FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Oramas, S
   Espinosa-Anke, L
   Gomez, F
   Serra, X
AF Oramas, Sergio
   Espinosa-Anke, Luis
   Gomez, Francisco
   Serra, Xavier
TI Natural language processing for music knowledge discovery
SO JOURNAL OF NEW MUSIC RESEARCH
LA English
DT Article
DE Musicology; natural language processing; information extraction; entity
   linking; sentiment analysis
ID INFORMATION EXTRACTION
AB Today, a massive amount of musical knowledge is stored in written form, with testimonies dated as far back as several centuries ago. In this work, we present different Natural Language Processing (NLP) approaches to harness the potential of these text collections for automatic music knowledge discovery, covering different phases in a prototypical NLP pipeline, namely corpus compilation, text-mining, information extraction, knowledge graph generation, and sentiment analysis. Each of these approaches is presented alongside different use cases (i.e. flamenco, Renaissance and popular music) where large collections of documents are processed, and conclusions stemming from data-driven analyses are presented and discussed.
C1 [Oramas, Sergio; Serra, Xavier] Univ Pompeu Fabra, Mus Technol Grp, Barcelona, Spain.
   [Espinosa-Anke, Luis] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, S Glam, Wales.
   [Gomez, Francisco] Tech Univ Madrid, Madrid, Spain.
RP Oramas, S (通讯作者)，Univ Pompeu Fabra, Mus Technol Grp, Barcelona, Spain.
EM sergio.oramas@upf.edu
RI Gómez, Francisco/ABF-7641-2021; Gómez, Francisco/AFH-9563-2022; Serra,
   Xavier/C-9299-2014
OI Serra, Xavier/0000-0003-1395-2345
FU Spanish Ministry of Economy and Competitiveness under the Maria de
   Maeztu Units of Excellence Programme [MDM-2015-0502]; COFLA2 research
   project (Proyectos de Excelencia de la Junta de Andalucia) [FEDER
   P12-TIC-1362]
FX This work was partially funded by the Spanish Ministry of Economy and
   Competitiveness under the Maria de Maeztu Units of Excellence Programme
   (MDM-2015-0502) and by the COFLA2 research project (Proyectos de
   Excelencia de la Junta de Andalucia, FEDER P12-TIC-1362).
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NR 42
TC 3
Z9 3
U1 2
U2 14
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0929-8215
EI 1744-5027
J9 J NEW MUSIC RES
JI J. New Music Res.
PY 2018
VL 47
IS 4
SI SI
BP 365
EP 382
DI 10.1080/09298215.2018.1488878
PG 18
WC Computer Science, Interdisciplinary Applications; Music
WE Science Citation Index Expanded (SCI-EXPANDED); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Computer Science; Music
GA HA4ZR
UT WOS:000450276800010
OA Green Submitted, Green Accepted
DA 2022-08-23
ER

PT J
AU Fu, JJ
   Jiang, ZX
   Hong, Y
   Liu, SQ
   Kong, DH
   Zhong, Z
   Luo, Y
AF Fu, Jingjing
   Jiang, Zixiao
   Hong, Yan
   Liu, Siqi
   Kong, Dehui
   Zhong, Zhu
   Luo, Yu
TI Global scientific research on social participation of older people from
   2000 to 2019: A bibliometric analysis
SO INTERNATIONAL JOURNAL OF OLDER PEOPLE NURSING
LA English
DT Article
ID PHYSICAL-ACTIVITY; EMERGING TRENDS; SCIENTOMETRIC ANALYSIS; REGENERATIVE
   MEDICINE; COGNITIVE FUNCTION; ASSOCIATION; ADULTS; HOME
AB Background Social participation is an indicator of successful ageing and important determinant of health outcomes. As more studies have been conducted on social participation of older people, a comprehensive and quantitative analysis of the current literature will contribute to a better understanding of the main and novel issues and improve existing geriatric care services in this domain. However, limited bibliometric analysis was employed in this research field. Therefore, we conducted this study to investigate the research trend and quantitatively and comprehensively characterise the landscapes of publications on social participation of older people via bibliometric analysis methods.
   Methods Data were obtained from the Web of Science Core Collection in January 2020. CiteSpace 5.5.R2 and VOSviewer software packages were used to generate knowledge maps and analyse the publication outputs, countries/regions, institutions, journals, research hot spots and research frontiers.
   Results A total of 7,029 publications between 2000 and 2019 were retrieved, and the publication number per year continues to increase. The United States held a leading position in this research field, and Duke University was the most productive institution. Co-cited reference cluster analysis and keyword co-occurrence analysis showed that research hot spots contained factors of healthy ageing, quality of life, psychological problems and health status, especially dementia, function (including cognitive function) and frailty. Burst detection of keywords revealed that social participation, social support, instrumental activity, frailty and loneliness have been new research frontiers since 2015.
   Conclusions By analysing publications over the past 20 years, we found publication trends and characteristics in this field. These findings will hopefully provide new insight into the scientific landscapes and further directions in the study of social participation of older people.
   Implications for Practice Social participation is strongly associated with physical and psychological problems and the well-being of older people. Related professionals, such as social workers, psychologists, nurses and many other health care practitioners, should raise widespread awareness and concern over research hot spots and frontiers on this topic to promote research knowledge translation and adoption into the practice of caring for older people.
C1 [Fu, Jingjing; Hong, Yan; Liu, Siqi; Kong, Dehui; Zhong, Zhu; Luo, Yu] Third Mil Med Univ, Sch Nursing, 30 Gaotanyan St, Chongqing 400038, Peoples R China.
   [Jiang, Zixiao] Southwest Univ, Coll Hort & Landscape Architecture, Chongqing, Peoples R China.
RP Luo, Y (通讯作者)，Third Mil Med Univ, Sch Nursing, 30 Gaotanyan St, Chongqing 400038, Peoples R China.
EM luoyuhlgl@tmmu.edu.cn
OI Liu, Siqi/0000-0001-5278-3156; Fu, Jingjing/0000-0003-1811-6637
FU National Social Science Fund of China [19XRK001]
FX The research is supported by the National Social Science Fund of China
   (Grant No. 19XRK001). Thanks for the foundation.
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NR 49
TC 13
Z9 14
U1 19
U2 51
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1748-3735
EI 1748-3743
J9 INT J OLDER PEOPLE N
JI Int. J. Older People Nurs.
PD JAN
PY 2021
VL 16
IS 1
AR e12349
DI 10.1111/opn.12349
EA SEP 2020
PG 14
WC Geriatrics & Gerontology; Gerontology; Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Geriatrics & Gerontology; Nursing
GA PV2XA
UT WOS:000570993600001
PM 32951349
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Zhong, SB
   Wen, YQ
   Huang, YM
   Cheng, XD
   Huang, L
AF Zhong, Shubin
   Wen, Yuanqiao
   Huang, Yamin
   Cheng, Xiaodong
   Huang, Liang
TI Ontological Ship Behavior Modeling Based on COLREGs for Knowledge
   Reasoning
SO JOURNAL OF MARINE SCIENCE AND ENGINEERING
LA English
DT Article
DE COLREGs; ship object; ship behavior; formal expression
ID COLLISION-AVOIDANCE
AB Formal expression of ship behavior is the basis for developing autonomous navigation systems, which supports the scene recognition, the intention inference, and the rule-compliant actions of the systems. The Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) offers experience-based expressions of ship behavior for human beings, helping the humans recognize the scene, infer the intention, and choose rule-compliant actions. However, it is still a challenge to teach a machine to interpret the COLREGs. This paper proposed an ontological ship behavior model based on the COLREGs using knowledge graph techniques, which aims at helping the machine interpret the COLREGs rules. In this paper, the ship is seen as a temporal-spatial object and its behavior is described as the change of object elements in time spatial scales by using Resource Description Framework (RDF), function mapping, and set expression methods. To demonstrate the proposed method, the Narrow Channel article (Rule 9) from COLREGs is introduced, and the ship objects and the ship behavior expression based on Rule 9 are shown. In brief, this paper lays a theoretical foundation for further constructing the ship behavior knowledge graph from COLREGs, which is helpful for the complete machine reasoning of ship behavior knowledge in the future.
C1 [Zhong, Shubin] Wuhan Univ Technol, Sch Nav, Wuhan 430063, Peoples R China.
   [Wen, Yuanqiao; Huang, Yamin; Cheng, Xiaodong; Huang, Liang] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China.
   [Wen, Yuanqiao; Huang, Yamin; Cheng, Xiaodong; Huang, Liang] Wuhan Univ Technol, Natl Engn Res Ctr Water Transport Safety, Wuhan 430063, Peoples R China.
RP Huang, YM (通讯作者)，Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China.; Huang, YM (通讯作者)，Wuhan Univ Technol, Natl Engn Res Ctr Water Transport Safety, Wuhan 430063, Peoples R China.
EM zhongzou@whut.edu.cn; wenyqwhut@foxmail.com; yaminhuang@whut.edu.cn;
   cxd921121@whut.edu.cn; leung.huang@whut.edu.cn
OI huang, liang/0000-0003-3233-4446; Huang, Yamin/0000-0001-5727-8829;
   ZHONG, Shubin/0000-0003-1105-2634
FU Key R&D Program of Zhejiang Province China [2021C01010]; National
   Natural Science Foundation of China (NSFC) [52072287, 52001241];
   Fundamental Research Funds for the Central Universities [WUT:
   2021IVA106]
FX This work was supported by the Key R&D Program of Zhejiang Province
   China through Grant No. 2021C01010, the National Natural Science
   Foundation of China (NSFC) through Grant No. 52072287 and 52001241, and
   "the Fundamental Research Funds for the Central Universities (WUT:
   2021IVA106)".
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NR 36
TC 2
Z9 2
U1 22
U2 22
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2077-1312
J9 J MAR SCI ENG
JI J. Mar. Sci. Eng.
PD FEB
PY 2022
VL 10
IS 2
AR 203
DI 10.3390/jmse10020203
PG 20
WC Engineering, Marine; Engineering, Ocean; Oceanography
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Oceanography
GA ZU3BU
UT WOS:000769718800001
OA gold
DA 2022-08-23
ER

PT J
AU Yang, R
   Wong, CWY
   Miao, X
AF Yang, Rui
   Wong, Christina W. Y.
   Miao, Xin
TI Analysis of the trend in the knowledge of environmental responsibility
   research
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Review
DE Environmental responsibility; Knowledge progress; Research
   opportunities; Knowledge graph
ID CORPORATE SOCIAL-RESPONSIBILITY; SUPPLY-CHAIN MANAGEMENT; CO-WORD
   ANALYSIS; FINANCIAL PERFORMANCE; ECO-INNOVATION; BIBLIOMETRIC
   CARTOGRAPHY; INFORMATION DISCLOSURE; INTELLECTUAL STRUCTURE; NETWORK
   ANALYSIS; MODERATING ROLE
AB With the rapid increase of environmental responsibility (ER) research in the past few years, it is common to see interdisciplinary studies in this field. Thus, it is important to conduct a comprehensive analysis of the knowledge progress of ER to better navigate the future research activities. This paper combines the advantages of the traditional bibliometric tools with the strengths of the latest bibliometric software CiteSpace to achieve a comprehensive knowledge review of ER. First, this work conducts a compre-hensive assessment and provides descriptive statistics on the sample papers. We extracted the top 52 keywords in 3656 ER papers from the Web of Science published in the past five years. By using co-word analysis, cluster analysis and social network analysis, this paper obtains keyword networks that cover five major categories of ER research: (1) stakeholder participation; (2) ER related theories; (3) management and performance; (4) sustainable development supply chain; (5) drivers. Using the method of multi-dimensional scaling, we proposed a spatial framework that identifies the main spatial structure of the current ER research by summarizing the high-frequency terminologies. We used the time-line mapping and strong citation burst in CiteSpace to present the knowledge evolution path of ER, revealing how ER-related research evolved over time, and to triangulate the results of cluster analysis. We also summarize three features of ER research and thus identifies research opportunities. At last, we build a knowledge graph model of ER research by integrating knowledge base, knowledge domain, and knowledge evolution in ER field. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Yang, Rui; Miao, Xin] Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
   [Wong, Christina W. Y.] Hong Kong Polytech Univ, Inst Text & Clothing, Business Div, Hunghom,Kowloon, Hong Kong, Peoples R China.
RP Miao, X (通讯作者)，Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
EM miaoxin@hit.edu.cn
OI WONG, Christina W.Y./0000-0002-6130-9613
FU National Natural Science Foundation of China [71471047]; Research Grants
   Council of Hong Kong Special Administration Region [GRF PolyU
   152031/17B]; Hong Kong Polytechnic University [SB1F]
FX This work is supported by the National Natural Science Foundation of
   China (Grant No. 71471047), the Research Grants Council of Hong Kong
   Special Administration Region (Grant No. GRF PolyU 152031/17B), and the
   Hong Kong Polytechnic University (Grant No.: SB1F).
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NR 162
TC 14
Z9 14
U1 39
U2 160
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0959-6526
EI 1879-1786
J9 J CLEAN PROD
JI J. Clean Prod.
PD JAN 1
PY 2021
VL 278
AR 123402
DI 10.1016/j.jclepro.2020.123402
PG 18
WC Green & Sustainable Science & Technology; Engineering, Environmental;
   Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Engineering; Environmental Sciences
   & Ecology
GA OV7NH
UT WOS:000592391200005
DA 2022-08-23
ER

PT J
AU Nova, P
   Calheiros, CSC
   Silva, M
AF Nova, Paulo
   Calheiros, Cristina S. C.
   Silva, Margarida
TI Glyphosate in Portuguese Adults - A Pilot Study
SO ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY
LA English
DT Article
DE Glyphosate; AMPA; Pesticides; Urine analysis; Environmental exposure;
   Human biomonitoring
ID AMINOMETHYLPHOSPHONIC ACID; HERBICIDE; EXPOSURE; DAMAGE
AB Background: Glyphosate is a broad-spectrum biocide and the active ingredient in the most widely used herbicides worldwide. Since 2015, when the International Agency for Research on Cancer classified it as a Class 2A carcinogen, global interest in this chemical spiked particularly as regards exposure of the general population. Objective: An exploratory glyphosate exposure assessment was conducted among Portuguese adults.
   Methods: Self-selected participants provided first morning urine which was tested for glyphosate and its metabolite aminomethylphosphonic acid (AMPA) at two distinct periods of time, by two different laboratories using gas chromatography with tandem mass spectrometry (GC-MS-MS) and high performance liquid chromatography linked to triple quadrupole mass spectrometry (HPLC-MS/MS), respectively.
   Results: In the first round of testing 28% and 50% presented detectable levels of glyphosate and AMPA respectively, with median values of 0.25 and 0.16 mu g/L. Systematically available internal dose values were 8.20E-06 mg/Kg (glyphosate) and 5.04-05 mg/Kg (AMPA). In the second round 73% and 97% presented detectable levels of glyphosate and AMPA respectively with median values of 0.13 and 0.10 mu g/L. Systematically available internal dose values were 4.00E-06 mg/Kg (glyphosate) and 3.00E-06 mg/Kg (AMPA).
   Conclusions: Glyphosate exposure was detected among Portuguese adults, with percentages of glyphosate and AMPA contaminated urine in both rounds of testing and above values from previous studies in other European countries. Systematically available internal doses values were below EFSA's risk assessment values (ADI or AOEL), and as such, the concentration values measured in this study are not per se a human health problem. Even though there were study limitations, it is the first assessment in Portugal and contributes to the overall knowledge map of glyphosate exposure in Europe.
C1 [Nova, Paulo; Silva, Margarida] Univ Catolica Portuguese, CBQF Ctr Biotecnol & Quim Fina, Lab Associado, Escola Super Biotecnol, Rua Diogo Botelho 1327, P-4169005 Porto, Portugal.
   [Calheiros, Cristina S. C.] Univ Porto, Interdisciplinary Ctr Marine & Environm Res CIIMA, Av Gen Norton de Matos S-N, P-4450208 Matosinhos, Portugal.
RP Nova, P (通讯作者)，Univ Catolica Portuguese, CBQF Ctr Biotecnol & Quim Fina, Lab Associado, Escola Super Biotecnol, Rua Diogo Botelho 1327, P-4169005 Porto, Portugal.
EM pnova@porto.ucp.pt
RI Calheiros, Cristina/A-1151-2011
OI Calheiros, Cristina/0000-0003-3159-3497; Nova, Paulo/0000-0002-6723-4974
FU FCT Foundation for Science and Technology [UID/Multi/50016/2019,
   UIDB/04423/2020, UIDP/04423/2020]
FX This research was supported by national funds through FCT Foundation for
   Science and Technology within the scope of UID/Multi/50016/2019,
   UIDB/04423/2020 and UIDP/04423/2020.
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   Van Bruggen AHC, 2018, SCI TOTAL ENVIRON, V616, P255, DOI 10.1016/j.scitotenv.2017.10.309
   Zoller O, 2020, INT J HYG ENVIR HEAL, V228, DOI 10.1016/j.ijheh.2020.113526
NR 42
TC 10
Z9 10
U1 3
U2 12
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 1382-6689
EI 1872-7077
J9 ENVIRON TOXICOL PHAR
JI Environ. Toxicol. Pharmacol.
PD NOV
PY 2020
VL 80
AR 103462
DI 10.1016/j.etap.2020.103462
PG 6
WC Environmental Sciences; Pharmacology & Pharmacy; Toxicology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Pharmacology & Pharmacy; Toxicology
GA OJ2DQ
UT WOS:000583776900021
PM 32755638
DA 2022-08-23
ER

PT J
AU Chen, JQ
   He, Q
   Jiang, BH
   Song, WJ
   Wu, ZH
   Yang, JY
   Huang, ZW
   Yu, XB
   Luo, J
   Tao, QW
AF Chen, Jiaqi
   He, Qian
   Jiang, Bohan
   Song, Weijiang
   Wu, Zihua
   Yang, Jianying
   Huang, Ziwei
   Yu, Xinbo
   Luo, Jing
   Tao, Qingwen
TI Research on primary Sjogren's syndrome in 2004-2021: a Web of
   Science-based cross-sectional bibliometric analysis
SO RHEUMATOLOGY INTERNATIONAL
LA English
DT Article; Early Access
DE Sjogren's syndrome; Bibliometric; Data visualization; Knowledge map;
   Research hotspot
ID SALIVARY-GLAND ULTRASONOGRAPHY; PATHOGENESIS; DIAGNOSIS; BIOPSY; TRENDS
AB The extent, range, and nature of available research in the field of primary Sjogren's syndrome (pSS) have not been understood fully. This study aimed to map the literature available on pSS, and identify global hotspots and trends in the research. Papers on pSS published between 2004 and 2021 were searched from Web of Science Core Collection. The quantity and citations of publications, and the research hotspots and trends in the field of pSS were analyzed and presented visually by Microsoft Excel and Citespace software. A total of 3606 papers mainly from 526 institutions in 83 countries/regions were included for analysis. The number of publications presented an overall upward trend in the field of pSS from 2004 to 2021. The USA ranked first in the number of publications (n = 661), followed by China (n = 491), Italy (n = 405), France (n = 351), and Japan (n = 292). Moreover, seven of the top ten countries by the number of publications on pSS were from Europe. The University of Groningen (n = 661), Xavier Mariette (n = 95), and Clinical and Experimental Rheumatology (n = 184) were the most prolific affiliation, author, and journal, respectively. Vitali C (n = 2009) and Arthritis and Rheumatism (n = 3918) held the record for the most cited papers by an author and journal, respectively. At present, the hot keywords in the field of pSS include disease activity, ultrasonography, management, consensus, and data-driven. Lymphoid organization, clinical phenotypes outcome, salivary gland ultrasonography, and Toll-like receptor are the emerging research trends in pSS. Research on pSS is flourishing. Current research of pSS mainly focuses on disease activity, ultrasonography, and management. While, the emerging research trends in pSS are lymphoid organization, clinical phenotypes outcome, salivary gland ultrasonography, and Toll-like receptor.
C1 [Chen, Jiaqi; He, Qian; Wu, Zihua; Yang, Jianying; Huang, Ziwei; Yu, Xinbo; Luo, Jing; Tao, Qingwen] China Japan Friendship Hosp, Tradit Chinese Med Dept Rheumatism, 2 Yinghua East Rd, Beijing 100029, Peoples R China.
   [Chen, Jiaqi; He, Qian; Jiang, Bohan; Wu, Zihua; Yang, Jianying; Huang, Ziwei; Yu, Xinbo] Beijing Univ Chinese Med, Grad Sch, Beijing, Peoples R China.
   [Song, Weijiang] Peking Univ Third Hosp, Tradit Chinese Med Dept, Beijing, Peoples R China.
   [Luo, Jing; Tao, Qingwen] Beijing Key Lab Immune Inflammatory Dis, Beijing, Peoples R China.
RP Luo, J; Tao, QW (通讯作者)，China Japan Friendship Hosp, Tradit Chinese Med Dept Rheumatism, 2 Yinghua East Rd, Beijing 100029, Peoples R China.; Luo, J; Tao, QW (通讯作者)，Beijing Key Lab Immune Inflammatory Dis, Beijing, Peoples R China.
EM jiaqichen19@outlook.com; zryhheqianbucm@163.com; vnjiang@foxmail.com;
   songwj0214@163.com; cancerpiscesvivien@hotmail.com; yangjy159@qq.com;
   hzw869516359@163.com; glah1005@163.com; luojinggg@sina.com;
   taoqg1@sina.com
OI Song, Wei-Jiang/0000-0002-2092-4775; Chen, Jiaqi/0000-0002-1643-5450;
   HE, qian/0000-0002-3203-696X; wu, zi hua/0000-0002-3385-7997
FU National Key Clinical Specialty Capacity Building Project
   [2011-ZDZK-001]; Capital's Funds for Health Improvement and Research
   [2020-4-40610]; Elite Medical Professionals project of China-Japan
   Friendship Hospital [ZRJY2021-QM14]
FX This work was supported by National Key Clinical Specialty Capacity
   Building Project (2011-ZDZK-001), Capital's Funds for Health Improvement
   and Research (2020-4-40610), and Elite Medical Professionals project of
   China-Japan Friendship Hospital (ZRJY2021-QM14).
CR Akyol A, 2021, RHEUMATOL INT, V41, P2007, DOI 10.1007/s00296-021-04836-0
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NR 33
TC 0
Z9 0
U1 3
U2 3
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0172-8172
EI 1437-160X
J9 RHEUMATOL INT
JI Rheumatol. Int.
DI 10.1007/s00296-022-05138-9
EA MAY 2022
PG 9
WC Rheumatology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Rheumatology
GA 1C3EM
UT WOS:000793006700001
PM 35536378
DA 2022-08-23
ER

PT J
AU Gou, XQ
   Liu, H
   Qiang, YJ
   Lang, ZH
   Wang, HN
   Ye, D
   Wang, ZW
   Wang, H
AF Gou, Xiaoqing
   Liu, Hui
   Qiang, Yujie
   Lang, Zhihui
   Wang, Haining
   Ye, Dong
   Wang, Zhiwen
   Wang, Han
TI In-depth analysis on safety and security research based on system
   dynamics: A bibliometric mapping approach-based study
SO SAFETY SCIENCE
LA English
DT Article
DE System dynamics; Safety and security; Bibliometric; System safety;
   Knowledge mapping
ID EVOLUTIONARY GAME; KNOWLEDGE DOMAIN; POWER; SIMULATION; MODEL;
   COCITATION; PATTERNS; TRENDS
AB Safety and security (SS) research based on system dynamics (SD) play a greater theoretical and technical support for hazard identification and accident prevention, which meant SD has become an important method to study the system safety, also that SS research based on SD has entered the application stage. It is a relatively macroscopic understanding of the application of SD to SS research by scholars, and difficult to capture the changes and characteristics of specific topics from the understanding. To get an in-depth understanding of the current research characteristics and development in the research domain, and help researchers dig into the more in-formation from a quantitative perspective, we retrieved 1091 documents about SS-related research based on SD published in the period 2000-2020 from the Web of Science Core Collection database in this paper, the bib-liometric analysis and science mapping were carried out. The USA, China, UK, Germany and Australia were the five most contributions to the literature associated with SS research based on SD. In terms of the major sources and co-citation of SS-related research publications, IEEE Transactions on Power System, Safety Science, Energy Policy, System Dynamics Review and Reliability Engineering & System Safety are the five most active journals in this domain. The knowledge base of SS research based on SD focuses on "safety management", "risk analysis" and "network and information security". There are some significant differences between the safety and security domains while they are very similar in "risk analysis". Currently, hotspot topics include "safety management", "energy security", "resource utilization" and "industry applications". "system safety and reliability", "energy consumption and security" and "power system dynamics and stability" have become the research frontier in the field.
C1 [Gou, Xiaoqing; Liu, Hui; Qiang, Yujie; Lang, Zhihui; Wang, Haining; Ye, Dong; Wang, Han] China Jiliang Univ, Coll Qual & Safety Engn, 258 Xueyuan St, Hangzhou 310018, Zhejiang, Peoples R China.
   [Wang, Zhiwen] Zhejiang Acad Emergency Management Sci, Hangzhou 310012, Peoples R China.
RP Liu, H (通讯作者)，China Jiliang Univ, Coll Qual & Safety Engn, 258 Xueyuan St, Hangzhou 310018, Zhejiang, Peoples R China.
EM liuhui2003@126.com
RI liu, hui/K-2142-2015
OI liu, hui/0000-0002-4265-7241
FU Zhejiang Provincial Natural Science Foundation of China [LY22E040001,
   LQ22E060003]; Science and Technology Project of Department of Education
   of Zhejiang Province [Y202045438]; Fundamental Research Funds for the
   Provincial Universities of Zhejiang [2020YW55, 2021YW92]
FX This work was supported in part by the Zhejiang Provincial Natural
   Science Foundation of China (No. LY22E040001,LQ22E060003), the Science
   and Technology Project of Department of Education of Zhejiang Province
   (Y202045438) and the Fundamental Research Funds for the Provincial
   Universities of Zhejiang (No. 2020YW55, 2021YW92).
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NR 74
TC 3
Z9 3
U1 11
U2 11
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-7535
EI 1879-1042
J9 SAFETY SCI
JI Saf. Sci.
PD MAR
PY 2022
VL 147
AR 105617
DI 10.1016/j.ssci.2021.105617
PG 16
WC Engineering, Industrial; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA 1L6BF
UT WOS:000799370700012
DA 2022-08-23
ER

PT J
AU de Oliveira, JF
   Pescarini, JM
   Rodrigues, MD
   Almeida, BD
   Henriques, CMP
   Gouveia, FC
   Rabello, ET
   Matta, GC
   Barreto, ML
   Sampaio, RB
AF de Oliveira, Juliane Fonseca
   Pescarini, Julia Moreira
   Rodrigues, Moreno de Souza
   Almeida, Bethania de Araujo
   Pessanha Henriques, Claudio Maierovitch
   Gouveia, Fabio Castro
   Rabello, Elaine Teixeira
   Matta, Gustavo Correa
   Barreto, Mauricio L.
   Sampaio, Ricardo Barros
TI The global scientific research response to the public health emergency
   of Zika virus infection
SO PLOS ONE
LA English
DT Article
ID BRAZIL
AB Background
   Science studies have been a field of research for different knowledge areas, and they have been successfully used to analyse the construction of scientific knowledge, practice and dissemination. In this study, we aimed to verify how the Zika epidemic has moulded the scientific articles published worldwide by analysing international collaborations and the knowledge landscape through time, as well as research topics and country involvement.
   Methodology
   We searched the Web of Science (WoS), Scopus and PubMed for studies published up to 31st December 2018 on Zika using the search terms "zika", "zkv" or "zikv". We analysed the scientific production regarding which countries have published the most, on which topics, as well as country level collaboration. We performed a scientometric analysis of research on Zika focusing on knowledge mapping and the scientific research path over time and space.
   Findings
   We found two well defined research areas divided into three subtopics accounting for six clusters. With regard to country analysis, the USA and Brazil were the countries with the highest numbers of publications on Zika. China entered as a new player focusing on specific research areas. When we took into consideration the epidemics and reported cases, Brazil and France were the leading research countries on related topics. As for international collaboration, the USA followed by England and France stand out as the main hubs. The research areas most published included public health-related topics from 2015 until the very beginning of 2016, followed by an increase in topics related to the clinical aspects of the disease in 2016 and the emergence of laboratory research in 2017/2018.
   Conclusions
   Mapping the response to Zika, a public health emergency, demonstrated a clear pattern of the participation of countries in the scientific advances. The pattern of knowledge production found in this study represented varying country perspectives, research capacity and interests based first on their level of exposure to the epidemic and second on their financial positions regarding science.
C1 [de Oliveira, Juliane Fonseca; Pescarini, Julia Moreira; Rodrigues, Moreno de Souza; Almeida, Bethania de Araujo; Barreto, Mauricio L.; Sampaio, Ricardo Barros] Fiocruz MS, Inst Goncalo Moniz, Ctr Integracao Dados & Conhecimentos Saude Cidacs, Salvador, BA, Brazil.
   [de Oliveira, Juliane Fonseca] Univ Porto, Ctr Matemat, Porto, Portugal.
   [Rodrigues, Moreno de Souza] Fiocruz MS, Lab Anal & Visualizacao Dados, Rondonia, Brazil.
   [Pessanha Henriques, Claudio Maierovitch; Sampaio, Ricardo Barros] Fiocruz MS, Gerencia Reg Brasilia, Brasilia, DF, Brazil.
   [Gouveia, Fabio Castro] Fiocruz MS, Casa Oswaldo Cruz, Rio De Janeiro, Brazil.
   [Rabello, Elaine Teixeira] Univ Estado Rio de Janeiro, Inst Med Social, Rio De Janeiro, Brazil.
   [Matta, Gustavo Correa] Fiocruz MS, Escola Nacl Saude Pobl, Rio De Janeiro, Brazil.
   [Rabello, Elaine Teixeira] Wageningen Univ & Res, Publ Adm & Policy Grp, Wageningen, Netherlands.
   [Barreto, Mauricio L.] Univ Fed Bahia, Inst Saude Colet, Salvador, BA, Brazil.
RP de Oliveira, JF; Sampaio, RB (通讯作者)，Fiocruz MS, Inst Goncalo Moniz, Ctr Integracao Dados & Conhecimentos Saude Cidacs, Salvador, BA, Brazil.; de Oliveira, JF (通讯作者)，Univ Porto, Ctr Matemat, Porto, Portugal.; Sampaio, RB (通讯作者)，Fiocruz MS, Gerencia Reg Brasilia, Brasilia, DF, Brazil.
EM julianlanzin@gmail.com; rsampaio.br@gmail.com
RI Henriques, Claudio/AAW-8044-2021; de Oliveira, Juliane
   Fonseca/W-6438-2018; Oliveira, Juliane/AAX-9476-2020; Rodrigues,
   Moreno/ABD-9852-2020; Sampaio, Ricardo Barros/F-9482-2018; L. Barreto,
   Mauricio/B-1752-2008
OI de Oliveira, Juliane Fonseca/0000-0002-7167-8754; Oliveira,
   Juliane/0000-0002-7167-8754; Rodrigues, Moreno/0000-0002-1594-2311;
   Sampaio, Ricardo Barros/0000-0002-6989-8555; Oliveira da Silva,
   Ilaydiany/0000-0002-3171-7878; L. Barreto, Mauricio/0000-0002-0215-4930;
   De Araujo Almeida, Bethania/0000-0001-8918-2661; Pescarini,
   Julia/0000-0001-8711-9589
FU Secretary of Health Surveillance, Brazilian Ministry of Health, through
   the Zika Platform; European Union [734548]; Oswaldo Cruz
   Foundation/Vice-Presidency of Research and Biological
   Collections-Fiocruz/VPPCB; Newton Fund/British Council
FX This work is supported by the Secretary of Health Surveillance,
   Brazilian Ministry of Health, through the Zika Platform - a long-term
   surveillance platform for the Zika virus and microcephaly in Brazil, at
   the Center of Data and Knowledge Integration for Health (CIDACS-Fiocruz)
   This work was partially supported by the European Union's Horizon 2020
   Research and Innovation Programme under ZIKAlliance Grant Agreement no.
   734548, and by the Oswaldo Cruz Foundation/Vice-Presidency of Research
   and Biological Collections-Fiocruz/VPPCB and the Newton Fund/British
   Council.
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NR 29
TC 14
Z9 14
U1 0
U2 9
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 MAR 12
PY 2020
VL 15
IS 3
AR e0229790
DI 10.1371/journal.pone.0229790
PG 16
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA LQ9AF
UT WOS:000535288600023
PM 32163439
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Tang, MJ
   Li, T
   Gao, W
   Xia, Y
AF Tang, Mingjing
   Li, Tong
   Gao, Wei
   Xia, Yu
TI AttenSy-SNER: software knowledge entity extraction with syntactic
   features and semantic augmentation information
SO COMPLEX & INTELLIGENT SYSTEMS
LA English
DT Article; Early Access
DE Entity extraction; Software knowledge graph; Attention mechanism;
   Syntactic dependency analysis; Semantic augmentation
ID RECOGNITION
AB Software knowledge community contains a large scale of software knowledge entity information, complex structure and rich semantic correlations. It is significant to recognize and extract software knowledge entity from software knowledge community, as it has great impact on entity-centric tasks such as software knowledge graph construction, software document generation and expert recommendation. Since the texts of the software knowledge community are unstructured by user-generated texts, it is difficult to apply the traditional entity extraction method in the domain of the software knowledge community due to the problems of entity variation, entity sparsity, entity ambiguity, out-of-vocabulary (OOV) words and the lack of annotated data sets. This paper proposes a novel software knowledge entity extraction model, named AttenSy-SNER, which integrates syntactic features and semantic augmentation information, to extract fine-grained software knowledge entities from unstructured user-generated content. The input representation layer utilizes Bidirectional Encoder Representations from Transformers (BERT) model to extract the feature representation of the input sequence. The contextual coding layer leverages the Bidirectional Long Short-Term Memory (BiLSTM) network and Graph Convolutional Network (GCN) for contextual information and syntactic dependency information, and a semantic augmentation strategy based on attention mechanism is introduced to enrich the semantic feature representation of sequences as well. The tag decoding layer leverages Conditional Random Fields (CRF) to solve the dependency between the output tags and obtain the global optimal label sequence. The results of model comparison experiments show that the proposed model has better performance than the benchmark model in software engineering domain.
C1 [Tang, Mingjing] Yunnan Normal Univ, Sch Life Sci, Kunming 650500, Peoples R China.
   [Tang, Mingjing] Yunnan Univ, Sch Software, Kunming 650091, Peoples R China.
   [Li, Tong] Yunnan Agr Univ, Sch Big Data, Kunming 650201, Peoples R China.
   [Gao, Wei; Xia, Yu] Yunnan Normal Univ, Sch Informat, Kunming 650500, Peoples R China.
RP Li, T (通讯作者)，Yunnan Agr Univ, Sch Big Data, Kunming 650201, Peoples R China.
EM tmj@ynnu.edu.cn; tli@ynu.edu.cn; gaowei@ynnu.edu.cn; woldcn@163.com
OI Tang, MingJing/0000-0003-0435-8854
FU Yunnan Science and Technology Major Project [202002AE090010]; Subproject
   5 of Yunnan Science and Technology Major [202002AD080002-5]
FX This work is supported by Yunnan Science and Technology Major Project
   (Grant no. 202002AE090010), the Subproject 5 of Yunnan Science and
   Technology Major (Grant no. 202002AD080002-5).
CR Alsaaran N, 2021, CMC-COMPUT MATER CON, V68, P471, DOI 10.32604/cmc.2021.016054
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NR 30
TC 0
Z9 0
U1 7
U2 7
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.
DI 10.1007/s40747-022-00742-5
EA JUN 2022
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1T9UH
UT WOS:000805068000001
OA gold
DA 2022-08-23
ER

PT J
AU Zhou, J
   Li, JX
   Wang, CS
   Wu, HR
   Zhao, CJ
   Teng, GF
AF Zhou, Ji
   Li, Jiuxi
   Wang, Chunshan
   Wu, Huarui
   Zhao, Chunjiang
   Teng, Guifa
TI Crop disease identification and interpretation method based on
   multimodal deep learning
SO COMPUTERS AND ELECTRONICS IN AGRICULTURE
LA English
DT Article
DE Multimodality; Knowledge graph; Image-text pairs; Semantic
   interpretation; Disease identification
AB Identification methods of crop diseases based on image modality alone have achieved relative success under limited and restricted conditions. As a data-driven technology, its performance depends on a large amount of image labeling data. Many of the existing methods neglected the role and value of other modal data except images and only relies on low-level image features for disease recognition without utilizing high-level domain knowledge, leading to poor credibility and interpretability of identification results. This paper targets tomato and cucumber common invasive diseases recognition as the research object. First, for the problem of insufficient utilization of multimodal data in existing models, the semantic embedding methods for disease images and disease description texts were examined, and the correlation and complementarity between the two types of modal data was utilized to realize the joint representation learning of disease features. Second, in response to the requirements of reliable identification and interpretability of diseases, the knowledge representation and knowledge embedding mechanism in the field of disease identification was studied, and the high-level domain knowledge graph was used as the external guidance for image feature learning and disease identification. Lastly, a disease identification model based on "image-text" multimodal collaborative representation and knowledge assistance (ITK-Net) was constructed. The proposed model achieved an identification accuracy, precision, sensitivity and specificity of 99.63%, 99%, 99.07% and 99.78% respectively on a dataset composed of "imagetext" pairs. Meanwhile, semantic interpretation was performed on the model inference process. The achievement of this paper can offer a new method for disease identification based on multimodal data and domain knowledge, which might help improve the intelligence level of crop disease identification.
C1 [Zhou, Ji; Wang, Chunshan; Teng, Guifa] Hebei Agr Univ, Sch Informat Sci & Technol, Baoding 071001, Peoples R China.
   [Zhou, Ji; Wang, Chunshan; Wu, Huarui; Zhao, Chunjiang] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China.
   [Wang, Chunshan; Wu, Huarui; Zhao, Chunjiang] Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China.
   [Li, Jiuxi] Hebei Agr Univ, Sch Mech & Elect Engn, Baoding 071001, Peoples R China.
   [Zhou, Ji; Li, Jiuxi; Wang, Chunshan; Teng, Guifa] Hebei Key Lab Agr Big Data, Baoding 071001, Peoples R China.
RP Li, JX; Wang, CS (通讯作者)，Hebei Agr Univ, Baoding 071001, Peoples R China.
EM chunshan9701@163.com
FU National Key Research and Development Program of China [2020YFD1100602];
   Beijing Municipal Science and Technology Project [Z191100004019007];
   China Agriculture Research System of MOF and MARA [CARS-23-C06]; Hebei
   Province Key Research and Development Project [20327402D, 19227210D];
   National Natural Science Foundation of China [61871041]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2020YFD1100602, and in part by
   the Beijing Municipal Science and Technology Project under Grant
   Z191100004019007, and in part by China Agriculture Research System of
   MOF and MARA under Grant CARS-23-C06, and in part by the Hebei Province
   Key Research and Development Project under Grant 20327402D,19227210D,
   and in part by the National Natural Science Foundation of China under
   Grant 61871041.
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NR 20
TC 3
Z9 3
U1 12
U2 27
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0168-1699
EI 1872-7107
J9 COMPUT ELECTRON AGR
JI Comput. Electron. Agric.
PD OCT
PY 2021
VL 189
AR 106408
DI 10.1016/j.compag.2021.106408
EA AUG 2021
PG 9
WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture; Computer Science
GA UR5LD
UT WOS:000696789900004
DA 2022-08-23
ER

PT J
AU Teschke, R
   Danan, G
AF Teschke, Rolf
   Danan, Gaby
TI Idiosyncratic Drug Induced Liver Injury, Cytochrome P450, Metabolic Risk
   Factors and Lipophilicity: Highlights and Controversies
SO INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
LA English
DT Review
DE cytochrome P450; CYP isoforms; DILI; daily drug dose; iDILI;
   idiosyncratic drug induced liver injury; drug metabolism; lipophilicity;
   metabolic risk factors; reactive oxygen species (ROS); Roussel Uclaf
   Causality Assessment Method; RUCAM
ID CAUSALITY ASSESSMENT METHODS; ORAL MEDICATIONS; STATIN HEPATOTOXICITY;
   ADVERSE REACTIONS; MECHANISMS; SUBSTRATE; DILEMMA; RUCAM
AB Progress in understanding the mechanisms of the idiosyncratic drug induced liver injury (iDILI) was highlighted in a scientometric investigation on the knowledge mapping of iDILI throughout the world, but uncertainty remained on metabolic risk factors of iDILI, the focus of the present review article. For the first time, a quantitative analysis of 3312 cases of iDILI assessed for causality with RUCAM (Roussel Uclaf Causality Assessment Method) showed that most drugs (61.1%) were metabolized by cytochrome P450 (CYP) isoforms: 49.6% by CYP 3A4/5, 24.6% by CYP 2C9, 13.2% by CYP 2E1, 7.3% by CYP 2C19, 3.5% by CYP 1A2 and 1.8% by CYP 2D6. Other studies showed high OR (odds ratio) for drugs metabolized by unspecified CYPs but the iDILI cases were not assessed for causality with RUCAM, a major shortcoming. In addition to critical comments on methodological flaws, several risk factors of iDILI were identified such as high but yet recommended daily drug doses, actual daily drug doses taken by the patients, hepatic drug metabolism and drug lipophilicity. These risk factors are subject to controversies by many experts seen critically also by others who outlined that none of these medication characteristics is able to predict iDILI with high confidence, leading to the statement of an outstanding caveat. It was also argued that all previous studies lacked comprehensive data because the number of examined drugs was relatively small as compared to the number of approved new molecular entities or currently used oral prescription drugs. In conclusion, trends are evident that some metabolic parameters are likely risk factors of iDILI but strong evidence can only be achieved when methodological issues will be successfully met.
C1 [Teschke, Rolf] Klinikum Hanau, Div Gastroenterol & Hepatol, Dept Internal Med 2, D-63450 Hanau, Germany.
   [Teschke, Rolf] Goethe Univ Frankfurt Main, Acad Teaching Hosp, Med Fac, D-60323 Frankfurt, Germany.
   [Danan, Gaby] Pharmacovigilance Consultancy, F-75020 Paris, France.
RP Teschke, R (通讯作者)，Klinikum Hanau, Div Gastroenterol & Hepatol, Dept Internal Med 2, D-63450 Hanau, Germany.; Teschke, R (通讯作者)，Goethe Univ Frankfurt Main, Acad Teaching Hosp, Med Fac, D-60323 Frankfurt, Germany.
EM rolf.teschke@gmx.de; gaby.danan@gmail.com
OI teschke, rolf/0000-0001-8910-1200; Danan, Gaby/0000-0001-7580-1505
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NR 48
TC 7
Z9 7
U1 2
U2 10
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1422-0067
J9 INT J MOL SCI
JI Int. J. Mol. Sci.
PD APR
PY 2021
VL 22
IS 7
AR 3441
DI 10.3390/ijms22073441
PG 16
WC Biochemistry & Molecular Biology; Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Chemistry
GA RL0FI
UT WOS:000638659900001
PM 33810530
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Han, J
   Dai, YH
   Rong, X
   Zhang, YM
   Li, H
AF Han, Jing
   Dai, Yuhong
   Rong, Xi
   Zhang, Yimei
   Li, Hong
TI Medical intelligent processor system and traditional Chinese medicine to
   treat endometriosis
SO MICROPROCESSORS AND MICROSYSTEMS
LA English
DT Article
DE Traditional chinese medication (TCM); Endometriosis; Medical issue;
   Medication and web embedded system
AB It is viewed as human sickness; an ongoing report shows that it very well might be a significant reason for metastatic malignant growth and barrenness. It additionally has been related to different illnesses identified with the capacity of the invulnerable framework. Hereditary and ecological variables are known to impact the turn of events and side effects of endometriosis. Danger elements of endometriosis in a ladies are a synopsis of the most recent exploration of analysis and treatment. As an effective and novel knowledge management technology, the Knowledge Graph provides a new way to inherit the traditional Chinese medicine development. However, the Traditional Chinese Medicine (TCM) knowledge graph is based on the current configuration data. An inserted Web worker is a blend of installed gadgets and Internet innovation. Focus on the advancement of clinical MSP430 and Acorn RISC Machine (ARM) 11 (Raspberry Pi) inside organization workers. For instance, blood oxygen, pulse, an information obtaining arrangement of the patient endometriosis, circulatory strain plan Utilized as a simple front end and the arm as Linux OS and a leading computerized group of the base station of the organization completely utilitarian In a restricted space worker, the fundamental thought, as specialists realize the medical issue of the patient, to realize the ailment of the patient, is the shading Occasionally, he is likewise a spot that doesn't arrive at the patient, as expected. Traditional Chinese Medication (TCM) is created, including different types of traditional Chinese medication. Needle therapy, just as development, depends on the idea of a typical act of the year and the customary drug stocks in a wide reach sustenance therapy. It, for the most part, is utilized as a reciprocal and elective clinical medicine. Since it is based is consumed into the philosophical hypothesis. Impact of Chinese philosophical works by the greater part of five components and a similar likeness hypothesis microcosm of the universe is grounded.
C1 [Han, Jing] Binzhou Peoples Hosp, Dept Endocrinol, Binzhou 262500, Shandong, Peoples R China.
   [Dai, Yuhong] Qingzhou Women & Childrens Hosp, Qingzhou 262500, Shandong, Peoples R China.
   [Rong, Xi] Weifang Yidu Cent Hosp, Dept Clin Pharm, Qingzhou 262500, Shandong, Peoples R China.
   [Zhang, Yimei] Weifang Maternal & Child Hlth Hosp, Dept Gynaecol & Obstet, Weifang 261011, Shandong, Peoples R China.
   [Li, Hong] Weifang Peoples Hosp, Dept Gynaecol, Weifang 261011, Shandong, Peoples R China.
RP Li, H (通讯作者)，Weifang Peoples Hosp, Dept Gynaecol, Weifang 261011, Shandong, Peoples R China.
EM sdwffklh@163.com
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NR 19
TC 1
Z9 1
U1 1
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0141-9331
EI 1872-9436
J9 MICROPROCESS MICROSY
JI Microprocess. Microsyst.
PD APR
PY 2021
VL 82
AR 103842
DI 10.1016/j.micpro.2021.103842
EA JAN 2021
PG 8
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA RY2QO
UT WOS:000647762200006
DA 2022-08-23
ER

PT J
AU Dai, JH
   Xu, JX
AF Dai, Jianhua
   Xu, Jingxin
TI Knowledge Graph Construction for Intelligent Media Based on Mobile
   Internet
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
ID COMMUNICATION; TECHNOLOGY; POWER; NEWS
AB Mobile Internet-based intelligent media has become a popular academic topic. This study uses the CiteSpace visualisation tool and Scientific Citation Index Expanded database to comb the existing research in the field of intelligent media from a quantitative perspective. A total of 7248 English papers were published on the topic of "intelligent media" from 2012 to 2021, and 145 highly cited papers refined were analysed. Scientific knowledge graphs were analysed from six dimensions: annual publication quantity, country of publication, institution of publication, author, keywords, and cited references. In the last 10 years, the research literature on intelligent media has been found to increase annually. Presently, the People's Republic of China and the United States of America have a high proportion of documents in this field. Chinese universities and institutions have achieved significantly in terms of the quantity and quality of documents. From the perspective of the whole intelligent media discipline, the high-yield author group has not been formed, and there is minimal cooperation amongst authors. Popular intelligent media topics include film, social media, machine learning, swarming motility, data mining, and artificial intelligence. Subject words of the main research directions are event recognition, fake news, Cable News Network model, reconfigurable intelligent surface, comprehensive survey, microblog message, strain sensor, and traffic event. Combined with popular topics and time zone maps, the future research frontier in the field of smart media is identified.
C1 [Dai, Jianhua; Xu, Jingxin] China Univ Polit Sci & Law, Business Sch, Beijing 100088, Peoples R China.
RP Dai, JH (通讯作者)，China Univ Polit Sci & Law, Business Sch, Beijing 100088, Peoples R China.
EM daijianhua66@163.com
FU National Social Science Fund Art Project [17BC059]
FX We thank Steven Pagel of Philadelphia, USA, for his help in writing this
   paper. This research was funded by the National Social Science Fund Art
   Project (17BC059).
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NR 25
TC 0
Z9 0
U1 7
U2 7
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD JAN 11
PY 2022
VL 2022
AR 4867220
DI 10.1155/2022/4867220
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA ZX7PZ
UT WOS:000772087200003
OA gold
DA 2022-08-23
ER

PT J
AU Fernandez-Alvarez, D
   Frey, J
   Gayo, JEL
   Gayo-Avello, D
   Hellmann, S
AF Fernandez-Alvarez, Daniel
   Frey, Johannes
   Labra Gayo, Jose Emilio
   Gayo-Avello, Daniel
   Hellmann, Sebastian
TI Approaches to measure class importance in Knowledge Graphs
SO PLOS ONE
LA English
DT Article
ID PAGERANK; RANKING
AB The amount, size, complexity, and importance of Knowledge Graphs (KGs) have increased during the last decade. Many different communities have chosen to publish their datasets using Linked Data principles, which favors the integration of this information with many other sources published using the same principles and technologies. Such a scenario requires to develop techniques of Linked Data Summarization. The concept of a class is one of the core elements used to define the ontologies which sustain most of the existing KGs. Moreover, classes are an excellent tool to refer to an abstract idea which groups many individuals (or instances) in the context of a given KG, which is handy to use when producing summaries of its content. Rankings of class importance are a powerful summarization tool that can be used both to obtain a superficial view of the content of a given KG and to prioritize many different actions over the data (data quality checking, visualization, relevance for search engines...). In this paper, we analyze existing techniques to measure class importance and propose a novel approach called ClassRank. We compare the class usage in SPARQL logs of different KGs with the importance ranking produced by the approaches evaluated. Then, we discuss the strengths and weaknesses of the evaluated techniques. Our experimentation suggests that ClassRank outperforms state-of-the-art approaches measuring class importance.
C1 [Fernandez-Alvarez, Daniel; Labra Gayo, Jose Emilio; Gayo-Avello, Daniel] Univ Oviedo, Dept Comp Sci, Oviedo, Spain.
   [Frey, Johannes; Hellmann, Sebastian] Univ Leipzig, AKSW Grp, Leipzig, Germany.
RP Fernandez-Alvarez, D (通讯作者)，Univ Oviedo, Dept Comp Sci, Oviedo, Spain.
EM fernandezalvdaniel@uniovi.es
RI Gayo-Avello, Daniel/D-3791-2014; Gayo, José Emilio Labra/L-9181-2014
OI Gayo-Avello, Daniel/0000-0002-4705-6891; Gayo, José Emilio
   Labra/0000-0001-8907-5348; Fernandez-Alvarez, Daniel/0000-0002-8666-7660
FU Severo Ochoa Research program [BP17-88]; Spanish Ministry of Economy and
   Competitiveness [302, TIN2017-88877-R]
FX This work is supported by the Severo Ochoa Research program (2017 call,
   exp. number BP17-88), and by the Spanish Ministry of Economy and
   Competitiveness 302 (Society challenges: TIN2017-88877-R).
CR Alistair KD., 2017, SEARCH ENGINE LINK B
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NR 64
TC 0
Z9 0
U1 1
U2 3
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD JUN 10
PY 2021
VL 16
IS 6
AR e0252862
DI 10.1371/journal.pone.0252862
PG 35
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA SW6SA
UT WOS:000664642700052
PM 34111187
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zucker, J
   Paneri, K
   Mohammad-Taheri, S
   Bhargava, S
   Kolambkar, P
   Bakker, C
   Teuton, J
   Hoyt, CT
   Oxford, K
   Ness, R
   Vitek, O
AF Zucker, Jeremy
   Paneri, Kaushal
   Mohammad-Taheri, Sara
   Bhargava, Somya
   Kolambkar, Pallavi
   Bakker, Craig
   Teuton, Jeremy
   Hoyt, Charles Tapley
   Oxford, Kristie
   Ness, Robert
   Vitek, Olga
TI Leveraging Structured Biological Knowledge for Counterfactual Inference:
   A Case Study of Viral Pathogenesis
SO IEEE TRANSACTIONS ON BIG DATA
LA English
DT Article
DE Biological system modeling; Proteins; Mathematical model; Stochastic
   processes; Biological processes; Data models; COVID-19; Biological
   expression language; structural causal model; counterfactual inference;
   causal biological knowledge graph; systems biology; SARS-CoV-2
ID PATHWAYS; DATABASE; ROLES
AB Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal knowledge that is qualitative in nature. This article proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. We demonstrate the feasibility, accuracy and versatility of this approach using two case studies in systems biology. The first demonstrates the appropriateness of the underlying assumptions and the accuracy of the results. The second demonstrates the versatility of the approach by querying a knowledge base for the molecular determinants of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to estimate the causal effect of medical countermeasures for severely ill patients.
C1 [Zucker, Jeremy; Bakker, Craig; Teuton, Jeremy; Oxford, Kristie] Pacific Northwest Natl Lab, Richland, WA 99354 USA.
   [Paneri, Kaushal] Microsoft, Redmond, WA 98052 USA.
   [Mohammad-Taheri, Sara; Bhargava, Somya; Kolambkar, Pallavi; Vitek, Olga] Northeastern Univ, Boston, MA 02115 USA.
   [Hoyt, Charles Tapley] Enveda Biosci, D-53225 Bonn, Germany.
   [Ness, Robert] Altdeep, Boston, MA 02115 USA.
RP Vitek, O (通讯作者)，Northeastern Univ, Boston, MA 02115 USA.
EM Jeremy.zucker@pnnl.gov; kaushalpaneri@gmail.com;
   mohammadtaheri.s@northeastern.edu; bhargavasomyav2@gmail.com;
   kolambkar.p@husky.neu.edu; craig.bakker@pnnl.gov;
   jeremy.Teuton@pnnl.gov; charles.hoyt@envedatx.com;
   kristie.oxford@pnnl.gov; robertness@gmail.com; o.vitek@northeastern.edu
RI Taheri, Sara/AAF-1637-2021
OI Bhargava, Somya/0000-0001-9926-517X; Mohammad Taheri,
   Sara/0000-0002-6554-9083; Teuton, Jeremy/0000-0002-7703-8870
FU PNNL Mathematics and Artificial Reasoning Systems Laboratory Directed
   Research and Development Initiative
FX This work was supported by funds from the PNNL Mathematics and
   Artificial Reasoning Systems Laboratory Directed Research and
   Development Initiative. Knowledge curation environments were provided by
   BioDati.com and Causaly.com. The authors would also like to acknowledge
   Jessica Stothers and Rose Glavin at CoronaWhy.org and Marek Ostaszewski
   at the COVID-19 Disease Map Initiative for providing valuable feedback
   about the IL6-AMP model. Jeremy Zucker, Kaushal Paneri, Sara
   Mohammad-Taheri contributed equally to this work.
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NR 60
TC 5
Z9 5
U1 2
U2 8
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 MAR 1
PY 2021
VL 7
IS 1
BP 25
EP 37
DI 10.1109/TBDATA.2021.3050680
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QT0ZS
UT WOS:000626322200002
OA Green Submitted, Bronze
DA 2022-08-23
ER

PT J
AU Jafarzadeh, P
   Ensan, F
AF Jafarzadeh, Parastoo
   Ensan, Faezeh
TI A semantic approach to post-retrieval query performance prediction
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Query performance prediction; Semantic-enabled prediction;
   Post-retrieval prediction; Semantic information retrieval
ID SIMILARITY; DOCUMENTS; MODELS; WEB
AB The importance of query performance prediction has been widely acknowledged in the literature, especially for query expansion, refinement, and interpolating different retrieval approaches. This paper proposes a novel semantics-based query performance prediction approach based on estimating semantic similarities between queries and documents. We introduce three post-retrieval predictors, namely (1) semantic distinction, (2) semantic query drift, and (3) semantic cohesion based on (1) the semantic similarity of a query to the top-ranked documents compared to the whole collection, (2) the estimation of non-query related aspects of the retrieved documents using semantic measures, and (3) the semantic cohesion of the retrieved documents. We assume that queries and documents are modeled as sets of entities from a knowledge graph, e.g., DBPedia concepts, instead of bags of words. With this assumption, semantic similarities between two texts are measured based on the relatedness between entities, which are learned from the contextual information represented in the knowledge graph. We empirically illustrate these predictors' effectiveness, especially when term-based measures fail to quantify query performance prediction hypotheses correctly. We report our findings on the proposed predictors' performance and their interpolation on three standard collections, namely ClueWeb09-B, ClueWeb12-B, and Robust04. We show that the proposed predictors are effective across different datasets in terms of Pearson and Kendall correlation coefficients between the predicted performance and the average precision measured by relevance judgments.
C1 [Jafarzadeh, Parastoo; Ensan, Faezeh] Ryerson Univ, Toronto, ON, Canada.
RP Ensan, F (通讯作者)，Ryerson Univ, Toronto, ON, Canada.
EM fensan@ryerson.ca
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NR 70
TC 3
Z9 3
U1 5
U2 11
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 102746
DI 10.1016/j.ipm.2021.102746
EA SEP 2021
PG 20
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 WV2UG
UT WOS:000717092800005
OA Bronze
DA 2022-08-23
ER

PT J
AU Qi, P
   Sun, Y
   Luo, H
AF Qi, Peng
   Sun, Yan
   Luo, Hong
TI Scratch-RL: A preference-driven adversarial reinforcement reasoning
   framework over knowledge graphs for explainable recommendation of
   Scratch
SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
LA English
DT Article; Early Access
DE a directive discriminator network; a preference-driven Actor-Critic
   network; explainable Scratch recommendation
AB Nowadays, Scratch, as a widely-used educational programming platform, has gathered a huge number of programming users all over the world. Facing massive programming resources, how to make satisfactory programming recommendations has attracted increasing attention, especially on explainable recommendations. Existing Scratch recommendation systems overlook to provide why a project is recommended, which prevents users from making better decisions and trusting in the system. To resolve this problem, we design the Scratch-RL, an explainable reinforcement learning framework over knowledge graphs for Scratch recommendation. First, we devise a preference-driven Actor-Critic network to simulate users' local preferences and explore the potential interested projects along the reasoning paths. In the Actor-Critic network, we elaborate a preference state function, a preference-based reward function, and a preference-conditional action pruning strategy for the agent. Then, we leverage a directive discriminator network to help evaluate the correctness of recommendations from the agent and return an extra guidance reward accordingly. A high guidance reward is given when the agent generates correct recommendations, which guarantees that the agent quickly and accurately comprehends the preferences of users. Finally, we jointly train the Actor-Critic network and the discriminator, when the whole training is done, the reasoning paths are taken as the interpretability of the recommendations. Extensive experiments on both the Scratch data set and public data set show that, Scratch-RL obtains favorable recommendation results compared with the state-of-the-art models.
C1 [Qi, Peng; Sun, Yan; Luo, Hong] Beijing Univ Posts & Telecommun, Dept Comp Sci, Beijing 100876, Peoples R China.
RP Sun, Y (通讯作者)，Beijing Univ Posts & Telecommun, Dept Comp Sci, Beijing 100876, Peoples R China.
EM sunyan@bupt.edu.cn
FU National Natural Science Foundation of China [61877005, 62172051]
FX National Natural Science Foundation of China, Grant/Award Numbers:
   61877005, 62172051
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NR 50
TC 0
Z9 0
U1 6
U2 6
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0884-8173
EI 1098-111X
J9 INT J INTELL SYST
JI Int. J. Intell. Syst.
DI 10.1002/int.22931
EA JUN 2022
PG 26
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1T2UR
UT WOS:000804590800001
DA 2022-08-23
ER

PT J
AU Li, JN
   Wu, J
   Li, JH
   Bashir, AK
   Piran, MJ
   Anjum, A
AF Li, Jianan
   Wu, Jun
   Li, Jianhua
   Bashir, Ali Kashif
   Piran, Md. Jalil
   Anjum, Ashiq
TI Blockchain-Based Trust Edge Knowledge Inference of Multi-Robot Systems
   for Collaborative Tasks
SO IEEE COMMUNICATIONS MAGAZINE
LA English
DT Article
AB Collaborative inference helps robots to complete large tasks with mutual collaboration in edge-assisted multi-robot systems. It is challenging to provide trusted edge collaborative inference in the presence of malicious nodes. In this article, we propose a blockchain-based collaborative edge knowledge inference (BCEI) framework for edge-assisted multi-robot systems. First, we formulate the inference process at the edge as the collaborative knowledge graph construction and sharing model. Second, to guarantee the trust of knowledge sharing, an efficient knowledge-based blockchain consensus method is presented. Finally, we conduct a case study on the emergency rescue application to evaluate the proposed framework. The experiment results demonstrate the efficiency of the proposed framework in terms of latency and accuracy.
C1 [Li, Jianan; Wu, Jun; Li, Jianhua] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai, Peoples R China.
   [Bashir, Ali Kashif] Manchester Metropolitan Univ, Dept Comp & Math, Manchester, Lancs, England.
   [Bashir, Ali Kashif] Natl Univ Sci & Technol, Islamabad, Pakistan.
   [Piran, Md. Jalil] Sejong Univ, Dept Comp Sci & Engn, Seoul, South Korea.
   [Anjum, Ashiq] Univ Leicester, Distributed Syst, Leicester, Leics, England.
RP Wu, J (通讯作者)，Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai, Peoples R China.
EM junwuhn@sjtu.edu.cn
FU National Natural Science Foundation of China [61972255, U20B2048]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant No. 61972255 and U20B2048.
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NR 15
TC 5
Z9 6
U1 13
U2 18
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0163-6804
EI 1558-1896
J9 IEEE COMMUN MAG
JI IEEE Commun. Mag.
PD JUL
PY 2021
VL 59
IS 7
BP 94
EP 100
DI 10.1109/MCOM.001.2000419
PG 7
WC Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Telecommunications
GA TS3CK
UT WOS:000679531500028
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Jaimini, U
   Sheth, A
AF Jaimini, Utkarshani
   Sheth, Amit
TI CausalKG: Causal Knowledge Graph Explainability Using Interventional and
   Counterfactual Reasoning
SO IEEE INTERNET COMPUTING
LA English
DT Article
DE Cognition; Internet
AB Humans use causality and hypothetical retrospection in their daily decision-making, planning, and understanding of life events.1 The human mind, while retrospecting a given situation, think about questions such as "What was the cause of the given situation?," "What would be the effect of my action?," "What would have happened if I had taken another action instead?," or "Which action led to this effect?" The human mind has an innate understanding of causality.15 It develops a causal model of the world, which learns with fewer data points, makes inferences, and contemplates counterfactual scenarios.8 The unseen and unknown scenarios are called "counterfactuals."2
C1 [Jaimini, Utkarshani; Sheth, Amit] Univ South Carolina, Artificial Intelligence Inst, Columbia, SC 29208 USA.
RP Sheth, A (通讯作者)，Univ South Carolina, Artificial Intelligence Inst, Columbia, SC 29208 USA.
EM ujaimini@email.sc.edu; amit@sc.edu
OI Sheth, Amit/0000-0002-0021-5293
FU National Science Foundation (NSF) [2133842, 2119654]
FX This work has greatly benefited from the discussions with Dr. Cory
   Henson. We also thank Dr. Christian O'reilly and Ruwan Wickramarachchi
   for their in-depth review. This research is support in part by National
   Science Foundation (NSF) Award #2133842 "EAGER: Advancing Neuro-symbolic
   AI with Deep Knowledgeinfused Learning," and Award #2119654, "RII Track
   2 FEC: Enabling Factory to Factory (F2F) Networking for Future
   Manufacturing." Any opinions, findings, and conclusions or
   recommendations expressed in this material are those of the author(s)
   and do not necessarily reflect the views of the NSF.
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NR 15
TC 0
Z9 0
U1 6
U2 6
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1089-7801
EI 1941-0131
J9 IEEE INTERNET COMPUT
JI IEEE Internet Comput.
PD JAN 1
PY 2022
VL 26
IS 1
BP 43
EP 50
DI 10.1109/MIC.2021.3133551
PG 8
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YW5GS
UT WOS:000753441900014
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Naja, I
   Markovic, M
   Edwards, P
   Pang, W
   Cottrill, C
   Williams, R
AF Naja, Iman
   Markovic, Milan
   Edwards, Peter
   Pang, Wei
   Cottrill, Caitlin
   Williams, Rebecca
TI Using Knowledge Graphs to Unlock Practical Collection, Integration, and
   Audit of AI Accountability Information
SO IEEE ACCESS
LA English
DT Article
DE Artificial intelligence; Ontologies; Rain; Law; Task analysis;
   Recording; Inspection; Accountability; AI systems; machine learning;
   ontology; provenance
ID PROVENANCE
AB To enhance trustworthiness of AI systems, a number of solutions have been proposed to document how such systems are built and used. A key facet of realizing trust in AI is how to make such systems accountable - a challenging task, not least due to the lack of an agreed definition of accountability and differing perspectives on what information should be recorded and how it should be used (e.g., to inform audit). Information originates across the life cycle stages of an AI system and from a variety of sources (individuals, organizations, systems), raising numerous challenges around collection, management, and audit. In our previous work, we argued that semantic Knowledge Graphs (KGs) are ideally suited to address those challenges and we presented an approach utilizing KGs to aid in the tasks of modelling, recording, viewing, and auditing accountability information related to the design stage of AI system development. Moreover, as KGs store data in a structured format understandable by both humans and machines, we argued that this approach provides new opportunities for building intelligent applications that facilitate and automate such tasks. In this paper, we expand our earlier work by reporting additional detailed requirements for knowledge representation and capture in the context of AI accountability; these extend the scope of our work beyond the design stage, to also include system implementation. Furthermore, we present the RAInS ontology which has been extended to satisfy these requirements. We evaluate our approach against three popular baseline frameworks, namely, Datasheets, Model Cards, and FactSheets, by comparing the range of information that can be captured by our KGs against these three frameworks. We demonstrate that our approach subsumes and extends the capabilities of the baseline frameworks and discuss how KGs can be used to integrate and enhance accountability information collection processes.
C1 [Naja, Iman; Markovic, Milan; Edwards, Peter] Univ Aberdeen, Dept Comp Sci, Aberdeen AB24 3UE, Scotland.
   [Pang, Wei] Heriot Watt Univ, Sch Math & Comp Sci, Edinburgh EH14 4AS, Midlothian, Scotland.
   [Cottrill, Caitlin] Univ Aberdeen, Sch Engn, Aberdeen AB24 3UE, Scotland.
   [Cottrill, Caitlin] Univ Aberdeen, Ctr Transport Res, Aberdeen AB24 3UE, Scotland.
   [Williams, Rebecca] Univ Oxford, Fac Law, Oxford OX1 1DW, England.
   [Williams, Rebecca] Univ Oxford, Pembroke Coll, Oxford OX1 1DW, England.
RP Markovic, M (通讯作者)，Univ Aberdeen, Dept Comp Sci, Aberdeen AB24 3UE, Scotland.
EM milan.markovic@abdn.ac.uk
RI Edwards, Peter/A-4808-2010
OI Edwards, Peter/0000-0002-4527-9186; Pang, Wei/0000-0002-1761-6659
FU Realising Accountable Intelligent Systems (RAInS) Project through the UK
   Research and Innovation (UKRI) Digital Economy Program [EP/R033846/1,
   EP/R03379X/1]
FX This work was supported by the Realising Accountable Intelligent Systems
   (RAInS) Project through the UK Research and Innovation (UKRI) Digital
   Economy Program under Award EP/R033846/1 and Award EP/R03379X/1.
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NR 54
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 74383
EP 74411
DI 10.1109/ACCESS.2022.3188967
PG 29
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3Q9AV
UT WOS:000838516400001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Vazquez, M
   Krallinger, M
   Leitner, F
   Kuiper, M
   Valencia, A
   Laegreid, A
AF Vazquez, Miguel
   Krallinger, Martin
   Leitner, Florian
   Kuiper, Martin
   Valencia, Alfonso
   Laegreid, Astrid
TI ExTRI: Extraction of transcription regulation interactions from
   literature
SO BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS
LA English
DT Article
DE Text-mining; Transcription factors; Gene regulation; Systems biology
ID DATABASE; TEXT; CLASSIFICATION; TFCLASS
AB The regulation of gene transcription by transcription factors is a fundamental biological process, yet the relations between transcription factors (TF) and their target genes (TG) are still only sparsely covered in databases. Text-mining tools can offer broad and complementary solutions to help locate and extract mentions of these biological relationships in articles. We have generated ExTRI, a knowledge graph of TF-TG relationships, by applying a high recall text-mining pipeline to MedLine abstracts identifying over 100,000 candidate sentences with TF-TG re-lations. Validation procedures indicated that about half of the candidate sentences contain true TF-TG re-lationships. Post-processing identified 53,000 high confidence sentences containing TF-TG relationships, with a cross-validation F1-score close to 75%. The resulting collection of TF-TG relationships covers 80% of the re-lations annotated in existing databases. It adds 11,000 other potential interactions, including relationships for ~100 TFs currently not in public TF-TG relation databases. The high confidence abstract sentences contribute 25,000 literature references not available from other resources and offer a wealth of direct pointers to functional aspects of the TF-TG interactions. Our compiled resource encompassing ExTRI together with publicly available resources delivers literature-derived TF-TG interactions for more than 900 of the 1500-1600 proteins considered to function as specific DNA binding TFs. The obtained result can be used by curators, for network analysis and modelling, for causal reasoning or knowledge graph mining approaches, or serve to benchmark text mining strategies.
C1 [Vazquez, Miguel; Krallinger, Martin; Valencia, Alfonso] Barcelona Supercomp Ctr, Barcelona, Spain.
   [Leitner, Florian] Data Catalyt, Madrid, Spain.
   [Kuiper, Martin] Norwegian Univ Sci & Technol NTNU, Dept Biol, Trondheim, Norway.
   [Valencia, Alfonso] ICREA, Barcelona, Spain.
   [Laegreid, Astrid] Norwegian Univ Sci & Technol NTNU, Dept Clin & Mol Med, N-7491 Trondheim, Norway.
RP Vazquez, M (通讯作者)，Barcelona Supercomp Ctr, Barcelona, Spain.; Laegreid, A (通讯作者)，Norwegian Univ Sci & Technol NTNU, Dept Clin & Mol Med, N-7491 Trondheim, Norway.
EM miguel.vazquez.g@bsc.es
OI Krallinger, Martin/0000-0002-2646-8782
FU COST (European Cooperation in Science and Technology) [CA15205]
FX We thank the participants of the COST Action GREEKC (CA15205) for
   fruitful discussions during workshops supported by COST (European
   Cooperation in Science and Technology) .
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NR 34
TC 0
Z9 0
U1 4
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1874-9399
EI 1876-4320
J9 BBA-GENE REGUL MECH
JI Biochim. Biophys. Acta-Gene Regul. Mech.
PD JAN
PY 2022
VL 1865
IS 1
AR 194778
DI 10.1016/j.bbagrm.2021.194778
PG 12
WC Biochemistry & Molecular Biology; Biophysics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biophysics
GA 0Y0VN
UT WOS:000790115400002
PM 34875418
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Kinyoki, DK
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CA Local Burden Dis Child Growth
TI Mapping child growth failure across low- and middle-income countries
SO NATURE
LA English
DT Article
ID HEALTH; MORTALITY; WEIGHT; AGE
AB Childhood malnutrition is associated with high morbidity and mortality globally(1). Undernourished children are more likely to experience cognitive, physical, and metabolic developmental impairments that can lead to later cardiovascular disease, reduced intellectual ability and school attainment, and reduced economic productivity in adulthood(2). Child growth failure (CGF), expressed as stunting, wasting, and underweight in children under five years of age (0-59 months), is a specific subset of undernutrition characterized by insufficient height or weight against age-specific growth reference standards(3-5). The prevalence of stunting, wasting, or underweight in children under five is the proportion of children with a height-for-age, weight-for-height, or weight-for-age z-score, respectively, that is more than two standard deviations below the World Health Organization's median growth reference standards for a healthy population(6). Subnational estimates of CGF report substantial heterogeneity within countries, but are available primarily at the first administrative level (for example, states or provinces)(7); the uneven geographical distribution of CGF has motivated further calls for assessments that can match the local scale of many public health programmes(8). Building from our previous work mapping CGF in Africa(9), here we provide the first, to our knowledge, mapped high-spatial-resolution estimates of CGF indicators from 2000 to 2017 across 105 low- and middle-income countries (LMICs), where 99% of affected children live(1), aggregated to policy-relevant first and second (for example, districts or counties) administrative-level units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the ambitious World Health Organization Global Nutrition Targets to reduce stunting by 40% and wasting to less than 5% by 2025. Large disparities in prevalence and progress exist across and within countries; our maps identify high-prevalence areas even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where the highest-need populations reside, these geospatial estimates can support policy-makers in planning interventions that are adapted locally and in efficiently directing resources towards reducing CGF and its health implications.
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RP Hay, SI (通讯作者)，Univ Washington, Inst Hlth Metr & Evaluat, Seattle, WA 98195 USA.; Hay, SI (通讯作者)，Univ Washington, Sch Med, Dept Hlth Metr Sci, Seattle, WA 98195 USA.
EM sihay@uw.edu
RI Mokdad, Ali H./AAD-1232-2022; Arabloo, Jalal/K-1829-2019; Zhang,
   Yunquan/M-9828-2017; Tantawi, Maha El/K-4336-2014; Samy,
   Abdallah/B-4375-2010; Yousefifard, Mahmoud/P-1621-2018; Bedi,
   Neeraj/AGR-0606-2022; Kisa, Adnan/Q-2081-2019; Palone, Marcos Roberto
   Tovani/J-6491-2014; Mohammed, Shafiu/P-2016-2014; Wang, Yuan
   Pang/A-4863-2008; Bhattacharyya, Krittika/AAA-8258-2021; Rajati,
   Fatemeh/S-1041-2017; T., Olagunju Andrew/B-4746-2017; Kononov, Aleksandr
   M/K-1614-2015; Martins-Melo, Francisco Rogerlandio/I-4868-2012; Tadesse,
   Zemenu/ABG-1690-2021; Khader, Yousef/AAE-9620-2019; Jha, Ravi
   Prakash/ABC-6588-2020; Butt, Zahid Ahmad/W-4292-2017; Radfar,
   Amir/I-8057-2019; Jakovljevic, Mihajlo B/B-3002-2014; Mini,
   GK/GMX-3520-2022; Mohebi, Farnam/AGB-7159-2022; Duraes, Andre
   R/N-2593-2017; Sudaryanto, Agus/G-5534-2014; Breitenbach,
   Sebastian/AAB-1420-2019; Mirrakhimov, Erkin/E-6900-2017; Ndwandwe,
   Duduzile/L-2296-2013; Bell, Michelle/Y-4608-2018; Moradi-Lakeh,
   Maziar/ABC-9793-2021; Waheed, Yasir/AAC-3896-2019; Alipour,
   Vahid/K-5099-2019; Veisani, Yousef/I-7684-2017; Herrero,
   Mario/A-6678-2015; KIM, YUN JIN/AAE-8281-2019; Sharafi,
   Kiomars/CAF-0682-2022; Salam, Nasir/C-8049-2015; Bikbov,
   Boris/I-4594-2013; Bassat, Quique/P-2341-2016; Awasthi,
   Ashish/P-3966-2018; Olusanya, Bolajoko/F-4504-2012; El-Khatib,
   Ziad/B-5161-2017; Bhutta, Zulfiqar/L-7822-2015; Moradi,
   Ghobad/R-1267-2016; /L-1271-2015; De Neve, Jan-Walter/D-7547-2017; Das
   Gupta, Rajat/E-5545-2018; Atique, Suleman/N-3469-2015; Rahimi-Movaghar,
   Vafa/T-7816-2017; Ajumobi, Olufemi/J-7389-2018; Soofi,
   Moslem/E-7665-2019; Hay, Simon/F-8967-2015; Mamun, Abdullah/A-4673-2011;
   Derbie, Engida Yisma/ABA-4170-2020; rostami, ali/R-3347-2017; Herteliu,
   Claudiu/K-4643-2014; Grada, Ayman/N-3691-2013; Sathian,
   Brijesh/G-7576-2014; Mousavi, Seyyed Meysam/P-7797-2015; Miazgowski,
   Tomasz/H-9448-2014; Kuate Defo, Barthelemy/I-4818-2017; Krishan,
   Kewal/I-3285-2014; Altirkawi, Khalid/D-7302-2017; Alahdab,
   Fares/N-6680-2013; Car, Josip/H-6755-2015; Kasaeian, Amir/C-8290-2017
OI Mokdad, Ali H./0000-0002-4994-3339; Arabloo, Jalal/0000-0003-1223-4528;
   Zhang, Yunquan/0000-0002-2618-5088; Tantawi, Maha
   El/0000-0003-4989-6584; Samy, Abdallah/0000-0003-3978-1134; Yousefifard,
   Mahmoud/0000-0001-5181-4985; Bedi, Neeraj/0000-0003-4709-8423; Kisa,
   Adnan/0000-0001-7825-3436; Palone, Marcos Roberto
   Tovani/0000-0003-1149-2437; Mohammed, Shafiu/0000-0001-5715-966X; Wang,
   Yuan Pang/0000-0001-7076-8312; Bhattacharyya,
   Krittika/0000-0002-9914-7031; Rajati, Fatemeh/0000-0001-6426-664X; T.,
   Olagunju Andrew/0000-0003-1736-9886; Kononov, Aleksandr
   M/0000-0003-2379-4782; Martins-Melo, Francisco
   Rogerlandio/0000-0003-3690-5023; Khader, Yousef/0000-0002-7830-6857;
   Jha, Ravi Prakash/0000-0001-5230-1436; Butt, Zahid
   Ahmad/0000-0002-2486-4781; Radfar, Amir/0000-0001-6177-3048;
   Jakovljevic, Mihajlo B/0000-0002-9160-6846; Mini,
   GK/0000-0003-2775-629X; Duraes, Andre R/0000-0002-1506-0327; Sudaryanto,
   Agus/0000-0002-5886-0295; Mirrakhimov, Erkin/0000-0003-2982-6108;
   Ndwandwe, Duduzile/0000-0001-7129-3865; Bell,
   Michelle/0000-0002-3965-1359; Moradi-Lakeh, Maziar/0000-0001-7381-5305;
   Waheed, Yasir/0000-0002-5789-4215; Alipour, Vahid/0000-0003-1010-9571;
   Herrero, Mario/0000-0002-7741-5090; KIM, YUN JIN/0000-0001-8853-6587;
   Salam, Nasir/0000-0001-9133-1304; Bikbov, Boris/0000-0002-1925-7506;
   Bassat, Quique/0000-0003-0875-7596; Awasthi, Ashish/0000-0002-9308-9782;
   Foigt, Nataliya/0000-0002-3613-5965; Olusanya,
   Bolajoko/0000-0002-3826-0583; Diaz, Daniel/0000-0003-2302-1982;
   El-Khatib, Ziad/0000-0003-0756-7280; Karami,
   Manoochehr/0000-0002-9026-3757; Tadesse, Zemenu/0000-0003-3878-7956;
   Bhutta, Zulfiqar/0000-0003-0637-599X; Moradi,
   Ghobad/0000-0003-2612-6528; Al-Raddadi, Rajaa/0000-0002-8921-9628;
   /0000-0002-7599-165X; Perico, Norberto/0000-0002-3147-4327;
   Asadi-Aliabadi, Mehran/0000-0003-1582-5489; De Neve,
   Jan-Walter/0000-0003-0090-8249; Quintana, Hedley
   Knewjen/0000-0002-5929-8791; Das Gupta, Rajat/0000-0002-7680-676X;
   Atique, Suleman/0000-0002-5149-0703; Rahimi-Movaghar,
   Vafa/0000-0001-7347-8767; Ajumobi, Olufemi/0000-0002-2652-1205; Li,
   Shanshan/0000-0002-9021-8470; Miller, Ted/0000-0002-0958-2639; Kimokoti,
   Ruth/0000-0002-4980-3256; Soofi, Moslem/0000-0003-4922-8412; Hay,
   Simon/0000-0002-0611-7272; Mamun, Abdullah/0000-0002-1535-8086; Britton,
   Gabrielle/0000-0002-1758-2495; Tabares-Seisdedos,
   Rafael/0000-0002-1089-2204; Ross, Jennifer/0000-0001-5677-939X; Mohebi,
   Farnam/0000-0002-6181-2008; Jozwiak, Jacek/0000-0002-2645-7274; Derbie,
   Engida Yisma/0000-0003-0703-1515; rostami, ali/0000-0002-1358-1321;
   Silva, Diego Augusto Santos/0000-0002-0489-7906; Aremu,
   Olatunde/0000-0002-5832-2403; Postma, Maarten/0000-0002-6306-3653;
   Effiong, Andem/0000-0003-2899-9656; Roever,
   Leonardo/0000-0002-7517-5548; Desalegn, Beruk
   Berhanu/0000-0001-7161-8260; Herteliu, Claudiu/0000-0001-8860-9547;
   Grada, Ayman/0000-0002-5321-0584; Yisma, Engida/0000-0002-7289-9946;
   Moodley, Yoshan/0000-0002-4119-1734; /0000-0001-5266-0201; Sathian,
   Brijesh/0000-0003-0851-4762; Ahmadi, Keivan/0000-0001-5674-2765;
   Chipeta, Michael Give/0000-0001-5882-9936; Pourshams,
   Akram/0000-0002-7950-3983; Sufiyan, Muawiyyah
   Babale/0000-0003-3630-6995; Zaman, Sojib Bin/0000-0002-3043-7954;
   Mousavi, Seyyed Meysam/0000-0002-6795-7224; Miazgowski,
   Tomasz/0000-0002-4819-9376; Chu, Dinh-Toi/0000-0002-4596-2022; Kuate
   Defo, Barthelemy/0000-0002-6589-0564; Kumar, Manasi/0000-0002-9773-8014;
   Krishan, Kewal/0000-0001-5321-0958; ansari,
   parastoo/0000-0003-1536-1565; Altirkawi, Khalid/0000-0002-7331-4196;
   Temsah, Mohamad-Hani/0000-0002-4389-9322; Alahdab,
   Fares/0000-0001-5481-696X; Noubiap, Jean Jacques/0000-0002-7722-9757;
   Moazen, Babak/0000-0002-2552-0438; Al-Eyadhy, Ayman/0000-0002-6051-9125;
   Mosser, Jonathan/0000-0001-9093-3186; Car, Josip/0000-0001-8969-371X;
   Marczak, Laurie/0000-0001-5979-4261; Bhattarai,
   Suraj/0000-0001-6843-6677; Guo, Yuming/0000-0002-1766-6592; Kasaeian,
   Amir/0000-0003-2018-9368; RENZAHO, ANDRE/0000-0002-6844-0833; Olusanya,
   Jacob/0000-0002-1566-9554; Nourollahpour Shiadeh,
   Malihe/0000-0002-2303-3054
FU Bill AMP; Melinda Gates Foundation [OPP1132415]
FX This work was primarily supported by grant OPP1132415 from the Bill &
   Melinda Gates Foundation.
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NR 40
TC 43
Z9 43
U1 11
U2 54
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 0028-0836
EI 1476-4687
J9 NATURE
JI Nature
PD JAN 9
PY 2020
VL 577
IS 7789
BP 231
EP +
DI 10.1038/s41586-019-1878-8
PG 21
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA KB7PM
UT WOS:000506682500039
PM 31915393
OA Green Accepted, Green Submitted, hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Zhao, ZB
   Wu, XL
   Qi, YC
   Nie, LQ
   Lv, B
AF Zhao, Zhenbing
   Wu, Xueliang
   Qi, Yincheng
   Nie, Liqiang
   Lv, Bin
TI A knowledge-guided model of fitting detection in aerial transmission
   line images
SO ENERGY REPORTS
LA English
DT Article; Proceedings Paper
CT 7th International Conference on Power and Energy Systems Engineering
   (CPESE)
CY SEP 26-29, 2020
CL Fukuoka, JAPAN
DE Fitting detection; Spatial attributes; Co-occurrence model; Graph; Deep
   learning
AB Transmission lines are one of the most important infrastructures of energy Internet in China, and the effective detection of fittings is a necessary prerequisite to ensure their safety and stability. The fitting detection method based on classic deep learning only focuses on a single region and extracts the visual features of the region proposal into the classifier to identify the target, but never considers the connection between fittings. There is a certain regularity between fittings when constructing a complete and intact transmission line, interdependent and interact with each other. Therefore, we propose a knowledge-guided aerial transmission line image fitting detection model (KGFD) which uses two modules to learn the regularity of the fittings. The first module is an implicit module, starting from the spatial layout of the fittings on the image and taking the relative geometric features of the region proposal as the input knowledge of the spatial position of the fittings on the image. The second module is an explicit one. By introducing the prior knowledge of the fittings expressed in co-occurrence mode and using the region proposal as the node while the prior knowledge as the edge, a knowledge graph is constructed. The information spreads on the knowledge graph via a gated graph neural network, which realizes a combination of the prior knowledge of the fitting with the features of the region proposal. These two modules are added to the deep learning framework to detect the fitting targets, and the typical fitting data set of aerial transmission lines are used for testing. The AP value increased by 4.4% when IoU={0.5:0.95}and the AP value by 3.9% when IoU=0.5 compared with those of Faster R-CNN classification. (C) 2020 The Authors. Published by Elsevier Ltd.
C1 [Zhao, Zhenbing; Wu, Xueliang; Qi, Yincheng] North China Elect Power Univ, Sch Elect & Elect Engn, Baoding 071003, Peoples R China.
   [Nie, Liqiang] Shandong Univ, Sch Comp Sci & Technol, Qingdao 266237, Peoples R China.
   [Lv, Bin] State Grid Zhejiang Hangzhou Xiaoshan Power Suppl, Hangzhou, Peoples R China.
RP Zhao, ZB (通讯作者)，North China Elect Power Univ, Sch Elect & Elect Engn, Baoding 071003, Peoples R China.
EM zhaozhenbing@ncepu.edu.cn
FU National Natural Science Foundation of China (NSFC) [61871182,
   61773160]; Beijing Natural Science Foundation [4192055]; Natural Science
   Foundation of Hebei Province of China [F2020502009]; Fundamental
   Research Funds for the Central Universities [2018MS095, 2020YJ006]; Open
   Project Program of the National Laboratory of Pattern Recognition(NLPR)
   [201900051]
FX This work is supported in part by the National Natural Science
   Foundation of China (NSFC) under grant number 61871182, 61773160, by
   Beijing Natural Science Foundation under grant number 4192055, by the
   Natural Science Foundation of Hebei Province of China under grant number
   F2020502009, by the Fundamental Research Funds for the Central
   Universities under grant number 2018MS095, 2020YJ006, and by the Open
   Project Program of the National Laboratory of Pattern Recognition(NLPR)
   under grant number 201900051.
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NR 13
TC 1
Z9 1
U1 4
U2 12
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-4847
J9 ENERGY REP
JI Energy Rep.
PD DEC
PY 2020
VL 6
SU 9
BP 1071
EP 1078
DI 10.1016/j.egyr.2020.11.075
PG 8
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Energy & Fuels
GA PN3OP
UT WOS:000604392100146
OA gold
DA 2022-08-23
ER

PT J
AU Sato, T
AF Sato, Taisuke
TI A linear algebraic approach to datalog evaluation
SO THEORY AND PRACTICE OF LOGIC PROGRAMMING
LA English
DT Article
DE Datalog; least model; matrix; vector space
ID ALGORITHMS
AB We propose a fundamentally new approach to Datalog evaluation. Given a linear Datalog program DB written using N constants and binary predicates, we first translate if-and-only-if completions of clauses in DB into a set Eq(DB) of matrix equations with a non-linear operation, where relations in MDB, the least Herbrand model of DB, are encoded as adjacency matrices. We then translate Eq(DB) into another, but purely linear matrix equations Eq(DB). It is proved that the least solution of Eq(DB) in the sense of matrix ordering is converted to the least solution of Eq(DB) and the latter gives MDB as a set of adjacency matrices. Hence, computing the least solution of Eq(DB) is equivalent to computing MDB specified by DB. For a class of tail recursive programs and for some other types of programs, our approach achieves O(N-3) time complexity irrespective of the number of variables in a clause since only matrix operations costing O(N-3) or less are used. We conducted two experiments that compute the least Herbrand models of linear Datalog programs. The first experiment computes transitive closure of artificial data and real network data taken from the Koblenz Network Collection. The second one compared the proposed approach with the state-of-the-art symbolic systems including two Prolog systems and two ASP systems, in terms of computation time for a transitive closure program and the same generation program. In the experiment, it is observed that our linear algebraic approach runs 10(1) similar to 10(4) times faster than the symbolic systems when data is not sparse. Our approach is inspired by the emergence of big knowledge graphs and expected to contribute to the realization of rich and scalable logical inference for knowledge graphs.
C1 [Sato, Taisuke] Natl Inst Informat, AI Res Ctr AIST, Tokyo, Japan.
RP Sato, T (通讯作者)，Natl Inst Informat, AI Res Ctr AIST, Tokyo, Japan.
EM satou.taisuke@aist.go.jp
RI Sato, Taisuke/Q-3245-2016
OI Sato, Taisuke/0000-0001-9062-0729
FU New Energy and Industrial Technology Development Organization (NEDO)
FX This research is supported in part by a project commissioned by the New
   Energy and Industrial Technology Development Organization (NEDO).
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   [No title captured]
NR 30
TC 12
Z9 12
U1 0
U2 0
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA
SN 1471-0684
EI 1475-3081
J9 THEOR PRACT LOG PROG
JI Theory Pract. Log. Program.
PD MAY
PY 2017
VL 17
IS 3
BP 244
EP 265
DI 10.1017/S1471068417000023
PG 22
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods; Logic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Science & Technology - Other Topics
GA EV5HR
UT WOS:000401795100001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Wang, JS
   Li, WJ
   Lin, PQ
   Mu, FT
AF Wang, Jiashuo
   Li, Wenjie
   Lin, Peiqin
   Mu, Feiteng
TI Empathetic Response Generation through Graph-based Multi-hop Reasoning
   on Emotional Causality
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Empathetic response generation; Commonsense knowledge graph; Multi-hop
   reasoning
AB Empathetic response generation aims to comprehend the user emotion and then respond to it appropriately. Most existing works merely focus on what the emotion is and ignore how the emotion is evoked, thus weakening the capacity of the model to understand the emotional experience of the user for generating empathetic responses. To tackle this problem, we consider the emotional causality, namely, what feelings the user expresses (i.e., emotion) and why the user has such feelings (i.e., cause). Then, we propose a novel graph-based model with multi-hop reasoning to model the emotional causality of the empathetic conversation. Finally, we demonstrate the effectiveness of our model on EMPATHETICDIALOGUES in comparison with several competitive models. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Wang, Jiashuo; Li, Wenjie; Lin, Peiqin; Mu, Feiteng] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China.
RP Wang, JS (通讯作者)，Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China.
EM jessiejs.wang@connect.polyu.hk; cswjli@comp.polyu.edu.hk;
   lpq29743@gmail.com; feitengmu.mu@connect.polyu.hk
RI Lin, Peiqin/AAD-1470-2022
OI Lin, Peiqin/0000-0003-2818-3008; , Jiashuo/0000-0002-8254-8138; Li,
   Wenjie/0000-0002-7360-8864
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NR 47
TC 1
Z9 1
U1 7
U2 9
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 107547
DI 10.1016/j.knosys.2021.107547
EA OCT 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WK7SG
UT WOS:000709922600003
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Huo, F
   Xu, L
   Li, YP
   Famiglietti, JS
   Li, ZH
   Kajikawa, Y
   Chen, F
AF Huo, Fei
   Xu, Li
   Li, Yanping
   Famiglietti, James S.
   Li, Zhenhua
   Kajikawa, Yuya
   Chen, Fei
TI Using big data analytics to synthesize research domains and identify
   emerging fields in urban climatology
SO WILEY INTERDISCIPLINARY REVIEWS-CLIMATE CHANGE
LA English
DT Review
DE citation analysis; knowledge mapping; text mining; thematic synthesis;
   urban climate
ID ORGANIC AEROSOL FORMATION; GREENHOUSE-GAS EMISSIONS; FOSSIL-FUEL CO2;
   LAND-USE CHANGE; HEAT-ISLAND; COASTAL PROTECTION; UNITED-STATES;
   ST-LOUIS; URBANIZATION; RAINFALL
AB The growing concerns over urbanization and climate change have resulted in an exponential growth in publications on urban climatology in recent decades. However, an advanced synthesis that characterizes the existing studies is lacking. In this review, we used citation network analysis and a text mining approach to identify research trends and extract common research topics and the emerging domains in urban climatology. Based on the clustered networks, we found that aerosols and ozone, and urban heat island are the most popular topics. Together with other clusters, four emerging topical fields were identified: secondary organic aerosols, urban precipitation, flood risk and adaptation, and greenhouse gas emissions. The city case studies' geographical information was analyzed to explore the spatial-temporal patterns, especially in the emerging topical fields. Interdisciplinary research grew in recent years as the field of urban climatology expanded to interact with urban hydrology, health, energy issues, and social sciences. A few knowledge gaps were proposed: the lack of long-term high-temporal-resolution observational data of organic aerosols for model validation and improvements, the need for predictions of urban effects on precipitation and extreme flooding events under climate change, and the lack of a framework for cooperation between physical sciences and social sciences under urban settings. To fill these gaps, we call for more observational data with high spatial and temporal resolution, using high-resolution models that adequately represent urban processes to conduct scenario analyses for urban planning, and the development of intellectual frameworks for better integration of urban climatology and social-economical systems in cities.
   This article is categorized under:
   Climate, History, Society, Culture > Disciplinary Perspectives
C1 [Huo, Fei; Xu, Li; Li, Yanping; Famiglietti, James S.; Li, Zhenhua] Univ Saskatchewan, Global Inst Water Secur, Saskatoon, SK, Canada.
   [Kajikawa, Yuya] Tokyo Inst Technol, Sch Environm & Soc, Tokyo, Japan.
   [Kajikawa, Yuya] Univ Tokyo, Inst Future Initiat, Tokyo, Japan.
   [Chen, Fei] Res Applicat Lab, Boulder, CO USA.
RP Li, YP (通讯作者)，Univ Saskatchewan, Global Inst Water Secur, Saskatoon, SK, Canada.
EM yanping.li@usask.ca
RI Chen, Fei/B-1747-2009; Huo, Fei/AAE-7664-2019; Famiglietti, James
   S/G-7383-2017; Xu, Li/G-3395-2014
OI Chen, Fei/0000-0003-2573-3828; Huo, Fei/0000-0002-4135-795X;
   Famiglietti, James S/0000-0002-6053-5379; Xu, Li/0000-0002-5580-8774
FU Natural Sciences and Engineering Research Council of Canada (NSERC);
   Global Water Futures; Canada 150 Research Chair in Hydrology and Remote
   Sensing
FX Natural Sciences and Engineering Research Council of Canada (NSERC);
   Global Water Futures; Canada 150 Research Chair in Hydrology and Remote
   Sensing
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NR 155
TC 3
Z9 3
U1 7
U2 51
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1757-7780
EI 1757-7799
J9 WIRES CLIM CHANGE
JI Wiley Interdiscip. Rev.-Clim. Chang.
PD JAN
PY 2021
VL 12
IS 1
AR e688
DI 10.1002/wcc.688
EA NOV 2020
PG 23
WC Environmental Studies; Meteorology & Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences
GA PH0UK
UT WOS:000588767100001
DA 2022-08-23
ER

PT J
AU Yang, G
   Li, ZY
   Ye, WH
   Huang, SX
   Liu, SH
   Liu, K
   Tan, Q
AF Yang, Ge
   Li, Zhuoyang
   Ye, Weihua
   Huang, Shengxiang
   Liu, Shaohua
   Liu, Kun
   Tan, Qian
TI Bibliometric Analysis of the 100 Most Cited Articles on Intervertebral
   Disk Research From 1900 to 2017 Year
SO CLINICAL SPINE SURGERY
LA English
DT Review
DE bibliometric analysis; spine; disk degeneration
ID BACK-PAIN; SCIENCE; PREVALENCE; CITATIONS
AB Study Design: A bibliometric review of the literature. Object: To analyze and quantify the most frequently cited papers in intervertebral disk research. Summary of Background Data: The number of citations that a paper has received reflects its impact in related research area. In the field of disk research, however, it remains unknown which papers are most cited. By searching related literature databases, we identified the most cited 100 articles that advanced the understanding of the intervertebral disk to provide a historic view of scientific research of the disk. Materials and Methods: Intervertebral disk original research-related publications from January 1, 1900 to December 31, 2017 were retrieved from the Web of Science database. Each retrieved article was analyzed using the Cited Reference Search tool to identify the most cited articles. The number of citations, year of publication, publishing journal, authorship, country of publication, and the knowledge maps of keywords were gathered and generated. Results: The number of citations of the 100 selected articles ranges from 209 to 1269, and they were published from 1953 to 2009. Basic research is the most common type of study (n=60), followed by epidemiological study (n=40). Spine published 57 of the most cited 100 papers. The greatest contribution came from the United States of America (n=41), followed by the United Kingdom (n=18) and Japan (n=9). Frequently appearing keywords classified into 3 clusters: "biological study," "clinical study," and "imageological study." The keyword "degeneration" was mentioned the most, 51 items, and the word "development" was the latest hot spot in the most cited articles. Conclusions: In the field of disk research, some papers were heavily cited more than 100 times, suggesting these studies have substantially contributed to the body of knowledge of the intervertebral disk and findings were widely accepted by related clinicians and scientists.
C1 [Yang, Ge; Ye, Weihua; Huang, Shengxiang; Liu, Shaohua; Liu, Kun; Tan, Qian] Hunan Childrens Hosp, Dept Orthoped Surg, 86 Ziyuan Rd, Changsha 410003, Peoples R China.
   [Li, Zhuoyang] Zhejiang Univ, Dept Orthoped Surg, Affiliated Hosp 1, Hangzhou, Peoples R China.
RP Yang, G (通讯作者)，Hunan Childrens Hosp, Dept Orthoped Surg, 86 Ziyuan Rd, Changsha 410003, Peoples R China.
EM yangorthop2018@163.com
FU China Postdoctoral Science Foundation [2019M652773]
FX Supported by the China Postdoctoral Science Foundation (grant no.
   2019M652773).
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NR 20
TC 2
Z9 2
U1 0
U2 0
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 2380-0186
J9 CLIN SPINE SURG
JI Clin. Spine Surg.
PD APR
PY 2020
VL 33
IS 3
BP 104
EP 110
DI 10.1097/BSD.0000000000000863
PG 7
WC Clinical Neurology; Orthopedics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology; Orthopedics
GA LB9LT
UT WOS:000524951000009
PM 31404016
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Atapattu, T
   Falkner, K
   Falkner, N
AF Atapattu, Thushari
   Falkner, Katrina
   Falkner, Nickolas
TI A comprehensive text analysis of lecture slides to generate concept maps
SO COMPUTERS & EDUCATION
LA English
DT Article
DE improving classroom teaching; Media in education; Pedagogical issues;
   Teaching/learning strategies
ID KNOWLEDGE MAPS; POWERPOINT
AB Current instructional methods widely support verbal learning through linear and sequential teaching materials, focusing on isolated pieces of information. However, an important aspect of learning design is to facilitate students in identifying relationships between information. The transformation of linearity in teaching resources into integrated network models such as concept maps facilitates effective knowledge organisation by constructing relationships between new and existing knowledge. However, the manual construction of concept maps from teaching materials places an additional workload on the academics involved. Consequently, this research investigates the effectiveness of automated approaches in extracting concept maps from lecture slides and the suitability of auto-generated concept maps as a pedagogical tool. We develop a set of Natural Language Processing (NLP) algorithms to support concept-relation-concept triple extraction to form concept maps. Structural and graph-based features are utilised to rank the triples according to their importance. The natural layout of the lecture slides is incorporated to organise the triples in a hierarchy, facilitating highly integrated structure. Our evaluation studies identify promising results, with several case studies demonstrating a statistically significant correlation (r(s) > 0.455) between auto-generated concept maps and human experts' judgment. Auto-generated concept maps were rated from 'good' to 'very good' by the academics on evaluation factors such as coverage, accuracy, and suitability as a pedagogical tool. Thus, auto-generated concept maps from this research can be utilised as a positive alternative to the manual construction of expert concept maps and further, it is possible to utilise these maps for a wider range of applications including knowledge organisation and reflective visualisation of course contents. Our research contributes to bridging the gap between linearity in teaching materials and the necessity of creating integrated network models from teaching resources. (C) 2017 Elsevier Ltd. All rights reserved.
C1 [Atapattu, Thushari; Falkner, Katrina; Falkner, Nickolas] Univ Adelaide, Sch Comp Sci, Adelaide, SA 5005, Australia.
RP Atapattu, T (通讯作者)，Univ Adelaide, Sch Comp Sci, Adelaide, SA 5005, Australia.
EM thushari.atapattu@adelaide.edu.au
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   [No title captured]
NR 67
TC 31
Z9 31
U1 4
U2 127
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0360-1315
EI 1873-782X
J9 COMPUT EDUC
JI Comput. Educ.
PD DEC
PY 2017
VL 115
BP 96
EP 113
DI 10.1016/j.compedu.2017.08.001
PG 18
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA FJ5IT
UT WOS:000412785900007
DA 2022-08-23
ER

PT J
AU Li, Q
   Yang, B
   Wang, SL
   Zhang, ZP
   Tang, XL
   Zhao, CY
AF Li, Qiu
   Yang, Bo
   Wang, Shilong
   Zhang, Zhengping
   Tang, Xiaoli
   Zhao, Chunyang
TI A fine-grained flexible graph convolution network for visual inspection
   of resistance spot welds using cross-domain features
SO JOURNAL OF MANUFACTURING PROCESSES
LA English
DT Article
DE Weld appearance quality; Knowledge graph; GCN
ID VISION
AB Resistance spot welding (RSW) is widely used in manufacturing processes of automobiles, aircraft, high-speed trains and other equipment, its appearance quality not only affects the product appearance, but also reflects the internal defects of welding spots and the health statuses of welding equipment to a great extent. Nowadays, a few researchers have tried to use deep learning algorithms to detect the appearance qualities of welding spots, however, the relationships between defect types and welding spot positions are ignored and the subtle visual differences among different welding spots are not fully utilized. To this end, a fine-grained flexible graph convolution network (FFGCN) is proposed for vision inspection of resistance spot welds in this paper, which combines natural language processing with computer vision. Specifically, the prior knowledge of relationships between weld appearance qualities and their positions is mapped to point-wise space by knowledge graph, so the features in point-wise space can be mined by a flexible graph convolution network (FGCN). In the FGCN, multihead attention mechanism is adopted to adaptively update the probabilistic matrix to generate multiple subgraphs, expand the spatial dimensions and increase feature information. Meanwhile, the optical features of weld images are excavated by a fine-grained network, in which the receptive field of model is enlarged and the number of pixels is ensured by dense atrous convolution. Besides, subtle visual differences between welding spots are captured by the bilinear attention convolution. Finally, the features in point-wise space and the visual features are combined by dot product to classify the appearances of welds. A six classification experiment for the vision inspection of resistance spot welds from engineering practice is implemented, results show that the proposed FFGCN has outstanding effect on the inspection of weld appearance qualities, it possesses faster convergence, more robustness, and its accuracy reaches 97.5%, which is higher than the commonly used visual classification algorithms.
C1 [Li, Qiu; Yang, Bo; Wang, Shilong] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China.
   [Zhang, Zhengping; Tang, Xiaoli] Chongqing Sokon Ind Grp Stock Co Ltd, Chongqing 400033, Peoples R China.
   [Zhao, Chunyang] Shenzhen Univ, Coll Mechatron & Control Engn, Shenzhen Key Lab High Performance Nontradit Mfg, Shenzhen 518060, Peoples R China.
   [Yang, Bo] Chongqing Univ, State Key Lab Mech Transmiss, 174 Shazheng St, Shapingba Dist, Chongqing 400044, Peoples R China.
RP Yang, B (通讯作者)，Chongqing Univ, State Key Lab Mech Transmiss, 174 Shazheng St, Shapingba Dist, Chongqing 400044, Peoples R China.
EM 202007021022@cqu.edu.cn; yangbo61@cqu.edu.cn; wang.sl@cqu.edu.cn;
   john.zhang@sokon.com; xiaoli.tang@dfsk.com.cn; zhaochunyang@szu.edu.cn
FU National Key R&D Pro-gram of China [2020YFB1713300]; National Natural
   Science Foundation of China [62003216]; Key Project of Technological
   Innovation and Application Development Plan of Chongqing
   [cstc2019jscx-mbdxX0056]; Natural Science Foundation of Chongq-ing
   [cstc2021jcyj-msxmX0732]; Self-Planned Task of State Key Laboratory of
   Mechanical Transmission [SKLMT-ZZKT-2021M03]; Shenzhen Stability Support
   Plan A [20200812104451001]
FX & nbsp;The presented work was supported by the National Key R & D
   Pro-gram of China (no. 2020YFB1713300) , the National Natural Science
   Foundation of China (no. 62003216) , the Key Project of Technological
   Innovation and Application Development Plan of Chongqing (no.
   cstc2019jscx-mbdxX0056) , the Natural Science Foundation of Chongq-ing
   (no. cstc2021jcyj-msxmX0732) and the Self-Planned Task of State Key
   Laboratory of Mechanical Transmission (no. SKLMT-ZZKT-2021M03) ,
   Shenzhen Stability Support Plan A (no. 20200812104451001) .
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NR 36
TC 0
Z9 0
U1 8
U2 8
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1526-6125
EI 2212-4616
J9 J MANUF PROCESS
JI J. Manuf. Process.
PD JUN
PY 2022
VL 78
BP 319
EP 329
DI 10.1016/j.jmapro.2022.04.025
PG 11
WC Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 1M1ZG
UT WOS:000799773400004
DA 2022-08-23
ER

PT J
AU Althebyan, Q
AF Althebyan, Qutaibah
TI Mitigating Insider Threats on the Edge: A Knowledgebase Approach
SO INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY
LA English
DT Article
DE Insider Threats; Fog; Mobile Edge; Cloud; Knowledge Graph; Dependency
   Graph; Database
AB Insider Threats, who are cloud internal users, cause very serious problems, which in terns, leads to devastating attacks for both individuals and organizations. Although, most of the attentions, in the real world, is for the outsider attacks, however, the most damaging attacks come from the Insiders. In cloud computing, the problem becomes worst in which the number of insiders are maximized and hence, the amount of data that can be breached and disclosed is also maximized. Consequently, insiders' threats in the cloud ought to be one of the top most issues that should be handled and settled. Classical solutions to defend against insiders' threats might fail short as it is not easy to track both activities of the insiders as well as the amount of knowledge an insider can accumulate through his/her privileged accesses. Such accumulated knowledge can be used to disclose critical information -which the insider is not privileged to- through expected dependencies that exist among different data items that reside in one or more nodes of the cloud. This paper provides a solution that suits well the specialized nature of the above mentioned problem. This solution takes advantage of knowledge bases by tracking accumulated knowledge of insiders through building Knowledge Graphs (KGs) for each insider. It also takes advantage of Mobile Edge Computing (MEC) by building a fog layer where a mitigation unit -resides on the edge- takes care of the insiders threats in a place that is as close as possible to the place where insiders reside. As a consequence, this gives continuous reactions to the insiders' threats in real-time, and at the same time, lessens the overhead in the cloud. The MEC model to be presented in this paper utilizes a knowledgebase approach where insiders' knowledge is tracked and modeled. In case an insider knowledge accumulates to a level that is expected to cause some potential disclosure of private data, an alarm will be raised so that expected actions should be taken to mitigate this risk. The knowledgebase approach involves generating Knowledge Graphs (KGs), Dependency Graphs (DGs) where a Threat Prediction Value (TPV) is evaluated to estimate the risk upon which alarms for potential disclosure are raised. Experimental analysis has been conducted using CloudExp simulator where the results have shown the ability of the proposed model to raise alarms for potential risks from insiders in a real time fashion with accurate precision.
C1 [Althebyan, Qutaibah] Al Ain Univ, Coll Engn, Al Ain, U Arab Emirates.
   [Althebyan, Qutaibah] Jordan Univ Sci & Technol, Software Engn Dept, Irbid, Jordan.
RP Althebyan, Q (通讯作者)，Al Ain Univ, Coll Engn, Al Ain, U Arab Emirates.; Althebyan, Q (通讯作者)，Jordan Univ Sci & Technol, Software Engn Dept, Irbid, Jordan.
OI Althebyan, Qutaibah/0000-0002-7474-4198
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NR 18
TC 1
Z9 1
U1 2
U2 3
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.
PY 2020
VL 17
IS 4A
SI SI
BP 621
EP 628
DI 10.34028/iajit/17/4A/6
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QF8WJ
UT WOS:000617170200006
OA gold
DA 2022-08-23
ER

PT J
AU Bellandi, V
   Ceravolo, P
   Maghool, S
   Siccardi, S
AF Bellandi, Valerio
   Ceravolo, Paolo
   Maghool, Samira
   Siccardi, Stefano
TI Graph embeddings in criminal investigation: towards combining precision,
   generalization and transparency Special issue on computational aspects
   of network science
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Knowledge graphs; Enquiry protocols; Criminal investigation; Graph
   embeddings
ID PERFORMANCE
AB Criminal investigation adopts Artificial Intelligence to enhance the volume of the facts that can be investigated and documented in trials. However, the abstract reasoning implied in legal justification and argumentation requests to adopt solutions providing high precision, low generalization error, and retrospective transparency. Three requirements that hardly coexist in today's Artificial Intelligence solutions. In a controlled experiment, we then investigated the use of graph embeddings procedures to retrieve potential criminal actions based on patterns defined in enquiry protocols. We observed that a significant level of accuracy can be achieved but different graph reformation procedures imply different levels of precision, generalization, and transparency.
C1 [Bellandi, Valerio; Ceravolo, Paolo; Maghool, Samira; Siccardi, Stefano] Univ Milan, Dept Comp Sci, Via Celoria 18, Milan, Italy.
RP Maghool, S (通讯作者)，Univ Milan, Dept Comp Sci, Via Celoria 18, Milan, Italy.
EM valerio.bellandi@unimi.it; paolo.ceravolo@unimi.it;
   samira.maghool@unimi.it; stefano.siccardi@unimi.it
RI bellandi, valerio/M-7582-2019
OI bellandi, valerio/0000-0003-4473-6258
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NR 38
TC 1
Z9 1
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
DI 10.1007/s11280-021-01001-2
EA FEB 2022
PG 24
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZA5NL
UT WOS:000756210100001
OA hybrid
DA 2022-08-23
ER

PT J
AU Chen, DW
   Zhou, JL
   Duan, PS
   Zhang, JQ
AF Chen, Dawei
   Zhou, Jianliang
   Duan, Pinsheng
   Zhang, Jiaqi
TI Integrating knowledge management and BIM for safety risk identification
   of deep foundation pit construction
SO ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
LA English
DT Article; Early Access
DE Safety risk identification; BIM; Deep foundation pit; Knowledge base;
   Pandemic
ID ONTOLOGY; SYSTEM; RECOGNITION
AB Purpose The outbreak of COVID-19 pandemic has posed severe challenges to infrastructure construction in China. Particularly, the complex technology and high process uncertainty of deep foundation pit construction make its safety risk identification a challenging issue of general concern. To address these challenges, Building Information Modeling (BIM) can be used as an important tool to enhance communication and decision-making among stakeholders during the pandemic. The purpose of this study is to propose a knowledge management and BIM-integrated safety risk identification method for deep foundation pit construction to improve the management efficiency of project participants. Design/methodology/approach This paper proposes a risk identification method that integrates BIM and knowledge management for deep foundation pit construction. In the framework of knowledge management, the topological relationships between objects in BIM are extracted and visualized in the form of knowledge mapping. After that, formal expressions of codes are established to realize the structured processing of specification provisions and special construction requirements. A comprehensive plug-in for deep foundation pit construction is designed based on the BIM software. Findings The proposed method was verified by taking a sub-project in deep foundation pit project construction as an example. The result showed the new method can make full use of the existing specification and special engineering requirements knowledge. In addition, the developed visual BIM plug-in proves the feasibility and applicability of the proposed method, which can help to increase the risk identification efficiency and refinement. Originality/value The deep foundation pit safety risk identification is challenged by the confusion of deep foundation pit construction safety knowledge and the complexity of the BIM model. By establishing the standardized expression of normative knowledge and special construction requirements, the efficiency and refinement of risk identification are improved while ensuring the comprehensiveness of results. Moreover, the topology-based risk identification method focuses on the project objects and their relations in the way of network, eliminating the problem of low efficiency from the direct BIM-based risk identification method due to massive data.
C1 [Chen, Dawei] Capital Univ Econ & Business, Sch Management Engn, Beijing, Peoples R China.
   [Zhou, Jianliang; Duan, Pinsheng; Zhang, Jiaqi] China Univ Min & Technol, Sch Mech & Civil Engn, Xuzhou, Jiangsu, Peoples R China.
RP Zhou, JL (通讯作者)，China Univ Min & Technol, Sch Mech & Civil Engn, Xuzhou, Jiangsu, Peoples R China.
EM zhoujianliang@cumt.edu.cn
FU National Natural Science Foundation of China [72171224, 71871116];
   Humanities and Social Sciences Foundation of China's Education Ministry
   [19YJAZH122]; Graduate Innovation Program of China University of Mining
   and Technology [2021WLKXJ033]
FX This work was supported by the National Natural Science Foundation of
   China (Grant number 72171224, 71871116), the Humanities and Social
   Sciences Foundation of China's Education Ministry (Grant number
   19YJAZH122) and Graduate Innovation Program of China University of
   Mining and Technology (Grant number 2021WLKXJ033). The authors would
   also like to thank the editors and reviewers for their valuable
   suggestions.
CR Bian Y., 2008, CHINESE J UNDERGROUN, V4, P1378
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   Fan WH, 2021, ADV CIV ENG, V2021, DOI 10.1155/2021/5539796
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NR 35
TC 0
Z9 0
U1 0
U2 0
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0969-9988
EI 1365-232X
J9 ENG CONSTR ARCHIT MA
JI Eng. Constr. Archit. Manag.
DI 10.1108/ECAM-10-2021-0934
EA AUG 2022
PG 17
WC Engineering, Industrial; Engineering, Civil; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics
GA 3P7WL
UT WOS:000837745500001
DA 2022-08-23
ER

PT J
AU Wu, HY
   Wang, YL
   Tong, LJ
   Yan, H
   Sun, ZM
AF Wu, Haiyang
   Wang, Yulin
   Tong, Linjian
   Yan, Hua
   Sun, Zhiming
TI The Global Research Trends and Hotspots on Developmental Dysplasia of
   the Hip: A Bibliometric and Visualized Study
SO FRONTIERS IN SURGERY
LA English
DT Article
DE developmental dysplasia of the hip; bibliometric analysis;
   visualization; research trends; hotspots
ID TRIPLE PELVIC OSTEOTOMY; CONGENITAL DISLOCATION; ARTHROPLASTY;
   DIAGNOSIS; RISK; ULTRASOUND; SONOGRAPHY; CHILDREN; SURGERY; COSTS
AB Background: Developmental dysplasia of the hip (DDH) is a common musculoskeletal disorder in newborns and also one of the most common causes of hip arthritis in women. Many topics concerning DDH still remain controversial, and the global research trend in this field has not been well-studied yet. The aim of the present study was to illustrate the overall knowledge structure, development trends, and research hotspots of DDH.
   Methods: The publications related to DDH from 1998 to 2020 were identified from the Web of Science Core Collection (WOSCC). Three bibliometric tools were used to conduct visualization and knowledge maps. Annual trends of publications, contributions of countries, institutions, authors, funding agencies and journals, and clustering of keywords were analyzed.
   Results: A total of 2,691 publications were included. The annual number of DDH publications showed an increasing trend worldwide. The United States has made the greatest contribution, with the largest number of publications and the highest H-index. The most prolific institutions were Shanghai Jiao Tong University, Children's Hospital of Philadelphia, and Shriners Hospital for Children. Professors Tonnis D, Harris WH, Crowe JF, Graf R, and Salter RB have made great achievements in this field. However, the collaboration between international institutions or researchers was relatively low and mainly conducted in European and American countries. All the keywords could be divided into five clusters: hip osteoarthritis study, hip replacement study, hip ultrasound study, osteotomy surgery study, and etiology study. A trend of balanced and diversified development existed in these clusters. Keywords with the ongoing bursts, including clinical outcome, risk factor, femoroacetabular impingement, predictor, arthroscopy, morphology, and anteversion may continue to be the research hotspots in the near future.
   Conclusions: There will be an increasing number of publications on DDH research, and the United States stay ahead in this field. International collaboration needs to be further strengthened. The information can provide helpful references for researchers to explore hot issues or target a specific field of DDH.
C1 [Wu, Haiyang; Wang, Yulin; Tong, Linjian; Yan, Hua; Sun, Zhiming] Tianjin Med Univ, Grad Sch, Tianjin, Peoples R China.
   [Wu, Haiyang; Wang, Yulin; Tong, Linjian] Tianjin Med Univ, Clin Coll Neurol Neurosurg & Neurorehabil, Tianjin, Peoples R China.
   [Yan, Hua] Tianjin Huanhu Hosp, Tianjin Neurosurg Inst, Tianjin Key Lab Cerebral Vasc & Neurodegenerat Di, Tianjin, Peoples R China.
   [Sun, Zhiming] Tianjin Huanhu Hosp, Dept Orthopaed Surg, Tianjin, Peoples R China.
RP Yan, H; Sun, ZM (通讯作者)，Tianjin Med Univ, Grad Sch, Tianjin, Peoples R China.; Yan, H (通讯作者)，Tianjin Huanhu Hosp, Tianjin Neurosurg Inst, Tianjin Key Lab Cerebral Vasc & Neurodegenerat Di, Tianjin, Peoples R China.; Sun, ZM (通讯作者)，Tianjin Huanhu Hosp, Dept Orthopaed Surg, Tianjin, Peoples R China.
EM yanhua20042007@sina.com; szhm618@163.com
FU Tianjin Municipal Health Bureau
FX This study was supported by the Tianjin Municipal Health Bureau (Grant
   No. 14KG115) and the Key projects of Tianjin Natural Science Foundation
   (Grant No. 20JCZDJC00730).
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NR 57
TC 2
Z9 2
U1 2
U2 6
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-875X
J9 FRONT SURG
JI Front. Surg.
PD OCT 25
PY 2021
VL 8
AR 671403
DI 10.3389/fsurg.2021.671403
PG 16
WC Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Surgery
GA WU7LY
UT WOS:000716724100001
PM 34760913
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Li, LF
   Wang, P
   Yan, J
   Wang, Y
   Li, SM
   Jiang, JP
   Sun, Z
   Tang, BZ
   Chang, TH
   Wang, SH
   Liu, YT
AF Li, Linfeng
   Wang, Peng
   Yan, Jun
   Wang, Yao
   Li, Simin
   Jiang, Jinpeng
   Sun, Zhe
   Tang, Buzhou
   Chang, Tsung-Hui
   Wang, Shenghui
   Liu, Yuting
TI Real-world data medical knowledge graph: construction and applications
SO ARTIFICIAL INTELLIGENCE IN MEDICINE
LA English
DT Article
DE real-world data; medical knowledge graph; CDSS; quadruplet; PSR
AB Objective: Medical knowledge graph (KG) is attracting attention from both academic and healthcare industry due to its power in intelligent healthcare applications. In this paper, we introduce a systematic approach to build medical KG from electronic medical records (EMRs) with evaluation by both technical experiments and end to end application examples.
   Materials and Methods: The original data set contains 16,217,270 de-identified clinical visit data of 3,767,198 patients. The KG construction procedure includes 8 steps, which are data preparation, entity recognition, entity normalization, relation extraction, property calculation, graph cleaning, related-entity ranking, and graph embedding respectively. We propose a novel quadruplet structure to represent medical knowledge instead of the classical triplet in KG. A novel related-entity ranking function considering probability, specificity and reliability (PSR) is proposed. Besides, probabilistic translation on hyperplanes (PrTransH) algorithm is used to learn graph embedding for the generated KG.
   Results: A medical KG with 9 entity types including disease, symptom, etc. was established, which contains 22,508 entities and 579,094 quadruplets. Compared with term frequency - inverse document frequency (TF/IDF) method, the normalized discounted cumulative gain (NDCG@10) increased from 0.799 to 0.906 with the proposed ranking function. The embedding representation for all entities and relations were learned, which are proven to be effective using disease clustering.
   Conclusion: The established systematic procedure can efficiently construct a high-quality medical KG from large-scale EMRs. The proposed ranking function PSR achieves the best performance under all relations, and the disease clustering result validates the efficacy of the learned embedding vector as entity's semantic representation. Moreover, the obtained KG finds many successful applications due to its statistics-based quadruplet.
   where N-infinity(min) is a minimum co-occurrence number and R is the basic reliability value. The reliability value can measure how reliable is the relationship between S-i and O-ij. The reason for the definition is the higher value of N-co(S-i,O-ij) the relationship is more reliable. However, the reliability values of the two relationships should not have a big difference if both of their co-occurrence numbers are very big. In our study, we finally set N-infinity(min) = 10 and R = 1 after some experiments. For instance, if co-occurrence numbers of three relationships are 1, 100 and 10000, their reliability values are 1, 2.96 and 5 respectively.
C1 [Li, Linfeng; Wang, Shenghui] Beijing Jiaotong Univ, Inst Informat Sci, Beijing, Peoples R China.
   [Li, Linfeng; Yan, Jun; Wang, Yao; Li, Simin; Jiang, Jinpeng; Sun, Zhe] Yidu Cloud Technol Inc, Beijing, Peoples R China.
   [Wang, Peng] Chongqing Univ, Coll Comp Sci, Chongqing, Peoples R China.
   [Wang, Peng] Southwest Hosp, Chongqing, Peoples R China.
   [Tang, Buzhou] Harbin Inst Technol, Shenzhen Grad Sch, Dept Comp Sci, Shenzhen, Peoples R China.
   [Chang, Tsung-Hui] Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen, Peoples R China.
   [Liu, Yuting] Beijing Jiaotong Univ, Sch Sci, Beijing, Peoples R China.
RP Liu, YT (通讯作者)，Beijing Jiaotong Univ, Beijing 100044, Peoples R China.
EM ytliu@bjtu.edu.cn
RI Chang, Tsung-Hui/J-3512-2014
OI Chang, Tsung-Hui/0000-0003-1349-2764; Wang, Yao/0000-0003-4121-7313
FU National Key Research Program [2018YFC0116700]
FX This work is supported in part by the National Key Research Program
   under Grant 2018YFC0116700.
CR [Anonymous], 2017, ARXIV PREPRINT ARXIV
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NR 34
TC 25
Z9 28
U1 13
U2 76
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 MAR
PY 2020
VL 103
AR 101817
DI 10.1016/j.artmed.2020.101817
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Biomedical;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Medical Informatics
GA KW4EG
UT WOS:000521117900015
PM 32143785
DA 2022-08-23
ER

PT J
AU Wu, HY
   Zhou, Y
   Wang, YL
   Tong, LJ
   Wang, FC
   Song, SR
   Xu, LX
   Liu, BL
   Yan, H
   Sun, ZM
AF Wu, Haiyang
   Zhou, Yan
   Wang, Yulin
   Tong, Linjian
   Wang, Fanchen
   Song, Sirong
   Xu, Lixia
   Liu, Baolong
   Yan, Hua
   Sun, Zhiming
TI Current State and Future Directions of Intranasal Delivery Route for
   Central Nervous System Disorders: A Scientometric and Visualization
   Analysis
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE scientometric; intranasal drug delivery; blood-brain barrier; CNS
   disorders; citespace; VOS viewer
ID NANOSTRUCTURED LIPID CARRIERS; TO-BRAIN DELIVERY; DRUG-DELIVERY;
   BIBLIOMETRIC ANALYSIS; ALZHEIMER-DISEASE; NEURAL CIRCUITRY; INSULIN;
   OXYTOCIN; NANOPARTICLES; TRANSPORT
AB Background: The management of various central nervous system (CNS) disorders has been challenging, due to highly compact blood-brain barrier (BBB) impedes the access of most pharmacological agents to the brain. Among multiple strategies proposed to circumvent this challenge, intranasal delivery route has sparked great interest for brain targeting in the past decades. The aim of this study was to apply scientometric method to estimate the current status and future trends of the field from a holistic perspective.
   Methods: All relevant publications during 1998-2020 were retrieved from the Web of Science Core Collection (SCIE, 1998-present). Two different scientometric software including VOS viewer and CiteSpace, and one online platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of journals, countries, institutes, authors, references and keywords.
   Results: A total of 2,928 documents, including 2,456 original articles and 472 reviews, were retrieved. Our analysis revealed a significant increasing trend in the total number of scientific publications over the past 2 decades (R-2 = 0.98). The United States dominated the field, reflecting in the largest amount of publications (971), the highest H-index (99), and extensive international collaboration. Jamia Hamdard contributed to most publications. Frey WH and Illum L were key researchers with the highest number of publications and citations, respectively. The International Journal of Pharmaceutics was the most influential academic journal, and Pharmacology/Pharmacy and Neurosciences/Neurology were the hottest research categories in this field. Based on keywords occurrence analysis, four main topics were identified, and the current research focus of this field has shifted from cluster 4 (pathways and mechanisms of intranasal delivery) to cluster 2 (the study of nasal drug delivery systems), especially the nanostructured and nano-sized carrier systems. Keywords burst detection revealed that the research focus on oxidative stress, drug delivery, neuroinflammation, nanostructured lipid carrier, and formulation deserves our continued attention.
   Conclusion: To the authors' knowledge, this is the first scientometric analysis regarding intranasal delivery research. This study has demonstrated a comprehensive knowledge map, development landscape and future directions of intranasal delivery research, which provides a practical and valuable reference for scholars and policymakers in this field.
C1 [Wu, Haiyang; Zhou, Yan; Wang, Yulin; Tong, Linjian; Wang, Fanchen; Song, Sirong; Yan, Hua; Sun, Zhiming] Tianjin Med Univ, Clin Coll Neurol Neurosurg & Neurorehabil, Tianjin, Peoples R China.
   [Xu, Lixia; Yan, Hua] Tianjin Huanhu Hosp, Tianjin Neurosurg Inst, Tianjin Key Lab Cerebral Vasc & Neurodegenerat D, Tianjin, Peoples R China.
   [Sun, Zhiming] Tianjin Huanhu Hosp, Dept Spine & Spinal Cord, Tianjin, Peoples R China.
   [Liu, Baolong] Tianjin Huanhu Hosp, Dept Ultrasound, Tianjin, Peoples R China.
RP Yan, H; Sun, ZM (通讯作者)，Tianjin Med Univ, Clin Coll Neurol Neurosurg & Neurorehabil, Tianjin, Peoples R China.; Yan, H (通讯作者)，Tianjin Huanhu Hosp, Tianjin Neurosurg Inst, Tianjin Key Lab Cerebral Vasc & Neurodegenerat D, Tianjin, Peoples R China.; Sun, ZM (通讯作者)，Tianjin Huanhu Hosp, Dept Spine & Spinal Cord, Tianjin, Peoples R China.
EM yanhua20042007@sina.com; szhm618@163.com
RI Xu, Lixia/CAJ-5305-2022
OI Xu, Lixia/0000-0002-1843-0127
FU Tianjin Municipal Health Bureau [14KG115]; Key Program of the Natural
   Science Foundation of Tianjin [20JCZDJC00730]
FX This work was supported by the Tianjin Municipal Health Bureau (grant
   numbers 14KG115) and Key Program of the Natural Science Foundation of
   Tianjin (grant numbers 20JCZDJC00730).
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NR 100
TC 9
Z9 9
U1 14
U2 38
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 JUL 12
PY 2021
VL 12
AR 717192
DI 10.3389/fphar.2021.717192
PG 20
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA TP8UG
UT WOS:000677869000001
PM 34322030
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Li, QW
   Long, RY
   Chen, H
   Chen, FY
   Wang, JQ
AF Li, Qianwen
   Long, Ruyin
   Chen, Hong
   Chen, Feiyu
   Wang, Jiaqi
TI Visualized analysis of global green buildings: Development, barriers and
   future directions
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Article
DE Green building; Subject analysis; Knowledge base; Visualization
   research; Literature review
ID WILLINGNESS-TO-PAY; INDOOR ENVIRONMENT QUALITY; ENERGY PERFORMANCE;
   SUSTAINABLE BUILDINGS; HOUSING EVIDENCE; SATISFACTION; LEED;
   PRODUCTIVITY; INFORMATION; SELECTION
AB Green building is an important measure to deal with energy and environment problems in the construction sector of the world. In this study, knowledge mapping analysis is used, and 3,060 articles are selected for further study (1900-2019). Based on CiteSpace (5.3.R4 SE 64-bit), the knowledge base, hot topics and research trends of green-building-related research are presented. The key highlights of the overall analysis results are: (1) Research on green buildings has received more extensive attention in the 21st century. During 2000-2010, research on green buildings showed a steady growth trend. Since 2011, the amount of literature has increased rapidly, and green building research is in a period of rapid growth. (2) In the macro-disciplinary analysis, the trends and focus of applied green building research is highlighted. In addition to the environmental, political, economic, and computing fields that green building research has traditionally belonged to, in recent years, some journals have published green-buildingrelated research in the fields of psychology, education, health, medicine, and animal science. (3) In the carrier analysis of meso-research findings, in terms of citation frequency, "Building & Environment," "Energy & Buildings" and the "Journal of Cleaner Production" were the top three journals. In terms of influence, "Building & Environment," "Building Research & Information" and "Solar Energy" were the top three journals. (4) In the microcosmic research topic analysis, a timeline visualization of a keyword co-occurrence network containing 14 clusters was generated. "Performance," "Green Building," "Sustainability," "System" and "Construction" were the five most frequently used keywords in this field. Next, the key analysis results were: (5) According to the literature clustering results, 12 research hotspots of early research, intermediate research and recent research on green building were ascertained. (6) The indicators of the research frontier were divided into burst terms and burst articles, wherein the former was divided into four stages according to the burst order. Taking the latest frontier research as an example (i.e., barriers), the "CAPITAL" framework was constructed to overcome the barriers that inhibited the promotion of green buildings from five levels. Finally, the shortcomings of this research are summarized, and some suggestions for future research are put forward. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Li, Qianwen; Long, Ruyin; Chen, Hong; Chen, Feiyu; Wang, Jiaqi] China Univ Min & Technol, Sch Management, Xuzhou 221116, Jiangsu, Peoples R China.
   [Long, Ruyin] Henan Polytech Univ, Sch Business Adm, Res Ctr Energy Econ, Jiaozuo 454000, Henan, Peoples R China.
RP Long, RY; Chen, H (通讯作者)，China Univ Min & Technol, Sch Management, Xuzhou 221116, Jiangsu, Peoples R China.
EM longruyin@163.com; hongchenxz@163.com
RI Chen, Feiyu/AAO-2258-2020
OI Chen, Hong/0000-0002-2035-6851; Li, Qianwen/0000-0001-5117-2695; Gong,
   Likun/0000-0001-8821-641X
FU Fundamental Research Funds for the Central Universities [2019BSCX15]
FX This work was supported by "the Fundamental Research Funds for the
   Central Universities" (2019BSCX15).
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NR 91
TC 33
Z9 34
U1 41
U2 230
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0959-6526
EI 1879-1786
J9 J CLEAN PROD
JI J. Clean Prod.
PD FEB 1
PY 2020
VL 245
AR 118775
DI 10.1016/j.jclepro.2019.118775
PG 15
WC Green & Sustainable Science & Technology; Engineering, Environmental;
   Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Engineering; Environmental Sciences
   & Ecology
GA JX4XQ
UT WOS:000503739400119
DA 2022-08-23
ER

PT J
AU Mei, P
   Ding, GY
   Jin, QK
   Zhang, FQ
   Jiao, YF
AF Mei, Peng
   Ding, GangYi
   Jin, QianKun
   Zhang, FuQuan
   Jiao, YangFan
TI Quantum-Based Creative Generation Method for a Dancing Robot
SO FRONTIERS IN NEUROROBOTICS
LA English
DT Article
DE creative generation; quantum simulation; information fidelity; M-3DQKG;
   QGAN; robot trajectory
ID SIMULATION; ENTANGLEMENT; INFORMATION; COMPUTATION; DYNAMICS; THINKING;
   TEAM
AB In this paper, we propose a creative generation process model based on the quantum modeling simulation method. This model is mainly aimed at generating the running trajectory of a dancing robot and the execution plan of the dancing action. First, we used digital twin technology to establish data mapping between the robot and the computer simulation environment to realize intelligent controllability of the robot's trajectory and the dance movements described in this paper. Second, we conducted many experiments and carried out a lot of research into information retrieval, information fidelity, and result evaluation. We constructed a multilevel three-dimensional spatial quantum knowledge map (M-3DQKG) based on the coherence and entangled states of quantum modeling and simulation. Combined with dance videos, we used regions with convolutional neural networks (R-CNNs) to extract character bones and movement features to form a movement library. We used M-3DQKG to quickly retrieve information from the knowledge base, action library, and database, and then the system generated action models through a holistically nested edge detection (HED) network. The system then rendered scenes that matched the actions through generative adversarial networks (GANs). Finally, the scene and dance movements were integrated, and the creative generation process was completed. This paper also proposes the creativity generation coefficient as a means of evaluating the results of the creative process, combined with artificial brain electroenchalographic data to assist in evaluating the degree of agreement between creativity and needs. This paper aims to realize the automation and intelligence of the creative generation process and improve the creative generation effect and usability of dance movements. Experiments show that this paper has significantly improved the efficiency of knowledge retrieval and the accuracy of knowledge acquisition, and can generate unique and practical dance moves. The robot's trajectory is novel and changeable, and can meet the needs of dance performances in different scenes. The creative generation process of dancing robots combined with deep learning and quantum technology is a required field for future development, and could provide a considerable boost to the progress of human society.
C1 [Mei, Peng; Ding, GangYi; Jin, QianKun] Beijing Inst Technol, Sch Comp Sci Technol, Digital Performance & Simulat Technol, Beijing, Peoples R China.
   [Zhang, FuQuan] Minjiang Univ, Fujian Prov Key Lab Informat Proc & Intelligent C, Fuzhou, Peoples R China.
   [Jiao, YangFan] Beijing Wanshide Technol Co Ltd, Beijing, Peoples R China.
RP Zhang, FQ (通讯作者)，Minjiang Univ, Fujian Prov Key Lab Informat Proc & Intelligent C, Fuzhou, Peoples R China.
EM 8528750@qq.com
RI Mei, Peng/AAO-2979-2020
OI Mei, Peng/0000-0001-8335-3909
FU Beijing Wanshide Technology Co., Ltd.
FX Special thanks to Beijing Wanshide Technology Co., Ltd. for its
   financial support for scientific research projects.
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NR 103
TC 0
Z9 0
U1 8
U2 20
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 DEC 1
PY 2020
VL 14
AR 559366
DI 10.3389/fnbot.2020.559366
PG 22
WC Computer Science, Artificial Intelligence; Robotics; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Robotics; Neurosciences & Neurology
GA PE6YO
UT WOS:000598509900001
PM 33335481
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Frisby, TS
   Baker, SJ
   Marcais, G
   Hoang, QM
   Kingsford, C
   Langmead, CJ
AF Frisby, Trevor S.
   Baker, Shawn J.
   Marcais, Guillaume
   Hoang, Quang Minh
   Kingsford, Carl
   Langmead, Christopher J.
TI Harvestman: a framework for hierarchical feature learning and selection
   from whole genome sequencing data
SO BMC BIOINFORMATICS
LA English
DT Article
DE Feature selection; Hierarchical feature spaces; Knowledge graphs;
   Integer linear programming; Machine learning
ID GENE ONTOLOGY; MACHINE; CANCER
AB Background Supervised learning from high-throughput sequencing data presents many challenges. For one, the curse of dimensionality often leads to overfitting as well as issues with scalability. This can bring about inaccurate models or those that require extensive compute time and resources. Additionally, variant calls may not be the optimal encoding for a given learning task, which also contributes to poor predictive capabilities. To address these issues, we present Harvestman, a method that takes advantage of hierarchical relationships among the possible biological interpretations and representations of genomic variants to perform automatic feature learning, feature selection, and model building. Results We demonstrate that Harvestman scales to thousands of genomes comprising more than 84 million variants by processing phase 3 data from the 1000 Genomes Project, one of the largest publicly available collection of whole genome sequences. Using breast cancer data from The Cancer Genome Atlas, we show that Harvestman selects a rich combination of representations that are adapted to the learning task, and performs better than a binary representation of SNPs alone. We compare Harvestman to existing feature selection methods and demonstrate that our method is more parsimonious-it selects smaller and less redundant feature subsets while maintaining accuracy of the resulting classifier. Conclusion Harvestman is a hierarchical feature selection approach for supervised model building from variant call data. By building a knowledge graph over genomic variants and solving an integer linear program , Harvestman automatically and optimally finds the right encoding for genomic variants. Compared to other hierarchical feature selection methods, Harvestman is faster and selects features more parsimoniously.
C1 [Frisby, Trevor S.; Baker, Shawn J.; Marcais, Guillaume; Kingsford, Carl; Langmead, Christopher J.] Carnegie Mellon Univ, Computat Biol Dept, Pittsburgh, PA 15213 USA.
   [Hoang, Quang Minh] Carnegie Mellon Univ, Comp Sci Dept, Pittsburgh, PA USA.
RP Kingsford, C; Langmead, CJ (通讯作者)，Carnegie Mellon Univ, Computat Biol Dept, Pittsburgh, PA 15213 USA.
EM carlk@cs.cmu.edu; cjl@cs.cmu.edu
OI Langmead, Christopher/0000-0001-7521-6736
FU Center for Machine Learning and Health at Carnegie Mellon University;
   CURE Grant from the Pennsylvania Department of Health (PA DOH)
   [4100070287]; National Science Foundation [ACI-1548562, TG-DMS160012];
   National Cancer Institute [P30CA047904]; NIH T32 training Grant as part
   of the HHMI-NIBIB Interfaces Initiative [T32 EB009403]; Gordon and Betty
   Moore Foundation's Data-Driven Discovery Initiative [GBMF4554]; US
   National Institutes of Health [R01GM122935]
FX This work was partially funded by the Center for Machine Learning and
   Health at Carnegie Mellon University and is supported by the CURE Grant
   4100070287 from the Pennsylvania Department of Health (PA DOH). The
   content of this paper is solely the responsibility of the authors and
   does not necessarily represent the official views of the funding
   agencies. The Pennsylvania Department of Health specifically disclaims
   responsibility for any analyses, interpretations or conclusions. This
   work used the Extreme Science and Engineering Discovery Environment
   [43], which is supported by National Science Foundation Grant Number
   ACI-1548562 through allocation TG-DMS160012. This project used the
   Hillman Cancer Bioinformatics Services, which is supported in part by
   the National Cancer Institute award P30CA047904. This research is
   supported by an NIH T32 training Grant T32 EB009403 as part of the
   HHMI-NIBIB Interfaces Initiative. This research is funded in part by the
   Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative
   through Grant GBMF4554 to C.K., and by the US National Institutes of
   Health (R01GM122935). None of the funding bodies played a role in the
   design, analysis, or interpretation of data, or in writing the
   manuscript. The CURE award provided access to the TCGA data.
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NR 43
TC 1
Z9 1
U1 0
U2 1
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD APR 1
PY 2021
VL 22
IS 1
AR 174
DI 10.1186/s12859-021-04096-6
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 RH8MB
UT WOS:000636464400001
PM 33794760
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU You, XD
   Yang, MJ
   Han, JM
   Ma, JW
   Xiao, G
   Lv, XQ
AF You, Xindong
   Yang, Meijing
   Han, Junmei
   Ma, Jiangwei
   Xiao, Gang
   Lv, Xueqiang
TI Attribute Extraction Study in the Field of Military Equipment Based on
   Distant Supervision
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB The effective organization and utilization of military equipment data is an important cornerstone for constructing knowledge system. Building a knowledge graph in the field of military equipment can effectively describe the relationship between entity and entity attribute information. Therefore, relevant personnel can obtain information quickly and accurately. Attribute extraction is an important part of building the knowledge graph. Given the lack of annotated data in the field of military equipment, we propose a new data annotation method, which adopts the idea of distant supervision to automatically build the attribute extraction dataset. We convert the attribute extraction task into a sequence annotation task. At the same time, we propose a RoBERTa-BiLSTM-CRF-SEL-based attribute extraction method. Firstly, a list of attribute name synonyms is constructed, then a corpus of military equipment attributes is obtained through automatic annotation of semistructured data in Baidu Encyclopedia. RoBERTa is used to obtain the vector encoding of the text. Then, input it into the entity boundary prediction layer to label the entity head and tail, and input the BiLSTM-CRF layer to predict the attribute label. The experimental results show that the proposed method can effectively perform attribute extraction in the military equipment domain. The F1 value of the model reaches 77% on the constructed attribute extraction dataset, which outperforms the current state-of-art model.
C1 [You, Xindong; Yang, Meijing; Ma, Jiangwei; Lv, Xueqiang] Beijing Informat Sci & Technol Univ, Beijing Key Lab Internet Culture Digital Dissemin, Beijing, Peoples R China.
   [Han, Junmei; Xiao, Gang] Inst Syst Engn, Natl Key Lab Complex Syst Simulat, Beijing, Peoples R China.
RP Lv, XQ (通讯作者)，Beijing Informat Sci & Technol Univ, Beijing Key Lab Internet Culture Digital Dissemin, Beijing, Peoples R China.
EM lxq@bistu.edu.cn
FU Natural Science Foundation of Beijing [4212020]; National Natural
   Science Foundation of China [62171043]; Defense-Related Science and
   Technology Key Lab Fund Project [6412006200404]; Qin Xin Talents
   Cultivation Program of Beijing Information Science and Technology
   University [QXTCP B201908]; Research Planning of Beijing Municipal
   Commission of Education [KM202111232001]
FX The authors also acknowledge the Natural Science Foundation of Beijing
   under grant no. 4212020, National Natural Science Foundation of China
   under grant no. 62171043, Defense-Related Science and Technology Key Lab
   Fund Project under grant no. 6412006200404, Qin Xin Talents Cultivation
   Program of Beijing Information Science and Technology University under
   grant no. QXTCP B201908, and Research Planning of Beijing Municipal
   Commission of Education under grant no. KM202111232001.
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NR 39
TC 0
Z9 0
U1 26
U2 26
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 NOV 23
PY 2021
VL 2021
AR 2549488
DI 10.1155/2021/2549488
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ZD4BC
UT WOS:000758143800001
OA gold
DA 2022-08-23
ER

PT J
AU MacAvaney, S
   Yates, A
   Cohan, A
   Soldaini, L
   Hui, K
   Goharian, N
   Frieder, O
AF MacAvaney, Sean
   Yates, Andrew
   Cohan, Arman
   Soldaini, Luca
   Hui, Kai
   Goharian, Nazli
   Frieder, Ophir
TI Overcoming low-utility facets for complex answer retrieval
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 Complex answer retrieval; Knowledge graphs; Neural information
   retrieval; Reranking
AB Many questions cannot be answered simply; their answers must include numerous nuanced details and context. Complex Answer Retrieval (CAR) is the retrieval of answers to such questions. These questions can be constructed from a topic entity (e.g., cheese') and a facet (e.g., health effects'). While topic matching has been thoroughly explored, we observe that some facets use general language that is unlikely to appear verbatim in answers, exhibiting low utility. In this work, we present an approach to CAR that identifies and addresses low-utility facets. First, we propose two estimators of facet utility: the hierarchical structure of CAR queries, and facet frequency information from training data. Then, to improve the retrieval performance on low-utility headings, we include entity similarity scores using embeddings trained from a CAR knowledge graph, which captures the context of facets. We show that our methods are effective by applying them to two leading neural ranking techniques, and evaluating them on the TREC CAR dataset. We find that our approach perform significantly better than the unmodified neural ranker and other leading CAR techniques, yielding state-of-the-art results. We also provide a detailed analysis of our results, verify that low-utility facets are indeed difficult to match, and that our approach improves the performance for these difficult queries.
C1 [MacAvaney, Sean; Cohan, Arman; Soldaini, Luca; Goharian, Nazli; Frieder, Ophir] Georgetown Univ, Comp Sci Dept, Informat Retrieval Lab, 3700 O St NW, Washington, DC 20057 USA.
   [Yates, Andrew] Max Planck Inst Informat, Saarland Informat Campus, Saarbrucken, Germany.
   [Cohan, Arman] Allen Inst Artificial Intelligence, Seattle, WA USA.
   [Hui, Kai] SAP SE Machine Learning R&D, Berlin, Germany.
RP MacAvaney, S (通讯作者)，Georgetown Univ, Comp Sci Dept, Informat Retrieval Lab, 3700 O St NW, Washington, DC 20057 USA.
EM sean@ir.cs.georgetown.edu
RI Hui, Kai/AAM-2600-2020
OI Hui, Kai/0000-0002-3110-7404
CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722, DOI 10.1007/978-3-540-76298-0_52
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NR 37
TC 1
Z9 1
U1 0
U2 7
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 395
EP 418
DI 10.1007/s10791-018-9343-0
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:000468918400007
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Xi, JN
   Miao, ZJ
   Liu, LZ
   Yang, XB
   Zhang, WS
   Huang, QH
   Li, XL
AF Xi, Jianing
   Miao, Zhaoji
   Liu, Longzhong
   Yang, Xuebing
   Zhang, Wensheng
   Huang, Qinghua
   Li, Xuelong
TI Knowledge tensor embedding framework with association enhancement for
   breast ultrasound diagnosis of limited labeled samples
SO NEUROCOMPUTING
LA English
DT Article
DE Breast ultrasound; Knowledge graph; Tensor decomposition; Limited
   labeled samples
ID COMPUTER-AIDED DIAGNOSIS; QUANTITATIVE-ANALYSIS; CLASSIFICATION;
   ALIGNMENT; LESIONS
AB In the AI diagnosis of breast cancer, instead of ultrasound images from non-standard acquisition process, the Breast Image Reporting and Data System (BI-RADS) reports are widely accepted as the input data since it can give standardized descriptions for the breast ultrasound samples. The BI-RADS reports are usually stored as the format of Knowledge Graph (KG) due to the flexibility, and the KG embedding is a common procedure for the AI analysis on BI-RADS data. However, since most existing embedding methods are based on the local connections in KG, in the situation of limited labeled samples, there is a clear need for embedding based diagnosis method which is capable of representing the global interactions among all entities/relations and associating the labeled/unlabeled samples. To diagnose the breast ultrasound samples with limited labels, in this paper we propose an efficient framework Knowledge Tensor Embedding with Association Enhancement Diagnosis (KTEAED), which adopts tensor decomposition into the embedding to achieve the global representation of KG entities/relations, and introduces the association enhancement strategy to prompt the similarities between embeddings of labeled/unlabeled samples. The embedding vectors are then utilized to diagnose the clinical outcomes of samples by predicting their links to outcomes entities. Through extensive experiments on BI-RADS data with different fractions of labels and ablation studies, our KTEAED displays promising performance in the situations of various fractions of labels. In summary, our framework demonstrates a clear advantage of tackling limited labeled samples of BI-RADS reports in the breast ultrasound diagnosis. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Xi, Jianing; Huang, Qinghua; Li, Xuelong] Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China.
   [Xi, Jianing; Huang, Qinghua; Li, Xuelong] Northwestern Polytech Univ, Minist Ind & Informat Technol, Key Lab Intelligent Interact & Applicat, Xian 710072, Peoples R China.
   [Miao, Zhaoji] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.
   [Liu, Longzhong] Sun Yat Sen Univ, Canc Ctr, Guangzhou 510060, Peoples R China.
   [Yang, Xuebing; Zhang, Wensheng] Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China.
   [Yang, Xuebing; Zhang, Wensheng] Univ Chinese Acad Sci, Beijing 101408, Peoples R China.
RP Huang, QH; Li, XL (通讯作者)，Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China.; Huang, QH; Li, XL (通讯作者)，Northwestern Polytech Univ, Minist Ind & Informat Technol, Key Lab Intelligent Interact & Applicat, Xian 710072, Peoples R China.
EM qhhuang@nwpu.edu.cn; xuelong_li@nwpu.edu.cn
RI Xi, Jianing/R-2778-2017
OI Xi, Jianing/0000-0001-6785-5618; Liu, Longzhong/0000-0003-4639-1767
FU National Key Research and Development Program of China [2018AAA0102104];
   National Natural Science Foundation of China [61901322, 62071382]; China
   Postdoctoral Science Foundation [2020M673494]; Innovation Capability
   Support Program of Shaanxi [2021TD-57]; Shaanxi Provincial Foundation
   for Distinguished Young Scholars [2019JC-13]
FX This work is supported by National Key Research and Development Program
   of China (Grant No. 2018AAA0102104), and partially by National Natural
   Science Foundation of China (Grant Nos. 61901322 and 62071382), and
   partially by China Postdoctoral Science Foundation (No. 2020M673494),
   and partially by Innovation Capability Support Program of Shaanxi
   (Program No. 2021TD-57), and partially by Shaanxi Provincial Foundation
   for Distinguished Young Scholars (No. 2019JC-13).
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NR 54
TC 0
Z9 0
U1 9
U2 22
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JAN 11
PY 2022
VL 468
BP 60
EP 70
DI 10.1016/j.neucom.2021.10.013
EA OCT 2021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WR8WI
UT WOS:000714774800006
DA 2022-08-23
ER

PT J
AU Yu, SP
   Wang, H
   Liu, TY
   Liang, C
   Luo, JW
AF Yu, Shengpeng
   Wang, Hong
   Liu, Tianyu
   Liang, Cheng
   Luo, Jiawei
TI A knowledge-driven network for fine-grained relationship detection
   between miRNA and disease
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article
DE knowledge-driven network; fine-grained relationship; miRNA and disease
ID ASSOCIATIONS; MICRORNA
AB Increasing biological evidence indicated that microRNAs (miRNAs) play a vital role in exploring the pathogenesis of various human diseases (especially in tumors). Mining disease-related miRNAs is of great significance for the clinical diagnosis and treatment of diseases. Compared with the traditional experimental methods with the significant limitations of high cost, long cycle and small scale, the methods based on computing have the advantages of being cost-effective. However, although the current methods based on computational biology can accurately predict the correlation between miRNAs and disease, they can not predict the detailed association information at a fine level. We propose a knowledge-driven approach to the fine-grained prediction of disease-related miRNAs (KDFGMDA). Different from the previous methods, this method can finely predict the clear associations between miRNA and disease, such as upregulation, downregulation or dysregulation. Specifically, KDFGMDA extracts triple information from massive experimental data and existing datasets to construct a knowledge graph and then trains a depth graph representation learning model based on knowledge graph to complete fine-grained prediction tasks. Experimental results show that KDFGMDA can predict the relationship between miRNA and disease accurately, which is of far-reaching significance for medical clinical research and early diagnosis, prevention and treatment of diseases. Additionally, the results of case studies on three types of cancers, Kaplan-Meier survival analysis and expression difference analysis further provide the effectiveness and feasibility of KDFGMDA to detect potential candidate miRNAs. Availability: Our work can be downloaded from https://github.com/ Sheng PengYu/KDFGMDA.
C1 [Yu, Shengpeng; Liang, Cheng] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China.
   [Liu, Tianyu] Shandong Normal Univ, Comp Sci, Jinan, Peoples R China.
   [Luo, Jiawei] Hunan Univ, Coll Informat Sci & Elect Engn, Changsha, Peoples R China.
RP Wang, H (通讯作者)，Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China.
EM wanghong106@163.com
FU National Science Foundation of China [61672329,62072290]; Shandong
   Provincial Project of Education Scientific Plan [SDYY18058]
FX National Science Foundation of China (No. 61672329,62072290); Shandong
   Provincial Project of Education Scientific Plan (No. SDYY18058).
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NR 41
TC 0
Z9 0
U1 12
U2 12
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 13
PY 2022
VL 23
IS 3
AR bbac058
DI 10.1093/bib/bbac058
EA MAR 2022
PG 14
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA 1Z0DH
UT WOS:000772866700001
PM 35323892
DA 2022-08-23
ER

PT J
AU Shang, FJ
   Ran, CF
AF Shang, Fengjun
   Ran, Chunfu
TI An Entity Recognition Model Based on Deep Learning Fusion of Text
   Feature
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Deep learning; Text features; Knowledge graph; Entity recognition;
   Relationship extraction
ID WEB
AB Nowadays a large amount of knowledge has been born on the Internet and the way of constructing knowledge graph is not uniform. Due to the recent outbreak of numerous diseases, the community has placed more importance on the healthcare system. Diabetes is a severe disease that affect people's health. To assist the health sector in combating this deadly disease, the authors developed a deep learning strategy for diabetes named entity extraction based on a fusion of text characteristic and relationship extraction utilizing text data as the object. This study aims to develop a multi-feature entity recognition model that considers the differences in text features across different fields. Firstly, in the word embedding layer, a multi-feature word embedding algorithm is proposed, which integrates Pinyin, radical, and the meaning of the character itself, so that the word embedding vector has the characteristics of Chinese characters and diabetes text. Then in modeling, CNN and BiLSTM are used to extract the local and global features before and after the text sequence, respectively, which solved the problem that the traditional method cannot capture the dependence before and after the text sequence. Finally, CRF is used to output the predicted tag sequence. The experimental results show that the multi-feature embedding algorithm and local features extracted by CNN can effectively improve the recognition effect of the entity recognition model.
C1 [Shang, Fengjun; Ran, Chunfu] Chongqing Univ Posts & Telecommun, Coll Comp Sci & Technol, Chongqing 400065, Peoples R China.
RP Shang, FJ (通讯作者)，Chongqing Univ Posts & Telecommun, Coll Comp Sci & Technol, Chongqing 400065, Peoples R China.
EM shangfj@cqupt.edu.cn
FU National Nature Science Foundation of China [61672004]
FX The work has been supported by the National Nature Science Foundation of
   China (No. 61672004).
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U2 44
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 MAR
PY 2022
VL 59
IS 2
AR 102841
DI 10.1016/j.ipm.2021.102841
EA DEC 2021
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA XV3ZB
UT WOS:000734882900006
DA 2022-08-23
ER

PT J
AU Belhadi, A
   Djenouri, Y
   Diaz, VG
   Houssein, EH
   Lin, JCW
AF Belhadi, Asma
   Djenouri, Youcef
   Diaz, Vicente Garcia
   Houssein, Essam H.
   Lin, Jerry Chun-Wei
TI Hybrid intelligent framework for automated medical learning
SO EXPERT SYSTEMS
LA English
DT Article
DE automated medical learning; distributed learning; multi&#8208; agent
   systems; ontology matching
ID ACTIVITY RECOGNITION; INDUSTRIAL INTERNET; AUGMENTED REALITY;
   BLOCKCHAIN; THINGS; OPTIMIZATION; POPULATION; ALGORITHM; IMPROVE
AB This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutions.
C1 [Belhadi, Asma] Kristiania Univ Coll, Dept Dept Technol, Oslo, Norway.
   [Djenouri, Youcef] SINTEF Digital, Math & Cybernet, Oslo, Norway.
   [Diaz, Vicente Garcia] Univ Oviedo, Dept Comp Sci, Oviedo, Spain.
   [Houssein, Essam H.] Minia Univ, Fac Comp & Informat, Al Minya, Egypt.
   [Lin, Jerry Chun-Wei] Western Norway Univ Appl Sci, Dept Comp Sci Elect Engn & Math Sci, Bergen, Norway.
RP Lin, JCW (通讯作者)，Western Norway Univ Appl Sci, Dept Comp Sci Elect Engn & Math Sci, Bergen, Norway.
EM jerrylin@ieee.org
RI Lin, Jerry Chun-Wei/C-1514-2011; Houssein, Essam H/C-8941-2016;
   Garcia-Diaz, Vicente/T-3938-2017
OI Houssein, Essam H/0000-0002-8127-7233; Garcia-Diaz,
   Vicente/0000-0003-2037-8548; Lin, Jerry Chun-Wei/0000-0001-8768-9709;
   Djenouri, Youcef/0000-0003-0135-7450
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NR 50
TC 4
Z9 4
U1 3
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0266-4720
EI 1468-0394
J9 EXPERT SYST
JI Expert Syst.
PD JUL
PY 2022
VL 39
IS 6
SI SI
DI 10.1111/exsy.12737
EA MAY 2021
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2I2CT
UT WOS:000653679800001
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Huang, WY
   Qu, Q
   Yang, M
AF Huang, Weiyi
   Qu, Qiang
   Yang, Min
TI Interactive knowledge-enhanced attention network for answer selection
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Question-answer selection; Knowledge graph; Attention mechanism; Deep
   learning
AB Answer selection which aims to select the most appropriate answers from a set of candidate answers plays a crucial role in various applications such as question answering (QA) and information retrieval. Recently, remarkable progress has been achieved on matching sequence pairs by deep neural networks. However, most of them focus on learning semantic representations for the contexts of QA pairs while the background information and facts beyond the context are neglected. In this paper, we propose an interactive knowledge-enhanced attention network for answer selection (IKAAS), which interactively learns the sentence representations of query-answer pairs by simultaneously considering the external knowledge from knowledge graphs and textual information of QA pairs. In this way, we can exploit the semantic compositionality of the input sequences and capture more comprehensive knowledge-enriched intra-document features within the question and answer. Specifically, we first propose a context-aware attentive mechanism to learn the knowledge representations guided by the corresponding context. The relations between the question and answer are then captured by computing the question-answer alignment matrix. We further employ self-attention to capture the global features of the input sequences, which are then used to calculate the relevance score of the question and answer. Experimental results on four real-life datasets demonstrate thatIKAASoutperforms the compared methods. In addition, a series of analyses shows the robust superiority and the extensive applicability of the proposed method.
C1 [Huang, Weiyi; Qu, Qiang; Yang, Min] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China.
   [Huang, Weiyi] Univ Chinese Acad Sci, Beijing, Peoples R China.
RP Qu, Q (通讯作者)，Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China.
EM wy.huang1@siat.ac.cn; qiang@siat.ac.cn; min.yang@siat.ac.cn
OI Qu, Qiang/0000-0001-5814-8460
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NR 52
TC 6
Z9 6
U1 3
U2 18
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 2020
VL 32
IS 15
BP 11343
EP 11359
DI 10.1007/s00521-019-04630-x
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ML7NA
UT WOS:000549646700039
DA 2022-08-23
ER

PT J
AU Deng, Y
AF Deng, Yue
TI Recommender Systems Based on Graph Embedding Techniques: A Review
SO IEEE ACCESS
LA English
DT Review
DE Recommender systems; Predictive models; Big Data; Task analysis;
   Scalability; Pipelines; Machine learning; Information retrieval;
   recommender systems; graph embedding; machine learning; knowledge
   graphs; graph neural networks
ID MATRIX FACTORIZATION; LINK-PREDICTION; NETWORK; ALGORITHM; LONG; TERM;
   REPRESENTATIONS; PERSPECTIVES; SIMILARITY; FRAMEWORK
AB As a pivotal tool to alleviate the information overload problem, recommender systems aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for alleviating the sparsity and cold start problems encountered by recommender systems, researchers generally resort to employing side information or knowledge in recommendation as a strategy for uncovering hidden (indirect) user-item relations, aiming to enrich observed information (or data) for recommendation. However, in the face of the high complexity and large scale of side information and knowledge, this strategy largely relies for efficient implementation on the scalability of recommendation models. Not until after the prevalence of machine learning did graph embedding techniques be a recent concentration, which can efficiently utilize complex and large-scale data. In light of that, equipping recommender systems with graph embedding techniques has been widely studied these years, appearing to outperform conventional recommendation implemented directly based on graph topological analysis (or resolution). As the focus, this article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs and knowledge graphs, and proposes a general design pipeline of that. In addition, after comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models on simulations, this article manifests that the conventional models can still overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the comparative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and conventional recommendation in different tasks, and puts forward some open questions.
C1 [Deng, Yue] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 611731, Peoples R China.
RP Deng, Y (通讯作者)，Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 611731, Peoples R China.
EM 201921210214@std.uestc.edu.cn
OI Deng, Yue/0000-0002-8263-1871
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NR 420
TC 0
Z9 0
U1 23
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 2022
VL 10
BP 51587
EP 51633
DI 10.1109/ACCESS.2022.3174197
PG 47
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 1I7MR
UT WOS:000797411900001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Mura, C
   Preissner, S
   Preissner, R
   Bourne, PE
AF Mura, Cameron
   Preissner, Saskia
   Preissner, Robert
   Bourne, Philip E.
TI A Birds-Eye (Re)View of Acid-Suppression Drugs, COVID-19, and the Highly
   Variable Literature
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE COVID-19; histamine (H2) receptor; famotidine; knowledge graph; disease
   ontology; antagonist action
ID CORONAVIRUS DISEASE 2019; FAMOTIDINE; ONTOLOGIES
AB This Perspective examines a recent surge of information regarding the potential benefits of acid-suppression drugs in the context of COVID-19, with a particular eye on the great variability (and, thus, confusion) that has arisen across the reported findings, at least as regards the popular antacid famotidine. The degree of inconsistency and discordance reflects contradictory conclusions from independent, clinical-based studies that took roughly similar approaches, in terms of both experimental design (retrospective, observational, cohort-based, etc.) and statistical analysis workflows (propensity-score matching and stratification into sub-cohorts, etc.). The contradictions and potential confusion have ramifications for clinicians faced with choosing therapeutically optimal courses of intervention: e.g., do any potential benefits of famotidine suggest its use in a particular COVID-19 case? (If so, what administration route, dosage regimen, duration, etc. are likely optimal?) As succinctly put this March in Freedberg et al. (2021), " horizontal ellipsis several retrospective studies show relationships between famotidine and outcomes in COVID-19 and several do not." Beyond the pressing issue of possible therapeutic indications, the conflicting data and conclusions related to famotidine must be resolved before its inclusion/integration in ontological and knowledge graph (KG)-based frameworks, which in turn are useful for drug discovery and repurposing. As a broader methodological issue, note that reconciling inconsistencies would bolster the validity of meta-analyses which draw upon the relevant data-sources. And, perhaps most broadly, developing a system for treating inconsistencies would stand to improve the qualities of both 1) real world evidence-based studies (retrospective), on the one hand, and 2) placebo-controlled, randomized multi-center clinical trials (prospective), on the other hand. In other words, a systematic approach to reconciling the two types of studies would inherently improve the quality and utility of each type of study individually.
C1 [Mura, Cameron; Bourne, Philip E.] Univ Virginia, Sch Data Sci, Charlottesville, VA 22903 USA.
   [Mura, Cameron; Bourne, Philip E.] Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22903 USA.
   [Preissner, Saskia] Charite Univ Med Berlin, Dept Oral & Maxillofacial Surg, Berlin, Germany.
   [Preissner, Saskia; Preissner, Robert] Free Univ Berlin, Berlin, Germany.
   [Preissner, Saskia; Preissner, Robert] Berlin Inst Hlth, Berlin, Germany.
   [Preissner, Saskia; Preissner, Robert] Humboldt Univ, Berlin, Germany.
   [Preissner, Robert] Charite Univ Med Berlin, Inst Physiol & Sci IT, Berlin, Germany.
RP Bourne, PE (通讯作者)，Univ Virginia, Sch Data Sci, Charlottesville, VA 22903 USA.; Bourne, PE (通讯作者)，Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22903 USA.; Preissner, R (通讯作者)，Free Univ Berlin, Berlin, Germany.; Preissner, R (通讯作者)，Berlin Inst Hlth, Berlin, Germany.; Preissner, R (通讯作者)，Humboldt Univ, Berlin, Germany.; Preissner, R (通讯作者)，Charite Univ Med Berlin, Inst Physiol & Sci IT, Berlin, Germany.
EM robert.preissner@charite.de; peb6a@virginia.edu
RI Bourne, Philip/ABG-8967-2021; Mura, Cameron/F-5872-2010
OI Bourne, Philip/0000-0002-7618-7292; Mura, Cameron/0000-0001-7985-2561;
   preissner, robert/0000-0002-2407-1087
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NR 40
TC 1
Z9 1
U1 1
U2 2
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1663-9812
J9 FRONT PHARMACOL
JI Front. Pharmacol.
PD AUG 11
PY 2021
VL 12
DI 10.3389/fphar.2021.700703
PG 7
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA UN1NA
UT WOS:000693787700001
PM 34456726
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Zhao, H
   Wang, YX
   Lin, AQ
   Hu, BY
   Yan, R
   McCusker, J
   Chen, W
   McGuinness, DL
   Schadler, L
   Brinson, LC
AF Zhao, He
   Wang, Yixing
   Lin, Anqi
   Hu, Bingyin
   Yan, Rui
   McCusker, James
   Chen, Wei
   McGuinness, Deborah L.
   Schadler, Linda
   Brinson, L. Catherine
TI NanoMine schema: An extensible data representation for polymer
   nanocomposites
SO APL MATERIALS
LA English
DT Article
ID DISPERSION; EXTRACTION; XML
AB Polymer nanocomposites consist of a polymer matrix and fillers with at least one dimension below 100 nanometers (nm) [L. Schadler et al., Jom 59(3), 53-60 (2007)]. A key challenge in constructing an effective data resource for polymer nanocomposites is building a consistent, coherent, and clear data representation of all relevant parameters and their interrelationships. The data resource must address (1) data representation for representing, saving, and accessing the data (e.g., a data schema used in a data resource such as a database management system), (2) data contribution and uploading (e.g., an MS Excel template file that users can use to input data), (3) concept and knowledge modeling in a computationally accessible form (e.g., generation of a knowledge graph and ontology), and (4) ultimately data analytics and mining for new materials discovery. This paper addresses the first three issues, paving the way for rich, nuanced data analysis. We present the NanoMine polymer nanocomposite schema as an XML-based data schema designed for nanocomposite materials data representation and distribution and discuss its relationship to a higher level polymer data core consistent with other centralized materials data efforts. We also demonstrate aspects of data entry in an accessible manner consistent with the XML schema and discuss our mapping and augmentation approach to provide a more comprehensive representation in the form of an ontology and an ontology-enabled knowledge graph framework for nanopolymer systems. The schema and ontology and their easy accessibility and compatibility with parallel material standards provide a platform for data storage and search, customized visualization, and machine learning tools for material discovery and design. (C) 2018 Author(s).
C1 [Zhao, He; Wang, Yixing; Chen, Wei] Northwestern Univ, Dept Mech Engn, Evanston, IL 60208 USA.
   [Lin, Anqi; Hu, Bingyin; Brinson, L. Catherine] Duke Univ, Dept Mech Engn & Mat Sci, Durham, NC 27708 USA.
   [Yan, Rui; McCusker, James; McGuinness, Deborah L.] Rensselaer Polytech Inst, Dept Comp Sci, Troy, NY 12180 USA.
   [Schadler, Linda] Rensselaer Polytech Inst, Dept Mat Sci & Engn, Troy, NY 12180 USA.
RP Brinson, LC (通讯作者)，Duke Univ, Dept Mech Engn & Mat Sci, Durham, NC 27708 USA.
EM cate.brinson@duke.edu
RI Brinson, L. Catherine/B-6678-2009
OI McCusker, Jamie/0000-0003-1085-6059; McGuinness,
   Deborah/0000-0001-7037-4567; Zhao, He/0000-0002-7909-8252; Hu,
   Bingyin/0000-0003-0366-8332; Brinson, Lynda/0000-0003-2551-1563
FU NSF [DMR-1310292, DIBBS A12761, 1640840, DMREF 1818574, 1729743, CMMI -
   1729452, ACI - 1640840]; NIST [70NANB14H012, 5]; CHiMaD center
FX The authors gratefully acknowledge support of NSF (DMR-1310292), NSF
   DIBBS A12761, 1640840, NSF DMREF 1818574, 1729743, CMMI - 1729452, ACI -
   1640840, NIST (70NANB14H012 Amd 5) and the CHiMaD center based at the
   Northwestern University.
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NR 46
TC 20
Z9 20
U1 0
U2 22
PU AMER INST PHYSICS
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 2166-532X
J9 APL MATER
JI APL Mater.
PD NOV
PY 2018
VL 6
IS 11
AR 111108
DI 10.1063/1.5046839
PG 16
WC Nanoscience & Nanotechnology; Materials Science, Multidisciplinary;
   Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics; Materials Science; Physics
GA HC3YD
UT WOS:000451738000008
OA gold
DA 2022-08-23
ER

PT J
AU Liu, Y
   Wu, J
   Li, JH
   Yang, W
   Chen, H
   Li, GL
AF Liu, Yi
   Wu, Jun
   Li, Jianhua
   Yang, Wu
   Chen, Hao
   Li, Gaolei
TI ISRF: interest semantic reasoning based fog firewall for
   information-centric Internet of Vehicles
SO IET INTELLIGENT TRANSPORT SYSTEMS
LA English
DT Article
DE computer network security; Internet of Things; Big Data; intelligent
   transportation systems; firewalls; interest semantic reasoning based fog
   firewall; information-centric network; information-centric IoV; ISRF;
   information-centric Internet of vehicles; intelligent transportation;
   next-generation network; content-oriented attributes; knowledge graph;
   context weight; semantic obfuscation; penetrated malicious content;
   content big data
ID ACCESS-CONTROL; NETWORKING; PRIVACY; EFFICIENT; SECURITY
AB Nowadays, Internet of Vehicles (IoV) has attracted lots of attention as an important component of intelligent transportation. Meanwhile, information-centric network is a meaningful next-generation network with content-oriented attributes, which has been introduced into IoV for more flexible services. In information-centric IoV, there are abundant semantic features because of the content big data in the network layer. As very few works of information-centric IoV consider semantic models, current security approaches in information-centric IoV cannot defence against efficiently novel threats with penetrated malicious content and semantic obfuscation, especially at the network edge. Thus an efficient firewall is a must for information-centric IoV. However, existing firewalls based on traditional computing resources cannot detect content threats implicated in semantics at the edge of networks. In this study, the authors propose an interest semantic reasoning based fog firewall for information-centric IoV. Firstly, a fog firewall is proposed to construct an edge isolation for information-centric IoV. Moreover, interest semantic reasoning with context weight and knowledge graph is proposed to mine implicit relations and illegal knowledge in names and contents. Based on knowledge constructed from reasoning, it aims to perceive threats from interest packet in information-centric IoV. Simulations verify its feasibility and efficiency of the proposed scheme.
C1 [Liu, Yi; Wu, Jun; Li, Jianhua; Chen, Hao; Li, Gaolei] Shanghai Key Lab Integrated Adm Technol Informat, Sch Cyber Secur, Shanghai 200240, Peoples R China.
   [Yang, Wu] Harbin Engn Univ, Informat Secur Res Ctr, Harbin 150001, Heilongjiang, Peoples R China.
RP Liu, Y (通讯作者)，Shanghai Key Lab Integrated Adm Technol Informat, Sch Cyber Secur, Shanghai 200240, Peoples R China.
EM ly2017@sjtu.edu.cn
RI li, Gaolei/E-9861-2017
FU National Natural Science Foundation of China [61431008, 61571300]
FX This work was supported by the National Natural Science Foundation of
   China (grant nos. 61431008 and 61571300).
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NR 40
TC 4
Z9 4
U1 5
U2 15
PU INST ENGINEERING TECHNOLOGY-IET
PI HERTFORD
PA MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND
SN 1751-956X
EI 1751-9578
J9 IET INTELL TRANSP SY
JI IET Intell. Transp. Syst.
PD JUN
PY 2019
VL 13
IS 6
BP 975
EP 982
DI 10.1049/iet-its.2018.5465
PG 8
WC Engineering, Electrical & Electronic; Transportation Science &
   Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Transportation
GA IM1DW
UT WOS:000477729700008
OA hybrid
DA 2022-08-23
ER

PT J
AU Dasgupta, S
   Jayagopal, A
   Hong, ALJ
   Mariappan, R
   Rajan, V
AF Dasgupta, Soham
   Jayagopal, Aishwarya
   Hong, Abel Lim Jun
   Mariappan, Ragunathan
   Rajan, Vaibhav
TI Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge
   Graphs: Algorithm Development and Validation
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE adverse drug event; knowledge graph; Embedding of Semantic Predications;
   biomedical literature
AB Background: Adverse drug events (ADEs) are unintended side effects of drugs that cause substantial clinical and economic burdens globally. Not all ADEs are discovered during clinical trials; therefore, postmarketing surveillance, called pharmacovigilance, is routinely conducted to find unknown ADEs. A wealth of information, which facilitates ADE discovery, lies in the growing body of biomedical literature. Knowledge graphs (KGs) encode information from the literature, where the vertices and the edges represent clinical concepts and their relations, respectively. The scale and unstructured form of the literature necessitates the use of natural language processing (NLP) to automatically create such KGs. Previous studies have demonstrated the utility of such literature-derived KGs in ADE prediction. Through unsupervised learning of the representations (features) of clinical concepts from the KG, which are used in machine learning models, state-of-the-art results for ADE prediction were obtained on benchmark data sets.
   Objective: Due to the use of NLP to infer literature-derived KGs, there is noise in the form of false positive (erroneous) and false negative (absent) nodes and edges. Previous representation learning methods do not account for such inaccuracies in the graph. NLP algorithms can quantify the confidence in their inference of extracted concepts and relations from the literature. Our hypothesis, which motivates this work, is that by using such confidence scores during representation learning, the learned embeddings would yield better features for ADE prediction models.
   Methods: We developed methods to use these confidence scores on two well-known representation learning methods-DeepWalk and Translating Embeddings for Modeling Multi-relational Data (TransE)-to develop their weighted versions: Weighted DeepWalk and Weighted TransE. These methods were used to learn representations from a large literature-derived KG, the Semantic MEDLINE Database, which contains more than 93 million clinical relations. They were compared with Embedding of Semantic Predications, which, to our knowledge, is the best reported representation learning method using the Semantic MEDLINE Database with state-of-the-art results for ADE prediction. Representations learned from different methods were used (separately) as features of drugs and diseases to build classification models for ADE prediction using benchmark data sets. The methods were compared rigorously over multiple cross-validation settings.
   Results: The weighted versions we designed were able to learn representations that yielded more accurate predictive models than the corresponding unweighted versions of both DeepWalk and TransE, as well as Embedding of Semantic Predications, in our experiments. There were performance improvements of up to 5.75% in the F 1 -score and 8.4% in the area under the receiver operating characteristic curve value, thus advancing the state of the art in ADE prediction from literature-derived KGs.
   Conclusions: Our classification models can be used to aid pharmacovigilance teams in detecting potentially new ADEs. Our experiments demonstrate the importance of modeling inaccuracies in the inferred KGs for representation learning.
C1 [Dasgupta, Soham] Mallya Aditi Int Sch, Bangalore, Karnataka, India.
   [Jayagopal, Aishwarya; Hong, Abel Lim Jun; Mariappan, Ragunathan] Natl Univ Singapore, Sch Comp, Singapore, Singapore.
   [Rajan, Vaibhav] Natl Univ Singapore, Dept Informat Syst & Analyt, Singapore, Singapore.
RP Rajan, V (通讯作者)，Natl Univ Singapore, Dept Informat Syst & Analyt, Sch Comp, Singapore 117417, Singapore.
EM vaibhav.rajan@nus.edu.sg
OI Lim, Abel Jun Hong/0000-0001-7503-0624
FU Singapore Ministry of Education Academic Research Fund
   [R-253-000-138-133]
FX This work is supported by the Singapore Ministry of Education Academic
   Research Fund [R-253-000-138-133], primary investigator: VR.
CR [Anonymous], KB EMBEDDINGS
   [Anonymous], 2011, J MACH LEARN RES
   [Anonymous], DATA FILES PREDICTIN
   [Anonymous], MEDLINE: Overview
   [Anonymous], DEEPWALK DEEP LEARNI
   [Anonymous], LEARNING PREDICTIVE
   [Anonymous], OPEN SOURCE PACKAGE
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U2 3
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
EI 2291-9694
J9 JMIR MED INF
JI JMIR Med. Inf.
PD OCT
PY 2021
VL 9
IS 10
AR e32730
DI 10.2196/32730
PG 20
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA WP1CG
UT WOS:000712877700003
PM 34694230
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, TS
   Lin, L
   Chen, RQ
   Hui, XL
   Wu, HF
AF Chen, Tianshui
   Lin, Liang
   Chen, Riquan
   Hui, Xiaolu
   Wu, Hefeng
TI Knowledge-Guided Multi-Label Few-Shot Learning for General Image
   Recognition
SO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
LA English
DT Article
DE Semantics; Task analysis; Training; Image recognition; Correlation;
   Neural networks; Proposals; Image recognition; multi-label learning;
   few-shot learning; knowledge graph; graph reasoning
AB Recognizing multiple labels of an image is a practical yet challenging task, and remarkable progress has been achieved by searching for semantic regions and exploiting label dependencies. However, current works utilize RNN/LSTM to implicitly capture sequential region/label dependencies, which cannot fully explore mutual interactions among the semantic regions/labels and do not explicitly integrate label co-occurrences. In addition, these works require large amounts of training samples for each category, and they are unable to generalize to novel categories with limited samples. To address these issues, we propose a knowledge-guided graph routing (KGGR) framework, which unifies prior knowledge of statistical label correlations with deep neural networks. The framework exploits prior knowledge to guide adaptive information propagation among different categories to facilitate multi-label analysis and reduce the dependency of training samples. Specifically, it first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features to initialize the graph, and it exploits a graph propagation network to explore graph node interactions, enabling learning contextualized image feature representations. Moreover, we initialize each graph node with the classifier weights for the corresponding label and apply another propagation network to transfer node messages through the graph. In this way, it can facilitate exploiting the information of correlated labels to help train better classifiers, especially for labels with limited training samples. We conduct extensive experiments on the traditional multi-label image recognition (MLR) and multi-label few-shot learning (ML-FSL) tasks and show that our KGGR framework outperforms the current state-of-the-art methods by sizable margins on the public benchmarks.
C1 [Chen, Tianshui; Lin, Liang; Chen, Riquan; Hui, Xiaolu; Wu, Hefeng] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Peoples R China.
   [Chen, Tianshui; Lin, Liang] DarkMatter AI Res, Guangzhou 511458, Peoples R China.
RP Lin, L (通讯作者)，Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Peoples R China.
EM tianshuichen@gmail.com; linliang@ieee.org; chenrq6@mail2.sysu.edu.cn;
   huixlu@mail2.sysu.edu.cn; wuhefeng@gmail.com
OI Wu, Hefeng/0000-0002-2132-6515; Liang, Lin/0000-0003-2248-3755
FU National Key Research and Development Program of China [2018YFC0830103];
   National Natural Science Foundation of China (NSFC) [61876045,
   61836012]; Zhujiang Science and Technology New Star Project of Guangzhou
   [201906010057]
FX This work was supported in part by theNational Key Research and
   Development Program of China under Grant No. 2018YFC0830103, in part by
   the National Natural Science Foundation of China (NSFC) under Grants
   61876045 and 61836012, and in part by Zhujiang Science and Technology
   New Star Project ofGuangzhou underGrants 201906010057.
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U1 33
U2 50
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0162-8828
EI 1939-3539
J9 IEEE T PATTERN ANAL
JI IEEE Trans. Pattern Anal. Mach. Intell.
PD MAR 1
PY 2022
VL 44
IS 3
BP 1371
EP 1384
DI 10.1109/TPAMI.2020.3025814
PG 14
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA YU4MA
UT WOS:000752018000022
PM 32986543
OA Green Submitted
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Vogt, L
AF Vogt, Lars
TI Organizing phenotypic dataa semantic data model for anatomy
SO JOURNAL OF BIOMEDICAL SEMANTICS
LA English
DT Article
DE Phenotypic data; Semantic data model for anatomy; Instance anatomy
   knowledge graph; Anatomy; ontology; Zoology; Knowledge management;
   Morphological description; Morphology
ID DATA STANDARDS; ONTOLOGIES; NEED; HOMOLOGY; BIOLOGY; RDF
AB BackgroundCurrently, almost all morphological data are published as unstructured free text descriptions. This not only brings about terminological problems regarding semantic transparency, which hampers their re-use by non-experts, but the data cannot be parsed by computers either, which in turn hampers their integration across many fields in the life sciences, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. With an ever-increasing amount of available ontologies and the development of adequate semantic technology, however, a solution to this problem becomes available. Instead of free text descriptions, morphological data can be recorded, stored, and communicated through the Web in the form of highly formalized and structured directed graphs (semantic graphs) that use ontology terms and URIs as terminology.ResultsAfter introducing an instance-based approach of recording morphological descriptions as semantic graphs (i.e., Semantic Instance Anatomy Knowledge Graphs) and discussing accompanying metadata graphs, I propose a general scheme of how to efficiently organize the resulting graphs in a tuple store framework based on instances of defined named graph ontology classes. The use of such named graph resources allows meaningful fragmentation of the data, which in turn enables subsequent specification of all kinds of data views for managing and accessing morphological data.ConclusionsMorphological data that comply with the here proposed semantic data model will not only be computer-parsable but also re-usable by non-experts and could be better integrated with other sources of data in the life sciences. This would allow morphology as a discipline to further participate in eScience and Big Data.
C1 [Vogt, Lars] Rheinische Friedrich Wilhelms Univ Bonn, Inst Evolut Biol & Okol, Immenburg 1, D-53121 Bonn, Germany.
RP Vogt, L (通讯作者)，Rheinische Friedrich Wilhelms Univ Bonn, Inst Evolut Biol & Okol, Immenburg 1, D-53121 Bonn, Germany.
EM lars.m.vogt@gmail.com
OI Vogt, Lars/0000-0002-8280-0487
FU German Research Foundation DFG [VO 1244/8-1]; Leibniz Competition
   [SAW-2016-SGN-2]
FX This work was supported by grant VO 1244/8-1 from the German Research
   Foundation DFG and grant SAW-2016-SGN-2 of the Leibniz Competition. I am
   also grateful to the taxpayers of Germany.
CR Apache Jena, FREE OP SOURC JAV FR
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NR 41
TC 5
Z9 5
U1 2
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 JUN 20
PY 2019
VL 10
AR 12
DI 10.1186/s13326-019-0204-6
PG 14
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA IE6PG
UT WOS:000472497500001
PM 31221226
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Alagha, I
AF Alagha, Iyad
TI Multihop Question Answering by Using Sequential Path Expansion With
   Backtracking
SO IEEE ACCESS
LA English
DT Article
DE Cognition; Semantics; Training; Public transportation; Films;
   Backtracking; Question answering (information retrieval); Multi-hop
   question answering; knowledge graph; backtracking; deep learning
AB Multi-hop question answering from knowledge graphs has gained a growing attention in the past few years due to its vast applications in AI. Existing approaches in this regard mostly rely on exhaustively traversing all paths in the graph until reaching a candidate answer entity, leading to high time complexity and reasoning errors when training the model. Recently, a few works have tried to reduce the search space by proposing sequential-decision techniques so that only the path that is likely to lead to the answer is traced. However, the sequential decision on the relations is likely to accumulate errors due to the potential incorrect matching with the question. Alternatively, this work proposes a method that leverages sequence matching and a backtracking algorithm to identify the correct path to the answer while minimizing the accumulated error. The process starts from the question entity and grows the path iteratively by reasoning over the outgoing relations. The aim is to find the relation with the highest similarity to the question and to transit through it to the next entity. Meanwhile, a termination/backtracking check is performed at each iteration to validate the path and act accordingly either by proceeding to the next entity, stopping the process, or backtracking to correct a wrongly expanded path. The proposed method does not require setting a maximum number of hops in advance and uses a compare-aggregate model with attention mechanism for effective sequence matching. Experimental results show that our method significantly outperforms existing methods on three benchmarking datasets.
C1 [Alagha, Iyad] Islamic Univ Gaza, Fac Informat Technol, Gaza 00972, Palestine.
RP Alagha, I (通讯作者)，Islamic Univ Gaza, Fac Informat Technol, Gaza 00972, Palestine.
EM ialagha@iugaza.edu.ps
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NR 58
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 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 76842
EP 76854
DI 10.1109/ACCESS.2022.3192858
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3I8IP
UT WOS:000832953400001
OA gold
DA 2022-08-23
ER

PT J
AU Katib, A
   Slavov, V
   Rao, P
AF Katib, Anas
   Slavov, Vasil
   Rao, Praveen
TI RIQ: Fast processing of SPARQL queries on RDF quadruples
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE RDF; Quadruples; SPARQL; Query processing; Knowledge graphs
ID WEB; ENGINE
AB In this paper, we address the problem of fast processing of SPARQL queries on a large RDF dataset, where the RDF statements are quadruples (or quads). Quads can capture provenance or other relevant information about facts. This is especially powerful in modeling knowledge graphs, which are becoming increasingly important on the Web to provide high quality search results to users. We propose a new approach called RIQ that employs a decrease-and-conquer strategy for fast SPARQL query processing. Rather than indexing the entire RDF dataset, RIQ identifies groups of similar RDF graphs and creates indexes on each group separately. It employs a new vector representation for RDF graphs and locality sensitive hashing to construct the groups efficiently. It constructs a novel filtering index on the groups and compactly represents the index as a combination of Bloom and Counting Bloom Filters. During query processing, RIQ employs a streamlined approach. It constructs a query plan for a SPARQL query (containing one or more graph patterns), searches the filtering index to quickly identify candidate groups that may contain matches for the query, and rewrites the original query to produce an optimized query for each candidate. The optimized queries are then executed using an existing SPARQL processor that supports quads to produce the final results. We conducted a comprehensive evaluation of RIQ using a real and synthetic dataset, each containing about 1.4 billion quads. Our results show that RIQ can outperform its competitors designed to support named graph queries on RDF quads (e.g., Jena TDB and Virtuoso) for a variety of queries. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Katib, Anas; Slavov, Vasil; Rao, Praveen] Univ Missouri, Dept Comp Sci & Elect Engn, Kansas City, MO 64110 USA.
RP Rao, P (通讯作者)，Univ Missouri, Dept Comp Sci & Elect Engn, Kansas City, MO 64110 USA.
EM anaskatib@mail.umkc.edu; vgslavov@mail.umkc.edu; raopr@umkc.edu
RI Rao, Praveen/AAP-9331-2020
OI Rao, Praveen/0000-0002-1859-0438
FU National Science Foundation [1115871]; Direct For Computer & Info Scie &
   Enginr [1115871] Funding Source: National Science Foundation
FX We are grateful to the anonymous reviewers for their comments and
   suggestions. This work was supported by the National Science Foundation
   under Grant No. 1115871.
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NR 68
TC 3
Z9 3
U1 0
U2 9
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
J9 J WEB SEMANT
JI J. Web Semant.
PD MAR
PY 2016
VL 37-38
BP 90
EP 111
DI 10.1016/j.websem.2016.03.005
PG 22
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:000376457600007
OA Bronze
DA 2022-08-23
ER

PT J
AU Wang, YL
   Wang, M
   Fujita, H
AF Wang, Yinglin
   Wang, Ming
   Fujita, Hamido
TI Word Sense Disambiguation: A comprehensive knowledge exploitation
   framework
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Word sense disambiguation; Background knowledge; Information retrieval;
   Relation exploitation; Semantic path
ID REPRESENTATION; WEB
AB Word Sense Disambiguation (WSD) has been a basic and on-going issue since its introduction in natural language processing (NLP) community. Its application lies in many different areas including sentiment analysis, Information Retrieval (IR), machine translation and knowledge graph construction. Solutions to WSD are mostly categorized into supervised and knowledge-based approaches. In this paper, a knowledge-based method is proposed, modeling the problem with semantic space and semantic path hidden behind a given sentence. The approach relies on the well-known Knowledge Base (KB) named WordNet and models the semantic space and semantic path by Latent Semantic Analysis (LSA) and PageRank respectively. Experiments has proven the method's effectiveness, achieving state-of-the-art performance in several WSD datasets. (C) 2019 The Author(s). Published by Elsevier B.V.
C1 [Wang, Yinglin; Wang, Ming] Shanghai Univ Finance & Econ, Sch Informat Management & Engn, Shanghai, Peoples R China.
   [Fujita, Hamido] Ho Chi Minh City Univ Technol HUTECH, Fac Informat Technol, Ho Chi Minh City, Vietnam.
RP Wang, YL (通讯作者)，Shanghai Univ Finance & Econ, Sch Informat Management & Engn, Shanghai, Peoples R China.
EM wang.yinglin@shufe.edu.cn; lwmlyy@163.com; h.fujita@hutech.edu.vn
RI Fujita, Hamido/D-6249-2012
OI Fujita, Hamido/0000-0001-5256-210X
FU National Natural Science Foundation of China [61375053]; graduate
   innovation fund of Shanghai University of Finance and Economics
   [CXJJ-2019-395]
FX The authors would like to express thanks to the National Natural Science
   Foundation of China (under Project No. 61375053) and the graduate
   innovation fund of Shanghai University of Finance and Economics (under
   Project No. CXJJ-2019-395) for their financial supports.
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NR 53
TC 46
Z9 46
U1 9
U2 64
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 FEB 29
PY 2020
VL 190
AR 105030
DI 10.1016/j.knosys.2019.105030
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KS7NC
UT WOS:000518492400007
OA hybrid
DA 2022-08-23
ER

PT J
AU Tomaszuk, D
   Angles, R
   Thakkar, H
AF Tomaszuk, Dominik
   Angles, Renzo
   Thakkar, Harsh
TI PGO: Describing Property Graphs in RDF
SO IEEE ACCESS
LA English
DT Article
DE Resource description framework; Ontologies; Data models; OWL; Database
   systems; Interoperability; RDF; property graphs; data transformation;
   OWL; ontology
ID DATABASES
AB RDF and Property Graphs are data models that are being used to represent Knowledge Graphs. The definition of methods to transform RDF data into Property graph data is fundamental to allow interoperability among the systems using these models. Although both models are based on a graph structure, they have special features that complicate the definition of data transformation methods. This article presents an ontology-based approach to transform (automatically) property graphs into RDF graphs. The ontology, called PGO, defines a set of terms that allows describing the elements of a property graph. The algorithm corresponding to the transformation method is described, and some properties of the method are discussed (complexity, data preservation, and monotonicity). The results of an experimental evaluation are also presented.
C1 [Tomaszuk, Dominik] Univ Bialystok, Inst Informat, PL-15245 Bialystok, Poland.
   [Angles, Renzo] Univ Talca, Dept Comp Sci, Fac Engn, Curico 3340000, Chile.
   [Thakkar, Harsh] OSTHUS GmbH, D-52068 Aachen, Germany.
   [Thakkar, Harsh] Univ Bonn, Informat Dept 3, D-53115 Bonn, Germany.
RP Tomaszuk, D (通讯作者)，Univ Bialystok, Inst Informat, PL-15245 Bialystok, Poland.
EM d.tomaszuk@uwb.edu.pl
RI Tomaszuk, Dominik/AFD-8572-2022
OI Tomaszuk, Dominik/0000-0003-1806-067X
FU National Science Center (NCN), Poland; Millennium Institute for
   Foundational Research on Data, Chile
FX The work of Dominik Tomaszuk was supported by the National Science
   Center (NCN), Poland under Grant Miniatura 2. The work of Renzo Angles
   was supported by the Millennium Institute for Foundational Research on
   Data, Chile.
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NR 70
TC 0
Z9 0
U1 1
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 118355
EP 118369
DI 10.1109/ACCESS.2020.3002018
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MK9WJ
UT WOS:000549129300001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, YN
   Benavides, R
   Diatchenko, L
   Grant, AV
   Li, Y
AF Wang, Yuening
   Benavides, Rodrigo
   Diatchenko, Luda
   Grant, Audrey V.
   Li, Yue
TI A graph-embedded topic model enables characterization of diverse pain
   phenotypes among UK biobank individuals
SO ISCIENCE
LA English
DT Article
ID MECHANISMS; DISEASE
AB Large biobank repositories of clinical conditions and medications data open opportunities to investigate the phenotypic disease network. We present a graph embedded topic model (GETM). We integrate existing biomedical knowledge graph information in the form of pre-trained graph embedding into the embedded topic model. Via a variational autoencoder framework, we infer patient phenotypic mixture by modeling multi-modal discrete patient medical records. We applied GETM to UK Biobank (UKB) self-reported clinical phenotype data, which contains 443 self-reported medical conditions and 802 medications for 457,461 individuals. Compared to existing methods, GETM demonstrates good imputation performance. With a more focused application on characterizing pain phenotypes, we observe that GETM-inferred phenotypes not only accurately predict the status of chronic musculoskeletal (CMK) pain but also reveal known pain-related topics. Intriguingly, medications and conditions in the cardiovascular category are enriched among the most predictive topics of chronic pain.
C1 [Wang, Yuening; Li, Yue] McGill Univ, Sch Comp Sci, Montreal, PQ, Canada.
   [Benavides, Rodrigo] Ctr Nacl Rehabil, Dept Anesthesiol, San Jose, Costa Rica.
   [Diatchenko, Luda; Grant, Audrey V.] McGill Univ, Dept Anesthesia, Montreal, PQ, Canada.
   [Diatchenko, Luda; Grant, Audrey V.] McGill Univ, Fac Dent, Montreal, PQ, Canada.
   [Diatchenko, Luda; Grant, Audrey V.] McGill Univ, Alan Edwards Ctr Res Pain, Montreal, PQ, Canada.
RP Li, Y (通讯作者)，McGill Univ, Sch Comp Sci, Montreal, PQ, Canada.; Grant, AV (通讯作者)，McGill Univ, Dept Anesthesia, Montreal, PQ, Canada.; Grant, AV (通讯作者)，McGill Univ, Fac Dent, Montreal, PQ, Canada.; Grant, AV (通讯作者)，McGill Univ, Alan Edwards Ctr Res Pain, Montreal, PQ, Canada.
EM audrey.grant@mcgill.ca; yueli@cs.mcgill.ca
OI Li, Yue/0000-0003-3844-4865
FU Natural Sciences and Engineering Research Council (NSERC)
   [RGPIN2019-0621]; Fonds de recherche Nature et technologies (FRQNT)
   [NC-268592]; Canada First Research Excellence Fund Healthy Brains for
   Healthy Life (HBHL) initiative New Investigator start-up award
   [G249591]; Canadian Excellence Research Chairs [CERC09]; Canadian
   Excellence Research Chairs fund [CERC09]; Pfizer Canada Professorship in
   Pain Research; CIHR [SCA-145102]
FX Y.L. is supported by Natural Sciences and Engineering Research Council
   (NSERC) Discovery Grant (RGPIN2019-0621), Fonds de recherche Nature et
   technologies (FRQNT) New Career (NC-268592), and Canada First Research
   Excellence Fund Healthy Brains for Healthy Life (HBHL) initiative New
   Investigator start-up award (G249591). AVG and YW were partly funded by
   the Canadian Excellence Research Chairs (CERC09). LD was supported by
   the Canadian Excellence Research Chairs fund (CERC09), a Pfizer Canada
   Professorship in Pain Research, and CIHR (SCA-145102) for Health
   Research's Strategy for Patient-Oriented Research (SPOR) in Chronic
   Pain. The current study was conducted under UK Biobank application
   20802.
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NR 37
TC 0
Z9 0
U1 2
U2 2
PU CELL PRESS
PI CAMBRIDGE
PA 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA
EI 2589-0042
J9 ISCIENCE
JI iScience
PD JUN 17
PY 2022
VL 25
IS 6
AR 104390
DI 10.1016/j.isci.2022.104390
PG 27
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 2A4TP
UT WOS:000809496800002
PM 35637735
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Gabas, A
   Macau, A
   Benjumea, B
   Bellmunt, F
   Figueras, S
   Vila, M
AF Gabas, A.
   Macau, A.
   Benjumea, B.
   Bellmunt, F.
   Figueras, S.
   Vila, M.
TI Combination of Geophysical Methods to Support Urban Geological Mapping
SO SURVEYS IN GEOPHYSICS
LA English
DT Review
DE Audiomagnetotelluric; Microtremor H/V; Ambient noise array; Urban
   geological mapping; Bedrock depth; Basin geometry; Soft soil-rock
   interface
ID SEISMIC NOISE; MICROTREMOR MEASUREMENTS; EXPLORATION TOOL; DC
   RESISTIVITY; SPECTRAL RATIO; DECOMPOSITION; CONSTRAINTS; INVERSION;
   THICKNESS; ZONE
AB Urban geological mapping is a key to assist management of new developed areas, conversion of current urban areas or assessment of urban geological hazards. Geophysics can have a pivotal role to yield subsurface information in urban areas provided that geophysical methods are capable of dealing with challenges related to these scenarios (e.g., low signal-to-noise ratio or special logistical arrangements). With this principal aim, a specific methodology is developed to characterize lithological changes, to image fault zones and to delineate basin geometry in the urban areas. The process uses the combination of passive and active techniques as complementary data: controlled source audio-magnetotelluric method (CSAMT), magnetotelluric method (MT), microtremor H/V analysis and ambient noise array measurements to overcome the limitations of traditional geophysical methodology. This study is focused in Girona and Salt surrounding areas (NE of Spain) where some uncertainties in subsurface knowledge (maps of bedrock depth and the isopach maps of thickness of quaternary sediments) need to be resolved to carry out the 1:5000 urban geological mapping. These parameters can be estimated using this proposed methodology. (1) Acoustic impedance contrast between Neogene sediments and Paleogene or Paleozoic bedrock is detected with microtremor H/V analysis that provides the soil resonance frequency. The minimum value obtained is 0.4 Hz in Salt city, and the maximum value is the 9.5 Hz in Girona city. The result of this first method is a fast scanner of the geometry of basement. (2) Ambient noise array constrains the bedrock depth using the measurements of shear-wave velocity of soft soil. (3) Finally, the electrical resistivity models contribute with a good description of lithological changes and fault imaging. The conductive materials (1-100 Omega m) are associated with Neogene Basin composed by unconsolidated detrital sediments; medium resistive materials (100-400 Omega m) correspond to Paleogene, and resistive materials (600-1,000 Omega m) are related with complex basement, granite of Paleozoic. The Neogene basin-basement boundary is constrained between surface and 500 m depth, approximately. The new geophysical methodology presented is an optimized and fast tool to refine geological mapping by adding 2D information to traditional geological data and improving the knowledge of subsoil.
C1 [Gabas, A.; Macau, A.; Benjumea, B.; Figueras, S.; Vila, M.] Inst Geol Catalunya, Barcelona 08006, Spain.
   [Bellmunt, F.] Univ Barcelona, E-08028 Barcelona, Spain.
RP Gabas, A (通讯作者)，Inst Geol Catalunya, C Balmes 209-211, Barcelona 08006, Spain.
EM agabas@igc.cat
RI Bellmunt, Fabian/G-2894-2016; Benjumea, Beatriz/L-5577-2014; Macau,
   Albert/L-3251-2017; Gabas i Gasa, Anna/L-5717-2014
OI Bellmunt, Fabian/0000-0003-4234-2394; Benjumea,
   Beatriz/0000-0002-0673-3411; Macau, Albert/0000-0001-8315-9831; Gabas i
   Gasa, Anna/0000-0002-2897-5550
FU SISPYR project
FX All of the authors would like to acknowledge all the persons helping
   during the stages of data acquisition and processing. We also thank Dr.
   J. Ledo for his suggestions and help to improve this paper. This
   fieldwork was part of microzonation project developed in Girona city
   during 2009-2010 and 2011 within the framework of SISPYR project
   (Seismic information system for the Pyrenees, INTERREG IV-A-2007-2013).
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NR 55
TC 24
Z9 25
U1 1
U2 48
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0169-3298
EI 1573-0956
J9 SURV GEOPHYS
JI Surv. Geophys.
PD JUL
PY 2014
VL 35
IS 4
BP 983
EP 1002
DI 10.1007/s10712-013-9248-9
PG 20
WC Geochemistry & Geophysics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geochemistry & Geophysics
GA AL3RY
UT WOS:000339047800005
DA 2022-08-23
ER

PT J
AU Ammar, N
   Shaban-Nejad, A
AF Ammar, Nariman
   Shaban-Nejad, Arash
TI Explainable Artificial Intelligence Recommendation System by Leveraging
   the Semantics of Adverse Childhood Experiences: Proof-of-Concept
   Prototype Development
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE mental health surveillance; semantic web; knowledge-based
   recommendation; digital assistant; explainable artificial intelligence;
   adverse childhood experiences
AB Background: The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence, thinking about how machine learning could benefit from knowledge graphs that combine "common sense" knowledge as well as semantic reasoning and causality models is a potential solution to this problem.
   Objective: In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance.
   Methods: We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology.
   Results: To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children's hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation.
   Conclusions: This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners' ability to provide explanations for the decisions they make.
C1 [Ammar, Nariman; Shaban-Nejad, Arash] Univ Tennessee Hlth Sci Ctr, Oak Ridge Natl Lab, Ctr Biomed Informat, Dept Pediat,Coll Med, Memphis, TN 38103 USA.
RP Shaban-Nejad, A (通讯作者)，Univ Tennessee Hlth Sci Ctr, Oak Ridge Natl Lab, Ctr Biomed Informat, Dept Pediat,Coll Med, Memphis, TN 38103 USA.
EM ashabann@uthsc.edu
RI Shaban-Nejad, Arash/ABG-4110-2020; Shaban-Nejad, Arash/ABH-1386-2021
OI Shaban-Nejad, Arash/0000-0003-2047-4759; Ammar,
   Nariman/0000-0002-5363-2541
FU Memphis Research Consortium
FX We would like to thank Dr Robert L Davis, Dr Jonathan A McCullers, Dr
   Jason Yaun, Dr Sandra R Arnold, and the entire team at the Family
   Resilience Initiative at Le Bonheur Children's Hospital, Memphis,
   Tennessee, for their support and insights. This research was partially
   supported by the Memphis Research Consortium.
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TC 5
Z9 5
U1 3
U2 19
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 e18752
DI 10.2196/18752
PG 15
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA PA5GG
UT WOS:000595663400025
PM 33146623
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Gao, H
   Li, BH
   Xie, WB
   Zhang, YX
   Guan, DH
   Chen, WT
   Cai, K
AF Gao, Han
   Li, Bohan
   Xie, Wenbin
   Zhang, Yuxin
   Guan, Donghai
   Chen, Weitong
   Cai, Ken
TI CSIP: Enhanced Link Prediction with Context of Social Influence
   Propagation
SO BIG DATA RESEARCH
LA English
DT Article
DE Link prediction; Social network; Influence propagation
AB Data mining in social networks brings an indispensable role for the construction of smart cities from the perspective of social development. Link prediction is an important task of data mining, especially in the knowledge graph, which is also called knowledge graph completion. Link prediction aims to find missing links or predict potential links according to the current social network. The most existing link prediction methods focus on static information in social networks, such as topology and node attributes, which are partly provided by users. When users are unwilling to provide or intentionally hide these static features, traditional link prediction methods cannot achieve ideal performance. The dynamic information of social influence propagation in social networks can avoid the user's subjective impact and better reflect the relationship between users. In addition, users show different degrees of interest and authority on various topics in the real world, leading to different influence propagation patterns. Therefore, we use context of social influence to optimize the topic-aware influence propagation model to improve the performance of link prediction. In this paper, we propose a new multi-output graph neural network framework to capture influence propagation in social networks and model the influence of users in different roles. In this way, the underlying information of influence between users can be used to construct new features to improve the performance of link prediction. Our experiments conduct the method on multiple benchmark datasets. The experimental results show that the modeling of context is effective, and our model outperforms the compared state-of-the-art link prediction methods. (C) 2021 Elsevier Inc. All rights reserved.
C1 [Gao, Han; Li, Bohan; Zhang, Yuxin; Guan, Donghai] Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China.
   [Li, Bohan] Minist Ind & Informat Technol, Key Lab Safety Crit Software, Nanjing, Peoples R China.
   [Li, Bohan] Collaborat Innovat Ctr Novel Software Technol & I, Nanjing, Peoples R China.
   [Xie, Wenbin] Army Engn Univ PLA, Nanjing, Peoples R China.
   [Chen, Weitong] Univ Queensland, Brisbane, Qld, Australia.
   [Cai, Ken] Zhongkai Univ Agr & Engn, Guangzhou, Peoples R China.
RP Xie, WB (通讯作者)，Army Engn Univ PLA, Nanjing, Peoples R China.
EM kingmorsun@sina.com
FU National Natural Science Foundation of China [61772268, 62003379];
   Fundamental Research Funds for the Central Universities [NT2018028,
   NS2018057]; China Degree and Graduate Education Fund; CCF-Huawei
   Database System Innovation Research Plan [CCF-HUAWEIDBIR2020001A]
FX This work was supported partly by National Natural Science Foundation of
   China (61772268, 62003379), Fundamental Research Funds for the Central
   Universities (NT2018028, NS2018057), China Degree and Graduate Education
   Fund, and CCF-Huawei Database System Innovation Research Plan
   (CCF-HUAWEIDBIR2020001A).
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NR 34
TC 2
Z9 2
U1 2
U2 11
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 100217
DI 10.1016/j.bdr.2021.100217
EA MAR 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:000642459200008
DA 2022-08-23
ER

PT J
AU Choudhury, N
   Faisal, F
   Khushi, M
AF Choudhury, Nazim
   Faisal, Fahim
   Khushi, Matloob
TI Mining Temporal Evolution of Knowledge Graphs and Genealogical Features
   for Literature-based Discovery Prediction
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Literature-based Knowledge Discovery; Dynamic Supervised Link
   Prediction; Keyword Co-occurrence Network (KCN); Genealogical Community;
   Weighted Temporal Citation
ID LINK PREDICTION; NETWORK; RANKING; SYSTEM
AB Literature-based discovery process identifies the important but implicit relations among information embedded in published literature. Existing techniques from Information Retrieval (IR) and Natural Language Processing (NLP) attempt to identify the hidden or unpublished connections between information concepts within published literature, however, these techniques overlooked the concept of predicting the future and emerging relations among scientific knowledge components such as author selected keywords encapsulated within the literature. Keyword Co-occurrence Network (KCN), built upon author selected keywords, is considered as a knowledge graph that focuses both on these knowledge components and knowledge structure of a scientific domain by examining the relationships between knowledge entities. Using data from two multidisciplinary research domains other than the bio-medical domain, and capitalizing on bibliometrics, the dynamicity of temporal KCNs, and a recurrent neural network, this study develops some novel features supportive for the prediction of the future literature-based discoveries - the emerging connections (co-appearances in the same article) among keywords. Temporal importance extracted from both bipartite and unipartite networks, communities defined by genealogical relations, and the relative importance of temporal citation counts were used in the feature construction process. Both node and edge-level features were input into a recurrent neural network to forecast the feature values and predict the future relations between different scientific concepts/topics represented by the author selected keywords. High performance rates, compared both against contemporary heterogeneous network-based method and preferential attachment process, suggest that these features complement both the prediction of future literature-based discoveries and emerging trend analysis. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Choudhury, Nazim] Univ S Florida, Dept Comp Sci & Engn, Tampa, FL 33620 USA.
   [Faisal, Fahim] George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA.
   [Khushi, Matloob] Univ Sydney, Sch Comp Sci, Sydney, NSW 2006, Australia.
RP Faisal, F (通讯作者)，George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA.
EM nachoudhury@usf.edu; ffaisal@gmu.edu; matloob.khushi@sydney.edu.au
RI Choudhury, Nazim/AAR-4944-2021; Khushi, Matloob/H-2725-2019
OI Khushi, Matloob/0000-0001-7792-2327; F, Faisal/0000-0001-6129-2084
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TC 2
Z9 2
U1 27
U2 99
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD AUG
PY 2020
VL 14
IS 3
AR 101057
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PG 23
WC Computer Science, Interdisciplinary Applications; Information Science &
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SC Computer Science; Information Science & Library Science
GA NM1BJ
UT WOS:000567839000004
DA 2022-08-23
ER

PT J
AU Roy, A
   Ghosal, D
   Cambria, E
   Majumder, N
   Mihalcea, R
   Poria, S
AF Roy, Abhinaba
   Ghosal, Deepanway
   Cambria, Erik
   Majumder, Navonil
   Mihalcea, Rada
   Poria, Soujanya
TI Improving Zero-Shot Learning Baselines with Commonsense Knowledge
SO COGNITIVE COMPUTATION
LA English
DT Article; Early Access
DE Commonsense knowledge; Zero-shot learning
AB Zero-shot learning - the problem of training and testing on a completely disjoint set of classes - relies greatly on its ability to transfer knowledge from train classes to test classes. Traditionally semantic embeddings consisting of human-defined attributes or distributed word embeddings are used to facilitate this transfer by improving the association between visual and semantic embeddings. In this paper, we take advantage of explicit relations between nodes defined in ConceptNet, a commonsense knowledge graph, to generate commonsense embeddings of the class labels by using a graph convolution network-based autoencoder. Our experiments performed on three standard benchmark datasets surpass the strong baselines when we fuse our commonsense embeddings with existing semantic embeddings, i.e., human-defined attributes and distributed word embeddings. This work paves the path to more brain-inspired approaches to zero-short learning.
C1 [Roy, Abhinaba; Cambria, Erik] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore.
   [Ghosal, Deepanway; Majumder, Navonil; Poria, Soujanya] Singapore Univ Technol & Design, Singapore, Singapore.
   [Mihalcea, Rada] Univ Michigan, Ann Arbor, MI 48109 USA.
RP Cambria, E (通讯作者)，Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore.
EM cambria@ntu.edu.sg
RI Cambria, Erik/C-2103-2013
OI Cambria, Erik/0000-0002-3030-1280
FU Agency for Science, Technology and Research (A*STAR) under its AME
   Programmatic Funding Scheme [A18A2b0046]; Singapore's MoE [T2MOE2008]
FX This research is supported by the Agency for Science, Technology and
   Research (A*STAR) under its AME Programmatic Funding Scheme (Project
   #A18A2b0046) and Project T2MOE2008 awarded by Singapore's MoE under its
   Tier-2 Grant Scheme.
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NR 36
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 1866-9956
EI 1866-9964
J9 COGN COMPUT
JI Cogn. Comput.
DI 10.1007/s12559-022-10044-0
EA JUL 2022
PG 11
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA 2X4HU
UT WOS:000825167500001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhao, YT
   Guo, JJ
   Bao, C
   Liang, CY
   Jain, HK
AF Zhao, Yating
   Guo, Jingjing
   Bao, Chao
   Liang, Changyong
   Jain, Hemant K.
TI Knowledge Graph Analysis of Human Health Research Related to Climate
   Change
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE climate change; human health; public health; knowledge evolution; visual
   analysis; hotspots
ID PUBLIC-HEALTH; HEAT WAVES; MORTALITY; IMPACT; VULNERABILITY; COCITATION;
   MORBIDITY; PATTERNS; DROUGHT
AB In order to explore the development status, knowledge base, research hotspots, and future research directions related to the impacts of climate change on human health, a systematic bibliometric analysis of 6719 published articles from 2003 to 2018 in the Web of Science was performed. Using data analytics tools such as HistCite and CiteSpace, the time distribution, spatial distribution, citations, and research hotspots were analyzed and visualized. The analysis revealed the development status of the research on the impacts of climate change on human health and analyzed the research hotspots and future development trends in this field, providing important knowledge support for researchers in this field.
C1 [Zhao, Yating] Hefei Normal Univ, Sch Econ & Management, Hefei 230601, Peoples R China.
   [Zhao, Yating; Guo, Jingjing; Bao, Chao; Liang, Changyong] Hefei Univ Technol, Sch Management, Hefei 230009, Peoples R China.
   [Jain, Hemant K.] Univ Tennessee, Coll Business, Chattanooga, TN USA.
RP Zhao, YT (通讯作者)，Hefei Normal Univ, Sch Econ & Management, Hefei 230601, Peoples R China.; Zhao, YT (通讯作者)，Hefei Univ Technol, Sch Management, Hefei 230009, Peoples R China.
EM 2012010126@mail.hfut.edu.cn; veralien@163.com; yancey1246@163.com;
   cyliang@163.com; hemant-jain@utc.edu
OI Jain, Hemant/0000-0001-6184-1565
FU National Natural Science Foundation of China [71771075, 71771077,
   72071063]
FX This research is partially supported in the collection, analysis, and
   interpretation of data by the National Natural Science Foundation of
   China under grant numbers 71771075, 71771077, and 72071063.
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NR 55
TC 2
Z9 2
U1 17
U2 45
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 OCT
PY 2020
VL 17
IS 20
AR 7395
DI 10.3390/ijerph17207395
PG 19
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA OL9IX
UT WOS:000585645000001
PM 33050582
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wu, HY
   Cheng, KM
   Guo, Q
   Yang, WG
   Tong, LJ
   Wang, YL
   Sun, ZM
AF Wu, Haiyang
   Cheng, Kunming
   Guo, Qiang
   Yang, Weiguang
   Tong, Linjian
   Wang, Yulin
   Sun, Zhiming
TI Mapping Knowledge Structure and Themes Trends of Osteoporosis in
   Rheumatoid Arthritis: A Bibliometric Analysis
SO FRONTIERS IN MEDICINE
LA English
DT Article
DE rheumatoid arthritis; osteoporosis; bibliometrics; CiteSpace; VOSviewer
ID BONE-MINERAL DENSITY; TUMOR-NECROSIS-FACTOR; KAPPA-B LIGAND; RECEPTOR
   ACTIVATOR; DOUBLE-BLIND; IDENTIFICATION; INTERLEUKIN-6; PREDNISOLONE;
   DESTRUCTION; MULTICENTER
AB Background: Rheumatoid arthritis is a chronic disabling disease characterized by chronic inflammation, articular cartilage destruction, and reduced bone mass. Multiple studies have revealed that the development of osteoporosis in rheumatoid arthritis (RA; ORA) patients could be led to a reduced quality of life and increased healthcare costs. Nevertheless, no attempt has been made to analyze the field of ORA research with the bibliometric method. This study aimed to provide a comprehensive overview of the knowledge structure and theme trends in the field of ORA research from a bibliometric perspective.Methods: Articles and reviews regarding ORA from 1998 to 2021 were identified from the Web of Science database. An online bibliometric platform, CiteSpace, and VOSviewer software were used to generate visualization knowledge maps including co-authorship, co-citation, and co-occurrence analysis. SPSS, R, and Microsoft Excel software were used to conduct curve fitting and correlation analysis, and to analyze quantitative indicators, such as publication and citation counts, h-index, and journal citation reports.Results: A total of 1,081 papers with 28,473 citations were identified. Publications were mainly concentrated in North America, Western Europe, and Eastern Asia. Economic strength is an important factor affecting scientific output. The United States contributed the most publications (213) with the highest h-index value (46) as of September 14, 2021. Diakonhjemmet Hospital and professor Haugeberg G were the most prolific institution and influential authors, respectively. Journal of Rheumatology was the most productive journal concerning ORA research. According to the burst references, "anti-citrullinated protein antibodies" and "preventing joint destruction" have been recognized as the hot research issues in the domain. The keywords co-occurrence analysis identified "teriparatide," "interleukin-6," "Wnt," and "vertebral fractures" as the important future research directions.Conclusion: This was the first bibliometric study comprehensively summarizing the trends and development of ORA research. Our findings could offer practical sources for scholars to understand the key information in this field, and identify the potential research frontiers and hot directions in the near future.
C1 [Wu, Haiyang; Yang, Weiguang; Tong, Linjian; Wang, Yulin; Sun, Zhiming] Tianjin Med Univ, Grad Sch, Dept Clin Med, Tianjin, Peoples R China.
   [Cheng, Kunming] Zhengzhou Univ, Affiliated Hosp 2, Dept Intens Care Unit, Zhengzhou, Peoples R China.
   [Guo, Qiang] Tianjin Med Univ, Baodi Clin Coll, Dept Orthopaed Surg, Tianjin, Peoples R China.
   [Sun, Zhiming] Tianjin Huanhu Hosp, Dept Orthopaed Surg, Tianjin, Peoples R China.
RP Sun, ZM (通讯作者)，Tianjin Med Univ, Grad Sch, Dept Clin Med, Tianjin, Peoples R China.; Sun, ZM (通讯作者)，Tianjin Huanhu Hosp, Dept Orthopaed Surg, Tianjin, Peoples R China.
EM szhm618@163.com
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NR 71
TC 12
Z9 12
U1 18
U2 26
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2296-858X
J9 FRONT MED-LAUSANNE
JI Front. Med.
PD NOV 23
PY 2021
VL 8
AR 787228
DI 10.3389/fmed.2021.787228
PG 17
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA XK7JQ
UT WOS:000727637500001
PM 34888333
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhang, NY
   Ye, HB
   Deng, SM
   Tan, CQ
   Chen, MS
   Huang, SF
   Huang, F
   Chen, HJ
AF Zhang, Ningyu
   Ye, Hongbin
   Deng, Shumin
   Tan, Chuanqi
   Chen, Mosha
   Huang, Songfang
   Huang, Fei
   Chen, Huajun
TI Contrastive Information Extraction With Generative Transformer
SO IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
LA English
DT Article
DE Task analysis; Data mining; Transformers; Information retrieval; Feature
   extraction; Speech processing; Pipelines; Event extraction; information
   extraction; triple extraction
ID ENTITY RECOGNITION
AB Information extraction tasks such as entity relation extraction and event extraction are of great importance for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end information extraction task for sequence generation. Since generative information extraction may struggle to capture long-term dependencies and generate unfaithful triples, we introduce a novel model, contrastive information extraction with a generative transformer. Specifically, we introduce a single shared transformer module for an encoder-decoder-based generation. To generate faithful results, we propose a novel triplet contrastive training object. Moreover, we introduce two mechanisms to further improve model performance (i.e., batch-wise dynamic attention-masking and triple-wise calibration). Experimental results on five datasets (i.e., NYT, WebNLG, MIE, ACE-2005, and MUC-4) show that our approach achieves better performance than baselines.(1)
C1 [Zhang, Ningyu; Ye, Hongbin; Deng, Shumin; Tan, Chuanqi; Chen, Mosha; Huang, Songfang; Huang, Fei] Alibaba Grp, Hangzhou 310007, Peoples R China.
   [Chen, Huajun] Zhejiang Univ, Hangzhou 311121, Peoples R China.
   [Chen, Huajun] Alibaba Grp, Hangzhou 311121, Peoples R China.
   [Chen, Huajun] Alibaba Zhejiang Univ Joint Res Inst Frontier Tec, Hangzhou 311121, Peoples R China.
RP Chen, HJ (通讯作者)，Zhejiang Univ, Hangzhou 311121, Peoples R China.; Chen, HJ (通讯作者)，Alibaba Grp, Hangzhou 311121, Peoples R China.; Chen, HJ (通讯作者)，Alibaba Zhejiang Univ Joint Res Inst Frontier Tec, Hangzhou 311121, Peoples R China.
EM zhangningyu@zju.edu.cn; yehongbin@zju.edu.cn; 231sm@zju.edu.cn;
   chuanqi.tcq@alibaba-inc.com; chenmosha.cms@alibaba-inc.com;
   songfang.hsf@alibaba-inc.com; f.huang@alibaba-inc.com;
   huajunsir@zju.edu.cn
OI Huang, Fei/0000-0002-3709-5053; Chen, Mosha/0000-0001-8815-6031; Deng,
   Shumin/0000-0002-4049-8478
FU National Key R&D Program of China [2018YFB1402800]; National Natural
   Science Foundation of China [U19B2027, 91846204]; Zhejiang University
   and Alibaba Group; Alibaba-Zhejiang University Joint Research Institute
   of Frontier Technologies (AZFT), Hangzhou, China
FX This work was supported in part by the National Key R&D Program of China
   under Grant 2018YFB1402800, and in part by the National Natural Science
   Foundation of China under Grants U19B2027 and 91846204. The work of
   Ningyu Zhang, Hongbin Ye, Huajun Chen, Shumin Deng was supported in part
   by the Zhejiang University and Alibaba Group, and in part by
   Alibaba-Zhejiang University Joint Research Institute of Frontier
   Technologies (AZFT), Hangzhou, China. The associate editor coordinating
   the review of this manuscript and approving it for publication was Dr.
   Nancy F. Chen. (Ningyu Zhang, Hongbin Ye, and Shumin Deng contributed
   equally to this work and are co-first authors.) (Corresponding author:
   Huajun Chen.)
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NR 67
TC 0
Z9 0
U1 12
U2 27
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 3077
EP 3088
DI 10.1109/TASLP.2021.3110126
PG 12
WC Acoustics; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Acoustics; Engineering
GA WH1IK
UT WOS:000707440600001
DA 2022-08-23
ER

PT J
AU Murali, V
   Konigs, C
   Deekshitula, S
   Nukala, S
   Santhi, MD
   Athri, P
AF Murali, Vidhya
   Koenigs, Cassandra
   Deekshitula, Sarvani
   Nukala, Saranya
   Santhi, Maddala Divya
   Athri, Prashanth
TI CompoundDB4j: Integrated Drug Resource of Heterogeneous Chemical
   Databases
SO MOLECULAR INFORMATICS
LA English
DT Article
ID CHEMBL; POLYPHARMACOLOGY; DISCOVERY
AB Computational approaches to analyze various drug/ compound centered analysis often present a need to map attributes from multiple drug databases. In this study, we provide a Neo4j repository that integrates two of the most prominent open source drug databases, DrugBank and ChEMBL, with a goal of establishing an integrated data visualization and analysis tool for drug discovery studies. The drugs present in DrugBank are mapped to their counterparts in ChEMBL. The integration of these resources and the harmonization using knowledge graph serialization using Neo4j lead to identification of relationships between drugs and other related features that are otherwise spread across two different resources. A common data format, a prerequisite to populate the Neo4j database, enables users to identify new relationships central to drug discovery research, like Drug Target Interactions (DTI). The resource is freely available at:https://github.com/ambf0632/CompoundDB4j
C1 [Murali, Vidhya; Athri, Prashanth] Amrita Vishwa Vidyapeetham, Dept Comp Sci & Engn, Amrita Sch Engn, Bengaluru, India.
   [Koenigs, Cassandra] Bielefeld Univ, Bio Informat & Med Informat, Northrhine Westphalia, Germany.
   [Deekshitula, Sarvani; Nukala, Saranya; Santhi, Maddala Divya] Amrita Vishwa Vidyapeetham, Dept Comp Sci & Engn, Amrita Sch Engn, Amritapuri, India.
RP Athri, P (通讯作者)，Amrita Vishwa Vidyapeetham, Dept Comp Sci & Engn, Amrita Sch Engn, Bengaluru, India.
EM prashanthathri@gmail.com
RI Athri, Prashanth/AFP-5474-2022
OI Athri, Prashanth/0000-0002-5731-0291; Konigs,
   Cassandra/0000-0001-8991-9272
FU Department of Biotechnology, Government of India
   [BT/16476/BID/7/631/2016]; Visvesvaraya PhD Scheme for Electronics and
   IT by the Ministry of Electronics and Information Technology, Government
   of India
FX This work was supported by the Department of Biotechnology, Government
   of India (Grant Reference Number: /BT/16476/BID/7/631/2016); and VM is
   supported by the Visvesvaraya PhD Scheme for Electronics and IT provided
   by the Ministry of Electronics and Information Technology, Government of
   India.
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NR 32
TC 3
Z9 3
U1 2
U2 10
PU WILEY-V C H VERLAG GMBH
PI WEINHEIM
PA POSTFACH 101161, 69451 WEINHEIM, GERMANY
SN 1868-1743
EI 1868-1751
J9 MOL INFORM
JI Mol. Inf.
PD SEP
PY 2020
VL 39
IS 9
DI 10.1002/minf.202000013
EA JUN 2020
PG 7
WC Chemistry, Medicinal; Computer Science, Interdisciplinary Applications;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy; Computer Science; Mathematical & Computational
   Biology
GA NN6HA
UT WOS:000540238500001
PM 32390334
DA 2022-08-23
ER

PT J
AU Tang, X
   Chen, L
   Cui, J
   Wei, BG
AF Tang Xing
   Chen Ling
   Cui Jun
   Wei Baogang
TI Knowledge representation learning with entity descriptions, hierarchical
   types, and textual relations
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Knowledge representation; Multi-source; Textual information
AB Knowledge representation learning methods usually only utilize triple facts, or just consider one kind of extra information. In this paper, we propose a multi-source knowledge representation learning (MKRL) model, which can combine entity descriptions, hierarchical types, and textual relations with triple facts. Specifically, for entity descriptions, a convolutional neural network is used to get representations. For hierarchical type, weighted hierarchy encoders are used to construct the projection matrixes of hierarchical types, and the projection matrix of an entity combines all hierarchical type projection matrixes of the entity with the relation-specific type constrains. For textual relations, a sentence-level attention mechanism is employed to get representations. We evaluate MKRL model on knowledge graph completion task with dataset FB15k-237, and experimental results demonstrate that our model outperforms the state-of-the-art methods, which indicates the effectiveness of multi-source information for knowledge representation.
C1 [Tang Xing; Chen Ling; Cui Jun; Wei Baogang] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Zhejiang, Peoples R China.
RP Chen, L (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Zhejiang, Peoples R China.
EM tangxing@cs.zju.edu.cn; lingchen@cs.zju.edu.cn; cuijun@cs.zju.edu.cn;
   wbg@cs.zju.edu.cn
RI Chen, Ling/AAY-3744-2020
FU National Key Research and Development Program of China [2018YFB0505000];
   National Natural Science Foundation of China [61572434]
FX This work was funded by National Key Research and Development Program of
   China (2018YFB0505000) and the National Natural Science Foundation of
   China (No. 61572434).
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NR 45
TC 23
Z9 28
U1 8
U2 41
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD MAY
PY 2019
VL 56
IS 3
BP 809
EP 822
DI 10.1016/j.ipm.2019.01.005
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 HR4OT
UT WOS:000463126500027
DA 2022-08-23
ER

PT J
AU Wang, NX
   Cai, XY
   Yang, LB
   Mei, X
AF Wang, Nanxin
   Cai, Xiaoyan
   Yang, Libin
   Mei, Xin
TI Safe medicine recommendation via star interactive enhanced-based
   transformer model
SO COMPUTERS IN BIOLOGY AND MEDICINE
LA English
DT Article
DE Medicine recommendation; Star interactive enhanced-based transformer;
   Graph embedding
ID ONTOLOGY; SYSTEM
AB With the rapid development of electronic medical records (EMRs), most existing medicine recommendation systems based on EMRs explore knowledge from the diagnosis history to help doctors prescribe medication correctly. However, due to the limitations of the EMRs' content, recommendation systems cannot explicitly reflect relevant medical data, such as drug interactions. In recent years, medicine recommendation approaches based on medical knowledge graphs and graph neural networks have been proposed, and the methods based on the Transformer model have been widely used in medicine recommendation systems. Transformer-based medicine recommendation approaches are readily applicable to inductive problems. Unfortunately, traditional Transformer-based medicine recommendation approaches require complex computing power and suffer information loss among the multi-heads in Transformer model, which causes poor performance. At the same time, these approaches have rarely considered the side effects of drug interaction in traditional medical recommendation approaches. To overcome the drawbacks of the current medicine recommendation approaches, we propose a Star Interactive Enhanced-based Transformer (SIET) model. It first constructs a high-quality heterogeneous graph by bridging EMR (MIMIC-III) and a medical knowledge graph (ICD-9 ontology and DrugBank). Then, based on the constructed heterogeneous graph, it extracts a disease homogeneous graph, a medicine homogeneous graph, and a negative factors homogeneous graph to get auxiliary information of disease or drug (named enhanced neighbors). These are fed into the SIET model in conjunction with the relevant information in the EMRs to obtain representations of diseases and drugs. It finally generates the recommended drug list by calculating the cosine similarity between disease combination representations and drug combination representations. Extensive experiments on the MIMIC-III, DrugBank, and ICD-9 ontology datasets demonstrate the outstanding performance of our proposed model. Meanwhile, we show that our SIET model outperforms strong baselines on an inductive medicine recommendation task.
C1 [Wang, Nanxin; Yang, Libin; Mei, Xin] Northwestern Polytech Univ, Sch Cyber Sci & Technol, Xian 710072, Shaanxi, Peoples R China.
   [Cai, Xiaoyan] Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China.
RP Yang, LB (通讯作者)，Northwestern Polytech Univ, Sch Cyber Sci & Technol, Xian 710072, Shaanxi, Peoples R China.
EM libiny@nwpu.edu.cn
FU National Key Research and Development Project of China [2018YFB1402604];
   National Natural Science Foundation of China [61 872 296, 61 772 429,
   U20B2065]; MOE (Ministry of Education in China) Project of Humanities
   and Social Sciences [18YJC870001]
FX This work was supported by National Key Research and Development Project
   of China (2018YFB1402604), National Natural Science Foundation of China
   (61 872 296, 61 772 429, U20B2065) and MOE (Ministry of Education in
   China) Project of Humanities and Social Sciences (18YJC870001).
CR Ahmed A., 2013, P 22 INT C WORLD WID, P37, DOI 10.1145/2488388.2488393
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NR 34
TC 0
Z9 0
U1 8
U2 10
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0010-4825
EI 1879-0534
J9 COMPUT BIOL MED
JI Comput. Biol. Med.
PD FEB
PY 2022
VL 141
AR 105159
DI 10.1016/j.compbiomed.2021.105159
EA DEC 2021
PG 12
WC Biology; Computer Science, Interdisciplinary Applications; Engineering,
   Biomedical; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics; Computer Science;
   Engineering; Mathematical & Computational Biology
GA YN6HS
UT WOS:000747358300004
PM 34971981
DA 2022-08-23
ER

PT J
AU Chen, LY
   Li, Z
   He, WD
   Cheng, G
   Xu, T
   Yuan, NJ
   Chen, EH
AF Chen, Liyi
   Li, Zhi
   He, Weidong
   Cheng, Gong
   Xu, Tong
   Yuan, Nicholas Jing
   Chen, Enhong
TI Entity Summarization via Exploiting Description Complementarity and
   Salience
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article; Early Access
DE Task analysis; Semantics; Neural networks; Tensors; Faces; Benchmark
   testing; Standards; Entity description; entity summarization; knowledge
   graph (KG); neural network
AB Entity summarization is a novel and efficient way to understand real-world facts and solve the increasing information overload problem in large-scale knowledge graphs (KG). Existing studies mainly rely on ranking independent entity descriptions as a list under a certain scoring standard such as importance. However, they often ignore the relatedness and even semantic overlap between individual descriptions. This may seriously interfere with the contribution judgment of descriptions for entity summarization. Actually, the entity summary is a whole to comprehensively integrate the main aspects of entity descriptions, which could be naturally treated as a set. Unfortunately, the exploration of these set characteristics for entity summarization is still an open issue with great challenges. To that end, we draw inspiration from a set completion perspective and propose an entity summarization method with complementarity and salience (ESCS) to deeply exploit description complementarity and salience in order to form a summary set for the target entity. Specifically, we first generate entity description representations with textual features in the description embedding module. For the purpose of learning complementary relationships within the entire summary set, we devise a bi-directional long short-term memory structure to capture global complementarity for each summary in the summary complementarity learning module. Meanwhile, in order to estimate the salience of individual descriptions, we calculate similarities between semantic embeddings of the target entity and its property-value pairs in the description salience learning module. Next, with a joint learning stage, we can optimize ESCS from a set completion perspective. Finally, a summary generation strategy is designed to infer the entire summary set step-by-step for the target entity. Extensive experiments on a public benchmark have clearly demonstrated the effectiveness of ESCS and revealed the potential of set completion in entity summarization task.
C1 [Chen, Liyi; Li, Zhi; He, Weidong; Xu, Tong; Chen, Enhong] Univ Sci & Technol China, Sch Data Sci, Hefei 230027, Peoples R China.
   [Cheng, Gong] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210093, Peoples R China.
   [Yuan, Nicholas Jing] Huawei Cloud & AI, Hangzhou 310007, Peoples R China.
RP Xu, T (通讯作者)，Univ Sci & Technol China, Sch Data Sci, Hefei 230027, Peoples R China.
EM liyichencly@gmail.com; zhili03@mail.ustc.edu.cn; hwd@mail.ustc.edu.cn;
   gcheng@nju.edu.cn; tongxu@ustc.edu.cn; nicholas.jing.yuan@gmail.com;
   cheneh@ustc.edu.cn
RI Cheng, Gong/O-4437-2015
OI Cheng, Gong/0000-0003-3539-7776; Chen, Liyi/0000-0003-2166-4386; Chen,
   Enhong/0000-0002-4835-4102
FU National Key Research and Development Program of China [2018YFB1402600];
   National Natural Science Foundation of China [62072423]; Huawei-USTC
   Joint Innovation Program
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018YFB1402600, in part by the
   National Natural Science Foundation of China under Grant 62072423, and
   in part by the Huawei-USTC Joint Innovation Program.
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NR 64
TC 0
Z9 0
U1 3
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
DI 10.1109/TNNLS.2022.3149047
EA FEB 2022
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA ZH9MO
UT WOS:000761253600001
PM 35196243
DA 2022-08-23
ER

PT J
AU Pal, JK
AF Pal, Jiban K.
TI Visualizing the knowledge outburst in global research on COVID-19
SO SCIENTOMETRICS
LA English
DT Article
DE Scientometrics; Scientific visualization; Research evaluation; Knowledge
   mapping; Quantitative analysis; Novel Coronavirus; COVID-19; 2019-nCoV;
   SARS-CoV-2
AB The scholarly output of the new coronavirus research has been proliferating. During five months, an amount of 14,588 scientific publications about nCoV-2 and COVID-19 has been generated intensively (as indexed in Scopus on 31 May 2020). Such a knowledge outburst has created ample interest in understanding the research landscape of this newly configured area. This paper demonstrates on scientometric dimensions of the novel coronavirus (2019-nCov) research using quantifiable characteristics of the publication dataset. Findings reveal that the rate of publication growth (1600%) is very significant to a synergic response of the researchers to combat with the most extended sequence of an RNA virus. Indeed their response has geared up to an average of 100 articles per day. Many scholarly publishers have disclosed their preprint servers to make the publications available immediately, even by enabling Open Access. The scientific contents have published in more than 500 journals from 240 academic publishers. While the top-ten publishers occupied almost 70% of the articles, then about 25% of the studies were sponsored by 300 funding agencies. Among the notable journals Lancet, Nature, BMJ, JAMA, JMV, and NEJM are prominent. Findings also reveal that majority of the contributions have occurred in Medical Science, focusing on virology, immunology, epidemiology, pharmacology, public health, critical care, and emergency medicine. However, the closely associated terms are virus transmission, infection control, asymptomatic, quarantine, pneumonia, human, disease severity, clinical trials, viral pathogenesis, pandemic, risk, and mortality. The study suggests that academic hubs are located mostly in the USA, China, Italy, and the UK. Among the productive institutions; Huazhong Univ (China), Tongji Med. College (China), Harvard Med. School (USA), Univ of Milan (Italy), INSERM (France), UCL (UK) are outstanding. The G7 countries together produced 50% of the global research output on nCov-2. It also noted an encouraging trend of collaborative research across many countries and disciplines, where the values of CI (6.46), DC (0.79), and CC (0.59) are very significant. It examines the geographical diversity of the collaborating authors, thereby visualized their linkages via co-authorship occurrences. Finally, it analyzed the publications' impact to showcase the most influential contributions of the new coronavirus research.
C1 [Pal, Jiban K.] Indian Stat Inst, Lib Documentat & Informat Sci Div, 203 BT Rd, Kolkata 700108, India.
RP Pal, JK (通讯作者)，Indian Stat Inst, Lib Documentat & Informat Sci Div, 203 BT Rd, Kolkata 700108, India.
EM jiban@isical.ac.in
RI Pal, Jiban K./B-4004-2010
OI Pal, Jiban K./0000-0002-2870-9180
CR AJIFERUKE I, 1988, SCIENTOMETRICS, V14, P421, DOI 10.1007/BF02017100
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NR 22
TC 13
Z9 13
U1 10
U2 29
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2021
VL 126
IS 5
BP 4173
EP 4193
DI 10.1007/s11192-021-03912-3
EA MAR 2021
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA RU1KG
UT WOS:000625725800009
PM 33716352
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Yao, Q
   Lyu, PH
   Yang, LP
   Yao, L
   Liu, ZY
AF Yao, Qiang
   Lyu, Peng-Hui
   Yang, Lian-Ping
   Yao, Lan
   Liu, Zhi-Yong
TI Current performance and future trends in health care sciences and
   services research
SO SCIENTOMETRICS
LA English
DT Article
DE Health care sciences and services research; Global research trends;
   Knowledge mapping; Scientometric; Web of Science
ID QUALITY-OF-LIFE; CONTINUING MEDICAL-EDUCATION; DECISION-MAKING;
   PALLIATIVE CARE; BIBLIOMETRIC ANALYSIS; PATIENT SATISFACTION; ECONOMIC
   EVALUATIONS; INFORMATION-SEEKING; GLOBAL TRENDS; COMMUNICATION
AB Health care sciences and services research (HCSSR) has come to the fore in recent years and related research literature increased rapidly over the last few decades. The main purpose of this study is to describe the global progress and to determine the current trends on HCSSR by using a scientometrics approach to survey related literature in the Web of Science database from 1900 to 2012. The document types, languages, publication patterns, subject categories, journals, geographic and institutional distributions, top cited articles, and the distribution of keywords were thoroughly examined. The results show that HCSSR has increased rapidly over the past 20 years, most notably in the last decade. In total, there are currently 128,728 research articles in 156 journals listed in 39 WoS subject categories. The top 20 most productive countries, and institutions were analyzed in detail, and 11 frequently cited papers and research foci were identified based on citation analysis. HCSSR spans many disciplines and focuses mainly on public, environmental & occupational health and education educational research. Medical Care, Academic Medicine, Health Affairs and Journal of School Health are the core journals with both high quantity and quality. High-income countries make up the leading nations, especially G7 countries. Meanwhile, "emerging economies" are also increasingly engaging this field. American and Canadian institutions have made greater advances in productions, citations, and cooperation, with stronger and better development prospects overall. The hot topics include internet use and decision making in health care, palliative care and end of life research, health status and quality of life, quality of healthcare and patient's satisfaction, medical education, and health communication. Also, most researchers tend to study health care sciences based on the topics of quality-of-life assessment, and their interest in quality-of-life measures has increased. Increasing attention has been paid to the developing countries, especially "emerging economies" like China. Although health research has made much progress, many questions still remain unanswered and there are few assessments of how well research systems carry out their essential functions. Hence, there is currently an urgent need for timely establishment of an effective health research system.
C1 [Yao, Qiang; Yang, Lian-Ping; Yao, Lan; Liu, Zhi-Yong] Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Med & Hlth Management, Wuhan 430030, Peoples R China.
   [Lyu, Peng-Hui] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China.
RP Liu, ZY (通讯作者)，Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Med & Hlth Management, Wuhan 430030, Peoples R China.
EM yaoqiang@hust.edu.cn; lvph@whu.edu.cn; zhiyongliu@hust.edu.cn
RI YAO, QIANG/X-9176-2019; Yao, Qiang/Q-5758-2019
OI Yao, Qiang/0000-0002-4408-087X
FU Centre for Health Statistics Information, Ministry of Health, People's
   Republic of China; Natural Science Foundation of China [71173249]
FX This work was supported by the Centre for Health Statistics Information,
   Ministry of Health, People's Republic of China and the Natural Science
   Foundation of China (Grant No. 71173249: Research on Formation Mechanism
   and Evolution Laws of Knowledge Networks). The authors are grateful to
   Li-xiang Li, Fei-Cheng Ma, Gerard Joseph White, Hassan Dib and Nathan D.
   Bobai for their helpful discussion and suggestions. The authors would
   also like to thank anonymous reviewers for their valuable comments.
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   1990, HLTH RES ESSENTIAL L
NR 100
TC 14
Z9 15
U1 3
U2 124
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 2014
VL 101
IS 1
BP 751
EP 779
DI 10.1007/s11192-014-1383-7
PG 29
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 AP6ZX
UT WOS:000342228300035
DA 2022-08-23
ER

PT J
AU Cho, SM
   Park, CU
   Song, M
AF Cho, Sun Mi
   Park, Chan-ung
   Song, Min
TI The evolution of social health research topics: A data-driven analysis
SO SOCIAL SCIENCE & MEDICINE
LA English
DT Article
DE Social health; Text mining; Latent Dirichlet allocation (LDA); Research
   trends; Social health knowledge mapping; Social health knowledge
   summarization; Social health research area; Scopus
ID INCOME INEQUALITY; ITEM BANK; IMPACTS; TRAJECTORIES; DEFINITION;
   MEDICINE; SUPPORT; QUALITY; PATIENT; WORLD
AB The realm of social health has not yet been properly established in terms of fixed definitions, concepts, and research areas. This study attempts to define social health using macro and micro perspectives and explores trends in social health research by mapping their topics and fields.
   We used Latent Dirichlet allocation (LDA) topic modeling, which allows the extraction of key terms and topics derived from a large volume of literature. We traced the evolution of research topics from past (the literature that "present" articles cited), present (existing journal articles on social health), to future (the literature which cited the articles) studies based on connections between citations. The datasets were collected by the query terms "social health" in the Scopus database, including title, abstract, and keywords of journal articles. We collected a total of 443 articles from recent social health literature, 6588 articles from past literature that the recent articles on social health cited, and 2680 articles from future literature in which recent social health articles were cited.
   We defined social health as positive interaction that increases individual engagement in social life at the micro level, and the high degree of social integration that deals with collective problems in society at the macro level. The results of LDA showed that social health research has developed into seven fields: Health Care Delivery; Vulnerable Groups; Measurement; Health Inequality; Social Network and Empowerment; Clinical/Physical Health; and Mental/Behavioral Health. Based on citation relationships, topics grounded in an individual/micro perspective have grown increasingly specialized and productive, while topics grounded in a social/macro perspective have stagnated or was underexplored. Our findings imply that social health studies should follow a more interdisciplinary approach to integrate current health models of individual-centered treatments with social science concerns on building collective capacity for social well-being.
C1 [Cho, Sun Mi] Yonsei Univ, Inst Social, Seoul, South Korea.
   [Park, Chan-ung] Yonsei Univ, Dept Sociol, Seoul, South Korea.
   [Song, Min] Yonsei Univ, Dept Lib & Informat Sci, 50 Yonsei Ro, Seoul 03722, South Korea.
RP Song, M (通讯作者)，Yonsei Univ, Dept Lib & Informat Sci, 50 Yonsei Ro, Seoul 03722, South Korea.
EM sunmicho@yonsei.ac.kr; chanung@yonsei.ac.kr; min.song@yonsei.ac.kr
FU Ministry of Education of the Republic of Korea; National Research
   Foundation of Korea [NRF-2018S1A3A2075114]
FX This work was supported by the Ministry of Education of the Republic of
   Korea and the National Research Foundation of Korea
   (NRF-2018S1A3A2075114). This funding source had no role in the design of
   this study and will not have any role during its execution, analyses,
   interpretation of the data, or decision to submit results.
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NR 75
TC 2
Z9 3
U1 7
U2 23
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0277-9536
EI 1873-5347
J9 SOC SCI MED
JI Soc. Sci. Med.
PD NOV
PY 2020
VL 265
AR 113299
DI 10.1016/j.socscimed.2020.113299
PG 10
WC Public, Environmental & Occupational Health; Social Sciences, Biomedical
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health; Biomedical Social Sciences
GA PI4JE
UT WOS:000601058000007
PM 32905964
DA 2022-08-23
ER

PT J
AU Chen, S
   Kong, JF
   Feng, L
AF Chen, Si
   Kong, Jinfeng
   Feng, Lei
TI The Trend of Drug Therapy on Uveitic Macular Edema: A Bibliometric
   Analysis of the 100 Most Cited Articles
SO FRONTIERS IN MEDICINE
LA English
DT Article
DE macular edema; uveitis; citations; bibliometric analysis; drug treatment
ID DEXAMETHASONE INTRAVITREAL IMPLANT; TRIAMCINOLONE; MULTICENTER; EFFICACY
AB BackgroundMacular edema is the most common cause of impaired vision due to uveitis. Although various medications are available, not all uveitis patients with macular edema are satisfied with the treatment results. Therefore, solving this gap becomes the utmost concern worldwide. This study attempted to use bibliometric analysis to compare the valuable information in the top 100 highly cited studies in the field of drug therapy for uveitic macular edema (UME) and then determine the research hot spots and trends in this field. MethodsIn this study, the Science Citation Index Expanded (SCIE) of Web of Science (WOS) was used to collect the top 100 most cited studies on UME and analyze the literature from different countries/regions, institutions, and journals. The visualization knowledge maps is generated by VOSviewer and Citespace software. ResultsThe top 100 highly cited studies are from 34 countries/regions. The United States has the largest number of publications, followed by the England, Spain and Germany. The top three institutions publishing highly cited literature are all from the England: University of London, University College London, and Moorfields Eye Hospital NHS Foundation Trust. Ophthalmology is the most widely published journal with 14 papers. The total number of citations is 1,371, meaning that Ophthalmology is the most authoritative journal in the field of UME drug therapy. The top two articles with the most cited times are from the United States, accounting for 36.5% of the total cited times of the top 10 articles. Keywords were divided into three clusters: the corticosteroid administration pathway, biological agents, and clinical trials. Uveitis, cystoid macular edema, efficacy, dexamethasone, and triamcinolone acetonide appeared more frequently in keywords. Researches on local and long-acting drug has gradually becoming the hot spots and trends. ConclusionThis study concludes that bibliometric analysis can intuitively and quickly obtain the frontiers and hot spots of research in the field of UME drug therapy. Corticosteroid administration, biological agents, and clinical trials are considered the potential focus of future research.
C1 [Chen, Si; Kong, Jinfeng; Feng, Lei] Zhejiang Univ, Ctr Eye, Affiliated Hosp 2, Sch Med, Hangzhou, Peoples R China.
   [Chen, Si] Shanghai Sixth Peoples Hosp, Dept Ophthalmol, Jinshan Branch, Shanghai, Peoples R China.
RP Feng, L (通讯作者)，Zhejiang Univ, Ctr Eye, Affiliated Hosp 2, Sch Med, Hangzhou, Peoples R China.
EM leifeng@zju.edu.cn
FU National Nature Science Foundation of China [81870648, 82070949]
FX Funding The work was supported by grants from the National Nature
   Science Foundation of China (Nos. 81870648 and 82070949).
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NR 32
TC 0
Z9 0
U1 6
U2 6
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2296-858X
J9 FRONT MED-LAUSANNE
JI Front. Med.
PD FEB 23
PY 2022
VL 9
AR 807319
DI 10.3389/fmed.2022.807319
PG 15
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA ZP3UA
UT WOS:000766349400001
PM 35280901
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ghio, D
   Lawes-Wickwar, S
   Tang, MY
   Epton, T
   Howlett, N
   Jenkinson, E
   Stanescu, S
   Westbrook, J
   Kassianos, AP
   Watson, D
   Sutherland, L
   Stanulewicz, N
   Guest, E
   Scanlan, D
   Carr, N
   Chater, A
   Hotham, S
   Thorneloe, R
   Armitage, CJ
   Arden, M
   Hart, J
   Byrne-Davis, L
   Keyworth, C
AF Ghio, Daniela
   Lawes-Wickwar, Sadie
   Tang, Mei Yee
   Epton, Tracy
   Howlett, Neil
   Jenkinson, Elizabeth
   Stanescu, Sabina
   Westbrook, Juliette
   Kassianos, Angelos P.
   Watson, Daniella
   Sutherland, Lisa
   Stanulewicz, Natalia
   Guest, Ella
   Scanlan, Daniel
   Carr, Natalie
   Chater, Angel
   Hotham, Sarah
   Thorneloe, Rachael
   Armitage, Christopher J.
   Arden, Madelynne
   Hart, Jo
   Byrne-Davis, Lucie
   Keyworth, Christopher
TI What influences people's responses to public health messages for
   managing risks and preventing infectious diseases? A rapid systematic
   review of the evidence and recommendations
SO BMJ OPEN
LA English
DT Review
DE public health; COVID-19; infection control
ID SOCIAL COGNITIVE THEORY; PANDEMIC INFLUENZA; VULNERABLE MIGRANTS; SWINE
   FLU; COMMUNICATION; EBOLA; KNOWLEDGE; INFORMATION; PERCEPTIONS;
   COMMUNITY
AB Background Individual behaviour changes, such as hand hygiene and physical distancing, are required on a population scale to reduce transmission of infectious diseases such as COVID-19. However, little is known about effective methods of communicating risk reducing information, and how populations might respond. Objective To synthesise evidence relating to what (1) characterises effective public health messages for managing risk and preventing infectious disease and (2) influences people's responses to messages. Design A rapid systematic review was conducted. Protocol is published on Prospero CRD42020188704. Data sources Electronic databases were searched: Ovid Medline, Ovid PsycINFO and Healthevidence.org, and grey literature (PsyarXiv, OSF Preprints) up to May 2020. Study selection All study designs that (1) evaluated public health messaging interventions targeted at adults and (2) concerned a communicable disease spread via primary route of transmission of respiratory and/or touch were included. Outcomes included preventative behaviours, perceptions/awareness and intentions. Non-English language papers were excluded. Synthesis Due to high heterogeneity studies were synthesised narratively focusing on determinants of intentions in the absence of measured adherence/preventative behaviours. Themes were developed independently by two researchers and discussed within team to reach consensus. Recommendations were translated from narrative synthesis to provide evidence-based methods in providing effective messaging. Results Sixty-eight eligible papers were identified. Characteristics of effective messaging include delivery by credible sources, community engagement, increasing awareness/knowledge, mapping to stage of epidemic/pandemic. To influence intent effectively, public health messages need to be acceptable, increase understanding/perceptions of health threat and perceived susceptibility. Discussion There are four key recommendations: (1) engage communities in development of messaging, (2) address uncertainty immediately and with transparency, (3) focus on unifying messages from sources and (4) frame messages aimed at increasing understanding, social responsibility and personal control. Embedding principles of behavioural science into public health messaging is an important step towards more effective health-risk communication during epidemics/pandemics.
C1 [Ghio, Daniela] Univ Salford, Fac Hlth & Soc, Dept Psychol, Manchester, Lancs, England.
   [Ghio, Daniela; Epton, Tracy; Stanulewicz, Natalia; Armitage, Christopher J.; Hart, Jo; Byrne-Davis, Lucie; Keyworth, Christopher] Univ Manchester, Manchester Ctr Hlth Psychol, Manchester, Lancs, England.
   [Lawes-Wickwar, Sadie] UCL, Dept Primary Care & Populat Hlth, London, England.
   [Tang, Mei Yee] Newcastle Univ, Populat Hlth Sci, Behav Sci Policy Res Unit, Newcastle Upon Tyne, Tyne & Wear, England.
   [Howlett, Neil] Univ Hertfordshire, Sch Life & Med Sci, Dept Psychol Sports & Geog, Hatfield, Herts, England.
   [Jenkinson, Elizabeth] Univ West England, Dept Hlth & Social Sci, Bristol, Avon, England.
   [Stanescu, Sabina] Univ Southampton, Sch Psychol, Southampton, Hants, England.
   [Westbrook, Juliette] Univ Bath, Dept Psychol, Bath, Avon, England.
   [Kassianos, Angelos P.] UCL, Dept Appl Hlth Res, London, England.
   [Watson, Daniella] Univ Southampton, Fac Med, Human Dev & Hlth, Southampton, Hants, England.
   [Sutherland, Lisa] Ipsos Mori, Behav Insight Publ Affairs, Edinburgh, Midlothian, Scotland.
   [Sutherland, Lisa] Ipsos Mori, Behav Insight Publ Affairs, London, England.
   [Stanulewicz, Natalia] De Montfort Univ, Sch Appl Social Sci, Fac Hlth & Life Sci, Leicester, Leics, England.
   [Guest, Ella] Univ West England, Fac Hlth & Appl Sci, Cte Appearance Res, Bristol, Avon, England.
   [Scanlan, Daniel] Educ Support, Dept Commun Policy & Res, London, England.
   [Carr, Natalie] Manchester Metropolitan Univ, Fac Hlth Psychol & Social Care, Manchester, Lancs, England.
   [Chater, Angel] Univ Bedfordshire, Ctr Hlth Wellbeing & Behav Change, Luton, Beds, England.
   [Hotham, Sarah] Univ Kent, Ctr Hlth Serv Studies, Canterbury, Kent, England.
   [Thorneloe, Rachael; Arden, Madelynne] Sheffield Hallam Univ, Ctr Behav Sci & Appl Psychol, Sheffield, S Yorkshire, England.
   [Armitage, Christopher J.] Manchester Univ NHS Fdn Trust, Manchester Acad Hlth Sci Ctr, Manchester, Lancs, England.
   [Armitage, Christopher J.] Univ Manchester, NIHR Greater Manchester Patient Safety Translat R, Manchester, Lancs, England.
   [Keyworth, Christopher] Univ Leeds, Sch Psychol, Leeds, W Yorkshire, England.
RP Epton, T (通讯作者)，Univ Manchester, Manchester Ctr Hlth Psychol, Manchester, Lancs, England.
EM tracy.epton@manchester.ac.uk
RI Jenkinson, Elizabeth/AAQ-7591-2021; Ghio, Daniela/H-5105-2015; Arden,
   Madelynne/B-8717-2009
OI Jenkinson, Elizabeth/0000-0001-5686-5132; Howlett,
   Neil/0000-0002-6502-9969; Chater, Angel Marie/0000-0002-9043-2565;
   Keyworth, Chris/0000-0002-7815-6174; Kassianos,
   Angelos/0000-0001-6428-2623; Ghio, Daniela/0000-0002-0580-0205; Arden,
   Madelynne/0000-0002-6199-717X; Lawes-Wickwar, Sadie/0000-0001-9307-4532
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NR 82
TC 4
Z9 4
U1 10
U2 15
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 NOV
PY 2021
VL 11
IS 11
AR e048750
DI 10.1136/bmjopen-2021-048750
PG 11
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC General & Internal Medicine
GA WY0RQ
UT WOS:000718994200009
PM 34764167
OA Green Accepted, Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Islam, K
   Aridhi, S
   Smail-Tabbone, M
AF Islam, Kamrul
   Aridhi, Sabeur
   Smail-Tabbone, Malika
TI Negative sampling and rule mining for explainable link prediction in
   knowledge graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; Link prediction; Negative sampling; Rule
   mining; Explainability
AB Several KG embedding methods were proposed to learn low dimensional vector representations of entities and relations of a KG. Such representations facilitate the link prediction task, in the service of inference and KG completion. In this context, it is important to achieve both an efficient KG embedding and explainable predictions. During learning of efficient embeddings, sampling negative triples was highlighted as an important step as KGs only have observed positive triples. We propose an efficient simple negative sampling (SNS) method based on the assumption that the entities which are closer in the embedding space to the corrupted entity are able to provide high-quality negative triples. As for explainability, it actually constitutes a thriving research question especially when it comes to analyze KGs with their rich semantics rooted in description logics. Hence, we propose in this paper a new rule mining method on the basis of learned embeddings. We extensively evaluate our proposals through several experiments. We evaluate our SNS sampling method plugged to several KG embedding models through link prediction task performances on well-known datasets. Experimental results show that the SNS improves the prediction performance of KG embedding models, and outperforms the existing sampling methods. To assess the performance of our rule mining method with and without SNS, we mine and evaluate rules on three popular datasets. The extracted rules are evaluated as knowledge nuggets extracted from the KG and also as support for explainable link prediction. The overall results are good and open the way to many improvements and new perspectives. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Islam, Kamrul; Aridhi, Sabeur; Smail-Tabbone, Malika] Univ Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France.
RP Islam, K (通讯作者)，Univ Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France.
EM kamrul.islam@loria.fr; sabeur.aridhi@loria.fr; malika.smail@loria.fr
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NR 31
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD AUG 17
PY 2022
VL 250
AR 109083
DI 10.1016/j.knosys.2022.109083
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2D1SE
UT WOS:000811334800006
DA 2022-08-23
ER

PT J
AU Daga, E
   Merono-Penuela, A
   Motta, E
AF Daga, Enrico
   Merono-Penuela, Albert
   Motta, Enrico
TI Sequential linked data: The state of affairs
SO SEMANTIC WEB
LA English
DT Article
DE Sequential linked data; benchmark; RDF; SPARQL
ID BENCHMARK; PERFORMANCE
AB Sequences are among the most important data structures in computer science. In the Semantic Web, however, little attention has been given to Sequential Linked Data. In previous work, we have discussed the data models that Knowledge Graphs commonly use for representing sequences and showed how these models have an impact on query performance and that this impact is invariant to triplestore implementations. However, the specific list operations that the management of Sequential Linked Data requires beyond the simple retrieval of an entire list or a range of its elements - e.g. to add or remove elements from a list -, and their impact in the various list data models, remain unclear. Covering this knowledge gap would be a significant step towards the realization of a Semantic Web list Application Programming Interface (API) that standardizes list manipulation and generalizes beyond specific data models. In order to address these challenges towards the realization of such an API, we build on our previous work in understanding the effects of various sequential data models for Knowledge Graphs, extending our benchmark and proposing a set of read-write Semantic Web list operations in SPARQL, with insert, update and delete support. To do so, we identify five classic list-based computer science sequential data structures (linked list, double linked list, stack, queue, and array), from which we derive nine atomic read-write operations for Semantic Web lists. We propose a SPARQL implementation of these operations with five typical RDF data models and compare their performance by executing them against six increasing dataset sizes and four different triplestores. In light of our results, we discuss the feasibility of our devised API and reflect on the state of affairs of Sequential Linked Data.
C1 [Daga, Enrico; Motta, Enrico] Open Univ, Knowledge Media Inst, Milton Keynes, Bucks, England.
   [Merono-Penuela, Albert] Kings Coll London, Dept Informat, London, England.
RP Daga, E (通讯作者)，Open Univ, Knowledge Media Inst, Milton Keynes, Bucks, England.
EM enrico.daga@open.ac.uk; albert.merono@kcl.ac.uk; enrico.motta@open.ac.uk
OI Motta, Enrico/0000-0003-0015-1952; Daga, Enrico/0000-0002-3184-5407
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NR 34
TC 0
Z9 0
U1 0
U2 0
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 6
BP 927
EP 958
DI 10.3233/SW-210436
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 WF0CZ
UT WOS:000705985000004
OA Green Published, Green Accepted, hybrid
DA 2022-08-23
ER

PT J
AU Meo, TJ
   Kim, C
   Raghavan, A
   Tozzo, A
   Salter, DA
   Tamrakar, A
   Amer, MR
AF Meo, Timothy J.
   Kim, Chris
   Raghavan, Aswin
   Tozzo, Alex
   Salter, David A.
   Tamrakar, Amir
   Amer, Mohamed R.
TI Aesop: A visual storytelling platform for conversational AI and common
   sense grounding
SO AI COMMUNICATIONS
LA English
DT Article
DE Visual storytelling; common sense grounding; natural language; knowledge
   representation; dialogue; language and vision; conversational AI;
   communication
AB We present a new collaborative visual storytelling platform, Aesop, for direction and animation. Our system operates in two main modes, common sense grounding (annotation) and conversation. The Aesop system senses the human state and input using a natural language parser and human gesture monitoring for natural interactions. The interface consists of a 3D animation software and a web controller to interact with the internal state of the system. For knowledge representation, we formulate novel knowledge graphs which enable spatio-temporal event representation. Aesop thus enables 3D spatial and temporal reasoning which are both essential for storytelling. Finally, the system utilizes a dialog manager to track the conversation state and manage goals. Aesop provides a rich platform that enables research in language, gestures, vision, and planning in the context of storytelling.
C1 [Meo, Timothy J.; Kim, Chris; Raghavan, Aswin; Tozzo, Alex; Salter, David A.; Tamrakar, Amir; Amer, Mohamed R.] SRI Int, 201 Washington Rd, Princeton, NJ 08540 USA.
   [Kim, Chris] Univ Ontario Inst Technol, Oshawa, ON, Canada.
RP Amer, MR (通讯作者)，SRI Int, 201 Washington Rd, Princeton, NJ 08540 USA.
EM mohamed.amer@sri.com
FU DARPA-CwC [W911NF-15-C-0246]
FX This work is funded by DARPA-CwC W911NF-15-C-0246. The views, opinions,
   and/or conclusions contained in this paper are those of the author and
   should not be interpreted as representing the official views or
   policies, either expressed or implied of the DARPA or the DoD.
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NR 32
TC 2
Z9 2
U1 0
U2 5
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 0921-7126
EI 1875-8452
J9 AI COMMUN
JI AI Commun.
PY 2019
VL 32
IS 1
BP 59
EP 76
DI 10.3233/AIC-180605
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HP2NC
UT WOS:000461511300004
DA 2022-08-23
ER

PT J
AU Dong, Y
   Liu, YL
   Yu, JG
   Qi, SX
   Liu, HJ
AF Dong, Yi
   Liu, Yanli
   Yu, Jianguo
   Qi, Shixin
   Liu, Huijuan
TI Mapping research trends in diabetic retinopathy from 2010 to 2019 A
   bibliometric analysis
SO MEDICINE
LA English
DT Review
DE bibliometric analysis; citespace; diabetic retinopathy; vosviewer
ID MAJOR RISK-FACTORS; FLUORESCEIN ANGIOGRAPHY; GLOBAL PREVALENCE;
   OXIDATIVE STRESS; OCT ANGIOGRAPHY; MACULAR EDEMA; METAANALYSIS;
   NEOVASCULARIZATION; PRETREATMENT; POLYMORPHISM
AB Background: Although many publications in diabetic retinopathy (DR) have been reported, there is no bibliometric analysis. Purpose: To perform a bibliometric analysis in the field of diabetic retinopathy (DR) research, to characterize the current international status of DR research, to identify the most effective factors involved in this field, and to explore research hotspots in DR research. Methods: Based on the Web of Science Core Collection (WoSCC), a bibliometric analysis was conducted to investigate the publication trends in research related to DR. Knowledge maps were constructed by VOSviewer v. 1. 6. 10 to visualize the publications, the distribution of countries, international collaborations, author productivity, source journals, cited references and keywords, and research hotspots in this field. Results: In total, 11,839 peer-reviewed papers were retrieved on DR from 2010 to 2019, and the annual research output increased with time. The United States ranks highest among countries with the most publications. The most active institution is the University of Melbourne. Wong, TY contributed the largest number of publications in this field. Investigative Ophthalmology & Visual Science was the most prolific journal in DR research. The top-cited references mainly investigated the use of anti-vascular endothelial growth factor (VEGF) medications in the management of DR, and the keywords formed 6 clusters: 1. pathogenesis of DR; 2. epidemiology and risk factors for DR; 3. treatments for DR; 4. screening of DR; 5. histopathology of DR; and 6. diagnostic methods for DR. Discussion: With the improvement of living standard, DR has gradually become one of the important causes of blindness, and has become a hot spot of public health research in many countries. The application of deep learning and artificial intelligence in diabetes screening and anti-VEGF medications in the management of DR have been the research hotspots in recent 10 years. Conclusions: Based on data extracted from the WoSCC, this study provides a broad view of the current status and trends in DR research and may provide clinicians and researchers with insight into DR research and valuable information to identify potential collaborators and partner institutions and better predict their dynamic directions.
C1 [Dong, Yi] Tianjin Med Univ, Tianjin Eye Hosp, Tianjin Key Lab Ophthalmol & Visual Sci, Tianjin Eye Inst,Clin Coll Ophthalmol, Gansu Rd 4, Tianjin 300020, Peoples R China.
   [Liu, Yanli; Yu, Jianguo; Qi, Shixin; Liu, Huijuan] Tianjin Med Univ, Tianjin Baodi Hosp, Dept Ophthalmol, Baodi Clin Coll, Tianjin, Peoples R China.
RP Dong, Y (通讯作者)，Tianjin Med Univ, Tianjin Eye Hosp, Tianjin Key Lab Ophthalmol & Visual Sci, Tianjin Eye Inst,Clin Coll Ophthalmol, Gansu Rd 4, Tianjin 300020, Peoples R China.
EM dongyifenghua@foxmail.com
OI Dong, Yi/0000-0002-0010-8172
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NR 52
TC 4
Z9 4
U1 12
U2 42
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0025-7974
EI 1536-5964
J9 MEDICINE
JI Medicine (Baltimore)
PD JAN 22
PY 2021
VL 100
IS 3
AR e23981
DI 10.1097/MD.0000000000023981
PG 12
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA PZ6FV
UT WOS:000612835100065
PM 33545985
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Du, JC
   Gui, L
   Xu, RF
   Xia, YQ
   Wang, X
AF Du, Jiachen
   Gui, Lin
   Xu, Ruifeng
   Xia, Yunqing
   Wang, Xuan
TI Commonsense Knowledge Enhanced Memory Network for Stance Classification
SO IEEE INTELLIGENT SYSTEMS
LA English
DT Article
AB Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification.
C1 [Du, Jiachen] Harbin Inst Technol, Shenzhen, Peoples R China.
   [Gui, Lin] Univ Warwick, Coventry, W Midlands, England.
   [Xu, Ruifeng; Wang, Xuan] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen, Peoples R China.
   [Xia, Yunqing] Microsoft, Search Technol Ctr, Beijing, Peoples R China.
RP Xia, YQ (通讯作者)，Microsoft, Search Technol Ctr, Beijing, Peoples R China.
EM dujiachen@stmail.hitsz.edu.cn; lin.gui@warwick.ac.uk;
   xuruifeng@hit.edu.cn; yxia@microsoft.com; wangxuan@cs.hitsz.edu.cn
OI Gui, Lin/0000-0002-8054-9524
FU National Natural Science Foundation of China [61632011, 61876053]; Key
   Technologies Research and Development Program of Shenzhen [JSGG20170
   817140856618]; Shenzhen Foundational Research Funding
   [JCYJ20180507183527919, JCYJ20180507183608379]; EU-H2020 [794196]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61632011 and Grant 61876053, in part by
   the Key Technologies Research and Development Program of Shenzhen (No.
   JSGG20170 817140856618), and in part by Shenzhen Foundational Research
   Funding (JCYJ20180507183527919 and JCYJ20180507183608379) and EU-H2020
   under Grant 794196.
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NR 30
TC 9
Z9 9
U1 1
U2 10
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1541-1672
EI 1941-1294
J9 IEEE INTELL SYST
JI IEEE Intell. Syst.
PD JUL-AUG
PY 2020
VL 35
IS 4
BP 102
EP 109
DI 10.1109/MIS.2020.2983497
PG 8
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA NG8WR
UT WOS:000564262600011
OA Green Accepted, Green Submitted
DA 2022-08-23
ER

PT J
AU Li, JR
   Horiguchi, Y
   Sawaragi, T
AF Li, Jiarui
   Horiguchi, Yukio
   Sawaragi, Tetsuo
TI Counterfactual inference to predict causal knowledge graph for
   relational transfer learning by assimilating expert knowledge
   --Relational feature transfer learning algorithm
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Transfer learning; Causality; Counterfactual inference; Knowledge
   networks; Explainable machine learning
ID OBSTRUCTIVE SLEEP-APNEA; AGE
AB Transfer learning (TL) is a machine learning (ML) method in which knowledge is transferred from the existing models of related problems to the model for solving the problem at hand. Relational TL enables the ML models to transfer the relationship networks from one domain to another. However, it has two critical issues. One is determining the proper way of extracting and expressing relationships among data features in the source domain such that the relationships can be transferred to the target domain. The other is how to do the transfer procedure. Knowledge graphs (KGs) are knowledge bases that use data and logic to graph-structured information; they are helpful tools for dealing with the first issue. The proposed relational feature transfer learning algorithm (RF-TL) embodies an extended structural equation modelling (SEM) as a method for constructing KGs. Additionally, in fields such as medicine, economics, and law related to people's lives and property safety and security, the knowledge of domain experts is a gold standard. This paper introduces the causal analysis and counterfactual inference in the TL domain that directs the transfer procedure. Different from traditional feature-based TL algorithms like transfer component analysis (TCA) and CORelation Alignment (CORAL), RF-TL not only considers relations between feature items but also utilizes causality knowledge, enabling it to perform well in practical cases. The algorithm was tested on two different healthcare-related datasets - sleep apnea questionnaire study data and COVID-19 case data on ICU admission - and compared its performance with TCA and CORAL. The experimental results show that RF-TL can generate better transferred models that give more accurate predictions with fewer input features.
C1 [Li, Jiarui; Sawaragi, Tetsuo] Kyoto Univ, Grad Sch Engn, Dept Mech Engn & Sci, Kyoto, Japan.
   [Horiguchi, Yukio] Kansai Univ, Fac Informat, Osaka, Japan.
RP Li, JR (通讯作者)，Kyoto Univ, Grad Sch Engn, Dept Mech Engn & Sci, Kyoto, Japan.
EM ljr10225008@gmail.com
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NR 45
TC 3
Z9 3
U1 4
U2 4
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 101516
DI 10.1016/j.aei.2021.101516
PG 18
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1K3ZE
UT WOS:000798542300007
OA hybrid
DA 2022-08-23
ER

PT J
AU Yue, Q
   Li, X
   Li, D
AF Yue, Qi
   Li, Xiang
   Li, Dan
TI Chinese Relation Extraction on Forestry Knowledge Graph Construction
SO COMPUTER SYSTEMS SCIENCE AND ENGINEERING
LA English
DT Article
DE Forestry; KG; RE; BiGRU; dual attention; FastText
ID NETWORK; MODEL
AB Forestry work has long been weak in data integration; its initial state will inevitably affect the forestry project development and decision-quality. Knowledge Graph (KG) can provide better abilities to organize, manage, and understand forestry knowledge. Relation Extraction (RE) is a crucial task of KG construction and information retrieval. Previous researches on relation extraction have proved the performance of using the attention mechanism. However, these methods focused on the representation of the entire sentence and ignored the loss of information. The lack of analysis of words and syntactic features contributes to sentences, especially in Chinese relation extraction, resulting in poor performance. Based on the above observations, we proposed an end-to-end relation extraction method that used Bi-directional Gated Recurrent Unit (BiGRU) neural network and dual attention mechanism in forestry KG construction. The dual attention includes sentence-level and word-level, capturing relational semantic words and direction words. To enhance the performance, we used the pre-training model FastText to provide word vectors, and dynamically adjusted the word vectors according to the context. We used forestry entities and relationships to build forestry KG and used Neo4j to store forestry KG. Our method can achieve better results than previous public models in the SemEval-2010 Task 8 dataset. By training the model on forestry dataset, results showed that the accuracy and precision of FastText-BiGRU-Dual Attention exceeded 0.8, which outperformed the comparison methods, thus the experiment confirmed the validity and accuracy of our model. In the future, we plan to apply forestry KG to question and answer system and achieve a recommendations system for forestry knowledge.
C1 [Yue, Qi; Li, Xiang; Li, Dan] Northeast Forestry Univ, Coll Informat & Comp Engn, Harbin 150040, Peoples R China.
RP Li, D (通讯作者)，Northeast Forestry Univ, Coll Informat & Comp Engn, Harbin 150040, Peoples R China.
EM ld725725@126.com
FU Heilongjiang Provincial Natural Science Foundation of China [LH2019G001]
FX This work is supported by Heilongjiang Provincial Natural Science
   Foundation of China (Grant No. LH2019G001), Q. Yue would like to thank
   the foundation for financial support.
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NR 77
TC 2
Z9 2
U1 13
U2 26
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 2021
VL 37
IS 3
BP 423
EP 442
DI 10.32604/csse.2021.014448
PG 20
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QU2LS
UT WOS:000627115300009
DA 2022-08-23
ER

PT J
AU Barrena, A
   Soroa, A
   Agirre, E
AF Barrena, Ander
   Soroa, Aitor
   Agirre, Eneko
TI Towards zero-shot cross-lingual named entity disambiguation
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Cross-lingual named entity disambiguation; Cross-lingual entity linking;
   Zero-shot learning; Transfer learning; Pre-trained language models;
   Low-resource languages
AB In cross-Lingual Named Entity Disambiguation (XNED) the task is to link Named Entity mentions in text in some native language to English entities in a knowledge graph. XNED systems usually require training data for each native language, limiting their application for low resource languages with small amounts of training data. Prior work have proposed so-called zero-shot transfer systems which are only trained in English training data, but required native prior probabilities of entities with respect to mentions, which had to be estimated from native training examples, limiting their practical interest. In this work we present a zero-shot XNED architecture where, instead of a single disambiguation model, we have a model for each possible mention string, thus eliminating the need for native prior probabilities. Our system improves over prior work in XNED datasets in Spanish and Chinese by 32 and 27 points, and matches the systems which do require native prior information. We experiment with different multilingual transfer strategies, showing that better results are obtained with a purpose-built multilingual pre-training method compared to state-of-the-art generic multilingual models such as XLM-R. We also discovered, surprisingly, that English is not necessarily the most effective zero-shot training language for XNED into English. For instance, Spanish is more effective when training a zero-shot XNED system that dis-ambiguates Basque mentions with respect to an English knowledge graph.
C1 [Barrena, Ander; Soroa, Aitor; Agirre, Eneko] Univ Basque Country, HiTZ Basque Ctr Language Technol Ixa NLP Grp, UPV EHU, Donostia San Sebastian, Basque Country, Spain.
RP Barrena, A (通讯作者)，Univ Basque Country, HiTZ Basque Ctr Language Technol Ixa NLP Grp, UPV EHU, Donostia San Sebastian, Basque Country, Spain.
EM ander.barrena@ehu.eus; a.soroa@ehu.eus; e.agirre@ehu.eus
OI Barrena, Ander/0000-0003-2024-0362
FU Basque Government [IT1343-19]; Project BigKnowledge (Ayudas Fundacion
   BBVA a equipos de investigacion cientifica 2018); IARPA BETTER Program
   [2019-19051600006]; UPV/EHU [ESPDOC18/101]; NVIDIA Corporation
FX This work has been partially funded by the Basque Government (IXA
   excellence research group (IT1343-19) and DeepText project), Project
   BigKnowledge (Ayudas Fundacion BBVA a equipos de investigacion
   cientifica 2018) and via the IARPA BETTER Program contract
   2019-19051600006 (ODNI, IARPA activity). Ander Barrena enjoys a
   post-doctoral grant ESPDOC18/101 from the UPV/EHU and also acknowledges
   the support of the NVIDIA Corporation with the donation of a Titan V GPU
   used for this research. The author thankfully acknowledges the computer
   resources at CTE-Power9 + V100 and technical support provided by
   Barcelona Supercomputing Center (RES-IM-2020-1-0020).
CR Agirre E., 2007, WORD SENSE DISAMBIGU
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NR 32
TC 0
Z9 0
U1 0
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 DEC 1
PY 2021
VL 184
AR 115542
DI 10.1016/j.eswa.2021.115542
EA JUL 2021
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 US1JF
UT WOS:000697191700004
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Mezni, H
   Driss, M
   Boulila, W
   Atitallah, SB
   Sellami, M
   Alharbi, N
AF Mezni, Haithem
   Driss, Maha
   Boulila, Wadii
   Atitallah, Safa Ben
   Sellami, Mokhtar
   Alharbi, Nouf
TI SmartWater: A Service-Oriented and Sensor Cloud-Based Framework for
   Smart Monitoring of Water Environments
SO REMOTE SENSING
LA English
DT Article
DE smart cities; smart water monitoring; sensor cloud services; knowledge
   graph; network representation learning; incremental embedding
ID MANAGEMENT
AB Due to the sharp increase in global industrial production, as well as the over-exploitation of land and sea resources, the quality of drinking water has deteriorated considerably. Furthermore, nowadays, many water supply systems serving growing human populations suffer from shortages since many rivers, lakes, and aquifers are drying up because of global climate change. To cope with these serious threats, smart water management systems are in great demand to ensure vigorous control of the quality and quantity of drinking water. Indeed, water monitoring is essential today since it allows to ensure the real-time control of water quality indicators and the appropriate management of resources in cities to provide an adequate water supply to citizens. In this context, a novel IoT-based framework is proposed to support smart water monitoring and management. The proposed framework, named SmartWater, combines cutting-edge technologies in the field of sensor clouds, deep learning, knowledge reasoning, and data processing and analytics. First, knowledge graphs are exploited to model the water network in a semantic and multi-relational manner. Then, incremental network embedding is performed to learn rich representations of water entities, in particular the affected water zones. Finally, a decision mechanism is defined to generate a water management plan depending on the water zones' current states. A real-world dataset has been used in this study to experimentally validate the major features of the proposed smart water monitoring framework.
C1 [Mezni, Haithem; Alharbi, Nouf] Taibah Univ, Coll Comp Sci & Engn, Medina 42353, Saudi Arabia.
   [Mezni, Haithem] Jendouba Univ, SMART Lab, Jendouba 8189, Tunisia.
   [Driss, Maha] Prince Sultan Univ, Secur Engn Lab, CCIS, Riyadh 12435, Saudi Arabia.
   [Driss, Maha; Boulila, Wadii; Atitallah, Safa Ben; Sellami, Mokhtar] Univ Manouba, RIADI Lab, Manouba 2010, Tunisia.
   [Boulila, Wadii] Prince Sultan Univ, Robot & Internet Things Lab, Riyadh 12435, Saudi Arabia.
RP Mezni, H (通讯作者)，Taibah Univ, Coll Comp Sci & Engn, Medina 42353, Saudi Arabia.; Mezni, H (通讯作者)，Jendouba Univ, SMART Lab, Jendouba 8189, Tunisia.
EM hmezni@taibahu.edu.sa; maha.idriss@riadi.rnu.tn;
   wadii.boulila@riadi.rnu.tn; safa.benatitallah@ensi-uma.tn;
   mokhtar.sellami@isetj.rnu.tn; nmoharbi@taibahu.edu.sa
OI Boulila, Wadii/0000-0003-2133-0757; Ben Atitallah,
   safa/0000-0003-0796-3507
FU Deputyship for Research & Innovation, Ministry of Education in Saudi
   Arabia [442/210]
FX This research was funded by the Deputyship for Research & Innovation,
   Ministry of Education in Saudi Arabia under the project number
   (442/210).
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NR 46
TC 2
Z9 2
U1 11
U2 11
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2072-4292
J9 REMOTE SENS-BASEL
JI Remote Sens.
PD FEB
PY 2022
VL 14
IS 4
AR 922
DI 10.3390/rs14040922
PG 26
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 ZN7YP
UT WOS:000765245200001
OA gold
DA 2022-08-23
ER

PT J
AU Rony, MDRA
   Chaudhuri, D
   Usbeck, R
   Lehmann, J
AF Rony, M. D. Rashad A. L. Hasan
   Chaudhuri, Debanjan
   Usbeck, Ricardo
   Lehmann, Jens
TI Tree-KGQA: An Unsupervised Approach for Question Answering Over
   Knowledge Graphs
SO IEEE ACCESS
LA English
DT Article
DE Task analysis; Joining processes; Training; Vegetation; Training data;
   Licenses; Indexing; Knowledge based systems; information retrieval;
   question answering; entity linking; relation linking; indexing;
   pre-trained language models
AB Most Knowledge Graph-based Question Answering (KGQA) systems rely on training data to reach their optimal performance. However, acquiring training data for supervised systems is both time-consuming and resource-intensive. To address this, in this paper, we propose Tree-KGQA, an unsupervised KGQA system leveraging pre-trained language models and tree-based algorithms. Entity and relation linking are essential components of any KGQA system. We employ several pre-trained language models in the entity linking task to recognize the entities mentioned in the question and obtain the contextual representation for indexing. Furthermore, for relation linking we incorporate a pre-trained language model previously trained for language inference task. Finally, we introduce a novel algorithm for extracting the answer entities from a KG, where we construct a forest of interpretations and introduce tree-walking and tree disambiguation techniques. Our algorithm uses the linked relation and predicts the tree branches that eventually lead to the potential answer entities. The proposed method achieves 4.5% and 7.1% gains in F1 score in entity linking tasks on LC-QuAD 2.0 and LC-QuAD 2.0 (KBpearl) datasets, respectively, and a 5.4% increase in the relation linking task on LC-QuAD 2.0 (KBpearl). The comprehensive evaluations demonstrate that our unsupervised KGQA approach outperforms other supervised state-of-the-art methods on the WebQSP-WD test set (1.4% increase in F1 score) - without training on the target dataset.
C1 [Rony, M. D. Rashad A. L. Hasan; Chaudhuri, Debanjan; Lehmann, Jens] Univ Bonn, Smart Data Analyt Res Grp, D-53115 Bonn, Germany.
   [Usbeck, Ricardo] Univ Hamburg, Semant Syst Grp, D-22527 Hamburg, Germany.
   [Rony, M. D. Rashad A. L. Hasan; Lehmann, Jens] Fraunhofer IAIS Dresden, D-01069 Dresden, Germany.
RP Rony, MDRA (通讯作者)，Univ Bonn, Smart Data Analyt Res Grp, D-53115 Bonn, Germany.; Rony, MDRA (通讯作者)，Fraunhofer IAIS Dresden, D-01069 Dresden, Germany.
EM rashad.rony@iais.fraunhofer.de
OI Rony, Md Rashad Al Hasan/0000-0003-0665-389X; Usbeck,
   Ricardo/0000-0002-0191-7211
FU SPEAKER under Grant BMWi FKZ [01MK20011A]; JOSEPH (Fraunhofer
   Zukunftsstiftung); OpenGPT-X under Grant BMWK [FKZ 68GX21007A];
   excellence clusters ML2R under Grant BmBF [FKZ 01 15 18038 A/B/C];
   ScaDS.AI [IS18026A-F]; TAILOR under Grant EU [GA 952215]; Federal
   Ministry for Economic Affairs and Energy of Germany in the Project CoyPu
   [01MK21007G]
FX This work was supported in part by the SPEAKER under Grant BMWi FKZ
   01MK20011A, in part by the JOSEPH (Fraunhofer Zukunftsstiftung), in part
   by the OpenGPT-X under Grant BMWK FKZ 68GX21007A, in part by the
   excellence clusters ML2R under Grant BmBF FKZ 01 15 18038 A/B/C, in part
   by the ScaDS.AI under Grant IS18026A-F, in part by the TAILOR under
   Grant EU GA 952215, and in part by the Federal Ministry for Economic
   Affairs and Energy of Germany in the Project CoyPu under Project
   01MK21007G.
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NR 54
TC 1
Z9 1
U1 7
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 2022
VL 10
BP 50467
EP 50478
DI 10.1109/ACCESS.2022.3173355
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1I7YW
UT WOS:000797444600001
OA gold
DA 2022-08-23
ER

PT J
AU Ni, P
   Okhrati, R
   Guan, S
   Chang, V
AF Ni, Pin
   Okhrati, Ramin
   Guan, Steven
   Chang, Victor
TI Knowledge Graph and Deep Learning-based Text-to-GQL Model for
   Intelligent Medical Consultation Chatbot
SO INFORMATION SYSTEMS FRONTIERS
LA English
DT Article; Early Access
DE Text-to-GQL; Semantic parsing; Knowledge graph; Natural language
   processing; Deep learning; Health informatics
AB Text-to-GQL (Text2GQL) is a task that converts the user's questions into GQL (Graph Query Language) when a graph database is given. That is a task of semantic parsing that transforms natural language problems into logical expressions, which will bring more efficient direct communication between humans and machines. The existing related work mainly focuses on Text-to-SQL tasks, and there is no available semantic parsing method and data set for the graph database. In order to fill the gaps in this field to serve the medical Human-Robot Interactions (HRI) better, we propose this task and a pipeline solution for the Text2GQL task. This solution uses the Adapter pre-trained by "the linking of GQL schemas and the corresponding utterances" as an external knowledge introduction plug-in. By inserting the Adapter into the language model, the mapping between logical language and natural language can be introduced faster and more directly to better realize the end-to-end human-machine language translation task. In the study, the proposed Text2GQL task model is mainly constructed based on an improved pipeline composed of a Language Model, Pre-trained Adapter plug-in, and Pointer Network. This enables the model to copy objects' tokens from utterances, generate corresponding GQL statements for graph database retrieval, and builds an adjustment mechanism to improve the final output. And the experiments have proved that our proposed method has certain competitiveness on the counterpart datasets (Spider, ATIS, GeoQuery, and 39.net) converted from the Text2SQL task, and the proposed method is also practical in medical scenarios.
C1 [Ni, Pin; Okhrati, Ramin] UCL, Inst Finance & Technol, London, England.
   [Guan, Steven] Xian Jiaotong Liverpool Univ, Dept Comp Sci & Software Engn, Suzhou, Peoples R China.
   [Chang, Victor] Aston Univ, Aston Business Sch, Dept Operat & Informat Management, Birmingham, W Midlands, England.
RP Chang, V (通讯作者)，Aston Univ, Aston Business Sch, Dept Operat & Informat Management, Birmingham, W Midlands, England.
EM victorchang.research@gmail.com
RI Chang, Victor/AAC-7582-2019
OI Chang, Victor/0000-0002-8012-5852; , Ramin/0000-0003-0103-7051; Ni,
   Pin/0000-0003-4516-1249
FU VC Research [VCR 0000162]
FX This research is partly supported by VC Research (VCR 0000162) for Prof
   Chang.
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NR 82
TC 0
Z9 0
U1 3
U2 3
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1387-3326
EI 1572-9419
J9 INFORM SYST FRONT
JI Inf. Syst. Front.
DI 10.1007/s10796-022-10295-0
EA JUL 2022
PG 19
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2W9EV
UT WOS:000824820100001
PM 35815295
OA Green Published, Green Accepted, hybrid
DA 2022-08-23
ER

PT J
AU Martin, T
AF Martin, Trevor
TI On the Need for Collaborative Intelligence in Cybersecurity
SO ELECTRONICS
LA English
DT Article
DE AI in cybersecurity; collaborative intelligence; graded knowledge
   representation; formal concept analysis; ontology; knowledge graph
ID ANOMALY DETECTION; CLASSIFICATION; EXPLANATIONS
AB The success of artificial intelligence (and particularly data-driven machine learning) in classifying and making predictions from large bodies of data has led to an expectation that autonomous AI systems can be deployed in cybersecurity applications. In this position paper we outline some of the problems facing machine learning in cybersecurity and argue for a collaborative approach where humans contribute insight and understanding, whilst machines are used to gather, filter and process data into a convenient and understandable form. In turn this requires a convenient representation for exchanging information between machine and human, and we argue that graded concepts are suitable, allowing summarisation at multiple levels of discernibility (granularity). We conclude with some suggestions for developing a hierarchical and graded representation.
C1 [Martin, Trevor] Univ Bristol, Engn Maths Dept, Machine Intelligence Unit, Bristol BS8 1UB, Avon, England.
RP Martin, T (通讯作者)，Univ Bristol, Engn Maths Dept, Machine Intelligence Unit, Bristol BS8 1UB, Avon, England.
EM trevor.martin@bristol.ac.uk
FU  [ML853886]
FX This research was partly funded by BT under agreement ML853886.
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NR 57
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD JUL
PY 2022
VL 11
IS 13
AR 2067
DI 10.3390/electronics11132067
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 2V7HZ
UT WOS:000824013700001
OA Green Published, gold
DA 2022-08-23
ER

PT S
AU Freedman, EG
   Shah, P
AF Freedman, EG
   Shah, P
BE Hegarty, M
   Meyer, B
   Narayanan, NH
TI Toward a model of knowledge-based graph comprehension
SO DIAGRAMMATIC REPRESENTATION AND INFERENCE
SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
LA English
DT Article; Proceedings Paper
CT 2nd International Conference on Theory and Application of Diagrams
CY APR 18-20, 2002
CL CALLAWAY GARDENS, GEORGIA
SP USN, Off Res, Amer Assoc Aritficial Intelligence, Cognit Sci Soc, Japanese Cognit Sci Soc, Japanese Soc Artificial Intelligence
ID INFORMATION INTEGRATION; SOCIAL THEORIES; LINE GRAPHS; PERSEVERANCE;
   CONSTRUCTION; COVARIATION; JUDGMENTS
AB Research on graph comprehension has been concerned with relatively low-level information extraction. However, laboratory studies often produce conflicting findings because real-world graph interpretation requires going beyond the data presentation to make inferences and solve problems. Furthermore, in real-world settings, graphical information is presented in the context of relevant prior knowledge. According to our model, knowledge-based graph comprehension involves an interaction of top-down-and bottom up processes. Several types of knowledge are brought to bear on graphs: domain knowledge, graphical skills, and explanatory skills. During the initial processing, people chunk the visual features in the graphs. Nevertheless, prior knowledge guides the processing of visual features. We outline the key assumptions of this model and show how this model explains the extant data and generates testable predictions.
C1 Univ Michigan, Dept Psychol, Flint, MI 48502 USA.
   Univ Michigan, Dept Psychol, Ann Arbor, MI 48109 USA.
RP Freedman, EG (通讯作者)，Univ Michigan, Dept Psychol, 411 Murchie Sci Bldg, Flint, MI 48502 USA.
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NR 41
TC 29
Z9 31
U1 1
U2 17
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-43561-1
J9 LECT NOTES ARTIF INT
PY 2002
VL 2317
BP 18
EP 30
PG 13
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BW16H
UT WOS:000181051900003
DA 2022-08-23
ER

PT J
AU Sun, GF
   Wong, YK
   Kankanhalli, MS
   Li, XD
   Geng, WD
AF Sun, Guofei
   Wong, Yongkang
   Kankanhalli, Mohan S.
   Li, Xiangdong
   Geng, Weidong
TI Enhanced 3D Shape Reconstruction With Knowledge Graph of Category
   Concept
SO ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
LA English
DT Article
DE Deep learning; 3D reconstruction; conceptual knowledge
ID SIMULTANEOUS LOCALIZATION
AB Reconstructing three-dimensional (3D) objects from images has attracted increasing attention due to its wide applications in computer vision and robotic tasks. Despite the promising progress of recent deep learning-based approaches, which directly reconstruct the full 3D shape without considering the conceptual knowledge of the object categories, existing models have limited usage and usually create unrealistic shapes. 3D objects have multiple forms of representation, such as 3D volume, conceptual knowledge, and so on. In this work, we show that the conceptual knowledge for a category of objects, which represents objects as prototype volumes and is structured by graph, can enhance the 3D reconstruction pipeline. We propose a novel multimodal framework that explicitly combines graph-based conceptual knowledge with deep neural networks for 3D shape reconstruction from a single RGB image. Our approach represents conceptual knowledge of a specific category as a structure-based knowledge graph. Specifically, conceptual knowledge acts as visual priors and spatial relationships to assist the 3D reconstruction framework to create realistic 3D shapes with enhanced details. Our 3D reconstruction framework takes an image as input. It first predicts the conceptual knowledge of the object in the image, then generates a 3D object based on the input image and the predicted conceptual knowledge. The generated 3D object satisfies the following requirements: (1) it is consistent with the predicted graph in concept, and (2) consistent with the input image in geometry. Extensive experiments on public datasets (i.e., ShapeNet, Pix3D, and Pascal3D+) with 13 object categories show that (1) our method outperforms the state-of-the-art methods, (2) our prototype volume-based conceptual knowledge representation is more effective, and (3) our pipeline-agnostic approach can enhance the reconstruction quality of various 3D shape reconstruction pipelines.
C1 [Sun, Guofei; Geng, Weidong] Zhejiang Univ, State Key Lab CAD & CG, Coll Comp Sci & Technol, Zheda Rd 38, Hangzhou 310027, Zhejiang, Peoples R China.
   [Wong, Yongkang] Natl Univ Singapore, Sch Comp, 3 Res Link,I4-0 Bldg, Singapore 117602, Singapore.
   [Kankanhalli, Mohan S.] Natl Univ Singapore, Sch Comp, 11 Comp Dr,AS6 Bldg, Singapore 117416, Singapore.
   [Li, Xiangdong] Zhejiang Univ, Coll Comp Sci & Technol, Zheda Rd 38, Hangzhou 310027, Zhejiang, Peoples R China.
RP Geng, WD (通讯作者)，Zhejiang Univ, State Key Lab CAD & CG, Coll Comp Sci & Technol, Zheda Rd 38, Hangzhou 310027, Zhejiang, Peoples R China.; Li, XD (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Zheda Rd 38, Hangzhou 310027, Zhejiang, Peoples R China.
EM guofeisun@zju.edu.cn; yongkang.wong@nus.edu.sg; mohan@comp.nus.edu.sg;
   axli@zju.edu.cn; gengwd@zju.edu.cn
OI Kankanhalli, Mohan/0000-0002-4846-2015; Wong,
   Yongkang/0000-0002-1239-4428
FU National Key Research and Development Program of China [2017YFB1303201,
   2017YFB1002802]; National Natural Science Foundation of China [61972346]
FX This work is supported by the National Key Research and Development
   Program of China (No. 2017YFB1303201 and No. 2017YFB1002802), and the
   National Natural Science Foundation of China (No. 61972346).
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NR 76
TC 0
Z9 0
U1 15
U2 15
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1551-6857
EI 1551-6865
J9 ACM T MULTIM COMPUT
JI ACM Trans. Multimed. Comput. Commun. Appl.
PD AUG
PY 2022
VL 18
IS 3
AR 71
DI 10.1145/3491224
PG 20
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZY5UA
UT WOS:000772650600005
DA 2022-08-23
ER

PT J
AU Feng, ZW
   Mayer, W
   He, KQ
   Kwashie, S
   Stumptner, M
   Grossmann, G
   Peng, R
   Huang, WY
AF Feng, Zaiwen
   Mayer, Wolfgang
   He, Keqing
   Kwashie, Selasi
   Stumptner, Markus
   Grossmann, Georg
   Peng, Rong
   Huang, Wangyu
TI A Schema-Driven Synthetic Knowledge Graph Generation Approach With
   Extended Graph Differential Dependencies (GDD(x)s)
SO IEEE ACCESS
LA English
DT Article
DE Synthetic graph data; graph dependencies; data modeling; semantic-based
   graph generation
ID SUBGRAPH
AB Knowledge Graphs (KGs), as one of the key trends which are driving the next wave of technologies, have now become a new form of knowledge representation, and a cornerstone for several applications from generic to specific industrial use cases. However, in some specific domains such as law enforcement, a real and large domain-oriented KG is often unavailable due to data privacy concerns. In such domains it is necessary to generate a synthetic KG which mimics the properties of a real KG in the domain. Although during the last two decades, a variety of graph data generators has been proposed to achieve the generation of different kinds of networks, the state-of-the-art synthetic graph data generators are not feasible to generate a realistic and synthetic KGs because KGs always contain data characteristics with specified semantics. In this work, we propose a schema-driven synthetic KG generation approach with extended graph differential dependencies (GDDx), which is an extension of the recently developed graph entity/differential dependencies that represent formal constraints for graph data to enable the generation of desired graph patterns in synthetic KG. Next, we develop an effective KG generation algorithm that employs the schema and the pre-defined GDDx s. Finally, we evaluate our synthetic KG generator and compare with several state-of-the-art synthetic graph generators. The results from the experiments show that our KG generation method can generate KGs that exhibit the desired graph patterns, node attributes and degree distributions associated with each entity type in the graph's schema.
C1 [Feng, Zaiwen] Huazhong Agr Univ, Coll Informat, Wuhan 430070, Peoples R China.
   [Feng, Zaiwen] Huazhong Agr Univ, Macro Agr Res Inst, Wuhan 430070, Peoples R China.
   [Feng, Zaiwen; Mayer, Wolfgang; Stumptner, Markus; Grossmann, Georg] Univ South Australia, Adv Comp Res Ctr, Adelaide, SA 5095, Australia.
   [He, Keqing; Peng, Rong] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
   [Kwashie, Selasi] CSIROs Data61, Adelaide, SA 5000, Australia.
   [Huang, Wangyu] Carnegie Mellon Univ Australia, Sch Informat Syst & Management, Heinz Coll, Adelaide, SA 5000, Australia.
RP He, KQ (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
EM hekeqing@whu.edu.cn
RI ; Stumptner, Markus/B-5558-2009
OI Grossmann, Georg/0000-0003-4415-2228; Stumptner,
   Markus/0000-0002-7125-3289; Huang, Wangyu/0000-0003-2029-5144; Mayer,
   Wolfgang/0000-0002-2154-2269
FU Data to Decisions Cooperative Research Centre (D2D CRC), Australia;
   National Key Research and Development Plan of China [2017YFB0503702];
   National Science Foundation of China [61672387]; Fundamental Research
   Funds for the Chinese Central Universities [2662020XXQD01]
FX This work was supported in part by the Data to Decisions Cooperative
   Research Centre (D2D CRC), Australia, in part by the National Key
   Research and Development Plan of China under Grant 2017YFB0503702, in
   part by the National Science Foundation of China under Grant 61672387,
   and in part by the Fundamental Research Funds for the Chinese Central
   Universities under Grant 2662020XXQD01.
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NR 50
TC 0
Z9 0
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 2021
VL 9
BP 5609
EP 5639
DI 10.1109/ACCESS.2020.3048186
PG 31
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PS8XN
UT WOS:000608206900001
OA gold, Green Accepted
DA 2022-08-23
ER

PT J
AU Ofoegbu, C
   New, MG
   Staline, K
AF Ofoegbu, Chidiebere
   New, Mark George
   Staline, Kibet
TI The Effect of Inter-Organisational Collaboration Networks on Climate
   Knowledge Flows and Communication to Pastoralists in Kenya
SO SUSTAINABILITY
LA English
DT Article
DE climate change; adaptation; livelihood; pastoralism
ID CATTLE FARMERS INTENTION; LIVESTOCK PRODUCTION; MANAGEMENT; ADAPTATION;
   GOVERNANCE; DROUGHT; CONSERVATION; STRATEGIES; DISTRICT; DISASTER
AB In Kenya, pastoralists have utilized natural grasslands using practices that often result in overgrazing, low productivity and low income. Such practices have caused environmental problems, which could be exacerbated by climate change. Although knowledge on practices that increase pastoralists' capacity to adapt to climate and environmental challenges is currently available, the adoption rate remains poor. Hence, there is growing interest in understanding how cross-scale inter-organizational collaboration process either facilitates or hinders climate knowledge communications to and uptake by pastoralists. This study used network analysis to identify how inter-organizational collaborations in knowledge production and dissemination shape knowledge flow and communication to pastoralists in Kenya. A knowledge mapping workshop, key informant interviews and questionnaire surveys were used to identify the key organizations involved in the generation, brokering, and dissemination of adaptation knowledge to pastoralists. Two networks of configurations were explored: (i) relations of collaboration in knowledge production and (ii) relations of collaboration in knowledge dissemination. Measure of clustering coefficient, density, core-periphery location, and degree centrality were used to analyze the network structure and cohesion, and its influence on knowledge flow and adoption. Findings revealed a strong integration across the network with research institutes, NGOs (Non-governmental organizations), and CBOs (Community based organizations) identified as among the central actors, based on their degree centrality. Further, we observed a higher density of ties among actors in the knowledge production network than the dissemination network. The lower density of the dissemination network indicates there are not that many activities by key organizations aimed at ensuring that knowledge reaches the users, compared to activities related to knowledge generation. This also results in poor feedback processes from local pastoralists to knowledge generators and brokers. Knowledge transfer and uptake could therefore be enhanced by improving dissemination activities and feedback mechanisms in the dissemination network as a means of capturing pastoralist perspectives on the relevance, reliability, and usability of knowledge for action. Reflection and revision can be used to improve knowledge so that it is more in sync with a pastoralist context.
C1 [Ofoegbu, Chidiebere; New, Mark George] Univ Cape Town, African Climate & Dev Initiat, ZA-7700 Rondebosch, South Africa.
   [New, Mark George] Univ East Anglia, Sch Int Dev, Norwich NR4 7TJ, Norfolk, England.
   [Staline, Kibet] Univ Nairobi Kenya, Dept Land Resource Management & Agr Technol, Nairobi 00100, Kenya.
RP Ofoegbu, C (通讯作者)，Univ Cape Town, African Climate & Dev Initiat, ZA-7700 Rondebosch, South Africa.
EM ofoegbu.c@gmail.com; mark.new@uct.ac.za; kibets3k@gmail.com
RI Ofoegbu, Chidiebere/Q-8372-2019; Kibet, Staline/AAA-9156-2019; New,
   Mark/A-7684-2008
OI Ofoegbu, Chidiebere/0000-0002-8920-9411; New, Mark/0000-0001-6082-8879
FU International Development Research Centre, Canada (IDRC); Dept. for
   International Development, United Kingdom (DFID) through the ASSAR
   project (Adaptation at Scale in the Semi-Arid Region) [ACDI-ASSAR-1034];
   ASSAR project
FX This research was funded by the International Development Research
   Centre, Canada (IDRC) and Dept. for International Development, United
   Kingdom (DFID) through the ASSAR project (Adaptation at Scale in the
   Semi-Arid Region), [ACDI-ASSAR-1034]. The APC was also funded through
   the ASSAR project.
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NR 62
TC 5
Z9 5
U1 1
U2 9
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD NOV
PY 2018
VL 10
IS 11
AR 4180
DI 10.3390/su10114180
PG 23
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA HC1AQ
UT WOS:000451531700354
OA Green Published, Green Submitted, Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Hu, W
   Chen, N
   Yan, WH
   Pei, P
   Wei, YX
   Zhan, XJ
AF Hu, Wen
   Chen, Na
   Yan, Weiheng
   Pei, Pei
   Wei, Yongxiang
   Zhan, Xiaojun
TI Knowledge Mapping of Olfactory Dysfunction: A Bibliometric Study
SO FRONTIERS IN SYSTEMS NEUROSCIENCE
LA English
DT Article
DE bibliometric study; CiteSpace; olfactory dysfunction; VOSviewer;
   hotspot; trend
ID PENNSYLVANIA SMELL; DISORDERS; IDENTIFICATION; ASSOCIATION; POPULATION;
   UNIVERSITY
AB BackgroundOlfaction is one of the five basic senses of human beings. As such, olfactory dysfunction seriously affects patients' quality of life and can even endanger them. In recent years, olfactory dysfunction has attracted greater research interest, and numerous studies have been published on olfactory dysfunction. However, there are few studies on olfactory dysfunction through bibliometric analysis. This study aims to describe the current situation and identify the foci and potential new research directions of olfactory dysfunction using a bibliometric approach. MethodsArticles related to olfactory dysfunction published from 2002 to 2021 were located in the Web of Science Core Collection of Clarivate Analytics (London, UK). Bibliometric analyses were conducted with the CiteSpace (Chaomei Chen, Drexel University, Philadelphia, PA, USA) and VOSviewer (Center for Science and Technology Studies, Leiden University, Leiden, Netherlands) software programs. ResultsThe number of articles published each year showed an upward trend, especially in 2020, where a sharp increase had occurred due to the coronavirus disease 2019 (COVID-19) pandemic. The United States was the country with the most publications and the strongest international cooperation. In terms of institutions, the greatest number of publications from a single institution came from Dresden University of Technology. Thomas Hummel was the author who had contributed the most articles. An analysis of co-citation networks and burst keywords in the field revealed a shift from "gonadotropin-releasing hormone" and "apoptosis" earlier on to "olfactory training," "COVID-19," and "Parkinson's disease" more recently. "Outcome," "COVID-19," "infection," and "pathogenesis" are topics of the research frontier and hotspots. ConclusionMore attention has been paid to olfactory dysfunction as the understanding of it has improved in the past 20 years. This study provides researchers with an objective, systematic, and comprehensive analysis of the literature on olfactory dysfunction. The current frontier areas and hotspots in the field focus on the pathological mechanisms of olfactory dysfunction after infection with COVID-19 and its different prognoses. The pathophysiological mechanism of olfactory dysfunction in neurodegenerative diseases and COVID-19 will be a primary future research direction.
C1 [Hu, Wen; Wei, Yongxiang] Capital Med Univ, Beijing Anzhen Hosp, Smell & Taste Ctr, Dept Otolaryngol, Beijing, Peoples R China.
   [Chen, Na] Capital Med Univ, Beijing Rehabil Hosp, Dept Otolaryngol, Beijing, Peoples R China.
   [Yan, Weiheng; Wei, Yongxiang; Zhan, Xiaojun] Capital Inst Pediat, Childrens Hosp, Dept Otorhinolaryngol Head & Neck Surg, Beijing, Peoples R China.
   [Yan, Weiheng] Chinese Acad Med Sci, Grad Sch, Peking Union Med Coll, Beijing, Peoples R China.
   [Pei, Pei] Capital Inst Pediat, Beijing Municipal Key Lab Child Dev & Nutri, Beijing, Peoples R China.
RP Wei, YX (通讯作者)，Capital Med Univ, Beijing Anzhen Hosp, Smell & Taste Ctr, Dept Otolaryngol, Beijing, Peoples R China.; Wei, YX; Zhan, XJ (通讯作者)，Capital Inst Pediat, Childrens Hosp, Dept Otorhinolaryngol Head & Neck Surg, Beijing, Peoples R China.
EM yongxw67@163.com; cnczhan81@163.com
FU Public service development and reform pilot project of Beijing Medical
   Research Institute [BMR2021-3]; Key Special Projects for International
   Cooperation in Science and Technology Innovation between the Ministry of
   Science and Technology [2019YFE0116000]; Key Program of National Nature
   Science Foundation of China [82130031]
FX This study was supported by Public service development and reform pilot
   project of Beijing Medical Research Institute (BMR2021-3), the Key
   Special Projects for International Cooperation in Science and Technology
   Innovation between the Ministry of Science and Technology (No.
   2019YFE0116000), and Key Program of National Nature Science Foundation
   of China (No. 82130031).
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NR 48
TC 0
Z9 0
U1 7
U2 7
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1662-5137
J9 FRONT SYST NEUROSCI
JI Front. Syst. Neurosci.
PD JUN 13
PY 2022
VL 16
AR 904982
DI 10.3389/fnsys.2022.904982
PG 12
WC Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA 2L2MN
UT WOS:000816854000001
PM 35770245
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Shen, JM
   Shen, H
   Ke, LX
   Chen, JL
   Dang, X
   Liu, BX
   Hua, YP
AF Shen, Jianming
   Shen, Hui
   Ke, Lixin
   Chen, Jialin
   Dang, Xi
   Liu, Baoxian
   Hua, Yunpeng
TI Knowledge Mapping of Immunotherapy for Hepatocellular Carcinoma: A
   Bibliometric Study
SO FRONTIERS IN IMMUNOLOGY
LA English
DT Article
DE HCC; Immunotherapy; Bibliometric study; VOSviewer; Citespace
ID SCIENTOMETRIC ANALYSIS; PUBLICATIONS; LANDSCAPE; REGORAFENIB; SORAFENIB;
   BLOCKADE; SCIENCE; WEB
AB BackgroundHepatocellular carcinoma (HCC) is one of the most common malignant tumors, and many patients are diagnosed with advanced disease. The treatment of advanced liver cancer has made significant strides in recent years, owing to the practice of immunotherapy drugs. Numerous studies have been published on immunotherapy for HCC; however, no relevant bibliometric study has been published. This study aims to gain a better understanding of the current situation and to identify potential new research directions by conducting a bibliometric analysis on immunotherapy for HCC. MethodsWe searched the Web of Science Core Collection (WoSCC) for articles related to immunotherapy for HCC. Three software (VOSviewer, CiteSpace, and python) were primarily used to assess the contribution and co-occurrence relationships of various countries/regions, institutes, journals, and, authors as well as to identify research hotspots and promising future trends in this research field. ResultsA total of 1,641 English articles published between 2011 and 2020 were collected, with the number of articles increasing nearly every year. The majority of publications originated from China (n = 893, 54.42%), followed by the United States and Japan. The Sun Yat-sen University contributed the most publications (n = 97, 5.91%). Nakatsura Tetsuya (n = 26) and Llovet JM (n = 366) were ranked first in the top ten authors and co-cited authors. Cancer Immunology Immunotherapy was the most productive academic journal on immunotherapy for HCC [n = 46, 2.80%; impact factor (IF) 2020 = 6.9679]. Aggregation and identification of critical nodes in the co-cited network demonstrated a shift in the field of HCC immunotherapy. Initially, the hotspots were predominantly "glypican-3", "cytokine-induced killer cells", and "ny-eso-1", while the emphasis has shifted in recent years to "landscape", "camrelizumab", "combination therapy", and "immune score". ConclusionIncreased attention has been paid to HCC with the advancement of immunotherapy. At the moment, the most active frontiers are focused on better understanding the immunological landscape of liver cancer, screening the population that can benefit from immunotherapy, and the clinical application of immune checkpoint inhibitors, particularly in combination with other therapeutic options (such as local therapy and targeted therapy).
C1 [Shen, Jianming; Ke, Lixin; Chen, Jialin; Dang, Xi; Hua, Yunpeng] Sun Yat Sen Univ, Affiliated Hosp 1, Hepatobiliary & Pancreat Ctr, Guangzhou, Peoples R China.
   [Shen, Hui; Liu, Baoxian] Sun Yat Sen Univ, Affiliated Hosp 1, Dept Med Ultrason, Guangzhou, Peoples R China.
RP Hua, YP (通讯作者)，Sun Yat Sen Univ, Affiliated Hosp 1, Hepatobiliary & Pancreat Ctr, Guangzhou, Peoples R China.; Liu, BX (通讯作者)，Sun Yat Sen Univ, Affiliated Hosp 1, Dept Med Ultrason, Guangzhou, Peoples R China.
EM liubxian@mail.sysu.edu.cn; hyp0427@163.com
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NR 56
TC 2
Z9 2
U1 43
U2 43
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 JAN 31
PY 2022
VL 13
AR 815575
DI 10.3389/fimmu.2022.815575
PG 12
WC Immunology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Immunology
GA ZE2TV
UT WOS:000758742000001
PM 35173728
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Faghihinia, M
   Xu, YY
   Liu, D
   Wu, NC
AF Faghihinia, Maede
   Xu, Yaoyang
   Liu, Dong
   Wu, Naicheng
TI Freshwater biodiversity at different habitats: Research hotspots with
   persistent and emerging themes
SO ECOLOGICAL INDICATORS
LA English
DT Article
DE Freshwater organisms; Biodiversity loss; Science mapping; Scientific
   production; Thematic areas
ID SERVICES
AB The immense value of ecosystem services provided by freshwater bodies is incomputable. However, the productivity and biodiversity of freshwater bodies are undergoing dramatic degradation as a result of climate- and anthropogenic-induced changes worldwide. Despite a well-documented literature on freshwater biodiversity, we still lack a comprehensive and objective overview of biodiversity research conducted in different types of freshwater bodies. Here, we conducted a comparative study of freshwater biodiversity in different ecosystem types at the global scale to describe the current state of knowledge, map the scientific geographical distribution and international collaboration network and identify persistent and emerging themes. Thus, metadata of biodiversity studies carried out in six freshwater ecosystem types - river, lake, wetland, stream, reservoir and pond- were extracted from SCI-E and SSCI databases of the Web of Science and then visualized for science mapping using bibliometric analysis. Our findings showed that in the field of freshwater biodiversity (i) the number of publications in various freshwater habitats has risen considerably over the last two decades with the highest average annual growth rate in river- and lake-related studies, (ii) there was a clear bias in the geographical distribution of the research with fewer scientific production and international collaboration in less developed parts of the world. The USA (20%) and China (19%) are the main leading countries and Europe (30%), Asia (26%, mainly China and India) and North America (24%) are major continents contributing to this field, (iii) taxonomic groups have been investigated disproportionally among different freshwater habitats, (iv) habitat heterogeneity and fragmentation, land use change, climate change, eutrophication and invasive species are the most frequent driving factors, (v) the persistent and emerging themes differ among water ecosystems, e.g. disproportionate assessment of biodiversity measures. Furthermore, functional and phylogenetic facets in this area are poorly understood. Overall, clear obtained distinctions in the hotspots and thematic areas of different freshwater environments can contribute to identify the missing pieces in current research, and put an effort to investigate the non-explored or under-explored areas of research.
C1 [Faghihinia, Maede; Xu, Yaoyang; Liu, Dong] Chinese Acad Sci, Fujian Key Lab Watershed Ecol, Inst Urban Environm, Key Lab Urban Environm & Hlth, Xiamen 361021, Fujian, Peoples R China.
   [Faghihinia, Maede; Xu, Yaoyang] Chinese Acad Sci, Ningbo Urban Environm Observat & Res Stn, Zhejiang Key Lab Urban Environm Proc & Pollu Cont, Ningbo 315800, Zhejiang, Peoples R China.
   [Liu, Dong] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Wu, Naicheng] Ningbo Univ, Dept Geog & Spatial Informat Tech, Ningbo 315211, Zhejiang, Peoples R China.
RP Xu, YY (通讯作者)，Chinese Acad Sci, Fujian Key Lab Watershed Ecol, Inst Urban Environm, Key Lab Urban Environm & Hlth, Xiamen 361021, Fujian, Peoples R China.
EM yyxu@iue.ac.cn
RI CAS, KLUEH/T-5743-2019; Faghihinia, Maede/AAW-6848-2021
OI Faghihinia, Maede/0000-0002-8953-1945
FU Ministry of Science and Technology of China (MSTC); National Key
   Research and Development Program [2017YFE0119000]; National Natural
   Science Foundation of China (NSFC) [31300397]
FX This work was supported financially by Ministry of Science and
   Technology of China (MSTC) with National Key Research and Development
   Program (2017YFE0119000) and National Natural Science Foundation of
   China (NSFC) with Young Scientists Programs (31300397). Any opinions,
   findings, and conclusions or recommendations expressed in this work are
   those of the authors and do not necessarily reflect the views of MSTC
   and NSFC.
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   Zhang J, 2016, J ASSOC INF SCI TECH, V67, P967, DOI 10.1002/asi.23437
NR 34
TC 0
Z9 0
U1 5
U2 24
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1470-160X
EI 1872-7034
J9 ECOL INDIC
JI Ecol. Indic.
PD OCT
PY 2021
VL 129
AR 107926
DI 10.1016/j.ecolind.2021.107926
EA JUN 2021
PG 7
WC Biodiversity Conservation; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biodiversity & Conservation; Environmental Sciences & Ecology
GA TZ2BU
UT WOS:000684281300004
OA gold
DA 2022-08-23
ER

PT J
AU Sunkle, S
   Saxena, K
   Patil, A
   Kulkarni, V
AF Sunkle, Sagar
   Saxena, Krati
   Patil, Ashwini
   Kulkarni, Vinay
TI AI-driven streamlined modeling: experiences and lessons learned from
   multiple domains
SO SOFTWARE AND SYSTEMS MODELING
LA English
DT Article
DE AI-driven; Domain modeling; Natural language processing; Information
   extraction; Knowledge graphs
AB Model-driven technologies (MD*), considered beneficial through abstraction and automation, have not enjoyed widespread adoption in the industry. In keeping with the recent trends, using AI techniques might help the benefits of MD* outweigh their costs. Although the modeling community has started using AI techniques, it is, in our opinion, quite limited and requires a change in perspective. We provide such a perspective through five industrial case studies where we use AI techniques in different modeling activities. We discuss our experiences and lessons learned, in some cases evolving purely modeling solutions with AI techniques, and in others considering the AI aids from the beginning. We believe that these case studies can help the researchers and practitioners make sense of various artifacts and data available to them and use applicable AI techniques to enhance suitable modeling activities.
C1 [Sunkle, Sagar; Saxena, Krati; Patil, Ashwini; Kulkarni, Vinay] Tata Consultancy Serv Res, Pune 411013, Maharashtra, India.
RP Sunkle, S (通讯作者)，Tata Consultancy Serv Res, Pune 411013, Maharashtra, India.
EM sagar.sunkle@tcs.com
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NR 90
TC 0
Z9 0
U1 1
U2 1
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 JUN
PY 2022
VL 21
IS 3
BP 1
EP 23
DI 10.1007/s10270-022-00982-6
EA FEB 2022
PG 23
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0U0DM
UT WOS:000757918200001
PM 35221860
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Starzyk, JA
   Jaszuk, M
   Maciura, L
   Horzyk, A
AF Starzyk, Janusz A.
   Jaszuk, Marek
   Maciura, Lukasz
   Horzyk, Adrian
TI Concurrent Associative Memories With Synaptic Delays
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article
DE Neurons; Logic gates; Delays; Speech recognition; Training; Biological
   neural networks; Associative memory; Concurrent associative memories;
   knowledge graphs; synaptic delays; synaptic efficacy
ID RECOGNITION
AB This article presents concurrent associative memories with synaptic delays useful for processing sequences of real vectors. Associative memories with synaptic delays were introduced by the authors for symbolic sequential inputs and demonstrated several advantages over other sequential memories. They were easy to organize and train. It was demonstrated that they were more robust than long short-term memories in recognition of damaged sequences. The associative memories can be applied in combination with deep neural networks to solve such symbol grounding problems, such as speech recognition, and support sequential memories triggered by sensory inputs. Several practical considerations for developed memories were discussed and illustrated. A continuous speech database was used to compare the developed method with LSTM memories. Tests demonstrated that the developed approach is more robust in recognition of speech sequences, particularly when the test sequences are damaged.
C1 [Starzyk, Janusz A.; Jaszuk, Marek; Maciura, Lukasz] Univ Informat Technol & Management Rzeszow, Fac Appl Comp Sci, PL-35225 Rzeszow, Poland.
   [Starzyk, Janusz A.] Ohio Univ, Sch Elect Engn & Comp Sci, Athens, OH 45701 USA.
   [Horzyk, Adrian] AGH Univ Sci & Technol Krakow, Dept Biocybernet & Biomed Engn, PL-30059 Krakow, Poland.
RP Horzyk, A (通讯作者)，AGH Univ Sci & Technol Krakow, Dept Biocybernet & Biomed Engn, PL-30059 Krakow, Poland.
EM horzyk@agh.edu.pl
RI ; Horzyk, Adrian/C-6661-2013
OI Starzyk, Janusz/0000-0003-2678-5515; Jaszuk, Marek/0000-0003-2680-0025;
   Horzyk, Adrian/0000-0001-9001-4198; Maciura, Lukasz/0000-0001-8657-3472
FU Polish National Science Centre [DEC-2016/21/B/ST7/02220, AGH
   16.16.120.773]
FX Manuscript received August 8, 2019; revised May 13, 2020 and September
   30, 2020; accepted November 23, 2020. Date of publication December 10,
   2020; date of current version August 4, 2021. This work was supported by
   the Polish National Science Centre, Twardowskiego 16, 30-312 Krakow,
   Poland, under Grant DEC-2016/21/B/ST7/02220 and Grant AGH 16.16.120.773.
   (Corresponding author: Adrian Horzyk.)
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NR 20
TC 0
Z9 0
U1 4
U2 6
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
PD AUG
PY 2021
VL 32
IS 8
BP 3736
EP 3747
DI 10.1109/TNNLS.2020.3041048
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA TU6UN
UT WOS:000681169500042
PM 33301411
OA Bronze
DA 2022-08-23
ER

PT J
AU Xu, Z
   Xuan, JY
   Liu, YH
   Choo, KKR
   Mei, L
   Hu, CP
AF Xu, Zheng
   Xuan, Junyu
   Liu, Yunhuai
   Choo, Kim-Kwang Raymond
   Mei, Lin
   Hu, Chuanping
TI Building spatial temporal relation graph of concepts pair using web
   repository
SO INFORMATION SYSTEMS FRONTIERS
LA English
DT Article
DE Knowledge graph; Semantic relations; Web repository; Temporal and
   spatial mining
ID DATA CENTERS; EXTRACTION; METRICS
AB Mining semantic relations between concepts underlies many fundamental tasks including natural language processing, web mining, information retrieval, and web search. In order to describe the semantic relation between concepts, in this paper, the problem of automatically generating spatial temporal relation graph (STRG) of semantic relation between concepts is studied. The spatial temporal relation graph of semantic relation between concepts includes relation words, relation sentences, relation factor, relation graph, faceted feature, temporal feature, and spatial feature. The proposed method can automatically generate the spatial temporal relation graph (STRG) of semantic relation between concepts, which is different from the manually generated annotation repository such as WordNet and Wikipedia. Moreover, the proposed method does not need any prior knowledge such as ontology or the hierarchical knowledge base such as WordNet. Empirical experiments on real dataset show that the proposed algorithm is effective and accurate.
C1 [Xu, Zheng; Liu, Yunhuai; Mei, Lin; Hu, Chuanping] Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China.
   [Xu, Zheng] Tsinghua Univ, Beijing, Peoples R China.
   [Xuan, Junyu] Shanghai Univ, Shanghai, Peoples R China.
   [Choo, Kim-Kwang Raymond] Univ Texas San Antonio, Dept Informat Syst & Cyber Secur, San Antonio, TX USA.
   [Choo, Kim-Kwang Raymond] Univ South Australia, Sch Informat Technol & Math Sci, Adelaide, SA, Australia.
   [Choo, Kim-Kwang Raymond] China Univ Geosci, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
RP Xu, Z (通讯作者)，Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China.; Xu, Z (通讯作者)，Tsinghua Univ, Beijing, Peoples R China.
EM xuzheng@shu.edu.cn
RI Choo, Kim-Kwang Raymond/A-3634-2009
OI Choo, Kim-Kwang Raymond/0000-0001-9208-5336; Xuan,
   Junyu/0000-0002-8367-6908
FU National Science and Technology Major Project [2013ZX01033002-003];
   National Science Foundation of China [61300202]; China Postdoctoral
   Science Foundation [2014 M560085]; Science Foundation of Shanghai
   [13ZR1452900]
FX This work was supported in part by the National Science and Technology
   Major Project under Grant 2013ZX01033002-003, in part by the National
   Science Foundation of China under Grant 61300202, in part by the China
   Postdoctoral Science Foundation under Grant 2014 M560085, and in part by
   the Science Foundation of Shanghai under Grant 13ZR1452900.
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NR 36
TC 7
Z9 7
U1 2
U2 10
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1387-3326
EI 1572-9419
J9 INFORM SYST FRONT
JI Inf. Syst. Front.
PD OCT
PY 2017
VL 19
IS 5
SI SI
BP 1029
EP 1038
DI 10.1007/s10796-016-9676-4
PG 10
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FG4EO
UT WOS:000410186100005
DA 2022-08-23
ER

PT J
AU Schutte, D
   Vasilakes, J
   Bompelli, A
   Zhou, YQ
   Fiszman, M
   Xu, H
   Kilicoglu, H
   Bishop, JR
   Adam, T
   Zhang, R
AF Schutte, Dalton
   Vasilakes, Jake
   Bompelli, Anu
   Zhou, Yuqi
   Fiszman, Marcelo
   Xu, Hua
   Kilicoglu, Halil
   Bishop, Jeffrey R.
   Adam, Terrence
   Zhang, Rui
TI Discovering novel drug-supplement interactions using SuppKG generated
   from the biomedical literature
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Dietary supplements; Drug supplement interactions; Knowledge discovery;
   Natural language processing; Text mining
ID METAMAP; KNOWLEDGE; COVERAGE
AB Objective: Develop a novel methodology to create a comprehensive knowledge graph (SuppKG) to represent a domain with limited coverage in the Unified Medical Language System (UMLS), specifically dietary supplement (DS) information for discovering drug-supplement interactions (DSI), by leveraging biomedical natural language processing (NLP) technologies and a DS domain terminology.Materials and Methods: We created SemRepDS (an extension of an NLP tool, SemRep), capable of extracting semantic relations from abstracts by leveraging a DS-specific terminology (iDISK) containing 28,884 DS terms not found in the UMLS. PubMed abstracts were processed using SemRepDS to generate semantic relations, which were then filtered using a PubMedBERT model to remove incorrect relations before generating SuppKG. Two discovery pathways were applied to SuppKG to identify potential DSIs, which are then compared with an existing DSI database and also evaluated by medical professionals for mechanistic plausibility.Results: SemRepDS returned 158.5% more DS entities and 206.9% more DS relations than SemRep. The fine-tuned PubMedBERT model (significantly outperformed other machine learning and BERT models) obtained an F1 score of 0.8605 and removed 43.86% of semantic relations, improving the precision of the relations by 26.4% over pre-filtering. SuppKG consists of 56,635 nodes and 595,222 directed edges with 2,928 DS-specific nodes and 164,738 edges. Manual review of findings identified 182 of 250 (72.8%) proposed DS-Gene-Drug and 77 of 100 (77%) proposed DS-Gene1-Function-Gene2-Drug pathways to be mechanistically plausible.Discussion: With added DS terminology to the UMLS, SemRepDS has the capability to find more DS-specific se-mantic relationships from PubMed than SemRep. The utility of the resulting SuppKG was demonstrated using discovery patterns to find novel DSIs. Conclusion: For the domain with limited coverage in the traditional terminology (e.g., UMLS), we demonstrated an approach to leverage domain terminology and improve existing NLP tools to generate a more comprehensive knowledge graph for the downstream task. Even this study focuses on DSI, the method may be adapted to other domains.
C1 [Schutte, Dalton; Vasilakes, Jake; Zhou, Yuqi; Adam, Terrence; Zhang, Rui] Univ Minnesota, Inst Hlth Informat, Minneapolis, MN USA.
   [Schutte, Dalton; Vasilakes, Jake; Bompelli, Anu; Zhou, Yuqi; Adam, Terrence; Zhang, Rui] Univ Minnesota, Dept Pharmaceut Care & Hlth Syst, Minneapolis, MN USA.
   [Vasilakes, Jake; Bishop, Jeffrey R.] Univ Minnesota, Dept Expt & Clin Pharm, Minneapolis, MN USA.
   [Kilicoglu, Halil] Univ Illinois, Sch Informat Sci, Champaign, IL USA.
   [Vasilakes, Jake] Univ Manchester, Natl Ctr Text Min, Sch Comp Sci, Manchester, England.
   [Fiszman, Marcelo] Pontifical Catholic Univ Rio de Janeiro, NITES Nucleo Inovacao & Tecnol Saude, Rio De Janeiro, Brazil.
   [Xu, Hua] Univ Texas Hlth Sci Ctr Houston, Sch Biomed Informat, Houston, TX USA.
   [Zhang, Rui] Univ Minnesota, 8-100 Phillips Wangensteen Bldg,516 Delaware St SE, Minneapolis, MN 55455 USA.
RP Zhang, R (通讯作者)，Univ Minnesota, 8-100 Phillips Wangensteen Bldg,516 Delaware St SE, Minneapolis, MN 55455 USA.
EM zhan1386@umn.edu
FU National Institutes of Health's National Center for Complementary and
   Integrative Health; Office of Dietary Supplements [R01AT009457]
FX This work was supported by the National Institutes of Health's National
   Center for Complementary and Integrative Health and the Office of
   Dietary Supplements grant number R01AT009457 (to RZ) . The con- tent is
   solely the responsibility of the authors and does not represent the
   official views of the National Institutes of Health.
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NR 44
TC 0
Z9 0
U1 0
U2 0
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD JUL
PY 2022
VL 131
AR 104120
DI 10.1016/j.jbi.2022.104120
PG 10
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA 3D4JT
UT WOS:000829270900001
PM 35709900
OA Green Accepted, hybrid
DA 2022-08-23
ER

PT J
AU Li, MY
   Xing, YQ
   Kong, F
   Zhou, GD
AF Li, Mingyang
   Xing, Yuqing
   Kong, Fang
   Zhou, Guodong
TI Towards better entity linking
SO FRONTIERS OF COMPUTER SCIENCE
LA English
DT Article
DE entity linking; knowledge base extraction; selfatention mechanism;
   highway network
ID KNOWLEDGE-BASE
AB As one of the most important components in knowledge graph construction, entity linking has been drawing more and more attention in the last decade. In this paper, we propose two improvements towards better entity linking. On one hand, we propose a simple but effective coarse-to-fine unsupervised knowledge base(KB) extraction approach to improve the quality of KB, through which we can conduct entity linking more efficiently. On the other hand, we propose a highway network framework to bridge key words and sequential information captured with a self-attention mechanism to better represent both local and global information. Detailed experimentation on six public entity linking datasets verifies the great effectiveness of both our approaches.
C1 [Li, Mingyang; Xing, Yuqing; Kong, Fang; Zhou, Guodong] Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China.
RP Kong, F (通讯作者)，Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China.
EM kongfang@suda.edu.cn
OI zhou, guodong/0000-0002-7887-5099
FU key project of the National Natural Science Foundation of China
   [61836007]; National Natural Science Foundation of China [61876118];
   Priority Academic Program Development of Jiangsu Higher Education
   Institutions
FX This work was supported by the key project of the National Natural
   Science Foundation of China (Grant No. 61836007), the normal project of
   the National Natural Science Foundation of China (Grant No. 61876118)
   and the project funded by the Priority Academic Program Development of
   Jiangsu Higher Education Institutions.
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NR 48
TC 0
Z9 0
U1 2
U2 13
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 2022
VL 16
IS 2
AR 162308
DI 10.1007/s11704-020-0192-9
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 WJ8ZW
UT WOS:000709327600001
DA 2022-08-23
ER

PT J
AU Moussallem, D
   Wauer, M
   Ngomo, ACN
AF Moussallem, Diego
   Wauer, Matthias
   Ngomo, Axel-Cyrille Ngonga
TI Machine Translation using Semantic Web Technologies: A Survey
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Machine Translation; Semantic Web; Ontology; Linked Data;
   Multilinguality; Knowledge Graphs
AB A large number of machine translation approaches have recently been developed to facilitate the fluid migration of content across languages. However, the literature suggests that many obstacles must still be dealt with to achieve better automatic translations. One of these obstacles is lexical and syntactic ambiguity. A promising way of overcoming this problem is using Semantic Web technologies. This article presents the results of a systematic review of machine translation approaches that rely on Semantic Web technologies for translating texts. Overall, our survey suggests that while Semantic Web technologies can enhance the quality of machine translation outputs for various problems, the combination of both is still in its infancy. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Moussallem, Diego; Wauer, Matthias] Univ Leipzig, Dept Comp Sci, AKSW Res Grp, Augustuspl 10, D-04109 Leipzig, Germany.
   [Moussallem, Diego; Ngomo, Axel-Cyrille Ngonga] Univ Paderborn, Data Sci Grp, Pohlweg 51, D-33098 Paderborn, Germany.
RP Moussallem, D (通讯作者)，Univ Leipzig, Dept Comp Sci, AKSW Res Grp, Augustuspl 10, D-04109 Leipzig, Germany.
EM moussallem@informatik.uni-leipzig.de; wauer@informatik.uni-leipzig.de;
   axel.ngonga@upb.de
FU Brazilian National Council for Scientific and Technological Development
   (CNPq) [206971/2014-1]; H2020 HOBBIT Project [688227]
FX This research has been partially supported by the Brazilian National
   Council for Scientific and Technological Development (CNPq) (no.
   206971/2014-1) and the H2020 HOBBIT Project (GA No. 688227).
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NR 213
TC 17
Z9 19
U1 5
U2 31
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 AUG
PY 2018
VL 51
BP 1
EP 19
DI 10.1016/j.websem.2018.07.001
PG 19
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 GQ0FU
UT WOS:000441293800001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhou, XY
   Zafarani, R
AF Zhou, Xinyi
   Zafarani, Reza
TI A Survey of Fake News: Fundamental Theories, Detection Methods, and
   Opportunities
SO ACM COMPUTING SURVEYS
LA English
DT Article
DE Fake news; news verification; disinformation; misinformation;
   fact-checking; knowledge graph; deception detection; information
   credibility; social media
ID KNOWLEDGE-BASE; WEB; CREDIBILITY; VALIDITY; BIAS; CUES
AB The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news detection and intervention. This survey reviews and evaluates methods that can detect fake news from four perspectives: the false knowledge it carries, its writing style, its propagation patterns, and the credibility of its source. The survey also highlights some potential research tasks based on the review. In particular, we identify and detail related fundamental theories across various disciplines to encourage interdisciplinary research on fake news. It is our hope that this survey can facilitate collaborative efforts among experts in computer and information sciences, social sciences, political science, and journalism to research fake news, where such efforts can lead to fake news detection that is not only efficient but, more importantly, explainable.
C1 [Zhou, Xinyi; Zafarani, Reza] Syracuse Univ, EECS Dept, Data Lab, Syracuse, NY 13244 USA.
RP Zhou, XY (通讯作者)，Syracuse Univ, EECS Dept, Data Lab, Syracuse, NY 13244 USA.
EM zhouxinyi@data.syr.edu; reza@data.syr.edu
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NR 180
TC 155
Z9 158
U1 42
U2 117
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 0360-0300
EI 1557-7341
J9 ACM COMPUT SURV
JI ACM Comput. Surv.
PD OCT
PY 2020
VL 53
IS 5
AR 109
DI 10.1145/3395046
PG 40
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA OH1YB
UT WOS:000582365900018
OA Green Submitted
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Wang, MX
   Ma, FK
   Wang, ZP
   Hu, DW
   Xu, XG
   Hao, XP
AF Wang, Mengxia
   Ma, Fukun
   Wang, Zhengping
   Hu, Dawei
   Xu, Xinguang
   Hao, Xiaopeng
TI Graphitic carbon nitride, a saturable absorber material for the visible
   waveband
SO PHOTONICS RESEARCH
LA English
DT Article
ID NONLINEAR-OPTICAL PROPERTIES; PHOTOCATALYTIC PERFORMANCE; NANOSHEETS;
   LASER; ABSORPTION; G-C3N4; WATER; NANOPARTICLES; FILMS; C3N4
AB For the first time to our knowledge, graphitic carbon nitride (g-C3N4) nanosheets are found to be an excellent saturable absorber material in the visible waveband. g-C3N4 exhibits much stronger saturable absorption in this region than in the near-infrared region, unlike other two-dimensional materials such as graphene and black phosphorus. By the Z-scan method, the nonlinear absorption coefficient beta of the material is first measured at three visible wavelengths, and for g-C3N4 it is -2.05, -0.34, and -0.11 cm.GW(-1) at 355, 532, and 650 nm, respectively. These are much larger than -0.06 cm.GW(-1) at 1064 nm. (c) 2018 Chinese Laser Press
C1 [Wang, Mengxia; Ma, Fukun; Wang, Zhengping; Xu, Xinguang; Hao, Xiaopeng] Shandong Univ, State Key Lab Crystal Mat, Jinan 250100, Shandong, Peoples R China.
   [Hu, Dawei] Shandong Univ, Res Inst Sci & Technol, Jinan 250100, Shandong, Peoples R China.
RP Wang, ZP (通讯作者)，Shandong Univ, State Key Lab Crystal Mat, Jinan 250100, Shandong, Peoples R China.
EM zpwang@sdu.edu.cn; xphao@sdu.edu.cn
OI Ma, Fukun/0000-0003-4610-6778
FU Natural Science Foundation of Shandong Province, China [ZR2017MF031]
FX Natural Science Foundation of Shandong Province, China (ZR2017MF031).
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Z9 9
U1 3
U2 36
PU OPTICAL SOC AMER
PI WASHINGTON
PA 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA
SN 2327-9125
J9 PHOTONICS RES
JI Photonics Res.
PD APR 1
PY 2018
VL 6
IS 4
BP 307
EP 313
DI 10.1364/PRJ.6.000307
PG 7
WC Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Optics
GA GB3MF
UT WOS:000428961400014
DA 2022-08-23
ER

PT J
AU Hossayni, H
   Khan, I
   Crespi, N
AF Hossayni, Hicham
   Khan, Imran
   Crespi, Noel
TI Data Anonymization for Maintenance Knowledge Sharing
SO IT PROFESSIONAL
LA English
DT Article
DE Manufacturing industries; Costs; Knowledge management; Maintenance
   engineering; Market research; Business continuity
AB Formerly considered part of general enterprise costs, industrial maintenance has become critical for business continuity and a real source of data. Despite the heavy investments made by companies in smart manufacturing, traditional maintenance practices still dominate the industrial landscape. Maintenance knowledge sharing between industries can significantly optimize maintenance activity and improve process efficiency. Different international standards and initiatives are promoting such an approach. However, this trend failed to gain ground in the manufacturing industry. In this article, we present the results of our investigation about the real roadblocks that obstruct the progress of the maintenance knowledge sharing approach. We determined that knowledge graphs and, more importantly, automated data anonymization techniques can facilitate the development of general-purpose solutions to share maintenance knowledge among concerned actors.
C1 [Hossayni, Hicham] Schneider Elect, Semant Web & Ind IoT, F-38000 Grenoble, France.
   [Khan, Imran] Schneider Elect, F-38000 Grenoble, France.
   [Crespi, Noel] Inst Polytech Paris, F-91764 Paris, France.
RP Hossayni, H (通讯作者)，Schneider Elect, Semant Web & Ind IoT, F-38000 Grenoble, France.
EM hicham.hossayni@se.com; imran@ieee.org; noel.crespi@telecom-sudparis.eu
RI Crespi, Noel/ABE-7052-2020
OI Crespi, Noel/0000-0003-2962-192X; Khan, Imran/0000-0001-5415-1785;
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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 SEPT 1
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VL 23
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WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA WG2LT
UT WOS:000706829300015
DA 2022-08-23
ER

PT J
AU Sun, X
   Bahmani, B
   Vlassis, NN
   Sun, WC
   Xu, YX
AF Sun, Xiao
   Bahmani, Bahador
   Vlassis, Nikolaos N.
   Sun, WaiChing
   Xu, Yanxun
TI Data-driven discovery of interpretable causal relations for deep
   learning material laws with uncertainty propagation
SO GRANULAR MATTER
LA English
DT Article
DE Causal discovery; Granular materials; Knowledge graph; Path-dependent
   responses; Soil mechanics
ID NEURAL-NETWORKS; MODEL; QUANTIFICATION; ANISOTROPY; KNOWLEDGE; STRESS;
   MEDIA; GAME
AB This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ). We first develop a causal discovery algorithm to infer causal relations among time-history data measured during each representative volume element (RVE) simulation through a directed acyclic graph. With multiple plausible sets of causal relationships estimated from multiple RVE simulations, the predictions are propagated in the derived causal graph while using a deep neural network equipped with dropout layers as a Bayesian approximation for UQ. We select two representative numerical examples (traction-separation laws for frictional interfaces, elastoplasticity models for granular assembles) to examine the accuracy and robustness of the proposed causal discovery method for the common material law predictions in civil engineering applications.
C1 [Sun, Xiao; Xu, Yanxun] Johns Hopkins Univ, Dept Appl Math & Stat, Baltimore, MD 21218 USA.
   [Bahmani, Bahador; Vlassis, Nikolaos N.; Sun, WaiChing] Columbia Univ, Dept Civil Engn & Engn Mech, New York, NY 10027 USA.
RP Xu, YX (通讯作者)，Johns Hopkins Univ, Dept Appl Math & Stat, Baltimore, MD 21218 USA.; Sun, WC (通讯作者)，Columbia Univ, Dept Civil Engn & Engn Mech, New York, NY 10027 USA.
EM yanxun.xu@jhu.edu
RI Sun, WaiChing/U-1654-2019
OI Sun, WaiChing/0000-0002-3078-5086; Bahmani, Bahador/0000-0002-2115-7109
FU National Science Foundation [CMMI-1846875, OAC-1940203, 1940107]; Earth
   Materials and Processes program from the US Army Research Office
   [W911NF-18-2-0306]; Dynamic Materials and Interactions Program from the
   Air Force Office of Scientific Research [FA9550-17-1-0169]
FX The members of the Columbia research group involved in this research are
   supported by National Science Foundation under Grant contracts
   CMMI-1846875 and OAC-1940203, the Earth Materials and Processes program
   from the US Army Research Office under Grant contract W911NF-18-2-0306,
   and the Dynamic Materials and Interactions Program from the Air Force
   Office of Scientific Research under Grant contracts FA9550-17-1-0169.
   The members of the Johns Hopkins University are supported by National
   Science Foundation under Grant contract 1940107. These supports are
   gratefully acknowledged. The authors would also like to thank Dr. Kun
   Wang from Los Alamos National Laboratory for providing the data for the
   traction-separation law. The views and conclusions contained in this
   document are those of the authors, and should not be interpreted as
   representing the official policies, either expressed or implied, of the
   sponsors, including the Army Research Laboratory or the U.S. Government.
   The U.S. Government is authorized to reproduce and distribute reprints
   for Government purposes notwithstanding any copyright notation herein.
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NR 100
TC 4
Z9 4
U1 13
U2 30
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1434-5021
EI 1434-7636
J9 GRANUL MATTER
JI Granul. Matter
PD FEB
PY 2022
VL 24
IS 1
AR 1
DI 10.1007/s10035-021-01137-y
PG 32
WC Materials Science, Multidisciplinary; Mechanics; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Materials Science; Mechanics; Physics
GA WQ8AS
UT WOS:000714034100001
OA Green Submitted
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Gao, H
   Ni, Y
   Mo, XY
   Li, DT
   Teng, S
   Huang, QS
   Huang, S
   Liu, GJ
   Zhang, S
   Tang, YP
   Lu, L
   Liang, HY
AF Gao, Huan
   Ni, Yuan
   Mo, Xueying
   Li, Dantong
   Teng, Shan
   Huang, Qingsheng
   Huang, Shuai
   Liu, Guangjian
   Zhang, Sheng
   Tang, Yaping
   Lu, Long
   Liang, Huiying
TI Drug repositioning based on network-specific core genes identifies
   potential drugs for the treatment of autism spectrum disorder in
   children
SO COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
LA English
DT Article
DE Autism spectrum disorder; Coexpression network; Drug repositioning;
   Knowledge graph; Natural language processing
ID CONNECTIVITY MAP; EXPRESSION; DYSFUNCTION; PACKAGE; BRAIN; DEFICIENCY;
   MODEL
AB Identification of exact causative genes is important for in silico drug repositioning based on drug-gene disease relationships. However, the complex polygenic etiology of the autism spectrum disorder (ASD) is a challenge in the identification of etiological genes. The network-based core gene identification method can effectively use the interactions between genes and accurately identify the pathogenic genes of ASD. We developed a novel network-based drug repositioning framework that contains three steps: network-specific core gene (NCG) identification, potential therapeutic drug repositioning, and candidate drug validation. First, through the analysis of transcriptome data for 178 brain tissues, gene network analysis identified 365 NCGs in 18 coexpression modules that were significantly correlated with ASD. Second, we evaluated two proposed drug repositioning methods. In one novel approach (dtGSEA), we used the NCGs to probe drug-gene interaction data and identified 35 candidate drugs. In another approach, we compared NCG expression patterns with drug-induced transcriptome data from the Connectivity Map database and found 46 candidate drugs. Third, we validated the candidate drugs using an in-house mental diseases and compounds knowledge graph (MCKG) that contained 7509 compounds, 505 mental diseases, and 123,890 edges. We found a total of 42 candidate drugs that were associated with mental illness, among which 10 drugs (baclofen, sulpiride, estradiol, entinostat, everolimus, fluvoxamine, curcumin, calcitriol, metronidazole, and zinc) were postulated to be associated with ASD. This study proposes a powerful network-based drug repositioning framework and also provides candidate drugs as well as potential drug targets for the subsequent development of ASD therapeutic drugs. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
C1 [Gao, Huan; Li, Dantong; Huang, Shuai; Liu, Guangjian; Liang, Huiying] Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Clin Data Ctr, Guangzhou 510080, Guangdong, Peoples R China.
   [Mo, Xueying; Lu, Long] Wuhan Univ, Sch Informat Management, Wuhan 430072, Hubei, Peoples R China.
   [Ni, Yuan; Zhang, Sheng] Ping An Technol, 20 Keji South 12 Rd, Shen Zhen 518063, Guangdong, Peoples R China.
   [Huang, Qingsheng; Tang, Yaping; Lu, Long] Guangzhou Med Univ, Guangzhou Women & Childrens Med Ctr, Inst Pediat, 9 Jinsui Rd, Guangzhou 510623, Guangdong, Peoples R China.
   [Teng, Shan] Southern Med Univ, Sch Publ Hlth, Dept Psychol, Guangzhou 510515, Guangdong, Peoples R China.
RP Liang, HY (通讯作者)，Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Clin Data Ctr, Guangzhou 510080, Guangdong, Peoples R China.; Lu, L (通讯作者)，Wuhan Univ, Sch Informat Management, Wuhan 430072, Hubei, Peoples R China.
EM bioinfo@gmail.com; lianghuiying@hotmail.com
FU Guangdong Key Project in ''Development of new tools for diagnosis and
   treatment of Autism" [2018B030335001]; National Natural Science
   Foundation of China [61772375, 18ZDA325]; Hubei Provincial Natural
   Science Foundation of China [2019CFA025]; National Key R&D Program of
   China [2019YFC010167]; Independent Research Project of School of
   Information Management Wuhan University [413100032]; Guangzhou Institute
   of Pediatrics/Guangzhou Women and Children's Medical Center
   [YIP-2019-026]
FX This work was supported by the Guangdong Key Project in ''Development of
   new tools for diagnosis and treatment of Autism"(2018B030335001). This
   research was partially funded by National Natural Science Foundation of
   China [No. 61772375, 18ZDA325]; Hubei Provincial Natural Science
   Foundation of China [No. 2019CFA025]; National Key R&D Program of China
   (2019YFC010167); and Independent Research Project of School of
   Information Management Wuhan University (No: 413100032). This work was
   supported by the Guangzhou Institute of Pediatrics/Guangzhou Women and
   Children's Medical Center (YIP-2019-026).
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NR 86
TC 1
Z9 1
U1 3
U2 7
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2001-0370
J9 COMPUT STRUCT BIOTEC
JI Comp. Struct. Biotechnol. J..
PY 2021
VL 19
BP 3908
EP 3921
DI 10.1016/j.csbj.2021.06.046
EA JUL 2021
PG 14
WC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology
GA UA0DY
UT WOS:000684837400013
PM 34306572
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Koho, M
   Ikkala, E
   Leskinen, P
   Tamper, M
   Tuominen, J
   Hyvonen, E
AF Koho, Mikko
   Ikkala, Esko
   Leskinen, Petri
   Tamper, Minna
   Tuominen, Jouni
   Hyvonen, Eero
TI WarSampo knowledge graph: Finland in the Second World War as Linked Open
   Data
SO SEMANTIC WEB
LA English
DT Article
DE Linked Open Data; semantic web; military history; World War II; Finland;
   cultural heritage; digital humanities
ID LINKING
AB The Second World War (WW2) is arguably the most devastating catastrophe of human history, a topic of great interest to not only researchers but the general public. However, data about the Second World War is heterogeneous and distributed in various organizations and countries making it hard to utilize. In order to create aggregated global views of the war, a shared ontology and data infrastructure is needed to harmonize information in various data silos. This makes it possible to share data between publishers and application developers, to support data analysis in Digital Humanities research, and to develop data-driven intelligent applications. As a first step towards these goals, this article presents the WarSampo knowledge graph (KG), a shared semantic infrastructure, and a Linked Open Data (LOD) service for publishing data about WW2, with a focus on Finnish military history. The shared semantic infrastructure is based on the idea of representing war as a spatio-temporal sequence of events that soldiers, military units, and other actors participate in. The used metadata schema is an extension of CIDOC CRM, supplemented by various military history domain ontologies. With an infrastructure containing shared ontologies, maintaining the interlinked data brings upon new challenges, as one change in an ontology can propagate across several datasets that use it. To support sustainability, a repeatable automatic data transformation and linking pipeline has been created for rebuilding the whole WarSampo KG from the individual source datasets. The WarSampo KG is hosted on a data service based on W3C Semantic Web standards and best practices, including content negotiation, SPARQL API, download, automatic documentation, and other services supporting the reuse of the data. The WarSampo KG, a part of the international LOD Cloud and totalling ca. 14 million triples, is in use in nine end-user application views of the WarSampo portal, which has had over 690 000 end users since its opening in 2015.
C1 [Koho, Mikko; Ikkala, Esko; Leskinen, Petri; Tamper, Minna; Tuominen, Jouni; Hyvonen, Eero] Aalto Univ, Dept Comp Sci, Semant Comp Res Grp SeCo, Espoo, Finland.
   [Koho, Mikko; Tamper, Minna; Tuominen, Jouni; Hyvonen, Eero] Univ Helsinki, HELDIG Helsinki Ctr Digital Humanities, Helsinki, Finland.
RP Koho, M (通讯作者)，Aalto Univ, Dept Comp Sci, Semant Comp Res Grp SeCo, Espoo, Finland.; Koho, M (通讯作者)，Univ Helsinki, HELDIG Helsinki Ctr Digital Humanities, Helsinki, Finland.
EM Mikko.Koho@aalto.fi; Esko.Ikkala@raalto.fi; Petri.Leskinen@aalto.fi;
   Minna.Tamper@aalto.fi; Jouni.Tuominen@helsinki.fi;
   Eero.Hyvonen@helsinki.fi
RI Hyvönen, Eero A/G-2252-2013; Tuominen, Jouni/E-9330-2019
OI Koho, Mikko/0000-0002-7373-9338; Tuominen, Jouni/0000-0003-4789-5676;
   Ikkala, Esko/0000-0002-9571-7260; Hyvonen, Eero
   Antero/0000-0003-1695-5840
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TC 4
Z9 4
U1 9
U2 27
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 2
SI SI
BP 265
EP 278
DI 10.3233/SW-200392
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QB1MB
UT WOS:000613905500007
DA 2022-08-23
ER

PT J
AU Ke, LX
   Lu, CC
   Shen, R
   Lu, TT
   Ma, B
   Hua, YP
AF Ke, Lixin
   Lu, Cuncun
   Shen, Rui
   Lu, Tingting
   Ma, Bin
   Hua, Yunpeng
TI Knowledge Mapping of Drug-Induced Liver Injury: A Scientometric
   Investigation (2010-2019)
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE scientometric; drug-induced liver injury; VOSviewer; CiteSpace;
   HLA-B*5701
ID CAUSALITY ASSESSMENT; BIBLIOMETRIC ANALYSIS; ADVERSE-REACTIONS; GLOBAL
   RESEARCH; FAILURE; OUTCOMES; SCIENCE; RISK; ACETAMINOPHEN; POPULATION
AB Background Drug-induced liver injury (DILI) is a common adverse event, which compromises the safety of numerous drugs, poses a significant risk to patient health, and enhances healthcare expenditures. Many articles have been recently published on DILI related research, though no relevant scientometric study has been published yet. This scientometric study was aimed at comprehensively analyzing the knowledge base and emerging topics on DILI. Methods The articles and reviews related to DILI, published from 2010 to 2019 in the Web of Science Core Collection (WoSCC), were retrieved on March 15, 2020, using relevant keywords. Four different scientometric software (HistCite, VOSviewer, CiteSpace, and R-bibliometrix) was used to conduct this scientometric study. Results A total of 1,995 publications were retrieved (including 1,550 articles and 445 reviews) from 592 academic journals with 56,273 co-cited references in 10 languages by 2,331 institutions from 79 countries/regions. The majority of publications (n= 727, 36.44%) were published in the United States, and the University of North Carolina contributed the most publications (n= 89, 4.46%). The most productive academic journal on DILI was theToxicological Sciences[n= 79, 3.96%; impact factor (IF) 2018 = 3.564], andHepatologywas the first co-cited journal (n= 7,383, IF 2018 = 14.971). Fontana RJ and Teschke R may have significant influence on DILI research, with more publications (n= 46;n= 39) and co-citations (n= 382;n= 945). Definition, incidence rate or clinical characteristics, etiology or pathogenesis (such as the character of the innate immune system, the regulation of cell-death pathways, and susceptible HLA-B*5701 genotype), identification of main drugs and causality assessment (criteria and methods) were the knowledge base for DILI research. Exploring the microscopic mechanism (such as the organelle dysfunction and cytotoxicity induced by drugs, and exploration of role of neutrophils in DILI using mouse models) and developed newer approaches to prevent DILI (such as the prospective HLA-B*5701 screening andin vitroapproaches for assessing the potential risk of candidate drugs for DILI) were the recent major topics for DILI research. Conclusion This scientometric study comprehensively reviewed the publications related to DILI during the past decade using quantitative and qualitative methods. This information would provide references for scholars, researching on DILI.
C1 [Ke, Lixin; Shen, Rui; Hua, Yunpeng] Sun Yat Sen Univ, Affiliated Hosp 1, Dept Liver Surg, Guangzhou, Peoples R China.
   [Lu, Cuncun; Lu, Tingting] Lanzhou Univ, Evidence Based Med Ctr, Sch Basic Med Sci, Lanzhou, Peoples R China.
   [Ma, Bin] Lanzhou Univ, Sch Basic Med Sci, Key Lab Preclin Study New Drug Gansu Prov, Lanzhou, Peoples R China.
RP Hua, YP (通讯作者)，Sun Yat Sen Univ, Affiliated Hosp 1, Dept Liver Surg, Guangzhou, Peoples R China.
EM hyp0427@163.com
RI Lu, cuncun/AAK-2842-2020; Ke, Lixin/AAT-8963-2021
OI Lu, cuncun/0000-0001-9541-9733; Hua, Yunpeng/0000-0003-4814-9392
FU Science and Technology Project of Guangzhou city [201707010387]
FX This study was supported by grants from the Science and Technology
   Project of Guangzhou city (no. 201707010387).
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NR 56
TC 34
Z9 34
U1 27
U2 64
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1663-9812
J9 FRONT PHARMACOL
JI Front. Pharmacol.
PD JUN 5
PY 2020
VL 11
AR 842
DI 10.3389/fphar.2020.00842
PG 14
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA MH8MZ
UT WOS:000546977700001
PM 32581801
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chinnathai, MK
   Alkan, B
   Harrison, R
AF Chinnathai, Malarvizhi Kaniappan
   Alkan, Bugra
   Harrison, Robert
TI A novel data-driven approach to support decision-making during
   production scale-up of assembly systems
SO JOURNAL OF MANUFACTURING SYSTEMS
LA English
DT Article
DE Manufacturing systems; Production planning; Scale-up; Demand
   amplification; Demand uncertainty; Data-driven method; Discrete-Event
   Simulation; DES; Multi-objective optimisation; Evolutionary optimisation
   algorithm; Genetic Algorithm; GA; Kinematic modelling
ID PRODUCTION RAMP-UP; OF-THE-ART; START-UP; DESIGN; FRAMEWORK;
   SCALABILITY; SIMULATION; OPERATION; PILOT; TECHNOLOGIES
AB In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scaleup can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.
C1 [Chinnathai, Malarvizhi Kaniappan; Harrison, Robert] Univ Warwick, Warwick Mfg Grp, Coventry CV4 7AL, W Midlands, England.
   [Alkan, Bugra] London South Bank Univ, Sch Engn, London SE1 0AA, England.
RP Chinnathai, MK (通讯作者)，Univ Warwick, Warwick Mfg Grp, Coventry CV4 7AL, W Midlands, England.
EM M.Kaniappan-Chinnathai.1@warwick.ac.uk
RI Kaniappan Chinnathai, Malarvizhi/AAX-1581-2021; Alkan, Bugra/K-7539-2019
OI Kaniappan Chinnathai, Malarvizhi/0000-0001-7044-3120; Alkan,
   Bugra/0000-0002-5994-4351
FU project AMPLiFII 2 [InnUK/104175/1]
FX The authors would like to thank Arun Gopal, Kaniappan Periakaruppa,
   Mus'ab Ahmad, Hala Abdelaziz, Rohin Titmarsh and Carlo Ferrifor
   evaluating the industrial benefits and reviewing the DDSM methodology.
   The presented work was conducted as part of research studies through the
   project AMPLiFII 2 (InnUK/104175/1) . The primary objective of the
   research is to provide a decision support framework to scaleup the
   battery module assembly facility present at WMG, University of Warwick.
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NR 71
TC 1
Z9 1
U1 3
U2 8
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0278-6125
EI 1878-6642
J9 J MANUF SYST
JI J. Manuf. Syst.
PD APR
PY 2021
VL 59
BP 577
EP 595
DI 10.1016/j.jmsy.2021.03.018
EA APR 2021
PG 19
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA SI3HQ
UT WOS:000654716600003
DA 2022-08-23
ER

PT J
AU Bhat, V
   Yadav, A
   Yadav, S
   Chandrasekaran, D
   Mago, V
AF Bhat, Vaibhav
   Yadav, Anita
   Yadav, Sonal
   Chandrasekaran, Dhivya
   Mago, Vijay
TI AdCOFE: Advanced Contextual Feature Extraction in conversations for
   emotion classification
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Emotion recognition; Chatbots; Dyadic conversation
AB Emotion recognition in conversations is an important step in various virtual chatbots which require opinion-based feedback, like in social media threads, online support, and many more applications. Current emotion recognition in conversations models face issues like: (a) loss of contextual information in between two dialogues of a conversation, (b) failure to give appropriate importance to significant tokens in each utterance, (c) inability to pass on the emotional information from previous utterances. The proposed model of Advanced Contextual Feature Extraction (AdCOFE) addresses these issues by performing unique feature extraction using knowledge graphs, sentiment lexicons and phrases of natural language at all levels (word and position embedding) of the utterances. Experiments on emotion recognition in conversations datasets show that AdCOFE is beneficial in capturing emotions in conversations.
C1 [Bhat, Vaibhav; Yadav, Anita; Yadav, Sonal; Chandrasekaran, Dhivya; Mago, Vijay] Lakehead Univ, Dept Comp Sci, Thunder Bay, ON, Canada.
RP Chandrasekaran, D (通讯作者)，Lakehead Univ, Dept Comp Sci, Thunder Bay, ON, Canada.
EM dchandra@lakeheadu.ca
FU NSERC [RGPIN-2017-05377]
FX Publication costs are funded by a NSERC Discovery Grant
   (RGPIN-2017-05377), held by Dr. Vijay Mago. The funders had no role in
   study design, data collection and analysis, decision to publish, or
   preparation of the manuscript.
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NR 28
TC 0
Z9 0
U1 7
U2 7
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 DEC 9
PY 2021
VL 7
AR e786
DI 10.7717/peerj-cs.786
PG 18
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XQ1GB
UT WOS:000731300400002
PM 34977351
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Liu, S
   Yang, H
   Li, JY
   Kolmanic, S
AF Liu, Shuang
   Yang, Hui
   Li, Jiayi
   Kolmanic, Simon
TI Chinese Named Entity Recognition Method in History and Culture Field
   Based on BERT
SO INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
LA English
DT Article
DE History and culture; Named entity recognition; Bert pre-training model;
   Bidirectional long short-term memory network
AB With rapid development of the Internet, people have undergone tremendous changes in the way they obtain information. In recent years, knowledge graph is becoming a popular tool for the public to acquire knowledge. For knowledge graph of Chinese history and culture, most researchers adopted traditional named entity recognition methods to extract entity information from unstructured historical text data. However, the traditional named entity recognition method has certain defects, and it is easy to ignore the association between entities. To extract entities from a large amount of historical and cultural information more accurately and efficiently, this paper proposes one named entity recognition model combining Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF). First, a BERT pre-trained language model is used to encode a single character to obtain a vector representation corresponding to each character. Then one Bidirectional Long Short-Term Memory (BiLSTM) layer is applied to semantically encode the input text. Finally, the label with the highest probability is output through the Conditional Random Field (CRF) layer to obtain each character's category. This model uses the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model to replace the static word vectors trained in the traditional way. In comparison, the BERT pre-trained language model can dynamically generate semantic vectors according to the context of words, which improves the representation ability of word vectors. The experimental results prove that the model proposed in this paper has achieved excellent results in the task of named entity recognition in the field of historical culture. Compared with the existing named entity identification methods, the precision rate, recall rate, and F-1 value have been significantly improved.
C1 [Liu, Shuang; Li, Jiayi] 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.
   [Kolmanic, Simon] Univ Maribor, Fac Elect Engn & Comp Sci, Koroska Cesta 46, Maribor 2000, Slovenia.
RP Yang, H (通讯作者)，Nanjing Inst Tourism & Hospitality, Sch Hotel Management, Nanjing 211100, Peoples R China.
EM liushuang@dlnu.edu.cn; yanghuimail2020@gmail.com; ygrace788@gmail.com;
   simon.kolmanic@gmail.com
FU Economic and social development research project of Liaoning province in
   2021 [2021lslybkt-022]; Jiangsu Provincial Social Science Applied
   Research Excellence Project [21SYB138]; Research Innovation Team Grant
   Project [2021KYTD04]
FX Thank all reviewers and editors for their helpful suggestions for the
   improvement of this paper. Thanks to Economic and social development
   research project of Liaoning province in 2021 (No. 2021lslybkt-022) for
   supporting this article.Thanks to Jiangsu Provincial Social Science
   Applied Research Excellence Project (No. 21SYB138); Research Innovation
   Team Grant Project (No. 2021KYTD04) for funding this article.
CR Ajees AP, 2019, INFORMATION, V10, DOI 10.3390/info10060186
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NR 29
TC 0
Z9 0
U1 7
U2 7
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 22
PY 2021
VL 14
IS 1
AR 163
DI 10.1007/s44196-021-00019-8
PG 10
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0G9YN
UT WOS:000778398000001
OA gold
DA 2022-08-23
ER

PT J
AU McCoy, K
   Gudapati, S
   He, L
   Horlander, E
   Kartchner, D
   Kulkarni, S
   Mehra, N
   Prakash, J
   Thenot, H
   Vanga, SV
   Wagner, A
   White, B
   Mitchell, CS
AF McCoy, Kevin
   Gudapati, Sateesh
   He, Lawrence
   Horlander, Elaina
   Kartchner, David
   Kulkarni, Soham
   Mehra, Nidhi
   Prakash, Jayant
   Thenot, Helena
   Vanga, Sri Vivek
   Wagner, Abigail
   White, Brandon
   Mitchell, Cassie S.
TI Biomedical Text Link Prediction for Drug Discovery: A Case Study with
   COVID-19
SO PHARMACEUTICS
LA English
DT Article
DE COVID-19; SARS-CoV-2; repurposed drugs; coronavirus; natural language
   processing; text mining; machine learning; literature review
AB Link prediction in artificial intelligence is used to identify missing links or derive future relationships that can occur in complex networks. A link prediction model was developed using the complex heterogeneous biomedical knowledge graph, SemNet, to predict missing links in biomedical literature for drug discovery. A web application visualized knowledge graph embeddings and link prediction results using TransE, CompleX, and RotatE based methods. The link prediction model achieved up to 0.44 hits@10 on the entity prediction tasks. The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, served as a case study to demonstrate the efficacy of link prediction modeling for drug discovery. The link prediction algorithm guided identification and ranking of repurposed drug candidates for SARS-CoV-2 primarily by text mining biomedical literature from previous coronaviruses, including SARS and middle east respiratory syndrome (MERS). Repurposed drugs included potential primary SARS-CoV-2 treatment, adjunctive therapies, or therapeutics to treat side effects. The link prediction accuracy for nodes ranked highly for SARS coronavirus was 0.875 as calculated by human in the loop validation on existing COVID-19 specific data sets. Drug classes predicted as highly ranked include anti-inflammatory, nucleoside analogs, protease inhibitors, antimalarials, envelope proteins, and glycoproteins. Examples of highly ranked predicted links to SARS-CoV-2: human leukocyte interferon, recombinant interferon-gamma, cyclosporine, antiviral therapy, zidovudine, chloroquine, vaccination, methotrexate, artemisinin, alkaloids, glycyrrhizic acid, quinine, flavonoids, amprenavir, suramin, complement system proteins, fluoroquinolones, bone marrow transplantation, albuterol, ciprofloxacin, quinolone antibacterial agents, and hydroxymethylglutaryl-CoA reductase inhibitors. Approximately 40% of identified drugs were not previously connected to SARS, such as edetic acid or biotin. In summary, link prediction can effectively suggest repurposed drugs for emergent diseases.
C1 [McCoy, Kevin; Gudapati, Sateesh; He, Lawrence; Horlander, Elaina; Kartchner, David; Kulkarni, Soham; Mehra, Nidhi; Prakash, Jayant; Thenot, Helena; Vanga, Sri Vivek; Wagner, Abigail; White, Brandon; Mitchell, Cassie S.] Georgia Inst Technol, Lab Pathol Dynam, Biomed Engn, Atlanta, GA 30332 USA.
   [McCoy, Kevin; Gudapati, Sateesh; He, Lawrence; Horlander, Elaina; Kartchner, David; Kulkarni, Soham; Mehra, Nidhi; Prakash, Jayant; Thenot, Helena; Vanga, Sri Vivek; Wagner, Abigail; White, Brandon; Mitchell, Cassie S.] Emory Univ, Atlanta, GA 30332 USA.
   [Gudapati, Sateesh; Prakash, Jayant] Georgia Inst Technol, Comp Sci, Atlanta, GA 30332 USA.
   [Kartchner, David; Vanga, Sri Vivek] Georgia Inst Technol, Comp Sci & Engn, Atlanta, GA 30332 USA.
   [Kulkarni, Soham] Georgia Inst Technol, Biochem, Atlanta, GA 30332 USA.
   [Mitchell, Cassie S.] Georgia Inst Technol, Inst Machine Learning, Atlanta, GA 30332 USA.
RP Mitchell, CS (通讯作者)，Georgia Inst Technol, Lab Pathol Dynam, Biomed Engn, Atlanta, GA 30332 USA.; Mitchell, CS (通讯作者)，Emory Univ, Atlanta, GA 30332 USA.; Mitchell, CS (通讯作者)，Georgia Inst Technol, Inst Machine Learning, Atlanta, GA 30332 USA.
EM kmccoy8@gatech.edu; sgudapati3@gatech.edu; lhe80@gatech.edu;
   ehorlander3@gatech.edu; david.kartchner@gatech.edu;
   skulkarni302@gatech.edu; nmehra3@gatech.edu; prakash1.jayant@gmail.com;
   hthenot3@gatech.edu; svanga3@gatech.edu; abbywagner@gatech.edu;
   brandonleewhitejr@gatech.edu; cassie.mitchell@bme.gatech.edu
RI McCoy, Kevin/AAR-2526-2021
OI McCoy, Kevin/0000-0002-3570-6826; Kulkarni, Soham/0000-0003-0546-7980;
   White, Brandon/0000-0002-8070-9778; He, Lawrence/0000-0003-0585-8762
FU Whitaker Biomedical Engineering Seed Grant for COVID-19, National
   Science Foundation CAREER grant [1944247]; National Institute of Health
   [R21-CA232249]; Alzheimer's Association Research Grant Award
   [2018-AARGD-591014]; Children's Hospital of Atlanta Aflac Pilot Grant;
   Georgia Institute of Technology President's Undergraduate Research
   Awards
FX Whitaker Biomedical Engineering Seed Grant for COVID-19, National
   Science Foundation CAREER grant 1944247, National Institute of Health
   grant R21-CA232249, Alzheimer's Association Research Grant Award
   2018-AARGD-591014, Children's Hospital of Atlanta Aflac Pilot Grant
   Award to CM, and Georgia Institute of Technology President's
   Undergraduate Research Awards to N.M. and B.W.
CR Abadi Martin, 2016, arXiv
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NR 53
TC 5
Z9 5
U1 5
U2 18
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1999-4923
J9 PHARMACEUTICS
JI Pharmaceutics
PD JUN
PY 2021
VL 13
IS 6
AR 794
DI 10.3390/pharmaceutics13060794
PG 22
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA SY5LI
UT WOS:000665928900001
PM 34073456
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Fan, M
   Zhou, Q
   Abel, A
   Zheng, TF
   Grishman, R
AF Fan, Miao
   Zhou, Qiang
   Abel, Andrew
   Zheng, Thomas Fang
   Grishman, Ralph
TI Probabilistic Belief Embedding for Large-Scale Knowledge Population
SO COGNITIVE COMPUTATION
LA English
DT Article
DE Knowledge population; Belief embedding; Entity inference; Relation
   prediction; Triplet classification
AB To populate knowledge repositories, such as WordNet, Freebase and NELL, two branches of research have grown separately for decades. On the one hand, corpus-based methods which leverage unstructured free texts have been explored for years; on the other hand, some recently emerged embedding-based approaches use structured knowledge graphs to learn distributed representations of entities and relations. But there are still few comprehensive and elegant models that can integrate those large-scale heterogeneous resources to satisfy multiple subtasks of knowledge population including entity inference, relation prediction and triplet classification.
   This paper contributes a novel embedding model which estimates the probability of each candidate belief < h,r,t,m > in a large-scale knowledge repository via simultaneously learning distributed representations for entities (h and t), relations (r) and the words in relation mentions (m). It facilitates knowledge population by means of simple vector operations to discover new beliefs. Given an imperfect belief, we can not only infer the missing entities and predict the unknown relations, but also identify the plausibility of the belief, just by leveraging the learned embeddings of remaining evidence.
   To demonstrate the scalability and the effectiveness of our model, experiments have been conducted on several large-scale repositories which contain millions of beliefs from WordNet, Freebase and NELL, and the results are compared with other cutting-edge approaches via comparing the performance assessed by the tasks of entity inference, relation prediction and triplet classification with their respective metrics. Extensive experimental results show that the proposed model outperforms the state of the arts with significant improvements.
   The essence of the improvements comes from the capability of our model that encodes not only structured knowledge graph information, but also unstructured relation mentions, into continuous vector spaces, so that we can bridge the gap of one-hot representations, and expect to discover certain relevance among entities, relations and even words in relation mentions.
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.
   [Abel, Andrew] Univ Stirling, Sch Nat Sci, Comp Sci & Math, Room 4B59,Cottrell Bldg, Stirling FK9 4LA, Scotland.
   [Grishman, Ralph] NYU, Courant Inst Math Sci, Dept Comp Sci, 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; zq-lxd@mail.tsinghua.edu.cn;
   aka@cs.stir.ac.uk; fzheng@tsinghua.edu.cn; grishman@cs.nyu.edu
RI Abel, Andrew/H-6337-2019
FU National Program on Key Basic Research Project (973 Program)
   [2013CB329304]; National Science Foundation of China (NSFC) [61433018,
   61373075]; Chinese Scholarship Council
FX The paper is dedicated to all the members of CSLT
   (http://cslt.riit.tsinghua.edu.cn/) and Proteus Group
   (http://nlp.cs.nyu.edu/index.shtml). It was supported by National
   Program on Key Basic Research Project (973 Program) Under Grant
   2013CB329304, National Science Foundation of China (NSFC) Under Grant
   Nos. 61433018 and 61373075, and Chinese Scholarship Council, when the
   first author was a joint-supervision Ph.D. candidate of Tsinghua
   University and New York University.
CR Bollacker K., 2008, P 2008 ACM SIGMOD IN, P1247
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NR 37
TC 5
Z9 5
U1 0
U2 7
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1866-9956
EI 1866-9964
J9 COGN COMPUT
JI Cogn. Comput.
PD DEC
PY 2016
VL 8
IS 6
BP 1087
EP 1102
DI 10.1007/s12559-016-9425-5
PG 16
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA EE0XW
UT WOS:000389304300007
DA 2022-08-23
ER

PT J
AU Li, H
AF Li, Han
TI Piano Teaching Knowledge Graph Construction Based on Cross-Media Data
   Analysis and Semantic Network
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID SOCIAL MEDIA
AB With the rapid development of information technology and mobile Internet, digital image, text, audio, video, and other cross-media data are growing explosively, which has changed people's way of life and work. In view of the issues of negative studying effectivity and challenging attention of college students in the modern-day piano instructing process, this paper puts forward the application of knowledge Atlas technology in piano teaching and constructs a multimodal knowledge Atlas of piano teaching based on deep neural network, so as to make piano teaching more intelligent and improve students' learning efficiency and learning interest. How to realize the semantic association understanding of cross-media data is the core problem of cross-media semantic analysis. First, this paper introduces the basic rules of ontology construction and the basic method of establishing general knowledge graph are introduced. Then, taking the piano teaching content as an example, natural language sentences can be expressed and stored with cross-media data using semantic network. The mathematical understanding is extracted in accordance to the herbal language processing technology, and the entities are fused in accordance to the frequent semantic similarity detection between extraordinary entities, so as to decrease the redundancy and repetition fee of entities and the complexity of the graph. The fused new knowledge is processed according to the quality evaluation rules, the qualified part is added to the knowledge base, and then the above steps are iterated to update the database. The great overall performance of piano instructing understanding graph mannequin primarily based on semantic network is validated through experiments.
C1 [Li, Han] Heze Univ, Mus & Dance Coll, Heze, Shandong, Peoples R China.
RP Li, H (通讯作者)，Heze Univ, Mus & Dance Coll, Heze, Shandong, Peoples R China.
EM rkh@key.edu.pl
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NR 27
TC 0
Z9 0
U1 4
U2 4
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD JUN 16
PY 2022
VL 2022
AR 5499593
DI 10.1155/2022/5499593
PG 11
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 2M5SW
UT WOS:000817760300011
PM 35755737
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Mu, SL
   Li, YL
   Zhao, WX
   Li, SQ
   Wen, JR
AF Mu, Shanlei
   Li, Yaliang
   Zhao, Wayne Xin
   Li, Siqing
   Wen, Ji-Rong
TI Knowledge-Guided Disentangled Representation Learning for Recommender
   Systems
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph; recommender system; representation learning;
   disentangled representation
AB In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue.
   In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel Knowledge-guided Disentangled Representations approach (KDR) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.
C1 [Mu, Shanlei] Renmin Univ China, Sch Informat, Beijing Key Lab Big Data Management & Anal Method, Beijing 100872, Peoples R China.
   [Li, Yaliang] Alibaba Grp, Bellevue, WA USA.
   [Zhao, Wayne Xin; Wen, Ji-Rong] Renmin Univ China, Gaoling Sch Artificial Intelligence, Beijing Key Lab Big Data Management & Anal Method, Beijing 100872, Peoples R China.
   [Li, Siqing] Renmin Univ China, Beijing 100872, Peoples R China.
RP Zhao, WX (通讯作者)，Renmin Univ China, Gaoling Sch Artificial Intelligence, Beijing Key Lab Big Data Management & Anal Method, Beijing 100872, Peoples R China.
EM slmu@ruc.edu.cn; yaliang.li@alibaba-inc.com; batmanfly@gmail.com;
   lisiqing@ruc.edu.cn; jirong.wen@gmail.com
FU National Natural Science Foundation of China [61872369, 61832017];
   Beijing Academy of Artificial Intelligence (BAAI) [BAAI2020ZJ0301];
   Beijing Outstanding Young Scientist Program [BJJWZYJH012019100020098];
   Fundamental Research Funds for the Central Universities; Renmin
   University of China [18XNLG22, 19XNQ047]; Alibaba Group through Alibaba
   Innovative Research Program; Alibaba Research Intern Program
FX This work was partially supported by the National Natural Science
   Foundation of China under Grant No. 61872369 and 61832017, Beijing
   Academy of Artificial Intelligence (BAAI) under Grant No.
   BAAI2020ZJ0301, Beijing Outstanding Young Scientist Program under Grant
   No. BJJWZYJH012019100020098, the Fundamental Research Funds for the
   Central Universities, and the Research Funds of Renmin University of
   China under Grant No.18XNLG22 and 19XNQ047. This work was also supported
   by Alibaba Group through Alibaba Innovative Research Program and Alibaba
   Research Intern Program.
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NR 69
TC 0
Z9 0
U1 2
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1046-8188
EI 1558-2868
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
PD JAN
PY 2022
VL 40
IS 1
AR 6
DI 10.1145/3464304
PG 26
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZV7AR
UT WOS:000770678900006
DA 2022-08-23
ER

PT J
AU Chen, YJ
   Ma, TF
   Yang, XX
   Wang, JM
   Song, BS
   Zeng, XX
AF Chen, Yujie
   Ma, Tengfei
   Yang, Xixi
   Wang, Jianmin
   Song, Bosheng
   Zeng, Xiangxiang
TI MUFFIN: multi-scale feature fusion for drug-drug interaction prediction
SO BIOINFORMATICS
LA English
DT Article
AB Motivation: Adverse drug-drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients and the society. Existing traditional machine learning models rely heavily on handcraft features and lack generalization. Recently, the deep learning approaches that can automatically learn drug features from the molecular graph or drug-related network have improved the ability of computational models to predict unknown DDIs. However, previous works utilized large labeled data and merely considered the structure or sequence information of drugs without considering the relations or topological information between drug and other biomedical objects (e.g. gene, disease and pathway), or considered knowledge graph (KG) without considering the information from the drug molecular structure.
   Results: Accordingly, to effectively explore the joint effect of drug molecular structure and semantic information of drugs in knowledge graph for DDI prediction, we propose a multi-scale feature fusion deep learning model named MUFFIN. MUFFIN can jointly learn the drug representation based on both the drug-self structure information and the KG with rich bio-medical information. In MUFFIN, we designed a bi-level cross strategy that includes cross- and scalar-level components to fuse multi-modal features well. MUFFIN can alleviate the restriction of limited labeled data on deep learning models by crossing the features learned from large-scale KG and drug molecular graph. We evaluated our approach on three datasets and three different tasks including binary-class, multi-class and multi-label DDI prediction tasks. The results showed that MUFFIN outperformed other state-of-the-art baselines.
C1 [Chen, Yujie; Ma, Tengfei; Yang, Xixi; Wang, Jianmin; Song, Bosheng; Zeng, Xiangxiang] Hunan Univ, Sch Comp Sci & Engn, Changsha 410012, Peoples R China.
RP Song, BS; Zeng, XX (通讯作者)，Hunan Univ, Sch Comp Sci & Engn, Changsha 410012, Peoples R China.
EM boshengsong@hnu.edu.cn; xzeng@hnu.edu.cn
RI ; , xiangxiang/H-3771-2014
OI Song, Bosheng/0000-0002-1479-5399; , xiangxiang/0000-0003-1081-7658;
   Wang, Jianmin/0000-0001-8910-0929
FU National Natural Science Foundation of China [61872309, 61972138];
   Fundamental Research Funds for the Central Universities [531118010355];
   Hunan Provincial Natural Science Foundation of China [2020JJ4215]
FX The work was supported in part by National Natural Science Foundation of
   China [61872309, 61972138], in part by the Fundamental Research Funds
   for the Central Universities [531118010355] and in part by Hunan
   Provincial Natural Science Foundation of China [2020JJ4215].
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NR 38
TC 23
Z9 23
U1 15
U2 32
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD SEP 1
PY 2021
VL 37
IS 17
BP 2651
EP 2658
DI 10.1093/bioinformatics/btab169
EA MAR 2021
PG 8
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA US4BT
UT WOS:000697377500017
PM 33720331
DA 2022-08-23
ER

PT J
AU Nanduri, J
   Jia, YT
   Oka, A
   Beaver, J
   Liu, YW
AF Nanduri, Jay
   Jia, Yuting
   Oka, Anand
   Beaver, John
   Liu, Yung-Wen
TI Microsoft Uses Machine Learning and Optimization to Reduce E-Commerce
   Fraud
SO INFORMS JOURNAL ON APPLIED ANALYTICS
LA English
DT Article
DE e-commerce fraud; fraud protection; knowledge graph; machine learning;
   dynamic prospective control; dynamic programming; multiple-party
   decisions
AB Many merchants conduct their businesses through e-commerce. One major challenge in tackling e-commerce fraud results from dynamic fraud patterns, which can degrade the detection power of risk models and can lead to them failing to detect fraud that has emerging unrecognized patterns. The problem is further exacerbated by the conventional decision frameworks that ignore the follow-up decisions made by other associated parties (e.g., payment-instrument-issuing banks and manual review agents). Microsoft developed a new fraud-management system (FMS) that effectively tackles these two challenges. It keeps features used by the machine learning (ML) risk models up to date by using real-time archiving, dynamic risk tables, and knowledge graphs. The FMS uses customized long-term and short-term sequential ML models to detect both historical and emerging fraud patterns. It also makes rapid real-time optimal decisions using a dynamic programming approach to optimize the long-term profit by taking into account the aforementioned multiple-party decisions. After implementing these innovations over a two-year period (2016-2018), Microsoft reduced its fraud loss by 0.52%, thus generating $75 million in additional savings; reduced the incorrect fraud rejection rate by 1.38%; and improved its bank authorization rate by 7.7 percentage points. The result was many millions of dollars in additional revenue. These innovations simultaneously prevent fraud and increase bank acceptance. In April 2019, Microsoft launched Microsoft Dynamics 365 Fraud Protection, a cloud-based service available for all e-commerce merchants.
C1 [Nanduri, Jay; Jia, Yuting; Oka, Anand; Beaver, John; Liu, Yung-Wen] Microsoft Corp, Dynam 365 Fraud Protect, Redmond, WA 98052 USA.
RP Nanduri, J (通讯作者)，Microsoft Corp, Dynam 365 Fraud Protect, Redmond, WA 98052 USA.
EM jay.nanduri@microsoft.com; yutjia@microsoft.com; anoka@microsoft.com;
   johnbea@microsoft.com; yungliu@microsoft.com
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PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 2644-0865
EI 2644-0873
J9 INFORMS J APPL ANAL
JI INFORMS J. Appl. Anal.
PD JAN-FEB
PY 2020
VL 50
IS 1
BP 64
EP 79
DI 10.1287/inte.2019.1017
PG 16
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA KF8EC
UT WOS:000509469100007
DA 2022-08-23
ER

PT J
AU Jing, FS
   Ren, H
   Cheng, WB
   Wang, X
   Zhang, QP
AF Jing, Fengshi
   Ren, Hao
   Cheng, Weibin
   Wang, Xin
   Zhang, Qingpeng
TI Knowledge-enhanced attentive learning for answer selection in community
   question answering systems
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge discovery; Answer selection; Recommender systems; Community
   question answering; Knowledge graph embedding
AB In a community question-answering (CQA) system, the answer selection task is used to identify the best answer for a specific question. This plays a key role in improving service quality by recommending appropriate answers to new questions. Recent advances in CQA answer selection have focused on enhancing performance by incorporating community information, and particularly the expertise (previous answers) and authority (position in the social network) of a respondent. However, existing approaches to incorporating this information are limited, as they (a) consider either the expertise or the authority, but not both; (b) ignore domain knowledge that could differentiate between the topics of previous answers; or (c) simply use authority information to adjust the similarity score, rather than fully integrating it into the process of measuring the similarity between the question and answer segments. We propose a new approach called the knowledge-enhanced attentive answer selection (KAAS) model, which enhances performance by (a) considering both the expertise and the authority of the answerer; (b) utilizing human-labeled tags, a taxonomy of tags, and votes as domain knowledge to infer the expertise of the respondent; (c) using a matrix decomposition of the social network (based on 'following' relationships) to infer the authority of the respondent and incorporating this information into the process of evaluating the similarity between segments. In addition, we incorporate an external knowledge graph to capture more professional information for CQA systems for vertical communities. We also adopt an attention mechanism to integrate our analysis of both questions and answers texts and the aforementioned community information. Experiments with both vertical and general CQA sites demonstrate the superior performance of the proposed KAAS model. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Jing, Fengshi; Ren, Hao; Cheng, Weibin] Guangdong Second Prov Gen Hosp, Inst Healthcare Artificial Intelligence, Guangzhou 510317, Peoples R China.
   [Jing, Fengshi; Cheng, Weibin; Zhang, Qingpeng] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China.
   [Jing, Fengshi] Univ North Carolina Chapel Hill, Sch Med, UNC Project China, UNC Global, China, NC USA.
   [Wang, Xin] Tianjin Univ, Coll Intelligence & Comp, Sch Artificial Intelligence, Tianjin, Peoples R China.
   [Wang, Xin] Tianjin Key Lab Cognit Comp & Applicat, Tianjin, Peoples R China.
RP Zhang, QP (通讯作者)，City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China.
EM qingpeng.zhang@cityu.edu.hk
RI Zhang, Qingpeng/D-4682-2011; Cheng, Weibin/C-9441-2018
OI Zhang, Qingpeng/0000-0002-6819-0686; Cheng, Weibin/0000-0002-9845-6676
FU National Key Research and Development Program of China, Ministry of
   Science and Technology of China [2019YFE0198600]; Innovation and
   Technology Fund of Innovation and Technology Commission of Hong Kong
   [MHP/081/19]; Doctoral Workstation Foundation of Guangdong Second
   Provincial General Hospital
FX This work is supported by National Key Research and Development Program
   of China, Ministry of Science and Technology of China: 2019YFE0198600;
   by Innovation and Technology Fund of Innovation and Technology
   Commission of Hong Kong: MHP/081/19; and by Doctoral Workstation
   Foundation of Guangdong Second Provincial General Hospital.
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NR 60
TC 0
Z9 0
U1 4
U2 4
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 109117
DI 10.1016/j.knosys.2022.109117
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2D1SE
UT WOS:000811334800003
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Timon-Reina, S
   Rincon, M
   Martinez-Tomas, R
AF Timon-Reina, Santiago
   Rincon, Mariano
   Martinez-Tomas, Rafael
TI An overview of graph databases and their applications in the biomedical
   domain
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Review
ID MOLECULAR-BIOLOGY; KNOWLEDGE GRAPHS; NETWORK; ONTOLOGIES; MODEL; GENE;
   REPRESENTATION; PERFORMANCE; CYTOSCAPE; BENCHMARK
AB Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.
C1 [Timon-Reina, Santiago; Rincon, Mariano; Martinez-Tomas, Rafael] Univ Nacl Educ Distancia UNED, Dept Inteligencia Artificial, C Juan del Rosal 16,Ciudad Univ, Madrid 28040, Spain.
RP Timon-Reina, S (通讯作者)，Univ Nacl Educ Distancia UNED, Dept Inteligencia Artificial, C Juan del Rosal 16,Ciudad Univ, Madrid 28040, Spain.
EM santiagotimon@dia.uned.es
RI rincón, mariano/N-2420-2014; Martinez-Tomas, Rafael/G-4403-2014
OI rincón, mariano/0000-0002-0138-4662; Timon Reina,
   Santiago/0000-0003-3652-1820; Martinez-Tomas, Rafael/0000-0003-4706-7168
FU Norwegian Research Council (Dementia Disease Initiation) [217780]; Helse
   Sor-ost, NASATS (Dementia Disease Initiation) [2013131]; State Research
   Program Oriented to the Challenges of Society [PID2019-110686RB-I00]
FX This work was supported by the Norwegian Research Council (Dementia
   Disease Initiation) [grant number 217780]; Helse Sor-ost, NASATS
   (Dementia Disease Initiation) under [grant number 2013131]; Research
   project PID2019-110686RB-I00 of the State Research Program Oriented to
   the Challenges of Society.
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NR 143
TC 3
Z9 3
U1 9
U2 9
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD MAY 18
PY 2021
AR baab026
DI 10.1093/database/baab026
PG 22
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA SL2KK
UT WOS:000656748700001
PM 34003247
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Li, PL
   Cheng, K
   Jiang, PY
   Katchasuwanmanee, K
AF Li, Pulin
   Cheng, Kai
   Jiang, Pingyu
   Katchasuwanmanee, Kanet
TI Investigation on industrial dataspace for advanced machining workshops:
   enabling machining operations control with domain knowledge and
   application case studies
SO JOURNAL OF INTELLIGENT MANUFACTURING
LA English
DT Article
DE Industrial dataspace; Machining knowledge; Machining operations control;
   Knowledge representation; Knowledge graph
ID CYBER-PHYSICAL SYSTEMS; BIG DATA; DIGITAL TWIN; VIRTUALIZATION;
   ARCHITECTURE; TECHNOLOGY
AB The machining processes on the advanced machining workshop floor are becoming more sophisticated with the interdependent intrinsic processes, generation of ever-increasing in-process data and machining domain knowledge. To manage and utilize those above effectively, an industrial dataspace for machining workshop (IDMW) is presented with a three-layer framework. The IDMW architecture isSchema Centralized-Data Distributed, which relies on Process-Workpiece-Centric knowledge schema description and data storage in decentralized data silos. Subsequently, the pre-processing method for the data silos driven by RFID event graphical deduction model is elaborated to associate decentralized data with knowledge schema. Furthermore, through two industrial case studies, it is found that IDMW is effective in managing heterogeneous data, interconnecting the resource entities, handling domain knowledge, and thereby enabling machining operations control on the machining workshop floor particularly.
C1 [Li, Pulin; Jiang, Pingyu] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian, Shaanxi, Peoples R China.
   [Cheng, Kai; Katchasuwanmanee, Kanet] Brunel Univ London, Coll Engn Design & Phys Sci, Uxbridge UB8 3PH, Middx, England.
RP Jiang, PY (通讯作者)，Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian, Shaanxi, Peoples R China.
EM pjiang@mail.xjtu.edu.cn
RI Cheng, Kai/AGD-2868-2022
OI Cheng, Kai/0000-0001-6872-9736
FU Natural Science Foundation of China [51975464, 71571142]; China
   Scholarships Council (CSC); Brunel University London
FX The research work is under the financial supports of the Natural Science
   Foundation of China with Grant Nos. 51975464 and 71571142. Thanks are
   also extended to the China Scholarships Council (CSC) and Brunel
   University London for hosting the academic scholarship.
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NR 52
TC 10
Z9 10
U1 7
U2 34
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0956-5515
EI 1572-8145
J9 J INTELL MANUF
JI J. Intell. Manuf.
PD JAN
PY 2022
VL 33
IS 1
BP 103
EP 119
DI 10.1007/s10845-020-01646-2
EA AUG 2020
PG 17
WC Computer Science, Artificial Intelligence; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA YC1KT
UT WOS:000563730000002
OA Green Published
DA 2022-08-23
ER

PT J
AU Mohri, T
   Yugami, N
   Okamoto, S
AF Mohri, Takao
   Yugami, Nobuhiro
   Okamoto, Seishi
TI Reliable Al that Develops the Society
SO FUJITSU SCIENTIFIC & TECHNICAL JOURNAL
LA English
DT Article
DE Takao Mohri; Nobuhiro Yugami; Seishi Okamoto
AB Al technology is playing a key role in the digitalization and transformation of many aspects of business and society. Since people support large segments of business and society, this transformation needs to be driven by an optimal combination of the best features of people and Al and the provision of high value. Fujitsu Laboratories is researching and developing Al technology on the basis of a human-centric way of thinking. The objective here is not just technology that can demonstrate a level of performance equal to or greater than experts but also that to explain Al output results, which is essential for establishing a close collaboration between people and Al. This paper describes an overall view of FUJITSU Human Centric Al Zinrai. It then introduces advanced technologies that focus on learning such as Deep Tensor, knowledge processing such as knowledge graphs, and "Explainable Al" that combines those technologies using practical examples in fields such as medical care and security.
C1 [Mohri, Takao; Yugami, Nobuhiro; Okamoto, Seishi] Fujitsu Labs Ltd, Tokyo, Japan.
   [Yugami, Nobuhiro; Okamoto, Seishi] Fujitsu Ltd, Minato, Japan.
RP Mohri, T (通讯作者)，Fujitsu Labs Ltd, Tokyo, Japan.
CR Fujitsu, 2017, NATURE
   Fujitsu, FUJ TAK SYST APPR AR
   Fujitsu Laboratories, FUJ INCR ACC MALW IN
   Fujitsu Laboratories, FUJ TECHN EL NEW INS
   Fujitsu Laboratories, FUJ DEV NEW DEEP LEA
   Fujitsu Laboratories Digital Enterprise Research Institute Fujitsu Laboratories of Europe, FUJ DERI REV ACC OP
   Fujitsu Laboratories Fujitsu, FUJ FUJ FUS DEEP TEN
   Fujitsu Laboratories Fujitsu Research and Development Center Fujitsu Laboratories of Europe, FUJ LAB EUR FUJ LAB
   Fujitsu Laboratories of Europe, FUJ HUM CENTR AL HEL
   Maruhashi K., 2018, P 32 AAAI C ART INT
   Maruhashi K, 2017, FUJITSU SCI TECH J, V53, P26
NR 11
TC 0
Z9 0
U1 1
U2 5
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.
PD OCT
PY 2018
VL 54
IS 5
BP 22
EP 29
PG 8
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA HA8ME
UT WOS:000450542900005
DA 2022-08-23
ER

PT J
AU Yu, J
   Zhu, ZH
   Wang, YJ
   Zhang, WF
   Hu, Y
   Tan, JL
AF Yu, Jing
   Zhu, Zihao
   Wang, Yujing
   Zhang, Weifeng
   Hu, Yue
   Tan, Jianlong
TI Cross-modal knowledge reasoning for knowledge-based visual question
   answering
SO PATTERN RECOGNITION
LA English
DT Article
DE Cross-modal knowledge reasoning; Multimodal knowledge graphs;
   Compositional reasoning module; Knowledge-based visual question
   answering; Explainable reasoning
AB Knowledge-based Visual Question Answering (KVQA) requires external knowledge beyond the visible content to answer questions about an image. This ability is challenging but indispensable to achieve general VQA. One limitation of existing KVQA solutions is that they jointly embed all kinds of information without fine-grained selection, which introduces unexpected noises for reasoning the correct answer. How to capture the question-oriented and information-complementary evidence remains a key challenge to solve the problem. Inspired by the human cognition theory, in this paper, we depict an image by multiple knowledge graphs from the visual, semantic and factual views. Thereinto, the visual graph and semantic graph are regarded as image-conditioned instantiation of the factual graph. On top of these new representations, we re-formulate Knowledge-based Visual Question Answering as a recurrent reasoning process for obtaining complementary evidence from multimodal information. To this end, we decompose the model into a series of memory-based reasoning steps, each performed by a Graph-based Read, Update, and Control (GRUC) module that conducts parallel reasoning over both visual and semantic information. By stacking the modules multiple times, our model performs transitive reasoning and obtains question-oriented concept representations under the constrain of different modalities. Finally, we perform graph neural networks to infer the global-optimal answer by jointly considering all the concepts. We achieve a new state-of-the-art performance on three popular benchmark datasets, including FVQA, Visual7W-KB and OK-VQA, and demonstrate the effectiveness and interpretability of our model with extensive experiments. The source code is available at: https://github.com/astro-zihao/gruc (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Yu, Jing; Zhu, Zihao; Hu, Yue; Tan, Jianlong] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China.
   [Yu, Jing; Zhu, Zihao; Hu, Yue; Tan, Jianlong] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China.
   [Wang, Yujing] Microsoft Res Asia, Beijing, Peoples R China.
   [Wang, Yujing] Peking Univ, Sch EECS, MOE, Key Lab Machine Percept, Beijing, Peoples R China.
   [Zhang, Weifeng] Jiaxing Univ, Coll Math Phys & Informat Engn, Jiaxing, Peoples R China.
RP Yu, J (通讯作者)，A3 Bldg,Minzhuang Rd 89, Beijing, Peoples R China.
EM yujing02@iie.ac.cn
OI Zhang, Weifeng/0000-0002-0280-2336
FU National Key Research and Development Program of China [2016YFB0801003]
FX This work was supported by the National Key Research and Development
   Program of China under grant no. 2016YFB0801003.
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NR 45
TC 17
Z9 17
U1 10
U2 67
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 DEC
PY 2020
VL 108
AR 107563
DI 10.1016/j.patcog.2020.107563
PG 13
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA NK8MN
UT WOS:000566985000003
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Bonch-Osmolovskaya, A
   Skorinkin, D
   Pavlova, I
   Kolbasov, M
   Orekhov, B
AF Bonch-Osmolovskaya, Anastasia
   Skorinkin, Daniil
   Pavlova, Irina
   Kolbasov, Matvey
   Orekhov, Boris
TI Tolstoy semanticized: Constructing a digital edition for knowledge
   discovery
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Digital edition; TEI; Semantic markup; Leo Tolstoy
AB The paper presents the results of a project devoted to the creation of a digital edition of Leo Tolstoy's complete works.(1) Our primary source is the 90-volume critical print edition of Tolstoy's oeuvre. We discuss the rationale for semantic markup of metadata for three classes of texts: works, letters and diaries. We extract information from the critical apparatus and supplement it with some new additional markups that enable visualizing the evolution of Tolstoy as a publicist. We show that the named entity index constitutes a valuable knowledge base, which can serve as a basis for generating a knowledge graph that is more detailed and systematic than the open linked databases like DBpedia. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Bonch-Osmolovskaya, Anastasia; Skorinkin, Daniil; Orekhov, Boris] Natl Res Univ, Higher Sch Econ, Moscow, Russia.
   [Pavlova, Irina] Univ Oxford, Oxford, England.
   [Kolbasov, Matvey] MrBot Moscow, Moscow, Russia.
RP Bonch-Osmolovskaya, A (通讯作者)，Natl Res Univ, Higher Sch Econ, Moscow, Russia.
EM abonch@hse.ru
RI Bonch-Osmolovskaya, Anastasia/L-5555-2015; Orekhov, Boris V/L-6206-2015;
   Skorinkin, Daniil/O-5528-2015
OI Bonch-Osmolovskaya, Anastasia/0000-0001-5826-8286; Orekhov, Boris
   V/0000-0002-9099-0436; Skorinkin, Daniil/0000-0002-1845-9974
CR Bonch-Osmolovskaya A., 1 C BIOGR DAT DIG WO
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NR 9
TC 0
Z9 0
U1 0
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 DEC
PY 2019
VL 59
AR 100483
DI 10.1016/j.websem.2018.12.001
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JT0RM
UT WOS:000500706800008
DA 2022-08-23
ER

PT J
AU Rouces, J
   de Melo, G
   Hose, K
AF Rouces, Jacobo
   de Melo, Gerard
   Hose, Katja
TI FrameBase: Enabling integration of heterogeneous knowledge
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge representation; semantic web; n-ary relations; frames;
   reification; semantic integration
ID ONTOLOGY DESIGN PATTERNS; ALIGNMENT
AB Large-scale knowledge graphs such as those in the Linked Open Data cloud are typically stored as subject-predicateobject triples. However, many facts about the world involve more than two entities. While n-ary relations can be converted to triples in a number of ways, unfortunately, the structurally different choices made in different knowledge sources significantly impede our ability to connect them. They also increase semantic heterogeneity, making it impossible to query the data concisely and without prior knowledge of each individual source. This article presents FrameBase, a wide-coverage knowledge base schema that uses linguistic frames to represent and query n-ary relations from other knowledge bases, providing multiple levels of granularity connected via logical entailment. Overall, this provides a means for semantic integration from heterogeneous sources under a single schema and opens up possibilities to draw on natural language processing techniques for querying and data mining.
C1 [Rouces, Jacobo; Hose, Katja] Aalborg Univ, Dept Comp Sci, Aalborg, Denmark.
   [de Melo, Gerard] Rutgers State Univ, Dept Comp Sci, Piscataway, NJ USA.
   [Rouces, Jacobo] Univ Gothenburg, Dept Swedish, Sprakbanken, Gothenburg, Sweden.
RP Rouces, J (通讯作者)，Aalborg Univ, Dept Comp Sci, Aalborg, Denmark.; Rouces, J (通讯作者)，Univ Gothenburg, Dept Swedish, Sprakbanken, Gothenburg, Sweden.
EM jrg@es.aau.dk; gdm@demelo.org; khose@cs.aau.dk
RI de Melo, Gerard/P-7886-2014
OI de Melo, Gerard/0000-0002-2930-2059; Hose, Katja/0000-0001-7025-8099
FU European Union Seventh Framework Programme (FP7) [FP7-SEC-2012-312651];
   Danish Council for Independent Research (DFF) [DFF-4093-00301]; China
   973 Program [2011CBA00300, 2011CBA00301]; NSFC [61033001, 61361136003,
   20141330245]
FX This research was partially funded by the European Union Seventh
   Framework Programme (FP7/2007-2013) under grant agreement No.
   FP7-SEC-2012-312651 (ePOOLICE project) as well as by the Danish Council
   for Independent Research (DFF) under grant agreement no. DFF-4093-00301.
   Additional funding was provided by China 973 Program Grants
   2011CBA00300, 2011CBA00301, and NSFC Grants 61033001, 61361136003,
   20141330245.
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   [No title captured]
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   [No title captured]
   [No title captured]
   [No title captured]
   [No title captured]
   [No title captured]
   [No title captured]
   [No title captured]
NR 71
TC 6
Z9 6
U1 0
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 2017
VL 8
IS 6
BP 817
EP 850
DI 10.3233/SW-170279
PG 34
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA FD1NM
UT WOS:000407304000004
OA Green Published
DA 2022-08-23
ER

PT J
AU Tang, XL
   Yang, JM
   Xiong, DY
   Luo, Y
   Wang, HM
   Peng, DG
AF Tang, Xianlun
   Yang, Jingming
   Xiong, Deyi
   Luo, Yang
   Wang, Huimin
   Peng, Deguang
TI Knowledge-enhanced graph convolutional network for recommendation
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Knowledge-enhanced recommendation; Graph convolutional network;
   Aggregator; Attention mechanism
AB Recommendation systems based on collaborative filtering (CF) are often accompanied by cold start and sparsity issues, which can be alleviated by using user information, item attributes and optimizing algorithms fully and reasonably. Empirically, the side information, such as item attributes, is not isolated but connected to each other in the form of the knowledge graph (KG). In this article, we put forward Knowledge-Enhanced Graph Convolutional Network (KE-GCN), a well-designed end-to-end architecture for recommendation, mining associated attributes of inter-item on the KG. To capture relatedness of inter-item effectively, we improve the attention mechanism to better probe into the correlation of the item's neighbors, and design a new aggregator to enhance feature aggregation. We conducted experiments on three public benchmarks, and empirical results of them show that KE-GCN is markedly superior to state-of-the-art methods.
C1 [Tang, Xianlun; Yang, Jingming; Luo, Yang; Wang, Huimin] Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing, Peoples R China.
   [Xiong, Deyi] Chongqing Acad Metrol & Qual Inspect, Chongqing, Peoples R China.
   [Peng, Deguang] Chongqing Megalight Technol Co LTD, Chongqing, Peoples R China.
RP Yang, JM (通讯作者)，Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing, Peoples R China.
EM yangjingming0601@qq.com
OI Yang, Jingming/0000-0001-6698-5381
FU National Nature Science Foundation of China [61673079]; Natural Science
   Foundation of Chongqing [cstc2018jcyjAX0160]; Innovation research group
   of universities in Chongqing
FX This work is supported by the National Nature Science Foundation of
   China under Project 61673079, the Natural Science Foundation of
   Chongqing under Project cstc2018jcyjAX0160 and the Innovation research
   group of universities in Chongqing.
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NR 34
TC 0
Z9 0
U1 10
U2 10
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD AUG
PY 2022
VL 81
IS 20
BP 28899
EP 28916
DI 10.1007/s11042-022-12272-w
EA MAR 2022
PG 18
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 3E6IF
UT WOS:000777375900001
DA 2022-08-23
ER

PT J
AU Li, XH
   Wang, ZL
   Zhang, ZH
AF Li, Xiaohui
   Wang, Zhiliang
   Zhang, Zhaohui
TI Complex Embedding with Type Constraints for Link Prediction
SO ENTROPY
LA English
DT Article
DE type constraint; link prediction; complex embedding; complex circular
   correlation
AB Large-scale knowledge graphs not only store entities and relations but also provide ontology-based information about them. Type constraints that exist in this information are of great importance for link prediction. In this paper, we proposed a novel complex embedding method, CHolE, in which complex circular correlation was introduced to extend the classic real-valued compositional representation HolE to complex domains, and type constraints were integrated into complex representational embeddings for improving link prediction. The proposed model consisted of two functional components, the type constraint model and the relation learning model, to form type constraints such as modulus constraints and acquire the relatedness between entities accurately by capturing rich interactions in the modulus and phase angles of complex embeddings. Experimental results on benchmark datasets showed that CHolE outperformed previous state-of-the-art methods, and the impartment of type constraints improved its performance on link prediction effectively.
C1 [Li, Xiaohui; Wang, Zhiliang] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.
   [Zhang, Zhaohui] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China.
RP Li, XH (通讯作者)，Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.
EM b20150293@xs.ustb.edu.cn; wzl@ustb.edu.cn; zhangzhaohui@ustb.edu.cn
FU National Key Research and Development Program [2019YFB2101902]
FX This research was funded by National Key Research and Development
   Program, grant number 2019YFB2101902.
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NR 40
TC 0
Z9 0
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD MAR
PY 2022
VL 24
IS 3
AR 330
DI 10.3390/e24030330
PG 19
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA 0D0TS
UT WOS:000775716900001
PM 35327841
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Jiang, TT
   Wang, H
   Luo, XF
   Xie, SR
   Wang, JC
AF Jiang, Tingting
   Wang, Hao
   Luo, Xiangfeng
   Xie, Shaorong
   Wang, Jingchao
TI MIFAS: Multi-source heterogeneous information fusion with adaptive
   importance sampling for link prediction
SO EXPERT SYSTEMS
LA English
DT Article
DE adaptive importance sampling; link prediction; multi-source
   heterogeneous information fusion
AB Link prediction plays an important role in constructing knowledge graph. Recently, graph representation learning models yield state-of-the-art results. However, existing models concentrate merely on triples or graph structures and mostly ignore textual descriptions, resulting in incomplete or partial information. In this paper, we propose a novel graph representation learning model to address this challenge, namely multi-source heterogeneous information fusion with adaptive importance sampling. Our model leverages multiple sources, such triple, graph structure and textual description, and generate rich-attribute embeddings for entities, encapsulating relations simultaneously. We also propose an adaptive importance sampling algorithm to boost aggregation of useful features from local neighbours. Additionally, we also boost node aggregation of useful features from local neighbours by adaptive importance sampling algorithm in our model. Experimental results on two benchmark datasets show that our proposed model significantly outperforms state-of-the-art methods.
C1 [Jiang, Tingting; Wang, Hao; Luo, Xiangfeng; Xie, Shaorong; Wang, Jingchao] Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China.
   [Wang, Hao; Luo, Xiangfeng; Xie, Shaorong] Shanghai Univ, Shanghai Inst Adv Commun & Data Sci, Shanghai, Peoples R China.
RP Wang, H; Luo, XF (通讯作者)，Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China.
EM wang-hao@shu.edu.cn; luoxf@shu.edu.cn
OI Wang, Hao/0000-0002-1089-9828
FU Shanghai Science and Technology Young Talents Sailing Program
   [21YF1413900]; National Natural Science Foundation of China [61991415];
   Ministry of Industry and Information Technology project of the
   Intelligent Ship Situation Awareness System [MC-201920-X01]; Natural
   Science Foundation of China [91746203]
FX Shanghai Science and Technology Young Talents Sailing Program,
   Grant/Award Number: 21YF1413900; National Natural Science Foundation of
   China, Grant/Award Number: 61991415; Ministry of Industry and
   Information Technology project of the Intelligent Ship Situation
   Awareness System, Grant/Award Number: MC-201920-X01; Natural Science
   Foundation of China, Grant/Award Number: 91746203
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   Yao, 2019, ARXIV190903193
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NR 31
TC 0
Z9 0
U1 8
U2 13
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 MAY
PY 2022
VL 39
IS 4
SI SI
AR e12888
DI 10.1111/exsy.12888
EA NOV 2021
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0F3NF
UT WOS:000719466200001
DA 2022-08-23
ER

PT J
AU Gunjan, VK
   Vijayalata, Y
   Valli, S
   Kumar, S
   Mohamed, MO
   Saravanan, V
AF Gunjan, Vinit Kumar
   Vijayalata, Y.
   Valli, Susmitha
   Kumar, Sumit
   Mohamed, M. O.
   Saravanan, V
TI Machine Learning and Cloud-Based Knowledge Graphs to Recognize Suicidal
   Mental Tendencies
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
AB To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users' psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user's multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper's accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues.
C1 [Gunjan, Vinit Kumar] CMR Inst Technol, Dept Comp Sci & Engn, Hyderabad, India.
   [Vijayalata, Y.; Valli, Susmitha] GRIET, Dept CSE, Hyderabad, Telangana, India.
   [Kumar, Sumit] Indian Inst Management, Kozhikode, India.
   [Mohamed, M. O.] Zagazig Univ, Fac Sci, Math Dept, Zagazig, Egypt.
   [Saravanan, V] Dambi Dollo Univ, Coll Engn & Technol, Dept Comp Sci, Dambi Dollo, Oromia Region, Ethiopia.
RP Gunjan, VK (通讯作者)，CMR Inst Technol, Dept Comp Sci & Engn, Hyderabad, India.; Saravanan, V (通讯作者)，Dambi Dollo Univ, Coll Engn & Technol, Dept Comp Sci, Dambi Dollo, Oromia Region, Ethiopia.
EM vinitkumargunjan@gmail.com; saravanan@dadu.edu.et
RI Gunjan, Vinit Kumar/I-1449-2014
OI Gunjan, Vinit Kumar/0000-0002-3222-4186; Y,
   Vijayalata/0000-0002-5215-9107
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NR 16
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 MAR 17
PY 2022
VL 2022
AR 3604113
DI 10.1155/2022/3604113
PG 10
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 0H0SO
UT WOS:000778450300006
PM 35341205
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Janev, V
   Vidal, ME
   Pujic, D
   Popadic, D
   Iglesias, E
   Sakor, A
   Campa, A
AF Janev, Valentina
   Vidal, Maria-Esther
   Pujic, Dea
   Popadic, Dusan
   Iglesias, Enrique
   Sakor, Ahmad
   Campa, Andrej
TI Responsible Knowledge Management in Energy Data Ecosystems
SO ENERGIES
LA English
DT Article
DE data integration systems; energy big data; knowledge graphs; data
   exchange; semantic interoperability; big data analytic
AB This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces.
C1 [Janev, Valentina; Pujic, Dea; Popadic, Dusan] Univ Belgrade, Mihajlo Pupin Inst, Belgrade 11060, Serbia.
   [Vidal, Maria-Esther; Sakor, Ahmad] TIB Leibniz Informat Ctr Sci & Technol, D-30167 Hannover, Germany.
   [Vidal, Maria-Esther; Iglesias, Enrique; Sakor, Ahmad] Leibniz Univ Hannover, L3S Res Ctr, D-30167 Hannover, Germany.
   [Campa, Andrej] ComSensus, Dob 1233, Slovenia.
RP Janev, V (通讯作者)，Univ Belgrade, Mihajlo Pupin Inst, Belgrade 11060, Serbia.
EM valentina.janev@institutepupin.com; maria.vidal@tib.eu;
   dea.pujic@pupin.rs; dusan.popadic@pupin.rs; enrique.iglesias@tib.eu;
   ahmad.sakor@tib.eu; andrej.campa@comsensus.eu
RI Janev, Valentina/M-9093-2014
OI Janev, Valentina/0000-0002-9794-8505
FU EU [872592, 809965, 952140]; Ministry of Science and Technological
   Development of the Republic of Serbia [451-03-9/2021-14/200034]; Science
   Fund of the Republic of Serbia [6527051]
FX This work was partially supported by the EU H2020 funded projects
   PLATOON (GA No. 872592), the EU project LAMBDA (GA No. 809965), the EU
   project SINERGY (GA No. 952140) and partly by the Ministry of Science
   and Technological Development of the Republic of Serbia (No.
   451-03-9/2021-14/200034) and the Science Fund of the Republic of Serbia
   (Artemis, No.6527051).
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NR 36
TC 0
Z9 0
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1073
J9 ENERGIES
JI Energies
PD JUN
PY 2022
VL 15
IS 11
AR 3973
DI 10.3390/en15113973
PG 17
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA 1Z2RC
UT WOS:000808676700001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, CL
   Wang, DJ
   Wang, Y
   Jiao, FY
AF Wang, Cuilan
   Wang, Deji
   Wang, Yu
   Jiao, Fangyuan
TI Evolution of the Internet's Support for Chinese Enterprises' Innovation
   Based on Big Data
SO INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Internet; big data; corporate innovation
AB In order to study the development trend of the Internet's role in supporting enterprise innovation, a new method based on deep learning algorithm and knowledge graph technology is proposed. Experiments show that the accuracy, F1 value, recall rate and precision of the algorithm are distinctly improved compared with the existing algorithms. A new algorithm is applied to analyze the innovation evolution of Chinese enterprises, using papers, patents and other documents between 1905 and 2020 as data sources. Based on the experimental results, the development stages can be divided into five stages. The research focus is on product R&D innovation, manufacturing innovation, marketing innovation, resource allocation innovation and organizational innovation. It can be seen that the development process of the Internet supporting enterprise innovation system is an evolutionary development process from point to line, to surface, to the body, and to the ecosystem.
C1 [Wang, Cuilan] Univ Sci & Technol China, Dept Hist Sci & Sci Archaeol, 96 Jinzhai Rd, Hefei 230026, Peoples R China.
   [Wang, Cuilan] Henan Vocat Coll Light Ind, 2 Yuantian Rd, Zhengzhou 450002, Peoples R China.
   [Wang, Deji] Staff Dev Inst CNTC, 7 Xinyuan Rd, Zhengzhou 450008, Peoples R China.
   [Wang, Yu] Univ Sci & Technol China, Dept Precis Machinery & Instrumentat, 96 Jinzhai Rd, Hefei 230026, Peoples R China.
   [Jiao, Fangyuan] Zhengzhou Univ, Sch Informat Engn, 100 Sci Ave, Zhengzhou 450001, Peoples R China.
RP Wang, DJ (通讯作者)，Staff Dev Inst CNTC, 7 Xinyuan Rd, Zhengzhou 450008, Peoples R China.
EM 574518303@qq.com; wangdeji@aliyun.com; 147796102@qq.com;
   2524809737@qq.com
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NR 28
TC 0
Z9 0
U1 2
U2 11
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-0014
EI 1793-6381
J9 INT J PATTERN RECOGN
JI Int. J. Pattern Recognit. Artif. Intell.
PD SEP 15
PY 2021
VL 35
IS 11
AR 2159042
DI 10.1142/S0218001421590424
PG 29
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA US1GT
UT WOS:000697185300009
DA 2022-08-23
ER

PT J
AU Saqi, M
   Lysenko, A
   Guo, YK
   Tsunoda, T
   Auffray, C
AF Saqi, Mansoor
   Lysenko, Artem
   Guo, Yi-Ke
   Tsunoda, Tatsuhiko
   Auffray, Charles
TI Navigating the disease landscape: knowledge representations for
   contextualizing molecular signatures
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Review
DE precision medicine; molecular medicine; multi-omics; disease modeling;
   integrated knowledge networks
ID GENE-EXPRESSION; EFFECT MODELS; DATABASE; PATHWAYS; NETWORK; ASTHMA;
   IDENTIFICATION; POLYMORPHISMS; INFLAMMATION; COEXPRESSION
AB Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.
C1 [Saqi, Mansoor; Guo, Yi-Ke] Imperial Coll London, Data Sci Inst, London, England.
   [Lysenko, Artem] RIKEN Lab Med Sci Math, Yokohama, Kanagawa, Japan.
   [Tsunoda, Tatsuhiko] Tokyo Med & Dent Univ, Tokyo, Japan.
   [Tsunoda, Tatsuhiko] RIKEN, Lab Med Sci Math, Yokohama, Kanagawa, Japan.
   [Auffray, Charles] European Inst Syst Biol & Med EISBM Lyon, Lyon, France.
RP Lysenko, A (通讯作者)，RIKEN Ctr Integrat Med Sci, Lab Med Sci Math, Tsurumi Ku, 1-7-22 Suehiro Cho, Yokohama, Kanagawa 2300045, Japan.
EM artem.lysenko@riken.jp
RI Tsunoda, Tatsuhiko/K-2061-2014
OI Tsunoda, Tatsuhiko/0000-0002-5439-7918; Saqi,
   Mansoor/0000-0003-1222-475X; Lysenko, Artem/0000-0002-1441-0604
FU Innovative Medicines Initiative Joint Undertaking eTRIKS Project from
   the European Union's Seventh Framework Programme (FP7/2007-2013) [IMI
   115446]; European Federation of Pharmaceutical Industries and
   Associations (EFPIA) companies; Core Research for Evolutional Science
   and Technology (CREST) Grant from the Japan Science and Technology
   Agency [JPMJCR1412]; Japan Society for the Promotion of Science KAKENHI
   [17H06307, 17H06299]
FX The research leading to these results has received support from the
   Innovative Medicines Initiative Joint Undertaking eTRIKS Project (Grant
   Number: IMI 115446) resources of which are composed of a financial
   contribution from the European Union's Seventh Framework Programme
   (FP7/2007-2013) and European Federation of Pharmaceutical Industries and
   Associations (EFPIA) companies' in kind contributions. The work was also
   partially supported by Core Research for Evolutional Science and
   Technology (CREST) Grant from the Japan Science and Technology Agency
   (Grant Number: JPMJCR1412), and Japan Society for the Promotion of
   Science KAKENHI (Grant Numbers: 17H06307 and 17H06299).
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NR 129
TC 8
Z9 8
U1 2
U2 6
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 2019
VL 20
IS 2
BP 609
EP 623
DI 10.1093/bib/bby025
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 IG4EI
UT WOS:000473756500005
PM 29684165
OA Green Submitted, Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Onishi, T
   Kadohira, T
   Watanabe, I
AF Onishi, Takeshi
   Kadohira, Takuya
   Watanabe, Ikumu
TI Relation extraction with weakly supervised learning based on
   process-structure-property-performance reciprocity
SO SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS
LA English
DT Article
DE Natural language processing; knowledge extraction; relation extraction;
   weakly supervised learning; materials informatics
AB In this study, we develop a computer-aided material design system to represent and extract knowledge related to material design from natural language texts. A machine learning model is trained on a text corpus weakly labeled by minimal annotated relationship data (similar to 100 labeled relationships) to extract knowledge from scientific articles. The knowledge is represented by relationships between scientific concepts, such as {annealing, grain size, strength}. The extracted relationships are represented as a knowledge graph formatted according to design charts, inspired by the process-structure-property-performance (PSPP) reciprocity. The design chart provides an intuitive effect of processes on properties and prospective processes to achieve the certain desired properties. Our system semantically searches the scientific literature and provides knowledge in the form of a design chart, and we hope it contributes more efficient developments of new materials.
   [GRAPHICS]
   .
C1 [Onishi, Takeshi] Toyota Technol Inst, Chicago, IL USA.
   [Kadohira, Takuya] Natl Inst Mat Sci, Res & Serv Div Mat Data & Integrated Syst, Tsukuba, Ibaraki, Japan.
   [Watanabe, Ikumu] Natl Inst Mat Sci, Res Ctr Struct Mat, 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, Japan.
RP Watanabe, I (通讯作者)，Natl Inst Mat Sci, Res Ctr Struct Mat, 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, Japan.
EM WATANABE.Ikumu@nims.go.jp
RI Watanabe, Ikumu/H-2601-2011
OI Watanabe, Ikumu/0000-0002-7693-1675
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NR 18
TC 10
Z9 10
U1 2
U2 10
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1468-6996
EI 1878-5514
J9 SCI TECHNOL ADV MAT
JI Sci. Technol. Adv. Mater.
PD SEP 19
PY 2018
VL 19
IS 1
BP 649
EP 659
DI 10.1080/14686996.2018.1500852
PG 11
WC Materials Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Materials Science
GA GU1FJ
UT WOS:000445002300001
PM 30245757
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Shen, YC
   Grivas, DA
AF Shen, YC
   Grivas, DA
TI Decision-support system for infrastructure preservation
SO JOURNAL OF COMPUTING IN CIVIL ENGINEERING
LA English
DT Article
ID PAVEMENT
AB This study presents the development of a decision-support system for the preservation of civil infrastructure. The system aims at providing assistance for decisions concerned with the three main tasks of infrastructure maintenance and rehabilitation: symptom observation, condition diagnosis, and treatment identification. Knowledge graphs and damage assessment are two methodologies incorporated into the system with a uniform representation scheme to organize the knowledge and data. The architecture of the system consists of knowledge bases, databases, analysis programs, and various interfaces. To illustrate the developed decision-support system, a case study involving the New York State Thruway is presented and provides a framework for a discussion of the functional integrity of the system in providing assistance for data and decision needs. It is concluded that the formalization of decision methodologies in achieving the functional integrity of a decision-support system is an important contribution of the system development.
C1 RENSSELAER POLYTECH INST,DEPT CIVIL ENGN,TROY,NY.
RP Shen, YC (通讯作者)，RENSSELAER POLYTECH INST,DEPT CIVIL ENGN,TROY,NY 12180, USA.
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TC 13
Z9 13
U1 2
U2 4
PU ASCE-AMER SOC CIVIL ENG
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017-2398
SN 0887-3801
J9 J COMPUT CIVIL ENG
JI J. Comput. Civil. Eng.
PD JAN
PY 1996
VL 10
IS 1
BP 40
EP 49
DI 10.1061/(ASCE)0887-3801(1996)10:1(40)
PG 10
WC Computer Science, Interdisciplinary Applications; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA TN833
UT WOS:A1996TN83300008
DA 2022-08-23
ER

PT J
AU Gao, Y
   You, SB
   Xu, YP
   Wang, N
AF Gao, Yu
   You, Shibing
   Xu, Yiping
   Wang, Na
TI The Hot Topics, Frontiers and Trends about Research on the Relationship
   between Air Pollution and Public Health-Visual Analysis Based on
   Knowledge Map
SO ATMOSPHERE
LA English
DT Article
DE air pollutants; sustained exposure to pollution; respiratory and
   cardiovascular diseases; CiteSpace; co-occurrence keywords; burst words
ID LONG-TERM EXPOSURE; INFANT-MORTALITY; LUNG-CANCER; SUSTAINED EXPOSURE;
   LIFE EXPECTANCY; PARTICULATE; DISEASE; IMPACT; INDICATORS; ADMISSIONS
AB It is of great practical significance to analyze the hot issues, research frontiers, and trends concerning the relationship between air pollution and public health and to adopt reasonable strategies to control air pollution and prevent health hazards for follow-up research in this field. Unlike traditional literature reviews, this paper adopts a visual, flexible, and scientifically systematic approach to the analysis, which makes these analysis results more intuitive and comprehensive. Based on the core collection of the Web of Science and CNKI databases, this paper uses CiteSpace software to draw and comment on the maps of Chinese and English keywords, publishing time, author, country, and research institutions in this field. The results show the following: (1) The number of studies on the relationship between air pollution and health has increased year by year; researchers have formed sub cooperation networks, and the trend of cooperation and exchange has become more and more obvious in recent years; the impact of air pollution on health is a hot topic in the world. (2) Research hot topics mainly focus on the selection of air pollutants, health economic consequences of air pollution and the global burden of disease it causes, health indicators, research samples, which are gradually being refined, the synergistic governance of air pollution, and climate change. (3) The analysis of research frontiers and trends reveals that, first, the study of air pollutants in the English literature has undergone a refinement from nitrogen dioxide to fine particulate matter, and the sources of air pollutants in the Chinese literature have undergone changes in the petrochemical industry, indoor formaldehyde pollution, and haze. Second, atmospheric pollution has a significant negative impact on health, increasing the incidence of respiratory and cardiovascular diseases, and even causing death. Third, sustained exposure to pollution then causes greater damage to health and will be a key direction for future research. Fourth, the literature not only studies the correlation but also emphasizes the causal inference between air pollution and health and measures the economic costs associated with health. Finally, air pollution and climate change need to be governed synergistically. The article points out that the three areas of sustained pollution exposure, indirect consequences of negative health effects of air pollution, and air pollution and climate change may be the future focus of the field.
C1 [Gao, Yu; You, Shibing] Wuhan Univ, Econ & Management Sch, Wuhan 430072, Peoples R China.
   [Xu, Yiping] Wuhan Business Univ, Econ Coll, Wuhan 430056, Peoples R China.
   [Wang, Na] Hubei Univ, Sch Business, Wuhan 430062, Peoples R China.
RP You, SB (通讯作者)，Wuhan Univ, Econ & Management Sch, Wuhan 430072, Peoples R China.; Wang, N (通讯作者)，Hubei Univ, Sch Business, Wuhan 430062, Peoples R China.
EM gao.yu@whu.edu.cn; sbyou@whu.edu.cn; 20160116@wbu.edu.cn;
   20110034@hubu.edu.cn
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NR 52
TC 0
Z9 0
U1 3
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4433
J9 ATMOSPHERE-BASEL
JI Atmosphere
PD JUN
PY 2022
VL 13
IS 6
AR 892
DI 10.3390/atmos13060892
PG 21
WC Environmental Sciences; Meteorology & Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences
GA 2M0KY
UT WOS:000817401100001
OA gold
DA 2022-08-23
ER

PT J
AU Zang, HY
   Han, DC
   Li, X
   Wan, ZF
   Wang, MZ
AF Zang, Hongyu
   Han, Dongcheng
   Li, Xin
   Wan, Zhifeng
   Wang, Mingzhong
TI CHA: Categorical Hierarchy-based Attention for Next POI Recommendation
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE Next POI recommendation; next new POI recommendation; categorical
   hierarchy-based attention
AB Next Point-of-interest (POI) recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial diversity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world location-based social networks (LBSNs) datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.
C1 [Zang, Hongyu; Han, Dongcheng; Li, Xin] Beijing Inst Technol, Sch Comp Sci, 5 South Zhongguancun St, Beijing 100081, Peoples R China.
   [Wan, Zhifeng] Beijing Inst Technol, Sch Mechatron Engn, 5 South Zhongguancun St, Beijing 100081, Peoples R China.
   [Wang, Mingzhong] Univ Sunshine Coast, Sch Sci, 90 Sippy Downs Dr, Sippy Downs, Qld, Australia.
RP Li, X (通讯作者)，Beijing Inst Technol, Sch Comp Sci, 5 South Zhongguancun St, Beijing 100081, Peoples R China.
EM zanghyu@bit.edu.cn; dchan@bit.edu.cn; xinli@bit.edu.cn;
   3120190232@bit.edu.cn; mwang@usc.edu.au
FU NSFC [U19B2020, 61772074]; National Key R&D Program of China
   [2017YFB0803300]
FX This research has been partially supported by NSFC under Grant (U19B2020
   and 61772074) and National Key R&D Program of China under Grant
   (2017YFB0803300).
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NR 47
TC 0
Z9 0
U1 2
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1046-8188
EI 1558-2868
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
PD JAN
PY 2022
VL 40
IS 1
AR 7
DI 10.1145/3464300
PG 22
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZV7AR
UT WOS:000770678900007
DA 2022-08-23
ER

PT J
AU Hu, LC
   Wei, QS
   Luo, ZH
   Wang, B
   Wu, ZF
   He, MC
   He, XM
   Luo, YW
AF Hu, Liuchao
   Wei, Qiushi
   Luo, Ziheng
   Wang, Bin
   Wu, Zhifang
   He, Mincong
   He, Xiaoming
   Luo, Yiwen
TI Mapping Knowledge Structure and Themes Trends of Post-operative
   Rehabilitation of Hip Fractures in the Elderly: A Bibliometrics and
   Visualization Study
SO FRONTIERS IN SURGERY
LA English
DT Article
DE hip fracture; postoperative rehabilitation; bibliometric analysis;
   research trends; hotspots
ID MORTALITY; MANAGEMENT; CARE; MORBIDITY; SURGERY
AB BackgroundHip fractures are a common type of fracture in the elderly and are characterized by many complications and high mortality. Many topics concerning postoperative rehabilitation of hip fracture in elderly people still remain controversial, and the global research trend in this field has not yet been well studied. The aim of the present study was to illustrate the overall knowledge structure, development trends, and research hot spots of postoperative rehabilitation of hip fracture in elderly people. MethodsArticles and reviews regarding postoperative rehabilitation of hip fracture in elderly people from 2001 to 2021 were identified from the Web of Science database. An online bibliometric platform, CiteSpace, and VOSviewer software were used to generate visualization knowledge maps, including annual trends of publications, contributions of countries, institutions, authors, funding agencies, and journals, and clustering of keywords. ResultsA total of 1,724 publications were identified from Web of Science Core Collection (WoSCC). In the past 20 years, the number of published studies on the rehabilitation of hip fracture in the elderly has exhibited an overall upward trend. The USA was the leading contributor in this field, with the largest number of publications (354, 20.65%) and the most citations (13,786 times). The international cooperation map among relevant countries/regions indicated that the USA collaborated most closely with Canada and China. The University of Maryland and Professor Marcantonio were the most prolific institution and influential author, respectively. Injury: International Journal of the Care of the Injured was the most productive journal concerning the research of postoperative rehabilitation of hip fracture in elderly people. The keyword co-occurrence analysis identified six clusters: quality of life study, rehabilitation and outcomes study, cognitive impairment study, operative approaches study, mortality study, and osteoporosis study. ConclusionsThere will be an increasing number of publications on the research of postoperative rehabilitation of hip fracture in elderly people, and the United States will stay ahead in this field. Our findings could offer practical sources for scholars to understand the current status and trend of studies on rehabilitation of hip fracture in the elderly and provide references and suggestions for the development of related research in future.
C1 [Hu, Liuchao; Wei, Qiushi; Luo, Ziheng; Wang, Bin; Wu, Zhifang; He, Mincong; He, Xiaoming; Luo, Yiwen] Guangzhou Univ Chinese Med, Affiliated Hosp 3, Guangzhou, Peoples R China.
   [Hu, Liuchao; Wei, Qiushi; Luo, Ziheng; Wang, Bin; Wu, Zhifang; He, Mincong; He, Xiaoming; Luo, Yiwen] Guangdong Res Inst Orthoped & Traumatol Chinese Me, Guangzhou, Peoples R China.
RP Luo, YW (通讯作者)，Guangzhou Univ Chinese Med, Affiliated Hosp 3, Guangzhou, Peoples R China.; Luo, YW (通讯作者)，Guangdong Res Inst Orthoped & Traumatol Chinese Me, Guangzhou, Peoples R China.
EM gzzyydxlyw@126.com
FU Foundations of the Guangdong Research Institute for Orthopedics &
   Traumatology of Chinese Medicine [GYH202101-02]; Guangdong Provincial
   Administration of Chinese Medicine Research Project [20201168]
FX Funding This study was sponsored by the Foundations of the Guangdong
   Research Institute for Orthopedics & Traumatology of Chinese Medicine
   (GYH202101-02) and the Guangdong Provincial Administration of Chinese
   Medicine Research Project (No. 20201168).
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NR 38
TC 0
Z9 0
U1 8
U2 8
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-875X
J9 FRONT SURG
JI Front. Surg.
PD MAY 3
PY 2022
VL 9
AR 881555
DI 10.3389/fsurg.2022.881555
PG 12
WC Surgery
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Surgery
GA 1I1FY
UT WOS:000796983200001
PM 35592118
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yulianto, F
   Tjahjono, B
   Anwar, S
AF Yulianto, Fajar
   Tjahjono, Boedi
   Anwar, Syaiful
TI The applications of Monte Carlo algorithm and energy cone model to
   produce the probability of block-and-ash flows of the 2010 eruption of
   Merapi volcano in Central Java, Indonesia
SO ARABIAN JOURNAL OF GEOSCIENCES
LA English
DT Article
DE Monte Carlo algorithm; Energy cone; InSAR method; Pyroclastic flows;
   Merapi volcano; Central Java; Indonesia
ID HAZARD; SUBSIDENCE; BEHAVIOR; GIS
AB Volcanic eruption hazard mapping is very important to fulfill information needs to prepare for emergency situations. Rapid mapping is one of the steps necessary for emergency response in disaster mitigation effort. Limitations of time, data, and knowledge mapping techniques can be a problem when performing the operational work. In this research, the combinations of the Monte Carlo algorithm and energy cone model have been applied to reproduce the probability of block-and-ash type of pyroclastic flows of the 2010 eruption of Merapi volcano. These approaches are applied as an alternative method of rapid, objective, and reproducible for hazard mapping of pyroclastic flows. In addition, the method of Interferometry Synthetic Aperture Radar (InSAR) has been used in this research to update the digital elevation model (DEM) data. The availability of DEM data updates was required as input of topography, which determines the pyroclastic flows. This research has produced DEM PALSAR 2010 pre-eruption of Merapi volcano, with a spatial resolution of 30 m. The result of the vertical accuracy calculations was performed using the root mean square error (RMSE) approach, which show the value of RMSE at 9.08 m. There are four eruptive phases, which have been used for the simulation scenarios, namely: phase 1 (period 26-29 October 2010), phase 2 (period 30 October-3 November 2010), phase 3 (period 4-5 November 2010), and phase 4 (period 6-23 November 2010). The results of the Monte Carlo algorithm to reproduce the effects of the 2010 eruption of Merapi volcano, has show that the height correction (hc) on the DEM data gives effect to the probability distribution of pyroclastic flows. At the hc=1, 2, 3, 4, and 5 m, the value of overall accuracy based on cross-correlation matrix of the reference map are 76.38, 77.38, 77.00, 77.75, and 77.25 %, respectively. In these scenarios, the hc=4 m can give the best accuracy. Meanwhile, the results of the comparison of the results of the difference of the average run out on the energy cone model obtained from the reference map is 843 m.
C1 [Yulianto, Fajar; Tjahjono, Boedi; Anwar, Syaiful] Bogor Agr Univ IPB, Fac Agr, Dept Soil Sci & Land Resources, Disaster Mitigat & Land Degradat MBK, Bogor 16680, West Java, Indonesia.
   [Yulianto, Fajar] Indonesian Natl Inst Aeronaut & Space LAPAN, Remote Sensing Applicat Ctr, Jakarta 13710, Indonesia.
RP Yulianto, F (通讯作者)，Indonesian Natl Inst Aeronaut & Space LAPAN, Remote Sensing Applicat Ctr, Jl Kalisari 8, Jakarta 13710, Indonesia.
EM fajar.yulianto@lapan.go.id
RI Yulianto, Fajar/G-2080-2016; Tjahjono, Boedi/GPP-3605-2022; Anwar,
   Syaiful/W-3806-2019; Yulianto, Fajar/Y-8432-2019
OI Yulianto, Fajar/0000-0002-3084-6694; Tjahjono,
   Boedi/0000-0003-1966-9479; Anwar, Syaiful/0000-0002-9928-5821; Yulianto,
   Fajar/0000-0002-3084-6694
FU Indonesian National Institute of Aeronautics and Space (LAPAN)
FX This article is derived from research in progress by F. Yulianto on "The
   potential risk of pyroclastic flows after the events of the 2010
   eruption of Merapi volcano using remotely sensed data in Central Java,
   Indonesia." This project is being done at the Disaster Mitigation and
   Land Degradation (MBK), Faculty of Agriculture, Bogor Agricultural
   University (IPB), Bogor, West Java, Indonesia. This study is supported
   by the Indonesian National Institute of Aeronautics and Space (LAPAN).
   ALOS PALSAR data were provided by the Japan Aerospace Exploration Agency
   (JAXA). Topographic maps were provided by the Geospatial Information
   Agency (BIG). The authors express their gratitude to the reviewers for
   their helpful advice.
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NR 45
TC 3
Z9 3
U1 0
U2 11
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1866-7511
EI 1866-7538
J9 ARAB J GEOSCI
JI Arab. J. Geosci.
PD JUL
PY 2015
VL 8
IS 7
BP 4717
EP 4739
DI 10.1007/s12517-014-1525-5
PG 23
WC Geosciences, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geology
GA CL6TV
UT WOS:000357101200034
DA 2022-08-23
ER

PT J
AU Boetto, E
   Fantini, MP
   Gangemi, A
   Golinelli, D
   Greco, M
   Nuzzolese, AG
   Presutti, V
   Rallo, F
AF Boetto, Erik
   Fantini, Maria Pia
   Gangemi, Aldo
   Golinelli, Davide
   Greco, Manfredi
   Nuzzolese, Andrea Giovanni
   Presutti, Valentina
   Rallo, Flavia
TI Using altmetrics for detecting impactful research in quasi-zero-day
   time-windows: the case of COVID-19
SO SCIENTOMETRICS
LA English
DT Article
DE Altmetrics; Research evaluation; Bibliometric indicators; COVID-19;
   SARS-CoV-2; nCoV-2019
AB On December 31st 2019, the World Health Organization China Country Office was informed of cases of pneumonia of unknown etiology detected in Wuhan City. The cause of the syndrome was a new type of coronavirus isolated on January 7th 2020 and named Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2). SARS-CoV-2 is the cause of the coronavirus disease 2019 (COVID-19). Since January 2020 an ever increasing number of scientific works related to the new pathogen have appeared in literature. Identifying relevant research outcomes at very early stages is challenging. In this work we use COVID-19 as a use-case for investigating: (1) which tools and frameworks are mostly used for early scholarly communication; (2) to what extent altmetrics can be used to identify potential impactful research in tight (i.e. quasi-zero-day) time-windows. A literature review with rigorous eligibility criteria is performed for gathering a sample composed of scientific papers about SARS-CoV-2/COVID-19 appeared in literature in the tight time-window ranging from January 15th 2020 to February 24th 2020. This sample is used for building a knowledge graph that represents the knowledge about papers and indicators formally. This knowledge graph feeds a data analysis process which is applied for experimenting with altmetrics as impact indicators. We find moderate correlation among traditional citation count, citations on social media, and mentions on news and blogs. Additionally, correlation coefficients are not inflated by indicators associated with zero values, which are quite common at very early stages after an article has been published. This suggests there is a common intended meaning of the citational acts associated with aforementioned indicators. Then, we define a method, i.e. the Comprehensive Impact Score (CIS), that harmonises different indicators for providing a multi-dimensional impact indicator. CIS shows promising results as a tool for selecting relevant papers even in a tight time-window. Our results foster the development of automated frameworks aimed at helping the scientific community in identifying relevant work even in case of limited literature and observation time.
C1 [Boetto, Erik; Fantini, Maria Pia; Golinelli, Davide; Greco, Manfredi; Rallo, Flavia] Univ Bologna, DIBINEM, Bologna, Italy.
   [Gangemi, Aldo; Nuzzolese, Andrea Giovanni] CNR, ISTC, STLab, Rome, Italy.
   [Gangemi, Aldo] Univ Bologna, FICLIT, Bologna, Italy.
   [Presutti, Valentina] Univ Bologna, LILEC, Bologna, Italy.
RP Nuzzolese, AG (通讯作者)，CNR, ISTC, STLab, Rome, Italy.
EM erik.boetto@gmail.com; mariapia.fantini@unibo.it; aldo.gangemi@cnr.it;
   davide.golinelli@unibo.it; manfredi.greco@studio.unibo.it;
   andrea.nuzzolese@cnr.it; valentina.presutti@unibo.it;
   flavia.rallo@studio.unibo.it
RI Greco, Manfredi/AAZ-7471-2021; Golinelli, Davide/Q-8059-2016; Golinelli,
   Davide/AEE-6114-2022; Gangemi, Aldo/C-7420-2013; Nuzzolese, Andrea
   Giovanni/S-2298-2016
OI Greco, Manfredi/0000-0001-6866-1428; Golinelli,
   Davide/0000-0001-7331-9520; GRECO, Manfredi/0000-0001-6886-0160;
   Gangemi, Aldo/0000-0001-5568-2684; Boetto, Erik/0000-0002-2509-159X;
   Nuzzolese, Andrea Giovanni/0000-0003-2928-9496
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NR 33
TC 3
Z9 3
U1 7
U2 25
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD FEB
PY 2021
VL 126
IS 2
BP 1189
EP 1215
DI 10.1007/s11192-020-03809-7
EA JAN 2021
PG 27
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA QR4LJ
UT WOS:000604526600001
PM 33424050
OA hybrid, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Karim, F
   Vidal, ME
   Auer, S
AF Karim, Farah
   Vidal, Maria-Esther
   Auer, Soeren
TI Compacting frequent star patterns in RDF graphs
SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
LA English
DT Article
DE Semantic Web; RDF compaction; Linked data; Knowledge graph
AB Knowledge graphs have become a popular formalism for representing entities and their properties using a graph data model, e.g., the Resource Description Framework (RDF). An RDF graph comprises entities of the same type connected to objects or other entities using labeled edges annotated with properties. RDF graphs usually contain entities that share the same objects in a certain group of properties, i.e., they match star patterns composed of these properties and objects. In case the number of these entities or properties in these star patterns is large, the size of the RDF graph and query processing are negatively impacted; we refer these star patterns as frequent star patterns. We address the problem of identifying frequent star patterns in RDF graphs and devise the concept of factorized RDF graphs, which denote compact representations of RDF graphs where the number of frequent star patterns is minimized. We also develop computational methods to identify frequent star patterns and generate a factorized RDF graph, where compact RDF molecules replace frequent star patterns. A compact RDF molecule of a frequent star pattern denotes an RDF subgraph that instantiates the corresponding star pattern. Instead of having all the entities matching the original frequent star pattern, a surrogate entity is added and related to the properties of the frequent star pattern; it is linked to the entities that originally match the frequent star pattern. Since the edges between the entities and the objects in the frequent star pattern are replaced by edges between these entities and the surrogate entity of the compact RDF molecule, the size of the RDF graph is reduced. We evaluate the performance of our factorization techniques on several RDF graph benchmarks and compare with a baseline built on top gSpan, a state-of-the-art algorithm to detect frequent patterns. The outcomes evidence the efficiency of proposed approach and show that our techniques are able to reduce execution time of the baseline approach in at least three orders of magnitude. Additionally, RDF graph size can be reduced by up to 66.56% while data represented in the original RDF graph is preserved.
C1 [Karim, Farah; Vidal, Maria-Esther; Auer, Soeren] Leibniz Univ Hannover, Welfengarten 1B, D-30167 Hannover, Germany.
   [Karim, Farah] MUST, Mirpur 10250, AJK, Pakistan.
RP Karim, F (通讯作者)，Leibniz Univ Hannover, Welfengarten 1B, D-30167 Hannover, Germany.; Karim, F (通讯作者)，MUST, Mirpur 10250, AJK, Pakistan.
EM karim@l3s.de
RI Vidal, Maria-Esther/AAU-8163-2020
OI Vidal, Maria-Esther/0000-0003-1160-8727
FU German Academic Exchange Service (DAAD); EU H2020 project IASiS [727658]
FX Farah Karim is supported by the German Academic Exchange Service (DAAD);
   this work is partially funded by the EU H2020 project IASiS (GA
   No.727658).
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NR 32
TC 2
Z9 2
U1 1
U2 4
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 DEC
PY 2020
VL 55
IS 3
BP 561
EP 585
DI 10.1007/s10844-020-00595-9
EA APR 2020
PG 25
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OH1OG
UT WOS:000526276700001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Wu, MH
   Wu, XD
AF Wu, Minghui
   Wu, Xindong
TI On big wisdom
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Human intelligence; AI; Organizational intelligence; Business
   intelligence; Bigdata; Big knowledge; Big wisdom
AB This paper defines big wisdom with a HAO/BIBLE framework, which integrates human intelligence (HI), artificial intelligence (AI), and organizational/business intelligence (O/BL) with Bigdata analytics in Large Environments, for industrial intelligence in organizational activities. Big wisdom starts with Bigdata, discovers big knowledge, and facilitates human and machine synergism for complex problem solving. When the HAO/BIBLE framework is applied to a regular (non-Bigdata) environment, it becomes the well-known PEAS agent structure, and when the knowledge graph in HAO/BIBLE relies on domain expertise (rather than big knowledge), HAO/BIBLE serves as an expert system. This paper compares and contrasts Bigdata, big knowledge, and big wisdom and instantiates HAO/BIBLE with a case study for intelligent catering services to illustrate synergized HAO intelligence (HI+AI+OI) for big wisdom.
C1 [Wu, Minghui; Wu, Xindong] Mininglamp Software Syst, Beijing, Peoples R China.
   [Wu, Xindong] Hefei Univ Technol, Res Inst Big Knowledge, Hefei, Anhui, Peoples R China.
RP Wu, XD (通讯作者)，Mininglamp Software Syst, Beijing, Peoples R China.; Wu, XD (通讯作者)，Hefei Univ Technol, Res Inst Big Knowledge, Hefei, Anhui, Peoples R China.
EM wuminghui@minginglamp.com; wuxindong@minginglamp.com
RI Wu, Xindong/AAB-6713-2022
OI Wu, Xindong/0000-0003-2396-1704
FU National Key Research and Development Program of China [2016YFB1000900];
   National Natural Science Foundation of China (NSFC) [91746209]; Program
   for Changjiang Scholars and Innovative Research Team in University
   (PCSIRT) of the Ministry of Education, China [IRT17R32]
FX This research is supported by the National Key Research and Development
   Program of China under Grant 2016YFB1000900, the National Natural
   Science Foundation of China (NSFC) under Grant 91746209, and the Program
   for Changjiang Scholars and Innovative Research Team in University
   (PCSIRT) of the Ministry of Education, China, under Grant IRT17R32.
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   Wu XD, 2014, IEEE T KNOWL DATA EN, V26, P97, DOI 10.1109/TKDE.2013.109
NR 11
TC 15
Z9 18
U1 3
U2 15
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 JAN
PY 2019
VL 58
IS 1
BP 1
EP 8
DI 10.1007/s10115-018-1282-y
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HI6YM
UT WOS:000456601400001
DA 2022-08-23
ER

PT J
AU Dai, ZQ
   Xu, SM
   Wu, X
   Hu, RX
   Li, HM
   He, HQ
   Hu, J
   Liao, X
AF Dai, Zeqi
   Xu, Simin
   Wu, Xue
   Hu, Ruixue
   Li, Huimin
   He, Haoqiang
   Hu, Jing
   Liao, Xing
TI Knowledge Mapping of Multicriteria Decision Analysis in Healthcare: A
   Bibliometric Analysis
SO FRONTIERS IN PUBLIC HEALTH
LA English
DT Review
DE bibliometric analysis; healthcare; multicriteria decision analysis;
   R-bibliometrix; VOSviewer; CiteSpace
ID TECHNOLOGY-ASSESSMENT; EVIDEM FRAMEWORK; ANALYSIS MCDA; MAKING METHODS;
   MEDICINES; IMPACT
AB ObjectiveMulticriteria decision analysis (MCDA) is a useful tool in complex decision-making situations, and has been used in medical fields to evaluate treatment options and drug selection. This study aims to provide valuable insights into MCDA in healthcare through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain. MethodsA bibliometric analysis was conducted on the publication related to MCDA in healthcare from the Web of Science Core Collection (WoSCC) database on 14 July 2021. Three bibliometric software (VOSviewer, R-bibliometrix, and CiteSpace) were used to conduct the analysis including years, countries, institutes, authors, journals, co-citation references, and keywords. ResultsA total of 410 publications were identified with an average yearly growth rate of 32% (1999-2021), from 196 academic journals with 23,637 co-citation references by 871 institutions from 70 countries/regions. The United States was the most productive country (n = 80). Universiti Pendidikan Sultan Idris (n = 16), Universite de Montreal (n = 13), and Syreon Research Institute (n = 12) were the top productive institutions. A A Zaidan, Mireille Goetghebeur and Zoltan Kalo were the biggest nodes in every cluster of authors' networks. The top journals in terms of the number of articles (n = 17) and citations (n = 1,673) were Value in Health and Journal of Medical Systems, respectively. The extant literature has focused on four aspects, including the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. COVID-19 and fuzzy TOPSIS received careful attention from MCDA applications recently. MCDA in big data, telemedicine, TOPSIS, and fuzzy AHP is well-developed and an important theme, which may be the trend in future research. ConclusionThis study uncovers a holistic picture of the performance of MCDA-related literature published in healthcare. MCDA has a broad application on different topics and would be helpful for practitioners, researchers, and decision-makers working in healthcare to advance the wheel of medical complex decision-making. It can be argued that the door is still open for improving the role of MCDA in healthcare, whether in its methodology (e.g., fuzzy TOPSIS) or application (e.g., telemedicine).
C1 [Dai, Zeqi; Xu, Simin; Wu, Xue; Hu, Ruixue; Li, Huimin; Liao, Xing] China Acad Chinese Med Sci, Inst Basic Res Clin Med, Ctr Evidence Based Chinese Med, Beijing, Peoples R China.
   [He, Haoqiang] China Acad Chinese Med Sci, Guanganmen Hosp, Dept Cardiol, Beijing, Peoples R China.
   [Hu, Jing] Capital Med Univ, Beijing Hosp Tradit Chinese Med, Beijing Inst Tradit Chinese Med, Evidence Based Med Ctr, Beijing, Peoples R China.
RP Liao, X (通讯作者)，China Acad Chinese Med Sci, Inst Basic Res Clin Med, Ctr Evidence Based Chinese Med, Beijing, Peoples R China.; Hu, J (通讯作者)，Capital Med Univ, Beijing Hosp Tradit Chinese Med, Beijing Inst Tradit Chinese Med, Evidence Based Med Ctr, Beijing, Peoples R China.
EM hujingebm@163.com; okfrom2008@hotmail.com
RI Hu, Rui-xue/AAC-3527-2019
OI Hu, Rui-xue/0000-0002-1333-0485; Dai, Zeqi/0000-0001-6928-4863
FU National Natural Science Foundation of China [82174239]; CACMS
   Innovation Fund [CI2021A00701-3]; Fundamental Research Funds for the
   Central Public Welfare Research Institutes [ZZ13-YQ-075]; China Center
   for Evidence Based Traditional Chinese Medicine [2020YJSZX-2]; 14th
   Basic Scientific Research Project of CACMS [Z0718, Z0724, Z0754]
FX This study was supported by the National Natural Science Foundation of
   China (No. 82174239), CACMS Innovation Fund (No. CI2021A00701-3), the
   Fundamental Research Funds for the Central Public Welfare Research
   Institutes (No. ZZ13-YQ-075), China Center for Evidence Based
   Traditional Chinese Medicine, CCEBTCM (No. 2020YJSZX-2), and the 14th
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NR 66
TC 0
Z9 0
U1 36
U2 36
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2296-2565
J9 FRONT PUBLIC HEALTH
JI Front. Public Health
PD JUN 9
PY 2022
VL 10
AR 895552
DI 10.3389/fpubh.2022.895552
PG 15
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA 2I4YA
UT WOS:000814984500001
PM 35757629
DA 2022-08-23
ER

PT J
AU Fares, M
   Moufarrej, A
   Jreij, E
   Tekli, J
   Grosky, W
AF Fares, Mireille
   Moufarrej, Angela
   Jreij, Eliane
   Tekli, Joe
   Grosky, William
TI Unsupervised word-level affect analysis and propagation in a lexical
   knowledge graph
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Sentiment analysis; Affect analysis; Knowledge base; Graph navigation;
   Sentiment lexicon; ANEW
ID SENTIMENT ANALYSIS; CLASSIFICATION; EMOTION; TEXT
AB Lexical sentiment analysis (LSA) is of central importance in extracting and analyzing user moods and views on the Web. Most existing LSA approaches have utilized supervised learning techniques applied on corpus-based statistics, requiring extensive training data, training time, and large statistical corpora which are not always available. Other studies have utilized unsupervised and lexicon-based approaches to match target words in a lexical knowledge base (KB) with seed words in a sentiment lexicon, usually suffering from the limited coverage or inconsistent connectivity of affective concepts. In this paper, we introduce LISA, an unsupervised word-level knowledge graph-based LSA framework. It uses different variants of shortest path graph navigation techniques to compute and propagate affective scores in a lexical-affective graph (LAG), created by connecting a typical lexical KB like WordNet, with a reliable affect KB like WordNet-Affect Hierarchy (where any other lexical or affective KB can be utilized). LISA was designed in two consecutive iterations, producing two main modules: i) LISA 1.0 for affect navigation, and ii) LISA 2.0 for affect propagation and lookup. LISA 1.0 suffered from the semantic connectivity problem shared by some existing lexicon-based methods, and required polynomial execution time. This led to the development of LISA 2.0, which i) processes affective relationships separately from lexical/semantic connections (solving the semantic connectivity problem of LISA 1.0), and ii) produces a sentiment lexicon which can be searched in logarithmic time (handling LISA 1.0's efficiency problem). Experimental results on the ANEW dataset show that our approach, namely LISA 2.0, while completely unsupervised, is on a par with existing (semi)supervised solutions, highlighting its quality and potential. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Fares, Mireille; Moufarrej, Angela; Jreij, Eliane; Tekli, Joe] Lebanese Amer Univ, Dept Elect & Comp Engn, Byblos, Lebanon.
   [Grosky, William] Univ Michigan UMich, Dept Comp & Informat Sci, Dearborn, MI USA.
RP Tekli, J (通讯作者)，Lebanese Amer Univ, Dept Elect & Comp Engn, Byblos, Lebanon.
EM mireille.fares@lau.edu; angela.moufarrej@lau.edu; Eliane.jreij@lau.edu;
   Joe.tekli@lau.edu.lb; wgrosky@umich.edu
RI Tekli, Joe/AAE-4591-2020
OI Tekli, Joe/0000-0003-3441-7974
FU National Council for Scientific Research - Lebanon (CNRS-L); Lebanese
   American University (LAU); Fulbright Visiting Scholar program (US
   Department of State)
FX This study is partly funded by the National Council for Scientific
   Research - Lebanon (CNRS-L), by the Lebanese American University (LAU),
   and by the Fulbright Visiting Scholar program (sponsored by the US
   Department of State). Special thanks go to LAU computer engineering
   graduates Ms. Souad Charafeddine and Mr. Ralph Abboud who partly
   contributed to USA's implementation.
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NR 131
TC 17
Z9 17
U1 1
U2 29
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 1
PY 2019
VL 165
BP 432
EP 459
DI 10.1016/j.knosys.2018.12.017
PG 28
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA HJ9GH
UT WOS:000457506400030
DA 2022-08-23
ER

PT J
AU Khalid, S
   Wu, SL
   Wahid, A
   Alam, A
   Ullah, I
AF Khalid, Shah
   Wu, Shengli
   Wahid, Abdul
   Alam, Aftab
   Ullah, Irfan
TI An Effective Scholarly Search by Combining Inverted Indices and
   Structured Search With Citation Networks Analysis
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Indexes; Internet; Search problems; Search engines; Computer
   science; Task analysis; Academic search; knowledge graph; inverted
   index; structure search; citation networks analysis
ID INFORMATION; RETRIEVAL; WEB
AB The rapid growth in the number of scholarly documents on the Web and in other digital platforms makes it challenging for researchers to find research publications most relevant to their information needs. This challenge has been mitigated to a greater extent by the major scholarly retrieval systems, such as Google Scholar, Semantic Scholar, PubMed, CiteSeerX, and others. The reason for the success of these retrieval solutions lies in the advances in ranking approaches. However, the existing studies advocate for the fact that we are still far from the method's effectiveness ceiling, leaving ample room for further improvement to meet the scholarly needs of users. The existing methods adopt different approaches; some use classical Information Retrieval (IR), others use semantics-aware methods, including Knowledge Graph (KG) to support scholarly search. However, we hypothesize that combining the best of both worlds can further improve search relevance. In this context, this work incorporates inverted index from the classical IR with BM25 as the weighting scheme, combined with Citation Networks Analysis (CNA) for the baseline search results, which are then re-ranked by passing the selected entities from the top-k initial search results as the search query to the KG. This way, not only the textual content but also the structural semantics of the research publications are well exploited in the retrieval processes. The goal is to exploit IR and KG-based retrieval techniques to gain insights into the behavior of both textual and structured information in the strategic ranking of scholarly articles. The proposed solution has been evaluated using the ACL Anthology Network (AAN) dataset. The results show that the proposed technique can comparatively improve the retrieval performance in terms of Normalized Discounted Cumulative Gain (nDCG) and precision rates.
C1 [Khalid, Shah; Wu, Shengli] Jiangsu Univ, Sch Comp Sci & Commun Engn, Zhenjiang 212013, Jiangsu, Peoples R China.
   [Khalid, Shah; Wahid, Abdul] Natl Univ Sci & Technol, Sch Elect Engn & Comp Sci, Islamabad 44000, Pakistan.
   [Wu, Shengli] Ulster Univ, Sch Comp, Belfast BT52 1SA, Antrim, North Ireland.
   [Alam, Aftab] Hamad Bin Khalifa Univ, Coll Sci & Engn, Ar Rayyan, Qatar.
   [Alam, Aftab] Kyung Hee Univ, Dept Comp Sci & Engn, Global Campus, Yongin 17104, South Korea.
   [Ullah, Irfan] Shaheed Benazir Bhutto Univ, Dept Comp Sci, Sheringal 18050, Pakistan.
RP Khalid, S (通讯作者)，Jiangsu Univ, Sch Comp Sci & Commun Engn, Zhenjiang 212013, Jiangsu, Peoples R China.; Khalid, S (通讯作者)，Natl Univ Sci & Technol, Sch Elect Engn & Comp Sci, Islamabad 44000, Pakistan.
EM shah.khalid@seecs.edu.pk
RI Ullah, Irfan/CAA-4310-2022; Khalid, Shah/AAC-8325-2021
OI Ullah, Irfan/0000-0003-0693-5467; Khalid, Shah/0000-0001-5735-5863
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NR 71
TC 0
Z9 0
U1 3
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 2021
VL 9
BP 120210
EP 120226
DI 10.1109/ACCESS.2021.3107939
PG 17
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 UK8OX
UT WOS:000692226300001
OA gold
DA 2022-08-23
ER

PT J
AU Alemu, EN
   Huang, JB
AF Alemu, Eyob N.
   Huang, Jianbin
TI HealthAid: Extracting domain targeted high precision procedural
   knowledge from on-line communities
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Commonsense know-how; Health informatics; Knowledge extraction;
   Procedural knowledge base/graph
AB Recent advances in semantic web have shown how entity related searches have benefited from entity-based knowledge graphs. However, much of the commonsense knowledge about the real world is in the form of procedures or sequences of actions. Also, search log analysis shows that 'how-to queries' make up a significant amount of users' queries. Unfortunately, these kinds of knowledge are missing from most knowledge graphs and commonsense knowledge bases in use. To empower semantic search, and other intelligent applications, computers need a much broader understanding of the world properties of everyday objects, human activities, and more. Luckily, such knowledge is abundantly available on-line and can be accessed from how-to communities. One domain of interest by on-line communities is the health domain, whereby users usually seek home remedies to common health-related issues. An example of such queries might be 'how to stop nausea using acupressure' or 'how to aid digestion naturally'. To answer such questions, we need systems that understand natural language and knowledge bases with task frames of solutions in a holistic approach, including the tools required, the agents involved, and the temporal order of the actions. Our goal is to construct a machine-readable domain targeted high precision procedural knowledge base containing task frames. We developed a pipeline of methods leveraging open information extraction tool to extract procedural knowledge by tapping into on-line communities. Also, we devised a mechanism to canonicalize the task frames into clusters based on the similarity of the problems they intend to solve. The resulting know-how knowledge base, HealthAidKB, consists of more than 71 K task frames which are structured hierarchically and categorically; and can be used in many applications such as semantic search, digital personal assistants, human-computer dialog and computer vision. A comprehensive evaluation of our knowledge base shows high accuracy.
C1 [Alemu, Eyob N.; Huang, Jianbin] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China.
   [Alemu, Eyob N.] 19 Ambo Univ, Ambo, Ethiopia.
RP Alemu, EN (通讯作者)，Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China.; Alemu, EN (通讯作者)，19 Ambo Univ, Ambo, Ethiopia.
EM eyobn@stu.xidian.edu.cn
FU National Science Foundation of China (NSFC) [61876138]
FX This work was partially supported by the National Science Foundation of
   China (NSFC) [Grant No 61876138].
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NR 54
TC 3
Z9 4
U1 2
U2 6
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD NOV
PY 2020
VL 57
IS 6
AR 102299
DI 10.1016/j.ipm.2020.102299
PG 12
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA OG9PN
UT WOS:000582206800028
DA 2022-08-23
ER

PT J
AU Montpetit-Tourangeau, K
   Dyer, JO
   Hudon, A
   Windsor, M
   Charlin, B
   Mamede, S
   van Gog, T
AF Montpetit-Tourangeau, Katherine
   Dyer, Joseph-Omer
   Hudon, Anne
   Windsor, Monica
   Charlin, Bernard
   Mamede, Silvia
   van Gog, Tamara
TI Fostering clinical reasoning in physiotherapy: comparing the effects of
   concept map study and concept map completion after example study in
   novice and advanced learners
SO BMC MEDICAL EDUCATION
LA English
DT Article
DE Clinical reasoning; Concept map; Cognitive load; Worked example; Near
   transfer; Far transfer; Physiotherapy
ID WORKED-OUT EXAMPLES; COGNITIVE LOAD THEORY; KNOWLEDGE MAPS;
   INSTRUCTIONAL EXPLANATIONS; SCRIPTED COOPERATION; DIAGNOSTIC KNOWLEDGE;
   SURGICAL COMPETENCE; EXPERTISE; STRATEGY; STUDENTS
AB Background: Health profession learners can foster clinical reasoning by studying worked examples presenting fully worked out solutions to a clinical problem. It is possible to improve the learning effect of these worked examples by combining them with other learning activities based on concept maps. This study investigated which combinaison of activities, worked examples study with concept map completion or worked examples study with concept map study, fosters more meaningful learning of intervention knowledge in physiotherapy students. Moreover, this study compared the learning effects of these learning activity combinations between novice and advanced learners.
   Methods: Sixty-one second-year physiotherapy students participated in the study which included a pre-test phase, a 130-min guided-learning phase and a four-week self-study phase. During the guided and self-study learning sessions, participants had to study three written worked examples presenting the clinical reasoning for selecting electrotherapeutic currents to treat patients with motor deficits. After each example, participants engaged in either concept map completion or concept map study depending on which learning condition they were randomly allocated to. Students participated in an immediate post-test at the end of the guided-learning phase and a delayed post-test at the end of the self-study phase. Post-tests assessed the understanding of principles governing the domain of knowledge to be learned (conceptual knowledge) and the ability to solve new problems that have similar (i.e., near transfer) or different (i.e., far transfer) solution rationales as problems previously studied in the examples.
   Results: Learners engaged in concept map completion outperformed those engaged in concept map study on near transfer (p = .010) and far transfer (p < .001) performance. There was a significant interaction effect of learners' prior ability and learning condition on conceptual knowledge but not on near and far transfer performance.
   Conclusions: Worked examples study combined with concept map completion led to greater transfer performance than worked examples study combined with concept map study for both novice and advanced learners. Concept map completion might give learners better insight into what they have and have not yet learned, allowing them to focus on those aspects during subsequent example study.
C1 [Montpetit-Tourangeau, Katherine; Dyer, Joseph-Omer; Hudon, Anne; Windsor, Monica] Univ Montreal, Sch Rehabil, Fac Med, POB 6128,Stn Ctr Ville, Montreal, PQ H3C 3J7, Canada.
   [Montpetit-Tourangeau, Katherine; Hudon, Anne] Ctr Interdisciplinary Res Rehabil Greater Montrea, Montreal, PQ, Canada.
   [Dyer, Joseph-Omer; Charlin, Bernard] Univ Montreal, CPASS, Montreal, PQ, Canada.
   [Charlin, Bernard] Montreal Gen Hosp, Dept Neurol, Montreal, PQ, Canada.
   [Mamede, Silvia] Erasmus MC, Inst Med Educ Res Rotterdam, Rotterdam, Netherlands.
   [Mamede, Silvia; van Gog, Tamara] Erasmus Univ, Dept Psychol, Rotterdam, Netherlands.
   [van Gog, Tamara] Univ Utrecht, Dept Educ, Utrecht, Netherlands.
RP Dyer, JO (通讯作者)，Univ Montreal, Sch Rehabil, Fac Med, POB 6128,Stn Ctr Ville, Montreal, PQ H3C 3J7, Canada.; Dyer, JO (通讯作者)，Univ Montreal, CPASS, Montreal, PQ, Canada.
EM joseph.omer.dyer@umontreal.ca
RI Mamede, Silvia/T-4053-2019; Hudon, Anne/X-8817-2019; Schmidt, Henk
   G/A-3410-2012
OI Dyer, Joseph-Omer/0000-0002-7570-9941
FU Centre de pedagogie appliquee aux sciences de la sante (CPASS) of
   Universite de Montreal
FX This study was funded by the Centre de pedagogie appliquee aux sciences
   de la sante (CPASS) of Universite de Montreal.
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NR 87
TC 7
Z9 7
U1 5
U2 18
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6920
J9 BMC MED EDUC
JI BMC Med. Educ.
PD DEC 1
PY 2017
VL 17
AR 238
DI 10.1186/s12909-017-1076-z
PG 23
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA FO5WN
UT WOS:000416932400001
PM 29191189
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Trouillon, T
   Gaussier, E
   Dance, CR
   Bouchard, G
AF Trouillon, Theo
   Gaussier, Eric
   Dance, Christopher R.
   Bouchard, Guillaume
TI On Inductive Abilities of Latent Factor Models for Relational Learning
SO JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
LA English
DT Article
AB Latent factor models are increasingly popular for modeling multi-relational knowledge graphs. By their vectorial nature, it is not only hard to interpret why this class of models works so well, but also to understand where they fail and how they might be improved. We conduct an experimental survey of state-of-the-art models, not towards a purely comparative end, but as a means to get insight about their inductive abilities. To assess the strengths and weaknesses of each model, we create simple tasks that exhibit first, atomic properties of binary relations, and then, common inter-relational inference through synthetic genealogies. Based on these experimental results, we propose new research directions to improve on existing models.
C1 [Trouillon, Theo; Gaussier, Eric] Univ Grenoble Alpes, 700 Ave Cent, F-38401 St Martin Dheres, France.
   [Dance, Christopher R.] NAVER LABS Europe, 6 Chemin Maupertuis, F-38240 Meylan, France.
   [Bouchard, Guillaume] Facebook, 1 Rathbone Sq, London WT1 1FB, England.
RP Trouillon, T (通讯作者)，Univ Grenoble Alpes, 700 Ave Cent, F-38401 St Martin Dheres, France.
EM theo.trouillon@imag.fr; eric.gaussier@imag.fr;
   chris.dance@naverlabs.com; gbouchard@fb.com
FU Association Nationale de la Recherche et de la Technologie through the
   CIFRE grant [2014/0121]
FX This work was supported in part by the Association Nationale de la
   Recherche et de la Technologie through the CIFRE grant 2014/0121.
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NR 70
TC 6
Z9 6
U1 0
U2 0
PU AI ACCESS FOUNDATION
PI MARINA DEL REY
PA USC INFORMATION SCIENCES INST, 4676 ADMIRALITY WAY, MARINA DEL REY, CA
   90292-6695 USA
SN 1076-9757
EI 1943-5037
J9 J ARTIF INTELL RES
JI J. Artif. Intell. Res.
PY 2019
VL 64
BP 21
EP 53
DI 10.1613/jair.1.11305
PG 33
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HW8YF
UT WOS:000466976900002
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Fathalla, S
AF Fathalla, Said
TI Detecting Human Diseases Relatedness: A Spreading Activation Approach
   Over Ontologies
SO INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS
LA English
DT Article
DE Bidirectional Search; Disease Ontology; Semantic Matching; Spreading
   Activation
ID SEMANTIC WEB
AB Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAGO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.
C1 [Fathalla, Said] Univ Bonn, Comp Sci, SDA, Bonn, Germany.
   [Fathalla, Said] Alexandria Univ, Fac Sci, Dept Math & Comp Sci, Alexandria, Egypt.
RP Fathalla, S (通讯作者)，Univ Bonn, Comp Sci, SDA, Bonn, Germany.; Fathalla, S (通讯作者)，Alexandria Univ, Fac Sci, Dept Math & Comp Sci, Alexandria, Egypt.
OI Fathalla, Said/0000-0002-2818-5890
FU Ministry of Higher Education (MoHE) of Egypt
FX Said Fathalla would like to acknowledge the Ministry of Higher Education
   (MoHE) of Egypt for providing a scholarship to conduct this study as
   well as the University of Bonn, Germany for offering all facilities and
   tools required to do this work.
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NR 52
TC 4
Z9 4
U1 0
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.
PD JUL-SEP
PY 2018
VL 14
IS 3
BP 120
EP 133
DI 10.4018/IJSWIS.2018070106
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HI1JS
UT WOS:000456202200006
DA 2022-08-23
ER

PT J
AU Pensa, RG
   Sapino, ML
   Schifanella, C
   Vignaroli, L
AF Pensa, Ruggero G.
   Sapino, Maria Luisa
   Schifanella, Claudio
   Vignaroli, Luca
TI Leveraging Cross-Domain Social Media Analytics to Understand TV Topics
   Popularity
SO IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
LA English
DT Article
ID RETRIEVAL
AB The way we watch television is changing with the introduction of attractive Web activities that move users away from TV to other media. The social multimedia and user-generated contents are dramatically changing all phases of the value chain of contents (production, distribution and consumption). We propose a concept-level integration framework in which users' activities on different social media are collectively represented, and possibly enriched with external knowledge, such as information extracted from the Electronic Program Guides, or available ontological domain knowledge. The integration framework has a knowledge graph as its core data model. It keeps track of active users, the television events they talk about, the concepts they mention in their activities, as well as different relationships existing among them. Temporal relationships are also captured to enable temporal analysis of the observed activity. The data model allows different types of analysis and the definition of global metrics in which the activity on different media concurs with the measure of success.
C1 [Pensa, Ruggero G.; Sapino, Maria Luisa] Univ Torino, Dept Comp Sci, Turin, Italy.
   [Schifanella, Claudio; Vignaroli, Luca] RAI CRIT, Turin, Italy.
RP Pensa, RG (通讯作者)，Univ Torino, Dept Comp Sci, Turin, Italy.
EM ruggero.pensa@unito.it
RI Pensa, Ruggero G./B-5994-2011
OI Pensa, Ruggero G./0000-0001-5145-3438; Schifanella,
   Claudio/0000-0001-7449-6529
CR Alashri S., 2015, P ASE IEEE INT C SOC
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NR 40
TC 8
Z9 8
U1 0
U2 17
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1556-603X
EI 1556-6048
J9 IEEE COMPUT INTELL M
JI IEEE Comput. Intell. Mag.
PD AUG
PY 2016
VL 11
IS 3
BP 11
EP 22
DI 10.1109/MCI.2016.2572518
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DS1FR
UT WOS:000380341700002
DA 2022-08-23
ER

PT J
AU Xiao, Y
   Li, CD
   Liu, VCZ
AF Xiao, Yan
   Li, Congdong
   Liu, Vincenzo
TI DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph
   Neural Network
SO MATHEMATICS
LA English
DT Article
DE DeepFM; GCN; knowledge graph; DNN; representation learning;
   recommendation systems
AB Among the inherent problems in recommendation systems are data sparseness and cold starts; the solutions to which lie in the introduction of knowledge graphs to improve the performance of the recommendation systems. The results in previous research, however, suffer from problems such as data compression, information damage, and insufficient learning. Therefore, a DeepFM Graph Convolutional Network (DFM-GCN) model was proposed to alleviate the above issues. The prediction of the click-through rate (CTR) is critical in recommendation systems where the task is to estimate the probability that a user will click on a recommended item. In many recommendation systems, the goal is to maximize the number of clicks so the items returned to a user can be ranked by an estimated CTR. The DFM-GCN model consists of three parts: the left part DeepFM is used to capture the interactive information between the users and items; the deep neural network is used in the middle to model the left and right parts; and the right one obtains a better item representation vector by the GCN. In an effort to verify the validity and precision of the model built in this research, and based on the public datasets ml1m-kg20m and ml1m-kg1m, a performance comparison experiment was designed. It used multiple comparison models and the MKR and FM_MKR algorithms as well as the DFM-GCN algorithm constructed in this paper. Having achieved a state-of-the-art performance, the experimental results of the AUC and f1 values verified by the CTR as well as the accuracy, recall, and f1 values of the top-k showed that the proposed approach was excellent and more effective when compared with different recommendation algorithms.
C1 [Xiao, Yan; Li, Congdong; Liu, Vincenzo] Macau Univ Sci & Technol, Sch Business, Macau 999078, Peoples R China.
   [Xiao, Yan] Chongqing Univ Technol, Coll Mech Engn, Chongqing 400054, Peoples R China.
   [Li, Congdong] Jinan Univ, Management Sch, Guangzhou 510632, Peoples R China.
   [Li, Congdong] Jinan Univ Zhuhai Campus, Inst Phys Internet, Zhuhai 519070, Peoples R China.
RP Li, CD; Liu, VCZ (通讯作者)，Macau Univ Sci & Technol, Sch Business, Macau 999078, Peoples R China.; Li, CD (通讯作者)，Jinan Univ, Management Sch, Guangzhou 510632, Peoples R China.; Li, CD (通讯作者)，Jinan Univ Zhuhai Campus, Inst Phys Internet, Zhuhai 519070, Peoples R China.
EM licd@jnu.edu.cn; licd@jnu.edu.cn; licd@jnu.edu.cn
FU National Natural Science Foundation of China [71672074, 72072072];
   Natural Science Foundation of Guangdong Province of China
   [2019A1515010045]; 2018 Guangzhou Leading Innovation Team Program
   (China) [201909010006]
FX The authors would like to acknowledge partial financial support from the
   National Natural Science Foundation of China, No. 71672074; National
   Natural Science Foundation of China, No. 72072072; Natural Science
   Foundation of Guangdong Province of China, No. 2019A1515010045; and 2018
   Guangzhou Leading Innovation Team Program (China), No. 201909010006.
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NR 24
TC 1
Z9 1
U1 18
U2 18
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD MAR
PY 2022
VL 10
IS 5
AR 721
DI 10.3390/math10050721
PG 14
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA ZW7WU
UT WOS:000771419800001
OA gold
DA 2022-08-23
ER

PT J
AU Jiang, YJ
   Liu, H
   Jin, JH
   Zhang, L
AF Jiang, Yanjie
   Liu, Hui
   Jin, Jiahao
   Zhang, Lu
TI Automated Expansion of Abbreviations Based on Semantic Relation and
   Transfer Expansion
SO IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
LA English
DT Article
DE Abbreviation; expansion; transfer; context; quality
ID RECOVERING TRACEABILITY LINKS; INFORMATION-RETRIEVAL; KNOWLEDGE GRAPH;
   CODE; COMPREHENSION
AB Although the negative impact of abbreviations in source code is well-recognized, abbreviations are common for various reasons. To this end, a number of approaches have been proposed to expand abbreviations in identifiers. However, such approaches are either inaccurate or confined to specific identifiers. To this end, in this paper, we propose a generic and accurate approach to expand identifier abbreviations by leveraging both semantic relation and transfer expansion. One of the key insights of the approach is that abbreviations in the name of software entity e have a great chance to find their full terms in names of software entities that are semantically related to e. Consequently, the proposed approach builds a knowledge graph to represent such entities and their relationships with c and searches the graph for full terms. Another key insight is that literally identical abbreviations within the same application are likely (but not necessary) to have identical expansions, and thus the semantics-based expansion in one place may be transferred to other places. To investigate when abbreviation expansion could be transferred safely, we conduct a case study on three open-source applications. The results suggest that a significant part (75 percent) of expansions could be transferred among lexically identical abbreviations within the same application. However, the risk of transfer varies according to various factors, e.g., length of abbreviations, the physical distance between abbreviations, and semantic relations between abbreviations. Based on these findings, we design nine heuristics for transfer expansion and propose a learning-based approach to prioritize both transfer heuristics and semantic-based expansion heuristics. Evaluation results on nine open-source applications suggest that the proposed approach significantly improves the state of the art, improving recall from 29 to 89 percent and precision from 39 to 92 percent.
C1 [Jiang, Yanjie; Liu, Hui; Jin, Jiahao] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.
   [Zhang, Lu] Peking Univ, Key Lab High Confidence Software Technol, Minist Educ, Beijing 100871, Peoples R China.
RP Liu, H (通讯作者)，Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.
EM jiangyanjie@bit.edu.cn; liuhui08@bit.edu.cn; jinjiahao1993@gmail.com;
   zhanglu@sei.pku.edu.cn
OI Liu, Hui/0000-0002-3267-6801; Zhang, Lu/0000-0001-8304-7055
FU National Natural Science Foundation of China [61772071, 61690205]; China
   Scholarship Council
FX The authors would like to thank the associate editor and anonymous
   reviewers for their insightful comments and constructive suggestions.
   This work was partially supported by the National Natural Science
   Foundation of China under Grant No. 61772071 and 61690205, and China
   Scholarship Council.
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NR 45
TC 2
Z9 2
U1 2
U2 2
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0098-5589
EI 1939-3520
J9 IEEE T SOFTWARE ENG
JI IEEE Trans. Softw. Eng.
PD FEB 1
PY 2022
VL 48
IS 2
BP 519
EP 537
DI 10.1109/TSE.2020.2995736
PG 19
WC Computer Science, Software Engineering; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA ZB4EB
UT WOS:000756796100010
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 Is diversity optimization always suitable? Toward a better understanding
   of diversity within recommendation approaches
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Recommender system; Diversity; Greedy optimization; DBpedia; Knowledge
   graph embedding; Deep learning
AB The diversity of the item list suggested by recommender systems has been proven to impact user satisfaction significantly. Most of the existing diversity optimization approaches re-rank the list of candidate items during a post-processing step. However, the diversity level of the candidate list strongly depends on the recommender system used. Hence, applying the same post-processing diversification strategy may not be as effective for different recommendation approaches. Moreover, individual users' diversity needs are usually ignored in the diversification post-processing.
   This article aims at providing an in-depth analysis of the diversity performances of different recommender systems. To the best of our knowledge, it is the first study to systematically compare diversity performances of the main types of recommendation models using benchmark datasets in different domains (movie, anime and book). Semantics related to items may be considered a key factor in measuring diversity within recommender systems. In this study, we leverage support from the knowledge engineering domain and take advantage of resources such as Linked Data and knowledge graphs, to assert the diversity of recommendations. We also propose a variant of the classic diversification post-processing objective that allows to take into account specific users' diversity needs. We measure the adequacy between the diversity levels a recommender system suggests to its users and those of users' profiles with the R-2 coefficient of determination.
   Our study indicates that (1) none of the tested recommender systems, even the most recent ones, provides items with levels of diversity that suit user profiles (R 2 < 0.2); (2) the classic post-processing diversification approach may lead to over-diversification compared to users' diversity needs and (3) the diversity adjustment that accounts for user profiles has more benefits (greater R-2 and smaller accuracy loss). All the source code and datasets used in our study are available to ensure the reproductibility of the study.
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,INRAE, F-34398 Montpellier, France.
RP Du, Y; 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;
   DU, Yu/0000-0002-1989-218X
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NR 59
TC 1
Z9 1
U1 5
U2 14
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD NOV
PY 2021
VL 58
IS 6
AR 102721
DI 10.1016/j.ipm.2021.102721
EA SEP 2021
PG 20
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 US8TR
UT WOS:000697699500012
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Ding, LL
   Xiao, ZY
   Gong, X
   Peng, YJ
AF Ding, Lulu
   Xiao, Zhenyu
   Gong, Xia
   Peng, Yaojin
TI Knowledge graphs of ethical concerns of cerebral organoids
SO CELL PROLIFERATION
LA English
DT Article
ID HUMAN-ANIMAL CHIMERAS; HUMAN BRAIN ORGANOIDS; ISSUES; CHALLENGES; MODEL
AB Objectives The rapid development of cerebral organoid technology and the gradual maturity of cerebral organoids highlight the necessity of foresighted research on relevant ethical concerns. We employed knowledge graphs and conducted statistical analysis with CiteSpace for a comprehensive analysis of the status quo of the research on the ethical concerns of cerebral organoids from a bibliometric perspective. Materials and Methods We performed a statistical analysis of published papers on cerebral organoid ethics, keyword co-occurrence graph, literature co-citation and knowledge clustering graph to examine the status of the ethics research, internal relationship between technological development and ethical research, and ethical concerns of the academia. Finally, we used a keyword time zone graph and related statistics to analyze and predict the trends and popular topics of future cerebral organoids ethics research. Results We demonstrated that although the ethical concerns of cerebral organoids have long been discussed, it was not until 2017 that the ethical issues began to receive more attention, when cerebral organoids were gradually mimicking the human brain more closely and increasingly being combined with chimera research. The recent key ethical concerns are primarily divided into three categories: concerns that are common in life sciences, specific to cerebral organoids, and present in cross-fields. These increasing ethical concerns are inherently related to the continual development of technology. The analysis pointed out that future research should focus on the ethical concerns of consciousness that are unique to cerebral organoids, ethical concerns of cross-fields, and construction and improvement of legislative and regulatory systems. Conclusions Although research on cerebral organoids can benefit the biomedicine field, the relevant ethical concerns are significant and have received increasing attention, which are inherently related to the continual development of technology. Future studies in ethics regarding cerebral organoid research should focus on the ethical concerns of consciousness, and cross-fields, as well as the improvement of regulatory systems.
C1 [Ding, Lulu; Gong, Xia; Peng, Yaojin] Chinese Acad Sci, Inst Zool, Beijing, Peoples R China.
   [Xiao, Zhenyu] Beijing Inst Technol, Sch Life Sci, Beijing, Peoples R China.
   [Xiao, Zhenyu; Peng, Yaojin] Beijing Inst Stem Cell & Regenerat Med, Beijing, Peoples R China.
   [Peng, Yaojin] Univ Chinese Acad Sci, Beijing, Peoples R China.
RP Peng, YJ (通讯作者)，Chinese Acad Sci, Inst Zool, Beijing, Peoples R China.
EM yaojin.peng@ioz.ac.cn
OI Peng, Yaojin/0000-0002-3909-6041
FU Chinese Academy of Sciences [XDA16040503]; Ministry of Science and
   Technology [2018YFE0204403]; Youth Innovation Promotion Association of
   the Chinese Academy of Sciences [2021080]
FX We would like to thank Dr. Qi Zhou, Baoyang Hu, Hongmei Wang and Wei Li
   (Chinese Academy of Sciences) for their invaluable comments and advice.
   This work was supported by the Chinese Academy of Sciences, Grant/Award
   Number: XDA16040503, the Ministry of Science and Technology, Grant/Award
   Number: 2018YFE0204403 and the Youth Innovation Promotion Association of
   the Chinese Academy of Sciences, Grant/Award Number: 2021080.
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NR 46
TC 1
Z9 1
U1 9
U2 9
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0960-7722
EI 1365-2184
J9 CELL PROLIFERAT
JI Cell Prolif.
PD AUG
PY 2022
VL 55
IS 8
SI SI
AR e13239
DI 10.1111/cpr.13239
EA MAY 2022
PG 8
WC Cell Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Cell Biology
GA 3O4XY
UT WOS:000796858200001
PM 35582763
OA Green Published
DA 2022-08-23
ER

PT J
AU Hu, HQ
   Yin, MY
   Li, JL
AF Hu, Hanqing
   Yin, Mengyue
   Li, Jianling
TI Evolution of Business Intelligence: An Analysis from the Perspective of
   Social Network
SO TEHNICKI VJESNIK-TECHNICAL GAZETTE
LA English
DT Article
DE Business Intelligence; Knowledge Atlas; Research Status; Social Network
   Analysis
ID BIG DATA; PERFORMANCE; ANALYTICS
AB Based on CiteSpace, Pajek and other software, this paper makes a visual analysis of the knowledge graph of the related literature of Business Intelligence and explores the future development trend of business intelligence. Taking the core periodicals of CNKI as the data source, key words are drawn and analyzed with the help of software. The total number of articles was 2938 from 2006 to 2020, and the number of articles published in the past 15 years was gradually levelled off. Among the 607 researchers, Yang Bingru is the representative; there are 424 journals, Journal of Information is the first, and 787 keywords are the most frequently used data mining. Our country still needs in-depth research in the field of business intelligence. Through the atlas, it directly shows that big data and machine learning are the frontier hot spots of future development, which provides research direction for researchers.
C1 [Hu, Hanqing; Yin, Mengyue] Beijing Informat Sci & Technol Univ, 12 Xiaoying East Rd,Xisanqi St, Beijing 100192, Peoples R China.
   [Li, Jianling] Beijing Union Univ, Business Coll, A3 Yanjingdongli, Beijing 100025, Peoples R China.
RP Li, JL (通讯作者)，Beijing Union Univ, Business Coll, A3 Yanjingdongli, Beijing 100025, Peoples R China.
EM hanqinghu@bistu.edu.cn; 2020020612@bistu.edu.cn; jianling.li@buu.edu.cn
FU Beijing Municipal Social Science Foundation key project [18YJA003];
   Top-notch plan project for strengthening schools with talents
   [BPHR2020BS01]; A series of research projects of the service center of
   China Association for Science and Technology [2020GGFWZLTS005]; Graduate
   Course construction project of Beijing Information Science & Technology
   University [5027011033]; Teaching Reform Project of Beijing Information
   Science & Technology University [5112110857]
FX (1) Beijing Municipal Social Science Foundation key project, 18YJA003.;
   (2) Top-notch plan project for strengthening schools with talents,
   BPHR2020BS01.; (3) A series of research projects of the service center
   of China Association for Science and Technology, 2020GGFWZLTS005.; (4)
   Graduate Course construction project of Beijing Information Science &
   Technology University, 5027011033.; (5) Teaching Reform Project of
   Beijing Information Science & Technology University, 5112110857.
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NR 42
TC 1
Z9 1
U1 17
U2 17
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 FEB
PY 2022
VL 29
IS 2
BP 497
EP 503
DI 10.17559/TV-20210819071232
PG 7
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA ZF8XJ
UT WOS:000759852300018
OA gold
DA 2022-08-23
ER

PT J
AU Zeng, WX
   Tang, JY
   Zhao, X
AF Zeng, Weixin
   Tang, Jiuyang
   Zhao, Xiang
TI Measuring Entity Relatedness via Entity and Text Joint Embedding
SO NEURAL PROCESSING LETTERS
LA English
DT Article
DE Entity relatedness; Joint embedding; Neural embedding network; Word
   embedding
AB As unique identifiers of objects and basic components of knowledge graphs, entities are crucial to many natural language processing related works, such as entity linking and question answering, in which the estimation of entity relatedness is required. Current entity relatedness measures either consider entities as words, which neglects the rich semantics entities contain, or are integrated into extrinsic applications, which fail to evaluate the intrinsic effectiveness. In this work, we propose E5, an effective entity relatedness measure taking into account of entity description text in a neural embedding manner. We first jointly map words and entities to the same high-dimensional vector space, the output of which is utilized as the input for the following joint entity and text embedding training. The well-trained entity and text embedding network is then leveraged to measure similarity between entities and entity descriptions, which in combination with a graph structure based method, constitute the eventual entity relatedness measure. The experimental results validate the usefulness of E5.
C1 [Zeng, Weixin; Tang, Jiuyang; Zhao, Xiang] Natl Univ Def Technol, Key Lab Sci & Technol Informat Syst Engn, Changsha, Hunan, Peoples R China.
   [Tang, Jiuyang; Zhao, Xiang] Collaborat Innovat Ctr Geospatial Technol, Wuhan, Hubei, Peoples R China.
RP Zhao, X (通讯作者)，Natl Univ Def Technol, Key Lab Sci & Technol Informat Syst Engn, Changsha, Hunan, Peoples R China.; Zhao, X (通讯作者)，Collaborat Innovat Ctr Geospatial Technol, Wuhan, Hubei, Peoples R China.
EM zengweixin13@nudt.edu.cn; jiuyang_genius@sina.com; xiangzhao@nudt.edu.cn
RI Zeng, Weixin/AAX-2993-2021; Zhao, Xiang/B-1303-2010
OI Zhao, Xiang/0000-0001-6339-0219; Zeng, Weixin/0000-0002-2690-3190
FU NSFC [61872446, 71690233]
FX The authors would like to thank the anonymous reviewers for their
   insightful and constructive comments, which greatly contributed to
   improving the quality of the paper. This work was partially supported by
   NSFC under Grants Nos. 61872446 and 71690233.
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NR 30
TC 2
Z9 2
U1 13
U2 19
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1370-4621
EI 1573-773X
J9 NEURAL PROCESS LETT
JI Neural Process. Lett.
PD OCT
PY 2019
VL 50
IS 2
BP 1861
EP 1875
DI 10.1007/s11063-018-9966-6
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JF0YI
UT WOS:000491112900044
DA 2022-08-23
ER

PT S
AU Schramm, M
   Fronhofer, B
AF Schramm, M
   Fronhofer, B
BE KernIsberner, G
   Rodder, W
   Kulmann, F
TI Completing incomplete Bayesian Networks
SO CONDITIONALS, INFORMATION, AND INFERENCE
SE Lecture Notes in Computer Science
LA English
DT Article; Proceedings Paper
CT International Workshop on Conditionals, Information, and Inference
CY MAY 13-15, 2002
CL Hagen, GERMANY
SP Univ Hagen, Dept Comp Sci, Univ Hagen, Dept Operat Res
DE probabilistic reasoning; maximum entropy; Bayesian Networks;
   conditionals
AB For reasoning with uncertain knowledge the use of probability theory has been broadly investigated. Two main approaches have been developed: Bayesian Networks and MaxEnt Completion.
   - Bayesian Networks allow to describe large probability distributions, but require the specification of a graph structure on the involved variables together with a complete specification of a set of conditional probabilities according to this graph. Both the graph and the set of conditional probabilities may be cumbersome to work out.
   - MaxEnt Completion is able to cope, in addition to uncertain knowledge, also with incomplete knowledge. Moreover, it does not depend on an ordering of knowledge (graph), which reduces the effort of specification.
   In this paper we investigate ways to combine these two approaches: We consider two kinds of incomplete Bayesian Networks - thus coping with incomplete (uncertain) knowledge - and study the usefulness of some variations of the MaxEnt Completion for processing or completing them. This analysis detected limits of the use of so-called update method 'ground".
C1 Tech Univ Munich, Inst Informat, D-8000 Munich, Germany.
   Tech Univ Dresden, Fak Informat, KI Inst, D-8027 Dresden, Germany.
RP Schramm, M (通讯作者)，Tech Univ Munich, Inst Informat, D-8000 Munich, Germany.
EM schramm@pit-systems.de; fronhoefer@pit-systems.de
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NR 10
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PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
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BN 3-540-25332-7
J9 LECT NOTES COMPUT SC
PY 2005
BP 200
EP 218
PG 19
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BCM44
UT WOS:000229999400012
DA 2022-08-23
ER

PT J
AU Wang, JH
   Guo, Y
   Wen, XX
   Wang, ZH
   Li, Z
   Tang, MW
AF Wang, Jiahui
   Guo, Yi
   Wen, Xinxiu
   Wang, Zhihong
   Li, Zhen
   Tang, Minwei
TI Improving graph-based label propagation algorithm with group partition
   for fraud detection
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Label propagation; Group partition; Semi-supervised; Knowledge graph;
   Fraud detection; Risk management
AB Fraudulent user detection is a crucial issue in financial risk management. Due to the lack of labeled data and the reliability of labeling, label propagation algorithms (LPA) are effective solutions in this scenario. Most existing models only propagate the risk probabilities for individual users through feature level, while ignoring the real-world graph structure and the characteristics of gang crime. This paper improves the graph-based LPA through group partition, which can be directly implemented on the graph at hand with full consideration of the group information. The exhaustive experimental results testify the performance of our proposed model KGLPA over other off-the-shelf models and amend the insufficiency of feature-based LPA with higher reliability and stability to improve the detection of fraudulent users and secure the marketing budgets.
C1 [Wang, Jiahui; Guo, Yi; Wen, Xinxiu; Wang, Zhihong] East China Univ Sci & Technol, Shanghai, Peoples R China.
   [Guo, Yi] Natl Engn Lab Big Data Distribut & Exchange Techn, Shanghai, Peoples R China.
   [Guo, Yi] Shanghai Engn Res Ctr Big Data & Internet Audienc, Shanghai, Peoples R China.
   [Li, Zhen; Tang, Minwei] China Telecom BestPay Co Ltd, Shanghai, Peoples R China.
RP Guo, Y (通讯作者)，East China Univ Sci & Technol, Shanghai, Peoples R China.; Guo, Y (通讯作者)，Natl Engn Lab Big Data Distribut & Exchange Techn, Shanghai, Peoples R China.; Guo, Y (通讯作者)，Shanghai Engn Res Ctr Big Data & Internet Audienc, Shanghai, Peoples R China.
EM wjh@piclass.cn; guoyi@ecust.edu.cn; werixinxiu@ecust.edu.cn;
   zhwang817@foxmail.com; lizhen@bestpay.com.cn; tangininwei@bestpay.com.cn
OI Jiahui, Wang/0000-0003-3217-3240
FU National Key Research and Development Program of China [2018YFC0807105];
   National Natural Science Foundation of China [61462073]; Science and
   Technology Committee of Shanghai Municipality (STCSM) [17DZ1101003,
   18511106602, 18DZ2252300]
FX This research is financially supported by The National Key Research and
   Development Program of China (grant number 2018YFC0807105), National
   Natural Science Foundation of China (grant number 61462073) and Science
   and Technology Committee of Shanghai Municipality (STCSM) (under grant
   numbers 17DZ1101003, 18511106602 and 18DZ2252300).
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PD OCT
PY 2020
VL 50
IS 10
BP 3291
EP 3300
DI 10.1007/s10489-020-01724-1
EA MAY 2020
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NI2ZM
UT WOS:000535665300001
DA 2022-08-23
ER

PT J
AU Gao, HH
   Duan, YC
   Shao, LX
   Sun, XB
AF Gao, Honghao
   Duan, Yucong
   Shao, Lixu
   Sun, Xiaobing
TI Transformation-based processing of typed resources for multimedia
   sources in the IoT environment
SO WIRELESS NETWORKS
LA English
DT Article
DE Service processing; Multimedia systems; Data Information Knowledge
   Graph; Everything as a service; Resource security protection
ID INFORMATION; KNOWLEDGE; PROTECTION
AB Web services are middleware designed to support the interoperation between different software systems and devices over the Web. Today, we encounter a variety of situations in which services deployed on the Internet of things (IoT), such as wireless sensor networks, ZigBee networks, and mobile edge computing frameworks, have become a widely used infrastructure that has become more flexible, intelligent and automated. This system supports multimedia applications, E-commerce transactions, business collaborations and information processing. However, how to manage these services has been a popular topic in IoT research. Existing research covers numerous resource models, based on sensors or human interactions. For everything as a service, things are available as a service include products, processes, resource management and security provision. To cope with the challenge of how to manage these services, we present an extension of Data, Information, Knowledge and Wisdom architecture as a resource expression model to construct a systematic approach to modeling both entity and relationship elements. The entity elements are formalized from a fully typed, multiple-related dimensions perspective to obtain a whole frequency-value-based representation of entities in the real world. A relationship model is extended and applied to define resource models based on relationships defined from a semantics perspective that is based on our proposed existence-level reasoning. Then, a processing framework is proposed that seeks to optimize the searching efficiency of typed resources in terms of IoT data, information and knowledge inside an integrated architecture, and the framework includes Data Graph, Information Graph and Knowledge Graph. We concentrate on improving performance in accessing and processing resources and providing resource security protection by utilizing the cost difference of both type conversions of resources and traversing on resources. Finally, an application scenario is simulated to illustrate the usage of the proposed framework. This scenario shows the feasibility and effectiveness of our method, considering the conversion, traversing and storage costs. Our method can help improve the optimization of services and scheduling resources of multimedia systems.
C1 [Gao, Honghao] Shanghai Univ, Comp Ctr, Shanghai, Peoples R China.
   [Duan, Yucong; Shao, Lixu] Hainan Univ, Coll Informat Sci & Technol, Haikou, Hainan, Peoples R China.
   [Sun, Xiaobing] Yangzhou Univ, Coll Comp & Informat, Yangzhou, Jiangsu, Peoples R China.
RP Duan, YC (通讯作者)，Hainan Univ, Coll Informat Sci & Technol, Haikou, Hainan, Peoples R China.
EM gaohonghao@shu.edu.cn; duanyucong@hotmail.com; 751486692@qq.com;
   sundomore@163.com
RI GAO, HONGHAO/AAX-4529-2020
OI Liu, Lu/0000-0003-1013-4507; Duan, Yucong/0000-0001-8417-892X
FU NSFC [61363007, 61662021, 61661019]; Cernet Project [NGII20180607,
   NGII20170513]
FX This paper is supported by NSFC under Grant (Nos. 61363007, 61662021,
   61661019), and Cernet Project Nos. NGII20180607 and NGII20170513.
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PU SPRINGER
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PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1022-0038
EI 1572-8196
J9 WIREL NETW
JI Wirel. Netw.
PD JUL
PY 2021
VL 27
IS 5
SI SI
BP 3377
EP 3393
DI 10.1007/s11276-019-02200-6
EA NOV 2019
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA TD4QO
UT WOS:000499548100001
DA 2022-08-23
ER

PT J
AU Zheng, WG
   Lian, X
   Zou, L
   Hong, L
   Zhao, DY
AF Zheng, Weiguo
   Lian, Xiang
   Zou, Lei
   Hong, Liang
   Zhao, Dongyan
TI Online Subgraph Skyline Analysis over Knowledge Graphs
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Subgraph skyline; feature encoding; skylayer; high dimensionality
ID ALGORITHM; JOIN; OPTIMIZATION; QUERIES
AB Subgraph search is very useful in many real-world applications. However, users may be overwhelmed by the masses of matches. In this paper, we propose a subgraph skyline analysis problem, denoted as S2A, to support more complicated analysis over graph data. Specifically, given a large graph G and a query graph q, we want to find all the subgraphs g in G, such that g is graph isomorphic to q and not dominated by any other subgraphs. In order to improve the efficiency, we devise a hybrid feature encoding incorporating both structural and numeric features based on a partitioning strategy, and discuss how to optimize the space partitioning. We also present a skylayer index to facilitate the dynamic subgraph skyline computation. Moreover, an attribute cluster-based method is proposed to deal with the curse of dimensionality. Extensive experiments over real datasets confirm the effectiveness and efficiency of our algorithm.
C1 [Zheng, Weiguo; Zou, Lei; Zhao, Dongyan] Peking Univ, Beijing 100080, Peoples R China.
   [Zheng, Weiguo] Chinese Univ Hong Kong, Hong Kong 999077, Hong Kong, Peoples R China.
   [Lian, Xiang] Univ Texas Rio Grande Valley, Edinburg, TX 78539 USA.
   [Hong, Liang] Wuhan Univ, Wuhan 430072, Peoples R China.
RP Zheng, WG (通讯作者)，Peking Univ, Beijing 100080, Peoples R China.; Zheng, WG (通讯作者)，Chinese Univ Hong Kong, Hong Kong 999077, Hong Kong, Peoples R China.
EM zhengweiguo@pku.edu.cn; xiang.lian@utrgv.edu; zoulei@pku.edu.cn;
   hong@whu.edu.cn; zhaody@pku.edu.cn
RI Lian, Xiang/T-2443-2019
OI Lian, Xiang/0000-0001-7681-3807
FU China 863 program [2015AA015402]; NSFC [61532010, 61370055, 61303025];
   Beijing Higher Education Young Elite Teacher Project [YETP0016]
FX This work was supported by China 863 program under Grant 2015AA015402,
   NSFC under Grants 61532010, 61370055, 61303025 and Beijing Higher
   Education Young Elite Teacher Project (YETP0016). This work was done
   when the first author was a PhD student at Peking University and now he
   is a Postdoc research fellow at The Chinese University of Hong Kong. Lei
   Zou is the corresponding author.
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NR 46
TC 8
Z9 8
U1 1
U2 29
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD JUL 1
PY 2016
VL 28
IS 7
BP 1805
EP 1819
DI 10.1109/TKDE.2016.2530063
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 DR7ZF
UT WOS:000380117500015
DA 2022-08-23
ER

PT J
AU Tian, LY
   Zhang, Y
   Zeng, JQ
   Wang, LQ
   Gao, HM
   Su, YN
   Li, YL
AF Tian, Lingyun
   Zhang, Ying
   Zeng, Jiaqi
   Wang, Liqian
   Gao, Huimin
   Su, Yanan
   Li, Yinglan
TI A bibliometric analysis of publications on venous thromboembolism in
   children from 1988 to 2019
SO MEDICINE
LA English
DT Review
DE bibliometric analysis; children; venous thromboembolic diseases; VTE
ID RANDOMIZED CONTROLLED-TRIAL; MOLECULAR-WEIGHT HEPARIN; POSTTHROMBOTIC
   SYNDROME; RISK-FACTORS; FACTOR-VIII; THROMBOSIS; THROMBOPHILIA;
   PREVENTION; IMPACT; METAANALYSIS
AB Introduction: Venous thromboembolism (VTE) in children not only imposes a heavy burden on the medical resources and economy of the society, but also seriously affects the growth and development of children, even threaten children's lives. A large number of publications have been performed in this field in recent years. In this bibliometric analysis, publications on VTE in children were examined and analyzed to explain the present research hotspots. Methods: Articles related to VTE in children published in the PubMed database from 1988 to June 18, 2019 were selected as the research sample. BICOMB software was used to retrieve the annual publications, journals, journal source countries and the high-frequency major medical subject headings (MeSH) terms on the articles. Then, the co-word matrix was constructed by BICOMB using the selected high-frequency MeSH terms. Next, gCLUTO software was used to analyze the matrix by double clustering and visual analysis in a strategy of hotspot identification. In addition, CiteSpace software was used to perform the knowledge map of co-authors to explore the core authors. Results: One thousand seven hundred seventy-nine articles on VTE in children were obtained. Seven hundred ninety academic journals distributed in 58 countries have published articles on VTE in children, and the developed countries and the United States were the major force in the journal source countries. Nowak-Gottl U occupied an important position in this field. We constructed a co-word matrix composing of 37 high-frequency MeSH terms, generated visual matrix and visual hill, and classified the hot-spots into 5 aspects based on 8 categories. Conclusion: The results show that the research trend of children's VTE has been increasing gradually, and the sound achievement has been obtained in these hotspots in relation to the area of inherited thrombophilia, prevention and control, treatment, diagnosis, prevalence, risk factors, and complication study. However, there is a lack of communication and cooperation in this field, and the gap of national and regional research results is huge. To sum up, this study provides evidence and guidance for researchers, clinicians, and educators.
C1 [Tian, Lingyun; Zeng, Jiaqi; Wang, Liqian; Gao, Huimin; Su, Yanan; Li, Yinglan] Cent South Univ, Xiangya Sch Nursing, Changsha, Peoples R China.
   [Tian, Lingyun] Anhui Univ Chinese Med, Sch Nursing, Hefei, Peoples R China.
   [Zhang, Ying] Cent South Univ, Xiangya Hosp, Infect Control Ctr, Changsha, Peoples R China.
   [Li, Yinglan] Cent South Univ, Xiangya Hosp, Dept Nursing, Changsha, Peoples R China.
RP Li, YL (通讯作者)，Cent South Univ, Xiangya Sch Nursing, Changsha, Peoples R China.; Li, YL (通讯作者)，Cent South Univ, Xiangya Hosp, Dept Nursing, Changsha, Peoples R China.
EM yuyan0202@sina.com
FU Hospital Management Research Fund of Xiangya Hospital, Central South
   University [2016GL08]; Xiangya clinical big data system construction
   project of Central South University [2013096]; Chinese Nursing
   Association [ZHKY201606]
FX This study was supported by the Hospital Management Research Fund of
   Xiangya Hospital, Central South University (No. 2016GL08), the Xiangya
   clinical big data system construction project of Central South
   University (No. 2013096) and Chinese Nursing Association (No.
   ZHKY201606).
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NR 47
TC 0
Z9 0
U1 6
U2 15
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0025-7974
EI 1536-5964
J9 MEDICINE
JI Medicine (Baltimore)
PD MAY 15
PY 2020
VL 99
IS 20
AR e20114
DI 10.1097/MD.0000000000020114
PG 9
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA MO4OD
UT WOS:000551506600037
PM 32443321
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Khamis, MA
   Ngo, HQ
   Pichler, R
   Suciu, D
   Wang, YR
AF Khamis, Mahmoud Abo
   Ngo, Hung Q.
   Pichler, Reinhard
   Suciu, Dan
   Wang, Yisu Remy
TI Datalog in Wonderland
SO SIGMOD RECORD
LA English
DT Article
ID VIEWS
AB Modern data analytics applications, such as knowledge graph reasoning and machine learning, typically involve recursion through aggregation. Such computations pose great challenges to both system builders and theoreticians: first, to derive simple yet powerful abstractions for these computations; second, to define and study the semantics for the abstractions; third, to devise optimization techniques for these computations.
   In recent work we presented a generalization of Datalog called Datalogffi, which addresses these challenges. Datalogffi is a simple abstraction, which allows aggregates to be interleaved with recursion, and retains much of the simplicity and elegance of Datalog. We define its formal semantics based on an algebraic structure called Partially Ordered Pre-Semirings, and illustrate through several examples how Datalogffi can be used for a variety of applications. Finally, we describe a new optimization rule for Datalogffi, called the FGH-rule, then illustrate the FGH-rule on several examples, including a simple magic-set rewriting, generalized semi-naive evaluation, and a bill-of-material example, and briefly discuss the implementation of the FGH-rule and present some experimental validation of its effectiveness.
C1 [Khamis, Mahmoud Abo; Ngo, Hung Q.] RelationalAI, Berkeley, CA 94704 USA.
   [Pichler, Reinhard] TU Wien, Vienna, Austria.
   [Suciu, Dan; Wang, Yisu Remy] Univ Washington, Seattle, WA 98195 USA.
RP Khamis, MA (通讯作者)，RelationalAI, Berkeley, CA 94704 USA.
FU NSF [IIS 1907997, IIS 1954222]; Austrian Science Fund (FWF) [P30930]
FX Suciu and Wang were partially supported by NSF IIS 1907997 and NSF IIS
   1954222. Pichler was supported by the Austrian Science Fund
   (FWF):P30930.
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NR 52
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 0163-5808
EI 1943-5835
J9 SIGMOD REC
JI Sigmod Rec.
PD JUN
PY 2022
VL 51
IS 2
BP 6
EP 17
PG 12
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3O4CO
UT WOS:000836782400002
DA 2022-08-23
ER

PT J
AU Paliwal, S
   de Giorgio, A
   Neil, D
   Michel, JB
   Lacoste, AMB
AF Paliwal, Saee
   de Giorgio, Alex
   Neil, Daniel
   Michel, Jean-Baptiste
   Lacoste, Alix M. B.
TI Preclinical validation of therapeutic targets predicted by tensor
   factorization on heterogeneous graphs
SO SCIENTIFIC REPORTS
LA English
DT Article
ID RHEUMATOID-ARTHRITIS; DISEASE GENES; PRIORITIZATION; APOPTOSIS;
   DISCOVERY
AB Incorrect drug target identification is a major obstacle in drug discovery. Only 15% of drugs advance from Phase II to approval, with ineffective targets accounting for over 50% of these failures(1-3). Advances in data fusion and computational modeling have independently progressed towards addressing this issue. Here, we capitalize on both these approaches with Rosalind, a comprehensive gene prioritization method that combines heterogeneous knowledge graph construction with relational inference via tensor factorization to accurately predict disease-gene links. Rosalind demonstrates an increase in performance of 18%-50% over five comparable state-of-the-art algorithms. On historical data, Rosalind prospectively identifies 1 in 4 therapeutic relationships eventually proven true. Beyond efficacy, Rosalind is able to accurately predict clinical trial successes (75% recall at rank 200) and distinguish likely failures (74% recall at rank 200). Lastly, Rosalind predictions were experimentally tested in a patient-derived in-vitro assay for Rheumatoid arthritis (RA), which yielded 5 promising genes, one of which is unexplored in RA.
C1 [Paliwal, Saee; Neil, Daniel; Michel, Jean-Baptiste; Lacoste, Alix M. B.] BenevolentAI, 1 Dock72 Way,7th Floor, Brooklyn, NY 11205 USA.
   [de Giorgio, Alex] BenevolentAI, 4-6 Maple St, London W1T 5HD, England.
RP Paliwal, S (通讯作者)，BenevolentAI, 1 Dock72 Way,7th Floor, Brooklyn, NY 11205 USA.
EM saee.paliwal@benevolent.ai
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NR 84
TC 5
Z9 5
U1 0
U2 1
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD OCT 26
PY 2020
VL 10
IS 1
AR 18250
DI 10.1038/s41598-020-74922-z
PG 19
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA QD2QT
UT WOS:000615370300005
PM 33106501
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Hua, R
   Bao, YW
   Chen, SA
   Zhuang, ZY
AF Hua, Rui
   Bao, Yongwen
   Chen, Shengan
   Zhuang, Ziyin
TI Optimization of the Dynamic Measure in Spillover Effect Based on
   Knowledge Graph
SO COMPUTER SYSTEMS SCIENCE AND ENGINEERING
LA English
DT Article
DE R&D; Spillover Effect; Improved Dynamic Feder Model
ID RESEARCH-AND-DEVELOPMENT; GROWTH
AB This paper improves the dynamic Feder model based on the characteristics of knowledge production and separates the direct effect and spillover effect of R&D in order to determine the relationship between spillover effect of R&D and economic growth, and accurately measure it by examining Chinese provincial panel data from 2008-2016. The theoretical analysis shows that the spillover effect of R&D promotes economic growth. Empirical analysis using a combination of OLS, sysGMM, 2SLS and GLS shows that basic research and application research have significant spillover effects; the marginal revenue of the basic research is lower than that of the production sector, while the marginal revenue of the application research is higher than that of the production sector; and knowledge stock does not significantly promote innovation in China. However, any study on the influence of knowledge stock on innovation entails more than basic research.
C1 [Hua, Rui; Zhuang, Ziyin] Wuhan Univ, Econ & Management Sch, Wuhan, Hubei, Peoples R China.
   [Hua, Rui; Chen, Shengan] Hubei Sci & Technol Univ, Math & Stat Sch, Xianning, Peoples R China.
   [Bao, Yongwen] Hubei Med Univ, Publ Hlth & Management, Shiyan, Peoples R China.
RP Zhuang, ZY (通讯作者)，Wuhan Univ, Econ & Management Sch, Wuhan, Hubei, Peoples R China.
EM zyzhuang1234@sina.com
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NR 25
TC 9
Z9 9
U1 1
U2 6
PU C R L PUBLISHING LTD
PI LEICESTER
PA 5 WEIR RD, KIBWORTH BEAUCHAMP, LEICESTER LE8 0LQ, ENGLAND
SN 0267-6192
J9 COMPUT SYST SCI ENG
JI Comput. Syst. Sci. Eng.
PD JUL
PY 2019
VL 34
IS 4
BP 215
EP 223
PG 9
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA JS9UG
UT WOS:000500644100007
DA 2022-08-23
ER

PT J
AU Zafar, H
   Dubey, M
   Lehmann, J
   Demidova, E
AF Zafar, Hamid
   Dubey, Mohnish
   Lehmann, Jens
   Demidova, Elena
TI IQA: Interactive query construction in semantic question answering
   systems
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE User interaction; Question answering; Knowledge graphs; Option gain
ID KNOWLEDGE-BASE
AB Semantic Question Answering (SQA) systems automatically interpret user questions expressed in a natural language in terms of semantic queries. This process involves uncertainty, such that the resulting queries do not always accurately match the user intent, especially for more complex and less common questions. In this article, we aim to empower users in guiding SQA systems towards the intended semantic queries through interaction. We introduce IQA - an interaction scheme for SQA pipelines. This scheme facilitates seamless integration of user feedback in the question answering process and relies on Option Gain - a novel metric that enables efficient and intuitive user interaction. Our evaluation shows that using the proposed scheme, even a small number of user interactions can lead to significant improvements in the performance of SQA systems. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Zafar, Hamid; Dubey, Mohnish; Lehmann, Jens] Univ Bonn, Smart Data Analyt Grp SDA, Bonn, Germany.
   [Dubey, Mohnish; Lehmann, Jens] Fraunhofer IAIS, Enterprise Informat Syst Dept, St Augustin, Germany.
   [Demidova, Elena] Leibniz Univ Hannover, L3S Res Ctr, Hannover, Germany.
RP Demidova, E (通讯作者)，Leibniz Univ Hannover, L3S Res Ctr, Hannover, Germany.
EM hzafarta@cs.uni-bonn.de; dubey@cs.uni-bonn.de;
   jens.lehmann@cs.uni-bonn.de; demidova@L3S.de
RI Demidova, Elena/G-4760-2017
OI Demidova, Elena/0000-0002-5134-9072
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   Zafar Hamid, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P714, DOI 10.1007/978-3-319-93417-4_46
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NR 40
TC 4
Z9 4
U1 0
U2 11
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 2020
VL 64
AR 100586
DI 10.1016/j.websem.2020.100586
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 NG1UI
UT WOS:000563771800002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Yang, JY
   Zhang, B
AF Yang, Jinyu
   Zhang, Bo
TI Artificial Intelligence in Intelligent Tutoring Robots: A Systematic
   Review and Design Guidelines
SO APPLIED SCIENCES-BASEL
LA English
DT Review
DE artificial intelligence; intelligent tutor system; robot; knowledge
   graph; decision-making; scene construction
ID AUGMENTED REALITY; VIRTUAL-REALITY; LEARNING ANALYTICS; EDUCATION;
   IMPLEMENTATION; LEARNERS; STYLES; MEDIA
AB This study provides a systematic review of the recent advances in designing the intelligent tutoring robot (ITR) and summarizes the status quo of applying artificial intelligence (AI) techniques. We first analyze the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu, and the curriculum. Then, we transform the relationship model into the perception-planning-action model to explore what AI techniques are suitable to be applied in the ITR. This article provides insights on promoting a human-robot teaching-learning process and AI-assisted educational techniques, which illustrates the design guidelines and future research perspectives in intelligent tutoring robots.
C1 [Yang, Jinyu] Natl Univ Def Technol, Sch Comp, Changsha 410072, Hunan, Peoples R China.
   [Zhang, Bo] Natl Innovat Inst Def Technol, Artificial Intelligence Res Ctr, Beijing 100071, Peoples R China.
RP Zhang, B (通讯作者)，Natl Innovat Inst Def Technol, Artificial Intelligence Res Ctr, Beijing 100071, Peoples R China.
EM dr.jinyu.yang@outlook.com; bo.zhang.airc@outlook.com
OI ZHANG, BO/0000-0001-5183-9867
FU National Natural Science Foundation of China [61601486, 91648204]
FX aThe National Natural Science Foundation of China, grant number 61601486
   and 91648204, funded this research.
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NR 73
TC 5
Z9 5
U1 33
U2 113
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 MAY 2
PY 2019
VL 9
IS 10
AR 2078
DI 10.3390/app9102078
PG 18
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 IG4BC
UT WOS:000473748100113
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zhu, Q
   Nguyen, DT
   Alyea, G
   Hanson, K
   Sid, E
   Pariser, A
AF Zhu, Qian
   Dac-Trung Nguyen
   Alyea, Gioconda
   Hanson, Karen
   Sid, Eric
   Pariser, Anne
TI Phenotypically Similar Rare Disease Identification from an Integrative
   Knowledge Graph for Data Harmonization: Preliminary Study
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE GARD; rare diseases; phenotypical similarity; data harmonization
ID GENES
AB Background: Although many efforts have been made to develop comprehensive disease resources that capture rare disease information for the purpose of clinical decision making and education, there is no standardized protocol for defining and harmonizing rare diseases across multiple resources. This introduces data redundancy and inconsistency that may ultimately increase confusion and difficulty for the wide use of these resources. To overcome such encumbrances, we report our preliminary study to identify phenotypical similarity among genetic and rare diseases (GARD) that are presenting similar clinical manifestations, and support further data harmonization.
   Objective: To support rare disease data harmonization, we aim to systematically identify phenotypically similar GARD diseases from a disease-oriented integrative knowledge graph and determine their similarity types.
   Methods: We identified phenotypically similar GARD diseases programmatically with 2 methods: (1) We measured disease similarity by comparing disease mappings between GARD and other rare disease resources, incorporating manual assessment; 2) we derived clinical manifestations presenting among sibling diseases from disease classifications and prioritized the identified similar diseases based on their phenotypes and genotypes.
   Results: For disease similarity comparison, approximately 87% (341/392) identified, phenotypically similar disease pairs were validated; 80% (271/392) of these disease pairs were accurately identified as phenotypically similar based on similarity score. The evaluation result shows a high precision (94%) and a satisfactory quality (86%F measure). By deriving phenotypical similarity from Monarch Disease Ontology (MONDO) and Orphanet disease classification trees, we identified a total of 360 disease pairs with at least 1 shared clinical phenotype and gene, which were applied for prioritizing clinical relevance. A total of 662 phenotypically similar disease pairs were identified and will be applied for GARD data harmonization.
   Conclusions: We successfully identified phenotypically similar rare diseases among the GARD diseases via 2 approaches, disease mapping comparison and phenotypical similarity derivation from disease classification systems. The results will not only direct GARD data harmonization in expanding translational science research but will also accelerate data transparency and consistency across different disease resources and terminologies, helping to build a robust and up-to-date knowledge resource on rare diseases.
C1 [Zhu, Qian; Dac-Trung Nguyen] NIH, Div Preclin Innovat, Natl Ctr Adv Translat Sci NCATS, 9800 Med Ctr Dr, Rockville, MD 20850 USA.
   [Alyea, Gioconda; Hanson, Karen] ICF Int Inc, Rockville, MD USA.
   [Sid, Eric; Pariser, Anne] NIH, Off Rare Dis Res ORDR, Natl Ctr Adv Translat Sci NCATS, Bldg 10, Bethesda, MD 20892 USA.
RP Zhu, Q (通讯作者)，NIH, Div Preclin Innovat, Natl Ctr Adv Translat Sci NCATS, 9800 Med Ctr Dr, Rockville, MD 20850 USA.
EM qian.zhu@nih.gov
OI Zhu, Qian/0000-0002-4858-6333; Pariser, Anne/0000-0002-9421-0126; Sid,
   Eric/0000-0001-7697-3026; Nguyen, Dac-Trung/0000-0003-2591-9948
FU Intramural research program of the NCATS, NIH
FX This research was supported by the Intramural research program of the
   NCATS, NIH.
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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 OCT
PY 2020
VL 8
IS 10
AR e18395
DI 10.2196/18395
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA OO6FW
UT WOS:000587474400010
PM 33006565
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, XB
   Wang, YK
   Xie, XF
   Hu, X
   Basak, A
   Liang, L
   Yan, MY
   Deng, L
   Ding, YF
   Du, ZD
   Xie, Y
AF Chen, Xiaobing
   Wang, Yuke
   Xie, Xinfeng
   Hu, Xing
   Basak, Abanti
   Liang, Ling
   Yan, Mingyu
   Deng, Lei
   Ding, Yufei
   Du, Zidong
   Xie, Yuan
TI Rubik: A Hierarchical Architecture for Efficient Graph Neural Network
   Training
SO IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND
   SYSTEMS
LA English
DT Article
DE Deep learning accelerator; graph neural network (GNN)
ID ACCELERATOR; DIANNAO
AB The graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs. However, learning from graphs is nontrivial because of its mixed computation model involving both graph analytics and neural network computing. To this end, we decompose the GCN learning into two hierarchical paradigms: 1) graph-level and 2) node-level computing. Such a hierarchical paradigm facilitates the software and hardware accelerations for GCN learning. We propose a lightweight graph reordering methodology, incorporated with a GCN accelerator architecture that equips a customized cache design to fully utilize the graph-level data reuse. We also propose a mapping methodology aware of data reuse and task-level parallelism to handle various graphs inputs effectively. The results show that Rubik accelerator design improves energy efficiency by 26.3x-1375.2x than GPU platforms across different datasets and GCN models.
C1 [Chen, Xiaobing; Hu, Xing; Yan, Mingyu; Du, Zidong] Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China.
   [Chen, Xiaobing] Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100190, Peoples R China.
   [Wang, Yuke; Ding, Yufei] Univ Calif Santa Barbara, Dept Comp Sci, Santa Barbara, CA 93106 USA.
   [Xie, Xinfeng; Basak, Abanti; Liang, Ling; Xie, Yuan] Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA.
   [Deng, Lei] Tsinghua Univ, Dept Precis Instrument, Ctr Brain Inspired Comp Res, Beijing 10084, Peoples R China.
RP Hu, X (通讯作者)，Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China.
EM chenxiaobing@ict.ac.cn; yuke_wang@ucsb.edu; xinfeng@ucsb.edu;
   huxing@ict.ac.cn; abasak@umail.ucsb.edu; lingliang@ucsb.edu;
   yanmingyu@ict.ac.cn; leideng@mail.tsinghua.edu.cn;
   yufeiding@cs.ucsb.edu; duzidong@ict.ac.cn; yuanxie@ucsb.edu
RI Yan, Mingyu/EUL-2389-2022
OI Xie, Xinfeng/0000-0001-7285-6682; Wang, Yuke/0000-0003-1634-8549
FU National Key Research and Development Program of China [2017YFA0700902];
   NSF of China [61925208, 62002338, 61732007, 61732002, 61906179,
   U19B2019, U20A20227]; Beijing Natural Science Foundation [JQ18013];
   Strategic Priority Research Program of Chinese Academy of Science
   [XDB32050200, XDC05010300]; Beijing Academy of Artificial Intelligence
   (BAAI); Beijing Nova Program of Science and Technology
   [Z191100001119093]; Youth Innovation Promotion Association CAS; Xplore
   Prize
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2017YFA0700902; in part by the
   NSF of China under Grant 61925208, Grant 62002338, Grant 61732007, Grant
   61732002, Grant 61906179, Grant U19B2019, and Grant U20A20227; in part
   by the Beijing Natural Science Foundation under Grant JQ18013; in part
   by the Strategic Priority Research Program of Chinese Academy of Science
   under Grant XDB32050200 and Grant XDC05010300; in part by the Youth
   Innovation Promotion Association CAS, Beijing Academy of Artificial
   Intelligence (BAAI); in part by the Beijing Nova Program of Science and
   Technology under Grant Z191100001119093; and in part by the Youth
   Innovation Promotion Association CAS and Xplore Prize. This article was
   recommended by Associate Editor P. A. Beerel.
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NR 52
TC 0
Z9 0
U1 7
U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0278-0070
EI 1937-4151
J9 IEEE T COMPUT AID D
JI IEEE Trans. Comput-Aided Des. Integr. Circuits Syst.
PD APR
PY 2022
VL 41
IS 4
BP 936
EP 949
DI 10.1109/TCAD.2021.3079142
PG 14
WC Computer Science, Hardware & Architecture; Computer Science,
   Interdisciplinary Applications; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA ZV5VG
UT WOS:000770597100014
DA 2022-08-23
ER

PT J
AU Alobaid, A
   Kacprzak, E
   Corcho, O
AF Alobaid, Ahmad
   Kacprzak, Emilia
   Corcho, Oscar
TI Typology-based semantic labeling of numeric tabular data
SO SEMANTIC WEB
LA English
DT Article; Proceedings Paper
CT 21st International Conference on Knowledge Engineering and Knowledge
   Management (EKAW)
CY NOV, 2018
CL Nancy, FRANCE
DE Semantic labeling; semantic annotation; levels of measurements; typology
   of numbers; fuzzy clustering; Semantic Web
ID TABLES
AB A lot of tabular data are being published on the Web. Semantic labeling of such data may help in their understanding and exploitation. However, many challenges need to be addressed to do this automatically. With numbers, it can be even harder due to the possible difference in measurement accuracy, rounding errors, and even the frequency of their appearance. Multiple approaches have been proposed in the literature to tackle the problem of semantic labeling of numeric values in existing tabular datasets. However, they also suffer from several shortcomings: closely coupled with entity-linking, rely on table context, need to profile the knowledge graph, and require manual training of the model. Above all, however, they all treat different types of numeric values evenly. In this paper, we tackle these problems and validate our hypothesis: whether taking into account the typology of numeric data in semantic labeling yields better results.
C1 [Alobaid, Ahmad; Corcho, Oscar] Univ Politecn Madrid, Dept Artificial Intelligence, Madrid 28223, Spain.
   [Kacprzak, Emilia] Univ Southampton, Dept Elect & Comp Sci, Southampton, Hants, England.
RP Alobaid, A (通讯作者)，Univ Politecn Madrid, Dept Artificial Intelligence, Madrid 28223, Spain.
EM aalobaid@fi.upm.es; e.kacprzak@soton.ac.uk; ocorcho@fi.upm.es
FU Agencia Estatal de Investigacion del MINECO [TIN2016-78011-C4-4-R]; ERDF
   [TIN2016-78011-C4-4-R]
FX This work has partially funded by Datos 4.0 (TIN2016-78011-C4-4-R)
   project, from Agencia Estatal de Investigacion del MINECO and ERDF.
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NR 31
TC 1
Z9 1
U1 1
U2 6
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2021
VL 12
IS 1
SI SI
BP 5
EP 20
DI 10.3233/SW-200397
PG 16
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA OW6HO
UT WOS:000592985300002
DA 2022-08-23
ER

PT J
AU Peng, YX
   Zhu, WW
   Zhao, Y
   Xu, CS
   Huang, QM
   Lu, HQ
   Zheng, QH
   Huang, TJ
   Gao, W
AF Peng, Yu-xin
   Zhu, Wen-wu
   Zhao, Yao
   Xu, Chang-sheng
   Huang, Qing-ming
   Lu, Han-qing
   Zheng, Qing-hua
   Huang, Tie-jun
   Gao, Wen
TI Cross-media analysis and reasoning: advances and directions
SO FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
LA English
DT Review
DE Cross-media analysis; Cross-media reasoning; Cross-media applications
ID KNOWLEDGE; IMAGES; RETRIEVAL; CODES
AB Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the state-of-the-art methods for cross-media analysis and reasoning or presented advances, challenges, and future directions for the field. To address these issues, we provide an overview as follows: (1) theory and model for cross-media uniform representation; (2) cross-media correlation understanding and deep mining; (3) cross-media knowledge graph construction and learning methodologies; (4) cross-media knowledge evolution and reasoning; (5) cross-media description and generation; (6) cross-media intelligent engines; and (7) cross-media intelligent applications. By presenting approaches, advances, and future directions in cross-media analysis and reasoning, our goal is not only to draw more attention to the state-of-the-art advances in the field, but also to provide technical insights by discussing the challenges and research directions in these areas.
C1 [Peng, Yu-xin] Peking Univ, Inst Comp Sci & Technol, Beijing 100871, Peoples R China.
   [Zhu, Wen-wu] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China.
   [Zhao, Yao] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China.
   [Xu, Chang-sheng; Lu, Han-qing] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China.
   [Huang, Qing-ming] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China.
   [Zheng, Qing-hua] Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Peoples R China.
   [Huang, Tie-jun; Gao, Wen] Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China.
RP Zhu, WW (通讯作者)，Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China.
EM pengyuxin@pku.edu.cn; wwzhu@tsinghua.edu.cn
RI Peng, Yuxin/U-7376-2019
FU National Natural Science Foundation of China [61371128, U1611461,
   61425025, 61532005]
FX Project supported by the National Natural Science Foundation of China
   (Nos. 61371128, U1611461, 61425025, and 61532005)
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NR 82
TC 48
Z9 64
U1 10
U2 55
PU ZHEJIANG UNIV
PI HANGZHOU
PA EDITORIAL BOARD, 20 YUGU RD, HANGZHOU, 310027, PEOPLES R CHINA
SN 2095-9184
EI 2095-9230
J9 FRONT INFORM TECH EL
JI Front. Inform. Technol. Elect. Eng.
PD JAN
PY 2017
VL 18
IS 1
BP 44
EP 57
DI 10.1631/FITEE.1601787
PG 14
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA EL3TC
UT WOS:000394541200004
OA Bronze
DA 2022-08-23
ER

PT J
AU Qiao, JY
AF Qiao, Jinyu
TI Intelligent Big Data Framework for the Technical Design of Public
   Management Applications in Sports
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID SERVICE MANAGEMENT; PHYSICAL-ACTIVITY; LEISURE-TIME
AB Public sports services (PSS) are an important element in the development of China's public service industry and promoting theoretical research on PSS at the present time has just become an inevitable requirement for the development of sports. The main purpose of the work in this paper is the study of public management data in sport through intelligent big data analysis, with the participation of knowledge graphs, migrating relevant algorithms from other application areas, and optimizing them on this basis. It provides an objective review and analysis of the weaknesses of current sport public management research, etc. Based on informative data sources and in-depth theoretical analysis, it provides an objective description and analysis of the current situation of the development of sport public management services and an outlook on the future development of public sport services.
C1 [Qiao, Jinyu] Puyang Vocat & Tech Coll, Sports Dept, Puyang 457000, Henan, Peoples R China.
RP Qiao, JY (通讯作者)，Puyang Vocat & Tech Coll, Sports Dept, Puyang 457000, Henan, Peoples R China.
EM qiaojinyu@mails.imnu.edu.cn
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NR 24
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 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD APR 13
PY 2022
VL 2022
AR 1900548
DI 10.1155/2022/1900548
PG 9
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 1W1MO
UT WOS:000806544000008
OA gold
DA 2022-08-23
ER

PT J
AU Chu, PL
   Wang, T
   Zheng, JL
   Xu, CQ
   Yan, YJ
   Ma, QS
   Meng-chen, Y
   Da-sheng, T
AF Chu, Pei-liny
   Wang, Tao
   Zheng, Jia-le
   Xu, Chong-qing
   Yan, Yin-jie
   Ma, Qing-shan
   Meng-chen, Yin
   Da-sheng, Tian
TI Global and Current Research Trends of Unilateral Biportal
   Endoscopy/Biportal Endoscopic Spinal Surgery in the Treatment of Lumbar
   Degenerative Diseases: A Bibliometric and Visualization Study
SO ORTHOPAEDIC SURGERY
LA English
DT Review
DE Bibliometric analysis; Research trends; Spinal surgery; UBE; BESS;
   Visualization
ID FORAMINAL DECOMPRESSION; TECHNICAL NOTE; STENOSIS
AB The study aimed to make a bibliometric analysis of the current research situation in unilateral biportal endoscopy/biportal endoscopic spinal surgery (UBE/BESS). Research data sets were acquired from the Web of Science database. The study chosed "biportal endoscopic spinal surgery" OR "two portal endoscopic spinal surgery" OR "percutaneous biportal endoscopic decompression" OR "unilateral biportal endoscopy" OR "irrigation endoscopic discectomy" as the search terms. The literature search was limited to articles published before March 5, 2021. We only included original articles and reviews. VOS viewer and Citespace software were used to analyze the data and generate visualization knowledge maps. Annual trend of publications, distribution, H-index status, co-authorship status, and research hotspots were analyzed. A total of 74 publications met the requirement. The sum number of citations was 31,204, in which 19,336 were no self-citations. The average citation of all the papers was 21.84 times. The H-index of all the publications was 85. South Korea's total number of articles was far higher than that of other countries and regions (61, 82.4%), followed by United Arab Emirates, Egypt, and Peoples Republic of China (three, ranking second, accounting for 12.2% of the total). For the most productive authors, Choi ranked first with 21 articles, Kim ranked second with 16 articles, and Heo ranked third with 12 articles. The journal with the greatest number of publications was World Neurosurgery, with a total of 18 (39.1%) papers. Clinics in Orthopedic Surgery ranked second with six (13.0%) papers. In third place, there were fix articles published by Asian Spine Journal and Neurospine, accounting for 21.8% of the total articles. These top three journals accounted for 73.9% of all the papers. Spondylolisthesis and endoscopic decompression were the research hotspots in recent years. The number of publications has showed an upward trend with a stable rise in recent years. South Korea is the country with the highest productivity, not only in quality, but also in quantity. Barun Hosp and Leon Wiltse Mem Hosphave published most articles. Choi is the most productive author. World Neurosurgery is the most productive journal. Spondylolisthesis and endoscopic decompression are the research hotspots in recent years. Indeed, this study provides new insight into the growth and development of UBE/BESS.
C1 [Chu, Pei-liny] Anhui Med Univ, Dept Orthopaed, Maanshan Gen Hosp Ranger Duree Healthcare, Hefei, Anhui, Peoples R China.
   [Wang, Tao; Da-sheng, Tian] Anhui Med Univ, Dept Orthopaed, Second Hosp, Hefei, Anhui, Peoples R China.
   [Zheng, Jia-le; Xu, Chong-qing; Yan, Yin-jie; Ma, Qing-shan; Meng-chen, Yin] Shanghai Univ Tradit Chinese Med, Longhua Hosp, Dept Orthopaed, Shanghai, Peoples R China.
RP Da-sheng, T (通讯作者)，Anhui Med Univ, Dept Orthopaed, Second Hosp, Hefei, Anhui, Peoples R China.; Meng-chen, Y (通讯作者)，Shanghai Univ Tradit Chinese Med, Longhua Hosp, Dept Orthopaed, Shanghai, Peoples R China.
EM yinmengchen0513@126.com; 13866725716@126.com
OI , Mengchen Yin/0000-0001-7404-1495
FU Research fund of Anhui Institute of Translation Medicine [2021zhyx-C49];
   Foundation of Anhui Medical University [2021xkj030]
FX The study was supported by the Research fund of Anhui Institute of
   Translation Medicine (2021zhyx-C49), Foundation of Anhui Medical
   University (2021xkj030).
CR Ahn DK, 2021, MEDICINE, V100, DOI 10.1097/MD.0000000000024685
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NR 38
TC 1
Z9 1
U1 10
U2 10
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1757-7853
EI 1757-7861
J9 ORTHOP SURG
JI Orthop. Surg.
PD APR
PY 2022
VL 14
IS 4
BP 635
EP 643
DI 10.1111/os.13216
EA MAR 2022
PG 9
WC Orthopedics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Orthopedics
GA 0M5YQ
UT WOS:000769411200001
PM 35293686
OA Green Published
DA 2022-08-23
ER

PT J
AU Zhang, D
   Kabuka, MR
AF Zhang, Da
   Kabuka, Mansur R.
TI Distributed Relationship Mining over Big Scholar Data
SO IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
LA English
DT Article
DE Task analysis; Distributed databases; Data mining; Big Data; Semantics;
   Knowledge engineering; Complexity theory; Scholarly big data;
   distributed system; heterogeneous information network; graph
   recommendation
AB In this paper, we propose a system infrastructure to construct the big scholar data as a large knowledge graph, discover the meta paths between the entities and calculate the relevancy between entities in the graph. The core infrastructure is established on the secured and private Amazon Elastic Compute Cloud(Amazon EC2) platform. The infrastructure maintains the data evenly across the repositories, processes the data parallel by utilizing open source Spark framework, manages computing resources optimally by utilizing YARN and Hadoop HDFS, and discovers the relationship distributedly between different types of entities. We incorporate four relationship discovery tasks including citation recommendation, potential collaborator discovery, similar venue measurement and paper to venue recommendation on top of this infrastructure. For relationship mining tasks, we propose a mixed and weighted meta path (MWMP) method to explore the potential relationship between different types of entities. To verify the accuracy and measure parallelization speedup of our algorithm, we set up clusters through Amazon EC2 platform.
C1 [Zhang, Da; Kabuka, Mansur R.] Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33146 USA.
RP Zhang, D (通讯作者)，Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33146 USA.
EM zhang.1855@miami.edu; m.kabuka@miami.edu
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NR 36
TC 1
Z9 1
U1 0
U2 11
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2168-6750
J9 IEEE T EMERG TOP COM
JI IEEE Trans. Emerg. Top. Comput.
PD JAN 1
PY 2021
VL 9
IS 1
BP 354
EP 365
DI 10.1109/TETC.2018.2829772
PG 12
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA QW8QH
UT WOS:000628912400032
DA 2022-08-23
ER

PT J
AU Banks, F
   Barlex, D
   Jarvinen, EM
   O'Sullivan, G
   Owen-Jackson, G
   Rutland, M
AF Banks, F
   Barlex, D
   Jarvinen, EM
   O'Sullivan, G
   Owen-Jackson, G
   Rutland, M
TI DEPTH - Developing professional thinking for technology teachers: an
   international study
SO INTERNATIONAL JOURNAL OF TECHNOLOGY AND DESIGN EDUCATION
LA English
DT Article
DE pedagogy; personal subject construct; self-awareness tool; subject
   knowledge; teacher knowledge
AB The tools to help teachers reflect on their professional knowledge are few in number, and often difficult to utilise. This paper reports on a study conducted with both primary and secondary technology initial teacher education students in a number of different countries who were given the same teacher-knowledge graphical framework as a tool to support reflection on their own professional knowledge. We wanted to investigate if, despite the different country contexts, student teachers of technology could take advantage of their experience with graphic visualisation to help them articulate abstract notions such as aspects of their developing teacher knowledge. We discovered that the graphical tool acted as a framework that enabled them to set out their subject knowledge, pedagogical knowledge and 'school' knowledge and was useful in helping them become more self-aware. In this paper, the framework itself is introduced, the way it was presented to the novice teachers is outlined and the relative impact of such 'self awareness' on their understandings, enabled by the framework, is then discussed.
C1 Open Univ, Fac Educ & Language Studies, Milton Keynes MK7 6AA, Bucks, England.
   Brunel Univ, Uxbridge UB8 3PH, Middx, England.
   Univ Oulu, Oulu, Finland.
   Massey Univ, Palmerston North, New Zealand.
   Univ Surrey, Roehampton, England.
RP Banks, F (通讯作者)，Open Univ, Fac Educ & Language Studies, Walton Hall, Milton Keynes MK7 6AA, Bucks, England.
EM f.banks@open.ac.uk
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   [No title captured]
NR 34
TC 17
Z9 17
U1 0
U2 7
PU KLUWER ACADEMIC PUBL
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0957-7572
J9 INT J TECHNOL DES ED
JI Int. J. Technol. Des. Educ.
PY 2004
VL 14
IS 2
BP 141
EP 157
DI 10.1023/B:ITDE.0000026475.55323.01
PG 17
WC Education & Educational Research; Education, Scientific Disciplines;
   Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA 817UX
UT WOS:000221203500004
DA 2022-08-23
ER

PT J
AU Arafeh, M
   Ceravolo, P
   Mourad, A
   Damiani, E
   Bellini, E
AF Arafeh, Mohamad
   Ceravolo, Paolo
   Mourad, Azzam
   Damiani, Ernesto
   Bellini, Emanuele
TI Ontology based recommender system using social network data
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Social network; Data miner; Big data; Data analysis; Data sampling;
   Ontology; Recommender system
AB Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Network (OSN). Domain knowledge is used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supports our filtering layer in evaluating the relatedness of nodes. Our approach demonstrates that the same mechanism can be advanced to prompt recommendations to users. Our test cases and experimental results emphasize the importance of the strategy definition step in our social miner and the application of ontologies on the knowledge graph in the domain of recommendation analysis. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Arafeh, Mohamad; Ceravolo, Paolo] Univ Milan, Dept Comp Sci, Milan, Italy.
   [Mourad, Azzam] Lebanese Amer Univ, Comp Sci & Math Dept, Beirut, Lebanon.
   [Damiani, Ernesto] Khalifa Univ, Ctr Cyber Phys Syst, Abu Dhabi, U Arab Emirates.
   [Bellini, Emanuele] Univ Campania Vanvitelli, Dept Math & Phys, Caserta, Italy.
RP Arafeh, M (通讯作者)，Univ Milan, Dept Comp Sci, Milan, Italy.
EM mohamad.dabberni@unimi.it
RI Mourad, Azzam/A-7204-2008; damiani, ernesto/AAI-5709-2020
OI Mourad, Azzam/0000-0001-9434-5322; damiani, ernesto/0000-0002-9557-6496
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NR 45
TC 9
Z9 9
U1 4
U2 61
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD FEB
PY 2021
VL 115
BP 769
EP 779
DI 10.1016/j.future.2020.09.030
PG 11
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OU3NP
UT WOS:000591438700002
PM 33071400
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Kaur, M
   Salim, FD
   Ren, YL
   Chan, J
   Tomko, M
   Sanderson, M
AF Kaur, Manpreet
   Salim, Flora D.
   Ren, Yongli
   Chan, Jeffrey
   Tomko, Martin
   Sanderson, Mark
TI Joint Modelling of Cyber Activities and Physical Context to Improve
   Prediction of Visitor Behaviors
SO ACM TRANSACTIONS ON SENSOR NETWORKS
LA English
DT Article
DE Wi-Fi; logs analysis; intent recognition; shopping behaviour;
   cyber-physical; context-aware computing; retail behaviour; user
   modelling; user profiling; recommender systems; indoor trajectory;
   location prediction; movement analysis; check-ins; knowledge graph;
   semantic enrichment
AB This article investigates the cyber-physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators. Our analysis shows that many users exhibit a high correlation between their cyber activities and their physical context. To find this correlation,propose a mechanism to semantically label a physical space with rich categorical information from DBPedia concepts and compute a contextual similarity that represents a user's activities with the mall context. We demonstrate the application of cyber-physical contextual similarity in two situations: user visit intent classification and future location prediction. The experimental results demonstrate that exploitation of contextual similarity significantly improves the accuracy of such applications.
C1 [Kaur, Manpreet; Salim, Flora D.; Ren, Yongli; Chan, Jeffrey; Sanderson, Mark] RMIT Univ, Sch Sci, Comp Sci & Informat Technol, Melbourne, Vic 3001, Australia.
   [Kaur, Manpreet] Tableau Software, Seattle, WA 98103 USA.
   [Tomko, Martin] Univ Melbourne, Dept Infrastruct Engn, Melbourne, Vic 3010, Australia.
RP Salim, FD (通讯作者)，RMIT Univ, Sch Sci, Comp Sci & Informat Technol, Melbourne, Vic 3001, Australia.
EM manpreet882@gmail.com; flora.salim@rmit.edu.au; yongli.ren@rmit.edu.au;
   jeffrey.chan@rmit.edu.au; tomkom@unimelb.edu.au;
   mark.sanderson@rmit.edu.au
OI Salim, Flora/0000-0002-1237-1664; Chan, Jeffrey/0000-0002-7865-072X;
   Sanderson, Mark/0000-0003-0487-9609
FU Australian Research Council (ARC) [LP120200413]; Alexander von Humboldt
   Foundation
FX We acknowledge the support of Australian Research Council (ARC) Linkage
   Project No. LP120200413. Flora Salim acknowledges the support of
   Alexander von Humboldt Foundation.
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NR 36
TC 2
Z9 2
U1 0
U2 4
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1550-4859
EI 1550-4867
J9 ACM T SENSOR NETWORK
JI ACM Trans. Sens. Netw.
PD AUG
PY 2020
VL 16
IS 3
AR 28
DI 10.1145/3393692
PG 25
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA OJ1PU
UT WOS:000583740500008
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Ribeiro, LFR
   Zhang, Y
   Gardent, C
   Gurevych, I
AF Ribeiro, Leonardo F. R.
   Zhang, Yue
   Gardent, Claire
   Gurevych, Iryna
TI Modeling Global and Local Node Contexts for Text Generation from
   Knowledge Graphs
SO TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
LA English
DT Article
AB Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding considers the relations between neighbor nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather both encoding strategies, proposing novel neural models that encode an input graph combining both global and local node contexts, in order to learn better contextualized node embeddings. In our experiments, we demonstrate that our approaches lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 18.01 on the AGENDA dataset, and 63.69 on the WebNLG dataset for seen categories, outperforming state-of-the-art models by 3.7 and 3.1 points, respectively.(1)
C1 [Ribeiro, Leonardo F. R.; Gurevych, Iryna] Tech Univ Darmstadt, Res Training Grp AIPHES, Darmstadt, Germany.
   [Ribeiro, Leonardo F. R.; Gurevych, Iryna] Tech Univ Darmstadt, UKP Lab, Darmstadt, Germany.
   [Zhang, Yue] Westlake Univ, Sch Engn, Hangzhou, Peoples R China.
   [Gardent, Claire] CNRS, LORIA, Nancy, France.
RP Ribeiro, LFR (通讯作者)，Tech Univ Darmstadt, Res Training Grp AIPHES, Darmstadt, Germany.; Ribeiro, LFR (通讯作者)，Tech Univ Darmstadt, UKP Lab, Darmstadt, Germany.
EM ribeiro@aiphes.tu-darmstadt.de; yue.zhang@wias.org.cn;
   claire.gardent@loria.fr; gurevych@ukp.informatik.tu-darmstadt.de
FU German Research Foundation, Research Training Group Adaptive Preparation
   of Information from Heterogeneous Sources (AIPHES) [GRK 1994/1]
FX We would like to thank Pedro Savarese, Markus Zopf, Mohsen Mesgar,
   Prasetya Ajie Utama, Ji-Ung Lee, andKevin Stowe for their feedback on
   this work, as well as the anonymous reviewers for detailed comments that
   improved this paper. This work has been supported by the German Research
   Foundation as part of the Research Training Group Adaptive Preparation
   of Information from Heterogeneous Sources (AIPHES) under grant No. GRK
   1994/1.
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NR 39
TC 3
Z9 3
U1 0
U2 0
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2307-387X
J9 T ASSOC COMPUT LING
JI Trans. Assoc. Comput. Linguist.
PY 2020
VL 8
BP 589
EP 604
DI 10.1162/tacl_a_00332
PG 16
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 XX8IL
UT WOS:000736531900038
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Mrdjenovich, D
   Horton, MK
   Montoya, JH
   Legaspi, CM
   Dwaraknath, S
   Tshitoyan, V
   Jain, A
   Persson, KA
AF Mrdjenovich, David
   Horton, Matthew K.
   Montoya, Joseph H.
   Legaspi, Christian M.
   Dwaraknath, Shyam
   Tshitoyan, Vahe
   Jain, Anubhav
   Persson, Kristin A.
TI propnet: A Knowledge Graph for Materials Science
SO MATTER
LA English
DT Article
ID REFRACTIVE-INDEX; ENERGY-GAP; HARDNESS; SEMICONDUCTORS
AB Data-driven materials science is bolstered by the recent growth of online materials databases. However, the current informatics infrastructure has yet to unlock the full knowledge available within existing datasets or to explore connections between different materials science domains. Here, we present a streamlined system for codifying and connecting materials properties in an open-source Python framework: propnet. We demonstrate the capability of this framework to augment existing datasets of materials properties: by consecutively applying a network of physical relationships to calculate related information, propnet connects disparate domain knowledge. Beyond an immediate increase in available information, the results allow for the examination of correlations between sets of properties and guide the design of multifunctional materials. By emphasizing code extensibility and simplicity, we offer this software to the materials science community for general application to any experimental or computationally derived materials database.
C1 [Mrdjenovich, David; Persson, Kristin A.] Univ Calif Berkeley, Dept Mat Sci & Engn, 210 Hearst Min Bldg, Berkeley, CA 94720 USA.
   [Horton, Matthew K.; Legaspi, Christian M.; Dwaraknath, Shyam; Tshitoyan, Vahe; Jain, Anubhav; Persson, Kristin A.] Lawrence Berkeley Natl Lab, 1 Cyclotron Rd, Berkeley, CA 94720 USA.
   [Montoya, Joseph H.] Toyota Res Inst, 4440 EL Camino Real, Los Altos, CA 94022 USA.
RP Persson, KA (通讯作者)，Univ Calif Berkeley, Dept Mat Sci & Engn, 210 Hearst Min Bldg, Berkeley, CA 94720 USA.; Persson, KA (通讯作者)，Lawrence Berkeley Natl Lab, 1 Cyclotron Rd, Berkeley, CA 94720 USA.
EM kapersson@lbl.gov
RI Persson, Kristin Aslaug/ABE-6343-2020
OI Jain, Anubhav/0000-0001-5893-9967; Persson, Kristin
   A./0000-0003-2495-5509; Dwaraknath, Shyam/0000-0003-0289-2607; Legaspi,
   Christian/0000-0002-5700-8586
FU Toyota Research Institute; Materials Project Program through the DOE
   Office of Basic Energy Sciences, Materials Sciences, and Engineering
   Division [KC23MP, DE-AC02-05CH11231]
FX We acknowledge the Toyota Research Institute for funding the development
   of this public codebase. Connections with the Materials Project were
   additionally supported by the Materials Project Program (grant no.
   KC23MP) through the DOE Office of Basic Energy Sciences, Materials
   Sciences, and Engineering Division, under contract DE-AC02-05CH11231. We
   acknowledge John Dagdelen, Leigh Weston, and Joseph Palakapilly for
   their input at the start of the project and John Dagdelen for the
   propnet name. We acknowledge Donny Winston and Patrick Huck for their
   input related to the website and database building.
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NR 46
TC 15
Z9 17
U1 6
U2 21
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2590-2393
EI 2590-2385
J9 MATTER-US
JI Matter
PD FEB 5
PY 2020
VL 2
IS 2
BP 464
EP 480
DI 10.1016/j.matt.2019.11.013
PG 17
WC Materials Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Materials Science
GA KU6UP
UT WOS:000519850300021
OA Bronze, Green Submitted
DA 2022-08-23
ER

PT J
AU Yu, Q
   Wang, Q
   Zhang, YF
   Chen, CY
   Ryu, H
   Park, N
   Baek, JE
   Li, KY
   Wu, YF
   Li, DF
   Xu, J
   Liu, MJ
   Yang, JJ
   Zhang, CW
   Lu, C
   Zhang, P
   Li, X
   Chen, BT
   Ebeid, IA
   Fensel, J
   Min, C
   Zhai, YJ
   Song, M
   Ding, Y
   Bu, Y
AF Yu, Qi
   Wang, Qi
   Zhang, Yafei
   Chen, Chongyan
   Ryu, Hyeyoung
   Park, Namu
   Baek, Jae-Eun
   Li, Keyuan
   Wu, Yifei
   Li, Daifeng
   Xu, Jian
   Liu, Meijun
   Yang, Jeremy J.
   Zhang, Chenwei
   Lu, Chao
   Zhang, Peng
   Li, Xin
   Chen, Baitong
   Ebeid, Islam Akef
   Fensel, Julia
   Min, Chao
   Zhai, Yujia
   Song, Min
   Ding, Ying
   Bu, Yi
TI Analyzing knowledge entities about COVID-19 using entitymetrics
SO SCIENTOMETRICS
LA English
DT Article
DE COVID-19; Knowledge graph; Entity; Entitymetrics; Scientific
   publications; Bibliometrics
ID WEB
AB COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity-entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.
C1 [Yu, Qi; Wang, Qi; Zhang, Yafei] Shanxi Med Univ, Inst Med Data Sci, Taiyuan, Peoples R China.
   [Yu, Qi] Shanxi Med Univ, Sch Management, Taiyuan, Peoples R China.
   [Wang, Qi] Shanxi Med Univ, Minist Educ, Key Lab Cellular Physiol, Taiyuan, Peoples R China.
   [Chen, Chongyan; Ebeid, Islam Akef; Ding, Ying] Univ Texas Austin, Sch Informat, Austin, TX 78712 USA.
   [Ryu, Hyeyoung] Univ Washington, Informat Sch, Seattle, WA 98195 USA.
   [Park, Namu] Yonsei Univ, Dept Digital Analyt, Seoul, South Korea.
   [Baek, Jae-Eun] Dae Gu Univ, Dept Lib & Informat Sci, Gyongsan, South Korea.
   [Li, Keyuan] Indiana Univ, Coll Arts & Sci, Dept Stat, Bloomington, IN USA.
   [Wu, Yifei; Zhang, Peng] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China.
   [Li, Daifeng; Xu, Jian] Sun Yat Sen Univ, Sch Informat Management, Guangzhou, Guangdong, Peoples R China.
   [Liu, Meijun] Fudan Univ, Inst Global Publ Policy, Shanghai, Peoples R China.
   [Liu, Meijun] Fudan Univ, LSE Fudan Res Ctr Global Publ Policy, Shanghai, Peoples R China.
   [Zhang, Chenwei] Univ Hong Kong, Fac Educ, Hong Kong, Peoples R China.
   [Yang, Jeremy J.] Univ New Mexico, Sch Med, Dept Internal Med, Albuquerque, NM 87131 USA.
   [Lu, Chao] Hohai Univ, Business Sch, Nanjing, Jiangsu, Peoples R China.
   [Li, Xin; Zhai, Yujia] Wuhan Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.
   [Chen, Baitong] Shanghai Univ, Dept Lib Informat & Arch, Shanghai, Peoples R China.
   [Fensel, Julia] Westlake High Sch, Austin, TX USA.
   [Min, Chao] Nanjing Univ, Sch Informat Management, Nanjing, Jiangsu, Peoples R China.
   [Zhai, Yujia] Tianjin Normal Univ, Dept Informat Resource Management, Tianjin, Peoples R China.
   [Song, Min] Yonsei Univ, Dept Lib & Informat Sci, Seoul, South Korea.
   [Ding, Ying] Univ Texas Austin, Dell Med Sch, Austin, TX 78712 USA.
   [Bu, Yi] Peking Univ, Dept Informat Management, Beijing, Peoples R China.
RP Song, M (通讯作者)，Yonsei Univ, Dept Lib & Informat Sci, Seoul, South Korea.; Bu, Y (通讯作者)，Peking Univ, Dept Informat Management, Beijing, Peoples R China.
EM min.song@yonsei.ac.kr; buyi@pku.edu.cn
RI Ebeid, Islam Akef/GLQ-8040-2022; Li, Xin/ABD-7192-2020; Min,
   Chao/N-9175-2015
OI Ebeid, Islam Akef/0000-0001-5345-0775; Li, Xin/0000-0002-8169-6059; Min,
   Chao/0000-0002-0627-995X; Ryu, Hyeyoung/0000-0003-2589-834X
FU National Natural Science Foundation of China [71573162]; Shanxi
   Scholarship Council of China [HGKY2019057]; Ministry of Education of the
   Republic of Korea; National Research Foundation of Korea
   [2020S1A5B1104865]; NSF RAPID [2028717]; Ministry of Education of China
   Project of Humanities and Social Sciences [18YJC870002]
FX The authors are grateful to Allen Institute of AI for publicly sharing
   the COVID-19 open research dataset and Drs. Zhiyong Lu and Chih-Hsuan
   Wei for their kind help on PubTator usage. The authors thank the
   anonymous reviewer to offer many suggestions to improve the quality of
   this paper. Qi Yu acknowledges financial supports from National Natural
   Science Foundation of China (Grant Number: 71573162) and the Shanxi
   Scholarship Council of China (Grant Number: HGKY2019057). This work was
   supported by Min Song acknowledges financial supports from the Ministry
   of Education of the Republic of Korea and the National Research
   Foundation of Korea (Grant Number: 2020S1A5B1104865). Ying Ding
   acknowledges financial support from the NSF RAPID (Grant Number:
   2028717). Baitong Chen acknowledges financial support from the Ministry
   of Education of China Project of Humanities and Social Sciences (Grant
   Number: 18YJC870002).
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U1 19
U2 49
PU SPRINGER
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PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2021
VL 126
IS 5
BP 4491
EP 4509
DI 10.1007/s11192-021-03933-y
EA MAR 2021
PG 19
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 RU1KG
UT WOS:000628128900008
PM 33746309
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Tsamoura, E
   Carral, D
   Malizia, E
   Urbani, J
AF Tsamoura, Efthymia
   Carral, David
   Malizia, Enrico
   Urbani, Jacopo
TI Materializing Knowledge Bases via Trigger Graphs
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB The chase is a well-established family of algorithms used to materialize Knowledge Bases (KBs) for tasks like query answering under dependencies or data cleaning. A general problem of chase algorithms is that they might perform redundant computations. To counter this problem, we introduce the notion of Trigger Graphs (TGs), which guide the execution of the rules avoiding redundant computations. We present the results of an extensive theoretical and empirical study that seeks to answer when and how TGs can be computed and what are the benefits of TGs when applied over real-world KBs. Our results include introducing algorithms that compute (minimal) TGs. We implemented our approach in a new engine, called GLog, and our experiments show that it can be significantly more efficient than the chase enabling us to materialize Knowledge Graphs with 17B facts in less than 40 min using a single machine with commodity hardware.
C1 [Tsamoura, Efthymia] Samsung AI Res, Cambridge, England.
   [Carral, David] Univ Montpellier, LIRMM, INRIA, CNRS, Montpellier, France.
   [Malizia, Enrico] Univ Bologna, Bologna, Italy.
   [Urbani, Jacopo] Vrije Univ Amsterdam, Amsterdam, Netherlands.
RP Tsamoura, E (通讯作者)，Samsung AI Res, Cambridge, England.
EM efi.tsamoura@samsung.com; david.carral@inria.fr;
   enrico.malizia@unibo.it; jacopo@cs.vu.nl
OI Carral, David/0000-0001-7287-4709
FU Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
   [389792660, KR 4381/1-1]; Bundesministerium fur Bildung und Forschung
   (BMBF, Federal Ministry of Education and Research) in the Center for
   Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)
FX David Carral conducted this research at TU Dresden, where he was
   supported by Deutsche Forschungsgemeinschaft (DFG, German Research
   Foundation) in projects number 389792660 (TRR 248, Center for
   Perspicuous Systems) and KR 4381/1-1 (Emmy Noether grant DIAMOND), and
   by the Bundesministerium fur Bildung und Forschung (BMBF, Federal
   Ministry of Education and Research) in the Center for Scalable Data
   Analytics and Artificial Intelligence (ScaDS.AI).
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TC 1
Z9 1
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 FEB
PY 2021
VL 14
IS 6
BP 943
EP 956
DI 10.14778/3447689.3447699
PG 14
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SN7WA
UT WOS:000658496900009
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Wang, P
   Zou, SY
   Liu, JJ
   Ke, WJ
AF Wang, Peng
   Zou, Shiyi
   Liu, Jiajun
   Ke, Wenjun
TI Matching biomedical ontologies with GCN-based feature propagation
SO MATHEMATICAL BIOSCIENCES AND ENGINEERING
LA English
DT Article
DE ontology matching; biomedical ontology; convolutional neural network;
   graph convolutional network
ID SYSTEM; AGGREGATION
AB With an increasing number of biomedical ontologies being evolved independently, matching these ontologies to solve the interoperability problem has become a critical issue in biomedical applications. Traditional biomedical ontology matching methods are mostly based on rules or similarities for concepts and properties. These approaches require manually designed rules that not only fail to address the heterogeneity of domain ontology terminology and the ambiguity of multiple meanings of words, but also make it difficult to capture structural information in ontologies that contain a large amount of semantics during matching. Recently, various knowledge graph (KG) embedding techniques utilizing deep learning methods to deal with the heterogeneity in knowledge graphs (KGs), have quickly gained massive attention. However, KG embedding focuses mainly on entity alignment (EA). EA tasks and ontology matching (OM) tasks differ dramatically in terms of matching elements, semantic information and application scenarios, etc., hence these methods cannot be applied directly to biomedical ontologies that contain abstract concepts but almost no entities. To tackle these issues, this paper proposes a novel approach called BioOntGCN that directly learns embeddings of ontology-pairs for biomedical ontology matching. Specifically, we first generate a pairwise connectivity graph (PCG) of two ontologies, whose nodes are concept-pairs and edges correspond to property-pairs. Subsequently, we learn node embeddings of the PCG to predicate the matching results through following phases: 1) A convolutional neural network (CNN) to extract the similarity feature vectors of nodes; 2) A graph convolutional network (GCN) to propagate the similarity features and obtain the final embeddings of concept-pairs. Consequently, the biomedical ontology matching problem is transformed into a binary classification problem. We conduct systematic experiments on real-world biomedical ontologies in Ontology Alignment Evaluation Initiative (OAEI), and the results show that our approach significantly outperforms other entity alignment methods and achieves stateof-the-art performance. This indicates that BioOntGCN is more applicable to ontology matching than the EA method. At the same time, BioOntGCN substantially achieves superior performance compared with previous ontology matching (OM) systems, which suggests that BioOntGCN based on the representation learning is more effective than the traditional approaches.
C1 [Wang, Peng; Liu, Jiajun] Southeast Univ, Sch Comp Sci & Engn, Nanjing 210018, Peoples R China.
   [Wang, Peng; Zou, Shiyi] Southeast Univ, Monash Univ, Joint Grad Sch, Suzhou 215123, Peoples R China.
   [Wang, Peng] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210018, Peoples R China.
   [Ke, Wenjun] Beijing Inst Comp Technol & Applicat, Beijing 100854, Peoples R China.
RP Wang, P (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing 210018, Peoples R China.; Wang, P (通讯作者)，Southeast Univ, Monash Univ, Joint Grad Sch, Suzhou 215123, Peoples R China.; Wang, P (通讯作者)，Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210018, Peoples R China.
EM pwang@seu.edu.cn
FU  [31514020501];  [31514020503]
FX The work was supported by the 13th Five-Year All-Army Common Information
   System Equipment Pre-Research Project (Grant Nos. 31514020501,
   31514020503).
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TC 0
Z9 0
U1 1
U2 1
PU AMER INST MATHEMATICAL SCIENCES-AIMS
PI SPRINGFIELD
PA PO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES
SN 1547-1063
EI 1551-0018
J9 MATH BIOSCI ENG
JI Math. Biosci. Eng.
PY 2022
VL 19
IS 8
BP 8479
EP 8504
DI 10.3934/mbe.2022394
PG 26
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA 3H3PK
UT WOS:000831950600001
PM 35801474
OA gold
DA 2022-08-23
ER

PT J
AU Doddahonnaiah, D
   Lenehan, PJ
   Hughes, TK
   Zemmour, D
   Garcia-Rivera, E
   Venkatakrishnan, AJ
   Chilaka, R
   Khare, A
   Kasaraneni, A
   Garg, A
   Anand, A
   Barve, R
   Thiagarajan, V
   Soundararajan, V
AF Doddahonnaiah, Deeksha
   Lenehan, Patrick J.
   Hughes, Travis K.
   Zemmour, David
   Garcia-Rivera, Enrique
   Venkatakrishnan, A. J.
   Chilaka, Ramakrishna
   Khare, Apoorv
   Kasaraneni, Akhil
   Garg, Abhinav
   Anand, Akash
   Barve, Rakesh
   Thiagarajan, Viswanathan
   Soundararajan, Venky
TI A Literature-Derived Knowledge Graph Augments the Interpretation of
   Single Cell RNA-seq Datasets
SO GENES
LA English
DT Article
DE single cell genomics; natural language processing
ID ENDOTHELIAL-CELLS; REVEALS; ATLAS; RECONSTRUCTION; HETEROGENEITY;
   MUTATIONS; CLIC6; TRANSCRIPTOMICS; LANDSCAPE; PROSTATE
AB Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p = 6.15 x 10(-76), r = 0.24; cohen's D = 2.6). Building on this, we developed an augmented annotation algorithm (single cell Annotation via Literature Encoding, or scALE) that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 133 clusters from nine datasets of human breast, colon, heart, joint, ovary, prostate, skin, and small intestine tissues. With the optimized settings, the true cellular identity matched the top prediction in 59% of tested clusters and was present among the top five predictions for 91% of clusters. scALE slightly outperformed an existing method for reference data driven automated cluster annotation, and we demonstrate that integration of scALE can meaningfully improve the annotations derived from such methods. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of well-studied cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature-derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.
C1 [Doddahonnaiah, Deeksha; Lenehan, Patrick J.; Hughes, Travis K.; Zemmour, David; Garcia-Rivera, Enrique; Venkatakrishnan, A. J.; Soundararajan, Venky] Nference, One Main St, Cambridge, MA 02142 USA.
   [Chilaka, Ramakrishna; Khare, Apoorv; Kasaraneni, Akhil; Garg, Abhinav; Anand, Akash; Barve, Rakesh; Thiagarajan, Viswanathan; Soundararajan, Venky] Nference Labs, Bengaluru 560017, Karnataka, India.
RP Soundararajan, V (通讯作者)，Nference, One Main St, Cambridge, MA 02142 USA.; Soundararajan, V (通讯作者)，Nference Labs, Bengaluru 560017, Karnataka, India.
EM deeksha@nference.net; patrick@nference.net; travis@nference.net;
   dzemmour@nference.net; enrique@nference.net; aj@nference.net;
   ramakrishna@nference.net; apoorykhare@nference.net;
   akhilkasaraneni@nference.net; abhinav@nference.net; akash@nference.net;
   rbarve@nference.net; vishy@nference.net; venky@nference.net
OI Garcia-Rivera, Enrique/0000-0003-1203-2918; Lenehan,
   Patrick/0000-0002-1950-9179
FU nference
FX This study was funded by nference.
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NR 110
TC 2
Z9 2
U1 2
U2 9
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4425
J9 GENES-BASEL
JI Genes
PD JUN
PY 2021
VL 12
IS 6
AR 898
DI 10.3390/genes12060898
PG 24
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA SZ4GQ
UT WOS:000666526000001
PM 34200671
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Powell, JH
   Swart, J
AF Powell, J. H.
   Swart, J.
TI Scaling knowledge: how does knowledge accrue in systems?
SO JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
LA English
DT Article
DE knowledge; scaling functions; entropy; information; uncertainty
ID ORGANIZATIONAL KNOWLEDGE; FIRM
AB This paper addresses the important and somewhat contentious matter of how knowledge accrues in a system. The matter has at its heart the establishment of a scaling function for knowledge (as distinct from the scaling used for information) which is related to the density of the knowledge structure at any point in the system. We commence with a discussion of whether it is possible at all to scale knowledge, dispensing with any concepts of knowledge as a simple finite resource and making a distinction between the establishment of a metric and the act of measurement itself. First, we draw on the Shannon - Weaver ( H) measure to establish how knowledge can be seen as contributing to the partitioning of message sets under the H-measure. This establishes how knowledge contributes to the quantity of information held within a system when viewed as a meta-structure for that information. Second, we build on the idea of knowledge as an endemic property of a structure of interconnections between concepts. We observe that knowledge content can be dense both in structures that are highly interconnected deploying a modest number of concepts and in those where the interconnections are more sparse but where the number of concepts deployed is high. A scaling function exhibiting appropriate properties is then proposed. It can be seen that the scaling associated with knowledge as meta-information and the scaling deriving from the interconnectivity point of view are connected. This scaling function is particularly useful in three ways. Firstly, it outlines the properties of knowledge itself which can be used as criteria for future knowledge-based research. Its application in practice creates the ability to identify areas of knowledge concentration within a system. Finally, this identification of knowledge 'hotspots' can be used to direct the investment of resources for the management of knowledge and it provides an indication of the appropriate approach for the management of this knowledge. We make some observations on the limitations of the approach, on its potential as a basis for managerial action (particularly in Knowledge Management) and on its relevance and applicability to OR practice (particularly in respect of systems approaches to knowledge mapping). Lastly, we offer a view on the likely line of research which may result from this work.
C1 [Powell, J. H.] Cardiff Univ, Cardiff Business Sch, Off EO1, Cardiff CF10 3EU, S Glam, Wales.
   [Swart, J.] Univ Bath, Bath BA2 7AY, Avon, England.
RP Powell, JH (通讯作者)，Cardiff Univ, Cardiff Business Sch, Off EO1, Aberconway Bldg,Colum Dr, Cardiff CF10 3EU, S Glam, Wales.
EM powelljh@cardiff.ac.uk
RI Wang, Charles/B-5565-2011
OI Wang, Charles/0000-0001-9331-8437; swart, juani/0000-0002-5365-0686
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NR 32
TC 3
Z9 3
U1 0
U2 7
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0160-5682
EI 1476-9360
J9 J OPER RES SOC
JI J. Oper. Res. Soc.
PD DEC
PY 2008
VL 59
IS 12
BP 1633
EP 1643
DI 10.1057/palgrave.jors.2602497
PG 11
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA 372TP
UT WOS:000260925200006
DA 2022-08-23
ER

PT J
AU Zhang, J
   Song, LX
   Jia, JD
   Tian, WD
   Lai, RM
   Zhang, ZH
   Li, JE
   Ju, JQ
   Xu, H
AF Zhang, Jie
   Song, Luxia
   Jia, Jundi
   Tian, Wende
   Lai, Runmin
   Zhang, Zihao
   Li, Jingen
   Ju, Jianqing
   Xu, Hao
TI Knowledge Mapping of Necroptosis From 2012 to 2021: A Bibliometric
   Analysis
SO FRONTIERS IN IMMUNOLOGY
LA English
DT Article
DE necroptosis; programmed cell death; CiteSpace; VOSviewer; bibliometric
ID MIXED LINEAGE KINASE; DOMAIN-LIKE PROTEIN; CELL-DEATH;
   MOLECULAR-MECHANISMS; PREVENTS NECROPTOSIS; PROGRAMMED NECROSIS;
   EMERGING TRENDS; RIP1 KINASE; EXPRESSION; ACTIVATION
AB BackgroundNecroptosis, a recently discovered programmed cell death, has been pathologically linked to various diseases and is thus a promising target for treating diseases. However, a comprehensive and objective report on the current status of entire necroptosis research is lacking. Therefore, this study aims to conduct a bibliometric analysis to quantify and identify the status quo and trending issues of necroptosis research in the last decade. MethodsArticles were acquired from the Web of Science Core Collection database. We used two bibliometric tools (CiteSpace and VOSviewer) to quantify and identify the individual impact and cooperation information by analyzing annual publications, journals, co-cited journals, countries/regions, institutions, authors, and co-cited authors. Afterwards, we identified the trending research areas of necroptosis by analyzing the co-occurrence and burst of keywords and co-cited references. ResultsFrom 2012 to 2021, a total of 3,111 research articles on necroptosis were published in 786 academic journals by 19,687 authors in 885 institutions from 82 countries/regions. The majority of publications were from China and the United States, of which the United States maintained the dominant position in necroptosis research; meanwhile, the Chinese Academy of Sciences and Ghent University were the most active institutions. Peter Vandenabeele published the most papers, while Alexei Degterev had the most co-citations. Cell Death & Disease published the most papers on necroptosis, while Cell was the top 1 co-cited journal, and the major area of these publications was molecular, biology, and immunology. High-frequency keywords mainly included those that are molecularly related (MLKL, TNF-alpha, NF-kappa B, RIPK3, RIPK1), pathological process related (cell-death, apoptosis, necroptosis, necrosis, inflammation), and disease related (cancer, ischemia/reperfusion injury, infection, carcinoma, Alzheimer's disease). ConclusionNecroptosis research had a stable stepwise growth in the past decade. Current necroptosis studies focused on its cross-talk with other types of cell death, potential applications in disease treatment, and further mechanisms. Among them, the synergy with ferroptosis, further RIPK1/RIPK3/MLKL studies, its association with inflammation and oxidative stress and translational applications, and the therapeutic potential to treat cancer and neurodegenerative diseases are the trending research area. These might provide ideas for further research in the necroptosis field.
C1 [Zhang, Jie; Song, Luxia; Jia, Jundi; Tian, Wende; Lai, Runmin; Zhang, Zihao; Li, Jingen; Ju, Jianqing; Xu, Hao] China Acad Chinese Med Sci, Xiyuan Hosp, Natl Clin Res Ctr Chinese Med Cardiol, Beijing, Peoples R China.
   [Zhang, Jie; Song, Luxia; Jia, Jundi; Zhang, Zihao] Beijing Univ Chinese Med, Grad Sch, Beijing, Peoples R China.
   [Tian, Wende; Lai, Runmin] China Acad Chinese Med Sci, Grad Sch, Beijing, Peoples R China.
   [Li, Jingen] Beijing Univ Chinese Med, Dongzhimen Hosp, Dept Cardiovasc Med, Beijing, Peoples R China.
RP Li, JE; Ju, JQ; Xu, H (通讯作者)，China Acad Chinese Med Sci, Xiyuan Hosp, Natl Clin Res Ctr Chinese Med Cardiol, Beijing, Peoples R China.; Li, JE (通讯作者)，Beijing Univ Chinese Med, Dongzhimen Hosp, Dept Cardiovasc Med, Beijing, Peoples R China.
EM lijingen198908@126.com; jujianqing@163.com; xuhaotcm@hotmail.com
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NR 107
TC 0
Z9 0
U1 10
U2 10
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 JUN 13
PY 2022
VL 13
AR 917155
DI 10.3389/fimmu.2022.917155
PG 16
WC Immunology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Immunology
GA 2K0MO
UT WOS:000816039700001
PM 35769473
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Younesi, E
   Malhotra, A
   Gundel, M
   Scordis, P
   Kodamullil, AT
   Page, M
   Muller, B
   Springstubbe, S
   Wullner, U
   Scheller, D
   Hofmann-Apitius, M
AF Younesi, Erfan
   Malhotra, Ashutosh
   Guendel, Michaela
   Scordis, Phil
   Kodamullil, Alpha Tom
   Page, Matt
   Mueller, Bernd
   Springstubbe, Stephan
   Wuellner, Ullrich
   Scheller, Dieter
   Hofmann-Apitius, Martin
TI PDON: Parkinson's disease ontology for representation and modeling of
   the Parkinson's disease knowledge domain
SO THEORETICAL BIOLOGY AND MEDICAL MODELLING
LA English
DT Article
DE Parkinson's disease; ontology; disease modeling; knowledge engineering
ID GENE ONTOLOGY; UNIFICATION; PROTEIN; TOOL
AB Background: Despite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson's disease. In the area of Parkinson's research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism.
   Methods: The Parkinson's disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology.
   Results: The Parkinson's disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson's knowledge map. The ontology was able to answer specific questions related to Parkinson's when evaluated by experts. Finally, the added value of the Parkinson's disease ontology is demonstrated by ontology-driven modeling of PINK1 and re-annotation of gene expression datasets relevant to Parkinson's disease.
   Conclusions: Parkinson's disease ontology delivers the knowledge domain of Parkinson's disease in a compact, computer-readable form, which can be further edited and enriched by the scientific community and also to be used to construct, represent and automatically extend Parkinson's-related computable models. A practical version of the Parkinson's disease ontology for browsing and editing can be publicly accessed at http://bioportal.bioontology.org/ontologies/PDON.
C1 [Younesi, Erfan; Malhotra, Ashutosh; Guendel, Michaela; Kodamullil, Alpha Tom; Mueller, Bernd; Springstubbe, Stephan; Hofmann-Apitius, Martin] Fraunhofer Inst Algorithms & Sci Comp, Dept Bionformat, D-53754 St Augustin, Germany.
   [Malhotra, Ashutosh; Guendel, Michaela; Kodamullil, Alpha Tom; Hofmann-Apitius, Martin] Univ Bonn, Bonn Aachen Int Ctr IT, D-53113 Bonn, Germany.
   [Wuellner, Ullrich] Univ Bonn, Dept Neurol, D-53105 Bonn, Germany.
   [Scordis, Phil; Page, Matt] UCB Pharma, Informat Grp, Slough, Berks, England.
   [Scheller, Dieter] UCB Pharma SA, Pharmacol Parkinsons Dis & Movement Disorders, B-1420 Braine Iallued, Belgium.
RP Younesi, E (通讯作者)，Fraunhofer Inst Algorithms & Sci Comp, Dept Bionformat, D-53754 St Augustin, Germany.
EM erfan.younesi@scai.fraunhofer.de
RI Wüllner, Ullrich/R-6790-2019; Müller, Bernd/AFE-4930-2022;
   Hofmann-Apitius, Martin/E-6123-2018
OI Müller, Bernd/0000-0003-3062-8192; Tom Kodamullil,
   Alpha/0000-0001-9896-3531; Scordis, Phil/0000-0003-3477-9959; Younesi,
   Erfan/0000-0001-8793-2692; Hofmann-Apitius, Martin/0000-0001-9012-6720
FU National Institute of General Medical Sciences of the United States
   National Institutes of Health [GM10331601]; German Federal Ministry of
   Education and Research (BMBF) within Biopharma Neuroallianz consortium;
   UCB Pharma GmbH (Monhein, Germany)
FX The authors wish to thank Jonathan Van Eyll, Javier garcia-Ladona, Anne
   Michel, Patrik Foerch, Michel Gillard and Frank Vanderdonck from UCB
   Pharma for their contribution to building the ontology. Theo Mevissen,
   Jurgen Klein and Christian Ebeling from Fraunhofer Institute SCAI are
   acknowledged for their support in automatic annotation of GEO entries
   and full-text publications. This work was conducted using the Protege
   resource, which is supported by grant GM10331601 from the National
   Institute of General Medical Sciences of the United States National
   Institutes of Health. This study was supported by the German Federal
   Ministry of Education and Research (BMBF) within the Biopharma
   Neuroallianz consortium, and by UCB Pharma GmbH (Monhein, Germany).
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NR 31
TC 14
Z9 14
U1 1
U2 12
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1742-4682
J9 THEOR BIOL MED MODEL
JI Theor. Biol. Med. Model.
PD SEP 22
PY 2015
VL 12
AR 20
DI 10.1186/s12976-015-0017-y
PG 17
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA CR9IP
UT WOS:000361668400001
PM 26395080
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Kalbarczyk, A
   Rao, A
   Mahendradhata, Y
   Majumdar, P
   Decker, E
   Anwar, HB
   Akinyemi, OO
   Rahimi, AO
   Kayembe, P
   Alonge, OO
AF Kalbarczyk, Anna
   Rao, Aditi
   Mahendradhata, Yodi
   Majumdar, Piyusha
   Decker, Ellie
   Anwar, Humayra Binte
   Akinyemi, Oluwaseun O.
   Rahimi, Ahmad Omid
   Kayembe, Patrick
   Alonge, Olakunle O.
TI Evaluating the process of partnership and research in global health:
   reflections from the STRIPE project
SO BMC PUBLIC HEALTH
LA English
DT Article
DE Process evaluation; Partnership; Collaboration; Global health research;
   Expectations
ID DISPARITIES; GOVERNANCE; FRAMEWORK
AB Background Thoughtful and equitable engagement with international partners is key to successful research. STRIPE, a consortium of 8 academic and research institutions across the globe whose objective is to map, synthesize, and disseminate lessons learned from polio eradication, conducted a process evaluation of this partnership during the project's first year which focused on knowledge mapping activities. Methods The STRIPE consortium is led by Johns Hopkins University (JHU) in partnership with 6 universities and 1 research consultancy organization in polio free, at-risk, and endemic countries. In December 2018 JHU team members submitted written reflections on their experiences (n = 9). We held calls with each consortium member to solicit additional feedback (n = 7). To establish the partnership evaluation criteria we conducted preliminary analyses based on Blackstock's framework evaluating participatory research. In April 2019, an in-person consortium meeting was held; one member from each institution was asked to join a process evaluation working group. This group reviewed the preliminary criteria, adding, subtracting, and combining as needed; the final evaluation criteria were applied to STRIPE's research process and partnership and illustrative examples were provided. Results Twelve evaluation criteria were defined and applied by each member of the consortium to their experience in the project. These includedaccess to resources,expectation setting,organizational context,external context,quality of information,relationship building,transparency,motivation,scheduling,adaptation,communication and engagement, andcapacity building. For each criteria members of the working group reflected on general and context-specific challenges and potential strategies to overcome them. Teams suggested providing more time for recruitment, training, reflection, pre-testing. and financing to alleviate resource constraints. Given the large scope of the project, competing priorities, and shifting demands the working group also suggested a minimum of one full-time project coordinator in each setting to manage resources. Conclusion Successful management of multi-country, multicentered implementation research requires comprehensive communication tools (which to our knowledge do not exist yet or are not readily available), expectation setting, and institutional support. Capacity building activities that address human resource needs for both individuals and their institutions should be incorporated into early project planning.
C1 [Kalbarczyk, Anna; Rao, Aditi; Decker, Ellie; Alonge, Olakunle O.] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Int Hlth, 615 N Wolfe St, Baltimore, MD 21205 USA.
   [Mahendradhata, Yodi] Univ Gadjah Mada, Fac Med Publ Hlth & Nursing, Yogyakarta, Indonesia.
   [Majumdar, Piyusha] Indian Inst Hlth Management Res, Jaipur, Rajasthan, India.
   [Anwar, Humayra Binte] BRAC Univ, BRAC James P Grant Sch Publ Hlth, Dhaka, Bangladesh.
   [Akinyemi, Oluwaseun O.] Univ Ibadan, Coll Med, Dept Hlth Policy & Management, Ibadan, Nigeria.
   [Rahimi, Ahmad Omid] Global Innovat Consultancy Serv, Kabul, Afghanistan.
   [Kayembe, Patrick] Univ Kinshasa, Sch Publ Hlth, Kinshasa, DEM REP CONGO.
RP Kalbarczyk, A (通讯作者)，Johns Hopkins Bloomberg Sch Publ Hlth, Dept Int Hlth, 615 N Wolfe St, Baltimore, MD 21205 USA.
EM akalbarc@jhu.edu
RI Mahendradhata, Yodi/AAQ-9044-2020
OI Mahendradhata, Yodi/0000-0002-4522-1785; Majumdar,
   Piyusha/0000-0003-0373-7887; Decker, Ellie/0000-0003-1317-9276; Rao,
   Aditi/0000-0003-4966-5778; Akinyemi, Oluwaseun/0000-0003-4135-1459;
   Kalbarczyk, Anna/0000-0002-6143-8634; Alonge,
   Olakunle/0000-0001-7642-2806
FU Bill and Melinda Gates Foundation - Bill and Melinda Gates Foundation
FX This study is funded by the Bill and Melinda Gates Foundation. The
   funder did not play any role in writing the protocol, interpreting the
   data, or in writing this manuscript. The funder provided and coordinated
   external peer-review for the study proposal. Publication costs were
   funded by the Bill and Melinda Gates Foundation.
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NR 15
TC 2
Z9 2
U1 1
U2 2
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1471-2458
J9 BMC PUBLIC HEALTH
JI BMC Public Health
PD AUG 12
PY 2020
VL 20
SU 2
SI SI
AR 1058
DI 10.1186/s12889-020-08591-y
PG 10
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA NE7KN
UT WOS:000562780600003
PM 32787895
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Mi, BH
   Zhang, WZ
   Xiao, YH
   Hong, WX
   Song, JL
   Tu, JF
   Jiang, BY
   Ye, C
   Shi, GX
AF Mi, Bao-Hong
   Zhang, Wen-Zheng
   Xiao, Yong-Hua
   Hong, Wen-Xue
   Song, Jia-Lin
   Tu, Jian-Feng
   Jiang, Bi-Yao
   Ye, Chen
   Shi, Guang-Xia
TI An exploration of new methods for metabolic syndrome examination by
   infrared thermography and knowledge mining
SO SCIENTIFIC REPORTS
LA English
DT Article
AB Metabolic syndrome (MS) is a clinical syndrome with multiple metabolic disorders. As the diagnostic criteria for MS still lacking of imaging laboratory method, this study aimed to explore the differences between healthy people and MS patients through infrared thermography (IRT). However, the observation region of the IRT image is uncertain, and the research tried to solve this problem with the help of knowledge mining technology. 43 MS participants were randomly included through a cross-sectional method, and 43 healthy participants were recruited through number matching. The IRT image of each participant was segmented into the region of interest (ROI) through the preprocessing method proposed in this research, and then the ROI features were granulated by the K-means algorithm to generate the formal background, and finally, the two formal background were separately built into a knowledge graph through the knowledge mining method based on the attribute partial order structure. The baseline data shows that there is no difference in age, gender, and height between the two groups (P > 0.05). The image preprocessing method can segment the IRT image into 18 ROI. Through the K-means method, each group of data can be separately established with a 43 x 36 formal background and generated a knowledge graph. It can be found through knowledge mining and independent-samples T test that the average temperature and maximum temperature difference between the chest and face of the two groups are statistically different (P < 0.01). IRT could reflect the difference between healthy people and MS people. The measurement regions were found by the method of knowledge mining on the premise of unknown. The method proposed in this paper may add a new imaging method for MS laboratory examinations, and at the same time, through knowledge mining, it can also expand a new idea for clinical research of IRT.
C1 [Mi, Bao-Hong; Tu, Jian-Feng; Shi, Guang-Xia] Beijing Univ Chinese Med, Sch Acupuncture Moxibust & Tuina, Beijing 100029, Peoples R China.
   [Zhang, Wen-Zheng; Xiao, Yong-Hua; Jiang, Bi-Yao; Ye, Chen] Beijing Univ Chinese Med, Dongzhimen Hosp, Beijing 100010, Peoples R China.
   [Hong, Wen-Xue; Song, Jia-Lin] Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Hebei, Peoples R China.
   [Mi, Bao-Hong; Tu, Jian-Feng; Shi, Guang-Xia] Beijing Univ Chinese Med, Int Acupuncture Innovat Inst, Beijing 100029, Peoples R China.
RP Shi, GX (通讯作者)，Beijing Univ Chinese Med, Sch Acupuncture Moxibust & Tuina, Beijing 100029, Peoples R China.; Shi, GX (通讯作者)，Beijing Univ Chinese Med, Int Acupuncture Innovat Inst, Beijing 100029, Peoples R China.
EM mibaohong12587@163.com; shiguangxia2008@126.com
FU Ethics Committee of Dongzhimen Hospital of Beijing University of Chinese
   Medicine [DZMEC-KY-2020-102]
FX This research passed the review of the Ethics Committee of Dongzhimen
   Hospital of Beijing University of Chinese Medicine (No.
   DZMEC-KY-2020-102), and conducted in accordance with the Declaration of
   Helsinki.
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NR 22
TC 0
Z9 0
U1 5
U2 5
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD APR 16
PY 2022
VL 12
IS 1
AR 6377
DI 10.1038/s41598-022-10422-6
PG 12
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 0N5XL
UT WOS:000782910400011
PM 35430598
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Cihan, C
   Kocabas, C
   Demirbas, U
   Sennaroglu, A
AF Cihan, Can
   Kocabas, Coskun
   Demirbas, Umit
   Sennaroglu, Alphan
TI Graphene mode-locked femtosecond Alexandrite laser
SO OPTICS LETTERS
LA English
DT Article
ID FLUORIDE GLASS-FIBER; SATURABLE ABSORBER; PRASEODYMIUM LASER; PULSE
   LASER; DIODE; NM; GENERATION
AB We report for the first time, to the best of our knowledge, graphene mode-locked operation of a femtosecond Alexandrite laser at 750 nm. A multipass-cavity configuration was employed to scale the output energy and to eliminate spectral/Q-switching instabilities. By using a monolayer graphene saturable absorber, mode locking could be obtained. With 5W of pinup at 532 nm, nearly transform-limited, 65 fs pulses with a time-bandwidth product of 0.319 were generated. The mode-locked laser operated at a pulse repetition rate of 5.56 MHz and produced 8 mW output power, corresponding to a pulse energy and peak power of 1.4 nJ and 22 kW, respectively. These experiments further show that graphene can be used to initiate mode locking at wavelengths as low as 750 nm. (C) 2018 Optical Society of America
C1 [Cihan, Can; Sennaroglu, Alphan] Koc Univ, Dept Phys, Laser Res Lab, TR-34450 Istanbul, Turkey.
   [Cihan, Can; Sennaroglu, Alphan] Koc Univ, Dept Elect Elect Engn, Laser Res Lab, TR-34450 Istanbul, Turkey.
   [Kocabas, Coskun] Bilkent Univ, Dept Phys, TR-06800 Ankara, Turkey.
   [Demirbas, Umit] Antalya Bilim Univ, Dept Elect & Elect Engn, Laser Technol Lab, TR-07190 Antalya, Turkey.
   [Sennaroglu, Alphan] Koc Univ Surface Sci & Technol Ctr KUYTAM, TR-34450 Istanbul, Turkey.
RP Sennaroglu, A (通讯作者)，Koc Univ, Dept Phys, Laser Res Lab, TR-34450 Istanbul, Turkey.; Sennaroglu, A (通讯作者)，Koc Univ, Dept Elect Elect Engn, Laser Res Lab, TR-34450 Istanbul, Turkey.; Sennaroglu, A (通讯作者)，Koc Univ Surface Sci & Technol Ctr KUYTAM, TR-34450 Istanbul, Turkey.
EM asennar@ku.edu.tr
RI Sennaroglu, Alphan/Y-9604-2018; Demirbas, Umit/AAZ-5119-2021; Demirbas,
   Umit/F-2981-2011
OI Sennaroglu, Alphan/0000-0003-4391-0189; Demirbas,
   Umit/0000-0001-8420-8965
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NR 37
TC 24
Z9 24
U1 1
U2 22
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 AUG 15
PY 2018
VL 43
IS 16
BP 3969
EP 3972
DI 10.1364/OL.43.003969
PG 4
WC Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Optics
GA GQ2RX
UT WOS:000441505100042
PM 30106928
OA Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Vogt, L
   Baum, R
   Bhatty, P
   Kohler, C
   Meid, S
   Quast, B
   Grobe, P
AF Vogt, Lars
   Baum, Roman
   Bhatty, Philipp
   Koehler, Christian
   Meid, Sandra
   Quast, Bjoern
   Grobe, Peter
TI SOCCOMAS: a FAIR web content management system that uses knowledge
   graphs and that is based on semantic programming
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Article
ID DATA STANDARDS; ONTOLOGIES; PATTERN; DESIGN; NEED; RDF
AB We introduce Semantic Ontology-Controlled application for web Content Management Systems (SOCCOMAS), a development framework for FAIR ('findable', 'accessible', 'interoperable', 'reusable') Semantic Web Content Management Systems (S-WCMSs). Each S-WCMS run by SOCCOMAS has its contents managed through a corresponding knowledge base that stores all data and metadata in the form of semantic knowledge graphs in a Jena tuple store. Automated procedures track provenance, user contributions and detailed change history. Each S-WCMS is accessible via both a graphical user interface (GUI), utilizing the JavaScript framework AngularJS, and a SPARQL endpoint. As a consequence, all data and metadata are maximally findable, accessible, interoperable and reusable and comply with the FAIR Guiding Principles. The source code of SOCCOMAS is written using the Semantic Programming Ontology (SPrO). SPrO consists of commands, attributes and variables, with which one can describe an S-WCMS. We used SPrO to describe all the features and workflows typically required by any S-WCMS and documented these descriptions in a SOCCOMAS source code ontology (SC-Basic). SC-Basic specifies a set of default features, such as provenance tracking and publication life cycle with versioning, which will be available in all S-WCMS run by SOCCOMAS. All features and workflows specific to a particular S-WCMS, however, must be described within an instance source code ontology (INST-SCO), defining, e.g. the function and composition of the GUI, with all its user interactions, the underlying data schemes and representations and all its workflow processes. The combination of descriptions in SC-Basic and a given INST-SCO specify the behavior of an S-WCMS. SOCCOMAS controls this S-WCMS through the Java-based middleware that accompanies SPrO, which functions as an interpreter. Because of the ontology-controlled design, SOCCOMAS allows easy customization with a minimum of technical programming background required, thereby seamlessly integrating conventional web page technologies with semantic web technologies. SOCCOMAS and the Java Interpreter are available from (https://github.com/SemanticProgramming).
C1 [Vogt, Lars; Baum, Roman; Koehler, Christian; Meid, Sandra] Rheinische Friedrich Wilhelms Univ Bonn, Inst Evolutionsbiol & Okol, Immenburg 1, D-53121 Bonn, Germany.
   [Bhatty, Philipp; Koehler, Christian; Quast, Bjoern; Grobe, Peter] Zool Forsch Museum Alexander Koenig, Adenauerallee 160, D-53113 Bonn, Germany.
RP Vogt, L (通讯作者)，Rheinische Friedrich Wilhelms Univ Bonn, Inst Evolutionsbiol & Okol, Immenburg 1, D-53121 Bonn, Germany.
EM lars.vogt@gmail.com
OI Vogt, Lars/0000-0002-8280-0487; Baum, Roman/0000-0001-5246-9351
FU Deutsche Forschungsgemeinschaft
FX Deutsche Forschungsgemeinschaft
   (http://gepris.dfg.de/gepris/projekt/248394582).
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NR 65
TC 5
Z9 5
U1 2
U2 6
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD AUG 8
PY 2019
AR baz067
DI 10.1093/database/baz067
PG 22
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA IT8KV
UT WOS:000483132100001
PM 31392324
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Almeida, A
   Bermejo, U
   Bilbao, A
   Azkune, G
   Aguilera, U
   Emaldi, M
   Dornaika, F
   Arganda-Carreras, I
AF Almeida, Aitor
   Bermejo, Unai
   Bilbao, Aritz
   Azkune, Gorka
   Aguilera, Unai
   Emaldi, Mikel
   Dornaika, Fadi
   Arganda-Carreras, Ignacio
TI A Comparative Analysis of Human Behavior Prediction Approaches in
   Intelligent Environments
SO SENSORS
LA English
DT Article
DE user behavior prediction; behavior modeling; transformers; attention;
   embeddings; graph neural networks; knowledge graphs; recurrent neural
   networks; convolutional neural networks; intelligent environments
ID TECHNOLOGIES
AB Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.
C1 [Almeida, Aitor; Bermejo, Unai; Bilbao, Aritz; Aguilera, Unai; Emaldi, Mikel] Univ Deusto, DeustoTech Inst Technol, Ave Univ 24, Bilbao 48007, Spain.
   [Azkune, Gorka; Dornaika, Fadi] Univ Basque Country, Dept Comp Sci & Artificial Intelligence, M Lardizabal 1, Donostia San Sebastian 20008, Spain.
   [Dornaika, Fadi; Arganda-Carreras, Ignacio] Basque Fdn Sci, Ikerbasque, Plaza Euskadi 5, Bilbao 48009, Spain.
   [Arganda-Carreras, Ignacio] Donostia Int Phys Ctr DIPC, Manuel Lardizabal 4, San Sebastian 20018, Spain.
RP Almeida, A (通讯作者)，Univ Deusto, DeustoTech Inst Technol, Ave Univ 24, Bilbao 48007, Spain.
EM aitor.almeida@deusto.es; unai.bermejo@deusto.es; aritzbilbao@deusto.es;
   gorka.azcune@ehu.eus; unai.aguilera@deusto.es; m.emaldi@deusto.es;
   fadi.dornaika@ehu.eus; ignacio.arganda@ehu.eus
RI Arganda-Carreras, Ignacio/L-4605-2014; Emaldi, Mikel/L-1589-2014;
   Aguilera, Unai/P-2953-2015
OI Arganda-Carreras, Ignacio/0000-0003-0229-5722; Emaldi,
   Mikel/0000-0003-0955-976X; Aguilera, Unai/0000-0001-7359-4864; Bilbao
   Jayo, Aritz/0000-0001-7743-6652
FU FuturAAL-Ego [RTI2018-101045-A-C22]; FuturAAL-Context
   [RTI2018-101045-B-C21]; Spanish Ministry of Science, Innovation and
   Universities
FX This work was carried out with the financial support of FuturAAL-Ego
   (RTI2018-101045-A-C22) and FuturAAL-Context (RTI2018-101045-B-C21)
   granted by Spanish Ministry of Science, Innovation and Universities.
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NR 45
TC 0
Z9 0
U1 7
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD FEB
PY 2022
VL 22
IS 3
AR 701
DI 10.3390/s22030701
PG 14
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA ZF8UR
UT WOS:000759845300001
PM 35161448
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ferilli, S
AF Ferilli, Stefano
TI Integration Strategy and Tool between Formal Ontology and Graph Database
   Technology
SO ELECTRONICS
LA English
DT Article
DE knowledge representation; formal ontologies; graph databases
AB Ontologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as a Knowledge Graph. On the other hand, database technology has often focused on the optimal organization of data so as to boost efficiency in their storage, management and retrieval. Graph databases are a recent technology specifically focusing on element-driven data browsing rather than on batch processing. While the complementarity and connections between these technologies are patent and intuitive, little exists to bring them to full integration and cooperation. This paper aims at bridging this gap, by proposing an intermediate format that can be easily mapped onto the formal ontology on one hand, so as to allow complex reasoning, and onto the graph database on the other, so as to benefit from efficient data handling.
C1 [Ferilli, Stefano] Univ Bari, Dept Comp Sci, I-70125 Bari, Italy.
RP Ferilli, S (通讯作者)，Univ Bari, Dept Comp Sci, I-70125 Bari, Italy.
EM stefano.ferilli@uniba.it
RI Ferilli, Stefano/E-8841-2014
OI Ferilli, Stefano/0000-0003-1118-0601
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NR 24
TC 3
Z9 3
U1 1
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD NOV
PY 2021
VL 10
IS 21
AR 2616
DI 10.3390/electronics10212616
PG 27
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA WX7UY
UT WOS:000718797700001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Gromann, D
AF Gromann, Dagmar
TI Neural language models for the multilingual, transcultural, and
   multimodal Semantic Web
SO SEMANTIC WEB
LA English
DT Article
DE Neural networks; multilingual representations; cross-linguistic modeling
ID TECHNOLOGIES
AB A vision of a truly multilingual Semantic Web has found strong support with the Linguistic Linked Open Data community. Standards, such as OntoLex-Lemon, highlight the importance of explicit linguistic modeling in relation to ontologies and knowledge graphs. Nevertheless, there is room for improvement in terms of automation, usability, and interoperability. Neural Language Models have achieved several breakthroughs and successes considerably beyond Natural Language Processing (NLP) tasks and recently also in terms of multimodal representations. Several paths naturally open up to port these successes to the Semantic Web, from automatically translating linguistic information associated with structured knowledge resources to multimodal question-answering with machine translation. Language is also an important vehicle for culture, an aspect that deserves considerably more attention. Building on existing approaches, this article envisions joint forces between Neural Language Models and Semantic Web technologies for multilingual, transcultural, and multimodal information access and presents open challenges and opportunities in this direction.
C1 [Gromann, Dagmar] Univ Vienna, Ctr Translat Studies, Vienna, Austria.
RP Gromann, D (通讯作者)，Univ Vienna, Ctr Translat Studies, Vienna, Austria.
EM dagmar.gromann@univie.ac.at
OI Gromann, Dagmar/0000-0003-0929-6103
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NR 71
TC 2
Z9 2
U1 2
U2 11
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 29
EP 39
DI 10.3233/SW-190373
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:000512298900005
DA 2022-08-23
ER

PT J
AU Ikkala, E
   Hyvonen, E
   Rantala, H
   Koho, M
AF Ikkala, Esko
   Hyvonen, Eero
   Rantala, Heikki
   Koho, Mikko
TI Sampo-UI: A full stack JavaScript framework for developing semantic
   portal user interfaces
SO SEMANTIC WEB
LA English
DT Article
DE Linked Data; semantic portal; user interface; web application;
   JavaScript; software framework
AB This paper presents a new software framework, SAMPO-UI, for developing user interfaces for semantic portals. The goal is to provide the end-user with multiple application perspectives to Linked Data knowledge graphs, and a two-step usage cycle based on faceted search combined with ready-to-use tooling for data analysis. For the software developer, the SAMPO-UI framework makes it possible to create highly customizable, user-friendly, and responsive user interfaces using current state-of-the-art JavaScript libraries and data from SPARQL endpoints, while saving substantial coding effort. SAMPO-UI is published on GitHub under the open MIT License and has been utilized in several internal and external projects. The framework has been used thus far in creating six published and five forth-coming portals, mostly related to the Cultural Heritage domain, that have had tens of thousands of end-users on the Web.
C1 [Ikkala, Esko; Hyvonen, Eero; Rantala, Heikki; Koho, Mikko] Aalto Univ, Dept Comp Sci, Semant Comp Res Grp SeCo, Espoo, Finland.
   [Hyvonen, Eero; Koho, Mikko] Univ Helsinki, HELDIG Helsinki Ctr Digital Humanities, Helsinki, Finland.
RP Ikkala, E (通讯作者)，Aalto Univ, Dept Comp Sci, Semant Comp Res Grp SeCo, Espoo, Finland.
EM esko.ikkala@aalto.fi
RI Hyvönen, Eero A/G-2252-2013
OI Koho, Mikko/0000-0002-7373-9338; Ikkala, Esko/0000-0002-9571-7260;
   Hyvonen, Eero Antero/0000-0003-1695-5840
FU Academy of Finland; University of Helsinki Future Fund
FX The SAMPO-UI framework have been developed gradually during several
   projects in 2018-2021. Thanks to the Academy of Finland and University
   of Helsinki Future Fund for funding. The authors wish to acknowledge CSC
   - IT Center for Science, Finland, for computational resources.
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NR 28
TC 1
Z9 1
U1 3
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 1
BP 69
EP 84
DI 10.3233/SW-210428
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 XE9KR
UT WOS:000723701000004
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Maass, W
   Storey, VC
AF Maass, Wolfgang
   Storey, Veda C.
TI Pairing conceptual modeling with machine learning
SO DATA & KNOWLEDGE ENGINEERING
LA English
DT Article
DE Conceptual modeling; Machine learning; Methodologies and tools; Models;
   Database management; Artificial intelligence; Framework for
   incorporating conceptual modeling into data science projects
ID INFORMATION-SYSTEMS; BIG DATA; NEURAL-NETWORKS; BLACK-BOX; QUALITY;
   REQUIREMENTS; ANALYTICS; SELECTION; GOAL; INTELLIGENCE
AB Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations and development cycle. We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects. The framework is illustrated by applying it to a healthcare application. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs. The pairing of conceptual modeling and machine learning in this way should help lay the foundations for future research.
C1 [Maass, Wolfgang] German Res Ctr Artificial Intelligence DFKI, D-66123 Saarbrucken, Germany.
   [Maass, Wolfgang] Saarland Univ, Saarland Informat Campus, D-66123 Saarbrucken, Germany.
   [Storey, Veda C.] Georgia State Univ, J Mack Robinson Coll Business, Atlanta, GA 30302 USA.
RP Maass, W (通讯作者)，German Res Ctr Artificial Intelligence DFKI, D-66123 Saarbrucken, Germany.
EM wolfgang.maass@dfki.de; vstorey@gsu.edu
RI Maass, Wolfgang/ACE-7055-2022
OI Maass, Wolfgang/0000-0003-4057-0924; Storey, Veda/0000-0002-8735-1553
FU German Ministry for Economic Affairs and Energy, project EVAREST
   [00586504]
FX This work was partially funded by the German Ministry for Economic
   Affairs and Energy, project EVAREST (grant ID: 00586504) .
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NR 228
TC 2
Z9 2
U1 2
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-023X
EI 1872-6933
J9 DATA KNOWL ENG
JI Data Knowl. Eng.
PD JUL
PY 2021
VL 134
AR 101909
DI 10.1016/j.datak.2021.101909
EA JUL 2021
PG 35
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 TQ6AQ
UT WOS:000678361100002
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Fang, Y
   Zhao, X
   Tan, Z
   Xiao, WD
AF Fang, Yang
   Zhao, Xiang
   Tan, Zhen
   Xiao, Weidong
TI TransPath: Representation Learning for Heterogeneous Information
   Networks via Translation Mechanism
SO IEEE ACCESS
LA English
DT Article
DE Heterogeneous information network; representation learning
AB In this paper, we propose a novel network representation learning model TransPath to encode heterogeneous information networks (HINs). Traditional network representation learning models aim to learn the embeddings of a homogeneous network. TransPath is able to capture the rich semantic and structure information of a HIN via meta-paths. We take advantage of the concept of translation mechanism in knowledge graph which regards a meta-path, instead of an edge, as a translating operation from the first node to the last node. Moreover, we propose a user-guided meta-path sampling strategy which takes users' preference as a guidance, which could explore the semantics of a path more precisely, and meanwhile improve model efficiency via the avoidance of other noisy and meaningless meta-paths. We evaluate our model on two large-scale real-world data sets database systems and logic programming (DBLP) and YELP, and two benchmark tasks similarity search and node classification. We observe that TransPath outperforms other state-of-the-art baselines consistently and significantly.
C1 [Fang, Yang; Zhao, Xiang; Tan, Zhen; Xiao, Weidong] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.
   [Zhao, Xiang] Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.
RP Zhao, X (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.; Zhao, X (通讯作者)，Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.
EM xiangzhao@nudt.edu.cn
RI Zhao, Xiang/B-1303-2010
OI Zhao, Xiang/0000-0001-6339-0219
FU NSFC [71331008, 71690233]
FX This work was supported by NSFC under Grant 71331008 and Grant 71690233.
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NR 36
TC 1
Z9 3
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 2018
VL 6
BP 20712
EP 20721
DI 10.1109/ACCESS.2018.2827121
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA GF1YQ
UT WOS:000431733200001
OA gold
DA 2022-08-23
ER

PT J
AU Huang, XF
   Guo, ZQ
   Zhang, JL
   Cao, H
   Yang, J
AF Huang, Xiaofeng
   Guo, Zhiqiang
   Zhang, Jialiang
   Cao, Hui
   Yang, Jie
TI RECA: Relation Extraction Based on Cross-Attention Neural Network
SO ELECTRONICS
LA English
DT Article
DE cross-attention neural network; dilated gated convolutional neural
   network; joint method; relation extraction
AB Extracting entities and relations, as a crucial part of many tasks in natural language processing, transforms the unstructured text information into structured information and provides corresponding data support for knowledge graph (KG) and knowledge vault (KV) construction. Nevertheless, the mainstream relation-extraction methods, the pipeline method and the joint method, ignore the dependency between the subject entity and the object entity. This work introduces a pre-trained BERT model and a dilated gated convolutional neural network (DGCNN) as an encoder to distinguish the long-range semantics representation from the input sequence. In addition, we propose a cross-attention neural network as a decoder to learn the importance of each subject word for each word of the input sequence. Experiments were undertaken with two extensive datasets, the New York Times Corpus (NYT) and WebNLG Corpus, and showed that our model performs significantly better than the CasRel model, outperforming the baseline by 1.9% and 0.7% absolute gain in terms of F1-score.
C1 [Huang, Xiaofeng; Guo, Zhiqiang; Zhang, Jialiang; Cao, Hui; Yang, Jie] Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Peoples R China.
RP Zhang, JL (通讯作者)，Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Peoples R China.
EM 259079@whut.edu.cn; guozhigiang@whut.edu.cn; zjliang@whut.edu.cn;
   iehuicao@whut.edu.cn; jieyang@whut.edu.cn
FU National Natural Science Foundation of China [51479159]
FX This research is supported by the National Natural Science Foundation of
   China (grant number 51479159).
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NR 31
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD JUL
PY 2022
VL 11
IS 14
AR 2161
DI 10.3390/electronics11142161
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 3J8PU
UT WOS:000833653800001
OA gold
DA 2022-08-23
ER

PT J
AU Fang, YX
   Cheng, R
   Li, XD
   Luo, SQ
   Hu, JF
AF Fang, Yixiang
   Cheng, Reynold
   Li, Xiaodong
   Luo, Siqiang
   Hu, Jiafeng
TI Effective Community Search over Large Spatial Graphs
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB Communities are prevalent in social networks, knowledge graphs, and biological networks. Recently, the topic of community search (CS) has received plenty of attention. Given a query vertex, CS looks for a dense subgraph that contains it. Existing CS solutions do not consider the spatial extent of a community. They can yield communities whose locations of vertices span large areas. In applications that facilitate the creation of social events (e.g., finding conference attendees to join a dinner), it is important to find groups of people who are physically close to each other. In this situation, it is desirable to have a spatial-aware community (or SAC), whose vertices are close structurally and spatially. Given a graph G and a query vertex q, we develop exact solutions for finding an SAC that contains q. Since these solutions cannot scale to large datasets, we have further designed three approximation algorithms to compute an SAC. We have performed an experimental evaluation for these solutions on both large real and synthetic datasets. Experimental results show that SAC is better than the communities returned by existing solutions. Moreover, our approximation solutions can find SACs accurately and efficiently.
C1 [Fang, Yixiang; Cheng, Reynold; Li, Xiaodong; Luo, Siqiang; Hu, Jiafeng] Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China.
RP Fang, YX (通讯作者)，Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China.
EM yxfang@cs.hku.hk; ckcheng@cs.hku.hk; xdli@cs.hku.hk; sqluo@cs.hku.hk;
   jhu@cs.hku.hk
RI Luo, Siqiang/AFM-6934-2022; Zhao, Fan/AAO-9771-2021; Luo,
   Siqiang/ABC-8964-2022
OI Zhao, Fan/0000-0001-9239-274X; Luo, Siqiang/0000-0001-8197-0903; LI,
   Xiaodong/0000-0002-5985-9886; , Yixiang/0000-0002-5047-8593
FU Research Grants Council of HK [HKU 17205115, 17229116]; HKU [102009508,
   104004129]
FX Reynold Cheng, Yixiang Fang, Xiaodong Li, Siqiang Luo, and Jiafeng Hu
   were supported by the Research Grants Council of HK (Project HKU
   17205115 and 17229116) and HKU (Projects 102009508 and 104004129). We
   would like to thank Dr. Mauro Sozio for his insightful comments.
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NR 30
TC 102
Z9 104
U1 2
U2 13
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 FEB
PY 2017
VL 10
IS 6
BP 709
EP 720
DI 10.14778/3055330.3055337
PG 12
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EP4ZX
UT WOS:000397389800007
OA Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Gupta, M
   Bendersky, M
AF Gupta, Manish
   Bendersky, Michael
TI Information Retrieval with Verbose Queries
SO FOUNDATIONS AND TRENDS IN INFORMATION RETRIEVAL
LA English
DT Article
ID SEARCH
AB Recently, the focus of many novel search applications has shifted from short keyword queries to verbose natural language queries. Examples include question answering systems and dialogue systems, voice search on mobile devices and entity search engines like Facebook's Graph Search or Google's Knowledge Graph. However the performance of textbook information retrieval techniques for such verbose queries is not as good as that for their shorter counterparts. Thus, effective handling of verbose queries has become a critical factor for adoption of information retrieval techniques in this new breed of search applications.
   Over the past decade, the information retrieval community has deeply explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations. However, thus far, there was not a coherent and organized survey on this topic. In this survey, we aim to put together various research pieces of the puzzle, provide a comprehensive and structured overview of various proposed methods, and also list various application scenarios where effective verbose query processing can make a significant difference.
C1 [Gupta, Manish] Microsoft Corp, Redmond, WA 98052 USA.
   [Bendersky, Michael] Google, Mountain View, CA USA.
RP Gupta, M (通讯作者)，Microsoft Corp, Redmond, WA 98052 USA.
EM gmanish@microsoft.com; bemike@google.com
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NR 94
TC 19
Z9 20
U1 0
U2 10
PU NOW PUBLISHERS INC
PI HANOVER
PA PO BOX 1024, HANOVER, MA 02339 USA
SN 1554-0669
EI 1554-0677
J9 FOUND TRENDS INF RET
JI Found. Trends Inf. Retr.
PY 2015
VL 9
IS 3-4
BP 209
EP 354
DI 10.1561/1500000050
PG 146
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA CO2SS
UT WOS:000359007500001
DA 2022-08-23
ER

PT J
AU Yang, XJ
   Wang, XY
   Li, XG
   Gu, DX
   Liang, CY
   Li, K
   Zhang, GR
   Zhong, JH
AF Yang, Xuejie
   Wang, Xiaoyu
   Li, Xingguo
   Gu, Dongxiao
   Liang, Changyong
   Li, Kang
   Zhang, Gongrang
   Zhong, Jinhong
TI Exploring emerging IoT technologies in smart health research: a
   knowledge graph analysis
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE Internet of things; Disease; Health; Bibliometrics; Visual analysis
ID THINGS IOT; INTERNET; CARE; EVOLUTION; NETWORKS; ENVIRONMENT; SCIENCE;
   DRIVEN
AB BackgroundAt present, Internet of Things technology has been widely used in various fields, and smart health is also one of its important application areas.MethodsWe use the core collection of Web of Science as a data source, using tools such as CiteSpace and bibliometric methods to visually analyze 9561 articles published in the field of smart health research based on the Internet of things (IoT) in 2003-2019, including time distribution, spatial distribution, and literature co-citation analysis and keyword analysis.ResultsThe field of smart health research based on IoT has developed rapidly since 2014, but has not yet formed a stable network of authors and institutions. In addition, the knowledge base in this field has been initially formed, and most of the published literatures are multi-theme research.ConclusionsThis study discusses the research status, research hotspots and future development trends in this field, and provides important knowledge support for subsequent research.
C1 [Yang, Xuejie; Li, Xingguo; Gu, Dongxiao; Liang, Changyong; Li, Kang; Zhang, Gongrang; Zhong, Jinhong] Hefei Univ Technol, Sch Management, 193 Tunxi Rd, Hefei 230009, Anhui, Peoples R China.
   [Wang, Xiaoyu] Anhui Univ Tradit Chinese Med, Affiliated Hosp 1, 117 Meishan Rd, Hefei 230031, Anhui, Peoples R China.
   [Gu, Dongxiao] Minist Educ, Key Lab Proc Optimizat & Intelligent Decis Making, Hefei 230009, Anhui, Peoples R China.
RP Wang, XY (通讯作者)，Anhui Univ Tradit Chinese Med, Affiliated Hosp 1, 117 Meishan Rd, Hefei 230031, Anhui, Peoples R China.
EM mikehfut0551@163.com
OI Gu, Dongxiao/0000-0003-3557-009X
FU National Natural Science Foundation of China (NSFC) [71771077, 71771075,
   72071063]; Fundamental Research Funds for the central universities
   [PA2020GDKC0020]
FX The dataset collection and analysis of this research were partially
   supported by the National Natural Science Foundation of China (NSFC)
   under grant Nos. 71771077, 71771075 and 72071063, and Fundamental
   Research Funds for the central universities under grant No.
   PA2020GDKC0020.
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NR 45
TC 7
Z9 7
U1 7
U2 39
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 OCT 8
PY 2020
VL 20
IS 1
AR 260
DI 10.1186/s12911-020-01278-9
PG 12
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Medical Informatics
GA OB6RB
UT WOS:000578595100001
PM 33032598
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Khan, A
   Bhowmick, SS
   Bonchi, F
AF Khan, Arijit
   Bhowmick, Sourav S.
   Bonchi, Francesco
TI Summarizing Static and Dynamic Big Graphs
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB Large-scale, highly-interconnected networks pervade our society and the natural world around us, including the World Wide Web, social networks, knowledge graphs, genome and scientific databases, medical and government records. The massive scale of graph data often surpasses the available computation and storage resources. Besides, users get overwhelmed by the daunting task of understanding and using such graphs due to their sheer volume and complexity. Hence, there is a critical need to summarize large graphs into concise forms that can be more easily visualized, processed, and managed. Graph summarization has indeed attracted a lot of interests from various research communities, such as sociology, physics, chemistry, bioinformatics, and computer science. Different ways of summarizing graphs have been invented that are often complementary to each other. In this tutorial, we discuss algorithmic advances on graph summarization in the context of both classical (e.g., static graphs) and emerging (e.g., dynamic and stream graphs) applications. We emphasize the current challenges and highlight some future research directions.
C1 [Khan, Arijit; Bhowmick, Sourav S.] Nanyang Technol Univ, Singapore, Singapore.
   [Bonchi, Francesco] ISI Fdn, Turin, Italy.
RP Khan, A (通讯作者)，Nanyang Technol Univ, Singapore, Singapore.
EM arijit.khan@ntu.edu.sg; assourav@ntu.edu.sg; francesco.bonchi@isi.it
OI Saha Bhowmick, Sourav/0000-0003-1957-8016; Khan,
   Arijit/0000-0002-7312-6312
FU AcRF MOE Tier-1 [M401020000]; NTU [M4081678]; Singapore-MOE AcRF Tier-2
   Grant [MOE2015-T2-1-040]
FX The research of Arijit Khan is supported by AcRF MOE Tier-1 M401020000
   and NTU M4081678. Sourav S Bhowmick was supported by the Singapore-MOE
   AcRF Tier-2 Grant MOE2015-T2-1-040. Any opinions, findings, and
   conclusions in this publication are those of the authors and do not
   necessarily reflect the views of the funding agencies.
CR Ahn K. J., 2012, SODA
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NR 56
TC 15
Z9 16
U1 2
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD AUG
PY 2017
VL 10
IS 12
BP 1981
EP 1984
DI 10.14778/3137765.3137825
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:000416494000057
DA 2022-08-23
ER

PT J
AU Wang, ZX
   Yang, LQ
   Yang, J
   Li, TL
   He, LT
   Li, ZJ
AF Wang, Zixiang
   Yang, Liqun
   Yang, Jian
   Li, Tongliang
   He, Longtao
   Li, Zhoujun
TI A Triple Relation Network for Joint Entity and Relation Extraction
SO ELECTRONICS
LA English
DT Article
DE entity and relation extraction; relational triple; triple relation
   network; implicit triples
AB Recent methods of extracting relational triples mainly focus on the overlapping problem and achieve considerable performance. Most previous approaches extract triples solely conditioned on context words, but ignore the potential relations among the extracted entities, which will cause incompleteness in succeeding Knowledge Graphs' (KGs) construction. Since relevant triples give a clue for establishing implicit connections among entities, we propose a Triple Relation Network (TRN) to jointly extract triples, especially handling extracting implicit triples. Specifically, we design an attention-based entity pair encoding module to identify all normal entity pairs directly. To construct implicit connections among these extracted entities in triples, we utilize our triple reasoning module to calculate relevance between two triples. Then, we select the top-K relevant triple pairs and transform them into implicit entity pairs to predict the corresponding implicit relations. We utilize a bipartite matching objective to match normal triples and implicit triples with the corresponding labels. Extensive experiments demonstrate the effectiveness of the proposed method on two public benchmarks, and our proposed model significantly outperforms previous strong baselines.
C1 [Wang, Zixiang; Yang, Jian; Li, Tongliang; Li, Zhoujun] Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.
   [Yang, Liqun] Beihang Univ, Sch Cyber Sci & Technol, Beijing 100191, Peoples R China.
   [He, Longtao] Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China.
RP Li, ZJ (通讯作者)，Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.
EM wangzixiang@buaa.edu.cn; lqyang@buaa.edu.cn; jiaya@buaa.edu.cn;
   tonyliangli@buaa.edu.cn; hlt@cert.org.cn; lizj@buaa.edu.cn
OI Wang, Zixiang/0000-0003-2329-231X
FU National Natural Science Foundation of China [U1636211, 61672081,
   61370126]; Fund of the State Key Laboratory of Software Development
   Environment [SKLSDE-2021ZX-18]
FX This research was funded by the National Natural Science Foundation of
   China (Grant Nos. U1636211, 61672081, 61370126), the 2020 Tencent Wechat
   Rhino-Bird Focused Research Program, and the Fund of the State Key
   Laboratory of Software Development Environment (Grant No.
   SKLSDE-2021ZX-18).
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NR 43
TC 0
Z9 0
U1 3
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD MAY
PY 2022
VL 11
IS 10
AR 1535
DI 10.3390/electronics11101535
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 1P3AC
UT WOS:000801884800001
OA gold
DA 2022-08-23
ER

PT J
AU Althar, RR
   Alahmadi, A
   Samanta, D
   Khan, MZ
   Alahmadi, AH
AF Althar, Raghavendra Rao
   Alahmadi, Abdulrahman
   Samanta, Debabrata
   Khan, Mohammad Zubair
   Alahmadi, Ahmed H.
TI Mathematical foundations based statistical modeling of software source
   code for software system evolution
SO MATHEMATICAL BIOSCIENCES AND ENGINEERING
LA English
DT Article
DE software development; requirements management; Bidirection encoders
   transformers; knowledge graphs; transfer learning
AB Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the pattern hidden in various software development processes and artifacts. This module is part of the smart requirements management system that is intended to be built. This system will have multiple modules to make the software requirements management phase more secure from vulnerabilities. Some of the critical challenges bothering the software development community are discussed. The background of Machine Learning approaches and their application in software development practices are explored. Some of the work done around modeling the source code and approaches used for vulnerabilities understanding in software systems are reviewed. Program representation is explored to understand some of the principles that would help in understanding the subject well. Further deeper dive into source code modeling possibilities are explored. Machine learning best practices are explored inline with the software source code modeling.
C1 [Althar, Raghavendra Rao] CHRIST Univ, Dept Data Sci, Bangalore, Karnataka, India.
   [Althar, Raghavendra Rao] First Amer India Private Ltd, QMS, Bangalore, Karnataka, India.
   [Alahmadi, Abdulrahman; Khan, Mohammad Zubair; Alahmadi, Ahmed H.] Taibah Univ, Dept Comp Sci & Informat, Madinah, Saudi Arabia.
   [Samanta, Debabrata] CHRIST Univ, Dept Comp Sci, Bangalore, Karnataka, India.
RP Khan, MZ (通讯作者)，Taibah Univ, Dept Comp Sci & Informat, Madinah, Saudi Arabia.; Samanta, D (通讯作者)，CHRIST Univ, Dept Comp Sci, Bangalore, Karnataka, India.
EM debabrata.samanta369@gmail.com; mkhanb@taibahu.edu.sa
OI Althar, Raghavendra Rao/0000-0001-5859-0662
CR Ahmad A., 2020, SECUR COMMUN NETWORK
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NR 42
TC 0
Z9 0
U1 2
U2 2
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 2022
VL 19
IS 4
BP 3701
EP 3719
DI 10.3934/mbe.2022170
PG 19
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA YZ9FK
UT WOS:000755775300008
PM 35341270
OA gold
DA 2022-08-23
ER

PT J
AU Wang, Q
   Ji, SY
   Peng, P
   Li, MD
   Huang, P
   Qin, Z
AF Wang, Qing
   Ji, Shengyi
   Peng, Peng
   Li, Mingdao
   Huang, Ping
   Qin, Zheng
TI Optimizing Distance Computation in Distributed Graph Systems
SO IEEE ACCESS
LA English
DT Article
DE Distributed information systems; graph theory; distributed computing;
   distance computation; distributed graph systems; landmark
ID CENTRALITY
AB Given a large graph, such as a social network or a knowledge graph, a fundamental query is how to find the distance from a source vertex to another vertex in the graph. As real graphs become very large and many distributed graph systems, such as Pregel, Pregel +, Giraph, and GraphX, are proposed, how to employ distributed graph systems to process single-source distance queries should attract more attention. In this paper, we propose a landmark-based framework to optimize the distance computation over distributed graph systems. We also use a measure called set betweenness to select the optimal set of landmarks for distance computation. Although we can prove that selecting the optimal set of landmarks is NP-hard, we propose a heuristic distributed algorithm that can guarantee the approximation ratio. Experiments on large real graphs confirm the superiority of our methods.
C1 [Wang, Qing; Ji, Shengyi; Peng, Peng; Li, Mingdao; Huang, Ping; Qin, Zheng] Hunan Univ, Changsha 410082, Peoples R China.
RP Peng, P (通讯作者)，Hunan Univ, Changsha 410082, Peoples R China.
EM hnu16pp@hnu.edu.cn
OI Qin, Zheng/0000-0003-0877-3887; Peng, Peng/0000-0002-8095-8061
FU National Key Research and Development Projects [2018YFB0704000];
   National Natural Science Foundation of China [61702171, 61772191]; Hunan
   Provincial Natural Science Foundation of China [2018JJ3065]; Science and
   Technology Key Projects of Hunan Province [2019WK2072]; Fundamental
   Research Funds for the Central Universities
FX This work was supported in part by the National Key Research and
   Development Projects under Grant 2018YFB0704000, in part by the National
   Natural Science Foundation of China under Grant 61702171 and Grant
   61772191, in part by the Hunan Provincial Natural Science Foundation of
   China under Grant 2018JJ3065, in part by the Science and Technology Key
   Projects of Hunan Province under Grant 2019WK2072, and in part by the
   Fundamental Research Funds for the Central Universities.
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NR 28
TC 0
Z9 0
U1 0
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 191673
EP 191682
DI 10.1109/ACCESS.2020.3032727
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA OP1GP
UT WOS:000587829600001
OA gold
DA 2022-08-23
ER

PT J
AU Lisena, P
   Laaksonen, J
   Troncy, R
AF Lisena, Pasquale
   Laaksonen, Jorma
   Troncy, Raphael
TI Understanding videos with face recognition: a complete pipeline and
   applications
SO MULTIMEDIA SYSTEMS
LA English
DT Article; Early Access
DE Computer vision; Face recognition; Web image retrieval; Knowledge graphs
AB When browsing or studying a video corpus, particularly relevant information consists in knowing who are the people appearing in the scenes. In this paper, we show how a combination of state of the art techniques can be organised in a pipeline for face recognition of celebrities. In particular, we propose a system which combines MTCNN for detecting faces and FaceNet for extracting face embeddings, which are used to train a set of classifiers. The face recognition results obtained at a frame level are then combined with those in consecutive frames, relying on automatic object tracking. Differently from previous work, we use images automatically retrieved by web search engines. We evaluate the systems one three datasets including historical videos from 1945 to 1969 and contemporary videos, obtaining a good precision score. In addition, we show how the obtained results can be applied to foster historical studies.
C1 [Lisena, Pasquale; Troncy, Raphael] EURECOM, Sophia Antipolis, France.
   [Laaksonen, Jorma] Aalto Univ, Espoo, Finland.
RP Lisena, P (通讯作者)，EURECOM, Sophia Antipolis, France.
EM pasquale.lisena@eurecom.fr; jorma.laaksonen@aalto.fi;
   raphael.troncy@eurecom.fr
FU French National Research Agency (ANR) within the ANTRACT project
   [ANR-17-CE38-0010]; Academy of Finland [329268, 345791]
FX The authors would like to thank Benedicte Pincemin for the valuable
   feedback, which helped to improve the paper. This work has been
   partially supported by the French National Research Agency (ANR) within
   the ANTRACT project (grant number ANR-17-CE38-0010) and by the Academy
   of Finland (project grants n. 329268 and 345791).
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NR 30
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0942-4962
EI 1432-1882
J9 MULTIMEDIA SYST
JI Multimedia Syst.
DI 10.1007/s00530-022-00959-x
EA JUN 2022
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2D2UM
UT WOS:000811408700001
DA 2022-08-23
ER

PT J
AU Liu, SK
   Xu, RL
   Geng, BY
   Sun, Q
   Duan, L
   Liu, YM
AF Liu, Shu-Kan
   Xu, Rui-Lin
   Geng, Bo-Ying
   Sun, Qiao
   Duan, Li
   Liu, Yi-Ming
TI Metaknowledge Extraction Based on Multi-Modal Documents
SO IEEE ACCESS
LA English
DT Article
DE Task analysis; Optical character recognition software; Layout; Object
   detection; Semantics; Knowledge based systems; Computational modeling;
   Metaknowledge; multi-modal; document layout analysis; knowledge graph
AB The triplet-based knowledge in large-scale knowledge bases is most likely lacking in structural logic and problematic of conducting knowledge hierarchy. In this paper, we introduce the concept of metaknowledge to knowledge engineering research for the purpose of structural knowledge construction. Therefore, the Metaknowledge Extraction Framework and Document Structure Tree model are presented to extract and organize metaknowledge elements (titles, authors, abstracts, sections, paragraphs, etc.), so that it is feasible to extract the structural knowledge from multi-modal documents. Experiment results have proved the effectiveness of metaknowledge elements extraction by our framework. Meanwhile, detailed examples are given to demonstrate what exactly metaknowledge is and how to generate it. At the end of this paper, we propose and analyze the task flow of metaknowledge applications and the associations between knowledge and metaknowledge.
C1 [Liu, Shu-Kan] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
   [Liu, Shu-Kan; Xu, Rui-Lin; Geng, Bo-Ying; Sun, Qiao; Duan, Li; Liu, Yi-Ming] PLA Naval Univ Engn, Sch Elect Engn, Wuhan 430033, Peoples R China.
   [Sun, Qiao] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310058, Peoples R China.
RP Geng, BY (通讯作者)，PLA Naval Univ Engn, Sch Elect Engn, Wuhan 430033, Peoples R China.
EM boying_geng@126.com
OI Xu, Ruilin/0000-0001-7468-8635; Liu, Shukan/0000-0002-7087-1069
FU Hubei Provincial Natural Science Foundation of China [2018CFC800]
FX This work was supported in part by the Hubei Provincial Natural Science
   Foundation of China under Grant 2018CFC800.
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NR 45
TC 2
Z9 2
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 2021
VL 9
BP 50050
EP 50060
DI 10.1109/ACCESS.2021.3068728
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA RJ3VE
UT WOS:000637524900001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Ren, JB
   He, GW
   Deng, XG
   Deng, XZ
   Yang, Y
   Yao, HQ
   Yang, SX
AF Ren, Jiangbo
   He, Gaowen
   Deng, Xiguang
   Deng, Xianze
   Yang, Yong
   Yao, Huiqiang
   Yang, Shengxiong
TI Metallogenesis of Co-rich ferromanganese nodules in the northwestern
   Pacific: Selective enrichment of metallic elements from seawater
SO ORE GEOLOGY REVIEWS
LA English
DT Article
DE Co-rich ferromanganese nodules; Selective enrichment; Rare earth
   elements and yttrium; Nd isotope; Northwestern Pacific; Knowledge map
ID RARE-EARTH-ELEMENTS; ISOTOPE COMPOSITION; MANGANESE NODULES; ND
   ISOTOPES; SM-ND; CRUSTS; COBALT; OCEAN; GEOCHEMISTRY; NEODYMIUM
AB Ferromanganese nodules in the northwestern Pacific are newly discovered submarine solid mineral resources with high economic value. To systematically ascertain the metallogenesis of these nodules, we investigate their microstructure, mineralogy, and elemental concentrations, as well as their Nd and Hf isotopic compositions. The seabed nodules in the study area are formed in a low-temperature marine environment, with low crystallinity and detrital components. The minerals identified by XRD contain a large amount of vernadite (mean 63.6%) and a small amount of todorokite (mean 7.2%), indicating an oxidized metallogenic environment. Moreover, the high contents of Co (0.31-0.51 wt%, mean 0.46%), Ti (mean 1.28%), rare earth elements and yttrium (REY, mean 1969 ppm), as well as the high delta Ce, and Fe/Mn ratios, differ from those of diagenetic and mixed-type nodules, confirming the typical hydrogenetic chemical composition of the nodules. Extended exposure to oxidizing deepwater environments, as a consequence of oligotrophic, continuous bottom current activities together with constant low sedimentation rates, provides optimal conditions for the Co-rich ferromanganese nodules (Co-rich nodules) to enrich multiple elements from seawater. Notably, Mn, Co, Ce and Pb are scavenged elements with low contents that decrease with water depth, and are primarily regulated by Mn-Fe oxyhydmxide in the water column. Nutrient-type elements, such as Fe, Ti, Cu, Ni, and REY (except Ce) can also be highly enriched in Corich nodules; however, they are mainly controlled by biological particles in water column. Collectively, coulombic interactions represent the major driving force for selective enrichment of metallic elements for Co-rich nodules from seawater. epsilon(Nd) and epsilon(Hf) also show that the Co-rich nodules are hydrogenetic ranging from -6.33 to -4.41 and from +5.00 to +6.18, respectively. The epsilon(Nd) of the Co-rich nodules was significantly lower than that of the Co-rich ferromanganese crusts observed on seamounts, indicating that the nodules are more affected by bottom currents. The nodules and crusts in the northwestern Pacific have rich radioactive Nd isotopic compositions, which is consistent with the high epsilon(Nd) observed along the Pacific margins, caused by the weathering of terrigenous components.
C1 [Ren, Jiangbo; He, Gaowen; Deng, Xiguang; Deng, Xianze; Yang, Yong; Yao, Huiqiang; Yang, Shengxiong] Southern Marine Sci & Engn Guangdong Lab Guangzho, Guangzhou 511458, Peoples R China.
   [Ren, Jiangbo; He, Gaowen; Deng, Xiguang; Deng, Xianze; Yang, Yong; Yao, Huiqiang; Yang, Shengxiong] Guangzhou Marine Geol Survey, Minist Nat Resources, Key Lab Marine Mineral Resources, Guangzhou 510075, Peoples R China.
RP He, GW; Deng, XG (通讯作者)，Southern Marine Sci & Engn Guangdong Lab Guangzho, Guangzhou 511458, Peoples R China.
EM hegaowen@163.com; dengxg68@163.com
FU Key Special Project for Introduced Talents Team of Southern Marine
   Science and Engineering Guangdong Laboratory (Guangzhou)
   [GML2019ZD0106]; National Natural Science Foundation of China
   [92162216]; China Ocean Mineral Resources R&D Association Research
   Program [DY135-R2-1-05, DY135-C1-1-01]; Geological Survey of China
   [DD20190629, DD20191009]
FX We are grateful for the hard work of the crew who participated in the
   scientific expedition. This work was financially supported by the Key
   Special Project for Introduced Talents Team of Southern Marine Science
   and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0106) , the
   National Natural Science Foundation of China (92162216) , China Ocean
   Mineral Resources R&D Association Research Program (DY135-R2-1-05,
   DY135-C1-1-01) , and Geological Survey of China (DD20190629, DD20191009)
   .
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NR 79
TC 1
Z9 1
U1 12
U2 12
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-1368
EI 1872-7360
J9 ORE GEOL REV
JI Ore Geol. Rev.
PD APR
PY 2022
VL 143
AR 104778
DI 10.1016/j.oregeorev.2022.104778
PG 13
WC Geology; Mineralogy; Mining & Mineral Processing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geology; Mineralogy; Mining & Mineral Processing
GA ZP9MW
UT WOS:000766741300002
OA hybrid
DA 2022-08-23
ER

PT J
AU Fecho, K
   Thessen, AE
   Baranzini, SE
   Bizon, C
   Hadlock, JJ
   Huang, S
   Roper, RT
   Southall, N
   Ta, C
   Watkins, PB
   Williams, MD
   Xu, H
   Byrd, W
   Dancik, V
   Duby, MP
   Dumontier, M
   Glusman, G
   Harris, NL
   Hinderer, EW
   Hyde, G
   Johs, A
   Su, AI
   Qin, GR
   Zhu, Q
AF Fecho, Karamarie
   Thessen, Anne E.
   Baranzini, Sergio E.
   Bizon, Chris
   Hadlock, Jennifer J.
   Huang, Sui
   Roper, Ryan T.
   Southall, Noel
   Ta, Casey
   Watkins, Paul B.
   Williams, Mark D.
   Xu, Hao
   Byrd, William
   Dancik, Vlado
   Duby, Marc P.
   Dumontier, Michel
   Glusman, Gustavo
   Harris, Nomi L.
   Hinderer, Eugene W.
   Hyde, Greg
   Johs, Adam
   Su, Andrew, I
   Qin, Guangrong
   Zhu, Qian
CA Biomed Data Translator Consortium
TI Progress toward a universal biomedical data translator
SO CTS-CLINICAL AND TRANSLATIONAL SCIENCE
LA English
DT Review
AB Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.
C1 [Fecho, Karamarie; Bizon, Chris; Xu, Hao] Univ N Carolina, Renaissance Comp Inst, Chapel Hill, NC 27515 USA.
   [Thessen, Anne E.] Univ Colorado, Ctr Hlth AI, Anschutz Med Campus, Aurora, CO USA.
   [Baranzini, Sergio E.] Univ Calif San Francisco, Dept Neurol, Weill Inst Neurosci, San Francisco, CA USA.
   [Hadlock, Jennifer J.; Huang, Sui; Roper, Ryan T.; Glusman, Gustavo; Qin, Guangrong] Inst Syst Biol, Seattle, WA USA.
   [Southall, Noel; Williams, Mark D.; Zhu, Qian] NIH, Div Preclin Innovat, Natl Ctr Adv Translat Sci, Rockville, MD USA.
   [Ta, Casey] Columbia Univ, Dept Biomed Informat, New York, NY USA.
   [Watkins, Paul B.] Univ N Carolina, Div Pharmacotherapy & Expt Therapeut, Eshelman Sch Pharm, Chapel Hill, NC 27515 USA.
   [Byrd, William] Univ Alabama Birmingham, Hugh Kaul Precis Med Inst, Birmingham, AL USA.
   [Dancik, Vlado] Broad Inst, Chem Biol & Therapeut Sci Program, Cambridge, MA USA.
   [Duby, Marc P.] Broad Inst, Med & Populat Genet Program, Cambridge, MA USA.
   [Dumontier, Michel] Maastricht Univ, Inst Data Sci, Maastricht, Netherlands.
   [Harris, Nomi L.] Lawrence Berkeley Natl Lab, Div Environm Genom & Syst Biol, Berkeley, CA USA.
   [Hinderer, Eugene W.] Tufts Med Ctr, Tufts Clin & Translat Sci Inst, Boston, MA 02111 USA.
   [Hyde, Greg] Dartmouth Coll, Thayer Sch Engn, Hanover, NH 03755 USA.
   [Johs, Adam] Drexel Univ, Coll Comp & Informat, Dept Informat Sci, Philadelphia, PA 19104 USA.
   [Su, Andrew, I] Scripps Res Inst, Dept Integrat Struct & Computat Biol, La Jolla, CA USA.
RP Fecho, K (通讯作者)，Renaissance Comp Inst, 100 Europa Dr,Suite 540, Chapel Hill, NC 27517 USA.
EM kfecho@renci.org
OI Hadlock, Jennifer/0000-0001-6103-7606; Fecho,
   Karamarie/0000-0002-6704-9306
FU National Center for Advancing Translational Sciences, Biomedical Data
   Translator Program [OT2TR003434, OT2TR003436, OT2TR003428, OT2TR003448,
   OT2TR003427, OT2TR003430, OT2TR003433, OT2TR003450, OT2TR003437,
   OT2TR003443, OT2TR003441, OT2TR003449, OT2TR003445, OT2TR003422,
   OT2TR003435]; National Center for Advancing Translational Sciences,
   Intramural Research Program [ZIA TR000276-05]; National Institute of
   Diabetes and Digestive and Kidney Diseases [5U01DK065201]; NCATS,
   Biomedical Data Translator Program (Other Transaction Awards)
   [OT3TR002026, OT3TR002020, OT3TR002025, OT3TR002019, OT3TR002027,
   OT2TR002517, OT2TR002514, OT2TR002515, OT2TR002584, OT2TR002520,
   75N95021P00636]
FX This work was supported by the National Center for Advancing
   Translational Sciences, Biomedical Data Translator Program (Other
   Transaction Awards OT2TR003434, OT2TR003436, OT2TR003428, OT2TR003448,
   OT2TR003427, OT2TR003430, OT2TR003433, OT2TR003450, OT2TR003437,
   OT2TR003443, OT2TR003441, OT2TR003449, OT2TR003445, OT2TR003422,
   OT2TR003435, OT3TR002026, OT3TR002020, OT3TR002025, OT3TR002019,
   OT3TR002027, OT2TR002517, OT2TR002514, OT2TR002515, OT2TR002584, and
   OT2TR002520; Contract number 75N95021P00636). Additional funding was
   provided by the National Center for Advancing Translational Sciences,
   Intramural Research Program (ZIA TR000276-05) and the National Institute
   of Diabetes and Digestive and Kidney Diseases (5U01DK065201).
CR Ackoff R.L., 1989, J APPL SYSTEMS ANAL, V16, P3, DOI DOI 10.1002/9781444303179.CH3
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NR 31
TC 1
Z9 1
U1 0
U2 0
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1752-8054
EI 1752-8062
J9 CTS-CLIN TRANSL SCI
JI CTS-Clin. Transl. Sci.
PD AUG
PY 2022
VL 15
IS 8
BP 1838
EP 1847
DI 10.1111/cts.13301
EA JUN 2022
PG 10
WC Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Research & Experimental Medicine
GA 3S3DN
UT WOS:000806299900001
PM 35611543
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Vanermen, I
   Muys, B
   Verheyen, K
   Vanwindekens, F
   Bouriaud, L
   Kardol, P
   Vranken, L
AF Vanermen, Iris
   Muys, Bart
   Verheyen, Kris
   Vanwindekens, Frederic
   Bouriaud, Laura
   Kardol, Paul
   Vranken, Liesbet
TI What do scientists and managers know about soil biodiversity?
   Comparative knowledge mapping for sustainable forest management
SO FOREST POLICY AND ECONOMICS
LA English
DT Article
DE Social-ecological system; Soil biodiversity; Sustainable forest
   management; Fuzzy cognitive mapping; Knowledge comparison; Context
   dependency
ID ECOSYSTEM SERVICES; DPSIR FRAMEWORK; ENVIRONMENTAL-CHANGE; TREE
   DIVERSITY; CLIMATE-CHANGE; MENTAL MODELS; PERCEPTIONS; EUROPE; POLICY;
   PROTECTION
AB Soil biodiversity is crucial for maintaining forest health and safeguarding forest ecosystem services delivery, but it is under increasing human pressure. Forest management puts pressure on soil biodiversity, but has also the potential to support soil biodiversity recovery, depending on which decisions forest managers make. These decisions are highly influenced by managers' perception and understanding. Nevertheless, insights into forest managers' understanding of soils and their biodiversity are largely lacking. This paper addresses this gap by studying private and public forest managers' understanding of soil biodiversity and comparing their level of knowledge with scientists' knowledge. In addition, this paper assesses the effects of context on understanding by comparing between two regions (NW of Flanders, Belgium, and NE of Romania). Specifically, knowledge was elicited using semi-structured interviews based on an integrated framework. The interviews were coded and analyzed using a Fuzzy Cognitive Mapping approach. In total, 24 interviews were conducted after selecting respondents using a purposive sampling design. Our results indicate that forest managers are aware of the crucial role of soil biodiversity and possess practical and context-specific understanding, but lack in-depth knowledge related to ecosystem processes and functions and soil state variables, compared to scientists. In addition, managers did not seem to explicitly consider soil biodiversity in their management decisions, but instead seemed to treat soil more as a black box. While scientists had a more detailed understanding, their understanding also depended on their background as researchers and mostly overlooked practical, site-specific implications. Moreover, we found that local context influenced respondents' understanding, especially related to drivers and pressures that affect soil biodiversity. Hence, communication strategies oriented towards forest managers seem suitable to maximize adoption of adaptive management practices that support soil biodiversity. These strategies should go beyond awareness raising and specifically explain ecosystem processes and functions linked to forest soil biodiversity to improve managers' in-depth understanding, while taking their socio-economic and forestry context into account. Further, policy design should enhance conditions for knowledge exchange and discussion about soil biodiversity. The methodology presented in this study might help such knowledge integration of scientists and forest managers in order to combine in-depth understanding of soil biodiversity and applicability of management practices in specific forest contexts.
C1 [Vanermen, Iris; Vranken, Liesbet] Katholieke Univ Leuven, Dept Earth & Environm Sci, Div Bioecon, Celestijnenlaan 200 E,Box 2411, B-3001 Leuven, Belgium.
   [Muys, Bart] Katholieke Univ Leuven, Dept Earth & Environm Sci, Div Forest Nat & Landscape, Celestijnenlaan 200 E,Box 2411, B-3001 Leuven, Belgium.
   [Verheyen, Kris] UGent, Dept Environm, Forest & Nat Lab, Geraadsbergsesteenweg 267, B-9090 Gontrode, Belgium.
   [Vanwindekens, Frederic] Walloon Agr Res Ctr, Agr & Nat Environm Dept, Rue Liroux 9, B-5030 Gembloux, Belgium.
   [Bouriaud, Laura] Univ Stefan Cel Mare Suceava, Fac Forestry, Univ 13, Suceava 720229, Romania.
   [Kardol, Paul] Swedish Univ Agr Sci, Dept Forest Ecol & Management, S-90183 Umea, Sweden.
RP Vanermen, I (通讯作者)，Katholieke Univ Leuven, Dept Earth & Environm Sci, Div Bioecon, Celestijnenlaan 200 E,Box 2411, B-3001 Leuven, Belgium.
EM iris.vanermen@kuleuven.be; bart.muys@kuleuven.be;
   kris.verheyen@ugent.be; f.vanwindekens@cra.wallonie.be; bouriaud@usv.ro;
   paul.kardol@slu.se; liesbet.vranken@kuleuven.be
RI Kardol, Paul/N-8383-2015; BITOUN, RACHEL/AAC-9538-2021; Vranken,
   Liesbet/Q-1710-2018; Muys, Bart/A-3194-2015
OI Kardol, Paul/0000-0001-7065-3435; Vranken, Liesbet/0000-0003-1170-1139;
   Muys, Bart/0000-0001-9421-527X; Vanwindekens,
   Frederic/0000-0002-9117-7543
FU SoilForEUROPE project funded through the BiodivERsA COFUND; Agence
   Nationale de la Recherche (ANR, France); Belgian Science Policy Office
   (BELSPO, Belgium); Deutsche Forschungsgemeinschaft (DFG, Germany);
   Research Foundation Flanders (FWO, Belgium); Swedish Research Council
   (FORMAS, Sweden)
FX We would like to thank the respondents for their contribution and
   effort. We would also like to thank Jan Goris, of the Flemish `bosgroep
   Houtland' for bringing us into contact with the Flemish private forest
   managers, and Cosmin Cosofret for translating during the Romanian
   interviews. This research was part of the SoilForEUROPE project funded
   through the 2015-2016 BiodivERsA COFUND call for research proposals,
   with the national funders Agence Nationale de la Recherche (ANR,
   France), Belgian Science Policy Office (BELSPO, Belgium), Deutsche
   Forschungsgemeinschaft (DFG, Germany), Research Foundation Flanders
   (FWO, Belgium), and the Swedish Research Council (FORMAS, Sweden).
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NR 86
TC 3
Z9 3
U1 2
U2 22
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1389-9341
EI 1872-7050
J9 FOREST POLICY ECON
JI Forest Policy Econ.
PD OCT
PY 2020
VL 119
AR 102264
DI 10.1016/j.forpol.2020.102264
PG 15
WC Economics; Environmental Studies; Forestry
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Environmental Sciences & Ecology; Forestry
GA NN3HS
UT WOS:000568682900010
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Xin, X
   Shi, TL
   Sohail, M
AF Xin, Xin
   Shi, Tianlei
   Sohail, Mishal
TI Knowledge-Based Intelligent Education Recommendation System with IoT
   Networks
SO SECURITY AND COMMUNICATION NETWORKS
LA English
DT Article
ID PERSONALIZED RECOMMENDATION; USERS
AB The intelligent education recommendation system can recommend knowledge suitable for students' personal learning. However, the traditional recommendation algorithm has generality problems, which lead to poor knowledge recommendation effects. In order to improve the performance of the education recommendation system, based on the machine learning algorithm, this paper combines the knowledge graph algorithm to improve the recommendation algorithm and decomposes the matrix with a higher dimension into several matrices with relatively small dimensions through matrix transformation. Moreover, this paper conducts in-depth mining of the potential attributes of users and items and improves the matrix decomposition formula based on knowledge recommendation requirements. In addition, this paper constructs the framework of the intelligent education recommendation system with IoT networks based on the analysis of functional requirements. Finally, this paper designs experiments to verify and analyze the model from the perspective of model performance and user satisfaction. The research results show that the algorithm model constructed in this paper is effective.
C1 [Xin, Xin] Jinlin Prov Inst Educ, Changchun 130022, Jilin, Peoples R China.
   [Shi, Tianlei] BOE Technol Grp Co Ltd, Beijing 100010, Peoples R China.
   [Sohail, Mishal] Int Ataturk Alatoo Univ, Dept Ind Engn, Bishkek, Kyrgyzstan.
RP Sohail, M (通讯作者)，Int Ataturk Alatoo Univ, Dept Ind Engn, Bishkek, Kyrgyzstan.
EM dr.mishalsohail@mail.cu.edu.kg
RI xin, xin/AGX-8597-2022
FU Educational Department of Jilin Province (the research of English online
   class of high vocational college on political character of curriculum)
   [2020ZCY349]
FX This study was supported by the Educational Department of Jilin Province
   (the research of English online class of high vocational college on
   political character of curriculum) (2020ZCY349).
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NR 25
TC 0
Z9 0
U1 11
U2 11
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1939-0114
EI 1939-0122
J9 SECUR COMMUN NETW
JI Secur. Commun. Netw.
PD MAR 7
PY 2022
VL 2022
AR 4140774
DI 10.1155/2022/4140774
PG 10
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 0A1FV
UT WOS:000773708000004
OA gold
DA 2022-08-23
ER

PT J
AU Xie, RB
   Heinrich, S
   Liu, ZY
   Weber, C
   Yao, Y
   Wermter, S
   Sun, MS
AF Xie, Ruobing
   Heinrich, Stefan
   Liu, Zhiyuan
   Weber, Cornelius
   Yao, Yuan
   Wermter, Stefan
   Sun, Maosong
TI Integrating Image-Based and Knowledge-Based Representation Learning
SO IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
LA English
DT Article
DE Visualization; Knowledge representation; Brain modeling; Task analysis;
   Head; Knowledge based systems; Computational modeling; Attention
   mechanisms and development; embodied cognition; generation of
   representation during development
AB A variety of brain areas is involved in language understanding and generation, accounting for the scope of language that can refer to many real-world matters. In this paper, we investigate how regularities among real-world entities impact emergent language representations. Specifically, we consider knowledge bases, which represent entities and their relations as structured triples, and image representations, which are obtained via deep convolutional networks. We combine these sources of information to learn representations of an image-based knowledge representation learning (IKRL) model. An attention mechanism lets more informative images contribute more to the image-based representations. Evaluation results show that the model outperforms all baselines on the tasks of knowledge graph (KG) completion and triple classification. In analyzing the learned models, we found that the structure-based and image-based representations integrate different aspects of the entities and the attention mechanism provides robustness during learning.
C1 [Xie, Ruobing] Tencent, WeChat Search Applicat Dept, Search Prod Ctr, Shenzhen 518000, Peoples R China.
   [Heinrich, Stefan; Weber, Cornelius; Wermter, Stefan] Univ Hamburg, Knowledge Technol Grp, Dept Informat, D-22527 Hamburg, Germany.
   [Liu, Zhiyuan; Yao, Yuan; Sun, Maosong] Tsinghua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China.
   [Liu, Zhiyuan; Yao, Yuan; Sun, Maosong] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China.
RP Heinrich, S (通讯作者)，Univ Hamburg, Knowledge Technol Grp, Dept Informat, D-22527 Hamburg, Germany.
EM xrbsnowing@163.com; heinrich@informatik.uni-hamburg.de;
   liuzy@tsinghua.edu.cn; weber@informatik.uni-hamburg.de;
   yao-y14@mails.tsinghua.edu.cn; wermter@informatik.uni-hamburg.de;
   sms@tsinghua.edu.cn
OI Heinrich, Stefan/0000-0001-9913-3206; Wermter,
   Stefan/0000-0003-1343-4775; Liu, Zhiyuan/0000-0002-7709-2543
FU German Research Foundation; National Science Foundation of China
   [TRR-169]
FX This work was supported in part by the German Research Foundation and in
   part by the National Science Foundation of China through Project
   "Crossmodal Learning" under Grant TRR-169.
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NR 56
TC 3
Z9 3
U1 4
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2379-8920
EI 2379-8939
J9 IEEE T COGN DEV SYST
JI IEEE Trans. Cogn. Dev. Syst.
PD JUN
PY 2020
VL 12
IS 2
BP 169
EP 178
DI 10.1109/TCDS.2019.2906685
PG 10
WC Computer Science, Artificial Intelligence; Robotics; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Robotics; Neurosciences & Neurology
GA MC0FF
UT WOS:000542972700005
OA hybrid
DA 2022-08-23
ER

PT J
AU Reyes-Magana, J
   Bel-Enguix, G
   Gomez-Adorno, H
   Sierra, G
AF Reyes-Magana, Jorge
   Bel-Enguix, Gemma
   Gomez-Adorno, Helena
   Sierra, Gerardo
TI A Lexical Search Model based on word association norms
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Information retrieval; word association norms; natural language graphs;
   lexical search
ID CENTRALITY
AB This work introduces a lexical search model based on a type of knowledge graphs, namely word association norms. The aim of the search is to retrieve a target word, given the description of a concept, i.e., the query. This differs from traditional information retrieval models were complete documents related to the query are retrieved. Our algorithm looks for the keywords of the definition in a graph, built over a corpus of word association norms for Mexican Spanish, and computes the centrality in order to find the relevant concept. We performed experiments over a corpus of human-definitions in order to evaluate our model. The results are compared with a Boolean information retrieval (IR) model, the BM25 text-retrieval algorithm, an algorithm based on word vectors and an online onomasiological dictionary-OneLook Reverse Dictionary. The experiments show that our lexical search method outperforms the IR models in our study case.
C1 [Reyes-Magana, Jorge; Bel-Enguix, Gemma; Sierra, Gerardo] Univ Nacl Autonoma Mexico, II, Mexico City, DF, Mexico.
   [Gomez-Adorno, Helena] Univ Nacl Autonoma Mexico, Inst Invest Matemat Aplicadas & Sistemas, Mexico City, DF, Mexico.
   [Reyes-Magana, Jorge] UADY, FM, Merida, Mexico.
RP Gomez-Adorno, H (通讯作者)，Univ Nacl Autonoma Mexico, Inst Invest Matemat Aplicadas & Sistemas, Mexico City, DF, Mexico.
EM helena.gomez@iimas.unam.mx
RI Martínez, Gerardo Sierra/J-3737-2016; Reyes-Magana, Jorge/ABC-1154-2021
OI Martínez, Gerardo Sierra/0000-0002-6724-1090; Reyes-Magana,
   Jorge/0000-0002-8296-1344
FU PAPIIT [IA400117, IN403016]; CONACYT Fronteras de la ciencia [002225]
FX This work has been supported by PAPIIT projects IA400117 and IN403016,
   and CONACYT Fronteras de la ciencia 002225.
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NR 51
TC 1
Z9 1
U1 0
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 2019
VL 36
IS 5
SI SI
BP 4587
EP 4597
DI 10.3233/JIFS-179010
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA HY1WD
UT WOS:000467907000056
DA 2022-08-23
ER

PT J
AU Aebeloe, C
   Montoya, G
   Setty, V
   Hose, K
AF Aebeloe, Christian
   Montoya, Gabriela
   Setty, Vinay
   Hose, Katja
TI Discovering Diversified Paths in Knowledge Bases
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB Vast amounts of world knowledge is now accessible through Knowledge Graphs (KGs) in RDF format and can be queried using SPARQL. Yet, finding paths between nodes in such graphs is not part of the official SPARQL 1.1 standard; only the simpler functionality of checking reachability is supported, i.e., assessing whether two nodes are connected based on certain conditions formalized as property paths but without providing information on how they are actually connected. To close this gap of functionality, we present JEDI, a system that extends a popular SPARQL engine, Jena, with the ability to compute paths connecting entities in a KG. JEDI shows the k most relevant results to the user where relevance is assessed as a trade-off between path length and diversification of the intermediate nodes in the path. Furthermore, our solution is not limited to a single property path pattern but supports queries containing multiple property path patterns. While JEDI supports arbitrary KGs, for demonstration purposes some predefined KGs, such as YAGO and DBLP, will be used.
C1 [Aebeloe, Christian; Montoya, Gabriela; Setty, Vinay; Hose, Katja] Aalborg Univ, Aalborg, Denmark.
   [Setty, Vinay] Univ Stavanger, Stavanger, Norway.
RP Aebeloe, C (通讯作者)，Aalborg Univ, Aalborg, Denmark.
EM caebel@cs.aau.dk; gmontoya@cs.aau.dk; vinay@cs.aau.dk; khose@cs.aau.dk
OI Hose, Katja/0000-0001-7025-8099; Setty, Vinay/0000-0002-9777-6758
FU Danish Council for Independent Research (DFF) [DFF-4093-00301]; Aalborg
   University's Talent Management Programme
FX This research was partially funded by the Danish Council for Independent
   Research (DFF) under grant agreement no. DFF-4093-00301 and Aalborg
   University's Talent Management Programme.
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NR 12
TC 1
Z9 1
U1 1
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD AUG
PY 2018
VL 11
IS 12
BP 2002
EP 2005
DI 10.14778/3229863.3236245
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:000452544100038
OA Green Published
DA 2022-08-23
ER

PT J
AU Huang, YC
   Gamborino, E
   Huang, YJ
   Qian, XB
   Fu, LC
   Yeh, SL
AF Huang, Yu-Cian
   Gamborino, Edwinn
   Huang, Yan-Jia
   Qian, Xiaobei
   Fu, Li-Chen
   Yeh, Su-Ling
TI Inferring Stressors from Conversation: Towards an Emotional Support
   Robot Companion
SO INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS
LA English
DT Article; Early Access
DE Social Robots; Emotional Support; Conversational Agents
ID SOCIAL SUPPORT; PERSONALITY; ASSOCIATION; PREFERENCES; IMPACT
AB People build relationships with each other through emotional support. Appropriate care provided by friends or family may critically help people extricate themselves from negative emotional states. In this work, we implement a conversational robot that can provide emotional support to people facing stressful situations in their daily lives. The proposed robot system extracts meaningful keywords from the support recipients' speech to identify the moments when people expose their concerns through self-disclosure. These keywords are then used to determine the underlying stressors in the recipients' minds. By using a novel approach through a commonsense knowledge graph-based Bayesian network, the proposed system leverages Gibbs sampling to infer the posterior probability of various documented stressors. Moreover, we trained a neural network to determine an appropriate support strategy that should be offered to a recipient with a specific personality given a detected stressor. We evaluated our implementation through several human-in-loop experiments. Results show that this system can correctly identify the underlying stressors from speech and provide appropriate emotional support required to help people cope with difficult situations in one's life.
C1 [Huang, Yu-Cian; Huang, Yan-Jia] Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan.
   [Qian, Xiaobei] Natl Taiwan Univ, Grad Inst Networking & Multimedia, Taipei, Taiwan.
   [Gamborino, Edwinn; Fu, Li-Chen] Natl Taiwan Univ, Res Ctr Artificial Intelligence & Adv Robot, Taipei, Taiwan.
   [Yeh, Su-Ling] Natl Taiwan Univ, Dept Psychol, Taipei, Taiwan.
RP Fu, LC (通讯作者)，Natl Taiwan Univ, Res Ctr Artificial Intelligence & Adv Robot, Taipei, Taiwan.
EM r06922072@ntu.edu.tw; r04921098@ntu.edu.tw; r08922139@ntu.edu.tw;
   d08944013@ntu.edu.tw; lichen@ntu.edu.tw; suling@ntu.edu.tw
RI ; Yeh, Su-Ling/D-6089-2016
OI Fu, Li-Chen/0000-0002-6947-7646; Yeh, Su-Ling/0000-0003-4597-8697
FU Ministry of Science and Technology of Taiwan [MOST 110-2634-F-002-049,
   MOST 110-2221-E-002-166MY3]; Center for Artificial Intelligence &
   Advanced Robotics, National Taiwan University [MOST 110-2634-F-002-049,
   MOST 110-2221-E-002-166MY3]
FX This research was supported by the Ministry of Science and Technology of
   Taiwan, and Center for Artificial Intelligence & Advanced Robotics,
   National Taiwan University, under the grant numbers MOST
   110-2634-F-002-049 & MOST 110-2221-E-002-166MY3.
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NR 71
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1875-4791
EI 1875-4805
J9 INT J SOC ROBOT
JI Int. J. Soc. Robot.
DI 10.1007/s12369-022-00902-0
EA JUL 2022
PG 15
WC Robotics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Robotics
GA 2Z0KO
UT WOS:000826274300001
DA 2022-08-23
ER

PT J
AU Bjerke, IE
   Puchades, MA
   Bjaalie, JG
   Leergaard, TB
AF Bjerke, Ingvild E.
   Puchades, Maja A.
   Bjaalie, Jan G.
   Leergaard, Trygve B.
TI Database of literature derived cellular measurements from the murine
   basal ganglia
SO SCIENTIFIC DATA
LA English
DT Article; Data Paper
ID WAXHOLM SPACE ATLAS; NEUROANATOMY; ARCHITECTURE; NEURONS; NUMBER
AB Quantitative measurements and descriptive statistics of different cellular elements in the brain are typically published in journal articles as text, tables, and example figures, and represent an important basis for the creation of biologically constrained computational models, design of intervention studies, and comparison of subject groups. Such data can be challenging to extract from publications and difficult to normalise and compare across studies, and few studies have so far attempted to integrate quantitative information available in journal articles. We here present a database of quantitative information about cellular parameters in the frequently studied murine basal ganglia. The database holds a curated and normalised selection of currently available data collected from the literature and public repositories, providing the most comprehensive collection of quantitative neuroanatomical data from the basal ganglia to date. The database is shared as a downloadable resource from the EBRAINS Knowledge Graph (https://kg.ebrains.eu), together with a workflow that allows interested researchers to update and expand the database with data from future reports.
C1 [Bjerke, Ingvild E.; Puchades, Maja A.; Bjaalie, Jan G.; Leergaard, Trygve B.] Univ Oslo, Inst Basic Med Sci, Dept Mol Med, Oslo, Norway.
RP Leergaard, TB (通讯作者)，Univ Oslo, Inst Basic Med Sci, Dept Mol Med, Oslo, Norway.
EM t.b.leergaard@medisin.uio.no
RI Bjaalie, Jan G. G./AAW-6445-2021; Puchades, Maja/D-5835-2013
OI Bjaalie, Jan G. G./0000-0001-7899-906X; Puchades,
   Maja/0000-0002-4536-8197; Bjerke, Ingvild Elise/0000-0002-2926-6836;
   Leergaard, Trygve B./0000-0001-5965-8470
FU European Union's Horizon 2020 Framework Programme for Research and
   Innovation Programme [785907, 945539]; Research Council of Norway
   [269774]
FX We thank Krister Andersson, Camilla Blixhavn, Heidi Kleven, Martin
   Ovsthus, Lyuba Zehl and Oliver Schmid for useful discussions, and Ulrike
   Schlegel for help with sharing datasets through the EBRAINS Knowledge
   Graph. This work was funded by the European Union's Horizon 2020
   Framework Programme for Research and Innovation Programme under the
   Specific Grant Agreement No. 785907 (Human Brain Project SGA2), Specific
   Grant Agreement No. 945539 (Human Brain Project SGA3), and The Research
   Council of Norway under Grant Agreement No. 269774 (INCF Norwegian
   Node). The funders had no role in study design, data collection and
   analysis, decision to publish, or preparation of the manuscript.
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NR 75
TC 4
Z9 4
U1 0
U2 0
PU NATURE PUBLISHING GROUP
PI LONDON
PA MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 2052-4463
J9 SCI DATA
JI Sci. Data
PD JUL 6
PY 2020
VL 7
IS 1
AR 211
DI 10.1038/s41597-020-0550-3
PG 14
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA MM2MO
UT WOS:000549991300001
PM 32632099
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhou, FG
   Wang, C
   Wang, JP
AF Zhou, Faguo
   Wang, Chao
   Wang, Jipeng
TI Named Entity Recognition of Ancient Poems Based on Albert-BiLSTM-MHA-CRF
   Model
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB The extraction and construction of the knowledge graph related to the entity of ancient poems are helpful to excavate the connection between ancient poems, and it is of great significance to inherit the traditional Chinese culture. This paper proposes an Albert-BiLSTM-MHA-CRF model for entity extraction in ancient poems. Based on the BiLSTM-CRF model, the author introduces the Albert pretraining model and the multihead self-attention mechanism to extract character vectors and enhance the generalization ability of word embedding vectors and the potential semantics between characters of the model, depending on weight and other feature extraction capabilities. The experiment is carried out in the corpus of ancient poetry, and the model is compared with Bert-BiLSTM-CRF, BiLSTM-CRF, and CRF model. The results show that the entity extraction effect of ancient poetry is significantly improved, and the harmonic average value is 97.17%. Compared with Bert model as the pretraining model, Albert model reduces the time by 19.56%.
C1 [Zhou, Faguo; Wang, Chao; Wang, Jipeng] China Univ Min & Technol, Sch Mechatron & Informat Engn, Beijing, Peoples R China.
RP Zhou, FG (通讯作者)，China Univ Min & Technol, Sch Mechatron & Informat Engn, Beijing, Peoples R China.
EM zhoufaguo@cumtb.edu.cn
OI Zhou, Faguo/0000-0002-2562-6494; Wang, Chao/0000-0002-8649-8182
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NR 38
TC 0
Z9 0
U1 17
U2 17
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 21
PY 2022
VL 2022
AR 6507719
DI 10.1155/2022/6507719
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1L9MG
UT WOS:000799604000009
OA gold
DA 2022-08-23
ER

PT J
AU Lin, PY
   Yu, SY
   Zhou, X
   Peng, P
   Li, KL
   Liao, XK
AF Lin, Peiying
   Yu, Siyang
   Zhou, Xu
   Peng, Peng
   Li, Kenli
   Liao, Xiangke
TI Community search over large semantic-based attribute graphs
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Community search; Data mining; Large graph; Semantic graph
AB Community search has attracted widespread attention in many fields, such as protein interaction networks, social networks, and knowledge graphs. It aims to find cohesive subgraphs that are closely related to a query vertex q in a graph G. Existing community search researches based on attribute graphs rarely consider the semantic information of attributes and interpretability of the community. In this paper, we study Community Search over Semantic-based Attribute Graphs (CSSAG), where the attribute of each vertex in the graph G is a semantic graph. To guarantee both the attribute and structure cohesiveness of the community, we introduce the maximal common subgraph and minimal degree metric to measure the cohesiveness of the attribute and structure, respectively. In this way, we can get more understandable and diverse cohesive subgraphs as depicted in the experiment. Also, we design three different online query algorithms by integrating new pruning strategies to shift the search space. Extensive experiments on real-world networks show that our approaches are effective and efficient.
C1 [Lin, Peiying; Zhou, Xu; Peng, Peng; Li, Kenli] Hunan Univ, Coll Comp Sci & Elect Endineering, Changsha 410082, Peoples R China.
   [Yu, Siyang] Hunan Univ Finance & Econ, Coll Informat Technol & Management, Changsha 410000, Peoples R China.
   [Liao, Xiangke] Natl Univ Def Technol, Coll Comp Sci & Technol, Changsha 410000, Peoples R China.
RP Yu, SY (通讯作者)，Hunan Univ Finance & Econ, Coll Informat Technol & Management, Changsha 410000, Peoples R China.
EM peiying_lin@hnu.edu.cn; yusiyang@hufe.edu.cn; zhxu@hnu.edu.cn;
   hnul6pp@hnu.edu.cn; lkl@hnu.edu.cn; xkliao@nudt.edu.cn
FU National Key Research and Development Program Project of China
   [2018YFB0204302]; Key Area Research Program of Hunan [2019GK2091];
   Outstanding Youth of Department of Education of Hunan Province [18B483];
   Zhejiang Lab [2021KD0AB02]
FX The research was supported by the National Key Research and Development
   Program Project of China (Grant No.2018YFB0204302), Key Area Research
   Program of Hunan (2019GK2091), Outstanding Youth of Department of
   Education of Hunan Province (18B483), and Zhejiang Lab (NO.2021KD0AB02).
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NR 42
TC 0
Z9 0
U1 6
U2 9
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD MAR
PY 2022
VL 25
IS 2
SI SI
BP 927
EP 948
DI 10.1007/s11280-021-00942-y
EA OCT 2021
PG 22
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZK2AM
UT WOS:000710091000001
DA 2022-08-23
ER

PT J
AU Vivar, JO
   Segarra, J
   Villazon-Terrazas, B
   Saquicela, V
AF Ortiz Vivar, Jose
   Segarra, Jose
   Villazon-Terrazas, Boris
   Saquicela, Victor
TI REDI: Towards knowledge graph-powered scholarly information management
   and research networking
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Academics; data integration; Linked Data; research networking; scholarly
   data; text mining
AB Academic data management has become an increasingly challenging task as research evolves over time. Essential tasks such as information retrieval and research networking have turned into extremely difficult operations due to an ever-growing number of researchers and scientific articles. Numerous initiatives have emerged in the IT environments to address this issue, especially focused on web technologies. Although those approaches have individually provided solutions for diverse problems, they still can not offer integrated knowledge bases nor flexibility to exploit adequately this information. In this article, we present REDI, a Linked Data-powered framework for academic knowledge management and research networking, which introduces a new perspective of integration. REDI combines information from multiple sources into a consolidated knowledge base through state-of-the-art procedures and leverages semantic web standards to represent the information. Moreover, REDI takes advantage of such knowledge for data visualisation and analysis, which ultimately improves and simplifies many activities including research networking.
C1 [Ortiz Vivar, Jose; Segarra, Jose; Saquicela, Victor] Univ Cuenca, Dept Comp Sci, April 12 Ave, EC-010203 Cuenca, Ecuador.
   [Villazon-Terrazas, Boris] Tinamica, Madrid, Spain.
RP Vivar, JO (通讯作者)，Univ Cuenca, Dept Comp Sci, April 12 Ave, EC-010203 Cuenca, Ecuador.
EM jose.ortizv@ucuenca.edu.ec
RI GALARZA, VICTOR HUGO SAQUICELA/T-2909-2019
OI GALARZA, VICTOR HUGO SAQUICELA/0000-0002-2438-9220
FU RED CEDIA9 in the project 'Ecuadorian Repository of Researchers'
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 RED CEDIA<SUP>9</SUP> in the project 'Ecuadorian
   Repository of Researchers'.
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NR 27
TC 0
Z9 0
U1 2
U2 37
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 APR
PY 2022
VL 48
IS 2
BP 167
EP 181
AR 0165551520944351
DI 10.1177/0165551520944351
EA AUG 2020
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 0Q2IK
UT WOS:000556303700001
DA 2022-08-23
ER

PT J
AU Yang, J
   Jiang, YZ
   Zhang, YP
AF Yang, Juan
   Jiang, Yizhang
   Zhang, Yuanpeng
TI Edge Fuzzy Clustering by Eliminating Undesirable Features in Garment
   Texture Image Segmentation
SO IEEE ACCESS
LA English
DT Article
DE Clustering algorithms; Linear programming; Entropy; Clothing; Image
   segmentation; Indexes; Feature extraction; Edge computing; unsupervised
   learning; clustering; feature elimination; garment texture image
   segmentation
ID WORKLOAD ALLOCATION; RESOURCE-ALLOCATION; TASK ASSIGNMENT; CLOUD; IOT;
   COMPUTATION; NETWORKING; PLACEMENT; MECHANISM; FRAMEWORK
AB Edge computing allows data to be processed at the nearest termination, reducing the requirement to transfer data back and forth between the cloud during the procedure of online knowledge graph construction for fashion design resources. In this paper, we proposed a DBI driven clustering technique for garment texture image segmentation installed on terminations which has two merits. One is that the technique embeds DBI index into the Shannon entropy term in the objective function to control feature weighting learning such that the learnt feature weights become more accurate. Another merit is that we propose a feature eliminating principle to filter undesirable features to thoroughly reduce the side effects caused by undesirable features. The proposed clustering technique is verified on different kinds of datasets including garment texture images and the experimental results indicate its promising performance.
C1 [Yang, Juan] Soochow Univ, Coll Text & Clothing Engn, Suzhou 215123, Peoples R China.
   [Yang, Juan] Nantong Univ, Sch Text & Clothing, Nantong 226001, Peoples R China.
   [Jiang, Yizhang] Jiangnan Univ, Sch Artificial Intelligent & Comp, Wuxi 224122, Jiangsu, Peoples R China.
   [Zhang, Yuanpeng] Nantong Univ, Dept Med Informat, Nantong 226001, Peoples R China.
RP Zhang, YP (通讯作者)，Nantong Univ, Dept Med Informat, Nantong 226001, Peoples R China.
EM maxbirdzhang@ntu.edu.cn
RI Jiang, Yizhang/V-2171-2019
OI Jiang, Yizhang/0000-0002-4558-9803; Zhang, Yuanpeng/0000-0003-1736-3425
FU National Natural Science Foundation of China [81701793]; Philosophy and
   Social Science Foundation of Jiangsu Province [18YSC009]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 81701793, and in part by the Philosophy
   and Social Science Foundation of Jiangsu Province under Grant 18YSC009.
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NR 59
TC 4
Z9 4
U1 2
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 45368
EP 45377
DI 10.1109/ACCESS.2020.2976793
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LB6GF
UT WOS:000524731600002
OA gold
DA 2022-08-23
ER

PT J
AU Xiong, Y
   Chen, S
   Chen, QC
   Yan, J
   Tang, BZ
AF Xiong, Ying
   Chen, Shuai
   Chen, Qingcai
   Yan, Jun
   Tang, Buzhou
TI Using Character-Level and Entity-Level Representations to Enhance
   Bidirectional Encoder Representation From Transformers-Based Clinical
   Semantic Textual Similarity Model: ClinicalSTS Modeling Study
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE natural language processing; deep learning; clinical semantic textual
   similarity; knowledge graph
ID SYSTEM
AB Background: With the popularity of electronic health records (EHRs), the quality of health care has been improved. However, there are also some problems caused by EHRs, such as the growing use of copy-and-paste and templates, resulting in EHRs of low quality in content. In order to minimize data redundancy in different documents, Harvard Medical School and Mayo Clinic organized a national natural language processing (NLP) clinical challenge (n2c2) on clinical semantic textual similarity (ClinicalSTS) in 2019. The task of this challenge is to compute the semantic similarity among clinical text snippets.
   Objective: In this study, we aim to investigate novel methods to model ClinicalSTS and analyze the results.
   Methods: We propose a semantically enhanced text matching model for the 2019 n2c2/Open Health NLP (OHNLP) challenge on ClinicalSTS. The model includes 3 representation modules to encode clinical text snippet pairs at different levels: (1) character-level representation module based on convolutional neural network (CNN) to tackle the out-of-vocabulary problem in NLP; (2) sentence-level representation module that adopts a pretrained language model bidirectional encoder representation from transformers (BERT) to encode clinical text snippet pairs; and (3) entity-level representation module to model clinical entity information in clinical text snippets. In the case of entity-level representation, we compare 2 methods. One encodes entities by the entity-type label sequence corresponding to text snippet (called entity I), whereas the other encodes entities by their representation in MeSH, a knowledge graph in the medical domain (called entity II).
   Results: We conduct experiments on the ClinicalSTS corpus of the 2019 n2c2/OHNLP challenge for model performance evaluation. The model only using BERT for text snippet pair encoding achieved a Pearson correlation coefficient (PCC) of 0.848. When character-level representation and entity-level representation are individually added into our model, the PCC increased to 0.857 and 0.854 (entity 1)/0.859 (entity II), respectively. When both character-level representation and entity-level representation are added into our model, the PCC further increased to 0.861 (entity I) and 0.868 (entity II).
   Conclusions: Experimental results show that both character-level information and entity-level information can effectively enhance the BERT-based STS model.
C1 [Xiong, Ying; Chen, Shuai; Chen, Qingcai; Tang, Buzhou] Harbin Inst Technol, HIT Campus, Shenzhen 518055, Peoples R China.
   [Chen, Qingcai; Tang, Buzhou] Peng Cheng Lab, Shenzhen, Peoples R China.
   [Yan, Jun] Yidu Cloud Technol Co Ltd, Beijing, Peoples R China.
RP Tang, BZ (通讯作者)，Harbin Inst Technol, HIT Campus, Shenzhen 518055, Peoples R China.
EM tangbuzhou@gmail.com
OI Tang, Buzhou/0000-0003-0271-8246; Xiong, Ying/0000-0001-7423-2937
FU National Natural Science Foundations of China [U1813215, 61876052,
   61573118]; Special Foundation for Technology Research Program of
   Guangdong Province [2015B010131010]; National Natural Science
   Foundations of Guangdong, China [2019A1515011158]; Guangdong Province
   Covid-19 Pandemic Control Research Fund [2020KZDZX1222]; Strategic
   Emerging Industry Development Special Funds of Shenzhen
   [JCYJ20180306172232154, JCYJ20170307150528934]; Innovation Fund of
   Harbin Institute of Technology [HIT.NSRIF.2017052]
FX This paper is supported in part by grants: National Natural Science
   Foundations of China (U1813215, 61876052, and 61573118), Special
   Foundation for Technology Research Program of Guangdong Province
   (2015B010131010), National Natural Science Foundations of Guangdong,
   China (2019A1515011158), Guangdong Province Covid-19 Pandemic Control
   Research Fund (2020KZDZX1222), Strategic Emerging Industry Development
   Special Funds of Shenzhen (JCYJ20180306172232154 and
   JCYJ20170307150528934), and Innovation Fund of Harbin Institute of
   Technology (HIT.NSRIF.2017052).
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NR 46
TC 2
Z9 2
U1 1
U2 10
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 DEC
PY 2020
VL 8
IS 12
AR e23357
DI 10.2196/23357
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Medical Informatics
GA PQ5NB
UT WOS:000606591100017
PM 33372664
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Waldrop, AM
   Cheadle, JB
   Bradford, K
   Preiss, A
   Chew, R
   Holt, JR
   Kebede, Y
   Braswell, N
   Watson, M
   Hench, V
   Crerar, A
   Ball, CM
   Schreep, C
   Linebaugh, PJ
   Hiles, H
   Boyles, R
   Bizon, C
   Krishnamurthy, A
   Cox, S
AF Waldrop, Alexander M.
   Cheadle, John B.
   Bradford, Kira
   Preiss, Alexander
   Chew, Robert
   Holt, Jonathan R.
   Kebede, Yaphet
   Braswell, Nathan
   Watson, Matt
   Hench, Virginia
   Crerar, Andrew
   Ball, Chris M.
   Schreep, Carl
   Linebaugh, P. J.
   Hiles, Hannah
   Boyles, Rebecca
   Bizon, Chris
   Krishnamurthy, Ashok
   Cox, Steve
TI Dug: a semantic search engine leveraging peer-reviewed knowledge to
   query biomedical data repositories
SO BIOINFORMATICS
LA English
DT Article; Early Access
ID INSTITUTE; SYSTEM
AB Motivation: As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned.
   Results: Developed through the National Heart, Lung and Blood Institute's (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15 911 study variables from public datasets. On a manually curated search dataset, Dug's total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch's total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results.
C1 [Waldrop, Alexander M.; Hench, Virginia; Crerar, Andrew] RTI Int, Ctr Genom Bioinformat & Translat Res, Res Triangle Pk, NC 27709 USA.
   [Cheadle, John B.; Braswell, Nathan; Ball, Chris M.; Boyles, Rebecca] RTI Int, Res Comp Div, Res Triangle Pk, NC 27709 USA.
   [Bradford, Kira; Kebede, Yaphet; Watson, Matt; Schreep, Carl; Linebaugh, P. J.; Hiles, Hannah; Bizon, Chris; Krishnamurthy, Ashok; Cox, Steve] Univ Chapel Hill, Renaissance Comp Inst, Chapel Hill, NC 27599 USA.
   [Preiss, Alexander; Chew, Robert; Holt, Jonathan R.] RTI Int, Ctr Data Sci, Res Triangle Pk, NC 27709 USA.
   [Krishnamurthy, Ashok] Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27599 USA.
RP Waldrop, AM (通讯作者)，RTI Int, Ctr Genom Bioinformat & Translat Res, Res Triangle Pk, NC 27709 USA.; Cox, S (通讯作者)，Univ Chapel Hill, Renaissance Comp Inst, Chapel Hill, NC 27599 USA.
EM awaldrop@rti.org; scox@renci.org
RI Chew, Rob/AAI-6054-2020
OI Chew, Rob/0000-0002-6979-1766; Hench, Virginia/0000-0002-3612-8036;
   Bradford, Kira/0000-0003-2403-817X; Ball,
   Christopher/0000-0003-3523-5312; Waldrop, Alexander/0000-0002-4502-7894;
   Boyles, Rebecca/0000-0003-0073-6854; Cheadle, John/0000-0002-0106-4415;
   Holt, Jonathan/0000-0001-6791-7858
FU National Heart, Lung, and Blood Institute (NHLBI) [1-OT3-HL142479-01];
   National Center for Advancing Translational Sciences (NCATS)
   [1-OT3-TR002020-01]; Helping to End Addiction Long-Term (HEAL) Office
   [1-OT2-OD031940-01]
FX This work was supported by the National Heart, Lung, and Blood Institute
   (NHLBI) [1-OT3-HL142479-01]; the National Center for Advancing
   Translational Sciences (NCATS) [1-OT3-TR002020-01]; and the Helping to
   End Addiction Long-Term (HEAL) Office [1-OT2-OD031940-01].
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NR 34
TC 0
Z9 0
U1 0
U2 0
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
DI 10.1093/bioinformatics/btac284
EA MAY 2022
PG 7
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 1J5VI
UT WOS:000797986200001
PM 35441678
DA 2022-08-23
ER

PT J
AU Sun, YB
   Tian, ZH
   Li, MH
   Zhu, CS
   Guizani, N
AF Sun, Yanbin
   Tian, Zhihong
   Li, Mohan
   Zhu, Chunsheng
   Guizani, Nadra
TI Automated Attack and Defense Framework toward 5G Security
SO IEEE NETWORK
LA English
DT Article
DE 5G mobile communication; Security; Software; Physical layer; Protocols;
   Internet of Things; Wireless communication
ID WIRELESS NETWORKS
AB 5G networks are currently being developed rapidly and are dedicated to connect all things of the Internet with new technologies, components and services. Due to the crucial role of 5G, as well as new architectures and designs, 5G faces a great number of security threats and demands suitable security technologies. This article proposes an automated attack and defense framework toward 5G security with the aim of providing exploratory guidance for 5G security research. We first review the security challenges of attack/defense objects from the perspective of a hierarchical structure, and then propose a hierarchical security model that supports both single-layer security and cross-layer security. According to the hierarchical model, an automated attack and defense framework based on a security knowledge graph is proposed for both known and unknown security threats of 5G and provides possible directions toward 5G security automation.
C1 [Sun, Yanbin; Li, Mohan] Guangzhou Univ, Guangzhou, Peoples R China.
   [Tian, Zhihong] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou, Peoples R China.
   [Zhu, Chunsheng] Southern Univ Sci & Technol, SUSTech Inst Future Networks, Shenzhen, Peoples R China.
   [Zhu, Chunsheng] Peng Cheng Lab, PCL Res Ctr Networks & Commun, Shenzhen, Peoples R China.
   [Guizani, Nadra] Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99164 USA.
RP Tian, ZH (通讯作者)，Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou, Peoples R China.
FU National Key Research and Development Plan [2018YFB0803504]; Guangdong
   Province Key Area RAMP;D Program of China [2019B010137004,
   2019B010136001]; National Natural Science Foundation of China [61702223,
   61702220, 61871140, U1636215]; Guangdong Province Universities and
   Colleges Pearl River Scholar Funded Scheme (2019); Natural Science
   Foundation of Guangdong Province [2020A151501450]; project PCL Future
   Greater-Bay Area Network Facilities for Large-scale Experiments and
   Applications [LZC0019]; Opening Project of Shanghai Trusted Industrial
   Control Platform [TICPSH202003014-ZC]
FX This work is supported by the National Key Research and Development Plan
   (no. 2018YFB0803504); the Guangdong Province Key Area R&D Program of
   China (no. 2019B010137004, 2019B010136001); the National Natural Science
   Foundation of China (no. 61702223, 61702220, 61871140, U1636215); the
   Guangdong Province Universities and Colleges Pearl River Scholar Funded
   Scheme (2019); the Natural Science Foundation of Guangdong Province
   (2020A151501450); the project PCL Future Greater-Bay Area Network
   Facilities for Large-scale Experiments and Applications (LZC0019); and
   the Opening Project of Shanghai Trusted Industrial Control Platform
   (TICPSH202003014-ZC).
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TC 9
Z9 9
U1 6
U2 18
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0890-8044
EI 1558-156X
J9 IEEE NETWORK
JI IEEE Netw.
PD SEP-OCT
PY 2020
VL 34
IS 5
BP 247
EP 253
DI 10.1109/MNET.011.1900635
PG 7
WC Computer Science, Hardware & Architecture; Computer Science, Information
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WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA OU1OI
UT WOS:000591303900036
DA 2022-08-23
ER

PT J
AU Toldo, L
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AF Toldo, L
   Rippmann, F
TI Integrated bioinformatics application for automated target discovery
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID DOCUMENT-RETRIEVAL; GENOME ANNOTATION; SEQUENCE; PERFORMANCE; RESOURCES;
   BIOLOGY; SYSTEM; TOOL
AB In this article we present an in silico method that automatically assigns putative functions to DNA sequences. The annotations are at an increasingly conceptual level, up to identifying general biomedical fields to which the sequences could contribute. This bioinformatics data-mining system makes substantial use of several resources: a locally stored MEDLINE(R) database; a manually built classification system; the MeSH(R) taxonomy; relational technology; and bioinformatics methods. Knowledge is generated from various data sources by using well-defined semantics, and by exploiting direct links between them. A two-dimensional "Concept Map(TM)" displays the knowledge graph, which allows causal connections to be followed. The use of this method has been valuable and has saved considerable time in our in-house projects, and can be generally exploited for any sequence-an notation or knowledge-condensation task.
C1 Merck KGaA, Global Preclin Res, Dept Bio & Chemoinformat, D-64293 Darmstadt, Germany.
RP Toldo, L (通讯作者)，Merck KGaA, Global Preclin Res, Dept Bio & Chemoinformat, Frankfurterstr 250, D-64293 Darmstadt, Germany.
EM luca.toldo@merck.de
RI Toldo, Luca/A-3784-2013
OI Toldo, Luca/0000-0003-2027-5717
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NR 51
TC 2
Z9 2
U1 1
U2 11
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 2005
VL 56
IS 5
BP 483
EP 492
DI 10.1002/asi.20137
PG 10
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 899NF
UT WOS:000227150500006
DA 2022-08-23
ER

PT J
AU Shenoy, K
   Ilievski, F
   Garijo, D
   Schwabe, D
   Szekely, P
AF Shenoy, Kartik
   Ilievski, Filip
   Garijo, Daniel
   Schwabe, Daniel
   Szekely, Pedro
TI A study of the quality of Wikidata
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Wikidata; Data quality; Knowledge graphs; Constraints; Crowdsourcing
AB Wikidata has been increasingly adopted by many communities for a wide variety of applications, which demand high-quality knowledge to deliver successful results. In this paper, we develop a framework to detect and analyze low-quality statements in Wikidata by shedding light on the current practices exercised by the community. We explore three indicators of data quality in Wikidata, based on: (1) community consensus on the currently recorded knowledge, assuming that statements that have been removed and not added back are implicitly agreed to be of low quality; (2) statements that have been deprecated; and (3) constraint violations in the data. We combine these indicators to detect lowquality statements, revealing challenges with duplicate entities, missing triples, violated type rules, and taxonomic distinctions. Our findings complement ongoing efforts by the Wikidata community to improve data quality, aiming to make it easier for users and editors to find and correct mistakes. (c) 2022 Published by Elsevier B.V.
C1 [Shenoy, Kartik; Ilievski, Filip; Szekely, Pedro] Univ Southern Calif, Informat Sci Inst, Los Angeles, CA 90007 USA.
   [Garijo, Daniel] Univ Politecn Madrid, Ontol Engn Grp, Madrid, Spain.
   [Schwabe, Daniel] Pontificia Univ Catolica Rio de Janeiro, Dept Informat, Rio De Janeiro, Brazil.
RP Ilievski, F (通讯作者)，Univ Southern Calif, Informat Sci Inst, Los Angeles, CA 90007 USA.
EM kshenoy@isi.edu; ilievski@isi.edu; daniel.garijo@upm.es;
   dschwabe@inf.puc-rio.br; pszekely@isi.edu
OI Shenoy, Kartik/0000-0002-8430-6554; Garijo, Daniel/0000-0003-0454-7145
FU Air Force Research Laboratory, USA [FA8750-202-10002, 309808/2017-0];
   CNPq -Brazil
FX This material is based on research sponsored by Air Force Research
   Laboratory, USA under agreement number FA8750-202-10002. The U.S.
   Government is authorized to reproduce and distribute reprints for
   Governmental purposes notwithstanding any copyright notation thereon.
   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 Air Force
   Research Laboratory or the U.S. Government. Daniel Schwabe was partially
   supported by grant 309808/2017-0 from CNPq -Brazil.
CR Balaraman V, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1787, DOI 10.1145/3184558.3191641
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NR 33
TC 0
Z9 0
U1 3
U2 3
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 100679
DI 10.1016/j.websem.2021.100679
PG 10
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:000761304400006
DA 2022-08-23
ER

PT J
AU Lu, GX
   Tian, L
   Zheng, X
   Hui, B
AF Lu, Guangxi
   Tian, Ling
   Zheng, Xu
   Hui, Bei
TI Integrating knowledge-based sparse representation for image detection
SO NEUROCOMPUTING
LA English
DT Article
DE Image detection; Deep reasoning; Knowledge graph; Gated graph neural
   network; Simrank; Dictionary learning
ID SIMRANK COMPUTATION
AB Image detection has been a primary step and widely adopted in diverse domains, such as autopilot, risk inspection, and robot vision. The main body of current models for image detection treat this task as the feature-based recognition of multiple independent objects. The adoption of semantic relationships, which can provide auxiliary information for joint recognition, is still in its early stage. Inspired by deep dictionary learning, this work proposes a novel framework named SEGNN, which aims at finding and using the sparse representation knowledge to improve the result of image detection. According to this design, both the efficiency and rationality of reasoning over semantic relationships are well captured. The framework includes an improved GGNN model. It can efficiently extract the semantic informations from the image, and map it to sparse vector. The framework also applies a novel SimRank method, to justify the rationality of the semantic reasoning. The performance of the framework is validated extensively on two datasets Visual Genome (VG) and COCO. The experimental results and case study have demonstrated the effectiveness of the proposed framework.
   (c) 2021 Published by Elsevier B.V.
C1 [Lu, Guangxi] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA.
   [Tian, Ling; Zheng, Xu] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China.
   [Hui, Bei] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China.
RP Tian, L (通讯作者)，Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China.
EM glu3@student.gsu.edu; lingtian@uestc.edu.cn; xzheng@uestc.edu.cn;
   bhui@uestc.edu.cn
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NR 42
TC 0
Z9 0
U1 5
U2 12
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 2021
VL 442
BP 173
EP 183
DI 10.1016/j.neucom.2020.09.089
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RW0SA
UT WOS:000646233500014
DA 2022-08-23
ER

PT J
AU Huang, HY
   Lei, M
   Feng, C
AF Huang, Heyan
   Lei, Ming
   Feng, Chong
TI Graph-based reasoning model for multiple relation extraction
SO NEUROCOMPUTING
LA English
DT Article
DE Relation extraction; Information extraction; Neural networks; Natural
   language processing
AB Linguistic knowledge is useful for various NLP tasks, but the difficulty lies in the representation and application. We consider that linguistic knowledge is implied in a large-scale corpus, while classification knowledge, the knowledge related to the definitions of entity and relation types, is implied in the labeled training data. Therefore, a corpus subgraph is proposed to mine more linguistic knowledge from the easily accessible unlabeled data, and sentence subgraphs are used to acquire classification knowledge. They jointly constitute a relation knowledge graph (RKG) to extract relations from sentences in this paper. On RKG, entity recognition can be regarded as a property value filling problem and relation classification can be regarded as a link prediction problem. Thus, the multiple relation extraction can be treated as a reasoning process for knowledge completion. We combine statistical reasoning and neural network reasoning to segment sentences into entity chunks and non-entity chunks, then propose a novel Chunk Graph LSTM network to learn the representations of entity chunks and infer the relations among them. The experiments on two standard datasets demonstrate our model outperforms the previous models for multiple relation extraction. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Huang, Heyan; Lei, Ming; Feng, Chong] 5 South Zhongguancun St, Beijing, Peoples R China.
RP Lei, M (通讯作者)，5 South Zhongguancun St, Beijing, Peoples R China.
EM 66529158@qq.com
OI Lei, Ming/0000-0002-5712-3285
FU National Key Research and Development Program of China [2016QY03D0602];
   National Natural Science Foundation of China [61751201]
FX We would like to thank the anonymous reviewers. This work is supported
   by the National Key Research and Development Program of China
   (No.2016QY03D0602) and the National Natural Science Foundation of China
   (No. 61751201).
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NR 35
TC 5
Z9 5
U1 6
U2 35
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JAN 8
PY 2021
VL 420
BP 162
EP 170
DI 10.1016/j.neucom.2020.09.025
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PI6QI
UT WOS:000601212800013
DA 2022-08-23
ER

PT J
AU Sagi, T
   Lissandrini, M
   Pedersen, TB
   Hose, K
AF Sagi, Tomer
   Lissandrini, Matteo
   Pedersen, Torben Bach
   Hose, Katja
TI A design space for RDF data representations
SO VLDB JOURNAL
LA English
DT Article
DE RDF; Data representation; SPARQL; Database; Query; Knowledge graphs
ID STORE; DATABASES; BENCHMARK
AB RDF triplestores' ability to store and query knowledge bases augmented with semantic annotations has attracted the attention of both research and industry. A multitude of systems offer varying data representation and indexing schemes. However, as recently shown for designing data structures, many design choices are biased by outdated considerations and may not result in the most efficient data representation for a given query workload. To overcome this limitation, we identify a novel three-dimensional design space. Within this design space, we map the trade-offs between different RDF data representations employed as part of an RDF triplestore and identify unexplored solutions. We complement the review with an empirical evaluation of ten standard SPARQL benchmarks to examine the prevalence of these access patterns in synthetic and real query workloads. We find some access patterns, to be both prevalent in the workloads and under-supported by existing triplestores. This shows the capabilities of our model to be used by RDF store designers to reason about different design choices and allow a (possibly artificially intelligent) designer to evaluate the fit between a given system design and a query workload.
C1 [Sagi, Tomer; Lissandrini, Matteo; Pedersen, Torben Bach; Hose, Katja] Aalborg Univ, Dept Comp Sci, Aalborg, Denmark.
RP Sagi, T (通讯作者)，Aalborg Univ, Dept Comp Sci, Aalborg, Denmark.
EM tsagi@cs.aau.dk; matteo@cs.aau.dk; tbp@cs.aau.dk; khose@cs.aau.dk
RI ; Sagi, Tomer/Q-2620-2016
OI Hose, Katja/0000-0001-7025-8099; Lissandrini,
   Matteo/0000-0001-7922-5998; Sagi, Tomer/0000-0002-8916-0128
FU Independent Research Fund Denmark [DFF-8048-00051B]; SEMIOTIC project,
   Aalborg University's Talent Programme; European Union's Horizon 2020
   research and innovation programme under the Marie Sklodowska-Curie Grant
   [838216]; Poul Due Jensen Foundation
FX This work is supported by the Independent Research Fund Denmark under
   Grant No. DFF-8048-00051B (RelWeb) and the SEMIOTIC project, Aalborg
   University's Talent Programme, the European Union's Horizon 2020
   research and innovation programme under the Marie Sklodowska-Curie Grant
   agreement No. 838216, and the Poul Due Jensen Foundation.
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NR 91
TC 0
Z9 0
U1 0
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.
PD MAR
PY 2022
VL 31
IS 2
SI SI
BP 347
EP 373
DI 10.1007/s00778-021-00725-x
EA JAN 2022
PG 27
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZU6ZC
UT WOS:000745344600002
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Hao, Y
   Li, GF
   Yuan, PP
   Jin, H
   Ding, XF
AF Hao, Yun
   Li, Gaofeng
   Yuan, Pingpeng
   Jin, Hai
   Ding, Xiaofeng
TI An Association-Oriented Partitioning Approach for Streaming Graph Query
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB The volumes of real-world graphs like knowledge graph are increasing rapidly, which makes streaming graph processing a hot research area. Processing graphs in streaming setting poses significant challenges from different perspectives, among which graph partitioning method plays a key role. Regarding graph query, a well-designed partitioning method is essential for achieving better performance. Existing offline graph partitioning methods often require full knowledge of the graph, which is not possible during streaming graph processing. In order to handle this problem, we propose an association-oriented streaming graph partitioning method named Assc. This approach first computes the rank values of vertices with a hybrid approximate PageRank algorithm. After splitting these vertices with an adapted variant affinity propagation algorithm, the process order on vertices in the sliding window can be determined. Finally, according to the level of these vertices and their association, the partition where the vertices should be distributed is decided. We compare its performance with a set of streaming graph partition methods and METIS, a widely adopted offline approach. The results show that our solution can partition graphs with hundreds of millions of vertices in streaming setting on a large collection of graph datasets and our approach outperforms other graph partitioning methods.
C1 [Hao, Yun; Li, Gaofeng; Yuan, Pingpeng; Jin, Hai; Ding, Xiaofeng] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Serv Comp Technol & Syst Lab, Wuhan 430074, Peoples R China.
   [Hao, Yun; Li, Gaofeng; Yuan, Pingpeng; Jin, Hai; Ding, Xiaofeng] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Cluster & Grid Comp Lab, Wuhan 430074, Peoples R China.
   [Hao, Yun; Li, Gaofeng; Yuan, Pingpeng; Jin, Hai; Ding, Xiaofeng] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Big Data Technol & Syst Lab, Wuhan 430074, Peoples R China.
RP Yuan, PP; Jin, H (通讯作者)，Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Serv Comp Technol & Syst Lab, Wuhan 430074, Peoples R China.; Yuan, PP; Jin, H (通讯作者)，Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Cluster & Grid Comp Lab, Wuhan 430074, Peoples R China.; Yuan, PP; Jin, H (通讯作者)，Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Big Data Technol & Syst Lab, Wuhan 430074, Peoples R China.
EM ppyuan@mail.hust.edu.cn; hjin@hust.edu.cn
FU Science and Technology Planning Project of Guangdong Province, China
   [2016B030306003, 2016B030305002]
FX The paper was recommended to be published in journals by International
   Workshop on Big Data Programming and System Software 2016 (BDPSS 2016).
   The research is supported by Science and Technology Planning Project of
   Guangdong Province, China (nos. 2016B030306003 and 2016B030305002).
CR Ackermann M. R., 2012, ACM J EXPT ALGORITHM, V17
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NR 26
TC 9
Z9 9
U1 0
U2 9
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PY 2017
VL 2017
AR 2573592
DI 10.1155/2017/2573592
PG 11
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EX1BG
UT WOS:000402953900001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Xiao, GC
   Liao, JZ
   Tan, Z
   Zhang, XN
   Zhao, X
AF Xiao, Guanchen
   Liao, Jinzhi
   Tan, Zhen
   Zhang, Xiaonan
   Zhao, Xiang
TI A Two-Stage Framework for Directed Hypergraph Link Prediction
SO MATHEMATICS
LA English
DT Article
DE hyperlink prediction; hypergraph; Tucker decomposition
AB Hypergraphs, as a special type of graph, can be leveraged to better model relationships among multiple entities. In this article, we focus on the task of hyperlink prediction in directed hypergraphs, which finds a wide spectrum of applications in knowledge graphs, chem-informatics, bio-informatics, etc. Existing methods handling the task overlook the order constraints of the hyperlink's direction and fail to exploit features of all entities covered by a hyperlink. To make up for the deficiency, we present a performant pipelined model, i.e., a two-stage framework for directed hyperlink prediction method (TF-DHP), which equally considers the entity's contribution to the form of hyperlinks, and emphasizes not only the fixed order between two parts but also the randomness inside each part. The TF-DHP incorporates two tailored modules: a Tucker decomposition-based module for hyperlink prediction, and a BiLSTM-based module for direction inference. Extensive experiments on benchmarks-WikiPeople, JF17K, and ReVerb15K-demonstrate the effectiveness and universality of our TF-DHP model, leading to state-of-the-art performance.
C1 [Xiao, Guanchen; Liao, Jinzhi; Tan, Zhen; Zhao, Xiang] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
   [Zhang, Xiaonan] Harbin Flight Acad, Harbin 150000, Peoples R China.
RP Zhao, X (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
EM xiaoguanchen20@nudt.edu.cn; liaojinzhi12@nudt.edu.cn;
   tanzhen08a@nudt.edu.cn; zhangxiaonan12@nudt.edu.cn;
   xiangzhao@nudt.edu.cn
FU NSFC [61872446, 61902417]; Science and Technology Innovation Program of
   Hunan Province [2020RC4046]
FX This work was partially supported by NSFC under grants Nos. 61872446,
   61902417, and The Science and Technology Innovation Program of Hunan
   Province under grant No. 2020RC4046.
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NR 31
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD JUL
PY 2022
VL 10
IS 14
AR 2372
DI 10.3390/math10142372
PG 18
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA 3H6TP
UT WOS:000832166800001
OA gold
DA 2022-08-23
ER

PT J
AU Moreira, J
   de Melo, T
   Barbosa, L
   da Silva, A
AF Moreira, Johny
   de Melo, Tiago
   Barbosa, Luciano
   da Silva, Altigran
TI A distantly supervised approach for enriching product graphs with user
   opinions
SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Product graphs; Subjective data; Opinion mining; Distant supervision
ID ASPECT EXTRACTION
AB Product Graphs (PGs) are knowledge graphs that structure the relationship of products and their characteristics. They have become very popular lately due to their potential to enable AI-related tasks in e-commerce. With the rise of social media, many dynamic and subjective information on products and their characteristics became widely available, creating an opportunity to aggregate such information to PGs. In this paper, we propose a method called PGOpi (Product Graph enriched with Opinions), whose goal is to enrich existing PGs with subjective information extracted from reviews written by customers. PGOpi uses a deep learning model to map opinions extracted from user reviews to nodes in the PG corresponding to targets of these opinions. To alleviate manual labor dependency for training the model, we devise a distant supervision strategy based on word embeddings. We have performed an extensive experimental evaluation on five product categories of two representative real-world datasets. The proposed unsupervised approach achieves superior micro F1 score over more complex unsupervised models. It also presents comparable results to a fully-supervised model.
C1 [Moreira, Johny; Barbosa, Luciano] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil.
   [de Melo, Tiago] Univ Estado Amazonas, Escola Super Tecnol, Manaus, Amazonas, Brazil.
   [da Silva, Altigran] Univ Fed Amazonas, Inst Comp, Manaus, Amazonas, Brazil.
RP Moreira, J (通讯作者)，Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil.
EM jms5@cin.ufpe.br; tmelo@uea.edu.br; luciano@cin.ufpe.br;
   alti@icomp.ufam.edu.br
OI da Silva, Johny/0000-0003-4705-9766
FU Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)
   [8882.347588/2019-01, 88887.130299/2017-01, 001]; Conselho Nacional de
   Desenvolvimento Cientifico e Tecnologico (CNPq) [307248/2019-4]; project
   MMBIAS (FAPESP MCTIC / CGI) [2020/05173-4]; Brazilian funding agency
   FAPEAM-POSGRAD 2020 [002/2020]; Gratificacao de Produtividade Academica
   (GPA) of the Universidade Estadual do Amazonas [086/2021]
FX This work was supported or partially supported by the Coordenacao de
   Aperfeicoamento de Pessoal de Nivel Superior (CAPES) under funding
   grants No. 8882.347588/2019-01, 88887.130299/2017-01, and financial code
   001; by the Conselho Nacional de Desenvolvimento Cientifico e
   Tecnologico (CNPq) funding grant No. 307248/2019-4; by the project
   MMBIAS (FAPESP MCTIC / CGI, 2020/05173-4); by the Brazilian funding
   agency FAPEAM-POSGRAD 2020 (Resolution 002/2020); and by the
   Gratificacao de Produtividade Academica (GPA) of the Universidade
   Estadual do Amazonas (Portaria 086/2021).
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NR 37
TC 0
Z9 0
U1 3
U2 3
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.
DI 10.1007/s10844-022-00717-5
EA MAY 2022
PG 20
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1N7DX
UT WOS:000800812700003
DA 2022-08-23
ER

PT J
AU Wu, XF
   Liu, MJ
   Xin, B
   Zhu, ZQ
   Wang, G
AF Wu, Xuefei
   Liu, Mingjiang
   Xin, Bo
   Zhu, Zhangqing
   Wang, Gang
TI Attention-Based Graph Convolutional Network for Zero-Shot Learning with
   Pre-Training
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
AB Zero-shot learning (ZSL) is a powerful and promising learning paradigm for classifying instances that have not been seen in training. Although graph convolutional networks (GCNs) have recently shown great potential for the ZSL tasks, these models cannot adjust the constant connection weights between the nodes in knowledge graph and the neighbor nodes contribute equally to classify the central node. In this study, we apply an attention mechanism to adjust the connection weights adaptively to learn more important information for classifying unseen target nodes. First, we propose an attention graph convolutional network for zero-shot learning (AGCNZ) by integrating the attention mechanism and GCN directly. Then, in order to prevent the dilution of knowledge from distant nodes, we apply the dense graph propagation (DGP) model for the ZSL tasks and propose an attention dense graph propagation model for zero-shot learning (ADGPZ). Finally, we propose a modified loss function with a relaxation factor to further improve the performance of the learned classifier. Experimental results under different pre-training settings verified the effectiveness of the proposed attention-based models for ZSL.
C1 [Wu, Xuefei; Liu, Mingjiang; Xin, Bo; Zhu, Zhangqing] Nanjing Univ, Sch Management & Engn, Dept Control & Syst Engn, Nanjing 210093, Peoples R China.
   [Wang, Gang] Minist Agr & Rural Area, Nanjing Res Inst Agr Mechanizat, Nanjing 210014, Peoples R China.
RP Xin, B; Zhu, ZQ (通讯作者)，Nanjing Univ, Sch Management & Engn, Dept Control & Syst Engn, Nanjing 210093, Peoples R China.
EM xuefeiwu@smail.nju.edu.cn; mjliu@smail.nju.edu.cn; xinbo@nju.edu.cn;
   zzqing@nju.edu.cn; 421404047@qq.com
FU National Key Research and Development Program of China [2018AAA0101100];
   National Natural Science Foundation of China [62073160]
FX AcknowledgmentsThis work was partially supported by the National Key
   Research and Development Program of China (No. 2018AAA0101100) and the
   National Natural Science Foundation of China (No. 62073160).
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NR 40
TC 0
Z9 0
U1 18
U2 31
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 DEC 7
PY 2021
VL 2021
AR 7480712
DI 10.1155/2021/7480712
PG 13
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA XW7IZ
UT WOS:000735789300009
OA gold
DA 2022-08-23
ER

PT J
AU Liu, SK
   Xu, RL
   Duan, L
   Li, MJ
   Liu, YM
AF Liu, Shukan
   Xu, Ruilin
   Duan, Li
   Li, Mingjie
   Liu, Yiming
TI Metaknowledge Enhanced Open Domain Question Answering with Wiki
   Documents
SO SENSORS
LA English
DT Article
DE metaknowledge; graph modeling; question answering; graph neural
   networks; knowledge graph
AB The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build a metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then, a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.
C1 [Liu, Shukan] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
   [Liu, Shukan; Xu, Ruilin; Duan, Li; Liu, Yiming] PLA Naval Univ Engn, Sch Elect Engn, Wuhan 430033, Peoples R China.
   [Li, Mingjie] PLA Naval Univ Engn, Ship Comprehens Test & Training Base, Wuhan 430033, Peoples R China.
RP Duan, L (通讯作者)，PLA Naval Univ Engn, Sch Elect Engn, Wuhan 430033, Peoples R China.
EM liusk@seu.edu.cn; kirinxu@foxmail.com; duanlidragon@126.com;
   jie19850625@126.com; liuyiming0507@foxmail.com
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NR 39
TC 0
Z9 0
U1 3
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD DEC
PY 2021
VL 21
IS 24
AR 8439
DI 10.3390/s21248439
PG 17
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA XZ7WT
UT WOS:000737860000001
PM 34960530
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Yang, JX
   Xiang, FY
   Li, R
   Zhang, LY
   Yang, XX
   Jiang, SX
   Zhang, HY
   Wang, D
   Liu, XL
AF Yang, Jianxi
   Xiang, Fangyue
   Li, Ren
   Zhang, Luyi
   Yang, Xiaoxia
   Jiang, Shixin
   Zhang, Hongyi
   Wang, Di
   Liu, Xinlong
TI Intelligent bridge management via big data knowledge engineering
SO AUTOMATION IN CONSTRUCTION
LA English
DT Review
DE Intelligent bridge management; Artificial intelligence; Big data
   knowledge engineering; Knowledge graph; Framework
ID STRUCTURAL DAMAGE DETECTION; CONVOLUTIONAL NEURAL-NETWORK; INTEGRATION;
   RECOGNITION; EXTRACTION; ONTOLOGIES; FRAMEWORK; GRAPH; PERFORMANCE;
   CHALLENGES
AB Fully combining the emerging intelligent technologies, such as big data and artificial intelligence, to realize the effective data fusion of bridge management with multi-source, autonomous, massive, heterogeneous features, and to further improve the capability of domain knowledge sharing and services have become the urgent demand and future development trend in the field of bridge engineering. This paper summarizes the business background and big data characteristics of bridge management, and represents a brief review of related work. According to the big data knowledge engineering paradigm, this paper proposes a novel BigKE-based intelligent bridge management and maintenance framework consisting of the layers of data-sources, storage and computing, knowledge representation, knowledge computing, and knowledge services. The corresponding main research contents involved in each layer are discussed as well. Finally, we point out some possible application scenarios and main challenges of the proposed framework, which elaborate on areas for future research.
C1 [Yang, Jianxi; Xiang, Fangyue; Li, Ren; Zhang, Luyi; Yang, Xiaoxia; Jiang, Shixin; Zhang, Hongyi; Wang, Di; Liu, Xinlong] Chongqing Jiaotong Univ, Sch Informat Sci & Engn, Chongqing 400074, Peoples R China.
RP Li, R (通讯作者)，Chongqing Jiaotong Univ, Sch Informat Sci & Engn, Chongqing 400074, Peoples R China.
EM renli@cqjtu.edu.cn
FU National Natural Science Foundation of China [62003063]; Natural Science
   Foundation of Chongqing, China [cstc2020jcyj-msxmX0047]; Science and
   Technology Research Program of Chongqing Municipal Education Commission
   of China [KJZD- M202000702, KJQN202000726]
FX This work was supported by the National Natural Science Foundation of
   China (62003063), Natural Science Foundation of Chongqing, China
   (cstc2020jcyj-msxmX0047), Science and Technology Research Program of
   Chongqing Municipal Education Commission of China (KJZD- M202000702,
   KJQN202000726)
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NR 114
TC 0
Z9 0
U1 32
U2 32
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0926-5805
EI 1872-7891
J9 AUTOMAT CONSTR
JI Autom. Constr.
PD MAR
PY 2022
VL 135
AR 104118
DI 10.1016/j.autcon.2021.104118
PG 14
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA 0K8LE
UT WOS:000781039500004
DA 2022-08-23
ER

PT J
AU Sun, Q
   Zhang, K
   Lv, LS
   Li, X
   Huang, K
   Zhang, T
AF Sun, Qi
   Zhang, Kun
   Lv, Laishui
   Li, Xun
   Huang, Kun
   Zhang, Ting
TI Joint extraction of entities and overlapping relations by improved graph
   convolutional networks
SO APPLIED INTELLIGENCE
LA English
DT Article
DE GCN; Multi-task learning; Attention mechanism; Overlapping relations
AB Joint extraction of entities and relations is to recognize entities and semantic relations simultaneously, which is significant for knowledge graph construction. Recently, many effective joint models use dependency trees to capture the structural information of sentences. However, most dependency-based methods cannot make full use of the dependency information. This is because these methods just consider the connection information of dependency trees and ignore the influence of different nodes on the connected edges. In this paper, we establish a novel model to extract entities and relations simultaneously by using improved Multi-task Graph Convolutional networks, called MGCN. Specifically, considering the importance of node information, we merge both node and edge information into Graph Convolutional networks (GCN). In addition, in order to recognize the overlapping relations, we propose an efficient strategy to map multiple relational labels of a sentence into a unique code. Finally, we evaluate our joint model on two public datasets, and the experimental results show that our model outperforms the state-of-art models.
C1 [Sun, Qi; Zhang, Kun; Lv, Laishui; Li, Xun; Huang, Kun; Zhang, Ting] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China.
RP Zhang, K (通讯作者)，Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China.
EM 319106003718@njust.edu.cn; zhangkun@njust.edu.cn; malslv@163.com;
   hardworkinglx@163.com; 319106003717@njust.edu.cn; yunshun_135@163.com
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NR 41
TC 4
Z9 4
U1 4
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.
PD MAR
PY 2022
VL 52
IS 5
BP 5212
EP 5224
DI 10.1007/s10489-021-02667-x
EA AUG 2021
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZN2PU
UT WOS:000682464100002
DA 2022-08-23
ER

PT J
AU Ristoski, P
   Rosati, J
   Di Noia, T
   De Leone, R
   Paulheim, H
AF Ristoski, Petar
   Rosati, Jessica
   Di Noia, Tommaso
   De Leone, Renato
   Paulheim, Heiko
TI RDF2Vec: RDF graph embeddings and their applications
SO SEMANTIC WEB
LA English
DT Article
DE Graph embeddings; linked open data; data mining; document semantic
   similarity; entity relatedness; recommender systems
ID LARGE-SCALE; WEB
AB Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks. However, most of the existing tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. We evaluate our approach on three different tasks: (i) standard machine learning tasks, (ii) entity and document modeling, and (iii) content-based recommender systems. The evaluation shows that the proposed entity embeddings outperform existing techniques, and that pre-computed feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.
C1 [Ristoski, Petar; Paulheim, Heiko] Univ Mannheim, Data & Web Sci Grp, B6 26, D-68159 Mannheim, Germany.
   [Rosati, Jessica; Di Noia, Tommaso] Polytech Univ Bari, Via Orabona 4, I-70125 Bari, Italy.
   [Rosati, Jessica; De Leone, Renato] Univ Camerino, Piazza Cavour 19-F, I-62032 Camerino, Italy.
RP Ristoski, P (通讯作者)，Univ Mannheim, Data & Web Sci Grp, B6 26, D-68159 Mannheim, Germany.
EM petar.ristoski@informatik.uni-mannheim.de; jessica.rosati@poliba.it;
   tommaso.dinoia@poliba.it; renato.deleone@unicam.it;
   heiko@informatik.uni-mannheim.de
RI Paulheim, Heiko/AAE-8132-2020; De Leone, Renato/AAR-6292-2020
OI Paulheim, Heiko/0000-0003-4386-8195; De Leone,
   Renato/0000-0001-9783-608X
FU German Research Foundation (DFG) [PA 2373/1-1]; Ministry of Science,
   Research and the Arts of the state of Baden-Wurttemberg; I.B.M. Ph.D.
   fellowship; Digital Services Ecosystem: DSE [PON03 PE_00136_1]
FX The work presented in this paper has been partly funded by the German
   Research Foundation (DFG) under grant number PA 2373/1-1 (Mine@LOD),
   partially funded by the project PON03 PE_00136_1 Digital Services
   Ecosystem: DSE, and by the Junior-professor funding programme of the
   Ministry of Science, Research and the Arts of the state of
   Baden-Wurttemberg (project "Deep semantic models for high-end NLP
   application"). Jessica Rosati also acknowledges support of I.B.M. Ph.D.
   fellowship 2015-2016.
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NR 96
TC 34
Z9 34
U1 1
U2 6
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2019
VL 10
IS 4
BP 721
EP 752
DI 10.3233/SW-180317
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 JA8DV
UT WOS:000488080900004
OA Green Published
DA 2022-08-23
ER

PT J
AU Wang, HW
   Liu, F
   Dong, TG
   Du, LK
   Zhang, DJ
   Gao, J
AF Wang, Hongwei
   Liu, Fang
   Dong, Tiange
   Du, Likai
   Zhang, Dongju
   Gao, Jun
TI Charge-Transfer Knowledge Graph among Amino Acids Derived from
   High-Throughput Electronic Structure Calculations for Protein Database
SO ACS OMEGA
LA English
DT Article
ID TUNNELING PATHWAYS; TRANSFER KINETICS; ROTAMER LIBRARY; DNA; PACKING;
   POLARIZATION; PARAMETERS; COMPLEXES; SECONDARY; MECHANISM
AB The charge-transfer coupling is an important component in tight-binding methods. Because of the highly complex chemical structure of biomolecules, the anisotropic feature of charge-transfer couplings in realistic proteins cannot be ignored. In this work, we have performed the first large-scale quantitative assessment of charge-transfer preference by calculating the charge-transfer couplings in all 20 x 20 possible amino acid side-chain combinations, which are extracted from available high-quality structures of thousands of protein complexes. The charge-transfer database quantitatively shows distinct features of charge-transfer couplings among millions of amino acid side-chain combinations. The overall distribution of charge-transfer couplings reveals that only one average or representative structure cannot be regarded as the typical charge-transfer preference in realistic proteins. This work provides us an alternative route to comprehensively understand the charge-transfer couplings for the overall distribution of realistic proteins in the foreseen big data scenario.
C1 [Wang, Hongwei; Liu, Fang; Dong, Tiange; Du, Likai; Gao, Jun] Huazhong Agr Univ, Coll Informat, Hubei Key Lab Agr Bioinformat, Wuhan 430070, Hubei, Peoples R China.
   [Zhang, Dongju] Shandong Univ, Inst Theoret Chem, Jinan 250100, Shandong, Peoples R China.
RP Du, LK (通讯作者)，Huazhong Agr Univ, Coll Informat, Hubei Key Lab Agr Bioinformat, Wuhan 430070, Hubei, Peoples R China.
EM dulikai@mail.hzau.edu.cn
OI Du, Likai/0000-0002-3198-276X
FU National Natural Science Foundation of China [21503249, 21373124];
   Huazhong Agricultural University Scientific & Technological
   Self-innovation Foundation [2015RC008]; Fundamental Funds for the
   Central Universities [2662016QD011, 2662015PY113]; Special Program for
   Applied Research on Super Computation of the NSFC-Guangdong Joint Fund
   [U1501501]
FX The work was supported by the National Natural Science Foundation of
   China (nos. 21503249, 21373124), Huazhong Agricultural University
   Scientific & Technological Self-innovation Foundation (program no.
   2015RC008), and Project 2662016QD011 and 2662015PY113 supported by the
   Fundamental Funds for the Central Universities. The authors also thank
   the support of Special Program for Applied Research on Super Computation
   of the NSFC-Guangdong Joint Fund (the second phase) under grant no.
   U1501501.
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NR 75
TC 6
Z9 6
U1 1
U2 9
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 2470-1343
J9 ACS OMEGA
JI ACS Omega
PD APR
PY 2018
VL 3
IS 4
BP 4094
EP 4104
DI 10.1021/acsomega.8b00336
PG 11
WC Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry
GA GD0OO
UT WOS:000430200300052
PM 31458645
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Zhou, JY
   Liu, L
   Wei, WQ
   Fan, JX
AF Zhou, Jingya
   Liu, Ling
   Wei, Wenqi
   Fan, Jianxi
TI Network Representation Learning: From Preprocessing, Feature Extraction
   to Node Embedding
SO ACM COMPUTING SURVEYS
LA English
DT Article
DE Network representation learning; data preprocessing; feature extraction;
   node embedding
ID LAPLACIAN EIGENMAPS
AB Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks. Dozens of NRL algorithms have been reported in the literature. Most of them focus on learning node embeddings for homogeneous networks, but they differ in the specific encoding schemes and specific types of node semantics captured and used for learning node embedding. This article reviews the design principles and the different node embedding techniques for NRL over homogeneous networks. To facilitate the comparison of different node embedding algorithms, we introduce a unified reference framework to divide and generalize the node embedding learning process on a given network into preprocessing steps, node feature extraction steps, and node embedding model training for an NRL task such as link prediction and node clustering. With this unifying reference framework, we highlight the representative methods, models, and techniques used at different stages of the node embedding model learning process. This survey not only helps researchers and practitioners gain an in-depth understanding of different NRL techniques but also provides practical guidelines for designing and developing the next generation of NRL algorithms and systems.
C1 [Zhou, Jingya; Fan, Jianxi] Soochow Univ, 1 Shizi St, Suzhou 215006, Jiangsu, Peoples R China.
   [Zhou, Jingya; Liu, Ling; Wei, Wenqi] Georgia Inst Technol, 801 Atlantic Dr, Atlanta, GA 30332 USA.
   [Zhou, Jingya] State Key Lab Math Engn & Adv Comp, Wuxi 214125, Jiangsu, Peoples R China.
RP Zhou, JY (通讯作者)，Soochow Univ, 1 Shizi St, Suzhou 215006, Jiangsu, Peoples R China.; Zhou, JY (通讯作者)，Georgia Inst Technol, 801 Atlantic Dr, Atlanta, GA 30332 USA.; Zhou, JY (通讯作者)，State Key Lab Math Engn & Adv Comp, Wuxi 214125, Jiangsu, Peoples R China.
EM jy_zhou@suda.edu.cn; lingliu@cc.gatech.edu; wenqiwei@gatech.edu;
   jxfan@suda.edu.cn
FU National Natural Science Foundation of China [61972272, 62172291,
   62072321, U1905211]; Open Project Program of the State Key Laboratory of
   Mathematical Engineering and Advanced Computing [2019A04]; Natural
   Science Foundation of the Jiangsu Higher Education Institutions of China
   [21KJA520008]; National Science Foundation [2038029, 1564097]; IBM
   faculty award
FX J. Zhou conducted this work during his 1-year visit at Georgia Institute
   of Technology under the Jiangsu Overseas Visiting Scholar Program for
   University Prominent Young & Middle-Aged Teachers and Presidents. J.
   Zhou and J. Fan were supported by the National Natural Science
   Foundation of China under grants 61972272, 62172291, 62072321, and
   U1905211, the Open Project Program of the State Key Laboratory of
   Mathematical Engineering and Advanced Computing under grant 2019A04, and
   the Natural Science Foundation of the Jiangsu Higher Education
   Institutions of China under grant 21KJA520008. L. Liu and W. Wei were
   partially supported by the National Science Foundation under grants
   2038029 and 1564097 and an IBM faculty award.
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NR 110
TC 0
Z9 0
U1 46
U2 46
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 0360-0300
EI 1557-7341
J9 ACM COMPUT SURV
JI ACM Comput. Surv.
PD MAR
PY 2023
VL 55
IS 2
AR 38
DI 10.1145/3491206
PG 35
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0H0VS
UT WOS:000778458900015
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Lan, GJ
   Liu, T
   Wang, X
   Pan, XL
   Huang, ZS
AF Lan, Gongjin
   Liu, Ting
   Wang, Xu
   Pan, Xueli
   Huang, Zhisheng
TI A semantic web technology index
SO SCIENTIFIC REPORTS
LA English
DT Article
ID KNOWLEDGE GRAPH; GENERATION; REASONERS; DBPEDIA
AB Semantic web (SW) technology has been widely applied to many domains such as medicine, health care, finance, geology. At present, researchers mainly rely on their experience and preferences to develop and evaluate the work of SW technology. Although the general architecture (e.g., Tim Berners-Lee's Semantic Web Layer Cake) of SW technology was proposed many years ago and has been well-known, it still lacks a concrete guideline for standardizing the development of SW technology. In this paper, we propose an SW technology index to standardize the development for ensuring that the work of SW technology is designed well and to quantitatively evaluate the quality of the work in SW technology. This index consists of 10 criteria that quantify the quality as a score of 0-10. We address each criterion in detail for a clear explanation from three aspects: (1) what is the criterion? (2) why do we consider this criterion and (3) how do the current studies meet this criterion? Finally, we present the validation of this index by providing some examples of how to apply the index to the validation cases. We conclude that the index is a useful standard to guide and evaluate the work in SW technology.
C1 [Lan, Gongjin] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China.
   [Liu, Ting; Wang, Xu; Pan, Xueli; Huang, Zhisheng] Vrije Univ Amsterdam, Dept Comp Sci, NL-1081 HV Amsterdam, Netherlands.
RP Liu, T (通讯作者)，Vrije Univ Amsterdam, Dept Comp Sci, NL-1081 HV Amsterdam, Netherlands.
EM t.liu@vu.nl
RI Wang, Xu/C-1588-2018
OI Wang, Xu/0000-0002-7585-759X
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NR 60
TC 0
Z9 0
U1 2
U2 2
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD MAR 7
PY 2022
VL 12
IS 1
AR 3672
DI 10.1038/s41598-022-07615-4
PG 10
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA ZO7QV
UT WOS:000765922600022
PM 35256665
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Dhoolia, P
   Chugh, P
   Costa, P
   Gantayat, N
   Gupta, M
   Kambhatla, N
   Kumar, R
   Mani, S
   Mitra, P
   Rogerson, C
   Saxena, M
AF Dhoolia, P.
   Chugh, P.
   Costa, P.
   Gantayat, N.
   Gupta, M.
   Kambhatla, N.
   Kumar, R.
   Mani, S.
   Mitra, P.
   Rogerson, C.
   Saxena, M.
TI A cognitive system for business and technical support: A case study
SO IBM JOURNAL OF RESEARCH AND DEVELOPMENT
LA English
DT Article
AB Business and technical support has traditionally been labor based. In this paper, we introduce a cognitive system for business and technical support. This cognitive system is aimed at answering, for example, "how to" and "how do I fix" questions that represent more than half of support help-desk queries. The standard method to build cognitive systems involves collecting the user questions, collecting and curating the domain knowledge, creating ground truth for learning, training, and testing, and continuous learning from user interactions and feedback. However, the lack of actual user questions, quality, and coverage of available enterprise knowledge, ambiguity in user communication, and user expectations on coverage and accuracy pose a challenge in applying the standard method to the domain of technical support. We address this by extracting and modeling users' support intents and questions from sources, such as help-desk tickets, discussion forums, and enterprise knowledge-extracting and using a domain knowledge graph to allow the cognitive system to have intentdisambiguating conversations with the user, and including a pool of human experts as a fall-back option to increase the effectiveness and acceptance of the solution (and as a source of learning). Users who participated in initial technology pilots found the system useful.
C1 [Dhoolia, P.; Chugh, P.; Gupta, M.; Kambhatla, N.; Saxena, M.] IBM Res, New Delhi 110070, India.
   [Costa, P.] IBM Global Business Serv Execut Partner, F-13002 Marseille, France.
   [Kambhatla, N.] IBM Res, Bangalore 560045, Karnataka, India.
   [Kumar, R.] IBM Global Business Serv, CH-1030 Bussigny, Switzerland.
   [Mani, S.] IBM Res, Bangalore 560077, Karnataka, India.
   [Mitra, P.] IBM Enterprise Applicat SAP Consulting Div, Kolkata 7000156, India.
   [Rogerson, C.] IBM Global Business Serv, CH-1008 Lausanne, Switzerland.
RP Dhoolia, P (通讯作者)，IBM Res, New Delhi 110070, India.
EM pdhoolia@in.ibm.com; palchugh@in.ibm.com; pierre.costa@fr.ibm.com;
   neelamadhav@in.ibm.com; monikgup@in.ibm.com; kambhatla@in.ibm.com;
   rajesh.kumar@ch.ibm.com; sentmani@in.ibm.com; pulak.mitra@in.ibm.com;
   cathy.rogerson@ch.ibm.com; mayur.saxena@in.ibm.com
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   [No title captured]
NR 35
TC 3
Z9 3
U1 2
U2 9
PU IBM CORP
PI ARMONK
PA 1 NEW ORCHARD ROAD, ARMONK, NY 10504 USA
SN 0018-8646
EI 2151-8556
J9 IBM J RES DEV
JI IBM J. Res. Dev.
PD JAN-FEB
PY 2017
VL 61
IS 1
AR 7
DI 10.1147/JRD.2016.2631398
PG 12
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Computer Science, Software Engineering; Computer Science,
   Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EO1WT
UT WOS:000396489300008
DA 2022-08-23
ER

PT J
AU Huang, RH
   Yang, XM
AF Huang, Ruihang
   Yang, Xiaoming
TI 8 Analysis and research hotspots of ceramic materials in textile
   application
SO JOURNAL OF CERAMIC PROCESSING RESEARCH
LA English
DT Article
DE Citespace; textile; ceramics; knowledge graph
ID TRENDS
AB From the perspective of scientometrics, comb the development history and research hotspots of the application of ceramics in textiles, the aim is to fully understand the global ceramic textile development direction and provide a credible reference for related researchers in China. By collecting the bibliographic data of 714 papers on ceramic textiles from 1990 to the present in the WoS core database, from the perspective of bibliometrics, analyze the time distribution, space distribution, research organization distribution, and research direction of ceramics in textile applications, then use methods such as subject word clustering, keyword clustering, and emergent word detection, explore the core content of the research on the application of ceramics in textiles, research hotspots, and future development trends. The study found that the research on ceramic textiles is currently in the extension stage, the papers published by Chinese scholars rank first in the world, but the importance is low. The research has gone through three stages, research hotspots include ceramic textile composite performance, preparation technology, heat preservation, composite material preparation, and application, etc, "microstructure", "refinement" and `temperature' are research hotspots in this field.
C1 [Huang, Ruihang; Yang, Xiaoming] Donghua Univ, Shanghai, Peoples R China.
RP Yang, XM (通讯作者)，Donghua Univ, Shanghai, Peoples R China.
EM 1037234058@qq.com
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NR 18
TC 1
Z9 1
U1 1
U2 1
PU KOREAN ASSOC CRYSTAL GROWTH, INC
PI SEOUL
PA SUNGDONG POST OFFICE, P O BOX 27, SEOUL 133-600, SOUTH KOREA
SN 1229-9162
J9 J CERAM PROCESS RES
JI J. Ceram. Process. Res.
PD JUN
PY 2022
VL 23
IS 3
BP 312
EP 319
DI 10.36410/jcpr.2022.23.3.312
PG 8
WC Materials Science, Ceramics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Materials Science
GA 3G0VA
UT WOS:000831073900012
DA 2022-08-23
ER

PT J
AU Yang, Y
   Zhuang, YT
   Pan, YH
AF Yang, Yi
   Zhuang, Yueting
   Pan, Yunhe
TI Multiple knowledge representation for big data artificial intelligence:
   framework, applications, and case studies
SO FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
LA English
DT Article
AB In this paper, we present a multiple knowledge representation (MKR) framework and discuss its potential for developing big data artificial intelligence (AI) techniques with possible broader impacts across different AI areas. Typically, canonical knowledge representations and modern representations each emphasize a particular aspect of transforming inputs into symbolic encoding or vectors. For example, knowledge graphs focus on depicting semantic connections among concepts, whereas deep neural networks (DNNs) are more of a tool to perceive raw signal inputs. MKR is an advanced AI representation framework for more complete intelligent functions, such as raw signal perception, feature extraction and vectorization, knowledge symbolization, and logical reasoning. MKR has two benefits: (1) it makes the current AI techniques (dominated by deep learning) more explainable and generalizable, and (2) it expands current AI techniques by integrating MKR to facilitate the mutual benefits of the complementary capacity of each representation, e.g., raw signal perception and symbolic encoding. We expect that MKR research and its applications will drive the evolution of AI 2.0 and beyond.
C1 [Yang, Yi; Zhuang, Yueting; Pan, Yunhe] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
RP Yang, Y (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
EM yangyics@zju.edu.cn; yzhuang@zju.edu.cn; panyh@zju.edu.cn
RI Yang, Yi/B-9273-2017
OI Yang, Yi/0000-0002-0512-880X
FU National Key R&D Program of China [2020AAA0108800]
FX Project supported by the National Key R&D Program of China (No.
   2020AAA0108800)
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   Xu DF, 2017, PROC CVPR IEEE, P3097, DOI 10.1109/CVPR.2017.330
   Yan Y, 2016, IEEE T MULTIMEDIA, V18, P2494, DOI 10.1109/TMM.2016.2602938
   Zhu LC, 2022, IEEE T MULTIMEDIA, V24, P668, DOI 10.1109/TMM.2021.3057503
NR 30
TC 21
Z9 21
U1 7
U2 8
PU ZHEJIANG UNIV PRESS
PI Hangzhou
PA Xixi Campus, Zhejiang University, No. 148 Tianmushan Road, Hangzhou,
   Zhejiang, PEOPLES R CHINA
SN 2095-9184
EI 2095-9230
J9 FRONT INFORM TECH EL
JI Front. Inform. Technol. Elect. Eng.
PD DEC
PY 2021
VL 22
IS 12
BP 1551
EP 1558
DI 10.1631/FITEE.2100463
PG 8
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA YA8ZI
UT WOS:000738614200001
DA 2022-08-23
ER

PT J
AU Huang, WH
   Mille, A
AF Huang, Weihong
   Mille, Alain
TI ConKMeL: a contextual knowledge management framework to support
   multimedia e-Learning
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article; Proceedings Paper
CT 5th IEEE International Symposium on Multimedia Software Engineeting
CY DEC 10-12, 2003
CL TAICHUNG, TAIWAN
SP IEEE Comp Soc, Taichung Healthcare & Management Univ, Natl Tsing Hua Univ, Natl Cent Univ, Bioinformat Soc Taiwan, Univ Calif Irvine
DE semantic context; e-Learning; knowledge management; multimedia; pedagogy
AB As the latest stage of learning and training evolution, e-Learning is supposed to provide intelligent functionalities not only in processing multi-media education resources but also in supporting context-sensitive pedagogical education processes. Towards providing an integrated solution for intelligent multimedia e-Learning, this paper presents a context-aware knowledge management framework named ConKMeL. Proposed framework features a semantic context-based approach for representing and integrating information and knowledge in e-Learning. Requirement analysis in university e-Learning environments shows that knowledge communications are usually in a hybrid mode across different conceptual levels. Based on the fact, a multi-layer contextual knowledge representation model called KG (knowledge graph) is presented. Corresponding key issues in development such as context-based knowledge retrieval and logical knowledge interpretation are discussed. On the application side, a scenario-based learning case study is shown to demonstrate the concepts and techniques developed in the ConKMeL framework.
C1 Kingston Univ, Fac Comp Informat Syst & Math, Kingston upon Thames KT1 2EE, Surrey, England.
   Univ Lyon 1, CNRS, FRE 2672, LIRIS, F-69622 Villeurbanne, France.
RP Huang, WH (通讯作者)，Kingston Univ, Fac Comp Informat Syst & Math, Kingston upon Thames KT1 2EE, Surrey, England.
EM W.Huang@kingston.ac.uk; alain.mille@liris.cnrs.fr
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   AKMAN V, 2002, CONTEX ARTIFICIAL IN
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   BERNERSLEE T, 2001, SEMANTIC WEB
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NR 28
TC 2
Z9 2
U1 1
U2 7
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD AUG
PY 2006
VL 30
IS 2
BP 205
EP 219
DI 10.1007/s11042-006-0024-4
PG 15
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 082CG
UT WOS:000240363500006
DA 2022-08-23
ER

PT J
AU Zheng, L
   Shen, ZM
   Wang, HZ
AF Zheng, Lei
   Shen, Ziming
   Wang, Hongzhi
TI GSBRL : Efficient RDF graph storage based on reinforcement learning
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph; Data management; Reinforcement learning; Markov
   decision process; Query rewriting
ID VIEW SELECTION; SEMANTIC WEB
AB Knowledge is the cornerstone of artificial intelligence, which is often represented as RDF graphs. The large-scale RDF graphs in various fields pose new challenges to graph data management. Due to the maturity and stability, relational database is a good choice for RDF graph storage. However, the management of the complex structure of RDF graphs in the relational database requires sophisticated storage structure design. To address this problem, this paper adopts reinforcement learning (RL) to optimize the storage partition method of RDF graph. To the best of our knowledge, this is the first work to adopt RL to solve this problem. Moreover, we propose the featurization method of RDF tables which guarantees adequacy of state representation and the query rewriting policy which ensures correct query results when the storage structure changes. Extensive experiments on various RDF benchmarks demonstrate that the proposed approach significantly outperforms the state-of-the-art storage strategies.
C1 [Zheng, Lei; Shen, Ziming; Wang, Hongzhi] Harbin Inst Technol, Sch Comp Sci, Harbin, Peoples R China.
RP Wang, HZ (通讯作者)，Harbin Inst Technol, Sch Comp Sci, Harbin, Peoples R China.
EM 19S103245@stu.hit.edu.cn; shenziming1120@gmail.com; wangzh@hit.edu.cn
OI Wang, Hongzhi/0000-0002-7521-2871
FU NSFC [U1866602, 71773025]; National Key Research and Development Program
   of China [2020YFB1006104]
FX This paper was supported by NSFC grant (U1866602,71773025). The National
   Key Research and Development Program of China (2020YFB1006104).
CR Agrawal R., 2001, Proceedings of the 27th International Conference on Very Large Data Bases, P149
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NR 37
TC 0
Z9 0
U1 0
U2 3
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD MAR
PY 2022
VL 25
IS 2
SI SI
BP 763
EP 784
DI 10.1007/s11280-021-00919-x
EA JUL 2021
PG 22
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZK2AM
UT WOS:000672266700001
DA 2022-08-23
ER

PT J
AU Chen, BF
   Hao, ZF
   Cai, XF
   Cai, RC
   Wen, W
   Zhu, J
   Xie, GQ
AF Chen, Bingfeng
   Hao, Zhifeng
   Cai, Xiaofeng
   Cai, Ruichu
   Wen, Wen
   Zhu, Jian
   Xie, Guangqiang
TI Embedding Logic Rules Into Recurrent Neural Networks
SO IEEE ACCESS
LA English
DT Article
DE RNN; logic rules; sentiment classification; named entity recognition
AB Incorporating prior knowledge into recurrent neural network (RNN) is of great importance for many natural language processing tasks. However, most of the prior knowledge is in the form of structured knowledge and is difficult to be exploited in the existing RNN framework. By extracting the logic rules from the structured knowledge and embedding the extracted logic rule into the RNN, this paper proposes an effective framework to incorporate the prior information in the RNN models. First, we demonstrate that commonly used prior knowledge could be decomposed into a set of logic rules, including the knowledge graph, social graph, and syntactic dependence. Second, we present a technique to embed a set of logic rules into the RNN by the way of feedback masks. Finally, we apply the proposed approach to the sentiment classification and named entity recognition task. The extensive experimental results verify the effectiveness of the embedding approach. The encouraging results suggest that the proposed approach has the potential for applications in other NLP tasks.
C1 [Chen, Bingfeng; Hao, Zhifeng; Cai, Xiaofeng; Cai, Ruichu; Wen, Wen; Zhu, Jian; Xie, Guangqiang] Guangdong Univ Technol, Dept Comp Sci, Guangzhou 510006, Guangdong, Peoples R China.
   [Hao, Zhifeng] Foshan Univ, Sch Math & Big Data, Foshan 528000, Peoples R China.
RP Hao, ZF (通讯作者)，Guangdong Univ Technol, Dept Comp Sci, Guangzhou 510006, Guangdong, Peoples R China.; Hao, ZF (通讯作者)，Foshan Univ, Sch Math & Big Data, Foshan 528000, Peoples R China.
EM zfhaomail@gmail.com
FU Natural Science Foundation of China (NSFC)-Guangdong Joint Fund
   [U1501254]; NSFC [61876043, 61472089]; Natural Science Foundation of
   Guangdong [2014A030306004, 2014A030308008]; Guangdong High-level
   Personnel of Special Support Program [2015TQ01X140]; Guangdong
   Provincial Key Laboratory of Cyber-Physical System [2016B030301008];
   Pearl River S&T Nova Program of Guangzhou [201610010101]
FX This work was supported in part by the Natural Science Foundation of
   China (NSFC)-Guangdong Joint Fund under Grant U1501254, in part by the
   NSFC under Grant 61876043 and Grant 61472089, in part by the Natural
   Science Foundation of Guangdong under Grant 2014A030306004 and Grant
   2014A030308008, in part by the Guangdong High-level Personnel of Special
   Support Program under Grant 2015TQ01X140, in part by the Guangdong
   Provincial Key Laboratory of Cyber-Physical System under Grant
   2016B030301008, and in part by the Pearl River S&T Nova Program of
   Guangzhou under Grant 201610010101.
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NR 32
TC 7
Z9 7
U1 0
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 2019
VL 7
BP 14938
EP 14946
DI 10.1109/ACCESS.2019.2892140
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HL5VD
UT WOS:000458797900060
OA gold
DA 2022-08-23
ER

PT J
AU Putman, TE
   Lelong, S
   Burgstaller-Muehlbacher, S
   Waagmeester, A
   Diesh, C
   Dunn, N
   Munoz-Torres, M
   Stupp, GS
   Wu, CL
   Su, AI
   Good, BM
AF Putman, Tim E.
   Lelong, Sebastien
   Burgstaller-Muehlbacher, Sebastian
   Waagmeester, Andra
   Diesh, Colin
   Dunn, Nathan
   Munoz-Torres, Monica
   Stupp, Gregory S.
   Wu, Chunlei
   Su, Andrew I.
   Good, Benjamin M.
TI WikiGenomes: an open web application for community consumption and
   curation of gene annotation data in Wikidata
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Article
ID LIFE SCIENCES; JBROWSE
AB With the advancement of genome-sequencing technologies, new genomes are being sequenced daily. Although these sequences are deposited in publicly available data warehouses, their functional and genomic annotations (beyond genes which are predicted automatically) mostly reside in the text of primary publications. Professional curators are hard at work extracting those annotations from the literature for the most studied organisms and depositing them in structured databases. However, the resources don't exist to fund the comprehensive curation of the thousands of newly sequenced organisms in this manner. Here, we describe WikiGenomes (wikigenomes. org), a web application that facilitates the consumption and curation of genomic data by the entire scientific community. WikiGenomes is based on Wikidata, an openly editable knowledge graph with the goal of aggregating published knowledge into a free and open database. WikiGenomes empowers the individual genomic researcher to contribute their expertise to the curation effort and integrates the knowledge into Wikidata, enabling it to be accessed by anyone without restriction.
C1 [Putman, Tim E.; Lelong, Sebastien; Burgstaller-Muehlbacher, Sebastian; Stupp, Gregory S.; Wu, Chunlei; Su, Andrew I.; Good, Benjamin M.] Scripps Res Inst, Dept Mol & Expt Med, La Jolla, CA 92037 USA.
   [Waagmeester, Andra] Micelio, Antwerp, Belgium.
   [Diesh, Colin] Univ Missouri, Div Anim Sci, Columbia, MO 65211 USA.
   [Dunn, Nathan; Munoz-Torres, Monica] Lawrence Berkeley Natl Lab, Environm Genom & Syst Biol Div, Berkeley, CA 94720 USA.
RP Su, AI; Good, BM (通讯作者)，Scripps Res Inst, Dept Mol & Expt Med, La Jolla, CA 92037 USA.
EM asu@scripps.edu; bgood@scripps.edu
RI Wu, Chunlei/ABF-4720-2020; Burgstaller-Muehlbacher,
   Sebastian/W-6686-2019
OI Wu, Chunlei/0000-0002-2629-6124; Burgstaller-Muehlbacher,
   Sebastian/0000-0003-4640-3510; Stupp, Gregory/0000-0002-0644-7212; Su,
   Andrew I./0000-0002-9859-4104; Putman, Timothy/0000-0002-4291-0737
FU National Institutes of General Medical Sciences [R01GM089820]; National
   Institutes for Health Common Fund [U54GM114833, U54DA036134]; National
   Institutes for Health from National Institute of General Medical
   Sciences [5R01GM080203]; National Human Genome Research Institute
   [5R01HG004483]; Office of Science, Office of Basic Energy Sciences, of
   the U.S. Department of Energy [DE-AC02-05CH11231]; NATIONAL HUMAN GENOME
   RESEARCH INSTITUTE [R01HG004483] Funding Source: NIH RePORTER; NATIONAL
   INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM080203, R01GM089820,
   U54GM114833] Funding Source: NIH RePORTER; NATIONAL INSTITUTE ON DRUG
   ABUSE [U54DA036134] Funding Source: NIH RePORTER
FX This project was supported by the National Institutes of General Medical
   Sciences (R01GM089820) and National Institutes for Health Common Fund
   programs for Big Data to Knowledge (U54GM114833) and Extracellular RNA
   Communication (U54DA036134). Nathan Dunn s and Monica Munoz-Torres s
   work with Apollo is supported by National Institutes for Health grants
   5R01GM080203 from National Institute of General Medical Sciences, and
   5R01HG004483 from National Human Genome Research Institute. Nathan Dunn
   and Monica Munoz-Torres are also supported by the Director, Office of
   Science, Office of Basic Energy Sciences, of the U.S. Department of
   Energy under Contract No. DE-AC02-05CH11231.
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NR 26
TC 16
Z9 16
U1 1
U2 7
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD MAR 24
PY 2017
AR bax025
DI 10.1093/database/bax025
PG 9
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA EP7CC
UT WOS:000397534300001
PM 28365742
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Wang, Z
   Xu, S
   Zhu, LJ
AF Wang, Zheng
   Xu, Shuo
   Zhu, Lijun
TI Semantic relation extraction aware of N-gram features from unstructured
   biomedical text
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Semantic relation extraction; N-gram features; Unstructured biomedical
   text; Prior knowledge; Rel-TNG model; Type-TNG model
AB Semantic relation extraction is a crucial step of automatically constructing a knowledge graph from unstructured biomedical text. Many real-world applications can benefit from it. As unsupervised relation extraction approaches, generative probabilistic models, Rel-LDA and Type-LDA, are receiving more attention in recent years. However, these two models inherit the bag-of-word assumption of the standard LDA model, which disable the exploitation of more distinguishable n-gram features. To overcome this limitation, two alternative models, named as Rel-TNG and Type-TNG, are proposed with the help of Topic N-Grams (TNG) model in this study, and collapsed Gibbs sampling algorithm is utilized for inference. Extensive experimental results on GENIA and EPI corpora indicate that Rel-TNG and Type-TNG models have similar performance with their unigram counterparts, but Rel-TNG and Type-TNG models outperform Rel-LDA and Type-LDA models when prior knowledge is available.
C1 [Wang, Zheng; Zhu, Lijun] Inst Sci & Tech Informat China, 15 Faxing Rd, Beijing 100038, Peoples R China.
   [Xu, Shuo] Beijing Univ Technol, 100 PingLeYuan, Beijing 100124, Peoples R China.
   [Xu, Shuo] Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, 2699 Qianjin St, Changchun 130012, Jilin, Peoples R China.
RP Xu, S (通讯作者)，Beijing Univ Technol, 100 PingLeYuan, Beijing 100124, Peoples R China.
EM wangz@istic.ac.cn; xushuo@bjut.edu.cn; zhulj@istic.ac.cn
FU Social Science Foundation of Beijing [17GLB074]; Science and Technology
   Project of Guangdong Province [2017A030303065]; Natural Science
   Foundation of China [71403255]
FX This work was supported partially by the Social Science Foundation of
   Beijing [Grant No. 17GLB074], Science and Technology Project of
   Guangdong Province [Grant No. 2017A030303065], and Natural Science
   Foundation of China [Grant No. 71403255]. Our gratitude also goes to the
   anonymous reviewers for their valuable comments.
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NR 36
TC 8
Z9 8
U1 2
U2 19
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 OCT
PY 2018
VL 86
BP 59
EP 70
DI 10.1016/j.jbi.2018.08.011
PG 12
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA HO0NQ
UT WOS:000460600800006
PM 30172761
OA Bronze
DA 2022-08-23
ER

PT J
AU Qazi, N
   William, WBL
AF Qazi, Nadeem
   William, Wong B. L.
TI An interactive human centered data science approach towards crime
   pattern analysis
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Interactive clustering; Linkage analysis; Crime matching; Text mining;
   Human-centred searching; Network visualization; Knowledge graph; Data
   science
ID FEATURE-SELECTION; VISUALIZATION; BURGLARIES; REDUCTION; KNOWLEDGE;
   LINKAGE
AB The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making.
   In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme.
   The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph.
   This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime.
   We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis.
C1 [Qazi, Nadeem] Brunel Univ, London, England.
   [William, Wong B. L.] Middlesex Univ, London, England.
RP Qazi, N (通讯作者)，Brunel Univ, London, England.
EM Nadeem.qazi@brunel.ac.uk
RI Furtado, Kássia/AAU-5007-2020
FU European Union Seventh Framework Programme through Project VALCRI,
   European Commission [FP7-IP-608142]
FX The research leading to the results reported here has received funding
   from the European Union Seventh Framework Programme through Project
   VALCRI, European Commission Grant Agreement N FP7-IP-608142, awarded to
   Middlesex University and partners. The data used in the examples were
   anonymized from actual data produced to support the project.
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NR 61
TC 9
Z9 9
U1 3
U2 43
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD NOV
PY 2019
VL 56
IS 6
AR 102066
DI 10.1016/j.ipm.2019.102066
PG 18
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA JA3YT
UT WOS:000487766200013
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Liu, JH
   Deng, AS
   Xie, XQ
   Xie, QJ
AF Liu, Jiahui
   Deng, Ansheng
   Xie, Xinqiang
   Xie, Qiuju
TI ExpRec: Deep knowledge-awared question routing in software question
   answering community
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Question routing; Knowledge graph; High-order embedding propagation;
   User capability; Attention mechanism
AB Software question answering community (SQAC) as an effective platform of knowledge sharing has achieved rapid development. In SQAC, one critical and challenging problem is question routing (or expert recommendation). To solve this problem, previous approaches focus on learning the relevance between the question and answerers. However, such approaches usually suffer from the data sparsity and noise issues which may reduce the accuracy of the question routing. Moreover, previous approaches also ignored the response quality and timeliness of the question routing. To tackle those issues, we study the question routing problem from two aspects: 1) the answerer's relevance to the given question, and 2) the answerer's capability. We first propose a deep knowledge-awared question routing framework (termed ExpRec) which leverages the attentive embedding propagates and their high-order connectivities to learn the answerer's relevance to the given question. Then we explicitly model the answerer's capability and incorporate it with the answerer's relevance to the given question. Finally, to evaluate the performance of ExpRec, we conduct extensive experiments on two real-world datasets. The experimental results show that ExpRec outperforms other five state-of-the-art approaches significantly.
C1 [Liu, Jiahui; Deng, Ansheng] Dalian Maritime Univ, Sch Informat Sci & Technol, Linghai Rd, Dalian 116026, Liaoning, Peoples R China.
   [Xie, Xinqiang] Northeastern Univ, Sch Comp Sci & Engn, Chuangxin Rd, Shenyang 110169, Liaoning, Peoples R China.
   [Xie, Qiuju] Northeastern Agr Univ, Coll Elect & Informat, Changjiang Rd, Harbin 150030, Heilongjiang, Peoples R China.
RP Liu, JH (通讯作者)，Dalian Maritime Univ, Sch Informat Sci & Technol, Linghai Rd, Dalian 116026, Liaoning, Peoples R China.
EM 183246593@qq.com; ashdeng@dlmu.edu.cn; xie.xq@foxmail.com;
   xqj197610@163.com
FU National Natural Science Foundation of China [61502068, 32072787]
FX This work was partially supported by the National Natural Science
   Foundation of China under Grant 61502068, 32072787.
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NR 35
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/s10489-022-03369-8
EA JUL 2022
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Q0BS
UT WOS:000820095500001
DA 2022-08-23
ER

PT J
AU Rodrigues, FB
   Giozza, WF
   Albuquerque, RD
   Villalba, LJG
AF Barros Rodrigues, Fillipe
   Ferreira Giozza, William
   de Oliveira Albuquerque, Robson
   Garcia Villalba, Luis Javier
TI Natural Language Processing Applied to Forensics Information Extraction
   With Transformers and Graph Visualization
SO IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
LA English
DT Article; Early Access
DE Digital forensics; Task analysis; Natural language processing; Data
   mining; Information retrieval; Transformers; Training; Digital
   forensics; named entity recognition (NER); natural language processing
   (NLP); relation extraction (RE); transformers
ID DIGITAL FORENSICS; SERVICE
AB Digital forensics analysis is a slow process mainly due to the large amount and variety of data. Some forensic tools help categorize files by type and allow automatization of tasks, like named entity recognition (NER). NER is a key component in many natural language processing (NLP) applications, such as relation extraction (RE) and information retrieval. The introduction of neural networks and transformer architectures in the last few years made it possible to develop more accurate models in different languages. This work proposes a reproducible setup to build a forensic pipeline for information extraction using NLP of texts. Our results show that it is possible to develop both NER and RE models in any language and tune its hyper-parameters to achieve state-of-art performance and build comprehensive knowledge graphs, decreasing the amount of time required for human supervision and review. We also find that solving this task in phases can further improve the performance, not only for digital investigation applications, but also for general-purpose information extraction and analysis.
C1 [Barros Rodrigues, Fillipe; Ferreira Giozza, William; de Oliveira Albuquerque, Robson] Univ Brasilia, Professional Postgrad Program Elect Engn, Dept Elect Engn, BR-70910900 Brasilia, DF, Brazil.
   [de Oliveira Albuquerque, Robson; Garcia Villalba, Luis Javier] Univ Complutense Madrid UCM, Fac Comp Sci & Engn, Dept Software Engn & Artificial Intelligence DISI, Grp Anal Secur & Syst GASS, Madrid 28040, Spain.
RP Villalba, LJG (通讯作者)，Univ Complutense Madrid UCM, Fac Comp Sci & Engn, Dept Software Engn & Artificial Intelligence DISI, Grp Anal Secur & Syst GASS, Madrid 28040, Spain.
EM fillipeibr@gmail.com; giozza@unb.br; robson@redes.unb.br;
   javiergv@fdi.ucm.es
RI Giozza, William F./AAH-4838-2019; Garcia Villalba, Luis
   Javier/N-4631-2014
OI Giozza, William F./0000-0002-3003-3458; Albuquerque,
   Robson/0000-0002-6717-3374; Garcia Villalba, Luis
   Javier/0000-0001-7573-6272; Rodrigues, Fillipe/0000-0002-0288-5987
FU Professional Postgraduate Program in Electrical Engineering of
   University of Brasilia; European Union [101021801]; HEROES Project;
   FAP-DF-Brazilian Federal District Research Support Foundation
   [00193-00000853/2021-28]; Brazilian National Council for Scientific and
   Technological Development-CNPq [312180/2019-5 PQ-2]; National Institute
   of Science and Technology (INCT) on Cybersecurity [465741/2014-2];
   Brazilian Ministry of the Economy [005/2016, 083/2016]; Administrative
   Council for Economic Defense-CADE [08700.000047/2019-14]; General
   Attorney of the Union-AGU [697.935/2019]; General Attorney's Office for
   the National Treasure-PGFN [23106.148934/2019-67]; National Auditing
   Department of the Brazilian Health System-DENASUS [23106.118410/2020-85]
FX This work was supported in part by the Professional Postgraduate Program
   in Electrical Engineering of University of Brasilia, in part by the
   HEROES Project, and in part by the European Union's Horizon 2020
   Research and Innovation Programme under Grant 101021801. The work of
   William Ferreira Giozza and Robson de Oliveira Albuquerque was supported
   in part by the FAP-DF-Brazilian Federal District Research Support
   Foundation under Grant 00193-00000853/2021-28, in part by the Brazilian
   National Council for Scientific and Technological Development-CNPq under
   Grant 312180/2019-5 PQ-2, in part by the National Institute of Science
   and Technology (INCT) on Cybersecurity under Grant 465741/2014-2, in
   part by the Brazilian Ministry of the Economy under Grant 005/2016 and
   Grant 083/2016, in part by the Administrative Council for Economic
   Defense-CADE under Grant 08700.000047/2019-14, in part by the General
   Attorney of the Union-AGU under Grant 697.935/2019, in part by the
   National Auditing Department of the Brazilian Health System-DENASUS
   under Grant 23106.118410/2020-85, and in part by the General Attorney's
   Office for the National Treasure-PGFN under Grant 23106.148934/2019-67.
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NR 50
TC 0
Z9 0
U1 5
U2 5
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2329-924X
J9 IEEE T COMPUT SOC SY
JI IEEE Trans. Comput. Soc. Syst.
DI 10.1109/TCSS.2022.3159677
EA APR 2022
PG 17
WC Computer Science, Cybernetics; Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0I8OG
UT WOS:000779672700001
DA 2022-08-23
ER

PT J
AU Phan, THV
   Do, P
AF Phan, Truong H., V
   Phuc Do
TI NER2QUES: combining named entity recognition and sequence to sequence to
   automatically generating Vietnamese questions
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Named entity recognition; Question generation; Question answering;
   Sequence to sequence; BERT
ID KNOWLEDGE GRAPH
AB Named entity recognition (NER) is an important task in natural language processing. NER is usually used to classify documents, extract information, and translate languages. However, few studies have used NER types to automatically generate questions. In this paper, we proposed a method named NER2QUES to solve the above problem for a low-resource language such as Vietnamese. NER2QUES was the combining pre-trained language model and sequence-to-sequence model. Specifically, we used BERT to detect NERs in a sentence and then applied a sequence-to-sequence model to automatically generate questions that corresponded to NER's types. We compared the accuracy of the proposed method to PhoBERT and spaCy on the NER task. Also, we used F1, BLEU, ROUGE, and METEOR to measure the effectiveness of this approach with the rules-based method, T5, and BERT on question generation tasks. The experiment results show that the accuracy of our method is more improved than previous methods' accuracy of 94% on SQuAD, 89% on XQuAD, and 95% on MLQA. This indicates that using NER to automatically generate questions may enrich question answering systems.
C1 [Phan, Truong H., V; Phuc Do] Ho Chi Minh Natl Univ, Univ Technol Informat, Ho Chi Minh City, Vietnam.
   [Phan, Truong H., V] Van Lang Univ, Ho Chi Minh City, Vietnam.
RP Do, P (通讯作者)，Ho Chi Minh Natl Univ, Univ Technol Informat, Ho Chi Minh City, Vietnam.
EM truongphv.ncs@grad.uit.edu.vn; phucdo@uit.edu.vn
OI H. V Phan, Truong/0000-0003-1773-6703
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
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NR 39
TC 0
Z9 0
U1 3
U2 5
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 JAN
PY 2022
VL 34
IS 2
SI SI
BP 1593
EP 1612
DI 10.1007/s00521-021-06477-7
EA SEP 2021
PG 20
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YK9QP
UT WOS:000695396500003
DA 2022-08-23
ER

PT J
AU Bai, LY
   Wang, JY
   Di, XF
   Li, N
AF Bai, Luyi
   Wang, Jinyao
   Di, Xiaofeng
   Li, Nan
TI Fixing the inconsistencies in fuzzy spatiotemporal RDF graph
SO INFORMATION SCIENCES
LA English
DT Article
DE Fuzzy spatiotemporal data; Inconsistency; RDF graph
ID QUERY
AB Since an increasing number of applications have urgent requirements for the management of fuzzy spatiotemporal information, it is challenging to model fuzzy spatiotemporal data. On the other hand, because Resource Description Framework (RDF) benefits greatly from web data, linked data, and knowledge graph, it has been widely accepted and has rapidly become a new choice to model and represent data in many application domains. The advent of RDF seems to provide an opportunity for dealing with fuzzy spatiotemporal data. However, RDF data is subject to continuously changing operations so that they may violate the integrity constraints and cause inconsistencies. In order to solve these problems, we propose an extended RDF model that can represent fuzzy spatiotemporal data, and then present the structure of fuzzy spatiotemporal data in RDF graph. After studying the con-straint conditions of fuzzy spatiotemporal data in RDF graph, we propose the correspond-ing fixing rules and algorithms to detect and repair the fuzzy spatiotemporal RDF graph. Finally, the experimental results demonstrate the effectiveness of our approach which can significantly solve the inconsistency problem of fuzzy spatiotemporal RDF graphs. (c) 2021 Elsevier Inc. All rights reserved.
C1 [Bai, Luyi; Wang, Jinyao; Di, Xiaofeng; Li, Nan] 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
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 Sci-entific 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 by the National Natural Science Foundation of
   China (61402087) , the Natural Science Foundation of Hebei Province
   (F2019501030) , the Natural Science Foundation of Liaoning Province
   (2019-MS-130) , the Key Project of Sci-entific Research Funds in
   Colleges and Universities of Hebei Education Department (ZD2020402) ,
   the Fundamental Research Funds for the Central Universities (N2023019) ,
   and in part by the Program for 333 Talents in Hebei Province
   (A202001066) . The authors would also like to express their gratitude to
   the anonymous reviewers for providing very helpful suggestions.
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NR 31
TC 1
Z9 1
U1 3
U2 4
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD NOV
PY 2021
VL 578
BP 166
EP 180
DI 10.1016/j.ins.2021.07.038
EA JUL 2021
PG 15
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UX8NO
UT WOS:000701095300010
DA 2022-08-23
ER

PT J
AU Oh, S
   Yoo, S
   Kim, Y
   Song, J
   Park, S
AF Oh, Solgil
   Yoo, Sujin
   Kim, Yuri
   Song, Jisoo
   Park, Seongbin
TI Implementation of a System That Helps Novice Users Work with Linked Data
SO ELECTRONICS
LA English
DT Article
DE Semantic Web; linked data; reasoning; knowledge graph
ID SEMANTIC WEB; FRAMEWORK; DIAGRAMS
AB On the Semantic Web, resources are connected to each other by the IRI. As the basic unit is comprised of linked data, machines can use semantic data and reason their relations without additional intervention on the Semantic Web. However, it is necessary for users who first encounter the Semantic Web to understand its underlying structure and some grammatical rules. This study suggests linking data sets of the Semantic Web through the Euler diagram, which does not require any prior knowledge. We performed a user study with our relationship-building system and verified that users could better understand linked data through the usage of the system. Users can indirectly be guided by using our Euler diagram-based data relationship-building system to understand the Semantic Web and its data linkage system. We also expect that the data sets defined through our system can be used in various applications.
C1 [Oh, Solgil; Kim, Yuri; Song, Jisoo; Park, Seongbin] Korea Univ, Dept Comp Sci, 145 Anam Ro, Seoul 02841, South Korea.
   [Yoo, Sujin] Hanyang Univ, Ctr Creat Convergence Educ, Software Educ Comm, 315 Engn Ctr,222 Wangsimni Ro, Seoul 04763, South Korea.
RP Park, S (通讯作者)，Korea Univ, Dept Comp Sci, 145 Anam Ro, Seoul 02841, South Korea.
EM hhldtfrf@gmail.com; sujinyoo@hanyang.ac.kr; fhrml1004@korea.ac.kr;
   y7116385@korea.ac.kr; hyperspace@korea.ac.kr
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NR 34
TC 0
Z9 0
U1 0
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD JUN
PY 2021
VL 10
IS 11
AR 1237
DI 10.3390/electronics10111237
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA SP4TV
UT WOS:000659664300001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wang, H
   Guo, B
   Wu, W
   Liu, SC
   Yu, ZW
AF Wang, Hao
   Guo, Bin
   Wu, Wei
   Liu, Sicong
   Yu, Zhiwen
TI Towards information-rich, logical dialogue systems with
   knowledge-enhanced neural models
SO NEUROCOMPUTING
LA English
DT Article
DE Text generation; Dialogue systems; Knowledge graphs; Neural network
   models
AB Dialogue systems have made massive promising progress contributed by deep learning techniques and have been widely applied in our life. However, existing end-to-end neural models suffer from the problem of tending to generate uninformative and generic responses because they cannot ground dialogue context with background knowledge. In order to solve this problem, many researchers begin to consider combining external knowledge in dialogue systems, namely knowledge-enhanced dialogue systems. The challenges of knowledge-enhanced dialogue systems include how to select the appropriate knowledge from large-scale knowledge bases, how to read and understand extracted knowledge, and how to integrate knowledge into responses generation process. Combined with external knowledge, dialogue systems can deeply understand the dialogue context, and generate more informative and logical responses. This survey gives a comprehensive review of knowledge-enhanced dialogue systems, summarizes research progress to solve these challenges and proposes some open issues and research directions. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Wang, Hao; Guo, Bin; Liu, Sicong; Yu, Zhiwen] Northwestern Polytech Univ, Xian, Peoples R China.
   [Wu, Wei] Microsoft Corp, Beijing, Peoples R China.
RP Guo, B (通讯作者)，Northwestern Polytech Univ, Xian, Peoples R China.
EM guob@nwpu.edu.cn
FU National Science Fund for Distinguished Young Scholars [62025205];
   National Natural Science Foundation of China [61772428, 61725205]
FX This work was partially supported by The National Science Fund for
   Distinguished Young Scholars (62025205), and The National Natural
   Science Foundation of China (Nos. 61772428, 61725205).
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NR 76
TC 2
Z9 2
U1 6
U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD NOV 20
PY 2021
VL 465
BP 248
EP 264
DI 10.1016/j.neucom.2021.08.131
EA SEP 2021
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WA4FU
UT WOS:000702843400016
DA 2022-08-23
ER

PT J
AU Rama-Maneiro, E
   Vidal, JC
   Lama, M
AF Rama-Maneiro, Efren
   Vidal, Juan C.
   Lama, Manuel
TI Collective disambiguation in entity linking based on topic coherence in
   semantic graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Entity linking; Semantic annotation; Topic coherence; Named entity
   disambiguation
AB Entity Linking (EL) consists of determinating the entities that best represent the mentions in a document. Mentions can be very ambiguous and can refer to different entities in different contexts.
   In this paper, we present ABACO, a semantic annotation system for Entity Linking (EL) which addresses name ambiguity assuming that the entity that annotates a mention should be coherent with the main topics of the document. ABACO extracts a sub-graph from a knowledge base which interconnects all the candidate entities to annotate each mention in the document. Candidate entities are scored according to their degree of centrality in the knowledge graph and their textual similarity with the topics of the document, and worst candidates are pruned from the sub-graph.
   The approach has been validated with 13 datasets and compared with other 11 annotation systems using the GERBIL platform. Results show that ABACO outperforms the other systems for medium/large documents. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Rama-Maneiro, Efren; Vidal, Juan C.; Lama, Manuel] Univ Santiago de Compostela, Ctr Singular Invest Tecnoloxias Intelixentes CiTI, Santiago De Compostela 15782, Spain.
RP Rama-Maneiro, E (通讯作者)，Univ Santiago de Compostela, Ctr Singular Invest Tecnoloxias Intelixentes CiTI, Santiago De Compostela 15782, Spain.
EM efren.rama.maneiro@usc.es; juan.vidal@usc.es; manuel.lama@usc.es
RI Vidal, Juan C./L-7375-2014
OI Vidal, Juan C./0000-0002-8682-6772; Rama-Maneiro,
   Efren/0000-0002-0338-4586
FU Spanish Ministry of Economy and Competitiveness [TIN2015-73566-JIN,
   TIN2017-84796-C21-R]; European Regional Development Fund (ERDF/FEDER);
   Conselleria de Cultura, Educacion e Ordenacion Universitaria [ED431G/08,
   ED431C 2018/29]
FX This work was supported by the Spanish Ministry of Economy and
   Competitiveness under the project TIN2015-73566-JIN and
   TIN2017-84796-C21-R, and by the European Regional Development Fund
   (ERDF/FEDER), the Conselleria de Cultura, Educacion e Ordenacion
   Universitaria (accreditation 2016-2019, ED431G/08, reference competitive
   group 2018-2020, ED431C 2018/29).
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PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
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JI Knowledge-Based Syst.
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PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LQ9ZH
UT WOS:000535355300010
DA 2022-08-23
ER

PT J
AU Bayerlein, B
   Hanke, T
   Muth, T
   Riedel, J
   Schilling, M
   Schweizer, C
   Skrotzki, B
   Todor, A
   Torres, BM
   Unger, JF
   Volker, C
   Olbricht, J
AF Bayerlein, Bernd
   Hanke, Thomas
   Muth, Thilo
   Riedel, Jens
   Schilling, Markus
   Schweizer, Christoph
   Skrotzki, Birgit
   Todor, Alexandru
   Torres, Benjami Moreno
   Unger, Jorg F.
   Voelker, Christoph
   Olbricht, Jurgen
TI A Perspective on Digital Knowledge Representation in Materials Science
   and Engineering
SO ADVANCED ENGINEERING MATERIALS
LA English
DT Article
DE data infrastructures; digital representations; digital workflows;
   knowledge graphs; materials informatics; ontologies; vocabulary
   providers
ID MATERIALS INFORMATICS; MATERIALS GENOME; ONTOLOGIES; INFERENCE;
   WORKFLOWS; SYSTEM; FIELD; MODEL; GENE
AB The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
C1 [Bayerlein, Bernd; Muth, Thilo; Riedel, Jens; Schilling, Markus; Skrotzki, Birgit; Torres, Benjami Moreno; Unger, Jorg F.; Voelker, Christoph; Olbricht, Jurgen] Bundesanstalt Mat Forsch & Prufung BAM, Eichen 87, D-12205 Berlin, Germany.
   [Hanke, Thomas] Fraunhofer Inst Mikrostruktur Werkstoffen & Syste, IMWS, Walter Hulse Str 1, D-06120 Halle, Saale, Germany.
   [Schweizer, Christoph] Fraunhofer Inst Werkstoffmech IWM, Wohlerstr 11, D-79108 Freiburg, Germany.
   [Todor, Alexandru] Fraunhofer Verbund Werkstoffe, Bauteile Mat, Anna Louisa Karsch Str 2, D-10178 Berlin, Germany.
RP Olbricht, J (通讯作者)，Bundesanstalt Mat Forsch & Prufung BAM, Eichen 87, D-12205 Berlin, Germany.
EM juergen.olbricht@bam.de
RI Skrotzki, Birgit/I-7661-2015; Schilling, Markus/AGZ-8982-2022
OI Skrotzki, Birgit/0000-0003-0727-5499; Schilling,
   Markus/0000-0002-7094-5371
FU Bundesanstalt fur Materialforschung und -prufung (BAM); Fraunhofer Group
   Materials and Components (MATERIALS); Projekt DEAL
FX Funding provided by Bundesanstalt fur Materialforschung und -prufung
   (BAM) and Fraunhofer Group Materials and Components (MATERIALS) is
   gratefully acknowledged. The authors wish to thank Manfred Futing for
   his careful review of the manuscript. Open Access funding enabled and
   organized by Projekt DEAL.
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NR 107
TC 0
Z9 0
U1 6
U2 6
PU WILEY-V C H VERLAG GMBH
PI WEINHEIM
PA POSTFACH 101161, 69451 WEINHEIM, GERMANY
SN 1438-1656
EI 1527-2648
J9 ADV ENG MATER
JI Adv. Eng. Mater.
PD JUN
PY 2022
VL 24
IS 6
AR 2101176
DI 10.1002/adem.202101176
EA APR 2022
PG 14
WC Materials Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Materials Science
GA 2G8HO
UT WOS:000781038300001
DA 2022-08-23
ER

PT J
AU Li, K
   Zhou, XF
   Wang, DZ
   Grant, C
   Dobra, A
   Dudley, C
AF Li, Kun
   Zhou, Xiaofeng
   Wang, Daisy Zhe
   Grant, Christan
   Dobra, Alin
   Dudley, Christopher
TI In-database batch and query-time inference over probabilistic graphical
   models using UDA-GIST
SO VLDB JOURNAL
LA English
DT Article
DE In-database analytics; Query-time inference; Batch inference;
   Data-parallel analytics; Graph-parallel analytics
ID MAD-SKILLS
AB To meet customers pressing demands, enterprise database vendors have been pushing advanced analytical techniques into databases. Most major DBMSes use user-defined aggregates (UDAs), a data-driven operator, to implement analytical techniques in parallel. However, UDAs alone are not sufficient to implement statistical algorithms where most of the work is performed by iterative transitions over a large state that cannot be naively partitioned due to data dependency. Typically, this type of statistical algorithm requires pre-processing to set up the large state in the first place and demands post-processing after the statistical inference. This paper presents general iterative state transition (GIST), a new database operator for parallel iterative state transitions over large states. GIST receives a state constructed by a UDA and then performs rounds of transitions on the state until it converges. A final UDA performs post-processing and result extraction. We argue that the combination of UDA and GIST (UDA-GIST) unifies data-parallel and state-parallel processing in a single system, thus significantly extending the analytical capabilities of DBMSes. We exemplify the framework through two high-profile batch applications: cross-document coreference, image denoising and one query-time inference application: marginal inference queries over probabilistic knowledge graphs. The 3 applications use probabilistic graphical models, which encode complex relationships of different variables and are powerful for a wide range of problems. We show that the in-database framework allows us to tackle a 27 times larger problem than a scalable distributed solution for the first application and achieves 43 times speedup over the state-of-the-art for the second application. For the third application, we implement query-time inference using the UDA-GIST framework and apply over a probabilistic knowledge graph, achieving 10 times speedup over sequential inference. To the best of our knowledge, this is the first in-database query-time inference engine over large probabilistic knowledge base. We show that the UDA-GIST framework for data-and graph-parallel computations can support both batch and query-time inference efficiently in databases.
C1 [Li, Kun; Zhou, Xiaofeng; Wang, Daisy Zhe; Dobra, Alin; Dudley, Christopher] Univ Florida, Dept Comp & Informat Sci & Engn, Gainesville, FL 32611 USA.
   [Grant, Christan] Univ Oklahoma, Sch Comp Sci, Norman, OK 73019 USA.
RP Zhou, XF (通讯作者)，Univ Florida, Dept Comp & Informat Sci & Engn, Gainesville, FL 32611 USA.
EM kli@cise.ufl.edu; xiaofeng@cise.ufl.edu; daisyw@cise.ufl.edu;
   cgrant@ou.edu; adobra@cise.ufl.edu; cdudley@cise.ufl.edu
RI Grant, Christan/J-2461-2018
OI Grant, Christan/0000-0002-6684-3620
FU NSF IIS [1526753]; DARPA [FA8750-12-2-0348-2]
FX This work was partially supported by NSF IIS Award No. 1526753, DARPA
   under FA8750-12-2-0348-2 (DEFT/CUBISM), and a generous gift from Google.
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NR 53
TC 4
Z9 5
U1 1
U2 6
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1066-8888
EI 0949-877X
J9 VLDB J
JI VLDB J.
PD APR
PY 2017
VL 26
IS 2
BP 177
EP 201
DI 10.1007/s00778-016-0446-1
PG 25
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EO6CZ
UT WOS:000396780700002
DA 2022-08-23
ER

PT J
AU He, B
   Zhang, JR
AF He, Bo
   Zhang, Jiru
TI An Association Rule Mining Method Based on Named Entity Recognition and
   Text Classification
SO ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
LA English
DT Article; Early Access
DE Chinese named entity recognition; Neural network; Text classification;
   Association rules
AB Using massive text data, building a knowledge graph to implement in-depth association analysis and mining can help identify entities and make decisions. The accuracy of traditional Chinese Named Entity Recognition methods is low, and traditional frequent itemset mining methods are also difficult to obtain different types of categories, and their novelty is not high. In this paper, we propose an association rule mining method based on named entity recognition and text classification (ARMTNER). First, the TextCNN model is used to extract the word vector information of the text data; secondly, bidirectional LSTM is used the model extracts the contextual features of the text; then the neural network model is used to automatically extract the word features and the global features of the text for text classification; finally, the text sequence labeling and entity recognition are performed. Then the association rules be used to mine frequent itemsets through two-level classification of text classification and entity classification. The experimental results showed that, our method can achieve an F1-score of 97.3% on the public data set in Chinese Named Entity Recognition, and the novelty of frequent itemsets increased by 0.279%.
C1 [He, Bo; Zhang, Jiru] Chongqing Univ Technol, Sch Comp Sci & Engn, Chongqing 400054, Peoples R China.
RP Zhang, JR (通讯作者)，Chongqing Univ Technol, Sch Comp Sci & Engn, Chongqing 400054, Peoples R China.
EM jrzhangjr@163.com
OI Zhang, Jiru/0000-0001-7210-8481
FU Humanities and Social Sciences of Ministry Education of China
   [19XJA910001]; Chongqing University of Technology [clgycx 20203114]
FX This research is supported by the Humanities and Social Sciences of
   Ministry Education of China (No.19XJA910001) and the postgraduate
   innovation fund project of Chongqing University of Technology (No.clgycx
   20203114).
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SN 2193-567X
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EA MAY 2022
PG 9
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 1I6EH
UT WOS:000797320700003
DA 2022-08-23
ER

PT J
AU Chen, L
   Tian, XX
   Tang, X
   Cui, J
AF Chen, Ling
   Tian, Xiaoxue
   Tang, Xing
   Cui, Jun
TI Multi-information embedding based entity alignment
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Entity alignment; Multi-information embedding; Weighted neighbor
   information encoding
AB Entity alignment refers to discovering two entities in different knowledge bases that represent the same thing in reality. Existing methods generally only adopt TransE or TransE-like knowledge graph representation learning models, which usually assume that there are enough training triples for each entity, and entities appearing in few triples are easily misaligned. In this paper, we propose a multi-information embedding based entity alignment method (MEEA), which utilizes embedding methods based on multi-information, including triple embedding and neighbor information embedding, to obtain the vector representations of each entity, which are then used for aligning entities. In addition, we propose a weighted neighbor information encoding method to make the neighbor information based vector representation suitable for entity alignment, which measures the effects of different neighbors on entity alignment from three aspects (i.e., the mapping cardinality of the neighbor, the relation association of the neighbor, and the attention mechanism) and gives corresponding weights. Experiments are conducted on two cross-lingual knowledge bases, and the experimental results show that MEEA is able to yield a better performance compared to the state-of-the-art methods.
C1 [Chen, Ling; Tian, Xiaoxue; Tang, Xing; Cui, Jun] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
RP Chen, L (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
EM lingchen@cs.zju.edu.cn; xxtian@cs.zju.edu.cn; tangxing@cs.zju.edu.cn;
   cuijun@cs.zju.edu.cn
FU National Key Research and Development Program of China [2018YFB0505000];
   Fundamental Research Funds for the Central Universities [2020QNA5017]
FX This work was funded by the National Key Research and Development
   Program of China (No. 2018YFB0505000) and the Fundamental Research Funds
   for the Central Universities (No.2020QNA5017).
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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 2021
VL 51
IS 12
BP 8896
EP 8912
DI 10.1007/s10489-021-02400-8
EA APR 2021
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WT4TD
UT WOS:000640755200004
DA 2022-08-23
ER

PT J
AU Li, H
   Liu, Y
   Mamoulis, N
   Rosenblum, DS
AF Li, Hui
   Liu, Ye
   Mamoulis, Nikos
   Rosenblum, David S.
TI Translation-Based Sequential Recommendation for Complex Users on Sparse
   Data
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Recommender systems; Task analysis; Data models; Bayes methods;
   Training; Recurrent neural networks; Sequential recommendation;
   sequential behavior; recommender systems
AB Sequential recommendation is one of the main tasks in recommender systems, where the next action (e.g., purchase, visit, and click) of the user is predicted based on his/her past sequence of actions. Translating Embeddings is a knowledge graph completion approach which was recently adapted to a translation-based sequential recommendation (TransRec) method. We observe a flaw of TransRec when handling complex translations, which hinders it from generating accurate suggestions. In view of this, we propose a translation-based recommender for complex users (CTransRec), which utilizes category-specific projection and temporal dynamic relaxation. Using our proposed Margin-based Pairwise Bayesian Personalized Ranking and Time-Aware Negative Sampling, CTransRec outperforms state-of-the-art methods for sequential recommendation on extremely sparse data. The superiority of CTransRec, which is confirmed by our extensive experiments on both public data and real data obtained from the industry, comes from not only the additional information used in training but also the fact that CTransRec makes good use of this additional information to model the complex translations.
C1 [Li, Hui] Xiamen Univ, Sch Informat Sci & Engn, Fujian Key Lab Sensing & Comp Smart City, Xiamen 361005, Fujian, Peoples R China.
   [Liu, Ye; Rosenblum, David S.] Natl Univ Singapore, Sch Comp, Singapore 119077, Singapore.
   [Mamoulis, Nikos] Univ Ioannina, Dept Comp Sci & Engn, Ioannina 45110, Epirus, Greece.
RP Li, H (通讯作者)，Xiamen Univ, Sch Informat Sci & Engn, Fujian Key Lab Sensing & Comp Smart City, Xiamen 361005, Fujian, Peoples R China.
EM hui@xmu.edu.cn; liuye@comp.nus.edu.sg; nikos@cs.uoi.gr;
   david@comp.nus.edu.sg
RI /C-1836-2009
OI /0000-0003-3423-4895
FU A*STAR SERC PSF [15212000]
FX This work was supported in part by A*STAR SERC PSF under grant 15212000.
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NR 55
TC 15
Z9 15
U1 1
U2 16
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 AUG. 1
PY 2020
VL 32
IS 8
BP 1639
EP 1651
DI 10.1109/TKDE.2019.2906180
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA MH7AX
UT WOS:000546878300015
DA 2022-08-23
ER

PT J
AU Benedetti, F
   Beneventano, D
   Bergamaschi, S
   Simonini, G
AF Benedetti, Fabio
   Beneventano, Domenico
   Bergamaschi, Sonia
   Simonini, Giovanni
TI Computing inter-document similarity with Context Semantic Analysis
SO INFORMATION SYSTEMS
LA English
DT Article; Proceedings Paper
CT 9th International Conference on Similarity Search and Applications
   (SISAP)
CY OCT 24-26, 2016
CL Tokyo, JAPAN
DE Knowledge base; Knowledge graph; Inter-document similarity; Similarity
   measures; Information Retrieval
ID INFORMATION-RETRIEVAL; DIVERGENCE; SYSTEM; MODELS
AB We propose a novel knowledge-based technique for inter-document similarity computation, called Context Semantic Analysis (CSA). Several specialized approaches built on top of specific knowledge base (e.g. Wikipedia) exist in literature, but CSA differs from them because it is designed to be portable to any RDF knowledge base. In fact, our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a Semantic Context Vector, a novel model for representing the context of a document, which is exploited by CSA to compute inter-document similarity effectively. Moreover, we show how CSA can be effectively applied in the Information Retrieval domain. Experimental results show that: (i) for the general task of inter-document similarity, CSA outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones built on top of specific knowledge bases; (ii) for Information Retrieval tasks, enriching documents with context (i.e., employing the Semantic Context Vector model) improves the results quality of the state-of-the-art technique that employs such similar semantic enrichment. (C) 2018 Elsevier Ltd. All rights reserved.
C1 [Benedetti, Fabio; Beneventano, Domenico; Bergamaschi, Sonia; Simonini, Giovanni] Univ Modena & Reggio Emilia, Dept Engn Enzo Ferrari, Modena, Italy.
RP Beneventano, D (通讯作者)，Univ Modena & Reggio Emilia, Dept Engn Enzo Ferrari, Modena, Italy.
EM Fabio.Benedetti@unimore.it; Domenico.Beneventano@unimore.it;
   Sonia.Bergamaschi@unimore.it; Giovanni.Simonini@unimore.it
RI BERGAMASCHI, Sonia/AAN-1712-2021; Beneventano, Domenico/K-9087-2015;
   Bergamaschi, Sonia/L-3095-2015; Simonini, Giovanni/L-6743-2018
OI BERGAMASCHI, Sonia/0000-0001-8087-6587; Beneventano,
   Domenico/0000-0001-6616-1753; Bergamaschi, Sonia/0000-0001-8087-6587;
   Simonini, Giovanni/0000-0002-3466-509X
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NR 45
TC 26
Z9 27
U1 0
U2 25
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4379
EI 1873-6076
J9 INFORM SYST
JI Inf. Syst.
PD FEB
PY 2019
VL 80
BP 136
EP 147
DI 10.1016/j.is.2018.02.009
PG 12
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA HG4SH
UT WOS:000454964800011
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Jiang, YX
   Perng, CS
   Sailer, A
   Silva-Lepe, I
   Zhou, Y
   Li, T
AF Jiang, Yexi
   Perng, Chang-Shing
   Sailer, Anca
   Silva-Lepe, Ignacio
   Zhou, Yang
   Li, Tao
TI CSM: A Cloud Service Marketplace for Complex Service Acquisition
SO ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
LA English
DT Article
DE Design; Algorithms; Cloud service; interactive search; semantic web
AB The cloud service marketplace (CSM) is an exploratory project aiming to provide "an AppStore for Services." It is an intelligent online marketplace that facilitates service discovery and acquisition for enterprise customers. Traditional service discovery and acquisition are time-consuming. In the era of OneClick Checkout and pay-as-you-go service plans, users expect services to be purchased online efficiently and conveniently. However, as services are complex and different from software apps, the currently prevailing App Store based on keyword search is inadequate for services.
   In CSM, exploring and configuring services are an iterative process. Customers provide their requirements in natural language and interact with the system through questioning and answering. Learning from the input, the system can incrementally clarify users' intention, narrow down the candidate services, and profile the configuration information for the candidates at the same time. CSM's back end is built around the Services Knowledge Graph (SKG) and leverages data mining technologies to enable the semantic understanding of customers' requirements. To quantitatively assess the value of CSM, empirical evaluation on real and synthetic datasets and case studies are given to demonstrate the efficacy and effectiveness of the proposed system.
C1 [Jiang, Yexi; Li, Tao] Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing, Jiangsu, Peoples R China.
   [Jiang, Yexi; Li, Tao] Florida Int Univ, Sch Comp & Informat Sci, 11200 SW 8th St, Miami, FL 33199 USA.
   [Perng, Chang-Shing; Sailer, Anca; Silva-Lepe, Ignacio] IBM TJ Watson Res Ctr, Yorktown Hts, NY 10598 USA.
   [Zhou, Yang] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA.
   [Perng, Chang-Shing] Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 USA.
RP Jiang, YX (通讯作者)，Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing, Jiangsu, Peoples R China.; Jiang, YX (通讯作者)，Florida Int Univ, Sch Comp & Informat Sci, 11200 SW 8th St, Miami, FL 33199 USA.
FU National Science Foundation [CNS-1126619, IIS-121302, CNS-1461926]
FX The work is partially supported by the National Science Foundation under
   grants CNS-1126619, IIS-121302, and CNS-1461926.
CR Akolkar Rahul, 2012, IEEE 8 WORLD C SERV
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NR 47
TC 2
Z9 2
U1 0
U2 12
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2157-6904
EI 2157-6912
J9 ACM T INTEL SYST TEC
JI ACM Trans. Intell. Syst. Technol.
PD OCT
PY 2016
VL 8
IS 1
AR 8
DI 10.1145/2894759
PG 25
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DZ1SW
UT WOS:000385621300008
DA 2022-08-23
ER

PT J
AU Ammi, M
   Adedugbe, O
   Alharby, FM
   Benkhelifa, E
AF Ammi, Meryem
   Adedugbe, Oluwasegun
   Alharby, Fahad M.
   Benkhelifa, Elhadj
TI Leveraging a cloud-native architecture to enable semantic
   interconnectedness of data for cyber threat intelligence
SO CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND
   APPLICATIONS
LA English
DT Article; Early Access
DE Cyber threat intelligence; Cyber Incident Response; Cloud Computing;
   Semantic knowledge graph; Data interconnectedness
AB Cloud technologies have several merits including the elimination of cost incurred when traditional technologies are adopted. Despite the benefits, the cloud is still facing security challenges thereby calling for cyber threat intelligence capable of identifying threats and providing possible solutions. However, dependence on traditional security mechanisms and approaches for security solutions within cloud environments presents challenges. This calls for cloud-native solutions which leverages cloud features for design and development of solutions for data and applications hosted and running within the cloud. Past studies have suggested the adoption of semantic technologies for cloud-based security mechanisms. However, the semantic processing of data faces challenges of data interconnectedness due to aggregation of data from diverse heterogenous sources. Hence, this study proposes a cloud-native architecture capable of connecting security-related data from different sources in the cloud to enhance cyber threat intelligence. It presents a proof-of-concept implementation of the proposed solution on Amazon AWS cloud, within an auto-scaling group for scalability and across multiple availability zones for high availability.
C1 [Ammi, Meryem; Adedugbe, Oluwasegun; Alharby, Fahad M.; Benkhelifa, Elhadj] Naif Arab Univ Secur Sci, Riyadh, Saudi Arabia.
   [Benkhelifa, Elhadj] Staffordshire Univ, Smart Syst AI & Cybersecur Res Ctr, Stoke On Trent ST4 2DE, Staffs, England.
RP Ammi, M; Alharby, FM (通讯作者)，Naif Arab Univ Secur Sci, Riyadh, Saudi Arabia.
EM mammi@nauss.edu.sa; Oluwasegun.Adedugbe1@staffs.ac.uk;
   fmalharby@nauss.edu.sa; e.benkhelifa@staffs.ac.uk
OI AMMI, MERYEM/0000-0001-6264-3720
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NR 46
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 1386-7857
EI 1573-7543
J9 CLUSTER COMPUT
JI Cluster Comput.
DI 10.1007/s10586-022-03576-5
EA APR 2022
PG 12
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0Q3RS
UT WOS:000784840800001
DA 2022-08-23
ER

PT J
AU Varadi, M
   Anyango, S
   Armstrong, D
   Berrisford, J
   Choudhary, P
   Deshpande, M
   Nadzirin, N
   Nair, SS
   Pravda, L
   Tanweer, A
   Al-Lazikani, B
   Andreini, C
   Barton, GJ
   Bednar, D
   Berka, K
   Blundell, T
   Brock, KP
   Carazo, JM
   Damborsky, J
   David, A
   Dey, S
   Dunbrack, R
   Recio, JF
   Fraternali, F
   Gibson, T
   Helmer-Citterich, M
   Hoksza, D
   Hopf, T
   Jakubec, D
   Kannan, N
   Krivak, R
   Kumar, M
   Levy, ED
   London, N
   Macias, JR
   Srivatsan, MM
   Marks, DS
   Martens, L
   McGowan, SA
   McGreig, JE
   Modi, V
   Parra, RG
   Pepe, G
   Piovesan, D
   Prilusky, J
   Putignano, V
   Radusky, LG
   Ramasamy, P
   Rausch, AO
   Reuter, N
   Rodriguez, LA
   Rollins, NJ
   Rosato, A
   Serrano, L
   Singh, G
   Skoda, P
   Sorzano, COS
   Stourac, J
   Sulkowska, JI
   Svobodova, R
   Tichshenko, N
   Tosatto, SCE
   Vranken, W
   Wass, MN
   Xue, DD
   Zaidman, D
   Thornton, J
   Sternberg, M
   Orengo, C
   Velankar, S
AF Varadi, Mihaly
   Anyango, Stephen
   Armstrong, David
   Berrisford, John
   Choudhary, Preeti
   Deshpande, Mandar
   Nadzirin, Nurul
   Nair, Sreenath S.
   Pravda, Lukas
   Tanweer, Ahsan
   Al-Lazikani, Bissan
   Andreini, Claudia
   Barton, Geoffrey J.
   Bednar, David
   Berka, Karel
   Blundell, Tom
   Brock, Kelly P.
   Maria Carazo, Jose
   Damborsky, Jiri
   David, Alessia
   Dey, Sucharita
   Dunbrack, Roland
   Fernandez Recio, Juan
   Fraternali, Franca
   Gibson, Toby
   Helmer-Citterich, Manuela
   Hoksza, David
   Hopf, Thomas
   Jakubec, David
   Kannan, Natarajan
   Krivak, Radoslav
   Kumar, Manjeet
   Levy, Emmanuel D.
   London, Nir
   Macias, Jose Ramon
   Srivatsan, Madhusudhan M.
   Marks, Debora S.
   Martens, Lennart
   McGowan, Stuart A.
   McGreig, Jake E.
   Modi, Vivek
   Gonzalo Parra, R.
   Pepe, Gerardo
   Piovesan, Damiano
   Prilusky, Jaime
   Putignano, Valeria
   Radusky, Leandro G.
   Ramasamy, Pathmanaban
   Rausch, Atilio O.
   Reuter, Nathalie
   Rodriguez, Luis A.
   Rollins, Nathan J.
   Rosato, Antonio
   Serrano, Luis
   Singh, Gulzar
   Skoda, Petr
   Sorzano, Carlos Oscar S.
   Stourac, Jan
   Sulkowska, Joanna, I
   Svobodova, Radka
   Tichshenko, Natalia
   Tosatto, Silvio C. E.
   Vranken, Wim
   Wass, Mark N.
   Xue, Dandan
   Zaidman, Daniel
   Thornton, Janet
   Sternberg, Michael
   Orengo, Christine
   Velankar, Sameer
CA PDBe-KB Consortium
TI PDBe-KB: collaboratively defining the biological context of structural
   data
SO NUCLEIC ACIDS RESEARCH
LA English
DT Article
ID WEB SERVER; PROTEIN; PREDICT
AB The Protein Data Bank in Europe - Knowledge Base (PDBe-KB, https://pdbe-kb.org) is an open collaboration between world-leading specialist data resources contributing functional and biophysical annotations derived from or relevant to the Protein Data Bank (PDB). The goal of PDBe-KB is to place macromolecular structure data in their biological context by developing standardised data exchange formats and integrating functional annotations from the contributing partner resources into a knowledge graph that can provide valuable biological insights. Since we described PDBe-KB in 2019, there have been significant improvements in the variety of available annotation data sets and user functionality. Here, we provide an overview of the consortium, highlighting the addition of annotations such as predicted covalent binders, phosphorylation sites, effects of mutations on the protein structure and energetic local frustration. In addition, we describe a library of reusable web-based visualisation components and introduce new features such as a bulk download data service and a novel superposition service that generates clusters of superposed protein chains weekly for the whole PDB archive.
C1 [Varadi, Mihaly; Anyango, Stephen; Armstrong, David; Berrisford, John; Choudhary, Preeti; Deshpande, Mandar; Nadzirin, Nurul; Nair, Sreenath S.; Pravda, Lukas; Tanweer, Ahsan; Thornton, Janet; Velankar, Sameer] European Bioinformat Inst EMBL EBI, European Mol Biol Lab, Wellcome Genome Campus, Hinxton, England.
   [Al-Lazikani, Bissan] Inst Canc Res, Div Canc Therapeut, Canc Res UK Canc Therapeut Unit, London, England.
   [Andreini, Claudia; Putignano, Valeria; Rosato, Antonio] Univ Florence, Sesto Fiorentino, Italy.
   [Andreini, Claudia; Putignano, Valeria; Rosato, Antonio] CIRMMP, Magnet Resonance Ctr, Sesto Fiorentino, Italy.
   [Barton, Geoffrey J.; McGowan, Stuart A.] Univ Dundee, Sch Life Sci, Dundee, Scotland.
   [Bednar, David; Damborsky, Jiri; Stourac, Jan] Masaryk Univ, Brno, Czech Republic.
   [Bednar, David; Damborsky, Jiri; Stourac, Jan] St Annes Univ Hosp Brno, Dept Expt Biol, Int Ctr Clin Res, Brno, Czech Republic.
   [Bednar, David; Damborsky, Jiri; Stourac, Jan] RECETOX, Brno, Czech Republic.
   [Berka, Karel] Palacky Univ Olomouc, Dept Phys Chem, Olomouc, Czech Republic.
   [Blundell, Tom] Univ Cambridge, Dept Biochem, Cambridge, England.
   [Blundell, Tom] MIT, Computat & Syst Biol, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
   [Maria Carazo, Jose; Macias, Jose Ramon; Sorzano, Carlos Oscar S.] CNB CSIC, Biocomp Unit, Instruct Image Proc Ctr, Madrid, Spain.
   [David, Alessia; Sternberg, Michael] Imperial Coll London, London, England.
   [Dey, Sucharita; Levy, Emmanuel D.; London, Nir; Prilusky, Jaime; Zaidman, Daniel] Weizmann Inst Sci, Rehovot, Israel.
   [Dunbrack, Roland; Modi, Vivek] Fox Chase Canc Ctr, Inst Canc Res, 7701 Burholme Ave, Philadelphia, PA 19111 USA.
   [Fernandez Recio, Juan; Rodriguez, Luis A.] Univ La Rioja, CSIC, Inst Ciencias Vid & Vino, Gobierno La Rioja,Oenol, Barcelona, Spain.
   [Fernandez Recio, Juan; Rodriguez, Luis A.] Univ La Rioja, Inst Ciencias Vid & Vino, CSIC, Gobierno La Rioja,Oenol, Logrono, Spain.
   [Fraternali, Franca] Kings Coll London, Randall Ctr Cell & Mol Biophys, London, England.
   [Gibson, Toby; Kumar, Manjeet] European Mol Biol Lab, Heidelberg, Germany.
   [Helmer-Citterich, Manuela; Pepe, Gerardo] Univ Roma Tor Vergata, Ctr Mol Bioinformat, Dept Biol, Rome, Italy.
   [Hoksza, David; Jakubec, David; Krivak, Radoslav; Skoda, Petr] Charles Univ Prague, Prague, Czech Republic.
   [Kannan, Natarajan] Univ Georgia, Dept Biochem & Mol Biol, Athens, GA 30602 USA.
   [Kannan, Natarajan] Inst Bioinformat, Athens, GA USA.
   [Srivatsan, Madhusudhan M.; Singh, Gulzar] Indian Inst Sci Educ & Res, Pune, Maharashtra, India.
   [Martens, Lennart; Ramasamy, Pathmanaban; Tichshenko, Natalia] Univ Ghent, Dept Biomol Med, Ghent, Belgium.
   [Martens, Lennart; Ramasamy, Pathmanaban; Tichshenko, Natalia] VIB UGent, Ctr Med Biotechnol, Ghent, Belgium.
   [McGreig, Jake E.; Wass, Mark N.] Univ Kent, Canterbury, Kent, England.
   [Gonzalo Parra, R.] Barcelona Supercomp Ctr, Life Sci Dept, Barcelona, Spain.
   [Piovesan, Damiano; Tosatto, Silvio C. E.] Univ Padua, Dept Biomed Sci, Padua, Italy.
   [Radusky, Leandro G.; Serrano, Luis] Ctr Genom Regulat, Syst Biol, Barcelona, Spain.
   [Ramasamy, Pathmanaban; Vranken, Wim] Vrije Univ Brussel, Dept Bioengn Sci, Brussels, Belgium.
   [Rausch, Atilio O.] Univ Nacl Entre Rios, Fac Ingn, Oro Verde, Argentina.
   [Reuter, Nathalie; Xue, Dandan] Univ Bergen, Dept Chem, Bergen, Norway.
   [Reuter, Nathalie; Xue, Dandan] Univ Bergen, Computat Biol Unit, Bergen, Norway.
   [Sulkowska, Joanna, I] Univ Warsaw, Ctr New Technol, Warsaw, Poland.
   [Svobodova, Radka] Masaryk Univ, CEITEC Cent European Inst Technol, Brno, Czech Republic.
   [Svobodova, Radka] Masaryk Univ, Natl Ctr Biomol, Fac Sci, Brno, Czech Republic.
   [Orengo, Christine] UCL, Dept Struct & Mol Biol, London, England.
RP Varadi, M (通讯作者)，European Bioinformat Inst EMBL EBI, European Mol Biol Lab, Wellcome Genome Campus, Hinxton, England.
EM mvaradi@ebi.ac.uk
RI S. Sorzano, Carlos Oscar/ABE-4884-2021; Rosato, Antonio/M-3250-2019;
   Piovesan, Damiano/H-4675-2016; Al-Lazikani, Bissan/AFK-6457-2022;
   Al-Lazikani, Bissan/AAG-4723-2022; Svobodová, Radka/E-2867-2012; Berka,
   Karel/E-2839-2010; Piovesan, Damiano/K-7647-2019; Fraternali,
   Franca/C-8912-2009; Damborsky, Jiri/H-3799-2012; Andreini,
   Claudia/D-5614-2014
OI S. Sorzano, Carlos Oscar/0000-0002-9473-283X; Rosato,
   Antonio/0000-0001-6172-0368; Piovesan, Damiano/0000-0001-8210-2390;
   Svobodová, Radka/0000-0002-3840-8760; Berka, Karel/0000-0001-9472-2589;
   Piovesan, Damiano/0000-0001-8210-2390; Fraternali,
   Franca/0000-0002-3143-6574; Anyango, Stephen/0000-0003-4838-443X;
   Ramasamy, Pathmanaban/0000-0003-4039-5230; Macias, Jose
   Ramon/0000-0003-2621-6806; Pepe, Gerardo/0000-0001-5308-2765; Nair,
   Sreenath/0000-0002-6861-7627; Velankar, Sameer/0000-0002-8439-5964;
   Thornton, Janet/0000-0003-0824-4096; Jakubec, David/0000-0003-4288-0344;
   Deshpande, Mandar/0000-0002-9043-7665; Varadi,
   Mihaly/0000-0002-3687-0839; Damborsky, Jiri/0000-0002-7848-8216;
   Rodriguez Lumbreras, Luis Angel/0000-0002-7502-3157; Choudhary,
   Preeti/0000-0003-2340-3278; Rubach, Pawel/0000-0001-5487-609X;
   Armstrong, David/0000-0002-4986-1229; Sternberg,
   Michael/0000-0002-1884-5445; Andreini, Claudia/0000-0003-4329-8225
FU ELIXIR; Biotechnology and Biological Sciences Research Council
   [BB/T01959X/1]; FunPDBe [BB/P024351/1]; European Molecular Biology
   Laboratory-European Bioinformatics Institute; Ministry of Education,
   Youth and Sport of the Czech Republic [INBIO CZ.02.1.01/0.0/0.0/16
   026/0008451, ELIXIR-CZ LM2018131]; European Union [823839, 819318];
   Research Founda-tion Flanders (FWO) [G032816N, G042518N, G028821N];
   Research Foundation Flanders (FWO) [G032816N, G028821N]; Fondazione
   Cassa Di Risparmio di Firenze [24316]; European Commission [101017567];
   AIRC project [IG 23539]; Spanish Ministry of Science and Innovation
   [PID2019110167RB-I00]; Norwegian Research Council (Norges Forskningsrad)
   [288008]; Wellcome Trust [104955/Z/14/Z, 218242/Z/19/Z]
FX ELIXIR [IDP implementation study]; Biotechnology and Biological Sciences
   Research Council via the 3DGateway [BB/T01959X/1]; FunPDBe
   [BB/P024351/1]; European Molecular Biology Laboratory-European
   Bioinformatics Institute who supported this work; J.D. acknowledges
   support from the Ministry of Education, Youth and Sport of the Czech
   Republic [INBIO CZ.02.1.01/0.0/0.0/16 026/0008451]; R.S., K.B. and J.D.
   also acknowledge support from the Ministry of Education, Youth and Sport
   of the Czech Republic [ELIXIR-CZ LM2018131]; L.M. acknowledges support
   from the European Union's Horizon 2020 Programme (H2020-INFRAIA-2018-1)
   [823839]; Research Founda-tion Flanders (FWO) [G032816N, G042518N,
   G028821N]; W.V. acknowledges support from the Research Foundation
   Flanders (FWO) [G032816N, G028821N]; A.R. acknowledges support from the
   Fondazione Cassa Di Risparmio di Firenze [24316]; European Commission
   [101017567]; M.H.C. acknowledges the AIRC project to MHC [IG 23539];
   J.F.-R. acknowledges support from the Spanish Ministry of Science and
   Innovation [PID2019110167RB-I00]; N.R. acknowledges support from the
   Norwegian Research Council (Norges Forskningsrad) [288008]; E.D.L.
   acknowledges support from the European Union's Horizon 2020 research and
   innovation programme [819318]; M.J.E.S. acknowledges support from the
   Wellcome Trust [104955/Z/14/Z, 218242/Z/19/Z]. Funding for open access
   charge: Biotechnology and Biological Sciences Research Council grant
   [BB/T01959X/1]; Wellcome Trust [104955/Z/14/Z and 218242/Z/19/Z].
   Conflict of interest statement. None declared.
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NR 51
TC 9
Z9 9
U1 5
U2 5
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0305-1048
EI 1362-4962
J9 NUCLEIC ACIDS RES
JI Nucleic Acids Res.
PD JAN 7
PY 2022
VL 50
IS D1
BP D534
EP D542
DI 10.1093/nar/gkab988
EA NOV 2021
PG 9
WC Biochemistry & Molecular Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology
GA YH9RO
UT WOS:000743496700065
PM 34755867
OA Green Published, Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Zeman, V
   Kliegr, T
   Svatek, V
AF Zeman, Vaclav
   Kliegr, Tomas
   Svatek, Vojtech
TI RDFRules: Making RDF rule mining easier and even more efficient
SO SEMANTIC WEB
LA English
DT Article
DE Rule mining; rule learning; exploratory data analysis; machine learning;
   inductive logical programming
ID ASSOCIATION RULES; WEB
AB AMIE+ is a state-of-the-art algorithm for learning rules from RDF knowledge graphs (KGs). Based on association rule learning, AMIE+ constituted a breakthrough in terms of speed on large data compared to the previous generation of ILP-based systems. In this paper we present several algorithmic extensions to AMIE+, which make it faster, and the support for data pre-processing and model post-processing, which provides a more comprehensive coverage of the linked data mining process than does the original AMIE+ implementation. The main contributions are related to performance improvement: (1) the top-k approach, which addresses the problem of combinatorial explosion often resulting from a hand-set minimum support threshold, (2) a grammar that allows to define fine-grained patterns reducing the size of the search space, and (3) a faster projection binding reducing the number of repetitive calculations. Other enhancements include the possibility to mine across multiple graphs, the support for discretization of continuous values, and the selection of the most representative rules using proven rule pruning and clustering algorithms. Benchmarks show reductions in mining time of up to several orders of magnitude compared to AMIE+.
C1 [Zeman, Vaclav; Kliegr, Tomas; Svatek, Vojtech] Prague Univ Econ & Business, Fac Informat & Stat, Dept Informat & Knowledge Engn, Nam W Churchilla 4, Prague 13067, Czech Republic.
RP Zeman, V (通讯作者)，Prague Univ Econ & Business, Fac Informat & Stat, Dept Informat & Knowledge Engn, Nam W Churchilla 4, Prague 13067, Czech Republic.
EM vaclav.zeman@vse.cz
RI Svátek, Vojtěch/K-5501-2015
OI Svátek, Vojtěch/0000-0002-2256-2982
FU project "`e-Infrastruktura CZ" program within the Projects of Large
   Research, Development and Innovations Infrastructures [e-INFRA
   LM2018140]; Prague Univer-sity of Economics and Business, Faculty of
   Informatics and Statistics; IGA [43/2020]; CSF [18-23964S]
FX We are grateful to our three reviewers for detailed feedback, which
   resulted in many improvements of the article. Computational resources
   were partly supplied by the project "`e-Infrastruktura CZ" (e-INFRA
   LM2018140) provided within the program Projects of Large Research,
   Development and Innovations Infrastructures. TK was supported by the
   Prague Univer-sity of Economics and Business, Faculty of Informatics and
   Statistics, via the long-term institutional support of research
   activities, and by the IGA 43/2020 project. VZ and VS were partially
   supported by the CSF project no. 18-23964S.
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NR 61
TC 0
Z9 0
U1 0
U2 1
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2021
VL 12
IS 4
BP 569
EP 602
DI 10.3233/SW-200413
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 SW1WL
UT WOS:000664311200003
DA 2022-08-23
ER

PT J
AU Leshcheva, I
   Begler, A
AF Leshcheva, Irina
   Begler, Alena
TI A method of semi-automated ontology population from multiple
   semi-structured data sources
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Data Source Ontology; ontology-based data integration; ontology
   population; semi-structured data
ID DESIGN SCIENCE; INFORMATION; SPREADSHEETS
AB Organisations use data in different formats: Word documents, Excel spreadsheets, databases, HTML pages and so on. It is not easy to make decisions with such data due to the lack of integration between the different sources and built-in decision-making rules. Decisions can be reached with knowledge bases, which, unlike databases, make it possible to store not only objects, facts and attributes but also more sophisticated patterns such as rules and axioms. The article proposes an ontology-based method for knowledge base creation that allows for the simultaneous integration of semi-structured data sources and extendibility while remaining context independent. At the initial steps of the method, data specification should be performed with the Data Sources Ontology developed by the authors. This ontology provides data structure description that forms supportive knowledge graph. The graph's schema should be mapped with the domain ontology to be populated. Finally, the data are inserted into the domain ontology according to the mapping rules. Manual input is needed during data specification and data-to-ontology schema mapping.
C1 [Leshcheva, Irina; Begler, Alena] St Petersburg Univ, Grad Sch Management, Volkhovsky Per 3, St Petersburg 199004, Russia.
RP Begler, A (通讯作者)，St Petersburg Univ, Grad Sch Management, Volkhovsky Per 3, St Petersburg 199004, Russia.
EM alena.begler@gmail.com
RI Leshcheva, Irina A/J-5922-2012; Begler, Alena/A-2902-2016
OI Leshcheva, Irina A/0000-0003-0132-3605; Begler,
   Alena/0000-0003-4375-1106
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NR 34
TC 1
Z9 1
U1 2
U2 14
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 APR
PY 2022
VL 48
IS 2
BP 223
EP 236
AR 0165551520950243
DI 10.1177/0165551520950243
EA AUG 2020
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 0Q2IK
UT WOS:000562028200001
DA 2022-08-23
ER

PT J
AU Bashir, MR
   Gill, AQ
   Beydoun, G
   Mccusker, B
AF Bashir, Muhammad Rizwan
   Gill, Asif Qumer
   Beydoun, Ghassan
   Mccusker, Brad
TI Big Data Management and Analytics Metamodel for IoT-Enabled Smart
   Buildings
SO IEEE ACCESS
LA English
DT Article
DE Big Data; Smart buildings; Data models; Architecture; Unified modeling
   language; Ecosystems; Biological system modeling; IoT; big data
   management; metamodel; smart buildings
ID INTEGRATION; MODEL
AB Big data management and analytics, in the context of IoT (Internet of Things)-enabled smart buildings, is a challenging task. It is a diffused and complex area of knowledge due to the diversity of IoT devices and the nature of data generated by the IoT devices. Many international bodies have developed metamodels for IoT-enabled ecosystems to allow knowledge sharing. However, these are often narrow in focus and deal with only the IoT aspects without taking into account the management and analytics of big data generated by the IoT devices. Hence, in this article we propose a metamodel for the Integrated Big Data Management and Analytics (IBDMA) framework for IoT-enabled smart buildings. The IBDMA Metamodel can be used to facilitate interoperability between existing big data management and analytics ecosystems deployed in smart buildings or other smart environments. We import the metamodel into a knowledge graph management tool and by considering a case study we validate the metamodel using this tool. The evaluation results demonstrate that IBDMA Metamodel is indeed suitable for its intended purpose.
C1 [Bashir, Muhammad Rizwan; Beydoun, Ghassan] Univ Technol Sydney, Sch Informat Syst & Modelling, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia.
   [Gill, Asif Qumer] Univ Technol Sydney, Sch Comp Sci, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia.
   [Mccusker, Brad] SURROUND Australia Pty Ltd, Sydney, NSW 2000, Australia.
RP Bashir, MR (通讯作者)，Univ Technol Sydney, Sch Informat Syst & Modelling, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia.
EM rizwan.bashir@student.uts.edu.au
RI Gill, Asif/K-4375-2017
OI Gill, Asif/0000-0001-6239-6280; Bashir, Muhammad
   Rizwan/0000-0001-6326-9267; Beydoun, Ghassan/0000-0001-8087-5445
FU Australian Government through the Research Training Program (RTP);
   SURROUND Australia Pty Ltd.
FX This work was supported in part by the Australian Government through the
   Research Training Program (RTP), and in part by the SURROUND Australia
   Pty Ltd.
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NR 26
TC 2
Z9 2
U1 5
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 169740
EP 169758
DI 10.1109/ACCESS.2020.3024066
PG 19
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NT5MK
UT WOS:000572984700001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Vileiniskis, T
   Butkiene, R
AF Vileiniskis, Tomas
   Butkiene, Rita
TI Applying Semantic Role Labeling and Spreading Activation Techniques for
   Semantic Information Retrieval
SO INFORMATION TECHNOLOGY AND CONTROL
LA English
DT Article
DE Semantic role labeling; spreading activation; information retrieval;
   information extraction; ontologies; RDF
AB Semantically enhanced information retrieval is aimed at improving classical information retrieval methods and goes way beyond plain Boolean keyword matching with the main goal of better serving implicit and ambiguous information needs. As a de-facto pre-requisite to semantic information retrieval, different information extraction techniques are used to mine unstructured text for underlying knowledge. In this paper, we present a method that combines both information extraction and information retrieval to enable semantic search in natural language texts. First, we apply semantic role labeling to automatically extract event-oriented information found in natural language texts to a Resource Description Framework knowledge graph leveraging semantic web technology. Second, we investigate how a custom flavored graph traversal spreading activation algorithm can be employed to interpret user's information needs on top of the previously extracted knowledge base. Finally, we present an assessment on the applicability of our method for semantically enhanced information retrieval. An experimental evaluation on partial WikiQA dataset shows the strengths of our approach and also unveils common pitfalls that we use as guidelines to draw further work directions in the open-domain semantic search field.
C1 [Vileiniskis, Tomas; Butkiene, Rita] Kaunas Univ Technol, Fac Informat, Studentu Str 50, LT-51368 Kaunas, Lithuania.
RP Vileiniskis, T (通讯作者)，Kaunas Univ Technol, Fac Informat, Studentu Str 50, LT-51368 Kaunas, Lithuania.
EM tomas.vileiniskis@ktu.lt; rita.butkiene@ktu.lt
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NR 29
TC 1
Z9 1
U1 0
U2 5
PU KAUNAS UNIV TECHNOLOGY
PI KAUNAS
PA KAUNAS UNIV TECHNOL, DEPT ELECTRONICS ENGINEERING, STUDENTU STR 50,
   KAUNAS, LT-51368, LITHUANIA
SN 1392-124X
J9 INF TECHNOL CONTROL
JI Inf. Technol. Control
PY 2020
VL 49
IS 2
BP 275
EP 288
DI 10.5755/j01.itc.49.2.24985
PG 14
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA OH7RN
UT WOS:000582791100006
OA gold
DA 2022-08-23
ER

PT J
AU Mungall, CJ
   McMurry, JA
   Kohler, S
   Balhoff, JP
   Borromeo, C
   Brush, M
   Carbon, S
   Conlin, T
   Dunn, N
   Engelstad, M
   Foster, E
   Gourdine, JP
   Jacobsen, JOB
   Keith, D
   Laraway, B
   Lewis, SE
   NguyenXuan, J
   Shefchek, K
   Vasilevsky, N
   Yuan, Z
   Washington, N
   Hochheiser, H
   Groza, T
   Smedley, D
   Robinson, PN
   Haendel, MA
AF Mungall, Christopher J.
   McMurry, Julie A.
   Koehler, Sebastian
   Balhoff, James P.
   Borromeo, Charles
   Brush, Matthew
   Carbon, Seth
   Conlin, Tom
   Dunn, Nathan
   Engelstad, Mark
   Foster, Erin
   Gourdine, J. P.
   Jacobsen, Julius O. B.
   Keith, Dan
   Laraway, Bryan
   Lewis, Suzanna E.
   NguyenXuan, Jeremy
   Shefchek, Kent
   Vasilevsky, Nicole
   Yuan, Zhou
   Washington, Nicole
   Hochheiser, Harry
   Groza, Tudor
   Smedley, Damian
   Robinson, Peter N.
   Haendel, Melissa A.
TI The Monarch Initiative: an integrative data and analytic platform
   connecting phenotypes to genotypes across species
SO NUCLEIC ACIDS RESEARCH
LA English
DT Article
ID ANATOMY ONTOLOGIES; MODEL ORGANISM; WEB SERVICES; DISEASE; GENE;
   INFORMATION; UNIFICATION; RESOURCE; CATALOG; BIOLOGY
AB The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype-phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype-phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.
C1 [Mungall, Christopher J.; Carbon, Seth; Dunn, Nathan; Lewis, Suzanna E.; NguyenXuan, Jeremy; Washington, Nicole] Lawrence Berkeley Natl Lab, Environm Genom & Syst Biol, Berkeley, CA 94720 USA.
   [McMurry, Julie A.; Brush, Matthew; Conlin, Tom; Engelstad, Mark; Foster, Erin; Gourdine, J. P.; Keith, Dan; Laraway, Bryan; Shefchek, Kent; Vasilevsky, Nicole; Haendel, Melissa A.] Oregon Hlth & Sci Univ, Dept Med Informat & Clin Epidemiol, Portland, OR 97239 USA.
   [McMurry, Julie A.; Brush, Matthew; Conlin, Tom; Engelstad, Mark; Foster, Erin; Gourdine, J. P.; Keith, Dan; Laraway, Bryan; Shefchek, Kent; Vasilevsky, Nicole; Haendel, Melissa A.] Oregon Hlth & Sci Univ, OHSU Lib, Portland, OR 97239 USA.
   [Koehler, Sebastian; Robinson, Peter N.] Charite, Inst Med Genet & Human Genet, Augustenburger Pl 1, D-13353 Berlin, Germany.
   [Balhoff, James P.] RTI Int, Res Triangle Pk, NC 27709 USA.
   [Borromeo, Charles; Yuan, Zhou; Hochheiser, Harry] Univ Pittsburgh, Dept Biomed Informat, Pittsburgh, PA 15260 USA.
   [Jacobsen, Julius O. B.; Smedley, Damian] Queen Mary Univ London, William Harvey Res Inst, Barts & London Sch Med & Dent, Charterhouse Sq, London EC1M 6BQ, England.
   [Groza, Tudor] Garvan Inst Med Res, Kinghorn Ctr Clin Genom, Darlinghurst, NSW 2010, Australia.
   [Robinson, Peter N.] Jackson Lab Genom Med, Farmington, CT 06032 USA.
RP Haendel, MA (通讯作者)，Oregon Hlth & Sci Univ, Dept Med Informat & Clin Epidemiol, Portland, OR 97239 USA.; Haendel, MA (通讯作者)，Oregon Hlth & Sci Univ, OHSU Lib, Portland, OR 97239 USA.
EM Haendel@ohsu.edu
RI Smedley, Damian/AAL-8172-2021; Vasilevsky, Nicole A/AAB-7589-2019;
   Mungall, Chris/I-6006-2019; Gourdine, Jean-Philippe/P-4323-2018
OI Vasilevsky, Nicole A/0000-0001-5208-3432; Mungall,
   Chris/0000-0002-6601-2165; Gourdine, Jean-Philippe/0000-0001-9969-8610;
   Dunn, Nathan/0000-0002-4862-3181; Foster, Erin D./0000-0001-6908-9849;
   Borromeo, Charles/0000-0003-4059-4547; Balhoff,
   James/0000-0002-8688-6599; McMurry, Julie/0000-0002-9353-5498; Conlin,
   Tom/0000-0003-0355-5581; Engelstad, Mark/0000-0001-5889-4463; Jacobsen,
   Julius/0000-0002-3265-1591; Washington, Nicole/0000-0001-8936-9143;
   Carbon, Seth/0000-0001-8244-1536; Groza, Tudor/0000-0003-2267-8333;
   Laraway, Bryan/0000-0002-0450-7074; Haendel, Melissa/0000-0001-9114-8737
FU National Institutes of Health (NIH) [1R24OD011883]; Wellcome Trust
   [098051]; NIH Undiagnosed Disease Program [HHSN268201300036C,
   HHSN268201400093P]; Phenotype RCN [NSF-DEB-0956049]; NCI/Leidos [GA4GH,
   15x143, BD2K U54HG007990-S2, BD2K PA-15-144-U01]; Office of Science,
   Office of Basic Energy Sciences of the U.S. Department of Energy
   [DE-AC02-05CH11231]; NIH [1R24OD011883]; NATIONAL HUMAN GENOME RESEARCH
   INSTITUTE [U54HG007990] Funding Source: NIH RePORTER; OFFICE OF THE
   DIRECTOR, NATIONAL INSTITUTES OF HEALTH [R24OD011883] Funding Source:
   NIH RePORTER; Div Of Biological Infrastructure [0956049] Funding Source:
   National Science Foundation
FX National Institutes of Health (NIH) [1R24OD011883]; Wellcome Trust
   [098051]; NIH Undiagnosed Disease Program [HHSN268201300036C,
   HHSN268201400093P]; Phenotype RCN [NSF-DEB-0956049]; NCI/Leidos [15x143,
   BD2K U54HG007990-S2 (Haussler; GA4GH), BD2K PA-15-144-U01 (Kesselman;
   FaceBase)]; Office of Science, Office of Basic Energy Sciences of the
   U.S. Department of Energy [DE-AC02-05CH11231 to J.N.Y., S.C., S.E.L. and
   C.J.M.]. Funding for open access charge: NIH [1R24OD011883].
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NR 62
TC 149
Z9 151
U1 9
U2 29
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0305-1048
EI 1362-4962
J9 NUCLEIC ACIDS RES
JI Nucleic Acids Res.
PD JAN 4
PY 2017
VL 45
IS D1
BP D712
EP D722
DI 10.1093/nar/gkw1128
PG 11
WC Biochemistry & Molecular Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology
GA EO3DQ
UT WOS:000396575500100
PM 27899636
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Xia, YC
   Wang, XY
   Gu, LC
   Gao, QJ
   Jiao, J
   Wang, C
AF Xia, Yingchun
   Wang, Xingyue
   Gu, Lichuan
   Gao, Qijuan
   Jiao, Jun
   Wang, Chao
TI A collective entity linking algorithm with parallel computing on
   large-scale knowledge base
SO JOURNAL OF SUPERCOMPUTING
LA English
DT Article
DE Collective entity linking; Topic model; Graph; Parallel computing
AB Entity linking is a central concern of automatic knowledge question answering and knowledge base population. Traditional collective entity linking approaches only consider one of the entity contexts or semantic relations between entities. Thus, these approaches always have poor performance on Web documents. The efficiency of collective entity linking needs to be improved as well. This paper proposes a collective entity linking algorithm based on topic model and graph. Constructing the topic model can represent mentions and candidate entities by using topic distributions. It makes full use of context in documents. Entity semantic relations are represented by document similarities which are computed through the topic model. Parallel computing is used to reduce long running time which is caused by topic model construction. Entity graph is constructed according to the relations between entities in the knowledge graph. Hypertext-Induced Topic Search exploits the entity graph to compute hub value and authority value of candidate entities. And the authority value is the basis for entity linking. Experimental results on open-domain corpus (NLPCC2014) demonstrate the validity of the proposed method. Experimental results show that the proposed approach has 5.2% improvement in F-1-measure than AGDISTIS on corp NLPCC2014.
C1 [Xia, Yingchun; Wang, Xingyue; Gu, Lichuan; Gao, Qijuan; Jiao, Jun; Wang, Chao] Anhui Agr Univ, Sch Informat & Comp, Hefei, Peoples R China.
   [Wang, Xingyue; Gu, Lichuan; Wang, Chao] Minist Agr, Key Lab Agr Elect Commerce, Hefei 230036, Peoples R China.
RP Gu, LC (通讯作者)，Anhui Agr Univ, Sch Informat & Comp, Hefei, Peoples R China.; Gu, LC (通讯作者)，Minist Agr, Key Lab Agr Elect Commerce, Hefei 230036, Peoples R China.
EM 1415220245@qq.com; glc@ahau.edu.cn
FU National Natural Science Foundation of China [31771679, 31371533,
   31671589]; Anhui Foundation for Science and Technology Major Project,
   China [16030701092, 18030901034]; Key Laboratory of Agricultural
   Electronic Commerce, Ministry of Agriculture of China [AEC2018003,
   AEC2018006]; 2016 Anhui Foundation for Natural Science Major Project of
   the Higher Education Institutions [KJ2016A836]; Hefei Major Research
   Project of Key Technology [J2018G14]
FX This work is partially supported by the National Natural Science
   Foundation of China under Grant (31771679, 31371533, 31671589), the
   Anhui Foundation for Science and Technology Major Project, China, under
   Grant (16030701092, 18030901034), the Key Laboratory of Agricultural
   Electronic Commerce, Ministry of Agriculture of China under Grant
   (AEC2018003, AEC2018006), the 2016 Anhui Foundation for Natural Science
   Major Project of the Higher Education Institutions under Grant
   (KJ2016A836), the Hefei Major Research Project of Key Technology
   (J2018G14).
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NR 30
TC 3
Z9 3
U1 1
U2 16
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-8542
EI 1573-0484
J9 J SUPERCOMPUT
JI J. Supercomput.
PD FEB
PY 2020
VL 76
IS 2
SI SI
BP 948
EP 963
DI 10.1007/s11227-019-03046-7
PG 16
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA KI9CX
UT WOS:000511655400012
DA 2022-08-23
ER

PT J
AU Fang, YX
   Wang, ZR
   Cheng, R
   Wang, HZ
   Hu, JF
AF Fang, Yixiang
   Wang, Zhongran
   Cheng, Reynold
   Wang, Hongzhi
   Hu, Jiafeng
TI Effective and Efficient Community Search Over Large Directed Graphs
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Community search; directed graphs; D-core; online queries
AB Communities are prevalent in social networks, knowledge graphs, and biological networks. Recently, the topic of community search (CS), extracting a dense subgraph containing a query vertex q from a graph, has received great attention. However, existing CS solutions are designed for undirected graphs, and overlook directions of edges which potentially lose useful information carried on directions. In many applications (e.g., Twitter), users' relationships are often modeled as directed graphs (e.g., if a user a follows another user b, then there is an edge from a to b). In this paper, we study the problem of CS on directed graph. Given a vertex q of a graph G, we aim to find a densely connected subgraph containing q from G, in which vertices have strong interactions and high similarities, by using the minimum in/out-degrees metric. We first develop a baseline algorithm based on the concept of D-core. We further propose three index structures and corresponding query algorithms. Our experimental results on seven real graphs show that our solutions are very effective and efficient. For example, on a graph with over 1 billion of edges, we only need around 40mins to index it and 1 similar to 2sec to answer a query.
C1 [Fang, Yixiang; Cheng, Reynold; Hu, Jiafeng] Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China.
   [Wang, Zhongran; Wang, Hongzhi] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China.
RP Wang, ZR (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China.
EM yxfang@cs.hku.hk; zhongran95@gmail.com; ckcheng@cs.hku.hk;
   wangzh@hit.edu.cn; jhu@cs.hku.hk
RI Wang, Hongzhi/F-2847-2011; Cheng, Chun Kong/C-1824-2009
OI Wang, Hongzhi/0000-0002-7521-2871; , Yixiang/0000-0002-5047-8593; Cheng,
   Chun Kong/0000-0002-9480-9809
FU Research Grants Council of Hong Kong (RGC) [HKU 17229116, 106150091,
   17205115]; University of Hong Kong [104004572, 102009508, 104004129];
   NSFC [U1509216]
FX Reynold Cheng, Yixiang Fang, and Jiafeng Hu were supported by the
   Research Grants Council of Hong Kong (RGC Projects HKU 17229116,
   106150091, and 17205115) and the University of Hong Kong (Projects
   104004572, 102009508, and 104004129). Hongzhi Wang was supported by NSFC
   grant U1509216. The authors thank the editors and reviewers for their
   insightful comments.
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NR 45
TC 25
Z9 27
U1 1
U2 10
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD NOV
PY 2019
VL 31
IS 11
BP 2093
EP 2107
DI 10.1109/TKDE.2018.2872982
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 JS4VM
UT WOS:000500304600005
DA 2022-08-23
ER

PT J
AU Luo, L
   Lai, PT
   Wei, CH
   Lu, ZY
AF Luo, Ling
   Lai, Po-Ting
   Wei, Chih-Hsuan
   Lu, Zhiyong
TI A sequence labeling framework for extracting drug-protein relations from
   biomedical literature
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Article
AB Automatic extracting interactions between chemical compound/drug and gene/protein are significantly beneficial to drug discovery, drug repurposing, drug design and biomedical knowledge graph construction. To promote the development of the relation extraction between drug and protein, the BioCreative VII challenge organized the DrugProt track. This paper describes the approach we developed for this task. In addition to the conventional text classification framework that has been widely used in relation extraction tasks, we propose a sequence labeling framework to drug-protein relation extraction. We first comprehensively compared the cutting-edge biomedical pre-trained language models for both frameworks. Then, we explored several ensemble methods to further improve the final performance. In the evaluation of the challenge, our best submission (i.e. the ensemble of models in two frameworks via major voting) achieved the F1-score of 0.795 on the official test set. Further, we realized the sequence labeling framework is more efficient and achieves better performance than the text classification framework. Finally, our ensemble of the sequence labeling models with majority voting achieves the best F1-score of 0.800 on the test set.
C1 [Luo, Ling; Lai, Po-Ting; Wei, Chih-Hsuan; Lu, Zhiyong] NIH, Natl Ctr Biotechnol Informat, Natl Lib Med, 8600 Rockville Pike, Bethesda, MD 20894 USA.
RP Lu, ZY (通讯作者)，NIH, Natl Ctr Biotechnol Informat, Natl Lib Med, 8600 Rockville Pike, Bethesda, MD 20894 USA.
EM zhiyong.lu@nih.gov
RI Luo, Ling/AAA-3476-2019
OI Luo, Ling/0000-0002-5141-0259
FU NIH Intramural Research Program, National Library of Medicine
FX This research is supported by the NIH Intramural Research Program,
   National Library of Medicine.
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NR 32
TC 0
Z9 0
U1 0
U2 0
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD JUL 19
PY 2022
VL 2022
AR baac058
DI 10.1093/database/baac058
PG 10
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA 3B2PN
UT WOS:000827788200001
PM 35856889
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Liu, C
   Chen, JP
   Li, S
   Qin, T
AF Liu, Chang
   Chen, Jianping
   Li, Shi
   Qin, Tao
TI Construction of Conceptual Prospecting Model Based on Geological Big
   Data: A Case Study in Songtao-Huayuan Area, Hunan Province
SO MINERALS
LA English
DT Article
DE geological big data; prospecting information; text mining;
   Songtao-Huayuan manganese deposits
ID MANGANESE DEPOSIT; LANGUAGE
AB With the era of big data, the prediction and evaluation of geological mineral resources have gradually entered into a new stage from digital prospecting to intelligent prospecting. The theoretical method of big data mining can contribute to deep mineral resource prediction and evaluation. This paper extracts ore-causing and ore-caused anomaly information based on text intelligent mining technology, and constructs a regional conceptual prospecting model based on geological prospecting big data. First, we set up a corpus based on text big data discovery and preprocessing technology. Second, we used CNN multiple scale text classification technology to analyze geological text data from the two main aspects: ore-causing anomalies and ore-caused anomalies. Third, we used a statistical method to analyze the semantic links between content-words, and we constructed chord diagrams and ternary diagrams to visualize the content-words and their links. Finally, we constructed a regional conceptual prospecting model based on the knowledge graphs.
C1 [Liu, Chang; Chen, Jianping; Qin, Tao] China Univ Geosci Beijing, Sch Earth Sci & Resources, Beijing 100083, Peoples R China.
   [Liu, Chang; Chen, Jianping; Qin, Tao] Beijing Key Lab Res & Explorat Informat Land Reso, Beijing 100083, Peoples R China.
   [Li, Shi] Beijing Wuzi Univ, Sch Informat, Beijing 101149, Peoples R China.
RP Chen, JP (通讯作者)，China Univ Geosci Beijing, Sch Earth Sci & Resources, Beijing 100083, Peoples R China.; Chen, JP (通讯作者)，Beijing Key Lab Res & Explorat Informat Land Reso, Beijing 100083, Peoples R China.
EM 3001190108@cugb.edu.cn; 3s@cugb.edu.cn; lishi@bwu.edu.cn;
   2101190057@cugb.edu.cn
FU Chinese MOST project "Methods and Models for Quantitative Prediction of
   Deep Metallogenic Geological Anomalies" [2017YFC0601502]
FX This research was financially supported by the Chinese MOST project
   "Methods and Models for Quantitative Prediction of Deep Metallogenic
   Geological Anomalies" (No.2017YFC0601502).
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NR 30
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-163X
J9 MINERALS-BASEL
JI Minerals
PD JUN
PY 2022
VL 12
IS 6
AR 669
DI 10.3390/min12060669
PG 21
WC Geochemistry & Geophysics; Mineralogy; Mining & Mineral Processing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geochemistry & Geophysics; Mineralogy; Mining & Mineral Processing
GA 2K7OI
UT WOS:000816520300001
OA gold
DA 2022-08-23
ER

PT J
AU Garcia-Silva, A
   Denaux, R
   Gomez-Perez, JM
AF Garcia-Silva, Andres
   Denaux, Ronald
   Gomez-Perez, Jose Manuel
TI On the impact of knowledge-based linguistic annotations in the quality
   of scientific embeddings
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Natural language processing; Linguistic analysis; Knowledge graphs;
   Embeddings
AB In essence, embedding algorithms work by optimizing the distance between a word and its usual context in order to generate an embedding space that encodes the distributional representation of words. In addition to single words or word pieces, other features which result from the linguistic analysis of text, including lexical, grammatical and semantic information, can be used to improve the quality of embedding spaces. However, until now we did not have a precise understanding of the impact that such individual annotations and their possible combinations may have in the quality of the embeddings. In this paper, we conduct a comprehensive study on the use of explicit linguistic annotations to generate embeddings from a scientific corpus and quantify their impact in the resulting representations. Our results show how the effect of such annotations in the embeddings varies depending on the evaluation task. In general, we observe that learning embeddings using linguistic annotations contributes to achieve better evaluation results. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Garcia-Silva, Andres; Denaux, Ronald; Gomez-Perez, Jose Manuel] Expert AI Res Lab, Prof Waskman 10, Madrid 28036, Spain.
RP Garcia-Silva, A (通讯作者)，Expert AI Res Lab, Prof Waskman 10, Madrid 28036, Spain.
EM agarcia@expert.ai; rdenaux@expert.ai; jmgomez@expert.ai
FU EU [825627]
FX We gratefully acknowledge the EU Horizon 2020 research and innovation
   programme under grant agreement No. 825627 (ELG).
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NR 52
TC 2
Z9 2
U1 2
U2 11
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 JUL
PY 2021
VL 120
BP 26
EP 35
DI 10.1016/j.future.2021.02.019
EA MAR 2021
PG 10
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RG5QM
UT WOS:000635592600003
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Bentley, RA
   Borycz, J
   Carrignon, S
   Ruck, DJ
   O'Brien, MJ
AF Bentley, R. Alexander
   Borycz, Joshua
   Carrignon, Simon
   Ruck, Damian J.
   O'Brien, Michael J.
TI Machine learning for rediscovering revolutionary ideas of the past
SO ADAPTIVE BEHAVIOR
LA English
DT Article
DE Artificial intelligence; machine learning; phylogenetic analysis;
   scientific revolution; social learning
AB The explosion of online knowledge has made knowledge, paradoxically, difficult to find. A web or journal search might retrieve thousands of articles, ranked in a manner that is biased by, for example, popularity or eigenvalue centrality rather than by informed relevance to the complex query. With hundreds of thousands of articles published each year, the dense, tangled thicket of knowledge grows even more entwined. Although natural language processing and new methods of generating knowledge graphs can extract increasingly high-level interpretations from research articles, the results are inevitably biased toward recent, popular, and/or prestigious sources. This is a result of the inherent nature of human social-learning processes. To preserve and even rediscover lost scientific ideas, we employ the theory that scientific progress is punctuated by means of inspired, revolutionary ideas at the origin of new paradigms. Using a brief case example, we suggest how phylogenetic inference might be used to rediscover potentially useful lost discoveries, as a way in which machines could help drive revolutionary science.
C1 [Bentley, R. Alexander; Carrignon, Simon] Univ Tennessee, Dept Anthropol, 502 Strong Hall,1621 Cumberland Ave, Knoxville, TN 37996 USA.
   [Bentley, R. Alexander; Carrignon, Simon] Univ Tennessee, Ctr Dynam Social Complex, Knoxville, TN USA.
   [Borycz, Joshua] Vanderbilt Univ, Sarah Shannon Stevenson Sci & Engn Lib, 221 Kirkland Hall, Nashville, TN 37235 USA.
   [Ruck, Damian J.] Northeastern Univ, Network Sci Inst, Boston, MA 02115 USA.
   [O'Brien, Michael J.] Texas A&M Univ, Off Provost, San Antonio, TX USA.
   [O'Brien, Michael J.] Univ Missouri, Dept Anthropol, Columbia, MO USA.
RP Bentley, RA (通讯作者)，Univ Tennessee, Dept Anthropol, 502 Strong Hall,1621 Cumberland Ave, Knoxville, TN 37996 USA.
EM rabentley@utk.edu
RI Carrignon, Simon/AFT-8918-2022; Borycz, Joshua/N-2010-2019
OI Carrignon, Simon/0000-0002-4416-1389; Borycz,
   Joshua/0000-0002-1505-148X; Bentley, R. Alexander/0000-0001-9086-2197
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NR 38
TC 0
Z9 0
U1 0
U2 5
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1059-7123
EI 1741-2633
J9 ADAPT BEHAV
JI Adapt. Behav.
PD JUN
PY 2022
VL 30
IS 3
BP 279
EP 286
AR 1059712320983045
DI 10.1177/1059712320983045
EA JAN 2021
PG 8
WC Computer Science, Artificial Intelligence; Psychology, Experimental;
   Social Sciences, Interdisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Psychology; Social Sciences - Other Topics
GA 1A7SS
UT WOS:000610292000001
DA 2022-08-23
ER

PT J
AU Gottschalk, S
   Demidova, E
AF Gottschalk, Simon
   Demidova, Elena
TI Tab2KG: Semantic table interpretation with lightweight semantic profiles
SO SEMANTIC WEB
LA English
DT Article
DE Semantic table interpretation; domain knowledge graphs; semantic
   profiles; one-shot learning
AB Tabular data plays an essential role in many data analytics and machine learning tasks. Typically, tabular data does not possess any machine-readable semantics. In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable. This article proposes Tab2KG - a novel method that targets at the interpretation of tables with previously unseen data and automatically infers their semantics to transform them into semantic data graphs. We introduce original lightweight semantic profiles that enrich a domain ontology's concepts and relations and represent domain and table characteristics. We propose a one-shot learning approach that relies on these profiles to map a tabular dataset containing previously unseen instances to a domain ontology. In contrast to the existing semantic table interpretation approaches, Tab2KG relies on the semantic profiles only and does not require any instance lookup. This propertymakes Tab2KG particularly suitable in the data analytics context, in which data tables typically contain new instances. Our experimental evaluation on several real-world datasets from different application domains demonstrates that Tab2KG outperforms state-of-the-art semantic table interpretation baselines.
C1 [Gottschalk, Simon] Leibniz Univ Hannover, L3S Res Ctr, Hannover, Germany.
   [Demidova, Elena] Univ Bonn, Data Sci & Intelligent Syst DSIS, Bonn, Germany.
RP Gottschalk, S (通讯作者)，Leibniz Univ Hannover, L3S Res Ctr, Hannover, Germany.
EM gottschalk@L3S.de; elena.demidova@cs.uni-bonn.de
FU DFG, German Research Foundation [424985896]; Federal Ministry of
   Education and Research (BMBF), Germany [01IS18054]; European Commission
   (EU H2020, "smashHit") [871477]
FX This work is partially funded by the DFG, German Research Foundation
   ("WorldKG", 424985896), the Federal Ministry of Education and Research
   (BMBF), Germany ("Simple-ML", 01IS18054) and the European Commission (EU
   H2020, "smashHit", 871477).
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NR 52
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 571
EP 597
DI 10.3233/SW-222993
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 0J3YH
UT WOS:000780041500012
OA hybrid
DA 2022-08-23
ER

PT J
AU Cao, QS
   Beden, S
   Beckmann, A
AF Cao, Qiushi
   Beden, Sadeer
   Beckmann, Arnold
TI A core reference ontology for steelmaking process knowledge modelling
   and information management
SO COMPUTERS IN INDUSTRY
LA English
DT Article
DE Industry 4; 0; Steelmaking; Knowledge graph; Ontology; Ontology-based
   data access; Condition-based maintenance
ID INTEROPERABILITY; SEMANTICS; SUPPORT
AB Following the trend of Industry 4.0, the business model of steel manufacturing is transforming from a historical inwardly focused supplier/customer relationship to one that embraces the wider end-to-end supply chain and improves productivity more holistically. However, the data and information required for supply chain planning and steelmaking process modelling are normally distributed over scattered sources across organisation boundaries and research communities. This leads to a major problem concerning se-mantic interoperability. To address this issue, this paper introduces a Common Reference Ontology for Steelmaking (CROS). CROS serves as a shared steelmaking resource and capability model that aims to fa-cilitate knowledge modelling, knowledge sharing and information management. In contrast to most of the existing steelmaking ontologies which merely focus on conceptual modelling, our work pays special at-tention to the real-world implementation and utilisation aspects of CROS. The functionality and usefulness of CROS is evaluated and tested on a real-world condition-based monitoring and maintenance task for cold rolling mills at Tata Steel in the United Kingdom. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Cao, Qiushi; Beden, Sadeer; Beckmann, Arnold] Swansea Univ, Dept Comp Sci, Swansea, W Glam, Wales.
RP Cao, QS (通讯作者)，Swansea Univ, Dept Comp Sci, Swansea, W Glam, Wales.
EM qiushi.cao@swansea.ac.uk
FU Engineering and Physical Sciences Research Council [EP/T517537/1]; Tata
   Steel
FX Q. Cao and A. Beckmann (in part) were supported by the Engineering and
   Physical Sciences Research Council [grant number EPSRC EP/S018107/1] .
   S. Beden was supported by the Engineering and Physical Sciences Research
   Council [grant number EP/T517537/1] and by Tata Steel.
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NR 43
TC 3
Z9 3
U1 8
U2 8
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 2022
VL 135
AR 103574
DI 10.1016/j.compind.2021.103574
EA DEC 2021
PG 13
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YX4MO
UT WOS:000754079700004
DA 2022-08-23
ER

PT J
AU Hou, DB
   Zhao, ZJ
   Hu, SY
AF Hou, Daibing
   Zhao, Zijian
   Hu, Sanyuan
TI Multi-Label Learning With Visual-Semantic Embedded Knowledge Graph for
   Diagnosis of Radiology Imaging
SO IEEE ACCESS
LA English
DT Article
DE Correlation; Diseases; Convolution; Visualization; Radiology; Medical
   diagnostic imaging; Solid modeling; Multi-label learning; chest-X-ray
   diagnosis; graph convolutional network; visual-semantic feature fusion;
   auxiliary nodes
AB A significant task of automatic diagnosis for radiology imaging, especially for chest X-rays, is to identify disease types, which can be viewed as a multi-label learning problem. Prior state-of-the-art approaches adopted the graph convolutional network to model the correlations among disease labels. However, the utilization of medical reports paired with radiology images is neglected in such approaches. Hence, at least two novel improvements are proposed in this paper. First, disease label embeddings are pre-trained on the total radiology reports, and these semantic features along with encoded X-ray features are fused in a transformer encoder for graph initialization. Second, to expand the representation ability of the graph, extra medical terms from radiology reports are mined and added to the graph model as auxiliary nodes without changing the size of the output space. Experiments conducted on two public chest-X-ray datasets demonstrate the outstanding performance over compared models and the advantages of the proposed improvements.
C1 [Hou, Daibing; Zhao, Zijian] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China.
   [Hu, Sanyuan] Shandong First Med Univ, Dept Gen Surg, Affiliated Hosp 1, Jinan 250010, Peoples R China.
RP Zhao, ZJ (通讯作者)，Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China.
EM zhaozijian@sdu.edu.cn
OI Zhao, Zijian/0000-0002-7849-814X; Hou, Daibing/0000-0002-4682-2187
FU National Key Research and Development Program of China [2019YFB1311300]
FX This work was supported by the National Key Research and Development
   Program of China under Grant 2019YFB1311300.
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NR 31
TC 3
Z9 3
U1 2
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 2021
VL 9
BP 15720
EP 15730
DI 10.1109/ACCESS.2021.3052794
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA QA1WH
UT WOS:000613239300001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, XZ
   Gao, TY
   Zhu, ZC
   Zhang, ZY
   Liu, ZY
   Li, JZ
   Tang, J
AF Wang, Xiaozhi
   Gao, Tianyu
   Zhu, Zhaocheng
   Zhang, Zhengyan
   Liu, Zhiyuan
   Li, Juanzi
   Tang, Jian
TI KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language
   Representation
SO TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
LA English
DT Article
AB Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) with informative entity embeddings, but conventional KE models cannot take full advantage of the abundant textual information. In this paper, we propose a unified model for Knowledge Embedding and Pre-trained LanguagE Representation (KEPLER), which can not only better integrate factual knowledge into PLMs but also produce effective text-enhanced KE with the strong PLMs. In KEPLER, we encode textual entity descriptions with a PLM as their embeddings, and then jointly optimize the KE and language modeling objectives. Experimental results show that KEPLER achieves state-of-the-art performances on various NLP tasks, and also works remarkably well as an inductive KE model on KG link prediction. Furthermore, for pre-training and evaluating KEPLER, we construct Wikidata5M(1), a large-scale KG dataset with aligned entity descriptions, and benchmark state-of-the-art KE methods on it. It shall serve as a new KE benchmark and facilitate the research on large KG, inductive KE, and KG with text. The source code can be obtained from https://github.com/THU-KEG/KEPLER.
C1 [Wang, Xiaozhi; Zhang, Zhengyan; Liu, Zhiyuan; Li, Juanzi] Tsinghua Univ, Dept CST, BNRist, Beijing, Peoples R China.
   [Liu, Zhiyuan; Li, Juanzi] Tsinghua Univ, Inst AI, KIRC, Beijing, Peoples R China.
   [Gao, Tianyu] Princeton Univ, Dept Comp Sci, Princeton, NJ 08544 USA.
   [Zhu, Zhaocheng; Tang, Jian] Mila Quebec AI Inst, Quebec City, PQ, Canada.
   [Zhu, Zhaocheng] Univ Montreal, Montreal, PQ, Canada.
   [Tang, Jian] HEC, Montreal, PQ, Canada.
   [Tang, Jian] CIFAR AI Res Chair, Montreal, PQ, Canada.
RP Liu, ZY (通讯作者)，Tsinghua Univ, Dept CST, BNRist, Beijing, Peoples R China.; Liu, ZY (通讯作者)，Tsinghua Univ, Inst AI, KIRC, Beijing, Peoples R China.; Tang, J (通讯作者)，Mila Quebec AI Inst, Quebec City, PQ, Canada.; Tang, J (通讯作者)，HEC, Montreal, PQ, Canada.; Tang, J (通讯作者)，CIFAR AI Res Chair, Montreal, PQ, Canada.
EM wangxz20@mails.tsinghua.edu.cn; tianyug@princeton.edu;
   zy-z19@mails.tsinghua.edu.cn; zhaocheng.zhu@umontreal.ca;
   liuzy@tsinghua.edu.cn; lijuanzi@tsinghua.edu.cn; jian.tang@hec.ca
OI Liu, Zhiyuan/0000-0002-7709-2543
FU National Key Research and Development Program of China [2018YFB1004503];
   National Natural Science Foundation of China (NSFC) [U1736204, 61533018,
   61772302, 61732008]; Institute for Guo Qiang, Tsinghua University
   [2019GQB0003]; Beijing Academy of Artificial Intelligence
   [BAAI2019ZD0502]; Natural Sciences and Engineering Research Council
   (NSERC); CanadaCIFAR AI Chair Program; Tsinghua University Initiative
   Scientific Research Program
FX This work is supported by the National Key Research and Development
   Program of China (No. 2018YFB1004503), the National Natural Science
   Foundation of China (NSFC No. U1736204, 61533018, 61772302, 61732008),
   grants from Institute for Guo Qiang, Tsinghua University (2019GQB0003),
   and Beijing Academy of Artificial Intelligence (BAAI2019ZD0502). Prof.
   Jian Tang is supported by the Natural Sciences and Engineering Research
   Council (NSERC) Discovery Grant and theCanadaCIFAR AI Chair Program.
   Xiaozhi Wang and Tianyu Gao are supported by Tsinghua University
   Initiative Scientific Research Program. We also thank our action editor,
   Prof. Doug Downey, and the anonymous reviewers for their consistent help
   and insightful suggestions.
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NR 61
TC 16
Z9 16
U1 9
U2 9
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2307-387X
J9 T ASSOC COMPUT LING
JI Trans. Assoc. Comput. Linguist.
PY 2021
VL 9
BP 176
EP 194
DI 10.1162/tacl_a_00360
PG 19
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 YU3MT
UT WOS:000751952200011
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Ponnalagu, K
AF Ponnalagu, K.
TI Ontology-driven root-cause analytics for user-reported symptoms in
   managed IT systems
SO IBM JOURNAL OF RESEARCH AND DEVELOPMENT
LA English
DT Article
ID VISUAL ANALYTICS
AB Enterprise users of IT services seek real-time contextual insights during system-failure scenarios in both cloud-provisioned and legacy management systems. Current IT management systems mostly provide front-office automation support, such as ticket categorization and scheduling, using a generalized set of troubleshooting instructions. Therefore, in this paper, we propose an IT management system that provides real-time insights on user-perceived failures (e.g., "Why is application not responding?") expressed in natural language texts. We achieve this through an underpinning of a knowledge graph that helps in discovering possible topology patterns comprising multiple interdependent systems for a specific purpose. Based on the detected list of topology patterns, the proposed system composes multiple debugging workflows to generate accurate operational insights. The user interactions are "system agnostic" in nature and do not depend on the knowledge of the underlying system topology. This significantly augments the self-assist scenarios of endusers and front-office agents, before they engage with IT support teams. We demonstrate our proposed approach, as a cloud application with a natural language interface, using an experimental setup involving a standard ticket management system.
C1 [Ponnalagu, K.] IBM Res, Bangalore 560045, Karnataka, India.
RP Ponnalagu, K (通讯作者)，IBM Res, Bangalore 560045, Karnataka, India.
EM karthikeyan.ponnalagu@in.ibm.com
CR Abele L., 2013, IFAC P VOLUMES, V46, P1843
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NR 14
TC 1
Z9 1
U1 1
U2 3
PU IBM CORP
PI ARMONK
PA 1 NEW ORCHARD ROAD, ARMONK, NY 10504 USA
SN 0018-8646
EI 2151-8556
J9 IBM J RES DEV
JI IBM J. Res. Dev.
PD JAN-FEB
PY 2017
VL 61
IS 1
AR 5
DI 10.1147/JRD.2016.2629319
PG 9
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Computer Science, Software Engineering; Computer Science,
   Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EO1WT
UT WOS:000396489300006
DA 2022-08-23
ER

PT J
AU Jia, YN
   Min, GC
   Xu, C
   Li, XS
   Zhang, DZ
AF Jia, Yongnan
   Min, Gaochen
   Xu, Cong
   Li, Xisheng
   Zhang, Dezheng
TI A Knowledge Driven Dialogue Model With Reinforcement Learning
SO IEEE ACCESS
LA English
DT Article
DE Learning (artificial intelligence); Task analysis; Knowledge
   engineering; Decision making; Information retrieval; Computational
   modeling; Cognition; Dialogue model; policy gradient; knowledge graph;
   transformer network
AB In recent decades, many researchers pay a lot of attention on generating informative responses in end-to-end neural dialogue systems. In order to output the responses with knowledge and fact, many works leverage external knowledge to guide the process of response generation. However, human dialogue is not a simple sequence to sequence task but a process heavily relying on their background knowledge about the topic. Thus, the key of generating informative responses is leveraging the appropriate knowledge associated with current topic. This paper focus on addressing incorporating the appropriate knowledge in response generation. We adopt the reinforcement learning to select the most proper knowledge as the input information of the response generation part. Then we design an end-to-end dialogue model consisting of the knowledge decision part and the response generation part. The proposed model is able to effectively complete the knowledge driven dialogue task with specific topic. Our experiments clearly demonstrate the superior performance of our model over other baselines.
C1 [Jia, Yongnan; Min, Gaochen; Xu, Cong; Li, Xisheng] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China.
   [Xu, Cong; Zhang, Dezheng] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China.
   [Zhang, Dezheng] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.
RP Xu, C (通讯作者)，Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China.; Xu, C (通讯作者)，Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China.
EM b20140357@xs.ustb.edu.cn
OI Jia, Yongnan/0000-0002-6297-9061; Xu, Cong/0000-0002-9288-1743
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NR 15
TC 1
Z9 1
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 2020
VL 8
BP 131741
EP 131749
DI 10.1109/ACCESS.2020.2993924
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MQ6EW
UT WOS:000552987200001
OA gold
DA 2022-08-23
ER

PT J
AU Meditskos, G
   Plans, PM
   Stavropoulos, TG
   Benois-Pineau, J
   Buso, V
   Kompatsiaris, I
AF Meditskos, Georgios
   Plans, Pierre-Marie
   Stavropoulos, Thanos G.
   Benois-Pineau, Jenny
   Buso, Vincent
   Kompatsiaris, Ioannis
TI Multi-modal activity recognition from egocentric vision, semantic
   enrichment and lifelogging applications for the care of dementia
SO JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
LA English
DT Article
DE Instrumental activity recognition; Egocentric camera; Mechanical
   measurements; Visual cues; Ontologies; Semantic knowledge graphs
ID FRAMEWORK; SENSORS; MODEL; HEVC
AB We describe a framework for lifelogging monitoring in the scope of dementia care, based on activity recognition from egocentric vision and semantic context-enrichment. As pure vision-based approaches appear to be already saturating in terms of recognition accuracy, we propose their enhancement with wearable bracelet accelerometer information. For that purpose, we design and study appropriate early and late fusion schemes to increase accuracy. The incorporation of mechanical variables, such as jerk, improves the recognition accuracy of activities that require fine motion. In addition, we describe a framework for semantic activity representation and interpretation, using Semantic Web technologies for building interoperable activity graphs. The system is personalized, as deployment-specific activity models are authored, while problems related to the disease are detected by rules. Complemented by lifelogging applications, the system is able to support interventions by clinicians, and endorse a feeling of safety and inclusion for end-users and their carers.
C1 [Meditskos, Georgios; Stavropoulos, Thanos G.; Kompatsiaris, Ioannis] Ctr Res & Technol Hellas, Informat Technol Inst, Thessaloniki, Greece.
   [Plans, Pierre-Marie; Benois-Pineau, Jenny; Buso, Vincent] Univ Bordeaux, LABRI, Bordeaux, France.
RP Meditskos, G (通讯作者)，Ctr Res & Technol Hellas, Informat Technol Inst, Thessaloniki, Greece.
EM gmeditsk@iti.gr; pplans@labri.fr; athstavr@iti.gr;
   jenny.benois@labri.fr; vincent.buso@labri.fr; ikom@iti.gr
RI Stavropoulos, Thanos G./O-5056-2019; Benois-Pineau, Jenny/ABG-6325-2020;
   Meditskos, Georgios/AGO-3230-2022; Kompatsiaris, Ioannis/P-8594-2015
OI Stavropoulos, Thanos G./0000-0003-2389-4329; Benois-Pineau,
   Jenny/0000-0003-0659-8894; Meditskos, Georgios/0000-0003-4242-5245;
   Kompatsiaris, Ioannis/0000-0001-6447-9020
FU EU [288199]
FX This work has been supported by the EU FP7 project Dem@Care: Dementia
   Ambient Care - Multi-Sensing Monitoring for Intelligent Remote
   Management and Decision Support under contract No. 288199.
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NR 60
TC 20
Z9 20
U1 0
U2 9
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 FEB
PY 2018
VL 51
BP 169
EP 190
DI 10.1016/j.jvcir.2018.01.009
PG 22
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FX8IB
UT WOS:000426334500017
DA 2022-08-23
ER

PT J
AU Duan, YC
   Sun, XB
   Che, HY
   Cao, CJ
   Li, Z
   Yang, XX
AF Duan, Yucong
   Sun, Xiaobing
   Che, Haoyang
   Cao, Chunjie
   Li, Zhao
   Yang, Xiaoxian
TI Modeling Data, Information and Knowledge for Security Protection of
   Hybrid IoT and Edge Resources
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph; security protection; typed resources; edge computing
ID SERVICE SELECTION; PRIVACY; PROTOCOL; REPRESENTATION; ALLOCATION;
   DEPLOYMENT; CHECKING; SMART
AB Currently, with the growth of the Internet of Things devices and the emergence of massive edge resources, security protection content has not only empowered IoT devices with the accumulation of networked computing and storage as a flexible whole but also enabled storing, transferring and processing DIKW (data, information, knowledge, and wisdom) content at the edge of the network from multiple devices in a mobile manner. However, understanding various DIKW content or resources poses a conceptual challenge in unifying the semantics of the core concepts as a starting point. Through building metamodels of the DIKW framework, we propose to cognitively formalize the semantics of the key elements of the DIKW in a conceptual process. The formalization centers on modeling the perceived world only by relationships or semantics as the prime atomic comprising elements. Based on this cognitive world model, we reveal the difference between relationships and entities during the conceptualization process as a foundation for distinguishing data and information. Thereafter, we show the initial case for using this formalization to construct security protection solutions for edge computing scenarios centering on type conversions among typed resources formalized through our proposed formalization of the DIKW.
C1 [Duan, Yucong; Cao, Chunjie] Hainan Univ, Coll Informat Sci & Technol, Haikou 570228, Hainan, Peoples R China.
   [Sun, Xiaobing] Yangzhou Univ, Sch Informat Engn, Yangzhou 225009, Jiangsu, Peoples R China.
   [Che, Haoyang] Autosmart Inc, Data Intelligence Ctr, Beijing 100086, Peoples R China.
   [Li, Zhao] Alibaba Grp, Hangzhou, Zhejiang, Peoples R China.
   [Yang, Xiaoxian] Shanghai Polytech Univ, Sch Comp & Informat Engn, Shanghai 310099, Peoples R China.
RP Yang, XX (通讯作者)，Shanghai Polytech Univ, Sch Comp & Informat Engn, Shanghai 310099, Peoples R China.
EM xxyang@sspu.edu.cn
OI Sun, Xiaobing/0000-0001-5165-5080; Li, Zhao/0000-0002-5056-0351; Duan,
   Yucong/0000-0001-8417-892X
FU NSFC [61662021, 61363007]; CERNET Innovation Project [NGII20180607]
FX This work was supported in part by the NSFC under Grant 61662021 and
   Grant 61363007, and in part by the CERNET Innovation Project under Grant
   NGII20180607.
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NR 74
TC 16
Z9 20
U1 3
U2 26
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 99161
EP 99176
DI 10.1109/ACCESS.2019.2931365
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IP8YT
UT WOS:000480334100002
OA gold
DA 2022-08-23
ER

PT J
AU Zhai, PJ
   Huang, X
   Zhang, BB
   Fang, Y
AF Zhai, Pengjun
   Huang, Xin
   Zhang, Beibei
   Fang, Yu
TI Relation Extraction Based on Fusion Dependency Parsing from Chinese EMRs
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
ID INFORMATION
AB The Electronic Medical Record (EMR) contains a great deal of medical knowledge related to patients, which has been widely used in the construction of medical knowledge graphs. Previous studies mainly focus on the features based on surface semantics of EMRs for relation extraction, such as contextual feature, but the features of sentence structure in Chinese EMRs have been neglected. In this paper, a fusion dependency parsing-based relation extraction method is proposed. Specifically, this paper extends basic features with medical record feature and indicator feature that are applicable to Chinese EMRs. Furthermore, dependency syntactic features are introduced to analyse the dependency structure of sentences. Finally, the F1 value of relation extraction based on extended features is 4.87% higher than that of relation extraction based on basic features. And compared with the former, the F1 value of relation extraction based on fusion dependency parsing is increased by 4.39%. The results of experiments performed on a Chinese EMR data set show that the extended features and dependency parsing all contribute to the relation extraction.
C1 [Zhai, Pengjun; Huang, Xin; Zhang, Beibei; Fang, Yu] Tongji Univ, Dept Comp Sci & Technol, Shanghai 200082, Peoples R China.
   [Huang, Xin] Jiangxi Normal Univ, Sch Software, Nanchang 330047, Jiangxi, Peoples R China.
RP Fang, Y (通讯作者)，Tongji Univ, Dept Comp Sci & Technol, Shanghai 200082, Peoples R China.
EM 1810369@tongji.edu.cn; 1610466@tongji.edu.cn; tassy520@163.com;
   fangyu@tongji.edu.cn
OI Huang, Xin/0000-0002-5470-1203
FU National Key Research and Development Program of China [2019YFB2101600]
FX The authors would like to thank the hospital for its contribution, which
   provides electronic medical records that are used as the data set for
   the experiments in this paper. This research was funded by the National
   Key Research and Development Program of China (No. 2019YFB2101600).
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NR 24
TC 0
Z9 0
U1 8
U2 15
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD JUN 8
PY 2020
VL 2020
AR 8658040
DI 10.1155/2020/8658040
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ME3YH
UT WOS:000544593900001
OA gold
DA 2022-08-23
ER

PT J
AU Guo, ZC
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TI Robust named entity disambiguation with random walks
SO SEMANTIC WEB
LA English
DT Article
DE Named entities; entity linking; entity disambiguation; relatedness
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AB Named Entity Disambiguation is the task of assigning entities from a Knowledge Graph ( KG) to mentions of such entities in a textual document. The state-of-the-art for this task balances two disparate sources of similarity: lexical, defined as the pairwise similarity between mentions in the text and names of entities in the KG; and semantic, defined through some graph-theoretic property of a subgraph of the KG induced by the choice of entities for each mention. Departing from previous work, our notion of semantic similarity is rooted in Information Theory and is defined as the mutual information between random walks on the disambiguation graph induced by choice of entities for each mention. We describe an iterative algorithm based on this idea, and show an extension that uses learning-to-rank, which yields further improvements. Our experimental evaluation demonstrates that this approach is robust and very competitive on well-known existing benchmarks. We also justify the need for new and more difficult benchmarks, and provide an extensive experimental comparison of our method and previous work on these new benchmarks.
C1 [Guo, Zhaochen; Barbosa, Denilson] Univ Alberta, Dept Comp Sci, Edmonton, AB, Canada.
RP Barbosa, D (通讯作者)，Univ Alberta, Dept Comp Sci, Edmonton, AB, Canada.
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RI Barbosa, Denilson/AAM-8499-2020
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NR 47
TC 29
Z9 33
U1 2
U2 9
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 4
BP 459
EP 479
DI 10.3233/SW-170273
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GL1QH
UT WOS:000436880000005
DA 2022-08-23
ER

PT J
AU Baclawski, K
   Bennett, M
   Berg-Cross, G
   Dickerson, L
   Schneider, T
   Seppala, S
   Sharma, R
   Sriram, RD
   Westerinen, A
AF Baclawski, Ken
   Bennett, Michael
   Berg-Cross, Gary
   Dickerson, Leia
   Schneider, Todd
   Seppala, Selja
   Sharma, Ravi
   Sriram, Ram D.
   Westerinen, Andrea
TI Ontology Summit 2021 Communique: Ontology generation and harmonization
SO APPLIED ONTOLOGY
LA English
DT Article
DE Ontology; machine learning; definitions; sustainability; harmonization
ID LANGUAGE
AB Advances in machine learning and the development of very large knowledge graphs have accompanied a proliferation of ontologies of many types and for many purposes. These ontologies are commonly developed independently, and as a result, it can be difficult to communicate about and between them. To address this difficulty of communication, ontologies and the communities they serve must agree on how their respective terminologies and formalizations relate to each other. The process of coming into accord and agreement is called "harmonization." The Ontology Summit 2021 examined the overall landscape of ontologies, the many kinds of ontology generation and harmonization, as well as the sustainability of ontologies. The Communique synthesizes and summarizes the findings of the summit as well as earlier summits on related issues. One of the major impediments to harmonization is the relatively poor quality of natural language definitions in many ontologies. The summit surveyed the state of the art in natural language definition development, based on lexicographic principles, as well as examples of ongoing projects that are explicitly dealing with harmonization and sustainability.
C1 [Baclawski, Ken] Northeastern Univ, Boston, MA 02115 USA.
   [Bennett, Michael] Hypercube Ltd, London, England.
   [Berg-Cross, Gary] ESIP Semant Harmonizat Colead, Severna Park, MD USA.
   [Dickerson, Leia] Govt Accountabil Off, Washington, DC USA.
   [Schneider, Todd] Engn Semant, Fairfax, VA USA.
   [Seppala, Selja] Univ Coll Cork, Cork, Ireland.
   [Sharma, Ravi] Senior Enterprise Architect, Elk Grove, CA USA.
   [Sriram, Ram D.] NIST, Gaithersburg, MD 20899 USA.
   [Westerinen, Andrea] OntoInsights LLC, Elkton, MD USA.
RP Baclawski, K (通讯作者)，Northeastern Univ, Boston, MA 02115 USA.
EM kenbaclawski@gmail.com; mbennett@hypercube.co.uk; gbergcross@gmail.com;
   ldickers@gmail.com; tschneider@engineeringsemantics.com;
   selja.seppala@ucc.ie; drravisharma@gmail.com; sriram@nist.gov;
   arwesterinen@gmail.com
FU European Union [713654]
FX Dr. Selja Seppala is a Marie Sklodowska-Curie Career-FIT Research Fellow
   under the number MF20180003. She is grateful for the funding received
   from the European Union's Marie Sklodowska-Curie grant agreement No.
   713654.
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NR 55
TC 0
Z9 0
U1 0
U2 0
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-5838
EI 1875-8533
J9 APPL ONTOL
JI Appl. Ontol.
PY 2022
VL 17
IS 2
BP 233
EP 248
DI 10.3233/AO-220266
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 1H3ED
UT WOS:000796426900001
DA 2022-08-23
ER

PT J
AU Kejriwal, M
AF Kejriwal, Mayank
TI Essential Features in a Theory of Context for Enabling Artificial
   General Intelligence
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE context; artificial general intelligence; context-rich AI; knowledge
   graphs; commonsense reasoning; semantic web; explainable AI;
   representation learning
ID KNOWLEDGE; RISK; AI
AB Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face recognition and natural language processing, the emergence of general machine intelligence continues to be elusive. Such an AI must overcome several challenges, one of which is the ability to be aware of, and appropriately handle, context. In this article, we argue that context needs to be rigorously treated as a first-class citizen in AI research and discourse for achieving true general machine intelligence. Unfortunately, context is only loosely defined, if at all, within AI research. This article aims to synthesize the myriad pragmatic ways in which context has been used, or implicitly assumed, as a core concept in multiple AI sub-areas, such as representation learning and commonsense reasoning. While not all definitions are equivalent, we systematically identify a set of seven features associated with context in these sub-areas. We argue that such features are necessary for a sufficiently rich theory of context, as applicable to practical domains and applications in AI.
C1 [Kejriwal, Mayank] Univ Southern Calif, Inst Informat Sci, 4676 Admiralty Way,Ste 1001, Marina Del Rey, CA 90292 USA.
RP Kejriwal, M (通讯作者)，Univ Southern Calif, Inst Informat Sci, 4676 Admiralty Way,Ste 1001, Marina Del Rey, CA 90292 USA.
EM kejriwal@isi.edu
OI Kejriwal, Mayank/0000-0001-5988-8305
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NR 103
TC 0
Z9 0
U1 3
U2 3
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 24
AR 11991
DI 10.3390/app112411991
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 XW3DL
UT WOS:000735504200001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Li, DY
   Zhu, XD
   Li, Y
   Wang, SG
   Li, DY
   Liao, J
   Zheng, JX
AF Li, Dayu
   Zhu, Xiaodan
   Li, Yang
   Wang, Suge
   Li, Deyu
   Liao, Jian
   Zheng, Jianxing
TI Enhancing emotion inference in conversations with commonsense knowledge
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Emotion analysis; Emotion inference in conversations; Conversation
   modeling; Commonsense knowledge integration
AB Existing studies on emotion analysis in conversations have mainly focused on recognizing the emotion of a given utterance. This paper investigates the task of emotion inference in conversations, which explores how the utterances affect the addressee's emotion, without knowing the addressee's response yet. While it is straightforward for humans to perceive and reason about the feelings of others in conversations, it is a severe challenge for machines, mainly due to the lack of commonsense knowledge. In this work, we propose to leverage external inferential knowledge to enhance the emotion inference in conversations. Specifically, a conversation modeling module is designed to accumulate information from the conversation history based on the emotional interaction between the addressee and writers. In addition, a knowledge integration strategy is also proposed to integrate the conversation-related commonsense knowledge generated from the event-based knowledge graph. The experiments on three different benchmark conversational datasets demonstrate the effectiveness of the proposed models, and prove the benefits of commonsense knowledge for emotion inference in conversations. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Li, Dayu; Wang, Suge; Li, Deyu; Liao, Jian; Zheng, Jianxing] Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Peoples R China.
   [Zhu, Xiaodan] Queens Univ, ECE & Ingenu Labs Res Inst, Kingston, ON, Canada.
   [Li, Yang] Shanxi Univ Finance & Econ, Sch Finance, Taiyuan, Peoples R China.
   [Wang, Suge; Li, Deyu] Shanxi Univ, Key Lab Computat Intelligence & Chinese Informat, Minist Educ, Taiyuan, Peoples R China.
RP Wang, SG (通讯作者)，Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Peoples R China.
EM wsg@sxu.edu.cn
OI Liao, Jian/0000-0002-9385-6873
FU National Natural Science Foundation of China [61632011, 62076158,
   62072294, 61906112]; Key Research and Development Program of Shanxi
   Province, China [201803D421024]; Natural Science Foundation of Shanxi
   Province, China [201901D211174]; Scientific and Technological Innovation
   Programs of Higher Education Institutions in Shanxi, China [2019L0008,
   2020L0001]
FX The authors would like to thank all anonymous reviewers for their
   valuable comments and suggestions which have significantly improved the
   quality and presentation of this paper. The works described in this
   paper are supported by the National Natural Science Foundation of China
   (61632011, 62076158, 62072294, 61906112), the Key Research and
   Development Program of Shanxi Province, China (201803D421024), Natural
   Science Foundation of Shanxi Province, China (201901D211174), Scientific
   and Technological Innovation Programs of Higher Education Institutions
   in Shanxi, China (2019L0008, 2020L0001).
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NR 52
TC 1
Z9 1
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 NOV 28
PY 2021
VL 232
AR 107449
DI 10.1016/j.knosys.2021.107449
EA SEP 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WB4VP
UT WOS:000703571500003
DA 2022-08-23
ER

PT J
AU Lou, PL
   Dong, YX
   Yepes, AJ
   Li, C
AF Lou, Peiliang
   Dong, YuXin
   Yepes, Antonio Jimeno
   Li, Chen
TI A representation model for biological entities by fusing structured
   axioms with unstructured texts
SO BIOINFORMATICS
LA English
DT Article
ID ONTOLOGY; SEQUENCE
AB Motivation: Structured semantic resources, for example, biological knowledge bases and ontologies, formally define biological concepts, entities and their semantic relationships, manifested as structured axioms and unstructured texts (e.g. textual definitions). The resources contain accurate expressions of biological reality and have been used by machine-learning models to assist intelligent applications like knowledge discovery. The current methods use both the axioms and definitions as plain texts in representation learning (RL). However, since the axioms are machine-readable while the natural language is human-understandable, difference in meaning of token and structure impedes the representations to encode desirable biological knowledge.
   Results: We propose ERBK, a RL model of bio-entities. Instead of using the axioms and definitions as a textual corpus, our method uses knowledge graph embedding method and deep convolutional neural models to encode the axioms and definitions respectively. The representations could not only encode more underlying biological knowledge but also be further applied to zero-shot circumstance where existing approaches fall short. Experimental evaluations show that ERBK outperforms the existing methods for predicting protein-protein interactions and gene-disease associations. Moreover, it shows that ERBK still maintains promising performance under the zero-shot circumstance. We believe the representations and the method have certain generality and could extend to other types of bio-relation.
C1 [Lou, Peiliang; Dong, YuXin; Li, Chen] Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xian 710049, Shaanxi, Peoples R China.
   [Lou, Peiliang] Xi An Jiao Tong Univ, Minist Educ, Key Lab Intelligent Networks & Network Secur, Xian 710049, Shaanxi, Peoples R China.
   [Yepes, Antonio Jimeno] IBM Res Australia, Southbank, Vic 3006, Australia.
   [Li, Chen] Xi An Jiao Tong Univ, Natl Engn Lab Big Data Analyt, Xian 710049, Shaanxi, Peoples R China.
RP Li, C (通讯作者)，Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xian 710049, Shaanxi, Peoples R China.; Li, C (通讯作者)，Xi An Jiao Tong Univ, Natl Engn Lab Big Data Analyt, Xian 710049, Shaanxi, Peoples R China.
EM cli@xjtu.edu.cn
OI Jimeno Yepes, Antonio Jose/0000-0002-6581-094X
FU National Key Research and Development Program of China [2018YFC0910404];
   National Natural Science Foundation of China [61772409]; Chinese Academy
   of Engineering (The Online and Offline Mixed Educational Service System
   for 'The Belt and Road' Training in MOOC China); Project of China
   Knowledge Centre for Engineering Science and Technology; Ministry of
   Education [IRT_17R86]; Innovative Research Group of the National Natural
   Science Foundation of China [61721002]; Professor Chen Li' s Recruitment
   Program for Young Professionals of 'The Thousand Talents Plan'
FX This work was supported by the National Key Research and Development
   Program of China [2018YFC0910404]; National Natural Science Foundation
   of China [61772409]; the consulting research project of the Chinese
   Academy of Engineering (The Online and Offline Mixed Educational Service
   System for 'The Belt and Road' Training in MOOC China); Project of China
   Knowledge Centre for Engineering Science and Technology; the Innovation
   Team from the Ministry of Education [IRT_17R86]; the Innovative Research
   Group of the National Natural Science Foundation of China [61721002];
   and Professor Chen Li' s Recruitment Program for Young Professionals of
   'The Thousand Talents Plan'.
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NR 45
TC 0
Z9 0
U1 2
U2 7
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 15
PY 2021
VL 37
IS 8
BP 1156
EP 1163
DI 10.1093/bioinformatics/btaa913
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 SR6OK
UT WOS:000661162100016
PM 33107905
DA 2022-08-23
ER

PT J
AU Yin, XY
   Gromann, D
   Rudolph, S
AF Yin, Xiaoyu
   Gromann, Dagmar
   Rudolph, Sebastian
TI Neural machine translating from natural language to SPARQL
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE SPARQL; Neural Machine Translation; Natural language queries; Learning
   structured knowledge
AB SPARQL is a highly powerful query language for an ever-growing number of resources and knowledge graphs represented in the Resource Description Framework (RDF) data format. Using it requires a certain familiarity with the entities in the domain to be queried as well as expertise in the language's syntax and semantics, none of which average human web users can be assumed to possess. To overcome this limitation, automatically translating natural language questions to SPARQL queries has been a vibrant field of research. However, to this date, the vast success of deep learning methods has not yet been fully propagated to this research problem. This paper contributes to filling this gap by evaluating the utilization of eight different Neural Machine Translation (NMT) models for the task of translating from natural language to the structured query language SPARQL. While highlighting the importance of high-quantity and high-quality datasets, the results show a dominance of a Convolutional Neural Network (CNN)-based architecture with a Bilingual Evaluation Understudy (BLEU) score of up to 98 and accuracy of up to 94%. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Yin, Xiaoyu; Rudolph, Sebastian] Tech Univ Dresden, Nothnitzer Str 46, D-01187 Dresden, Germany.
   [Gromann, Dagmar] Univ Vienna, Gymnasiumstr 50, A-1190 Vienna, Austria.
RP Gromann, D (通讯作者)，Univ Vienna, Gymnasiumstr 50, A-1190 Vienna, Austria.
EM dagmar.gromann@univie.ac.at
RI Rudolph, Sebastian/AAK-7281-2020
OI Rudolph, Sebastian/0000-0002-1609-2080; Gromann,
   Dagmar/0000-0003-0929-6103
FU NexusLinguarum COST-Action for Web-centered linguistic data science
   [CA18209]; project Text2TCS - European Language Grid [825627]
FX We would like to thank the reviewers and guest editors for their
   valuable comments. Part of this work has been supported by the
   NexusLinguarum COST-Action (CA18209) for Web-centered linguistic data
   science and the pilot project Text2TCS funded by the European Language
   Grid (Grant agreement Nr. 825627).
CR Bahdanau Dzmitry, 2015, CORRABS14090473
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NR 21
TC 5
Z9 5
U1 6
U2 20
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 APR
PY 2021
VL 117
BP 510
EP 519
DI 10.1016/j.future.2020.12.013
EA JAN 2021
PG 10
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PY5TQ
UT WOS:000612107200019
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Hur, Y
   Jo, J
AF Hur, Yuna
   Jo, Jaechoon
TI Development of Intelligent Information System for Digital Cultural
   Contents
SO MATHEMATICS
LA English
DT Article
DE knowledge graph; traditional culture; ontology; classification; relation
   extraction; education; artificial intelligence
ID GENDER; WEB
AB A significant amount of digital cultural contents is shared online, but learners do not know where subject matter content is or how to find it. Therefore, there is a need for a service to improve educational quality by effectively providing relevant information in response to searches for content that is useful to learners. This study developed and tested the usability and utility of an intelligent information system that effectively searches and visualizes digital cultural contents. The system collects data on digital cultural contents, automatically classifies them, and creates content triple data to automatically display the results with a 3D timeline, knowledge network map, and keyword relation network map through content search, triple search, and keyword search. We also conducted a survey and in-depth interviews to verify users' satisfaction with respect to the use and utility of the system. For the experiment, we developed survey questions to measure user satisfaction and conducted in-depth interviews regarding the system's utility with a total of 65 subjects. The results show that the response for satisfaction with regard to the use and utility was generally "satisfied". In addition, the system stability was evaluated as "high".
C1 [Hur, Yuna] Korea Univ, Dept Comp Sci & Engn, Seoul 02841, South Korea.
   [Jo, Jaechoon] Hanshin Univ, Div Comp Engn, Osan 18101, South Korea.
RP Jo, J (通讯作者)，Hanshin Univ, Div Comp Engn, Osan 18101, South Korea.
EM yj72722@korea.ac.kr; jachoon@hs.ac.kr
OI Hur, Yuna/0000-0001-5997-1627
FU MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information
   Technology Research Center) support program [IITP-2020-2018-0-01405];
   Institute for Information & communications Technology Planning &
   Evaluation (IITP) grant - Korean government (MSIT) [2020-0-00368]
FX This research was supported by the MSIT (Ministry of Science and ICT),
   Korea, under the ITRC (Information Technology Research Center) support
   program (IITP-2020-2018-0-01405) supervised by the IITP (Institute for
   Information & Communications Technology Planning & Evaluation).
   Additionally, it was also supported by Institute for Information &
   communications Technology Planning & Evaluation (IITP) grant funded by
   the Korean government (MSIT) (No. 2020-0-00368, A Neural-Symbolic Model
   for Knowledge Acquisition and Inference Techniques).
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NR 32
TC 2
Z9 2
U1 2
U2 10
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD FEB
PY 2021
VL 9
IS 3
AR 238
DI 10.3390/math9030238
PG 15
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematics
GA QD2TQ
UT WOS:000615377800001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Song, ZY
   Duan, YC
   Wan, SX
   Sun, XB
   Zou, Q
   Gao, HH
   Zhu, DH
AF Song, Zhengyang
   Duan, Yucong
   Wan, Shixiang
   Sun, Xiaobing
   Zou, Quan
   Gao, Honghao
   Zhu, Donghai
TI Processing Optimization of Typed Resources with Synchronized Storage and
   Computation Adaptation in Fog Computing
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
ID BANDWIDTH ALLOCATION; RISK MODEL; EDGE; STATE
AB Wide application of the Internet of Things (IoT) system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wireless-network-based three-tier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources.
C1 [Song, Zhengyang; Duan, Yucong; Zhu, Donghai] Hainan Univ, Coll Informat Sci & Technol, State Key Lab Marine Resource Utilizat South Chin, Haikou, Hainan, Peoples R China.
   [Wan, Shixiang; Zou, Quan] Tianjin Univ, Sch Comp Sci & Engn, Tianjin, Peoples R China.
   [Sun, Xiaobing] Yangzhou Univ, Sch Informat Engn, Yangzhou, Jiangsu, Peoples R China.
   [Gao, Honghao] Shanghai Univ, Comp Ctr, Shanghai, Peoples R China.
   [Gao, Honghao] Shanghai Key Lab Intelligent Mfg & Robot, Shanghai, Peoples R China.
RP Duan, YC (通讯作者)，Hainan Univ, Coll Informat Sci & Technol, State Key Lab Marine Resource Utilizat South Chin, Haikou, Hainan, Peoples R China.
EM duanyucong@hotmail.com
RI GAO, HONGHAO/AAX-4529-2020; Sun, Xiaobing/L-7423-2019
OI Sun, Xiaobing/0000-0001-5165-5080; Duan, Yucong/0000-0001-8417-892X
FU Hainan Key Development Program [ZDYF2017128]; NSFC [61662021, 61363007,
   61502294]; IIOT Innovation and Development Special Foundation of
   Shanghai [2017-GYHLW-01037]
FX This paper is supported by Hainan Key Development Program (no.
   ZDYF2017128), NSFC (no. 61662021, no. 61363007, and no. 61502294), and
   the IIOT Innovation and Development Special Foundation of Shanghai (no.
   2017-GYHLW-01037).
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   Zins C, 2007, J AM SOC INF SCI TEC, V58, P479, DOI 10.1002/asi.20508
NR 39
TC 7
Z9 7
U1 1
U2 8
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.
PY 2018
AR 3794175
DI 10.1155/2018/3794175
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA GI8XM
UT WOS:000434809200001
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Helal, A
   Helali, M
   Ammar, K
   Mansour, E
AF Helal, Ahmed
   Helali, Mossad
   Ammar, Khaled
   Mansour, Essam
TI A Demonstration of KGLac: A Data Discovery and Enrichment Platform for
   Data Science
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article; Proceedings Paper
CT 47th International Conference on Very Large Data Bases (VLDB)
CY AUG 16-20, 2021
CL Copenhagen, DENMARK
AB Data science growing success relies on knowing where a relevant dataset exists, understanding its impact on a specific task, finding ways to enrich a dataset, and leveraging insights derived from it. With the growth of open data initiatives, data scientists need an extensible set of effective discovery operations to find relevant data from their enterprise datasets accessible via data discovery systems or open datasets accessible via data portals. Existing portals and systems suffer from limited discovery support and do not track the use of a dataset and insights derived from it. We will demonstrate KGLac, a system that captures metadata and semantics of datasets to construct a knowledge graph (GLac) interconnecting data items, e.g., tables and columns. KGLac supports various data discovery operations via SPARQL queries for table discovery, unionable and joinable tables, plus annotation with related derived insights. We harness a broad range of Machine Learning (ML) approaches with GLac to enable automatic graph learning for advanced and semantic data discovery. The demo will showcase how KGLac facilitates data discovery and enrichment while developing an ML pipeline to evaluate potential gender salary bias in IT jobs.
C1 [Helal, Ahmed; Helali, Mossad; Mansour, Essam] Concordia Univ, Montreal, PQ, Canada.
   [Ammar, Khaled] Borealis AI, Toronto, ON, Canada.
RP Helal, A (通讯作者)，Concordia Univ, Montreal, PQ, Canada.
EM Ahmed.Helal@concordia.ca; Mossad.Helali@concordia.ca;
   Khaled.Ammar@borealisAI.com; Essam.Mansour@concordia.ca
CR Bogatu Alex, 2020, ICDE
   Christodoulakis C, 2020, PROC VLDB ENDOW, V13, P2075, DOI 10.14778/3407790.3407810
   Fernandez RC, 2018, PROC INT CONF DATA, P1001, DOI 10.1109/ICDE.2018.00094
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   Zhu EK, 2019, INT CONF MANAGE DATA, P847, DOI 10.1145/3299869.3300065
NR 13
TC 1
Z9 1
U1 1
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD AUG
PY 2021
VL 14
IS 12
BP 2675
EP 2678
DI 10.14778/3476311.3476317
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA YH1AX
UT WOS:000742908500007
DA 2022-08-23
ER

PT J
AU Hou, WF
   Liu, Q
   Cao, LB
AF Hou, Wenfeng
   Liu, Qing
   Cao, Longbing
TI Cognitive Aspects-Based Short Text Representation with Named Entity,
   Concept and Knowledge
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE short text representation; semantic representation; short text
   classification; knowledge graph; convolutional neural network; attention
   network
AB Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation model, which extracts the semantic features from the word, entity, concept and knowledge levels by CNN, respectively. Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related attentive representations from the multi-level textual features, respectively. The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification. Experiments on three tasks demonstrate our method significantly outperforms the state-of-the-art methods.
C1 [Hou, Wenfeng; Liu, Qing; Cao, Longbing] Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia.
RP Hou, WF (通讯作者)，Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia.
EM wenfeng.hou@student.uts.edu.au; qingliu-7@studentuts.edu.au;
   longbing.cao@uts.edu.au
OI Hou, Wenfeng/0000-0003-4586-2168; cao, longbing/0000-0003-1562-9429
FU Australian Research Council [DP190101079]
FX This work is partly sponsored by the Australian Research Council
   Discovery grant DP190101079.
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NR 32
TC 0
Z9 0
U1 2
U2 14
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 14
AR 4893
DI 10.3390/app10144893
PG 13
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA MW0SA
UT WOS:000556756300001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Yang, YJ
   Li, ZF
   Wang, X
   Hu, QH
AF Yang, Yajun
   Li, Zhongfei
   Wang, Xin
   Hu, Qinghua
TI Finding the Shortest Path with Vertex Constraint over Large Graphs
SO COMPLEXITY
LA English
DT Article
ID TRAVELING SALESMAN PROBLEM; ALGORITHM; OPTIMIZATION
AB Graph is an important complex network model to describe the relationship among various entities in real applications, including knowledge graph, social network, and traffic network. Shortest path query is an important problem over graphs and has been well studied. This paper studies a special case of the shortest path problem to find the shortest path passing through a set of vertices specified by user, which is NP-hard. Most existing methods calculate all permutations for given vertices and then find the shortest one from these permutations. However, the computational cost is extremely expensive when the size of graph or given set of vertices is large. In this paper, we first propose a novel exact heuristic algorithm in best-first search way and then give two optimizing techniques to improve efficiency. Moreover, we propose an approximate heuristic algorithm in polynomial time for this problem over large graphs. We prove the ratio bound is 3 for our approximate algorithm. We confirm the efficiency of our algorithms by extensive experiments on real-life datasets. The experimental results validate that our algorithms always outperform the existing methods even though the size of graph or given set of vertices is large.
C1 [Yang, Yajun; Li, Zhongfei; Wang, Xin; Hu, Qinghua] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China.
   [Yang, Yajun] State Key Lab Digital Publishing Technol, Beijing, Peoples R China.
RP Wang, X (通讯作者)，Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China.
EM yjyang@tju.edu.cn; zfli@tju.edu.cn; wangx@tju.edu.cn;
   huqinghua@tju.edu.cn
OI Hu, Qinghua/0000-0001-7765-8095
FU National Natural Science Foundation of China [61402323, 61572353,
   U1736103]; Opening Project of State Key Laboratory of Digital Publishing
   Technology; Australian Research Council [DP130103051]
FX This work is supported by the grants of the National Natural Science
   Foundation of China nos. 61402323, 61572353, and U1736103, the Opening
   Project of State Key Laboratory of Digital Publishing Technology, and
   the Australian Research Council Discovery Grant DP130103051.
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NR 32
TC 2
Z9 2
U1 4
U2 12
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
PY 2019
AR 8728245
DI 10.1155/2019/8728245
PG 13
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA HN5OG
UT WOS:000460233700001
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Melotte, S
   Ilievski, F
   Zhang, LL
   Malte, A
   Mutha, N
   Morstatter, F
   Mehrabi, N
AF Melotte, Sara
   Ilievski, Filip
   Zhang, Linglan
   Malte, Aditya
   Mutha, Namita
   Morstatter, Fred
   Mehrabi, Ninareh
TI Where Does Bias in Common Sense Knowledge Models Come From?
SO IEEE INTERNET COMPUTING
LA English
DT Article
DE Comets; Computational modeling; Predictive models; Internet; Commonsense
   reasoning; Analytical models; Adaptation models; Information bias;
   language models; knowledge graphs; commonsense knowledge
AB Common Sense knowledge bases and models have been shown to embed bias. We investigate the source of such bias in a knowledge model called common sense transformer (COMET) by training it on various combinations of language models and knowledge bases. We experiment with three language models of different sizes and architectures, and two knowledge bases with different modeling principles. We use sentiment and regard as proxy measures of bias and analyze bias using three methods: overgeneralization and disparity, keyword outliers, and relational dimensions. Our results show that larger models tend to be more nuanced in their biases but are more biased than smaller models in certain categories (e.g., utility of religions), which can be attributed to the larger knowledge accumulated during pretraining. We also observe that training on a larger set of common sense knowledge typically leads to more bias, and that models generally have stronger negative regard than positive.
C1 [Melotte, Sara; Ilievski, Filip; Zhang, Linglan; Malte, Aditya; Mutha, Namita; Morstatter, Fred; Mehrabi, Ninareh] Univ Southern Calif, Informat Sci Inst, Marina Del Rey, CA 90007 USA.
RP Melotte, S (通讯作者)，Univ Southern Calif, Informat Sci Inst, Marina Del Rey, CA 90007 USA.
EM melotte@usc.edu; ilievski@isi.edu; linglan@usc.edu; malte@usc.edu;
   nmutha@usc.edu; fredmors@isi.edu; ninarehm@usc.edu
CR Bosselut A., 2019, ARXIV
   Caliskan A, 2017, SCIENCE, V356, DOI 10.1126/science.aal4230
   Fisher J., 2019, ARXIV
   Hutto C.J., 2014, 8 INT C WEBL SOC MED
   Hwang J. D., 2020, ARXIV
   Ilievski F., 2021, PROC AAAI TUT COMMON, P12
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   Ilievski F., 2020, ARXIV
   Kejriwal M., 2021, EXPERT EXPLAINS WHY
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   Mehrabi N., 2021, ARXIV
   Radford A, 2019, OPENAI BLOG, V1, P8
   Raffel C., 2019, ARXIV
   Sheng E., 2019, ARXIV
   Speer R., 2017, ARXIV
   Valentini F., 2021, ARXIV
NR 18
TC 0
Z9 0
U1 1
U2 1
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1089-7801
EI 1941-0131
J9 IEEE INTERNET COMPUT
JI IEEE Internet Comput.
PD JUL-AUG
PY 2022
VL 26
IS 4
BP 12
EP 20
DI 10.1109/MIC.2022.3170914
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3G1OG
UT WOS:000831124400009
DA 2022-08-23
ER

PT J
AU Ji, WF
   Jiang, TH
   Wang, M
   Tang, XY
   Chen, G
   Yang, S
AF Ji, Wenfei
   Jiang, Tonghai
   Wang, Meng
   Tang, Xinyu
   Chen, Guang
   Yang, Shan
TI Online Knowledge Learning Model Based on Gravitational Field Theory
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph; knowledge representation; online learning;
   gravitational field; knowledge space
AB Currently, low-dimensional embedded representation learning models are the mainstream approach in knowledge representation research, due to ease of calculation and ability to utilize the spatial relationship between knowledge areas, which benefit from static knowledge learning. However, these models cannot update and learn knowledge online. Although using update strategies to update the knowledge base has been proposed by some scholars, this still requires retraining of knowledge and does not use the previous learning parameters and models. TransOnLine, an online knowledge learning method based on the theory of gravitational field, inspired by the fact that the forces acting on two objects in a gravitational field are only related to the distances between objects, rebalances the knowledge space caused by new knowledge through dynamic programming via introducing the spatial energy function and energy transfer function to solve the above problems. TransOnLine can reuse the parameters and models of previous learning. Experiments show that the performance of the TransOnLine method is close to state-of-the-art methods, and it is suitable for online learning and updating a relational-intensive knowledge base.
C1 [Ji, Wenfei; Jiang, Tonghai; Wang, Meng; Tang, Xinyu; Chen, Guang; Yang, Shan] Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China.
   [Ji, Wenfei; Jiang, Tonghai; Wang, Meng; Tang, Xinyu; Chen, Guang; Yang, Shan] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Ji, Wenfei; Jiang, Tonghai; Wang, Meng; Tang, Xinyu; Chen, Guang; Yang, Shan] Chinese Acad Sci, Xinjiang Lab Minor Speech & Language Informat Pro, Urumqi 830011, Peoples R China.
   [Ji, Wenfei; Wang, Meng; Tang, Xinyu; Chen, Guang; Yang, Shan] Jiangsu CAS Nor West Star Informat Technol Co Ltd, Wuxi 214135, Jiangsu, Peoples R China.
RP Tang, XY (通讯作者)，Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China.; Tang, XY (通讯作者)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.; Tang, XY (通讯作者)，Chinese Acad Sci, Xinjiang Lab Minor Speech & Language Informat Pro, Urumqi 830011, Peoples R China.; Tang, XY (通讯作者)，Jiangsu CAS Nor West Star Informat Technol Co Ltd, Wuxi 214135, Jiangsu, Peoples R China.
EM tangxy@ciotc.org
OI wenfei, ji/0000-0003-1299-9627
FU Western Light of the Chinese Academy of Sciences-Class A Project for
   Young Scholars in Western China [2018-XBQNXZ-A-003]
FX This research was funded by the Western Light of the Chinese Academy of
   Sciences-Class A Project for Young Scholars in Western China, grant
   number 2018-XBQNXZ-A-003.
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NR 37
TC 0
Z9 0
U1 0
U2 3
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 1
PY 2019
VL 9
IS 15
AR 3195
DI 10.3390/app9153195
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 IS4PB
UT WOS:000482134500250
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Wang, QB
   Zhang, QC
   Zuo, M
   He, SY
   Zhang, BY
AF Wang, Qingbang
   Zhang, Qingchuan
   Zuo, Min
   He, Siyu
   Zhang, Baoyu
TI A Entity Relation Extraction Model with Enhanced Position Attention in
   Food Domain
SO NEURAL PROCESSING LETTERS
LA English
DT Article
DE Entity-relationship extraction; Attention mechanism; Position awareness;
   Semantic role labeling
AB Entity-relationship extraction is a fine-grained task for constructing a knowledge graph of food public opinion in the field of food public opinion, and it is also an important research topic in the field of current information extraction. This paper aims at the multi-entity-to-relationship problem that often occurs in food public opinion, the entity-relationship types are extracted from the BERT (Bidirectional Encoder Representation from Transformers) network model; In the bidirectional long short-term memory network (BLSTM), the entity-relationship types extracted by BERT model are integrated, and the semantic role attention mechanism based on position awareness is introduced to construct a model BERT-BLSTM-based entity-relationship extraction model for food public opinion at the same time. In this paper, comparative experiments were conducted on the food sentiment data set. The experimental results show that the accuracy of the BERT-BLSTM-based food sentiment entity-relationship extraction model proposed in this paper is 8.7 similar to 13.94% higher than several commonly used deep neural network models on the food sentiment data set, which verifies the rationality and effectiveness of the model proposed in this paper.
C1 [Wang, Qingbang; Zhang, Qingchuan; Zuo, Min; He, Siyu; Zhang, Baoyu] Beijing Technol & Business Univ, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100048, Peoples R China.
RP Zhang, QC (通讯作者)，Beijing Technol & Business Univ, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100048, Peoples R China.
EM zqc1982@126.com
FU Beijing Natural Science Foundation [4202014]; Natural Science Foundation
   of China [61873027]; Humanity and Social Science Youth Foundation of
   Ministry of Education of China [20YJCZH229]; R&D Program of Beijing
   Municipal Education Commission [KM202010011011]
FX This study is supported by Beijing Natural Science Foundation (No.
   4202014), Natural Science Foundation of China (61873027), Humanity and
   Social Science Youth Foundation of Ministry of Education of China (No.
   20YJCZH229), the R&D Program of Beijing Municipal Education Commission
   (No. KM202010011011).
CR [Anonymous], 2015, SCI TECH ENG
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NR 24
TC 1
Z9 1
U1 15
U2 15
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1370-4621
EI 1573-773X
J9 NEURAL PROCESS LETT
JI Neural Process. Lett.
PD APR
PY 2022
VL 54
IS 2
BP 1449
EP 1464
DI 10.1007/s11063-021-10690-9
EA JAN 2022
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0J0UT
UT WOS:000743407500002
DA 2022-08-23
ER

PT J
AU Ding, YL
   Xu, ZW
   Zhu, Q
   Li, HK
   Luo, Y
   Bao, Y
   Tang, LJ
   Zeng, S
AF Ding, Yulin
   Xu, Zhaowen
   Zhu, Qing
   Li, Hankan
   Luo, Yan
   Bao, Ying
   Tang, Lingjun
   Zeng, Sen
TI Integrated data-model-knowledge representation for natural resource
   entities
SO INTERNATIONAL JOURNAL OF DIGITAL EARTH
LA English
DT Article
DE Natural resources; data-model-knowledge; interactional relationships
ID GEOGRAPHIC INFORMATION; TOPOLOGICAL RELATIONS; CLASSIFICATION; SYSTEM;
   SPACE; GIS; CONSTRUCTION; FRAMEWORK
AB The unified management and planning of national or provincial natural resources distributed both aboveground and underground have become increasingly important. Accurate depictions of natural resource elements and their interactions are key to achieving integrated and systematic management of natural resources. However, current spatiotemporal data models are based only on data descriptions, attribute records, and other model knowledge of a more general basis, without intuitively describing relationships between these elements and natural resources. This paper, therefore, proposes an integrated data-model-knowledge representation model to explicitly describe the time, space, and interaction of natural resource entities through an integrated knowledge graph. First, this study constructs a conceptual model using the aspects of semantics, scale, and data-model-knowledge, thereby explicitly describing the relationships of natural resources. Second, a logical model of natural resource representation is proposed, that is integrated with time, space, attributes, and relationships. Finally, taking the management of water resources as an example, this paper realizes the meticulous presentation of the levels of detail and rich semantic relations of natural resource entities. The findings of this study lay the foundation for a more efficient, precise, and lucid perception of the distribution laws and complicated interactional relationships of natural resources, both aboveground and underground.
C1 [Ding, Yulin; Xu, Zhaowen; Zhu, Qing; Li, Hankan; Luo, Yan] Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 611756, Peoples R China.
   [Bao, Ying; Tang, Lingjun] Shenzhen Municipal Bur Planning & Nat Resources, Municipal Planning & Land Estate Informat Ctr, Shenzhen, Peoples R China.
   [Zeng, Sen] Dept Nat Resources Sichuan Prov, Informat Ctr, Chengdu, Peoples R China.
RP Ding, YL (通讯作者)，Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 611756, Peoples R China.
EM dingyulin@swjtu.edu.cn
FU National Natural Science Foundation of China [41871314, 4187010232];
   Program of the Department of Natural Resources of Sichuan Province
   [KJ20206]
FX This study was supported by the National Natural Science Foundation of
   China [Projects No. 41871314, 4187010232] and the Program of the
   Department of Natural Resources of Sichuan Province [Grant Number
   KJ20206].
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NR 63
TC 0
Z9 0
U1 25
U2 25
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 31
PY 2022
VL 15
IS 1
BP 653
EP 678
DI 10.1080/17538947.2022.2047802
PG 26
WC Geography, Physical; Remote Sensing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Physical Geography; Remote Sensing
GA ZU3WP
UT WOS:000769773900001
OA gold
DA 2022-08-23
ER

PT J
AU Reich, Y
   Kapeliuk, A
AF Reich, Y
   Kapeliuk, A
TI A framework for organizing the space of decision problems with
   application to solving subjective, context-dependent problems
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE case-based reasoning; software development; influence knowledge graph;
   decision problems characteristics; clustering
ID INFORMATION-SYSTEMS; DESIGN; DSS; SUBGRAPH; SUPPORT
AB Developing Decision Support Systems (DSS) is a difficult task, prone to errors and susceptible to colossal failures. In order to alleviate this difficulty and its consequences, we propose a framework for organizing the space of decision problems based oil problem characteristics. This organization classifies decision problems into classes that exhibit intra-class similarity. We show how classifying a new decision problem into one of the existing classes provides assistance in designing the DSS for the new problem. The classification could be based simply on problem characteristics or oil the structure of the problem-solution relationships. We further show that a DSS whose goal is to support the development of DSS can be developed with the assistance of related DSS.
   The framework we propose could best serve researchers and practitioners if the reporting of DSS projects would follow the structure presented in this paper. In this way, additional data would be accumulated and would be available for refining the classification of the space of DSS. This in turn, would improve the support for building new DSS. (c) 2004 Elsevier B.V All rights reserved.
C1 Tel Aviv Univ, Fac Engn, Sch Mech Engn, IL-69978 Tel Aviv, Israel.
RP Reich, Y (通讯作者)，Tel Aviv Univ, Fac Engn, Sch Mech Engn, IL-69978 Tel Aviv, Israel.
EM yoram@eng.tau.ac.il
RI Reich, Yoram/AAE-6262-2020; Reich, Yoram/G-4894-2010
OI Reich, Yoram/0000-0002-0922-8381; 
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NR 42
TC 24
Z9 24
U1 2
U2 12
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0167-9236
EI 1873-5797
J9 DECIS SUPPORT SYST
JI Decis. Support Syst.
PD NOV
PY 2005
VL 41
IS 1
BP 1
EP 19
DI 10.1016/j.dss.2004.05.001
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Operations Research & Management Science
GA 976DC
UT WOS:000232712000001
DA 2022-08-23
ER

PT J
AU de Diego, IM
   Gonzalez-Fernandez, C
   Fernandez-Isabel, A
   Fernandez, RR
   Cabezas, J
AF Martin de Diego, Isaac
   Gonzalez-Fernandez, Cesar
   Fernandez-Isabel, Alberto
   Fernandez, Ruben R.
   Cabezas, Javier
TI System for evaluating the reliability and novelty of medical scientific
   papers
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Reliability estimation; Novelty detection; Article evaluation; Knowledge
   graph; Medical articles
ID KNOWLEDGE; TEXT; DATABASE; METAMAP; INDEX; UMLS
AB As society develops, the number of published research articles raises. Besides, the pressure to publish has been increased because the competitiveness between researchers working on similar topics. Although this increment is desirable, it leads to multiple issues. At a reader level, insurmountable barriers to keep up with the state of the art appear. From a publisher perspective, it is a very demanding task to determine which research articles are worth publishing. Automatizing these tasks appears as a core solution. In the case of readers, a previous evaluation of articles would simplify the filtering process. As for publishers, they could perform preliminary selections or estimate the reviewing effort. This paper presents Medical Evaluator System for Scientific Interoperability (MESSI) system to overcome all these issues. It is able to evaluate the novelty and reliability of health-related texts. The novelty calculation is based on previously acquired knowledge after processing more than 500,000 papers. The reliability estimation is based on the reputations of similar articles calculated based on previously defined metrics. Multiple experiments have been addressed to illustrate the viability of the proposal. The obtained results show a good performance that encourage to continue evolving the system.
C1 [Martin de Diego, Isaac; Gonzalez-Fernandez, Cesar; Fernandez-Isabel, Alberto; Fernandez, Ruben R.; Cabezas, Javier] Rey Juan Carlos Univ, Data Sci Lab, C Tulipan S-N, Mostoles 28933, Spain.
RP de Diego, IM (通讯作者)，Rey Juan Carlos Univ, Data Sci Lab, C Tulipan S-N, Mostoles 28933, Spain.
EM isaac.martin@urjc.es; cesar.gonzalezf@urjc.es;
   alberto.fernandez.isabel@urjc.es; ruben.rodriguez@urjc.es;
   javier.cabezas@urjc.es
RI Martín, Isaac/P-3479-2014
OI Martín, Isaac/0000-0001-5197-2932; Gonzalez Fernandez,
   Cesar/0000-0003-0724-0819
FU Spanish Ministry of Economy and Competitiveness [RTC-2017-6253-1]
FX Research supported by grant from the Spanish Ministry of Economy and
   Competitiveness, under the Retos-Colaboracion program: SABERMED (Ref:
   RTC-2017-6253-1), medical corpus provided by MMG and donation of the
   Titan V GPU by NVIDIA Corporation.
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NR 74
TC 0
Z9 0
U1 12
U2 24
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD NOV
PY 2021
VL 15
IS 4
AR 101188
DI 10.1016/j.joi.2021.101188
EA JUL 2021
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 WB4NH
UT WOS:000703549900017
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Milis, GM
   Panayiotou, CG
   Polycarpou, MM
AF Milis, George M.
   Panayiotou, Christos G.
   Polycarpou, Marios M.
TI IoT-Enabled Automatic Synthesis of Distributed Feedback Control Schemes
   in Smart Buildings
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Buildings; Heating systems; Internet of Things; Feedback control;
   Architecture; Technological innovation; Cyber&#8211; physical systems
   (CPSs); semantic data and services; service-oriented architecture
AB There is currently a rapid evolution of technologies enabling the "Internet-of-Things" paradigm, which leads to the design of systems with augmented sensing, analysis, and actuation capabilities. The intelligent control research community is beginning to recognize the challenge of designing flexible and adaptable control systems that will take advantage of the online evolution of the number and types of "things" in a large-scale system. Responding to this challenge, in a previous article, we designed and presented a semantically enhanced control system supervisor that integrates a knowledge graph and deductive inference capabilities, to achieve the online (re)configuration of feedback control schemes. In this article, we show that by exploiting the capabilities of this supervisor, we facilitate the automatic synthesis and plugging of model-based controllers for the space heating problem in multizone buildings. The supervisor retrieves stored knowledge about the building and the available control system components and uses it to configure online a scheme of distributed feedback controllers, one per building zone. The controllers are designed to utilize information online and optimally allocate the control signal to multiple heating devices in each building zone.
C1 [Milis, George M.] PHOEBE Res & Innovat Ltd, CY-2027 Nicosia, Cyprus.
   [Panayiotou, Christos G.; Polycarpou, Marios M.] Univ Cyprus, KIOS Res & Innovat Ctr Excellence, Dept Elect & Comp Engn, CY-2109 Nicosia, Cyprus.
RP Milis, GM (通讯作者)，PHOEBE Res & Innovat Ltd, CY-2027 Nicosia, Cyprus.
EM george.milis@phoebeinnovations.com; christosp@ucy.ac.cy;
   mpolycar@ucy.ac.cy
OI M. Milis, George/0000-0001-6730-2714; Polycarpou,
   Marios/0000-0001-6495-9171
FU European Union [739551]; Government of the Republic of Cyprus through
   the Directorate General for European Programmes, Coordination and
   Development; Cyprus Research and Innovation Foundation
   [ENTERPRISES/0618/0132]
FX This work was supported in part by the European Union's Horizon 2020
   Research and Innovation Programme under Grant 739551 (KIOS CoE); in part
   by the Government of the Republic of Cyprus through the Directorate
   General for European Programmes, Coordination and Development; and in
   part by the Cyprus Research and Innovation Foundation under Grant
   ENTERPRISES/0618/0132 (Domognostics+).
CR [Anonymous], 2017, INT THINGS MOV NOT M
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NR 29
TC 0
Z9 0
U1 0
U2 4
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 2615
EP 2626
DI 10.1109/JIOT.2020.3019662
PG 12
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:000616317000040
DA 2022-08-23
ER

PT J
AU Zhu, QN
   Zhou, XF
   Tan, JL
   Guo, L
AF Zhu, Qiannan
   Zhou, Xiaofei
   Tan, Jianlong
   Guo, Li
TI Knowledge Base Reasoning with Convolutional-Based Recurrent Neural
   Networks
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Knowledge base embedding; knowledge base reasoning; knowledge
   representation learning; knowledge graph completion
AB Recurrent neural network(RNN) has achieved remarkable performances in complex reasoning on knowledge bases, which usually takes as inputs vector embeddings of relations along a path between an entity pair. However, it is insufficient to extract local correlations of a path due to RNN is better at capturing global sequential information of a path. In this paper, we take full advantages of convolutional neural network that can effectively extract local features, and propose a convolutional-based RNN architecture denoted as C-RNN to perform reasoning. C-RNN first utilizes CNN to extract local high-level correlation features of a path, and then feeds the correlation features into recurrent neural network to model the path representation. Our C-RNN architecture is adaptable to obtain not only local features but also global sequential features of a path. Based on C-RNN architecture, we devise two models, the unidirectional C-RNN and bidirectional C-RNN. We empirically evaluate them on a large-scale FreeBase+ClueWeb prediction task. Experimental results show that C-RNN models achieve state-of-the-art predictive performance.
C1 [Zhu, Qiannan; Zhou, Xiaofei; Tan, Jianlong; Guo, Li] Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China.
   [Zhu, Qiannan; Zhou, Xiaofei; Tan, Jianlong; Guo, Li] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China.
RP Zhou, XF (通讯作者)，Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China.
EM zhuqiannan@iie.ac.cn; zhouxiaofei@iie.ac.cn; tanjianlong@iie.ac.cn;
   guoli@iie.ac.cn
FU National Key RD Program [2016YFB0801300]; National Natural Science
   Foundation of China [61202226]
FX This work is supported by National Key R&D Program 2016
   (No.2016YFB0801300), and the National Natural Science Foundation of
   China (No.61202226). We thank all anonymous reviewers for their
   constructive comments.
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NR 53
TC 2
Z9 3
U1 13
U2 32
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 2021
VL 33
IS 5
BP 2015
EP 2028
DI 10.1109/TKDE.2019.2951103
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 RJ3XT
UT WOS:000637532700016
DA 2022-08-23
ER

PT J
AU Feng, YX
   Li, KJ
   Gao, YC
   Qiu, J
AF Feng, Yixiong
   Li, Kangjie
   Gao, Yicong
   Qiu, Jian
TI Hierarchical graph attention networks for semi-supervised node
   classification
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Graph convolutional networks; Hierarchical representation;
   Semi-supervisded
AB Recently, there has been a promising tendency to generalize convolutional neural networks (CNNs) to graph domain. However, most of the methods cannot obtain adequate global information due to their shallow structures. In this paper, we address this challenge by proposing a hierarchical graph attention network (HGAT) for semi-supervised node classification. This network employs a hierarchical mechanism for the learning of node features. Thus, more information can be effectively obtained of the node features by iteratively using coarsening and refining operations on different hierarchical levels. Moreover, HGAT combines with the attention mechanism in the input and prediction layer. It can assign different weights to different nodes in a neighborhood, which helps to improve accuracy. Experiment results demonstrate that state-of-the-art performance was achieved by our method, not only on Cora, Citeseer, and Pubmed citation datasets, but also on the simplified NELL knowledge graph dataset. The sensitive analysis further verifies that HGAT can capture global structure information by increasing the receptive field, as well as the effective transfer of node features.
C1 [Feng, Yixiong; Li, Kangjie; Gao, Yicong] Zhejiang Univ, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China.
   [Qiu, Jian] Hangzhou Dianzi Univ, Sch Cyberspace, Hangzhou 310027, Peoples R China.
RP Feng, YX (通讯作者)，Zhejiang Univ, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China.
EM fyxtv@zju.edu.cn; 21825116@zju.edu.cn; gaoyicong@zju.edu.cn;
   qiujian@hdu.edu.cn
FU National Key R&D Program of China [2018YFB1701702]; National Natural
   Science Foundation of China [51675477, 51775489]; Zhejiang Provincial
   Natural Science Foundation of China [LZ18E050001]
FX This work is supported by the National Key R&D Program of China (No.
   2018YFB1701702), the National Natural Science Foundation of China (Nos.
   51675477, 51775489), Zhejiang Provincial Natural Science Foundation of
   China (No. LZ18E050001).
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NR 37
TC 7
Z9 8
U1 9
U2 29
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 OCT
PY 2020
VL 50
IS 10
BP 3441
EP 3451
DI 10.1007/s10489-020-01729-w
EA JUN 2020
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NI2ZM
UT WOS:000539520200001
DA 2022-08-23
ER

PT J
AU Sun, C
   Yang, ZH
   Wang, L
   Zhang, Y
   Lin, HF
   Wang, J
   Yang, L
   Xu, K
   Zhang, YJ
AF Sun, Cong
   Yang, Zhihao
   Wang, Lei
   Zhang, Yin
   Lin, Hongfei
   Wang, Jian
   Yang, Liang
   Xu, Kan
   Zhang, Yijia
TI Chemical-protein interaction extraction from biomedical literature: a
   hierarchical recurrent convolutional neural network method
SO INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
LA English
DT Article
DE chemical-protein interaction; data mining; relation extraction; HNN;
   hierarchical neural network; RCNN; recurrent convolutional neural
   network
ID DRUG INTERACTION EXTRACTION
AB Mining chemical-protein interactions between chemicals and proteins plays vital roles in biomedical tasks, such as knowledge graph, pharmacology, and clinical research. Although chemical-protein interactions can be manually curated from the biomedical literature, the process is difficult and time-consuming. Hence, it is of great value to automatically obtain the chemical-protein interactions from biomedical literature. Recently, the most popular methods are based on the neural network to avoid complex manual processing. However, the performance is usually limited because of the lengthy and complicated sentences. To address this limitation, we propose a novel model, Hierarchical Recurrent Convolutional Neural Network (HRCNN), to learn hidden semantic and syntactic features from sentence sub-sequences effectively. Our approach achieves an F-score of 65.56% on the CHEMPROT corpus and outperforms the state-of-the-art systems. The experimental results demonstrate that our approach can greatly alleviate the defect of existing methods due to the existence of long sentences.
C1 [Sun, Cong; Yang, Zhihao; Lin, Hongfei; Wang, Jian; Yang, Liang; Xu, Kan; Zhang, Yijia] 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 suncong132@mail.dlut.edu.cn; yangzh@dlut.edu.cn;
   wangleibihami@gmail.com; zhangyinbihami@gmail.com; hflin@dlut.edu.cn;
   wangjian@dlut.edu.cn; liang@dlut.edu.cn; xukan@dlut.edu.cn;
   zhyj@dlut.edu.cn
FU National Key Research and Development Program of China [2016YFC0901902]
FX This work was supported by the grants from the National Key Research and
   Development Program of China (No. 2016YFC0901902).
CR Bengio Y, 2001, ADV NEUR IN, V13, P932
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NR 26
TC 3
Z9 3
U1 2
U2 8
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
   GENEVA, SWITZERLAND
SN 1748-5673
EI 1748-5681
J9 INT J DATA MIN BIOIN
JI Int. J. Data Min. Bioinform.
PY 2019
VL 22
IS 2
BP 113
EP 130
PG 18
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA HZ3HN
UT WOS:000468739200002
DA 2022-08-23
ER

PT J
AU Hopp, MT
   Domingo-Fernandez, D
   Gadiya, Y
   Detzel, MS
   Graf, R
   Schmalohr, BF
   Kodamullil, AT
   Imhof, D
   Hofmann-Apitius, M
AF Hopp, Marie-Therese
   Domingo-Fernandez, Daniel
   Gadiya, Yojana
   Detzel, Milena S.
   Graf, Regina
   Schmalohr, Benjamin F.
   Kodamullil, Alpha T.
   Imhof, Diana
   Hofmann-Apitius, Martin
TI Linking COVID-19 and Heme-Driven Pathophysiologies: A Combined
   Computational-Experimental Approach
SO BIOMOLECULES
LA English
DT Article
DE biomarkers; cardiovascular risk; coagulation; COVID-19; heme;
   heme-binding motifs; hemolysis; inflammation; pathway networks;
   SARS-CoV-2
ID ACE2; SARS-COV-2; INFECTION; EXPRESSION; RECEPTOR; PROTEIN; TMPRSS2
AB The SARS-CoV-2 outbreak was declared a worldwide pandemic in 2020. Infection triggers the respiratory tract disease COVID-19, which is accompanied by serious changes in clinical biomarkers such as hemoglobin and interleukins. The same parameters are altered during hemolysis, which is characterized by an increase in labile heme. We present two computational-experimental approaches aimed at analyzing a potential link between heme-related and COVID-19 pathophysiologies. Herein, we performed a detailed analysis of the common pathways induced by heme and SARS-CoV-2 by superimposition of knowledge graphs covering heme biology and COVID-19 pathophysiology. Focus was laid on inflammatory pathways and distinct biomarkers as the linking elements. In a second approach, four COVID-19-related proteins, the host cell proteins ACE2 and TMPRSS2 as well as the viral proteins 7a and S protein were computationally analyzed as potential heme-binding proteins with an experimental validation. The results contribute to the understanding of the progression of COVID-19 infections in patients with different clinical backgrounds and may allow for a more individual diagnosis and therapy in the future.
C1 [Hopp, Marie-Therese; Detzel, Milena S.; Graf, Regina; Schmalohr, Benjamin F.; Imhof, Diana] Univ Bonn, Pharmaceut Inst, Pharmaceut Biochem & Bioanalyt, Immenburg 4, D-53121 Bonn, Germany.
   [Domingo-Fernandez, Daniel; Gadiya, Yojana; Kodamullil, Alpha T.; Hofmann-Apitius, Martin] Fraunhofer Inst Algorithms & Sci Comp SCAI, Dept Bioinformat, D-53757 St Augustin, Germany.
   [Domingo-Fernandez, Daniel] Enveda Biosci Inc, San Francisco, CA 94080 USA.
   [Kodamullil, Alpha T.] Causal Biomodels, Kinfra Hi Tech Pk, Cochin 683503, Kerala, India.
RP Imhof, D (通讯作者)，Univ Bonn, Pharmaceut Inst, Pharmaceut Biochem & Bioanalyt, Immenburg 4, D-53121 Bonn, Germany.; Hofmann-Apitius, M (通讯作者)，Fraunhofer Inst Algorithms & Sci Comp SCAI, Dept Bioinformat, D-53757 St Augustin, Germany.
EM mhopp@uni-bonn.de; Daniel.domingo.fernandez@scai.fraunhofer.de;
   yojanagadiya@gmail.com; milena.detzel@uni-bonn.de; s6regraf@uni-bonn.de;
   ben.s@uni-bonn.de; alpha.tom.kodamullil@scai.fraunhofer.de;
   dimhof@uni-bonn.de; martin.hofmann-apitius@scai.fraunhofer.de
RI Domingo-Fernández, Daniel/AGH-3497-2022
OI Domingo-Fernández, Daniel/0000-0002-2046-6145; Hopp,
   Marie-T./0000-0002-0883-7564; Graf, Regina/0000-0002-7560-9991; Gadiya,
   Yojana/0000-0002-7683-0452
FU MAVO program of the Fraunhofer Society; ICON program of the Fraunhofer
   Society; University of Bonn
FX This work was supported by the MAVO and ICON programs of the Fraunhofer
   Society. Financial support by the University of Bonn (Argelander grant
   to M.-T.H.) is gratefully acknowledged.
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NR 101
TC 2
Z9 2
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2218-273X
J9 BIOMOLECULES
JI Biomolecules
PD MAY
PY 2021
VL 11
IS 5
AR 644
DI 10.3390/biom11050644
PG 18
WC Biochemistry & Molecular Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology
GA SG4MT
UT WOS:000653416000001
PM 33925394
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Braines, D
   Stockdill-Mander, J
   Lee, E
AF Braines, Dave
   Stockdill-Mander, Jane
   Lee, Eunjin
TI The Science Library: Curation and visualization of a science gateway
   repository
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article
DE Controlled English; human&#8208; computer interaction; science gateways;
   Science Library; visualization
ID INTERFACE
AB Scientific publications from a group or consortium often form a coherent larger body of work with underlying threads and relationships. Rich social, structural, and topical networks between authors and organizations can be identified, and to convey these we have created the publicly available "Science Library" as a user-centric, interactive portal. A key consideration in this endeavor is rapid and efficient curation of the corpus of publications, both in terms of assuring quality, as well minimizing the effort required. For this to be sustainable it must offer substantial benefits to the community and avoid excessive operational cost through cumbersome or complex processes. We describe the agility of the Science Library implementation as a controlled natural language (CNL) semantic knowledge graph and describe the different roles within the community to ensure efficient curation, validation, and provenance of the content. By describing the process of curation and validation, alongside the CNL-based definition of the model we show how relatively non-technical users are able to interact with, and contribute to the Science Library. This provides an extensible approach, initially based around digital library and virtual community capabilities, that can be applied more broadly to support other desired capabilities of Science Gateways.
C1 [Braines, Dave; Stockdill-Mander, Jane] IBM Res Europe, Hursley Pk, Winchester SO21 2JN, Hants, England.
   [Braines, Dave] Cardiff Univ, Crime & Secur Res Inst, Cardiff, Wales.
   [Lee, Eunjin] IBM Corp, Winchester, Hants, England.
RP Braines, D (通讯作者)，IBM Res Europe, Hursley Pk, Winchester SO21 2JN, Hants, England.
EM dave_braines@uk.ibm.com
OI Braines, Dave/0000-0003-3296-0842
FU U.S. Army Research Laboratory; UK Ministry of Defence [W911NF-16-3-0001]
FX U.S. Army Research Laboratory and the UK Ministry of Defence,
   Grant/Award Number: W911NF-16-3-0001
CR [Anonymous], [No title captured]
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NR 21
TC 1
Z9 1
U1 2
U2 6
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-0626
EI 1532-0634
J9 CONCURR COMP-PRACT E
JI Concurr. Comput.-Pract. Exp.
PD OCT 10
PY 2021
VL 33
IS 19
SI SI
AR e6100
DI 10.1002/cpe.6100
EA DEC 2020
PG 16
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UP9KH
UT WOS:000603411800001
OA hybrid
DA 2022-08-23
ER

PT J
AU Ihsan, I
   Ullah, M
   Khan, RU
   Uddin, MI
   Alharbi, A
   Alosaimi, W
AF Ihsan, Imran
   Ullah, Mohib
   Khan, Rafi Ullah
   Uddin, M. Irfan
   Alharbi, Abdullah
   Alosaimi, Wael
TI SEAL: Semantically Enriched Authoring in LATEX-A Model for Scientific
   Discourse
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Annotations; Ontologies; Typesetting; Resource description
   framework; Publishing; Ecosystems; Semantic annotation; semantic
   authoring; scientific discourse; citation Indicators; citation
   behaviors; ontology
AB Semantic tags can enrich citation graphs by inter-connecting papers with citation reasons. One of the best sources of knowledge to tell the reason for citation is the author himself. Integrating these reasons in an authoring system can help authors to choose a reason while citing. We examined various Human and Automatic authoring systems for integration of citation reasons. However, to the best of our knowledge, no such system exists that facilities authors to integrate the reasons while citing. Same is the case with Cite Packages. This research proposes integration of CCRO:Citations' Context and Reasons Ontology's (Ihsan and Qadir, 2019) taxonomic hierarchy of reasons within document. We have developed a CCRO Package to semantically tag citations with reasons and to create an intra-discourse relation between research articles. Furthermore, embedding these structures within RDF Data Store enables the creation of knowledge graphs that become a foundation artifact for the Semantic & Scientific Discourse.
C1 [Ihsan, Imran] Air Univ, Dept Creat Technol, FCAI, Islamabad 44000, Pakistan.
   [Ullah, Mohib; Khan, Rafi Ullah] Univ Agr Peshawar, Inst Comp Sci & Informat Technol, Peshawar 25120, Pakistan.
   [Uddin, M. Irfan] Kohat Univ Sci & Technol, Inst Comp, Kohat 2600, Pakistan.
   [Alharbi, Abdullah; Alosaimi, Wael] Taif Univ, Coll Comp & Informat Technol, Dept Informat Technol, At Taif 21944, Saudi Arabia.
RP Ihsan, I (通讯作者)，Air Univ, Dept Creat Technol, FCAI, Islamabad 44000, Pakistan.
EM iimranihsan@gmail.com
RI ULLAH, MOHIB/GPS-8783-2022
OI Khan, Rafi Ullah/0000-0002-0229-7747; Alosaimi,
   Wael/0000-0002-6940-9483; Ullah, Mohib/0000-0003-0534-8826
FU Taif University Researchers Supporting through Taif University, Taif,
   Saudi Arabia [TURSP-2020/231]
FX This work was supported by Taif University Researchers Supporting
   through Taif University, Taif, Saudi Arabia, under Project
   TURSP-2020/231.
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NR 25
TC 0
Z9 0
U1 3
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 13525
EP 13535
DI 10.1109/ACCESS.2022.3145954
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA YW4RL
UT WOS:000753401500001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, CY
   Fan, Y
   He, XF
   Zhou, AY
AF Wang, Chengyu
   Fan, Yan
   He, Xiaofeng
   Zhou, Aoying
TI Predicting hypernym-hyponym relations for Chinese taxonomy learning
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Hypernym-hyponym relation extraction; Taxonomy expansion; Weakly
   supervised learning; Word embedding; User-generated categories
ID CLASSIFICATION
AB Hypernym-hyponym (is-a) relations are key components in taxonomies, object hierarchies and knowledge graphs. Robustly harvesting of such relations requires the analysis of the linguistic characteristics of is-a word pairs in the target language. While there is abundant research on is-a relation extraction in English, it still remains a challenge to accurately identify such relations from Chinese knowledge sources due to the flexibility of language expression and the significant differences between the two language families. In this paper, we introduce a weakly supervised framework to extract Chinese is-a relations from user-generated categories. It employs piecewise linear projection models trained on an existing Chinese taxonomy built from Wikipedia and an iterative learning algorithm to update model parameters incrementally. A pattern-based relation selection method is proposed to prevent semantic drift in the learning process using bi-criteria optimization. Experimental results on the publicly available test set illustrate that the proposed approach outperforms state-of-the-art methods.
C1 [Wang, Chengyu; Fan, Yan; He, Xiaofeng] East China Normal Univ, Sch Comp Sci & Software Engn, Shanghai 200062, Peoples R China.
   [Zhou, Aoying] East China Normal Univ, Sch Data Sci & Engn, Shanghai 200062, Peoples R China.
RP He, XF (通讯作者)，East China Normal Univ, Sch Comp Sci & Software Engn, Shanghai 200062, Peoples R China.
EM chywang2013@gmail.com; eileen940531@gmail.com; xfhe@sei.ecnu.edu.cn;
   ayzhou@sei.ecnu.edu.cn
FU National Key Research and Development Program of China [2016YFB1000904];
   ECNU Outstanding Doctoral Dissertation Cultivation Plan of Action
   [YB2016040]
FX We thank anonymous reviewers for their very useful comments and
   suggestions. This work is supported by the National Key Research and
   Development Program of China under Grant No. 2016YFB1000904. Chengyu
   Wang is partially supported by the ECNU Outstanding Doctoral
   Dissertation Cultivation Plan of Action under Grant No. YB2016040. This
   manuscript is an extended version of the paper "Chinese Hypernym-Hyponym
   Extraction from User Generated Categories" presented at COLING 2016
   [30]. The Chinese taxonomy construction technique is based on our
   previous work, which was presented at APWeb 2015, entitled "User
   Generated Content Oriented Chinese Taxonomy Construction" [18].
CR Cai JS, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P155
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NR 38
TC 7
Z9 7
U1 1
U2 8
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 MAR
PY 2019
VL 58
IS 3
BP 585
EP 610
DI 10.1007/s10115-018-1166-1
PG 26
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HN2LO
UT WOS:000460016900004
DA 2022-08-23
ER

PT J
AU Abel, M
   Silva, LAL
   Campbell, JA
   De Ros, LF
AF Abel, M
   Silva, LAL
   Campbell, JA
   De Ros, LF
TI Knowledge acquisition and interpretation problem-solving methods for
   visual expertise: A study of petroleum-reservoir evaluation
SO JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
LA English
DT Article; Proceedings Paper
CT International Workshop on Intelligent Computing in the Petroleum
   Industry (ICPI 2003)
CY AUG, 2003
CL Acapulco, MEXICO
DE knowledge engineering; knowledge acquisition; expertise; problem-solving
   method; sedimentary petrography
AB The paper presents a practical study of expertise in sedimentary petrography and its incorporation into a knowledge-based computing system, PetroGrapher. The study identifies features of expert geologists' essentially visual knowledge that limit the usefulness of traditional logic-oriented symbolic methods for developing relevant knowledge-based systems. In particular, the experts' expressions of knowledge differ significantly from what is taught through the literature about their subject. Also, the means (ontology) of describing the explicit objects of geological data are not well-suited to describe how experts combine such objects and make inferences about them. The paper indicates how to deal with those two different levels of ontology, proposes "knowledge graphs" as an effective medium for linking them and explains their place in a general descriptive model of how experts solve problems in interpretation based on visual evidence. The model is being validated through the use of PetroGrapher, for management of data and knowledge about reservoir rocks, in a petroleum company. (c) 2005 Elsevier B.V. All rights reserved.
C1 Univ Fed Rio Grande do Sul, Inst Informat, BR-91501970 Porto Alegre, RS, Brazil.
   UCL, Dept Comp Sci, London WC1E 6BT, England.
   Univ Fed Rio Grande do Sul, Inst Geociencias, BR-91501970 Porto Alegre, RS, Brazil.
RP Abel, M (通讯作者)，Univ Fed Rio Grande do Sul, Inst Informat, Av Bento Goncalves,9500 Campus Vale,Caixa Postal, BR-91501970 Porto Alegre, RS, Brazil.
EM marabel@inf.ufrgs.br; l.silva@cs.ucl.ac.uk; j.campbell@cs.ucl.ac.uk;
   lfderos@inf.ufrgs.br
RI Abel, Mara/L-5392-2015; De Ros, Luiz F/Q-5439-2018; De Lima Silva, Luis
   Alvaro/ABD-8123-2021
OI Abel, Mara/0000-0002-9589-2616; De Ros, Luiz F/0000-0003-2651-8097; De
   Lima Silva, Luis Alvaro/0000-0002-6025-5270
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NR 38
TC 13
Z9 13
U1 0
U2 7
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0920-4105
EI 1873-4715
J9 J PETROL SCI ENG
JI J. Pet. Sci. Eng.
PD MAY 15
PY 2005
VL 47
IS 1-2
BP 51
EP 69
DI 10.1016/j.petrol.2004.11.007
PG 19
WC Energy & Fuels; Engineering, Petroleum
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Energy & Fuels; Engineering
GA 934GM
UT WOS:000229694900006
DA 2022-08-23
ER

PT J
AU Nagdev, S
   Radke, MA
   Ramanath, M
AF Nagdev, Swati
   Radke, Mansi A.
   Ramanath, Maya
TI Automatically Solving Elementary Science Questions: A Survey
SO IETE TECHNICAL REVIEW
LA English
DT Article; Early Access
DE Question answering (QA); Science question answering; Knowledge graph;
   Resource description framework (RDF); Textual entailment; Expression
   tree
AB Traditionally, Question Answering (QA) has been an important task in the field of Artificial Intelligence (AI) and a substantial amount of research has been done in the past proposing several methods to answer questions in an automated way. The process of QA has been viewed differently by different researchers in the past. Till date, the current / existing systems cannot answer elementary science questions which are obvious and easy to answer even for a primary school student. This paper documents various methodologies proposed in the research area of QA specifically for elementary science domain and includes the discussion of each technique used by various methodologies. We briefly present the overview of three components of generalized QA system and discuss various metrics used for evaluating the QA systems. The paper also presents an exhaustive review of various datasets used for the purpose with their respective description and details. We conclude the paper by identifying the limitations of the methodologies used till date and pointing out some future directions for research in the arena of science QA.
C1 [Nagdev, Swati; Radke, Mansi A.] Visvesvaraya Natl Inst Technol, Comp Sci & Engn Dept, Nagpur, Maharashtra, India.
   [Ramanath, Maya] Indian Inst Technol, Comp Sci & Engn Dept, Delhi, India.
RP Nagdev, S (通讯作者)，Visvesvaraya Natl Inst Technol, Comp Sci & Engn Dept, Nagpur, Maharashtra, India.
EM swati.nagdev123@gmail.com; mansi.radke@cse.vnit.ac.in;
   ramanath@cse.iitd.ac.in
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NR 81
TC 0
Z9 0
U1 2
U2 2
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0256-4602
EI 0974-5971
J9 IETE TECH REV
JI IETE Tech. Rev.
DI 10.1080/02564602.2022.2048716
EA MAR 2022
PG 20
WC Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Telecommunications
GA ZZ3CR
UT WOS:000773151800001
DA 2022-08-23
ER

PT J
AU Yan, BN
   Lee, TS
   Lee, TP
AF Yan, Bei-Ni
   Lee, Tian-Shyug
   Lee, Tsung-Pei
TI Analysis of research papers on E-commerce (2000-2013): based on a text
   mining approach
SO SCIENTOMETRICS
LA English
DT Article
DE E-commerce; Research papers; Co-word; Text mining
ID CO-WORD ANALYSIS; E-BUSINESS; NETWORK; CENTRALITY; RETRIEVAL; CITATION;
   FIELD
AB E-commerce (EC) is sweep across the globe and has become a most important commercial activity. Accordingly, EC also causes the academia's research interests. A lot of research achievements have been gained in recent years. This paper takes these achievements as research object and collects 8488 research papers published in academic journals during 2000-2013 included in Web of Science database. Using text mining techniques, 68 terms are identified as the main keywords of EC field. Then the scientific structure of the EC is mapped through multidimensional scaling, based upon the co-occurrence of the main terms in the academic journals. The results show that the EC domain is composed of three main fields, such as technology, management and customer. Furthermore, knowledge graph based on the EC research network is visualized and it shows that the whole EC research papers covering seven important subnets, which are: internet, consumer behaviour, customer satisfaction, online shopping, reputation, Taiwan and knowledge management.
C1 [Yan, Bei-Ni] Anhui Univ, Coll Management, Hefei 230039, Anhui, Peoples R China.
   [Yan, Bei-Ni; Lee, Tian-Shyug; Lee, Tsung-Pei] Fu Jen Catholic Univ, Coll Management, Taipei 24205, Taiwan.
RP Yan, BN (通讯作者)，Anhui Univ, Coll Management, Hefei 230039, Anhui, Peoples R China.
EM yanbenney@163.com
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NR 70
TC 4
Z9 5
U1 0
U2 135
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 2015
VL 105
IS 1
BP 403
EP 417
DI 10.1007/s11192-015-1675-6
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 CS3RP
UT WOS:000361992800026
DA 2022-08-23
ER

PT J
AU Domingo-Fernandez, DB
   Gadiya, Y
   Patel, A
   Mubeen, S
   Rivas-Barragan, D
   Diana, C
   Misra, B
   Healey, D
   Rokicki, J
   Colluru, V
   Mac Gabhann, F
   Herrmann, C
AF Domingo-Fernandez, Daniel B.
   Gadiya, Yojana
   Patel, Abhishek
   Mubeen, Sarah
   Rivas-Barragan, Daniel
   Diana, Chris
   Misra, Biswapriya
   Healey, David
   Rokicki, Joe
   Colluru, Viswa
   Mac Gabhann, Feilim
   Herrmann, Carl
TI Causal reasoning over knowledge graphs leveraging drug-perturbed and
   disease-specific transcriptomic signatures for drug discovery
SO PLOS COMPUTATIONAL BIOLOGY
LA English
DT Article
ID SIGNALING NETWORKS; TARGET
AB Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.
   Author summaryDifferent types of interactions between various biological elements (e.g., proteins, drugs and diseases) can be modeled using networks for various applications, including drug discovery and finding novel use cases of known drugs. Nevertheless, we are far from having a complete picture of all possible biological interactions that can occur in humans, and so, current networks modeling human biology remain incomplete. To try and compensate for this shortcoming, researchers are beginning to use both knowledge of biological interactions, alongside experimental data. In this work, we show how we can deduce which drugs may be good candidates for treatments by using networks to estimate how a drug can affect a disease, and overlaying elements in our network with those in experimental datasets. These experimental datasets can help guide us through the network, showing us which interactions are likely occurring and which are not. Finally, we show that the approach we take can also help us to come up with new research questions and determine which proteins a drug must actually target to produce a therapeutic effect in a patient.
C1 [Domingo-Fernandez, Daniel B.; Gadiya, Yojana; Patel, Abhishek; Diana, Chris; Misra, Biswapriya; Healey, David; Rokicki, Joe; Colluru, Viswa; Mac Gabhann, Feilim; Herrmann, Carl] Enveda Biosci, Boulder, CO 80301 USA.
   [Mubeen, Sarah] Rhein Friedrich Wilhelms Univ Bonn, Bonn Aachen Int Ctr IT, Bonn, Germany.
   [Rivas-Barragan, Daniel] Barcelona Supercomp Ctr, Barcelona, Spain.
RP Domingo-Fernandez, DB; Colluru, V (通讯作者)，Enveda Biosci, Boulder, CO 80301 USA.
EM daniel.domingo-fernandez@envedabio.com; viswa.colluru@envedabio.com
RI Domingo-Fernández, Daniel/AGH-3497-2022; Misra, Biswapriya/H-5136-2013
OI Domingo-Fernández, Daniel/0000-0002-2046-6145; Diana,
   Chris/0000-0002-1864-8150; Rivas, Daniel/0000-0003-0163-7890; Mubeen,
   Sarah/0000-0002-9858-3755; Gadiya, Yojana/0000-0002-7683-0452; Healey,
   David/0000-0002-9584-9757; Misra, Biswapriya/0000-0003-2589-6539
FU Enveda Biosciences
FX DDF,YG, AP, CWD,BBM,DH, JR, and VC have been funded by Enveda
   Biosciences. This work has been funded by
   EnvedaBiosciences(https://www.envedabio.com/).The funders had no role in
   study design ,data collection and analysis ,decision to publish ,or
   preparation of themanuscript.SM and DRB received no specific funding for
   this work
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NR 57
TC 0
Z9 0
U1 10
U2 10
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1553-734X
EI 1553-7358
J9 PLOS COMPUT BIOL
JI PLoS Comput. Biol.
PD FEB
PY 2022
VL 18
IS 2
AR e1009909
DI 10.1371/journal.pcbi.1009909
PG 18
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA ZI3HQ
UT WOS:000761515900001
PM 35213534
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Lu, DL
   Sun, YD
   Dai, QR
   Li, XF
   Zhu, DJ
   Du, HW
   Wang, YS
   Qu, RN
   Cao, N
   O'Hare, GMP
AF Lu, Donglei
   Sun, Yundong
   Dai, Qinrui
   Li, Xiaofang
   Zhu, Dongjie
   Du, Haiwen
   Wang, Yansong
   Qu, Rongning
   Cao, Ning
   O'Hare, Gregory M. P.
TI MSM: A Method of Multi-Neighborhood Sampling Matching for Entity
   Alignment
SO INTELLIGENT AUTOMATION AND SOFT COMPUTING
LA English
DT Article
DE Entity alignment; representation learning; heterogeneous network
AB The heterogeneity of knowledge graphs brings great challenges to entity alignment. In particular, the attributes of network entities in the real world are complex and changeable. The key to solving this problem is to expand the neighborhoods in different ranges and extract the neighborhood information efficiently. Based on this idea, we propose Multi-neighborhood Sampling Matching Network (MSM), a new KG alignment network, aiming at the structural heterogeneity challenge. MSM constructs a multi-neighborhood network representation learning method to learn the KG structure embedding. It then adopts a unique sampling and cosine cross-matching method to solve different sizes of neighborhoods and distinct topological structures in two entities. To choose the right neighbors, we apply a down-sampling process to select the most informative entities towards the central target entity from its one-hop and two-hop neighbors. To verify the effectiveness of matching this neighborhood with any neighborhood in the corresponding node, we give a cosine cross-graph neighborhood matching method and conduct detailed research and analysis on three entity matching data sets, which proves the effectiveness of MSM.
C1 [Lu, Donglei; Cao, Ning] Wuxi Vocat Coll Sci & Technol, Sch Artificial Intelligence, Wuxi 214028, Jiangsu, Peoples R China.
   [Sun, Yundong; Dai, Qinrui; Du, Haiwen] Harbin Inst Technol, Sch Astronaut, Harbin 150001, Peoples R China.
   [Li, Xiaofang; Qu, Rongning] Harbin Inst Technol, Dept Math, Weihai 264209, Peoples R China.
   [Zhu, Dongjie; Wang, Yansong] Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China.
   [O'Hare, Gregory M. P.] Univ Coll Dublin, Sch Comp Sci, Dublin 4, Dublin, Ireland.
RP Li, XF (通讯作者)，Harbin Inst Technol, Dept Math, Weihai 264209, Peoples R China.
EM lixiaofang@hit.edu.cn
RI O'Hare, Gregory M.P./AAA-7756-2022
OI O'Hare, Gregory M.P./0000-0002-5124-1686
FU State Grid Shandong Electric Power Company Science and Technology
   Project [520613200001, 520613180002, 62061318C002]; Weihai Scientific
   Research and Innovation Fund; Sanming University [19YG02]
FX This work is supported by State Grid Shandong Electric Power Company
   Science and Technology Project Funding under Grant Nos. 520613200001,
   520613180002, 62061318C002, Weihai Scientific Research and Innovation
   Fund (2020) and the Grant 19YG02, Sanming University.
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NR 20
TC 0
Z9 0
U1 4
U2 7
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.
PY 2022
VL 32
IS 2
BP 1141
EP 1151
DI 10.32604/iasc.2022.020218
PG 11
WC Automation & Control Systems; Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA XA7TM
UT WOS:000720844200006
OA hybrid
DA 2022-08-23
ER

PT J
AU Lv, CY
   Pan, D
   Li, YX
   Li, JX
   Wang, Z
AF Lv, Chengyao
   Pan, Deng
   Li, Yaxiong
   Li, Jianxin
   Wang, Zong
TI A Novel Chinese Entity Relationship Extraction Method Based on the
   Bidirectional Maximum Entropy Markov Model
SO COMPLEXITY
LA English
DT Article
AB To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. To address this problem, a novel Chinese relationship extraction method is proposed in this paper. Firstly, the triple is treated as an entity relation chain and can identify the entity before the relationship and predict its corresponding relationship and the entity after the relationship. Secondly, the Joint Extraction of Entity Mentions and Relations model is based on the Bidirectional Long Short-Term Memory and Maximum Entropy Markov Model (Bi-MEMM). Experimental results indicate that the proposed model can achieve a precision of 79.2% which is much higher than that of traditional models.
C1 [Lv, Chengyao] China Univ Geosci, Sch Foreign Languages, Wuhan 430074, Peoples R China.
   [Pan, Deng] Hubei Univ Sci & Technol, Sch Foreign Languages, Xianning 437000, Peoples R China.
   [Li, Yaxiong] Hubei Univ Sci & Technol, Informat Ctr, Xianning 437000, Peoples R China.
   [Li, Jianxin] Deakin Univ, Sch Informat Technol, Geelong, Vic 3220, Australia.
   [Wang, Zong] China Univ Geosci, Sch Publ Adm, Wuhan 430074, Peoples R China.
RP Li, YX (通讯作者)，Hubei Univ Sci & Technol, Informat Ctr, Xianning 437000, Peoples R China.
EM 83520583@qq.com; dylanpan@hbust.edu.cn; liyaxiong@hbust.edu.cn;
   jianxin.li@deakin.edu.au; wangzhong@cug.edu.cn
FU National Natural Science Foundation of China [U1711266, 62076224,
   41925007]; Department of Education Neo-Generation Information Technology
   Innovation Project [2018A03006, 2018A02021]; General Project of
   Education Humanities and Social Sciences in Hubei Province [18Y38]
FX This paper was partially supported by the National Natural Science
   Foundation of China (Nos. U1711266, 62076224, and 41925007), Department
   of Education Neo-Generation Information Technology Innovation Project
   (Nos. 2018A03006 and 2018A02021), and General Project of Education
   Humanities and Social Sciences in Hubei Province (No. 18Y38).
CR Aitken JS, 2002, FRONT ARTIF INTEL AP, V77, P355
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NR 35
TC 1
Z9 1
U1 10
U2 24
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 JAN 19
PY 2021
VL 2021
AR 6610965
DI 10.1155/2021/6610965
PG 8
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA QE3KJ
UT WOS:000616107800005
OA gold
DA 2022-08-23
ER

PT J
AU Yin, D
   Gao, H
AF Yin, Dan
   Gao, Hong
TI A flexible aggregation framework on large-scale heterogeneous
   information networks
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Aggregation; entropy; heterogeneous information networks; OLAP
ID GRAPH ENTROPY; DISCOVERY
AB OLAP (On-line Analytical Processing) can provide users with aggregate results from different perspectives and granularities. With the advent of heterogeneous information networks that consist of multi-type, interconnected nodes, such as bibliographic networks and knowledge graphs, it is important to study flexible aggregation in such networks. The aggregation results by existing work are limited to one type of node, which cannot be applied to aggregation on multi-type nodes, and relations in large-scale heterogeneous information networks. In this paper, we investigate the flexible aggregation problem on large-scale heterogeneous information networks, which is defined on multi-type nodes and relations. Moreover, by considering both attributes and structures, we propose a novel function based on graph entropy to measure the similarities of nodes. Further, we prove that the aggregation problem based on the function is NP-hard. Therefore, we develop an efficient heuristic algorithm for aggregation in two phases: informational aggregation and structural aggregation. The algorithm has linear time and space complexity. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm.
C1 [Yin, Dan; Gao, Hong] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China.
RP Yin, D (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China.
EM yindan630@gmail.com
FU National Grand Fundamental Research 973 Program [2012CB316200]; Major
   Program of National Science Foundation [61190115]; General Program of
   Natural Science Foundation [61173023]; Key Program of Natural Science
   Foundation [61532015]
FX This work is supported by the National Grand Fundamental Research 973
   Program (grant number 2012CB316200), the Major Program of National
   Science Foundation (grant number 61190115), the General Program of
   Natural Science Foundation (grant number 61173023) and the Key Program
   of Natural Science Foundation (grant number 61532015).
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NR 60
TC 3
Z9 3
U1 0
U2 24
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 APR
PY 2017
VL 43
IS 2
BP 186
EP 203
DI 10.1177/0165551516630237
PG 18
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA EM6AQ
UT WOS:000395395600004
DA 2022-08-23
ER

PT J
AU Abedini, F
   Keyvanpour, MR
   Menhaj, MB
AF Abedini, Farhad
   Keyvanpour, Mohammad Reza
   Menhaj, Mohammad Bagher
TI EPCI: An Embedding Method for Post-Correction of Inconsistency in the
   RDF Knowledge Bases
SO IETE JOURNAL OF RESEARCH
LA English
DT Article
DE Embedding; Error correction; Inconsistency; Knowledge graph; RDF
   knowledge base
ID WEB
AB There are many methods for the RDF knowledge base correction in which errors are detected and then deleted or repaired if necessary. Although most of them do only detection and deletion of the errors and do not pay attention to repair. There are only a few methods to repair these errors that their accuracy is not acceptable. To solve this problem, a method will be proposed with a suitable accuracy that is called EPCI. The focus of EPCI is on inconsistency errors correction because previous correction methods have paid less attention to these kinds of errors. Most of its existing studies avoid these errors before knowledge base creation in the form of pre-correction. Although in some cases, it is needed to correct these errors after its creation as post-correction. For this reason, in EPCI a post-correction method is proposed for this objective. In the studied post-correction methods so far it has not been mentioned that inconsistency can be solved with the embedding methods and most of them solved only numeric inconsistencies. Nowadays, embedding methods are rapidly growing with high precision. So in EPCI, this technique will be used to solve also the problem of non-numeric inconsistent triples.
C1 [Abedini, Farhad] Islamic Azad Univ, Fac Comp & Informat Technol Engn, Qazvin, Iran.
   [Keyvanpour, Mohammad Reza] Alzahra Univ, Dept Comp Engn, Tehran, Iran.
   [Menhaj, Mohammad Bagher] Amirkabir Univ Technol, Dept Elect Engn, Ctr Excellence Control & Robot, 424 Hafez Ave, Tehran, Iran.
RP Abedini, F (通讯作者)，Islamic Azad Univ, Fac Comp & Informat Technol Engn, Qazvin, Iran.
EM abedini.ac@gmail.com; keyvanpour@alzahra.ac.ir; menhaj@aut.ac.ir
RI Keyvanpour, Mohammad Reza/AAL-5574-2020; Abedini, Farhad/ABG-9635-2021
OI Keyvanpour, Mohammad Reza/0000-0003-2115-9099; Abedini,
   Farhad/0000-0002-1475-9427
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NR 72
TC 1
Z9 1
U1 0
U2 2
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0377-2063
EI 0974-780X
J9 IETE J RES
JI IETE J. Res.
PD MAR 4
PY 2022
VL 68
IS 2
BP 1043
EP 1055
DI 10.1080/03772063.2019.1634495
EA JUL 2019
PG 13
WC Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Telecommunications
GA 2E3MH
UT WOS:000475168700001
DA 2022-08-23
ER

PT J
AU Nourani, E
AF Nourani, Esmaeil
TI GoVec: Gene Ontology Representation Learning Using Weighted
   Heterogeneous Graph and Meta-Path
SO JOURNAL OF COMPUTATIONAL BIOLOGY
LA English
DT Article; Early Access
DE Gene Ontology; heterogeneous graph; meta-path; representation learning
ID SEMANTIC SIMILARITY MEASURES; INFORMATION-CONTENT; GO TERMS
AB Biomedical knowledge graphs are crucial to support data-intensive applications in the life sciences and health care. These graphs can be extended by generating a heterogeneous graph that contains both ontology terms and biomedical entities. However, state-of-the-art approaches for Gene Ontology representation learnings are constrained to homogeneous graphs that cannot represent different node types and relations. To address this limitation, we present GoVec to produce representations seamlessly for both ontologies and biological entities by utilizing meta-path-based representation learning in the heterogeneous graph. The resulting vectors can be used in many bioinformatics applications, particularly for calculating semantic similarity and extracting relations among biological entities. We verify the approach's usefulness by comparing the resulting semantic similarities with the manually produced similarities by the experts. Furthermore, the superiority of the GoVec is shown by an extensive set of quantitative and qualitative evaluations. Two downstream tasks, including protein-protein interaction and protein family similarity, are evaluated in comparison with many state-of-the-art approaches. Finally, as a qualitative visual representation, the separability of various protein families is examined and visually separable groups of proteins are generated, which shows the capability of GoVec representations to embed functional semantics into the vectors.
C1 [Nourani, Esmaeil] Azarbaijan Shahid Madani Univ, Fac Comp Engn & Informat Technol, Dept Informat Technol, 35 Km Tabriz Maragheh Rd, Tabriz 53714161, Iran.
   [Nourani, Esmaeil] Univ Copenhagen, Novo Nordisk Fdn, Fac Hlth Sci, Ctr Prot Res, Copenhagen, Denmark.
RP Nourani, E (通讯作者)，Azarbaijan Shahid Madani Univ, Fac Comp Engn & Informat Technol, Dept Informat Technol, 35 Km Tabriz Maragheh Rd, Tabriz 53714161, Iran.
EM ac.nourani@azaruniv.ac.ir
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NR 47
TC 0
Z9 0
U1 2
U2 5
PU MARY ANN LIEBERT, INC
PI NEW ROCHELLE
PA 140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA
SN 1066-5277
EI 1557-8666
J9 J COMPUT BIOL
JI J. Comput. Biol.
DI 10.1089/cmb.2021.0069
EA NOV 2021
PG 12
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA XI4GT
UT WOS:000726072900001
PM 34847734
DA 2022-08-23
ER

PT J
AU Corcoglioniti, F
   Rospocher, M
   Aprosio, AP
AF Corcoglioniti, Francesco
   Rospocher, Marco
   Aprosio, Alessio Palmero
TI Frame-Based Ontology Population with PIKES
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Ontology population; Semantic Web; natural language processing; semantic
   role labeling; FrameBase
ID KNOWLEDGE-BASE
AB We present an approach for ontology population from natural language English texts that extracts RDF triples according to FrameBase, a Semantic Web ontology derived from FrameNet. Processing is decoupled in two independently-tunable phases. First, text is processed by several NLP tasks, including Semantic Role Labeling (SRL), whose results are integrated in an RDF graph of mentions, i.e., snippets of text denoting some entity/fact. Then, the mention graph is processed with SPARQL-like rules using a specifically created mapping resource from NomBank/PropBank/FrameNet annotations to FrameBase concepts, producing a knowledge graph whose content is linked to DBpedia and organized around semantic frames, i.e., prototypical descriptions of events and situations. A single RDF/OWL representation is used where each triple is related to the mentions/tools it comes from. We implemented the approach in PIKES, an open source tool that combines two complementary SRL systems and provides a working online demo. We evaluated PIKES on a manually annotated gold standard, assessing precision/recall in (i) populating FrameBase ontology, and (ii) extracting semantic frames modeled after standard predicate models, for comparison with state-of-the-art tools for the Semantic Web. We also evaluated (iii) sampled precision and execution times on a large corpus of 110 K Wikipedia-like pages.
C1 [Corcoglioniti, Francesco; Rospocher, Marco; Aprosio, Alessio Palmero] Fdn Bruno Kessler, I-38122 Trento, Italy.
RP Corcoglioniti, F (通讯作者)，Fdn Bruno Kessler, I-38122 Trento, Italy.
EM corcoglio@fbk.eu; rospocher@fbk.eu; aprosio@fbk.eu
RI Rospocher, Marco/AGW-8382-2022; Aprosio, Alessio Palmero/ABD-9105-2020;
   Rospocher, Marco/AAD-9108-2022
OI Aprosio, Alessio Palmero/0000-0002-1484-0882; Rospocher,
   Marco/0000-0001-9391-3201; Corcoglioniti, Francesco/0000-0002-0656-606X
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NR 36
TC 23
Z9 23
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 DEC 1
PY 2016
VL 28
IS 12
BP 3261
EP 3275
DI 10.1109/TKDE.2016.2602206
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 EC5ZB
UT WOS:000388214700010
DA 2022-08-23
ER

PT J
AU Bellandi, V
   Ceravolo, P
   Maghool, S
   Siccardi, S
AF Bellandi, Valerio
   Ceravolo, Paolo
   Maghool, Samira
   Siccardi, Stefano
TI Toward a General Framework for Multimodal Big Data Analysis
SO BIG DATA
LA English
DT Article; Early Access
DE Big Data; big graph; data fusion; multimodal analysis
ID MODEL
AB Multimodal Analytics in Big Data architectures implies compounded configurations of the data processing tasks. Each modality in data requires specific analytics that triggers specific data processing tasks. Scalability can be reached at the cost of an attentive calibration of the resources shared by the different tasks searching for a trade-off with the multiple requirements they impose. We propose a methodology to address multimodal analytics within the same data processing approach to get a simplified architecture that can fully exploit the potential of the parallel processing of Big Data infrastructures. Multiple data sources are first integrated into a unified knowledge graph (KG). Different modalities of data are addressed by specifying ad hoc views on the KG and producing a rewriting of the graph containing merely the data to be processed. Graph traversal and rule extraction are this way boosted. Using graph embeddings methods, the different ad hoc views can be transformed into low-dimensional representation following the same data format. This way a single machine learning procedure can address the different modalities, simplifying the architecture of our system. The experiments we executed demonstrate that our approach reduces the cost of execution and improves the accuracy of analytics.
C1 [Bellandi, Valerio; Ceravolo, Paolo; Maghool, Samira; Siccardi, Stefano] Univ Milan, Dept Comp Sci, Milan, Italy.
   [Bellandi, Valerio; Ceravolo, Paolo] CINI Consorzio Interuniv Nazl Informat, Rome, Italy.
RP Bellandi, V (通讯作者)，Univ Milan, Dept Comp Sci, Milan, Italy.; Bellandi, V (通讯作者)，CINI Consorzio Interuniv Nazl Informat, Rome, Italy.
EM valerio.bellandi@unimi.it
RI bellandi, valerio/M-7582-2019
OI bellandi, valerio/0000-0003-4473-6258; Siccardi,
   Stefano/0000-0002-6477-3876
FU University of Milan
FX This work is partially funded by "Sostegno alla Ricerca (PSR 2020)" of
   the University of Milan.
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NR 38
TC 0
Z9 0
U1 2
U2 2
PU MARY ANN LIEBERT, INC
PI NEW ROCHELLE
PA 140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA
SN 2167-6461
EI 2167-647X
J9 BIG DATA
JI Big Data
DI 10.1089/big.2021.0326
EA JUN 2022
PG 17
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1X2FI
UT WOS:000807275200001
PM 35666602
DA 2022-08-23
ER

PT J
AU Jian, M
   Zhang, CL
   Fu, X
   Wu, LF
   Wang, ZQ
AF Jian, Meng
   Zhang, Chenlin
   Fu, Xin
   Wu, Lifang
   Wang, Zhangquan
TI Knowledge-Aware Multispace Embedding Learning for Personalized
   Recommendation
SO SENSORS
LA English
DT Article
DE collaborative filtering; user interest; knowledge graph; recommender
   system
AB Recommender systems help users filter items they may be interested in from massive multimedia content to alleviate information overload. Collaborative filtering-based models perform recommendation relying on users' historical interactions, which meets great difficulty in modeling users' interests with extremely sparse interactions. Fortunately, the rich semantics hidden in items may be promising in helping to describing users' interests. In this work, we explore the semantic correlations between items on modeling users' interests and propose knowledge-aware multispace embedding learning (KMEL) for personalized recommendation. KMEL attempts to model users' interests across semantic structures to leverage valuable knowledge. High-order semantic collaborative signals are extracted in multiple independent semantic spaces and aggregated to describe users' interests in each specific semantic. The semantic embeddings are adaptively integrated with a target-aware attention mechanism to learn cross-space multisemantic embeddings for users and items, which are fed to the subsequent pairwise interaction layer for personalized recommendation. Experiments on real-world datasets demonstrate the effectiveness of the proposed KMEL model.
C1 [Jian, Meng; Zhang, Chenlin; Wu, Lifang] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China.
   [Fu, Xin] Univ Jinan, Sch Water Conservancy & Environm, Jinan 250022, Peoples R China.
   [Wang, Zhangquan] Inner Mongolia Aerosp Power Machinery Testing Ins, Hohhot 010076, Peoples R China.
RP Wu, LF (通讯作者)，Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China.
EM mjian@bjut.edu.cn; zhangchenlin@emails.bjut.edu.cn; stu_fux@ujn.edu.cn;
   lfwu@bjut.edu.cn; zhangquanw@163.com
OI Jian, Meng/0000-0001-5659-5128
FU National Natural Science Foundation of China [62176011, 61702022,
   61802011, 61976010]; Beijing Municipal Education Committee Science
   Foundation [KM201910005024]; Inner Mongolia Autonomous Region Science
   and Technology Foundation [2021GG0333]; Beijing Postdoctoral Research
   Foundation [Q6042001202101]
FX This research was funded by the National Natural Science Foundation of
   China under Grant NO. 62176011, NO. 61702022, NO. 61802011, and NO.
   61976010, Beijing Municipal Education Committee Science Foundation under
   Grant NO. KM201910005024, Inner Mongolia Autonomous Region Science and
   Technology Foundation under Grant NO. 2021GG0333, and Beijing
   Postdoctoral Research Foundation under Grant NO. Q6042001202101.
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NR 30
TC 0
Z9 0
U1 12
U2 12
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD MAR
PY 2022
VL 22
IS 6
AR 2212
DI 10.3390/s22062212
PG 14
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA 0B4QR
UT WOS:000774621800001
PM 35336383
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Said, A
   Hassan, SU
   Tuarob, S
   Nawaz, R
   Shabbir, M
AF Said, Anwar
   Hassan, Saeed-Ul
   Tuarob, Suppawong
   Nawaz, Raheel
   Shabbir, Mudassir
TI DGSD: Distributed graph representation via graph statistical properties
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Graph embedding; Distributed computing; Batch processing; Graph
   classification
ID KNOWLEDGE GRAPH
AB Graph encoding methods have been proven exceptionally useful in many classification tasks - from molecule toxicity prediction to social network recommendations. However, most of the existing methods are designed to work in a centralized environment that requires the whole graph to be kept in memory. Moreover, scaling them on very large networks remains a challenge. In this work, we propose a distributed and permutation invariant graph embedding method denoted as Distributed Graph Statistical Distance (DGSD) that extracts graph representation on independently distributed machines. DGSD finds nodes' local proximity by considering only nodes' degree, common neighbors and direct connectivity that allows it to run in the distributed environment. On the other hand, the linear space complexity of DGSD makes it suitable for processing large graphs. We show the scalability of DGSD on sufficiently large random and real-world networks and evaluate its performance on various bioinformatics and social networks with the implementation in a distributed computing environment. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Said, Anwar; Hassan, Saeed-Ul; Shabbir, Mudassir] Informat Technol Univ, Dept Comp Sci, Lahore, Pakistan.
   [Tuarob, Suppawong] Mahidol Univ, Fac Informat & Commun Technol, Nakhon Pathom, Thailand.
   [Nawaz, Raheel] Manchester Metropolitan Univ, Dept Operat Technol Events & Hospitality Manageme, Manchester, Lancs, England.
RP Hassan, SU (通讯作者)，Informat Technol Univ, Dept Comp Sci, Lahore, Pakistan.
EM anwar.said@itu.edu.pk; saeed-ul-nassan@itu.edu.pk;
   suppawong.tua@mahidol.edu; r.nawaz@mmu.ac.uk;
   mudassir.shabbir@itu.edu.pk
RI Nawaz, Raheel/AAX-5293-2021; shabbir, mudassir/AAC-1396-2022; Said,
   Anwar/AAG-3660-2019; Hassan, Saeed-Ul/G-1889-2016
OI Nawaz, Raheel/0000-0001-9588-0052; Said, Anwar/0000-0002-6715-0068;
   Hassan, Saeed-Ul/0000-0002-6509-9190
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   Zhang Z.-L., ARXIV PREPRINT ARXIV
NR 51
TC 3
Z9 3
U1 2
U2 4
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 2021
VL 119
BP 166
EP 175
DI 10.1016/j.future.2021.02.005
EA FEB 2021
PG 10
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RB3WX
UT WOS:000632046100014
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Shi, BX
   Yang, L
   Weninger, T
AF Shi, Baoxu
   Yang, Lin
   Weninger, Tim
TI Forward backward similarity search in knowledge networks
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Similarity measures; Graph search
ID LINK-PREDICTION
AB Similarity search is a fundamental problem in social and knowledge networks like GitHub, DBLP, Wikipedia, etc. Existing network similarity measures are limited because they only consider similarity from the perspective of the query node. However, due to the complicated topology of real-world networks, ignoring the preferences of target nodes often results in odd or unintuitive performance. In this work, we propose a dual perspective similarity metric called Forward Backward Similarity (FBS) that efficiently computes topological similarity from the perspective of both the query node and the perspective of candidate nodes. The effectiveness of our method is evaluated by traditional quantitative ranking metrics and large-scale human judgement on four large real world networks. The proposed method matches human preference arid outperforms other similarity search algorithms on community overlap and link prediction. Finally, we demonstrate top-5 rankings for five famous researchers on an academic collaboration network to illustrate how our approach captures semantics more intuitively than other approaches. (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.
   [Yang, Lin] Univ Notre Dame, Dept Comp Sci & Engn, 326E Cushing 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; lyang5@nd.edu; tweninge@nd.edu
OI Weninger, Tim/0000-0003-3164-2615
FU Templeton Foundation [FP053369-M/O]
FX This work is supported by the Templeton Foundation under grant
   FP053369-M/O.
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   [No title captured]
NR 54
TC 6
Z9 6
U1 0
U2 21
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
PY 2017
VL 119
BP 20
EP 31
DI 10.1016/j.knosys.2016.11.025
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EJ9FQ
UT WOS:000393532400003
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Tan, L
   Wang, G
   Jia, FY
   Lian, XF
AF Tan, Li
   Wang, Ge
   Jia, Feiyang
   Lian, Xiaofeng
TI Research status of deep learning methods for rumor detection
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article; Early Access
DE Rumor detection; Deep learning; Social media; Research status
ID FAKE NEWS DETECTION; DETECTION FRAMEWORK; PROPAGATION; INFORMATION;
   CONTEXT
AB To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks. To comprehensively sort out the research status of rumor detection from multiple perspectives, this paper analyzes the highly focused work from three perspectives: Feature Selection, Model Structure, and Research Methods. From the perspective of feature selection, we divide methods into content feature, social feature, and propagation structure feature of the rumors. Then, this work divides deep learning models of rumor detection into CNN, RNN, GNN, Transformer based on the model structure, which is convenient for comparison. Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods, based knowledge graph, and other methods for the first time. And compare the advantages of different methods to detect rumors. In addition, this review enumerate datasets available and discusses the potential issues and future work to help researchers advance the development of field.
C1 [Tan, Li; Wang, Ge; Jia, Feiyang] Beijing Technol & Business Univ, Sch Comp Sci & Engn, Beijing 100048, Peoples R China.
   [Lian, Xiaofeng] Beijing Technol & Business Univ, Sch Artificial Intelligence, Beijing 100048, Peoples R China.
RP Tan, L (通讯作者)，Beijing Technol & Business Univ, Sch Comp Sci & Engn, Beijing 100048, Peoples R China.
EM tanli@th.btbu.edu.cn; wanggestu@163.com; jfy539@yeah.net;
   lianxf@th.btbu.edu.cn
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NR 127
TC 0
Z9 0
U1 16
U2 16
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/s11042-022-12800-8
EA APR 2022
PG 42
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 0Q1IV
UT WOS:000784679300021
PM 35469150
OA Bronze, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Cheng, WN
   Khoo, CSG
AF Cheng, Wei-Ning
   Khoo, Christopher S. G.
TI Information structures in sociology research papers: Modeling
   cause-effect and comparison relations in research objective and result
   statements(1)
SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID CITATION
AB When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause-effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause-effect and comparison relations, and used as coding schemes to annotate the text for different information types. Six link patterns between the two frames were identified-showing how comparisons are used to support the claim that the cause-effect relation is valid. This study demonstrated how semantic frames can be incorporated in discourse analysis to identify deep structures underlying the argument structure. The results carry implications for the knowledge representation of academic research in knowledge graphs, for semantic relation extraction, and teaching of academic writing.
C1 [Cheng, Wei-Ning] Shanghai Univ, Sch Management, Dept Lib Informat & Arch, Shanghai, Peoples R China.
   [Khoo, Christopher S. G.] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, 31 Nanyang Link, Singapore 637718, Singapore.
RP Khoo, CSG (通讯作者)，Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, 31 Nanyang Link, Singapore 637718, Singapore.
EM chriskhoo@pmail.ntu.edu.sg
OI Cheng, Wei-Ning/0000-0001-6197-1503
FU Singapore Ministry of Education [MOE2015-1-TR05]
FX Singapore Ministry of Education, Grant/Award Number: MOE2015-1-TR05
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NR 31
TC 0
Z9 0
U1 4
U2 15
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2330-1635
EI 2330-1643
J9 J ASSOC INF SCI TECH
PD NOV
PY 2021
VL 72
IS 11
BP 1367
EP 1385
DI 10.1002/asi.24486
EA APR 2021
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 UZ6FQ
UT WOS:000645303100001
DA 2022-08-23
ER

PT J
AU Yuste, R
   Hawrylycz, M
   Aalling, N
   Aguilar-Valles, A
   Arendt, D
   Arnedillo, RA
   Ascoli, GA
   Bielza, C
   Bokharaie, V
   Bergmann, TB
   Bystron, I
   Capogna, M
   Chang, Y
   Clemens, A
   de Kock, CPJ
   DeFelipe, J
   Dos Santos, SE
   Dunville, K
   Feldmeyer, D
   Fiath, R
   Fishell, GJ
   Foggetti, A
   Gao, XF
   Ghaderi, P
   Goriounova, NA
   Gunturkun, O
   Hagihara, K
   Hall, VJ
   Helmstaedter, M
   Herculano, S
   Hilscher, MM
   Hirase, H
   Hjerling-Leffler, J
   Hodge, R
   Huang, J
   Huda, R
   Khodosevich, K
   Kiehn, O
   Koch, H
   Kuebler, ES
   Kuhnemund, M
   Larranaga, P
   Lelieveldt, B
   Louth, EL
   Lui, JH
   Mansvelder, HD
   Marin, O
   Martinez-Trujillo, J
   Chameh, HM
   Nath, A
   Nedergaard, M
   Nemec, P
   Ofer, N
   Pfisterer, UG
   Pontes, S
   Redmond, W
   Rossier, J
   Sanes, JR
   Scheuermann, R
   Serrano-Saiz, E
   Steiger, JF
   Somogyi, P
   Tamas, G
   Tolias, AS
   Tosches, MA
   Garcia, MT
   Vieira, HM
   Wozny, C
   Wuttke, TV
   Yong, L
   Yuan, J
   Zeng, HK
   Lein, E
AF Yuste, Rafael
   Hawrylycz, Michael
   Aalling, Nadia
   Aguilar-Valles, Argel
   Arendt, Detlev
   Arnedillo, Ruben Armananzas
   Ascoli, Giorgio A.
   Bielza, Concha
   Bokharaie, Vahid
   Bergmann, Tobias Borgtoft
   Bystron, Irina
   Capogna, Marco
   Chang, Yoonjeung
   Clemens, Ann
   de Kock, Christiaan P. J.
   DeFelipe, Javier
   Dos Santos, Sandra Esmeralda
   Dunville, Keagan
   Feldmeyer, Dirk
   Fiath, Richard
   Fishell, Gordon James
   Foggetti, Angelica
   Gao, Xuefan
   Ghaderi, Parviz
   Goriounova, Natalia A.
   Guentuerkuen, Onur
   Hagihara, Kenta
   Hall, Vanessa Jane
   Helmstaedter, Moritz
   Herculano, Suzana
   Hilscher, Markus M.
   Hirase, Hajime
   Hjerling-Leffler, Jens
   Hodge, Rebecca
   Huang, Josh
   Huda, Rafiq
   Khodosevich, Konstantin
   Kiehn, Ole
   Koch, Henner
   Kuebler, Eric S.
   Kuhnemund, Malte
   Larranaga, Pedro
   Lelieveldt, Boudewijn
   Louth, Emma Louise
   Lui, Jan H.
   Mansvelder, Huibert D.
   Marin, Oscar
   Martinez-Trujillo, Julio
   Moradi Chameh, Homeira
   Nath, Alok
   Nedergaard, Maiken
   Nemec, Pavel
   Ofer, Netanel
   Pfisterer, Ulrich Gottfried
   Pontes, Samuel
   Redmond, William
   Rossier, Jean
   Sanes, Joshua R.
   Scheuermann, Richard
   Serrano-Saiz, Esther
   Steiger, Jochen F.
   Somogyi, Peter
   Tamas, Gabor
   Tolias, Andreas Savas
   Tosches, Maria Antonietta
   Garcia, Miguel Turrero
   Vieira, Hermany Munguba
   Wozny, Christian
   Wuttke, Thomas V.
   Yong, Liu
   Yuan, Juan
   Zeng, Hongkui
   Lein, Ed
TI A community-based transcriptomics classification and nomenclature of
   neocortical cell types
SO NATURE NEUROSCIENCE
LA English
DT Article
ID RNA-SEQ; VISUAL-CORTEX; INTERNEURONS; EVOLUTION; NEURONS; ARCHITECTURE;
   RECONSTRUCTION; ORGANIZATION; TAXONOMY; IDENTITY
AB To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.
C1 [Yuste, Rafael; Pontes, Samuel; Tosches, Maria Antonietta] Columbia Univ, New York, NY 10027 USA.
   [Hawrylycz, Michael; Hodge, Rebecca; Zeng, Hongkui; Lein, Ed] Allen Inst Brain Sci, Seattle, WA 98109 USA.
   [Aalling, Nadia; Bergmann, Tobias Borgtoft; Hall, Vanessa Jane; Khodosevich, Konstantin; Kiehn, Ole; Pfisterer, Ulrich Gottfried] Univ Copenhagen, Copenhagen, Denmark.
   [Aguilar-Valles, Argel] Carleton Univ, Ottawa, ON, Canada.
   [Arendt, Detlev] European Mol Biol Lab Heidelberg, Heidelberg, Germany.
   [Arnedillo, Ruben Armananzas; Ascoli, Giorgio A.] George Mason Univ, Fairfax, VA USA.
   [Bielza, Concha; Larranaga, Pedro] Tech Univ Madrid, Madrid, Spain.
   [Bokharaie, Vahid] Max Planck Inst, Tubingen, Germany.
   [Bystron, Irina; Somogyi, Peter] Univ Oxford, Oxford, England.
   [Capogna, Marco; Louth, Emma Louise] Aarhus Univ, Aarhus, Denmark.
   [Chang, Yoonjeung; Fishell, Gordon James; Sanes, Joshua R.; Garcia, Miguel Turrero] Harvard Med Sch, Cambridge, MA USA.
   [Clemens, Ann] Univ Edinburgh, Edinburgh, Scotland.
   [de Kock, Christiaan P. J.; Goriounova, Natalia A.; Mansvelder, Huibert D.] Vrije Univ Amsterdam, Amsterdam, Netherlands.
   [DeFelipe, Javier] Cajal Inst Neurobiol, Madrid, Spain.
   [Dos Santos, Sandra Esmeralda; Herculano, Suzana] Vanderbilt Univ, Nashville, TN USA.
   [Dunville, Keagan] Scuola Normale Super Pisa, Pisa, Italy.
   [Feldmeyer, Dirk] JARA Brain Inst Neurosci & Med, Julich, Germany.
   [Fiath, Richard] Res Ctr Nat Sci, Budapest, Budapest, Hungary.
   [Foggetti, Angelica] Christian Albrechts Univ Kiel, Kiel, Germany.
   [Gao, Xuefan] European Mol Biol Lab, Hamburg, Germany.
   [Ghaderi, Parviz] Ecole Polytech Fed Lausanne, Lausanne, Switzerland.
   [Guentuerkuen, Onur] Ruhr Univ Bochum, Bochum, Germany.
   [Hagihara, Kenta] Friedrich Miescher Inst Biol Res, Basel, Switzerland.
   [Helmstaedter, Moritz] Max Planck Inst Brain Res, Frankfurt, Germany.
   [Hilscher, Markus M.; Hjerling-Leffler, Jens; Vieira, Hermany Munguba; Yuan, Juan] Karolinska Inst, Stockholm, Sweden.
   [Hirase, Hajime] RIKEN Ctr Brain Sci, Saitama, Japan.
   [Huang, Josh] Cold Spring Harbor Lab, Laurel, MD USA.
   [Huda, Rafiq] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
   [Koch, Henner; Wuttke, Thomas V.] Rhein Westfal TH Aachen, Aachen, Germany.
   [Kuebler, Eric S.] Robarts Res Inst, London, ON, Canada.
   [Kuhnemund, Malte] CARTANA, Stockholm, Sweden.
   [Lelieveldt, Boudewijn] Leiden Univ, Leiden, Netherlands.
   [Lui, Jan H.] Stanford Univ, Stanford, CA 94305 USA.
   [Marin, Oscar] Kings Coll London, London, England.
   [Martinez-Trujillo, Julio] Univ Western Ontario, London, ON, Canada.
   [Moradi Chameh, Homeira] Krembil Res Inst, Toronto, ON, Canada.
   [Nath, Alok] Univ Haifa, Haifa, Palestine.
   [Nedergaard, Maiken] Univ Rochester, Rochester, NY USA.
   [Nemec, Pavel] Charles Univ Prague, Prague, Czech Republic.
   [Ofer, Netanel] Bar Ilan Univ, Ramat Gan, Palestine.
   [Redmond, William] Macquarie Univ, Sydney, NSW, Australia.
   [Rossier, Jean] Sarbonne Univ, Paris, France.
   [Scheuermann, Richard] J Craig Venter Inst, La Jolla, CA USA.
   [Serrano-Saiz, Esther] Severo Ochoa Ctr Mol Biol, Madrid, Spain.
   [Steiger, Jochen F.] Univ Gottingen, Gottingen, Germany.
   [Tamas, Gabor] Univ Szeged, Szeged, Szeged, Hungary.
   [Tolias, Andreas Savas] Baylor Coll Med, Houston, TX 77030 USA.
   [Wozny, Christian] Univ Strathclyde, Glasgow, Lanark, Scotland.
   [Yong, Liu] NYU, Sch Engn, New York, NY USA.
RP Yuste, R (通讯作者)，Columbia Univ, New York, NY 10027 USA.; Hawrylycz, M; Zeng, HK; Lein, E (通讯作者)，Allen Inst Brain Sci, Seattle, WA 98109 USA.
EM rmy5@columbia.edu; MikeH@alleninstitute.org;
   HongkuiZ@alleninstitute.org; EdL@alleninstitute.org
RI Wozny, Christian/A-1498-2015; Hirase, Hajime/A-3709-2012; Bielza,
   Concha/F-9277-2013; Hilscher, Markus M./I-5941-2013; Nemec,
   Pavel/K-1163-2012; Mansvelder, Huibert/N-6138-2018; Goriounova,
   Natalia/ABE-6759-2020; Louth, Emma/AAN-7517-2020; Dunville,
   Keagan/ABA-3136-2021; Larranaga, Pedro/F-9293-2013; kiehn,
   ole/AAT-7881-2021; Aalling, Nadia/AAG-5669-2020; Serrano-Saiz,
   Esther/AAO-7791-2021; Ofer, Netanel/AAB-9345-2019; Armananzas,
   Ruben/C-2735-2013; Hjerling Leffler, Jens/E-3775-2012
OI Wozny, Christian/0000-0003-4220-2033; Hirase,
   Hajime/0000-0003-3806-6905; Bielza, Concha/0000-0001-7109-2668;
   Hilscher, Markus M./0000-0001-7782-0830; Nemec,
   Pavel/0000-0003-0277-0239; Mansvelder, Huibert/0000-0003-1365-5340;
   Louth, Emma/0000-0001-5357-1315; Larranaga, Pedro/0000-0003-0652-9872;
   Ofer, Netanel/0000-0002-7244-2348; Gunturkun, Onur/0000-0003-4173-5233;
   Kiehn, Ole/0000-0002-5954-469X; Armananzas, Ruben/0000-0003-4049-0000;
   Clemens, Ann/0000-0001-5488-0902; Hjerling Leffler,
   Jens/0000-0002-4539-1776; Fishell, Gordon/0000-0002-9640-9278; Serrano
   Saiz, Esther/0000-0003-0077-878X; Dunville, Keagan/0000-0001-9050-8278;
   Aalling, Nadia/0000-0001-6995-1590; Fiath, Richard/0000-0001-8732-2691;
   Aguilar-Valles, Argel/0000-0002-2272-1138; Turrero Garcia,
   Miguel/0000-0002-7294-169X; Bergmann, Tobias/0000-0002-4610-2585;
   Capogna, Marco/0000-0002-2742-5485; Wuttke, Thomas/0000-0001-5655-8490;
   Goriounova, Natalia/0000-0002-5917-983X; Khodosevich,
   Konstantin/0000-0001-7232-5558; Hall, Vanessa/0000-0002-3598-2214;
   Mohapatra, Alok Nath/0000-0002-9994-6407; Redmond, William
   John/0000-0002-6250-1594; Gao, Xuefan/0000-0001-8114-6404
FU FENS; Brain Prize Foundation
FX This document resulted from group discussions at the FENS/Brain Prize
   meeting, 'The necessity of cell types for brain function', that took
   place in Copenhagen, Denmark, on 7-10 October 2018. We thank the FENS
   and Brain Prize Foundation and staff for help and the Lundbeck
   Foundation for support. This paper is dedicated to the memory of Sydney
   Brenner.
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TC 76
Z9 76
U1 10
U2 30
PU NATURE RESEARCH
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 1097-6256
EI 1546-1726
J9 NAT NEUROSCI
JI Nat. Neurosci.
PD DEC
PY 2020
VL 23
IS 12
BP 1456
EP 1468
DI 10.1038/s41593-020-0685-8
EA AUG 2020
PG 13
WC Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA PC2QZ
UT WOS:000562723600001
PM 32839617
OA Green Submitted, Green Published, hybrid, Green Accepted
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Du, YJ
   Su, FH
   Yang, AZ
   Li, XY
   Fan, YQ
AF Du, Yajun
   Su, Fanghong
   Yang, Anzheng
   Li, Xianyong
   Fan, Yongquan
TI Extracting Deep Personae Social Relations in Microblog Posts
SO IEEE ACCESS
LA English
DT Article
DE Personae relation; relation feature word; dependency trigram kernel;
   relation classification; knowledge graph; microblog post
ID SEARCH; SYSTEM
AB Numerous studies have been conducted to extract relationships from different documents. However, extracting relationships from microblog posts is rarely studied. In this paper, we improve a novel kernel-based learning algorithm to mine the personae social relationships from microblog posts by combining the syntax and semantic meanings of the dependency trigram kernels (DTK). To deeply extract the personal social relationships of microblog posts, we define the relation feature words, provide seven rules for extracting these feature words, and propose a rule-based approach that mines these relation feature words from microblog posts. We construct relation feature word dictionaries for different relation types because of the lack of prominent relation features in microblog posts. We propose an algorithm to classify relation feature words by considering two features of the relation feature words, namely, syntax and semantic similarities between relation feature words in microblog posts and by using relation feature word dictionaries. Experimental results show that the average recall, precision, and F-measure of our proposed approach outperforms the original DTK in sentence selection, personae social relation extraction, and personae social relation classification. Finally, the relation graphs of five topics clarify that our proposed approach is effective for extracting personae social relations from microblog posts.
C1 [Du, Yajun; Yang, Anzheng; Li, Xianyong; Fan, Yongquan] Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Peoples R China.
   [Su, Fanghong] Sichuan Lewei Technol Co Ltd, Chengdu 610041, Peoples R China.
RP Du, YJ (通讯作者)，Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Peoples R China.
EM duyajun@mail.xhu.edu.cn
OI Li, Xianyong/0000-0003-0097-1643
FU National Nature Science Foundation [61872298, 61532009, 61472329];
   Sichuan Science and Technology Program [2018GZ0096, 2019GFW115]
FX This work was supported in part by the National Nature Science
   Foundation under Grant 61872298, Grant 61532009, and Grant 61472329, and
   in part by the Sichuan Science and Technology Program under Grant
   2018GZ0096 and Grant 2019GFW115.
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NR 40
TC 2
Z9 2
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 5488
EP 5501
DI 10.1109/ACCESS.2019.2960659
PG 14
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:000524677500026
OA gold
DA 2022-08-23
ER

PT J
AU Qian, TY
   Liu, B
   Nguyen, QVH
   Yin, HZ
AF Qian, Tieyun
   Liu, Bei
   Quoc Viet Hung Nguyen
   Yin, Hongzhi
TI Spatiotemporal Representation Learning for Translation-Based POI
   Recommendation
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE POI recommendation; location-based social networks; spatiotemporal
   aware; contextual modeling
AB The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the user's decision-making for choosing a POI to visit. In this article, we focus on the spatiotemporal context-aware POI recommendation, which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a spatiotemporal context-aware and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a "transition space" where spatiotemporal contexts (i.e., a <time, location> pair) are modeled as translation vectors operating on users and POIs. We further develop a series of strategies to exploit various correlation information to address the data sparsity and cold-start issues for new spatiotemporal contexts, new users, and new POIs. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate that our STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem.
C1 [Qian, Tieyun; Liu, Bei] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
   [Quoc Viet Hung Nguyen] Griffith Univ, Sch Informat & Commun Technol, Nathan, Qld 4215, Australia.
   [Yin, Hongzhi] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia.
RP Yin, HZ (通讯作者)，Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia.
EM qty@whu.edu.cn; beiliu@whu.edu.cn; quocviethung1@gmail.com;
   h.yin1@uq.edu.au
OI Yin, Hongzhi/0000-0003-1395-261X; Nguyen, Quoc Viet
   Hung/0000-0002-9687-1315
FU NSFC [61572376]; 111 project [B07037]; ARC Discovery Early Career
   Researcher Award [DE160100308]; ARC Discovery Project [DP170103954]
FX The work has been supported in part by the NSFC projects (61572376), the
   111 project(B07037), ARC Discovery Early Career Researcher Award (Grant
   No. DE160100308), and ARC Discovery Project (Grant No. DP170103954).
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NR 51
TC 64
Z9 70
U1 5
U2 38
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1046-8188
EI 1558-2868
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
PD MAR
PY 2019
VL 37
IS 2
AR 18
DI 10.1145/3295499
PG 24
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IH9YE
UT WOS:000474861800005
DA 2022-08-23
ER

PT J
AU Chawla, T
   Singh, G
   Pilli, ES
AF Chawla, Tanvi
   Singh, Girdhari
   Pilli, Emmanuel S.
TI MuSe: a multi-level storage scheme for big RDF data using MapReduce
SO JOURNAL OF BIG DATA
LA English
DT Article
DE RDF; SPARQL; Hadoop; HDFS; MapReduce; Storage
ID BENCHMARK
AB Resource Description Framework (RDF) model owing to its flexible structure is increasingly being used to represent Linked data. The rise in amount of Linked data and Knowledge graphs has resulted in an increase in the volume of RDF data. RDF is used to model metadata especially for social media domains where the data is linked. With the plethora of RDF data sources available on the Web, scalable RDF data management becomes a tedious task. In this paper, we present MuSe-an efficient distributed RDF storage scheme for storing and querying RDF data with Hadoop MapReduce. In MuSe, the Big RDF data is stored at two levels for answering the common triple patterns in SPARQL queries. MuSe considers the type of frequently occuring triple patterns and optimizes RDF storage to answer such triple patterns in minimum time. It accesses only the tables that are sufficient for answering a triple pattern instead of scanning the whole RDF dataset. The extensive experiments on two synthetic RDF datasets i.e. LUBM and WatDiv, show that MuSe outperforms the compared state-of-the art frameworks in terms of query execution time and scalability.
C1 [Chawla, Tanvi; Singh, Girdhari; Pilli, Emmanuel S.] Malaviya Natl Inst Technol, Dept Comp Sci & Engn, Jaipur, India.
RP Pilli, ES (通讯作者)，Malaviya Natl Inst Technol, Dept Comp Sci & Engn, Jaipur, India.
EM espilli.cse@mnit.ac.in
RI CHAWLA, Dr. TANVI/AAF-3822-2021; Pilli, Emmanuel/H-7984-2017
OI CHAWLA, Dr. TANVI/0000-0001-9168-1793; Pilli,
   Emmanuel/0000-0002-6056-1147
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NR 30
TC 0
Z9 0
U1 1
U2 3
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 OCT 9
PY 2021
VL 8
IS 1
AR 130
DI 10.1186/s40537-021-00519-6
PG 26
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WD9AN
UT WOS:000705225400002
OA gold
DA 2022-08-23
ER

PT J
AU Weis, JW
   Jacobson, JM
AF Weis, James W.
   Jacobson, Joseph M.
TI Learning on knowledge graph dynamics provides an early warning of
   impactful research
SO NATURE BIOTECHNOLOGY
LA English
DT Article
ID CITATION
AB Biotechnology-related papers predicted to be of long-term impact are identified in a machine learning framework (DELPHI) that analyzes relationships among a range of features from the scientific literature over time.
   The scientific ecosystem relies on citation-based metrics that provide only imperfect, inconsistent and easily manipulated measures of research quality. Here we describe DELPHI (Dynamic Early-warning by Learning to Predict High Impact), a framework that provides an early-warning signal for 'impactful' research by autonomously learning high-dimensional relationships among features calculated across time from the scientific literature. We prototype this framework and deduce its performance and scaling properties on time-structured publication graphs from 1980 to 2019 drawn from 42 biotechnology-related journals, including over 7.8 million individual nodes, 201 million relationships and 3.8 billion calculated metrics. We demonstrate the framework's performance by correctly identifying 19/20 seminal biotechnologies from 1980 to 2014 via a blinded retrospective study and provide 50 research papers from 2018 that DELPHI predicts will be in the top 5% of time-rescaled node centrality in the future. We propose DELPHI as a tool to aid in the construction of diversified, impact-optimized funding portfolios.
C1 [Weis, James W.; Jacobson, Joseph M.] MIT, Media Lab, Cambridge, MA 02139 USA.
   [Weis, James W.] MIT, Dept Computat & Syst Biol, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
   [Jacobson, Joseph M.] MIT, MIT Ctr Bits & Atoms, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
RP Weis, JW (通讯作者)，MIT, Media Lab, Cambridge, MA 02139 USA.; Weis, JW (通讯作者)，MIT, Dept Computat & Syst Biol, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM jww@mit.edu
OI Jacobson, Joseph/0000-0002-1332-3197
FU MIT Media Lab; MIT Center for Bits and Atoms
FX This work was supported by the consortia of sponsors of the MIT Media
   Lab and the MIT Center for Bits and Atoms. We thank the AWS Cloud
   Credits for Research program for computational infrastructure and the
   Lens Lab for providing publication data.
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Z9 10
U1 6
U2 39
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 OCT
PY 2021
VL 39
IS 10
BP 1300
EP +
DI 10.1038/s41587-021-00907-6
EA MAY 2021
PG 13
WC Biotechnology & Applied Microbiology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biotechnology & Applied Microbiology
GA WF4RT
UT WOS:000651336800001
PM 34002098
DA 2022-08-23
ER

PT J
AU Liao, J
   Wang, SG
   Li, DY
   Li, XL
AF Liao, Jian
   Wang, Suge
   Li, Deyu
   Li, Xiaoli
TI FREERL: Fusion relation embedded representation learning framework for
   aspect extraction
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Fusion learning; Structure-based embedding; Language expression feature;
   Entity representation
AB Opinion object-attribute extraction is one of the fundamental tasks of fine-grained sentiment analysis. It is accomplished by identifying opinion aspect entities (including object entities and attribute entities) and then aligning object entities to attribute entities. Recent studies on knowledge graphs have shown that by adding the embeddings of semantic structures between opinion aspect entities, structure-based learning models can achieve better performance in link-prediction than traditional methods. The studies, however, focused only on learning semantic structures between aspect entities, did not take language expression features into account. In this paper, we propose the Fusion RElation Embedded Representation Learning (FREERL) framework, by which, one can fuse semantic structures and language expression features such as statistical co-occurrence or dependency syntax, into the embeddings of object entities and attribute entities. The obtained embeddings are then used to align object-attribute pairs and to predict new pairs in a zero-shot scenario. Experimental results on the datasets of COAE2014 and COAE2015 show that the best results in our framework achieve 12.1% and 32.1% improvements over the baselines, respectively. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Liao, Jian; Wang, Suge; Li, Deyu] Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Peoples R China.
   [Wang, Suge; Li, Deyu] Shanxi Univ, Minist Educ, Key Lab Computat Intelligence & Chinese Informat, Taiyuan, Peoples R China.
   [Li, Xiaoli] ASTAR, Inst Infocomm Res, Singapore, Singapore.
RP Wang, SG (通讯作者)，Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Peoples R China.; Wang, SG (通讯作者)，Shanxi Univ, Minist Educ, Key Lab Computat Intelligence & Chinese Informat, Taiyuan, Peoples R China.
EM wsg@sxu.edu.cn
RI Li, Xiaoli/C-9739-2012
OI Li, Xiaoli/0000-0002-0762-6562; Li, Deyu/0000-0003-2489-9404; Liao,
   Jian/0000-0002-9385-6873; Wang, Suge/0000-0002-1553-2937
FU National Natural Science Foundation of China (NSFC) [61632011, 61573231,
   61672331, 61432011, 61603229, U1435212]; National High-Tech Research and
   Development Program (863 Program) [2015AA011808]
FX The authors would like to thank all anonymous reviewers and Dr.Yanhui
   Zhai for their valuable comments and suggestions which have
   significantly improved the quality and presentation of this paper. The
   works described in this paper are supported by the National Natural
   Science Foundation of China (NSFC nos. 61632011, 61573231, 61672331,
   61432011, 61603229, U1435212), the National High-Tech Research and
   Development Program (863 Program) (2015AA011808).
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TC 13
Z9 13
U1 2
U2 29
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 NOV 1
PY 2017
VL 135
BP 9
EP 17
DI 10.1016/j.knosys.2017.07.015
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FJ9AB
UT WOS:000413058700002
DA 2022-08-23
ER

PT J
AU Bai, ZB
   Bai, XM
AF Bai, Zhongbo
   Bai, Xiaomei
TI Sports Big Data: Management, Analysis, Applications, and Challenges
SO COMPLEXITY
LA English
DT Review
ID SOCIAL NETWORK ANALYSIS; PARTICIPATION; TAXONOMY; TEAM; IOT
AB With the rapid growth of information technology and sports, analyzing sports information has become an increasingly challenging issue. Sports big data come from the Internet and show a rapid growth trend. Sports big data contain rich information such as athletes, coaches, athletics, and swimming. Nowadays, various sports data can be easily accessed, and amazing data analysis technologies have been developed, which enable us to further explore the value behind these data. In this paper, we first introduce the background of sports big data. Secondly, we review sports big data management such as sports big data acquisition, sports big data labeling, and improvement of existing data. Thirdly, we show sports data analysis methods, including statistical analysis, sports social network analysis, and sports big data analysis service platform. Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes' performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. This paper should help the researchers obtaining a broader understanding of sports big data and provide some potential research directions.
C1 [Bai, Zhongbo] Anshan Normal Univ, Sch Sports Sci, Anshan, Peoples R China.
   [Bai, Xiaomei] Anshan Normal Univ, Ctr Comp, Anshan, Peoples R China.
RP Bai, XM (通讯作者)，Anshan Normal Univ, Ctr Comp, Anshan, Peoples R China.
EM zhongbobai@outlook.com; xiaomeibai@outlook.com
FU Liaoning Provincial Key R&D Guidance Project [2018104021]
FX This work was partially supported by the Liaoning Provincial Key R&D
   Guidance Project (2018104021).
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NR 86
TC 2
Z9 2
U1 40
U2 82
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 JAN 30
PY 2021
VL 2021
AR 6676297
DI 10.1155/2021/6676297
PG 11
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA QG5FR
UT WOS:000617611500005
OA gold
DA 2022-08-23
ER

PT J
AU Fang, YX
   Yang, YX
   Zhang, WJ
   Lin, XM
   Cao, X
AF Fang, Yixiang
   Yang, Yixing
   Zhang, Wenjie
   Lin, Xuemin
   Cao, Xin
TI Effective and Efficient Community Search over Large Heterogeneous
   Information Networks
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB Recently, the topic of community search (CS) has gained plenty of attention. Given a query vertex, CS looks for a dense subgraph that contains it. Existing studies mainly focus on homogeneous graphs in which vertices are of the same type, and cannot be directly applied to heterogeneous information networks (HINs) that consist of multi-typed, interconnected objects, such as the bibliographic networks and knowledge graphs. In this paper, we study the problem of community search over large HINs; that is, given a query vertex q, find a community from an HIN containing q, in which all the vertices are with the same type of q and have close relationships.
   To model the relationship between two vertices of the same type, we adopt the well-known concept of meta-path, which is a sequence of relations defined between different types of vertices. We then measure the cohesiveness of the community by extending the classic minimum degree metric with a meta-path. We further propose efficient query algorithms for finding communities using these cohesiveness metrics. We have performed extensive experiments on five real large HINs, and the results show that the proposed solutions are effective for searching communities. Moreover, they are much faster than the baseline solutions.
C1 [Fang, Yixiang; Yang, Yixing; Zhang, Wenjie; Lin, Xuemin; Cao, Xin] Univ New South Wales, Sydney, NSW, Australia.
RP Fang, YX (通讯作者)，Univ New South Wales, Sydney, NSW, Australia.
EM yixiang.fang@unsw.edu.au; yixing.yang@unsw.edu.au;
   wenjie.zhang@unsw.edu.au; lxue@cse.unsw.edu.au; xin.cao@unsw.edu.au
OI Cao, Xin/0000-0002-3519-7013
FU ARC [PS53783, DP200101116, DP18010 3096, NSFC61232006, 2018YFB10 03504,
   U1636215, DP200101338, DP180103096, DP1701016 28];  [DE190100663]
FX We would like to thank Prof. Maria E. Orlowska for her insightful
   suggestions on improving the paper. Wenjie Zhang was supported by
   PS53783, DP200101116 and DP18010 3096. Xuemin Lin was supported by
   NSFC61232006, 2018YFB10 03504, U1636215, DP200101338, DP180103096, and
   DP1701016 28. Xin Cao was supported by ARC DE190100663.
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NR 80
TC 37
Z9 38
U1 8
U2 13
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 FEB
PY 2020
VL 13
IS 6
BP 854
EP 867
DI 10.14778/3380750.3380756
PG 14
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NU9KR
UT WOS:000573955200006
DA 2022-08-23
ER

PT J
AU Ji, ZZ
   Dai, L
   Pang, J
   Shen, TT
AF Ji, Zizheng
   Dai, Lin
   Pang, Jin
   Shen, Tingting
TI Leveraging Concept-Enhanced Pre-Training Model and Masked-Entity
   Language Model for Named Entity Disambiguation
SO IEEE ACCESS
LA English
DT Article
DE Context modeling; Task analysis; Semantics; Predictive models;
   Adaptation models; Natural language processing; Neural networks; Named
   entity disambiguation; pre-training; lexical knowledge
AB Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in an input-text sequence to their correct references in a knowledge graph. We tackle NED problem by leveraging two novel objectives for pre-training framework, and propose a novel pre-training NED model. Especially, the proposed pre-training NED model consists of: (i) concept-enhanced pre-training, aiming at identifying valid lexical semantic relations with the concept semantic constraints derived from external resource Probase; and (ii) masked entity language model, aiming to train the contextualized embedding by predicting randomly masked entities based on words and non-masked entities in the given input-text. Therefore, the proposed pre-training NED model could merge the advantage of pre-training mechanism for generating contextualized embedding with the superiority of the lexical knowledge (e.g., concept knowledge emphasized here) for understanding language semantic. We conduct experiments on the CoNLL dataset and TAC dataset, and various datasets provided by GERBIL platform. The experimental results demonstrate that the proposed model achieves significantly higher performance than previous models.
C1 [Ji, Zizheng; Dai, Lin; Shen, Tingting] Beijing Inst Technol, Sch Comp, Beijing 100081, Peoples R China.
   [Pang, Jin] State Grid Corp China, Beijing 100031, Peoples R China.
RP Ji, ZZ (通讯作者)，Beijing Inst Technol, Sch Comp, Beijing 100081, Peoples R China.
EM jzz6891@163.com
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NR 63
TC 3
Z9 3
U1 0
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 2020
VL 8
BP 100469
EP 100484
DI 10.1109/ACCESS.2020.2994247
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MH0EN
UT WOS:000546404100001
OA gold
DA 2022-08-23
ER

PT J
AU Hu, SZ
   Tan, Z
   Zeng, WX
   Ge, B
   Xiao, WD
AF Hu, Shengze
   Tan, Zhen
   Zeng, Weixin
   Ge, Bin
   Xiao, Weidong
TI Entity Linking via Symmetrical Attention-Based Neural Network and Entity
   Structural Features
SO SYMMETRY-BASEL
LA English
DT Article
DE symmetrical neural network; entity linking; entity structural features;
   prior probability; information integration
AB In the process of knowledge graph construction, entity linking is a pivotal step, which maps mentions in text to a knowledge base. Existing models only utilize individual information to represent their latent features and ignore the correlation between entities and their mentions. Besides, in the process of entity feature extraction, only partial latent features, i.e., context features, are leveraged to extract latent features, and the pivotal entity structural features are ignored. In this paper, we propose SA-ESF, which leverages the symmetrical Bi-LSTM neural network with the double attention mechanism to calculate the correlation between mentions and entities in two aspects: (1) entity embeddings and mention context features; (2) mention embeddings and entity description features; furthermore, the context features, structural features, and entity ID feature are integrated to represent entity embeddings jointly. Finally, we leverage (1) the similarity score between each mention and its candidate entities and (2) the prior probability to calculate the final ranking results. The experimental results on nine benchmark dataset validate the performance of SA-ESF where the average F1 score is up to 0.866.
C1 [Hu, Shengze; Tan, Zhen; Zeng, Weixin; Ge, Bin; Xiao, Weidong] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.
RP Tan, Z (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.
EM springsun.hu@gmail.com; tanzhen08a@nudt.edu.cn;
   zengweixin13@nudt.edu.cn; gebin@nudt.edu.cn; wdxiao@nudt.edu.cn
RI Zeng, Weixin/AAX-2993-2021
OI Zeng, Weixin/0000-0002-2690-3190; Tan, Zhen/0000-0001-8643-4683
FU NSFC [61872466, 71690233, 71331008]
FX This research was funded by NSFC grant number 61872466, 71690233,
   71331008.
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NR 41
TC 8
Z9 8
U1 1
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-8994
J9 SYMMETRY-BASEL
JI Symmetry-Basel
PD APR
PY 2019
VL 11
IS 4
AR 453
DI 10.3390/sym11040453
PG 18
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA HX3TA
UT WOS:000467314400012
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Melo, A
   Volker, J
   Paulheim, H
AF Melo, Andre
   Voelker, Johanna
   Paulheim, Heiko
TI Type Prediction in Noisy RDF Knowledge Bases Using Hierarchical
   Multilabel Classification with Graph and Latent Features
SO INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
LA English
DT Article
DE Knowledge base; type prediction; hierarchical multilabel classification
AB Semantic Web knowledge bases, in particular large cross-domain data, are often noisy, incorrect, and incomplete with respect to type information. This incompleteness can be reduced, as previous work shows, with automatic type prediction methods. Most knowledge bases contain an ontology defining a type hierarchy, and, in general, entities are allowed to have multiple types (classes of an instance assigned with the rdf : type relation). In this paper, we exploit these characteristics and formulate the type prediction problem as hierarchical multi classification, where the labels are types. We evaluate different sets of features, including entity embeddings, which can be extracted from the knowledge graph exclusively. We propose SLCN, a modification of the local classifier per node approach, which performs feature selection, instance sampling, and class balancing for each local classifier with the objective of improving scalability. Furthermore, we explore different variants of creating features for the classifier, including both graph and latent features. We compare the performance of our proposed method with the stateof-the-art type prediction approach and popular hierarchical multilabel classifiers, and report on experiments with large-scale cross-domain RDF datasets.
C1 [Melo, Andre; Voelker, Johanna; Paulheim, Heiko] Univ Mannheim, B6 26, D-68159 Mannheim, Germany.
RP Melo, A (通讯作者)，Univ Mannheim, B6 26, D-68159 Mannheim, Germany.
EM andre@informatik.uni-mannheim.de; johanna@informatik.uni-mannheim.de;
   heiko@informatik.uni-mannheim.de
RI Paulheim, Heiko/AAE-8132-2020
OI Paulheim, Heiko/0000-0003-4386-8195
FU Ministry of Science, Research and the Arts Baden-Wurttemberg
FX The work presented in this paper has been partly supported by the
   Ministry of Science, Research and the Arts Baden-Wurttemberg in the
   project SyKo<SUP>2</SUP>W<SUP>2</SUP> (Synthesis of Completion and
   Correction of Knowledge Graphs on the Web).
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NR 58
TC 12
Z9 12
U1 1
U2 37
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-2130
EI 1793-6349
J9 INT J ARTIF INTELL T
JI Int. J. Artif. Intell. Tools
PD APR
PY 2017
VL 26
IS 2
SI SI
AR 1760011
DI 10.1142/S0218213017600119
PG 32
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ES2YX
UT WOS:000399394900003
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhu, HM
   He, CH
   Fang, Y
   Xiao, WD
AF Zhu, Huiming
   He, Chunhui
   Fang, Yang
   Xiao, Weidong
TI Fine Grained Named Entity Recognition via Seq2seq Framework
SO IEEE ACCESS
LA English
DT Article
DE Tagging; Encyclopedias; Electronic publishing; Internet; Task analysis;
   Decoding; Named entity recognition; fine-grained; seq2seq framework
AB Fine-grained Named entity recognition (NER) is crucial to natural language processing (NLP) applications like relation extraction and knowledge graph construction. Most existing fine-grained NER systems suffer from inefficiency problem as they use manually annotated training datasets. To address such issue, our NER system could automatically generate datasets from Wikipedia in distant supervision paradigm through mapping hyperlinks in Wikipedia documents to Freebase. In addition, previous NER models can not effectively process fine-grained labels with more than 100 types. So we introduce a "BIO" tagging strategy which can identify the position and type attributes simultaneously. Such tagging scheme transfers NER problem into a sequence-to-sequence (seq2seq) based issue. We propose a seq2seq framework to comprehend the input sentence in a comprehensive way. Specifically, we adopt a Bi-LSTM as the encoder to equally process the past and future information of the input. Then we add a self-attention mechanism to handle the long-term dependency problem in a long sequence. When classifying the entity tags, we choose CRF model as it adds more constraints to avoid position logical problem. Experiments are performed on large-scale datasets for fine-grained NER tasks. Experimental results verify the effectiveness of FSeqC, and it outperforms other state-of-the-art alternatives consistently and significantly.
C1 [Zhu, Huiming; He, Chunhui; Fang, Yang; Xiao, Weidong] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
   [Zhu, Huiming] Changsha Commerce & Tourism Coll, Dept Econ & Trade, Changsha 410116, Peoples R China.
RP Zhu, HM (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.; Zhu, HM (通讯作者)，Changsha Commerce & Tourism Coll, Dept Econ & Trade, Changsha 410116, Peoples R China.
EM zhuhuiming_1983@163.com
OI He, Chunhui/0000-0003-1505-1620
FU NSFC [61872446, 61902417, 71690233, 71971212]; NSF of Hunan province
   [2019JJ20024]
FX This work was supported in part by the NSFC under Grant 61872446, Grant
   61902417, Grant 71690233, and Grant 71971212, and in part by the NSF of
   Hunan province under Grant 2019JJ20024.
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NR 36
TC 3
Z9 3
U1 4
U2 16
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 53953
EP 53961
DI 10.1109/ACCESS.2020.2980431
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LB6NA
UT WOS:000524749300005
OA gold
DA 2022-08-23
ER

PT J
AU Zanon, AL
   da Rocha, LCD
   Manzato, MG
AF Zanon, Andre Levi
   da Rocha, Leonardo Chaves Dutra
   Manzato, Marcelo Garcia
TI Balancing the trade-off between accuracy and diversity in recommender
   systems with personalized explanations based on Linked Open Data
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Recommender systems; Collaborative filtering; Linked open data;
   Explainable AI
AB Collaborative filtering recommendation algorithms generate suggestions based on similar interactions between users. Although it provides accurate recommendations, the approach has two limitations: the popularity bias, which frequently suggests a small set of the most interacted items, and the systems' black box functioning, as they are grounded on complex mathematical models. To improve such aspects in collaborative filtering algorithms, this paper introduces a multi-domain item reordering system based on the best explanation for an item, which are the best ranked paths extracted from a Linked Open Data knowledge graph connecting recommended and interacted items. To order paths, the algorithm assigns a value to the node attributes connecting two items by calculating the popularity of the property between interacted items that are rare in the full set of items. Results from two datasets of the movie and music domains comparing the proposed reordering system with six baselines of different collaborative filtering families showed that our easy-to-explain approach improved diversity and/or accuracy metrics. (C) 2022 Elsevier B.V. All rights reserved.
C1 [Zanon, Andre Levi; Manzato, Marcelo Garcia] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Sao Carlos, SP, Brazil.
   [da Rocha, Leonardo Chaves Dutra] Univ Fed Sao Joao del Rei, Dept Ciencia Comp, Sao Joao Del Rei, MG, Brazil.
RP Zanon, AL (通讯作者)，Univ Sao Paulo, Inst Ciencias Matemat & Comp, Sao Carlos, SP, Brazil.
EM andrezanon@usp.br
OI Zanon, Andre/0000-0003-0526-2678
FU CAPES, Brazil; CNPq, Brazil; Fapesp, Brazil; Fapemig, Brazil
FX The authors acknowledge CAPES, Brazil, CNPq, Brazil, Fapesp, Brazil and
   Fapemig, Brazil for their funding and support of this research.
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NR 79
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 SEP 27
PY 2022
VL 252
AR 109333
DI 10.1016/j.knosys.2022.109333
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3D5WR
UT WOS:000829372500001
DA 2022-08-23
ER

PT J
AU Liu, M
   Zhang, JX
AF Liu, Ming
   Zhang, Jinxu
TI Chinese Neural Question Generation: Augmenting Knowledge into Multiple
   Neural Encoders
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE natural language processing; question generation; deep learning
AB Neural question generation (NQG) is the task of automatically generating a question from a given passage and answering it with sequence-to-sequence neural models. Passage compression has been proposed to address the challenge of generating questions from a long passage text by only extracting relevant sentences containing the answer. However, it may not work well if the discarded irrelevant sentences contain the contextual information for the target question. Therefore, this study investigated how to incorporate knowledge triples into the sequence-to-sequence neural model to reduce such contextual information loss and proposed a multi-encoder neural model for Chinese question generation. This approach has been extensively evaluated in a large Chinese question and answer dataset. The study results showed that our approach outperformed the state-of-the-art NQG models by 5.938 points on the BLEU score and 7.120 points on the ROUGE-L score on the average since the proposed model is answer focused, which is helpful to produce an interrogative word matching the answer type. In addition, augmenting the information from the knowledge graph improves the BLEU score by 10.884 points. Finally, we discuss the challenges remaining for Chinese NQG.
C1 [Liu, Ming] Southwest Univ, Fac Educ, Sch Educ Technol, Chongqing 400716, Peoples R China.
   [Zhang, Jinxu] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400716, Peoples R China.
RP Liu, M (通讯作者)，Southwest Univ, Fac Educ, Sch Educ Technol, Chongqing 400716, Peoples R China.
EM mingliu@swu.edu.cn; zhangjinxu@email.swu.edu.cn
FU National Natural Science Foundation (NSF) [61977054]; Fundamental
   Research Funds for the Central Universities [XDJK2019B023]
FX FundingThis research was funded by the Fundamental Research Funds for
   the Central Universities grant number XDJK2019B023 and National Natural
   Science Foundation grant number 61977054.
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NR 50
TC 0
Z9 0
U1 4
U2 4
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 2022
VL 12
IS 3
AR 1032
DI 10.3390/app12031032
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 ZA5WF
UT WOS:000756233000001
OA gold
DA 2022-08-23
ER

PT J
AU Cheng, DW
   Yang, FZ
   Xiang, S
   Liu, J
AF Cheng, Dawei
   Yang, Fangzhou
   Xiang, Sheng
   Liu, Jin
TI Financial time series forecasting with multi-modality graph neural
   network
SO PATTERN RECOGNITION
LA English
DT Article
DE Graph neural network; Graph attention; Deep learning; Quantitative
   investment
AB Financial time series analysis plays a central role in hedging market risks and optimizing investment de-cisions. This is a challenging task as the problems are always accompanied by multi-modality streams and lead-lag effects. For example, the price movements of stock are reflections of complicated market states in different diffusion speeds, including historical price series, media news, associated events, etc. Furthermore, the financial industry requires forecasting models to be interpretable and compliant. There-fore, in this paper, we propose a multi-modality graph neural network (MAGNN) to learn from these multimodal inputs for financial time series prediction. The heterogeneous graph network is constructed by the sources as nodes and relations in our financial knowledge graph as edges. To ensure the model interpretability, we leverage a two-phase attention mechanism for joint optimization, allowing end-users to investigate the importance of inner-modality and inter-modality sources. Extensive experiments on real-world datasets demonstrate the superior performance of MAGNN in financial market prediction. Our method provides investors with a profitable as well as interpretable option and enables them to make informed investment decisions. (c) 2021 Elsevier Ltd. All rights reserved.
C1 [Cheng, Dawei] Tongji Univ, Dept Comp Sci & Technol, Shanghai, Peoples R China.
   [Yang, Fangzhou; Liu, Jin] Emoney Inc, AI Lab, Shanghai, Peoples R China.
   [Xiang, Sheng] Univ Technol Sydney, Ctr Artificial Intelligence, Sydney, NSW, Australia.
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 [2018YFB2100801]; National Science
   Foundation of China [6210070838]; Shanghai "Scientific and Technological
   Innovation Action Plan" High and New Technology Projects [19511101300]
FX The work is supported by the National Key R&D Program of China
   (2018YFB2100801), the National Science Foundation of China (6210070838),
   the Shanghai "Scientific and Technological Innovation Action Plan" High
   and New Technology Projects (19511101300).
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NR 45
TC 9
Z9 9
U1 29
U2 81
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 JAN
PY 2022
VL 121
AR 108218
DI 10.1016/j.patcog.2021.108218
EA AUG 2021
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA US8JT
UT WOS:000697672400001
DA 2022-08-23
ER

PT J
AU Buraga, SC
   Dospinescu, O
AF Buraga, Sabin C.
   Dospinescu, Octavian
TI A Knowledge-Based Pilot Study on Assessing the Music Influence
SO CMC-COMPUTERS MATERIALS & CONTINUA
LA English
DT Article
DE HCI; knowledge; conceptual model; methodology; music; pilot study
ID BACKGROUND MUSIC
AB A knowledge-driven approach is proposed for assessing the music influence on university students. The proposed method of modeling and conducting the interactive pilot study can be useful to convey other surveys, interviews, and experiments created in various phases of the user interface (UI) design processes, as part of a general human-computer interaction (HCI) methodology. Benefiting from existing semantic Web and linked data standards, best practices, and tools, a microservice-oriented system is developed as a testbed platform able to generate playlists in a smart way according to users' music preferences. This novel approach could bring also benefits for user interface adaptation by using semantic Web techniques. Statistical analysis based on the ANOVA method and post-experiment survey data led to the conclusion that music listened has a significant impact on students' cognitive abilities in various contexts. All obtained results were semantically enhanced by using different conceptual models in order to create a knowledge graph providing support for automated reasoning. Also, a knowledge-based persona Web smart editor was implemented in order to include music preferences for certain categories of the potential users operating a specific interactive system.
C1 [Buraga, Sabin C.] Univ Alexandru Ioan Cuza, Fac Comp Sci, Iasi 700706, Romania.
   [Dospinescu, Octavian] Univ Alexandru Ioan Cuza, Fac Econ & Business Adm, Iasi 700706, Romania.
RP Dospinescu, O (通讯作者)，Univ Alexandru Ioan Cuza, Fac Econ & Business Adm, Iasi 700706, Romania.
EM doctav@uaic.ro
RI Buraga, Sabin/AAI-1804-2019
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NR 40
TC 3
Z9 3
U1 2
U2 11
PU TECH SCIENCE PRESS
PI HENDERSON
PA 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA
SN 1546-2218
EI 1546-2226
J9 CMC-COMPUT MATER CON
JI CMC-Comput. Mat. Contin.
PY 2021
VL 66
IS 3
BP 2857
EP 2873
DI 10.32604/cmc.2021.014429
PG 17
WC Computer Science, Information Systems; Materials Science,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Materials Science
GA PN6WA
UT WOS:000604616100006
OA gold
DA 2022-08-23
ER

PT J
AU Liu, JS
   Yu, D
   Hu, Y
   Yu, HY
   He, WW
   Zhang, LP
AF Liu, Jinsong
   Yu, Dong
   Hu, Yi
   Yu, Haoyu
   He, Wuwei
   Zhang, Lipeng
TI CNC Machine Tool Fault Diagnosis Integrated Rescheduling Approach
   Supported by Digital Twin-Driven Interaction and Cooperation Framework
SO IEEE ACCESS
LA English
DT Article
DE Fault diagnosis; Production; Digital twin; Real-time systems;
   Manufacturing; Job shop scheduling; Machine tools; Digital twin; fault
   diagnosis; knowledge graph; MCTS; rescheduling
ID NEURAL-NETWORKS; INDUSTRY 4.0; BIG DATA; SYSTEM
AB The problems of CNC machine tool (CNCMT) fault diagnosis and production rescheduling have attracted continuous attention because of their great significance to the manufacturing industry. Digital twin is a supporting technology for achieving smart manufacturing and provides a new paradigm for solving these problems. This paper explores a digital twin-driven interaction and cooperation framework and proposes the architecture and implementation mechanism to enable the sharing of data, knowledge, and resource, to realize the fusion of physical space and cyber space, and to improve the accuracy of fault diagnosis. Under this framework, aiming at the influence of CNCMT failure on the initial production planning, a self-adaptation rescheduling method based on Monte Carlo Tree Search (MCTS) algorithm is proposed to provide support for developing more efficient production planning. Finally, the effectiveness of the proposed framework is validated by experimental study. The framework and integrated rescheduling approach can provide guidance for enterprises in implementing CNCMT maintenance and production scheduling to meet high accuracy and reliability requirements.
C1 [Liu, Jinsong; Yu, Haoyu; He, Wuwei; Zhang, Lipeng] Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China.
   [Liu, Jinsong; Yu, Dong; Hu, Yi; Yu, Haoyu; He, Wuwei; Zhang, Lipeng] Chinese Acad Sci, Shenyang Inst Comp Technol, Shenyang 110168, Peoples R China.
   [Hu, Yi] Shenyang CASNC Technol Co Ltd, Shenyang 110168, Peoples R China.
RP Liu, JS (通讯作者)，Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China.; Liu, JS (通讯作者)，Chinese Acad Sci, Shenyang Inst Comp Technol, Shenyang 110168, Peoples R China.
EM liujinsong@sict.ac.cn
OI Liu, Jinsong/0000-0001-9308-0502
FU National Science and Technology Major Project of China [2018ZX04035001];
   High-Level Innovation and Entrepreneurship Team in Liaoning Province
   [XLYC1808002]
FX This work was supported in part by the National Science and Technology
   Major Project of China under Grant 2018ZX04035001, and in part by the
   High-Level Innovation and Entrepreneurship Team in Liaoning Province
   under Grant XLYC1808002.
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NR 46
TC 1
Z9 1
U1 23
U2 50
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 118801
EP 118814
DI 10.1109/ACCESS.2021.3106797
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA UK7WN
UT WOS:000692176800001
OA gold
DA 2022-08-23
ER

PT J
AU Zhong, YM
   Cao, JH
   Lu, HZ
   Huang, ZH
   Liu, L
   Wen, CB
   Chen, J
AF Zhong, Yanmei
   Cao, Jihui
   Lu, Haizhen
   Huang, Zonghai
   Liu, Lu
   Wen, Chuanbiao
   Chen, Ji
TI Publication Trends in Rehabilitative Effects of Acupuncture: A Visual
   Analysis of the Literature
SO EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE
LA English
DT Review
ID MYOFASCIAL TRIGGER POINTS; STROKE REHABILITATION; COGNITIVE IMPAIRMENT;
   SYSTEMATIC REVIEWS; SCALP ACUPUNCTURE; FUNCTIONAL RECOVERY; RAT MODEL;
   ELECTROACUPUNCTURE; PAIN; STIMULATION
AB Objectives. To conduct a comprehensive analysis of scientific outputs in 2011-2021 regarding the rehabilitative effects of acupuncture on diseases. Methods. The study was conducted in the form of knowledge graph and data visualization, with data being drawn from the Web of Science Core Collection database. Results. Articles and reviews were the dominant types; China, Guangzhou University of Chinese Medicine and Medicine ranked was the active country, institution, and journal, respectively, in terms of issued articles. Systematic reviews and the meta-analyses of stroke and pain were extensively carried out in the past decade, whose principal interventions were manual acupuncture, electroacupuncture, scalp acupuncture, and dry needling correspondingly at Baihui (DU20) and Zusanli (ST36). And most frequently utilized rehabilitation assessment criteria were the Fugl-Meyer Assessment Scale and the Barthel Index. More recently, motor function and chronic obstructive pulmonary disease have captured researchers' attention, which might be the futuristic frontier. Conclusions. This article provided a relatively panoramic picture of the scientific outputs in acupuncture for disease rehabilitation, which may help readers embrace the heated topic and grasp the recent research focus on this field.
C1 [Zhong, Yanmei; Huang, Zonghai; Wen, Chuanbiao] Chengdu Univ Tradit Chinese Med, Sch Med Informat Engn, Chengdu 610075, Peoples R China.
   [Cao, Jihui] Renshou Cty Hosp Tradit Chinese Med, Dept Orthoped, Chongqing 620500, Peoples R China.
   [Lu, Haizhen] Chengdu Univ Tradit Chinese Med, Basic Med Sch, Chengdu 610000, Peoples R China.
   [Liu, Lu; Wen, Chuanbiao] Chengdu Univ Tradit Chinese Med, Sch Acupuncture Moxibust & Tuina, Chengdu 610075, Peoples R China.
   [Chen, Ji] Chengdu Univ Tradit Chinese Med, Sch Foreign Languages, Chengdu 610075, Peoples R China.
RP Wen, CB (通讯作者)，Chengdu Univ Tradit Chinese Med, Sch Med Informat Engn, Chengdu 610075, Peoples R China.; Wen, CB (通讯作者)，Chengdu Univ Tradit Chinese Med, Sch Acupuncture Moxibust & Tuina, Chengdu 610075, Peoples R China.; Chen, J (通讯作者)，Chengdu Univ Tradit Chinese Med, Sch Foreign Languages, Chengdu 610075, Peoples R China.
EM 1422311457@qq.com; cjh9752339@163.com; 824289582@qq.com;
   774778307@qq.com; 756132259@qq.com; wcb@cdutcm.edu.cn;
   jeanelle_chin@126.com
OI Huang, Zonghai/0000-0003-3891-8354; Lu, Haizhen/0000-0002-1893-3090;
   Chen, Ji/0000-0002-4294-0370; , yanked/0000-0002-5089-2348
FU National Natural Science Foundation of China [81590950, 82174236];
   National Social Science Fund of China [19XXW005]
FX AcknowledgmentsYanmei Zhong was responsible for writing the first draft.
   Jihui Cao, Lu Liu, Zonghai Huang, and Haizhen Lu collected the data.
   Chuanbiao Wen provided ideas and supervised this study. Ji Chen gave
   expert comments on the study design and finalization of the manuscript.
   This study was funded by the National Natural Science Foundation of
   China (Grant nos: 81590950 and 82174236) and the National Social Science
   Fund of China (Grant no: 19XXW005).
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NR 115
TC 0
Z9 0
U1 7
U2 7
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1741-427X
EI 1741-4288
J9 EVID-BASED COMPL ALT
JI Evid.-based Complement Altern. Med.
PD APR 11
PY 2022
VL 2022
AR 7705256
DI 10.1155/2022/7705256
PG 20
WC Integrative & Complementary Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Integrative & Complementary Medicine
GA 1W1OW
UT WOS:000806550000010
PM 35449821
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Westphal, P
   Grubenmann, T
   Collarana, D
   Bin, SM
   Buehmann, L
   Lehmann, J
AF Westphal, Patrick
   Grubenmann, Tobias
   Collarana, Diego
   Bin, Simon
   Buehmann, Lorenz
   Lehmann, Jens
TI Spatial concept learning and inference on geospatial polygon data
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Spatial analytics; Concept learning; Description logics; Spatial
   knowledge graphs
ID ALGORITHM; COMPLEXITY; KNOWLEDGE; LOGICS
AB Geospatial knowledge has always been an essential driver for many societal aspects. This concerns in particular urban planning and urban growth management. To gain insights from geospatial data and guide decisions usually authoritative and open data sources are used, combined with user or citizen sensing data. However, we see a great potential for improving geospatial analytics by combining geospatial data with the rich terminological knowledge, e.g., provided by the Linked Open Data Cloud. Having semantically explicit, integrated geospatial and terminological knowledge, expressed by means of established vocabularies and ontologies, cross-domain spatial analytics can be performed. One analytics technique working on terminological knowledge is inductive concept learning, an approach that learns classifiers expressed as logical concept descriptions. In this paper, we extend inductive concept learning to infer and make use of the spatial context of entities in spatio-terminological data. We propose a formalism for extracting and making spatial relations explicit such that they can be exploited to learn spatial concept descriptions, enabling 'spatially aware' concept learning. We further provide an implementation of this formalism and demonstrate its capabilities in different evaluation scenarios.(c) 2022 Elsevier B.V. All rights reserved.
C1 [Westphal, Patrick; Collarana, Diego; Lehmann, Jens] Fraunhofer Inst Intelligent Anal & Informat Syst I, Zwickauer Str 46, D-01069 Dresden, Germany.
   [Westphal, Patrick; Bin, Simon; Buehmann, Lorenz; Lehmann, Jens] Univ Leipzig, Inst Appl Informat InfAI, Goerdelerring 9, D-04109 Leipzig, Germany.
   [Grubenmann, Tobias; Lehmann, Jens] Univ Bonn, Comp Sci Inst, Friedrich Hirzebruch Allee 8, D-53115 Bonn, Germany.
   [Collarana, Diego] Univ Privada Boliviana, Inst Computat Intelligence ICI, Ave Juan Pablo II, Cochabamba, Bolivia.
   [Bin, Simon] Paderborn Univ, Data Sci DICE Grp, Warburger Str 100, D-33098 Paderborn, Germany.
RP Westphal, P (通讯作者)，Fraunhofer Inst Intelligent Anal & Informat Syst I, Zwickauer Str 46, D-01069 Dresden, Germany.
EM patrick.westphal@informatik.uni-leipzig.de
OI Grubenmann, Tobias/0000-0003-0391-584X; Lehmann,
   Jens/0000-0001-9108-4278; Collarana, Diego/0000-0002-2583-0778
FU EU [870228]; CALLISTO [101004152]
FX This work was supported by grants from the EU H2020 Programme for the
   projects Opertus Mundi (GA no. 870228) and CALLISTO (GA no. 101004152) .
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NR 46
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 APR 6
PY 2022
VL 241
AR 108233
DI 10.1016/j.knosys.2022.108233
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V2EK
UT WOS:000788156700017
DA 2022-08-23
ER

PT J
AU Yan, L
   Sheng, J
   Tu, YF
   Zhou, XS
   Ma, ZM
AF Yan, Li
   Sheng, Jie
   Tu, Yaofeng
   Zhou, Xiangsheng
   Ma, Zongmin
TI Automatic construction of RDF with web tables
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Web tables; RDF; Construction; Web page parsing
ID XML
AB Being a metadata model, the Resource Description Framework (RDF) has been widely used in the context of semantic extraction, unified organization and intelligent processing of a large number of data because RDF's machine understandability. Knowledge graph based on RDF model, for example, is predominantly generated from massive data with various models and formats. While many efforts have been carried out in converting databases and XML into RDF, the generation of RDF from Web tables currently receives less attention due to their flexible and complex structure as well as difficult understanding. Given that billions of Web tables are widely used in real-world applications, in this paper, we devote to the automatic construction of RDF with diverse Web tables. We recognize total six representative types of Web tables that have been extensively applied in Web-based applications, including Web tables with simple structure and complex structure. For each type of Web tables, we propose our approach for converting it into RDF. Our approaches can well identify the semantics of Web tables with complex structure and then generate the corresponding RDF. We implement an RDF generation system and experimental results show the feasibility of our proposed approaches.
C1 [Yan, Li; Ma, Zongmin] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China.
   [Sheng, Jie; Tu, Yaofeng; Zhou, Xiangsheng] Zhongxing Telecommun Equipment Corp, Nanjing 210012, Peoples R China.
   [Ma, Zongmin] Collaborat Innovat Ctr Novel Software Technol & I, Nanjing 210000, Peoples R China.
RP Ma, ZM (通讯作者)，Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China.
EM yanli@nuaa.edu.cn; sheng.jie@zte.com.cn; tu.yaofeng@zte.com.cn;
   zhou.xiangsheng@zte.com.cn; zongminma@nuaa.edu.cn
FU Basic Research Program of Jiangsu Province [BK20191274]; National
   Natural Science Foundation of China [61772269]
FX The work was supported by the Basic Research Program of Jiangsu Province
   (BK20191274) and in part by the National Natural Science Foundation of
   China (61772269) .
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NR 48
TC 1
Z9 1
U1 0
U2 4
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD NOV 15
PY 2021
VL 182
AR 115200
DI 10.1016/j.eswa.2021.115200
EA MAY 2021
PG 22
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 UF3DM
UT WOS:000688457300010
DA 2022-08-23
ER

PT J
AU Ge, YY
   Tian, TZ
   Huang, SL
   Wan, FP
   Li, JX
   Li, SY
   Wang, XT
   Yang, H
   Hong, LX
   Wu, N
   Yuan, EM
   Luo, Y
   Cheng, LL
   Hu, CL
   Lei, YP
   Shu, HT
   Feng, XL
   Jiang, ZY
   Wu, YF
   Chi, Y
   Guo, XL
   Cui, LB
   Xiao, L
   Li, Z
   Yang, CH
   Miao, ZH
   Chen, LG
   Li, HT
   Zeng, HN
   Zhao, D
   Zhu, FC
   Shen, XK
   Zeng, JY
AF Ge, Yiyue
   Tian, Tingzhong
   Huang, Suling
   Wan, Fangping
   Li, Jingxin
   Li, Shuya
   Wang, Xiaoting
   Yang, Hui
   Hong, Lixiang
   Wu, Nian
   Yuan, Enming
   Luo, Yunan
   Cheng, Lili
   Hu, Chengliang
   Lei, Yipin
   Shu, Hantao
   Feng, Xiaolong
   Jiang, Ziyuan
   Wu, Yunfu
   Chi, Ying
   Guo, Xiling
   Cui, Lunbiao
   Xiao, Liang
   Li, Zeng
   Yang, Chunhao
   Miao, Zehong
   Chen, Ligong
   Li, Haitao
   Zeng, Hainian
   Zhao, Dan
   Zhu, Fengcai
   Shen, Xiaokun
   Zeng, Jianyang
TI An integrative drug repositioning framework discovered a potential
   therapeutic agent targeting COVID-19
SO SIGNAL TRANSDUCTION AND TARGETED THERAPY
LA English
DT Article
ID RESPIRATORY SYNDROME-CORONAVIRUS; POLY(ADP-RIBOSE) POLYMERASE PARP;
   NF-KAPPA-B; STRUCTURAL BASIS; IN-VITRO; NUCLEOCAPSID PROTEIN; CYTOKINE
   STORM; INHIBITORS; INFECTION; PJ34
AB The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.
C1 [Ge, Yiyue; Tian, Tingzhong; Wan, Fangping; Li, Shuya; Hong, Lixiang; Wu, Nian; Yuan, Enming; Shu, Hantao; Zhao, Dan; Zeng, Jianyang] Tsinghua Univ, Inst Interdisciplinary Informat Sci, Beijing, Peoples R China.
   [Ge, Yiyue; Tian, Tingzhong; Li, Jingxin; Chi, Ying; Guo, Xiling; Cui, Lunbiao; Zhu, Fengcai] Jiangsu Prov Ctr Dis Control & Prevent, NHC Key Lab Enter Pathogen Microbiol, Nanjing, Jiangsu, Peoples R China.
   [Huang, Suling; Yang, Chunhao; Miao, Zehong] Chinese Acad Sci, Shanghai Inst Mat Med, Shanghai, Peoples R China.
   [Wang, Xiaoting; Yang, Hui; Lei, Yipin; Zeng, Hainian] Silexon Technol Co Ltd, Nanjing, Jiangsu, Peoples R China.
   Univ Illinois, Dept Comp Sci, Urbana, IL USA.
   [Cheng, Lili; Hu, Chengliang; Chen, Ligong] Tsinghua Univ, Sch Pharmaceut Sci, Beijing, Peoples R China.
   [Feng, Xiaolong] Huazhong Univ Sci & Technol, Sch Elect Informat & Communicat, Wuhan, Hubei, Peoples R China.
   [Feng, Xiaolong] Huazhong Univ Sci & Technol, Inst Pathol, Tongji Hosp, Tongji Med Coll, Wuhan, Hubei, Peoples R China.
   [Jiang, Ziyuan] Tsinghua Univ, Dept Automat, Beijing, Peoples R China.
   [Wu, Yunfu] Inner Mongolia Alashan League Org Estab Comm Off, Alashan, Inner Mongolia, Peoples R China.
   [Xiao, Liang; Li, Zeng; Shen, Xiaokun] Convalife Shanghai Co Ltd, Shanghai, Peoples R China.
   [Chen, Ligong] Capital Med Univ, Beijing Tiantan Hosp, Adv Innovat Ctr Human Brain Protect, Beijing, Peoples R China.
   [Li, Haitao] Tsinghua Univ, Beijing Adv Innovat Ctr Struct Biol, Sch Med, Dept Basic Med Sci, Beijing, Peoples R China.
   [Zhu, Fengcai] Nanjing Med Univ, Ctr Global Hlth, Nanjing, Jiangsu, Peoples R China.
RP Zhao, D; Zeng, JY (通讯作者)，Tsinghua Univ, Inst Interdisciplinary Informat Sci, Beijing, Peoples R China.; Zhu, FC (通讯作者)，Jiangsu Prov Ctr Dis Control & Prevent, NHC Key Lab Enter Pathogen Microbiol, Nanjing, Jiangsu, Peoples R China.; Shen, XK (通讯作者)，Convalife Shanghai Co Ltd, Shanghai, Peoples R China.; Zhu, FC (通讯作者)，Nanjing Med Univ, Ctr Global Hlth, Nanjing, Jiangsu, Peoples R China.
EM zhaodan2018@tsinghua.edu.cn; jszfc@vip.sina.com;
   steve.shen@convalife.com; zengjy321@tsinghua.edu.cn
RI ge, yiyue/AAW-9914-2021; Huang, Suling/AGJ-5708-2022
OI Huang, Suling/0000-0003-2338-8004; Tian, Tingzhong/0000-0003-4899-0632;
   Wan, Fangping/0000-0003-1647-3278; Cheng, Lili/0000-0001-8009-1069; ge,
   yiyue/0000-0002-4839-5334
FU National Natural Science Foundation of China [61872216, 81630103,
   31900862, 31725014]; Jiangsu Provincial Emergency Project on Prevention
   and Control of COVID-19 Epidemic [BE2020601]; Nation Science and
   Technology Major Projects for Major New Drugs Innovation and Development
   [2018ZX09711003-004-002, 2019ZX09301010]; Pudong New Area Science and
   Technology Development Foundation [PKX2019-S08]; Turing AI Institute of
   Nanjing; Zhongguancun Haihua Institute for Frontier Information
   Technology
FX This work was supported in part by the National Natural Science
   Foundation of China (61872216, 81630103, 31900862, 31725014), Jiangsu
   Provincial Emergency Project on Prevention and Control of COVID-19
   Epidemic (BE2020601), the Nation Science and Technology Major Projects
   for Major New Drugs Innovation and Development (2018ZX09711003-004-002,
   2019ZX09301010), Pudong New Area Science and Technology Development
   Foundation (PKX2019-S08), and the Turing AI Institute of Nanjing, and
   the Zhongguancun Haihua Institute for Frontier Information Technology.
   We thank Dr. Yu Zhou for providing gene expression profile data of
   COVID-19 patients. We thank Dr. Feixiong Cheng for email communications
   on the connectivity map analysis.
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NR 110
TC 19
Z9 20
U1 4
U2 11
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 2095-9907
EI 2059-3635
J9 SIGNAL TRANSDUCT TAR
JI Signal Transduct. Target. Ther.
PD APR 24
PY 2021
VL 6
IS 1
AR 165
DI 10.1038/s41392-021-00568-6
PG 16
WC Biochemistry & Molecular Biology; Cell Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Cell Biology
GA XS9TS
UT WOS:000733242600003
PM 33895786
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Yang, J
   Wang, R
   Guan, X
   Hassan, MM
   Almogren, A
   Alsanad, A
AF Yang, Jun
   Wang, Rui
   Guan, Xin
   Hassan, Mohammad Mehedi
   Almogren, Ahmad
   Alsanad, Ahmed
TI AI-enabled emotion-aware robot: The fusion of smart clothing, edge
   clouds and robotics
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Artificial intelligence; Emotion-aware; Personal robot; Smart clothing
AB Mental health has become a severe problem that significantly influences people's living quality. With the rapid development of science and technology, a completely new direction for mental health improving by using the interaction between robots and people has emerged. As an intelligent personal agent, a robot can be easily accepted in people's daily life, meeting users' behavior and mental demands to a certain extent. Nevertheless, the existing robot design is very limited, and a household personal robot is too large to be carried anywhere. The usage of wearable devices is simple, but these devices cannot offer diversified services. Therefore, this paper puts forward an emotion-aware system that integrates a personal robot, smart clothing, and cloud terminal. A new 'people-centered' emotion interaction mode is realized. Namely, personal robot and smart clothing supplement each other seamlessly and interact jointly with users. Artificial intelligence technology and knowledge graph are used to design emotion perception and interaction algorithms including intelligent recommendation, relation recognition, emotional expression recognition. Also, different scenarios are analyzed. Finally, a testbed is built to carry out relevant tests to verify the effectiveness of the proposed algorithms and emotion-aware system. According to the obtained test results, the system can be widely used to serve people and improve people's mental health. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Yang, Jun; Wang, Rui] Huazhong Univ Sci & Technol, Wuhan, Hubei, Peoples R China.
   [Guan, Xin] Heilongjiang Univ, Sch Data Sci & Technol, Harbin, Heilongjiang, Peoples R China.
   [Hassan, Mohammad Mehedi; Almogren, Ahmad; Alsanad, Ahmed] King Saud Univ, Coll Comp & Informat Sci, Riyadh 11543, Saudi Arabia.
RP Hassan, MM (通讯作者)，King Saud Univ, Coll Comp & Informat Sci, Riyadh 11543, Saudi Arabia.
EM junyang_cs@hust.edu.cn; ruiwang2018@hust.edu.cn; guanxin@hlju.edu.cn;
   mmhassan@ksu.edu.sa; ahalmogren@ksu.edu.sa; aasanad@ksu.edu.sa
RI Hassan, Mohammad Mehedi/D-4946-2016; Almogren, Ahmad S/F-1365-2014
OI Hassan, Mohammad Mehedi/0000-0002-3479-3606; Almogren, Ahmad
   S/0000-0002-8253-9709
FU Deanship of Scientific Research at King Saud University, Saudi Arabia
   [RGP-281]
FX The authors extend their appreciation to the Deanship of Scientific
   Research at King Saud University, Saudi Arabia for funding this work
   through research group no (RGP-281). Mohammad Mehedi Hassan is the
   corresponding author of this paper.
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JI Futur. Gener. Comp. Syst.
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WC Computer Science, Theory & Methods
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SC Computer Science
GA JU8RD
UT WOS:000501936300058
DA 2022-08-23
ER

PT J
AU Zhou, XC
   Nurkowski, D
   Mosbach, S
   Akroyd, J
   Kraft, M
AF Zhou, Xiaochi
   Nurkowski, Daniel
   Mosbach, Sebastian
   Akroyd, Jethro
   Kraft, Markus
TI Question Answering System for Chemistry
SO JOURNAL OF CHEMICAL INFORMATION AND MODELING
LA English
DT Article
ID ONTOLOGY LOOKUP SERVICE; LARGE-SCALE; INTERFACE; DBPEDIA
AB This paper describes the implementation and evaluation of a proof-of-concept Question Answering (QA) system for accessing chemical data from knowledge graphs (KGs) which offer data from chemical kinetics to the chemical and physical properties of species. We trained the question classification and named the entity recognition models that specialize in interpreting chemistry questions. The system has a novel design which applies a topic model to identify the question-to-ontology affiliation to handle ontologies with different structures. The topic model also helps the system to provide answers with a higher quality. Moreover, a new method that automatically generates training questions from ontologies is also implemented. The question set generated for training contains 432,989 questions under 11 types. Such a training set has been proven to be effective for training both the question classification model and the named entity recognition model. We evaluated the system using other KGQA systems as baselines. The system outperforms the chosen KGQA system answering chemistry-related questions. The QA system is also compared to the Google search engine and the WolframAlpha engine. It shows that the QA system can answer certain types of questions better than the search engines.
C1 [Zhou, Xiaochi; Mosbach, Sebastian; Akroyd, Jethro; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.
   [Nurkowski, Daniel] CMCL Innovat, Cambridge CB3 0AX, England.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.
   [Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore, CARES, Singapore 138602, Singapore.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.; Kraft, M (通讯作者)，Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.; Kraft, M (通讯作者)，Cambridge Ctr Adv Res & Educ Singapore, CARES, Singapore 138602, Singapore.
EM mk306@cam.ac.uk
RI ; Kraft, Markus/D-7243-2016; Akroyd, Jethro/A-8698-2010
OI Mosbach, Sebastian/0000-0001-7018-9433; Kraft,
   Markus/0000-0002-4293-8924; Akroyd, Jethro/0000-0002-2143-8656
FU National Research Foundation, Prime Minister's Office, Singapore under
   its Campus for Research Excellence and Technological Enterprise (CREATE)
   program; CMCL Innovations; Alexander von Humboldt Foundation
FX This research was supported by the National Research Foundation, Prime
   Minister's Office, Singapore under its Campus for Research Excellence
   and Technological Enterprise (CREATE) program. X.Z. acknowledges
   financial support provided CMCL Innovations. M.K. gratefully
   acknowledges the support of the Alexander von Humboldt Foundation.
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NR 59
TC 3
Z9 3
U1 3
U2 5
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 3868
EP 3880
DI 10.1021/acs.jcim.1c00275
EA AUG 2021
PG 13
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:000688241800013
PM 34338504
DA 2022-08-23
ER

PT J
AU Nagabhushan, P
   Anami, BS
AF Nagabhushan, P
   Anami, BS
TI A knowledge-based approach for recognition of handwritten Pitman
   shorthand language strokes
SO SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
LA English
DT Article
DE Pitman shorthand language; character recognition; English text
   production; primitives; knowledge base
AB The Pitman shorthand language (PSL) is a recording medium practised in all organizations, where English is the transaction medium. It has the practical advantage of high speed of recording, more than 120-200 words per minute, because of which it is universally acknowledged. This recording medium has its continued existence in spite of considerable developments in speech processing systems, which are not universally established yet. In order to exploit the vast transcribing potential of PSL a new area of research on automation of PSL processing is conceived. It has three major steps, namely, shape recognition of PSL strokes; their validation and English text production from these strokes. The paper describes a knowledge-based approach for the recognition of PSL strokes. Information about location and the direction of the starting point and final point of strokes are considered the knowledge base for recognition of strokes. The work comprises preprocessing, determination of starting and final points, acquisition of quadrant knowledge, graph-based traversal and finally a rule-based inference process for generating phonetic equivalent of English language characters for the strokes. The proposed work is thoroughly tested for a large number of handwritten strokes.
C1 Univ Mysore, Dept Studies Comp Sci, Mysore 570006, Karnataka, India.
RP Nagabhushan, P (通讯作者)，Univ Mysore, Dept Studies Comp Sci, Manasgangotri, Mysore 570006, Karnataka, India.
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NR 15
TC 2
Z9 2
U1 0
U2 3
PU INDIAN ACADEMY SCIENCES
PI BANGALORE
PA P B 8005 C V RAMAN AVENUE, BANGALORE 560 080, INDIA
SN 0256-2499
J9 SADHANA-ACAD P ENG S
JI Sadhana-Acad. Proc. Eng. Sci.
PD DEC
PY 2002
VL 27
BP 685
EP 698
DI 10.1007/BF02703359
PN 6
PG 14
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 643BY
UT WOS:000180843100008
OA Bronze
DA 2022-08-23
ER

PT J
AU Zeng, ZM
   Sun, SQ
   Li, TT
   Yin, J
   Shen, YY
   Huang, Q
AF Zeng, Ziming
   Sun, Shouqiang
   Li, Tingting
   Yin, Jie
   Shen, Yueyan
   Huang, Qian
TI Exploring the topic evolution of Dunhuang murals through image
   classification
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article; Early Access
DE BOW_HSV_SVM; classification and machine learning; cultural heritage;
   digital humanities; Dunhuang murals; graph theory; topic map
ID GRAPH-THEORY; ART; IDENTIFICATION; REPRESENTATION; RETRIEVAL; BOW
AB Dunhuang is a unique art treasure and a world heritage site. In order to organise and manage Dunhuang cultural heritage resources, this article studies the classification of Dunhuang murals in different dynasties, and explores the topic distribution characteristics and evolution rules of them. First, image features are extracted through scale-invariant feature transform (SIFT) and Canny and scale-invariant feature transform (CSIFT), a visual dictionary is generated through the k-means clustering algorithm, and the term frequency-inverse document frequency (TF-IDF) vector is calculated and combined with the colour feature vector extracted via hue, saturation and value (HSV). Second, Dunhuang mural images are collected and the support vector machine (SVM) classifier is built. Finally, the knowledge graph-based topic maps are constructed, and graph theory is introduced to analyse the topic distribution and evolution of Dunhuang murals in different dynasties. The results show that the Dunhuang murals of different dynasties can be effectively classified through the bag of words, HSV and support vector machine (BOW_HSV_SVM) based on their visual features. Through topic maps, the topic distribution characteristics and evolution rules of Dunhuang murals with the dynasties are revealed.
C1 [Zeng, Ziming] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China.
   [Sun, Shouqiang; Li, Tingting; Yin, Jie; Shen, Yueyan; Huang, Qian] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.
RP Zeng, ZM (通讯作者)，Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China.
EM zmzeng1977@aliyun.com
OI sun, shou qiang/0000-0001-8760-9254; li, Tingting/0000-0002-5651-5260;
   Shen, Yueyan/0000-0002-7200-2991
FU National Natural Science Foundation of China [71673203]; Key Research
   Institutes of Philosophy and Social Science by Ministry of Education, PR
   China [16JJD870003]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship and/or publication of this article: This
   research was supported by the National Natural Science Foundation of
   China (grant no. 71673203) and the Key Research Institutes of Philosophy
   and Social Science by Ministry of Education, PR China (grant no.
   16JJD870003).
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NR 84
TC 0
Z9 0
U1 33
U2 33
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
AR 01655515221074336
DI 10.1177/01655515221074336
EA FEB 2022
PG 18
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA ZK1QG
UT WOS:000762770000001
DA 2022-08-23
ER

PT J
AU Stromert, P
   Hunold, J
   Castro, A
   Neumann, S
   Koepler, O
AF Stroemert, Philip
   Hunold, Johannes
   Castro, Andre
   Neumann, Steffen
   Koepler, Oliver
TI Ontologies4Chem: the landscape of ontologies in chemistry
SO PURE AND APPLIED CHEMISTRY
LA English
DT Article
DE Cheminformatics; FAIR data; linked data; ontology; research data;
   terminology
ID PRINCIPLES
AB For a long time, databases such as CAS, Reaxys, PubChem or ChemSpider mostly rely on unique numerical identifiers or chemical structure identifiers like InChI, SMILES or others to link data across heterogeneous data sources. The retrospective processing of information and fragmented data from text publications to maintain these databases is a cumbersome process. Ontologies are a holistic approach to semantically describe data, information and knowledge of a domain. They provide terms, relations and logic to semantically annotate and link data building knowledge graphs. The application of standard taxonomies and vocabularies from the very beginning of data generation and along research workflows in electronic lab notebooks (ELNs), software tools, and their final publication in data repositories create FAIR data straightforwardly. Thus a proper semantic description of an investigation and the why, how, where, when, and by whom data was produced in conjunction with the description and representation of research data is a natural outcome in contrast to the retrospective processing of research publications as we know it. In this work we provide an overview of ontologies in chemistry suitable to represent concepts of research and research data. These ontologies are evaluated against several criteria derived from the FAIR data principles and their possible application in the digitisation of research data management workflows.
C1 [Stroemert, Philip; Hunold, Johannes; Castro, Andre; Koepler, Oliver] TIB Leibniz Informat Ctr Sci & Technol, Welfengarten 1 B, D-30167 Hannover, Germany.
   [Neumann, Steffen] Leibniz Inst Plant Biochem, Weinberg 3, D-06120 Halle, Germany.
RP Stromert, P (通讯作者)，TIB Leibniz Informat Ctr Sci & Technol, Welfengarten 1 B, D-30167 Hannover, Germany.
EM philip.stroemert@tib.eu
OI Hunold, Johannes/0000-0002-4378-6061; Stromert,
   Philip/0000-0002-1595-3213; Koepler, Oliver/0000-0003-3385-4232; Castro,
   Andre/0000-0002-7839-3698
FU Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
   [441958208]
FX The presented work was conducted as part of the NFDI4Chem project (DFG
   project no. 441958208). The authors would like to thank the Deutsche
   Forschungsgemeinschaft (DFG, German Research Foundation) for funding and
   support.
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NR 68
TC 0
Z9 0
U1 4
U2 4
PU WALTER DE GRUYTER GMBH
PI BERLIN
PA GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY
SN 0033-4545
EI 1365-3075
J9 PURE APPL CHEM
JI Pure Appl. Chem.
PD JUN 27
PY 2022
VL 94
IS 6
BP 605
EP 622
DI 10.1515/pac-2021-2007
EA MAR 2022
PG 18
WC Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry
GA 3N0WF
UT WOS:000772131800001
OA hybrid
DA 2022-08-23
ER

PT J
AU Jimmy
   Zuccon, G
   Koopman, B
AF Jimmy
   Zuccon, Guido
   Koopman, Bevan
TI Payoffs and pitfalls in using knowledge-bases for consumer health search
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 Knowledge base; Knowledge graph; Query expansion; Consumer health search
ID INFORMATION; RETRIEVAL; UMLS
AB Consumer health search (CHS) is a challenging domain with vocabulary mismatch and considerable domain expertise hampering peoples' ability to formulate effective queries. We posit that using knowledge bases for query reformulation may help alleviate this problem. How to exploit knowledge bases for effective CHS is nontrivial, involving a swathe of key choices and design decisions (many of which are not explored in the literature). Here we rigorously empirically evaluate the impact these different choices have on retrieval effectiveness. A state-of-the-art knowledge-base retrieval modelthe Entity Query Feature Expansion modelwas used to evaluate these choices, which include: which knowledge base to use (specialised vs. general purpose), how to construct the knowledge base, how to extract entities from queries and map them to entities in the knowledge base, what part of the knowledge base to use for query expansion, and if to augment the knowledge base search process with relevance feedback. While knowledge base retrieval has been proposed as a solution for CHS, this paper delves into the finer details of doing this effectively, highlighting both payoffs and pitfalls. It aims to provide some lessons to others in advancing the state-of-the-art in CHS.
C1 [Jimmy; Zuccon, Guido] QUT, Brisbane, Qld, Australia.
   [Jimmy] Univ Surabaya UBAYA, Surabaya, Indonesia.
   [Koopman, Bevan] CSIRO, Australian E Hlth Res Ctr, Canberra, ACT, Australia.
RP Zuccon, G (通讯作者)，QUT, Brisbane, Qld, Australia.
EM jimmy@hdr.qut.edu.au; g.zuccon@uq.edu.au; bevan.koopman@csiro.au
RI Zuccon, Guido/AAI-6206-2021; Koopman, Bevan/AAF-3038-2021
OI Zuccon, Guido/0000-0003-0271-5563
FU Indonesia Endowment Fund for Education (Lembaga Pengelola Dana
   Pendidikan/LPDP); Australian Research Council DECRA Research Fellowship
   [DE180101579]; Google Faculty Research Award
FX Jimmy is sponsored by the Indonesia Endowment Fund for Education
   (Lembaga Pengelola Dana Pendidikan/LPDP). Guido Zuccon is the recipient
   of an Australian Research Council DECRA Research Fellowship
   (DE180101579) and a Google Faculty Research Award.
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NR 38
TC 1
Z9 1
U1 0
U2 8
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 350
EP 394
DI 10.1007/s10791-018-9344-z
PG 45
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA HZ5UO
UT WOS:000468918400006
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Cao, TH
   Huynh, DT
AF Tru H. Cao
   Dat T. Huynh
TI Subsumption degrees between entity types and names for approximate
   knowledge retrieval
SO INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED
   SYSTEMS
LA English
DT Article
DE semantic distance; partial subsumption; query modification; graph
   matching; information retrieval
ID LINKAGE
AB The Web has become a huge and indispensable source of information to be used and shared globally, where knowledge is commonly represented and stored in RDF, or alternatively, in conceptual graphs. Managing and searching for web information have gone beyond the relational database model, as the data are semi-structured and inexact answers are often the case. Usually, approximate searching results are due to mismatching between entity types and names in a query and an answer. Firstly, this research work focuses on partial subsumption of a query graph to an answer graph, which is an unsymmetric measure in contrast to similarity. Secondly, it proposes a population-based method for defining subsumption degrees between entity types, one to another, and a class-sensitive soft TF-IDF method for entity names. Lastly, on the one hand, for a user-friendly interface and easily readable query expressions, conceptual graphs are employed at the front-end. On the other hand, in order to take the advantage of the existing platform of SeRQL, an exact RDF query language, the query modification tactic is used to retrieve the knowledge graphs that are close to a query graph, before the subsumption degrees of the query graph to those answer graphs are calculated.
EM Tru@dit.hcmut.edu.vn; Htdat@dit.hcmut.edu.vn
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NR 38
TC 2
Z9 2
U1 1
U2 8
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 FEB
PY 2007
VL 15
IS 1
SU S
BP 21
EP 42
DI 10.1142/S0218488507004443
PG 22
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 307GK
UT WOS:000256307200003
DA 2022-08-23
ER

PT J
AU Zhang, QC
   Li, MH
   Dong, W
   Zuo, M
   Wei, SW
   Song, SY
   Ai, DM
AF Zhang, Qingchuan
   Li, Menghan
   Dong, Wei
   Zuo, Min
   Wei, Siwei
   Song, Shaoyi
   Ai, Dongmei
TI An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food
   Safety Domain
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
AB Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the food safety field is constructed to discover the relationship between food safety issues. To solve the problem of multi-entity relationships in food safety incident sentences for few-shot learning, this paper adopts the pipeline-type extraction method. Entity relationship is extracted from Bidirectional Encoder Representation from Transformers (BERTs) joined Bidirectional Long Short-Term Memory (BLSTM), namely, the BERT-BLSTM network model. Based on the entity relationship types extracted from the BERT-BLSTM model and the introduction of Chinese character features, an entity pair extraction model based on the BERT-BLSTM-conditional random field (CRF) is established. In this paper, several common deep neural network models are compared with the BERT-BLSTM-CRF model with a food public opinion events dataset. Experimental results show that the precision of the entity relationship extraction model based on BERT-BLSTM-CRF is 3.29%similar to 23.25% higher than that of other models in the food public opinion events dataset, which verifies the validity and rationality of the model proposed in this paper.
C1 [Zhang, Qingchuan; Li, Menghan; Dong, Wei; Zuo, Min; Wei, Siwei; Song, Shaoyi] Beijing Technol & Business Univ, Natl Engn Res Ctr Agriprod Qual Traceabil, Beijing 100048, Peoples R China.
   [Zhang, Qingchuan; Li, Menghan; Dong, Wei; Zuo, Min; Wei, Siwei; Song, Shaoyi] Beijing Technol & Business Univ, Sch E Business & Logist, Beijing 100048, Peoples R China.
   [Ai, Dongmei] Univ Sci & Technol Beijing, Basic Expt Nat Sci, Beijing 100083, Peoples R China.
RP Dong, W; Zuo, M (通讯作者)，Beijing Technol & Business Univ, Natl Engn Res Ctr Agriprod Qual Traceabil, Beijing 100048, Peoples R China.; Dong, W; Zuo, M (通讯作者)，Beijing Technol & Business Univ, Sch E Business & Logist, Beijing 100048, Peoples R China.
EM dongwei2019@btbu.edu.cn; zuomin@btbu.edu.cn
OI Wei, Siwei/0000-0003-1718-9981; Zuo, Min/0000-0003-3336-2940; meng han,
   li/0000-0002-8669-6146; Wei, Dong/0000-0002-3634-0598
FU National Key Technology R&D Program of China [2021YFD2100605]; Beijing
   Natural Science Foundation [4202014]; Natural Science Foundation of
   China [62006008, 61873027]; Humanity and Social Science Youth Foundation
   of Ministry of Education of China [20YJCZH229, AQT-2020-YB6]; Social
   Science Research Common Program of Beijing Municipal Commission of
   Education [SM202010011013]; Research Foundation for Youth Scholars of
   Beijing Technology and Business University [QNJJ2020-28]
FX This research was supported by the National Key Technology R&D Program
   of China (grant no. 2021YFD2100605), Beijing Natural Science Foundation
   (grant no. 4202014), Natural Science Foundation of China (grant nos.
   62006008 and 61873027), Humanity and Social Science Youth Foundation of
   Ministry of Education of China (grant no. 20YJCZH229), Open Project
   Program of National Engineering Laboratory of Agri-Product Quality
   Traceability (grant no. AQT-2020-YB6), Social Science Research Common
   Program of Beijing Municipal Commission of Education (grant no.
   SM202010011013), and Research Foundation for Youth Scholars of Beijing
   Technology and Business University (grant no. QNJJ2020-28).
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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 APR 28
PY 2022
VL 2022
AR 7773259
DI 10.1155/2022/7773259
PG 13
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 2S1RS
UT WOS:000821577800020
PM 35528358
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Upham, NS
   Poelen, JH
   Paul, D
   Groom, QJ
   Simmons, NB
   Vanhove, MPM
   Bertolino, S
   Reeder, DM
   Bastos-Silveira, C
   Sen, A
   Sterner, B
   Franz, NM
   Guidoti, M
   Penev, L
   Agosti, D
AF Upham, Nathan S.
   Poelen, Jorrit H.
   Paul, Deborah
   Groom, Quentin J.
   Simmons, Nancy B.
   Vanhove, Maarten P. M.
   Bertolino, Sandro
   Reeder, DeeAnn M.
   Bastos-Silveira, Cristiane
   Sen, Atriya
   Sterner, Beckett
   Franz, Nico M.
   Guidoti, Marcus
   Penev, Lyubomir
   Agosti, Donat
TI Liberating host-virus knowledge from biological dark data
SO LANCET PLANETARY HEALTH
LA English
DT Article
ID SPILLOVER
AB Connecting basic data about bats and other potential hosts of SARS-CoV-2 with their ecological context is crucial to the understanding of the emergence and spread of the virus. However, when lockdowns in many countries started in March, 2020, the world's bat experts were locked out of their research laboratories, which in turn impeded access to large volumes of offline ecological and taxonomic data. Pandemic lockdowns have brought to attention the longstanding problem of so-called biological dark data: data that are published, but disconnected from digital knowledge resources and thus unavailable for high-throughput analysis. Knowledge of host-to-virus ecological interactions will be biased until this challenge is addressed. In this Viewpoint, we outline two viable solutions: first, in the short term, to interconnect published data about host organisms, viruses, and other pathogens; and second, to shift the publishing framework beyond unstructured text (the so-called PDF prison) to labelled networks of digital knowledge. As the indexing system for biodiversity data, biological taxonomy is foundational to both solutions. Building digitally connected knowledge graphs of host-pathogen interactions will establish the agility needed to quickly identify reservoir hosts of novel zoonoses, allow for more robust predictions of emergence, and thereby strengthen human and planetary health systems.
C1 [Upham, Nathan S.; Sterner, Beckett; Franz, Nico M.] Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA.
   [Poelen, Jorrit H.] Ronin Inst Independent Scholarship, Montclair, NJ USA.
   [Poelen, Jorrit H.] Univ Calif Santa Barbara, Cheadle Ctr Biodivers & Ecol Restorat, Santa Barbara, CA 93106 USA.
   [Paul, Deborah] Univ Illinois, Illinois Nat Hist Survey, Champaign, IL USA.
   [Groom, Quentin J.] Meise Bot Garden, Meise, Belgium.
   [Simmons, Nancy B.] Amer Museum Nat Hist, Dept Mammal, Div Vertebrate Zool, New York, NY 10024 USA.
   [Vanhove, Maarten P. M.] Hasselt Univ, Ctr Environm Sci, Zool Biodivers & Toxicol, Diepenbeek, Belgium.
   [Bertolino, Sandro] Univ Turin, Dept Life Sci & Syst Biol, Turin, Italy.
   [Reeder, DeeAnn M.] Bucknell Univ, Dept Biol, Lewisburg, PA 17837 USA.
   [Bastos-Silveira, Cristiane] Univ Lisbon, Ctr Ecol Evolut & Environm Changes, Lisbon, Portugal.
   [Sen, Atriya] Univ New Orleans, Dept Comp Sci, New Orleans, LA 70148 USA.
   [Guidoti, Marcus] Plazi, Porto Alegre, RS, Brazil.
   [Penev, Lyubomir] Pensoft Publishers, Sofia, Bulgaria.
   [Agosti, Donat] Plazi, Bern, Switzerland.
RP Upham, NS (通讯作者)，Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA.
EM nathan.upham@asu.edu
OI Sterner, Beckett/0000-0001-5219-7616; Groom,
   Quentin/0000-0002-0596-5376; Simmons, Nancy B./0000-0001-8807-7499;
   Poelen, Jorrit/0000-0003-3138-4118; Vanhove,
   Maarten/0000-0003-3100-7566; Sen, Atriya/0000-0002-1640-5905; Reeder,
   DeeAnn/0000-0001-8651-2012; Bertolino, Sandro/0000-0002-1063-8281
FU Biodiversity Knowledge Integration Center at Arizona State University;
   US National Institutes of Health [1R01AI151144-01A1, 1R21AI164268-01];
   US National Science Foundation [RAPID 2032774, DBI-1547229, DBI:1901932,
   DBI:1901926]; Arizona State University President's Special Initiative
   Funds; SYNTHESYS+ Research and Innovation action grant Horizon
   [2020-EU.1.4.1.2823827]; Arcadia Fund; Special Research Fund of Hasselt
   University [BOF20TT06]
FX NSU is supported by the Biodiversity Knowledge Integration Center at
   Arizona State University and the US National Institutes of Health (grant
   number 1R01AI151144-01A1). DMR is supported by the US National Science
   Foundation (RAPID 2032774) and the US National Institutes of Health
   (grant number 1R01AI151144-01A1). NMF and BS are supported by the
   Arizona State University President's Special Initiative Funds. QJG is
   supported by the SYNTHESYS+ Research and Innovation action grant Horizon
   2020-EU.1.4.1.2823827. DA is supported by the Arcadia Fund. DP is
   supported by the US National Science Foundation Advancing Digitization
   of Biodiversity Collections Program grant number DBI-1547229. MPMV is
   supported by the Special Research Fund of Hasselt University (grant
   number BOF20TT06). JHP is supported by the US National Science
   Foundation award Collaborative Research: Digitization TCN: Digitizing
   collections to trace parasite-host associations and predict the spread
   of vector-borne disease (award numbers DBI:1901932 and DBI:1901926).
   NSU, DMR, BS, AS, JHP, and DA are supported by the US National
   Institutes of Health (1R21AI164268-01). We thank Ana Casino, Dimitris
   Koureas, and Wouter Addink for organising the Consortium of European
   Taxonomic Facilities-Distributed System of Scientific Collections
   COVID-19 Taskforce that resulted in this Viewpoint, Esther Florsheim for
   valuable conversations, and BioRender, Pipat Soisook, and Emily Damstra
   for access to images or illustrations.
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NR 27
TC 2
Z9 2
U1 5
U2 6
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
EI 2542-5196
J9 LANCET PLANET HEALTH
JI Lancet Planet. Health
PD OCT
PY 2021
VL 5
IS 10
BP E746
EP E750
DI 10.1016/S2542-5196(21)00196-0
PG 5
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 WK4UJ
UT WOS:000709722100015
PM 34562356
OA Green Submitted, Green Published, gold, Green Accepted
DA 2022-08-23
ER

PT J
AU Ren, JT
   Long, JW
   Xu, ZK
AF Ren, Jiangtao
   Long, Jiawei
   Xu, Zhikang
TI Financial news recommendation based on graph embeddings
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE News recommendation; Knowledge graph; Graph embeddings
AB Most of the existing methods are not enough for securities companies to make their decisions on recommending the most suitable financial news to a specific user. On the one hand, such news articles often contain external knowledge related to companies and stocks. On the other hand, it is important for financial news recommendations to dynamically measure users' interests since people are usually interested in multiple specific concepts, companies, stocks and industry categories. To address the above challenges, we start by building a heterogeneous graph consisting of users, news, companies, concepts, and industry categories. Then, the graph embeddings of the nodes are generated using node2vec, and user-news relatedness can be computed based on them. Since financial news articles are time-sensitive, we propose an incremental method for alleviating the computational efficiency problem. The combination of a node2vec-based recommendation method and the incremental method can achieve a good balance between time efficiency and recommendation accuracy in the financial news recommendation task. Our methods are evaluated on a real-world dataset from a Chinese securities company, are shown to outperform other commonly used baseline models, can provide decision support for companies choosing news to be recommended to target users and allow users to obtain personalized real-time news recommendations.
C1 [Ren, Jiangtao; Long, Jiawei; Xu, Zhikang] Sun Yat Sen Univ, Guangdong Prov Key Lab Computat Sci, Sch Data & Comp Sci, Guangzhou 510275, Guangdong, Peoples R China.
RP Ren, JT (通讯作者)，Sun Yat Sen Univ, Guangdong Prov Key Lab Computat Sci, Sch Data & Comp Sci, Guangzhou 510275, Guangdong, Peoples R China.
EM issrjt@mail.sysu.edu.cn
OI Ren, Jiangtao/0000-0003-2827-8322; Long, Jiawei/0000-0001-8273-3280
FU National Natural Science Foundation of China [U1811462, U1711263]
FX This research is partially supported by the National Natural Science
   Foundation of China (no. U1811462 and U1711263).
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   Beres F., 2018, 7 INT C COMPL NETW T, P178
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NR 26
TC 17
Z9 17
U1 9
U2 76
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-9236
EI 1873-5797
J9 DECIS SUPPORT SYST
JI Decis. Support Syst.
PD OCT
PY 2019
VL 125
AR 113115
DI 10.1016/j.dss.2019.113115
PG 11
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Operations Research & Management Science
GA JA1HP
UT WOS:000487568400006
DA 2022-08-23
ER

PT J
AU Ding, YH
   Yu, HT
   Zhang, JP
   Li, HR
   Gu, YJ
AF Ding, Yuehang
   Yu, Hongtao
   Zhang, Jianpeng
   Li, Huanruo
   Gu, Yunjie
TI A Knowledge Representation Based User-Driven Ontology Summarization
   Method
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article
DE ontology; ontology summarization; semantic distance; K-means plus
AB As the superstructure of knowledge graph, ontology has been widely applied in knowledge engineering. However, it becomes increasingly difficult to be practiced and comprehended due to the growing data size and complexity of schemas. Hence, ontology summarization surfaced to enhance the comprehension and application of ontology. Existing summarization methods mainly focus on ontology's topology without taking semantic information into consideration, while human understand information based on semantics. Thus, we proposed a novel algorithm to integrate semantic information and topological information, which enables ontology to be more understandable. In our work, semantic and topological information are represented by concept vectors, a set of high-dimensional vectors. Distances between concept vectors represent concepts' similarity and we selected important concepts following these two criteria: 1) the distances from important concepts to normal concepts should be as short as possible, which indicates that important concepts could summarize normal concepts well; 2) the distances from an important concept to the others should be as long as possible which ensures that important concepts are not similar to each other. K-means++ is adopted to select important concepts. Lastly, we performed extensive evaluations to compare our algorithm with existing ones. The evaluations prove that our approach performs better than the others in most of the cases.
C1 [Ding, Yuehang; Yu, Hongtao; Zhang, Jianpeng; Li, Huanruo; Gu, Yunjie] Informat Engn Univ, Zhengzhou, Henan, Peoples R China.
RP Ding, YH (通讯作者)，Informat Engn Univ, Zhengzhou, Henan, Peoples R China.
EM data_rabbit@163.com; lizardwhite@163.com
FU National Natural Science Foundation of China [61521003]
FX This work was supported by the National Natural Science Foundation of
   China (61521003).
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NR 5
TC 2
Z9 2
U1 1
U2 7
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 SEP
PY 2019
VL E102D
IS 9
BP 1870
EP 1873
DI 10.1587/transinf.2019EDL8069
PG 4
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IV1BX
UT WOS:000484013400033
OA gold
DA 2022-08-23
ER

PT J
AU Li, GL
   Zhu, T
   Hua, J
   Yuan, T
   Niu, ZY
   Li, T
   Zhang, HB
AF Li, Guangli
   Zhu, Tao
   Hua, Jin
   Yuan, Tian
   Niu, Zhengyu
   Li, Tao
   Zhang, Hongbin
TI Asking Images: Hybrid Recommendation System for Tourist Spot by
   Hierarchical Sampling Statistics and Multimodal Visual Bayesian
   Personalized Ranking
SO IEEE ACCESS
LA English
DT Article
DE Recommendation system; tourist spots; hierarchical sampling statistics;
   multimodal visual Bayesian personalized ranking; data sparseness
ID NETWORK; MODEL
AB "Data sparseness" is a key issue in current research works on recommendation systems. However, additional information, such as texts, images, knowledge graph, and audios, that is correlated to items helps alleviate the problem to some extent. We focus our research on designing a novel hybrid recommendation system for tourist spots. Tourist spot images are utilized to suppress the "data sparseness" problem in the recommendation procedure. First, a novel multimodal visual bayesian personalized ranking algorithm is proposed to fully utilize the cross-modal semantic correlations among different image features. Then, a new recommendation list called L-A is generated accordingly from the multimodal perspective. Second, user preference is acquired using the hierarchical sampling statistics model. A new recommendation list called L-H is generated in turn from the statistical perspective. Finally, hybrid recommendation results are obtained on the basis of L-H and L-A. Experimental results demonstrate that the proposed hybrid recommendation system for tourist spots is effective and robust. It is superior to other competitive baselines. More importantly, the proposed hybrid recommendation system is good at recommending a group of tourist spots and more stable than baselines, indicating its high practical value.
C1 [Li, Guangli; Zhu, Tao; Hua, Jin; Yuan, Tian] East China Jiaotong Univ, Sch Informat Engn, Nanchang 330013, Jiangxi, Peoples R China.
   [Niu, Zhengyu] Baidu China Co Ltd, Beijing 100085, Peoples R China.
   [Li, Tao] Florida Int Univ, Sch Comp & Informat Sci, Miami, FL 33101 USA.
   [Zhang, Hongbin] East China Jiaotong Univ, Software Sch, Nanchang 330013, Jiangxi, Peoples R China.
RP Zhang, HB (通讯作者)，East China Jiaotong Univ, Software Sch, Nanchang 330013, Jiangxi, Peoples R China.
EM zhanghongbin@whu.edu.cn
OI zhang, hongbin/0000-0002-8375-0039
FU National Natural Science Foundation of China [61762038, 61861016];
   Natural Science Foundation of Jiangxi Province [20171BAB202023,
   20192ACB21004]; Key Research and Development Plan of Jiangxi Provincial
   Science and Technology Department [20171BBG70093, 20192BBE50071];
   Humanity and Social Science Foundation of the Ministry of Education
   [17YJAZH117, 16YJAZH029]; Humanity and Social Science Foundation of the
   Jiangxi Province [16TQ02]; Science and Technology Projects of Jiangxi
   Provincial Department of Education [GJJ180320]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61762038 and Grant 61861016, in part by
   the Natural Science Foundation of Jiangxi Province under Grant
   20171BAB202023 and Grant 20192ACB21004, in part by the Key Research and
   Development Plan of Jiangxi Provincial Science and Technology Department
   under Grant 20171BBG70093 and Grant 20192BBE50071, in part by the
   Humanity and Social Science Foundation of the Ministry of Education
   under Grant 17YJAZH117 and Grant 16YJAZH029, in part by the Humanity and
   Social Science Foundation of the Jiangxi Province under Grant 16TQ02,
   and in part by the Science and Technology Projects of Jiangxi Provincial
   Department of Education under Grant GJJ180320.
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NR 67
TC 7
Z9 7
U1 1
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 126539
EP 126560
DI 10.1109/ACCESS.2019.2937375
PG 22
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA JA2SG
UT WOS:000487666800006
OA gold
DA 2022-08-23
ER

PT J
AU Mao, QR
   Li, X
   Peng, H
   Li, JX
   He, DX
   Guo, S
   He, M
   Wang, LH
AF Mao, Qianren
   Li, Xi
   Peng, Hao
   Li, Jianxin
   He, Dongxiao
   Guo, Shu
   He, Min
   Wang, Lihong
TI Event prediction based on evolutionary event ontology knowledge
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Event prediction; Evolutionary event; Event knowledge; Event ontology
   knowledge; Event knowledge graph
AB The evolution and development of breaking news events usually present regular patterns, leading to the happening of sequential events. Therefore, the analysis of such evolutionary patterns among events and prediction to breaking news events from free text is a valuable capability for decision support systems. Traditional systems tend to focus on contents distribution information but ignore the inherent regularity of evolutionary events. We introduce evolutionary event ontology knowledge (EEOK) structuring the evolutionary patterns in five different event domains, namely Explosion, Conflagration, Geological Hazard, Traffic Accident, Personal Injury. Based on EEOK which provides a representing general-purpose ontology knowledge, we also explore a framework with a pipeline semantic analysis procedure of event extraction, evolutionary event recognition, and event prediction. Since the evolutionary event under each event domain has different evolution patterns, our proposed event prediction model combines the event types to capture the inherent regulation of evolutionary events. Comparative analyses are presented to show the effectiveness of the proposed prediction model compared to other alternative methods. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Mao, Qianren; Li, Xi; Peng, Hao; Li, Jianxin] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China.
   [Mao, Qianren; Li, Xi; Peng, Hao; Li, Jianxin] Beihang Univ, State Key Lab Software Dev Environm, Beijing, Peoples R China.
   [He, Dongxiao] Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China.
   [Guo, Shu; He, Min; Wang, Lihong] Natl Comp Network Emergency Response Tech Team, Coordinat Ctr China, Beijing, Peoples R China.
RP Wang, LH (通讯作者)，Natl Comp Network Emergency Response Tech Team, Coordinat Ctr China, Beijing, Peoples R China.
EM maoqr@act.buaa.edu.cn; lixi@act.buaa.edu.cn; penghao@act.buaa.edu.cn;
   lijx@act.buaa.edu.cn; hedongxiao@tju.edu.cn; guoshu@cert.org.cn;
   hemin@cert.org.cn; wlh@isc.org.cn
RI Peng, Hao/L-7364-2019
OI Peng, Hao/0000-0001-7422-630X
FU National Key R&D Program of China [2018YFC0830804, 2017YFB0803305]; NSFC
   [61772151, 61872022]; Academic Excellence Foundation of BUAA for Ph.D.
   Students
FX This work is supported by the National Key R&D Program of China
   (2018YFC0830804), NSFC (No.61772151 and No.61872022), Academic
   Excellence Foundation of BUAA for Ph.D. Students. The co-author Min He
   is supported by National Key R&D Program of China (No. 2017YFB0803305).
   We also thank our anonymous reviewers for their constructive comments.
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NR 53
TC 7
Z9 8
U1 12
U2 73
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD FEB
PY 2021
VL 115
BP 76
EP 89
DI 10.1016/j.future.2020.07.041
PG 14
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OU3NQ
UT WOS:000591438800006
DA 2022-08-23
ER

PT J
AU Hascoet, T
   Ariki, Y
   Takiguchi, T
AF Hascoet, Tristan
   Ariki, Yasuo
   Takiguchi, Tetsuya
TI Semantic embeddings of generic objects for zero-shot learning
SO EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
LA English
DT Article
DE ZSL; Semantic embedding
AB Zero-shot learning (ZSL) models use semantic representations of visual classes to transfer the knowledge learned from a set of training classes to a set of unknown test classes. In the context of generic object recognition, previous research has mainly focused on developing custom architectures, loss functions, and regularization schemes for ZSL using word embeddings as semantic representation of visual classes. In this paper, we exclusively focus on the affect of different semantic representations on the accuracy of ZSL. We first conduct a large scale evaluation of semantic representations learned from either words, text documents, or knowledge graphs on the standard ImageNet ZSL benchmark. We show that, using appropriate semantic representations of visual classes, a basic linear regression model outperforms the vast majority of previously proposed approaches. We then analyze the classification errors of our model to provide insights into the relevance and limitations of the different semantic representations we investigate. Finally, our investigation helps us understand the reasons behind the success of recently proposed approaches based on graph convolution networks (GCN) which have shown dramatic improvements over previous state-of-the-art models.
C1 [Hascoet, Tristan; Ariki, Yasuo; Takiguchi, Tetsuya] Kobe Univ, Nada Ward, 1-1 Rokkodaicho, Kobe, Hyogo 6570013, Japan.
   [Ariki, Yasuo; Takiguchi, Tetsuya] Assoc Adv Sci & Technol, Nada Ward, 1-1 Rokkodaicho, Kobe, Hyogo 6570013, Japan.
RP Hascoet, T (通讯作者)，Kobe Univ, Nada Ward, 1-1 Rokkodaicho, Kobe, Hyogo 6570013, Japan.
EM tristan.hascoet@me.cs.scitec.kobe-u.ac.jp
FU scholarship MEXT from the Japanese Ministry of Education, Culture,
   Sports, Science, and Technology; JSPS [JP 17K00236]; PRESTO, JST
   [JPMJPR15D2]
FX This work was supported by a scholarship MEXT from the Japanese Ministry
   of Education, Culture, Sports, Science, and Technology. A part of this
   study is subsidized by JSPS Grant-in-Aid for Scientific Research and
   Research granted JP 17K00236. This work was supported in part by PRESTO,
   JST (Grant No. JPMJPR15D2).
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NR 39
TC 3
Z9 3
U1 0
U2 6
PU SPRINGEROPEN
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 1687-5176
EI 1687-5281
J9 EURASIP J IMAGE VIDE
JI EURASIP J. Image Video Process.
PD JAN 15
PY 2019
AR 13
DI 10.1186/s13640-018-0371-x
PG 14
WC Engineering, Electrical & Electronic; Imaging Science & Photographic
   Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Imaging Science & Photographic Technology
GA HH6CO
UT WOS:000455816600002
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Binder, J
   Ursu, O
   Bologa, C
   Jiang, SY
   Maphis, N
   Dadras, S
   Chisholm, D
   Weick, J
   Myers, O
   Kumar, P
   Yang, JJ
   Bhaskar, K
   Oprea, TI
AF Binder, Jessica
   Ursu, Oleg
   Bologa, Cristian
   Jiang, Shanya
   Maphis, Nicole
   Dadras, Somayeh
   Chisholm, Devon
   Weick, Jason
   Myers, Orrin
   Kumar, Praveen
   Yang, Jeremy J.
   Bhaskar, Kiran
   Oprea, Tudor, I
TI Machine learning prediction and tau-based screening identifies potential
   Alzheimer's disease genes relevant to immunity
SO COMMUNICATIONS BIOLOGY
LA English
DT Article
ID NMDA RECEPTORS; RISK-FACTOR; PROTEIN; CELL; EXPRESSION; BRAIN;
   PHOSPHORYLATION; SUPPRESSOR; CONTRIBUTE; TRANSPORT
AB With increased research funding for Alzheimer's disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1 beta-TNF alpha, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.
   Jessica Binder et al. developed a machine learning model to discover potential drug targets for Alzheimer's disease. They validated their 20 top candidates in several in vitro models, and highlight FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2 as potential AD risk genes.
C1 [Binder, Jessica; Ursu, Oleg; Bologa, Cristian; Myers, Orrin; Kumar, Praveen; Yang, Jeremy J.; Oprea, Tudor, I] Univ New Mexico, Dept Internal Med, Sch Med, Albuquerque, NM 87131 USA.
   [Jiang, Shanya; Maphis, Nicole; Dadras, Somayeh; Chisholm, Devon; Bhaskar, Kiran] Univ New Mexico, Dept Mol Genet & Microbiol, Sch Med, Albuquerque, NM 87131 USA.
   [Weick, Jason] Univ New Mexico, Dept Neurosci, Sch Med, Albuquerque, NM 87131 USA.
   [Bhaskar, Kiran] Univ New Mexico, Dept Neurol, Sch Med, Albuquerque, NM 87131 USA.
   [Oprea, Tudor, I] Gothenburg Univ, Dept Rheumatol & Inflammat Res, Inst Med, Sahlgrenska Acad, S-40530 Gothenburg, Sweden.
   [Oprea, Tudor, I] Univ Copenhagen, Novo Nordisk Fdn, Fac Hlth & Med Sci, Ctr Prot Res, DK-2200 Copenhagen, Denmark.
   [Ursu, Oleg] Merck & Co Inc, Computat & Struct Chem, 2000 Galloping Hill Rd, Kenilworth, NJ 07033 USA.
   [Oprea, Tudor, I] Roivant Discovery Sci Inc, 451 D St, Boston, MA 02210 USA.
RP Oprea, TI (通讯作者)，Univ New Mexico, Dept Internal Med, Sch Med, Albuquerque, NM 87131 USA.; Bhaskar, K (通讯作者)，Univ New Mexico, Dept Mol Genet & Microbiol, Sch Med, Albuquerque, NM 87131 USA.; Bhaskar, K (通讯作者)，Univ New Mexico, Dept Neurol, Sch Med, Albuquerque, NM 87131 USA.; Oprea, TI (通讯作者)，Gothenburg Univ, Dept Rheumatol & Inflammat Res, Inst Med, Sahlgrenska Acad, S-40530 Gothenburg, Sweden.; Oprea, TI (通讯作者)，Univ Copenhagen, Novo Nordisk Fdn, Fac Hlth & Med Sci, Ctr Prot Res, DK-2200 Copenhagen, Denmark.; Oprea, TI (通讯作者)，Roivant Discovery Sci Inc, 451 D St, Boston, MA 02210 USA.
EM kbhaskar@salud.unm.edu; toprea@salud.unm.edu
RI ; Bologa, Cristian/A-7952-2010
OI Binder, Jessica/0000-0003-2091-3950; Bologa,
   Cristian/0000-0003-2232-4244
FU NIH [U24 CA224370-01S1, U24 TR002278]; UNM Health Sciences Center; UNM
   Department of Molecular Genetics and Microbiology; Alzheimer's Disease
   Core Center from the National Institute on Aging to Northwestern
   University, Chicago Illinois [P30 AG013854]; IDG KMC application from
   the University of New Mexico [NIH CA224370];  [RF1NS083704-05A1]; 
   [R01NS083704];  [R21NS077089];  [R21NS093442]
FX This work was primarily funded by the NIH Common Fund U24 CA224370-01S1
   AD/ADRD supplement. Additional funding for this study was from
   RF1NS083704-05A1, R01NS083704, R21NS077089, and R21NS093442; UNM Health
   Sciences Center Bridge Funding, UNM Department of Molecular Genetics and
   Microbiology intradepartmental grant funding, Dr. Stephanie Ruby travel
   award (to J.B.). This study was also supported in part by an Alzheimer's
   Disease Core Center grant (P30 AG013854) from the National Institute on
   Aging to Northwestern University, Chicago Illinois. We gratefully
   acknowledge the assistance of the Northwestern Cognitive Neurology &
   Alzheimer's Disease Center (CNADC) Neuropathology Core for postmortem
   tissue samples. The general machine learning, informatics, and data
   science framework development was supported by the IDG KMC application
   from the University of New Mexico (NIH CA224370). Additional funding for
   T.I.O., C.B., and J.J.Y. was provided by NIH grant U24 TR002278.
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NR 101
TC 2
Z9 2
U1 6
U2 7
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
EI 2399-3642
J9 COMMUN BIOL
JI Commun. Biol.
PD FEB 11
PY 2022
VL 5
IS 1
AR 125
DI 10.1038/s42003-022-03068-7
PG 15
WC Biology; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics; Science & Technology - Other
   Topics
GA YX6WS
UT WOS:000754241500005
PM 35149761
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ilievski, F
   Oltramari, A
   Ma, KX
   Zhang, B
   McGuinness, DL
   Szekely, P
AF Ilievski, Filip
   Oltramari, Alessandro
   Ma, Kaixin
   Zhang, Bin
   McGuinness, Deborah L.
   Szekely, Pedro
TI Dimensions of commonsense knowledge
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Commonsense knowledge; Semantics; Knowledge graphs; Reasoning
AB Commonsense knowledge is essential for many AI applications, including those in natural language processing, visual processing, and planning. Consequently, many sources that include commonsense knowledge have been designed and constructed over the past decades. Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization. Efforts to consolidate commonsense knowledge have yielded partial success, with no clear path towards a comprehensive solution. We aim to organize these sources around a common set of dimensions of commonsense knowledge. We survey a wide range of popular commonsense sources with a special focus on their relations. We consolidate these relations into 13 knowledge dimensions. This consolidation allows us to unify the separate sources and to compute indications of their coverage, overlap, and gaps with respect to the knowledge dimensions. Moreover, we analyze the impact of each dimension on downstream reasoning tasks that require commonsense knowledge, observing that the temporal and desire/goal dimensions are very beneficial for reasoning on current downstream tasks, while distinctness and lexical knowledge have little impact. These results reveal preferences for some dimensions in current evaluation, and potential neglect of others. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Ilievski, Filip; Zhang, Bin; Szekely, Pedro] Univ Southern Calif, Informat Sci Inst, Marina Del Rey, CA 90292 USA.
   [Oltramari, Alessandro] Bosch Res & Technol Ctr, Intelligent Internet Things, Pittsburgh, PA USA.
   [Ma, Kaixin] Carnegie Mellon Univ, Language Technol Inst, Pittsburgh, PA 15213 USA.
   [McGuinness, Deborah L.] Rensselaer Polytech Inst, Tetherless World Constellat, Troy, NY USA.
RP Ilievski, F (通讯作者)，Univ Southern Calif, Informat Sci Inst, Marina Del Rey, CA 90292 USA.
EM ilievski@isi.edu; Alessandro.Oltramari@us.bosch.com;
   kaixinm@andrew.cmu.edu; binzhang@isi.edu; dlm@cs.rpi.edu;
   pszekely@isi.edu
OI Ilievski, Filip/0000-0002-1735-0686; McGuinness,
   Deborah/0000-0001-7037-4567
FU DARPA MCS program [N660011924033]; United States Office Of Naval
   Research
FX Four of the authors (Filip Ilievski, Bin Zhang, Deborah McGuinness, and
   Pedro Szekely) are sponsored by the DARPA MCS program under Contract No.
   N660011924033 with the United States Office Of Naval Research.
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U1 1
U2 8
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD OCT 11
PY 2021
VL 229
AR 107347
DI 10.1016/j.knosys.2021.107347
EA AUG 2021
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UO5NV
UT WOS:000694743300008
OA Green Submitted, Bronze
DA 2022-08-23
ER

PT J
AU Laha, A
   Jain, P
   Mishra, A
   Sankaranarayanan, K
AF Laha, Anirban
   Jain, Parag
   Mishra, Abhijit
   Sankaranarayanan, Karthik
TI Scalable Micro-planned Generation of Discourse from Structured Data
SO COMPUTATIONAL LINGUISTICS
LA English
DT Article
AB We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically use end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. Rather, it relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains.
   Our system utilizes a three-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent, and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain data set curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular data sets covering diverse data types such as knowledge graphs and key-value maps.
C1 [Laha, Anirban] Univ Montreal, Mila, Montreal, PQ, Canada.
   [Jain, Parag] Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland.
   [Mishra, Abhijit; Sankaranarayanan, Karthik] IBM Res, Yorktown Hts, NY USA.
RP Laha, A (通讯作者)，Univ Montreal, Mila, Montreal, PQ, Canada.
EM anirbanlaha@gmail.com; parag.jain@ed.ac.uk; abhijimi@in.ibm.com;
   kartsank@in.ibm.com
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NR 33
TC 1
Z9 2
U1 0
U2 3
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
SN 0891-2017
EI 1530-9312
J9 COMPUT LINGUIST
JI Comput. Linguist.
PD DEC
PY 2019
VL 45
IS 4
BP 737
EP 763
DI 10.1162/COLI_a_00363
PG 27
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; 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 KJ8WG
UT WOS:000512336200005
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zhu, JW
   Li, ZX
   Wei, JH
   Zeng, YF
   Ma, HF
AF Zhu, Jianwei
   Li, Zhixin
   Wei, Jiahui
   Zeng, Yufei
   Ma, Huifang
TI Fine-grained bidirectional attentional generation and knowledge-assisted
   networks for cross-modal retrieval
SO IMAGE AND VISION COMPUTING
LA English
DT Article
DE Cross-modal retrieval; Graph convolutional network; Knowledge embedding;
   Cross-attention; Attentional generative network
AB Generally, most existing cross-modal retrieval methods only consider global or local semantic embeddings, lacking fine-grained dependencies between objects. At the same time, it is usually ignored that the mutual transformation between modalities also facilitates the embedding of modalities. Given these problems, we propose a method called BiKA (Bidirectional Knowledge-assisted embedding and Attention-based generation). The model uses a bidirectional graph convolutional neural network to establish dependencies between objects. In addition, it employs a bidirectional attention-based generative network to achieve the mutual transformation between modalities. Specifically, the knowledge graph is used for local matching to constrain the local expression of the modalities, in which the generative network is used for mutual transformation to constrain the global expression of the modalities. In addition, we also propose a new position relation embedding network to embed position relation information between objects. The experiments on two public datasets show that the performance of our method has been dramatically improved compared to many state-of-the-art models.(c) 2022 Elsevier B.V. All rights reserved.
C1 [Zhu, Jianwei; Li, Zhixin; Wei, Jiahui; Zeng, Yufei] Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China.
   [Ma, Huifang] Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou 730070, Peoples R China.
RP Li, ZX (通讯作者)，Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China.
EM zhujw@stu.gxnu.edu.cn; lizx@gxnu.edu.cn; weijh@stu.gxnu.edu.cn;
   zengyf@stu.gxnu.edu.cn; mahuifang@nwnu.edu.cn
FU National Natural Science Foundation of China [61966004, 61866004];
   Guangxi Natural Science Foundation [2019GXNSFDA245018]; Innovation
   Project of Guangxi Graduate Education [XYCBZ2021002]; Guangxi "Bagui
   Scholar" Teams for Innovation and Research Project, Guangxi Talent
   Highland Project of Big Data Intelligence and Application; Guangxi
   Collaborative Innovation Center of Multi-source Information Integration
   and Intelligent Processing
FX This work is supported by National Natural Science Foundation of China
   (Nos. 61966004, 61866004) , Guangxi Natural Science Foundation (No.
   2019GXNSFDA245018) , Innovation Project of Guangxi Graduate Education
   (No.XYCBZ2021002) , Guangxi "Bagui Scholar" Teams for Innovation and
   Research Project, Guangxi Talent Highland Project of Big Data
   Intelligence and Application, and Guangxi Collaborative Innovation
   Center of Multi-source Information Integration and Intelligent
   Processing.
CR Anderson P, 2018, PROC CVPR IEEE, P6077, DOI 10.1109/CVPR.2018.00636
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NR 46
TC 0
Z9 0
U1 3
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0262-8856
EI 1872-8138
J9 IMAGE VISION COMPUT
JI Image Vis. Comput.
PD AUG
PY 2022
VL 124
AR 104507
DI 10.1016/j.imavis.2022.104507
PG 11
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic; Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Optics
GA 2J7XL
UT WOS:000815864800006
DA 2022-08-23
ER

PT J
AU Koho, M
   Burrows, T
   Hyvonen, E
   Ikkala, E
   Page, K
   Ransom, L
   Tuominen, J
   Emery, D
   Fraas, M
   Heller, B
   Lewis, D
   Morrison, A
   Porte, G
   Thomson, E
   Velios, A
   Wijsman, H
AF Koho, Mikko
   Burrows, Toby
   Hyvonen, Eero
   Ikkala, Esko
   Page, Kevin
   Ransom, Lynn
   Tuominen, Jouni
   Emery, Doug
   Fraas, Mitch
   Heller, Benjamin
   Lewis, David
   Morrison, Andrew
   Porte, Guillaume
   Thomson, Emma
   Velios, Athanasios
   Wijsman, Hanno
TI Harmonizing and publishing heterogeneous premodern manuscript metadata
   as Linked Open Data
SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID INTEROPERABILITY
AB Manuscripts are a crucial form of evidence for research into all aspects of premodern European history and culture, and there are numerous databases devoted to describing them in detail. This descriptive information, however, is typically available only in separate data silos based on incompatible data models and user interfaces. As a result, it has been difficult to study manuscripts comprehensively across these various platforms. To address this challenge, a team of manuscript scholars and computer scientists worked to create "Mapping Manuscript Migrations" (MMM), a semantic portal, and a Linked Open Data service. MMM stands as a successful proof of concept for integrating distinct manuscript datasets into a shared platform for research and discovery with the potential for future expansion. This paper will discuss the major products of the MMM project: a unified data model, a repeatable data transformation pipeline, a Linked Open Data knowledge graph, and a Semantic Web portal. It will also examine the crucial importance of an iterative process of multidisciplinary collaboration embedded throughout the project, enabling humanities researchers to shape the development of a digital platform and tools, while also enabling the same researchers to ask more sophisticated and comprehensive research questions of the aggregated data.
C1 [Koho, Mikko; Hyvonen, Eero; Ikkala, Esko; Tuominen, Jouni] Aalto Univ, Semant Comp Res Grp SeCo, Espoo, Finland.
   [Koho, Mikko; Hyvonen, Eero; Tuominen, Jouni] Univ Helsinki, HELDIG Helsinki Ctr Digital Humanities, Helsinki, Finland.
   [Burrows, Toby; Page, Kevin; Lewis, David; Velios, Athanasios] Univ Oxford, Oxford E Res Ctr, Oxford, England.
   [Ransom, Lynn; Emery, Doug; Fraas, Mitch; Heller, Benjamin; Thomson, Emma] Univ Penn, Schoenberg Inst Manuscript Studies, Philadelphia, PA 19104 USA.
   [Morrison, Andrew] Univ Oxford, Bodleian Lib, Oxford, England.
   [Porte, Guillaume; Wijsman, Hanno] Inst Rech & Hist Textes, Aubervilliers, France.
   [Velios, Athanasios] Univ Arts London, Ligatus, London, England.
RP Koho, M (通讯作者)，Univ Helsinki, Fac Arts, Dept Digital Humanities, PL 4, Helsinki 00014, Finland.
EM mikko.koho@helsinki.fi
RI Hyvönen, Eero A/G-2252-2013; Thomson, Emma C/M-8690-2017; Burrows,
   Toby/C-3644-2009; Tuominen, Jouni/E-9330-2019
OI Thomson, Emma C/0000-0003-1482-0889; Porte,
   Guillaume/0000-0001-8656-8227; Morrison, Andrew/0000-0002-1074-8616;
   Emery, Doug/0000-0002-5147-7736; Ikkala, Esko/0000-0002-9571-7260;
   Ransom, Lynn/0000-0002-5231-3602; Koho, Mikko/0000-0002-7373-9338;
   Hyvonen, Eero Antero/0000-0003-1695-5840; Fraas,
   Arthur/0000-0003-3228-9876; Burrows, Toby/0000-0002-0469-7584; Tuominen,
   Jouni/0000-0003-4789-5676
FU Trans-Atlantic Platform under its Digging into Data Challenge
FX Trans-Atlantic Platform under its Digging into Data Challenge
CR Baierer K, 2017, SEMANT WEB, V8, P733, DOI 10.3233/SW-160234
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NR 38
TC 2
Z9 2
U1 7
U2 21
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2330-1635
EI 2330-1643
J9 J ASSOC INF SCI TECH
PD FEB
PY 2022
VL 73
IS 2
SI SI
BP 240
EP 257
DI 10.1002/asi.24499
EA MAY 2021
PG 18
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA XY3HN
UT WOS:000654263000001
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Guimaraes, V
   Paes, A
   Zaverucha, G
AF Guimaraes, Victor
   Paes, Aline
   Zaverucha, Gerson
TI Online probabilistic theory revision from examples with ProPPR
SO MACHINE LEARNING
LA English
DT Article
DE Online learning; Statistical relational learning; Theory revision from
   examples; Inductive logic programming; Mining data streams
AB Handling relational data streams has become a crucial task, given the availability of pervasive sensors and Internet-produced content, such as social networks and knowledge graphs. In a relational environment, this is a particularly challenging task, since one cannot assure that the streams of examples are independent along the iterations. Thus, most relational learning systems are still designed to learn only from closed batches of data. Furthermore, in case there is a previously acquired model, these systems either would discard it or assuming it as correct. In this work, we propose an online relational learning algorithm that can handle continuous, open-ended streams of relational examples as they arrive. We employ techniques of theory revision to take advantage of the previously acquired model as a starting point, by finding where it should be modified to cope with the new examples, and automatically update it. We rely on the Hoeffding's bound statistical theory to decide if the model must, in fact, be updated in accordance with the new examples. The proposed algorithm is built upon ProPPR statistical relational language, aiming at contemplating the uncertainty inherent to real data. Experimental results in social networks and entity co-reference datasets show the potential of the proposed approach compared to other relational learners.
C1 [Guimaraes, Victor; Zaverucha, Gerson] Univ Fed Rio de Janeiro, COPPE, Dept Syst Engn & Comp Sci, Rio de Janeiro, RJ, Brazil.
   [Paes, Aline] Univ Fed Fluminense, Dept Comp Sci, Niteroi, RJ, Brazil.
RP Paes, A (通讯作者)，Univ Fed Fluminense, Dept Comp Sci, Niteroi, RJ, Brazil.
EM guimaraes@cos.ufrj.br; alinepaes@ic.uff.br; gerson@cos.ufrj.br
FU Brazilian Research Agency CNPq
FX We would like to thank the Brazilian Research Agency CNPq for the
   financial support and the authors of RDN-Boost, ProPPR, and HTilde for
   making their code available to us.
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NR 46
TC 5
Z9 5
U1 0
U2 2
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0885-6125
EI 1573-0565
J9 MACH LEARN
JI Mach. Learn.
PD JUL
PY 2019
VL 108
IS 7
SI SI
BP 1165
EP 1189
DI 10.1007/s10994-019-05798-y
PG 25
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IC0XY
UT WOS:000470684600006
OA Bronze
DA 2022-08-23
ER

PT J
AU Yang, P
   Liu, ZJ
   Li, B
   Zhang, PH
AF Yang, Peng
   Liu, Zijian
   Li, Bing
   Zhang, Penghui
TI Implicit Relation Inference with Deep Path Extraction for Commonsense
   Question Answering
SO NEURAL PROCESSING LETTERS
LA English
DT Article; Early Access
DE Commonsense question answering; Evidence fusion; Knowledge reasoning;
   Dynamic interaction
AB Natural language inference plays an essential role in Commonsense Question Answering. Conventional models usually adopt keywords in questions and choices as queries to retrieve static and explicit evidence that is used to obtain final answers, where dynamic interaction between different keywords and implicit relations inference of deeper information are often neglected. In this paper, we propose a novel joint model, the Graph Relation retrieval Reasoning Network (GRRN), to explicitly introduce the dynamic interaction among different keywords and generate informative features that contribute to representation updating. In addition, to pursue in-depth relations between different keywords, we develop an optimised Path Evidence Fusion in the GRRN to obtain evidence based on deep paths and implicit relations with comprehensive knowledge by making full use of the original paths in external knowledge graphs. The experimental results show that compared with the baselines, our approach achieves remarkable improvement of 1.74% for precision on the CommonsenseQA dataset, thereby demonstrating the superiority of our state-of-the-art approach on implicit relation inference with deep paths.
C1 [Yang, Peng; Liu, Zijian; Li, Bing] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Yang, Peng; Zhang, Penghui] Southeast Univ, Sch Cyber Sci & Engn, Nanjing, Peoples R China.
   [Yang, Peng; Liu, Zijian; Li, Bing; Zhang, Penghui] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, 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, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.
EM pengyang@seu.edu.cn
FU Consulting Project of Chinese Academy of Engineering [2020-XY-5,
   2018-XY-07]; Fundamental Research Funds for the Central Universities;
   Academy-Locality Cooperation Project of Chinese Academy of Engineering
   [JS2021ZT05]
FX This work was supported in part by the Consulting Project of Chinese
   Academy of Engineering under Grant 2020-XY-5, 2018-XY-07, and in part by
   the Fundamental Research Funds for the Central Universities and the
   Academy-Locality Cooperation Project of Chinese Academy of Engineering
   under Grant JS2021ZT05.
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NR 66
TC 0
Z9 0
U1 6
U2 6
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1370-4621
EI 1573-773X
J9 NEURAL PROCESS LETT
JI Neural Process. Lett.
DI 10.1007/s11063-022-10831-8
EA MAY 2022
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1D3IC
UT WOS:000793696100001
DA 2022-08-23
ER

PT J
AU Wang, SF
   Dang, DP
   Guo, ZX
   Chen, CX
   Yu, WH
AF Wang, Shaofei
   Dang, Depeng
   Guo, Zixian
   Chen, Chuangxia
   Yu, Wenhui
TI Knowledge Enhanced Quality Estimation for Crowdsourcing
SO IEEE ACCESS
LA English
DT Article
DE Quality estimation; crowdsourcing; knowledge representation learning
ID MECHANISMS
AB Estimating the quality of answers is one of the challenges in crowdsourcing. The previous methods focus on the quality estimation for objective tasks, whereas subjective tasks, as a common type of crowdsourcing tasks, have not been well studied. In this paper, we focus on the quality estimation for subjective crowdsourcing tasks. Considering the high uncertainty of answers for subjective tasks, in this paper, we propose a background knowledge enhanced quality estimation method. More specifically, first we learn the distributed knowledge representation from knowledge graphs and text corpora by utilizing the multi-task learning framework. Then, we construct a pseudo-gold answer set for each task. Next, by comparing the provided answer with the derived pseudo-gold answer set, we calculate two different scores for each answer: 1) symbolic score, which measures the symbolic similarity and 2) embedding score, which indicates the embedding similarity. Finally, we get the final scores for each answer by combining these two scores. The extensive experiments on both universal and domain-specific crowdsourcing tasks show that our method can obtain better performance than other baselines.
C1 [Wang, Shaofei; Dang, Depeng; Guo, Zixian; Chen, Chuangxia; Yu, Wenhui] Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China.
RP Dang, DP (通讯作者)，Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China.
EM ddepeng@bnu.edu.cn
OI Wang, Shaofei/0000-0002-6378-4792
FU National Natural Science Foundation of China [61672102, 61073034,
   61370064, 60940032]; National Social Science Foundation of China
   [BCA150050]; Program for New Century Excellent Talents in the University
   of Ministry of Education of China [NCET-10-0239]; Open Project Sponsor
   of Beijing Key Laboratory of Intelligent Communication Software and
   Multimedia [ITSM201493]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61672102, Grant 61073034, Grant
   61370064, and Grant 60940032, in part by the National Social Science
   Foundation of China under Grant BCA150050, in part by the Program for
   New Century Excellent Talents in the University of Ministry of Education
   of China under Grant NCET-10-0239, and in part by the Open Project
   Sponsor of Beijing Key Laboratory of Intelligent Communication Software
   and Multimedia under Grant ITSM201493.
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NR 50
TC 0
Z9 0
U1 3
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 106693
EP 106703
DI 10.1109/ACCESS.2019.2932149
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IS2GX
UT WOS:000481972100110
OA gold
DA 2022-08-23
ER

PT J
AU Lisena, P
   Merono-Penuela, A
   Troncy, R
AF Lisena, Pasquale
   Merono-Penuela, Albert
   Troncy, Raphael
TI MIDI2vec: Learning MIDI embeddings for reliable prediction of symbolic
   music metadata
SO SEMANTIC WEB
LA English
DT Article
DE Music; metadata; metadata prediction; graph embeddings; neural networks
ID INFORMATION-RETRIEVAL; PERFORMANCE
AB An important problem in large symbolic music collections is the low availability of high-quality metadata, which is essential for various information retrieval tasks. Traditionally, systems have addressed this by relying either on costly human annotations or on rule-based systems at a limited scale. Recently, embedding strategies have been exploited for representing latent factors in graphs of connected nodes. In this work, we propose MIDI2vec, a new approach for representing MIDI files as vectors based on graph embedding techniques. Our strategy consists of representing the MIDI data as a graph, including the information about tempo, time signature, programs and notes. Next, we run and optimise node2vec for generating embeddings using random walks in the graph. We demonstrate that the resulting vectors can successfully be employed for predicting the musical genre and other metadata such as the composer, the instrument or the movement. In particular, we conduct experiments using those vectors as input to a Feed-Forward Neural Network and we report good comparable accuracy scores in the prediction with respect to other approaches relying purely on symbolic music, avoiding feature engineering and producing highly scalable and reusable models with low dimensionality. Our proposal has real-world applications in automated metadata tagging for symbolic music, for example in digital libraries for musicology, datasets for machine learning, and knowledge graph completion.
C1 [Lisena, Pasquale; Troncy, Raphael] EURECOM, Sophia Antipolis, France.
   [Merono-Penuela, Albert] Kings Coll London, London, England.
RP Lisena, P (通讯作者)，EURECOM, Sophia Antipolis, France.
EM pasquale.lisena@eurecom.fr; albert.merono@kcl.ac.uk;
   raphael.troncy@eurecom.fr
OI Merono-Penuela, Albert/0000-0003-4646-5842
FU Dutch Research Council NWO; European Union [101004746]
FX This work is partly supported by the CLARIAH project funded by the Dutch
   Research Council NWO. This work is part of a project that has received
   funding from the European Union's Horizon 2020 Research and Innovation
   Programme under grant agreement No. 101004746 (Polifonia: a digital
   harmoniser for musical heritage knowledge, H2020-SC6-TRANSFORMATIONS).
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NR 64
TC 1
Z9 1
U1 0
U2 0
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 357
EP 377
DI 10.3233/SW-210446
PG 21
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:000780041500004
OA hybrid
DA 2022-08-23
ER

PT J
AU Dong, NN
   Ren, BQ
   Li, HJ
   Zhong, XD
   Gong, XW
   Han, JM
   Lv, JZ
   Cheng, JH
AF Dong, Nannan
   Ren, Baoquan
   Li, Hongjun
   Zhong, Xudong
   Gong, Xiangwu
   Han, Junmei
   Lv, Jiazheng
   Cheng, Jianhua
TI A Novel Network-Node-Embedded Network Cognition Model Based on Knowledge
   Module for Strengthening the Thinking Capability of Intelligent Network
SO IEEE SENSORS JOURNAL
LA English
DT Article
DE Knowledge engineering; Cognition; Intelligent networks; Correlation;
   Decision making; Technological innovation; Optimization; Network
   intellectualization; network cognition model; multi-domain perception;
   network knowledge
ID ARCHITECTURE; 5G
AB Network intellectualization enables networks to adapt to the dynamic environment by perceiving, thinking, and interpreting the overall environmental changes like a human. However, the thinking capability of existing intelligent networks is generally limited or even absent. To further strengthen the thinking capability, we propose a novel network-node-embedded network cognition model based on a knowledge module to provide superior network intelligence. The proposed model is designed based on the OODA cycle, which is named from its components of "Observe", "Orient", "Decide", and "Act" modules, and is widely applied to improve the network intellectualization. We firstly reorganize the OODA cycle by extending the perceptive range of the "Observe" module and classifying the other three modules as "Learning Analysis" and "Optimization Decision-making" modules. Furthermore, we innovatively introduce a "Network Knowledge" module using the knowledge graph, which constructs the correlation between the network and its overall environmental changes to generate effective decisions. Besides, the proposed model is designed to be directly embedded in network nodes to verify and execute decisions simultaneously, eliminating the time delay between them. The use cases that illustrate the applicability of the proposed model are presented, and simulation results can demonstrate that the proposed model is feasible and effective.
C1 [Dong, Nannan; Lv, Jiazheng; Cheng, Jianhua] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin 150001, Peoples R China.
   [Ren, Baoquan; Li, Hongjun; Zhong, Xudong; Gong, Xiangwu; Han, Junmei] Acad Mil Med Sci, Inst Syst Engn, Beijing 100101, Peoples R China.
RP Dong, NN (通讯作者)，Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin 150001, Peoples R China.
EM dongnannan_ins@hrbeu.edu.cn; renbq88@126.com; lee.hongjun@hotmail.com;
   zxd148367@163.com; xiangwugong@hotmail.com; hjm.han@163.com;
   lvjiazheng@hrbeu.edu.cn; ins_cheng@163.com
FU China Postdoctoral Science Foundation [2020M673687]
FX This work was supported by the China Postdoctoral Science Foundation
   under Grant 2020M673687.
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NR 27
TC 0
Z9 0
U1 3
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1530-437X
EI 1558-1748
J9 IEEE SENS J
JI IEEE Sens. J.
PD JUL 1
PY 2022
VL 22
IS 13
BP 13727
EP 13738
DI 10.1109/JSEN.2022.3178111
PG 12
WC Engineering, Electrical & Electronic; Instruments & Instrumentation;
   Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Instruments & Instrumentation; Physics
GA 2P6BH
UT WOS:000819823500134
DA 2022-08-23
ER

PT J
AU Dethlefs, N
   Schoene, A
   Cuayahuitl, H
AF Dethlefs, Nina
   Schoene, Annika
   Cuayahuitl, Heriberto
TI A divide-and-conquer approach to neural natural language generation from
   structured data
SO NEUROCOMPUTING
LA English
DT Article
DE Neural networks; Artificial intelligence; Natural language processing
AB Current approaches that generate text from linked data for complex real-world domains can face problems including rich and sparse vocabularies as well as learning from examples of long varied sequences. In this article, we propose a novel divide-and-conquer approach that automatically induces a hierarchy of "generation spaces" from a dataset of semantic concepts and texts. Generation spaces are based on a notion of similarity of partial knowledge graphs that represent the domain and feed into a hierarchy of sequence-to-sequence or memory-to-sequence learners for concept-to-text generation. An advantage of our approach is that learning models are exposed to the most relevant examples during training which can avoid bias towards majority samples. We evaluate our approach on two common benchmark datasets and compare our hierarchical approach against a flat learning setup. We also conduct a comparison between sequence-to-sequence and memory-to-sequence learning models. Experiments show that our hierarchical approach overcomes issues of data sparsity and learns robust lexico-syntactic patterns, consistently outperforming flat baselines and previous work by up to 30%. We also find that while memory to-sequence models can outperform sequence-to-sequence models in some cases, the latter are generally more stable in their performance and represent a safer overall choice. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Dethlefs, Nina; Schoene, Annika] Univ Hull, Big Data Analyt Res Grp, Dept Comp Sci & Technol, Cottingham Rd, Kingston Upon Hull HU6 7RX, N Humberside, England.
   [Cuayahuitl, Heriberto] Univ Lincoln, Sch Comp Sci, Lincoln Ctr Autonomous Syst L CAS, Lincoln LN6 7TS, England.
RP Dethlefs, N (通讯作者)，Univ Hull, Big Data Analyt Res Grp, Dept Comp Sci & Technol, Cottingham Rd, Kingston Upon Hull HU6 7RX, N Humberside, England.
EM n.dethlefs@hull.ac.uk
FU Diffbot
FX We gratefully acknowledge funding from Diffbot to carry out this
   project. We also acknowledge the VIPER high-performance computing
   facility of the University of Hull and its support team, and thanks to
   Andy Hancock for helping us with access to the DGX-1 for most of our
   experiments.
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NR 44
TC 5
Z9 5
U1 0
U2 8
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD APR 14
PY 2021
VL 433
BP 300
EP 309
DI 10.1016/j.neucom.2020.12.083
EA JAN 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QQ1TR
UT WOS:000624310100009
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Loseto, G
   Scioscia, F
   Ruta, M
   Gramegna, F
   Ieva, S
   Pinto, A
   Scioscia, C
AF Loseto, Giuseppe
   Scioscia, Floriano
   Ruta, Michele
   Gramegna, Filippo
   Ieva, Saverio
   Pinto, Agnese
   Scioscia, Crescenzio
TI Knowledge-Based Decision Support in Healthcare via Near Field
   Communication
SO SENSORS
LA English
DT Article
DE decision support; ubiquitous healthcare; knowledge graph; automated
   reasoning; near-field communication
ID PHYSICIAN ORDER ENTRY; ONTOLOGY; SERVICES; DATABASE
AB The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient's case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care.
C1 [Loseto, Giuseppe; Scioscia, Floriano; Ruta, Michele; Gramegna, Filippo; Ieva, Saverio; Pinto, Agnese] Polytech Univ Bari, Dept Elect & Informat Engn, Via E Orabona 4, I-70125 Bari, Italy.
   [Scioscia, Crescenzio] Univ Bari, Dept Emergency & Organ Transplantat DETO, Rheumatol Unit, Piazza G Cesare 11, I-70124 Bari, Italy.
RP Ruta, M (通讯作者)，Polytech Univ Bari, Dept Elect & Informat Engn, Via E Orabona 4, I-70125 Bari, Italy.
EM giuseppe.loseto@poliba.it; floriano.scioscia@poliba.it;
   michele.ruta@poliba.it; filippo.gramegna@poliba.it;
   saverio.ieva@poliba.it; agnese.pinto@poliba.it;
   crescenzio.scioscia@policlinico.ba.it
RI ; Scioscia, Floriano/E-2205-2017; Scioscia, Crescenzio/M-1588-2015
OI RUTA, Michele/0000-0003-2125-327X; Scioscia,
   Floriano/0000-0002-7859-9602; Loseto, Giuseppe/0000-0002-7995-8494;
   Scioscia, Crescenzio/0000-0003-0941-8182; Gramegna,
   Filippo/0000-0003-4162-957X
FU Apulia Region InnoNetwork project Si-Ca.Re; Italian national PON project
   AMICO
FX This work was supported in part by Apulia Region InnoNetwork project
   Si-Ca.Re and Italian national PON project AMICO.
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NR 68
TC 2
Z9 2
U1 2
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD SEP
PY 2020
VL 20
IS 17
AR 4923
DI 10.3390/s20174923
PG 20
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry; Engineering; Instruments & Instrumentation
GA NO6KV
UT WOS:000569599300001
PM 32878204
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wang, K
   Sun, WC
   Du, Q
AF Wang, Kun
   Sun, WaiChing
   Du, Qiang
TI A cooperative game for automated learning of elasto-plasticity knowledge
   graphs and models with AI-guided experimentation
SO COMPUTATIONAL MECHANICS
LA English
DT Article
DE Directed multigraph; Data-driven constitutive modeling; Multi-agent deep
   reinforcement learning; Combinatorial optimization; Computational
   combinatorics
ID CONSISTENT CLUSTERING ANALYSIS; GENERALIZED PLASTICITY MODEL; ARTIFICIAL
   NEURAL-NETWORK; UNIQUE CRITICAL-STATE; NUMERICAL-INTEGRATION;
   POROUS-MEDIA; CONSTITUTIVE MODEL; HYDRO-MECHANICS; PART I; SAND
AB We introduce a multi-agent meta-modeling game to generate data, knowledge, and models that make predictions on constitutive responses of elasto-plastic materials. We introduce a new concept from graph theory where a modeler agent is tasked with evaluating all the modeling options recast as a directed multigraph and find the optimal path that links the source of the directed graph (e.g. strain history) to the target (e.g. stress) measured by an objective function. Meanwhile, the data agent, which is tasked with generating data from real or virtual experiments (e.g. molecular dynamics, discrete element simulations), interacts with the modeling agent sequentially and uses reinforcement learning to design new experiments to optimize the prediction capacity. Consequently, this treatment enables us to emulate an idealized scientific collaboration as selections of the optimal choices in a decision tree search done automatically via deep reinforcement learning.
C1 [Wang, Kun; Sun, WaiChing] Columbia Univ, Dept Civil Engn & Engn Mech, 614 SW Mudd,Mail Code 4709, New York, NY 10027 USA.
   [Du, Qiang] Columbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA.
   [Du, Qiang] Columbia Univ, Data Sci Inst, New York, NY 10027 USA.
RP Sun, WC (通讯作者)，Columbia Univ, Dept Civil Engn & Engn Mech, 614 SW Mudd,Mail Code 4709, New York, NY 10027 USA.
EM kw2534@columbia.edu; wsun@columbia.edu; qd2125@columbia.edu
RI Sun, WaiChing/U-1654-2019; Du, Qiang/B-1021-2008
OI Sun, WaiChing/0000-0002-3078-5086; Du, Qiang/0000-0002-1067-8937
FU Earth Materials and Processes program from the US Army Research Office
   [W911NF-18-2-0306]; Dynamic Materials and Interactions Program from the
   Air Force Office of Scientific Research [FA9550-17-1-0169]; nuclear
   energy university program from Department of Energy [DE-NE0008534];
   Mechanics of Material program at National Science Foundation
   [CMMI-1462760]; Columbia SEAS Interdisciplinary Research Seed Grant; NSF
   [CCF-1704833, DMS-1719699, DMR-1534910]; ARO MURI [W911NF-15-1-0562]
FX The work of KW and WCS is supported by the Earth Materials and Processes
   program from the US Army Research Office under Grant Contract
   W911NF-18-2-0306, the Dynamic Materials and Interactions Program from
   the Air Force Office of Scientific Research under Grant Contract
   FA9550-17-1-0169, the nuclear energy university program from Department
   of Energy under Grant Contract DE-NE0008534, the Mechanics of Material
   program at National Science Foundation under Grant Contract
   CMMI-1462760, and the Columbia SEAS Interdisciplinary Research Seed
   Grant. The work of QD is supported in part by NSF CCF-1704833,
   DMS-1719699, DMR-1534910, and ARO MURI W911NF-15-1-0562. These supports
   are gratefully acknowledged. The views and conclusions contained in this
   document are those of the authors, and should not be interpreted as
   representing the official policies, either expressed or implied, of the
   sponsors, including the Army Research Laboratory or the U.S. Government.
   The U.S. Government is authorized to reproduce and distribute reprints
   for Government purposes notwithstanding any copyright notation herein.
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   [No title captured]
NR 104
TC 19
Z9 23
U1 2
U2 32
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0178-7675
EI 1432-0924
J9 COMPUT MECH
JI Comput. Mech.
PD AUG
PY 2019
VL 64
IS 2
SI SI
BP 467
EP 499
DI 10.1007/s00466-019-01723-1
PG 33
WC Mathematics, Interdisciplinary Applications; Mechanics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Mechanics
GA IG3WC
UT WOS:000473735100014
OA Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Xu, Z
   Zhang, H
   Hu, CP
   Mei, L
   Xuan, JY
   Choo, KKR
   Sugumaran, V
   Zhu, YW
AF Xu, Zheng
   Zhang, Hui
   Hu, Chuanping
   Mei, Lin
   Xuan, Junyu
   Choo, Kim-Kwang Raymond
   Sugumaran, Vijayan
   Zhu, Yiwei
TI Building knowledge base of urban emergency events based on crowdsourcing
   of social media
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article
DE crowdsourcing; emergency events; social media; knowledge base
ID SYSTEM
AB An emergency event is an unexceptional event that exceeds the capacity of normal resources and organization to cope and a situation that poses an immediate risk to health, life, property, or environment. Crowdsourcing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create solutions that improve urban environment, human life quality, and city operation systems. The crowdsourcing on social media can be used to detect and analyze urban emergency events. In this paper, in order to detect and describe the real-time urban emergency event, the knowledge base model is proposed. The crowdsourcing-based knowledge base model is firstly introduced, which uses the information from social media. Secondly, the basic definition of the proposed knowledge base model including keywords, patterns, positive sentences, and knowledge graph is given. Thirdly, the temporal information is added to the proposed knowledge base model. The case study on real data sets shows that the proposed algorithm has good performance and high effectiveness in the analysis and detection of emergency events. Copyright (C) 2016 John Wiley & Sons, Ltd.
C1 [Xu, Zheng; Hu, Chuanping; Mei, Lin] Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China.
   [Xu, Zheng; Zhang, Hui] Tsinghua Univ, Beijing, Peoples R China.
   [Xuan, Junyu] Shanghai Univ, Shanghai, Peoples R China.
   [Zhu, Yiwei] Zhejiang Business Technol Inst, Ningbo, Zhejiang, Peoples R China.
   [Choo, Kim-Kwang Raymond] Univ South Australia, Adelaide, SA, Australia.
   [Sugumaran, Vijayan] Oakland Univ, Rochester, MI 48063 USA.
RP Xu, Z (通讯作者)，Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China.
EM xuzheng@shu.edu.cn
RI Choo, Kim-Kwang Raymond/A-3634-2009
OI Choo, Kim-Kwang Raymond/0000-0001-9208-5336; Xuan,
   Junyu/0000-0002-8367-6908
CR [Anonymous], [No title captured]
   [Anonymous], [No title captured]
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NR 31
TC 60
Z9 63
U1 5
U2 142
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-0626
EI 1532-0634
J9 CONCURR COMP-PRACT E
JI Concurr. Comput.-Pract. Exp.
PD OCT
PY 2016
VL 28
IS 15
BP 4038
EP 4052
DI 10.1002/cpe.3780
PG 15
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DZ4VF
UT WOS:000385858000007
DA 2022-08-23
ER

PT J
AU Ma, JB
   Wu, X
   Huang, LH
AF Ma, Jianbing
   Wu, Xi
   Huang, Lihong
TI The Use of Artificial Intelligence in Literature Search and Selection of
   the PubMed Database
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB Background. A vast number of research papers are published every day on PubMed, making it difficult for scientists to retrieve relevant articles in a timely manner. Keyword-based searches are currently the most popular method, but determining a suitable set of keywords can be challenging. Moreover, searches based on keywords typically retrieve many irrelevant papers. We developed a natural language processing (NLP)-based keyword augmentation and screening (NKAS) method to help scientists easily refine their keywords in topic searches. This method can extract meaningful candidate keywords from the titles and abstracts of an initial search using prior knowledge, knowledge graphs, and machine learning. The method was tested on three atrial fibrillation topics. When the NKAS was applied, the number of remaining papers was less than those in the original search but showed much higher precision (73.83% vs. 34.6%) and recall (98.4% vs. 59.93%) compared with those of the original search results. In conclusion, the NKAS method showed that NLP and other artificial intelligence techniques can help enhance both the search comprehensiveness and accuracy. These results suggest a great potential for the application of artificial intelligence methods in medical publication searches and other text-based applications.
C1 [Ma, Jianbing; Wu, Xi] Chengdu Univ Informat Technol, Sch Comp Sci, Chengdu 610225, Peoples R China.
   [Huang, Lihong] Fudan Univ, Zhongshan Hosp, Dept Biostat, Shanghai 200032, Peoples R China.
   [Huang, Lihong] Fudan Univ, Evidence Based Med Ctr, Shanghai 200032, Peoples R China.
RP Huang, LH (通讯作者)，Fudan Univ, Zhongshan Hosp, Dept Biostat, Shanghai 200032, Peoples R China.; Huang, LH (通讯作者)，Fudan Univ, Evidence Based Med Ctr, Shanghai 200032, Peoples R China.
EM huang.lihong@zs-hospital.sh.cn
OI Ma, Jianbing/0000-0002-1665-1428; wu, xi/0000-0002-7659-1631
FU National Natural Science Foundation of China [81903407]; Sichuan
   Provincial Key Research and Development Program [2021YFG0345]; Chengdu
   University of Information Technology [376245]
FX AcknowledgmentsThe present study was supported by the National Natural
   Science Foundation of China (Grant no. 81903407 to Lihong Huang), the
   Sichuan Provincial Key Research and Development Program (Grant no.
   2021YFG0345 to Jianbing Ma), and the starting research fund from Chengdu
   University of Information Technology (Grant no. 376245 to Jianbing Ma).
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NR 22
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 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD MAR 22
PY 2022
VL 2022
AR 8855307
DI 10.1155/2022/8855307
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1D0BZ
UT WOS:000793477000001
OA gold
DA 2022-08-23
ER

PT J
AU Pham, HV
   Thanh, DH
   Moore, P
AF Hai Van Pham
   Dat Hoang Thanh
   Moore, Philip
TI Hierarchical Pooling in Graph Neural Networks to Enhance Classification
   Performance in Large Datasets
SO SENSORS
LA English
DT Article
DE knowledge graphs; graph classification; graph neural networks; graph
   convolutional network; hierarchical graph pooling; FPool
AB Deep learning methods predicated on convolutional neural networks and graph neural networks have enabled significant improvement in node classification and prediction when applied to graph representation with learning node embedding to effectively represent the hierarchical properties of graphs. An interesting approach (DiffPool) utilises a differentiable graph pooling technique which learns 'differentiable soft cluster assignment' for nodes at each layer of a deep graph neural network with nodes mapped on sets of clusters. However, effective control of the learning process is difficult given the inherent complexity in an 'end-to-end' model with the potential for a large number parameters (including the potential for redundant parameters). In this paper, we propose an approach termed FPool, which is a development of the basic method adopted in DiffPool (where pooling is applied directly to node representations). Techniques designed to enhance data classification have been created and evaluated using a number of popular and publicly available sensor datasets. Experimental results for FPool demonstrate improved classification and prediction performance when compared to alternative methods considered. Moreover, FPool shows a significant reduction in the training time over the basic DiffPool framework.
C1 [Hai Van Pham; Dat Hoang Thanh] Hanoi Univ Sci & Technol, Sch Informat & Commun Technol, 1 Dai Co Viet, Hanoi 10000, Vietnam.
   [Moore, Philip] Lanzhou Univ, Sch Informat Sci & Engn, Feiyun Bldg,222 Tianshui S Rd, Lanzhou 730030, Peoples R China.
RP Pham, HV (通讯作者)，Hanoi Univ Sci & Technol, Sch Informat & Commun Technol, 1 Dai Co Viet, Hanoi 10000, Vietnam.
EM haipv@soict.hust.edu.vn; thanhdath97@gmail.com; ptmbcu@gmail.com
RI Van Pham, Hai/AAA-5116-2022; Pham, Hai V./Q-8380-2019; Moore,
   Philip/F-3981-2019
OI Pham, Hai V./0000-0001-8325-1662; Hoang, Thanh Dat/0000-0002-5177-794X;
   Van Pham, Hai/0000-0003-1547-9782; Moore, Philip/0000-0003-3874-8981
FU Vietnam National Foundation for Science and Technology Development
   (NAFOSTED) [102.05-2019.316]
FX This research is funded by the Vietnam National Foundation for Science
   and Technology Development (NAFOSTED) under grant number:
   102.05-2019.316.
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NR 45
TC 1
Z9 1
U1 4
U2 18
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD SEP
PY 2021
VL 21
IS 18
AR 6070
DI 10.3390/s21186070
PG 20
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA UX8NL
UT WOS:000701095000001
PM 34577277
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Xie, K
   Wang, C
   Wang, P
AF Xie, Kai
   Wang, Chao
   Wang, Peng
TI A Domain-Independent Ontology Learning Method Based on Transfer Learning
SO ELECTRONICS
LA English
DT Article
DE ontology learning; transfer learning; ontology
ID WEB
AB Ontology plays a critical role in knowledge engineering and knowledge graphs (KGs). However, building ontology is still a nontrivial task. Ontology learning aims at generating domain ontologies from various kinds of resources by natural language processing and machine learning techniques. One major challenge of ontology learning is reducing labeling work for new domains. This paper proposes an ontology learning method based on transfer learning, namely TF-Mnt, which aims at learning knowledge from new domains that have limited labeled data. This paper selects Web data as the learning source and defines various features, which utilizes abundant textual information and heterogeneous semi-structured information. Then, a new transfer learning model TF-Mnt is proposed, and the parameters' estimation is also addressed. Although there exist distribution differences of features between two domains, TF-Mnt can measure the relevance by calculating the correlation coefficient. Moreover, TF-Mnt can efficiently transfer knowledge from the source domain to the target domain and avoid negative transfer. Experiments in real-world datasets show that TF-Mnt achieves promising learning performance for new domains despite the small number of labels in it, by learning knowledge from a proper existing domain which can be automatically selected.
C1 [Xie, Kai; Wang, Chao; Wang, Peng] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
   [Xie, Kai] NR Elect Co Ltd, Nanjing 211102, Peoples R China.
RP Wang, P (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
EM xiekai@nrec.com; pwang@seu.edu.cn; 220194862@seu.edu.cn
OI Wang, Peng/0000-0001-8782-857X
FU National Key R&D Program of China [2018YFD1100302]; National Natural
   Science Foundation of China [61972082]; All-Army Common Information
   System Equipment Pre-Research Project [31514020501, 31514020503]
FX The work is supported by National Key R&D Program of China
   (2018YFD1100302), National Natural Science Foundation of China
   (No.61972082), and All-Army Common Information System Equipment
   Pre-Research Project (No. 31514020501, No. 31514020503).
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Z9 0
U1 3
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PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD AUG
PY 2021
VL 10
IS 16
AR 1911
DI 10.3390/electronics10161911
PG 23
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA UG0RZ
UT WOS:000688972100001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Lu, RQ
   Jin, XL
   Zhang, SM
   Qiu, MK
   Wu, XD
AF Lu, Ruqian
   Jin, Xiaolong
   Zhang, Songmao
   Qiu, Meikang
   Wu, Xindong
TI A Study on Big Knowledge and Its Engineering Issues
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Big data; knowledge engineering; big data knowledge engineering; big
   knowledge; massiveness characteristics; big-knowledge system;
   big-knowledge engineering; life cycle
ID CHALLENGES; CATALOG
AB After entering the big data era, a new term of 'big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed.
C1 [Lu, Ruqian; Zhang, Songmao] Chinese Acad Sci, Key Lab MADIS, AMSS, Inst Math, Beijing 100190, Peoples R China.
   [Lu, Ruqian; Zhang, Songmao] Chinese Acad Sci, Key Lab IIP, Beijing 100190, Peoples R China.
   [Jin, Xiaolong] Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100864, Peoples R China.
   [Jin, Xiaolong] Univ CAS, Sch Comp & Control Engn, Beijing 100190, Peoples R China.
   [Qiu, Meikang] Columbia Univ, Dept Elect Engn, New York, NY 10027 USA.
   [Wu, Xindong] Hefei Univ Technol, Res Inst Big Knowledge, Hefei 230009, Anhui, Peoples R China.
   [Wu, Xindong] Univ Louisiana Lafayette, Sch Comp & Informat, Lafayette, LA 70504 USA.
RP Lu, RQ (通讯作者)，Chinese Acad Sci, Key Lab MADIS, AMSS, Inst Math, Beijing 100190, Peoples R China.; Lu, RQ (通讯作者)，Chinese Acad Sci, Key Lab IIP, Beijing 100190, Peoples R China.
EM ruqian@math.ac.cn; jinxiaolong@ict.ac.cn; smzhang@math.ac.cn;
   qiumeikang@yahoo.com; xwu@hfut.edu.cn
RI Wu, Xindong/AAB-6713-2022
OI Wu, Xindong/0000-0003-2396-1704; Qiu, Meikang/0000-0002-1004-0140
FU National Key Research and Development Program of China [2016YFB1000902];
   NSFC [61472412, 61572473, 61621003, 91746209, 61772501]; US National
   Science Foundation (NSF) [1763620, 1652107]; Program for Yangtze River
   Scholars and Innovative Research Team in University (PCSIRT) of the
   Ministry of Education (China) [IRT17R32]; Beijing Science and Technology
   Project on Machine Learning based Stomatology;
   Tsinghua-Tencent-AMSSCJoint Project on WWW Knowledge Structure and its
   Application
FX The authors would like to thank Mr. Xikun Huang and Mr. Guilong Chen for
   providing experimental data on harmonic connections of MC2, and Prof.
   Cungen Cao, Ms. Shuhan Zhang, and Prof. Fei Wang for their kind help.
   The core content about BK definition and KG analysis has been given by
   the first author as invited talks at numerous conferences. This work has
   been supported by the National Key Research and Development Program of
   China under grant 2016YFB1000902, NSFC Projects (61472412, 61572473,
   61621003, 91746209, and 61772501), the US National Science Foundation
   (NSF) under grants 1763620 and 1652107, the Program for Yangtze River
   Scholars and Innovative Research Team in University (PCSIRT) of the
   Ministry of Education (China) under grant IRT17R32 (Phase 2), Beijing
   Science and Technology Project on Machine Learning based Stomatology,
   and Tsinghua-Tencent-AMSSCJoint Project on WWW Knowledge Structure and
   its Application.
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NR 91
TC 62
Z9 65
U1 9
U2 45
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 SEP
PY 2019
VL 31
IS 9
BP 1630
EP 1644
DI 10.1109/TKDE.2018.2866863
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 IP9EK
UT WOS:000480352800001
DA 2022-08-23
ER

PT J
AU Liang, K
   Wu, SF
   Gu, JY
AF Liang, Ke
   Wu, Sifan
   Gu, Jiayi
TI MKA: A Scalable Medical Knowledge-Assisted Mechanism for Generative
   Models on Medical Conversation Tasks
SO COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
LA English
DT Article
AB Using natural language processing (NLP) technologies to develop medical chatbots makes the diagnosis of the patient more convenient and efficient, which is a typical application in healthcare AI. Because of its importance, lots of researches have come out. Recently, the neural generative models have shown their impressive ability as the core of chatbot, while it cannot scale well when directly applied to medical conversation due to the lack of medical-specific knowledge. To address the limitation, a scalable medical knowledge-assisted mechanism (MKA) is proposed in this paper. The mechanism is aimed at assisting general neural generative models to achieve better performance on the medical conversation task. The medical-specific knowledge graph is designed within the mechanism, which contains 6 types of medical-related information, including department, drug, check, symptom, disease, and food. Besides, the specific token concatenation policy is defined to effectively inject medical information into the input data. Evaluation of our method is carried out on two typical medical datasets, MedDG and MedDialog-CN. The evaluation results demonstrate that models combined with our mechanism outperform original methods in multiple automatic evaluation metrics. Besides, MKA-BERT-GPT achieves state-of-the-art performance.
C1 [Liang, Ke] Penn State Univ, State Coll, PA 16801 USA.
   [Liang, Ke] Natl Univ Def Technol, Changsha 410073, Peoples R China.
   [Wu, Sifan] Nvidia, Shanghai 201210, Peoples R China.
   [Gu, Jiayi] TMiRob, Shanghai 201203, Peoples R China.
RP Liang, K (通讯作者)，Penn State Univ, State Coll, PA 16801 USA.; Liang, K (通讯作者)，Natl Univ Def Technol, Changsha 410073, Peoples R China.
EM kul660@psu.edu
RI LIANG, KEKE/ADR-5898-2022
OI Liang, Ke/0000-0002-4837-455X
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NR 40
TC 0
Z9 0
U1 3
U2 3
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1748-670X
EI 1748-6718
J9 COMPUT MATH METHOD M
JI Comput. Math. Method Med.
PD DEC 23
PY 2021
VL 2021
AR 5294627
DI 10.1155/2021/5294627
PG 10
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA 1I5DG
UT WOS:000797248700001
PM 34976109
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, DH
   Che, NN
   Le, JJ
   Pan, Q
AF Chen, Dehua
   Che, Nannan
   Le, Jiajin
   Pan, Qiao
TI A co-training based entity recognition approach for cross-disease
   clinical documents
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article
DE clinical document; co-training; cross-disease; entity recognition;
   LSTM-CRF; semi-supervised
ID OF-THE-ART; EXTRACTION
AB Entity recognition plays an important role in building the electronic medical records (EMRs) based medical knowledge graph, which is significant for building Clinical decision support (CDS) system. Cross-disease clinical documents are context-related and have different interrelated semantic structures, which bring challenges for entity recognition using traditional methods. In order to solve these problems, this paper proposes a co-training based entity recognition approach for cross-disease clinical documents. In this model, we first build partial annotation corpus of the single disease using dependency syntax analysis and the medical statement rule unifies. Then, according to the partial annotation corpus of different diseases, the sentence level features are extracted through the Bi-LSTM layer with memory unit and CRF methods, which optimize the whole sequence and improve the combination probability of sequence labels. Finally, the results with higher confidence are selected by cross feedback to label the corpus, which enlarges the size of corpus and improves the accuracy of the document entity recognition. The experiment result proves the availability and high efficiency of our method.
C1 [Chen, Dehua; Che, Nannan; Le, Jiajin; Pan, Qiao] Donghua Univ, Coll Comp Sci & Technol, Shanghai 201600, Peoples R China.
RP Chen, DH (通讯作者)，Donghua Univ, Coll Comp Sci & Technol, Shanghai 201600, Peoples R China.
EM chendehua@dhu.edu.cn
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NR 23
TC 1
Z9 1
U1 0
U2 11
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 DEC 10
PY 2019
VL 31
IS 23
SI SI
AR e4505
DI 10.1002/cpe.4505
PG 9
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JM8OO
UT WOS:000496467600017
DA 2022-08-23
ER

PT J
AU Xie, C
   Yang, P
   Yang, Y
AF Xie, Cheng
   Yang, Po
   Yang, Yun
TI Open Knowledge Accessing Method in IoT-Based Hospital Information System
   for Medical Record Enrichment
SO IEEE ACCESS
LA English
DT Article
DE Internet of Thing; health care; knowledge graph; linked open data;
   semantic technology; data engineering; data sciences
ID INTERNET; THINGS
AB For a medical treatment with IoT-based facilities, physicians always have to pay much more attentions to the raw medical records of target patients instead of directly making medical advice, conclusions or diagnosis from their experiences. Because the medical records in IoT-based hospital information system (HIS) are dispersedly obtained from distributed devices such as tablet computer, personal digital assistant, automated analyzer, and other medical devices, they are raw, simple, weak-content, and massive. Such medical records cannot be used for further analyzing and decision supporting due to that they are collected in a weak-semantic manner. In this paper, we propose a novel approach to enrich IoT-based medical records by linking them with the knowledge in linked open data. A case study is conducted on a real-world IoT-based HIS system in association with our approach, the experimental results show that medical records in the local HIS system are significantly enriched and useful for healthcare analysis and decision making, and further demonstrate the feasibility and effectiveness of our approach for knowledge accessing.
C1 [Xie, Cheng; Yang, Yun] Yunnan Univ, Sch Software, Kunming 650540, Yunnan, Peoples R China.
   [Yang, Po] Liverpool John Moores Univ, Dept Comp Sci, Liverpool L3 5UA, Merseyside, England.
RP Yang, Y (通讯作者)，Yunnan Univ, Sch Software, Kunming 650540, Yunnan, Peoples R China.
EM yangyan19@hotmail.com
RI Yang, Po/C-9936-2011
OI Yang, Po/0000-0002-8553-7127; Yang, Yun/0000-0002-9893-3436
FU Natural Science Foundation China [61663046]; Yunnan Applied Fundamental
   Research Project [2016FB104]; Yunnan Provincial Young Academic and
   Technical Leaders Reserve Talents [2017HB005]; Science Foundation of
   Yunnan University [2017YDQN11]
FX This work was supported in part by the Natural Science Foundation China
   under Grant 61663046, in part by the Yunnan Applied Fundamental Research
   Project under Grant 2016FB104, in part by the Yunnan Provincial Young
   Academic and Technical Leaders Reserve Talents under Grant 2017HB005,
   and in part by the Science Foundation of Yunnan University under Grant
   2017YDQN11.
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NR 40
TC 34
Z9 35
U1 0
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 2018
VL 6
BP 15202
EP 15211
DI 10.1109/ACCESS.2018.2810837
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA GB8BS
UT WOS:000429299500001
OA Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Li, K
   Zhou, HC
   Tu, Z
   Liu, FY
   Zhang, HK
AF Li, Kun
   Zhou, Huachun
   Tu, Zhe
   Liu, Feiyang
   Zhang, Hongke
TI Blockchain Empowered Federated Learning for Distributed Network Security
   Behaviour Knowledge Base in 6G
SO SECURITY AND COMMUNICATION NETWORKS
LA English
DT Article
ID PRIVACY; INTELLIGENT
AB The malicious flow originating from massive access devices in 6G network will increase sharply. In order to effectively reduce malicious flow, we hope to establish a new framework for coordination of security monitoring and malicious behaviour control in 6G network. Federated learning provides data and privacy protection for the distributed network security behaviour knowledge base. However, since the equipment of its participants needs to upload the original data to the central server for model training, this may lead to data leakage in the knowledge base. Therefore, in this article, we first use the knowledge graph to describe network security behaviours, then build a universal network security malicious behaviour knowledge base, and discuss its application scenarios. Then, we propose a blockchain empowered federated learning (BeFL) for distributed network security malicious behaviour knowledge base architecture to ensure the security of knowledge transmission. Finally, we deployed the designed distributed knowledge base in the prototype system and compared it with the other two baseline methods to verify the performance. Relevant results show that our method outperforms other methods in terms of user identification, flow detection, and attack source tracing.
C1 [Li, Kun; Zhou, Huachun; Tu, Zhe; Liu, Feiyang; Zhang, Hongke] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China.
RP Zhou, HC (通讯作者)，Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China.
EM hchzhou@bjtu.edu.cn
OI Tu, Zhe/0000-0001-9758-6869; Li, Kun/0000-0001-9164-7691
FU National Key R&D Program of China [2018YFA0701604]; Fundamental Research
   Funds for the Central Universities [2021YJS008, 2021YJS012]
FX This paper was supported by the National Key R&D Program of China under
   Grant no. 2018YFA0701604 and in part by the Fundamental Research Funds
   for the Central Universities under Grant nos. 2021YJS008 and 2021YJS012.
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NR 33
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 21
PY 2022
VL 2022
AR 4233238
DI 10.1155/2022/4233238
PG 11
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 1C8PK
UT WOS:000793374700006
OA gold
DA 2022-08-23
ER

PT J
AU Xie, AZ
   Carlsson, A
   Mohoney, J
   Waleffe, R
   Peters, S
   Rekatsinas, T
   Venkataraman, S
AF Xie, Anze
   Carlsson, Anders
   Mohoney, Jason
   Waleffe, Roger
   Peters, Shanan
   Rekatsinas, Theodoros
   Venkataraman, Shivaram
TI Demo of Marius: A System for Large-scale Graph Embeddings
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article; Proceedings Paper
CT 47th International Conference on Very Large Data Bases (VLDB)
CY AUG 16-20, 2021
CL Copenhagen, DENMARK
AB Graph embeddings have emerged as the de facto representation for modern machine learning over graph data structures. The goal of graph embedding models is to convert high-dimensional sparse graphs into low-dimensional, dense and continuous vector spaces that preserve the graph structure properties. However, learning a graph embedding model is a resource intensive process, and existing solutions rely on expensive distributed computation to scale training to instances that do not fit in GPU memory. This demonstration showcases Marius: a new open-source engine for learning graph embedding models over billion-edge graphs on a single machine. Marius is built around a recently-introduced architecture for machine learning over graphs that utilizes pipelining and a novel data replacement policy to maximize GPU utilization and exploit the entire memory hierarchy (including disk, CPU, and GPU memory) to scale to large instances. The audience will experience how to develop, train, and deploy graph embedding models using Marius' configuration-driven programming model. Moreover, the audience will have the opportunity to explore Marius' deployments on applications including link-prediction on WikiKG90M and reasoning queries on a paleobiology knowledge graph. Marius is available as open source software at https://marius-project.org.
C1 [Xie, Anze; Carlsson, Anders; Mohoney, Jason; Waleffe, Roger; Rekatsinas, Theodoros; Venkataraman, Shivaram] UW Madison, Dept Comp Sci, Madison, WI 53706 USA.
   [Peters, Shanan] UW Madison, Dept Geosci, Madison, WI USA.
RP Xie, AZ (通讯作者)，UW Madison, Dept Comp Sci, Madison, WI 53706 USA.
EM axie7@wisc.edu; awcarlsson@wisc.edu; mohoney2@wisc.edu;
   waleffe@wisc.edu; peters@geology.wisc.edu; rekatsinas@wisc.edu;
   shivaram@cs.wisc.edu
RI Peters, Shanan E/A-5620-2013
OI Peters, Shanan E/0000-0002-3346-4317
FU NSF [1815538]; DARPA [ASKE HR00111990013]; National Science Foundation
   [CNS-1838733]; Facebook faculty research award; Office of the Vice
   Chancellor for Research and Graduate Education at UW-Madison; Wisconsin
   Alumni Research Foundation
FX This work was supported by NSF under grant 1815538 and DARPA under grant
   ASKE HR00111990013. The U.S. Government is authorized to reproduce and
   distribute reprints for Governmental purposes notwithstanding any
   copyright notation thereon. Any opinions, findings, and conclusions or
   recommendations expressed in this material are those of the authors and
   do not necessarily reflect the views, policies, or endorsements, either
   expressed or implied, of DARPA or the U.S. Government. This work is also
   supported by the National Science Foundation grant CNS-1838733, a
   Facebook faculty research award and by the Office of the Vice Chancellor
   for Research and Graduate Education at UW-Madison with funding from the
   Wisconsin Alumni Research Foundation.
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NR 15
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 AUG
PY 2021
VL 14
IS 12
BP 2759
EP 2762
DI 10.14778/3476311.3476338
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA YH1AX
UT WOS:000742908500027
DA 2022-08-23
ER

PT J
AU Wang, C
   Zhu, HY
AF Wang, Cheng
   Zhu, Hangyu
TI Representing Fine-Grained Co-Occurrences for Behavior-Based Fraud
   Detection in Online Payment Services
SO IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
LA English
DT Article
DE Data models; Feature extraction; Predictive models; Heterogeneous
   networks; Computer crime; Semantics; Online payment services; fraud
   detection; network embedding; user behavioral modeling
ID IDENTIFICATION; RECOGNITION; USERS; MODEL
AB The vigorous development of e-commerce breeds cybercrime. Online payment fraud detection, a challenge faced by online service, plays an important role in rapidly evolving e-commerce. Behavior-based methods are recognized as a promising method for online payment fraud detection. However, it is a big challenge to build high-resolution behavioral models by using low-quality behavioral data. In this work, we mainly address this problem from data enhancement for behavioral modeling. We extract fine-grained co-occurrence relationships of transactional attributes by using a knowledge graph. Furthermore, we adopt the heterogeneous network embedding to learn and improve representing comprehensive relationships. Particularly, we explore customized network embedding schemes for different types of behavioral models, such as the population-level models, individual-level models, and generalized-agent-based models. The performance gain of our method is validated by the experiments over the real dataset from a commercial bank. It can help representative behavioral models improve significantly the performance of online banking payment fraud detection. To the best of our knowledge, this is the first work to realize data enhancement for diversified behavior models by implementing network embedding algorithms on attribute-level co-occurrence relationships.
C1 [Wang, Cheng; Zhu, Hangyu] Tongji Univ, Dept Comp Sci & Engn, Key Lab Embedded Syst & Serv Comp, Minist Educ, Shanghai 200092, Peoples R China.
   [Wang, Cheng; Zhu, Hangyu] Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 200092, Peoples R China.
RP Wang, C (通讯作者)，Tongji Univ, Dept Comp Sci & Engn, Key Lab Embedded Syst & Serv Comp, Minist Educ, Shanghai 200092, Peoples R China.; Wang, C (通讯作者)，Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 200092, Peoples R China.
EM cwang@tongji.edu.cn; 1830823@tongji.edu.cn
OI Zhu, Hangyu/0000-0001-7221-9130
FU National Natural Science Foundation of China (NSFC) [61972287]; Major
   Project of Ministry of Industry and Information Technology of China
   [[2018] 282]; Municipal Human Resources Development Program for
   Outstanding Young Talents in Shanghai
FX The authors sincerely thank the anonymous referees for their helpful
   comments and suggestions. The research of authors was partially
   supported by the National Natural Science Foundation of China (NSFC)
   under Grants 61972287, the Major Project of Ministry of Industry and
   Information Technology of China under Grant [2018] 282, and Municipal
   Human Resources Development Program for Outstanding Young Talents in
   Shanghai.
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NR 48
TC 2
Z9 2
U1 6
U2 6
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 JAN 1
PY 2022
VL 19
IS 1
BP 301
EP 315
DI 10.1109/TDSC.2020.2991872
PG 15
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YG8KO
UT WOS:000742730400022
OA hybrid
DA 2022-08-23
ER

PT J
AU Wang, Y
   Sun, YN
   Ma, ZC
   Gao, LS
   Xu, Y
AF Wang, Yu
   Sun, Yining
   Ma, Zuchang
   Gao, Lisheng
   Xu, Yang
TI A Hybrid Model for Named Entity Recognition on Chinese Electronic
   Medical Records
SO ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION
   PROCESSING
LA English
DT Article
DE Named entity recognition; Chinese electronic medical records; neural
   networks; hybrid models
ID INFORMATION
AB Electronic medical records (EMRs) contain valuable information about the patients, such as clinical symptoms, diagnostic results, and medications. Named entity recognition (NER) aims to recognize entities from unstructured text, which is the initial step toward the semantic understanding of the EMRs. Extracting medical information from Chinese EMRs could be a more complicated task because of the difference between English and Chinese. Some researchers have noticed the importance of Chinese NER and used the recurrent neural network or convolutional neural network (CNN) to deal with this task. However, it is interesting to know whether the performance could be improved if the advantages of the RNN and CNN can be both utilized. Moreover, RoBERTa-WWM, as a pre-training model, can generate the embeddings with word-level features, which is more suitable for Chinese NER compared with Word2Vec. In this article, we propose a hybrid model. This model first obtains the entities identified by bidirectional long short-term memory and CNN, respectively, and then uses two hybrid strategies to output the final results relying on these entities. We also conduct experiments on raw medical records from real hospitals. This dataset is provided by the China Conference on Knowledge Graph and Semantic Computing in 2019 (CCKS 2019). Results demonstrate that the hybrid model can improve performance significantly.
C1 [Wang, Yu; Sun, Yining; Ma, Zuchang; Gao, Lisheng; Xu, Yang] Inst Intelligent Machines, Hefei, Anhui, Peoples R China.
   [Wang, Yu; Sun, Yining] Univ Sci & Technol China, Hefei, Anhui, Peoples R China.
   [Wang, Yu; Sun, Yining] Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, AnHui Prov Key Lab Med Phys & Technol, Hefei, Anhui, Peoples R China.
   [Ma, Zuchang; Gao, Lisheng; Xu, Yang] Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Anhui, Peoples R China.
RP Sun, YN (通讯作者)，Inst Intelligent Machines, Hefei, Anhui, Peoples R China.; Sun, YN (通讯作者)，Univ Sci & Technol China, Hefei, Anhui, Peoples R China.; Sun, YN (通讯作者)，Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, AnHui Prov Key Lab Med Phys & Technol, Hefei, Anhui, Peoples R China.
EM briskyu@mail.ustc.edu.cn; ynsun@iim.ac.cn; zcma@iim.ac.cn;
   lsgao@iim.ac.cn; yxu@hfcas.ac.cn
OI Wang, Yu/0000-0003-3096-2481
FU Major Special Project of Anhui Science and Technology Department
   [18030801133]; Science and Technology Service Network Initiative
   [KFJ-STS-ZDTP-079]
FX This work was supported by the Major Special Project of Anhui Science
   and Technology Department under grant 18030801133, and the Science and
   Technology Service Network Initiative under grant KFJ-STS-ZDTP-079.
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NR 38
TC 0
Z9 0
U1 15
U2 43
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 2375-4699
EI 2375-4702
J9 ACM T ASIAN LOW-RESO
JI ACM Trans. Asian Low-Resour. Lang. Inf. Process.
PD APR
PY 2021
VL 20
IS 2
AR 35
DI 10.1145/3436819
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RZ6OF
UT WOS:000648719600017
DA 2022-08-23
ER

PT J
AU Buitelaar, P
   Wood, ID
   Negi, S
   Arcan, M
   McCrae, JP
   Abele, A
   Robin, C
   Andryushechkin, V
   Ziad, H
   Sagha, H
   Schmitt, M
   Schuller, BW
   Sanchez-Rada, JF
   Iglesias, CA
   Navarro, C
   Giefer, A
   Heise, N
   Masucci, V
   Danza, FA
   Caterino, C
   Smrz, P
   Hradis, M
   Povolny, F
   Klimes, M
   Matejka, P
   Tummarello, G
AF Buitelaar, Paul
   Wood, Ian D.
   Negi, Sapna
   Arcan, Mihael
   McCrae, John P.
   Abele, Andrejs
   Robin, Cecile
   Andryushechkin, Vladimir
   Ziad, Housam
   Sagha, Hesam
   Schmitt, Maximilian
   Schuller, Bjoern W.
   Fernando Sanchez-Rada, J.
   Iglesias, Carlos A.
   Navarro, Carlos
   Giefer, Andreas
   Heise, Nicolaus
   Masucci, Vincenzo
   Danza, Francesco A.
   Caterino, Ciro
   Smrz, Pavel
   Hradis, Michal
   Povolny, Filip
   Klimes, Marek
   Matejka, Pavel
   Tummarello, Giovanni
TI MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis
SO IEEE TRANSACTIONS ON MULTIMEDIA
LA English
DT Article
DE Emotion analysis; open source toolbox; affective computing; linked data;
   audio processing; text processing; video processing
ID RECOGNITION; SPEECH; MODEL
AB Recently, there is an increasing tendency to embed functionalities for recognizing emotions from user-generated media content in automated systems such as call-centre operations, recommendations, and assistive technologies, providing richer and more informative user and content profiles. However, to date, adding these functionalities was a tedious, costly, and time-consuming effort, requiring identification and integration of diverse tools with diverse interfaces as required by the use case at hand. The MixedEmotions Toolbox leverages the need 14 such functionalities by providing tools for text, audio, video, and linked data processing within an easily integrable plug-and-play platform. These functionalities include: 1) for text processing: emotion and sentiment recognition; 2) for audio processing: emotion, age, and gender recognition; 3) for video processing: face detection and tracking, emotion recognition, facial landmark localization, head pose estimation, face alignment, and body pose estimation; and 4) for linked data: knowledge graph integration. Moreover, the MixedEmotions Toolbox is open-source and free. In this paper, we present this toolbox in the context of the existing landscape, and provide a range of detailed benchmarks on standard test-beds showing its state-of-the-art performance. Furthermore, three real-world use cases show its effectiveness, namely, emotion-driven smart TV, call center monitoring, and brand reputation analysis.
C1 [Buitelaar, Paul; Wood, Ian D.; Negi, Sapna; Arcan, Mihael; McCrae, John P.; Abele, Andrejs; Robin, Cecile; Andryushechkin, Vladimir; Ziad, Housam] Natl Univ Ireland Galway, Galway, Ireland.
   [Sagha, Hesam; Schmitt, Maximilian; Schuller, Bjoern W.] Univ Passau, Chair Complex & Intelligent Syst, D-94032 Passau, Germany.
   [Sagha, Hesam] AudEERING GmbH, D-82205 Gilching, Germany.
   [Schuller, Bjoern W.] Imperial Coll London, Dept Comp, London SW7 2AZ, England.
   [Fernando Sanchez-Rada, J.; Iglesias, Carlos A.] GSI Univ Politecn Madrid, Madrid 28040, Spain.
   [Navarro, Carlos] Paradigma Digital, Madrid 28224, Spain.
   [Giefer, Andreas; Heise, Nicolaus] Deutsch Welle, D-53113 Bonn, Germany.
   [Masucci, Vincenzo; Danza, Francesco A.; Caterino, Ciro] Expert Syst, I-41123 Modena, Italy.
   [Smrz, Pavel; Hradis, Michal] Brno Univ Technol, Brno 60190, Czech Republic.
   [Povolny, Filip; Klimes, Marek; Matejka, Pavel] Phonexia, Brno 61200, Czech Republic.
   [Tummarello, Giovanni] Siren Solut, Dublin, Ireland.
RP Sagha, H (通讯作者)，AudEERING GmbH, D-82205 Gilching, Germany.
EM paul.buitelaar@insight-centre.org; ian.wood@insight-centre.org;
   sapna.negi@insight-centre.org; mihael.arcan@insight-centre.org;
   John.McCrae@insight-centre.org; andrejs.abele@insight-centre.org;
   cecile.robin@insight-centre.org;
   vladimir.andryushechkin@insight-centre.org;
   housam.ziad@insight-centre.org; hesamsga81@gmail.com;
   maximilian.schmitt@uni-passau.de; bjoern.schuller@imperial.ac.uk;
   jf.sanchez@upm.es; cif@dit.upm.es; cnavarro@paradigmadigital.com;
   andreas.giefer@dw.com; nicolaus.heise@dw.com; vmasucci@expertsystem.com;
   fadanza@gmail.com; ccaterino@expertsystem.com; smrz@fit.vutbr.cz;
   ihradis@fit.vutbr.cz; filip.povolny@phonexia.com; klimes@phonexia.com;
   matejka@phonexia.com; giovanni@siren.solutions
RI Matejka, Pavel/AAP-7982-2020; Hradiš, Michal/G-9365-2016; FERNANDEZ,
   CARLOS ANGEL A. IGLESIAS/I-2181-2015; Schmitt, Maximilian/ABD-4551-2020;
   Wood, Ian/N-1901-2019
OI Matejka, Pavel/0000-0002-0404-8672; Hradiš, Michal/0000-0002-6364-129X;
   FERNANDEZ, CARLOS ANGEL A. IGLESIAS/0000-0002-1755-2712; Schmitt,
   Maximilian/0000-0001-7453-5612; Wood, Ian/0000-0002-6094-0358; Arcan,
   Mihael/0000-0002-3116-621X; Schuller, Bjorn/0000-0002-6478-8699; Sagha,
   Hesam/0000-0002-8644-9591; Ziad, Housam/0000-0001-6132-4082; Smrz,
   Pavel/0000-0002-5638-1362
FU European Unions Horizon 2020 Programme research and innovation programme
   [644632]
FX This work was supported by the European Unions Horizon 2020 Programme
   research and innovation programme under Grant 644632 (MixedEmotions).
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NR 114
TC 12
Z9 13
U1 6
U2 41
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1520-9210
EI 1941-0077
J9 IEEE T MULTIMEDIA
JI IEEE Trans. Multimedia
PD SEP
PY 2018
VL 20
IS 9
BP 2454
EP 2465
DI 10.1109/TMM.2018.2798287
PG 12
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Telecommunications
GA GR1ZX
UT WOS:000442358200017
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Srivastava, P
   Bej, S
   Schultz, K
   Yordanova, K
   Wolkenhauer, O
AF Srivastava, Prashant
   Bej, Saptarshi
   Schultz, Kristian
   Yordanova, Kristina
   Wolkenhauer, Olaf
TI Attention Retrieval Model for Entity Relation Extraction From Biological
   Literature
SO IEEE ACCESS
LA English
DT Article
DE Biological system modeling; Proteins; Task analysis; Biology;
   Annotations; Data models; Diseases; Attention models; biological
   literature mining; deep learning; knowledge graphs
AB Natural Language Processing (NLP) has contributed to extracting relationships among biological entities, such as genes, their mutations, proteins, diseases, processes, phenotypes, and drugs, for a comprehensive and concise understanding of information in the literature. Self-attention-based models for Relationship Extraction (RE) have played an increasingly important role in NLP. However, self-attention models for RE are framed as a classification problem, which limits its practical usability in several ways. We present an alternative framework called the Attention Retrieval Model (ARM), which enhances the applicability of attention-based models compared to the regular classification approach, for RE. Given a text sequence containing related entities/keywords, ARM learns the association between a chosen entity/keyword with the other entities present in the sequence, using an underlying self-attention mechanism. ARM provides a flexible framework for a modeller to customise their model, facilitate data integration, and integrate expert knowledge to provide a more practical approach for RE. ARM can extract unseen relationships that are not annotated in the training data, analogous to zero-shot learning. To sum up, ARM provides an alternative self-attention-based deep learning framework for RE, that can capture directed entity relationships.
C1 [Srivastava, Prashant; Bej, Saptarshi; Schultz, Kristian; Yordanova, Kristina; Wolkenhauer, Olaf] Univ Rostock, Inst Comp Sci, Dept Syst Biol & Bioinformat, D-18057 Rostock, Germany.
   [Bej, Saptarshi; Wolkenhauer, Olaf] Tech Univ Munich, Leibniz Inst Food Syst Biol, D-85354 Freising Weihenstephan, Germany.
RP Wolkenhauer, O (通讯作者)，Univ Rostock, Inst Comp Sci, Dept Syst Biol & Bioinformat, D-18057 Rostock, Germany.; Wolkenhauer, O (通讯作者)，Tech Univ Munich, Leibniz Inst Food Syst Biol, D-85354 Freising Weihenstephan, Germany.
EM olaf.wolkenhauer@uni-rostock.de
OI Yordanova, Kristina/0000-0002-6428-1062; Srivastava,
   Prashant/0000-0002-0383-8330
CR Bahdanau D., 2014, ARXIV
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NR 28
TC 0
Z9 0
U1 4
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 22429
EP 22440
DI 10.1109/ACCESS.2022.3154820
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ZM9VG
UT WOS:000764694600001
OA gold
DA 2022-08-23
ER

PT J
AU Xu, JJ
   Sun, JB
   Zhou, R
   Liu, CF
   Yin, LH
AF Xu, Jiajie
   Sun, Jiabao
   Zhou, Rui
   Liu, Chengfei
   Yin, Lihua
TI CISK: An interactive framework for conceptual inference based spatial
   keyword query
SO NEUROCOMPUTING
LA English
DT Article
DE self-driving; Spatial database; Spatial keyword query; Interactive query
AB Spatial keyword query is an important technique for recommending users their desired POIs in self-driving services. Inferring query intention has been recognized as an important yet challenging issue for spatial keyword search. However, existing methods are inadequate to discover qualified results due to the inability to capture the intention of short-text input keywords. In this paper, we adopt a conceptual inference based method to deduce implicit intentions of users and thus are able to find more meaningful answers. Firstly, a locality-aware inference model is designed to generate concepts by considering typicality, granularity and spatial distribution, taking into account the hypernym-hyponym relationships in knowledge graphs. Afterwards, we propose a novel interactive framework to learn conceptual preferences for users, by using k-skyband to prune unpromising objects at the beginning and employing dense subgraph to select promising candidates in each interaction round. After a small number of rounds of learning, all objects can be rationally ordered by a user's personalized ranking function which is unknown in advance. Empirical study on two real datasets demonstrates the effectiveness of our proposed conceptual inference and preference learning based methods. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Xu, Jiajie; Yin, Lihua] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou, Peoples R China.
   [Xu, Jiajie; Sun, Jiabao] Soochow Univ, Sch Comp Sci & Technol, Suzhou, Peoples R China.
   [Zhou, Rui; Liu, Chengfei] Swinburne Univ Technol, Sch Software & Elect Engn, Hawthorn, Vic, Australia.
RP Xu, JJ (通讯作者)，Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou, Peoples R China.; Xu, JJ (通讯作者)，Soochow Univ, Sch Comp Sci & Technol, Suzhou, Peoples R China.
EM xujj@suda.edu.cn
OI Liu, Chengfei/0000-0003-1675-1389
FU Chinese NSFC [61872258]; Blockshine Corporation
FX This work was partially supported by Chinese NSFC project under the
   grant number 61872258 and Blockshine Corporation.
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NR 22
TC 7
Z9 7
U1 1
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD MAR 7
PY 2021
VL 428
BP 368
EP 375
DI 10.1016/j.neucom.2020.02.129
EA JAN 2021
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PW9MI
UT WOS:000610989800017
DA 2022-08-23
ER

PT J
AU Fang, YX
   Wang, Z
   Cheng, R
   Li, XD
   Luo, SQ
   Hu, JF
   Chen, XJ
AF Fang, Yixiang
   Wang, Zheng
   Cheng, Reynold
   Li, Xiaodong
   Luo, Siqiang
   Hu, Jiafeng
   Chen, Xiaojun
TI On Spatial-Aware Community Search
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Community search; spatial graphs; geo-social networks; online queries
ID EFFICIENT; FRAMEWORK; QUERIES
AB Communities are prevalent in social networks, knowledge graphs, and biological networks. Recently, the topic of community search (CS) has received plenty of attention. The CS problem aims to look for a dense subgraph that contains a query vertex. Existing CS solutions do not consider the spatial extent of a community. They can yield communities whose locations of vertices span large areas. In applications that facilitate setting social events (e.g., finding conference attendees to join a dinner), it is important to find groups of people who are physically close to each other, so it is desirable to have a spatial-aware community (or SAC), whose vertices are close structurally and spatially. Given a graph G and a query vertex q, we develop an exact solution to find the SAC containing q, but it cannot scale to large datasets, so we design three approximation algorithms. We further study the problem of continuous SAC search on a "dynamic spatial graph," whose vertices' locations change with time, and propose three fast solutions. We evaluate the solutions on both real and synthetic datasets, and the results show that SACs are better than communities returned by existing solutions. Moreover, our approximation solutions perform accurately and efficiently.
C1 [Fang, Yixiang; Wang, Zheng; Cheng, Reynold; Li, Xiaodong; Luo, Siqiang; Hu, Jiafeng] Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China.
   [Chen, Xiaojun] Shenzhen Univ, Coll Comp Sci & Software, Shenzhen 518060, Peoples R China.
RP Chen, XJ (通讯作者)，Shenzhen Univ, Coll Comp Sci & Software, Shenzhen 518060, Peoples R China.
EM yxfang@cs.hku.hk; zwang@cs.hku.hk; ckcheng@cs.hku.hk; xdli@cs.hku.hk;
   sqluo@cs.hku.hk; jhu@cs.hku.hk; xjchen@szu.edu.cn
RI Luo, Siqiang/ABC-8964-2022; Luo, Siqiang/AFM-6934-2022; Zhao,
   Fan/AAO-9771-2021
OI Luo, Siqiang/0000-0001-8197-0903; Zhao, Fan/0000-0001-9239-274X; wang,
   zheng/0000-0002-7064-6267; , Yixiang/0000-0002-5047-8593; LI,
   Xiaodong/0000-0002-5985-9886
FU Research Grants Council of Hong Kong (RGC Projects) [HKU 17229116,
   17205115]; HKU [104004572, 102009508, 104004129, 104005000]; NSFC
   [61773268]
FX Reynold Cheng, Yixiang Fang, Xiaodong Li, and Jiafeng Hu were supported
   by the Research Grants Council of Hong Kong (RGC Projects HKU 17229116
   and 17205115) and HKU (Projects 104004572, 102009508, 104004129,
   104005000). Xiaojun Chen was supported by NSFC under Grant no. 61773268.
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NR 46
TC 27
Z9 28
U1 2
U2 11
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 APR 1
PY 2019
VL 31
IS 4
BP 783
EP 798
DI 10.1109/TKDE.2018.2845414
PG 16
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA HO1NP
UT WOS:000460674900013
DA 2022-08-23
ER

PT J
AU Zhu, YF
   Hu, LM
   Ning, NW
   Zhang, W
   Wu, B
AF Zhu, Yangfu
   Hu, Linmei
   Ning, Nianwen
   Zhang, Wei
   Wu, Bin
TI A lexical psycholinguistic knowledge-guided graph neural network for
   interpretable personality detection
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Personality detection; Psycholinguistic knowledge; Heterogeneous
   network; Graph neural network
ID RECOGNITION
AB With the blossoming of online social media, personality detection based on user-generated content has a significant impact on information scientific and industrial applications. Most existing approaches rely heavily on semantic features or superficial psycholinguistic statistical features calculated by existing tools and fail to effectively exploit psycholinguistic knowledge that can help determine and interpret peoples personality traits. In this paper, we propose a novel lexical psycholinguistic knowledge guided graph neural model for interpretable personality detection, which leverages the personality lexicons as a bridge for injecting relevant external knowledge to enrich the semantics of a document. Specifically, we learn a kind of personality-aware word embedding, that encodes psycholinguistic information in the continuous representations of words. Then, a Heterogeneous Personality word graph is constructed by aligning the personality lexicons with the personality knowledge graph, which is fed into a Message-passing graph Network (HPMN) to extract explicit lexicon and knowledge relations through the interactions among heterogeneous graph nodes. Finally, through a carefully designed readout function, all heterogeneous nodes are selectively incorporated as knowledge-guided document embeddings for user-generated text personality understanding and interpretation. Experiments show that our model effectively detects personality traits. Moreover, it provides a certain level of support for lexical hypotheses in psycholinguistic research from a computational linguistics perspective. (C) 2022 Published by Elsevier B.V.
C1 [Zhu, Yangfu; Hu, Linmei; Zhang, Wei; Wu, Bin] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligence Telecommun Software &, Beijing 100876, Peoples R China.
   [Ning, Nianwen] Henan Univ, Sch Artificial Intelligence, Zhengzhou 450046, Peoples R China.
RP Wu, B (通讯作者)，Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligence Telecommun Software &, Beijing 100876, Peoples R China.
EM wubin@bupt.edu.cn
OI Zhu, Yangfu/0000-0003-2880-9517
FU NSFC-General Technology Basic Research Joint Funds, China [U1936220];
   Na-tional Natural Science Foundation of China [61972047]; National Key
   Research and Development Program of China [2018YFC0831500]
FX This work is supported by the NSFC-General Technology Basic Research
   Joint Funds, China under Grant (U1936220) , the Na-tional Natural
   Science Foundation of China under Grant (61972047) and the National Key
   Research and Development Program of China (2018YFC0831500) .
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NR 44
TC 0
Z9 0
U1 4
U2 4
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 5
PY 2022
VL 249
AR 108952
DI 10.1016/j.knosys.2022.108952
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1W5DY
UT WOS:000806795100010
DA 2022-08-23
ER

PT J
AU Jiang, JX
   Choi, B
   Xu, JL
   Bhowmick, SS
AF Jiang, Jiaxin
   Choi, Byron
   Xu, Jianliang
   Bhowmick, Sourav S.
TI A Generic Ontology Framework for Indexing Keyword Search on Massive
   Graphs
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Keyword search; ontology graphs; graphs; indexing
ID SIMILARITY
AB Due to the unstructuredness and the lack of schema information of knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. Recently, various keyword search semantics have been designed. In this paper, we propose a generic ontology-based indexing framework for keyword search, called Bisimulation of Generalized Graph Index (BiG-index), to enhance the search performance. The novelties of BiG-index reside in using an ontology graph G(Ont) to summarize and index a data graph G iteratively, to form a hierarchical index structure G. BiG-index is generic since it only requires keyword search algorithms to generate query answers from summary graphs having two simple properties. Regarding query evaluation, we transform a keyword search q into Q according to G(Ont) in runtime. The transformed query is searched on the summary graphs in G. The efficiency is due to the small sizes of the summary graphs and the early pruning of semantically irrelevant subgraphs. To illustrate BiG-index's applicability, we show popular indexing techniques for keyword search (e.g., Blinks and r-clique) can be easily implemented on top of BiG-index. Our extensive experiments show that BiG-index reduced the runtimes of popular keyword search work Blinks by 50.5 percent and r-clique by 29.5 percent.
C1 [Jiang, Jiaxin; Choi, Byron; Xu, Jianliang] Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China.
   [Bhowmick, Sourav S.] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore.
RP Jiang, JX (通讯作者)，Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China.
EM jxjian@comp.hkbu.edu.hk; bchoi@comp.hkbu.edu.hk; xujl@comp.hkbu.edu.hk;
   assourav@ntu.edu.sg
OI Choi, Byron (Koon Kau)/0000-0002-8381-336X; Xu,
   Jianliang/0000-0001-9404-5848
FU HKRGC GRF [12201119, 12232716, 12201518, 12200817, 12201018]; NSFC
   [61602395]
FX This work is partly supported by HKRGC GRF 12201119, 12232716, 12201518,
   12200817, and 12201018, and NSFC 61602395.
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NR 33
TC 1
Z9 1
U1 1
U2 7
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 JUN 1
PY 2021
VL 33
IS 6
BP 2322
EP 2336
DI 10.1109/TKDE.2019.2956535
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 SC1TM
UT WOS:000650462800001
DA 2022-08-23
ER

PT J
AU Jurasky, W
   Moder, P
   Milde, M
   Ehm, H
   Reinhart, G
AF Jurasky, Wiking
   Moder, Patrick
   Milde, Michael
   Ehm, Hans
   Reinhart, Gunther
TI Transformation of semantic knowledge into simulation-based decision
   support
SO ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
LA English
DT Article
DE Knowledge transformation; Decision support; Ontologies; Hybrid modeling;
   Pandemic simulation; Supply chain simulation
ID ONTOLOGY
AB Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, indepth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.
C1 [Jurasky, Wiking] Tech Univ Munich, TUM Sch Management, Arcisstr 21, D-80333 Munich, Germany.
   [Moder, Patrick; Ehm, Hans] Infineon Technol AG, Supply Chain Innovat, Campeon 1-15, D-85579 Neubiberg, Germany.
   [Milde, Michael; Reinhart, Gunther] Tech Univ Munich, Inst Machine Tools & Ind Management, Boltzmannstr 15, D-85748 Garching, Germany.
RP Jurasky, W (通讯作者)，Tech Univ Munich, TUM Sch Management, Arcisstr 21, D-80333 Munich, Germany.
EM wiking.jurasky@mail.de
RI Moder, Patrick/ABF-5631-2021
OI Moder, Patrick/0000-0003-4576-7128
FU EU ECSEL JU under the H2020 Framework Programme, JU grant [783163,
   826452]
FX The research has received funding from the EU ECSEL JU under the H2020
   Framework Programme, JU grant no. 783163 (project iDev40), no. 826452
   (project Arrowhead Tools), and from the partner's national
   programs/funding authorities.
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NR 35
TC 0
Z9 0
U1 3
U2 22
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0736-5845
EI 1879-2537
J9 ROBOT CIM-INT MANUF
JI Robot. Comput.-Integr. Manuf.
PD OCT
PY 2021
VL 71
AR 102174
DI 10.1016/j.rcim.2021.102174
EA APR 2021
PG 11
WC Computer Science, Interdisciplinary Applications; Engineering,
   Manufacturing; Robotics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Robotics
GA SU7UG
UT WOS:000663336900001
DA 2022-08-23
ER

PT J
AU Wu, YL
   Gamborino, E
   Fu, LC
AF Wu, Yi-Luen
   Gamborino, Edwinn
   Fu, Li-Chen
TI Interactive Question-Posing System for Robot-Assisted Reminiscence From
   Personal Photographs
SO IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
LA English
DT Article
DE Task analysis; Visualization; Robot sensing systems; Knowledge based
   systems; Data models; Interactive question-posing; knowledge graph;
   Markov random fields (MRFs); reminiscence; social companion robot
AB Reminiscence is a lifelong activity that happens throughout our lifespan. While memories can serve as topics in people's everyday conversations, recalling the past can also help us build self-esteem and increase our level of happiness. In this paper, we aim to develop a robot companion that helps people to recollect their memories from personal photographs. We focus on how a robot can associate concepts relevant to the content in the photographs and evoke people's memories by asking questions that are both relatable and engaging. To understand the content of a picture, we applied deep learning techniques in order to recognize events, objects, and scenes in it. Then, these observations and any user utterances are considered in a Markov random field (MRF)-based algorithm that contains common sense knowledge of a number of events, with loopy belief propagation being used to infer possible associated concepts and topics. Afterward, the robot poses appropriate questions about the selected topics, guiding the user to reminisce through conversation. Our results show that the proposed system can pose related and appropriate questions to interact with the user, and has the potential guide the user to recall the past in an organized way.
C1 [Wu, Yi-Luen; Fu, Li-Chen] Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan.
   [Gamborino, Edwinn; Fu, Li-Chen] Natl Taiwan Univ, Ctr Artificial Intelligence & Adv Robot, Taipei 106, Taiwan.
   [Fu, Li-Chen] Natl Taiwan Univ, Dept Elect Engn, Taipei 106, Taiwan.
RP Fu, LC (通讯作者)，Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan.; Fu, LC (通讯作者)，Natl Taiwan Univ, Ctr Artificial Intelligence & Adv Robot, Taipei 106, Taiwan.; Fu, LC (通讯作者)，Natl Taiwan Univ, Dept Elect Engn, Taipei 106, Taiwan.
EM r04922068@ntu.edu.tw; r04921098@ntu.edu.tw; lichen@ntu.edu.tw
OI Fu, Li-Chen/0000-0002-6947-7646; Gamborino, Edwinn/0000-0003-3199-0708
FU Joint Research Center for Artificial Intelligence Technology; All Vista
   Healthcare through Ministry of Science and Technology of Taiwan; Center
   for Artificial Intelligence and Advanced Robotics, National Taiwan
   University [MOST 108-2634-F-002-016, MOST 107-2218-E-002-009]
FX This work was supported in part by the Joint Research Center for
   Artificial Intelligence Technology and All Vista Healthcare through
   Ministry of Science and Technology of Taiwan and the Center for
   Artificial Intelligence and Advanced Robotics, National Taiwan
   University under Grant MOST 108-2634-F-002-016 and Grant MOST
   107-2218-E-002-009.
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NR 46
TC 0
Z9 0
U1 1
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2379-8920
EI 2379-8939
J9 IEEE T COGN DEV SYST
JI IEEE Trans. Cogn. Dev. Syst.
PD SEPT
PY 2020
VL 12
IS 3
BP 439
EP 450
DI 10.1109/TCDS.2019.2917030
PG 12
WC Computer Science, Artificial Intelligence; Robotics; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Robotics; Neurosciences & Neurology
GA NN3AB
UT WOS:000568663000006
DA 2022-08-23
ER

PT J
AU Zheng, CY
   Fan, H
   Singh, R
   Shi, YY
AF Zheng, Chenyu
   Fan, Hong
   Singh, Rohit
   Shi, Yuanyuan
TI A Domain Expertise and Word-Embedding Geometric Projection Based
   Semantic Mining Framework for Measuring the Soft Power of Social
   Entities
SO IEEE ACCESS
LA English
DT Article
DE Computational social science; soft power; word-embedding; semantic
   mining; intelligence fusion
ID SCIENCE; IMAGE
AB Social science research (communication studies in particular) often involves measuring the soft power of social entities from the related big data of media texts. This work proposes a soft power measurement framework that integrates the domain-expertise-driven conceptualization of the traditional social science paradigm and the data-driven operationalization of the computational social science paradigm. Based on the geometric projections of word vectors from the social entity keywords to the soft power dimension keywords, the weights of social entity keywords in the full-sample media texts are projected to the corresponding soft power dimensions in the high-dimensional word-embedding semantic space and accumulated to the total and dimensional values of the soft power of social entities respectively. We demonstrate the applicability, compatibility, computational complexity, validity, adaptability, and scalability of the proposed framework by showing a standard instantiation (measuring the national image carrying capacity of Chinese elite private entrepreneurs from the full-sample WeChat articles, each of which has more than 100,000 unique views) and a variational instantiation (measuring the city brand influence of global cities from the big data of the Global News Knowledge Graph in the Global Database of Events, Language, and Tone (GDELT) of Google) of the proposed framework.
C1 [Zheng, Chenyu] Fudan Univ, Sch Journalism, Commun & Data Sci Lab, Shanghai 200433, Peoples R China.
   [Fan, Hong] Tsinghua Univ, Sch Journalism & Commun, Beijing 100084, Peoples R China.
   [Singh, Rohit] Univ Colorado, Dept Comp Sci & Engn, Denver, CO 80204 USA.
   [Shi, Yuanyuan] Fudan Univ, Dept Tourism, Shanghai 200433, Peoples R China.
RP Shi, YY (通讯作者)，Fudan Univ, Dept Tourism, Shanghai 200433, Peoples R China.
EM shiyy@fudan.edu.cn
OI Shi, Yuanyuan/0000-0003-0005-7106; Singh, Rohit/0000-0002-3978-2332
FU Shanghai Philosophy and Social Science Research Project "A Study on
   Shanghai's Global City Brand Competitiveness, Evolution, and Improvement
   Strategies Based on Word-Embedding Semantic Mining on the Big Data of
   the GDELT Global News'' [2019EXW007]
FX This work was supported by the Shanghai Philosophy and Social Science
   Research Project "A Study on Shanghai's Global City Brand
   Competitiveness, Evolution, and Improvement Strategies Based on
   Word-Embedding Semantic Mining on the Big Data of the GDELT Global
   News'' under Grant 2019EXW007.
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NR 47
TC 0
Z9 0
U1 0
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 204597
EP 204611
DI 10.1109/ACCESS.2020.3037462
PG 15
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 OS8YC
UT WOS:000590443200001
OA gold
DA 2022-08-23
ER

PT J
AU Singh, AK
   Kailasam, L
AF Singh, Ashwini Kumar
   Kailasam, Lakshmanan
TI PILHNB: Popularity, interests, location used hidden Naive Bayesian-based
   model for link prediction in dynamic social networks
SO NEUROCOMPUTING
LA English
DT Article
DE Dynamic attributed social networks; Link prediction; Latent Dirichlet
   allocation; Hidden Naive Bayesian
ID COMMUNITIES; RECOMMENDATION; INFERENCE; FRIENDS; FINITE
AB Link prediction aims to predict the missing interactions in evolving networks that may appear in the future. It has practical importance in various real-world applications, ranging from friendship recommen-dation, knowledge graph completion, target advertising, and protein-protein interaction prediction. Most of the recent efforts focus on the structure of the network while ignoring many other essential factors. In this paper, we present a modified Latent Dirichlet Allocation (LDA), and Hidden Naive Bayesian (HNB) based link prediction technique named PILHNB model for link prediction in dynamic social networks by considering behavioral controlling elements like relationship network structure, nodes' attributes, location-based information of nodes, nodes' popularity, users' interests, and learning the evolution pat-tern of these factors in the networks. Experimental results on six real-world networks demonstrate our proposed models' effectiveness and efficiency compared with existing state-of-the-art link prediction techniques. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Singh, Ashwini Kumar; Kailasam, Lakshmanan] Indian Inst Technol BHU, Varanasi, Uttar Pradesh, India.
RP Singh, AK (通讯作者)，Indian Inst Technol BHU, Varanasi, Uttar Pradesh, India.
EM ashwinikrsingh.rs.cse17@iitbhu.ac.in; lakshmanank.cse@iitbhu.ac.in
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NR 86
TC 4
Z9 4
U1 7
U2 10
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD OCT 21
PY 2021
VL 461
BP 562
EP 576
DI 10.1016/j.neucom.2021.02.101
EA SEP 2021
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA WC2IT
UT WOS:000704086300007
DA 2022-08-23
ER

PT J
AU Tu, JC
   Tu, YW
   Wang, TR
AF Tu, Jui-Che
   Tu, Ya-Wen
   Wang, Tai-Ran
TI An Investigation of the Effects of Infographics and Green Messages on
   the Environmental Attitudes of Taiwanese Online Shoppers
SO SUSTAINABILITY
LA English
DT Article
DE green messages; postal packaging; infographics; environmental protection
   attitudes; online shoppers
ID MODEL; FOOD
AB The rapid growth of e-commerce in Taiwan has increased the use of postal packaging; this contributes to the destruction of our environment, because it cannot be re-used, and may contain toxic substances. To address this understudied issue, we examined the effects of attaching green messages about environmental attitudes in the form of infographics on postal packaging. To improve our understanding of consumer preferences with respect to green messages, first, a questionnaire survey of online shoppers was conducted. Based on a statistical analysis of the results, it was found that green messages require excellent infographics in order to deliver environmental knowledge. Graphical information was considered superior to text-only information in the presentation of green messages and the visualization of green information by online shoppers. This result indicates that infographics have emerged as a better visual approach to deliver green messages. We hope that these findings will serve as a reference in the design of environmentally-friendly postal packaging.
C1 [Tu, Jui-Che; Tu, Ya-Wen; Wang, Tai-Ran] Natl Yunlin Univ Sci & Technol, Grad Sch Design, Touliu 640, Yunlin, Taiwan.
   [Tu, Ya-Wen] Chien Kuo Technol Univ, Dept Commercial Design, Changhua 500, Taiwan.
RP Tu, YW (通讯作者)，Natl Yunlin Univ Sci & Technol, Grad Sch Design, Touliu 640, Yunlin, Taiwan.; Tu, YW (通讯作者)，Chien Kuo Technol Univ, Dept Commercial Design, Changhua 500, Taiwan.
EM tujc@yuntech.edu.tw; arwentu888@gmail.com; Steiner0720@gmail.com
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NR 46
TC 3
Z9 3
U1 2
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD NOV
PY 2018
VL 10
IS 11
AR 3993
DI 10.3390/su10113993
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 HC1AQ
UT WOS:000451531700167
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Sun, C
   Yang, ZH
   Wang, L
   Zhang, Y
   Lin, HF
   Wang, J
AF Sun, Cong
   Yang, Zhihao
   Wang, Lei
   Zhang, Yin
   Lin, Hongfei
   Wang, Jian
TI Deep learning with language models improves named entity recognition for
   PharmaCoNER
SO BMC BIOINFORMATICS
LA English
DT Article; Proceedings Paper
CT 5th Workshop on BioNLP Open Shared Tasks
CY NOV 04, 2019
CL Hong Kong, HONG KONG
DE Named entity recognition; NER; Language model; BERT; Text mining
AB Background The recognition of pharmacological substances, compounds and proteins is essential for biomedical relation extraction, knowledge graph construction, drug discovery, as well as medical question answering. Although considerable efforts have been made to recognize biomedical entities in English texts, to date, only few limited attempts were made to recognize them from biomedical texts in other languages. PharmaCoNER is a named entity recognition challenge to recognize pharmacological entities from Spanish texts. Because there are currently abundant resources in the field of natural language processing, how to leverage these resources to the PharmaCoNER challenge is a meaningful study. Methods Inspired by the success of deep learning with language models, we compare and explore various representative BERT models to promote the development of the PharmaCoNER task. Results The experimental results show that deep learning with language models can effectively improve model performance on the PharmaCoNER dataset. Our method achieves state-of-the-art performance on the PharmaCoNER dataset, with a max F1-score of 92.01%. Conclusion For the BERT models on the PharmaCoNER dataset, biomedical domain knowledge has a greater impact on model performance than the native language (i.e., Spanish). The BERT models can obtain competitive performance by using WordPiece to alleviate the out of vocabulary limitation. The performance on the BERT model can be further improved by constructing a specific vocabulary based on domain knowledge. Moreover, the character case also has a certain impact on model performance.
C1 [Sun, Cong; Yang, Zhihao; Lin, Hongfei; Wang, Jian] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian, Peoples R China.
   [Wang, Lei; Zhang, Yin] Beijing Inst Hlth Adm & Med Informat, Beijing, Peoples R China.
RP Yang, ZH (通讯作者)，Dalian Univ Technol, Sch Comp Sci & Technol, Dalian, Peoples R China.; Wang, L (通讯作者)，Beijing Inst Hlth Adm & Med Informat, Beijing, Peoples R China.
EM yangzh@dlut.edu.cn; wanglei.wlbihami@gmail.com
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NR 33
TC 0
Z9 0
U1 4
U2 5
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD DEC 17
PY 2021
VL 22
IS SUPPL 1
SU 1
SI SI
AR 602
DI 10.1186/s12859-021-04260-y
PG 16
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA XQ0PK
UT WOS:000731257100004
PM 34920700
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Saenz-Adan, C
   Garcia-Izquierdo, FJ
   Perez, B
   Huynh, TD
   Moreau, L
AF Saenz-Adan, Carlos
   Garcia-Izquierdo, Francisco J.
   Perez, Beatriz
   Huynh, Trung Dong
   Moreau, Luc
TI Automated and non-intrusive provenance capture with UML2PROV
SO COMPUTING
LA English
DT Article
DE Data provenance; Application logging; Technology audits
AB Data provenance is a form of knowledge graph providing an account of what a system performs, describing the data involved, and the processes carried out over them. It is crucial to ascertaining the origin of data, validating their quality, auditing applications behaviours, and, ultimately, making them accountable. However, instrumenting applications, especially legacy ones, to track the provenance of their operations remains a significant technical hurdle, hindering the adoption of provenance technology. UML2PROV is a software-engineering methodology that facilitates the instrumentation of provenance recording in applications designed with UML diagrams. It automates the generation of (1) templates for the provenance to be recorded and (2) the code to capture values required to instantiate those templates from an application at run time, both from the application's UML diagrams. By so doing, UML2PROV frees application developers from manual instrumentation of provenance capturing while ensuring the quality of recorded provenance. In this paper, we present in detail UML2PROV's approach to generating application code for capturing provenance values via the means of Bindings Generation Module (BGM). In particular, we propose a set of requirements for BGM implementations and describe an event-based design of BGM that relies on the Aspect-Oriented Programming (AOP) paradigm to automatically weave the generated code into an application. Finally, we present three different BGM implementations following the above design and analyze their pros and cons in terms of computing/storage overheads and implications to provenance consumers.
C1 [Saenz-Adan, Carlos; Garcia-Izquierdo, Francisco J.; Perez, Beatriz] Univ La Rioja, Dept Math & Comp Sci, La Rioja, Spain.
   [Huynh, Trung Dong; Moreau, Luc] Kings Coll London, Dept Informat, London, England.
RP Perez, B (通讯作者)，Univ La Rioja, Dept Math & Comp Sci, La Rioja, Spain.
EM beatriz.perez@unirioja.es
RI ; Huynh, Trung Dong/E-4185-2018
OI Moreau, Luc/0000-0002-3494-120X; Huynh, Trung Dong/0000-0003-4937-2473
FU CRUE-CSIC agreement; Springer Nature
FX Open Access funding provided thanks to the CRUE-CSIC agreement with
   Springer Nature.
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NR 44
TC 0
Z9 0
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U2 2
PU SPRINGER WIEN
PI WIEN
PA SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA
SN 0010-485X
EI 1436-5057
J9 COMPUTING
JI Computing
PD APR
PY 2022
VL 104
IS 4
SI SI
BP 767
EP 788
DI 10.1007/s00607-021-01012-x
EA DEC 2021
PG 22
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0K3DA
UT WOS:000729025100001
OA hybrid
DA 2022-08-23
ER

PT J
AU Bauer, TJ
   Gombocz, E
   Wehland, M
   Bauer, J
   Infanger, M
   Grimm, D
AF Bauer, Thomas J.
   Gombocz, Erich
   Wehland, Markus
   Bauer, Johann
   Infanger, Manfred
   Grimm, Daniela
TI Insight in Adhesion Protein Sialylation and Microgravity Dependent Cell
   Adhesion-An Omics Network Approach
SO INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
LA English
DT Article
DE linked open database; graphical SPARQL; semantic knowledgebase;
   proteome; integrin; cadherin
ID I-LIKE DOMAIN; BETA-GALACTOSIDE ALPHA-2,6-SIALYLTRANSFERASE;
   THYROID-CANCER CELLS; SIALIC ACIDS; N-GLYCANS; SPHEROID FORMATION;
   ENDOTHELIAL-CELLS; POLYSIALIC ACID; HOMOPHILIC INTERACTIONS;
   GENE-EXPRESSION
AB The adhesion behavior of human tissue cells changes in vitro, when gravity forces affecting these cells are modified. To understand the mechanisms underlying these changes, proteins involved in cell-cell or cell-extracellular matrix adhesion, their expression, accumulation, localization, and posttranslational modification (PTM) regarding changes during exposure to microgravity were investigated. As the sialylation of adhesion proteins is influencing cell adhesion on Earth in vitro and in vivo, we analyzed the sialylation of cell adhesion molecules detected by omics studies on cells, which change their adhesion behavior when exposed to microgravity. Using a knowledge graph created from experimental omics data and semantic searches across several reference databases, we studied the sialylation of adhesion proteins glycosylated at their extracellular domains with regards to its sensitivity to microgravity. This way, experimental omics data networked with the current knowledge about the binding of sialic acids to cell adhesion proteins, its regulation, and interactions in between those proteins provided insights into the mechanisms behind our experimental findings, suggesting that balancing the sialylation against the de-sialylation of the terminal ends of the adhesion proteins' glycans influences their binding activity. This sheds light on the transition from two- to three-dimensional growth observed in microgravity, mirroring cell migration and cancer metastasis in vivo.
C1 [Bauer, Thomas J.; Wehland, Markus; Infanger, Manfred; Grimm, Daniela] Otto von Guericke Univ, Clin Plast Aesthet & Hand Surg, D-39120 Magdeburg, Germany.
   [Gombocz, Erich] Melissa Informat, 2550 Ninth St,Suite 114, Berkeley, CA 94710 USA.
   [Bauer, Johann] Max Planck Inst Biochem, D-82152 Martinsried, Germany.
   [Grimm, Daniela] Aarhus Univ Hosp, Dept Biomed, DK-8000 Aarhus C, Denmark.
   [Grimm, Daniela] Otto von Guericke Univ, Dept Micrograv & Translat Regenerat Med, Pfalzer Pl, D-39106 Magdeburg, Germany.
RP Bauer, J (通讯作者)，Max Planck Inst Biochem, D-82152 Martinsried, Germany.
EM thomas.bauer@med.ovgu.de; egombocz@ix.netcom.com;
   markus.wehland@med.ovgu.de; jbauer@biochem.mpg.de;
   manfred.infanger@med.ovgu.de; dgg@biomed.au.dk
OI Wehland, Markus/0000-0002-8160-859X; Gombocz, Erich/0000-0002-2921-4454;
   Grimm, Daniela/0000-0002-4991-3105
FU (Bundesministerium furWirtschaft und Energie, und Deutsches Zentrum fur
   Luft-und Raumfahrt) [50WB1524, 50WB1924]
FX This research was funded byBMWi/DLR (Bundesministerium furWirtschaft und
   Energie, und Deutsches Zentrum fur Luft-und Raumfahrt) grants 50WB1524
   and 50WB1924 (DG).
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NR 144
TC 6
Z9 6
U1 1
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1422-0067
J9 INT J MOL SCI
JI Int. J. Mol. Sci.
PD MAR
PY 2020
VL 21
IS 5
AR 1749
DI 10.3390/ijms21051749
PG 23
WC Biochemistry & Molecular Biology; Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Chemistry
GA LB8VL
UT WOS:000524908500200
PM 32143440
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhao, XW
   Wang, DQ
   Zhao, ZY
   Liu, W
   Lu, CW
   Zhuang, FZ
AF Zhao, Xiaowei
   Wang, Deqing
   Zhao, Zhengyang
   Liu, Wei
   Lu, Chenwei
   Zhuang, Fuzhen
TI A neural topic model with word vectors and entity vectors for short
   texts
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Topic model; Short text; Variational auto-encoder; Word embedding;
   Entity embedding
ID KNOWLEDGE
AB Traditional topic models are widely used for semantic discovery from long texts. However, they usually fail to mine high-quality topics from short texts (e.g. tweets) due to the sparsity of features and the lack of word co-occurrence patterns. In this paper, we propose a Variational Auto-Encoder Topic Model (VAETM for short) by combining word vector representation and entity vector representation to address the above limitations. Specifically, we first learn embedding representations of each word and each entity by employing a large-scale external corpora and a large and manually edited knowledge graph, respectively. Then we integrated the embedding representations into the variational auto-encoder framework and propose an unsupervised model named VAETM to infer the latent representation of topic distributions. To further boost VAETM, we propose an improved supervised VAETM (SVAETM for short) by considering label information in training set to supervise the inference of latent representation of topic distributions and the generation of topics. Last, we propose KL-divergence-based inference algorithms to infer approximate posterior distribution for our two models. Extensive experiments on three common short text datasets demonstrate our proposed VAETM and SVAETM outperform various kinds of state-of-the-art models in terms of perplexity, NPMI, and accuracy.
C1 [Zhao, Xiaowei; Wang, Deqing; Zhao, Zhengyang; Lu, Chenwei] Beihang Univ, Sch Comp Sci, Beijing 100191, Peoples R China.
   [Liu, Wei] Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China.
   [Zhuang, Fuzhen] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, CAS, Beijing 100190, Peoples R China.
   [Zhuang, Fuzhen] Chinese Acad Sci, Xiamen Data Intelligence Acad ICT, Beijing, Peoples R China.
RP Zhuang, FZ (通讯作者)，Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, CAS, Beijing 100190, Peoples R China.; Zhuang, FZ (通讯作者)，Chinese Acad Sci, Xiamen Data Intelligence Acad ICT, Beijing, Peoples R China.
EM fuzhenzhuang@ict.ac.cn
FU National Key R&D Program of China [2019YFA0707204]; National Natural
   Science Foundation of China [U1836206]
FX This research was supported by National Key R&D Program of China under
   Grant 2019YFA0707204. Dr. Zhuang was supported by the National Natural
   Science Foundation of China under Grant No. U1836206.
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NR 28
TC 11
Z9 11
U1 14
U2 26
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 MAR
PY 2021
VL 58
IS 2
AR 102455
DI 10.1016/j.ipm.2020.102455
PG 11
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 PY7NV
UT WOS:000612229800005
DA 2022-08-23
ER

PT J
AU Sarica, S
   Luo, JX
   Wood, KL
AF Sarica, Serhad
   Luo, Jianxi
   Wood, Kristin L.
TI TechNet: Technology semantic network based on patent data
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge discovery; Word embedding; Technology semantic network;
   Knowledge representation
ID WORD EMBEDDINGS; METHODOLOGY
AB The growing developments in general semantic networks, knowledge graphs and ontology databases have motivated us to build a large-scale comprehensive semantic network of technology-related data for engineering knowledge discovery, technology search and retrieval, and artificial intelligence for engineering design and innovation. Specially, we constructed a technology semantic network (TechNet) that covers the elemental concepts in all domains of technology and their semantic associations by mining the complete U.S. patent database from 1976. To derive the TechNet, natural language processing techniques were utilized to extract terms from massive patent texts and recent word embedding algorithms were employed to vectorize such terms and establish their semantic relationships. We report and evaluate the TechNet for retrieving terms and their pairwise relevance that is meaningful from a technology and engineering design perspective. The TechNet may serve as an infrastructure to support a wide range of applications, e.g., technical text summaries, search query predictions, relational knowledge discovery, and design ideation support, in the context of engineering and technology, and complement or enrich existing semantic databases. To enable such applications, the TechNet is made public via an online interface and APIs for public users to retrieve technology-related terms and their relevancies. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Sarica, Serhad; Luo, Jianxi; Wood, Kristin L.] Singapore Univ Technol & Design, Engn Prod Dev Pillar, 8 Somapah Rd, Singapore 487372, Singapore.
   [Luo, Jianxi; Wood, Kristin L.] Singapore Univ Technol & Design, SUTD MIT Int Design Ctr, 8 Somapah Rd, Singapore 487372, Singapore.
RP Sarica, S (通讯作者)，Singapore Univ Technol & Design, Engn Prod Dev Pillar, 8 Somapah Rd, Singapore 487372, Singapore.
EM serhad_sarica@mymail.sutd.edu.sg; luo@sutd.edu.sg;
   kristinwood@sutd.edu.sg
RI Luo, Jianxi/AAA-8002-2021
OI Luo, Jianxi/0000-0001-5892-8432; SARICA, Serhad/0000-0003-0359-0360
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U1 20
U2 167
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 112995
DI 10.1016/j.eswa.2019.112995
PG 15
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 JQ2AZ
UT WOS:000498755200004
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Alshehri, AS
   You, FQ
AF Alshehri, Abdulelah S.
   You, Fengqi
TI Deep learning to catalyze inverse molecular design
SO CHEMICAL ENGINEERING JOURNAL
LA English
DT Article
DE Inverse molecular design; Computational molecular design; Deep learning;
   Representation learning; Property prediction; Molecular optimization
AB The discovery of superior molecular solutions through computational methods is critical for innovative technologies and their role in addressing pressing resources, health, and environmental issues. Despite its short timespan, the synergetic application of deep learning to inverse molecular design has outpaced decades of theoretical efforts, bearing promise to transform current molecular design paradigms. Herein, we provide an overview of the element of computational inverse molecular design and offer our views on current limitations and outstanding challenges. In our perspective, three main directions are identified for each element and analyzed in terms of their merits and relevant novel deep learning developments. For the molecular representations element, Graph Neural Networks (GNNs), grids, and knowledge graphs (KGs) are discussed for enhancing the expressivity, complexity, descriptivity of relevant molecular information, respectively. Second, chemical text mining, accelerated quantum chemical calculations, and transfer learning are explored to augment the size and the accuracy of current property data and predictive models. Last, emerging trends in design methods including generative modeling, reinforcement learning (RL), and active learning (AL) are examined for optimizing not only computational costs, but also experimental and simulation efforts. The presented discussions are aimed at catalyzing progress and interdisciplinary collaborations toward general-purpose inverse design frameworks.
C1 [Alshehri, Abdulelah S.; You, Fengqi] Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14853 USA.
   [Alshehri, Abdulelah S.] King Saud Univ, Coll Engn, Dept Chem Engn, Riyadh 11421, Saudi Arabia.
RP You, FQ (通讯作者)，Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14853 USA.
EM fengqi.you@cornell.edu
RI Alshehri, Abdulelah S./AAZ-5921-2021; You, Fengqi/B-5040-2011
OI Alshehri, Abdulelah S./0000-0001-5213-3575; You,
   Fengqi/0000-0001-9609-4299
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NR 79
TC 0
Z9 0
U1 8
U2 8
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 1385-8947
EI 1873-3212
J9 CHEM ENG J
JI Chem. Eng. J.
PD SEP 15
PY 2022
VL 444
AR 136669
DI 10.1016/j.cej.2022.136669
PG 8
WC Engineering, Environmental; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 1N0YT
UT WOS:000800390200001
DA 2022-08-23
ER

PT J
AU Shang, JY
   Sun, YN
AF Shang, Jiayu
   Sun, Yanni
TI CHERRY: a Computational metHod for accuratE pRediction of
   virus-pRokarYotic interactions using a graph encoder-decoder model
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article; Early Access
DE phage host prediction; link prediction; graph convolutional network;
   deep learning
ID HOSTS
AB Prokaryotic viruses, which infect bacteria and archaea, are key players in microbial communities. Predicting the hosts of prokaryotic viruses helps decipher the dynamic relationship between microbes. Experimental methods for host prediction cannot keep pace with the fast accumulation of sequenced phages. Thus, there is a need for computational host prediction. Despite some promising results, computational host prediction remains a challenge because of the limited known interactions and the sheer amount of sequenced phages by high-throughput sequencing technologies. The state-of-the-art methods can only achieve 43% accuracy at the species level. In this work, we formulate host prediction as link prediction in a knowledge graph that integrates multiple protein and DNA-based sequence features. Our implementation named CHERRY can be applied to predict hosts for newly discovered viruses and to identify viruses infecting targeted bacteria. We demonstrated the utility of CHERRY for both applications and compared its performance with 11 popular host prediction methods. To our best knowledge, CHERRY has the highest accuracy in identifying virus-prokaryote interactions. It outperforms all the existing methods at the species level with an accuracy increase of 37%. In addition, CHERRY's performance on short contigs is more stable than other tools.
C1 [Shang, Jiayu] City Univ Hong Kong, Hong Kong, Peoples R China.
   [Sun, Yanni] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China.
RP Sun, YN (通讯作者)，City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China.
EM yannisun@cityu.edu.hk
RI SUN, Yanni/AHC-4163-2022
OI SUN, Yanni/0000-0003-1373-8023; Shang, Jiayu/0000-0001-5974-4985
FU City University of Hong Kong [9678241]; Hong Kong Institute of Data
   Science [9360163]; Hong Kong Innovation and Technology Commission
FX This work was supported by City University of Hong Kong (Project
   9678241), Hong Kong Institute of Data Science (9360163), and the Hong
   Kong Innovation and Technology Commission (InnoHK Project CIMDA).
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NR 53
TC 0
Z9 0
U1 4
U2 4
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1467-5463
EI 1477-4054
J9 BRIEF BIOINFORM
JI Brief. Bioinform.
DI 10.1093/bib/bbac182
EA MAY 2022
PG 16
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA 1K0VR
UT WOS:000798329600001
PM 35595715
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Yang, XY
   Hu, F
   Ye, L
   Chang, ZM
   Li, JY
AF Yang, Xinyan
   Hu, Fei
   Ye, Long
   Chang, Zhiming
   Li, Jiyin
TI A system of configurable 3D indoor scene synthesis via semantic relation
   learning
SO DISPLAYS
LA English
DT Article
DE 3D indoor scene synthesis; Graph neural network; Contextual message
   passing; Knowledge graph completion
AB When describing a complicated scene, natural language usually has extra meanings beyond the actual descriptions based on the context of objects and relations. Thus, synthesizing 3D scene from natural language description can be regarded as an ill-posed problem. To solve this challenge, we build a novel system named Text to Scene (T2S) via relation learning for the language-driven scene synthesis. In our paper, we propose a novel graph-based contextual completion method Contextual ConvE(CConvE) to enrich the 3D indoor scene and visualize the graph by arranging 3D models under an object location protocol. Besides, we integrate them into T2S system which synthesizes 3D scene from text. Given a text, T2S system will organize inclusive semantic message to a graph template, complete the graph with CConvE and visualize the graph by retrieving and arranging 3D models under the protocol. CConvE is a convolutional neural network which can infer object categories and spatial relations from the contextual message passing. The experimental result shows the competitive performance of CConvE compared with the state-of-the-art approach and proves that introducing semantic object relation learning method to 3D scene synthesis task can make the virtual visualized results in line with real life.
C1 [Yang, Xinyan; Ye, Long] Commun Univ China, Minist Educ, Key Lab Media Audio & Video, Beijing 100024, Peoples R China.
   [Hu, Fei] Commun Univ China, State Key Lab Media Convergence & Commun, Beijing 100024, Peoples R China.
   [Chang, Zhiming; Li, Jiyin] Henan Broadcasting Syst, Zhengzhou 450002, Peoples R China.
RP Ye, L (通讯作者)，Commun Univ China, Minist Educ, Key Lab Media Audio & Video, Beijing 100024, Peoples R China.
EM yelong@cuc.edu.cn
FU National Key R&D Program of China [2021YFF0900504]; National Natural
   Science Foun-dation of China [61971383, 62001432]; Fundamental Research
   Funds for the Central Universities, China [CUC21GZ007]
FX Acknowledgments This work is supported by National Key R&D Program of
   China under Grant No. 2021YFF0900504, the National Natural Science
   Foun-dation of China under Grant Nos. 61971383 and 62001432, and the
   Fundamental Research Funds for the Central Universities, China under
   Grant No. CUC21GZ007.
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NR 44
TC 1
Z9 1
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0141-9382
EI 1872-7387
J9 DISPLAYS
JI Displays
PD SEP
PY 2022
VL 74
AR 102168
DI 10.1016/j.displa.2022.102168
PG 10
WC Computer Science, Hardware & Architecture; Engineering, Electrical &
   Electronic; Instruments & Instrumentation; Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Instruments & Instrumentation; Optics
GA 1C1PJ
UT WOS:000792899500004
DA 2022-08-23
ER

PT J
AU Zhou, FQ
   Li, WJ
   Yang, Y
   Feng, L
   Yu, P
   Zhao, MY
   Yan, XQ
   Wu, JJ
AF Zhou, Fanqin
   Li, Wenjing
   Yang, Yang
   Feng, Lei
   Yu, Peng
   Zhao, Mingyu
   Yan, Xueqiang
   Wu, Jianjun
TI Intelligence-Endogenous Networks: Innovative Network Paradigm for 6G
SO IEEE WIRELESS COMMUNICATIONS
LA English
DT Article
DE 6G mobile communication; Knowledge engineering; Base stations; Heuristic
   algorithms; Transportation; Maintenance engineering; Manufacturing
   processing
ID COMMUNICATION; CHALLENGES; SYSTEM
AB Feature applications envisioned in the 6G era, represented by holographic communication, full-sensing communication, intelligent transportation, and intelligent manufacturing in terms of virtual and physical integration, immersion, contextualization, and personalization, have placed high demands on the intelligentization of 6G networks. The current network operating mechanism based on rule-based algorithms is limited by rigid preset rules. It is difficult to adapt dynamically to constantly changing user needs and network environments. In order to enable the network to effectively accumulate and independently utilize the operating experience and realize the independent iterative evolution of network operation, management, and control capabilities, this article proposes the concept of the intelligence-endogenous network (IEN). The IEN aims to introduce knowledge graph and artificial intelligence technologies into networks to characterize, construct, learn, and apply the network's multidimensional subjective and objective knowledge. Based on the learned knowledge, an IEN can realize stereo perception, decision deduction, and dynamic adjustment, so the network can change as it needs for whatever new services we want. The article introduces the basic concepts, goals, and meanings of the IEN, together with its key principles and technologies, and illustrates how it innovatively responds to the current network's multifaceted challenges with an intuitive use case.
C1 [Zhou, Fanqin; Feng, Lei; Yu, Peng] BUPT, State Key Lab Networking & Switching Technol, Beijing, Peoples R China.
   [Li, Wenjing] BUPT, Key Lab Network Management Res Ctr, Beijing, Peoples R China.
   [Yang, Yang] BUPT, Beijing, Peoples R China.
   [Zhao, Mingyu; Wu, Jianjun] Huawei Technol, Shenzhen, Peoples R China.
   [Yan, Xueqiang] Huawei Technol, Wireless Technol Lab, Shenzhen, Peoples R China.
RP Li, WJ (通讯作者)，BUPT, Key Lab Network Management Res Ctr, Beijing, Peoples R China.
FU National Key R&D Program of China [2018YFE0205502]; 2018 Youth Backbone
   Individual Program of Beijing Excellent Talents Training Project
FX This work is supported by the National Key R&D Program of China
   (2018YFE0205502) and the 2018 Youth Backbone Individual Program of
   Beijing Excellent Talents Training Project.
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NR 15
TC 0
Z9 0
U1 4
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1536-1284
EI 1558-0687
J9 IEEE WIREL COMMUN
JI IEEE Wirel. Commun.
PD FEB
PY 2022
VL 29
IS 1
BP 40
EP 47
DI 10.1109/MWC.004.00320
PG 8
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1R0ZB
UT WOS:000803106900018
DA 2022-08-23
ER

PT J
AU Xie, ZW
   Zhu, RJ
   Liu, J
   Zhou, GY
   Huang, JX
AF Xie, Zhiwen
   Zhu, Runjie
   Liu, Jin
   Zhou, Guangyou
   Huang, Jimmy Xiangji
TI Hierarchical Neighbor Propagation With Bidirectional Graph Attention
   Network for Relation Prediction
SO IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
LA English
DT Article
DE Natural language processing; text mining; knowledge graph;
   representation learning
ID KNOWLEDGE; EMBEDDINGS
AB The graph attention network (GAT) [1] has started to become a mainstream neural network architecture since 2018, yielding remarkable performance gains in various natural language processing (NLP) tasks. Although GAT has reached the state-of-the-art (SOTA) performance as a recent success in relation prediction in know ledge graph, the current model is still limited by the following two aspects: (1) the existing model only considers the neighbors from the inbound-direction of the given entity, but ignores the rich neighborhood information from outbound-directions; (2) the existing model only uses the k-th hop output to learn the multi-hop embeddings, which leads to the loss of a large amount of early-stage embedding information (e.g., one-hop) at the graph attention step. In this study, we propose a novel bidirectional graph attention network (BiCAT) to learn the hierarchical neighbor propagation. In our proposed BiGAT, an inbound-directional GAT and an outbound-directional GAT are introduced to capture sufficient neighborhood information before propagating the bidirectional neighborhood information to learn the multi-hop feature embeddings in a hierarchical manner. Experiments conducted on the four publicly available datasets show that BiGAT achieves the competitive results in comparison to other SOTA methods.
C1 [Xie, Zhiwen; Liu, Jin] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
   [Zhu, Runjie] York Univ, Lassonde Sch Engn, Toronto, ON M3J 1P3, 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 M3J 1P3, Canada.
RP Liu, J (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.; Zhou, GY (通讯作者)，Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
EM xiezhiwen@whu.edu.cn; sherryzh@cse.yorku.ca; jinliu@whu.edu.cn;
   gyzhou@mail.ccnu.edu.cn; jhuang@yorku.ca
OI Huang, Jimmy Xiangji/0000-0003-1292-1491
FU National Natural Science Foundation of China [61972173, 61972290];
   National Key R&D Program of China [2018YFC1604000]; Natural Sciences and
   Engineering Research Council (NSERC) of Canada; York Research Chairs
   (YRC) program
FX Thisworkwas supported by the National Natural Science Foundation of
   China under Grants 61972173 and 61972290, and also supported by the
   National Key R&D Program of China underGrant 2018YFC1604000 and by the
   research grant from Natural Sciences and Engineering Research Council
   (NSERC) of Canada and York Research Chairs (YRC) program.
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NR 74
TC 0
Z9 0
U1 5
U2 11
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 1762
EP 1773
DI 10.1109/TASLP.2021.3079812
PG 12
WC Acoustics; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Acoustics; Engineering
GA SN5JW
UT WOS:000658326800003
DA 2022-08-23
ER

PT J
AU Qiu, J
   Chai, YH
   Liu, Y
   Gu, ZQ
   Li, SD
   Tian, ZH
AF Qiu, Jing
   Chai, Yuhan
   Liu, Yan
   Gu, Zhaoquan
   Li, Shudong
   Tian, Zhihong
TI Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City
SO IEEE ACCESS
LA English
DT Article
DE Non-taxonomic relations; semantic graph; dependency relations; smart
   city
ID TRUST
AB The explosive data growth in smart city is making domain big data a hot topic for knowledge extraction. Non-taxonomic relations refer to any relations between concept pairs except the is-a relation, which is an important part of Knowledge Graph. In this paper, toward big data in smart city, we present a multi-phase correlation search framework to automatically extract non-taxonomic relations from domain documents. Different kinds of semantic information are used to improve the performance of the system. First, inspired by the works of network representation; we propose a Semantic Graph-Based method to combine structure information of semantic graph and context information of terms together for non-taxonomic relationships identification. Second, different semantic types of verb sets are extracted based on the dependency syntactic information, which are ranked to act as non-taxonomic relationship labels. Extensive experiments demonstrate the efficiency of the proposed framework. The F1 value reaches 81.4% for identification of non-taxonomic relationships. The total precision of the non-taxonomic relationship labels extraction is 73.4%, and 87.8% non-taxonomic relations can be provided with "good" labels. We hope this article can provide a useful way for domain big data knowledge extraction in smart city.
C1 [Qiu, Jing; Chai, Yuhan; Gu, Zhaoquan; Li, Shudong; Tian, Zhihong] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Guangdong, Peoples R China.
   [Chai, Yuhan; Liu, Yan] Hebei Univ Sci & Technol, Sch Informat Sci & Engn, Shijiazhuang 050026, Hebei, Peoples R China.
RP Tian, ZH (通讯作者)，Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Guangdong, Peoples R China.
EM tianzhihong@gzhu.edu.cn
OI tian, zhihong/0000-0002-9409-5359
FU National Natural Science Foundation of China [61871140, 61572153,
   U1636215, 61572492, 61672020]; National Key Research and Development
   Plan [2018YFB0803504]; Open Fund of Beijing Key Laboratory of IoT
   Information Security Technology [J6V0011104]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61871140, Grant 61572153, Grant
   U1636215, Grant 61572492, and Grant 61672020, in part by the National
   Key Research and Development Plan under Grant 2018YFB0803504, and in
   part by the Open Fund of Beijing Key Laboratory of IoT Information
   Security Technology under Grant J6V0011104.
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NR 44
TC 30
Z9 30
U1 3
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 74854
EP 74864
DI 10.1109/ACCESS.2018.2881422
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HF7BF
UT WOS:000454392300001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, XL
   Wang, Y
   Gao, CC
   Lin, KH
   Li, YD
AF Wang, Xiaoli
   Wang, Yuan
   Gao, Chuchu
   Lin, Kunhui
   Li, Yadi
TI Automatic Diagnosis With Efficient Medical Case Searching Based on
   Evolving Graphs
SO IEEE ACCESS
LA English
DT Article
DE Automatic diagnosis; multimodal medical data; graph similarity search;
   evolving graphs
ID HEALTH-CARE; SYSTEM
AB The clinical data are often multimodal and consist of both structured data and unstructured data. The modeling of clinical data has become a very important and challenging problem in healthcare big data analytics. Most existing systems focus on only one type of data. In this paper, we propose a knowledge graph-based method to build the linkage between various types of multimodal data. First, we build a semantic-rich knowledge base using both medical dictionaries and practical clinical data collected from hospitals. Second, we propose a graph modeling method to bridge the gap between different types of data, and the multimodal clinical data of each patient are fused and modeled as one unified profile graph. To capture the temporal evolution of the patient's clinical case, the profile graph is represented as a sequence of evolving graphs. Third, we develop a lazy learning algorithm for automatic diagnosis based on graph similarity search. To evaluate our method, we conduct experimental studies on ICU patient diagnosis and Orthopaedics patient classification. The results show that our method could outperform the baseline algorithms. We also implement a real automatic diagnosis system for clinical use. The results obtained from the hospital demonstrate high precision.
C1 [Wang, Xiaoli; Gao, Chuchu; Lin, Kunhui] Xiamen Univ, Sch Software, Xiamen, Peoples R China.
   [Wang, Yuan] Natl Univ Singapore, Dept Ind Syst Engn & Management, Singapore, Singapore.
   [Li, Yadi] Beijing Tongren Hosp, Dept Dermatol, Beijing, Peoples R China.
RP Wang, Y (通讯作者)，Natl Univ Singapore, Dept Ind Syst Engn & Management, Singapore, Singapore.
EM jessicawang36@gmail.com
FU National Natural Science Foundation of China [61702432]; Fundamental
   Research Funds for the Central Universities of China [20720180070];
   International Cooperation Projects of Fujian Province in China
   [2018I0016]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61702432, in part by the Fundamental
   Research Funds for the Central Universities of China under Grant
   20720180070, and in part by the International Cooperation Projects of
   Fujian Province in China under Grant 2018I0016.
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NR 49
TC 8
Z9 8
U1 0
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 53307
EP 53318
DI 10.1109/ACCESS.2018.2871769
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA GX7VY
UT WOS:000447988900001
OA gold
DA 2022-08-23
ER

PT J
AU Ma, YH
   Chen, AR
   Wang, BJ
AF Ma, Yihu
   Chen, Airong
   Wang, Benjin
TI Establishment and application of a fatigue crack database for steel box
   girders
SO STRUCTURE AND INFRASTRUCTURE ENGINEERING
LA English
DT Article; Early Access
DE Long-span bridge; steel box girder; orthotropic steel deck; inspection;
   fatigue crack; database; standardization; long-term data
ID DATA-MANAGEMENT
AB Fatigue cracks commonly occur on the steel box girders of long-span bridges, while are barely summarized and analyzed in a statistic way due to the non-standard records on reports. This paper proposes to establish a standardized database of cracks on steel box girders from multiple data sources. The data are collected from 7 bridges, while 4 of them provide the inspection reports for 28 years in accumulation. By conducting the data specification and standardization, a tuple containing 15 parameters is proposed to store the information of each crack. The crack categorization is then carried out, resulting in 6 series and 34 types. In addition, the analysis module is proposed for the database to show the crack properties in various aspects, including the longitudinal and transverse distribution of the cracks, and the growing process of the crack length. It can be concluded that the database in its current form can benefit bridge management and maintenance in practice. Moreover, it lays the foundation for building the knowledge graph of cracks on steel box girders in the future, which can be helpful in predicting the crack behavior in both data-driven and mechanical ways.
C1 [Ma, Yihu; Chen, Airong] Tongji Univ, Dept Bridge Engn, Shanghai, Peoples R China.
   [Wang, Benjin] Tongji Univ, Sch Aerosp Engn & Appl Mech, Shanghai 200092, Peoples R China.
RP Wang, BJ (通讯作者)，Tongji Univ, Sch Aerosp Engn & Appl Mech, Shanghai 200092, Peoples R China.
EM Benjin.Wang@tongji.edu.cn
OI WANG, BENJIN/0000-0003-3546-3235
FU National Natural Science Foundation of China [51908423, 51678437,
   51978514, 51878493]
FX The authors are grateful for the financial support provided by the
   National Natural Science Foundation of China (No. 51908423, 51678437,
   51978514, 51878493).
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NR 19
TC 0
Z9 0
U1 11
U2 11
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1573-2479
EI 1744-8980
J9 STRUCT INFRASTRUCT E
JI Struct. Infrastruct. Eng.
DI 10.1080/15732479.2022.2058023
EA MAR 2022
PG 16
WC Engineering, Civil; Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 0J0GV
UT WOS:000779789200001
DA 2022-08-23
ER

PT J
AU Lin, X
   Peng, Y
   Choi, B
   Xu, JL
AF Lin, Xin
   Peng, Yun
   Choi, Byron
   Xu, Jianliang
TI Human-Powered Data Cleaning for Probabilistic Reachability Queries on
   Uncertain Graphs
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
AB Uncertain graph models are widely used in real-world applications such as knowledge graphs and social networks. To capture the uncertainty, each edge in an uncertain graph is associated with an existential probability that signifies the likelihood of the existence of the edge. One notable issue of querying uncertain graphs is that the results are sometimes uninformative because of the edge uncertainty. In this paper, we consider probabilistic reachability queries, which are one of the fundamental classes of graph queries. To make the results more informative, we adopt a crowdsourcing-based approach to clean the uncertain edges. However, considering the time and monetary cost of crowdsourcing, it is a problem to efficiently select a limited set of edges for cleaning that maximizes the quality improvement. We prove that the edge selection problem is #P-hard. In light of the hardness of the problem, we propose a series of edge selection algorithms, followed by a number of optimization techniques and pruning heuristics for reducing the computation time. Our experimental results demonstrate that our proposed techniques outperform a random selection by up to 27 times in terms of the result quality improvement and the brute-force solution by up to 60 times in terms of the elapsed time.
C1 [Lin, Xin] East China Normal Univ, Shanghai Key Lab Multidimens Informat Proc, Shanghai 200241, Peoples R China.
   [Lin, Xin; Peng, Yun; Choi, Byron; Xu, Jianliang] Hong Kong Baptist Univ, Dept Comp Sci, Kowloon Tong, Hong Kong, Peoples R China.
RP Lin, X (通讯作者)，East China Normal Univ, Shanghai Key Lab Multidimens Informat Proc, Shanghai 200241, Peoples R China.; Lin, X (通讯作者)，Hong Kong Baptist Univ, Dept Comp Sci, Kowloon Tong, Hong Kong, Peoples R China.
EM xlin@cs.ecnu.edu.cn; ypen@comp.hkbu.edu.cn; bchoi@comp.hkbu.edu.cn;
   xujl@comp.hkbu.edu.hk
OI Choi, Byron (Koon Kau)/0000-0002-8381-336X; Xu,
   Jianliang/0000-0001-9404-5848; Peng, Yun/0000-0001-6358-2333
FU National High-tech 863 Program of China [2015AA015801]; IIK-RGC grant
   [12244916, 12200114, HKBU12201315]; NSFC [61572193, 61502258, 71402083];
   NSF of Shanghai [17ZR1444900]; NSF of Shandong [ZR2014FQ007]
FX This work was supported by the National High-tech 863 Program of China
   (No. 2015AA015801) and IIK-RGC grants 12244916, 12200114, and
   HKBU12201315. Xin Lin's work was also supported by NSFC (No. 61572193)
   and NSF of Shanghai (No. 17ZR1444900). Yun Peng's work was supported by
   NSFC (No. 61502258, 71402083) and NSF of Shandong (No. ZR2014FQ007). Yun
   Peng is the corresponding author.
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NR 39
TC 8
Z9 9
U1 1
U2 11
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD JUL 1
PY 2017
VL 29
IS 7
BP 1452
EP 1465
DI 10.1109/TKDE.2017.2684166
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 EX2PG
UT WOS:000403068000008
OA hybrid
DA 2022-08-23
ER

PT J
AU Sun, XM
   Zhang, R
   Liu, SM
   Lv, QB
   Bao, JS
   Li, J
AF Sun, Xuemin
   Zhang, Rong
   Liu, Shimin
   Lv, Qibing
   Bao, Jinsong
   Li, Jie
TI A digital twin-driven human-robot collaborative assembly-commissioning
   method for complex products
SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Digital twin; Human-robot collaboration; Human motion intention; DDPG;
   Assembly-commissioning; Complex products
AB The process of complex product assembly-commissioning has the characteristics of high flexibility and firm dynamics. To overcome the drawbacks of manual assembly, deploying automated and intelligent techniques can greatly boost efficiency, improve flexibility, and enhance the quality control. The human-robot collaborative (HRC) technology combines the advantages of human capabilities and the efficiency and precision of robots. However, current HRC technology lacks of perception and cognitive ability, especially in dynamic environments. Therefore, this paper proposed a digital twin-driven HRC assembly-commissioning framework. In this framework, a virtual-physical mapping environment for HRC is constructed. In order to improve the cognitive ability of robot units to tasks, a motion intention recognition approach is proposed which integrates the feature of part into human joint sequences. To improve the adaptability of the robot unit to tasks, the assembly-commissioning task knowledge graph is developed to extract the action sequence of the robot unit in a timely manner. Moreover, the deep deterministic policy gradient (DDPG) is used to adaptively adjust the robot unit movement path in the process of assembly-commissioning. Finally, the effectiveness of the proposed method is verified by taking a particular type of automobile generator as a case study product.
C1 [Sun, Xuemin; Zhang, Rong; Liu, Shimin; Lv, Qibing; Bao, Jinsong; Li, Jie] Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China.
RP Bao, JS (通讯作者)，Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China.
EM bao@dhu.edu.cn
FU National Key Research and Development Plan of China [2019YFB1706300];
   Graduate Student Innovation Fund of Donghua University
   [CUSF-DH-D-2020051]; Fundamental Research Funds for the Central
   Universities [2232019D3-32]; Shanghai Sailing Program [19YF1401600];
   Fundamental Research Funds for the Central Universities of Donghua
   University [CUSF-DH-D-2020051]
FX This work is financially supported by National Key Research and
   Development Plan of China (Grant 2019YFB1706300), in part by the
   Fundamental Research Funds for the Central Universities and Graduate
   Student Innovation Fund of Donghua University (Grant No.
   CUSF-DH-D-2020051), in part by Fundamental Research Funds for the
   Central Universities (No. 2232019D3-32), and in part by Shanghai Sailing
   Program (19YF1401600).
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NR 30
TC 1
Z9 1
U1 33
U2 63
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 FEB
PY 2022
VL 118
IS 9-10
BP 3389
EP 3402
DI 10.1007/s00170-021-08211-y
EA OCT 2021
PG 14
WC Automation & Control Systems; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering
GA YI8NY
UT WOS:000707295900002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Hogan, A
AF Hogan, Aidan
TI The Semantic Web: Two decades on
SO SEMANTIC WEB
LA English
DT Article
DE Semantic Web; ontologies; Linked Data; knowledge graphs
ID LINKED DATA; INFERENCE ENGINE; DBPEDIA; SCALE
AB More than two decades have passed since the establishment of the initial cornerstones of the Semantic Web. Since its inception, opinions have remained divided regarding the past, present and potential future impact of the Semantic Web. In this paper - and in light of the results of over two decades of development on both the Semantic Web and related technologies - we reflect on the current status of the Semantic Web, the impact it has had thus far, and future challenges. We first review some of the external criticism of this vision that has been put forward by various authors; we draw together the individual critiques, arguing both for and against each point based on the current state of adoption. We then present the results of a questionnaire that we have posed to the SemanticWeb mailing list in order to understand respondents' perspective(s) regarding the degree to which the original Semantic Web vision has been realised, the impact it can potentially have on the Web (and other settings), its success stories thus far, as well as the degree to which they agree with the aforementioned critiques of the SemanticWeb in terms of both its current state and future feasibility. We conclude by reflecting on future challenges and opportunities in the area.
C1 [Hogan, Aidan] IMFD, Santiago, Chile.
   [Hogan, Aidan] Univ Chile, Dept Comp Sci, Santiago, Chile.
RP Hogan, A (通讯作者)，IMFD, Santiago, Chile.; Hogan, A (通讯作者)，Univ Chile, Dept Comp Sci, Santiago, Chile.
EM ahogan@dcc.uchile.cl
FU Millennium Institute for Foundational Research on Data (IMFD); Fondecyt
   [1181896]
FX We thank the respondents to the questionnaire and the reviewers for
   their helpful comments. This work was funded in part by the Millennium
   Institute for Foundational Research on Data (IMFD) and Fondecyt, Grant
   No. 1181896.
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   2019, AMAZON NEPTUNE FAST
NR 89
TC 5
Z9 5
U1 3
U2 8
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2020
VL 11
IS 1
BP 169
EP 185
DI 10.3233/SW-190387
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 KJ8IH
UT WOS:000512298900018
DA 2022-08-23
ER

PT J
AU Gortzis, LG
   Nikiforidis, G
AF Gortzis, L. G.
   Nikiforidis, G.
TI Tracing and cataloguing knowledge in an e-health cardiology environment
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE scalable knowledge process; e-Health; knowledge engineering;
   collaborative system
ID COLLABORATION; MANAGEMENT; FEEDBACK
AB In an e-health cardiology environment, the current knowledge engineering systems can support two knowledge processes; the knowledge tracing, and the knowledge cataloguing.
   We have developed an n-tier system capable of supporting these processes by enabling human collaboration in each phase along with, a prototype scalable knowledge engineering tactic. A knowledge graph is used as a dynamic information structure. Biosignal data (values of HR, QRS, and ST variables) from 86 patients were used; two general practitioners defined and updated the patients' clinical management protocols; and feedback was inserted retrospectively. Several calibration tests were also performed.
   The system succeeded in formulating three knowledge catalogues per patient, namely, the "patient in life", the "patient in time", and the "patient in action".
   For each patient the clinically accepted normal limits of each variable were predicted with an accuracy of approximately 95%. The patients' risk-levels were identified accurately, and in turn, the errors were reduced. The data and the expert-oriented feedback were also time-stamped correctly and synchronized under a common time-framework.
   Knowledge processes optimization necessitates human collaboration and scalable knowledge engineering tactics. Experts should be responsible for resenting or rejecting a process if it downgrades the provided healthcare quality. (c) 2007 Elsevier Inc. All rights reserved.
C1 [Gortzis, L. G.; Nikiforidis, G.] Univ Patras, Sch Med, Telemed Unit, Rion 26500, Greece.
RP Gortzis, LG (通讯作者)，Univ Patras, Sch Med, Telemed Unit, Rion 26500, Greece.
EM gortzis@med.upatras.gr
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NR 31
TC 6
Z9 6
U1 0
U2 3
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 APR
PY 2008
VL 41
IS 2
BP 217
EP 223
DI 10.1016/j.jbi.2007.09.001
PG 7
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA 293UE
UT WOS:000255360000001
PM 17945541
OA Bronze
DA 2022-08-23
ER

PT J
AU Lin, Q
AF Lin, Qin
TI Music Score Recognition Method Based on Deep Learning
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
AB In recent years, the recommendation application of artificial intelligence and deep music has gradually become a research hotspot. As a complex machine learning algorithm, deep learning can extract features with value laws through training samples. The rise of deep learning network will promote the development of artificial intelligence and also provide a new idea for music score recognition. In this paper, the improved deep learning algorithm is applied to the research of music score recognition. Based on the traditional neural network, the attention weight value improved convolutional neural network (CNN) and high execution efficiency deep belief network (DBN) are introduced to realize the feature extraction and intelligent recognition of music score. Taking the feature vector set extracted by CNN-DBN as input set, a feature learning algorithm based on CNN&DBN was established to extract music score. Experiments show that the proposed model in a variety of different types of polyphony music recognition showed more accurate recognition and good performance; the recognition rate of the improved algorithm applied to the soundtrack identification is as high as 98.4%, which is significantly better than those of other classic algorithms, proving that CNN&DBN can achieve better effect in music information retrieval. It provides data support for constructing knowledge graph in music field and indicates that deep learning has great research value in music retrieval field.
C1 [Lin, Qin] Guizhou Univ Finance & Econ, Art Coll, Guiyang 550001, Guizhou, Peoples R China.
RP Lin, Q (通讯作者)，Guizhou Univ Finance & Econ, Art Coll, Guiyang 550001, Guizhou, Peoples R China.
EM linqin@mail.gufe.edu.cn
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NR 31
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 7
PY 2022
VL 2022
AR 3022767
DI 10.1155/2022/3022767
PG 12
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 3F7IW
UT WOS:000830839300005
PM 35845890
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Wang, JW
   Chen, DY
AF Wang, Jianwei
   Chen, Deyun
TI Few-Shot Object Detection Method Based on Knowledge Reasoning
SO ELECTRONICS
LA English
DT Article
DE convolutional neural network (CNN); graph neural network (GNN); few-shot
   object detection; knowledge reasoning; knowledge graphs (KGs)
AB Human beings have the ability to quickly recognize novel concepts with the help of scene semantics. This kind of ability is meaningful and full of challenge for the field of machine learning. At present, object recognition methods based on deep learning have achieved excellent results with the use of large-scale labeled data. However, the data scarcity of novel objects significantly affects the performance of these recognition methods. In this work, we investigated utilizing knowledge reasoning with visual information in the training of a novel object detector. We trained a detector to project the image representations of objects into an embedding space. Knowledge subgraphs were extracted to describe the semantic relation of the specified visual scenes. The spatial relationship, function relationship, and the attribute description were defined to realize the reasoning of novel classes. The designed few-shot detector, named KR-FSD, is robust and stable to the variation of shots of novel objects, and it also has advantages when detecting objects in a complex environment due to the flexible extensibility of KGs. Experiments on VOC and COCO datasets showed that the performance of the detector was increased significantly when the novel class was strongly associated with some of the base classes, due to the better knowledge propagation between the novel class and the related groups of classes.
C1 [Wang, Jianwei; Chen, Deyun] Harbin Univ Sci & Technol, Coll Comp Sci & Technol, Harbin 150080, Peoples R China.
   [Wang, Jianwei] Heilongjiang Inst Technol, Coll Comp Sci & Technol, Harbin 150050, Peoples R China.
RP Chen, DY (通讯作者)，Harbin Univ Sci & Technol, Coll Comp Sci & Technol, Harbin 150080, Peoples R China.
EM 1910400003@stu.hrbust.edu.cn; chendeyun@hrbust.edu.cn
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NR 32
TC 0
Z9 0
U1 6
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD MAY
PY 2022
VL 11
IS 9
AR 1327
DI 10.3390/electronics11091327
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 1E7DW
UT WOS:000794645400001
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, JH
   Zhang, LZ
   Hui, B
   Tian, L
AF Zhang, Jinhao
   Zhang, Lizong
   Hui, Bei
   Tian, Ling
TI Improving complex knowledge base question answering via structural
   information learning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge base; Knowledge graph; Question answering; Attention
AB Responding to complex questions is one of the most difficult and valuable goals of KBQA. Current efforts mainly follow two approaches to extract the in-depth semantics of questions. Information retrieval-based methods tend to encode questions directly and ignore the explicit analysis of the question structure. Besides, although retaining the analysis ability of question structure, semantic parsing-based methods rely on the expensive query graph labels and suffer from sparse reward due to wrong explorations. To benefit from both sides, this paper proposes a novel semantic parsing model, structural information restraint (SIR) for KBQA. SIR applies structural information of questions for reinforcement-based path reasoning for the first time. Specifically, SIR synthesizes the dependency tree, constituency tree, and the first token to build a composited structural attention (StrucAtt) and realizes reasoning without expensive query graphs labels. Such an attention mechanism improves the efficiency of path reasoning by distinguishing different knowledge paths, based on the relevance between path features and question structure. In addition, we also design a type-assisted reward based on answer concepts (person, location, etc.) instead of simple variable types (string, number, etc.), which alleviates the sparse reward problem effectively. Besides, the experiment results clearly show that our model achieves SOTA on CWQ, CQ, and WQSP datasets. (C) 2022 Elsevier B.V. All rights reserved.
C1 [Zhang, Jinhao; Zhang, Lizong; Tian, Ling] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Peoples R China.
   [Hui, Bei] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu, Peoples R China.
   [Zhang, Lizong] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610000, Sichuan, Peoples R China.
RP Zhang, LZ (通讯作者)，Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610000, Sichuan, Peoples R China.
EM l.zhang@uestc.edu.cn
OI Jinhao, Zhang/0000-0002-6229-8565
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NR 48
TC 0
Z9 0
U1 6
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD APR 22
PY 2022
VL 242
AR 108252
DI 10.1016/j.knosys.2022.108252
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V1XS
UT WOS:000788138900015
DA 2022-08-23
ER

PT J
AU Kuang, HL
   Chen, HR
   Ma, XL
   Liu, XH
AF Kuang, Hailan
   Chen, Haoran
   Ma, Xiaolin
   Liu, Xinhua
TI A Keyword Detection and Context Filtering Method for Document Level
   Relation Extraction
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE relation extraction; keyword detection; Self-Attention; ConvLSTM;
   context extraction; transformer
AB Relation extraction (RE) is the core link of downstream tasks, such as information retrieval, question answering systems, and knowledge graphs. Most of the current mainstream RE technologies focus on the sentence-level corpus, which has great limitations in practical applications. Moreover, the previously proposed models based on graph neural networks or transformers try to obtain context features from the global text, ignoring the importance of local features. In practice, the relation between entity pairs can usually be inferred just through a few keywords. This paper proposes a keyword detection and context filtering method based on the Self-Attention mechanism for document-level RE. In addition, a Self-Attention Memory (SAM) module in ConvLSTM is introduced to process the document context and capture keyword features. By searching for word embeddings with high cross-attention of entity pairs, we update and record critical local features to enhance the performance of the final classification model. The experimental results on three benchmark datasets (DocRED, CDR, and GBA) show that our model achieves advanced performance within open and specialized domain relationship extraction tasks, with up to 0.87% F1 value improvement compared to the state-of-the-art methods. We have also designed experiments to demonstrate that our model can achieve superior results by its stronger contextual filtering capability compared to other methods.
C1 [Kuang, Hailan; Chen, Haoran; Ma, Xiaolin; Liu, Xinhua] Wuhan Univ Technol, Sch Informat Engn, Hubei Key Lab Broadband Wireless Commun & Sensor, Wuhan 430070, Peoples R China.
RP Chen, HR; Ma, XL (通讯作者)，Wuhan Univ Technol, Sch Informat Engn, Hubei Key Lab Broadband Wireless Commun & Sensor, Wuhan 430070, Peoples R China.
EM kuanghailan@whut.edu.cn; harry_chen@whut.edu.cn;
   maxiaolin0615@whut.edu.cn; liuxinhua@whut.edu.cn
OI Ma, Xiaolin/0000-0001-7538-772X; liu, xinhua/0000-0003-4577-2250
FU National Natural Science Foundation of China [61772088]
FX FundingThis work was supported by the National Natural Science
   Foundation of China (No.61772088).
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NR 31
TC 1
Z9 1
U1 8
U2 8
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 2022
VL 12
IS 3
AR 1599
DI 10.3390/app12031599
PG 13
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA YZ2SD
UT WOS:000755330100001
OA gold
DA 2022-08-23
ER

PT J
AU Zhao, XY
   Chen, LW
   Chen, HH
AF Zhao, Xinyan
   Chen, Liangwei
   Chen, Huanhuan
TI A Weighted Heterogeneous Graph-Based Dialog System
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article; Early Access
DE Diseases; Medical diagnosis; Knowledge based systems; Task analysis;
   Natural languages; Medical diagnostic imaging; Learning systems; Deep
   reinforcement learning (RL); dialog system; graph neural network;
   knowledge; natural language process
AB Knowledge-based dialog systems have attracted increasing research interest in diverse applications. However, for disease diagnosis, the widely used knowledge graph (KG) is hard to represent the symptom-symptom and symptom-disease relations since the edges of traditional KG are unweighted. Most research on disease diagnosis dialog systems highly relies on data-driven methods and statistical features, lacking profound comprehension of symptom-symptom and symptom-disease relations. To tackle this issue, this work presents a weighted heterogeneous graph-based dialog system for disease diagnosis. Specifically, we build a weighted heterogeneous graph based on symptom co-occurrence and the proposed symptom frequency-inverse disease frequency. Then, this work proposes a graph-based deep Q-network (graph-DQN) for dialog management. By combining graph convolutional network (GCN) with DQN to learn the embeddings of diseases and symptoms from both the structural and attribute information in the weighted heterogeneous graph, graph-DQN could capture the symptom-disease relations and symptom-symptom relations better. Experimental results show that the proposed dialog system rivals the state-of-the-art models. More importantly, the proposed dialog system can complete the task with fewer dialog turns and possess a better distinguishing capability on diseases with similar symptoms.
C1 [Zhao, Xinyan; Chen, Liangwei] Univ Sci & Technol China, Sch Data Sci, Hefei 230027, Peoples R China.
   [Chen, Huanhuan] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Peoples R China.
RP Chen, HH (通讯作者)，Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Peoples R China.
EM sa516458@mail.ustc.edu.cn; clw2018@mail.ustc.edu.cn; hchen@ustc.edu.cn
FU NSFC [62176245, 62137002]; Fundamental Research Funds for the Central
   Universities; Anhui Province [202104a05020011, 202103a07020002]
FX This work was supported in part by NSFC under Grant 62176245 and Grant
   62137002, in part by the Fundamental Research Funds for the Central
   Universities, and in part by the Anhui Province under Grant
   202104a05020011 and Grant 202103a07020002.
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NR 29
TC 1
Z9 1
U1 5
U2 10
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.2021.3124640
EA NOV 2021
PG 6
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 XR7AV
UT WOS:000732378100001
PM 34793308
DA 2022-08-23
ER

PT J
AU Lu, YH
   Luo, JY
   Xiao, Y
   Zhu, H
AF Lu, Yonghe
   Luo, Jiayi
   Xiao, Ying
   Zhu, Hou
TI Text representation model of scientific papers based on fusing
   multi-viewpoint information and its quality assessment
SO SCIENTOMETRICS
LA English
DT Article
DE Scientific papers; Text representation; Multi-viewpoint information;
   Part-of-speech; Word meaning
AB Text representation is the preliminary work for in-depth analysis and mining of information in scientific papers. It directly affects the effects of downstream tasks such as, scientific papers classification, clustering, and similarity calculation. However, recent researches mainly considered citation network and partial structural information, which is insufficient when representing scientific papers. Therefore, in order to improve the performance of text representation model, this paper proposed MV-HATrans, a text representation model that combines multi-viewpoint information, such as the semantic information of knowledge graph and structural information. This model extracts word information from three aspects, including contextual content, part of speech, and word meaning of WordNet. Based on combination of hierarchical attention mechanism and transformer, the model achieves the full text representation of scientific papers. Finally, this paper uses the binary experimental dataset AAPR, which indicates whether scientific papers are accepted or not, and applies the proposed model of text representation to achieve the goal of automatic quality assessment. Results show that in the quality classification of scientific papers, adopting part-of-speech information and semantic information based on WordNet definitions can effectively achieve the accuracy of prediction as 70.14%. Among all the structural modules, authors and abstracts contributes the most to the quality classification of scientific papers, especially authors as 9.51%.
C1 [Lu, Yonghe; Luo, Jiayi; Xiao, Ying; Zhu, Hou] Sun Yat Sen Univ, Sch Informat Management, Guangzhou, Peoples R China.
RP Zhu, H (通讯作者)，Sun Yat Sen Univ, Sch Informat Management, Guangzhou, Peoples R China.
EM luyonghe@mail.sysu.edu.cn; luojy25@foxmail.com;
   xiaoy88@mail2.sysu.edu.cn; zhuhou3@mail.sysu.edu.cn
FU Basic and Applied Basic Research Fund of Guangdong Province
   [2019B1515120085]; National Natural Science Foundation of China
   [71373291]; Science and Technology Planning Project of Guangdong
   Province (China) [2016B030303003]
FX The authors warmly thank reviewers for their valuable suggestions. This
   research was partly supported by Basic and Applied Basic Research Fund
   of Guangdong Province (No. 2019B1515120085), National Natural Science
   Foundation of China [Grant Number: 71373291], and Science and Technology
   Planning Project of Guangdong Province (China) [Grant Number:
   2016B030303003].Jiayi Luo and Ying Xiao are the co second authors.
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NR 63
TC 1
Z9 1
U1 26
U2 48
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD AUG
PY 2021
VL 126
IS 8
BP 6937
EP 6963
DI 10.1007/s11192-021-04028-4
EA JUN 2021
PG 27
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 TM5GC
UT WOS:000664849500001
DA 2022-08-23
ER

PT J
AU Bin, CZ
   Gu, TL
   Jia, ZH
   Zhu, GM
   Xiao, CH
AF Bin, Chenzhong
   Gu, Tianlong
   Jia, Zhonghao
   Zhu, Guimin
   Xiao, Cihan
TI A neural multi-context modeling framework for personalized attraction
   recommendation
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Attraction recommendation; Multi-context modeling; Network
   representation learning; Trajectory mining
AB In attraction recommendation scenarios, how to model multifaceted tourism contexts so as to accurately learn tourist preferences and attraction tourism features is a keystone of generating personalized recommendations. However, most of existing works generally focused on modeling spatiotemporal contexts of historical travel trajectories to learn tourists' preferences, while neglected rich heterogeneous tourism side information, i.e., personal tourism constraints of tourists and tourism attributes of attractions. To this end, we propose a Neural Multi-context Modeling Framework (NMMF) to learn tourism feature representations of tourists and attractions by modeling multiple tourism contexts. Initially, we leverage a travel knowledge graph and massive original travelogues to construct the tourism attribute context of attractions and the travel trajectory context of tourists. Then, we design two context embedding models, named TKG2vec and Traj2vec, to model two kinds of context respectively. Both models learn feature vectors of tourist and attraction in contexts by elaborating neural networks to project each tourist and attraction into a uniform latent feature space. Finally, our framework integrates feature vectors derived from two models to acquire complete feature representations of tourists and attractions, and recommends personalized attractions by calculating the similarity between tourist and candidate attractions in the latent space. Experimental results on a real-world tourism dataset demonstrate our framework outperforms state-of-the-art methods in two personalized attraction recommendation tasks.
C1 [Bin, Chenzhong] Guilin Univ Elect Technol, Sch Informat & Commun, Guilin 541004, Peoples R China.
   [Bin, Chenzhong; Gu, Tianlong; Jia, Zhonghao; Zhu, Guimin] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China.
   [Xiao, Cihan] Virginia Polytech Inst & State Univ, Sch Comp Sci, Blacksburg, VA 24061 USA.
RP Bin, CZ (通讯作者)，Guilin Univ Elect Technol, Sch Informat & Commun, Guilin 541004, Peoples R China.; Bin, CZ (通讯作者)，Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China.
EM binchenzhong@guet.edu.cn; cctlgu@guet.edu.cn; 1090994959@qq.com;
   18770914075@163.com; borrisonxiao@gmail.com
OI Bin, Chenzhong/0000-0002-7200-0929; Gu, Tianlong/0000-0002-1662-7110
CR [Anonymous], 2013, P ICLR
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NR 45
TC 3
Z9 3
U1 12
U2 28
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 JUN
PY 2020
VL 79
IS 21-22
BP 14951
EP 14979
DI 10.1007/s11042-019-08554-5
PG 29
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 LV8HQ
UT WOS:000538675900036
DA 2022-08-23
ER

PT J
AU Wang, HW
   Leskovec, J
AF Wang, Hongwei
   Leskovec, Jure
TI Combining Graph Convolutional Neural Networks and Label Propagation
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE Graph convolutional neural networks; label propagation algorithm;
   semi-supervised learning
AB Label Propagation Algorithm (LPA) and Graph Convolutional Neural Networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification, but LPA propagates node label information across the edges of the graph, while GCN propagates and transforms node feature information. However, while conceptually similar, theoretical relationship between LPA and GCN has not yet been systematically investigated. Moreover, it is unclear how LPA and GCN can be combined under a unified framework to improve the performance. Here we study the relationship between LPA and GCN in terms of feature/label Influence, in which we characterize how much the initial feature/label of one node influences the final feature/label of another node in GCN/LPA. Based on our theoretical analysis, we propose an end-to-end model that combines GCN and LPA. In our unified model, edge weights are learnable, and the LPA serves as regularization to assist the GCN in learning proper edge weights that lead to improved performance. Our model can also be seen as learning the weights of edges based on node labels, which is more direct and efficient than existing feature-based attention models or topology-based diffusion models. In a number of experiments for semi-supervised node classification and knowledge-graph-aware recommendation, our model shows superiority over state-of-the-art baselines.
C1 [Wang, Hongwei; Leskovec, Jure] Stanford Univ, Comp Sci Dept, Stanford, CA 94305 USA.
RP Wang, HW (通讯作者)，Stanford Univ, Comp Sci Dept, Stanford, CA 94305 USA.
EM hongweiw@cs.stanford.edu; jure@cs.stanford.edu
FU DARPA; ARO; NSF; NIH; Stanford Data Science Initiative; Wu Tsai
   Neurosciences Institute; Chan Zuckerberg Biohub; Amazon; JPMorgan Chase;
   Docomo; Hitachi; Intel; JD.com; KDDI; NVIDIA; Dell; Toshiba; Visa;
   UnitedHealth Group
FX This research has been supported in part by DARPA, ARO, NSF, NIH,
   Stanford Data Science Initiative, Wu Tsai Neurosciences Institute, Chan
   Zuckerberg Biohub, Amazon, JPMorgan Chase, Docomo, Hitachi, Intel,
   JD.com, KDDI, NVIDIA, Dell, Toshiba, Visa, and UnitedHealth Group.
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NR 49
TC 0
Z9 0
U1 4
U2 4
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 OCT
PY 2022
VL 40
IS 4
AR 73
DI 10.1145/3490478
PG 27
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1H7SA
UT WOS:000796738200010
DA 2022-08-23
ER

PT J
AU Zangeneh, P
   McCabe, B
AF Zangeneh, Pouya
   McCabe, Brenda
TI Ontology-based knowledge representation for industrial megaprojects
   analytics using linked data and the semantic web
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Industrial megaprojects; Knowledge representation; Project analytics;
   Risk analysis; Ontology; Semantic web
ID MODEL VIEWS; INFORMATION; RISK; MANAGEMENT; INFRASTRUCTURE;
   TECHNOLOGIES; PROJECTS; EXCHANGE; EXPRESS; IFCOWL
AB The fourth industrial revolution has affected most industries, including construction and those within the delivery chain of megaprojects. These major paradigm shifts, however, did not considerably improve the track record in predicting project outcomes and estimating required resources. One reason is the lack of unified data definitions and expandable knowledge representation across project lifecycle to represent megaprojects for analytics. This paper proposes and evaluates a unified ontology for project knowledge representation that facilitates data collection, processing, and utilization for industrial megaprojects through their lifecycle. The proposed Uniform Project Ontology, or UPonto, provides a data infrastructure for project analytics by enabling logical deductions and inferences, and flexible expansion and partitioning of the data utilizing linked data and the semantic web. The ontology facilitates cost normalization processes, temporal queries, and graph queries using SPARQL, while defining universal semantics for a wide range of project risk factors and characteristics based on comprehensive research of the empirical project risk and success literature augmented by practical considerations gained through expert consultations. UPonto forms the basis for a project knowledge graph to utilize unstructured data; it as well provides semantic definitions for smart IoT agents to consume project risk data and knowledge.
C1 [Zangeneh, Pouya; McCabe, Brenda] Univ Toronto, Dept Civil & Mineral Engn, 35 St George St,Room 107, Toronto, ON M5S 1A4, Canada.
RP Zangeneh, P (通讯作者)，Univ Toronto, Dept Civil & Mineral Engn, 35 St George St,Room 107, Toronto, ON M5S 1A4, Canada.
EM p.zangeneh@mail.utoronto.ca; brenda.mccabe@utoronto.ca
OI Zangeneh, Pouya/0000-0001-5074-302X
FU Hatch Ltd.; Natural Sciences and Engineering Research Council (NSERC) of
   Canada [CRDPH 491877-15]
FX We benefited from the comments of Murray Pearson, Nick Mason, and
   several other project experts at Hatch Ltd. in defining and collecting
   project success factors, risk factors, and characteristics. The project
   is funded by Hatch Ltd. and Natural Sciences and Engineering Research
   Council (NSERC) of Canada grant CRDPH 491877-15.
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NR 133
TC 7
Z9 7
U1 9
U2 26
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 2020
VL 46
AR 101164
DI 10.1016/j.aei.2020.101164
PG 18
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA PA9FZ
UT WOS:000595934400003
DA 2022-08-23
ER

PT J
AU Weissenborn, D
   Schroeder, M
   Tsatsaronis, G
AF Weissenborn, Dirk
   Schroeder, Michael
   Tsatsaronis, George
TI Discovering relations between indirectly connected biomedical concepts
SO JOURNAL OF BIOMEDICAL SEMANTICS
LA English
DT Article
DE Relation discovery; Biomedical concepts; Text mining
AB Background: The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation.
   Results: It is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely "has target", and "may treat", are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach.
   Conclusions: Analysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.
C1 [Weissenborn, Dirk] DFKI Projektburo Berlin, D-10559 Berlin, Germany.
   [Weissenborn, Dirk; Schroeder, Michael; Tsatsaronis, George] Tech Univ Dresden, Biotechnol Ctr, D-01307 Dresden, Germany.
RP Weissenborn, D (通讯作者)，DFKI Projektburo Berlin, Alt Moabit 91c, D-10559 Berlin, Germany.
EM dirk.weissenborn@dfki.de
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NR 27
TC 9
Z9 9
U1 0
U2 10
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 JUL 6
PY 2015
VL 6
AR 28
DI 10.1186/s13326-015-0021-5
PG 19
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA CW2FU
UT WOS:000364807300001
PM 26150906
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Starzyk, JA
   Maciura, L
   Horzyk, A
AF Starzyk, Janusz A.
   Maciura, Lukasz
   Horzyk, Adrian
TI Associative Memories With Synaptic Delays
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article
DE Neurons; Logic gates; Delays; Organizations; Biological neural networks;
   Memory management; Delay effects; Associative knowledge graphs;
   associative memories; synaptic delays; synaptic efficacy
ID SEQUENCES
AB In this paper, we introduce a new concept of associative memories in which synaptic connections of the self-organizing neural network learn time delays between input sequence elements. Synaptic connections represent both the synaptic weights and expected delays between the network inputs. This property of synaptic connections facilitates recognition of time sequences and provides context-based associations between sequence elements. Characteristics of time delays are learned and are updated each time an input sequence is presented. There are no separate learning and testing modes typically used in other neural networks, as the network starts to predict the next input element as soon as there is no expected input signal. The network generates output signals useful for associative recall and prediction. These output signals depend on the presented input context and the knowledge stored in the graph. Such a mode of operation is preferred for the organization of episodic memories used to store the observed episodes and to recall them if a sufficient context is provided. The associative sequential recall is useful for the operation of working memory in a cognitive agent. Test results demonstrate that the network correctly recognizes the input sequences with variable delays and that it is more efficient than other recently developed sequential memory networks based on associative neurons.
C1 [Starzyk, Janusz A.] Ohio Univ, Sch Elect Engn & Comp Sci, Athens, OH 45701 USA.
   [Maciura, Lukasz] Univ Informat Technol & Management, Dept Appl Informat Syst, PL-35225 Rzeszow, Poland.
   [Horzyk, Adrian] AGH Univ Sci & Technol, Dept Biocybernet & Biomed Engn, PL-30059 Krakow, Poland.
RP Horzyk, A (通讯作者)，AGH Univ Sci & Technol, Dept Biocybernet & Biomed Engn, PL-30059 Krakow, Poland.
RI ; Horzyk, Adrian/C-6661-2013
OI Maciura, Lukasz/0000-0001-8657-3472; Horzyk, Adrian/0000-0001-9001-4198
FU National Science Centre, Poland [DEC-2016/21/B/ST7/02220, AGH
   11.11.120.612]
FX This work was supported by the National Science Centre, Poland, under
   Grant DEC-2016/21/B/ST7/02220 and Grant AGH 11.11.120.612.
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NR 28
TC 5
Z9 5
U1 4
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
PD JAN
PY 2020
VL 31
IS 1
BP 331
EP 344
DI 10.1109/TNNLS.2019.2921143
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 KB6NH
UT WOS:000506609000028
PM 31295124
DA 2022-08-23
ER

PT J
AU Hu, S
   Zou, L
   Yu, JX
   Wang, HX
   Zhao, DY
AF Hu, Sen
   Zou, Lei
   Yu, Jeffrey Xu
   Wang, Haixun
   Zhao, Dongyan
TI Answering Natural Language Questions by Subgraph Matching over Knowledge
   Graphs
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE RDF; graph database; question answering
AB RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a natural language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. More specifically, we propose two different frameworks to build the semantic query graph, one is relation (edge)-first and the other one is node-first. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.
C1 [Hu, Sen] Peking Univ, Beijing 100080, Peoples R China.
   [Zou, Lei] Peking Univ, Big Data Ctr, Beijing 100080, Peoples R China.
   [Zhao, Dongyan] Peking Univ, Inst Comp Sci & Technol, Beijing 100080, Peoples R China.
   [Zou, Lei] Beijing Inst Big Data Res, Beijing, Peoples R China.
   [Yu, Jeffrey Xu] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China.
   [Wang, Haixun] Facebook, Menlo Pk, CA 94025 USA.
RP Zou, L (通讯作者)，Peking Univ, Big Data Ctr, Beijing 100080, Peoples R China.; Zou, L (通讯作者)，Beijing Inst Big Data Res, Beijing, Peoples R China.
EM husen@pku.edu.cn; zoulei@pku.edu.cn; yu@se.cuhk.edu.hk;
   haixun@google.com; zhaody@pku.edu.cn
RI Zhou, Xiaofang/C-6169-2013
OI Zhou, Xiaofang/0000-0001-6343-1455; Hu, Sen/0000-0002-4201-5919
FU National Key Research and Development Program of China [2016YFB1000603];
   NSFC [61622201, 61532010, 61370055]; Research Grants Council of the Hong
   Kong SAR, China [14221716, 12258116]
FX This work was supported by The National Key Research and Development
   Program of China under grant 2016YFB1000603 and NSFC under grants
   61622201, 61532010, and 61370055. Jeffrey Xu Yu was supported by the
   Research Grants Council of the Hong Kong SAR, China No. 14221716 and No.
   12258116.
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NR 32
TC 90
Z9 91
U1 4
U2 89
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
PY 2018
VL 30
IS 5
BP 824
EP 837
DI 10.1109/TKDE.2017.2766634
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 GB4KH
UT WOS:000429028500002
DA 2022-08-23
ER

PT J
AU Ma, Y
   Xie, Z
   Li, G
   Ma, K
   Huang, Z
   Qiu, QJ
   Liu, H
AF Ma, Ying
   Xie, Zhong
   Li, Gang
   Ma, Kai
   Huang, Zhen
   Qiu, Qinjun
   Liu, Hui
TI Text visualization for geological hazard documents via text mining and
   natural language processing
SO EARTH SCIENCE INFORMATICS
LA English
DT Article
DE Geological disaster report; Text mining; Natural language processing;
   Text visualization analysis
ID VISUAL ANALYTICS; CONSTRUCTION; KNOWLEDGE; EXTRACTION; IDENTIFICATION;
   INTEGRATION; ALGORITHMS
AB An increasing number of geological hazard documents about the mechanism and occurrence process of geological disasters contain unstructured geoscientific data that are not fully utilized. Text mining and visualization techniques offer opportunities to leverage this wealth of data and extract valuable information from dense, abstract geological disaster reports to quickly focus on the core information in geological reports and improve the efficiency of report usage. In this research, a flow framework for the automatic extraction of key information and its transformation to a simple and intuitive form for managers/researchers to quickly navigate, understand and make more informed decisions based on the key information are described. To automatically extract key information from text, an optimized term frequency-inverse document frequency algorithm is proposed to analyze text characteristics. The important information extracted from a case study document is demonstrated using a word cloud. Co-occurrence network analysis is used to present key content from geological reports and describe the correlations between words. We use the dependency grammar technique to extract triads of geological report text information and we visualize them using knowledge graphs. The results show that text visualization analysis can be used to identify the types and locations of geological disasters in reports, highlight key information from survey reports as an auxiliary public resource, and more rapidly analyze the key contents of a large number of geological disaster survey reports.
C1 [Ma, Ying; Xie, Zhong; Huang, Zhen; Qiu, Qinjun] Natl Engn Res Ctr Geog Informat Syst, Wuhan 430074, Peoples R China.
   [Xie, Zhong; Qiu, Qinjun] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China.
   [Li, Gang] Jinan Rail Transit Grp Co Ltd, Jinan 250000, Peoples R China.
   [Ma, Kai] China Three Gorges Univ, Coll Comp & Informat Technol, Yichang 443002, Peoples R China.
   [Liu, Hui] Wuhan Zondy Cyber Sci Sr Technol Co Ltd, Wuhan 430074, Peoples R China.
RP Qiu, QJ (通讯作者)，Natl Engn Res Ctr Geog Informat Syst, Wuhan 430074, Peoples R China.; Qiu, QJ (通讯作者)，China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China.
EM qiuqinjun@cug.edu.cn
FU National Natural Science Foundation of China [42050101, U1711267,
   41871311, 41871305]; National Key Research and Development Program
   [2018YFB0505500, 2018YFB0505504]; Fundamental Research Funds for the
   Central Universities, China University of Geosciences (Wuhan)
   [CUG2106116]
FX This study was financially supported by the National Natural Science
   Foundation of China (42050101, U1711267, 41871311, 41871305), National
   Key Research and Development Program (2018YFB0505500, 2018YFB0505504)
   and the Fundamental Research Funds for the Central Universities, China
   University of Geosciences (Wuhan) (No. CUG2106116)).
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NR 53
TC 0
Z9 0
U1 10
U2 11
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1865-0473
EI 1865-0481
J9 EARTH SCI INFORM
JI Earth Sci. Inform.
PD MAR
PY 2022
VL 15
IS 1
BP 439
EP 454
DI 10.1007/s12145-021-00732-0
EA JAN 2022
PG 16
WC Computer Science, Interdisciplinary Applications; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Geology
GA ZC5BI
UT WOS:000741972400001
DA 2022-08-23
ER

PT J
AU Skrlj, B
   Martinc, M
   Lavrac, N
   Pollak, S
AF Skrlj, Blaz
   Martinc, Matej
   Lavrac, Nada
   Pollak, Senja
TI autoBOT: evolving neuro-symbolic representations for explainable low
   resource text classification
SO MACHINE LEARNING
LA English
DT Article
DE Representation learning; Natural language processing; AutoML;
   Neuro-symbolic computing
AB Learning from texts has been widely adopted throughout industry and science. While state-of-the-art neural language models have shown very promising results for text classification, they are expensive to (pre-)train, require large amounts of data and tuning of hundreds of millions or more parameters. This paper explores how automatically evolved text representations can serve as a basis for explainable, low-resource branch of models with competitive performance that are subject to automated hyperparameter tuning. We present autoBOT (automatic Bags-Of-Tokens), an autoML approach suitable for low resource learning scenarios, where both the hardware and the amount of data required for training are limited. The proposed approach consists of an evolutionary algorithm that jointly optimizes various sparse representations of a given text (including word, subword, POS tag, keyword-based, knowledge graph-based and relational features) and two types of document embeddings (non-sparse representations). The key idea of autoBOT is that, instead of evolving at the learner level, evolution is conducted at the representation level. The proposed method offers competitive classification performance on fourteen real-world classification tasks when compared against a competitive autoML approach that evolves ensemble models, as well as state-of-the-art neural language models such as BERT and RoBERTa. Moreover, the approach is explainable, as the importance of the parts of the input space is part of the final solution yielded by the proposed optimization procedure, offering potential for meta-transfer learning.
C1 [Skrlj, Blaz; Martinc, Matej; Lavrac, Nada; Pollak, Senja] Jozef Stefan Inst, Jamova 39, Ljubljana 1000, Slovenia.
   [Skrlj, Blaz; Martinc, Matej] Jozef Stefan Int Postgrad Sch, Jamova 39, Ljubljana 1000, Slovenia.
   [Lavrac, Nada] Univ Nova Gorica, Glavni Trg 8, Vipava 5271, Slovenia.
RP Skrlj, B (通讯作者)，Jozef Stefan Inst, Jamova 39, Ljubljana 1000, Slovenia.; Skrlj, B (通讯作者)，Jozef Stefan Int Postgrad Sch, Jamova 39, Ljubljana 1000, Slovenia.
EM blaz.skrlj@ijs.si
OI Lavrac, Nada/0000-0002-9995-7093; Skrlj, Blaz/0000-0002-9916-8756
FU Slovenian Research Agency (ARRS) [P2-0103, N2-0078]; European Union
   [825153]; TAILOR (EU Horizon 2020 research and innovation programme)
   [952215]; AI4EU [825619]; Slovenian Research Agency
FX The work of the first author was funded by the Slovenian Research Agency
   through a young researcher grant. The work of other authors was
   supported by the Slovenian Research Agency (ARRS) core research
   programme Knowledge Technologies (P2-0103), an ARRS funded research
   project Semantic Data Mining for Linked Open Data (financed under the
   ERC Complementary Scheme, N2-0078) and European Union's Horizon 2020
   research and innovation programme under grant agreement No 825153,
   project EMBEDDIA (Cross-Lingual Embeddings for Less-Represented
   Languages in European News Media). We also gratefully acknowledge the
   support of NVIDIA Corporation for the donation of Titan-XP GPU. This
   research was also partially supported by TAILOR (a project funded by the
   EU Horizon 2020 research and innovation programme under GA No 952215)
   and AI4EU (GA No 825619). We would also like to thank the reviewers for
   their valuable comments.
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NR 76
TC 2
Z9 2
U1 0
U2 1
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0885-6125
EI 1573-0565
J9 MACH LEARN
JI Mach. Learn.
PD MAY
PY 2021
VL 110
IS 5
BP 989
EP 1028
DI 10.1007/s10994-021-05968-x
EA APR 2021
PG 40
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SG9VK
UT WOS:000640146200005
PM 34720391
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Han, HH
   Wang, J
   Wang, XW
AF Han, Huihui
   Wang, Jian
   Wang, Xiaowen
TI A Relation-Oriented Model With Global Context Information for Joint
   Extraction of Overlapping Relations and Entities
SO FRONTIERS IN NEUROROBOTICS
LA English
DT Article
DE joint extraction of entities and relations; multi-label classification;
   relation extraction; entity recognition; overlapping triples
ID RECOGNITION; TEXT
AB The entity relation extraction in the form of triples from unstructured text is a key step for self-learning knowledge graph construction. Two main methods have been proposed to extract relation triples, namely, the pipeline method and the joint learning approach. However, these models do not deal with the overlapping relation problem well. To overcome this challenge, we present a relation-oriented model with global context information for joint entity relation extraction, namely, ROMGCJE, which is an encoder-decoder model. The encoder layer aims to build long-term dependencies among words and capture rich global context representation. Besides, the relation-aware attention mechanism is applied to make use of the relation information to guide the entity detection. The decoder part consists of a multi-relation classifier for the relation classification task, and an improved long short-term memory for the entity recognition task. Finally, the minimum risk training mechanism is introduced to jointly train the model to generate final relation triples. Comprehensive experiments conducted on two public datasets, NYT and WebNLG, show that our model can effectively extract overlapping relation triples and outperforms the current state-of-the-art methods.
C1 [Han, Huihui; Wang, Jian] Tongji Univ, Coll Elect & Informat Engn, Country Comp Integrated Mfg Syst Res Ctr, Shanghai, Peoples R China.
   [Wang, Xiaowen] Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen, Peoples R China.
RP Han, HH (通讯作者)，Tongji Univ, Coll Elect & Informat Engn, Country Comp Integrated Mfg Syst Res Ctr, Shanghai, Peoples R China.; Wang, XW (通讯作者)，Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen, Peoples R China.
EM hanhuihui@tongji.edu.cn; wangxw20@mails.tsinghua.edu.cn
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NR 43
TC 0
Z9 0
U1 4
U2 4
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 JUL 4
PY 2022
VL 16
AR 914705
DI 10.3389/fnbot.2022.914705
PG 18
WC Computer Science, Artificial Intelligence; Robotics; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Robotics; Neurosciences & Neurology
GA 3C1TX
UT WOS:000828414100001
PM 35859657
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Popa, A
   Gonzalez, APR
   Jaglan, G
   Fensel, A
AF Popa, Angela
   Gonzalez, Alfonso P. Ramallo
   Jaglan, Gaurav
   Fensel, Anna
TI A Semantically Data-Driven Classification Framework for Energy
   Consumption in Buildings
SO ENERGIES
LA English
DT Article
DE near-zero energy buildings; energy efficiency; semantic technology;
   knowledge graphs; energy performance certificates; energy performance
   certification
AB Encouraged by the European Union, all European countries need to enforce solutions to reduce non-renewable energy consumption in buildings. The reduction of energy (heating, domestic hot water, and appliances consumption) aims for the vision of near-zero energy consumption as a requirement goal for constructing buildings. In this paper, we review the available standards, tools and frameworks on the energy performance of buildings. Additionally, this work investigates if energy performance ratings can be obtained with energy consumption data from IoT devices and if the floor size and energy consumption values are enough to determine a dwellings' energy performance rating. The essential outcome of this work is a data-driven prediction tool for energy performance labels that can run automatically. The tool is based on the cutting edge kNN classification algorithm and trained on open datasets with actual building data such as those coming from the IoT paradigm. Additionally, it assesses the results of the prediction by analysing its accuracy values. Furthermore, an approach to semantic annotations for energy performance certification data with currently available ontologies is presented. Use cases for an extension of this work are also discussed in the end.
C1 [Popa, Angela; Jaglan, Gaurav; Fensel, Anna] Univ Innsbruck, STI Semant Technol Inst Innsbruck, Dept Comp Sci, A-6020 Innsbruck, Austria.
   [Gonzalez, Alfonso P. Ramallo] Univ Murcia, Fac Informat, Murcia 30100, Spain.
   [Fensel, Anna] Wageningen Univ & Res, Wageningen Data Competence Ctr WDCC, NL-6708 PB Wageningen, Netherlands.
   [Fensel, Anna] Wageningen Univ & Res, Consumpt & Hlth Lifestyles Grp, NL-6708 PB Wageningen, Netherlands.
RP Gonzalez, APR (通讯作者)，Univ Murcia, Fac Informat, Murcia 30100, Spain.
EM popa.angela@gmail.com; alfonsop.ramallo@um.es; jaglan.gaurav@gmail.com;
   anna.fensel@sti2.at
OI Fensel, Anna/0000-0002-1391-7104
FU ERDF funds [EQC2019-006176-P]; Horizon 2020 Project PHOENIX [893079];
   project ONOFRE of the Ministry of Science and Innovation of the
   Government of Spain [PID2020-112675RB-C44]
FX UMU-CAMPUS LIVING LAB EQC2019-006176-P funded by ERDF funds, Horizon
   2020 Project PHOENIX (grant number 893079) and project ONOFRE of the
   Ministry of Science and Innovation of the Government of Spain, with
   code: PID2020-112675RB-C44.
CR [Anonymous], 2013, THESIS U EXETER STOC
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NR 32
TC 0
Z9 0
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1073
J9 ENERGIES
JI Energies
PD MAY
PY 2022
VL 15
IS 9
AR 3155
DI 10.3390/en15093155
PG 22
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA 1L5KI
UT WOS:000799326500001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Liu, SM
   Lu, YQ
   Li, J
   Song, DQ
   Sun, XM
   Bao, JS
AF Liu, Shimin
   Lu, Yuqian
   Li, Jie
   Song, Dengqiang
   Sun, Xuemin
   Bao, Jinsong
TI Multi-scale evolution mechanism and knowledge construction of a digital
   twin mimic model
SO ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
LA English
DT Article
DE Digital twin; Model evolution; Knowledge graph; Quality control
ID COMPENSATION; STRATEGY
AB Metal products are susceptible to factors such as cutting force, clamping force and heat in the machining process, resulting in product quality problems, such as geometric deformation and surface defects. The real-time observation and control of product quality are integral to optimizing machining process. Digital twin technologies can be used to monitor and control the quality of products via multi-scale based quality analysis. However, previous research on digital twin lacks a fine-grained expression and generation method for product multi-scale quality, making it impossible to carry out an in-depth analysis of product quality. Aiming at addressing this challenge, we study the multi-scale evolution mechanism of the digital twin model and explore the knowledge generation method of the digital twin data. The proposed method constructed the digital twin quality knowledge model from the macro, meso, and micro levels by utilizing the data of the digital twin mimic model. These multi-scale quality knowledge models can express product quality in a fine-grained way and provide data support for digital twin-based decision-making. Finally, we tested the method in monitoring and controlling the machining quality of an air rudder to verify the feasibility of the proposed method.
C1 [Liu, Shimin; Li, Jie; Song, Dengqiang; Sun, Xuemin; Bao, Jinsong] Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China.
   [Lu, Yuqian] Univ Auckland, Dept Mech Engn, Auckland 1010, New Zealand.
RP Bao, JS (通讯作者)，Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China.
EM bao@dhu.edu.cn
RI Lu, Yuqian/U-6820-2019
OI Lu, Yuqian/0000-0001-5954-0421
FU National Key Research and Development Plan of China [2019YFB1706301];
   Fundamental Research Funds for the Central Universities [2232019D332,
   CUSFDHD2020056]; Graduate Student Innovation Fund of Donghua University
   [CUSFDHD2020056]; Shanghai Sailing Program [19YF1401600]
FX This work is financially supported in part by National Key Research and
   Development Plan of China (Grant 2019YFB1706301) , in part by the
   Fundamental Research Funds for the Central Universities and Graduate
   Student Innovation Fund of Donghua University (Grant No. CUSFDHD2020056)
   , in part by Fundamental Research Funds for the Central Universities
   (No. 2232019D332) , and in part by Shanghai Sailing Program
   (19YF1401600) .
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NR 46
TC 9
Z9 10
U1 38
U2 115
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0736-5845
EI 1879-2537
J9 ROBOT CIM-INT MANUF
JI Robot. Comput.-Integr. Manuf.
PD OCT
PY 2021
VL 71
AR 102123
DI 10.1016/j.rcim.2021.102123
EA FEB 2021
PG 17
WC Computer Science, Interdisciplinary Applications; Engineering,
   Manufacturing; Robotics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Robotics
GA SU7UM
UT WOS:000663337500002
DA 2022-08-23
ER

PT J
AU Zhu, RC
   Hu, XF
   Bai, YP
   Li, X
AF Zhu, Rongchen
   Hu, Xiaofeng
   Bai, Yiping
   Li, Xin
TI Risk analysis of terrorist attacks on LNG storage tanks at ports
SO SAFETY SCIENCE
LA English
DT Article
DE port LNG storage tanks; Terrorist attack; Risk analysis; Bayesian
   network
ID DECISION-MAKING APPROACH; NETWORK-BASED APPROACH; NATURAL-GAS;
   VULNERABILITY ASSESSMENT; SECURITY ASSESSMENT; PROBABILISTIC RISK;
   SAFETY; FRAMEWORK; MANAGEMENT; FACILITIES
AB In recent years, with the increasing demand for liquefied natural gas (LNG) worldwide, a large amount of LNG is stored, processed, and transported in ports. The port's LNG storage tanks may become a hot target for terrorist attacks because the failure or destruction of these facilities will put markets, cities, and the environment at high risk. This article presents a new risk analysis method of terrorist attacks on the port's LNG Storage tanks. The steps are as follows: (1) Identify risk factors from a multidimensional perspective. (2) Analyze the risk using the Bayesian network and event tree. (3) Evaluate and quantify the accident consequence. (4) Use knowledge graph to store risk knowledge. We verify the effectiveness of the method through case studies. The defence capabilities of the police and different prevention strategies were studied. The results show significant differences in accident consequence between different defensive and emergency response forces. We also conducted nine scenario analysis, which could provide a theoretical basis and method support for public security and urban risk management departments' security prevention decisions.
C1 [Zhu, Rongchen; Hu, Xiaofeng; Li, Xin] Peoples Publ Secur Univ China, Sch Informat Technol & Network Secur, Beijing 100038, Peoples R China.
   [Hu, Xiaofeng] Peoples Publ Secur Univ China, Publ Secur Behav Sci Lab, Beijing 100038, Peoples R China.
   [Bai, Yiping] China Univ Min & Technol, Sch Emergency Management & Safety Engn, Beijing 100083, Peoples R China.
RP Hu, XF (通讯作者)，Peoples Publ Secur Univ China, 1 Muxidinanli, Beijing 100038, Peoples R China.
EM zhurongchen@stu.ppsuc.edu.cn; huxiaofeng@ppsuc.edu.cn;
   1410120101@student.cumtb.edu.cn; lixin@ppsuc.edu.cn
OI Bai, Yiping/0000-0001-6699-4827
FU National Natural Science Foundation of China [71704183]
FX research was funded by the National Natural Science Foundation of China
   (Grant No. 71704183).
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NR 88
TC 5
Z9 5
U1 15
U2 33
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-7535
EI 1879-1042
J9 SAFETY SCI
JI Saf. Sci.
PD MAY
PY 2021
VL 137
AR 105192
DI 10.1016/j.ssci.2021.105192
EA FEB 2021
PG 15
WC Engineering, Industrial; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Operations Research & Management Science
GA RZ8VV
UT WOS:000648878700017
DA 2022-08-23
ER

PT J
AU Chen, S
   Xie, SY
   Chen, QQ
AF Chen, Samuel
   Xie, Shengyi
   Chen, Qingqiang
TI Integrated Embedding Approach for Knowledge Base Completion with CNN
SO INFORMATION TECHNOLOGY AND CONTROL
LA English
DT Article
DE Knowledge Representation; Knowledge Base Completion; CNN; Translation
   Mechanism; Link Prediction
ID PERSONALIZATION
AB In knowledge graph embedding, sophisticated models may suffer from over-fitting and high computational costs. On the contrary, transitional models come with lower complexity but struggle with complex relations while integrating relational attributes or semantic information could help with embedding representation. Convolutional neural networks employed in recent researches are able to model interactions between entities and relations efficiently but may ignore global dependencies. To tackle such problems, a model called Integrated Embedding Approach for Knowledge Base Completion (IEAKBC) is proposed. In this model, embedding representations of entities and relations are put together to constitute a three-column, k dimensional matrix for each triplet. Afterwards, features from different relations are integrated into head and tail entities thus forming fused triplet matrices. Both sets of matrices are used as inputs to a convolutional neural network (CNN) framework. In CNN, kernels go over each row of the matrices for feature extraction. Feature maps are subsequently concatenated and weighted for output scores to discern whether the original triplet holds or not. Experiments on four benchmark datasets show that our model performs well on complex relations while retaining transitional characteristics. Finally, we apply the model to a personalized search application, verifying its practicality in real-world scenarios.
C1 [Chen, Samuel] Melbourne Polytech, Fac Informat Technol, Preston, Vic 3072, Australia.
   [Xie, Shengyi] Fujian Vocat Coll Agr, Expt Training Ctr, Fuzhou 350181, Fujian, Peoples R China.
   [Chen, Qingqiang] Fujian Univ Technol, Informat Sci & Engn Coll, Fuzhou 350118, Fujian, Peoples R China.
RP Chen, QQ (通讯作者)，Fujian Univ Technol, Informat Sci & Engn Coll, Fuzhou 350118, Fujian, Peoples R China.
EM samuelchen@melbournepolytechnic.edu.au; fjnyxsy@qq.com; 516040610@qq.com
FU Fujian Provincial Educational and Scientific Project [FJJKCG20-402]
FX This paper is sponsored by Fujian Provincial Educational and Scientific
   Project(FJJKCG20-402)
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NR 81
TC 0
Z9 0
U1 1
U2 3
PU KAUNAS UNIV TECHNOLOGY
PI KAUNAS
PA KAUNAS UNIV TECHNOL, DEPT ELECTRONICS ENGINEERING, STUDENTU STR 50,
   KAUNAS, LT-51368, LITHUANIA
SN 1392-124X
J9 INF TECHNOL CONTROL
JI Inf. Technol. Control
PY 2020
VL 49
IS 4
BP 622
EP 642
DI 10.5755/j01.itc.49.4.25366
PG 21
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA WK2PD
UT WOS:000709572200012
OA gold
DA 2022-08-23
ER

PT J
AU Le-Phuoc, D
   Quoc, HNM
   Quoc, HN
   Nhat, TT
   Hauswirth, M
AF Danh Le-Phuoc
   Hoan Nguyen Mau Quoc
   Hung Ngo Quoc
   Tuan Tran Nhat
   Hauswirth, Manfred
TI The Graph of Things: A step towards the Live Knowledge Graph of
   connected things
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Internet of Things; Graph of Things; Linked Stream Data; Real-time
   search engine
AB The Internet of Things (IoT) with billions of connected devices has been generating an enormous amount of data every hour. Connecting every data item generated by IoT to the rest of the digital world to turn this data into meaningful actions will create new capabilities, richer experiences, and unprecedented economic opportunities for businesses, individuals, and countries. However, providing an integrated view for exploring and querying such data at real-time is extremely challenging due to its Big Data natures: big volume, fast real-time update and messy data sources. To address this challenge, we provide a unified integrated and live view for heterogeneous IoT data sources using Linked Data, called the Graph of Things (GoT). GoT is backed by a scalable and elastic software stack to deal with billions of records of historical and static datasets in conjunction with millions of triples being fetched and enriched to connect to GoT per hour in real time. GoT makes approximately a half of million stream data sources queryable via a SPARQL endpoint and a continuous query channel that enable us to create a live explorer of GoT (http://graphofthings.org/) with just HTML and Javascript. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Danh Le-Phuoc; Hoan Nguyen Mau Quoc; Hung Ngo Quoc; Tuan Tran Nhat] Natl Univ Ireland, Insight Ctr Data Analyt, Galway, Ireland.
   [Hauswirth, Manfred] Tech Univ Berlin, Inst Telekommunikat Syst, Berlin, Germany.
RP Le-Phuoc, D (通讯作者)，Natl Univ Ireland, Insight Ctr Data Analyt, Galway, Ireland.
EM danh@danhlephuoc.info
RI Ngo, Hung Quoc/ABH-4892-2020; Le Phuoc, Danh/AAL-9278-2021
OI Ngo, Hung Quoc/0000-0001-8246-8392; Le Phuoc, Danh/0000-0003-2480-9261
FU Irish Research Council [GOIPD/2013/104]; Science Foundation Ireland
   (SFI) [SFI/12/RC/2289]
FX This publication has emanated from research supported in part by Irish
   Research Council under Grant No. GOIPD/2013/104 and by a research grant
   from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.
CR Abadi DJ, 2007, P 33 INT C VER LARG, P411
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NR 33
TC 37
Z9 37
U1 4
U2 31
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 MAR
PY 2016
VL 37-38
BP 25
EP 35
DI 10.1016/j.websem.2016.02.003
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 DM6JK
UT WOS:000376457600002
DA 2022-08-23
ER

PT J
AU Huang, C
   Wang, YZ
   Yu, YQ
   Hao, YJ
   Liu, YB
   Zhao, XJ
AF Huang, Chao
   Wang, Yuzhu
   Yu, Yuqing
   Hao, Yujia
   Liu, Yuebin
   Zhao, Xiujian
TI Chinese Named Entity Recognition of Geological News Based on BERT Model
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE BERT; named entity recognition; geological news; CRF
AB With the ongoing progress of geological survey work and the continuous accumulation of geological data, extracting accurate information from massive geological data has become increasingly difficult. To fully mine and utilize geological data, this study proposes a geological news named entity recognition (GNNER) method based on the bidirectional encoder representations from transformers (BERT) pre-trained language model. This solves the problems of traditional word vectors that are difficult to represent context semantics and the single extraction effect and can also help construct the knowledge graphs of geological news. First, the method uses the BERT pre-training model to embed words in the geological news text, and the dynamically obtained word vector is used as the model's input. Second, the word vector is sent to a bidirectional long short-term memory model for further training to obtain contextual features. Finally, the corresponding six entity types are extracted using conditional random field sequence decoding. Through experiments on the constructed Chinese geological news dataset, the average F1 score identified by the model is 0.839. The experimental results show that the model can better identify news entities in geological news.
C1 [Huang, Chao; Wang, Yuzhu; Yu, Yuqing; Hao, Yujia; Liu, Yuebin; Zhao, Xiujian] China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China.
RP Wang, YZ (通讯作者)，China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China.
EM huangchao@cugb.edu.cn; wangyz@cugb.edu.cn; yuyq@cugb.edu.cn;
   haoyj@cugb.edu.cn; liuyb@cugb.edu.cn; zhaoxj@cugb.edu.cn
FU China University of Geosciences (Beijing) College Students' Innovation
   and Entrepreneurship Training Program [X202211415100]
FX This work was supported by the China University of Geosciences (Beijing)
   College Students' Innovation and Entrepreneurship Training Program under
   Grant X202211415100.
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NR 24
TC 0
Z9 0
U1 0
U2 0
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 2022
VL 12
IS 15
AR 7708
DI 10.3390/app12157708
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 3T3DN
UT WOS:000840159500001
OA gold
DA 2022-08-23
ER

PT J
AU Liang, K
   Zhou, BX
   Zhang, YY
   He, YS
   Guo, XY
   Zhang, B
AF Liang, Kun
   Zhou, Baoxian
   Zhang, Yiying
   He, Yeshen
   Guo, Xiaoyan
   Zhang, Bo
TI A Multi-Entity Knowledge Joint Extraction Method of Communication
   Equipment Faults for Industrial IoT
SO ELECTRONICS
LA English
DT Article
DE knowledge graph; entity recognition; relationship extraction; joint
   learning; multi-entity segmentation
ID LSTM
AB The Industrial Internet of Things (IIoT) deploys massive communication devices for information collection and process control. Once it reaches failure, it will seriously affect the operation of the industrial system. This paper proposes a new method for multi-entity knowledge joint extraction (MEKJE) of IIoT communication equipment faults. This method constructs a multi-task tightly coupled model of fault entity and relationship extraction. We use it to implement word embedding and bidirectional semantic capture to generate computable text vectors. At the same time, a multi-entity segmentation method is proposed, which uses noise filtering to distinguish the multi-fault relationship of single corpus. We constructed a dataset of communication failures in power IIoT and conducted experiments. The experimental results show that the method performs best in tests with the Faulty Text dataset and the CLUENER dataset. In particular, the model achieves an F1 value of 78.6% in the evaluation of relationship extraction for multiple entities, and a significant improvement of 5-8% in its accuracy and recall. It enables effective mapping and accurate extraction of fault knowledge data.
C1 [Liang, Kun; Zhou, Baoxian; Zhang, Yiying] Tianjin Univ Sci & Technol, Coll Artificial Intelligence, Tianjin 300457, Peoples R China.
   [He, Yeshen] China Gridcom Co Ltd, Shenzhen 518109, Peoples R China.
   [Guo, Xiaoyan] State Grid Tianjin Elect Power Co, Informat & Commun Co, Tianjin 300140, Peoples R China.
   [Zhang, Bo] State Grid Smart Grid Res Inst Co Ltd, Nanjing 210003, Peoples R China.
RP Zhou, BX (通讯作者)，Tianjin Univ Sci & Technol, Coll Artificial Intelligence, Tianjin 300457, Peoples R China.
EM liangkun@tust.edu.cn; zhoubaoxian@yeah.net; yiyingzhang@tust.edu.cn;
   heyeshen@sgitg.sgcc.com.cn; xiaoyan.guo@tj.sgcc.com.cn;
   zhangbo@geiri.sgcc.com.cn
OI Zhou, Baoxian/0000-0003-4207-9178
FU National Natural Science Foundation of China [61807024]
FX This research was funded by the National Natural Science Foundation of
   China, grant number 61807024.
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TC 0
Z9 0
U1 5
U2 5
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 979
DI 10.3390/electronics11070979
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 0L5SY
UT WOS:000781534300001
OA gold
DA 2022-08-23
ER

PT J
AU Li, CT
   Hsu, C
   Zhang, Y
AF Li, Cheng-Te
   Hsu, Cheng
   Zhang, Yang
TI FairSR: Fairness-aware Sequential Recommendation through Multi-Task
   Learning with Preference Graph Embeddings
SO ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
LA English
DT Article
DE Fairness-aware models; sequential recommendation; knowledge graph
   embedding; multi-task learning
AB Sequential recommendation (SR) learns from the temporal dynamics of user-item interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of algorithmic biases in the learning of user preferences. This article aims at bringing a marriage between SR and algorithmic fairness. We propose a novel fairness-aware sequential recommendation task, in which a new metric, interaction fairness, is defined to estimate how recommended items are fairly interacted by users with different protected attribute groups. We propose a multi-task learning-based deep end-to-end model, FairSR, which consists of two parts. One is to learn and distill personalized sequential features from the given user and her item sequence for SR. The other is fairness-aware preference graph embedding (FPGE). The aim of FPGE is two-fold: incorporating the knowledge of users' and items' attributes and their correlation into entity representations, and alleviating the unfair distributions of user attributes on items. Extensive experiments conducted on three datasets show FairSR can outperform state-of-the-art SR models in recommendation performance. In addition, the recommended items by FairSR also exhibit promising interaction fairness.
C1 [Li, Cheng-Te; Hsu, Cheng] Natl Cheng Kung Univ, 1 Univ Rd, Tainan 701, Taiwan.
   [Zhang, Yang] CISPA Helmholtz Ctr Informat Secur, Stuhlsatzenhaus 5, D-66123 Saarbrucken, Germany.
RP Li, CT (通讯作者)，Natl Cheng Kung Univ, 1 Univ Rd, Tainan 701, Taiwan.
EM chengte@mail.ncku.edu.tw; hsuchengmath@gmail.com; zhang@cispa.de
FU Ministry of Science and Technology (MOST) of Taiwan
   [110-2221-E-006-136-MY3, 110-2221-E-006-001, 110-2634-F-002-051]
FX This work is supported by Ministry of Science and Technology (MOST) of
   Taiwan under grants 110-2221-E-006-136-MY3, 110-2221-E-006-001 and
   110-2634-F-002-051.
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NR 49
TC 0
Z9 0
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 FEB
PY 2022
VL 13
IS 1
AR 16
DI 10.1145/3495163
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZF0XS
UT WOS:000759299400015
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Hsieh, KL
   Wang, YY
   Chen, LY
   Zhao, ZM
   Savitz, S
   Jiang, XQ
   Tang, J
   Kim, YJ
AF Hsieh, Kanglin
   Wang, Yinyin
   Chen, Luyao
   Zhao, Zhongming
   Savitz, Sean
   Jiang, Xiaoqian
   Tang, Jing
   Kim, Yejin
TI Drug repurposing for COVID-19 using graph neural network and harmonizing
   multiple evidence
SO SCIENTIFIC REPORTS
LA English
DT Article
ID CONNECTIVITY MAP
AB Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug's representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.
C1 [Hsieh, Kanglin; Chen, Luyao; Jiang, Xiaoqian; Kim, Yejin] Univ Texas Hlth Sci Ctr Houston, Ctr Secure Artificial Intelligence Healthcare, Sch Biomed Informat, Houston, TX 77030 USA.
   [Wang, Yinyin; Tang, Jing] Univ Helsinki, Fac Med, Res Program Syst Oncol, Helsinki, Finland.
   [Zhao, Zhongming] Univ Texas Hlth Sci Ctr Houston, Sch Biomed Informat, Ctr Precis Hlth, Houston, TX 77030 USA.
   [Savitz, Sean] Univ Texas Hlth Sci Ctr Houston, Inst Stroke & Cerebrovasc Dis, Houston, TX 77030 USA.
RP Kim, YJ (通讯作者)，Univ Texas Hlth Sci Ctr Houston, Ctr Secure Artificial Intelligence Healthcare, Sch Biomed Informat, Houston, TX 77030 USA.
EM yejin.kim@uth.tmc.edu
RI Tang, Jing/W-1764-2019
OI Tang, Jing/0000-0001-7480-7710
FU CPRIT [RR180012, R01AG066749, R01AG066749-01S1, RP180734]; ERC [716063];
   Academy of Finland [3176880]; NIH [R01LM012806]; Christopher Sarofim
   Family Professorship; UT Stars award; UTHealth startup; National
   Institute of Health (NIH) [R01AG066749, R01AG066749-01S1]
FX K. was supported in part by CPRIT RR180012, R01AG066749, and
   R01AG066749-01S1. K. H. was supported by CPRIT RR180012. Y. W. and J. T.
   were supported by ERC starting Grant No. 716063 and Academy of Finland
   funding No. 3176880. Z. Z. is partially supported by NIH grant
   R01LM012806 and CPRIT grant CPRIT RP180734. X. J. is CPRIT Scholar in
   Cancer Research (RR180012), and he was supported in part by Christopher
   Sarofim Family Professorship, UT Stars award, UTHealth startup, the
   National Institute of Health (NIH) under Award Number R01AG066749 and
   R01AG066749-01S1.
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NR 63
TC 6
Z9 6
U1 3
U2 7
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD NOV 30
PY 2021
VL 11
IS 1
AR 23179
DI 10.1038/s41598-021-02353-5
PG 13
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA XG0VJ
UT WOS:000724479000018
PM 34848761
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Kang, X
   Wu, YN
   Ren, FJ
AF Kang, Xin
   Wu, Yunong
   Ren, Fuji
TI Toward action comprehension for searching: Mining actionable intents in
   query entities
SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID WEB
AB Understanding search engine users' intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users' future actions. In this article, we present a novel research method for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, that is, the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment, based on the Action Mining (AM) query entity data set from the Actionable Knowledge Graph (AKG) task at NTCIR-13, suggests that the proposed approach is effective in generating an informative and early-satisfying ranking of potential actions for search users.
C1 [Kang, Xin; Wu, Yunong; Ren, Fuji] Tokushima Univ, Fac Engn, Tokushima, Japan.
RP Kang, X (通讯作者)，Tokushima Univ, Fac Engn, Tokushima, Japan.
EM kang-xin@is.tokushima-u.ac.jp; wuyunong@tokushima-u.ac.jp;
   ren@is.tokushima-u.ac.jp
FU Japan Society for the Promotion of Science (JSPS) [15H01712]
FX This research was partially supported by Japan Society for the Promotion
   of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) Grant
   Number, 15H01712.
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NR 41
TC 0
Z9 0
U1 0
U2 11
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2330-1635
EI 2330-1643
J9 J ASSOC INF SCI TECH
PD FEB
PY 2020
VL 71
IS 2
BP 143
EP 157
DI 10.1002/asi.24220
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA JZ7GB
UT WOS:000505275600002
DA 2022-08-23
ER

PT J
AU Saeed, MR
   Chelmis, C
   Prasanna, VK
AF Saeed, Muhammad Rizwan
   Chelmis, Charalampos
   Prasanna, Viktor K.
TI Extracting entity-specific substructures for RDF graph embeddings
SO SEMANTIC WEB
LA English
DT Article
DE Relevance metrics; graph embedding; Linked Open Data; data mining;
   recommender systems; RDF; SPARQL; Semantic Web; DBpedia
ID SEMANTIC WEB
AB Knowledge Graphs (KGs) have become useful sources of structured data for information retrieval and data analytics tasks. Enabling complex analytics, however, requires entities in KGs to be represented in a way that is suitable for Machine Learning tasks. Several approaches have been recently proposed for obtaining vector representations of KGs based on identifying and extracting relevant graph substructures using both uniform and biased random walks. However, such approaches lead to representations comprising mostly popular, instead of relevant, entities in the KG. In KGs, in which different types of entities often exist (such as in Linked Open Data), a given target entity may have its own distinct set of most relevant nodes and edges. We propose specificity as an accurate measure of identifying most relevant, entity-specific, nodes and edges. We develop a scalable method based on bidirectional random walks to compute specificity. Our experimental evaluation results show that specificity-based biased random walks extract more meaningful (in terms of size and relevance) substructures compared to the state-of-the-art and the graph embedding learned from the extracted substructures perform well against existing methods in common data mining tasks.
C1 [Saeed, Muhammad Rizwan; Prasanna, Viktor K.] Univ Southern Calif, Ming Hseih Dept Elect Engn, Los Angeles, CA 90007 USA.
   [Chelmis, Charalampos] SUNY Albany, Dept Comp Sci, Albany, NY 12222 USA.
RP Saeed, MR (通讯作者)，Univ Southern Calif, Ming Hseih Dept Elect Engn, Los Angeles, CA 90007 USA.
EM saeedm@usc.edu; cchelmis@albany.edu; prasanna@usc.edu
FU Chevron Corp. under the joint project, Center for Interactive Smart
   Oilfield Technologies (CiSoft), at the University of Southern
   California; University of Southern California
FX This work is supported by Chevron Corp. under the joint project, Center
   for Interactive Smart Oilfield Technologies (CiSoft), at the University
   of Southern California.
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NR 52
TC 0
Z9 0
U1 0
U2 1
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2019
VL 10
IS 6
BP 1087
EP 1108
DI 10.3233/SW-190359
PG 22
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:000492839300007
DA 2022-08-23
ER

PT J
AU Freitas, A
   da Silva, JCP
   Curry, E
   Buitelaar, P
AF Freitas, Andre
   da Silva, Joao C. P.
   Curry, Edward
   Buitelaar, Paul
TI Approximate and selective reasoning on knowledge graphs: A
   distributional semantics approach
SO DATA & KNOWLEDGE ENGINEERING
LA English
DT Article
DE Commonsense reasoning; Selective reasoning; Distributional semantics;
   Hybrid distributional-relation models; Semantic approximation
AB Tasks such as question answering and semantic search are dependent on the ability of querying and reasoning over large-scale commonsense knowledge bases (KBs). However, dealing with commonsense data demands coping with problems such as the increase in schema complexity, semantic inconsistency, incompleteness and scalability. This paper proposes a selective graph navigation mechanism based on a distributional relational semantic model which can be applied to querying and reasoning over heterogeneous knowledge bases (KBs). The approach can be used for approximative reasoning, querying and associational knowledge discovery. In this paper we focus on commonsense reasoning as the main motivational scenario for the approach. The approach focuses on addressing the following problems: (i) providing a semantic selection mechanism for facts which are relevant and meaningful in a specific reasoning and querying context and (ii) allowing coping with information incompleteness in large KBs. The approach is evaluated using ConceptNet as a commonsense KB, and achieved high selectivity, high selectivity scalability and high accuracy in the selection of meaningful navigational paths. Distributional semantics is also used as a principled mechanism to cope with information incompleteness. (C) 2015 Elsevier B.V. All rights reserved.
C1 [Freitas, Andre] Univ Passau, Dept Comp Sci & Math, D-94032 Passau, Germany.
   [da Silva, Joao C. P.; Curry, Edward; Buitelaar, Paul] Natl Univ Ireland, Insight Ctr Data Analyt, Galway, Ireland.
   [da Silva, Joao C. P.] Univ Fed Rio de Janeiro, Dept Comp Sci, BR-21941916 Rio De Janeiro, RJ, Brazil.
   [Buitelaar, Paul] Univ S Africa, Coll Grad Studies, ZA-0001 Pretoria, South Africa.
RP Freitas, A (通讯作者)，Univ Passau, Dept Comp Sci & Math, Dr Hans Kapfinger Str 12,R-109, D-94032 Passau, Germany.
EM andre.freitas@uni-passau.de; jcps.ufrj.br@gmail.com
RI Freitas, Andre/AAT-9885-2020
OI Freitas, Andre/0000-0002-4430-4837; Curry, Edward/0000-0001-8236-6433
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NR 13
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U2 21
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0169-023X
EI 1872-6933
J9 DATA KNOWL ENG
JI Data Knowl. Eng.
PD NOV
PY 2015
VL 100
SI SI
BP 211
EP 225
DI 10.1016/j.datak.2015.06.010
PN B
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA CY6RK
UT WOS:000366536300003
DA 2022-08-23
ER

PT J
AU Yu, HL
   Li, ZQ
   Bi, CG
   Chen, HL
AF Yu, Helong
   Li, Ziqing
   Bi, Chunguang
   Chen, Huiling
TI An effective deep learning method with multi-feature and attention
   mechanism for recognition of Chinese rice variety information
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Rice; Named entity recognition; Multi-head attention mechanism; Radical
   features; Word segmentation boundary features; Deep learning
ID NAMED ENTITY RECOGNITION; NETWORK
AB In the process of Chinese rice variety information named entity recognition, traditional methods cannot extract potential semantic information from data and cannot capture long-distance dependence. So, this paper proposes a Chinese rice variety information named entity recognition method based on a bidirectional long short-term memory network and conditional random field (BiLSTM-CRF), which combines radical features, word segmentation boundary features, and multi-head attention mechanism. First, the radical features and word segmentation boundary features are encoded and integrated into a pre-trained character vector as the model embedding to solve the disadvantage of the lack of semantic information. Then, the multi-head attention mechanism is introduced to assist the bidirectional long short-term memory network (BiLSTM) in acquiring long-distance context-dependence. Finally, a conditional random field (CRF) is used to realize character-level sequence annotation and then realize the named entity recognition task of Chinese rice variety information. The experimental results show that this model's precision, recall, and F1-score are 95.78%, 97.07%, and 96.42%, respectively. The three evaluation indices are better than those of the other models. The model proposed in this paper can effectively identify Chinese rice variety information entities and provides method support for the subsequent construction of a Chinese rice variety information knowledge graph.
C1 [Yu, Helong; Li, Ziqing; Bi, Chunguang] Jilin Agr Univ, Coll Informat Technol, Changchun 130118, Peoples R China.
   [Chen, Huiling] Wenzhou Univ, Dept Comp Sci & Artificial Intelligence, Wenzhou 325035, Peoples R China.
RP Bi, CG (通讯作者)，Jilin Agr Univ, Coll Informat Technol, Changchun 130118, Peoples R China.; Chen, HL (通讯作者)，Wenzhou Univ, Dept Comp Sci & Artificial Intelligence, Wenzhou 325035, Peoples R China.
EM chunguangb@jlau.edu.cn; chenhuiling.jlu@gmail.com
FU National Natural Science Foundation of China [U19A2061]; National Key
   R&D Program of China [2019YFC1710700]; Science and Technology
   Development Program of Jilin Province [20190301024NY]; Jilin Provincial
   Development and Reform Commission Project [2020C005]
FX This research was funded by the National Natural Science Foundation of
   China (U19A2061), the National Key R&D Program of China
   (2019YFC1710700), the Science and Technology Development Program of
   Jilin Province (20190301024NY), and the Jilin Provincial Development and
   Reform Commission Project (2020C005).
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NR 73
TC 2
Z9 2
U1 22
U2 22
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 MAY
PY 2022
VL 81
IS 11
BP 15725
EP 15745
DI 10.1007/s11042-022-12458-2
EA MAR 2022
PG 21
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 0X7NC
UT WOS:000762902200001
DA 2022-08-23
ER

PT J
AU Wang, HW
   Liu, YW
   Yin, P
   Zhang, H
   Xu, X
   Wen, QY
AF Wang, Huawei
   Liu, Yiwei
   Yin, Peng
   Zhang, Hua
   Xu, Xin
   Wen, Qiaoyan
TI Label specificity attack: Change your label as I want
SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
LA English
DT Article; Early Access
DE graph neural networks; label specificity attack; node classification;
   structural entropy
ID NETWORKS; GRAPHS
AB Graph neural networks (GNN) have been widely used in many machine learning tasks, such as text classification, sequence labeling, protein interface prediction, and knowledge graph. With increasing security concerns, GNN have been proved to be vulnerable and unreliable. Recent years, inspired by adversarial model in computer vision, various attacks on graph data begin to emerge. However, the exist attacks mostly focus on security violation and attack specificity, and very few attacks concern error specificity. In this paper, we focus on dealing with this kind of attack on one node of the graph by slightly manipulating the graph structure. Our goal is to change the label of the node to what we want after attack. We formulate this case as label specificity attack problem. The biggest challenge in solving this problem is the lack of theoretical guidance to perform this attack. For this, we reinterpret structural entropy and define differential structural entropy (DS-entropy) to guide the manipulation. Based on DS-entropy, we propose the target-label principle and max-degree principle to execute our attack, and then design the corresponding algorithm DSEM. We compare our algorithm DSEM with two benchmarks on three classical graph data sets. Results show that our algorithm is effective in performing label specificity attacks.
C1 [Wang, Huawei; Zhang, Hua; Wen, Qiaoyan] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China.
   [Liu, Yiwei; Yin, Peng; Xu, Xin] Def Ind Secrecy Examinat & Certificat Ctr, Beijing 100089, Peoples R China.
   [Yin, Peng] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China.
RP Liu, YW (通讯作者)，Def Ind Secrecy Examinat & Certificat Ctr, Beijing 100089, Peoples R China.
EM yiweiliu_disecc@163.com
OI Liu, Yiwei/0000-0003-3400-3632
FU NSFC [62072051, 61976024, 61972048]; Fundamental Research Funds for the
   Central Universities [2019XD-A01]; Key Project Plan of Blockchain in
   Ministry of Education of the People's Republic of China [2020KJ010802];
   Defense Industrial Technology Development Program [JCKY2021906A001]
FX NSFC, Grant/Award Numbers: 62072051, 61976024, 61972048; Fundamental
   Research Funds for the Central Universities, Grant/Award Number:
   2019XD-A01; Key Project Plan of Blockchain in Ministry of Education of
   the People's Republic of China, Grant/Award Number: 2020KJ010802; The
   Defense Industrial Technology Development Program, Grant/Award Number:
   JCKY2021906A001
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NR 63
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PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0884-8173
EI 1098-111X
J9 INT J INTELL SYST
JI Int. J. Intell. Syst.
DI 10.1002/int.22902
EA MAY 2022
PG 20
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1C3IY
UT WOS:000793018300001
DA 2022-08-23
ER

PT J
AU Zhang, LY
   Liu, SC
   Liu, DH
   Zeng, PP
   Li, XP
   Song, JK
   Gao, LL
AF Zhang, Liyang
   Liu, Shuaicheng
   Liu, Donghao
   Zeng, Pengpeng
   Li, Xiangpeng
   Song, Jingkuan
   Gao, Lianli
TI Rich Visual Knowledge-Based Augmentation Network for Visual Question
   Answering
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article
DE Feature extraction; Visualization; Knowledge based systems; Task
   analysis; Knowledge discovery; Semantics; Cognition; Knowledge base;
   object detection; self-attention; visual question answering (VQA)
AB Visual question answering (VQA) that involves understanding an image and paired questions develops very quickly with the boost of deep learning in relevant research fields, such as natural language processing and computer vision. Existing works highly rely on the knowledge of the data set. However, some questions require more professional cues other than the data set knowledge to answer questions correctly. To address such an issue, we propose a novel framework named a knowledge-based augmentation network (KAN) for VQA. We introduce object-related open-domain knowledge to assist the question answering. Concretely, we extract more visual information from images and introduce a knowledge graph to provide the necessary common sense or experience for the reasoning process. For these two augmented inputs, we design an attention module that can adjust itself according to the specific questions, such that the importance of external knowledge against detected objects can be balanced adaptively. Extensive experiments show that our KAN achieves state-of-the-art performance on three challenging VQA data sets, i.e., VQA v2, VQA-CP v2, and FVQA. In addition, our open-domain knowledge is also beneficial to VQA baselines. Code is available at https://github.com/yyyanglz/KAN.
C1 [Zhang, Liyang; Zeng, Pengpeng; Li, Xiangpeng; Song, Jingkuan; Gao, Lianli] Univ Elect Sci & Technol China, Future Media Ctr, Chengdu 611731, Peoples R China.
   [Zhang, Liyang; Zeng, Pengpeng; Li, Xiangpeng; Song, Jingkuan; Gao, Lianli] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China.
   [Liu, Shuaicheng] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China.
   [Liu, Shuaicheng; Liu, Donghao] Megvii Technol Ltd, Chengdu 611730, Peoples R China.
RP Gao, LL (通讯作者)，Univ Elect Sci & Technol China, Future Media Ctr, Chengdu 611731, Peoples R China.
EM lianli.gao@uestc.edu.cn
OI song, jingkuan/0000-0002-2549-8322; Zeng, Pengpeng/0000-0002-0672-3790;
   Zhang, Liyang/0000-0003-2675-9763
FU Fundamental Research Funds for the Central Universities [ZYGX2019J073];
   National Natural Science Foundation of China [61772116, 61872064,
   61632007, 61602049, 61872067]; Sichuan Science and Technology Program
   [2019JDTD0005, 2019YFH0016]; Open Project of Zhejiang Lab [2019KD0AB05];
   Zhejiang Lab's International Talent Fund for Young Professionals
FX This work was supported in part by the Fundamental Research Funds for
   the Central Universities under Grant ZYGX2019J073, in part by the
   National Natural Science Foundation of China under Grant 61772116, Grant
   61872064, Grant 61632007, Grant 61602049, and Grant 61872067; in part by
   the Sichuan Science and Technology Program under Grant 2019JDTD0005 and
   Grant 2019YFH0016, in part by the Open Project of Zhejiang Lab under
   Grant 2019KD0AB05, and in part by the Zhejiang Lab's International
   Talent Fund for Young Professionals.
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NR 46
TC 5
Z9 5
U1 9
U2 17
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
PD OCT
PY 2021
VL 32
IS 10
BP 4362
EP 4373
DI 10.1109/TNNLS.2020.3017530
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA WC2SG
UT WOS:000704111000011
PM 32941156
DA 2022-08-23
ER

PT J
AU Zhang, WT
   Yang, Z
   Wang, YX
   Shen, Y
   Li, Y
   Wang, L
   Cui, B
AF Zhang, Wentao
   Yang, Zhi
   Wang, Yexin
   Shen, Yu
   Li, Yang
   Wang, Liang
   Cui, Bin
TI GRAIN: Improving Data Efficiency of Graph Neural Networks via
   Diversified Influence Maximization
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article; Proceedings Paper
CT 47th International Conference on Very Large Data Bases (VLDB)
CY AUG 16-20, 2021
CL Copenhagen, DENMARK
AB Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets. However, recent deep learning models have moved forward from independent and identically distributed data to graph-structured data, such as social networks, e-commerce user-item graphs, and knowledge graphs. This evolution has led to the emergence of Graph Neural Networks (GNNs) that go beyond the models existing data selection methods are designed for. Therefore, we present Grain, an efficient framework that opens up a new perspective through connecting data selection in GNNs with social influence maximization. By exploiting the common patterns of GNNs, Grain introduces a novel feature propagation concept, a diversified influence maximization objective with novel influence and diversity functions, and a greedy algorithm with an approximation guarantee into a unified framework. Empirical studies on public datasets demonstrate that Grain significantly improves both the performance and efficiency of data selection (including active learning and core-set selection) for GNNs. To the best of our knowledge, this is the first attempt to bridge two largely parallel threads of research, data selection, and social influence maximization, in the setting of GNNs, paving new ways for improving data efficiency.
C1 [Zhang, Wentao; Yang, Zhi; Wang, Yexin; Shen, Yu; Li, Yang; Wang, Liang; Cui, Bin] Peking Univ, Sch EECS, Beijing, Peoples R China.
   [Zhang, Wentao; Yang, Zhi; Wang, Yexin; Shen, Yu; Li, Yang; Wang, Liang; Cui, Bin] Peking Univ, Key Lab High Confidence Software Technol, Beijing, Peoples R China.
   [Zhang, Wentao] Tencent Inc, Shenzhen, Peoples R China.
   [Yang, Zhi; Cui, Bin] Peking Univ, Ctr Data Sci, Beijing, Peoples R China.
   [Yang, Zhi; Cui, Bin] Natl Engn Lab Big Data Anal & Applicat, Beijing, Peoples R China.
RP Zhang, WT (通讯作者)，Peking Univ, Sch EECS, Beijing, Peoples R China.; Zhang, WT (通讯作者)，Peking Univ, Key Lab High Confidence Software Technol, Beijing, Peoples R China.; Zhang, WT (通讯作者)，Tencent Inc, Shenzhen, Peoples R China.
EM wentao.zhang@pku.edu.cn; yangzhi@pku.edu.cn; yexinwang@pku.edu.cn;
   shenyu@pku.edu.cn; liyang.cs@pku.edu.cn; liang_wang_now@yahoo.com;
   bin.cui@pku.edu.cn
OI CUI, Bin/0000-0003-1681-4677
FU National Key Research and Development Program of China [2018YFB1004403];
   NSFC [61832001, 6197200]; Beijing Academy of Artificial Intelligence
   (BAAI); PKU-Tencent joint research Lab
FX This work is supported by the National Key Research and Development
   Program of China (No. 2018YFB1004403), NSFC (No. 61832001, 6197200),
   Beijing Academy of Artificial Intelligence (BAAI), and PKU-Tencent joint
   research Lab. Wentao Zhang and Zhi Yang contributed equally, and Bin Cui
   is the corresponding author.
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NR 62
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 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD JUL
PY 2021
VL 14
IS 11
BP 2473
EP 2482
DI 10.14778/3476249.3476295
PG 10
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA YH0UF
UT WOS:000742891100045
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Joshi, M
   Joshi, KP
   Finin, T
AF Joshi, Maithilee
   Joshi, Karuna P.
   Finin, Tim
TI Delegated Authorization Framework for EHR Services Using Attribute-Based
   Encryption
SO IEEE TRANSACTIONS ON SERVICES COMPUTING
LA English
DT Article
DE Encryption; Authorization; Cloud computing; Electronic medical records;
   Ontologies; Organizations; Attribute based encryption (ABE); attribute
   based access control (ABAC); electronic health record (EHR); cloud
   storage; Semantic Web; access broker; knowledge graph (ontology); cloud
   computing
AB Medical organizations find it challenging to adopt cloud-based Electronic Health Records (EHR) services due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient-centric approach for EHR management, where the responsibility of authorizing data access is handled at the patients end. This creates a significant overhead for the patient who must authorize every access of their health record. This is not practical given that multiple personnel are typically involved in providing care and that the patient may not always be in a state to provide this authorization. Hence there is a need to develop a proper authorization delegation mechanism for safe, secure and easy to use cloud-based EHR Service management. We present a novel, centralized, attribute-based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHRs access authority to medical providers.
C1 [Joshi, Maithilee; Finin, Tim] Univ Maryland Baltimore Cty, Comp Sci & Elect Engn Dept, Baltimore, MD 21250 USA.
   [Joshi, Karuna P.] Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21250 USA.
RP Joshi, M (通讯作者)，Univ Maryland Baltimore Cty, Comp Sci & Elect Engn Dept, Baltimore, MD 21250 USA.
EM maithi1@umbc.edu; karuna.joshi@umbc.edu; finin@umbc.edu
OI Joshi, Karuna/0000-0002-6354-1686; Finin, Tim/0000-0002-6593-1792
FU Office of Naval Research [N00014-15-1-2228, N00014-16-WX01489,
   GM10331601]; National Institute of General Medical Sciences of the
   United States National Institutes of Health
FX This research was supported by the Office of Naval Research under grants
   N00014-15-1-2228 and N00014-16-WX01489. We thank Dr. Seung Geol Choi
   (USNA), Dr. Eliot Siegel (University of Maryland Medical Center) and
   members of the Ebiquity Research Group for their vital input. This work
   was conducted using the Protege resource, which is supported by grant
   GM10331601 from the National Institute of General Medical Sciences of
   the United States National Institutes of Health.
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TC 0
Z9 0
U1 1
U2 4
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 NOV 1
PY 2021
VL 14
IS 6
BP 1612
EP 1623
DI 10.1109/TSC.2019.2917438
PG 12
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XM6GK
UT WOS:000728922900001
OA Green Published
DA 2022-08-23
ER

PT J
AU Zeng, WX
   Zhao, X
   Tang, JY
   Lin, XM
   Groth, P
AF Zeng, Weixin
   Zhao, Xiang
   Tang, Jiuyang
   Lin, Xuemin
   Groth, Paul
TI Reinforcement Learning-based Collective Entity Alignment with Adaptive
   Features
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE Entity alignment; reinforcement learning; adaptive feature fusion
ID ALGORITHMS; RESOLUTION
AB Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs). For entities to be aligned, existing EA solutions treat them separately and generate alignment results as ranked lists of entities on the other side. Nevertheless, this decision-making paradigm fails to take into account the interdependence among entities. Although some recent efforts mitigate this issue by imposing the 1-to-1 constraint on the alignment process, they still cannot adequately model the underlying interdependence and the results tend to be sub-optimal.
   To fill in this gap, in this work, we delve into the dynamics of the decision-making process, and offer a reinforcement learning (RL)-based model to align entities collectively. Under the RL framework, we devise the coherence and exclusiveness constraints to characterize the interdependence and restrict collective alignment. Additionally, to generate more precise inputs to the RL framework, we employ representative features to capture different aspects of the similarity between entities in heterogeneous KGs, which are integrated by an adaptive feature fusion strategy. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks and compared against state-of-the-art solutions. The empirical results verify its effectiveness and superiority.
C1 [Zeng, Weixin; Zhao, Xiang; Tang, Jiuyang] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410072, Peoples R China.
   [Lin, Xuemin] Univ New South Wales, Sydney, NSW 2052, Australia.
   [Groth, Paul] Univ Amsterdam, NL-1090 GH Amsterdam, Netherlands.
RP Zeng, WX; Zhao, X (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410072, Peoples R China.
EM zengweixin13@nudt.edu.cn; xiangzhao@nudt.edu.cn;
   jiuyang_tang@nudt.edu.cn; lxue@cse.unsw.edu.au; p.groth@uva.nl
RI ; Groth, Paul/F-1577-2010
OI Zeng, Weixin/0000-0002-2690-3190; Groth, Paul/0000-0003-0183-6910
FU Ministry of Science and Technology of China [2020AAA0108802]; NSFC
   [61872446, 71971212]; NSF of Hunan Province [2019JJ20024]; Science and
   Technology Innovation Program of Hunan Province [2020RC4046];
   Postgraduate Scientific Research Innovation Project of Hunan Province
   [CX20190033];  [ARC DP200101338];  [ARC DP180103096];  [ARC DP170101628]
FX Zhao and J. Tang were partially supported by Ministry of Science and
   Technology of China under Grant No. 2020AAA0108802, NSFC under Grants
   No. 61872446 and No. 71971212, NSF of Hunan Province under Grant No.
   2019JJ20024. X. Zhao and J. Tang were also supported by The Science and
   Technology Innovation Program of Hunan Province under Grant No.
   2020RC4046. W. Zeng was partially supported by Postgraduate Scientific
   Research Innovation Project of Hunan Province under Grant No.
   CX20190033. X. Lin was partially supported by Grants No. ARC
   DP200101338, No. ARC DP180103096, and No. ARC DP170101628. All content
   represents the opinion of the authors, which is not necessarily shared
   or endorsed by their respective employers and/or sponsors.
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NR 63
TC 4
Z9 4
U1 11
U2 13
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1046-8188
EI 1558-2868
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
PD JUL
PY 2021
VL 39
IS 3
AR 26
DI 10.1145/3446428
PG 31
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WV5UX
UT WOS:000717303100005
OA Bronze, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Shalaby, W
   Zadrozny, W
   Jin, HX
AF Shalaby, Walid
   Zadrozny, Wlodek
   Jin, Hongxia
TI Beyond word embeddings: learning entity and concept representations from
   large scale knowledge bases
SO INFORMATION RETRIEVAL JOURNAL
LA English
DT Article
DE Entity and concept embeddings; Entity identification; Concept
   categorization; Skip-gram; Probase; Knowledge graph representations
AB Text representations using neural word embeddings have proven effective in many NLP applications. Recent researches adapt the traditional word embedding models to learn vectors of multiword expressions (concepts/entities). However, these methods are limited to textual knowledge bases (e.g., Wikipedia). In this paper, we propose a novel and simple technique for integrating the knowledge about concepts from two large scale knowledge bases of different structure (Wikipedia and Probase) in order to learn concept representations. We adapt the efficient skip-gram model to seamlessly learn from the knowledge in Wikipedia text and Probase concept graph. We evaluate our concept embedding models on two tasks: (1) analogical reasoning, where we achieve a state-of-the-art performance of 91% on semantic analogies, (2) concept categorization, where we achieve a state-of-the-art performance on two benchmark datasets achieving categorization accuracy of 100% on one and 98% on the other. Additionally, we present a case study to evaluate our model on unsupervised argument type identification for neural semantic parsing. We demonstrate the competitive accuracy of our unsupervised method and its ability to better generalize to out of vocabulary entity mentions compared to the tedious and error prone methods which depend on gazetteers and regular expressions.
C1 [Shalaby, Walid; Zadrozny, Wlodek] Univ N Carolina, Dept Comp Sci, 9201 Univ City Blvd, Charlotte, NC 28223 USA.
   [Jin, Hongxia] Samsung Res Amer, 665 Clyde Ave, Mountain View, CA 94043 USA.
RP Shalaby, W (通讯作者)，Univ N Carolina, Dept Comp Sci, 9201 Univ City Blvd, Charlotte, NC 28223 USA.
EM wshalaby@uncc.edu; wzadrozn@uncc.edu; hongxia.jin@samsung.com
RI Zadrozny, Wlodek/AAZ-3972-2020
OI Zadrozny, Wlodek/0000-0003-4844-9117
FU National Science Foundation [1624035]
FX This work was partially supported by the National Science Foundation
   under Grant Number 1624035. Any opinions, findings, and conclusions or
   recommendations expressed in this material are those of the authors and
   do not necessarily reflect the views of the National Science Foundation.
   The authors would like to thank Avik Ray and Yilin Shen from Samsung
   Research America for their constructive feedback and discussions while
   developing the case study on the argument type identification task. The
   authors also appreciate the reviewers valuable and profound comments.
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NR 31
TC 8
Z9 8
U1 1
U2 11
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 DEC
PY 2019
VL 22
IS 6
BP 525
EP 542
DI 10.1007/s10791-018-9340-3
PG 18
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JI8CV
UT WOS:000493691800001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Yuan, Y
   Wang, GR
   Chen, L
   Ning, B
AF Yuan, Ye
   Wang, Guoren
   Chen, Lei
   Ning, Bo
TI Efficient pattern matching on big uncertain graphs
SO INFORMATION SCIENCES
LA English
DT Article
DE Uncertain data; Graph data
ID REACHABILITY; SEARCH; REPRESENTATION; COMPUTATION; LABEL
AB A significant amount of research has been devoted to seeking efficient solutions to the problem of pattern matching over graphs. This interest is largely due to the many applications that require such efficient solutions, including protein complex prediction, social network analysis, and structural pattern recognition. However, in many real applications, the graph data are often noisy, incomplete, and inaccurate. In other words, there exist many uncertain graphs. Therefore, in this paper, we study pattern matching in the context of large uncertain graphs. Specifically, we want to retrieve all qualified matches of a query pattern in the uncertain graph. Though pattern matching over uncertain graphs is NP-hard, we employ a filtering-and-verification framework to speed up the search. In the filtering phase, we propose a probabilistic matching tree (PM-tree) built from match cuts obtained by a cut selection process. Based on the PM-tree, we devise a collective pruning strategy to prune a large number of unqualified matches. During the verification phase, we develop an efficient sampling algorithm to validate the remaining candidates. Extensive experimental results demonstrate the effectiveness and efficiency of the proposed algorithms. Finally, we show how our solution can be applied to querying knowledge graphs. (C) 2016 Elsevier Inc. All rights reserved.
C1 [Yuan, Ye; Wang, Guoren] Northeastern Univ, Dept Comp Sci, Shenyang 110816, Peoples R China.
   [Chen, Lei] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China.
   [Ning, Bo] Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian, Peoples R China.
RP Yuan, Y (通讯作者)，Northeastern Univ, Dept Comp Sci, Shenyang 110816, Peoples R China.
EM linuxyy@gmail.com
OI Chen, Lei/0000-0002-8257-5806
FU NSFC [61100024, 61173029, 61025007, 61328202, U1401256, 61202083];
   Fundamental Research Funds for the Central Universities [N130504006];
   National Basic Research Program of China (973) [2011CB302200-G];
   National High Technology Research and Development 863 Program of China
   [2012AA011004]
FX Ye Yuan is supported by the NSFC (grant nos. 61100024 and 61173029) and
   the Fundamental Research Funds for the Central Universities (grant no.
   N130504006). Guoren Wang is supported by the NSFC (grant no. 61025007,
   61328202 and U1401256), National Basic Research Program of China (973,
   grant no. 2011CB302200-G), National High Technology Research and
   Development 863 Program of China (grant no. 2012AA011004). Lei Chen is
   supported by the NSFC (grant no. 61328202). Bo Ning is supported by the
   NSFC (grant no. 61202083).
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NR 77
TC 13
Z9 13
U1 0
U2 46
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 APR 20
PY 2016
VL 339
BP 369
EP 394
DI 10.1016/j.ins.2015.12.034
PG 26
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DE8TY
UT WOS:000370910800023
DA 2022-08-23
ER

PT J
AU Zheng, KH
   Sun, LY
   Wang, X
   Zhou, SL
   Li, HB
   Li, S
   Zeng, LK
   Gong, QH
AF Zheng, Kaihong
   Sun, Lingyun
   Wang, Xin
   Zhou, Shangli
   Li, Hanbin
   Li, Sheng
   Zeng, Lukun
   Gong, Qihang
TI Named Entity Recognition in Electric Power Metering Domain Based on
   Attention Mechanism
SO IEEE ACCESS
LA English
DT Article
DE Feature extraction; Hidden Markov models; Task analysis; Data models;
   Semantics; Power systems; Neural networks; Power metering; attention
   mechanism; joint feature; named entity recognition
AB Named Entity Recognition (NER) is one key step for constructing power domain knowledge graph which is increasingly urgent in building smart grid. This paper proposes a new NER model called Att-CNN-BiGRU-CRF which consists of the following five layers. The prefix Att means the model is based on attention mechanism. A joint feature embedding layer combines the character embedding and word embedding based on BERT to obtain more semantic information. A convolutional attention layer combines the local attention mechanism and CNN to capture the relationship of local context. A BiGRU layer extracts higher-level features of power metering text. A global multi-head attention layer optimizes the processing of sentence level information. A CRF layer obtains the output tag sequences. This paper also constructs a corresponding power metering corpus data set with a new entity classification method. The novelties of our work are the five layer model structure and the attention mechanism. Experimental results show that the proposed model has high recall rate 88.16% and precision rate 89.33% which is better than the state-of-the-art models.
C1 [Zheng, Kaihong; Zhou, Shangli; Li, Sheng; Zeng, Lukun; Gong, Qihang] China Southern Power Grid, Digital Grid Res Inst, Guangzhou 510663, Guangdong, Peoples R China.
   [Zheng, Kaihong; Sun, Lingyun; Wang, Xin] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310058, Zhejiang, Peoples R China.
   [Sun, Lingyun; Wang, Xin] Zhejiang Univ, Zhejiang Univ China Southern Power Grid Joint Res, Hangzhou 310058, Zhejiang, Peoples R China.
   [Wang, Xin; Li, Hanbin] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310014, Zhejiang, Peoples R China.
RP Wang, X (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310058, Zhejiang, Peoples R China.; Wang, X (通讯作者)，Zhejiang Univ, Zhejiang Univ China Southern Power Grid Joint Res, Hangzhou 310058, Zhejiang, Peoples R China.; Wang, X (通讯作者)，Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310014, Zhejiang, Peoples R China.
EM wangxin2009@zju.edu.cn
FU National Key Research and Development Program of China [2020YFB0906004,
   2020YFB0906000]
FX This work was supported by the National Key Research and Development
   Program of China under Grant 2020YFB0906004 and Grant 2020YFB0906000.
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NR 37
TC 0
Z9 0
U1 13
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 2021
VL 9
BP 152564
EP 152573
DI 10.1109/ACCESS.2021.3123154
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA XA2TJ
UT WOS:000720505800001
OA gold
DA 2022-08-23
ER

PT J
AU Jiang, JY
   Zhou, YC
   Chen, XS
   Jhou, YR
   Zhao, LQ
   Liu, S
   Yang, PC
   Ahmar, J
   Wang, W
AF Jiang, Jyun-Yu
   Zhou, Yichao
   Chen, Xiusi
   Jhou, Yan-Ru
   Zhao, Liqi
   Liu, Sabrina
   Yang, Po-Chun
   Ahmar, Jule
   Wang, Wei
TI COVID-19 Surveiller: toward a robust and effective pandemic surveillance
   system basedon social media mining
SO PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL
   AND ENGINEERING SCIENCES
LA English
DT Article
DE pandemic surveillance; social media mining; knowledge graph; natural
   language processing
ID SIR-MODEL; TEMPORAL EVOLUTION; EPIDEMIC MODEL; PART
AB The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available at http://scaiweb.cs.ucla.edu/covidsurveiller/. This article is part of the theme issue 'Data science approachs to infectious disease surveillance'.
C1 [Jiang, Jyun-Yu; Zhou, Yichao; Chen, Xiusi; Jhou, Yan-Ru; Zhao, Liqi; Liu, Sabrina; Yang, Po-Chun; Ahmar, Jule; Wang, Wei] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA.
RP Wang, W (通讯作者)，Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA.
EM weiwang@cs.ucla.edu
OI Wang, Wei/0000-0002-8180-2886
FU National Science Foundation [NSF-DGE-1829071, NSF-IIS-2031187]; National
   Institutes of Health [NIH-R35-HL135772, NIH/NIBIBR-01-EB027650]
FX This work was partially supported by the National Science Foundation
   (grant no. NSF-DGE-1829071 and NSF-IIS-2031187) and the National
   Institutes of Health (grant no. NIH-R35-HL135772 and
   NIH/NIBIBR-01-EB027650).
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TC 2
Z9 2
U1 7
U2 15
PU ROYAL SOC
PI LONDON
PA 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND
SN 1364-503X
EI 1471-2962
J9 PHILOS T R SOC A
JI Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci.
PD JAN 10
PY 2022
VL 380
IS 2214
AR 20210125
DI 10.1098/rsta.2021.0125
PG 13
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA XA7TO
UT WOS:000720844400001
PM 34802278
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Pornprasit, C
   Liu, X
   Kiattipadungkul, P
   Kertkeidkachorn, N
   Kim, KS
   Noraset, T
   Hassan, SU
   Tuarob, S
AF Pornprasit, Chanathip
   Liu, Xin
   Kiattipadungkul, Pattararat
   Kertkeidkachorn, Natthawut
   Kim, Kyoung-Sook
   Noraset, Thanapon
   Hassan, Saeed-Ul
   Tuarob, Suppawong
TI Enhancing citation recommendation using citation network embedding
SO SCIENTOMETRICS
LA English
DT Article
DE Citation recommendation; Knowledge graph embedding; Convolutional neural
   networks; Graph representation learning
ID PAPER RECOMMENDATION; IMPACT
AB Automatic recommendation of citations has been a focal point of research in scholarly digital libraries. Many graph-based citation recommendation algorithms have been proposed; however, most of them utilize local citation behavior from the citation network that results in recommending papers in the same proximity as the query article. In this paper, we propose to capture the global citation behavior in the citation network and use it to enhance the citation recommendation performance. Specifically, we develop a novel citation network embedding algorithm, ConvCN, to encode the citation relationship among papers. We then propose to enhance existing graph-based citation recommendation algorithms by incorporating ConvCN to improve the recommendation efficacy. ConvCN has been shown to improve the citation recommendation performance by 44.86% and 34.87% on average in terms of Bpref and F-measure@20, respectively. The findings from this research not only confirm that global citation behavior could be additionally useful for improving the performance of traditional citation recommendation algorithms but also shed light on the possibility to adapt the proposed ConvCN algorithm for other recommendation tasks that rely on graph-like information such as items recommendation in social networks and people recommendation in referral networks.
C1 [Pornprasit, Chanathip; Kiattipadungkul, Pattararat; Noraset, Thanapon; Tuarob, Suppawong] Mahidol Univ, Fac Informat & Commun Technol, Salaya, Nakhon Pathom, Thailand.
   [Liu, Xin; Kertkeidkachorn, Natthawut; Kim, Kyoung-Sook] Natl Inst Adv Ind Sci & Technol, Tokyo, Japan.
   [Hassan, Saeed-Ul] Manchester Metropolitan Univ, Dept Comp & Math, Manchester, Lancs, England.
RP Tuarob, S (通讯作者)，Mahidol Univ, Fac Informat & Commun Technol, Salaya, Nakhon Pathom, Thailand.
EM suppawong.tua@mahidol.edu
OI Tuarob, Suppawong/0000-0002-5201-5699
FU Thailand Science Research and Innovation (TSRI) [RSA6280105]; JSPS
   [21K12042]; New Energy and Industrial Technology Development
   Organization [JPNP20006]
FX This research is supported by the Thailand Science Research and
   Innovation (TSRI), formerly known as Thailand Research Fund (TRF),
   through Grant RSA6280105. In addition, we would also like to acknowledge
   partial support from JSPS Grant-in-Aid for Scientific Research (Grant
   No. 21K12042) and the New Energy and Industrial Technology Development
   Organization (Grant ANo. JPNP20006). This manuscript is an extension of
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NR 84
TC 1
Z9 1
U1 22
U2 27
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 2022
VL 127
IS 1
BP 233
EP 264
DI 10.1007/s11192-021-04196-3
EA JAN 2022
PG 32
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 YK9II
UT WOS:000741622100005
DA 2022-08-23
ER

PT J
AU Bellini, V
   Di Noia, T
   Di Sciascio, E
   Schiavone, A
AF Bellini, Vito
   Di Noia, Tommaso
   Di Sciascio, Eugenio
   Schiavone, Angelo
TI Semantics-Aware Autoencoder
SO IEEE ACCESS
LA English
DT Article
DE Autoencoder neural network; cold start problem; deep learning;
   explanation; knowledge graph; recommender system
ID RECOMMENDATION
AB Recommender Systems are widely adopted in nowadays services such as e-commerce websites, multimedia streaming platforms, and many others. They help users to find what they are looking for by suggesting relevant items leveraging their past preferences. Deep Learning models are very effective in solving the recommendation problem; as a matter of fact, many deep learning architectures have been proposed over the years. Even if deep learning models outperform many state-of-the-art algorithms, the worst disadvantage is about their interpretability: explaining the reason a specific item has been recommended to a user is quite a difficult task since the model is not interpretable. Accuracy in the recommendation is no more enough since users are also expecting a useful explanation for the suggested items. Users, on the other hand, want to know why. In this paper, we present SemAuto, a novel approach based on an Autoencoder Neural Network that makes it possible to semantically label neurons in hidden layers, thus paving the way to the model's interpretability and consequently to the explanation of a recommendation. We tested our semantics aware approach with respect to other state-of-the-art algorithms to prove the recommendation's accuracy. Furthermore, we performed an extensive AB test with real users to evaluate the explanation we generate.
C1 [Bellini, Vito; Di Noia, Tommaso; Di Sciascio, Eugenio; Schiavone, Angelo] Polytech Univ Bari, Dept Elect & Informat Engn, I-70126 Bari, Italy.
RP Bellini, V; Di Noia, T (通讯作者)，Polytech Univ Bari, Dept Elect & Informat Engn, I-70126 Bari, Italy.
EM vito.bellini@poliba.it; tommaso.dinoia@poliba.it
FU PON FLET4.0 Project
FX This work was supported in part by the PON FLET4.0 Project.
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NR 45
TC 3
Z9 3
U1 0
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 166122
EP 166137
DI 10.1109/ACCESS.2019.2953308
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA JQ1MO
UT WOS:000498717500001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Yang, B
   Yang, MF
AF Yang, Bo
   Yang, Meifang
TI Research on enterprise knowledge service based on semantic reasoning and
   data fusion
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Semantic reasoning; Knowledge fusion; Enterprise knowledge service; Risk
   management
ID DECISION-SUPPORT; FRAMEWORK
AB In the era of big data, the field of enterprise risk is facing considerable challenges brought by massive multisource heterogeneous information sources. In view of the proliferation of multisource and heterogeneous enterprise risk information, insufficient knowledge fusion capabilities, and the low level of intelligence in risk management, this article explores the application process of enterprise knowledge service models for rapid responses to risk incidents from the perspective of semantic reasoning and data fusion and clarifies the elements of the knowledge service model in the field of risk management. Based on risk data, risk decision making as the standard, risk events as the driving force, and knowledge graph analysis methods as the power, the risk domain knowledge service process is decomposed into three stages: prewarning, in-event response, and postevent summary. These stages are combined with the empirical knowledge of risk event handling to construct a three-level knowledge service model of risk domain knowledge acquisition-organization-application. This model introduces the semantic reasoning and data fusion method to express, organize, and integrate the knowledge needs of different stages of risk events; provide enterprise managers with risk management knowledge service solutions; and provide new growth points for the innovation of interdisciplinary knowledge service theory.
C1 [Yang, Bo; Yang, Meifang] Jiangxi Univ Finance & Econ, Sch Informat Management, Nanchang 30013, Jiangxi, Peoples R China.
   [Yang, Bo; Yang, Meifang] Jiangxi Univ Finance & Econ, Inst Informat Resources Management, Nanchang 330013, Jiangxi, Peoples R China.
RP Yang, MF (通讯作者)，Jiangxi Univ Finance & Econ, Sch Informat Management, Nanchang 30013, Jiangxi, Peoples R China.; Yang, MF (通讯作者)，Jiangxi Univ Finance & Econ, Inst Informat Resources Management, Nanchang 330013, Jiangxi, Peoples R China.
EM yangbo@jxufe.edu.cn; yangmeifang@jxufe.edu.cn
FU National Natural Science Foundation of China [72064015]; 13th Five-Year
   Plan Project of Social Sciences in Jiangxi Province-Key Projects
   [19TQ01]
FX This work is supported by the National Natural Science Foundation of
   China(Grant No. 72064015), the 13th Five-Year Plan Project of Social
   Sciences in Jiangxi Province-Key Projects(Grant No. 19TQ01).
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TC 2
Z9 2
U1 18
U2 29
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 12
SI SI
BP 9455
EP 9470
DI 10.1007/s00521-021-06382-z
EA AUG 2021
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 1H6NM
UT WOS:000687925600001
PM 34456516
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Kong, XL
   Han, WN
   Liao, L
   Li, BX
AF Kong, Xianglong
   Han, Weina
   Liao, Li
   Li, Bixin
TI An analysis of correctness for API recommendation: are the unmatched
   results useless?
SO SCIENCE CHINA-INFORMATION SCIENCES
LA English
DT Article
DE API recommendation; onsite programming; correctness; evaluation of
   recommendation
AB API recommendation is a promising approach which is widely used during software development. However, the evaluation of API recommendation is not explored with sufficient rigor. The current evaluation of API recommendation mainly focuses on correctness, the measurement is conducted by matching recommended results with ground-truth results. In most cases, there is only one set of ground-truth APIs for each recommendation attempt, but the object code can be implemented in dozens of ways. The neglect of code diversity results in a possible defect in the evaluation. To address the problem, we invite 15 developers to analyze the unmatched results in a user study. The online evaluation confirms that some unmatched APIs can also benefit to programming due to the functional correlation with ground-truth APIs. Then we measure the API functional correlation based on the relationships extracted from API knowledge graph, API method name, and API documentation. Furthermore, we propose an approach to improve the measurement of correctness based on API functional correlation. Our measurement is evaluated on a dataset of 6141 requirements and historical code fragments from related commits. The results show that 28.2% of unmatched APIs can contribute to correctness in our experiments.
C1 [Kong, Xianglong; Han, Weina; Liao, Li; Li, Bixin] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
RP Li, BX (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
EM bx.li@seu.edu.cn
OI Kong, Xianglong/0000-0002-2448-2214
FU National Key R&D Program of China [2018YFB100-3900]; National Natural
   Science Foundation of China [61402103, 61572126, 61872078]; Open
   Research Fund of Key Laboratory of Safety-Critical Software Fund
   (Nanjing University of Aeronautics and Astronautics) [NJ2019006]; Key
   Laboratory of Computer Network and Information Integration of the
   Ministry of Education of China [93K-9]
FX This work was supported in part by National Key R&D Program of China
   (Grant No. 2018YFB100-3900), in part by National Natural Science
   Foundation of China (Grant Nos. 61402103, 61572126, 61872078), in part
   by Open Research Fund of Key Laboratory of Safety-Critical Software Fund
   (Nanjing University of Aeronautics and Astronautics) (Grant No.
   NJ2019006), and in part by Key Laboratory of Computer Network and
   Information Integration of the Ministry of Education of China (Grant No.
   93K-9).
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NR 28
TC 1
Z9 1
U1 0
U2 11
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1674-733X
EI 1869-1919
J9 SCI CHINA INFORM SCI
JI Sci. China-Inf. Sci.
PD AUG 13
PY 2020
VL 63
IS 9
AR 190103
DI 10.1007/s11432-019-2929-9
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA NG9SW
UT WOS:000564322700001
DA 2022-08-23
ER

PT J
AU Banerjee, S
   Khapra, MM
AF Banerjee, Suman
   Khapra, Mitesh M.
TI Graph Convolutional Network with Sequential Attention for Goal-Oriented
   Dialogue Systems
SO TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
LA English
DT Article
AB Domain-specific goal-oriented dialogue systems typically require modeling three types of inputs, namely, (i) the knowledge-base associated with the domain, (ii) the history of the conversation, which is a sequence of utterances, and (iii) the current utterance for which the response needs to be generated. While modeling these inputs, current state-of-the-art models such as Mem2Seq typically ignore the rich structure inherent in the knowledge graph and the sentences in the conversation context. Inspired by the recent success of structure-aware Graph Convolutional Networks (GCNs) for various NLP tasks such as machine translation, semantic role labeling, and document dating, we propose a memory-augmented GCN for goal-oriented dialogues. Our model exploits (i) the entity relation graph in a knowledge-base and (ii) the dependency graph associated with an utterance to compute richer representations for words and entities. Further, we take cognizance of the fact that in certain situations, such as when the conversation is in a code-mixed language, dependency parsers may not be available. We show that in such situations we could use the global word co-occurrence graph to enrich the representations of utterances. We experiment with four datasets: (i) the modified DSTC2 dataset, (ii) recently released code-mixed versions of DSTC2 dataset in four languages, (iii) Wizard-of-Oz style CAM676 dataset, and (iv) Wizard-of-Oz style MultiWOZ dataset. On all four datasets our method outperforms existing methods, on a wide range of evaluation metrics.
C1 [Banerjee, Suman; Khapra, Mitesh M.] Indian Inst Technol Madras, Robert Bosch Ctr Data Sci & Artificial Intelligen, Dept Comp Sci & Engn, Chennai, Tamil Nadu, India.
RP Banerjee, S (通讯作者)，Indian Inst Technol Madras, Robert Bosch Ctr Data Sci & Artificial Intelligen, Dept Comp Sci & Engn, Chennai, Tamil Nadu, India.
EM suman@cse.iitm.ac.in; miteshk@cse.iitm.ac.in
FU Accenture Technology Labs, India
FX We would like to thank the anonymous reviewers and the action editor for
   their insightful comments and suggestions. We would like to thank the
   Department of Computer Science and Engineering, IIT Madras and Robert
   Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI),
   IIT Madras for providing the necessary resources. We would also like to
   thank Accenture Technology Labs, India, for supporting our work through
   their generous academic research grant.
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NR 42
TC 11
Z9 12
U1 1
U2 1
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2307-387X
J9 T ASSOC COMPUT LING
JI Trans. Assoc. Comput. Linguist.
PY 2019
VL 7
BP 485
EP 500
DI 10.1162/tacl_a_00284
PG 16
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 VK5UX
UT WOS:000736523200031
OA gold
DA 2022-08-23
ER

PT J
AU Necasky, M
   Skoda, P
   Bernhauer, D
   Klimek, J
   Skopal, T
AF Necasky, Martin
   Skoda, Petr
   Bernhauer, David
   Klimek, Jakub
   Skopal, Tomas
TI Modular framework for similarity-based dataset discovery using external
   knowledge
SO DATA TECHNOLOGIES AND APPLICATIONS
LA English
DT Article; Early Access
DE Dataset; Discovery; Search; Framework; Similarity; Knowledge graph
AB Purpose Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth. Design/methodology/approach In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery. Findings The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework. Originality/value To the best of authors' knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.
C1 [Necasky, Martin; Skoda, Petr; Bernhauer, David; Klimek, Jakub; Skopal, Tomas] Charles Univ Prague, Fac Math & Phys, Dept Software Engn, Prague, Czech Republic.
RP Klimek, J (通讯作者)，Charles Univ Prague, Fac Math & Phys, Dept Software Engn, Prague, Czech Republic.
EM jakub.klimek@matfyz.cuni.cz
RI Klímek, Jakub/K-1134-2014; Skoda, Petr/D-8810-2017; Necasky,
   Martin/A-4873-2009
OI Klímek, Jakub/0000-0001-7234-3051; Skoda, Petr/0000-0002-2732-9370;
   Necasky, Martin/0000-0002-5186-7734
FU Czech Science Foundation (GAR) [19-01641S]
FX This work was supported by the Czech Science Foundation (GAR), Grant
   Number 19-01641S.
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   Zezula P, 2015, MOBILE NETW APPL, V20, P487, DOI 10.1007/s11036-014-0547-2
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NR 51
TC 0
Z9 0
U1 3
U2 3
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2514-9288
EI 2514-9318
J9 DATA TECHNOL APPL
JI Data Technol. Appl.
DI 10.1108/DTA-09-2021-0261
EA FEB 2022
PG 30
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA ZF5VE
UT WOS:000759634600001
OA hybrid
DA 2022-08-23
ER

PT J
AU Son, NT
   Jaafar, J
   Aziz, IA
   Anh, BN
AF Son, Ngo Tung
   Jaafar, Jafreezal
   Aziz, Izzatdin Abdul
   Anh, Bui Ngoc
TI Meta-Heuristic Algorithms for Learning Path Recommender at MOOC
SO IEEE ACCESS
LA English
DT Article
DE Optimization; Electronic learning; Data mining; Genetic algorithms;
   Computer aided instruction; Java; Ant colony optimization; Learning
   path; Knowledge-based recommendation; Knowledge graph; multi-objective
   optimization; compromise programming; genetic algorithm; ant colony
   optimization algorithm
AB Online learning platforms, such as Coursera, Edx, Udemy, etc., offer thousands of courses with different content. These courses are often of discrete content. It leads the learner not to find a learning path in a vast volume of courses and contents, especially when they have no experience in advance. Streamlining the order of courses to create a well-defined learning path can help e-learners achieve their learning goals effectively and systematically. The learners usually ask the necessary skills that they expect to earn (query). The need is to develop a recommender system that can search for suitable learning paths. This study proposes a multi-objective optimization model as a knowledge-based recommender. Our model can generate an appropriate learning path for learners based on their background and job goals. The recommended studying path satisfies several learner criteria, such as the critical learning path, number of enrollments, learning duration, popularity, rating of previous learners, and cost. We have developed Metaheuristic algorithms includes the Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), to solve the proposed model. Finally, we tested proposed methods with a dataset consisting of Coursera's courses and Vietnam work's jobs. The test results show the effectiveness of the proposed method.
C1 [Son, Ngo Tung; Jaafar, Jafreezal; Aziz, Izzatdin Abdul] Univ Teknol Petronas, Dept Comp & Informat Sci, Seri Iskandar 32610, Perak, Malaysia.
   [Son, Ngo Tung; Anh, Bui Ngoc] FPT Univ, Informat & Commun Technol Dept, Hanoi 100000, Vietnam.
RP Son, NT (通讯作者)，Univ Teknol Petronas, Dept Comp & Informat Sci, Seri Iskandar 32610, Perak, Malaysia.; Son, NT (通讯作者)，FPT Univ, Informat & Commun Technol Dept, Hanoi 100000, Vietnam.
EM sonnt69@fe.edu.vn
RI Aziz, Izzatdin Abdul/AAT-3037-2021
OI Aziz, Izzatdin Abdul/0000-0003-2654-4463; Ngo, Tung
   Son/0000-0003-4098-3147; Anh, Bui Ngoc/0000-0001-6703-3868
FU FPT University, Hanoi, Vietnam [1097/QD-DHFPT]
FX This work was supported by the FPT University, Hanoi, Vietnam under
   Grant 1097/QD-DHFPT.
CR Aguaded-Gomez JI, 2013, COMUNICAR, V21, P7, DOI 10.3916/C41-2013-a1
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NR 34
TC 8
Z9 8
U1 7
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 2021
VL 9
BP 59093
EP 59107
DI 10.1109/ACCESS.2021.3072222
PG 15
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 RQ9SP
UT WOS:000642751600001
OA gold
DA 2022-08-23
ER

PT J
AU Huang, LA
   Luo, XF
AF Huang, Li-An
   Luo, Xiangfeng
TI EASA: Entity Alignment Algorithm Based on Semantic Aggregation and
   Attribute Attention
SO IEEE ACCESS
LA English
DT Article
DE Entity alignment; semantic aggregation; attribute attention; knowledge
   base embedding
AB Numerous knowledge bases have been published on the web, and there are serious heterogeneous problems among them. Unifying these knowledge bases at the semantic level can better promote the development of the Linked Data Project. Various effective methods, the mainstream one of which is the iterative entity alignment algorithm based on TransE (Translation-based Embedding, an efficient knowledge graph embedding representation algorithm), have been put forward to solve the heterogeneous problems among knowledge bases. Although the TransE-based iterative entity alignment algorithm can shorten the distance between two entities with the same semantics, it has low accuracy because it ignores the importance of semantic aggregation generated by many attributes of entities in the entity alignment process. To solve this problem, a novel entity alignment algorithm based on semantic aggregation and attribute attention, named EASA, is proposed in this paper. On the one hand, semantic aggregation of entities can be generated by different attributes and attribute values. On the other hand, the addition of attribute attention can be used to distinguish the different roles of different attributes in the entity alignment process. The experimental results show that our method achieves significant improvements compared to baselines for entity alignment on Chinese and English datasets. The data and source code for this paper can be obtained from https://www.github.com/xinan711456/EASA.
C1 [Huang, Li-An; Luo, Xiangfeng] Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China.
RP Huang, LA; Luo, XF (通讯作者)，Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China.
EM xinan@i.shu.edu.cn; luoxf@shu.edu.cn
FU National Science Foundation of China [91746203, 61471232, 61625304];
   Shanghai Science International Cooperation Project [16550720400]
FX This work was supported in part by the National Science Foundation of
   China under Grant 91746203, Grant 61471232, and Grant 61625304, and in
   part by the Shanghai Science International Cooperation Project under
   Grant 16550720400.
CR [Anonymous], 2009, LDOW
   [Anonymous], [No title captured]
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NR 28
TC 2
Z9 3
U1 4
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 18162
EP 18170
DI 10.1109/ACCESS.2020.2968620
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LB6PC
UT WOS:000524754700023
OA gold
DA 2022-08-23
ER

PT J
AU Li, YB
   Ma, ZC
   Gao, LS
   Wu, YC
   Xie, F
   Ren, XY
AF Li, Yibing
   Ma, Zuchang
   Gao, Lisheng
   Wu, Yichen
   Xie, Fei
   Ren, Xiaoye
TI Enhance prototypical networks with hybrid attention and confusing loss
   function for few-shot relation classification
SO NEUROCOMPUTING
LA English
DT Article
DE Relation classification; Few-shot learning; Hybrid attention; Loss; BERT
AB Relation classification (RC) is a fundamental task to building knowledge graphs and describing semantic formalization. It aims to classify a relation between the head and the tail entities in a sentence. The existing RC method mainly adopts the distant supervision (DS) scheme. However, DS still has the problem of long-tail and suffers from data sparsity. Recently, few-shot learning (FSL) has attracted people's attention. It solves the long-tail problem by learning from few-shot samples. The prototypical networks have a better effect on FSL, which classifies a relation by distance. However, the prototypical networks and their related variants did not consider the critical role of entity words. In addition, not all sentences in support set equally contributed to classifying relations. Furthermore, an entity pair in a sentence may have true and confusing relations, which is difficult for the RC model to distinguish them. A new context encoder BERT_FE is proposed to address those problems, which uses the BERT model as pre-training and fuses the information of head and tail entities by entity word-level attention (WLA). At the same time, the sentence-level attention (SLA) is proposed to give more weight to sentences of the support set similar to the query instance and improve the classification accuracy. A confusing loss function (CLF) is designed to enhance the model's ability to distinguish between true and confusing relations. The experiment results demonstrate that our proposed model (HACLF) is better than several baseline models. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Li, Yibing; Ma, Zuchang; Gao, Lisheng; Wu, Yichen] Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Anhui Prov Key Lab Med Phys & Technol, Hefei 230031, Peoples R China.
   [Li, Yibing; Wu, Yichen] Univ Sci & Technol China, Sci Isl Branch Grad Sch, Hefei 230026, Peoples R China.
   [Li, Yibing; Xie, Fei; Ren, Xiaoye] Hefei Normal Univ, Sch Comp Sci & Technol, Hefei 230601, Peoples R China.
   [Wu, Yichen] Anhui Jianzhu Univ, Sch Elect & Informat Engn, Hefei 230601, Peoples R China.
RP Ma, ZC (通讯作者)，Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Anhui Prov Key Lab Med Phys & Technol, Hefei 230031, Peoples R China.
EM zcma@iim.ac.cn
FU National Key Research and Development Program of China [2017YFB1002200,
   2020YFC2005603]; National Natural Science Foundation of China (NSFC)
   [61701482]; Key Projects of the National Natural Science Foundation of
   Universities in Anhui Province [KJ2020A0112]; High-level Talents
   Research Start-up Fund of Hefei Normal University [2020rcjj45]; Natural
   Science Foundation of Anhui Province, China [1808085MF191]
FX Acknowledgments This research was supported by the National Key Research
   and Development Program of China (grant numbers 2017YFB1002200 and
   2020YFC2005603) , the National Natural Science Foundation of China
   (NSFC) (grant numbers 61701482) , the Key Projects of the National
   Natural Science Foundation of Universities in Anhui Province (grant
   number KJ2020A0112) , the High-level Talents Research Start-up Fund of
   Hefei Normal University (grant number 2020rcjj45) and the Natural
   Science Foundation of Anhui Province, China (grant number 1808085MF191)
   . In addition, we would like to thank the anonymous reviewers who have
   helped to improve the paper.
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NR 40
TC 0
Z9 0
U1 18
U2 18
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JUL 7
PY 2022
VL 493
BP 362
EP 372
DI 10.1016/j.neucom.2022.04.067
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1G9TL
UT WOS:000796189800004
DA 2022-08-23
ER

PT J
AU Angioni, S
   Salatino, A
   Osborne, F
   Recupero, DR
   Motta, E
AF Angioni, Simone
   Salatino, Angelo
   Osborne, Francesco
   Recupero, Diego Reforgiato
   Motta, Enrico
TI The AIDA Dashboard: A Web Application for Assessing and Comparing
   Scientific Conferences
SO IEEE ACCESS
LA English
DT Article
DE Ontologies; Computer science; Metadata; Measurement; Industries;
   Licenses; Collaboration; Scholarly data; knowledge graph; conference
   analytics; bibliographic data; scholarly ontologies; science of science
ID WORKFLOW
AB Scientific conferences are essential for developing active research communities, promoting the cross-pollination of ideas and technologies, bridging between academia and industry, and disseminating new findings. Analyzing and monitoring scientific conferences is thus crucial for all users who need to take informed decisions in this space. However, scholarly search engines and bibliometric applications only provide a limited set of analytics for assessing research conferences, preventing us from performing a comprehensive analysis of these events. In this paper, we introduce the AIDA Dashboard, a novel web application, developed in collaboration with Springer Nature, for analyzing and comparing scientific conferences. This tool introduces three major new features: 1) it enables users to easily compare conferences within specific fields (e.g., Digital Libraries) and time-frames (e.g., the last five years); 2) it characterises conferences according to a 14K research topics from the Computer Science Ontology (CSO); and 3) it provides several functionalities for assessing the involvement of commercial organizations, including the ability to characterize industrial contributions according to 66 industrial sectors (e.g., automotive, financial, energy, electronics) from the Industrial Sectors Ontology (INDUSO). We evaluated the AIDA Dashboard by performing both a quantitative evaluation and a user study, obtaining excellent results in terms of quality of the analytics and usability.
C1 [Angioni, Simone; Recupero, Diego Reforgiato] Univ Cagliari, Dept Math & Comp Sci, I-09124 Cagliari, Italy.
   [Salatino, Angelo; Osborne, Francesco; Motta, Enrico] Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England.
   [Osborne, Francesco] Univ Milano Bicocca, Dept Business & Law, I-20126 Milan, Italy.
RP Angioni, S (通讯作者)，Univ Cagliari, Dept Math & Comp Sci, I-09124 Cagliari, Italy.
EM simone.angioni@unica.it
OI Osborne, Francesco/0000-0001-6557-3131; Angioni,
   Simone/0000-0002-6682-3419; Motta, Enrico/0000-0003-0015-1952;
   Reforgiato Recupero, Diego/0000-0001-8646-6183; Salatino, Angelo
   A./0000-0002-4763-3943
FU ASTRID project (Fondazione di Sardegna) [CUP: F75F21001220007, 7]
FX This work was partially supported by the ASTRID project (Fondazione di
   Sardegna, L.R. 7 agosto 2007, no7, CUP: F75F21001220007).
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NR 52
TC 0
Z9 0
U1 0
U2 0
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 39471
EP 39486
DI 10.1109/ACCESS.2022.3166256
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3Q7IZ
UT WOS:000838401400001
OA Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Shen, Y
   Zhang, LZ
   Zhang, J
   Yang, M
   Tang, BZ
   Li, YL
   Lei, K
AF Shen, Ying
   Zhang, Lizhu
   Zhang, Jin
   Yang, Min
   Tang, Buzhou
   Li, Yaliang
   Lei, Kai
TI CBN: Constructing a clinical Bayesian network based on data from the
   electronic medical record
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Bayesian network; Ontology; Probabilistic inference; Disease diagnosis
ID DIAGNOSIS; INFERENCE; LANGUAGE
AB The process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise statistics. We explore CBN, a Clinical Bayesian Network construction for medical ontology probabilistic inference, to learn high-quality Bayesian topology and complete ontology directly from EMRs. Specifically, we first extract medical entity relationships from over 10,000 deidentified patient records and adopt the odds ratio (OR value) calculation and the K2 greedy algorithm to automatically construct a Bayesian topology. Then, Bayesian estimation is used for the probability distribution. Finally, we employ a Bayesian network to complete the causal relationship and probability distribution of ontology to enhance the ontology inference capability. By evaluating the learned topology versus the expert opinions of physicians and entropy calculations and by calculating the ontology-based diagnosis classification, our study demonstrates that the direct and automated construction of a high-quality health topology and ontology from medical records is feasible.
   Our results are reproducible, and we will release the source code and CN-Stroke knowledge graph of this work after publication.
C1 [Shen, Ying; Zhang, Lizhu; Zhang, Jin; Lei, Kai] Peking Univ, Sch Elect & Comp Engn, ICNLAB, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China.
   [Yang, Min] Chinese Acad Sci, SIAT, Shenzhen 518055, Peoples R China.
   [Tang, Buzhou] Harbin Inst Technol Shenzhen, Sch Comp Sci & Technol, Shenzhen 518055, Peoples R China.
   [Li, Yaliang] Tencent Med AI Lab, Palo Alto, CA USA.
RP Lei, K (通讯作者)，Peking Univ, Sch Elect & Comp Engn, ICNLAB, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China.
EM shenying@pkusz.edu.cn; 1501213982@pkusz.edu.cn; zhangjin@sz.pku.edu.cn;
   min.yang@siat.ac.cn; tangbuzhou@hit.edu.cn; yaliangli@tencent.com;
   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]; National
   Natural Science Foundation of Guangdong [2018A030313017]; Shenzhen Key
   Fundamental Research Projects [JCYJ2015030154330711,
   JCYJ20151014093505032, JCYJ20170818091546869]
FX This work was financially supported by the National Natural Science
   Foundation of China (No. 61602013), National Natural Science Foundation
   of Guangdong (No. 2018A030313017) and the Shenzhen Key Fundamental
   Research Projects (Grant No. JCYJ2015030154330711, JCYJ20151014093505032
   and JCYJ20170818091546869).
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NR 38
TC 16
Z9 17
U1 1
U2 16
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD DEC
PY 2018
VL 88
BP 1
EP 10
DI 10.1016/j.jbi.2018.10.007
PG 10
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA HO0NK
UT WOS:000460600200001
PM 30399432
OA Bronze
DA 2022-08-23
ER

PT J
AU Jalandoni, A
   Zhang, YS
   Zaidi, NA
AF Jalandoni, Andrea
   Zhang, Yishuo
   Zaidi, Nayyar A.
TI On the use of Machine Learning methods in rock art research with
   application to automatic painted rock art identification
SO JOURNAL OF ARCHAEOLOGICAL SCIENCE
LA English
DT Article
DE Rock art; Pictograms; Machine Learning; Deep learning; Classification
ID NORTHERN AUSTRALIA; CHALLENGES; HERITAGE
AB Rock art is globally recognized as significant, yet the resources allocated to the study and exploration of this important form of cultural heritage are often scarce. In areas where numerous rock art sites exist, much of the rock art is unidentified and therefore remains, unrecorded and unresearched. Manually identifying rock art is time-consuming, tedious, and expensive. Therefore, it is necessary to automate many processes in rock art research, which can be accomplished by Machine Learning. Artificial Intelligence (AI) and Machine Learning (ML) can greatly facilitate rock art research in many ways, such as through Object Recognition and Detection, Motif Extraction, Object Reconstruction, Image Knowledge Graphs, and Representations. This article is a reflective work on the future of ML for rock art research. As a proof-of-concept, it presents a machine learning method based on recent advances in deep learning to train a model to identify images with painted rock art (pictograms). The efficacy of the proposed method is shown using data collected from fieldwork in Australia. Furthermore, our proposed method can be used to train models that are specific to the rock art found in different regions. We provide the code and the trained models in the supplementary section.
C1 [Jalandoni, Andrea] Griffith Univ, Griffith Ctr Social & Cultural Res, Gold Coast, Qld 4222, Australia.
   [Zhang, Yishuo; Zaidi, Nayyar A.] Deakin Univ, Fac Sci, Sch Informat Technol, Engn & Built Environm, Burwood, Vic 3220, Australia.
RP Jalandoni, A (通讯作者)，Griffith Univ, Griffith Ctr Social & Cultural Res, Gold Coast, Qld 4222, Australia.
EM a.jalandoni@griffith.edu.au
OI Jalandoni, Andrea/0000-0002-4821-7183
FU Australian Research Council; Australian rock art: history, conservation
   and Indigenous well-being, part of Paul Tacons ARC Laureate Project
   [FL160100123]
FX This research was undertaken as part of the Pathways: People, land-scape
   and rock art project and was funded by the Australian Research Council
   grant Australian rock art: history, conservation and Indigenous
   well-being, part of Paul Tacons ARC Laureate Project (FL160100123).As
   partners in this research, Jeffrey Lee and Parks Australia have provided
   much support, and we thank them for their collaboration. Wewould also
   like to thank Paul S. C. Tacon, Chantelle Bayes, Sally K. May,Tracey and
   Oz Sahin, Susan Jarvis, Joakim Goldhahn and the GriffithCentre for
   Social and Cultural Research for their contributions.
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NR 71
TC 0
Z9 0
U1 4
U2 4
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 0305-4403
EI 1095-9238
J9 J ARCHAEOL SCI
JI J. Archaeol. Sci.
PD AUG
PY 2022
VL 144
AR 105629
DI 10.1016/j.jas.2022.105629
PG 14
WC Anthropology; Archaeology; Geosciences, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Anthropology; Archaeology; Geology
GA 2T7LT
UT WOS:000822652100006
DA 2022-08-23
ER

PT J
AU Makni, B
   Hendler, J
AF Makni, Bassem
   Hendler, James
TI Deep learning for noise-tolerant RDFS reasoning
SO SEMANTIC WEB
LA English
DT Article
DE Deep learning; Semantic Web; RDFS reasoning; noise-tolerance; neural
   machine translation; graph words
ID GRAPH; KNOWLEDGE; ONTOLOGIES; WEB
AB Since the 2001 envisioning of the Semantic Web (SW) (Scientific American 284(5) (2001) 34-43), the main research focus in SW reasoning has been on the soundness and completeness of reasoners. While these reasoners assume the veracity of input data, the reality is that the Web of data is inherently noisy. Although there has been recent work on noise-tolerant reasoning, it has focused on type inference rather than full RDFS reasoning. Even though RDFS closure generation can be seen as a Knowledge Graph (KG) completion problem, the problem setting is different - making KG embedding techniques that were designed for link prediction not suitable for RDFS reasoning. This paper documents a novel approach that extends noise-tolerance in the SW to full RDFS reasoning. Our embedding technique - that is tailored for RDFS reasoning - consists of layering RDF graphs and encoding them in the form of 3D adjacency matrices where each layer layout forms a graph word. Each input graph and its entailments are then represented as sequences of graph words, and RDFS inference can be formulated as translation of these graph words sequences, achieved through neural machine translation. Our evaluation on LUBM1 synthetic dataset shows 97% validation accuracy and 87.76% on a subset of DBpedia while demonstrating a noise-tolerance unavailable with rule-based reasoners.
C1 [Makni, Bassem] IBM Res, AI Reasoning, Yorktown Hts, NY 10598 USA.
   [Hendler, James] Rensselaer Polytech Inst, TWC, Troy, NY 12180 USA.
RP Makni, B (通讯作者)，IBM Res, AI Reasoning, Yorktown Hts, NY 10598 USA.
EM bassem.makni@ibm.com; hendler@cs.rpi.edu
OI Hendler, James/0000-0003-3056-1960
FU DARPA SMISC; AFRL NS-CTA; IBM HEALS
FX We would like to thank DARPA SMISC, AFRL NS-CTA and IBM HEALS for
   sponsoring different stages of this research.
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NR 97
TC 8
Z9 8
U1 1
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 2019
VL 10
IS 5
BP 823
EP 862
DI 10.3233/SW-190363
PG 40
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:000488082100002
DA 2022-08-23
ER

PT J
AU Yang, LY
   Wang, X
   Dai, YX
   Xin, KJ
   Zheng, XL
   Ding, WP
   Zhang, J
   Wang, FY
AF Yang, Linyao
   Wang, Xiao
   Dai, Yuxin
   Xin, Kejun
   Zheng, Xiaolong
   Ding, Weiping
   Zhang, Jun
   Wang, Fei-Yue
TI HackRL: Reinforcement learning with hierarchical attention for
   cross-graph knowledge fusion and collaborative reasoning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge fusion; Knowledge reasoning; Decision-making; Hierarchical
   graph attention; Reinforcement learning
ID FRAMEWORK
AB Reasoning aiming at inferring implicit facts over knowledge graphs (KGs) is a critical and fundamental task for various intelligent knowledge-based services. With multiple distributed and complementary KGs, the effective and efficient capture and fusion of knowledge from different KGs is becoming an increasingly important topic, which has not been well studied. To fill this gap, we propose to explore cross-KG relation paths with the anchor links identified by entity alignment for the knowledge fusion and collaborative reasoning of multiple KGs. To address the heterogeneity of different KGs, this paper proposes a novel reasoning model named HackRL based on the reinforcement learning framework, which incorporates the long short-term memory and hierarchical graph attention in the policy network to infer indicative cross-KG relation paths from the history trajectory and the heterogeneous environment for predicting corresponding relations. Meanwhile, an entity alignment oriented representation learning method is utilized to embed different KGs into a unified vector space based on the anchor links to reduce the impact of distinct vector spaces, and two training mechanisms, action mask and retrain with sampled paths, are proposed to optimize the training process to learn more successful indicative paths. The proposed HackRL is validated on three cross-lingual datasets built from DBpedia on the link prediction and fact prediction tasks. Experimental results demonstrate that HackRL achieves better performance on most tasks than existing methods. This work provides an industrially-applicable framework for fusing distributed KGs to make better decisions. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Yang, Linyao; Wang, Xiao; Zheng, Xiaolong; Zhang, Jun; Wang, Fei-Yue] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
   [Yang, Linyao] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China.
   [Wang, Xiao; Xin, Kejun; Zhang, Jun; Wang, Fei-Yue] Qingdao Acad Intelligent Ind, Qingdao 266000, Peoples R China.
   [Dai, Yuxin; Zhang, Jun] Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China.
   [Xin, Kejun] Nanjing Joinmap Data Res Inst, Nanjing 211100, Peoples R China.
   [Ding, Weiping] Nantong Univ, Sch Informat Sci & Technol, Nantong 226019, Peoples R China.
RP Wang, X (通讯作者)，Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
EM yanglinyao2017@ia.ac.cn; x.wang@ia.ac.cn; YuxinDai@whu.edu.cn;
   xinkj@joinmap.ai; xiaolong.zheng@ia.ac.cn; ding.wp@ntu.edu.cn;
   jun.zhang.ee@whu.edu.cn; feiyue.wang@ia.ac.cn
OI yang, lin yao/0000-0003-0826-9453; Dai, Yuxin/0000-0001-8805-7236; wang,
   xiao/0000-0002-0008-0659
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NR 62
TC 2
Z9 2
U1 18
U2 27
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 107498
DI 10.1016/j.knosys.2021.107498
EA SEP 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WK7QW
UT WOS:000709919000011
DA 2022-08-23
ER

PT J
AU Gumma, MK
   Thenkabail, PS
   Teluguntla, P
   Rao, MN
   Mohammed, IA
   Whitbread, AM
AF Gumma, Murali Krishna
   Thenkabail, Prasad S.
   Teluguntla, Pardharsadhi
   Rao, Mahesh N.
   Mohammed, Irshad A.
   Whitbread, Anthony M.
TI Mapping rice-fallow cropland areas for short-season grain legumes
   intensification in South Asia using MODIS 250 m time-series data
SO INTERNATIONAL JOURNAL OF DIGITAL EARTH
LA English
DT Article
DE Croplands; cropland fallow; seasonal rice mapping; rice-fallow;
   intensification; kharif; rabi; remote sensing; double-cropping; MODIS
   250 m; NDVI; spectral matching techniques; ground survey data; grain
   legumes; potential cropland areas; South Asia
ID SPECTRAL MATCHING TECHNIQUES; USE/LAND-COVER LULC; FOOD SECURITY;
   IRRIGATED AREAS; CROPPING INTENSITY; RESOLUTION; INDIA; EXTENT; BASIN;
   WATER
AB The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season ( June-October), followed by a fallow during the rabi season ( November-February). These cropland areas are not suitable for growing rabi-season rice due to their high water needs, but are suitable for a short -season (<= 3 months), low water-consuming grain legumes such as chickpea ( Cicer arietinum L.), black gram, green gram, and lentils. Intensification ( double-cropping) in this manner can improve smallholder farmer's incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands. Several grain legumes, primarily chickpea, are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region. The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers: ( a) rice crop is grown during the primary ( kharif) crop growing season or during the north-west monsoon season ( June-October); ( b) same croplands are left fallow during the second ( rabi) season or during the south-east monsoon season ( November-February); and ( c) ability to support low water-consuming, short-growing season (<= 3 months) grain legumes ( chickpea, black gram, green gram, and lentils) during rabi season. Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season, because the moisture/ water demand of these crops is too high. The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index ( NDVI) time series for one year ( June 2010-May 2011) of Moderate Resolution Imaging Spectroradiometer ( MODIS) data, using spectral matching techniques ( SMTs), and extensive field knowledge. Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics. The producers' and users' accuracies of the cropland fallow classes were between 75% and 82%. The overall accuracy and the kappa coefficient estimated for rice classes were 82% and 0.79, respectively. The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia, with 88.3% in India, 0.5% in Pakistan, 1.1% in Sri Lanka, 8.7% in Bangladesh, 1.4% in Nepal, and 0.02% in Bhutan. Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.
C1 [Gumma, Murali Krishna; Mohammed, Irshad A.; Whitbread, Anthony M.] Int Crops Res Inst Semi Arid Trop, Patancheru, Andhra Pradesh, India.
   [Thenkabail, Prasad S.; Teluguntla, Pardharsadhi] US Geol Survey, Western Geog Sci Ctr, Flagstaff, AZ 86001 USA.
   [Rao, Mahesh N.] Humboldt State Univ, Arcata, CA 95521 USA.
RP Gumma, MK (通讯作者)，Int Crops Res Inst Semi Arid Trop, Patancheru, Andhra Pradesh, India.
EM gummamk@gmail.com
OI Gumma, Murali Krishna/0000-0002-3760-3935; Teluguntla,
   Pardhasaradhi/0000-0001-8060-9841
FU CGIAR; WLE; NASA MEaSUREs through NASA ROSES solicitation [GFSAD30,
   NNH13AV82I]; Land Change Science (LCS); Land Remote Sensing (LRS);
   Climate Land Use Change Mission Area Programs of the U.S. Geological
   Survey (USGS)
FX This research was supported by two CGIAR Research Programs: Dryland
   Cereals, Grain legumes and WLE. The research was also supported by the
   global food security support analysis data at 30 m project (GFSAD30;
   http://geography.wr.usgs.gov/science/croplands/; https://croplands.org/)
   funded by the NASA MEaSUREs [grant number: NNH13AV82I] (Making Earth
   System Data Records for Use in Research Environments) funding obtained
   through NASA ROSES solicitation as well as by the Land Change Science
   (LCS), Land Remote Sensing (LRS), and Climate Land Use Change Mission
   Area Programs of the U.S. Geological Survey (USGS).
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   [No title captured]
NR 51
TC 68
Z9 69
U1 4
U2 23
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
PY 2016
VL 9
IS 10
BP 981
EP 1003
DI 10.1080/17538947.2016.1168489
PG 23
WC Geography, Physical; Remote Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physical Geography; Remote Sensing
GA DV5IR
UT WOS:000382961000004
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Majumdar, S
   Bansal, A
   Das, PP
   Clough, PD
   Datta, K
   Ghosh, SK
AF Majumdar, Srijoni
   Bansal, Ayush
   Das, Partha Pratim
   Clough, Paul D.
   Datta, Kausik
   Ghosh, Soumya Kanti
TI Automated evaluation of comments to aid software maintenance
SO JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS
LA English
DT Article
DE code comprehension; comment quality; knowledge graph; machine learning;
   ontology
ID INFORMATION
AB Approaches to evaluate comments based on whether they increase code comprehensibility for software maintenance tasks are important, but largely missing. We propose Comment Probe$$ Probe $$ for automated classification and quality evaluation of code comments of C codebases based on how they can help to understand existing code. We conduct surveys and document developers' perceptions on the type of comments that prove useful to maintaining software in the form of comment categories. A total of 20,206 comments have been collected from open-source Github projects and annotated with assistance from industry experts. We develop features to semantically analyze comments to locate concepts related to categories of usefulness. Additionally, features based on code and comment correlation are designed to infer whether the comment is also consistent and not superfluous. Using neural networks, comments are classified as useful, partially useful, and not useful with precision and recall scores of 86.27% and 86.42%, respectively. The proposed framework for comment quality evaluation incorporates industry practices and adds significant value to companies wanting to formulate better code commenting strategies. Furthermore, large codebases can be de-cluttered by removing comments not helpful in maintaining code.
C1 [Majumdar, Srijoni] Indian Inst Technol Kharagpur, Adv Technol Dev Ctr, Kharagpur, W Bengal, India.
   [Majumdar, Srijoni] TCG Crest Inst Adv Intelligence, Ctr Res & Educ, Kolkata, India.
   [Bansal, Ayush; Das, Partha Pratim; Ghosh, Soumya Kanti] Indian Inst Technol Kharagpur, Dept Comp Sci & Engn, Kharagpur, W Bengal, India.
   [Clough, Paul D.] Peak Indicators, Chesterfield, England.
   [Clough, Paul D.] Univ Sheffield, Informat Sch, Sheffield, S Yorkshire, England.
   [Datta, Kausik] A Siemens Business, Mentor, Kolkata, W Bengal, India.
RP Majumdar, S (通讯作者)，Indian Inst Technol Kharagpur, Adv Technol Dev Ctr, Kharagpur, W Bengal, India.
EM srijoni.majumdar@iitkgp.ac.in
OI majumdar, srijoni/0000-0003-3935-4087
FU National Digital Library of India, Ministry of Education, Government of
   India
FX National Digital Library of India, Ministry of Education, Government of
   India
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NR 73
TC 0
Z9 0
U1 2
U2 2
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2047-7473
EI 2047-7481
J9 J SOFTW-EVOL PROC
JI J. Softw.-Evol. Proc.
PD JUL
PY 2022
VL 34
IS 7
AR e2463
DI 10.1002/smr.2463
EA MAY 2022
PG 32
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Q5LR
UT WOS:000800460000001
DA 2022-08-23
ER

PT J
AU Yang, Y
   Rao, YL
   Yu, MH
   Kang, Y
AF Yang, Yun
   Rao, Yulong
   Yu, Minghao
   Kang, Yan
TI Multi-layer information fusion based on graph convolutional network for
   knowledge-driven herb recommendation
SO NEURAL NETWORKS
LA English
DT Article
DE Traditional Chinese Medicine; Herb recommendation; Graph convolutional
   network; Representation learning
AB Prescription of Traditional Chinese Medicine (TCM) is a precious treasure accumulated in the long-term development of TCM. Artificial intelligence (AI) technology is used to build herb recommendation models to deeply understand regularities in prescriptions, which is of great significance to clinical application of TCM and discovery of new prescriptions. Most of herb recommendation models constructed in the past ignored the nature information of herbs, and most of them used statistical models based on bag-of-words for herb recommendation, which makes it difficult for the model to perceive the complex correlation between symptoms and herbs. In this paper, we introduce the properties of herbs as additional auxiliary information by constructing herb knowledge graph, and propose a graph convolution model with multi-layer information fusion to obtain symptom feature representations and herb feature representations with rich information and less noise. We apply the proposed model to the TCM prescription dataset, and the experiment results show that our model outperforms the baseline models in terms of Precision@5 by 6.2%, Recall@5 by 16.0% and F1-Score@5 by 12.0%. (C) 2021 Elsevier Ltd. All rights reserved.
C1 [Yang, Yun; Rao, Yulong; Yu, Minghao; Kang, Yan] Yunnan Univ, Natl Pilot Sch Software, Kunming 650091, Yunnan, Peoples R China.
RP Kang, Y (通讯作者)，Yunnan Univ, Natl Pilot Sch Software, Kunming 650091, Yunnan, Peoples R China.
EM kangyan@ynu.edu.cn
OI Yang, Yun/0000-0002-9893-3436
FU Natural Science Foundation of China (NSFC) [61876166, 61663046]
FX The authors would like to acknowledge the financial support provided by
   the Natural Science Foundation of China (NSFC) under Grant No. 61876166,
   No. 61663046.
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NR 47
TC 1
Z9 1
U1 23
U2 46
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 2022
VL 146
BP 1
EP 10
DI 10.1016/j.neunet.2021.11.010
EA NOV 2021
PG 10
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA XJ2ET
UT WOS:000726608700001
PM 34826774
DA 2022-08-23
ER

PT J
AU Shang, JY
   Jiang, JZ
   Sun, YN
AF Shang, Jiayu
   Jiang, Jingzhe
   Sun, Yanni
TI Bacteriophage classification for assembled contigs using graph
   convolutional network
SO BIOINFORMATICS
LA English
DT Article; Proceedings Paper
CT Biennial Joint Meeting of 29th Annual Conference on Intelligent Systems
   for Molecular Biology (ISMB) / 20th European Conference on Computational
   biology (ECCB)
CY JUL 25-30, 2021
CL ELECTR NETWORK
ID METAGENOMIC DATA; PHAGE; DISCOVERY; VIRUSES
AB Motivation: Bacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high-throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data.
   Results: In this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools.
C1 [Shang, Jiayu; Sun, Yanni] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China.
   [Jiang, Jingzhe] Chinese Acad Fishery Sci, South China Sea Fisheries Res Inst, Key Lab South China Sea Fishery Resources Exploit, Minist Agr, Guangzhou, Guangdong, Peoples R China.
RP Sun, YN (通讯作者)，City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China.
EM yannisun@cityu.edu.hk
RI SUN, Yanni/AHC-4163-2022; Shang, Jiayu/AAS-1130-2021; Jiang,
   Jing-Zhe/CAI-9184-2022
OI SUN, Yanni/0000-0003-1373-8023; Shang, Jiayu/0000-0001-5974-4985; Jiang,
   Jing-Zhe/0000-0001-5260-7822
FU Research Grants Council of the Hong Kong Special Administrative Region,
   China [CityU 11206819]; HKIDS [9360163]; NSF of China [31972847]
FX This work was supported by the Research Grants Council of the Hong Kong
   Special Administrative Region, China (Project No. CityU 11206819) and
   HKIDS (9360163) and NSF of China (31972847).
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NR 35
TC 5
Z9 5
U1 11
U2 17
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD JUL
PY 2021
VL 37
SU 1
BP I25
EP I33
DI 10.1093/bioinformatics/btab293
PG 9
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA US8VF
UT WOS:000697703700005
PM 34252923
OA Green Published, hybrid, Green Submitted
DA 2022-08-23
ER

PT J
AU Ouyang, YR
   Cai, YP
   Guo, HJ
AF Ouyang, Yiran
   Cai, Yanpeng
   Guo, Hongjiang
TI Visualization and Analysis of Mapping Knowledge Domains for Food Waste
   Studies
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE food waste; mapping knowledge domains; bibliometric analysis; evolution
   path
ID ANAEROBIC CO-DIGESTION; HYDROGEN FERMENTATION; BIOHYDROGEN PRODUCTION;
   KITCHEN WASTE; CONSUMPTION; PRETREATMENT; FEASIBILITY; SUBSTRATE
AB Food waste and loss is a global issue involving ethics, society, the environment, and the economy. However, there is a lack of systematic and visual scientific knowledge and graph methods to study the precedents of this field's development over time. The article is based on the scientific knowledge graph research of articles published in the past 22 years to review the latest food waste research developments. The study will be conducted from the following perspectives: country/region, institution, author, journal, keyword co-occurrence, and article co-citation. It turns out that in the past eight years, food waste research has grown rapidly. A total of 8298 research articles have been published in 8064 journals and 176 Web of Science (WOS) subject categories. Research shows in the past 20 years. The main research hotspots were anaerobic digestion, biogas production, composting, biological hydrogen production, and innovation in system management methods. In the future, efficient and multitask biological value-added conversion technology, systematization of food-supply-chain decision-making aid models, and research on differences in management strategies may become the frontiers of research.
C1 [Ouyang, Yiran; Cai, Yanpeng; Guo, Hongjiang] Guangdong Univ Technol, Inst Environm & Ecol Engn, Guangdong Prov Key Lab Water Qual Improvement & E, Guangzhou 510006, Peoples R China.
   [Cai, Yanpeng] Univ Regina, Inst Energy Environm & Sustainable Communities, Regina, SK S4S 0A2, Canada.
RP Cai, YP (通讯作者)，Guangdong Univ Technol, Inst Environm & Ecol Engn, Guangdong Prov Key Lab Water Qual Improvement & E, Guangzhou 510006, Peoples R China.; Cai, YP (通讯作者)，Univ Regina, Inst Energy Environm & Sustainable Communities, Regina, SK S4S 0A2, Canada.
EM Yiran.Ouyang@foxmail.com; yanpeng.cai@gdut.edu.cn;
   guohongjiangzzl@163.com
OI Ouyang, Yiran/0000-0002-0234-307X
FU Beijing Municipal Natural Science Foundation [JQ18028]; Guangdong
   Provincal Science and Technology Project [2020B1111380003]; National
   Natural Science Foundation of China [51879007, U20A20117,51970943]
FX Please add: This research was supported by the Beijing Municipal Natural
   Science Foundation (JQ18028), Guangdong Provincal Science and Technology
   Project (2020B1111380003) and the National Natural Science Foundation of
   China (51879007, U20A20117,51970943).
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NR 43
TC 3
Z9 4
U1 19
U2 44
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD MAY
PY 2021
VL 18
IS 10
AR 5143
DI 10.3390/ijerph18105143
PG 17
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 SI5ZU
UT WOS:000654906100001
PM 34066262
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Golovnev, A
   Regev, O
   Weinstein, O
AF Golovnev, Alexander
   Regev, Oded
   Weinstein, Omri
TI The Minrank of Random Graphs
SO IEEE TRANSACTIONS ON INFORMATION THEORY
LA English
DT Article; Proceedings Paper
CT 21st International Workshop on Randomization and Computation
CY AUG 16-18, 2017
CL Berkeley, CA
DE Index coding; minrank; linear index coding
ID COMMUNICATION COMPLEXITY; CHROMATIC NUMBER; INFORMATION; BROADCAST;
   DEMAND; ISCOD
AB The minrank of a directed graph G is the minimum rank of a matrix M that can be obtained from the adjacency matrix of G by switching some ones to zeros (i.e., deleting edges) and then setting all diagonal entries to one. This quantity is closely related to the fundamental information-theoretic problems of (linear) index coding (Bar-Yossef et al.), network coding (Effros et al.), and distributed storage (Mazumdar, ISIT, 2014). We prove tight bounds on the minrank of directed Erdos-Renyi random graphs G(n, p) for all regimes of p is an element of[0, 1]. In particular, for any constant p, we show that minrk(G) = Theta (n/log n) with high probability, where G is chosen from G(n, p). This bound gives a near quadratic improvement over the previous best lower bound of Omega (root n) (Haviv and Langberg), and partially settles an open problem raised by Lubetzky and Stav. Our lower bound matches the well-known upper bound obtained by the "clique covering" solution and settles the linear index coding problem for random knowledge graphs.
C1 [Golovnev, Alexander] Yahoo Res, New York, NY 10036 USA.
   [Regev, Oded] NYU, Courant Inst Math Sci, 251 Mercer St, New York, NY 10012 USA.
   [Weinstein, Omri] Columbia Univ, New York, NY 10027 USA.
RP Golovnev, A (通讯作者)，Yahoo Res, New York, NY 10036 USA.
EM alexgolovnev@gmail.com
FU National Science Foundation [CCF-1320188]; Simons Junior Fellowship;
   Simons Collaboration on Algorithms and Geometry
FX This paper was presented at the Proceedings of the 2017 21st
   International Workshop on Randomization and Computation. This work was
   supported in part by the Simons Collaboration on Algorithms and Geometry
   and in part by the National Science Foundation under Grant CCF-1320188.
   Any opinions, findings, and conclusions or recommendations expressed in
   this material are those of the authors and do not necessarily reflect
   the views of the NSF. O. Weinstein was supported by a Simons Junior
   Fellowship.
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NR 24
TC 5
Z9 5
U1 0
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9448
EI 1557-9654
J9 IEEE T INFORM THEORY
JI IEEE Trans. Inf. Theory
PD NOV
PY 2018
VL 64
IS 11
BP 6990
EP 6995
DI 10.1109/TIT.2018.2810384
PG 6
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA GX8IT
UT WOS:000448029300005
OA hybrid
DA 2022-08-23
ER

PT J
AU Jing, Y
   Xu, ZW
   Gao, GL
AF Jing, Yun
   Zhiwei Xu
   Guanglai Gao
TI Context-Driven Image Caption With Global Semantic Relations of the Named
   Entities
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Visualization; Task analysis; Computational modeling;
   Knowledge based systems; Correlation; Organizations; Image caption;
   named entity; semantic relation
ID GENERATION
AB Automatic image captioning has achieved a great progress. However, the existing captioning frameworks basically enumerate the objects in the image. The generated captions lack the real-world knowledge about named entities and their relations, such as the relations among famous persons, organizations and buildings. On the contrary, humans interpret images in a specific way by providing real-world knowledge with relations of the aforementioned named entities. To generate human-like captions, we focus on captioning the images of news, which could provide real-world knowledge of the whole story behind the images. Then we propose a novel model that makes captions closer to the human-like description of the image, by leveraging the semantic relevance of the named entities. The named entities are not only extracted from news under the guidance of the image content, but also extended with external knowledge based on the semantic relations. In detail, we propose a sentence correlation analysis algorithm to selectively draw the contextual information from news, and use entity-linking algorithm based on knowledge graph to discover the relations of entities with a global sight. The results of extensive experiments based on real-world dataset which is collected from the news show that our model generates image captions closer to the corresponding real-world captions.
C1 [Jing, Yun; Guanglai Gao] Inner Mongolia Univ, Dept Comp Sci, Hohhot 010021, Peoples R China.
   [Zhiwei Xu] Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China.
   [Jing, Yun] Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 010080, Peoples R China.
RP Jing, Y (通讯作者)，Inner Mongolia Univ, Dept Comp Sci, Hohhot 010021, Peoples R China.; Jing, Y (通讯作者)，Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 010080, Peoples R China.
EM yunjing_zoe@163.com
FU Natural Science Foundation of China [61650205, 61540004]; Natural
   Science Foundation of Inner Mongolia Autonomous Region [2018MS06003,
   2020MS06025]; Natural Science Foundation of Education Department Inner
   Mongolian Autonomous Region [NJZY19083]
FX This work was supported in part by the Natural Science Foundation of
   China under Grant 61650205 and Grant 61540004, in part by the Natural
   Science Foundation of Inner Mongolia Autonomous Region under Grant
   2018MS06003 and Grant 2020MS06025, and in part by the Natural Science
   Foundation of Education Department Inner Mongolian Autonomous Region
   under Grant NJZY19083.
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NR 47
TC 4
Z9 4
U1 3
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 143584
EP 143594
DI 10.1109/ACCESS.2020.3013321
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NB2GU
UT WOS:000560332200001
OA gold
DA 2022-08-23
ER

PT J
AU Xue, B
   Tong, NN
AF Xue, Bin
   Tong, Ningning
TI Real-World ISAR Object Recognition Using Deep Multimodal Relation
   Learning
SO IEEE TRANSACTIONS ON CYBERNETICS
LA English
DT Article
DE Convolution; Object recognition; Kernel; Image resolution; Feature
   extraction; Cybernetics; Convolutional neural networks; Attribute
   learning; deep learning; deformable; image matching; object recognition;
   radar; real-world environment; relation learning; statistic
ID NETWORKS
AB Real-world inverse synthetic aperture radar (ISAR) object recognition is a critical and challenging problem in computer vision tasks. In this article, an efficient real-world ISAR object recognition method is proposed, namely, real-world ISAR object recognition (RIOR), based on deep multimodal relation learning (DMRL). It cannot only handle the complex multimodal recognition problem efficiently but also exploit the relations among the features, attributes, labels, and classes with semantic knowledge: 1) an adaptive multimodal mechanism (AMM) is proposed in convolutional neural network (CNN) to substantially promote the CNN sampling and transformation capability and significantly raise the output feature map resolutions by keeping almost all of the information; 2) deep attribute relation graph learning (DARGL) is proposed to jointly estimate the large numbers of heterogeneous attributes and collaboratively explore the relations among the features, attributes, labels, and classes with common knowledge graphs; and 3) relational-regularized convolutional sparse learning (RCSL) is proposed to further achieve good translation invariance and improve the accuracy and speed of the entire system. Extensive qualitative and quantitative experiments are performed on two real-world ISAR datasets, demonstrating that RIOR outperforms the state-of-the-art methods while running quickly.
C1 [Xue, Bin; Tong, Ningning] Air Force Engn Univ, Grad Sch, Xian 710051, Peoples R China.
RP Xue, B (通讯作者)，Air Force Engn Univ, Grad Sch, Xian 710051, Peoples R China.
EM xxbbxl@sina.com
FU National Natural Science Foundation of China [61571459, 61631019,
   61701526]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61571459, Grant 61631019, and Grant
   61701526. This article was recommended by Associate Editor M. Shin.
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NR 35
TC 19
Z9 20
U1 5
U2 28
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2168-2267
EI 2168-2275
J9 IEEE T CYBERNETICS
JI IEEE T. Cybern.
PD OCT
PY 2020
VL 50
IS 10
BP 4256
EP 4267
DI 10.1109/TCYB.2019.2933224
PG 12
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA NT0EM
UT WOS:000572625500006
PM 31449040
DA 2022-08-23
ER

PT J
AU Wu, HH
   Zhong, YC
   Tian, YJ
   Jiang, S
   Luo, LY
AF Wu, Huanhuan
   Zhong, Yichen
   Tian, Yingjie
   Jiang, Shan
   Luo, Lingyun
TI Automatic diagnosis of COVID-19 infection based on ontology reasoning
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE COVID-19; Ontology; Diagnostic rules; SWRL rules; Automated diagnosis
AB Background 2019-nCoV has been spreading around the world and becoming a global concern. To prevent further widespread of 2019-nCoV, confirmed and suspected cases of COVID-19 infection are suggested to be kept in quarantine. However, the diagnose of COVID-19 infection is quite time-consuming and labor-intensive. To alleviate the burden on the medical staff, we have done some research on the intelligent diagnosis of COVID-19. Methods In this paper, we constructed a COVID-19 Diagnosis Ontology (CDO) by utilizing Protege, which includes the basic knowledge graph of COVID-19 as well as diagnostic rules translated from Chinese government documents. Besides, SWRL rules were added into the ontology to infer intimate relationships between people, thus facilitating the efficient diagnosis of the suspected cases of COVID-19 infection. We downloaded real-case data and extracted patients' syndromes from the descriptive text, so as to verify the accuracy of this experiment. Results After importing those real instances into Protege, we demonstrated that the COVID-19 Diagnosis Ontology showed good performances to diagnose cases of COVID-19 infection automatically. Conclusions In conclusion, the COVID-19 Diagnosis Ontology will not only significantly reduce the manual input in the diagnosis process of COVID-19, but also uncover hidden cases and help prevent the widespread of this epidemic.
C1 [Wu, Huanhuan; Zhong, Yichen; Tian, Yingjie; Jiang, Shan; Luo, Lingyun] Univ South China, Sch Comp Sci, 28 West Changsheng Rd, Hengyang 421001, Peoples R China.
   [Luo, Lingyun] Hunan Med Big Data Int Sci & Technol Innovat Coop, Hengyang 421001, Peoples R China.
RP Luo, LY (通讯作者)，Univ South China, Sch Comp Sci, 28 West Changsheng Rd, Hengyang 421001, Peoples R China.
EM luoly@usc.edu.cn
FU Hunan Provincial Natural Science Foundation of China [2019JJ50520];
   National Science Foundation of China [61502221]
FX Publication costs are funded by the Hunan Provincial Natural Science
   Foundation of China (No. 2019JJ50520) and the National Science
   Foundation of China (No. 61502221). 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 [Anonymous], WHO DIRECTOR GEN REM
   Brickley D., FOAF VOCABULARY SPEC
   Brickley DM, FOAF FRIEND FRIEND
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NR 13
TC 0
Z9 0
U1 7
U2 11
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 16
PY 2021
VL 21
IS SUPPL 9
SU 9
SI SI
AR 271
DI 10.1186/s12911-021-01629-0
PG 13
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA WZ3VM
UT WOS:000719898500003
PM 34789243
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yu, SW
   Yang, XB
   Zhang, WS
AF Yu, Shaowei
   Yang, Xuebing
   Zhang, Wensheng
TI PKGCN: prior knowledge enhanced graph convolutional network for
   graph-based semi-supervised learning
SO INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
LA English
DT Article
DE Graph convolutional network; Semi-supervised learning; Prior knowledge;
   Node classification
ID PERFORMANCE; INFORMATION
AB Graph is a widely existed data structure in many real world scenarios, such as social networks, citation networks and knowledge graphs. Recently, Graph Convolutional Network (GCN) has been proposed as a powerful method for graph-based semi-supervised learning, which has the similar operation and structure as Convolutional Neural Networks (CNNs). However, like many CNNs, it is often necessary to go through a lot of laborious experiments to determine the appropriate network structure and parameter settings. Fully exploiting and utilizing the prior knowledge that nearby nodes have the same labels in graph-based neural network is still a challenge. In this paper, we propose a model which utilizes the prior knowledge on graph to enhance GCN. To be specific, we decompose the objective function of semi-supervised learning on graphs into a supervised term and an unsupervised term. For the unsupervised term, we present the concept of local inconsistency and devise a loss term to describe the property in graphs. The supervised term captures the information from the labeled data while the proposed unsupervised term captures the relationships among both labeled data and unlabeled data. Combining supervised term and unsupervised term, our proposed model includes more intrinsic properties of graph-structured data and improves the GCN model with no increase in time complexity. Experiments on three node classification benchmarks show that our proposed model is superior to GCN and seven existing graph-based semi-supervised learning methods.
C1 [Yu, Shaowei; Yang, Xuebing; Zhang, Wensheng] Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China.
   [Yu, Shaowei] Univ Chinese Acad Sci, Beijing, Peoples R China.
RP Yu, SW (通讯作者)，Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China.; Yu, SW (通讯作者)，Univ Chinese Acad Sci, Beijing, Peoples R China.
EM yushaowei2017@ia.ac.cn; yangxuebing2013@ia.ac.cn;
   zhangwenshengia@hotmail.com
FU National Natural Science Foundation of China [U1636220, 61532006];
   Beijing Municipal Natural Science Foundation [4172063]
FX This work was funded by the National Natural Science Foundation of China
   under Grant No. U1636220 and 61532006, and Beijing Municipal Natural
   Science Foundation under Grant No. 4172063.
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NR 44
TC 5
Z9 6
U1 0
U2 0
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1868-8071
EI 1868-808X
J9 INT J MACH LEARN CYB
JI Int. J. Mach. Learn. Cybern.
PD NOV
PY 2019
VL 10
IS 11
BP 3115
EP 3127
DI 10.1007/s13042-019-01003-7
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JK4GT
UT WOS:000494802500009
DA 2022-08-23
ER

PT J
AU Radovic, M
   Petrovic, N
   Tosic, M
AF Radovic, Maja
   Petrovic, Nenad
   Tosic, Milorad
TI An Ontology-Driven Learning Assessment Using the Script Concordance Test
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE ontology; learning assessment platform; script concordance test
ID CLINICAL REASONING ASSESSMENT; GENERATION; EDUCATION; TOOL
AB Assessing the level of domain-specific reasoning acquired by students is one of the major challenges in education particularly in medical education. Considering the importance of clinical reasoning in preclinical and clinical practice, it is necessary to evaluate students' learning achievements accordingly. The traditional way of assessing clinical reasoning includes long-case exams, oral exams, and objective structured clinical examinations. However, the traditional assessment techniques are not enough to answer emerging requirements in the new reality due to limited scalability and difficulty for adoption in online education. In recent decades, the script concordance test (SCT) has emerged as a promising tool for assessment, particularly in medical education. The question is whether the usability of SCT could be raised to a level high enough to match the current education requirements by exploiting opportunities that new technologies provide, particularly semantic knowledge graphs (SCGs) and ontologies. In this paper, an ontology-driven learning assessment is proposed using a novel automated SCT generation platform. SCTonto ontology is adopted for knowledge representation in SCT question generation with the focus on using electronic health records data for medical education. Direct and indirect strategies for generating Likert-type scores of SCT are described in detail as well. The proposed automatic question generation was evaluated against the traditional manually created SCT, and the results showed that the time required for tests creation significantly reduced, which confirms significant scalability improvements with respect to traditional approaches.
C1 [Radovic, Maja] Univ Kragujevac, Fac Tech Sci Cacak, Svetog Save 65, Cacak 32000, Serbia.
   [Petrovic, Nenad; Tosic, Milorad] Univ Nis, Fac Elect Engn, Aleksandra Medvedeva 14, Nish 18000, Serbia.
RP Radovic, M (通讯作者)，Univ Kragujevac, Fac Tech Sci Cacak, Svetog Save 65, Cacak 32000, Serbia.
EM maja.radovic@ftn.kg.ac.rs; nenad.petrovic@elfak.ni.ac.rs;
   milorad.tosic@elfak.ni.ac.rs
OI Radovic, Maja/0000-0002-1983-1707; Petrovic, Nenad/0000-0003-2264-7369;
   Tosic, Milorad/0000-0001-8142-5788
FU Serbian Ministry of Education, Science and Technological Development
   [451-03-9/2021-14/200132, 32051, 47003]
FX FundingThis research received no external funding.
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NR 43
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 FEB
PY 2022
VL 12
IS 3
AR 1472
DI 10.3390/app12031472
PG 16
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA ZE9EY
UT WOS:000759181000001
OA gold
DA 2022-08-23
ER

PT J
AU Iadanza, C
   Trigila, A
   Starace, P
   Dragoni, A
   Biondo, T
   Roccisano, M
AF Iadanza, Carla
   Trigila, Alessandro
   Starace, Paolo
   Dragoni, Alessio
   Biondo, Tommaso
   Roccisano, Marco
TI IdroGEO: A Collaborative Web Mapping Application Based on REST API
   Services and Open Data on Landslides and Floods in Italy
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE web mapping; API; progressive web application; open data; open source;
   landslide inventory; natural hazards
AB The new national IdroGEO web platform allows the navigation, social sharing and download of data, maps, reports of the Italian Landslide Inventory, national hazard maps, and risk indicators. It is a tool for communication and dissemination of information to support decisions in risk mitigation policies, land use planning, preliminary design of infrastructures, prioritization of mitigation measures, management of civil protection emergencies, and environmental impact assessment. The challenges that have been faced during the design and development of the platform concern usability, access on multiple devices (smartphones, tablets, desktops), interoperability, transparency, reuse of information and software in the public sector, and improvement of the updating of the Italian Landslide Inventory. The methodologies and solutions adopted to address them include Progressive Web Application (PWA), Application Programming Interface (API), open standards, open libraries, and software. A landslide inventory management system has been developed via REST API for data entry and approval workflow in order to maintain the inventory in a distributed and collaborative manner. As a result, IdroGEO provides a public service for citizens, public administration, and professionals, using the "mobile first" approach and with scalable and reliable architecture. IdroGEO represents a solid infrastructure for the interoperability of data that serves as the foundation for creating a first knowledge-graph on landslides and the community who manages them.
C1 [Iadanza, Carla; Trigila, Alessandro] ISPRA Italian Inst Environm Protect & Res, Via Vitaliano Brancati 48, I-00144 Rome, Italy.
   [Starace, Paolo; Dragoni, Alessio; Biondo, Tommaso; Roccisano, Marco] SciamLab Srl, Via Circo Massimo 7, I-00153 Rome, Italy.
RP Iadanza, C (通讯作者)，ISPRA Italian Inst Environm Protect & Res, Via Vitaliano Brancati 48, I-00144 Rome, Italy.
EM carla.iadanza@isprambiente.it; alessandro.trigila@isprambiente.it;
   paolo@sciamlab.com; ad@sciamlab.com; tbiondo@sciamlab.com;
   mroccisano@sciamlab.com
OI Iadanza, Carla/0000-0002-7763-4840; Dragoni,
   Alessio/0000-0001-8276-6971; Trigila, Alessandro/0000-0001-8145-1804;
   Starace, Paolo/0000-0003-4126-8049
FU European Union, National Operational Programme on Governance, and
   Institutional Capacity 2014-2020; Institutional Capacity 2014-2020
FX The IdroGEO platform has been realized with the co-financing of the
   European Union, National Operational Programme on Governance, and
   Institutional Capacity 2014-2020.
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   WKT, WELL KNOWN TEXT REPR
NR 81
TC 5
Z9 5
U1 5
U2 13
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 FEB
PY 2021
VL 10
IS 2
AR 89
DI 10.3390/ijgi10020089
PG 20
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA QN6SU
UT WOS:000622587400001
OA gold
DA 2022-08-23
ER

PT J
AU Yu, R
   Gadiraju, U
   Fetahu, B
   Lehmberg, O
   Ritze, D
   Dietze, S
AF Yu, Ran
   Gadiraju, Ujwal
   Fetahu, Besnik
   Lehmberg, Oliver
   Ritze, Dominique
   Dietze, Stefan
TI KnowMore - knowledge base augmentation with structured web markup
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge base augmentation; Web markup; microdata; data fusion; entity
   resolution; structured data
AB Knowledge bases are in widespread use for aiding tasks such as information extraction and information retrieval, for example in Web search. However, knowledge bases are known to be inherently incomplete, where in particular tail entities and properties are under-represented. As a complimentary data source, embedded entity markup based on Microdata, RDFa, and Microformats have become prevalent on the Web and constitute an unprecedented source of data with significant potential to aid the task of knowledge base augmentation (KBA). RDF statements extracted from markup are fundamentally different from traditional knowledge graphs: entity descriptions are flat, facts are highly redundant and of varied quality, and, explicit links are missing despite a vast amount of coreferences. Therefore, data fusion is required in order to facilitate the use of markup data for KBA. We present a novel data fusion approach which addresses these issues through a combination of entity matching and fusion techniques geared towards the specific challenges associated with Web markup. To ensure precise and non-redundant results, we follow a supervised learning approach based on a set of features considering aspects such as quality and relevance of entities, facts and their sources. We perform a thorough evaluation on a subset of the Web Data Commons dataset and show significant potential for augmenting existing knowledge bases. A comparison with existing data fusion baselines demonstrates superior performance of our approach when applied to Web markup data.
C1 [Yu, Ran; Gadiraju, Ujwal; Fetahu, Besnik; Dietze, Stefan] L3S Res Ctr, Appelstr 4, D-30167 Hannover, Germany.
   [Lehmberg, Oliver; Ritze, Dominique] Univ Mannheim, Data & Web Sci Grp, B6,26, D-68159 Mannheim, Germany.
RP Yu, R; Dietze, S (通讯作者)，L3S Res Ctr, Appelstr 4, D-30167 Hannover, Germany.
EM yu@l3s.de; gadiraju@l3s.de; fetahu@l3s.de;
   oli@informatik.uni-mannheim.de; dominique@informatik.uni-mannheim.de;
   dietze@l3s.de
FU European Union [687916 - AFEL]
FX This work has been partially funded by the European Union's Horizon 2020
   research and innovation program (Grant No. 687916 - AFEL project). The
   authors would like to thank Robert Meusel for his inspiring feedback and
   support with the presented work.
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NR 43
TC 4
Z9 4
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 1
BP 159
EP 180
DI 10.3233/SW-180304
PG 22
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HS1WW
UT WOS:000463654000008
DA 2022-08-23
ER

PT J
AU Malawade, AV
   Yu, SY
   Hsu, B
   Kaeley, H
   Karra, A
   Al Faruque, MA
AF Malawade, Arnav Vaibhav
   Yu, Shih-Yuan
   Hsu, Brandon
   Kaeley, Harsimrat
   Karra, Anurag
   Al Faruque, Mohammad Abdullah
TI ROADSCENE2VEC: A tool for extracting and embedding road scene-graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Scene-graph; Autonomous vehicles; Graph embedding; Vehicle safety; Graph
   learning; Knowledge graph
AB Recently, road scene-graph representations used in conjunction with graph learning techniques have been shown to outperform state-of-the-art deep learning techniques in tasks including action classification, risk assessment, and collision prediction. To enable the exploration of applications of road scene-graph representations, we introduce ROADSCENE2VEC: an open-source tool for extracting and embedding road scene-graphs. The goal of ROADSCENE2VEC is to enable research into the applications and capabilities of road scene-graphs by providing tools for generating scene-graphs, graph learning models to create spatitemporal scene-graph embeddings, and tools for visualizing and analyzing scene-graph-based methodologies. The capabilities of ROADSCENE2VEC include (i) customized scene-graph generation from either video clips or data from the CARLA simulator, (ii) multiple configurable spatiotemporal graph embedding models and baseline CNN-based models, (iii) built-in functionality for using graph and sequence embeddings for risk assessment and collision prediction applications, (iv) tools for evaluating transfer learning, and (v) utilities for visualizing scene-graphs and analyzing the explainability of graph learning models. We demonstrate the utility of ROADSCENE2VEC for these use cases with experimental results and qualitative evaluations for both graph learning models and CNN-based models. ROADSCENE2VEC is available at https://github.com/AICPS/roadscene2vec. (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 [Malawade, Arnav Vaibhav; Yu, Shih-Yuan; Hsu, Brandon; Kaeley, Harsimrat; Karra, Anurag; Al Faruque, Mohammad Abdullah] Univ Calif Irvine, Dept Elect Engn & Comp Sci, Irvine, CA 92697 USA.
RP Malawade, AV (通讯作者)，Univ Calif Irvine, Dept Elect Engn & Comp Sci, Irvine, CA 92697 USA.
EM malawada@uci.edu; shihyuay@uci.edu; bdhsu@uci.edu; kaeleyh@uci.edu;
   karraa@uci.edu; alfaruqu@uci.edu
OI Al Faruque, Mohammad Abdullah/0000-0002-5390-0497; Malawade,
   Arnav/0000-0002-4974-1622
FU National Science Foundation (NSF) , USA [CMMI-1739503]; Grad-uate
   Assistance in Areas of National Need (GAANN) , USA [P200A180052]
FX Acknowledgments This work was partially supported by the National
   Science Foundation (NSF) , USA under award CMMI-1739503 and by Grad-uate
   Assistance in Areas of National Need (GAANN) , USA under award
   P200A180052. Any opinions, findings, conclusions, or rec-ommendations
   expressed in this paper are those of the authors and do not necessarily
   reflect the views of the funding agency.
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NR 50
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD APR 22
PY 2022
VL 242
AR 108245
DI 10.1016/j.knosys.2022.108245
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 0V1XS
UT WOS:000788138900011
OA hybrid, Green Submitted
DA 2022-08-23
ER

PT J
AU Heling, L
   Acosta, M
AF Heling, Lars
   Acosta, Maribel
TI Robust query processing for linked data fragments
SO SEMANTIC WEB
LA English
DT Article
DE SPARQL; query processing; robustness; adaptivity; linked data fragments
ID OPTIMIZATION; COST
AB Linked Data Fragments (LDFs) refer to interfaces that allow for publishing and querying Knowledge Graphs on the Web. These interfaces primarily differ in their expressivity and allow for exploring different trade-offs when balancing the workload between clients and servers in decentralized SPARQL query processing. To devise efficient query plans, clients typically rely on heuristics that leverage the metadata provided by the LDF interface, since obtaining fine-grained statistics from remote sources is a challenging task. However, these heuristics are prone to potential estimation errors based on the metadata which can lead to inefficient query executions with a high number of requests, large amounts of data transferred, and, consequently, excessive execution times. In this work, we investigate robust query processing techniques for Linked Data Fragment clients to address these challenges. We first focus on robust plan selection by proposing CROP, a query plan optimizer that explores the cost and robustness of alternative query plans. Then, we address robust query execution by proposing a new class of adaptive operators: Polymorphic Join Operators. These operators adapt their join strategy in response to possible cardinality estimation errors. The results of our first experimental study show that CROP outperforms state-of-the-art clients by exploring alternative plans based on their cost and robustness. In our second experimental study, we investigate how different planning approaches can benefit from polymorphic join operators and find that they enable more efficient query execution in the majority of cases.
C1 [Heling, Lars] Karlsruhe Inst Technol, Inst AIFB, Karlsruhe, Germany.
   [Heling, Lars] Robert Bosch GmbH, Corp Res, Gerlingen, Germany.
   [Acosta, Maribel] Ruhr Univ Bochum, Fac Comp Sci, Bochum, Germany.
RP Heling, L (通讯作者)，Karlsruhe Inst Technol, Inst AIFB, Karlsruhe, Germany.; Heling, L (通讯作者)，Robert Bosch GmbH, Corp Res, Gerlingen, Germany.
EM heling@kit.edu; maribel.acosta@rub.de
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NR 31
TC 0
Z9 0
U1 0
U2 0
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 4
BP 623
EP 657
DI 10.3233/SW-212888
PG 35
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1V5NS
UT WOS:000806136700002
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Vo, T
AF Vo, Tham
TI An integrated network embedding with reinforcement learning for
   explainable recommendation
SO SOFT COMPUTING
LA English
DT Article
DE HIN; BERT; Network embedding; Reinforcement learning; Recommendation
AB Within the explainable recommendation field, most of recent knowledge graphs (KG)-oriented recommendation techniques mainly focus on the direct interactions between entities in a given KG. These interactions are considered as the rich information sources for leveraging the quality of recommendation outputs. However, these recent recommendation techniques are still hindered by the heterogeneity, type-varied entities and their relationships in a given KG as the heterogeneous information networks (HIN). This limitation seems challenging to build up an effective approach for the KG-based recommendation system in both semantic path-based exploitation and heterogeneous information extraction. In order to overcome these challenges, we proposed a novel integrated HIN embedding with reinforcement learning (RL)-based feature engineering for recommendation, called as: HINRL4Rec. First of all, we apply the combined textual meta-path-based embedding approach for learning multiple-rich-schematic representations of user/item and their associated entities. Then, these extracted multi-typed embeddings of user and item entities are fused into the unified embedding spaces during the KG embedding process. In the end, the combined representations of users and items are used to facilitate the RL-based policy-driven searching process in the next steps for performing the explainable recommendation task. Extensive experiments in real-world datasets demonstrate the effectiveness of our proposed model in comparing with recent state-of-the-art recommendation baselines.
C1 [Vo, Tham] Thu Dau Mot Univ, Binh Duong, Vietnam.
RP Vo, T (通讯作者)，Thu Dau Mot Univ, Binh Duong, Vietnam.
EM thamvth@tdmu.edu.vn
FU Thu Dau Mot University, Binh Duong, Vietnam
FX This research is funded by Thu Dau Mot University, Binh Duong, Vietnam.
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NR 32
TC 0
Z9 0
U1 3
U2 3
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1432-7643
EI 1433-7479
J9 SOFT COMPUT
JI Soft Comput.
PD APR
PY 2022
VL 26
IS 8
BP 3757
EP 3775
DI 10.1007/s00500-022-06843-0
EA MAR 2022
PG 19
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZY3OS
UT WOS:000765228300001
DA 2022-08-23
ER

PT J
AU Mercorio, F
   Mezzazanica, M
   Moscato, V
   Picariello, A
   Sperli, G
AF Mercorio, Fabio
   Mezzazanica, Mario
   Moscato, Vincenzo
   Picariello, Antonio
   Sperli, Giancarlo
TI DICO: A Graph-DB Framework for Community Detection on Big Scholarly Data
SO IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
LA English
DT Article
DE Big scholarly data; knowledge graphs; semantic network mining; community
   mining
AB The widespread use of online social networks has also involved the scientific field in which researchers interact each other by publishing or citing a given paper. The huge amount of information about scientific research documents has been described through the term Big Scholarly Data. In this article we propose a framework, namely Discovery Information using COmmunity detection (DICO), for identifying overlapped communities of authors from Big Scholarly Data by modeling authors' interactions through a novel graph-based data model combining jointly document metadata with semantic information. In particular, DICO presents three distinctive characteristics: i) the coauthorship network has been built from publication records using a novel approach for estimating relationships weight between users; ii) a new community detection algorithm based on Node Location Analysis has been developed to identify overlapped communities; iii) some built-in queries are provided to browse the generated network, though any graph-traversal query can be implemented through the Cypher query language. The experimental evaluation has been carried out to evaluate the efficacy of the proposed community detection algorithm on benchmark networks. Finally, DICO has been tested on a real-world Big Scholarly Dataset to show its usefulness working on the DBLP+AMiner dataset, that contains 1.7M+ distinct authors, 3M+ papers, handling 25M+ citation relationships.
C1 [Mercorio, Fabio; Mezzazanica, Mario] Univ Milano Bicocca, Dept Stat & Quantitat Methods DISMEQ, I-20126 Milan, Italy.
   [Moscato, Vincenzo; Picariello, Antonio; Sperli, Giancarlo] Univ Naples Federico II, Dept Informat Technol & Elect Engn DIETI, I-80138 Naples, Italy.
RP Sperli, G (通讯作者)，Univ Naples Federico II, Dept Informat Technol & Elect Engn DIETI, I-80138 Naples, Italy.
EM giancarlo.sperli@unina.it
OI Moscato, Vincenzo/0000-0002-0754-7696; Sperli,
   Giancarlo/0000-0003-4033-3777; MEZZANZANICA, MARIO/0000-0003-0399-2810
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NR 29
TC 22
Z9 22
U1 10
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2168-6750
J9 IEEE T EMERG TOP COM
JI IEEE Trans. Emerg. Top. Comput.
PD OCT 1
PY 2021
VL 9
IS 4
BP 1987
EP 2003
DI 10.1109/TETC.2019.2952765
PG 17
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA XI0IO
UT WOS:000725807100028
DA 2022-08-23
ER

PT J
AU Wang, S
   Mao, WJ
   Wei, PH
   Zeng, DD
AF Wang, Shuai
   Mao, Wenji
   Wei, Penghui
   Zeng, Daniel D.
TI Knowledge structure driven prototype learning and verification for fact
   checking
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Fact checking; Knowledge structure; Hierarchical prototype learning;
   Relation enhancement; Verification
AB To inhibit the spread of rumorous information, fact checking aims at retrieving relevant evidence to verify the veracity of a given claim. Previous work on fact checking typically uses knowledge graphs (KGs) as external repositories and develop reasoning methods to retrieve evidence from KGs. Domain knowledge structure, including category hierarchy and attribute relationships, can be utilized as discriminative information to facilitate KG based learning and verification. However, in previous fact checking research, category hierarchy and attribute information was often scattered in a KG and treated as the ordinary triple facts in the learning process like other types of information, or was utilized in a limited way without the consideration of category hierarchy or the combination of category hierarchy with the learning process. Thus to better utilize category hierarchy and attribute relationships, in this paper, we propose an end-to-end knowledge structure driven prototype learning and verification method for fact checking. For improving intra-category compactness and inter-category separation, we develop a hierarchical prototype learning technique that jointly learns a prototype for each sub-category to enhance entity embeddings and optimize embedding representations using highlevel category. For further enhancing embedding learning, we propose a graph attention network to aggregate information from neighboring attribute nodes. We construct a real-world dataset on food domain, and experimental results on the benchmark datasets and our domain dataset show the effectiveness of our method compared to both previous fact checking methods and representative KG reasoning methods. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Mao, Wenji] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
   Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China.
RP Mao, WJ (通讯作者)，Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
EM wenji.mao@ia.ac.cn
FU Ministry of Science and Technology of China [2020AAA0108405]; National
   Natural Science Foundation of China [1183 2001, 71621002]
FX Acknowledgments This work is supported in part by the Ministry of
   Science and Technology of China under Grant #2020AAA0108405, and
   National Natural Science Foundation of China under Grants #1183 2001 and
   #71621002.
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NR 45
TC 0
Z9 0
U1 5
U2 5
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 FEB 28
PY 2022
VL 238
AR 107910
DI 10.1016/j.knosys.2021.107910
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0I1IU
UT WOS:000779180700013
DA 2022-08-23
ER

PT J
AU Saber-Ayad, M
   Hammoudeh, S
   Abu-Gharbieh, E
   Hamoudi, R
   Tarazi, H
   Al-Tel, TH
   Hamid, Q
AF Saber-Ayad, Maha
   Hammoudeh, Sarah
   Abu-Gharbieh, Eman
   Hamoudi, Rifat
   Tarazi, Hamadeh
   Al-Tel, Taleb H.
   Hamid, Qutayba
TI Current Status of Baricitinib as a Repurposed Therapy for COVID-19
SO PHARMACEUTICALS
LA English
DT Review
DE bioinformatics; transcriptomics; gene expression; baricitinib; JAK
   inhibitors; COVID-19; clinical trial
ID KINASE INHIBITORS; JAK2 INHIBITOR; DOUBLE-BLIND; EFFICACY; SAFETY;
   PHASE-2; DRUGS; RUXOLITINIB; SARS-COV-2
AB The emergence of the COVID-19 pandemic has mandated the instant (re)search for potential drug candidates. In response to the unprecedented situation, it was recognized early that repurposing of available drugs in the market could timely save lives, by skipping the lengthy phases of preclinical and initial safety studies. BenevolentAI's large knowledge graph repository of structured medical information suggested baricitinib, a Janus-associated kinase inhibitor, as a potential repurposed medicine with a dual mechanism; hindering SARS-CoV2 entry and combatting the cytokine storm; the leading cause of mortality in COVID-19. However, the recently-published Adaptive COVID-19 Treatment Trial-2 (ACTT-2) positioned baricitinib only in combination with remdesivir for treatment of a specific category of COVID-19 patients, whereas the drug is not recommended to be used alone except in clinical trials. The increased pace of data output in all life sciences fields has changed our understanding of data processing and manipulation. For the purpose of drug design, development, or repurposing, the integration of different disciplines of life sciences is highly recommended to achieve the ultimate benefit of using new technologies to mine BIG data, however, the final say remains to be concluded after the drug is used in clinical practice. This review demonstrates different bioinformatics, chemical, pharmacological, and clinical aspects of baricitinib to highlight the repurposing journey of the drug and evaluates its placement in the current guidelines for COVID-19 treatment.
C1 [Saber-Ayad, Maha; Hammoudeh, Sarah; Abu-Gharbieh, Eman; Hamoudi, Rifat; Hamid, Qutayba] Univ Sharjah, Coll Med, Dept Clin Sci, Sharjah 27272, U Arab Emirates.
   [Saber-Ayad, Maha; Hammoudeh, Sarah; Abu-Gharbieh, Eman; Hamoudi, Rifat; Al-Tel, Taleb H.; Hamid, Qutayba] Univ Sharjah, Sharjah Inst Med Res, Sharjah 27272, U Arab Emirates.
   [Saber-Ayad, Maha] Cairo Univ, Fac Med, Dept Med Pharmacol, Cairo 11559, Egypt.
   [Hamoudi, Rifat] UCL, Div Surg & Intervent Sci, London WC1E 6BT, England.
   [Tarazi, Hamadeh; Al-Tel, Taleb H.] Univ Sharjah, Coll Pharm, Dept Med Chem, Sharjah 27272, U Arab Emirates.
   [Hamid, Qutayba] McGill Univ, Res Inst, Meakins Christie Labs, Hlth Ctr, Montreal, PQ H4A 3J1, Canada.
RP Saber-Ayad, M; Abu-Gharbieh, E (通讯作者)，Univ Sharjah, Coll Med, Dept Clin Sci, Sharjah 27272, U Arab Emirates.; Saber-Ayad, M; Abu-Gharbieh, E (通讯作者)，Univ Sharjah, Sharjah Inst Med Res, Sharjah 27272, U Arab Emirates.; Saber-Ayad, M (通讯作者)，Cairo Univ, Fac Med, Dept Med Pharmacol, Cairo 11559, Egypt.
EM msaber@sharjah.ac.ae; sara-hammoudeh@hotmail.com;
   eabugharbieh@sharjah.ac.ae; rhamoudi@sharjah.ac.ae;
   htarazi@sharjah.ac.ae; taltal@sharjah.ac.ae; qalheialy@sharjah.ac.ae
RI Abu-Gharbieh, Eman/F-3635-2010
OI Abu-Gharbieh, Eman/0000-0002-5972-0681; Saber-Ayad,
   Maha/0000-0002-9287-7621; Hamoudi, Rifat/0000-0002-1402-0868; AlTel,
   Taleb/0000-0003-4914-9677
FU University of Sharjah [0306, 0304, 0308]
FX M.S.-A., E.A.-G, and R.H. are funded by the University of Sharjah
   Research Grant (CoV-19 #0304). R.H. and M.S.-A. are funded by the
   University of Sharjah Research Grant (CoV-19 #0308). T.H.A.-T. is funded
   by the University of Sharjah Research Grant (CoV-19 #0306).
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NR 62
TC 1
Z9 1
U1 2
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8247
J9 PHARMACEUTICALS-BASE
JI Pharmaceuticals
PD JUL
PY 2021
VL 14
IS 7
AR 680
DI 10.3390/ph14070680
PG 13
WC Chemistry, Medicinal; Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA TO3BZ
UT WOS:000676793000001
PM 34358107
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Peroni, S
   Vitali, F
AF Peroni, Silvio
   Vitali, Fabio
TI Interfacing fast-fashion design industries with Semantic Web
   technologies The case of Imperial Fashion
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE OWL; Semantic Web; Data conversion; Data reengineering; Data
   visualisation; Fast-fashion design; Ontology
AB With the advent of the fourth industrial revolution, several enterprises worldwide have started to evolve their production processes with new automation and data exchange policies by means of the adoption of emergent technologies, i.e. knowledge graphs, sensor networks, big data, and cloud computing. While several domains have been already involved in this revolution, other parties, such as the fast-fashion industries, have started to move the first steps in that direction since few months. In this article we describe the first outcomes of a project, i.e. Refactoring Imperial Selling Data (RISED), which aims at using Semantic Web technologies so as to simplify the management and the enrichment of a huge set of data that are continuously collected by Imperial Fashion, one of the most important fast-fashion companies in Italy. In particular, we introduce the process adopted for the development of a unifying model (i.e. an OWL 2 DL ontology) for the description of all the Imperial Fashion data and we propose some mechanisms for converting the original data stored in existing databases according to the new ontology. Finally, we introduce some prototypical visual tools that use the converted data for addressing some of the questions that have been raised by Imperial Fashion employees during several informal meetings we had about the project RISED. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Peroni, Silvio; Vitali, Fabio] Univ Bologna, DISI, DASPLab, Bologna, Italy.
RP Peroni, S (通讯作者)，Univ Bologna, DISI, DASPLab, Bologna, Italy.
EM silvio.peroni@unibo.it
OI Peroni, Silvio/0000-0003-0530-4305
FU University of Bologna; Imperial Fashion
FX This work has been funded by the "CreativeLab'' project started on June
   2015 as consequence of a collaboration agreement between the University
   of Bologna and Imperial Fashion.
CR BERNERSLEE T, 2001, SEMANTIC WEB
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NR 24
TC 4
Z9 4
U1 3
U2 24
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 MAY
PY 2017
VL 44
BP 37
EP 53
DI 10.1016/j.websem.2017.06.001
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 FQ6LQ
UT WOS:000418476400003
DA 2022-08-23
ER

PT J
AU Pang, SC
   Zhang, Y
   Song, T
   Zhang, XD
   Wang, X
   Rodriguez-Paton, A
AF Pang, Shanchen
   Zhang, Ying
   Song, Tao
   Zhang, Xudong
   Wang, Xun
   Rodriguez-Paton, Alfonso
TI AMDE: a novel attention-mechanism-based multidimensional feature encoder
   for drug-drug interaction prediction
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article
DE DDI prediction; multidimensional feature; encoder; deep learning
ID TORASEMIDE
AB The properties of the drug may be altered by the combination, which may cause unexpected drug-drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs for systematic and effective treatment. In most of deep learning-based methods for predicting DDI, encoded information about the drugs is insufficient in some extent, which limits the performances of DDIs prediction. In this work, we propose a novel attention-mechanism-based multidimensional feature encoder for DDIs prediction, namely attention-based multidimensional feature encoder (AMDE). Specifically, in AMDE, we encode drug features from multiple dimensions, including information from both Simplified Molecular-Input Line-Entry System sequence and atomic graph of the drug. Data experiments are conducted on DDI data set selected from Drugbank, involving a total of 34 282 DDI relationships with 17 141 positive DDI samples and 17 141 negative samples. Experimental results show that our AMDE performs better than some state-of-the-art baseline methods, including Random Forest, One-Dimension Convolutional Neural Networks, DeepDrug, Long Short-Term Memory, Seq2seq, Deepconv, DeepDDI, Graph Attention Networks and Knowledge Graph Neural Networks. In practice, we select a set of 150 drugs with 3723 DDIs, which are never appeared in training, validation and test sets. AMDE performs well in DDIs prediction task, with AUROC and AUPRC 0.981 and 0.975. As well, we use Torasemide (DB00214) as an example and predict the most likely drug to interact with it. The top 15 scores all have been reported with clear interactions in literatures.
C1 [Pang, Shanchen] Tongji Univ, Comp Software & Theory, Shanghai, Peoples R China.
   [Pang, Shanchen; Song, Tao; Wang, Xun] China Univ Petr, Qingdao, Peoples R China.
   [Zhang, Ying; Zhang, Xudong] China Univ Petr, Dept Comp Sci & Engn, Qingdao, Peoples R China.
   [Song, Tao] Huazhong Univ Sci & Technol, Wuhan, Peoples R China.
   [Song, Tao] Univ Politecn Madrid, Madrid, Spain.
   [Wang, Xun] Tsukuba Univ, Tsukuba, Ibaraki, Japan.
   [Wang, Xun] Chinese Acad Sci, Inst Comp Technol, China High Performance Comp Res Ctr, Beijing, Peoples R China.
   [Rodriguez-Paton, Alfonso] Univ Santiago de Compostela, Elect & Computat, Santiago De Compostela, Spain.
   [Rodriguez-Paton, Alfonso] Politecn Univ Madrid, Comp Sci, Madrid, Spain.
   [Rodriguez-Paton, Alfonso] Politecn Univ Madrid, Fac Informat, Madrid, Spain.
RP Song, T (通讯作者)，China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China.
EM t.song@upm.es
RI Song, Tao/T-7360-2018
OI Song, Tao/0000-0002-0130-3340; Zhang, Xudong/0000-0003-2671-8275
FU Natural Science Foundation of China [61873280, 61972416, 61873281];
   Taishan Scholarship [tsqn201812029]; Natural Science Foundation of
   Shandong Province [2019GGX101067]; Foundation of Science and Technology
   Development of Jinan [201907116]; Fundamental Research Funds for the
   Central Universities [18CX02152A, 19CX05003A-6]; Intelligent Diagnosis
   of Early Lung Cancer Classification Based on Self-learning Pulsed Neural
   Membrane System and Its Clinical Application [05E19070003]; 3D
   Reconstruction and Accurate Volume Measurement of White Matter High
   Signal Based on Deep Learning and Its Clinical Application
   [05tc2107001]; Research on Topological Representation and ultra -low
   complexity computability of large-scale graph data [05E18070010];
   TalentoComunidad de Madrid
FX Natural Science Foundation of China (grant nos 61873280, 61972416,
   61873281); Taishan Scholarship (grant no. tsqn201812029); Natural
   Science Foundation of Shandong Province (grant no. 2019GGX101067);
   Foundation of Science and Technology Development of Jinan (grant no.
   201907116); Fundamental Research Funds for the Central Universities
   (grant no. 18CX02152A, 19CX05003A-6); Intelligent Diagnosis of Early
   Lung Cancer Classification Based on Self-learning Pulsed Neural Membrane
   System and Its Clinical Application (grant no. 05E19070003); 3D
   Reconstruction and Accurate Volume Measurement of White Matter High
   Signal Based on Deep Learning and Its Clinical Application (grant no.
   05tc2107001); Research on Topological Representation and ultra -low
   complexity computability of large-scale graph data (grant no.
   05E18070010); Juan de la Cierva and TalentoComunidad de Madrid.
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NR 52
TC 5
Z9 5
U1 11
U2 15
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 bbab545
DI 10.1093/bib/bbab545
PG 12
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:000763000800097
PM 34965586
DA 2022-08-23
ER

PT J
AU Chu, Y
   Wang, GY
   Cao, L
   Qiao, LS
   Liu, MX
AF Chu, Ying
   Wang, Guangyu
   Cao, Liang
   Qiao, Lishan
   Liu, Mingxia
TI Multi-Scale Graph Representation Learning for Autism Identification With
   Functional MRI
SO FRONTIERS IN NEUROINFORMATICS
LA English
DT Article
DE functional connectivity; graph convolutional networks; autism;
   resting-state functional MRI; classification
ID CONVOLUTIONAL NEURAL-NETWORKS; BRAIN NETWORKS; CONNECTIVITY; REGIONS
AB Resting-state functional MRI (rs-fMRI) has been widely used for the early diagnosis of autism spectrum disorder (ASD). With rs-fMRI, the functional connectivity networks (FCNs) are usually constructed for representing each subject, with each element representing the pairwise relationship between brain region-of-interests (ROIs). Previous studies often first extract handcrafted network features (such as node degree and clustering coefficient) from FCNs and then construct a prediction model for ASD diagnosis, which largely requires expert knowledge. Graph convolutional networks (GCNs) have recently been employed to jointly perform FCNs feature extraction and ASD identification in a data-driven manner. However, existing studies tend to focus on the single-scale topology of FCNs by using one single atlas for ROI partition, thus ignoring potential complementary topology information of FCNs at different spatial scales. In this paper, we develop a multi-scale graph representation learning (MGRL) framework for rs-fMRI based ASD diagnosis. The MGRL consists of three major components: (1) multi-scale FCNs construction using multiple brain atlases for ROI partition, (2) FCNs representation learning via multi-scale GCNs, and (3) multi-scale feature fusion and classification for ASD diagnosis. The proposed MGRL is evaluated on 184 subjects from the public Autism Brain Imaging Data Exchange (ABIDE) database with rs-fMRI scans. Experimental results suggest the efficacy of our MGRL in FCN feature extraction and ASD identification, compared with several state-of-the-art methods.
C1 [Chu, Ying; Wang, Guangyu; Qiao, Lishan] Liaocheng Univ, Sch Math Sci, Liaocheng, Shandong, Peoples R China.
   [Chu, Ying; Liu, Mingxia] Taishan Univ, Dept Informat Sci & Technol, Tai An, Shandong, Peoples R China.
   [Cao, Liang] Taian Tumor Prevent & Treatment Hosp, Tai An, Shandong, Peoples R China.
RP Qiao, LS (通讯作者)，Liaocheng Univ, Sch Math Sci, Liaocheng, Shandong, Peoples R China.; Liu, MX (通讯作者)，Taishan Univ, Dept Informat Sci & Technol, Tai An, Shandong, Peoples R China.
EM qiaolishan@lcu.edu.cn; mxliu1226@gmail.com
FU National Natural Science Foundation of China [62176112, 61976110,
   11931008]; Natural Science Foundation of Shandong Province [ZR2018MF020,
   ZR2019YQ27]; Taishan Scholar Program of Shandong Province; Open Project
   of Liaocheng University Animal Husbandry Discipline [319312101-01]
FX Funding YC, GW, and LQ were partly supported by National Natural Science
   Foundation of China (Nos. 62176112, 61976110, and 11931008), Natural
   Science Foundation of Shandong Province (Nos. ZR2018MF020 and
   ZR2019YQ27), and Taishan Scholar Program of Shandong Province and the
   Open Project of Liaocheng University Animal Husbandry Discipline (No.
   319312101-01).
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NR 62
TC 0
Z9 0
U1 16
U2 18
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1662-5196
J9 FRONT NEUROINFORM
JI Front. Neuroinformatics
PD JAN 13
PY 2022
VL 15
AR 802305
DI 10.3389/fninf.2021.802305
PG 14
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA YO6KF
UT WOS:000748047200001
PM 35095453
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Banks, F
AF Banks, Frank
TI Learning in DEPTH: developing a graphical tool for professional thinking
   for technology teachers
SO INTERNATIONAL JOURNAL OF TECHNOLOGY AND DESIGN EDUCATION
LA English
DT Article
DE knowledge metaphors; teacher knowledge; subject knowledge; pedagogical
   knowledge; personal subject construct
AB In this issue of the International Journal of Technology and Design, we report on a series of case studies from the second phase of an international project-Developing Professional Thinking for Technology Teachers (DEPTH2). The first phase of the project was a study conducted with both primary and secondary technology pre-service teacher education students in a number of different countries who were given the same teacher-knowledge graphical framework as a tool to support reflection on their professional knowledge. We discovered that, despite the different country contexts, student teachers of technology could articulate aspects of their developing teacher knowledge using the same framework for teacher professional development. As previously reported in this journal (Banks et al. International Journal of Technology and Design Education, 14, 141-157, 2004), the common graphical tool enabled them to set out their subject knowledge, pedagogical knowledge and 'school' knowledge and was useful in helping them become more self-aware. In this second phase of the project we have developed this line of research in two ways. First, we extended the range of participants to include experienced teachers involved in in-service work connected to curriculum development. Second, we looked at the inter-relationship for pre-service teachers between their developing professional knowledge and their own 'personal subject construct'. In this article, the theoretical framework for the subsequent papers is described and set in the context of recent debates surrounding the nature and importance of teacher knowledge; and the way such professional knowledge can be articulated by teachers.
C1 Open Univ, Fac Educ & Language Studies, Ctr Res Educ & Educ Technol, Milton Keynes MK7 6AA, Bucks, England.
RP Banks, F (通讯作者)，Open Univ, Fac Educ & Language Studies, Ctr Res Educ & Educ Technol, Stuart Hall Bldg,Level 3,Walton Hall, Milton Keynes MK7 6AA, Bucks, England.
EM f.banks@open.ac.uk
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   [No title captured]
   [No title captured]
NR 33
TC 2
Z9 3
U1 0
U2 9
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0957-7572
J9 INT J TECHNOL DES ED
JI Int. J. Technol. Des. Educ.
PD JUL
PY 2008
VL 18
IS 3
BP 221
EP 229
DI 10.1007/s10798-008-9050-z
PG 9
WC Education & Educational Research; Education, Scientific Disciplines;
   Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Engineering
GA 311TC
UT WOS:000256622300001
DA 2022-08-23
ER

PT J
AU Li, X
   Li, YW
   Yang, JN
   Liu, H
   Hu, PJ
AF Li, Xiang
   Li, Yuwei
   Yang, Junan
   Liu, Hui
   Hu, Pengjiang
TI A relation aware embedding mechanism for relation extraction
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Relation extraction; Information extraction; Knowledge graph; Attention
   mechanism
ID MENTION HYPERGRAPH
AB Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction.
C1 [Li, Xiang; Li, Yuwei; Yang, Junan; Liu, Hui; Hu, Pengjiang] Natl Univ Def Technol, Coll Elect Engn, Hefei, Peoples R China.
RP Li, YW; Yang, JN (通讯作者)，Natl Univ Def Technol, Coll Elect Engn, Hefei, Peoples R China.
EM lix_21@163.com; liyuwei@zju.edu.cn; yangjunan@ustc.edu;
   christ592604@163.com; pjhu2012@126.com
CR Chan Y. S., 2011, P 49 ANN M ASS COMP, V1, P551
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NR 36
TC 0
Z9 0
U1 19
U2 19
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 JUL
PY 2022
VL 52
IS 9
BP 10022
EP 10031
DI 10.1007/s10489-021-02699-3
EA JAN 2022
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2I2IM
UT WOS:000741637600004
DA 2022-08-23
ER

PT J
AU Zhao, X
   Zeng, WX
   Tang, JY
   Wang, W
   Suchanek, FM
AF Zhao, Xiang
   Zeng, Weixin
   Tang, Jiuyang
   Wang, Wei
   Suchanek, Fabian M.
TI An Experimental Study of State-of-the-Art Entity Alignment Approaches
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Task analysis; Erbium; Robustness; Mirrors; Benchmark testing; Training;
   Natural languages
AB Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs (KGs), which is an essential step to enhance the quality of KGs, and hence of significance to downstream applications (e.g., question answering and recommendation). Recent years have witnessed a rapid increase of EA approaches, yet the relative performance of them remains unclear, partly due to the incomplete empirical evaluations, as well as the fact that comparisons were carried out under different settings (i.e., datasets, information used as input, etc.). In this paper, we fill in the gap by conducting a comprehensive evaluation and detailed analysis of state-of-the-art EA approaches. We first propose a general EA framework that encompasses all the current methods, and then group existing methods into three major categories. Next, we judiciously evaluate these solutions on a wide range of use cases, based on their effectiveness, efficiency and robustness. Finally, we construct a new EA dataset to mirror the real-life challenges of alignment, which were largely overlooked by existing literature. This study strives to provide a clear picture of the strengths and weaknesses of current EA approaches, so as to inspire quality follow-up research.
C1 [Zhao, Xiang; Zeng, Weixin; Tang, Jiuyang] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.
   [Wang, Wei] Univ New South Wales, Sydney, NSW 2052, Australia.
   [Suchanek, Fabian M.] Telecom Paris Univ, F-75005 Paris, France.
RP Zhao, X (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.
EM xiangzhao@nudt.edu.cn; zengweixin13@nudt.edu.cn;
   jiuyang_tang@nudt.edu.cn; weiw@cse.unsw.edu.au; fabian@suchanek.name
OI Zeng, Weixin/0000-0002-2690-3190
FU NSFC [61872446, 71690233, 71971212]; NSF of Hunan Province
   [2019JJ20024]; ARC [170103710, 180103411, D2DCRCDC25002, DC25003]; 
   [ANR-17-CE23-0018]
FX X. Zhao, W. Zeng and J. Tang were partially supported by NSFC under
   grants Nos. 61872446, 71690233, and 71971212, and NSF of Hunan Province
   under grant No. 2019JJ20024. W. Wang was partially funded by ARC DPs
   170103710 and 180103411, andD2DCRCDC25002 andDC25003. F. M. Suchanekwas
   partially funded byANR-17-CE23-0018 DirtyData.
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NR 69
TC 15
Z9 15
U1 5
U2 5
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 JUN 1
PY 2022
VL 34
IS 6
BP 2610
EP 2625
DI 10.1109/TKDE.2020.3018741
PG 16
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 0W4MV
UT WOS:000789003800006
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Liao, J
   Wang, M
   Chen, X
   Wang, SG
   Zhang, K
AF Liao, Jian
   Wang, Min
   Chen, Xin
   Wang, Suge
   Zhang, Kai
TI Dynamic commonsense knowledge fused method for Chinese implicit
   sentiment analysis
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Chinese implicit sentiment analysis; Sentimental knowledge; Knowledge
   fusion; Multi-polarity orthogonal attention; Chinese implicit sentiment
   analysis; Sentimental knowledge; Knowledge fusion; Multi-polarity
   orthogonal attention
AB Compared with explicit sentiment analysis that attracts considerable attention, implicit sen-timent analysis is a more difficult task due to the lack of sentimental words. The abundant information in an external sentimental knowledge base can play a significant complementary and expansion role. In this paper, a sentimental commonsense knowledge graph embedded multi-polarity orthogonal attention model is proposed to learn the implication of the implicit sentiment. We analyzed the effectiveness of different knowledge relations in the ConceptNet knowledge base in detail, and proposed a matching and filtering method to distill useful knowledge tuples for implicit sentiment analysis automatically. By introducing the sentimental information in the knowledge base, the proposed model can extend the semantic of a sentence with an implicit sentiment. Then, a bi-directional long-short term memory model with multi-polarity orthogonal attention is adopted to fuse the distilled sentimental knowledge with the semantic embedding, effectively enriching the representation of sentences. Experiments on the SMP2019-ECISA implicit sentiment dataset show that our model fully utilizes the information of the knowledge base and improves the performance of Chinese implicit sentiment analysis.
C1 [Liao, Jian; Wang, Min; Wang, Suge; Zhang, Kai] Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Peoples R China.
   [Chen, Xin] Taiyuan Univ Sci & Technol, Coll Comp Sci & Technol, Taiyuan, Peoples R China.
   [Wang, Suge] Shanxi Univ, Key Lab Computat Intelligence & Chinese Informat, Minist Educ, Taiyuan, Peoples R China.
RP Wang, SG (通讯作者)，Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Peoples R China.
EM wsg@sxu.edu.cn
FU National Natural Science Foundation of China [61906112, 61632011,
   62076158]; Natural Science Foundation of Shanxi Province, China
   [201901D211174]; Scientific and Technological Innovation Programs of
   Higher Education Institutions in Shanxi Province, China [2019L0008,
   2020L0001]
FX Acknowledgments The authors would like to thank all anonymous reviewers
   for their valuable comments and suggestions which have signifi-cantly
   improved the quality and presentation of this paper. The works described
   in this paper are supported by the National Natural Science Foundation
   of China (61906112, 61632011, 62076158) , Natural Science Foundation of
   Shanxi Province, China (201901D211174) , Scientific and Technological
   Innovation Programs of Higher Education Institutions in Shanxi Province,
   China (2019L0008, 2020L0001) .
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NR 46
TC 0
Z9 0
U1 9
U2 9
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD MAY
PY 2022
VL 59
IS 3
AR 102934
DI 10.1016/j.ipm.2022.102934
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 1C1MM
UT WOS:000792892000001
DA 2022-08-23
ER

PT J
AU Wu, HL
   French, T
   Liu, W
   Hodkiewicz, M
AF Wu, Haolin
   French, Tim
   Liu, Wei
   Hodkiewicz, Melinda
TI Automatic semantic knowledge extraction from electronic forms
SO PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF
   RISK AND RELIABILITY
LA English
DT Article; Early Access
DE Knowledge graph; procedures; maintenance; semantic relationship
AB Electronic tabular forms are an intuitive way for organisations to collect, present and store structured information for human readers. Forms use features such as fonts, colours and cell positioning to help readers navigate and find information. Millions of forms, typically in Portable Document Format (PDF), are generated by businesses as part of routine operations. Unlike human readers, machines are not able to directly 'understand' the implicit cues contained in the fonts, colours and use of boxes without explicit processing. In this paper, a supervised computer vision model is proposed to decompose the PDF form document into nested microtables. The cells within these microtables are then processed using a customisable rule bank for meaningful table content and semantic relationship extraction. The process is demonstrated on an industry dataset of 37 maintenance procedure documents containing 373 pages and 1016 unique microtables. A web application EMU (Extracting Machine Understandable Semantics from Forms) demonstrates how data captured in tables with different dimensions in procedural forms can be automatically extracted and stored in JavaScript Object Notation (JSON). Identifying and extracting nested tables is a critical fundamental step for future applications to support machine-automated search and extraction of data at scale for both maintenance and other procedural documentation.
C1 [Wu, Haolin; French, Tim; Liu, Wei] Univ Western Australia, Dept Comp Sci & Software Engn, Perth, WA, Australia.
   [Hodkiewicz, Melinda] Univ Western Australia, Sch Engn, Perth, WA, Australia.
RP Hodkiewicz, M (通讯作者)，Univ Western Australia, Sch Engn, M050, Crawley, WA 6009, Australia.
EM melinda.hodkiewicz@uwa.edu.au
OI Liu, Wei/0000-0002-7409-0948; French, Tim/0000-0002-0748-8040;
   Hodkiewicz, Melinda/0000-0002-7336-3932
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NR 13
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 1748-006X
EI 1748-0078
J9 P I MECH ENG O-J RIS
JI Proc. Inst. Mech. Eng. Part O-J. Risk Reliab.
AR 1748006X221098272
DI 10.1177/1748006X221098272
EA MAY 2022
PG 12
WC Engineering, Multidisciplinary; Engineering, Industrial; Operations
   Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA 1U4ZD
UT WOS:000805421900001
DA 2022-08-23
ER

PT J
AU Bosdurmaz, EB
   Hajian, H
   Ercaglar, V
   Ozbay, E
AF Bosdurmaz, Ekin Bircan
   Hajian, Hodjat
   Ercaglar, Veysel
   Ozbay, Ekmel
TI Graphene-based metasurface absorber for the active and broadband
   manipulation of terahertz radiation
SO JOURNAL OF THE OPTICAL SOCIETY OF AMERICA B-OPTICAL PHYSICS
LA English
DT Article
ID METAMATERIAL ABSORBER; PERFECT; LIGHT; REFLECTION; LAYER
AB Graphene-based metasurface nearly perfect absorbers (MPAs) can be used as an efficient tool for the active control and manipulation of waves in the terahertz (THz) gap. Here, we propose a novel, to the best of our knowledge, graphene-based MPA that is designed based on a simple configuration and is capable of absorbing THz radiation within a broad bandwidth of almost 3 THz with polarization-insensitive and omnidirectional characteristics. The MPA comprises a periodic array of graphene patches with two different dimensions that are separated from a gold bottom reflector with an SiO2 spacer layer. The broadband spectral response of the MPA, which is also verified by analytical calculations, is due to the support of propagating surface plasmon excitations and can be either actively tuned via changes in the chemical potential of graphene or passively adjusted by the modification of the geometrical parameters of the patches and thickness of the spacer layer. As a complement to the previous studies in the literature, due to the simplicity of its design and broad spectral response, it is believed that the suggested graphene-based MPA will find potential applications in THz spectroscopy and communications. (C) 2021 Optical Society of America
C1 [Bosdurmaz, Ekin Bircan; Hajian, Hodjat; Ercaglar, Veysel; Ozbay, Ekmel] Bilkent Univ, NANOTAM Nanotechnol Res Ctr, TR-06800 Ankara, Turkey.
   [Bosdurmaz, Ekin Bircan; Ercaglar, Veysel; Ozbay, Ekmel] Bilkent Univ, Dept Elect & Elect Engn, TR-06800 Ankara, Turkey.
   [Ozbay, Ekmel] Bilkent Univ, Dept Phys, TR-06800 Ankara, Turkey.
   [Ozbay, Ekmel] Bilkent Univ, UNAM Inst Mat Sci & Nanotechnol, TR-06800 Ankara, Turkey.
RP Hajian, H (通讯作者)，Bilkent Univ, NANOTAM Nanotechnol Res Ctr, TR-06800 Ankara, Turkey.
EM hodjat.hajian@bilkent.edu.tr; ozbay@bilkent.edu.tr
RI ; Hajian, Hodjat/A-8656-2015
OI Ercaglar, Veysel/0000-0002-7355-1925; Hajian, Hodjat/0000-0001-6564-6273
FU Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [120E422]
FX Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) (120E422).
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NR 56
TC 1
Z9 1
U1 3
U2 15
PU Optica Publishing Group
PI WASHINGTON
PA 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA
SN 0740-3224
EI 1520-8540
J9 J OPT SOC AM B
JI J. Opt. Soc. Am. B-Opt. Phys.
PD SEP 1
PY 2021
VL 38
IS 9
BP C160
EP C167
DI 10.1364/JOSAB.427975
PG 8
WC Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Optics
GA UK8FM
UT WOS:000692200200020
OA Green Published
DA 2022-08-23
ER

PT J
AU Zhang, X
   Yang, LY
   Zhang, B
   Liu, Y
   Jiang, D
   Qin, XH
   Hao, MM
AF Zhang, Xun
   Yang, Lanyan
   Zhang, Bin
   Liu, Ying
   Jiang, Dong
   Qin, Xiaohai
   Hao, Mengmeng
TI Multi-Scale Aggregation Graph Neural Networks Based on Feature
   Similarity for Semi-Supervised Learning
SO ENTROPY
LA English
DT Article
DE graph analysis; graph neural network; semi-supervised learning;
   neighborhood aggregation
AB The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.
C1 [Zhang, Xun; Yang, Lanyan; Zhang, Bin; Liu, Ying; Qin, Xiaohai] Beijing Technol & Business Univ, Sch Comp Sci & Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China.
   [Jiang, Dong; Hao, Mengmeng] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Resources Utilizat & Environm Remediat, Beijing 100101, Peoples R China.
RP Liu, Y (通讯作者)，Beijing Technol & Business Univ, Sch Comp Sci & Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China.; Jiang, D (通讯作者)，Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Resources Utilizat & Environm Remediat, Beijing 100101, Peoples R China.
EM zhangxun@btbu.edu.cn; yly_951010@yeah.net; zb18041963006@163.com;
   liu_ying@th.btbu.edu.cn; jiangd@igsnrr.ac.cn; qxh_4929@163.com;
   haomm.16b@igsnrr.ac.cn
RI Zhang, Xun/C-8375-2016
OI Zhang, Xun/0000-0002-5502-2974; jiang, dong/0000-0002-4154-5969
FU National Key Research and Development Program of China [2020YFB1806500];
   Support Project of High-level Teachers in Beijing Municipal Universities
   in the Period of 13th Five-year Plan [CITTCD201904037]; R&D Program of
   Beijing Municipal Education Commission [KM202010011012]
FX This research was funded by the National Key Research and Development
   Program of China (grant number 2020YFB1806500); the Support Project of
   High-level Teachers in Beijing Municipal Universities in the Period of
   13th Five-year Plan (grant number CIT&TCD201904037); the R&D Program of
   Beijing Municipal Education Commission (grant number KM202010011012).
CR Belkin M, 2006, J MACH LEARN RES, V7, P2399
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NR 41
TC 0
Z9 0
U1 2
U2 8
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD APR
PY 2021
VL 23
IS 4
AR 403
DI 10.3390/e23040403
PG 17
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA RR3IT
UT WOS:000642997100001
PM 33800642
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Quiroz-Mercado, JI
   Barron-Fernandez, R
   Ramirez-Salinas, MA
AF Isaias Quiroz-Mercado, Job
   Barron-Fernandez, Ricardo
   Antonio Ramirez-Salinas, Marco
TI Semantic Similarity Estimation Using Vector Symbolic Architectures
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Benchmark testing; Computational modeling; Correlation;
   Computer architecture; Natural language processing; Estimation; Concept
   representation; semantic similarity; vector symbolic architectures; word
   embeddings
ID SIMLEX-999; LANGUAGE; CONTEXT; MODELS
AB For many natural language processing applications, estimating similarity and relatedness between words are key tasks that serve as the basis for classification and generalization. Currently, vector semantic models (VSM) have become a fundamental language modeling tool. VSMs represent words as points in a high-dimensional space and follow the distributional hypothesis of meaning, which assumes that semantic similarity is related to the context. In this paper, we propose a model whose representations are based on the semantic features associated with a concept within the ConceptNet knowledge graph. The proposed model is based on a vector symbolic architecture framework, which defines a set of arithmetic operations to encode the semantic features within a single high-dimensional vector. In addition to word distribution, these vector representations consider several types of information. Moreover, owing to the properties of high-dimensional spaces, they have the additional advantage of being interpretable. We analyze the model's performance on the SimLex-999 dataset, a dataset where commonly used distributional models (e.g., word2vec or GloVe) perform poorly. Our results are similar to those of other hybrid models, and they surpass several state-of-the-art distributional and knowledge-based models.
C1 [Isaias Quiroz-Mercado, Job; Barron-Fernandez, Ricardo; Antonio Ramirez-Salinas, Marco] Inst Politecn Nacl, Ctr Invest Comp, Mexico City 07738, DF, Mexico.
RP Quiroz-Mercado, JI (通讯作者)，Inst Politecn Nacl, Ctr Invest Comp, Mexico City 07738, DF, Mexico.
EM jobquiroz@hotmail.com
RI ; Ramirez Salinas, Marco Antonio/N-2780-2018
OI Quiroz Mercado, Job Isaias/0000-0003-2988-472X; Ramirez Salinas, Marco
   Antonio/0000-0002-9376-2893
FU Instituto Politecnico Nacional (Secretaria de Investigacion y Posgrado y
   Comision de Operacion y Fomento de Actividades Academicas)
   [SIP-20201415]; Consejo Nacional de Ciencia y Tecnologia (CONACYT)
   [666415]
FX This work was supported in part by Instituto Politecnico Nacional
   (Secretaria de Investigacion y Posgrado y Comision de Operacion y
   Fomento de Actividades Academicas) under grant SIP-20201415, and in part
   by Consejo Nacional de Ciencia y Tecnologia (CONACYT) under grant
   666415.
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NR 65
TC 0
Z9 0
U1 1
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 109120
EP 109132
DI 10.1109/ACCESS.2020.3001765
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA MM0MM
UT WOS:000549854400024
OA gold
DA 2022-08-23
ER

PT J
AU Stadler, C
   Bin, S
   Wenige, L
   Buhmann, L
   Lehmann, J
AF Stadler, Claus
   Bin, Simon
   Wenige, Lisa
   Buehmann, Lorenz
   Lehmann, Jens
TI Schema-agnostic SPARQL-driven faceted search benchmark generation
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Faceted search; Benchmark; SPARQL; RDF; Benchmark generator; Triple
   store
AB In this work, we present a schema-agnostic faceted browsing benchmark generation framework for RDF data and SPARQL engines. Faceted search is a technique that allows narrowing down sets of information items by applying constraints over their properties, whereas facets correspond to properties of these items. While our work can be used to realise real-world faceted search user interfaces, our focus lies on the construction and benchmarking of faceted search queries over knowledge graphs. The RDF model exhibits several traits that seemingly make it a natural foundation for faceted search: all information items are represented as RDF resources, property values typically already correspond to meaningful semantic classifications, and with SPARQL there is a standard language for uniformly querying instance and schema information.
   However, although faceted search is ubiquitous today, it is typically not performed on the RDF model directly. Two major sources of concern are the complexity of query generation and the query performance. To overcome the former, our framework comes with an intermediate domain-specific language. Thereby our approach is SPARQL-driven which means that every faceted search information need is intensionally expressed as a single SPARQL query. In regard to the latter, we investigate the possibilities and limits of real-time SPARQL-driven faceted search on contemporary triple stores. We report on our findings by evaluating systems performance and correctness characteristics when executing a benchmark generated using our generation framework.
   All components, namely the benchmark generator, the benchmark runners and the underlying faceted search framework, are published freely available as open source. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Stadler, Claus; Bin, Simon; Wenige, Lisa; Buehmann, Lorenz; Lehmann, Jens] Inst Appl Informat, Goerdelerring 9, Leipzig, Germany.
   [Lehmann, Jens] Univ Bonn, Endenicher Allee 19a, Bonn, Germany.
   [Lehmann, Jens] Fraunhofer IAIS, Zwickauer Str 46, Dresden, Germany.
RP Stadler, C (通讯作者)，Inst Appl Informat, Goerdelerring 9, Leipzig, Germany.
EM cstadler@informatik.uni-leipzig.de; jens.lehmann@iais.fraunhofer.de
FU EU [688227, 732194]; Federal Ministry of Transport and Digital
   Infrastructure (BMVI) [19F2029G]
FX This work was supported by grants from the EU H2020 Programme for the
   projects HOBBIT (GA no. 688227) and QROWD (GA no. 732194) and the
   Federal Ministry of Transport and Digital Infrastructure (BMVI) for the
   LIMBO project (GA no. 19F2029G).
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NR 34
TC 2
Z9 2
U1 0
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 DEC
PY 2020
VL 65
AR 100614
DI 10.1016/j.websem.2020.100614
PG 20
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PE4MD
UT WOS:000598338300007
DA 2022-08-23
ER

PT J
AU Achichi, M
   Bellahsene, Z
   Ben Ellefi, M
   Todorov, K
AF Achichi, Manel
   Bellahsene, Zohra
   Ben Ellefi, Mohamed
   Todorov, Konstantin
TI Linking and disambiguating entities across heterogeneous RDF graphs
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE RDF data linking; Knowledge graphs; Linked open data; Data
   heterogeneities
ID DISCOVERY
AB Establishing identity links across RDF datasets is a central and challenging task on the way to realising the Data Web project. It is well-known that data supplied by different sources can be highly heterogeneous-two entities referring to the same real world object are often described, structured and valued differently, or in a complementary fashion. In this paper, we explore the origins and the multiplicity of data heterogeneity problems, proposing a novel classification that allows to isolate challenges and to position our and future work. Many state-of-the-art data linking approaches rely on sets of discriminative properties, provided by the user or by specialised tools, which, in the lack of knowledge of the nature of the data, do not allow to account automatically for a large number of structural heterogeneities. In addition, similarity measures and thresholds need to be selected and tuned manually or learned by specialised algorithms. We propose a solution covering an important number of heterogeneities, attempting to reduce the user configuration effort, based on: (i) Property filtering, or automatic data cleaning of "problematic" attributes; (ii) Instance profiling allowing to represent each resource by a sub-graph considered relevant for the comparison task; and (iii) Instance vector representation allowing to compare resources. To reduce the false positives rate, we apply a (iv) Post-processing step based on hierarchical clustering and key ranking techniques aiming to disambiguate highly similar, though not identical instances. This pipeline is implemented in Legato-a data linking tool, showing to outperform or to perform as well as state-of-the-art tools on highly heterogeneous and diverse benchmark datasets, yet keeping the user configuration effort low. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Achichi, Manel; Bellahsene, Zohra; Todorov, Konstantin] Univ Montpellier, LIRMM, CNRS, 161 Rue Ada, F-34000 Montpellier, France.
   [Ben Ellefi, Mohamed] Aix Marseille Univ, LIS, 163 Ave Luminy, F-13288 Marseille, France.
RP Todorov, K (通讯作者)，Univ Montpellier, LIRMM, CNRS, 161 Rue Ada, F-34000 Montpellier, France.
EM todorov@lirmm.fr
OI BEN ELLEFI, Mohamed/0000-0003-4458-0919
FU French National Research Agency (ANR) within the DOREMUS Project
   [ANR-14-CE24-0020]
FX This work has been partially supported by the French National Research
   Agency (ANR) within the DOREMUS Project, under grant number
   ANR-14-CE24-0020.
CR Achichi M., 2016, REV SCI TECHNOL INF
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NR 56
TC 9
Z9 9
U1 0
U2 8
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
J9 J WEB SEMANT
JI J. Web Semant.
PD MAR
PY 2019
VL 55
BP 108
EP 121
DI 10.1016/j.websem.2018.12.003
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 HQ1OB
UT WOS:000462166300008
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Mora-Cantallops, M
   Sanchez-Alonso, S
   Garcia-Barriocanal, E
AF Mora-Cantallops, Marcal
   Sanchez-Alonso, Salvador
   Garcia-Barriocanal, Elena
TI A systematic literature review on Wikidata
SO DATA TECHNOLOGIES AND APPLICATIONS
LA English
DT Review
DE Literature review; Survey; Applications; Empirical studies; Wikidata;
   Knowledge graphs
ID INFORMATION; MODEL
AB Purpose The purpose of this paper is to review the current status of research on Wikidata and, in particular, of articles that either describe applications of Wikidata or provide empirical evidence, in order to uncover the topics of interest, the fields that are benefiting from its applications and which researchers and institutions are leading the work. Design/methodology/approach A systematic literature review is conducted to identify and review how Wikidata is being dealt with in academic research articles and the applications that are proposed. A rigorous and systematic process is implemented, aiming not only to summarize existing studies and research on the topic, but also to include an element of analytical criticism and a perspective on gaps and future research. Findings Despite Wikidata's potential and the notable rise in research activity, the field is still in the early stages of study. Most research is published in conferences, highlighting such immaturity, and provides little empirical evidence of real use cases. Only a few disciplines currently benefit from Wikidata's applications and do so with a significant gap between research and practice. Studies are dominated by European researchers, mirroring Wikidata's content distribution and limiting its Worldwide applications. Originality/value The results collect and summarize existing Wikidata research articles published in the major international journals and conferences, delivering a meticulous summary of all the available empirical research on the topic which is representative of the state of the art at this time, complemented by a discussion of identified gaps and future work.
C1 [Mora-Cantallops, Marcal; Sanchez-Alonso, Salvador; Garcia-Barriocanal, Elena] Univ Alcala de Henares, Alcala De Henares, Spain.
RP Mora-Cantallops, M (通讯作者)，Univ Alcala de Henares, Alcala De Henares, Spain.
EM marcal.mora@uah.es
RI Mora-Cantallops, Marçal/Z-4249-2019; Sanchez-Alonso,
   Salvador/M-2950-2014
OI Mora-Cantallops, Marçal/0000-0002-2480-1078; Sanchez-Alonso,
   Salvador/0000-0002-9949-4797
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NR 68
TC 5
Z9 5
U1 1
U2 10
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2514-9288
EI 2514-9318
J9 DATA TECHNOL APPL
JI Data Technol. Appl.
PD JUL 1
PY 2019
VL 53
IS 3
BP 250
EP 268
DI 10.1108/DTA-12-2018-0110
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 IU7KY
UT WOS:000483764000001
DA 2022-08-23
ER

PT J
AU Ficel, H
   Haddad, MR
   Zghal, HB
AF Ficel, Hemza
   Haddad, Mohamed Ramzi
   Zghal, Hajer Baazaoui
TI A graph-based recommendation approach for highly interactive platforms
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Recommender systems; Stream analysis; Consumption behavior modeling
   and&nbsp; prediction; Ranking recommendation approach; Knowledge graphs
ID NEWS RECOMMENDATION; PERCEIVED VALUE; INFORMATION; SYSTEMS; GENDER;
   MODEL; DECISION
AB Highly interactive and dynamic marketplaces shake the underlying hypothesis of established recommendation approaches assuming static offerings and sparse interaction data. Such markets show different dynamics due to goods/contents volatility and public accessibility. This imposes new challenges to recommender systems since they are required to handle unbounded data streams with high velocity, volume and variability while operating at scale and in real-time. Moreover, due to the high rates of new offerings introduction and obsolescence, low latency modeling and inference are needed to keep an up-to-date understanding of the market. In this paper, we propose a recommendation approach addressing the specific challenges of real-time stream recommendation in highly interactive marketplaces. The approach is based on several psychological theories to model consumers' perceived value towards items. Besides, a ranking measure is defined on a heterogeneous information network to infer the factors that drive consumers' decisions. With data relationships at its center, this data model is strongly efficient while ensuring free and flexible knowledge evolution as data evolves. It allows low latency incremental learning at scale while providing dynamic recommendations. Several comparative experiments were conducted to validate the potential of this approach in different use cases requiring offline, online, static or dynamic recommendations.
C1 [Ficel, Hemza; Haddad, Mohamed Ramzi; Zghal, Hajer Baazaoui] Univ Manouba, Riadi Lab, Ecole Natl Sci Informat, Manouba, Tunisia.
RP Haddad, MR (通讯作者)，Univ Manouba, Riadi Lab, Ecole Natl Sci Informat, Manouba, Tunisia.
EM hemza.ficel@ensi-uma.tn; haddad.medramzi@gmail.com;
   hajer.baazaouizghal@riadi.rnu.tn
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NR 108
TC 1
Z9 1
U1 4
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 DEC 15
PY 2021
VL 185
AR 115555
DI 10.1016/j.eswa.2021.115555
EA JUL 2021
PG 19
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 WH0YJ
UT WOS:000707414500013
DA 2022-08-23
ER

PT J
AU Picart-Armada, S
   Fernandez-Albert, F
   Vinaixa, M
   Yanes, O
   Perera-Lluna, A
AF Picart-Armada, Sergio
   Fernandez-Albert, Francesc
   Vinaixa, Maria
   Yanes, Oscar
   Perera-Lluna, Alexandre
TI FELLA: an R package to enrich metabolomics data
SO BMC BIOINFORMATICS
LA English
DT Article
DE Metabolomics; Pathways; Network analysis; Data mining; Knowledge
   representation
ID FUMARATE-HYDRATASE; REVEALS ALTERATIONS; LIVER; CANCER; CELLS
AB BackgroundPathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.ResultsWe introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets -three from Homo sapiens, two from Danio rerio and one from Mus musculus- and has reproduced findings from the original studies and from independent literature.ConclusionsThe R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.
C1 [Picart-Armada, Sergio; Fernandez-Albert, Francesc; Perera-Lluna, Alexandre] Univ Politecn Cataluna, Dept Engn Sistemes Automat & Informat Ind, B2SLab, E-08028 Barcelona, Spain.
   [Picart-Armada, Sergio; Fernandez-Albert, Francesc; Perera-Lluna, Alexandre] CIBER, BBN, Networking Biomed Res Ctr, Subject Area Bioengn Biomat & Nanomed, Madrid 28029, Spain.
   [Picart-Armada, Sergio; Perera-Lluna, Alexandre] Hosp St Joan Deu, Inst Recerca Pediat, Barcelona 08950, Spain.
   [Vinaixa, Maria; Yanes, Oscar] Univ Rovira & Virgili, DEEA, Dept Elect Engn, Metabol Platform,IISPV, Tarragona 43003, Spain.
   [Vinaixa, Maria; Yanes, Oscar] CIBERDEM, CIBER Diabet & Associated Metab Dis, Madrid 28029, Spain.
   [Fernandez-Albert, Francesc] Takeda Cambridge Ltd, Cambridge CB4 0PZ, England.
RP Picart-Armada, S (通讯作者)，Univ Politecn Cataluna, Dept Engn Sistemes Automat & Informat Ind, B2SLab, E-08028 Barcelona, Spain.; Picart-Armada, S (通讯作者)，CIBER, BBN, Networking Biomed Res Ctr, Subject Area Bioengn Biomat & Nanomed, Madrid 28029, Spain.
EM sergi.picart@upc.edu
RI Yanes, Oscar/N-3630-2016; Vinaixa, Maria/AAB-1176-2020; Vinaixa,
   Maria/L-7236-2014
OI Yanes, Oscar/0000-0003-3695-7157; Vinaixa, Maria/0000-0001-9804-0171;
   Vinaixa, Maria/0000-0001-9804-0171; Picart-Armada,
   Sergio/0000-0002-6426-8204; Fernandez-Albert,
   Francesc/0000-0001-5561-0701
FU Spanish Ministry of Economy and Competitiveness (MINECO)
   [BFU2014-57466-P, TEC2014-60337-R, DPI2017-89827-R]; CIBERDEM of
   Instituto de Investigacion Carlos III (ISCIII); CIBER-BBN of Instituto
   de Investigacion Carlos III (ISCIII)
FX This work was supported by the Spanish Ministry of Economy and
   Competitiveness (MINECO) [BFU2014-57466-P to OY, TEC2014-60337-R and
   DPI2017-89827-R to AP]. OY, AP and SP thank for funding CIBERDEM and
   CIBER-BBN, both initiatives of Instituto de Investigacion Carlos III
   (ISCIII). SP thanks the AGAUR FI-scholarship programme. The funding body
   had no role in the design of the study and collection, analysis, and
   interpretation of data and in writing the manuscript.
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Z9 24
U1 4
U2 15
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD DEC 22
PY 2018
VL 19
AR 538
DI 10.1186/s12859-018-2487-5
PG 9
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 HF4NG
UT WOS:000454209300001
PM 30577788
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Alvarez-Rodriguez, JM
   Mendieta, R
   de la Vara, JL
   Fraga, A
   Llorens, J
AF Maria Alvarez-Rodriguez, Jose
   Mendieta, Roy
   Luis de la Vara, Jose
   Fraga, Anabel
   Llorens, Juan
TI Enabling System Artefact Exchange and Selection through a Linked Data
   Layer
SO JOURNAL OF UNIVERSAL COMPUTER SCIENCE
LA English
DT Article
DE Linked data; OSLC; data shapes; interoperability; retrieval systems
ID SOFTWARE; REUSE; SUPPORT
AB The use of different techniques and tools is a common practice to cover all stages in the systems development lifecycle, generating a very good number of system artefacts. Moreover, these artefacts are commonly encoded in different formats and can only be accessed, in most cases, through proprietary and non-standard protocols. This scenario can be considered a real nightmare for software or systems reuse. Possible solutions imply the creation of a real collaborative development environment where tools can exchange and share data, information and knowledge. In this context, the OSLC (Open Services for Lifecycle Collaboration) initiative pursues the creation of public specifications (data shapes) to exchange any artefact generated during the development lifecycle, by applying the principles of the Linked Data initiative. In this paper, the authors present a solution to provide a real multi-format system artefact reuse by means of an OSLC-based specification to share and exchange any artefact under the principles of the Linked Data initiative. Finally, two experiments are conducted to demonstrate the advantages of enabling an input/output interface based on an OSLC implementation on top of an existing commercial tool (the Knowledge Manager). Thus, it is possible to enhance the representation and retrieval capabilities of system artefacts by considering the whole underlying knowledge graph generated by the different system artefacts and their relationships. After performing 45 different queries over logical and physical models stored in Papyrus, IBM Rhapsody and Simulink, results of precision and recall are promising showing average values between 70-80%.
C1 [Maria Alvarez-Rodriguez, Jose; Mendieta, Roy; Luis de la Vara, Jose; Fraga, Anabel; Llorens, Juan] Carlos III Univ Madrid, Madrid, Spain.
RP Alvarez-Rodriguez, JM (通讯作者)，Carlos III Univ Madrid, Madrid, Spain.
EM josemaria.alvarez@uc3m.es; roy.mendieta@kr.inf.uc3m.es;
   jvara@inf.uc3m.es; afraga@inf.uc3m.es; juan.llorens@uc3m.es
RI de la Vara, Jose Luis/A-7678-2018
OI de la Vara, Jose Luis/0000-0003-1813-398X; FRAGA,
   ANABEL/0000-0001-5837-920X; LLORENS, JUAN/0000-0001-7951-6357
FU AMASS project (H2020-ECSEL grant) [692474]; AMASS project (Spain's
   MINECO) [PCIN-2015-262]; CRYSTAL project (ARTEMIS FP7-CRitical sYSTem
   engineering AcceLeration project) [332830-CRYSTAL]; CRYSTAL project
   (Spanish Ministry of Industry)
FX The research leading to these results has received funding from the
   AMASS project (H2020-ECSEL grant agreement no 692474; Spain's MINECO
   ref. PCIN-2015-262) and the CRYSTAL project (ARTEMIS FP7-CRitical sYSTem
   engineering AcceLeration project no 332830-CRYSTAL and the Spanish
   Ministry of Industry).
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U1 1
U2 1
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 2018
VL 24
IS 11
BP 1536
EP 1560
PG 25
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HG5QC
UT WOS:000455032500004
DA 2022-08-23
ER

PT J
AU Wang, BL
   Jing, J
   Huang, XC
   Hua, C
   Qin, Q
   Jia, Y
   Wang, ZY
   Jiang, L
   Gao, B
   Wu, LL
   Zeng, XF
   Wang, FB
   Mao, CB
   Liu, SR
AF Wang, Beilei
   Jing, Jie
   Huang, Xiaochun
   Hua, Cheng
   Qin, Qin
   Jia, Yin
   Wang, Zhiyong
   Jiang, Lei
   Gao, Bai
   Wu, Lele
   Zeng, Xianfei
   Wang, Fubo
   Mao, Chuanbin
   Liu, Shanrong
TI Establishment of a Knowledge-and-Data-Driven Artificial Intelligence
   System with Robustness and Interpretability in Laboratory Medicine
SO ADVANCED INTELLIGENT SYSTEMS
LA English
DT Article
DE artificial intelligence; diagnosis; knowledge and data driven;
   laboratory medicine; machine learning
ID DIABETIC-RETINOPATHY; MACHINE; CANCER
AB Laboratory medicine plays an important role in clinical diagnosis. However, no laboratory-based artificial intelligence (AI) diagnostic system has been applied in current clinical practice due to the lack of robustness and interpretability. Although many attempts have been made, it is still difficult for doctors to adopt the existing machine learning (ML) patterns in interpreting laboratory (lab) big data. Here, a knowledge-and-data-driven laboratory diagnostic system is developed, termed AI-based Lab tEst tO diagNosis (AI LEON), by integrating an innovative knowledge graph analysis framework and "mixed XGboost and Genetic Algorithm (MiXG)" technique to simulate the doctor's laboratory-based diagnosis. To establish AI LEON, we included 89 116 949 laboratory data and 10 423 581 diagnosis data points from 730 113 participants. Among them, 686 626 participants were recruited for training and validating purposes with the remaining for testing purposes. AI LEON automatically identified and analyzed 2071 lab indexes, resulting in multiple disease recommendations that involved 441 common diseases in ten organ systems. AI LEON exhibited outstanding transparency and interpretability in three universal clinical application scenarios and outperformed human physicians in interpreting lab reports. AI LEON is an advanced intelligent system that enables a comprehensive interpretation of lab big data, which substantially improves the clinical diagnosis.
C1 [Wang, Beilei; Jing, Jie; Huang, Xiaochun; Qin, Qin; Jia, Yin; Liu, Shanrong] Navy Mil Med Univ, Dept Lab Diagnost, Changhai Hosp, 168 Changhai Rd, Shanghai 200433, Peoples R China.
   [Wang, Beilei] Naval Special Med Ctr, Dept Marine Biomed & Polar Med, 880 Xiangyin Rd, Shanghai 200433, Peoples R China.
   [Hua, Cheng] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, 1954 Huashan Rd, Shanghai 200031, Peoples R China.
   [Wang, Zhiyong; Gao, Bai; Wu, Lele] Navy Mil Med Univ, Dept Informat, Changhai Hosp, 168 Changhai Rd, Shanghai 200433, Peoples R China.
   [Wang, Zhiyong] Tongji Univ, Dept Informat, Shanghai Peoples Hosp 4, Sch Med, 1279 Sanmen Rd, Shanghai 200081, Peoples R China.
   [Jiang, Lei] Navy Mil Med Univ, Dept Rheumatol & Immunol, Shanghai Changzheng Hosp, 415 Fengyang Rd, Shanghai 200003, Peoples R China.
   [Zeng, Xianfei] Northwest Univ, Sch Med, Xian Area Med Lab Ctr, 15 Gaoxin Rd, Xian 710000, Peoples R China.
   [Wang, Fubo] Guangxi Med Univ, Ctr Genom & Personalized Med, 22 Shuangyong Rd, Nanning 530021, Peoples R China.
   [Mao, Chuanbin] Univ Oklahoma, Dept Chem & Biochem, Stephenson Life Sci Res Ctr, 101 Stephenson Pkwy, Norman, OK 73019 USA.
RP Liu, SR (通讯作者)，Navy Mil Med Univ, Dept Lab Diagnost, Changhai Hosp, 168 Changhai Rd, Shanghai 200433, Peoples R China.; Wang, FB (通讯作者)，Guangxi Med Univ, Ctr Genom & Personalized Med, 22 Shuangyong Rd, Nanning 530021, Peoples R China.; Mao, CB (通讯作者)，Univ Oklahoma, Dept Chem & Biochem, Stephenson Life Sci Res Ctr, 101 Stephenson Pkwy, Norman, OK 73019 USA.
EM wangfubo@gxmu.edu.cn; cbmao@ou.edu; shanrongliu@smmu.edu.cn
RI wang, fubo/GPF-4834-2022; wang, fubo/I-4093-2014
OI wang, fubo/0000-0002-2209-9932
FU Shanghai Science and Technology Committee Program [20511101200]
FX B.W., J.J., X.H., and C.H. contributed equally to this work. The authors
   thank Shanghai Science and Technology Committee Program (20511101200)
   for financial support. Thanks to the clinicians from different
   Departments of Changhai Hospital: Prof. Xianbao Zhan, Dr. Yingyi Zhang,
   Dr. Jie Li, Dr. Meihong Wu (Oncology); Prof. Xiaoying Bi, Dr. Chunhua
   Feng, Dr. Shu Zhou, Dr. Zhenyu Liu, Dr. Youbei Liu (Neurology); Prof
   Zhiyong Guo, Dr. Juan Li, Dr. Qi Bian, Dr. Jing Sun (Nephrology); Dr.
   Jie Gao, Dr. Dongbao Zhao, Dr. Wei Wan, Dr. Lianmei Ji, Dr. Ju Zhang
   (Rheumatology); Dr. Xin Zhang, Dr. Rui Zheng (General Surgery); Prof.
   Yan Shang, Dr. Jiayi Zhao, Dr. Junjie Wu, Dr. Yang Jiao (General
   Medicine); Dr. Lei Li, Dr. Xiangyu Kong (Gastroenterology); Prof. Maojin
   Xu (Endocrinology); Prof. Fubo Wang (Urology); and Dr. Xiaoliang Cong
   (Cardiology, Shanghai Changzheng Hospital). The authors also want to
   thank to Dr. Zhang Jianfeng (Nantong Tumor Hospital); Dr. Yunhan Ji
   (Orthopedic Surgery Tongren Hospital); Prof. Cheng Wu (Department of
   Health Statistics); Prof. Jun Zheng and Prof. Wenxin Hu (School of Data
   Science & Engineering, East China Normal University); and Mr. Chao Xiong
   and Mr. Nan Zhang (Hitales, Shanghai) for their professional guidance
   and support to the establishment of the AI LEON. Moreover, thanks to all
   the general practitioners from the standardized training organization of
   general medicine who participated actively in the Examination Report
   Interpretation Challenge with AI LEON.
CR Ahmadian L, 2011, INT J MED INFORM, V80, P81, DOI 10.1016/j.ijmedinf.2010.11.006
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NR 26
TC 0
Z9 0
U1 9
U2 9
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
EI 2640-4567
J9 ADV INTELL SYST-GER
JI Adv. Intell. Syst.
PD MAY
PY 2022
VL 4
IS 5
AR 2100204
DI 10.1002/aisy.202100204
EA FEB 2022
PG 17
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Robotics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science; Robotics
GA 1I4SG
UT WOS:000753293800001
OA gold
DA 2022-08-23
ER

PT J
AU Xian, XP
   Wu, T
   Qiao, SJ
   Wang, W
   Wang, C
   Liu, YB
   Xu, GX
AF Xian, Xingping
   Wu, Tao
   Qiao, Shaojie
   Wang, Wei
   Wang, Chao
   Liu, Yanbing
   Xu, Guangxia
TI DeepEC: Adversarial attacks against graph structure prediction models
SO NEUROCOMPUTING
LA English
DT Article
DE Graph data; Adversarial attacks; Link prediction; Structural
   perturbation; Deep ensemble coding
ID LINK-PREDICTION; COMPLEX NETWORKS
AB Inspired by the practical importance of graph structured data, link prediction, one of the most frequently applied tasks on graph data, has garnered considerable attention in recent years, and they have been widely applied in item recommendation, privacy inference attack, knowledge graph completion, fraud detection, and other fields. However, recent studies show that machine learning-based intelligent systems are vulnerable to adversarial attacks, which has recently inspired much research on the security problems of machine learning in the context of computer vision, natural language processing, physical world, etc. Nonetheless, there is a lack of understanding of the vulnerability of link prediction methods in face of adversarial attacks. To unveil the weaknesses and aid in the development of robust link prediction methods, we propose a deep architecture-based adversarial attack method, called Deep Ensemble Coding, against link prediction. In particular, based on the assumption that links play different structural roles in structure organization, we propose a deep linear coding-based structure enhancement mechanism to generate adversarial examples. We also empirically investigate other adversarial attack methods for graph data, including heuristic and evolutionary perturbation methods. Based on the comprehensive experiments conducted on various real-world networks, we can conclude that the proposed adversarial attack method has satisfactory performance for link prediction. Moreover, we can observe that state-of-the-art link prediction algorithms are vulnerable to adversarial attacks and, for adversarial defense, the attack can be viewed as a robustness evaluation for the construction of robust link prediction methods.
   Inspired by the practical importance of graph structured data, link prediction, one of the most fre-quently applied tasks on graph data, has garnered considerable attention in recent years, and they have been widely applied in item recommendation, privacy inference attack, knowledge graph completion, fraud detection, and other fields. However, recent studies show that machine learning-based intelligent systems are vulnerable to adversarial attacks, which has recently inspired much research on the secu-rity problems of machine learning in the context of computer vision, natural language processing, phys-ical world, etc. Nonetheless, there is a lack of understanding of the vulnerability of link prediction methods in face of adversarial attacks. To unveil the weaknesses and aid in the development of robust link prediction methods, we propose a deep architecture-based adversarial attack method, called Deep Ensemble Coding, against link prediction. In particular, based on the assumption that links play differ-ent structural roles in structure organization, we propose a deep linear coding-based structure enhancement mechanism to generate adversarial examples. We also empirically investigate other adversarial attack methods for graph data, including heuristic and evolutionary perturbation methods. Based on the comprehensive experiments conducted on various real-world networks, we can conclude that the proposed adversarial attack method has satisfactory performance for link prediction. Moreover, we can observe that state-of-the-art link prediction algorithms are vulnerable to adversarial attacks and, for adversarial defense, the attack can be viewed as a robustness evaluation for the construction of robust link prediction methods.
   (c) 2020 Elsevier B.V. All rights reserved.
C1 [Xian, Xingping; Wu, Tao] Chongqing Univ Posts & Telecommun, Sch Cybersecur & Informat Law, Chongqing, Peoples R China.
   [Qiao, Shaojie] Chengdu Univ Informat Technol, Sch Software Engn, Chengdu, Peoples R China.
   [Wang, Wei] Sichuan Univ, Inst Cybersecur, Chengdu, Peoples R China.
   [Wang, Chao] Chongqing Univ, Inst Elect Engn, Chongqing, Peoples R China.
   [Liu, Yanbing] Chongqing Univ Posts & Telecommun, Chongqing Engn Lab Internet & Informat Secur, Chongqing, Peoples R China.
   [Xu, Guangxia] Chongqing Univ Posts & Telecommun, Dept Software Engn, Chongqing, Peoples R China.
RP Wu, T (通讯作者)，Chongqing Univ Posts & Telecommun, Sch Cybersecur & Informat Law, Chongqing, Peoples R China.
EM wutaoadeny@gmail.com
RI Wang, Wei/A-1091-2016
OI Wang, Wei/0000-0003-4088-5395
FU National Natural Science Foundation of China [61802039, 61772098,
   61772091]; Chongqing Municipal Natural Science Foundation
   [cstc2020jcyjmsxmX0804]; National Key R&D Program of China
   [2018YFB0904900, 2018YFB0904905]; CCFHuawei Database System Innovation
   Researc h Plan [CCFHuaweiDBIR2020004A]
FX This work is partially supported by the National Natural Science
   Foundation of China under Grant No. 61802039, 61772098, 61772091;
   Chongqing Municipal Natural Science Foundation under Grant No.
   cstc2020jcyjmsxmX0804; National Key R&D Program of China under Grant No.
   2018YFB0904900, 2018YFB0904905; CCFHuawei Database System Innovation
   Researc h Plan under Grant No. CCFHuaweiDBIR2020004A. We thank the
   anonymous reviewers for their thorough review and highly appreciate
   their useful comments and suggestions.
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NR 86
TC 4
Z9 4
U1 2
U2 20
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD MAY 21
PY 2021
VL 437
BP 168
EP 185
DI 10.1016/j.neucom.2020.07.126
EA FEB 2021
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RE8DJ
UT WOS:000634377200015
DA 2022-08-23
ER

PT J
AU Chaves-Fraga, D
   Priyatna, F
   Alobaid, A
   Corcho, O
AF Chaves-Fraga, David
   Priyatna, Freddy
   Alobaid, Ahmad
   Corcho, Oscar
TI Exploiting Declarative Mapping Rules for Generating GraphQL Servers with
   Morph-GraphQL
SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
LA English
DT Article
DE OBDA; Declarative Mappings; GraphQL
ID SEMANTICS
AB In the last decade, REST has become the most common approach to provide web services, yet it was not originally designed to handle typical modern applications (e.g. mobile apps). GraphQL was proposed to reduce the number of queries and data exchanged in comparison with REST. Since its release in 2015, it has gained momentum as an alternative approach to REST. However, generating and maintaining GraphQL resolvers is not a simple task. First, a domain expert has to analyze a dataset, design the corresponding GraphQL schema and map the dataset to the schema. Then, a software engineer (e.g. GraphQL developer) implements the corresponding GraphQL resolvers in a specific programming language. In this paper, we present an approach to exploit the information from mappings rules (relation between target and source schema) and generate a GraphQL server. These mapping rules construct a virtual knowledge graph which is accessed by the generated GraphQL resolvers. These resolvers translate the input GraphQL queries into the queries supported by the underlying dataset. Domain experts or software developers may benefit from our approach: a domain expert does not need to involve software developers to implement the resolvers, and software developers can generate the initial version of the resolvers to be implemented. We implemented our approach in the Morph-GraphQL framework and evaluated it using the LinGBM benchmark.
C1 [Chaves-Fraga, David; Priyatna, Freddy; Alobaid, Ahmad; Corcho, Oscar] Univ Politecn Madrid, Ontol Engn Grp, Madrid, Spain.
RP Chaves-Fraga, D (通讯作者)，Univ Politecn Madrid, Ontol Engn Grp, Madrid, Spain.
EM dchaves@fi.upm.es; fpriyatna@fi.upm.es; aalobaid@fi.upm.es;
   ocorcho@fi.upm.es
RI Corcho, Oscar/C-4000-2011; Fraga, David Chaves/AAL-1548-2020
OI Corcho, Oscar/0000-0002-9260-0753; Fraga, David
   Chaves/0000-0003-3236-2789
FU Spanish Ministerio de Economia, Industria y Competitividad
   [TIN2016-78011-C4-4-R]; EU FEDER funds under the DATOS 4.0: RETOS Y
   SOLUCIONES - UPM Spanish National Project [TIN2016-78011-C4-4-R]; FPI
   Grant [BES-2017-082511]
FX We would like to acknowledge David Moreno and Alvaro Sainz for helping
   in the development of Morph-GraphQL and Daniel Dona for helping in the
   setup of the performed experiments. The work presented in this paper is
   supported by the Spanish Ministerio de Economia, Industria y
   Competitividad and EU FEDER funds under the DATOS 4.0: RETOS Y
   SOLUCIONES - UPM Spanish National Project (TIN2016-78011-C4-4-R) and by
   an FPI Grant (BES-2017-082511).
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U2 5
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 JUN
PY 2020
VL 30
IS 6
SI SI
BP 785
EP 803
DI 10.1142/S0218194020400070
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA MY0IM
UT WOS:000558101900004
OA hybrid
DA 2022-08-23
ER

PT J
AU Zhang, Z
   Zhuang, FZ
   Li, XB
   Niu, ZY
   He, J
   He, Q
   Xiong, H
AF Zhang, Zhao
   Zhuang, Fuzhen
   Li, Xuebing
   Niu, Zheng-Yu
   He, Jia
   He, Qing
   Xiong, Hui
TI Knowledge triple mining via multi-task learning
SO INFORMATION SYSTEMS
LA English
DT Article
DE Multi-task learning; Knowledge mining; Relation extraction; Knowledge
   graph construction
AB Recent years have witnessed the rapid development of knowledge bases (KBs) such as WordNet, Yago and DBpedia, which are useful resources in Al-related applications. However, most of the existing KBs are suffering from incompleteness and manually adding knowledge into KBs is inefficient. Therefore, automatically mining knowledge becomes a critical issue. To this end, in this paper, we propose to develop a model (S-2 AMT) to extract knowledge triples, such as <Barack Obama, wife, Michelle Obama>, from the Internet and add them to KBs to support many downstream applications. Particularly, because the seed instances' for every relation is difficult to obtain, our model is capable of mining knowledge triples with limited available seed instances. To be more specific, we treat the knowledge triple mining task for each relation as a single task and use multi-task learning (MTL) algorithms to solve the problem, because MTL algorithms can often get better results than single-task learning (STL) ones with limited training data. Moreover, since finding proper task groups is a fatal problem in MTL which can directly influences the final results, we adopt a clustering algorithm to find proper task groups to further improve the performance. Finally, we conduct extensive experiments on real-world data sets and the experimental results clearly validate the performance of our MTL algorithms against STL ones. (C) 2018 Elsevier Ltd. All rights reserved.
C1 [Zhang, Zhao; Zhuang, Fuzhen; Li, Xuebing; He, Jia; He, Qing] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China.
   [Zhang, Zhao; Zhuang, Fuzhen; He, Jia; He, Qing] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Niu, Zheng-Yu] Baidu Inc, Beijing, Peoples R China.
   [Xiong, Hui] Rutgers State Univ, Rutgers Business Sch, Management Sci & Informat Syst Dept, Newark, NJ USA.
RP Zhuang, FZ (通讯作者)，6 Kexueyuan South Rd, Beijing 100190, Peoples R China.
EM zhuangfuzhen@ict.ac.cn
FU National Key Research and Development Program of China [2018YFB1004300];
   National Natural Science Foundation of China [61773361, 61473273,
   91546122]; Guangdong provincial science and technology plan projects
   [2015 B010109005]; Project of Youth Innovation Promotion Association CAS
   [2017146]
FX The research work is 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. 61773361,
   61473273, 91546122, Guangdong provincial science and technology plan
   projects under Grant No. 2015 B010109005, the Project of Youth
   Innovation Promotion Association CAS under Grant No. 2017146.
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NR 39
TC 2
Z9 2
U1 2
U2 17
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4379
EI 1873-6076
J9 INFORM SYST
JI Inf. Syst.
PD FEB
PY 2019
VL 80
BP 64
EP 75
DI 10.1016/j.is.2018.09.003
PG 12
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HG4SH
UT WOS:000454964800005
DA 2022-08-23
ER

PT J
AU Sloof, M
AF Sloof, M
TI Automated modelling of physiological processes during postharvest
   distribution of agricultural products
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE automated modelling; postharvest physiology; simulation models
AB In this paper, we present an approach to automated modelling of physiological processes occurring during postharvest distribution of agricultural products. The approach involves reasoning about the reuse of both qualitative and mathematical models for physiological processes, and constructs quantitative simulation models for the postharvest behaviour of agricultural products. The qualitative models are used to bridge the gap between the modeller's knowledge about the physiological phenomenon and the mathematical models. The qualitative models are represented by knowledge graphs, that are conceptual graphs containing only causal relations, aggregation relations, and generalisation relations between domain quantities. The relationships between the mathematical models and the qualitative models are explicitly represented in application frames.
   The automated modelling task consists of two subtasks. In the first subtask, Qualitative Process Analysis, a process structure graph is constructed using the qualitative models as building blocks. The process structure graph is a qualitative description of the phenomenon under study, that contains the processes that are responsible for the behaviour of the phenomenon. The process structure graph serves as a focus for the second subtask, Simulation Model Construction. This subtask uses a library of mathematical models to compose a quantitative simulation model that corresponds to the process structure graph constructed in the first subtask. The approach is illustrated with the construction of a model for the occurrence of chilling injury in bell peppers.
C1 Vrije Univ Amsterdam, Artificial Intelligence Grp, Amsterdam, Netherlands.
   DLO, ATO, Agrotechnol Res Inst, NL-6700 AA Wageningen, Netherlands.
RP Sloof, M (通讯作者)，Vrije Univ Amsterdam, Artificial Intelligence Grp, Amsterdam, Netherlands.
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Z9 1
U1 1
U2 7
PU KLUWER ACADEMIC PUBL
PI DORDRECHT
PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS
SN 0269-2821
J9 ARTIF INTELL REV
JI Artif. Intell. Rev.
PD FEB
PY 1998
VL 12
IS 1-3
BP 39
EP 70
DI 10.1023/A:1006588305364
PG 32
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZY047
UT WOS:000074581300004
DA 2022-08-23
ER

PT J
AU Zhang, ZS
   Li, JL
   Zhao, H
AF Zhang, Zhuosheng
   Li, Junlong
   Zhao, Hai
TI Multi-Turn Dialogue Reading Comprehension With Pivot Turns and Knowledge
SO IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
LA English
DT Article
DE Context modeling; Task analysis; Speech processing; History; Bit error
   rate; Computational modeling; Benchmark testing; Multi-turn dialogue
   comprehension; response selection; utterance selection; commonsense
   modeling
ID MATCHING NETWORK
AB Multi-turn dialogue reading comprehension aims to teach machines to read dialogue contexts and solve tasks such as response selection and answering questions. The major challenges involve noisy history contexts and especial prerequisites of commonsense knowledge that is unseen in the given material. Existing works mainly focus on context and response matching approaches. This work thus makes the first attempt to tackle the above two challenges by extracting substantially important turns as pivot utterances and utilizing external knowledge to enhance the representation of context. We propose a pivot-oriented deep selection model (PoDS) on top of the Transformer-based language models for dialogue comprehension. In detail, our model first picks out the pivot utterances from the conversation history according to the semantic matching with the candidate response or question, if any. Besides, knowledge items related to the dialogue context are extracted from a knowledge graph as external knowledge. Then, the pivot utterances and the external knowledge are combined together with a well-designed mechanism for refining predictions. Experimental results on four dialogue comprehension benchmark tasks show that our proposed model achieves great improvements on baselines. A series of empirical comparisons are conducted to show how our selection strategies and the extra knowledge injection influence the results.
C1 [Zhang, Zhuosheng; Li, Junlong; Zhao, Hai] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China.
   [Zhang, Zhuosheng; Li, Junlong; Zhao, Hai] Shanghai Jiao Tong Univ, Key Lab Shanghai Educ Commiss Intelligent Interac, Shanghai 200240, Peoples R China.
   [Zhang, Zhuosheng; Li, Junlong; Zhao, Hai] Shanghai Jiao Tong Univ, MoE Key Lab Artificial Intelligence, AI Inst, Shanghai 200240, Peoples R China.
RP Zhao, H (通讯作者)，Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China.; Zhao, H (通讯作者)，Shanghai Jiao Tong Univ, Key Lab Shanghai Educ Commiss Intelligent Interac, Shanghai 200240, Peoples R China.
EM zhangzs@sjtu.edu.cn; lockonn@sjtu.edu.cn; zhaohai@cs.sjtu.edu.cn
OI Zhang, Zhuosheng/0000-0002-4183-3645
FU National Key Research and Development Program of China [2017YFB0304100];
   Key Projects of National Natural Science Foundation of China [U1836222,
   61733011]; Huawei-SJTU long termAI project, "Cutting-Edge Machine
   Reading Comprehension and Language Model"
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2017YFB0304100, and in part by
   the Key Projects of National Natural Science Foundation of China
   (U1836222, and 61733011), Huawei-SJTU long termAI project, "Cutting-Edge
   Machine Reading Comprehension and Language Model."
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U2 7
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 1161
EP 1173
DI 10.1109/TASLP.2021.3058616
PG 13
WC Acoustics; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Acoustics; Engineering
GA RE9HQ
UT WOS:000634461600001
OA Green Accepted
DA 2022-08-23
ER

EF