, IEEE INT C BIOINFORM, P1237, DOI 10.1109/BIBM47256.2019.8983248
   Schuster T., 2019, P 2019 C N AM CHAPT
   Sun X, 2018, IEEE INT C BIOINFORM, P1662, DOI 10.1109/BIBM.2018.8621405
   Tenney I., 2019, P 7 INT C LEARNING R
   Dang TH, 2018, BIOINFORMATICS, V34, P3539, DOI 10.1093/bioinformatics/bty356
   Trouillon T, 2016, PR MACH LEARN RES, V48
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Vig J, 2019, PREPRINT ARXIV190604
   Wang X, 2019, BIOINFORMATICS, V35, P1745, DOI 10.1093/bioinformatics/bty869
   Wang Z, 2014, P C EMP METH NAT LAN, P1591
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wu, 2019, ABS190409223 CORR
   Yang B., 2015, ICLR
   Yang Z., 2019, ABS190608237
   Yoon W, 2019, BMC BIOINFORMATICS, V20, DOI 10.1186/s12859-019-2813-6
   Zaremoodi P, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P656
   Zhang YJ, 2011, J BIOMED INFORM, V44, P1086, DOI 10.1016/j.jbi.2011.08.011
   Zhang ZY, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P1441
NR 68
TC 0
Z9 0
U1 1
U2 1
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 MAY 20
PY 2021
VL 22
IS 3
AR bbaa110
DI 10.1093/bib/bbaa110
PG 14
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA 2V9HT
UT WOS:000824148400012
DA 2022-08-23
ER

PT J
AU Tang, TC
   Yuan, TY
   Tang, XH
   Chen, DL
AF Tang, Tiancheng
   Yuan, Tianyi
   Tang, Xinhuai
   Chen, Delai
TI Incorporating External Knowledge into Unsupervised Graph Model for
   Document Summarization
SO ELECTRONICS
LA English
DT Article
DE document summarization; knowledge graph; Textrank; unsupervised model
AB Supervised neural network models have achieved outstanding performance in the document summarization task in recent years. However, it is hard to get enough labeled training data with a high quality for these models to generate different types of summaries in reality. In this work, we mainly focus on improving the performance of the popular unsupervised Textrank algorithm that requires no labeled training data for extractive summarization. We first modify the original edge weight of Textrank to take the relative position of sentences into account, and then combine the output of the improved Textrank with K-means clustering to improve the diversity of generated summaries. To further improve the performance of our model, we innovatively incorporate external knowledge from open-source knowledge graphs into our model by entity linking. We use the knowledge graph sentence embedding and the tf-idf embedding as the input of our improved Textrank, and get the final score for each sentence by linear combination. Evaluations on the New York Times data set show the effectiveness of our knowledge-enhanced approach. The proposed model outperforms other popular unsupervised models significantly.
C1 [Tang, Tiancheng; Yuan, Tianyi; Tang, Xinhuai] Shanghai Jiao Tong Univ, Sch Software, Shanghai 200240, Peoples R China.
   [Chen, Delai] China Telecom Shanghai Branch, Shanghai Key Lab Adv Mfg Environm, Shanghai 200030, Peoples R China.
RP Tang, XH (通讯作者)，Shanghai Jiao Tong Univ, Sch Software, Shanghai 200240, Peoples R China.
EM tangtian_cheng@sjtu.edu.cn; gran_turismo@sjtu.edu.cn;
   tang-xh@cs.sjtu.edu.cn; dl.chen@163.com
OI Tang, Tiancheng/0000-0003-3842-715X
FU National Key R&D Program of China [2018YFB1003801]
FX This research is supported by National Key R&D Program of China (No.
   2018YFB1003801).
CR Akter S., 2017, IEEE INT C IM VIS PA, P1
   [Anonymous], 2004, P EMNLP
   Balabantaray R.C., ARXIV150207938
   Bouscarrat L., ARXIV190707323
   Chen Y.C., ARXIV180511080
   Cheng Jianpeng, ARXIV160307252
   Devlin J., 2018, P 2019 C N AM CHAPTE, DOI DOI 10.18653/V1/N19-1423
   Erkan G, 2004, J ARTIF INTELL RES, V22, P457, DOI 10.1613/jair.1523
   Hou SL, 2020, INFORM SYST, V94, DOI 10.1016/j.is.2020.101615
   Huang L., ARXIV200501159
   Kavuluru Ramakanth, 2013, Adv Artif Intell, V7884, P77, DOI 10.1007/978-3-642-38457-8_7
   Kim SE, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10165422
   Kiros R., 2015, ADV NEURAL INFORM PR, V28, P3294
   Krishna Kundan, 2018, P 2018 C N AM CHAPTE, V1, P1697
   Lan Z., ARXIV190911942
   Li X., 2010, P ACM INT C INF KNOW, P1765
   Lin C.Y., 2004, P M ASS COMP LING AC, P74
   Liu Y., ARXIV190310318
   Mallick C, 2019, ADV INTELL SYST, V758, P137, DOI 10.1007/978-981-13-0514-6_14
   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]
   Narayan S., ARXIV170404530
   Narayan S., ARXIV180208636
   Page Lawrence, 1998, PAGERANK CITATION CI
   Paulus R., ARXIV170504304
   Radev DR, 2004, INFORM PROCESS MANAG, V40, P919, DOI 10.1016/j.ipm.2003.10.006
   Sandhaus E., 2008, LINGUISTIC DATA CONS, V6, P26752
   See A., ARXIV170404368
   Sun F, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P7, DOI 10.1145/3269206.3271722
   Vaswani A., 2017, ADV NEUR IN, V30, P6000
   Wang K., ARXIV190605012
   Wang QC, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9214701
   Xiang B., ARXIV160206023
   Zheng H., ARXIV190603508
   Zhou Q., 2017, NAT CCF C NAT LANG P, P662, DOI DOI 10.1007/978-3-319-73618-156
NR 34
TC 1
Z9 1
U1 1
U2 8
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD SEP
PY 2020
VL 9
IS 9
AR 1520
DI 10.3390/electronics9091520
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA OE2HM
UT WOS:000580358200001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Giarelis, N
   Karacapilidis, N
AF Giarelis, Nikolaos
   Karacapilidis, Nikos
TI Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE Horizon 2020; Natural Language Processing; text mining; knowledge graph;
   graph analytics; unsupervised learning; keyphrase extraction; named
   entity recognition
AB This paper aims to meaningfully analyse the Horizon 2020 data existing in the CORDIS repository of EU, and accordingly offer evidence and insights to aid organizations in the formulation of consortia that will prepare and submit winning research proposals to forthcoming calls. The analysis is performed on aggregated data concerning 32,090 funded projects, 34,295 organizations participated in them, and 87,067 public deliverables produced. The modelling of data is performed through a knowledge graph-based approach, aiming to semantically capture existing relationships and reveal hidden information. The main contribution of this work lies in the proper utilization and orchestration of keyphrase extraction and named entity recognition models, together with meaningful graph analytics on top of an efficient graph database. The proposed approach enables users to ask complex questions about the interconnection of various entities related to previously funded research projects. A set of representative queries demonstrating our data representation and analysis approach are given at the end of the paper.
C1 [Giarelis, Nikolaos; Karacapilidis, Nikos] Univ Patras, MEAD, Ind Management & Informat Syst Lab, Patras 26504, Greece.
RP Giarelis, N (通讯作者)，Univ Patras, MEAD, Ind Management & Informat Syst Lab, Patras 26504, Greece.
EM giarelis@ceid.upatras.gr; karacap@upatras.gr
OI Karacapilidis, Nikos/0000-0002-6581-6831; Giarelis,
   Nikolaos/0000-0003-2611-3129
FU inPOINT project; European Union; Greek national funds through the
   Operational Program Competitiveness, Entrepreneurship and Innovation,
   under the call RESEARCH-CREATE-INNOVATE [T2EDK-04389]
FX The work presented in this paper is supported by the inPOINT project
   (https://inpointproject.eu/ (accessed on 27 November 2021)), which is
   co-financed by the European Union and Greek national funds through the
   Operational Program Competitiveness, Entrepreneurship and Innovation,
   under the call RESEARCH-CREATE-INNOVATE (Project id: T2EDK-04389).
CR Akbik A., 2019, P 2019 C N AM CHAPT, P54, DOI [10.18653/v1/N19- 4010, DOI 10.18653/V1/N19-4010]
   Angles R, 2017, ACM COMPUT SURV, V50, DOI 10.1145/3104031
   Arenas M, 2021, INT CONF MANAGE DATA, P2821, DOI 10.1145/3448016.3457545
   Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Bird S, 2008, P 3 WORKSH ISS TEACH, P62, DOI DOI 10.3115/1627306.1627317
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Calignano G, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12114696
   Campos R, 2020, INFORM SCIENCES, V509, P257, DOI 10.1016/j.ins.2019.09.013
   Comito C, 2019, 2019 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2019), P192, DOI 10.1145/3350546.3352518
   Cunningham H., 1996, P 16 INT C COMP LING, P1057
   Ehrlinger L., 2016, P JOINT POST DEM TRA
   Forestiero Agostino, 2010, Proceedings 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), P428, DOI 10.1109/CCGRID.2010.48
   Giarelis N., ARTIF INTELL, V627, P635
   Gong FM, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0207595
   Gusfield Dan, 1997, ALGORITHMS STRINGS T
   Haase P, 2019, SEMANT WEB, V10, P1109, DOI 10.3233/SW-190360
   Hasan KS, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1262
   Jo S, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11093862
   Kapetanios E., 2013, NATURAL LANGUAGE PRO
   Malloci FM, 2020, LECT NOTES COMPUT SC, V12343, P143, DOI 10.1007/978-3-030-62008-0_10
   Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
   Merrouni ZA, 2020, J INTELL INF SYST, V54, P391, DOI 10.1007/s10844-019-00558-9
   Mihalcea R., 2004, P 2004 C EMP METH NA, P404, DOI DOI 10.3115/1219044.1219064
   Nobre C, 2019, IEEE T VIS COMPUT GR, V25, P544, DOI 10.1109/TVCG.2018.2865149
   Papagiannopoulou E, 2020, WIRES DATA MIN KNOWL, V10, DOI 10.1002/widm.1339
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Perez-Fernandez D, 2019, PROCES LENG NAT, P193, DOI 10.26342/2019-63-28
   Qi P, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020): SYSTEM DEMONSTRATIONS, P101
   RATCLIFF JW, 1988, DR DOBBS J, V13, P46
   Rinaldi AM, 2021, MULTIMED TOOLS APPL, V80, P3885, DOI 10.1007/s11042-020-09761-1
   Schmitt X, 2019, 2019 SIXTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORKS ANALYSIS, MANAGEMENT AND SECURITY (SNAMS), P338, DOI 10.1109/SNAMS.2019.8931850
   Sheng SJ, 2019, 2019 10TH IEEE INTERNATIONAL CONFERENCE ON BIG KNOWLEDGE (ICBK 2019), P221, DOI 10.1109/ICBK.2019.00037
   Wan XJ, 2010, ACM T INFORM SYST, V28, DOI 10.1145/1740592.1740596
NR 33
TC 0
Z9 0
U1 2
U2 2
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 2021
VL 11
IS 23
AR 11425
DI 10.3390/app112311425
PG 17
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA ZJ3VC
UT WOS:000762234600001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Lei, Y
   Duan, YC
AF Lei, Yu
   Duan, Yucong
TI Trusted Service Provider Discovery Based on Data, Information,
   Knowledge, and Wisdom
SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
LA English
DT Article
DE Knowledge graphs; trusted service discovery; reasoning
ID PROTECTION; FRAMEWORK; SYSTEM
AB Data, information, knowledge, and wisdom forms a progressive relationship. Information is formed by data collation. Knowledge is filtered, refined, and processed from relevant information. Wisdom is based on knowledge and is accumulated through experience. This paper uses the progressive relationship of service data, information, knowledge, and wisdom to explain the expression of service knowledge graph. It is an increasingly challenging demand to discover trusted Cloud service providers with service data, information, and knowledge. We propose an efficient method of trusted service provider discovery based on service knowledge graphs, called PDG (Provider Discovery based on Graphs), to ensure that each service instance of composite services in Cloud systems is trustworthy. PDG evaluates the outputs of service providers in service classes with the help of additional service information. According to the additional service information, service knowledge is generated and trusted service providers can be found easily. PDG improves the accuracy of processing results by automatically replacing data provided by untrusted service providers with results provided by trusted service providers.
C1 [Lei, Yu] Inner Mongolia Univ, Dept Comp Sci, Hohhot, Inner Mongolia, Peoples R China.
   [Duan, Yucong] Hainan Univ, Sch Comp & Cyberspace Secur, Haikou, Hainan, Peoples R China.
RP Lei, Y (通讯作者)，Inner Mongolia Univ, Dept Comp Sci, Hohhot, Inner Mongolia, Peoples R China.
EM yuleiimu@sohu.com
FU NSFC [61662021, 61962040]; CERNet Innovation Project [NGII20180607]
FX This work was supported by grants from NSFC (Grant Nos. 61662021 and
   61962040) and CERNet Innovation Project No. NGII20180607.
CR Bellomarini L, 2018, PROC VLDB ENDOW, V11, P975, DOI 10.14778/3213880.3213888
   Ben Mahmoud C, 2016, INT J SEMANT WEB INF, V12, P23, DOI 10.4018/IJSWIS.2016010102
   Bhardwaj KC, 2015, J WEB ENG, V14, P196
   Cassar G, 2014, IEEE T SERV COMPUT, V7, P654, DOI 10.1109/TSC.2013.28
   Chen FZ, 2017, ENTERP INF SYST-UK, V11, P452, DOI 10.1080/17517575.2015.1081987
   Chen FZ, 2017, EXPERT SYST APPL, V67, P19, DOI 10.1016/j.eswa.2016.09.028
   Chen HK, 2018, IEEE TETCI, V2, P13, DOI 10.1109/TETCI.2017.2755691
   Chen WH, 2015, IEEE T SERV COMPUT, V8, P284, DOI 10.1109/TSC.2013.20
   Cheng B, 2017, IEEE T KNOWL DATA EN, V29, P950, DOI 10.1109/TKDE.2016.2645769
   Dai CC, 2018, IEEE T SERV COMPUT, V11, P507, DOI 10.1109/TSC.2016.2586062
   Duan YC, 2019, 2019 20TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD), P133, DOI 10.1109/SNPD.2019.8935653
   Duan YC, 2019, IEEE ACCESS, V7, P99161, DOI 10.1109/ACCESS.2019.2931365
   Duan YC, 2019, ENG APPL ARTIF INTEL, V81, P323, DOI 10.1016/j.engappai.2019.03.002
   Feuerlicht G, 2016, LECT NOTES COMPUT SC, V9586, P15, DOI 10.1007/978-3-662-50539-7_2
   Girolami M, 2015, COMPUT NETW, V88, P51, DOI 10.1016/j.comnet.2015.06.006
   HUANG YM, 2018, ENTROPY-SWITZ, V20, DOI DOI 10.3390/e20110858
   Jiao HJ, 2017, INT J COMPUT INTEG M, V30, P433, DOI 10.1080/0951192X.2014.1002812
   Krotzsch M, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5309
   Li J, 2017, IEEE ACCESS, V5, P19154, DOI 10.1109/ACCESS.2017.2756446
   Li QX, 2018, ENTROPY-SWITZ, V20, DOI 10.3390/e20120970
   Liang W, 2020, IEEE T IND INFORM, V16, P6543, DOI 10.1109/TII.2020.2966069
   Liang W, 2020, IEEE T IND INFORM, V16, P2063, DOI 10.1109/TII.2019.2946791
   Liu B, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017), P468, DOI 10.1109/ICWS.2017.54
   Liu CX, 2017, DESTECH TRANS COMP, P566
   Liu L, 2016, ACM T EMBED COMPUT S, V15, DOI 10.1145/2811264
   Ma SP, 2016, FUTURE GENER COMP SY, V60, P1, DOI 10.1016/j.future.2016.01.011
   Mao C., 2017, P 24 AS PAC SOFTW EN
   Narock T, 2016, DECIS SUPPORT SYST, V87, P105, DOI 10.1016/j.dss.2016.04.002
   Rodriguez-Mier P, 2016, IEEE T SERV COMPUT, V9, P537, DOI 10.1109/TSC.2015.2402679
   Zapater JJS, 2015, EXPERT SYST APPL, V42, P3833, DOI 10.1016/j.eswa.2015.01.005
   Sharma V, 2015, 2015 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS (ICNC), P374, DOI 10.1109/ICCNC.2015.7069372
   Siddiqui ZA, 2018, IET SOFTW, V12, P76, DOI 10.1049/iet-sen.2016.0141
   Song MM, 2020, EURASIP J WIREL COMM, V2020, DOI 10.1186/s13638-019-1635-6
   Song ZY, 2018, WIREL COMMUN MOB COM, DOI 10.1155/2018/3794175
   Surianarayanan C, 2016, IEEE T SERV COMPUT, V9, P524, DOI 10.1109/TSC.2015.2399301
   Trivedi R, 2017, PR MACH LEARN RES, V70
   Versteeg S, 2016, INTERNATIONAL WORKSHOP ON CONTINUOUS SOFTWARE EVOLUTION AND DELIVERY, CSED 2016, P49, DOI [10.1109/CSED.2016.018, 10.1145/2896941.2896950]
   Wang Y, 2015, 2015 IEEE CONFERENCE ON COLLABORATION AND INTERNET COMPUTING (CIC), P252, DOI 10.1109/CIC.2015.28
   Wang Z., 2018, P 27 INT JOINT C ART, P1021
   Xue X, 2021, IEEE T SERV COMPUT, V14, P1598, DOI 10.1109/TSC.2019.2917445
   Xue X, 2019, IEEE T IND INFORM, V15, P3343, DOI 10.1109/TII.2018.2871167
   Yin JW, 2017, IEEE T PARALL DISTR, V28, P1271, DOI 10.1109/TPDS.2016.2619715
   Zeshan F, 2017, IET SOFTW, V11, P64, DOI 10.1049/iet-sen.2016.0048
   Zhang Q, 2018, IEEE T NEUR NET LEAR, V29, P1637, DOI 10.1109/TNNLS.2017.2673243
   Zhang S, 2018, NEUROCOMPUTING, V272, P439, DOI 10.1016/j.neucom.2017.07.011
   Zhang Y, 2015, ENTROPY-SWITZ, V17, P509, DOI 10.3390/e17020509
   Zhang YY, 2018, AAAI CONF ARTIF INTE, P6069
NR 47
TC 3
Z9 3
U1 3
U2 8
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-1940
EI 1793-6403
J9 INT J SOFTW ENG KNOW
JI Int. J. Softw. Eng. Knowl. Eng.
PD JAN
PY 2021
VL 31
IS 01
BP 3
EP 19
DI 10.1142/S0218194021400015
PG 17
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QD4ID
UT WOS:000615483100003
DA 2022-08-23
ER

PT J
AU Man, N
   Wang, KC
   Liu, L
AF Man, Na
   Wang, Kechao
   Liu, Lin
TI Using Computer Cognitive Atlas to Improve Students' Divergent Thinking
   Ability
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE Artificial Intelligence; Cognitive Atlas; Divergent Thinking; Knowledge
   Atlas
ID INTELLIGENCE
AB Human society has entered the era of intelligence. Social development in the era of intelligence has spawned a large number of intelligent applications. Intelligent applications have put forward unprecedented requirements on the level of cognitive intelligence of machines, and the realization of machine cognitive intelligence depends on knowledge map technology. Divergent thinking is an important part of thinking and an important indicator for measuring innovative thinking. The research in this article found that after the experiment, the associated probabilities of the F values of fluency, flexibility, uniqueness, semantic divergence, graphical divergence, and problem divergence were 0.389, 0.442, 0.594, 0.267, 0.319, and 0.478, which were all greater than the significance level of 0.05, that is, the divergent thinking ability of the experimental group has been significantly improved. The results of this study show that the use of computer cognitive maps can improve students' divergent thinking ability.
C1 [Man, Na; Wang, Kechao; Liu, Lin] Harbin Univ, Sch Informat Engn, Harbin 150086, Heilongjiang, Peoples R China.
RP Man, N (通讯作者)，Harbin Univ, Sch Informat Engn, Harbin 150086, Heilongjiang, Peoples R China.
EM mannajiaoshou@163.com
FU Natural Science Foundation of Heilongjiang Province [LH2019F046]; Harbin
   science and technology innovation talents research project
   [2016RAQXJ013]
FX This work was supported by Natural Science Foundation of Heilongjiang
   Province (Grant No. LH2019F046), Harbin science and technology
   innovation talents research project (Grant No. 2016RAQXJ013)
CR Buckley P, 2019, J ORGAN END USER COM, V31, P22, DOI 10.4018/JOEUC.2019010102
   Fabisiak L, 2018, J ORGAN END USER COM, V30, P1, DOI 10.4018/JOEUC.2018100101
   Fan XY, 2019, IEEE WIREL COMMUN, V26, P19, DOI 10.1109/MWC.2019.1800405
   Gangurde SR, 2018, BENCHMARKING, V25, P450, DOI 10.1108/BIJ-08-2016-0131
   Jia Y, 2018, ENGINEERING-PRC, V4, P53, DOI 10.1016/j.eng.2018.01.004
   Khatwani G, 2018, J ORGAN END USER COM, V30, P63, DOI 10.4018/JOEUC.2018070104
   Liu W, 2019, RENEW SUST ENERG REV, V116, DOI 10.1016/j.rser.2019.109436
   Lu D, 2016, NEUROREPORT, V27, P379, DOI 10.1097/WNR.0000000000000547
   Ludolph R, 2016, J MED INTERNET RES, V18, DOI 10.2196/jmir.5430
   Ma HL, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-21208-0
   Pardeller S, 2017, PSYCHOL HEALTH MED, V22, P1001, DOI 10.1080/13548506.2016.1255766
   Song Q, 2018, IEEE T KNOWL DATA EN, V30, P1887, DOI 10.1109/TKDE.2018.2807442
   [滕静 Teng Jing], 2018, [心理科学进展, Advances in Psychological Science], V26, P411
   Zhang XM, 2017, COMPUT PHYS COMMUN, V211, P98, DOI 10.1016/j.cpc.2016.07.005
   Zhang X, 2020, ULTRASON SONOCHEM, V62, DOI 10.1016/j.ultsonch.2019.03.006
NR 15
TC 6
Z9 6
U1 6
U2 9
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PD NOV-DEC
PY 2021
VL 33
IS 6
DI 10.4018/JOEUC.20211101.oa25
PG 16
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 XN9GJ
UT WOS:000729805400018
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, B
   Hu, Y
   Xu, D
   Li, MZ
   Li, MZ
AF Zhang, Bo
   Hu, Yue
   Xu, Duo
   Li, Maozhen
   Li, Meizi
TI SKG-Learning: a deep learning model for sentiment knowledge graph
   construction in social networks
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Sentiment analysis; Sentiment knowledge graph; Named entity recognition;
   Relation extraction
ID RELATION EXTRACTION; ENTITY; ATTENTION
AB Traditional sentiment analysis methods pay little attention to the inseparable relations between evaluation words and evaluation aspects, and the relations between evaluation words and topics. There have been many studies of knowledge graph (KG), which can effectively store and manage massive amounts of information and is suitable to associate emotion words with evaluation aspects and topics. This study proposes SKG-Learning based on a deep learning model to construct a sentiment knowledge graph (SKG) for sentiment analysis. Entity and relation are the cornerstones of SKG; thus, the task of SKG-Learning is divided into named entity recognition and relation extraction. We propose a bidirectional long short-term memory model (Bi-LSTM) with background knowledge embedding and co-extraction of features (BBC-LSTM) to extract entities. BBC-LSTM completes the embedding of background knowledge such as topic and emotion information and uses three-dimensional tensors to co-extract the deep features of aspect entities and sentiment entities. It solves the problems that it is difficult to recognize entities from insufficient context, and traditional models usually neglect the relevance between sentiment entities and aspect entities. A relation extraction model based on an encoder-decoder model (ED-Learning) is proposed to extract and classify the relation between sentiment and aspect entity, that is, the emotional tendency of sentiment entity toward aspect entity. Experiments show that the proposed methods can more efficiently extract entities and relations from social network texts. We confirm the validity of an SKG constructed by the SKG-Learning model in an emotional analysis task.
C1 [Zhang, Bo; Hu, Yue; Xu, Duo; Li, Maozhen; Li, Meizi] Shanghai Normal Univ, Coll Informat Mech & Elect Engn, Shanghai 200234, Peoples R China.
   [Zhang, Bo] Shanghai Normal Univ, Inst Artificial Intelligence Educ, Shanghai 200234, Peoples R China.
   [Li, Maozhen] Brunel Univ London, Dept Elect & Elect Engn, Uxbridge UB8 3PH, Middx, England.
   [Zhang, Bo] Shanghai Normal Univ, Shanghai Engn Res Ctr Intelligent Educ & Bigdata, Shanghai 200234, Peoples R China.
RP Li, MZ (通讯作者)，Shanghai Normal Univ, Coll Informat Mech & Elect Engn, Shanghai 200234, Peoples R China.
EM zhangbo@shnu.edu.cn; 1000479097@smail.shnu.edu.cn;
   maozhen.li@brunel.ac.uk; limeizi@shnu.edu.cn
OI , li mei zi/0000-0001-7000-5905
FU National Natural Science Foundation of China [61802258, 61572326];
   Natural Science Foundation of Shanghai [18ZR1428300]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61802258, Grant 61572326, in part by the
   Natural Science Foundation of Shanghai under Grant 18ZR1428300.
CR Al-Khatib K, 2020, AAAI CONF ARTIF INTE, V34, P7367
   Ballesteros M., 2016, P 2016 C N AM CHAPT, P260, DOI [10.18653/v1/N16-1030, DOI 10.18653/V1/N16-1030]
   Basiri ME, 2021, FUTURE GENER COMP SY, V115, P279, DOI 10.1016/j.future.2020.08.005
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Cai Y, 2019, INT J MACH LEARN CYB, V10, P2131, DOI 10.1007/s13042-017-0757-6
   Cambria E, 2016, COLING, P2666, DOI DOI 10.1007/978-3-319-55209-5_5
   Chen J., 2019, NAACL HLT, P2509, DOI DOI 10.18653/V1/N19-1259
   Chiu J.P.C., 2015, ABS151108308 CORR
   Dietz L, 2018, ACM/SIGIR PROCEEDINGS 2018, P1387, DOI 10.1145/3209978.3210187
   Do HH, 2019, EXPERT SYST APPL, V118, P272, DOI 10.1016/j.eswa.2018.10.003
   EKMAN P, 1992, COGNITION EMOTION, V6, P169, DOI 10.1080/02699939208411068
   Fares M, 2019, KNOWL-BASED SYST, V165, P432, DOI 10.1016/j.knosys.2018.12.017
   Guo X, 2020, NEURAL COMPUT APPL, V32, P5409, DOI 10.1007/s00521-019-04686-9
   Hasegawa Takaaki, 2004, P 42 ANN M ASS COMP, DOI DOI 10.3115/1218955.1219008
   He RD, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P579
   Nguyen HL, 2020, INFORM FUSION, V61, P56, DOI 10.1016/j.inffus.2020.03.014
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   Jia Y, 2018, ENGINEERING-PRC, V4, P53, DOI 10.1016/j.eng.2018.01.004
   Kanimozhi U, 2017, 2017 SECOND INTERNATIONAL CONFERENCE ON RECENT TRENDS AND CHALLENGES IN COMPUTATIONAL MODELS (ICRTCCM), P335, DOI 10.1109/ICRTCCM.2017.23
   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
   Li PF, 2019, EXPERT SYST APPL, V115, P512, DOI 10.1016/j.eswa.2018.08.009
   Ma RX, 2019, FUTURE GENER COMP SY, V92, P879, DOI 10.1016/j.future.2017.11.036
   Manurung R, 2008, P INT C ADV COMPUTAT, P1
   Pontiki M., 2014, P 8 INT WORKSH SEM E, P27, DOI DOI 10.3115/V1/S14-2004
   Qin Y, 2019, FRONT INFORM TECH EL, V20, P872, DOI 10.1631/FITEE.1800520
   Qiu LR, 2017, IEEE INT C COMPUT, P894, DOI 10.1109/CSE-EUC.2017.181
   Qu JF, 2018, NEURAL NETWORKS, V100, P59, DOI 10.1016/j.neunet.2018.01.006
   Saedi C, 2018, REPRESENTATION LEARNING FOR NLP, P122
   Sun C, 2018, IEEE INT C BIOINFORM, P765, DOI 10.1109/BIBM.2018.8621159
   Sun TT, 2019, IEEE ACCESS, V7, P98023, DOI 10.1109/ACCESS.2019.2930340
   Syachrul MAKRM, 2019, PROCEDIA COMPUT SCI, V157, P214, DOI 10.1016/j.procs.2019.08.160
   Taboada M, 2011, COMPUT LINGUIST, V37, P267, DOI 10.1162/COLI_a_00049
   Unanue IJ, 2017, J BIOMED INFORM, V76, P102, DOI 10.1016/j.jbi.2017.11.007
   Wang D, 2017, SHORT PAPERS, V2, P383
   Wang WY, 2017, AAAI CONF ARTIF INTE, P3316
   Xie C, 2021, IEEE INTERNET THINGS, V8, P2635, DOI 10.1109/JIOT.2020.3019707
   Xu GX, 2019, IEEE ACCESS, V7, P43749, DOI 10.1109/ACCESS.2019.2907772
   Zeng D., 2014, P COLING, P2335
   Zhao Y, 2020, P 28 INT C COMP LING, P4495
   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
   Zhou J, 2020, KNOWL-BASED SYST, V205, DOI 10.1016/j.knosys.2020.106292
NR 42
TC 0
Z9 0
U1 30
U2 30
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 JUL
PY 2022
VL 34
IS 13
SI SI
BP 11015
EP 11034
DI 10.1007/s00521-022-07028-4
EA FEB 2022
PG 20
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2J4QQ
UT WOS:000761267800001
DA 2022-08-23
ER

PT J
AU Brzozowska-Rup, K
   Nowakowska, M
AF Brzozowska-Rup, Katarzyna
   Nowakowska, Marzena
TI Bibliometric Studies on Renewable Energy-Poland Compared to Other EU
   Countries
SO ENERGIES
LA English
DT Article
DE renewable energy; knowledge mapping; research trends; visual
   bibliometric analysis; VOSviewer
AB The necessity to use renewable energy sources (RES), especially in EU countries, is becoming more and more urgent in the face of environmental degradation. It is reflected not only in technological solutions for obtaining energy from renewable sources, but also in scientific research supporting RES technology development. There are an increasing number of papers on renewable energy. The aim of the study was to compare research areas concerning renewable energy in Poland and other EU countries by analyzing scientific works. A selected collection of publications available in the Scopus scientific databases was selected as the subject of the study. After cleaning the data and elaborating the thesaurus, the analysis of the article content was conducted applying text processing methods. Conceptual maps of keywords and keyword co-occurrences were created, which enables arranging and classifying knowledge from the subject area. Research hotspots and the directions of science development in the field of renewable energy were identified. The computer program VOSviewer was used in the investigation. Lorenz curves and Gini coefficients were used to identify quite a strong concentration of RES articles in leading journals on the publication market.
C1 [Brzozowska-Rup, Katarzyna; Nowakowska, Marzena] Kielce Univ Technol, Fac Management & Comp Modelling, PL-25314 Kielce, Poland.
RP Brzozowska-Rup, K (通讯作者)，Kielce Univ Technol, Fac Management & Comp Modelling, PL-25314 Kielce, Poland.
EM krup@tu.kielce.pl; spimn@tu.kielce.pl
FU KIELCE UNIVERSITY OF TECHNOLOGY [04.0.06.00/1.02.001 SUBB.ZKIS.22.001]
FX This research was funded by KIELCE UNIVERSITY OF TECHNOLOGY, POLAND,
   grant number 04.0.06.00/1.02.001 SUBB.ZKIS.22.001.
CR Andreo-Martinez P, 2020, APPL ENERG, V264, DOI 10.1016/j.apenergy.2020.114753
   Reyes-Belmonte MA, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11073015
   [Anonymous], 2019, COMMUNICATION COMMIS
   [Anonymous], 2021, ENERGY TRANSITION PO
   [Anonymous], AG RZECZ ZROWN ROZW
   Bortoluzzi M, 2021, RENEW SUST ENERG REV, V143, DOI 10.1016/j.rser.2021.110958
   Chanchetti LF, 2020, INT J HYDROGEN ENERG, V45, P5356, DOI 10.1016/j.ijhydene.2019.06.093
   Communication from the Commission to European Parliament The European Council The Council, COM2019640 COMM COMM
   elsevier, ELSEVIER PRICING
   elsevier, SCOPUS QUICK REFEREN
   ELSEVIER, ONL ART PUBL TIM
   elsevier, ELSEVIER REV SPEED
   enerdynamics, RENEWABLE ENERGY GLO
   europa, EUROPEAN STRATEGY EN
   europa, DIRECTIVE EU 2018 20
   GASTWIRT.JL, 1972, REV ECON STAT, V54, P306, DOI 10.2307/1937992
   gov, POLSKA STRATEGIA WOD
   gov.wales, MEANING TERM TERMINO
   He K, 2019, SCI TOTAL ENVIRON, V670, P236, DOI 10.1016/j.scitotenv.2019.03.184
   mdpi, MDPI OPEN ACCESS ART
   Osborne J.W., 2013, BEST PRACTICES DATA
   Radhakrishnan S, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0172778
   Rosokhata Anna, 2021, E3S Web of Conferences, V250, DOI 10.1051/e3sconf/202125003002
   Salmeron-Manzano E, 2017, PUBLICATIONS, V5, DOI 10.3390/publications5040025
   scimagojr, SCIMAGO J COUNTRY RA
   Scopus, **DROPPED REF**
   Shi SQ, 2021, ENVIRON IMPACT ASSES, V89, DOI 10.1016/j.eiar.2021.106571
   The European Union Leading in Renewables, EUR COM RE EN FUT CO
   Union E., 2001, OFFICIAL J EUR UNION, V283, P27
   Waltman L., 2013, VOSVIEWER MANUAL 1 6, P1
   Waltman L, 2010, J INFORMETR, V4, P629, DOI 10.1016/j.joi.2010.07.002
   Web of Science Group, US
   Wuni IY, 2019, ENERG BUILDINGS, V190, P69, DOI 10.1016/j.enbuild.2019.02.010
   Ziabina Y., 2020, VIRTUAL ECON, V3, P147, DOI [10.34021/ve.2020.03.04(8), DOI 10.34021/VE.2020.03.04(8)]
NR 34
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1073
J9 ENERGIES
JI Energies
PD JUL
PY 2022
VL 15
IS 13
AR 4577
DI 10.3390/en15134577
PG 20
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA 2Y1ZB
UT WOS:000825689100001
OA gold
DA 2022-08-23
ER

PT J
AU Denaux, R
   Gomez-Perez, JM
AF Denaux, Ronald
   Manuel Gomez-Perez, Jose
TI Vecsigrafo: Corpus-based word-concept embeddings
SO SEMANTIC WEB
LA English
DT Article
DE Joint word and concept embeddings; corpus-based embeddings; knowledge
   graphs; analysis and benchmarking
ID KNOWLEDGE; REPRESENTATION
AB The proliferation of knowledge graphs and recent advances in Artificial Intelligence have raised great expectations related to the combination of symbolic and distributional semantics in cognitive tasks. This is particularly the case of knowledge-based approaches to natural language processing as near-human symbolic understanding relies on expressive, structured knowledge representations. Engineered by humans, such knowledge graphs are frequently well curated and of high quality, but at the same time can be labor-intensive, brittle or biased. The work reported in this paper aims to address such limitations, bringing together bottom-up, corpus-based knowledge and top-down, structured knowledge graphs by capturing as embeddings in a joint space the semantics of both words and concepts from large document corpora. To evaluate our results, we perform the largest and most comprehensive empirical study around this topic that we are aware of, analyzing and comparing the quality of the resulting embeddings over competing approaches. We include a detailed ablation study on the different strategies and components our approach comprises and show that our method outperforms the previous state of the art according to standard benchmarks.
C1 [Denaux, Ronald; Manuel Gomez-Perez, Jose] Expert Syst Cogito Labs, Madrid 28036, Spain.
RP Gomez-Perez, JM (通讯作者)，Expert Syst Cogito Labs, Madrid 28036, Spain.
EM rdenaux@expertsystem.com; jmgomez@expertsystem.com
FU EU [DANTE-700367]; Spanish Centre for the Development of Industrial
   Technology, CDTI [GRESLADIX-IDI-20160805]
FX The research presented herein was funded by Expert System, the EU
   Research and Innovation Horizon 2020 programme (project DANTE-700367)
   and the Spanish Centre for the Development of Industrial Technology,
   CDTI (project GRESLADIX-IDI-20160805). We gratefully acknowledge their
   continued support.
CR Agirre E, 2009, P HUM LANG TECHN 200, P19, DOI DOI 10.3115/1620754.1620758
   Asher N, 2013, SEMANT PRAGMAT, V6, DOI 10.3765/sp.6.2
   Bahdanau D., 2014, NEURAL MACHINE TRANS
   Baker S., 2014, AN UNSUPERVISED MODE, P278, DOI [10.3115/v1/D14-1034, DOI 10.3115/V1/D14-1034]
   Baroni M, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P238
   Baroni M, 2010, COMPUT LINGUIST, V36, P673, DOI 10.1162/coli_a_00016
   Belinkov Y, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P861, DOI 10.18653/v1/P17-1080
   Bender E. M., 2018, TECHNICAL REPORT
   Bruni Elia, 2012, P 50 ANN M ASS COMP, P136, DOI DOI 10.1109/ICRA.2016.7487801
   Camacho-Collados J., 2017, SEMEVAL ACL
   Camacho-Collados J, 2016, ARTIF INTELL, V240, P36, DOI 10.1016/j.artint.2016.07.005
   Chen X., 2014, P 2014 C EMP METH NA, P1025, DOI DOI 10.3115/V1/D14-1110
   Cho K., 2014, P SSST 8 8 WORKSH SY, P103
   Denaux R., 2017, INTERNATIONAL WORKSH
   Devlin J., 2018, P NAACL HLT 2019 2 7
   Domingos P, 2012, COMMUN ACM, V55, P78, DOI 10.1145/2347736.2347755
   Dubossarsky H., 2018, P 2018 C EMP METH NA, P1732
   Duong L, 2016, P 2016 C EMP METH NA, P1285, DOI DOI 10.18653/V1/D16-1136
   Europarl P., 2005, P MT SUMM, P79
   Faruqui M., 2016, P 1 WORKSH EV VECT S, P30, DOI DOI 10.18653/V1/W16-2506
   Feigenbaum E. A., 1977, TECHNICAL REPORT
   Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110
   Gerz D., 2016, ARXIV160800869, DOI DOI 10.18653/V1/D16-1235
   Gomez-Perez J. M., 2017, P WORKSH TUT 9 INT C, P47
   Gunning D, 2010, AI MAG, V31, P33, DOI 10.1609/aimag.v31i3.2302
   Halawi G, 2012, ACM SIGKDD, DOI 10.1145/2339530.2339751
   Han L., 2013, 2 JOINT C LEX COMP S
   Hill F, 2015, COMPUT LINGUIST, V41, P665, DOI 10.1162/COLI_a_00237
   Iacobacci I, 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, P95
   Kalchbrenner N, 2013, P 2013 C EMP METH NA, P1700
   Khot T., 2018, PROCEEDINGS OF THE 3
   Kim Y., 2014, P 2014 C EMP METH NA, P1746
   Levy O., 2014, P 18 C COMP NAT LANG, P171, DOI DOI 10.3115/V1/W14-1618
   Levy O, 2015, T ASSOC COMPUT LING, P3, DOI DOI 10.1162/TACL_A_00134
   Liu V., 2006, 11TH CONFERENCE OF T
   Luong T, 2013, P 17 C COMP NAT LANG, P104, DOI DOI 10.1007/BF02579642
   Mancini M., 2017, P 21 C COMP NAT LANG, P100, DOI DOI 10.18653/V1/K17-1012
   Melamud O., 2016, P 20 SIGNLL C COMP N, P51, DOI DOI 10.18653/V1/K16-1006
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Mikolov T., 2018, PROCEEDINGS OF THE 1
   MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936
   Miller George A., 1993, HLT, DOI DOI 10.3115/1075671.1075742
   Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355
   NEWELL A, 1982, ARTIF INTELL, V18, P87, DOI 10.1016/0004-3702(82)90012-1
   Nickel M, 2016, AAAI CONF ARTIF INTE, P1955
   Olah C., 2017, DISTILL, DOI [10.23915/distill.00007, DOI 10.23915/DISTILL.00007]
   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
   Peters M., 2018, IMPROVING LANGUAGE U
   Radinsky K., 2011, P 20 INT WORLD WID W, P337, DOI [10.1145/1963405.1963455, DOI 10.1145/1963405.1963455]
   Resnik P, 1995, INT JOINT CONF ARTIF, P448
   Ristoski P, 2016, LECT NOTES COMPUT SC, V9981, P498, DOI 10.1007/978-3-319-46523-4_30
   Rothe S, 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, P1793
   RUBENSTEIN H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657
   Schlichtkrull M. S., 2018, MODELING RELATIONAL, P593, DOI 10.1007/978-3-319-93417-4_38
   Schnabel T, 2015, P 2015 C EMP METH NA, P298, DOI DOI 10.18653/V1/D15-1036
   Seo M. J., 2017, 5TH INTERNATIONAL CO
   Shazeer N., 2016, CORR
   Sheth A., 2017, P 2017 IEEE 3 INT FO, P1
   Shi B., 2018, PROCEEDINGS OF THE 3
   Shoham Y, 2016, COMMUN ACM, V59, P47, DOI 10.1145/2803170
   Shortliffe E. H., 1977, The First Annual Symposium on Computer Application in Medical Care, P66
   Sutskever I., 2014, PROCEEDINGS OF THE 2
   Toutanova K., 2015, REPRESENTING TEXT JO, P1499, DOI [10.18653/v1/D15-1174, DOI 10.18653/V1/D15-1174]
   Weston J., 2013, CORR
   Weston J.A., 2013, P ADV NEUR INF PROC, V26, P2787
   Yang Dongqiang, 2006, P 3 INT WORDNET C GW, P121
   Ziemski M., 2016, PROCEEDINGS OF THE T
NR 68
TC 5
Z9 5
U1 1
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 2019
VL 10
IS 5
BP 881
EP 908
DI 10.3233/SW-190361
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 JA8EF
UT WOS:000488082100004
DA 2022-08-23
ER

PT J
AU Xie, C
   Yu, BB
   Zeng, ZY
   Yang, Y
   Liu, Q
AF Xie, Cheng
   Yu, Beibei
   Zeng, Zuoying
   Yang, Yun
   Liu, Qing
TI Multilayer Internet-of-Things Middleware Based on Knowledge Graph
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Middleware; Ontologies; Internet of Things; Protocols; Standards;
   Resource description framework; Machine-to-machine communications;
   Internet of Things (IoT); IoT application; IoT middleware; knowledge
   graph; ontology; Web service
AB Internet of Things (IoT) provides ubiquitous intelligence and pervasive interconnections to diverse physical objects. A key technology to seamlessly integrate different IoT devices into an IoT system is IoT middleware, a software system layer designed to be the intermediary between IoT devices and applications. However, two issues, communication gap and heterogeneous access, prevent the existing IoT middleware from effective application in an IoT system with heterogeneous standards and interfaces. To address this problem, inspired by a graph-based knowledge system for eliminating heterogeneity in business systems, we, in this article, propose a knowledge graph-based multilayer IoT middleware. The proposed multilayer IoT middleware introduces a new layer to bridge the gap between IoT devices with different communication protocols. It is able to uniformly manage all IoT devices by using an IoT knowledge graph. We evaluate the applicability of the proposed approach by a real-life IoT project, a remote monitoring project of rural sewage treatment stations located in Yunnan Province, China. We find that the proposed approach effectively resolves the communication gap and heterogeneous access problems that occurred in the system.
C1 [Xie, Cheng; Yu, Beibei; Zeng, Zuoying; Yang, Yun; Liu, Qing] Yunnan Univ, Sch Software, Kunming 650504, Yunnan, Peoples R China.
RP Yang, Y (通讯作者)，Yunnan Univ, Sch Software, Kunming 650504, Yunnan, Peoples R China.
EM yangyan19@hotmail.com
OI Yang, Yun/0000-0002-9893-3436
FU National Natural Science Foundation of China [61876166, 61663046]; Youth
   Talent Promotion Project of China Association for Science and Technology
   [W8193209]; Department of Science and Technology of Yunnan Province
   [202002AB080001-5, K264202002220]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61876166 and Grant 61663046; in part by
   the Youth Talent Promotion Project of China Association for Science and
   Technology under Grant W8193209; and in part by the Department of
   Science and Technology of Yunnan Province under Grant 202002AB080001-5
   and Grant K264202002220.
CR Aberer K, 2006, P 32 INT C VER LARG, P1199
   Al-Fuqaha A, 2015, IEEE COMMUN SURV TUT, V17, P2347, DOI 10.1109/COMST.2015.2444095
   Annervaz K. M., 2018, PROC C N AM CHAPTER, P313
   [Anonymous], 2012, STANDARDS M2M INTERN
   [Anonymous], 2018, FIWARE NGSI V2
   [Anonymous], 2020, ORION CONTEXT BROKER
   [Anonymous], 2016, LIGHTWEIGHT M2M
   [Anonymous], 2019, OPEN SOURCE IOT DATA
   Canas C, 2015, PROCEEDINGS OF THE 16TH ANNUAL MIDDLEWARE CONFERENCE, P1, DOI 10.1145/2814576.2814812
   Carlson Andrew, 2010, P 3 ACM INT C WEB SE, P101, DOI 10.1145/ 1718487.1718501
   Cheng B, 2018, IEEE T NETW SERV MAN, V15, P1063, DOI 10.1109/TNSM.2018.2827933
   da Cruz MAA, 2018, J NETW COMPUT APPL, V109, P53, DOI 10.1016/j.jnca.2018.02.013
   da Cruz MAA, 2018, IEEE INTERNET THINGS, V5, P871, DOI 10.1109/JIOT.2018.2796561
   Dan Y., 2018, QUESTION ANSWERING F
   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
   Eisenhauer M, 2009, 2009 6TH ANNUAL IEEE COMMUNICATION SOCIETY CONFERENCE ON SENSOR, MESH AND AD HOC COMMUNICATIONS AND NETWORKS WORKSHOPS, P143
   Farahzadi A, 2018, DIGIT COMMUN NETW, V4, P176, DOI 10.1016/j.dcan.2017.04.005
   Fiware- IoT-Agent, 2019, FIW AG
   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Ngu AH, 2017, IEEE INTERNET THINGS, V4, P1, DOI 10.1109/JIOT.2016.2615180
   Paulheim H, 2017, SEMANT WEB, V8, P489, DOI 10.3233/SW-160218
   Qin YR, 2016, J NETW COMPUT APPL, V64, P137, DOI 10.1016/j.jnca.2015.12.016
   Raggett D, 2015, COMPUTER, V48, P26, DOI 10.1109/MC.2015.149
   Razzaque MA, 2016, IEEE INTERNET THINGS, V3, P70, DOI 10.1109/JIOT.2015.2498900
   Savaglio C, 2014, P UBICITEC, V1156, P86
   Sezer OB, 2018, IEEE INTERNET THINGS, V5, P1, DOI 10.1109/JIOT.2017.2773600
   Soldatos J, 2015, LECT NOTES COMPUT SC, V9001, P13, DOI 10.1007/978-3-319-16546-2_3
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Tanganelli G, 2018, IEEE INTERNET THINGS, V5, P425, DOI 10.1109/JIOT.2017.2767381
   Tiburski RT, 2015, IEEE COMMUN MAG, V53, P20, DOI 10.1109/MCOM.2015.7355580
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xie C, 2018, IEEE ACCESS, V6, P15202, DOI 10.1109/ACCESS.2018.2810837
   Xie C, 2017, IEEE T IND INFORM, V13, P3338, DOI 10.1109/TII.2017.2723501
   Yao L, 2015, IEEE INTERNET COMPUT, V19, P60, DOI 10.1109/MIC.2015.77
   Zhang DH, 2019, IEEE ACCESS, V7, P57678, DOI 10.1109/ACCESS.2019.2912627
   Zhang YY, 2018, AAAI CONF ARTIF INTE, P6069
NR 38
TC 9
Z9 9
U1 13
U2 38
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2327-4662
J9 IEEE INTERNET THINGS
JI IEEE Internet Things J.
PD FEB 15
PY 2021
VL 8
IS 4
BP 2635
EP 2648
DI 10.1109/JIOT.2020.3019707
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA QE6LK
UT WOS:000616317000042
DA 2022-08-23
ER

PT J
AU Lu, GM
   Zhang, LZ
   Jin, MJ
   Li, PC
   Huang, X
AF Lu, Guoming
   Zhang, Lizong
   Jin, Minjie
   Li, Pancheng
   Huang, Xi
TI Entity alignment via knowledge embedding and type matching constraints
   for knowledge graph inference
SO JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
LA English
DT Article; Early Access
DE Entity alignment; Knowledge graph; Knowledge embedding; Type matching
   constraints
AB The prosperous development of cognition and human-level intelligence have made the creation of large-scale domain knowledge graphs (KGs) a hot research field. Entity alignment (EA), which discovers entity pairs that represent the same real object in different KGs, is the key technology for building large scale knowledge graph. Existing EA methods rely mainly on the vector representation of the entity and ignoring the semantic features of an entity in a KG, result in poor alignment quality. To address this limitation, we propose a new EA framework based on knowledge embeddings (KEs) and type matching constraints. By embedding two KGs into a unified vector space, we combine the similarity of the entity vector and the degree of the entity type matching to perform more accurate cross-KG EA. The experimental results show that our proposed method has very good performance on both cross-KG and cross-language data sets. On the DBP-YG dataset, in terms of Hit@1, Hits@10, and MRR, our methods have an average increase of 27% over the baseline method MTransE. In most cases, our method significantly improves the accuracy of EA compared with state-of-the-art methods.
C1 [Lu, Guoming; Zhang, Lizong; Li, Pancheng] Univ Elect Sci & Technol China, Sch Engn & Comp Sci, Chengdu, Peoples R China.
   [Jin, Minjie] Univ British Columbia, Sch Food Nutr & Hlth, Vancouver, BC, Canada.
   [Huang, Xi] Chengdu Pvirtech Technol Co Ltd, Chengdu, Peoples R China.
RP Zhang, LZ (通讯作者)，Univ Elect Sci & Technol China, Sch Engn & Comp Sci, Chengdu, Peoples R China.
EM l.zhang@uestc.edu.cn
RI Zhang, Lizong/AAU-5095-2021
OI Zhang, Lizong/0000-0002-0719-9556
FU National Development and Reform Commission 2018 Digital Economy Pilot
   Project [2018FGW005]; Key Research Plan for State Commission of the
   Science and Technology of China [2018YFC0807501]; Foundation of Science
   & Technology Department of Sichuan Province [2018HH0075, 2018JY0605,
   2018JY0073, 2017KP035, 2017JZ0031]
FX The National Development and Reform Commission 2018 Digital Economy
   Pilot Project (2018FGW005) the Key Research Plan for State Commission of
   the Science and Technology of China (2018YFC0807501) the Foundation of
   Science & Technology Department of Sichuan Province, under Grant Nos.
   (2018HH0075, 2018JY0605, 2018JY0073, 2017KP035, 2017JZ0031).
CR Bordes A., 2013, ADV NEURAL INFORM PR, V26, P1
   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
   Hao YC, 2016, COMM COM INF SC, V650, P3, DOI 10.1007/978-981-10-3168-7_1
   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 TN, 2017, ICLR 2017
   Lacoste-Julien S, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P572
   Lan RS, 2021, IEEE T CYBERNETICS, V51, P1443, DOI 10.1109/TCYB.2020.2970104
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Lu HM, 2018, IEEE INTERNET THINGS, V5, P2315, DOI 10.1109/JIOT.2017.2737479
   Lu WP, 2021, IEEE INTELL SYST, V36, P6, DOI 10.1109/MIS.2020.3021188
   Lu WP, 2020, J VIS COMMUN IMAGE R, V71, DOI 10.1016/j.jvcir.2020.102794
   Mahdisoltani F, 2015, CIDR 2015 7 BIENN C
   Rinser D, 2013, INFORM SYST, V38, P887, DOI 10.1016/j.is.2012.10.003
   Suchanek FM, 2011, PROC VLDB ENDOW, V5, P157, DOI 10.14778/2078331.2078332
   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
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Wang Zhichun, 2018, P EMPIRICAL METHODS, P349
   Xiang LY, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8091558
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   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 Y, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1395, DOI 10.1145/2783258.2783353
   Zhu H., 2017, IJCAI, P4258, DOI DOI 10.24963/IJCAI.2017/595
   Zhuang Y, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1917, DOI 10.1145/3132847.3132912
NR 27
TC 0
Z9 0
U1 5
U2 21
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1868-5137
EI 1868-5145
J9 J AMB INTEL HUM COMP
JI J. Ambient Intell. Humaniz. Comput.
DI 10.1007/s12652-020-02821-2
EA FEB 2021
PG 11
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA QG8TA
UT WOS:000617852400001
DA 2022-08-23
ER

PT J
AU Zhang, X
   Cheng, Z
   Tang, LL
   Xi, JL
AF Zhang, Xia
   Cheng, Zhi
   Tang, Luliang
   Xi, Jinglong
TI Research and application of space-time behavior maps: a review
SO JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING
LA English
DT Review
DE Behavior visualization; behavior maps; space-time behavior
ID PUBLIC SPACE; ACCESSIBILITY; GENDER
AB Today, in the space-time big data environment, the research into behavior maps, as an important spatial analysis and behavior visualization method, has gradually increased in dimension, depth and breadth. In this study, we used the China National Knowledge Infrastructure (CNKI) database and the Web of Science (WOS) Core Collection database as the main literature search engines, through the literature review, the behavior maps analysis type, visual expression, and its method evolution are summarized. And we used CiteSpace and VOSviewer scientific knowledge mapping software to reveal the hotspots, development trends, and field dynamics of space-time behavior maps research in the collected documents. We find that the current research literature on space-time behavior maps shows three areas of clustering: post-occupancy evaluation, public open space, and time geography. By establishing the typical application cases in the three fields, the applications and development frontiers are summarized and considered.
C1 [Zhang, Xia; Cheng, Zhi; Xi, Jinglong] Wuhan Univ, Sch Urban Design, Wuhan 430072, Peoples R China.
   [Tang, Luliang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China.
RP Cheng, Z (通讯作者)，Wuhan Univ, Sch Urban Design, Wuhan 430072, Peoples R China.
EM chengzhi93@yeah.net
FU National Natural Science Foundation of China [41671442]; Human Behavior
   Patterns Analysis and Space Optimization based on the Big Trace
   Space-time Data
FX This work was funded by the National Natural Science Foundation of China
   [Grant No. 41671442]: Human Behavior Patterns Analysis and Space
   Optimization based on the Big Trace Space-time Data.
CR Barker R. G., 1980, SOCIAL BEHAV SCI
   Bayramzadeh S, 2018, HERD-HEALTH ENV RES, V11, P124, DOI 10.1177/1937586717751124
   BEEKEN D, 1978, AM EDUC RES J, V15, P507
   Bell P.A., 1997, AM J ALZHEIMERS DIS, V12, P184
   Bill H., 2005, WORLD ARCHITECTURE, DOI [10.16414/j.wa.2005.11.008, DOI 10.16414/J.WA.2005.11.008]
   Blennerhassett JM, 2018, HERD-HEALTH ENV RES, V11, P109, DOI 10.1177/1937586718758444
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Cosco NG, 2010, MED SCI SPORT EXER, V42, P513, DOI 10.1249/MSS.0b013e3181cea27a
   Donghan Z., 2006, ACTA SCI NATURALIUM, DOI [10.13209/j.0479-8023.2007.127, DOI 10.13209/J.0479-8023.2007.127]
   Ellegard K., 2019, THINKING TIME GEOGRA
   Elsheshtawy Y, 2013, CITIES, V31, P382, DOI 10.1016/j.cities.2012.12.001
   Fernandes Odara de Sá, 2008, Paidéia (Ribeirão Preto), V18, P41, DOI 10.1590/S0103-863X2008000100005
   Gharib RY, 2014, FRONT ARCHIT RES, V3, P468, DOI 10.1016/j.foar.2014.06.004
   Gillott M., 2006, ARCHITECTURAL ENG DE, V2
   Golicnik B., 2005, 18 C INT ASS PEOPL E
   Golicnik B, 2010, LANDSCAPE URBAN PLAN, V94, P38, DOI 10.1016/j.landurbplan.2009.07.016
   Golicnik Marusic B, 2011, URBAN DES INT, V16, P36, DOI 10.1057/udi.2010.20
   Handy SL, 1997, ENVIRON PLANN A, V29, P1175, DOI 10.1068/a291175
   Ittelson W. H., 1976, WORLD HOSP
   Ittelson WH, 1996, PSYCHON B REV, V3, P171, DOI 10.3758/BF03212416
   ITTELSON WH, 1961, PSYCHOSOM MED, V23, P219, DOI 10.1097/00006842-196105000-00003
   Jan G., 2002, COMMUNICATION SPACE
   Jan G., 2003, PUBLIC SPACE B PUBLI
   Jialin Z., 2018, CHINA SPORT SCI TECH, V54, P91
   Jianhua C., 2007, ARCH SCI, DOI [10.13614/j.cnki .11-1962/tu.2007.09.026, DOI 10.13614/J.CNKI.11-1962/TU.2007.09.026]
   Jing G., 2018, MATCH PHYS SPACE BEH
   Jing H., 2005, ARCHITECTURAL J
   Kevin L., 2001, URBAN IMAGERY
   Klein Camila, 2018, Trends Psychol., V26, P593, DOI 10.9788/tp2018.2-03pt
   Kwan M. P., 2004, GEOGR ANN B, V86, P267, DOI DOI 10.1111/J.0435-3684.2004.00167.X
   Kwan M. P., 2010, INT URBAN PLANNING, V25
   [关美宝 Kwan Meipo], 2013, [地理科学进展, Progress in Geography], V32, P1316
   Kwan MP, 1998, GEOGR ANAL, V30, P191, DOI 10.1111/j.1538-4632.1998.tb00396.x
   Kwan MP, 1999, ECON GEOGR, V75, P370, DOI 10.2307/144477
   Kwan MP, 1999, PROF GEOGR, V51, P210
   Lestan KA, 2014, INT J ENV RES PUB HE, V11, P6547, DOI 10.3390/ijerph110606547
   Liu A. M. M., 2003, CONSTRUCTION MANAGEM, V21, P147, DOI DOI 10.1080/0144619032000049700
   Luliang T., 2019, ACTA GEODAETICA CART, V48
   Massey D, 1999, T I BRIT GEOGR, V24, P261, DOI 10.1111/j.0020-2754.1999.00261.x
   MILLER HJ, 1991, INT J GEOGR INF SYST, V5, P287, DOI 10.1080/02693799108927856
   Ngesan MR, 2015, PROCD SOC BEHV, V170, P452, DOI 10.1016/j.sbspro.2015.01.008
   Nicol F, 2005, BUILD RES INF, V33, P338, DOI 10.1080/09613210500161885
   Ning L., 2008, J XIAN U ARCHITECTUR, DOI [10.15986/j.1006-7930.2008.04.017, DOI 10.15986/J.1006-7930.2008.04.017]
   Oliveira S, 2007, INT J BIOMETEOROL, V52, P69, DOI 10.1007/s00484-007-0100-0
   Peters K, 2010, LEISURE SCI, V32, P418, DOI 10.1080/01490400.2010.510987
   Petrenko A., 2013, HUMAN SPATIAL BEHAV, DOI [10.1007/978-3-319-01790-7, DOI 10.1007/978-3-319-01790-7]
   Raymundo L. S., 2010, PSICOLOGIA EM REVIST, V16
   Shaw S. L., 2016, J GEOINFORMATION SCI, V18
   Shaw SL, 2009, J TRANSP GEOGR, V17, P141, DOI 10.1016/j.jtrangeo.2008.11.012
   Shaw Shihlung, 2014, Geomatics and Information Science of Wuhan University, V39, P667, DOI 10.13203/j.whugis20140127
   Shaw SL., 2008, T GIS, V12, P425, DOI [10.1111/j.1467-9671.2008.01114.x, DOI 10.1111/J.1467-9671.2008.01114.X]
   Shuoxian W., 2004, SICHUAN BUILDING SCI, V30, P128
   Smith W. R., 2014, ENVIRON BEHAV
   Weixin H., 2018, ARCHITECT TECHNOLOGY
   Weixin H., 2016, WORLD ARCHITECTURE, DOI [10.16414/j.wa.2016.04.023, DOI 10.16414/J.WA.2016.04.023]
   Xia Z., 2018, NEW ARCHITECTURE, DOI [10.12069/j.na.201805024, DOI 10.12069/J.NA.201805024]
   Xia Z., 2016, J TONGJI U, V44
   Yanwei C., 2000, HUMAN GEOGRAPHY, DOI [10.13959/j.issn.1003-2398.2000.06.013, DOI 10.13959/J.ISSN.1003-2398.2000.06.013]
   Yanwei C., 2000, B CHINESE ACAD SCI, DOI [10.16418/j.issn.1000-3045.2000.06.004, DOI 10.16418/J.ISSN.1000-3045.2000.06.004]
   Yanwei C., 2014, SPACE BEHAV BEHAV SP
   Yanwei C., 1998, SCI GEOGRAPHIC SCI, DOI [10.13249/j.cnki.sgs.1998.01.010, DOI 10.13249/J.CNKI.SGS.1998.01.010]
   Yanwei C., 1997, ECON GEOGR, DOI [10.15957/j.cnki.jjdl.1997.03.010, DOI 10.15957/J.CNKI.JJDL.1997.03.010]
   Yanwei C., 2009, GEOGRAPHIC GEOINFORM, V25
   Yu H, 2008, INT J GEOGR INF SCI, V22, P409, DOI 10.1080/13658810701427569
   Zube E. H., 2013, ADV ENV BEHAV DESIGN, V2
NR 65
TC 0
Z9 0
U1 32
U2 78
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 SEP 3
PY 2021
VL 20
IS 5
BP 581
EP 595
DI 10.1080/13467581.2020.1800473
EA AUG 2020
PG 15
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 UM2GM
UT WOS:000561454900001
OA gold
DA 2022-08-23
ER

PT J
AU Chumachenko, AV
   Kreminskyi, BG
   Mosenkis, IL
   Yakimenko, AI
AF Chumachenko, A., V
   Kreminskyi, B. G.
   Mosenkis, Iu L.
   Yakimenko, A., I
TI Dynamics of topic formation and quantitative analysis of hot trends in
   physical science
SO SCIENTOMETRICS
LA English
DT Article
DE Dynamical complex networks; Topic detection; Mutual information
   dynamics; Topic structure; Concept mapping
ID BIBLIOMETRIC ANALYSIS; CO-WORD; GALAXIES; CLUSTERS; NETWORK; INDEX
AB Successful research in the face of increasing complexity of modern scientific knowledge together with diversity and depth of the studied problems requires an understanding of the structure and evolution of trends in science. Available digital records open wide possibilities for statistical analysis of scientific publications and related metadata for topic modeling and evolution, knowledge mapping, citation indexing, etc. We investigate dynamical properties of the physical topics using analysis of temporal evolution of proximity measure for word pairs related to the mutual information. We use full-text conceptualization of content of scientific documents provided by the ScienceWISE platform for topic mapping, trend analysis and detection of hot topics together with relevant papers retrieval. We found that time evolution of relative mutual information distance reveals a hidden topic structure and could be used for quantitative analysis of current trends in scientific research.
C1 [Chumachenko, A., V; Yakimenko, A., I] Taras Shevchenko Natl Univ Kyiv, Dept Phys, 64-13 Volodymyrska St, UA-01601 Kiev, Ukraine.
   [Kreminskyi, B. G.; Yakimenko, A., I] Inst Educ Content Modernizat, State Sci Inst, 36 Mytropolyta Vasylia Lypkivskogo Str, UA-03035 Kiev, Ukraine.
   [Mosenkis, Iu L.; Yakimenko, A., I] Taras Shevchenko Natl Univ Kyiv, Inst Philol, 64-13 Volodymyrska St, UA-01601 Kiev, Ukraine.
RP Chumachenko, AV (通讯作者)，Taras Shevchenko Natl Univ Kyiv, Dept Phys, 64-13 Volodymyrska St, UA-01601 Kiev, Ukraine.
EM chumachenko.a@gmail.com
OI Chumachenko, Artem/0000-0002-1801-8933
FU Swiss National Science Foundation [IZ74Z0-160497/1]
FX The authors are grateful to Stanislav Vilchynsky, Oleg Ruchaisky and
   Alexey Boiarskyi for their helpful discussion and suggestions and Andrey
   Magalich for his help with the data preparation. This work was supported
   by the Swiss National Science Foundation Grant "Complex Information
   Network Manipulation" (SCOPES Grant IZ74Z0-160497/1).
CR Abdalgader K., 2012, ACM T SPEECH LANGUAG, DOI [10.1145/2168748.2168750, DOI 10.1145/2168748.2168750]
   Abe K, 2018, PROG THEOR EXP PHYS, V2018, DOI 10.1093/ptep/pty044
   Abe K, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.092006
   Aggarwal C., 2012, SURVEY TEXT CLUSTERI, P77, DOI DOI 10.1007/978-1-4614-3223-4_4
   Ahmed S., 2017, PRAMANA, V88, P79
   Alvarez-Ruso L, 2018, PROG PART NUCL PHYS, V100, P1, DOI 10.1016/j.ppnp.2018.01.006
   Amat CB, 2008, SCIENTOMETRICS, V74, P379, DOI 10.1007/s11192-007-1823-8
   Amelio A, 2015, PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), P1584, DOI 10.1145/2808797.2809344
   Anderberg M. R., 1973, CLUSTER ANAL APPL, P1
   Antusch S, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2018)124
   Astafiev A., 2012, LECT NOTES COMPUTER, V7540, P392
   Banks MG, 2006, SCIENTOMETRICS, V69, P161, DOI 10.1007/s11192-006-0146-5
   Barranco RC, 2018, IEEE INT CONF BIG DA, P2088, DOI 10.1109/BigData.2018.8621902
   Berlind AA, 2006, ASTROPHYS J SUPPL S, V167, P1, DOI 10.1086/508170
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Boyarsky A., 2012, SCI PAPERS SCI ONTOL
   Bybee J. L, 2007, OXFORD HDB COGNITIVE
   Cai D, 2011, IEEE T KNOWL DATA EN, V23, P902, DOI 10.1109/TKDE.2010.165
   CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
   Chen HB, 2015, SCIENTOMETRICS, V105, P1005, DOI 10.1007/s11192-015-1714-3
   DEGAETANOORTLIE.S, 2013, NEW METHODS HIST COR, V3, P93
   Ding WY, 2014, J ASSOC INF SCI TECH, V65, P2084, DOI 10.1002/asi.23134
   Ding Y, 2011, J AM SOC INF SCI TEC, V62, P236, DOI 10.1002/asi.21452
   Dong BS, 2012, SCIENTOMETRICS, V93, P1101, DOI 10.1007/s11192-012-0730-9
   Dridi A, 2019, IEEE ACCESS, V7, P176414, DOI 10.1109/ACCESS.2019.2957440
   Fukui K, 2005, LECT NOTES ARTIF INT, V3684, P745
   Gan CM, 2015, SCIENTOMETRICS, V105, P1167, DOI 10.1007/s11192-015-1723-2
   Giganti C, 2018, PROG PART NUCL PHYS, V98, P1, DOI 10.1016/j.ppnp.2017.10.001
   Glanzel W, 1996, SCIENTOMETRICS, V37, P195, DOI 10.1007/BF02093621
   Hagedorn C, 2018, INT J MOD PHYS A, V33, DOI 10.1142/S0217751X1842006X
   HAVEL TF, 1983, B MATH BIOL, V45, P665
   Havemann F, 2017, SCIENTOMETRICS, V111, P1089, DOI 10.1007/s11192-017-2302-5
   He Q, 1999, LIB TRENDS, V48
   Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950
   Hric D, 2014, PHYS REV E, V90, DOI 10.1103/PhysRevE.90.062805
   HUCHRA JP, 1982, ASTROPHYS J, V257, P423, DOI 10.1086/160000
   Jin GP, 2008, I C WIREL COMM NETW, P14, DOI 10.1109/ITAB.2008.4570501
   Lee L., 1997, CMPLG9708011 ARXIV
   Li H., 2017, J COMPUTER COMMUNICA, V5, P63, DOI [10.4236/jcc.2017.512007, DOI 10.4236/JCC.2017.512007]
   Liu GY, 2012, SCIENTOMETRICS, V91, P203, DOI 10.1007/s11192-011-0586-4
   Lu Y, 2011, INFORM RETRIEVAL, V14, P178, DOI 10.1007/s10791-010-9141-9
   Mane KK, 2004, P NATL ACAD SCI USA, V101, P5287, DOI 10.1073/pnas.0307626100
   Mao N, 2010, HUM ECOL RISK ASSESS, V16, P801, DOI 10.1080/10807039.2010.501248
   McDaid A.F., 2011, NORMALIZED MUTUAL IN
   Mikolov T., 2013, ADV NEURAL INFORM PR
   Ng A., 2002, ADV NEURAL INFORM PR, V14
   Padilla Stefano, 2014, CHI 14 HUM FACT COMP, P815, DOI [DOI 10.1145/2559206.2578867, 10.1145/2559206.2578867]
   Peel L, 2017, SCI ADV, V3, DOI 10.1126/sciadv.1602548
   Peng TQ, 2012, J AM SOC INF SCI TEC, V63, P1789, DOI 10.1002/asi.22649
   PRESS WH, 1982, ASTROPHYS J, V259, P449, DOI 10.1086/160183
   Prokofyev Roman, 2013, Advances in Information Retrieval. 35th European Conference on IR Research, ECIR 2013. Proceedings, P594, DOI 10.1007/978-3-642-36973-5_50
   Qian X, 2019, REP PROG PHYS, V82, DOI 10.1088/1361-6633/aae881
   Renals S., 2007, FORMAL MODELING COGN
   Rudolph M, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P1003, DOI 10.1145/3178876.3185999
   Shannon C.E., 1948, Bell System Technical Journal, V27, P379
   Steuer R, 2005, STUD CLASS DATA ANAL, P81, DOI 10.1007/3-540-26981-9_11
   Su XN, 2014, SCIENTOMETRICS, V98, P1567, DOI 10.1007/s11192-012-0921-4
   Tan J, 2014, SCIENTOMETRICS, V98, P1473, DOI 10.1007/s11192-013-1125-2
   Velden T, 2017, SCIENTOMETRICS, V111, P1169, DOI 10.1007/s11192-017-2306-1
   Wang X., 2016, ARXIV160805517
   Wei Xu, 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P202
   Wen H, 2012, SCIENTOMETRICS, V91, P51, DOI 10.1007/s11192-011-0535-2
   Xie P., 2013, P 29 C UNC ART INT, P694, DOI 10.5555/3023638.3023709
   Xie P, 2015, SCIENTOMETRICS, V105, P611, DOI 10.1007/s11192-015-1689-0
   Xu W., 2003, PROC 26 ANN INT ACM, P267, DOI [DOI 10.1145/860435.860485, 10.1145/860435.860485]
   Yan EJ, 2012, J AM SOC INF SCI TEC, V63, P1313, DOI 10.1002/asi.22680
   Yao Z., 2017, ARXIV170300607
   Ye CL, 2015, 2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), P2648, DOI 10.1109/FSKD.2015.7382375
   Ye FY, 2013, CURR SCI INDIA, V104, P160
   Zheng LK, 2019, SCIENTOMETRICS, V119, P1695, DOI 10.1007/s11192-019-03098-9
   Zheng TL, 2015, SCIENTOMETRICS, V105, P863, DOI 10.1007/s11192-015-1736-x
NR 72
TC 3
Z9 3
U1 4
U2 37
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 739
EP 753
DI 10.1007/s11192-020-03610-6
EA JUL 2020
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 NL7AC
UT WOS:000548155000002
DA 2022-08-23
ER

PT J
AU Mertins, K
   Heisig, P
   Alwert, K
AF Mertins, K
   Heisig, P
   Alwert, K
TI Process-oriented knowledge structuring
SO JOURNAL OF UNIVERSAL COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 3rd International Conference on Knowledge Management (I-Know 03)
CY JUL 02-04, 2003
CL Graz, AUSTRIA
SP Know Ctr
DE knowledge management; knowledge structure; visualization; process
   context; browsing; business process; knowledge base; knowledge bearer;
   knowledge carrier; knowledge domain; knowledge map; knowledge navigator;
   post-structuring; pre-structuring; process assistant; process modeling
   tool; key word based search; topic map engine
AB Within a business environment, where the fast and reliable access to knowledge is a key success factor, an efficient handling of the organizational knowledge is crucial. Therefore the need for methods and techniques, which allow to structure and maintain complex knowledge bases according to the requirements emerging from the daily work have a high priority. This article provides a business process oriented approach to structure organizational knowledge and information bases. The approach was developed within applied research in the industrial, service and administrative sector. Following this approach, three different types of knowledge structures and their visualization have been developed by the Fraunhofer IPK and are currently applied and tested in organizations. Beside the approach itself, these three types of knowledge structure and the cases of application shall be introduced here.
C1 Fraunhofer IPK Berlin, Berlin, Germany.
RP Mertins, K (通讯作者)，Fraunhofer IPK Berlin, Berlin, Germany.
EM Kai.Mertins@ipk.fhg.de; Peter.Heisig@ipk.fhg.de; Kay.Alwert@ipk.fhg.de
RI Heisig, Peter/GOK-1407-2022; Heisig, Peter/E-5617-2013
OI Heisig, Peter/0000-0001-8720-6375; Heisig, Peter/0000-0001-8720-6375
CR Bair J. H., 1998, J KNOWLEDGE MANAGEME, V2, P20
   FISCHER M, 1998, VISUALISIERUNG MANAG
   HEISIG P, 2003, IN PRESS KNOWLEDGE M
   HOFFMANN I, 2001, KNOWLEDGE MANAGEMENT
   ISSING LJ, 1988, WISSENSPSYCHOLOGIE P
   MERTINS K, 2001, KNOWLEDGE MANAGEMENT
   PARK J, 2002, XML TOPICS MAPS CREA
   SPUR G, 1993, INTEGRIERTE UNTERNEH
NR 8
TC 8
Z9 8
U1 0
U2 6
PU SPRINGER
PI NEW YORK
PA 233 SPRING STREET, NEW YORK, NY 10013 USA
SN 0948-695X
J9 J UNIVERS COMPUT SCI
JI J. Univers. Comput. Sci.
PY 2003
VL 9
IS 6
BP 542
EP 550
PG 9
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 958FO
UT WOS:000231430300011
DA 2022-08-23
ER

PT J
AU Xu, GW
   Jia, GY
   Shi, L
   Zhang, ZJ
AF Xu, Gongwen
   Jia, Guangyu
   Shi, Lin
   Zhang, Zhijun
TI Personalized Course Recommendation System Fusing with Knowledge Graph
   and Collaborative Filtering
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID IMAGES
AB Personalized courses recommendation technology is one of the hotspots in online education field. A good recommendation algorithm can stimulate learners' enthusiasm and give full play to different learners' learning personality. At present, the popular collaborative filtering algorithm ignores the semantic relationship between recommendation items, resulting in unsatisfactory recommendation results. In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed. Firstly, the knowledge graph representation learning method is used to embed the semantic information of the items into a low-dimensional semantic space; then, the semantic similarity between the recommended items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm. This algorithm increases the performance of recommendation at the semantic level. The results show that the proposed algorithm can effectively recommend courses for learners and has higher values on precision, recall, and F1 than the traditional recommendation algorithm.
C1 [Xu, Gongwen; Jia, Guangyu; Shi, Lin] Shandong Jianzhu Univ, Sch Business, Jinan 250101, Peoples R China.
   [Zhang, Zhijun] Shandong Jianzhu Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China.
RP Xu, GW (通讯作者)，Shandong Jianzhu Univ, Sch Business, Jinan 250101, Peoples R China.
EM xugongwen@sdjzu.edu.cn
OI Xu, Gongwen/0000-0003-4518-9911
FU Shandong Education Department Teaching Reform Project [Z2016M014,
   Z2016M016, Z2016Z013]
FX This work was supported in part by the Shandong Education Department
   Teaching Reform Project (Z2016M014, Z2016M016, and Z2016Z013).
CR Aguilar Jose, 2017, Applied Computing and Informatics, V13, P147, DOI 10.1016/j.aci.2016.08.002
   Bhaskaran S, 2019, CLUSTER COMPUT, V22, P1137, DOI 10.1007/s10586-017-1160-5
   Chen YX, 2018, APPL PSYCH MEAS, V42, P24, DOI 10.1177/0146621617697959
   China Internet Network Information Center (CNNIC), 2020, 46 STAT REP CURR SIT
   Cinar Ahmet, 2020, Ingenierie des systemes d'information, V25, P35, DOI 10.18280/isi.250105
   Demircan S, 2020, TRAIT SIGNAL, V37, P51, DOI 10.18280/ts.370107
   Doppala B.P., 2020, REV INTELLIGENCE ART, V34, P377, DOI [10.18280/ria.340401, DOI 10.18280/RIA.340401]
   Ebisu T, 2020, IEEE T KNOWL DATA EN, V32, P941, DOI 10.1109/TKDE.2019.2893920
   Ebisu T, 2018, AAAI CONF ARTIF INTE, P1819
   Fan M, 2017, PATTERN RECOGN LETT, V93, P31, DOI 10.1016/j.patrec.2016.09.005
   Gope J, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA), P133, DOI 10.1109/CCAA.2017.8229786
   Huang ZY, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1261, DOI 10.1145/3357384.3357995
   Jia BX, 2018, EDUC SCI-THEOR PRACT, V18, P2958, DOI 10.12738/estp.2018.6.195
   Lin JJ, 2018, PROCEDIA COMPUT SCI, V129, P449, DOI 10.1016/j.procs.2018.03.023
   Lin Y., 2018, CORR
   Liu Q, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P627, DOI 10.1145/3292500.3330922
   Liu Y., 2020, INGENIERIE SYSTEMES, V25, P359, DOI [10.18280/isi.250310, DOI 10.18280/ISI.250310]
   Meng WL, 2019, TRAIT SIGNAL, V36, P575, DOI 10.18280/ts.360613
   Obeidat Raghad, 2019, 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). Proceedings, P49, DOI 10.1109/Deep-ML.2019.00018
   Pang YX, 2017, COMPUT APPL ENG EDUC, V25, P120, DOI 10.1002/cae.21785
   Sattar A, 2017, ARAB J SCI ENG, V42, P3229, DOI 10.1007/s13369-016-2410-1
   Shen J, 2020, INT J COMPUT SCI ENG, V21, P219
   Subramaniyaswamy V., 2017, International Journal of High Performance Computing and Networking, V10, P54
   Tan ZH, 2017, IEEE ACCESS, V5, P27211, DOI 10.1109/ACCESS.2017.2778424
   Thakkar P, 2019, SMART INNOV SYST TEC, V107, P173, DOI 10.1007/978-981-13-1747-7_17
   Tian YH, 2019, PROC CIRP, V83, P490, DOI 10.1016/j.procir.2019.04.126
   Wen J., 2020, INGENIERIE SYSTEMES, V25, P595, DOI [10.18280/isi.250506, DOI 10.18280/ISI.250506]
   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
   Xiao J, 2018, J AMB INTEL HUM COMP, V9, P667, DOI 10.1007/s12652-017-0466-8
   Xu GW, 2020, ALEX ENG J, V59, P1333, DOI 10.1016/j.aej.2020.02.034
   Xu GW, 2020, IEEE ACCESS, V8, P14278, DOI 10.1109/ACCESS.2020.2966220
   Xu GW, 2019, TEH VJESN, V26, P1707, DOI 10.17559/TV-20190730110003
   Xu KYL, 2018, PR MACH LEARN RES, V80
   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
   Zarzour H, 2018, INT CONF INFORM COMM, P102, DOI 10.1109/IACS.2018.8355449
   Zhang H, 2018, MULTIMED TOOLS APPL, V77, P7051, DOI 10.1007/s11042-017-4620-2
   Zhang H, 2017, 2017 INTERNATIONAL SYMPOSIUM ON EDUCATIONAL TECHNOLOGY (ISET 2017), P106, DOI 10.1109/ISET.2017.33
   Zhang Q, 2021, TRAIT SIGNAL, V38, P197, DOI 10.18280/ts.380121
NR 38
TC 1
Z9 1
U1 21
U2 50
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 SEP 26
PY 2021
VL 2021
AR 9590502
DI 10.1155/2021/9590502
PG 8
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA WB1AY
UT WOS:000703313300002
PM 34616447
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wu, TX
   Khan, A
   Yong, ML
   Qi, GL
   Wang, M
AF Wu, Tianxing
   Khan, Arijit
   Yong, Melvin
   Qi, Guilin
   Wang, Meng
TI Efficiently embedding dynamic knowledge graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Dynamic embedding; Online learning
ID DBPEDIA; SEARCH; SCALE
AB Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems. In real world, knowledge graphs (KGs) are dynamic and evolve over time with addition or deletion of triples. However, most existing models focus on embedding static KGs while neglecting dynamics. To adapt to the changes in a KG, these models need to be retrained on the whole KG with a high time cost. In this paper, to tackle the aforementioned problem, we propose a new context-aware Dynamic Knowledge Graph Embedding (DKGE) method which supports the embedding learning in an online fashion. DKGE introduces two different representations (i.e., knowledge embedding and contextual element embedding) for each entity and each relation, in the joint modeling of entities and relations as well as their contexts, by employing two attentive graph convolutional networks, a gate strategy, and translation operations. This effectively helps limit the impacts of a KG update in certain regions, not in the entire graph, so that DKGE can rapidly acquire the updated KG embedding by a proposed online learning algorithm. Furthermore, DKGE can also learn KG embedding from scratch. Experiments on the tasks of link prediction and question answering in a dynamic environment demonstrate the effectiveness and efficiency of DKGE. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Wu, Tianxing; Qi, Guilin; Wang, Meng] Southeast Univ, Nanjing, Peoples R China.
   [Khan, Arijit] Aalborg Univ, Aalborg, Denmark.
   [Yong, Melvin] Natl Univ Singapore, Singapore, Singapore.
RP Wu, TX (通讯作者)，Southeast Univ, Nanjing, Peoples R China.
EM tianxingwu@seu.edu.cn
FU NSFC, China [62006040, U21A20488]; Novo Nordisk Foundation, Denmark
   [NNF22OC0072415]; Project for the Doctor of Entrepreneurship and
   Innovation in Jiangsu Province [JSSCBS20210126]; Fundamental Research
   Funds for the Central Universities, China; ZhiShan Young Scholar Program
   of Southeast University
FX This work is supported by the NSFC, China (Grant No. 62006040,
   U21A20488) , the Novo Nordisk Foundation, Denmark (Grant No.
   NNF22OC0072415) , the Project for the Doctor of Entrepreneurship and
   Innovation in Jiangsu Province (Grant No. JSSCBS20210126) , the
   Fundamental Research Funds for the Central Universities, China, and
   ZhiShan Young Scholar Program of Southeast University.
CR Bahdanau D., 2015, ICLR
   Barros CD., 2021, ACM COMPUT SURVEYS C, V55, P1
   Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
   Bruna J., 2013, P 2 INT C LEARN REPR
   Cai B., 2022, ARXIV PREPRINT ARXIV
   Cai L, 2018, NAACL, P1470
   Caruana R, 2001, ADV NEUR IN, V13, P402
   Daruna A, 2021, IEEE ROBOT AUTOM LET, V6, P1128, DOI 10.1109/LRA.2021.3056071
   Dasgupta SS, 2018, P 2018 C EMPIRICAL M, P2001
   Defferrard M., 2016, NIPS, P3844
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Dong XL, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2869, DOI 10.1145/3219819.3219938
   Du L, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2086
   Fei L., 2021, COMPLEX NETWORKS, P523
   Feng J., 2016, COLING, P641
   Gao Z., 2022, ARXIV
   Goel R, 2020, AAAI CONF ARTIF INTE, V34, P3988
   Hamaguchi T, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1802
   Hamilton Will, 2017, NEURIPS
   Han X., 2018, P C EMP METH NAT LAN, P139
   Hellmann S, 2009, LECT NOTES COMPUT SC, V5871, P1209, DOI 10.1007/978-3-642-05151-7_33
   Henaff M., 2015, ARXIV150605163
   Huang X, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P105, DOI 10.1145/3289600.3290956
   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
   Jia YT, 2018, ACM T WEB, V12, DOI 10.1145/3132733
   Jiang T., 2016, P 26 INT C COMP LING, P1715
   Jin JH, 2017, IEEE T PARALL DISTR, V28, P2342, DOI 10.1109/TPDS.2017.2665478
   Jin Woojeong, 2020, P EMNLP, P6669
   Khan A, 2013, PROC VLDB ENDOW, V6, P181, DOI 10.14778/2535569.2448952
   Kipf T. N., 2017, P 5 INT C LEARN REPR, P1
   Lehmann J, 2015, SEMANT WEB, V6, P167, DOI 10.3233/SW-140134
   Lesort T., 2020, ARXIV
   Li JD, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P387, DOI 10.1145/3132847.3132919
   Li Z., WORLD WIDE J, DOI [10.1007/s11280-022-01016-3, DOI 10.1007/S11280-022-01016-3]
   Li ZX, 2021, SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P408, DOI 10.1145/3404835.3462963
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Liu P., 2022, KNOWL-BASED SYST
   Liu ZJ, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P1629, DOI 10.1145/3442381.3449821
   Liu ZJ, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P1005, DOI 10.1145/3340531.3411945
   Ma JX, 2018, AAAI CONF ARTIF INTE, P370
   Mahdisoltani F., 2015, CIDR
   Nickel M, 2011, P 28 INT C MACH LEAR, P809, DOI DOI 10.5555/3104482.3104584
   Schlichtkrull M., 2018, EUR SEM WEB C, P593, DOI DOI 10.1007/978-3-319-93417-4_38
   Sun Z., 2019, 7 INT C LEARN REPR
   Tay Y, 2017, AAAI CONF ARTIF INTE, P1243
   Trivedi R., 2019, P 7 INT C LEARN REPR
   Trivedi R, 2017, PR MACH LEARN RES, V70
   Trouillon T, 2016, PR MACH LEARN RES, V48
   Wang PF, 2018, AAAI CONF ARTIF INTE, P2005
   Wang Q, 2017, IEEE T KNOWL DATA EN, V29, P2724, DOI 10.1109/TKDE.2017.2754499
   Wang Z, 2014, AAAI CONF ARTIF INTE, P1112
   Xu JC, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1318
   Yang B., 2015, ICLR
   Zhang YQ, 2019, PROC INT CONF DATA, P614, DOI 10.1109/ICDE.2019.00061
   Zheng WG, 2018, PROC VLDB ENDOW, V11, P1373, DOI 10.14778/3236187.3236192
   Zhou LK, 2018, AAAI CONF ARTIF INTE, P571
   Zhu DY, 2018, IEEE T KNOWL DATA EN, V30, P2134, DOI 10.1109/TKDE.2018.2822283
NR 59
TC 0
Z9 0
U1 11
U2 11
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 109124
DI 10.1016/j.knosys.2022.109124
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2D1SE
UT WOS:000811334800004
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Dai, QY
   Wu, XM
   Fan, L
   Li, QM
   Liu, H
   Zhang, XT
   Wang, D
   Lin, GL
   Yang, KP
AF Dai, Quanyu
   Wu, Xiao-Ming
   Fan, Lu
   Li, Qimai
   Liu, Han
   Zhang, Xiaotong
   Wang, Dan
   Lin, Guli
   Yang, Keping
TI Personalize d knowle dge-aware recommendation with collaborative and
   attentive graph convolutional networks
SO PATTERN RECOGNITION
LA English
DT Article
DE Recommender system; Graph convolutional network; Attention mechanism;
   Knowledge graph
AB Knowledge graphs (KGs) are increasingly used to solve the data sparsity and cold start problems of col-laborative filtering. Recently, graph neural networks (GNNs) have been applied to build KG-based rec-ommender systems and achieved competitive performance. However, existing GNN-based methods are either limited in their ability to capture fine-grained semantics in a KG, or insufficient in effectively mod-eling user-item interactions. To address these issues, we propose a novel framework with collaborative and attentive graph convolutional networks for personalized knowledge-aware recommendation. Partic-ularly, we model the user-item graph and the KG separately and simultaneously with an efficient graph convolutional network and a personalized knowledge graph attention network, where the former aims to extract informative collaborative signals, while the latter is designed to capture fine-grained semantics. Collectively, they are able to learn meaningful node representations for predicting user-item interactions. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method compared with state-of-the-arts.(c) 2022 Elsevier Ltd. All rights reserved.
C1 [Dai, Quanyu] Noahs Ark Lab, Shenzhen 518129, Huawei, Peoples R China.
   [Wu, Xiao-Ming; Fan, Lu; Li, Qimai; Wang, Dan] Hong Kong Polytech Univ, Dept Comp, Hong Kong 999077, Peoples R China.
   [Liu, Han; Zhang, Xiaotong] Dalian Univ Technol, Sch Software, Dalian 116024, Peoples R China.
   [Lin, Guli; Yang, Keping] Alibaba Grp, Hangzhou 311121, Peoples R China.
RP Wu, XM; Wang, D (通讯作者)，Hong Kong Polytech Univ, Dept Comp, Hong Kong 999077, Peoples R China.
EM csxmwu@comp.polyu.edu.hk; csdwang@comp.polyu.edu.hk
OI Wu, Xiao-Ming/0000-0002-3130-0554; LI, Qimai/0000-0002-6705-7939
FU PolyU (UGC) [P0001175, P0030935]; Alibaba Group [P0034058, P0038850]
FX This research was supported by the grants of P0001175 (ZVJJ) and
   P0030935 (ZVPY) funded by PolyU (UGC) and P0034058 (ZGAL) and P0038850
   (ZGD1) funded by [1-45] Alibaba Group.
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
   Bahdanau D., 2014, ARXIV
   Bai PZ, 2019, PATTERN RECOGN, V88, P729, DOI 10.1016/j.patcog.2018.12.003
   BAMBER D, 1975, J MATH PSYCHOL, V12, P387, DOI 10.1016/0022-2496(75)90001-2
   Bruna J., 2014, 2 INT C LEARNING REP
   Cao YX, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P151, DOI 10.1145/3308558.3313705
   Chen SH, 2015, IEEE T SIGNAL PROCES, V63, P6510, DOI 10.1109/TSP.2015.2469645
   Cheng JH, 2018, CHI 2018: EXTENDED ABSTRACTS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3170427.3188467
   Defferrard M., 2016, 30 INT C NEURAL INFO, P3844
   Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776
   Diederik J.B., 2015, ICLR
   Guo HF, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1725
   Hamilton WL, 2017, ADV NEUR IN, V30
   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
   Hu YF, 2008, IEEE DATA MINING, P263, DOI 10.1109/ICDM.2008.22
   Kipf T.N., 2017, P 5 INT C LEARN REPR
   Li QM, 2019, PROC CVPR IEEE, P9574, DOI 10.1109/CVPR.2019.00981
   Lin YK, 2015, AAAI CONF ARTIF INTE, P2181
   Maas A.L., 2013, P ICML, VVolume 30, P3, DOI DOI 10.1016/0010-0277(84)90022-2
   Nathani D, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4710
   Rendle S, 2011, PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), P635
   Rong Y., 2020, INT C LEARN REPR
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Shuman DI, 2013, IEEE SIGNAL PROC MAG, V30, P83, DOI 10.1109/MSP.2012.2235192
   Srivastava N, 2014, J MACH LEARN RES, V15, P1929
   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
   Vashishth S., 2020, INT C LEARN REPR
   Velickovi Petar, 2018, ICLR
   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 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 X, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2022, DOI 10.1145/3308558.3313562
   Xu K, 2015, PR MACH LEARN RES, V37, P2048
   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 X, 2019, PATTERN RECOGN, V94, P96, DOI 10.1016/j.patcog.2019.05.030
   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 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 ZW, 2022, IEEE T KNOWL DATA EN, V34, P249, DOI 10.1109/TKDE.2020.2981333
NR 45
TC 1
Z9 1
U1 9
U2 9
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 AUG
PY 2022
VL 128
AR 108628
DI 10.1016/j.patcog.2022.108628
PG 12
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 0V6CZ
UT WOS:000788430900004
DA 2022-08-23
ER

PT J
AU Yang, LH
   Shi, Y
AF Yang, Lihong
   Shi, Yu
TI The Cultivation of Political Identity Literacy in Ideological and
   Political Classroom Based on IOT and Knowledge Map
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB The learning depth and training temperature of citizen research course are realized through preclass preview and exploration of life situation and strengthen emotional noncognitive learning by guiding students through preschool preview, life situation exploration, hot topic dialogue and questions, experience transfer and application, and creating practical activities. In order to achieve the purpose of critical understanding and real problem-solving, I integrated and refined the learning content, from emotion to internal political identity and realized the organic combination of in-depth learning and classroom training. Combined with the development of Multimedia Internet of Things in the classroom, a new software is developed to solve the problem of multimedia network management that connects to other central media control devices and auxiliary devices through the Internet of Things. This paper introduces the design idea and overall structure of the system and discusses the changes brought by the network structure to the multimedia network. The network architecture of multimedia classroom based on SDN and Internet of Things helps to solve the practical problems in the use of existing multimedia classroom, improve the intelligence of multimedia classroom, and better serve teachers and students.
C1 [Yang, Lihong] Hebei Chem & Pharmaceut Coll, Dept Marxism, Shijiazhuang 050026, Hebei, Peoples R China.
   [Shi, Yu] Hebei Acad Fine Arts, Shijiazhuang 050700, Hebei, Peoples R China.
RP Shi, Y (通讯作者)，Hebei Acad Fine Arts, Shijiazhuang 050700, Hebei, Peoples R China.
EM sy@hbafa.edu.cn
CR Altass P., 2017, J CANADIAN ASS CURRI, V15, P48
   Calzada I, 2018, REG STUD REG SCI, V5, P267, DOI 10.1080/21681376.2018.1507754
   Castaneda L, 2018, INT J EDUC TECHNOL H, V15, DOI 10.1186/s41239-018-0109-y
   de Roock RS, 2019, COGNITION INSTRUCT, V37, P285, DOI 10.1080/07370008.2019.1624545
   Feilei Yan, 2021, 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), P786, DOI 10.1109/ICECA52323.2021.9676054
   Garcia-Orosa B, 2021, SOC SCI-BASEL, V10, DOI 10.3390/socsci10100357
   Graham M, 2017, TRANSFER-LONDON, V23, P135, DOI 10.1177/1024258916687250
   Habib B, 2018, INT STUD PERSPECT, V19, P218, DOI 10.1093/isp/ekx015
   Jethani S, 2014, COMMUN POLITICS CULT, V47, P34
   Kassab Mohamad, 2020, Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment. Second International Workshop, DEVOPS 2019. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 12055), P152, DOI 10.1007/978-3-030-39306-9_11
   Maillard D., 2021, INT J APPL INFORM MA, V1, P99, DOI [10.47738/ijaim.v1i3.13, DOI 10.47738/IJAIM.V1I3.13]
   Markham AN, 2019, QUAL INQ, V25, P754, DOI 10.1177/1077800418809470
   Moinuddin S., 2021, MAPPING DIGITAL POLI, DOI [10.1007/978-3-030-67888-3_1, DOI 10.1007/978-3-030-67888-3_1]
   Nichols TP, 2019, MEDIA COMMUN-LISBON, V7, P14, DOI 10.17645/mac.v7i2.1946
   Parvulescu C, 2021, CAN J FILM STUD, V30, P49, DOI 10.3138/cjfs-2020-0034
   Turow J, 2018, J COMMUN, V68, P415, DOI 10.1093/joc/jqx011
   Valtonen T., 2019, J MEDIA LITERACY ED, V11, P20
   Verma A., 2021, SN COMPUTER SCI, V2, P1, DOI [10.1007/s42979-021-00635-3, DOI 10.1007/S42979-021-00635-3]
   Yao F., 2021, ASP T PSYCHOL ED, V1, P31, DOI [10.52810/TPE.2021.100085, DOI 10.52810/TPE.2021.100085]
   Zhang K, 2021, CONT ED RES J, V11, P210, DOI [10.18844/cerj.v11i4.5974, DOI 10.18844/CERJ.V11I4.5974]
   [张争万 Zhang Zhengwan], 2020, [计算机应用, Journal of Computer Applications], V40, P3571
NR 21
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 APR 5
PY 2022
VL 2022
AR 9904314
DI 10.1155/2022/9904314
PG 6
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1X2MU
UT WOS:000807294700003
OA gold
DA 2022-08-23
ER

PT J
AU Zhou, ZY
   Zheng, QY
   Ding, C
   Yan, JY
   Piao, ZY
AF Zhou Zhen-yu
   Zheng Qiu-yan
   Ding Cong
   Yan Ju-yu
   Piao Zhong-yu
TI A review of the development of surface burnishing process technique
   based on bibliometric analysis and visualization
SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
LA English
DT Review
DE Surface burnishing process; Scientific knowledge map; Bibliometric;
   Visualization; Web of Science
ID TI-6AL-4V ALLOY; FATIGUE PERFORMANCE; STAINLESS-STEEL;
   MECHANICAL-PROPERTIES; RESIDUAL-STRESS; WEAR-RESISTANCE; ALUMINUM-ALLOY;
   ANALYTICAL PREDICTION; PHASE-TRANSFORMATION; CORROSION-RESISTANCE
AB The surface burnishing process (SBP), by which the surface layer with high hardness, residual compressive pressure, and nanocrystalline structure are obtained, is a practical approach to enhance the metal/alloy surface. The present paper collects a data set in the SBP field from 1990 to 2020 through the Science Citation Index Expanded (Web of Science Core Collection) database to identify and describe the research history, hotspots, and frontiers based on bibliometric analysis and visualization. Firstly, the temporal and spatial distribution of publications are summarized, and the corresponding countries, institutions, journals, and influential authors are also described. Secondly, the keystone publications are identified and analyzed based on a citation network. Thirdly, cluster analysis representing a hot research region field is carried out for all keywords. Finally, after analyzing the evolution route of the SBP field, the future development direction and trend are discussed. This paper is hopefully to help scholars understanding the research patterns of SBP.
C1 [Zhou Zhen-yu; Zheng Qiu-yan; Ding Cong; Yan Ju-yu; Piao Zhong-yu] Zhejiang Univ Technol, Coll Mech Engn, Hangzhou 310012, Zhejiang, Peoples R China.
   [Zhou Zhen-yu; Zheng Qiu-yan; Ding Cong; Yan Ju-yu; Piao Zhong-yu] Zhejiang Univ Technol, Key Lab Special Purpose Equipment & Adv Proc Tech, Minist Educ & Zhejiang Prov, Hangzhou 310012, Peoples R China.
RP Piao, ZY (通讯作者)，Zhejiang Univ Technol, Coll Mech Engn, Hangzhou 310012, Zhejiang, Peoples R China.; Piao, ZY (通讯作者)，Zhejiang Univ Technol, Key Lab Special Purpose Equipment & Adv Proc Tech, Minist Educ & Zhejiang Prov, Hangzhou 310012, Peoples R China.
EM piaozy@zjut.edu.cn
FU National Natural Science Foundation of China (NSFC) [51675483, 52075047,
   51705028]; Fundamental Research Funds for the Provincial Universities of
   Zhejiang [RF-A2019008]
FX This article was financially supported by National Natural Science
   Foundation of China (NSFC) (51675483, 52075047, 51705028), and
   Fundamental Research Funds for the Provincial Universities of Zhejiang
   (Grant No. RF-A2019008)
CR Altenberger I, 1999, MAT SCI ENG A-STRUCT, V264, P1, DOI 10.1016/S0921-5093(98)01121-6
   Altenberger I, 2012, INT J FATIGUE, V44, P292, DOI 10.1016/j.ijfatigue.2012.03.008
   Amini C, 2020, METALS-BASEL, V10, DOI 10.3390/met10050638
   Ao N, 2019, APPL SURF SCI, V489, P595, DOI 10.1016/j.apsusc.2019.06.006
   Ao N, 2019, SURF COAT TECH, V361, P35, DOI 10.1016/j.surfcoat.2019.01.045
   Ao N, 2019, MAT SCI ENG A-STRUCT, V742, P820, DOI 10.1016/j.msea.2018.10.098
   Ao N, 2018, MATER CHARACT, V145, P527, DOI 10.1016/j.matchar.2018.09.004
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Aviles R, 2013, INT J FATIGUE, V55, P230, DOI 10.1016/j.ijfatigue.2013.06.024
   Baecker V, 2010, IOP C SERIES MAT SCI, V10
   Bertini L, 2015, INT J FATIGUE, V81, P179, DOI 10.1016/j.ijfatigue.2015.08.007
   Blanco-Mesa F, 2017, J INTELL FUZZY SYST, V32, P2033, DOI 10.3233/JIFS-161640
   Bouzid W, 2004, INT J ADV MANUF TECH, V24, P120, DOI 10.1007/s00170-003-1761-4
   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 Chaomei, 2009, Journal of the China Society for Scientific and Technical Information, V28, P401, DOI 10.3772/j.issn.1000-0135.2009.03.012
   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
   Cheng ML, 2016, INT J ADV MANUF TECH, V83, P123, DOI 10.1007/s00170-015-7485-4
   Cheng ML, 2014, J MATER PROCESS TECH, V214, P2395, DOI 10.1016/j.jmatprotec.2014.05.019
   Chomienne V, 2016, CIRP J MANUF SCI TEC, V13, P90, DOI 10.1016/j.cirpj.2015.12.003
   Ding C, 2021, J TRIBOL-T ASME, V143, DOI 10.1115/1.4049267
   El-Taweel TA, 2009, INT J ADV MANUF TECH, V41, P301, DOI 10.1007/s00170-008-1485-6
   Fang TH, 2011, SCIENCE, V331, P1587, DOI 10.1126/science.1200177
   Fu CH, 2013, PROCEEDINGS OF THE ASME 8TH INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE - 2013, VOL 1
   Fu CH, 2014, J MATER PROCESS TECH, V214, P3122, DOI 10.1016/j.jmatprotec.2014.07.019
   Garfield E, 2004, J INF SCI, V30, P119, DOI 10.1177/0165551504042802
   Garfield E, 2003, INFORM TECHNOL LIBR, V22, P183
   Garfield E, 2009, J INFORMETR, V3, P173, DOI 10.1016/j.joi.2009.03.009
   Hadadian A, 2020, INT J ADV MANUF TECH, V108, P2103, DOI 10.1007/s00170-020-05481-w
   Hamadache H, 2006, J MATER PROCESS TECH, V180, P130, DOI 10.1016/j.jmatprotec.2006.05.013
   Hassan AM, 1997, INT J MACH TOOL MANU, V37, P813, DOI 10.1016/S0890-6955(96)00058-2
   Hassan AM, 1999, J MATER PROCESS TECH, V96, P73
   Hassan AM, 1996, J MATER PROCESS TECH, V59, P250, DOI 10.1016/0924-0136(95)02149-3
   Hassan AM, 1996, WEAR, V199, P1, DOI 10.1016/0043-1648(95)06847-3
   Hassan AM, 2000, J MATER PROCESS TECH, V102, P115, DOI 10.1016/S0924-0136(00)00464-7
   Hassan AM, 1997, J MATER PROCESS TECH, V72, P385, DOI 10.1016/S0924-0136(97)00199-4
   Hassan AM, 1998, J MATER PROCESS TECH, V83, P176, DOI 10.1016/S0924-0136(98)00058-2
   He D, 2018, INT J ADV MANUF TECH, V95, P1, DOI 10.1007/s00170-017-0640-3
   Heradio R, 2016, COMPUT EDUC, V98, P14, DOI 10.1016/j.compedu.2016.03.010
   Hiegemann L, 2015, J MATER PROCESS TECH, V217, P193, DOI 10.1016/j.jmatprotec.2014.11.008
   Hiegemann L, 2016, J MATER PROCESS TECH, V232, P63, DOI 10.1016/j.jmatprotec.2016.01.024
   Hiegemann L, 2014, PROCEDIA ENGINEER, V81, P1921, DOI 10.1016/j.proeng.2014.10.257
   Hua Y, 2019, SURF COAT TECH, V375, P508, DOI 10.1016/j.surfcoat.2019.07.057
   Huang HW, 2015, ACTA MATER, V87, P150, DOI 10.1016/j.actamat.2014.12.057
   John MRS, 2017, J BRAZ SOC MECH SCI, V39, P3089, DOI 10.1007/s40430-016-0674-8
   Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314
   Klocke F, 1998, INT J MACH TOOL MANU, V38, P419, DOI 10.1016/S0890-6955(97)00085-0
   Korzynski M, 2007, INT J MACH TOOL MANU, V47, P1956, DOI 10.1016/j.ijmachtools.2007.03.002
   Krishna KG, 2012, CORROS SCI, V60, P82, DOI 10.1016/j.corsci.2012.04.009
   Kumar S, 2019, GREY SYST, V9, P449, DOI 10.1108/GS-03-2019-0006
   Kumar S, 2020, BENCHMARKING, V27, P760, DOI 10.1108/BIJ-07-2019-0314
   Kuznetsov VP, 2015, J MATER PROCESS TECH, V217, P327, DOI 10.1016/j.jmatprotec.2014.11.023
   Laengle S, 2017, EUR J OPER RES, V262, P803, DOI 10.1016/j.ejor.2017.04.027
   Li FL, 2012, INT J MACH TOOL MANU, V62, P67, DOI 10.1016/j.ijmachtools.2012.06.001
   Li J, 2014, ACTA BIOMATER, V10, P2866, DOI 10.1016/j.actbio.2014.02.032
   Li J, 2018, J MATER SCI TECHNOL, V34, P1660, DOI 10.1016/j.jmst.2018.01.008
   Li L, 2018, INT J ADV MANUF TECH, V96, P1255, DOI 10.1007/s00170-018-1666-x
   Li WL, 2008, SCRIPTA MATER, V59, P546, DOI 10.1016/j.scriptamat.2008.05.003
   Liu CS, 2019, INT J FATIGUE, V125, P249, DOI 10.1016/j.ijfatigue.2019.03.042
   Liu CS, 2019, J MATER SCI TECHNOL, V35, P1555, DOI 10.1016/j.jmst.2019.03.036
   Liu D, 2018, MAT SCI ENG A-STRUCT, V726, P69, DOI 10.1016/j.msea.2018.04.033
   Liu X, 2016, POWDER METALL, V59, P329, DOI 10.1080/00325899.2016.1242880
   Liu XC, 2013, SCIENCE, V342, P337, DOI 10.1126/science.1242578
   Liu X, 2020, SURF TOPOGR-METROL, V8, DOI 10.1088/2051-672X/ab58b8
   Liu Y, 2011, J MATER PROCESS TECH, V211, P2106, DOI 10.1016/j.jmatprotec.2011.07.009
   Luca L, 2005, PRECIS ENG, V29, P253, DOI 10.1016/j.precisioneng.2004.02.002
   Luo HY, 2005, INT J ADV MANUF TECH, V25, P454, DOI 10.1007/s00170-003-1959-5
   Lv JL, 2013, APPL SURF SCI, V273, P192, DOI 10.1016/j.apsusc.2013.02.012
   Ma C, 2017, J MATER PROCESS TECH, V249, P433, DOI 10.1016/j.jmatprotec.2017.06.038
   Ma Y, 2020, ENG FRACT MECH, V226, DOI 10.1016/j.engfracmech.2020.106877
   Majzoobi GH, 2009, MAT SCI ENG A-STRUCT, V516, P235, DOI 10.1016/j.msea.2009.03.020
   Maximov JT, 2020, J BRAZ SOC MECH SCI, V42, DOI 10.1007/s40430-020-02276-8
   Maximov JT, 2019, INT J ADV MANUF TECH, V104, P785, DOI 10.1007/s00170-019-03881-1
   Maximov JT, 2019, FATIGUE FRACT ENG M, V42, P363, DOI 10.1111/ffe.12915
   Munoz-Cubillos J, 2019, WEAR, V428, P24, DOI 10.1016/j.wear.2019.03.001
   Nalla RK, 2003, MAT SCI ENG A-STRUCT, V355, P216, DOI 10.1016/S0921-5093(03)00069-8
   Nemat M, 2000, INT J ADV MANUF TECH, V16, P469, DOI 10.1007/s001700070054
   Nestler A, 2018, METALS-BASEL, V8, DOI 10.3390/met8020095
   Pang C, 2018, PROG NAT SCI-MATER, V28, P54, DOI 10.1016/j.pnsc.2017.11.006
   Perianes-Rodriguez A, 2016, J INFORMETR, V10, P1178, DOI 10.1016/j.joi.2016.10.006
   Piao ZY, 2020, CRIT REV SOLID STATE, V45, P429, DOI 10.1080/10408436.2019.1671798
   Piao ZY, 2019, TRIBOL LETT, V67, DOI 10.1007/s11249-018-1126-7
   Prevey PS, 2004, INT J FATIGUE, V26, P975, DOI 10.1016/j.ijfatigue.2004.01.010
   Primee SY, 2020, CHIANG MAI J SCI, V47, P304
   Qu Sheng-guan, 2016, Journal of South China University of Technology (Natural Science Edition), V44, P1, DOI 10.3969/j.issn.1000-565X.2016.03.001
   Ramesh S, 2021, SILICON-NETH, V13, P1549, DOI 10.1007/s12633-020-00541-y
   Randhawa K, 2016, J PROD INNOVAT MANAG, V33, P750, DOI 10.1111/jpim.12312
   Randjelovic S, 2015, INT J ADV MANUF TECH, V81, P1509, DOI 10.1007/s00170-015-7319-4
   Ren ZJ, 2020, SURF COAT TECH, V389, DOI 10.1016/j.surfcoat.2020.125612
   Revankar GD, 2014, MEASUREMENT, V58, P256, DOI 10.1016/j.measurement.2014.08.043
   Revankar GD, 2017, J MATER RES TECHNOL, V6, P13, DOI 10.1016/j.jmrt.2016.03.007
   Rodriguez A, 2012, SURF COAT TECH, V206, P2817, DOI 10.1016/j.surfcoat.2011.11.045
   Rotella G, 2020, INT J ADV MANUF TECH, V106, P431, DOI 10.1007/s00170-019-04631-z
   Sagbas A, 2011, ADV ENG SOFTW, V42, P992, DOI 10.1016/j.advengsoft.2011.05.021
   Sayahi M, 2013, INT J ADV MANUF TECH, V67, P1665, DOI 10.1007/s00170-012-4599-9
   Shen XH, 2019, INT J ADV MANUF TECH, V105, P1189, DOI 10.1007/s00170-019-04128-9
   Shiou FJ, 2003, J MATER PROCESS TECH, V140, P248, DOI 10.1016/S0924-0136(03)00750-7
   Sun YG, 2019, SURF COAT TECH, V368, P215, DOI 10.1016/j.surfcoat.2019.03.045
   Tajane RS, 2020, MATER MANUF PROCESS, V35, P817, DOI 10.1080/10426914.2020.1743848
   Tang J, 2017, INT J ADV MANUF TECH, V88, P2785, DOI 10.1007/s00170-016-9000-y
   Tang J, 2018, SURF COAT TECH, V345, P123, DOI 10.1016/j.surfcoat.2018.03.102
   Teimouri R, 2019, P I MECH ENG E-J PRO, V233, P630, DOI 10.1177/0954408918781481
   Teimouri R, 2019, MAT SCI ENG A-STRUCT, V747, P208, DOI 10.1016/j.msea.2019.01.007
   Teimouri R, 2019, INT J MECH SCI, V151, P118, DOI 10.1016/j.ijmecsci.2018.11.008
   Teimouri R, 2019, MEASUREMENT, V131, P654, DOI 10.1016/j.measurement.2018.09.023
   Tian YG, 2007, INT J MACH TOOL MANU, V47, P14, DOI 10.1016/j.ijmachtools.2006.03.002
   Nguyen TT, 2020, J CLEAN PROD, V245, DOI 10.1016/j.jclepro.2019.119328
   Nguyen TT, 2018, MATER MANUF PROCESS, V33, P1233, DOI 10.1080/10426914.2018.1453159
   Uddin MS, 2017, INT J ADV MANUF TECH, V90, P2095, DOI 10.1007/s00170-016-9536-x
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Wagner L, 1999, MAT SCI ENG A-STRUCT, V263, P210, DOI 10.1016/S0921-5093(98)01168-X
   Wang C, 2019, COATINGS, V9, DOI 10.3390/coatings9040276
   Wang HB, 2016, J ALLOY COMPD, V681, P146, DOI 10.1016/j.jallcom.2016.04.067
   Wang WR, 2018, INT J UNCERTAIN FUZZ, V26, P169, DOI 10.1142/S0218488518500095
   Wang Z, 2020, SURF COAT TECH, V381, DOI 10.1016/j.surfcoat.2019.125122
   Xu XC, 2020, J MATER SCI TECHNOL, V40, P88, DOI 10.1016/j.jmst.2019.08.030
   Xu XC, 2020, MATERIALS, V13, DOI 10.3390/ma13030738
   Xu XC, 2019, INT J FATIGUE, V125, P237, DOI 10.1016/j.ijfatigue.2019.04.005
   Yang J, 2020, INT J FATIGUE, V133, DOI 10.1016/j.ijfatigue.2019.105373
   Yang ZC, 2020, INT J MACH TOOL MANU, V156, DOI 10.1016/j.ijmachtools.2020.103594
   Ye H, 2019, SURF COAT TECH, V372, P288, DOI 10.1016/j.surfcoat.2019.05.035
   Ye YD, 2018, MATER DESIGN, V149, P214, DOI 10.1016/j.matdes.2018.04.027
   Yen YC, 2005, CIRP ANN-MANUF TECHN, V54, P237, DOI 10.1016/S0007-8506(07)60092-4
   Yu XB, 1999, INT J MACH TOOL MANU, V39, P459, DOI 10.1016/S0890-6955(98)00033-9
   Zhang PR, 2017, APPL SURF SCI, V409, P169, DOI 10.1016/j.apsusc.2017.03.028
   Zhang QL, 2017, SURF COAT TECH, V321, P64, DOI 10.1016/j.surfcoat.2017.04.052
   Zhao J, 2020, J MATER RES TECHNOL, V9, P2387, DOI 10.1016/j.jmrt.2019.12.071
   Zhao J, 2020, METALL MATER TRANS A, V51, P1320, DOI 10.1007/s11661-019-05594-2
   Zheng JX, 2020, INT J ADV MANUF TECH, V106, P503, DOI 10.1007/s00170-019-04640-y
   Zhou ZY, 2020, J MANUF PROCESS, V51, P1, DOI 10.1016/j.jmapro.2020.01.026
   Zhou ZY, 2021, CHIN J MECH ENG-EN, V34, DOI 10.1186/s10033-021-00540-z
   Zhuang WZ, 2001, INT J FATIGUE, V23, pS31
   Zyoud SH, 2017, EXPERT SYST APPL, V78, P158, DOI 10.1016/j.eswa.2017.02.016
NR 134
TC 1
Z9 1
U1 10
U2 20
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 JUL
PY 2021
VL 115
IS 5-6
BP 1955
EP 1999
DI 10.1007/s00170-021-06967-x
EA MAY 2021
PG 45
WC Automation & Control Systems; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering
GA TD7SQ
UT WOS:000651805100001
DA 2022-08-23
ER

PT J
AU Chhetri, TR
   Kurteva, A
   Adigun, JG
   Fensel, A
AF Chhetri, Tek Raj
   Kurteva, Anelia
   Adigun, Jubril Gbolahan
   Fensel, Anna
TI Knowledge Graph Based Hard Drive Failure Prediction
SO SENSORS
LA English
DT Article
DE hard drive; failure prediction; knowledge graphs; machine learning;
   predictive maintenance; reliability
ID BIG DATA ANALYTICS; CLASSIFICATION; MAINTENANCE; TREES
AB The hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have been conducted and many are still ongoing to improve hard drive failure prediction. Most of those studies rely solely on machine learning, and a few others on semantic technology. The studies based on machine learning, despite promising results, lack context-awareness such as how failures are related or what other factors, such as humidity, influence the failure of hard drives. Semantic technology, on the other hand, by means of ontologies and knowledge graphs (KGs), is able to provide the context-awareness that machine learning-based studies lack. However, the studies based on semantic technology lack the advantages of machine learning, such as the ability to learn a pattern and make predictions based on learned patterns. Therefore, in this paper, leveraging the benefits of both machine learning (ML) and semantic technology, we present our study, knowledge graph-based hard drive failure prediction. The experimental results demonstrate that our proposed method achieves higher accuracy in comparison to the current state of the art.
C1 [Chhetri, Tek Raj; Kurteva, Anelia; Fensel, Anna] Univ Innsbruck, Semant Technol Inst STI, Dept Comp Sci, A-6020 Innsbruck, Austria.
   [Adigun, Jubril Gbolahan] Univ Innsbruck, Qual Engn QE Lab, Dept Comp Sci, A-6020 Innsbruck, Austria.
   [Fensel, Anna] Wageningen Univ & Res, Wageningen Data Competence Ctr, NL-6708 PB Wageningen, Netherlands.
   [Fensel, Anna] Wageningen Univ & Res, Consumpt & Hlthy 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;
   jubril.adigun@uibk.ac.at; anna.fensel@wur.nl
RI Chhetri, Tek Raj/AFE-8526-2022; Adigun, Jubril Gbolahan/AFG-4270-2022
OI Chhetri, Tek Raj/0000-0002-3905-7878; Adigun, Jubril
   Gbolahan/0000-0002-5494-3958; Fensel, Anna/0000-0002-1391-7104; Kurteva,
   Anelia/0000-0003-4512-5969
CR Abdelmageed Nora, 2020, The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020 Satellite Events. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 12124), P217, DOI 10.1007/978-3-030-62327-2_37
   Akosa J., 2017, SAS GLOBAL FORUM, V12, P2
   Ali MI, 2019, IEEE INT CONF DISTR, P370, DOI 10.1109/DCOSS.2019.00079
   Lam AN, 2019, IEEE INTL CONF IND I, P206, DOI 10.1109/INDIN41052.2019.8972295
   Bellatreche L., 2021, IEEE T KNOWL DATA EN, DOI [10.1109/TKDE.2021.3059506, DOI 10.1109/TKDE.2021.3059506]
   Berrar D., 2018, ENCY BIOINFORMATICS, V1, P542, DOI DOI 10.1016/B978-0-12-809633-8.20349-X
   Bonnaud S., 2019, IND 4 0 COGNITIVE MA
   Cao H, 2013, IEEE T KNOWL DATA EN, V25, P2809, DOI 10.1109/TKDE.2013.37
   Cao QS, 2020, SEMANT WEB, V11, P927, DOI 10.3233/SW-200406
   Cao QS, 2019, PROCEDIA COMPUT SCI, V159, P630, DOI 10.1016/j.procs.2019.09.218
   Carvalho TP, 2019, COMPUT IND ENG, V137, DOI 10.1016/j.cie.2019.106024
   Chawla NV, 2002, J ARTIF INTELL RES, V16, P321, DOI 10.1613/jair.953
   Chhetri TR, 2021, LECT NOTES COMPUT SC, V12739, P165, DOI 10.1007/978-3-030-80418-3_29
   Dabiri S, 2018, TRANSPORT RES REC, V2672, P136, DOI 10.1177/0361198118772689
   Dalzochio J, 2020, COMPUT IND, V123, DOI 10.1016/j.compind.2020.103298
   De Santo A, 2022, IEEE T COMPUT, V71, P69, DOI 10.1109/TC.2020.3042053
   El-Sappagh S, 2018, IEEE ACCESS, V6, P37371, DOI 10.1109/ACCESS.2018.2852004
   Fensel D., 2020, P QURATOR
   Franklin P.H., 2017, P 2017 ANN REL MAINT, P1
   Freire N, 2019, IEEE INT CONF BIG DA, P3072, DOI 10.1109/BigData47090.2019.9005491
   Gal Y, 2016, ADV NEUR IN, V29
   Ganguly S, 2016, PROCEEDINGS 2016 IEEE SECOND INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING SERVICE AND APPLICATIONS (BIGDATASERVICE 2016), P105, DOI 10.1109/BigDataService.2016.10
   Gao C, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P385, DOI 10.1145/3340531.3411889
   Gilmer J, 2017, PR MACH LEARN RES, V70
   Goodfellow I. J., 2016, ADVERSARIAL MACHINE
   Gruber T, 2009, ENCY DATABASE SYSTEM, P1963, DOI [DOI 10.1007/978-0-387-39940-9_1318, 10.1007/978-0-387-39940-9_1318]
   Han SJ, 2020, INT CON DISTR COMP S, P628, DOI 10.1109/ICDCS47774.2020.00044
   Hogan A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447772
   Iglesias E, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P3039, DOI 10.1145/3340531.3412881
   Johansson A, MACHINE LEARNING IS
   Juba B, 2019, AAAI CONF ARTIF INTE, P4039
   Jung H, 2016, CLUSTER COMPUT, V19, P2261, DOI 10.1007/s10586-016-0672-8
   Kainzner M., 2021, CEUR WORKSHOP P SERI, V2941
   Kroese DP, 2014, WIRES COMPUT STAT, V6, P386, DOI 10.1002/wics.1314
   Krupitzer C., 2020, ARXIV200208224
   Kumari A, 2019, IET NETW, V8, P155, DOI 10.1049/iet-net.2018.5187
   Lakehal A., 2019, INT J ADV COMPUTER R, V9, P212, DOI DOI 10.19101/IJACR.PID33
   LeDell E., 2020, P AUTOML WORKSH ICML
   Li J, 2017, RELIAB ENG SYST SAFE, V164, P55, DOI 10.1016/j.ress.2017.03.004
   Li J, 2014, I C DEPEND SYS NETWO, P383, DOI 10.1109/DSN.2014.44
   Liu DS, 2020, LECT NOTES COMPUT SC, V11944, P373, DOI 10.1007/978-3-030-38991-8_25
   Liu WJ, 2020, AAAI CONF ARTIF INTE, V34, P2901
   Mamoutova Olga V., 2019, 2019 IEEE 17th International Symposium on Intelligent Systems and Informatics (SISY). Proceedings, P207, DOI 10.1109/SISY47553.2019.9111617
   Mamoutova O, 2021, ACTA POLYTECH HUNG, V18, P27
   Manousakis I, 2016, 14TH USENIX CONFERENCE ON FILE AND STORAGE TECHNOLOGIES (FAST '16), P53
   Mashhadi AR, 2018, PROCEDIA MANUF, V26, P1107, DOI 10.1016/j.promfg.2018.07.147
   Murphy K.P, 2022, PROBABILISTIC MACHIN
   Nahavandi S, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11164371
   Nwankpa CE, 2021, 2 INT C COMP SCI TEC, P124
   Piccarreta R, 2008, COMPUTATION STAT, V23, P407, DOI 10.1007/s00180-007-0077-5
   Quinlan JR, 1996, ACM COMPUT SURV, V28, P71, DOI 10.1145/234313.234346
   Rajashekarappa, 2011, COMM COM INF SC, V157, P463
   Ravichandiran S., 2019, HANDS ON DEEP LEARNI, V1st
   Richardson M, 2006, MACH LEARN, V62, P107, DOI 10.1007/s10994-006-5833-1
   Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
   Schoenfisch J, 2018, INFORM SYST, V74, P103, DOI 10.1016/j.is.2017.11.003
   Selcuk S, 2017, P I MECH ENG B-J ENG, V231, P1670, DOI 10.1177/0954405415601640
   Shen J, 2018, INT J DISTRIB SENS N, V14, DOI 10.1177/1550147718806480
   Speybroeck N, 2012, INT J PUBLIC HEALTH, V57, P243, DOI 10.1007/s00038-011-0315-z
   Su CJ, 2018, COMPUT ELECTR ENG, V71, P93, DOI 10.1016/j.compeleceng.2018.07.025
   Su SF, 2017, IEEE ACCESS, V5, P12257, DOI 10.1109/ACCESS.2017.2704758
   Sutton O., 2012, INTRO K NEAREST NEIG, V1
   Wang M., 2019, ICLR WORKSH REPR LEA
   Wu F, 2019, PR MACH LEARN RES, V97
   Xu C, 2016, IEEE T COMPUT, V65, P3502, DOI 10.1109/TC.2016.2538237
   Yang WJ, 2015, SYM REL DIST SYST, P13, DOI 10.1109/SRDSW.2015.15
   Zhang JG, 2018, IEEE DATA MINING, P1428, DOI 10.1109/ICDM.2018.00197
   Zhdanova A.V., 2006, P 15 IST MOB WIR COM, V53
   Zonta T, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106889
   Zufle Marwin, 2020, Measurement, Modelling and Evaluation of Computing Systems. 20th International GI/ITG Conference, MMB 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12040), P19, DOI 10.1007/978-3-030-43024-5_2
NR 70
TC 1
Z9 1
U1 9
U2 9
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD FEB
PY 2022
VL 22
IS 3
AR 985
DI 10.3390/s22030985
PG 21
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA ZA1ZC
UT WOS:000755964400001
PM 35161730
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Kim, H
AF Kim, Haklae
TI Towards a sales assistant using a product knowledge graph
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Product knowledge; Knowledge graph; Knowledge base; Ontology;
   Intelligent assistant
ID ONTOLOGY; WEB
AB The phenomenal growth in the consumer electronics industry over the last decade is mainly due to rapid advances in integration technologies. The market is ever evolving with new products made available on a frequent basis. However, typically, the average consumer tends to have insufficient knowledge of the features and functions of an electronic device. Although most manufacturers provide product specifications, they are often difficult for consumers to understand and it is hard for customers to grasp the details of a product. This study introduces the concept of a product knowledge graph, which is constructed by transforming a set of data from a product management system and external data. This study overcomes the technical barriers for customers with the use of knowledge graph technologies. To construct large-scale product knowledge, an ontology model is designed and a platform for knowledge extraction and transformation is also developed. Then, a sales assistant called God of Sales is demonstrated. This study provides a novel approach to explaining product features and functions in a comprehensive manner with semantic recommendations. (c) 2017 Elsevier B.V. All rights reserved.
C1 [Kim, Haklae] Korea Inst Sci & Technol Informat, 245 Daehak Ro, Daejeon 34141, South Korea.
RP Kim, H (通讯作者)，Korea Inst Sci & Technol Informat, 245 Daehak Ro, Daejeon 34141, South Korea.
EM haklaekim@gmail.com
CR [Anonymous], 2015, DISRUPTION AHEAD DEL
   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
   Bouzidi S., 2013, P 8 INT C KNOWL MAN, P439
   Carlson A, 2010, AAAI CONF ARTIF INTE, P1306
   Cisco Systems Inc, 2013, SELF SERV SHOPP GROW
   Heath T., 2011, SYNTHESIS LECT SEMAN
   Hepp M, 2008, LECT NOTES ARTIF INT, V5268, P329, DOI 10.1007/978-3-540-87696-0_29
   Hysing M, 2015, BMJ OPEN, V5, DOI 10.1136/bmjopen-2014-006748
   Jahangir A. K. M., 2013, RESNA ANN C 2013 RES, P1
   Kasneci G, 2008, PROC INT CONF DATA, P953, DOI 10.1109/ICDE.2008.4497504
   Kim H. L., 2015, LECT NOTES COMPUTER, V9544, P301
   Kim Haklae, 2016, EAI ENDORSED T IND N, V16, P151
   Lee Hong Jae, 2015, P 12 EAI INT C MOB U, P289
   Mark Bergen, 2014, SAMSUNG PUTS FRIDGES
   Nederstigt LJ, 2014, DECIS SUPPORT SYST, V59, P296, DOI 10.1016/j.dss.2014.01.001
   Nickel M, 2016, P IEEE, V104, P11, DOI 10.1109/JPROC.2015.2483592
   Soo David, 2014, IND OUTLOOK CONSUMER, P1
   Stankiewicz Zofia, 2012, HLT NAACL, P13
   SUCHANEK Fabian M., 2007, 16 INT WORLD WID WEB, V16, P697, DOI DOI 10.1145/1242572.1242667
   Vermaas R, 2014, LECT NOTES COMPUT SC, V8786, P534
   Vrandecic D., 2012, P 21 INT C WORLD WID, P1063, DOI [DOI 10.1145/2187980.2188242, 10.1145/2187980.2188242]
NR 22
TC 7
Z9 9
U1 3
U2 20
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 OCT
PY 2017
VL 46-47
BP 14
EP 19
DI 10.1016/j.websem.2017.03.001
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FQ6MF
UT WOS:000418477900003
DA 2022-08-23
ER

PT J
AU Lever, J
   Gakkhar, S
   Gottlieb, M
   Rashnavadi, T
   Lin, S
   Siu, C
   Smith, M
   Jones, MR
   Krzywinski, M
   Jones, SJM
AF Lever, Jake
   Gakkhar, Sitanshu
   Gottlieb, Michael
   Rashnavadi, Tahereh
   Lin, Santina
   Siu, Celia
   Smith, Maia
   Jones, Martin R.
   Krzywinski, Martin
   Jones, Steven J. M.
TI A collaborative filtering-based approach to biomedical knowledge
   discovery
SO BIOINFORMATICS
LA English
DT Article
ID FISH-OIL
AB Motivation: The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers.
   Results: We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations.
C1 [Lever, Jake; Gakkhar, Sitanshu; Gottlieb, Michael; Rashnavadi, Tahereh; Lin, Santina; Siu, Celia; Smith, Maia; Jones, Martin R.; Krzywinski, Martin; Jones, Steven J. M.] Canadas Michael Smith Genome Sci Ctr, Vancouver, BC V5Z 4S6, Canada.
   [Lever, Jake; Jones, Steven J. M.] Univ British Columbia, Vancouver, BC V6T 1Z1, Canada.
   [Jones, Steven J. M.] Simon Fraser Univ, Burnaby, BC V5A 1S6, Canada.
RP Jones, SJM (通讯作者)，Canadas Michael Smith Genome Sci Ctr, Vancouver, BC V5Z 4S6, Canada.; Jones, SJM (通讯作者)，Univ British Columbia, Vancouver, BC V6T 1Z1, Canada.; Jones, SJM (通讯作者)，Simon Fraser Univ, Burnaby, BC V5A 1S6, Canada.
EM sjones@bcgsc.ca
RI Jones, Steven J/C-3621-2009
OI Jones, Steven J/0000-0003-3394-2208; Lever, Jake/0000-0001-8198-2939
FU Vanier Canada Graduate Scholarship
FX This work was supported by a Vanier Canada Graduate Scholarship.
CR Ananiadou S, 2006, TRENDS BIOTECHNOL, V24, P571, DOI 10.1016/j.tibtech.2006.10.002
   Bennett J., 2007, P KDD CUP WORKSH NEW, P35, DOI 10.1145/1345448.1345459
   Bruskiewich R., 2016, BIORXIV
   Chu J, 2013, TRANSL PSYCHIAT, V3, DOI 10.1038/tp.2013.106
   Davies M, 2009, INT J CORPUS LINGUIS, V14, P159, DOI 10.1075/ijcl.14.2.02dav
   DIGIACOMO RA, 1989, AM J MED, V86, P158, DOI 10.1016/0002-9343(89)90261-1
   Eckart C, 1936, PSYCHOMETRIKA, V1, P211, DOI 10.1007/BF02288367
   Frijters R, 2008, NUCLEIC ACIDS RES, V36, pW406, DOI 10.1093/nar/gkn215
   Funk C, 2014, BMC BIOINFORMATICS, V15, DOI 10.1186/1471-2105-15-59
   Gordon MD, 1998, J AM SOC INFORM SCI, V49, P674
   Hersh W, 2008, INFORM SCI KNOWL MAN, V15, P153
   Hettne KM, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0149621
   Hristovski Dimitar, 2013, Cardiovascular & Hematological Agents in Medicinal Chemistry, V11, P14
   Jelier R, 2008, INT J MED INFORM, V77, P354, DOI 10.1016/j.ijmedinf.2007.07.004
   Jelier R, 2008, GENOME BIOL, V9, DOI 10.1186/gb-2008-9-6-r96
   Kang R, 2014, INT ORTHOP, V38, P1011, DOI 10.1007/s00264-014-2319-9
   Kilicoglu H, 2008, P 3 INT S SEM MIN BI, P69
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Lichtenwalter R, 2012, 2012 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), P376, DOI 10.1109/ASONAM.2012.68
   Lin YC, 2015, ADV SOC SCI EDUC HUM, V39, P2181
   Loper E., 2002, P ACL WORKSH EFF TOO
   Low Y, 2014, CORR ABS 1408 2041
   Pan R, 2008, IEEE DATA MINING, P502, DOI 10.1109/ICDM.2008.16
   Press W.H., 2007, NUMERICAL RECIPES AR
   Quinn CT, 2011, BRIT J HAEMATOL, V153, P111, DOI 10.1111/j.1365-2141.2010.08477.x
   SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7
   Swanson DR, 1997, ARTIF INTELL, V91, P183, DOI 10.1016/S0004-3702(97)00008-8
   Tsuruoka Y, 2005, LECT NOTES COMPUT SC, V3746, P382
   Tsuruoka Y, 2011, BIOINFORMATICS, V27, pI111, DOI 10.1093/bioinformatics/btr214
   Van Landeghem S, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0055814
   Yetisgen-Yildiz M, 2009, J BIOMED INFORM, V42, P633, DOI 10.1016/j.jbi.2008.12.001
NR 31
TC 10
Z9 11
U1 1
U2 23
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 2018
VL 34
IS 4
BP 652
EP 659
DI 10.1093/bioinformatics/btx613
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 FV9EF
UT WOS:000424889300014
PM 29028901
OA Bronze
DA 2022-08-23
ER

PT J
AU Farber, M
   Bartscherer, F
   Menne, C
   Rettinger, A
AF Faerber, Michael
   Bartscherer, Frederic
   Menne, Carsten
   Rettinger, Achim
TI Linked Data Quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge Graph; Linked Data Quality; Data Quality Metrics; Comparison;
   DBpedia; Freebase; OpenCyc; Wikidata; YAGO
ID WEB
AB In recent years, several noteworthy large, cross-domain, and openly available knowledge graphs (KGs) have been created. These include DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Although extensively in use, these KGs have not been subject to an in-depth comparison so far. In this survey, we provide data quality criteria according to which KGs can be analyzed and analyze and compare the above mentioned KGs. Furthermore, we propose a framework for finding the most suitable KG for a given setting.
C1 [Faerber, Michael; Bartscherer, Frederic; Menne, Carsten; Rettinger, Achim] Karlsruhe Inst Technol, Inst AIFB, D-76131 Karlsruhe, Germany.
RP Farber, M (通讯作者)，Karlsruhe Inst Technol, Inst AIFB, D-76131 Karlsruhe, Germany.
EM michael.faerber@kit.edu
RI Färber, Michael/AAA-4789-2021
OI Färber, Michael/0000-0001-5458-8645; Bartscherer,
   Frederic/0000-0002-2816-7932
FU German Federal Ministry of Education and Research (BMBF) within the
   Software Campus project SUITE [01IS12051]; European Union Seventh
   Framework Programme (FP7) [611346]
FX This work was carried out with the support of the German Federal
   Ministry of Education and Research (BMBF) within the Software Campus
   project SUITE (Grant 01IS12051).; The research leading to these results
   has received funding from the European Union Seventh Framework Programme
   (FP7/20072013) under grant agreement no. 611346.
CR Acosta M, 2013, LECT NOTES COMPUT SC, V8219, P260, DOI 10.1007/978-3-642-41338-4_17
   Acosta Maribel, 2017, SEMANTIC WE IN PRESS
   Acosta Maribel, 2015, P 8 INT C KNOWL CAP
   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 S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
   Batini Carlo, 2009, ACM Computing Surveys, V41, DOI 10.1145/1541880.1541883
   Berners Lee T., 2006, LINKED DATA
   Berners-Lee T, 2001, SCI AM, V284, P34, DOI 10.1038/scientificamerican0501-34
   Bingham, 1974, QUALITY CONTROL HDB
   Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002
   Bizer Christian, 2007, QUALITY DRIVEN INFOR
   Dean M, 2004, OWL WEB ONTOLOGY LAN
   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
   Duan S., 2011, P ACM SIGMOD INT C M, P145, DOI DOI 10.1145/1989323.1989340
   Ell B, 2011, LECT NOTES COMPUT SC, V7031, P162, DOI 10.1007/978-3-642-25073-6_11
   Erxleben F, 2014, LECT NOTES COMPUT SC, V8796, P50, DOI 10.1007/978-3-319-11964-9_4
   Farber Michael, 2017, SEMANTIC WE IN PRESS
   Fellbaum Christiane, 1998, WORDNET ELECT LEXICA
   Flemming Annika, 2011, THESIS
   FREEDMAN G, 1980, READ TEACH, V33, P677
   Furber Christian, 2011, 19 EUR C INF SYST EC, P76
   Guns R, 2013, J AM SOC INF SCI TEC, V64, P2173, DOI 10.1002/asi.22907
   Halpin H, 2010, LECT NOTES COMPUT SC, V6496, P305, DOI 10.1007/978-3-642-17746-0_20
   Heath T., 2011, SYNTHESIS LECT SEMAN, DOI [DOI 10.2200/S00334ED1V01Y201102WBE001, 10.2200/s00334ed1v01y201102wbe001]
   Hernandez D., 2015, P 11 INT WORKSH SCAL, P32
   Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001
   Hogan A., 2010, CEUR WORKSHOP P, V628
   Hogan A, 2012, J WEB SEMANT, V14, P14, DOI 10.1016/j.websem.2012.02.001
   Jain Prateek, 2013, TECHNICAL REPORT
   Kobilarov G, 2009, LECT NOTES COMPUT SC, V5554, P723, DOI 10.1007/978-3-642-02121-3_53
   Kontokostas D, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P747, DOI 10.1145/2566486.2568002
   Kontokostas D, 2013, COMM COM INF SC, V394, P265
   Matuszek C., 2006, AAAI SPRING S FORM C, P44
   Mecella M, 2002, LECT NOTES COMPUT SC, V2519, P486
   Medelyan O., 2008, P WIKIAI WIK AI WORK, V8, P13
   Naumann F., 2002, LECT NOTES COMPUTER, V2261, DOI Quality-Driven Query Answering for Integrated Information Systems
   Pipino LL., 2002, COMMUN ACM, V45, P211, DOI [DOI 10.1145/505248.506010, 10.1145/505248.506010]
   Sandhaus E, 2010, LECT NOTES COMPUT SC, V6497, P355, DOI 10.1007/978-3-642-17749-1_27
   Singhal Amit, 2012, OFFICIAL GOOGLE BLOG
   Srinivas Kavitha, 2015, P 10 INT WORKSH ONT, P25
   Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001
   Tanon TP, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P1419, DOI 10.1145/2872427.2874809
   Tartir S., 2005, ONTOQA METRIC BASED
   Wang R. Y., 1996, Journal of Management Information Systems, V12, P5
   WANG RY, 1995, DECIS SUPPORT SYST, V13, P349, DOI 10.1016/0167-9236(93)E0050-N
   Zaveri A., 2013, P 9 INT C SEM SYST, P97, DOI DOI 10.1145/2506182.2506195
   Zaveri A, 2016, SEMANT WEB, V7, P63, DOI 10.3233/SW-150175
NR 47
TC 86
Z9 87
U1 2
U2 29
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 2018
VL 9
IS 1
SI SI
PG 53
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FZ4MU
UT WOS:000427567100005
DA 2022-08-23
ER

PT J
AU Yuan, XZ
   Chang, CT
   Chen, XR
   Li, K
AF Yuan, Xingzhu
   Chang, Chengting
   Chen, Xinrong
   Li, Ka
TI Emerging trends and focus of human gastrointestinal microbiome research
   from 2010-2021: a visualized study
SO JOURNAL OF TRANSLATIONAL MEDICINE
LA English
DT Review
DE Gastrointestinal microbiome; Human; Bibliometric; Visualized study;
   Scopus; Emerging trends; Research focus
ID HUMAN GUT MICROBIOME; IRRITABLE-BOWEL-SYNDROME; INTESTINAL MICROBIOTA;
   DOUBLE-BLIND; ENTEROTYPES; SUPPLEMENTATION; RESISTANCE; POPULATION;
   RICHNESS; CHILDREN
AB Background The gastrointestinal microbiome is an important component of the human body and is closely related to human health and disease. This study describes the hotspots of the human gastrointestinal microbiome research and its evolution in the past decade, evaluates the scientific cooperation network, and finally predicts the field's future development trend using bibliometric analysis and a visualized study. Methods We searched the original articles from January 2010 to February 2021 in the Scopus database using the term "gastrointestinal microbiome" and its synonyms. CiteSpace was used to construct country and author co-occurrence map; conduct journal, citation cocitation analysis, and reference co-citation knowledge map; and form a keywords co-occurrence map, a clustering knowledge map, timeline view of keywords, and burst term map. Result A total of 4444 documents published from January 2010 to February 2021 were analysed. In approximately the past decade, the number of articles on the human gastrointestinal microbiome has increased rapidly, and the research topics focus on different populations, research methods, and detection methods. All countries and regions in the world, led by the US, are studying the human gastrointestinal microbiome, and many research teams with close cooperation have been formed. The research has been published extensively in microbiology journals and clinical medicine journals, and the highly cited articles mainly describe the relationship between gastrointestinal microorganisms and human health and disease. Regarding the research emphasis, researchers' exploration of the human gastrointestinal microbiome (2011-2013) was at a relatively macro and superficial stage and sought to determine how the gastrointestinal microbiome relates to humans. From 2014 to 2017, increasingly more studies were conducted to determine the interaction between human gastrointestinal flora and various organs and systems. In addition, researchers (2018-2021) focused on the gastrointestinal microbial community and the diversity of certain types of microbes. Conclusion Over time, the scope of the research on the clinical uses of the gastrointestinal microbiome gradually increased, and the contents were gradually deepened and developed towards a more precise level. The study of the human gastrointestinal microbiome is an ongoing research hotspot and contributes to human health.
C1 [Yuan, Xingzhu; Chang, Chengting; Chen, Xinrong; Li, Ka] Sichuan Univ, West China Hosp, West China Sch Nursing, 8 Teaching Bldg, Chengdu 610041, Sichuan, Peoples R China.
RP Li, K (通讯作者)，Sichuan Univ, West China Hosp, West China Sch Nursing, 8 Teaching Bldg, Chengdu 610041, Sichuan, Peoples R China.
EM likalika127127@163.com
FU Key research and development project of Science & Technology Department
   of Sichuan Province [2021YFS0022]
FX This work was supported by the Key research and development project of
   Science & Technology Department of Sichuan Province (No. 2021YFS0022).
   The funder had no role in the study design, data collection and
   analysis, decision to publish, or preparation of the manuscript.
CR Adams JB, 2011, BMC GASTROENTEROL, V11, DOI 10.1186/1471-230X-11-22
   Al Assal K, 2020, NUTRIENTS, V12, DOI 10.3390/nu12020278
   Aleixandre-Tudo JL, 2020, PROBIOTICS ANTIMICRO, V12, P785, DOI 10.1007/s12602-019-09591-0
   Almeida A, 2019, NATURE, V568, P499, DOI 10.1038/s41586-019-0965-1
   Arumugam M, 2011, NATURE, V473, P174, DOI 10.1038/nature09944
   Beaumont M, 2017, AM J CLIN NUTR, V106, P1005, DOI 10.3945/ajcn.117.158816
   Berentsen B, 2020, MEDICINE, V99, DOI 10.1097/MD.0000000000021950
   Bervoets L, 2013, GUT PATHOG, V5, DOI 10.1186/1757-4749-5-10
   Bingula R, 2017, J ONCOL, V2017, DOI 10.1155/2017/5035371
   Bingula R, 2018, MEDICINE, V97, DOI 10.1097/MD.0000000000013676
   Boehme M, 2020, MOL PSYCHIATR, V25, P2567, DOI 10.1038/s41380-019-0425-1
   Brown JM, 2018, NAT REV MICROBIOL, V16, P171, DOI 10.1038/nrmicro.2017.149
   Callahan BJ, 2016, NAT METHODS, V13, P581, DOI [10.1038/NMETH.3869, 10.1038/nmeth.3869]
   Chaomei C, 2020, HOW TO USE CITESPACE
   Charoenngam N, 2020, ANTICANCER RES, V40, P551, DOI 10.21873/anticanres.13984
   Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100
   Christensen L, 2018, AM J CLIN NUTR, V108, P645, DOI 10.1093/ajcn/nqy175
   Conterno L, 2019, EUR J NUTR, V58, P63, DOI 10.1007/s00394-017-1572-2
   Craven L, 2020, AM J GASTROENTEROL, V115, P1055, DOI 10.14309/ajg.0000000000000661
   Cryan JF, 2019, PHYSIOL REV, V99, P1877, DOI 10.1152/physrev.00018.2018
   Cui HX, 2019, FRONT MICROBIOL, V10, DOI 10.3389/fmicb.2019.01423
   David LA, 2014, NATURE, V505, P559, DOI 10.1038/nature12820
   de la Cuesta-Zuluaga J, 2019, NUTRIENTS, V11, DOI 10.3390/nu11010051
   Desai MS, 2016, CELL, V167, P1339, DOI 10.1016/j.cell.2016.10.043
   Dethlefsen L, 2011, P NATL ACAD SCI USA, V108, P4554, DOI 10.1073/pnas.1000087107
   Dillon SM, 2014, MUCOSAL IMMUNOL, V7, P983, DOI 10.1038/mi.2013.116
   Duan RQ, 2019, CLIN TRANSL GASTROEN, V10, DOI 10.14309/ctg.0000000000000012
   Durk RP, 2019, INT J SPORT NUTR EXE, V29, P249, DOI 10.1123/ijsnem.2018-0024
   El-Salhy M, 2019, NUTRIENTS, V11, DOI 10.3390/nu11081824
   Fallani M, 2010, J PEDIATR GASTR NUTR, V51, P77, DOI 10.1097/MPG.0b013e3181d1b11e
   Falony G, 2016, SCIENCE, V352, P560, DOI 10.1126/science.aad3503
   Fodor AA, 2019, GUT MICROBES, V10, P22, DOI 10.1080/19490976.2018.1460013
   Fouhy F, 2012, ANTIMICROB AGENTS CH, V56, P5811, DOI 10.1128/AAC.00789-12
   Franzosa EA, 2019, NAT MICROBIOL, V4, P293, DOI 10.1038/s41564-018-0306-4
   Gasparrini AJ, 2019, NAT MICROBIOL, V4, P2285, DOI 10.1038/s41564-019-0550-2
   Ghosh TS, 2020, GUT, V69, P1218, DOI 10.1136/gutjnl-2019-319654
   Gopalakrishnan V, 2018, SCIENCE, V359, P97, DOI 10.1126/science.aan4236
   Gopalakrishnan V, 2018, CANCER CELL, V33, P570, DOI 10.1016/j.ccell.2018.03.015
   Harbison JE, 2019, PEDIATR DIABETES, V20, P574, DOI 10.1111/pedi.12865
   Hou JH, 2018, SCIENTOMETRICS, V115, P869, DOI 10.1007/s11192-018-2695-9
   Huang XQ, 2019, J TRANSL MED, V17, DOI 10.1186/s12967-019-1810-x
   Jamshidi P, 2019, GUT PATHOG, V11, DOI 10.1186/s13099-019-0332-7
   Jeffery IB, 2012, NAT REV MICROBIOL, V10, P591, DOI 10.1038/nrmicro2859
   Jobira B, 2020, J CLIN ENDOCR METAB, V105, pE2134, DOI 10.1210/clinem/dgz263
   Kakiuchi T, 2020, HELICOBACTER, V25, DOI 10.1111/hel.12690
   Karlsson CLJ, 2012, OBESITY, V20, P2257, DOI 10.1038/oby.2012.110
   Kim YS, 2020, WORLD J MENS HEALTH, V38, P48, DOI 10.5534/wjmh.190009
   Koren O, 2013, PLOS COMPUT BIOL, V9, DOI 10.1371/journal.pcbi.1002863
   Langille MGI, 2013, NAT BIOTECHNOL, V31, P814, DOI 10.1038/nbt.2676
   Larsen N, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0009085
   Lavelle A, 2020, NAT REV GASTRO HEPAT, V17, P223, DOI 10.1038/s41575-019-0258-z
   Le Chatelier E, 2013, NATURE, V500, P541, DOI 10.1038/nature12506
   Le Roy T, 2019, BMC BIOL, V17, DOI 10.1186/s12915-019-0715-8
   Leong KSW, 2020, JAMA NETW OPEN, V3, DOI 10.1001/jamanetworkopen.2020.30415
   Li M, 2018, FRONT PHARMACOL, V8, DOI 10.3389/fphar.2017.00959
   Liou JM, 2019, LANCET INFECT DIS, V19, P1109, DOI 10.1016/S1473-3099(19)30272-5
   Liu FT, 2019, TRANSL PSYCHIAT, V9, DOI 10.1038/s41398-019-0389-6
   Liu H, 2020, FRONT MICROBIOL, V11, DOI 10.3389/fmicb.2020.00932
   Michail S, 2011, PROBIOTICS ANTIMICRO, V3, P1, DOI 10.1007/s12602-010-9059-y
   Million M, 2012, INT J OBESITY, V36, P817, DOI 10.1038/ijo.2011.153
   National Natural science Foundation of China, NATL NAT SCI FDN PRO
   Pan XL, 2018, J INFORMETR, V12, P481, DOI 10.1016/j.joi.2018.03.005
   Partty A, 2013, J PEDIATR-US, V163, P1272, DOI 10.1016/j.jpeds.2013.05.035
   Pathirana MM, 2020, J DEV ORIG HLTH DIS, V11, P244, DOI 10.1017/S2040174420000185
   Pittayanon R, 2020, GASTROENTEROLOGY, V158, P930, DOI 10.1053/j.gastro.2019.11.294
   Pittayanon R, 2019, GASTROENTEROLOGY, V157, P97, DOI 10.1053/j.gastro.2019.03.049
   Ponziani FR, 2019, HEPATOLOGY, V69, P107, DOI 10.1002/hep.30036
   Proctor LM, 2019, NATURE, V569, P641, DOI 10.1038/s41586-019-1238-8
   Qin JJ, 2012, NATURE, V490, P55, DOI 10.1038/nature11450
   Qin JJ, 2010, NATURE, V464, P59, DOI 10.1038/nature08821
   Qin N, 2014, NATURE, V513, P59, DOI 10.1038/nature13568
   Rinninella E, 2019, MICROORGANISMS, V7, DOI 10.3390/microorganisms7010014
   Roager HM, 2014, APPL ENVIRON MICROB, V80, P1142, DOI 10.1128/AEM.03549-13
   Sanna S, 2019, NAT GENET, V51, P600, DOI 10.1038/s41588-019-0350-x
   Scheperjans F, 2015, MOVEMENT DISORD, V30, P350, DOI 10.1002/mds.26069
   Sharpton SR, 2019, AM J CLIN NUTR, V110, P139, DOI 10.1093/ajcn/nqz042
   Shen Y, 2018, SCHIZOPHR RES, V197, P470, DOI 10.1016/j.schres.2018.01.002
   Shields GS, 2020, JAMA PSYCHIAT, V77, P1031, DOI 10.1001/jamapsychiatry.2020.0431
   Sondergaard B, 2011, SCAND J GASTROENTERO, V46, P663, DOI 10.3109/00365521.2011.565066
   Song MY, 2020, GASTROENTEROLOGY, V158, P322, DOI 10.1053/j.gastro.2019.06.048
   Strandwitz P, 2019, NAT MICROBIOL, V4, P396, DOI 10.1038/s41564-018-0307-3
   Strati F, 2017, MICROBIOME, V5, DOI 10.1186/s40168-017-0242-1
   Suez Jotham, 2014, Nature, V514, P181, DOI 10.1038/nature13793
   Sun JF, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17041277
   The Central People's Government of the People's Republic of China, 14 5 YEAR PLAN NATL
   Turnbaugh PJ, 2007, NATURE, V449, P804, DOI 10.1038/nature06244
   Valles-Colomer M, 2019, NAT MICROBIOL, V4, P623, DOI 10.1038/s41564-018-0337-x
   Vandeputte D, 2016, GUT, V65, P57, DOI 10.1136/gutjnl-2015-309618
   Venturelli OS, 2018, MOL SYST BIOL, V14, DOI 10.15252/msb.20178157
   Wang Z, 2019, CHINESE MED J-PEKING, V132, P889, DOI 10.1097/CM9.0000000000000192
   Wu GD, 2011, SCIENCE, V334, P105, DOI 10.1126/science.1208344
   Xi WJ, 2021, J CHILD PSYCHOL PSYC, V62, P1246, DOI 10.1111/jcpp.13409
   Xiong JQ, 2020, J AFFECT DISORDERS, V277, P55, DOI 10.1016/j.jad.2020.08.001
   Yatsunenko T, 2012, NATURE, V486, P222, DOI 10.1038/nature11053
   Yue YY, 2020, WORLD J CLIN CASES, V8, P2817, DOI 10.12998/wjcc.v8.i13.2817
   Zanella A, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0217296
   Zeb F, 2020, BRIT J NUTR, V123, P1216, DOI 10.1017/S0007114519003428
   Zhang X, 2015, NAT MED, V21, P895, DOI 10.1038/nm.3914
   Zimmermann MB, 2010, AM J CLIN NUTR, V92, P1406, DOI 10.3945/ajcn.110.004564
   Zmora N, 2018, CELL, V174, P1388, DOI 10.1016/j.cell.2018.08.041
NR 100
TC 4
Z9 4
U1 20
U2 49
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1479-5876
J9 J TRANSL MED
JI J. Transl. Med.
PD JUL 31
PY 2021
VL 19
IS 1
AR 327
DI 10.1186/s12967-021-03009-8
PG 16
WC Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Research & Experimental Medicine
GA TU9XR
UT WOS:000681383300003
PM 34332587
OA Green Published, gold
DA 2022-08-23
ER

EF