FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Li, JH
   Wang, ZQ
   Wang, Y
   Hua, ZY
   Jing, WF
AF Li, Jihong
   Wang, Zhiqiang
   Wang, Yuan
   Hua, Zhaoyun
   Jing, Wenfeng
TI Research on Distributed Search Technology of Multiple Data Sources
   Intelligent Information Based on Knowledge Graph
SO JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO
   TECHNOLOGY
LA English
DT Article
DE Knowledge graph; Multiple data sources intelligent information;
   Distributed search technology; Feedback correction
AB The traditional information search technology performs full-text indexing on the data in the Internet, searches for information by means of keyword matching index, and returns information to the user. This retrieval method does not accurately understand the user's needs, and returns relevant links rather than the information the user needs. The user needs to find relevant information from the linked documents. In order to improve the shortcomings of the above traditional search technology, this paper is based on the knowledge map of multi-data source intelligent information distributed search technology, through data acquisition in the Internet, complete the transformation of data to knowledge to form a knowledge network and provide information retrieval. This paper studies the construction of knowledge maps for application domain representation, proposes a semantic similarity model with constraints and implicit feedback correction mechanism, and explores the realization of intelligent information search under certain conditions. Through the analysis of prototype experimental data in the application field, the accuracy of information search based on knowledge map can reach 90%, which has strong practicability.
C1 [Li, Jihong; Wang, Zhiqiang] State Grid Zhejiang Power Co Ltd, Hangzhou, Peoples R China.
   [Wang, Yuan] NARI Grp Co Ltd, Nanjing, Jiangsu, Peoples R China.
   [Hua, Zhaoyun] State Grid Anhui Power Co Ltd, Langxi, Xuancheng, Peoples R China.
   [Jing, Wenfeng] Sch Math & Stat, Xian, Peoples R China.
RP Jing, WF (通讯作者)，Sch Math & Stat, Xian, Peoples R China.
EM li_jihong@zj.sgcc.com.cn; wang_zhiqiang@zj.sgcc.com.cn;
   wangyuan1@sgepri.sgcc.com.cn; hzy215@foxmail.com; wfjing@xjtu.edu.cn
FU Science and Technology Project of State Grid Corporation of China
   [5211XT180045]
FX This work was supported by the Science and Technology Project of State
   Grid Corporation of China (Project number: 5211XT180045).
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NR 23
TC 1
Z9 2
U1 3
U2 16
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1939-8018
EI 1939-8115
J9 J SIGNAL PROCESS SYS
JI J. Signal Process. Syst. Signal Image Video Technol.
PD MAR
PY 2021
VL 93
IS 2-3
SI SI
BP 239
EP 248
DI 10.1007/s11265-020-01592-5
EA SEP 2020
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QK8GI
UT WOS:000571108600002
DA 2022-08-23
ER

PT J
AU Cui, M
   Yang, S
   Yu, T
   Yang, C
   Gao, YH
   Zhu, HY
AF Cui, Meng
   Yang, Shuo
   Yu, Tong
   Yang, Ce
   Gao, Yonghong
   Zhu, Haiyan
TI Expert knowledge maps for knowledge management: a case study in
   Traditional Chinese Medicine research
SO JOURNAL OF TRADITIONAL CHINESE MEDICINE
LA English
DT Article
DE Medicine, Chinese traditional; Information retrieval; Knowledge
   management; Knowledge element; Expert knowledge map
AB OBJECTIVE: To design a model to capture information on the state and trends of knowledge creation, at both an individual and an organizational level, in order to enhance knowledge management.
   METHODS: We designed a graph-theoretic knowledge model, the expert knowledge map (EKM), based on literature-based annotation. A case study in the domain of Traditional Chinese Medicine research was used to illustrate the usefulness of the model.
   RESULTS: The EKM successfully captured various aspects of knowledge and enhanced knowledge management within the case-study organization through the provision of knowledge graphs, expert graphs, and expert-knowledge biography.
   CONCLUSION: Our model could help to reveal the hot topics, trends, and products of the research done by an organization. It can potentially be used to facilitate knowledge learning, sharing and decision-making among researchers, academicians, students, and administrators of organizations. (C) 2013 JTCM. All rights reserved.
C1 [Cui, Meng; Yang, Shuo; Yu, Tong; Yang, Ce; Zhu, Haiyan] China Acad Chinese Med Sci, Inst Informat Tradit Chinese Med, Beijing 100700, Peoples R China.
   [Gao, Yonghong] Beijing Univ Chinese Med, Dongzhimen Hosp Lab, Beijing 100700, Peoples R China.
RP Yang, S (通讯作者)，China Acad Chinese Med Sci, Inst Informat Tradit Chinese Med, Beijing 100700, Peoples R China.
EM y1y6y3@139.com
RI zhu, xueqiong/U-1320-2019
FU Ministry of Science and Technology Support Projects [12116BAI14A21]
FX Supported by the Ministry of Science and Technology Support Projects
   (No. 12116BAI14A21)
CR Cui M, 2008, ZHONG GUO ZHONG YI Y, V49, P267
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   2009, CHINESE NATL STANDAR
NR 13
TC 1
Z9 1
U1 1
U2 38
PU JOURNAL TRADITIONAL CHINESE MED
PI BEIJING
PA 16 NANXIAOJIE, DONGZHIMEN NEI, BEIJING, 100700, PEOPLES R CHINA
SN 0255-2922
EI 1577-7014
J9 J TRADIT CHIN MED
JI J. Tradit. Chin. Med.
PD OCT 15
PY 2013
VL 33
IS 5
BP 698
EP 702
DI 10.1016/S0254-6272(14)60045-5
PG 5
WC Integrative & Complementary Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Integrative & Complementary Medicine
GA 230AX
UT WOS:000325310100025
PM 24660599
OA hybrid
DA 2022-08-23
ER

PT J
AU Wang, L
   Wang, K
AF Wang, Lan
   Wang, Kai
TI Econometric Analysis of the Hot Research of Marxist Theoretical Journals
   Based on Knowledge Map
SO DISCRETE DYNAMICS IN NATURE AND SOCIETY
LA English
DT Article
ID LAYOUT OPTIMIZATION; SYSTEM
AB In recent years, the research on Marxist theory has received a lot of attention. Many studies use quantitative research methods such as statistical keywords and constructing knowledge map to carry out Marxist theoretical analysis. In this paper, by constructing a self-expanding Chinese word separation and self-expanding address data knowledge graph, matching Marxist Chinese addresses based on the full-text indexing knowledge graph, incorporating a weighted pinyin full-text search mechanism to improve the matching accuracy of misspelled addresses, constructing a multiple matching mechanism for Marxist addresses by combining an online geographic parsing interface, and performing semisupervised matching for a small number of difficult addresses, a complete system of Marxist address matching methods is formed. By studying and analyzing the basic orientation and attention to academic hotspots, we can gain insight into the characteristics, rules, and problems of academic hotspots in Marxist theory journals.
C1 [Wang, Lan] Weinan Normal Univ, Sch Marxism, Weinan 714099, Shaanxi, Peoples R China.
   [Wang, Kai] Weinan Normal Univ, Sch Phys & Elect Engn, Weinan 714099, Shaanxi, Peoples R China.
RP Wang, L (通讯作者)，Weinan Normal Univ, Sch Marxism, Weinan 714099, Shaanxi, Peoples R China.
EM wanglan@wnu.edu.cn
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NR 33
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 1026-0226
EI 1607-887X
J9 DISCRETE DYN NAT SOC
JI Discrete Dyn. Nat. Soc.
PD MAR 30
PY 2022
VL 2022
AR 3806576
DI 10.1155/2022/3806576
PG 10
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA 2T8OW
UT WOS:000822728500001
OA gold
DA 2022-08-23
ER

PT J
AU Yang, JB
AF Yang, Jyh-Bin
TI Developing a knowledge map for construction scheduling using a novel
   approach
SO AUTOMATION IN CONSTRUCTION
LA English
DT Article
DE knowledge map; knowledge management; construction management; scheduling
   technique
ID MANAGEMENT
AB A knowledge map is a vital tool for better knowledge management and learning. While application of knowledge maps in the construction domain remains in the initial stages of development, the construction industry is experience-oriented and therefore suited to knowledge maps. This study presents a novel approach for developing a knowledge map for construction scheduling. According to framework-based classification, this study utilizes a science-specific search engine to search for literature on construction scheduling knowledge. Search results are then used to develop a file cabinet knowledge map consisting of a contour map, and several trend and density charts. This map representation compensates for the lack of various meanings in a single knowledge map. For novices interested in learning construction scheduling knowledge, results of this study provide constructive information to know the key issues and research trends in the construction domain. In summary, this study presents a suitable procedure for extracting knowledge from public knowledge sources for development of a knowledge map. The proposed approach can be used for rapid generation of knowledge maps. (c) 2007 Elsevier B.V. All rights reserved.
C1 Chung Hua Univ, Inst Construct Management, Hsinchu 300, Taiwan.
RP Yang, JB (通讯作者)，Chung Hua Univ, Inst Construct Management, 707 Sec 2 Wu Fu Rd, Hsinchu 300, Taiwan.
EM jyhbin@chu.edu.tw
RI Yang, Jyh-Bin/AAO-2806-2021
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NR 31
TC 13
Z9 14
U1 7
U2 36
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0926-5805
EI 1872-7891
J9 AUTOMAT CONSTR
JI Autom. Constr.
PD SEP
PY 2007
VL 16
IS 6
BP 806
EP 815
DI 10.1016/j.autcon.2007.02.005
PG 10
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA 189ZM
UT WOS:000248027500009
DA 2022-08-23
ER

PT J
AU Tserng, HP
   Yin, SYL
   Lee, MH
AF Tserng, H. Ping
   Yin, Samuel Yen-Liang
   Lee, Meng-Hsueh
TI THE USE OF KNOWLEDGE MAP MODEL IN CONSTRUCTION INDUSTRY
SO JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
LA English
DT Article
DE knowledge map; construction industry; information technology
ID BRIDGE MANAGEMENT-SYSTEM; DOCUMENT MANAGEMENT; MAINTENANCE; INFORMATION;
   PROJECT; METHODOLOGY; CREATION; TAIWAN; REUSE; NEWS
AB The construction industry consists of many unstructured documents, which accumulate a large volume of tacit knowledge. In the general domain, a Knowledge Map can illustrate connections of knowledge and transfer tacit knowledge to explicit knowledge, but this is unsuitable on specific domain such as construction industry. This research generalized existing mapping rules to be applied to general and specific domain; and presented a novel and practical model for building knowledge map, named Knowledge Map Model (KMM). This model comprised of five procedures, including identifying problems, discussing with experts and users, establishing the classification structure, establishing the document base, and deciding the display model. Furthermore, this research establishes Knowledge Map Model System (KMMS) as a major tool to improve the knowledge map reused and shared among the practical process. Finally, this research validated the KMM in a real project of bridge maintenance.
C1 [Tserng, H. Ping; Lee, Meng-Hsueh] Natl Taiwan Univ, Dept Civil Engn, Div Construct Engn & Management, Taipei 10764, Taiwan.
   [Yin, Samuel Yen-Liang] Ruentex Grp, Taipei, Taiwan.
RP Tserng, HP (通讯作者)，Natl Taiwan Univ, Dept Civil Engn, Div Construct Engn & Management, 1 Roosevelt Rd,Sec 4, Taipei 10764, Taiwan.
EM hptserng@ntu.edu.tw; Samuel@mail.ruentex.com.tw; d92521016@ntu.edu.tw
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   Wang YM, 2008, EXPERT SYST APPL, V34, P689, DOI 10.1016/j.eswa.2006.10.006
   Woo JH, 2004, AUTOMAT CONSTR, V13, P203, DOI 10.1016/j.autcon.2003.09.003
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   Yin SYL, 2008, AUTOMAT CONSTR, V17, P536, DOI 10.1016/j.autcon.2007.10.002
NR 39
TC 19
Z9 20
U1 9
U2 40
PU VILNIUS GEDIMINAS TECH UNIV
PI VILNIUS
PA SAULETEKIO AL 11, VILNIUS, LT-10223, LITHUANIA
SN 1392-3730
EI 1822-3605
J9 J CIV ENG MANAG
JI J. Civ. Eng. Manag.
PY 2010
VL 16
IS 3
BP 332
EP 344
DI 10.3846/jcem.2010.38
PG 13
WC Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 660OH
UT WOS:000282652400003
OA hybrid
DA 2022-08-23
ER

PT S
AU Handzic, M
AF Handzic, M
TI The role of knowledge mapping in electronic government
SO KNOWLEDGE MANAGEMENT IN ELECTRONIC GOVERNMENT, PROCEEDINGS
SE Lecture Notes in Computer Science
LA English
DT Article; Proceedings Paper
CT 5th IFIP Interenational Working Conference on Knowledge Management in
   Electronic Government
CY MAY 17-19, 2004
CL Krems, AUSTRIA
SP IFIP WG 8 3, IFIP WG 8 5, IFIP WG 6 2, Univ Linz, Inst Informat Business & Govt, Danube Univ Krems
AB This paper explores the role of knowledge mapping in electronic government. It begins by outlining the rationale for knowledge mapping. It then reviews different conceptualisations of knowledge maps. These include concept, competency and process based maps. Then, it illustrates the application of these maps in a series of examples from the Australian government websites. The paper concludes by identifying some major issues and challenges for the future of knowledge mapping in electronic government.
C1 Univ New S Wales, Sch Informat Syst Technol & Management, Sydney, NSW 2052, Australia.
RP Handzic, M (通讯作者)，Univ New S Wales, Sch Informat Syst Technol & Management, Sydney, NSW 2052, Australia.
EM m.handzic@unsw.edu.au
CR [Anonymous], 1990, MAPPING STRATEGIC TH
   CHUNG G, 1999, 507 UNSW CSE
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NR 20
TC 3
Z9 3
U1 0
U2 7
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 3-540-22002-X
J9 LECT NOTES COMPUT SC
PY 2004
VL 3025
BP 9
EP 17
PG 9
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BAE35
UT WOS:000221801000002
OA Bronze
DA 2022-08-23
ER

PT J
AU Vail, EF
AF Vail, EF
TI Knowledge mapping: Getting started with knowledge management
SO INFORMATION SYSTEMS MANAGEMENT
LA English
DT Article
AB Knowledge mapping can be a powerful tool for CIOs in implementing knowledge management in their organizations. A knowledge map can bridge the gap between business and information technology (IT) people. It can also capture and integrate the knowledge collected in an initial knowledge management identification process. The value of these initial knowledge maps can then be extended to incorporate the rest of the intellectual capital of an enterprise.
C1 Arthur Andersen & Co, Chicago, IL 60602 USA.
   Coopers & Lybrand, New York, NY 10020 USA.
NR 0
TC 67
Z9 68
U1 2
U2 37
PU AUERBACH PUBLICATIONS
PI BOCA RATON
PA C/O CRC PRESS L L C, 2000 CORPORATE BLVD NW, BOCA RATON, FL 33431 USA
SN 1058-0530
J9 INFORM SYST MANAGE
JI Inf. Syst. Manage.
PD FAL
PY 1999
VL 16
IS 4
BP 16
EP 23
DI 10.1201/1078/43189.16.4.19990901/31199.3
PG 8
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 232BC
UT WOS:000082345000003
DA 2022-08-23
ER

PT J
AU Brozova, H
   Subrt, T
   Bartoska, J
AF Brozova, Helena
   Subrt, T.
   Bartoska, J.
TI Knowledge maps in agriculture and rural development
SO AGRICULTURAL ECONOMICS-ZEMEDELSKA EKONOMIKA
LA English
DT Article
DE knowledge formalisation; system approach; decision-making; map creation
   process; model creation process
AB The possibility of knowledge maps use in the decision-making process in agriculture and rural development is discussed in this paper. Each knowledge map presents a visualization of knowledge using different tools, where a mathematical model can be applied as one of them. The hierarchical structure of a knowledge map conforms to the general structure of a mathematical model. The mathematical model, when successfully solved and correctly read, is a knowledge map in itself. The parallels exist also between the creation process of a mathematical model and of a knowledge map. In general, every phase of a system approach can lead to a special knowledge map. The following paper explains this process and demonstrates it on the farm production structure optimisation problem solved by using a linear programming model.
C1 [Brozova, Helena; Subrt, T.; Bartoska, J.] Czech Univ Life Sci, Fac Econ & Management, Dept Syst Engn, Prague 16521 6, Suchdol, Czech Republic.
RP Brozova, H (通讯作者)，Czech Univ Life Sci, Fac Econ & Management, Dept Syst Engn, Prague 16521 6, Suchdol, Czech Republic.
EM brozova@pef.czu.cz; subrt@pef.czu.cz; bartoska@pef.czu.cz
RI Bartoška, Jan/C-8992-2017; Brozova, Helena/K-2834-2017; Subrt,
   Tomas/H-8542-2018
OI Bartoška, Jan/0000-0003-0935-5978; Brozova, Helena/0000-0002-0322-251X;
   Subrt, Tomas/0000-0002-4437-1431
FU Ministry of Education, Youth and Sports of the Czech Republic [MSM
   6046070904]
FX Supported by the Ministry of Education, Youth and Sports of the Czech
   Republic (Grant No. MSM 6046070904 - Information and knowledge support
   of strategic management).
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NR 18
TC 2
Z9 2
U1 1
U2 15
PU CZECH ACADEMY AGRICULTURAL SCIENCES
PI PRAGUE
PA TESNOV 17, PRAGUE, 117 05, CZECH REPUBLIC
SN 0139-570X
EI 1805-9295
J9 AGR ECON-CZECH
JI Agric. Econ.
PY 2008
VL 54
IS 11
BP 546
EP 553
DI 10.17221/263-AGRICECON
PG 8
WC Agricultural Economics & Policy; Economics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Agriculture; Business & Economics
GA 393QH
UT WOS:000262387300006
OA gold
DA 2022-08-23
ER

PT J
AU Wang, SH
AF Wang, SH
TI Knowledge maps for managing Web-based business
SO INDUSTRIAL MANAGEMENT & DATA SYSTEMS
LA English
DT Article
DE knowledge workers; internet; computer users; management
ID MANAGEMENT
AB Knowledge management has recently received considerable attention in the Web-based business community. This paper discusses the key concepts of human-computer interaction in knowledge development, and identifies new challenges of knowledge management for Web-based business. Based on theories of knowledge representations and semantic networks, this paper proposes a structure of knowledge maps for knowledge management in the Web-based business environment. An example of knowledge maps for online auctions is used to illustrate the application of the proposed structure of knowledge maps.
C1 Univ Massachusetts, Charlton Coll Business, Dept Mkt Business Informat Syst, Dartmouth, MA USA.
RP Wang, SH (通讯作者)，Univ Massachusetts, Charlton Coll Business, Dept Mkt Business Informat Syst, Dartmouth, MA USA.
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NR 33
TC 17
Z9 17
U1 0
U2 8
PU EMERALD GROUP PUBLISHING LIMITED
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0263-5577
EI 1758-5783
J9 IND MANAGE DATA SYST
JI Ind. Manage. Data Syst.
PY 2002
VL 102
IS 7
BP 357
EP 364
DI 10.1108/02635570210439445
PG 8
WC Computer Science, Interdisciplinary Applications; Engineering,
   Industrial
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA 605ZW
UT WOS:000178707800001
DA 2022-08-23
ER

PT J
AU Lin, FR
   Hsueh, CM
AF Lin, FR
   Hsueh, CM
TI Knowledge map creation and maintenance for virtual communities of
   practice
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE knowledge map; information retrieval; clustering; community of practice;
   professional community
ID TEXT RETRIEVAL; MODEL; INFORMATION
AB This paper proposes a knowledge map management system to facilitate knowledge management in virtual communities of practice. To realize the proposed knowledge map management, we develop knowledge map creation and maintenance functions by utilizing information retrieval and data mining techniques. The knowledge maps created respectively from the documents of the teachers' professional community, SCTNet, and the thesis repository at Taiwan's National Central Library, are evaluated by experts of these two domains. Knowledge maps generated by the system are accepted by domain experts from the evaluation since the degree of their modification of the automatically created knowledge maps is proportionally small. The knowledge structure representing the categories of community documents maintains its high purity, diversity, specificity, and structure adaptation by using the knowledge map maintenance function with limited Computational cost. Thus, the knowledge map creation and maintenance mechanisms developed in this research enable the dynamic knowledge management of communities of practice on the Internet. (c) 2005 Elsevier Ltd. All rights reserved.
C1 Natl Tsing Hua Univ, Inst Technol Management, Hsinchu 300, Taiwan.
   Natl Sun Yat Sen Univ, Dept Informat Management, Kaohsiung 804, Taiwan.
RP Lin, FR (通讯作者)，Natl Tsing Hua Univ, Inst Technol Management, Hsinchu 300, Taiwan.
EM frlin@mx.nthu.edu.tw
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NR 33
TC 60
Z9 65
U1 1
U2 65
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4573
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD MAR
PY 2006
VL 42
IS 2
BP 551
EP 568
DI 10.1016/j.ipm.2005.03.026
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 981BF
UT WOS:000233061300014
DA 2022-08-23
ER

PT J
AU Li, LH
   Zhang, Z
   Zhang, SD
AF Li, Lianhuan
   Zhang, Zheng
   Zhang, Shaoda
TI Knowledge Graph Entity Similarity Calculation under Active Learning
SO COMPLEXITY
LA English
DT Article
AB To address the objectives of the adaptive learning platform, the requirements of the system in terms of business, functionality, and performance are mainly analysed, and the design of functions and database is completed; then, an updatable learner model is constructed based on the cognitive diagnosis model and resource preference attributes; then, the construction of the knowledge map is completed based on embedding to achieve knowledge point alignment, and based on this, the target knowledge points of learners are located with the help of deep learning; at the same time, the target knowledge points are taken as the starting point to generate the best learning path by traversing the knowledge map, and the corresponding learning resources and test questions are recommended for them with the help of the architecture; finally, the adaptive learning platform is developed in the environment using the architecture. Also, the target knowledge point is used as the starting point to traverse the knowledge map to generate the best learning path, and the corresponding learning resources and test questions are recommended for the learner in combination with the learner model; finally, this study adopts an architecture for the development of an adaptive learning platform in the environment to realize online tests, score analysis, resource recommendation, and other functions. A knowledge graph fusion system supporting interactive facilitation between entity alignment and attribute alignment is implemented. Under a unified conceptual layer, this system can combine entity alignment and attribute alignment to promote each other and truly achieve the final fusion of the two graphs. Our experimental results on real datasets show that the entity alignment algorithm proposed in this paper has a great improvement in accuracy compared with the previous mainstream alignment algorithms. Also, the attribute alignment algorithm proposed in this paper, which calculates the similarity based on associated entities, outperforms the traditional methods in terms of accuracy and recall.
C1 [Li, Lianhuan] Nanyang Med Coll, Sch Int Educ, Nanyang 473000, Henan, Peoples R China.
   [Zhang, Zheng] Nanyang Inst Technol, Sch Comp & Software, Nanyang 473000, Henan, Peoples R China.
   [Zhang, Shaoda] Cofoe Med Technol Co Ltd, Shenzhen 518101, Guangdong, Peoples R China.
RP Zhang, Z (通讯作者)，Nanyang Inst Technol, Sch Comp & Software, Nanyang 473000, Henan, Peoples R China.; Zhang, SD (通讯作者)，Cofoe Med Technol Co Ltd, Shenzhen 518101, Guangdong, Peoples R China.
EM nymcllh@sina.com; zhangzheng@nyist.edu.cn; zhangsd@stu.xmu.edu.cn
FU Henan Soft Science Research Program [212400410192]
FX This work was supported by Henan Soft Science Research Program Project
   (212400410192), Research on the Application of Recommendation Algorithm
   Based on Multivariate Collaborative Filtering in Medical Practice
   Qualification Examination.
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NR 28
TC 1
Z9 1
U1 6
U2 18
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 JUN 12
PY 2021
VL 2021
AR 3522609
DI 10.1155/2021/3522609
PG 11
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA TA4QN
UT WOS:000667234200003
OA gold
DA 2022-08-23
ER

PT J
AU Lee, B
   Noh, KR
   Won, DK
   Kwon, OJ
AF Lee, Bangrae
   Noh, Kyung-Ran
   Won, Dong-Kyu
   Kwon, Oh-Jin
TI A Knowledge Map Development Based on Collaboration Network of Social
   Science in Korea
SO INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL
LA English
DT Article
DE Knowledge map; Co-author; Collaboration network; Social network
   analysis; Power law
AB This paper will explore developing knowledge map which can be used in the establishment of research support policy. We analyzed the collaboration network of social science in Korea and found some properties of the network. We suggest that our knowledge map can help the establishment of research support policy.
C1 [Lee, Bangrae; Won, Dong-Kyu; Kwon, Oh-Jin] Korea Inst Sci & Technol Informat, Informat Anal Ctr, Seoul 130741, South Korea.
   [Noh, Kyung-Ran] Korea Inst Sci & Technol Informat, Knowledge Informat Ctr, Seoul 130741, South Korea.
RP Kwon, OJ (通讯作者)，Korea Inst Sci & Technol Informat, Informat Anal Ctr, Cheongryangri Dong 206-9, Seoul 130741, South Korea.
EM brlee@kisti.re.kr; infor@kisti.re.kr; dkwon@kisti.re.kr;
   dbajin@kisti.re.kr
OI Kwon, Oh Jin/0000-0002-0548-2630
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NR 14
TC 0
Z9 0
U1 0
U2 8
PU INT INFORMATION INST
PI TOKYO
PA FAC ENG, HOSEI UNIV, KOGANEI, TOKYO, 184-8584, JAPAN
SN 1343-4500
J9 INFORMATION-TOKYO
JI Information
PD MAY
PY 2010
VL 13
IS 3B
BP 941
EP 946
PG 6
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA V23WY
UT WOS:000208374100013
DA 2022-08-23
ER

PT J
AU Liu, J
   Wang, JC
   Zheng, QH
   Zhang, W
   Jiang, L
AF Liu, Jun
   Wang, Jincheng
   Zheng, Qinghua
   Zhang, Wei
   Jiang, Lu
TI Topological analysis of knowledge maps
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge map; Complex network; Learning-dependency; Topological
   property; Topological analysis
ID SMALL-WORLD NETWORKS; METABOLIC NETWORKS; SCIENTIFIC PAPERS; COMPLEX
   NETWORKS; SEMANTIC NETWORK; ONTOLOGY; ORGANIZATION; COMMUNITIES; WEB
AB A knowledge map can be viewed as a directed graph, in which each node is a knowledge unit (KU), and each edge is a learning-dependency between two KUs. Understanding the topological properties of knowledge map can help us gain better insights into human cognition structure and its mechanism, design better knowledge map construction algorithms, and guide learners' navigational learning through knowledge map. In this paper, we perform topological analysis on 12 knowledge maps from computer science, mathematics, and physics. We discover that they exhibit small-world and scale-free properties like many other networks. Specifically, we show the locality of learning-dependency and hierarchical modular structure in the 12 knowledge maps. In addition, we study how KUs affect the network efficiency by removing KUs based on different centrality measures. We find that the importance of KUs varies greatly. (C) 2012 Elsevier B.V. All rights reserved.
C1 [Liu, Jun; Wang, Jincheng; Zheng, Qinghua; Jiang, Lu] Xi An Jiao Tong Univ, SPKLSTN Lab, Dept Comp Sci, Xian 710049, Peoples R China.
   [Zhang, Wei] Amazon Com Inc, Seattle, WA 98109 USA.
RP Liu, J (通讯作者)，Xi An Jiao Tong Univ, SPKLSTN Lab, Dept Comp Sci, Xian 710049, Peoples R China.
EM liukeen@mail.xjtu.edu.cn
FU National High-Tech R&D Program of China [2012AA011003]; National Science
   Foundation of China [60825202, 61173112, 60921003]; Cheung Kong Scholars
   Program, Key Projects in the National Science & Technology Pillar
   Program [2011BAK08B02, 2011BAK08B05]
FX The research was supported in part by the National High-Tech R&D Program
   of China under Grant No. 2012AA011003, the National Science Foundation
   of China under Grant Nos. 60825202, 61173112, 60921003, Cheung Kong
   Scholars Program, Key Projects in the National Science & Technology
   Pillar Program under Grant Nos. 2011BAK08B02, 2011BAK08B05.
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NR 34
TC 17
Z9 18
U1 5
U2 65
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD DEC
PY 2012
VL 36
BP 260
EP 267
DI 10.1016/j.knosys.2012.07.011
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 046OV
UT WOS:000311775200026
DA 2022-08-23
ER

PT J
AU Wang, H
   Tian, XM
   Wang, XR
   Wang, Y
AF Wang, Hui
   Tian, Xuemei
   Wang, Xianrui
   Wang, Yun
TI Evolution and Emerging Trends in Depression Research From 2004 to 2019:
   A Literature Visualization Analysis
SO FRONTIERS IN PSYCHIATRY
LA English
DT Article
DE depression; major depressive disorder; bibliometrics; visual analysis;
   knowledge graphs; CiteSpace
ID C-REACTIVE PROTEIN; ADVERSE CHILDHOOD EXPERIENCES; DOUBLE-BLIND; MAJOR
   DEPRESSION; RISK-FACTORS; WOMEN; CELECOXIB; SYMPTOMS; DISORDER; ANXIETY
AB Depression has become a major threat to human health, and researchers around the world are actively engaged in research on depression. In order to promote closer research, the study of the global depression knowledge map is significant. This study aims to map the knowledge map of depression research and show the current research distribution, hotspots, frontiers, and trends in the field of depression research, providing researchers with worthwhile information and ideas. Based on the Web of Science core collection of depression research from 2004 to 2019, this study systematically analyzed the country, journal, category, author, institution, cited article, and keyword aspects using bibliometric and data visualization methods. A relationship network of depression research was established, highlighting the highly influential countries, journals, categories, authors, institutions, cited articles, and keywords in this research field. The study identifies great research potential in the field of depression, provides scientific guidance for researchers to find potential collaborations through collaboration networks and coexistence networks, and systematically and accurately presents the hotspots, frontiers, and shortcomings of depression research through the knowledge map of global research on depression with the help of information analysis and fusion methods, which provides valuable information for researchers and institutions to determine meaningful research directions.
C1 [Wang, Hui; Wang, Xianrui; Wang, Yun] Beijing Univ Chinese Med, Sch Tradit Chinese Med, Beijing, Peoples R China.
   [Tian, Xuemei; Wang, Yun] Beijing Univ Chinese Med, Sch Life Sci, Beijing, Peoples R China.
RP Wang, Y (通讯作者)，Beijing Univ Chinese Med, Sch Tradit Chinese Med, Beijing, Peoples R China.; Wang, Y (通讯作者)，Beijing Univ Chinese Med, Sch Life Sci, Beijing, Peoples R China.
EM wangyun@bucm.edu.cn
FU National Natural Science Foundation of China [81973495]
FX Funding This work was supported by the National Natural Science
   Foundation of China under Grant No. 81973495.
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NR 98
TC 3
Z9 3
U1 22
U2 35
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-0640
J9 FRONT PSYCHIATRY
JI Front. Psychiatry
PD OCT 29
PY 2021
VL 12
AR 705749
DI 10.3389/fpsyt.2021.705749
PG 20
WC Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Psychiatry
GA WW3JT
UT WOS:000717818000001
PM 34777037
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Nguyen, SH
   Chowdhury, G
AF Son Hoang Nguyen
   Chowdhury, Gobinda
TI Interpreting the knowledge map of digital library research (1990-2010)
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
DE literary warrant; digital libraries; information science
AB A knowledge map of digital library (DL) research shows the semantic organization of DL research topics and also the evolution of the field. The research reported in this article aims to find the core topics and subtopics of DL research in order to build a knowledge map of the DL domain. The methodology is comprised of a four-step research process, and two knowledge organization methods (classification and thesaurus building) were used. A knowledge map covering 21 core topics and 1,015 subtopics of DL research was created and provides a systematic overview of DL research during the last two decades (1990-2010). We argue that the map can work as a knowledge platform to guide, evaluate, and improve the activities of DL research, education, and practices. Moreover, it can be transformed into a DL ontology for various applications. The research methodology can be used to map any human knowledge domain; it is a novel and scientific method for producing comprehensive and systematic knowledge maps based on literary warrant.
C1 [Son Hoang Nguyen; Chowdhury, Gobinda] Univ Technol Sydney, Ultimo, NSW 2007, Australia.
   [Son Hoang Nguyen] Vietnam Natl Univ, Fac Informat & Lib Sci, Hanoi, Vietnam.
RP Nguyen, SH (通讯作者)，Univ Technol Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.
EM n_hoangson@yahoo.com; Gobinda.Chowdhury@uts.edu.au
RI chowdhury, gobinda/K-3518-2019
OI chowdhury, gobinda/0000-0002-2122-9547
FU ALA scholarship (AusAID)
FX The ALA scholarship (AusAID funding) supporting the PhD study of the
   first author is gratefully acknowledged. The authors would like to thank
   the anonymous reviewers for their valuable comments on the earlier
   versions of this article.
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   [No title captured]
NR 52
TC 7
Z9 8
U1 2
U2 82
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-2882
EI 1532-2890
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD JUN
PY 2013
VL 64
IS 6
BP 1235
EP 1258
DI 10.1002/asi.22830
PG 24
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 204OR
UT WOS:000323377600012
DA 2022-08-23
ER

PT J
AU Li, M
   Lu, XZ
   Chen, LS
   Wang, J
AF Li, Ming
   Lu, Xiuzhi
   Chen, Lisheng
   Wang, Jun
TI Knowledge map construction for question and answer archives
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge map; Community question answering; Artificial neural network
ID RETRIEVAL; SELECTION; NETWORK
AB The rapid increase in the number of community-based question-and-answer services has built up large archives of questions and answers. These archives deliver a plethora of valuable knowledge to users who primarily browse to locate information. Question-answer pairs, which not only include knowledge content but also indicate knowledge needs, are a new form of information presentation. To facilitate browsing question and answer archives and alleviate information overload during the browsing process, this paper constructs a knowledge map for question and answer archives by exploiting question-answer pair characteristics to determine a more precise location of question-answer pairs. First, the questions and answers are modeled and then the knowledge map structure is completed. Questions and answers are the two main dimensions of this knowledge map, and their intersection comprises a cluster of corresponding question-answer pairs. The knowledge map can be widely and deeply extended to provide a comprehensive representation of the question and answer archive. When labeling the knowledge map to aid in interpretation and understanding, the key words are identified, and typical question-answer pair extraction methods are proposed. Finally, we conduct extensive experiments on a real dataset, and the results show that the proposed approach is feasible and performs well. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Li, Ming; Lu, Xiuzhi; Chen, Lisheng] China Univ Petr, Sch Econ & Management, Beijing 102249, Peoples R China.
   [Wang, Jun] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China.
RP Li, M (通讯作者)，China Univ Petr, Sch Econ & Management, Beijing 102249, Peoples R China.
EM limingzyq@cup.edu.cn
FU National Natural Science Foundation of China [91646122, 71871005];
   Humanity and Social Science Youth Foundation of the Ministry of
   Education in China [15YJCZH081]
FX The research is supported by the National Natural Science Foundation of
   China [grant numbers 71571191], the Humanity and Social Science Youth
   Foundation of the Ministry of Education in China[grant number
   15YJCZH081] and National Natural Science Foundation of China [grant
   numbers 91646122 and 71871005].
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NR 58
TC 12
Z9 12
U1 9
U2 59
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 1
PY 2020
VL 141
AR 112923
DI 10.1016/j.eswa.2019.112923
PG 16
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 JM6PT
UT WOS:000496334800001
DA 2022-08-23
ER

PT J
AU Shaw, RS
AF Shaw, Ruey-Shiang
TI A study of learning performance of e-learning materials design with
   knowledge maps
SO COMPUTERS & EDUCATION
LA English
DT Article
DE Authoring tools and methods; Media in education; Teaching/learning
   strategies
ID SELF-EFFICACY
AB This research investigated the application of knowledge maps in e-learning materials design and hypothesized that knowledge maps would be more effective than e-learning in general at improving the performance and satisfaction of e-learning. In order to test the hypotheses, we conducted an experiment with 175 participants and randomly assigned them into knowledge map-based and browse-based groups. Both groups of participants needed to acquire specific skills and knowledge to write the target ADO.NET program. In the end of each training session subjects received an assessment to understand their learning score, satisfaction level, and computer self-efficacy. Our statistical analysis result showed that knowledge map-based learning group outperformed browse-based group in these three measurements. Thus, the proposed hypotheses were supported. We concluded that if knowledge map-based materials design approach were employed novice users would acquire the ADO.NET programming in a more effective manner. In addition, the satisfaction and computer self-efficacy of users could be improved substantially as a result of knowledge map-based materials design approach.
   Thus, we propose that if knowledge maps are employed to adequately present the relationships of learning contents that knowledge could be better understood and learning performance could be improved. (C) 2009 Elsevier Ltd. All rights reserved.
C1 Tamkang Univ, Dept Informat Management, Danshui Township 25137, Taipei County, Taiwan.
RP Shaw, RS (通讯作者)，Tamkang Univ, Dept Informat Management, 151 Yingzhuan Rd, Danshui Township 25137, Taipei County, Taiwan.
EM rsshaw@mail.tku.edu.tw
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NR 34
TC 43
Z9 47
U1 6
U2 40
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0360-1315
J9 COMPUT EDUC
JI Comput. Educ.
PD JAN
PY 2010
VL 54
IS 1
BP 253
EP 264
DI 10.1016/j.compedu.2009.08.007
PG 12
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 526RI
UT WOS:000272310100024
DA 2022-08-23
ER

PT J
AU Yang, Q
   Song, B
   Lu, WF
   Zhang, Y
AF Yang, Q.
   Song, B.
   Lu, W. F.
   Zhang, Y.
TI Integrating knowledge maps in design process configurations for
   concurrently engineered product development
SO PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF
   ENGINEERING MANUFACTURE
LA English
DT Article; Proceedings Paper
CT 12th International Conference on Concurrent Engineering
CY 2005
CL Dallas, TX
SP ISPE
DE product design process; concurrent engineering; knowledge map; process
   configuration; new product introduction
AB Concurrently engineered product development requires effective inter- functional communication and knowledge sharing across multidisciplinary teams and heterogeneous processes. This paper presents a semantics -enabled approach to the integration of knowledge. maps with design process configurations to support more meaningful communication and seamless collaboration between cross- functional teams and concurrent workflows in new product introduction (NPI) processes. The proposed approach focuses on: explicit and formalized process description and configuration of NPI activities; process knowledge capture and reuse through knowledge maps; integration of knowledge maps into design process configurations; and ontology-based process model mapping and knowledge sharing to support seamless process collaboration. It thereby contributes to the development of the next generation concurrent engineering methodologies in order to achieve NPI process advances for meaningful inter- functional communication and collaboration. The approach has been implemented in a software prototype to provide the concurrently engineered product development with the capabilities of semantic process description and configuration, knowledge reuse, and process collaboration.
C1 Singapore Inst Mfg Technol, Singapore, Singapore.
   Natl Univ Singapore, Dept Mech Engn, Singapore 117548, Singapore.
   Stanfield Coll, Singapore, Singapore.
RP Yang, Q (通讯作者)，Singapore Inst Mfg Technol, Singapore, Singapore.
EM qyang@SIMTech.a-star.edu.sg
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NR 25
TC 6
Z9 6
U1 0
U2 14
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0954-4054
EI 2041-2975
J9 P I MECH ENG B-J ENG
JI Proc. Inst. Mech. Eng. Part B-J. Eng. Manuf.
PD MAR
PY 2007
VL 221
IS 3
BP 431
EP 445
DI 10.1243/09544054JEM621
PG 15
WC Engineering, Manufacturing; Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA 176XT
UT WOS:000247118900007
DA 2022-08-23
ER

PT J
AU Gardner, SF
   Monaghan, MS
   Peel, J
AF Gardner, SF
   Monaghan, MS
   Peel, J
TI Knowledge maps improve comprehension in therapeutics
SO AMERICAN JOURNAL OF PHARMACEUTICAL EDUCATION
LA English
DT Article
AB The purpose of this study was to examine knowledge mapping as an educational tool to improve comprehension in two modules of a therapeutics course. Two groups of students attended either the gastrointestinal or cardiovascular modules taught with traditional outlines. After testing, they crossed over to the opposite module which was taught with knowledge maps. This module was followed by an examination testing identical concepts as the previous groups. Students also completed a Likert-scale questionnaire to assess their attitude toward knowledge mapping. The mean score on the gastrointestinal exam increased from 80.2 to 84.7 (P<0.05). Likewise, mean score on the cardiovascular exam increased from 78.3 to 88.2 (P<0.001). Questionnaires indicated that students found knowledge maps easier to study than traditional outlines. The students also stated that knowledge maps were useful in logically grouping information and in depicting interrelationships between concepts.
C1 UNIV TEXAS,MED BRANCH,OFF EDUC DEV,GALVESTON,TX 77550.
RP Gardner, SF (通讯作者)，UNIV ARKANSAS MED SCI HOSP,COLL PHARM,4301 W MARKHAM,SLOT 522,LITTLE ROCK,AR 72205, USA.
CR ARMBRUSTER BB, 1984, SPATIALLEARNING STRA
   *COMM IMPL CHANG P, 1991, 2 AM ASS COLL PHARM
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NR 10
TC 4
Z9 4
U1 0
U2 1
PU AMER ASSOC COLL PHARMACY
PI ALEXANDRIA
PA 1426 PRINCE STREET, ALEXANDRIA, VA 22314-2815
SN 0002-9459
J9 AM J PHARM EDUC
JI Am. J. Pharm. Educ.
PD SPR
PY 1996
VL 60
IS 1
BP 56
EP 59
PG 4
WC Education, Scientific Disciplines; Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Pharmacology & Pharmacy
GA UE205
UT WOS:A1996UE20500010
DA 2022-08-23
ER

PT J
AU Gordon, JL
AF Gordon, JL
TI Creating knowledge maps by exploiting dependent relationships
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article; Proceedings Paper
CT 19th SGES International Conference on Knowledge-Based Systems and
   Applied Artificial Intelligence (ES99)
CY DEC 13-15, 1999
CL CAMBRIDGE, ENGLAND
SP SGES, Appl AI
DE knowledge-mapping; knowledge-management; learning-dependency
AB Knowledge is an interesting concept that has attracted the attention of philosophers for thousands of years. In more recent times, researchers have investigated knowledge in a more applied way with the chief aim of bringing knowledge to life in machines. Artificial Intelligence has provided some degree of rigour to the study of knowledge and Expert Systems are able to use knowledge to solve problems and answer questions.
   Current business, social, political and technological pressures have forced organisations to take greater control of the knowledge asset. Software suppliers and others offering valuable solutions in this area have unfortunately clouded the issue of knowledge. Information and data control are seen as implicit knowledge management tools and many have abandoned the search for explicit knowledge management methods.
   Knowledge representation schemes help to identify knowledge. They allow for human understanding and machine application and they can support the automated use of knowledge in problem solving. Some of these representation methods also employ spatial techniques that add an extra dimension to human understanding.
   Knowledge mapping defined in this work uses learning dependency to organise the map and draws on the ideas of what knowledge is and on spatial representation structures. Knowledge maps can support metrics that provide information about the knowledge asset. Knowledge maps create a visible knowledge framework that supports the explicit management of knowledge by organisation managers and directors. Knowledge maps also offer other advantages to the organisation, the individual and to educational institutions. (C) 2000 Elsevier Science B.V. All rights reserved.
C1 Blackburn Coll, NWAIAG, Blackburn, Lancs, England.
RP Gordon, JL (通讯作者)，Blackburn Coll, NWAIAG, Feilden St, Blackburn, Lancs, England.
RI Wang, Charles/B-5565-2011
OI Wang, Charles/0000-0001-9331-8437
CR FRAWLEY W, 1991, KNOWLEDGE DISCOVERY
   Genesereth Michael R., 1992, KNOWLEDGE INTERCHANG
   GORDON JL, 1997, SGES PUBLICATIONS, P207
   HUTCHINSON A, 1994, ALGORITHMIC LEARNING
   Shortliffe E. H., 1976, COMPUTER BASED MED C
   Sowa J. F., 1993, Conceptual Graphs for Knowledge Representation. First International Conference on Conceptual Structures, ICCS '93 Proceeding, P1
   SOWA JF, 1984, CONCEPTUAL STRUCTURE
   THORNTON CJ, 1992, TECHNIQUES COMPUTATI
NR 8
TC 59
Z9 67
U1 1
U2 18
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0950-7051
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD APR
PY 2000
VL 13
IS 2-3
BP 71
EP 79
DI 10.1016/S0950-7051(00)00048-4
PG 9
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 324VB
UT WOS:000087641100004
DA 2022-08-23
ER

PT J
AU Zhong, XQ
   Fu, HG
   Xia, HD
   Yang, LN
   Shang, MS
AF Zhong, Xiuqin
   Fu, Hongguang
   Xia, Huadong
   Yang, Leina
   Shang, Mingsheng
TI A hybrid cognitive assessment based on ontology knowledge map and skills
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Ontology; Knowledge map; Skill; Cognitive model; Assessment
ID DECISION
AB An intelligent tutoring system plays vital role in education and its importance is constantly increasing, meanwhile the key challenge in the teaching learning process is assessing students' learning efficiently. In this paper, a hybrid assessment based-on ACT-R cognitive learning theory, combining ontology knowledge map with skills is proposed. In order to assess how well students master knowledge structure, an ontology knowledge map is constructed to describe declarative knowledge; and in order to assess how well students master knowledge skills, a problem solving process is constructed to describe procedural knowledge based on ACT-R. Finally, a student's mastery of knowledge is assessed through both the knowledge map and skills in the problem solving process, as well as auxiliary indicators like time usage, prior knowledge level, self-assessment, etc. This method is implemented in a geometric intelligent assessment system and is evaluated in a junior high school. Experiments show that the assessment results are consistent with students' actual learning levels. The hybrid cognitive assessment method can not only obtain the score of students' mastery of knowledge points and the structure through knowledge map, but also assess the learning skills in problem solving process through exercises quantitatively. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Zhong, Xiuqin; Fu, Hongguang; Yang, Leina; Shang, Mingsheng] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China.
   [Xia, Huadong] Virginia Tech, Dept Comp Sci, Blacksburg, VA USA.
RP Zhong, XQ (通讯作者)，Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China.
EM zhongxiuqin2009@gmail.com
FU National Natural Science Foundation of China [61073099, 61202257];
   Sichuan Provincial Science and Technology Department [2012FZ0120]
FX The work in this paper was supported by National Natural Science
   Foundation of China (Grant Nos. 61073099 and 61202257) and the Sichuan
   Provincial Science and Technology Department (Grant No. 2012FZ0120).
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NR 27
TC 12
Z9 14
U1 4
U2 71
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JAN
PY 2015
VL 73
BP 52
EP 60
DI 10.1016/j.knosys.2014.09.004
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA AW4AW
UT WOS:000346224900005
DA 2022-08-23
ER

PT J
AU Li, J
   Wu, FD
   Yang, X
   Chen, YJ
AF Li, Jie
   Wu, Fada
   Yang, Xu
   Chen, Yijun
TI APPQKW: An Efficient Algorithm for Processing Path Queries on the
   Knowledge Graph under Ecosystem
SO EKOLOJI
LA English
DT Article
DE path queries; equivalent rewriting; knowledge graph; path view
AB Path queries on the knowledge graph have recently received a lot of attention in artificial intelligence community. At present, most of the research works are directly based on the large-scale knowledge graph which is stored on hard disks. And they cannot effectively use the pre-stored path views to improve the processing efficiency of path queries. To tackle with this problem, in this paper, we present an efficient algorithm APPQKW (Algorithm for Processing Path Queries on the Knowledge Graph) which utilizes several path views to equivalently rewrite real-time path queries on the knowledge maps. APPQKW algorithm can efficiently overcome the three performance drawbacks of existing methods: (1) they need to perform exponential number of nesting cycles, (2) they need to produce exponential number of candidate path queries, and (3) for each candidate path query, and they need to require exponential number of equivalent matches. Moreover, we give the correctness and validity of the ACPQKW algorithm in theory, and gives detailed experiments to verify the theoretical results.
C1 [Li, Jie] Yantai Vocat Coll, Off Educ Adm, Yantai 264005, Peoples R China.
   [Wu, Fada] Yantai Vocat Coll, Yantai Radio & Televis Univ, Yantai 264670, Peoples R China.
   [Yang, Xu] Tongji Univ, Dept Comp Sci & Technol, Shanghai 201804, Peoples R China.
   [Chen, Yijun] Tongji Univ, Natl Maglev Transportat Engn R&D Ctr, Shanghai 201804, Peoples R China.
RP Yang, X (通讯作者)，Tongji Univ, Dept Comp Sci & Technol, Shanghai 201804, Peoples R China.; Chen, YJ (通讯作者)，Tongji Univ, Natl Maglev Transportat Engn R&D Ctr, Shanghai 201804, Peoples R China.
EM yx_yt@126.com; chenyijun@tongji.edu.cn
FU Shandong Radio and Television University: Research on the Construction
   and Application of Education and Teaching Practice Base of "Quality
   Promotion Project" for Grass-roots Cadres in Shandong Province
   [2017SD06]
FX Shandong Radio and Television University: Research on the Construction
   and Application of Education and Teaching Practice Base of "Quality
   Promotion Project" for Grass-roots Cadres in Shandong Province
   (2017SD06).
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NR 18
TC 0
Z9 0
U1 0
U2 2
PU FOUNDATION ENVIRONMENTAL PROTECTION & RESEARCH-FEPR
PI BASMANE KONAK-IZMIR
PA MURESELPASA BULVARI 1265 SOKAK 10-10, H MEVLUT SUSUZLU IS MERKESI, PK
   63, BASMANE KONAK-IZMIR, 35230, TURKEY
SN 1300-1361
J9 EKOLOJI
JI Ekoloji
PY 2019
VL 28
IS 107
BP 2031
EP 2040
AR UNSP e107234
PG 10
WC Ecology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA HP4WZ
UT WOS:000461678300233
DA 2022-08-23
ER

PT J
AU Lin, FR
   Yu, JH
AF Lin, Fu-ren
   Yu, Jen-Hung
TI Visualized cognitive knowledge map integration for P2P networks
SO DECISION SUPPORT SYSTEMS
LA English
DT Article; Proceedings Paper
CT 11th Pacific Asia Conference on Information Systems
CY JUL 03-06, 2007
CL Auckland, NEW ZEALAND
DE Self-organizing map (SOM); Knowledge map; Peer-to-peer (P2P); Egocentric
   SOM (ESOM)
ID COMMUNITIES; WEB
AB This study proposes a visualized cognitive knowledge map integration system, called VisCog, to facilitate knowledge management on P2P networks. By using the SOM (self-organized map)-like model, Egocentric SOM (ESOM), VisCog can merge the other peers' knowledge artifacts (e.g., documents) under a focal peer's knowledge structure and visually present the cognitive knowledge map of the P2P network. The experimental results from evaluating VisCog performance show that VisCog can retain an individual peer's knowledge structure while articulating with those of other peers to build its cognitive knowledge map. (C) 2008 Elsevier B.V. All rights reserved.
C1 [Lin, Fu-ren; Yu, Jen-Hung] Natl Tsing Hua Univ, Inst Technol Management, Hsinchu 300, Taiwan.
RP Lin, FR (通讯作者)，Natl Tsing Hua Univ, Inst Technol Management, 101 Sec 2,Kuangfu Rd, Hsinchu 300, Taiwan.
EM frlin@mx.nthu.edu.tw; byendyu@gmail.com
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NR 31
TC 8
Z9 10
U1 3
U2 22
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 MAR
PY 2009
VL 46
IS 4
SI SI
BP 774
EP 785
DI 10.1016/j.dss.2008.11.020
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Operations Research & Management Science
GA 426IK
UT WOS:000264701000004
DA 2022-08-23
ER

PT J
AU Wang, H
   Wei, AM
AF Wang, Hui
   Wei, Aimin
TI Construction of a Knowledge Map Based on Text CNN Algorithm for Maritime
   English Subjects
SO JOURNAL OF FUNCTION SPACES
LA English
DT Article
AB Knowledge map is a new method of knowledge management with the information revolution. This paper is aimed at forming a systematic and standardized huge redundant knowledge structure, which can be used to mine the knowledge structure and the relationship between knowledge and visualize it in a graphical way, in order to obtain more representative information and improve the classification accuracy of text classification model. In this paper, a knowledge map construction method based on the Text CNN algorithm is proposed for the subject of Nautical English. It is of practical significance and academic value to make use of knowledge map to study Chinese Maritime English, which is helpful to the development of Chinese Maritime English and provides guidance. In order to maintain the diversity of particle swarm optimization, the Text CNN algorithm is combined with the construction of Maritime English subject knowledge map, and the network parameters and structure are optimized. Using knowledge map to study China Maritime English has important practical significance and academic value and has certain guiding significance for the development of China Maritime English.
C1 [Wang, Hui; Wei, Aimin] Hainan Vocat Univ Sci & Technol, Marine Sch, Haikou 571126, Hainan, Peoples R China.
RP Wei, AM (通讯作者)，Hainan Vocat Univ Sci & Technol, Marine Sch, Haikou 571126, Hainan, Peoples R China.
EM whjulia@hvust.edu.cn; captalf@hvust.edu.cn
FU Education and Teaching Reform Research Foundation of Higher Education
   Institution of Hainan Province of China: Evaluation of Nautical English
   Ability Based on Network Learning Platform and Teaching Improvement
   Research [Hnjg2021-117]; Hainan Province Philosophy and Social Science
   Planning Project: A Study on the International Ship Registration System
   under the Background of Free Trade Port [(ZX)21-90]
FX This research was financially supported by the Education and Teaching
   Reform Research Foundation of Higher Education Institution of Hainan
   Province of China: Evaluation of Nautical English Ability Based on
   Network Learning Platform and Teaching Improvement Research (Grant No.
   Hnjg2021-117) and the Hainan Province Philosophy and Social Science
   Planning Project: A Study on the International Ship Registration System
   under the Background of Free Trade Port (Grant No. HNSK(ZX)21-90).
CR Hongxin D., 2020, CAIZHI, V1, P1
   Hui C., 2020, CHINA ED INFORM, V7, P5
   Kuo Q., 2017, MODERN COMMUNICATION, V18, P2
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   Zhu X., 2021, J JILIN U ENG EDITIO, V51, P6
NR 11
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 2314-8896
EI 2314-8888
J9 J FUNCT SPACE
JI J. Funct. space
PD JUL 31
PY 2022
VL 2022
AR 6578682
DI 10.1155/2022/6578682
PG 9
WC Mathematics, Applied; Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA 3P9VY
UT WOS:000837883500001
OA gold
DA 2022-08-23
ER

PT J
AU Browne, GJ
   Curley, SP
   Benson, PG
AF Browne, GJ
   Curley, SP
   Benson, PG
TI Evoking information in probability assessment: Knowledge maps and
   reasoning-based directed questions
SO MANAGEMENT SCIENCE
LA English
DT Article
DE probability assessment; decision analysis; knowledge maps; practical
   reasoning; knowledge evocation; protocol analysis
ID DECOMPOSITION; PERFORMANCE; PERSPECTIVE
AB To assess probabilities in decision analysis, and for decision making in general, decision variety of structuring tools to aid decision makers in these tasks, including influence diagrams and knowledge maps. However, despite their pervasive use in practice, there have been no reported empirical tests of these tools. One goal of the present research was to provide an empirical test of the evocative knowledge map methodology. Second, a theoretical analysis of probability assessment was used to develop a new prescriptive elicitation technique. This technique uses a theoretically-grounded set of directed questions to help decision makers evoke information for probability assessment. Experimental results showed that both the knowledge map and the new directed questions methodology elicited a higher quantity and quality of information from decision makers engaged in probability assessment tasks than did a control condition. Further, the information elicited by the two techniques was qualitatively different, suggesting that the two methods might profitably be used as complementary elicitation techniques.
C1 UNIV MINNESOTA,CARLSON SCH MANAGEMENT,MINNEAPOLIS,MN 55455.
   RUTGERS STATE UNIV,GRAD SCH MANAGEMENT,NEWARK,NJ 07102.
RP Browne, GJ (通讯作者)，UNIV MARYLAND,DEPT INFORMAT SYST,CATONSVILLE,MD 21228, USA.
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   [No title captured]
NR 43
TC 47
Z9 48
U1 0
U2 16
PU INST OPERATIONS RESEARCH  MANAGEMENT SCIENCES
PI LINTHICUM HTS
PA 901 ELKRIDGE LANDING RD, STE 400, LINTHICUM HTS, MD 21090-2909
SN 0025-1909
J9 MANAGE SCI
JI Manage. Sci.
PD JAN
PY 1997
VL 43
IS 1
BP 1
EP 14
DI 10.1287/mnsc.43.1.1
PG 14
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 WG421
UT WOS:A1997WG42100001
DA 2022-08-23
ER

PT J
AU Yang, Y
   Hao, MJ
   Huo, YH
   Chen, LD
   Gao, ZP
AF Yang, Yang
   Hao, Maojie
   Huo, Yonghua
   Chen, Liandong
   Gao, Zhipeng
TI Entity Alignment Algorithm Based on Dual-Attention and Incremental
   Learning Mechanism
SO IEEE ACCESS
LA English
DT Article
DE Training; Predictive models; Learning systems; Data models;
   Approximation algorithms; Prediction algorithms; Solids; Entity
   alignment; knowledge map; attention; neural network; binary
   classification
AB With the development of artificial intelligence and big data technology, large-scale general knowledge map construction is becoming increasingly important. One of the most efficient methods is undoubtedly the integration of existing knowledge maps, and entity alignment is the key in the process of knowledge map fusion. The merits of the entity alignment algorithm directly affect the efficiency and accuracy of the knowledge map fusion. However, there are some problems with the current Chinese knowledge map entity alignment algorithm, such as its low accuracy, difficulty in generating solid vectors, and difficulty in obtaining a priori alignment data. In this paper, the entity alignment algorithm is understood to be a neural network binary classification model, and we propose an entity alignment algorithm based on the dual-attention mechanism. The algorithm improves the entity vector training process, proposes a dual-attention mechanism, and applies an incremental learning mechanism. The experiments show that the improvements proposed in this paper effectively improve the classification accuracy of the algorithm, and the overall effect of the algorithm is better than that of the existing physical alignment algorithm.
C1 [Yang, Yang; Hao, Maojie; Gao, Zhipeng] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China.
   [Huo, Yonghua] CETC, Res Inst 54, Shijiazhuang 050002, Hebei, Peoples R China.
   [Chen, Liandong] State Grid Hebei Elect Power Co Ltd, Informat & Telecommun Branch, Shijiazhuang 050022, Hebei, Peoples R China.
RP Yang, Y (通讯作者)，Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China.
EM yyang@bupt.edu.cn
OI Yang, Yang/0000-0001-7848-5421
FU NSFC [61672108]; Open Subject Funds of Science and Technology on
   Information Transmission and Dissemination in the Communication Networks
   Laboratory [SKX182010049]; Fundamental Research Funds for the Central
   Universities [500419319 2019PTB-019]; Industrial Internet Innovation and
   Development Project 2018 of China
FX This work was supported in part by the NSFC under Grant 61672108, in
   part by the Open Subject Funds of Science and Technology on Information
   Transmission and Dissemination in the Communication Networks Laboratory
   under Grant SKX182010049, in part by the Fundamental Research Funds for
   the Central Universities under Grant 500419319 2019PTB-019, and in part
   by the Industrial Internet Innovation and Development Project 2018 of
   China.
CR [Anonymous], 2018, LONG PAPERS
   Bordes A., 2013, ADV NEURAL INFORM PR, P2787
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NR 22
TC 0
Z9 0
U1 0
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 2019
VL 7
BP 162179
EP 162191
DI 10.1109/ACCESS.2019.2951785
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA JN8VQ
UT WOS:000497169800099
OA gold
DA 2022-08-23
ER

PT J
AU Yoon, B
   Lee, S
   Lee, G
AF Yoon, Byungun
   Lee, Sungjoo
   Lee, Gwanghee
TI Development and application of a keyword-based knowledge map for
   effective R&D planning
SO SCIENTOMETRICS
LA English
DT Article
DE Knowledge map; Research proposal database; Text mining; Network
   analysis; R&D
ID BIBLIOMETRIC METHODS; UNIVERSITIES; NETWORKS
AB With the growing recognition of the importance of knowledge creation, knowledge maps are being regarded as a critical tool for successful knowledge management. However, the various methods of developing knowledge maps mostly depend on unsystematic processes and the judgment of domain experts with a wide range of untapped information. Thus, this research aims to propose a new approach to generate knowledge maps by mining document databases that have hardly been examined, thereby enabling an automatic development process and the extraction of significant implications from the maps. To this end, the accepted research proposal database of the Korea Research Foundation (KRF), which includes a huge knowledge repository of research, is investigated for inducing a keyword-based knowledge map. During the developmental process, text mining plays an important role in extracting meaningful information from documents, and network analysis is applied to visualize the relations between research categories and measure the value of network indices. Five types of knowledge maps (core R&D map, R&D trend map, R&D concentration map, R&D relation map, and R&D cluster map) are developed to explore the main research themes, monitor research trends, discover relations between R&D areas, regions, and universities, and derive clusters of research categories. The results can be used to establish a policy to support promising R&D areas and devise a long-term research plan.
C1 [Lee, Sungjoo] Ajou Univ, Dept Ind & Informat Syst Engn, Suwon 443749, South Korea.
   [Yoon, Byungun] Dongguk Univ Seoul, Dept Ind & Syst Engn, Seoul 100715, South Korea.
   [Lee, Gwanghee] Natl Res Fdn Korea, Publ Adm, Taejon 305350, South Korea.
RP Lee, S (通讯作者)，Ajou Univ, Dept Ind & Informat Syst Engn, San 5, Suwon 443749, South Korea.
EM postman3@dongguk.edu; sungjoo@ajou.ac.kr; thomas@nrf.go.kr
RI Yoon, Byungun/AAC-6608-2020; White, Howard D/A-7034-2009; Lee,
   Sungjoo/R-4279-2019
OI Yoon, Byungun/0000-0002-1110-4011
FU Ministry of Education, Science, and Technology [2009-0073285]
FX This research was supported by the Basic Science Research Program
   through the National Research Foundation (NRF) and funded by the
   Ministry of Education, Science, and Technology (Grant No. 2009-0073285).
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NR 59
TC 42
Z9 43
U1 2
U2 95
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2010
VL 85
IS 3
BP 803
EP 820
DI 10.1007/s11192-010-0294-5
PG 18
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 666DA
UT WOS:000283090300012
DA 2022-08-23
ER

PT J
AU Lv, QN
   Zhang, YY
   Li, YY
   Yu, Y
AF Lv, Qingna
   Zhang, Yanyun
   Li, Yanyan
   Yu, Yang
TI Research on a Health Care Personnel Training Model Based on Multilayered
   Knowledge Mapping for the Integration of Nursing Courses and
   Examinations
SO JOURNAL OF HEALTHCARE ENGINEERING
LA English
DT Article
ID SKILLS
AB While nursing courses provide a convenient and quick way to learn, they can also be overloaded with resources that can cause learners to become cognitively disoriented or have difficulty choosing nursing course. This paper proposes to fully explore learners' interests in the case of sparse data by fusing knowledge graph technology and deep recommendation models and adopt knowledge graph to model nursing courses at the semantic level so as to correspond the set of nursing courses to the knowledge graph and solve the problem of lack of logical knowledge relationships. Due to the specificity of its positions, the nursing profession must accurately position the nursing professional curriculum standards in the process of determining the talent cultivation model based on the nursing professional positions and the admission requirements for nursing practice qualification. Through linear feature mining based on the knowledge graph, entities and relationships are used to intuitively display the interest paths of nursing professional learners and enhance the interpretability of recommendations.
C1 [Lv, Qingna; Yu, Yang] Dalian Med Univ, Affiliated Hosp 2, Nursing Dept, Dalian 116023, Liaoning, Peoples R China.
   [Zhang, Yanyun] Inner Mongolia Minzu Univ, Coll Nursing, Tongliao 028000, Inner Mongolia, Peoples R China.
   [Li, Yanyan] Chongqing Med & Pharmaceut Coll, Coll Nursing, Chongqing 401331, Peoples R China.
RP Yu, Y (通讯作者)，Dalian Med Univ, Affiliated Hosp 2, Nursing Dept, Dalian 116023, Liaoning, Peoples R China.
EM qingna0522@dmu.edu.cn; zhangyanyunxin@163.com; liyanyanbbyxy@163.com;
   yuyang1983@dmu.edu.cn
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NR 28
TC 0
Z9 0
U1 4
U2 4
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 2040-2295
EI 2040-2309
J9 J HEALTHC ENG
JI J. Healthc. Eng.
PD FEB 9
PY 2022
VL 2022
AR 3826413
DI 10.1155/2022/3826413
PG 10
WC Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services
GA ZR1MT
UT WOS:000767557000005
PM 35186230
OA Green Published, gold
DA 2022-08-23
ER

PT S
AU Horn, RE
AF Horn, RE
BE Roco, MC
   Montemagno, CD
TI To think bigger thoughts - Why the human cognome project requires visual
   language tools to address social messes
SO COEVOLUTION OF HUMAN POTENTIAL AND CONVERGING TECHNOLOGIES
SE ANNALS OF THE NEW YORK ACADEMY OF SCIENCES
LA English
DT Article; Proceedings Paper
CT NBIC Convergence 2003 Conference on Converging Technologies for
   Improving Human Performance
CY FEB 05-07, 2003
CL Los Angeles, CA
SP Infocast Inc, Natl Sci Fdn
DE Human Cognome Project; cognome; visual language; social messes;
   communicating complexity; knowledge maps; knowledge mapping
AB The need and use of visual languages in complex situations is outlined.
C1 Stanford Univ, Ctr Study Language & Informat, Program People Comp & Design, Stanford, CA 94305 USA.
RP Horn, RE (通讯作者)，2819 Jackson St,101, San Francisco, CA 94115 USA.
EM hornbob@earthlink.net
CR Horn R. E., 2001, NAT SCI FDN C CONV T
   Horn RE, 1998, VISUAL LANGUAGE GLOB
   HORN RE, VISUAL LANGUAGE PATH
   HORN RE, MAPPING GREAT DEBATE
   [No title captured]
NR 5
TC 5
Z9 6
U1 0
U2 2
PU NEW YORK ACAD SCIENCES
PI NEW YORK
PA 2 EAST 63RD ST, NEW YORK, NY 10021 USA
SN 0077-8923
BN 1-57331-500-1
J9 ANN NY ACAD SCI
JI Ann.NY Acad.Sci.
PY 2004
VL 1013
BP 212
EP 220
DI 10.1196/annals.1305.015
PG 9
WC Multidisciplinary Sciences
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA BAH02
UT WOS:000222177300014
PM 15194616
DA 2022-08-23
ER

PT J
AU Liu, LQ
   Wang, B
   Ma, FQ
   Zheng, Q
   Yao, LZ
   Zhang, C
   Mohamed, MA
AF Liu, Liqing
   Wang, Bo
   Ma, Fuqi
   Zheng, Quan
   Yao, Liangzhong
   Zhang, Chi
   Mohamed, Mohamed A.
TI A Concurrent Fault Diagnosis Method of Transformer Based on Graph
   Convolutional Network and Knowledge Graph
SO FRONTIERS IN ENERGY RESEARCH
LA English
DT Article
DE knowledge graph; graph convolutional neural network; fault diagnosis;
   concurrent failures; failures analysis
AB In complex power systems, when power equipment fails, multiple concurrent failures usually occur instead of a single failure. Concurrent failures are so common and hidden in complex systems that diagnosis requires not only analysis of failure characteristics, but also correlation between failures. Therefore, in this paper, a concurrent fault diagnosis method is proposed for power equipment based on graph neural networks and knowledge graphs. First, an electrical equipment failure knowledge map is created based on operational and maintenance records to emphasize the relevance of the failed equipment or component. Next, a lightweight graph neural network model is built to detect concurrent faults in the graph data. Finally, a city's transformer concurrent fault is taken as an example for simulation and validation. Simulation results show that the accuracy and acquisition rate of graph neural network mining in Knowledge Graph is superior to traditional algorithms such as convolutional neural networks, which can achieve the effectiveness and robustness of concurrent fault mining.
C1 [Liu, Liqing; Zhang, Chi] State Grid Tianjin Elect Power Res Inst, Tianjin, Peoples R China.
   [Liu, Liqing; Zhang, Chi] Tianjin Key Lab Internet Things Elect, Tianjin, Peoples R China.
   [Wang, Bo; Ma, Fuqi; Yao, Liangzhong] Wuhan Univ, Sch Elect & Automat, Wuhan, Peoples R China.
   [Zheng, Quan] State Grid Tianjin Elect Power Co, Tianjin, Peoples R China.
   [Mohamed, Mohamed A.] Minia Univ, Fac Engn, Elect Engn Dept, Al Minya, Egypt.
RP Ma, FQ (通讯作者)，Wuhan Univ, Sch Elect & Automat, Wuhan, Peoples R China.; Mohamed, MA (通讯作者)，Minia Univ, Fac Engn, Elect Engn Dept, Al Minya, Egypt.
EM whumfq@whu.edu.cn; dr.mohamed.abdelaziz@mu.edu.eg
RI Mohamed, Mohamed A./AAD-3846-2021
OI Mohamed, Mohamed A./0000-0001-8700-0270
FU Science and Technology Program of the Headquarters of State Grid
   Corporation of China, Research on Knowledge Discovery, Reasoning and
   Decision-making for Electric Power Operation and Maintenance Based on
   Graph Machine Learning and Its Applications [5700-202012488A-0-0-00]
FX I declare that all sources of funding received for the research have
   been submitted. This work was supported by the Science and Technology
   Program of the Headquarters of State Grid Corporation of China, Research
   on Knowledge Discovery, Reasoning and Decision-making for Electric Power
   Operation and Maintenance Based on Graph Machine Learning and Its
   Applications, under Grant 5700-202012488A-0-0-00.
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NR 22
TC 2
Z9 2
U1 50
U2 50
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-598X
J9 FRONT ENERGY RES
JI Front. Energy Res.
PD FEB 21
PY 2022
VL 10
AR 837553
DI 10.3389/fenrg.2022.837553
PG 8
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA ZY3OY
UT WOS:000772498600001
OA gold
DA 2022-08-23
ER

PT J
AU Li, M
   Cao, S
   Qin, ZG
AF Li, Meng
   Cao, Sheng
   Qin, Zhiguang
TI Creation Method and Evolution Evaluation of Concept Knowledge Maps
SO JOURNAL OF INTERNET TECHNOLOGY
LA English
DT Article
DE Knowledge map; Evolution model; Link prediction; Learning efficiency
AB A concept knowledge map provides dependency among related knowledge and is used to help learners perform study navigation. The diversity of knowledge in the field of education and the discrepancy between subjects and learners extremely complicates the dependency among related knowledge. The rapid emergence of a number of educational information resources has significantly increased the size of knowledge maps. Moreover, the internal logical relations in various types of knowledge become less clear. This paper proposes a new knowledge unit centrality algorithm and evaluation method for the evolution model of concept knowledge maps. Results show that the dependency among related types of knowledge has an essential feature of complex network link directivity. Link prediction and maximum likelihood means are introduced to prove the existence of an ideal concept knowledge map. A method for quantifying the dependency among related knowledge is also developed to analyse the influence mechanism on learning efficiency. This method provides a new way for the comprehensive integration of information technology and educational science.
C1 [Li, Meng; Cao, Sheng; Qin, Zhiguang] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Peoples R China.
RP Cao, S (通讯作者)，Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Peoples R China.
EM mengli@uestc.edu.cn; caosheng@uestc.edu.cn; qinzg@uestc.edu.cn
FU Chinese Ministry of Education & China Mobile Communications Corporation
   Research Funds [MCM20121041]; Sichuan Key Technology Support Program
   [2015GZ0111, 2016GZ0095]; Open Project Program of Laboratory of
   Metrorological information sharing and data mining of Sichuan Province
   [QGX16008]
FX This research is supported by the Chinese Ministry of Education & China
   Mobile Communications Corporation Research Funds (Grant No.
   MCM20121041), Sichuan Key Technology Support Program (Grant No.
   2015GZ0111 and 2016GZ0095), and the Open Project Program of Laboratory
   of Metrorological information sharing and data mining of Sichuan
   Province (Grant No. QGX16008)
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NR 24
TC 3
Z9 3
U1 2
U2 29
PU LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV
PI HUALIEN
PA LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV, HUALIEN, 00000, TAIWAN
SN 1607-9264
EI 2079-4029
J9 J INTERNET TECHNOL
JI J. Internet Technol.
PD MAR
PY 2016
VL 17
IS 2
BP 179
EP 189
DI 10.6138/JIT.2016.17.2.20140710
PG 11
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA DI7CQ
UT WOS:000373657100002
DA 2022-08-23
ER

PT J
AU Zhao, XY
   Sheng, L
   Diao, TX
   Zhang, Y
   Wang, L
   Yanjun, Z
AF Zhao, X. Y.
   Sheng, L.
   Diao, T. X.
   Zhang, Y.
   Wang, L.
   Yanjun, Z.
TI Knowledge mapping analysis of Ebola research
SO BRATISLAVA MEDICAL JOURNAL-BRATISLAVSKE LEKARSKE LISTY
LA English
DT Article
DE Ebola; visualization; bibliometrics
ID VIRUS; INFECTION
AB OBJECTIVE: To reveal the overall situation of research on Ebola virus via visual knowledge mapping analyses of the literatures regarding Ebola virus all around the world, e.g., literature metrology and topic evolvement.
   METHODS: On the basis of the database Web of Knowledge (SCI), a knowledge graph was built by comprehensively utilizing software such as BIBEXCEL, GEPHI, VOSVIEWER, and HistCite.
   RESULTS: According to the results of literature analysis, publications on Ebola virus boomed in 2014, and the research focus transferred gradually from basic research (viral glycoprotein) in 1999 to the prevention and treatment research (vaccine). The United States are in a dominant position in the field of Ebola virus research, wherein the US army (United States Army Medical Research Institute of Infectious Diseases) has the strongest research capacity.
   DISCUSSION: Ebola virus is an important potential biological warfare agent, and some researches may not be published in the form of literature. So the result of literature metrology can only partially reflect the situation of Ebola virus research. In addition, close attention should be paid to the biosafety risk of Ebola research (Tab. 5, Fig. 5, Ref. 14). Text in PDF www.elis.sk.
C1 [Zhao, X. Y.; Sheng, L.; Diao, T. X.; Zhang, Y.; Wang, L.] Inst Hlth Serv & Med Informat, Beijing, Peoples R China.
   [Yanjun, Z.] Beijing Univ Technol, Ctr Biomed Engn, Beijing, Peoples R China.
RP Diao, TX (通讯作者)，Inst Hlth Serv & Med Informat, Beijing, Peoples R China.
EM diaotx@263.com
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NR 12
TC 6
Z9 7
U1 5
U2 43
PU COMENIUS UNIV
PI BRATISLAVA I
PA SCH MEDICINE, SPITALSKA 24, BRATISLAVA I, SK-813 72, SLOVAKIA
SN 0006-9248
EI 1336-0345
J9 BRATISL MED J
JI Bratisl. Med. J.
PY 2015
VL 116
IS 12
BP 729
EP 734
DI 10.4149/BLL_2015_143
PG 6
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA CZ7LL
UT WOS:000367280800008
PM 26924143
OA Bronze
DA 2022-08-23
ER

PT J
AU Chen, T
   Zhang, SJ
   Wang, Y
   Chen, ZB
   Jing, WF
AF Chen, Tong
   Zhang, Shujuan
   Wang, Yuan
   Chen, Zhengbo
   Jing, Wenfeng
TI Construction Methods of Knowledge Mapping for Full Service Power Data
   Semantic Search System
SO JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO
   TECHNOLOGY
LA English
DT Article
DE Power dispatch; Fault recovery; Semantic web; Semantic search; Knowledge
   mapping
AB The power sector continues to accumulate a large amount of data resources, including relevant standard specifications, technical documents, management documents, fault resolution records. How to quickly query and intelligently search these documents is of great value for grid dispatching and fault recovery. The domain search system of traditional power grid is based on keywords, and has the problems of low precision and recall rate. It cannot understand the business language and cannot support semantic reasoning. This paper designs and implements a method based on knowledge mapping to construct the power domain semantic search system. Semantic knowledge extraction of unstructured data is carried out by intelligent domain segmentation technology, organized and stored as knowledge mapping, and semantic search for support reasoning is realized based on knowledge mapping. The process of constructing domain semantic search system is introduced. Experiments show that the accuracy rate and recall rate of the method have been greatly improved.
C1 [Chen, Tong; Chen, Zhengbo] State Grid Zhejiang Power Co Ltd, 8 Huanglong Rd, Hangzhou 310007, Peoples R China.
   [Zhang, Shujuan] State Grid Anhui Power Co Ltd, 33 Xinjian St, Xuancheng 242100, Langxi County, Peoples R China.
   [Wang, Yuan] NARI Grp Co Ltd, 19 Chengxin Ave, Nanjing 211000, Peoples R China.
   [Jing, Wenfeng] Xi An Jiao Tong Univ, Sch Math & Stat, 28 Xianning West Rd, Xian 710049, Shaanxi, Peoples R China.
RP Chen, T (通讯作者)，State Grid Zhejiang Power Co Ltd, 8 Huanglong Rd, Hangzhou 310007, Peoples R China.
EM chentong@zj.sgcc.com.cn; zhang_shujuan@ah.sgcc.com.cn;
   wangyuan1@sgepri.sgcc.com.cn; chen_zhengbo@zj.sgcc.com.cn
FU Science and Technology Project of State Grid Corporation of China
   [5211XT180045]
FX This work was supported by the Science and Technology Project of State
   Grid Corporation of China (Project number: 5211XT180045).
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TC 1
Z9 1
U1 0
U2 6
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1939-8018
EI 1939-8115
J9 J SIGNAL PROCESS SYS
JI J. Signal Process. Syst. Signal Image Video Technol.
PD MAR
PY 2021
VL 93
IS 2-3
SI SI
BP 275
EP 284
DI 10.1007/s11265-020-01591-6
EA SEP 2020
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QK8GI
UT WOS:000571108600001
DA 2022-08-23
ER

PT J
AU Lee, JH
   Segev, A
AF Lee, Jae Hwa
   Segev, Aviv
TI Knowledge maps for e-learning
SO COMPUTERS & EDUCATION
LA English
DT Article
DE Knowledge maps; E-learning; Concept relation
AB Maps such as concept maps and knowledge maps are often used as learning materials. These maps have nodes and links, nodes as key concepts and links as relationships between key concepts. From a map, the user can recognize the important concepts and the relationships between them. To build concept or knowledge maps, domain experts are needed. Therefore, since these experts are hard to obtain, the cost of map creation is high. In this study, an attempt was made to automatically build a domain knowledge map for e-learning using text mining techniques. From a set of documents about a specific topic, keywords are extracted using the TF/IDF algorithm. A domain knowledge map (K-map) is based on ranking pairs of keywords according to the number of appearances in a sentence and the number of words in a sentence. The experiments analyzed the number of relations required to identify the important ideas in the text. In addition, the experiments compared K-map learning to document learning and found that K-map identifies the more important ideas. (C) 2012 Elsevier Ltd. All rights reserved.
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RP Segev, A (通讯作者)，Korea Adv Inst Sci & Technol, Dept Knowledge Serv Engn, Taejon, South Korea.
EM aviv@kaist.edu
RI Segev, Aviv/C-2060-2011
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NR 35
TC 43
Z9 44
U1 7
U2 95
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 SEP
PY 2012
VL 59
IS 2
BP 353
EP 364
DI 10.1016/j.compedu.2012.01.017
PG 12
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 955QQ
UT WOS:000305036400018
DA 2022-08-23
ER

PT J
AU Lv, YJ
   Zhao, G
   Yu, Y
AF Lv, Yanjie
   Zhao, Gang
   Yu, Yong
TI A novel method for adaptive knowledge map construction in the aircraft
   development
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Transportation facilities; Aircraft; Knowledge map; Knowledge overload;
   Domain ontology
ID INFORMATION-RETRIEVAL; DOMAIN KNOWLEDGE; MANAGEMENT; SERVICES; INDUSTRY;
   MODEL
AB Aircraft is a typical transportation facility and its development need to refer to the existing knowledge. With the rapid increase of knowledge, a knowledge map may deliver excess knowledge to users that they cannot manage at once, thereby causing the problem of knowledge overload. Hence, a novel method for adaptive knowledge map construction was proposed to solve this problem. First, the knowledge was semantically annotated and stored with the domain ontology and a knowledge model that integrates context. Then, user requirement was described by the context of product design, and knowledge nodes that met users' requirement could be extracted from the knowledge retrieval technology on the basis of context similarity. Finally, the connection between knowledge nodes was constructed with a composite connection model, and the knowledge map was visualized using a hierarchical approach. To verify the effectiveness of the proposed method, the constructed knowledge map was applied in an airplane wing design to assist users in browsing the knowledge base. Results indicate that the proposed method can change the displayed contents according to user requirement and identify the displayed knowledge nodes at a highly acceptable level, the constructed knowledge map can guide users efficiently, and the knowledge overload can be reduced significantly.
C1 [Lv, Yanjie; Zhao, Gang; Yu, Yong] Beihang Univ, Sch Mech Engn & Automat, Beijing, Peoples R China.
RP Lv, YJ; Zhao, G (通讯作者)，Beihang Univ, Sch Mech Engn & Automat, Beijing, Peoples R China.
EM lvyanjie_std@163.com; zhaog@buaa.edu.cn
FU Chinese 863 - program - "the High Technology Research and Development
   Program" [2009AA043302]
FX The research was supported by Chinese 863 - program - "the High
   Technology Research and Development Program". The project number is
   2009AA043302.
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NR 37
TC 5
Z9 5
U1 5
U2 24
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 DEC
PY 2016
VL 75
IS 24
BP 17465
EP 17486
DI 10.1007/s11042-015-3113-4
PG 22
WC Computer Science, Information Systems; Computer Science, Software
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   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA EE4VV
UT WOS:000389604600033
DA 2022-08-23
ER

PT J
AU Dang, Y
   Zhang, YL
   Hu, PJH
   Brown, SA
   Chen, HC
AF Dang, Yan
   Zhang, Yulei
   Hu, Paul Jen-Hwa
   Brown, Susan A.
   Chen, Hsinchun
TI Knowledge mapping for rapidly evolving domains: A design science
   approach
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE Knowledge mapping; Design science; Information systems
ID INFORMATION-SYSTEMS SUCCESS; NANOTECHNOLOGY DEVELOPMENT; TECHNOLOGY;
   WEB; VISUALIZATION; INSTITUTION; COUNTRY; SEARCH; FIELD
AB Knowledge mapping can provide comprehensive depictions of rapidly evolving scientific domains. Taking the design science approach, we developed a Web-based knowledge mapping system (i.e., Nano Mapper) that provides interactive search and analysis on various scientific document sources in nanotechnology. We conducted multiple studies to evaluate Nano Mapper's search and analysis functionality respectively. The search functionality appears more effective than that of the benchmark systems. Subjects exhibit favorable satisfaction with the analysis functionality. Our study addresses several gaps in knowledge mapping for nanotechnology and illustrates desirability of using the design science approach to design, implement, and evaluate an advanced information system. (C) 2010 Elsevier B.V. All rights reserved.
C1 [Dang, Yan; Zhang, Yulei] Univ Arkansas, Dept Informat Syst, Sam M Walton Coll Business, Fayetteville, AR 72701 USA.
   [Brown, Susan A.; Chen, Hsinchun] Univ Arizona, Dept Management Informat Syst, Eller Coll Management, Tucson, AZ 85721 USA.
   [Hu, Paul Jen-Hwa] Univ Utah, Operat & Informat Syst Dept, David Eccles Sch Business, Salt Lake City, UT 84112 USA.
RP Zhang, YL (通讯作者)，Univ Arkansas, Dept Informat Syst, Sam M Walton Coll Business, Fayetteville, AR 72701 USA.
EM ydang@walton.uark.edu; yzhang@walton.uark.edu;
   paul.hu@business.utah.edu; suebrown@eller.arizona.edu;
   hchen@eller.arizona.edu
RI White, Howard D/A-7034-2009
FU NSF [CMMI-0738803, DMI-0533749]
FX This research is supported by the following awards: NSF, "SGER:
   Inter-Repository Patent Analysis to Understand Worldwide Nanotechnology
   Research and Development" CMMI-0738803, and "Mapping Nanotechnology
   Development," DMI-0533749. We would like to thank USPTO, EPO, JPO, and
   NSF for making their databases available for research purposes.
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NR 65
TC 31
Z9 31
U1 7
U2 58
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 JAN
PY 2011
VL 50
IS 2
BP 415
EP 427
DI 10.1016/j.dss.2010.10.003
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Operations Research & Management Science
GA 714ZX
UT WOS:000286851300006
DA 2022-08-23
ER

PT J
AU Hao, J
   Yan, Y
   Gong, L
   Wang, GX
   Lin, JJ
AF Hao, Jia
   Yan, Yan
   Gong, Lin
   Wang, Guoxin
   Lin, Jianjun
TI Knowledge map-based method for domain knowledge browsing
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE Information overload; Knowledge browsing; Knowledge map; Social network
   analysis
ID INFORMATION OVERLOAD; CATEGORY MAP; WEB
AB The exponential growth of available information and the deployment of knowledge management systems delivers excessive information to the end users that they cannot manage at once. This problem has led to an increased emphasis on solutions to information overload. Searching and browsing are two methods to locate information. Many studies have focused on solving the information overload problem in the searching process, but the methods to alleviate information overload in browsing process have not been adequately studied. Hence, a method that addresses information overload in the browsing process is presented in this paper. The aim is to reduce the information overload during browsing domain knowledge for new knowledge users who have little understanding of the information. In this method, a knowledge map and social network analysis are utilized to navigate the knowledge users. Technologies first construct a knowledge map from text mining and the important knowledge that includes more information about the domain is then identified via social network analysis. Based on this process, the knowledge user can browse the domain knowledge starting from the important knowledge and navigate via the knowledge map. We applied the method to assist new knowledge users in browsing the Computer Numerical Control (CNC) domain knowledge base to validate the method. The results indicate that the method can identify the important knowledge at a highly acceptable level, the constructed knowledge map can efficiently navigate the knowledge users, and the information overload can be significantly decreased. (C) 2014 Elsevier B.V. All rights reserved.
C1 Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China.
   Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA.
RP Hao, J (通讯作者)，3635 Green Brier Blvd,Apt 120A, Ann Arbor, MI 48105 USA.
EM haojia632@gmail.com; yanyan331@bit.edu.cn; gonglin@bit.edu.cn;
   wangguoxin@bit.edu.cn; bitlin@sina.cn
OI Hao, Jia/0000-0003-0321-7021
FU National Defense Basic Scientific Research of China [A222011001];
   National Natural Science Foundation of China [51375049]
FX The authors would like to thank the anonymous reviewers for their
   valuable comments and thank the strong support provided by the National
   Defense Basic Scientific Research of China (A222011001) and the National
   Natural Science Foundation of China (51375049).
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NR 31
TC 31
Z9 31
U1 3
U2 92
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 MAY
PY 2014
VL 61
BP 106
EP 114
DI 10.1016/j.dss.2014.02.001
PG 9
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 AG0OB
UT WOS:000335113700010
DA 2022-08-23
ER

PT J
AU Zhao, L
   Xu, J
AF Zhao, Lin
   Xu, Juan
TI Architectural design of Residential Buildings in Qinba Mountains based
   on knowledge mapping
SO CASE STUDIES IN THERMAL ENGINEERING
LA English
DT Article
DE Knowledge mapping; Qinba mountain area; Residential design;
   Co-occurrence network
AB This article has selected knowledge mapping as the research method and China HowNet as the references source, 222 related research papers are selected to analyze and summarize the keyword co-occurrence network, aiming at the problems of residential architecture research in Qinba Mountains. For the clustered hotspots, taking the traditional residential buildings in Qinba Mountains as an example, this paper carries out a survey and summarizes the ways of residential design in Qinba Mountains, with a view to providing a better reference for the design of residential buildings in this area.
C1 [Zhao, Lin] Xian Aeronaut Univ, Sch Comp, Xian 710000, Shaanxi, Peoples R China.
   [Xu, Juan] Changan Univ, Sch Arch, Xian 710000, Shaanxi, Peoples R China.
RP Zhao, L (通讯作者)，Xian Aeronaut Univ, Sch Comp, Xian 710000, Shaanxi, Peoples R China.
EM 1240047820@qq.com
RI H, D/AFW-2362-2022
FU Ministry of Housing and Urban-Rural Development of the People's Republic
   of China [2016K6021]
FX This paper supported by the fund of Ministry of Housing and Urban-Rural
   Development of the People's Republic of China (No. 2016K6021).
CR Chen C., 2016, CITE SPACE PRACTICAL
   Gaoyuan, 2013, A C, V10
   Jiao Xiaojing, 2015, LIB INF WORK, V59, P5
   Xu juan, 2014, IND ARCHITECT, V9, P40
   Zhang Guoxin, 2016, A C FUND PROJ, V3, P102
   Zhang Tong, 2013, SHANXI ARCHITECT, V8, P8
NR 6
TC 0
Z9 0
U1 5
U2 7
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-157X
J9 CASE STUD THERM ENG
JI Case Stud. Therm. Eng.
PD SEP
PY 2019
VL 14
AR 100412
DI 10.1016/j.csite.2019.100412
PG 7
WC Thermodynamics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Thermodynamics
GA JA4UT
UT WOS:000487833400011
OA gold
DA 2022-08-23
ER

PT J
AU Huang, YW
   Jiang, ZH
   He, CN
   Liu, JF
   Song, B
   Liu, LJ
AF Huang, Yongwen
   Jiang, Zuhua
   He, Chengneng
   Liu, Jianfeng
   Song, Bo
   Liu, Lijun
TI A semantic-based visualised wiki system (SVWkS) for lesson-learned
   knowledge reuse situated in product design
SO INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
LA English
DT Article
DE knowledge map; knowledge reuse; knowledge management; product design;
   wiki; lesson-learned; semantic
ID MANUFACTURING KNOWLEDGE; WEB 2.0; MANAGEMENT; METHODOLOGY; FRAMEWORK
AB In the process of product design, engineers usually find it is difficult to precisely find and reuse others' empirical knowledge resources, especially the lesson-learned knowledge, which is usually not well collected by the organisation. This study proposes a novel approach, which uses a semantic-based visualised wiki system (SVWkS) to support lesson-learned knowledge reuse. The core of visualised knowledge search framework is a semantic-based topic knowledge map. The architecture of this knowledge map creation method is designed, which has five major modules: lesson-learned items pre-processing, topic extraction, topic relation computation, topic weight computation and topic knowledge map generation modules. Then a working scenario of SVWkS is briefly introduced. We have conducted three sets of experiments to evaluate quality of visualised results-knowledge map, the effectiveness of semantic-based visualised searching mechanisms and the performance of utilising SVWkS for knowledge reuse in outfitting design of a ship-building company. The first experiment shows that knowledge maps generated by SVWkS are accepted by domain experts from the evaluation since precision and recall are high. The second experiment shows a semantic-based visualised searching mechanism supported by semantic relations is more useful than a traditional keyword search in terms of precision and recall. The third experiment shows that SVWkS-based group outperforms keyword search-based group in both learning score and satisfaction level, which are two measurements of performance of utilising SVWkS. The promising results confirm the feasibility of SVWkS in helping engineers to find needed lesson-learned knowledge and reuse-related knowledge.
C1 [Huang, Yongwen; Jiang, Zuhua; Song, Bo; Liu, Lijun] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200030, Peoples R China.
   [He, Chengneng; Liu, Jianfeng] Shanghai Waigaoqiao Ship Bldg Co, Inst Prod Design, Shanghai, Peoples R China.
RP Jiang, ZH (通讯作者)，Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200030, Peoples R China.
EM zhjiang@sjtu.edu.cn
RI Song, Bo/K-5020-2015
OI Song, Bo/0000-0002-1850-7841
FU National Nature Science Foundation of China [70971085, 71271133];
   Research Fund for the Doctoral Programme of Higher Education of China
   [20100073110035]; Shanghai Municipal Education Commission [13ZZ012]
FX The research is supported by National Nature Science Foundation of China
   [grant number 70971085], [grant number 71271133]; the Research Fund for
   the Doctoral Programme of Higher Education of China [grant number
   20100073110035] and Innovation Programme of Shanghai Municipal Education
   Commission [grant number 13ZZ012].
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NR 63
TC 13
Z9 15
U1 2
U2 56
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0020-7543
EI 1366-588X
J9 INT J PROD RES
JI Int. J. Prod. Res.
PD APR 18
PY 2015
VL 53
IS 8
BP 2524
EP 2541
DI 10.1080/00207543.2014.975861
PG 18
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA CB1NW
UT WOS:000349395700016
DA 2022-08-23
ER

PT J
AU LU, SCY
AF LU, SCY
TI KNOWLEDGE MAP - AN APPROACH TO KNOWLEDGE ACQUISITION IN DEVELOPING
   ENGINEERING EXPERT SYSTEMS
SO ENGINEERING WITH COMPUTERS
LA English
DT Article
RP LU, SCY (通讯作者)，UNIV ILLINOIS,DEPT MECH & IND ENGN,KNOWLEDGE BASED ENGN SYST RES LAB,URBANA,IL 61801, USA.
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NR 10
TC 1
Z9 1
U1 0
U2 11
PU SPRINGER VERLAG
PI NEW YORK
PA 175 FIFTH AVE, NEW YORK, NY 10010
SN 0177-0667
J9 ENG COMPUT
JI Eng. Comput.
PY 1987
VL 3
IS 2
BP 59
EP 68
DI 10.1007/BF01206303
PG 10
WC Computer Science, Interdisciplinary Applications; Engineering,
   Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA L3571
UT WOS:A1987L357100001
DA 2022-08-23
ER

PT J
AU Wang, QJY
   Wang, DJ
   Bai, GY
   Yu, Q
AF Wang, Qiaojiayu
   Wang, Dejiang
   Bai, Gengyuan
   Yu, Qian
TI Co-Occurrence and Cyclical Growth Law Analysis of User Innovation
   Knowledge Map Based on Temporal-Weighted Network
SO IEEE ACCESS
LA English
DT Article
DE Co-occurrence analysis; user innovation; temporal-weighted user
   innovation knowledge map; collaborative creativity
ID SOCIAL NETWORKS; DYNAMICS
AB The construction of user innovation knowledge map can clearly describe the co-occurrence relationship among innovative knowledge points. Different types of weighted knowledge map models have been applied by enterprises to promote their innovative knowledge development. Moreover, the temporal-weighted user innovation knowledge map can reveal the hidden hot knowledge network model accurately and reflect the general evolution law of the hot topic of innovation knowledge in a certain period. In this paper, we proposed a method based on a knowledge network for constructing the temporal-weighted user innovation knowledge map. In order to model the temporal-weighted co-occurrence relationship among knowledge points, we assigned different weights to the knowledge points at different periods. Then, we carried out centrality analysis and core subgroup analysis for knowledge points through using a time-dynamic analysis method to explore the dynamic laws. Based on that, we proposed the periodical growth rule of user innovation hotspots. It was convenient to predict the hot spots of innovation and grasp the pace of innovation. Besides, we conducted an empirical study of the "Arduino" community, which is the most professional electronic enthusiast community in China. We applied the established model to explore the sensitivity and accuracy of the knowledge map after adding the temporal factor to the user innovation knowledge map, and we found out the evolution process of co-occurrence relationship between user innovation knowledge points. Finally, we concluded that it takes time to ultimately establish a core-edge innovation knowledge model to represent specific innovation fields. More importantly, the development of hotspots of innovative knowledge presented the general growth cycle of the four stages: the initial stage, the explosive development stage, the mature stage, and the recession stage. Discovery of this law has a major impact on the company's trending of the future "frontier research" field and the entry choice of innovation.
C1 [Wang, Qiaojiayu; Wang, Dejiang; Bai, Gengyuan; Yu, Qian] Wuhan Univ Technol, Sch Econ, Wuhan 430070, Hubei, Peoples R China.
RP Yu, Q (通讯作者)，Wuhan Univ Technol, Sch Econ, Wuhan 430070, Hubei, Peoples R China.
EM yuqian@whut.edu.cn
OI wang, dejiang/0000-0002-1462-2156
FU National Natural Science Foundation of China [71774128]; Fundamental
   Research Funds for the Central Universities of China [WUT 2019VI022];
   National Undergraduate Training Programs for Innovation and
   Entrepreneurship [201810497042]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 71774128, in part by the Fundamental
   Research Funds for the Central Universities of China under Grant WUT
   2019VI022, and in part by the National Undergraduate Training Programs
   for Innovation and Entrepreneurship under Grant 201810497042.
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NR 49
TC 1
Z9 1
U1 3
U2 17
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 60026
EP 60041
DI 10.1109/ACCESS.2019.2914234
PG 16
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 HZ2IF
UT WOS:000468671800001
OA gold
DA 2022-08-23
ER

PT J
AU Liu, DR
   Ke, CK
   Lee, JY
   Lee, CF
AF Liu, Duen-Ren
   Ke, Chih-Kun
   Lee, Jia-Yuan
   Lee, Chun-Feng
TI Knowledge maps for composite e-services: A mining-based system platform
   coupling with recommendations
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE composite e-service; knowledge maps; topic maps; data mining;
   recommendation
ID WEB SERVICES
AB Providing various c-services on the Internet by enterprises is an important trend in c-business. Composite e-services, which consist of various e-services provided by different e-service providers, are complex processes that require the cooperation among cross-organizational e-service providers. The flexibility and success of e-business depend on effective knowledge support to access related information resources of composite e-services. Thus, providing effective knowledge support for accessing composite e-services is a challenging task. This work proposes a knowledge map platform to provide an effective knowledge support for utilizing composite e-services. A data mining approach is applied to extract knowledge patterns from the usage records of composite e-services. Based on the mining result, topic maps are employed to construct the knowledge map. Meanwhile, the proposed knowledge map is integrated with recommendation capability to generate recommendations for composite e-services via data mining and collaborative filtering techniques. A prototype system is implemented to demonstrate the proposed platform. The proposed knowledge map enhanced with recommendation capability can provide users customized decision support to effectively utilize composite e-services. (c) 2006 Elsevier Ltd. All rights reserved.
C1 Natl Chiao Tung Univ, Inst Informat Management, Hsinchu 300, Taiwan.
RP Liu, DR (通讯作者)，Natl Chiao Tung Univ, Inst Informat Management, Hsinchu 300, Taiwan.
EM dliu@iim.netu.edu.tw
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NR 41
TC 25
Z9 26
U1 3
U2 16
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD JAN
PY 2008
VL 34
IS 1
BP 700
EP 716
DI 10.1016/j.eswa.2006.10.005
PG 17
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 222KP
UT WOS:000250295300072
DA 2022-08-23
ER

PT J
AU Chiu, DY
   Pan, YC
AF Chiu, Deng-Yiv
   Pan, Ya-Chen
TI Topic knowledge map and knowledge structure constructions with genetic
   algorithm, information retrieval, and multi-dimension scaling method
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge structure; Topic knowledge map; Information retrieval; Genetic
   algorithm; Independent chi-square; Multi-dimension scaling
ID COGNITIVE MAPS
AB This work presents a novel automated approach to construct topic knowledge maps with knowledge structures, followed by its application to an internationally renowned journal. Knowledge structures are diagrams showing the important components of knowledge in study. Knowledge maps identify the locations of objects and illustrate the relationship among objects. In our study, the important components derived from knowledge structures are used as objects to be spotted in a topic knowledge map. The purpose of our knowledge structures is to find out the major topics serving as subjects of article collections as well as related methods employed in the published papers. The purpose of topic knowledge maps is to transform high-dimensional objects (topic, paper, and cited frequency) into a 2-dimensional space to help understand complicated relatedness among high-dimensional objects, such as the related degree between an article and a topic.
   First, we adopt independent chi-square test to examine the independence of topics and apply genetic algorithm to choose topics selection with best fitness value to construct knowledge structures.
   Additionally, high-dimensional relationships among objects are transformed into a 2-dimensional space using the multi-dimension scaling method. The optimal transformation coordinate matrix is also determined by using a genetic algorithm to preserve the original relations among objects and construct appropriate topic knowledge maps. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Chiu, Deng-Yiv; Pan, Ya-Chen] ChungHua Univ, Dept Informat Management, Hsinchu 300, Taiwan.
RP Chiu, DY (通讯作者)，ChungHua Univ, Dept Informat Management, 707,Sec 2,Wu Fu Rd, Hsinchu 300, Taiwan.
EM chiuden@chu.edu.tw; d09403019@cc.chu.edu.tw
FU National Science Council of the Republic of China, Taiwan (NSC)
   [98-2221-E-216-038]
FX The authors would like to thank the National Science Council of the
   Republic of China, Taiwan, for financially supporting this research
   under Contract No. NSC 98-2221-E-216-038.
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NR 31
TC 10
Z9 11
U1 2
U2 43
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
PY 2014
VL 67
BP 412
EP 428
DI 10.1016/j.knosys.2014.03.008
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA AM9SP
UT WOS:000340221600032
DA 2022-08-23
ER

PT J
AU Huang, Y
   Jiang, Z
AF Huang, Ywen
   Jiang, Zhua
TI A Semantic-Based Topic Knowledge Map System (STKMS) for Lesson-Learned
   Documents Reuse in Product Design
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article
DE knowledge management; topic knowledge map; lesson-learned documents;
   semantic
ID RETRIEVAL
AB In the process of production design, engineers usually find it is difficult to seek and reuse others' empirical knowledge which is in the forms of lesson-learned documents. This study proposed a novel approach, which uses a semantic-based topic knowledge map system (STKMS) to support timely and precisely lesson-learned documents finding and reusing. The architecture of STKMS is designed, which has five major functional modules: lesson-learned documents pm-processing, topic extraction, topic relation computation, topic weights computation, and topic knowledge map generation modules. Then STKMS implementation is briefly introduced. We have conducted two sets of experiments to evaluate quality of knowledge map and the performance of utilizing STKMS in outfitting design of a ship-building company. The first experiment shows that knowledge maps generated by STKMS are accepted by domain experts from the evaluation since precision and recall are high. The second experiment shows that STKMS-based group outperforms browse-based group in both learning score and satisfaction level, which are two measurements of performance of utilizing STKMS. The promising results confirm the feasibility of STKMS in helping engineers to find needed lesson-learned documents and reuse related knowledge easily and precisely.
C1 [Huang, Ywen; Jiang, Zhua] Shanghai Jiao Tong Univ, Dept Ind Engn, Shanghai 200030, Peoples R China.
RP Huang, Y (通讯作者)，Shanghai Jiao Tong Univ, Dept Ind Engn, Shanghai 200030, Peoples R China.
EM hywen2004@163.com
FU National Nature Science Foundation of China [70971085, 71271133];
   Research Fund for the Doctoral Program of Higher Education of China
   [20100073110035]; Innovation Program of Shanghai Municipal Education
   Commission [13ZZ012]
FX The research is supported by National Nature Science Foundation of China
   (No. 70971085, 71271133), the Research Fund for the Doctoral Program of
   Higher Education of China (No. 20100073110035), and Innovation Program
   of Shanghai Municipal Education Commission (13ZZ012). The authors would
   like to thank all the participants for their efforts in our experiments
   and the viewers for their helpful comments.
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NR 28
TC 1
Z9 1
U1 1
U2 24
PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
PI TOKYO
PA KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011,
   JAPAN
SN 1745-1361
J9 IEICE T INF SYST
JI IEICE Trans. Inf. Syst.
PD MAY
PY 2014
VL E97D
IS 5
BP 1049
EP 1057
DI 10.1587/transinf.E97.D.1049
PG 9
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA AQ4QW
UT WOS:000342784200005
OA gold
DA 2022-08-23
ER

PT J
AU Shou, WH
   Fan, WH
   Liu, BY
   Lai, YY
AF Shou, Wenhui
   Fan, Wenhui
   Liu, Boyuan
   Lai, Yuyang
TI Knowledge Map Mining of Financial Data
SO TSINGHUA SCIENCE AND TECHNOLOGY
LA English
DT Article
DE Knowledge Map (KM); data mining; health monitoring; crisis warning
ID FUZZY COGNITIVE MAPS
AB The Knowledge Map (KM) concept, which was derived from the Fuzzy Cognitive Map (FCM), is used to describe and manage knowledge. KM provides insight into the interdependencies and uncertainties contained in the system. This paper uses a model-free method to mine KMs in historical data to analyze component stock corporations of the Shanghai Stock 50 index. The analyses use static and time-domain analyses. The results indicate that a knowledge map is useful for representing knowledge and for monitoring the health of companies. Furthermore, sudden changes of the key features of the KMs should be taken seriously by policymakers as an alarm of a crisis.
C1 [Shou, Wenhui; Fan, Wenhui; Liu, Boyuan] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China.
   [Lai, Yuyang] SOYOTEC Technol Co Ltd, Beijing 100081, Peoples R China.
RP Fan, WH (通讯作者)，Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China.
EM sylvia0816@gmail.com; fanwenhui@tsinghua.edu.cn
FU National Natural Science Foundation of China [60874066]; National
   High-Tech Research and Development (863) Program of China [2009AA110302]
FX This work was supported in part by the National Natural Science
   Foundation of China (No. 60874066) and the National High-Tech Research
   and Development (863) Program of China (No. 2009AA110302).
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NR 16
TC 5
Z9 5
U1 1
U2 8
PU TSINGHUA UNIV PRESS
PI BEIJING
PA B605D, XUE YAN BUILDING, BEIJING, 100084, PEOPLES R CHINA
SN 1007-0214
EI 1878-7606
J9 TSINGHUA SCI TECHNOL
JI Tsinghua Sci. Technol.
PD FEB
PY 2013
VL 18
IS 1
BP 68
EP 76
DI 10.1109/TST.2013.6449410
PG 9
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA V41EH
UT WOS:000209529000009
DA 2022-08-23
ER

PT J
AU Xu, Z
   Chen, HY
AF Xu, Zheng
   Chen, Haiyan
TI The semantic analysis of knowledge map for the traffic violations from
   the surveillance video big data
SO COMPUTER SYSTEMS SCIENCE AND ENGINEERING
LA English
DT Article
DE the surveillance video; traffic violations; semantic; knowledge map
ID DATA SETS; CLOUD; COST
AB With the high speed development of accessing and producing data, the new research area named the big data is emerging. With the pervasive of the definition of the smart city, the surveillance system, huge number of video surveillance devices such as surveillance cameras has been built. The rapid increase in the available amount of surveillance video data has caused an urgent need to develop intelligent model to extract the video content.The surveillance video data has grown tremendously in recent years, but traffic violations still cause a lot of accidents and personal injury every year. There are the problems of "cannot find", "difficult to understand" for the mass surveillance video retrieval system. Knowledge map is a huge knowledge database, which can solve the above question. In this paper, the knowledge map in the traffic violations area is constructed. The Knowledge acquisition connection model is proposed. Then we study a knowledge management and rule system based on knowledge map which can detect traffic violations.
C1 [Xu, Zheng] Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China.
   [Xu, Zheng] Tsinghua Univ, Beijing 100084, Peoples R China.
   [Chen, Haiyan] East China Univ Polit Sci & Law, Shanghai, Peoples R China.
RP Xu, Z (通讯作者)，Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China.
EM xuzheng@shu.edu.cn
FU National Science and Technology Major Project [2013ZX01033002-003];
   National High Technology Research and Development Program of China (863
   Program) [2013AA014601, 2013AA014603]; National Key Technology Support
   Program [2012BAH07B01]; National Science Foundation of China [61300202,
   61300028]; Ministry of Public Security [2014JSYJB009]; China
   Postdoctoral Science Foundation [2014M560085]; 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
   High Technology Research and Development Program of China (863 Program)
   under Grant 2013AA014601, 2013AA014603, in part by National Key
   Technology Support Program under Grant 2012BAH07B01, in part by the
   National Science Foundation of China under Grant 61300202, 61300028, in
   part by the Project of the Ministry of Public Security under Grant
   2014JSYJB009, in part by the China Postdoctoral Science Foundation under
   Grant 2014M560085, and in part by the Science Foundation of Shanghai
   under Grant 13ZR1452900.
CR Babaguchi N, 2003, IEEE IMAGE PROC, P13
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NR 20
TC 5
Z9 5
U1 1
U2 22
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 SEP
PY 2015
VL 30
IS 5
SI SI
BP 403
EP 410
PG 8
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DB2EY
UT WOS:000368322300009
DA 2022-08-23
ER

PT J
AU Ebener, S
   Khan, A
   Shademani, R
   Compernolle, L
   Beltran, M
   Lansang, MA
   Lippman, M
AF Ebener, S.
   Khan, A.
   Shademani, R.
   Compernolle, L.
   Beltran, M.
   Lansang, M. A.
   Lippman, M.
TI Knowledge mapping as a technique to support knowledge translation
SO BULLETIN OF THE WORLD HEALTH ORGANIZATION
LA English
DT Article
AB This paper explores the possibility of integrating knowledge mapping into a conceptual framework that could serve as a tool for understanding the many complex processes, resources and people involved in a health system, and for identifying potential gaps within knowledge translation processes in order to address them.
   After defining knowledge mapping, this paper presents various examples of the application of this process in health, before looking at the steps that need to be taken to identify potential gaps, to determine to what extent these gaps affect the knowledge translation process and to establish their cause, This is followed by proposals for interventions aimed at strengthening the overall process. Finally, potential limitations on the application of this framework at the country level are addressed.
C1 WHO, Knowledge Management & Sharing Dept, CH-1211 Geneva 27, Switzerland.
   Hlth Policy Dev Planning Bur, Dept Hlth, Manila, Philippines.
   Univ Philippines, Coll Med, Dept Clin Epidemiol, Manila, Philippines.
RP Ebener, S (通讯作者)，WHO, Knowledge Management & Sharing Dept, CH-1211 Geneva 27, Switzerland.
EM ebeners@who.int
OI Lansang, Mary Ann/0000-0002-6606-0466
CR BURKHARD RA, 2005, KNOWLEDGE VISUALIZAT
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NR 20
TC 33
Z9 34
U1 0
U2 13
PU WORLD HEALTH ORGANIZATION
PI GENEVA 27
PA MARKETING AND DISSEMINATION, CH-1211 GENEVA 27, SWITZERLAND
SN 0042-9686
EI 1564-0604
J9 B WORLD HEALTH ORGAN
JI Bull. World Health Organ.
PD AUG
PY 2006
VL 84
IS 8
BP 636
EP 642
DI 10.2471/BLT.06.029736
PG 7
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Public, Environmental & Occupational Health
GA 071ON
UT WOS:000239611600013
PM 16917651
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Zhang, C
   Zhang, J
   Long, C
   Zheng, J
   Su, C
   Hu, W
   Duan, Z
AF Zhang, C.
   Zhang, J.
   Long, C.
   Zheng, J.
   Su, C.
   Hu, W.
   Duan, Z.
TI Analyses of research on the health of college students based on a
   perspective of knowledge mapping
SO PUBLIC HEALTH
LA English
DT Article
ID ADOLESCENTS; AMERICAN
C1 [Zhang, C.; Zhang, J.; Zheng, J.; Su, C.; Hu, W.; Duan, Z.] Shanxi Med Univ, Sch Publ Hlth, Taiyuan, Peoples R China.
   [Long, C.] Stanford Univ, Sch Med, Stanford, CA 94305 USA.
RP Duan, Z (通讯作者)，56 South Xinjian Rd, Taiyuan, Peoples R China.
EM dzg52827@aliyun.com
RI Zhang, Chichen/I-6881-2016
OI Zhang, Chichen/0000-0003-1095-9939; Duan, zhiguang/0000-0002-2063-5236
FU National Natural Science Foundation of China [71473154, 71403155];
   Ministry of Education in China (Research Projects of Humanities and
   Social Sciences on Young Fund) [13YJCZH239]; Shanxi Province Education
   Science's 'Twelfth Five-Year Plan' Project [GH-13028]
FX The research was supported by the National Natural Science Foundation of
   China (No. 71473154; 71403155), the Ministry of Education in China
   (Research Projects of Humanities and Social Sciences on Young Fund, No.
   13YJCZH239), and Shanxi Province Education Science's 'Twelfth Five-Year
   Plan' Project (No. GH-13028).
CR Donadiki EM, 2014, PUBLIC HEALTH, V128, P268, DOI 10.1016/j.puhe.2013.12.004
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NR 9
TC 2
Z9 4
U1 1
U2 20
PU W B SAUNDERS CO LTD
PI LONDON
PA 32 JAMESTOWN RD, LONDON NW1 7BY, ENGLAND
SN 0033-3506
EI 1476-5616
J9 PUBLIC HEALTH
JI Public Health
PD AUG
PY 2016
VL 137
BP 188
EP 191
DI 10.1016/j.puhe.2015.11.002
PG 4
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA DT3HJ
UT WOS:000381371400029
PM 26715319
DA 2022-08-23
ER

PT J
AU Yang, C
   Liu, Z
   Wang, HB
   Shen, JQ
AF Yang, Cheng
   Liu, Zheng
   Wang, Haobai
   Shen, Jiaoqi
TI Reusing design knowledge based on design cases and knowledge map
SO INTERNATIONAL JOURNAL OF TECHNOLOGY AND DESIGN EDUCATION
LA English
DT Article
DE Design case; Ontology model; Knowledge map; Implicit knowledge; Reuse of
   knowledge
ID ENGINEERING DESIGN; MANAGEMENT
AB Design knowledge was reused for innovative design work to support designers with product design knowledge and help designers who lack rich experiences to improve their design capacity and efficiency. First, based on the ontological model of product design knowledge constructed by taxonomy, implicit and explicit knowledge was extracted from some design cases. The design knowledge was expressed using a constructional organization. With the knowledge map, design knowledge was illustrated to help novice designers reconstruct specific design cases, thus, encouraging innovative design. Four groups of designers were invited to participate in an experiment for the design knowledge map system. The experiment results verified the effectiveness of the method.
C1 [Yang, Cheng; Wang, Haobai; Shen, Jiaoqi] Zhejiang Univ City Coll, 51 Huzhou St, Hangzhou 310003, Zhejiang, Peoples R China.
   [Liu, Zheng] China Acad Art, Hangzhou, Zhejiang, Peoples R China.
RP Yang, C (通讯作者)，Zhejiang Univ City Coll, 51 Huzhou St, Hangzhou 310003, Zhejiang, Peoples R China.
EM yangchengyc@126.com
FU National Natural Science Fund of China [51005203]; Humanities and Social
   Science Planning Fund of the Ministry of Education of China [11YJAZH110]
FX This research was supported by the National Natural Science Fund of
   China (No. 51005203) and the Humanities and Social Science Planning Fund
   of the Ministry of Education of China (No. 11YJAZH110).
CR Chen H., 2005, PRODUCT DESIGN
   Cynthia J. A., 2005, DESIGN STUDIES, V26, P325
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   Tomi H., 2004, J KNOWLEDGE MANAGEME, V8, P36
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NR 17
TC 8
Z9 10
U1 10
U2 67
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0957-7572
EI 1573-1804
J9 INT J TECHNOL DES ED
JI Int. J. Technol. Des. Educ.
PD NOV
PY 2013
VL 23
IS 4
BP 1063
EP 1077
DI 10.1007/s10798-013-9239-7
PG 15
WC Education & Educational Research; Education, Scientific Disciplines;
   Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA 245HS
UT WOS:000326454900016
DA 2022-08-23
ER

PT S
AU Zhuge, H
   Luo, XF
AF Zhuge, H
   Luo, XF
BE JIn, H
   Pan, Y
   Xiao, N
   Sun, J
TI Knowledge map model
SO GRID AND COOPERATIVE COMPUTING GCC 2004, PROCEEDINGS
SE LECTURE NOTES IN COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 3rd International Conference on Grid and Cooperative Computing (GCC
   2004)
CY OCT 21-24, 2004
CL Wuhan, PEOPLES R CHINA
AB Knowledge representation and inference is a key issue of Knowledge Grid. This paper proposes Knowledge Map (KM) model for representing and reasoning causal knowledge as an overlay in Knowledge Grid. It extends Fuzzy Cognitive Map (FCM) to represent and infer not only cause-effect causal relations, but also time-delay causal relations, conditional probabilistic causal relations and sequential relations. Mathematical model and inference rules of KM are presented. Simulations show that KM is more powerful than FCM in emulating real world.
C1 Chinese Acad Sci, Inst Comp Technol, Ley Lab Intelligent Informat Proc, Knowledge Grid Res Grp, Beijing 100080, Peoples R China.
   Hunan Univ Sci & Technol, Hunan Knowledge Grid Lab, Hunan, Peoples R China.
RP Zhuge, H (通讯作者)，Chinese Acad Sci, Inst Comp Technol, Ley Lab Intelligent Informat Proc, Knowledge Grid Res Grp, Beijing 100080, Peoples R China.
EM zhuge@ict.ac.cn; luoxf@kg.ict.ac.cn
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   Liu ZQ, 1999, IEEE T FUZZY SYST, V7, P495, DOI 10.1109/91.797975
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NR 8
TC 4
Z9 4
U1 1
U2 5
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-23564-7
J9 LECT NOTES COMPUT SC
PY 2004
VL 3251
BP 381
EP 388
PG 8
WC Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BBE85
UT WOS:000225161200047
DA 2022-08-23
ER

PT J
AU Paul, S
   Kokossis, A
   Gage, H
   Storey, L
   Lawrenson, R
   Trend, P
   Walmsley, K
   Morrison, S
   Kaye, J
   Gradwell, E
   Baker, M
AF Paul, S.
   Kokossis, A.
   Gage, H.
   Storey, L.
   Lawrenson, R.
   Trend, P.
   Walmsley, K.
   Morrison, S.
   Kaye, J.
   Gradwell, E.
   Baker, M.
TI A semantically enabled formalism for the knowledge management of
   Parkinson's disease
SO MEDICAL INFORMATICS AND THE INTERNET IN MEDICINE
LA English
DT Article
DE ontology; Parkinson's disease; knowledge map; axioms; symptoms and
   drugs; physiotherapy; speech and language therapy; diet
ID ONTOLOGIES; DIAGNOSIS
AB Bio-ontology is a formal representation of biological concepts that is used in the interchange of communication between computers and humans alike. They can then be used in the formulation and retrieval of knowledge. In developing a knowledge-based system for Parkinson's Disease, a procedure of knowledge map was used to capture and harness the intellectual resources of an organization, and new paradigms for knowledge mapping were also formulated. Knowledge bases for symptoms and drugs, physiotherapy, speech and language therapy, and dieting that affect patient care were developed. Finally, the knowledge bases were merged to form a single central repository of knowledge base.
C1 Univ Surrey, Proc Informat Syst Engn Ctr, Guildford GU2 7XH, Surrey, England.
   Univ Surrey, Dept Econ, Guildford GU2 7XH, Surrey, England.
   Univ Surrey, Post Grad Med Sch, Guildford GU2 7XX, Surrey, England.
   Royal Surrey Cty Hosp NHS Trust, Surrey, England.
   Guildford & Waverly Primary Care Trust, Farnham GU9 8QT, Surrey, England.
   European Parkinson Dis Assoc, Sevenoaks TN13 3NJ, Kent, England.
RP Paul, S (通讯作者)，Univ Surrey, Proc Informat Syst Engn Ctr, Guildford GU2 7XH, Surrey, England.
EM s.paul@surrey.ac.uk
OI Gage, Heather/0000-0002-2049-9406
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NR 32
TC 4
Z9 4
U1 0
U2 4
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1463-9238
J9 MED INFORM INTERNET
JI Med. Inform. Internet Med.
PD JUN
PY 2006
VL 31
IS 2
BP 101
EP 120
DI 10.1080/14639230500299220
PG 20
WC Computer Science, Information Systems; Health Care Sciences & Services;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Health Care Sciences & Services; Medical Informatics
GA 053LP
UT WOS:000238306900002
PM 16777785
DA 2022-08-23
ER

PT J
AU Irani, Z
   Sharif, A
   Kamal, MM
   Love, PED
AF Irani, Zahir
   Sharif, Amir
   Kamal, Muhammad Mustafa
   Love, Peter E. D.
TI Visualising a knowledge mapping of information systems investment
   evaluation
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge management; Knowledge mapping; Knowledge components; Supply
   chain management; IS investment evaluation; Expert Systems
ID CRITICAL SUCCESS FACTORS; ORGANIZATIONAL FIT; MANAGEMENT-PRACTICES; ERP
   IMPLEMENTATION; ENTERPRISE SYSTEMS; E-GOVERNMENT; TECHNOLOGY; MODEL;
   FRAMEWORK; JUSTIFICATION
AB Information systems (IS) facilitate organisations to increase responsiveness and reduce the costs of their supply chain. This paper seeks to make a contribution through exploring and visualising knowledge mapping from the perspective of IS investment evaluation. The evaluation of IS is regarded as a challenging and complex process, which becomes even more difficult with the increased complexity of IS. The intricacy of IS evaluation, however, is due to numerous interrelated factors (e.g. costs, benefits and risks) that have human or organisational dimensions. With this in mind, there appears to be an increasing need to assess investment decision-making processes, to better understand the often far-reaching implications associated with technology adoption and interrelated knowledge components (KC). Through the identification and extrapolation of key learning issues from the literature and empirical findings, organisations can better improve their business processes and thereby their effectiveness and efficiency, while preventing others from making costly oversights that may not necessarily be only financial. In seeking to enlighten the often obscure evaluation of IS investments, this paper attempts to inductively emphasise the dissemination of knowledge and learning through the application of a fuzzy Expert System (ES) based knowledge mapping technique (i.e. Fuzzy Cognitive Map [FCM]). The rationale for exploring knowledge and IS investment evaluation is that a knowledge map will materialise for others to exploit during their specific technology evaluation. This is realised through conceptualising the explicit and tacit investment drivers. Among the several findings drawn from this research, the key resulting knowledge mapping through FCM demonstrated the complex, multifaceted and emergent behaviour of causal relationships within the knowledge area. The principal relationships and knowledge within IS investment evaluation are illustrated as being determined by a blend of managerial and user perspectives. (C) 2013 Elsevier Ltd. All rights reserved.
C1 [Irani, Zahir; Sharif, Amir] Brunel Univ, Brunel Business Sch, Uxbridge UB8 3PH, Middx, England.
   [Irani, Zahir] Brunel Univ, Sch Profess Dev, Uxbridge UB8 3PH, Middx, England.
   [Kamal, Muhammad Mustafa] Brunel Univ, Uxbridge UB8 3PH, Middx, England.
   [Love, Peter E. D.] Curtin Univ, Sch Built Environm, Perth, WA, Australia.
RP Irani, Z (通讯作者)，Brunel Univ, Brunel Business Sch, Uxbridge UB8 3PH, Middx, England.
EM zahir.irani@brunel.ac.uk; Amir.Sharif@brunel.ac.uk;
   Muhammad.Kamal@brunel.ac.uk; P.Love@curtine.edu.au
RI Sharif, Amir M./AAX-4672-2020; Love, Peter/D-7418-2017
OI Sharif, Amir M./0000-0003-4293-7929; Love, Peter/0000-0002-3239-1304
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NR 94
TC 30
Z9 32
U1 3
U2 119
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD JAN
PY 2014
VL 41
IS 1
BP 105
EP 125
DI 10.1016/j.eswa.2013.07.015
PG 21
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 242CK
UT WOS:000326214800011
OA Green Accepted, Green Submitted
DA 2022-08-23
ER

PT J
AU Menaouer, B
   Nada, M
AF Menaouer, Brahami
   Nada, Matta
TI The relationship between knowledge mapping and the open innovation
   process: the case of education system
SO AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND
   MANUFACTURING
LA English
DT Article
DE BKMDM method; data source; knowledge management; knowledge mapping;
   knowledge visualization; open innovation process; project memory
ID MANAGEMENT
AB In an increasingly competitive economic environment, innovation has become an invaluable asset to the organization and for effective knowledge management (KM). Nowadays, organizations are knowledge based and their success and survival depend on creativity, diversity, and innovation. A knowledge map is a vital tool for better KM and innovation. To this effect, the innovation processes on KMin education system through knowledge transfer activities will facilitate the shift from teaching as knowledge transmission to teaching as learning facilitation. In this context, we present a new approach based on, the one hand, the critical knowledge mapping being based on an extraction work of the expert knowledge and on the other hand, the description of the conceptual framework design which allows one to exploit and integrate knowledge capitalized and external knowledge by the open innovation process. In addition, this practice makes it possible to examine how KM, in particular, the knowledge mapping, can be used to establish the flow of the internal and external information in order to increase the efficiency of creativity and invention.
C1 [Menaouer, Brahami] Natl Polytech Sch Oran, Syst Engn Dept, BP 1523 El Mnaouer, Oran 31000, Algeria.
   [Nada, Matta] Univ Technol Troyes, Lab ICD TechC, Troyes, France.
RP Menaouer, B (通讯作者)，Natl Polytech Sch Oran, Syst Engn Dept, BP 1523 El Mnaouer, Oran 31000, Algeria.
EM menaouer.brahami@enp-oran.dz
RI Menaouer, BRAHAMI/AAE-4845-2019
OI Menaouer, BRAHAMI/0000-0003-0045-9797
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NR 51
TC 1
Z9 1
U1 0
U2 4
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA
SN 0890-0604
EI 1469-1760
J9 AI EDAM
JI AI EDAM-Artif. Intell. Eng. Des. Anal. Manuf.
PD FEB
PY 2020
VL 34
IS 1
SI SI
BP 17
EP 29
AR PII S0890060419000325
DI 10.1017/S0890060419000325
PG 13
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Engineering, Multidisciplinary;
   Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA LN1OD
UT WOS:000532713900003
DA 2022-08-23
ER

PT J
AU Park, I
   Yoon, B
AF Park, Inchae
   Yoon, Byungun
TI Exploring Potential fusion Disciplines by Developing a Keyword-based
   Research Knowledge Map
SO INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL
LA English
DT Article
DE fusion research; knowledge map; fusion keyword; network analysis;
   potential fusion discipline
AB The phenomenon of fusion among technologies, disciplines, and industries is emerging in diverse fields across the globe. However, many pieces of fusion research have tended to focus on technologies, while fusion research in other academic fields has rarely been conducted. Furthermore, the subject matter of existing fusion research has largely been chosen according to the relevant experts' opinions and intuitions. This paper aims to evaluate the status of fusion research and to explore potential fusion disciplines by developing a keyword-based research knowledge map. Therefore, exploring potential fusion disciplines without the expert's intuition can help governmental policy makers to identify promising fusion disciplines, and provide insight regarding new fusion research areas for scientists.
C1 [Park, Inchae; Yoon, Byungun] Dongguk Univ Seoul, Dept Ind & Syst Engn, Seoul 100715, South Korea.
RP Yoon, B (通讯作者)，Dongguk Univ Seoul, Dept Ind & Syst Engn, 3-26 Pil Dong, Seoul 100715, South Korea.
EM inchae.j.park@gmail.com; postman3@dongguk.edu
RI Yoon, Byungun/AAC-6608-2020
OI Yoon, Byungun/0000-0002-1110-4011
FU National Research Foundation of Korea (NRF); Ministry of Education,
   Science and Technology [2009-0073258]
FX This work is supported by basic science research program through the
   National Research Foundation of Korea (NRF) funded the Ministry of
   Education, Science and Technology (NO. 2009-0073258).
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   Roco M.C., 2003, CONVERGING TECHNOLOG
NR 6
TC 0
Z9 0
U1 0
U2 11
PU INT INFORMATION INST
PI TOKYO
PA FAC ENG, HOSEI UNIV, KOGANEI, TOKYO, 184-8584, JAPAN
SN 1343-4500
J9 INFORMATION-TOKYO
JI Information
PD JUN
PY 2012
VL 15
IS 6
SI SI
BP 2473
EP 2478
PG 6
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 965MS
UT WOS:000305772500018
DA 2022-08-23
ER

PT J
AU Lai, JY
   Wang, CT
   Chou, CY
AF Lai, Jung-Yu
   Wang, Chao-Te
   Chou, Chun-Yi
TI How knowledge map fit and personalization affect success of KMS in
   high-tech firms
SO TECHNOVATION
LA English
DT Article
DE Effectiveness; IS success; Knowledge management systems (KMS); Knowledge
   map fit; Personalization; User satisfaction
ID INFORMATION-SYSTEMS SUCCESS; USER SATISFACTION; MANAGEMENT; MODEL;
   TECHNOLOGY; ACCEPTANCE; BARRIERS; DELONE
AB The shift from it product-based to a knowledge-based economy has resulted in all increasing demand for organizations to implement knowledge management systems (KMS) at in accelerating pace. However, factors influencing success of KMS have seldom been empirically examined by prior research, particularly how knowledge map fit and personalization influence employee satisfaction with KMS, which is it surrogate measure of the success/effectiveness of information systems (IS). Results from a sample of 133 employees, mostly from four international high-tech companies in the Hsin-Chu Science-based Industrial Park in Taiwan, help us better understand what factors affect employee satisfaction with KMS. The result shows that KMS with a higher level of knowledge map fit and personalization will satisfy employees directly or indirectly through the mediation effects of increased perceptions of case of use and usefulness of KMS. Our findings could serve as useful references for researchers and practitioners interested in investigating issues related to the successful implementation of KMS. (C) 2008 Elsevier Ltd. All rights reserved.
C1 [Lai, Jung-Yu; Wang, Chao-Te; Chou, Chun-Yi] Natl Chung Hsing Univ, Inst Management Technol, Taichung 402, Taiwan.
RP Lai, JY (通讯作者)，Natl Chung Hsing Univ, Inst Management Technol, 250 Kuo Kuang Rd, Taichung 402, Taiwan.
EM jylai@nchu.edu.tw
RI 范, 京/H-3271-2016
FU National Science Council of Taiwan [NSC 96-2416-H-005-008-MY3]
FX The author thanks the Editor-in-Chief. Dr Jonathan Won, and anonymous
   reviewers for their assistance in editing the paper. Moreover, the
   author would like to thank the National Science Council of Taiwan for
   financially supporting this research under Contract No. NSC
   96-2416-H-005-008-MY3.
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NR 75
TC 33
Z9 35
U1 0
U2 38
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-4972
EI 1879-2383
J9 TECHNOVATION
JI Technovation
PD APR
PY 2009
VL 29
IS 4
BP 313
EP 324
DI 10.1016/j.technovation.2008.10.007
PG 12
WC Engineering, Industrial; Management; Operations Research & Management
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics; Operations Research & Management
   Science
GA 429EZ
UT WOS:000264905000006
DA 2022-08-23
ER

PT J
AU Hola, B
   Sawicki, M
   Skibniewski, M
AF Hola, Bozena
   Sawicki, Marek
   Skibniewski, Miroslaw
TI AN IT MODEL OF A KNOWLEDGE MAP WHICH SUPPORTS MANAGEMENT IN SMALL AND
   MEDIUM-SIZED COMPANIES USING SELECTED POLISH CONSTRUCTION ENTERPRISES AS
   AN EXAMPLE
SO JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
LA English
DT Article
DE civil engineering; knowledge management; knowledge Map; process
   approach; investment process; enterprise
ID TACIT KNOWLEDGE; PERFORMANCE; SYSTEM; INFORMATION
AB Knowledge is one of the most important resources of an enterprise. Skillful knowledge management can bring significant benefits including labour productivity growth, an increase in quality of provided services and also a strengthening of the competitive position of an enterprise. This paper presents an IT model of a knowledge Map which supports knowledge management in a construction enterprise. The model was developed on the basis of the results of surveys and analyses carried out in small and medium sized construction enterprises in Poland. The process approach in management was included in the proposed model and it involves analysis of an enterprise activity as a set of processes. The model contains processes occurring at enterprise level and also in the widely considered investment process in the construction industry. This assumption made the proposed knowledge Map universal and able to be applied in construc- tion enterprises operating in both the design and executive field.
C1 [Hola, Bozena; Sawicki, Marek] Wroclaw Univ Technol, Fac Civil Engn, Ul Wybrzeze Wyspianskiego 27, PL-50370 Wroclaw, Poland.
   [Skibniewski, Miroslaw] Univ Maryland, Dept Civil & Environm Engn, College Pk, MD 20742 USA.
RP Hola, B (通讯作者)，Wroclaw Univ Technol, Fac Civil Engn, Ul Wybrzeze Wyspianskiego 27, PL-50370 Wroclaw, Poland.
EM bozena.hola@pwr.wroc.pl
RI Skibniewski, Miroslaw J./P-5310-2018
OI Hola, Bozena/0000-0001-6630-8065; Skibniewski,
   Miroslaw/0000-0002-7102-753X
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NR 60
TC 19
Z9 20
U1 2
U2 34
PU VILNIUS GEDIMINAS TECH UNIV
PI VILNIUS
PA SAULETEKIO AL 11, VILNIUS, LT-10223, LITHUANIA
SN 1392-3730
EI 1822-3605
J9 J CIV ENG MANAG
JI J. Civ. Eng. Manag.
PD NOV 17
PY 2015
VL 21
IS 8
BP 1014
EP 1026
DI 10.3846/13923730.2015.1030865
PG 13
WC Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA CX3FG
UT WOS:000365580900004
OA hybrid
DA 2022-08-23
ER

PT J
AU Suthers, DD
   Vatrapu, R
   Medina, R
   Joseph, S
   Dwyer, N
AF Suthers, Daniel D.
   Vatrapu, Ravi
   Medina, Richard
   Joseph, Samuel
   Dwyer, Nathan
TI Beyond threaded discussion: Representational guidance in asynchronous
   collaborative learning environments
SO COMPUTERS & EDUCATION
LA English
DT Article
DE computer-mediated communication; cooperative/collaborative learning;
   human-computer interface; knowledge maps; representational guidance
AB Although most online learning environments are predominately text based, researchers have argued that representational support for the conceptual structure of a problem would address problems of coherence and convergence that have been shown to be associated with threaded discussions and more effectively support collaborative knowledge construction. The study described in this paper sets out to investigate the merits of knowledge mapping representations as an adjunct to or replacement for threaded discussion in problem solving by asynchronously communicating dyads. Results show that users of knowledge maps created more hypotheses earlier in the experimental sessions and elaborated on them more than users of threaded discussions. Participants using knowledge maps were more likely to converge on the same conclusion and scored significantly higher on post-test questions that required integration of information distributed across dyads in a hidden profile design, suggesting that there was greater collaboration during the session. These results were most consistent when a knowledge map with embedded notes was the primary means of interaction rather than when it augmented a threaded discussion.
   The paper also offers a methodological contribution: a paradigm for practical experimental study of asynchronous collaboration. It is crucial to understand how to support collaborative knowledge construction in the asynchronous settings prevalent in online learning, yet prior experimental research has focused on face-to-face and synchronous collaboration due to the pragmatic problems of conducting controlled studies of asynchronous interaction. A protocol is outlined that enables study of asynchronous collaboration in a controlled setting. (C) 2006 Elsevier Ltd. All rights reserved.
C1 [Suthers, Daniel D.; Vatrapu, Ravi; Medina, Richard; Joseph, Samuel; Dwyer, Nathan] Univ Hawaii Manoa, Dept Informat & Comp Sci, Honolulu, HI 96822 USA.
RP Suthers, DD (通讯作者)，Univ Hawaii Manoa, Dept Informat & Comp Sci, 1680 E W Rd,POST 309, Honolulu, HI 96822 USA.
EM suthers@hawaii.edu; vatrapu@hawaii.edu; rmedina@hawaii.edu;
   srjoseph@hawaii.edu; ndwyer@hawaii.edu
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NR 71
TC 98
Z9 98
U1 2
U2 35
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 MAY
PY 2008
VL 50
IS 4
BP 1103
EP 1127
DI 10.1016/j.compedu.2006.10.007
PG 25
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 300BE
UT WOS:000255798500001
DA 2022-08-23
ER

PT J
AU Park, I
   Lee, K
   Yoon, B
AF Park, Inchae
   Lee, Keeeun
   Yoon, Byungun
TI Exploring Promising Research Frontiers Based on Knowledge Maps in the
   Solar Cell Technology Field
SO SUSTAINABILITY
LA English
DT Article
DE bibliographic analysis; patent map; research frontier; solar cell
   technology
ID DESIGN PATENT MAP; COCITATION ANALYSIS; SCIENCE MAPS; INNOVATION;
   CITATION; REPRESENTATIONS; EVOLUTION; NETWORKS; PATTERNS; SECTOR
AB Given the challenging environmental issues in the energy sector, the importance of strategic research and development (R&D) planning has been emphasized to manage a turbulent business situation. This study aims to propose a methodology for exploring promising research frontiers in the energy sector. To this end, first, core documents are collected from scientific documents such as patents and academic papers. Second, the research frontiers are extracted by clustering the core documents based on the bibliographic relations. Third, a knowledge map is generated by mapping the relations between research frontiers. Finally, the promising research frontiers (RFs) are identified by conducting dynamic analyses and the contents of promising RFs are suggested. As an illustration of the method, the field of solar cell technology is chosen and analyzed As a result, the promising research frontiers from the patent knowledge map are related to development (D) themes and promising research frontiers from scientific paper knowledge map are related to the research (R) themes. The proposed method and results can be utilized by researchers, R&D policy makers, and administrations in practice.
C1 [Park, Inchae; Lee, Keeeun; Yoon, Byungun] Dongguk Univ Seoul, Dept Ind & Syst Engn, Seoul 100715, South Korea.
RP Yoon, B (通讯作者)，Dongguk Univ Seoul, Dept Ind & Syst Engn, 30,Pildong Ro 1 Gil, Seoul 100715, South Korea.
EM inchae@dongguk.edu; kelee@dongguk.edu; postman3@dongguk.edu
RI Yoon, Byungun/AAC-6608-2020; Lee, Sungjoo/R-4279-2019
OI Yoon, Byungun/0000-0002-1110-4011
FU National Research Foundation of Korea Grant - Korean Government
   [NRF-2014R1A1A2054892]
FX This work was supported by the National Research Foundation of Korea
   Grant funded by the Korean Government (NRF-2014R1A1A2054892).
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NR 59
TC 8
Z9 10
U1 6
U2 45
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD OCT
PY 2015
VL 7
IS 10
BP 13660
EP 13689
DI 10.3390/su71013660
PG 30
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 CV5RI
UT WOS:000364328300036
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Li, J
   Tan, X
   Hu, YH
AF Li, Jun
   Tan, Xu
   Hu, Yanhui
TI Research on the framework of intelligent classroom based on artificial
   intelligence
SO INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION
LA English
DT Article; Early Access
DE Artificial intelligence; intelligent classroom; learner modeling;
   knowledge graph
AB The emergence of artificial intelligence has greatly improved the imbalance of resource allocation in different regions of China, and promoted education equity. Video is an important form of learning resources in the field of education. Educational video analysis and innovative application based on artificial intelligence technology greatly promote the innovation of education and teaching, and promote the deep integration of artificial intelligence and education. In this paper, firstly, the system architecture is introduced, secondly, the model of education knowledge map which can establish the mapping relationship among knowledge, problem and ability is proposed, and finally, the evaluation index of intelligent classroom is designed. Intelligent classroom based on artificial intelligence technology is helpful to enhance interactive learning and promote the construction of intelligent campus.
C1 [Li, Jun] Hunan Inst Traff Engn, Acad Hi Tech Res, Hengyang, Peoples R China.
   [Tan, Xu] Shenzhen Inst Informat Technol, Sch Software Engn, Shenzhen 518172, Peoples R China.
   [Hu, Yanhui] Yanshan Univ, Sch Econ & Management, Qinhuangdao, Hebei, Peoples R China.
RP Tan, X (通讯作者)，Shenzhen Inst Informat Technol, Sch Software Engn, Shenzhen 518172, Peoples R China.
EM tanx@sziit.edu.cn
FU Natural Science Foundation of Hunan Province [2020JJ5149, 2020JJ5150];
   Pengcheng Scholar Funded Scheme; Key Project of Shenzhen Science and
   Technology Basic Research Plan [JCYJ20200109141218676]; Innovation Team
   of Guangdong Provincial Department of Education [2018KCXTD031]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This
   research work is supported by the Natural Science Foundation of Hunan
   Province (2020JJ5149, 2020JJ5150), the Pengcheng Scholar Funded Scheme,
   the Key Project of Shenzhen Science and Technology Basic Research Plan
   (JCYJ20200109141218676), and the Innovation Team of Guangdong Provincial
   Department of Education (2018KCXTD031).
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NR 27
TC 1
Z9 1
U1 22
U2 48
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0020-7209
EI 2050-4578
J9 INT J ELEC ENG EDUC
JI Int. J. Elec. Eng. Educ.
AR 0020720920984000
DI 10.1177/0020720920984000
EA JAN 2021
PG 10
WC Education, Scientific Disciplines; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA RD6VM
UT WOS:000633612900001
DA 2022-08-23
ER

PT J
AU Kelm, K
   Bolduc, FV
AF Kelm, Karen
   Bolduc, Francois V.
TI How Knowledge Mapping Can Bridge the Communication Gap Between
   Caregivers and Health Professionals Supporting Individuals With Complex
   Medical Needs: A Study in Fragile X Syndrome
SO FRONTIERS IN PSYCHIATRY
LA English
DT Review
DE Fragile X syndrome; care mapping; communication; mental model; medical
   complexity; knowledge mapping; concept mapping
ID NEURODEVELOPMENTAL DISORDERS; MOTHERS PERSPECTIVES; CARE COORDINATION;
   DECISION-MAKING; MENTAL MODELS; DOWN-SYNDROME; CHILDREN; DISABILITIES;
   LITERACY; AUTISM
AB The challenges of caring for children with complex health needs, such as intellectual disability (ID) and autism spectrum disorder (ASD), are multiple and experienced by both caregivers and health professionals. Fragile X syndrome (FXS) is the most common single gene cause of ID and ASD, and provides a pertinent model to understand these complexities of care, as well as the communication challenges experienced between caregivers and healthcare professionals. In recent years both caregivers and healthcare professionals have recognized the need for enhancing communication both in clinical and research settings. Knowledge mapping has emerged as a tool to support quality communication between team participants. Here we review how differences in mental models, as well as challenges related to health literacy and knowledge transfer can have an impact on communication. Next, we present different knowledge mapping approaches used in complex situations, with a focus on concept maps and care maps. Finally, we highlight the potential benefits and limitations of mapping to improve communication issues related to caring for individuals with FXS and potentially other neurodevelopmental disorders (NDDs).
C1 [Kelm, Karen; Bolduc, Francois V.] Univ Alberta, Dept Pediat, Edmonton, AB, Canada.
   [Bolduc, Francois V.] Univ Alberta, Dept Med Genet, Edmonton, AB, Canada.
   [Bolduc, Francois V.] Univ Alberta, Neurosci & Mental Hlth Inst, Edmonton, AB, Canada.
   [Bolduc, Francois V.] Univ Alberta, Women & Children Hlth Res Inst, Edmonton, AB, Canada.
RP Bolduc, FV (通讯作者)，Univ Alberta, Dept Pediat, Edmonton, AB, Canada.; Bolduc, FV (通讯作者)，Univ Alberta, Dept Med Genet, Edmonton, AB, Canada.; Bolduc, FV (通讯作者)，Univ Alberta, Neurosci & Mental Hlth Inst, Edmonton, AB, Canada.; Bolduc, FV (通讯作者)，Univ Alberta, Women & Children Hlth Res Inst, Edmonton, AB, Canada.
EM fbolduc@ualberta.ca
OI Kelm, Karen/0000-0002-1022-0774
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NR 86
TC 0
Z9 0
U1 1
U2 3
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-0640
J9 FRONT PSYCHIATRY
JI Front. Psychiatry
PD NOV 24
PY 2021
VL 12
AR 731011
DI 10.3389/fpsyt.2021.731011
PG 9
WC Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Psychiatry
GA YA4FH
UT WOS:000738290600001
PM 34899415
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Wang, YF
   Wang, T
   Wang, JH
   Zhou, X
   Gao, M
   Liu, RM
AF Wang, Yanfeng
   Wang, Tao
   Wang, Junhui
   Zhou, Xin
   Gao, Ming
   Liu, Runmin
TI Military Chain: Construction of Domain Knowledge Graph of Kill Chain
   Based on Natural Language Model
SO MOBILE INFORMATION SYSTEMS
LA English
DT Article
AB With the advent of the Big Data era, the specialized data in the kill chain domain has increased dramatically, and the engine-based method of retrieving information can hardly meet the users' need for more accurate answers. The kill chain domain includes four components: control equipment, sensor equipment, strike equipment (weapon and platform), and evaluator equipment, as well as related data which contain a large amount of valuable information such as the parameter information contained in each component. If these fragmented and confusing data are integrated and effective query methods are established, they can help professionals complete the military kill chain knowledge system. The knowledge system constructed in this paper is based on the Neo4j graph database and the US Command simulation system to establish a target-oriented knowledge map of kill chain, aiming to provide data support for the Q&A system. Secondly, in order to facilitate the query, this paper establishes entity and relationship/attribute mining based on the continuous bag-of-words (CBOW) encoding model, bidirectional long short-term memory-conditional random field (BiLSTM-CRF) named entity model, and bidirectional gated recurrent neural network (BiGRU) intent recognition model for Chinese kill chain question and answer; returns the corresponding entity or attribute values in combination with the knowledge graph triad form; and finally constructs the answer return. The constructed knowledge map of the kill chain contains 2767 items (including sea, land, and air), and the number of parameters involved is 30124. The number of model parameters of the deep learning network is 27.9 M for the Q&A system built this time, and the accuracy rate is 85.5% after 200 simulated queries.
C1 [Wang, Yanfeng; Wang, Tao; Zhou, Xin] Natl Univ Def Technol, Coll Syst Engn, Changsha 410082, Peoples R China.
   [Wang, Junhui] Natl Univ Def Technol, Coll Comp, Changsha 410082, Peoples R China.
   [Gao, Ming] Wuhan Sports Univ, Coll Sports Sci & Technol, Wuhan 430079, Peoples R China.
   [Liu, Runmin] Wuhan Sports Univ, Coll Sports Engn & Informat Technol, Wuhan 430079, Hubei, Peoples R China.
RP Wang, JH (通讯作者)，Natl Univ Def Technol, Coll Comp, Changsha 410082, Peoples R China.
EM wangyanfeng@nudt.edu.cn; wangtao1976@nudt.edu.cn;
   wangjunhui@nudt.edu.cn; zhouxin09@nudt.edu.cn; gaoming418@whsu.edu.cn;
   shirley@whsu.edu.cn
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NR 26
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 1574-017X
EI 1875-905X
J9 MOB INF SYST
JI Mob. Inf. Syst.
PD JUL 14
PY 2022
VL 2022
AR 7097385
DI 10.1155/2022/7097385
PG 11
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 3H3OK
UT WOS:000831948000004
OA gold
DA 2022-08-23
ER

PT J
AU Shakiba, A
   Hooshmandasl, MR
AF Shakiba, Ali
   Hooshmandasl, Mohammad Reza
TI Neighborhood system S-approximation spaces and applications
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE S-approximation spaces; Neighborhood systems; Three-way decisions;
   Partial monotonicity; Two universal approximation; Multigranulation
   rough set theory
ID ROUGH SET-THEORY; GRANULATION
AB In this paper, we will study neighborhood system S-approximation spaces, i.e., combination of S-approximation spaces with identical elements except that they have different knowledge mappings, e.g., the knowledge mappings differ due to different experimental conditions and/or sampling methodology. In such situations, there is a risk of contradictory knowledge sets which can lead to different decisions by the same query. These situations are studied in this paper in detail. Moreover, neighborhood system S-approximation spaces are investigated from a three-way decisions viewpoint with respect to different deciders. In addition, completeness results are shown for optimistic and pessimistic neighborhood system S-approximation spaces, i.e., these constructions can be represented by an ordinary S-approximation space. Also, the concept of knowledge significance is proposed and studied in detail, and we have shown that computing a minimal set of knowledge mappings for a neighborhood system S-approximation space is -hard. Finally, the paper is concluded by two illustrative medical examples.
C1 [Shakiba, Ali; Hooshmandasl, Mohammad Reza] Yazd Univ, Dept Comp Sci, Yazd, Iran.
   [Shakiba, Ali; Hooshmandasl, Mohammad Reza] Yazd Univ, Lab Quantum Informat Proc, Yazd, Iran.
RP Hooshmandasl, MR (通讯作者)，Yazd Univ, Dept Comp Sci, Yazd, Iran.; Hooshmandasl, MR (通讯作者)，Yazd Univ, Lab Quantum Informat Proc, Yazd, Iran.
EM ali.shakiba@stu.yazd.ac.ir; hooshmandasl@yazd.ac.ir
RI Hooshmandasl, Mohammad Reza/AAA-6999-2022; Shakiba, Ali/J-6420-2016
OI Hooshmandasl, Mohammad Reza/0000-0002-3834-3610; Shakiba,
   Ali/0000-0002-2253-1166
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NR 54
TC 7
Z9 7
U1 0
U2 6
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0219-1377
EI 0219-3116
J9 KNOWL INF SYST
JI Knowl. Inf. Syst.
PD NOV
PY 2016
VL 49
IS 2
BP 749
EP 794
DI 10.1007/s10115-015-0913-9
PG 46
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DY6BG
UT WOS:000385190100012
DA 2022-08-23
ER

PT J
AU Woo, JH
   Clayton, MJ
   Johnson, RE
   Flores, BE
   Ellis, C
AF Woo, JH
   Clayton, MJ
   Johnson, RE
   Flores, BE
   Ellis, C
TI Dynamic Knowledge Map: reusing experts' tacit knowledge in the AEC
   industry
SO AUTOMATION IN CONSTRUCTION
LA English
DT Article; Proceedings Paper
CT Annual Conference of the
   Association-for-Computer-Aided-Design-In-Architecture (ACADIA 2002)
CY 2002
CL Pomona, CA
SP Assoc Comp Aided Design Architecture
DE Internet; Dynamic Knowledge Map; AEC
AB Much knowledge in the Architecture, Engineering and Construction (AEC) industry is experience-based and tacit. Nevertheless, the typical strategy for knowledge management is focused on computer-based approaches for capturing and disseminating explicit knowledge. AEC firms have been successful at collecting and storing explicit information in enterprise databases, but they are poor at knowledge retrieval and exchange. Consequently, AEC professionals find it difficult to reuse core experts' knowledge for highly knowledge-intensive AEC activities. This situation calls for a method for disseminating tacit knowledge from experts' brains to achieve higher quality AEC projects.
   The primary purpose of this paper is to set a theoretical foundation for clarifying the contribution of experts' tacit knowledge in the AEC industry. The secondary purpose is to describe the concept for prototype software, Dynamic Knowledge Map, that can assist in the reuse of experts' tacit knowledge. Dynamic Knowledge Map is a Web-based knowledge navigator that searches for experts and facilitates communication with those experts by using internet technology. Higher performance levels theoretically can be achieved while accelerating the knowledge transfer processes. Future research will test the suitability of Dynamic Knowledge Map for tacit knowledge utilization in AEC organizations. (C) 2003 Elsevier B.V. All rights reserved.
C1 Texas A&M Univ, Dept Architecture, College Stn, TX 77843 USA.
RP Woo, JH (通讯作者)，Texas A&M Univ, Dept Architecture, College Stn, TX 77843 USA.
EM jwoo@tamu.edu
RI Wang, Charles/B-5565-2011
OI Wang, Charles/0000-0001-9331-8437
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NR 17
TC 111
Z9 115
U1 5
U2 48
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0926-5805
J9 AUTOMAT CONSTR
JI Autom. Constr.
PD MAR
PY 2004
VL 13
IS 2
BP 203
EP 207
DI 10.1016/j.autcon.2003.09.003
PG 5
WC Construction & Building Technology; Engineering, Civil
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA 804TM
UT WOS:000220321000008
DA 2022-08-23
ER

PT J
AU Gao, Q
   Wang, DX
   Gao, F
AF Gao, Qin
   Wang, Dunxing
   Gao, Fan
TI Impact of Knowledge Representations on Problem-Oriented Learning in
   Online Environments
SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
LA English
DT Article
ID NATURAL-SCIENCE COURSES; RELATIONAL KNOWLEDGE; MAPS; LEARNERS; STUDENTS;
   DESIGN; SYSTEM; NAVIGATION; STRATEGY; SKILLS
AB The objective of this study was to investigate the impact of knowledge representations on problem-oriented learning in online learning environments. The study compared the impact of knowledge map representation with traditional hierarchical representation with regard to learning memory and problem-solving performance. Twenty-nine students participated in an experiment in which they studied online materials with the goal of solving two programming problems (simple and complex). It was found that participants who used the hierarchical representation read in the depth-first sequence, whereas participants who used the knowledge map representation read in a sequence reflecting the system running mechanism implied by the graphical representation. In addition, participants who used the knowledge map representation had better memory of the learning content, especially about relations between knowledge nodes. When solving the complex problem, participants who used the knowledge map representation made a deeper analysis of the problem and had better problem-solving performance. These results were not significant in the simple problem-solving task.
C1 [Gao, Qin; Wang, Dunxing] Tsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China.
   [Gao, Fan] Qyer Com, Beijing, Peoples R China.
RP Gao, Q (通讯作者)，Tsinghua Univ, Dept Ind Engn, Room 610,Shunde Bldg, Beijing 100084, Peoples R China.
EM gaoqin@tsinghua.edu.cn
RI Gao, Qin/D-4595-2009
OI Gao, Qin/0000-0003-2436-5410; Wang, Dunxing/0000-0002-9138-3793
FU National Natural Science Foundation of China [71401087]; Beijing Higher
   Education Young Elite Teacher Project
FX This study was supported by the National Natural Science Foundation of
   China (Project no. 71401087) and Beijing Higher Education Young Elite
   Teacher Project.
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NR 72
TC 4
Z9 4
U1 3
U2 19
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1044-7318
EI 1532-7590
J9 INT J HUM-COMPUT INT
JI Int. J. Hum.-Comput. Interact.
PY 2015
VL 31
IS 12
BP 922
EP 938
DI 10.1080/10447318.2015.1072788
PG 17
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA CY5AG
UT WOS:000366419600007
DA 2022-08-23
ER

PT J
AU Fionda, V
   Gutierrez, C
   Pirro, G
AF Fionda, Valeria
   Gutierrez, Claudio
   Pirro, Giuseppe
TI Building knowledge maps of Web graphs
SO ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Maps of the web; Navigation; Web of data; Linked data; Semantic web
ID MINIMUM SPANNING-TREES
AB We research the problem of building knowledge maps of graph-like information. There exist well-consolidated cartographic principles and techniques for mapping physical landscapes. However, we live in the digital era and similarly to the Earth, the Web is simply too large and its interrelations too complex for anyone to grasp much of it through direct observation. Thus, the problem of applying cartographic principles also to digital landscapes is intriguing. We introduce a mathematical formalism that captures the general notion of map of a graph and enables its development and manipulation in a semi automated way. We present an implementation of our formalism on the Web of Linked Data graph and discuss algorithms that efficiently generate and combine (via an algebra) regions and maps. We present the MaGe tool, implementing the map framework, and discuss examples of knowledge maps. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Fionda, Valeria] Univ Calabria, DeMaCS, I-87030 Commenda Di Rende, Italy.
   [Gutierrez, Claudio] Univ Chile, DCC, Santiago, Chile.
   [Pirro, Giuseppe] CNR, ICAR, Arcavacata Di Rende, CS, Italy.
RP Pirro, G (通讯作者)，CNR, ICAR, Arcavacata Di Rende, CS, Italy.
EM fionda@matunical.it; cgutierr@dcc.uchile.cl; pirro@icar.cnr.it
RI Pirrò, Giuseppe/AAF-1993-2019; Pirrò, Giuseppe/AAZ-4500-2020
OI Pirrò, Giuseppe/0000-0002-7499-5798; 
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NR 57
TC 11
Z9 12
U1 0
U2 42
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0004-3702
EI 1872-7921
J9 ARTIF INTELL
JI Artif. Intell.
PD OCT
PY 2016
VL 239
BP 143
EP 167
DI 10.1016/j.artint.2016.07.003
PG 25
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DV5XS
UT WOS:000383004600005
DA 2022-08-23
ER

PT J
AU Zhang, C
   Zhou, GH
   Lu, Q
   Chang, FT
AF Zhang, Chao
   Zhou, Guanghui
   Lu, Qi
   Chang, Fengtian
TI Graph-based knowledge reuse for supporting knowledge-driven
   decision-making in new product development
SO INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
LA English
DT Article
DE knowledge-driven decision-making; knowledge reuse; knowledge management;
   ontologies; knowledge map; knowledge navigation; new product development
ID SEMANTIC RETRIEVAL; DESIGN; ONTOLOGY; MAINTENANCE; INFORMATION;
   MANAGEMENT
AB Pre-existing knowledge buried in manufacturing enterprises can be reused to help decision-makers develop good judgements to make decisions about the problems in new product development, which in turn speeds up and improves the quality of product innovation. This paper presents a graph-based approach to knowledge reuse for supporting knowledge-driven decision-making in new product development. The paper first illustrates the iterative process of knowledge-driven decision-making in new product development. Then, a novel framework is proposed to facilitate this process, where knowledge maps and knowledge navigation are involved. Here, OWL ontologies are employed to construct knowledge maps, which appropriately capture and organise knowledge resources generated at various stages of product lifecycle; the Personalised PageRank algorithm is used to perform knowledge navigation, which finds the most relevant knowledge in knowledge maps for a given problem in new product development. Finally, the feasibility and effectiveness of the proposed approach are demonstrated through a case study and two performance evaluation experiments.
C1 [Zhang, Chao; Zhou, Guanghui; Lu, Qi; Chang, Fengtian] Xi An Jiao Tong Univ, Sch Mech Engn, Xian, Shaanxi, Peoples R China.
   [Zhou, Guanghui] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian, Shaanxi, Peoples R China.
RP Zhou, GH (通讯作者)，Xi An Jiao Tong Univ, Sch Mech Engn, Xian, Shaanxi, Peoples R China.; Zhou, GH (通讯作者)，Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian, Shaanxi, Peoples R China.
EM ghzhou@mail.xjtu.edu.cn
RI Lu, Qi/AAA-9829-2019
OI Zhang, Chao/0000-0001-8260-1210
FU Innovation Method Fund of China [2015IM020600]; Science and Technology
   Programme of Guangdong of China [2010A080408007]
FX This work was supported by the Innovation Method Fund of China [grant
   number 2015IM020600]; and Science and Technology Programme of Guangdong
   of China [grant number 2010A080408007].
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NR 33
TC 39
Z9 42
U1 19
U2 96
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0020-7543
EI 1366-588X
J9 INT J PROD RES
JI Int. J. Prod. Res.
PY 2017
VL 55
IS 23
BP 7187
EP 7203
DI 10.1080/00207543.2017.1351643
PG 17
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA FT4OJ
UT WOS:000423134100018
DA 2022-08-23
ER

PT J
AU SANDOVAL, J
AF SANDOVAL, J
TI TEACHING IN SUBJECT-MATTER AREAS - SCIENCE
SO ANNUAL REVIEW OF PSYCHOLOGY
LA English
DT Review
DE CONCEPTUAL CHANGE; INTEREST; INSTRUCTION
ID LEARNING SCIENTIFIC CONCEPTS; ADVANCE ORGANIZERS; KNOWLEDGE MAPS;
   META-ANALYSIS; STUDENTS; INSTRUCTION; ANALOGY; RECALL; ACHIEVEMENT;
   EDUCATION
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NR 80
TC 26
Z9 26
U1 1
U2 8
PU ANNUAL REVIEWS INC
PI PALO ALTO
PA 4139 EL CAMINO WAY, PO BOX 10139, PALO ALTO, CA 94303-0139
SN 0066-4308
J9 ANNU REV PSYCHOL
JI Annu. Rev. Psychol.
PY 1995
VL 46
BP 355
EP 374
DI 10.1146/annurev.ps.46.020195.002035
PG 20
WC Psychology; Psychology, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Psychology
GA QF077
UT WOS:A1995QF07700014
DA 2022-08-23
ER

PT J
AU Buniyamin, N
   Barber, KD
AF Buniyamin, N
   Barber, KD
TI The intranet: a platform for knowledge management system based on
   knowledge mapping
SO INTERNATIONAL JOURNAL OF TECHNOLOGY MANAGEMENT
LA English
DT Article
DE knowledge management systems; knowledge mapping; knowledge management
   platform; intranet
ID PERFORMANCE
AB This paper presents a discussion based on a literature review and a case study on the suitability of using an intranet as a platform to implement Knowledge Management System (KMS). A description of Knowledge Management (KM) and the current research carried out in this area, with examples of web-based KMS systems currently implemented in organisations, are presented. Further, this paper then describes how knowledge mapping of an organisation's intranet as a form of a KMS can be used to promote the re-utilisation of knowledge, which will contribute to the competitiveness of the organisation. A case study that illustrates and presents evidence of the need and suitability of such a system is provided. The paper ends with a proposal for future research to be carried out in this area.
C1 Univ Manchester, Total Technol Ctr, Manchester M60 1QD, Lancs, England.
RP Buniyamin, N (通讯作者)，Univ Manchester, Total Technol Ctr, POB 88, Manchester M60 1QD, Lancs, England.
EM N.Buniyamin@postgrad.umist.ac.uk; Kevin.Barber@umist.ac.uk
OI Buniyamin, Norlida/0000-0002-4256-6809
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NR 43
TC 13
Z9 13
U1 0
U2 21
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
   GENEVA, SWITZERLAND
SN 0267-5730
EI 1741-5276
J9 INT J TECHNOL MANAGE
JI Int. J. Technol. Manage.
PY 2004
VL 28
IS 7-8
BP 729
EP 746
DI 10.1504/IJTM.2004.005780
PG 18
WC Engineering, Multidisciplinary; Management; Operations Research &
   Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics; Operations Research & Management
   Science
GA 890DE
UT WOS:000226491100007
DA 2022-08-23
ER

PT J
AU Zins, C
AF Zins, Chaim
TI Knowledge map of information science
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID SCHOLARLY CLASSIFICATIONS; DOMAIN ANALYSIS; BEGTHOL 2003; NAIVE; FIELD
AB This article is part of a group of four articles that resulted from a Critical Delphi study conducted in 2003-2005. The study, "Knowledge Map of Information Science," was aimed at exploring the foundations of information science. The international panel was composed of 57 leading scholars from 16 countries who represent nearly all the major subfields and important aspects of the field. This article presents a systematic and comprehensive knowledge map of the field, and is grounded on the panel discussions. The map has 10 basic categories: (1) Foundations, (2) Resources, (3) Knowledge Workers, (4) Contents, (5) Applications, (6) Operations and Processes, (7) Technologies, (8) Environments, (9) Organizations, and (10) Users. The model establishes the groundwork for formulating theories of information science, as well as developing and evaluating information science academic programs and bibliographic resources.
C1 Knowledge Mapping Res, IL-97852 Jerusalem, Israel.
RP Zins, C (通讯作者)，Knowledge Mapping Res, 26 Hahaganah St, IL-97852 Jerusalem, Israel.
EM chaim.zins@gmail.com
RI huang, jane/C-5019-2009
OI Buckland, Michael K./0000-0002-1527-8350
CR Beghtol C, 2004, KNOWL ORGAN, V31, P62
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NR 23
TC 45
Z9 51
U1 3
U2 67
PU JOHN WILEY & SONS INC
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN, NJ 07030 USA
SN 1532-2882
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD FEB 15
PY 2007
VL 58
IS 4
BP 526
EP 535
DI 10.1002/asi.20505
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 166LF
UT WOS:000246379600007
DA 2022-08-23
ER

PT J
AU Zhang, BY
   Qi, Q
   Li, LG
AF Zhang, Baoyu
   Qi, Qian
   Li, Linguo
TI Influence of Ideological and Political Education Strategies on College
   Students' Entrepreneurship Based on Wireless Network and Artificial
   Intelligence Knowledge Map
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
ID DESIGN
AB Technological advancements have paved the path for utilizing artificial intelligence and wireless networks in every field. In this study, people will research the ideological and political education (IPE) strategies based on wireless networks and artificial intelligence knowledge maps on college students' entrepreneurship. IPE deals with a country's political status, forming a social and economic study. This education utilizes wireless network technology and artificial intelligence for teaching and learning. The proposed system studies IPE strategies that use artificial intelligence and wireless networks in a combined way of intelligent wireless network (IWN) in a knowledge map on college students' entrepreneurship. The idea is then adapted to the college student's entrepreneurship and found efficient. Recursive algorithms (RA) call themselves out by lowering input quantities and returning values for the input signal by performing basic processes in a repeated iteration. Artificial intelligence utilizes recursion to develop entrepreneurship skills among college students. The proposed algorithm is compared with the traditional remote supervision algorithm (RSA), and the study results that RA has obtained an accuracy of 98%.
C1 [Zhang, Baoyu; Li, Linguo] Fuyang Normal Univ, Fuyang 236041, Anhui, Peoples R China.
   [Qi, Qian] Beijing Univ Posts & Telecommun, Beijing 100876, Haidian, Peoples R China.
RP Qi, Q (通讯作者)，Beijing Univ Posts & Telecommun, Beijing 100876, Haidian, Peoples R China.
EM qiqian@bupt.edu.cn
FU Anhui Province University Excellent Young Talent Support Program
   Project; Quality Engineering Project of Higher Education Institutions in
   Anhui Province [2019kfkc322]; Natural Science Foundation of Anhui
   Province [1908085MF207]
FX This paper is supported by the Anhui Province University Excellent Young
   Talent Support Program Project (No. gxyq2019172), Quality Engineering
   Project of Higher Education Institutions in Anhui Province (No.
   2019kfkc322), and the Natural Science Foundation of Anhui Province under
   Grant (No. 1908085MF207).
CR Arshad M, 2019, MANAGE DECIS, V57, P649, DOI 10.1108/MD-04-2016-0248
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NR 21
TC 0
Z9 0
U1 1
U2 1
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD MAY 21
PY 2022
VL 2022
AR 5726099
DI 10.1155/2022/5726099
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1V3WT
UT WOS:000806025000004
OA gold
DA 2022-08-23
ER

PT J
AU Su, N
   Wang, ZB
AF Su, Na
   Wang, Zhenbo
TI Visual Analysis of Global Carbon Mitigation Research Based on Scientific
   Knowledge Graphs
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE scientific knowledge graphs; carbon mitigation; bibliometrics; hot spot
   analysis; trend analysis
ID LAND-USE; CLIMATE; EMISSIONS; BIOCHAR; SOIL; CO2
AB Global temperature change is related to the destiny of all mankind, and carbon mitigation, as well as greenhouse gases control, are key points. In order to explore the basic knowledge, research hotspots and trends in global carbon mitigation research, this paper, based on 15,304 carbon mitigation articles from Web of Science, from 1991 to 2021, conducts spatio-temporal distribution, country distribution, research hotspots and cooperation network analysis, and draws frontier knowledge graphs of carbon mitigation by using CiteSpace, Gephi and other scientific knowledge mapping and literature analysis software. The key scholars, important literature, main contribution institutions and countries/regions in the field of carbon mitigation research were extracted, and the research basis and evolution law were displayed. The study found that: (1) The research on carbon mitigation is increasing every year, which can be roughly divided into four stages: germination, low-speed development, medium-speed development and high-speed development. (2) The spatial distribution of carbon mitigation research is unbalanced, mainly showing a characteristic of "one super and many strong" centered on the United States. (3) The research hotspots of carbon mitigation have gradually evolved from phenomenon analysis, policy guidance, method exploration to mechanism improvement. Further research might focus on ocean carbon sink, carbon trading and carbon-negative technology.
C1 [Su, Na; Wang, Zhenbo] Chinese Acad Sci, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China.
   [Wang, Zhenbo] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China.
RP Wang, ZB (通讯作者)，Chinese Acad Sci, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China.; Wang, ZB (通讯作者)，Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China.
EM 18366186920@163.com; wangzb@igsnrr.ac.cn
OI Wang, Zhenbo/0000-0001-5342-0613
FU National Natural Science Foundation of China [42130713]
FX This research was funded by the National Natural Science Foundation of
   China, grant number 42130713.
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NR 33
TC 0
Z9 0
U1 23
U2 23
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD MAY
PY 2022
VL 19
IS 9
AR 5766
DI 10.3390/ijerph19095766
PG 15
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA 1E7PI
UT WOS:000794675200001
PM 35565156
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Fu, QJ
   Kuang, SZ
AF Fu, Qijun
   Kuang, Shouzhong
TI Mind Map Construction for English Grammar Teaching Based on Knowledge
   Map
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
ID LAYOUT OPTIMIZATION; SYSTEM
AB The analysis of the frontier issues of the English language teaching method in China is of great guidance for English language teaching. Based on the ontology model of English teaching domain, the knowledge map of English teaching in colleges and universities is constructed by fusing heterogeneous English subject data from multiple sources. Firstly, we obtain domain knowledge from relevant websites and existing documents through web crawlers and other techniques and clean the data based on BERT model; then, we use Word2Vec to judge the similarity between the research directions of characters and solve the entity alignment problem; based on the scientific knowledge map theory, we count the frequency of keywords in each year and analyze them to describe the association and union between keywords. It can explain the current situation and trend, rise and fall, disciplinary growth points, and breakthroughs of ELT. Through keyword analysis, the hot issues mainly revolve around ELT, English teaching, college English, grammar-translation method, curriculum reform, and so forth, to realize the quick query and resource statistics of ELT basic data, in order to promote the subsequent English discipline assessment work to be completed more efficiently.
C1 [Fu, Qijun; Kuang, Shouzhong] Hunan Univ Informat & Technol, Gen Educ Sch, Changsha 410151, Hunan, Peoples R China.
RP Kuang, SZ (通讯作者)，Hunan Univ Informat & Technol, Gen Educ Sch, Changsha 410151, Hunan, Peoples R China.
EM fuqijun2021@163.com; kuangshouzhong@nudt.edu.cn
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NR 26
TC 1
Z9 1
U1 8
U2 10
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD DEC 15
PY 2021
VL 2021
AR 4921825
DI 10.1155/2021/4921825
PG 10
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA XY9RZ
UT WOS:000737303300002
OA gold
DA 2022-08-23
ER

PT J
AU Yang, B
   Sun, Y
AF Yang, Bo
   Sun, Ying
TI An exploration of link-based knowledge map in academic web space
SO SCIENTOMETRICS
LA English
DT Article
DE Web knowledge map; Subject-based community; Link analysis; Knowledge
   discovery
ID COMMUNITY STRUCTURE; COLLABORATION; VISUALIZATION; SITES
AB The World Wide Web has become an important source of academic information. The linking feature of the Web has been used to study the structure of academic web, as well as the presence of academic and research institutes on the Web. In this paper, we propose an integrated model for exploring the subject macrostructure of a specific academic topic on the Web and automatically depicting the knowledge map that is closer to what a domain expert would expect. The model integrates a hyperlink-induced topic search (HITS)-based link network extending strategy and a semantic based clustering algorithm with the aid of co-link analysis and social network analysis (SNA) to discover subject-based communities in the academic web space. We selected to use websites as analytical units rather than web pages because of the subject stability of a website. Compared with traditional techniques in Webometrics and SNA that have been used for such analyses, our model has the advantages of working on open web space (capability to explore unknown web resources and identify important ones) and of automatically building an extendable and hierarchical web knowledge map. The experiment in the area of Information Retrieval shows the effectiveness of the integrated model in analyzing and portraying of subject clustering phenomenon in academic web space.
C1 [Yang, Bo] Nanjing Agr Univ, Dept Informat Management, Nanjing, Jiangsu, Peoples R China.
   [Sun, Ying] SUNY Buffalo, Dept Lib & Informat Studies, Buffalo, NY 14260 USA.
RP Yang, B (通讯作者)，Nanjing Agr Univ, Dept Informat Management, Weigang 1, Nanjing, Jiangsu, Peoples R China.
EM mail.boyang@gmail.com; sun3@buffalo.edu
FU Fund of Humanities and Social Sciences from the Ministry of Education of
   China [11YJC870030]
FX This research has been supported by the Fund of Humanities and Social
   Sciences from the Ministry of Education of China under Grant No.
   11YJC870030 ("Research on building of Web knowledge map based on
   community discovery"). The authors would like to thank Professor
   Dagobert Soergel from University at Buffalo for his great suggestions to
   this paper.
CR [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
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NR 32
TC 9
Z9 9
U1 1
U2 117
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUL
PY 2013
VL 96
IS 1
BP 239
EP 253
DI 10.1007/s11192-012-0919-y
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 164IB
UT WOS:000320401500014
DA 2022-08-23
ER

PT J
AU Zhang, S
   Zou, H
   Sun, J
AF Zhang, Shuo
   Zou, Hua
   Sun, Jian
TI Knowledge Mapping Analysis of Manufacturing Product Innovation Based on
   CiteSpace
SO JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
LA English
DT Article
DE Product innovation; CiteSpace; knowledge mapping; manufacturing
ID MARKET ORIENTATION; FIRM PERFORMANCE; EMPIRICAL-EVIDENCE; TECHNOLOGY;
   BUSINESS; NETWORKS; DEMAND; IMPACT; CAPABILITY; VENTURES
AB Manufacturing product innovation is increasingly showing a green, low-carbon, systematic ecological innovation development trend. In this paper, using CiteSpace analysis method and assisted by BICOMB2 software, the literature of CNKI (1992-2019) and WOS (1995-2019) database was analyzed to construct the knowledge mapping of manufacturing product innovation, and the research findings were as follows: (i) The research is mainly concentrated in China, the United States, the United Kingdom, Germany and other countries. The number of Chinese scholars' research is the largest, but the American scholars' research is the first in novelty and prospective, and its sigma value is as high as 294.17. (ii) In recent years, Shanghai Jiaotong University, Xi'an University of Technology, Hunan University and other research institutions have recently produced more research results on product innovation. Among them, Shanghai Jiaotong University can be ranked first. (ii) "Green product innovation" and "product innovation performance" can continue to be the hot spots for future manufacturing product innovation and development.
C1 [Zhang, Shuo; Zou, Hua; Sun, Jian] Shenyang Univ Technol, Shenyang 110870, Peoples R China.
   [Sun, Jian] Shenyang Polytech Coll, Sch Business Adm, Shenyang 110000, Peoples R China.
RP Zhang, S (通讯作者)，Shenyang Univ Technol, Shenyang 110870, Peoples R China.
EM zhangshuo1820124@sut.edu.cn
OI shuo, zhang/0000-0002-3340-5771
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NR 57
TC 1
Z9 1
U1 28
U2 28
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-1266
EI 1793-6454
J9 J CIRCUIT SYST COMP
JI J. Circuits Syst. Comput.
PD MAY 15
PY 2022
VL 31
IS 07
AR 2250121
DI 10.1142/S0218126622501213
PG 26
WC Computer Science, Hardware & Architecture; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA 0Q3AY
UT WOS:000784796300002
DA 2022-08-23
ER

PT J
AU Huang, SL
   Lin, SC
   Chan, YC
AF Huang, Shiu-Li
   Lin, Sheng-Cheng
   Chan, Yung-Chun
TI Investigating effectiveness and user acceptance of semantic social
   tagging for knowledge sharing
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Knowledge management system; Knowledge map; Ontology; Social tagging;
   Technology acceptance
ID MANAGEMENT; SYSTEM; INFORMATION; MAPS
AB Social tagging systems enable users to assign arbitrary tags to various digital resources. However, they face vague-meaning problems when users retrieve or present resources with the keyword-based tags. In order to solve these problems, this study takes advantage of Semantic Web technology and the topological characteristics of knowledge maps to develop a system that comprises a semantic tagging mechanism and triple-pattern and visual searching mechanisms. A field experiment was conducted to evaluate the effectiveness and user acceptance of these mechanisms in a knowledge sharing context. The results show that the semantic social tagging system is more effective than a keyword-based system. The visualized knowledge map helps users capture an overview of the knowledge domain, reduce cognitive effort for the search, and obtain more enjoyment. Traditional keyword tagging with a keyword search still has the advantage of ease of use and the users had higher intention to use it. This study also proposes directions for future development of semantic social tagging systems. (C) 2011 Elsevier Ltd. All rights reserved.
C1 [Huang, Shiu-Li] Natl Taipei Univ, Dept Business Adm, New Taipei City 23741, Taiwan.
   [Lin, Sheng-Cheng] Tunghai Univ, Dept Informat Management, Taichung 40704, Taiwan.
   [Chan, Yung-Chun] Ming Chuan Univ, Dept Informat Management, Gwei Shan Dist 333, Taoyuan County, Taiwan.
RP Huang, SL (通讯作者)，Natl Taipei Univ, Dept Business Adm, 151 Univ Rd, New Taipei City 23741, Taiwan.
EM slhuang@mail.ntpu.edu.tw; sclin@thu.edu.tw; jacky08102728@hotmail.com
RI Huang, Shiu-Li/CAF-0381-2022
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NR 55
TC 19
Z9 19
U1 1
U2 76
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD JUL
PY 2012
VL 48
IS 4
BP 599
EP 617
DI 10.1016/j.ipm.2011.07.004
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 957NY
UT WOS:000305170900001
DA 2022-08-23
ER

PT J
AU Gomez, A
   Moreno, A
   Pazos, J
   Sierra-Alonso, A
AF Gomez, A
   Moreno, A
   Pazos, J
   Sierra-Alonso, A
TI Knowledge maps: An essential technique for conceptualisation
SO DATA & KNOWLEDGE ENGINEERING
LA English
DT Article
DE conceptual models; conceptual modelling; knowledge maps
AB The process of conceptualisation is a fundamental problem-solving activity and, hence: is an essential activity for solving the problem of software systems construction. This paper first analyses the process of conceptualisation generally, that is, not as applied specifically to software systems, and establishes a general-purpose conceptualisation process, composed of three activities: analysis, synthesis and holistic testing. A proposed instantiation of this framework for the process of conceptualisation in knowledge-based systems (KBS) construction is then presented. The paper focuses on an activity that is frequently overlooked in conceptualisation, that is, holistic testing, and on a technique that is proposed to address this phase, known as the knowledge map (KM). This technique integrates the static and dynamic perspectives of the reasoning employed by the expert to solve the problem. This paper discusses the foundations and an application of this technique. (C) 2000 Published by Elsevier Science B.V. All rights reserved.
C1 Univ Carlos III Madrid, Escuela Politecn Super, Madrid 28911, Spain.
   Univ Politecn Madrid, Fac Informat, E-28660 Madrid, Spain.
RP Sierra-Alonso, A (通讯作者)，Univ Carlos III Madrid, Escuela Politecn Super, Avda Univ 30,C Butarque 15, Madrid 28911, Spain.
RI Moreno, Ana M./AAC-1166-2020; Sierra-Alonso, Almudena/H-7040-2015;
   Gomez-Perez, Asuncion/I-9382-2012
OI Moreno, Ana M./0000-0001-8397-6794; Sierra-Alonso,
   Almudena/0000-0002-2409-4617; Gomez-Perez, Asuncion/0000-0002-3037-0331
CR Ares J, 1998, KNOWL-BASED SYST, V11, P87, DOI 10.1016/S0950-7051(98)00048-3
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NR 9
TC 51
Z9 57
U1 1
U2 19
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0169-023X
J9 DATA KNOWL ENG
JI Data Knowl. Eng.
PD MAY
PY 2000
VL 33
IS 2
BP 169
EP 190
DI 10.1016/S0169-023X(99)00050-6
PG 22
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 299HR
UT WOS:000086193400004
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Zhu, HP
   Tian, F
   Wu, K
   Shah, N
   Chen, Y
   Ni, YF
   Zhang, XH
   Chao, KM
   Zheng, QH
AF Zhu, Haiping
   Tian, Feng
   Wu, Ke
   Shah, Nazaraf
   Chen, Yan
   Ni, Yifu
   Zhang, Xinhui
   Chao, Kuo-Ming
   Zheng, Qinghua
TI A multi-constraint learning path recommendation algorithm based on
   knowledge map
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE E-learning; Knowledge map; Learning scenario; Learning path
   recommendation
ID SEQUENTIAL PATTERN
AB It is difficult for e-learners to make decisions on how to learn when they are facing with a large amount of learning resources, especially when they have to balance available limited learning time and multiple learning objectives in various learning scenarios. This research presented in this paper addresses this challenge by proposing a new multi-constraint learning path recommendation algorithm based on knowledge map. The main contributions of the paper are as follows. Firstly, two hypotheses on e-learners' different learning path preferences for four different learning scenarios (initial learning, usual review, pre-exam learning and pre-exam review) are verified through questionnaire-based statistical analysis. Secondly, according to learning behavior characteristics of four types of the learning scenarios, a multi constraint learning path recommendation model is proposed, in which the variables and their weighted coefficients considers different learning path preferences of the learners in different learning scenarios as well as learning resource organization and fragmented time. Thirdly, based on the proposed model and knowledge map, the design and implementation of a multi-constraint learning path recommendation algorithm is described. Finally, it is shown that the questionnaire results from over 110 e-learners verify the effectiveness of the proposed algorithm and show the similarity between the learners' self-organized learning paths and the recommended learning paths. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Zhu, Haiping; Wu, Ke; Chen, Yan; Ni, Yifu; Zhang, Xinhui; Zheng, Qinghua] Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian, Shaanxi, Peoples R China.
   [Zhu, Haiping; Tian, Feng; Chen, Yan; Ni, Yifu; Zheng, Qinghua] Xi An Jiao Tong Univ, Natl Engn Lab Big Data Analyt, Xian 710049, Peoples R China.
   [Wu, Ke] Xi An Jiao Tong Univ, Shaanxi Prov Key Lab Satellite & Terr Network Tec, R&D, Xian 710049, Peoples R China.
   [Shah, Nazaraf; Chao, Kuo-Ming] Coventry Univ, Dept Comp, Coventry CV1 2JH, W Midlands, England.
RP Tian, F (通讯作者)，Xi An Jiao Tong Univ, Natl Engn Lab Big Data Analyt, Xian 710049, Peoples R China.
EM fengtian@mail.xjtu.edu.cn
OI Chao, Kuo-Ming/0000-0002-4501-9739; Shah, Nazaraf/0000-0002-4140-8200
FU National Key Research and Development Program of China [2016YFB1000903];
   Innovative Research Group of the National Natural Science Foundation of
   China [61721002]; Innovation Research Team of Ministry of Education
   [IRT_17R86]; National Science Foundation of China [61472315, 61402392,
   61428206, 61532015, 61532004]; project of China Knowledge Centre for
   Engineering Science and Technology; Natural Science Basic Research Plan
   in Shaanxi Province of China [2016JM6027, 2016JM6080]; Online Education
   Research Foundation of MOE Research Center for Online Education
   [2016YB165, 2016YB169]
FX It is a project supported by the National Key Research and Development
   Program of China under Grant No. 2016YFB1000903, Innovative Research
   Group of the National Natural Science Foundation of China (61721002),
   Innovation Research Team of Ministry of Education (IRT_17R86), the
   National Science Foundation of China under Grant No. 61472315, 61402392,
   61428206, 61532015 and 61532004, the project of China Knowledge Centre
   for Engineering Science and Technology, the Natural Science Basic
   Research Plan in Shaanxi Province of China under Grant No. 2016JM6027,
   2016JM6080, Online Education Research Foundation of MOE Research Center
   for Online Education under Grant No. 2016YB165, 2016YB169.
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NR 32
TC 38
Z9 41
U1 9
U2 81
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD MAR 1
PY 2018
VL 143
BP 102
EP 114
DI 10.1016/j.knosys.2017.12.011
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA FW3II
UT WOS:000425199600010
OA Green Published
DA 2022-08-23
ER

PT J
AU Cheng, YT
   Chuang, HM
   Pei, C
AF Cheng, Yu-Tien
   Chuang, Huan-Ming
   Pei, Chun
TI Risk management of developing assistive devices for elderly
SO ARCHIVES OF GERONTOLOGY AND GERIATRICS
LA English
DT Article
DE Assistive devices; New product development; Knowledge maps; Mind mapping
AB This study uses the Delphi method in interviewing experts to identify critical risks in the development of assistive devices. Critical risks included product planning, technology development, production, performance, schedule management, and cost management, comprising 26 risk factors in 6 constructs. This study determined which factors were of high importance, finding a total of 7 market analysis errors. Mind mapping was used to create a knowledge map; and layer expansion was used to understand risk distribution to facilitate risk mitigation and monitoring. Organizational risk strategies could be developed to reduce risk and maintain stability while achieving the objective of developing new products. (C) 2010 Elsevier Ireland Ltd. All rights reserved.
C1 [Pei, Chun] Nan Kai Univ Technol, Grad Sch Geront Technol & Serv Management, Tsaotun 54243, Nantou County, Taiwan.
   [Cheng, Yu-Tien; Chuang, Huan-Ming] Natl Yunlin Univ Sci & Technol, Dept Informat Management, Touliu 64002, Yunlin County, Taiwan.
RP Pei, C (通讯作者)，Nan Kai Univ Technol, Grad Sch Geront Technol & Serv Management, 568 Chung Cheng Rd, Tsaotun 54243, Nantou County, Taiwan.
EM pantonpei@nkut.edu.tw
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NR 27
TC 5
Z9 5
U1 0
U2 11
PU ELSEVIER IRELAND LTD
PI CLARE
PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND
SN 0167-4943
EI 1872-6976
J9 ARCH GERONTOL GERIAT
JI Arch. Gerontol. Geriatr.
PD MAY-JUN
PY 2011
VL 52
IS 3
BP E145
EP E151
DI 10.1016/j.archger.2010.10.001
PG 7
WC Geriatrics & Gerontology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Geriatrics & Gerontology
GA 743BC
UT WOS:000288989400010
PM 21035205
DA 2022-08-23
ER

PT J
AU Li, XL
   Chen, MR
   Xie, GQ
   Jiang, YR
AF Li, Xiulai
   Chen, Mingrui
   Xie, Gengquan
   Jiang, Yirui
TI Design of Knowledge Map Construction Based on Convolutional Neural
   Network
SO INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Knowledge map; ontology; CNN
AB With the advent of the Web era, data has exploded, with tens of thousands of textual data being generated every day. Traditional text sentiment analysis methods are mainly based on lexicon and machine learning-based methods. These methods show certain limitations as the data size increases. Here, we propose a new knowledge map design method based on convolutional neural network. For the academic literature data crawled from HowNet and Baidu Academic Website with the theme of computer, the corresponding ontology database, the fusion application of multiple data sources, and the mapping of different ontology libraries through data fusion are constructed for data sources in different fields. The global ontology library then uses the entity alignment and entity link methods for knowledge acquisition and fusion. Finally, the convolutional neural network is used for training and testing. The experimental results show that the subject search task can not only obtain the book through the convolution network, the effective academic literature in the question bank can also be used to obtain the relevance of the keyword in the search results, which verifies the effectiveness of the method.
C1 [Li, Xiulai; Chen, Mingrui; Xie, Gengquan; Jiang, Yirui] Hainan Univ, Haikou, Hainan, Peoples R China.
   [Li, Xiulai] Chuang Technol Co Ltd, Haikou, Hainan, Peoples R China.
RP Chen, MR (通讯作者)，Hainan Univ, Haikou, Hainan, Peoples R China.
EM lixiulai@hainu.edu.cn; 17786967707@163.com; 171615358@qq.com;
   864694132@qq.com
FU  [20167238];  [ZDYF2016136]
FX This work was supported by the funding project of A study on multiple
   mapping fusion Chinese and English automatic word alignment system based
   on multi-preprocessing mechanism that takes the establishment of
   parallel corpus on the Chinese-English translation website of Hainan
   tourism text as an example (No. 20167238) and the funding project of
   Construction and application of female health system in Hainan (No.
   ZDYF2016136).
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NR 29
TC 0
Z9 0
U1 0
U2 21
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 NOV
PY 2019
VL 33
IS 12
AR 1951008
DI 10.1142/S021800141951008X
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JR6HE
UT WOS:000499723300005
DA 2022-08-23
ER

PT J
AU Li, ST
   Chang, WC
AF Li, Sheng-Tun
   Chang, Won-Chen
TI DESIGN AND EVALUATION OF A LAYERED THEMATIC KNOWLEDGE MAP SYSTEM
SO JOURNAL OF COMPUTER INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge map; Knowledge management; Visualization; Hierarchical
   self-organizing map network; Text mining
ID VECTOR-SPACE MODEL; SELF-ORGANIZATION; TREE; VISUALIZATIONS; EXPLORATION
AB Knowledge maps (K-maps) have been recognized as an expressive, efficient and effective way to enhance access and navigation of information in a large knowledge repository. However, its capability is limited by the lack of a way to systematically discover the hidden structure of codified knowledge and compactly visualize the map morphology to improve effective navigation. This paper presents a new hybrid approach to tackle these issues and proposes a layered thematic K-map system. The knowledge objects arc firstly categorized and labeled according to their hidden conceptual architecture using a hierarchical self-organizing map network and then thematically navigated through a spatially expandable stacked view with corresponding information. The system is tested with a real-world soil remediation patent corpus with various map topologies. A pair-wise evaluation consisting of clustering performance and usability assessment was conducted. Compared with a typical file/directory view, the results of usability evaluation showed that the proposed system enhances the efficiency of comparison tasks while preserving comparable performance of identification and association tasks.
C1 [Li, Sheng-Tun; Chang, Won-Chen] Natl Cheng Kung Univ, Tainan 701, Taiwan.
RP Li, ST (通讯作者)，Natl Cheng Kung Univ, Tainan 701, Taiwan.
FU NSC, Taiwan [NSC95-2416-H-006-039]
FX This study was supported in part by NSC95-2416-H-006-039, Taiwan, R.O.C.
   The authors would like to thank Uranus Huang, who assisted us in
   integrating the IT tools and developing the prototype system for this
   study. Appreciation also goes to the anonymous reviewers for their
   constructive comments.
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NR 57
TC 1
Z9 1
U1 0
U2 10
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0887-4417
EI 2380-2057
J9 J COMPUT INFORM SYST
JI J. Comput. Inf. Syst.
PD WIN
PY 2008
VL 49
IS 2
BP 92
EP 103
PG 12
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 400AY
UT WOS:000262841600011
DA 2022-08-23
ER

PT J
AU Ji, YL
   Zhan, Y
   Yang, Y
   Xu, X
   Shen, FM
   Shen, HT
AF Ji, Yanli
   Zhan, Yue
   Yang, Yang
   Xu, Xing
   Shen, Fumin
   Shen, Heng Tao
TI A Context Knowledge Map Guided Coarse-to-Fine Action Recognition
SO IEEE TRANSACTIONS ON IMAGE PROCESSING
LA English
DT Article
DE Human action recognition; multi-modal fusion; coarse-to-fine
   classification; knowledge map
ID FUSION
AB Human actions involve a wide variety and a large number of categories, which leads to a big challenge in action recognition. However, according to similarities on human body poses, scenes, interactive objects, human actions can be grouped into some semantic groups, i.e. sports, cooking, etc. Therefore, in this paper, we propose a novel approach which recognizes human actions from coarse to fine. Taking full advantage of contributions from high-level semantic contexts, a context knowledge map guided recognition method is designed to realize the coarse-to-fine procedure. In the approach, we define semantic contexts with interactive objects, scenes and body motions in action videos, and build a context knowledge map to automatically define coarse-grained groups. Then fine-grained classifiers are proposed to realize accurate action recognition. The coarse-to-fine procedure narrows action categories in target classifiers, so it is beneficial to improving recognition performance. We evaluate the proposed approach on the CCV, the HMDB-51, and the UCF101 database. Experiments verify its significant effectiveness, on average, improving more than 5 of recognition precisions than current approaches. Compared with the state-of-the-art, it also obtains outstanding performance. The proposed approach achieves higher accuracies of 93.1, 95.4 and 74.5 in the CCV, the UCF-101 and the HMDB51 database, respectively.
C1 [Ji, Yanli; Zhan, Yue; Yang, Yang; Xu, Xing; Shen, Fumin; Shen, Heng Tao] Univ Elect Sci & Technol China, Ctr Future Media, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China.
RP Shen, HT (通讯作者)，Univ Elect Sci & Technol China, Ctr Future Media, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China.
EM yanliji@uestc.edu.cn; shenhengtao@hotmail.com
RI Shen, Heng Tao/ABD-5331-2021
OI Zhan, Yue/0000-0002-6599-5390
FU Natural Science Foundation of China (NSFC) [61673088, 61305043]; 111
   Project [B17008]
FX This work was supported in part by the Natural Science Foundation of
   China (NSFC) under Grant 61673088 and Grant 61305043 and in part by the
   111 Project under Grant B17008.
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NR 52
TC 16
Z9 16
U1 7
U2 36
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1057-7149
EI 1941-0042
J9 IEEE T IMAGE PROCESS
JI IEEE Trans. Image Process.
PY 2020
VL 29
BP 2742
EP 2752
DI 10.1109/TIP.2019.2952088
PG 11
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA KH6EV
UT WOS:000510744400020
PM 31725381
DA 2022-08-23
ER

PT J
AU Ding, YY
AF Ding, Yuanyi
TI Correlation Analysis Model of Social Capital and Innovation Performance
   Based on Knowledge Mapping
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID COMMUNITIES; RESILIENCE; MANAGEMENT; NETWORKS; SYSTEMS
AB In this paper, the correlation between social capital and innovation performance is analyzed using a knowledge graph approach, and a correlation analysis model is designed for practical use. Given the advantages of knowledge graph technology and the fact that most recommendation models fail to make full use of the correlation between knowledge graph and recommended items, this paper proposes a cross-attention fusion-based knowledge graph recommendation algorithm (CAKR). The CAKR model contains a cross-fusion module, a recommendation task module, and a knowledge graph embedding task module, and the inputs of the two task modules are alternately learned in a low-dimensional space by the cross-fusion module. The input of the two task modules is alternately learned in the low-dimensional space by the cross-fusion module to interact with the two embedding vectors of items and entities, and then, the obtained feature vectors are fed into the relevant task modules, respectively, and then, the results are calculated by the respective prediction functions. There is a mediating role of knowledge management capabilities in the innovation ecology perspective between social capital and innovation performance of SMEs. Among them, it fully mediates the relationship dimension of technical social capital, the cognitive dimension of institutional social capital, the relationship and cognitive dimension of market-based social capital, and innovation performance, and partially mediates the structural and cognitive dimension of technical social capital, the structural and relationship dimension of institutional social capital, and innovation performance. The relationship between knowledge management capability and innovation performance is positively mediated by environmental dynamics in the innovation ecology perspective, and the positive effect of knowledge management capability on innovation performance is more significant when the environmental dynamics are stronger, and vice versa. Finally, based on the results of the empirical study, some management suggestions are provided on how SMEs can reasonably utilize social capital to enhance their innovation performance.
C1 [Ding, Yuanyi] Nanjing Audit Univ, Nanjing 211815, Jiangsu, Peoples R China.
RP Ding, YY (通讯作者)，Nanjing Audit Univ, Nanjing 211815, Jiangsu, Peoples R China.
EM 221286@nau.edu.cn
FU Jiangsu Social Science Fund Project "Research on the Mechanism of
   Government Governance and Social Capital on Technological Innovation
   Quality" [20EYD003]
FX This work was supported by research results of Jiangsu Social Science
   Fund Project "Research on the Mechanism of Government Governance and
   Social Capital on Technological Innovation Quality," 20EYD003.
CR [Anonymous], 2015, DIABETES CARE, V38, pS1, DOI 10.2337/dc15-S001
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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 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD JUN 6
PY 2022
VL 2022
AR 2138200
DI 10.1155/2022/2138200
PG 12
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 2I5ZM
UT WOS:000815056400017
PM 35707194
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Choy, SY
   Lee, WB
   Cheung, CF
AF Choy, SY
   Lee, WB
   Cheung, CF
TI A systematic approach for knowledge audit analysis: Integration of
   knowledge inventory, mapping and knowledge flow analysis
SO JOURNAL OF UNIVERSAL COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 4th International Conference on Knowledge Management
CY JUN 30-JUL 02, 2004
CL Graz, AUSTRIA
DE knowledge audit; knowledge inventory; knowledge map; social network
   analysis; knowledge flow analysis
AB Knowledge audit lays a concrete foundation for any knowledge management programs. The central topic of this paper is to integrate various knowledge audit related techniques into pre-audit preparation, in-audit process and post-audit analysis in a systematic manner. Culture assessment, in the form of surveys and radar charts, along with orientation program make up the pre-audit preparation. Structured interviews are carried out to capture process-critical knowledge. Knowledge inventory, knowledge maps and knowledge flow analysis compose of post-audit analysis. Knowledge inventory is then built for stocktaking knowledge assets and thus revealing the key knowledge assets by measuring them against four performance criteria. Knowledge mapping together with social network analysis are to show the knowledge exchange path and make the key knowledge suppliers and customers visible. They are then being further applied into knowledge flow analysis, which serves to reveal the strength and weakness of the current knowledge flow. A case study of applying the designed instruments in the Engineering Division of the Hong Kong Dragon Airlines Limited and the related analysis are also present in this paper.
C1 Hong Kong Polytech Univ, Hong Kong, Hong Kong, Peoples R China.
RP Choy, SY (通讯作者)，Hong Kong Polytech Univ, Hong Kong, Hong Kong, Peoples R China.
EM Eric.choy@inet.polyu.edu.hk; mfwblee@inet.polyu.edu.hk;
   mfbenny@inet.polyu.edu.hk
RI Lee, Wing Bun/P-6257-2014; Fai, Cheung Chi/ABC-1100-2020; Cheung, Chi
   Fai/B-1146-2008
OI Lee, Wing Bun/0000-0002-3413-4029; Fai, Cheung Chi/0000-0002-6066-7419;
   Cheung, Chi Fai/0000-0002-6066-7419
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NR 11
TC 25
Z9 32
U1 0
U2 7
PU GRAZ UNIV TECHNOLGOY, INST INFORMATION SYSTEMS COMPUTER MEDIA-IICM
PI GRAZ
PA INFFELDGASSE 16C, GRAZ, A-8010, AUSTRIA
SN 0948-695X
J9 J UNIVERS COMPUT SCI
JI J. Univers. Comput. Sci.
PY 2004
VL 10
IS 6
BP 674
EP 682
PG 9
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA 958GP
UT WOS:000231433000002
DA 2022-08-23
ER

PT J
AU Dong, JW
   Jing, XW
   Lu, X
   Liu, JF
   Li, HP
   Cao, XW
   Du, CX
   Li, J
   Li, L
AF Dong, Jianwei
   Jing, Xuwen
   Lu, Xiang
   Liu, Jinfeng
   Li, Haipeng
   Cao, Xuwu
   Du, Chenxiao
   Li, Jun
   Li, Lei
TI Process knowledge graph modeling techniques and application methods for
   ship heterogeneous models
SO SCIENTIFIC REPORTS
LA English
DT Article
AB In the process design and reuse of marine component products, there are a lot of heterogeneous models, causing the problem that the process knowledge and process design experience contained in them are difficult to express and reuse. Therefore, a process knowledge representation model for ship heterogeneous model is proposed in this paper. Firstly, the multi-element process knowledge graph is constructed, and the heterogeneous ship model is described in a unified way. Then, the multi-strategy ontology mapping method is applied, and the semantic expression between the process knowledge graph and the entity model is realized. Finally, by obtaining implicit semantics based on case-based reasoning and checking the similarity of the matching results, the case knowledge reuse is achieved, to achieve rapid design of the process. This method provides reliable technical support for the design of ship component assembly and welding process, greatly shortens the design cycle, and improves the working efficiency. In addition, taking the double-deck bottom segment of a ship as an example, the process knowledge map of the heterogeneous model is constructed to realize the rapid design of ship process, which shows that the method can effectively acquire the process knowledge in the design case and improve the efficiency and intelligence of knowledge reuse in the process design of the heterogeneous model of a ship.
C1 [Dong, Jianwei; Jing, Xuwen; Liu, Jinfeng; Cao, Xuwu; Du, Chenxiao; Li, Lei] Jiangsu Univ Sci & Technol, Sch Mech Engn, Zhenjiang 212100, Jiangsu, Peoples R China.
   [Lu, Xiang; Li, Haipeng; Li, Jun] China Merchants Cruise Shipbldg Co LTD, Nantong 226000, Peoples R China.
RP Liu, JF (通讯作者)，Jiangsu Univ Sci & Technol, Sch Mech Engn, Zhenjiang 212100, Jiangsu, Peoples R China.
EM liujinfeng@just.edu.cn
FU National Defense Basic Scientific Research Program [JCKY2018414C002];
   National Natural Science Foundation of China [52075229]; Natural Science
   foundation of the Higher Education Institutions of Jiangsu Province
   [20KJA460009]; High-tech Ship Scientific Research Project of the
   Ministry of Industry and Information Technology [CJ04N20]
FX This work was supported in part by the National Defense Basic Scientific
   Research Program under Grant JCKY2018414C002 and the National Natural
   Science Foundation of China (grants NO. 52075229), and The Natural
   Science foundation of the Higher Education Institutions of Jiangsu
   Province (grants NO. 20KJA460009), and the High-tech Ship Scientific
   Research Project of the Ministry of Industry and Information Technology
   (NO. CJ04N20).
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NR 32
TC 0
Z9 0
U1 11
U2 11
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD FEB 21
PY 2022
VL 12
IS 1
AR 2911
DI 10.1038/s41598-022-06940-y
PG 14
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA ZE7UK
UT WOS:000759084600052
PM 35190625
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Rouse, WB
   Thomas, BS
   Boff, KR
AF Rouse, WB
   Thomas, BS
   Boff, KR
TI Knowledge maps for knowledge mining: Application to R & D/technology
   management
SO IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND
   REVIEWS
LA English
DT Article
DE information systems; knowledge management; R & D management; technology
   management
ID INFORMATION; DESIGNERS
AB Most of the world's archive of knowledge sources remains untouched by those who could greatly benefit from accessing and utilizing this information. The fundamental barriers to access and utilization by these users involve the content and format of this information. These users are not particularly interested in data compilations, research methods, and discussions of related literature. Instead, they are interested in the knowledge available from these sources in terms of concepts and principles that can enhance their planning, decision making, and allocation of resources. This article focuses on meeting these needs via a six-step knowledge mining methodology that relies upon user-oriented knowledge maps, This concept is illustrated in the context of R&D/technology management.
C1 Enterprise Support Syst, Norcross, GA 30092 USA.
   Knowledge Au Courant, Decatur, GA 30030 USA.
   USAF, Res Lab, AF Mat Command, Wright Patterson AFB, OH 45433 USA.
RP Rouse, WB (通讯作者)，Enterprise Support Syst, Norcross, GA 30092 USA.
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   [No title captured]
NR 31
TC 10
Z9 10
U1 0
U2 5
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA
SN 1094-6977
J9 IEEE T SYST MAN CY C
JI IEEE Trans. Syst. Man Cybern. Part C-Appl. Rev.
PD AUG
PY 1998
VL 28
IS 3
BP 309
EP 317
DI 10.1109/5326.704556
PG 9
WC Computer Science, Artificial Intelligence; Computer Science,
   Cybernetics; Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 104JF
UT WOS:000075010400001
DA 2022-08-23
ER

PT J
AU Liu, DR
   Hsu, CY
AF Liu, DR
   Hsu, CY
TI Project-based knowledge maps: combining project mining and XML-enabled
   topic maps
SO INTERNET RESEARCH-ELECTRONIC NETWORKING APPLICATIONS AND POLICY
LA English
DT Article
DE knowledge management; extensible markup language; topic maps; Internet;
   data handling
ID MANAGEMENT; SYSTEM; AGENT
AB Many enterprises implement various business projects on the Internet in the global knowledge economy. The task of managing distributed and heterogeneous project knowledge is very important in increasing the knowledge assets of enterprises. Accordingly, this work presents a project-based knowledge map system to properly organize project knowledge into topic maps, from which users can obtain in-depth concepts to facilitate further project development. A two-phase data mining approach involving the ISO/ISEC 13250 topic maps and Extensible Markup Language (XML) is used to establish the proposed system, which can determine knowledge patterns from previous projects and transform these patterns into a navigable knowledge map. The map can help users to locate required information and also offers subject-related information easily and rapidly over the Internet.
C1 Natl Chiao Tung Univ, Inst Informat Management, Hsinchu, Taiwan.
   Overseas Chinese Inst Technol, Dept Informat Management, Taichung, Taiwan.
   Overseas Chinese Inst Technol, Inst Informat Management, Taichung, Taiwan.
RP Liu, DR (通讯作者)，Natl Chiao Tung Univ, Inst Informat Management, Hsinchu, Taiwan.
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NR 42
TC 5
Z9 5
U1 1
U2 10
PU EMERALD GROUP PUBLISHING LIMITED
PI BRADFORD
PA 60/62 TOLLER LANE, BRADFORD BD8 9BY, W YORKSHIRE, ENGLAND
SN 1066-2243
J9 INTERNET RES
JI Internet Res.-Electron. Netw. Appl. Policy
PY 2004
VL 14
IS 3
BP 254
EP 266
DI 10.1108/10662240410542689
PG 13
WC Business; Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Business & Economics; Computer Science; Telecommunications
GA 840GS
UT WOS:000222845900007
DA 2022-08-23
ER

PT J
AU Irani, Z
   Sharif, AM
   Love, PED
AF Irani, Z.
   Sharif, A. M.
   Love, P. E. D.
TI Knowledge mapping for information systems evaluation in manufacturing
SO INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
LA English
DT Article
DE information systems; knowledge management; knowledge mapping;
   organizational learning
ID INTEGRATION; MANAGEMENT; GOVERNMENT; SUCCESS
AB The evaluation of information systems is regarded as a complex process due to the large number of interacting factors that culminate in respective costs, benefits and risks that have human or organizational dimensions. With this in mind, there is much need to examine investment decision-making processes to better understand the often far-reaching implications associated with technology adoption and associated knowledge constructs. Through the identification and extrapolation of key learning issues from the literature and empirical findings, organizations can better improve their business processes and thereby their effectiveness, whilst preventing others from making costly mistakes that may not necessarily be purely financial. In seeking to shed light on the often elusive evaluation of information systems, this paper will, through the adoption of a case enquiry research strategy that uses qualitative research methods, present the experiences of a manufacturing organization that sought to evaluate its information system. The authors will inductively show the propagation of knowledge and learning of a company in the manufacturing sector, via the application of a knowledge mapping technique. The rationale for exploring knowledge and information systems evaluation in manufacturing is that through empirically extrapolating explicit and tacit knowledge drivers, a knowledge map will emerge for others to use during their technology evaluation.
C1 Brunel Univ, Sch Informat Syst Comp & Math, Informat Syst Evaluat & Integrat Grp, Uxbridge UB8 3PH, Middx, England.
   Edith Cowan Univ, Sch Management Informat Syst, We B Ctr, Perth, WA 6018, Australia.
RP Irani, Z (通讯作者)，Brunel Univ, Sch Informat Syst Comp & Math, Informat Syst Evaluat & Integrat Grp, Uxbridge UB8 3PH, Middx, England.
EM Zahir.Irani@Brunel.ac.uk
RI Love, Peter/D-7418-2017; Sharif, Amir M./AAX-4672-2020
OI Love, Peter/0000-0002-3239-1304; Sharif, Amir M./0000-0003-4293-7929;
   Irani, Zahir/0000-0002-6332-6118
CR Beynon-Davies P, 2004, LOCAL GOV STUD, V30, P214, DOI 10.1080/0300303042000267245
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NR 37
TC 16
Z9 16
U1 0
U2 14
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0020-7543
EI 1366-588X
J9 INT J PROD RES
JI Int. J. Prod. Res.
PY 2007
VL 45
IS 11
BP 2435
EP 2457
DI 10.1080/00207540601020452
PG 23
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA 168UN
UT WOS:000246549600004
DA 2022-08-23
ER

PT J
AU Rao, L
   Mansingh, G
   Osei-Bryson, KM
AF Rao, Lila
   Mansingh, Gunjan
   Osei-Bryson, Kweku-Muata
TI Building ontology based knowledge maps to assist business process
   re-engineering
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE Business process reengineering; Ontology; Knowledge maps; Knowledge
   management
ID INFORMATION-SYSTEMS; MANAGEMENT; FRAMEWORK; QUALITY; DESIGN
AB Business Process Re-engineering (BPR) is being used to improve the efficiency of the organizational processes, however, a number of obstacles have prevented its full potential from being realised. One of these obstacles is caused by an emphasis on the business process itself at the exclusion of considering other important knowledge of the organization. Another is due to the lack of tools for identifying the cause of the inefficiencies and inconsistencies in BPR. In this paper we propose a methodology for BPR that overcomes these two obstacles through the use of a formal organizational ontology and knowledge structure and source maps. These knowledge maps are represented formally to facilitate an inferencing mechanism which helps to automatically identify the causes of the inefficiencies and inconsistencies. We demonstrate the applicability of this methodology through the use of a case study of a university domain. (C) 2011 Elsevier B.V. All rights reserved.
C1 [Mansingh, Gunjan] Univ W Indies, Dept Comp, Kingston 7, Jamaica.
   [Rao, Lila] Univ W Indies, Mona Sch Business, Kingston 7, Jamaica.
   [Osei-Bryson, Kweku-Muata] Virginia Commonwealth Univ, Sch Business, Richmond, VA USA.
RP Mansingh, G (通讯作者)，Univ W Indies, Dept Comp, Mona Campus, Kingston 7, Jamaica.
EM lilasao@uwimona.edu.jm; gunjan.mansingh@uwimona.edu.jm;
   kmuata@isy.vcu.edu
FU School of Business at Virginia Commonwealth University
FX This research was supported in part by a grant from the 2011 Summer
   Research Program of the School of Business at Virginia Commonwealth
   University.
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   [No title captured]
NR 43
TC 53
Z9 58
U1 2
U2 67
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0167-9236
J9 DECIS SUPPORT SYST
JI Decis. Support Syst.
PD FEB
PY 2012
VL 52
IS 3
BP 577
EP 589
DI 10.1016/j.dss.2011.10.014
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Operations Research & Management Science
GA 897JZ
UT WOS:000300648300003
DA 2022-08-23
ER

PT J
AU Chung, W
   Chen, H
   Nunamaker, JF
AF Chung, W
   Chen, H
   Nunamaker, JF
TI A visual framework for knowledge discovery on the Web: An empirical
   study of business intelligence exploration
SO JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
LA English
DT Article
DE business intelligence; genetic algorithm; knowledge map;
   multidimensional scaling; visualization; Web browsing; Web community
ID CATEGORIZATION; ALGORITHMS
AB Information overload often hinders knowledge discovery on the Web. Existing tools lack analysis and visualization capabilities. Search engine displays often overwhelm users with irrelevant information. This research proposes a visual framework for knowledge discovery on the Web. The framework incorporates Web mining, clustering, and visualization techniques to support effective exploration of knowledge. Two new browsing methods were developed and applied to the business intelligence domain: Web community uses a genetic algorithm to organize Web sites into a tree format; knowledge map uses a multidimensional scaling algorithm to place Web sites as points on a screen. Experimental results show that knowledge map outperformed Kartoo, a commercial search engine with graphical display, in terms of effectiveness and efficiency. Web community was found to be more effective, efficient, and usable than result list. Our visual framework thus helps to alleviate information overload on the Web and offers practical implications for search engine developers.
C1 Univ Texas, Coll Business Adm, Dept Informat & Decis Sci, El Paso, TX 79968 USA.
   Univ Arizona, Eller Coll, Tucson, AZ 85721 USA.
   Univ Arizona, Ctr Management Informat, Tucson, AZ 85721 USA.
RP Chung, W (通讯作者)，Univ Texas, Coll Business Adm, Dept Informat & Decis Sci, El Paso, TX 79968 USA.
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   [No title captured]
NR 39
TC 102
Z9 103
U1 2
U2 78
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0742-1222
EI 1557-928X
J9 J MANAGE INFORM SYST
JI J. Manage. Inform. Syst.
PD SPR
PY 2005
VL 21
IS 4
BP 57
EP 84
DI 10.1080/07421222.2005.11045821
PG 28
WC Computer Science, Information Systems; Information Science & Library
   Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
   Economics
GA 916CG
UT WOS:000228362400005
DA 2022-08-23
ER

PT J
AU Ong, TH
   Chen, HC
   Sung, WK
   Zhu, B
AF Ong, TH
   Chen, HC
   Sung, WK
   Zhu, B
TI Newsmap: a knowledge map for online news
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE knowledge management; knowledge map; online news; neural networks;
   experimental research
ID SELF-ORGANIZING APPROACH; SYSTEM; RETRIEVAL
AB Information technology has made possible the capture and accessing of a large number of data and knowledge bases, which in turn has brought about the problem of information overload. Text mining to turn textual information into knowledge has become a very active research area, but much of the research remains restricted to the English language. Due to the differences in linguistic characteristics and methods of natural language processing, many existing text analysis approaches have yet to be shown to be useful for the Chinese language. This research focuses on the automatic generation of a hierarchical knowledge map NewsMap, based on online Chinese news, particularly the finance and health sections. Whether in print or online, news still represents one important knowledge source that people produce and consume on a daily basis. The hierarchical knowledge map can be used as a tool for browsing business intelligence and medical knowledge hidden in news articles. In order to assess the quality of the map, an empirical study was conducted which shows that the categories of the hierarchical knowledge map generated by NewsMap are better than those generated by regular news readers, both in terms of recall and precision, on the sub-level categories but not on the top-level categories. NewsMap employs an improved interface combining a ID alphabetical hierarchical list and a 2D Self-Organizing Map (SOM) island display. Another empirical study compared the two visualization displays and found that users' performances can be improved by taking advantage of the visual cues of the 2D SOM display, (c) 2004 Elsevier B.V. All rights reserved.
C1 Calif State Univ Sacramento, Coll Business Adm, Dept Management Informat Sci, Sacramento, CA 95819 USA.
   Univ Arizona, Eller Coll Management, Dept Management Informat Syst, Tucson, AZ 85721 USA.
   Boston Univ, Sch Management, Dept Informat Syst, Boston, MA 02215 USA.
RP Ong, TH (通讯作者)，Calif State Univ Sacramento, Coll Business Adm, Dept Management Informat Sci, Sacramento, CA 95819 USA.
EM ongt@csus.edu; hchen@eller.arizona.edu; wai-ki@eller.arizona.edu;
   bzhu@bu.edu
RI Wang, Charles/B-5565-2011
OI Wang, Charles/0000-0001-9331-8437
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   [No title captured], DOI DOI 10.1109/2.222119
   [No title captured]
NR 41
TC 70
Z9 76
U1 1
U2 61
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 JUN
PY 2005
VL 39
IS 4
BP 583
EP 597
DI 10.1016/j.dss.2004.03.008
PG 15
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 925JM
UT WOS:000229049400004
DA 2022-08-23
ER

PT S
AU Bidarra, J
   Dias, A
AF Bidarra, J
   Dias, A
BE Bubak, M
   VanAlbada, GD
   Sloot, PMA
   Dongarra, JJ
TI Ecological strategies and knowledge mapping
SO COMPUTATIONAL SCIENCE - ICCS 2004, PT 3, PROCEEDINGS
SE LECTURE NOTES IN COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 4th International Conference on Computational Science (ICCS 2004)
CY JUN 06-09, 2004
CL Krakow, POLAND
SP Hewlett-Packard, Intel, SGI, ATM, Sun Microsyst, IBM, Polish Airlines LOT, ACC CYFRONET AGH, Inst Comp Sci AGH, Polish Minist Sci Res & Informat Technol, Spring Verlag
AB In this paper we discuss the concept of ecological strategy in relation to cognitive artefacts that reflect the expressive power of learners engaged in the development of significant knowledge paths. Our experiments show that learners strive to make sense of the fragmentary information surrounding them and in this endeavour they may benefit from the creation of knowledge maps, i.e. branched structures of knowledge that depart from a central node, a starting point or a homepage. In the course of creating these fractal hyperspaces on the Web, with integration of varied multimedia materials, the range of cognitive strategies used during the learning process becomes evident, both at individual and collective level. Perhaps ecological strategies may encourage a new research approach regarding the study of knowledge construction within complex hypermedia environments.
C1 Univ Aberta, P-1269001 Lisbon, Portugal.
   Univ Minho, TecMinho, P-4800058 Guimaraes, Portugal.
RP Bidarra, J (通讯作者)，Univ Aberta, Rua Escola Politecn 147, P-1269001 Lisbon, Portugal.
EM bidarra@univ-ab.pt; anadias@tecminho.uminho.pt
RI Bidarra, Jose/M-9115-2013
OI Bidarra, Jose/0000-0002-2082-5996
CR BIDARRA J, 1981, CINTEC 2001
   GAINES BR, 1995, CONCEPTS MAP HYPERME
   Guimaraes N, 2000, INTERACTIVE MULTIMED
   RESNICK M, 2002, THINKING LIKE TREE O
   SALOMON G, 2000, ED MEDIA 2000 MONTR
   Suzan T., 1995, THE MINDMAP BOOK
NR 6
TC 0
Z9 0
U1 0
U2 3
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-22116-6
J9 LECT NOTES COMPUT SC
PY 2004
VL 3038
BP 1025
EP 1029
PG 5
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA BAG08
UT WOS:000222048900132
DA 2022-08-23
ER

PT J
AU Li, Q
   Shen, CQ
   Chen, L
   Zhu, ZK
AF Li, Qi
   Shen, Changqing
   Chen, Liang
   Zhu, Zhongkui
TI Knowledge mapping-based adversarial domain adaptation: A novel fault
   diagnosis method with high generalizability under variable working
   conditions
SO MECHANICAL SYSTEMS AND SIGNAL PROCESSING
LA English
DT Article
DE Adversarial transfer learning; Variable working condition; Fault
   diagnosis; Knowledge mapping; Adversarial domain adaptation
ID CONVOLUTIONAL NEURAL-NETWORK; ROTATING MACHINERY; AUTOENCODER; FUSION
AB Artificial intelligence-based fault diagnosis has recently been the subject of extensive research. However, the model learned from source data exhibits poor performance in target pattern recognition due to different data distributions caused by variable working conditions. Therefore, the transfer learning (TL) method, which reuses acquired knowledge and diagnoses the target domain fault without labels, has elicited the attention of researchers. The common deep TL method reduces the distance between the source and target domains in accordance with a certain divergence criterion that should be designed differently for specific tasks, leading to poor generalization results. In this study, we propose a knowledge mapping-based adversarial domain adaptation (KMADA) method with a discriminator and a feature extractor to generalize knowledge from target to source domain. The discriminator achieves the distance metric of the neural network wherein the target feature extractor maps the target data into the source feature space to explore domain-invariant knowledge. To accelerate the adversarial training process, KMADA fully utilizes the parameters obtained from the supervised pre-training. In addition, comparison analysis with other TL methods indicates the irreplaceable superiority of the KMADA, which achieves the highest diagnosis accuracy. Moreover, the visualization results demonstrate that the proposed model extracts the domain-invariant feature to realize knowledge mapping diagnosis, and thus, the model exhibits considerable research prospects. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Li, Qi; Shen, Changqing; Chen, Liang; Zhu, Zhongkui] Soochow Univ, Sch Mech & Elect Engn, Suzhou 215131, Peoples R China.
RP Shen, CQ; Chen, L (通讯作者)，Soochow Univ, Sch Mech & Elect Engn, Suzhou 215131, Peoples R China.
EM cqshen@suda.edu.cn; chenl@suda.edu.cn
RI Shen, Changqing/AAG-9637-2019
OI Shen, Changqing/0000-0002-5143-8366; Li, Qi/0000-0001-7105-2818
FU National Natural Science Foundation of China [51875375, 51875376];
   Suzhou Science Foundation [SYG201802]
FX This work is financially supported by the National Natural Science
   Foundation of China (Nos. 51875375, 51875376) and the Suzhou Science
   Foundation (No. SYG201802).
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NR 46
TC 40
Z9 40
U1 31
U2 176
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 0888-3270
J9 MECH SYST SIGNAL PR
JI Mech. Syst. Signal Proc.
PD JAN 15
PY 2021
VL 147
AR 107095
DI 10.1016/j.ymssp.2020.107095
PG 20
WC Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA NS6HH
UT WOS:000572359600007
HC Y
HP N
DA 2022-08-23
ER

PT J
AU MacMurdo, MG
   Mulloy, KB
   Culver, DA
   Felix, CW
   Curtis, AJ
   Ajayakumar, J
   Curtis, J
AF MacMurdo, Maeve G.
   Mulloy, Karen B.
   Culver, Daniel A.
   Felix, Charles W.
   Curtis, Andrew J.
   Ajayakumar, Jayakrishnan
   Curtis, Jacqueline
TI Mapping Mobility: Utilizing Local-Knowledge-Derived Activity Space to
   Estimate Exposure to Ambient Air Pollution among Individuals
   Experiencing Unsheltered Homelessness
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE homelessness; local knowledge mapping; environmental justice;
   particulate matter; homeless persons; vulnerable populations
ID MODEL
AB Individuals experiencing homelessness represent a growing population in the United States. Air pollution exposure among individuals experiencing homelessness has not been quantified. Utilizing local knowledge mapping, we generated activity spaces for 62 individuals experiencing homelessness residing in a semi-rural county within the United States. Satellite derived measurements of fine particulate matter (PM2.5) were utilized to estimate annual exposure to air pollution experienced by our participants, as well as differences in the variation in estimated PM2.5 at the local scale compared with stationary monitor data and point location estimates for the same period. Spatial variation in exposure to PM2.5 was detected between participants at both the point and activity space level. Among all participants, annual median PM2.5 exposure was 16.22 mu g/m(3), exceeding the National Air Quality Standard. Local knowledge mapping represents a novel mechanism to capture mobility patterns and investigate exposure to air pollution within vulnerable populations. Reliance on stationary monitor data to estimate air pollution exposure may lead to exposure misclassification, particularly in rural and semirural regions where monitoring is limited.
C1 [MacMurdo, Maeve G.; Culver, Daniel A.] Cleveland Clin, Resp Inst, Cleveland, OH 44195 USA.
   [Mulloy, Karen B.] Case Western Reserve Univ, Dept Family Med & Community Hlth, Cleveland, OH 44106 USA.
   [Felix, Charles W.] Tulare Cty Counsel, Visalia, CA 93291 USA.
   [Curtis, Andrew J.; Ajayakumar, Jayakrishnan; Curtis, Jacqueline] Case Western Reserve Univ, Dept Populat & Quantitat Hlth Sci, Cleveland, OH 44106 USA.
RP MacMurdo, MG (通讯作者)，Cleveland Clin, Resp Inst, Cleveland, OH 44195 USA.
EM macmurm@ccf.org; kbm30@case.edu; culverd@ccf.org;
   cwfelix@tularecounty.ca.gov; ajc321@case.edu; jxa421@case.edu;
   jxc1546@case.edu
OI Mulloy, Karen/0000-0002-0201-3353
CR [Anonymous], STATE HOMELESSNESS N
   [Anonymous], Agri-BusinessTulareDevelopment
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NR 40
TC 1
Z9 1
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD MAY
PY 2022
VL 19
IS 10
AR 5842
DI 10.3390/ijerph19105842
PG 9
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 1O7QL
UT WOS:000801521700001
PM 35627378
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhu, HP
   Liu, Y
   Tian, F
   Ni, YF
   Wu, K
   Chen, Y
   Zheng, QH
AF Zhu, Haiping
   Liu, Yu
   Tian, Feng
   Ni, Yifu
   Wu, Ke
   Chen, Yan
   Zheng, Qinghua
TI A Cross-Curriculum Video Recommendation Algorithm Based on a
   Video-Associated Knowledge Map
SO IEEE ACCESS
LA English
DT Article
DE Online learning; collaborative filtering; video-associated knowledge
   map; curriculum video recommendation
ID SYSTEM
AB Learning resource recommendation, such as curriculum video recommendation, is an effective way to reduce cognitive overload in online learning. The existing curriculum video recommendation systems are generally limited to one course, ignoring the knowledge correlation between courses. In this paper, we propose a two-stage cross-curriculum video recommendation algorithm that considers both the learners' implicit feedback and the knowledge association between course videos. First, we use collaborative filtering to generate a video seed set, which is based on the learner's implicit video feedback, such as video learning frequencies, video learning duration, and video pausing and dragging frequencies. Second, we construct a cross-curriculum video-associated knowledge map and use a random walk algorithm to measure the relevance of the course videos. The relevance is based on each video seed as a starting node and is extended to a video subgraph. Then, several cross-curricular video-oriented subgraphs are recommended for the learners. The experimental results indicate that our cross-curriculum video recommendation algorithm performs better than the traditional collaborative filtering-based recommendation algorithms in terms of accuracy, recall rate, and knowledge relevance.
C1 [Zhu, Haiping; Liu, Yu; Ni, Yifu; Wu, Ke; Chen, Yan; Zheng, Qinghua] Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Shaanxi, Peoples R China.
   [Zhu, Haiping; Liu, Yu; Tian, Feng; Chen, Yan; Zheng, Qinghua] Xi An Jiao Tong Univ, MOEKLINNS Lab, Xian 710049, Shaanxi, Peoples R China.
   [Zhu, Haiping; Liu, Yu; Ni, Yifu; Wu, Ke; Chen, Yan; Zheng, Qinghua] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Shaanxi, Peoples R China.
   [Tian, Feng] Xi An Jiao Tong Univ, Syst Engn Inst, Xian 710049, Shaanxi, Peoples R China.
   [Ni, Yifu] Xi An Jiao Tong Univ, Shaanxi Prov Key Lab Satellite & Terr Network Tec, R&D, Xian 710049, Shaanxi, Peoples R China.
RP Tian, F (通讯作者)，Xi An Jiao Tong Univ, MOEKLINNS Lab, Xian 710049, Shaanxi, Peoples R China.; Tian, F (通讯作者)，Xi An Jiao Tong Univ, Syst Engn Inst, Xian 710049, Shaanxi, Peoples R China.
EM fengtian@mail.xjtu.edu.cn
FU National Key Research and Development Program of China [2016YFB1000903];
   National Science Foundation of China [61472315]; Innovative Research
   Group of the National Natural Science Foundation of China [61721002];
   Innovation Research Team of Ministry of Education [IRT_17R86]; Project
   of China Knowledge Centre for Engineering Science and Technology
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2016YFB1000903, in part by the
   National Science Foundation of China under Grant 61472315, in part by
   the Innovative Research Group of the National Natural Science Foundation
   of China under Grant 61721002, in part by the Innovation Research Team
   of Ministry of Education under Grant IRT_17R86, and in part by the
   Project of China Knowledge Centre for Engineering Science and
   Technology.
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NR 35
TC 6
Z9 7
U1 6
U2 25
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 57562
EP 57571
DI 10.1109/ACCESS.2018.2873106
PG 10
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 GY8ZB
UT WOS:000448927000001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, L
   Cheng, YM
AF Wang, Liang
   Cheng, Yiming
TI Exploring a comprehensive knowledge map for promoting safety management
   research in the construction industry
SO ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
LA English
DT Article
DE Safety management; Construction industry; Scientometric analysis;
   Knowledge map; Evolution trend
ID INFORMATION MODELING BIM; VISUALIZATION TECHNOLOGY; IDENTIFICATION
   SYSTEM; RISK IDENTIFICATION; WARNING SYSTEM; SITE SAFETY; CLIMATE;
   PERFORMANCE; BEHAVIOR; DESIGN
AB Purpose The purpose of this paper was to map the safety management research of construction industry by scientometric analysis, which can predict important highlights and future research directions of safety management research in the construction industry. As an important issue in the construction industry, safety management issues have been researched from different perspectives. Although previous studies make knowledge contributions to the safety management research of construction industry, there are still huge obstacles to distinguish the comprehensive knowledge map of safety management research in the construction industry. Design/methodology/approach This study applies three scientometric analysis methods, collaboration network analysis, co-occurrence network analysis and cocitation network analysis, to the safety management research of construction industry. 5,406 articles were retrieved from the core collection database of the Web of Science. CiteSpace was used for constructing a comprehensive analysis framework to analyze and visualize the safety management research of construction industry. According to integrating the analysis results, a knowledge map for the safety management research of construction industry can be constructed. Findings The analysis results revealed the academic communities, key research topics and knowledge body of safety management research in the construction industry. The evolution paths of safety management research in the construction industry were divided into three development stages: "construction safety management", "multi-objective safety management" and "comprehensive safety management". Five research directions were predicted on the future safety management research of construction industry, including (1) comprehensive assessment indicators system; (2) intelligent safety management; (3) cross-organization collaboration of safety management; (4) multilevel safety behavior perception and (5) comparative analysis of safety climate. Originality/value The findings can reveal the overall status of safety management research in the construction industry and represent a high-quality knowledge body of safety management research in the construction industry that accurately reflects the comprehensive knowledge map on the safety management research of construction industry. The findings also predict important highlights and future research directions of safety management research in the construction industry, which will help researchers in the safety management research of construction industry for future collaboration and work.
C1 [Wang, Liang; Cheng, Yiming] Dalian Maritime Univ, Sch Maritime Econ & Management, Dalian, Peoples R China.
RP Wang, L (通讯作者)，Dalian Maritime Univ, Sch Maritime Econ & Management, Dalian, Peoples R China.
EM liangwang@dlmu.edu.cn; chengyiming@dlmu.edu.cn
OI Wang, Liang/0000-0002-4063-6842
FU National Social Science Fund of China [18ZDA043]; National Natural
   Science Foundation of China (NSFC) [71671053, 71841024, 71771067,
   71390522]; Social Science Planning Foundation of Liaoning Province
   [L20BGL056]; Economic and Social Development Research Foundation of
   Liaoning Province [20211slqnkt-014]
FX This research was funded by the National Social Science Fund of China
   (No. 18ZDA043). The work described in this paper was also supported by
   the National Natural Science Foundation of China (NSFC) (NO. 71671053,
   NO. 71841024, NO. 71771067, No. 71390522), the Social Science Planning
   Foundation of Liaoning Province (No. L20BGL056) and the Economic and
   Social Development Research Foundation of Liaoning Province (No.
   20211slqnkt-014).
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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.
PD APR 8
PY 2022
VL 29
IS 4
BP 1678
EP 1714
DI 10.1108/ECAM-11-2020-0984
EA APR 2021
PG 37
WC Engineering, Industrial; Engineering, Civil; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics
GA 0H8TF
UT WOS:000645257100001
DA 2022-08-23
ER

PT J
AU Chen, Y
   Wang, ZQ
   Song, K
   Lin, DM
   Kretschmer, H
AF Chen, Yue
   Wang, Zhiqi
   Song, Kai
   Lin, Deming
   Kretschmer, Hildrun
TI Growing with collaboration: footprint of WISE Lab
SO SCIENTOMETRICS
LA English
DT Article
DE WISE lab; Citation aging; Theme evolution; Collaboration; Knowledge
   mapping; Prof; Zeyuan Liu
AB The paper explores the development of WISE Lab during the past 15 years from three complementary perspectives: citation aging characteristics, themes evolution and academic collaboration, based on the research results produced by WISE Lab. The results show that, the half-life of the papers in WISE Lab reaches about 5 years, and the highly cited papers have a longer impact than mainstream papers; WISE Lab is focused on six themes, "Knowledge mapping", "Visual analysis", "Scientometrics", "Patentometrics", "Information retrieval", and "Innovation management", which developed alternately over time; WISE Lab is greatly driven by collaboration, and Prof. Liu contributed in a central way to the team cohesion, his academic spirit and thought influenced widely.
C1 [Chen, Yue; Wang, Zhiqi; Song, Kai; Lin, Deming] Dalian Univ Technol, Inst Sci Sci & S&T Management, WISE Lab, Dalian, Peoples R China.
   [Kretschmer, Hildrun] COLLNET Ctr, Borgsdorfer Str 5, D-16540 Hohen Neuendorf, Germany.
RP Chen, Y (通讯作者)，Dalian Univ Technol, Inst Sci Sci & S&T Management, WISE Lab, Dalian, Peoples R China.
EM chenyuedlut@163.com; zhiqi_wang90@126.com; Songkai1105@mail.dlut.edu.cn;
   deminglin@dlut.edu.cn; kretschmer.h@onlinehome.de
FU Economic and social development Foundation of Liaoning Province
   [20201slktyb-012]
FX This study was funded by Economic and social development Foundation of
   Liaoning Province (Grant No. 20201slktyb-012).
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NR 15
TC 0
Z9 0
U1 5
U2 26
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUL
PY 2021
VL 126
IS 7
SI SI
BP 6147
EP 6167
DI 10.1007/s11192-020-03687-z
EA SEP 2020
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 SY3VS
UT WOS:000569300100011
DA 2022-08-23
ER

PT J
AU Shi, PS
AF Shi, PeiShuang
TI Research on the Strategy of Autonomous Learning under the Dual-Class
   Model of Ideological and Political Courses Based on the Knowledge Map
   Route
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB The two-part classroom teaching mode organically combines teachers' lectures with students' discussions, which can not only ensure the dominance of ideological and political teachers' classroom teaching but also give full play to students' autonomy in learning. The role of students' autonomous learning as a bridge between teachers' lectures and students' discussions cannot be ignored. Through the study of the knowledge map, the self-learning strategies of ideological and political courses calculated from the aspects of resource push and path planning, accurate learning resource search, in-depth reading, and answering robots will maximize the benefits of students' self-learning.
C1 [Shi, PeiShuang] Chongqing Coll Elect Engn, Chongqing 401331, Peoples R China.
RP Shi, PS (通讯作者)，Chongqing Coll Elect Engn, Chongqing 401331, Peoples R China.
EM 201029020@cqcet.edu.cn
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NR 14
TC 0
Z9 0
U1 4
U2 4
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD JUN 13
PY 2022
VL 2022
AR 2854233
DI 10.1155/2022/2854233
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 2M3ZE
UT WOS:000817640900001
OA gold
DA 2022-08-23
ER

PT J
AU Luo, XF
   Hu, QL
   Xu, WM
   Yu, Z
AF Luo, Xiangfeng
   Hu, Qingliang
   Xu, Weimin
   Yu, Zhian
TI Discovery of textual knowledge flow based on the management of knowledge
   maps
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article; Proceedings Paper
CT 2nd International Workshop on Workflow Management and Applications in
   Grid Environments (WaGe 2007)
CY AUG 17, 2007
CL Urumqi, PEOPLES R CHINA
DE textual knowledge flow; knowledge map; semantic link network; knowledge
   grid
ID MODEL
AB Textual knowledge flow (TKF) provides an effective technique and theoretical support for file discovery and cooperation of knowledge innovation, intelligent browsing, and personalized recommendation in Web services and e-Science Knowledge Grid. For the discovery of TKF firstly knowledge map (KM) is proposed to represent the textual knowledge; then a hash algorithm is used to code KMs in (order to form an Island which contains enormous KMs belonging to a domain. Under the control of the Island, C-Location and R-Location are introduced to manage those KMs belonging to in Island. KM-Chord is proposed to manage the number of Islands, C-Locations and R-Locations in Web or a library. With the help of the management of KMs, similar relation and associated relation between KMs are found to build the semantic link network (SLN) between KMs. Based on the SLN and users' profile and input, similar or associated TKF with the user's different demands is activated. Experiments show that the proposed method can effectively discover TKF for Web services and e-Science Knowledge Grid. Copyright (E) 2008 John Wile & Sons. Ltd.
C1 [Luo, Xiangfeng; Hu, Qingliang; Xu, Weimin; Yu, Zhian] Shanghai Univ, Key Lab Grid Technol, Digital Content Anal & Semant Grid Lab, Shanghai 200072, Peoples R China.
RP Luo, XF (通讯作者)，Shanghai Univ, Key Lab Grid Technol, Digital Content Anal & Semant Grid Lab, Shanghai 200072, Peoples R China.
EM luoxiangfeng@163.com
CR CHEN J, 2007, CONCURR COMP-PRACT E, DOI DOI 10.1001/CPE.1220
   Chen JJ, 2007, ACM T AUTON ADAP SYS, V2, DOI 10.1145/1242060.1242063
   DABEK F, 2001, P 8 WORKSH HOT TOP O
   DOU W, 2006, P 2 INT C SEM KNOWL
   GUO W, 2005, INT C SEM KNOWL GRID
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NR 18
TC 22
Z9 24
U1 0
U2 25
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 2008
VL 20
IS 15
BP 1791
EP 1806
DI 10.1002/cpe.1319
PG 16
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA 355KK
UT WOS:000259707600006
DA 2022-08-23
ER

PT J
AU Lee, PC
   Xie, W
   Lo, TP
   Long, DB
   Tang, XF
AF Lee, Pin-Chan
   Xie, Wei
   Lo, Tzu-Ping
   Long, Danbing
   Tang, Xiaofei
TI A Cloud Model-based Knowledge Mapping Method for Historic Building
   Maintenance based on Building Information Modelling and Ontology
SO KSCE JOURNAL OF CIVIL ENGINEERING
LA English
DT Article
DE BIM; cloud model; FMEA; historic building; knowledge mapping; ontology
ID SEMANTIC WEB; RISK-ASSESSMENT; ARCHITECTURAL HERITAGE; CONSTRUCTION;
   BIM; MANAGEMENT; DESIGN; COST; SUSTAINABILITY; VULNERABILITY
AB The maintenance of historic buildings requires a systematic approach to construct and reuse maintenance knowledge. Maintenance knowledge of historic buildings has extensive and specific knowledge, but a small number of experts provide most of experience. With the rapid development of building information modelling (BIM), it can facilitate maintenance management of historic buildings. Recently, research on connecting ontology and BIM was discussed to promote building knowledge management (BKM). However, BKM is less applied in historic buildings and connection strength is also less discussed. Connection strength of knowledge mapping can increase the performance of knowledge retrieval. Therefore, this study aims to build connection between maintenance ontology of historic buildings and BIM, and also proposes a cloud model-based knowledge mapping method to evaluate the connection strength. This study uses FMEA (failure mode and effects analysis) to connect ontology and BIM, and the grey relational analysis to evaluate the connection strength. Meanwhile, cloud model is integrated into FMEA to better deal with uncertain information to increase the reliability. This study adopts a real case to valid the practicability. The results show the proposed method can evaluate the connection strengths with uncertain information and obtain the maintenance knowledge more efficiently.
C1 [Lee, Pin-Chan; Xie, Wei; Long, Danbing] Southwest Jiaotong Univ, Sch Civil Engn, Chengdu 610031, Sichuan, Peoples R China.
   [Lo, Tzu-Ping] Chengdu Liangzi Intelligent Technol Ltd, Chengdu 610041, Sichuan, Peoples R China.
   [Tang, Xiaofei] Chengdu Hitech Zone Management Off, Chengdu 610041, Sichuan, Peoples R China.
RP Lo, TP (通讯作者)，Chengdu Liangzi Intelligent Technol Ltd, Chengdu 610041, Sichuan, Peoples R China.
EM alanlee@swjtu.edu.cn; 963257827@qq.com; 2731368845@qq.com;
   lornalong@swjtu.edu.cn; tangxiaofei@cdht-freetrade.gov.cn
OI Long, Lorna/0000-0001-6232-6055
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NR 71
TC 7
Z9 7
U1 10
U2 57
PU KOREAN SOCIETY OF CIVIL ENGINEERS-KSCE
PI SEOUL
PA 3-16 JUNGDAE-RO 25-GIL, SONGPA-GU, SEOUL, 05661, SOUTH KOREA
SN 1226-7988
EI 1976-3808
J9 KSCE J CIV ENG
JI KSCE J. Civ. Eng.
PD AUG
PY 2019
VL 23
IS 8
BP 3285
EP 3296
DI 10.1007/s12205-019-2457-0
PG 12
WC Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA IJ4EM
UT WOS:000475857300003
DA 2022-08-23
ER

PT J
AU Qiu, HH
   Liu, LG
AF Qiu, Hong-Hua
   Liu, Lu-Ge
TI A Study on the Evolution of Carbon Capture and Storage Technology Based
   on Knowledge Mapping
SO ENERGIES
LA English
DT Article
DE carbon capture and storage; knowledge mapping; technological evolution;
   CiteSpace
ID POSTCOMBUSTION CO2 CAPTURE; DIOXIDE CAPTURE; FACILE SYNTHESIS; CARBIDE
   SLAG; ORGANIC FRAMEWORKS; EMERGING TRENDS; GRAPHENE OXIDE; POWER-PLANTS;
   ADSORPTION; PERFORMANCE
AB As a useful technical measure to deal with the problem of carbon dioxide (CO2) emissions, carbon capture and storage (CCS) technology has been highly regarded in both theory and practice under the promotion of the Intergovernmental Panel on Climate Change (IPCC). Knowledge mapping is helpful for understanding the evolution in terms of research topics and emerging trends in a specific domain. In this work knowledge mapping of CCS technology was investigated using CiteSpace. Several aspects of the outputs of publications in the CCS research area were analyzed, such as annual trends, countries, and institutions. The research topics in this particular technology area were analyzed based on their co-occurring keyword networks and co-citation literature networks, while, the emerging trends and research frontiers were studied through the analysis of burst keywords and citation bursts. The results indicated that the annual number of publications in the research field of CCS technology increased rapidly after 2005. There are more CCS studies published in countries from Asia, North America, and Europe, especially in the United States and China. The Chinese Academy of Sciences not only has the largest number of publications, but also has a greater impact on the research area of CCS technology, however, there are more productive institutions located in developed countries. In the research area of CCS technology, the main research topics include carbon emissions and environmental protection, research and development activities, and social practical issues, meanwhile, the main emerging trends include emerging techniques and processes, emerging materials, evaluation of technological performance, and socioeconomic analysis.
C1 [Qiu, Hong-Hua; Liu, Lu-Ge] Northwest Univ, Sch Law, Xian 710127, Shaanxi, Peoples R China.
   [Qiu, Hong-Hua] Max Planck Inst Innovat & Competit, Marstallplaz 1, D-80539 Munich, Germany.
RP Qiu, HH (通讯作者)，Northwest Univ, Sch Law, Xian 710127, Shaanxi, Peoples R China.; Qiu, HH (通讯作者)，Max Planck Inst Innovat & Competit, Marstallplaz 1, D-80539 Munich, Germany.
EM qiuhonghua@nwu.edu.cn; liuluge@stumail.nwu.edu.cn
OI qiu, hong hua/0000-0001-5526-2786
FU National Social Science Foundation of China [15BTQ047]; Chinese
   Scholarship Council (CSC)
FX This research is supported by the National Social Science Foundation of
   China under Grant no. 15BTQ047. The corresponding author is grateful for
   the financial sponsorship of Chinese Scholarship Council (CSC) to
   support his study in Max Planck Institute for Innovation and Competition
   as a visiting scholar. Finally, special thanks to my supervisor, Dietmar
   Harhoff, the director of Max Planck Institute for Innovation and
   Competition. He not only provides me with very good research conditions
   but also gives me many valuable comments for my research when I carry
   out my research at MPI.
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NR 102
TC 16
Z9 16
U1 12
U2 68
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1073
J9 ENERGIES
JI Energies
PD MAY
PY 2018
VL 11
IS 5
AR 1103
DI 10.3390/en11051103
PG 25
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Energy & Fuels
GA GJ7ZY
UT WOS:000435610300081
OA gold
DA 2022-08-23
ER

PT J
AU Li, JJ
   Sun, XH
   Dai, X
   Zhang, JY
   Liu, BF
AF Li, Junjie
   Sun, Xuehang
   Dai, Xin
   Zhang, Junying
   Liu, Bangfan
TI Knowledge Map Analysis of Industry-University Research Cooperation
   Policy Research Based on CNKI and WOS Visualization in China
SO SUSTAINABILITY
LA English
DT Review
DE knowledge graph; industry-university research cooperation; CiteSpace;
   NoteExpress; visualization
ID COMMERCIALIZATION
AB The field of industry-university research cooperation policy is about to enter a new stage. It is necessary to systematically sort and summarize the literature, both at home and abroad, that has been produced in recent years. Through knowledge map analysis of the current situation regarding the development of industry-university research cooperation policy, from both domestic and foreign perspectives, taking the core documents included in the Web of Science and China National Knowledge Infrastructure database as samples, and through visual research on the time context, author, organization, theme, and other key nodes of industry-university research cooperation policy research, with the help of CiteSpace and NoteExpress measurement software, we can draw the following conclusions. Domestic research is relatively focused on colleges and universities; that is, industry-university research cooperation as a mechanism to cultivate innovative talents and improve scientific research and enhance social services, while foreign countries pay more attention to the technological utilization and technological innovation of enterprises and their accompanying economic benefits. Domestic and foreign research have their own characteristics, and the research quality of the two is equal. Generally speaking, research on the policy of domestic university research cooperation should focus on the following three points in the future: It must be closely related to the functions of colleges and universities, the nature of enterprises, and social needs; we must attach great importance to empirical research; and we must pay attention to cross-organizational research.
C1 [Li, Junjie; Sun, Xuehang; Zhang, Junying; Liu, Bangfan] Yanshan Univ, Sch Publ Adm, Qinhuangdao 066004, Hebei, Peoples R China.
   [Li, Junjie; Sun, Xuehang; Dai, Xin; Zhang, Junying; Liu, Bangfan] Hebei Publ Policy Evaluat & Res Ctr, Qinhuangdao 066004, Hebei, Peoples R China.
   [Dai, Xin] Chinese Acad Social Sci, Inst Marxism, Beijing 100000, Peoples R China.
RP Sun, XH; Liu, BF (通讯作者)，Yanshan Univ, Sch Publ Adm, Qinhuangdao 066004, Hebei, Peoples R China.; Sun, XH; Liu, BF (通讯作者)，Hebei Publ Policy Evaluat & Res Ctr, Qinhuangdao 066004, Hebei, Peoples R China.
EM lijunjie@stumail.ysu.edu.cn; s18830380782@stumail.ysu.edu.cn;
   daixin860227@163.com; zhangjunying@stumail.ysu.edu.cn;
   liubangfan@ysu.edu.cn
FU Hebei Social Science Foundation Project "Research on the implementation
   of policies to curb agricultural land conversion in the development of
   new agriculture in Hebei Province [HB19Gl015]; Hebei Provincial
   Department of Education Science Research Plan Project-a major research
   project of humanities and social sciences-"Research on the promotion
   path of industrial chain of Hebei coastal counties (districts and
   cities) from the perspective of c [ZD202104]
FX This research was funded by the Hebei Social Science Foundation Project
   "Research on the implementation of policies to curb agricultural land
   conversion in the development of new agriculture in Hebei Province"
   (Grant No. HB19Gl015), and by the Hebei Provincial Department of
   Education Science Research Plan Project-a major research project of
   humanities and social sciences-"Research on the promotion path of
   industrial chain of Hebei coastal counties (districts and cities) from
   the perspective of coordinated development of Beijing, Tianjin and
   Hebei" (Grant No. ZD202104).
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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 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUL
PY 2022
VL 14
IS 13
AR 7862
DI 10.3390/su14137862
PG 26
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 2W4MZ
UT WOS:000824501100001
OA gold
DA 2022-08-23
ER

PT J
AU Choi, JY
   Lee, JH
   Cho, Y
   Kim, S
   Kim, JA
   Lee, SY
AF Choi, Jae-Young
   Lee, Jee-Hyong
   Cho, Younghwa
   Kim, Suntae
   Kim, Jeong-Ah
   Lee, Seang-Yong
TI Visualization of knowledge map for monitoring knowledge diagnoses
SO JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
LA English
DT Article
DE Knowledge visualization; Knowledge diagnosis; Ontology; Deep Sparse
   Neural Network
AB In order to evaluate the level of knowledge acquired by students, this study presents the contents of the students' knowledge using a proposed novel method to monitor a knowledge map and to diagnose them. To do so, we propose a method to improve the accuracy of diagnoses by comparing the level of the surrounding knowledge related to the knowledge, rather than the level of the knowledge itself, to correct the evaluation value. Moreover, in visualizing the relationship between knowledge objects based on knowledge evaluation data using an ontological structure, the Deep Sparse Neural Network model of deep learning is applied, and the map is regarded as one neural network and is proposed to express the quantitative value using the weight instead of the qualitative concept of ontology. The proposed knowledge map visualization can monitor the relationship and relevance (weight) of the related knowledge level at a glance, so that it is possible to intuitively grasp the result with the quantitative knowledge diagnosis and to improve the efficiency of knowledge evaluation and the accurate evaluation of knowledge diagnoses.
C1 [Choi, Jae-Young; Lee, Jee-Hyong; Cho, Younghwa] Sungkyunkwan Univ, Dept Comp Engn, Suwon, Gyeonggi Do, South Korea.
   [Kim, Suntae] Chonbuk Natl Univ, Dept Software Engn, Jeonju, Jeollabuk Do, South Korea.
   [Kim, Jeong-Ah] Catholic Kwandong Univ, Dept Comp Educ, Kangnung, Kangwon Do, South Korea.
   [Lee, Seang-Yong] Cardinal Info Tech Co Ltd, Seoul, South Korea.
RP Lee, SY (通讯作者)，Cardinal Info Tech Co Ltd, Seoul, South Korea.
EM jaeychoi@skku.edu; john@skku.edu; choyh2285@skku.edu; stkim@jbnu.ac.kr;
   clara@cku.ac.kr; cadpower@citsoft.net
FU National Research Foundation of Korea (NRF) - Korea Government (MSIT)
   [NRF-2014M3C4A7030503]; Institute of Information and Communications
   Technology Planning and Evaluation (IITP) - Korea Government (MSIT)
   [2019-0-00421]
FX This research was partly supported by National Research Foundation of
   Korea (NRF) Grant funded by Korea Government (MSIT)
   (NRF-2014M3C4A7030503, Next-Generation Information Computing Development
   Program) and Institute of Information and Communications Technology
   Planning and Evaluation (IITP) Grant funded by the Korea Government
   (MSIT) (no. 2019-0-00421, AI Graduate School Support Program).
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NR 11
TC 2
Z9 2
U1 4
U2 4
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1868-5137
EI 1868-5145
J9 J AMB INTEL HUM COMP
JI J. Ambient Intell. Humaniz. Comput.
PD MAR
PY 2022
VL 13
IS 3
SI SI
BP 1615
EP 1623
DI 10.1007/s12652-019-01407-x
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA ZK2TC
UT WOS:000762845300033
DA 2022-08-23
ER

PT J
AU Zhang, JQ
   Fan, YS
   Zhang, J
   Bai, B
AF Zhang, Junqi
   Fan, YuShun
   Zhang, Jia
   Bai, Bing
TI Learning to Build Accurate Service Representations and Visualization
SO IEEE TRANSACTIONS ON SERVICES COMPUTING
LA English
DT Article
DE Mashups; Ecosystems; Visualization; Knowledge engineering; Resource
   management; Fans; Service representation; service visualization; service
   ecosystem; topic model; knowledge map
ID KNOWLEDGE MAP; NETWORK
AB With the boom of Web services, there is a growing need for visualizing service ecosystems to help people browse services and understand their functionalities and positions in the systems. One foundational step of building a proper visualization is to ensure accurate representations for the comprising services. However, it is not a trivial task as service profiles may not be sufficient for two significant reasons. First, while the services themselves being used in various scenarios, their profiles may not always precisely reflect all of them. Second, service profiles usually comprise quite a few universal background terms that cannot distinguish services. To address these two issues, we apply machine learning techniques to incrementally learn service representations in a whole. A tailored topic model is developed, named Service Representation-Latent Dirichlet Allocation (SR-LDA). The core idea is to learn more comprehensive and up-to-date information about services from the profiles of the involved service compositions (i.e., mashup profiles), while introducing a global filter to identify and filter out background terms. Both quantitative and qualitative experiments on a real-world dataset demonstrate that the proposed SR-LDA builds higher-quality service representations comparing with baselines. We further generate a knowledge map to visualize a service ecosystem based on the learned service representations. Such a knowledge map directly leads to the detection of four typical functionality patterns of Web services and serves the purpose of mashup creation.
C1 [Zhang, Junqi; Fan, YuShun] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China.
   [Zhang, Jia] Carnegie Mellon Univ, Dept Elect & Comp Engn, Moffett Field, CA 94035 USA.
   [Bai, Bing] Tencent, Cloud & Smart Ind Grp, Beijing 100193, Peoples R China.
RP Fan, YS (通讯作者)，Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China.
EM jq-zhang15@mails.tsinghua.edu.cn; fanyus@tsinghua.edu.cn;
   jia.zhang@sv.cmu.edu; icebai@tencent.com
FU National Key Research and Development Program of China [2018YFB1402500];
   National Natural Science Foundation of China [61673230]; High-Tech Ship
   Research Project of China [17GC26102.01]
FX This research was supported in part by the National Key Research and
   Development Program of China (No.2018YFB1402500), the National Natural
   Science Foundation of China (No.61673230), and the High-Tech Ship
   Research Project of China (No.17GC26102.01). Yushun Fan is the
   corresponding author.
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NR 44
TC 0
Z9 0
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1939-1374
J9 IEEE T SERV COMPUT
JI IEEE Trans. Serv. Comput.
PD MAY-JUN
PY 2022
VL 15
IS 3
BP 1551
EP 1563
DI 10.1109/TSC.2020.3001307
PG 13
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2E9HU
UT WOS:000812532400030
DA 2022-08-23
ER

PT J
AU Wu, XH
   Qin, ZH
AF Wu, Xiaohang
   Qin, Zhihua
TI Multivariate Statistical Analysis of Quality Improvement Effect of
   Innovation and Entrepreneurship Education Based on Random Matrix Theory
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID CLASSIFICATION
AB The reform can better improve students' innovation and entrepreneurial skills and comprehensively improve the innovation and entrepreneurial competitiveness of college students, which is conducive to their survival and development in the market. Therefore, this paper analyzes the data generated in the educational process from the perspective of educational data mining and studies students' entrepreneurial interests and motivations so as to improve the quality of students' entrepreneurial guidance. The knowledge map, an important visualization tool in personalized learning systems, is used as a research object to analyze students' online evaluation data. We develop a prototype system for improving teaching quality-a knowledge map generation and knowledge point recommendation path analysis system. We hope to further explore new ways and methods to promote the reform of innovation and entrepreneurship education in colleges and universities, giving full play to the proper role and value of innovation and entrepreneurship education.
C1 [Wu, Xiaohang] Chongqing Normal Univ, Sch Econ & Management, Chongqing 401331, Peoples R China.
   [Qin, Zhihua] Guangxi Transport Vocat & Tech Coll, Traff Informat Engn Inst, Nanning 530023, Guangxi, Peoples R China.
RP Qin, ZH (通讯作者)，Guangxi Transport Vocat & Tech Coll, Traff Informat Engn Inst, Nanning 530023, Guangxi, Peoples R China.
EM xiaohangwu@cqnu.edu.cn; 123560423@gxjtc.edu.cn
FU special topic of the "14th Five-Year Plan" of Guangxi Education Science
   in 2021 (Research on the Path of Innovation and Entrepreneurship
   Education in Colleges and Universities in Guangxi Ethnic Minority Areas)
   [2021ZJY1482]
FX THis study was supported by the special topic of the "14th Five-Year
   Plan" of Guangxi Education Science in 2021 (Research on the Path of
   Innovation and Entrepreneurship Education in Colleges and Universities
   in Guangxi Ethnic Minority Areas to Serve Rural Revitalization)
   (2021ZJY1482).
CR Arlbjoslashrn Jan S., 2011, Logistics Research, V3, P3, DOI 10.1007/s12159-010-0044-3
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NR 29
TC 0
Z9 0
U1 0
U2 0
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD JUL 22
PY 2022
VL 2022
AR 3789315
DI 10.1155/2022/3789315
PG 8
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 3L9IM
UT WOS:000835074000001
OA gold
DA 2022-08-23
ER

PT J
AU Zhao, YQ
   Huang, L
   Liu, MJ
   Gao, H
   Li, WT
AF Zhao, Yanqing
   Huang, Li
   Liu, Meijuan
   Gao, Han
   Li, Wentao
TI Scientific Knowledge Graph of Acupuncture for Migraine: A Bibliometric
   Analysis from 2000 to 2019
SO JOURNAL OF PAIN RESEARCH
LA English
DT Article
DE acupuncture; migraine; scientific knowledge graph; CiteSpace;
   bibliometric analysis
ID TENSION-TYPE HEADACHE; RANDOMIZED CONTROLLED-TRIAL; PROPHYLACTIC
   TREATMENT; PAIN; OSTEOARTHRITIS; TOPIRAMATE; EFFICACY; CARE; KNEE
AB Objective: This study aims to explore the trend and knowledge mapping of acupuncture for migraine through bibliometrics.
   Methods: It retrieved the literature on acupuncture for migraine in the Web of Science database from 2000 to 2019, and then resorted to CiteSpace to conduct bibliometric analysis to attain the knowledge mapping.
   Results: The total number of publications each year has increased year by year, and the average annual growth rate from 2000 to 2009 was 15.57%, while from 2010 to 2019 was 6.35%, with a faster growth rate from 2000 to 2009. According to the cluster analysis of institutions, authors, cited references, and keywords, 10, 7, 12, and 10 categories were gained from 2000 to 2019. The most productive countries, institutions, and authors are the USA and China, Technical University of Munich and Beijing University of Chinese Medicine, Linde K and Liang FR from 2000 to 2019, whose frequency is 119/103, 28/24, and 28/24, respectively. However, the most important of them are Canada, Sichuan University, and Witt CM. Owing to their highest centrality, they are 0.86, 0.54, and 0.27 separately. Moreover, cited references that contributed to the most co-citations are Linde K (2005), yet, the most vital cited reference is Karst M (2001). Keywords such as migraine, acupuncture, headache, pain, and randomized controlled trial are the most frequently used. However, needle acupuncture is the crucial keyword. In the cluster analysis of institutions, authors, cited references, and keywords from 2000 to 2019, the largest cluster categories are #0 migraine prophylaxis, #1 randomized controlled trial, #0 episodic migraine, and #0 topiramate treatment. Then, randomized controlled trials of acupuncture prevention and treatment of migraine are the most important research content in this field.
   Conclusion: Through the bibliometric analysis of the research on acupuncture for migraine in the Web of Science database in the past 20 years, the trends and the Knowledge Graph of the country, institution, author, cited reference, and the keyword are acquired, which have an important guiding significance for quickly and accurately positioning the key information in the field.
C1 [Zhao, Yanqing; Liu, Meijuan; Gao, Han; Li, Wentao] Shanghai Municipal Hosp Tradit Chinese Med, Internal Med Tradit Chinese Med, Shanghai, Peoples R China.
   [Huang, Li] Shanghai Univ Tradit Chinese Med, Putuo Hosp, Internal Med Tradit Chinese Med, Shanghai, Peoples R China.
   [Li, Wentao] Shanghai Univ Tradit Chinese Med, Shanghai Municipal Hosp Tradit Chinese Med, Encephalopathy Dept, Shanghai, Peoples R China.
RP Li, WT (通讯作者)，Shanghai Municipal Hosp Tradit Chinese Med, Encephalopathy Dept, 274 Zhijiang Middle Rd, Shanghai 200071, Peoples R China.
EM lwt1132@163.com
RI zhao, yanqing/AAU-4377-2021
OI zhao, yanqing/0000-0003-4003-0063
FU Shanghai Science and Technology Commission, "science and technol-ogy
   innovation action plan" clinical medicine project [18401971200];
   Shanghai 13th five years plan key clinical specialty construction
   project [shslczdzk04901]; Scientific research project of Putuo Hospital,
   Shanghai University of Traditional Chinese Medicine [2020304A]; Budget
   Project of Shanghai University of Traditional Chinese Medicine
   [2019WK118]
FX This study was supported by grants from the Shanghai Science and
   Technology Commission, "science and technol-ogy innovation action plan"
   clinical medicine project (no. 18401971200), Shanghai 13th five years
   plan key clinical specialty construction project (no. shslczdzk04901).
   Scientific research project of Putuo Hospital, Shanghai University of
   Traditional Chinese Medicine (no. 2020304A). Budget Project of Shanghai
   University of Traditional Chinese Medicine (no. 2019WK118).
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NR 65
TC 0
Z9 0
U1 16
U2 40
PU DOVE MEDICAL PRESS LTD
PI ALBANY
PA PO BOX 300-008, ALBANY, AUCKLAND 0752, NEW ZEALAND
SN 1178-7090
J9 J PAIN RES
JI J. Pain Res.
PY 2021
VL 14
BP 1985
EP 2000
DI 10.2147/JPR.S314174
PG 16
WC Clinical Neurology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA TD0JB
UT WOS:000669021300001
PM 34234545
OA Green Published, gold
DA 2022-08-23
ER

PT S
AU Kim, HG
   Fillies, C
   Smith, B
   Wikarski, D
AF Kim, HG
   Fillies, C
   Smith, B
   Wikarski, D
BE Han, Y
   Wikarski, D
   Tai, S
TI Visualizing a dynamic knowledge map using semantic web technology
SO ENGINEERING AND DEPLOYMENT OF COOPERATIVE INFORMATION SYSTEMS,
   PROCEEDINGS
SE LECTURE NOTES IN COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 1st International Conference on Engineering and Deployment of
   Cooperative Information Systems
CY SEP 17-20, 2002
CL BEIJING, PEOPLES R CHINA
SP China Comp Federat, EASST, IEEE, Chinese Acad Sci, Inst Comp, Fraunhofer Inst Software & Syst Technol, Brandenburg Univ Appl Sci, Tsinghua Univ, IBM Res
ID CONCEPTUALIZATION
AB Visual knowledge(1) maps are being used to improve the communication processes within global organizations. Knowledge maps are graphical presentations of ontological knowledge as well as of business processes. Especially for enterprises working in a multi-cultural space the explicit formalization of knowledge and business rules using graphical models seems to be a very promising approach in order to improve discussion and learning processes. Publishing and automatic inference or search techniques are becoming available due to the latest standards for Semantic Web worked out by W3C. This article gives an impression how to create end user interfaces for the "Corporate Knowledge Base" using MS Office and Visio with the modeling tool SemTalk. Several problems on capturing and maintaining large scale knowledge bases are discussed. Specific attention is given to the problem of weighting and association of information from orthogonal ontologies, which arises while using the same concepts in different graphical scenarios.
C1 Dankook Univ, Choongman, South Korea.
   Semtat GmbH, Schwabach, Germany.
   Univ Appl Sci, Brandenburg, Germany.
RP Kim, HG (通讯作者)，Dankook Univ, Choongman, South Korea.
CR BERNERSLEE T, 2001, SCI AM           MAI
   BRICKLEY D, RDFS WEB SERVICE WOR
   DOAN A, 2002, WWW2002
   FILLIES C, 2002, WWW2002
   FLYNN J, DAML VISIO SHAPES
   Gomez A, 2000, DATA KNOWL ENG, V33, P169, DOI 10.1016/S0169-023X(99)00050-6
   Gordon JL, 2000, KNOWL-BASED SYST, V13, P71, DOI 10.1016/S0950-7051(00)00048-4
   Kim HG, 1997, MIND MACH, V7, P249, DOI 10.1023/A:1008234817601
   KIM HG, 1997, P 39 HAW INT C SYST, V5, P347
   *W3C, 1999, RDF SCHEM SPEC
NR 10
TC 2
Z9 2
U1 0
U2 7
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-44222-7
J9 LECT NOTES COMPUT SC
PY 2002
VL 2480
BP 130
EP 140
PG 11
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BY01V
UT WOS:000187254100010
DA 2022-08-23
ER

PT J
AU Chen, CT
   Pai, PF
   Hung, WZ
AF Chen, Chen-Tung
   Pai, Ping-Feng
   Hung, Wei-Zhan
TI A New Decision-Making Process for Selecting Project Leader Based on
   Social Network and Knowledge Map
SO INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
LA English
DT Article
DE Project Leader Selection; Interval Linguistic TOPSIS; Maximizing
   Deviation Method; Social Network Analysis; Knowledge Mapping
ID PERSONNEL-SELECTION; EVALUATION MODEL; INFORMATION
AB In general, the characteristics of a project leader may influence the possible success of a project. Therefore, project leader selection is one of the most important tasks for project management. In the matrix of an organization, a project leader is difficult to select from within a single department of an organization. A good project leader must try to cooperate easily with each member in a project team. In this study, the general ability, professional ability and the social network strength of each employee will be examined for the purpose of selecting a project leader. Accordingly, interval linguistic TOPSIS is combined with a maximizing deviation method to evaluate the general ability of each employee. Knowledge mapping is used to evaluate the professional ability of each employee. The comprehensive ability of each employee is defined by combining each one's general and professional abilities. Based on the concept of social networking, the cooperation capacity of each employee is defined to indicate the strength of the degree of cooperation with other members in the enterprise. Therefore, a project leader can be selected by considering the employees' comprehensive ability and their cooperation capacity simultaneously. Next, a numerical example is created to illustrate the process of the proposed method. Finally, some conclusions are discussed at the end of this study.
C1 [Chen, Chen-Tung] Natl United Univ, Dept Informat Management, Miaoli, Taiwan.
   [Pai, Ping-Feng] Natl Chi Nan Univ, Dept Informat Management, Nantou County, Taiwan.
RP Chen, CT (通讯作者)，Natl United Univ, Dept Informat Management, 1 Lienda Rd, Miaoli, Taiwan.
EM ctchen@nuu.edu.tw; paipf@ncnu.edu.tw; steady_2006@hotmail.com
OI Pai, Ping-Feng/0000-0002-4020-3326
FU National Taiwan Science Council [NSC101-2410-H-260-005-MY2, NSC
   101-2410-H-239-004-MY2, NSC100-2221-E-260-008]
FX This work was supported by National Taiwan Science Council projects
   "NSC101-2410-H-260-005-MY2", "NSC 101-2410-H-239-004-MY2" and
   "NSC100-2221-E-260-008".
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NR 24
TC 4
Z9 4
U1 2
U2 23
PU TAIWAN FUZZY SYSTEMS ASSOC
PI TAIPEI
PA NATL TAIPEI UNIV TECHNOL, 1 SEC 3 CHUNG-HSIAO E RD, TAIPEI, ROC 00000,
   TAIWAN
SN 1562-2479
J9 INT J FUZZY SYST
JI Int. J. Fuzzy Syst.
PD MAR
PY 2013
VL 15
IS 1
BP 36
EP 46
PG 11
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 134FI
UT WOS:000318195300004
DA 2022-08-23
ER

PT J
AU Zhao, L
   Zhang, W
   Wang, WS
AF Zhao, Liang
   Zhang, Wei
   Wang, Wenshun
TI Recognition of Energy-Saving Influencing Factors of Construction Project
   Lifecycle Based on Knowledge Map
SO INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Construction project; energy saving; influencing factors; knowledge map;
   cluster analysis
ID EFFICIENCY; BARRIERS; CARBON; PERFORMANCE; BUILDINGS; WATER; RETROFITS;
   POLICIES; OPTIONS; DESIGN
AB With the purpose of clarifying the energy-saving influencing factors of the participants in the construction project, qualitative analysis and scientific measurement analysis were combined in this paper to define and identify the energy-saving influencing factors of the construction project lifecycle. First, a qualitative analysis of the research literature of the natural science index (SCI) and social science citation index (SSCI) databases was conducted through a combination of literature analysis and expert interviews, constructing a list of the energy-saving influencing factors in the lifecycle of construction projects. Second, the knowledge map of the energy-saving research literature of construction projects in the past 20 years from the Web of Science (WOS) database was drawn through Citespace. Then the hotspots of energy-saving research in international construction projects were identified through keyword co-occurrence map as energy saving, energy efficiency, and energy consumption. The main research subjects were divided into four categories: energy-saving management and incentive policies, project users' energy-saving behaviors and awareness, various energy-saving new materials and technologies, and energy-saving management and control at the operation and maintenance stage. Next, nine main clusters on energy-saving research were identified through cluster analysis, mainly distributed in various energy-saving technology research, energy-saving behavior and power research of owners and residents, policy and regulation research, and energy-saving management and control research in the operation and maintenance stage. Finally, the knowledge mapping analysis results were classified and defined according to the action scope of each stage of the construction project, and the identification of the energy-saving influencing factors in the construction project lifecycle was completed.
C1 [Zhao, Liang] Jiangsu Vocat Inst Architectural Technol, Sch Telecommun Engn, Xuzhou 221000, Jiangsu, Peoples R China.
   [Zhang, Wei] Jiangsu Vocat Inst Architectural Technol, Inst Bldg Intelligence, Xuzhou 221000, Jiangsu, Peoples R China.
   [Wang, Wenshun] China Univ Min & Technol, Sch Mech & Civil Engn, Xuzhou 221000, Jiangsu, Peoples R China.
RP Zhang, W (通讯作者)，Jiangsu Vocat Inst Architectural Technol, Inst Bldg Intelligence, Xuzhou 221000, Jiangsu, Peoples R China.
EM godswish2006@126.com; 22605181@qq.com; kdwangws@126.com
FU Jiangsu Provincial Department of Housing and Urban Rural Construction
   Project [2018ZD328]; Jiangsu Construction System Science and Technology
   Project [2018ZD295]; Xuzhou Science and Technology Project [KC19198];
   Jiangsu Province "Qing Lan" Academic Leader Project [2020-0519]
FX This research was financially supported by Jiangsu Provincial Department
   of Housing and Urban Rural Construction Project (Grant No. 2018ZD328),
   Jiangsu Construction System Science and Technology Project (Grant No.
   2018ZD295), Xuzhou Science and Technology Project(Grant No.
   KC19198).Jiangsu Province "Qing Lan" Academic Leader Project (Grant No.
   2020-0519).
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NR 54
TC 2
Z9 2
U1 19
U2 29
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 MAY
PY 2021
VL 35
IS 06
AR 2159017
DI 10.1142/S0218001421590175
PG 21
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SK8SZ
UT WOS:000656490200004
DA 2022-08-23
ER

PT J
AU Lee, KW
   Wu, KL
   Kuo, HP
   Yuan, PL
AF Lee, Kuo-Wei
   Wu, Kang-Li
   Kuo, Hsing-Ping
   Yuan, Pen-Li
TI Design and validation of a knowledge map systemthe case of construction
   industry in Taiwan
SO HUMAN FACTORS AND ERGONOMICS IN MANUFACTURING & SERVICE INDUSTRIES
LA English
DT Article
DE knowledge management (km); knowledge map system (kms); user acceptance
ID INFORMATION-TECHNOLOGY; PERCEIVED USEFULNESS; ACCEPTANCE; MANAGEMENT;
   MODEL; ALLIANCES; VARIABLES; ASSETS; USAGE; EASE
AB Knowledge map systems (KMSs) have recently received widespread attention. A review of the literature shows that relatively few studies explicitly address the issues of implementation and user evaluation of the system and are limited in application. In view of this dilemma, this study aims to integrate the key features of knowledge assets maps, knowledge sources maps, knowledge structure maps, and knowledge task maps in order to develop an operational KMS. Also, the technology acceptance model was used as the theoretical basis for the assessment of user acceptance of the system, which is validated using structural equation modeling. In terms of user acceptance assessment, our empirical results shows that the perceived usefulness and perceived ease of use will positively affect user attitude toward the proposed KMS. Also, our results suggest that social defense negatively moderates the effect of attitude toward the system on behavior intention. Furthermore, we found that the management system also positively moderates the effect of attitude toward the system on behavior intention. On the basis of theoretical evaluation and empirical investigation, we hope that the proposed construction of a knowledge system can serve as references for both academic and related practical fields.
C1 [Lee, Kuo-Wei; Yuan, Pen-Li] Natl Taichung Univ Sci & Technol, Dept Business Adm, Taichung, Taiwan.
   [Wu, Kang-Li] Natl Quemoy Univ, Dept Urban Planning & Landscape, Kinmen, Taiwan.
   [Kuo, Hsing-Ping] Southern Taiwan Univ Sci & Technol, Dept Business Adm, Tainan, Taiwan.
RP Lee, KW (通讯作者)，Natl Taichung Univ Sci & Technol, Dept Business Adm, Taichung, Taiwan.
EM kuowei@nutc.edu.tw
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NR 80
TC 7
Z9 7
U1 1
U2 29
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1090-8471
EI 1520-6564
J9 HUM FACTOR ERGON MAN
JI Hum. Factors Ergonom. Manuf. Serv. Ind.
PD JAN
PY 2017
VL 27
IS 1
BP 30
EP 44
DI 10.1002/hfm.20409
PG 15
WC Engineering, Manufacturing; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering
GA EF9WZ
UT WOS:000390683300003
DA 2022-08-23
ER

PT J
AU Cargnel, M
   Bianchini, J
   Welby, S
   Koenen, F
   Van der Stede, Y
   De Clercq, K
   Saegerman, C
AF Cargnel, Mickael
   Bianchini, Juana
   Welby, Sarah
   Koenen, Frank
   Van der Stede, Yves
   De Clercq, Kris
   Saegerman, Claude
TI Improving laboratory diagnostic capacities of emerging diseases using
   knowledge mapping
SO TRANSBOUNDARY AND EMERGING DISEASES
LA English
DT Article
DE capacity; diagnostic; emerging diseases; epizootic diseases; knowledge
   mapping; laboratories; preparedness; prioritization
ID INFLUENZA SURVEILLANCE; BLUETONGUE; EPIDEMIC; PCR; DIFFERENTIATION;
   SEROGROUP; BELGIUM; AKABANE; ASSAYS; SWINE
AB Over the last decade, European countries faced several emerging and re-emerging animal diseases as well as zoonotic diseases. During these episodes, the laboratory diagnostic capabilities were a key factor to rapidly control and/or eradicate them. Because of the associated socio-economic and health consequences, it is crucial to react rapidly and efficiently, not only during crisis but also in peacetime (i.e. preparedness). However, to date, there is no published method to identify diseases with diagnostic gaps and to prioritize assays to be implemented. This study was conducted based on the outcome of a prioritization exercise in which 29 epizootic and exotic diseases with high risk of emergence or re-emergence in Belgium (Bianchini et al., [2020]Transboundary and Emerging Diseases, 67(1), 344-376) were listed. Knowledge mapping was used to visualize and identify gaps in the diagnostic procedures for different epidemiological scenarios at national level. To fill these gaps, an overview of diagnostic capabilities at national and international level (laboratories and kits providers or manufacturers) as well as the published assays in the scientific literature and the prescribed assays by international institutions and kits providers was carried out. The outcome of this study revealed the usefulness of knowledge mapping as a tool to identify gaps and ultimately gain insight on alternatives for better preparedness and responsiveness. While this exercise was limited to Belgium, we believe this exercise can benefit other countries and thereby enhancing knowledge sharing and collaboration to increase diagnostic capabilities for a common list of (re-) emerging diseases in crisis situation.
C1 [Cargnel, Mickael; Welby, Sarah; Koenen, Frank; Van der Stede, Yves; De Clercq, Kris] Sciensano, Brussels, Belgium.
   [Cargnel, Mickael; Bianchini, Juana; Saegerman, Claude] Fac Vet Med Fundamental & Appl Res Anim & Hlth FA, Res Unit Epidemiol & Risk Anal Appl Vet Sci UREAR, Liege, Belgium.
   [Van der Stede, Yves] European Food Safety Author, Parma, Italy.
RP Saegerman, C (通讯作者)，Univ Liege, Res Unit Epidemiol & Risk Anal Appl Vet Sci UREAR, Fundamental & Appl Res Anim & Hlth FARAH Ctr, Quartier Vallee 2,Ave Cureghem 7A,B42, B-4000 Liege, Belgium.
EM claude.saegerman@uliege.be
OI Cargnel, Mickael/0000-0002-7490-7903; Saegerman,
   Claude/0000-0001-9087-7436
FU Belgian Federal Public Service of Health, Food Chain Safety and
   Environment [RT13/3 EPIDIACAP]
FX Belgian Federal Public Service of Health, Food Chain Safety and
   Environment, Grant/Award Number: RT13/3 EPIDIACAP
CR Agence nationale de securite sanitaire de l'alimentation de l'environnement et du travail, 2012, METH HIER MAL AN APP
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   World Organisation for Animal Health, 2015, VES STOM
   World Organisation for Animal Health, 2018, NIPAH HENDR VIR DIS
NR 45
TC 1
Z9 1
U1 2
U2 6
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1865-1674
EI 1865-1682
J9 TRANSBOUND EMERG DIS
JI Transbound. Emerg. Dis.
PD MAY
PY 2021
VL 68
IS 3
BP 1175
EP 1189
DI 10.1111/tbed.13768
EA AUG 2020
PG 15
WC Infectious Diseases; Veterinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Infectious Diseases; Veterinary Sciences
GA SH7ZM
UT WOS:000561094000001
PM 32750203
OA Green Published
DA 2022-08-23
ER

PT J
AU Sharples, M
   Corlett, D
   Westmancott, O
AF Sharples, Mike
   Corlett, Dan
   Westmancott, Oliver
TI The Design and Implementation of a Mobile Learning Resource
SO PERSONAL AND UBIQUITOUS COMPUTING
LA English
DT Article
DE Collaborative learning; Handheld learning device; Interactive learning
   environments; Knowledge map; Mobile learning
AB The convergence of mobile communications and handheld computers offers the opportunity to develop technology that will assist individuals and groups to learn anytime, anywhere. We describe the theory-informed design, implementation and evaluation of a handheld learning device. It is intended to support children to capture everyday events such as images, notes and sounds, to relate them to web-based learning resources, to organise these into a visual knowledge map, and to share them with other learners and teachers. A working prototype system, for children aged 9-11, is discussed and evaluated, as an exemplar of personal mobile systems for life-long learning.
C1 [Sharples, Mike; Corlett, Dan; Westmancott, Oliver] Univ Birmingham, Dept Elect Elect & Comp Engn, Educ Technol Res Grp, Birmingham, W Midlands, England.
RP Sharples, M (通讯作者)，Univ Birmingham, Dept Elect Elect & Comp Engn, Educ Technol Res Grp, Edgbaston B15 2TT, W Midlands, England.
EM m.sharples@bham.ac.uk
CR [Anonymous], 1987, LEARNING EXPANDING A
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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]
   [No title captured]
   [No title captured]
NR 26
TC 177
Z9 177
U1 4
U2 45
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 1617-4909
EI 1617-4917
J9 PERS UBIQUIT COMPUT
JI Pers. Ubiquitous Comput.
PD MAY
PY 2002
VL 6
IS 3
BP 220
EP 234
DI 10.1007/s007790200021
PG 15
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA V11VG
UT WOS:000207558500007
DA 2022-08-23
ER

PT J
AU Xiao, H
   Hao, SY
AF Xiao, Han
   Hao, Shengyue
TI Public participation in infrastructure projects: an integrative review
   and prospects for the future research
SO ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
LA English
DT Review; Early Access
DE Public participation; Infrastructure projects; Literature review;
   Research framework; Bibliometric
ID CONSTRUCTION PROJECTS; STAKEHOLDER MANAGEMENT; ENERGY INFRASTRUCTURE;
   MAJOR INFRASTRUCTURE; GOVERNANCE; ENGAGEMENT; PERSPECTIVE; PERFORMANCE;
   FRAMEWORK; ENGLAND
AB Purpose In the past few decades, traditional project management theories mainly focused on quality, cost and schedule. However, with larger scale and wider influence of infrastructure projects, especially in megaprojects, social problems have gradually become pressing issues in the field of project governance. As the public is the main body of society, public participation has attracted the attention from both the academic field and industry field. Despite people are gradually realizing the significance of public participation, a deeper and more systematic understanding is still needed to be developed in this field. Therefore, this paper aims to conduct a systematic literature review on public participation in infrastructure projects. Based on existing literatures, this paper draws a knowledge map and develops a research framework in this field. Also, this paper tries to provide some theoretical and practical directions for the future. Design/methodology/approach In total, 142 papers from Web of Science Core collection were selected to be used in this systematic review. Quantitative analysis of this paper is concentrated on the knowledge map with bibliometric tools. Qualitative analysis is concentrated on identifying the research framework. Findings This paper reviewed existing literatures and the results are as below. Firstly, the results show a knowledge map containing knowledge domains, knowledge frontiers and knowledge evolution of research fields in public participation. Secondly, the results indicate a research framework in this field, including situational dimension, implementation dimension, individual dimension and influence dimension. Moreover, each dimension has several research topics, respectively. Finally, according to the results, the paper proposes practical suggestions for stakeholders and research directions for further study. Originality/value It is the first paper to draw a holistic knowledge map and build up a multilevel framework for public participation in the field of infrastructure projects. This paper bridges the gap in both quantitative and qualitative identification of existing literatures in this field. Besides, this paper proposes some practical suggestions and research directions for further study and development of public participation.
C1 [Xiao, Han; Hao, Shengyue] Beijing Jiaotong Univ, Beijing, Peoples R China.
RP Xiao, H (通讯作者)，Beijing Jiaotong Univ, Beijing, Peoples R China.
EM 19113057@bjtu.edu.cn
FU Fundamental Research Funds for the Central Universities [2021YJS063]
FX This paper is supported by the Fundamental Research Funds for the
   Central Universities (2021YJS063).
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NR 86
TC 1
Z9 1
U1 21
U2 35
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-06-2021-0495
EA NOV 2021
PG 22
WC Engineering, Industrial; Engineering, Civil; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics
GA XD6EU
UT WOS:000722800500001
DA 2022-08-23
ER

PT J
AU Guo, YL
   Geng, XL
   Chen, DH
   Chen, YF
AF Guo, Yanlong
   Geng, Xinlei
   Chen, Denghang
   Chen, Yufei
TI Sustainable Building Design Development Knowledge Map: A Visual Analysis
   Using CiteSpace
SO BUILDINGS
LA English
DT Article
DE sustainable; architecture; design; visualization; knowledge mapping
ID REINFORCED CONCRETE UHPFRC; LIFE-CYCLE ASSESSMENT; EMERGING TRENDS;
   ENERGY; PERFORMANCE; OPTIMIZATION; CONSTRUCTION; STRATEGIES; CAPACITY;
   PATTERNS
AB Based on the Web of Science (WoS) core collection database, this article compares the research results in this subject area since 2000 with the literature data on the theme of sustainable architectural design and conducts an in-depth investigation into the research themes, basic literature, development trends, and research frontiers. Qualitative and quantitative analyses were conducted through the CiteSpace scientific visualization software, and the degree of collaboration between authors, institutions, and countries was analysed through research power. The topical research hotspots and their evolution were explored through a word frequency analysis, cluster analysis, and timeline analysis; the origins and development of a particular issue in sustainable building design were explored in conjunction with mutation analysis; and the frontier hotspots were explored. The analysis of co-citations was used to identify important knowledge bases in the field; the flow of knowledge between disciplines was explored through biplot overlay analysis. By interpreting the scientific visualization knowledge map, it was concluded that the research trends in sustainable building design are mainly in the areas of resource control, energy consumption, renewable building materials, evaluation systems, and computer-aided tools, and so on. The major topics of future research related to sustainable building design are discussed and summarized.
C1 [Guo, Yanlong; Geng, Xinlei] Anhui Univ, Social Innovat Design Res Ctr, Dept Design, Hefei 203106, Peoples R China.
   [Chen, Denghang] Univ Sci & Technol China, Dept Sci & Technol Commun, Hefei 203106, Peoples R China.
   [Chen, Yufei] Hefei Univ Technol, Sch Architecture & Art, Hefei 203106, Peoples R China.
RP Guo, YL (通讯作者)，Anhui Univ, Social Innovat Design Res Ctr, Dept Design, Hefei 203106, Peoples R China.; Chen, DH (通讯作者)，Univ Sci & Technol China, Dept Sci & Technol Commun, Hefei 203106, Peoples R China.
EM 230601@ahu.edu.cn; n20301074@stu.ahu.edu.cn; hahn1122@mail.ustc.edu.cn;
   ustc0111@163.com
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NR 59
TC 0
Z9 0
U1 6
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-5309
J9 BUILDINGS-BASEL
JI BUILDINGS-BASEL
PD JUL
PY 2022
VL 12
IS 7
AR 969
DI 10.3390/buildings12070969
PG 22
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA 3J3ZN
UT WOS:000833336700001
OA gold
DA 2022-08-23
ER

PT S
AU Fong, S
   Poh, CK
AF Fong, S
   Poh, CK
BE Hui, LCK
   Lee, DL
TI Knowledge Beads - Approach for knowledge representation and market place
   negotiations for Internet agents
SO INTERNET APPLICATIONS
SE LECTURE NOTES IN COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 5th International Computer Science Conference (ICSC 99)
CY DEC 13-15, 1999
CL HONG KONG, PEOPLES R CHINA
SP IEEE, HK Sect, Comp Chapter
AB Knowledge Representation can be modeled using rule-based semantics with self-learning capabilities based on given feedback. There are 3 approaches expounded in this paper to achieve this model - a) knowledge-bead b) bill-of-knowledge and c) self learning knowledge mapping. Knowledge-Bead is the fundamental building blocks for rule-based systems that allow for synchronised attribute analysis and comparisons in an agent driven negotiation process. The bill of knowledge is the structure for collections of knowledge-beads that will allow inheritance of rules and attributes. This approach allows probabilistic weightage based knowledge maps so that an infinite level of states can be modelled. This model fits well with the negotiation process is made up of continuous non-binary states. We are also proposing alternative modesl for market place negotiations using Sensing Agent.
C1 Nanyang Technol Univ, Sch Appl Sci, Singapore 2263, Singapore.
RP Fong, S (通讯作者)，Nanyang Technol Univ, Sch Appl Sci, Singapore 2263, Singapore.
RI Fong, Simon/C-9388-2009
OI Fong, Simon/0000-0002-1848-7246
CR CAGLAYAN AK, 1997, AGENT SOURCEBOOK, P179
NR 1
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-66903-5
J9 LECT NOTES COMPUT SC
PY 1999
VL 1749
BP 467
EP 473
PG 7
WC Computer Science, Theory & Methods; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA BQ97D
UT WOS:000165175400053
DA 2022-08-23
ER

PT J
AU Wei, CP
   Yang, CC
   Lin, CM
AF Wei, Chih-Ping
   Yang, Christopher C.
   Lin, Chia-Min
TI A Latent Semantic Indexing-based approach to multilingual document
   clustering
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE document management; text mining; document clustering; multilingual
   document clustering; multilingual knowledge management; Latent Semantic
   Indexing
ID KNOWLEDGE MANAGEMENT; WEB
AB The creation and deployment of knowledge repositories for managing, sharing, and reusing tacit knowledge within an organization has emerged as a prevalent approach in current knowledge management practices. A knowledge repository typically contains vast amounts of formal knowledge elements, which generally are available as documents. To facilitate users' navigation of documents within a knowledge repository, knowledge maps, often created by document clustering techniques, represent an appealing and promising approach. Various document clustering techniques have been proposed in the literature, but most deal with monolingual documents (i.e., written in the same language). However, as a result of increased globalization and advances in Internet technology, an organization often maintains documents in different languages in its knowledge repositories, which necessitates multilingual document clustering (MLDC) to create organizational knowledge maps. Motivated by the significance of this demand, this study designs a Latent Semantic Indexing (LSI)-based MLDC technique capable of generating knowledge maps (i.e., document clusters) from multilingual documents. The empirical evaluation results show that the proposed LSI-based MLDC technique achieves satisfactory clustering effectiveness, measured by both cluster recall and cluster precision, and is capable of maintaining a good balance between monolingual and cross-lingual clustering effectiveness when clustering a multilingual document corpus. (c) 2007 Elsevier B.V. All rights reserved.
C1 [Wei, Chih-Ping] Natl Tsing Hua Univ, Coll Technol Management, Inst Technol Management, Hsinchu, Taiwan.
   [Yang, Christopher C.] Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Hong Kong, Peoples R China.
   [Lin, Chia-Min] Openfind Informat Technol Inc, Taipei, Taiwan.
RP Wei, CP (通讯作者)，Natl Tsing Hua Univ, Coll Technol Management, Inst Technol Management, Hsinchu, Taiwan.
EM cpwei@mx.nthu.edu.tw; yang@se.cuhk.edu.hk; alucard.lin@gmail.com
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NR 41
TC 50
Z9 53
U1 2
U2 24
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 JUN
PY 2008
VL 45
IS 3
BP 606
EP 620
DI 10.1016/j.dss.2007.07.008
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Operations Research & Management Science
GA 325CX
UT WOS:000257566900020
DA 2022-08-23
ER

PT J
AU Xue, WZ
   Li, H
   Ali, R
   Rehman, RU
AF Xue, Wuzhao
   Li, Hua
   Ali, Rizwan
   Rehman, Ramiz Ur
TI Knowledge Mapping of Corporate Financial Performance Research: A Visual
   Analysis Using Cite Space and Ucinet
SO SUSTAINABILITY
LA English
DT Review
DE corporate financial performance; knowledge mapping; cite Space; Ucinet
ID SOCIAL PERFORMANCE; RESPONSIBILITY; MANAGEMENT; DETERMINANTS;
   INDUSTRIAL; STRATEGY
AB Financial performance is an indicator of the financial stability and the health of a firm. It is a measure of how well a firm uses its assets to generate revenues, a firm's credibility, and its ability to pay off debts. To study the theoretical development, empirical examinations, and growing trend of financial performance research, this study reviewed the financial performance literature based on 875 journal articles sourced from the Web of Science, ACM, and Scopus databases from 2005 to 2019. To carry out the review, we applied Cite Space and Ucinet software for visualization and social network analysis, statistical analysis, co-occurrence analysis, and other research methods. We obtained a panoramic depiction of the posts, authors, journals, and keywords, and also reviewed financial performance using the field of knowledge maps. Accordingly, this study highlighted the recent and future research trends, and found that corporate social responsibility, and the green and sustainable corporate environment are potential research areas. These findings, through the combination of bibliometric methods and a systematic review, provide a better understanding of the development of corporate financial performance for both researchers and practitioners.
C1 [Xue, Wuzhao; Li, Hua] Xidian Univ, Sch Econ & Management, Xian 710126, Peoples R China.
   [Ali, Rizwan; Rehman, Ramiz Ur] Univ Lahore, Lahore Business Sch, Lahore 54100, Pakistan.
   [Rehman, Ramiz Ur] Xian Polytech Univ, Sch Management, Xian 710048, Peoples R China.
RP Ali, R (通讯作者)，Univ Lahore, Lahore Business Sch, Lahore 54100, Pakistan.
EM xuewz@xidian.edu.cn; lihua@xidian.edu.cn; rizwan.ali@lbs.uol.edu.pk;
   ramiz_rehman@xpu.edu.cn
RI Rehman, Ramiz ur/N-9684-2019
OI Rehman, Ramiz ur/0000-0001-6019-4781; Ali, Rizwan/0000-0002-4439-6815
CR [Anonymous], BANK AM J APPL CORP
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NR 46
TC 7
Z9 7
U1 37
U2 67
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAY
PY 2020
VL 12
IS 9
AR 3554
DI 10.3390/su12093554
PG 21
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA LU0TK
UT WOS:000537476200053
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Feng, JW
   Lin, X
AF Feng, Jianwen
   Lin, Xuan
TI Research on Online Learners' Course Recommendation System Based on
   Knowledge Atlas in Smart Education Cloud Platform
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB At present, there are still some problems in the application of recommendation algorithm in course, such as data sparseness, lack of knowledge logical relationship, and lack of explanation. Therefore, in this paper, the recommendation system of course for online learners based on knowledge map is implemented where different functional modules are designed, and the recommendation model is optimized based on the loss function. In addition, a recommendation algorithm is designed, which selects the data set, tests the algorithm based on knowledge map, and mainly evaluates the performance from the rate of click-through predicted evaluation, accuracy rate, and recall rate. The results show that the overall performance of online learner course recommendation system based on knowledge is excellent.
C1 [Feng, Jianwen] Hanshan Normal Univ, Deans Off, Chaozhou 521041, Guangdong, Peoples R China.
   [Lin, Xuan] Hanshan Normal Univ, Sch Comp & Informat Engn, Chaozhou 521041, Guangdong, Peoples R China.
RP Lin, X (通讯作者)，Hanshan Normal Univ, Sch Comp & Informat Engn, Chaozhou 521041, Guangdong, Peoples R China.
EM linxuan@hstc.edu.cn
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NR 20
TC 0
Z9 0
U1 0
U2 0
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD MAY 27
PY 2022
VL 2022
AR 5043838
DI 10.1155/2022/5043838
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 2Z3KM
UT WOS:000826481400005
OA gold
DA 2022-08-23
ER

PT J
AU Popescu, G
AF Popescu, Gheorghe
TI ACHIEVING COMPETITIVE ADVANTAGE THROUGH SHAPING SHARP BRAINS: TOWARDS
   STRATEGIC DYNAMICS
SO METALURGIA INTERNATIONAL
LA English
DT Article
DE competitive advantage; sharp brains; applied research; innovation;
   strategic dynamics; SME
ID PERFORMANCE
AB Knowledge mapping is a process, in which we use methods and tools for analyzing knowledge areas in order to discover features or meaning and to visualize these in a comprehensive, transparent form. Knowledge maps are created by transferring certain aspects of knowledge into a graphical form that is easily understandable. Research and innovation need a predictable and favorable regulative frame to attract the private investments and to allow the capitalization of the new ideas on the market. It should also take into account the sectorial specificities, the SME's needs, and the role of the public research organizations. In the same time it should contribute to the achievement of the political objectives in fields such as environment, safety, health and transport. In this context, all the policies at National and Community level should be harmonized so to support the excellence in fundamental and applied research.
C1 Dimitrie Cantemir Univ, Bucharest, Romania.
RP Popescu, G (通讯作者)，Dimitrie Cantemir Univ, Bucharest, Romania.
EM popescu_ucdc@yahoo.com
CR Amit R., 1999, EUROPEAN MANAGEMENT, V17, P174, DOI DOI 10.1016/S0263-2373(98)00076-0
   Buller PF, 2012, HUM RESOUR MANAGE R, V22, P43, DOI 10.1016/j.hrmr.2011.11.002
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NR 10
TC 0
Z9 0
U1 0
U2 7
PU EDITURA STIINTIFICA FMR
PI BUCHAREST
PA CALEA GRIVITEI, NR 83, SECTOR 1, O P 12, BUCHAREST, 010705, ROMANIA
SN 1582-2214
J9 METAL INT
JI Metal. Int.
PY 2013
VL 18
IS 2
BP 190
EP 192
PG 3
WC Metallurgy & Metallurgical Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Metallurgy & Metallurgical Engineering
GA 069XD
UT WOS:000313469500038
DA 2022-08-23
ER

PT J
AU Su, CY
   Wang, TI
AF Su, C. Y.
   Wang, T. I.
TI Construction and analysis of educational assessments using knowledge
   maps with weight appraisal of concepts
SO COMPUTERS & EDUCATION
LA English
DT Article
DE Applications in subject areas; Architectures for educational technology
   system; Elementary education; Human-computer interface; Improving
   classroom teaching
ID TECHNOLOGY ACCEPTANCE MODEL; DIAGNOSIS; TEACHERS; SYSTEM
AB The rapid advance of information and communication technologies (ICT) has important impacts on teaching and learning, as well as on the educational assessment. Teachers may create assessments utilizing some developed assessment software or test authoring tools. However, problems could occur, such as neglecting key concepts in the curriculum or having disproportionate course topics distribution, when teachers create assessments or test items. This study proposes a novel approach, which uses knowledge map with appraisal of concept weights and other ICTs, and implements an assessment system KMAAS to help primary school teachers in Taiwan, or elsewhere, create educational assessments properly. When compiling an assessment, KMAAS ensures that teachers can include all important course concepts intended for assessing and maintain correct balance between course concepts among test items. It does so first by analyzing course material of the assessment range and displaying a concept-weight-annotated knowledge map which concretize and visualize the importance of and the relationships among concepts in the range. It then analyzes the test sheet which is being complied and displays another similar real-time updated knowledge map containing balance between course concepts among the test items. Teachers may cross-refer to these maps to help them adjust concept balances and even select appropriate test items from test banks. The system has being evaluated in both the accuracy of learning concepts extraction and the degree of user satisfaction, as measured by questionnaires given to the teachers who tested the system. The promising results confirm the feasibility of this system in helping teachers compile their educational assessments easily and precisely. Other results of the formative evaluations on techniques have being used to improve the system in order to make it more effective and efficient. The methodology and technologies KMAAS employed are all well developed and are domain independent, which makes it highly flexible to transfer to other course subject domain too. (C) 2010 Elsevier Ltd. All rights reserved.
C1 [Su, C. Y.; Wang, T. I.] Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan.
RP Su, CY (通讯作者)，Natl Cheng Kung Univ, Dept Engn Sci, 1 Univ Rd, Tainan 701, Taiwan.
EM n9897108@mail.ncku.edu.tw
OI Su, Chien-Yuan/0000-0003-1639-3948
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NR 60
TC 15
Z9 15
U1 2
U2 39
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 NOV
PY 2010
VL 55
IS 3
BP 1300
EP 1311
DI 10.1016/j.compedu.2010.05.027
PG 12
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 639PB
UT WOS:000280985600036
DA 2022-08-23
ER

PT J
AU Tian, M
   Li, J
AF Tian, Miao
   Li, Jun
TI Knowledge mapping of protective clothing research-a bibliometric
   analysis based on visualization methodology
SO TEXTILE RESEARCH JOURNAL
LA English
DT Article
DE protective clothing; knowledge mapping; bibliometric; visual analysis;
   CiteSpace
ID HEAT-TRANSFER; PERFORMANCE; DESIGN; SIMULATION; MANNEQUIN; NETWORKS;
   EXPOSURE; EXERCISE; DEVICES; ENERGY
AB In order to further understanding of the research status and fronts, a novel method was adopted in the textile and apparel field to perform knowledge mapping of protective clothing research over the last 20 years. The database of 1735 articles was built based on records retrieved from the Web of Science. Visualization software, CiteSpace, combined with Google Earth was applied to determine intellectual basis and research fronts for the protective clothing domain. Research area analysis indicated that the top ranked field was the "Materials Science" with a number of articles of 427. Publication distribution revealed that the Textile Research Journal was the most popular cited and citing journal of the protective clothing articles. The USA and China were the two primary countries contributing to the protective clothing research evidenced by the frequency, bursts and centrality. Donghua University, North Carolina State University and the University of Alberta, with a high publication frequency and centrality, were identified to be the main research drivers. The intensity of red nodes in the geographical visualization map proved the core status of Europe and America in the global cooperation network. According to the co-occurrence analysis, the three keywords of exposure, performance and heat stress were detected to be the most popular research topics over the last 20 years, corresponding to the study of exposure environment, performance evaluation and thermal physiology. The keywords in recent years suggested the research trend of enhancing the mechanism and fundamental investigation of the heat transfer process and fabrics.
C1 [Tian, Miao; Li, Jun] Donghua Univ, Coll Fash & Design, 1882 West Yan An Rd, Shanghai 200051, Peoples R China.
   [Tian, Miao; Li, Jun] Donghua Univ, Protect Clothing Res Ctr, Coll Fash & Design, Shanghai, Peoples R China.
   [Tian, Miao; Li, Jun] Donghua Univ, Key Lab Clothing Design & Technol, Minist Educ, Shanghai, Peoples R China.
RP Li, J (通讯作者)，Donghua Univ, Coll Fash & Design, 1882 West Yan An Rd, Shanghai 200051, Peoples R China.
EM lijun@dhu.edu.cn
FU National Nature Science Foundation [51576038]; Fundamental Research
   Funds for the Central Universities [2232018G-08]; Shanghai Municipal
   Natural Science Foundation [17ZR1400500]; Start-up Research Funding for
   Young Teachers of Donghua University [107-07-0053017]; Shanghai Summit
   Discipline in Design
FX The authors disclosed receipt of the following financial support for the
   research, authorship, and/or publication of this article: This work was
   supported by the National Nature Science Foundation (Grant Number
   51576038), the Fundamental Research Funds for the Central Universities
   (Grant Number 2232018G-08), the Shanghai Municipal Natural Science
   Foundation (Grant Number 17ZR1400500), Start-up Research Funding for
   Young Teachers of Donghua University (Grant Number 107-07-0053017) and
   the Shanghai Summit Discipline in Design.
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NR 68
TC 7
Z9 7
U1 8
U2 102
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0040-5175
EI 1746-7748
J9 TEXT RES J
JI Text. Res. J.
PD AUG
PY 2019
VL 89
IS 16
BP 3203
EP 3220
DI 10.1177/0040517518809044
PG 18
WC Materials Science, Textiles
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Materials Science
GA IM0ZX
UT WOS:000477719300001
DA 2022-08-23
ER

PT J
AU Chen, L
   Xia, CH
   Lei, SW
   Wang, TB
AF Chen, Long
   Xia, Chunhe
   Lei, Shengwei
   Wang, Tianbo
TI Detection, Traceability, and Propagation of Mobile Malware Threats
SO IEEE ACCESS
LA English
DT Article
DE Android mobile malware; threat traceability; family chronology;
   propagation models; detection analysis; infected system environment;
   knowledge map construction; architecture of mobile malware security
   analysis
ID MODEL
AB In recent years, the application of smartphones, Android operating systems and mobile applications have become more prevalent worldwide. To study the traceability, propagation, and detection of the threats, we perform research on all aspects of the end-to-end environment. With machine learning based on the mobile malware detection algorithms that integrate the dynamic and static research of the identification algorithm, application software samples are collected to study sentences. Through knowledge labeling and knowledge construction, the association relationship of knowledge is extracted to realize the research of knowledge map construction. Flooding is closely correlated with the complexity of the Android mobile version of the kernel and malicious programs. A static dynamic analysis of the mobile malicious program is carried out, and the social network social diagram is constructed to model the propagation of the mobile malicious program. We extended the approach of deriving common malware behavior through graph clustering. On this basis, Android behavior analysis is performed through our virtual machine execution engine. We extend the family characteristics to the concept of DNA race genes. By studying SMS/MMS, Bluetooth, 5G base station networks, metropolitan area networks, social networks, homogeneous communities, telecommunication networks, and application market ecosystem propagation scenarios, we discovered the law of propagation. In addition, we studied the construction of the mobile Internet big data knowledge graph. Quantitative data for the main family chronology of mobile malware are obtained. We conducted detailed research and comprehensive analysis of Android application package (APK) details and behavior, relationship, resource-centric, and syntactic aspects. Furthermore, we summarized the architecture of mobile malware security analysis. We also discuss encryption of malware traffic discrimination. These precise modeling and quantified research results constitute the architecture of mobile malware analysis.
C1 [Chen, Long; Xia, Chunhe; Lei, Shengwei; Wang, Tianbo] Beihang Univ, Beijing Key Lab Network Technol, Beijing 100191, Peoples R China.
   [Chen, Long] Beijing Topsec Network Secur Technol Co Ltd, Innovat Technol Res Inst, Beijing 100085, Peoples R China.
   [Chen, Long] China United Network Commun Ltd, Home Internet Operat Ctr, Beijing 100032, Peoples R China.
   [Xia, Chunhe] Guangxi Normal Univ, Sch Comp Sci & Informat Technol, Guilin 541004, Peoples R China.
   [Wang, Tianbo] Beihang Univ, Sch Cyber Sci & Technol, Beijing 100191, Peoples R China.
RP Wang, TB (通讯作者)，Beihang Univ, Beijing Key Lab Network Technol, Beijing 100191, Peoples R China.; Wang, TB (通讯作者)，Beihang Univ, Sch Cyber Sci & Technol, Beijing 100191, Peoples R China.
EM wangtb@buaa.edu.cn
OI Chen, Long/0000-0002-2535-4978
FU National Natural Science Foundation of China [61862008, U1636208,
   61902013]; Shijiazhuang Science and Technology Research and Development
   Plan [201130351A]; Beihang Youth Top Talent Support Program
   [YWF-20-BJ-J-1038]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61862008, Grant U1636208, and Grant
   61902013; in part by the Shijiazhuang Science and Technology Research
   and Development Plan under Grant 201130351A; and in part by the Beihang
   Youth Top Talent Support Program under Grant YWF-20-BJ-J-1038.
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NR 85
TC 2
Z9 2
U1 3
U2 14
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 14576
EP 14598
DI 10.1109/ACCESS.2021.3049819
PG 23
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA QA1QO
UT WOS:000613224400001
OA gold
DA 2022-08-23
ER

PT J
AU Huang, L
   Zhao, YQ
   Xiang, MH
AF Huang, Li
   Zhao, Yanqing
   Xiang, Minhong
TI Knowledge Mapping of Acupuncture for Cancer Pain: A Scientometric
   Analysis (2000-2019)
SO JOURNAL OF PAIN RESEARCH
LA English
DT Article
DE knowledge mapping; acupuncture; cancer pain; CiteSpace; scientometric
   analysis
ID INDUCED PERIPHERAL NEUROPATHY; CHEMOTHERAPY-INDUCED NAUSEA; STRESS
   REDUCTION PROGRAM; QUALITY-OF-LIFE; BREAST-CANCER; ALTERNATIVE MEDICINE;
   CONTROLLED-TRIAL; JOINT SYMPTOMS; CLINICAL-TRIAL; MANAGEMENT
AB Objective: This study aimed to demonstrate the state of the present situation and trends concerning the global use of acupuncture for cancer pain in the past 20 years.
   Methods: Searched the Web of Science database from 2000 to 2019 related to acupuncture for cancer pain, and then used CiteSpace to conduct scientometric analysis to acquire the knowledge mapping.
   Results: Yearly output has increased year by year, and the growth rate has become faster after 2012. According to the cluster analysis of institutions, authors, cited references, and keywords, 4, 4, 15, and 14 categories were obtained, respectively. The most productive countries, institutions, and authors are the USA, Mem Sloan Kettering Cancer Center, and Mao JJ, whose frequencies are 196, 24, and 17, respectively. However, the most important of them are Australia, Univ. Maryland, and Bao T, owing to their highest centrality, they are 0.90, 0.21, and 0.09 separately. Moreover, cited references that contributed to the most cocitations are Crew KD (2010), however, the most key cited reference is Roscoe JA (2003). Keywords such as acupuncture, pain, breast cancer, palliative care, and quality of life are the most frequently used. But auricular acupuncture is the crucial keyword. In the cluster analysis of institutions, authors, cited references, and keywords, the more convincing research categories are multiple myeloma, placebo effect, neck malignancies, and early breast cancer, with S values of 0.990, 0.991, 0.990, and 0.923, respectively. Therefore, they can be regarded as research hotspots in this field.
   Conclusion: Based on the scientometric analysis in the past 20 years, the knowledge mapping of the country, institution, author, cited reference, and the keyword is gained, which has an important guiding significance for quickly and accurately positioning the trend in this field.
C1 [Huang, Li; Zhao, Yanqing; Xiang, Minhong] Shanghai Univ Tradit Chinese Med, Putuo Hosp, 164 Lanxi Rd, Shanghai 200062, Peoples R China.
RP Xiang, MH (通讯作者)，Shanghai Univ Tradit Chinese Med, Putuo Hosp, 164 Lanxi Rd, Shanghai 200062, Peoples R China.
EM xiangminhong1977@126.com
OI zhao, yanqing/0000-0003-4003-0063
FU National Natural Science Foundation of China [82,074,495]; Shanghai
   University of Chinese Medicine [2019WK118]; Scientific Research Project
   of Putuo Hospital, Shanghai University of Traditional Chinese Medicine
   [2020304A]
FX This study was supported by grants from the National Natural Science
   Foundation of China (no. 82,074,495), Budget Project of Shanghai
   University of Chinese Medicine (no. 2019WK118), and Scientific Research
   Project of Putuo Hospital, Shanghai University of Traditional Chinese
   Medicine (No.2020304A).
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NR 82
TC 3
Z9 3
U1 4
U2 14
PU DOVE MEDICAL PRESS LTD
PI ALBANY
PA PO BOX 300-008, ALBANY, AUCKLAND 0752, NEW ZEALAND
SN 1178-7090
J9 J PAIN RES
JI J. Pain Res.
PY 2021
VL 14
BP 343
EP 358
DI 10.2147/JPR.S292657
PG 16
WC Clinical Neurology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA QI1PN
UT WOS:000618745500001
PM 33574698
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yu, J
   Tao, C
   Xu, LY
   Wu, HQ
   Liu, FF
AF Yu, Jie
   Tao, Chao
   Xu, Lingyu
   Wu, Haiqiao
   Liu, Fangfang
TI Construction of Hierarchical Cognitive Academic Map
SO IEEE ACCESS
LA English
DT Article
DE Knowledge representation; cognition; entropy; clustering algorithms;
   Bayesian Rose Tree
ID KNOWLEDGE MAP; COUNTRY
AB Nowadays, mobile devices have been considered as a new platform for information services, and have been widely used in many fields. In mobile application services, the processing and representation of data is a key issue which has a great impact on the service quality. Knowledge map is regarded as an effective method and has been widely utilized in mobile devices. However, traditional knowledge maps employed in mobile devices are subject to a lack of cognition characteristics, which results in corresponding information services' being unable to match the users' cognition level, thus affecting the quality of services. In this paper, we propose a hierarchical cognitive academic map (HCAM) for the specific academic domain application background. HCAM can meet the needs of three basic levels of Bloom's cognition taxonomy model by distinguishing the academic attributes of nodes and relations between nodes. First, academic concepts are the basic units in HCAM and are classified into research object concepts and method/technique concepts, which meet the human's remembering cognition levels. Second, HCAM provides the implementation and collaboration relation between concepts, which satisfies the human's applying and understanding cognition levels. Third, technique/method concepts are organized in the form of hierarchical structure from the top down of which concepts' specificity for the domain get higher and higher. In addition, Bayesian rose tree clustering is adopted in the construction of this hierarchical structure and acquiring the cognition depth for each concept. Furthermore, experiments on information retrieval field and data mining field are performed to demonstrate the effectiveness and cognition characteristics of HCAM.
C1 [Yu, Jie; Tao, Chao; Xu, Lingyu; Wu, Haiqiao; Liu, Fangfang] Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China.
RP Xu, LY (通讯作者)，Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China.
EM xly@shu.edu.cn
OI Tao, Chao/0000-0003-4496-9510
FU National Key Research and Development Plan [2016YFC1401902]; Shanghai
   Natural Science Foundation [12zr1411000]
FX This work was supported in part by National Key Research and Development
   Plan under Grant 2016YFC1401902 and in part by Shanghai Natural Science
   Foundation under Grant 12zr1411000.
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   Zhang W, 2015, QUAL QUANT, V49, P1023, DOI 10.1007/s11135-014-0032-9
NR 31
TC 3
Z9 3
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 2017
VL 5
BP 2141
EP 2151
DI 10.1109/ACCESS.2017.2657790
PG 11
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 EP1HV
UT WOS:000397136900123
OA gold
DA 2022-08-23
ER

PT J
AU Vimal, R
   Morgans, C
AF Vimal, Ruppert
   Morgans, Courtney
TI Using knowledge mapping to rethink the gap between science and action
SO CONSERVATION BIOLOGY
LA English
DT Article
DE biodiversity; conservation network; knowledge management; biodiversidad;
   manejo del conocimiento; red de conservacion; (sic)(sic)(sic)(sic);
   (sic)(sic)(sic)(sic)(sic); (sic)(sic)(sic)(sic)
ID TROPICAL NATIONAL-PARKS; STAKEHOLDER ANALYSIS; POLICY-RELEVANT;
   CONSERVATION; BIODIVERSITY; PARTICIPATION; EXCHANGE; DECLINE; SYSTEMS;
   AGENDA
AB Scholars have long stressed the need to bridge the gap between science and action and seek the most efficient use of knowledge for decision making. Many contributors have attempted to consider and understand the sociopolitical forces involved in knowledge generation and exchange. We argue, however, that a model is still needed to adequately conceptualize and frame the knowledge networks in which these processes are embedded. We devised a model for knowledge mapping as a prerequisite for knowledge management in the context of conservation. Using great ape conservation to frame our approach, we propose that knowledge mapping should be based on 2 key principles. First, each conservation network results from the conglomeration of subnetworks of expertise producing and using knowledge. Second, beyond the research-management gradient, other dimensions, such as the scale of operation, geographic location, and organizational characteristics, must also be considered. Assessing both knowledge production and trajectory across different dimensions of the network opens new space for investigating and reducing the gap between science and action.
C1 [Vimal, Ruppert] Univ Jean Jaures, CNRS, GEODE UMR 5602, 5 Allee Antonio Machado, F-31058 Toulouse, France.
   [Vimal, Ruppert] German Ctr Integrat Biodivers Res iDiv, Deutsch Pl 5e, D-04103 Leipzig, Germany.
   [Morgans, Courtney] Univ Queensland, Sch Biol Sci, Ctr Biodivers & Conservat Sci, Brisbane, Qld 4072, Australia.
RP Vimal, R (通讯作者)，Univ Jean Jaures, CNRS, GEODE UMR 5602, 5 Allee Antonio Machado, F-31058 Toulouse, France.; Vimal, R (通讯作者)，German Ctr Integrat Biodivers Res iDiv, Deutsch Pl 5e, D-04103 Leipzig, Germany.
EM ruppert.vimal@univ-tlse2.fr
RI Morgans, Courtney/D-4087-2014
OI Morgans, Courtney/0000-0002-2562-1482
FU German Centre for Integrative Biodiversity (iDiv); Max Planck Institute
   for Evolutionary Anthropology; Australian Research Training award
FX This project was supported by the German Centre for Integrative
   Biodiversity (iDiv) and the Max Planck Institute for Evolutionary
   Anthropology. C.L.M. was supported by an Australian Research Training
   award.
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NR 74
TC 2
Z9 2
U1 3
U2 26
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0888-8892
EI 1523-1739
J9 CONSERV BIOL
JI Conserv. Biol.
PD DEC
PY 2020
VL 34
IS 6
BP 1433
EP 1443
DI 10.1111/cobi.13563
EA SEP 2020
PG 11
WC Biodiversity Conservation; Ecology; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biodiversity & Conservation; Environmental Sciences & Ecology
GA OW1GY
UT WOS:000569112900001
PM 32506700
OA Green Published
DA 2022-08-23
ER

PT J
AU Yang, M
   Joo, W
   Choi, K
   Kim, Y
   Kim, Y
AF Yang, MyungSeok
   Joo, WonKyun
   Choi, KiSeok
   Kim, YoungKuk
   Kim, YunJeong
TI Development of Platform-Based Knowledge Map Service to get Data Insights
   of R&D Institution on User-Interested Subjects
SO WIRELESS PERSONAL COMMUNICATIONS
LA English
DT Article
DE NTIS; Knowledge Map; R&D Information; R&D Institute Map; Data Insight
ID INFORMATION; SYSTEM; SECURITY
AB Generally, any database is developed for the purpose of providing certain services for retrieval or management of data. If rules or processes designed to extract and analyze data in consideration of data characteristics are simplified and services are provided to users by visualizing analysis results in an easy-to-understand fashion, users will be able to get diverse insights easily without much cost or effort. If this kind of database is constructed and provided in NTIS database as well (e.g., project data, research outcome data such as paper and patent, personnel information, etc.) users might be able to get varied insights on national R&D data. The approach proposed in this study is significant in that it provides platform-based analysis services which can create a knowledge map after extracting and analyzing user-interested data and subsequently visualizing the results. Furthermore, this study is intended to make users obtain insight on a research institute more easily by providing analyzable additional information, not only as a list of research institutes in user-preferred subjects after providing the results of the analysis on R&D expenses, but also in regional distribution and annual changes in core research institutes, focusing on those taking part in national R&D programs in certain fields.
C1 [Yang, MyungSeok; Joo, WonKyun; Choi, KiSeok; Kim, YunJeong] Korea Inst Sci & Technol Informat, NTIS Ctr, DaeHackRo 245, Daejeon 34141, South Korea.
   [Kim, YoungKuk; Kim, YunJeong] Chungnam Natl Univ, Dept Comp Sci & Engn, 99 Daehak Ro, Daejeon 34141, South Korea.
RP Kim, Y (通讯作者)，Korea Inst Sci & Technol Informat, NTIS Ctr, DaeHackRo 245, Daejeon 34141, South Korea.; Kim, Y (通讯作者)，Chungnam Natl Univ, Dept Comp Sci & Engn, 99 Daehak Ro, Daejeon 34141, South Korea.
EM msyang@kisti.re.kr; joo@kisti.re.kr; choi@kisti.re.kr; ykim@cnu.ac.kr;
   miso@kisti.re.kr
OI Kim, Young-Kuk/0000-0002-8329-9348
FU 'Maximize the Value of National Science and Technology by Strengthen
   Sharing/Collaboration of National R&D Information' - Korea Institute of
   Science and Technology Information
FX This research was supported by 'Maximize the Value of National Science
   and Technology by Strengthen Sharing/Collaboration of National R&D
   Information' funded by the Korea Institute of Science and Technology
   Information.
CR Chae CJ, 2016, WIRELESS PERS COMMUN, V89, P819, DOI 10.1007/s11277-015-3113-8
   Gim J., 2016, WIRELESS PERSONAL CO
   Kim YK, 2016, WIRELESS PERS COMMUN, V89, P977, DOI 10.1007/s11277-016-3359-9
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NR 9
TC 1
Z9 1
U1 1
U2 14
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 0929-6212
EI 1572-834X
J9 WIRELESS PERS COMMUN
JI Wirel. Pers. Commun.
PD FEB
PY 2018
VL 98
IS 4
BP 3265
EP 3285
DI 10.1007/s11277-017-5097-z
PG 21
WC Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Telecommunications
GA FW0US
UT WOS:000425011700016
DA 2022-08-23
ER

PT J
AU Lin, YC
   Wang, LC
   Tserng, HP
AF Lin, Yu-Cheng
   Wang, Lung-Chuang
   Tserng, H. Ping
TI Enhancing knowledge exchange through web map-based knowledge management
   system in construction: Lessons learned in Taiwan
SO AUTOMATION IN CONSTRUCTION
LA English
DT Article
DE knowledge management; knowledge map; web-based application; construction
   projects
AB Knowledge management involves creating, securing, coordinating, combining, retrieving and distributing knowledge. Knowledge can be reused and shared among engineers and experts to enhance construction processes and decrease the time and cost of solving problems. This study presents a novel and practical method to capture and represent construction project knowledge by using network knowledge maps. Network Knowledge Maps (NKM) gives users an overview of available and missing knowledge in core project areas, enabling tacit and explicit knowledge to be managed appropriately. This study addresses application of knowledge management in the construction phase of construction projects, and presents a construction Map-based Knowledge Management (MBKM) concept and system for contractors. The MBKM system is then utilized in selected case studies involving a High-Tech factory building enterprise in Taiwan to verify the proposed methodology and indicate the effectiveness of sharing knowledge, particularly in the construction phase. Knowledge can be captured and managed to benefit future projects by effectively utilizing information and web technologies during the construction phase of a project. The results of this study demonstrate that an MBKM-like system can be applied effectively in knowledge management systems in the construction industry by using map-based knowledge management and web technology. (C) 2005 Elsevier B.V. All rights reserved.
C1 Ching Yun Univ, Inst Civil Engn & Disaster Reduct Technol, Jhongli, Taiwan.
   Natl Taipei Univ Technol, Inst Civil & Disaster Prevent Engn, Taipei, Taiwan.
   Natl Taiwan Univ, Dept Civil Engn, Div Construct Engn & Management, Taipei 10764, Taiwan.
RP Lin, YC (通讯作者)，Ching Yun Univ, Inst Civil Engn & Disaster Reduct Technol, 229 Chien Hsin Rd, Jhongli, Taiwan.
EM d89521016@ntu.edu.tw; lcwang@ntut.edu.tw; hptserng@ce.ntu.edu.tw
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NR 21
TC 61
Z9 61
U1 1
U2 36
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0926-5805
EI 1872-7891
J9 AUTOMAT CONSTR
JI Autom. Constr.
PD NOV
PY 2006
VL 15
IS 6
BP 693
EP 705
DI 10.1016/j.autcon.2005.09.006
PG 13
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA 090EJ
UT WOS:000240933100003
DA 2022-08-23
ER

PT S
AU Hansen, BH
   Kautz, K
AF Hansen, BH
   Kautz, K
BE Dingsoyr, T
TI Knowledge mapping: A technique for identifying knowledge flows in
   software organisations
SO SOFTWARE PROCESS IMPROVEMENT, PROCEEDINGS
SE Lecture Notes in Computer Science
LA English
DT Article; Proceedings Paper
CT 11th European Software Process Improvement Conference
CY NOV 10-12, 2004
CL Trondheim, NORWAY
AB Knowledge is a key parameter for software companies' survival, as the ability to continuously become better at producing services relies on the organisation's abilities to develop and utilise the intellectual competencies, which its employees possess. These abilities depend highly on the organisation's capability to share knowledge and thus on the way knowledge flows in the organisation. In this paper we present a knowledge management perspective on software process improvement, and describe a technique for mapping the organisational knowledge flows in a software company. The results show that the technique successfully helps the organisation to select relevant focus areas for planning future improvement initiatives. The study further explains four distinct critical situations, which can be identified in a knowledge map; Hubs, Black Holes, Springs, and Missing Links. Each covers potential problems in the organisational flows and therefore can provide guidance for organisational process improvements.
C1 Copenhagen Sch Econ & Business Adm, Dept Informat, DK-2000 Frederiksberg, Denmark.
RP Hansen, BH (通讯作者)，Copenhagen Sch Econ & Business Adm, Dept Informat, Howitzvej 60, DK-2000 Frederiksberg, Denmark.
EM bo@cbs.dk; kautz@cbs.dk
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NR 22
TC 16
Z9 16
U1 1
U2 7
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 3-540-23725-9
J9 LECT NOTES COMPUT SC
PY 2004
VL 3281
BP 126
EP 137
PG 12
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BBD98
UT WOS:000225083200012
DA 2022-08-23
ER

PT J
AU Mohammadi, E
AF Mohammadi, Ehsan
TI Knowledge mapping of the Iranian nanoscience and technology: a text
   mining approach
SO SCIENTOMETRICS
LA English
DT Article
DE Science map; Knowledge map; Nano science and technology; Text mining
ID CO-WORD ANALYSIS; NEURAL-NETWORK RESEARCH; SCIENCE-AND-TECHNOLOGY;
   NANOTECHNOLOGY LITERATURE; INFORMATION-SCIENCE; FIELD; SCIENTOMETRICS;
   EMERGENCE; MAPS; REPRESENTATIONS
AB Nanoscience and technology (NST) is a relatively new interdisciplinary scientific domain, and scholars from a broad range of different disciplines are contributing to it. However, there is an ambiguity in its structure and in the extent of multidisciplinary scientific collaboration of NST. This paper investigates the multidisciplinary patterns of Iranian research in NST based on a selection of 1,120 ISI-indexed articles published during 1974-2007. Using text mining techniques, 96 terms were identified as the main terms of the Iranian publications in NST. Then the scientific structure of the Iranian NST was mapped through multidimensional scaling, based upon the co-occurrence of the main terms in the academic publications. The results showed that the NST domain in Iranian publications has a multidisciplinary structure which is composed of different fields, such as pure physics, analytical chemistry, chemistry physics, material science and engineering, polymer science, biochemistry and new emerging topics.
C1 Islamic Azad Univ, Lib & Informat Sci Dept, Tehran, Iran.
RP Mohammadi, E (通讯作者)，Islamic Azad Univ, Lib & Informat Sci Dept, Sci & Res Campus, Tehran, Iran.
EM ehsan.mohammadi@wlv.ac.uk
RI Mohammadi, Ehsan/V-3149-2019
OI Mohammadi, Ehsan/0000-0003-3481-6991
CR Bhattacharya S, 1998, SCIENTOMETRICS, V43, P359, DOI 10.1007/BF02457404
   Blake C., 2011, ANN REV INFORM SCI T, V45
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NR 68
TC 12
Z9 13
U1 4
U2 108
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
J9 SCIENTOMETRICS
JI Scientometrics
PD SEP
PY 2012
VL 92
IS 3
BP 593
EP 608
DI 10.1007/s11192-012-0644-6
PG 16
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 988UZ
UT WOS:000307517500005
DA 2022-08-23
ER

PT S
AU Novak, J
   Wurst, M
   Fleischmann, M
   Strauss, W
AF Novak, J
   Wurst, M
   Fleischmann, M
   Strauss, W
BE VanElst, L
   Dignum, V
   Abecker, A
TI Discovering, visualizing, and sharing knowledge through personalized
   learning knowledge maps
SO AGENT-MEDIATED KNOWLEDGE MANAGEMENT
SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
LA English
DT Article; Proceedings Paper
CT International Symposium on Agent-Mediated Knowledge Management
CY MAR 24-26, 2003
CL Stanford Univ, Stanford, CA
HO Stanford Univ
AB This paper presents an agent-based approach to semantic exploration and knowledge discovery in large information spaces by means of capturing, visualizing and making usable implicit knowledge structures of a group of users. The focus is on the developed conceptual model and system for creation and collaborative use of personalized learning knowledge maps. We use the paradigm of agents on the one hand as model for our approach, on the other hand it serves as a basis for an efficient implementation of the system. We present an unobtrusive model for profiling personalised user agents based on two dimensional semantic maps that provide 1) a medium of implicit communication between human users and the agents, 2) form of visual representation of resulting knowledge structures. Concerning the issues of implementation we present an agent architecture, consisting of two sets of asynchronously operating agents, which enables both sophisticated processing, as well as short respond times necessary for enabling interactive use in real-time.
C1 Fraunhofer Inst Media Commun, MARS Exploratory Media Lab, D-53754 St Augustin, Germany.
   Univ Dortmund, Artificial Intelligence Dept, D-44221 Dortmund, Germany.
RP Novak, J (通讯作者)，Fraunhofer Inst Media Commun, MARS Exploratory Media Lab, Schloss Birlinghoven, D-53754 St Augustin, Germany.
EM jasminko.novak@imk.fhg.de; wurst@1s8.cs.uni-dortmund.de
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NR 20
TC 3
Z9 3
U1 0
U2 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-20868-2
J9 LECT NOTES ARTIF INT
PY 2004
VL 2926
BP 213
EP 228
PG 16
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BY60G
UT WOS:000189418700015
DA 2022-08-23
ER

PT S
AU Kumar, NP
   Bandopadhyay, T
AF Kumar, NP
   Bandopadhyay, T
BE Chakraborty, G
TI Capturing market intelligence from customer feedback e-mails using
   self-enhancing Boltzmann Machine-based network of knowledge maps
SO DISTRIBUTED COMPUTING AND INTERNET TECHNOLOGY, PROCEEDINGS
SE Lecture Notes in Computer Science
LA English
DT Article; Proceedings Paper
CT 2nd International Conference on Distributed Computing and Internet
   Technology
CY DEC 22-24, 2005
CL Bhubaneswar, INDIA
ID WEB
AB With the proliferation of the Web, capture of market intelligence data has become more difficult in reality from the system's point of view, as data sources on the web are voluminous, heterogeneous in terms of structures and semantics, and some part of it may be irrelevant to a specific organizations' marketing decision making context, which is the primary premises of market intelligence (MI) systems. To address these requirements of MI, we are proposing a method for creating an MI network using customer feedback messages and e-mails as inputs. We have proposed the use of knowledge map (KM) method for representing textual and unstructured resources as a network using KMs and clustering and then incrementally enhance itself as the new customer e-mails keep coming. At last, we have proposed a self-enhancing network using Bolzmann Machines concept where the new messages are treated as new hypotheses, and they get absorbed into the MI network based on their similarity values.
EM pkgarg@ibsdel.org; tapati@ibsdel.org
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NR 12
TC 0
Z9 0
U1 0
U2 4
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 3-540-30999-3
J9 LECT NOTES COMPUT SC
PY 2005
VL 3816
BP 553
EP 560
PG 8
WC Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BDW25
UT WOS:000235804200062
DA 2022-08-23
ER

PT J
AU Liu, KH
   Guan, XJ
   Li, GL
   Duan, M
   Li, Y
   Hong, YY
   Lin, MM
   Fu, RX
   Yu, FM
AF Liu, Kehui
   Guan, Xiaojin
   Li, Guangluan
   Duan, Min
   Li, Yi
   Hong, Yanyan
   Lin, Meimei
   Fu, Rongxin
   Yu, Fangming
TI Publication characteristics, topic trends and knowledge domains of karst
   ecological restoration: a bibliometric and knowledge mapping analysis
   from 1991 to 2021
SO PLANT AND SOIL
LA English
DT Article
DE Bibliometrics; Research hotspots; Karst ecological restoration;
   Knowledge base; Knowledge mapping analysis
ID SOIL ORGANIC-CARBON; ROCKY DESERTIFICATION; VEGETATION SUCCESSION;
   ECOSYSTEM SERVICES; SOUTHWEST CHINA; CLIMATE-CHANGE; BIOCHAR;
   BIODIVERSITY; BACTERIAL; IMPACTS
AB Aims Karst ecological restoration (KER) publication characteristics, topic trends, and knowledge domains were analysed to provide a global perspective. Methods Bibliometric and knowledge map analyses were used to analyse KER documents (319) in the WoSCC. The whole period was divided into the germination (GP), slow growth (SGP) and rapid growth (RGP) periods based on the number of annual publications (NAP). Results (1) The NAP increased over time, with China contributing the most. (2) The study topics varied and flourished over time. Plant was the most studied class and Animal the least studied in the Biology category. In the Environment category, Water was the most studied medium in the GP, while Soil was the most studied in the SGP and RGP. Remote sensing and Model were the leading methods in the SGP and RGP; Field investigation and/or experimental methods, including Leaf traits, Vegetation investigation, and Chlorophyll fluorescence, were the main methods in the GP. Ecological engineering was the leading restoration method overall. Planting pattern was a new restoration method in the SGP, while Biochar, Seed bank, and Soil translocation were new methods in the RGP. (3) Eleven knowledge domains and 10 vital documents well support studies on KER. Future research directions are proposed. Conclusions The results provide a new, global, objective perspective for understanding KER research over the past 3 decades.
C1 [Liu, Kehui; Guan, Xiaojin; Li, Guangluan; Duan, Min; Li, Yi; Hong, Yanyan; Lin, Meimei; Fu, Rongxin; Yu, Fangming] Guangxi Normal Univ, Guilin, Guangxi, Peoples R China.
RP Fu, RX; Yu, FM (通讯作者)，Guangxi Normal Univ, Guilin, Guangxi, Peoples R China.
EM rxfu@mailbox.gxnu.edu.cn; fmyu1215@163.com
FU National Natural Science Foundation of China [41967019]; Guangxi Key
   Research and Development Program [AB21220057]; Special Project of
   Science and Technology Base and Talents in Guangxi Province
   [GuikeAD20159055]; Scientific Research Basic Ability Improvement Project
   for Young and Middle-aged University Teachers in Guangxi Province
   [2020KY02027]
FX This project was supported by the National Natural Science Foundation of
   China (41967019), Guangxi Key Research and Development Program
   (AB21220057), Special Project of Science and Technology Base and Talents
   in Guangxi Province (GuikeAD20159055) and Scientific Research Basic
   Ability Improvement Project for Young and Middle-aged University
   Teachers in Guangxi Province (2020KY02027).
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NR 76
TC 1
Z9 1
U1 22
U2 22
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0032-079X
EI 1573-5036
J9 PLANT SOIL
JI Plant Soil
PD JUN
PY 2022
VL 475
IS 1-2
SI SI
BP 169
EP 189
DI 10.1007/s11104-022-05345-0
EA MAR 2022
PG 21
WC Agronomy; Plant Sciences; Soil Science
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SC Agriculture; Plant Sciences
GA 2L9EI
UT WOS:000767739400001
DA 2022-08-23
ER

PT J
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   Fan, H
   Wang, X
   Zhao, K
AF Liu, Hong
   Jiang, Ying
   Fan, Hua
   Wang, Xin
   Zhao, Kang
TI Visualization Analysis of Knowledge Network Research Based on Mapping
   Knowledge
SO JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO
   TECHNOLOGY
LA English
DT Article
DE Knowledge network; Knowledge map; Research hotspot; Visual analysis
ID RDF
AB With the acceleration of economic information, networking, globalization and the new technological revolution, more and more scholars abroad have begun to pay attention to the research of knowledge networks. Some scholars even regard knowledge networks as a new field in the field of knowledge management research theoretical paradigm. This paper takes the research literature of knowledge network in Web of Science as the research object, and uses the information visualization software Cite Space, which is the world's leading information, to visualize the geographical distribution of relevant knowledge networks in the international context by using traditional bibliometric methods and information visualization tools. High-impact authors, core literature, research hotspots for quantitative research and visual analysis, showing the classic literature and research hotspots and frontier fields of international knowledge management research in the form of maps, and analyzing knowledge maps and literature materials, content analysis, and induction The combination of methods and other methods to analyze and summarize the current research status of international knowledge management can provide useful reference for knowledge networks and related research.
C1 [Liu, Hong] Xi An Jiao Tong Univ, Xian, Peoples R China.
   [Jiang, Ying; Fan, Hua] State Grid Zhejiang Power Co Ltd, Jinhua, Zhejiang, Peoples R China.
   [Wang, Xin] State Grid Anhui Power Co Ltd, Hefei, Peoples R China.
   [Zhao, Kang] NARI Grp Co Ltd, Nanjing, Peoples R China.
RP Jiang, Y (通讯作者)，State Grid Zhejiang Power Co Ltd, Jinhua, Zhejiang, Peoples R China.
EM jiang_ying@zj.sgcc.com.cn; fanhua@zj.sgcc.com.cn;
   ustcwx@mail.ustc.edu.cn; zhaokang@sepri.sgcc.com.cn
FU Science and Technology Project of State Grid Corporation of China
   [5211XT180045]
FX This work was supported by the Science and Technology Project of State
   Grid Corporation of China (Project number: 5211XT180045).
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NR 34
TC 1
Z9 1
U1 8
U2 23
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1939-8018
EI 1939-8115
J9 J SIGNAL PROCESS SYS
JI J. Signal Process. Syst. Signal Image Video Technol.
PD MAR
PY 2021
VL 93
IS 2-3
SI SI
BP 333
EP 344
DI 10.1007/s11265-020-01595-2
EA SEP 2020
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QK8GI
UT WOS:000571108600003
DA 2022-08-23
ER

PT J
AU Hou, JQ
   Li, X
   Zhu, RC
   Zhu, CQ
   Wei, ZY
   Zhang, C
AF Hou, Jiaqi
   Li, Xin
   Zhu, Rongchen
   Zhu, Chongqiang
   Wei, Zeyu
   Zhang, Chao
TI A Neural Relation Extraction Model for Distant Supervision in
   Counter-Terrorism Scenario
SO IEEE ACCESS
LA English
DT Article
DE Bit error rate; Data mining; Encoding; Feature extraction; Data models;
   Big Data; Training; BERT; relation extraction; distant supervision;
   selective attention mechanism; BERT entity encoding
AB Natural language processing (NLP) is the best solution to extensive, unstructured, complex, and diverse network big data for counter-terrorism. Through the text analysis, it is the basis and the most critical step to quickly extract the relationship between the relevant entities pairs in terrorism. Relation extraction lays a foundation for constructing a knowledge graph (KG) of terrorism and provides technical support for intelligence analysis and prediction. This paper takes the distant-supervised relation extraction as the starting point, breaks the limitation of artificial data annotation. Combining the Bidirectional Encoder Representation from Transformers (BERT) pre-training model and the sentence-level attention over multiple instances, we proposed the relation extraction model named BERT-att. Experiments show that our model is more efficient and better than the current leading baseline model over each evaluative metrics. Our model applied to the construction of anti-terrorism knowledge map, it used in regional security risk assessment, terrorist event prediction and other scenarios.
C1 [Hou, Jiaqi; Li, Xin; Zhu, Rongchen] Peoples Publ Secur Univ China, Sch Informat Technol & Cyber Secur, Beijing 100038, Peoples R China.
   [Zhu, Chongqiang] Lianyungang Publ Secur Bur, Tech Invest Detachment, Lianyungang 222006, Peoples R China.
   [Wei, Zeyu] Shaoxing Publ Secur Bur, Patrol Special Police Brigade Keqiao, Shaoxing 312000, Peoples R China.
   [Zhang, Chao] Lianyungang Publ Secur Bur, Network Secur Detachment, Lianyungang 222000, Peoples R China.
RP Li, X (通讯作者)，Peoples Publ Secur Univ China, Sch Informat Technol & Cyber Secur, Beijing 100038, Peoples R China.
EM lixin@ppsuc.edu.cn
RI Zhu, Rongchen/AAZ-2501-2020
OI Zhu, Rongchen/0000-0002-8379-5579
FU National Key Research and Development Program of China [2017YFC0803700];
   People's Public Security University of China through 2019 Basic Research
   Operating Expenses New Teacher Research Startup Fund Project
   [2019JKF424]; Natural Science Foundation of China [41971367]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2017YFC0803700, in part by the
   People's Public Security University of China through 2019 Basic Research
   Operating Expenses New Teacher Research Startup Fund Project under Grant
   2019JKF424, and in part by the Natural Science Foundation of China under
   Grant 41971367.
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NR 28
TC 1
Z9 1
U1 5
U2 14
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 225088
EP 225096
DI 10.1109/ACCESS.2020.3042672
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PM3NR
UT WOS:000603710900001
OA gold
DA 2022-08-23
ER

PT J
AU Xiao, X
   Skitmore, M
   Li, H
   Xia, B
AF Xiao, Xue
   Skitmore, Martin
   Li, Heng
   Xia, Bo
TI Mapping Knowledge in the Economic Areas of Green Building Using
   Scientometric Analysis
SO ENERGIES
LA English
DT Review
DE economic area; green building; scientometric; review; knowledge map
ID LIFE-CYCLE ASSESSMENT; INFORMATION MODELING BIM; EMERGING TRENDS;
   TECHNOLOGIES ADOPTION; DEVELOPING-COUNTRIES; ENERGY-CONSUMPTION; COST
   PREMIUM; CONSTRUCTION; BARRIERS; SYSTEM
AB This paper presents the first inclusive scientometric review of the economic areas of green building (GBE). The aim is to methodically examine and summarize the state-of-the-art of the GBE body of knowledge. To this end, this study analyses 1713 GBE-related bibliographic records retrieved from the Web of Science by using the quantitative method of knowledge mapping. The knowledge base, knowledge domain, and knowledge evolution of how they interacted with each other are explored using document co-citation analysis and keywords co-citation analysis of the existing body of literature. The research findings are informative in recognizing and interpreting the underlying structure and trends in GBE. A knowledge map provides a valuable and instructive understanding of the evolution and status quo of the GBE knowledge body, as well as assisting in recognizing the gaps and deficiencies involved. The results will help in understanding how GBE knowledge is evolving and its role played in green building, and thus provide suggestions of how academic research can enhance sustainability practices in terms of economic area in the future.
C1 [Xiao, Xue; Skitmore, Martin; Xia, Bo] Queensland Univ Technol, Sci & Engn Fac, Brisbane, Qld 4000, Australia.
   [Xiao, Xue; Li, Heng] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Peoples R China.
   [Skitmore, Martin] Birmingham City Univ, Fac Comp Engn & Built Environm, Birmingham B4 7BD, W Midlands, England.
RP Xiao, X (通讯作者)，Queensland Univ Technol, Sci & Engn Fac, Brisbane, Qld 4000, Australia.; Xiao, X (通讯作者)，Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Peoples R China.
EM xue.xiao@hdr.qut.edu.au
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NR 107
TC 18
Z9 18
U1 7
U2 33
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1073
J9 ENERGIES
JI Energies
PD AUG 1
PY 2019
VL 12
IS 15
AR 3011
DI 10.3390/en12153011
PG 22
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Energy & Fuels
GA IS5EK
UT WOS:000482174800170
OA Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Yao, Q
   Chen, J
   Lyu, PH
   Zhang, SJ
   Ma, FC
   Fang, JG
AF Yao, Qiang
   Chen, Jing
   Lyu, Peng-Hui
   Zhang, Shi-Jing
   Ma, Fei-Cheng
   Fang, Jian-Guo
TI Knowledge map of artemisinin research in SCI and Medline database
SO JOURNAL OF VECTOR BORNE DISEASES
LA English
DT Article
DE Artemisinin; bibliometric analysis; co-authorship; co-words; knowledge
   mapping; research trend
ID ENDOPEROXIDE ANTIMALARIAL; DRUG; DERIVATIVES; QINGHAOSU; MALARIA; DESIGN
AB Background & objectives: Artemisinin was first extracted from the herb Artemisia annua which has been used for many centuries in Chinese traditional medicine as a treatment for fever and malaria. It has been given the 2011 Lasker-DeBakey clinical medical research award. In this paper, knowledge map of artemisinin research was drawn to provide some information for global researchers interested in artemisinin and its relevant references.
   Methods: In this work, bibliometric analysis and knowledge visualization technology were applied to evaluate global scientific production and developing trend of artemisinin research through Science Citation Index (SCI) papers and Medline papers with online version published as following aspects: publication outputs, subject categories, journals, countries, international collaboration, citations, authorship and co-authorship, author key words and co-words analysis. The Thomson Data Analyzer (TDA). Netdraw and Aureka software were employed to analyze the SCI as well as Medline papers data for knowledge mapping.
   Results: Global literature of artemisinin research has increased rapidly over the past 30 years and has boosted in recent years. Seen from the statistical study in many aspects. Pharmacology & Pharmacy, and Chemistry are still the main subjects of artemisinin research with parasitology and tropical medicine increasing quickly. Malaria Journal and American Journal of Tropical Medicine are top productive journals both in SCI and Medline databases. Quantity and quality of papers in US are in a leading position, although papers quantity and active degree in developing countries such as P.R. China. Thailand and India are relatively high, the quality of papers from these countries needs to be improved. New emerging key words and co-words remind us that mechanism of action, pharmacokinetics. artemisinin-based alternatives, etc. are the future trends of artemisinin research.
   Conclusion: Through bibliometric analysis the development trends of artemisinin research are predicted. With further development of artemisinin research, it is presumed that scientists might concentrate mainly on the synthesis of new compounds with activity, action mechanism, new artemisinin-based combination therapy regimens, etc.
C1 [Yao, Qiang; Zhang, Shi-Jing] Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Med & Hlth Management, Wuhan 430030, Peoples R China.
   [Chen, Jing; Fang, Jian-Guo] Huazhong Univ Sci & Technol, Tongji Med Coll, Tongji Hosp, Dept Pharm, Wuhan 430030, Peoples R China.
   [Chen, Jing] Wuhan Brain Hosp, Dept Pharm, Wuhan, Peoples R China.
   [Lyu, Peng-Hui; Ma, Fei-Cheng] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China.
   [Lyu, Peng-Hui] Hubei Acad Sci & Tech Informat, Emanu Ctr, Wuhan, Peoples R China.
RP Zhang, SJ (通讯作者)，Huazhong Univ Sci & Technol, Tongji Med Coll, Wuhan 430030, Peoples R China.
EM ph@scinet.cn; zhangsj@scinet.cn
RI LYU, Penghui/A-6864-2013; Pieroni, Andrea/AAE-9578-2020; Yao,
   Qiang/Q-5758-2019; González-Tejero, Reyes/AAB-6132-2019; YAO,
   QIANG/X-9176-2019
OI LYU, Penghui/0000-0002-7542-4591; Pieroni, Andrea/0000-0002-2302-6380;
   Yao, Qiang/0000-0002-4408-087X; González-Tejero,
   Reyes/0000-0001-6700-8056; 
FU Natural Science Foundation of China [71173249]; Research project with
   combination of traditional Chinese and Western medicine in Hubei
   Province of China [2012Z-Z05]
FX This work was supported by the Natural Science Foundation of China
   (Grant No. 71173249) and Research project with combination of
   traditional Chinese and Western medicine in Hubei Province of China
   (Grant No. 2012Z-Z05). The authors are grateful to Peiyang Xu, Lijun Guo
   and Kathleen Diane Connors for their helpful discussion and suggestions.
   The authors would also like to thank anonymous reviewers for their
   valuable comments.
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NR 35
TC 11
Z9 11
U1 0
U2 62
PU WOLTERS KLUWER MEDKNOW PUBLICATIONS
PI MUMBAI
PA WOLTERS KLUWER INDIA PVT LTD , A-202, 2ND FLR, QUBE, C T S  NO 1498A-2
   VILLAGE MAROL, ANDHERI EAST, MUMBAI, 400059, INDIA
SN 0972-9062
J9 J VECTOR DIS
JI J. Vector Borne Dis.
PD DEC
PY 2012
VL 49
IS 4
BP 205
EP 216
PG 12
WC Infectious Diseases; Parasitology; Tropical Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Infectious Diseases; Parasitology; Tropical Medicine
GA V33MT
UT WOS:000209023600002
PM 23428519
DA 2022-08-23
ER

PT J
AU Du, S
   Li, H
AF Du, Shan
   Li, Hua
TI The Knowledge Mapping of Mobile Commerce Research: A Visual Analysis
   Based on I-Model
SO SUSTAINABILITY
LA English
DT Article
DE mobile commerce; CiteSpace; knowledge mapping; I-Model
ID SOCIAL MEDIA; VISUALIZATION ANALYSIS; CUSTOMER SATISFACTION; BEHAVIORAL
   INTENTION; CONSUMER ACCEPTANCE; EMPIRICAL-ANALYSIS; PAYMENT SERVICES;
   CITESPACE II; ADOPTION; TRUST
AB With the advance of 5G communication technologies and Internet+ strategy, mobile commerce has experienced rapid growth and needs urgent attention from researchers. It is the aim of this article to analyze the literature on mobile commerce to address the following question: With the wide application of artificial intelligence and big data, what are the latest technology, models and problems in the background of the new era that researchers and practitioners need to understand in order to grasp the research frontier in this field quickly? Therefore, to achieve these objectives, this paper reviews 1130 m-commerce articles with 25,502 associated references from the SCI-EXPANDED, SSCI, CPCI-S, CPCI-SSH database and develops a framework of m-commerce value by analyzing the most influential authors, institutions, countries, journals and keywords in m-commerce. We apply three types of knowledge mapping to our studycluster view, timezone view and timeline view. Frequency statistics, clustering coefficient as well as centrality calculation are employed to analyze by CiteSpace. We use the strength of citation bursts to analyze keywords and put result into the I-Modelwhich provide an important framework for classifying m-commerce activities and theories. In this study, we explore the knowledge structure, development and the future trend of mobile commerce for researchers. We identify the main technology and models to improve customer satisfaction and adoption behavior in the background of the new era which provide decision support for practitioners. Compared with the existing literature reviews of mobile commerce, we make a set of knowledge maps to show the future trend of mobile commerce and analyze visual results based on I-model. It is the first study to present the major clusters to reveal their associated intellectual bases and research fronts.
C1 [Du, Shan; Li, Hua] Xidian Univ, Sch Econ & Management, Xian 710071, Peoples R China.
   [Du, Shan; Li, Hua] Xidian Univ, Shaanxi Soft Sci Inst Informatizat & Digita Econ, Xian 710071, Peoples R China.
RP Li, H (通讯作者)，Xidian Univ, Sch Econ & Management, Xian 710071, Peoples R China.; Li, H (通讯作者)，Xidian Univ, Shaanxi Soft Sci Inst Informatizat & Digita Econ, Xian 710071, Peoples R China.
EM dushan_19860617@126.com; lihua@xidian.edu.cn
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NR 143
TC 11
Z9 11
U1 8
U2 44
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAR 2
PY 2019
VL 11
IS 6
AR 1580
DI 10.3390/su11061580
PG 26
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 HU9KJ
UT WOS:000465613000075
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Ekanayake, EMAC
   Shen, G
   Kumaraswamy, MM
AF Ekanayake, E. M. A. C.
   Shen, Geoffrey
   Kumaraswamy, Mohan M.
TI Mapping the knowledge domains of value management: a bibliometric
   approach
SO ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
LA English
DT Review
DE Value management; Management; Literature review; Construction;
   Methodology; Bibliometric analysis; CiteSpace
ID INFORMATION MODELING BIM; CONSTRUCTION; PROJECT; PERSPECTIVE
AB Purpose The purpose of this paper is to review the state-of-the-art of literature of value management (VM) and to map the VM domain to provide a launch pad for further knowledge development and dissemination. Design/methodology/approach This study employed CiteSpace bibliometric analysis software to systematically, comprehensively and accurately review the VM-related literature and to map the VM knowledge domain. Findings The results reveal the current VM knowledge base, clusters, research hotspot, and the evolutionary trajectory while contributing to the development of VM knowledge by providing a dynamic platform for integrating future developments in research. Originality/value The contribution of this paper to scientific VM knowledge is, therefore, a quantitative and accurate VM knowledge map based on a bibliometric analysis of data from the VM knowledge base, domains and evolution. The findings can be generalised and used as an effective knowledge mapping tool in a specific field of study that could complement and add significant value to the often adopted more traditional literature reviews. This study further recommends that the proposed knowledge map be frequently updated by similar future studies to fill gaps that arise with changing needs, priorities and contexts, as well as to identify corresponding future demands in the VM research domain.
C1 [Ekanayake, E. M. A. C.] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Kowloon, Hong Kong, Peoples R China.
   [Shen, Geoffrey] Hong Kong Polytech Univ, Kowloon, Hong Kong, Peoples R China.
   [Kumaraswamy, Mohan M.] Univ Hong Kong, Dept Civil Engn, Pokfulam, Hong Kong, Peoples R China.
RP Shen, G (通讯作者)，Hong Kong Polytech Univ, Kowloon, Hong Kong, Peoples R China.
EM geoffrey.shen@polyu.edu.hk
RI Shen, Geoffrey Qiping/A-1250-2014; Ekanayake, E.M.A.C./AAS-2703-2020
OI Shen, Geoffrey Qiping/0000-0002-3111-2019; Ekanayake,
   E.M.A.C./0000-0003-2557-4070
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NR 67
TC 15
Z9 15
U1 8
U2 97
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.
PD APR 15
PY 2019
VL 26
IS 3
BP 499
EP 514
DI 10.1108/ECAM-06-2018-0252
PG 16
WC Engineering, Industrial; Engineering, Civil; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics
GA HR5NI
UT WOS:000463193300009
DA 2022-08-23
ER

PT J
AU Zhuang, QD
   Hussein, MK
   Ariffin, NFM
   Yunos, MYM
AF Zhuang, Q. D.
   Hussein, M. K.
   Ariffin, N. F. M.
   Yunos, M. Y. M.
TI Landscape character: a knowledge mapping analysis using CiteSpace
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
LA English
DT Review; Early Access
DE Landscape character; Knowledge mapping analysis; CiteSpace; landscape
   character assessment
ID PUBLIC-PARTICIPATION GIS; SCIENTOMETRIC ANALYSIS; EMERGING TRENDS;
   RURAL-AREAS; LAND; CLASSIFICATION; MODEL; SERVICES; QUALITY; REGION
AB The purpose of this study is to determine the overall structure and relative comprehensive understanding of the development status of landscape character research visually. A knowledge mapping analysis of 308 academic papers in Web of Science published between 1967 and 2021 related to landscape character using CiteSpace from multiple perspectives including collaboration network, co-citation, and co-occurrence was conducted in this study. The results suggest that the quantity of publications about landscape character has increased rapidly and its staged characteristics have been exposed. The distribution of disciplines, countries, and research organizations, as well as the productive authors involved in landscape character research were identified. The research revealed the high-frequency themes sorted out in current literature including classification, biodiversity, perception, GIS, management, conservation, etc. Finally, the study visualized emerging research frontiers and deeply depicted six of them which consist of the cultural landscape, nonexpert-based landscape aesthetics evaluation, understanding public interest, landscape metrics, practical application, and using public participation GIS. This study provided a comprehensive prospect for the further study of landscape character and filled the gap in this field. It demonstrated that through the approach of CiteSpace can draw a sketch structure and potential relationship of landscape character researches. All these provide readers especially potential researchers with an overall and in-depth understanding of landscape character, indicating the research development and characteristics in this field.
C1 [Zhuang, Q. D.; Hussein, M. K.; Ariffin, N. F. M.; Yunos, M. Y. M.] Univ Putra Malaysia, Dept Landscape Architecture, Fac Design & Architecture, Serdang 43400, Selangor Darul, Malaysia.
RP Zhuang, QD (通讯作者)，Univ Putra Malaysia, Dept Landscape Architecture, Fac Design & Architecture, Serdang 43400, Selangor Darul, Malaysia.
EM 53346001@qq.com
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NR 85
TC 0
Z9 0
U1 45
U2 45
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1735-1472
EI 1735-2630
J9 INT J ENVIRON SCI TE
JI Int. J. Environ. Sci. Technol.
DI 10.1007/s13762-022-04279-5
EA MAY 2022
PG 16
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 1S0XH
UT WOS:000803781900002
DA 2022-08-23
ER

PT J
AU Wang, SD
AF Wang, Shoudao
TI Development of Online Political and Ideological Education System Based
   on Personalized Recommendation
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
AB This paper aims to design and implement the online political and ideological teaching system based on personalized recommendation in order to more accurately recommend teaching resources appropriate for students' learning, thus improving the learning efficiency and teaching quality of the online political and ideological teaching system. First, the design of the online political and ideological education system is detailed, along with its basic framework, functional modules, hierarchical structure, and database. A personalized recommendation approach based on knowledge map is proposed. The algorithm is applied to the online political and ideological teaching system to understand the differences of students' interests in different teaching resources, establish a student interest transfer model, and effectively improve the transfer of students' interests. On the basis of knowledge map, the matrix decomposition method is introduced, matched with the knowledge map to obtain the recommendation prediction score, and the feedback model is established and extended. Measure the dynamic transformation of the recommended ideological and political teaching content, and comprehensively consider the long-term and short-term preferences of students, so as to realize the personalized recommendation of ideological and political teaching resources. Experiments show that the personalized recommendation online political and ideological teaching system designed in this paper has good overall performance, the accuracy of the proposed recommendation approach is high, and the recommendation time is fast, so as to improve the teaching quality of the teaching system.
C1 [Wang, Shoudao] Lanzhou City Univ, Sch Marxism, Lanzhou 730070, Gansu, Peoples R China.
RP Wang, SD (通讯作者)，Lanzhou City Univ, Sch Marxism, Lanzhou 730070, Gansu, Peoples R China.
EM shoudaoabc@lzcu.edu.cn
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NR 20
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 20
PY 2022
VL 2022
AR 8161596
DI 10.1155/2022/8161596
PG 9
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 3M0LW
UT WOS:000835150400004
PM 35909827
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Liu, KH
   Guan, XJ
   Li, CM
   Zhao, KY
   Yang, XH
   Fu, RX
   Li, Y
   Yu, FM
AF Liu, Kehui
   Guan, Xiaojin
   Li, Chunming
   Zhao, Keyi
   Yang, Xiaohua
   Fu, Rongxin
   Li, Yi
   Yu, Fangming
TI Global perspectives and future research directions for the
   phytoremediation of heavy metal-contaminated soil: A knowledge mapping
   analysis from 2001 to 2020
SO FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING
LA English
DT Review
DE Heavy metal-contaminated soil; Hot topics; Knowledge mapping analysis;
   Knowledge base; Phytoremediation
ID PLANT-GROWTH; REGENERATIVE MEDICINE; EMERGING TRENDS; PHOSPHATE ROCK;
   ARSENIC UPTAKE; CD; HYPERACCUMULATOR; ACCUMULATION; STRESS; WATER
AB In total, 9,552 documents were extracted from the Web of Science Core Collection and subjected to knowledge mapping and visualization analysis for the field of phytoremediation of HM-contaminated soil (PHMCS) with CiteSpace 5.7 R3 software. The results showed that (1) the number of publications increased linearly over the studied period. The top 10 countries/regions, institutions and authors contributing to this field were exhibited. (2) Keyword co-occurrence cluster analysis revealed a total of 8 clusters, including "Bioremediation," "Arsenic," "Biochar," "Oxidative stress," "Hyperaccumulation," "EDTA," "Arbuscular mycorrhizal fungi," and "Environmental pollution" clusters (3) In total, 334 keyword bursts were obtained, and the 25 strongest, longest duration, and newest keyboard bursts were analyzed in depth. The strongest keyword burst test showed that the hottest keywords could be divided into 7 groups, i.e., "Plant bioremediation materials," "HM types," "Chelating amendments," "Other improved strategies," "Technical terminology," "Risk assessment," and "Other." Almost half of the newest topics had emerged in the past 3 years, including "biochar," "drought," "health risk assessment," "electrokinetic remediation," "nanoparticle," and "intercropping." (4) In total, 9 knowledge base clusters were obtained in this study. The studies of Ali et al. (2013), Blaylock et al. (1997), Huang et al. (1997), van der Ent et al. (2013), Salt et al. (1995), and Salt (1998), which had both high frequencies and the strongest burst scores, have had the most profound effects on PHMCS research. Finally, future research directions were proposed.
C1 [Liu, Kehui; Guan, Xiaojin; Li, Chunming; Zhao, Keyi; Yang, Xiaohua; Li, Yi; Yu, Fangming] Guangxi Normal Univ, Minist Educ, Key Lab Ecol Rare & Endangered Species & Environm, Guilin 541004, Peoples R China.
   [Li, Chunming] Fudan Univ, Sch Life Sci, Shanghai 200438, Peoples R China.
   [Fu, Rongxin] Guangxi Normal Univ, Guangxi Normal Univ Lib, Guilin 541004, Peoples R China.
RP Li, Y; Yu, FM (通讯作者)，Guangxi Normal Univ, Minist Educ, Key Lab Ecol Rare & Endangered Species & Environm, Guilin 541004, Peoples R China.
EM Liyi412@mailbox.gxnu.edu.cn; fmyu1215@163.com
FU National Natural Science Foundation of China [41967019]; National Social
   Science Foundation Project of China [16BTQ033]
FX This project was supported by the National Natural Science Foundation of
   China (Grant No. 41967019) and the National Social Science Foundation
   Project of China (No. 16BTQ033).
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NR 107
TC 4
Z9 5
U1 205
U2 575
PU HIGHER EDUCATION PRESS
PI BEIJING
PA CHAOYANG DIST, 4, HUIXINDONGJIE, FUSHENG BLDG, BEIJING 100029, PEOPLES R
   CHINA
SN 2095-2201
EI 2095-221X
J9 FRONT ENV SCI ENG
JI Front. Env. Sci. Eng.
PD JUN
PY 2022
VL 16
IS 6
AR 73
DI 10.1007/s11783-021-1507-2
PG 20
WC Engineering, Environmental; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Environmental Sciences & Ecology
GA WF2LD
UT WOS:000706141500003
DA 2022-08-23
ER

PT J
AU Zhang, SN
AF Zhang, Sining
TI Knowledge Mapping Analysis of the Study of Ecosystem Services and
   Landscape Architecture
SO JOURNAL OF URBAN PLANNING AND DEVELOPMENT
LA English
DT Article
DE Knowledge mapping; Bibliometric analysis; Ecosystem services; Landscape
   architecture; CiteSpace
ID CONCEPTUAL-FRAMEWORK; BIODIVERSITY; TRADEOFFS; INTENSIFICATION;
   POLLINATION; INDICATORS; SCIENCE
AB Ecosystem services (ES) and landscape architecture (LA) research have received increasing attention since the important role of landscape sustainability has been developed. Yet bibliometric and visual analyses of the scientific literature in this field are still scarce. A total of 5,689 publications related to ES and LA research were systematically analyzed and visualized, including basic characteristics, research topics, hot spots, and frontiers, using CiteSpace in the Web of Science core database from 2000 to 2020. Literature cocitation analysis finds 15 cocitation clusters that reflect the research intellectual base. Keyword cooccurrence analysis reveals the top five high-frequency keywords. Analysis of burst detection reveals the research frontiers: integration studies of ES and biodiversity, the application of ES methods in agricultural studies, new technologies and tools that are constantly applied, and the implication for landscape management and decision-making progress. This paper aims to provide a reference for the integration of ES and LA research.
C1 [Zhang, Sining] Southwest Jiaotong Univ SWJTU, Sch Architecture, Bldg 8,Xipu Campus,West Pk Hitech Zone, Chengdu 611756, Sichuan, Peoples R China.
RP Zhang, SN (通讯作者)，Southwest Jiaotong Univ SWJTU, Sch Architecture, Bldg 8,Xipu Campus,West Pk Hitech Zone, Chengdu 611756, Sichuan, Peoples R China.
EM ZSNing@swjtu.edu.cn
FU National Natural Science Foundation of China (NSFC) [52008345];
   Fundamental Research Funds for the Central Universities, China
   [2682020CX43]
FX This work was supported by the National Natural Science Foundation of
   China (NSFC) (Grant No. 52008345), and the Fundamental Research Funds
   for the Central Universities, China (Grant No. 2682020CX43). The author
   sincerely thanks all anonymous reviewers and editors for their comments
   and help.
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   Rodiek JE, 2006, LANDSCAPE URBAN PLAN, V76, P291, DOI 10.1016/j.landurbplan.2004.09.037
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NR 56
TC 0
Z9 0
U1 5
U2 5
PU ASCE-AMER SOC CIVIL ENGINEERS
PI RESTON
PA 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA
SN 0733-9488
EI 1943-5444
J9 J URBAN PLAN DEV
JI J. Urban Plan. Dev
PD SEP 1
PY 2022
VL 148
IS 3
AR 03122001
DI 10.1061/(ASCE)UP.1943-5444.0000851
PG 11
WC Engineering, Civil; Regional & Urban Planning; Urban Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Public Administration; Urban Studies
GA 2Y2UK
UT WOS:000825756700028
DA 2022-08-23
ER

PT J
AU Qian, G
AF Qian, Ge
TI Computational and visual analysis of the development stage of theories
   in the social sciences: a case in the entrepreneurship field
SO CURRENT SCIENCE
LA English
DT Article
DE Co-citation; entrepreneurship field; knowledge domains; mapping method
ID SCIENTIFIC LITERATURE; VISUALIZATION; NETWORKS
AB The aim of this article is to develop a method combining calculation, visualization and intuitive analysis, which will help social scientists to study the history of a theory in a particular discipline. Considering that the typical type of knowledge-domain map is too complicated and cumbersome for social scientists who possess little knowledge about scientometrics, a new and simplified 3D knowledge-mapping method is designed and illustrated using examples from the entrepreneurship field. The major difference between the orthodox knowledge mapping method and the method we introduce is that co-citation network is not marked, but only co-citation analysis results. Using an alternative method, our map shows the three prominent researchers in the entrepreneurship field as well as three stages: the first from approximately 1920 to 1960, the second from 1960 to 2000, and the third beginning in 2000.
C1 Shanghai Univ Finance & Econ, Human Coll, Theoret Teaching & Res Dept, Shanghai 200433, Peoples R China.
RP Qian, G (通讯作者)，Shanghai Univ Finance & Econ, Human Coll, Theoret Teaching & Res Dept, Shanghai 200433, Peoples R China.
EM qiange@mail.shufe.edu.cn
RI Qian, Guangren/G-8500-2019; Qian, Ge/B-6267-2015
OI Qian, Guangren/0000-0001-6021-5212; 
CR [Anonymous], 1942, CAPITALISM SOCIALISM
   Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317
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NR 16
TC 3
Z9 3
U1 0
U2 35
PU INDIAN ACAD SCIENCES
PI BANGALORE
PA C V RAMAN AVENUE, SADASHIVANAGAR, P B #8005, BANGALORE 560 080, INDIA
SN 0011-3891
J9 CURR SCI INDIA
JI Curr. Sci.
PD DEC 10
PY 2014
VL 107
IS 11
BP 1795
EP 1799
PG 5
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA AX1EO
UT WOS:000346691100012
DA 2022-08-23
ER

PT J
AU Wang, N
   Tang, T
   Zhang, XY
   Xi, ZP
   Li, JC
   Xie, L
AF Wang, Nan
   Tang, Tian
   Zhang, Xiaoyu
   Xi, Zhipeng
   Li, Jingchi
   Xie, Lin
TI Knowledge Areas and New Trends in Lumbar Disc Herniation Research:
   Bibliometrics and Knowledge Mapping Analysis
SO INDIAN JOURNAL OF ORTHOPAEDICS
LA English
DT Article; Early Access
DE Lumbar disc herniation; LDH; Bibliometric analysis; CiteSpace;
   Visualization; VOSviewer
ID INTERVERTEBRAL DISC; PROPIONIBACTERIUM-ACNES; DISKECTOMY; PUBLICATIONS;
   DEGENERATION; MANAGEMENT; INFECTION; PATTERNS; EFFICACY; OUTCOMES
AB Objective To determine the coalitions and impact of authors, countries, institutions, and journals, evaluate the knowledge base, find the hotspot trends, and identify the emerging topics in lumbar disc herniation (LDH). Method The articles related to LDH were obtained from the Web of Science Core Collection on August 21, 2021. Two scientometric software (CiteSpace 5.8.R.1 and VOSviewer 1.6.17) were used to perform bibliometric and knowledge-map analysis. Results From the set parameters, 4642 articles were included in the literature. Although the total number of publications fluctuated between 2001 and 2020, a general trend toward increase was observed. Respectively, the most productive country and institution in the field were the United States and Wooridul Spine Hospital. The most active and cited authors were Lee and Weinstein. Spine was the most impactful and cited journal. Weinstein (JAMA 296:2441-2450, 2006) had the highest number of co-citations and Weinstein(N Engl J Med 358:794-810, 2008) had the highest number of citations. The keyword "low back pain" was ranked first for frequency and total link strength, whereas "risk factor" was ranked first for centrality. Topics including pathogenesis (disc herniation), examination methods (MRI), treatment methods (non-surgical treatment, surgical treatment), surgical options (laminectomy, discectomy), clinical observations (double-blind, efficacy, outcome, learning curve), and evaluation of efficacy (meta-analysis) of LDH have been the focus of leading-edge research in 2001-2020. Conclusion Using bibliometric methods, this study mapped the knowledge map of LDH research in the past 20 years. The study identifies existing trends to provide a framework for further research.
C1 [Wang, Nan; Tang, Tian; Zhang, Xiaoyu; Xi, Zhipeng; Li, Jingchi; Xie, Lin] Nanjing Univ Chinese Med, Affiliated Hosp Integrated Tradit Chinese & Weste, Dept Spine Surg, Nanjing 210028, Peoples R China.
RP Xie, L (通讯作者)，Nanjing Univ Chinese Med, Affiliated Hosp Integrated Tradit Chinese & Weste, Dept Spine Surg, Nanjing 210028, Peoples R China.
EM xielin6508@163.com
OI Xie, Lin/0000-0002-2538-347X
FU Jiangsu Provincial Traditional Chinese Medicine Science and Technology
   Development Plan Project [2020 ZD202008]; Science and technology
   projects in Jiangsu Province [2019 BE2019765]
FX The current research was funded by Jiangsu Provincial Traditional
   Chinese Medicine Science and Technology Development Plan Project (2020
   ZD202008), and Science and technology projects in Jiangsu Province (2019
   BE2019765).
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NR 66
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0019-5413
EI 1998-3727
J9 INDIAN J ORTHOP
JI Indian J. Orthop.
DI 10.1007/s43465-022-00702-8
EA AUG 2022
PG 19
WC Orthopedics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Orthopedics
GA 3N5DT
UT WOS:000836168500002
DA 2022-08-23
ER

PT J
AU Zhuge, H
   Luo, XF
AF Zhuge, H
   Luo, XF
TI Knowledge map: Mathematical model and dynamic behaviors
SO JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
LA English
DT Article
DE knowledge representation; causal relation; fuzzy cognitive maps;
   knowledge grid
AB Knowledge representation and reasoning is a key issue of the Knowledge Grid. This paper proposes a Knowledge Map (KM) model for representing and reasoning causal knowledge as an overlay in the Knowledge Grid. It extends Fuzzy Cognitive, Maps (FCMs) to represent and reason not only simple cause-effect relations, but also time-delay causal relations, conditional probabilistic causal relations and sequential relations. The mathematical model and dynamic behaviors of KM are presented. Experiments show that, under certain conditions, the dynamic behaviors of KM can translate between different states. Knowing this condition, experts can control or modify the constructed KM while its dynamic behaviors do not accord with their expectation. Simulations and applications show that KM is more powerful and natural than FCM in emulating real world.
C1 Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, China Knowledge Grid Res Grp, Beijing 100080, Peoples R China.
RP Zhuge, H (通讯作者)，Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, China Knowledge Grid Res Grp, Beijing 100080, Peoples R China.
EM zhuge@ict.ac.cn; luoxf@kg.ict.ac.cn
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NR 17
TC 8
Z9 12
U1 0
U2 17
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1000-9000
EI 1860-4749
J9 J COMPUT SCI TECH-CH
JI J. Comput. Sci. Technol.
PD MAY
PY 2005
VL 20
IS 3
BP 289
EP 295
DI 10.1007/s11390-005-0289-5
PG 7
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 928SP
UT WOS:000229292300001
DA 2022-08-23
ER

PT J
AU Xu, WQ
   Zou, ZM
   Pei, J
   Huang, LF
AF Xu, Wanqi
   Zou, Zhongmei
   Pei, Jin
   Huang, Linfang
TI Longitudinal trend of global artemisinin research in chemistry subject
   areas (1983-2017)
SO BIOORGANIC & MEDICINAL CHEMISTRY
LA English
DT Review
DE Atemisinin; QingHaoSu; Bibliometric analysis; Chemical synthesis;
   Evolutionary trend; Knowledge map
ID BIBLIOMETRIC ANALYSIS; REGENERATIVE MEDICINE; EMERGING TRENDS; PRODUCT;
   INDEX
AB Artemisinin, the initial and main drug for malaria prevention and treatment internationally, was first extracted from the plant Artemisia annua L. by Chinese scientists in 1972. Research on artemisinin in chemistry subject areas shows a rapid growth since the 1980s. To evaluate the evolutionary trends and draw the knowledge map of artemisinin research, 1316 relevant publications are analysed based on bibliometrics. The global research status, emerging trends and future directions are also visualised and discussed. Furthermore, a historical overview of chemical synthesis on artemisinin is illustrated via timeline in terms of industrialisation. Overall, this study provides a novel method to visualise further information about artemisinin research and a comprehensive perspective to understand the longitudinal trend over the last 30 years.
C1 [Xu, Wanqi; Zou, Zhongmei; Huang, Linfang] Chinese Acad Med Sci, Peking Union Med Coll, Inst Med Plant Dev, Beijing, Peoples R China.
   [Pei, Jin] Chengdu Univ Tradit Chinese Med, Coll Pharm, Chengdu, Sichuan, Peoples R China.
RP Huang, LF (通讯作者)，Chinese Acad Med Sci, Peking Union Med Coll, Inst Med Plant Dev, Beijing, Peoples R China.
EM lfhuang@implad.ac.cn
FU National Natural Science Foundation of China [81473315]; Public Welfare
   Scientific Research Project of State Administration of Traditional
   Chinese Medicine [201507004-2-1]; CAMS Innovation Fund for Medical
   Sciences [2016-I2M-3-015]
FX The study was supported by grants from the National Natural Science
   Foundation of China (81473315), Public Welfare Scientific Research
   Project of State Administration of Traditional Chinese Medicine
   (201507004-2-1) and CAMS Innovation Fund for Medical Sciences (NO.
   2016-I2M-3-015).
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NR 37
TC 6
Z9 7
U1 5
U2 45
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0968-0896
EI 1464-3391
J9 BIOORGAN MED CHEM
JI Bioorg. Med. Chem.
PD NOV 1
PY 2018
VL 26
IS 20
BP 5379
EP 5387
DI 10.1016/j.bmc.2018.09.030
PG 9
WC Biochemistry & Molecular Biology; Chemistry, Medicinal; Chemistry,
   Organic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Pharmacology & Pharmacy; Chemistry
GA HB0ZH
UT WOS:000450747500001
PM 30279043
DA 2022-08-23
ER

PT S
AU Dazeley, R
   Kang, BH
AF Dazeley, R
   Kang, BH
BE Gedeon, TD
   Fung, LCC
TI Weighted MCRDR: Deriving information about relationships between
   classifications in MCRDR
SO AI 2003: ADVANCES IN ARTIFICIAL INTELLIGENCE
SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
LA English
DT Article; Proceedings Paper
CT 16th Australian Conference on Artificial Intelligence
CY DEC 03-05, 2003
CL UNIV WESTERN AUSTRALIA, PERTH, AUSTRALIA
SP Natl Comm Artificial Intellignece & Expert Syst
HO UNIV WESTERN AUSTRALIA
ID SYSTEM
AB Multiple Classification Ripple Down Rules (MCRDR) is a knowledge acquisition technique that produces representations, or knowledge maps, of a human expert's knowledge of a particular domain. However, work on gaining an understanding of the knowledge acquired at a deeper meta-level or using the knowledge to derive new information is still in its infancy. This paper will introduce a technique called Weighted MCRDR (WM), which looks at deriving and learning information about the relationships between multiple classifications within MCRDR by calculating a meaningful rating for the task at hand, This is not intended to reduce the knowledge acquisition effort for the expert. Rather, it is attempting to use the knowledge received in the MCRDR knowledge map to derive additional information that can allow improvements in functionality of MCRDR in many problem domains. Preliminary testing shows that there exists a strong potential for WM to quickly and effectively learn meaningful weightings.
C1 Univ Tasmania, Sch Comp, Hobart, Tas 7001, Australia.
   Smart Internet Technol Cooperat Res Ctr, Eveleigh, NSW 1430, Australia.
RP Dazeley, R (通讯作者)，Univ Tasmania, Sch Comp, Hobart, Tas 7001, Australia.
OI Dazeley, Richard/0000-0002-6199-9685; Kang, Byeong
   Ho/0000-0003-3476-8838
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NR 16
TC 3
Z9 3
U1 0
U2 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-20646-9
J9 LECT NOTES ARTIF INT
PY 2003
VL 2903
BP 245
EP 255
PG 11
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BY08N
UT WOS:000187551700021
DA 2022-08-23
ER

PT J
AU Sun, L
   Xu, X
   Yang, YB
   Liu, W
   Jin, JH
AF Sun, Ling
   Xu, Xu
   Yang, Yanbin
   Liu, Wei
   Jin, Jiahao
TI Knowledge mapping of supply chain risk research based on CiteSpace
SO COMPUTATIONAL INTELLIGENCE
LA English
DT Article
DE CiteSpace; knowledge mapping analysis; supply chain risk
ID FUZZY TOPSIS; MANAGEMENT; SUSTAINABILITY; RESILIENCE; COCITATION;
   OPTIMIZATION; FLEXIBILITY; TRENDS; ISSUES; LEVEL
AB Supply chain risk is a relatively new research field, and research results have been published in journals, and in master and doctoral articles. In this article, we first performed a quantitative analysis of existing literature on supply chain risk indexed in Web of Science (WOS). Then, we analyzed the author, institution, keywords, research hotspot, and cocited literature of existing publications in the research field of supply chain risk by using CiteSpace. The aim of this study is to investigate the trend, geographical distribution, author distribution, research field, keywords, keyword clustering, and other features of scientific articles on supply chain risk published in global journals, using the most advanced bibliometric analysis tools. The results of this study can be used to determine the most influential research institutions and authors in the research field of supply chain risk, as well as the research hotspots in different periods, and different research directions in this field.
C1 [Sun, Ling; Yang, Yanbin; Liu, Wei; Jin, Jiahao] Shanghai Maritime Univ, Coll Transport & Commun, Shanghai, Peoples R China.
   [Xu, Xu] Shanghai DianJi Univ, Business Sch, 300 Shuihua Rd, Shanghai, Peoples R China.
RP Xu, X (通讯作者)，Shanghai DianJi Univ, Business Sch, 300 Shuihua Rd, Shanghai, Peoples R China.
EM xuxu007@qq.com
OI Yang, Yanbin/0000-0003-0258-628X
FU Key Research Topics of Decision-making Consultation of Shanghai
   Municipal People's Government [2019-AZ-004]; Shanghai Philosophy and
   Social Science Planning Project [2019ECK005]
FX Key Research Topics of Decision-making Consultation of Shanghai
   Municipal People's Government, Grant/Award Number: 2019-AZ-004; Shanghai
   Philosophy and Social Science Planning Project, Grant/Award Number:
   2019ECK005
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TC 2
Z9 2
U1 41
U2 124
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0824-7935
EI 1467-8640
J9 COMPUT INTELL-US
JI Comput. Intell.
PD NOV
PY 2020
VL 36
IS 4
BP 1686
EP 1703
DI 10.1111/coin.12306
EA MAR 2020
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA OV6UR
UT WOS:000561633100001
DA 2022-08-23
ER

PT J
AU Qiu, YJ
   Lu, J
AF Qiu, Yongjian
   Lu, Jing
TI A visualization algorithm for medical big data based on deep learning
SO MEASUREMENT
LA English
DT Article
DE Convolutional neural networks; Medical big data; Visualization; Deep
   learning
ID DATA ANALYTICS; INTERNET; NETWORK
AB This paper proposes a visualization algorithm model based on deep learning. First, through entity link disambiguation and knowledge map embedding and extraction, the structured disease knowledge in the medical knowledge map is obtained, and the disease feature word vector and the corresponding knowledge entity vector in the disease description text are used as the multi-channel input of Convolutional Neural Networks (CNN). Second, in the convolution process, different types of diseases are represented from two levels of semantics and knowledge. Finally, through training and testing on multiple types of disease description text data sets. The experimental results show that the sensitivity of the method is 0.94, and the accuracy rate reaches 0.87, which verifies that this method of joint training of knowledge and data is more suitable for the initial diagnosis of the disease type described by the condition.
C1 [Qiu, Yongjian] Xingxiang Med Univ, Sanquan Coll, Xinxiang 453000, Henan, Peoples R China.
   [Lu, Jing] Xinxiang Med Univ, Lib, Xinxiang 453003, Henan, Peoples R China.
RP Lu, J (通讯作者)，Xinxiang Med Univ, Lib, Xinxiang 453003, Henan, Peoples R China.
EM 349305731@qq.com
FU Henan Provincial Department of Education Humanities and Social Sciences
   research project "college, academy" General Education Curriculum Mode
   Construction Research [2019-ZZJH-269]
FX The work was supported by Henan Provincial Department of Education
   Humanities and Social Sciences research project "college, academy"
   General Education Curriculum Mode Construction Research under the dual
   system education pattern (No.: 2019-ZZJH-269).
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NR 38
TC 3
Z9 3
U1 2
U2 16
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0263-2241
EI 1873-412X
J9 MEASUREMENT
JI Measurement
PD OCT
PY 2021
VL 183
AR 109808
DI 10.1016/j.measurement.2021.109808
EA JUN 2021
PG 8
WC Engineering, Multidisciplinary; Instruments & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Instruments & Instrumentation
GA UO5CR
UT WOS:000694712600006
DA 2022-08-23
ER

PT J
AU Miao, LL
   Zhang, J
   Zhang, ZC
   Wang, S
   Tang, FT
   Teng, MZ
   Li, YM
AF Miao, Lele
   Zhang, Juan
   Zhang, Zhengchao
   Wang, Song
   Tang, Futian
   Teng, Muzhou
   Li, Yumin
TI A Bibliometric and Knowledge-Map Analysis of CAR-T Cells From 2009 to
   2021
SO FRONTIERS IN IMMUNOLOGY
LA English
DT Article
DE CAR-T cell; Citespace; VOSviewer; bibliometric; knowledge-map; hotspots;
   topics
ID CYTOKINE RELEASE SYNDROME; B-CELL; ANTITUMOR-ACTIVITY; RECEPTOR;
   THERAPY; MANAGEMENT; IMMUNOTHERAPY; GLIOBLASTOMA; EFFICACY; CANCER
AB ObjectivesA bibliometric and knowledge-map analysis is used to explore hotspots' evolution and development trends in the CAR-T cell field. By looking for research hotspots and new topics, we can provide new clues and ideas for researchers in this field. MethodsThe articles and reviews regarding CAR-T cells were retrieved and obtained from the Web of Science Core Collection (WOSCC) on October 28th, 2021. CtieSpace [version 5.8.R3 (64-bit)] and VOSviewer (version 1.6.17) were used to conduct the bibliometric and knowledge-map analysis. Results660 authors from 488 institutions in 104 countries/regions published 6,867 papers in 1,212 academic journals. The United States was absolutely in the leading position in this research field. The institution that contributed the most publications was the University of Pennsylvania. Carl H June published the most articles, while Shannon L Maude had the most co-citations. However, there was little cooperation between countries. After 2012, cooperation among various institutions was also small. The journals that published the most CAR-T cell-related papers were Frontiers in immunology and Cancers. Nevertheless, Blood and The New England Journal of Medicine were the most commonly co-cited journals. The most influential research hotspots were the research of CAR-T cells in hematological malignancies, the related research of cytokine release syndrome (CRS), CD19, and the anti-tumor activity and efficacy of CAR-T cells. The latest hotspots and topics included the study of CAR-T cells in solid tumors, universal CAR-T cells, CAR-NK cells, CD22, and anakinra (the IL-1 receptor antagonist). The research of CAR-T cells in solid tumors was a rapidly developing hot field. Emerging topics in this field mainly included the study of CAR-T cells in glioblastoma (related targets: IL13R alpha 2, EGFRvIII, and HER2), neuroblastoma (related target: GD2), sarcoma (related target: HER2), and pancreatic cancer (related target: mesothelin), especially glioblastoma. ConclusionAs an anti-tumor therapy with great potential and clinical application prospects, CAR-T cell therapy is still in a stage of rapid development. The related field of CAR-T cells will remain a research hotspot in the future.
C1 [Miao, Lele; Zhang, Zhengchao; Wang, Song; Li, Yumin] Lanzhou Univ, Dept Gen Surg, Hosp 2, Lanzhou, Peoples R China.
   [Miao, Lele; Zhang, Zhengchao; Wang, Song; Tang, Futian; Teng, Muzhou; Li, Yumin] Key Lab Digest Syst Tumors Gansu Prov, Lanzhou, Peoples R China.
   [Zhang, Juan] Chinese Peoples Liberat Army PLA Gen Hosp, Med Ctr 5, Dept Hematol, Beijing, Peoples R China.
   [Teng, Muzhou; Li, Yumin] Lanzhou Univ, Lanzhou, Peoples R China.
RP Li, YM (通讯作者)，Lanzhou Univ, Dept Gen Surg, Hosp 2, Lanzhou, Peoples R China.; Teng, MZ; Li, YM (通讯作者)，Key Lab Digest Syst Tumors Gansu Prov, Lanzhou, Peoples R China.; Teng, MZ; Li, YM (通讯作者)，Lanzhou Univ, Lanzhou, Peoples R China.
EM tengmz@lzu.edu.cn; liym@lzu.edu.cn
FU Special Research Project of Lanzhou University Serving the Economic
   Social Development of Gansu Province [054000282]; Lanzhou Talent
   Innovation and Entrepreneurship Project [2020-RC-38]; Fundamental
   Research Funds for the Central Universities [lzujbky-2020-kb14]; Major
   Science and Technology Special Project of Gansu Province [20ZD7FA003]
FX Funding This work was funded by Special Research Project of Lanzhou
   University Serving the Economic Social Development of Gansu Province
   (054000282) Lanzhou Talent Innovation and Entrepreneurship Project
   (2020-RC-38), the Fundamental Research Funds for the Central
   Universities (lzujbky-2020-kb14), and Major Science and Technology
   Special Project of Gansu Province (20ZD7FA003).
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NR 98
TC 0
Z9 0
U1 21
U2 21
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 MAR 18
PY 2022
VL 13
AR 840956
DI 10.3389/fimmu.2022.840956
PG 20
WC Immunology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Immunology
GA 0Y4AZ
UT WOS:000790335100001
PM 35371087
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Wang, TI
   Su, CY
   Hsieh, TC
AF Wang, Tzone-I
   Su, Chien-Yuan
   Hsieh, Tung-Cheng
TI Accumulating and visualising tacit knowledge of teachers on educational
   assessments
SO COMPUTERS & EDUCATION
LA English
DT Article
DE Applications in subject areas; Architectures for educational technology
   system; Elementary education; Human-computer interface; Improving
   classroom teaching
ID CONCEPT MAPS; CONSTRUCTION; SYSTEM
AB Assessments, embedded with teachers' implicit (i.e. tacit) domain knowledge, play an important role in evaluating 'comprehension of a subject. The knowledge on the importance of both the concepts and their relationships of a subject, if captured, made explicit, and shared around, may greatly help teachers construct more effective assessments. This study establishes a methodology to accumulate tacit knowledge of specific topics from collected assessments by using an implicit knowledge extraction mechanism and, visualises the overall importance distribution of concepts by using knowledge maps for helping teachers compile their assessments. Several two stage experiments, scheduled for one semester, were conducted in the third grade natural science courses at elementary schools in Taiwan. Eighteen teachers who actually teach the courses participated in the experiments, and thirty students were in each course. In the first stage, teachers compiled assessments without using IKMAAS's knowledge map features while in the second stage, they did use them. System usage records, questionnaires and interview results were used for evaluating the usability of the methodology and the satisfaction of using IKMAAS. The results indicate the potential of the methodology, as each of the teachers agreed that the visualised assessment knowledge helped them to comprehend the proportions of concepts they intended to test easily and, additionally, helped them to clearly notice concepts they may have ignored. Yet the results in this study also show the potential of using knowledge maps and knowledge accumulating methodology in pedagogy paradigm. (C) 2011 Elsevier Ltd. All rights reserved.
C1 [Wang, Tzone-I; Su, Chien-Yuan] Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan.
   [Hsieh, Tung-Cheng] Taipei Coll Maritime Technol, Dept Multimedia & Game Sci, Taipei 111, Taiwan.
RP Su, CY (通讯作者)，Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan.
EM n9897108@mail.ncku.edu.tw
OI Su, Chien-Yuan/0000-0003-1639-3948
FU National Science Council, Taiwan [NSC 98-2511-S-006-001-MY3]
FX This research was supported by the National Science Council, Taiwan,
   under Grant No. NSC 98-2511-S-006-001-MY3.
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NR 35
TC 8
Z9 8
U1 0
U2 45
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 2011
VL 57
IS 4
BP 2212
EP 2223
DI 10.1016/j.compedu.2011.06.018
PG 12
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 833CT
UT WOS:000295860100002
DA 2022-08-23
ER

PT J
AU Zhou, L
AF Zhou, Lu
TI Event Scene Method of Legal Domain Knowledge Map Based on Neural Network
   Hybrid Model
SO APPLIED BIONICS AND BIOMECHANICS
LA English
DT Article
ID PREDICTION MODEL; SOLAR-RADIATION
AB Event extraction technology is one of the important researches in the field of information extraction, which helps people accurately retrieve, find, classify, and summarize effective information from a large amount of information streams. This paper uses the neural network hybrid model to identify the trigger words and event categories of the legal domain knowledge graph events, extracts the events of interest from a large amount of free text, and displays them in a structured format. First, the original text is preprocessed, and then, the distributed semantic word vector is combined with the dependent syntactic structure and location attributes to create a semantic representation in the form of a vector. The combined deep learning model is used to extract activated words, the long-term memory loop neural network uses temporal semantics to extract deep features, and the convergent neural network completes the extraction of activated words and event categories. Finally, the experimental results show that the accuracy of event extraction of the neural network hybrid model designed in this paper has reached 77.1%, and the recall rate has reached 76.8%, which is greatly improved compared with the traditional model.
C1 [Zhou, Lu] Hunan Univ, Sch Law, Changsha, Hunan, Peoples R China.
RP Zhou, L (通讯作者)，Hunan Univ, Sch Law, Changsha, Hunan, Peoples R China.
EM b131900018@hnu.edu.cn
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NR 30
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 1176-2322
EI 1754-2103
J9 APPL BIONICS BIOMECH
JI Appl. Bionics Biomech.
PD JUN 18
PY 2022
VL 2022
AR 5880595
DI 10.1155/2022/5880595
PG 12
WC Engineering, Biomedical; Robotics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Robotics
GA 2M2UD
UT WOS:000817560200001
PM 35761859
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Lin, FR
   Lin, SC
AF Lin, FR
   Lin, SC
TI A conceptual model for virtual organizational learning
SO JOURNAL OF ORGANIZATIONAL COMPUTING AND ELECTRONIC COMMERCE
LA English
DT Article; Proceedings Paper
CT 4th Pacific Asia Conference on Information Systems
CY JUN 01-03, 2000
CL HONG KONG, PEOPLES R CHINA
DE virtual organization; organizational learning; organizational memory;
   transactive memory; knowledge map; social network; cyber community
ID JOINT-VENTURES; STRATEGIC ALLIANCES; TRANSACTIVE MEMORY;
   ABSORPTIVE-CAPACITY; INFORMATION-SYSTEMS; KNOWLEDGE; MANAGEMENT;
   PERSPECTIVE; PERFORMANCE; INDIVIDUALS
AB As a trend toward virtual organizations (VOs) emerges in the era of electronic commerce, an understanding of knowledge processing is essential to sustain performance. Active learning is the key to achieving flexibility in a VO; however, few studies have focused on this dimension. In this article we attempt to assemble insights about learning in a VO by developing a conceptual model for virtual organizational learning (VOL). The VOL model is focused on transactive memory, which is composed of knowledge maps, social networks, and mnemonic functions. The initialization of modeling learning in VOs in this article has been erecting the foundation for transactive memory systems in a cyber community. A case study, Smart Creative Teachers Network, is used for demonstrating the transactive memory system for a teacher community to share and create knowledge and form VOs across school boundaries.
C1 Natl Sun Yat Sen Univ, Dept Informat Management, Comp & Network Ctr, Applicat Div, Kaohsiung 804, Taiwan.
RP Lin, FR (通讯作者)，Natl Sun Yat Sen Univ, Dept Informat Management, Comp & Network Ctr, Applicat Div, Kaohsiung 804, Taiwan.
EM frlin@cc.nsysu.edu.tw
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NR 104
TC 16
Z9 18
U1 2
U2 16
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1091-9392
EI 1532-7744
J9 J ORG COMP ELECT COM
JI J. Organ. Comp. Electron. Commer.
PY 2001
VL 11
IS 3
BP 155
EP 178
DI 10.1207/S15327744JOCE1103_02
PG 24
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA 561VU
UT WOS:000176164400002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Chung, GKWK
   Harmon, TC
   Baker, EL
AF Chung, GKWK
   Harmon, TC
   Baker, EL
TI The impact of a simulation-based learning design project on student
   learning
SO IEEE TRANSACTIONS ON EDUCATION
LA English
DT Article
DE assessment; capstone; concept mapping; evaluation; hazardous waste;
   knowledge mapping; problem-based learning; simulation
ID FRAMEWORK; TEAMWORK
AB This study examined the effects of a sophisticated simulation-based task on students' learning of course-related content, ability to deal with complex, open-ended problems, and attitudes toward the Interactive Site Investigation Software (ISIS) and the course. Students were required to conduct a hazardous waste site investigation using the simulation software designed specifically for the course. ISIS simulated physical processes as well as red-world engineering processes. Assessment of student outcomes was based on the use of constructed-response knowledge maps to measure content understanding, and surveys to measure student attitudes and use of cognitive processes. Students reported very positive attitudes toward the ISIS experience, appeared to have gained substantial knowledge over the course of ISIS use, and perceived the ISIS activity as being generally effective in improving their skills in dealing with complex projects, linking theory to real-world applications, and improving their problem-solving performance. Our assessment of student outcomes was a successful first attempt, but more work is needed to validate our measures with advanced students, particularly when the task is complex and requires interdisciplinary knowledge.
C1 Univ Calif Los Angeles, CSE, CRESST, Ctr Res Evaluat Stand & Student Testing, Los Angeles, CA 90095 USA.
   Univ Calif Los Angeles, Dept Civil & Elect Engn, Los Angeles, CA 90095 USA.
RP Chung, GKWK (通讯作者)，Univ Calif Los Angeles, CSE, CRESST, Ctr Res Evaluat Stand & Student Testing, 301 GSE&IS, Los Angeles, CA 90095 USA.
EM greg@ucla.edu
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NR 41
TC 29
Z9 29
U1 1
U2 22
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9359
EI 1557-9638
J9 IEEE T EDUC
JI IEEE Trans. Educ.
PD NOV
PY 2001
VL 44
IS 4
BP 390
EP 398
DI 10.1109/13.965789
PG 9
WC Education, Scientific Disciplines; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA 492NZ
UT WOS:000172175600015
DA 2022-08-23
ER

PT J
AU Wu, ZS
   Xue, R
   Shao, MY
AF Wu, Zongsheng
   Xue, Ru
   Shao, Meiyun
TI Knowledge graph analysis and visualization of AI technology applied in
   COVID-19
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article
DE COVID-19; Coronavirus disease; Knowledge graph; AI; Data visualization;
   Visual analysis
AB With the global outbreak of coronavirus disease (COVID-19) all over the world, artificial intelligence (AI) technology is widely used in COVID-19 and has become a hot topic. In recent 2 years, the application of AI technology in COVID-19 has developed rapidly, and more than 100 relevant papers are published every month. In this paper, we combined with the bibliometric and visual knowledge map analysis, used the WOS database as the sample data source, and applied VOSviewer and CiteSpace analysis tools to carry out multi-dimensional statistical analysis and visual analysis about 1903 pieces of literature of recent 2 years (by the end of July this year). The data is analyzed by several terms with the main annual article and citation count, major publication sources, institutions and countries, their contribution and collaboration, etc. Since last year, the research on the COVID-19 has sharply increased; especially the corresponding research fields combined with the AI technology are expanding, such as medicine, management, economics, and informatics. The China and USA are the most prolific countries in AI applied in COVID-19, which have made a significant contribution to AI applied in COVID-19, as the high-level international collaboration of countries and institutions is increasing and more impactful. Moreover, we widely studied the issues: detection, surveillance, risk prediction, therapeutic research, virus modeling, and analysis of COVID-19. Finally, we put forward perspective challenges and limits to the application of AI in the COVID-19 for researchers and practitioners to facilitate future research on AI applied in COVID-19.
C1 [Wu, Zongsheng] Xianyang Normal Univ, Sch Comp Sci, Xianyang 712000, Shaanxi, Peoples R China.
   [Xue, Ru; Shao, Meiyun] Xizang Minzu Univ, Sch Informat Engn, Xianyang 712082, Shaanxi, Peoples R China.
RP Wu, ZS (通讯作者)，Xianyang Normal Univ, Sch Comp Sci, Xianyang 712000, Shaanxi, Peoples R China.
EM wuzs2005@163.com
FU National Natural Science Foundation of China [62,073,218]; Natural
   Science Foundation of Tibet Autonomous Region [XZ202001ZR0048G];
   Scientific Research Project of Xianyang Science and Technology Bureau
   [2020K02-14]; Scientific Research Fund Project of Xianyang Normal
   University [XSYK20025]
FX This work is supported in part by four foundations:; National Natural
   Science Foundation of China (Grant: 62,073,218); Natural Science
   Foundation of Tibet Autonomous Region (Grant: XZ202001ZR0048G);
   Scientific Research Project of Xianyang Science and Technology Bureau
   (Grant: 2020K02-14); Scientific Research Fund Project of Xianyang Normal
   University (Grant: XSYK20025)
CR Aa B, 2020, INFORM MED UNLOCKED, V19
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NR 32
TC 2
Z9 2
U1 29
U2 47
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
PD APR
PY 2022
VL 29
IS 18
BP 26396
EP 26408
DI 10.1007/s11356-021-17800-z
EA DEC 2021
PG 13
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 0I5GL
UT WOS:000725470500006
PM 34859342
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Zhang, Y
AF Zhang, Yan
TI Intelligent Recommendation Model of Contemporary Pop Music Based on
   Knowledge Map
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
AB With the advent of the era of big data, the rise of Web2.0 completely subverts the traditional Internet model and becomes the trend of today's information age. Simultaneously, massive amounts of data and information have infiltrated various Internet companies, resulting in an increase in the problem of information overload. In the online world, learning how to quickly and accurately select the parts we are interested in from a variety of data has become a hot topic. Intelligent music recommendation has become a current research hotspot in music services as a viable solution to the problem of information overload in the digital music field. On the basis of precedents, this paper examines the characteristics of music in a comprehensive and detailed manner. A knowledge graph-based intelligent recommendation algorithm for contemporary popular music is proposed. User-defined tags are described as the free genes of music in this paper, making it easier to analyze user behavior and tap into user interests. It has been confirmed that this algorithm's recommendation quality is relatively high, and it offers a new development path for improving the speed of searching for health information services.
C1 [Zhang, Yan] Sejong Univ, Gen Grad Sch, Dept Mus, Seoul 05006, South Korea.
RP Zhang, Y (通讯作者)，Sejong Univ, Gen Grad Sch, Dept Mus, Seoul 05006, South Korea.
EM zymusic1987@163.com
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NR 30
TC 0
Z9 0
U1 11
U2 11
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD FEB 9
PY 2022
VL 2022
AR 1756585
DI 10.1155/2022/1756585
PG 8
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA ZQ2GP
UT WOS:000766929200002
PM 35186055
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ranjbari, M
   Esfandabadi, ZS
   Ferraris, A
   Quatraro, F
   Rehan, M
   Nizami, AS
   Gupta, VK
   Lam, SS
   Aghbashlo, M
   Tabatabaei, M
AF Ranjbari, Meisam
   Esfandabadi, Zahra Shams
   Ferraris, Alberto
   Quatraro, Francesco
   Rehan, Mohammad
   Nizami, Abdul-Sattar
   Gupta, Vijai Kumar
   Lam, Su Shiung
   Aghbashlo, Mortaza
   Tabatabaei, Meisam
TI Biofuel supply chain management in the circular economy transition: An
   inclusive knowledge map of the field
SO CHEMOSPHERE
LA English
DT Article
DE Biofuel; Supply chain management; Circular economy; Biomass; Anaerobic
   digestion; Biogas
ID SUSTAINABILITY ASSESSMENT; FOREST RESIDUES; BIOMASS; OPTIMIZATION;
   ELECTRICITY; MICROALGAE; LOGISTICS; RESOURCES; BIOENERGY; PROSPECTS
AB Investment in biofuels, as sustainable alternatives for fossil fuels, has gained momentum over the last decade due to the global environmental and health concerns regarding fossil fuel consumption. Hence, effective management of biofuel supply chain (BSC) components, including biomass feedstock production, biomass logistics, biofuel production in biorefineries, and biofuel distribution to consumers, is crucial in transitioning towards a lowcarbon and circular economy (CE). The present study aims to render an inclusive knowledge map of the BSCrelated scientific production. In this vein, a systematic review, supported by a keywords co-occurrence analysis and qualitative content analysis, was carried out on a total of 1,975 peer-reviewed journal articles in the target literature. The analysis revealed four major research hotspots in the BSC literature, namely (1) biomass-tobiofuel supply chain design and planning, (2) environmental impacts of biofuel production, (3) biomass to bioenergy, and (4) techno-economic analysis of biofuel production. Besides, the findings showed that the following subject areas of research in the BSC research community have recently attracted more attention: (i) global warming and climate change mitigation, (ii) development of the third-generation biofuels produced from algal biomass, which has recently gained momentum in the CE debate, and (iii) government incentives, pricing, and subsidizing policies. The provided insights shed light on the understanding of researchers, stakeholders, and policy-makers involved in the sustainable energy sector by outlining the main research backgrounds, developments, and tendencies within the BSC arena. Looking at the provided knowledge map, potential research directions in BSCs towards implementing the CE model, including (i) integrative policy convergence at macro, meso, and micro levels, and (ii) industrializing algae-based biofuel production towards the CE transition, were proposed.
C1 [Ranjbari, Meisam; Lam, Su Shiung; Aghbashlo, Mortaza; Tabatabaei, Meisam] Henan Agr Univ, Henan Prov Forest Resources Sustainable Dev, Sch Forestry, Zhengzhou 450002, Peoples R China.
   [Ranjbari, Meisam; Lam, Su Shiung; Aghbashlo, Mortaza; Tabatabaei, Meisam] Henan Agr Univ, High Value Utilizat Engn Res Ctr, Sch Forestry, Zhengzhou 450002, Peoples R China.
   [Ranjbari, Meisam; Quatraro, Francesco] Univ Turin, Dept Econ & Stat Cognetti Martiis, Turin, Italy.
   [Esfandabadi, Zahra Shams] Politecn Torino, Dept Environm Land & Infrastruct Engn DIATI, Turin, Italy.
   [Esfandabadi, Zahra Shams] Politecn Torino, Energy Ctr Lab, Turin, Italy.
   [Ferraris, Alberto] Univ Turin, Dept Management, Turin, Italy.
   [Ferraris, Alberto] Ural Fed Univ, Grad Sch Econ & Management, Lab Int & Reg Econ, Moscow, Russia.
   [Ferraris, Alberto] Univ Rijeka, Fac Econ & Business, Rijeka, Croatia.
   [Quatraro, Francesco] Coll Carlo Alberto, BRICK, Piazza Arbarello 8, I-10123 Turin, Italy.
   [Rehan, Mohammad; Nizami, Abdul-Sattar] King Abdulaziz Univ, Ctr Excellence Environm Studies CEES, Jeddah, Saudi Arabia.
   [Nizami, Abdul-Sattar] Govt Coll Univ, Sustainable Dev Study Ctr, Lahore, Pakistan.
   [Gupta, Vijai Kumar] Scotlands Rural Coll SRUC, Biorefining & Adv Mat Res Ctr, Kings Bldg,West Mains Rd, Edinburgh EH9 3JG, Midlothian, Scotland.
   [Gupta, Vijai Kumar] Scotlands Rural Coll SRUC, Ctr Safe & Improved Foods, Kings Bldg,West Mains Rd, Edinburgh EH9 3JG, Midlothian, Scotland.
   [Lam, Su Shiung; Tabatabaei, Meisam] Univ Malaysia Terengganu, Higher Inst Ctr Excellence HICoE, Inst Trop Aquaculture & Fisheries AKUATROP, Terengganu 21030, Malaysia.
   [Aghbashlo, Mortaza] Univ Tehran, Coll Agr & Nat Resources, Fac Agr Engn & Technol, Dept Mech Engn Agr Machinery, Karaj, Iran.
   [Tabatabaei, Meisam] Biofuel Res Team BRTeam, Terengganu, Malaysia.
RP Ranjbari, M; Lam, SS; Aghbashlo, M; Tabatabaei, M (通讯作者)，Henan Agr Univ, Henan Prov Forest Resources Sustainable Dev, Sch Forestry, Zhengzhou 450002, Peoples R China.; Ranjbari, M; Lam, SS; Aghbashlo, M; Tabatabaei, M (通讯作者)，Henan Agr Univ, High Value Utilizat Engn Res Ctr, Sch Forestry, Zhengzhou 450002, Peoples R China.
EM meisam.ranjbari@unito.it; lam@umt.edu.my; maghbashlo@ut.ac.ir;
   meisam.tabatabaei@umt.edu.my
RI Rehan, Mohammad/I-5609-2012; Shams Esfandabadi, Zahra/AAW-9671-2020;
   Ranjbari, Meisam/AAW-9409-2020; Lam, Su Shiung/K-7436-2012
OI Rehan, Mohammad/0000-0002-1019-8677; Shams Esfandabadi,
   Zahra/0000-0001-9320-4712; Ranjbari, Meisam/0000-0002-2666-9154; Lam, Su
   Shiung/0000-0002-5318-1760; FERRARIS, ALBERTO/0000-0003-3125-1710
FU Universiti Malaysia Terengganu [UMT/CRIM/2-2/2/23 (23), 55302]; Henan
   Agricultural University under a Research Collaboration Agreement (RCA);
   Ministry of Higher Education, Malaysia, under the Higher Institution
   Centre of Excellence (HICoE), Institute of Tropical Aquaculture and
   Fisheries (AKUATROP) program [56051, 56052]; Program for Innovative
   Research Team (in Science and Technology) in the University of Henan
   Province [21IRTSTHN020]; Central Plain Scholar Funding Project of Henan
   Province [212101510005]; University of Tehran; Biofuel Research Team
   (BRTeam)
FX The authors would like to thank Universiti Malaysia Terengganu under
   International Partnership Research Grant (UMT/CRIM/2-2/2/23 (23), Vot
   55302) for supporting this joint project with Henan Agricultural
   University under a Research Collaboration Agreement (RCA). This work is
   also supported by the Ministry of Higher Education, Malaysia, under the
   Higher Institution Centre of Excellence (HICoE), Institute of Tropical
   Aquaculture and Fisheries (AKUATROP) program (Vot. No. 56051,
   UMT/CRIM/2-2/5 Jilid 2 (10) and Vot. No. 56052, UMT/CRIM/2-2/5 Jilid 2
   (11)). The manuscript is also supported by the Program for Innovative
   Research Team (in Science and Technology) in the University of Henan
   Province (No. 21IRTSTHN020) and Central Plain Scholar Funding Project of
   Henan Province (No. 212101510005). The authors would also like to extend
   their sincere appreciation to the University of Tehran and the Biofuel
   Research Team (BRTeam) for their support throughout this project.
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NR 128
TC 2
Z9 2
U1 10
U2 10
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0045-6535
EI 1879-1298
J9 CHEMOSPHERE
JI Chemosphere
PD JUN
PY 2022
VL 296
AR 133968
DI 10.1016/j.chemosphere.2022.133968
PG 14
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 2P7WC
UT WOS:000819945200003
PM 35181422
DA 2022-08-23
ER

PT J
AU Ren, HL
   Li, HL
   Peng, KY
AF Ren, Haoli
   Li, Hailan
   Peng, Kongyang
TI Model Research on In-Service Learning of Intellectualization of
   Aerospace Knowledge
SO MOBILE INFORMATION SYSTEMS
LA English
DT Article
AB With the development of vocational education, it is necessary to construct the pattern of lifelong learning. To push delivery learning resources and provide a learning environment, it is necessary to innovate in-service learning mode. According to the characteristics of the aerospace position, the capacity model was studied and proposed. Based on the ability model, the intelligent in-service learning model is studied and proposed to improve the precision service quality. From the angle of principle and learning process, this paper discusses the intelligent in-service learning mode of including the learning model based on knowledge map and the learning model based on seminar hall. The framework of the job knowledge map is constructed according to the post ability model which is based on professional knowledge, professional skills, and professional quality. The intelligent on-the-job learning model includes four elements: (i) learning platform, (ii) learning resources, (iii) learning methods, and (iv) learning evaluation. The learning portrait can record and visualize the information of learning, including content, activities, and effects.
C1 [Ren, Haoli] Space Engn Univ, Space Secur Res Ctr, Beijing, Peoples R China.
   [Li, Hailan; Peng, Kongyang] Space Engn Univ, Sergeant Sch, Beijing, Peoples R China.
RP Ren, HL (通讯作者)，Space Engn Univ, Space Secur Res Ctr, Beijing, Peoples R China.
EM rholy@sina.com
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   Jiang Q., 2005, VOCATIONAL TECHNICAL, V26, P46
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   Zhang N. Y., 2003, P C MACH LEARN CYB I
NR 9
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 1574-017X
EI 1875-905X
J9 MOB INF SYST
JI Mob. Inf. Syst.
PD DEC 18
PY 2021
VL 2021
AR 4643744
DI 10.1155/2021/4643744
PG 6
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA ZV1ZA
UT WOS:000770332200003
OA gold
DA 2022-08-23
ER

PT J
AU Yang, DC
   Guo, JE
   Li, J
   Wang, SY
   Sun, SL
AF Yang, Dongchuan
   Guo, Ju-e
   Li, Jie
   Wang, Shouyang
   Sun, Shaolong
TI Knowledge Mapping in Electricity Demand Forecasting: A Scientometric
   Insight
SO FRONTIERS IN ENERGY RESEARCH
LA English
DT Article
DE electricity demand forecasting; scientometric; visualization; citespace;
   knowledge mapping
ID SHORT-TERM LOAD; SUPPORT VECTOR REGRESSION; NEURAL-NETWORK; EMERGING
   TRENDS; GREY PREDICTION; MODEL; MACHINES; SYSTEMS
AB Electricity demand forecasting plays a fundamental role in the operation and planning procedures of power systems, and the publications related to electricity demand forecasting have attracted more and more attention in the past few years. To have a better understanding of the knowledge structure in the field of electricity demand forecasting, we applied scientometric methods to analyze the current state and the emerging trends based on the 831 publications from the Web of Science Core Collection during the past 20 years (1999-2018). Employing statistical description analysis, cooperative network analysis, keyword co-occurrence analysis, co-citation analysis, cluster analysis, and emerging trend analysis techniques, this study gives a comprehensive overview of the most critical countries, institutions, journals, authors, and publications in this field, cooperative networks relationships, research hotspots, and emerging trends. The results can provide meaningful guidance and helpful insights for researchers to enhance the understanding of crucial research, emerging trends, and new developments in electricity demand forecasting.</p>
C1 [Yang, Dongchuan; Guo, Ju-e; Sun, Shaolong] Xi An Jiao Tong Univ, Sch Management, Xian, Peoples R China.
   [Li, Jie] Chinese Acad Sci, Natl Sci Lib, Beijing, Peoples R China.
   [Li, Jie] Liaoning Tech Univ, Coll Safety Sci & Engn, Fuxing, Peoples R China.
   [Wang, Shouyang] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China.
   [Wang, Shouyang] Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China.
   [Wang, Shouyang] Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China.
RP Sun, SL (通讯作者)，Xi An Jiao Tong Univ, Sch Management, Xian, Peoples R China.
EM sunshaolong@xjtu.edu.cn
OI Yang, Dongchuan/0000-0002-5249-3922
FU National Natural Science Foundation of China [72101197, 71988101];
   Fundamental Research Funds for the Central Universities [SK2021007]
FX Funding This research work was partly supported by the National Natural
   Science Foundation of China under Grants No. 72101197 and No. 71988101,
   and by the Fundamental Research Funds for the Central Universities under
   Grant No. SK2021007.
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NR 49
TC 2
Z9 2
U1 6
U2 12
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-598X
J9 FRONT ENERGY RES
JI Front. Energy Res.
PD OCT 14
PY 2021
VL 9
AR 771433
DI 10.3389/fenrg.2021.771433
PG 14
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA WQ2JT
UT WOS:000713647200001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Agossou, V
   Suh, HW
   Lee, H
   Lee, JH
AF Agossou, Valentin
   Suh, Hyo-Won
   Lee, Heejung
   Lee, Jae Hyun
TI Development of a Framework to Understand Tables in Engineering
   Specification Documents
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE engineering specification documents; table understanding; domain
   knowledge map
ID EXTRACTION
AB Several works have been done in the last decades for understanding tables in documents, but most of them were not specifically designed to understand tables in engineering specification documents. Tables in engineering specifications have characteristics such as various table structures with restricted terms. A framework is developed to address the issues in understanding tables in engineering specification documents. The framework consists of three steps: (1) Identifying minimal tables, (2) classifying cells, and (3) extending a domain knowledge map. A modified XY-tree algorithm was developed to find minimal tables, and a neural network algorithm was adopted to classify cells into labels and data. Then, specific domain rules were developed to discover concepts and relationships from terms in the classified cells. It is assumed a domain ontology is given, and it is extended with new concepts and relationships extracted from tables. We illustrated how each step performed with engineering table examples. The proposed framework could be used for searching product specification and for discovering hidden knowledge from tables in engineering specification documents.
C1 [Agossou, Valentin; Lee, Jae Hyun] Daegu Univ, Dept Ind Engn, 201 Daegudae Ro, Kyongsan Si 712714, Kyongbuk, South Korea.
   [Suh, Hyo-Won] Korea Adv Inst Sci & Technol, Dept Ind & Syst Engn, 291 Daehak Ro, Daejeon 34141, South Korea.
   [Lee, Heejung] Hanyang Univ, Div Interdisciplinary Ind Studies, 222 Wangsimni Ro, Seoul 04763, South Korea.
RP Lee, JH (通讯作者)，Daegu Univ, Dept Ind Engn, 201 Daegudae Ro, Kyongsan Si 712714, Kyongbuk, South Korea.
EM valentin@daegu.ac.kr; hw_suh@kaist.ac.kr; stdream@hanyang.ac.kr;
   jaehyun.lee@daegu.ac.kr
RI Lee, Heejung/K-7823-2015
OI Lee, Heejung/0000-0001-7548-9291; Lee, Jae Hyun/0000-0001-8778-8707
FU Ministry of Trade, Industry and Energy in South Korea [20009324,
   20009185]
FX This research was funded by two projects of the Ministry of Trade,
   Industry and Energy in South Korea. The first grant number is 20009324
   (project name: "Development of Vertical Integrated Smart Factory Package
   for Chemical Plant") and the second one is 20009185 (project name:
   "Development of NPD service platform based on AI analytics and user
   comments on consumer goods").
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NR 24
TC 0
Z9 0
U1 2
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 SEP
PY 2020
VL 10
IS 18
AR 6182
DI 10.3390/app10186182
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 OF9BH
UT WOS:000581492900001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zeng, Y
   Xu, XL
AF Zeng, Yong
   Xu, Xiaoli
TI TOWARD ENVIRONMENT-AWARE 6G COMMUNICATIONS VIA CHANNEL KNOWLEDGE MAP
SO IEEE WIRELESS COMMUNICATIONS
LA English
DT Article
ID DESIGN
AB This article proposes the concept of channel knowledge map (CKM) as an enabler toward environment-aware wireless communications. CKM is a site-specific database, tagged with the locations of the transmitters and/or receivers, that contains channel-related information useful to enhance environment-awareness and facilitate or even obviate sophisticated real-time channel state information (CSI) acquisition. Therefore, CKM is expected to play an important role for 6G networks targeting for super high capacity, extremely low latency, and ultra- massive connectivity, by offering potential solutions to practical challenges brought by the drastically increased channel dimensions and training overhead. In this article, the motivations of environment-awareness enabled by CKM are first discussed, followed by the key techniques to build and utilize CKM. In particular, it is highlighted that CKM is especially appealing for four channel types: channels for yet-to-reach locations, channels for non-cooperative nodes, channels with large dimensions, and channels with severe hardware/processing limitations. Two case studies with extensive numerical results are presented to demonstrate the great potential of environment-aware communications enabled by CKM.
C1 [Zeng, Yong; Xu, Xiaoli] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing, Peoples R China.
   [Zeng, Yong] Purple Mt Labs, Nanjing, Peoples R China.
RP Xu, XL (通讯作者)，Southeast Univ, Natl Mobile Commun Res Lab, Nanjing, Peoples R China.
FU National Key R&D Program of China [2019YFB1803400]
FX This work was supported by the National Key R&D Program of China with
   Grant number 2019YFB1803400.
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NR 15
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 1536-1284
EI 1558-0687
J9 IEEE WIREL COMMUN
JI IEEE Wirel. Commun.
PD JUN
PY 2021
VL 28
IS 3
BP 84
EP 91
DI 10.1109/MWC.001.2000327
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 TL9WB
UT WOS:000675202200014
DA 2022-08-23
ER

PT J
AU Wang, YP
   Jiang, RG
   Xie, JC
   Zhao, Y
   Li, FW
AF Wang, Yinping
   Jiang, Rengui
   Xie, Jiancang
   Zhao, Yong
   Li, Fawen
TI Water Resources Management under Changing Environment: A Systematic
   Review
SO JOURNAL OF COASTAL RESEARCH
LA English
DT Review
DE Water resources management; knowledge mapping analysis; changing
   environment; climate change; sustainability; CiteSpace
ID STOCHASTIC-PROGRAMMING-MODEL; CLIMATE-CHANGE; RIVER-BASIN; OPTIMIZATION
   MODEL; EMERGING TRENDS; EXTREME PRECIPITATION; REGENERATIVE MEDICINE;
   USE EFFICIENCY; PART 1; IMPACTS
AB Under the influences of climate change, human activities and socioeconomic development, the water resources management (WRM) has become more complex and uncertain. The paper visualizes and quantifies the knowledge base, domain and structure of the WRM using the bibliometric analysis and knowledge map. The visualized knowledge maps are used to investigate the development trend, scientific collaboration, subject categories, core research topics and the newest research fronts of the WRM. 9028 publications on the WRM were retrieved from the Web of Science Core Collection databases for the period of 1990-2018. Results indicated that the research on the WRM maintained an increasing trend for the past 29 years. China contributed the most publication, and Germany cooperated the most with other countries/regions, which were two active contributors of the WRM research. The co-word analysis and burst detective analysis of keywords showed that climate change and ecosystem were the research hotspot of the WRM knowledge base. The co-occurrence analysis and burst detective analysis of subject categories found that the Environmental Sciences & Ecology and Green & Sustainable Science & Technology were the core direction of the WRM knowledge domain. Solving the practical problems through model simulation was the core of the WRM knowledge structure under the changing environment using the cluster analysis and document co- citation analysis. It is suggested that the WRM managers should take the sustainable and adaptive development as goal, ensure the health and integrity of the ecosystem, integrate stakeholders into the WRM, which should help to make the WRM more adaptable under changing environment.
C1 [Wang, Yinping; Jiang, Rengui; Xie, Jiancang] Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg, Xian 710048, Peoples R China.
   [Jiang, Rengui; Zhao, Yong] China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China.
   [Li, Fawen] Tianjin Univ, State Key Lab Hydraul Engn Simulat & Safety, Tianjin 300072, Peoples R China.
RP Jiang, RG (通讯作者)，Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg, Xian 710048, Peoples R China.; Jiang, RG (通讯作者)，China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China.
EM jrengui@163.com
FU National Key Research and Development Program of China [2016YFC0401409];
   National Natural Science Foundation of China [51679188, 51509201]; Young
   Technology Star in Shaanxi Province of China [2020KJXX-092]
FX This paper was partly funded by the National Key Research and
   Development Program of China (Grant No. 2016YFC0401409), National
   Natural Science Foundation of China (Grant No. 51679188, 51509201),
   Young Technology Star in Shaanxi Province of China (Grant No.
   2020KJXX-092). The publications data were download from Web of Science
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   was obtained from http://cluster.ischool.drexel.edu/similar to
   cchen/citespace/download/.The Cattor2 was acquired from
   http://project.carrot2.org/.
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TC 2
Z9 2
U1 15
U2 46
PU COASTAL EDUCATION & RESEARCH FOUNDATION
PI COCONUT CREEK
PA 5130 NW 54TH STREET, COCONUT CREEK, FL 33073 USA
SN 0749-0208
EI 1551-5036
J9 J COASTAL RES
JI J. Coast. Res.
PD FAL
PY 2020
SI 104
BP 29
EP 41
DI 10.2112/JCR-SI104-006.1
PG 13
WC Environmental Sciences; Geography, Physical; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Physical Geography; Geology
GA OK2VS
UT WOS:000584510900006
DA 2022-08-23
ER

PT J
AU Sheikhattar, MR
   Nezafati, N
   Shokouhyar, S
AF Sheikhattar, Mohammad Reza
   Nezafati, Navid
   Shokouhyar, Sajjad
TI A thematic analysis-based model for identifying the impacts of natural
   crises on a supply chain for service integrity: a text analysis approach
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article; Early Access
DE Supply chain crisis management; Supply chain risk monitoring; Pandemic
   crisis; Thematic analysis model; Text-mining
ID TECHNOLOGY; MANAGEMENT; SENSEMAKING; ANALYTICS
AB Numerous studies have been conducted to identify the effects of natural crises on supply chain performance. Conventional analysis methods are based on either manual filter methods or data-driven methods. The manual filter methods suffer from validation problems due to sampling limitations, and data-driven methods suffer from the nature of crisis data which are vague and complex. This study aims to present an intelligent analysis model to automatically identify the effects of natural crises such as the COVID-19 pandemic on the supply chain through metadata generated on social media. This paper presents a thematic analysis framework to extract knowledge under user steering. This framework uses a text-mining approach, including co-occurrence term analysis and knowledge map construction. As a case study to approve our proposed model, we retrieved, cleaned, and analyzed 1024 online textual reports on supply chain crises published during the COVID-19 pandemic in 2019-2021. We conducted a thematic analysis of the collected data and achieved a knowledge map on the impact of the COVID-19 crisis on the supply chain. The resultant knowledge map consists of five main areas (and related sub-areas), including (1) food retail, (2) food services, (3) manufacturing, (4) consumers, and (5) logistics. We checked and validated the analytical results with some field experts. This experiment achieved 53 crisis knowledge propositions classified from 25,272 sentences with 631,799 terms and 31,864 unique terms using just three user-system interaction steps, which shows the model's high performance. The results lead us to conclude that the proposed model could be used effectively and efficiently as a decision support system, especially for crises in the supply chain analysis.
C1 [Sheikhattar, Mohammad Reza; Nezafati, Navid; Shokouhyar, Sajjad] Shahid Beheshti Univ, Dept Management & Accounting, Velenjak Ave, Tehran 1983969411, Iran.
RP Nezafati, N (通讯作者)，Shahid Beheshti Univ, Dept Management & Accounting, Velenjak Ave, Tehran 1983969411, Iran.
EM m_sheikhattar@sbu.ac.ir; n_nezafati@sbu.ac.ir; s_shokouhyar@sbu.ac.ir
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NR 70
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
DI 10.1007/s11356-022-21380-x
EA JUN 2022
PG 21
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 2E8QU
UT WOS:000812488200011
PM 35713832
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Sayers, B
AF Sayers, B
TI Knowledge-based technology in the service of health
SO WORLD HEALTH FORUM
LA English
DT Article
AB The significance of numerical information for decision-making usually has to be assessed on the basis of knowledge that does not readily lend itself to quantification. Computational logic can help to handle the complexity of this process of interpretation without sacrificing the necessary rigour. When this approach is used, health indicators are expressed in the form of a "knowledge map".
RP Sayers, B (通讯作者)，Univ London Imperial Coll Sci Technol & Med, William Penney Bldg, London SW7 2AZ, England.
NR 0
TC 0
Z9 0
U1 0
U2 1
PU WORLD HEALTH ORGANIZATION
PI GENEVA 27
PA DISTRIBUTION AND SALES, CH-1211 GENEVA 27, SWITZERLAND
SN 0251-2432
J9 WORLD HEALTH FORUM
JI World Health Forum
PY 1998
VL 19
IS 1
BP 15
EP 20
PG 6
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Public, Environmental & Occupational Health
GA ZL183
UT WOS:000073407100005
PM 9610237
DA 2022-08-23
ER

PT J
AU Liu, WQ
   Liu, J
   Wu, MM
   Abbas, S
   Hu, W
   Wei, BF
   Zheng, QH
AF Liu, Wenqiang
   Liu, Jun
   Wu, Mengmeng
   Abbas, Samar
   Hu, Wei
   Wei, Bifan
   Zheng, Qinghua
TI Representation learning over multiple knowledge graphs for knowledge
   graphs alignment
SO NEUROCOMPUTING
LA English
DT Article
DE Representation learning; Knowledge graph embedding; Knowledge graph
AB The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces. Mostly current works have demonstrated the benefits of knowledge graph embedding in single knowledge graph completion, such as relation extraction. The most significant distinction between multiple knowledge graphs embedding and single knowledge graph embedding is that the former must consider the alignments between multiple knowledge graphs which is very helpful to some applications built on multiple KGs, such as KB-QA and KG integration. In this paper, we proposed a new automatic representation learning model over Multiple Knowledge Graphs (MGTransE) by adopting a bootstrapping method. More specifically, MGTransE consists of three core components: Structure Model, Semantically Smooth Embedding Model and Iterative Smoothness Model. The experiment results on two real-world datasets show that our method achieves better performance on two new multiple KGs tasks compared with state-of-the-art KG embedding models and also preserves the key properties of knowledge graph embedding on traditional single KG tasks as compared to those methods learned from single KG. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Liu, Wenqiang; Liu, Jun; Abbas, Samar; Wei, Bifan; Zheng, Qinghua] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Dept Comp Sci & Technol, Xian, Shaanxi, Peoples R China.
   [Wu, Mengmeng] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China.
   [Hu, Wei] Nanjing Univ, Dept Comp Sci & Technol, Nanjing, Jiangsu, Peoples R China.
RP Liu, WQ (通讯作者)，Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Dept Comp Sci & Technol, Xian, Shaanxi, Peoples R China.
EM liuwenqiangcs@gmail.com; liukeen@xjtu.edu.cn; wmmthu@foxmail.com;
   ammarhashmi@gmail.com; whu@nju.edu.cn; weibifan@gmail.com;
   ghzheng@mail.xjtu.edu.cn
FU Fundamental Theory and Applications of Big Data with Knowledge
   Engineering under the National Key Research and Development Program of
   China [2016YFB1000903]; National Science Foundation of China [61672419,
   61672418, 61532004, 61532015]; Ministry of Education Innovation Research
   Team [IRT17R86]
FX This work is sponsored by The Fundamental Theory and Applications of Big
   Data with Knowledge Engineering under the National Key Research and
   Development Program of China with grant number 2016YFB1000903; National
   Science Foundation of China under Grant Nos. 61672419, 61672418,
   61532004, and 61532015; Ministry of Education Innovation Research Team
   No. IRT17R86.
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NR 33
TC 12
Z9 16
U1 26
U2 144
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD DEC 3
PY 2018
VL 320
BP 12
EP 24
DI 10.1016/j.neucom.2018.08.070
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GW1US
UT WOS:000446667300002
DA 2022-08-23
ER

PT J
AU Li, LZ
   Liao, SN
   Yuan, JF
   Wang, ED
   She, JJ
AF Li, Lingzhi
   Liao, Shuni
   Yuan, Jingfeng
   Wang, Endong
   She, Jianjun
TI Analyzing Healthcare Facility Resilience: Scientometric Review and
   Knowledge Map
SO FRONTIERS IN PUBLIC HEALTH
LA English
DT Review
DE healthcare facility; hospitals; resilience; bibliometrics; delivery of
   health care; knowledge map; epidemics; disasters
ID SEISMIC RESILIENCE; REGENERATIVE MEDICINE; DISASTER RESILIENCE; HOSPITAL
   SYSTEM; EMERGING TRENDS; INFRASTRUCTURE; PERFORMANCE; FRAMEWORK;
   NETWORK; CHAIN
AB In contemporary "high-risk" society, unexpected disasters (epidemics and extreme weather) and chronic pressures (aging problems) put tremendous pressure on healthcare facilities. Enhancing the healthcare facilities' resilience ability to resist, absorb, and respond to disaster disruptions is urgent. This study presents a scientometric review for healthcare facility resilience research. A total of 374 relevant articles published between 2000 and 2020, collected from Web of Science (WoS) core collection database, Scopus database and MEDLINE database were reviewed and analyzed. The results indicated that research on resilience in healthcare facilities went through three development periods, and the research involved countries or institutions that are relatively scattered. The studies have been focused on the subject categories of engineering, public, environmental, and occupational health. The keywords of "resilience," "hospital," "disaster," "healthcare," and "healthcare facility" had the most frequency. Furthermore, based on the literature co-citation networks and content analysis, the detected seven co-citation clusters were grouped into four knowledge domains: climate change impact, strengthening resilience in response to war and epidemic, resilience assessment of healthcare facility, and the applications of information system. Moreover, the timeline view of literature reflected the evolution of each domain. Finally, a knowledge map for resilience of healthcare facilities was put forward, in which critical research contents, current knowledge gaps, and future research work were discussed. This contribution will promote researchers and practitioners to detect the hot topics, fill the knowledge gaps, and extend the body of research on resilience of healthcare facilities.
C1 [Li, Lingzhi; Liao, Shuni; She, Jianjun] Nanjing Tech Univ, Res Ctr Smart City, Nanjing, Peoples R China.
   [Yuan, Jingfeng] Southeast Univ, Sch Civil Engn, Dept Construct & Real Estate, Nanjing, Peoples R China.
   [Wang, Endong] SUNY Syracuse, Dept Sustainable Resources Management, Syracuse, NY USA.
RP Yuan, JF (通讯作者)，Southeast Univ, Sch Civil Engn, Dept Construct & Real Estate, Nanjing, Peoples R China.
EM jingfeng-yuan@outlook.com
FU National Natural Science Foundation of China [NSFC-71901120, 72072031];
   Humanities and Social Science Youth Foundation, Ministry of Education of
   the People's Republic of China [19YJCZH080]
FX This research was supported by the National Natural Science Foundation
   of China (Grant Number: NSFC-71901120 and - 72072031), and Humanities
   and Social Science Youth Foundation, Ministry of Education of the
   People's Republic of China (Grant Number: 19YJCZH080).
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NR 129
TC 1
Z9 1
U1 19
U2 28
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 NOV 8
PY 2021
VL 9
AR 764069
DI 10.3389/fpubh.2021.764069
PG 19
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA XB1BQ
UT WOS:000721070500001
PM 34820352
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Painter, K
   McConnell, ER
   Sahasrabudhe, S
   Burgoon, L
   Powers, CM
AF Painter, Kyle
   McConnell, Emma R.
   Sahasrabudhe, Samir
   Burgoon, Lyle
   Powers, Christina M.
TI What Do the Data Show? Knowledge Map Development for Comprehensive
   Environmental Assessment
SO INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT
LA English
DT Article
DE Knowledge maps; Data visualization; Data gaps; Engineered nanomaterials;
   Comprehensive environmental assessment
AB Environmental and human health risk assessments benefit from using data that cross multiple scientific domains. Although individual data points may often be readily understood, the total picture can be difficult to envision. This is especially true with gaps in the data (e.g., with emerging substances such as engineered nanomaterials [ENM]), such that simply presenting only known information can result in a skewed picture. This study describes a method for building knowledge maps (KM) to visually summarize factors relevant to risk assessment in a relatively easy to interpret format. The KMs were created in the context of the comprehensive environmental assessment (CEA) approach for research planning and risk management of environmental contaminants. Recent applications of CEA to emerging substances such as engineered nanomaterials that have numerous data gaps have suggested that a more visually based depiction of information would improve the approach. We developed KM templates as a pilot project, to represent pertinent aspects of conceptual domains, and to highlight gaps in available information for one particular portion of a specific CEA application: the comparison of environmental transport, transformation, and fate of multiwalled carbon nanotubes (MWCNTs) and decabromodiphenyl ether as flame retardants. The results are 3 KM templates representing Physical Properties, Transport, and Transformation. The 3 templates were applied to both substances, resulting in a total of 6 KMs. In addition to presenting the KMs, this paper details the process used to generate them, to aid KM development for other sections of CEA applied to MWCNTs, or to apply the process to new CEA applications. Integr Environ Assess Manag 2014;10:37-47. (c) 2013 SETAC
RP Burgoon, L (通讯作者)，US EPA, Natl Ctr Environm Assessment, Off Res & Dev, Res Triangle Pk, NC 27711 USA.
EM burgoon.lyle@epa.gov
RI Burgoon, Lyle D./I-2894-2019
OI Burgoon, Lyle D./0000-0003-4977-5352
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NR 19
TC 5
Z9 6
U1 1
U2 20
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1551-3777
EI 1551-3793
J9 INTEGR ENVIRON ASSES
JI Integr. Environ. Assess. Manag.
PD JAN
PY 2014
VL 10
IS 1
BP 37
EP 47
DI 10.1002/ieam.1486
PG 11
WC Environmental Sciences; Toxicology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Toxicology
GA 279BM
UT WOS:000328934100006
PM 24327299
DA 2022-08-23
ER

PT J
AU Lv, PH
   Wang, GF
   Wan, Y
   Liu, J
   Liu, Q
   Ma, FC
AF Lv, Peng Hui
   Wang, Gui-Fang
   Wan, Yong
   Liu, Jia
   Liu, Qing
   Ma, Fei-cheng
TI Bibliometric trend analysis on global graphene research
SO SCIENTOMETRICS
LA English
DT Article
DE Graphene; Bibliometric analysis; Research trend; Co-words;
   Co-authorship; Knowledge mapping
ID EPITAXIAL GRAPHENE; CARBON-FILMS; SCIENCE
AB Graphene is a rising star as one of the promising materials with many applications. Its global literature increased fast in recent years. In this work, bibliometric analysis and knowledge visualization technology were applied to evaluate global scientific production and developing trend of graphene research. The data were collected from 1991 to 2010 from the Science Citation Index database, Conference Proceeding Citation Index database and Derwent Innovation Index database integrated by Thomson Reuters. The published papers from different subjects, journals, authors, countries and keywords distributed in several aspects of research topics proved that graphene research increased rapidly over past 20 years and boosted in recent 5 years. The distinctions in knowledge map show that the clusters distributed regularly in keywords of applied patents in recent 5 years due to the potential applications of graphene research gradually found. The analytical results provided several key findings of bibliometrics trend.
C1 [Lv, Peng Hui; Wang, Gui-Fang; Wan, Yong; Liu, Jia] Chinese Acad Sci, Wuhan Documentat & Informat Ctr, Wuhan 430071, Peoples R China.
   [Liu, Qing] Chinese Acad Sci, Bur Planning & Strategy, Beijing 100864, Peoples R China.
   [Ma, Fei-cheng] Wuhan Univ, Informat Resource Res Ctr, Wuhan 430072, Peoples R China.
RP Lv, PH (通讯作者)，Chinese Acad Sci, Wuhan Documentat & Informat Ctr, Wuhan 430071, Peoples R China.
EM ph@nimte.ac.cn
RI LYU, Penghui/A-6864-2013
OI LYU, Penghui/0000-0002-7542-4591; Wan, Yong/0000-0002-3745-3447
FU Chinese Academy of Sciences [2009QNRC05]
FX This work is supported by the knowledge innovation program of the
   Chinese Academy of Sciences (No. 2009QNRC05). The authors are grateful
   to Lei Cui, Xingfu Wang, Hongyan Yang, Jun Zhang and Yongheng Zhong for
   their helpful discussions and suggestions. The authors would also like
   to thank the chief editor of Scientometrics and anonymous reviewers for
   their valuable comments.
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PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
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JI Scientometrics
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BP 399
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DI 10.1007/s11192-011-0386-x
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 785EO
UT WOS:000292210400005
DA 2022-08-23
ER

PT J
AU Liao, SH
   Chen, YJ
   Deng, MY
AF Liao, Shu-hsien
   Chen, Yin-Ju
   Deng, Min-yi
TI Mining customer knowledge for tourism new product development and
   customer relationship management
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Tourism management; New product development; Data mining; Customer
   relationship management; Apriori algorithm; Clustering analysis;
   Knowledge map
ID DEMAND; SYSTEM
AB In recent years tourism has become one of the fastest growing sectors of the world economy and is widely recognized for its contribution to regional and national economic development. Tourism product design and development have become important activities in many areas/countries as a growing source of foreign and domestic earnings. On the other hand, customer relationship management is a competitive strategy that businesses need in order to stay focused on the needs of their customers and to integrate a customer-oriented approach throughout the organization. Thus, this paper uses the Apriori algorithm as a methodology for association rules and clustering analysis for data mining, which is implemented for mining customer knowledge from the case firm, Phoenix Tours International, in Taiwan. Knowledge extraction from data mining results is illustrated as knowledge patterns, rules, and knowledge maps in order to propose suggestions and solutions to the case firm for new product development and customer relationship management. (C) 2009 Elsevier Ltd. All rights reserved.
C1 [Liao, Shu-hsien; Chen, Yin-Ju] Tamkang Univ, Dept Management Sci & Decis Making, Taipei 251, Taiwan.
   [Deng, Min-yi] Natl Def Univ, Grad Sch Resource Management, Coll Management, Jon Ho, Taipei County, Taiwan.
RP Liao, SH (通讯作者)，Tamkang Univ, Dept Management Sci & Decis Making, 151 Yingjuan Rd, Taipei 251, Taiwan.
EM michael@mail.tku.edu.tw
RI Liao, Shu-hsien/AAW-1885-2020
OI Liao, Shu-hsien/0000-0002-3667-2867
FU National Science Council, Taiwan, Republic of China [NSC
   98-2410-H-032-038-MY2]
FX This research was funded by the National Science Council, Taiwan,
   Republic of China, under contract No. NSC 98-2410-H-032-038-MY2.
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NR 24
TC 60
Z9 61
U1 3
U2 64
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 JUN
PY 2010
VL 37
IS 6
BP 4212
EP 4223
DI 10.1016/j.eswa.2009.11.081
PG 12
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 581OE
UT WOS:000276532600021
DA 2022-08-23
ER

PT J
AU Naghizadeh, R
   Elahi, S
   Manteghi, M
   Ghazinoory, S
   Ranga, M
AF Naghizadeh, Reza
   Elahi, Shaban
   Manteghi, Manoochehr
   Ghazinoory, Sepehr
   Ranga, Marina
TI Through the magnifying glass: an analysis of regional innovation models
   based on co-word and meta-synthesis methods
SO QUALITY & QUANTITY
LA English
DT Article
DE Regional innovation models; Knowledge map; Co-word analysis;
   Meta-synthesis; Clusters; Regional typology
ID KNOWLEDGE PRODUCTION FUNCTION; TRIPLE-HELIX; SYSTEMS; DYNAMICS;
   DIMENSION; NETWORKS
AB This paper performs a fine-grained analysis of regional innovation models discussed in the literature, using a combination of quantitative (co-word analysis) and qualitative (meta-synthesis) methods applied to 300 papers from the Scopus and Sage databases covering the period 1990-2013. The co-word analysis produced knowledge maps that identify the most frequently occurring regional innovation models and group them into three clusters: Cluster 1 (industrial districts, local production systems), Cluster 2 (industrial clusters and regional clusters), and Cluster 3 (innovative milieus, regional innovation systems, innovation networks and learning regions). The meta-synthesis analysis used for exploring the content of these models identified three main themes, each with several sub-themes, as well as distinct features of the regions implementing them. Based on these distinctions, a typology of regions was derived, distinguishing between Early Innovators, Transitional Innovators and Advanced Innovators, which can serve as a useful instrument for academic researchers, policy-makers and practitioners involved in regional innovation.
C1 [Naghizadeh, Reza; Elahi, Shaban; Ghazinoory, Sepehr] Tarbiat Modares Univ, Sch Management & Econ, Dept Informat Technol Management, Tehran, Iran.
   [Manteghi, Manoochehr] Univ Tehran, Coll Engn, Dept Ind Engn, Tehran, Iran.
   [Ranga, Marina] Stanford Univ, H STAR Inst, Stanford, CA 94304 USA.
RP Elahi, S (通讯作者)，Tarbiat Modares Univ, Sch Management & Econ, Dept Informat Technol Management, Tehran, Iran.
EM rezanaghizadeh@yahoo.com; elahi@modares.ac.ir; manteghi@ut.ac.ir;
   ghazinoory@modares.ac.ir; marina.ranga@stanford.edu
RI Ranga, Marina/AAS-2159-2021; Ghazinoory, Sepehr/M-3915-2019; Naghizadeh,
   Reza/R-3268-2019; Ghazinoory, Sepehr/D-6543-2011; Elahi,
   Shaban/AAD-2198-2022
OI Ranga, Marina/0000-0002-9502-0746; Elahi, Shaban/0000-0002-1598-4431;
   Naghizadeh, Reza/0000-0002-9311-4327
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   [No title captured]
   [No title captured]
NR 110
TC 13
Z9 15
U1 1
U2 50
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0033-5177
EI 1573-7845
J9 QUAL QUANT
JI Qual. Quant.
PD NOV
PY 2015
VL 49
IS 6
BP 2481
EP 2505
DI 10.1007/s11135-014-0123-7
PG 25
WC Social Sciences, Interdisciplinary; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Mathematics
GA CS4JA
UT WOS:000362040900015
DA 2022-08-23
ER

PT J
AU Chen, HR
   Fang, TT
   Liu, F
   Pang, LM
   Wen, Y
   Chen, S
   Gu, XY
AF Chen, Huaruo
   Fang, Tingting
   Liu, Fan
   Pang, Liman
   Wen, Ya
   Chen, Shi
   Gu, Xueying
TI Career Adaptability Research: A Literature Review with Scientific
   Knowledge Mapping in Web of Science
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE career adaptability; literature review; scientific knowledge mapping;
   CiteSpace
ID LIFE SATISFACTION; PROACTIVE PERSONALITY; FUTURES INVENTORY;
   SELF-EFFICACY; CONSTRUCTION; INTERVENTION; INTENTIONS; SUPPORT; DESIGN;
   COMPETENCE
AB With the rapid development of society and technology, personal adaptability is becoming more and more important. Learning how to adapt to a changing world is becoming one of the necessary conditions for success. Career adaptability can help individuals to smoothly adapt to changes when coping with their career roles, and maintain their ability to balance their career roles, which will affect their important psychological resources for career development and achieve more meaning in life. In recent years, career adaptability has gradually attracted the attention of researchers. Therefore, in order to explore the main factors, such as research focus, the main researchers, its evolution, and the important results of career adaptability in the last ten years, this study used the scientific knowledge mapping software CiteSpace as a research tool, and select related articles from the Web of Science between 2010 to 2020 under the theme of "career adaptability" for data analysis, which can help future researchers to understand current and future career adaptability research and control the research direction of career adaptability. The results of this research indicate that there are direct or indirect connections between different themes, such as the career adaptability scale, career construction, positive personalities, and so on, but few articles integrate multiple research topics. At the same time, the main researchers, research frontiers and network relationships were also obtained. Based on the above findings, the correlative main concept, theoretical structure, evolution, and research progress of career adaptability in the past ten years are discussed.
C1 [Chen, Huaruo; Liu, Fan; Pang, Liman; Wen, Ya; Chen, Shi; Gu, Xueying] Nanjing Normal Univ, Sch Educ Sci, Nanjing 210046, Peoples R China.
   [Chen, Huaruo] Johns Hopkins Univ, Ctr Res & Reform Educ, Baltimore, MA 21286 USA.
   [Fang, Tingting] Nanjing Normal Univ, Sch Psychol, Nanjing 210046, Peoples R China.
RP Gu, XY (通讯作者)，Nanjing Normal Univ, Sch Educ Sci, Nanjing 210046, Peoples R China.
EM 190601021@stu.njnu.edu.cn; 192302037@stu.njnu.edu.cn;
   190602093@stu.njnu.edu.cn; 190602089@stu.njnu.edu.cn;
   170601026@stu.njnu.edu.cn; 180601021@stu.njnu.edu.cn; 02099@njnu.edu.cn
OI Chen, Huaruo/0000-0002-3106-8038; Chen, Shi/0000-0001-8352-1810
FU Jiangsu Graduate Innovation Project [KYCX20_1145]; Jiangsu Province
   Basic Education Prospective Teaching Reform Experiment Project; Jiangsu
   Province University's Advantageous Discipline Construction Project;
   China National Fund for Study Abroad
FX This research was funded by a Jiangsu Graduate Innovation Project, grant
   number "KYCX20_1145", Jiangsu Province Basic Education Prospective
   Teaching Reform Experiment Project, China National Fund for Study
   Abroad, and Jiangsu Province University's Advantageous Discipline
   Construction Project, grant number "PAPD".
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NR 85
TC 16
Z9 16
U1 37
U2 98
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 AUG
PY 2020
VL 17
IS 16
AR 5986
DI 10.3390/ijerph17165986
PG 21
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 NI0SC
UT WOS:000565068100001
PM 32824717
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Lamirel, JC
   Gueddari, Y
   Wang, YQ
   Cuxac, P
   Perez, A
   Dugue, N
AF Lamirel, Jean-Charles
   Gueddari, Younes
   Wang, Yuqi
   Cuxac, Pascal
   Perez, Anthony
   Dugue, Nicolas
TI Analysis of the dynamics and influence of the research work of Prof. Liu
   Zeyuan in China featuring a new hybrid approach combining community
   detection with topic tracking
SO SCIENTOMETRICS
LA English
DT Article
DE Knowledge mapping; Citation analysis; Clustering; Topic tracking;
   Community; detection
ID NETWORK
AB The influence of authors is mostly based on their capacity to form specific self-contained and/or active research communities or topics while also inspiring fruitful spin-off research derived from those communities or topics. Accurately estimating author influence and its indirect effect in inspiring external research creativity must thus be based on very precise community roles as well as topic-role analysis on citing material. In this paper we thus implement, compare, and combine two different kinds of knowledge mapping approaches used to track authors' influence over time-a recent topic-based mapping approach and an original hybrid community-based mapping approach. The experimental context of our study is the analysis of the dynamics and of the influence of the research of Prof. Liu Zeyuan, the most important contributor in the field of Science of Science in China. This analysis focuses on papers citing Professor Liu Zeyuan's research and highlights the full extent of his creative thinking.
C1 [Lamirel, Jean-Charles] INRIA Nancy Grand Est, LORIA, SYNALP Team, Vandoeuvre Les Nancy, France.
   [Gueddari, Younes] Ecole Natl Super Mines, St Etienne, France.
   [Wang, Yuqi] Dalian Univ Technol, WISELAB, Dalian, Peoples R China.
   [Cuxac, Pascal; Perez, Anthony] CNRS, INIST, Vandoeuvre Les Nancy, France.
   [Dugue, Nicolas] Univ Orleans, LIFO, Orleans, France.
   [Dugue, Nicolas] Univ Mans, LIUM, Le Mans, France.
RP Lamirel, JC (通讯作者)，INRIA Nancy Grand Est, LORIA, SYNALP Team, Vandoeuvre Les Nancy, France.
EM jean-charles.lamirel@loria.fr; younes.gueddari@etu.emse.fr;
   wanqyuqi2020@dlut.edu.cn; pascal.cuxac@inist.fr;
   anthony.perez@univ-orleans.fr; nicolas.dugue@univ-lemans.fr
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NR 28
TC 0
Z9 0
U1 5
U2 13
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUL
PY 2021
VL 126
IS 7
SI SI
BP 6273
EP 6300
DI 10.1007/s11192-021-04010-0
EA MAY 2021
PG 28
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA SY3VS
UT WOS:000651351500005
DA 2022-08-23
ER

PT J
AU Radhakrishnan, S
   Erbis, S
   Isaacs, JA
   Kamarthi, S
AF Radhakrishnan, Srinivasan
   Erbis, Serkan
   Isaacs, Jacqueline A.
   Kamarthi, Sagar
TI Novel keyword co-occurrence network-based methods to foster systematic
   reviews of scientific literature
SO PLOS ONE
LA English
DT Review
ID CO-WORD ANALYSIS; SCIENTOMETRICS; ACIDIFICATION; TRENDS
AB Systematic reviews of scientific literature are important for mapping the existing state of research and highlighting further growth channels in a field of study, but systematic reviews are inherently tedious, time consuming, and manual in nature. In recent years, keyword cooccurrence networks (KCNs) are exploited for knowledge mapping. In a KCN, each keyword is represented as a node and each co-occurrence of a pair of words is represented as a link. The number of times that a pair of words co-occurs in multiple articles constitutes the weight of the link connecting the pair. The network constructed in this manner represents cumulative knowledge of a domain and helps to uncover meaningful knowledge components and insights based on the patterns and strength of links between keywords that appear in the literature. In this work, we propose a KCN-based approach that can be implemented prior to undertaking a systematic review to guide and accelerate the review process. The novelty of this method lies in the new metrics used for statistical analysis of a KCN that differ from those typically used for KCN analysis. The approach is demonstrated through its application to nano-related Environmental, Health, and Safety (EHS) risk literature. The KCN approach identified the knowledge components, knowledge structure, and research trends that match with those discovered through a traditional systematic review of the nanoEHS field. Because KCN-based analyses can be conducted more quickly to explore a vast amount of literature, this method can provide a knowledge map and insights prior to undertaking a rigorous traditional systematic review. This two-step approach can significantly reduce the effort and time required for a traditional systematic literature review. The proposed KCN-based pre-systematic review method is universal. It can be applied to any scientific field of study to prepare a knowledge map.
C1 [Radhakrishnan, Srinivasan; Erbis, Serkan; Isaacs, Jacqueline A.; Kamarthi, Sagar] Northeastern Univ, Dept Mech & Ind Engn, Boston, MA 02115 USA.
RP Kamarthi, S (通讯作者)，Northeastern Univ, Dept Mech & Ind Engn, Boston, MA 02115 USA.
EM Sagar@coe.neu.edu
RI Isaacs, Jacqueline/I-7558-2015; Kamarthi, Sagar/P-7226-2014
OI Isaacs, Jacqueline/0000-0001-8406-0083; Kamarthi,
   Sagar/0000-0001-5845-7714
FU NSF; NSF Scalable Nanomanufacturing Award [CMMI-1120329]; NSEC
   [EEC-0832785]; National Institute of Standards and Technology
   [70NANB15H028]; Directorate For Engineering [1120329] Funding Source:
   National Science Foundation
FX This work was supported by NSF Scalable Nanomanufacturing Award
   CMMI-1120329 and in part by NSEC Award EEC 0832785. We also thank the
   National Institute of Standards and Technology for providing funding for
   this research under sponsor award number 70NANB15H028. The funders had
   no role in study design, data collection and analysis, decision to
   publish, or preparation of the manuscript.; We thank members of the
   NSF-funded Nanoscale Science and Engineering Center for Highrate
   Nanomanufacturing at Northeastern University for their technical
   insights. S. Abbasi, C. Bosso, M. Eckelman, L. Pourzahedi, W. Walker and
   P. Zhai at Northeastern contributed to discussions. This work was
   supported by NSF Scalable Nanomanufacturing Award CMMI-1120329 and in
   part by NSEC Award EEC-0832785. We also thank the National Institute of
   Standards and Technology for providing funding for this research under
   sponsor award number 70NANB15H028.
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NR 28
TC 78
Z9 80
U1 5
U2 39
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 22
PY 2017
VL 12
IS 3
AR e0172778
DI 10.1371/journal.pone.0172778
PG 16
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA ER8SZ
UT WOS:000399094700010
PM 28328983
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Liang, HY
   Zhang, WK
   Chen, Z
   Chen, XX
AF Liang, Hanyuan
   Zhang, Weikun
   Chen, Zhe
   Chen, Xuexue
TI Visualizing a Field of Research for the Coronavirus Replication in
   Humans with Knowledge Mapping: Evidence from Web of Science
SO INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
LA English
DT Article
DE Coronavirus replication; Humans; Knowledge mapping; Spike protein; Web
   of Science
ID RESPIRATORY SYNDROME CORONAVIRUS; ANGIOTENSIN-CONVERTING ENZYME-2;
   SARS-CORONAVIRUS; EVOLUTION; VIRUS
AB Background The outbreak of COVID-19 sweeping the globe in 2020 has caused widespread fear and threatened global health security. Compared to SARS and MERS, COVID-19 also causes severe respiratory diseases and even fatal diseases but have many differences, such as the unidentified gene sequence and replication mechanism. From SARS to MERS, and then to COVID-19, coronaviruses have significant variations in host adaptation, virus evolution, infectivity, spread, and pathogenicity due to its unique replication mechanism.
   Methods A field of research for the coronavirus replication in humans was visualized with a database covering 9177 kinds of literature in Web of Science from 2002 through October 2021 to provide cognitive direction for the epidemic situation of virus infection. Knowledge Mapping by CiteSpace and Bibliometrix Package in R Software was drawn to depict the underlying features of viral replication and changing trends of studies, with these analyses including co-citation, density visualization, keyword clustering, and time zone.
   Results The keyword frequencies of "replication," ''infection," and ''spike protein" repeatedly appeared in published papers. Coronavirus can promote or inhibit apoptosis, depending on the balance between viral protein and apoptotic factors. When the living environment of cells is irreversibly damaged by the virus, cells have to start the apoptosis mechanism to prevent the replication, transmission, and spread of the virus. The replication, assembly and transmission of coronavirus can inhibit cells from entering the apoptosis prematurely with the fusion of spike protein and cell receptor in human.
   Conclusion Our results indicated that "viral infection," spike protein," and "mutation" might be future research hotspots on coronavirus replication in humans. The attention should be paid to the mutations of S protein and these mutants carrying mutations.
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C1 [Liang, Hanyuan; Chen, Zhe] Zhanjiang Univ Sci & Technol, Sch Econ & Finance, Zhanjiang 524088, Guangdong, Peoples R China.
   [Zhang, Weikun] Lingnan Normal Univ, Sch Social & Publ Adm, Zhanjiang 524088, Guangdong, Peoples R China.
   [Chen, Xuexue] Cent Peoples Hosp Zhanjiang, Zhanjiang 524037, Guangdong, Peoples R China.
RP Zhang, WK (通讯作者)，Lingnan Normal Univ, Sch Social & Publ Adm, Zhanjiang 524088, Guangdong, Peoples R China.
EM weikunz@sids.org.cn
OI Zhang, Weikun/0000-0002-9983-7788; liang, hanyuan/0000-0003-3385-7011
FU Humanities and Social Sciences Foundation; Universities of Guangdong
   China [2019KZDZX2030]; Major Project of Innovation and Strengthening
   School, Department of Education of Guangdong China [2017WZDXM013]
FX This work was supported by Humanities and Social Sciences Foundation,
   Universities of Guangdong China (Grant numbers 2019KZDZX2030), and the
   Major Project of Innovation and Strengthening School, Department of
   Education of Guangdong China (Grant number 2017WZDXM013).
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PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1913-2751
EI 1867-1462
J9 INTERDISCIP SCI
JI Interdiscip. Sci.
PD JUN
PY 2022
VL 14
IS 2
BP 471
EP 484
DI 10.1007/s12539-022-00504-4
EA FEB 2022
PG 14
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA 1K2WJ
UT WOS:000758043500001
PM 35150388
OA Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Zhang, J
   Song, LX
   Xu, LY
   Fan, YX
   Wang, T
   Tian, WD
   Ju, JQ
   Xu, H
AF Zhang, Jie
   Song, Luxia
   Xu, Liyan
   Fan, Yixuan
   Wang, Tong
   Tian, Wende
   Ju, Jianqing
   Xu, Hao
TI Knowledge Domain and Emerging Trends in Ferroptosis Research: A
   Bibliometric and Knowledge-Map Analysis
SO FRONTIERS IN ONCOLOGY
LA English
DT Article
DE ferroptosis; bibliometric; knowledge-map; CiteSpace; VOSviewer
ID CELL-DEATH; GPX4; NANOPARTICLES; INHIBITION; MECHANISMS; RECEPTOR;
   TOURISM; BIOLOGY; FIELD; FORM
AB Objectives
   To identify the cooperation and impact of authors, countries, institutions, and journals, evaluate the knowledge base, find the hotspot trends, and detect the emerging topics regarding ferroptosis research.
   Methods
   The articles and reviews related to ferroptosis were obtained from the Web of Science Core Collection on November 1, 2020. Two scientometric software (CiteSpace 5.7 and VOSviewer 1.6.15) were used to perform bibliometric and knowledge-map analysis.
   Results
   A total of 1,267 papers were included, in 466 academic journals by 6,867 authors in 438 institutions from 61 countries/regions. The ferroptosis-related publications were increasing rapidly. Cell Death & Disease published the most papers on ferroptosis, while Cell was the top co-cited journal, publication journals and co-cited journals were major in the molecular and biology fields. The United States and China were the most productive countries; meanwhile, the University of Pittsburgh, Columbia University and Guangzhou Medical University were the most active institutions. Brent R Stockwell published the most papers, while Scott J Dixon had the most co-citations; simultaneously, active cooperation existed in ferroptosis researchers. Ten references on reviews, mechanisms, and diseases were regarded as the knowledge base. Five main aspects of ferroptosis research included regulation mechanisms, nervous system injury, cancer, relationships with other types of cell death, and lipid peroxidation. The latest hotspots were nanoparticle, cancer therapy, iron metabolism, and in-depth mechanism. Notably Nrf2 might have turning significance. The emerging topics on ferroptosis research were the further molecular mechanism of ferroptosis and the wider application of ferroptosis-related disease with advanced technology.
   Conclusion
   This study performed a full overview of the ferroptosis research using bibliometric and visual methods. The information would provide helpful references for scholars focusing on ferroptosis.
C1 [Zhang, Jie; Song, Luxia; Fan, Yixuan; Wang, Tong] Beijing Univ Chinese Med, Grad Sch, Beijing, Peoples R China.
   [Zhang, Jie; Song, Luxia; Fan, Yixuan; Wang, Tong; Tian, Wende; Ju, Jianqing; Xu, Hao] China Acad Chinese Med Sci, Xiyuan Hosp, Natl Clin Res Ctr Chinese Med Cardiol, Beijing, Peoples R China.
   [Xu, Liyan] Emory Univ, Dept Comp Sci, Atlanta, GA 30322 USA.
   [Tian, Wende] China Acad Chinese Med Sci, Grad Sch, Beijing, Peoples R China.
RP 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.
EM jujianqing@163.com; xuhaotcm@hotmail.com
FU National Natural Science Foundation of China [82004145]; Beijing Natural
   Science Foundation [7204298]; Central Public Welfare Research Institutes
   of China Academy of Chinese Medical Sciences [ZZ13-YQ-017]
FX The work was supported by the National Natural Science Foundation of
   China (No. 81874412), National Natural Science Foundation of China
   (No.82004145), Beijing Natural Science Foundation (No.7204298), and
   Central Public Welfare Research Institutes of China Academy of Chinese
   Medical Sciences (No. ZZ13-YQ-017).
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NR 87
TC 13
Z9 13
U1 21
U2 39
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2234-943X
J9 FRONT ONCOL
JI Front. Oncol.
PD JUN 3
PY 2021
VL 11
AR 686726
DI 10.3389/fonc.2021.686726
PG 16
WC Oncology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Oncology
GA ST1KN
UT WOS:000662209300001
PM 34150654
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhang, GR
   Li, K
   Gu, DX
   Wang, XY
   Yang, XJ
   Zhu, KY
   Liang, GQ
AF Zhang, Gongrang
   Li, Kang
   Gu, Dongxiao
   Wang, Xiaoyu
   Yang, Xuejie
   Zhu, Keyu
   Liang, Guoqiang
TI Visualizing Knowledge Evolution and Hotspots of Rural Environment and
   Health: A Systematic Review and Research Direction
SO IEEE ACCESS
LA English
DT Review
DE Rural; health; environment; pollution; bibliometric analysis
ID PHYSICAL-ACTIVITY; BIBLIOMETRIC ANALYSIS; DRINKING-WATER; AIR-POLLUTION;
   OBESITY; TRENDS; ADULTS; URBAN; URBANIZATION; COCITATION
AB With global warming, energy scarcity, water shortages, and air, soil, and water pollution, the situation of environments in countries around the world is getting more and more serious and in some countries, rural environmental issues are more prominent. Health problems in rural areas also cannot be ignored, chronic diseases and infectious diseases have become the greatest threat to human life, while good environment and human health are the foundation of social and economic sustainable development. This paper adopts the bibliometrics method to conduct a visual analysis of 6,971 studies in the field of the rural environment and health published on the Web of Science between 2000 and 2017, including time knowledge map analysis, space knowledge map analysis, knowledge base analysis, and research focus analysis. This paper reveals the development status of research in the field of rural environment and health, analyzes, and discusses the research hotspots and future development trends in this field, and provides important knowledge support for researchers to carry out follow-up research.
C1 [Zhang, Gongrang; Li, Kang; Gu, Dongxiao; Yang, Xuejie; Zhu, Keyu] Hefei Univ Technol, Sch Management, Hefei 230009, Anhui, Peoples R China.
   [Wang, Xiaoyu] Anhui Univ Tradit Chinese Med, Affiliated Hosp 1, Dept Pharm, Hefei 230031, Anhui, Peoples R China.
   [Liang, Guoqiang] Dalian Univ Technol, Wise Lab, Dalian 116023, Peoples R China.
   [Liang, Guoqiang] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN 47408 USA.
RP Gu, DX (通讯作者)，Hefei Univ Technol, Sch Management, Hefei 230009, Anhui, Peoples R China.
EM gudongxiao@hfut.edu.cn
RI , 梁国强/S-7662-2019
OI , 梁国强/0000-0002-9669-4048; Gu, Dongxiao/0000-0003-3557-009X
FU National Natural Science Foundation of China [71331002, 71771075,
   71771077, 71501054, 71573071]; Anhui Province Science and Technology
   Innovation Strategy and Soft Science Research Foundation
   [201806a02020040]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 71331002, Grant 71771075, Grant
   71771077, Grant 71501054, and Grant 71573071, and in part by the Anhui
   Province Science and Technology Innovation Strategy and Soft Science
   Research Foundation under Grant 201806a02020040.
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NR 67
TC 11
Z9 11
U1 6
U2 23
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 72538
EP 72550
DI 10.1109/ACCESS.2019.2919549
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 IE2CA
UT WOS:000472190900001
OA gold
DA 2022-08-23
ER

PT J
AU Caraballo, Y
   Mesa, D
   Herrera, JA
AF Caraballo, Yeter
   Mesa, Dianelis
   Herrera, J. A.
TI Tools of knowledge management: convergences toward organizational
   learning
SO CUBAN JOURNAL OF AGRICULTURAL SCIENCE
LA English
DT Review
DE knowledge management; knowledge audit; knowledge map; conceptual map;
   organizational learning
AB A review on the origin, object of application, and forms of expression on knowledge management is conducted, as well-as on the strategic processes associated with this activity. Also, considerations are presented about the knowledge audit, and the most used tools in the organization to identify knowledge. Emphasis is laid on the existing convergences between the conceptual maps and the knowledge maps. Criteria are exposed to support the idea that the former is associated more with knowledge management in general and the latter, with aspects led by knowledge audit. Among the main conclusions, knowledge management is noteworthy because it is the basis for conducting the rest of the organizational processes (that is, intellectual capital management and organizational knowledge acquisition), for the institution may be in conditions for learning more through a continuous improvement process. It is recommended that research institutions, whose more significant principle is knowledge, should have among their premises to identify where the knowledge is located and how to manage it, in function of the welfare of the organization itself.
C1 [Caraballo, Yeter; Mesa, Dianelis; Herrera, J. A.] Inst Ciencia Anim, Havana, Cuba.
RP Caraballo, Y (通讯作者)，Inst Ciencia Anim, Apartado Postal 24, Havana, Cuba.
EM jeter@ica.co.cu
OI Mesa Travieso, Dianelis/0000-0001-6076-0579
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NR 29
TC 0
Z9 1
U1 1
U2 8
PU CUBAN JOURNAL AGR SCI
PI HAVANA
PA TULIPAN 1011 E/47 Y LOMA NUEVO VEDADO, HAVANA, CUBA
SN 0864-0408
EI 2079-3480
J9 CUBAN J AGR SCI
JI Cuba. J. Agric. Sci.
PD MAR
PY 2009
VL 43
IS 1
BP 1
EP 12
PG 12
WC Agriculture, Dairy & Animal Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture
GA 615XT
UT WOS:000279172700001
DA 2022-08-23
ER

PT J
AU Barirani, A
   Agard, B
   Beaudry, C
AF Barirani, Ahmad
   Agard, Bruno
   Beaudry, Catherine
TI Competence maps using agglomerative hierarchical clustering
SO JOURNAL OF INTELLIGENT MANUFACTURING
LA English
DT Article
DE Knowledge mapping; Innovation; Citation networks analysis; Data mining;
   Agglomerative hierarchical clustering; Vector space model;
   Nanotechnology
ID KNOWLEDGE; SCIENCE
AB Knowledge management from a strategic planning point of view often requires having an accurate understanding of a firm's or a nation's competences in a given technological discipline. Knowledge maps have been used for the purpose of discovering the location, ownership and value of intellectual assets. The purpose of this article is to develop a new method for assessing national and firm-level competences in a given technological discipline. To achieve this goal, we draw a competence map by applying agglomerative hierarchical clustering on a sample of patents. Considering the top levels of the resulting dendrogram, each cluster represents one of the technological branches of nanotechnology and its children branches are those that are most technologically proximate. We also assign a label to each branch by extracting the most relevant words found in each of them. From the information about patents inventors' cities, we are able to identify where the largest invention communities are located. Finally, we use information regarding patent assignees and identify the most productive firms. We apply our method to the case of the emerging and multidisciplinary Canadian nanotechnology industry.
C1 [Barirani, Ahmad; Agard, Bruno; Beaudry, Catherine] Ecole Polytech, Dept Math & Genie Ind, Montreal, PQ H3C 3A7, Canada.
RP Agard, B (通讯作者)，Ecole Polytech, Dept Math & Genie Ind, CP 6079,Succ Ctr Ville, Montreal, PQ H3C 3A7, Canada.
EM ahmad.barirani@polymtl.ca; bruno.agard@polymtl.ca;
   catherine.beaudry@polymtl.ca
RI Agard, Bruno/R-2104-2017
OI Beaudry, Catherine/0000-0002-2110-3151
FU SSHRC; CIHR; NSERC
FX We would like to thank the SSHRC, the CIHR and the NSERC for their
   financial support. We thank the OST and particularly Vincent Lariviere
   for access and support to the USPTO citation data. We thank
   Ricard-Olivier Moreau and Terence-Shawn Ramine Kananian for their
   rigorous contribution in cleaning and administrating our patent
   database. Finally, we thank Ilse-Carolina Monila Gutierrez for preparing
   the nanotechnology keyword list.
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NR 45
TC 9
Z9 9
U1 2
U2 59
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 APR
PY 2013
VL 24
IS 2
BP 373
EP 384
DI 10.1007/s10845-011-0600-y
PG 12
WC Computer Science, Artificial Intelligence; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA 113SP
UT WOS:000316689000012
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Yu, HZ
   Wang, QC
   Wu, WY
   Zeng, WZ
   Feng, Y
AF Yu, Haozhe
   Wang, Qicong
   Wu, Wenyu
   Zeng, Weizhen
   Feng, Yun
TI Therapeutic Effects of Melatonin on Ocular Diseases: Knowledge Map and
   Perspective
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE melatonin; circadian rhythms; ocular diseases; therapeutics;
   pharmacology
ID RETINAL GANGLION-CELLS; MESENCHYMAL STEM-CELLS; INTRAOCULAR-PRESSURE;
   EXPERIMENTAL UVEITIS; RECEPTOR EXPRESSION; CIRCADIAN-RHYTHMS;
   CATARACT-SURGERY; DIURNAL RHYTHMS; PINEAL ORGAN; MELANOPSIN
AB Melatonin plays a critical role in the pathophysiological process including circadian rhythm, apoptosis, and oxidative stress. It can be synthesized in ocular tissues, and its receptors are also found in the eye, triggering more investigations concentrated on the role of melatonin in the eye. In the past decades, the protective and therapeutic potentials of melatonin for ocular diseases have been widely revealed in animal models. Herein, we construct a knowledge map of melatonin in treating ocular diseases through bibliometric analysis and review its current understanding and clinical evidence. The overall field could be divided into twelve topics through keywords co-occurrence analysis, in which the glaucoma, myopia, and retinal diseases were of greatest research interests according to the keywords burst detection. The existing clinical trials of melatonin in ocular diseases mainly focused on the glaucoma, and more research should be promoted, especially for various diseases and drug administration. We also discuss its bioavailability and further research topics including developing melatonin sensors for personalized medication, acting as stem cell therapy assistant drug, and consuming food-derived melatonin for facilitating its clinical transformation.
C1 [Yu, Haozhe; Wu, Wenyu; Zeng, Weizhen; Feng, Yun] Peking Univ Third Hosp, Dept Ophthalmol, Beijing, Peoples R China.
   [Yu, Haozhe; Feng, Yun] Peking Univ, Inst Med Technol, Hlth Sci Ctr, Beijing, Peoples R China.
   [Wang, Qicong] Beijing Univ Chinese Med, Dept Chinese Med Taiwan Hong Kong & Macao, Beijing, Peoples R China.
RP Feng, Y (通讯作者)，Peking Univ Third Hosp, Dept Ophthalmol, Beijing, Peoples R China.; Feng, Y (通讯作者)，Peking Univ, Inst Med Technol, Hlth Sci Ctr, Beijing, Peoples R China.
EM fengyun@bjmu.edu.cn
FU National Natural Science Foundation of China [81700799, 82070926]
FX Funding This study was supported by the National Natural Science
   Foundation of China grants (Nos. 81700799; 82070926).
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NR 131
TC 3
Z9 3
U1 8
U2 11
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 NOV 2
PY 2021
VL 12
AR 721869
DI 10.3389/fphar.2021.721869
PG 9
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA WY9QL
UT WOS:000719611300001
PM 34795578
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Huang, L
   Kelly, S
   Lv, KJ
   Giurco, D
AF Huang, Li
   Kelly, Scott
   Lv, Kangjuan
   Giurco, Damien
TI A systematic review of empirical methods for modelling sectoral carbon
   emissions in China
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Review
DE Climate change; Carbon emissions; China; Knowledge mapping; Literature
   review; Modelling
ID STRUCTURAL DECOMPOSITION ANALYSIS; SPATIAL ECONOMETRIC-ANALYSIS;
   INPUT-OUTPUT-ANALYSIS; CO2 EMISSIONS; ENERGY-CONSUMPTION;
   CLIMATE-CHANGE; HOUSEHOLD CONSUMPTION; INDUSTRIAL-STRUCTURE;
   SENSITIVITY-ANALYSIS; GUANGDONG PROVINCE
AB A number of empirical methods have been developed to study China's sectoral carbon emissions (CSCE). Measuring these emissions is important for climate change mitigation. While several articles have reviewed specific methods, few attempts conduct a systematic analysis of all the major research methods. In total 807 papers were published on CSCE research between 1997 and 2017. The primary source of literature for this analysis was taken from the Web of Science database. Based on a bibliometric analysis using knowledge mapping with the software CiteSpace, the review identified five common families of methods: 1) environmentally-extended input-output analysis (EE-IOA), 2) index decomposition analysis (IDA), 3) econometrics, 4) carbon emission control efficiency evaluation and 5) simulation. The research revealed the main trends in each family of methods and has visualized this research into ten research clusters. In addition, the paper provides a direct comparison of all methods. The research results can help scholars quickly identify and compare different methods for addressing specific research questions. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Huang, Li] Shanghai Univ, Sch Econ, Shanghai 200444, Peoples R China.
   [Huang, Li; Lv, Kangjuan] Shanghai Univ, SHU UTS SILC Business Sch, Shanghai 201800, Peoples R China.
   [Huang, Li; Kelly, Scott; Giurco, Damien] Univ Technol Sydney, Inst Sustainable Futures, Sydney, NSW 2007, Australia.
RP Lv, KJ (通讯作者)，Shanghai Univ, SHU UTS SILC Business Sch, Shanghai 201800, Peoples R China.
EM lilyhuang@shu.edu.cn; Scott.Kelly@uts.edu.au; lvkangjuan@shu.edu.cn;
   Damien.Giurco@uts.edu.au
RI Kelly, Scott/ABC-7526-2020; Kelly, Scott/F-8774-2011
OI Kelly, Scott/0000-0002-8454-6144; Giurco, Damien/0000-0002-1707-9531;
   Huang, Li/0000-0002-1018-7919
FU National Natural Science Foundation of China [71774108]; China
   Scholarship Council [201606890043]
FX This work was supported by National Natural Science Foundation of China
   (Grant no. 71774108) and China Scholarship Council (Grant no.
   201606890043). The authors thank the editors and three anonymous
   reviewers for their constructive suggestions.
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NR 142
TC 27
Z9 27
U1 38
U2 212
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0959-6526
EI 1879-1786
J9 J CLEAN PROD
JI J. Clean Prod.
PD APR 1
PY 2019
VL 215
BP 1382
EP 1401
DI 10.1016/j.jclepro.2019.01.058
PG 20
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 HM3FE
UT WOS:000459358300116
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Cheng, BJ
   Zhang, J
   Liu, H
   Cai, ML
   Wang, Y
AF Cheng, Binjie
   Zhang, Jin
   Liu, Hong
   Cai, Meiling
   Wang, Ying
TI Research on Medical Knowledge Graph for Stroke
SO JOURNAL OF HEALTHCARE ENGINEERING
LA English
DT Article
AB Knowledge graph can effectively analyze and construct the essential characteristics of data. At present, scholars have proposed many knowledge graph models from different perspectives, especially in the medical field, but there are still relatively few studies on stroke diseases using medical knowledge graphs. Therefore, this paper will build a medical knowledge graph model for stroke. Firstly, a stroke disease dictionary and an ontology database are built through the international standard medical term sets and semiautomatic extraction-based crowdsourcing website data. Secondly, the external data are linked to the nodes of the existing knowledge graph via the entity similarity measures and the knowledge representation is performed by the knowledge graph embedded model. Thirdly, the structure of the established knowledge graph is modified continuously through iterative updating. Finally, in the experimental part, the proposed stroke medical knowledge graph is applied to the real stroke data and the performance of the proposed knowledge graph approach on the series of Trans * models is compared.
C1 [Cheng, Binjie; Zhang, Jin; Liu, Hong; Cai, Meiling] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Peoples R China.
   [Wang, Ying] Hunan Univ Chinese Med, Sch Humanities & Management, Changsha 410208, Peoples R China.
RP Zhang, J (通讯作者)，Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Peoples R China.
EM chengbinjie@126.com; jinzhang@hunnu.edu.cn; 1711677469@qq.com;
   cai.meiling@hunnu.edu.cn; wy101223@163.com
OI Zhang, Jin/0000-0001-6925-9246
FU Hunan Provincial Education Department [JG2018A012, 291-410, 248-27, 370,
   9, 90, 233 HNKCSZ-2020-0122]; Ministry of Education of the People's
   Republic of China [201901051021]; Science and Technology Progress and
   Innovation Project of Hunan Provincial Department of Transportation
   [201927]; Hunan University of Chinese Medicine [2019XJJJ030]
FX This work was supported by the education and research projects of Hunan
   Provincial Education Department (JG2018A012, XiangJiaoTong, 2019: nos.
   291-410, 248-27, and 370; 2020: nos. 9, 90, and 233 HNKCSZ-2020-0122),
   the projects of the Ministry of Education of the People's Republic of
   China (201901051021), the Science and Technology Progress and Innovation
   Project of Hunan Provincial Department of Transportation (201927), and
   economic burden and policy of traditional Chinese and western medicine
   for stroke patients in Hunan Province: 2019 scientific research project
   of Hunan University of Chinese Medicine (2019XJJJ030).
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NR 20
TC 3
Z9 3
U1 40
U2 93
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 2040-2295
EI 2040-2309
J9 J HEALTHC ENG
JI J. Healthc. Eng.
PD MAR 25
PY 2021
VL 2021
AR 5531327
DI 10.1155/2021/5531327
PG 10
WC Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services
GA RJ7SR
UT WOS:000637802900002
PM 33833858
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Gu, DX
   Yang, XJ
   Deng, SY
   Liang, CY
   Wang, XY
   Wu, J
   Guo, JJ
AF Gu, Dongxiao
   Yang, Xuejie
   Deng, Shuyuan
   Liang, Changyong
   Wang, Xiaoyu
   Wu, Jiao
   Guo, Jingjing
TI Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge
   Map and Common Word Analysis
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE cloud health care; cloud computing; health care informatics;
   cybermetrics; co-word analysis
ID INTERNET; SERVICE; COCITATION; MOBILE
AB Background: With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars in the medical informatics community need to understand the extent of the evolution of and possible trends in cloud health care research to inform their future research.
   Objective: Drawing on the cloud health care literature, this study aimed to describe the development and evolution of research themes in cloud health care through a knowledge map and common word analysis.
   Methods: A total of 2878 articles about cloud health care was retrieved from the Web of Science database. We used cybermetrics to analyze and visualize the keywords in these articles. We created a knowledge map to show the evolution of cloud health care research. We used co-word analysis to identify the hotspots and their evolution in cloud health care research.
   Results: The evolution and development of cloud health care services are described. In 2007-2009 (Phase I), most scholars used cloud computing in the medical field mainly to reduce costs, and grid computing and cloud computing were the primary technologies. In 2010-2012 (Phase II), the security of cloud systems became of interest to scholars. In 2013-2015 (Phase III), medical informatization enabled big data for health services. In 2016-2017 (Phase IV), machine learning and mobile technologies were introduced to the medical field.
   Conclusions: Cloud health care research has been rapidly developing worldwide, and technologies used in cloud health research are simultaneously diverging and becoming smarter. Cloud-based mobile health, cloud-based smart health, and the security of cloud health data and systems are three possible trends in the future development of the cloud health care field.
C1 [Gu, Dongxiao; Yang, Xuejie; Liang, Changyong; Guo, Jingjing] Hefei Univ Technol, Sch Management, 193 Tunxi Rd, Hefei 230009, Peoples R China.
   [Deng, Shuyuan] Grand Valley State Univ, Seidman Coll Business, Grand Rapids, MI USA.
   [Wang, Xiaoyu] Anhui Univ Tradit Chinese Med, Affiliated Hosp 1, Hefei, Peoples R China.
   [Wu, Jiao] Cent Michigan Univ, Coll Business Adm, Mt Pleasant, MI 48859 USA.
RP Yang, XJ (通讯作者)，Hefei Univ Technol, Sch Management, 193 Tunxi Rd, Hefei 230009, Peoples R China.
EM xuejie_y@126.com
OI Gu, Dongxiao/0000-0003-3557-009X
FU National Natural Science Foundation of China (NSFC) [71771075, 71771077]
FX The dataset collection and analysis of this research were partially
   supported by the National Natural Science Foundation of China (NSFC)
   under grant numbers 71771075 and 71771077.
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NR 67
TC 9
Z9 10
U1 5
U2 26
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD FEB 25
PY 2020
VL 22
IS 2
AR e15142
DI 10.2196/15142
PG 18
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services; Medical Informatics
GA KO4UY
UT WOS:000515547800001
PM 32130115
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Celen, I
   Ross, KE
   Arighi, CN
   Wu, CH
AF Celen, Irem
   Ross, Karen E.
   Arighi, Cecilia N.
   Wu, Cathy H.
TI Bioinformatics Knowledge Map for Analysis of Beta-Catenin Function in
   Cancer
SO PLOS ONE
LA English
DT Article
ID CYCLIN-DEPENDENT KINASE-5; POSTTRANSLATIONAL MODIFICATIONS; ANDROGEN
   RECEPTOR; PROTEIN ONTOLOGY; ACTIVATION; CELLS; CDK5; PHOSPHORYLATION;
   RESOURCE; NETWORK
AB Given the wealth of bioinformatics resources and the growing complexity of biological information, it is valuable to integrate data from disparate sources to gain insight into the role of genes/proteins in health and disease. We have developed a bioinformatics framework that combines literature mining with information from biomedical ontologies and curated databases to create knowledge "maps" of genes/proteins of interest. We applied this approach to the study of beta-catenin, a cell adhesion molecule and transcriptional regulator implicated in cancer. The knowledge map includes post-translational modifications (PTMs), protein-protein interactions, disease-associated mutations, and transcription factors co-activated by beta-catenin and their targets and captures the major processes in which beta-catenin is known to participate. Using the map, we generated testable hypotheses about beta-catenin biology in normal and cancer cells. By focusing on proteins participating in multiple relation types, we identified proteins that may participate in feedback loops regulating beta-catenin transcriptional activity. By combining multiple network relations with PTM proteoform-specific functional information, we proposed a mechanism to explain the observation that the cyclin dependent kinase CDK5 positively regulates beta-catenin co-activator activity. Finally, by overlaying cancer-associated mutation data with sequence features, we observed mutation patterns in several beta-catenin PTM sites and PTM enzyme binding sites that varied by tissue type, suggesting multiple mechanisms by which beta-catenin mutations can contribute to cancer. The approach described, which captures rich information for molecular species from genes and proteins to PTM proteoforms, is extensible to other proteins and their involvement in disease.
C1 [Celen, Irem; Ross, Karen E.; Arighi, Cecilia N.; Wu, Cathy H.] Univ Delaware, Ctr Bioinformat & Computat Biol, Newark, DE 19716 USA.
   [Ross, Karen E.] Georgetown Univ, Med Ctr, Dept Biochem & Mol & Cellular Biol, Washington, DC 20007 USA.
RP Ross, KE (通讯作者)，Univ Delaware, Ctr Bioinformat & Computat Biol, Newark, DE 19716 USA.
EM ross@dbi.udel.edu
RI Celen, Irem/K-5329-2019; Arighi, Cecilia N/J-2381-2015
OI Arighi, Cecilia N/0000-0002-0803-4817; Wu, Cathy/0000-0001-6379-8601
FU Republic of Turkey Ministry of National Education; National Science
   Foundation [ABI-1062520]; National Institutes of Health [5R01GM080646,
   P20GM103446]; NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
   [R13GM109648, R01GM080646, P20GM103446] Funding Source: NIH RePORTER;
   Div Of Biological Infrastructure [1062520] Funding Source: National
   Science Foundation
FX IC acknowledges Republic of Turkey Ministry of National Education for
   funding her during Master's studies. This work has been supported by the
   National Science Foundation (grant number: ABI-1062520 to CHW) and the
   National Institutes of Health (grant number: 5R01GM080646 and
   P20GM103446 to CHW). 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 45
TC 6
Z9 6
U1 0
U2 14
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD OCT 28
PY 2015
VL 10
IS 10
AR e0141773
DI 10.1371/journal.pone.0141773
PG 19
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA CV0DL
UT WOS:000363918100130
PM 26509276
OA gold, Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Wu, MF
   Long, RY
   Bai, Y
   Chen, H
AF Wu, Meifen
   Long, Ruyin
   Bai, Yun
   Chen, Hong
TI Knowledge mapping analysis of international research on environmental
   communication using bibliometrics
SO JOURNAL OF ENVIRONMENTAL MANAGEMENT
LA English
DT Article
DE Environmental communication; Knowledge map; CiteSpace; Bibliometrics
ID CLIMATE-CHANGE; SOCIAL MEDIA; NEWS; GREEN; TRENDS; PRESS; PARTICIPATION;
   METAANALYSIS; DISCOURSE; MESSAGES
AB Environmental communication (EC) is of great significance to the practice of environmental protection by human society, as it involves disseminating ecological and environmental information, communicating environmental risks, and increasing environmental awareness among the audience. In this study, knowledge mapping analysis was used to systematically review research in the field of EC. A scientometric analysis (1900-2020) was carried out on 2219 journal articles obtained from the Web of Science database to explore the basic characteristics, research hotspots, and research frontiers of EC research. The results revealed that: (1) Research on EC received widespread attention since the beginning of the 21st century, and 2010 was an important turning point in the study of EC. EC research shows the trend of interdisciplinary development. (2) Well-known universities in western countries and from around the world constitute the main body of current EC research. However, there is still a lack of international cooperation in the field of EC research. (3) Climate change, as a complex scientific issue, is not only a global environmental issue, but also the most concerned and hot issue in the field of EC. It has a strong political attribute and has become a major issue that cannot be ignored in politics. (4) According to the timeline analysis of research hotspots, EC research was innovatively divided into an initial stage, a development stage, and a rising stage. Combined the keyword bursts result with the research hotspots analysis, the research frontier of EC was divided into four periods. Finally, the shortcomings of this study are summarized and directions for future research of EC are proposed by considering the following four aspects: research perspective, research content, research paradigm and method, and research context.
C1 [Wu, Meifen] China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Jiangsu, Peoples R China.
   [Long, Ruyin; Chen, Hong] Jiangnan Univ, Sch Business, Wuxi 214122, Jiangsu, Peoples R China.
   [Long, Ruyin] Jiangnan Univ, Inst Jiangnan Culture, Wuxi 214122, Jiangsu, Peoples R China.
   [Bai, Yun] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China.
   [Bai, Yun] Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China.
   [Chen, Hong] Jiangnan Univ, Inst Natl Secur & Green Dev, Wuxi 214122, Jiangsu, Peoples R China.
RP Long, RY; Chen, H (通讯作者)，Jiangnan Univ, Sch Business, Wuxi 214122, Jiangsu, Peoples R China.
EM longruyin@163.com; hongchenxz@163.com
OI Chen, Hong/0000-0002-2035-6851
FU Major Project of National Social Science Foundation of China [19ZDA107];
   Key Project of National Social Sciences Foundation of China [18AZD014]
FX This work was supported by the Major Project of National Social Science
   Foundation of China (No. 19ZDA107) , and the Key Project of National
   Social Sciences Foundation of China (No. 18AZD014) .
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NR 85
TC 5
Z9 5
U1 26
U2 91
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 0301-4797
EI 1095-8630
J9 J ENVIRON MANAGE
JI J. Environ. Manage.
PD NOV 15
PY 2021
VL 298
AR 113475
DI 10.1016/j.jenvman.2021.113475
EA AUG 2021
PG 12
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA UR8WJ
UT WOS:000697022000003
PM 34365181
DA 2022-08-23
ER

PT J
AU Yu, CM
   Wang, F
   Liu, YH
   An, L
AF Yu, Chuanming
   Wang, Feng
   Liu, Ying-Hsang
   An, Lu
TI Research on knowledge graph alignment model based on deep learning
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Deep learning; Domain knowledge alignment; Knowledge graph; Knowledge
   representation
ID INFORMATION; ONTOLOGY; WEB
AB The construction of large-scale knowledge graphs from heterogeneous sources is fundamental to knowledge-driven applications. To solve the problem of redundancy and inconsistency in the process of domain knowledge fusion, this paper reports studies of domain knowledge alignment from the perspective of a knowledge graph. A novel knowledge graph alignment (KGA) model is proposed, based on knowledge graph deep representation learning. To assess the validity of the model, comparative experiments are conducted on the datasets of heterogeneous, cross-lingual, and domain-specific knowledge graphs. Our results of experiments suggest significant improvement on all of these datasets. We discuss the implications for improving the alignment effect of knowledge graph entities, enhancing the coverage and correctness of knowledge graphs, and promoting the performance of knowledge graphs in knowledge-driven applications.
C1 [Yu, Chuanming; Wang, Feng] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China.
   [Liu, Ying-Hsang] Oslo Metropolitan Univ, Dept Archivist Lib & Informat Sci, N-0167 Oslo, Norway.
   [An, Lu] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China.
   [An, Lu] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.
RP An, L (通讯作者)，Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China.; An, L (通讯作者)，Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.
EM yucm@zuel.edu.cn; Ying-Hsang.Liu@oslomet.no; anlu2009@whu.edu.cn
RI LIU, Ying-Hsang/C-6092-2009
OI LIU, Ying-Hsang/0000-0001-6504-4598
FU Natural Science Foundation of China [71974202, 71921002, 71790612,
   72174153]; Ministry of Education of China [19YJC870029, 17JZD034];
   Fundamental Research Funds for the Central Universities, Zhongnan
   University of Economics and Law [2722021AJ011]
FX This work was supported by the Natural Science Foundation of China
   (71974202, 71921002, 71790612, and 72174153), the project of the
   Ministry of Education of China (Grant No. 19YJC870029 and 17JZD034), and
   the Fundamental Research Funds for the Central Universities, Zhongnan
   University of Economics and Law (Grant No. 2722021AJ011).
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NR 50
TC 2
Z9 2
U1 65
U2 143
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD DEC 30
PY 2021
VL 186
AR 115768
DI 10.1016/j.eswa.2021.115768
EA AUG 2021
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 WE3LM
UT WOS:000705531600010
DA 2022-08-23
ER

PT J
AU Wiharja, K
   Pan, JZ
   Kollingbaum, MJ
   Deng, Y
AF Wiharja, Kemas
   Pan, Jeff Z.
   Kollingbaum, Martin J.
   Deng, Yu
TI Schema aware iterative Knowledge Graph completion
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge Graph completion; Schema aware; Knowledge Graph reasoning;
   Approximate reasoning; SHACL constraint; Correctness and coverage
ID DATATYPES; WORDNET; DBPEDIA
AB Recent success of Knowledge Graph has spurred widespread interests in methods for the problem of Knowledge Graph completion. However, efforts to understand the quality of the candidate triples from these methods, in particular from the schema aspect, have been limited. Indeed, most existing Knowledge Graph completion methods do not guarantee that the expanded Knowledge Graphs are consistent with the ontological schema of the initial Knowledge Graph. In this work, we challenge the silver standard method, by proposing the notion of schema-correctness. A fundamental challenge is how to make use of different types of Knowledge Graph completion methods together to improve the production of schema-correct triples. To address this, we analyse the characteristics of different methods and propose a schema aware iterative approach to Knowledge Graph completion. Our main findings are: (i) Some popular Knowledge Graph completion methods have surprisingly low schema-correctness ratio; (ii) Different types of Knowledge Graph completion methods can work with each other to help overcame individual limitations; (iii) Some iterative sequential combinations of Knowledge Graph completion methods have significantly better schema-correctness and coverage ratios than other combinations; (iv) All the MapReduce based iterative methods outperform involved single-pass methods significantly over the tested Knowledge Graphs in terms of productivity of schema-correct triples. Our findings and infrastructure can help further work on evaluating Knowledge Graph completion methods, more fine-grained approaches for schema aware iterative knowledge graph completion, as well as new approximate reasoning approaches based Knowledge Graph completion methods. (C) 2020 Published by Elsevier B.V.
C1 [Wiharja, Kemas; Pan, Jeff Z.; Kollingbaum, Martin J.] Univ Aberdeen, Dept Comp Sci, Aberdeen, Scotland.
   [Wiharja, Kemas] Telkom Univ, Sch Comp, Bandung, Indonesia.
   [Pan, Jeff Z.] Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland.
   [Deng, Yu] IBM Res, Yorktown Hts, NY USA.
RP Wiharja, K (通讯作者)，Telkom Univ, Sch Comp, Bandung, Indonesia.
EM bagindokemas@telkomuniversity.ac.id
RI Wiharja, Kemas/GNM-9613-2022
FU Lembaga Pengelola Dana Pendidikan (LPDP); Ministry of Finance of
   Indonesia; IBM Faculty Award; EU Marie Currie K-Drive project [286348]
FX This work was supported by Lembaga Pengelola Dana Pendidikan (LPDP), the
   Ministry of Finance of Indonesia, IBM Faculty Award and the EU Marie
   Currie K-Drive project (286348). We would like to thank NELL2RDF ([82])
   for providing us the tool for transforming NELL-995 Knowledge Graph into
   OWL format. Furthermore, we want to thank the anonymous reviewers for
   their valuable comments and suggestions for improving the quality of the
   paper.
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NR 82
TC 4
Z9 4
U1 13
U2 44
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 100616
DI 10.1016/j.websem.2020.100616
PG 16
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:000598338300003
DA 2022-08-23
ER

PT J
AU Dang, Y
   Zhang, YL
   Chen, HC
   Brown, SA
   Hu, PJH
   Nunamaker, JF
AF Dang, Yan
   Zhang, Yulei
   Chen, Hsinchun
   Brown, Susan A.
   Hu, Paul Jen-Hwa
   Nunamaker, Jay F., Jr.
TI Theory-Informed Design and Evaluation of an Advanced Search and
   Knowledge Mapping System in Nanotechnology
SO JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
LA English
DT Article
DE cognitive fit; cognitive load; information system evaluation; knowledge
   mapping; searching; visualization
ID COGNITIVE FIT; USER ACCEPTANCE; INFORMATION-TECHNOLOGY; SCIENCE;
   NETWORK; IMPACT; WEB; VISUALIZATION; COMPREHENSION; ANIMATION
AB Effective search support is an important tool for helping individuals deal with the problem of information overload. This is particularly true in the field of nanotechnology, where information from patents, grants, and research papers is growing rapidly. Guided by cognitive fit and cognitive load theories, we develop an advanced Web-based system, Nano Mapper, to support users' search and analysis of nanotechnology developments. We perform controlled experiments to evaluate the functions of Nano Mapper. We examine users' search effectiveness, efficiency, and evaluations of system usefulness, ease of use, and satisfaction. Our results demonstrate that Nano Mapper enables more effective and efficient searching, and users consider it to be more useful and easier to use than the benchmark systems. Users are also more satisfied with Nano Mapper and have higher intention to use it in the future. User evaluations of the analysis functions are equally positive.
C1 [Dang, Yan; Zhang, Yulei] No Arizona Univ, WA Franke Coll Business, Flagstaff, AZ 86011 USA.
   [Chen, Hsinchun] Univ Arizona, Artificial Intelligence Lab, Tucson, AZ 85721 USA.
   [Brown, Susan A.] Univ Arizona, Eller Coll Management, Tucson, AZ 85721 USA.
   [Hu, Paul Jen-Hwa] Univ Utah, David Eccles Sch Business, Salt Lake City, UT 84112 USA.
   [Nunamaker, Jay F., Jr.] Univ Arizona, Ctr Management Informat, Tucson, AZ 85721 USA.
RP Dang, Y (通讯作者)，No Arizona Univ, WA Franke Coll Business, Flagstaff, AZ 86011 USA.
RI White, Howard D/A-7034-2009
FU National Science Foundation [CMMI-0738803, DMI-0533749]
FX This research is supported by the following awards: National Science
   Foundation, "SGER: Inter-Repository Patent Analysis to Understand
   Worldwide Nanotechnology Research and Development" (CMMI-0738803) and
   "Mapping Nanotechnology Development" (DMI-0533749). The authors thank
   the United States Patent and Trademark Office, the European Patent
   Office, and the Japan Patent Office for making their databases available
   for research purposes.
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   [No title captured]
NR 74
TC 16
Z9 17
U1 2
U2 74
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0742-1222
EI 1557-928X
J9 J MANAGE INFORM SYST
JI J. Manage. Inform. Syst.
PD SPR
PY 2012
VL 28
IS 4
BP 99
EP 127
DI 10.2753/MIS0742-1222280405
PG 29
WC Computer Science, Information Systems; Information Science & Library
   Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
   Economics
GA 936QA
UT WOS:000303603900006
DA 2022-08-23
ER

PT J
AU Zeng, HB
   Shao, BL
   Bian, GQ
   Song, D
   Li, XJ
AF Zeng, Huibin
   Shao, Bilin
   Bian, Genqing
   Song, Dan
   Li, Xiaojun
TI A Survey of Research Progress and Hot Front of Natural Gas Load
   Forecasting From Technical Perspective
SO IEEE ACCESS
LA English
DT Article
DE Natural gas; Load forecasting; Forecasting; Market research;
   Visualization; Tools; Software; Natural gas load forecasting; prediction
   techniques; CiteSpace; knowledge mapping; visual analysis
ID SHORT-TERM; NEURAL-NETWORK; DEMAND; MODELS; CONSUMPTION; COMBINATION;
   PREDICTION
AB With economic development and scientific and technological progress, people's requirements for the ecological environment are increasing day by day. As a clean energy, natural gas is favored by many countries all over the world. As an important part of natural gas industry planning, load forecasting plays a vital role in the optimal dispatching and operation of the natural gas network. From the perspective of prediction technology, this paper selects the literature related to natural gas load prediction from the Web of Science and CNKI database as the research object. Firstly, the literature chronology distribution and background, research institutions, and academic communities were analyzed by CiteSpace scientific knowledge mapping software. Secondly, the research hotspots and cutting-edge technologies were presented and analyzed visually, and the hot spot migration process in this field was summarized. In the end, the paper puts forward some problems that should be paid attention to in natural gas load prediction, including data processing in prediction, prediction of prediction model, adaptive prediction method and combination prediction method, which are also the direction of development in the future.
C1 [Zeng, Huibin; Shao, Bilin; Bian, Genqing; Song, Dan; Li, Xiaojun] Xian Univ Architecture & Technol, Sch Management, Xian 710055, Peoples R China.
   [Bian, Genqing] Xian Univ Architecture & Technol, Sch Informat & Control Engn, Xian 710055, Peoples R China.
RP Shao, BL (通讯作者)，Xian Univ Architecture & Technol, Sch Management, Xian 710055, Peoples R China.
EM sblin0462@sina.com
OI LI, Xiaojun/0000-0002-6275-8808; zeng, huibin/0000-0001-5499-9217
FU National Natural Science Foundation of China [62072363]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 62072363.
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NR 71
TC 4
Z9 4
U1 11
U2 35
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 222824
EP 222840
DI 10.1109/ACCESS.2020.3044052
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PM4EH
UT WOS:000603754100001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, YH
   Xiao, WD
   Tan, Z
   Zhao, X
AF Wang, Yuhan
   Xiao, Weidong
   Tan, Zhen
   Zhao, Xiang
TI Caps-OWKG: a capsule network model for open-world knowledge graph
SO INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
LA English
DT Article
DE Capsule network; Open-world knowledge graph; Link prediction; Knowledge
   graph completion
AB Knowledge graphs are typical multi-relational structures, which is consisted of many entities and relations. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is utilized to embed entities and relations into low-dimensional spaces. Many existing knowledge graphs embedding models focus on learning latent features in close-world assumption but omit the changeable of each knowledge graph.In this paper, we propose a knowledge graph representation learning model, called Caps-OWKG, which leverages the capsule network to capture the both known and unknown triplets features in open-world knowledge graph. It combines the descriptive text and knowledge graph to get descriptive embedding and structural embedding, simultaneously. Then, the both above embeddings are used to calculate the probability of triplet authenticity. We verify the performance of Caps-OWKG on link prediction task with two common datasets FB15k-237-OWE and DBPedia50k. The experimental results are better than other baselines, and achieve the state-of-the-art performance.
C1 [Wang, Yuhan; Xiao, Weidong; Tan, Zhen; Zhao, Xiang] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, 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.
EM xiangzhao@nudt.edu.cn
FU NSFC [61872446, 61902417, 71971212]; NSF of Hunan Province [2019JJ20024]
FX This work was supported by NSFC under grants Nos. 61872446, 61902417 and
   71971212, NSF of Hunan Province under grant No. 2019JJ20024.
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NR 23
TC 0
Z9 0
U1 6
U2 14
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 JUN
PY 2021
VL 12
IS 6
BP 1627
EP 1637
DI 10.1007/s13042-020-01259-4
EA JAN 2021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SF3KD
UT WOS:000604799700001
OA hybrid
DA 2022-08-23
ER

PT J
AU Zhou, XG
   Gong, RB
   Shi, FG
   Wang, ZF
AF Zhou, Xiang-Guang
   Gong, Ren-Bin
   Shi, Fu-Geng
   Wang, Zhe-Feng
TI PetroKG: Construction and Application of Knowledge Graph in Upstream
   Area of PetroChina
SO JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
LA English
DT Article
DE knowledge graph; natural language processing; oil and gas industry
AB There is a large amount of heterogeneous data distributed in various sources in the upstream of PetroChina. These data can be valuable assets if we can fully use them. Meanwhile, the knowledge graph, as a new emerging technique, provides a way to integrate multi-source heterogeneous data. In this paper, we present one application of the knowledge graph in the upstream of PetroChina. Specifically, we first construct a knowledge graph from both structured and unstructured data with multiple NLP (natural language progressing) methods. Then, we introduce two typical knowledge graph powered applications and show the benefit that the knowledge graph brings to these applications: compared with the traditional machine learning approach, the well log interpretation method powered by knowledge graph shows more than 7.69% improvement of accuracy.
C1 [Zhou, Xiang-Guang; Gong, Ren-Bin; Shi, Fu-Geng] PetroChina Res Inst Petr Explorat & Dev, Beijing 100083, Peoples R China.
   [Wang, Zhe-Feng] Huawei Technol, Hangzhou 310007, Peoples R China.
RP Zhou, XG (通讯作者)，PetroChina Res Inst Petr Explorat & Dev, Beijing 100083, Peoples R China.
EM zhouxg69@petrochina.com.cn; gongrb@petrochina.com.cn;
   sfg@petrochina.com.cn; wangzhefeng@huawei.com
CR Angeli G, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P344
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NR 39
TC 2
Z9 2
U1 7
U2 36
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1000-9000
EI 1860-4749
J9 J COMPUT SCI TECH-CH
JI J. Comput. Sci. Technol.
PD MAR
PY 2020
VL 35
IS 2
BP 368
EP 378
DI 10.1007/s11390-020-9966-7
PG 11
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LQ1XN
UT WOS:000534804000012
DA 2022-08-23
ER

PT J
AU Wang, YJ
   Qiao, YQ
   Ma, JT
   Hu, GW
   Zhang, CQ
   Sangaiah, AK
   Zhang, HP
   Ren, K
AF Wang, Yanjun
   Qiao, Yaqiong
   Ma, Jiangtao
   Hu, Guangwu
   Zhang, Chaoqin
   Sangaiah, Arun Kumar
   Zhang, Hongpo
   Ren, Kai
TI A Novel Time Constraint-Based Approach for Knowledge Graph Conflict
   Resolution
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph; knowledge conflict resolution; neural recurrent network
AB Knowledge graph conflict resolution is a method to solve the knowledge conflict problem in constructing knowledge graphs. The existing methods ignore the time attributes of facts and the dynamic changes of the relationships between entities in knowledge graphs, which is liable to cause high error rates in dynamic knowledge graph construction. In this article, we propose a knowledge graph conflict resolution method, knowledge graph evolution algorithm based on deep learning (Kgedl), which can resolve facts confliction with high precision by combing time attributes, semantic embedding representations, and graph structure features. Kgedl first trains the semantic embedding vector through the relationships between entities. Then, the path embedding vector is trained from the graph structures of knowledge graphs, and the time attributes of entities are combined with the semantic and path embedding vectors. Finally, Kgedl uses a recurrent neural network to make the inconsistent facts appear in the dynamic evolution of the knowledge graph consistent. A large number of experiments on real datasets show that Kgedl outperforms the state-of-the-art methods. Especially, Kgedl achieves 23% higher performance than the classical method numerical Probabilistic Soft Logic (nPSL).in the metric HITS@10. Also, extensive experiments verified that our proposal possess better robustness by adding noise data.
C1 [Wang, Yanjun; Qiao, Yaqiong; Zhang, Hongpo] State Key Lab Math Engn & Adv Comp, Zhengzhou 450000, Henan, Peoples R China.
   [Wang, Yanjun; Ma, Jiangtao; Zhang, Chaoqin] Zhengzhou Univ Light Ind, Sch Comp & Commun Engn, Zhengzhou 450002, Henan, Peoples R China.
   [Ma, Jiangtao; Zhang, Chaoqin] Natl Digital Switching Syst Engn & Technol R&D Ct, Zhengzhou 450002, Henan, Peoples R China.
   [Hu, Guangwu] Shenzhen Inst Informat Technol, Sch Comp Sci, Shenzhen 518172, Peoples R China.
   [Sangaiah, Arun Kumar] VIT Univ, Sch Comp Sci & Engn, Vellore 632014, Tamil Nadu, India.
   [Zhang, Hongpo; Ren, Kai] Zhengzhou Univ, Cooperat Innovat Ctr Internet Healthcare, Zhengzhou 450000, Henan, Peoples R China.
RP Hu, GW (通讯作者)，Shenzhen Inst Informat Technol, Sch Comp Sci, Shenzhen 518172, Peoples R China.
EM wyj@zzuli.edu.cn; kitesmile@126.com; majiangtao@zzuli.edu.cn;
   hugw@sziit.edu.cn; 2000007@zzuli.edu.cn; arunkumarsangaiah@gmail.com;
   zhp@zzu.edu.cn; renkai@zzu.edu.cn
RI Sangaiah, Arun Kumar/U-6785-2019
OI Sangaiah, Arun Kumar/0000-0002-0229-2460; HU,
   Guangwu/0000-0003-3947-9998; , Jiangtao/0000-0001-5181-4045
FU National Natural Science Foundation of China [61702462, 61672470,
   61802352, 61866008]; Doctoral Research Fund of Zhengzhou University of
   Light Industry [2017BSJJ046, 2018BSJJ039, 13501050045]; Scientific AMP;
   Technological Project of Henan Province [182102210607]; Foundation of
   Henan Province Educational Committee [17A520064]; Fundamental Research
   Project of Shenzhen Municipality [JCYJ20170817115335418]
FX This research was funded by the National Natural Science Foundation of
   China (No. 61702462, No. 61672470, No. 61802352, No. 61866008), the
   Doctoral Research Fund of Zhengzhou University of Light Industry
   (2017BSJJ046, 2018BSJJ039, 13501050045), Scientific & Technological
   Project of Henan Province (No. 182102210607), Foundation of Henan
   Province Educational Committee (No. 17A520064) and Fundamental Research
   Project of Shenzhen Municipality (No. JCYJ20170817115335418).
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NR 18
TC 0
Z9 0
U1 7
U2 25
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD OCT
PY 2019
VL 9
IS 20
AR 4399
DI 10.3390/app9204399
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 JM5QU
UT WOS:000496269400202
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ma, RX
   Li, ZY
   Ma, YL
   Wu, H
   Yu, MF
   Zhao, L
AF Ma, Ruixin
   Li, Zeyang
   Ma, Yunlong
   Wu, Hao
   Yu, Mengfei
   Zhao, Liang
TI Adaptive Attentional Network for Few-Shot Relational Learning of
   Knowledge Graphs
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE few-shot; one-shot; knowledge graph reasoning; Transformer
AB Few-shot knowledge graph reasoning is a research focus in the field of knowledge graph reasoning. At present, in order to expand the application scope of knowledge graphs, a large number of researchers are devoted to the study of the multi-shot knowledge graph model. However, as far as we know, the knowledge graph contains a large number of missing relations and entities, and there are not many reference examples at the time of training. In this paper, our goal is to be able to infer the correct entity given a few training instances, or even only one training instance is available. Therefore, we propose an adaptive attentional network for few-shot relational learning of knowledge graphs, extracting knowledge based on traditional embedding methods, using the Transformer mechanism and hierarchical attention mechanism to obtain hidden attributes of entities, and then using a noise checker to filter out unreasonable candidate entities. Our model produces large performance improvements on the NELL-One dataset.
C1 [Ma, Ruixin; Li, Zeyang; Ma, Yunlong; Wu, Hao; Yu, Mengfei; Zhao, Liang] Dalian Univ Technol, Sch Software Technol, Dalian 116024, Peoples R China.
   [Ma, Ruixin; Li, Zeyang; Ma, Yunlong; Wu, Hao; Yu, Mengfei; Zhao, Liang] Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116600, Peoples R China.
RP Zhao, L (通讯作者)，Dalian Univ Technol, Sch Software Technol, Dalian 116024, Peoples R China.; Zhao, L (通讯作者)，Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116600, Peoples R China.
EM maruixin@dlut.edu.cn; lizeyang@mail.dlut.edu.cn;
   m1804069847@mail.dlut.edu.cn; wuhao15904655726@mail.dlut.edu.cn;
   yumengfei@mail.dlut.edu.cn; liangzhao@dlut.edu.cn
FU National Natural Science Foundation of China [61906030]; Science and
   Technology Project of Liaoning Province [2021JH2/10300064]; Natural
   Science Foundation of Liaoning Province [2020-BS-063]; Youth Science and
   Technology Star Support Program of Dalian City [2021RQ057]
FX This work is supported by the National Natural Science Foundation of
   China (61906030), the Science and Technology Project of Liaoning
   Province (2021JH2/10300064), the Natural Science Foundation of Liaoning
   Province (2020-BS-063) and the Youth Science and Technology Star Support
   Program of Dalian City (2021RQ057).
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NR 35
TC 0
Z9 0
U1 6
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD MAY
PY 2022
VL 12
IS 9
AR 4284
DI 10.3390/app12094284
PG 17
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 1F0CE
UT WOS:000794843900001
OA gold
DA 2022-08-23
ER

PT J
AU Romero-Perdomo, F
   Carvajalino-Umana, JD
   Moreno-Gallego, JL
   Ardila, N
   Gonzalez-Curbelo, MA
AF Romero-Perdomo, Felipe
   Carvajalino-Umana, Juan David
   Moreno-Gallego, Jaime Leonardo
   Ardila, Natalia
   Gonzalez-Curbelo, Miguel Angel
TI Research Trends on Climate Change and Circular Economy from a Knowledge
   Mapping Perspective
SO SUSTAINABILITY
LA English
DT Article
DE sustainable development goals; sustainability; greenhouse gases;
   bioeconomy; renewable energy; nature-based solutions
AB The circular economy (CE) has been proposed as a potentially significant catalyst to enhance the current response to the global climate crisis. The objective of this study was to investigate the scientific literature of the research between climate change and CE adopting a knowledge mapping approach. Based on a total of 789 peer-reviewed publications extracted from Scopus, we found that research on climate change and CE is continually growing and interdisciplinary in nature. Europe notably leads scientific production. Keyword evolution shows that CE has been influenced by more lines of research than climate change. We also found that waste management is the CE approach most associated with climate change, mitigation is the climate action most impacted by CE, and food is the most reported greenhouse gas (GHG)-emitting material. However, there are knowledge gaps in the integration of the social dimension, the promotion of climate change adaptation, and the association of sustainable development goal (SDG) 13. Finally, we identified four potentially valuable directions for future studies: (i) CE practices, (ii) bioeconomy, (iii) climate and energy, and (iv) sustainability and natural resources, in which carbon recovery technologies, green materials, regional supply chains, circular agriculture models, and nature-based solutions are promising themes.
C1 [Romero-Perdomo, Felipe; Carvajalino-Umana, Juan David; Gonzalez-Curbelo, Miguel Angel] Univ EAN, Fac Ingn, Dept Ciencias Basicas, Bogota 110221, Colombia.
   [Romero-Perdomo, Felipe] Corp Colombiana Invest Agr AGROSAVIA Tibaitata, Mosquera 250047, Colombia.
   [Moreno-Gallego, Jaime Leonardo] Univ Andes, Dept Biol Sci, Max Planck Tandem Grp Computat Biol, Bogota 111711, Colombia.
   [Moreno-Gallego, Jaime Leonardo] Max Planck Inst Dev Biol, Dept Microbiome Sci, D-72076 Tubingen, Germany.
   [Ardila, Natalia] Org Naciones Unidas, Pacto Global Red Colombia, Bogota 110221, Colombia.
RP Gonzalez-Curbelo, MA (通讯作者)，Univ EAN, Fac Ingn, Dept Ciencias Basicas, Bogota 110221, Colombia.
EM fromero44146@universidadean.edu.co; jcarvaj14023@universidadean.edu.co;
   jl.moreno116@uniandes.edu.co; natalia.ardila@pactoglobal-colombia.org;
   magonzalez@universidadean.edu.co
RI Curbelo, Miguel Ángel González/I-7165-2016
OI Curbelo, Miguel Ángel González/0000-0001-6047-4302
FU Universidad EAN
FX This research was funded by Universidad EAN.
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NR 63
TC 1
Z9 1
U1 19
U2 19
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JAN
PY 2022
VL 14
IS 1
AR 521
DI 10.3390/su14010521
PG 17
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA ZE8YW
UT WOS:000759165000001
OA gold
DA 2022-08-23
ER

PT J
AU Yang, Q
   Zheng, XZ
   Jin, LH
   Lei, XH
   Shao, B
   Chen, Y
AF Yang, Qiu
   Zheng, Xiazhong
   Jin, Lianghai
   Lei, Xiaohui
   Shao, Bo
   Chen, Yun
TI Research Progress of Urban Floods under Climate Change and Urbanization:
   A Scientometric Analysis
SO BUILDINGS
LA English
DT Article
DE urban floods; stormwater management; citation analysis; visualization;
   CiteSpace; VOSviewer
ID LOW IMPACT DEVELOPMENT; BUILT ENVIRONMENT; CONSTRUCTION; MANAGEMENT;
   CITIES; RUNOFF; FUTURE; CHINA; SUSTAINABILITY; PREDICTION
AB Urban floods research has been attracting extensive attention with the increasing threat of flood risk and environmental hazards due to global climate change and urbanization. However, there is rarely a comprehensive review of this field and it remains unclear how the research topics on urban floods have evolved. In this study, we analyzed the development of urban floods research and explored the hotspots and frontiers of this field by scientific knowledge mapping. In total, 3314 published articles from 2006 to 2021 were analyzed. The results suggest that the number of published articles in the field of urban floods generally has an upward trend year by year, and the research focus has shifted from exploring hydrological processes to adopting advanced management measures to solve urban flood problems. Moreover, urban stormwater management and low impact development in the context of climate change and urbanization have gradually become research hotspots. Future research directions based on the status and trends of the urban floods field were also discussed. This research can not only inspire other researchers and policymakers, but also demonstrates the effectiveness of scientific knowledge mapping analysis by the use of the software CiteSpace and VOSviewer.
C1 [Yang, Qiu; Zheng, Xiazhong; Jin, Lianghai; Shao, Bo; Chen, Yun] China Three Gorges Univ, Coll Hydraul & Environm Engn, Yichang 443002, Peoples R China.
   [Zheng, Xiazhong; Jin, Lianghai] China Three Gorges Univ, Hubei Key Lab Construct & Management Hydropower E, Yichang 443002, Peoples R China.
   [Lei, Xiaohui] China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China.
RP Zheng, XZ; Jin, LH (通讯作者)，China Three Gorges Univ, Coll Hydraul & Environm Engn, Yichang 443002, Peoples R China.; Zheng, XZ; Jin, LH (通讯作者)，China Three Gorges Univ, Hubei Key Lab Construct & Management Hydropower E, Yichang 443002, Peoples R China.
EM 201908710021010@ctgu.edu.cn; zhengxz@126.com; jinlianghai@ctgu.edu.cn;
   lxh@iwhr.com; shaobo910@whu.edu.cn; yunchen@ctgu.edu.cn
OI Shao, Bo/0000-0003-4566-2836
FU National Natural Science Foundation of China [51878385]; Open Fund of
   Hubei Key Laboratory of Construction and Management in Hydropower
   Engineering [2019KSD05]
FX FundingThis research was supported by the National Natural Science
   Foundation of China (Grant number 51878385), and Open Fund of Hubei Key
   Laboratory of Construction and Management in Hydropower Engineering
   (Grant number 2019KSD05).
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NR 141
TC 5
Z9 5
U1 41
U2 48
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-5309
J9 BUILDINGS-BASEL
JI BUILDINGS-BASEL
PD DEC
PY 2021
VL 11
IS 12
AR 628
DI 10.3390/buildings11120628
PG 25
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Construction & Building Technology; Engineering
GA XX2KH
UT WOS:000736131000001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Guo, C
   Tang, H
   Niu, B
   Lee, CBP
AF Guo, Chen
   Tang, Heng
   Niu, Ben
   Lee, Chang Boon Patrick
TI A survey of bacterial foraging optimization
SO NEUROCOMPUTING
LA English
DT Article
DE Bacterial foraging optimization; Swarm intelligence; CiteSpace;
   Knowledge mapping
ID PARTICLE SWARM OPTIMIZATION; OPTIMAL POWER-FLOW; ADAPTIVE COMPUTATIONAL
   CHEMOTAXIS; HYBRID GENETIC ALGORITHM; DISTRIBUTION-SYSTEM; EMERGING
   TRENDS; DISTRIBUTED OPTIMIZATION; REGENERATIVE MEDICINE; CONGESTION
   MANAGEMENT; ECONOMIC-DISPATCH
AB Bacterial foraging optimization algorithm (BFO) is a biological-inspired swarm intelligence optimization algorithm that simulates the foraging behavior of bacteria to obtain maximal energy during the searching process. Since its inception, it has evoked wide attention from researchers. The number of articles about BFO and its variants has grown significantly over the past decades and continues to grow. Hence, there is a clear need for a scientific and comparative review of extant BFO literature based on a common framework that offers a quick glimpse of the development of BFO. In light of this, we develop a bibliometric review via using CiteSpace to construct a knowledge mapping, and then conduct a thorough literature review of BFO research. Based on the analytical results, we present the influential researchers, journals or proceedings, and articles, and discuss various key issues of BFO. Finally, we suggest serval research directions that require further attention. This survey is expected to exhibit a lucid outline and useful guidance for the researchers of BFO. (c) 2020 Elsevier B.V. All rights reserved.
C1 [Guo, Chen; Niu, Ben] Shenzhen Univ, Coll Management, Shenzhen 518060, Peoples R China.
   [Guo, Chen; Tang, Heng; Lee, Chang Boon Patrick] Univ Macau, Fac Business Adm, Macau, Peoples R China.
   [Niu, Ben] Shenzhen Univ, Inst Big Data Intelligent Management & Decis, Shenzhen, Peoples R China.
RP Niu, B (通讯作者)，Shenzhen Univ, Coll Management, Shenzhen 518060, Peoples R China.
EM drniuben@gmail.com
OI CHEN, GUO/0000-0001-6252-342X
FU Natural Science Foundation of China [71971143, 71571120]; Major Research
   Plan for National Natural Science Foundation of China [91846301]; Major
   Project for National Natural Science Foundation of China [71790615];
   University of Macau [MYRG201800051FBA]; Natural Science Foundation of
   Guangdong Province [2020A1515010749]; Major Projects of Universities of
   Guangdong Provincial Department of Education [2019KZDXM030]
FX The work described in this paper was supported by The Natural Science
   Foundation of China (Grant No. 71971143, 71571120) ; Major Research Plan
   for National Natural Science Foundation of China (No. 91846301) ; Major
   Project for National Natural Science Foundation of China (No. 71790615)
   ; University of Macau under Grant [MYRG201800051FBA] ; The Natural
   Science Foundation of Guangdong Province (No. 2020A1515010749) ; Major
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NR 182
TC 4
Z9 4
U1 10
U2 47
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD SEP 10
PY 2021
VL 452
BP 728
EP 746
DI 10.1016/j.neucom.2020.06.142
EA JUN 2021
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SU4ED
UT WOS:000663092100019
DA 2022-08-23
ER

PT J
AU Li, WZ
   Qi, GL
   Ji, Q
AF Li, Weizhuo
   Qi, Guilin
   Ji, Qiu
TI Hybrid reasoning in knowledge graphs: Combing symbolic reasoning and
   statistical reasoning
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge graphs; hybrid reasoning; knowledge representation
AB Knowledge graphs (KGs) contain rich resources that represent human knowledge in the world. There are mainly two kinds of reasoning techniques in knowledge graphs, symbolic reasoning and statistical reasoning. However, both of them have their merits and limitations. Therefore, it is desirable to combine them to provide hybrid reasoning in a knowledge graph. In this paper, we present the first work on the survey of methods for hybrid reasoning in knowledge graphs. We categorize existing methods based on applications of reasoning techniques, and introduce the key ideas of them. Finally, we re-examine the remaining research problems to be solved and provide an outlook to future directions for hybrid reasoning in knowledge graphs.
C1 [Li, Weizhuo; Qi, Guilin] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Li, Weizhuo; Qi, Guilin] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.
   [Ji, Qiu] Nanjing Univ Posts & Telecommun, Sch Modern Posts, Nanjing, Peoples R China.
   [Ji, Qiu] Nanjing Univ Posts & Telecommun, Inst Modern Posts, Nanjing, Peoples R China.
RP Qi, GL (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.; Qi, GL (通讯作者)，Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.
EM liweizhuo@amss.ac.cn; gqi@seu.edu.cn; qiuji@njupt.edu.cn
FU National Key Research and Development Program of China [2018YFC0830200,
   2017YFB1002801]; Natural Science Foundation of China [U1736204,
   61602259]; Fundamental Research Funds for the Central Universities
   [3209009601]; Judicial Big Data Research Centre, School of Law at
   Southeast University
FX The authors would like to thank all the reviewers for their insightful
   and valuable suggestions, which significantly improve the quality of
   this survey. Research presented in this paper was partially supported by
   the National Key Research and Development Program of China under grants
   (2018YFC0830200, 2017YFB1002801), the Natural Science Foundation of
   China grants (U1736204, 61602259), the Fundamental Research Funds for
   the Central Universities (3209009601), and the Judicial Big Data
   Research Centre, School of Law at Southeast University.
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NR 62
TC 2
Z9 5
U1 24
U2 82
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 53
EP 62
DI 10.3233/SW-190375
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KJ8IH
UT WOS:000512298900007
DA 2022-08-23
ER

PT J
AU Haase, P
   Herzig, DM
   Kozlov, A
   Nikolov, A
   Trame, J
AF Haase, Peter
   Herzig, Daniel M.
   Kozlov, Artem
   Nikolov, Andriy
   Trame, Johannes
TI metaphactory: A platform for knowledge graph management
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge graph; data management; end-user platform; industrial
   application
ID RDF
AB In this system paper we describe metaphactory, a platform for building knowledge graph applications. The metaphactory platform aims at supporting different categories of knowledge graph users within the organization by realizing relevant services for knowledge graph data management tasks, providing a rich and customizable user interface, and enabling rapid building of use case-specific applications. The paper discusses how the platform architecture design built on open standards enables its reusability in various application domains and use cases as well as facilitates integration of the knowledge graph with other parts of the organizational data and software infrastructure. We highlight the capabilities of the platform by describing its usage in four different knowledge graph application domains and share the lessons learnt from the practical experience of building knowledge graph applications in the enterprise context.
C1 [Haase, Peter; Herzig, Daniel M.; Kozlov, Artem; Nikolov, Andriy; Trame, Johannes] Metaphacts GmbH, Walldorf, Germany.
RP Nikolov, A (通讯作者)，Metaphacts GmbH, Walldorf, Germany.
EM ph@metaphacts.com; dh@metaphacts.com; ak@metaphacts.com;
   an@metaphacts.com
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NR 30
TC 11
Z9 11
U1 7
U2 30
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 1109
EP 1125
DI 10.3233/SW-190360
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 JH5WI
UT WOS:000492839300008
DA 2022-08-23
ER

PT J
AU Zhao, QQ
   Zhang, WX
   Wang, RM
   Wang, YT
   Ouyang, DF
AF Zhao, Qianqian
   Zhang, Weixiang
   Wang, Runmiao
   Wang, Yitao
   Ouyang, Defang
TI Research Advances in Molecular Modeling in Cyclodextrins
SO CURRENT PHARMACEUTICAL DESIGN
LA English
DT Review
DE Cyclodextrin; Molecular modeling; Knowledge map; Molecular dynamics;
   Inclusion complexes
ID CHIRAL RECOGNITION MECHANISM; CARLO DOCKING SIMULATIONS; FREE-ENERGY
   CALCULATIONS; AMINO-ACID ENANTIOMERS; BETA-CYCLODEXTRIN; INCLUSION
   COMPLEXES; ALPHA-CYCLODEXTRIN; CAPILLARY-ELECTROPHORESIS; DYNAMICS
   SIMULATIONS; PHARMACEUTICAL APPLICATIONS
AB Background: Cyclodextrins (CDs), as one type of the novel pharmaceutical excipients, have been widely used in drug delivery and pharmaceutical industry. Over the past decades, a large amount of molecular modeling studies in CDs were reported for profound understanding of structural, dynamic and energetic features of CDs systems. Thus, this review is focused on qualitative and quantitative analysis of research outputs on molecular modeling in CDs.
   Methods: The original data were collected from Web of Science and analyzed by scientific knowledge mapping tools, including Citespace, Science of Science, VOSviewer, GPSvisualizer and Gephi software. Scientific knowledge mapping, as an emerging approach for literature analysis, was employed to identify the knowledge structure and capture the development of the science in a visual way.
   Results: The results of analysis included research outputs landscape, collaboration patterns, knowledge structure and research frontiers shift with time. China had the largest contributions to the publication number in this area, while USA dominated the high quality research outputs. International collaboration between USA and Europe was much stronger than that within Asia. J American Chemical Society, as one of the most important journals, played a pivotal role in linking different research fields. Furthermore, seven important thematic clusters were identified by the research cluster analysis with visualization tools and demonstrated from three different perspectives including: (1) the mostly-used CD molecules: beta-Cyclodextrin, (2) preferred modeling tools: docking calculation and molecular dynamic, (3) hot research fields: structural properties, solubility, chiral recognition and solidstate inclusion complexes. Moreover, research frontier shift in the past three decades was traced by detecting keywords bursts with high citation.
   Conclusion: The current review provided us a macro-perspective and intellectual landscape to molecular modeling in CDs.
C1 [Zhao, Qianqian; Zhang, Weixiang; Wang, Runmiao; Wang, Yitao; Ouyang, Defang] Univ Macau, Inst Chinese Med Sci, State Key Lab Qual Res Chinese Med, Taipa, Macau, Peoples R China.
RP Ouyang, DF (通讯作者)，Univ Macau, Inst Chinese Med Sci, State Key Lab Qual Res Chinese Med, Taipa, Macau, Peoples R China.
EM defangouyang@umac.mo
RI Ouyang, Defang/AAW-1087-2020; Wang, Yitao/O-5184-2016; Ouyang,
   Defang/L-8279-2019
OI Ouyang, Defang/0000-0002-8052-4773
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NR 77
TC 25
Z9 29
U1 8
U2 131
PU BENTHAM SCIENCE PUBL LTD
PI SHARJAH
PA EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB
   EMIRATES
SN 1381-6128
EI 1873-4286
J9 CURR PHARM DESIGN
JI Curr. Pharm. Design
PY 2017
VL 23
IS 3
BP 522
EP 531
DI 10.2174/1381612822666161208142617
PG 10
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA EL4EN
UT WOS:000394574600015
PM 27928960
DA 2022-08-23
ER

PT J
AU Wang, FR
   Tan, B
   Chen, Y
   Fang, XY
   Jia, GW
   Wang, HY
   Cheng, G
   Shao, ZZ
AF Wang, Feiran
   Tan, Bo
   Chen, Yue
   Fang, Xiyang
   Jia, Guowei
   Wang, Haiyan
   Cheng, Gang
   Shao, Zhuangzhuang
TI A visual knowledge map analysis of mine fire research based on CiteSpace
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article; Early Access
DE Coal fire; CiteSpace; Coal spontaneous combustion; Visual analysis
ID COAL SPONTANEOUS COMBUSTION; LOW-TEMPERATURE OXIDATION; COMPOSITE
   INHIBITOR; FUNCTIONAL-GROUPS; THERMAL-BEHAVIOR; MECHANISM; LIGNITE; HEAT
AB Mine fire has always been a serious disaster in coal industry; many academic achievements have poured out in the past two decades for solving this problem. In this study, visual analysis was conducted to grasp the hotspots and development trend of mine fire research. Papers that published in 1999-2020 were retrieved as the data basis from Web of Science, and CiteSpace was used to carry out knowledge map analysis. The results shown that number of papers has increased steadily since 2005 and achieved explosive growth since 2014. Deng J is the first published author among many scholars. China, the USA, and Australia are active areas in mine fire research and China University of Mining and Technology ranks first in this field. The highest co-occurrence frequency keyword is "spontaneous combustion." International Journal of Coal Geology and Fuel provide guidance for mine fire research. Fire prevention technology, low carbon, ecology, and sustainable development are the hot research in recent years. The prevention and control of mine fire from combustion mechanism should be further strengthened.
C1 [Wang, Feiran; Tan, Bo; Chen, Yue; Fang, Xiyang; Cheng, Gang; Shao, Zhuangzhuang] China Univ Min & Technol Beijing, Sch Emergency Management & Safety Engn, Beijing 100083, Peoples R China.
   [Wang, Haiyan] Univ Sci & Technol Beijing, Sch Civil & Resources Engn, Beijing 100083, Peoples R China.
   [Jia, Guowei] Adm Ctr Chinas Agenda 21, Beijing 100036, Peoples R China.
RP Tan, B (通讯作者)，China Univ Min & Technol Beijing, Sch Emergency Management & Safety Engn, Beijing 100083, Peoples R China.
EM tanbo709@126.com
FU National Nature Science Foundation of China [52074156, 51804355]
FX This work was supported by the financial support of the National Nature
   Science Foundation of China (52074156 and 51804355).
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NR 68
TC 0
Z9 0
U1 28
U2 28
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
DI 10.1007/s11356-022-20993-6
EA JUN 2022
PG 16
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 1Y9DB
UT WOS:000808436200013
PM 35680744
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Baclawski, K
   Bennett, M
   Berg-Cross, G
   Schneider, T
   Sharma, R
   Singer, J
   Sriram, RD
AF Baclawski, Ken
   Bennett, Michael
   Berg-Cross, Gary
   Schneider, Todd
   Sharma, Ravi
   Singer, Janet
   Sriram, Ram D.
TI Ontology summit 2020 communique: Knowledge graphs
SO APPLIED ONTOLOGY
LA English
DT Article
DE Knowledge graph; knowledge graph architecture; ontology; semantics;
   semantic networks
AB An increasing amount of data is now available from public and private sources. Furthermore, the types, formats, and number of sources of data are also increasing. Techniques for extracting, storing, processing, and analyzing such data have been developed in the last few years for managing this bewildering variety based on a structure called a knowledge graph. Industry has devoted a great deal of effort to the development of knowledge graphs, and knowledge graphs are now critical to the functions of intelligent virtual assistants such as Siri, Alexa, and Google Assistant. The goal of the Ontology Summit 2020 was to understand not only what knowledge graphs are but also where they originated, why they are so popular, the current issues, and their future prospects. The summit sessions examined many examples of knowledge graphs and surveyed the relevant standards that exist and are in development for knowledge graphs. The purpose of this Communique is to summarize our understanding from the Summit in order to foster research and development of knowledge graphs.
C1 [Baclawski, Ken] Northeastern Univ, Boston, MA 02115 USA.
   [Bennett, Michael] Hypercube Ltd, London, England.
   [Schneider, Todd] Engn Semant, Fairfax, VA USA.
   [Singer, Janet] INCOSE, Scotts Valley, CA USA.
   [Sriram, Ram D.] NIST, Gaithersburg, MD 20899 USA.
RP Baclawski, K (通讯作者)，Northeastern Univ, Boston, MA 02115 USA.
EM Ken@Baclawski.com; mbennett@hypercube.co.uk; gbergcross@gmail.com;
   tsch@engsem.net; drravisharma@gmail.com; jsinger@soe.ucsc.edu;
   ramdsriram@gmail.com
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NR 43
TC 1
Z9 1
U1 8
U2 20
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 2021
VL 16
IS 2
BP 229
EP 247
DI 10.3233/AO-210249
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RU8VA
UT WOS:000645418100005
DA 2022-08-23
ER

PT J
AU Liu, XX
   Bai, XS
   Wang, LH
   Ren, BY
   Lu, SH
   Li, L
AF Liu Xiaoxue
   Bai Xuesong
   Wang Longhe
   Ren Bingyuan
   Lu Shuhan
   Li Lin
TI Review and Trend Analysis of Knowledge Graphs for Crop Pest and Diseases
SO IEEE ACCESS
LA English
DT Review
DE Knowledge graph; crop disease and pest; knowledge extraction; knowledge
   fusion; knowledge reasoning
ID SEMANTIC SIMILARITY; ONTOLOGY; SYSTEM; EXTRACTION; CONSTRUCTION; DESIGN;
   ENTITY
AB Current techniques of knowledge management have some common defects in efficiency, scalability, and applicability. Knowledge graph provides a new way for knowledge management and is a more flexible knowledge management method. Considering the specific features of crop diseases and pest data, this paper analyzed and classified the key techniques and methods of knowledge graph technology in the field of crop diseases and pest in recent years. We introduced the definition and connotation of the crop diseases and pest knowledge, and the current construction method of it was analyzed in detail from four aspects: knowledge representation, extraction, fusion, and reasoning. Furthermore, the application of crop diseases and pest knowledge graph was introduced in detail in the expert system, search engine, and knowledge question-answering system. At last, this paper summarized the challenges and important problems of diseases and pest knowledge graph and forecasted the prospect of knowledge graph according to the key points and difficulties of current knowledge graph research.
C1 [Liu Xiaoxue; Bai Xuesong; Wang Longhe; Li Lin] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China.
   [Ren Bingyuan] Natl Agr Technol Serv Ctr, Prevent & Control Dept, Beijing 100125, Peoples R China.
   [Lu Shuhan] Ohio State Univ, Comp & Informat Sci, Columbus, OH 43210 USA.
RP Li, L (通讯作者)，China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China.
EM lilincau@126.com
OI Liu, Xiaoxue/0000-0001-6818-4377
FU National Key Research and Development Program of China [2016YFD0300710]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2016YFD0300710.
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NR 87
TC 16
Z9 21
U1 28
U2 92
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 62251
EP 62264
DI 10.1109/ACCESS.2019.2915987
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IA2GF
UT WOS:000469378900001
OA gold
DA 2022-08-23
ER

PT J
AU Yong, C
   Dong, JL
   Fei, G
   Bin, T
   Tao, Z
   Hao, F
   Li, W
   Zhan, QH
AF Yong, Chen
   Dong, Jinlong
   Fei, Guo
   Bin, Tong
   Tao, Zhou
   Hao, Fang
   Li, Wang
   Qinghua, Zhan
TI Review of landslide susceptibility assessment based on knowledge mapping
SO STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
LA English
DT Review; Early Access
DE Web of science; CiteSpace; Knowledge mapping; Landslide susceptibility
ID LOGISTIC-REGRESSION; INVENTORY; MODELS; HAZARD; EARTHQUAKE; FREQUENCY;
   PROBABILITY; VALIDATION; PREDICTION; MAGNITUDE
AB Landslide susceptibility assessment is highly valuable for disaster prevention and mitigation. This study utilized the aspects of data and content to comprehensively examine the research status of landslide susceptibility. First, we used CiteSpace to visually analyze papers with "landslide susceptibility" as their theme word in the Web of Science database from 1991 to 2020. Next, we summarized the characteristics of quantitative trends, journals, authors, organization types, and keywords, and created a map of authors, institutions, and keywords. Then we presented the common methods and their advantages and disadvantages of landslide inventory, evaluation index, evaluation unit, evaluation model and verification method, combs the shortcomings of each part at the present stage, and looks forward to the possible research direction in the future. Finally, the research difficulties of landslide susceptibility in spatial scale, qualitative and quantitative problems, and spatial representation of landslide information are discussed. We find that the development of 3S and new technology in computer field promotes the development of landslide susceptibility research, makes landslide inventory more efficient and accurate, makes the assessment factor system considering factor contribution more reasonable, and makes more intelligent models applied to landslide susceptibility research. The results are beneficial for researchers to understand the current landslide susceptibility condition and can provide a reference for subsequent landslide susceptibility studies.
C1 [Yong, Chen; Dong, Jinlong; Fei, Guo; Li, Wang; Qinghua, Zhan] China Three Gorges Univ, Minist Educ, Key Lab Geol Hazards Three Gorges Reservoir Area, Yichang 443002, Hubei, Peoples R China.
   [Fei, Guo] Three Gorges Univ, Coll Civil Engn & Architecture, Yichang 443002, Peoples R China.
   [Bin, Tong; Hao, Fang] China Inst Geol Environm Monitoring, Beijing 100081, Peoples R China.
   [Tao, Zhou] Beijing Inst Hydrogeol & Engn Geol, Beijing 100195, Peoples R China.
RP Fei, G (通讯作者)，China Three Gorges Univ, Minist Educ, Key Lab Geol Hazards Three Gorges Reservoir Area, Yichang 443002, Hubei, Peoples R China.; Fei, G (通讯作者)，Three Gorges Univ, Coll Civil Engn & Architecture, Yichang 443002, Peoples R China.
EM ybbnui.2008@163.com
RI Guo, Fei/I-5023-2015
OI Guo, Fei/0000-0003-3006-3478
FU Natural Science Research Project of Yichang City [A21-3-006]; China
   Institute of Geological Environment Monitoring Project [DD20190638];
   National Natural Science Foundation of China [42107489]
FX This work is supported by the Natural Science Research Project of
   Yichang City (No. A21-3-006), the China Institute of Geological
   Environment Monitoring Project (No. DD20190638), and the National
   Natural Science Foundation of China (No. 42107489).
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NR 102
TC 4
Z9 4
U1 79
U2 90
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1436-3240
EI 1436-3259
J9 STOCH ENV RES RISK A
JI Stoch. Environ. Res. Risk Assess.
DI 10.1007/s00477-021-02165-z
EA JAN 2022
PG 19
WC Engineering, Environmental; Engineering, Civil; Environmental Sciences;
   Statistics & Probability; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Environmental Sciences & Ecology; Mathematics; Water
   Resources
GA YE7EG
UT WOS:000741283500001
DA 2022-08-23
ER

PT J
AU Barbu, L
   Mihaiu, DM
   Serban, RA
   Opreana, A
AF Barbu, Liliana
   Mihaiu, Diana Marieta
   Serban, Radu-Alexandru
   Opreana, Alin
TI Knowledge Mapping of Optimal Taxation Studies: A Bibliometric Analysis
   and Network Visualization
SO SUSTAINABILITY
LA English
DT Article
DE bibliometric analysis; optimal taxation; tax optimization; CiteSpace
ID OPTIMAL INCOME TAXATION; OPTIMAL TAX THEORY; EMERGING TRENDS;
   SCIENTOMETRIC ANALYSIS; REGENERATIVE MEDICINE; GENERAL EQUILIBRIUM;
   PUBLIC PRODUCTION; SOCIAL INSURANCE; MONETARY-POLICY; LABOR-MARKET
AB The problem of tax policy design has been an important concern over the years, involving comprehensive scientific research. In this study, our major goal was to examine and map the optimal taxation research thematic structure by using bibliometric analysis. The analysis was carried out with the CiteSpace software on publications indexed by Web of Science (WoS) between 1975 and 2021. This document offers an actual bibliometric analysis of the current research climate in terms of optimal taxation, based on the following aspects: (1) descriptive characteristics of publication outputs (distribution by years, authors, countries, journals); (2) collaboration analysis of authors, institutions, and countries; (3) co-citation analysis of cited journals, cited authors, and cited references; and (4) keywords' co-occurrence analysis. We constructed a knowledge map about optimal taxation research to provide a wide visual brief of the actual research in the domain of optimal tax policy. The current study adds knowledge by presenting the state of the art of the most significant studies published in the field of optimal taxation research.
C1 [Barbu, Liliana; Mihaiu, Diana Marieta] Lucian Blaga Univ Sibiu, Fac Econ Sci, Dept Finance & Accounting, Sibiu 550024, Romania.
   [Serban, Radu-Alexandru; Opreana, Alin] Lucian Blaga Univ Sibiu, Fac Econ Sci, Dept Management Mkt & Business Adm, Sibiu 550024, Romania.
RP Barbu, L (通讯作者)，Lucian Blaga Univ Sibiu, Fac Econ Sci, Dept Finance & Accounting, Sibiu 550024, Romania.
EM liliana.barbu@ulbsibiu.ro; diana.mihaiu@ulbsibiu.ro;
   radu.serban@ulbsibiu.ro; alin.opreana@ulbsibiu.ro
RI Barbu, Liliana/AAM-8373-2021; Opreana, Alin/I-1178-2016
OI Opreana, Alin/0000-0001-8860-1609; Serban,
   Radu-Alexandru/0000-0003-0765-2346
FU Lucian Blaga University of Sibiu & Hasso Plattner Foundation
   [LBUS-IRG-2021-07]
FX FundingThis research was funded by Lucian Blaga University of Sibiu and
   Hasso Plattner Foundation research grants LBUS-IRG-2021-07.
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NR 97
TC 1
Z9 1
U1 11
U2 11
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JAN
PY 2022
VL 14
IS 2
AR 1043
DI 10.3390/su14021043
PG 36
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 ZB7WT
UT WOS:000757048600001
OA gold
DA 2022-08-23
ER

PT J
AU Chen, XL
   Xie, HR
   Li, ZX
   Cheng, G
AF Chen, Xieling
   Xie, Haoran
   Li, Zongxi
   Cheng, Gary
TI Topic analysis and development in knowledge graph research: A
   bibliometric review on three decades
SO NEUROCOMPUTING
LA English
DT Review
DE Knowledge graphs; Bibliometric analysis; Structural topic modeling;
   Research topics; Scientific collaboration
ID RELATION EXTRACTION; INFORMATION; MODELS; GPU
AB Knowledge graph as a research topic is increasingly popular to represent structural relations between entities. Recent years have witnessed the release of various open-source and enterprise-supported knowledge graphs with dramatic growth in applying knowledge representation and reasoning into different areas like natural language processing and computer vision. This study aims to comprehensively explore the status and trends - particularly the thematic research structure - of knowledge graphs. Specifically, based on 386 research articles published from 1991 to 2020, we conducted analyses in terms of the (1) visualization of the trends of annual article and citation counts, (2) recognition of major institutions, countries/regions, and publication sources, (3) visualization of scientific collaborations of major institutions and countries/regions, and (4) detection of major research themes and their developmental tendencies. Interest in knowledge graph research has clearly increased from 1991 to 2020 and is continually expanding. China is the most prolific country in knowledge graph research. Moreover, countries/regions and institutions that have higher levels of international collaboration are more impactful. Several widely studied issues such as knowledge graph embedding, search and query based on knowledge graphs, and knowledge graphs for intangible cultural heritage are highlighted. Based on the results, we further summarize perspective directions and suggestions for researchers, practitioners, and project managers to facilitate future research on knowledge graphs. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Chen, Xieling; Cheng, Gary] Educ Univ, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
   [Li, Zongxi] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China.
RP Xie, HR (通讯作者)，Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; hrxie2@gmail.com;
   zongxili2-c@my.cityu.edu.hk; chengks@eduhk.hk
RI Xie, Haoran/AFS-3515-2022
OI LI, Zongxi/0000-0002-1708-7099; Cheng, Gary/0000-0002-5614-3348
FU General Research Fund of Research Grants Council of Hong Kong SAR, China
   [18601118]; Faculty Research Grant of Lingnan University, Hong Kong
   [DB21B6]; One-off Special Fund from Central and Faculty Fund
   [MIT02/19-20]; Research Cluster Fund [RG 78/2019-2020R];
   Interdisciplinary Research Scheme of the Dean's Research Fund of The
   Education University of Hong Kong [FLASS/DRF/IDS-2]
FX The research described in this article has been supported by General
   Research Fund (No. 18601118) of Research Grants Council of Hong Kong
   SAR, China; the Faculty Research Grant (DB21B6) of Lingnan University,
   Hong Kong; the One-off Special Fund from Central and Faculty Fund in
   Support of Research from 2019/20 to 2021/22 (MIT02/19-20) , the Research
   Cluster Fund (RG 78/2019-2020R) , and the Interdisciplinary Research
   Scheme of the Dean's Research Fund 2019-20 (FLASS/DRF/IDS-2) of The
   Education University of Hong Kong.
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NR 115
TC 3
Z9 3
U1 44
U2 80
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 497
EP 515
DI 10.1016/j.neucom.2021.02.098
EA SEP 2021
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA WC2IT
UT WOS:000704086300002
DA 2022-08-23
ER

PT J
AU Bai, LY
   Zhang, MC
   Zhang, H
   Zhang, H
AF Bai, Luyi
   Zhang, Mingcheng
   Zhang, Han
   Zhang, Heng
TI FTMF: Few-shot temporal knowledge graph completion based on
   meta-optimization and fault-tolerant mechanism
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Few-shot temporal knowledge graph; Long-tail relations;
   Meta-optimization
AB Traditional knowledge graph completion mainly focuses on static knowledge graph. Although there are efforts studying temporal knowledge graph completion, they assume that each relation has enough entities to train, ignoring the influence of long tail relations. Moreover, many relations only have a few samples. In that case, how to handle few-shot temporal knowledge graph completion still merits further attention. This paper aims to propose a framework for completing few-shot temporal knowledge graph. We use self-attention mechanism to encode entities, use cyclic recursive aggregation network to aggregate reference sets, use fault-tolerant mechanism to deal with error information, and use similarity network to calculate similarity scores. Experimental results show that our proposed model outperforms the baseline models and has better stability.
C1 [Bai, Luyi; Zhang, Mingcheng; Zhang, Han; Zhang, Heng] Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China.
   [Bai, Luyi] Univ Leicester, Sch Informat, Leicester LE1 7RH, Leics, England.
RP Bai, LY (通讯作者)，Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China.; Bai, LY (通讯作者)，Univ Leicester, Sch Informat, Leicester LE1 7RH, Leics, England.
EM baily@neuq.edu.cn
FU National Natural Science Foundation of China [61402087]; Natural Science
   Foundation of Hebei Province [F2022501015]; Key Project of Scientific
   Research Funds in Colleges and Universities of Hebei Education
   Department [ZD2020402]; 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
   (F2022501015), the Key Project of Scientific Research Funds in Colleges
   and Universities of Hebei Education Department (ZD2020402), and in part
   by the Program for 333 Talents in Hebei Province (A202001066).
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NR 44
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
DI 10.1007/s11280-022-01091-6
EA AUG 2022
PG 28
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3N4QB
UT WOS:000836132700001
DA 2022-08-23
ER

PT J
AU Zhang, Z
   Yu, JT
   Hu, CH
AF Zhang, Zhen
   Yu, Jintian
   Hu, Chunhua
TI Evolution of path dependence and lock-in with the knowledge mining
   analysis of Web of Science literature
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Path dependence; lock-in; knowledge mining; evolution; knowledge mapping
ID QWERTY ALWAYS WINS; INCREASING RETURNS; HISTORY; RERUN; TAPE
AB Studies on path dependence (PD) and lock-in have indicated several limitations, such as chaotic literature information, interdisciplinarity complexity, and evolution vagueness. In this study, literature on path dependence and lock-in are obtained from the database of Web of Science Core Collection from 2001 to 2017. The knowledge mining model of the evolution of path dependence and lock-in is constructed based on knowledge mining process and content. The evolution of this area is also explored. Results show that the evolution of path dependence and lock-in can be categorized as follows: (1) English, American, and German scholars have led the research on path dependence and lock-in. (2) The research topics have undergone four stages. (3) The research levels have been classified as policy system (macro-level), regional economy (meso-level), and organizational path dependence (micro-level). (4) The research design can be represented as theory basis, case discussion, lock-in or reformation, and path creation. (5) The research method can be transformed from case study into lock-in mathematical modeling. A panoramic knowledge mapping of path dependence and lock-in is displayed by the research system to enhance understanding on research trajectories and future research directions.
C1 [Zhang, Zhen; Yu, Jintian] Wuhan Univ Technol, Sch Management, Wuhan, Hubei, Peoples R China.
   [Hu, Chunhua] Wuhan Donghu Univ, Sch Management, Wuhan, Hubei, Peoples R China.
RP Hu, CH (通讯作者)，Wuhan Donghu Univ, Sch Management, Wuhan, Hubei, Peoples R China.
EM zztuaner@163.com
FU National Key Technology Research and Development Program of the Ministry
   of Science and Technology of China [2015BAG08B04]; Soft Science Research
   Program of Science and Technology Department of Hubei Province, China
   [2017ADD009]
FX The authors acknowledge the National Key Technology Research and
   Development Program of the Ministry of Science and Technology of China
   (Grant: 2015BAG08B04), Soft Science Research Program of Science and
   Technology Department of Hubei Province, China (Grant: 2017ADD009).
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NR 21
TC 1
Z9 1
U1 3
U2 26
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 2018
VL 35
IS 3
BP 2951
EP 2963
DI 10.3233/JIFS-169651
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA GV6QR
UT WOS:000446239400038
DA 2022-08-23
ER

PT J
AU Nowacki, M
AF Nowacki, Marek
TI Heritage Interpretation and Sustainable Development: A Systematic
   Literature Review
SO SUSTAINABILITY
LA English
DT Review
DE knowledge mapping; text mining; topic extraction; literature review;
   sustainable tourism
ID TOURISM; AUTHENTICITY; VISITORS
AB Heritage interpretation is an educational activity that provides information on natural and cultural heritage to people visiting heritage sites and objects. The article aims to identify different ways of using heritage interpretation as a tool for managing areas of sustainable development. The study consisted of a systematic literature review (knowledge mapping) in the field of heritage interpretation and sustainable development. Using the most important databases of scientific publications in the world (Scopus, Web of Science, Ebsco, Proquest), publications on the interpretation of heritage and sustainable development were searched. A total of 106 articles and scientific monographs were identified. Then, quantitative and qualitative data analysis was performed (text mining, topic extraction, content analysis). As a result, six thematic areas were identified: opportunities and threats for the area related to the implementation of heritage interpretation programs; the effectiveness of heritage interpretation in the areas of sustainable development; sustainable tourism development planning in heritage areas; the role of a guide-interpreter in the sustainable development of the area; the role of creativity in the heritage interpretation; and strategies for managing the visitor traffic in heritage areas. These thematic areas were discussed by citing examples from the literature on the subject.
C1 [Nowacki, Marek] WSB Univ Poznan, Dept Appl Sci, PL-61874 Poznan, Poland.
RP Nowacki, M (通讯作者)，WSB Univ Poznan, Dept Appl Sci, PL-61874 Poznan, Poland.
EM marek.nowacki@wsb.poznan.pl
OI Nowacki, Marek/0000-0002-6981-7698
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NR 70
TC 0
Z9 0
U1 19
U2 46
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD APR
PY 2021
VL 13
IS 8
AR 4383
DI 10.3390/su13084383
PG 16
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA RU7LB
UT WOS:000645324100001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Myklebust, EB
   Jimenez-Ruiz, E
   Chen, JY
   Wolf, R
   Tollefsen, KE
AF Myklebust, Erik B.
   Jimenez-Ruiz, Ernesto
   Chen, Jiaoyan
   Wolf, Raoul
   Tollefsen, Knut Erik
TI Prediction of adverse biological effects of chemicals using knowledge
   graph embeddings
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge graph; ecotoxicology; risk assessment; adverse effects;
   embedding; chemicals; species
ID LARGE-SCALE
AB We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment, namely chemical effect prediction. We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, and convolutional models on this prediction task. We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. Furthermore, we have implemented a fine-tuning architecture which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.
C1 [Myklebust, Erik B.; Wolf, Raoul; Tollefsen, Knut Erik] Norwegian Inst Water Res, Oslo, Norway.
   [Myklebust, Erik B.; Jimenez-Ruiz, Ernesto] Univ Oslo, SIRIUS, Oslo, Norway.
   [Myklebust, Erik B.] NORSAR, Gunnar Randers Vei 15, N-2007 Kjeller, Norway.
   [Jimenez-Ruiz, Ernesto] City Univ London, Coll Bldg,Northampton Sq, London EC1V 0HB, England.
   [Chen, Jiaoyan] Univ Oxford, Oxford, England.
   [Wolf, Raoul] Norwegian Geotech Inst, Oslo, Norway.
   [Tollefsen, Knut Erik] Norwegian Univ Life Sci, As, Norway.
RP Myklebust, EB (通讯作者)，NORSAR, Gunnar Randers Vei 15, N-2007 Kjeller, Norway.; Jimenez-Ruiz, E (通讯作者)，City Univ London, Coll Bldg,Northampton Sq, London EC1V 0HB, England.
EM erik@norsar.no; ernesto.jimenez-ruiz@city.ac.uk
FU Research Council of Norway (RCN) [272414]; Samsung Research UK [268294,
   237889]; Siemens AG; EPSRC [EP/P025943/1]; OASIS; AIDA project (Alan
   Turing Institute) [EP/S032347/1, EP/S019111/1]
FX This work is supported by the grant 272414 from the Research Council of
   Norway (RCN), the MixRisk project (Research Council of Norway, project
   268294), SIRIUS Centre for Scalable Data Access (Research Council of
   Norway, project 237889), Samsung Research UK, Siemens AG, and the EPSRC
   projects AnaLOG (EP/P025943/1), OASIS (EP/S032347/1), UK FIRES
   (EP/S019111/1) and the AIDA project (Alan Turing Institute).
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NR 84
TC 1
Z9 1
U1 15
U2 15
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 299
EP 338
DI 10.3233/SW-222804
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 0J3YH
UT WOS:000780041500002
OA Green Accepted, hybrid, Green Submitted
DA 2022-08-23
ER

PT J
AU Duan, JL
   Wang, GY
   Hu, X
   Bao, HA
AF Duan, Jiangli
   Wang, Guoyin
   Hu, Xin
   Bao, Huanan
TI Hierarchical quotient space-based concept cognition for knowledge graphs
SO INFORMATION SCIENCES
LA English
DT Article
DE Granular computing; Cognitive intelligence; Concept cognition; Knowledge
   graph; Hierarchical quotient space
ID FORMAL CONCEPT ANALYSIS; ATTRIBUTE REDUCTION; QUERY; MODEL; SIMILARITY
AB As part of cognitive intelligence, concept cognition aims to view a concept from a comprehensive perspective and clearly grasp the concept intension (i.e., the typical attributes of the things referred to by the concept). Since knowledge graphs contain many entities that are beneficial for concept cognition, concept cognition for knowledge graphs is a promising research branch of concept cognition. Unlike general concept cognition, which selects all the attributes belonging to the concept and simultaneously eliminates other attributes, concept cognition for knowledge graphs focuses on determining the significance of concept-relevant attributes. Therefore, we propose a method of processing concept cognition for knowledge graphs that utilizes the hierarchical quotient space to determine attribute significance. Specifically, 1) this is the first study to propose and address concept cognition for knowledge graphs. 2) We propose a method of building a hierarchical quotient space for a concept in a knowledge graph that fully considers the characteristics of the concept-relevant attributes in the knowledge graph. 3) We propose using the distance between two hierarchical quotient spaces with and without a certain attribute to determine the significance of the concept-relevant attributes. Finally, the performance of our solution is measured from multiple aspects.(c) 2022 Elsevier Inc. All rights reserved.
C1 [Duan, Jiangli; Wang, Guoyin; Bao, Huanan] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing 400065, Peoples R China.
   [Duan, Jiangli; Hu, Xin] Yangtze Normal Univ, Coll Big Data & Intelligent Engn, Chongqing 408100, Peoples R China.
RP Wang, GY (通讯作者)，Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing 400065, Peoples R China.; Hu, X (通讯作者)，Yangtze Normal Univ, Coll Big Data & Intelligent Engn, Chongqing 408100, Peoples R China.
EM wanggy@cqupt.edu.cn; huxin@yznu.edu.cn
FU National Key Research and Development Program of China [2016YFB1000901];
   National Natural Science Foundation of China [61936001, 62106024,
   61772096, 62006029]; key cooperation project of chongqing municipal
   education commission [HZ2021008]; youth project of science and
   tech-nology research program of Chongqing Education Commission of China
   [KJQN201901414]; Startup Foundation for Introducing Talent of Yangtze
   Normal University [0107/011160052]; PhD Candidate Talent Development
   Project [BYJS201908]; Natural Science Foundation of Chongqing
   [cstc2021ycjh-bgzxm0013]
FX This work is supported by the National Key Research and Development
   Program of China (No. 2016YFB1000901) , the National Natural Science
   Foundation of China (NSFC No. 61936001, No. 62106024, No. 61772096 and
   No. 62006029) , the key cooperation project of chongqing municipal
   education commission (HZ2021008) , the youth project of science and
   tech-nology research program of Chongqing Education Commission of China
   (No. KJQN201901414) , the Startup Foundation for Introducing Talent of
   Yangtze Normal University (No. 0107/011160052) , the PhD Candidate
   Talent Development Project (No. BYJS201908) and the Natural Science
   Foundation of Chongqing (No.cstc2019jcyj-cxttX0002, No.
   cstc2021ycjh-bgzxm0013) .
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NR 48
TC 0
Z9 0
U1 3
U2 3
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD JUN
PY 2022
VL 597
BP 300
EP 317
DI 10.1016/j.ins.2022.03.029
PG 18
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1C0GI
UT WOS:000792806800002
DA 2022-08-23
ER

PT J
AU Lin, JJ
   Zhao, YZ
   Huang, WY
   Liu, CF
   Pu, HT
AF Lin, Jinjiao
   Zhao, Yanze
   Huang, Weiyuan
   Liu, Chunfang
   Pu, Haitao
TI Domain knowledge graph-based research progress of knowledge
   representation
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Domain knowledge graph; Knowledge representation; Entity; Relationship;
   Property
AB Domain knowledge graph has become a research topic in the era of artificial intelligence. Knowledge representation is the key step to construct domain knowledge graph. There have been quite a few well-established general knowledge graphs. However, there are still gaps on the domain knowledge graph construction. The research introduces the related concepts of the knowledge representation and analyzes knowledge representation of knowledge graphs by category, which includes some classical general knowledge graphs and several typical domain knowledge graphs. The paper also discusses the development of knowledge representation in accordance with the difference of entities, relationships and properties. It also presents the unsolved problems and future research trends in the knowledge representation of domain knowledge graph study.
C1 [Lin, Jinjiao; Zhao, Yanze; Liu, Chunfang] Shandong Univ Finance & Econ, Sch Management Sci & Engn, Jinan 250000, Peoples R China.
   [Huang, Weiyuan] Shandong Univ Finance & Econ, Sch Marxism, Jinan 250000, Peoples R China.
   [Pu, Haitao] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Jinan 250000, Peoples R China.
RP Pu, HT (通讯作者)，Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Jinan 250000, Peoples R China.
EM pht@sdust.edu.cn
FU Teaching Reform Research Project of Undergraduate Colleges and
   Universities of Shandong Province [Z2016Z036]; Teaching Reform Research
   Project of Shandong University of Finance and Economics
   [jy2018062891470, jy201830, jy201810]; Shandong Provincial Social
   Science Planning Research Project [18CHLJ08]; Scientific Research
   Projects of Universities in Shandong Province [J18RA136]; Youth
   Innovative on Science and Technology Project of Shandong Province
   [2019RWF013]; SDUST Excellent Teaching Team Construction Plan
   [JXTD20160512, JXTD20180510]; Jinan campus of SDUST Excellent Teaching
   Team Construction Plan [JNJXTD201711]; Teaching research project of
   Shandong University of Science and Technology [JNJG2017104]; National
   Natural Science Foundation of China [61703243]
FX Teaching Reform Research Project of Undergraduate Colleges and
   Universities of Shandong Province (Z2016Z036), the Teaching Reform
   Research Project of Shandong University of Finance and Economics
   (jy2018062891470, jy201830, jy201810), Shandong Provincial Social
   Science Planning Research Project (18CHLJ08), Scientific Research
   Projects of Universities in Shandong Province (J18RA136), Youth
   Innovative on Science and Technology Project of Shandong Province
   (2019RWF013), SDUST Excellent Teaching Team Construction Plan
   (JXTD20160512 and JXTD20180510), Jinan campus of SDUST Excellent
   Teaching Team Construction Plan (JNJXTD201711), Teaching research
   project of Shandong University of Science and Technology (JNJG2017104),
   National Natural Science Foundation of China (61703243).
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NR 56
TC 12
Z9 14
U1 179
U2 454
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 2021
VL 33
IS 2
BP 681
EP 690
DI 10.1007/s00521-020-05057-5
EA JUN 2020
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QC5JF
UT WOS:000541688500003
DA 2022-08-23
ER

PT J
AU Page, RDM
AF Page, Roderic D. M.
TI Ozymandias: a biodiversity knowledge graph
SO PEERJ
LA English
DT Article
DE Knowledge graph; Biodiversity informatics; Linked data; Identifiers;
   Challenge
ID AMPLIRHAGADA IREDALE; KIMBERLEY; NEEDS
AB Enormous quantities of biodiversity data are being made available online, but much of this data remains isolated in silos. One approach to breaking these silos is to map local, often database-specific identifiers to shared global identifiers. This mapping can then be used to construct a knowledge graph, where entities such as taxa, publications, people, places, specimens, sequences, and institutions are all part of a single, shared knowledge space. Motivated by the 2018 GBIF Ebbe Nielsen Challenge I explore the feasibility of constructing a "biodiversity knowledge graph" for the Australian fauna. The data cleaning and reconciliation steps involved in constructing the knowledge graph are described in detail. Examples are given of its application to understanding changes in patterns of taxonomic publication over time. A web interface to the knowledge graph (called "Ozymandias") is available at https://ozymandias-demo.herokuapp.com.
C1 [Page, Roderic D. M.] Univ Glasgow, IBAHCM, MVLS, Glasgow, Lanark, Scotland.
RP Page, RDM (通讯作者)，Univ Glasgow, IBAHCM, MVLS, Glasgow, Lanark, Scotland.
EM roderic.page@glasgow.ac.uk
RI Page, Roderic/A-1375-2009
OI Page, Roderic/0000-0002-7101-9767
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NR 39
TC 3
Z9 3
U1 6
U2 17
PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
SN 2167-8359
J9 PEERJ
JI PeerJ
PD APR 8
PY 2019
VL 7
AR e6739
DI 10.7717/peerj.6739
PG 25
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA HS2MS
UT WOS:000463695300006
PM 30993051
OA Green Published, Green Accepted, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zhou, B
   Shen, XW
   Lu, YQ
   Li, XY
   Hua, B
   Liu, TY
   Bao, JS
AF Zhou, Bin
   Shen, Xingwang
   Lu, Yuqian
   Li, Xinyu
   Hua, Bao
   Liu, Tianyuan
   Bao, Jinsong
TI Semantic-aware event link reasoning over industrial knowledge graph
   embedding time series data
SO INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
LA English
DT Article; Early Access
DE Time series data; data modelling; semantic fusion; industrial knowledge
   graph; link reasoning
ID WEB
AB The time series data in the manufacturing process reflects the sequential state of the manufacturing system, and the fusion of temporal features into the industrial knowledge graph will undoubtedly significantly improve the knowledge process efficiency of the manufacturing system. This paper proposes a semantic-aware event link reasoning over an industrial knowledge graph embedding time series data. Its knowledge graph skeleton is constructed through a specific manufacturing process. NLTK is used to transform technical documents into a structured industrial knowledge graph. We employ deep learning (DL)-based models to obtain semantic information related to product quality prediction using time series data collected from IoT devices. Then the prediction information is attached to the specified node in the knowledge graph. Thus, the knowledge graph will describe the dynamic semantic information of manufacturing contexts. Meanwhile, a dynamic event link reasoning model that uses graph embedding to aggregate manufacturing processes information is proposed. The implicit information with industrial temporal knowledge can be further mined and inferred. The case study has shown that the proposed knowledge graph link reasoning reflects dynamic temporal characteristics. Compared to the classical knowledge graph prediction models, our model is superior to the baseline methods.
C1 [Zhou, Bin; Shen, Xingwang; Li, Xinyu; Hua, Bao; Liu, Tianyuan; Bao, Jinsong] Donghua Univ, Coll Mech Engn, Shanghai, Peoples R China.
   [Lu, Yuqian] Univ Auckland, Dept Mech Engn, Auckland, New Zealand.
RP Bao, JS (通讯作者)，Donghua Univ, Coll Mech Engn, Shanghai, Peoples R China.
EM bao@dhu.edu.cn
OI BAO, Jinsong/0000-0003-1999-1003
FU National Key Research and Development Programof China [2019YFB1706 300];
   Graduate Student Innovation Fund of Donghua University
   [CUSF-DH-D-2021043]; Fundamental Research Funds for the Central
   Universities
FX This work is supported by the National Key Research and Development
   Programof China [grant number: 2019YFB1706 300], and the Fundamental
   Research Funds for theCentralUniversities and Graduate Student
   Innovation Fund of Donghua University [grant number: CUSF-DH-D-2021043].
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NR 41
TC 1
Z9 1
U1 39
U2 44
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0020-7543
EI 1366-588X
J9 INT J PROD RES
JI Int. J. Prod. Res.
DI 10.1080/00207543.2021.2022803
EA JAN 2022
PG 18
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA YJ3GI
UT WOS:000744421800001
DA 2022-08-23
ER

PT J
AU Song, HJ
   Park, SB
AF Song, Hyun-Je
   Park, Seong-Bae
TI Enriching Translation-Based Knowledge Graph Embeddings Through Continual
   Learnings
SO IEEE ACCESS
LA English
DT Article
DE Continual learning; knowledge graph enriching; translation-based
   knowledge graph embeddings
AB This paper addresses an enrichment of translation-based knowledge graph embeddings. When new knowledge triples become available after a knowledge graph is embedded onto a vector space, the embedding should be enriched with the new triples, but without the triples used in training the embedding. The main challenge is that the enrichment of new triples should be accomplished without forgetting the knowledge of current embedding. This paper achieves the goal by minimizing a risk over the new triples penalized by rapid parameter change between old and new embedding models. The effectiveness of the proposed method is shown by learning a translation-based knowledge graph embedding trained incrementally using a series of knowledge triples. The experimental results from two tasks of knowledge graph embedding prove that the proposed method not only incorporates new knowledge of new triples into the existing embedding successfully but also preserves the knowledge of the current embedding.
C1 [Song, Hyun-Je] Naver Corp, Search & Clova Team, Seongnam 13561, South Korea.
   [Park, Seong-Bae] Kyung Hee Univ, Dept Comp Sci & Engn, Yongin 17104, South Korea.
RP Park, SB (通讯作者)，Kyung Hee Univ, Dept Comp Sci & Engn, Yongin 17104, South Korea.
EM sbpark71@khu.ac.kr
OI Song, Hyun-Je/0000-0002-9109-2611
FU Institute for Information AMP; communications Technology Promotion
   (IITP) grant through the Korean Government (WiseKB: Big data based
   self-evolving knowledge base and reasoning platform) [2013-0-00109]
FX This work was supported by the Institute for Information &
   communications Technology Promotion (IITP) grant through the Korean
   Government (WiseKB: Big data based self-evolving knowledge base and
   reasoning platform) under Grant 2013-0-00109.
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NR 41
TC 7
Z9 7
U1 1
U2 21
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2018
VL 6
BP 60489
EP 60497
DI 10.1109/ACCESS.2018.2874656
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HA3ZE
UT WOS:000450196000001
OA gold
DA 2022-08-23
ER

PT J
AU Alvarez-Meaza, I
   Zarrabeitia-Bilbao, E
   Rio-Belver, RM
   Garechana-Anacabe, G
AF Alvarez-Meaza, Izaskun
   Zarrabeitia-Bilbao, Enara
   Rio-Belver, Rosa Maria
   Garechana-Anacabe, Gaizka
TI Fuel-Cell Electric Vehicles: Plotting a Scientific and Technological
   Knowledge Map
SO SUSTAINABILITY
LA English
DT Article
DE fuel cell electric vehicle; bibliometric analysis; patent analysis
ID COLLABORATION; HYDROGEN; SCIENCE; ENERGY; SUSTAINABILITY; CHINA;
   CONVERGENCE; EXPLORATION; TENDENCIES; INDICATORS
AB The fuel-cell electric vehicle (FCEV) has been defined as a promising way to avoid road transport greenhouse emissions, but nowadays, they are not commercially available. However, few studies have attempted to monitor the global scientific research and technological profile of FCEVs. For this reason, scientific research and technological development in the field of FCEV from 1999 to 2019 have been researched using bibliometric and patent data analysis, including network analysis. Based on reports, the current status indicates that FCEV research topics have reached maturity. In addition, the analysis reveals other important findings: (1) The USA is the most productive in science and patent jurisdiction; (2) both Chinese universities and their authors are the most productive in science; however, technological development is led by Japanese car manufacturers; (3) in scientific research, collaboration is located within the tri-polar world (North America-Europe-Asia-Pacific); nonetheless, technological development is isolated to collaborations between companies of the same automotive group; (4) science is currently directing its efforts towards hydrogen production and storage, energy management systems related to battery and hydrogen energy, Life Cycle Assessment, and greenhouse gas (GHG) emissions. The technological development focuses on technologies related to electrically propelled vehicles; (5) the International Journal of Hydrogen Energy and SAE Technical Papers are the two most important sources of knowledge diffusion. This study concludes by outlining the knowledge map and directions for further research.
C1 [Alvarez-Meaza, Izaskun; Zarrabeitia-Bilbao, Enara; Rio-Belver, Rosa Maria; Garechana-Anacabe, Gaizka] Univ Basque Country, Foresight Technol & Management FTM Grp, Dept Ind Org & Management Engn, Pl Ingeniero Torres Quevedo, Bilbao 48013, Spain.
RP Alvarez-Meaza, I (通讯作者)，Univ Basque Country, Foresight Technol & Management FTM Grp, Dept Ind Org & Management Engn, Pl Ingeniero Torres Quevedo, Bilbao 48013, Spain.
EM izaskun.alvarez@ehu.es; enara.zarrabeitia@ehu.eus;
   rosamaria.rio@ehu.eus; gaizka.garechana@ehu.eus
RI Rio-Belver, Rosa M/C-9742-2009; Alvarez-Meaza, Izaskun/AAA-2481-2021;
   Zarrabeitia-Bilbao, Enara/L-4671-2017
OI Rio-Belver, Rosa M/0000-0002-4244-9098; Alvarez-Meaza,
   Izaskun/0000-0002-2110-0719; Zarrabeitia-Bilbao,
   Enara/0000-0002-2347-3885; Garechana, Gaizka/0000-0002-1913-3239
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NR 74
TC 9
Z9 9
U1 9
U2 49
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAR 2
PY 2020
VL 12
IS 6
AR 2334
DI 10.3390/su12062334
PG 25
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 LA1YW
UT WOS:000523751400181
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Li, WD
   Peng, R
   Li, Z
AF Li, Weidong
   Peng, Rong
   Li, Zhi
TI Improving knowledge graph completion via increasing embedding
   interactions
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Knowledge graph completion; Interaction embeddings; Knowledge graph
   embedding; Inception network
AB Knowledge graphs usually consist of billions of triplet facts describing the real world. Although most of the existing knowledge graphs are huge in scale, they are still far from completion. As a result, varieties of knowledge graph embedding approaches have emerged, which have been proven to be an effective and efficient solution for knowledge graph completion. In this paper, we devise a novel knowledge graph embedding model named InterERP, which aims to improve model performance by increasing Inter actions between the embeddings of E ntities, R elations and relation P aths. Specifically, we first introduce the interaction matrix to obtain the interaction embeddings of entities and relations. Then, we employ the Inception network to learn the query embedding, which can further increase the interactions between entities and relations. Furthermore, we resort to logical rules to construct semantic relation paths and are committed to modeling the interactions between different relations in a relation path. The experimental results on four commonly used datasets, demonstrate that the proposed InterERP matches or outperforms the state-of-the-art approaches.
C1 [Li, Weidong; Li, Zhi] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
   [Peng, Rong] Guangxi Normal Univ, Coll Comp Sci & Informat Technol, Guilin, Guangxi, Peoples R China.
RP Peng, R (通讯作者)，Guangxi Normal Univ, Coll Comp Sci & Informat Technol, Guilin, Guangxi, Peoples R China.
EM weidonghappy@whu.edu.cn; rongpeng@whu.edu.cn; zhili@gxnu.edu.cn
OI Peng, Rong/0000-0003-2330-9409
FU National Key Research and Development Plan of China [2017YFB0503702,
   2016YFB0501801]; National Natural Science Foundation of China
   [61862009]; Guangxi Natural Science Foundation [2018GXNSFAA281314]
FX This work is supported by the National Key Research and Development Plan
   of China under Grant No. 2017YFB0503702, 2016YFB0501801, and National
   Natural Science Foundation of China under Grant No. 61862009, and
   Guangxi Natural Science Foundation under Grant No. 2018GXNSFAA281314.
   Rong Peng is the corresponding author of this paper.
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NR 49
TC 0
Z9 0
U1 17
U2 17
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
PD JUN
PY 2022
VL 52
IS 8
BP 9289
EP 9307
DI 10.1007/s10489-021-02947-6
EA JAN 2022
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1U7XE
UT WOS:000737750700004
DA 2022-08-23
ER

PT J
AU Weiss, J
   Hof, B
   Burkhatter, H
AF Weiss, Johanna
   Hof, Bernadette
   Burkhatter, Hanna
TI Interprofessional corporation between registered community nurses and
   general practitioner: A qualitative study
SO PFLEGE
LA German
DT Article; Early Access
DE Professional cooperation; outpatient; knowledge mapping group
   discussions; qualitative research
ID COLLABORATION
AB Background: The quality and effectiveness of the work of certified nurses and physicians in the inpatient setting is significantly influenced by interprofessional collaboration (IPC). There is a lack of knowledge about IPC in outpatient primary care at home. Aim: In highlighting the differences between inpatient and outpatient settings, the challenges of IPC for the outpatient setting should become clear. Method: The data for this study was collected in interprofessional group discussions. The data were methodically extracted by knowledge mapping and structuring content analysis according to Kuckartz (2018). The results were compared with the meta-matrix for interprofessional collaboration in inpatient settings by Scharli et al. (2017). Results: The qualitative analysis showed that the outpatient IPC of qualified nurses and general practitioners is challenged by structural conditions, which are given in the inpatient area by the form of organization. Lack of agreements and coordination of processes complicate IPC in outpatient health care, as there is no recognized common management structure. Conclusions: In order to ensure that IPC in the outpatient setting is not dependent on the individual commitment of individuals, nurses and GPs must jointly define criteria for their IPC, define processes and communication channels, and develop a common approach and team spirit.
C1 [Weiss, Johanna] Spitex Chur, Pflege, Gartenstr 2, CH-7000 Chur, Switzerland.
   [Hof, Bernadette] Spitex Oberthurgau, Pflege & Entwicklung, Amriswil, Switzerland.
   [Burkhatter, Hanna] Kantonsspital Graubunden, Dept Pflege & Fachsupport, Pflegeentwicklung, Chur, Switzerland.
RP Weiss, J (通讯作者)，Spitex Chur, Pflege, Gartenstr 2, CH-7000 Chur, Switzerland.
EM johanna.weiss@spitex-chur.ch
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NR 25
TC 0
Z9 0
U1 0
U2 0
PU HOGREFE AG-HOGREFE AG SUISSE
PI BERN
PA LANGGASSSTRASSE 76, BERN, SWITZERLAND
SN 1012-5302
EI 1664-283X
J9 PFLEGE
JI Pflege
DI 10.1024/1012-5302/a000900
EA JUL 2022
PG 10
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA 3I3XB
UT WOS:000832653000001
PM 35904460
DA 2022-08-23
ER

PT J
AU Zhong, DX
   Fan, JS
   Yang, GJ
   Tian, BZ
   Zhang, YK
AF Zhong, Daixing
   Fan, Jiashuang
   Yang, Gangjun
   Tian, Baozhen
   Zhang, Yukun
TI <p>Knowledge management of product design: A requirements-oriented
   knowledge management framework based on Kansei engineering and knowledge
   map</p>
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Knowledge innovation; Knowledge management; Kansei engineering;
   Knowledge map
ID DECISION-MAKING; REPRESENTATION; ACQUISITION; METHODOLOGY; EVOLUTION;
   SYSTEMS; OBJECT
AB For the knowledge management of product design, knowledge innovation is the foundation and motivation for the independent innovation and enhancing the core competitiveness. Most of the product knowledge exists in the brain of designers. How to obtain the required knowledge accurately in massive knowledge database becomes the key to knowledge innovation. However, the design knowledge based on consumer's requirement has not been extensively studied. There is no consensus on the reasonable and effective implementation of the knowledge management framework to select the optimum design knowledge based on the consumer's requirements. In this study, to efficiently realize the knowledge acquisition and knowledge selection, a requirements-oriented knowledge management model is established, with the advantage of Kansei engineering in knowledge acquisition and multi-objective decision-making in knowledge selection. Finally, the outdoor leisure chairs design is used as a case study to explain the implementation of the knowledge management framework. To reveal the advantages of the framework, it was compared with other frameworks. The results show that the proposed knowledge management framework is more efficient and provided a method of designers to acquire design knowledge based on the consumer's requirements.
C1 [Zhong, Daixing] AF Med Univ, Tangdu Hosp, Dept Thorac Surg, Xian 710038, Peoples R China.
   [Fan, Jiashuang; Yang, Gangjun] Taiyuan Univ Sci & Technol, Sch Art, Taiyuan 030024, Peoples R China.
   [Tian, Baozhen] Taiyuan Univ Sci & Technol, Sch Mech Engn, Taiyuan 030024, Peoples R China.
   [Zhang, Yukun] AF Med Univ, Tangdu Hosp, Dept Cardiol, Xian 710038, Peoples R China.
RP Fan, JS (通讯作者)，Taiyuan Univ Sci & Technol, Sch Art, Taiyuan 030024, Peoples R China.; Zhang, YK (通讯作者)，AF Med Univ, Tangdu Hosp, Dept Cardiol, Xian 710038, Peoples R China.
EM 1450630672@qq.com; 305679823@qq.com
FU Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry
   and Information Technology (Northwestern Polytechnical University,
   Xi'an, Shaanxi, China)
FX The authors would like to acknowledge the support of the Key Laboratory
   of Industrial Design and Ergonomics, Ministry of Industry and
   Information Technology (Northwestern Polytechnical University, Xi'an,
   Shaanxi, China) for their assistance in data collection and analysis.
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NR 59
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 1474-0346
EI 1873-5320
J9 ADV ENG INFORM
JI Adv. Eng. Inform.
PD APR
PY 2022
VL 52
AR 101541
DI 10.1016/j.aei.2022.101541
PG 18
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1K8DE
UT WOS:000798825800005
DA 2022-08-23
ER

PT J
AU Liu, G
   Tan, YT
   Huang, ZJ
AF Liu, Guo
   Tan, Yongtao
   Huang, Zhijia
TI Knowledge Mapping of Homeowners' Retrofit Behaviors: An Integrative
   Exploration
SO BUILDINGS
LA English
DT Review
DE existing residential buildings; energy retrofit; knowledge mapping;
   integrative framework
ID ENERGY-EFFICIENCY RETROFIT; RESIDENTIAL HEATING-SYSTEMS;
   DECISION-MAKING; GREEN RETROFIT; RENOVATIONS; BUILDINGS; REFURBISHMENT;
   PARTICIPATION; INVESTMENTS; MOTIVATIONS
AB Energy retrofitting of existing residential buildings has a great potential to achieve a sustainable future. One important way to reach this potential is to understand homeowners' retrofit behaviors due to their crucial roles in retrofit adoption and retrofit effects. Despite many attempts, researchers and governments still know less about the holistic profile of homeowners' retrofit behaviors, which brought little success in trigging renovation activities and achieving expected retrofit effects. This study tries to fill this research gap by a comprehensive review of the body of existing research. A keyword-based scientometric analysis was performed based on a set of 152 journal articles. By further refining keywords, main research domains pertaining to investment-decision-energy behaviors, policy instruments, retrofit types, construction & services, and methods & methodologies were mapped to show relevant research knowledge and research topics. Based on these research results, a further integrated framework was developed, which explains homeowners' retrofit behaviors in a systematic way of cross-disciplinary knowledge interactions. Furthermore, implications for retrofit policies in existing buildings were provided. This study is useful for facilitating future research to deepen homeowners' retrofit behaviors, and also provides valuable references for policy makers to successfully promote home energy retrofit.
C1 [Liu, Guo; Huang, Zhijia] Anhui Univ Technol, Sch Civil Engn & Architecture, Maanshan 243002, Peoples R China.
   [Tan, Yongtao] RMIT Univ, Sch Engn, GPO Box 2476, Melbourne, Vic 3001, Australia.
RP Liu, G (通讯作者)，Anhui Univ Technol, Sch Civil Engn & Architecture, Maanshan 243002, Peoples R China.; Tan, YT (通讯作者)，RMIT Univ, Sch Engn, GPO Box 2476, Melbourne, Vic 3001, Australia.
EM lgjycqu@163.com; yongtao.tan@rmit.edu.au; hzj@ahut.edu.cn
RI Tan, Yongtao/J-6829-2014
OI Tan, Yongtao/0000-0001-7321-4251
FU National Natural Science Foundation of China [72001003]
FX This research work was supported by the National Natural Science
   Foundation of China (No. 72001003).
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NR 90
TC 2
Z9 2
U1 4
U2 17
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-5309
J9 BUILDINGS-BASEL
JI BUILDINGS-BASEL
PD JUL
PY 2021
VL 11
IS 7
AR 273
DI 10.3390/buildings11070273
PG 17
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA TR8EJ
UT WOS:000679192900001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Wang, YQ
   Dong, LY
   Li, YL
   Zhang, H
AF Wang, YueQun
   Dong, LiYan
   Li, YongLi
   Zhang, Hao
TI Multitask feature learning approach for knowledge graph enhanced
   recommendations with RippleNet
SO PLOS ONE
LA English
DT Article
AB Introducing a knowledge graph into a recommender system as auxiliary information can effectively solve the sparse and cold start problems existing in traditional recommender systems. In recent years, many researchers have performed related work. A recommender system with knowledge graph embedding learning characteristics can be combined with a recommender system of the following three forms: one-by-one learning, joint learning, and alternating learning. For current knowledge graph embedding, a deep learning framework only has one embedding mode, which fails to excavate the potential information from the knowledge graph thoroughly. To solve this problem, this paper proposes the Ripp-MKR model, a multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet, which combines joint learning and alternating learning of knowledge graphs and recommender systems. Ripp-MKR is a deep end-to-end framework that utilizes a knowledge graph embedding task to assist recommendation tasks. Similar to the MKR model, in the Ripp-MKR model, two tasks are associated with cross and compress units, which automatically share latent features and learn the high-order interactions among items in recommender systems and entities in the knowledge graph. Additionally, the model borrows ideas from RippleNet and combines the knowledge graph with the historical interaction record of a user's historically clicked items to represent the user's characteristics. Through extensive experiments on real-world datasets, we demonstrate that Ripp-MKR achieves substantial gains over state-of-the-art baselines in movie, book, and music recommendations.
C1 [Wang, YueQun; Dong, LiYan; Zhang, Hao] Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China.
   [Dong, LiYan; Zhang, Hao] Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun, Peoples R China.
   [Li, YongLi] Northeast Normal Univ, Sch Comp Sci & Technol, Changchun, Peoples R China.
RP Zhang, H (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China.; Zhang, H (通讯作者)，Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun, Peoples R China.
EM zhangh@jlu.edu.cn
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NR 36
TC 5
Z9 5
U1 13
U2 43
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 MAY 14
PY 2021
VL 16
IS 5
AR e0251162
DI 10.1371/journal.pone.0251162
PG 21
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA ST0XR
UT WOS:000662175000023
PM 33989299
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Wang, S
   Huang, CC
   Li, JJ
   Yuan, Y
   Wang, FY
AF Wang, Shuai
   Huang, Chenchen
   Li, Juanjuan
   Yuan, Yong
   Wang, Fei-Yue
TI Decentralized Construction of Knowledge Graphs for Deep Recommender
   Systems Based on Blockchain-Powered Smart Contracts
SO IEEE ACCESS
LA English
DT Article
DE Decentralized knowledge graph; deep recommender system; crowdsourcing;
   blockchain; smart contracts
AB Since first coined by Google in 2012, knowledge graph has received extensive attention from both industry and academia, and has been widely used in many scenarios with success, e.g. information retrieval, online recommendation, question-answering, and so on. However, traditional centralized construction of knowledge graph faces many challenges, such as laborious and time-consuming, vulnerable to manipulation or tampering, lacking scrutiny, among others. Therefore, in this paper, we propose a novel decentralized knowledge graph construction method by means of crowdsourcing, and the business logic of crowdsourcing is implemented by blockchain-powered smart contracts to guarantee the transparency, integrity, and auditability. On this basis, the decentralized knowledge graph is used for a deep recommender system, and case studies validate the effectiveness of the system. This paper is aimed at providing a novel decentralized approach for constructing knowledge graph and serving as reference and guidance for future research and practical applications of knowledge graph.
C1 [Wang, Shuai; Huang, Chenchen; Li, Juanjuan; Yuan, Yong; Wang, Fei-Yue] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
   [Wang, Shuai; Huang, Chenchen; Wang, Fei-Yue] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Li, Juanjuan; Yuan, Yong; Wang, Fei-Yue] Qingdao Acad Intelligent Ind, Qingdao 266109, Shandong, Peoples R China.
RP Yuan, Y (通讯作者)，Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.; Yuan, Y (通讯作者)，Qingdao Acad Intelligent Ind, Qingdao 266109, Shandong, Peoples R China.
EM yong.yuan@ia.ac.cn
FU National Natural Science Foundation of China [61533019, 71702182];
   National Key R&D Program of China [2018AAA0101400]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61533019 and Grant 71702182, and in part
   by the National Key R&D Program of China under Grant 2018AAA0101400.
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NR 38
TC 21
Z9 24
U1 7
U2 31
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 136951
EP 136961
DI 10.1109/ACCESS.2019.2942338
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA JQ1HA
UT WOS:000498703100002
OA gold
DA 2022-08-23
ER

PT J
AU Li, X
   Liu, HY
   Zhao, X
   Zhang, GG
   Xing, CX
AF Li, Xin
   Liu, Haoyang
   Zhao, Xu
   Zhang, Guigang
   Xing, Chunxiao
TI Automatic approach for constructing a knowledge graph of knee
   osteoarthritis in Chinese
SO HEALTH INFORMATION SCIENCE AND SYSTEMS
LA English
DT Article
DE Electronic medical record; Entity recognition; Entity relation
   extraction; Knowledge graph; Knee osteoarthritis
ID EXTRACTION
AB In this study, a medical knowledge graph is constructed from the electronic medical record text of knee osteoarthritis patients to support intelligent medical applications such as knowledge retrieval and decision support, and to promote the sharing of medical resources. After constructing the domain ontology of knee osteoarthritis and manually labeling, we trained a machine learning model to automatically perform entity recognition and entity relation extraction, and then used a graph database to construct the knowledge graph of knee osteoarthritis. The experiment proves that the knowledge graph is comprehensive and reliable, and the knowledge graph construction method proposed in this study is effective.
C1 [Li, Xin] Tsinghua Univ, Beijing Tsinghua Changgung Hosp, Dept Rehabil, Med Ctr, Beijing, Peoples R China.
   [Liu, Haoyang] Beijing Univ Posts & Telecommun, Beijing, Peoples R China.
   [Zhao, Xu] Tsinghua Univ, Inst Internet Ind, Dept Comp Sci & Technol, RIIT,BNRist, Beijing 100084, Peoples R China.
   [Zhang, Guigang] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China.
   [Xing, Chunxiao] Tsinghua Univ, Dept Comp Sci & Technol, RIIT, TNList, Beijing, Peoples R China.
RP Li, X (通讯作者)，Tsinghua Univ, Beijing Tsinghua Changgung Hosp, Dept Rehabil, Med Ctr, Beijing, Peoples R China.
EM horsebackdancing@sina.com
FU NSFC [91646202]; National Key RD Program of China [2018YFB1404400,
   2018YFB1402700]
FX This work was supported by NSFC (91646202), National Key RD Program of
   China (2018YFB1404400, 2018YFB1402700).
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NR 12
TC 9
Z9 10
U1 17
U2 87
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2047-2501
J9 HEALTH INF SCI SYST
JI Health Inf. Sci. Syst.
PD FEB 27
PY 2020
VL 8
IS 1
AR 12
DI 10.1007/s13755-020-0102-4
PG 8
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA KV6LM
UT WOS:000520593800002
PM 32175080
OA Green Published
DA 2022-08-23
ER

PT J
AU Hertling, S
   Paulheim, H
AF Hertling, Sven
   Paulheim, Heiko
TI DBkWik: extracting and integrating knowledge from thousands of Wikis
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph creation; Information extraction; Linked open data;
   Knowledge graph matching
AB Popular cross-domain knowledge graphs, such as DBpedia and YAGO, are built from Wikipedia, and therefore similar in coverage. In contrast, Wikifarms like Fandom contain Wikis for specific topics, which are often complementary to the information contained in Wikipedia, and thus DBpedia and YAGO. Extracting these Wikis with the DBpedia extraction framework is possible, but results in many isolated knowledge graphs. In this paper, we show how to create one consolidated knowledge graph, called DBkWik, from thousands of Wikis. We perform entity resolution and schema matching, and show that the resulting large-scale knowledge graph is complementary to DBpedia. Furthermore, we discuss the potential use of DBkWik as a benchmark for knowledge graph matching.
C1 [Hertling, Sven; Paulheim, Heiko] Univ Mannheim, Data & Web Sci Grp, Mannheim, Germany.
RP Hertling, S (通讯作者)，Univ Mannheim, Data & Web Sci Grp, Mannheim, Germany.
EM sven@informatik.uni-mannheim.de; heiko@informatik.uni-tnannheim.de
OI Hertling, Sven/0000-0003-0333-5888; Paulheim, Heiko/0000-0003-4386-8195
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NR 53
TC 6
Z9 6
U1 10
U2 20
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 JUN
PY 2020
VL 62
IS 6
BP 2169
EP 2190
DI 10.1007/s10115-019-01415-5
EA NOV 2019
PG 22
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LK4NO
UT WOS:000493668300001
OA hybrid
DA 2022-08-23
ER

PT J
AU Zhou, W
   Xu, ZS
   Skackauskas, P
AF Zhou, Wei
   Xu, Zeshui
   Skackauskas, Paulius
TI MAPPING KNOWLEDGE DOMAIN OF "TRANSPORT": A BIBLIOMETRIC STUDY OF ITS
   STATUS QUO AND EMERGING TRENDS
SO TRANSPORT
LA English
DT Article
DE transport; publishing; bibliometric; knowledge map; emerging trend
ID DECISION-MAKING; EVOLUTION; ECONOMY; SYSTEMS
AB Transport plays an important role in human society. Due to its significance, the research journal TRANSPORT was established with the aim of developing and reinforcing the performance of national transport system based on theoretical and empirical investigations. The analyses such as transport policy, transport system fundamentals, multiple transport methods and traffic safety are all within the research scope of TRANSPORT. Therefore, this paper focuses on learning the development of this multidisciplinary journal by using the bibliometric tools. After analysing 704 papers published from January 2007 to June 2019 on TRANSPORT, the development of this journal is comprehensively analysed from the perspectives of its status quo and emerging trends. Specifically, the current status is introduced based on the general journal information, the publication and citation number, the citation structure and the significant contributors in terms of author, journal, institution and country. As for the emerging trends, the citation burst detections and the timeline view analysis are presented to give some deep insights of the hot research streams in certain time periods. This paper makes the contribution to provide a knowledge map of the journal TRANSPORT's research domain to help researchers learn this journal and transport-related issues clearly and directly. It can be also considered as the reference source for further investigations.
C1 [Zhou, Wei] Yunnan Univ Finance & Econ, Sch Finance, Kunming, Yunnan, Peoples R China.
   [Xu, Zeshui] Sichuan Univ, Business Sch, State Key Lab Hydraul & Mt River Engn, Chengdu, Sichuan, Peoples R China.
   [Skackauskas, Paulius] Vilnius Gediminas Tech Univ, Lab Transport Engn & Logist, Vilnius, Lithuania.
RP Xu, ZS (通讯作者)，Sichuan Univ, Business Sch, State Key Lab Hydraul & Mt River Engn, Chengdu, Sichuan, Peoples R China.
EM xuzeshui@263.net
OI Xu, Zeshui/0000-0003-3547-2908; Skackauskas, Paulius/0000-0002-2082-9372
FU Natural Science Foundation of China [71561026, 71571123, 71840001]
FX This work was supported by the Natural Science Foundation of China (No
   71561026, No 71571123 and No 71840001).
CR Audi R., 2015, CAMBRIDGE DICT PHILO
   Brauers WKM, 2008, TRANSPORT-VILNIUS, V23, P183, DOI 10.3846/1648-4142.2008.23.183-193
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NR 27
TC 4
Z9 4
U1 4
U2 14
PU VILNIUS GEDIMINAS TECH UNIV
PI VILNIUS
PA SAULETEKIO AL 11, VILNIUS, LT-10223, LITHUANIA
SN 1648-4142
EI 1648-3480
J9 TRANSPORT-VILNIUS
JI Transport
PY 2019
VL 34
IS 6
BP 741
EP 753
DI 10.3846/transport.2019.11774
PG 13
WC Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Transportation
GA KA1YQ
UT WOS:000505595300010
OA gold
DA 2022-08-23
ER

PT J
AU Chen, LS
   Wei, Q
   Li, J
   Liao, DX
   Feng, DL
AF Chen, Lisu
   Wei, Qiong
   Li, Jie
   Liao, Dexiang
   Feng, Daolun
TI A scientometric visualization analysis for global toxicology and
   pharmacology research of natural products from 1962 to 2018
SO PHYTOMEDICINE
LA English
DT Article
DE Bibliometric analysis; TPNP; Knowledge map; Medicine
ID BIBLIOMETRIC ANALYSIS; SCIENTIFIC PUBLICATIONS; INFORMATION SCIENCES;
   KNOWLEDGE; CATEGORY; WEB
AB Background: A growing number of studies have been focused on the medicinal potential of natural products since 1962, while few scholars have analyzed the existing documents comprehensively.
   Purpose: Aiming to visualize the researches on toxicology and pharmacology of natural products (TPNP) published between 1962 and 2018, as well as to reveal their spatiotemporal patterns, a scientometric analysis with 3210 relevant documents collected from Web of Science was conducted in this study.
   Results: The most prominent contributors of TPNP research are mainly from the USA, China, Brazil, India and Germany. The knowledge domains of TPNP research focus mainly on the topics of (1) traditional Chinese medicine, (2) richardia grandiflora, (3) chemical conversion, (4) new generation, (5) modern medicine, (6) intelligent mixture, (7) hplc-based activity. Most countries have recognized the pharmaceutical potential of natural products, and have paid more attention to the pharmacological and toxicological characteristics of natural products in the past decade. Future TPNP research tends to focus more on complex analysis of mechanisms for diseases treatment, such as toxicology and pharmacology.
   Conclusion: This research has firstly demonstrated a comprehensive knowledge map for the existing toxicological and pharmacological researches of natural products, which offered essential instructions on medical application of natural products to future research.
C1 [Chen, Lisu; Wei, Qiong; Li, Jie; Liao, Dexiang; Feng, Daolun] Shanghai Maritime Univ, Coll Ocean Sci & Engn, Shanghai, Peoples R China.
RP Li, J (通讯作者)，Shanghai Maritime Univ, Coll Ocean Sci & Engn, Shanghai, Peoples R China.
EM cls512@zju.edu.cn; weiqiong2019@163.com; jiel@shmtu.edu.cn;
   dxliao@shmtu.edu.cn; dlfeng@shmtu.edu.cn
FU National Natural Science Foundation of China [31801255]; Principal
   Investigator for Zhejiang Province Postdoctoral Fund
FX This research was funded by the National Natural Science Foundation of
   China (No.31801255) and the Principal Investigator for Zhejiang Province
   Postdoctoral Fund(No.BSH1502132). Additionally, I would like to express
   my gratitude to Shihan Huang for the preliminary edition of English
   language.
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NR 53
TC 3
Z9 3
U1 4
U2 23
PU ELSEVIER GMBH
PI MUNICH
PA HACKERBRUCKE 6, 80335 MUNICH, GERMANY
SN 0944-7113
EI 1618-095X
J9 PHYTOMEDICINE
JI Phytomedicine
PD MAR
PY 2020
VL 68
AR 153190
DI 10.1016/j.phymed.2020.153190
PG 14
WC Plant Sciences; Chemistry, Medicinal; Integrative & Complementary
   Medicine; Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Plant Sciences; Pharmacology & Pharmacy; Integrative & Complementary
   Medicine
GA LD2GA
UT WOS:000525848500028
PM 32109739
DA 2022-08-23
ER

PT J
AU Huang, J
   Zhang, TH
   Zhu, J
   Yu, WH
   Tang, Y
   He, Y
AF Huang, Jin
   Zhang, TingHua
   Zhu, Jia
   Yu, Weihao
   Tang, Yong
   He, Yang
TI A deep embedding model for knowledge graph completion based on attention
   mechanism
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Knowledge graph; Knowledge graph completion; Link prediction; Attention
   mechanism
AB Knowledge graph completion has become a well-studied problem and a non-trivial task with the broad application of the knowledge graphs. Previously, a lot of works have been proposed to solve the knowledge graph completion problem, for example, a series of Trans model, semantic matching models, convolutional neural networks based methods and so on. However, a series of Trans models and semantic matching models only focused on the shadow information of the knowledge graph, thus failed to capture the implicit fine-grained feature in the triple of knowledge graphs; convolutional neural networks based methods learned more expressive feature for knowledge graph completion, and it also ignored the directional relation characteristic and implicit fine-grained feature in the triple. In this paper, we propose a novel knowledge graph completion model named directional multi-dimensional attention convolution model that explores directional information and an inherent deep expressive characteristic of the triple. At last, we evaluate our directional multi-dimensional attention convolution model based on three standard evaluation criteria in two robust datasets, and the experiment shows that our model achieves state-of-the-art MeanRank.
C1 [Huang, Jin; Zhang, TingHua; Yu, Weihao; Tang, Yong] South China Normal Univ, 55 Zhongshan Ave West, Guangzhou, Peoples R China.
   [Zhu, Jia] ZheJiang Normal Univ, 688 Yingbin Ave, Jinhua, Zhejiang, Peoples R China.
   [He, Yang] CEIEC, Tower A,Guohai Plaza 17,Fuxing Rd, Beijing 100036, Peoples R China.
RP Zhu, J (通讯作者)，ZheJiang Normal Univ, 688 Yingbin Ave, Jinhua, Zhejiang, Peoples R China.
EM huangjin@m.scnu.edu.cn; tinghuazhang@m.scnu.edu.cn; jzhu@m.scnu.edu.cn;
   whyu@m.scnu.edu.cn; ytang@m.scnu.edu.cn; heyang@ceiec.com.cn
OI Zhu, Jia/0000-0002-5959-390X; Yu, Weihao/0000-0003-0727-4744
FU National Natural Science Foundation of China [61877020, 62077015]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 61877020 and 62077015.
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NR 28
TC 2
Z9 2
U1 15
U2 40
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
PD AUG
PY 2021
VL 33
IS 15
BP 9751
EP 9760
DI 10.1007/s00521-021-05742-z
EA MAR 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TG5EF
UT WOS:000629516200006
DA 2022-08-23
ER

PT J
AU Wu, TX
   Qi, GL
   Li, C
   Wang, M
AF Wu, Tianxing
   Qi, Guilin
   Li, Cheng
   Wang, Meng
TI A Survey of Techniques for Constructing Chinese Knowledge Graphs and
   Their Applications
SO SUSTAINABILITY
LA English
DT Article
DE knowledge graph; intelligent applications; OBOR
AB With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the "One Belt One Road (OBOR)" initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.
C1 [Wu, Tianxing; Qi, Guilin; Li, Cheng; Wang, Meng] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Jiangsu, Peoples R China.
   [Wu, Tianxing] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore.
RP Qi, GL (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Jiangsu, Peoples R China.
EM wutianxing@seu.edu.cn; gqi@seu.edu.cn; licheng1205@seu.edu.cn;
   wangmengsd@outlook.com
FU National Natural Science Foundation of China (NSFC) [U1736204]
FX This research was funded by National Natural Science Foundation of China
   (NSFC) under Grant No. U1736204.
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NR 33
TC 16
Z9 20
U1 67
U2 231
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD SEP
PY 2018
VL 10
IS 9
AR 3245
DI 10.3390/su10093245
PG 26
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 GW3DA
UT WOS:000446770200288
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zhang, TT
   Ye, Y
   Peng, YZ
   Ren, RY
   Ye, MN
   Zhang, Q
AF Zhang, Tingting
   Ye, Ying
   Peng, Yangzhi
   Ren, Runyuan
   Ye, Manni
   Zhang, Qi
TI Knowledge mapping visualization analysis of Tai chi research published
   in web of science over the past 20 years
SO INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL MEDICINE
LA English
DT Article
DE Tai chi; knowledge mapping; visualization analysis; CiteSpace; hotpot
ID QUALITY-OF-LIFE; BONE-MINERAL DENSITY; CHRONIC HEART-FAILURE;
   OLDER-ADULTS; FALL PREVENTION; MUSCLE STRENGTH; CHALLENGES INHERENT;
   ARTERIAL COMPLIANCE; PARKINSONS-DISEASE; EXERCISE PROGRAM
AB Objective: The aim of this study was to demonstrate state of the art trends concerning the worldwide use of Tai chi in prevention, treatment, and rehabilitation. Methods: Literature relating to Tai chi from 1999 to 2018 was retrieved from Web of Science Core Collection (WoSCC). CiteSpace was used to analyze authors, institutions, countries, keywords, cited references/authors/journals, core journals, active authors, foundation references, hot topics, and frontiers, according to time period. Results: A total of 794 references were obtained. The total number of publications increased before 2014 and decreased in the past 4 years. Journal articles were the only document type included. The most productive countries and institutions in this field were the USA (334), Chinese University of Hongkong (38), and Harvard University (37), respectively. Evidence-Based Complementary and Alternative Medicine (27) was the most productive journal. Journal of the American Geriatrics Society (497) was the most co-cited journal, which reflected the nature of the research. The six hot topics of Tai chi included balance, musculoskeletal system, chronic diseases, psychological well-being, research methods, and cost-effectiveness. Conclusion: This study provides insight into Tai chi, containing valuable information for researchers in identifying new perspectives on potential collaborators and cooperative institutions, hot topics, and research frontiers.
C1 [Zhang, Tingting; Ye, Ying; Peng, Yangzhi; Ren, Runyuan; Zhang, Qi] Chengdu Univ Tradit Chinese Med, Basic Med Sch, 37 Shi Er Qiao Rd, Chengdu 610075, Sichuan, Peoples R China.
   [Ye, Manni] Wuhan Univ, Inst Educ Sci, Wuhan 430072, Hubei, Peoples R China.
RP Zhang, Q (通讯作者)，Chengdu Univ Tradit Chinese Med, Basic Med Sch, 37 Shi Er Qiao Rd, Chengdu 610075, Sichuan, Peoples R China.
EM zhangqi@cdutcm.edu.cn
FU National Natural Science Foundation of China [61801058]
FX The authors would like to express their appreciation to professor CM
   Chen. He invented CiteSpace, which is free to use. Tingting Zhang and
   Ying Ye are co-first authors. ZTT and YY collected the literature and
   wrote the article. PYZ, RRY, and YMN revised the article, while ZQ
   designed the study. All authors read and approved the final article. All
   authors contributed toward data analysis, drafting, and critically
   revising the paper and agree to be accountable for all aspects of the
   work. This work was supported by a grant from National Natural Science
   Foundation of China (No 61801058).
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NR 83
TC 0
Z9 0
U1 7
U2 23
PU E-CENTURY PUBLISHING CORP
PI MADISON
PA 40 WHITE OAKS LN, MADISON, WI 53711 USA
SN 1940-5901
J9 INT J CLIN EXP MED
JI Int. J. Clin. Exp. Med.
PY 2019
VL 12
IS 6
BP 6617
EP 6635
PG 19
WC Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Research & Experimental Medicine
GA IF7UF
UT WOS:000473289500018
DA 2022-08-23
ER

PT J
AU Zhao, XF
   Lei, M
   Gu, RY
AF Zhao, Xiaofeng
   Lei, Mei
   Gu, Runyao
TI Knowledge Mapping of the Phytoremediation of Cadmium-Contaminated Soil:
   A Bibliometric Analysis from 1994 to 2021
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE phytoremediation; cadmium; hyperaccumulator; bibliometric analysis; Web
   of Science
ID HEAVY-METAL POLLUTION; SEDUM-PLUMBIZINCICOLA; BACTERIAL ENDOPHYTES;
   THLASPI-CAERULESCENS; AGRICULTURAL SOILS; CHINA POLLUTION;
   SOLANUM-NIGRUM; PLANT-GROWTH; TOLERANCE; BIOCHAR
AB Cadmium pollution of soil threatens the environmental quality and human health. Phytoremediation of cadmium-contaminated soil has attracted global attention in recent decades. This study aimed to conduct a comprehensive and systematic review of the literature on phytoremediation of cadmium-contaminated soil based on bibliometric analysis. A total of 5494 articles published between 1994 and 2021 were retrieved from the Web of Science Core Collection. Our knowledge mapping presented the authors, journals, countries, institutions, and other basic information to understand the development status of phytoremediation of cadmium-contaminated soil. Based on a keyword cluster analysis, the identified major research domains were "biochar", "Thlaspi caerulescens", "endophytic bacteria", "oxidative stress", "EDTA", and "bioconcentration factor". Overall, this study provided a detailed summary of research trends and hotspots. Based on the keyword co-occurrence and burst analysis, the core concepts and basic theories of this field were completed in 2011. However, the pace of theoretical development has been relatively slow. Finally, future research trends/frontiers were proposed, such as biochar addition, rhizosphere bacterial community manipulation, cadmium subcellular distribution, and health risk assessment.
C1 [Zhao, Xiaofeng; Lei, Mei] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China.
   [Zhao, Xiaofeng; Lei, Mei] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Gu, Runyao] Guizhou Minzu Univ, Coll Ecoenvironm Engn, Guiyang 550025, Peoples R China.
RP Lei, M (通讯作者)，Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China.; Lei, M (通讯作者)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
EM zhaoxf.15b@igsnrr.ac.cn; leim@igsnrr.ac.cn; gu.ry@gzmu.edu.cn
OI Zhao, Xiaofeng/0000-0002-2790-0135
FU Innovation Academy for Green Manufacture of the Chinese Academy of
   Sciences [IAGM-2019-A16-5]
FX This research was funded by Innovation Academy for Green Manufacture of
   the Chinese Academy of Sciences, grant number IAGM-2019-A16-5.
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NR 105
TC 0
Z9 0
U1 18
U2 18
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD JUN
PY 2022
VL 19
IS 12
AR 6987
DI 10.3390/ijerph19126987
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 2O5LA
UT WOS:000819098200001
PM 35742236
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Paez, O
   Vilches-Blazquez, LM
AF Paez, Oswaldo
   Vilches-Blazquez, Luis M.
TI Bringing Federated Semantic Queries to the GIS-Based Scenario
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE GeoSPARQL; SPARQL; federated query; knowledge graph; geospatial data
ID LINKED DATA; SPATIAL DATA; WEB; INFORMATION; LINKEDGEODATA
AB Geospatial data is increasingly being made available on the Web as knowledge graphs using Linked Data principles. This entails adopting the best practices for publishing, retrieving, and using data, providing relevant initiatives that play a prominent role in the Web of Data. Despite the appropriate progress related to the amount of geospatial data available, knowledge graphs still face significant limitations in the GIScience community since their use, consumption, and exploitation are scarce, especially considering that just a few developments retrieve and consume geospatial knowledge graphs from within GIS. To overcome these limitations and address some critical challenges of GIScience, standards and specific best practices for publishing, retrieving, and using geospatial data on the Web have appeared. Nevertheless, there are few developments and experiences that support the possibility of expressing queries across diverse knowledge graphs to retrieve and process geospatial data from different and distributed sources. In this scenario, we present an approach to request, retrieve, and consume (geospatial) knowledge graphs available at diverse and distributed platforms, prototypically implemented on Apache Marmotta, supporting SPARQL 1.1 and GeoSPARQL standards. Moreover, our approach enables the consumption of geospatial knowledge graphs through a lightweight web application or QGIS. The potential of this work is shown with two examples that use GeoSPARQL-based knowledge graphs.
C1 [Paez, Oswaldo] Inst Politecn Nacl, Unidad Profes Interdisciplinaria Ingn & Tecnol Av, Mexico City 07340, DF, Mexico.
   [Vilches-Blazquez, Luis M.] Inst Politecn Nacl, Ctr Invest Comp, Av Miguel Othon Mendizabal S-N, Mexico City 07738, DF, Mexico.
RP Vilches-Blazquez, LM (通讯作者)，Inst Politecn Nacl, Ctr Invest Comp, Av Miguel Othon Mendizabal S-N, Mexico City 07738, DF, Mexico.
EM opaezo1500@alumno.ipn.mx; lmvilches@cic.ipn.mx
OI Vilches-Blazquez, Luis M./0000-0001-5799-469X
FU Instituto Panamericano de Geografia e Historia, Red
   GeoLIBERO-Consolidacion de una red de geomatica libre aplicada a las
   necesidades de Iberoamerica (CYTED) [520RT0010]; Generacion de grafos de
   conocimiento sobre eventos meteorologicos urbanos [SIP-IPN 20210677]
FX This work was partially supported by the Instituto Panamericano de
   Geografia e Historia, Red GeoLIBERO-Consolidacion de una red de
   geomatica libre aplicada a las necesidades de Iberoamerica (CYTED
   program-520RT0010), and Generacion de grafos de conocimiento sobre
   eventos meteorologicos urbanos (SIP-IPN 20210677).
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NR 53
TC 0
Z9 0
U1 0
U2 0
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 2022
VL 11
IS 2
AR 86
DI 10.3390/ijgi11020086
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 0H0TA
UT WOS:000778451700001
OA gold
DA 2022-08-23
ER

PT J
AU He, L
   Shao, B
   Xiao, YH
   Li, YT
   Liu, TY
   Chen, EH
   Xia, HH
AF He, Liang
   Shao, Bin
   Xiao, Yanghua
   Li, Yatao
   Liu, Tie-Yan
   Chen, Enhong
   Xia, Huanhuan
TI Neurally-Guided Semantic Navigation in Knowledge Graph
SO IEEE TRANSACTIONS ON BIG DATA
LA English
DT Article
DE Semantic navigation; knowledge graph; path finding; neural network
ID NETWORKS
AB In this big data era, knowledge becomes increasingly linked, along with the rapid growth in data volume. Connected knowledge is naturally represented and stored as knowledge graphs, which are of more and more importance for many frontier research areas such as machine intelligence. Effectively finding relations between entities in a large knowledge graph plays a key role in many knowledge graph applications, as the most valuable part of a knowledge graph is its rich connectedness, which captures rich information about the objects in the real world. However, due to the intrinsic complexity of real-world knowledge, finding semantically close relations by navigation in a large knowledge graph is very challenging. Canonical graph exploration methods inevitably result in combinatorial explosion especially when the paths connecting two entities are long: the search space is O(d(l)), where d is the average graph node degree and l is the length of the path. In this paper, we will systematically study the semantic navigation problem for large knowledge graphs. Inspired by AlphaGo, which was overwhelmingly successful in the game Go, we designed an efficient semantic navigation method based on a well-tailored Monte Carlo Tree Search algorithm with the unique characteristics of knowledge graphs considered. Extensive experiments on different real-life knowledge bases show that our method is not only effective but also very efficient.
C1 [He, Liang; Chen, Enhong] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Anhui, Peoples R China.
   [Shao, Bin; Li, Yatao; Liu, Tie-Yan; Xia, Huanhuan] Microsoft Res Asia, Beijing 100080, Peoples R China.
   [Xiao, Yanghua] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China.
RP Chen, EH (通讯作者)，Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Anhui, Peoples R China.
EM hshl05@mail.ustc.edu.cn; binshao@microsoft.com; shawyh@fudan.edu.cn;
   yatli@microsoft.com; tie-yan.liu@microsoft.com; cheneh@ustc.edu.cn;
   lexi@microsoft.com
RI XIAO, YANG/GPW-5529-2022
OI Chen, Enhong/0000-0002-4835-4102
FU National Science Foundation for Distinguished Young Scholars of China
   [61325010]; National Natural Science Foundation of China [U1605251];
   National Key Foundation for Exploring Scientific Instrument of China
   [61727809]; National NSFC [61472085, U1509213]; Shanghai Municipal
   Science and Technology Commission foundation key project [15JC1400900];
   Shanghai Municipal Science and Technology project [16511102102,
   16JC1420401]
FX This work was partially supported by grants from the National Science
   Foundation for Distinguished Young Scholars of China (Grant No.
   61325010), the National Natural Science Foundation of China (Grant No.
   U1605251), and the National Key Foundation for Exploring Scientific
   Instrument of China (Grant No. 61727809). Yanghua Xiao was supported by
   the National NSFC (No. 61472085, U1509213), by Shanghai Municipal
   Science and Technology Commission foundation key project under No.
   15JC1400900, by Shanghai Municipal Science and Technology project under
   No. 16511102102, No. 16JC1420401. This work was done in Microsoft
   Research Asia.
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NR 36
TC 3
Z9 3
U1 4
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2332-7790
J9 IEEE T BIG DATA
JI IEEE Trans. Big Data
PD JUN 1
PY 2022
VL 8
IS 3
BP 607
EP 615
DI 10.1109/TBDATA.2018.2805363
PG 9
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1F3YX
UT WOS:000795107500003
DA 2022-08-23
ER

PT J
AU Agibetov, A
   Samwald, M
AF Agibetov, Asan
   Samwald, Matthias
TI Benchmarking neural embeddings for link prediction in knowledge graphs
   under semantic and structural changes
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge graphs; Neural embeddings; Benchmarks; Evaluation; Link
   prediction
AB Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion. While algorithmic advances have strongly focused on efficient ways of learning embeddings, fewer attention has been drawn to the different ways their performance and robustness can be evaluated. In this work we propose an open-source evaluation pipeline, which benchmarks the accuracy of neural embeddings in situations where knowledge graphs may experience semantic and structural changes. We define relation-centric connectivity measures that allow us to connect the link prediction capacity to the structure of the knowledge graph. Such an evaluation pipeline is especially important to simulate the accuracy of embeddings for knowledge graphs that are expected to be frequently updated. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Agibetov, Asan; Samwald, Matthias] Med Univ Vienna, Sect Artificial Intelligence & Decis Support, Vienna, Austria.
RP Samwald, M (通讯作者)，Med Univ Vienna, Sect Artificial Intelligence & Decis Support, Vienna, Austria.
EM asan.agibetov@meduniwien.ac.at
RI Agibetov, Asan/AAE-5207-2021
OI Agibetov, Asan/0000-0003-0096-0143; Samwald,
   Matthias/0000-0002-4855-2571
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NR 29
TC 3
Z9 3
U1 2
U2 25
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 100590
DI 10.1016/j.websem.2020.100590
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 NG1UI
UT WOS:000563771800003
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Huang, WJ
   Wu, JH
   Song, WH
   Wang, ZH
AF Huang, Weijian
   Wu, Jianhua
   Song, Weihu
   Wang, Zehua
TI Cross attention fusion for knowledge graph optimized recommendation
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Recommendation systems; Knowledge graph; Multi-task learning; Feature
   cross
AB Knowledge Graph has attracted a wide range of attention in the field of recommendation, which is usually applied as auxiliary information to solve the problem of data sparsity. However, most recommendation models cannot effectively mine the associations between the items to be recommended and the entities in the Knowledge Graph. In this paper, we propose CAKR, a knowledge graph recommendation method based on the cross attention unit, which is similar to MKR, a multi-task feature learning general framework that uses knowledge graph embedding tasks to assist recommendation tasks. Specifically, we design a new method to optimize the feature interaction between the items and the corresponding entities in the Knowledge Graph and propose a feature cross-unit combined with the attention mechanism to enhance the recommendation effect. Through extensive experiments on the public datasets of movies, books, and music, we prove that CAKR is better than MKR and other knowledge graph recommendation methods so that the new feature cross-unit designed in this paper is effective in improving the accuracy of the recommendation system.
C1 [Huang, Weijian; Wu, Jianhua; Song, Weihu; Wang, Zehua] Hebei Univ Engn, Sch Informat & Elect Engn, Handan 056038, Peoples R China.
RP Wu, JH (通讯作者)，Hebei Univ Engn, Sch Informat & Elect Engn, Handan 056038, Peoples R China.
EM cutem0615@gmail.com
OI Wu, Jianhua/0000-0002-9257-2350
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
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NR 27
TC 0
Z9 0
U1 26
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 JUL
PY 2022
VL 52
IS 9
BP 10297
EP 10306
DI 10.1007/s10489-021-02930-1
EA JAN 2022
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2I2IM
UT WOS:000741961900004
DA 2022-08-23
ER

PT J
AU Chen, Z
   Wang, YH
   Zhao, B
   Cheng, J
   Zhao, X
   Duan, ZT
AF Chen, Zhe
   Wang, Yuehan
   Zhao, Bin
   Cheng, Jing
   Zhao, Xin
   Duan, Zongtao
TI Knowledge Graph Completion: A Review
SO IEEE ACCESS
LA English
DT Review
DE Cognition; Computational modeling; Semantics; Task analysis; Knowledge
   representation; Natural language processing; Knowledge graph; knowledge
   graph completion; entity prediction; relation prediction; deep learning
ID REPRESENTATIONS; COMBINATION
AB Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and related applications, which aims to complete the structure of knowledge graph by predicting the missing entities or relationships in knowledge graph and mining unknown facts. Starting from the definition and types of KGC, existing technologies for KGC are analyzed in categories. From the evolving point of view, the KGC technologies could be divided into traditional and representation learning based methods. The former mainly includes rule-based reasoning method, probability graph model, such as Markov logic network, and graph computation based method. The latter further includes translation model based, semantic matching model based, representation learning based and other neural network model based methods. In this article, different KGC technologies are introduced, including their advantages, disadvantages and applicable fields. Finally the main challenges and problems faced by the KGC are discussed, as well as the potential research directions.
C1 [Chen, Zhe; Wang, Yuehan; Zhao, Bin; Cheng, Jing; Zhao, Xin; Duan, Zongtao] Changan Univ, Sch Informat Engn, Xian 710064, Peoples R China.
RP Duan, ZT (通讯作者)，Changan Univ, Sch Informat Engn, Xian 710064, Peoples R China.
EM ztduana@chd.edu.cn
OI Chen, Zhe/0000-0003-2286-4988
FU Funds for Key Research and Development Plan Project of the Shaanxi
   Province, China [2017GY-072, 2019ZDLGY17-08, 2020ZDLGY09-02]
FX This work was supported in part by the Funds for Key Research and
   Development Plan Project of the Shaanxi Province, China, under Grant
   2017GY-072, Grant 2019ZDLGY17-08, and Grant 2020ZDLGY09-02.
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NR 119
TC 7
Z9 9
U1 66
U2 157
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 192435
EP 192456
DI 10.1109/ACCESS.2020.3030076
PG 22
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA OK6YT
UT WOS:000584794100001
OA gold
DA 2022-08-23
ER

PT J
AU Dudezert, A
   Leidner, DE
AF Dudezert, Aurelie
   Leidner, Dorothy E.
TI Illusions of control and social domination strategies in knowledge
   mapping system use
SO EUROPEAN JOURNAL OF INFORMATION SYSTEMS
LA English
DT Article
DE knowledge management; knowledge management systems; illusion of control;
   social domination; case study
ID INFORMATION-SYSTEMS; DYNAMIC THEORY; COMPETENCE; MANAGEMENT;
   PERSPECTIVE; TECHNOLOGY; PRINCIPLES
AB In recent years, the overload of information has driven companies to develop visualization technologies as a way to graphically represent knowledge for decision making. On the basis of visualization technologies and on knowledge modeling techniques, knowledge maps (Kmaps) help represent the knowledge assets of a company as graphical discrete objects. Yet such systems, because of their visual representation of knowledge, are prone to creating illusions. Using a case study of three firms' use of Kmaps in their Human Resource departments and drawing upon Bourdieu's social theory, illusion of control theory, and IS as signal and symbol theory, our study provides a critical perspective on the use of Kmaps by groups of actors to reinforce their social domination in business organizations. We find that Kmap use can lead to illusions of knowledge control and illusions of executive control and that in response to the potential for knowledge illusions, three social domination strategies a signal and symbol strategy, an avoidance strategy and an evidence strategy are employed. Implications of these results on Knowledge Management and Information Systems Management future research are discussed. European Journal of Information Systems (2011) 20, 574-588. doi:10.1057/ejis.2011.17; published online 31 May 2011
C1 [Dudezert, Aurelie] Lab Genie Ind Ecole Cent Paris, Knowledge Management Res Grp, F-92295 Chatenay Malabry, France.
   [Leidner, Dorothy E.] Baylor Univ, Hankamer Sch Business, Waco, TX 76798 USA.
RP Dudezert, A (通讯作者)，Lab Genie Ind Ecole Cent Paris, Knowledge Management Res Grp, F-92295 Chatenay Malabry, France.
EM aurelie.dudezert@ecp.fr
RI Leidner, Dorothy E/F-5746-2014
OI Leidner, Dorothy E/0000-0002-7159-6273
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NR 46
TC 8
Z9 8
U1 1
U2 38
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0960-085X
EI 1476-9344
J9 EUR J INFORM SYST
JI Eur. J. Inform. Syst.
PD SEP
PY 2011
VL 20
IS 5
BP 574
EP 588
DI 10.1057/ejis.2011.17
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
   Economics
GA 804NL
UT WOS:000293660400007
DA 2022-08-23
ER

PT J
AU Singh, S
   Siwach, M
AF Singh, Sushmita
   Siwach, Manvi
TI Handling Heterogeneous Data in Knowledge Graphs: A Survey
SO JOURNAL OF WEB ENGINEERING
LA English
DT Article
DE Knowledge graph; knowledge fusion; heterogeneous data; entity linking;
   entity extraction; entity alignment; ontology; knowledge base
AB In this era of information where everything is digital, data tends to be ubiquitous. Data Analytics is a term that covers all the areas that deal with the logical analysis of raw data Graph analytics is one of the emerging domains of data analytics that represents and analyses data in the form of knowledge graphs. Knowledge graphs play a vital role in analysing and processing data in order to make decisions. In knowledge graphs the data is stored in the form of entities, relationships between the entities and the attributes of entities as well as attributes of relationships. Construction of knowledge graph and its analytics face multiple challenges like data redundancy, heterogeneity of data, missing data, dynamic nature of real-world data etc. This paper focuses on the issue related to heterogeneity of data while constructing a knowledge graph, and it provides a systematic literature review over construction of knowledge graphs from heterogeneous data sources. This review compiles state-of-the-art knowledge fusion techniques. To conduct this systematic literature review, an exhaustive approach has been adopted to identify various procedures and algorithms included and adapted by different research works for knowledge graph construction.
C1 [Singh, Sushmita; Siwach, Manvi] JC BOSE Univ Sci & Technol, YMCA, Dept Comp Engn, Faridabad, Haryana, India.
RP Singh, S (通讯作者)，JC BOSE Univ Sci & Technol, YMCA, Dept Comp Engn, Faridabad, Haryana, India.
EM sushmi278@gmail.com
RI siwach, manvi/GPG-3142-2022
OI siwach, manvi/0000-0002-0035-1164
CR [Anonymous], [No title captured]
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NR 64
TC 0
Z9 0
U1 6
U2 6
PU RIVER PUBLISHERS
PI GISTRUP
PA ALSBJERGVEJ 10, GISTRUP, 9260, DENMARK
SN 1540-9589
EI 1544-5976
J9 J WEB ENG
JI J. Web Eng.
PY 2022
VL 21
IS 4
BP 1145
EP 1186
DI 10.13052/jwe1540-9589.2147
PG 42
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0O8DM
UT WOS:000783753900008
DA 2022-08-23
ER

PT J
AU Wang, MH
   Qiu, LL
   Wang, XL
AF Wang, Meihong
   Qiu, Linling
   Wang, Xiaoli
TI A Survey on Knowledge Graph Embeddings for Link Prediction
SO SYMMETRY-BASEL
LA English
DT Article
DE link prediction; knowledge graph embedding; knowledge graph completion;
   survey
ID ENTITIES; PATHS
AB Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform the link-prediction task based on various representation techniques. Among them, KG-embedding models have significantly advanced the state of the art in the past few years. In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs. We first provide a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding. Then, we investigate several representative models that are classified into five categories. Finally, we conducted experiments on two benchmark datasets to report comprehensive findings and provide some new insights into the strengths and weaknesses of existing models.
C1 [Wang, Meihong; Qiu, Linling; Wang, Xiaoli] Xiamen Univ, Sch Informat, Xiamen 361000, Peoples R China.
RP Wang, XL (通讯作者)，Xiamen Univ, Sch Informat, Xiamen 361000, Peoples R China.
EM wangmh@xmu.edu.cn; qiulinling@stu.xmu.edu.cn; xlwang@xmu.edu.cn
OI Wang, Xiaoli/0000-0002-8677-9080; Wang, Meihong/0000-0001-8639-7675
FU Fujian Provincial Department of Science and Technology [2019H0001];
   National Natural Science Foundation of China [61702432]; Fundamental
   Research Funds for Central Universities of China [20720180070];
   International Cooperation Projects of Fujian Province in China
   [2018I0016]
FX This research was funded by the Fujian Provincial Department of Science
   and Technology under Grant No. 2019H0001, the National Natural Science
   Foundation of China under Grant No. 61702432, the Fundamental Research
   Funds for Central Universities of China under Grant No. 20720180070, and
   the International Cooperation Projects of Fujian Province in China under
   Grant No. 2018I0016.
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NR 190
TC 9
Z9 9
U1 14
U2 39
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-8994
J9 SYMMETRY-BASEL
JI Symmetry-Basel
PD MAR
PY 2021
VL 13
IS 3
AR 485
DI 10.3390/sym13030485
PG 29
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA RE5JI
UT WOS:000634189800001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Tang, CF
   Rao, Y
   Yu, HL
   Sun, L
   Cheng, JM
   Wang, YT
AF TANG, Caifang
   RAO, Yuan
   YU, Hualei
   SUN, Ling
   CHENG, Jiamin
   WANG, Yutian
TI Improving Knowledge Graph Completion Using Soft Rules and Adversarial
   Learning
SO CHINESE JOURNAL OF ELECTRONICS
LA English
DT Article
DE Knowledge graph; Knowledge embedding; Knowledge graph completion;
   Adversarial learning
AB Knowledge graph is a useful resources and tools for describing entities and relationships in natural language processing tasks. However, the existing knowledge graph are incomplete. Therefore, knowledge graph completion technology has become a research hotspot in the field of artificial intelligence, but the traditional knowledge graph embedding method does not fully take into account the role of logic rules and the effect of false negative samples on knowledge embedding. Based on the logic rules of knowledge and the role of adversarial learning in knowledge embedding, we proposes a model to improve the completion of knowledge graph: soft Rules and graph adversarial learning (RUGA). Firstly, the traditional knowledge graph embedding model is trained as generator and discriminator by using adversarial learning method, and high-quality negative samples are obtained. Then these negative samples and the existing positive samples together constitute the label triple in the injection rule model. The whole model will benefit from both high-quality samples and logical rules. In addition, we evaluated the performance of link prediction task and triple classification task on Freebase and Yago datasets respectively. Finally, the experimental results show that the model can effectively improve the effect of knowledge graph completion.}
C1 [TANG, Caifang; RAO, Yuan; YU, Hualei; SUN, Ling; CHENG, Jiamin] Xi An Jiao Tong Univ, Sch Software Engn, Xian Key Lab Social Intelligence & Complex Data P, Xian 710049, Peoples R China.
   [TANG, Caifang; RAO, Yuan; YU, Hualei; SUN, Ling; CHENG, Jiamin] Xi An Jiao Tong Univ, Sch Software Engn, Shaanxi Joint Key Lab Artifact Intelligence, Xian 710049, Peoples R China.
   [WANG, Yutian] Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China.
RP Rao, Y (通讯作者)，Xi An Jiao Tong Univ, Sch Software Engn, Xian Key Lab Social Intelligence & Complex Data P, Xian 710049, Peoples R China.; Rao, Y (通讯作者)，Xi An Jiao Tong Univ, Sch Software Engn, Shaanxi Joint Key Lab Artifact Intelligence, Xian 710049, Peoples R China.
EM tang_c_fang@163.com; yuanrao@163.com; 13227805056@163.com;
   sunling@stu.xjtu.edu.cn; cheng1044642385@163.com
FU National Key Research and Development Program in China [2019YFB2102300];
   World-Class Universities (Disciplines) and the Characteristic
   Development Guidance Funds for the Central Universities of China
   [PY3A022]; Ministry of Education [18JZD022, 2017B00030]; Shenzhen
   Science and Technology Project [JCYJ20180306170836595]; Basic Scientific
   Research Operating Expenses of Central Universities [ZDYF2017006]; Xi'an
   Navinfo Corp.& Engineering Center of Xi'an Intelligence Spatial-temporal
   Data Analysis Project [C2020103]; Beilin District of Xi'an Science &
   Technology Project [GX1803]
FX The research work is supported by National Key Research and Development
   Program in China (No.2019YFB2102300), the World-Class Universities
   (Disciplines) and the Characteristic Development Guidance Funds for the
   Central Universities of China (No.PY3A022), Ministry of Education Fund
   Projects (No.18JZD022, No.2017B00030); Shenzhen Science and Technology
   Project (No.JCYJ20180306170836595), Basic Scientific Research Operating
   Expenses of Central Universities (No.ZDYF2017006), Xi'an Navinfo Corp.&
   Engineering Center of Xi'an Intelligence Spatial-temporal Data Analysis
   Project (No.C2020103), and Beilin District of Xi'an Science & Technology
   Project (No.GX1803).
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PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1022-4653
EI 2075-5597
J9 CHINESE J ELECTRON
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PD JUL
PY 2021
VL 30
IS 4
BP 623
EP 633
DI 10.1049/cje.2021.05.004
PG 11
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA TT5RH
UT WOS:000680404600004
DA 2022-08-23
ER

PT J
AU Liu, K
   Wang, F
   Ding, ZY
   Liang, S
   Yu, ZF
   Zhou, Y
AF Liu, Kai
   Wang, Fei
   Ding, Zhaoyun
   Liang, Sheng
   Yu, Zhengfei
   Zhou, Yun
TI Recent Progress of Using Knowledge Graph for Cybersecurity
SO ELECTRONICS
LA English
DT Review
DE cybersecurity knowledge graph; construction technology; application
   scenarios; cyberthreat intelligence extraction; cyberattack analysis;
   cyber threat prediction
AB In today's dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. In this article, we give a quick overview of the cybersecurity knowledge graph's core concepts, schema, and building methodologies. We also give a relevant dataset review and open-source frameworks on the information extraction and knowledge creation job to aid future studies on cybersecurity knowledge graphs. We perform a comparative assessment of the many works that expound on the recent advances in the application scenarios of cybersecurity knowledge graph in the majority of this paper. In addition, a new comprehensive classification system is developed to define the linked works from 9 core categories and 18 subcategories. Finally, based on the analyses of existing research issues, we have a detailed overview of various possible research directions.
C1 [Liu, Kai; Wang, Fei; Ding, Zhaoyun; Yu, Zhengfei; Zhou, Yun] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
   [Liang, Sheng] Univ Munich LMU, Ctr Informat & Language Proc CIS, D-80538 Munich, Germany.
RP Ding, ZY (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
EM liukai18@nudt.edu.cn; wangfei@nudt.edu.cn; yuzhengfei19@nudt.edu.cn;
   shengliang@cis.lmu.de; yuzhengfei19@nudt.edu.cn; zhouyun@nudt.edu.cn
FU Science and Technology Innovation Program of Hunan Province
   [2021RC3076]; Training Program for Excellent Young Innovators of
   Changsha [KQ2009009]
FX This research was partially funded by The Science and Technology
   Innovation Program of Hunan Province, grant number 2021RC3076 and
   Training Program for Excellent Young Innovators of Changsha, grant
   number KQ2009009.
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   Zhang D., 2015, ARXIV
   Zhang Q, 2018, AAAI CONF ARTIF INTE, P5674
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   Zhou P, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, P207, DOI 10.18653/v1/p16-2034
   Zhu Z, 2018, 2018 IEEE 18TH INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT), P870, DOI 10.1109/ICCT.2018.8600108
NR 134
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 AUG
PY 2022
VL 11
IS 15
AR 2287
DI 10.3390/electronics11152287
PG 28
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA 3R8IL
UT WOS:000839149600001
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, JL
   Han, R
   Chen, WJ
   Zhang, WX
   Li, Y
   Ji, YH
   Chen, LJ
   Pan, H
   Yang, XG
   Pan, WS
   Ouyang, DF
AF Zhang, Jinglu
   Han, Run
   Chen, Weijie
   Zhang, Weixiang
   Li, Ying
   Ji, Yuanhui
   Chen, Lijiang
   Pan, Hao
   Yang, Xinggang
   Pan, Weisan
   Ouyang, Defang
TI Analysis of the Literature and Patents on Solid Dispersions from 1980 to
   2015
SO MOLECULES
LA English
DT Article
DE solid dispersion; big data; literature; patent; scientific knowledge
   mapping; CiteSpace
ID TARGET INTERACTION PREDICTION; RESEARCH-AND-DEVELOPMENT; STATE
   CHARACTERIZATION; EMERGING TRENDS; DRUG DISCOVERY; SOLUBLE DRUGS;
   SOLUBILITY; STABILITY; STABILIZATION; CRYSTALLIZATION
AB Background: Solid dispersions are an effective formulation technique to improve the solubility, dissolution rate, and bioavailability of water-insoluble drugs for oral delivery. In the last 15 years, increased attention was focused on this technology. There were 23 marketed drugs prepared by solid dispersion techniques. Objective: This study aimed to report the big picture of solid dispersion research from 1980 to 2015. Method: Scientific knowledge mapping tools were used for the qualitative and the quantitative analysis of patents and literature from the time and space dimensions. Results: Western Europe and North America were the major research areas in this field with frequent international cooperation. Moreover, there was a close collaboration between universities and industries, while research collaboration in Asia mainly existed between universities. The model drugs, main excipients, preparation technologies, characterization approaches and the mechanism involved in the formulation of solid dispersions were analyzed via the keyword burst and co-citation cluster techniques. Integrated experimental, theoretical and computational tools were useful techniques for in silico formulation design of the solid dispersions. Conclusions: Our research provided the qualitative and the quantitative analysis of patents and literature of solid dispersions in the last three decades.
C1 [Zhang, Jinglu; Han, Run; Chen, Weijie; Zhang, Weixiang; Ouyang, Defang] Univ Macau, ICMS, State Key Lab Qual Res Chinese Med, Macau 999078, Peoples R China.
   [Li, Ying] Shenzhen Univ, Sch Med, Dept Pharm, Shenzhen 518060, Peoples R China.
   [Ji, Yuanhui] Southeast Univ, Sch Chem & Chem Engn, Jiangsu Prov Hitech Key Lab Biomed Res, Nanjing 211189, Jiangsu, Peoples R China.
   [Chen, Lijiang; Pan, Hao] Liaoning Univ, Sch Pharm, Shenyang 110000, Liaoning, Peoples R China.
   [Yang, Xinggang; Pan, Weisan] Shenyang Pharmaceut Univ, Sch Pharm, Shenyang 110000, Liaoning, Peoples R China.
RP Ouyang, DF (通讯作者)，Univ Macau, ICMS, State Key Lab Qual Res Chinese Med, Macau 999078, Peoples R China.; Li, Y (通讯作者)，Shenzhen Univ, Sch Med, Dept Pharm, Shenzhen 518060, Peoples R China.
EM zhangjinglu01@hotmail.com; hanrun1307@126.com; mb55813@umac.mo;
   13980967387@163.com; li.ying@szu.edu.cn; yuanhui.ji@seu.edu.cn;
   chlj16@163.com; haopan0330@163.com; yangxg123@163.com; ppwwss@163.com;
   defangouyang@umac.mo
RI Li, Ying/AAC-1331-2021; Ouyang, Defang/L-8279-2019; Li,
   Wang/M-1612-2019; Ouyang, Defang/AAW-1087-2020
OI Ouyang, Defang/0000-0002-8052-4773; Pan, Hao/0000-0002-4432-3835
FU University of Macau [MYRG2016-00038-ICMS-QRCM,
   MYRG2016-00040-ICMS-QRCM]; Basic Research Program of the Science,
   Industry, Trade and Information Technology Commission of Shenzhen
   Municipality [JCYJ20150410112709457]
FX This research was funded by the University of Macau research grants
   (MYRG2016-00038-ICMS-QRCM and MYRG2016-00040-ICMS-QRCM) and Basic
   Research Program of the Science, Industry, Trade and Information
   Technology Commission of Shenzhen Municipality (Grant no.
   JCYJ20150410112709457).
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NR 64
TC 25
Z9 25
U1 8
U2 35
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1420-3049
J9 MOLECULES
JI Molecules
PD JUL
PY 2018
VL 23
IS 7
AR 1697
DI 10.3390/molecules23071697
PG 19
WC Biochemistry & Molecular Biology; Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biochemistry & Molecular Biology; Chemistry
GA GU5CE
UT WOS:000445301800200
PM 30002275
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Do, P
   Le, H
   Pham, AB
   Nguyen, CH
AF Phuc Do
   Hung Le
   Pham, An B.
   Nguyen, Cuong H.
TI Using BERT and Knowledge Graph for detecting triples in Vietnamese text
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article; Early Access
DE Natural Language Processing; BERT; Knowledge Graph; Contextual
   understanding; Triple classification
AB One of the challenges in constructing Knowledge Graphs from text is verifying the correctness of the produced results. Each language has its unique characteristics, so a Knowledge Graphs construction system may perform better on certain languages and worse on others. In order to detect the most suitable Knowledge Graph construction systems for Vietnamese, in this paper, we propose a method to classify triples extracted from such systems into two categories: Existent and Non-existent. Vietnamese is a low-resource language with limited natural language processing tools and datasets. By combining BERT with a self-constructed Vietnamese Knowledge Graph, we build a classification model to verify the existence of triples in paragraphs. Our results suggest that BERT can learn contextual relations between words from a large amount of text, even for a low-resource language like Vietnamese. BERT's adaptive capability to detect meaningful triples is also shown and discussed. The outcome of this paper could potentially be used to build more sophisticated systems to solve Knowledge Graph construction and Triple Classification tasks in low resource languages.
C1 [Phuc Do; Hung Le; Pham, An B.; Nguyen, Cuong H.] Vietnam Natl Univ Ho Chi Minh City, Univ Informat Technol, Quarter 6, Ho Chi Minh City, Vietnam.
   [Hung Le] Japan Adv Inst Sci & Technol, Human Informat Sci, 1-1 Asahidai, Nomi, Ishikawa 9231292, Japan.
RP Do, P (通讯作者)，Vietnam Natl Univ Ho Chi Minh City, Univ Informat Technol, Quarter 6, Ho Chi Minh City, Vietnam.
EM phucdo@uit.edu.vn; 15520283@gm.uit.edu.vn; 16520016@gm.uit.edu.vn;
   16520148@gm.uit.edu.vn
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 Grant Number DS2020-26-01.
CR An B, 2018, ACCURATE TEXT ENHANC
   Chen DG, 2021, COMPUT INTEL NEUROSC, V2021, DOI 10.1155/2021/8810366
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NR 30
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
DI 10.1007/s00521-022-07439-3
EA JUN 2022
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1Y1MN
UT WOS:000807908300002
DA 2022-08-23
ER

PT J
AU Huang, J
   Lu, T
   Zhu, J
   Yu, WH
   Zhang, TH
AF Huang, Jin
   Lu, Tian
   Zhu, Jia
   Yu, Weihao
   Zhang, Tinghua
TI Multi-relational knowledge graph completion method with local
   information fusion
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Knowledge graph completion; Graph neural networks
AB Knowledge graph completion(KGC) has attracted increasing attention in recent years, aiming at complementing missing relationships between entities in a Knowledge Graph(KG). While the existing KGC approaches utilizing the knowledge within KG could only complement a very limited number of missing relations, more and more approaches tend to study the completion of the multi-relationship knowledge graph. However, the existing completion methods of multi-relation knowledge graph regard knowledge graph as an undirected graph, which ignores the directionality of knowledge graph, so that the potential characteristics of multi-relation cannot be learned. Besides, most algorithms fail to explore the local information of knowledge because they ignore the different importance of entity adjacencies. In this paper, we propose to use local information fusion to join the entity and its adjacency relation, to acquiring the multi-relation representation. In addition, we try to specify distinct weights to model the direction of the relationship and apply the attention mechanism between entity nodes to obtain local information between entity nodes. Experiments conducted on three benchmark datasets and a medical domain knowledge graph dataset that we collect demonstrate the effectiveness of the proposed framework.
C1 [Huang, Jin; Lu, Tian; Zhang, Tinghua] South China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China.
   [Zhu, Jia] Zhejiang Normal Univ, Coll Teacher Educ, Jinhua, Zhejiang, Peoples R China.
   [Yu, Weihao] China Telecom Corp Ltd, Res Inst, Guangzhou, Peoples R China.
RP Zhu, J (通讯作者)，Zhejiang Normal Univ, Coll Teacher Educ, Jinhua, Zhejiang, Peoples R China.
EM huangjin@m.scnu.edu.cn; tianlu@m.scnu.edu.cn; jiazhu@zjnu.edu.cn;
   yuwh3@chinatelecom.cn; tinghuazhang@m.scnu.edu.cn
OI Lu, Tian/0000-0002-7786-8321; Yu, Weihao/0000-0003-0727-4744
FU National Natural Science Foundation of China [62077015, 62177015]; Key
   Laboratory of Intelligent Education Technology and Application of
   Zhejiang Province; Zhejiang Normal University, Jinhua, Zhejiang, China
FX This work was supported by the National Natural Science Foundation of
   China under Grant 62077015, No. 62177015, and the Key Laboratory of
   Intelligent Education Technology and Application of Zhejiang Province,
   Zhejiang Normal University, Jinhua, Zhejiang, China.
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NR 35
TC 0
Z9 0
U1 18
U2 28
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
PD MAY
PY 2022
VL 52
IS 7
BP 7985
EP 7994
DI 10.1007/s10489-021-02876-4
EA OCT 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1A0YQ
UT WOS:000707527500003
DA 2022-08-23
ER

PT J
AU Ji, SX
   Pan, SR
   Cambria, E
   Marttinen, P
   Yu, PS
AF Ji, Shaoxiong
   Pan, Shirui
   Cambria, Erik
   Marttinen, Pekka
   Yu, Philip S.
TI A Survey on Knowledge Graphs: Representation, Acquisition, and
   Applications
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article
DE Cognition; Knowledge based systems; Semantics; Knowledge acquisition;
   Task analysis; Taxonomy; Extraterrestrial measurements; Deep learning;
   knowledge graph completion (KGC); knowledge graph; reasoning; relation
   extraction; representation learning
AB Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.
C1 [Ji, Shaoxiong; Marttinen, Pekka] Aalto Univ, Dept Comp Sci, Aalto 02150, Finland.
   [Pan, Shirui] Monash Univ, Dept Data Sci & AI, Fac Informat Technol, Clayton, Vic 3800, Australia.
   [Cambria, Erik] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore.
   [Yu, Philip S.] Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA.
RP Pan, SR (通讯作者)，Monash Univ, Dept Data Sci & AI, Fac Informat Technol, Clayton, Vic 3800, Australia.
EM shaoxiong.ji@aalto.fi; shirui.pan@monash.edu; cambria@ntu.edu.sg;
   pekka.marttinen@aalto.fi; psyu@uic.edu
RI Pan, Shirui/K-6763-2018; Ji, Shaoxiong/AAI-9628-2020; Cambria,
   Erik/C-2103-2013; Marttinen, Pekka/N-6234-2015
OI Pan, Shirui/0000-0003-0794-527X; Ji, Shaoxiong/0000-0003-3281-8002;
   Cambria, Erik/0000-0002-3030-1280; Yu, Philip/0000-0002-3491-5968;
   Marttinen, Pekka/0000-0001-7078-7927
FU NSF [III1763325, III-1909323, SaTC-1930941]; Agency for Science,
   Technology and Research (A*STAR) through its AME Programmatic Funding
   Scheme [A18A2b0046]; Academy of Finland [336033, 315896]; Business
   Finland [884/31/2018]; EU H2020 [101016775]
FX This work was supported in part by NSF under Grant III1763325, Grant
   III-1909323, and Grant SaTC-1930941; in part by the Agency for Science,
   Technology and Research (A*STAR) through its AME Programmatic Funding
   Scheme under Project A18A2b0046; in part by the Academy of Finland under
   Grant 336033 and Grant 315896; in part by Business Finland under Grant
   884/31/2018; and in part by EU H2020 under Grant 101016775.
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NR 210
TC 200
Z9 205
U1 383
U2 474
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 FEB
PY 2022
VL 33
IS 2
BP 494
EP 514
DI 10.1109/TNNLS.2021.3070843
EA APR 2021
PG 21
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 YU4LL
UT WOS:000733458000001
PM 33900922
OA Green Published, Green Submitted
HC Y
HP Y
DA 2022-08-23
ER

PT J
AU Breitfuss, A
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   Kurteva, A
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AF Breitfuss, Arno
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TI Representing emotions with knowledge graphs for movie recommendations
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Knowledge graph; Artificial intelligence; Machine learning; Bias;
   Emotions; Ontology; Movies; Recommender system
ID ONTOLOGY
AB Consumption of media, and movies in particular, is increasing and is influenced by a number of factors. One important and overlooked factor that affects the media consumption choices is the emotional state of the user and the decision making based on it. To include this factor in movie recommendation processes, we propose a knowledge graph representing human emotions in the domain of movies. The knowledge graph has been built by extracting emotions out of pre-existing movie reviews using machine learning techniques. To show how the knowledge graph can be used, a chatbot prototype has been developed. The chatbot's reasoning mechanism derives movie recommendations for the user by combining the user's emotions, which have been extracted from chat messages, with the knowledge graph. The developed approach for movie recommendations based on sentiment represented as a knowledge graph has been proven to be technically feasible, however, it requires more information about the emotions associated with the movies than currently available online. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Breitfuss, Arno; Errou, Karen; Kurteva, Anelia; Fensel, Anna] Univ Innsbruck, Semant Technol Inst STI Innsbruck, Dept Comp Sci, Technikerstr 21a, A-6020 Innsbruck, Austria.
RP Kurteva, A (通讯作者)，Univ Innsbruck, Semant Technol Inst STI Innsbruck, Dept Comp Sci, Technikerstr 21a, A-6020 Innsbruck, Austria.
EM Arno.Breitfuss@student.uibk.ac.at; Karen.Errou@student.uibk.ac.at;
   anelia.kurteva@sti2.at; anna.fensel@sti2.at
FU Interreg [AB292]
FX This work has been partly supported by an Interreg funded project KI-Net
   (AB292) . We thank our colleague Juliette Opden-platz for collaborating
   on previous early versions of this research.
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TC 3
Z9 3
U1 5
U2 37
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.
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PY 2021
VL 125
BP 715
EP 725
DI 10.1016/j.future.2021.06.001
EA JUL 2021
PG 11
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA UE5YZ
UT WOS:000687964600020
DA 2022-08-23
ER

PT J
AU Zeng, P
   Tan, QP
   Meng, XK
   Zhang, HY
   Xu, JJ
AF Zeng, Ping
   Tan, Qingping
   Meng, Xiankai
   Zhang, Haoyu
   Xu, Jianjun
TI Modeling Complex Relationship Paths for Knowledge Graph Completion
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article
DE knowledge graph completion; knowledge representation learning; knowledge
   graph
AB Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion. So far, the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph. Among them, translationmodels obtain excellent performance. However, the proposed translation models do not adequately consider the indirect relationships among entities, affecting the precision of the representation. Based on the long short-term memory neural network and existing translation models, we propose a multiple-module hybrid neural network model called TransP. By modeling the entity paths and their relationship paths, TransP can effectively excavate the indirect relationships among the entities, and thus, improve the quality of knowledge graph completion tasks. Experimental results show that TransP outperforms state-of-the-art models in the entity prediction task, and achieves the comparable performance with previous models in the relationship prediction task.
C1 [Zeng, Ping; Tan, Qingping; Meng, Xiankai; Zhang, Haoyu; Xu, Jianjun] Natl Univ Def Technol, Coll Comp, Changsha, Hunan, Peoples R China.
   [Zeng, Ping; Tan, Qingping; Meng, Xiankai; Zhang, Haoyu; Xu, Jianjun] Natl Key Lab Parallel & Distributed Proc, Changsha, Hunan, Peoples R China.
RP Tan, QP (通讯作者)，Natl Univ Def Technol, Coll Comp, Changsha, Hunan, Peoples R China.; Tan, QP (通讯作者)，Natl Key Lab Parallel & Distributed Proc, Changsha, Hunan, Peoples R China.
EM eric_tan@nudt.edu.cn
FU Scientific Research Fund of Hunan Provincial Education Department
   [15A007]; National Natural Science Foundation of China [61202116]
FX This study is supported by the Scientific Research Fund of Hunan
   Provincial Education Department (No. 15A007) and the National Natural
   Science Foundation of China (No. 61202116).
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SC Computer Science
GA GF2MA
UT WOS:000431772900020
OA gold
DA 2022-08-23
ER

PT J
AU Yuan, JB
   Jin, ZW
   Guo, H
   Jin, HX
   Zhang, XC
   Smith, T
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AF Yuan, Jianbo
   Jin, Zhiwei
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   Luo, Jiebo
TI Constructing biomedical domain-specific knowledge graph with minimum
   supervision
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph construction; Biomedical; Domain-specific; Minimum
   supervision
AB Domain-specific knowledge graph is an effective way to represent complex domain knowledge in a structured format and has shown great success in real-world applications. Most existing work on knowledge graph construction and completion shares several limitations in that sufficient external resources such as large-scale knowledge graphs and concept ontologies are required as the starting point. However, such extensive domain-specific labeling is highly time-consuming and requires special expertise, especially in biomedical domains. Therefore, knowledge extraction from unstructured contexts with minimum supervision is crucial in biomedical fields. In this paper, we propose a versatile approach for knowledge graph construction with minimum supervision based on unstructured biomedical domain-specific contexts including the steps of entity recognition, unsupervised entity and relation embedding, latent relation generation via clustering, relation refinement and relation assignment to assign cluster-level labels. The experimental results based on 24,687 unstructured biomedical science abstracts show that the proposed framework can effectively extract 16,192 structured facts with high precision. Moreover, we demonstrate that the constructed knowledge graph is a sufficient resource for the task of knowledge graph completion and new knowledge inference from unseen contexts.
C1 [Yuan, Jianbo; Luo, Jiebo] Univ Rochester, Dept Comp Sci, Rochester, NY 14623 USA.
   [Jin, Zhiwei; Guo, Han] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China.
   [Jin, Hongxia] Samsung Res Amer, Mountain View, CA USA.
   [Zhang, Xianchao] Dalian Univ Technol, Sch Software Technol, Dalian, Peoples R China.
   [Smith, Tristram] Univ Rochester, Med Ctr, Dept Pediat, Rochester, NY 14642 USA.
RP Yuan, JB (通讯作者)，Univ Rochester, Dept Comp Sci, Rochester, NY 14623 USA.
EM jyuan10@cs.rochester.edu
RI Luo, Jiebo/AAI-7549-2020; Yuan, Jianbo/AAY-5982-2021
OI Luo, Jiebo/0000-0002-4516-9729
FU New York State through the Goergen Institute for Data Science;
   Carestream Health; NSF [1704309, 1722847]
FX This work is supported in part by the New York State through the Goergen
   Institute for Data Science and our corporate sponsors, Carestream Health
   and NSF awards #1704309 and #1722847.
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PU SPRINGER LONDON LTD
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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 2020
VL 62
IS 1
BP 317
EP 336
DI 10.1007/s10115-019-01351-4
PG 20
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KI9CR
UT WOS:000511654800011
DA 2022-08-23
ER

PT J
AU You, MS
   Yin, J
   Wang, H
   Cao, JL
   Wang, KT
   Miao, Y
   Bertino, E
AF You, Mingshan
   Yin, Jiao
   Wang, Hua
   Cao, Jinli
   Wang, Kate
   Miao, Yuan
   Bertino, Elisa
TI A knowledge graph empowered online learning framework for access control
   decision-making
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Knowledge graph; Online learning; Access control; High cardinality
   categorical data
AB Knowledge graph, as an extension of graph data structure, is being used in a wide range of areas as it can store interrelated data and reveal interlinked relationships between different objects within a large system. This paper proposes an algorithm to construct an access control knowledge graph from user and resource attributes. Furthermore, an online learning framework for access control decision-making is proposed based on the constructed knowledge graph. Within the framework, we extract topological features to represent high cardinality categorical user and resource attributes. Experimental results show that topological features extracted from knowledge graph can improve the access control performance in both offline learning and online learning scenarios with different degrees of class imbalance status.
C1 [You, Mingshan; Yin, Jiao; Wang, Hua] Victoria Univ, Inst Sustainable Ind Liveable Cities, Melbourne, Vic 3083, Australia.
   [Cao, Jinli] La Trobe Univ, Dept Comp Sci & Informat Technol, Melbourne, Vic 3083, Australia.
   [Wang, Kate] RMIT Univ, Sch Hlth & Biomed Sci, Melbourne, Vic, Australia.
   [Miao, Yuan] Victoria Univ, Coll Engn & Sci, Melbourne, Vic 3083, Australia.
   [Bertino, Elisa] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA.
RP Yin, J (通讯作者)，Victoria Univ, Inst Sustainable Ind Liveable Cities, Melbourne, Vic 3083, Australia.
EM j.yin@latrobe.edu.au
OI Wang, Kate/0000-0001-5208-1090; Yin, Jiao/0000-0002-0269-2624
FU CAUL
FX Open Access funding enabled and organized by CAUL and its Member
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NR 44
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
DI 10.1007/s11280-022-01076-5
EA JUN 2022
PG 22
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2I4DX
UT WOS:000814931900001
OA hybrid
DA 2022-08-23
ER

PT J
AU Lu, RQ
   Fei, CQ
   Wang, CQ
   Gao, SF
   Qiu, H
   Zhang, SM
   Cao, CG
AF Lu, Ruqian
   Fei, Chaoqun
   Wang, Chuanqing
   Gao, Shunfeng
   Qiu, Han
   Zhang, Songmao
   Cao, Cungen
TI HAPE: A programmable big knowledge graph platform
SO INFORMATION SCIENCES
LA English
DT Article
DE Big knowledge; Big knowledge system; Big knowledge graph; Knowledge
   graph browser; Knowledge graph operating system; Knowledge scripting
   language; Big knowledge security
AB Heaven Ape (HAPE) is an integrated big knowledge graph platform supporting the construction, management, and operation of large to massive scale knowledge graphs. Its current version described in this paper is a prototype, which consists of three parts: a big knowledge graph knowledge base, a knowledge graph browser on the client side, and a knowledge graph operating system on the server side. The platform is programmed in two high level scripting languages: JavaScript for programming the client side functions and Python for the server side functions. For making the programming more suitable for big knowledge processing and more friendly to knowledge programmers, we have developed two versions of knowledge scripting languages, namely K-script-c and K-script-s, for performing very high level knowledge programming of client resp. server side functions. HAPE borrows ideas from some well-known knowledge graph processing techniques and also invents some new ones as our creation. As an experiment, we transformed a major part of the DBpedia knowledge base and reconstructed it as a big knowledge graph. It works well in some application tests and provides acceptable efficiency. (C) 2019 Elsevier Inc. All rights reserved.
C1 [Lu, Ruqian; Fei, Chaoqun; Gao, Shunfeng; Cao, Cungen] Chinese Acad Sci, ICT, Beijing 100190, Peoples R China.
   [Lu, Ruqian; Fei, Chaoqun; Gao, Shunfeng; Cao, Cungen] Chinese Acad Sci, Key Lab IIP, Beijing 100190, Peoples R China.
   [Lu, Ruqian; Wang, Chuanqing; Zhang, Songmao] Chinese Acad Sci, AMSS, Beijing 100190, Peoples R China.
   [Lu, Ruqian; Wang, Chuanqing; Zhang, Songmao] Chinese Acad Sci, Key Lab MADIS, Beijing 100190, Peoples R China.
   [Fei, Chaoqun; Wang, Chuanqing] Univ Chinese Acad Sci, Beijing 100190, Peoples R China.
   [Gao, Shunfeng] Jiangsu Univ Sci & Technol, Zhenjiang 210003, Jiangsu, Peoples R China.
   [Qiu, Han] Telecom Paris, F-75013 Paris, France.
RP Lu, RQ (通讯作者)，Chinese Acad Sci, ICT, Beijing 100190, Peoples R China.
EM rqlu@math.ac.cn; feichaoqun@ict.ac.cn; wangchuanqing15@mails.ucas.ac.cn;
   gaoshunfeng@stu.just.edu.cn; han.qiu@telecom-paris.fr;
   smzhang@math.ac.cn; cgcao@ict.ac.cn
OI Qiu, Han/0000-0003-2678-8070; Fei, Chaoqun/0000-0001-9164-0019
FU National Key Research and Development Program of China [2016YFB1000902];
   National Natural Science Foundation of China [61232015, 61472412,
   61621003]; Tsinghua-Tencent-AMSS Joint Project: WWW Knowledge Structure
   and its Application
FX The authors wish to thank the anonymous reviewers sincerely for their
   valuable comments and suggestions. This work has been supported by
   National Key Research and Development Program of China under grant
   2016YFB1000902, National Natural Science Foundation of China (Nos.
   61232015, 61472412, and 61621003), Tsinghua-Tencent-AMSS Joint Project:
   WWW Knowledge Structure and its Application.
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NR 47
TC 10
Z9 12
U1 23
U2 108
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 JAN
PY 2020
VL 509
BP 87
EP 103
DI 10.1016/j.ins.2019.08.051
PG 17
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JK5LA
UT WOS:000494883700006
DA 2022-08-23
ER

PT J
AU Ma, YP
   Tresp, V
   Daxberger, EA
AF Ma, Yunpu
   Tresp, Volker
   Daxberger, Erik A.
TI Embedding models for episodic knowledge graphs
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge graph; Temporal knowledge graph; Semantic memory; Episodic
   memory; Tensor models
ID HIPPOCAMPAL NEUROGENESIS; SEMANTIC MEMORY
AB In recent years a number of large-scale triple-oriented knowledge graphs have been generated and various models have been proposed to perform learning in those graphs. Most knowledge graphs are static and reflect the world in its current state. In reality, of course, the state of the world is changing: a healthy person becomes diagnosed with a disease and a new president is inaugurated. In this paper, we extend models for static knowledge graphs to temporal knowledge graphs. This enables us to store episodic data and to generalize to new facts (inductive learning). We generalize leading learning models for static knowledge graphs (i.e., Tucker, RESCAL, HolE, ComplEx, DistMult) to temporal knowledge graphs. In particular, we introduce a new tensor model, ConT, with superior generalization performance. The performances of all proposed models are analyzed on two different datasets: the Global Database of Events, Language, and Tone (GDELT) and the database for Integrated Conflict Early Warning System (ICEWS). We argue that temporal knowledge graph embeddings might be models also for cognitive episodic memory (facts we remember and can recollect) and that a semantic memory (current facts we know) can be generated from episodic memory by a marginalization operation. We validate this episodic-to-semantic projection hypothesis with the ICEWS dataset. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Ma, Yunpu; Tresp, Volker] Siemens AG, Corp Technol, Munich, Germany.
   [Ma, Yunpu; Tresp, Volker] Ludwig Maximilian Univ Munich, Munich, Germany.
   [Daxberger, Erik A.] Swiss Fed Inst Technol, Zurich, Switzerland.
   [Daxberger, Erik A.] Siemens AG, Munich, Germany.
RP Ma, YP (通讯作者)，Siemens Corp Technol, Otto Hahn Ring 6, D-81739 Munich, Germany.
EM yunpu.ma@siemens.com
FU Cognitive Deep Learning, Germany research project in Siemens AG
FX This work is funded by the Cognitive Deep Learning, Germany research
   project in Siemens AG.
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U2 38
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 100490
DI 10.1016/j.websem.2018.12.008
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:000500706800003
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Asgari-Bidhendi, M
   Hadian, A
   Minaei-Bidgoli, B
AF Asgari-Bidhendi, Majid
   Hadian, Ali
   Minaei-Bidgoli, Behrouz
TI FarsBase: The Persian knowledge graph
SO SEMANTIC WEB
LA English
DT Article
DE Semantic Web; Linked Date; Persian; knowledge graph
ID RELATION EXTRACTION; BASE CONSTRUCTION; LARGE-SCALE; WEB; WIKIDATA;
   DBPEDIA; YAGO
AB Over the last decade, extensive research has been done on automatic construction of knowledge graphs from Web resources, resulting in a number of large-scale knowledge graphs such as YAGO, DBpedia, BabelNet, and Wikidata. Despite that some of these knowledge graphs are multilingual, they contain few or no linked data in Persian, and do not support tools for extracting knowledge from Persian information sources. FarsBase (available at http://farsbase.net/about) is the first Persian multi-source knowledge graph, which is specifically designed for semantic search engines to support Persian knowledge. FarsBase uses a diverse set of hybrid and flexible techniques to extract and integrate knowledge from various sources, such as Wikipedia, Web tables and unstructured texts. It also supports entity linking, which allows integration with other knowledge graphs. To maintain high accuracy for triples, we adopt a low-cost mechanism for verifying candidate knowledge by human experts, where the candidates for human verification are prioritized using different heuristics. FarsBase is being used as the semantic-search system of a Persian search engine and efficiently answers hundreds of semantic queries per second.
C1 [Asgari-Bidhendi, Majid; Hadian, Ali; Minaei-Bidgoli, Behrouz] Iran Univ Sci & Technol, Dept Comp Engn, Tehran, Iran.
RP Minaei-Bidgoli, B (通讯作者)，Iran Univ Sci & Technol, Dept Comp Engn, Tehran, Iran.
EM majid.asgari@live.com; ali.hadian@gmail.com; b_minaei@iust.ac.ir
RI Hadian, Ali/ABB-6349-2021; Minaei-Bidgoli, Behrouz/L-2779-2018
OI Hadian, Ali/0000-0003-2010-0765; Minaei-Bidgoli,
   Behrouz/0000-0002-9327-7345
FU Iran Telecommunication Research Center (ITRC)
FX This research is funded by the Iran Telecommunication Research Center
   (ITRC). We thank all Fars-Base contributors, including Mohammad-Bagher
   Sajadi, Hossein Khademi-Khaledi, Mostafa Mahdavi, Abolfazl Mahdizadeh,
   Nasim Damirchi, Leila Oskooei, Ensieh Hemmatan, Morteza Khaleghi, Razieh
   FarjamFard, Mohammad Abdous, Mohsen Rahimi, Ehsan Shahshahani, Yousef
   Alizadeh, and Dr. Samad Paydar. We also thank the editors and the
   reviewers for their valuable comments.
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EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2019
VL 10
IS 6
BP 1169
EP 1196
DI 10.3233/SW-190369
PG 28
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JH5WI
UT WOS:000492839300010
DA 2022-08-23
ER

PT J
AU Gilchrist, A
AF Gilchrist, A
TI Corporate taxonomies: report on a survey of current practice
SO ONLINE INFORMATION REVIEW
LA English
DT Article
DE classification; information; literacy; information retrieval; companies;
   terminology
AB Presents a report of a survey, based on case studies, of current practice in the building and use of taxonomies, with particular emphasis on corporate taxonomies to support enterprise information portals. Of the 22 case studies, six were designated as "core" and four are discussed in this paper: British Broadcasting Corporation, Glare Wellcome, Microsoft, and Unilever. Discusses key factors leading enterprises to consider the building of corporate taxonomies and describes the uses of taxonomies, principally as, inter alia, a source of authority for tagging, to aid navigation, to support search engines, as knowledge maps, and as a depository of enterprise retrieval languages. Key findings are presented.
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PI BRADFORD
PA 60/62 TOLLER LANE, BRADFORD BD8 9BY, W YORKSHIRE, ENGLAND
SN 1468-4527
J9 ONLINE INFORM REV
JI Online Inf. Rev.
PY 2001
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DI 10.1108/14684520110390033
PG 9
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 435UW
UT WOS:000168901400003
DA 2022-08-23
ER

PT J
AU Li, P
   Li, TC
   Wang, X
   Zhang, SZ
   Jiang, YC
   Tang, Y
AF Li, Pu
   Li, Tianci
   Wang, Xin
   Zhang, Suzhi
   Jiang, Yuncheng
   Tang, Yong
TI Scholar Recommendation Based on High-Order Propagation of Knowledge
   Graphs
SO INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS
LA English
DT Article
DE Dual Aggregation; High-Order Propagation; Knowledge Graph; Scholar
   Recommendation
AB In a big data environment, traditional recommendation methods have limitations such as data sparseness and cold start, etc. In view of the rich semantics, excellent quality, and good structure of knowledge graphs, many researchers have introduced knowledge graphs into the research about recommendation systems and studied interpretable recommendations based on knowledge graphs. Along this line, this paper proposes a scholar recommendation method based on the high-order propagation of knowledge graph (HoPKG), which analyzes the high-order semantic information in the knowledge graph and generates richer entity representations to obtain users' potential interest by distinguishing the importance of different entities. On this basis, a dual aggregation method of high-order propagation is proposed to enable entity information to be propagated more effectively. Through experimental analysis, compared with some baselines, such as Ripplenet, RKGE, and CKE, the method has certain advantages in the evaluation indicators AUC and F-1.
C1 [Li, Pu; Zhang, Suzhi] Zhengzhou Univ Light Ind, Software Engn Coll, Zhengzhou, Peoples R China.
   [Li, Tianci] Zhengzhou Univ Light Ind, Coll Software Engn, Software Engn, Zhengzhou, Peoples R China.
   [Wang, Xin] Zhengzhou Univ Light Ind, Coll Software Engn, Elect Informat, Zhengzhou, Peoples R China.
   [Jiang, Yuncheng; Tang, Yong] South China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China.
RP Li, P (通讯作者)，Zhengzhou Univ Light Ind, Software Engn Coll, Zhengzhou, Peoples R China.
FU National Natural Science Foundation of China [61802352, 61772211,
   61772210, 62072414, 61975187, 62006212, 62102372]; Program for Young Key
   Teachers of Henan Province [2021GGJS095]; Project of collaborative
   innovation in Zhengzhou [2021ZDPY0208]; Ph.D. Research Foundation of
   Zhengzhou University of Light Industry [2017BSJJ052]; Project of Science
   and Technology in Henan Province [202102210178, 222102210030,
   212102210418, 222102210027, 212102210104]
FX The works described in this paper are supported by The National Natural
   Science Foundation of China under Grant Nos. 61802352, 61772211,
   61772210, 62072414, 61975187, 62006212, 62102372; The Program for Young
   Key Teachers of Henan Province under Grant No. 2021GGJS095; The Project
   of collaborative innovation in Zhengzhou under Grant No. 2021ZDPY0208;
   The Ph.D. Research Foundation of Zhengzhou University of Light Industry
   under Grant No. 2017BSJJ052; The Project of Science and Technology in
   Henan Province under Grant Nos. 202102210178, 222102210030,
   212102210418, 222102210027, 212102210104.
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NR 30
TC 0
Z9 0
U1 0
U2 0
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1552-6283
EI 1552-6291
J9 INT J SEMANT WEB INF
JI Int. J. Semant. Web Inf. Syst.
PY 2022
VL 18
IS 1
DI 10.4018/IJSWIS.297146
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3G6ZO
UT WOS:000831500300027
OA gold
DA 2022-08-23
ER

PT B
AU Hoede, C
AF Hoede, C
BE Ellis, G
   Levinson, R
   Rich, W
   Sowa, JF
TI On the ontology of knowledge graphs
SO CONCEPTUAL STRUCTURES: APPLICATIONS, IMPLEMENTATION AND THEORY
SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
LA English
DT Article; Proceedings Paper
CT 3rd International Conference on Conceptual Structures (ICCS 95)
CY AUG 14-18, 1995
CL UNIV CALIF SANTA CRUZ, SANTA CRUZ, CA
SP IBM, Santa Teresa Lab, San Jose, Univ Calif Santa Cruz, Royal Melbourne Univ Technol, Austr, Amer Assoc Artificial Intelligence
HO UNIV CALIF SANTA CRUZ
AB Knowledge graphs are a special kind of conceptual graphs. They were developed independently from 1982 on and are special in that. a very restricted set of types of relationships is used. In this paper the ontology of knowledge graphs and the philosophy behind its choice are discussed.
RP Hoede, C (通讯作者)，UNIV TWENTE,DEPT APPL MATH,POB 217,7500 AE ENSCHEDE,NETHERLANDS.
CR BAKKER RR, 1987, THESIS U TWENTE ENSC
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NR 14
TC 0
Z9 0
U1 2
U2 4
PU SPRINGER-VERLAG BERLIN
PI BERLIN 33
PA HEIDELBERGER PLATZ 3, W-1000 BERLIN 33, GERMANY
BN 3-540-60161-9
J9 LECT NOTES ARTIF INT
PY 1995
VL 954
BP 308
EP 322
PG 15
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BE73G
UT WOS:A1995BE73G00022
DA 2022-08-23
ER

PT J
AU Jia, YT
   Wang, YZ
   Jin, XL
   Lin, HL
   Cheng, XQ
AF Jia, Yantao
   Wang, Yuanzhuo
   Jin, Xiaolong
   Lin, Hailun
   Cheng, Xueqi
TI Knowledge Graph Embedding: A Locally and Temporally Adaptive
   Translation-Based Approach
SO ACM TRANSACTIONS ON THE WEB
LA English
DT Article
DE Locally and temporally adaptive translation; knowledge graph embedding;
   optimal margin; convergence
ID STABILITY
AB A knowledge graph is a graph with entities of different types as nodes and various relations among them as edges. The construction of knowledge graphs in the past decades facilitates many applications, such as link prediction, web search analysis, question answering, and so on. Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, for example, TransE, TransH, and TransR, learn the embedding representation by defining a global margin-based loss function over the data. However, the loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidates, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this article, a locally adaptive translation method for knowledge graph embedding, called TransA, is proposed to find the loss function by adaptively determining its margin over different knowledge graphs. Then the convergence of TransA is verified from the aspect of its uniform stability. To make the embedding methods up-to-date when new vertices and edges are added into the knowledge graph, the incremental algorithm for TransA, called iTransA, is proposed by adaptively adjusting the optimal margin over time. Experiments on four benchmark data sets demonstrate the superiority of the proposed method, as compared to the state-of-the-art ones.
C1 [Jia, Yantao; Wang, Yuanzhuo; Jin, Xiaolong; Cheng, Xueqi] Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China.
   [Lin, Hailun] Chinese Acad Sci, Inst Informat Engn, Beijing 100193, Peoples R China.
RP Jia, YT (通讯作者)，Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China.
EM jiayantao@ict.ac.cn; wangyuanzhuo@ict.ac.cn; jinxiaolong@ict.ac.cn;
   linhailun@iie.ac.cn; cxq@ict.ac.cn
FU National Key Research and Development Program of China [2016YFB1000902];
   National Grand Fundamental Research 973 Program of China [2012CB316303,
   2014CB340401]; National High-tech R&D Program (863 Program)
   [2015AA020104]; National Natural Science Foundation of China [61402442,
   61572469, 91646120, 61572473, 61303244, 61402124]
FX This work is supported by National Key Research and Development Program
   of China under grant 2016YFB1000902, National Grand Fundamental Research
   973 Program of China (No. 2012CB316303, 2014CB340401), National
   High-tech R&D Program (863 Program) (No. 2015AA020104), National Natural
   Science Foundation of China (No. 61402442, 61572469, 91646120, 61572473,
   61303244, 61402124).
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NR 55
TC 9
Z9 9
U1 0
U2 31
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1559-1131
EI 1559-114X
J9 ACM T WEB
JI ACM Trans. Web
PD JUN
PY 2018
VL 12
IS 2
AR 8
DI 10.1145/3132733
PG 33
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GK9MB
UT WOS:000436573100002
DA 2022-08-23
ER

PT J
AU Ding, LL
   Li, SS
   Li, M
   Chen, Z
   Zhang, HL
   Luo, H
   Yuan, GY
AF Ding, Linlin
   Li, Sisi
   Li, Mo
   Chen, Ze
   Zhang, Hanlin
   Luo, Hao
   Yuan, George Y.
TI Example query on ontology-labels knowledge graph based on filter-refine
   strategy
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Knowledge graph; Ontology-labels knowledge graph; Example query;
   Filter-refine strategy
ID SEARCH; RDF
AB The query processing on knowledge graphs has attracted significant attention in the past years. Different from the traditional query processing on knowledge graphs, the example query method can capture the users' query intentions by providing examples. But regrettably, it does not consider the semantic relevance of entities. Therefore, we first define an example query on the ontology-labels knowledge graph to better capture the query interest of users and improve the semantic relevance of query results. Second, a Filter-Refine Strategy-based method is proposed to solve the example queries. Specifically, we propose the ontology-labels tree index to reduce the search space and the bidirectional index to improve query efficiency. Then, an effective candidate results combination technology is used to return top-k results directly. Extensive experiments over two real-world data sets have shown our proposed algorithm is superior to three existing algorithms in terms of efficiency and effectiveness.
C1 [Ding, Linlin; Li, Sisi; Li, Mo; Chen, Ze; Zhang, Hanlin; Luo, Hao] Liaoning Univ, Sch Informat, Shenyang 110036, Liaoning, Peoples R China.
   [Yuan, George Y.] Thinvent Digital Technol Co Ltd, Nanchang 330096, Jiangxi, Peoples R China.
RP Luo, H (通讯作者)，Liaoning Univ, Sch Informat, Shenyang 110036, Liaoning, Peoples R China.
EM dinglinlin@lnu.edu.cn; luohao8711@163.com
FU National Natural Science Foundation of China [62072220, 61502215];
   Central Government Guides Local Science and Technology Development
   Foundation Project of Liaoning Province [2022JH6/100100032]; China
   Postdoctoral Science Foundation Funded Project [2020M672134]
FX This study was funded by the National Natural Science Foundation of
   China (No. 62072220, 61502215). Central Government Guides Local Science
   and Technology Development Foundation Project of Liaoning Province (No.
   2022JH6/100100032). China Postdoctoral Science Foundation Funded Project
   (No. 2020M672134).
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NR 44
TC 0
Z9 0
U1 4
U2 4
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
DI 10.1007/s11280-022-01020-7
EA MAR 2022
PG 31
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZV7WP
UT WOS:000770735900002
DA 2022-08-23
ER

PT J
AU Shao, TY
   Li, XY
   Zhao, X
   Xu, H
   Xiao, WD
AF Shao, Tianyang
   Li, Xinyi
   Zhao, Xiang
   Xu, Hao
   Xiao, Weidong
TI DSKRL: A dissimilarity-support-aware knowledge representation learning
   framework on noisy knowledge graph
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph; Knowledge graph completion; Noise detection;
   Dissimilarity-support
AB Knowledge graphs are widely used in a variety of knowledge-driven applications. Due to the inefficiency of manual construction of knowledge graphs, automatic construction mechanism has become the dominant method, which introduces noise. However, most knowledge representation learning approaches assume that there is no noise in the knowledge graph and hence ignore noise detection. In this paper, we propose a dissimilarity-support-aware knowledge representation learning framework, which accomplishes knowledge representation learning and noise detection simultaneously. Specifically, we introduce triple dissimilarity and triple support to construct the model energy function which is based on translation based methods. The triple dissimilarity measures the matching extent of entities and relations in triples and the triple support measures the credibility of the matching extent. In order to make triple dissimilarity and triple support estimation effective and comprehensive, we synthesize structural information and auxiliary information (entity hierarchical type and relation path information) in triple dissimilarity and triple support. We conduct experiments on three datasets for the knowledge graph noise detection task and the knowledge graph completion task. The experimental results show that our model achieves significant and consistent improvements compared to all baseline methods. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Shao, Tianyang; Li, Xinyi; Zhao, Xiang; Xu, Hao; Xiao, Weidong] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.
   [Zhao, Xiang; Xiao, Weidong] Collaborat Innovat Ctr Geospatial Technol, Wuhan, Peoples R China.
RP Li, XY; Xiao, WD (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.
EM xinyili@nudt.edu.cn; wdxiao@nudt.edu.cn
FU NSFC [71971212, 62002373, 61872446]; NSF of Hunan province [2019JJ20024]
FX This work was partially supported by NSFC under Grant Nos. 71971212,
   62002373 and 61872446, and NSF of Hunan province under Grant No.
   2019JJ20024.
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NR 37
TC 0
Z9 0
U1 6
U2 11
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD OCT 21
PY 2021
VL 461
BP 608
EP 617
DI 10.1016/j.neucom.2021.02.099
EA SEP 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WC2IT
UT WOS:000704086300011
DA 2022-08-23
ER

PT J
AU Fettach, Y
   Ghogho, M
   Benatallah, B
AF Fettach, Yousra
   Ghogho, Mounir
   Benatallah, Boualem
TI Knowledge Graphs in Education and Employability: A Survey on
   Applications and Techniques
SO IEEE ACCESS
LA English
DT Article
DE Education; Feature extraction; Semantics; Taxonomy; Task analysis;
   Representation learning; Object recognition; Educational technologies;
   employability; knowledge graphs; representation learning
ID SYSTEM
AB Studies on the relationship between education and employability are of paramount importance for policy makers, training institutions, companies and students. The availability of large scale data on the Internet, such as online job ads, has been leveraged to better investigate this relationship. Recent studies have shown that representing this data using knowledge graphs can be useful in identifying the job market needs and establishing better assessment methods. Such studies also highlight the skill mismatch between education and the job market and help mitigate it. In this paper, we provide a comprehensive review of the applications of knowledge graphs in education and employability. We review knowledge graph based frameworks that have been proposed in previous work on different aspects of education and employability. The survey introduces new taxonomies for these applications and their methodologies, and provides a thorough outlook on promising research directions. We also present a use case to illustrate how knowledge graphs can be used to investigate the relationship between education and the job market.
C1 [Fettach, Yousra; Ghogho, Mounir] Int Univ Rabat, Coll Engn & Architecture, TIC Lab, Sale 11103, Morocco.
   [Ghogho, Mounir] Univ Leeds, Fac Engn, Leeds LS2 9JT, W Yorkshire, England.
   [Benatallah, Boualem] Dublin City Univ, Sch Comp, Dublin D09 W6Y4 9, Ireland.
   [Benatallah, Boualem] UNSW Sydney, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia.
RP Fettach, Y (通讯作者)，Int Univ Rabat, Coll Engn & Architecture, TIC Lab, Sale 11103, Morocco.
EM yousra.fettach@uir.ac.ma
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NR 66
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 80174
EP 80183
DI 10.1109/ACCESS.2022.3194063
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3R0PN
UT WOS:000838622700001
OA gold
DA 2022-08-23
ER

PT J
AU Yang, Y
   Li, M
   Wang, J
   Huang, WX
   Wang, Y
AF Yang, Yi
   Li, Meng
   Wang, Jian
   Huang, Weixing
   Wang, Yun
TI Entity Recommendation With Negative Feedback Memory Networks for
   Topic-Oriented Knowledge Graph Exploration
SO IEEE TRANSACTIONS ON RELIABILITY
LA English
DT Article
DE Semantics; Adaptation models; Task analysis; Feature extraction;
   Transformers; Data mining; Recommender systems; Entity recommendation;
   knowledge graph; knowledge graph exploration; memory network; negative
   feedback
AB Knowledge graph exploration is an interactive knowledge discovery process over the knowledge graph. Entity recommendation deals with the information overflow issue when exploring the large-scale unfamiliar knowledge graphs. The traditional personalized entity recommendation methods for knowledge graph explorations rarely consider the adaptive topic-oriented long-term positive- and negative intent modeling. In this article, we propose a topic-oriented entity recommendation method during the knowledge graph exploration. We build a negative feedback memory network model for obtaining the user's long-term negative intents. We propose a transformer-based sequence encoder for the positive intents. We dynamically obtain the adaptive intents by aggregating the positive- and negative intents by the proposed intent attention mechanism. Experiments show that our method has advantages in TopK entity recommendations.
C1 [Yang, Yi; Li, Meng; Wang, Jian; Huang, Weixing; Wang, Yun] Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China.
RP Wang, J (通讯作者)，Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China.
EM yangyi@ia.ac.cn; meng_li@ia.ac.cn; jian.wang@ia.ac.cn;
   weixing.huang@ia.ac.cn; y.wang@ia.ac.cn
FU National Science and Technology Major Project [2017-I-0006-0007]
FX This work was supported by the National Science and Technology Major
   Project under Grant 2017-I-0006-0007.
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NR 56
TC 0
Z9 0
U1 10
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9529
EI 1558-1721
J9 IEEE T RELIAB
JI IEEE Trans. Reliab.
PD JUN
PY 2022
VL 71
IS 2
BP 788
EP 802
DI 10.1109/TR.2022.3169092
EA MAY 2022
PG 15
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1V0SX
UT WOS:000795468100001
DA 2022-08-23
ER

PT J
AU Su, HN
   Lee, PC
AF Su, Hsin-Ning
   Lee, Pei-Chun
TI Mapping knowledge structure by keyword co-occurrence: a first look at
   journal papers in Technology Foresight
SO SCIENTOMETRICS
LA English
DT Article
DE Keyword; Network theory; Knowledge structure; Technology Foresight
ID SCIENCE-AND-TECHNOLOGY; CO-WORD ANALYSIS; DISCOVERY LRD; BIBLIOMETRIC
   ANALYSIS; POTENTIAL TREATMENTS; NETWORK; CARTOGRAPHY; CHINA;
   INFRASTRUCTURE; INTELLIGENCE
AB This study proposes an approach for visualizing a knowledge structure, the proposed approach creates a three-dimensional "Research focused parallelship network", a "Keyword Co-occurrence Network", and a two-dimensional knowledge map to facilitate visualization of the knowledge structure created by journal papers from different perspectives. The networks and knowledge maps can be depicted differently by choosing different information as the network actor, e.g. author, institute or country keyword, to reflect knowledge structures in micro-, meso-, and macro-levels, respectively. Technology Foresight is selected as an example to illustrate the method proposed in this study. A total of 556 author keywords contained in 181 Technology Foresight related papers have been analyzed. European countries, China, India and Brazil are located at the core of Technology Foresight research. Quantitative ways of mapping journal papers are investigated in this study to unveil emerging elements as well as to demonstrate dynamics and visualization of knowledge. The quantitative method provided in this paper shows a possible way of visualizing and evaluating knowledge structure; thus a computerized calculation is possible for potential quantitative applications, e.g. R&D resource allocation, research performance evaluation, science map, etc.
C1 [Lee, Pei-Chun] Natl Chengchi Univ, Grad Inst Technol & Innovat Management, 64,Sec 2,Chih Nan Rd, Taipei 116, Taiwan.
   [Su, Hsin-Ning; Lee, Pei-Chun] Natl Appl Res Labs, Sci & Technol Policy Res & Informat Ctr, Taipei 106, Taiwan.
   [Lee, Pei-Chun] Univ Sussex, Freeman Ctr, SPRU Sci & Technol Policy Res, Brighton BN1 9QE, E Sussex, England.
RP Lee, PC (通讯作者)，Natl Chengchi Univ, Grad Inst Technol & Innovat Management, 64,Sec 2,Chih Nan Rd, Taipei 116, Taiwan.
EM hnsu@mail.stpi.org.tw; pclee@mail.stpi.org.tw
RI Su, Hsin-Ning/H-6209-2011
OI Su, Hsin-Ning/0000-0002-4325-0793
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NR 70
TC 321
Z9 330
U1 21
U2 259
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 2010
VL 85
IS 1
BP 65
EP 79
DI 10.1007/s11192-010-0259-8
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 639CC
UT WOS:000280947400006
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Smallenburg, K
   Halman, JIM
   vanMal, HH
AF Smallenburg, K
   Halman, JIM
   vanMal, HH
TI Towards re-use of knowledge in the concept stage of development
SO INTERNATIONAL JOURNAL OF TECHNOLOGY MANAGEMENT
LA English
DT Article; Proceedings Paper
CT 5th International Forum on Technology Management
CY JUN 07-08, 1995
CL HELSINKI, FINLAND
DE development of product concepts; process knowledge maps; technology
   management; innovation management
AB A framework for specifying the intended result of a product development project is introduced in the paper. This framework has been developed for the concept stage and facilitates the establishment and re-use of acquired knowledge. In this way, core capabilities naturally grow stronger with each development project, and are exploited in a way that enables a company to do things its competitors cannot. Implementation of the framework within Colorproofing Systems - an organization developing and manufacturing industrial printing systems - improved the learning capabilities of the firm.
RP Smallenburg, K (通讯作者)，EINDHOVEN UNIV TECHNOL,GRAD SCH IND ENGN & MANAGEMENT SCI,POB 513,5600 MB EINDHOVEN,NETHERLANDS.
CR BOWEN HK, 1994, HARVARD BUSINESS SEP, P110
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   [No title captured]
NR 10
TC 1
Z9 2
U1 0
U2 7
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA AEROPORT
PA WORLD TRADE CENTER BLDG 110 AVE LOUIS CASAI CP 306, CH-1215 GENEVA
   AEROPORT, SWITZERLAND
SN 0267-5730
J9 INT J TECHNOL MANAGE
JI Int. J. Technol. Manage.
PY 1996
VL 11
IS 3-4
BP 343
EP 353
PG 11
WC Engineering, Multidisciplinary; Management; Operations Research &
   Management Science
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH); Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics; Operations Research & Management
   Science
GA UJ963
UT WOS:A1996UJ96300009
DA 2022-08-23
ER

PT J
AU Gao, WX
   Wang, W
   Yu, JH
AF Gao, Weixia
   Wang, Wei
   Yu, Jihong
TI Insights into Sustainable Design Education: A Bibliometric and Visual
   Analysis
SO INTERNATIONAL JOURNAL OF ENGINEERING EDUCATION
LA English
DT Article
DE bibliometric analysis; sustainable design education; Bibexcel;
   CiteSpace; knowledge graph
ID SOCIAL-RESPONSIBILITY; CAMPUS SUSTAINABILITY; CITATION; COLLABORATION;
   UNIVERSITIES; EXPERIENCES; INDICATORS; CHALLENGES; COURSES; IMPACT
AB In recent years, research on sustainable design education (SDE) has attracted worldwide attention, but the current situation, theme and development trend of this field are still unclear. Based on the Web of Science Core Collection (WOSCC), this paper draws a knowledge map with 1265 papers published in Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI) and Science Citation Index Expanded (SCI-E) database as the source, and uses the visualization software Bibexcel and CiteSpace to systematically examine the knowledge output characteristics, cooperation networks, emerging hotspots, thematic context, knowledge base and frontiers of global SDE research from the perspective of bibliometrics. It is found that the number of SDE publications is increasing rapidly, and the interdisciplinary nature is strong. The cooperation between authors and institutions is relatively weak, and cooperation among countries focuses on the USA, Japan, South Korea and member states of the European Union (EU). Hotspots form a four mainstream research themes distributing at content, relation, practice and object level. 22 papers play a key role in SDE field development. The frontiers are divided into three evolution stages: the concept-oriented from 2010 to 2014, the path-oriented from 2015 to 2017, and the organization-oriented stage from 2018 to 2020.The discussion of this study can provide reference for those interested in design education (DE) , engineering education (EE) and other fields related to sustainable development (SD).
C1 [Gao, Weixia; Wang, Wei] Changshu Inst Technol, Sch Text Garment & Design, Changshu 215500, Jiangsu, Peoples R China.
   [Yu, Jihong] Donghua Univ, Coll Fash & Design, Shanghai 200051, Peoples R China.
RP Yu, JH (通讯作者)，Donghua Univ, Coll Fash & Design, Shanghai 200051, Peoples R China.
EM 1046978972@qq.com; 18201855821@163.com
FU Philosophy and Social Science Foundation of China [17ZDA020]; Excellent
   Innovation Team of Philosophy and Social Sciences in Jiangsu
   Universities [2017ZSTD015]; Changshu Institute of Technology [GJ2011]
FX This research was funded by the Philosophy and Social Science Foundation
   of China (17ZDA020) , the Excellent Innovation Team of Philosophy and
   Social Sciences in Jiangsu Universities (2017ZSTD015) and the Research
   Project funded by Changshu Institute of Technology (GJ2011) .
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NR 68
TC 0
Z9 0
U1 9
U2 12
PU TEMPUS PUBLICATIONS
PI DURRUS, BANTRY
PA IJEE , ROSSMORE,, DURRUS, BANTRY, COUNTY CORK 00000, IRELAND
SN 0949-149X
J9 INT J ENG EDUC
JI Int. J. Eng. Educ
PY 2021
VL 37
IS 5
BP 1186
EP 1200
PG 15
WC Education, Scientific Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Engineering
GA XO4CF
UT WOS:000730133900005
DA 2022-08-23
ER

PT J
AU Zhu, HP
   Ni, YF
   Tian, F
   Feng, P
   Chen, Y
   Zheng, QH
AF Zhu, Haiping
   Ni, Yifu
   Tian, Feng
   Feng, Pei
   Chen, Yan
   Zheng, Qinghua
TI A Group-Oriented Recommendation Algorithm Based on Similarities of
   Personal Learning Generative Networks
SO IEEE ACCESS
LA English
DT Article
DE Graph similarity; group recommendation; knowledge map; learning
   generative network
AB To solve the lack of consideration of the learning time sequence and knowledge dependencies in group-based recommendation, we proposed a novel group-oriented recommendation algorithm which is characterized by mapping the user's learning log to a personal learning generative network (PLGN) based on a knowledge map. In this paper, we first provide calculation methods of similarity and temporal correlation between knowledge points, where we provide the construction method of the PLGN. Second, a method for measuring the similarities between any two PLGNs is proposed. According to the similarities, we perform the CURE clustering algorithm to obtain learning groups. Third, based on the group clustering, the group learning generative network using a graph overlay method is generated. We calculate the importance of the vertices on the different learning needs and propose a group-oriented recommendation algorithm. Finally, we compare the effect of the proposed recommendation to that of a group-based collaborative filtering recommendation for the aspects of precision rate, recall rate, normalized discounted cumulative gain, and the average accuracy of parameters (MAP). The experimental results show that the group-oriented learning recommendation based on the learning generated network outperforms the group recommendation-based collaborative filtering when the amount of data is large enough.
C1 [Zhu, Haiping; Ni, Yifu; Chen, Yan; Zheng, Qinghua] Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Shaanxi, Peoples R China.
   [Zhu, Haiping; Ni, Yifu; Tian, Feng; Feng, Pei; Chen, Yan; Zheng, Qinghua] Xi An Jiao Tong Univ, MOEKLINNS Lab, Xian 710049, Shaanxi, Peoples R China.
   [Zhu, Haiping; Feng, Pei; Chen, Yan; Zheng, Qinghua] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Shaanxi, Peoples R China.
   [Tian, Feng] Xi An Jiao Tong Univ, Syst Engn Inst, Xian 710049, Shaanxi, Peoples R China.
RP Tian, F (通讯作者)，Xi An Jiao Tong Univ, MOEKLINNS Lab, Xian 710049, Shaanxi, Peoples R China.; Tian, F (通讯作者)，Xi An Jiao Tong Univ, Syst Engn Inst, Xian 710049, Shaanxi, Peoples R China.
EM fengtian@mail.xjtu.edu.cn
OI tian, feng/0000-0001-7888-0587
FU National Key Research and Development Program of China [2018YFB1004500];
   National Science Foundation of China [61472315]; Innovative Research
   Group of the National Natural Science Foundation of China [61721002];
   Innovation Research Team of the Ministry of Education [IRT_17R86];
   Project of the China Knowledge Centre for Engineering Science and
   Technology
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018YFB1004500, in part by the
   National Science Foundation of China under Grant 61472315, in part by
   the Innovative Research Group of the National Natural Science Foundation
   of China under Grant 61721002, in part by the Innovation Research Team
   of the Ministry of Education under Grant IRT_17R86, and in part by the
   Project of the China Knowledge Centre for Engineering Science and
   Technology.
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NR 30
TC 7
Z9 8
U1 0
U2 20
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2018
VL 6
BP 42729
EP 42739
DI 10.1109/ACCESS.2018.2856753
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA GR2LW
UT WOS:000442404500043
OA gold
DA 2022-08-23
ER

PT J
AU Ballandies, MC
   Pournaras, E
AF Ballandies, Mark C.
   Pournaras, Evangelos
TI Mobile link prediction: Automated creation and crowdsourced validation
   of knowledge graphs
SO MICROPROCESSORS AND MICROSYSTEMS
LA English
DT Article
DE Knowledge graph; Ontology; Cyber-physical-social system; Link
   prediction; Genetic programming; Crowdsourcing
ID PHYSICAL-SOCIAL SYSTEMS; AUTONOMY; TRUST
AB Building trustworthy knowledge graphs for cyber-physical social systems (CPSS) is a challenge. In particular, current approaches relying on human experts have limited scalability, while automated approaches are often not validated by users resulting in knowledge graphs of questionable quality. This paper introduces a novel pervasive knowledge graph builder for mobile devices that brings together automation, experts' and crowdsourced citizens' knowledge. The knowledge graph grows via automated link predictions using genetic programming that are validated by humans for improving transparency and calibrating accuracy. The knowledge graph builder is designed for pervasive devices such as smartphones and preserves privacy by localizing all computations. The accuracy, practicality, and usability of the knowledge graph builder is evaluated in a real-world social experiment that involves a smartphone implementation and a Smart City application scenario. The proposed methodology of knowledge graph building outperforms a baseline method in terms of accuracy while demonstrating its efficient calculations on smartphones and the feasibility of the pervasive human supervision process in terms of high interactions throughput. These findings promise new opportunities to crowdsource and operate pervasive reasoning systems for cyber-physical social systems in Smart Cities.
C1 [Ballandies, Mark C.] Swiss Fed Inst Technol, Computat Social Sci, Zurich, Switzerland.
   [Pournaras, Evangelos] Univ Leeds, Sch Comp, Leeds LS2 9JT, W Yorkshire, England.
RP Ballandies, MC (通讯作者)，Swiss Fed Inst Technol, Computat Social Sci, Zurich, Switzerland.
EM bmark@ethz.ch; E.Pournaras@leeds.ac.uk
OI Ballandies, Mark Christopher/0000-0001-9350-9823
FU Swiss National Science Foundation [170226]
FX This work is supported by the Swiss National Science Foundation (grant
   no. 170226) for the European FLAG ERA project `FuturICT 2.0 -Large scale
   experiments and simulations for the second generation of FuturICT'
   (https://futurict2.eu/).
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NR 87
TC 1
Z9 1
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0141-9331
EI 1872-9436
J9 MICROPROCESS MICROSY
JI Microprocess. Microsyst.
PD NOV
PY 2021
VL 87
AR 104335
DI 10.1016/j.micpro.2021.104335
PG 14
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA ZY3JI
UT WOS:000772483700012
OA Green Submitted, hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Arnaout, H
   Elbassuoni, S
AF Arnaout, Hiba
   Elbassuoni, Shady
TI Effective searching of RDF knowledge graphs
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE RDF; Ranking; Diversity; Relaxation
ID QUERY; WEB
AB RDF knowledge graphs are typically searched using triple-pattern queries. Often, triple-pattern queries will return too many or too few results, making it difficult for users to find relevant answers to their information needs. To remedy this, we propose a general framework for effective searching of RDF knowledge graphs. Our framework extends both the searched knowledge graph and triple-pattern queries with keywords to allow users to form a wider range of queries. In addition, it provides result ranking based on statistical machine translation, and performs automatic query relaxation to improve query recall. Finally, we also define a notion of result diversity in the setting of RDF data and provide mechanisms to diversify RDF search results using Maximal Marginal Relevance. We evaluate the effectiveness of our retrieval framework using various carefully-designed user studies on DBpedia, a large and real-world RDF knowledge graph. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Arnaout, Hiba; Elbassuoni, Shady] Amer Univ Beirut, Comp Sci Dept, Riad El Solh 1107, Beirut 2020, Lebanon.
RP Elbassuoni, S (通讯作者)，Amer Univ Beirut, Comp Sci Dept, Riad El Solh 1107, Beirut 2020, Lebanon.
EM hka22@aub.edu.lb; se58@aub.edu.lb
FU American University of Beirut's research board (URB) [22161.102848]
FX We would like to thank the American University of Beirut's research
   board (URB) (grant no. 22161.102848) for funding this project.
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NR 63
TC 15
Z9 15
U1 0
U2 35
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 JAN
PY 2018
VL 48
BP 66
EP 84
DI 10.1016/j.websem.2017.12.001
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FX2JO
UT WOS:000425886000004
DA 2022-08-23
ER

PT J
AU Valencia, M
   Lauth, C
   Menasalvas, E
AF Valencia, Maria
   Lauth, Codrina
   Menasalvas, Ernestina
TI EMERGING USER INTENTIONS: MATCHING USER QUERIES WITH TOPIC EVOLUTION IN
   NEWS TEXT STREAMS
SO INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED
   SYSTEMS
LA English
DT Article
DE Topic and trend detection; query enrichment; query categorization;
   knowledge mapping; knowledge acquisition
AB Trend detection analysis from unstructured data poses a huge challenge to current advanced, web-enabled knowledge-based systems (KBS). Consolidated studies in topic and trend detection from text streams have concentrated so far mainly on identifying and visualizing dynamically evolving text patterns. From the knowledge modeling perspective identifying and de. ning new, relevant features that are able to synchronize the emergent user intentions to the dynamicity of the system's structure is a need. Additionally the advanced KBS have to remain highly sensitive to the content change, marked by evolution of trends in topics extracted from text streams. In this paper, we are describing a three-layered approach called the "user-system-content method" that is helping us to identify the most relevant knowledge mapping features derived from the USER, SYSTEM and CONTENT perspectives into an overall "context model", that will enable the advanced KBS to automatically streamline the query enrichment process in a much more user-centered, dynamical and flexible way. After a general introduction to our three-layered approach, we will describe into detail the necessary process steps for the implementation of our method and will present a case study for its integration on a real multimedia web-content portal using news streams as major source of unstructured information.
C1 [Valencia, Maria; Menasalvas, Ernestina] Univ Politecn Madrid, Fac Informat, Boadilla Del Monte 28660, Spain.
   [Lauth, Codrina] Fraunhofer Inst Intelligent Anal & Informat Syst, Knowledge Discovery Grp, D-53754 St Augustin, Germany.
RP Valencia, M (通讯作者)，Univ Politecn Madrid, Fac Informat, Campus Montegancedo, Boadilla Del Monte 28660, Spain.
EM mvalencia@alumnos.fi.upm.es; codrina.lauth@iais.fraunhofer.de;
   emenasalvas@fi.upm.es
OI menasalvas, ernestina/0000-0002-5615-6798
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Z9 1
U1 0
U2 4
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 AUG
PY 2009
VL 17
SU 1
BP 59
EP 80
DI 10.1142/S0218488509006030
PG 22
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 476MW
UT WOS:000268447700005
DA 2022-08-23
ER

PT J
AU Hogan, A
   Blomqvist, E
   Cochez, M
   D'Amato, C
   de Melo, G
   Gutierrez, C
   Kirrane, S
   Gayo, JEL
   Navigli, R
   Neumaier, S
   Ngomo, ACN
   Polleres, A
   Rashid, SM
   Rula, A
   Schmelzeisen, L
   Sequeda, J
   Staab, S
   Zimmermann, A
AF Hogan, Aidan
   Blomqvist, Eva
   Cochez, Michael
   D'Amato, Claudia
   de Melo, Gerard
   Gutierrez, Claudio
   Kirrane, Sabrina
   Labra Gayo, Jose Emilio
   Navigli, Roberto
   Neumaier, Sebastian
   Ngomo, Axel-Cyrille Ngonga
   Polleres, Axel
   Rashid, Sabbir M.
   Rula, Anisa
   Schmelzeisen, Lukas
   Sequeda, Juan
   Staab, Steffen
   Zimmermann, Antoine
TI Knowledge Graphs
SO ACM COMPUTING SURVEYS
LA English
DT Article
DE Knowledge graphs; graph databases; graph query languages; shapes;
   ontologies; graph algorithms; embeddings; graph neural networks; rule
   mining
ID SCALE
AB In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.
C1 [Hogan, Aidan; Gutierrez, Claudio] Univ Chile, DCC, Beauchef 851, Santiago, Chile.
   [Hogan, Aidan; Gutierrez, Claudio] IMFD, Santiago, Chile.
   [Blomqvist, Eva] Linkoping Univ, S-58183 Linkoping, Sweden.
   [Cochez, Michael] Vrije Univ Amsterdam, De Boelelaan 1111, NL-1081 HV Amsterdam, Netherlands.
   [Cochez, Michael] Elsevier, Discovery Lab, Amsterdam, Netherlands.
   [D'Amato, Claudia] Univ Bari, Dipartimento Informat, Campus Univ,Via Orabona 4, I-70126 Bari, Italy.
   [de Melo, Gerard] Rutgers State Univ, New Brunswick, NJ USA.
   [Kirrane, Sabrina; Neumaier, Sebastian; Polleres, Axel] WU Vienna, Vienna, Austria.
   [Labra Gayo, Jose Emilio] Univ Oviedo, Dept Comp Sci, Oviedo 33007, Spain.
   [Navigli, Roberto] Sapienza Univ Rome, Rome, Italy.
   [Ngomo, Axel-Cyrille Ngonga] Paderborn Univ, DICE, Warburgerstr 100, D-33098 Paderborn, Germany.
   [Rashid, Sabbir M.] Rensselaer Polytech Inst, Tetherless World Constellat, 164 James St, Worcester, MA 01603 USA.
   [Rula, Anisa] Univ Milano Bicocca, Milan, Italy.
   [Rula, Anisa] Univ Bonn, Bonn, Germany.
   [Schmelzeisen, Lukas; Staab, Steffen] Univ Stuttgart, IPVS, Univ Str 32, D-70569 Stuttgart, Germany.
   [Sequeda, Juan] Dataworld, 7000 N Mopac Expy Suite 425, Austin, TX 78731 USA.
   [Staab, Steffen] Univ Southampton, Southampton, Hants, England.
   [Zimmermann, Antoine] Ecole Mines St Etienne, St Etienne, France.
   [de Melo, Gerard] HPI, Prof Dr Helmert Str 2, D-14482 Potsdam, Germany.
   [Kirrane, Sabrina] Vienna Univ Econ & Business, Weldhandelspl 1, A-1020 Vienna, Austria.
   [Neumaier, Sebastian] FH St Polten, A-3100 St Polten, Austria.
   [Rula, Anisa] Dept Informat Engn, Via Branze 38, I-25121 Brescia, Italy.
RP Hogan, A (通讯作者)，Univ Chile, DCC, Beauchef 851, Santiago, Chile.; Hogan, A (通讯作者)，IMFD, Santiago, Chile.
EM ahogan@dcc.uchile.cl; eva.blomqvist@liu.se; m.cochez@vu.nl;
   claudia.damato@uniba.it; gerard.demelo@rutgers.edu; skirrane@wu.ac.at;
   labra@uniovi.es; navigli@di.uniroma1.it; sebastian.neumaier@wu.ac.at;
   axel.ngonga@uni-paderborn.de; apollere@wu.ac.at; rashis2@rpi.edu;
   anisa.rula@unimib.it; lukas@uni-koblenz.de; juan@data.world;
   steffen.staab@ipvs.uni-stuttgart.de; antoine.zimmermann@emse.fr
RI Gayo, José Emilio Labra/L-9181-2014
OI Gayo, José Emilio Labra/0000-0001-8907-5348; Navigli,
   Roberto/0000-0003-3831-9706; Kirrane, Sabrina/0000-0002-6955-7718;
   Hogan, Aidan/0000-0001-9482-1982; Neumaier,
   Sebastian/0000-0002-9804-4882
FU Fondecyt [1181896]; ANID -Millennium Science Initiative Program
   [ICN17_002]; Elsevier's Discovery Lab; European Union [860801, 731601];
   Spanish Ministry of Economy and Competitiveness [TIN2017-88877-R];
   MOUSSE ERC [726487]; IBM Research AI through the AI Horizons Network;
   German Research Foundation (DFG) [STA 572/18-1]
FX Hogan was supported by Fondecyt Grant No. 1181896. Hogan and Gutierrez
   were funded by ANID -Millennium Science Initiative Program - Code
   ICN17_002. Cochez did part of the work while employed at Fraunhofer FIT,
   Germany and was later partially funded by Elsevier's Discovery Lab.
   Kirrane, Ngonga Ngomo, Polleres and Staab received funding through the
   project "KnowGraphs" from the European Union's Horizon programme under
   the Marie Sklodowska-Curie grant agreement No. 860801. Kirrane and
   Polleres were supported by the European Union's Horizon 2020 research
   and innovation programme under grant 731601. Labra was supported by the
   Spanish Ministry of Economy and Competitiveness (Society challenges:
   TIN2017-88877-R). Navigli was supported by the MOUSSE ERC Grant No.
   726487 under the European Union's Horizon 2020 research and innovation
   programme. Rashid was supported by IBM Research AI through the AI
   Horizons Network. Schmelzeisen was supported by the German Research
   Foundation (DFG) grant STA 572/18-1.
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NR 156
TC 54
Z9 56
U1 144
U2 316
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 0360-0300
EI 1557-7341
J9 ACM COMPUT SURV
JI ACM Comput. Surv.
PD JUL
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DI 10.1145/3447772
PG 37
WC Computer Science, Theory & Methods
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GA TF3DS
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DA 2022-08-23
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PT J
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   Qi, Guilin
   Zheng, Qiushuo
TI Richpedia: A Large-Scale, Comprehensive Multi-Modal Knowledge Graph
SO BIG DATA RESEARCH
LA English
DT Article
DE Knowledge graph; Multi-modal; Wikidata; Ontology
ID LANGUAGE
AB Large-scale knowledge graphs such as Wikidata and DBpedia have become a powerful asset for semantic search and question answering. However, most of the knowledge graph construction works focus on organizing and discovering textual knowledge in a structured representation, while paying little attention to the proliferation of visual resources on the Web. To consolidate this recent trend, in this paper, we present Richpedia, aiming to provide a comprehensive multi-modal knowledge graph by distributing sufficient and diverse images to textual entities in Wikidata. We also set Resource Description Framework links (visual semantic relations) between image entities based on the hyperlinks and descriptions in Wikipedia. The Richpedia resource is accessible on the Web via a faceted query endpoint, which provides a pathway for knowledge graph and computer vision tasks, such as link prediction and visual relation detection. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Wang, Meng; Qi, Guilin] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Wang, Meng; Qi, Guilin] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.
   [Wang, Haofen] Tongji Univ, Intelligent Big Data Visualizat Lab, Shanghai, Peoples R China.
   [Zheng, Qiushuo] Southeast Univ, Sch Cyber Sci & Engn, Nanjing, Peoples R China.
RP Qi, GL (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.; Wang, HF (通讯作者)，Tongji Univ, Intelligent Big Data Visualizat Lab, Shanghai, Peoples R China.
EM carter.whfcarter@gmail.com; gqi@seu.edu.cn
FU National Natural Science Foundation of China [61906037, U1736204];
   National Key Research and Development Program of China [2018YFC0830201,
   2017YFB1002801]; Judicial Big Data Research Centre, School of Law at
   Southeast University [4313059291]; Fundamental Research Funds for the
   Central Universities [4009009106, 22120200184]
FX This work was supported by National Natural Science Foundation of China
   with Grant Nos. 61906037 and U1736204; National Key Research and
   Development Program of China with Grant Nos. 2018YFC0830201 and
   2017YFB1002801; the Judicial Big Data Research Centre, School of Law at
   Southeast University with Grant No. 4313059291; the Fundamental Research
   Funds for the Central Universities with Grant No. 4009009106 and
   22120200184.
CR Addis M., 2010, INT DPIF, P3
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   Zhu Y, 2015, ARXIV150705670
NR 20
TC 4
Z9 5
U1 18
U2 64
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-5796
J9 BIG DATA RES
JI Big Data Res.
PD DEC
PY 2020
VL 22
AR 100159
DI 10.1016/j.bdr.2020.100159
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 OU4WS
UT WOS:000591531100004
DA 2022-08-23
ER

PT J
AU Zhang, YH
   Wang, J
   Luo, J
AF Zhang, Yuhang
   Wang, Jun
   Luo, Jie
TI Knowledge Graph Embedding Based Collaborative Filtering
SO IEEE ACCESS
LA English
DT Article
DE Collaboration; Recommender systems; Neural networks; Computational
   modeling; Measurement; Task analysis; Collaborative filtering; link
   prediction; knowledge graph embedding; recommender system
AB Along with the rapidly increasing massive online data, recommender systems have been used as an effective approach for filtering useful information, which have been widely adopted in many web applications. In recent years, new techniques such as neural network and deep learning have demonstrated its effectiveness in studies of recommender systems. In this paper, we propose a novel approach for collaborative filtering with implicit feedback based on knowledge graph embedding. The basic ideal is to model the interactions between users and items as an interaction knowledge graph with a single relation, whose vector representation is learned through knowledge graph embedding. Base on the learned representation, the collaborative filtering problem is converted into link prediction in the interaction knowledge graph. KGECF, a neural network for knowledge graph embedding based collaborative filtering, is proposed based on this ideal and the RotatE knowledge graph embedding model. Experimental results on five datasets with different characteristics show that the KGECF model achieves the state-of-the-art (SOTA) performance on all datasets. And unlike other SOTA models that have obvious performance drops on AMusic and AToy datasets, our model's performance is very stable across all datasets.
C1 [Zhang, Yuhang; Wang, Jun] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China.
   [Luo, Jie] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.
RP Wang, J (通讯作者)，Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China.; Luo, J (通讯作者)，Beihang Univ, Sch Comp Sci & Engn, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.
EM king.wang@buaa.edu.cn; luojie@nlsde.buaa.edu.cn
OI Luo, Jie/0000-0002-4157-9931
FU National Natural Science Foundation of China [71871005, 71531001]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 71871005 and Grant 71531001.
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NR 43
TC 7
Z9 7
U1 13
U2 29
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 134553
EP 134562
DI 10.1109/ACCESS.2020.3011105
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MS6EL
UT WOS:000554366600001
OA gold
DA 2022-08-23
ER

PT J
AU MacLean, F
AF MacLean, Finlay
TI Knowledge graphs and their applications in drug discovery
SO EXPERT OPINION ON DRUG DISCOVERY
LA English
DT Review
DE Biomedical knowledge graphs; drug repurposing; drug repositioning;
   heterogeneous information networks; graph machine learning; network
   embeddings; knowledge graph embedding; network pharmacology; network
   medicine
ID DISEASE
AB Introduction
   Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation.
   Areas covered
   In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility; differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies.
   Expert opinion
   Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery.
C1 [MacLean, Finlay] BenevolentAI, Target Identificat, London, England.
RP MacLean, F (通讯作者)，BenevolentAI, 4-8 Maple St, London W1T 5HD, England.
EM finlay.maclean@benevolent.ai
FU BenevolentAI
FX This manuscript was supported by BenevolentAI.
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NR 108
TC 3
Z9 3
U1 37
U2 115
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1746-0441
EI 1746-045X
J9 EXPERT OPIN DRUG DIS
JI Expert. Opin. Drug Discov.
PD SEP 2
PY 2021
VL 16
IS 9
SI SI
BP 1057
EP 1069
DI 10.1080/17460441.2021.1910673
EA APR 2021
PG 13
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA UO1PC
UT WOS:000639690800001
PM 33843398
DA 2022-08-23
ER

PT J
AU Chen, LS
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AF Chen, Lisu
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   Wang, Tianzhen
TI Evolution Trend Research of Global Ocean Power Generation Based on a
   45-Year Scientometric Analysis
SO JOURNAL OF MARINE SCIENCE AND ENGINEERING
LA English
DT Article
DE scientometric analysis; ocean power generation; knowledge map; clean
   energy; sustainable development
AB Since 1973, studies have explored ocean power generation from different perspectives. However, in the past 45 years, few studies have attempted to comprehensively review the existing studies on ocean power generation using wave energy, tidal current energy, ocean thermal energy, salinity gradient energy, bio-mass energy, and gas hydrates. In this study, we collected 5262 studies published from 1973 to 2018 for scientometric visualization analysis and drew a knowledge map of the ocean power generation field. The results show that the most important contributions to the research of ocean power generation mainly came from the United States, China, Britain, Italy, Spain, Japan, Norway, Germany, France, and Denmark. Ocean power generation research is mainly divided into two stages. From 1973 to 2007, there were relatively few studies and no obvious hot topics. From 2008 to 2018, the knowledge fields mainly focused on ocean biomass power generation, the exploitation of natural gas hydrates, the utilization of wave energy and tidal energy, the research and optimization of energy generators, the storage and management of ocean energy, and numerical simulations of marine climates. In addition, the joint utilization of wind energy and wave energy is also a current research topic of interest, including joint assessment of the two energy potentials, the research and development of equipment, and numerical simulations of joint power generation projects.
C1 [Chen, Lisu; Li, Wei] Shanghai Maritime Univ, Coll Ocean Sci & Engn, Shanghai 201306, Peoples R China.
   [Li, Jie] Liaoning Tech Univ, Coll Safety Sci & Engn, Fuxing 125000, Peoples R China.
   [Fu, Qiang] Shanghai Maritime Univ, Coll Transport & Commun, Shanghai 201306, Peoples R China.
   [Wang, Tianzhen] Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China.
RP Wang, TZ (通讯作者)，Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China.
EM cls512@zju.edu.cn; 202030410153@stu.shmtu.edu.cn; lijie@lntu.edu.cn;
   fuqiang@shmtu.edu.cn; tzwang@shmtu.edu.cn
OI wang, tianzhen/0000-0002-7525-8466
FU National Natural Science Foundation of China [31801255]
FX This work was supported by the National Natural Science Foundation of
   China, grant number 31801255.
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NR 47
TC 4
Z9 4
U1 8
U2 21
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 2021
VL 9
IS 2
AR 218
DI 10.3390/jmse9020218
PG 17
WC Engineering, Marine; Engineering, Ocean; Oceanography
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Oceanography
GA QN7GI
UT WOS:000622622700001
OA gold
DA 2022-08-23
ER

PT J
AU Choi, SJ
   Song, HJ
   Park, SB
AF Choi, Su Jeong
   Song, Hyun-Je
   Park, Seong-Bae
TI An Approach to Knowledge Base Completion by a Committee-Based Knowledge
   Graph Embedding
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge base completion; knowledge graph construction; knowledge graph
   embedding; committee machine
AB Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks. Nevertheless, they are not normally complete and have many missing facts. Such missing facts keep them from being used in diverse applications in spite of their usefulness. Therefore, it is significant to complete knowledge bases. Knowledge graph embedding is one of the promising approaches to completing a knowledge base and thus many variants of knowledge graph embedding have been proposed. It maps all entities and relations in knowledge base onto a low dimensional vector space. Then, candidate facts that are plausible in the space are determined as missing facts. However, any single knowledge graph embedding is insufficient to complete a knowledge base. As a solution to this problem, this paper defines knowledge base completion as a ranking task and proposes a committee-based knowledge graph embedding model for improving the performance of knowledge base completion. Since each knowledge graph embedding has its own idiosyncrasy, we make up a committee of various knowledge graph embeddings to reflect various perspectives. After ranking all candidate facts according to their plausibility computed by the committee, the top-k facts are chosen as missing facts. Our experimental results on two data sets show that the proposed model achieves higher performance than any single knowledge graph embedding and shows robust performances regardless of k. These results prove that the proposed model considers various perspectives in measuring the plausibility of candidate facts.
C1 [Choi, Su Jeong] KT, Inst Convergence Technol, 151 Taebong Ro, Seoul 06763, South Korea.
   [Song, Hyun-Je] Jeonbuk Natl Univ, Dept Informat Technol, 567 Baekje Daero, Jeonju Si 54896, Jeollabuk Do, South Korea.
   [Park, Seong-Bae] Kyung Hee Univ, Dept Comp Sci & Engn, 1732 Deogyeong Daero, Yongin 17104, Gyeonggi Do, South Korea.
RP Park, SB (通讯作者)，Kyung Hee Univ, Dept Comp Sci & Engn, 1732 Deogyeong Daero, Yongin 17104, Gyeonggi Do, South Korea.
EM sujeong.choi@kt.com; hyunje.song@jbnu.ac.kr; sbpark71@khu.ac.kr
OI Choi, Su Jeong/0000-0002-8787-280X
FU Institute of Information & Communications Technology Planning &
   Evaluation(IITP) - Korean government(MSIT); (WiseKB: Big data based
   self-evolving knowledge base and reasoning platform)
FX This work was supported by Institute of Information & Communications
   Technology Planning & Evaluation(IITP) grant funded by the Korean
   government(MSIT)(No.2013-2-00109, WiseKB: Big data based self-evolving
   knowledge base and reasoning platform).
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NR 39
TC 2
Z9 3
U1 10
U2 18
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD FEB
PY 2020
VL 10
IS 8
AR 2651
DI 10.3390/app10082651
PG 12
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA LO0XE
UT WOS:000533352100015
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, YZ
   Liu, JS
   Yang, HY
   Chen, JX
   Xiong, J
AF Li, Youzhu
   Liu, Jinsi
   Yang, Hongyu
   Chen, Jianxin
   Xiong, Jason
TI A Bibliometric Analysis of Literature on Vegetable Prices at Domestic
   and International Markets-A Knowledge Graph Approach
SO AGRICULTURE-BASEL
LA English
DT Article
DE vegetable price; knowledge graph; visualization; cluster analysis
ID FRUIT; FARMERS; SYSTEM; CONSTRUCTION; WHOLESALE; QUALITY; DEMAND
AB Vegetable prices play an important role in people's daily lives globally. However, there is very little research that has systematically analyzed and discussed the existing research. Therefore, in order to narrow this gap in the literature, there is a need for an extensive and macro-level study of vegetable price research. By sorting out a large volume of documents, This paper presents a visual representation of the vegetable price research literature in the Web of Science and CNKI databases; it includes three perspectives of the cooperative network, co-occurrence network and co-cited network, and draws scientific knowledge map, to acquire a good understanding of the current situation of vegetable price research, hot issues, clearly shows the focus of the research, summaries the fluctuation pattern of vegetable price, deeply analyses the influencing factors of vegetable price fluctuation, and find out the connection between related scholars and the correlation existing in each research content. The results show that scholars at home and abroad have conducted research from the perspectives of vegetable price influencing factors, forecasting and early warning, and insurance systems, but they are only limited to some regions, and some vegetable varieties, and the forecasting and early warning of vegetable prices are not accurate enough, the insurance system is not perfect, and a complete price influencing mechanism has not yet been proposed. The aim of this study is to make a significant contribution to the scientific community and industry practitioners by providing a comprehensive understanding of the latest status, popular keywords and topics, and emerging areas of vegetable price research. In addition, this study will be a useful and valuable reference and guide for researchers interested in this field.</p>
C1 [Li, Youzhu; Liu, Jinsi; Yang, Hongyu] Huazhong Agr Univ, Coll Publ Adm, Wuhan 430070, Peoples R China.
   [Chen, Jianxin] Wuhan Univ Technol, Coll Art & Design, Wuhan 430070, Peoples R China.
   [Xiong, Jason] Appalachian State Univ, Walker Coll Business, Boone, NC 28607 USA.
RP Chen, JX (通讯作者)，Wuhan Univ Technol, Coll Art & Design, Wuhan 430070, Peoples R China.
EM liyz@mail.hzau.edu.cn; louis.hzau.edu.cn@webmail.hzau.edu.cn;
   yanghy.hzau.edu.cn@webmail.hzau.edu.cn; chenjianxin@whut.edu.cn;
   xiongjj@appstate.edu
OI Xiong, Jason/0000-0001-9213-5698
FU National Social Science Foundation of China [21BGL168]; MOE (Ministry of
   Education in China) Project of Humanities and Social Sciences
   [20YJC790069]; National Social Science Foundation of China General
   Program of Art Studies [20BG133]; Earmarked Fund for Modern
   Agro-industry Technology Research System [CARS-23-F01, CARS-21]
FX FundingThis work was supported partly the National Social Science
   Foundation of China (Project No. 21BGL168), the MOE (Ministry of
   Education in China) Project of Humanities and Social Sciences (Project
   No. 20YJC790069), the National Social Science Foundation of China
   General Program of Art Studies (Project No. 20BG133), the Earmarked Fund
   for Modern Agro-industry Technology Research System (Project No.
   CARS-23-F01 and No. CARS-21).
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NR 46
TC 2
Z9 2
U1 9
U2 20
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2077-0472
J9 AGRICULTURE-BASEL
JI Agriculture-Basel
PD OCT
PY 2021
VL 11
IS 10
AR 951
DI 10.3390/agriculture11100951
PG 17
WC Agronomy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture
GA WN5OO
UT WOS:000711817000001
OA gold
DA 2022-08-23
ER

PT J
AU Yu, HZ
   Li, HS
   Mao, DH
   Cai, Q
AF Yu, Haoze
   Li, Haisheng
   Mao, Dianhui
   Cai, Qiang
TI A relationship extraction method for domain knowledge graph construction
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Domain knowledge graph; Convolution residual network; Relationship
   extraction; Cross-entropy
AB As a semantic knowledge base, knowledge graph is a powerful tool for managing large-scale knowledge consists with instances, concepts and relationships between them. In view that the existing domain knowledge graphs can not obtain relationships in various structures through targeted approaches in the process of construction which resulting in insufficient knowledge utilization, this paper proposes a relationship extraction method for domain knowledge graph construction. We obtain upper and lower relationships from structured data in the classification system of network encyclopedia and semi-structured data in the classification labels of web pages, and non-superordinate relationships are extracted from unstructured text through the proposed convolution residual network based on improved cross-entropy loss function. We verify the effectiveness of the designed method by comparing with existing relationship extraction methods and constructing a food domain knowledge graph.
C1 [Yu, Haoze; Li, Haisheng; Mao, Dianhui; Cai, Qiang] Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing, Peoples R China.
   [Yu, Haoze; Li, Haisheng; Mao, Dianhui; Cai, Qiang] Beijing Key Lab Big Data Technol Food Safety, Beijing, Peoples R China.
   [Yu, Haoze; Li, Haisheng; Mao, Dianhui; Cai, Qiang] Natl Engn Lab Agriprod Qual Traceabil, Beijing, Peoples R China.
RP Li, HS (通讯作者)，Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing, Peoples R China.; Li, HS (通讯作者)，Beijing Key Lab Big Data Technol Food Safety, Beijing, Peoples R China.; Li, HS (通讯作者)，Natl Engn Lab Agriprod Qual Traceabil, Beijing, Peoples R China.
EM 2628740743@qq.com; Lihsh@th.btbu.edu.cn; Maodh@th.btbu.edu.cn;
   Caiq@th.btbu.edu.en
RI LI, Haisheng/AAM-5232-2020
OI LI, Haisheng/0000-0003-4861-0513
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NR 35
TC 13
Z9 15
U1 14
U2 69
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 2020
VL 23
IS 2
SI SI
BP 735
EP 753
DI 10.1007/s11280-019-00765-y
PG 19
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KT5KY
UT WOS:000519054500004
DA 2022-08-23
ER

PT J
AU Huang, JJ
   Chen, W
   Liu, A
   Wang, WQ
   Yin, HZ
   Zhao, L
AF Huang, Jinjing
   Chen, Wei
   Liu, An
   Wang, Weiqing
   Yin, Hongzhi
   Zhao, Lei
TI Cluster query: a new query pattern on temporal knowledge graph
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Cluster query; Temporal knowledge graph; Graph pattern matching
ID TIME; WEB
AB A temporal knowledge graph (TKG) is theoretically a temporal graph. Recently, systems have been developed to support snapshot queries over temporal graphs. However, snapshot queries can only give separate answers. To retrieve forward-backward correlation facts from temporal knowledge graph, cluster query is proposed in this paper. To deal with the query, the logical view and physical model are presented. Subsequently, five corresponding basic query patters of unit matching are studied, and then the complete matchings are also addressed. To improve the query performance, index-based methods and pruning strategies are adopted. Experiments are conducted to evaluate cluster queries on three real datasets. The experimental results show the effectiveness and efficiency of cluster queries on temporal knowledge graphs.
C1 [Huang, Jinjing; Chen, Wei; Liu, An; Zhao, Lei] Soochow Univ, Sch Comp Sci & Technol, Suzhou, Peoples R China.
   [Huang, Jinjing] Suzhou Vocat Inst Ind Technol, Sch Software & Serv Outsourcing, Suzhou, Peoples R China.
   [Wang, Weiqing] Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia.
   [Yin, Hongzhi] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld, Australia.
RP Zhao, L (通讯作者)，Soochow Univ, Sch Comp Sci & Technol, Suzhou, Peoples R China.
EM huangjj@siitedu.cn; robertchen@suda.edu.cn; anliu@suda.edu.cn;
   Teresa.Wang@monash.edu; h.yin1@uq.edu.au; zhaol@suda.edu.cn
OI Yin, Hongzhi/0000-0003-1395-261X
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NR 39
TC 1
Z9 1
U1 3
U2 24
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 2020
VL 23
IS 2
SI SI
BP 755
EP 779
DI 10.1007/s11280-019-00754-1
PG 25
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KT5KY
UT WOS:000519054500005
DA 2022-08-23
ER

PT J
AU Wang, YQ
   Dong, LY
   Zhang, H
   Ma, XT
   Li, YL
   Sun, MH
AF Wang, Yuequn
   Dong, Liyan
   Zhang, Hao
   Ma, Xintao
   Li, Yongli
   Sun, Minghui
TI An Enhanced Multi-Modal Recommendation Based on Alternate Training With
   Knowledge Graph Representation
SO IEEE ACCESS
LA English
DT Article
DE Training; Feature extraction; Motion pictures; Deep learning; Neural
   networks; Data models; Data mining; Feature learning; graph
   representations; knowledge graph; recommendation system
AB Deep network recommendation is a cutting-edge topic in current recommendation system research, which as a combination of recommendation systems and deep learning theory can effectively improve recommendation accuracy. In a real recommendation scenario, all the effective information in a data set should be extracted, both explicit and implicit, because the comprehensive degree of information is proportional to the recommendation performance. This article proposes an enhanced multi-modal recommendation based on alternate training with knowledge graph representation (SI-MKR) based on the MKR deep learning recommendation model. Our framework is an enhanced recommendation system based on knowledge graph representation, using valuable external knowledge as multi-modal information. The SI-MKR model solves the problem of ignoring the diversity of data types in the multi-modal knowledge-based recommendation system, which adds user and item attribute information from a knowledge graph as an enhancement recommendation multi-tasking training. By analysing the content of the item and user attributes, the SI-MKR model classifies the attributes of the items and users, processes the text type attributes and multi-value type attributes separately for feature extraction, and other types of attributes are used as inputs to the knowledge graph embedding unit. In addition, the knowledge graph data form a triplet unit, thus continuing the knowledge graph data training process. The feature extraction unit of the knowledge graph and the recommended unit are connected through the cross-compression unit for alternate training. During the deep learning framework training process, the recommendation system's item has a potential correlation with the head entity in the knowledge graph which embodies the idea of multi-tasking. Through extensive experiments on real-world datasets, we demonstrate that SI-MKR achieves substantial gains in movie recommendation over advanced model baselines. Even user-item interactions are sparse, SI-MKR maintains better performance than the MKR model.
C1 [Wang, Yuequn; Dong, Liyan; Zhang, Hao; Ma, Xintao; Sun, Minghui] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China.
   [Wang, Yuequn; Dong, Liyan; Zhang, Hao; Ma, Xintao; Sun, Minghui] Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Peoples R China.
   [Li, Yongli] Northeast Normal Univ, Sch Informat Sci & Technol, Changchun 130117, Peoples R China.
RP Sun, MH (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China.; Sun, MH (通讯作者)，Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Peoples R China.
EM smh@jlu.edu.cn
OI Wang, YueQun/0000-0001-7154-0539; dong, liyan/0000-0001-7491-5893
FU National Natural Science Foundation of China [61872164]; Program of
   Science and Technology Development Plan for Jilin Province, China
   [20190302032GX]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61872164, and in part by the Program of
   Science and Technology Development Plan for Jilin Province, China, under
   Grant 20190302032GX.
CR Gomez JA, 2018, ENVIRON SCI POLLUT R, V25, P977, DOI 10.1007/s11356-016-8339-9
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NR 33
TC 0
Z9 1
U1 9
U2 46
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 213012
EP 213026
DI 10.1109/ACCESS.2020.3039388
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PC7LF
UT WOS:000597177500001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, J
AF Wang, Jun
TI Knowledge Graph Analysis of Internal Control Field in Colleges
SO TEHNICKI VJESNIK-TECHNICAL GAZETTE
LA English
DT Article
DE CiteSpace; Construction Methods; Internal Control; Knowledge Graph;
   Visualization
AB Knowledge graph is a new method to describe concepts, instances and their relationships in the objective world. In recent years, it has attracted people's wide attention. Knowledge graph can effectively expand the breadth of search results. At present, in the field of internal control of colleges and universities, the key word search technology is mainly used to retrieve relevant knowledge content. It is difficult to retrieve information by using the relation between objects. Therefore, this paper first proposes a knowledge graph construction method for internal control in universities, through which the knowledge graph of internal control policy in universities can be constructed. Then, this paper proposes a knowledge inference method based on inference rules, which USES the knowledge hidden in the knowledge graph of internal control policy to realize intelligent data retrieval. Finally, the CiteSpace software is used to realize the visualization display in the field of internal control in universities and realize the construction of internal control knowledge graph and visualization research. The system can effectively utilize the relationship between knowledge objects of internal control and give full play to the value of information resources of internal control.
C1 [Wang, Jun] Beijing Jiaotong Univ, Sch Econ & Management, 3 Shangyuancun, Beijing, Peoples R China.
RP Wang, J (通讯作者)，Beijing Jiaotong Univ, Sch Econ & Management, 3 Shangyuancun, Beijing, Peoples R China.
EM 15113125@bjtu.edu.cn
CR AICPA, 1949, INT CONTR SPEC REP C
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NR 15
TC 2
Z9 3
U1 7
U2 23
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.
PY 2020
VL 27
IS 1
BP 67
EP 72
DI 10.17559/TV-20191004092659
PG 6
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA KN4GP
UT WOS:000514797400010
OA gold
DA 2022-08-23
ER

PT J
AU Hui, B
   Zhang, LZ
   Zhou, X
   Wen, X
   Nian, YH
AF Hui, Bei
   Zhang, Lizong
   Zhou, Xue
   Wen, Xiao
   Nian, Yuhui
TI Personalized recommendation system based on knowledge embedding and
   historical behavior
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Collaborative filtering; Recommendation system; Knowledge graph;
   Historical behavior
AB Collaborative filtering (CF) usually suffers from limited performance in recommendation systems due to the sparsity of user-item interactions and cold start problems. To address these issues, auxiliary information from knowledge graphs, such as social networks and item properties, is typically used to boost performance. The current recommended algorithms based on knowledge graphs fail to utilize rich semantic associations. In this paper, we regard knowledge graphs as heterogeneous networks to add auxiliary information, propose a recommendation system with unified embeddings of behavior and knowledge features, and mine user preferences from their historical behavior and knowledge graphs to provide more accurate and diverse recommendations to the users. Our proposed ReBKC shows a significant improvement on three datasets compared to state-of-the-art methods. These results verify the effectiveness of learning short-term and long-term user preferences from their historical behavior and by integrating knowledge graphs to deeply identify user preferences.
C1 [Hui, Bei; Zhang, Lizong] Univ Elect Sci & Technol China, Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 611731, Peoples R China.
   [Zhou, Xue; Nian, Yuhui] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 611731, Peoples R China.
   [Wen, Xiao] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China.
RP Zhang, LZ (通讯作者)，Univ Elect Sci & Technol China, Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 611731, Peoples R China.
EM l.zhang@uestc.edu.cn
RI Zhang, Lizong/AAU-5095-2021
OI Zhang, Lizong/0000-0002-0719-9556
FU National Key R&D Program of China [2018YFC0807500]; National Natural
   Science Foundation of China [U19A2059]; Ministry of Science and
   Technology of Sichuan Province Program [2018GZDZX0048,20ZDYF0343]
FX This work was supported by the National Key R&D Program of China (No.
   2018YFC0807500), by National Natural Science Foundation of China (No.
   U19A2059), and by Ministry of Science and Technology of Sichuan Province
   Program (No. 2018GZDZX0048,20ZDYF0343).
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NR 28
TC 12
Z9 12
U1 25
U2 58
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 JAN
PY 2022
VL 52
IS 1
BP 954
EP 966
DI 10.1007/s10489-021-02363-w
EA MAY 2021
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YG8PL
UT WOS:000650309500001
DA 2022-08-23
ER

PT J
AU Jiang, K
   Ashworth, P
AF Jiang, Kai
   Ashworth, Peta
TI The development of Carbon Capture Utilization and Storage (CCUS)
   research in China: A bibliometric perspective
SO RENEWABLE & SUSTAINABLE ENERGY REVIEWS
LA English
DT Article
DE China; Carbon capture utilization and storage (CCUS); Citespace;
   Knowledge mapping; Research and development; Research and policy
AB Carbon Capture, Utilization and Storage (CCUS) is considered a critical carbon dioxide reduction technology for climate change mitigation. More recently, it has been attracting significant attention from the Chinese government, industry and academia in relation to its research, development and deployment. This paper screens 1,202 unique scientific articles from Web of Science related to China's CCUS and employs Citespace to conduct a knowledge mapping of the domain. The results show that the number of publications have increased and CCUS has become a multidisciplinary area of study. The paper visually demonstrates the most productive institutions, main authors, and primary sources of China's CCUS research domain. Moreover, the paper explains how the research topics have changed over time and discovers the current research frontiers including "pipeline", "shale gas" and "steel". Of significant interest is the finding that China's CCUS research progress appears to be directly related to the increasing policy support through China's five-year planning process. However, research regarding public perception, financial mechanisms and appropriate business models for CCUS deployment is found to be less prevalent. Recommendations to improve research in these areas are provided from the perspective of government intervention. This paper also highlights the importance of continued government support for CCUS as part of the national development strategy for carbon emissions reduction and transitioning towards a low carbon future.
C1 [Jiang, Kai; Ashworth, Peta] Univ Queensland, Sch Chem Engn, Brisbane, Qld, Australia.
   [Ashworth, Peta] Univ Queensland, Ctr Nat Gas, Brisbane, Qld, Australia.
RP Jiang, K (通讯作者)，Univ Queensland, Sch Chem Engn, Brisbane, Qld, Australia.
EM kai.jiang@uq.edu.au; p.ashworth@uq.edu.au
RI ; Ashworth, Peta/I-6503-2013
OI Jiang, Kai/0000-0001-8510-3743; Ashworth, Peta/0000-0003-4648-7531
FU University of Queensland Surat Deep Aquifer Appraisal Project (UQ-SDAAP)
   through the UQ Center for Coal Seam Gas; China Scholarship Council
   (CSC); Australian Government through the Carbon Capture and Storage
   Research Development & Demonstration (CCS RDD) Fund; COAL21 Fund;
   University of Queensland
FX This research is supported by the University of Queensland Surat Deep
   Aquifer Appraisal Project (UQ-SDAAP) through the UQ Center for Coal Seam
   Gas and the China Scholarship Council (CSC). The UQ-SDAAP project is
   funded by the Australian Government through the Carbon Capture and
   Storage Research Development & Demonstration (CCS RD&D) Fund, COAL21
   Fund and the University of Queensland. The CSC is a non-profit
   organization established by the Chinese Ministry of Education to provide
   support for international academic exchange with China. The authors
   thank the editors and four anonymous reviewers for their helpful
   comments.
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NR 107
TC 28
Z9 30
U1 65
U2 198
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1364-0321
EI 1879-0690
J9 RENEW SUST ENERG REV
JI Renew. Sust. Energ. Rev.
PD MAR
PY 2021
VL 138
AR 110521
DI 10.1016/j.rser.2020.110521
EA JAN 2021
PG 16
WC Green & Sustainable Science & Technology; Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Energy & Fuels
GA QC0IB
UT WOS:000614516400003
DA 2022-08-23
ER

PT J
AU Guan, NN
   Song, DD
   Liao, LJ
AF Guan, Niannian
   Song, Dandan
   Liao, Lejian
TI Knowledge graph embedding with concepts
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; Concept space; Knowledge graph completion
AB Knowledge graph embedding aims to embed the entities and relationships of a knowledge graph in low dimensional vector spaces, which can be widely applied to many tasks. Existing models for knowledge graph embedding primarily concentrate on entity-relation-entity triplets, or interact with the text corpus. However, triplets are less informative, and the in-domain text corpus is not always available, making the embedding results deviate from the actual meaning. At the same time, our mental world contains many concepts about worldly facts. For human cognition, compared to knowledge that we learned, common-sense concepts are more basic and general, and they play important roles in human knowledge accumulation. In this paper, based on common-sense concepts information of entities from a concept graph, we propose a Knowledge Graph Embedding with Concepts (KEC) model that embeds entities and concepts of entities jointly into a semantic space. The fact triplets from a knowledge graph are adjusted by the common-sense concept information of entities from a concept graph. Our model not only focuses on the relevance between entities but also focuses on their concepts. Thus, this model offers precise semantic embedding. We evaluate our method on the tasks of knowledge graph completion and entity classification. Experimental results show that our model outperforms other baselines on the two tasks. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Guan, Niannian; Song, Dandan; Liao, Lejian] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China.
RP Song, DD (通讯作者)，Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China.
EM sdd@bit.edu.cn
FU National Key Research and Development Program of China [2016YFB1000902];
   National Natural Science Foundation of China [61472040]
FX This work was supported by National Key Research and Development Program
   of China (Grant No. 2016YFB1000902) and National Natural Science
   Foundation of China (Grant No. 61472040).
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PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JAN 15
PY 2019
VL 164
BP 38
EP 44
DI 10.1016/j.knosys.2018.10.008
PG 7
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HJ9HG
UT WOS:000457508900004
DA 2022-08-23
ER

PT J
AU Jiang, ZX
   Chi, CY
   Zhan, YY
AF Jiang, Zhixue
   Chi, Chengying
   Zhan, Yunyun
TI Research on Medical Question Answering System Based on Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Medical diagnostic imaging; Diseases; Knowledge discovery; Knowledge
   based systems; Data mining; Clustering algorithms; Semantics; Natural
   language processing; knowledge graph; question and answer system;
   medical knowledge
AB To meet the high-efficiency question answering needs of existing patients and doctors, this system integrates medical professional knowledge, knowledge graphs, and question answering systems that conduct man-machine dialogue through natural language. This system locates the medical field, uses crawler technology to use vertical medical websites as data sources, and uses diseases as the core entity to construct a knowledge graph containing 44,000 knowledge entities of 7 types and 300,000 entities of 11 kinds. It is stored in the Neo4j graph database, using rule-based matching methods and string-matching algorithms to construct a domain lexicon to classify and query questions. This system has specific practical value in the medical field knowledge graph and question answering system.
C1 [Jiang, Zhixue; Chi, Chengying] Univ Sci & Technol LiaoNing, Sch Comp & Software, Anshan 114031, Peoples R China.
   [Zhan, Yunyun] Technol Univ Dublin, Coll Sci & Hlth, Dublin D08 X622, Ireland.
RP Chi, CY (通讯作者)，Univ Sci & Technol LiaoNing, Sch Comp & Software, Anshan 114031, Peoples R China.
EM chichengying@ustl.edu.cn
OI jiang, zhixue/0000-0002-5606-0200
FU National Natural Science Foundation of China (NSFC) [61672138]
FX This work was supported by the National Natural Science Foundation of
   China (NSFC) under Grant 61672138.
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NR 23
TC 3
Z9 3
U1 33
U2 96
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 21094
EP 21101
DI 10.1109/ACCESS.2021.3055371
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA QE6DK
UT WOS:000616295900001
DA 2022-08-23
ER

PT J
AU Hu, KR
   Liu, H
   Zhan, CJ
   Tang, Y
   Hao, TY
AF Hu, Kairong
   Liu, Hai
   Zhan, Choujun
   Tang, Yong
   Hao, Tianyong
TI Learning knowledge graph embedding with a bi-directional relation
   encoding network and a convolutional autoencoder decoding network
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Bi-directional relation; Convolutional autoencoder; Knowledge graph;
   Link prediction
ID DIMENSIONALITY
AB Derived from knowledge bases, knowledge graphs represent knowledge expressions in graphs, which utilize nodes and edges to denote entities and relations conceptually. Knowledge graph can be described in textual triple form, consisting of head entities, tail entities and relations between entities. In order to represent elements in knowledge graphs, knowledge graph embedding techniques are proposed to map entities and relations into continuous vector spaces as numeric vectors for computational efficiency. Convolution-based knowledge graph embedding models have promising performance for knowledge graph representation learning. However, the input of those neural network-based models is frequently in handmade forms and may suffer from low efficiency in feature extraction procedure of the models. In this paper, a convolutional autoencoder is proposed for knowledge graph representation learning with entity pairs as input, aiming to obtain corresponding hidden relation representation. In addition, a bi-directional relation encoding network is utilized to represent semantic of entities in different directional relation patterns, as an encoder to output representation for initialization of the convolutional autoencoder. Experiments are conducted on standard datasets including, WN18RR, Kinship, NELL-995 and FB15k-237 as a link prediction task. Besides, input embedding matrix composed of different ingredients is designed to evaluate performances of the convolutional autoencoder. The results demonstrate that our model is effective in learning representation from entity feature interactions.
C1 [Hu, Kairong; Liu, Hai; Zhan, Choujun; Tang, Yong; Hao, Tianyong] South China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China.
RP Hao, TY (通讯作者)，South China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China.
EM 2018022615@m.scnu.edu.cn; namelh@gmail.com; zchoujun2@gmail.com;
   ytang@m.scnu.edu.cn; haoty@m.scnu.edu.cn
OI Hao, Tianyong/0000-0002-9792-3949
FU National Natural Science Foundation of China [61772146, 61772211,
   U1811263]; Natural Science Foundation of Guangdong [c20140500000225]
FX This work is supported by National Natural Science Foundation of China
   (No. 61772146, No. 61772211, No. U1811263) and Natural Science
   Foundation of Guangdong (No. c20140500000225).
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NR 55
TC 1
Z9 1
U1 1
U2 14
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
PD SEP
PY 2021
VL 33
IS 17
SI SI
BP 11157
EP 11173
DI 10.1007/s00521-020-05654-4
EA JAN 2021
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TZ1GX
UT WOS:000605918300006
DA 2022-08-23
ER

PT J
AU Rozanec, JM
   Lu, JZ
   Rupnik, J
   Skrjanc, M
   Mladenic, D
   Fortuna, B
   Zheng, XC
   Kiritsis, D
AF Rozanec, Joze M.
   Lu, Jinzhi
   Rupnik, Jan
   Skrjanc, Maja
   Mladenic, Dunja
   Fortuna, Blaz
   Zheng, Xiaochen
   Kiritsis, Dimitris
TI Actionable cognitive twins for decision making in manufacturing
SO INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
LA English
DT Article
DE Knowledge graph; cognitive digital twin; smart manufacturing; production
   planning; demand forecasting
ID DIGITAL TWIN; KNOWLEDGE MANAGEMENT; INDUSTRY 4.0; ONTOLOGY; SYSTEMS;
   CAPABILITY; PRODUCT
AB Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. This paper proposes a knowledge graph modelling approach to construct actionable cognitive twins for capturing specific knowledge related to production planning and demand forecasting in a manufacturing plant. The knowledge graph provides semantic descriptions and contextualisation of the production lines and processes, including data identification and simulation or artificial intelligence algorithms and forecasts used to support them. Such semantics provide ground for inferencing, relating different knowledge types: creative, deductive, definitional, and inductive. To develop the knowledge graph models for describing the use case thoroughly, systems thinking approach is proposed to design and verify the ontology, develop a knowledge graph and build an actionable cognitive twin. Finally, we evaluate our approach in two use cases developed for a European original equipment manufacturer related to the automotive industry as part of the European Horizon 2020 project FACTLOG.
C1 [Rozanec, Joze M.] Jozef Stefan Int Postgrad Sch, Ljubljana, Slovenia.
   [Rozanec, Joze M.; Rupnik, Jan; Skrjanc, Maja; Mladenic, Dunja; Fortuna, Blaz] Jozef Stefan Inst, Ljubljana, Slovenia.
   [Rozanec, Joze M.; Rupnik, Jan; Skrjanc, Maja; Fortuna, Blaz] Qlector doo, Ljubljana, Slovenia.
   [Lu, Jinzhi; Zheng, Xiaochen; Kiritsis, Dimitris] EPFL SCI STI DK, Stn 9, CH-1015 Lausanne, Switzerland.
RP Lu, JZ (通讯作者)，EPFL SCI STI DK, Stn 9, CH-1015 Lausanne, Switzerland.
EM jinzhi.lu@epfl.ch
RI ; Fortuna, Blaz/K-5427-2014
OI Lu, Jinzhi/0000-0001-5044-2921; Fortuna, Blaz/0000-0002-8585-9388;
   Zheng, Xiaochen/0000-0003-1506-3314; Karlovcec,
   Mario/0000-0003-4480-082X
FU Slovenian Research Agency; European Union [H2020-869951]
FX This work was supported by the Slovenian Research Agency and the
   European Union's Horizon 2020 program project FACTLOG under grant
   agreement H2020-869951.
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NR 143
TC 7
Z9 7
U1 40
U2 60
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0020-7543
EI 1366-588X
J9 INT J PROD RES
JI Int. J. Prod. Res.
PD JAN 17
PY 2022
VL 60
IS 2
SI SI
BP 452
EP 478
DI 10.1080/00207543.2021.2002967
EA NOV 2021
PG 27
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA ZE3SE
UT WOS:000719242500001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Nguyen, NT
   Zhang, XL
   Wu, C
   Lange, RA
   Chilton, RJ
   Lindsey, ML
   Jin, YF
AF Nguyen, Nguyen T.
   Zhang, Xiaolin
   Wu, Cathy
   Lange, Richard A.
   Chilton, Robert J.
   Lindsey, Merry L.
   Jin, Yu-Fang
TI Integrative Computational and Experimental Approaches to Establish a
   Post-Myocardial Infarction Knowledge Map
SO PLOS COMPUTATIONAL BIOLOGY
LA English
DT Article
ID ACUTE MYOCARDIAL-INFARCTION; ENDOTHELIAL GROWTH-FACTOR; NITRIC-OXIDE
   SYNTHASE; LEFT-VENTRICULAR DYSFUNCTION; FRIZZLED-RELATED PROTEIN-2;
   BRAIN NATRIURETIC PEPTIDE; PLASMA OSTEOPONTIN LEVELS;
   CORONARY-ARTERY-DISEASE; GENOME-WIDE ASSOCIATION; MESENCHYMAL STEM-CELLS
AB Vast research efforts have been devoted to providing clinical diagnostic markers of myocardial infarction (MI), leading to over one million abstracts associated with "MI" and "Cardiovascular Diseases" in PubMed. Accumulation of the research results imposed a challenge to integrate and interpret these results. To address this problem and better understand how the left ventricle (LV) remodels post-MI at both the molecular and cellular levels, we propose here an integrative framework that couples computational methods and experimental data. We selected an initial set of MI-related proteins from published human studies and constructed an MI-specific protein-protein-interaction network (MIPIN). Structural and functional analysis of the MIPIN showed that the post-MI LV exhibited increased representation of proteins involved in transcriptional activity, inflammatory response, and extracellular matrix (ECM) remodeling. Known plasma or serum expression changes of the MIPIN proteins in patients with MI were acquired by data mining of the PubMed and UniProt knowledgebase, and served as a training set to predict unlabeled MIPIN protein changes post-MI. The predictions were validated with published results in PubMed, suggesting prognosticative capability of the MIPIN. Further, we established the first knowledge map related to the post-MI response, providing a major step towards enhancing our understanding of molecular interactions specific to MI and linking the molecular interaction, cellular responses, and biological processes to quantify LV remodeling.
C1 [Nguyen, Nguyen T.; Zhang, Xiaolin; Jin, Yu-Fang] Univ Texas San Antonio, Dept Elect & Comp Engn, San Antonio, TX 78249 USA.
   [Nguyen, Nguyen T.; Lange, Richard A.; Chilton, Robert J.; Lindsey, Merry L.; Jin, Yu-Fang] Univ Texas Hlth Sci Ctr San Antonio, San Antonio Cardiovasc Prote Ctr, San Antonio, TX 78229 USA.
   [Wu, Cathy] Univ Delaware, Ctr Bioinformat & Computat Biol & Prot Informat R, Newark, DE USA.
   [Lange, Richard A.; Chilton, Robert J.] Univ Texas Hlth Sci Ctr San Antonio, Dept Med, San Antonio, TX 78229 USA.
   [Lindsey, Merry L.] Univ Mississippi, Med Ctr, Mississippi Ctr Heart Res, Jackson, MS 39216 USA.
   [Lindsey, Merry L.] GV Sonny Montgomery Vet Affairs Med Ctr, Res Serv, Jackson, MS USA.
RP Nguyen, NT (通讯作者)，Univ Texas San Antonio, Dept Elect & Comp Engn, San Antonio, TX 78249 USA.
EM yufang.jin@utsa.edu
RI Lange, Richard/AAM-9594-2021; Lindsey, Merry L/B-2650-2012
OI Lindsey, Merry L/0000-0002-4090-0391; Wu, Cathy/0000-0001-6379-8601
FU Rapoport Foundation for Cardiovascular Research; National Institutes of
   Health NHLBI from HHSN [268201000036C (N01-HV-00244), R01HL075360,
   HL051971]; Biomedical Laboratory Research and Development Service of the
   Veterans Affairs Office of Research and Development Award
   [5I01BX000505]; NIH [EB009496, 1SC2 HL101430]; NATIONAL HEART, LUNG, AND
   BLOOD INSTITUTE [SC2HL101430, P01HL051971, R01HL075360] Funding Source:
   NIH RePORTER; NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND
   BIOENGINEERING [R03EB009496] Funding Source: NIH RePORTER; Veterans
   Affairs [I01BX000505] Funding Source: NIH RePORTER
FX We acknowledge funding support from Rapoport Foundation for
   Cardiovascular Research to RAL, from the National Institutes of Health
   NHLBI from HHSN 268201000036C (N01-HV-00244) for the San Antonio
   Cardiovascular Proteomics Center and R01HL075360, HL051971, and from the
   Biomedical Laboratory Research and Development Service of the Veterans
   Affairs Office of Research and Development Award 5I01BX000505 to MLL;
   and NIH EB009496 and 1SC2 HL101430 to YFJ. 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 163
TC 8
Z9 8
U1 0
U2 16
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 MAR
PY 2014
VL 10
IS 3
AR e1003472
DI 10.1371/journal.pcbi.1003472
PG 19
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA AI0AQ
UT WOS:000336509000010
PM 24651374
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, H
   Li, GY
   Jiang, W
   Sun, YH
AF Chen, Heng
   Li, Guanyu
   Jiang, Wei
   Sun, Yunhao
TI Dynamic dual quaternion knowledge graph embedding
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Knowledge graph; Knowledge graph embedding; Dual quaternion; Dynamic
   mapping mechanism
AB Knowledge graph aims to describe the concepts, the entities and the complex relations of them in the real world. Recently, a series of quaternion rotation models that usually considering the relation vector as the rotation between head and tail entities, have been extensively studied. The results showed these models had the advantages of simplicity and efficiency. However, they are quite weak in capturing the representation and the feature interaction between entities and relations, resulting in insufficient expressiveness of the underlying models, because these models only focus on the linear relations between entities and relations. In order to solve this problem, this paper proposes a novel knowledge graph embedding model called DualDE, which dynamically maps the dual quaternions to the knowledge graph. Specifically, DualDE uses a dynamic mapping mechanism to construct the entity transition vector and the relation transition vector, and continuously adjusts the embedding position of the entity vector in the dual quaternion space according to the dual quaternion multiplication rules. In addition, DualDE can dynamically construct a variety of complex relation types, such as one-to-many, many-to-one and many-to-many. The experimental results based on three standard data sets show that the DualDE model is superior to the existing knowledge graph embedding models on many metrics.
C1 [Chen, Heng; Li, Guanyu; Jiang, Wei; Sun, Yunhao] Dalian Maritime Univ, Fac Informat Sci & Technol, Dalian 116026, Peoples R China.
   [Chen, Heng] Dalian Univ Foreign Languages, Res Ctr Language Intelligence, Dalian 116044, Peoples R China.
RP Li, GY (通讯作者)，Dalian Maritime Univ, Fac Informat Sci & Technol, Dalian 116026, Peoples R China.
EM chenheng@dlufl.edu.cn; rabitlee@163.com; syh8086@163.com
FU National Natural Science Foundation of China [61976032, 61806038];
   Scientific Research Funding Project of Education Department of Liaoning
   Province [2020JYT03]; Innovative Talents in Colleges and Universities of
   Liaoning Province
FX This work was supported by the National Natural Science Foundation of
   China (No.61976032 and No.61806038), the Scientific Research Funding
   Project of Education Department of Liaoning Province (No.2020JYT03) and
   the Innovative Talents in Colleges and Universities of Liaoning Province
   (No.WR2019005).
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NR 36
TC 0
Z9 0
U1 12
U2 12
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/s10489-021-03069-9
EA MAR 2022
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZL7GO
UT WOS:000763842000008
DA 2022-08-23
ER

PT J
AU Wang, RJ
   Wang, M
   Liu, J
   Cochez, M
   Decker, S
AF Wang, Ruijie
   Wang, Meng
   Liu, Jun
   Cochez, Michael
   Decker, Stefan
TI Structured query construction via knowledge graph embedding
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph; Query construction; Knowledge graph embedding; Natural
   language question answering
AB In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.
C1 [Wang, Ruijie] Xi An Jiao Tong Univ, Natl Engn Lab Big Data Analyt, Xian, Peoples R China.
   [Wang, Ruijie; Liu, Jun] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Peoples R China.
   [Wang, Meng] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Liu, Jun] Guang Dong Xian Jiaotong Univ Acad, Shunde, Peoples R China.
   [Cochez, Michael] Vrije Univ Amsterdam, Amsterdam, Netherlands.
   [Cochez, Michael; Decker, Stefan] Fraunhofer FIT, St Augustin, Germany.
   [Wang, Ruijie; Decker, Stefan] Rhein Westfal TH Aachen, Informat 5, Aachen, Germany.
   [Cochez, Michael] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla, Finland.
RP Wang, M (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
EM meng.wang@seu.edu.cn
OI Cochez, Michael/0000-0001-5726-4638
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NR 49
TC 1
Z9 1
U1 15
U2 56
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0219-1377
EI 0219-3116
J9 KNOWL INF SYST
JI Knowl. Inf. Syst.
PD MAY
PY 2020
VL 62
IS 5
BP 1819
EP 1846
DI 10.1007/s10115-019-01401-x
PG 28
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LG2WX
UT WOS:000527968600006
OA Green Submitted, Green Accepted
DA 2022-08-23
ER

PT J
AU Markazi-Moghaddam, N
   Arab, M
   Ravaghi, H
   Rashidian, A
   Khatibi, T
   Zargar Balaye Jame, S
AF Markazi-Moghaddam, Nader
   Arab, Mohammad
   Ravaghi, Hamid
   Rashidian, Arash
   Khatibi, Toktam
   Zargar Balaye Jame, Sanaz
TI A Knowledge Map for Hospital Performance Concept: Extraction and
   Analysis: A Narrative Review Article
SO IRANIAN JOURNAL OF PUBLIC HEALTH
LA English
DT Review
DE Hospital performance; Knowledge mapping; Social network analysis;
   Co-word analysis; Text mining
ID CO-WORD ANALYSIS; READMISSION; QUALITY; POLICY; PATH
AB Background: Performance is a multi-dimensional and dynamic concept. During the past 2 decades, considerable studies were performed in developing the hospital performance concept. To know literature key concepts on hospital performance, the knowledge visualization based on co-word analysis and social network analysis has been used.
   Methods: Documents were identified through "PubMed" searching from1945 to 2014 and 2350 papers entered the study after omitting unrelated articles, the duplicates, and articles without abstract. After pre-processing and preparing articles, the key words were extracted and terms were weighted by TF-IDF weighting schema. Support as an interestingness measure, which considers the co-occurrence of the extracted keywords and "hospital performance" phrase was calculated. Keywords having high support with "hospital performance" are selected. Term-term matrix of these selected keywords is calculated and the graph is extracted.
   Results: The most high frequency words after "Hospital Performance" were "mortality" and "efficiency". The major knowledge structure of hospital performance literature during these years shows that the keyword "mortality" had the highest support with hospital performance followed by "quality of care", "quality improvement", "discharge", "length of stay" and "clinical outcome". The strongest relationship is seen between "electronic medical record" and "readmission rate".
   Conclusion: Some dimensions of hospital performance are more important such as "efficiency", "effectiveness", "quality" and "safety" and some indicators are more highlighted such as "mortality", "length of stay", "readmission rate" and "patient satisfaction". In the last decade, some concepts became more significant in hospital performance literature such as "mortality", "quality of care" and "quality improvement".
C1 [Markazi-Moghaddam, Nader; Arab, Mohammad; Rashidian, Arash; Zargar Balaye Jame, Sanaz] Univ Tehran Med Sci, Sch Publ Hlth, Dept Hlth Management & Econ, Tehran, Iran.
   [Markazi-Moghaddam, Nader; Zargar Balaye Jame, Sanaz] AJA Univ Med Sci, Sch Med, Dept Publ Hlth, Tehran, Iran.
   [Ravaghi, Hamid] Iran Univ Med Sci, Sch Hlth Management & Informat Sci, Dept Hlth Serv Management, Tehran, Iran.
   [Rashidian, Arash] Univ Tehran Med Sci, Res Ctr Knowledge Utilizat, Tehran, Iran.
   [Khatibi, Toktam] Tarbiat Modares Univ, Dept Ind Engn, Tehran, Iran.
RP Markazi-Moghaddam, N (通讯作者)，Univ Tehran Med Sci, Sch Publ Hlth, Dept Hlth Management & Econ, Tehran, Iran.
EM arabmoha@tums.ac.ir
RI Ravaghi, Hamid/GOH-0893-2022; Ravaghi, Hamid/AAA-3867-2022
OI Ravaghi, Hamid/0000-0003-2717-920X; Rashidian,
   Arash/0000-0002-4005-5183; Markazi-Moghaddam, nader/0000-0003-2861-2765
FU Tehran University of Medical Sciences
FX The authors would like to thank the Deputy of Research at Tehran
   University of Medical Sciences for their help and financial support. The
   authors declare that there is no conflict of interests.
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NR 45
TC 4
Z9 4
U1 2
U2 27
PU IRANIAN SCIENTIFIC SOCIETY MEDICAL ENTOMOLOGY
PI TEHRAN
PA SCHOOL PUBLIC HEALTH & INST HEALTH RESEARCH, TEHRAN UNIV MEDICAL
   SCIENCES, P O BOX  6446-14155, TEHRAN, 00000, IRAN
SN 2251-6085
EI 2251-6093
J9 IRAN J PUBLIC HEALTH
JI Iran J. Public Health
PD JUL
PY 2016
VL 45
IS 7
BP 843
EP 854
PG 12
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA DZ7OA
UT WOS:000386054400001
PM 27516990
DA 2022-08-23
ER

PT J
AU Abu-Salih, B
AF Abu-Salih, Bilal
TI Domain-specific knowledge graphs: A survey
SO JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
LA English
DT Article
DE Knowledge graph; Domain-specific knowledge graph; Knowledge graph
   construction; Knowledge graph embeddings; Knowledge graph evaluation;
   Domain ontology; Survey
ID ONTOLOGY; EXTRACTION; CONSTRUCT; PATHWAY
AB Knowledge Graphs (KGs) have made a qualitative leap and effected a real revolution in knowledge representation. This is leveraged by the underlying structure of the KG which underpins a better comprehension, reasoning and interpretation of knowledge for both human and machine. Therefore, KGs continue to be used as the main means of tackling a plethora of real-life problems in various domains. However, there is no consensus in regard to a plausible and inclusive definition of a domain-specific KG. Further, in conjunction with several limitations and deficiencies, various domain-specific KG construction approaches are far from perfect. This survey is the first to offer a comprehensive definition of a domain-specific KG. Also, the paper presents a thorough review of the state-of-the-art approaches drawn from academic works relevant to seven domains of knowledge. An examination of current approaches reveals a range of limitations and deficiencies. At the same time, uncharted territories on the research map are highlighted to tackle extant issues in the literature and point to directions for future research.
C1 [Abu-Salih, Bilal] Univ Jordan, King Abdullah II Sch Informat Technol, Amman, Jordan.
RP Abu-Salih, B (通讯作者)，Univ Jordan, King Abdullah II Sch Informat Technol, Amman, Jordan.
EM b.abusalih@ju.edu.jo
RI Abu-Salih, Bilal/Q-4020-2016
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NR 217
TC 17
Z9 20
U1 55
U2 108
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 1084-8045
EI 1095-8592
J9 J NETW COMPUT APPL
JI J. Netw. Comput. Appl.
PD JUL 1
PY 2021
VL 185
AR 103076
DI 10.1016/j.jnca.2021.103076
EA APR 2021
PG 21
WC Computer Science, Hardware & Architecture; Computer Science,
   Interdisciplinary Applications; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SN6JI
UT WOS:000658393600001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Yang, X
   Huan, ZY
   Zhai, YS
   Lin, T
AF Yang, Xu
   Huan, Ziyi
   Zhai, Yisong
   Lin, Ting
TI Research of Personalized Recommendation Technology Based on Knowledge
   Graphs
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph; representation learning; personalized recommendation;
   Bayesian personalized ranking
AB Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs' construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs (KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.
C1 [Yang, Xu; Huan, Ziyi; Zhai, Yisong; Lin, Ting] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.
RP Lin, T (通讯作者)，Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.
EM yangxu@tsinghua.edu.cn; 3220200891@bit.edu.cn; 3220180899@bit.edu.cn;
   linting@bit.edu.cn
OI Yang, Xu/0000-0001-7983-6473
FU National Natural Science Foundation of China [91846303]; Beijing
   Municipal Natural Science Foundation [4212043]
FX This work is supported by the National Natural Science Foundation of
   China under Grant No. 91846303, and the Beijing Municipal Natural
   Science Foundation under Grand No. 4212043.
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NR 36
TC 3
Z9 3
U1 35
U2 80
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD AUG
PY 2021
VL 11
IS 15
AR 7104
DI 10.3390/app11157104
PG 25
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA TV6RL
UT WOS:000681848200001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Omran, PG
   Wang, KW
   Wang, Z
AF Omran, Pouya Ghiasnezhad
   Wang, Kewen
   Wang, Zhe
TI An Embedding-Based Approach to Rule Learning in Knowledge Graphs
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Knowledge engineering; Task analysis; Scalability; Standards; Knowledge
   based systems; Predictive models; Sampling methods; Rule learning;
   knowledge graphs
AB It is natural and effective to use rules for representing explicit knowledge in knowledge graphs. However, it is challenging to learn rules automatically from very large knowledge graphs such as Freebase and YAGO. This paper presents a new approach, RLvLR (Rule Learning via Learning Representations), to learning rules from large knowledge graphs by using the technique of embedding in representation learning together with a new sampling method. Based on RLvLR, a new method RLvLR-Stream is developed for learning rules from streams of knowledge graphs. Both RLvLR and RLvLR-Stream have been implemented and experiments conducted to validate the proposed methods regarding the tasks of rule learning and link prediction. Experimental results show that our systems are able to handle the task of rule learning from large knowledge graphs with high accuracy and outperform some state-of-the-art systems. Specifically, for massive knowledge graphs with hundreds of predicates and over 10M facts, RLvLR is much faster and can learn much more quality rules than major systems for rule learning in knowledge graphs such as AMIE+. In the setting of knowledge graph streams, RLvLR-Stream significantly improved RLvLR for both rule learning and link prediction.
C1 [Omran, Pouya Ghiasnezhad; Wang, Kewen; Wang, Zhe] Griffith Univ, Sch Informat & Commun Technol, Mt Gravatt, Qld 4122, Australia.
RP Omran, PG (通讯作者)，Griffith Univ, Sch Informat & Commun Technol, Mt Gravatt, Qld 4122, Australia.
EM pouya.ghiasnezhadomran@griffithuni.edu.au; k.wang@griffith.edu.au;
   zhe.wang@griffith.edu.au
RI Wang, Zhe/N-3635-2018
OI Wang, Zhe/0000-0002-1367-7139; Wang, Kewen/0000-0002-0542-3761;
   Ghiasnezhad Omran, Pouya/0000-0002-4473-3877
FU Australian Research Council (ARC) [DP130102302]
FX The authors would like to thank three anonymous referees for their
   helpful comments. This work was supported by the Australian Research
   Council (ARC) under the grant DP130102302. This work was done while
   Pouya Ghiasnezhad Omran was working at Griffith University.
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NR 34
TC 7
Z9 8
U1 15
U2 54
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 2021
VL 33
IS 4
BP 1348
EP 1359
DI 10.1109/TKDE.2019.2941685
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QT5HG
UT WOS:000626617900003
OA Green Published
DA 2022-08-23
ER

PT J
AU Zhang, DH
   Liu, LN
   Wei, Q
   Yang, Y
   Yang, P
   Liu, Q
AF Zhang, Dehai
   Liu, Linan
   Wei, Qi
   Yang, Yun
   Yang, Po
   Liu, Qing
TI Neighborhood Aggregation Collaborative Filtering Based on Knowledge
   Graph
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE recommendation system; collaborative filtering; knowledge graph; graph
   convolutional neural network; attention mechanism
AB In recent years, the research of combining a knowledge graph with recommendation systems has caused widespread concern. By studying the interconnections in knowledge graphs, potential connections between users and items can be discovered, which provides abundant and complementary information for recommendation of items. However, most existing studies have not effectively established the relation between entities and users. Therefore, the recommendation results may be affected by some unrelated entities. In this paper, we propose a neighborhood aggregation collaborative filtering (NACF) based on knowledge graph. It uses the knowledge graph to spread and extract the user's potential interest, and iteratively injects them into the user features with attentional deviation. We conducted a large number of experiments on three public datasets; we verifyied that NACF is ahead of the most advanced models in top-k recommendation and click-through rate (CTR) prediction.
C1 [Zhang, Dehai; Liu, Linan; Wei, Qi; Yang, Yun; Liu, Qing] Yunnan Univ, Sch Software, Kunming 650000, Yunnan, Peoples R China.
   [Yang, Po] Univ Sheffield, Dept Comp Sci, Sheffield S1 1DA, S Yorkshire, England.
RP Yang, Y (通讯作者)，Yunnan Univ, Sch Software, Kunming 650000, Yunnan, Peoples R China.
EM dhzhang@ynu.edu.cn; liulinan@mail.ynu.edu.cn; weiqi199704@163.com;
   yangyun@ynu.edu.cn; po.yang@sheffield.ac.uk; liuqing@ynu.edu.cn
RI Yang, Po/C-9936-2011
OI Yang, Po/0000-0002-8553-7127; Yang, Yun/0000-0002-9893-3436; Zhang,
   Dehai/0000-0002-8110-1158
FU Natural Science Foundation China (NSFC) [61402397, 61263043, 61562093,
   61663046]; Yunnan Provincial Young Academic and Technical Leaders
   Reserve Talents [2017HB005]; Yunnan Provincial Innovation Team
   [2017HC012]; Youth Talent Project of China Association for Science and
   Technology [W8193209]; Science Foundation of Yunnan University
   [2017YDQN11]; Yunnan Provincial Science Research Project of the
   Department of Education [2018JS008]
FX This research was funded by (i) Natural Science Foundation China (NSFC)
   under Grant No. 61402397, 61263043, 61562093 and 61663046; (ii) Yunnan
   Provincial Young Academic and Technical Leaders Reserve Talents under
   Grant No. 2017HB005; (iii) Yunnan Provincial Innovation Team under Grant
   No. 2017HC012; (iv) Youth Talent Project of China Association for
   Science and Technology under Grant No. W8193209; (v) Science Foundation
   of Yunnan University under Grant No. 2017YDQN11; and (vi) Yunnan
   Provincial Science Research Project of the Department of Education under
   the Grant No.2018JS008.
CR Andreas J., 2016, ARXIV160101705
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NR 34
TC 9
Z9 9
U1 7
U2 29
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD JUN
PY 2020
VL 10
IS 11
AR 3818
DI 10.3390/app10113818
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 MC6HQ
UT WOS:000543385900155
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Yan, JH
   Xu, C
   Li, N
   Gao, M
   Zhou, AY
AF Yan, Jihong
   Xu, Chen
   Li, Na
   Gao, Ming
   Zhou, Aoying
TI Optimizing model parameter for entity summarization across knowledge
   graphs
SO JOURNAL OF COMBINATORIAL OPTIMIZATION
LA English
DT Article
DE Entity summarization; Word embedding; Expectation maximization (EM)
   algorithm; Parameter optimization
AB Knowledge graphs, which belongs to the category of semantic networks, are considered as a new method of knowledge representation of health care data. It establishes a semantic explanation model for human perception and health care information processing. Each knowledge graph is composed of massive entities and relationships. However, it is an arduous work to search and visualize users' interested entities and attributes since there are many attributes for an entity across different knowledge graphs. It is a natural problem how to summarize an entity based on multiple knowledge graphs. We propose a three-stage algorithm to solve the problem of entity summarization across knowledge graphs, including candidate generation, knowledge graph linkage, and entity summarization. We propose an unsupervised framework to link different knowledge graphs based on the semantic and structure information of entities. To further reduce the computational cost, we employ word embedding technique to find the similar entities in semantic, and filter some pairs of unmatched entities. Finally, we model entity summarization as personalized ranking problem in a knowledge graph. We conduct a set of experiments to evaluate our proposed method on four real datasets: historical data for user query, two English knowledge graphs (YAGO and DBpeida) and an English corpus. Experimental results demonstrate the effectiveness of our proposed method by comparing with baselines.
C1 [Li, Na; Gao, Ming; Zhou, Aoying] East China Normal Univ, Inst Data Sci & Engn, Shanghai 200062, Peoples R China.
   [Yan, Jihong] Shanghai Polytech Univ, Coll Comp & Informat Engn, Shanghai 201209, Peoples R China.
   [Xu, Chen] Shanghai Agr Comm, Informat Ctr, Shanghai 200335, Peoples R China.
RP Gao, M (通讯作者)，East China Normal Univ, Inst Data Sci & Engn, Shanghai 200062, Peoples R China.
EM mgao@sei.ecnu.edu.cn
FU National Key Research and Development Program of China [2016YFB1000905];
   NSFC [U1401256, 61672234, 61402180, 61472321]; Key Disciplines of
   Software Engineering of Shanghai Polytechnic University [XXKZD1604]
FX This work has been supported by the National Key Research and
   Development Program of China under Grant 2016YFB1000905, NSFC under
   Grant Nos. U1401256, 61672234, 61402180 and 61472321. The author would
   also like to thank Key Disciplines of Software Engineering of Shanghai
   Polytechnic University under Grant No. XXKZD1604.
CR [Anonymous], 2016, THESIS CLEMSON U
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NR 32
TC 0
Z9 0
U1 1
U2 17
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1382-6905
EI 1573-2886
J9 J COMB OPTIM
JI J. Comb. Optim.
PD JAN
PY 2019
VL 37
IS 1
BP 293
EP 318
DI 10.1007/s10878-017-0225-y
PG 26
WC Computer Science, Interdisciplinary Applications; Mathematics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Mathematics
GA HI1PA
UT WOS:000456216200016
DA 2022-08-23
ER

PT J
AU Lin, ZQ
   Xie, B
   Zou, YZ
   Zhao, JF
   Li, XD
   Wei, J
   Sun, HL
   Yin, G
AF Lin, Ze-Qi
   Xie, Bing
   Zou, Yan-Zhen
   Zhao, Jun-Feng
   Li, Xuan-Dong
   Wei, Jun
   Sun, Hai-Long
   Yin, Gang
TI Intelligent Development Environment and Software Knowledge Graph
SO JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
LA English
DT Article
DE intelligent development environment; software big data; software
   knowledge graph; semantic
AB Software intelligent development has become one of the most important research trends in software engineering. In this paper, we put forward two key concepts - intelligent development environment (IntelliDE) and software knowledge graph - for the first time. IntelliDE is an ecosystem in which software big data are aggregated, mined and analyzed to provide intelligent assistance in the life cycle of software development. We present its architecture and discuss its key research issues and challenges. Software knowledge graph is a software knowledge representation and management framework, which plays an important role in IntelliDE. We study its concept and introduce some concrete details and examples to show how it could be constructed and leveraged.
C1 [Lin, Ze-Qi; Xie, Bing; Zou, Yan-Zhen; Zhao, Jun-Feng] Peking Univ, Minist Educ, Key Lab High Confidence Software Technol, Beijing 100871, Peoples R China.
   [Lin, Ze-Qi; Xie, Bing; Zou, Yan-Zhen; Zhao, Jun-Feng] Beida Binhai Informat Res, Tianjin 300450, Peoples R China.
   [Li, Xuan-Dong] Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing 210023, Jiangsu, Peoples R China.
   [Wei, Jun] Chinese Acad Sci, Inst Software, Technol Ctr Software Engn, Beijing 100190, Peoples R China.
   [Sun, Hai-Long] Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China.
   [Yin, Gang] Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China.
RP Xie, B (通讯作者)，Peking Univ, Minist Educ, Key Lab High Confidence Software Technol, Beijing 100871, Peoples R China.; Xie, B (通讯作者)，Beida Binhai Informat Res, Tianjin 300450, Peoples R China.
EM linzeqi@pku.edu.cn; xiebing@pku.edu.cn; zouyz@pku.edu.cn;
   zhaojf@pku.edu.cn; lxd@nju.edu.cn; weijun@iscas.ac.cn;
   sunhl@act.buaa.edu.cn; jack_nudt@163.com
FU National Key Research and Development Program of China [2016YFB1000800];
   National Science Fund for Distinguished Young Scholars of China
   [61525201]
FX This work is supported by the National Key Research and Development
   Program of China under Grant No. 2016YFB1000800, and the National
   Science Fund for Distinguished Young Scholars of China under Grant No.
   61525201.
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NR 17
TC 19
Z9 25
U1 6
U2 29
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1000-9000
EI 1860-4749
J9 J COMPUT SCI TECH-CH
JI J. Comput. Sci. Technol.
PD MAR
PY 2017
VL 32
IS 2
BP 242
EP 249
DI 10.1007/s11390-017-1718-y
PG 8
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EQ1MZ
UT WOS:000397835500004
DA 2022-08-23
ER

PT J
AU Zhao, MX
   Wang, H
   Guo, J
   Liu, D
   Xie, C
   Liu, Q
   Cheng, ZB
AF Zhao, Mingxiong
   Wang, Han
   Guo, Jin
   Liu, Di
   Xie, Cheng
   Liu, Qing
   Cheng, Zhibo
TI Construction of an Industrial Knowledge Graph for Unstructured Chinese
   Text Learning
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE social network; industry 4.0; industrial knowledge graph; deep learning;
   industrial big data; intellectualization of industrial information
AB The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field.
C1 [Zhao, Mingxiong; Wang, Han; Guo, Jin; Liu, Di; Xie, Cheng; Liu, Qing; Cheng, Zhibo] Yunnan Univ, Sch Software, Kunming 650500, Yunnan, Peoples R China.
RP Xie, C (通讯作者)，Yunnan Univ, Sch Software, Kunming 650500, Yunnan, Peoples R China.
EM chengxie@sjtu.edu.cn
RI 迪, 刘/ABU-9202-2022
OI 迪, 刘/0000-0002-4365-2768
FU Science Foundation of Yunnan University [2017YDQN11]; Yunnan Provincial
   Science Research Project of the Department of Education [2018JS008];
   Youth Talent Project of the China Association for Science and Technology
   [W8193209]
FX Science Foundation of Yunnan University: No. 2017YDQN11.; Yunnan
   Provincial Science Research Project of the Department of Education: No.
   2018JS008.; Youth Talent Project of the China Association for Science
   and Technology: No. W8193209.
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NR 40
TC 11
Z9 11
U1 10
U2 58
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 1
PY 2019
VL 9
IS 13
AR 2720
DI 10.3390/app9132720
PG 22
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA IL1AS
UT WOS:000477031900139
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zhao, F
   Sun, HR
   Jin, LJQ
   Jin, H
AF Zhao, Feng
   Sun, Haoran
   Jin, Langjunqing
   Jin, Hai
TI Structure-augmented knowledge graph embedding for sparse data with rule
   learning
SO COMPUTER COMMUNICATIONS
LA English
DT Article
DE Knowledge graph completion; Embedding; Rule learning; Sparse data
AB In recent years, knowledge graphs have received considerable attention because of their ability to express rich information and their potential for use in knowledge-based reasoning. For example, they can assist in-depth knowledge discovery related to user associations, switching policies, and traffic content in mobile services. Knowledge graph embeddings project the entities and relations of knowledge graphs into vectors that are dense and low dimensional, thus allowing the complex semantic information and relations between these entities to be measured efficiently. However, traditional knowledge graph embedding methods consider only direct facts, making it difficult to achieve reasonable embedding learning of entities and relations when faced with sparse data. To settle this issue, this paper proposes a novel knowledge graph embedding method based on tensor decomposition combined with rule learning. First, rules are inferred and scored based on the initial embeddings of entities and relations. Then, new triples for sparse entities are inferred from rules with high scores. Finally, these new triples are iteratively embedded into the model. Experimental results obtained on the WN18 and FB15k datasets indicate that the proposed model achieves significantly better performance than other state-of-the-art knowledge graph embedding models when faced with sparse data.
C1 [Zhao, Feng; Sun, Haoran; Jin, Langjunqing; Jin, Hai] Huazhong Univ Sci & Technol, Natl Engn Res Ctr Big Data Technol & Syst, Sch Comp Sci & Technol, Serv Comp Technol & Syst Lab,Cluster & Grid Comp, Wuhan, Peoples R China.
RP Zhao, F (通讯作者)，Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.
EM zhaof@hust.edu.cn
FU National Key R&D Program of China [2018YFB1404302]; National Natural
   Science Foundation of China [61672256]
FX This work was supported in part by National Key R&D Program of China
   under Grants no. 2018YFB1404302 and National Natural Science Foundation
   of China under Grants No. 61672256.
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NR 25
TC 5
Z9 5
U1 4
U2 22
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0140-3664
EI 1873-703X
J9 COMPUT COMMUN
JI Comput. Commun.
PD JUN 1
PY 2020
VL 159
BP 271
EP 278
DI 10.1016/j.comcom.2020.05.017
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LY7NW
UT WOS:000540715200024
DA 2022-08-23
ER

PT J
AU Van Den Besselaar, P
   Heimeriks, G
AF Van Den Besselaar, Peter
   Heimeriks, Gaston
TI Mapping research topics using word-reference co-occurrences: A method
   and an exploratory case study
SO SCIENTOMETRICS
LA English
DT Article; Proceedings Paper
CT 10th International Conference of the
   International-Society-for-Scientometrics-and-Informetrics
CY JUL 24-28, 2005
CL Stockholm, SWEDEN
SP Int Soc Scientometr & Informetr
ID COMBINED COCITATION; SCIENCE
AB Mapping of science and technology can be done at different levels of aggregation, using a variety of methods. In this paper, we propose a method in which title words are used as indicators for the content of a research topic, and cited references are used as the context in which words get their meaning. Research topics are represented by sets of papers that are similar in terms of these word-reference combinations. In this way we use words without neglecting differences and changes in their meanings. The method has several advantages, such as high coverage of publications. As an illustration we apply the method to produce knowledge maps of information science.
C1 Rathenau Inst, Sci Syst Assessment Ctr, NL-2509 CJ The Hague, Netherlands.
   Univ Amsterdam, Amsterdam Sch Commun Res, Amsterdam, Netherlands.
   IPTS, Seville, Spain.
RP Van Den Besselaar, P (通讯作者)，Rathenau Inst, Sci Syst Assessment Ctr, POB 95366, NL-2509 CJ The Hague, Netherlands.
EM p.vandenbesselaar@rathenau.nl
RI Heimeriks, Gaston/G-1057-2012; van den Besselaar, Peter A A/E-5938-2013;
   van den Besselaar, Peter/A-8945-2011
OI Heimeriks, Gaston/0000-0002-0577-6938; van den Besselaar, Peter A
   A/0000-0002-8304-8565; 
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NR 15
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Z9 107
U1 2
U2 64
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2006
VL 68
IS 3
BP 377
EP 393
DI 10.1007/s11192-006-0118-9
PG 17
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Information Science & Library Science
GA 067KK
UT WOS:000239300600005
DA 2022-08-23
ER

PT J
AU Siddharth, L
   Blessing, LTM
   Wood, KL
   Luo, JX
AF Siddharth, L.
   Blessing, Lucienne T. M.
   Wood, Kristin L.
   Luo, Jianxi
TI Engineering Knowledge Graph From Patent Database
SO JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
LA English
DT Article
DE information management; knowledge engineering
ID DESIGN; INFORMATION; METHODOLOGY; SYSTEM; TOOL
AB We propose a large, scalable engineering knowledge graph, comprising sets of real-world engineering "facts" as < entity, relationship, entity > triples that are found in the patent database. We apply a set of rules based on the syntactic and lexical properties of claims in a patent document to extract facts. We aggregate these facts within each patent document and integrate the aggregated sets of facts across the patent database to obtain an engineering knowledge graph. Such a knowledge graph is expected to support inference, reasoning, and recalling in various engineering tasks. The knowledge graph has a greater size and coverage in comparison with the previously used knowledge graphs and semantic networks in the engineering literature.
C1 [Siddharth, L.; Blessing, Lucienne T. M.; Luo, Jianxi] Singapore Univ Technol & Design SUTD, Engn Prod Dev EPD, 8 Somapah Rd, Singapore 487372, Singapore.
   [Wood, Kristin L.] Univ Colorado, Coll Engn Design & Comp, 1201 Larimer St, Denver, CO 80204 USA.
RP Siddharth, L (通讯作者)，Singapore Univ Technol & Design SUTD, Engn Prod Dev EPD, 8 Somapah Rd, Singapore 487372, Singapore.
EM siddharth_l@mymail.sutd.edu.sg; lucienne_blessing@sutd.edu.sg;
   kristinwood@sutd.edu.sg; luo@sutd.edu.sg
OI Luo, Jianxi/0000-0001-5892-8432
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NR 66
TC 4
Z9 4
U1 31
U2 31
PU ASME
PI NEW YORK
PA TWO PARK AVE, NEW YORK, NY 10016-5990 USA
SN 1530-9827
EI 1944-7078
J9 J COMPUT INF SCI ENG
JI J. Comput. Inf. Sci. Eng.
PD APR 1
PY 2022
VL 22
IS 2
AR 021008
DI 10.1115/1.4052293
PG 11
WC Computer Science, Interdisciplinary Applications; Engineering,
   Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA ZL6GY
UT WOS:000763774700005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Li, H
   Wang, WJ
AF Li, Hua
   Wang, Weijun
TI Knowledge Domain and Emerging Trends of Social Vulnerability Research: A
   Bibliometric Analysis (1991-2021)
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE social vulnerability; CiteSpace; knowledge mapping; research hotspot;
   development trend
ID CLIMATE-CHANGE; MULTIPLE STRESSORS; ADAPTIVE CAPACITY; ADAPTATION;
   RESILIENCE; INDICATORS; LEVEL; MODEL; RISK; PERSPECTIVES
AB Carrying out social vulnerability research has become an important way to understand the sustainable development of resources, the environment, populations, and societies. Clarifying the research context and development trend of social vulnerability is of great significance to the follow-up theoretical research on and practical exploration of sustainable social development. With the help of a CiteSpace knowledge map analysis, this study reveals the research hotspots and their evolution in different periods and puts forward the key problems in and future direction of social vulnerability research in the future. This study found that the number of articles on social vulnerability research showed a steady upward trend and that the research experienced roughly three stages: an embryonic stage, a development stage, and a stable stage. The United States, Brazil, the United Kingdom, and China accounted for the majority of the research, but the intensity of cooperation between them is still weak. Vulnerability assessment and risk assessment in the context of policy and environmental change are hot topics in the current research. In the future, it is necessary to focus on the comprehensive research on the integrated and cross-scale research on social vulnerability, research on its occurrence and evolution, and on the dynamic monitoring as well as optimal regulation of social vulnerability under multiple pressures.
C1 [Li, Hua] Northwest Normal Univ, Coll Econ, Lanzhou 730070, Peoples R China.
   [Wang, Weijun] Northwest Normal Univ, Coll Tourism, Lanzhou 730070, Peoples R China.
RP Wang, WJ (通讯作者)，Northwest Normal Univ, Coll Tourism, Lanzhou 730070, Peoples R China.
EM lihua0529@nwnu.edu.cn; wjwang@nwnu.edu.cn
FU National Nature Sciences Foundation of China [41971268]; Higher
   Education Innovation Fund Projects in Gansu Province [2021B-086];
   Project of Improving Young Teachers' Scientific Research Ability in
   Northwest Normal University [NWNU-SKQN2021-44, NWNU-SKQN2022-42]
FX This research was funded by National Nature Sciences Foundation of
   China, grant number 41971268, Higher Education Innovation Fund Projects
   in Gansu Province, grant number 2021B-086 and Project of Improving Young
   Teachers' Scientific Research Ability in Northwest Normal University,
   grant number NWNU-SKQN2021-44, NWNU-SKQN2022-42.
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NR 52
TC 0
Z9 0
U1 2
U2 2
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 JUL
PY 2022
VL 19
IS 14
AR 8342
DI 10.3390/ijerph19148342
PG 15
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA 3K9WU
UT WOS:000834424200001
PM 35886193
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Chen, QM
   Wang, Y
   Zhang, JH
   Wang, ZF
AF Chen, Qimei
   Wang, Yan
   Zhang, Jianhan
   Wang, Zhifeng
TI The Knowledge Mapping of Concentrating Solar Power Development Based on
   Literature Analysis Technology
SO ENERGIES
LA English
DT Article
DE concentrating solar power; renewable energy; domain development trend;
   bibliometrics; knowledge mapping
ID THERMAL-ENERGY STORAGE; CONFIGURATIONS; ELECTRICITY; RECEIVER; SYSTEMS;
   PLANTS
AB Decreasing the levelized cost of renewable energy and improving the stability of power systems are the key requirements for realizing the sustainable growth of power production capacity. Concentrating solar power (CSP) technology with thermal energy storage can overcome the intermittent and unstable nature of solar energy, and its development is of great significance for the sustainable development of human society. In this paper, topic discovery and clustering were studied using bibliometric, social network analysis and information visualization technology based on the Web of Science database (SCI-Expanded) and the incoPat global patent database. The technology searched for papers and patents related to CSP technology to reveal the development trends of CSP technology and provide the references for related technical layout and hot spot tracking. The results show that the global output of CSP technology papers has continued to grow steadily, whereas the number of patent applications showed a significant downtrend. CSP technology, which is at the initial stage of commercialization, still needs technological breakthroughs. Technological innovation that integrates thermal engineering, control engineering, physics, chemistry, materials, and other disciplines may become an effective path for CSP technology development in the future. CSP technology research shows increasing research and development trends in high-temperature receivers, phase-change thermal energy storage, the overall performance of thermal power generation systems, and a development trend from a single technology to multi-energy complementary systems.
C1 [Chen, Qimei] Chinese Acad Sci, Natl Sci Lib, Beijing 100190, Peoples R China.
   [Chen, Qimei; Wang, Zhifeng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Wang, Yan; Zhang, Jianhan; Wang, Zhifeng] Chinese Acad Sci, Inst Elect Engn, Beijing 100190, Peoples R China.
RP Chen, QM (通讯作者)，Chinese Acad Sci, Natl Sci Lib, Beijing 100190, Peoples R China.; Chen, QM; Wang, ZF (通讯作者)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.; Wang, ZF (通讯作者)，Chinese Acad Sci, Inst Elect Engn, Beijing 100190, Peoples R China.
EM chenqm@mail.las.ac.cn; wangyan955@126.com; jhzh@tsinghua.edu.cn;
   zhifeng@vip.sina.com
OI wang, yan/0000-0001-5880-5416
FU National Key Research and Development Program Key Project on "key basic
   problems of supercritical CO2 solar thermal power generation"
   [2018YFB1501005]; Ministry of Science and Technology of China
FX This research was funded by National Key Research and Development
   Program Key Project on "key basic problems of supercritical CO2 solar
   thermal power generation" (2018YFB1501005) from Ministry of Science and
   Technology of China.
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NR 45
TC 10
Z9 10
U1 9
U2 29
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1073
J9 ENERGIES
JI Energies
PD APR
PY 2020
VL 13
IS 8
AR 1988
DI 10.3390/en13081988
PG 15
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA LU9BJ
UT WOS:000538041800131
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Pujara, J
   Miao, H
   Getoor, L
   Cohen, WW
AF Pujara, Jay
   Miao, Hui
   Getoor, Lise
   Cohen, William W.
TI Using Semantics and Statistics to Turn Data into Knowledge
SO AI MAGAZINE
LA English
DT Article
ID INFORMATION EXTRACTION
AB Many information-extraction and knowledge base construction systems are addressing the challenge of deriving knowledge from text. A key problem in constructing these knowledge bases from sources like the web is overcoming the erroneous and incomplete information found in millions of candidate extractions. To solve this problem, we turn to semantics - using ontological constraints between candidate facts to eliminate errors. In this article, we represent the desired knowledge base as a knowledge graph and introduce the problem of knowledge graph identification, collectively resolving the entities, and relations present in the knowledge graph. Knowledge graph identification requires reasoning jointly over trillions of extractions simultaneously, posing a scalability challenge to many approaches. We use probabilistic soft logic (PSL), a recently introduced statistical relational learning framework, to implement an efficient solution to knowledge graph identification and present state-of-the-art results for knowledge graph construction while performing an order of magnitude faster than competing methods.
C1 [Pujara, Jay; Miao, Hui] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA.
   [Getoor, Lise] Univ Calif Santa Cruz, Dept Comp Sci, Santa Cruz, CA 95064 USA.
   [Getoor, Lise] Stanford Univ, Stanford, CA 94305 USA.
   [Cohen, William W.] Carnegie Mellon Univ, Machine Learning Dept, Pittsburgh, PA 15213 USA.
   [Cohen, William W.] Carnegie Mellon Univ, Language Technol Inst, Pittsburgh, PA 15213 USA.
RP Pujara, J (通讯作者)，Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA.
FU NSF [0746930, IIS1218488, CCF0937094]; Google Research grants
FX We would like to thank Shangpu Jiang and Daniel Lowd for sharing their
   data and offering enthusiastic assistance. This work was partially
   supported by NSF CAREER grant 0746930, NSF grants IIS1218488 and
   CCF0937094, and Google Research grants. 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.
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NR 14
TC 9
Z9 9
U1 1
U2 11
PU AMER ASSOC ARTIFICIAL INTELL
PI MENLO PK
PA 445 BURGESS DRIVE, MENLO PK, CA 94025-3496 USA
SN 0738-4602
J9 AI MAG
JI AI Mag.
PD SPR
PY 2015
VL 36
IS 1
BP 65
EP 74
DI 10.1609/aimag.v36i1.2568
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA CG2BK
UT WOS:000353079700007
OA Bronze
DA 2022-08-23
ER

PT J
AU Lan, N
   Yang, SQ
   Yin, L
   Gao, YB
AF Lan, Ning
   Yang, Shuqun
   Yin, Ling
   Gao, Yongbin
TI Research on Knowledge Graphs with Concept Lattice Constraints
SO SYMMETRY-BASEL
LA English
DT Article
DE attribute implication; formal context; concept lattice; ontology;
   knowledge graph
ID DOMAIN ONTOLOGY; PRINCIPLES
AB The application of knowledge graphs has been restricted in some domains, especially the industrial and academic domains. One of the reasons is that they require a high reliability of knowledge, which cannot be satisfied by the existing knowledge graph research. By comparison, traditional knowledge engineering has a high correctness, but low efficiency is an inevitable drawback. Therefore, it is meaningful to organically connect traditional knowledge engineering and knowledge graphs. Therefore, we propose a theory from Attribute Implications to Knowledge Graphs, named AIs-KG, which can construct knowledge graphs based on implications. The theory connects formal concept analysis and knowledge graphs. We firstly analyze the mutual transformation based on the ideas of symmetry with a strict proof among the attribute implication, the formal context and the concept lattice, which forms the closed cycle between the three. Particularly, we propose an Augment algorithm (IFC-A) to generate the Implication Formal Context through the attribute implication, which can make knowledge more complete. Furthermore, we regard ontology as a bridge to realize the transformation from the concept lattice to the knowledge graph through some mapping methods. We conduct our experiments on the attribute implication from the rule base of an animal recognition expert system to prove the feasibility of our algorithms.
C1 [Lan, Ning; Yang, Shuqun; Yin, Ling; Gao, Yongbin] Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai 201620, Peoples R China.
RP Yang, SQ (通讯作者)，Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai 201620, Peoples R China.
EM nlan@sues.edu.cn; shqyang@sues.edu.cn; yl@sues.edu.cn;
   gaoyongbin@sues.edu.cn
FU National Key R&D Program of China [2020AAA0109400]; National Natural
   Science Foundation of China [61802251]
FX FundingThis research was funded by the National Key R&D Program of
   China, grant number 2020AAA0109400 and National Natural Science
   Foundation of China, grant number 61802251. And The APC was funded by
   61802251.
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NR 40
TC 0
Z9 0
U1 12
U2 12
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-8994
J9 SYMMETRY-BASEL
JI Symmetry-Basel
PD DEC
PY 2021
VL 13
IS 12
AR 2363
DI 10.3390/sym13122363
PG 19
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA XZ1HE
UT WOS:000737410700001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Popping, R
AF Popping, Roel
TI Text analysis for knowledge graphs
SO QUALITY & QUANTITY
LA English
DT Article
DE text analysis; knowledge graphs
ID NETWORK
AB The concept of knowledge graphs is introduced as a method to represent the state of the art in a specific scientific discipline. Next the text analysis part in the construction of such graphs is considered. Here the 'translation' from text to graph takes place. The method that is used here is compared to methods used in other approaches in which texts are analysed.
C1 Univ Groningen, Dept Sociol, NL-9712 TG Groningen, Netherlands.
RP Popping, R (通讯作者)，Univ Groningen, Dept Sociol, Grote Rozenstr 31, NL-9712 TG Groningen, Netherlands.
EM r.popping@rug.nl
RI Popping, Roel/E-3974-2013
OI Popping, Roel/0000-0003-4646-9472
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NR 31
TC 3
Z9 3
U1 3
U2 27
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0033-5177
J9 QUAL QUANT
JI Qual. Quant.
PD OCT
PY 2007
VL 41
IS 5
BP 691
EP 709
DI 10.1007/s11135-006-9020-z
PG 19
WC Social Sciences, Interdisciplinary; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Mathematics
GA 210MV
UT WOS:000249461200006
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Li, ZH
   Guo, LP
   Sha, YZ
   Yang, KH
AF Li, Zhihong
   Guo, Liping
   Sha, Yongzhong
   Yang, Kehu
TI Knowledge map and global trends in extreme weather research from 1980 to
   2019: a bibliometric analysis
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article
DE Extreme weather; Trends; Climate change; Knowledge mapping; Bibliometric
   analysis
ID CLIMATE
AB There is an increasing number of studies focusing on extreme weather all over the world, but global trends and research topics related to extreme weather are still unclear. This study aimed to explore the current situation, research themes, and future trends in the field of extreme weather. Publications published from 1980 to 2019 were identified and retrieved from the Web of Science Core Collection databases by using keywords on May 5, 2020. Excel 2019, VOSviewer, R, and CiteSpace were used for scientific analysis. The results showed that (1) the number of publications on extreme weather research has rapidly increased and expanded continually, shifting from core disciplines to interdisciplinary fields; (2) the International Journal of Climatology was the most productive journal, and climate and environment were the most popular subject categories. Most studies were carried out in the USA, China, Germany, and other nations, and the Chinese Academy of Sciences was the most productive institution; (3) the main research topics were summarized as (a) climate change; (b) variability; (c) trends; (d) rainfall; (e) temperature; and (f) maximum. At the same time, (4) keyword bursts analysis showed that the domain focused on changes to atmospheric rivers, the impacts of global nitrogen content on extreme weather, and the relationship between water quality, soil moisture content, and extreme weather. Based on the in-depth analysis of extreme weather research, this paper developed a further understanding of the developments in this field over the past 39 years and also provided a reference for future research.
C1 [Li, Zhihong; Guo, Liping; Yang, Kehu] Lanzhou Univ, Sch Basic Med Sci, Evidence Based Med Ctr, Lanzhou 730000, Peoples R China.
   [Li, Zhihong; Sha, Yongzhong] Lanzhou Univ, Crisis Management Res Ctr, Lanzhou 730000, Peoples R China.
   [Li, Zhihong; Guo, Liping; Sha, Yongzhong; Yang, Kehu] Lanzhou Univ, Sch Publ Hlth, Evidence Based Social Sci Res Ctr, Lanzhou 730000, Peoples R China.
RP Li, ZH (通讯作者)，Lanzhou Univ, Sch Basic Med Sci, Evidence Based Med Ctr, Lanzhou 730000, Peoples R China.; Li, ZH (通讯作者)，Lanzhou Univ, Crisis Management Res Ctr, Lanzhou 730000, Peoples R China.; Li, ZH (通讯作者)，Lanzhou Univ, Sch Publ Hlth, Evidence Based Social Sci Res Ctr, Lanzhou 730000, Peoples R China.
EM lizhh16@lzu.edu.cn
FU NSFC [19ZDA142]
FX This work was supported by the Major projects of NSFC: Theoretical
   System, International Experience and Chinese Path of Evidence-based
   Social Sciences (grant no. 19ZDA142).
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NR 78
TC 2
Z9 2
U1 18
U2 65
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
PD SEP
PY 2021
VL 28
IS 36
BP 49755
EP 49773
DI 10.1007/s11356-021-13825-6
EA MAY 2021
PG 19
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA UR2RO
UT WOS:000646587500020
PM 33939084
DA 2022-08-23
ER

PT J
AU Wang, HF
   Liu, ZQ
AF Wang, Hui-fang
   Liu, Zi-quan
TI An error recognition method for power equipment defect records based on
   knowledge graph technology
SO FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
LA English
DT Article
DE Error recognition; Power equipment defect record; Knowledge graph;
   Machine learning; TM76; TP181
AB To recognize errors in the power equipment defect records in real time, we propose an error recognition method based on knowledge graph technology. According to the characteristics of power equipment defect records, a method for constructing a knowledge graph of power equipment defects is presented. Then, a graph search algorithm is employed to recognize different kinds of errors in defect records, based on the knowledge graph of power equipment defects. Finally, an error recognition example in terms of transformer defect records is given, by comparing the precision, recall, F-1-score, accuracy, and efficiency of the proposed method with those of machine learning methods, and the factors influencing the error recognition effects of various methods are analyzed. Results show that the proposed method performs better in error recognition of defect records than machine learning methods, and can satisfy real-time requirements.
C1 [Wang, Hui-fang; Liu, Zi-quan] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China.
RP Wang, HF (通讯作者)，Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China.
EM huifangwang@zju.edu.cn
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NR 33
TC 8
Z9 9
U1 5
U2 18
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 NOV
PY 2019
VL 20
IS 11
BP 1564
EP 1577
DI 10.1631/FITEE.1800260
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 KI9UD
UT WOS:000511703300009
DA 2022-08-23
ER

PT J
AU Yu, CM
   Zhang, ZG
   An, L
   Li, G
AF Yu, Chuanming
   Zhang, Zhengang
   An, Lu
   Li, Gang
TI A knowledge graph completion model integrating entity description and
   network structure
SO ASLIB JOURNAL OF INFORMATION MANAGEMENT
LA English
DT Article; Early Access
DE Knowledge graph completion; Knowledge graph; Deep learning; Pre-trained
   language model
ID EMBEDDINGS
AB Purpose In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets. Design/methodology/approach The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model. Findings The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task. Originality/value The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.
C1 [Yu, Chuanming; Zhang, Zhengang] Zhongnan Univ Econ & Law, Sch Informat Safety & Engn, Wuhan, Peoples R China.
   [An, Lu; Li, Gang] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.
RP Yu, CM (通讯作者)，Zhongnan Univ Econ & Law, Sch Informat Safety & Engn, Wuhan, Peoples R China.
EM yucm@zuel.edu.cn
OI zhang, zhengang/0000-0002-7676-5101
FU Natural Science Foundation of China [71974202, 71921002, 71790612,
   72174153]; Ministry of Education of China [19YJC870029]; Fundamental
   Research Funds for the Central Universities, Zhongnan University of
   Economics and Law [2722021AJ011]
FX This research was supported by the Natural Science Foundation of China
   (Grant Nos. 71974202, 71921002, 71790612 and 72174153), the project of
   the Ministry of Education of China (Grant No. 19YJC870029) and the
   Fundamental Research Funds for the Central Universities, Zhongnan
   University of Economics and Law (Grant No. 2722021AJ011).
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NR 55
TC 0
Z9 0
U1 4
U2 4
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2050-3806
EI 1758-3748
J9 ASLIB J INFORM MANAG
JI Aslib J. Inf. Manag.
DI 10.1108/AJIM-01-2022-0031
EA JUL 2022
PG 23
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 2W9SQ
UT WOS:000824856500001
DA 2022-08-23
ER

PT J
AU Shan, X
   Qiu, JY
   Wang, B
   Dang, YC
   Lu, TX
   Zheng, YM
AF Shan, Xin
   Qiu, Jingyi
   Wang, Bo
   Dang, Yongcheng
   LU, Tingxiang
   Zheng, Yiming
TI Place Retrieval in Knowledge Graph
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
ID SEARCH
AB With the rapid development of Internet and big data, place retrieval has become an indispensable part of daily life. However, traditional retrieval technology cannot meet the semantic needs of users. Knowledge graph has been introduced into the new-generation retrieval systems to improve retrieval performance. Knowledge graph abstracts things into entities and establishes relationships among entities, which are expressed in the form of triples. However, with the expansion of knowledge graph and the rapid increase of data volume, traditional place retrieval methods on knowledge graph have low performance. This paper designs a place retrieval method in order to improve the efficiency of place retrieval. Firstly, perform data preprocessing and problem model building in the offline stage. Meanwhile, build semantic distance index, spatial quadtree index, and spatial semantic hybrid index according to semantic and spatial information. At the same time, in the online retrieval stage, this paper designs an efficient query algorithm and ranking model based on the index information constructed in the offline stage, aiming at improving the overall performance of the retrieval system. Finally, we use experiment to verify the effectiveness and feasibility of the place retrieval method based on knowledge graph in terms of retrieval accuracy and retrieval efficiency under the real data.
C1 [Shan, Xin] Hohai Univ, Sch Energy & Elect Engn, Nanjing 210000, Peoples R China.
   [Shan, Xin; Wang, Bo; LU, Tingxiang; Zheng, Yiming] NARI Grp Corp, State Grid Elect Power Res Inst, Nanjing 210000, Peoples R China.
   [Qiu, Jingyi; Dang, Yongcheng] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
RP Wang, B (通讯作者)，NARI Grp Corp, State Grid Elect Power Res Inst, Nanjing 210000, Peoples R China.
EM shanxin@sgepri.sgcc.com.cn; 1261613202@qq.com; wwwbo1981@163.com;
   220161505@seu.edu.cn; lutingxiang@sgepri.sgcc.com.cn;
   zhengyiming@sgepri.sgcc.com.cn
FU Research and Application of Intelligent Technology [52460817A029]
FX This work was supported by the Research and Application of Intelligent
   Technology for Real-time Dispatching of Power Grid Based on Artificial
   Intelligence (52460817A029).
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NR 12
TC 0
Z9 0
U1 7
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 JUL 1
PY 2020
VL 2020
AR 5060635
DI 10.1155/2020/5060635
PG 10
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MQ2YW
UT WOS:000552764600002
OA gold
DA 2022-08-23
ER

PT J
AU Rotmensch, M
   Halpern, Y
   Tlimat, A
   Horng, S
   Sontag, D
AF Rotmensch, Maya
   Halpern, Yoni
   Tlimat, Abdulhakim
   Horng, Steven
   Sontag, David
TI Learning a Health Knowledge Graph from Electronic Medical Records
SO SCIENTIFIC REPORTS
LA English
DT Article
ID DIAGNOSIS; PROGRAM
AB Demand for clinical decision support systems in medicine and self-diagnostic symptom checkers has substantially increased in recent years. Existing platforms rely on knowledge bases manually compiled through a labor-intensive process or automatically derived using simple pairwise statistics. This study explored an automated process to learn high quality knowledge bases linking diseases and symptoms directly from electronic medical records. Medical concepts were extracted from 273,174 de-identified patient records and maximum likelihood estimation of three probabilistic models was used to automatically construct knowledge graphs: logistic regression, naive Bayes classifier and a Bayesian network using noisy OR gates. A graph of disease-symptom relationships was elicited from the learned parameters and the constructed knowledge graphs were evaluated and validated, with permission, against Google's manually-constructed knowledge graph and against expert physician opinions. Our study shows that direct and automated construction of high quality health knowledge graphs from medical records using rudimentary concept extraction is feasible. The noisy OR model produces a high quality knowledge graph reaching precision of 0.85 for a recall of 0.6 in the clinical evaluation. Noisy OR significantly outperforms all tested models across evaluation frameworks (p < 0.01).
C1 [Rotmensch, Maya] NYU, Ctr Data Sci, New York, NY USA.
   [Halpern, Yoni] NYU, Dept Comp Sci, New York, NY USA.
   [Tlimat, Abdulhakim; Horng, Steven] Beth Israel Deaconess Med Ctr, Dept Emergency Med, Boston, MA 02215 USA.
   [Horng, Steven] Beth Israel Deaconess Med Ctr, Div Clin Informat, Boston, MA 02215 USA.
   [Sontag, David] MIT, Dept Elect Engn & Comp Sci, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
   [Sontag, David] MIT, Inst Med Engn & Sci, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
RP Sontag, D (通讯作者)，MIT, Dept Elect Engn & Comp Sci, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA.; Sontag, D (通讯作者)，MIT, Inst Med Engn & Sci, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM dsontag@mit.edu
FU Google, Inc.
FX The authors thank Yacine Jernite for helpful discussions and for
   providing his code for identifying concepts from clinical text, and
   Google, Inc. for permitting the use of the Google health knowledge graph
   within our evaluation. This study was approved by our institution's
   Institutional Review Board, Committee on Clinical Investigations
   Protocol # 2011P-000356. A waiver of informed consent and authorization
   was granted by the Committee on Clinical Investigation as described in
   45 CFR 46.116(d). This work is supported by a grant from Google, Inc.
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NR 40
TC 113
Z9 121
U1 12
U2 98
PU NATURE PUBLISHING GROUP
PI LONDON
PA MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD JUL 20
PY 2017
VL 7
AR 5994
DI 10.1038/s41598-017-05778-z
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA FB1MN
UT WOS:000405907800048
PM 28729710
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhao, F
   Xu, T
   Jin, LJQ
   Jin, H
AF Zhao, Feng
   Xu, Tao
   Jin, Langjunqing
   Jin, Hai
TI Convolutional Network Embedding of Text-Enhanced Representation for
   Knowledge Graph Completion
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Semantics; Internet of Things; Task analysis; Knowledge representation;
   Feature extraction; Data mining; Uncertainty; Attention mechanism; joint
   representation learning; knowledge graph
AB In recent years, knowledge graph representation learning has prompted extensive research. Machine learning models are used to map entity and relational data in knowledge graphs to vector representations in low-dimensional spaces to predict and analyze potential relationships. Current works mainly focus on the knowledge representation of the triple structure and relationship path in knowledge graphs without fully utilizing external textual information to semantically supplement knowledge representation. However, the existing knowledge inventory, such as that for smart health and emotion care systems, is relatively meager, and structural knowledge is incomplete; therefore, knowledge graph completion is essential. In this article, we propose a novel joint representation learning model that introduces text description information and extracts reliable feature information from text data by using a convolutional neural network (CNN) model. Furthermore, being based on the attention mechanism, the proposed model distinguishes the characteristic credibility of different relationships, enhances the representation of the entity relationship structure vector in the existing knowledge graph, and obtains rich semantic information. Finally, the 2-D convolution operation is used to process the joint representation vectors of entities and relationships to obtain nonlinear features, and the knowledge graph is completed by completing the calculation of the score function of the joint representation vector of the entity and the relationship. Experiments performing tasks, such as link prediction and triple classification, on the FreeBase (FB15k), WordNet (WN18) and Yet Another Great Ontology (YAGO3-10) data sets reveal that our model performs better than the benchmark model and has some degree of scalability.
C1 [Zhao, Feng; Xu, Tao; Jin, Langjunqing; Jin, Hai] Huazhong Univ Sci & Technol, Natl Engn Res Ctr Big Data Technol & Syst, Serv Comp Technol & Syst Lab, Cluster & Grid Comp Lab, Wuhan 430074, Peoples R China.
   [Zhao, Feng; Xu, Tao; Jin, Langjunqing; Jin, Hai] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China.
RP Zhao, F (通讯作者)，Huazhong Univ Sci & Technol, Natl Engn Res Ctr Big Data Technol & Syst, Serv Comp Technol & Syst Lab, Cluster & Grid Comp Lab, Wuhan 430074, Peoples R China.; Zhao, F (通讯作者)，Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China.
EM zhaof@hust.edu.cn; xu_tao@hust.edu.cn; langjunqingj@hust.edu.cn;
   hjin@hust.edu.cn
FU National Key Research and Development Program of China [2018YFB1404302];
   National Natural Science Foundation of China [62072203]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018YFB1404302, and in part by
   the National Natural Science Foundation of China under Grant 62072203.
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NR 36
TC 2
Z9 2
U1 13
U2 19
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 DEC 1
PY 2021
VL 8
IS 23
BP 16758
EP 16769
DI 10.1109/JIOT.2020.3039750
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA XA2YL
UT WOS:000720519000010
DA 2022-08-23
ER

PT J
AU Ma, RX
   Li, ZY
   Guo, FQ
   Zhao, L
AF Ma, Ruixin
   Li, Zeyang
   Guo, Fangqing
   Zhao, Liang
TI Hybrid attention mechanism for few-shot relational learning of knowledge
   graphs
SO IET COMPUTER VISION
LA English
DT Article
DE attention mechanism; few-shot; knowledge graph reasoning; one-shot
AB Few-shot knowledge graph (KG) reasoning is the main focus in the field of knowledge graph reasoning. In order to expand the application fields of the knowledge graph, a large number of studies are based on a large number of training samples. However, we have learnt that there are actually many missing relationships or entities in the knowledge graph, and in most cases, there are not many training instances when implementing new relationships. To tackle it, in this study, the authors aim to predict a new entity given few reference instances, even only one training instance. A few-shot learning framework based on a hybrid attention mechanism is proposed. The framework employs traditional embedding models to extract knowledge, and uses an attenuated attention network and a self-attention mechanism to obtain the hidden attributes of entities. Thus, it can learn a matching metric by considering both the learnt embeddings and one-hop graph structures. The experimental results present that the model has achieved significant performance improvements on the NELL-One and Wiki-One datasets.
C1 [Ma, Ruixin; Li, Zeyang; Guo, Fangqing; Zhao, Liang] Dalian Univ Technol, Sch Software Technol, Dalian 116024, Peoples R China.
   [Ma, Ruixin; Li, Zeyang; Guo, Fangqing; Zhao, Liang] Dalian Univ Technol, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian, Peoples R China.
RP Zhao, L (通讯作者)，Dalian Univ Technol, Sch Software Technol, Dalian 116024, Peoples R China.
EM liangzhao@dlut.edu.cn
FU Natural Science Foundation of Liaoning Province [2020-BS-063]; National
   Natural Science Foundation of China [61906030]; Fundamental Research
   Funds for the Central Universities [DUT20RC(4)009]
FX Natural Science Foundation of Liaoning Province, Grant/Award Number:
   2020-BS-063; National Natural Science Foundation of China, Grant/Award
   Number: 61906030; Fundamental Research Funds for the Central
   Universities, Grant/Award Number: DUT20RC(4)009
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NR 40
TC 2
Z9 2
U1 5
U2 10
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1751-9632
EI 1751-9640
J9 IET COMPUT VIS
JI IET Comput. Vis.
PD DEC
PY 2021
VL 15
IS 8
SI SI
BP 561
EP 572
DI 10.1049/cvi2.12066
EA SEP 2021
PG 12
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA WJ3IE
UT WOS:000696572000001
OA gold
DA 2022-08-23
ER

PT J
AU Xiong, J
   Liu, GY
   Liu, YG
   Liu, MT
AF Xiong, Jing
   Liu, Guoying
   Liu, Yongge
   Liu, Mengting
TI Oracle Bone Inscriptions information processing based on multi-modal
   knowledge graph
SO COMPUTERS & ELECTRICAL ENGINEERING
LA English
DT Article
DE Oracle Bone Inscriptions; Knowledge graph; Multi-modal data; Detection
   and recognition; Deep learning
AB To solve the problems of the great learning difficulty, the long learning period, wide range of knowledge points but weak knowledge connection, and low sharing of Oracle Bone Studies (OBS), a solution of constructing a multi-modal knowledge graph is proposed. Because OBS research involves various kinds of modal data, and these modalities need to be combined together to solve some problems. The OBS multi-modal knowledge graph can provide a unified semantic space for multi-source heterogeneous data. Through multi-modal fusion and information complementation, the defects of a single modality in information processing can be resolved. This multi-modal knowledge graph organizes and manages the basic data better to serve Oracle Bone Inscriptions (OBI) information processing research. Taking OBI detection and recognition as examples, we studied the applications of OBS multi-modal knowledge graph. The experimental results demonstrate that the proposed method reaches 81.3% accuracy in detection and 80.43% accuracy in recognition, and it has 3.7% in detection and 14.8% in recognition improved to the conventional methods.
C1 [Xiong, Jing; Liu, Guoying; Liu, Yongge] Anyang Normal Univ, Sch Comp & Informat Engn, Anyang 455000, Henan, Peoples R China.
   [Xiong, Jing; Liu, Guoying; Liu, Yongge; Liu, Mengting] Minist Educ, Key Lab OBI Informat Proc, Anyang 455000, Henan, Peoples R China.
RP Xiong, J (通讯作者)，Anyang Normal Univ, Sch Comp & Informat Engn, Anyang 455000, Henan, Peoples R China.
EM jingxiong@aynu.edu.cn
OI Xiong, Jing/0000-0002-3604-9645
FU National Natural Science Foundation of China [U1504612, U1804153,
   61806007]; National Language Commission Special Project for Research and
   Application of Oracle Bone Inscriptions and other ancient characters
   [YWZJ023, YWZJ010]; Development Projects of Henan Province Science and
   Technology, China [182102310039]; Major Commissioned Project of National
   Social Science Foundation, China [16 @ZH017A3]; Program for Ministry of
   Education Innovation Research Team, China [2017PT35]; Chinese Ministry
   of Education [YWZJ023, YWZJ010]
FX This work is supported by the National Natural Science Foundation of
   China (U1504612, U1804153, 61806007) , the Chinese Ministry of Education
   and National Language Commission Special Project for Research and
   Application of Oracle Bone Inscriptions and other ancient characters
   (YWZJ023, YWZJ010) , the Development Projects of Henan Province Science
   and Technology, China (182102310039) , the Major Commissioned Project of
   National Social Science Foundation, China (16 @ZH017A3) , and the
   Program for Ministry of Education Innovation Research Team, China
   (2017PT35) .
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NR 25
TC 2
Z9 2
U1 27
U2 47
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0045-7906
EI 1879-0755
J9 COMPUT ELECTR ENG
JI Comput. Electr. Eng.
PD JUN
PY 2021
VL 92
AR 107173
DI 10.1016/j.compeleceng.2021.107173
EA MAY 2021
PG 13
WC Computer Science, Hardware & Architecture; Computer Science,
   Interdisciplinary Applications; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA SV3EX
UT WOS:000663706800002
DA 2022-08-23
ER

PT J
AU Ma, RX
   Guo, FQ
   Zhao, L
   Mei, B
   Bu, XY
   Wu, H
   Song, E
AF Ma, Ruixin
   Guo, Fangqing
   Zhao, Liang
   Mei, Biao
   Bu, Xiya
   Wu, Hao
   Song, Enxin
TI Knowledge Graph Extrapolation Network with Transductive Learning for
   Recommendation
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph; recommendation; transductive learning; collaborative
   signal
AB A knowledge graph is introduced into the personalized recommendation algorithm due to its strong ability to express structural information and exploit side information. However, there is a long tail phenomenon and data sparsity in real knowledge graphs, and most items are related to only a few triples. This results in a significant reduction in the amount of data available for training, and makes it difficult to make accurate recommendations. Motivated by these limitations, the Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation (KGET) is proposed to improve recommendation quality. To be specific, the method first learns the embedding of users and items by knowledge propagation combined with collaborative signal to obtain high-order structural information, and the attention mechanism is used to distinguish the contributions of different neighbor nodes in propagation. In order to better solve with data sparsity and long tail phenomenon, transductive learning is designed to model links between unknown items to enrich feature representation to further extrapolate the knowledge graph. We conduct experiments with two datasets about music and books, the experiment results reveal that our proposed method outperforms state-of-the-art recommendation methods. KGET also achieves strong and stable performance in sparse data scenarios where items have merely a few triples.
C1 [Ma, Ruixin; Guo, Fangqing; Zhao, Liang; Mei, Biao; Bu, Xiya; Wu, Hao; Song, Enxin] Dalian Univ Technol, Sch Software Technol, Dalian 116024, Peoples R China.
RP Zhao, L (通讯作者)，Dalian Univ Technol, Sch Software Technol, Dalian 116024, Peoples R China.
EM maruixin@dlut.edu.cn; guofangqing@mail.dlut.edu.cn;
   liangzhao@dlut.edu.cn; aliezc0411@163.com; buxiya1999@163.com;
   wuhao990115@163.com; 2019192405@mail.dlut.edu.cn
FU National Natural Science Foundation of China [61906030]; Youth Science
   and Technology Star Support Program of Dalian [2021RQ057]; Natural
   Science Foundation of Liaoning Province [2020-BS-063]
FX This work is supported by the National Natural Science Foundation of
   China (61906030), the Youth Science and Technology Star Support Program
   of Dalian, 2021RQ057 and the Natural Science Foundation of Liaoning
   Province (2020-BS-063).
CR Ba L.J., 2015, INT C LEARN REPR
   Baek J., 2020, ADV NEURAL INF PROCE, V33, P546
   Cao XS, 2021, SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P203, DOI 10.1145/3404835.3462900
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   Xue F, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3314578
   Ying R, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P974, DOI 10.1145/3219819.3219890
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NR 28
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD MAY
PY 2022
VL 12
IS 10
AR 4899
DI 10.3390/app12104899
PG 17
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 1Q2DG
UT WOS:000802504400001
OA gold
DA 2022-08-23
ER

PT J
AU Palumbo, E
   Monti, D
   Rizzo, G
   Troncy, R
   Baralis, E
AF Palumbo, Enrico
   Monti, Diego
   Rizzo, Giuseppe
   Troncy, Raphael
   Baralis, Elena
TI entity2rec: Property-specific knowledge graph embeddings for item
   recommendation
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Recommender systems; Knowledge graph embeddings; Knowledge graphs
ID SYSTEMS
AB Knowledge graphs have shown to be highly beneficial to recommender systems, providing an ideal data structure to generate hybrid recommendations using both content-based and collaborative filtering. Most knowledge-aware recommender systems are based on manually engineered features, typically relying on path counting and/or on random walks. Recently, knowledge graph embeddings have proven to be extremely effective at learning features for prediction tasks, reducing the complexity and time required to manually design effective features. In this work, we present entity2rec, which learns user-item relatedness for item recommendation through property-specific knowledge graph embeddings. A key element of entity2rec is the construction of property-specific subgraphs. Through an extensive evaluation on three datasets, we show that: (1) hybrid property-specific subgraphs consistently enhance the quality of recommendations with respect to collaborative and content-based subgraphs; (2) entity2rec generates accurate and non-obvious recommendations, compared to a set of state-of-the-art recommender systems, achieving high accuracy, serendipity and novelty. More in detail, entity2rec is particularly effective when the dataset is sparse and has a low popularity bias; (3) entity2rec is easily interpretable and can thus be configured for a particular recommendation problem. (C) 2020 Published by Elsevier Ltd.
C1 [Palumbo, Enrico; Rizzo, Giuseppe] Ist Super Mario Boella, Via Pier Carlo Boggio 61, Turin 10138, Italy.
   [Palumbo, Enrico; Troncy, Raphael] EURECOM, Campus SophiaTech,450 Route Chappes, Biot 06410, France.
   [Palumbo, Enrico; Monti, Diego; Baralis, Elena] Politecn Torino, Corso Duca Abruzzi 24, Turin 10129, Italy.
RP Palumbo, E (通讯作者)，Ist Super Mario Boella, Via Pier Carlo Boggio 61, Turin 10138, Italy.; Palumbo, E (通讯作者)，EURECOM, Campus SophiaTech,450 Route Chappes, Biot 06410, France.; Palumbo, E (通讯作者)，Politecn Torino, Corso Duca Abruzzi 24, Turin 10129, Italy.
EM enrico.palumbo@polito.it; diego.monti@polito.it;
   giuseppe.rizzo@linksfoundation.com; raphael.troncy@eurecom.fr;
   elena.baralis@polito.it
RI Troncy, Raphaël/ABE-7222-2021
OI Troncy, Raphaël/0000-0003-0457-1436; BARALIS, ELENA
   MARIA/0000-0001-9231-467X; Monti, Diego/0000-0002-3821-5379
FU EIT Digital within the innovation activity PasTime [17164]
FX This work was partially supported by EIT Digital within the innovation
   activity PasTime (grant number 17164).
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   [No title captured]
NR 56
TC 18
Z9 18
U1 9
U2 95
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD AUG 1
PY 2020
VL 151
AR 113235
DI 10.1016/j.eswa.2020.113235
PG 18
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA LJ3LT
UT WOS:000530070100001
OA Bronze
DA 2022-08-23
ER

PT J
AU Yu, SY
   Chhetri, SR
   Canedo, A
   Goyal, P
   Al Faruque, MA
AF Yu, Shih-Yuan
   Chhetri, Sujit Rokka
   Canedo, Arquimedes
   Goyal, Palash
   Al Faruque, Mohammad Abdullah
TI Pykg2vec: A Python Library for Knowledge Graph Embedding
SO JOURNAL OF MACHINE LEARNING RESEARCH
LA English
DT Article
DE Knowledge Graph Embedding; Representation Learning
AB Pykg2vec is a Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec's flexible and modular software architecture currently implements 25 state-of-the-art knowledge graph embedding algorithms, and is designed to easily incorporate new algorithms. The goal of pykg2vec is to provide a practical and educational platform to accelerate research in knowledge graph representation learning. Pykg2vec is built on top of PyTorch and Python's multiprocessing framework and provides modules for batch generation, Bayesian hyperparameter optimization, evaluation of KGE tasks, embedding, and result visualization. Pykg2vec is released under the MIT License and is also available in the Python Package Index (PyPI). The source code of pykg2vec is available at https://github.com/Sujit-0/pykg2vec(dagger).
C1 [Yu, Shih-Yuan; Chhetri, Sujit Rokka; Al Faruque, Mohammad Abdullah] Univ Calif Irvine, Dept Elect Engn & Comp Sci, Irvine, CA 92697 USA.
   [Canedo, Arquimedes] Siemens Corp Technol, Princeton, NJ USA.
   [Goyal, Palash] Univ Southern Calif, Dept Comp Sci, Los Angeles, CA 90007 USA.
RP Yu, SY (通讯作者)，Univ Calif Irvine, Dept Elect Engn & Comp Sci, Irvine, CA 92697 USA.
EM SHIHYUAY@UCI.EDU; SCHHETRI@UCI.EDU; ARQUIMEDES.GANEDO@SIEMENS.COM;
   PALASHGO@USC.EDU; ALFARUQU@UCI.EDU
OI Goyal, Palash/0000-0003-2455-2160
CR Ali Mehdi, 2018, BIORXIV, P475202
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   Lin Yankai, 2015, LEARNING ENTITY RELA
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NR 20
TC 0
Z9 0
U1 1
U2 8
PU MICROTOME PUBL
PI BROOKLINE
PA 31 GIBBS ST, BROOKLINE, MA 02446 USA
SN 1532-4435
J9 J MACH LEARN RES
JI J. Mach. Learn. Res.
PY 2021
VL 22
PG 6
WC Automation & Control Systems; Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA QW3BP
UT WOS:000628529500016
DA 2022-08-23
ER

PT J
AU Yang, MS
   Kang, NK
   Kim, YJ
   Kim, JS
   Choi, KN
   Kim, YK
AF Yang, Myung-seok
   Kang, Nam-kyu
   Kim, Yun-jeong
   Kim, Jae-soo
   Choi, Kwang-nam
   Kim, Young-kuk
TI Expert recommendation system based on analyzing expertise and networks
   of human resources in National Science & Technology Information Service
SO JOURNAL OF CENTRAL SOUTH UNIVERSITY
LA English
DT Article
DE human networks; network analysis; NTIS; R&D information
AB This work aims to implement expert and collaborative group recommendation services through an analysis of expertise and network relations NTIS. First of all, expertise database has been constructed by extracting keywords after indexing national R&D information in Korea (human resources, project and outcome) and applying expertise calculation algorithm. In consideration of the characteristics of national R&D information, weight values have been selected. Then, expertise points were calculated by applying weighted values. In addition, joint research and collaborative relations were implemented in a knowledge map format through network analysis using national R&D information.
C1 [Yang, Myung-seok; Kang, Nam-kyu; Kim, Yun-jeong; Kim, Jae-soo; Choi, Kwang-nam] NTIS Ctr Korea Inst Sci & Technol Informat, Taejon 306806, South Korea.
   [Kim, Young-kuk] Chungnam Natl Univ, Dept Comp Sci & Engn, Taejon 305764, South Korea.
RP Choi, KN (通讯作者)，NTIS Ctr Korea Inst Sci & Technol Informat, Taejon 306806, South Korea.
EM knchoi@kisti.re.kr
OI Kim, Young-Kuk/0000-0002-8329-9348
FU Construction of NTIS (National Science & Technology Information Service)
   Program [N-12-NM-LU01-C01]; National Science & Technology Commission
   (NSTC), Korea
FX Foundation item: Project(N-12-NM-LU01-C01) supported by Construction of
   NTIS (National Science & Technology Information Service) Program Funded
   by the National Science & Technology Commission (NSTC), Korea
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NR 8
TC 6
Z9 6
U1 0
U2 35
PU JOURNAL OF CENTRAL SOUTH UNIV
PI HUNAN
PA EDITORIAL OFF, CHANGSHA, CHINA MAINLAND, HUNAN 410083, PEOPLES R CHINA
SN 2095-2899
EI 2227-5223
J9 J CENT SOUTH UNIV
JI J. Cent. South Univ.
PD AUG
PY 2013
VL 20
IS 8
BP 2212
EP 2218
DI 10.1007/s11771-013-1726-1
PG 7
WC Metallurgy & Metallurgical Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Metallurgy & Metallurgical Engineering
GA 192YE
UT WOS:000322523100017
DA 2022-08-23
ER

PT J
AU Zhang, Y
   Sheng, M
   Zhou, R
   Wang, Y
   Han, GJ
   Zhang, H
   Xing, CX
   Dong, J
AF Zhang, Yong
   Sheng, Ming
   Zhou, Rui
   Wang, Ye
   Han, Guangjie
   Zhang, Han
   Xing, Chunxiao
   Dong, Jing
TI HKGB: An Inclusive, Extensible, Intelligent, Semi-auto-constructed
   Knowledge Graph Framework for Healthcare with Clinicians' Expertise
   Incorporated
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Health knowledge graph; clinician-in-the-loop; platform; knowledge
   island; healthcare; health informatics
ID SYSTEM; INFORMATION; PATIENT
AB Health knowledge graph provides an ideal technical means to integrate heterogeneous data resources and enhance knowledge-based services. There are many challenges for the construction of health knowledge graph such as complex concepts and relationships, various medical standards, heterogeneous data structures, poor data quality, highly accurate and interpretable services, etc.
   In this paper, firstly, we propose Health Knowledge Graph Builder (HKGB), an end-to-end platform which could be used to construct disease-specific and extensible health knowledge graphs from multiple sources. Secondly, we analyze the capabilities and requirements of clinicians, design the tasks to involve the clinicians and implement a clinician-in-the-loop toolset to integrate the clinicians prior knowledge into the construction of health knowledge graphs. Thirdly, we design an extensible mechanism to add new diseases to an existing knowledge graph. Fourthly, we present a quantitative effort estimation algorithm to quantitatively evaluate the effort of clinicians during the construction, and use it to calculate the workloads such as 44.27 person days for knee osteoarthritis domain. Finally, we have developed several knowledge graph based tools to facilitate real applications.
C1 [Zhang, Yong; Sheng, Ming; Xing, Chunxiao] Tsinghua Univ, RIIT, BNRist, DCST, Beijing 100084, Peoples R China.
   [Zhou, Rui] Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Vic 3122, Australia.
   [Wang, Ye] MoST, Ctr Sci & Technol Talents, Beijing 100045, Peoples R China.
   [Han, Guangjie] Hohai Univ, Changzhou 213022, Peoples R China.
   [Zhang, Han] Univ Pittsburgh, Pittsburgh, PA 15260 USA.
   [Dong, Jing] Univ Queensland, St Lucia, Qld 4072, Australia.
RP Sheng, M (通讯作者)，Tsinghua Univ, RIIT, BNRist, DCST, Beijing 100084, Peoples R China.
EM shengming@tsinghua.edu.cn; rzhou@swin.edu.au
OI Xing, Chunxiao/0000-0001-9390-3097
FU ARC Discovery Projects [DP170104747, DP180100212, DP200103700]
FX This work is supported by ARC Discovery Projects under Grant No.
   DP170104747, DP180100212 and DP200103700.
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NR 65
TC 9
Z9 10
U1 20
U2 61
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 102324
DI 10.1016/j.ipm.2020.102324
PG 16
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA OG9PN
UT WOS:000582206800050
OA hybrid
DA 2022-08-23
ER

PT J
AU Hu, ZZ
   Leng, S
   Lin, JR
   Li, SW
   Xiao, YQ
AF Hu, Zhen-Zhong
   Leng, Shuo
   Lin, Jia-Rui
   Li, Sun-Wei
   Xiao, Ya-Qi
TI Knowledge Extraction and Discovery Based on BIM: A Critical Review and
   Future Directions
SO ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
LA English
DT Review
ID ONTOLOGY-BASED REPRESENTATION; SEMANTIC WEB TECHNOLOGIES; SPATIAL QUERY
   LANGUAGE; FAULT-DETECTION; INFORMATION EXTRACTION; FACILITY MANAGEMENT;
   LINKED DATA; CONSTRUCTION; SYSTEM; MODEL
AB In the past, knowledge in the fields of Architecture, Engineering and Construction (AEC) industries mainly come from experiences and are documented in hard copies or specific electronic databases. In order to make use of this knowledge, a lot of studies have focused on retrieving and storing this knowledge in a systematic and accessible way. The Building Information Modeling (BIM) technology proves to be a valuable media in extracting data because it provides physical and functional digital models for all the facilities within the life-cycle of the project. Therefore, the combination of the knowledge science with BIM shows great potential in constructing the knowledge map in the field of the AEC industry. Based on literature reviews, this article summarizes the latest achievements in the fields of knowledge science and BIM, in the aspects of (1) knowledge description, (2) knowledge discovery, (3) knowledge storage and management, (4) knowledge inference and (5) knowledge application, to show the state-of-arts and suggests the future directions in the application of knowledge science and BIM technology in the fields of AEC industries. The review indicates that BIM is capable of providing information for knowledge extraction and discovery, by adopting semantic network, knowledge graph and some other related methods. It also illustrates that the knowledge is helpful in the design, construction, operation and maintenance periods of the AEC industry, but now it is only at the beginning stage.
C1 [Hu, Zhen-Zhong; Li, Sun-Wei] Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China.
   [Hu, Zhen-Zhong; Leng, Shuo; Lin, Jia-Rui; Xiao, Ya-Qi] Tsinghua Univ, Dept Civil Engn, Beijing 100084, Peoples R China.
RP Hu, ZZ (通讯作者)，Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China.; Hu, ZZ (通讯作者)，Tsinghua Univ, Dept Civil Engn, Beijing 100084, Peoples R China.
EM huzhenzhong@tsinghua.edu.cn
RI Lin, Jia-Rui/D-5238-2012; Lin, Jia-Rui/L-7191-2019
OI Lin, Jia-Rui/0000-0003-2195-8675; Lin, Jia-Rui/0000-0003-2195-8675; Hu,
   Zhen-Zhong/0000-0001-9653-0097
FU National Natural Science Foundation of China [51778336]; Tsinghua
   University-Glodon Joint Research Centre for Building Information Model
   (RCBIM)
FX This study was funded by the National Natural Science Foundation of
   China (No. 51778336) and the Tsinghua University-Glodon Joint Research
   Centre for Building Information Model (RCBIM).
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NR 205
TC 5
Z9 5
U1 22
U2 68
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1134-3060
EI 1886-1784
J9 ARCH COMPUT METHOD E
JI Arch. Comput. Method Eng.
PD JAN
PY 2022
VL 29
IS 1
BP 335
EP 356
DI 10.1007/s11831-021-09576-9
EA APR 2021
PG 22
WC Computer Science, Interdisciplinary Applications; Engineering,
   Multidisciplinary; Mathematics, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Mathematics
GA YA1CE
UT WOS:000639061200001
OA hybrid
DA 2022-08-23
ER

PT J
AU Buchgeher, G
   Gabauer, D
   Martinez-Gil, J
   Ehrlinger, L
AF Buchgeher, Georg
   Gabauer, David
   Martinez-Gil, Jorge
   Ehrlinger, Lisa
TI Knowledge Graphs in Manufacturing and Production: A Systematic
   Literature Review
SO IEEE ACCESS
LA English
DT Review
DE Manufacturing; Production; Systematics; Peer-to-peer computing;
   Internet; Companies; Bibliometrics; Knowledge graphs; manufacturing;
   production; systematic literature review
ID LARGE-SCALE; SIMILARITY
AB Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applied in the field of manufacturing and production is needed. Therefore, we have conducted a systematic literature review as an attempt to characterize the state-of-the-art in this field, i.e., by identifying existing research and by identifying gaps and opportunities for further research. We have focused on finding the primary studies in the existing literature, which were classified and analyzed according to four criteria: bibliometric key facts, research type facets, knowledge graph characteristics, and application scenarios. Besides, an evaluation of the primary studies has also been carried out to gain deeper insights in terms of methodology, empirical evidence, and relevance. As a result, we can offer a complete picture of the domain, which includes such interesting aspects as the fact that knowledge fusion is currently the main use case for knowledge graphs, that empirical research and industrial application are still missing to a large extent, that graph embeddings are not fully exploited, and that technical literature is fast-growing but still seems to be far from its peak.
C1 [Buchgeher, Georg; Gabauer, David; Martinez-Gil, Jorge; Ehrlinger, Lisa] Software Competence Ctr Hagenberg GmbH, A-4232 Hagenberg, Austria.
   [Ehrlinger, Lisa] Johannes Kepler Univ Linz, Inst Applicat Oriented Knowledge Proc FAW, A-4040 Linz, Austria.
RP Buchgeher, G (通讯作者)，Software Competence Ctr Hagenberg GmbH, A-4232 Hagenberg, Austria.
EM georg.buchgeher@scch.at
RI Martinez-Gil, Jorge/O-4784-2016; Gabauer, David/E-1817-2018
OI Martinez-Gil, Jorge/0000-0002-5730-7965; Gabauer,
   David/0000-0003-2465-5741; Buchgeher, Georg/0000-0002-8565-6257
FU Interreg Osterreich-Bayern 2014-2020 Programme [AB292]; Federal Ministry
   for Climate Action, Environment, Energy, Mobility, Innovation and
   Technology (BMK); Federal Ministry for Digital and Economic Affairs
   (BMDW); Province of Upper Austria in the frame of the COMET - Competence
   Centers for Excellent Technologies Programme
FX This work was supported in part by the Interreg Osterreich-Bayern
   2014-2020 Programme funded under Grant (AB292), in part by the Federal
   Ministry for Climate Action, Environment, Energy, Mobility, Innovation
   and Technology (BMK), in part by the Federal Ministry for Digital and
   Economic Affairs (BMDW), and in part by the Province of Upper Austria in
   the frame of the COMET - Competence Centers for Excellent Technologies
   Programme managed by Austrian Research Promotion Agency FFG.
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NR 72
TC 5
Z9 5
U1 31
U2 88
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 55537
EP 55554
DI 10.1109/ACCESS.2021.3070395
PG 18
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA RO4KZ
UT WOS:000641015200001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zang, XM
   Zhu, YY
   Zhong, YG
   Chu, T
AF Zang, Xinming
   Zhu, Yuanyuan
   Zhong, Yongguang
   Chu, Tao
TI CiteSpace-Based Bibliometric Review of Pickup and Delivery Problem from
   1995 to 2021
SO APPLIED SCIENCES-BASEL
LA English
DT Review
DE bibliometric analysis; pickup and delivery; evolution trend; CiteSpace;
   knowledge maps
ID VEHICLE-ROUTING PROBLEM; LARGE NEIGHBORHOOD SEARCH; A-RIDE PROBLEM;
   PARTICLE SWARM OPTIMIZATION; SHARING REBALANCING PROBLEM;
   TRAVELING-SALESMAN PROBLEM; GRANULAR TABU SEARCH; TIME WINDOWS; MEMETIC
   ALGORITHM; HYBRID ALGORITHM
AB In this paper, we adopt the bibliometric analysis software CiteSpace to analyze the research status quo and evolution trend of pickup and delivery problem (PDP), an important real-world issue occurring in logistics and transportation. We obtain 819 documents with the topic of PDP that were published in the Web of Science core collection during the period 1995-2021, and acquire their basic situation of posting trend and category distribution. Next, we employ CiteSpace to draw scientific knowledge maps and perform the corresponding visualization analysis, which mainly include the following aspects: (a) collaboration analysis of author, country, and institution; (b) co-citation analysis of author, journal, and reference; (c) citation burst detection of keyword; (d) co-citation clustering analysis of reference. The results show that PDP research has gradually become interdisciplinary and highly comprehensive, and the evolution trend of hot topics also reflects that the research directions involve multiple academic disciplines and professional areas ranging from algorithm design to logistics management. The changing knowledge components reveal the fact that the development of PDP research is highly related to the diversity and uncertainty of realistic logistics industry contexts. Study in this paper provides comprehensive understandings of PDP research for scholars and logistics practitioners, inspiring its further investigation.
C1 [Zang, Xinming; Zhu, Yuanyuan; Zhong, Yongguang; Chu, Tao] Qingdao Univ, Sch Business, Dept Management Sci & Engn, Qingdao 266071, Peoples R China.
RP Zhong, YG (通讯作者)，Qingdao Univ, Sch Business, Dept Management Sci & Engn, Qingdao 266071, Peoples R China.
EM zangxinming@qdu.edu.cn; 2017022561@qdu.edu.cn;
   zhongyongguang@qdu.edu.cn; chutao@qdu.edu.cn
FU National Natural Science Foundation of China [71974104]
FX This research was funded by National Natural Science Foundation of China
   (71974104).
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NR 105
TC 0
Z9 0
U1 31
U2 31
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
PY 2022
VL 12
IS 9
AR 4607
DI 10.3390/app12094607
PG 31
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 1F8HN
UT WOS:000795403200001
OA gold
DA 2022-08-23
ER

PT J
AU Wen, CH
   Liao, SH
   Chang, WL
   Hsu, PY
AF Wen, Chih-Hao
   Liao, Shu-Hsien
   Chang, Wei-Ling
   Hsu, Ping-Yu
TI Mining shopping behavior in the Taiwan luxury products market
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Luxury products; Data mining; Brand spectrum; Product spectrum;
   Association rules; Clustering
ID TECHNOLOGIES; KNOWLEDGE; SYSTEM; RULES
AB The rapid growth of Taiwan's economy has been accompanied by the country's developing market for luxury products. To successfully establish the new market demand chain for the luxury industry in Taiwan, it is essential to understand customer preferences. Thus, this study uses an association rules approach and clustering analysis for data mining to mine knowledge among luxury product-buying customers in Taiwan. The results of knowledge extraction from data mining, illustrated as knowledge patterns, rules and knowledge maps, are used to make recommendations for future developments in the luxury products industry. (C) 2012 Elsevier Ltd. All rights reserved.
C1 [Wen, Chih-Hao; Hsu, Ping-Yu] Natl Cent Univ, Dept Business Adm, Jhongli, Taiwan.
   [Liao, Shu-Hsien; Chang, Wei-Ling] Tamkang Univ, Dept Management Sci & Decis Making, Taipei 251, Taiwan.
RP Wen, CH (通讯作者)，Natl Cent Univ, Dept Business Adm, 300 Jhongda Rd, Jhongli, Taiwan.
EM chwen@mgt.ncu.edu.tw
RI Hsu, Ping-Yu/ABB-6953-2021; Liao, Shu-hsien/AAW-1885-2020
OI Hsu, Ping-Yu/0000-0002-7153-2579; Liao, Shu-hsien/0000-0002-3667-2867;
   Wen, Chih-Hao/0000-0001-5021-5243
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NR 25
TC 4
Z9 4
U1 1
U2 40
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD SEP 15
PY 2012
VL 39
IS 12
BP 11257
EP 11268
DI 10.1016/j.eswa.2012.03.072
PG 12
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 966UQ
UT WOS:000305863300097
DA 2022-08-23
ER

PT J
AU Wei, CC
   Choy, CS
   Yeow, PHP
AF Wei, Chong Chin
   Choy, Chong Siong
   Yeow, Paul Heng Ping
TI KM implementation in Malaysian telecommunication industry - An empirical
   analysis
SO INDUSTRIAL MANAGEMENT & DATA SYSTEMS
LA English
DT Article
DE knowledge management; business planning; organizational structures;
   telecommunications; Malaysia
ID CRITICAL SUCCESS FACTORS; KNOWLEDGE MANAGEMENT; STRATEGIES; COMPANIES;
   CREATION; WILL
AB Purpose - This paper aims assess to the perceived importance (PI) and actual implementation (AI) of five preliminary knowledge management (KM) success factors, i.e. business strategy, organizational structure, knowledge team, knowledge audit, and knowledge map in the Malaysian telecommunication industry.
   Design/methodology/approach - A questionnaire survey was conducted on telecommunication organizations located in the capital of Malaysia. Data were analyzed using indices and parametric statistics.
   Findings - The results show that the organizations are aware of the importance of all the KM factors but fall short of implementation. The implemented factors consist of business strategy, organizational structure, and knowledge team. Knowledge audit and knowledge map are perceived as important but are the least implemented factors.
   Research limitations/implications - This study was conducted in only one industry in Malaysia. Furthermore, it focuses on the preliminary success factors of KM implementation rather than on learning and knowledge utilization.
   Practical implications - Telecommunication organizations have to overcome resources problems and enhance implementation level in order to narrow the gaps for effective, full scale KM implementation in the later stage. Such viable practice will significantly help the industry not only to compete more effectively within Malaysia, but also to position itself as a global player in the world.
   Originality/value - This study is perhaps one of the first to address the preliminary steps to be dealt with prior to KM implementation. Moreover, it attempts to compare the PI and Al of the five proposed success factors, which has received very little attention to date.
C1 Multimedia Univ, Fdn Studies & Extens Educ, Melaka, Malaysia.
   Multimedia Univ, Fac Business & Law, Melaka, Malaysia.
RP Wei, CC (通讯作者)，Multimedia Univ, Fdn Studies & Extens Educ, Melaka, Malaysia.
EM cwchong@mmu.edu.my
RI Yeow, Paul H.P./I-5264-2019
OI Yeow, Paul H.P./0000-0003-2277-8916
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   [No title captured]
NR 72
TC 38
Z9 38
U1 1
U2 15
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0263-5577
EI 1758-5783
J9 IND MANAGE DATA SYST
JI Ind. Manage. Data Syst.
PY 2006
VL 106
IS 8
BP 1112
EP 1132
DI 10.1108/02635570610710782
PG 21
WC Computer Science, Interdisciplinary Applications; Engineering,
   Industrial
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA 097WK
UT WOS:000241480000003
DA 2022-08-23
ER

PT S
AU Sanchez, D
   Isern, D
   Moreno, A
AF Sanchez, David
   Isern, David
   Moreno, Antonio
BE Gabrys, B
   Howlett, RJ
   Jain, LC
TI Integrated agent-based approach for ontology-driven web filtering
SO KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 3,
   PROCEEDINGS
SE Lecture Notes in Artificial Intelligence
LA English
DT Article; Proceedings Paper
CT 10th International Conference on Knowledge-Based and Intelligent
   Information and Engineering Systems
CY OCT 09-11, 2006
CL Bournemouth, ENGLAND
AB For knowledge-intensive industries it is of paramount importance to keep an up-to-date knowledge map of their domain in order to take the most appropriate strategic decisions. The Web offers a huge amount of valuable information, but its interaction is very hard and time consuming for humans because it requires to filter, analyse all related web pages and integrate it in a knowledge repository. This paper describes an integrated agent-based ontology-driven approach to retrieve web pages that contain data relevant to each of the main concepts of the domain of interest in a completely automatic, unsupervised and domain independent way.
C1 Univ Rovira & Virgili, Comp Sci & Math Dept, Artificial Intelligence Res Grp, BANZAI, Tarragona 43007, Catalonia, Spain.
RP Sanchez, D (通讯作者)，Univ Rovira & Virgili, Comp Sci & Math Dept, Artificial Intelligence Res Grp, BANZAI, Tarragona 43007, Catalonia, Spain.
EM david.sanchez@urv.net; david.isern@urv.net; antonio.moreno@urv.net
RI Sanchez, David/B-5275-2012; Moreno, Antonio/N-6972-2019; Isern,
   David/X-1653-2019
OI Sanchez, David/0000-0001-7275-7887; Moreno, Antonio/0000-0003-3945-2314;
   Isern, David/0000-0003-1270-9466
CR ABRAMOWICZ W, 2003, KNOWLEDGE BASED INFO
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   Turney P, 2001, 12 EUR C MACH LEARN
   Wooldridge M., 2002, INTRO MULTIAGENT SYS
NR 11
TC 2
Z9 2
U1 0
U2 8
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 3-540-46542-1
J9 LECT NOTES ARTIF INT
PY 2006
VL 4253
BP 758
EP 765
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BFI90
UT WOS:000242123100096
DA 2022-08-23
ER

PT J
AU Wang, Q
   Ji, YD
   Hao, YS
   Cao, J
AF Wang, Qi
   Ji, Yuede
   Hao, Yongsheng
   Cao, Jie
TI GRL: Knowledge graph completion with GAN-based reinforcement learning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Knowledge graph completion; Reinforcement learning;
   Deep learning
ID FRAMEWORK
AB Knowledge graph completion intends to infer the entities that need to be queried through the entities and relations known in the knowledge graphs. It is used in many applications, such as question and answer systems, and searching engines. As the completion process can be represented as a Markov process, existing works would solve this problem with reinforcement learning. However, there are three issues blocking them from achieving high accuracy, which are reward sparsity, missing specific domain rules, and ignoring the generation of knowledge graphs. In this paper, we design a generative adversarial net (GAN)-based reinforcement learning model, named GRL, for knowledge graph completion. First, GRL employs the graph convolutional network to embed the knowledge graphs into the low-dimensional space. Second, GRL employs both GAN and long short-term memory (LSTM) to record trajectory sequences obtained by the agent from traversing the knowledge graph and generate new trajectory sequences if needed. At the same time, GRL applies domain-specific rules accordingly. Finally, GRL employs the deep deterministic policy gradient method to optimize both rewards and adversarial loss. The experiments show that GRL is able to both generate better policies and outperform traditional methods for several tasks. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Wang, Qi] Fudan Univ, Shanghai, Peoples R China.
   [Ji, Yuede] George Washington Univ, Dept Elect & Comp Engn, Washington, DC 20052 USA.
   [Hao, Yongsheng] Nanjing Univ Informat Sci & Technol, Network Ctr, Nanjing, Peoples R China.
   [Cao, Jie] Xuzhou Univ Technol, Xuzhou, Jiangsu, Peoples R China.
RP Wang, Q (通讯作者)，Fudan Univ, Shanghai, Peoples R China.
EM qiwang1101@163.com
RI Wang, Qi/CAH-9366-2022
OI Wang, Qi/0000-0003-3249-8459
FU Chinese National Funding of Social Sciences [16ZDA054]; National Natural
   Science Foundation of China (NSFC) [41475089]
FX The work was partly supported by the Chinese National Funding of Social
   Sciences (No. 16ZDA054), the National Natural Science Foundation of
   China (NSFC) under grant (NO. 41475089).
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NR 51
TC 8
Z9 10
U1 16
U2 96
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 17
PY 2020
VL 209
AR 106421
DI 10.1016/j.knosys.2020.106421
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OH4CI
UT WOS:000582518100004
DA 2022-08-23
ER

PT J
AU Du, QS
   Cui, J
   Zhang, CJ
   He, K
AF Du, Quan-sheng
   Cui, Jie
   Zhang, Chun-jie
   He, Ke
TI Visualization analysis of CRISPR/Cas9 gene editing technology studies
SO JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B
LA English
DT Article
DE CRISPR/Cas9; CiteSpaceV; Visualization analysis
ID ESCHERICHIA-COLI; REPEATS; PROKARYOTES; DNA; IDENTIFICATION;
   BETWEENNESS; CENTRALITY; SEQUENCES; PATTERNS; BACTERIA
AB The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system is an adaptive immune defense system that resists the invasion of viruses and plasmids heterologous genetic material in bacteria and archaea. Taking the literature related to gene editing technology of CRISPR/Cas9 from the Web of Science database from 2002 to 2015, we use the software CiteSpaceV to analyze co-cited literature in order to establish the research hotspots and fronts recently in this field by knowledge mapping.
C1 [Du, Quan-sheng] Natl Nat Sci Fdn China, Dept Life Sci, Beijing 100085, Peoples R China.
   [Cui, Jie] Guangdong Technoecon Res & Dev Ctr, Guangzhou 510070, Guangdong, Peoples R China.
   [Zhang, Chun-jie] Hangzhou Dianzi Univ, Sch Sci, Hangzhou 310018, Zhejiang, Peoples R China.
   [He, Ke] Zhejiang A&F Univ, Coll Anim Sci & Technol, Linan 311300, Peoples R China.
RP Du, QS (通讯作者)，Natl Nat Sci Fdn China, Dept Life Sci, Beijing 100085, Peoples R China.
EM duqs@nsfc.gov.cn
FU Zhejiang Provincial Natural Science Foundation of China [LY16A010013]
FX Project supported by the Zhejiang Provincial Natural Science Foundation
   of China (No. LY16A010013)
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NR 29
TC 3
Z9 4
U1 6
U2 34
PU ZHEJIANG UNIV
PI HANGZHOU
PA EDITORIAL BOARD, 20 YUGU RD, HANGZHOU, 310027, PEOPLES R CHINA
SN 1673-1581
EI 1862-1783
J9 J ZHEJIANG UNIV-SC B
JI J. Zhejiang Univ.-SCI. B
PD OCT
PY 2016
VL 17
IS 10
BP 798
EP 806
DI 10.1631/jzus.B1601985
PG 9
WC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Research & Experimental Medicine
GA EA3TJ
UT WOS:000386527600007
PM 27704749
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Li, J
   Xia, EJ
   Wang, LL
   Yan, K
   Zhu, L
   Huang, JP
AF Li, Jun
   Xia, Enjun
   Wang, Lingling
   Yan, Kuan
   Zhu, Li
   Huang, Jieping
TI Knowledge domain and emerging trends of climate-smart agriculture: a
   bibliometric study
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article; Early Access
DE Climate-smart agriculture; Food security; Bibliometric analysis;
   CiteSpace; Knowledge map
ID INDO-GANGETIC PLAINS; FOOD SECURITY; SMALLHOLDER FARMERS; CHANGE
   IMPACTS; ADAPTATION; INTERVENTIONS; MITIGATION; ADOPTION; OPTIONS;
   AFRICA
AB Climate-smart agriculture (CSA) is a new agricultural development pattern to address future food crises. Since CSA was proposed in 2010, it has attracted the attention of scholars from all over the world. It is of great significance to scientifically summarize the overview and emerging trends of CSA research, providing ideas for scholars concerned about CSA to engage in research in this field. Based on bibliometrics and CSA-related literature data in the WOS database, this paper used CiteSpace software to draw knowledge maps to scientifically analyze publications in the field of CSA. Our study found that (1) CSA research is showing a rapid upward trend, focusing on the environmental sciences and agricultural economic management; (2) international organizations such as the FAO, World Bank, and the international agricultural research institute have made significant contributions to CSA research; (3) among the nine clusters in the CSA literature, CSA practice, conservation agriculture, smallholder farmers, and sub-Saharan Africa have been consistently given high attention; (4) CSA research can be divided into three phases, and the research hotspots have transferred from essential elements of CSA to household and carbon emissions. We believe that in future research, more attention should be paid to the trade-off and synergy of the three pillars of CSA, as well as the investment, finance, and evaluation criteria of CSA. Such strengthening is of great significance to the sustainable promotion of CSA.
C1 [Li, Jun; Xia, Enjun; Wang, Lingling; Yan, Kuan; Zhu, Li; Huang, Jieping] Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China.
RP Huang, JP (通讯作者)，Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China.
EM lijuncampus@bit.edu.cn
OI Yan, Kuan/0000-0002-3275-8137
FU National Natural Science Foundation of China [71572012, 7217020877];
   Beijing Social Science Foundation [15JDJGB022]
FX This study was financially supported by the National Natural Science
   Foundation of China (No. 71572012), the National Natural Science
   Foundation of China (No. 7217020877), and the Beijing Social Science
   Foundation (No. 15JDJGB022). Additional thanks are extended to editors
   and reviewers for their careful and constructive suggestions on this
   paper.
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NR 69
TC 1
Z9 1
U1 11
U2 11
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
DI 10.1007/s11356-022-20796-9
EA MAY 2022
PG 20
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 1J2XG
UT WOS:000797784100018
PM 35589899
DA 2022-08-23
ER

PT J
AU Wu, JT
   Orlandi, F
   O'Sullivan, D
   Pisoni, E
   Dev, S
AF Wu, Jiantao
   Orlandi, Fabrizio
   O'Sullivan, Declan
   Pisoni, Enrico
   Dev, Soumyabrata
TI Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs
SO IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE
   SENSING
LA English
DT Article
DE Meteorology; Machine learning; Resource description framework;
   Ontologies; Task analysis; Semantics; Linked data; Climate data;
   knowledge graphs (KGs); linked data; machine learning; semantic webs
ID CLASSIFICATION; ENHANCEMENT
AB Nowadays, the fast expansion of heterogeneous climate data resources accessible on the Internet has led to substantial data fragmentation on the web. For example, station-based sensor data from different sources are likely to be interrelated but may be stored in disparate formats, such as CSV, JSON, and XML. To address the data isolation problem, several semantically uplifted knowledge graphs are proposed for climate data exchange. While these knowledge graphs improve data interoperability, the advancement in multisource data interchange is limited to data included inside knowledge graphs. As a result, the exclusive interoperability of current climatic knowledge graphs hampers the flow of data into typical climate analysis workflows in contexts, where analytical models often need data in nonknowledge graph formats. This article addresses this issue by focusing on enhancing climate analysis workflows within the context of the Python machine learning environment, with a preference for tabular data. We propose an analysis workflow able to automatically integrate remote climate knowledge graph data with local tabular data so as to enhance the data usability with respect to certain climate analysis tasks. To underscore the importance of our study, we illustrate how the workflow streamlines the access to multisource climatic variables in the Python environment from a semantic perspective. The additional knowledge graph data have the potential to augment local datasets in the climate domain, as evidenced by an improvement in accuracy of up to 10% for machine learning geared on rainfall detection.
C1 [Wu, Jiantao; Dev, Soumyabrata] Univ Coll Dublin, ADAPT SFI Res Ctr, Sch Comp Sci, Dublin 4, Ireland.
   [Orlandi, Fabrizio; O'Sullivan, Declan] Trinity Coll Dublin, ADAPT SFI Res Ctr, Sch Comp Sci & Stat, Dublin 2, Ireland.
   [Pisoni, Enrico] European Commiss Joint Res Ctr, I-21027 Ispra, Italy.
RP Dev, S (通讯作者)，Univ Coll Dublin, ADAPT SFI Res Ctr, Sch Comp Sci, Dublin 4, Ireland.
EM jiantao.wu@ucdconnect.ie; fabrizio.orlandi@tcd.ie;
   declan.osullivan@tcd.ie; enrico.pisoni@ec.europa.eu;
   soumyabrata.dev@ucd.ie
OI Pisoni, Enrico/0000-0001-5484-5744
FU Science Foundation Ireland at the ADAPT SFI Research Centre at
   University College Dublin. ADAPT [13/RC/2106_P2]; SFI Research Centre
   for AI-Driven Digital Content Technology; Science Foundation Ireland
   through the SFI Research Centre's Program; EU H2020 Research and
   Innovation Program under the Marie Sklodowska-Curie at the ADAPT SFI
   Research Centre at Trinity College Dublin [713567]
FX This work was supported in part by the Science Foundation Ireland under
   Grant 13/RC/2106_P2 at the ADAPT SFI Research Centre at University
   College Dublin. ADAPT, in part by the SFI Research Centre for AI-Driven
   Digital Content Technology, in part by the Science Foundation Ireland
   through the SFI Research Centre's Program, and in part by the EU H2020
   Research and Innovation Program under the Marie Sklodowska-Curie under
   Grant 713567 at the ADAPT SFI Research Centre at Trinity College Dublin.
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NR 24
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 1939-1404
EI 2151-1535
J9 IEEE J-STARS
JI IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.
PY 2022
VL 15
BP 4708
EP 4718
DI 10.1109/JSTARS.2022.3177463
PG 11
WC Engineering, Electrical & Electronic; Geography, Physical; Remote
   Sensing; Imaging Science & Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Physical Geography; Remote Sensing; Imaging Science &
   Photographic Technology
GA 2I0KD
UT WOS:000814673000008
DA 2022-08-23
ER

PT J
AU Hong, C
   Rush, E
   Liu, ML
   Zhou, DD
   Sun, JH
   Sonabend, A
   Castro, VM
   Schubert, P
   Panickan, VA
   Cai, TR
   Costa, L
   He, ZL
   Link, N
   Hauser, R
   Gaziano, JM
   Murphy, SN
   Ostrouchov, G
   Ho, YL
   Begoli, E
   Lu, JW
   Cho, K
   Liao, KP
   Cai, TX
AF Hong, Chuan
   Rush, Everett
   Liu, Molei
   Zhou, Doudou
   Sun, Jiehuan
   Sonabend, Aaron
   Castro, Victor M.
   Schubert, Petra
   Panickan, Vidul A.
   Cai, Tianrun
   Costa, Lauren
   He, Zeling
   Link, Nicholas
   Hauser, Ronald
   Gaziano, J. Michael
   Murphy, Shawn N.
   Ostrouchov, George
   Ho, Yuk-Lam
   Begoli, Edmon
   Lu, Junwei
   Cho, Kelly
   Liao, Katherine P.
   Cai, Tianxi
CA VA Million Vet Program
TI Clinical knowledge extraction via sparse embedding regression (KESER)
   with multi-center large scale electronic health record data
SO NPJ DIGITAL MEDICINE
LA English
DT Article
ID RXNORM
AB The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.
C1 [Hong, Chuan; Panickan, Vidul A.; Gaziano, J. Michael; Cho, Kelly; Liao, Katherine P.; Cai, Tianxi] Harvard Med Sch, Boston, MA 02115 USA.
   [Hong, Chuan; Schubert, Petra; Cai, Tianrun; Costa, Lauren; Link, Nicholas; Gaziano, J. Michael; Ho, Yuk-Lam; Lu, Junwei; Cho, Kelly; Liao, Katherine P.; Cai, Tianxi] VA Boston Healthcare Syst, Boston, MA 02130 USA.
   [Rush, Everett; Ostrouchov, George; Begoli, Edmon] Oak Ridge Natl Lab, Dept Energy, Oak Ridge, TN USA.
   [Liu, Molei; Sonabend, Aaron; Lu, Junwei; Cai, Tianxi] Harvard TH Chan Sch Publ Hlth, Boston, MA 02115 USA.
   [Zhou, Doudou] Univ Calif Davis, Davis, CA 95616 USA.
   [Sun, Jiehuan] Univ Illinois, Chicago, IL USA.
   [Castro, Victor M.; Cai, Tianrun; He, Zeling; Murphy, Shawn N.] Mass Gen Brigham, Boston, MA USA.
   [Hauser, Ronald] West Haven VA Med Ctr, West Haven, CT USA.
   [Gaziano, J. Michael; Cho, Kelly; Liao, Katherine P.] Brigham & Womens Hosp, 75 Francis St, Boston, MA 02115 USA.
RP Cai, TX (通讯作者)，Harvard Med Sch, Boston, MA 02115 USA.; Cai, TX (通讯作者)，VA Boston Healthcare Syst, Boston, MA 02130 USA.; Cai, TX (通讯作者)，Harvard TH Chan Sch Publ Hlth, Boston, MA 02115 USA.
EM tcai@hsph.harvard.edu
RI Castro, Victor M/CAF-9395-2022; Castro, Victor M/CAH-3428-2022
OI Castro, Victor M/0000-0001-7390-6354; Castro, Victor
   M/0000-0001-7390-6354; Schubert, Petra/0000-0002-6135-0173; Rush,
   Everett/0000-0002-5632-5723; Ayakulangara Panickan,
   Vidul/0000-0003-0616-0403; Sonabend, Aaron/0000-0002-6165-9082
FU UT-Battelle, LLC [DE-AC0500OR22725]; Office of Science of the U.S.
   Department of Energy [DE-AC05-00OR22725];  [MVP000]
FX We would like to acknowledge that this work would not have been possible
   without the collaboration between VA and the Department of Energy which
   provided the computing infrastructure necessary to develop and test
   these approaches at scale using nationwide VA EHR data. We would also
   like to thank Ms. Hope Cook, SQL Database Administrator, and Mr. Ian
   Goethert, Data Engineer, for their assistance with optimizing the
   computing environment required for this study. This manuscript has been
   in part co-authored by UT-Battelle, LLC under Contract No.
   DE-AC0500OR22725, and under a joint program with the Department of
   Veterans Affairs under the Million Veteran Project Computational Health
   Analytics for Medical Precision to Improve Outcomes Now. Part of this
   research is based on data from the Million Veteran Program, Office of
   Research and Development, Veterans Health Administration, and was
   supported by award #MVP000. The United States Government retains and the
   publisher, by accepting the article for publication, acknowledges that
   the United States Government retains a nonexclusive, paid-up,
   irrevocable, world-wide license to publish or reproduce the published
   form of this manuscript, or allow others to do so, for United States
   Government purposes. The Department of Energy will provide public access
   to these results of federally sponsored research in accordance with the
   DOE Public Access Plan
   (http://energy.gov/downloads/doe-public-access-plan).This research used
   resources of the Knowledge Discovery Infrastructure at the Oak Ridge
   National Laboratory, which is supported by the Office of Science of the
   U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
CR Abraham M., 2010, CPT 2011 STANDARD ED
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NR 36
TC 0
Z9 0
U1 0
U2 2
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2398-6352
J9 NPJ DIGIT MED
JI npj Digit. Med.
PD OCT 27
PY 2021
VL 4
IS 1
AR 151
DI 10.1038/s41746-021-00519-z
PG 11
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA WO6RL
UT WOS:000712579200002
PM 34707226
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, XL
   Zheng, Y
   Xia, ML
   Wu, YN
   Liu, XJ
   Xie, SK
   Wu, YF
   Wang, M
AF Zhang, Xiang-Long
   Zheng, Yu
   Xia, Meng-Lei
   Wu, Ya-Nan
   Liu, Xiao-Jing
   Xie, San-Kuan
   Wu, Yan-Fang
   Wang, Min
TI Knowledge Domain and Emerging Trends in Vinegar Research: A Bibliometric
   Review of the Literature from WoSCC
SO FOODS
LA English
DT Review
DE vinegar; intellectual structure; knowledge mapping; CiteSpace;
   bibliometrics
ID ACETIC-ACID BACTERIA; TRADITIONAL BALSAMIC VINEGAR; SHANXI AGED VINEGAR;
   ZHENJIANG AROMATIC VINEGAR; SOLID-PHASE MICROEXTRACTION; BAR SORPTIVE
   EXTRACTION; HS-SPME/GC-MS; ANTIOXIDANT ACTIVITY; VOLATILE COMPOUNDS;
   MICROBIAL COMMUNITY
AB Vinegar is one of the most widely used acidic condiments. In recent decades, rapid advances have been made in the area of vinegar research, and the intellectual structure pertaining to this domain has significantly evolved. Thus, it is important that scientists keep abreast of associated developments to ensure an appropriate understanding of this field. To facilitate this current study, a bibliometric analysis method was adopted to visualize the knowledge map of vinegar research based on literature data retrieved from the Web of Science Core Collection (WoSCC) database. In total, 883 original research and review articles from between 1998 and 2019 with 19,663 references were analyzed by CiteSpace. Both a macroscopical sketch and microscopical characterization of the whole knowledge domain were realized. According to the research contents, the main themes that underlie vinegar research can be divided into six categories, that is, microorganisms, substances, health functions, production technologies, adjuvant medicines, and vinegar residues. In addition to the latter analysis, emerging trends and future research foci were predicted. Finally, the evolutionary stage of vinegar research was discerned according to Shneider's four-stage theory. This review will help scientists to discern the dynamic evolution of vinegar research, as well as highlight areas for future research.
C1 [Zhang, Xiang-Long; Zheng, Yu; Xia, Meng-Lei; Wu, Ya-Nan; Liu, Xiao-Jing; Xie, San-Kuan; Wu, Yan-Fang; Wang, Min] Tianjin Univ Sci & Technol, State Key Lab Food Nutr & Safety, Key Lab Ind Fermentat Microbiol, Minist Educ,Coll Biotechnol, Tianjin 300457, Peoples R China.
RP Wang, M (通讯作者)，Tianjin Univ Sci & Technol, State Key Lab Food Nutr & Safety, Key Lab Ind Fermentat Microbiol, Minist Educ,Coll Biotechnol, Tianjin 300457, Peoples R China.
EM xl_zhang@mail.tust.edu.cn; yuzheng@tust.edu.cn; mlxia@tust.edu.cn;
   18820944@mail.tust.edu.cn; lxj9704@163.com; xskuan@163.com;
   amwyvonne@163.com; minw@tust.edu.cn
OI Zhang, Xianglong/0000-0002-1253-3539
FU National Key R&D Program of China [2016YFD0400505]; National Natural
   Science Foundation of China [31671851]; Tianjin Municipal Science and
   Technology Commission [19YFLHSN00030, 18PTSYJC00140]; Tianjin Municipal
   Education Commission [2018ZD08, TD13-5013]; Tianjin Municipal Human
   Resource and Social Security Bureau [TJQYBSH2018010]; Key Laboratory of
   Industrial Fermentation Microbiology, Education Ministry of China
   [2018KF005]
FX This research was funded by the National Key R&D Program of China
   (2016YFD0400505), the National Natural Science Foundation of China
   (31671851), Tianjin Municipal Science and Technology Commission
   (19YFLHSN00030, 18PTSYJC00140), Tianjin Municipal Education Commission
   (2018ZD08, TD13-5013), Tianjin Municipal Human Resource and Social
   Security Bureau (TJQYBSH2018010), and Key Laboratory of Industrial
   Fermentation Microbiology, Education Ministry of China (2018KF005).
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NR 128
TC 16
Z9 16
U1 21
U2 55
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2304-8158
J9 FOODS
JI Foods
PD FEB
PY 2020
VL 9
IS 2
AR 166
DI 10.3390/foods9020166
PG 34
WC Food Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Food Science & Technology
GA KX6KX
UT WOS:000521989200023
PM 32050682
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Li, LF
   Wang, P
   Wang, Y
   Wang, SH
   Yan, J
   Jiang, JP
   Tang, BZ
   Wang, CL
   Liu, YT
AF Li, Linfeng
   Wang, Peng
   Wang, Yao
   Wang, Shenghui
   Yan, Jun
   Jiang, Jinpeng
   Tang, Buzhou
   Wang, Chengliang
   Liu, Yuting
TI A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph:
   Algorithm Development
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE probabilistic medical knowledge graph; representation learning; graph
   embedding; PrTransX; decision support systems; clinical; knowledge
   graph; medical informatics; electronic health records; natural language
   processing
AB Background: Knowledge graph embedding is an effective semantic representation method for entities and relations in knowledge graphs. Several translation-based algorithms, including TransE, TransH, TransR, TransD, and TranSparse, have been proposed to learn effective embedding vectors from typical knowledge graphs in which the relations between head and tail entities are deterministic. However, in medical knowledge graphs, the relations between head and tail entities are inherently probabilistic. This difference introduces a challenge in embedding medical knowledge graphs.
   Objective: We aimed to address the challenge of how to learn the probability values of triplets into representation vectors by making enhancements to existing TransX (where Xis E, H, R, D, or Sparse) algorithms, including the following: (1) constructing a mapping function between the score value and the probability, and (2) introducing probability-based loss of triplets into the original margin-based loss function.
   Methods: We performed the proposed PrTransX algorithm on a medical knowledge graph that we built from large-scale real-world electronic medical records data. We evaluated the embeddings using link prediction task.
   Results: Compared with the corresponding TransX algorithms, the proposed PrTransX performed better than the TransX model in all evaluation indicators, achieving a higher proportion of corrected entities ranked in the top 10 and normalized discounted cumulative gain of the top 10 predicted tail entities, and lower mean rank.
   Conclusions: The proposed PrTransX successfully incorporated the uncertainty of the knowledge triplets into the embedding vectors.
C1 [Li, Linfeng; Wang, Shenghui] Beijing Jiaotong Univ, Inst Informat Sci, Beijing, Peoples R China.
   [Li, Linfeng; Wang, Yao; Yan, Jun; Jiang, Jinpeng] Yidu Cloud Technol Inc, Beijing, Peoples R China.
   [Wang, Peng; Wang, Chengliang] Chongqing Univ, Coll Comp Sci, Chongqing, Peoples R China.
   [Wang, Peng] Southwest Hosp, Chongqing, Peoples R China.
   [Tang, Buzhou] Harbin Inst Technol, Dept Comp Sci, Shenzhen Grad Sch, Shenzhen, Peoples R China.
   [Liu, Yuting] Beijing Jiaotong Univ, Sch Sci, 3 Shangyuancun Haidian Dist, Beijing, Peoples R China.
RP Liu, YT (通讯作者)，Beijing Jiaotong Univ, Sch Sci, 3 Shangyuancun Haidian Dist, Beijing, Peoples R China.
EM ytliu@bjtu.edu.cn
OI Wang, Shenghui/0000-0003-3325-0754; li, linfeng/0000-0003-4949-8906;
   Wang, Yao/0000-0003-4121-7313; Tang, Buzhou/0000-0003-0271-8246; Liu,
   Yuting/0000-0002-3707-982X
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
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NR 19
TC 1
Z9 1
U1 2
U2 15
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
EI 2291-9694
J9 JMIR MED INF
JI JMIR Med. Inf.
PD MAY
PY 2020
VL 8
IS 5
AR e17645
DI 10.2196/17645
PG 12
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA MH0CX
UT WOS:000546399900011
PM 32436854
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Dai, SL
   Duan, X
   Zhang, W
AF Dai, Shengli
   Duan, Xin
   Zhang, Wei
TI Knowledge map of environmental crisis management based on keywords
   network and co-word analysis, 2005-2018
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Article
DE Environmental crisis management; Social network; Co-word analysis;
   Multi-dimensional scaling; Mapping knowledge
ID INFORMATION-RETRIEVAL; CLIMATE-CHANGE; CHINA; POLLUTION; SCIENCE;
   COCITATION; HEALTH; IMPACT; LEAD; WEB
AB Currently, the whole world is facing various environmental crises, which have caused serious harm to human society. Environmental crisis management is extremely important in tackling environmental crises. A large number of scholars have conducted extensive research on environmental crisis management, but the knowledge map about the environmental crisis management research is unknown. Therefore, analyzing the spatial structure of environmental crisis management not only helps to understand the specific distribution of the research paradigm of environmental crisis management but is also helpful in understanding the important topics. Based on the Web of Science (WOS) database. The software of NoteExpress, Bibexcel, and Ucinet and the analysis methods of social network (SN), K-core, co-word analysis, and multi-dimensional scaling (MDS) were employed to explore the spatial structure of keywords in environmental crisis management literature. The study revealed that climate change is the hotspot of environmental crisis management. Moreover, the five main research topics on environmental crisis management have been identified, which are, respectively, different types of environmental crisis, the impact areas of the environmental crisis, the governance of environmental crisis, the application of technology in environmental crisis management, the characteristics of environmental crisis, and micro-governance. The spatial structure in the research field of environmental crisis management can be divided into application of environmental crisis management technology, management practice of environmental crisis, specific types of environmental crisis, and management theory of environmental crisis management. Finally, this study puts forward four research topics which should be attached importantly in the future. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Dai, Shengli; Duan, Xin] Cent China Normal Univ, Sch Publ Adm, Bldg Gongguan,152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
   [Zhang, Wei] Cent China Normal Univ, Sch Publ Adm, Rm 431,Bldg Gongguan,152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
   [Zhang, Wei] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, 1954 Huashan Rd, Shanghai 150010, Peoples R China.
RP Zhang, W (通讯作者)，Cent China Normal Univ, Sch Publ Adm, Rm 431,Bldg Gongguan,152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
EM daishengli1@mail.ccnu.edu.cn; duanxin@mails.ccnu;
   zhangwei2017@mail.ccnu.edu.cn
RI Zhang, Wei/AAA-6273-2022
OI Zhang, Wei/0000-0001-7758-9675
FU National Natural Science Foundation of China [71804056, 71672111,
   71932004]; Humanities and Social Sciences Research Project of the
   Ministry of Education of China [18YJC630250]; China Postdoctoral Science
   Foundation [2018M642033]; National Social Science Fund Key Project of
   China [18AZD004]
FX This research was supported by National Natural Science Foundation of
   China (#71804056; #71672111, #71932004), Humanities and Social Sciences
   Research Project of the Ministry of Education of China (#18YJC630250),
   China Postdoctoral Science Foundation (#2018M642033) and National Social
   Science Fund Key Project of China (#18AZD004).
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NR 55
TC 18
Z9 18
U1 0
U2 0
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 JUL 20
PY 2020
VL 262
AR 121168
DI 10.1016/j.jclepro.2020.121168
PG 8
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 LT2AZ
UT WOS:000536876500007
DA 2022-08-23
ER

PT J
AU Zhu, HM
   Wang, XW
   Jiang, YZ
   Fan, HF
   Du, BW
   Liu, Q
AF Zhu, Hongming
   Wang, Xiaowen
   Jiang, Yizhi
   Fan, Hongfei
   Du, Bowen
   Liu, Qin
TI FTRLIM: Distributed Instance Matching Framework for Large-Scale
   Knowledge Graph Fusion
SO ENTROPY
LA English
DT Article
DE knowledge graph; instance matching; blocking algorithm; FTRL
ID ALIGNMENT; BLOCKING
AB Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects' features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.
C1 [Zhu, Hongming; Wang, Xiaowen; Jiang, Yizhi; Fan, Hongfei; Du, Bowen; Liu, Qin] Tongji Univ, Sch Software Engn, Shanghai 201804, Peoples R China.
   [Du, Bowen] Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England.
RP Du, BW; Liu, Q (通讯作者)，Tongji Univ, Sch Software Engn, Shanghai 201804, Peoples R China.; Du, BW (通讯作者)，Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England.
EM zhu_hongming@tongji.edu.cn; 2110211@tongji.edu.cn;
   1931566@tongji.edu.cn; fanhongfei@tongji.edu.cn; B.Du@Warwick.ac.uk;
   qin.liu@tongji.edu.cn
OI Wang, Xiaowen/0000-0003-1880-7921; Du, Bowen/0000-0002-3755-4870; Liu,
   Qin/0000-0002-9352-1694; Zhu, Hongming/0000-0001-5795-5279; jiang, yi
   zhi/0000-0002-9409-521X
FU Science and Technology Commission of Shanghai Municipality
   [20511102703]; National Natural Science Foundation of China [61702374]
FX This research was funded by the Science and Technology Commission of
   Shanghai Municipality (No.20511102703) and the National Natural Science
   Foundation of China (No. 61702374).
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NR 67
TC 0
Z9 1
U1 2
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD MAY
PY 2021
VL 23
IS 5
AR 602
DI 10.3390/e23050602
PG 27
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA SH1JI
UT WOS:000653891600001
PM 34068208
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wan, GJ
   Du, B
   Pan, SR
   Wu, J
AF Wan, Guojia
   Du, Bo
   Pan, Shirui
   Wu, Jia
TI Adaptive knowledge subgraph ensemble for robust and trustworthy
   knowledge graph completion
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Trustworthy knowledge graph; Knowledge graph completion; Link
   prediction; Knowledge graph embedding; Never-ending language learning
ID TASK ASSIGNMENT; WORKER
AB Knowledge graph (KG) embedding approaches are widely used to infer underlying missing facts based on intrinsic structure information. However, the presence of noisy facts in automatically extracted or crowdsourcing KGs significantly reduces the reliability of various embedding learners. In this paper, we thoroughly study the underlying reasons for the performance drop in dealing with noisy knowledge graphs, and we propose an ensemble framework, Adaptive Knowledge Subgraph Ensemble (AKSE), to enhance the robustness and trust of knowledge graph completion. By employing an effective knowledge subgraph extraction approach to re-sample the sub-components from the original knowledge graph, AKSE generates different representations for learning diversified base learners (e.g., TransE and DistMult), which substantially alleviates the noise effect of KG embedding. All embedding learners are integrated into a unified framework to reduce generalization errors via our simple or adaptive weighting schemes, where the weight is allocated based on each individual learner's prediction capacity. Experimental results show that the robustness of our ensemble framework outperforms exiting knowledge graph embedding approaches on manually injected noise as well as inherent noisy extracted KGs.
C1 [Wan, Guojia; Du, Bo] Wuhan Univ, Sch Comp Sci, Natl Engn Res Ctr MultimediaSoftware, Wuhan, Hubei, Peoples R China.
   [Pan, Shirui] Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia.
   [Wu, Jia] Macquarie Univ, Dept Comp, Sydney, NSW, Australia.
RP Du, B (通讯作者)，Wuhan Univ, Sch Comp Sci, Natl Engn Res Ctr MultimediaSoftware, Wuhan, Hubei, Peoples R China.; Pan, SR (通讯作者)，Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia.
EM guojiawan@whu.edu.cn; remoteking@whu.edu.cn; Shirui.Pan@monash.edu;
   jia.wu@mq.edu.au
RI Pan, Shirui/K-6763-2018
OI Pan, Shirui/0000-0003-0794-527X; Wu, Jia/0000-0002-1371-5801
FU National Natural Science Foundation of China [61822113]; Natural Science
   Foundation of Hubei Province [2018CFA050]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grants 61822113, the Natural Science
   Foundation of Hubei Province under Grants 2018CFA050.
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NR 42
TC 8
Z9 9
U1 7
U2 30
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 JAN
PY 2020
VL 23
IS 1
SI SI
BP 471
EP 490
DI 10.1007/s11280-019-00711-y
EA OCT 2019
PG 20
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KU0NY
UT WOS:000490196100001
DA 2022-08-23
ER

PT J
AU Shen, GW
   Wang, WL
   Mu, QL
   Pu, YH
   Qin, Y
   Yu, M
AF Shen, Guowei
   Wang, Wanling
   Mu, Qilin
   Pu, Yanhong
   Qin, Ya
   Yu, Miao
TI Data-Driven Cybersecurity Knowledge Graph Construction for Industrial
   Control System Security
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB Industrial control systems (ICS) involve many key industries, which once attacked will cause heavy losses. However, traditional passive defense methods of cybersecurity have difficulty effectively dealing with increasingly complex threats; a knowledge graph is a new idea to analyze and process data in cybersecurity analysis. We propose a novel overall framework of data-driven industrial control network security defense, which integrated fragmented multisource threat data with an industrial network layout by a cybersecurity knowledge graph. In order to better correlate data to construct a knowledge graph, we propose a distant supervised relation extraction model ResPCNN-ATT; it is based on a deep residual convolutional neural network and attention mechanism, reduces the influence of noisy data in distant supervision, and better extracts deep semantic features in sentences by using deep residuals. We empirically demonstrate the performance of the proposed method in the field of general cybersecurity by using dataset CSER; the model proposed in this paper achieves higher accuracy than other models. And then, the dataset ICSER was used to construct a cybersecurity knowledge graph (CSKG) on the basis of analyzing specific industrial control scenarios, visualizing the knowledge graph for further security analysis to the industrial control system.
C1 [Shen, Guowei; Wang, Wanling; Qin, Ya] Guizhou Univ, Coll Comp Sci & Technol, Guizhou Prov Key Lab Publ Big Data, Guiyang 550025, Peoples R China.
   [Shen, Guowei; Mu, Qilin; Pu, Yanhong] Big Data Applicat Improving Govt Governance Capab, Guiyang 550022, Peoples R China.
   [Shen, Guowei; Mu, Qilin; Pu, Yanhong] CETC Big Data Res Inst Co Ltd, Guiyang 550022, Peoples R China.
   [Yu, Miao] Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China.
RP Yu, M (通讯作者)，Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China.
EM gwshen@gzu.edu.cn; wl_wang@foxmail.com; muqilin@cetcbigdata.com;
   puyanhong@cetcbigdata.com; qyamail@163.com; yumiao@iie.ac.cn
OI shen, guowei/0000-0002-1329-4058; Yu, Miao/0000-0003-1720-9882
FU National Natural Science Foundation of China [61802081]; Big Data
   Application on Improving Government Governance Capabilities National
   Engineering Laboratory Open Fund Project [W-2018023]
FX This work is supported by the National Natural Science Foundation of
   China under Grant 61802081 and Big Data Application on Improving
   Government Governance Capabilities National Engineering Laboratory Open
   Fund Project (No.W-2018023).
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NR 33
TC 4
Z9 4
U1 6
U2 20
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 DEC 28
PY 2020
VL 2020
AR 8883696
DI 10.1155/2020/8883696
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PU7MY
UT WOS:000609486600006
OA gold
DA 2022-08-23
ER

PT J
AU Qi, YL
   Jiang, R
   Jia, Y
   Li, AP
AF Qi, Yulu
   Jiang, Rong
   Jia, Yan
   Li, Aiping
TI Attack Analysis Framework for Cyber-Attack and Defense Test Platform
SO ELECTRONICS
LA English
DT Article
DE cyber security knowledge graph; self-defined rule reasoning; threat
   element association statistics; composite attack rule base
AB In 2012, Google first proposed the knowledge graph and applied it in the field of intelligent searching. Subsequently, knowledge graphs have been used for in-depth association analysis in different fields. In recent years, composite attacks have been discovered through association analysis in the field of cyber security. This paper proposes an attack analysis framework for cyber-attack and defense test platforms, which stores prior knowledge in a cyber security knowledge graph and attack rule base as data that can be understood by a computer, sets the time interval of analysis on the Spark framework, and then mines attack chains from massive data with spatiotemporal constraints, so as to achieve the balance between automated analysis and real-time accurate performance. The experimental results show that the analysis accuracy depends on the completeness of the cyber security knowledge graph and the precision of the detection results from security equipment. With the rational expectation about more exposure of attacks and faster upgrade of security equipment, it is necessary and meaningful to constantly improve the cyber security knowledge graph in the attack analysis framework.
C1 [Qi, Yulu; Jiang, Rong; Jia, Yan; Li, Aiping] Natl Univ Def Technol, Coll Comp, Changsha 410073, Peoples R China.
RP Jiang, R (通讯作者)，Natl Univ Def Technol, Coll Comp, Changsha 410073, Peoples R China.
EM qiyulu1103@163.com; jiangrong@nudt.edu.cn; jiayanjy@vip.sina.com;
   liaiping@nudt.edu.cn
OI QI, Yulu/0000-0001-9891-2427
FU National Key Research and Development Program of China [2019QY1406,
   2017YFB0802204, 2017YFB0803301]; Key R&D Program of Guangdong Province
   [2019B010136003]; National Natural Science Foundation of China
   [61732004, 61732022, 61502517, 61472433, 61672020]
FX This research was partially supported by the National Key Research and
   Development Program of China, grant numbers 2019QY1406, 2017YFB0802204,
   and 2017YFB0803301, and the Key R&D Program of Guangdong Province, grant
   number No.2019B010136003. This research was also supported by the
   National Natural Science Foundation of China, grant numbers No.
   61732004, 61732022, 61502517, 61472433, and 61672020.
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NR 25
TC 4
Z9 4
U1 3
U2 13
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD SEP
PY 2020
VL 9
IS 9
AR 1413
DI 10.3390/electronics9091413
PG 18
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA OE0FF
UT WOS:000580217100001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Alshahrani, M
   Khan, MA
   Maddouri, O
   Kinjo, AR
   Queralt-Rosinach, N
   Hoehndorf, R
AF Alshahrani, Mona
   Khan, Mohammad Asif
   Maddouri, Omar
   Kinjo, Akira R.
   Queralt-Rosinach, Nuria
   Hoehndorf, Robert
TI Neuro-symbolic representation learning on biological knowledge graphs
SO BIOINFORMATICS
LA English
DT Article
ID ONTOLOGY; INFORMATION; DISEASE; OWL
AB Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics.
C1 [Alshahrani, Mona; Khan, Mohammad Asif; Maddouri, Omar; Hoehndorf, Robert] King Abdullah Univ Sci & Technol, Computat Biosci Res Ctr, Comp Elect & Math Sci & Engn Div, Thuwal 239556900, Saudi Arabia.
   [Maddouri, Omar] Hamad Bin Khalifa Univ, Coll Sci & Engn, Life Sci Div, Doha, Qatar.
   [Kinjo, Akira R.] Osaka Univ, Inst Prot Res, 3-2 Yamadaoka, Suita, Osaka 5650871, Japan.
   [Queralt-Rosinach, Nuria] Scripps Res Inst, Dept Integrat Struct & Computat Biol, La Jolla, CA 92037 USA.
RP Hoehndorf, R (通讯作者)，King Abdullah Univ Sci & Technol, Computat Biosci Res Ctr, Comp Elect & Math Sci & Engn Div, Thuwal 239556900, Saudi Arabia.
EM robert.hoehndorf@kaust.edu.sa
RI Hoehndorf, Robert/H-6127-2019
OI Hoehndorf, Robert/0000-0001-8149-5890; Alshahrani,
   Mona/0000-0002-9848-8248; Kinjo, Akira/0000-0002-4006-8208; Maddouri,
   Omar/0000-0003-0305-0348
FU King Abdullah University of Science and Technology (KAUST); Database
   Integration Coordination Program (Upgrading and Integrative Management
   of Protein Data Bank Japan) from the National Bioscience Database Center
   (NBDC); Drug Discovery and Life Science Research (Platform for Drug
   Discovery, Informatics, and Structural Life Science) from Japan Agency
   for Medical Research and Development (AMED); ISCIII-FEDER [PI13/00082,
   CP10/00524, CPII16/00026]; IMI-JU from EU-FP7 (FP7) [115191, 115372];
   IMI-JU from EFPIA companies [115191, 115372]; EU H Programme [634143,
   676559]; Spanish Ministry of Economy and Competitiveness, through the
   "Maria de Maeztu" Programme for Units of Excellence in RD
   [MDM-2014-0370]; PE I+D+i - ISCIII [PT13/0001/0023]; PE I+D+i - FEDER
   [PT13/0001/0023]
FX M.A., M.A.K., O.M. and R.H. were supported by funding from King Abdullah
   University of Science and Technology (KAUST). A.R.K. was supported by
   Database Integration Coordination Program (Upgrading and Integrative
   Management of Protein Data Bank Japan) from the National Bioscience
   Database Center (NBDC) and Platform Project for Supporting in Drug
   Discovery and Life Science Research (Platform for Drug Discovery,
   Informatics, and Structural Life Science) from Japan Agency for Medical
   Research and Development (AMED). N.Q.R. acknowledges support from
   ISCIII-FEDER (PI13/00082, CP10/00524, CPII16/00026), IMI-JU under grants
   agreements no. 115191 (Open PHACTS), and no. 115372 (EMIF), resources of
   which are composed of financial contribution from the EU-FP7
   (FP7/2007-2013) and EFPIA companies in kind contribution, and the EU
   H2020 Programme 2014-2020 under grant agreements no. 634143
   (MedBioinformatics) and no. 676559 (Elixir-Excelerate), and the Spanish
   Ministry of Economy and Competitiveness, through the "Maria de Maeztu"
   Programme for Units of Excellence in R&D (MDM-2014-0370). The Research
   Programme on Biomedical Informatics (GRIB) is a member of the Spanish
   National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by
   grant PT13/0001/0023, of the PE I+D+i 2013-2016, funded by ISCIII and
   FEDER.
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NR 53
TC 57
Z9 57
U1 10
U2 24
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 2017
VL 33
IS 17
BP 2723
EP 2730
DI 10.1093/bioinformatics/btx275
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 FF3CI
UT WOS:000408772700015
PM 28449114
OA Green Published, hybrid, Green Submitted
DA 2022-08-23
ER

PT J
AU Chen, PF
   Wang, YS
   Yu, Q
   Fan, Y
   Feng, RY
AF Chen, Panfeng
   Wang, Yisong
   Yu, Quan
   Fan, Yi
   Feng, Renyan
TI Hamming Distance Encoding Multihop Relation Knowledge Graph Completion
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph; knowledge graph completion; tensor factorization;
   matrix factorization; Hamming distance; multihop relations
AB Knowledge graphs (KGs) play an important role in many real-world applications like information retrieval, question answering, relation extraction, etc. To reveal implicit knowledge from a knowledge graph (KG), viz. knowledge graph completion (KGC), is a crucial task for the downstream applications based on KG. For this purpose various embedding-based approaches have been proposed recently. This paper proposes a new approach named HRESCAL to KGC. It extends the well-known embedding-based approach RESCAL by introducing Hamming distance-based encoder to capture implicit multihop and partial inverse relation features in a KG. Experimental results on widely used KGC benchmarks show that the new approach achieves state-of-the-art or is competitive AUC performance.
C1 [Chen, Panfeng; Wang, Yisong; Feng, Renyan] Guizhou Univ, Dept Comp Sci & Technol, Guiyang 550025, Peoples R China.
   [Yu, Quan; Fan, Yi] Qiannan Normal Univ Nationalities, Sch Math & Stat, Duyun 558200, Peoples R China.
   [Fan, Yi] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China.
RP Wang, YS (通讯作者)，Guizhou Univ, Dept Comp Sci & Technol, Guiyang 550025, Peoples R China.
EM yswang@gzu.edu.cn
OI Chen, Panfeng/0000-0002-6155-7043
FU National Natural Science Foundation of China (NSFC) [61976065]; Guizhou
   Talents'Growth Foundation of Science and Technology [KY[2019]201]; 
   [[2019]QNSYXM05];  [QNSY2018JS010];  [QianNan Science [2018]10]
FX This work was supported in part by the National Natural Science
   Foundation of China (NSFC) under Grant 61976065, and in part by the
   Guizhou Talents'Growth Foundation of Science and Technology under Grant
   KY[2019]201. The work of Yu Quan was supported by the Foundations under
   Grant [2019]QNSYXM05, Grant QNSY2018JS010, and Grant QianNan Science
   [2018]10.
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NR 44
TC 2
Z9 2
U1 1
U2 8
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 117146
EP 117158
DI 10.1109/ACCESS.2020.3004448
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MI6JK
UT WOS:000547511400001
OA gold
DA 2022-08-23
ER

PT J
AU Qiao, B
   Fang, K
   Chen, YM
   Zhu, XH
AF Qiao, Bo
   Fang, Kui
   Chen, Yiming
   Zhu, Xinghui
TI Building thesaurus-based knowledge graph based on schema layer
SO CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND
   APPLICATIONS
LA English
DT Article
DE Knowledge graph; Agricultural thesaurus; Schema layer; Triple;
   Mathematical model
AB Google proposed the concept of knowledge graph to improve the quality of searching results in 2012, so the knowledge graph brought a hot topic in the field of academic and industry. The knowledge graph can effectively improve the searching quality and the accuracy of Q & A system, which is a hot issue. With the help of agricultural experts, this paper based on the Agricultural Thesaurus, determines the rules for judging whether a thesaurus is a concept or an entity, establishes maps from Agricultural Thesaurus to the agricultural knowledge graph schema layer and the data layer. Therefore, the large-scale automatic building is realized from the Agricultural Thesaurus to the agricultural knowledge graph. In order to effectively manage and utilize triples, this paper proposes a mathematical model for the management of triples with the RDF-based triple storage pattern, which lays the solid foundation for the semantic-based agricultural information retrieving and the construction of the Q & A system.
C1 [Qiao, Bo; Fang, Kui; Chen, Yiming; Zhu, Xinghui] Hunan Agr Univ, Coll Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.
RP Fang, K (通讯作者)，Hunan Agr Univ, Coll Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.
EM qiaobo@hunau.edu.cn; fk@hunau.net; chenym@hunau.edu.cn; 152062475@qq.com
FU National Science and Technology Funding Program [2012BAD35807]; Hunan
   Province's Key Science and Technology Project [2016NK2118]; Hunan
   Education Authority's Science Research Project [13C389]
FX Our work has been fully supported by the National Science and Technology
   Funding Program (2012BAD35807), the Hunan Province's Key Science and
   Technology Project (2016NK2118), as well as the Hunan Education
   Authority's Science Research Project (13C389).
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NR 28
TC 5
Z9 5
U1 8
U2 63
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1386-7857
EI 1573-7543
J9 CLUSTER COMPUT
JI Cluster Comput.
PD MAR
PY 2017
VL 20
IS 1
SI SI
BP 81
EP 91
DI 10.1007/s10586-016-0725-z
PG 11
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EQ3JU
UT WOS:000397969100008
DA 2022-08-23
ER

PT J
AU Shen, LS
   He, RB
   Huang, SB
AF Shen, Linshan
   He, Rongbo
   Huang, Shaobin
TI Entity alignment with adaptive margin learning knowledge graph embedding
SO DATA & KNOWLEDGE ENGINEERING
LA English
DT Article
DE Entity matching; Entity alignment; Knowledge representation; Knowledge
   embedding
AB A large number of knowledge graphs have been constructed at present. However, there is diversity and heterogeneity among different knowledge graphs. The relation and attribute of the knowledge graph contain rich semantic information, which helps construct the potential semantic representation of the knowledge graph. At present, the method based on knowledge representation is an important method of entity alignment, which can align entities by transforming them into spatial vectors. And it helps to reduce the heterogeneity among different knowledge domains. However, existing methods use the same optimization goal for triples under different relations, ignoring the difference between relationships. In this article, we put forward a kind of entity alignment method based on the TransE model and use adaptive margin strategies in training. At the same time, this paper studies the LSTM encoder model and the BERT pretraining model in the application of entity alignment. To enhance the model's robustness, we put forward the triple selection strategy based on attribute similarity. Experimental results on real datasets show that this method is significantly improved compared with the baseline model.
C1 [Shen, Linshan; He, Rongbo; Huang, Shaobin] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150001, Peoples R China.
RP He, RB (通讯作者)，Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150001, Peoples R China.
EM brian@hrbeu.edu.cn
FU Basic Research Program project, China [JCKY2020210B019]
FX Funding statement This article is supported by the Basic Research
   Program project, China (No. JCKY2020210B019) .
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NR 22
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-023X
EI 1872-6933
J9 DATA KNOWL ENG
JI Data Knowl. Eng.
PD MAY
PY 2022
VL 139
AR 101987
DI 10.1016/j.datak.2022.101987
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1K7UR
UT WOS:000798803300005
DA 2022-08-23
ER

PT J
AU Song, YX
AF Song, Yixin
TI Construction of Event Knowledge Graph based on Semantic Analysis
SO TEHNICKI VJESNIK-TECHNICAL GAZETTE
LA English
DT Article
DE corporate credit events; text data; knowledge graph; semantic analysis
AB At present, the research and application of enterprise credit event information mainly takes the data of enterprise credit events as a dimension of enterprise credit evaluation, and lacks in-depth analysis and mining of the content of special events. On the basis of sorting out the connotation of enterprise credit events, the article firstly proposes a model with evolutionary features, network structured features and unstructured features of text data for the knowledge graph of enterprise credit events; then, the events in enterprise credit events are extracted in the form of case study, the named entities and dependency relationships in the text statements are analyzed, and the events with subject-predicate object relationship as the main form are extracted; secondly, the statements are analyzed. Finally, the extracted events and relationships are matched to form a knowledge graph of corporate credit events. The study applies the mapping research method to the field of corporate credit event research, and realizes the process analysis of the evolution of corporate credit events using knowledge graph.
C1 [Song, Yixin] Zhongnan Univ Econ & Law, Accounting Dept, 182 Nanhu Ave, Wuhan, Peoples R China.
RP Song, YX (通讯作者)，Zhongnan Univ Econ & Law, Accounting Dept, 182 Nanhu Ave, Wuhan, Peoples R China.
EM 2295195085@qq.com
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NR 9
TC 0
Z9 0
U1 13
U2 37
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 OCT
PY 2021
VL 28
IS 5
BP 1640
EP 1646
DI 10.17559/TV-20210427063132
PG 7
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA UD0JW
UT WOS:000686904600028
OA gold
DA 2022-08-23
ER

PT J
AU Jiang, D
   Wang, RG
   Yang, J
   Xue, LX
AF Jiang, Dan
   Wang, Ronggui
   Yang, Juan
   Xue, Lixia
TI Kernel multi-attention neural network for knowledge graph embedding
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Link prediction; Entity kernel; Relation kernel; Multi-attention neural
   network; Knowledge graph embedding
AB Link prediction is the problem of predicting missing link between entities and relations for knowledge graph. In recent years, some tasks have achieved great success for link prediction, but these tasks are far from expanding entity relation vectors, and cannot predict missing links more efficiently. In this paper, we propose a novel link prediction method called kernel multi-attention neural network for knowledge graph embedding (KMAE) which is able to extend kernel separately in entity and relation attributes. The kernel function uses Gaussian kernel function to expand into more robust entity kernel and relation kernel. In addition, we constructed a novel multi-attention neural network that acts on the entity kernel and relation kernel which can capture local important characteristics. Experiments on FB15k-237 and WN18RR, show that multi-attention fully reflect excellent performance in the task of knowledge graph embedding. Our proposed KMAE achieves better results than previous state-of-the-art link prediction methods. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Jiang, Dan; Wang, Ronggui; Yang, Juan; Xue, Lixia] Hefei Univ Technol, Sch Comp & Informat, Hefei 230601, Peoples R China.
RP Yang, J (通讯作者)，Hefei Univ Technol, Sch Comp & Informat, Hefei 230601, Peoples R China.
EM yangjuan_hfut@126.com
FU National Key RAMP;D Program of China of the Ministry of Science and
   Technology of the People's Republic of China [2020YFC1512601]
FX This project was supported by a grant from the National Key R&D Program
   of China of the Ministry of Science and Technology of the People's
   Republic of China (No. 2020YFC1512601).
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NR 41
TC 2
Z9 2
U1 8
U2 16
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD SEP 5
PY 2021
VL 227
AR 107188
DI 10.1016/j.knosys.2021.107188
EA JUN 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TS0WX
UT WOS:000679379400023
DA 2022-08-23
ER

PT J
AU Schuetz, CG
   Bozzato, L
   Neumayr, B
   Schrefl, M
   Serafini, L
AF Schuetz, Christoph G.
   Bozzato, Loris
   Neumayr, Bernd
   Schrefl, Michael
   Serafini, Luciano
TI Knowledge Graph OLAP A multidimensional model and query operations for
   contextualized knowledge graphs
SO SEMANTIC WEB
LA English
DT Article
DE Contextualized Knowledge Repository; knowledge graph management system;
   knowledge graph summarization; Resource Description Framework;
   ontologies
ID LARGE-SCALE; TECHNOLOGIES; REPOSITORIES; BASE; OWL
AB A knowledge graph (KG) represents real-world entities and their relationships. The represented knowledge is often context-dependent, leading to the construction of contextualized KGs. The multidimensional and hierarchical nature of context invites comparison with the OLAP cube model from multidimensional data analysis. Traditional systems for online analytical processing (OLAP) employ multidimensional models to represent numeric values for further analysis using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of the traditional OLAP query operations for the purposes of performing analysis over KGs. In particular, we decompose the roll-up operation from traditional OLAP into a merge and an abstraction operation. The merge operation corresponds to the selection of knowledge from different contexts whereas abstraction replaces entities with more general entities. The result of such a query is a more abstract, high-level view - a management summary - of the knowledge.
C1 [Schuetz, Christoph G.; Neumayr, Bernd; Schrefl, Michael] Johannes Kepler Univ Linz, Inst Business Informat Data & Knowledge Engn, Linz, Austria.
   [Bozzato, Loris; Serafini, Luciano] Fdn Bruno Kessler, Ctr Informat & Commun Technol, Trento, Italy.
RP Schuetz, CG (通讯作者)，Johannes Kepler Univ Linz, Inst Business Informat Data & Knowledge Engn, Linz, Austria.
EM christoph.schuetz@jku.at; bozzato@fbk.eu; bernd.neumayr@jku.at;
   michael.schrefl@jku.at; serafini@fbk.eu
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NR 89
TC 2
Z9 2
U1 10
U2 17
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2021
VL 12
IS 4
BP 649
EP 683
DI 10.3233/SW-200419
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 SW1WL
UT WOS:000664311200006
OA hybrid
DA 2022-08-23
ER

PT J
AU Zhang, SH
   Sun, ZY
   Zhang, WS
AF Zhang, Siheng
   Sun, Zhengya
   Zhang, Wensheng
TI Improve the translational distance models for knowledge graph embedding
SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; Translational distance model; Positional
   encoding; Self-attention
AB Knowledge graph embedding techniques can be roughly divided into two mainstream, translational distance models and semantic matching models. Though intuitive, translational distance models fail to deal with the circle structure and hierarchical structure in knowledge graphs. In this paper, we propose a general learning framework named TransX-pa, which takes various models (TransE, TransR, TransH and TransD) into consideration. From this unified viewpoint, we analyse the learning bottlenecks are: (i) the common assumption that the inverse of a relation r is modelled as its opposite - r; and (ii) the failure to capture the rich interactions between entities and relations. Correspondingly, we introduce position-aware embeddings and self-attention blocks, and show that they can be adapted to various translational distance models. Experiments are conducted on different datasets extracted from real-world knowledge graphs Freebase and WordNet in the tasks of both triplet classification and link prediction. The results show that our approach makes a great improvement, showing a better, or comparable, performance with state-of-the-art methods.
C1 [Zhang, Siheng; Sun, Zhengya; Zhang, Wensheng] Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing, Peoples R China.
   [Zhang, Siheng] Univ Chinese Acad Sci, Beijing, Peoples R China.
   [Zhang, Wensheng] Foshan Univ, Sch Math & Big Data, Foshan, Peoples R China.
RP Zhang, WS (通讯作者)，Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing, Peoples R China.; Zhang, WS (通讯作者)，Foshan Univ, Sch Math & Big Data, Foshan, Peoples R China.
EM zhangsiheng2015@ia.ac.cn; zhengya.sun@ia.ac.cn; wensheng.zhang@ia.ac.cn
FU National Key Research and Development Program of China [2016QY03D0500];
   National Natural Science Foundation of China [U1636220, 61876183,
   61976212]
FX The authors are thankful for the financial support from the National Key
   Research and Development Program of China (2016QY03D0500), as well as
   the National Natural Science Foundation of China (U1636220, 61876183,
   61976212).
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NR 35
TC 2
Z9 2
U1 2
U2 19
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 445
EP 467
DI 10.1007/s10844-019-00592-7
EA JAN 2020
PG 23
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OH1OG
UT WOS:000515591800001
DA 2022-08-23
ER

PT J
AU Sun, Y
   Chen, AD
   Chen, CF
   Xia, TC
   Zhao, XB
AF Sun, Yuan
   Chen, Andong
   Chen, Chaofan
   Xia, Tianci
   Zhao, Xiaobing
TI A Joint Model for Representation Learning of Tibetan Knowledge Graph
   Based on Encyclopedia
SO ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION
   PROCESSING
LA English
DT Article
DE Tibetan; knowledge graph; representation learning; joint model;
   encyclopedia
AB Learning the representation of a knowledge graph is critical to the field of natural language processing. There is a lot of research for English knowledge graph representation. However, for the low-resource languages, such as Tibetan, how to represent sparse knowledge graphs is a key problem. In this article, aiming at scarcity of Tibetan knowledge graphs, we extend the Tibetan knowledge graph by using the triples of the high-resource language knowledge graphs and Point of Information map information. To improve the representation learning of the Tibetan knowledge graph, we propose a joint model to merge structure and entity description information based on the Translating Embeddings and Convolution Neural Networks models. In addition, to solve the segmentation errors, we use character and word embedding to learn more complex information in Tibetan. Finally, the experimental results show that our model can make a better representation of the Tibetan knowledge graph than the baseline.
C1 [Sun, Yuan; Chen, Andong; Chen, Chaofan; Xia, Tianci; Zhao, Xiaobing] Minzu Univ China, Sch Informat Engn, Beijing 100081, Peoples R China.
RP Sun, Y (通讯作者)，Minzu Univ China, Sch Informat Engn, Beijing 100081, Peoples R China.
EM tracy.yuan.sun@gmail.com; ands691119@gmail.com; chaofanalex@qq.com;
   muctianciking@163.com; nmzxb_cn@163.com
FU National Natural Science Foundation of China [61972436]
FX This work was supported by the National Natural Science Foundation of
   China under Grant No. 61972436.
CR Aone C, 2000, 6TH APPLIED NATURAL LANGUAGE PROCESSING CONFERENCE/1ST MEETING OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE AND PROCEEDINGS OF THE ANLP-NAACL 2000 STUDENT RESEARCH WORKSHOP, P76
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NR 42
TC 0
Z9 0
U1 11
U2 30
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 27
DI 10.1145/3447248
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RZ6OF
UT WOS:000648719600009
DA 2022-08-23
ER

PT J
AU Ristoski, P
   Gentile, AL
   Alba, A
   Gruhl, D
   Welch, S
AF Ristoski, Petar
   Gentile, Anna Lisa
   Alba, Alfredo
   Gruhl, Daniel
   Welch, Steven
TI Large-scale relation extraction from web documents and knowledge graphs
   with human-in-the-loop
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Relation extraction; Web mining; Knowledge graph mining;
   Human-in-the-loop
ID SEMANTIC WEB
AB The Semantic Web movement has produced a wealth of curated collections of entities and facts, often referred as Knowledge Graphs. Creating and maintaining such Knowledge Graphs is far from being a solved problem: it is crucial to constantly extract new information from the vast amount of heterogeneous sources of data on the Web. In this work we address the task of Knowledge Graph population. Specifically, given any target relation between two entities, we propose an approach to extract positive instances of the relation from various Web sources. Our relation extraction approach introduces a human-in-the-loop component in the extraction pipeline, which delivers significant advantage with respect to other solely automatic approaches. We test our solution on the ISWC 2018 Semantic Web Challenge, with the objective to identify supply-chain relations among organizations in the Thomson Reuters Knowledge Graph. Our human-in-the-loop extraction pipeline achieves top performance among all competing systems. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Ristoski, Petar; Gentile, Anna Lisa; Alba, Alfredo; Gruhl, Daniel; Welch, Steven] IBM Res Almaden, 650 Harry Rd, San Jose, CA 95120 USA.
RP Ristoski, P (通讯作者)，IBM Res Almaden, 650 Harry Rd, San Jose, CA 95120 USA.
EM petar.ristoski@ibm.com
RI Gentile, Anna Lisa/R-8187-2016
OI Gentile, Anna Lisa/0000-0002-6401-4175
CR [Anonymous], [No title captured]
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NR 40
TC 6
Z9 6
U1 10
U2 22
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 JAN
PY 2020
VL 60
AR 100546
DI 10.1016/j.websem.2019.100546
PG 7
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KN2GE
UT WOS:000514658800002
DA 2022-08-23
ER

PT J
AU Carriero, VA
   Gangemi, A
   Mancinelli, ML
   Nuzzolese, AG
   Presutti, V
   Veninata, C
AF Carriero, Valentina Anita
   Gangemi, Aldo
   Mancinelli, Maria Letizia
   Nuzzolese, Andrea Giovanni
   Presutti, Valentina
   Veninata, Chiara
TI Pattern-based design applied to cultural heritage knowledge graphs
SO SEMANTIC WEB
LA English
DT Article
DE Cultural heritage knowledge graph; ontology design patterns; ontology
   testing; ontology evaluation
ID ONTOLOGY; CRITERIA
AB Ontology Design Patterns (ODPs) have become an established and recognised practice for guaranteeing good quality ontology engineering. There are several ODP repositories where ODPs are shared as well as ontology design methodologies recommending their reuse. Performing rigorous testing is recommended as well for supporting ontology maintenance and validating the resulting resource against its motivating requirements. Nevertheless, it is less than straightforward to find guidelines on how to apply such methodologies for developing domain-specific knowledge graphs. ArCo is the knowledge graph of Italian Cultural Heritage and has been developed by using eXtreme Design (XD), an ODP- and test-driven methodology. During its development, XD has been adapted to the need of the CH domain e.g. gathering requirements from an open, diverse community of consumers, a new ODP has been defined and many have been specialised to address specific CH requirements. This paper presents ArCo and describes how to apply XD to the development and validation of a CH knowledge graph, also detailing the (intellectual) process implemented for matching the encountered modelling problems to ODPs. Relevant contributions also include a novel web tool for supporting unit-testing of knowledge graphs, a rigorous evaluation of ArCo, and a discussion of methodological lessons learned during ArCo's development.
C1 [Carriero, Valentina Anita] Univ Bologna, DISI, Bologna, Italy.
   [Gangemi, Aldo] Univ Bologna, FICLIT, Bologna, Italy.
   [Gangemi, Aldo; Nuzzolese, Andrea Giovanni; Presutti, Valentina] ISTC CNR, STLab, Rome, Italy.
   [Mancinelli, Maria Letizia; Veninata, Chiara] MiBAC, ICCD, Rome, Italy.
   [Presutti, Valentina] Univ Bologna, LILEC, Bologna, Italy.
RP Carriero, VA (通讯作者)，Univ Bologna, DISI, Bologna, Italy.
EM valentina.carriero3@unibo.it; aldo.gangemi@unibo.it;
   marialetizia.mancinelli@beniculturali.it;
   andreagiovanni.nuzzolese@cnr.it; valentina.presutti@unibo.it;
   chiara.veninata@beniculturali.it
OI Carriero, Valentina Anita/0000-0003-1427-3723
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NR 66
TC 1
Z9 1
U1 8
U2 18
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2021
VL 12
IS 2
SI SI
BP 313
EP 357
DI 10.3233/SW-200422
PG 45
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:000613905500010
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Li, XL
   Zhang, SS
   Huang, R
   Huang, B
   Xu, CH
   Kuang, BW
AF Li, Xiuling
   Zhang, Shusheng
   Huang, Rui
   Huang, Bo
   Xu, Changhong
   Kuang, Bowen
TI Structured modeling of heterogeneous CAM model based on process
   knowledge graph
SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Process knowledge graph; Heterogeneous; CAM model; Structured modeling
ID MACHINING FEATURES; RETRIEVAL APPROACH; PRISMATIC PARTS; REUSE;
   INTEGRATION; DBPEDIA; SYSTEM
AB With the rapid development of customized production and globalization, a vast number of heterogeneous CAM models are generated and stored in enterprises, which contain lots of experts' knowledge and experience. Sharing and reusing these models is a favorable method to improve the NC process planning efficiency, and the structuralized representation of these heterogeneous CAM models is the precondition. In this paper, a structured heterogeneous CAM models based on process knowledge graph is presented. Firstly, the multilevel and multifactor process knowledge graph is constructed, which describes the CAD models and NC processes in a unified way to guide the data mapping and exchange. Then, the CAM system ontology is established to organize the inherent concepts/terms of heterogeneous CAM systems accurately and consistently. And the concept/semantic match between ontology model and process knowledge graph is achieved through the multistrategy ontology mapping. Finally, the implicit semantics is obtained through rule-based reasoning and the precision of match results is checked, which enriches and guarantees the unity of the process knowledge graph. A prototype system has been developed to verify the effectiveness of the proposed approach.
C1 [Li, Xiuling; Zhang, Shusheng; Huang, Bo; Kuang, Bowen] Northwestern Polytech Univ, Key Lab Contemporary Designing & Integrated Mfg T, Minist Educ, Xian 710072, Shaanxi, Peoples R China.
   [Li, Xiuling] Zhengzhou Railway Vocat & Tech Coll, Zhengzhou 450052, Henan, Peoples R China.
   [Huang, Rui] HoHai Univ, Coll IOT Engn, Changzhou 213022, Peoples R China.
   [Xu, Changhong] Nanjing Res Inst Elect Technol, Nanjing 210000, Jiangsu, Peoples R China.
RP Zhang, SS (通讯作者)，Northwestern Polytech Univ, Key Lab Contemporary Designing & Integrated Mfg T, Minist Educ, Xian 710072, Shaanxi, Peoples R China.
EM zssnwpu@163.com
FU National Science Foundation of China [51605142, 51375397]; Equipment
   Pre-Research Domain Foundation of China [61409230102-3]
FX We thank the anonymous reviewers for their constructive comments. The
   authors are very grateful for the financial support from the National
   Science Foundation of China (Nos. 51605142, 51375397) and Equipment
   Pre-Research Domain Foundation of China (61409230102-3).
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NR 41
TC 15
Z9 18
U1 6
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 JUN
PY 2018
VL 96
IS 9-12
BP 4173
EP 4193
DI 10.1007/s00170-018-1862-8
PG 21
WC Automation & Control Systems; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering
GA GG8UK
UT WOS:000432974100091
DA 2022-08-23
ER

PT J
AU Huang, XQ
   Tang, JY
   Tan, Z
   Zeng, WX
   Wang, J
   Zhao, X
AF Huang, Xuqian
   Tang, Jiuyang
   Tan, Zhen
   Zeng, Weixin
   Wang, Ji
   Zhao, Xiang
TI Knowledge graph embedding by relational and entity rotation
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; Relation pattern; Multi-fold relation;
   Relational and entity rotation
AB Knowledge graphs are typical large-scale multi-relational structures and useful for many artificial intelligence tasks. However, knowledge graphs often have missing facts, which limits the development of downstream tasks. To refine the knowledge graphs, knowledge graph embedding models have been developed. Knowledge graph embedding models aim to learn distributed representations for entities and relations and predict unknown triplets by scoring candidate triplets. Nevertheless, state-of-the-art works either aim to capture different relation patterns, or to model the multi-fold relations, and yet fail to consider these two aspects simultaneously. To fill this gap, in this paper, we propose a novel knowledge graph embedding model, MRotatE. It exploits triplet features from the perspective of relational and entity rotations, which can model and infer various relation patterns and handle with multi-fold relations at the same time. The experimental results demonstrate that MRotatE outperforms existing approaches and attains the state-of-the-art performance. (C) 2021 The Author(s). Published by Elsevier B.V.
C1 [Huang, Xuqian; Tang, Jiuyang; Tan, Zhen; Zeng, Weixin; Wang, Ji; Zhao, Xiang] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
RP Tan, Z (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
EM tanzhen08a@nudt.edu.cn
OI Zeng, Weixin/0000-0002-2690-3190
FU NSFC, China [61872446, 61902417, 71690233, 71971212]; NSF of Hunan
   province, China [2019JJ20024]
FX This work was partially supported by NSFC, China under grants Nos.
   61872446, 61902417, 71690233, and 71971212, and NSF of Hunan province,
   China under grant No. 2019JJ20024.
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NR 47
TC 1
Z9 1
U1 6
U2 20
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD OCT 11
PY 2021
VL 229
AR 107310
DI 10.1016/j.knosys.2021.107310
EA AUG 2021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UO7YW
UT WOS:000694909500018
OA hybrid
DA 2022-08-23
ER

PT J
AU Li, GD
   Qiu, Z
   Ding, YK
   Wang, Z
AF Li Guodong
   Qiu Zhang
   Ding, Yongkai
   Wang Zhe
TI Research on Service Discovery Methods Based on Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Syntactics; Web services; Knowledge engineering; Semantics; Semantic
   Web; Data mining; Service discovery; knowledge graph; template matching
AB Service discovery is one of the main goals of a service-oriented architecture, helping to discover complex applications. Knowledge graph is a cross-cutting research hotspot in the fields of computer, knowledge engineering and information science. This paper makes in-depth research on the key issues of knowledge graph matching for service-oriented discovery. Firstly, construct a knowledge graph for service discovery, and then design a template to match the knowledge graph. By designing a matching template, the service discovery mechanism is transformed into a knowledge graph knowledge query. Finally, the validity of the knowledge graph matching method proposed in this paper is verified by experiments.
C1 [Li Guodong; Qiu Zhang; Ding, Yongkai; Wang Zhe] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China.
RP Li, GD (通讯作者)，North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China.
EM lgd@ncepu.edu.cn
FU National Key Research and Development Program of China [2018YFC0831404]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018YFC0831404.
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PI PISCATAWAY
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PY 2020
VL 8
BP 138934
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WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MX6DL
UT WOS:000557811300001
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DA 2022-08-23
ER

PT J
AU Wang, J
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AF Wang, Jian
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LA English
DT Article
ID REAL-TIME; BIG DATA; MULTIOBJECTIVE OPTIMIZATION; DECISION-MAKING;
   ENERGY-STORAGE; PROCESS MODEL; POWER; SYSTEM; ONTOLOGY; NETWORK
AB With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.
C1 [Wang, Jian; Wang, Xi; Ma, Chaoqun] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 611756, Peoples R China.
   [Kou, Lei] Northeast Elect Power Univ, Sch Elect Engn, Jilin, Jilin, Peoples R China.
RP Wang, J (通讯作者)，Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 611756, Peoples R China.
EM JianWangzx@163.com
RI kou, lei/AGW-8926-2022
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NR 186
TC 4
Z9 5
U1 12
U2 57
PU INST ENGINEERING TECHNOLOGY-IET
PI HERTFORD
PA MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND
SN 1751-8687
EI 1751-8695
J9 IET GENER TRANSM DIS
JI IET Gener. Transm. Distrib.
PD FEB
PY 2021
VL 15
IS 3
BP 383
EP 407
DI 10.1049/gtd2.12040
EA DEC 2020
PG 25
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA PV3QW
UT WOS:000596403200001
OA hybrid
DA 2022-08-23
ER

PT J
AU Wang, LH
   Gong, ZW
   Shi, LN
   Hu, ZW
   Shah, AA
AF Wang, Lihong
   Gong, Zaiwu
   Shi, Linna
   Hu, Zewen
   Shah, Ashfaq Ahmad
TI Knowledge mapping analysis of research progress and frontiers in
   integrated disaster risk management in a changing climate
SO NATURAL HAZARDS
LA English
DT Article
DE Climate change; Integrated disaster risk; CiteSpace; Bibliometrics;
   Knowledge mapping
ID INTELLECTUAL STRUCTURE; SCIENTOMETRIC ANALYSIS; COCITATION
AB Integrated disaster risk management in a changing climate is a key concern for disaster reduction and global sustainable development now and in the future. This study conducted descriptive statistical and bibliometric analysis of 488 articles on integrated disaster risk in a changing climate from 2008 to 2020 using Excel, CiteSpace, and Gephi. The research process of integrated disaster risk in a changing climate was divided into three periods, 2008-2010, 2011-2015, and 2016-2020, which is similar to the changing characteristic of the number of publications. Results showed that, first, although the number of papers was increasing exponentially, the number of publications in each period remained relatively small. Second, the main features of the cooperation network of authors across the world showed a small concentration of groups, large dispersion, and weak connections. Third, the literature focused on five key issues, including basic disaster theory research; the construction of databases; research on underlying concepts, relationships, determinants and influences of vulnerability, risk, adaptability and resilience; the construction of frameworks and models of integrated disaster risk; and impact analysis of integrated disaster risk assessment and governance in regard to the relationships and interactions among disasters, nature, society, and human behavior. Fourth, the priorities, foci, and frontiers of integrated disaster risk research were mainly reflected in five aspects, including disasters; climate change adaptation; climate change impacts; health, insurance, critical infrastructure, communities, and various frameworks; and research on key regions and countries.
C1 [Wang, Lihong] Nanjing Univ Informat Sci & Technol, Minist Educ, Sch Appl Meteorol, Nanjing, Peoples R China.
   [Wang, Lihong; Gong, Zaiwu] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing, Peoples R China.
   [Gong, Zaiwu] Nanjing Univ Informat Sci & Technol, Minist Educ, Nanjing, Peoples R China.
   [Shi, Linna] Chinese Acad Sci, Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing, Peoples R China.
   [Hu, Zewen; Shah, Ashfaq Ahmad] Nanjing Univ Informat Sci & Technol, Sch Management Sci & Engn, Nanjing, Peoples R China.
RP Gong, ZW (通讯作者)，Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing, Peoples R China.
EM zwgong26@163.com
RI Shah, Ashfaq Ahmad/J-2476-2019; Shah, Ashfaq Ahmad/AAT-7073-2021
FU National Natural Science Foundation of China [71971121, 71571104]; Major
   project of the national social science foundation [6ZDA047]; NUISTUoR
   International Research Institute, the Major Project Plan of Philosophy
   and Social Sciences Research in Jiangsu Universities [2018SJZDA038];
   Jiangsu Province Policy Guidance Program (Soft Science Research)
   [BR2019064]; Jiangsu Province Science and Technology Planning Program
   (Soft Science Research) [SBR2019000126]; Postgraduate Research &
   Practice Innovation Program of Jiangsu Province [KYCX19_1038]; Project
   the Impact of Weather Conditions on the Spread of Large-Scale Influenza
   Virus
FX This research is partially supported by the National Natural Science
   Foundation of China (71971121, 71571104), the Major project of the
   national social science foundation (grant no.6ZDA047), NUISTUoR
   International Research Institute, the Major Project Plan of Philosophy
   and Social Sciences Research in Jiangsu Universities (2018SJZDA038), the
   2019 Jiangsu Province Policy Guidance Program (Soft Science Research)
   (BR2019064), the 2019 Jiangsu Province Science and Technology Planning
   Program (Soft Science Research) (SBR2019000126), the Postgraduate
   Research & Practice Innovation Program of Jiangsu Province
   (KYCX19_1038), and the support of the special Project "the Impact of
   Weather Conditions on the Spread of Large-Scale Influenza Virus.
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Z9 4
U1 26
U2 66
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0921-030X
EI 1573-0840
J9 NAT HAZARDS
JI Nat. Hazards
PD JUL
PY 2021
VL 107
IS 3
SI SI
BP 2033
EP 2052
DI 10.1007/s11069-020-04465-z
EA JAN 2021
PG 20
WC Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences;
   Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geology; Meteorology & Atmospheric Sciences; Water Resources
GA TB9ES
UT WOS:000605135400002
DA 2022-08-23
ER

PT J
AU Liu, PK
   Wang, X
   Fu, Q
   Yang, YJ
   Li, YF
   Zhang, QP
AF Liu, Pengkai
   Wang, Xin
   Fu, Qiang
   Yang, Yajun
   Li, Yuan-Fang
   Zhang, Qingpeng
TI KGVQL: A knowledge graph visual query language with bidirectional
   transformations
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graphs; Visual query language; Bidirectional transformation;
   Query graph pattern
AB With the rapid development of artificial intelligence, knowledge graphs have been widely recognized as a critical component in many AI techniques and systems. A complex knowledge graph may contain hundreds of millions of nodes and edges, thus is challenging for end-users to understand and query. In this paper, we present a knowledge graph interactive visual query language, KGVQL, to improve the efficiency of end-users' understanding and querying of knowledge graphs. Furthermore, KGVQL realizes the novel capability of flexible bidirectional transformations between query graphs and query results, therefore significantly assisting end-users in constructing queries over large and unfamiliar knowledge graphs in an incremental way. We present the visual syntax of KGVQL, discuss our design rationale behind this interactive visual query language, and illustrate a number of case studies. We empirically evaluate the effectiveness of a visual query system based on KGVQL against a number of textual and visual query environments over a large knowledge graph, DBpedia. Our evaluation demonstrates the superiority of KGVQL in effectiveness and accuracy. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
C1 [Liu, Pengkai; Wang, Xin; Fu, Qiang; Yang, Yajun] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China.
   [Li, Yuan-Fang] AI Monash Univ, Dept Data Sci, Clayton, Vic, Australia.
   [Zhang, Qingpeng] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China.
RP Wang, X (通讯作者)，Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China.
EM liupengkai@tju.edu.cn; wangx@tju.edu.cn; tomqcust@tju.edu.cn;
   yjyang@tju.edu.cn; liupengkai@tju.edu.cn; qingpeng.zhang@cityu.edu.hk
OI Li, Yuan-Fang/0000-0003-4651-2821
FU National Key Research and Development Program of China [2019YFE0198600]
FX Acknowledgments This work is supported by the National Key Research and
   Development Program of China (2019YFE0198600) .
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NR 32
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD AUG 17
PY 2022
VL 250
AR 108870
DI 10.1016/j.knosys.2022.108870
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2D1KA
UT WOS:000811313600009
OA hybrid
DA 2022-08-23
ER

PT J
AU Kong, YH
   Liu, X
   Zhao, ZB
   Zhang, DX
   Duan, JK
AF Kong, Yinghui
   Liu, Xu
   Zhao, Zhenbing
   Zhang, Dongxia
   Duan, Jikun
TI Bolt defect classification algorithm based on knowledge graph and
   feature fusion
SO ENERGY REPORTS
LA English
DT Article; Proceedings Paper
CT 8th International Conference on Power and Energy Systems Engineering
   (CPESE)
CY SEP 10-12, 2021
CL Fukuoka, JAPAN
DE Bolt and nut pair; Knowledge graph; Feature fusion; Decision fusion;
   Defect classification
AB At present, there is a problem of insufficient utilization of regional features in the application of the existing knowledge graph based on GGNN in the defect classification of the bolt and nut pair of transmission lines. Therefore, a decision-making method of combining the bolt and nut pair with the original regional feature and the bolt and nut pair knowledge graph feature is proposed. For this reason, a method is proposed to combine the decision-making of the bolt and nut pair on the original regional features and the bolt and nut pair on the characteristics of the knowledge graph. First, the characteristics of the bolt and nut pair knowledge graph are combined with the adaptive normalization of the bolt and nut pair to the features of the original area. Then, the classification score vector based on fusion features and the classification score vector based on the bolt and nut pair to the original area feature are derived from the classifier respectively; Finally, the classification score vector of the fusion feature and the bolt and nut pair are fused to the classification score vector of the original region feature to obtain the final classification result. On this basis, this article uses bolt and nut pair to conduct multiple sets of defect classification experiments on the data set of the knowledge graph experiment. The experimental results show that the method of decision fusion of the bolt and nut pair to the original regional feature fusion the bolt and nut pair to the knowledge graph feature is better than the bolt and nut pair to the knowledge graph average precision, precision, and recall rate. It is effective Prove the improvement of the algorithm. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
C1 [Kong, Yinghui; Liu, Xu; Zhao, Zhenbing; Duan, Jikun] North China Elect Power Univ, Dept Elect & Commun Engn, Baoding 071003, Hebei, Peoples R China.
   [Kong, Yinghui; Liu, Xu; Zhao, Zhenbing; Duan, Jikun] North China Elect Power Univ, Hebei Key Lab Power Internet Things Technol, Baoding 071003, Hebei, Peoples R China.
   [Zhang, Dongxia] China Elect Power Res Inst, Beijing 100192, Haidian Distric, Peoples R China.
RP Zhao, ZB (通讯作者)，North China Elect Power Univ, Dept Elect & Commun Engn, Baoding 071003, Hebei, Peoples R China.
EM zhaozhenbing@ncepu.edu.cn
FU National Natural Science Foundation of China [61871182, 61773160];
   Beijing Natural Science Foundation, China [4192055]; Natural Science
   Foundation of Hebei Province [F2020502009]; Fundamental Research Funds
   for the Central Universities [2020YJ006]
FX This work was supported in part by the National Natural Science
   Foundation of China (61871182, 61773160), Beijing Natural Science
   Foundation, China (4192055), Natural Science Foundation of Hebei
   Province (F2020502009), and Fundamental Research Funds for the Central
   Universities (2020YJ006).
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NR 13
TC 1
Z9 1
U1 14
U2 20
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-4847
J9 ENERGY REP
JI Energy Rep.
PD APR
PY 2022
VL 8
SU 1
SI SI
BP 856
EP 863
DI 10.1016/j.egyr.2021.11.127
EA DEC 2021
PG 8
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Energy & Fuels
GA XP5MF
UT WOS:000730908700001
OA gold
DA 2022-08-23
ER

PT J
AU Ma, XG
   Ma, C
   Wang, CB
AF Ma, Xiaogang
   Ma, Chao
   Wang, Chengbin
TI A new structure for representing and tracking version information in a
   deep time knowledge graph
SO COMPUTERS & GEOSCIENCES
LA English
DT Article
DE Knowledge graph; Geologic time scale; Ontology; Version control; Open
   data
ID GEOLOGIC TIME; SEMANTIC WEB; VISUALIZATION; SCALE; EARTH
AB Ontologies and vocabularies are an effective way to promote data interoperability in open data and open science. The deep time knowledge graph is one of the most discussed and studied topics in geoscience ontologies and vocabularies. The continuous evolution of deep time concepts calls for a mechanism of version control and organization to reduce the semantic ambiguity. In this paper we propose a new structure for version control and tracking of concepts, attributes and topological relationships in the deep time knowledge graph. In our work we have reused the existing ontologies for geologic time scale and vocabularies for the International (Chrono) stratigraphic Chart (ISC). Through the new structure, we are able to represent the whole version history of the ISC charts (from 2004 to 2018) in a single knowledge graph. Moreover, the resulting knowledge graph is consistent with the existing ontologies and vocabularies. Experiments of SPARQL queries prove the efficiency of this structure for version tracking of concepts and attributes. We are now extending the knowledge graph with concepts from regional and local geologic time standards, such as North America, Europe, Britain, China, and Australia, and building a graphic user interface for the services. In a future work, we will implement the knowledge graph in data integration workflows. We hope this research will spur more discussion and development of methods for version control of knowledge graphs in geoscience and other disciplines.
C1 [Ma, Xiaogang; Ma, Chao] Univ Idaho, Dept Comp Sci, 875 Perimeter Dr, Moscow, ID 83844 USA.
   [Wang, Chengbin] China Univ Geosci, State Key Lab Geol Proc & Mineral Resources, Wuhan 430074, Peoples R China.
   [Wang, Chengbin] China Univ Geosci, Sch Earth Resources, Wuhan 430074, Peoples R China.
RP Ma, XG (通讯作者)，Univ Idaho, Dept Comp Sci, 875 Perimeter Dr, Moscow, ID 83844 USA.
EM max@uidaho.edu
RI Wang, Chengbin/ABG-2166-2021
FU National Science Foundation, United States [1835717]
FX The work presented in this paper was supported by the National Science
   Foundation, United States (No. 1835717). Additional support was provided
   by the IUGS Deep-time Digital Earth (DDE) Big Science Program, the Deep
   Carbon Observatory, the Alfred P. Sloan Foundation, and the Carnegie
   Institution for Science for communicating the research progress at
   several workshops and meetings. We thank the Computers & Geoscience
   editor and four anonymous reviewers for their constructive comments on
   an earlier version of the manuscript.
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NR 54
TC 6
Z9 7
U1 12
U2 22
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0098-3004
EI 1873-7803
J9 COMPUT GEOSCI-UK
JI Comput. Geosci.
PD DEC
PY 2020
VL 145
AR 104620
DI 10.1016/j.cageo.2020.104620
PG 12
WC Computer Science, Interdisciplinary Applications; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Geology
GA OO4LL
UT WOS:000587352100015
OA hybrid
DA 2022-08-23
ER

PT J
AU Chang, C
   Tang, Y
   Long, YX
   Hu, K
   Li, Y
   Li, JG
   Wang, CD
AF Chang, Chao
   Tang, Yong
   Long, Yongxu
   Hu, Kun
   Li, Ying
   Li, Jianguo
   Wang, Chang-Dong
TI Multi-Information Preprocessing Event Extraction With BiLSTM-CRF
   Attention for Academic Knowledge Graph Construction
SO IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
LA English
DT Article; Early Access
DE Feature extraction; Data mining; Blogs; Knowledge engineering;
   Semantics; Task analysis; Social networking (online); Academic knowledge
   graph; event extraction; knowledge graph embedding; SCHOLAT
AB Academic knowledge graph is an important application of knowledge graph in the vertical field of academia. At present, the construction of the academic knowledge graph is mainly completed by extracting published academic papers, authors, publications, and other information from related databases. However, academic information is not just information of published papers. Scholars' academic activities include participation in academic conferences, visiting and making presentation, and so on. However, the above academic information is hidden in natural language texts and cannot be directly stored in academic knowledge graph. This article proposes an approach named construct-SCHOLAT knowledge graph to construct an academic event knowledge graph based on academic social network SCHOLAT. The construction framework mainly consists of two parts: data preprocessing and event extraction. In the data preprocessing, we propose a knowledge graph embedding method to represent scholars' academic social feature. In the event extraction, we concatenate the preprocessed scholar vector with academic we-media blog text into the extraction model based on BiLSTM-CRF fused with attention mechanism. The extracted events are added to academic knowledge graph, and a public relationship exists between the event and the scholar. Compared to the previous methods, our framework has an excellent performance after experimental verification. To the best of our knowledge, this is the first study to use the scholar academic social information of the scholar who edited the text as the event extraction input information. In addition, we publish a Chinese event extraction dataset SCHOLAT academic event extraction.https://www.scholat.com/research/opendata The dataset includes academic we-media blog and the social behavior embedding vector of the scholar. All the data in this dataset are derived from the academic social network SCHOLAT.
C1 [Chang, Chao; Tang, Yong; Long, Yongxu; Hu, Kun; Li, Ying; Li, Jianguo] South China Normal Univ, Sch Comp Sci, Guangzhou 510632, Guangdong, Peoples R China.
   [Wang, Chang-Dong] Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510275, Peoples R China.
RP Tang, Y (通讯作者)，South China Normal Univ, Sch Comp Sci, Guangzhou 510632, Guangdong, Peoples R China.
EM changchao@m.scnu.edu.cn; ytang@m.scnu.edu.cn; longyongxu@m.scun.edu.cn;
   lkome@m.scun.edu.cn; 2345903@qq.com; jianguoli@m.scnu.edu.cn;
   changdongwang@hotmail.com
FU National Natural Science Foundation of China [U1811263, 62077045,
   61876193]
FX This work was supported by the National Natural Science Foundation of
   China under Grant U1811263, Grant 62077045, and Grant 61876193.
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NR 50
TC 0
Z9 0
U1 1
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2329-924X
J9 IEEE T COMPUT SOC SY
JI IEEE Trans. Comput. Soc. Syst.
DI 10.1109/TCSS.2022.3183685
EA JUL 2022
PG 12
WC Computer Science, Cybernetics; Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2W7RE
UT WOS:000824716700001
DA 2022-08-23
ER

PT J
AU Luo, J
   Wang, YF
   Jiang, DC
AF Luo, Jie
   Wang, Yifei
   Jiang, Dongchen
TI Rule-based hidden relation recognition for large scale knowledge graphs
SO PATTERN RECOGNITION LETTERS
LA English
DT Article
DE Hidden relation; Knowledge graph; Reasoning; OWL2 RL
AB Knowledge graphs usually contain much implicit semantic information, which need to be further recognized through semantic inference. However, existing approaches are either not good at processing large scale data or not powerful enough for digging hidden relations thoroughly. This paper proposes a distributed OWL2 RL/RDF rule-based theory closure reasoning algorithm, named KGRL, for recognizing hidden relations in knowledge graphs. Since hidden relations derived from knowledge graph usually contain a lot of redundancies, a redundancy reduction strategy is proposed for eliminating redundant data without effect further queries on the knowledge graph. Extensive experiments and comprehensive evaluations are conducted. The experimental result shows that KGRL recognizes more hidden relations efficiently than Cichlid at different scales of the LUBM benchmark, and it only has a constant increase of runtime. Further more, the redundancy reduction strategy effectively reduces the size of the resulting knowledge graphs of hidden relation recognition on both synthetic and real-world knowledge graphs. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Luo, Jie; Wang, Yifei] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.
   [Jiang, Dongchen] Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China.
RP Jiang, DC (通讯作者)，Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China.
EM jiangdongchen@bjfu.edu.cn
OI Luo, Jie/0000-0002-4157-9931
FU National Natural Science Foundation of China [61502022]; State Key
   Laboratory of Software Development Environment [SKLSDE-2017ZX-17]
FX This work was supported by National Natural Science Foundation of China
   (Grand No. 61502022) and State Key Laboratory of Software Development
   Environment (Grand No. SKLSDE-2017ZX-17).
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TC 3
Z9 3
U1 4
U2 25
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8655
EI 1872-7344
J9 PATTERN RECOGN LETT
JI Pattern Recognit. Lett.
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PY 2019
VL 125
BP 13
EP 20
DI 10.1016/j.patrec.2019.03.012
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IS8CA
UT WOS:000482374500003
DA 2022-08-23
ER

PT J
AU Franco-Salvador, M
   Rosso, P
   Gomez, MMY
AF Franco-Salvador, Marc
   Rosso, Paolo
   Montes-y-Gomez, Manuel
TI A systematic study of knowledge graph analysis for cross-language
   plagiarism detection
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Cross-language; Plagiarism detection; Knowledge graphs; Multilingual
   semantic network; Distributed representations; Evaluation
AB Cross-language plagiarism detection aims to detect plagiarised fragments of text among documents in different languages. In this paper, we perform a systematic examination of Cross-language Knowledge Graph Analysis; an approach that represents text fragments using knowledge graphs as a language independent content model. We analyse the contributions to cross-language plagiarism detection of the different aspects covered by knowledge graphs: word sense disambiguation, vocabulary expansion, and representation by similarities with a collection of concepts. In addition, we study both the relevance of concepts and their relations when detecting plagiarism. Finally, as a key component of the knowledge graph construction, we present a new weighting scheme of relations between concepts based on distributed representations of concepts. Experimental results in Spanish-English and German-English plagiarism detection show state-of-the-art performance and provide interesting insights on the use of knowledge graphs. (C) 2015 Elsevier Ltd. All rights reserved.
C1 [Franco-Salvador, Marc; Rosso, Paolo] Univ Politecn Valencia, Pattern Recognit & Human Language Technol PRHLT R, Camino Vera S-N, E-46022 Valencia, Spain.
   [Montes-y-Gomez, Manuel] Inst Nacl Astrofis Opt & Electr, Dept Comp Sci, Luis Enrique Erro 1, Puebla 72840, Mexico.
RP Franco-Salvador, M (通讯作者)，Univ Politecn Valencia, Pattern Recognit & Human Language Technol PRHLT R, Camino Vera S-N, E-46022 Valencia, Spain.
EM mfranco@prhlt.upv.es
FU European Commission [269180, TIN2012-38603-C02-01]
FX This research has been carried out in the framework of the European
   Commission WIQ-EI IRSES (No. 269180) and DIANA APPLICATIONS - Finding
   Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) projects.
   We would like to thank Tomas Mikolov, Martin Potthast, and Luis A. Leiva
   for their support and comments during this research.
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ER

PT J
AU Bellomarini, L
   Fayzrakhmanov, RRR
   Gottlob, G
   Kravchenko, A
   Laurenza, E
   Nenov, Y
   Reissfelder, S
   Sallinger, E
   Sherkhonov, E
   Vahdati, S
   Wu, LL
AF Bellomarini, Luigi
   Fayzrakhmanov, Ruslan R. R.
   Gottlob, Georg
   Kravchenko, Andrey
   Laurenza, Eleonora
   Nenov, Yavor
   Reissfelder, Stephane
   Sallinger, Emanuel
   Sherkhonov, Evgeny
   Vahdati, Sahar
   Wu, Lianlong
TI Data science with Vadalog: Knowledge Graphs with machine learning and
   reasoning in practice
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Knowledge Graphs; Data science; Machine learning; Reasoning;
   Probabilistic reasoning
ID DATA EXTRACTION; DATA EXCHANGE; SYSTEM; OXPATH; WEB
AB Following the recent successful examples of large technology companies, many modern enterprises seek to build Knowledge Graphs to provide a unified view of corporate knowledge, and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modeling, and systems for reasoning with domain knowledge. In this paper, we demonstrate how to perform a broad spectrum of data science tasks in a unified Knowledge Graph environment. This includes data wrangling, complex logical and probabilistic reasoning, and machine learning. We base our work on the state-of-the-art Knowledge Graph Management System Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits such as the Jupyter platform. We argue that this is a significant step forward towards practical, holistic data science workflows that combine machine learning and reasoning in data science.(c) 2021 Elsevier B.V. All rights reserved.
C1 [Bellomarini, Luigi; Fayzrakhmanov, Ruslan R. R.; Gottlob, Georg; Kravchenko, Andrey; Nenov, Yavor; Reissfelder, Stephane; Sallinger, Emanuel; Sherkhonov, Evgeny; Vahdati, Sahar; Wu, Lianlong] Univ Oxford, Oxford, England.
   [Bellomarini, Luigi; Laurenza, Eleonora] Banca Italia, Rome, Italy.
   [Gottlob, Georg; Sallinger, Emanuel] TU Wien, Vienna, Austria.
RP Gottlob, G (通讯作者)，Univ Oxford, Oxford, England.
EM georg.gottlob@cs.ox.ac.uk
RI Laurenza, Eleonora/AGQ-4979-2022
OI Laurenza, Eleonora/0000-0002-2786-8163
FU EPSRC, United Kingdom programme grant [EP/M025268/1 VADA]; Vienna
   Science and Technology Fund (WWTF), Austria [VRG18-013]; EPSRC, United
   Kingdom iCASE studentship grant [EP/P510609/1 2370505]; EU [809965];
   Royal Society Research Professorship, United Kingdom [RP/R1/201074]
FX This work is supported by the EPSRC, United Kingdom programme grant
   EP/M025268/1 VADA, the Vienna Science and Technology Fund (WWTF),
   Austria grant VRG18-013, EPSRC, United Kingdom iCASE studentship grant
   EP/P510609/1 2370505, the EU Horizon 2020 grant 809965. Georg Gottlob
   was supported by his Royal Society Research Professorship, United
   Kingdom, Grant ``RAISON DATA'' RP/R1/201074.
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NR 70
TC 1
Z9 1
U1 8
U2 8
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 2022
VL 129
BP 407
EP 422
DI 10.1016/j.future.2021.10.021
PG 16
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZV6TX
UT WOS:000770661300010
DA 2022-08-23
ER

PT J
AU Li, MD
   Sun, ZY
   Zhang, WS
AF Li, Mingda
   Sun, Zhengya
   Zhang, Wensheng
TI SLAN: Similarity-aware aggregation network for embedding
   out-of-knowledge-graph entities
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Out-of-knowledge-graph entities; Knowledge
   graph completion; Similarity search
AB Knowledge graph embedding acts as a pivotal role in predicting the missing information in knowledge graphs (KGs). Due to the evolving nature of real-world KGs, one requires the ability to make predictions for newly emerging entities besides those already observed at training time. Current studies have made great efforts to develop a neighborhood aggregator and embed out-of-knowledge-graph (OOKG) entities inductively, with less focus on exploiting the similarity between the existing and newly emerging enti-ties. Attaching importance to such similarity helps facilitate semantic transfer. In this work, we propose a similarity-aware aggregation network for embedding out-of-knowledge-graph entities. Motivated by the fact that similar entities are likely to occur in common graph context, we skillfully design a similarity-aware function, which measures the distance of each entity pair based on the contextual gap. Moreover, we aggregate the neighborhood surrounding the target entity and its similarity information by query-specific attention weights, which are optimized during the learning process. Extensive experi-ments on knowledge graph completion task show that our method achieves substantial improvements over baselines.(c) 2022 Elsevier B.V. All rights reserved.
C1 [Li, Mingda; Sun, Zhengya; Zhang, Wensheng] Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China.
   [Li, Mingda; Sun, Zhengya; Zhang, Wensheng] Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China.
RP Sun, ZY (通讯作者)，Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China.; Sun, ZY (通讯作者)，Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China.
EM zhengya.sun@ia.ac.cn
FU National Key Research and Development Program of China [2018AAA0102100];
   National Natural Science Foundation of China [61876183, 61961160707,
   61976212]
FX Acknowledgements The authors are thankful for the financial support from
   the National Key Research and Development Program of China (No.
   2018AAA0102100) and the National Natural Science Foundation of China
   (Nos. 61876183, 61961160707, 61976212)
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NR 39
TC 0
Z9 0
U1 5
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JUN 28
PY 2022
VL 491
BP 186
EP 196
DI 10.1016/j.neucom.2022.03.063
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V1ZN
UT WOS:000788143600016
DA 2022-08-23
ER

PT J
AU Liu, JT
   Schmid, F
   Li, KP
   Zheng, W
AF Liu, Jintao
   Schmid, Felix
   Li, Keping
   Zheng, Wei
TI A knowledge graph-based approach for exploring railway operational
   accidents
SO RELIABILITY ENGINEERING & SYSTEM SAFETY
LA English
DT Article
DE Network analysis; Topological analysis; Knowledge graph; Accident
   analysis; Railway operational accident
ID ORGANIZATIONAL ANALYSIS METHOD; FAULT-TREE ANALYSIS; SAFETY ANALYSIS;
   CAUSATION MODEL; RISK-MANAGEMENT; LEVEL-CROSSINGS; PROCESS SYSTEMS;
   HYBRID HUMAN; BOW-TIE; TELL US
AB Drawing lessons from past accidents is an essential way to improve the operational safety of railways. Various railway operational accidents and their related hazards constitute a causation network due to the interactions among the hazards. Some useful lessons can be captured from such a network. In this paper, a new knowledge graph-based approach to explore railway operational accidents is proposed, aiming to reveal the potential rules of accidents by depicting accidents and hazards in a heterogeneous network. This work serves as an extension and complement to classical homogeneous network-based accident analyses. Its originality is to apply the knowledge graph theory to railway operational accident analysis, by means of some topological indicators adapting to the heterogeneous structural features of knowledge graphs. To facilitate the construction of the accident knowledge graph, a modelling method is developed. The outcomes of the knowledge graph-based analysis provide railway operators with the decision-making basis for the investment of accident prevention efforts. An application on real railway operational accidents in the UK is presented. The results show the effectiveness of the proposed approach in terms of discovering the latent features of the corresponding railway operational accidents and assisting in formulating targeted preventive measures.
C1 [Liu, Jintao; Zheng, Wei] Beijing Jiaotong Univ, Natl Res Ctr Railway Safety Assessment, Beijing, Peoples R China.
   [Liu, Jintao; Zheng, Wei] Beijing Jiaotong Univ, Beijing Key Lab Secur & Privacy Intelligent Trans, Beijing, Peoples R China.
   [Schmid, Felix] Univ Birmingham, Birmingham Ctr Railway Res & Educ, Birmingham, W Midlands, England.
   [Li, Keping] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing, Peoples R China.
RP Liu, JT (通讯作者)，Beijing Jiaotong Univ, Natl Res Ctr Railway Safety Assessment, Beijing, Peoples R China.
EM ljt@bjtu.edu.cn
FU National Natural Science Foundation of China [61803019, 61803020]; China
   Railway Corporation Foundation [K2019X010]; Beijing Natural Science
   Foundation [8202039]
FX This work is co-supported by the National Natural Science Foundation of
   China (No. 61803019, No. 61803020), the China Railway Corporation
   Foundation (No. K2019X010) and the Beijing Natural Science Foundation
   (No. 8202039). The authors also gratefully acknowledge the insightful
   comments of the editor and the reviewers that substantially improved the
   paper.
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NR 75
TC 9
Z9 9
U1 27
U2 101
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0951-8320
EI 1879-0836
J9 RELIAB ENG SYST SAFE
JI Reliab. Eng. Syst. Saf.
PD MAR
PY 2021
VL 207
AR 107352
DI 10.1016/j.ress.2020.107352
PG 16
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 PQ6VH
UT WOS:000606682100029
DA 2022-08-23
ER

PT J
AU Zhang, DZ
   Jia, Q
   Yang, SB
   Han, XL
   Xu, C
   Liu, X
   Xie, YH
AF Zhang, Dezheng
   Jia, Qi
   Yang, Shibing
   Han, Xinliang
   Xu, Cong
   Liu, Xin
   Xie, Yonghong
TI Traditional Chinese Medicine Automated Diagnosis Based on Knowledge
   Graph Reasoning
SO CMC-COMPUTERS MATERIALS & CONTINUA
LA English
DT Article
DE Traditional Chinese medicine; automated diagnosis; knowledge graph;
   Naive Bayes; syndrome differentiation
ID SYNDROME DIFFERENTIATION
AB Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine (TCM). We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM. We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph. There are two kinds of path patterns in the knowledge graph: one-hop and two-hop. The one-hop path pat-tern maps the symptom to syndromes immediately. The two-hop path pattern maps the symptom to syndromes through the nature of disease, etiology, and pathomechanism to support the diagnostic reasoning. Considering the different support strengths for the knowledge paths in reasoning, we design a dynamic weight mechanism. We utilize Naive Bayes and TF-IDF to imple-ment the reasoning method and the weighted score calculation. The proposed method reasons the syndrome results by calculating the possibility accord-ing to the weighted score of the path in the knowledge graph based on the reasoning path patterns. We evaluate the method with clinical records and clinical practice in hospitals. The preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in practice. Meanwhile, the method is robust and explainable under the guide of the knowledge graph. It could help TCM physicians, especially primary physicians in rural areas, and provide clinical decision support in clinical practice.
C1 [Zhang, Dezheng; Jia, Qi; Yang, Shibing; Liu, Xin; Xie, Yonghong] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.
   [Zhang, Dezheng; Jia, Qi; Yang, Shibing; Han, Xinliang; Xie, Yonghong] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China.
   [Xu, Cong] Inspur Elect Informat Ind Co Ltd, Jinan 250101, Peoples R China.
   [Xu, Cong] State Key Lab High End Server & Storage Technol, Jinan 250101, Peoples R China.
   [Liu, Xin] Univ Alberta, Dept Surg, Surg Simulat Res Lab, Edmonton, AB T6G 2E1, Canada.
RP Xie, YH (通讯作者)，Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China.; Xie, YH (通讯作者)，Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China.
EM xieyh@ustb.edu.cn
FU National Key Research and Development Program of China [2017YFB1002304];
   China Scholarship Council [201906465021]
FX This work is supported by the National Key Research and Development
   Program of China under Grant 2017YFB1002304 and the China Scholarship
   Council under Grant 201906465021.
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NR 23
TC 0
Z9 0
U1 19
U2 42
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 2022
VL 71
IS 1
BP 159
EP 170
DI 10.32604/cmc.2022.017295
PG 12
WC Computer Science, Information Systems; Materials Science,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Materials Science
GA WW0LD
UT WOS:000717617700010
OA gold
DA 2022-08-23
ER

PT J
AU Ji, ZZ
   Lei, ZC
   Shen, TT
   Zhang, J
AF Ji, Zizheng
   Lei, Zhengchao
   Shen, Tingting
   Zhang, Jing
TI Joint Representations of Knowledge Graphs and Textual Information via
   Reference Sentences
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article
DE joint representation learning; knowledge graph; reference sentence;
   attention mechanism
AB The joint representations of knowledge graph have become an important approach to improve the quality of knowledge graph, which is beneficial to machine learning, data mining, and artificial intelligence applications. However, the previous work suffers severely from the noise in text when modeling the text information. To overcome this problem, this paper mines the high-quality reference sentences of the entities in the knowledge graph, to enhance the representation ability of the entities. A novel framework for joint representation learning of knowledge graphs and text information based on reference sentence noise-reduction is proposed, which embeds the entity, the relations, and the words into a unified vector space. The proposed framework consists of knowledge graph representation learning module, textual relation representation learning module, and textual entity representation learning module. Experiments on entity prediction, relation prediction, and triple classification tasks are conducted, results show that the proposed framework can significantly improve the performance of mining and fusing the text information. Especially, compared with the state-of-the-art method [15], the proposed framework improves the metric of H@10 by 5.08% and 3.93% in entity prediction task and relation prediction task, respectively, and improves the metric of accuracy by 5.08% in triple classification task.
C1 [Ji, Zizheng; Lei, Zhengchao] Beijing Inst Technol, Sch Comp Sci, Beijing 100081, Peoples R China.
   [Shen, Tingting] China United Network Commun Grp Co Ltd, Hong Kong, Peoples R China.
   [Zhang, Jing] Capital Univ Econ & Business, Beijing, Peoples R China.
RP Ji, ZZ (通讯作者)，Beijing Inst Technol, Sch Comp Sci, Beijing 100081, Peoples R China.
EM jzz@bit.edu.cn; leizhengchao@bit.edu.cn; 17801036883@163.com;
   cathy64882584@163.com
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TC 2
Z9 2
U1 1
U2 5
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 JUN
PY 2020
VL E103D
IS 6
BP 1362
EP 1370
DI 10.1587/transinf.2019EDP7229
PG 9
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LU5LZ
UT WOS:000537798100018
OA gold
DA 2022-08-23
ER

PT J
AU Chen, H
   Wang, WM
   Li, GY
   Shi, YM
AF Chen, Heng
   Wang, Weimei
   Li, Guanyu
   Shi, Yimin
TI A Quaternion-Embedded Capsule Network Model for Knowledge Graph
   Completion
SO IEEE ACCESS
LA English
DT Article
DE Quaternions; Knowledge engineering; Task analysis; Convolutional neural
   networks; Predictive models; Solid modeling; Capsule network; knowledge
   graph; knowledge graph completion; link prediction; quaternion
AB Knowledge graphs are collections of factual triples. Link prediction aims to predict lost factual triples in knowledge graphs. In this paper, we present a novel capsule network method for link prediction taking advantages of quaternion. More specifically, we explore two methods, including a relational rotation model called QuaR and a deep capsule neural model called CapS-QuaR to encode semantics of factual triples. QuaR model defines each relation as a rotation from the head entity to the tail entity in the hyper-complex vector space, which could be used to infer and model diverse relation patterns, including: symmetry/anti-symmetry, reversal and combination. Based on these characteristics of quaternions, we use the embeddings of entities and relations trained from QuaR as the input to CapS-QuaR model. Experimental results on multiple benchmark knowledge graphs show that the proposed method is not only scalable, but also able to predict the correctness of triples in knowledge graphs and significantly outperform the existing state-of-the-art models for link prediction. Finally, the evaluation of a real dataset for search personalization task is conducted to prove the effectiveness of our model.
C1 [Chen, Heng; Wang, Weimei; Li, Guanyu; Shi, Yimin] Dalian Maritime Univ, Fac Informat Sci & Technol, Dalian 116026, Peoples R China.
   [Chen, Heng] Dalian Univ Foreign Languages, Res Ctr Language Intelligence, Dalian 116044, Peoples R China.
RP Li, GY (通讯作者)，Dalian Maritime Univ, Fac Informat Sci & Technol, Dalian 116026, Peoples R China.
EM rabitlee@163.com
FU National Natural Science Foundation of China [61976032, 61806038,
   61702072, 61602076, 61371090]; National Social Science Foundation of
   China [15BYY028]; Natural Science Foundation of Liaoning Province
   [20170540232]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61976032, Grant 61806038, Grant
   61702072, Grant 61602076, and Grant 61371090, in part by the National
   Social Science Foundation of China under Grant 15BYY028, and in part by
   the Natural Science Foundation of Liaoning Province under Grant
   20170540232.
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NR 43
TC 9
Z9 10
U1 3
U2 13
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 100890
EP 100904
DI 10.1109/ACCESS.2020.2997177
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LZ3LG
UT WOS:000541129100022
OA gold
DA 2022-08-23
ER

PT J
AU Wang, H
   Zu, QN
   Lu, M
   Chen, RF
   Yang, ZR
   Gao, YQ
   Ding, JW
AF Wang, Hong
   Zu, Quannan
   Lu, Ming
   Chen, Rongfa
   Yang, Zhiren
   Gao, Yongqiang
   Ding, Jiawang
TI Application of Medical Knowledge Graphs in Cardiology and Cardiovascular
   Medicine: A Brief Literature Review
SO ADVANCES IN THERAPY
LA English
DT Review; Early Access
DE Medical knowledge graphs; Advances in therapy; Cardiology;
   Cardiovascular diseases; Artificial intelligence
AB A knowledge graph is defined as a collection of interlinked descriptions of concepts, relationships, entities and events. Medical knowledge graphs have been the most recent advances in technology, therapy and medicine. Nowadays, a number of specific uses and applications rely on knowledge graphs. The application of the knowledge graph, another form of artificial intelligence (AI) in cardiology and cardiovascular medicine, is a new concept, and only a few studies have been carried out on this particular aspect. In this brief literature review, the use and importance of disease-specific knowledge graphs in exploring various aspects of Kawasaki disease were described. A vision of individualized knowledge graphs (iKGs) in cardiovascular medicine was also discussed. Such iKGs would be based on a modern informatics platform of exchange and inquiry that could comprehensively integrate biologic knowledge with medical histories and health outcomes of individual patients. This could transform how clinicians and scientists discover, communicate and apply new knowledge. In addition, we also described how a study based on the comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease used a knowledge graph to show the dietary factors associated with cardiovascular diseases in Nurses' Health Study data. To conclude, in this fast-developing world, medical knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. They could be a major and effective tool in cardiology and cardiovascular medicine and play an important role in reaching effective clinical decisions during treatment and management of patients in the cardiology department.
C1 [Wang, Hong; Gao, Yongqiang] Peoples Hosp Guangxi Zhuang Autonomous Reg, Dept Cardiol, Nanning 530021, Guangxi, Peoples R China.
   [Wang, Hong] Jinan Univ, Guangzhou 510632, Guangdong, Peoples R China.
   [Zu, Quannan; Lu, Ming] Tianjin Univ, Coll Management & Econ, Tianjin 300072, Peoples R China.
   [Chen, Rongfa; Yang, Zhiren] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
   [Ding, Jiawang] Beijing Chaoyang Hosp, Dept Internal Med, Beijing 100020, Peoples R China.
RP Wang, H (通讯作者)，Jinan Univ, Guangzhou 510632, Guangdong, Peoples R China.
EM iriswh2014@163.com; 59474566@qq.com; China.luming_cn@tju.edu.cn;
   rongfa.chen@ia.ac.cn; zhiren.yang@ia.ac.cn; 1737883971@qq.com;
   liuhaomd2013@163.com
FU Guangxi Key Research and Development Program; Guangxi Medical and Health
   Appropriate Technology Development and Promotion Application Project
   [S2017077]; Guangxi Nanning Qingxiu District Science and Technology
   Development Project [2014S06]
FX This research was supported by the Guangxi Key Research and Development
   Program (grant no. AB22035078); Guangxi Medical and Health Appropriate
   Technology Development and Promotion Application Project (grant no.
   S2017077) and the Guangxi Nanning Qingxiu District Science and
   Technology Development Project (grant no. 2014S06). No funding or
   sponsorship was received for the publication of this article.
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NR 21
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0741-238X
EI 1865-8652
J9 ADV THER
JI Adv. Ther.
DI 10.1007/s12325-022-02254-7
EA JUL 2022
PG 9
WC Medicine, Research & Experimental; Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Research & Experimental Medicine; Pharmacology & Pharmacy
GA 3J6OH
UT WOS:000833512400002
PM 35908002
OA hybrid
DA 2022-08-23
ER

PT J
AU Dang, FR
   Tang, JT
   Pang, KY
   Wang, T
   Li, SS
   Li, X
AF Dang, Fu-Rong
   Tang, Jin-Tao
   Pang, Kun-Yuan
   Wang, Ting
   Li, Sha-Sha
   Li, Xiao
TI Constructing an Educational Knowledge Graph with Concepts Linked to
   Wikipedia
SO JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
LA English
DT Article
DE concept extraction; educational resource; knowledge graph; massive open
   online course (MOOC); prerequisite
AB To use educational resources efficiently and dig out the nature of relations among MOOCs (massive open online courses), a knowledge graph was built for MOOCs on four major platforms: Coursera, EDX, XuetangX, and ICourse. This paper demonstrates the whole process of educational knowledge graph construction for reference. And this knowledge graph, the largest knowledge graph of MOOC resources at present, stores and represents five classes, 11 kinds of relations and 52 779 entities with their corresponding properties, amounting to more than 300 000 triples. Notably, 24 188 concepts are extracted from text attributes of MOOCs and linked them directly with corresponding Wikipedia entries or the closest entries calculated semantically, which provides the normalized representation of knowledge and a more precise description for MOOCs far more than enriching words with explanatory links. Besides, prerequisites discovered by direct extractions are viewed as an essential supplement to augment the connectivity in the knowledge graph. This knowledge graph could be considered as a collection of unified MOOC resources for learners and the abundant data for researchers on MOOC-related applications, such as prerequisites mining.
C1 [Dang, Fu-Rong; Tang, Jin-Tao; Pang, Kun-Yuan; Wang, Ting; Li, Sha-Sha] Natl Univ Def Technol, Coll Comp Sci & Technol, Changsha 410073, Hunan, Peoples R China.
   [Li, Xiao] Natl Univ Def Technol, Informat Ctr, Changsha 410073, Hunan, Peoples R China.
RP Wang, T (通讯作者)，Natl Univ Def Technol, Coll Comp Sci & Technol, Changsha 410073, Hunan, Peoples R China.
EM 8mimixi8@163.com; tangjintao@nudt.edu.cn; pangkunyuan@nudt.edu.cn;
   tingwang@nudt.edu.cn; shashali@nudt.edu.cn; xiaoli@nudt.edu.cn
FU National Key Research and Development Program of China [2018YFB1004502];
   National Natural Science Foundation of China [61532001, 61702532,
   61303190]
FX This work was supported by the National Key Research and Development
   Program of China under Grant No. 2018YFB1004502, and the National
   Natural Science Foundation of China under Grant Nos. 61532001, 61702532
   and 61303190.
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NR 37
TC 2
Z9 2
U1 13
U2 22
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1000-9000
EI 1860-4749
J9 J COMPUT SCI TECH-CH
JI J. Comput. Sci. Technol.
PD OCT
PY 2021
VL 36
IS 5
BP 1200
EP 1211
DI 10.1007/s11390-020-0328-2
PG 12
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WO6QC
UT WOS:000712575700018
DA 2022-08-23
ER

PT J
AU He, P
   Zhou, G
   Liu, HB
   Xia, Y
   Wang, L
AF He, Peng
   Zhou, Gang
   Liu, Hongbo
   Xia, Yi
   Wang, Ling
TI Hyperplane-based time-aware knowledge graph embedding for temporal
   knowledge graph completion
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Temporal knowledge graph; knowledge graph completion; knowledge graph
   embedding
AB Knowledge Graph (KG) embedding approaches have been proved effective to infer new facts for a KG based on the existing ones-a problem known as KG completion. However, most of them have focused on static KGs, in fact, relational facts in KGs often show temporal dynamics, e.g., the fact (US, has president, Barack Obama, [2009-2017]) is only valid from 2009 to 2017. Therefore, utilizing available time information to develop temporal KG embedding models is an increasingly important problem. In this paper, we propose a new hyperplane-based time-aware KG embedding model for temporalKGcompletion. By employing the method of time-specific hyperplanes, our model could explicitly incorporate time information in the entity-relation space to predict missing elements in the KG more effectively, especially temporal scopes for facts with missing time information. Moreover, in order to model and infer four important relation patterns including symmetry, antisymmetry, inversion and composition, we map facts happened at the same time into a polar coordinate system. During training procedure, a time-enhanced negative sampling strategy is proposed to get more effective negative samples. Experimental results on datasets extracted from real-world temporal KGs show that our model significantly outperforms existing state-of-the-art approaches for the KG completion task.
C1 [He, Peng; Zhou, Gang; Liu, Hongbo; Xia, Yi; Wang, Ling] Informat Engn Univ, Zhengzhou, Peoples R China.
   [He, Peng] Zhengzhou Univ Technol, Zhengzhou, Peoples R China.
RP Zhou, G (通讯作者)，Informat Engn Univ, Zhengzhou, Peoples R China.
EM gzhougzhou@126.com
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NR 49
TC 0
Z9 0
U1 0
U2 0
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2022
VL 42
IS 6
BP 5457
EP 5469
DI 10.3233/JIFS-211950
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0Y9GE
UT WOS:000790690300046
OA hybrid
DA 2022-08-23
ER

PT J
AU Chai, XQ
AF Chai, Xuqing
TI Diagnosis Method of Thyroid Disease Combining Knowledge Graph and Deep
   Learning
SO IEEE ACCESS
LA English
DT Article
DE Medical diagnostic imaging; Diseases; Medical diagnosis; Machine
   learning; Databases; Electronic medical records; Knowledge graph; deep
   learning; thyroid disease; disease diagnosis
ID CLASSIFICATION; SYSTEM
AB The scale of medical data is growing rapidly, and these data come from different data sources. The amount of data is huge, the production speed is fast, and the format is different. Case data is very important because it contains a lot of medical knowledge about diseases, drugs, treatments, etc. It can provide important support for the development of smart medicine. Knowledge graph is a graph-based data structure, which can well represent the relationship between these medical data in reality and form a semantic network. This research uses knowledge graph technology to connect trivial and scattered knowledge in various medical information systems to assist in disease diagnosis. This research takes thyroid disease as an example, constructs a medical knowledge graph and applies it to intelligent medical diagnosis. First, extract the relationships between biomedical entities to construct a biomedical knowledge graph. Then, the entities and relationships in the knowledge graph are transformed into low-dimensional continuous vectors through the knowledge graph embedding method. Finally, the known pathological disease relationship data is used to train the disease diagnosis model of the bidirectional long short-term memory network (BSTLM). Experiments show that the thyroid disease diagnosis method that combines knowledge graphs and deep learning has a better diagnostic effect. This shows that smart medical care based on the knowledge graph will provide a solution path for alleviating the shortage of domestic high-quality medical resources.
C1 [Chai, Xuqing] Henan Normal Univ, Coll Comp & Informat Engn, Xinxiang 453000, Henan, Peoples R China.
RP Chai, XQ (通讯作者)，Henan Normal Univ, Coll Comp & Informat Engn, Xinxiang 453000, Henan, Peoples R China.
EM chaixuqing@htu.edu.cn
FU National Natural Science Foundation of China [U1804164]; Key Project of
   Science and Technology of Henan Provincial Science and Technology
   Department [192102310020]; Henan Provincial Federation of Social
   Sciences [SKL-2018-771]; Research Project on Curriculum Reform of
   Teacher Education in Henan Province [2018-JSJYYB-020]; Education Science
   Research Fund of Henan Normal University [2018JK10]; High Performance
   Computing Centre of Henan Normal University; Supercomputing Center of
   University of Science and Technology of China
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant U1804164, in part by the Key Project of
   Science and Technology of Henan Provincial Science and Technology
   Department under Grant 192102310020, in part by the Henan Provincial
   Federation of Social Sciences under Grant SKL-2018-771, in part by the
   Research Project on Curriculum Reform of Teacher Education in Henan
   Province under Grant 2018-JSJYYB-020, in part by the Education Science
   Research Fund of Henan Normal University under Grant 2018JK10, in part
   by the High Performance Computing Centre of Henan Normal University, and
   in part by the Supercomputing Center of University of Science and
   Technology of China.
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NR 46
TC 3
Z9 3
U1 15
U2 67
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 149787
EP 149795
DI 10.1109/ACCESS.2020.3016676
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ND7IY
UT WOS:000562077800001
OA gold
DA 2022-08-23
ER

PT J
AU Padia, A
   Kalpakis, K
   Ferraro, F
   Finin, T
AF Padia, Ankur
   Kalpakis, Kostantinos
   Ferraro, Francis
   Finin, Tim
TI Knowledge graph fact prediction via knowledge-enriched tensor
   factorization
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge graph; Knowledge graph embedding; Tensor decomposition; Tensor
   factorization; Representation learning; Fact prediction
ID WEB
AB We present a family of novel methods for embedding knowledge graphs into real-valued tensors. These tensor-based embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background knowledge provided by users or extracted automatically from existing knowledge graphs. In addition to providing more robust methods for knowledge graph embedding, we provide a provably-convergent, linear tensor factorization algorithm. We demonstrate the efficacy of our models for the task of predicting new facts across eight different knowledge graphs, achieving between 5% and 50% relative improvement over existing state-of-the-art knowledge graph embedding techniques. Our empirical evaluation shows that all of the tensor decomposition models perform well when the average degree of an entity in a graph is high, with constraint-based models doing better on graphs with a small number of highly similar relations and regularization-based models dominating for graphs with relations of varying degrees of similarity. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Padia, Ankur; Kalpakis, Kostantinos; Ferraro, Francis; Finin, Tim] Univ Maryland, Baltimore, MD 21201 USA.
RP Finin, T (通讯作者)，Univ Maryland, Baltimore, MD 21201 USA.
EM ankurpadia@umbc.edu; kalpakis@umbc.edu; ferraro@umbc.edu; finin@umbc.edu
OI Finin, Tim/0000-0002-6593-1792
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NR 54
TC 3
Z9 3
U1 8
U2 53
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
EI 1873-7749
J9 J WEB SEMANT
JI J. Web Semant.
PD DEC
PY 2019
VL 59
AR 100497
DI 10.1016/j.websem.2019.01.004
PG 19
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:000500706800005
OA Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Qiao, C
   Hu, X
AF Qiao, Chen
   Hu, Xiao
TI A neural knowledge graph evaluator: Combining structural and semantic
   evidence of knowledge graphs for predicting supportive knowledge in
   scientific QA
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Graph neural networks; Knowledge graph; Network analysis; Scientific
   question answering; Text entailment analysis
ID REPRESENTATION; EMBEDDINGS
AB Effectively detecting supportive knowledge of answers is a fundamental step towards automated question answering. While pre-trained semantic vectors for texts have enabled semantic computation for background-answer pairs, they are limited in representing structured knowledge relevant for question answering. Recent studies have shown interests in enrolling structured knowledge graphs for text processing, however, their focus was more on semantics than on graph structure. This study, by contrast, takes a special interest in exploring the structural patterns of knowledge graphs. Inspired by human cognitive processes, we propose novel methods of feature extraction for capturing the local and global structural information of knowledge graphs. These features not only exhibit good indicative power, but can also facilitate text analysis with explainable meanings. Moreover, aiming to better combine structural and semantic evidence for prediction, we propose a Neural Knowledge Graph Evaluator (NKGE) which showed superior performance over existing methods. Our contributions include a novel set of interpretable structural features and the effective NKGE for compatibility evaluation between knowledge graphs. The methods of feature extraction and the structural patterns indicated by the features may also provide insights for related studies in computational modeling and processing of knowledge.
C1 [Qiao, Chen; Hu, Xiao] Univ Hong Kong, R209 Runme Shaw Bldg, Hong Kong, Peoples R China.
RP Hu, X (通讯作者)，Univ Hong Kong, R209 Runme Shaw Bldg, Hong Kong, Peoples R China.
EM xiaoxhu@hku.hk
RI Hu, Xiao/A-7645-2013
OI Qiao, Chen/0000-0002-4382-3221
FU Early Career Scheme grant from the Research Grants Council; Hong Kong
   S.A.R., China [HKU 27401114]
FX This study is partially supported by an Early Career Scheme grant from
   the Research Grants Council (https://www.ugc.edu.hk/eng/rgc/) of the
   Hong Kong S.A.R., China (Project No.: HKU 27401114).
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NR 75
TC 9
Z9 10
U1 12
U2 19
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD NOV
PY 2020
VL 57
IS 6
AR 102309
DI 10.1016/j.ipm.2020.102309
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 OG9PN
UT WOS:000582206800037
DA 2022-08-23
ER

PT J
AU Ebisu, T
   Ichise, R
AF Ebisu, Takuma
   Ichise, Ryutaro
TI Generalized Translation-Based Embedding of Knowledge Graph
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Knowledge graph embedding
AB Knowledge graphs are useful for many AI tasks but often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. TransE is one of such models and the first translation-based method. TransE is well known because the principle of TransE can effectively capture the rules of a knowledge graph although it seems very simple. However, TransE has problems with its regularization and an unchangeable ratio of negative sampling. In this paper, we generalize TransE to solve these problems by proposing knowledge graph embedding on a Lie group (KGLG) and the Weighted Negative Part (WNP) method for the objective function of translation-based models. KGLG is the novel translation-based method which embeds entities and relations of a knowledge graph on any Lie group. It allows us not to employ regularization during training of the model if we choose a compact lie group for the embedding space. The WNP method is for changing the ratio of negative sampling, which enhances translation-based models. Our approach outperforms other state-of-the-art approaches such as TransE, DistMult, and ComplEx on a standard link prediction task. We show that TorusE, KGLG on a torus, is scalable to large-size knowledge graphs and faster than the original TransE.
C1 [Ebisu, Takuma] SOKENDAI, Dept Informat, Tokyo 2400193, Japan.
   [Ichise, Ryutaro] Natl Inst Informat, Tokyo 1000003, Japan.
RP Ebisu, T (通讯作者)，SOKENDAI, Dept Informat, Tokyo 2400193, Japan.
EM takuma@nii.ac.jp; ichise@nii.ac.jp
FU New Energy and Industrial Technology Development Organization (NEDO)
FX This work was partially supported by the New Energy and Industrial
   Technology Development Organization (NEDO).
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NR 38
TC 15
Z9 15
U1 3
U2 43
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 2020
VL 32
IS 5
BP 941
EP 951
DI 10.1109/TKDE.2019.2893920
PG 11
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA LB0WA
UT WOS:000524357000009
DA 2022-08-23
ER

PT J
AU Liu, JX
   Zhang, C
AF Liu, Jiexian
   Zhang, Chen
TI A Neural Network Model for Digitizing Enterprise Carbon Assets Based on
   Multimodal Knowledge Mapping
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID SMART CITIES
AB In this paper, a multimodal knowledge mapping approach is used to digitize enterprise carbon assets, and a corresponding neural network model is designed for use in the practical process. Rich textual entity labels associated with images are obtained using an entity annotation system. A topology-based data fusion method is also designed based on the hierarchical relationship between WordNet and DBpedia to fuse the knowledge obtained from image visualization and text description mining. Existing neural network-based entity linking methods ignore the semantic gap between the context of sequential entity denotative items and the context of graph-structured entities, thus affecting the accuracy of entity linking. It is observed that the importance of words in the context of entity denotative items is different, and the importance of content in the entity context is also different. To solve the above problems, this paper proposes an entity linking method that combines a common attention mechanism with a graph convolutional neural network. Secondly, based on the basic theory of value assessment, the characteristics of classical asset valuation methods and their inapplicability to the valuation of carbon assets are analyzed, and thus the real option valuation method and its two classical models are introduced; after demonstrating the real option characteristics of carbon assets of power enterprise projects, a real option model-based carbon asset valuation model for power enterprise projects is constructed and its applicability is verified with case studies. Through analyzing the current situation and problems of carbon asset valuation work in power enterprises, targeted practical suggestions are put forward to further strengthen and enhance the carbon asset valuation work in power enterprises in the future.
C1 [Liu, Jiexian; Zhang, Chen] Hefei Univ Technol, Sch Management, Hefei 230009, Anhui, Peoples R China.
   [Liu, Jiexian] Tongling Univ, Sch Accounting, Tongling 244061, Anhui, Peoples R China.
RP Liu, JX (通讯作者)，Hefei Univ Technol, Sch Management, Hefei 230009, Anhui, Peoples R China.; Liu, JX (通讯作者)，Tongling Univ, Sch Accounting, Tongling 244061, Anhui, Peoples R China.
EM ljx1988@mail.hfut.edu.cn; sm.zhangchen@hfut.edu.cn
FU National Natural Science Foundation of China [71971071, 71702042,
   71373065]; Philosophy and Social Science Foundation of China [18BGL069]
FX AcknowledgmentsThe National Natural Science Foundation of China
   (71971071; 71702042; and 71373065) and Philosophy and Social Science
   Foundation of China (18BGL069) supported this study.
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NR 20
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 JUN 20
PY 2022
VL 2022
AR 4485168
DI 10.1155/2022/4485168
PG 12
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 3K6AB
UT WOS:000834155800008
PM 35769277
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Cao, X
   Wu, QJ
   Chang, Q
   Zhang, TN
   Li, XS
   Chen, YX
   Zhao, YH
AF Cao, Xia
   Wu, Qi-Jun
   Chang, Qing
   Zhang, Tie-Ning
   Li, Xiang-Sen
   Chen, Yun-Xiang
   Zhao, Yu-Hong
TI Knowledge Mapping of Dietary Factors of Metabolic Syndrome Research:
   Hotspots, Knowledge Structure, and Theme Trends
SO FRONTIERS IN NUTRITION
LA English
DT Article
DE metabolic syndrome; diet; knowledge mapping; bibliometric analysis;
   biclustering analysis; social network analysis; evolutionary analysis
ID LOW-DENSITY-LIPOPROTEIN; CO-WORD ANALYSIS; RISK; ASSOCIATION; WOMEN;
   INDEX; FAT; CONSUMPTION; PREVALENCE; NETWORK
AB Background: The global incidence of metabolic syndrome (MetS) is continuously increasing, making it a potential worldwide public health concern. Research on dietary factors related to MetS has attracted considerable attention in the recent decades. However, the research hotspots, knowledge structure, and theme trends for the dietary factors associated with MetS remain unknown, and have not yet been systematically mapped. This study aimed to review the research status of diet as a risk factor for MetS through bibliometric methods. Bibliometric analysis was conducted using the Web of Science database. Research hotspots were identified using biclustering analysis with the gCLUTO software, and knowledge structure was explored via social network analysis using the Ucinet software. Theme trends were investigated using evolutionary analysis with the SciMAT software. In total, 1,305 papers were analyzed. The research output on the dietary factors associated with MetS increased steadily. The research scope was gradually expanding and diverse. Overall, eight research hot spots, four key dietary nodes, and four motor themes on the dietary factors associated with MetS were identified. Fatty acids, dietary fiber, and polyphenols have been the focus of research in this field over the years. Evolutionary analysis showed that fish oil and vitamin C were well-developed research foci recently. Prebiotics was recognized as an emerging theme with certain developmental potential. These findings provide a better understanding of the research status of the dietary factors associated with MetS and a reference for future investigations.
C1 [Cao, Xia; Li, Xiang-Sen; Chen, Yun-Xiang] China Med Univ, Shengjing Hosp, Dept Lib, Shenyang, Peoples R China.
   [Cao, Xia; Wu, Qi-Jun; Chang, Qing; Zhang, Tie-Ning; Zhao, Yu-Hong] China Med Univ, Shengjing Hosp, Dept Clin Epidemiol, Shenyang, Peoples R China.
   [Zhang, Tie-Ning] China Med Univ, Shengjing Hosp, Dept Pediat, Pediat Intens Care Unit, Shenyang, Peoples R China.
RP Zhao, YH (通讯作者)，China Med Univ, Shengjing Hosp, Dept Clin Epidemiol, Shenyang, Peoples R China.
EM zhaoyuhong@sj-hospital.org
RI Zhang, Tie-Ning/GPK-5202-2022
OI cao, xia/0000-0001-9228-276X
FU National Key R&D Program of China [2017YFC0907400]
FX This study was supported by the National Key R&D Program of China
   (2017YFC0907400 to Y-HZ).
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NR 60
TC 1
Z9 1
U1 11
U2 25
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-861X
J9 FRONT NUTR
JI Front. Nutr.
PD MAY 31
PY 2021
VL 8
AR 655533
DI 10.3389/fnut.2021.655533
PG 14
WC Nutrition & Dietetics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nutrition & Dietetics
GA SR4LO
UT WOS:000661012700001
PM 34136515
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zou, QF
   Lu, CZ
AF Zou, Qifeng
   Lu, Chaoze
TI An Accurate Matching Query Method of Natural Language Knowledge Graph
   Based on Hierarchical Graph Topological Sequence
SO IEEE ACCESS
LA English
DT Article
DE Indexes; Natural languages; Knowledge engineering; Feature extraction;
   Search problems; Complexity theory; Semantics; Knowledge graph query;
   knowledge matching; graph matching; graph hierarchical topological
   sequence; natural language processing
ID COMPLETION; ALGORITHM; DOMAIN
AB In recent years, although the application of knowledge graph in natural language processing has made some progress, there are still some key problems to be solved, especially the matching query problem in natural language knowledge graph. Since the basic data model of knowledge graph is graph, the matching query problem in natural language knowledge graph is usually transformed into graph matching query problem. However, at present, the traditional graph matching technology applied in knowledge graph consumes too much time and has low query efficiency, which cannot meet the needs of users for large-scale natural language knowledge graph query. Based on the full analysis of the defects of the traditional graph matching technology applied in the knowledge graph, according to the characteristics of the natural language knowledge graph, in order to improve the query efficiency, we propose an accurate matching query method of graph hierarchical topological sequence based on the graph model of knowledge graph. Through experiment analysis, compared with the traditional graph model matching algorithm applied in knowledge graph query, this method can quickly filter the unqualified knowledge graph candidate sets, effectively reduce the number of knowledge graph candidate sets, and make it have more advantages in matching efficiency and time performance. In addition, compared with two algorithms of GIndex and FG-Index, this method has better performance in index construction time, average size of candidate set and average running time of online update.
C1 [Zou, Qifeng] Ningbo Univ Finance & Econ, Sch Humanities, Ningbo 315175, Zhejiang, Peoples R China.
   [Lu, Chaoze] Ningbo Univ Technol, Sch Elect & Informat Engn, Ningbo 315211, Zhejiang, Peoples R China.
RP Lu, CZ (通讯作者)，Ningbo Univ Technol, Sch Elect & Informat Engn, Ningbo 315211, Zhejiang, Peoples R China.
EM luchaoze@126.com
CR Abu-Salih B, 2021, J NETW COMPUT APPL, V185, DOI 10.1016/j.jnca.2021.103076
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   Zou L., 2008, P 11 INT C EXT DAT T, P181, DOI DOI 10.1145/1353343.1353369
NR 34
TC 0
Z9 0
U1 52
U2 52
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 24080
EP 24094
DI 10.1109/ACCESS.2022.3155520
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ZP6OQ
UT WOS:000766540500001
OA gold
DA 2022-08-23
ER

PT J
AU Cao, YK
   Miao, ZY
AF Cao, Yukun
   Miao, Zeyu
TI Complex-InversE: Improving bilinear knowledge graph embeddings by
   mapping into complex space
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
AB Knowledge graph link prediction uses known fact links to infer the missing link information in the knowledge graph, which is of great significance to the completion of the knowledge graph. Generating low-dimensional embeddings of entities and relations which are used to make inferences is a popular way for such link prediction problems. This paper proposes a knowledge graph link prediction method called Complex-InversE in the complex space, which maps entities and relations into the complex space. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. The Complex-InversE effectively captures the antisymmetric relations and introduces Dropout and Early-Stopping technologies into deal with the problem of small numbers of relationships and entities, thus effectively alleviates the model's overfitting. The results of comparison experiment on the public knowledge graph datasets show that the Complex-InversE achieves good results on multiple benchmark evaluation indicators and outperforms previous methods. Complex-InversE's code is available on GitHub at https://github.com/ZeyuMiao97/Complex-InversE.
C1 [Cao, Yukun; Miao, Zeyu] Shanghai Univ Elect Power, 1851 Huchenghuan Rd, Shanghai, Peoples R China.
RP Cao, YK (通讯作者)，Shanghai Univ Elect Power, 1851 Huchenghuan Rd, Shanghai, Peoples R China.
EM marilyn_cao@163.com
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NR 22
TC 0
Z9 0
U1 1
U2 1
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2022
VL 42
IS 6
BP 6079
EP 6089
DI 10.3233/JIFS-212374
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0Y9GE
UT WOS:000790690300087
DA 2022-08-23
ER

PT J
AU Zhao, FK
   Du, FK
   Zhang, JS
   Xu, J
AF Zhao, Fangkun
   Du, Fengkun
   Zhang, Jinsong
   Xu, Jun
TI Trends in Research Related to Keratoconus From 2009 to 2018: A
   Bibliometric and Knowledge Mapping Analysis
SO CORNEA
LA English
DT Article
DE keratoconus; bibliometric analysis; mapping knowledge domain; VOSviewer
ID COLLAGEN CROSS-LINKING; ANTERIOR LAMELLAR KERATOPLASTY; IN-SITU
   KERATOMILEUSIS; PHOTOREFRACTIVE KERATECTOMY; BIOMECHANICAL PROPERTIES;
   PROGRESSIVE KERATOCONUS; CORNEAL; ASSOCIATION; THICKNESS; ECTASIA
AB Purpose: To map the publication trends in and explore hotspots of keratoconus research.
   Methods: A bibliometric analysis based on the Web of Science Core Collection was conducted to investigate the publication trends in research related to keratoconus. The records extracted were analyzed, and a knowledge map was constructed using VOSviewer v.1.6.10 to visualize the annual publication number, distribution of countries, international collaborations, author productivity, source journals, intellectual base, and research hotspots in the field of keratoconus.
   Results: In total, 3194 peer-reviewed publications on keratoconus published between 2009 and 2018 were retrieved, and the annual research output increased with time. The United States ranked the highest among the countries with the most publications, and Tehran University of Medical Sciences was the most active institution. JL Alio contributed to the most number of publications on keratoconus, and Cornea was the most prolific journal publishing keratoconus research. The top cited references mainly focused on corneal collagen cross-linking. The keywords formed 6 clusters: 1) pathogenesis of keratoconus, 2) corneal collagen cross-linking, 3) management for early-stage keratoconus, 4) corneal parameter measurement, 5) surgical treatment of keratoconus, and 6) corneal biomechanics-related research.
   Conclusions: On the basis of the data extracted from the Web of Science Core Collection, the quantity and quality of publications on keratoconus were assessed using bibliometric techniques. The cited references and research hotspots could provide insights into keratoconus research as well as valuable information to cornea specialists for performing research in this field and discovering potential collaborators.
C1 [Zhao, Fangkun; Zhang, Jinsong; Xu, Jun] China Med Univ, Eye Hosp China Med Univ, Affiliated Hosp 4, Dept Ophthalmol,Key Lab Lens, 11 Xinhua Rd, Shenyang, Liaoning, Peoples R China.
   [Du, Fengkun] Xavier Univ Louisiana, Dept Biol, New Orleans, LA USA.
RP Xu, J (通讯作者)，China Med Univ, Eye Hosp China Med Univ, Affiliated Hosp 4, Dept Ophthalmol,Key Lab Lens, 11 Xinhua Rd, Shenyang, Liaoning, Peoples R China.
EM xujun@cmu.edu.cn
FU Basic Research Fund for Young Instructors of Higher Education in
   Liaoning Province [LQNK201717]; Shenyang Science and Technology Plan
   Projects [18-014-4-46]
FX The work was supported by Basic Research Fund for Young Instructors of
   Higher Education in Liaoning Province (Grant No. LQNK201717). Shenyang
   Science and Technology Plan Projects (Grant No. 18-014-4-46).
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NR 34
TC 11
Z9 12
U1 0
U2 23
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0277-3740
EI 1536-4798
J9 CORNEA
JI Cornea
PD JUL
PY 2019
VL 38
IS 7
BP 847
EP 854
DI 10.1097/ICO.0000000000001984
PG 8
WC Ophthalmology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Ophthalmology
GA IQ3WF
UT WOS:000480681700016
PM 31033693
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU van Luijt, B
   Verhagen, M
AF van Luijt, Bob
   Verhagen, Micha
TI Bringing Semantic Knowledge Graph Technology to Your Data
SO IEEE SOFTWARE
LA English
DT Article
DE Companies; Semantics; Google; Data models; Software; Database languages
AB The most used knowledge graph today is Google Search. What makes Google Search powerful is its ability to derive knowledge by finding a correlation between the concepts in a search query as opposed to a correlation between words (also known as from strings to things).(1) In a Google search, Google is able to increase the relevance of search results by making connections in its knowledge graph that exceed the possibilities of textual search.
C1 [van Luijt, Bob; Verhagen, Micha] SeMI Technol, Amsterdam, Netherlands.
RP van Luijt, B (通讯作者)，SeMI Technol, Amsterdam, Netherlands.
EM bob@semi.technology; micha@semi.technology
OI Verhagen, Micha/0000-0003-2365-3154
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   Google, 2012, INTRO KNOWLEDGE GRAP
   GraphQL, QUER LANG YOUR API
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   Pennington J., GLOVE GLOBAL VECTORS
   SeMI Technologies, WEAV OP SOURC KNOWL
   SeMI Technologies, CONT
   SeMI Technologies Amsterdam North Holland, INST GUID
   SeMI Technologies Amsterdam North Holland, INTR WEAV
   Verborgh R, 2015, IEEE SOFTWARE, V32, P23, DOI 10.1109/MS.2015.63
NR 13
TC 3
Z9 3
U1 2
U2 9
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0740-7459
EI 1937-4194
J9 IEEE SOFTWARE
JI IEEE Softw.
PD MAR-APR
PY 2020
VL 37
IS 2
BP 89
EP 94
DI 10.1109/MS.2019.2957526
PG 6
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KV0ZB
UT WOS:000520152900015
DA 2022-08-23
ER

PT J
AU Chen, XJ
   Jia, SB
   Ding, L
   Xiang, Y
AF Chen, Xiaojun
   Jia, Shengbin
   Ding, Ling
   Xiang, Yang
TI Reasoning over temporal knowledge graph with temporal consistency
   constraints
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Knowledge graph reasoning; temporal information; temporal consistency
   constraints; integer linear programming
AB Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporal knowledge graph reasoning that performs inference on the graph over time. Most existing reasoning models ignore the time information when learning entities and relations representations. For example, the fact (Scarlett Johansson, spouse Of, Ryan Reynolds) was true only during 2008 - 2011. To facilitate temporal reasoning, we present TA-TransR(ILP), which involves temporal information by utilizing RNNs and takes advantage of Integer Linear Programming Specifically, we utilize a character-level long short-term memory network to encode relations with sequences of temporal tokens, and combine it with common reasoning model. To achieve more accurate reasoning, we further deploy temporal consistency constraints to basic model, which can help in assessing the validity of a fact better. We conduct entity prediction and relation prediction on YAGO11k and Wikidata12k datasets. Experimental results demonstrate that TA-TransR(ILP) can make more accurate predictions by taking time information and temporal consistency constraints into account, and outperforms existing methods with a significant improvement about 6-8% on Hits @ 10.
C1 [Chen, Xiaojun; Jia, Shengbin; Ding, Ling; Xiang, Yang] Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China.
RP Xiang, Y (通讯作者)，Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China.
EM tjdxxiangyang@gmail.com
FU National Natural Science Foundation of China [72071145]; National Key
   R&D Program of China [2019YFB1704402]
FX We thank the anonymous reviewers for valuable comments. This work is
   supported by the National Natural Science Foundation of China under
   Grant No. 72071145 and the National Key R&D Program of China under Grant
   No. 2019YFB1704402.
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NR 30
TC 1
Z9 1
U1 8
U2 24
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2021
VL 40
IS 6
BP 11941
EP 11950
DI 10.3233/JIFS-210064
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TA8PR
UT WOS:000667508800108
DA 2022-08-23
ER

PT J
AU Zhang, DH
   Yang, XB
   Liu, LA
   Liu, Q
AF Zhang, Dehai
   Yang, Xiaobo
   Liu, Linan
   Liu, Qing
TI A Knowledge Graph-Enhanced Attention Aggregation Network for Making
   Recommendations
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph; personalized recommendation; attention aggregation
AB In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KANR, a knowledge graph-enhanced attention aggregation network for making recommendations. This is an end-to-end deep learning model using knowledge graph embedding to enhance the attention aggregation network for making recommendations. It consists of three main parts. The first is the attention aggregation network, which collect the user's interaction history and captures the user's preference for each item. The second is the knowledge graph-embedded model, which aims to integrate the knowledge. The semantic information of the nodes and edges in the graph is mapped to the low-dimensional vector space. The final part is the information interaction unit, which is used for fusing the features of two vectors. Experiments showed that our model achieved a stable improvement compared to the baseline model in making recommendations for movies, books, and music.
C1 [Zhang, Dehai; Yang, Xiaobo; Liu, Linan; Liu, Qing] Yunnan Univ, Coll Software, Kunming 650504, Yunnan, Peoples R China.
RP Liu, Q (通讯作者)，Yunnan Univ, Coll Software, Kunming 650504, Yunnan, Peoples R China.
EM dhzhang@ynu.edu.cn; xb_yang@mail.ynu.edu.cn; liulinan@mail.ynu.edu.cn;
   liuqing@ynu.edu.cn
FU Natural Science Foundation China (NSFC) [61402397, 61263043, 61562093,
   61663046]; Open Foundation of Key Laboratory in Software Engineering of
   Yunnan Province [2020SE304]
FX FundingThis research was funded by (i) Natural Science Foundation China
   (NSFC) under Grant No. 61402397, 61263043, 61562093 and 61663046; (ii)
   Open Foundation of Key Laboratory in Software Engineering of Yunnan
   Province: 2020SE304.
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NR 31
TC 0
Z9 0
U1 9
U2 23
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 NOV
PY 2021
VL 11
IS 21
AR 10432
DI 10.3390/app112110432
PG 12
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA WY1XG
UT WOS:000719077200001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ratajczak, F
   Joblin, M
   Ringsquandl, M
   Hildebrandt, M
AF Ratajczak, Florin
   Joblin, Mitchell
   Ringsquandl, Martin
   Hildebrandt, Marcel
TI Task-driven knowledge graph filtering improves prioritizing drugs for
   repurposing
SO BMC BIOINFORMATICS
LA English
DT Article
DE Knowledge graphs; Knowledge graph embeddings; Drug repurposing; Link
   prediction
AB Background Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. For example, if we want to learn embeddings to predict links between compounds and diseases but almost the entirety of relations in the graph is incident to other pairs of entity types, then the resulting embeddings are likely not optimised to predict links between compounds and diseases. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency. Results We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 14.2% on DRKG, with an average improvement of 20.6% on Hetionet and 8.9% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied. Conclusion Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process.
C1 [Ratajczak, Florin] Helmholtz Zentrum Munchen, Deutsch Forschungszentrum Gesundheit & Umwelt Gmb, Munich, Germany.
   [Ratajczak, Florin] Siemens Healthineers, Digital Technol & Innovat, Erlangen, Germany.
   [Joblin, Mitchell; Ringsquandl, Martin; Hildebrandt, Marcel] Siemens AG, Corp Technol, Munich, Germany.
RP Ratajczak, F (通讯作者)，Helmholtz Zentrum Munchen, Deutsch Forschungszentrum Gesundheit & Umwelt Gmb, Munich, Germany.; Ratajczak, F (通讯作者)，Siemens Healthineers, Digital Technol & Innovat, Erlangen, Germany.
EM florin.ratajczak@helmholtz-muenchen.de
OI Ratajczak, Florin/0000-0003-3246-5032
FU Siemens Healthineers, Erlangen, Germany; Siemens AG, Munich, Germany;
   Helmholtz Association under the joint research school "Munich School for
   Data Science MUDS"
FX Funding has been provided by Siemens Healthineers, Erlangen, Germany and
   Siemens AG, Munich, Germany. Florin Ratajczak is supported by the
   Helmholtz Association under the joint research school "Munich School for
   Data Science MUDS". The funding bodies did not play any roles in the
   design of the study and collection, analysis, and interpretation of data
   and in writing the manuscript.
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NR 42
TC 0
Z9 0
U1 8
U2 8
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD MAR 4
PY 2022
VL 23
IS 1
AR 84
DI 10.1186/s12859-022-04608-y
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 ZM9VS
UT WOS:000764695800001
PM 35246025
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Sun, YH
   Li, GY
   Du, JJ
   Ning, B
   Chen, H
AF Sun, Yunhao
   Li, Guanyu
   Du, Jingjing
   Ning, Bo
   Chen, Heng
TI A subgraph matching algorithm based on subgraph index for knowledge
   graph
SO FRONTIERS OF COMPUTER SCIENCE
LA English
DT Article
DE knowledge graph; subgraph matching; subgraph index; matching tree
ID ISOMORPHISM
AB The problem of subgraph matching is one fundamental issue in graph search, which is NP-Complete problem. Recently, subgraph matching has become a popular research topic in the field of knowledge graph analysis, which has a wide range of applications including question answering and semantic search. In this paper, we study the problem of subgraph matching on knowledge graph. Specifically, given a query graph q and a data graph G, the problem of subgraph matching is to conduct all possible subgraph isomorphic mappings of q on G. Knowledge graph is formed as a directed labeled multi-graph having multiple edges between a pair of vertices and it has more dense semantic and structural features than general graph. To accelerate subgraph matching on knowledge graph, we propose a novel subgraph matching algorithm based on subgraph index for knowledge graph, called as FGq(T)-Match. The subgraph matching algorithm consists of two key designs. One design is a subgraph index of matching-driven flow graph (FGq(T)), which reduces redundant calculations in advance. Another design is a multi-label weight matrix, which evaluates a near-optimal matching tree for minimizing the intermediate candidates. With the aid of these two key designs, all subgraph isomorphic mappings are quickly conducted only by traversing FGq(T). Extensive empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms.
C1 [Sun, Yunhao; Li, Guanyu; Du, Jingjing; Ning, Bo; Chen, Heng] Dalian Maritime Univ, Fac Informat Sci & Technol, Dalian 116026, Liaoning, Peoples R China.
   [Chen, Heng] Dalian Univ Foreign Languages, Fac Software, Dalian 116044, Liaoning, Peoples R China.
RP Li, GY (通讯作者)，Dalian Maritime Univ, Fac Informat Sci & Technol, Dalian 116026, Liaoning, Peoples R China.
EM rabitlee@163.com
FU National Natural Science Foundation of China [61976032, 62002039]
FX This work was supported by the National Natural Science Foundation of
   China (Grant Nos. 61976032, 62002039).
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NR 31
TC 3
Z9 3
U1 24
U2 82
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 JUN
PY 2022
VL 16
IS 3
AR 163606
DI 10.1007/s11704-020-0360-y
PG 18
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UW8JI
UT WOS:000700398800003
DA 2022-08-23
ER

PT J
AU Stewart, SA
   Abidi, SSR
AF Stewart, Samuel Alan
   Abidi, Syed Sibte Raza
TI Leveraging medical taxonomies to improve knowledge management within
   online communities of practice: The knowledge maps system
SO COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
LA English
DT Article
DE Knowledge management; Medical taxonomies; Online communities; Natural
   language processing; Semantic mapping
AB Background and Objective: Online communities of practice contain a wealth of information, stored in the free text of shared communications between community members. The Knowledge Maps (KMaps) system is designed to facilitate Knowledge Translation in online communities through multi-level analyses of the shared messages of these communications.
   Methods: Using state-of-the-art semantic mapping technologies (Metamap) the contents of the messages shared within an online community are mapped to terms from the MeSH medical lexicon, providing a multi-level topic-specific summary of the knowledge being shared within the community. Using the inherent hierarchical structure of the lexicon important insights can be found within the community.
   Results: The KMaps system was applied to two medical mailing lists, the PPML (archives from 2009-02 to 2013-02) and SURGINET (archives from 2012-01 to 2013-04), identifying 27,924 and 50,597 medical terms respectively. KMaps identified content areas where both communities found interest, specifically around Diseases, 22% and 24% of the total terms, while also identifying field-specific areas that were more popular: SURGINET expressed an interest in Anatomy (14% vs 4%) while the PPML was more interested in Drugs (19% vs 9%). At the level of the individual KMaps identified 6 PPML users and 9 SURGINET users that had noticeably more contributions to the community than their peers, and investigated their personal areas of interest.
   Conclusion: The KMaps system provides valuable insights into the structure of both communities, identifying topics of interest/shared content areas and defining content-profiles for individual community members. The system provides a valuable addition to the online KT process. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Stewart, Samuel Alan] Fac Med, Dept Community Hlth & Epidemiol, Med Informat, Halifax, NS, Canada.
   [Abidi, Syed Sibte Raza] Dalhousie Univ, Fac Comp Sci, NICHE Res Grp, Halifax, NS, Canada.
RP Stewart, SA (通讯作者)，Dalhousie Univ, Ctr Clin Res, 5790 Univ Ave, Halifax, NS B3H 1V7, Canada.
EM sam.stewart@dal.ca
OI Stewart, Samuel Alan/0000-0001-6299-7967; Abidi, Syed Sibte
   Raza/0000-0003-3075-7736
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NR 19
TC 3
Z9 3
U1 1
U2 21
PU ELSEVIER IRELAND LTD
PI CLARE
PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND
SN 0169-2607
EI 1872-7565
J9 COMPUT METH PROG BIO
JI Comput. Meth. Programs Biomed.
PD MAY
PY 2017
VL 143
BP 121
EP 127
DI 10.1016/j.cmpb.2017.03.003
PG 7
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods; Engineering, Biomedical; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Medical Informatics
GA ET8EP
UT WOS:000400531900013
PM 28391809
DA 2022-08-23
ER

PT J
AU Shao, PP
   Zhang, DW
   Yang, GH
   Tao, JH
   Che, FH
   Liu, T
AF Shao, Pengpeng
   Zhang, Dawei
   Yang, Guohua
   Tao, Jianhua
   Che, Feihu
   Liu, Tong
TI Tucker decomposition-based temporal knowledge graph completion
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Temporal knowledge graphs; Tucker decomposition; Reconstruction;
   Contrastive learning
AB Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the knowledge graphs. To enrich the existing knowledge graphs, recent years have witnessed that many algorithms for link prediction and knowledge graphs embedding have been designed to infer new facts. But most of these studies focus on the static knowledge graphs and ignore the temporal information which reflects the validity of knowledge. Developing the model for temporal knowledge graphs completion is an increasingly important task. In this paper, we build a new tensor decomposition model for temporal knowledge graphs completion inspired by the Tucker decomposition of order-4 tensor. Furthermore, to further improve the basic model performance, we provide three kinds of methods including cosine similarity, contrastive learning, and reconstruction-based to incorporate the prior knowledge into the proposed model. Because the core tensor contains a large number of parameters on the proposed model, thus we present two embedding regularization schemes to avoid the over-fitting problem. By combining these two kinds of regularization with the proposed model, our model outperforms baselines with an explicit margin on three temporal datasets (i.e. ICEWS2014, ICEWS05-15, GDELT).
   (c) 2021 Published by Elsevier B.V.
C1 [Shao, Pengpeng; Zhang, Dawei; Yang, Guohua; Tao, Jianhua; Che, Feihu; Liu, Tong] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China.
   [Tao, Jianhua] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China.
   [Tao, Jianhua] CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China.
RP Tao, JH (通讯作者)，Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China.
EM jhtao@nlpr.ia.ac.cn
FU National Key Research & Development Plan of China [2017YFC0820602];
   National Natural Science Foundation of China (NSFC) [61831022, 61771472,
   61773379, 61901473]
FX Acknowledgments We gratefully acknowledge the help and assistance from
   Bin Liu, Xiao Wang, and Xinke Wang. Moreover, this work is sup-ported by
   the National Key Research & Development Plan of China (No.
   2017YFC0820602) , the National Natural Science Foundation of China
   (NSFC) (No. 61831022, No. 61771472, No. 61773379, No. 61901473) .
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NR 36
TC 2
Z9 2
U1 11
U2 11
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 107841
DI 10.1016/j.knosys.2021.107841
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0I1IU
UT WOS:000779180700014
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Gupta, S
   Tiwari, S
   Ortiz-Rodriguez, F
   Panchal, R
AF Gupta, Shivansh
   Tiwari, Sanju
   Ortiz-Rodriguez, Fernando
   Panchal, Ronak
TI KG4ASTRA: question answering over Indian Missiles Knowledge Graph
SO SOFT COMPUTING
LA English
DT Article
DE Query answering; Knowledge graph; Knowledge extraction; Neo4j; Cypher
AB Natural language, being unstructured, makes the tedious task for building a model to parse it into a query language successfully. An incorrect query for a particular question would lead to an absurd answer. As seen in many semantic parsing approaches, the inaccurate answering of complex questions increases significantly. This leads to many novel effective strategies that can apply to semantic parsing approaches to accurately parse complex natural language questions and generate an appropriate answer over a large knowledge graph. The defense system in a particular country is one of the most crucial components, and the lack of a proper defensive domain interface makes it a significant motivation. Hence, a knowledge graph has been constructed to collect and enquire about all information in one place. In this paper, KG4ASTRA has been designed with a Missile Knowledge Graph consisting of 177 entities linked using 400 relationships. A query-answering model then utilizes this manually created Missile Knowledge Graph, which generates tabular or graph representation for the natural language question. Neo4j platform has been used to prepare the knowledge graph, and Cypher queries are used to execute queries. The modeled knowledge has been evaluated by executing natural language queries on KG4ASTRA query-answering model and compared the search results with other existing knowledge graphs. As of our best knowledge, neither the knowledge graph nor the question-answering model has been designed for Indian Missiles. As a future scope, the proposed knowledge graph will be connected with the existing knowledge graph and extended to automatically extract domain-specific entities.
C1 [Gupta, Shivansh] Jaypee Inst Informat Technol, Noida, India.
   [Tiwari, Sanju; Ortiz-Rodriguez, Fernando] Univ Autonoma Tamaulipas, Ciudad Victoria, Tamaulipas, Mexico.
   [Panchal, Ronak] Vidyabharti Trust Coll BCA, Bardoli, Gujarat, India.
RP Tiwari, S (通讯作者)，Univ Autonoma Tamaulipas, Ciudad Victoria, Tamaulipas, Mexico.
EM shivanshgupta171999@gmail.com; sanju.tiwari@uat.edu.mx;
   ferortiz@uat.edu.mx; ronak.panchal@vtcbb.edu.in
RI Ortiz-Rodriguez, Fernando/A-5224-2011
OI Ortiz-Rodriguez, Fernando/0000-0003-2084-3462
CR Abdelkawi A, 2019, LECT NOTES COMPUT SC, V11877, P571, DOI 10.1007/978-3-030-33246-4_36
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NR 31
TC 0
Z9 0
U1 6
U2 11
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 NOV
PY 2021
VL 25
IS 22
BP 13841
EP 13855
DI 10.1007/s00500-021-06233-y
EA SEP 2021
PG 15
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WL6HB
UT WOS:000702246200002
DA 2022-08-23
ER

PT J
AU Wang, ZD
   Bai, C
   Hu, TY
   Luo, CY
   Yu, H
   Ma, XY
   Liu, TG
   Gu, XH
AF Wang, Zhendong
   Bai, Chen
   Hu, Tingyao
   Luo, Changyong
   Yu, He
   Ma, Xueyan
   Liu, Tiegang
   Gu, Xiaohong
TI Emerging trends and hotspot in gut-lung axis research from 2011 to 2021:
   a bibliometrics analysis
SO BIOMEDICAL ENGINEERING ONLINE
LA English
DT Article
DE Bibliometric; Knowledge map; Gut-lung axis; Inflammation
ID ALLERGIC AIRWAY DISEASE; COMMENSAL MICROBIOTA; EARLY-LIFE; INFECTION;
   MEDICINE; IMMUNITY; OUTCOMES; DEFENSE; RISK
AB Background Increasing attention has been paid to the potential relationship between gut and lung. The bacterial dysbiosis in respiratory tract and intestinal tract is related to inflammatory response and the progress of lung diseases, and the pulmonary diseases could be improved by regulating the intestinal microbiome. This study aims to generate the knowledge map to identify major the research hotspots and frontier areas in the field of gut-lung axis. Materials and methods Publications related to the gut-lung axis from 2011 to 2021 were identified from the Web of Science Core Collection. CiteSpace 5.7.R2 software was used to analyze the publication years, journals, countries, institutions, and authors. Reference co-citation network has been plotted, and the keywords were used to analyze the research hotspots and trends. Results A total of 3315 publications were retrieved and the number of publications per year increased over time. Our results showed that Plos One (91 articles) was the most active journal and The United States (1035 articles) published the most articles. We also observed the leading institution was the University of Michigan (48 articles) and Huffnagle Gary B, Dickson Robert P and Hansbro Philip M, who have made outstanding contributions in this field. Conclusion The Inflammation, Infection and Disease were the hotspots, and the regulation of intestinal flora to improve the efficacy of immunotherapy in lung cancer was the research frontier. The research has implications for researchers engaged in gut-lung axis and its associated fields.
C1 [Wang, Zhendong; Bai, Chen; Yu, He; Ma, Xueyan; Liu, Tiegang; Gu, Xiaohong] Beijing Univ Chinese Med, Sch Tradit Chinese Med, Beijing 100029, Peoples R China.
   [Hu, Tingyao] Beijing Univ Chinese Med, Sch Acupuncture Moxibust & Tuina, Beijing 100029, Peoples R China.
   [Luo, Changyong] Beijing Univ Chinese Med, Dept Infect Dis, Dongfang Hosp, Beijing 100078, Peoples R China.
RP Liu, TG; Gu, XH (通讯作者)，Beijing Univ Chinese Med, Sch Tradit Chinese Med, Beijing 100029, Peoples R China.
EM liutiegang2009@163.com; guxiaohong1962@163.com
FU National Natural Science Foundation of China [81874421]; Innovation Team
   and Talents Cultivation Program of National Administration of
   Traditional Chinese Medicine [ZYYCXTD-C-202006]
FX This study was funded by the National Natural Science Foundation of
   China, Grant Number 81874421 and Innovation Team and Talents Cultivation
   Program of National Administration of Traditional Chinese Medicine,
   Grant Number ZYYCXTD-C-202006.
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NR 74
TC 0
Z9 0
U1 11
U2 11
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1475-925X
J9 BIOMED ENG ONLINE
JI Biomed. Eng. Online
PD APR 21
PY 2022
VL 21
IS 1
AR 27
DI 10.1186/s12938-022-00987-8
PG 20
WC Engineering, Biomedical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 0P9IV
UT WOS:000784541500001
PM 35449051
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhang, ZL
   Li, ZF
   Liu, H
   Xiong, NN
AF Zhang, Zhaoli
   Li, Zhifei
   Liu, Hai
   Xiong, Neal N.
TI Multi-Scale Dynamic Convolutional Network for Knowledge Graph Embedding
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Computational modeling; Convolution; Semantics; Predictive models;
   Feature extraction; Knowledge engineering; Computer architecture;
   Knowledge graphs; knowledge graph embedding; complex relations; link
   prediction; convolutional network
AB Knowledge graphs are large graph-structured knowledge bases with incomplete or partial information. Numerous studies have focused on knowledge graph embedding to identify the embedded representation of entities and relations, thereby predicting missing relations between entities. Previous embedding models primarily regard (subject entity, relation, and object entity) triplet as translational distance or semantic matching in vector space. However, these models only learn a few expressive features and hard to handle complex relations, i.e., 1-to-N, N-to-1, and N-to-N, in knowledge graphs. To overcome these issues, we introduce a multi-scale dynamic convolutional network (M-DCN) model for knowledge graph embedding. This model features topnotch performance and an ability to generate richer and more expressive feature embeddings than its counterparts. The subject entity and relation embeddings in M-DCN are composed in an alternating pattern in the input layer, which helps extract additional feature interactions and increase the expressiveness. Multi-scale filters are generated in the convolution layer to learn different characteristics among input embeddings. Specifically, the weights of these filters are dynamically related to each relation to model complex relations. The performance of M-DCN on the five benchmark datasets is tested via experiments. Results show that the model can effectively handle complex relations and achieve state-of-the-art link prediction results on most evaluation metrics.
C1 [Zhang, Zhaoli; Li, Zhifei; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
   [Xiong, Neal N.] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.
RP Li, ZF (通讯作者)，Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
EM zl.zhang@ccnu.edu.cn; zhifei1993@mails.ccnu.edu.cn;
   hailiu0204@ccnu.edu.cn; 18766362@qq.com
FU National Natural Science Foundation of China [61875068, 61505064];
   Fundamental Research Funds for the Central Universities [CCNU20ZT017,
   CCNU2020ZN008]
FX The authors Zhaoli Zhang and Zhifei Li contributed equally to this work.
   The authors sincerely thank anonymous reviewers for their constructive
   comments, which helped improve this paper. This work was supported in
   part by the National Natural Science Foundation of China under Grant
   61875068, and Grant 61505064, and the Fundamental Research Funds for the
   Central Universities under Grant CCNU20ZT017 and Grant CCNU2020ZN008.
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NR 50
TC 26
Z9 26
U1 25
U2 25
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD MAY 1
PY 2022
VL 34
IS 5
BP 2335
EP 2347
DI 10.1109/TKDE.2020.3005952
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 0F4LD
UT WOS:000777332000024
DA 2022-08-23
ER

PT J
AU Zhang, XD
   Wang, CX
   Jiang, HH
   Jing, SL
   Zhao, JY
   Yu, ZY
AF Zhang, Xiao-Dan
   Wang, Chun-Xia
   Jiang, Hong-Hu
   Jing, Shuo-Lan
   Zhao, Jiang-Yue
   Yu, Zi-Yan
TI Trends in research related to high myopia from 2010 to 2019: a
   bibliometric and knowledge mapping analysis
SO INTERNATIONAL JOURNAL OF OPHTHALMOLOGY
LA English
DT Article
DE high myopia; pathological myopia; bibliometric analysis; VOSviewer
ID TOPOGRAPHY-GUIDED LASIK; INCISION LENTICULE EXTRACTION; IMPLANTABLE
   COLLAMER LENS; HIGHER-ORDER ABERRATIONS; CHOROIDAL NEOVASCULARIZATION;
   REFRACTIVE ERROR; VISUAL IMPAIRMENT; GENE-EXPRESSION; SCHOOL-CHILDREN;
   CROSS-LINKING
AB AIM: To evaluate the global trends in and explore hotspots of high myopia (HM) research.
   METHODS: This bibliometric analysis was used to reveal the publication trends in HM research field based on the Web of Science Core Collection (WoSCC). VOSviewer version 1.6.13 software was used to analyze the data and construct a knowledge map including the yearly publication number, journals, countries, international collaborations, authors, research hotspots, and intellectual base in HM.
   RESULTS: The search engine found 3544 peer-reviewed publications on HM between 2010 and 2019, and the yearly research output substantially elevated over the past decade. China is the top publishing country, and Sun Yatsen University was the most active academic institution. Jonas JB is the top publishing scientist, and Investigative Ophthalmology and Visual Science (IOVS) was the most productive journal. The highest cited references mainly focused on epidemiology and management. The keywords formed 6 clusters: 1) refractive surgery; 2) etiology and clinical characteristics; 3) the mechanism of eye growth; 4) management for myopic maculopathy; 5) vitrectomy surgical treatment; 6) myopia-associated glaucoma-like optic neuropathy..
   CONCLUSION: The evaluation of development trends based on the data extracted from WoSCC can provide valuable information and guidance for ophthalmologists and public health researchers to improve management procedures in HM field.
C1 [Zhang, Xiao-Dan; Wang, Chun-Xia; Zhao, Jiang-Yue; Yu, Zi-Yan] China Med Univ, Affiliated Hosp 4, Dept Ophthalmol, 11 Xinhua Rd, Shenyang 110005, Liaoning, Peoples R China.
   [Zhang, Xiao-Dan; Wang, Chun-Xia; Zhao, Jiang-Yue; Yu, Zi-Yan] China Med Univ, Eye Hosp, 11 Xinhua Rd, Shenyang 110005, Liaoning, Peoples R China.
   [Zhang, Xiao-Dan; Wang, Chun-Xia; Zhao, Jiang-Yue; Yu, Zi-Yan] Key Lens Res Lab Liaoning Prov, 11 Xinhua Rd, Shenyang 110005, Liaoning, Peoples R China.
   [Jiang, Hong-Hu; Jing, Shuo-Lan] China Med Univ, Shenyang 110122, Liaoning, Peoples R China.
RP Zhao, JY; Yu, ZY (通讯作者)，China Med Univ, Affiliated Hosp 4, Dept Ophthalmol, 11 Xinhua Rd, Shenyang 110005, Liaoning, Peoples R China.; Zhao, JY; Yu, ZY (通讯作者)，China Med Univ, Eye Hosp, 11 Xinhua Rd, Shenyang 110005, Liaoning, Peoples R China.; Zhao, JY; Yu, ZY (通讯作者)，Key Lens Res Lab Liaoning Prov, 11 Xinhua Rd, Shenyang 110005, Liaoning, Peoples R China.
EM jyzhao@cmu.edu.cn; yuziyan@hotmail.com
FU Natural Science Key Research Project of the Education Department of
   Liaoning Province; Natural Science Foundation of Liaoning Province
FX Supported by Natural Science Key Research Project of the Education
   Department of Liaoning Province (No.ZD2020003); Natural Science
   Foundation of Liaoning Province (No.2019-MS-376).
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NR 116
TC 1
Z9 2
U1 3
U2 18
PU IJO PRESS
PI XI AN
PA NO 269 YOUYI EAST RD, XI AN, 710054, PEOPLES R CHINA
SN 2222-3959
EI 2227-4898
J9 INT J OPHTHALMOL-CHI
JI Int. J. Ophthalmol.
PD APR 18
PY 2021
VL 14
IS 4
BP 589
EP 599
DI 10.18240/ijo.2021.04.17
PG 11
WC Ophthalmology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Ophthalmology
GA RK3LM
UT WOS:000638201100018
PM 33875953
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Thomson, G
   Diop, MQ
   Stuijfzand, S
   Horsch, A
AF Thomson, Gill
   Diop, Magali Quillet
   Stuijfzand, Suzannah
   Horsch, Antje
CA COST Birth Consortium
TI Policy, service, and training provision for women following a traumatic
   birth: an international knowledge mapping exercise
SO BMC HEALTH SERVICES RESEARCH
LA English
DT Article
DE Traumatic birth; Services; Policy; Training; Education; Survey
ID POSTTRAUMATIC-STRESS-DISORDER; PERINATAL-PERIOD; EXPERIENCES;
   CHILDBIRTH; FUTURE; PERCEPTIONS; PREVALENCE; SYMPTOMS; OUTCOMES; COSTS
AB Background High numbers of women experience a traumatic birth, which can lead to childbirth-related post-traumatic stress disorder (CB-PTSD) onset, and negative and pervasive impacts for women, infants, and families. Policies, suitable service provision, and training are needed to identify and treat psychological morbidity following a traumatic birth experience, but currently there is little insight into whether and what is provided in different contexts. The aim of this knowledge mapping exercise was to map policy, service and training provision for women following a traumatic birth experience in different European countries. Methods A survey was distributed as part of the COST Action "Perinatal mental health and birth-related trauma: Maximizing best practice and optimal outcomes". Questions were designed to capture country level data; care provision (i.e., national policies or guidelines for the screening, treatment and/or prevention of a traumatic birth, service provision), and nationally mandated pre-registration and post-registration training for maternity professionals. Results Eighteen countries participated. Only one country (the Netherlands) had national policies regarding the screening, treatment, and prevention of a traumatic birth experience/CB-PTSD. Service provision was provided formally in six countries (33%), and informally in the majority (78%). In almost all countries (89%), women could be referred to specialist perinatal or mental health services. Services tended to be provided by midwives, although some multidisciplinary practice was apparent. Seven (39%) of the countries offered 'a few hours' professional/pre-registration training, but none offered nationally mandated post-registration training. Conclusions A traumatic birth experience is a key public health concern. Evidence highlights important gaps regarding formalized care provision and training for care providers.
C1 [Thomson, Gill] Univ Cent Lancashire, Sch Community Hlth & Midwifery, Preston, Lancs, England.
   [Diop, Magali Quillet; Stuijfzand, Suzannah; Horsch, Antje] Univ Lausanne, Inst Higher Educ & Res Healthcare, Lausanne, Switzerland.
   [Horsch, Antje] Lausanne Univ Hosp, Dept Woman Mother Child, Lausanne, Switzerland.
   [COST Birth Consortium] Trinity Coll Dublin, Sch Nursing & Midwifery, Dublin, Ireland.
RP Thomson, G (通讯作者)，Univ Cent Lancashire, Sch Community Hlth & Midwifery, Preston, Lancs, England.
EM GThomson@uclan.ac.uk
RI Topalidou, Anastasia/AFS-0887-2022; Karlsdottir, Dr. Sigfridur
   Inga/D-3063-2018
OI Topalidou, Anastasia/0000-0003-0280-6801; Abreu,
   Wilson/0000-0002-0847-824X; EL-HAGE, WISSAM/0000-0003-3877-0855;
   Karlsdottir, Dr. Sigfridur Inga/0000-0001-5886-9196; Avignon,
   Valerie/0000-0003-0955-6534; Baranowska, Barbara/0000-0003-2723-9604;
   Thomson, Gill/0000-0003-3392-8182; Pehlke-Milde,
   Jessica/0000-0001-9549-7723; Healy, Maria/0000-0003-3729-7285; Horsch,
   Antje/0000-0002-9950-9661
FU COST (European Cooperation in Science and Technology)
FX This article/publication is based upon work from COST Action "Perinatal
   mental health and birth-related trauma: Maximizing best practice and
   optimal outcomes", supported by COST (European Cooperation in Science
   and Technology). We would like to thank Gulcan Tecirli for her
   assistance in data collection in Turkey.
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NR 41
TC 1
Z9 1
U1 1
U2 2
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6963
J9 BMC HEALTH SERV RES
JI BMC Health Serv. Res.
PD NOV 6
PY 2021
VL 21
IS 1
AR 1206
DI 10.1186/s12913-021-07238-x
PG 10
WC Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services
GA WS2PO
UT WOS:000715029400003
PM 34742293
OA Green Published, Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Chen, Y
   Wu, C
AF Chen, Ying
   Wu, Can
TI The hot spot transformation in the research evolution of maker
SO SCIENTOMETRICS
LA English
DT Article
DE Maker; Maker movement; Makerspace; Maker culture; Scientometric review;
   Knowledge mapping
ID CITESPACE
AB The rapid development of digital technology together with the grassroots innovation has led to a new landmark of maker culture. Recently, a search with the query of ''makerspace'', ''maker movement'' and ''maker culture'' in Google Scholar found over 10,000 documents in total. As the volume of the literature grows rapidly, thus, a systematic review of maker research and its current challenges becomes essential. This study surveys the literature of maker between 1975 and 2016. The overall structure of its intellectual landscape is illustrated in terms of thematic concentrations of knowledge clusters of maker study, hot research categories and keywords, major contributing factors including contributing countries, organizations, and authors. Our review is based on 836 bibliographic records retrieved from the ISI Web of Science and visualized by the scientometric software of CiteSpace. This application results in comprehensive knowledge maps of maker research. The study identifies major intellectual cooperation network, co-occurrence keywords, research clusters and landmark articles, including: (1) the thematic clusters of ''maker movement'', ''DIY culture and extended milieu'', ''craftsman hero'' reflects the biggest knowledge base clusters of maker study. (2) "Physical computing", "maker space", "maker movement", "3D printer" are the major frontiers of maker research based on analysis of citation burst. (3) In terms of contributing countries, institutions, authors, the major driving force of maker research is from the United States of America, England and Germany, with University of California Berkeley, Georgia Institute of Technology and Massachusetts Institute of Technology being major contributing institutions. Paulo Blikstein from Stanford University USA, Vasilis Kostakis from Tallinn University of Technology Estonia, Hans-Joachim Bohme from Technische Universitat Ilmenau German all have published three articles and rank at the top of the author list.
C1 [Chen, Ying] Zhejiang Univ Finance & Econ, Sch Business Adm, Hangzhou 310018, Zhejiang, Peoples R China.
   [Wu, Can] Hangzhou Dianzi Univ, Sch Mech Engn, Hangzhou 310018, Zhejiang, Peoples R China.
RP Chen, Y (通讯作者)，Zhejiang Univ Finance & Econ, Sch Business Adm, Hangzhou 310018, Zhejiang, Peoples R China.
EM yc@zufe.edu.cn
FU Key Research Base of Philosophy and Social Sciences of Zhejiang Province
   [13JDCB02Z]; State Scholarship Fund of China Scholarship Council
   [201508330629]; High Education Association of Zhejiang Province [17];
   Zhejiang University of Finance and Economics High Education Research
   Project [JK201509]; Hangzhou Dianzi University High Education Research
   Project [YB201620]
FX I would like to give great thanks to Professor Michael Keane of Curtin
   University Australia for valuable discussions and suggestions throughout
   this research. This work is supported by Key Research Base of Philosophy
   and Social Sciences of Zhejiang Province (13JDCB02Z); State Scholarship
   Fund of China Scholarship Council (201508330629); 2015 High Education
   Research Project of High Education Association of Zhejiang Province (No.
   17); Zhejiang University of Finance and Economics High Education
   Research Project (No. JK201509); Hangzhou Dianzi University High
   Education Research Project (YB201620).
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NR 38
TC 22
Z9 23
U1 9
U2 190
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2017
VL 113
IS 3
BP 1307
EP 1324
DI 10.1007/s11192-017-2542-4
PG 18
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA FN1AK
UT WOS:000415715100004
DA 2022-08-23
ER

PT J
AU Huang, G
   Yuan, M
   Li, CS
   Wei, YH
AF Huang, Gang
   Yuan, Man
   Li, Chun-Sheng
   Wei, Yong-he
TI Personalized Knowledge Recommendation Based on Knowledge Graph in
   Petroleum Exploration and Development
SO INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Recommendation algorithm; knowledge graph; petroleum exploration and
   development
AB Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where K value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users' potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.
C1 [Huang, Gang; Yuan, Man; Li, Chun-Sheng] Northeast Petr Univ, Coll Comp & Informat Technol, Daqing, Peoples R China.
   [Wei, Yong-he] JiBei Elect Power Ltd Co, State Grid Corp China, Management Training Ctr, Beijing, Peoples R China.
RP Huang, G (通讯作者)，Northeast Petr Univ, Coll Comp & Informat Technol, Daqing, Peoples R China.
EM huanggangmail@126.com; yuanman@nepu.edu.cn; lichunsheng@nepu.edu.cn;
   1735176043@qq.com
FU Construction Method and Application of Knowledge Graph in Petroleum
   Exploration and Development - Scientific Research Fund of Heilongjiang
   Provincial Education Department [2019YDL]; Research on Semantic
   Standardization Integration Model of Data and Big Data - Scientific
   Research Fund of Heilongjiang Provincial Education Department
   [2017PYYL-06]
FX Construction Method and Application of Knowledge Graph in Petroleum
   Exploration and Development (2019YDL) supported by Scientific Research
   Fund of Heilongjiang Provincial Education Department; Research on
   Semantic Standardization Integration Model of Data and Big Data
   (2017PYYL-06) supported by Scientific Research Fund of Heilongjiang
   Provincial Education Department.
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NR 34
TC 1
Z9 1
U1 11
U2 55
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-0014
EI 1793-6381
J9 INT J PATTERN RECOGN
JI Int. J. Pattern Recognit. Artif. Intell.
PD SEP
PY 2020
VL 34
IS 10
AR 2059033
DI 10.1142/S0218001420590338
PG 24
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NR6IC
UT WOS:000571664900014
DA 2022-08-23
ER

PT J
AU YE, Z
   KUMAR, YJ
   SING, GO
   SONG, FY
   WANG, JS
AF YE, Z., I
   KUMAR, Y. O. G. A. N. J. A. Y. A.
   SING, G. O. H. O. N. G.
   SONG, F. E. N. G. Y. A. N.
   WANG, J. U. N. S. O. N. G.
TI A Comprehensive Survey of Graph Neural Networks for Knowledge Graphs
SO IEEE ACCESS
LA English
DT Article
DE Knowledge engineering; Graph neural networks; Task analysis; Predictive
   models; Internet; Deep learning; Cognition; Deep learning; distributed
   embedding; graph neural network; knowledge graph; representation
   learning
AB The Knowledge graph, a multi-relational graph that represents rich factual information among entities of diverse classifications, has gradually become one of the critical tools for knowledge management. However, the existing knowledge graph still has some problems which form hot research topics in recent years. Numerous methods have been proposed based on various representation techniques. Graph Neural Network, a framework that uses deep learning to process graph-structured data directly, has significantly advanced the state-of-the-art in the past few years. This study firstly is aimed at providing a broad, complete as well as comprehensive overview of GNN-based technologies for solving four different KG tasks, including link prediction, knowledge graph alignment, knowledge graph reasoning, and node classification. Further, we also investigated the related artificial intelligence applications of knowledge graphs based on advanced GNN methods, such as recommender systems, question answering, and drug-drug interaction. This review will provide new insights for further study of KG and GNN.
C1 [YE, Z., I] Wenzhou Polytech, Sch Artificial Intelligence, Wenzhou 325000, Peoples R China.
   [KUMAR, Y. O. G. A. N. J. A. Y. A.; SING, G. O. H. O. N. G.] Univ Tekn Malaysia, Fac Informat & Commun Technol, Melaka 76100, Malaysia.
   [SONG, F. E. N. G. Y. A. N.] Shanghai Gen Cong Informat Technol Co Ltd, Shanghai 200000, Peoples R China.
   [WANG, J. U. N. S. O. N. G.] Shenzhen Technol Univ, Coll Big Data & Internet, Shenzhen 518000, Peoples R China.
RP WANG, JS (通讯作者)，Shenzhen Technol Univ, Coll Big Data & Internet, Shenzhen 518000, Peoples R China.
EM wjsong2004@126.com
FU Basic Research Project of Wenzhou, China [G2020019]; National Natural
   Science Foundation of China [61473208, 61876132]
FX This work was supported in part by the Basic Research Project of
   Wenzhou, China, under Grant G2020019; and in part by the National
   Natural Science Foundation of China under Grant 61473208 and Grant
   61876132.
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NR 81
TC 0
Z9 0
U1 1
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 75729
EP 75741
DI 10.1109/ACCESS.2022.3191784
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3G0OH
UT WOS:000831056400001
OA gold
DA 2022-08-23
ER

PT J
AU Yan, VKC
   Li, XD
   Ye, XX
   Ou, M
   Luo, RB
   Zhang, QP
   Tang, B
   Cowling, BJ
   Hung, I
   Siu, CW
   Wong, ICK
   Cheng, RCK
   Chan, EW
AF Yan, Vincent K. C.
   Li, Xiaodong
   Ye, Xuxiao
   Ou, Min
   Luo, Ruibang
   Zhang, Qingpeng
   Tang, Bo
   Cowling, Benjamin J.
   Hung, Ivan
   Siu, Chung Wah
   Wong, Ian C. K.
   Cheng, Reynold C. K.
   Chan, Esther W.
TI Drug Repurposing for the Treatment of COVID-19: A Knowledge Graph
   Approach
SO ADVANCED THERAPEUTICS
LA English
DT Article
DE COVID-19; drug repurposing; knowledge graph; motif scores; ranking
AB Identifying effective drug treatments for COVID-19 is essential to reduce morbidity and mortality. Although a number of existing drugs have been proposed as potential COVID-19 treatments, effective data platforms and algorithms to prioritize drug candidates for evaluation and application of knowledge graph for drug repurposing have not been adequately explored. A COVID-19 knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, symptoms and their linkages is developed. An algorithm is developed to extract hidden linkages connecting drugs and COVID-19 from the knowledge graph, to generate and rank proposed drug candidates for repurposing as treatments for COVID-19 by integrating three scores for each drug: motif scores, knowledge graph PageRank scores, and knowledge graph embedding scores. The knowledge graph contains over 48 000 nodes and 13 37 000 edges, including 13 563 molecules in the DrugBank database. From the 5624 molecules identified by the motif-discovery algorithms, ranking results show that 112 drug molecules had the top 2% scores, of which 50 existing drugs with other indications approved by health administrations reported. The proposed drug candidates serve to generate hypotheses for future evaluation in clinical trials and observational studies.
C1 [Yan, Vincent K. C.; Ye, Xuxiao; Wong, Ian C. K.; Chan, Esther W.] Univ Hong Kong, LKS Fac Med, Dept Pharmacol & Pharm, Ctr Safe Medicat Practice & Res,Pokfulam, 1-F Jockey Club Bldg Interdisciplinary Res, Hong Kong, Peoples R China.
   [Li, Xiaodong; Ou, Min; Luo, Ruibang; Cheng, Reynold C. K.] Univ Hong Kong, Fac Engn, Dept Comp Sci, Pokfula, CB303 Chow Yei Ching Bldg, Hong Kong, Peoples R China.
   [Zhang, Qingpeng] City Univ Hong Kong, Sch Data Sci, Kowloon, 83 Tat Chee Ave, Hong Kong, Peoples R China.
   [Tang, Bo] Southern Univ Sci & Technol, Dept Comp Sci & Engn, 1088 Xueyuan Ave, Shenzhen 518055, Guangdong, Peoples R China.
   [Cowling, Benjamin J.] Univ Hong Kong, Sch Publ Hlth, Div Epidemiol & Biostat, 21 Sassoon Rd Pokfulam, Hong Kong, Peoples R China.
   [Hung, Ivan] Univ Hong Kong, LKS Fac Med, Dept Med, Div Infect Dis, 102 Pokfulam Rd, Hong Kong, Peoples R China.
   [Siu, Chung Wah] Univ Hong Kong, Dept Med, Div Cardiol, 102 Pokfulam Rd, Hong Kong, Peoples R China.
   [Wong, Ian C. K.; Chan, Esther W.] Lab Data Discovery Hlth, 12-F,Bldg 19W,19 Sci Pk West Ave, Hong Kong, Peoples R China.
   [Wong, Ian C. K.; Chan, Esther W.] Univ Hong Kong, Shenzhen Hopsital, Dept Pharm, 1 Haiyuanyi Rd, Shenzhen 518009, Peoples R China.
   [Wong, Ian C. K.; Chan, Esther W.] Univ Hong Kong, Shenzhen Inst Res & Inovat, 5-F Key Lab Platform Bldg, Shenzhen 518057, Peoples R China.
   [Wong, Ian C. K.] UCL Sch Pharm, 29-39 Brunswick Sq, London, England.
RP Chan, EW (通讯作者)，Univ Hong Kong, LKS Fac Med, Dept Pharmacol & Pharm, Ctr Safe Medicat Practice & Res,Pokfulam, 1-F Jockey Club Bldg Interdisciplinary Res, Hong Kong, Peoples R China.; Chan, EW (通讯作者)，Univ Hong Kong, Shenzhen Hopsital, Dept Pharm, 1 Haiyuanyi Rd, Shenzhen 518009, Peoples R China.; Chan, EW (通讯作者)，Univ Hong Kong, Shenzhen Inst Res & Inovat, 5-F Key Lab Platform Bldg, Shenzhen 518057, Peoples R China.
EM ewchan@hku.hk
RI Siu, Chung Wah/C-6947-2011; Chan, Esther Wai Yin/B-3522-2012
OI Chan, Esther Wai Yin/0000-0002-7602-9470; OU, Min/0000-0002-1381-8683;
   Yan, Vincent K.C./0000-0001-5225-3032; Luo, Ruibang/0000-0001-9711-6533;
   Ye, Xuxiao/0000-0002-9316-4666
FU Li Ka Shing Faculty of Medicine, The University of Hong Kong
FX The authors thank Prof. Chuan Wu and Prof. Kevin Chang for their
   valuable input on the concept and design of this study. The authors also
   thank Ms Lisa Y Lam for proofreading the manuscript. This study was
   supported through funding from the Li Ka Shing Faculty of Medicine, The
   University of Hong Kong.
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NR 47
TC 3
Z9 3
U1 9
U2 26
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
EI 2366-3987
J9 ADV THER-GERMANY
JI Adv. Therap.
PD JUL
PY 2021
VL 4
IS 7
AR 2100055
DI 10.1002/adtp.202100055
EA MAY 2021
PG 12
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA TL1EB
UT WOS:000652290000001
PM 34179346
OA Green Submitted, Green Published, Bronze
DA 2022-08-23
ER

PT J
AU Wang, YS
   Zhang, HH
   Shi, G
   Liu, ZR
   Zhou, Q
AF Wang, Yashen
   Zhang, Huanhuan
   Shi, Ge
   Liu, Zhirun
   Zhou, Qiang
TI A Model of Text-Enhanced Knowledge Graph Representation Learning With
   Mutual Attention
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph representation; textual relation representation; mutual
   attention mechanism; representation learning
ID WEB
AB Recently, it has gained lots of interests to jointly learn the embeddings of knowledge graph (KG) and text information. However, previous work fails to incorporate the complex structural signals (from structure representation) and semantic signals (from text representation). This paper proposes a novel text-enhanced knowledge graph representation model, which can utilize textual information to enhance the knowledge representations. Especially, a mutual attention mechanism between KG and text is proposed to learn more accurate textual representations for further improving knowledge graph representation, within a unified parameter sharing semantic space. Different from conventional joint models, no complicated linguistic analysis or strict alignments between KG and text are required to train our model. Besides, the proposed model could fully incorporate the multi-direction signals. Experimental results show that the proposed model achieves the state-of-the-art performance on both link prediction and triple classification tasks, and significantly outperforms previous text-enhanced knowledge representation models.
C1 [Wang, Yashen; Zhang, Huanhuan] China Acad Elect & Informat Technol, Natl Engn Lab Publ Safety Risk Percept & Control, Beijing 100041, Peoples R China.
   [Shi, Ge; Liu, Zhirun; Zhou, Qiang] Beijing Inst Technol, Sch Comp, Beijing Engn Res Ctr High Volume Language Informa, Beijing 100041, Peoples R China.
RP Wang, YS (通讯作者)，China Acad Elect & Informat Technol, Natl Engn Lab Publ Safety Risk Percept & Control, Beijing 100041, Peoples R China.
EM yswang@bit.edu.cn
FU National Key Research and Development Project [2017YFC0820503]; National
   Natural Science Foundation of China [U19B2026]; China Postdoctoral
   Science Foundation [2018M641436]; New Generation of Artificial
   Intelligence Special Action Project [AI20191125008]; National Integrated
   Big Data Center Pilot Project [17111001, 17111002]; Joint Advanced
   Research Foundation of China Electronics Technology Group Corporation
   (CETC) [6141B08010102]
FX This work was supported in part by the National Key Research and
   Development Project under Grant 2017YFC0820503, in part by the National
   Natural Science Foundation of China under Grant U19B2026, in part by the
   China Postdoctoral Science Foundation under Grant 2018M641436, in part
   by the New Generation of Artificial Intelligence Special Action Project
   under Grant AI20191125008, in part by the National Integrated Big Data
   Center Pilot Project under Grant 17111001 and Grant 17111002, and in
   part by the Joint Advanced Research Foundation of China Electronics
   Technology Group Corporation (CETC) under Grant 6141B08010102.
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NR 62
TC 6
Z9 6
U1 1
U2 17
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 52895
EP 52905
DI 10.1109/ACCESS.2020.2981212
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LB6MV
UT WOS:000524748800004
OA gold
DA 2022-08-23
ER

PT J
AU Gutierrez, C
   Sequeda, JF
AF Gutierrez, Claudio
   Sequeda, Juan F.
TI Knowledge Graphs
SO COMMUNICATIONS OF THE ACM
LA English
DT Review
ID PRINCIPLES; COMPUTERS
C1 [Gutierrez, Claudio] Univ Chile, DCC, Santiago, Chile.
   [Gutierrez, Claudio] IMFD, Santiago, Chile.
   [Sequeda, Juan F.] Dataworld, Austin, TX USA.
RP Gutierrez, C (通讯作者)，Univ Chile, DCC, Santiago, Chile.; Gutierrez, C (通讯作者)，IMFD, Santiago, Chile.
EM cgutierr@dcc.uchile.cl; juan@data.world
FU ANID - Millennium Science Initiative Program [ICN17_002]
FX This work was funded by ANID - Millennium Science Initiative Program
   Code ICN17_002.
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NR 40
TC 14
Z9 14
U1 41
U2 88
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 0001-0782
EI 1557-7317
J9 COMMUN ACM
JI Commun. ACM
PD MAR
PY 2021
VL 64
IS 3
BP 96
EP 104
DI 10.1145/3418294
PG 9
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QQ5AI
UT WOS:000624534800022
OA Bronze
DA 2022-08-23
ER

PT J
AU Huang, ZS
   Hu, Q
   Liao, MQ
   Miao, C
   Wang, CY
   Liu, GH
AF Huang, Zhisheng
   Hu, Qing
   Liao, Mingqun
   Miao, Cong
   Wang, Chengyi
   Liu, Guanghua
TI Knowledge Graphs of Kawasaki Disease
SO HEALTH INFORMATION SCIENCE AND SYSTEMS
LA English
DT Article
DE Knowledge graph; Kawasaki Disease; Semantic technology
AB Kawasaki Disease is a vasculitis syndrome that is extremely harmful to children. Kawasaki Disease can cause severe symptoms of ischemic heart disease or develop into ischemic heart disease, leading to death in children. Researchers and clinicians need to analyze various knowledge and data resources to explore aspects of Kawasaki Disease. Knowledge Graphs have become an important AI approach to integrating various types of complex knowledge and data resources. In this paper, we present an approach for the construction of Knowledge Graphs of Kawasaki Disease. It integrates a wide range of knowledge resources related to Kawasaki Disease, including clinical guidelines, clinical trials, drug knowledge bases, medical literature, and others. It provides a basic integration foundation of knowledge and data concerning Kawasaki Disease for clinical study. In this paper, we will show that this disease-specific Knowledge Graphs are useful for exploring various aspects of Kawasaki Disease.
C1 [Huang, Zhisheng; Hu, Qing] Vrije Univ Amsterdam, Knowledge Representat & Reasoning KR&R Grp, Amsterdam, Netherlands.
   [Huang, Zhisheng; Hu, Qing] Wuhan Univ Sci & Technol, Sch Comp Sci & Engn, Wuhan, Peoples R China.
   [Huang, Zhisheng; Hu, Qing] Ztone Int BV, Purmerend, Netherlands.
   [Liao, Mingqun] Ztone Fujian, Fuzhou, Peoples R China.
   [Miao, Cong; Wang, Chengyi; Liu, Guanghua] Fujian Med Univ, Affiliated Hosp, Fujian Prov Matern & Childrens Hosp, Fuzhou, Peoples R China.
   [Wang, Chengyi] Engn Res Ctr Med Data Min & Applicat Fujian, Xiamen, Fujian, Peoples R China.
RP Huang, ZS (通讯作者)，Ztone Int BV, Purmerend, Netherlands.; Miao, C; Liu, GH (通讯作者)，Fujian Med Univ, Affiliated Hosp, Fujian Prov Matern & Childrens Hosp, Fuzhou, Peoples R China.
EM z.huang@vu.nl; 68484088@qq.com; liugh1962815@hotmail.com
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NR 16
TC 1
Z9 1
U1 21
U2 42
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2047-2501
J9 HEALTH INF SCI SYST
JI Health Inf. Sci. Syst.
PD FEB 27
PY 2021
VL 9
IS 1
AR 11
DI 10.1007/s13755-020-00130-8
PG 8
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA QN8NS
UT WOS:000622709500001
PM 33680447
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Yu, B
   Zhou, CY
   Zhang, C
   Wang, GD
   Fan, YM
AF Yu, Bin
   Zhou, Chenyu
   Zhang, Chen
   Wang, Guodong
   Fan, Yiming
TI A Privacy-Preserving Multi-Task Framework for Knowledge Graph Enhanced
   Recommendation
SO IEEE ACCESS
LA English
DT Article
DE Recommendation system; differential privacy; multi-task learning
AB Multi-task learning (MTL) is a learning paradigm which can improve generalization performance by transferring knowledge among multiple tasks. Traditional collaborative filtering recommendation methods suffer from cold start, sparsity and scalability problems. The latest research has shown that applying side information of knowledge graph can not only solve the problems above, but also improve the accuracy of recommendation. However, existing multi-task methods for knowledge graph enhanced recommendation expose obvious issues of disclosing the private information of training samples. In order to solve these problems, we put forward a privacy-preserving multi-task framework for knowledge graph enhanced recommendation. In specific, Laplacian noise is added into the recommendation module to guarantee the privacy of sensitive data and knowledge graph is utilized to improve the accuracy of recommendation. Extensive experimental results on three datasets demonstrate that the proposed method can not only preserve the privacy of sensitive training data, but also have little effect on the prediction accuracy of the model.
C1 [Yu, Bin; Zhou, Chenyu; Zhang, Chen; Wang, Guodong; Fan, Yiming] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China.
RP Zhang, C (通讯作者)，Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China.
EM zhangc@xidian.edu.cn
FU Key Research and Development Program of Shaanxi Province
   [2019ZDLGY17-01, 2019GY-042]
FX This work was supported by the Key Research and Development Program of
   Shaanxi Province under Grant 2019ZDLGY17-01 and Grant 2019GY-042.
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NR 42
TC 3
Z9 3
U1 4
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 2020
VL 8
BP 115717
EP 115727
DI 10.1109/ACCESS.2020.3004250
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MI6DW
UT WOS:000547497000001
OA gold
DA 2022-08-23
ER

PT J
AU Wilcke, WX
   de Boer, V
   de Kleijn, MTM
   van Harmelen, FAH
   Scholten, HJ
AF Wilcke, W. X.
   de Boer, V
   de Kleijn, M. T. M.
   van Harmelen, F. A. H.
   Scholten, H. J.
TI User-centric pattern mining on knowledge graphs: An archaeological case
   study
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE User-centric; Pattern mining; Generalized association rules; Knowledge
   graphs; Digital humanities; Archaeology
ID ASSOCIATION RULES; DISCOVERY
AB In recent years, there has been a growing interest from the digital humanities in knowledge graphs as data modelling paradigm. Already, this has led to the creation of many such knowledge graphs, many of which are now available as part of the Linked Open Data cloud. This presents new opportunities for data mining. In this work, we develop, implement, and evaluate (both data-driven and user-driven) an end-to-end pipeline for user-centric pattern mining on knowledge graphs in the humanities. This pipeline combines constrained generalized association rule mining with natural language output and facet rule browsing to allow for transparency and interpretability-two key domain requirements. Experiments in the archaeological domain show that domain experts were positively surprised by the range of patterns that were discovered and were overall optimistic about the future potential of this approach. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Wilcke, W. X.; de Kleijn, M. T. M.; Scholten, H. J.] Vrije Univ, Dept Spatial Econ, Amsterdam, Netherlands.
   [Wilcke, W. X.; de Boer, V; van Harmelen, F. A. H.] Vrije Univ, Dept Comp Sci, Amsterdam, Netherlands.
RP Wilcke, WX (通讯作者)，Vrije Univ, Dept Comp Sci, Amsterdam, Netherlands.
EM w.x.wilcke@vu.nl; v.de.boer@vu.nl; mtm.de.kleijn@vu.nl;
   frank.van.harmelen@vu.nl; h.j.scholten@vu.nl
OI Wilcke, Xander/0000-0003-2415-8438; de Boer, Victor/0000-0001-9079-039X;
   van Harmelen, Frank/0000-0002-7913-0048
FU ARIADNE project through the European Commission under the Community's
   Seventh Framework Programme [FP7-INFRASTRUCTURES2012-1-313193]
FX We wish to express our deep gratitude to domain experts Milco Wansleeben
   and Rein van 't Veer for their enthusiastic encouragement and useful
   critiques during the various steps that have lead to this work. We also
   wish to thank all domain experts who participated in our survey for
   their willingness to sacrifice their free time, and without whom we
   would not have been able to complete this research. This research has
   been partially funded by the ARIADNE project through the European
   Commission under the Community's Seventh Framework Programme, contract
   no. FP7-INFRASTRUCTURES2012-1-313193.
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NR 31
TC 2
Z9 2
U1 2
U2 21
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 100486
DI 10.1016/j.websem.2018.12.004
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 JT0RM
UT WOS:000500706800009
OA Green Published
DA 2022-08-23
ER

PT J
AU Ye, N
   Kueh, TB
   Hou, LS
   Liu, YX
   Yu, H
AF Ye, Nan
   Kueh, Tung-Boon
   Hou, Lisong
   Liu, Yongxin
   Yu, Hang
TI A bibliometric analysis of corporate social responsibility in
   sustainable development
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Article
DE Corporate social responsibility; Sustainable development; Knowledge map;
   Bibliometric analysis; CiteSpace
ID SUPPLY CHAIN MANAGEMENT; CONCEPTUAL-FRAMEWORK; INTERNATIONAL-BUSINESS;
   STRATEGIC MANAGEMENT; CSR; PERSPECTIVE; STAKEHOLDERS; PERFORMANCE;
   EVOLUTION; INDUSTRY
AB The involvement of corporate social responsibility (CSR) in sustainable development (SD) is becoming a popular topic on research and business domain. However, the co-themed research is still rather new and hasn't been fully studied. An in-depth bibliometric analysis using the 'CiteSpace' software is applied to analyze and visualize the knowledge map of the CSR research related to SD. Main findings show that the CSR involvement in SD is a lasting but recent prosperous research topic. The top 3 influential journals in this area are Corporate Social Responsibility and Environment Management; Sustainability; and Journal of Cleaner Production. Porter ME, Carroll AB, etc., are the most impactful authors. The co-author network is fragmented, while cross-national co-operations occur in groups. 11 clusters are identified to be highly concerned, among which, "stakeholder" and "NGO" are long lasting till now. 13 burst terms has changed over 15 years (2005-2019) indicated the research frontiers evolution in this field, with the earliest "sustainability" to "strategy", "performance" and then "stakeholder", "developing country", "disclosure" and "supply chain management", etc., and "climate change" being the newest but strongest. Four stages of the evolution can be identified: initial phase (1997-2004), debating phase (2005-20 09), rapid developing phase (2010-2013), and research specialization phase (2014-2019). Finally, contributions, limitations and further research directions are discussed. (c) 2020 Elsevier Ltd. All rights reserved.
C1 [Ye, Nan; Kueh, Tung-Boon; Hou, Lisong; Liu, Yongxin; Yu, Hang] Jiangsu Normal Univ, Business Sch, Dept Mkt, 101 Shanghai Rd, Xuzhou 221116, Jiangsu, Peoples R China.
RP Ye, N; Kueh, TB (通讯作者)，Jiangsu Normal Univ, Business Sch, Dept Mkt, 101 Shanghai Rd, Xuzhou 221116, Jiangsu, Peoples R China.
EM nye@jsnu.edu.cn; seath87@yahoo.com; abc@jsnu.edu.cn; lyx151@126.com;
   1872567@qq.com
FU National Natural Science Foundation of China [71502072]; National social
   science foundation of China [16BJY073, 18BJY034]; Jiangsu Province
   Postgraduate Research and Practice Innovation Project [KYCX18_2067]
FX This work was financially supported by the National Natural Science
   Foundation of China (71502072), National social science foundation of
   China (16BJY073, 18BJY034), and Jiangsu Province Postgraduate Research
   and Practice Innovation Project (No. KYCX18_2067). The authors would
   like to extend special thanks to the editors and three anonymous
   reviewers for their constructive comments and suggestions for improving
   the quality of this study. They also appreciate Joseph Atherley for his
   revisions on the language of this article.
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NR 72
TC 61
Z9 63
U1 42
U2 223
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 NOV 1
PY 2020
VL 272
AR 122679
DI 10.1016/j.jclepro.2020.122679
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 NR8PU
UT WOS:000571823200014
DA 2022-08-23
ER

PT J
AU Guo, S
   Niu, GC
   Wang, ZW
   Pun, MO
   Yang, K
AF Guo, Sheng
   Niu, Guanchong
   Wang, Zewei
   Pun, Man-On
   Yang, Kai
TI An Indoor Knowledge Graph Framework for Efficient Pedestrian
   Localization
SO IEEE SENSORS JOURNAL
LA English
DT Article
DE Ontologies; Wireless fidelity; Semantics; Indoor environments; Magnetic
   sensors; Indoor navigation; sensor fusion; knowledge representation
AB A large amount of information including indoor spatial structure information and smartphone sensor information can be utilized for indoor localization. However, it remains an open challenge to efficiently organize and effectively integrate this data to achieve accurate indoor localization. Knowledge Graphs have powerful intuitive data representation capabilities, semantic and relational processing capabilities, and efficient data storage and feedback mechanisms. Introducing Knowledge Graphs to indoor localization can effectively improve positioning accuracy and subsequently, enrich indoor location-based services. This article presents a Knowledge Graph framework that integrates the basic structure of the indoor environment and various types of smartphone sensing data for indoor localization. This framework consists of two sections: indoor space ontology and mobile sensing data. The indoor space ontology expresses the indoor spatial structure and relationship data, while the sensor sensing data includes a large amount of sensor information generated by pedestrians during indoor activities. Experimental results confirm that the proposed Knowledge Graph framework can achieve efficient indoor localization with good scalability and flexibility under various indoor circumstances.
C1 [Guo, Sheng; Niu, Guanchong; Wang, Zewei; Pun, Man-On] Chinese Univ Hong Kong Shenzhen, Sch Sci & Engn, Shenzhen 518172, Peoples R China.
   [Guo, Sheng; Niu, Guanchong; Pun, Man-On] Chinese Univ Hong Kong Shenzhen, Shenzhen Key Lab IoT Intelligent Syst & Wireless, Shenzhen 518172, Peoples R China.
   [Niu, Guanchong; Pun, Man-On] Chinese Univ Hong Kong Shenzhen, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China.
   [Yang, Kai] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.
RP Pun, MO (通讯作者)，Chinese Univ Hong Kong Shenzhen, Sch Sci & Engn, Shenzhen 518172, Peoples R China.
EM simonpun@cuhk.edu.cn
RI cao, xiaoxiang/AAR-9291-2021
OI PUN, Man-On/0000-0003-3316-5381
FU Shenzhen Science and Technology Innovation Committee
   [ZDSYS20170725140921348, JCYJ20190813170803617]; National Natural
   Science Foundation of China [61731018]
FX This work was supported in part by the Shenzhen Science and Technology
   Innovation Committee under Grant ZDSYS20170725140921348 and Grant
   JCYJ20190813170803617 and in part by the National Natural Science
   Foundation of China under Grant 61731018.
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NR 30
TC 4
Z9 4
U1 11
U2 33
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 FEB 15
PY 2021
VL 21
IS 4
BP 5151
EP 5163
DI 10.1109/JSEN.2020.3029098
PG 13
WC Engineering, Electrical & Electronic; Instruments & Instrumentation;
   Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Instruments & Instrumentation; Physics
GA PX1OT
UT WOS:000611133100122
DA 2022-08-23
ER

PT J
AU Amador-Dominguez, E
   Serrano, E
   Manrique, D
   De Paz, JF
AF Amador-Dominguez, Elvira
   Serrano, Emilio
   Manrique, Daniel
   De Paz, Juan F.
TI Prediction and Decision-Making in Intelligent Environments Supported by
   Knowledge Graphs, A Systematic Review
SO SENSORS
LA English
DT Review
DE knowledge base; knowledge graph; intelligent environment; ambient
   intelligence; reasoning model; knowledge graph embedding
ID ONTOLOGY; GENERATION
AB Ambient Intelligence is currently a lively application domain of Artificial Intelligence and has become the central subject of multiple initiatives worldwide. Several approaches inside this domain make use of knowledge bases or knowledge graphs, both previously existing and ad hoc. This form of representation allows heterogeneous data gathered from diverse sources to be contextualized and combined to create relevant information for intelligent systems, usually following higher level constraints defined by an ontology. In this work, we conduct a systematic review of the existing usages of knowledge bases in intelligent environments, as well as an in-depth study of the predictive and decision-making models employed. Finally, we present a use case for smart homes and illustrate the use and advantages of Knowledge Graph Embeddings in this context.
C1 [Amador-Dominguez, Elvira; Serrano, Emilio] Univ Politecn Madrid, ETSI Informat, Dept Artificial Intelligence, Ontol Engn Grp, E-28660 Madrid, Spain.
   [Manrique, Daniel] Univ Politecn Madrid, ETSI Informat, Dept Artificial Intelligence, Artificial Intelligence Lab, E-28660 Madrid, Spain.
   [De Paz, Juan F.] Univ Salamanca, Fac Sci, Expert Syst & Applicat Lab, Salamanca 37007, Spain.
RP Serrano, E (通讯作者)，Univ Politecn Madrid, ETSI Informat, Dept Artificial Intelligence, Ontol Engn Grp, E-28660 Madrid, Spain.
EM eamador@fi.upm.es; emilioserra@fi.upm.es; dmanrique@fi.upm.es;
   fcofds@usal.es
RI De Paz, Juan F./C-7040-2009; Amador-Domínguez, Elvira/AAR-8801-2020;
   Manrique, Daniel/F-9625-2013; Serrano, Emilio/AAH-7093-2019; Manrique,
   Daniel/N-8806-2019
OI De Paz, Juan F./0000-0001-9461-7922; Amador-Domínguez,
   Elvira/0000-0001-6838-1266; Manrique, Daniel/0000-0002-0792-4156;
   Serrano, Emilio/0000-0001-7587-0703; Manrique,
   Daniel/0000-0002-0792-4156
FU Spanish Ministry of Economy, Industry and Competitiveness under the R&D
   project Datos 4.0: Retos y soluciones (AEI/FEDER, UE)
   [TIN2016-78011-C4-4-R]; "Universidad Politecnica de Madrid" under the
   program "Ayudas para Contratos Predoctorales para la Realizacion del
   Doctorado"
FX This research work is supported by the Spanish Ministry of Economy,
   Industry and Competitiveness under the R&D project Datos 4.0: Retos y
   soluciones (TIN2016-78011-C4-4-R, AEI/FEDER, UE); and by the
   "Universidad Politecnica de Madrid" under the program "Ayudas para
   Contratos Predoctorales para la Realizacion del Doctorado".
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NR 79
TC 3
Z9 3
U1 25
U2 80
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD APR 2
PY 2019
VL 19
IS 8
AR 1774
DI 10.3390/s19081774
PG 21
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA HX8GO
UT WOS:000467644500028
PM 31013899
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, QJ
   Wang, RG
   Yang, J
   Xue, LX
AF Zhang, Qianjin
   Wang, Ronggui
   Yang, Juan
   Xue, Lixia
TI Structural context-based knowledge graph embedding for link prediction
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Link prediction; Relational
   structure-context; Edge structure-context
AB Knowledge graph embedding, which aims to address the limitation of symbolic representation of knowledge, has become an effective method for many AI downstream tasks, such as relation extraction, question answering. Existing knowledge graph embedding models mainly consider triples individually, and ignore the structural information connected with other entities. However, the connectivity between entities not only provides explicit structural information represented in triples, but also embodies a lot of implicit structure information. In this paper, a new knowledge graph embedding model is proposed, which can capture both the information of relational structure-context and edge structure-context by two-interaction. In addition, in order to model complex relations, we define different score function for different relation types. Moreover, the four relation connectivity types in knowledge graph (i.e. symmetry/antisymmetry, inversion, and composition) also can be modeled and inferred by StructurE. We evaluate our StructurE for knowledge graph link prediction task. Benefiting from the structural context and the relation-type-specific score function, compared with conventional geometric transformation based knowledge graph embedding models StructurE achieves state-of-the-art results for link prediction. Moreover, compared with GCN-based models StructurE also achieves state-of-the-art results on more challenging dataset WN18RR which contains more symmetric relations. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Zhang, Qianjin; Wang, Ronggui; Yang, Juan; Xue, Lixia] Hefei Univ Technol, Sch Comp & Informat, Hefei 230601, Peoples R China.
RP Yang, J (通讯作者)，Hefei Univ Technol, Sch Comp & Informat, Hefei 230601, Peoples R China.
EM yangjuan_a1002@126.com
OI Zhang, Qianjin/0000-0002-1321-4329
FU National Key R&D Program of China [2020YFC1512601]
FX This work is supported by the National Key R&D Program of China under
   Grant No. 2020YFC1512601.
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NR 39
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PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JAN 22
PY 2022
VL 470
BP 109
EP 120
DI 10.1016/j.neucom.2021.10.088
EA NOV 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XC9BK
UT WOS:000722305600010
DA 2022-08-23
ER

PT J
AU Stylianou, N
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   Bassiliades, N
   Peristeras, V
AF Stylianou, Nikolaos
   Vlachava, Danai
   Konstantinidis, Ioannis
   Bassiliades, Nick
   Peristeras, Vassilios
TI Doc2KG: Transforming Document Repositories to Knowledge Graphs
SO INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS
LA English
DT Article
DE eGovernment; Government Portals; Linked Data; Machine Learning; Natural
   Language Processing; Open Data; Semantic Web
ID FRAMEWORK
AB Document management systems (DMS) have been used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information enriched with metadata to support searchability. However, this exhibits limitations as it treats textual information as a black box and is based exclusively on user-created metadata, a process that suffers from quality and completeness shortcomings. The use of knowledge graphs in DMS can substantially improve searchability, providing the ability to link data and enabling semantic searching. Recent approaches focus on either creating knowledge graphs from document collections or updating existing ones. In this paper, the authors introduce Doc2KG (Document-to-Knowledge-Graph), an intelligent framework that handles both creation and real-time updating of a knowledge graph, while also exploiting domain-specific ontology standards. They use DIAVGEIA (clarity), an award-winning Greek open government portal, as the case-study and discuss new capabilities for the portal by implementing Doc2KG.
C1 [Stylianou, Nikolaos; Bassiliades, Nick] Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki, Greece.
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   [Peristeras, Vassilios] Int Hellen Univ, Sch Sci & Technol Thessaloniki, Thessaloniki, Greece.
RP Stylianou, N (通讯作者)，Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki, Greece.
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NR 56
TC 0
Z9 0
U1 1
U2 1
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1552-6283
EI 1552-6291
J9 INT J SEMANT WEB INF
JI Int. J. Semant. Web Inf. Syst.
PY 2022
VL 18
IS 1
DI 10.4018/IJSWIS.295552
PG 20
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3G6ZO
UT WOS:000831500300011
OA gold
DA 2022-08-23
ER

PT J
AU Xiong, F
   Wang, HZ
AF Xiong, Feng
   Wang, Hongzhi
TI Mining Simple Path Traversal Patterns in Knowledge Graph
SO JOURNAL OF WEB ENGINEERING
LA English
DT Article
DE Knowledge graph; path traversal; data mining
ID SEQUENTIAL PATTERNS; FREQUENT; CONSTRUCTION; BEHAVIOR
AB The data mining has remained a subject of unfailing charm for research. The knowledge graph is rising and showing infinite life force and strong developing potential in recent years, where it is observed that acyclic knowledge graph has capacity for enhancing usability. Though the development of knowledge graphs has provided an ample scope for appearing the abilities of data mining, related researches are still insufficient. In this paper, we introduce path traversal patterns mining to knowledge graph. We design a novel simple path traversal pattern mining framework for improving the representativeness of result. A divide-and-conquer approach of combining each path is proposed to discover the most frequent traversal patterns in knowledge graph. To support the algorithm, we design a linked list structure indexed by the length of sequences with handy operations. The correctness of algorithm is proven. Experiments show that our algorithm reaches a high coverage with low output amounts compared to existing frequent sequence mining algorithms.
C1 [Xiong, Feng; Wang, Hongzhi] Harbin Inst Technol, Dept Comp Sci, Harbin, Peoples R China.
RP Wang, HZ (通讯作者)，Harbin Inst Technol, Dept Comp Sci, Harbin, Peoples R China.
EM fxiong@hit.edu.cn; wangzh@hit.edu.cn
FU NSFC [U1866602]; CCF-Huawei Database System Innovation Research Plan
   [CCF-HuaweiDBIR2020007B]
FX This paper was supported by NSFC grant U1866602. CCF-Huawei Database
   System Innovation Research Plan CCF-HuaweiDBIR2020007B.
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NR 41
TC 0
Z9 0
U1 5
U2 5
PU RIVER PUBLISHERS
PI GISTRUP
PA ALSBJERGVEJ 10, GISTRUP, 9260, DENMARK
SN 1540-9589
EI 1544-5976
J9 J WEB ENG
JI J. Web Eng.
PY 2022
VL 21
IS 2
BP 307
EP 336
DI 10.13052/jwe1540-9589.2128
PG 30
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YY6ZI
UT WOS:000754935900008
OA Bronze
DA 2022-08-23
ER

PT J
AU Chen, JF
   Xu, J
   Bo, MH
   Tang, HW
AF Chen, Junfan
   Xu, Jie
   Bo, Manhui
   Tang, Hongwu
TI Augmenting Embedding Projection With Entity Descriptions for Knowledge
   Graph Completion
SO IEEE ACCESS
LA English
DT Article
DE Task analysis; Electronic mail; Predictive models; Licenses; Urban
   areas; Solid modeling; Probabilistic logic; Knowledge graph completion;
   knowledge graph embedding; text representation; attention
AB Extra information, such as hierarchical entity types, entity descriptions or some text corpus are recently used to enhance Knowledge Graph Completion (KGC). A typical task in this setting is building entities' description information into some embedding models. Existing approaches under this task usually use simple embedding models, which have difficulty in handling the complex structures of the knowledge graphs. These models are also limited in the way where description representation is combined with structure representation, which requires an impractical large set of weight parameters increasing in proportion to the number of entities in the knowledge graph. This paper aims at developing more effective embedding models that jointly represent the structure information of the knowledge base with the description of entities and efficiently reduce the model parameters. We propose more principled approaches named Dimensional Attentive Combination (DAC) for the composition of structure representation and description representation with fixed-size parameters independent of entity amount, and the composition builds upon more powerful knowledge graph embedding models. The proposed model significantly reduces the weight parameters and can extend to KGs with a large set of entities or involving sparse data. Experimental comparison on link prediction and relation prediction shows that our approaches, even under a simple description-encoding model, improve upon the baselines by a significant margin.
C1 [Chen, Junfan] Beihang Univ, Sch Comp Sci & Engn, Beijing 100083, Peoples R China.
   [Xu, Jie] Univ Leeds, Sch Comp, Leeds LS2 9JT, W Yorkshire, England.
   [Bo, Manhui; Tang, Hongwu] Travelsky Mobile Technol Ltd, Beijing 100029, Peoples R China.
RP Chen, JF (通讯作者)，Beihang Univ, Sch Comp Sci & Engn, Beijing 100083, Peoples R China.
EM chenjf@act.buaa.edu.cn
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   Xiao H, 2017, AAAI CONF ARTIF INTE, P3104
   Xiao H, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2316
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xie RB, 2016, AAAI CONF ARTIF INTE, P2659
   Xiong W., 2017, P 2017 C EMPIRICAL M, P564
   Xu JC, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1318
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NR 44
TC 0
Z9 0
U1 2
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 159955
EP 159964
DI 10.1109/ACCESS.2021.3132071
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA XM6FM
UT WOS:000728920400001
OA gold
DA 2022-08-23
ER

PT J
AU Shang, FH
   Ding, QY
   Du, RS
   Cao, MJ
   Chen, HY
AF Shang, Fuhua
   Ding, Qiuyu
   Du, Ruishan
   Cao, Maojun
   Chen, Huanyu
TI Construction and Application of the User Behavior Knowledge Graph in
   Software Platforms
SO JOURNAL OF WEB ENGINEERING
LA English
DT Article
DE User behavior knowledge graph; user knowledge extraction; graph
   database; knowledge graph construction
ID MANAGEMENT
AB The analysis of user behavior provides a large amount of useful information. After being extracted, this information is called user knowledge. User knowledge plays a guiding role in implementing user-centric updates for software platforms. A good representation and application of user knowledge can accelerate the development of a software platform and improve its quality. This paper aims to further the utilization of user knowledge by mining the user knowledge that is implicit in user behavior and then constructing a knowledge graph of this behavior. First, the association between a software bug and a software component is mined from the user knowledge. Then, the knowledge entity extraction and relationship extraction are performed from the development code and the user behavior. Finally, the knowledge is stored in the graph database, from which it can be visually retrieved. Relevant experiments on CIFLog, an integrated logging processing software platform, have proved the effectiveness of this research. Constructing a user behavior knowledge graph can improve the utilization of user knowledge as well as the quality of software platform development.
C1 [Shang, Fuhua; Ding, Qiuyu; Du, Ruishan; Cao, Maojun; Chen, Huanyu] Northeast Petr Univ, Sch Comp & Informat Technol, Daqing, Peoples R China.
RP Du, RS (通讯作者)，Northeast Petr Univ, Sch Comp & Informat Technol, Daqing, Peoples R China.
EM ruishan_du@163.com
FU Heilongjiang Provincial Philosophy and Social Science Foundation of
   China [19SHE280]
FX The research described in this paper was fully supported by Heilongjiang
   Provincial Philosophy and Social Science Foundation of China under Grant
   No. 19SHE280.
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NR 25
TC 1
Z9 1
U1 13
U2 23
PU RIVER PUBLISHERS
PI GISTRUP
PA ALSBJERGVEJ 10, GISTRUP, 9260, DENMARK
SN 1540-9589
EI 1544-5976
J9 J WEB ENG
JI J. Web Eng.
PY 2021
VL 20
IS 2
BP 387
EP 411
DI 10.13052/jwe1540-9589.2027
PG 25
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RA0LH
UT WOS:000631110300007
OA Bronze
DA 2022-08-23
ER

PT J
AU Kwon, S
   Monnier, LV
   Barbau, R
   Bernstein, WZ
AF Kwon, Soonjo
   Monnier, Laetitia V.
   Barbau, Raphael
   Bernstein, William Z.
TI Enriching standards-based digital thread by fusing as-designed and
   as-inspected data using knowledge graphs
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Digital thread; Knowledge graph; Ontology; Industrial data standard;
   Product lifecycle; Data fusion
ID PRODUCT; ONTOLOGY; INTEROPERABILITY; ARCHITECTURE; INFORMATION;
   CAD/CAM/CAI; SEMANTICS; FRAMEWORK; EXPRESS; MODELS
AB Realizing the digital thread is essential for linking and orchestrating data across the product lifecycle in smart manufacturing. Linking heterogeneous lifecycle data is critical to maintain associativity and traceability in a digital thread. Recently, researchers have successfully leveraged ontology models with knowledge graphs in engineering domains for threading different lifecycle data. One of the most successful of such efforts is OntoSTEP which enables the formal capture of information embedded in the STandard for Exchange of Product model data (STEP) data representation, or ISO 10303. Meanwhile, an emerging inspection standard, called the Quality Information Framework (QIF), has garnered significant attention as it can bring quality information into the digital thread. Implementing more automated methods for product quality assurance is challenging due to the lack of unified information models from design to inspection. To this end, we propose an approach to fuse as designed data represented in STEP and as-inspected data represented in QIF in a standards-based digital thread based on ontology with knowledge graphs. Specifically, we present an automated pipeline for generating knowledge graphs representing STEP and QIF data, a mapping implementation to integrate STEP and QIF knowledge graphs, and rules and queries to demonstrate the integration's potential for better decision making with respect to product quality assurance.
C1 [Kwon, Soonjo; Monnier, Laetitia V.; Barbau, Raphael; Bernstein, William Z.] NIST, Engn Lab, Gaithersburg, MD 20899 USA.
   [Kwon, Soonjo] Korea Adv Inst Sci & Technol, Daejeon 34141, South Korea.
   [Monnier, Laetitia V.] Univ Bourgogne, Dijon, France.
   [Barbau, Raphael] Engisis LLC, Bethesda, MD 20817 USA.
RP Bernstein, WZ (通讯作者)，NIST, Engn Lab, Gaithersburg, MD 20899 USA.
EM wzb@nist.gov
OI Kwon, Soonjo/0000-0003-1578-9262
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NR 50
TC 17
Z9 17
U1 7
U2 28
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 101102
DI 10.1016/j.aei.2020.101102
PG 12
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA PA9FZ
UT WOS:000595934400002
DA 2022-08-23
ER

PT J
AU Qiao, LJ
AF Qiao, Lijun
TI Teaching Design of Online Ideological and Political Course Based on Deep
   Learning Model Evaluation
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB In practical terms, teachers are supported to use more straightforward teaching methods, such as creating real-life contextual problems, to help students develop deep learning skills. In this paper, using Bayesian theory and Bayesian classifier research methods, a machine learning model was constructed using Python to establish the correspondence between online teaching of civics and high-level semantic features and to achieve computer learning through text and teaching design evaluation research that can identify high-frequency knowledge points. The inter-relationship model knowledge mapping, the accuracy is 90%, and the continuous knowledge update help to improve the model accuracy.
C1 [Qiao, Lijun] Anyang Presch Educ Coll, Teaching Dept Ideol & Polit Theory Course, Anyang 455000, Peoples R China.
RP Qiao, LJ (通讯作者)，Anyang Presch Educ Coll, Teaching Dept Ideol & Polit Theory Course, Anyang 455000, Peoples R China.
EM qiaolijun@ayyz.edu.cn
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NR 25
TC 1
Z9 1
U1 1
U2 1
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 JAN 12
PY 2022
VL 2022
AR 4754972
DI 10.1155/2022/4754972
PG 8
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0Y7SF
UT WOS:000790585900002
OA gold
DA 2022-08-23
ER

PT J
AU Liu, HY
   Zhao, SK
   Xin, O
AF Liu, Hongyu
   Zhao, Shukuan
   Xin, Ouyang
TI Analysis on the Evolution Path and Hotspot of Knowledge Innovation Study
   Based on Knowledge Map
SO SUSTAINABILITY
LA English
DT Article
DE knowledge innovation; CiteSpace IV; knowledge map; evolution path; hot
   topics
ID RESEARCH-AND-DEVELOPMENT; ABSORPTIVE-CAPACITY; PRODUCT DEVELOPMENT;
   MANAGING KNOWLEDGE; MANAGEMENT; NETWORKS; PERFORMANCE; ORGANIZATION;
   INTEGRATION; TECHNOLOGY
AB Innovation is the main driving force for promoting high-quality economic development. The ability for knowledge innovation and knowledge transformation productivity reflects the ability for scientific research transformation and the comprehensive strength of the science and technology of the country. This paper takes the knowledge innovation literature collected in the Web of Science database as its research object, carries on the knowledge atlas analysis with Citespace IV software, makes a knowledge atlas of the space-time distribution, document co-citation and keyword co-occurrence, respectively, and further explores the distribution status, changing trend and hot areas of knowledge innovation research. (1) American and British scholars have led the research in knowledge innovation, and Chinese scholars have followed the international frontier in this field; (2) The research evolution path of knowledge innovation can be summarized as follows: the research object is gradually detailed from the macro-enterprise level to the micro-enterprise level. The research hotspots are as follows: (3) The four hotspots of knowledge innovation research are based on research on the knowledge innovation system within enterprises, research on the knowledge innovation mode based on innovation cooperation among enterprises, and research on the knowledge innovation among enterprises based on inter-organizational knowledge. Research on the knowledge innovation path of knowledge sharing and enterprise knowledge innovation development is based on the global strategic orientation. This paper clarifies the evolution path and development trend of knowledge innovation research, offering a summary for further research and motivating professionals to research future knowledge innovation.
C1 [Liu, Hongyu; Zhao, Shukuan] Jilin Univ, Sch Management, Changchun 130012, Jilin, Peoples R China.
   [Xin, Ouyang] Univ Chinese Acad Social Sci, Sch Econ, Beijing 102488, Peoples R China.
RP Zhao, SK (通讯作者)，Jilin Univ, Sch Management, Changchun 130012, Jilin, Peoples R China.
EM hongyul17@mails.jlu.edu.cn; zhaosk@jlu.edu.cn; ouyangxin@ucass.edu.cn
OI liu, hong yu/0000-0002-5926-3513
FU Construction of Regional Integrated Science and Technology Service
   Ecosystem and Service Mode [2017YFB1401800]
FX This research was funded by Construction of Regional Integrated Science
   and Technology Service Ecosystem and Service Mode, grant number
   2017YFB1401800 and The APC was funded by 2017YFB1401800.
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TC 2
Z9 2
U1 8
U2 43
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD OCT 1
PY 2019
VL 11
IS 19
AR 5528
DI 10.3390/su11195528
PG 14
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA JI5RS
UT WOS:000493525500387
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Liang, L
   Li, Y
   Wen, M
   Liu, Y
AF Liang, Lu
   Li, Yong
   Wen, Ming
   Liu, Ying
TI KG4Py: A toolkit for generating Python knowledge graph and code semantic
   search
SO CONNECTION SCIENCE
LA English
DT Article
DE Knowledge graph; static code analysis; information extraction; deep
   learning; code semantic search
AB In the era of big data, there are numerous duplicate code snippets on the Internet, it is especially necessary to make use of them to build new software projects. In this paper, we present a toolkit (KG4Py) for generating a knowledge graph of Python files in GitHub repositories and conducting semantic search with the knowledge graph. In KG4Py, we remove all duplicate files in 317 K Python files and perform static code analyses of these files by using a concrete syntax tree (CST) to build a code knowledge graph of Python functions. We integrate a pre-trained model with an unsupervised model to generate a new model, and combine this new model with a code knowledge graph for the purpose of searching code snippets with natural language descriptions. The experimental results show that KG4Py achieves good performance in both the construction of the code knowledge graph and the semantic search of code snippets.
C1 [Liang, Lu; Li, Yong; Liu, Ying] Xinjiang Normal Univ, Coll Comp Sci & Technol, Urumqi, Peoples R China.
   [Liang, Lu; Li, Yong; Wen, Ming; Liu, Ying] Xinjiang Elect Res Inst Ltd Share Ltd, Urumqi, Peoples R China.
   [Li, Yong] Nanjing Univ Aeronaut & Astronaut, Key Lab Safety Crit Software, Minist Ind & Informat Technol, Nanjing, Peoples R China.
RP Li, Y (通讯作者)，Xinjiang Normal Univ, Coll Comp Sci & Technol, Urumqi, Peoples R China.; Li, Y (通讯作者)，Xinjiang Elect Res Inst Ltd Share Ltd, Urumqi, Peoples R China.; Li, Y (通讯作者)，Nanjing Univ Aeronaut & Astronaut, Key Lab Safety Crit Software, Minist Ind & Informat Technol, Nanjing, Peoples R China.
EM liyong@live.com
FU Xinjiang Tianshan Youth Project of China [2020Q019]; National Natural
   Science Foundation of China [61562087]; Doctoral Scientific Research
   Foundation of Xinjiang Normal University [XJNUBS1905]
FX This work is supported by the Xinjiang Tianshan Youth Project of China
   [grant number 2020Q019], the National Natural Science Foundation of
   China [grant number 61562087], and the Doctoral Scientific Research
   Foundation of Xinjiang Normal University [grant number XJNUBS1905].
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NR 36
TC 0
Z9 0
U1 9
U2 9
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0954-0091
EI 1360-0494
J9 CONNECT SCI
JI Connect. Sci.
PD DEC 31
PY 2022
VL 34
IS 1
BP 1384
EP 1400
DI 10.1080/09540091.2022.2072471
PG 17
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1D1NR
UT WOS:000793575200001
OA gold
DA 2022-08-23
ER

PT J
AU Djenouri, Y
   Srivastava, G
   Belhadi, A
   Lin, JCW
AF Djenouri, Youcef
   Srivastava, Gautam
   Belhadi, Asma
   Lin, Jerry Chun-Wei
TI Intelligent blockchain management for distributed knowledge graphs in
   IoT 5G environments
SO TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES
LA English
DT Article; Early Access
ID INDUSTRIAL INTERNET; ONTOLOGY; THINGS; SYSTEMS
AB This article introduces a new problem of distributed knowledge graph, in IoT 5G setting. We developed an end-to-end solution for solving such problem by exploring the blockchain management and intelligent method for producing the better matching of the concepts and relations of the set of knowledge graphs. The concepts and the relations of the knowledge graphs are divided into several components, each of which contains similar concepts and relations. Instead of exploring the whole concepts and the relations of the knowledge graphs, only the representative of these components is compared during the matching process. The framework has outperformed state-of-the-art knowledge graph matching algorithms using different scenarios as input in the experiments. In addition, to confirm the usability of our suggested framework, an in-depth experimental analysis has been done; the results are very promising in both runtime and accuracy.
C1 [Djenouri, Youcef] SINTEF Digital, Oslo, Norway.
   [Srivastava, Gautam] Brandon Univ, Brandon, MB, Canada.
   [Belhadi, Asma] Kristiania Univ Coll, Oslo, Norway.
   [Lin, Jerry Chun-Wei] Western Norway Univ Appl Sci, Bergen, Norway.
RP Lin, JCW (通讯作者)，Western Norway Univ Appl Sci, Bergen, Norway.
EM jerrylin@ieee.org
OI Lin, Jerry Chun-Wei/0000-0001-8768-9709; Srivastava,
   Gautam/0000-0001-9851-4103; Djenouri, Youcef/0000-0003-0135-7450
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NR 50
TC 13
Z9 13
U1 6
U2 14
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2161-3915
J9 T EMERG TELECOMMUN T
JI Trans. Emerg. Telecommun. Technol.
AR e4332
DI 10.1002/ett.4332
EA JUL 2021
PG 13
WC Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Telecommunications
GA TR5XX
UT WOS:000679038300001
OA hybrid, Green Published
DA 2022-08-23
ER

PT S
AU Kashihara, A
   Kumei, K
   Hasegawa, S
   Toyoda, J
AF Kashihara, A
   Kumei, K
   Hasegawa, S
   Toyoda, J
BE Cerri, SA
   Gouarderes, G
   Paraguacu, F
TI Adaptive postviewer for constructive learning in hyperspace
SO INTELLIGENT TUTORING SYSTEMS
SE LECTURE NOTES IN COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 6th International Conference on Intelligent Tutoring Systems
CY JUN 02-07, 2002
CL SAN SEBASTIAN, SPAIN
SP Assoc Comp Machinery, Int Federat Informat Processing, Artificial Intelligence Educ Soc, IEEE CS Learning Technol Task Force, French Direct Gen Armement, Asociac Espanola Inteligencia Artificial
AB Self-directed learning in hyperspace requires learners to monitor their navigation process involving knowledge construction, which they have carried out so far, since what and how they have learned becomes hazy as the navigation progresses. Although the reflective monitoring is an important activity in the self-directed learning, it is hard for them to keep it during navigating pages. The main issue addressed here is how to support the reflective monitoring. Our approach to this issue is to provide learners with the postviews of their knowledge construction adapted to their navigation process. Following the idea of adaptive postviewing, we have proposed an adaptive postviewer for self-directed teaming with existing web-based resources. It generates page postviews and knowledge map, which display information suitable for their reflective monitoring to promote the constructive learning in hyperspace.
C1 Osaka Univ, Inst Sci & Ind Res, Ibaraki, Osaka 5670047, Japan.
RP Kashihara, A (通讯作者)，Osaka Univ, Inst Sci & Ind Res, 8-1 Mihogaoka, Ibaraki, Osaka 5670047, Japan.
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NR 8
TC 0
Z9 0
U1 0
U2 2
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-43750-9
J9 LECT NOTES COMPUT SC
PY 2002
VL 2363
BP 219
EP 228
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BV79M
UT WOS:000180067900026
DA 2022-08-23
ER

PT J
AU Kurbatova, N
   Swiers, R
AF Kurbatova, Natalja
   Swiers, Rowan
TI Disease ontologies for knowledge graphs
SO BMC BIOINFORMATICS
LA English
DT Article
DE Ontologies; Knowledge graph; Data integration
AB BackgroundData integration to build a biomedical knowledge graph is a challenging task. There are multiple disease ontologies used in data sources and publications, each having its hierarchy. A common task is to map between ontologies, find disease clusters and finally build a representation of the chosen disease area. There is a shortage of published resources and tools to facilitate interactive, efficient and flexible cross-referencing and analysis of multiple disease ontologies commonly found in data sources and research.ResultsOur results are represented as a knowledge graph solution that uses disease ontology cross-references and facilitates switching between ontology hierarchies for data integration and other tasks.ConclusionsGrakn core with pre-installed "Disease ontologies for knowledge graphs" facilitates the biomedical knowledge graph build and provides an elegant solution for the multiple disease ontologies problem.
C1 [Kurbatova, Natalja] AstraZeneca, R&D, Data Infrastruct & Tools Data Sci & Artificial In, Cambridge, England.
   [Swiers, Rowan] AstraZeneca, BioPharmaceut R&D, Quantitat Biol, Cambridge, England.
RP Kurbatova, N (通讯作者)，AstraZeneca, R&D, Data Infrastruct & Tools Data Sci & Artificial In, Cambridge, England.
EM natalie.kurbatova@astrazeneca.com
OI Kurbatova, Natalja/0000-0002-3973-2909
FU AstraZeneca
FX The work was supported by AstraZeneca internal funding. The funding body
   did not play any roles in the design of the study and collection,
   analysis, and interpretation of data and in writing the manuscript.
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NR 22
TC 0
Z9 0
U1 27
U2 51
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD JUL 21
PY 2021
VL 22
IS 1
AR 377
DI 10.1186/s12859-021-04173-w
PG 7
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 TP5AP
UT WOS:000677610700001
PM 34289807
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Sun, WJ
   Huang, PX
   Song, HL
   Feng, DX
AF Sun, Wenjie
   Huang, Pinxian
   Song, Hualing
   Feng, Dianxu
TI Bibliometric analysis of acute pancreatitis in Web of Science database
   based on CiteSpace software
SO MEDICINE
LA English
DT Review
DE acute pancreatitis; CiteSpace; knowledge map; web of science
ID EMERGING TRENDS; PATTERNS
AB Objectives: Recent years have witnessed some controversial viewpoints in clinical and basic research, which exert a great influence on the research trend of acute pancreatitis (AP). We aimed to analyze the literature on AP by metrology, co-occurrence, co-citation, and visualization, and to explore the research status and trend in this field in the past 5 years. Methods: The relevant literature collected in Web of Science Core Collection (WoSCC) database from 2015 to 2019 was searched using "acute pancreatitis" as the title word, and the co-occurrence analysis of authors, institutions, countries, and keywords was carried out by using CiteSpace V. On this basis, the keywords were clustered and analyzed by using VOSviewer 1.6.8 and Carrot 2 software, and a visual map was drawn. Results: A total of 2035 articles were included, with an average annual volume of more than 400. The high-yield authors were mainly Chinese, among which Li WQ was the most prominent. Most of these articles were from universities and institutions of high-yielding countries including China, the United States, and India. The main sources of journals were professional journals, among which Pancreas and Pancreatology have the most literature collection volume (both over 100), including clinical and basic research. Among the funds, the National Natural Science Foundation of China and NIH were the main 2 sponsors. Disciplinary attributes involved multiple subjects such as gastroenterology, internal medicine, and surgery. Keyword co-occurrence and clustering results showed that the classification, mortality, and risk factors of AP were still more concerned, and the research trend of this disease was the molecular mechanism of the severity of AP. Conclusions: CiteSpace can be used to analyze the knowledge graph of AP, to show its development status initially and intuitively, and to provide a reference for topic content and its further development.
C1 [Sun, Wenjie; Huang, Pinxian; Song, Hualing] Shanghai Univ Tradit Chinese Med, Shanghai 201203, Peoples R China.
   [Sun, Wenjie; Feng, Dianxu] Shanghai Univ Tradit Chinese Med, Putuo Hosp, Dept Gen Surg, Shanghai, Peoples R China.
   [Sun, Wenjie] Xiamen Univ, Affiliated Hosp 1, Xiamen, Peoples R China.
RP Song, HL (通讯作者)，Shanghai Univ Tradit Chinese Med, Shanghai 201203, Peoples R China.
EM hsong@shutcm.edu.cn
FU National Natural Science Foundation of China [81673789]; Continuing
   Education Course Construction Project of Shanghai University of
   Traditional Chinese Medicine, Medical Statistics Network Course
   Construction [SHUTCMKCJSZD2018165]; 17th course construction of Shanghai
   University of Traditional Chinese Medicine [SHUTCMKCJSZD2018057]
FX Funded by National Natural Science Foundation of China (81673789),
   Continuing Education Course Construction Project of Shanghai University
   of Traditional Chinese Medicine, Medical Statistics Network Course
   Construction (SHUTCMKCJSZD2018165), and the 17th course construction of
   Shanghai University of Traditional Chinese Medicine
   (SHUTCMKCJSZD2018057).
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NR 40
TC 6
Z9 7
U1 33
U2 85
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 DEC 4
PY 2020
VL 99
IS 49
AR e23208
DI 10.1097/MD.0000000000023208
PG 11
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA PZ6GR
UT WOS:000612838500033
PM 33285696
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wu, JJ
AF Wu, Jiajing
TI Construct a Knowledge Graph for China Coronavirus (COVID-19) Patient
   Information Tracking
SO RISK MANAGEMENT AND HEALTHCARE POLICY
LA English
DT Article
DE COVID-19; knowledge graph; Neo4j; CRF; tracking
ID INFECTIOUS-DISEASE SURVEILLANCE; BIG-DATA; SYSTEMS
AB Since first outbreak of respiratory disease in China, the Coronavirus epidemic (COVID-19) spread on a large scale, causing huge losses to individuals, families, communities and society in the country. The conventional research on the transmission process is basically to study the law or trend of the transmission of infectious diseases from a macro perspective. For in-depth study of the critical data of the newly confirmed patients, one effective way to improve the social isolation measures requires the formation of an organized tracking knowledge system for the confirmed patients and the personnel who have been removed, and the deep data mining and application. Knowledge graph (KG) is one of the irreplaceable techniques to quickly gather patient contact information and outbreak event, which reflecting the relationship between knowledge evolution and structure of novel Coronavirus. Therefore, this paper proposes a method for the analysis of COVID-19 epidemic situation using knowledge graph combined with interactive visual analysis. Firstly, based on the key factors of novel Coronavirus disease, the entity model of the patient, the relationship type of the patient and the expression of knowledge modeling were proposed, and the knowledge graph of the action track of the COVID-19 patient was deeply explored and comparative summarized. Secondly, in the process of constructing knowledge graph, conditional random field (CRF) algorithm is used to extract entity and knowledge. Meanwhile, to better analyze the disease relationship between patients, the semantic relationship of knowledge graph was combined with the visualization of knowledge graph, and the semantic model was verified by deep learning calculation and node attribute similarity. To discover the community detection of patients in the patient knowledge graph, this paper uses PageRank combined with Label propagation algorithms to discover community propagation in the network. Finally, COVID-19 epidemic situation was analyzed from confirmed patient data and multi-view collaborative interactions, such as map distribution visualization, knowledge graph visualization, and track visualization. The results show that the construction of a knowledge graph of COVID-19 patient activity is feasible for the transmission process, analysis of key nodes and tracing of activity tracks.
C1 [Wu, Jiajing] Ocean Univ China, Sch Informat Sci & Engn, Qingdao 265100, Peoples R China.
   [Wu, Jiajing] Chifeng Univ, Sch Math & Comp Sci, Chifeng City 024000, Inner Mongolia, Peoples R China.
RP Wu, JJ (通讯作者)，Ocean Univ China, Sch Informat Sci & Engn, Qingdao 265100, Peoples R China.; Wu, JJ (通讯作者)，Chifeng Univ, Sch Math & Comp Sci, Chifeng City 024000, Inner Mongolia, Peoples R China.
EM wujiajing@stu.ouc.edu.cn
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NR 33
TC 1
Z9 1
U1 15
U2 17
PU DOVE MEDICAL PRESS LTD
PI ALBANY
PA PO BOX 300-008, ALBANY, AUCKLAND 0752, NEW ZEALAND
EI 1179-1594
J9 RISK MANAG HEALTHC P
JI RISK MANAG. HEALTHC. POLICY
PY 2021
VL 14
BP 4321
EP 4337
DI 10.2147/RMHP.S309732
PG 17
WC Health Care Sciences & Services; Health Policy & Services
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services
GA ZT4YK
UT WOS:000769163100001
PM 34707418
DA 2022-08-23
ER

PT J
AU Chen, Y
   Mensah, S
   Ma, F
   Wang, H
   Jiang, ZA
AF Chen, Ya
   Mensah, Samuel
   Ma, Fei
   Wang, Hao
   Jiang, Zhongan
TI Collaborative filtering grounded on knowledge graphs
SO PATTERN RECOGNITION LETTERS
LA English
DT Article
DE Collaborative filtering; Knowledge graph; Recommender system
AB Matrix Factorization (MF) is a widely used collaborative filtering technique for effectively modeling a user-item interaction in recommender system. Despite the successful application of MF and its variants, the method proves to be effective only in situations where there is an abundance of user-item interactions. However, user-item interaction data are usually sparse, limiting the effectiveness of the method. In addressing this problem, recent methods have proposed to use knowledge graphs (KGs) as additional information to complement the sparse user-item interaction data. This has proved challenging given the complexity of the KG structure. In this paper, we propose a collaborative filtering method that takes advantage of knowledge graphs. More specifically, the embedding of a user and item are both grounded on the item's attributes in the knowledge graph, and are aggregated with generic user and item representations modeled by MF for implicit recommendation. Our model has demonstrated to outperform the recent state-of-the-art method KGCN [18] in very sparse settings, showing an effective integration of KGs in recommender systems. (c) 2021 Published by Elsevier B.V.
C1 [Chen, Ya; Ma, Fei] Univ Sci & Technol Beijing, Sch Mech Engn, Beijing, Peoples R China.
   [Wang, Hao] Beihang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China.
   [Jiang, Zhongan] Univ Sci & Technol Beijing, Sch Civil & Resource Engn, Beijing, Peoples R China.
   [Mensah, Samuel] Univ Sheffield, Sch Comp Sci, Sheffield, S Yorkshire, England.
RP Ma, F (通讯作者)，Univ Sci & Technol Beijing, Sch Mech Engn, Beijing, Peoples R China.
EM chenya913@sina.com; s.mensah@sheffield.ac.uk; yeke@ustb.edu.cn;
   wanghao@act.buaa.edu.cn; jza1963@263.net
FU Ministry of Science and Technology of the Peoples Republic of China
   [2018YFC0810500]; National Natural Science Foundation of China
   [51774019]; Fundamental Research Funds for the Central Universities
   [FRF-TP-19-045A1]
FX This work is supported by the Ministry of Science and Technology of the
   Peoples Republic of China (No. 2018YFC0810500), the National Natural
   Science Foundation of China (No. 51774019) and the Fundamental Research
   Funds for the Central Universities (FRF-TP-19-045A1).
CR Bai T, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1979, DOI 10.1145/3132847.3133083
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NR 21
TC 5
Z9 5
U1 10
U2 21
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8655
EI 1872-7344
J9 PATTERN RECOGN LETT
JI Pattern Recognit. Lett.
PD NOV
PY 2021
VL 151
BP 55
EP 61
DI 10.1016/j.patrec.2021.07.022
EA AUG 2021
PG 7
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UW6SN
UT WOS:000700283100009
DA 2022-08-23
ER

PT J
AU Minervini, P
   d'Amato, C
   Fanizzi, N
AF Minervini, Pasquale
   d'Amato, Claudia
   Fanizzi, Nicola
TI Efficient energy-based embedding models for link prediction in knowledge
   graphs
SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
LA English
DT Article
DE Energy-based embedding models; Link predictions; RDF knowledge graphs
AB We focus on the problem of link prediction in Knowledge Graphs, with the goal of discovering new facts. To this purpose, Energy-Based Models for Knowledge Graphs that embed entities and relations in continuous vector spaces have been largely used. The main limitation in their applicability lies in the parameter learning phase, which may require a large amount of time for converging to optimal solutions. In this article, we first propose an unified view on different Energy-Based Embedding Models. Hence, for improving the model training phase, we propose the adoption of adaptive learning rates. We show that, by adopting adaptive learning rates during training, we can improve the efficiency of the parameter learning process by an order of magnitude, while leading to more accurate link prediction models in a significantly lower number of iterations. We extensively evaluate the proposed learning procedure on a variety of new models: our result show a significant improvement over state-of-the-art link prediction methods on two large Knowledge Graphs, namely WordNet and Freebase.
C1 [Minervini, Pasquale; d'Amato, Claudia; Fanizzi, Nicola] Univ Bari, Dept Comp Sci, Bari, Italy.
RP d'Amato, C (通讯作者)，Univ Bari, Dept Comp Sci, Bari, Italy.
EM claudia.damato@uniba.it
RI Fanizzi, Nicola/A-4517-2010; d'Amato, Claudia/C-1142-2013
OI Fanizzi, Nicola/0000-0001-5319-7933; d'Amato,
   Claudia/0000-0002-3385-987X
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NR 29
TC 9
Z9 10
U1 0
U2 26
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0925-9902
EI 1573-7675
J9 J INTELL INF SYST
JI J. Intell. Inf. Syst.
PD AUG
PY 2016
VL 47
IS 1
BP 91
EP 109
DI 10.1007/s10844-016-0414-7
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DR9YE
UT WOS:000380251300005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Mao, YM
AF Mao, Yanmei
TI Summary and Evaluation of the Application of Knowledge Graphs in
   Education 2007-2020
SO DISCRETE DYNAMICS IN NATURE AND SOCIETY
LA English
DT Review
AB Since 2007, knowledge graphs, an important research tool, have been applied to education and many other disciplines. This paper firstly overviews the application of knowledge graphs in education and then samples the knowledge graph applications in CSSCI- (Chinese Social Sciences Citation Index-) indexed journals in the past two years. These samples were classified and analyzed in terms of research institute, data source, visualization software, and analysis perspective. Next, the situation of knowledge graph applications in education was summarized and evaluated in detail. Furthermore, the authors discussed and assessed the normalization of knowledge graph applications in education. The results show that in the past 15 years, knowledge graphs have been widely used in education. The academia has reached a consensus on the paradigm of the research tool: examining the hotspots, topics, and trends in the related fields from the angles of keyword cooccurrence network (KCN), time zone map, clustering network, and literature/author cocitation, with the aid of CiteSpace and other visualization software and text analysis. However, there is not yet a thorough understanding of the limitations of the visualization software. The relevant research should be improved in terms of scientific level, normalization level, and quality.
C1 [Mao, Yanmei] Zhejiang Univ, Coll Educ, Hangzhou 310058, Peoples R China.
   [Mao, Yanmei] Zhejiang Business Coll, Int Studies Sch, Hangzhou 310053, Peoples R China.
RP Mao, YM (通讯作者)，Zhejiang Univ, Coll Educ, Hangzhou 310058, Peoples R China.; Mao, YM (通讯作者)，Zhejiang Business Coll, Int Studies Sch, Hangzhou 310053, Peoples R China.
EM 11803018@zju.edu.cn
FU project of China Vocational Education Society in Zhejiang Province
   [ZJCV2021B31]
FX This paper is phased achievement of the project of China Vocational
   Education Society in Zhejiang Province (ZJCV2021B31).
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NR 30
TC 0
Z9 0
U1 34
U2 62
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1026-0226
EI 1607-887X
J9 DISCRETE DYN NAT SOC
JI Discrete Dyn. Nat. Soc.
PD SEP 28
PY 2021
VL 2021
AR 6304109
DI 10.1155/2021/6304109
PG 10
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA WF2PG
UT WOS:000706152200001
OA gold
DA 2022-08-23
ER

PT J
AU ROGERS, E
AF ROGERS, E
TI VIA-RAD - A BLACKBOARD-BASED SYSTEM FOR DIAGNOSTIC-RADIOLOGY
SO ARTIFICIAL INTELLIGENCE IN MEDICINE
LA English
DT Article
DE INTELLIGENT SYSTEMS; BLACKBOARD ARCHITECTURE; DIAGNOSTIC RADIOLOGY;
   COOPERATIVE DECISION-MAKING ASSISTANCE; PERCEPTION AND PROBLEM-SOLVING
AB The work described in this article presents an approach to the integration of computer-displayed radiological images with cooperative computerized assistance for decision-making. The VIA-RAD system (Visual Interaction Assistant for Radiology) is a blackboard-based architecture, founded on extensive data collection and analysis in the domain of diagnostic radiology, together with cognitive modeling of the interaction between perception and problem-solving. The details of this system are presented in terms of domain knowledge representation and domain knowledge mapping. A small prototype of the system has been implemented and tested with radiology subjects, and the results of this study are also described.
RP ROGERS, E (通讯作者)，CLARK ATLANTA UNIV,DEPT COMP & INFORMAT SCI,223 JAMES P BRAWLEY DR SW,ATLANTA,GA 30314, USA.
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NR 25
TC 13
Z9 13
U1 0
U2 2
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0933-3657
J9 ARTIF INTELL MED
JI Artif. Intell. Med.
PD AUG
PY 1995
VL 7
IS 4
BP 343
EP 360
DI 10.1016/0933-3657(95)00009-U
PG 18
WC Computer Science, Artificial Intelligence; Engineering, Biomedical;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Medical Informatics
GA RP716
UT WOS:A1995RP71600003
PM 7581628
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Li, MD
   Sun, ZY
   Zhang, SH
   Zhang, WS
AF Li, Mingda
   Sun, Zhengya
   Zhang, Siheng
   Zhang, Wensheng
TI Enhancing knowledge graph embedding with relational constraints
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Translational model; Relational constraints;
   Knowledge graph completion
AB Knowledge graph embedding is studied to embed entities and relations of a knowledge graph into continuous vector spaces, which benefits a variety of real-world applications. Among existing solutions, translational models, which employ geometric translation to design score function, have drawn much attention. However, these models primarily concentrate on evidence from observing whether the triplets are plausible, and ignore the fact that the relation also implies certain semantic constraints on its subject or object entity. In this paper, we present a general framework for enhancing knowledge graph embedding with relational constraints (KRC). Specifically, we elaborately design the score function by encoding regularities between a relation and its arguments into the translational embedding space. Additionally, we propose a soft margin-based ranking loss for effectively training the KRC model, which characterizes different semantic distances between negative and positive triplets. Furthermore, we combine regularities with distributional representations to predict the missing triplets, which can possess certain robust guarantee. We evaluate our method on the tasks of knowledge graph completion and entity classification. Extensive experiments show that KRC achieves a better, or comparable performance against state-of-the-art methods. Besides, KRC makes a great improvement when dealing with long-tail entities, which have few instances in the knowledge graph. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Zhang, Wensheng] Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing, Peoples R China.
   [Zhang, Wensheng] Univ Chinese Acad Sci, Beijing, Peoples R China.
RP Zhang, WS (通讯作者)，Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing, Peoples R China.; Zhang, WS (通讯作者)，Univ Chinese Acad Sci, Beijing, Peoples R China.
EM zhangwenshengia@hotmail.com
FU National Natural Science Foundation of China [U1636220, 61876183]
FX The authors are thankful for the financial support from the National
   Natural Science Foundation of China (U1636220 and 61876183).
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NR 36
TC 5
Z9 5
U1 5
U2 22
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD MAR 14
PY 2021
VL 429
BP 77
EP 88
DI 10.1016/j.neucom.2020.12.012
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QD2QC
UT WOS:000615368600008
DA 2022-08-23
ER

PT J
AU Huang, WJ
   Li, QM
   Meng, SM
AF Huang, Weijia
   Li, Qianmu
   Meng, Shunmei
TI KG2Rec: LSH-CF recommendation method based on knowledge graph for cloud
   services
SO WIRELESS NETWORKS
LA English
DT Article; Early Access
DE Cloud services; Recommendation; Knowledge graph; Collaborative
   filtering; LSH
ID SYSTEM; ONTOLOGY
AB With the rapid development of cloud computing paradigm and mobile devices, the number of cloud services and user interest data are growing explosively. It becomes harder for users to find suitable cloud services within satisfying response time. Thus a suitable cloud service automatic recommendation system is needed to solve the above problem. In this work, we propose KG2Rec, a novel recommendation method based on Knowledge Graph to provide users with appropriate recommendations to meet their real-time needs. The property-specific knowledge graph model is designed to model user-item and item-item bipartite relations. Based on the property-specific knowledge graph model, property-specific user-item relation features can be mined from it. Then the user-item relation features will be integrated into an improved collaborative filtering algorithm based on the local sensitive hash technique for Top-N item recommendation. Finally, we evaluate the proposed method in terms of Top-N recommendation on the MovieLens dataset, and prove it outperforms numbers of state-of-the-art recommendation systems. In addition, we prove it has well performance in term of long tail recommendation and big data processing.
C1 [Huang, Weijia; Li, Qianmu; Meng, Shunmei] Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing, Peoples R China.
RP Meng, SM (通讯作者)，Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing, Peoples R China.
EM weijia@njust.edu.cn; qianmu@njust.edu.cn; mengshunmei@njust.edu.cn
FU National Science Youth Foundation of China [61702264]; Fundamental
   Research Funds for the Central Universities [30918014108]; Postdoctoral
   Science Foundation of China [2019M651835]
FX This paper is supported by the National Science Youth Foundation of
   China under Grant No. 61702264, the Fundamental Research Funds for the
   Central Universities No. 30919011282, the Fundamental Research Funds for
   the Central Universities No. 30918014108, the Postdoctoral Science
   Foundation of China, No. 2019M651835.
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NR 48
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U2 24
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1022-0038
EI 1572-8196
J9 WIREL NETW
JI Wirel. Netw.
DI 10.1007/s11276-020-02387-z
EA JUN 2020
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
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WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LV1QA
UT WOS:000538215200001
DA 2022-08-23
ER

PT J
AU Zhan, XL
   Lu, C
   Hu, GM
AF Zhan, Xianglin
   Lu, Cai
   Hu, Guangmin
TI 3D structural modeling for seismic exploration based on knowledge graphs
SO GEOPHYSICS
LA English
DT Article
ID GEOLOGICAL MODELS; TOPOLOGY; CONSTRUCTION; UNCERTAINTY; EXTRACTION;
   FAULTS
AB Three-dimensional structural models provide valuable references tiff studying exploration challenges and often are used for survey design and seismic method validation as well as to assist with reservoir interpretation. The current structural modeling strategy primarily relies on seismic images, which can suffer from degraded image quality in areas of complex structures. This leads to less-reliable structural interpretations. It is difficult to build appropriate models with unreliable inputs by data-driven modeling methods when the input data contain no other constraints. To mitigate this problem. we have adopted a knowledge-data-driven structural modeling method for seismic exploration based on knowledge graphs that contain certain digitized prior information related to the subsurface structures. The knowledge graph of the structural models formalizes the knowledge of geoscientists by describing the structural model entities and their interrelations in the form of a graph. We first establish the knowledge graph of the structural model to constrain the model topology, then estimate the geologic intersections (lines) between the geologic surfaces under the guidance of the knowledge graph, and finally reconstruct the surfaces under the constraints of the intersections and generate closed geologic bodies. The knowledge graph defines the overall structure of the model, which sets up a priori constraint for the modeling and plays a role in transferring expertise to computers. We use two field models to demonstrate our structural modeling process and prove the effectiveness and feasibility of the method.
C1 [Zhan, Xianglin; Lu, Cai] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu, Peoples R China.
   [Hu, Guangmin] Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu, Peoples R China.
RP Lu, C (通讯作者)，Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu, Peoples R China.
EM xianglin_zhan@163.com; lucai@uestc.edu.cn; hgm@uestc.edu.cn
OI Zhan, Xianglin/0000-0003-2859-6590
FU National Natural Science Foundation of China [41974147, 41804162]
FX This work was supported by the National Natural Science Foundation of
   China (grant nos. 41974147 and 41804162) .
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   Zheng K., 2010, J GEOGRAPHIC INFORM, V2, P6, DOI [10.4236/jgis.2010.21002, DOI 10.4236/JGIS.2010.21002]
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NR 65
TC 0
Z9 0
U1 5
U2 5
PU SOC EXPLORATION GEOPHYSICISTS
PI TULSA
PA 8801 S YALE ST, TULSA, OK 74137 USA
SN 0016-8033
EI 1942-2156
J9 GEOPHYSICS
JI Geophysics
PD MAY-JUN
PY 2022
VL 87
IS 3
BP IM81
EP IM100
DI 10.1190/GEO2020-0924.1
PG 20
WC Geochemistry & Geophysics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geochemistry & Geophysics
GA 1C9XB
UT WOS:000793464200004
DA 2022-08-23
ER

PT J
AU Jermol, M
   Lavrac, N
   Urbancic, T
AF Jermol, M
   Lavrac, N
   Urbancic, T
TI Managing business intelligence in a virtual enterprise: A case study and
   knowledge management lessons learned
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
AB This paper presents a virtual enterprise model used in networking international expert teams from academia and business in the area of data mining and decision support. The knowledge management aspects of business intelligence as implemented in the virtual enterprise model are analyzed in terms of (a) the appropriate business, organizational and management models, (b) the need for knowledge management and the construction of a knowledge map of the available tools, expertise and collaborative work procedures, (c) the cognitive authority in collaborative work management, as well as (d) the network intelligence aspect of the virtual enterprise endeavor. The project Data Mining and Decision Support for Business Competitiveness: A European Virtual Enterprise (SolEuNet) is used as a case study and the source of lessons learned.
C1 Jozef Stefan Inst, Ljubljana 1000, Slovenia.
   Nova Gorica Polytech, Nova Gorica 5001, Slovenia.
RP Jermol, M (通讯作者)，Jozef Stefan Inst, Jamova 39, Ljubljana 1000, Slovenia.
EM mitja.jermol@ijs.si
OI Jermol, Mitja/0000-0001-5469-8385
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NR 27
TC 14
Z9 14
U1 0
U2 9
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 2003
VL 14
IS 3
BP 121
EP 136
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 818YI
UT WOS:000221280000002
DA 2022-08-23
ER

PT J
AU De Liddo, A
   Sandor, A
   Shum, SB
AF De Liddo, Anna
   Sandor, Agnes
   Shum, Simon Buckingham
TI Contested Collective Intelligence: Rationale, Technologies, and a
   Human-Machine Annotation Study
SO COMPUTER SUPPORTED COOPERATIVE WORK-THE JOURNAL OF COLLABORATIVE
   COMPUTING AND WORK PRACTICES
LA English
DT Article
DE collective intelligence; discourse; human annotation; knowledge mapping;
   machine annotation; learning; sensemaking; network visualization; social
   software; social annotation
ID ARGUMENTATION; DESIGN; SENSE; TOOLS
AB We propose the concept of Contested Collective Intelligence (CCI) as a distinctive subset of the broader Collective Intelligence design space. CCI is relevant to the many organizational contexts in which it is important to work with contested knowledge, for instance, due to different intellectual traditions, competing organizational objectives, information overload or ambiguous environmental signals. The CCI challenge is to design sociotechnical infrastructures to augment such organizational capability. Since documents are often the starting points for contested discourse, and discourse markers provide a powerful cue to the presence of claims, contrasting ideas and argumentation, discourse and rhetoric provide an annotation focus in our approach to CCI. Research in sensemaking, computer-supported discourse and rhetorical text analysis motivate a conceptual framework for the combined human and machine annotation of texts with this specific focus. This conception is explored through two tools: a social-semantic web application for human annotation and knowledge mapping (Cohere), plus the discourse analysis component in a textual analysis software tool (Xerox Incremental Parser: XIP). As a step towards an integrated platform, we report a case study in which a document corpus underwent independent human and machine analysis, providing quantitative and qualitative insight into their respective contributions. A promising finding is that significant contributions were signalled by authors via explicit rhetorical moves, which both human analysts and XIP could readily identify. Since working with contested knowledge is at the heart of CCI, the evidence that automatic detection of contrasting ideas in texts is possible through rhetorical discourse analysis is progress towards the effective use of automatic discourse analysis in the CCI framework.
C1 [De Liddo, Anna; Shum, Simon Buckingham] Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England.
   [Sandor, Agnes] Xerox Res Ctr Europe, F-38240 Meylan, France.
RP De Liddo, A (通讯作者)，Open Univ, Knowledge Media Inst, Walton Hall, Milton Keynes MK7 6AA, Bucks, England.
EM a.deliddo@open.ac.uk; agnes.sandor@xrce.xerox.com;
   s.buckingham.shum@open.ac.uk
RI SHUM, Simon BUCKINGHAM/AAN-9238-2020
OI SHUM, Simon BUCKINGHAM/0000-0002-6334-7429; De Liddo,
   Anna/0000-0003-0301-1154
FU William and Flora Hewlett Foundation; OLnet Visiting Fellowship at the
   Open University
FX Cohere is being developed as part of the OLnet Project
   (http://olnet.org) funded by The William and Flora Hewlett Foundation.
   This work was conducted as part of an OLnet Visiting Fellowship for
   Agnes Sandor at the Open University. Cohere uses the open source Prefuse
   visualization code from PARC: http://prefuse.org The authors also thank
   the anonymous referees for their thorough and helpful reviews.
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NR 54
TC 26
Z9 27
U1 1
U2 22
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0925-9724
EI 1573-7551
J9 COMPUT SUPP COOP W J
JI Comput. Support. Coop. Work
PD OCT
PY 2012
VL 21
IS 4-5
SI SI
BP 417
EP 448
DI 10.1007/s10606-011-9155-x
PG 32
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 964GK
UT WOS:000305681800004
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Sun, QT
   Han, J
   Ma, DF
AF Sun, Qitong
   Han, Jun
   Ma, Dianfu
TI A Framework for Service Semantic Description Based on Knowledge Graph
SO ELECTRONICS
LA English
DT Article
DE web service; knowledge graph; ontology; deep neural network
AB To construct a large-scale service knowledge graph is necessary. We propose a method, namely semantic information extension, for service knowledge graphs. We insist on the information of services described by Web Services Description Language (WSDL) and we design the ontology layer of web service knowledge graph and construct the service graph, and using the WSDL document data set, the generated service knowledge graph contains 3738 service entities. In particular, our method can give a full performance to its effect in service discovery. To evaluate our approach, we conducted two sets of experiments to explore the relationship between services and classify services that develop by service descriptions. We constructed two experimental data sets, then designed and trained two different deep neural networks for the two tasks to extract the semantics of the natural language used in the service discovery task. In the prediction task of exploring the relationship between services, the prediction accuracy rate reached 95.1%, and in the service classification experiment, the accuracy rate of TOP5 reached 60.8%. Our experience shows that the service knowledge graph has additional advantages over traditional file storage when managing additional semantic information is effective and the new service representation method is helpful for service discovery and composition tasks.
C1 [Sun, Qitong; Han, Jun; Ma, Dianfu] Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China.
RP Han, J (通讯作者)，Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China.
EM sunqt@buaa.edu.cn; jun_han@buaa.edu.cn; dfma@buaa.edu.cn
FU National Key Research and Development Program of China [2018YFB0203802]
FX This work was supported by the National Key Research and Development
   Program of China under Grant No. 2018YFB0203802.
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NR 20
TC 0
Z9 0
U1 2
U2 9
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD MAY
PY 2021
VL 10
IS 9
AR 1017
DI 10.3390/electronics10091017
PG 19
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA SB4SK
UT WOS:000649985900001
OA gold
DA 2022-08-23
ER

PT J
AU Ebisu, T
   Ichise, R
AF Ebisu, Takuma
   Ichise, Ryutaro
TI Triple Prediction from Texts by Using Distributed Representations of
   Words
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article
DE distributed representations of words; knowledge extraction; knowledge
   graph completion
AB Knowledge graphs have been shown to be useful to many tasks in artificial intelligence. Triples of knowledge graphs are traditionally structured by human editors or extracted from semi-structured information; however, editing is expensive, and semi-structured information is not common. On the other hand, most such information is stored as text. Hence, it is necessary to develop a method that can extract knowledge from texts and then construct or populate a knowledge graph; this has been attempted in various ways. Currently, there are two approaches to constructing a knowledge graph. One is open information extraction (Open IE), and the other is knowledge graph embedding; however, neither is without problems. Stanford Open IE, the current best such system, requires labeled sentences as training data, and knowledge graph embedding systems require numerous triples. Recently, distributed representations of words have become a hot topic in the field of natural language processing, since this approach does not require labeled data for training. These require only plain text, but Mikolov showed that it can perform well with the word analogy task, answering questions such as, "a is to b as c is to __?." This can be considered as a knowledge extraction task from a text for finding the missing entity of a triple. However, the accuracy is not sufficiently high when applied in a straightforward manner to relations in knowledge graphs, since the method uses only one triple as a positive example. In this paper, we analyze why distributed representations perform such tasks well; we also propose a new method for extracting knowledge from texts that requires much less annotated data. Experiments show that the proposed method achieves considerable improvement compared with the baseline; in particular, the improvement in HITS@10 was more than doubled for some relations.
C1 [Ebisu, Takuma; Ichise, Ryutaro] SOKENDAI Grad Univ Adv Studies, Tokyo 1018430, Japan.
   [Ebisu, Takuma] Natl Inst Informat, Tokyo 1018430, Japan.
   [Ichise, Ryutaro] Natl Inst Informat, Principles Informat Res Div, Tokyo 1018430, Japan.
RP Ebisu, T (通讯作者)，SOKENDAI Grad Univ Adv Studies, Tokyo 1018430, Japan.; Ebisu, T (通讯作者)，Natl Inst Informat, Tokyo 1018430, Japan.
EM takuma@nii.ac.jp
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NR 27
TC 3
Z9 3
U1 0
U2 5
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 DEC
PY 2017
VL E100D
IS 12
BP 3001
EP 3009
DI 10.1587/transinf.2017EDP7112
PG 9
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FP9TB
UT WOS:000417990300029
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, CH
   Zhou, M
   Han, X
   Hu, Z
   Ji, Y
AF Zhang, Chunhong
   Zhou, Miao
   Han, Xiao
   Hu, Zheng
   Ji, Yang
TI Knowledge Graph Embedding for Hyper-Relational Data
SO TSINGHUA SCIENCE AND TECHNOLOGY
LA English
DT Article
DE distributed representation; transfer matrix; knowledge graph embedding
AB Knowledge graph representation has been a long standing goal of artificial intelligence. In this paper, we consider a method for knowledge graph embedding of hyper-relational data, which are commonly found in knowledge graphs. Previous models such as Trans (E, H, R) and CTransR are either insufficient for embedding hyper-relational data or focus on projecting an entity into multiple embeddings, which might not be effective for generalization nor accurately reflect real knowledge. To overcome these issues, we propose the novel model TransHR, which transforms the hyper-relations in a pair of entities into an individual vector, serving as a translation between them. We experimentally evaluate our model on two typical tasks-link prediction and triple classification. The results demonstrate that TransHR significantly outperforms Trans (E, H, R) and CTransR, especially for hyperrelational data.
C1 [Zhang, Chunhong; Hu, Zheng] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China.
   [Zhou, Miao; Han, Xiao; Ji, Yang] Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Universal Wireless Commun, Beijing 100876, Peoples R China.
RP Zhang, CH (通讯作者)，Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China.
EM zhangch@bupt.edu.cn; zm10211060@bupt.edu.cn; hanxiao1007@bupt.edu.cn;
   huzheng@bupt.edu.cn; jiyang@bupt.edu.cn
FU National Natural Science Foundation of China [61302077, 61520106007,
   61421061, 61602048]
FX This work was partially supported by the National Natural Science
   Foundation of China (Nos. 61302077, 61520106007, 61421061, and
   61602048).
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NR 40
TC 12
Z9 13
U1 1
U2 15
PU TSINGHUA UNIV PRESS
PI BEIJING
PA TSINGHUA UNIV, RM A703, XUEYAN BLDG, BEIJING, 100084, PEOPLES R CHINA
SN 1007-0214
EI 1878-7606
J9 TSINGHUA SCI TECHNOL
JI Tsinghua Sci. Technol.
PD APR
PY 2017
VL 22
IS 2
BP 185
EP 197
PG 13
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA ES1II
UT WOS:000399280900006
DA 2022-08-23
ER

PT J
AU Wang, L
   Xie, HM
   Han, WT
   Yang, X
   Shi, LL
   Dong, JC
   Jiang, K
   Wu, HQ
AF Wang, Lei
   Xie, Huimin
   Han, Wentao
   Yang, Xiao
   Shi, Lili
   Dong, Jiancheng
   Jiang, Kui
   Wu, Huiqun
TI Construction of a knowledge graph for diabetes complications from
   expert-reviewed clinical evidences
SO COMPUTER ASSISTED SURGERY
LA English
DT Article
DE Diabetes; knowledge graph; evidence-based medicine; risk prediction
ID METAANALYSIS; RETINOPATHY
AB A knowledge graph is a structured representation of data that can express entity and relational knowledge. More attention has been paid to the study of a clinical knowledge graph, especially in the field of chronic diseases. However, knowledge graph construction is based mainly on electronic medical records and other data sources, and the authority of the constructed knowledge graph presents some problems. Therefore, regarding the quality of evidence, this study, in combination with experimental research on system evaluation and meta-analysis presents some new information, On the basis of evidence-based medicine (EBM), the secondary results of systematic evaluation and meta-analyses of social, psychological, and behavioral aspects were extracted as data for the core nodes and edges of a knowledge graph to construct a graph of type 2 diabetes (T2D) and its complications. In this study, relevant life-style evidence that are factors for the risk of diabetic retinopathy (DR), diabetic nephropathy (DN), diabetic foot (DF), and diabetic depression (DD), and the results of several of the relevant clinical test, including bariatric surgery, myopia, lipid-lowering drugs, lipid-lowering drug duration, blood glucose control, disease course, glycosylated hemoglobin, fasting blood glucose, hypertension, sex, smoking and other common lifestyle characteristics were finally extracted. The evidence-based knowledge graph of the DM complications was constructed by extracting relevant disease, risk factors, risk outcomes, and other diabetes entities and the strength of the data for the odds ratio (OR) or relative risk (RR) correlations from clinical evidence. Moreover, the risk prediction models constructed using a logistic model were incorporated into the knowledge graph to visualize the risk score of DM complications for each user. In short, the EBM-powered construction of the knowledge graph could provide high-quality information to support decisions for the prevention and control of diabetes and its complications.
C1 [Wang, Lei; Xie, Huimin; Han, Wentao; Yang, Xiao; Shi, Lili; Dong, Jiancheng; Jiang, Kui; Wu, Huiqun] Nantong Univ, Dept Med Informat, Med Sch, Nantong, Peoples R China.
RP Jiang, K; Wu, HQ (通讯作者)，Nantong Univ, Dept Med Informat, Med Sch, Nantong, Peoples R China.
EM kuij@ntu.edu.cn; wuhuiqun@ntu.edu.cn
OI DONG, Jiancheng/0000-0002-9646-2853; Han, Wentao/0000-0003-2650-0233
FU National Key R&D Program of China [2018YFC1314900, 2018YFC1314902];
   Nantong '226 Project', Excellent Key Teachers in the 'Qing Lan Project'
   of Jiangsu Colleges and Universities, Jiangsu Students' Platform for
   innovation and entrepreneurship training program [201910304108Y];
   Jiangsu postgraduate research and innovation program [KYCX19_2070,
   KYCX20_2836]
FX This work was supported by the grant from National Key R&D Program of
   China [2018YFC1314900, 2018YFC1314902], Nantong '226 Project', Excellent
   Key Teachers in the 'Qing Lan Project' of Jiangsu Colleges and
   Universities, Jiangsu Students' Platform for innovation and
   entrepreneurship training program [201910304108Y] and Jiangsu
   postgraduate research and innovation program [KYCX19_2070, KYCX20_2836].
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NR 29
TC 2
Z9 3
U1 11
U2 35
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
EI 2469-9322
J9 COMPUT ASSIST SURG
JI COMPUT. ASSIST. SURG.
PD NOV 16
PY 2020
VL 25
IS 1
BP 29
EP 35
DI 10.1080/24699322.2020.1850866
PG 7
WC Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Surgery
GA PC7UQ
UT WOS:000597202000001
PM 33275462
OA gold
DA 2022-08-23
ER

PT J
AU Necasky, M
AF Necasky, Martin
TI Interactive and iterative visual exploration of knowledge graphs based
   on shareable and reusable visual configurations
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge graph; Graph visualization; Linked data; Open data; Ontology
ID LINKED DATA VISUALIZATION; SYSTEM; SCALE
AB Knowledge graphs denote structured data which represent entities and relationships between them in a form of a graph, often expressed in the RDF data model. It may be hard for lay users to explore existing knowledge graphs, especially when graphs from different data sources need to be integrated. In this paper, we present an approach to knowledge graph visual exploration based on the concept of shareable and reusable visual configurations. A visual configuration comprises domain specific views on a knowledge graph which define operations such as node detail or expansion. These operations are easy to understand for lay users who can use them to explore a graph while complexities unnecessary in a given application context remain hidden. We introduce an ontology which enables to express and publish visual configurations and reuse their components in other configurations. We also provide an experimental implementation called KGBrowser. We evaluate the proposed approach with real users. We also compare our implementation KGBrowser with other existing tools for knowledge graph visualization and exploration. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Necasky, Martin] Charles Univ Prague, Fac Math & Phys, Prague, Czech Republic.
RP Necasky, M (通讯作者)，Charles Univ Prague, Fac Math & Phys, Prague, Czech Republic.
EM martin.necasky@matfyz.cuni.cz
OI Stenchlak, Stepan/0000-0003-4843-2470
FU Czech Science Foundation [19-01641S]
FX This work was supported by the Czech Science Foundation (GACR) , grant
   number 19-01641S.
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TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
EI 1873-7749
J9 J WEB SEMANT
JI J. Web Semant.
PD JUL
PY 2022
VL 73
AR 100713
DI 10.1016/j.websem.2022.100713
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1F0HO
UT WOS:000794858500001
DA 2022-08-23
ER

PT J
AU Paulheim, H
AF Paulheim, Heiko
TI Knowledge Graph Refinement: A Survey of Approaches and Evaluation
   Methods
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge Graphs; Refinement; Completion; Correction; Error Detection;
   Evaluation
ID LINKED DATA; WORDNET; QUALITY; SET
AB In the recent years, different Web knowledge graphs, both free and commercial, have been created. While Google coined the term "Knowledge Graph" in 2012, there are also a few openly available knowledge graphs, with DBpedia, YAGO, and Freebase being among the most prominent ones. Those graphs are often constructed from semi-structured knowledge, such as Wikipedia, or harvested from the web with a combination of statistical and linguistic methods. The result are large-scale knowledge graphs that try to make a good trade-off between completeness and correctness. In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces of information. In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.
C1 [Paulheim, Heiko] Univ Mannheim, Data & Web Sci Grp, B6 26, D-68159 Mannheim, Germany.
RP Paulheim, H (通讯作者)，Univ Mannheim, Data & Web Sci Grp, B6 26, D-68159 Mannheim, Germany.
EM heiko@informatik.uni-mannheim.de
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   [No title captured]
NR 113
TC 391
Z9 405
U1 75
U2 394
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 3
BP 489
EP +
DI 10.3233/sw-160218
PG 23
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EO7IF
UT WOS:000396863700010
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Fan, SX
   Liu, XW
   Chen, Y
   Liao, ZF
   Zhao, YQ
   Luo, HM
   Fan, HW
AF Fan, Shixiong
   Liu, Xingwei
   Chen, Ying
   Liao, Zhifang
   Zhao, Yiqi
   Luo, Huimin
   Fan, Haiwei
TI How to Construct a Power Knowledge Graph with Dispatching Data?
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of power dispatching behaviors by semi-automated labeling. And then, we propose a model, called the BiLSTM-CRF model, to extract entities and identify the dispatching behavior relationship patterns. Finally, we construct a knowledge graph of power dispatching data. The knowledge graph provides an underlying knowledge model for automated power dispatching and related services and helps dispatchers perform better power dispatch knowledge retrieval and other operations during the dispatch process.
C1 [Fan, Shixiong; Liu, Xingwei] China Elect Power Res Inst, Beijing Key Lab Res & Syst Evaluat Power Dispatch, Beijing 100192, Peoples R China.
   [Chen, Ying; Liao, Zhifang; Zhao, Yiqi; Luo, Huimin] Cent South Univ, Sch Comp Sci & Engn, Hunan 410000, Hunan, Peoples R China.
   [Fan, Haiwei] State Grid Fujian Elect Power Co Ltd, Fuzhou 350003, Peoples R China.
RP Chen, Y; Liao, ZF (通讯作者)，Cent South Univ, Sch Comp Sci & Engn, Hunan 410000, Hunan, Peoples R China.
EM 469509415@qq.com; 1069581482@qq.com; 1074647728@qq.com;
   zfliao@csu.edu.cn; 595593794@qq.com; sirenslytherin@qq.com;
   2193927906@qq.com
FU Basic Prospective Project of SGCC [5442DZ180017]; Science and Technology
   Research Foundation of SGCC [5442DZ180024-I]
FX This work was supported in part by the Basic Prospective Project of SGCC
   (no. 5442DZ180017) and in part by the Science and Technology Research
   Foundation of SGCC (5442DZ180024-I).
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NR 18
TC 5
Z9 5
U1 7
U2 10
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 JUL 14
PY 2020
VL 2020
AR 8842463
DI 10.1155/2020/8842463
PG 10
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ND2KJ
UT WOS:000561733800001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, YH
   Zhao, EP
   Wang, W
AF Wang, Yuhao
   Zhao, Erping
   Wang, Wei
TI A Knowledge Graph Completion Method Based on Fusing Association
   Information
SO IEEE ACCESS
LA English
DT Article
DE Tail; Representation learning; Reliability; Animals; Semantics;
   Knowledge representation; Data mining; Knowledge graph; knowledge graph
   completion; associated information; link prediction
ID DOMAIN
AB Knowledge graph is a carrier of knowledge. The knowledge graph completion task is to predict the links between entities to make the knowledge graph more complete. Currently, some completion method based on knowledge representation learning do not fully consider the association information between each relationship in the multiple-step relation paths and the direct relations, and the association information between the head and tail entity types and direct relations. In this study, we extract and utilize these associated information, and propos AiTransE model for knowledge graph completion, which uses the frequency of each relationship in the multiple-step relation paths between head and tail entities to calculate the degree of association with the direct relations, and uses head and tail entity types and direct relation types for matching to obtain the degree of association between them. Finally, the two association degrees are linearly weighted and merged and then introduced into the objective function, so that the model can give different degrees of attention to different triples, and improve model knowledge representation learning performance. The experiment with link prediction was carried out on the Tibet Animal Husbandry Dataset and the WN18 dataset, and compared with the TransE, TransH, TransR, and other models. The experimental results show that this model has a significant improvement over other models in the indicators Hits@10 and Mean Rank.
C1 [Wang, Yuhao; Zhao, Erping; Wang, Wei] Xizang Minzu Univ, Coll Informat Engn, Xianyang 712082, Shaanxi, Peoples R China.
RP Zhao, EP (通讯作者)，Xizang Minzu Univ, Coll Informat Engn, Xianyang 712082, Shaanxi, Peoples R China.
EM xdzep@163.com
FU National Natural Science Foundation of China [61762082]; Science and
   Technology Project of Tibet Autonomous Region [XZ202001ZY0055G]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61762082, and in part by the Science and
   Technology Project of Tibet Autonomous Region under Grant
   XZ202001ZY0055G.
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NR 28
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 50500
EP 50507
DI 10.1109/ACCESS.2022.3174110
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1I7ME
UT WOS:000797410600001
OA gold
DA 2022-08-23
ER

PT J
AU Shi, DQ
   Wang, T
   Xing, H
   Xu, H
AF Shi, Daqian
   Wang, Ting
   Xing, Hao
   Xu, Hao
TI A learning path recommendation model based on a multidimensional
   knowledge graph framework for e-learning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Learning path recommendation; Knowledge graph; e-learning; Learning
   needs
ID SYSTEM; CREATIVITY
AB E-learners face a large amount of fragmented learning content during e-learning. How to extract and organize this learning content is the key to achieving the established learning target, especially for non-experts. Reasonably arranging the order of the learning objects to generate a well-defined learning path can help the e-learner complete the learning target efficiently and systematically. Currently, knowledge-graph-based learning path recommendation algorithms are attracting the attention of researchers in this field. However, these methods only connect learning objects using single relationships, which cannot generate diverse learning paths to satisfy different learning needs in practice. To overcome this challenge, this paper proposes a learning path recommendation model based on a multidimensional knowledge graph framework. The main contributions of this paper are as follows. Firstly, we have designed a multidimensional knowledge graph framework that separately stores learning objects organized in several classes. Then, we have proposed six main semantic relationships between learning objects in the knowledge graph. Secondly, a learning path recommendation model is designed for satisfying different learning needs based on the multidimensional knowledge graph framework, which can generate and recommend customized learning paths according to the e-learner's target learning object. The experiment results indicate that the proposed model can generate and recommend qualified personalized learning paths to improve the learning experiences of e-learners. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Shi, Daqian; Wang, Ting; Xing, Hao; Xu, Hao] Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China.
   [Xu, Hao] Jilin Univ, Sch Management, Changchun, Peoples R China.
   [Xu, Hao] Jilin Univ, Dept Comp Sci & Technol, Zhuhai Coll, Changchun, Peoples R China.
   [Xu, Hao] Jilin Univ, Symbol Computat & Knowledge Engineer, Minist Educ, Changchun, Peoples R China.
RP Xu, H (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China.
EM xuhao@jlu.edu.cn
FU Development of Sicence and Technology of Jilin Province, China
   [20170203002GX, 20170101006JC]; National Natural Science Foundation of
   China [61472159, 71620107001, 71232011]
FX This work is supported by the Development of Sicence and Technology of
   Jilin Province, China (No. 20170203002GX, 20170101006JC), the National
   Natural Science Foundation of China (No. 61472159, 71620107001,
   71232011).
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NR 41
TC 39
Z9 40
U1 33
U2 113
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD MAY 11
PY 2020
VL 195
AR 105618
DI 10.1016/j.knosys.2020.105618
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA KZ9EN
UT WOS:000523561500024
DA 2022-08-23
ER

PT J
AU Ma, RX
   Guo, FQ
   Li, ZY
   Zhao, L
AF Ma, Ruixin
   Guo, Fangqing
   Li, Zeyang
   Zhao, Liang
TI Knowledge Graph Random Neural Networks for Recommender Systems
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Recommendation; Knowledge graph; Graph neural networks; Feature
   propagation
AB In recent years, knowledge graph networks for recommendation have attracted extensive attention, since these methods can capture structured information by linking items with their attributes instead of using only interaction data between users and items. However, existing Graph Neural Networks Methods on Knowledge Graph suffer from over-smoothing and data sparsity, leading to the inability to build deeper networks. To address these problems, the Knowledge Graph Random Neural Networks for Recommender Systems (KRNN) is proposed. Specifically, a random dropout strategy is designed to generate the perturbed entities feature matrices. Then, a feature propagation method is proposed over the perturbed feature matrices for capturing high-order neighbor information, and locating the novel entities representation. The data augmentation matrices are generated by using the new entity representation from the previous step. The consistency regularization is designed to optimize the prediction across different data augmentation matrices in multiple random dropout. Extensive experiments on real datasets demonstrate the proposed method is superior to state of the baselines in alleviating over-smoothing and predicting user preferences, especially in data sparsity scenarios.
C1 [Ma, Ruixin; Guo, Fangqing; Li, Zeyang; Zhao, Liang] Dalian Univ Technol, Sch Software Technol, Dalian 116024, Peoples R China.
RP Zhao, L (通讯作者)，Dalian Univ Technol, Sch Software Technol, Dalian 116024, Peoples R China.
EM maruixin@dlut.edu.cn; guofangqing@mail.dlut.edu.cn;
   lizeyang@mail.dlut.edu.cn; liangzhao@dlut.edu.cn
FU National Natural Science Foundation of China [61906030]; Science and
   Technology Project of Liaoning Province [2021JH2/10300064]; Natural
   Science Foundation of Liaoning Province [2020-BS-063]; Youth Science and
   Technology Star Support Program of Dalian City, China [2021RQ057]
FX This work is supported by the National Natural Science Foundation of
   China (61906030), the Science and Technology Project of Liaoning
   Province (2021JH2/10300064), the Natural Science Foundation of Liaoning
   Province (2020-BS-063) and the Youth Science and Technology Star Support
   Program of Dalian City, China (2021RQ057)
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NR 45
TC 0
Z9 0
U1 0
U2 0
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD SEP 1
PY 2022
VL 201
AR 117120
DI 10.1016/j.eswa.2022.117120
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 3E7OD
UT WOS:000830169300012
DA 2022-08-23
ER

PT J
AU Chen, PH
   Lu, Y
   Zheng, VW
   Chen, XY
   Yang, BD
AF Chen, Penghe
   Lu, Yu
   Zheng, Vincent W.
   Chen, Xiyang
   Yang, Boda
TI KnowEdu: A System to Construct Knowledge Graph for Education
SO IEEE ACCESS
LA English
DT Article
DE Educational knowledge graph; instructional concept; educational
   relation; pedagogical data; learning assessment; educational data mining
AB Motivated by the vast applications of knowledge graph and the increasing demand in education domain, we propose a system, called KnowEdu, to automatically construct knowledge graph for education. By leveraging on heterogeneous data (e.g., pedagogical data and learning assessment data) from the education domain, this system first extracts the concepts of subjects or courses and then identifies the educational relations between the concepts. More specifically, it adopts the neural sequence labeling algorithm on pedagogical data to extract instructional concepts and employs probabilistic association rule mining on learning assessment data to identify the relations with educational significance. We detail all the abovementioned efforts through an exemplary case of constructing a demonstrative knowledge graph for mathematics, where the instructional concepts and their prerequisite relations are derived from curriculum standards and concept-based performance data of students. Evaluation results show that the F1 score for concept extraction exceeds 0.70, and for relation identification, the area under the curve and mean average precision achieve 0.95 and 0.87, respectively.
C1 [Chen, Penghe; Chen, Xiyang; Yang, Boda] Beijing Normal Univ, Adv Innovat Ctr Future Educ, Beijing 100875, Peoples R China.
   [Lu, Yu] Beijing Normal Univ, Fac Educ, Sch Educ Technol, Beijing 100875, Peoples R China.
   [Zheng, Vincent W.] Adv Digital Sci Ctr, Singapore 138602, Singapore.
RP Lu, Y (通讯作者)，Beijing Normal Univ, Fac Educ, Sch Educ Technol, Beijing 100875, Peoples R China.
EM luyu@bnu.edu.cn
OI Lu, Yu/0000-0003-2378-4971
FU National Natural Science Foundation of China [61702039]; Humanities and
   Social Sciences Foundation, Ministry of Education of China [17YJCZH116]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61702039 and in part by the Humanities
   and Social Sciences Foundation, Ministry of Education of China, under
   Grant 17YJCZH116.
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NR 38
TC 44
Z9 50
U1 22
U2 63
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 31553
EP 31563
DI 10.1109/ACCESS.2018.2839607
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA GM1US
UT WOS:000437858900001
OA gold
DA 2022-08-23
ER

PT J
AU Wan, XL
   Wang, HZ
   Li, JZ
AF Wan, Xiaolong
   Wang, Hongzhi
   Li, Jianzhong
TI LKAQ: Large-scale knowledge graph approximate query algorithm
SO INFORMATION SCIENCES
LA English
DT Article
DE Large-scale knowledge graph; Query; Memory limited
ID GSTORE; REUSE; WEB
AB The problems of storing and processing queries for knowledge graphs (KGs) have always been a hot topic in the database community. Various tools, for example, 3store, RDF-3X, Jena2, and gStore, have been proposed. Recently, KGs have gradually shown a non-strict structure, and their volumes continue to grow. As a result, current KG storage and query tools cannot handle the intricate relationships in KGs or support massive data in limited memory space. In addition, an increasing number of users want to use KGs under limited computing resources. Therefore, to meet the current needs of KGs and solve the above problems, we propose a large-scale knowledge graph approximate query algorithm (LKAQ) adopting the idea of an approximate query processing algorithm. LKAQ gives users the ability to control the trade-off among query time, accuracy, and in-memory usage. From extensive experiments, we demonstrate that LKAQ outperforms state-of-the-art approaches with memory constraints. (C) 2019 Elsevier Inc. All rights reserved.
C1 [Wan, Xiaolong; Wang, Hongzhi; Li, Jianzhong] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China.
RP Wang, HZ (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China.
EM wxl@hit.edu.cn; wangzh@hit.edu.cn; lijzh@hit.edu.cn
RI Wang, Hongzhi/F-2847-2011; Li, Jianzhong/AAJ-2912-2021
OI Wang, Hongzhi/0000-0002-7521-2871; Wan, Xiaolong/0000-0003-4502-085X
FU NSFC [U1509216, U1866602, 61602129]; Microsoft Research Asia
FX This paper was partially supported by NSFC grant U1509216, U1866602,
   61602129 and Microsoft Research Asia.
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NR 47
TC 5
Z9 5
U1 1
U2 60
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 DEC
PY 2019
VL 505
BP 306
EP 324
DI 10.1016/j.ins.2019.07.087
PG 19
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IZ6BV
UT WOS:000487167700017
DA 2022-08-23
ER

PT J
AU Seo, S
   Oh, B
   Lee, KH
AF Seo, Seungmin
   Oh, Byungkook
   Lee, Kyong-Ho
TI Reliable Knowledge Graph Path Representation Learning
SO IEEE ACCESS
LA English
DT Article
DE Reliability; Knowledge representation; Semantics; Task analysis;
   Learning systems; Frequency measurement; Cognition; Knowledge graph
   embedding; representation learning; path reasoning; link prediction
AB Knowledge graphs, which have been widely utilized in various intelligent applications, are highly incomplete. Many valid facts can be inferred from existing facts in knowledge graphs. A promising approach for this task is a knowledge graph representation learning, which aims to represent entities and relations into low-dimensional vector spaces. Most of the existing methods mainly focus on direct relationships between entities and do not reflect the semantics of multi-hop relation paths. Although a few methods have studied the problem of multi-hop path-based representation learning, they fail to distinguish reliable relation paths among a majority of meaningless relation paths. In this paper, we propose a reliable path-based knowledge graph representation learning method, called RKRL. Specifically, we combine the representations of intermediate entities and relations on relation paths to learn more meaningful knowledge representations. Also, we present a reliable knowledge graph path ranking method to avoid the unnecessary computation of unreliable paths and find semantically valid relation paths. Experimental results on benchmark datasets show that our method achieves consistent improvement on typical evaluation tasks for knowledge representations, compared with the classical and state-of-the-art representation learning baselines.
C1 [Seo, Seungmin; Oh, Byungkook; Lee, Kyong-Ho] Yonsei Univ, Dept Comp Sci, Seoul 03722, South Korea.
RP Lee, KH (通讯作者)，Yonsei Univ, Dept Comp Sci, Seoul 03722, South Korea.
EM khlee89@yonsei.ac.kr
OI LEE, KYONG-HO/0000-0002-1581-917X; Seo, Seungmin/0000-0001-5772-7997
FU Korea Electric Power Corporation [R18XA05]
FX This work was supported by the Korea Electric Power Corporation under
   Grant R18XA05.
CR [Anonymous], [No title captured]
   [Anonymous], [No title captured]
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NR 47
TC 2
Z9 4
U1 5
U2 27
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 32816
EP 32825
DI 10.1109/ACCESS.2020.2973923
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LC6CZ
UT WOS:000525419700024
OA gold
DA 2022-08-23
ER

PT J
AU Yu, T
   Li, JH
   Yu, Q
   Tian, Y
   Shun, XF
   Xu, LL
   Zhu, L
   Gao, HJ
AF Yu, Tong
   Li, Jinghua
   Yu, Qi
   Tian, Ye
   Shun, Xiaofeng
   Xu, Lili
   Zhu, Ling
   Gao, Hongjie
TI Knowledge graph for TCM health preservation: Design, construction, and
   applications
SO ARTIFICIAL INTELLIGENCE IN MEDICINE
LA English
DT Article
DE Knowledge graph; Ontology; Knowledge services; Traditional Chinese
   medicine; Health preservation
AB Traditional Chinese Medicine (TCM) is one of the important non-material cultural heritages of the Chinese nation. It is an important development strategy of Chinese medicine to collect, analyzes, and manages the knowledge assets of TCM health care. As a novel and massive knowledge management technology, knowledge graph provides an ideal technical means to solve the problem of "Knowledge Island" in the field of traditional Chinese medicine. In this study, we construct a large-scale knowledge graph, which integrates terms, documents, databases and other knowledge resources. This knowledge graph can facilitate various knowledge services such as knowledge visualization, knowledge retrieval, and knowledge recommendation, and helps the sharing, interpretation, and utilization of TCM health care knowledge. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Yu, Tong; Li, Jinghua; Yu, Qi; Tian, Ye; Shun, Xiaofeng; Xu, Lili; Zhu, Ling; Gao, Hongjie] China Acad Chinese Med Sci, Inst Informat Tradit Chinese Med, 16 Dongzhimen South Alley, Beijing 100700, Peoples R China.
RP Yu, T; Li, JH (通讯作者)，China Acad Chinese Med Sci, Inst Informat Tradit Chinese Med, 16 Dongzhimen South Alley, Beijing 100700, Peoples R China.
EM yutongoracle@hotmail.com; zingarlee@hotmail.com
FU Fundamental Research Funds for the Central public welfare research
   institutes [ZZ090305, ZZ070804, ZZ090317]; Beijing science and
   technology development fund of traditional Chinese Medicine [JJ2014-61]
FX This work is supported by "the Fundamental Research Funds for the
   Central public welfare research institutes (NO. ZZ090305, NO. ZZ070804,
   NO. ZZ090317)" and "Beijing science and technology development fund of
   traditional Chinese Medicine (NO. JJ2014-61)".
CR CUI Meng, 2006, CHIN J INF TRADITION, V13, P104
   FAN Wei-yu, 2008, INT J CHIN MAT MED, V30, P250
   Qi Yu, 2015, CHIN J DIGITAL MED, V10, P73
   Tong Yu, 2010, CHIN J DIGITAL MED, V10, P80
   Wang Hao-fen, 2014, CHIN SOC COMPUT SCI, V10, P64
   WHO Regional Office for the Western Pacific, WHO INT STAND TERM T
   Yu Tong, 2016, 3 CHIN MED INF C ORD
NR 7
TC 53
Z9 58
U1 29
U2 180
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0933-3657
EI 1873-2860
J9 ARTIF INTELL MED
JI Artif. Intell. Med.
PD MAR
PY 2017
VL 77
BP 48
EP 52
DI 10.1016/j.artmed.2017.04.001
PG 5
WC Computer Science, Artificial Intelligence; Engineering, Biomedical;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Medical Informatics
GA EX0XG
UT WOS:000402943500005
PM 28545611
DA 2022-08-23
ER

PT J
AU Arenas, M
   Grau, BC
   Kharlamov, E
   Marciuska, S
   Zheleznyakov, D
AF Arenas, Marcelo
   Grau, Bernardo Cuenca
   Kharlamov, Evgeny
   Marciuska, Sarunas
   Zheleznyakov, Dmitriy
TI Faceted search over RDF-based knowledge graphs
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Faceted search; Ontology; OWL 2; RDF; SPARQL; Algorithms
AB Knowledge graphs such as Yago and Freebase have become a powerful asset for enhancing search, and are being intensively used in both academia and industry. Many existing knowledge graphs are either available as Linked Open Data, or they can be exported as RDF datasets enhanced with background knowledge in the form of an OWL 2 ontology. Faceted search is the de facto approach for exploratory search in many online applications, and has been recently proposed as a suitable paradigm for querying RDF repositories. In this paper, we provide rigorous theoretical underpinnings for faceted search in the context of RDF-based knowledge graphs enhanced with OWL 2 ontologies. We identify well-defined fragments of SPARQL that can be naturally captured using faceted search as a query paradigm, and establish the computational complexity of answering such queries. We also study the problem of updating faceted interfaces, which is critical for guiding users in the formulation of meaningful queries during exploratory search. We have implemented our approach in a fully-fledged faceted search system, SemFacet, which we have evaluated over the Yago knowledge graph. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Arenas, Marcelo] Pontificia Univ Catolica Chile, Vicuna Mackenna 4860,Edificio San Agustin, Santiago 7820436, Chile.
   [Grau, Bernardo Cuenca; Kharlamov, Evgeny; Marciuska, Sarunas; Zheleznyakov, Dmitriy] Univ Oxford, Dept Comp Sci, Informat Syst Grp, Wolfson Bldg,Pk Rd, Oxford OX1 3QD, England.
RP Zheleznyakov, D (通讯作者)，Univ Oxford, Dept Comp Sci, Informat Syst Grp, Wolfson Bldg,Pk Rd, Oxford OX1 3QD, England.
EM marenas@ing.puc.cl; bernardo.cuenca.grau@cs.ox.ac.uk;
   evgeny.kharlamov@cs.ox.ac.uk; sarunas.marciuska@cs.ox.ac.uk;
   dmitriy.zheleznyakov@cs.ox.ac.uk
FU Royal Society; EPSRC project Score!; EPSRC project DBOnto; EPSRC project
   MaSI<SUP>3</SUP>; EU FP7 project Optique [318338]; EPSRC [EP/J020214/1]
   Funding Source: UKRI; Engineering and Physical Sciences Research Council
   [EP/J020214/1] Funding Source: researchfish
FX This research was supported by the Royal Society, the EPSRC projects
   Score!, DBOnto, and MaSI<SUP>3</SUP> and the EU FP7 project Optique (n.
   318338).
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NR 69
TC 51
Z9 51
U1 4
U2 65
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 55
EP 74
DI 10.1016/j.websem.2015.12.002
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 DM6JK
UT WOS:000376457600005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Alshahrani, M
   Thafar, MA
   Essack, M
AF Alshahrani, Mona
   Thafar, Maha A.
   Essack, Magbubah
TI Application and evaluation of knowledge graph embeddings in biomedical
   data
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Knowledge graphs; Embeddings methods; Biomedicine; Comparative
   evaluation; Performance studies; Linked data; Bio-ontologies
ID HUMAN PHENOTYPE ONTOLOGY; GENE ONTOLOGY; DBPEDIA; DISEASE; TOOL
AB Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases. That is, linked data and bio-ontologies are employed in databases to maintain data integrity, data organization, and to empower search capabilities. However, linked data and bio-ontologies are more recently being used to represent information as multi-relational heterogeneous graphs, "knowledge graphs". The reason being, entities and relations in the knowledge graph can be represented as embedding vectors in semantic space, and these embedding vectors have been used to predict relationships between entities. Such knowledge graph embedding methods provide a practical approach to data analytics and increase chances of building machine learning models with high prediction accuracy that can enhance decision support systems. Here, we present a comparative assessment and a standard benchmark for knowledge graph-based representation learning methods focused on the link prediction task for biological relations. We systematically investigated and compared state-of-the-art embedding methods based on the design settings used for training and evaluation. We further tested various strategies aimed at controlling the amount of information related to each relation in the knowledge graph and its effects on the final performance. We also assessed the quality of the knowledge graph features through clustering and visualization and employed several evaluation metrics to examine their uses and differences. Based on this systematic comparison and assessments, we identify and discuss the limitations of knowledge graph-based representation learning methods and suggest some guidelines for the development of more improved methods.
C1 [Alshahrani, Mona] Jubail Univ Coll, Dept Comp Sci & Engn, Jubail Ind City, Saudi Arabia.
   [Thafar, Maha A.; Essack, Magbubah] King Abdullah Univ Sci & Technol KAUST, Computat Biosci Res Ctr CBRC, Comp Elect & Math Sci & Engn Div CEMSE, Thuwal, Saudi Arabia.
   [Thafar, Maha A.] Taif Univ, Coll Comp & Informat Technol, At Taif, Saudi Arabia.
RP Alshahrani, M (通讯作者)，Jubail Univ Coll, Dept Comp Sci & Engn, Jubail Ind City, Saudi Arabia.
EM mona.alshahrani@kaust.edu.sa
RI Thafar, Maha/AGY-3745-2022
OI Thafar, Maha/0000-0003-0539-7361
FU Jubail University College (JUC) in Saudi Arabia; KAUST Office of
   Sponsored Research (OSR) [FCC/1/1976-17-01]
FX Mona Alshahrani is supported by Jubail University College (JUC) in Saudi
   Arabia. Magbubah Essack has been supported by the KAUST Office of
   Sponsored Research (OSR) grant no. FCC/1/1976-17-01. 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 102
TC 7
Z9 7
U1 5
U2 11
PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
SN 2376-5992
J9 PEERJ COMPUT SCI
JI PeerJ Comput. Sci.
PD FEB 18
PY 2021
AR e341
DI 10.7717/peerj-cs.341
PG 28
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QL1GH
UT WOS:000620827900001
PM 33816992
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Li, HT
   Wang, Y
   Zhang, SH
   Song, YQ
   Qu, HM
AF Li, Haotian
   Wang, Yong
   Zhang, Songheng
   Song, Yangqiu
   Qu, Huamin
TI KG4Vis: A Knowledge Graph-Based Approach for Visualization
   Recommendation
SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
LA English
DT Article
DE Data visualization; Feature extraction; Tools; Manuals; Data models;
   Visualization; Interviews; Data visualization; Visualization
   recommendation; Knowledge graph
AB Visualization recommendation or automatic visualization generation can significantly lower the barriers for general users to rapidly create effective data visualizations, especially for those users without a background in data visualizations. However, existing rule-based approaches require tedious manual specifications of visualization rules by visualization experts. Other machine learning-based approaches often work like black-box and are difficult to understand why a specific visualization is recommended, limiting the wider adoption of these approaches. This paper fills the gap by presenting KG4Vis, a knowledge graph (KG)-based approach for visualization recommendation. It does not require manual specifications of visualization rules and can also guarantee good explainability. Specifically, we propose a framework for building knowledge graphs, consisting of three types of entities (i.e., data features, data columns and visualization design choices) and the relations between them, to model the mapping rules between data and effective visualizations. A TransE-based embedding technique is employed to learn the embeddings of both entities and relations of the knowledge graph from existing dataset-visualization pairs. Such embeddings intrinsically model the desirable visualization rules. Then, given a new dataset, effective visualizations can be inferred from the knowledge graph with semantically meaningful rules. We conducted extensive evaluations to assess the proposed approach, including quantitative comparisons, case studies and expert interviews. The results demonstrate the effectiveness of our approach.
C1 [Li, Haotian; Song, Yangqiu; Qu, Huamin] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China.
   [Li, Haotian; Wang, Yong; Zhang, Songheng] Singapore Management Univ, Singapore, Singapore.
RP Wang, Y (通讯作者)，Singapore Management Univ, Singapore, Singapore.
EM haotian.li@connect.ust.hk; yongwang@snw.edu.sg;
   shzhang.2021@phdcs.smu.edu.sg; yqsong@cse.ust.hk; huamin@cse.ust.hk
FU Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier
   1 grant [20-C220-SMU-011]
FX This research was supported by the Singapore Ministry of Education (MOE)
   Academic Research Fund (AcRF) Tier 1 grant (Grant number:
   20-C220-SMU-011). We would like to thank the experts in our expert
   interviews and anonymous reviewers for their feedback.
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NR 59
TC 1
Z9 1
U1 24
U2 29
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1077-2626
EI 1941-0506
J9 IEEE T VIS COMPUT GR
JI IEEE Trans. Vis. Comput. Graph.
PD JAN
PY 2022
VL 28
IS 1
BP 195
EP 205
DI 10.1109/TVCG.2021.3114863
PG 11
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XU0IB
UT WOS:000733959000034
PM 34587080
OA Green Submitted, Green Accepted
DA 2022-08-23
ER

PT J
AU Ma, T
   Lv, SW
   Huang, LT
   Hu, SL
AF Ma, Ting
   Lv, Shangwen
   Huang, Longtao
   Hu, Songlin
TI HiAM: A Hierarchical Attention based Model for knowledge graph multi-hop
   reasoning
SO NEURAL NETWORKS
LA English
DT Article
DE Knowledge graph reasoning; Predecessor paths; Hierarchical Attention
ID LOGIC
AB Learning to reason in large-scale knowledge graphs has attracted much attention from research communities recently. This paper targets a practical task of multi-hop reasoning in knowledge graphs, which can be applied in various downstream tasks such as question answering, and recommender systems. A key challenge in multi-hop reasoning is to synthesize structural information (e.g., paths) in knowledge graphs to perform deeper reasoning. Existing methods usually focus on connection paths between each entity pair. However, these methods ignore predecessor paths before connection paths and regard entities and relations within every single path as equally important. With our observations, predecessor paths before connection paths can provide more accurate semantic representations. Furthermore, entities and relations in a single path contribute variously to the right answers. To this end, we propose a novel model HiAM (Hierarchical Attention based Model) for knowledge graph multi-hop reasoning. HiAM makes use of predecessor paths to provide more accurate semantics for entities and explores the effects of different granularities. Firstly, we extract predecessor paths of head entities and connection paths between each entity pair. Then, a hierarchical attention mechanism is designed to capture the information of different granularities, including entity/relation-level and path-level features. Finally, multi-granularity features are fused together to predict the right answers. We go one step further to select the most significant path as the explanation for predicted answers. Comprehensive experimental results demonstrate that our method achieves competitive performance compared with the baselines on three benchmark datasets. (C) 2021 Elsevier Ltd. All rights reserved.
C1 [Ma, Ting; Lv, Shangwen; Hu, Songlin] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Ma, Ting; Lv, Shangwen; Hu, Songlin] Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China.
   [Huang, Longtao] Alibaba Grp, Beijing 100102, Peoples R China.
RP Hu, SL (通讯作者)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
EM mating@iie.ac.cn; lvshangwen@iie.ac.cn; kaiyang.hlt@alibaba-inc.com;
   husonglin@iie.ac.cn
FU Beijing Municipal Science and Technology Project [Z191100007119008]
FX This research is supported in part by the Beijing Municipal Science and
   Technology Project under Grant Z191100007119008.
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NR 40
TC 0
Z9 0
U1 1
U2 10
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 NOV
PY 2021
VL 143
BP 261
EP 270
DI 10.1016/j.neunet.2021.06.008
EA JUN 2021
PG 10
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA WB4HD
UT WOS:000703533900015
PM 34157650
DA 2022-08-23
ER

PT J
AU Li, XD
   Wang, Y
   Wang, DW
   Yuan, W
   Peng, DZ
   Mei, QZ
AF Li, Xuedong
   Wang, Yue
   Wang, Dongwu
   Yuan, Walter
   Peng, Dezhong
   Mei, Qiaozhu
TI Improving rare disease classification using imperfect knowledge graph
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article; Proceedings Paper
CT 2nd International Workshop on Health Natural Language Processing
   (HealthNLP)
CY JUN 10, 2019
CL Xian, PEOPLES R CHINA
DE Rare disease diagnosis; Knowledge graph; Machine learning; Text
   classification; Extremely imbalanced data
AB Background: Accurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the lack of historical data poses a great challenge to machine learning-based approaches. On the other hand, medical knowledge in automatically constructed knowledge graphs (KGs) has the potential to compensate the lack of labeled training examples. This work aims to develop a rare disease classification algorithm that makes effective use of a knowledge graph, even when the graph is imperfect.
   Method: We develop a text classification algorithm that represents a document as a combination of a "bag of words" and a "bag of knowledge terms," where a "knowledge term" is a term shared between the document and the subgraph of KG relevant to the disease classification task. We use two Chinese disease diagnosis corpora to evaluate the algorithm. The first one, HaoDaiFu, contains 51,374 chief complaints categorized into 805 diseases. The second data set, ChinaRe, contains 86,663 patient descriptions categorized into 44 disease categories.
   Results: On the two evaluation data sets, the proposed algorithm delivers robust performance and outperforms a wide range of baselines, including resampling, deep learning, and feature selection approaches. Both classification-based metric (macro-averaged F-1 score) and ranking-based metric (mean reciprocal rank) are used in evaluation.
   Conclusion: Medical knowledge in large-scale knowledge graphs can be effectively leveraged to improve rare diseases classification models, even when the knowledge graph is incomplete.
C1 [Li, Xuedong; Peng, Dezhong] Sichuan Univ, Coll Comp Sci, Chengdu, Peoples R China.
   [Wang, Yue] Univ North Carolina Chapel Hill, Sch Informat & Lib Sci, Chapel Hill, NC USA.
   [Wang, Dongwu; Yuan, Walter] MobLab Inc, Pasadena, CA USA.
   [Mei, Qiaozhu] Univ Michigan, Sch Informat, Ann Arbor, MI 48109 USA.
RP Mei, QZ (通讯作者)，Univ Michigan, Sch Informat, Ann Arbor, MI 48109 USA.
EM qmei@umich.edu
OI Mei, Qiaozhu/0000-0002-8640-1942
FU National Science Foundation [1633370]; National Library of Medicine
   [2R01LM010681-05]; China Scholarship Council; Kilgour Research Grant
   Award by UNC SILS; NATIONAL LIBRARY OF MEDICINE [R01LM010681] Funding
   Source: NIH RePORTER
FX This work and the publication cost of this article was funded in part by
   the National Science Foundation under grant number 1633370 and by the
   National Library of Medicine under grant number 2R01LM010681-05. Xuedong
   Li was visiting the University of Michigan under the support of the
   China Scholarship Council. Yue Wang was under the support of Kilgour
   Research Grant Award by UNC SILS.
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NR 32
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U2 30
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD DEC 5
PY 2019
VL 19
IS 1
SU 5
AR 238
DI 10.1186/s12911-019-0938-1
PG 10
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Medical Informatics
GA KF2UE
UT WOS:000509102400007
PM 31801534
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Chen, XJ
   Jia, SB
   Xiang, Y
AF Chen, Xiaojun
   Jia, Shengbin
   Xiang, Yang
TI A review: Knowledge reasoning over knowledge graph
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Review
DE Knowledge graph; Reasoning; Rule-based reasoning; Distributed
   representation-based reasoning; Neural network-based reasoning
ID INFERENCE; ONTOLOGY; EFFICIENT
AB Mining valuable hidden knowledge from large-scale data relies on the support of reasoning technology. Knowledge graphs, as a new type of knowledge representation, have gained much attention in natural language processing. Knowledge graphs can effectively organize and represent knowledge so that it can be efficiently utilized in advanced applications. Recently, reasoning over knowledge graphs has become a hot research topic, since it can obtain new knowledge and conclusions from existing data. Herein we review the basic concept and definitions of knowledge reasoning and the methods for reasoning over knowledge graphs. Specifically, we dissect the reasoning methods into three categories: rule-based reasoning, distributed representation-based reasoning and neural network-based reasoning. We also review the related applications of knowledge graph reasoning, such as knowledge graph completion, question answering, and recommender systems. Finally, we discuss the remaining challenges and research opportunities for knowledge graph reasoning. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Chen, Xiaojun; Jia, Shengbin; Xiang, Yang] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.
RP Xiang, Y (通讯作者)，Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.
EM xiaojunchen@tongji.edu.cn; shengbinjia@tongji.edu.cn;
   tjdxxiangyang@gmail.com
OI , Xiaojun/0000-0003-1091-1361
FU National Natural Science Foundation of China [71571136]; Project of
   Science and Technology Commission of Shanghai Municipality [16JC1403000]
FX We would like to thank all the reviewers for their insightful and
   valuable comments, which significantly improve the quality of this
   paper. This work is supported by the National Natural Science Foundation
   of China under Grant no. 71571136 and the Project of Science and
   Technology Commission of Shanghai Municipality under Grant no.
   16JC1403000.
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NR 176
TC 94
Z9 102
U1 152
U2 660
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 1
PY 2020
VL 141
AR 112948
DI 10.1016/j.eswa.2019.112948
PG 21
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 JM6PT
UT WOS:000496334800020
DA 2022-08-23
ER

PT J
AU Li, Q
   Dong, JQ
   Zhong, J
   Li, Q
   Wang, C
AF Li, Qi
   Dong, JunQi
   Zhong, Jiang
   Li, Qing
   Wang, Chen
TI A neural model for type classification of entities for text
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Neural network; Entity classification; Entity mention;
   Machine learning
ID KNOWLEDGE-BASE; LARGE-SCALE; SYNCHRONIZATION; NETWORKS
AB Entity classification has become an increasingly crucial component in the development of knowledge graphs. Due to the incompleteness of the knowledge graph, the semantic relation features of entities in the knowledge graph are generally incomplete, leading to some entities cannot be complete classified. To overcome the weakness of existing research, in this study, we investigated the problem of classifying entities in knowledge graph from the text and proposed an end-to-end entity classification system based on the neural network model. To be specific, firstly, the mention model used long short-term memory to identify the types of each entity mention from the sentences that it contains. Secondly, we proposed a fusion model to fuse the types of multiple mentions to compensate for the existing systems of entity classification. The experimental results demonstrated the necessity and effectiveness of each module in the system. We believe that our proposed method posed a good complement for the existing systems of entity classification. (C) 2019 Published by Elsevier B.V.
C1 [Li, Qi; Zhong, Jiang; Li, Qing; Wang, Chen] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China.
   [Dong, JunQi] Chongqing Univ, Sch Econ & Business Adm, Chongqing 400044, Peoples R China.
   [Zhong, Jiang] Chongqing Univ, Key Lab Dependable Serv Comp Cyber Phys Soc, Chongqing 400044, Peoples R China.
RP Zhong, J (通讯作者)，Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China.
EM zhongjiang@cqu.edu.cn
FU National Key Research and Development Program of China [2017YFB1402400];
   Frontier and Application Foundation Research Program, PR China of CQ
   CSTC [cstc2017jcyjAX0340]; Key Industries Common Key Technologies
   Innovation Projects, PR China of CQ CSTC [cstc2017zdcy-zdyxx0047];
   Chongqing Technological Innovation and Application Demonstration
   Project, PR China [cstc2018jszx-cyzdX0086]; Social Undertakings and
   Livelihood Security Science and Technology Innovation Fund, PR China of
   CQ CSTC [cstc2017shmsA0641]; Fundamental Research Funds for the Central
   Universities [2018CDYJSY0055]; Graduate Research and Innovation
   Foundation of Chongqing, PR China [CYB18058]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2017YFB1402400, in part by the
   Frontier and Application Foundation Research Program, PR China of CQ
   CSTC under Grant cstc2017jcyjAX0340,in part by the Key Industries Common
   Key Technologies Innovation Projects, PR China of CQ CSTC under Grant
   cstc2017zdcy-zdyxx0047, in part by the Chongqing Technological
   Innovation and Application Demonstration Project, PR China under Grant
   cstc2018jszx-cyzdX0086, in part by the Social Undertakings and
   Livelihood Security Science and Technology Innovation Fund, PR China of
   CQ CSTC under Grant cstc2017shmsA0641, in part by the Fundamental
   Research Funds for the Central Universities under Grant 2018CDYJSY0055,
   and in part by the Graduate Research and Innovation Foundation of
   Chongqing, PR China under Grant CYB18058.
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Z9 8
U1 1
U2 30
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JUL 15
PY 2019
VL 176
BP 122
EP 132
DI 10.1016/j.knosys.2019.03.025
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HZ9CK
UT WOS:000469153200010
DA 2022-08-23
ER

PT J
AU Abu-Salih, B
   Al-Tawil, M
   Aljarah, I
   Faris, H
   Wongthongtham, P
   Chan, KY
   Beheshti, A
AF Abu-Salih, Bilal
   Al-Tawil, Marwan
   Aljarah, Ibrahim
   Faris, Hossam
   Wongthongtham, Pornpit
   Chan, Kit Yan
   Beheshti, Amin
TI Relational Learning Analysis of Social Politics using Knowledge Graph
   Embedding
SO DATA MINING AND KNOWLEDGE DISCOVERY
LA English
DT Article
DE Domain-specific Knowledge Graphs; Social Politics; Knowledge Graph
   Embedding; Knowledge Graph Completion; Knowledge Graph Construction;
   Semantic Analytics; Trustworthy Knowledge Graphs
ID DOMAIN; INFORMATION; TWITTER
AB Knowledge Graphs (KGs) have gained considerable attention recently from both academia and industry. In fact, incorporating graph technology and the copious of various graph datasets have led the research community to build sophisticated graph analytics tools, which has extended the application of KGs to tackle a plethora of real-life problems in dissimilar domains. Despite the abundance of the currently proliferated generic KGs, there is a vital need to construct domain-specific KGs. Further, quality and credibility should be assimilated in the process of constructing and augmenting KGs, particularly those propagated from mixed-quality resources such as social media data. For example, the amount of the political discourses in social media is overwhelming yet can be hijacked and misused by spammers to spread misinformation and false news. This paper presents a novel credibility domain-based KG Embedding framework. This framework involves capturing a fusion of data related to politics domain and obtained from heterogeneous resources into a formal KG representation depicted by a politics domain ontology. The proposed approach makes use of various knowledge-based repositories to enrich the semantics of the textual contents, thereby facilitating the interoperability of information. The proposed framework also embodies a domain-based social credibility module to ensure data quality and trustworthiness. The utility of the proposed framework is verified by means of experiments conducted on two constructed KGs. The KGs are then embedded in low-dimensional semantically-continuous space using several embedding techniques. The effectiveness of embedding techniques and social credibility module is further demonstrated and substantiated on link prediction, clustering, and visualisation tasks.
C1 [Abu-Salih, Bilal; Al-Tawil, Marwan; Aljarah, Ibrahim; Faris, Hossam] Univ Jordan, King Abdullah II Sch Informat Technol, Amman, Jordan.
   [Faris, Hossam] Al Hussein Tech Univ, Sch Comp & Informat, Amman, Jordan.
   [Wongthongtham, Pornpit] Univ Western Australia, Perth, WA, Australia.
   [Chan, Kit Yan] Curtin Univ, Perth, WA, Australia.
   [Beheshti, Amin] Macquarie Univ, Melbourne, Vic, Australia.
RP Abu-Salih, B (通讯作者)，Univ Jordan, King Abdullah II Sch Informat Technol, Amman, Jordan.
EM b.abusalih@ju.edu.jo
RI Abu-Salih, Bilal/Q-4020-2016; Aljarah, Ibrahim/J-4719-2013; Faris,
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OI Abu-Salih, Bilal/0000-0001-9875-4369; Aljarah,
   Ibrahim/0000-0002-9265-9819; Faris, Hossam/0000-0003-4261-8127;
   Beheshti, Amin/0000-0002-5988-5494; Wongthongtham,
   Pornpit/0000-0002-3413-6868
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NR 105
TC 5
Z9 5
U1 17
U2 29
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1384-5810
EI 1573-756X
J9 DATA MIN KNOWL DISC
JI Data Min. Knowl. Discov.
PD JUL
PY 2021
VL 35
IS 4
SI SI
BP 1497
EP 1536
DI 10.1007/s10618-021-00760-w
EA MAY 2021
PG 40
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SX2CO
UT WOS:000650090800003
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Allen, BL
AF Allen, BL
TI Boolean browsing in an information system: An experimental test
SO INFORMATION TECHNOLOGY AND LIBRARIES
LA English
DT Article
ID INDIVIDUAL-DIFFERENCES; KNOWLEDGE MAPS; TEXT; MEMORY
AB Boolean browsing presents a two-dimensional word map in response to an initial user query. Users interact with this word map to obtain a list of suggested search terms, which then can be used in information retrieval. This experiment tested the impact of Boolean browsing on retrieval effectiveness with users of standard bibliographic data who had different levels of cognitive abilities, and who were working on different tasks. The results showed a positive impact for this approach to vocabulary selection in users with higher levels of cognitive abilities (particularly spatial scanning abilities), who were accomplishing a high-precision, low-recall task. The implications of this result for the design of information systems are discussed.
C1 Univ Missouri, Sch Informat Sci & Learning Technol, Columbia, MO 65211 USA.
RP Allen, BL (通讯作者)，Univ Missouri, Sch Informat Sci & Learning Technol, Columbia, MO 65211 USA.
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   [No title captured]
NR 24
TC 3
Z9 3
U1 0
U2 3
PU AMER LIBRARY ASSOC
PI CHICAGO
PA 50 E HURON ST, CHICAGO, IL 60611 USA
SN 0730-9295
J9 INFORM TECHNOL LIBR
JI Inf. Technol. Libr.
PD MAR
PY 2001
VL 20
IS 1
BP 12
EP 20
PG 9
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 410WC
UT WOS:000167463200003
DA 2022-08-23
ER

PT J
AU Joshi, KP
   Elluri, L
   Nagar, A
AF Joshi, Karuna Pande
   Elluri, Lavanya
   Nagar, Ankur
TI An Integrated Knowledge Graph to Automate Cloud Data Compliance
SO IEEE ACCESS
LA English
DT Article
DE Cloud computing; cloud security; security domains; security compliance
   models; cloud security models
AB To address data protection concerns, authorities and standards bodies worldwide have released a plethora of regulations, guidelines, and software controls to be applied to Cloud data. As a result, service providers maintaining their end-user's private attributes have seen a surge in compliance requirements. Since most of these regulations are not available in a machine-processable format, it requires significant manual effort to adhere to them. Often many of the laws have overlapping rules, but as they are not referencing each other, providers must duplicate efforts to comply with each regulation. We have done a detailed study of all the data protection regulations that apply to Cloud data. We have developed an integrated, semantically rich knowledge graph that captures these various data compliance regulations. It includes the data threats and security controls that are needed to mitigate the risks. In this paper, we present this knowledge graph in detail, along with the system that we have developed to evaluate it. We have validated our knowledge graph against the privacy policies of various Cloud service providers like Amazon, Google, IBM, and Rackspace. This knowledge graph is available in the public domain and can be used by organizations to automate their compliance processes and set their enterprise Cloud security policies.
C1 [Joshi, Karuna Pande; Elluri, Lavanya; Nagar, Ankur] Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21250 USA.
   [Nagar, Ankur] UBS, New York, NY 10019 USA.
RP Joshi, KP; Elluri, L (通讯作者)，Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21250 USA.
EM karuna.joshi@umbc.edu; lelluri1@umbc.edu
OI Joshi, Karuna/0000-0002-6354-1686; , Lavanya/0000-0002-8881-3369
FU DoD Supplement to NSF Phase I Industry-University Cooperative Research
   Centers (IUCRC) UMBC: Center for Accelerated Real-time Analytics (CARTA)
   [1747724]
FX This work was supported in part by a DoD Supplement to NSF Phase I
   Industry-University Cooperative Research Centers (IUCRC) UMBC: Center
   for Accelerated Real-time Analytics (CARTA) under Award 1747724.
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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 148541
EP 148555
DI 10.1109/ACCESS.2020.3008964
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ND7OJ
UT WOS:000562092000001
OA gold
DA 2022-08-23
ER

PT J
AU Liu, Y
   Zeng, QG
   Mere, JO
   Yang, HR
AF Liu, Yang
   Zeng, Qingguo
   Mere, Joaquin Ordieres
   Yang, Huanrui
TI Anticipating Stock Market of the Renowned Companies: A Knowledge Graph
   Approach
SO COMPLEXITY
LA English
DT Article
ID TECHNICAL ANALYSIS; NEURAL-NETWORKS; NEWS; PREDICTION; SENTIMENT;
   EMBEDDINGS; MEDIA; MODEL
AB An increasing number of the renowned company's investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.
C1 [Liu, Yang; Mere, Joaquin Ordieres] Univ Politecn Madrid, Escuela Tecnica Super Ingn Ind, Dept Ind Engn Business Adm & Stat, E-28006 Madrid, Spain.
   [Zeng, Qingguo] South China Normal Univ, Sch Math Sci, Guangzhou 510631, Guangdong, Peoples R China.
   [Yang, Huanrui] Duke Univ, Elect & Comp Engn Dept, Durham, NC 27708 USA.
RP Liu, Y (通讯作者)，Univ Politecn Madrid, Escuela Tecnica Super Ingn Ind, Dept Ind Engn Business Adm & Stat, E-28006 Madrid, Spain.
EM yang.liu00@alumnos.upm.es; domceng@gmail.com; j.ordieres@upm.es;
   huanrui.yang@duke.edu
RI Ordieres-Meré, Joaquín/B-9677-2011; Zeng, Qingguo/ABE-5040-2021
OI Ordieres-Meré, Joaquín/0000-0002-9677-6764; Liu,
   Yang/0000-0002-9410-1755; Zeng, Qingguo/0000-0002-3365-5690
FU China Scholarship Council (CSC) [201508390019]; EU [847202]
FX This work is supported by the China Scholarship Council (CSC) throughout
   the grant number: 201508390019. The authors also want to thank the
   research program RFCS from the EU, which partially supported this
   research through the research project AutoSurveillance, with project ID
   847202.
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NR 49
TC 3
Z9 3
U1 18
U2 72
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 AUG 7
PY 2019
VL 2019
AR 9202457
DI 10.1155/2019/9202457
PG 15
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematics; Science & Technology - Other Topics
GA IS5NV
UT WOS:000482200300001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ren, L
   Li, YJ
   Wang, XK
   Cui, J
   Zhang, L
AF Ren, Lei
   Li, Yingjie
   Wang, Xiaokang
   Cui, Jin
   Zhang, Lin
TI An ABGE-aided manufacturing knowledge graph construction approach for
   heterogeneous IIoT data integration
SO INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
LA English
DT Article; Early Access
DE Industrial internet of things; smart manufacturing; knowledge graph;
   graph embedding; big data
ID OF-THE-ART
AB The Industrial Internet of Things (IIoT) provides a foundation for the development of emerging digital servitization paradigm in smart manufacturing. The deep integration of massive heterogeneous IIOT data plays a critical role in realising manufacturing digital servitization. However, there is a knowledge gap between different manufacturing fields, which brings a challenge for efficient integration and leverage of industrial big data. For this purpose, a Framework of Manufacturing Knowledge Graph (FMKG) is proposed, which is used to extracts industry knowledge triples from multi-source heterogeneous data to integrate domain knowledge. Also, an attention-based graph embedding model (ABGE) is proposed to discover and complement the implicit missing relationships in the knowledge graph to obtain a complete industrial knowledge graph. The effectiveness of the ABGE model has been verified on several knowledge graph data sets. And an aerospace enterprise production process was taken as an example to establish a product quality knowledge graph, which proved the feasibility of the proposed method.
C1 [Ren, Lei; Li, Yingjie; Zhang, Lin] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China.
   [Wang, Xiaokang] St Francis Xavier Univ, Dept Comp Sci, Antigonish, NS, Canada.
   [Cui, Jin] Beihang Univ, Res Inst Frontier Sci, Beijing, Peoples R China.
RP Ren, L (通讯作者)，Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China.
EM renlei@buaa.edu.cn
OI Wang, Xiaokang/0000-0002-0981-6204; Cui, Jin/0000-0003-1579-6526
FU National Key Research and Development Program of China [2018YFB1700403];
   NSFC (National Science Foundation of China) [92167108, 62173023,
   61836001]
FX The research is supported by The National Key Research and Development
   Program of China No. 2018YFB1700403, and the NSFC (National Science
   Foundation of China) project No. 92167108, 62173023, and 61836001.
CR Belkin M, 2002, ADV NEUR IN, V14, P585
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NR 25
TC 0
Z9 0
U1 23
U2 23
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0020-7543
EI 1366-588X
J9 INT J PROD RES
JI Int. J. Prod. Res.
DI 10.1080/00207543.2022.2042416
EA MAR 2022
PG 15
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA ZR3IS
UT WOS:000767681800001
DA 2022-08-23
ER

PT J
AU Zhao, HX
   Pan, YL
   Yang, F
AF Zhao, Huaxuan
   Pan, Yueling
   Yang, Feng
TI Research on Information Extraction of Technical Documents and
   Construction of Domain Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Proposals; Knowledge engineering; Information retrieval; Semantics; Data
   mining; 3GPP; Data models; Domain knowledge graph; information
   extraction; graph database; TextCNN; Neo4j; resource retrieval
ID WEB
AB With the rapid development of knowledge graph related technologies, domain knowledge graph has become a research hotspot in academia and industry. However, the domain knowledge graph for technical documents is not mature enough, and the semantic information implicit in unstructured technical documents has not been fully tapped. Combining the characteristics of technical documents, the paper proposes a TextCNN-based topic information extraction model and constructs a domain knowledge graph for technical documents. It uses the graph database Neo4j for knowledge storage and visualization. The information extraction model based on TextCNN can automatically extract the subject information of the document and the summary information such as title, ID, status, meeting, organization, etc. Experiments show that the model has high accuracy on the technical document dataset, which can effectively reduce the cost of manual annotation and data collation. At the same time, knowledge graph visualization can facilitate scientific researchers to search, track and update technical documents, which can show the evolution of technology more clearly.
C1 [Zhao, Huaxuan; Pan, Yueling; Yang, Feng] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China.
RP Yang, F (通讯作者)，Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China.
EM yangfeng@sdnu.edu.cn
OI ZHAO, HUAXUAN/0000-0002-9962-597X
FU Natural Science Foundation of China [61801277, 61373081]
FX This work was supported by the Natural Science Foundation of China under
   Grant 61801277 and Grant 61373081.
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NR 23
TC 0
Z9 0
U1 20
U2 63
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 168087
EP 168098
DI 10.1109/ACCESS.2020.3024070
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NT5MT
UT WOS:000572985600001
OA gold
DA 2022-08-23
ER

PT J
AU Nelayah, J
   Gu, L
   Sigle, W
   Koch, CT
   Pastoriza-Santos, I
   Liz-Marzan, LM
   van Aken, PA
AF Nelayah, J.
   Gu, L.
   Sigle, W.
   Koch, C. T.
   Pastoriza-Santos, I.
   Liz-Marzan, L. M.
   van Aken, P. A.
TI Direct imaging of surface plasmon resonances on single triangular silver
   nanoprisms at optical wavelength using low-loss EFTEM imaging
SO OPTICS LETTERS
LA English
DT Article
ID NANOPARTICLES; SPECTROSCOPY
AB Using low-loss energy-filtering transmission electron microscopy (EFTEM) imaging, we map surface plasmon resonances (SPRs) at optical wavelengths on single triangular silver nanoprisms. We show that EFTEM imaging combining high spatial sampling and high energy resolution enables the detection and for the first time, to the best of our knowledge, mapping at the nanoscale of an extra multipolar SPR on these nanoparticles. As illustrated on a 276.5 nm long nanoprism, this eigenmode is found to be enhanced on the three edges where it exhibits a two-lobe distribution. (C) 2009 Optical Society of America
C1 [Nelayah, J.; Gu, L.; Sigle, W.; Koch, C. T.; van Aken, P. A.] Max Planck Inst Met Res, D-70569 Stuttgart, Germany.
   [Pastoriza-Santos, I.; Liz-Marzan, L. M.] Univ Vigo, Dept Quim Fis, Vigo 36310, Spain.
RP Nelayah, J (通讯作者)，Max Planck Inst Met Res, Heisenbergstr 3, D-70569 Stuttgart, Germany.
EM nelayah@mf.mpg.de
RI Koch, Christoph T./E-9689-2011; Santos, Isabel pastoriza/D-4015-2013;
   pastoriza-santos, isabel/N-2729-2019; Liz-Marzán, Luis M/B-8910-2011;
   Gu, Lin/D-9631-2011; van Aken, Peter A./AAC-9287-2019
OI Koch, Christoph T./0000-0002-3984-1523; Santos, Isabel
   pastoriza/0000-0002-1091-1364; pastoriza-santos,
   isabel/0000-0002-1091-1364; Liz-Marzán, Luis M/0000-0002-6647-1353; Gu,
   Lin/0000-0002-7504-031X; van Aken, Peter A./0000-0003-1890-1256;
   nelayah, jaysen/0000-0001-7349-7394
FU European Union [026019]
FX The authors acknowledge financial support from the European Union (E4)
   under the Framework 6 program under a contract for an Integrated
   Infrastructure Initiative (reference 026019 ESTEEM).
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NR 14
TC 75
Z9 75
U1 0
U2 57
PU OPTICAL SOC AMER
PI WASHINGTON
PA 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA
SN 0146-9592
J9 OPT LETT
JI Opt. Lett.
PD APR 1
PY 2009
VL 34
IS 7
BP 1003
EP 1005
DI 10.1364/OL.34.001003
PG 3
WC Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Optics
GA 436QI
UT WOS:000265429100049
PM 19340200
DA 2022-08-23
ER

PT J
AU Chen, T
   Zhang, YM
   Qian, XW
   Li, J
AF Chen, Tian
   Zhang, Yimu
   Qian, Xinwu
   Li, Jian
TI A knowledge graph-based method for epidemic contact tracing in public
   transportation
SO TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
LA English
DT Article
DE Digital Contact Tracing; Contact Network; Knowledge Graph; Public
   Transportation; Epidemic Control
ID DESTINATION ESTIMATION; OUTBREAK; ORIGIN; SYSTEM; INFORM; SPREAD; CITY
AB Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.
C1 [Chen, Tian; Li, Jian] Tongji Univ, Key Lab Rd Traff Engn, Minist Educ, 4800 Caoan Rd, Shanghai 201804, Peoples R China.
   [Zhang, Yimu] Tongji Univ, Urban Mobil Inst, 4800 Caoan Rd, Shanghai 201804, Peoples R China.
   [Qian, Xinwu] Univ Alabama, Tuscaloosa, AL 35487 USA.
RP Li, J (通讯作者)，Tongji Univ, Key Lab Rd Traff Engn, Minist Educ, 4800 Caoan Rd, Shanghai 201804, Peoples R China.
EM 1831348@tongji.edu.cn; 1931361@tongji.edu.cn; xinwu.qian@ua.edu;
   jianli@tongji.edu.cn
FU National Key R&D Program of China [2018YFB1601100]
FX This research is support by the National Key R&D Program of China (Grant
   No. 2018YFB1601100). The authors also thank the anonymous reviewers'
   constructive comments and suggestions.
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NR 49
TC 0
Z9 0
U1 26
U2 26
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0968-090X
EI 1879-2359
J9 TRANSPORT RES C-EMER
JI Transp. Res. Pt. C-Emerg. Technol.
PD APR
PY 2022
VL 137
AR 103587
DI 10.1016/j.trc.2022.103587
PG 22
WC Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Transportation
GA 0F4NC
UT WOS:000777337100006
PM 35153392
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Kraft, M
   Eibeck, A
AF Kraft, Markus
   Eibeck, Andreas
TI J-Park Simulator: Wissensgraph fur Industrie 4.0J-Park Simulator:
   Knowledge Graph for Industry 4.0
SO CHEMIE INGENIEUR TECHNIK
LA German
DT Article
DE Knowledge Graphs; Industrie 4.0; Ontology; Semantic web
ID ONTOLOGY; DESIGN; TECHNOLOGIES
AB This article introduces ontologies, Knowledge Graphs, agents and their role in the context of Industry 4.0. The importance of interoperability of agents in cross-domain scenarios using the J-Park Simulator (JPS), which is part of the Knowledge Graph () is shown. The dispersion of air pollutants from a power plant situated in Berlin is investigated with the help of JPS. The article describes how interoperability between agents in cross-domain scenarios can be achieved and discusses related open problems.
C1 [Kraft, Markus; Eibeck, Andreas] Cambridge Ctr Adv Res & Educ Singapore, CARES, 1 Create Way,CREATE Tower,05-05, Singapore 138602, Singapore.
   [Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, Philippa Fawcett Dr, Cambridge CB3 0AS, England.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, 62 Nanyang Dr, Singapore 637459, Singapore.
RP Kraft, M (通讯作者)，Cambridge Ctr Adv Res & Educ Singapore, CARES, 1 Create Way,CREATE Tower,05-05, Singapore 138602, Singapore.; Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, Philippa Fawcett Dr, Cambridge CB3 0AS, England.; Kraft, M (通讯作者)，Nanyang Technol Univ, Sch Chem & Biomed Engn, 62 Nanyang Dr, Singapore 637459, Singapore.
EM mk306@cam.ac.uk
RI Kraft, Markus/D-7243-2016
OI Kraft, Markus/0000-0002-4293-8924
CR Anderl R., 2016, WORKING PAPER
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NR 40
TC 2
Z9 2
U1 0
U2 15
PU WILEY-V C H VERLAG GMBH
PI WEINHEIM
PA POSTFACH 101161, 69451 WEINHEIM, GERMANY
SN 0009-286X
EI 1522-2640
J9 CHEM-ING-TECH
JI Chem. Ing. Tech.
PD JUL
PY 2020
VL 92
IS 7
SI SI
BP 967
EP 977
DI 10.1002/cite.202000002
EA MAY 2020
PG 11
WC Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA MB1CB
UT WOS:000531404200001
DA 2022-08-23
ER

PT J
AU Li, C
   Peng, XT
   Niu, YH
   Zhang, SH
   Peng, H
   Zhou, C
   Li, JX
AF Li, Chen
   Peng, Xutan
   Niu, Yuhang
   Zhang, Shanghang
   Peng, Hao
   Zhou, Chuan
   Li, Jianxin
TI Learning graph attention-aware knowledge graph embedding
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Graph attention mechanism; Entity typing;
   Link prediction
AB The knowledge graph, which utilizes graph structure to represent multi-relational data, has been widely used in the reasoning and prediction tasks, attracting considerable research efforts recently. However, most existing works still concentrate on learning knowledge graph embeddings straightforwardly and intuitively without subtly considering the context of knowledge. Specifically, recent models deal with each single triple independently or consider contexts indiscriminately, which is one-sided as each knowledge unit (i.e., triple) can be derived from its partial surrounding triples. In this paper, we propose a graph-attention-based model to encode entities, which formulates a knowledge graph as an irregular graph and explores a number of concrete and interpretable knowledge compositions by integrating the graph-structured information via multiple independent channels. To measure the correlation between entities from different angles (i.e., entity pair, relation, and structure), we respectively develop three attention metrics. By making use of our enhanced entity embeddings, we further introduce several improved factorization functions for updating relation embeddings and evaluating candidate triples. We conduct extensive experiments on downstream tasks including entity classification, entity typing, and link prediction to validate our methods. Empirical results validate the importance of our introduced attention metrics and demonstrate that our proposed method can improve the performance of factorization models on large-scale knowledge graphs. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Li, Chen; Niu, Yuhang; Peng, Hao; Li, Jianxin] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China.
   [Niu, Yuhang] Beihang Univ, State Key Lab Software Dev Environm Lab, Beijing, Peoples R China.
   [Zhang, Shanghang] Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA USA.
   [Zhou, Chuan] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China.
   [Peng, Hao] Beihang Univ, Sch Cyber Sci & Technol, Beijing, Peoples R China.
   [Peng, Xutan] Univ Sheffield, Dept Comp Sci, Sheffield, S Yorkshire, England.
RP Li, C (通讯作者)，Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China.
OI Niu, Yuhang/0000-0003-1291-9746
FU Natural Science Foundation of China program [U20B2053, 62002007,
   61772151]; S&T Program of Hebei [20310101D, SKLSDE-2020ZX-12]
FX The corresponding author is Jianxin Li. This work was supported by the
   Natural Science Foundation of China program (No. U20B2053, 62002007 and
   61772151) , S&T Program of Hebei through grant 20310101D, and
   SKLSDE-2020ZX-12.
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NR 70
TC 2
Z9 2
U1 20
U2 32
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 516
EP 529
DI 10.1016/j.neucom.2021.01.139
EA SEP 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WC2IT
UT WOS:000704086300003
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Xiu, XL
   Qian, Q
   Wu, SZ
AF Xiu, Xiaolei
   Qian, Qing
   Wu, Sizhu
TI Construction of a Digestive System Tumor Knowledge Graph Based on
   Chinese Electronic Medical Records: Development and Usability Study
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE Chinese electronic medical records; knowledge graph; digestive system
   tumor; graph evaluation
AB Background: With the increasing incidences and mortality of digestive system tumor diseases in China, ways to use clinical experience data in Chinese electronic medical records (CEMRs) to determine potentially effective relationships between diagnosis and treatment have become a priority. As an important part of artificial intelligence, a knowledge graph is a powerful tool for information processing and knowledge organization that provides an ideal means to solve this problem.
   Objective: This study aimed to construct a semantic-driven digestive system tumor knowledge graph (DSTKG) to represent the knowledge in CEMRs with fine granularity and semantics.
   Methods: This paper focuses on the knowledge graph schema and semantic relationships that were the main challenges for constructing a Chinese tumor knowledge graph. The DSTKG was developed through a multistep procedure. As an initial step, a complete DSTKG construction framework based on CEMRs was proposed. Then, this research built a knowledge graph schema containing 7 classes and 16 kinds of semantic relationships and accomplished the DSTKG by knowledge extraction, named entity linking, and drawing the knowledge graph. Finally, the quality of the DSTKG was evaluated from 3 aspects: data layer, schema layer, and application layer.
   Results: Experts agreed that the DSTKG was good overall (mean score 4.20). Especially for the aspects of "rationality of schema structure," "scalability," and "readability of results," the DSTKG performed well, with scores of 4.72, 4.67, and 4.69, respectively, which were much higher than the average. However, the small amount of data in the DSTKG negatively affected its "practicability" score. Compared with other Chinese tumor knowledge graphs, the DSTKG can represent more granular entities, properties, and semantic relationships. In addition, the DSTKG was flexible, allowing personalized customization to meet the designer's focus on specific interests in the digestive system tumor.
   Conclusions: We constructed a granular semantic DSTKG. It could provide guidance for the construction of a tumor knowledge graph and provide a preliminary step for the intelligent application of knowledge graphs based on CEMRs. Additional data sources and stronger research on assertion classification are needed to gain insight into the DSTKG's potential.
C1 [Xiu, Xiaolei; Qian, Qing; Wu, Sizhu] Chinese Acad Med Sci & Peking Union Med Coll, Inst Med Informat, Med Lib, 3 Yabao Rd, Beijing 100020, Peoples R China.
RP Wu, SZ (通讯作者)，Chinese Acad Med Sci & Peking Union Med Coll, Inst Med Informat, Med Lib, 3 Yabao Rd, Beijing 100020, Peoples R China.
EM wu.sizhu@imicams.ac.cn
OI wu, si zhu/0000-0003-4540-9910
FU Construction of Online Service System and Standard Penetration of
   National Population Health Science Data Center [NCMI-KDOIN-201905];
   Precise Medical Database Group of Major Diseases of the Key Special
   Project of Precision Medical Research in China's National Key RD
   Programmes [2016YFC0901602]
FX The authors would like to thank the CCKS 2018 CNER challenge organizers
   for providing the data source. The research is supported by the
   Construction of Online Service System and Standard Penetration of
   National Population Health Science Data Center (grant
   #NCMI-KDOIN-201905) and the Precise Medical Database Group of Major
   Diseases of the Key Special Project of Precision Medical Research in
   China's National Key R&D Programmes (grant #2016YFC0901602).
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NR 49
TC 3
Z9 4
U1 7
U2 35
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 e18287
DI 10.2196/18287
PG 18
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA OO6FW
UT WOS:000587474400008
PM 33026359
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Huang, ZB
   Hou, ZK
   Liu, FB
   Zhang, M
   Hu, W
   Xu, SF
AF Huang, Zhibang
   Hou, Zhengkun
   Liu, Fengbin
   Zhang, Mei
   Hu, Wen
   Xu, Shaofen
TI Scientometric Analysis of Medicinal and Edible Plant Coptis
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE Coptis; berberine; scientometric; CiteSpace; VOSviewer
ID BINDING CASSETTE PROTEIN; ALKALOID BIOSYNTHESIS; MOLECULAR-CLONING;
   CHINENSIS FRANCH; EMERGING TRENDS; BERBERINE; PUBLICATIONS; DECOCTION
AB Objective: A scientometric analysis to obtain knowledge mapping of Coptis revealed the current research situation, knowledge base and research hotspots in Coptis research.</p>
   Methods: Coptis-related documents published from 1987 to 2020 were selected through the Web of Science Core Collection. CiteSpace, VOSviewer and Microsoft Excel were used to construct knowledge maps of the Coptis research field.</p>
   Results: A total of 367 documents and their references were analyzed. These papers were primarily published in mainland China (214), followed by Japan (57) and South Korea (52), and they each formed respective cooperation networks. The document co-citation analysis suggested that the identification of Coptis Salisb. species, the production of alkaloids, and the mechanisms of action of these alkaloids formed the knowledge bases in this field. A keyword analysis further revealed that the research hotspots were primarily concentrated in three fields of research involving berberine, Coptis chinensis Franch, and Coptis japonica (Thunb) Makino. Oxidative stress, rat plasma (for the determination of plasma alkaloid contents), and Alzheimer's disease are recent research hotspots associated with Coptis.</p>
   Conclusion: Coptis research was mainly distributed in three countries: China, Japan, and South Korea. Researchers were concerned with the identification of Coptis species, the production of Coptis alkaloids, and the efficacy and pharmacological mechanism of the constituent alkaloids. In addition, the anti-oxidative stress, pharmacokinetics, and Alzheimer's disease treatment of Coptis are new hotspots in this field. This study provides a reference for Coptis researchers.</p>
C1 [Huang, Zhibang; Xu, Shaofen] Guangzhou Univ Chinese Med, Postgrad Coll, Guangzhou, Peoples R China.
   [Hou, Zhengkun; Liu, Fengbin] Guangzhou Univ Chinese Med, Affiliated Hosp 1, Dept Gastroenterol, Guangzhou, Peoples R China.
   [Liu, Fengbin] Guangzhou Univ Chinese Med, Baiyun Hosp, Affiliated Hosp 1, Guangzhou, Peoples R China.
   [Zhang, Mei] Univ South China, Changsha Cent Hosp, Dept Integrat Med, Changsha, Peoples R China.
   [Hu, Wen] Huanggang Hosp Tradit Chinese Med, Intens Care Unit, Huanggang, Peoples R China.
RP Hou, ZK (通讯作者)，Guangzhou Univ Chinese Med, Affiliated Hosp 1, Dept Gastroenterol, Guangzhou, Peoples R China.
EM fenghou5128@126.com
OI Huang, Zhibang/0000-0003-4052-2308
FU National Natural Science Foundation of China [81774450]; Key Field
   Project of Ordinary Colleges and Universities in Guangdong Province
   [2020ZDZX3011]; Pearl River S&T Nova Program of Guangzhou, China
   [201710010077]; High-Level University Discipline Collaborative
   Innovation Team Project of Guangzhou University of Chinese Medicine
   [2021XK18]
FX This work was supported by the National Natural Science Foundation of
   China (No. 81774450), the Key Field Project of Ordinary Colleges and
   Universities in Guangdong Province (No. 2020ZDZX3011), the Pearl River
   S&T Nova Program of Guangzhou, China (No. 201710010077), and the "Double
   topclass" and High-Level University Discipline Collaborative Innovation
   Team Project of Guangzhou University of Chinese Medicine (No. 2021XK18).
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NR 60
TC 1
Z9 1
U1 16
U2 36
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 12
PY 2021
VL 12
AR 725162
DI 10.3389/fphar.2021.725162
PG 15
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Pharmacology & Pharmacy
GA WU5IO
UT WOS:000716578900001
PM 34456737
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Si, HY
   Shi, JG
   Wu, GD
   Chen, JD
   Zhao, XB
AF Si, Hongyun
   Shi, Jian-gang
   Wu, Guangdong
   Chen, Jindao
   Zhao, Xianbo
TI Mapping the bike sharing research published from 2010 to 2018: A
   scientometric review
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Review
DE Bike sharing; Review; Scientometric; Knowledge map; Sustainable
   transportation
ID INFORMATION MODELING BIM; CONGESTION EVIDENCE; BICYCLE SCHEME; SYSTEMS;
   IMPACT; MONTREAL; SUSTAINABILITY; BARRIERS; INFRASTRUCTURE; INNOVATION
AB An increasing number of studies since 2010 have examined bike sharing from diverse perspectives to provide the best travel practices of "the last mile". However, few studies have attempted to comprehensively review existing literature over the past decade. The present study aims to map bike sharing research published between 2010 and 2018. A total of 208 relevant articles were collected to conduct scientometric analysis. The results revealed that the most significant contributions in bike sharing research primarily originated from the US, China, Canada, England and Australia. Critical institutions, publications and articles were also identified. The knowledge domains of bike sharing research focus mainly on topic categories of factors & barrier, system optimization, behavior & impact, safety & health, and sharing economy. Evolutionary trends in bike sharing research tend to move from the safety and benefits of bike usage to more complex external impacts, system optimization, design and integration with public transit. Furthermore, increasing interests and outputs in the new generation of dockless bike sharing programs were observed from the research community for the past two years. The present study contributes to the existing body of knowledge on bike sharing by presenting a new, integrated and holistic knowledge map. This study offers valuable guidance and in-depth understanding to researchers, operators and policy makers who wish to promote bike sharing sustainability, as well as for follow-up studies, updates and management. (C) 2018 Elsevier Ltd. All rights reserved.
C1 [Si, Hongyun; Shi, Jian-gang; Chen, Jindao] Tongji Univ, Sch Econ & Management, Shanghai, Peoples R China.
   [Wu, Guangdong] Jiangxi Univ Finance & Econ, Sch Tourism & Urban Management, Nanchang, Jiangxi, Peoples R China.
   [Chen, Jindao] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Kowloon, Hung Hom, Hong Kong, Peoples R China.
   [Zhao, Xianbo] Cent Queensland Univ, Sch Engn & Technol, Sydney, NSW, Australia.
RP Shi, JG (通讯作者)，Tongji Univ, Sch Econ & Management, Shanghai, Peoples R China.
EM sjg126com@126.com
RI Zhao, Xianbo/H-1540-2017; Chen, Jindao/L-8522-2019; Si,
   Hongyun/AAY-2908-2021
OI Zhao, Xianbo/0000-0003-0153-5173; Chen, Jindao/0000-0002-6294-829X; si,
   hong yun/0000-0003-2367-4756
FU Fundamental Research Funds for the Central Universities [22120170137]
FX This work was supported by the Fundamental Research Funds for the
   Central Universities (22120170137). The authors wish to thank four
   anonymous reviewers, whose insightful comments and helpful suggestions
   significantly contributed to improve this paper.
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NR 88
TC 110
Z9 110
U1 40
U2 255
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 MAR 10
PY 2019
VL 213
BP 415
EP 427
DI 10.1016/j.jclepro.2018.12.157
PG 13
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 HO7MU
UT WOS:000461132600037
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Ali, M
   Berrendorf, M
   Hoyt, CT
   Vermue, L
   Sharifzadeh, S
   Tresp, V
   Lehmann, J
AF Ali, Mehdi
   Berrendorf, Max
   Hoyt, Charles Tapley
   Vermue, Laurent
   Sharifzadeh, Sahand
   Tresp, Volker
   Lehmann, Jens
TI PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph
   Embeddings
SO JOURNAL OF MACHINE LEARNING RESEARCH
LA English
DT Article
DE Knowledge Graphs; Knowledge Graph Embeddings; Relational Learning
AB Recently, knowledge graph embeddings (KGEs) have received significant attention, and several software libraries have been developed for training and evaluation. While each of them addresses specific needs, we report on a community effort to a re-design and re-implementation of PyKEEN, one of the early KGE libraries. PyKEEN 1.0 enables users to compose knowledge graph embedding models based on a wide range of interaction models, training approaches, loss functions, and permits the explicit modeling of inverse relations. It allows users to measure each component's influence individually on the model's performance. Besides, an automatic memory optimization has been realized in order to optimally exploit the provided hardware. Through the integration of Optuna, extensive hyper-parameter optimization (HPO) functionalities are provided.
C1 [Ali, Mehdi; Lehmann, Jens] Univ Bonn, Smart Data Analyt Grp, Bonn, Germany.
   [Ali, Mehdi; Lehmann, Jens] Fraunhofer IAIS, St Augustin, Germany.
   [Berrendorf, Max; Sharifzadeh, Sahand; Tresp, Volker] Ludwig Maximilians Univ Munchen, Munich, Germany.
   [Hoyt, Charles Tapley] Enveda Biosci, Bonn, Germany.
   [Vermue, Laurent] Tech Univ Denmark, Lyngby, Denmark.
   [Tresp, Volker] Siemens AG, Munich, Germany.
RP Ali, M (通讯作者)，Univ Bonn, Smart Data Analyt Grp, Bonn, Germany.; Ali, M (通讯作者)，Fraunhofer IAIS, St Augustin, Germany.
EM MEHDI.ALI@CS.UNI-BONN.DE; BERRENDORF@DES.IFI.LMU.DE;
   CHARLES.HOYT@ENVEDATX.COM; LAUVE@DTU.DK; SHARIFZADEH@DBS.IFI.LMU.DE;
   VOLKER.TRESP@SIEMENS.COM; JENS.LEHMANN@CS.UNI-BONN.DE
FU Smart Data Analytics research group (University of Bonn); Fraunhofer
   IAIS; Munich Center for Machine Learning (MCML); Siemens; Technical
   University of Denmark; German Federal Ministry of Education and Research
   (BMBF) [01IS18036A, 01IS18050D]; Innovation Fund Denmark; Danish Center
   for Big Data Analytics driven Innovation (DABAI)
FX PyKEEN 1.0 is publicly available under the MIT License at
   https://github.com/pykeen/pykeen, and is distributed through the Python
   Package Index. It will be maintained by the core developer team that is
   supported by the Smart Data Analytics research group (University of
   Bonn), Fraunhofer IAIS, Munich Center for Machine Learning (MCML),
   Siemens, and the Technical University of Denmark (section for Cognitive
   Systems and section for Statistics and Data Analysis). The project is
   funded by the German Federal Ministry of Education and Research (BMBF)
   under Grant No. 01IS18036A and Grant No. 01IS18050D (project MLWin) as
   well as the Innovation Fund Denmark with the Danish Center for Big Data
   Analytics driven Innovation (DABAI) which ensures the maintenance of the
   project in the next years.
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NR 18
TC 11
Z9 11
U1 3
U2 6
PU MICROTOME PUBL
PI BROOKLINE
PA 31 GIBBS ST, BROOKLINE, MA 02446 USA
SN 1532-4435
J9 J MACH LEARN RES
JI J. Mach. Learn. Res.
PY 2021
VL 22
AR 2021
PG 6
WC Automation & Control Systems; Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA SK7MQ
UT WOS:000656400900001
DA 2022-08-23
ER

PT J
AU Lee, WK
   Shin, WC
   Jagvaral, B
   Roh, JS
   Kim, MS
   Lee, MH
   Park, HK
   Park, YT
AF Lee, Wan-Kon
   Shin, Won-Chul
   Jagvaral, Batselem
   Roh, Jae-Seung
   Kim, Min-Sung
   Lee, Min-Ho
   Park, Hyun-Kyu
   Park, Young-Tack
TI A path-based relation networks model for knowledge graph completion
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge graph completion; Relation network; Link prediction; Triple
   classification
AB We consider the problem of learning and inference in a large-scale knowledge graph containing incomplete knowledge. We show that a simple neural network module for relational reasoning through the path extracted from the knowledge base can be used to reliably infer new facts for the missing link. In our work, we used path ranking algorithm to extract the relation path from knowledge graph and use it to build train data. In order to learn the characteristics of relation, a detour path between nodes was created as training data using the extracted relation path. Using this, we trained a model that can predict whether a given triple (Head entity, relation, tail entity) is valid or not. Experiments show that our model obtains better link prediction, relation prediction and triple classification results than previous state-of-the-art models on benchmark datasets WN18RR, FB15k-237, WN11 and FB13.
C1 [Lee, Wan-Kon; Shin, Won-Chul; Jagvaral, Batselem; Roh, Jae-Seung; Kim, Min-Sung; Lee, Min-Ho; Park, Hyun-Kyu; Park, Young-Tack] Soongsil Univ, Dept Comp Sci, 1-1 Sangdo Dong, Seoul 156743, South Korea.
RP Park, YT (通讯作者)，Soongsil Univ, Dept Comp Sci, 1-1 Sangdo Dong, Seoul 156743, South Korea.
EM ocshin1201@naver.com; park@ssu.ac.kr
FU Institute of Information & communications Technology Planning &
   Evaluation (IITP) - Korea government (MSIT) [2017-0-00162]
FX This work was supported by the Institute of Information & communications
   Technology Planning & Evaluation (IITP) grant funded by the Korea
   government(MSIT) (No. 2017-0-00162, Development of Humancare Robot
   Technology for Aging Society).
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NR 32
TC 0
Z9 0
U1 9
U2 16
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD NOV 15
PY 2021
VL 182
AR 115273
DI 10.1016/j.eswa.2021.115273
EA JUN 2021
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA UO7RL
UT WOS:000694890100012
DA 2022-08-23
ER

PT J
AU Moshiul, AM
   Mohammad, R
   Hira, FA
   Maarop, N
AF Moshiul, Alam Md
   Mohammad, Roslina
   Hira, Fariha Anjum
   Maarop, Nurazean
TI Alternative Marine Fuel Research Advances and Future Trends: A
   Bibliometric Knowledge Mapping Approach
SO SUSTAINABILITY
LA English
DT Review
DE alternative marine fuel; bibliometric analysis; biblioshiny;
   decarbonization; maritime shipping; VOSviewer
ID LIFE-CYCLE ASSESSMENT; DECISION-MAKING; NATURAL-GAS; TECHNOLOGIES;
   TRANSPORTATION; SUSTAINABILITY; EMISSIONS; POLLUTION; BIOFUELS; CRITERIA
AB While the marine sector contributes significantly to the global economy, its environmental impact is a cause for apprehension due to growing concerns about ship emissions. The International Maritime Organization (IMO) has set decarbonization strategies consistent with sustainable development goals. The impending legislation aimed at reducing greenhouse gas (GHG) emissions from maritime shipping by at least half by 2050 and to zero by the end of the century. A growing body of research has focused on alternative marine fuel selection; hence, this bibliometric review is timely. We assess the global scientific research on alternative marine fuel for knowledge mapping based on the articles available on the Scopus database since 1973. A total of 749 publications associated with alternative marine fuel has been subjected to a range of bibliometric analyses to explore this research field quantitatively and qualitatively. The study utilized the R-studio bibliometrics package and VOSviewer bibliometric tools to generate the results. The field of study has a growth rate of 7.05%, having a significant contribution to knowledge from the USA, the United Kingdom, India, and China. Recent trends indicate that researchers are increasingly focused on alternative fuel oil choices. By analyzing commonly used keywords, it was possible to trace a multi-criteria decision analysis process that might be utilized to construct decision support systems for alternative maritime fuel selection. According to relevant articles, the research community has concentrated on the possibility of alternative fuels being utilized in place of traditional marine fuels to reduce emissions from the shipping sector from an environmental, technological, and economic standpoint. The current study offers an updated and comprehensive overview of research trends on alternative marine fuels. Researchers interested in data mapping work in this area can begin with this endeavor.
C1 [Moshiul, Alam Md; Mohammad, Roslina; Maarop, Nurazean] Univ Teknol Malaysia, Razak Fac Technol & Informat, Kuala Lumpur 54100, Malaysia.
   [Hira, Fariha Anjum] Univ Teknol Malaysia, Azman Hashim Int Business Sch, Kuala Lumpur 54100, Malaysia.
RP Hira, FA (通讯作者)，Univ Teknol Malaysia, Azman Hashim Int Business Sch, Kuala Lumpur 54100, Malaysia.
EM mamd4@graduate.utm.my; mroslina.kl@utm.my; hira-1992@graduate.utm.my;
   nurazean.kl@utm.my
OI HIRA, FARIHA ANJUM/0000-0002-8912-0847; Alam,
   Moshiul/0000-0002-3351-912X
FU Universiti Teknologi Malaysia (UTM) [19J48]; Ministry of Higher
   Education (MOHE) under the Fundamental Research Grant Scheme (FRGS)
   [FRGS/1/2019/TK03/UTM/02/14]; Razak Faculty of Technology and
   Informatics (UTM); Azman Hashim International Business School (UTM);
   Universiti Teknologi Malaysia (UTM)
FX This study was financially supported by the Universiti Teknologi
   Malaysia (UTM) Encouragement Research Grant (19J48), the Ministry of
   Higher Education (MOHE) under the Fundamental Research Grant Scheme
   (FRGS) (grant number: FRGS/1/2019/TK03/UTM/02/14), Razak Faculty of
   Technology and Informatics (UTM), Azman Hashim International Business
   School (UTM), Universiti Teknologi Malaysia (UTM); for all the support
   towards making this study a success.; This research was funded by and
   the Universiti Teknologi Malaysia (UTM) Encouragement Research Grant
   (19J48) and the Ministry of Higher Education (MOHE) under the
   Fundamental Research Grant Scheme (FRGS) (grant number:
   FRGS/1/2019/TK03/UTM/02/14).
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NR 44
TC 1
Z9 1
U1 8
U2 8
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAY
PY 2022
VL 14
IS 9
AR 4947
DI 10.3390/su14094947
PG 27
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 1E6TY
UT WOS:000794619600001
OA gold
DA 2022-08-23
ER

PT J
AU Li, SY
   Jiang, S
   Tian, M
   Su, Y
   Li, J
AF Li, Shuyang
   Jiang, Shu
   Tian, Miao
   Su, Yun
   Li, Jun
TI Mapping the research status and dynamic frontiers of functional
   clothing: a review via bibliometric and knowledge visualization
SO INTERNATIONAL JOURNAL OF CLOTHING SCIENCE AND TECHNOLOGY
LA English
DT Review
DE Functional clothing; Visual analysis; Knowledge mapping; Dynamic
   frontiers
ID COMPRESSION GARMENTS; NUMERICAL-SIMULATION; WATERPROOF; MEMBRANES; HEAT;
   PERFORMANCE; MOTION; MANAGEMENT; TEXTILES; COMFORT
AB Purpose The purpose of this paper is to gain an in-depth understanding of the research progress, hotspots and future trends in the field of functional clothing. Design/methodology/approach The records of 4,153 pieces of literature related to functional clothing were retrieved from Web of Science by using a comprehensive retrieval strategy. A piece of software, CiteSpace was used as a tool to visualize the results of specific terms, such as author, institution and keyword. By analyzing the knowledge maps with several indicators, the intellectual basis and research fronts for the functional clothing domain could then be demonstrated. Findings The result indicated that functional clothing was a popular research field, with approximately 500 papers published worldwide in 2020. Its main research area was material science and involved public environmental and occupational health, engineering, etc. showing the characteristic of multi-interdisciplinary. Textile Research Journal and International Journal of Clothing Science and Technology were the top two journals in this field. The USA, China, Australia, England and Germany have been active and frequently cooperating with each other. Donghua University, the Hong Kong Polytechnic University and NASA, with the largest number of publications, were identified as the main research drivers. According to the co-citation analysis, thermal stress, nanogenerator and electrospinning were the topics of most cited articles during the past 20 years. Practical implications The findings identified smart clothing and protective clothing to be the research frontiers in the field of functional clothing, which deserved further study in the future. Originality/value The outcomes offered an overview of the research status and future trends of the functional clothing field. It could not only provide scholars with convenience in identifying research hotspots and building potential cooperation in the follow-up research, but also assist beginners in searching core scholars and literature of great significance.
C1 [Li, Shuyang; Jiang, Shu; Tian, Miao; Su, Yun; Li, Jun] Donghua Univ, Coll Fash & Design, Shanghai, Peoples R China.
   [Tian, Miao; Su, Yun; Li, Jun] Donghua Univ, Key Lab Clothing Design & Technol, Minist Educ, Shanghai, Peoples R China.
RP Tian, M (通讯作者)，Donghua Univ, Coll Fash & Design, Shanghai, Peoples R China.; Tian, M (通讯作者)，Donghua Univ, Key Lab Clothing Design & Technol, Minist Educ, Shanghai, Peoples R China.
EM tianmiao@dhu.edu.cn
OI Su, Yun/0000-0001-8697-4284; Jiang, Shu/0000-0002-1010-8352
FU Shanghai Education Development Foundation; Shanghai Municipal Education
   Commission [18CG76]; Fundamental Research Funds for the Central
   Universities [2232022G-08]; International Cooperation Fund of Science
   and Technology Commission of Shanghai Municipality [21130750100]
FX The authors would like to acknowledge the financial support from the
   Chenguang Program supported by Shanghai Education Development Foundation
   and Shanghai Municipal Education Commission (Grant No. 18CG76),
   Fundamental Research Funds for the Central Universities (Grant NO.
   2232022G-08) and International Cooperation Fund of Science and
   Technology Commission of Shanghai Municipality (Grant NO. 21130750100).
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NR 59
TC 0
Z9 0
U1 20
U2 20
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0955-6222
EI 1758-5953
J9 INT J CLOTH SCI TECH
JI Int. J. Cloth. Sci. Technol.
PD AUG 2
PY 2022
VL 34
IS 5
BP 697
EP 715
DI 10.1108/IJCST-10-2021-0151
EA APR 2022
PG 19
WC Materials Science, Textiles
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Materials Science
GA 3L8JM
UT WOS:000782836400001
DA 2022-08-23
ER

PT J
AU Zhang, QJ
   Wang, RG
   Yang, J
   Xue, LX
AF Zhang, Qianjin
   Wang, Ronggui
   Yang, Juan
   Xue, Lixia
TI Knowledge graph embedding by translating in time domain space for link
   prediction
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; Link prediction; Cross-operation;
   Self-interaction
AB Knowledge graph embedding, which aims to learn distributed representations of entities and relations, has been proven to be an effective method for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly consider that the space where head and tail entities are located has the same properties. However head and tail entities can be different types of objects, and they should not be located in the vector space with the same properties. In this paper, we propose a novel knowledge graph embedding model called TimE, which presents each entry of the head (or tail) entity embeddings as a point in time domain space, and the corresponding entry of tail (or head) entity embeddings as a point in frequency domain space. Specifically, TimE defines each relation as a composition relation, which consists of a translation between entities and a diagonal projection matrix that projects the entities into the time domain space. In addition, we propose a cross-operation to model inverse and symmetric relations. Experimental results show that TimE not only outperforms existing state-of-the-art models on several large-scale benchmark datasets for the link prediction task, but also can better capture diversity distribution of entity embeddings for different relation patterns effectively, and can also model all relation patterns (including symmetry/antisymmetry, inversion, and composition). (C) 2020 Elsevier B.V. All rights reserved.Y
C1 [Zhang, Qianjin; Wang, Ronggui; Yang, Juan; Xue, Lixia] Hefei Univ Technol, Sch Comp & Informat, Hefei 230601, Peoples R China.
RP Yang, J (通讯作者)，Hefei Univ Technol, Sch Comp & Informat, Hefei 230601, Peoples R China.
EM yangjuan_a1002@126.com
OI Zhang, Qianjin/0000-0002-1321-4329
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NR 40
TC 7
Z9 8
U1 1
U2 11
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JAN 5
PY 2021
VL 212
AR 106564
DI 10.1016/j.knosys.2020.106564
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PN5NK
UT WOS:000604525200010
DA 2022-08-23
ER

PT J
AU Nguyen, T
   Duong, QH
   Van Nguyen, T
   Zhu, Y
   Zhou, L
AF Nguyen, Tiep
   Duong, Quang Huy
   Van Nguyen, Truong
   Zhu, You
   Zhou, Li
TI Knowledge mapping of digital twin and physical internet in Supply Chain
   Management: A systematic literature review
SO INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
LA English
DT Review
DE Digital twin; Physical internet; Supply chain; Bibliometric analysis
ID INDUSTRY 4.0; INFORMATION-SCIENCE; BIG DATA; DESIGN; SERVICE;
   SIMULATION; COCITATION; FRAMEWORK; VIRTUALIZATION; OPTIMIZATION
AB Physical Internet (PI) is an open global logistics system of which components are hyperconnected for increased efficiency and sustainability. Digital twin (DT), referring to the virtual representation of a physical object, is well-perceived as a key driver in the development of PI-based Supply Chain Management (SCM). Due to the capabilities of real-time monitoring and evaluation of large-scale complex systems, significant research efforts have been made to exploit values of PI/DT in SCM. Despite this, the current literature remained largely unstructured and scattered due to a lack of systematic literature reviews to synergise research findings, analyse the evolution of research fronts and extract emerging trends in the field. To address this issue, the paper deploys a bibliometric knowledge mapping approach to provide a bird's eye view of the current research status in the PI/DT-SCM area. Using CiteSpace's keyword co-occurrence network, 518 journal articles are clustered into 10 key research streams on PI/DT applications in: job shop scheduling, smart manufacturing design, PI-based SCM, manufacturing virtualisation, information management, sustainability development, data analytics, manufacturing operations management, simulation and optimisation, and assembly process planning. Based on citation burst rate, keywords representing research frontiers of the PI/DT are detected and their temporal evolutions are discussed. Likewise, some identified emerging research trends are production process and system, robotics, computer architecture, and cost. Finally, seven future research directions are suggested, which emphasise on several PI/DT-related issues, including business ecosystem, sustainability development, SC downstream management, cognitive thinking in Industry 5.0, citizen twin in digital society, and SC resilience.
C1 [Nguyen, Tiep; Duong, Quang Huy; Zhou, Li] Univ Greenwich, Business Sch, London SE10 9LS, England.
   [Van Nguyen, Truong] Brunel Univ London, Business Sch, Uxbridge UB8 3PH, Middx, England.
   [Zhu, You] Hunan Univ, Coll Business Adm, Changsha 410082, Peoples R China.
RP Zhu, Y (通讯作者)，Hunan Univ, Coll Business Adm, Changsha 410082, Peoples R China.
EM zy19@hnu.edu.cn
RI ; Zhou, Li/B-3035-2011
OI Duong, Quang Huy/0000-0003-2108-2976; Zhou, Li/0000-0001-7132-5935
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NR 178
TC 4
Z9 4
U1 38
U2 38
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-5273
EI 1873-7579
J9 INT J PROD ECON
JI Int. J. Prod. Econ.
PD FEB
PY 2022
VL 244
AR 108381
DI 10.1016/j.ijpe.2021.108381
PG 20
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA 1L1AP
UT WOS:000799024900001
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Shen, T
   Gao, C
   Nagai, Y
   Ou, W
AF Shen, Tao
   Gao, Chan
   Nagai, Yukari
   Ou, Wei
TI Deriving Design Knowledge Graph for Complex Sociotechnical Systems Using
   the AIA Design Thinking
SO MOBILE INFORMATION SYSTEMS
LA English
DT Article
ID INFORMATION OVERLOAD; CONSTRUCTION
AB The AIA design thinking has been validated in complex design tasks, which includes three overlapping design thinking fields and uses the knowledge field theory as a theoretical mechanism of knowledge flow among design thinking fields. Meanwhile, the design of complex sociotechnical systems highly relies on multidisciplinary knowledge and design methods. Despite the emergence of knowledge management techniques (ontology, expert system, text mining, etc.), designers continue to store knowledge in unstructured ways. To facilitate the integration of knowledge graph and design thinking, we introduce an integrated approach to structure design knowledge graph with the AIA design thinking, which organizes existing design knowledge through Agent (concept)-Interaction (relation)-Adaptation (concept) framework. The approach uses an optimized convolutional neural network to accomplish two tasks: building concept graph from text and stimulating design thinking information processing for complex sociotechnical system tasks. Based on our knowledge graph, the validation experiment demonstrates the advantages of promoting the designer's extension of idea space and idea quality.
C1 [Shen, Tao] Tongji Univ, Coll Design & Innovat, Shanghai, Peoples R China.
   [Gao, Chan] Huzhou Univ, Dept Architecture, Huzhou, Zhejiang, Peoples R China.
   [Gao, Chan; Nagai, Yukari] Japan Adv Inst Sci & Technol, Knowledge Sci, Nomi City, Ishikawa, Japan.
   [Ou, Wei] Zhejiang Gongshang Univ, Int Business Sch, Hangzhou, Zhejiang, Peoples R China.
RP Ou, W (通讯作者)，Zhejiang Gongshang Univ, Int Business Sch, Hangzhou, Zhejiang, Peoples R China.
EM studyouwei@126.com
FU China Postdoctoral Science Foundation [2020M681405]
FX The authors acknowledge the funding support for this work received from
   China Postdoctoral Science Foundation (2020M681405).
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NR 65
TC 0
Z9 0
U1 12
U2 12
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1574-017X
EI 1875-905X
J9 MOB INF SYST
JI Mob. Inf. Syst.
PD NOV 12
PY 2021
VL 2021
AR 6416061
DI 10.1155/2021/6416061
PG 11
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA ZP9YF
UT WOS:000766771000007
OA gold
DA 2022-08-23
ER

PT J
AU Wan, H
   Liang, JR
   Du, JF
   Liu, YN
   Ou, JL
   Wang, BY
   Pan, JZ
   Zeng, J
AF Wan, Hai
   Liang, Jinrui
   Du, Jianfeng
   Liu, Yanan
   Ou, Jialing
   Wang, Baoyi
   Pan, Jeff Z.
   Zeng, Juan
TI Iterative Visual Relationship Detection via Commonsense Knowledge Graph
SO BIG DATA RESEARCH
LA English
DT Article
DE Scene graph generation; Commonsense knowledge graph; Visual relationship
   detection; Visual Genome
AB Scene Graph Generation, which discovers the interaction between pairs of entities in an image, plays a significant role in image understanding. Most recent studies only consider visual features, ignoring the implicit effect of commonsense. We propose a novel model to take the advantage of commonsense knowledge in Scene Graph Generation, named Iterative Visual Relationship Detection with Commonsense Knowledge Graph (IVRDC). IVRDC consists of two modules: a feature module that predicts predicates by visual features and semantic features with a bi-directional recurrent neural network; and a commonsense knowledge module that constructs a specific commonsense knowledge graph for predicate prediction. These two modules roll out iteratively and cross-feed predictions from and to each other. The final predictions are made by taking the result of every iteration into account with an attention mechanism. Experimental results on the Visual Relationship Detection (VRD) dataset and the Visual Genome (VG) dataset demonstrate that our proposed model is competitive. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Wan, Hai; Liang, Jinrui; Liu, Yanan; Ou, Jialing; Wang, Baoyi] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Peoples R China.
   [Du, Jianfeng] Guangdong Univ Foreign Studies, Guangzhou Key Lab Multilingual Intelligent Proc, Guangzhou, Peoples R China.
   [Pan, Jeff Z.] Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland.
   [Zeng, Juan] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou, Peoples R China.
RP Du, JF (通讯作者)，Guangdong Univ Foreign Studies, Guangzhou Key Lab Multilingual Intelligent Proc, Guangzhou, Peoples R China.
EM wanhai@mail.sysu.edu.cn; liangjr9@mail2.sysu.edu.cn; jfdu@gdufs.edu.cn;
   liuyn56@mail2.sysu.edu.cn; oujl5@mail2.sysu.edu.cn;
   wangby9@mail2.sysu.edu.cn; j.z.pan@ed.ac.uk; zengjuan@mail.sysu.edu.cn
OI Wan, Hai/0000-0001-5357-9130
FU National Key R&D Program of China [2018YFC0830600, 2018YFC0830601];
   National Natural Science Foundation of China [61375056, 61573386,
   61876204, 61976232, 51978675]; Guangdong Province Natural Science
   Foundation [2016A030313292, 2017A070706010, 2018A030313086]; Guangdong
   Province Science and Technology Plan projects [2016B030305007,
   2017B01011-0011]; Guangzhou Science and Technology Project
   [201804010-435, 201804010496]; All-China Federation of Returned Overseas
   Chinese Research Project [17BZQK216]; MOE Foundation of Humanities and
   Social Sciences Project [18YJCZH006]
FX This paper was supported by the National Key R&D Program of China (No.
   2018YFC0830600; 2018YFC0830601), the National Natural Science Foundation
   of China (No. 61375056, 61573386, 61876204, 61976232, and 51978675),
   Guangdong Province Natural Science Foundation (No. 2016A030313292,
   2017A070706010 (soft science), 2018A030313086), Guangdong Province
   Science and Technology Plan projects (No. 2016B030305007 and
   2017B01011-0011), Guangzhou Science and Technology Project (No.
   201804010-435 and 201804010496), All-China Federation of Returned
   Overseas Chinese Research Project (No. 17BZQK216), and MOE Foundation of
   Humanities and Social Sciences Project (No. 18YJCZH006).
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NR 35
TC 2
Z9 2
U1 4
U2 10
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-5796
J9 BIG DATA RES
JI Big Data Res.
PD FEB 15
PY 2021
VL 23
AR 100175
DI 10.1016/j.bdr.2020.100175
EA JAN 2021
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PU2WL
UT WOS:000609166100004
DA 2022-08-23
ER

PT J
AU Yan, JH
   Wang, CY
   Cheng, WL
   Gao, M
   Zhou, AY
AF Yan, Jihong
   Wang, Chengyu
   Cheng, Wenliang
   Gao, Ming
   Zhou, Aoying
TI A retrospective of knowledge graphs
SO FRONTIERS OF COMPUTER SCIENCE
LA English
DT Review
DE knowledge graph; knowledge base; information extraction; logical
   reasoning; graph database
ID WRAPPER INDUCTION; EXTRACTION; BASE
AB Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for machines, and even for humans. Knowledge graphs have become prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and finally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner.
   In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via logical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph systems and discuss the future research directions.
C1 [Yan, Jihong; Wang, Chengyu; Cheng, Wenliang; Gao, Ming] East China Normal Univ, Inst Data Sci & Engn, Shanghai 200062, Peoples R China.
   [Yan, Jihong] Shanghai Second Polytech Univ, Inst Comp & Informat Engn, Shanghai 201209, Peoples R China.
   [Zhou, Aoying] East China Normal Univ, Shanghai Key Lab Trustworthy Comp, Shanghai 200062, Peoples R China.
RP Gao, M (通讯作者)，East China Normal Univ, Inst Data Sci & Engn, Shanghai 200062, Peoples R China.
EM mgao@sei.ecnu.edu.cn
RI Zhou, Xiaofang/C-6169-2013
OI Zhou, Xiaofang/0000-0001-6343-1455
FU National Key Research and Development Program of China [2016YFB1000905];
   National Natural Science Foundation of China [U1401256, 61402177,
   61402180]; Natural Science Foundation of Shanghai [14ZR1412600];
   CCF-Tecent Research Program of China [AGR20150114]; Key Disciplines of
   Software Engineering of Shanghai Second Polytechnic University
   [XXKZD1301]
FX This work has been supported by the National Key Research and
   Development Program of China (2016YFB1000905), the National Natural
   Science Foundation of China (Grant Nos. U1401256, 61402177, 61402180)
   and the Natural Science Foundation of Shanghai (14ZR1412600). This work
   was also supported by CCF-Tecent Research Program of China
   (AGR20150114). The author would also like to thank Key Disciplines of
   Software Engineering of Shanghai Second Polytechnic University
   (XXKZD1301).
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NR 120
TC 47
Z9 57
U1 121
U2 532
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 FEB
PY 2018
VL 12
IS 1
BP 55
EP 74
DI 10.1007/s11704-016-5228-9
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 FS5UR
UT WOS:000419865200005
DA 2022-08-23
ER

PT J
AU Guo, P
   Tian, W
   Li, HM
   Zhang, GM
   Li, JH
AF Guo, Ping
   Tian, Wei
   Li, Huimin
   Zhang, Guangmin
   Li, Jianhui
TI Global characteristics and trends of research on construction dust:
   based on bibliometric and visualized analysis
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article
DE Construction dust (CD); Bibliometrics; Knowledge mapping; CiteSpace
ID PARTICULATE AIR-POLLUTION; MARBLE POWDER; FUGITIVE DUST; ROAD DUST;
   HEALTH; EMISSIONS; EXPOSURE; WORKERS; INFLUENZA; MORTALITY
AB The booming construction industry has led to many environmental and occupational health and safety problems. Construction dust caused irreversible damage to the health of frontline workers and polluted the surrounding air environment, which has attracted the attention of researchers and practitioners. In this study, to systematically sort and analyze the distribution of construction dust (CD) research, its hot areas, and the evolution of its fronts, papers with "construction dust" as the subject term in the Web of Science Core Collection Database since 2010 are visually analyzed using CiteSpace. The characteristics of these papers, including the quantity trend, quality, author group, affiliated institution type, and journal type, are summarized, and keyword co-appearance and paper co-citation knowledge maps are produced. The results show that (1) China is the backbone of CD research, and the research results account for a considerable proportion of the total. (2) Respiratory dust and atmospheric aerosols, marble dust, PM2.5, and other hot issues have always attracted international attention. And exposure assessment and spatial distribution were the main focuses in the study of CD. (3) The direction of CD research will explore in a more subtle and intelligent direction in the future, for example, monitoring and control equipment under the technical support of big data technology and machine learning and face recognition. By combining bibliometrics with a systematic review, we aim to analyze the research foci and future development direction deeply, providing scholars with a comprehensive view of the field.
C1 [Guo, Ping; Tian, Wei; Li, Huimin; Zhang, Guangmin] Xian Univ Architecture & Technol, Sch Civil Engn, Xian 710055, Peoples R China.
   [Li, Jianhui] Beijing Shougang Construct Grp Co Ltd, Beijing 100041, Peoples R China.
RP Guo, P (通讯作者)，Xian Univ Architecture & Technol, Sch Civil Engn, Xian 710055, Peoples R China.
EM guoping@xauat.edu.cn; lijh0035@shougang.com.cn
FU National Natural Science Foundation of China [51908452]; Special
   Scientific Research Plan of Education Department of Shaanxi Province
   [18JK0470]
FX This research was funded by National Natural Science Foundation of
   China, grant number 51908452, and Special Scientific Research Plan of
   Education Department of Shaanxi Province, grant number 18JK0470.
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NR 89
TC 9
Z9 10
U1 21
U2 86
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
PD OCT
PY 2020
VL 27
IS 30
BP 37773
EP 37789
DI 10.1007/s11356-020-09723-y
EA JUL 2020
PG 17
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA NM3DR
UT WOS:000544853000006
PM 32613507
DA 2022-08-23
ER

PT J
AU Ma, JT
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   Hu, GW
   Wang, YJ
   Zhang, CQ
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   Sangaiah, AK
   Wu, HG
   Zhang, HP
   Ren, K
AF Ma, Jiangtao
   Qiao, Yaqiong
   Hu, Guangwu
   Wang, Yanjun
   Zhang, Chaoqin
   Huang, Yongzhong
   Sangaiah, Arun Kumar
   Wu, Huaiguang
   Zhang, Hongpo
   Ren, Kai
TI ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph
   Completion
SO SYMMETRY-BASEL
LA English
DT Article
DE relation completion; knowledge graph completion; link prediction;
   probabilistic soft logic
AB Link prediction in knowledge graph is the task of utilizing the existing relations to infer new relations so as to build a more complete knowledge graph. The inferred new relations plus original knowledge graph is the symmetry of completion knowledge graph. Previous research on link predication only focuses on path or semantic-based features, which can hardly have a full insight of features between entities and may result in a certain ratio of false inference results. To improve the accuracy of link predication, we propose a novel approach named Entity Link Prediction for Knowledge Graph (ELPKG), which can achieve a high accuracy on large-scale knowledge graphs while keeping desirable efficiency. ELPKG first combines path and semantic-based features together to represent the relationships between entities. Then it adopts a probabilistic soft logic-based reasoning method that effectively solves the problem of non-deterministic knowledge reasoning. Finally, the relation between entities is completed based on the entity link prediction algorithm. Extensive experiments on real dataset show that ELPKG outperforms baseline methods on hits@1, hits@10, and MRR.
C1 [Ma, Jiangtao; Wang, Yanjun; Zhang, Chaoqin; Wu, Huaiguang] Zhengzhou Univ Light Ind, Sch Comp & Commun Engn, Zhengzhou 450002, Henan, Peoples R China.
   [Ma, Jiangtao; Zhang, Chaoqin] Natl Digital Switching Syst Engn & Technol R&D Ct, Zhengzhou 450002, Henan, Peoples R China.
   [Qiao, Yaqiong; Wang, Yanjun; Zhang, Hongpo] State Key Lab Math Engn & Adv Comp, Zhengzhou 450001, Henan, Peoples R China.
   [Hu, Guangwu] Shenzhen Inst Informat Technol, Sch Comp Sci, Shenzhen 518172, Peoples R China.
   [Huang, Yongzhong] Guilin Univ Elect Technol, Sch Comp Sci & Informat Secur, Guilin 541004, Peoples R China.
   [Sangaiah, Arun Kumar] VIT Univ, Sch Comp Sci & Engn, Vellore 632014, Tamil Nadu, India.
   [Zhang, Hongpo; Ren, Kai] Zhengzhou Univ, Cooperat Innovat Ctr Internet Healthcare, Zhengzhou 450000, Henan, Peoples R China.
RP Hu, GW (通讯作者)，Shenzhen Inst Informat Technol, Sch Comp Sci, Shenzhen 518172, Peoples R China.
EM hugw@sziit.edu.cn
RI Sangaiah, Arun Kumar/U-6785-2019
OI Sangaiah, Arun Kumar/0000-0002-0229-2460; ,
   Jiangtao/0000-0001-5181-4045; zhang, hongpo/0000-0003-3485-8470
FU National Nature Science Foundation of China [61702462, 61672470,
   61802352, 61866008]; Doctoral Research Fund of Zhengzhou University of
   Light Industry [2017BSJJ046, 2018BSJJ039, 13501050045]; Scientific &
   Technological Project of Henan Province [182102210607]; Second Education
   Fund for Industry and Education project "Digital Science and Technology,
   Wisdom for the Future" [2018A01094]; Foundation of Henan Province
   Educational Committee [17A520064]; Fundamental Research Project of
   Shenzhen Municipality [JCYJ20170817115335418]
FX This research was funded by National Nature Science Foundation of China
   (No. 61702462, No. 61672470, No. 61802352, No. 61866008), the Doctoral
   Research Fund of Zhengzhou University of Light Industry (2017BSJJ046,
   2018BSJJ039, 13501050045), Scientific & Technological Project of Henan
   Province (No. 182102210607), the Second Education Fund for Industry and
   Education project "Digital Science and Technology, Wisdom for the
   Future"(2018A01094), Foundation of Henan Province Educational Committee
   (No. 17A520064) and Fundamental Research Project of Shenzhen
   Municipality (No. JCYJ20170817115335418).
CR Ayala-Gomez F, 2018, J INTELL FUZZY SYST, V34, P3089, DOI 10.3233/JIFS-169493
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NR 24
TC 5
Z9 5
U1 13
U2 40
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-8994
J9 SYMMETRY-BASEL
JI Symmetry-Basel
PD SEP
PY 2019
VL 11
IS 9
AR 1096
DI 10.3390/sym11091096
PG 13
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA JC3KP
UT WOS:000489177900032
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Rospocher, M
   van Erp, M
   Vossen, P
   Fokkens, A
   Aldabe, I
   Rigau, G
   Soroa, A
   Ploeger, T
   Bogaard, T
AF Rospocher, Marco
   van Erp, Marieke
   Vossen, Piek
   Fokkens, Antske
   Aldabe, Itziar
   Rigau, German
   Soroa, Aitor
   Ploeger, Thomas
   Bogaard, Tessel
TI Building event-centric knowledge graphs from news
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Event-centric knowledge; Natural language processing; Event extraction;
   Information integration; Big data; Real world data
AB Knowledge graphs have gained increasing popularity in the past couple of years, thanks to their adoption in everyday search engines. Typically, they consist of fairly static and encyclopedic facts about persons and organizations - e.g. a celebrity's birth date, occupation and family members - obtained from large repositories such as Freebase or Wikipedia.
   In this paper, we present a method and tools to automatically build knowledge graphs from news articles. As news articles describe changes in the world through the events they report, we present an approach to create Event-Centric Knowledge Graphs (ECKGs) using state-of-the-art natural language processing and semantic web techniques. Such ECKGs capture long-term developments and histories on hundreds of thousands of entities and are complementary to the static encyclopedic information in traditional knowledge graphs.
   We describe our event-centric representation schema, the challenges in extracting event information from news, our open source pipeline, and the knowledge graphs we have extracted from four different news corpora: general news (Wikinews), the FIFA world cup, the Global Automotive Industry, and Airbus A380 airplanes. Furthermore, we present an assessment on the accuracy of the pipeline in extracting the triples of the knowledge graphs. Moreover, through an event-centered browser and visualization tool we show how approaching information from news in an event-centric manner can increase the user's understanding of the domain, facilitates the reconstruction of news story lines, and enable to perform exploratory investigation of news hidden facts. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Rospocher, Marco] Fdn Bruno Kessler, Trento, Italy.
   [van Erp, Marieke; Vossen, Piek; Fokkens, Antske] Vrije Univ Amsterdam, Amsterdam, Netherlands.
   [Aldabe, Itziar; Rigau, German; Soroa, Aitor] Univ Basque Country, Donostia San Sebastian, Spain.
   [Ploeger, Thomas; Bogaard, Tessel] SynerScope BV, Helvoirt, Netherlands.
RP van Erp, M (通讯作者)，Vrije Univ Amsterdam, Amsterdam, Netherlands.
EM marieke.van.erp@vu.nl
RI Fokkens, Antske/AAF-7199-2019; Rospocher, Marco/AAD-9108-2022; Rigau,
   German/H-7235-2015; Rospocher, Marco/AGW-8382-2022
OI Rospocher, Marco/0000-0001-9391-3201; Rigau, German/0000-0003-1119-0930;
   van Erp, Marieke/0000-0001-9195-8203
FU European Union's 7th Framework Programme via the NewsReader project
   [ICT-316404]
FX This research was funded by European Union's 7th Framework Programme via
   the NewsReader project (ICT-316404).
CR Agerri R., 2015, EVENT DETECTION VERS
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NR 58
TC 49
Z9 54
U1 8
U2 81
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 132
EP 151
DI 10.1016/j.websem.2015.12.004
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 DM6JK
UT WOS:000376457600009
DA 2022-08-23
ER

PT J
AU Cao, ZY
   Qiao, XH
   Jiang, S
   Zhang, XG
AF Cao, Zhiying
   Qiao, Xinghao
   Jiang, Shuo
   Zhang, Xiuguo
TI An Efficient Knowledge-Graph-Based Web Service Recommendation Algorithm
SO SYMMETRY-BASEL
LA English
DT Article
DE Web service; Web services relationships; knowledge graph; representation
   learning; recommender systems
AB Using semantic information can help to accurately find suitable services from a variety of available (different semantics) services, and the semantic information of Web services can be described in detail in a Web service knowledge graph. In this paper, a Web service recommendation algorithm based on knowledge graph representation learning (kg-WSR) is proposed. The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. By calculating the distance between service entities in low-dimensional space, the relationship information of services which is not considered in recommendation approaches using a collaborative filtering algorithm is incorporated into the recommendation algorithm to enhance the accurateness of the result. The experimental results show that this algorithm can not only effectively improve the accuracy rate, recall rate, and coverage rate of recommendation but also solve the cold start problem to some extent.
C1 [Cao, Zhiying; Qiao, Xinghao; Jiang, Shuo; Zhang, Xiuguo] Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian 116033, Peoples R China.
RP Qiao, XH; Zhang, XG (通讯作者)，Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian 116033, Peoples R China.
EM czysophy@dlmu.edu.cn; qxh@dlmu.edu.cn; jsgg@dlmu.edu.cn;
   Zhangxg@dlmu.edu.cn
FU National Natural Science Foundation of China [51679025, 51479021];
   Fundamental Liaoning Provincial Natural Science Foundation of China
   [20170520196]; Fundamental Research Funds for the Central Universities
   [3132016308]
FX This work was supported by the National Natural Science Foundation of
   China (Grant No. 51679025, 51479021), the Fundamental Liaoning
   Provincial Natural Science Foundation of China (Grant No. 20170520196)
   and the Fundamental Research Funds for the Central Universities (Grant
   No. 3132016308).
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NR 27
TC 9
Z9 9
U1 7
U2 41
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2073-8994
J9 SYMMETRY-BASEL
JI Symmetry-Basel
PD MAR 18
PY 2019
VL 11
IS 3
AR 392
DI 10.3390/sym11030392
PG 16
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA HT2PB
UT WOS:000464403700004
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yu, HZ
   Li, HS
   Mao, DH
   Cai, Q
AF Yu, Haoze
   Li, Haisheng
   Mao, Dianhui
   Cai, Qiang
TI A domain knowledge graph construction method based on Wikipedia
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Co-word analysis; domain knowledge graph; relationship extraction;
   vector variance algorithm; Wikipedia
AB In order to achieve real-time updating of the domain knowledge graph and improve the relationship extraction ability in the construction process, a domain knowledge graph construction method is proposed. Based on the structured knowledge in Wikipedia's classification system, we acquire concepts and instances contained in subject areas. A relationship extraction algorithm based on co-word analysis is intended to extract the classification relationships in semi-structured open labels. A Bi-GRU remote supervised relationship extraction model based on a multiple-scale attention mechanism and an improved cross-entropy loss function is proposed to obtain the non-classification relationships of concepts in unstructured texts. Experiments show that the proposed model performs better than the existing methods. Based on the obtained concepts, instances and relationships, a domain knowledge graph is constructed and the domain-independent nodes and relationships contained in them are removed through a vector variance algorithm. The effectiveness of the proposed method is verified by constructing a food domain knowledge graph based on Wikipedia.
C1 [Yu, Haoze; Li, Haisheng; Mao, Dianhui; Cai, Qiang] Beijing Technol & Business Univ, Sch Comp & Informat Engn, Fucheng Rd, Beijing 100048, Peoples R China.
   [Yu, Haoze; Li, Haisheng; Mao, Dianhui; Cai, Qiang] Natl Engn Lab Agriprod Qual Traceabil, Beijing, Peoples R China.
   [Yu, Haoze; Li, Haisheng; Mao, Dianhui; Cai, Qiang] Beijing Key Lab Big Data Technol Food Safety, Beijing, Peoples R China.
RP Li, HS (通讯作者)，Beijing Technol & Business Univ, Sch Comp & Informat Engn, Fucheng Rd, Beijing 100048, Peoples R China.
EM lihsh@btbu.edu.cn
FU National Natural Science Foundation of China [61877002]; Ministry of
   Science and Technology's Innovative Methodology Work Project
   [2018IM020200]; Beijing Natural Science Foundation - Traffic Control
   Technology Funding Project (Railway Transportation Joint Fund Project)
   [L191009]; National Social Science Fund of China [18BGL202]; Social
   Science and Humanity on Young Fund of the Ministry of Education
   [17YJCZH127]; Beijing Municipal Commission of Education
   [PXM2019_014213_000007]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship and/or publication of this article: This
   research received financial support from the National Natural Science
   Foundation of China (No. 61877002), the Ministry of Science and
   Technology's Innovative Methodology Work Project (No. 2018IM020200),
   Beijing Natural Science Foundation - Traffic Control Technology Funding
   Project (Railway Transportation Joint Fund Project, No. L191009), the
   National Social Science Fund of China (No. 18BGL202), the Social Science
   and Humanity on Young Fund of the Ministry of Education (No. 17YJCZH127)
   and Beijing Municipal Commission of Education (No.
   PXM2019_014213_000007).
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NR 31
TC 7
Z9 7
U1 21
U2 69
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 DEC
PY 2021
VL 47
IS 6
BP 783
EP 793
AR 0165551520932510
DI 10.1177/0165551520932510
EA JUN 2020
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 WW0WF
UT WOS:000544678300001
DA 2022-08-23
ER

PT J
AU Faralli, S
   Finocchi, I
   Ponzetto, SP
   Velardi, P
AF Faralli, Stefano
   Finocchi, Irene
   Ponzetto, Simone Paolo
   Velardi, Paola
TI CrumbTrail: An efficient methodology to reduce multiple inheritance in
   knowledge graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Semantic networks; Ontologies; Knowledge graph pruning
ID ALGORITHMS; WEB
AB In this paper we present CRUMBTRAIL, an algorithm to clean large and dense knowledge graphs. CRUMB TRAIL removes cycles, out-of-domain nodes and non-essential nodes, i.e., those that can be safely removed without breaking the knowledge graph's connectivity. It achieves this through a bottom-up topological pruning on the basis of a set of input concepts that, for instance, a user can select in order to identify a domain of interest. Our technique can be applied to both noisy hypernymy graphs-typically generated by ontology learning algorithms as intermediate representations-as well as crowdsourced resources like Wikipedia, in order to obtain clean, domain-focused concept hierarchies. CRUMBTRAIL overcomes the time and space complexity limitations of current state-of-art algorithms. In addition, we show in a variety of experiments that it also outperforms them in tasks such as pruning automatically acquired taxonomy graphs, and domain adaptation of the Wikipedia category graph. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Faralli, Stefano; Ponzetto, Simone Paolo] Univ Mannheim, Mannheim, Germany.
   [Finocchi, Irene; Velardi, Paola] Sapienza Univ Rome, Rome, Italy.
RP Faralli, S (通讯作者)，Univ Mannheim, Mannheim, Germany.
EM stefano@informatik.uni-mannheim.de; finocchi@di.uniroma1.it;
   simone@informatik.uni-mannheim.de; velardi@di.uniroma1.it
RI Faralli, Stefano/M-6407-2019; Faralli, Stefano/M-7020-2018
OI Faralli, Stefano/0000-0003-3684-8815; Faralli,
   Stefano/0000-0003-3684-8815; Velardi, Paola/0000-0003-0884-1499;
   FINOCCHI, Irene/0000-0002-6394-6798
FU Deutsche Forschungsgemeinschaft (DFG) [PO 1900/1-1]; IBM Faculty Award
   [2305895190]; MIUR
FX This work has been partially supported by grant PO 1900/1-1 of the
   Deutsche Forschungsgemeinschaft (DFG) under the JOIN-T project, by the
   IBM Faculty Award #2305895190, and by the MIUR under grant "Dipartimenti
   di eccellenza 2018-2022" of the Department of Computer Science of
   Sapienza University.
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NR 88
TC 1
Z9 1
U1 1
U2 19
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JUL 1
PY 2018
VL 151
BP 180
EP 197
DI 10.1016/j.knosys.2018.03.030
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GH7TF
UT WOS:000433655600014
DA 2022-08-23
ER

PT J
AU Yoo, S
   Jeong, O
AF Yoo, SoYeop
   Jeong, OkRan
TI Automating the expansion of a knowledge graph
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Auto expansion; Knowledge graph; Neologisms; Semantic analysis;
   Multilingual BERT model
AB In order to make computers understand human languages and to reason, human knowledge needs to be represented and stored in a form that can be processed by computers. Knowledge graphs have been developed for use as a form of the knowledge base for words and general relationships among words. However, they have two limitations. One is that the knowledge graph is limited in size and scope for most of the human languages. Another is that they are not able to deal with neologisms that form a part of the human common sense. Addressing these problems, we have developed and validated PolarisX which can automatically expand a knowledge graph, by crawling and analyzing the news sites and social media in real-time. We utilize and fine-tune the pre-trained multilingual BERT model for the construction of knowledge graphs without language dependencies. We extract new relationships using the BERT-based relation extraction model and integrate them into the knowledge graph. We verify the novelty and accuracy of PolarisX. It deals with neologisms and does not have language dependencies. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Yoo, SoYeop; Jeong, OkRan] Gachon Univ, 1342 Seongnam Daero, Seongnam Si, Gyeonggi Do, South Korea.
RP Jeong, O (通讯作者)，Gachon Univ, 1342 Seongnam Daero, Seongnam Si, Gyeonggi Do, South Korea.
EM orjeong@gachon.ac.kr
FU Basic Science Research Program through the NRF (National Research
   Foundation of Korea); MSIT (Ministry of Science and ICT), Korea, under
   the National Program for Excellence in SW; Copyright Technology R&D
   through the MCST (Ministry of Culture, Sports and Tourism), Korea;
   Copyright Technology R&D through KCC (Korea Copyright Commission) [NRF
   2019R1A2C1008412, 2015-0-00932, 2019-CONTEXT-9500]
FX This research was supported by Basic Science Research Program through
   the NRF (National Research Foundation of Korea), and the MSIT (Ministry
   of Science and ICT), Korea, under the National Program for Excellence in
   SW supervised by the IITP (Institute for Information & communications
   Technology Promotion), and Copyright Technology R&D through the MCST
   (Ministry of Culture, Sports and Tourism), Korea and the KCC (Korea
   Copyright Commission) (Nos. NRF 2019R1A2C1008412, 2015-0-00932,
   2019-CONTEXT-9500).
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NR 47
TC 16
Z9 16
U1 3
U2 102
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 1
PY 2020
VL 141
AR 112965
DI 10.1016/j.eswa.2019.112965
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA JM6PT
UT WOS:000496334800036
DA 2022-08-23
ER

PT J
AU Zins, C
AF Zins, Chaim
TI Conceptual approaches for defining data, information, and knowledge
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID SCIENCE
AB The field of Information Science is constantly changing. Therefore, information scientists are required to regularly review-and if necessary-redefine its fundamental building blocks. This article is one of a group of four articles, which resulted from a Critical Delphi study conducted in 2003-2005. The study, "Knowledge Map of Information Science," was aimed at exploring the foundations of information science. The international panel was composed of 57 leading scholars from 16 countries, who represent (almost) all the major subfields and important aspects of the field. This particular article documents 130 definitions of data, information, and knowledge formulated by 45 scholars, and maps the major conceptual approaches for defining these three key concepts.
C1 Knowledge Mapping Res, IL-97852 Jerusalem, Israel.
RP Zins, C (通讯作者)，Knowledge Mapping Res, 26 Hahaganah St, IL-97852 Jerusalem, Israel.
EM chaim.zins@gmail.com
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   [No title captured]
NR 42
TC 287
Z9 301
U1 5
U2 78
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-2882
EI 1532-2890
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD FEB 15
PY 2007
VL 58
IS 4
BP 479
EP 493
DI 10.1002/asi.20508
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 166LF
UT WOS:000246379600003
DA 2022-08-23
ER

PT J
AU Kasztler, A
   Leitner, KH
AF Kasztler, A
   Leitner, KH
TI Bibliometric analysis and visualisation of intellectual capital
SO JOURNAL OF UNIVERSAL COMPUTER SCIENCE
LA English
DT Article
DE Intellectual Capital Report; relational capital; knowledge map; network
AB On the basis of an example gained from the perspective of a person reading Intellectual Capital (IC) reports this paper explains the method of BibTechMon(TM) which is based on an analysis of the co-occurrence of different terms within databases and the algorithm to visualise the results [Kopcsa, A., Schiebel, E. (1998b)]. The application of this method for the IC report is currently a major step in improving the IC reporting system within ARC Seibersdorf research GmbH. In this paper the advantages and potentials of using BibTechMon (TM) in the context of IC reporting will be demonstrated by means of the 2001 IC report of ARC Seibersdorf research GmbH.
C1 ARC Seibersdorf Res GmbH, Seibersdorf, Austria.
RP Kasztler, A (通讯作者)，ARC Seibersdorf Res GmbH, Seibersdorf, Austria.
OI Leitner, Karl-Heinz/0000-0002-3885-9465
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   SCHNEIDER U, 1999, BERICHT ERSTELLUNG W
   Steward T. A., 1997, INTELLECTUAL CAPITAL
   Sveiby K. E., 1997, NEW ORG WEALTH MANAG
NR 11
TC 5
Z9 5
U1 0
U2 3
PU SPRINGER-VERLAG
PI NEW YORK
PA 175 FIFTH AVE, NEW YORK, NY 10010 USA
J9 J UNIVERS COMPUT SCI
JI J. Univers. Comput. Sci.
PY 2002
VL 8
IS 5
BP 516
EP 525
PG 10
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 578JE
UT WOS:000177113000006
DA 2022-08-23
ER

PT J
AU Jiang, BC
   You, X
   Li, K
   Li, TT
   Zhou, XJ
   Tan, LH
AF Jiang, Bingchuan
   You, Xiong
   Li, Ke
   Li, Tingting
   Zhou, Xiaojun
   Tan, Liheng
TI Interactive Analysis of Epidemic Situations Based on a Spatiotemporal
   Information Knowledge Graph of COVID-19
SO IEEE ACCESS
LA English
DT Article
DE Epidemics; Spatiotemporal phenomena; Knowledge engineering; Analytical
   models; COVID-19; Semantic Web; Data models; COVID-19; geographic
   knowledge graph; spatiotemporal big data; visual analysis
ID PROPAGATION; DYNAMICS; MOBILITY; MODELS; AGENT
AB In view of the lack of data association in spatiotemporal information analysis and the lack of spatiotemporal situation analysis in knowledge graphs, this article combines the semantic web of the geographic knowledge graph with the visual analysis model of spatial information and puts forward the comprehensive utilization of the related technologies of the geographic knowledge graph and big data visual analysis. Then, it realizes the situational analysis of COVID-19 (Coronavirus Disease 2019) and the exploration of patient relationships through interactive collaborative analysis. The main contributions of the paper are as follows. (1) Based on the characteristics of the geographic knowledge graph, a patient entity model and an entity relationship type and knowledge representation method are proposed, and a knowledge graph of the spatiotemporal information of COVID-19 is constructed. (2) To analyse the COVID-19 patients' situations and explore their relationships, an analytical framework is designed. The framework, combining the semantic web of the geographic knowledge graph and the visual analysis model of geographic information, allows one to analyse the semantic web by using the node attribute similarity calculation, key stage mining, community prediction and other methods. (3)An efficient epidemic prevention and anti-epidemic method is proposed which is of referential significance. It is based on experiments and the collaborative analysis of the semantic web and spatial information, allowing for real-time situational understanding, the discovery of patients' relationships, the analysis of the spatiotemporal distribution of patients, super spreader mining, key node analysis, and the prevention and control of high-risk groups.
C1 [Jiang, Bingchuan; You, Xiong; Li, Ke; Li, Tingting; Zhou, Xiaojun; Tan, Liheng] PLA Strateg Support Force Informat Engn Univ, Inst Geospatial Informat, Zhengzhou 450052, Peoples R China.
RP You, X (通讯作者)，PLA Strateg Support Force Informat Engn Univ, Inst Geospatial Informat, Zhengzhou 450052, Peoples R China.
EM youarexiong@163.com
OI You, Xiong/0000-0002-2683-1879; Li, Ke/0000-0002-7873-1554
FU National Natural Science Foundation of China (NSFC) [41801319,
   61671141]; National Key Research and Development Program of China
   [2018YFB1702000]; Fundamental Research Funds for the Central
   Universities [N171612014, N170308028, N180708009]
FX This work was supported in part by the National Natural Science
   Foundation of China (NSFC) under Grant 41801319 and Grant 61671141, in
   part by the National Key Research and Development Program of China under
   Grant 2018YFB1702000, and in part by the Fundamental Research Funds for
   the Central Universities under Grant N171612014, Grant N170308028, and
   Grant N180708009.
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NR 53
TC 1
Z9 1
U1 12
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 2022
VL 10
BP 46782
EP 46795
DI 10.1109/ACCESS.2020.3033997
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1A4CK
UT WOS:000791706800001
PM 35937640
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yu, M
   Guo, JJ
   Yu, J
   Xu, TY
   Zhao, MK
   Liu, HW
   Li, XW
   Yu, RG
AF Yu, Mei
   Guo, Jiujiang
   Yu, Jian
   Xu, Tianyi
   Zhao, Mankun
   Liu, Hongwei
   Li, Xuewei
   Yu, Ruiguo
TI TBDRI: block decomposition based on relational interaction for temporal
   knowledge graph completion
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Temporal KGC; Inverse relation; Tensor decomposition
ID TENSOR
AB Knowledge graph completion (KGC) can be interpreted as the task of missing inferences to real-world facts. Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static knowledge graphs. The data they are applied to usually evolves with time, such as friend graphs in social networks. Therefore, developing temporal knowledge graph completion (temporal KGC) models is an increasingly important topic, although it is difficult due to data non-stationarity, and its complex temporal dependencies. In this paper, we propose block decomposition based on relational interaction for temporal knowledge graph completion (TBDRI), a novel model based on block term decomposition (which can be seen as a special variant of CP decomposition and Tucker decomposition) of the binary tensor representation of knowledge graph quadruples. TBDRI considers that inverse relations, as one of the most important types of relations, occupy an important share in the real world. Although some existing models introduce inverse relation into the model, it is not enough to only learn the inverse relation independently. TBDRI learns inverse relation in an enhanced way to strengthen the binding of forward and inverse relation. Furthermore, TBDRI first uses the core tensor as temporal information to bind timestamps more adequately. We prove TBDRI is full expressiveness and derive the bound on its entity, relation, and timestamp embedding dimensionality. We show that TBDRI is able to outperform most previous state-of-the-art models on the four benchmark datasets for temporal knowledge graph completion.
C1 [Yu, Mei; Guo, Jiujiang; Yu, Jian; Xu, Tianyi; Zhao, Mankun; Li, Xuewei; Yu, Ruiguo] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China.
   [Yu, Mei; Guo, Jiujiang; Yu, Jian; Xu, Tianyi; Zhao, Mankun; Li, Xuewei; Yu, Ruiguo] Tianjin Key Lab Adv Networking TANKLab, Tianjin, Peoples R China.
   [Yu, Mei; Guo, Jiujiang; Yu, Jian; Xu, Tianyi; Zhao, Mankun; Li, Xuewei; Yu, Ruiguo] Tianjin Key Lab Cognit Comp & Applicat, Tianjin, Peoples R China.
   [Liu, Hongwei] Tianjin Foreign Studies Univ, Foreign Language Literature & Culture Studies Ctr, Tianjin, Peoples R China.
RP Li, XW (通讯作者)，Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China.; Li, XW (通讯作者)，Tianjin Key Lab Adv Networking TANKLab, Tianjin, Peoples R China.; Li, XW (通讯作者)，Tianjin Key Lab Cognit Comp & Applicat, Tianjin, Peoples R China.
EM yumei@tju.edu.cn; 353745517@qq.com; yujian@tju.edu.cn;
   tianyi.xu@tju.edu.cn; zmk@tju.edu.cn; liuhongwei@tjfsu.edu.cn;
   lixuewei@tju.edu.cn; rgyu@tju.edu.cn
FU National Natural Science Foundation of China [61877043]; National
   Natural Science of China [61877044]
FX This work is jointly supported by National Natural Science Foundation of
   China (61877043) and National Natural Science of China (61877044).
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NR 47
TC 0
Z9 0
U1 7
U2 7
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
DI 10.1007/s10489-022-03601-5
EA JUN 2022
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2E8NY
UT WOS:000812480800007
DA 2022-08-23
ER

PT J
AU Niu, YT
   Feng, XJ
   Kou, SQ
   Xiang, P
AF Niu, Yingtao
   Feng, Xijin
   Kou, Shaoqin
   Xiang, Peng
TI A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph
   Technology
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE communication anti-jamming; knowledge graph; time-frequency domain
   feature extraction; anti-jamming decision-making
ID WEB
AB Wireless communications are increasingly vulnerable to malicious jamming attacks due to the inherent openness and broadcast nature. In this regard, a novel anti-jamming scheme based on knowledge graph is proposed in this paper, where the knowledge graph which contains the prior knowledge of the commonly-used jamming patterns is utilized to make the fast anti-jamming decision with low complexity. Specifically, for the known jamming patterns, the anti-jamming strategy is directly extracted based on the search from the knowledge graph by using the sensing information. On the other hand, for the unknown jamming patterns, a time-frequency domain feature extraction method is first proposed to extract the jamming feature and match the jamming patterns in the knowledge graph. As such, multiple anti-jamming strategies can be obtained by the knowledge graph search. Finally, the optimal strategy can be achieved by the two communication decision criteria-based strategy fusion. The simulation results showed that the time-frequency domain feature extraction method can well extract the time-frequency domain feature of the jamming pattern, and the proposed algorithm has high decision accuracy and robustness.
C1 [Niu, Yingtao; Kou, Shaoqin] Natl Univ Def Technol, Sixty Third Res Inst, Nanjing 210007, Peoples R China.
   [Feng, Xijin] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China.
   [Xiang, Peng] Army Engn Univ, Sch Commun Engn, Nanjing 210000, Peoples R China.
RP Niu, YT (通讯作者)，Natl Univ Def Technol, Sixty Third Res Inst, Nanjing 210007, Peoples R China.
EM niuyingtao78@hotmail.com; xjfeng@nuist.edu.cn; koushaoqin97@163.com;
   sunshinetim@126.com
OI Feng, Xijin/0000-0001-9246-2410; Niu, YIngtao/0000-0001-6366-3774
FU National Science Foundation of China [U19B2014]
FX This research is supported by the National Science Foundation of China
   under contract No. U19B2014.
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NR 27
TC 0
Z9 0
U1 7
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD MAY
PY 2022
VL 12
IS 10
AR 4960
DI 10.3390/app12104960
PG 17
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 1Q0EI
UT WOS:000802372000001
OA gold
DA 2022-08-23
ER

PT J
AU Lamurias, A
   Ferreira, JD
   Clarke, LA
   Couto, FM
AF Lamurias, Andre
   Ferreira, Joao D.
   Clarke, Luka A.
   Couto, Francisco M.
TI Generating a Tolerogenic Cell Therapy Knowledge Graph from Literature
SO FRONTIERS IN IMMUNOLOGY
LA English
DT Article
DE tolerogenic therapy; text mining; knowledge graph; cytokines; machine
   learning
ID ANTIGEN-PRESENTING CELLS; DENDRITIC CELLS; RHEUMATOID-ARTHRITIS;
   INFORMATION; RESPONSES; DATABASE; ONSET; MICE; APCS
AB Tolerogenic cell therapies provide an alternative to conventional immunosuppressive treatments of autoimmune disease and address, among other goals, the rejection of organ or stem cell transplants. Since various methodologies can be followed to develop tolerogenic therapies, it is important to be aware and up to date on all available studies that may be relevant to their improvement. Recently, knowledge graphs have been proposed to link various sources of information, using text mining techniques. Knowledge graphs facilitate the automatic retrieval of information about the topics represented in the graph. The objective of this work was to automatically generate a knowledge graph for tolerogenic cell therapy from biomedical literature. We developed a system, ICReI, based on machine learning to extract relations between cells and cytokines from abstracts. Our system retrieves related documents from PubMed, annotates each abstract with cell and cytokine named entities, generates the possible combinations of cell cytokine pairs cooccurring in the same sentence, and identifies meaningful relations between cells and cytokines. The extracted relations were used to generate a knowledge graph, where each edge was supported by one or more documents. We obtained a graph containing 647 cell cytokine relations, based on 3,264 abstracts. The modules of ICReI were evaluated with cross-validation and manual evaluation of the relations extracted. The relation extraction module obtained an F-measure of 0.789 in a reference database, while the manual evaluation obtained an accuracy of 0.615. Even though the knowledge graph is based on information that was already published in other articles about immunology, the system we present is more efficient than the laborious task of manually reading all the literature to find indirect or implicit relations. The ICRel graph will help experts identify implicit relations that may not be evident in published studies.
C1 [Lamurias, Andre; Ferreira, Joao D.; Couto, Francisco M.] Univ Lisbon, Fac Ciencias, LaSIGE, Lisbon, Portugal.
   [Clarke, Luka A.] Univ Lisbon, Fac Ciencias, BioISI Biosyst & Integrat Sci Inst, Lisbon, Portugal.
RP Lamurias, A (通讯作者)，Univ Lisbon, Fac Ciencias, LaSIGE, Lisbon, Portugal.
EM alamurias@lasige.di.fc.ul.pt
RI Lamúrias, André/U-9729-2017; Clarke, Luka A/E-5843-2012; Couto,
   Francisco M/A-4066-2010
OI Lamúrias, André/0000-0001-7965-6536; Clarke, Luka A/0000-0003-3254-9121;
   Couto, Francisco M/0000-0003-0627-1496; Ferreira,
   Joao/0000-0002-6900-3168
FU Portuguese Fundacao para a Ciencia e Tecnologia [PD/BD/106083/2015,
   UID/MULTI/04046/2013, UID/CEC/00408/2013]
FX This work was supported by the Portuguese Fundacao para a Ciencia e
   Tecnologia (http://www.fct.pt/) through the PhD Grant ref.
   PD/BD/106083/2015 to AL, UID/MULTI/04046/2013 (BioISI) to LC, and
   UID/CEC/00408/2013 (LaSIGE) to AL, JF, and FE. 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 40
TC 3
Z9 4
U1 2
U2 11
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA PO BOX 110, EPFL INNOVATION PARK, BUILDING I, LAUSANNE, 1015,
   SWITZERLAND
SN 1664-3224
J9 FRONT IMMUNOL
JI Front. Immunol.
PD NOV 29
PY 2017
VL 8
AR 1656
DI 10.3389/fimmu.2017.01656
PG 12
WC Immunology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Immunology
GA FO0BB
UT WOS:000416406700001
PM 29238346
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yang, B
   Liao, YM
AF Yang, Bo
   Liao, Yi-ming
TI Research on enterprise risk knowledge graph based on multi-source data
   fusion
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Enterprise dynamic risk; Knowledge graph; Knowledge fusion; Dynamic
   knowledge reasoning
AB The construction of risk knowledge graphs aims at the effective organization and utilization of enterprise knowledge resources in big data environments. To address the problem of static mapping in existing enterprise knowledge graphs, this paper introduces the time dimension to describe the evolutionary characteristics of enterprise risk events, such as dynamics, suddenness and timeliness. Through information extraction, knowledge fusion, ontology construction and dynamic knowledge reasoning about risk knowledge, a bottom-up enterprise dynamic risk knowledge graph is systematically constructed. In the knowledge fusion link, aiming at the imbalanced classification problem for the entity samples of a data set, this paper proposes the ResNet dynamic knowledge reasoning method to improve the loss balance function of the Multi-Net model. The experiments show that the new model can effectively improve the accuracy of entity and relationship prediction. Finally, the knowledge graph is applied to an intelligent question-answering system.
C1 [Yang, Bo; Liao, Yi-ming] Jiang Xi Univ Finance & Econ, Sch Informat Management, Nanchang, Jiangxi, Peoples R China.
RP Yang, B (通讯作者)，Jiang Xi Univ Finance & Econ, Sch Informat Management, Nanchang, Jiangxi, Peoples R China.
EM yangbo@jxufe.edu.cn
FU National Natural Science Foundation Project [72064015]; Social Science
   Planning Project of Jiangxi Province [19TQ01]; Jiangxi Provincial
   Department of Education Science and Technology Research Key Project
   [GJJ180249]; Jiangxi university humanities and social science research
   Project [GL18103]
FX This work was financially supported by National Natural Science
   Foundation Project (72064015), Social Science Planning Project of
   Jiangxi Province (19TQ01), Jiangxi Provincial Department of Education
   Science and Technology Research Key Project (Grant No: GJJ180249),
   Jiangxi university humanities and social science research Project
   (GL18103).
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NR 25
TC 3
Z9 4
U1 32
U2 82
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 FEB
PY 2022
VL 34
IS 4
SI SI
BP 2569
EP 2582
DI 10.1007/s00521-021-05985-w
EA APR 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YV1BC
UT WOS:000641663000007
DA 2022-08-23
ER

PT J
AU Chen, C
   Lou, Y
   Li, XY
   Lv, ZT
   Zhang, LQ
   Mao, W
AF Chen, Chan
   Lou, Yang
   Li, Xin-Yi
   Lv, Zheng-Tian
   Zhang, Lu-Qiu
   Mao, Wei
TI Mapping current research and identifying hotspots on mesenchymal stem
   cells in cardiovascular disease
SO STEM CELL RESEARCH & THERAPY
LA English
DT Article
DE Mesenchymal stem cells; Cardiovascular; Knowledge mapping analysis;
   Visualization
ID ACUTE MYOCARDIAL-INFARCTION; SMOOTH-MUSCLE-CELL; BONE-MARROW;
   HEART-FAILURE; IN-VITRO; PROMOTE ANGIOGENESIS; TRANSENDOCARDIAL
   INJECTION; CARDIAC DIFFERENTIATION; THERAPEUTIC-EFFICACY; KNOWLEDGE
   DOMAIN
AB BackgroundMesenchymal stem cells (MSCs) have important research value and broad application prospects in the cardiovascular disease. This study provides information on the latest progress, evolutionary path, frontier research hotspots, and future research developmental trends in this field.MethodsA knowledge map was generated by CiteSpace and VOSviewer analysis software based on data obtained from the literature on MSCs in the cardiovascular field.ResultsThe USA and China ranked at the top in terms of the percentage of articles, accounting for 34.306% and 28.550%, respectively. The institution with the highest number of research publications in this field was the University of Miami, followed by the Chinese Academy of Medical Sciences and Harvard University. The research institution with the highest ACI value was Harvard University, followed by the Mayo Clinic and the University of Cincinnati.The top three subjects in terms of the number of published articles were cell biology, cardiovascular system cardiology, and research experimental medicine. The journal with the most publications in this field was Circulation Research, followed by Scientific Reports and Biomaterials. The direction of research on MSCs in the cardiovascular system was divided into four parts: (1) tissue engineering, scaffolds, and extracellular matrix research; (2) cell transplantation, differentiation, proliferation, and signal transduction pathway research; (3) assessment of the efficacy of stem cells from different sources and administration methods in the treatment of acute myocardial infarction, myocardial hypertrophy, and heart failure; and (4) exosomes and extracellular vesicles research. Tissue research is the hotspot and frontier in this field.ConclusionMSC research has presented a gradual upward trend in the cardiovascular field. Multidisciplinary intersection is a characteristic of this field. Engineering and materials disciplines are particularly valued and have received attention from researchers. The progress in multidisciplinary research will provide motivation and technical support for the development of this field.
C1 [Chen, Chan] Zhejiang Chinese Med Univ, Hangzhou Xiaoshan Dist Hosp TCM, Jiangnan Hosp, Hangzhou 311201, Zhejiang, Peoples R China.
   [Chen, Chan] Zhejiang Chinese Med Univ, Hangzhou 310053, Zhejiang, Peoples R China.
   [Lou, Yang; Li, Xin-Yi; Lv, Zheng-Tian; Zhang, Lu-Qiu; Mao, Wei] Zhejiang Chinese Med Univ, Affiliated Hosp 1, Hangzhou 311006, Zhejiang, Peoples R China.
RP Chen, C (通讯作者)，Zhejiang Chinese Med Univ, Hangzhou Xiaoshan Dist Hosp TCM, Jiangnan Hosp, Hangzhou 311201, Zhejiang, Peoples R China.; Chen, C (通讯作者)，Zhejiang Chinese Med Univ, Hangzhou 310053, Zhejiang, Peoples R China.; Mao, W (通讯作者)，Zhejiang Chinese Med Univ, Affiliated Hosp 1, Hangzhou 311006, Zhejiang, Peoples R China.
EM chenchan2009@126.com; maoweilw@163.com
FU Hangzhou Municipal Health Commission [20181228Y90]; Zhejiang Medical
   Science and Technology Projects [2020RC104]; National Natural Science
   Foundation of China [81673706]
FX This work was supported by Hangzhou Municipal Health Commission Projects
   (No.20181228Y90), Zhejiang Medical Science and Technology Projects
   (No.2020RC104), and National Natural Science Foundation of China
   (81673706).
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NR 116
TC 8
Z9 8
U1 12
U2 36
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1757-6512
J9 STEM CELL RES THER
JI Stem Cell Res. Ther.
PD DEC 25
PY 2020
VL 11
IS 1
AR 498
DI 10.1186/s13287-020-02009-7
PG 16
WC Cell & Tissue Engineering; Cell Biology; Medicine, Research &
   Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Cell Biology; Research & Experimental Medicine
GA PA6US
UT WOS:000595768600005
PM 33239082
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Meng, FQ
   Yang, SS
   Wang, JD
   Xia, L
   Liu, H
AF Meng, Fanqi
   Yang, Shuaisong
   Wang, Jingdong
   Xia, Lei
   Liu, Han
TI Creating Knowledge Graph of Electric Power Equipment Faults Based on
   BERT-BiLSTM-CRF Model
SO JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
LA English
DT Article
DE Knowledge graph; Electric power equipment; Fault diagnosis; Entity
   recognition; Relation extraction
ID NETWORKS
AB Creating a large-scale knowledge graph of electric power equipment faults will facilitate the development of automatic fault diagnosis and intelligent question answering (QA) in the electric power industry. However, most existing methods have lower accuracy in Chinese entity recognition, thus it is hard to build such a high-quality knowledge graph by extracting knowledge from Chinese technical literature. To solve the problem, a novel model called BERT-BiLSTM-CRF is proposed. It blends Bi-directional Encoder Representation from Transformers (BERT), Bi-directional Long Short-Term Memory (BiLSTM), and Conditional Random Field (CRF). The model firstly identifies and extracts electric power equipment entities from pre-processed Chinese technical literature. Then, the semantic relations between the entities are extracted based on the relation classification method based on dependency parsing. Finally, the extracted knowledge is stored in the Neo4j database in the form of the triplet and visualized in the form of a graph. Through the above steps, a Chinese knowledge graph of electric power equipment faults can be built. The novelty of the model just lies in its subtle blend: the BERT module can not only learn phrase-level information representation, but also learn rich semantic information features; the CRF module realizes the constraint on the label prediction value and reduces the irregular recognition rate, so the accuracy rate of entity recognition is improved. Taking the Chinese technological literature, which is about fault diagnosis of electric power equipment as the experimental object, the experimental results show that the model identifies and extracts Chinese entities more accurately than traditional methods. Thus, a comprehensive and accurate Chinese knowledge graph of electric power equipment faults could be constructed more easily.
C1 [Meng, Fanqi; Yang, Shuaisong; Wang, Jingdong; Xia, Lei; Liu, Han] Northeast Elect Power Univ, Sch Comp Sci, Jilin 132012, Jilin, Peoples R China.
   [Meng, Fanqi] Guangdong Atv Acad Performing Arts, Sch Informat Engn, Dongguan 523710, Guangdong, Peoples R China.
RP Yang, SS; Wang, JD (通讯作者)，Northeast Elect Power Univ, Sch Comp Sci, Jilin 132012, Jilin, Peoples R China.
EM yangshuaisong@163.com; 286604374@qq.com
RI Meng, Fanqi/GLU-4878-2022
OI Meng, Fanqi/0000-0002-5699-592X
FU Plan of Science and Technical Innovation Development of Jilin City
   [201901041146]; Chinese Scholarship Council [201908220190]; National Key
   Research and Development Program [2020YFB1707804]; Science and Technical
   Research Project of Jilin Provincial Education Department
   [JJKH20190711KJ]
FX This work was supported in part by the Plan of Science and Technical
   Innovation Development of Jilin City (No. 201901041146), the Chinese
   Scholarship Council (No. 201908220190), National Key Research and
   Development Program (No. 2020YFB1707804), and the Science and Technical
   Research Project of Jilin Provincial Education Department (No.
   JJKH20190711KJ).
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NR 26
TC 15
Z9 15
U1 39
U2 39
PU SPRINGER SINGAPORE PTE LTD
PI SINGAPORE
PA #04-01 CENCON I, 1 TANNERY RD, SINGAPORE 347719, SINGAPORE
SN 1975-0102
EI 2093-7423
J9 J ELECTR ENG TECHNOL
JI J. Electr. Eng. Technol.
PD JUL
PY 2022
VL 17
IS 4
BP 2507
EP 2516
DI 10.1007/s42835-022-01032-3
EA MAR 2022
PG 10
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 2L9GW
UT WOS:000764594400005
DA 2022-08-23
ER

PT J
AU Beaudoin, C
   Mistry, I
   Young, N
AF Beaudoin, Christine
   Mistry, Isha
   Young, Nathan
TI Collaborative knowledge mapping to inform environmental policy-making:
   The case of Canada's Rideau Canal National Historic Site
SO ENVIRONMENTAL SCIENCE & POLICY
LA English
DT Article
DE Collaboration; Knowledge mapping; Resilience; Social-ecological systems;
   Network analysis; Rideau Canal
ID NETWORK; ENGAGEMENT; SYSTEM
AB The Rideau Canal National Historic Site is a complex social-ecological system that connects the Ottawa River and the St-Lawrence River in Eastern Ontario, Canada. As an interjurisdictional waterway, it presents interconnected social-ecological challenges. In this article, we analyze findings from five participatory workshops held with stakeholder groups in the Rideau Canal system. Participants in the workshops co-produced social-ecological relational maps that represent their knowledge and views about determinants of environmental health in the system. The maps were merged by the authors to create an aggregated, collective map grouping all factors that participants perceived as influencing the environmental health of the Rideau Canal. Our analysis focuses on two dimensions of these co-production exercises. First, we use concepts and methods from social-ecological systems and social network analysis to analyze the content of the maps, examining convergences and divergences in stakeholder perceptions of the system and outlining insights into social-ecological linkages that might be relevant for decision- and policy-makers. Most factors cited were social, emphasizing the need for more careful integration of the social and ecological in our understanding of complex historic waterway systems. Second, we examine the value of our workshops, anchored in collaborative systems thinking and network analysis, as a method for eliciting highly variable local knowledge and experiences, and for summarizing these in a consumable form for decision- and policy-makers. We argue that this method facilitates inclusion of various knowledges and perspectives in decision- and policymaking processes and provides pathways to improve socialecological resilience.
C1 [Beaudoin, Christine; Young, Nathan] Univ Ottawa, Sch Sociol & Anthropol Studies, 120 Univ Private,Social Sci Bldg Room 10005, University, ON K1N 6N5, Canada.
   [Mistry, Isha; Young, Nathan] Univ Ottawa, Inst Environm, 1 Stewart St,Room 301, Ottawa, ON K1N 6N5, Canada.
RP Beaudoin, C (通讯作者)，Univ Ottawa, Sch Sociol & Anthropol Studies, 120 Univ Private,Social Sci Bldg Room 10005, University, ON K1N 6N5, Canada.
EM cbeau143@uottawa.ca; imist092@uottawa.ca; nyoung@uottawa.ca
FU Natural Science and Engineering Research Council of Canada [STPGP
   506352]
FX This work was supported by the Natural Science and Engineering Research
   Council of Canada through a Strategic Partnership Grant under File STPGP
   506352. The funding agency had no role in study design, data collection
   or analysis.
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NR 58
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1462-9011
EI 1873-6416
J9 ENVIRON SCI POLICY
JI Environ. Sci. Policy
PD FEB
PY 2022
VL 128
BP 299
EP 309
DI 10.1016/j.envsci.2021.12.001
PG 11
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA 0T9BK
UT WOS:000787256200005
DA 2022-08-23
ER

PT J
AU Maureira, C
   Pinto, H
   Yepes, V
   Garcia, J
AF Maureira, Carlos
   Pinto, Hernan
   Yepes, Victor
   Garcia, Jose
TI Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping:
   An Adaptive Methodology for Macroscopic Conceptual Analysis
SO IEEE ACCESS
LA English
DT Article
DE Industries; Artificial intelligence; Optimization; Machine learning
   algorithms; Ecosystems; Bibliometrics; Machine learning; Architecture;
   engineering and construction; AEC; artificial intelligence; literature
   corpus; machine learning; optimization algorithms; knowledge mapping and
   structure
ID INFORMATION MODELING BIM; CONSTRUCTION-INDUSTRY; THINGS IOT; R PACKAGE;
   INTERNET; SAFETY; ENVIRONMENT; SOFTWARE; SYSTEM; HEALTH
AB The Architecture, Engineering, and Construction (AEC) Industry is one of the most important productive sectors, hence also produce a high impact on the economic balances, societal stability, and global challenges in climate change. Regarding its adoption of technologies, applications and processes is also recognized by its status-quo, its slow innovation pace, and the conservative approaches. However, a new technological era - Industry 4.0 fueled by AI- is driving productive sectors in a highly pressurized global technological competition and sociopolitical landscape. In this paper, we develop an adaptive approach to mining text content in the literature research corpus related to the AEC and AI (AEC-AI) industries, in particular on its relation to technological processes and applications. We present a first stage approach to an adaptive assessment of AI algorithms, to form an integrative AI platform in the AEC industry, the AEC-AI industry 4.0. At this stage, a macroscopic adaptive method is deployed to characterize "Optimization," a key term in AEC-AI industry, using a mixed methodology incorporating machine learning and classical evaluation process. Our results show that effective use of metadata, constrained search queries, and domain knowledge allows getting a macroscopic assessment of the target concept. This allows the extraction of a high-level mapping and conceptual structure characterization of the literature corpus. The results are comparable, at this level, to classical methodologies for the literature review. In addition, our method is designed for an adaptive assessment to incorporate further stages.
C1 [Maureira, Carlos] Univ Amer, Fac Econ & Negocios, Santiago 7500975, Chile.
   [Maureira, Carlos] Univ Bernardo OHiggins, Fac Ingn Ciencias & Tecnol, Santiago 8370993, Chile.
   [Pinto, Hernan; Garcia, Jose] Pontificia Univ Catolica Valparaiso, Escuela Ingn Construcc, Valparaiso 2362804, Chile.
   [Yepes, Victor] Univ Politecn Valencia, Inst Concrete Sci & Technol ICITECH, Valencia 46022, Spain.
RP Maureira, C (通讯作者)，Univ Amer, Fac Econ & Negocios, Santiago 7500975, Chile.; Maureira, C (通讯作者)，Univ Bernardo OHiggins, Fac Ingn Ciencias & Tecnol, Santiago 8370993, Chile.
EM carlos.maureira@gmail.com
RI Yepes, Víctor/K-9763-2014; Maureira, Carlos/AAV-7709-2021
OI Yepes, Víctor/0000-0001-5488-6001; Maureira, Carlos/0000-0002-2245-9053;
   GARCIA, JOSE/0000-0003-3126-8352
FU CONICYT/FONDECYT/INICIACION [11180056]; Spanish Ministry of Science and
   Innovation through the FEDER Funding [PID2020-117056RB-I00]
FX This work was supported by the CONICYT/FONDECYT/INICIACION under Grant
   11180056 to Jose Garcia and the Spanish Ministry of Science and
   Innovation through the FEDER Funding under Project PID2020-117056RB-I00
   to Victor Yepes.
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NR 99
TC 1
Z9 1
U1 14
U2 24
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 110842
EP 110879
DI 10.1109/ACCESS.2021.3102215
PG 38
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 TZ7TU
UT WOS:000684675700001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhang, Z
   Zhuang, FZ
   Qu, M
   Niu, ZY
   Xiong, H
   He, Q
AF Zhang, Zhao
   Zhuang, Fuzhen
   Qu, Meng
   Niu, Zheng-Yu
   Xiong, Hui
   He, Qing
TI Knowledge graph embedding with shared latent semantic units
SO NEURAL NETWORKS
LA English
DT Article
DE Knowledge graph; Reinforcement learning; Embedding
AB Knowledge graph embedding (KGE) aims to project both entities and relations into a continuous low-dimensional space. However, for a given knowledge graph (KG), only a small number of entities and relations occur many times, while the vast majority of entities and relations occur less frequently. This data sparsity problem has largely been ignored by most of the existing KGE models. To this end, in this paper, we propose a general technique to enable knowledge transfer among semantically similar entities or relations. Specifically, we define latent semantic units (LSUs), which are the sub-components of entity and relation embeddings. Semantically similar entities or relations are supposed to share the same LSUs, and thus knowledge can be transferred among entities or relations. Finally, extensive experiments show that the proposed technique is able to enhance existing KGE models and can provide better representations of KGs. (C) 2021 Elsevier Ltd. All rights reserved.
C1 [Zhang, Zhao; Zhuang, Fuzhen; He, Qing] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China.
   [Zhang, Zhao; Zhuang, Fuzhen; He, Qing] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Qu, Meng; Xiong, Hui] Rutgers State Univ, Management Sci & Informat Syst, New Brunswick, NJ USA.
   [Niu, Zheng-Yu] Baidu Inc, Beijing, Peoples R China.
RP Zhuang, FZ (通讯作者)，Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China.
EM zhangzhao2017@ict.ac.cn; zhuangfuzhen@ict.ac.cn;
   mengqu@business.rutgers.edu; niuzhengyu@baidu.com; hxiong@rutgers.edu;
   heqing@ict.ac.cn
FU National Key Research and Development Program of China [2017YFB1002104];
   National Natural Science Foundation of China [U1836206, U1811461,
   61773361]; Project of Youth Innovation Promotion Association CAS, China
   [2017146]
FX The research work is supported by the National Key Research and
   Development Program of China under Grant No. 2017YFB1002104, the
   National Natural Science Foundation of China under Grant Nos. U1836206,
   U1811461, 61773361, the Project of Youth Innovation Promotion
   Association CAS, China under Grant No. 2017146.
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NR 35
TC 1
Z9 1
U1 2
U2 14
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 JUL
PY 2021
VL 139
BP 140
EP 148
DI 10.1016/j.neunet.2021.02.013
EA MAR 2021
PG 9
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA SF4TL
UT WOS:000652750100011
PM 33706227
DA 2022-08-23
ER

PT J
AU Mao, YY
   Chen, HH
AF Mao, Yanying
   Chen, Honghui
TI Rule-Guided Compositional Representation Learning on Knowledge Graphs
   with Hierarchical Types
SO MATHEMATICS
LA English
DT Article
DE knowledge graph; representation learning; hierarchical types; logic
   rules
AB The representation learning of the knowledge graph projects the entities and relationships in the triples into a low-dimensional continuous vector space. Early representation learning mostly focused on the information contained in the triplet itself but ignored other useful information. Since entities have different types of representations in different scenarios, the rich information in the types of entity levels is helpful for obtaining a more complete knowledge representation. In this paper, a new knowledge representation frame (TRKRL) combining rule path information and entity hierarchical type information is proposed to exploit interpretability of logical rules and the advantages of entity hierarchical types. Specifically, for entity hierarchical type information, we consider that entities have multiple representations of different types, as well as treat it as the projection matrix of entities, using the type encoder to model entity hierarchical types. For rule path information, we mine Horn rules from the knowledge graph to guide the synthesis of relations in paths. Experimental results show that TRKRL outperforms baselines on the knowledge graph completion task, which indicates that our model is capable of using entity hierarchical type information, relation paths information, and logic rules information for representation learning.
C1 [Mao, Yanying; Chen, Honghui] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
RP Chen, HH (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
EM maoyanying@nudt.edu.cn; chenhonghui@nudt.edu.cm
OI Mao, Yanying/0000-0002-4746-9346
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NR 26
TC 0
Z9 0
U1 4
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD AUG
PY 2021
VL 9
IS 16
AR 1978
DI 10.3390/math9161978
PG 11
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA UG9PI
UT WOS:000689574300001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Song, Q
   Wu, YH
   Lin, P
   Dong, LX
   Sun, H
AF Song, Qi
   Wu, Yinghui
   Lin, Peng
   Dong, Luna Xin
   Sun, Hui
TI Mining Summaries for Knowledge Graph Search
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Graph summarization; pattern mining; parallel algorithm
ID RDF DATA; SUBGRAPH; QUERIES
AB Querying heterogeneous and large-scale knowledge graphs is expensive. This paper studies a graph summarization framework to facilitate knowledge graph search. (1) We introduce a class of reduced summaries. Characterized by approximate graph pattern matching, these summaries are capable of summarizing entities in terms of their neighborhood similarity up to a certain hop, using small and informative graph patterns. (2) We study a diversified graph summarization problem. Given a knowledge graph, it is to discover top-k summaries that maximize a bi-criteria function, characterized by both informativeness and diversity. We show that diversified summarization is feasible for large graphs, by developing both sequential and parallel summarization algorithms. (a) We show that there exists a 2-approximation algorithm to discover diversified summaries. We further develop an anytime sequential algorithm which discovers summaries under resource constraints. (b) We present a new parallel algorithm with quality guarantees. The algorithm is parallel scalable, which ensures its feasibility in distributed graphs. (3) We also develop a summary-based query evaluation scheme, which only refers to a small number of summaries. Using real-world knowledge graphs, we experimentally verify the effectiveness and efficiency of our summarization algorithms, and query processing using summaries.
C1 [Song, Qi; Wu, Yinghui; Lin, Peng] Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99163 USA.
   [Wu, Yinghui] Pacific Northwest Natl Lab, 902 Battelle Blvd, Richland, WA 99354 USA.
   [Dong, Luna Xin] Amazon Inc, Seattle, WA 98101 USA.
   [Sun, Hui] Renmin Univ, Beijing 100872, Peoples R China.
RP Song, Q (通讯作者)，Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99163 USA.
EM qsong@eecs.wsu.eduk; yinghui@eecs.wsu.edu; plin1@eecs.wsu.edu;
   lunadong@amazon.com; sun_h@ruc.edu.cn
RI Song, Qi/AAG-8844-2020; Lin, Peng/AAW-2519-2021
FU NSF [IIS-1633629]; Google Faculty Research Award
FX Qi Song and Yinghui Wu are supported in part by NSF IIS-1633629 and a
   Google Faculty Research Award.
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NR 38
TC 34
Z9 37
U1 3
U2 55
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD OCT 1
PY 2018
VL 30
IS 10
BP 1887
EP 1900
DI 10.1109/TKDE.2018.2807442
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 GT6ER
UT WOS:000444603900005
OA hybrid
DA 2022-08-23
ER

PT J
AU Zins, C
   Santos, PLVAC
AF Zins, Chaim
   Santos, Placida L. V. A. C.
TI Mapping the Knowledge Covered by Library Classification Systems
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID INFORMATION
AB This study explores, in 3 steps, how the 3 main library classification systems, the Library of Congress Classification, the Dewey Decimal Classification, and the Universal Decimal Classification, cover human knowledge. First, we mapped the knowledge covered by the 3 systems. We used the "10 Pillars of Knowledge: Map of Human Knowledge," which comprises 10 pillars, as an evaluative model. We mapped all the subject-based classes and subclasses that are part of the first 2 levels of the 3 hierarchical structures. Then, we zoomed into each of the 10 pillars and analyzed how the three systems cover the 10 knowledge domains. Finally, we focused on the 3 library systems. Based on the way each one of them covers the 10 knowledge domains, it is evident that they failed to adequately and systematically present contemporary human knowledge. They are unsystematic and biased, and, at the top 2 levels of the hierarchical structures, they are incomplete.
C1 [Zins, Chaim; Santos, Placida L. V. A. C.] UNESP Univ Estadual Paulista, Fac Filosofia & Ciencias, Informat Sci Postgrad Program, Sao Paulo, Brazil.
   [Zins, Chaim] Knowledge Mapping Res, Jerusalem, Israel.
RP Zins, C (通讯作者)，UNESP Univ Estadual Paulista, Fac Filosofia & Ciencias, Informat Sci Postgrad Program, Sao Paulo, Brazil.
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   *UDC CONS, 2011, UN DEC CLASS SUMM
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   ZINS C, 2008, 10 PILLARS KNOWLEDGE
   ZINS C, 2009, DESAFIOS IMPRESSO AO, P169
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NR 7
TC 8
Z9 8
U1 0
U2 31
PU WILEY-BLACKWELL
PI MALDEN
PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA
SN 1532-2882
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD MAY
PY 2011
VL 62
IS 5
BP 877
EP 901
DI 10.1002/asi.21481
PG 25
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 749RG
UT WOS:000289486100006
DA 2022-08-23
ER

PT J
AU Nicholson, DN
   Greene, CS
AF Nicholson, David N.
   Greene, Casey S.
TI Constructing knowledge graphs and their biomedical applications
SO COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
LA English
DT Review
DE knowledge graphs; Network embeddings; Text mining; Natural language
   processing; Machine learning; Lterature review
ID EXTRACTION; INFORMATION; ANNOTATION; NETWORKS; DISEASES; ENTITY
AB Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated systems. A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications. This representation is designed to preserve a knowledge graph's local and/or global structure. Additional machine learning methods can be applied to this representation to make predictions within genomic, pharmaceutical, and clinical domains. We frame our discussion first around knowledge graph construction and then around unifying representational learning techniques and unifying applications. Advances in machine learning for biomedicine are creating new opportunities across many domains, and we note potential avenues for future work with knowledge graphs that appear particularly promising. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
C1 [Nicholson, David N.] Univ Penn, Dept Syst Pharmacol & Translat Therapeut, Philadelphia, PA 19104 USA.
   [Greene, Casey S.] Univ Penn, Dept Syst Pharmacol & Translat Therapeut, Perelman Sch Med, Childhood Canc Data Lab,Alexs Lemonade Stand Fdn, Philadelphia, PA 19104 USA.
RP Greene, CS (通讯作者)，Univ Penn, Dept Syst Pharmacol & Translat Therapeut, Perelman Sch Med, Childhood Canc Data Lab,Alexs Lemonade Stand Fdn, Philadelphia, PA 19104 USA.
EM greenescientist@gmail.com
OI Nicholson, David/0000-0003-0002-5761
FU Gordon and Betty Moore Foundation [GBMF4552]; National Institutes of
   Health [T32 HG000046, R01 HG010067]
FX David Nicholson Funded by The Gordon and Betty Moore Foundation
   (GBMF4552) and the National Institutes of Health (T32 HG000046); Casey
   S. Greene Funded by The Gordon and Betty Moore Foundation (GBMF4552) and
   the National Institutes of Health (R01 HG010067).
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SN 2001-0370
J9 COMPUT STRUCT BIOTEC
JI Comp. Struct. Biotechnol. J..
PY 2020
VL 18
BP 1414
EP 1428
DI 10.1016/j.csbj.2020.05.017
PG 15
WC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology
GA PR6NH
UT WOS:000607350300016
PM 32637040
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Liu, H
   Hu, KR
   Wang, FL
   Hao, TY
AF Liu, Hai
   Hu, Kairong
   Wang, Fu-Lee
   Hao, Tianyong
TI Aggregating neighborhood information for negative sHarm
   probabilityampling for knowledge graph embedding
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Negative sampling; Knowledge graph; Neighborhood; NKSGAN; Link
   prediction
ID ATTENTION
AB Knowledge graphs, as linked data, can be extracted from texts in triple form that illustrate the structure of "entity-relation-entity." Knowledge graph embedding (KGE) models are used to map entities and relations into a continuous vector space with semantic constraints so as to learn a knowledge graph with fact triples. In the KGE model training process, both positive and negative triples are necessarily provided. Thus, negative sampling methods are meaningful in generating negative samples based on the representations of entities and relations. This paper proposes an innovative neighborhood knowledge selective adversarial network (NKSGAN), which leverages the representation of aggregating neighborhood information to generate high-quality negative samples for enhancing the performances of the discriminator. Experiments are conducted on widely used standard datasets such as FB15k, FB15k-237, WN18 and WN18RR to evaluate our model for link prediction task. The results present the superiority of our proposed NKSGAN than other baseline methods, indicating that the negative sampling process in NKSGAN is effective in generating high-quality negative samples for boosting KGE models.
C1 [Liu, Hai; Hu, Kairong; Hao, Tianyong] South China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China.
   [Wang, Fu-Lee] Open Univ Hong Kong, Sch Sci & Technol, Hong Kong, Peoples R China.
RP Hao, TY (通讯作者)，South China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China.
EM liuhai@m.senu.edu.cn; 2018022615@m.senu.edu.cn; pwang@ouhk.edu.hk;
   haoty@m.scnu.edu.cn
RI Wang, Fu Lee/AAD-9782-2021
OI Wang, Fu Lee/0000-0002-3976-0053
FU National Natural Science Foundation of China [61772146]; Guangdong
   Natural Science Foundation [2016A030313441, 2018A030310051]; Guangzhou
   Science Technology and Innovation Commission [201803010063]
FX This work is supported by National Natural Science Foundation of China
   (No. 61772146), Guangdong Natural Science Foundation (Nos.
   2016A030313441, 2018A030310051), and Guangzhou Science Technology and
   Innovation Commission (No. 201803010063).
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NR 68
TC 2
Z9 2
U1 1
U2 8
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 DEC
PY 2020
VL 32
IS 23
SI SI
BP 17637
EP 17653
DI 10.1007/s00521-020-04940-5
EA MAY 2020
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OS0SU
UT WOS:000531524600002
DA 2022-08-23
ER

PT J
AU Mohamed, A
   Abuoda, G
   Ghanem, A
   Kaoudi, Z
   Aboulnaga, A
AF Mohamed, Aisha
   Abuoda, Ghadeer
   Ghanem, Abdurrahman
   Kaoudi, Zoi
   Aboulnaga, Ashraf
TI RDFFrames: knowledge graph access for machine learning tools
SO VLDB JOURNAL
LA English
DT Article
DE Knowledge graphs; RDF; SPARQL; PyData; Data preparation; Machine
   learning
ID CONSTRUCTION
AB Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.
C1 [Mohamed, Aisha; Aboulnaga, Ashraf] HBKU, Qatar Comp Res Inst, Education City, Qatar.
   [Abuoda, Ghadeer] HBKU, Coll Sci & Engn, Education City, Qatar.
   [Ghanem, Abdurrahman] Bluescape, San Carlos, CA USA.
   [Kaoudi, Zoi] Tech Univ Berlin, Berlin, Germany.
RP Abuoda, G (通讯作者)，HBKU, Coll Sci & Engn, Education City, Qatar.
EM ahmohamed@hbku.edu.qa; gabuoda@hbku.edu.qa; ghanemabdo@gmail.com;
   zoi.kaoudi@tu-berlin.de; aaboulnaga@hbku.edu.qa
OI Kaoudi, Zoi/0000-0003-4520-5360
FU Qatar National Library
FX Open access funding provided by the Qatar National Library.
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NR 48
TC 1
Z9 1
U1 6
U2 8
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 321
EP 346
DI 10.1007/s00778-021-00690-5
EA AUG 2021
PG 26
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZU6ZC
UT WOS:000689650700001
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Farazi, F
   Salamanca, M
   Mosbach, S
   Akroyd, J
   Eibeck, A
   Aditya, LK
   Chadzynski, A
   Pan, K
   Zhou, XC
   Zhang, SC
   Lim, MQ
   Kraft, M
AF Farazi, Feroz
   Salamanca, Maurin
   Mosbach, Sebastian
   Akroyd, Jethro
   Eibeck, Andreas
   Aditya, Leonardus Kevin
   Chadzynski, Arkadiusz
   Pan, Kang
   Zhou, Xiaochi
   Zhang, Shaocong
   Lim, Mei Qi
   Kraft, Markus
TI Knowledge Graph Approach to Combustion Chemistry and Interoperability
SO ACS OMEGA
LA English
DT Article
ID ONTOLOGY; MANAGEMENT
AB In this paper, we demonstrate through examples how the concept of a Semantic Web based knowledge graph can be used to integrate combustion modeling into cross-disciplinary applications and in particular how inconsistency issues in chemical mechanisms can be addressed. We discuss the advantages of linked data that form the essence of a knowledge graph and how we implement this in a number of interconnected ontologies, specifically in the context of combustion chemistry. Central to this is OntoKin, an ontology we have developed for capturing both the content and the semantics of chemical kinetic reaction mechanisms. OntoKin is used to represent the example mechanisms from the literature in a knowledge graph, which itself is part of the existing, more general knowledge graph and ecosystem of autonomous software agents that are acting on it. We describe a web interface, which allows users to interact with the system, upload and compare the existing mechanisms, and query species and reactions across the knowledge graph. The utility of the knowledge-graph approach is demonstrated for two use-cases: querying across multiple mechanisms from the literature and modeling the atmospheric dispersion of pollutants emitted by ships. As part of the query use-case, our ontological tools are applied to identify variations in the rate of a hydrogen abstraction reaction from methane as represented by 10 different mechanisms.
C1 [Farazi, Feroz; Salamanca, Maurin; Mosbach, Sebastian; Akroyd, Jethro; Zhou, Xiaochi; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.
   [Salamanca, Maurin; Mosbach, Sebastian; Akroyd, Jethro; Eibeck, Andreas; Chadzynski, Arkadiusz; Pan, Kang; Zhang, Shaocong; Lim, Mei Qi; Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore CARES, Singapore 138602, Singapore.
   [Aditya, Leonardus Kevin; Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.; Kraft, M (通讯作者)，Cambridge Ctr Adv Res & Educ Singapore CARES, Singapore 138602, Singapore.; Kraft, M (通讯作者)，Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.
EM mk306@cam.ac.uk
RI Salamanca, Maurin/AAL-3974-2021; Pan, Kang/AAU-2468-2020; Akroyd,
   Jethro/A-8698-2010
OI Salamanca, Maurin/0000-0001-6584-9097; Pan, Kang/0000-0002-4107-0748;
   Akroyd, Jethro/0000-0002-2143-8656; Farazi, Feroz/0000-0002-6786-7309;
   Mosbach, Sebastian/0000-0001-7018-9433
FU National Research Foundation (NRF), Prime Minister's Office, Singapore,
   under its Campus for Research Excellence and Technological Enterprise
   (CREATE) program; European Union Horizon 2020 Research and Innovation
   Program [646121]; Alexander von Humboldt foundation; EPSRC
   [EP/R029369/1]; ARCHER
FX This work was partly funded by the National Research Foundation (NRF),
   Prime Minister's Office, Singapore, under its Campus for Research
   Excellence and Technological Enterprise (CREATE) program, and by the
   European Union Horizon 2020 Research and Innovation Program under grant
   agreement 646121. Markus Kraft gratefully acknowledges the support of
   the Alexander von Humboldt foundation. The authors are grateful to EPSRC
   (grant number: EP/R029369/1) and ARCHER for financial and computational
   support as a part of their funding to the UK Consortium on Turbulent
   Reacting Flows (www.ukctrf.com) (https://doi.org/10.17863/CAM.54397).
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NR 42
TC 11
Z9 11
U1 7
U2 11
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 JUL 28
PY 2020
VL 5
IS 29
BP 18342
EP 18348
DI 10.1021/acsomega.0c02055
PG 7
WC Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry
GA MX5GC
UT WOS:000557750100055
PM 32743209
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Shan, SB
   Cao, BY
AF Shan, Shubing
   Cao, Buyang
TI Follow a guide to solve urban problems: the creation and application of
   urban knowledge graph
SO IET SOFTWARE
LA English
DT Article
DE knowledge acquisition; inference mechanisms; graph theory;
   expectation-maximisation algorithm; urban problems; urban knowledge
   graph; urban knowledge acquisition; hybrid reasoning algorithm; EG;
   expectation-maximisation algorithm; Gibbs algorithm
AB It is a hot research topic today to find out the potential knowledge from the scattered urban data and take advantage of the relationship between the knowledge to solve the challenges of urban governance and smart city construction. The urban knowledge graph is an effective way to establish the relationship between the knowledge and address the urban issues. This study proposes the methodology to create an urban knowledge graph and its framework. It elaborates the approaches of urban knowledge acquisition, reasoning and expression. Furthermore, a hybrid reasoning algorithm known as EG is given based on expectation-maximisation algorithm and Gibbs algorithm, which has the complementary advantages of the both methods. Through a study case, this study illustrates constructing and working process of an urban knowledge graph. The case shows that the urban knowledge graph has a good application prospect.
C1 [Shan, Shubing; Cao, Buyang] Tongji Univ, Sch Software Engn, 4800 Caoan Rd, Shanghai, Peoples R China.
   [Shan, Shubing; Cao, Buyang] China Intelligent Urbanizat Cocreat Ctr, 1239 Siping Rd, Shanghai, Peoples R China.
RP Shan, SB (通讯作者)，Tongji Univ, Sch Software Engn, 4800 Caoan Rd, Shanghai, Peoples R China.; Shan, SB (通讯作者)，China Intelligent Urbanizat Cocreat Ctr, 1239 Siping Rd, Shanghai, Peoples R China.
EM shanshubing@msn.com
FU CIUC [CIUC20150011]; TJAD [CIUC20150011]; National Natural Science
   Foundation of China [61271351]
FX This work was supported by CIUC and TJAD (grant no.CIUC20150011) and
   National Natural Science Foundation of China (grant no. 61271351).
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PU INST ENGINEERING TECHNOLOGY-IET
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PA MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND
SN 1751-8806
EI 1751-8814
J9 IET SOFTW
JI IET Softw.
PD JUN
PY 2017
VL 11
IS 3
BP 126
EP 134
DI 10.1049/iet-sen.2016.0189
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FA1OZ
UT WOS:000405210600008
DA 2022-08-23
ER

PT J
AU Wang, P
   Zhou, J
   Liu, YZ
   Zhou, XC
AF Wang, Peng
   Zhou, Jing
   Liu, Yuzhang
   Zhou, Xingchen
TI TransET: Knowledge Graph Embedding with Entity Types
SO ELECTRONICS
LA English
DT Article
DE knowledge graph embedding; entity type; circular convolution
AB Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.
C1 [Wang, Peng; Liu, Yuzhang; Zhou, Xingchen] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
   [Wang, Peng] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 211189, Peoples R China.
   [Zhou, Jing] Nanjing Fiberhome Starrysky Co Ltd, Nanjing 211100, Peoples R China.
RP Wang, P (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.; Wang, P (通讯作者)，Southeast Univ, Sch Cyber Sci & Engn, Nanjing 211189, Peoples R China.
EM pwang@seu.edu.cn; jingzhou0201@gmail.com; yuzhangliu@seu.edu;
   xczhou_2021@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 [31511110310, 31514020501,
   31514020503]
FX The work is supported by the National Key R&D Program of China
   (2018YFD1100302), the National Natural Science Foundation of China (No.
   61972082), and All-Army Common Information System Equipment Pre-Research
   Project (No. 31511110310, No. 31514020501, No. 31514020503).
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NR 29
TC 4
Z9 4
U1 2
U2 7
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 12
AR 1407
DI 10.3390/electronics10121407
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA TB4EM
UT WOS:000667898300001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Biswas, S
   Mitra, P
   Rao, KS
AF Biswas, Saikat
   Mitra, Pabitra
   Rao, Krothapalli Sreenivasa
TI Relation Prediction of Co-Morbid Diseases Using Knowledge Graph
   Completion
SO IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
LA English
DT Article
DE Diseases; Databases; Task analysis; Drugs; Tensors; Proteins; Disease
   co-morbidity; knowledge graph; markov clustering; embedding;
   protein-protein interaction
ID EXPRESSION; GENES
AB Co-morbid disease condition refers to the simultaneous presence of one or more diseases along with the primary disease. A patient suffering from co-morbid diseases possess more mortality risk than with a disease alone. So, it is necessary to predict co-morbid disease pairs. In past years, though several methods have been proposed by researchers for predicting the co-morbid diseases, not much work is done in prediction using knowledge graph embedding using tensor factorization. Moreover, the complex-valued vector-based tensor factorization is not being used in any knowledge graph with biological and biomedical entities. We propose a tensor factorization based approach on biological knowledge graphs. Our method introduces the concept of complex-valued embedding in knowledge graphs with biological entities. Here, we build a knowledge graph with disease-gene associations and their corresponding background information. To predict the association between prevalent diseases, we use ComplEx embedding based tensor decomposition method. Besides, we obtain new prevalent disease pairs using the MCL algorithm in a disease-gene-gene network and check their corresponding inter-relations using edge prediction task.
C1 [Biswas, Saikat] Indian Inst Technol, Adv Technol Dev Ctr, Kharagpur 721302, W Bengal, India.
   [Mitra, Pabitra; Rao, Krothapalli Sreenivasa] Indian Inst Technol, Dept Comp Sci & Engn, Kharagpur 721302, W Bengal, India.
RP Biswas, S (通讯作者)，Indian Inst Technol, Adv Technol Dev Ctr, Kharagpur 721302, W Bengal, India.
EM saikatbiswas17@iitkgp.ac.in; pabitra@cse.iitkgp.ac.in;
   ksrao@cse.iitkgp.ac.in
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U1 10
U2 33
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1545-5963
EI 1557-9964
J9 IEEE ACM T COMPUT BI
JI IEEE-ACM Trans. Comput. Biol. Bioinform.
PD MAR-APR
PY 2021
VL 18
IS 2
BP 708
EP 717
DI 10.1109/TCBB.2019.2927310
PG 10
WC Biochemical Research Methods; Computer Science, Interdisciplinary
   Applications; Mathematics, Interdisciplinary Applications; Statistics &
   Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Computer Science; Mathematics
GA RK6JM
UT WOS:000638400100016
PM 31295118
DA 2022-08-23
ER

PT J
AU Zhou, CH
   Wang, H
   Wang, CS
   Hou, ZQ
   Zheng, ZM
   Shen, SZ
   Cheng, QM
   Feng, ZQ
   Wang, XB
   Lv, HR
   Fan, JX
   Hu, XM
   Hou, MC
   Zhu, YQ
AF Zhou, Chenghu
   Wang, Hua
   Wang, Chengshan
   Hou, Zengqian
   Zheng, Zhiming
   Shen, Shuzhong
   Cheng, Qiuming
   Feng, Zhiqiang
   Wang, Xinbing
   Lv, Hairong
   Fan, Junxuan
   Hu, Xiumian
   Hou, Mingcai
   Zhu, Yunqiang
TI Prospects for the research on geoscience knowledge graph in the Big Data
   Era
SO SCIENCE CHINA-EARTH SCIENCES
LA English
DT Article
DE Geoscience knowledge graph; All-domain geoscience knowledge
   representation model; Federated crowd intelligence collaboration;
   High-precision geological time scale
ID GLOBAL STRATOTYPE SECTION; CHRONOSTRATIGRAPHY; POINT
AB Since the beginning of the 21st century, the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means. It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph. Based on adopting the graph pattern of general knowledge representation, the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge, and integrates geoscience knowledge elements such as map, text, number, to establish an all-domain geoscience knowledge representation model. A federated, crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here, which realizes the construction of high-quality professional knowledge graph in collaboration with global geoscientists. We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis, which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph. A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis, but also advance the construction of the high-precision geological time scale driven by big data, the compilation of intelligent maps driven by rules and data, and the geoscience knowledge evolution and reasoning analysis, and others. It will further expand the new directions of geoscience research driven by both data and knowledge, break new ground where geoscience, information science, and data science intersect, and realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.
C1 [Zhou, Chenghu; Wang, Hua; Zhu, Yunqiang] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.
   [Zhou, Chenghu; Wang, Hua; Zhu, Yunqiang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Wang, Chengshan; Cheng, Qiuming] China Univ Geosci Beijing, Sch Earth Sci & Resources, Beijing 100083, Peoples R China.
   [Hou, Zengqian] Chinese Acad Geol Sci, Inst Geol, Minist Nat Resources, Key Lab Deep Earth Dynam, Beijing 100037, Peoples R China.
   [Zheng, Zhiming] Beihang Univ, Inst Artificial Intelligence, Beijing 100191, Peoples R China.
   [Shen, Shuzhong; Fan, Junxuan; Hu, Xiumian] Nanjing Univ, Sch Earth Sci & Engn, State Key Lab Mineral Deposits Res, Nanjing 210023, Peoples R China.
   [Feng, Zhiqiang] Sinopec Petr Explorat & Prod Res Inst, Beijing 100083, Peoples R China.
   [Wang, Xinbing] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China.
   [Lv, Hairong] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China.
   [Hou, Mingcai] Chengdu Univ Technol, Inst Sedimentary Geol, Chengdu 610059, Peoples R China.
RP Zhou, CH; Wang, H (通讯作者)，Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.; Zhou, CH; Wang, H (通讯作者)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
EM zhouch@lreis.ac.cn; wangh@lreis.ac.cn
RI Lv, Hairong/ABD-4100-2021; chen, shu zhong/D-8214-2011
OI chen, shu zhong/0000-0001-8380-0692
FU National Natural Science Foundation of China [41421001, 42050101,
   42050105]
FX This paper benefitted from the guidances and supports from many domestic
   and overseas colleagues. The theme of this paper comes from our
   collective thinking. So this paper should be a piece of collective work.
   The authors want to specially thank the experts and scholars who
   participated in the Shuang Qing Forum themed "Data-driven Geoscience:
   from Tradition to the Data Age" in 2019 and Deep Time Digital Earth
   Series Symposiums. The authors appreciate Prof. Yupeng Yao and Chaolin
   Zhao from the Geoscience Department of NSFC for their guidance on the
   overall design and research directions in the original exploration
   project of NSFC. Many thanks are also given to the anonymous peer
   reviewers for their valuable comments and suggestions. This work was
   supported by the National Natural Science Foundation of China (Grant
   Nos. 41421001, 42050101, and 42050105).
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NR 39
TC 6
Z9 6
U1 30
U2 70
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1674-7313
EI 1869-1897
J9 SCI CHINA EARTH SCI
JI Sci. China-Earth Sci.
PD JUL
PY 2021
VL 64
IS 7
BP 1105
EP 1114
DI 10.1007/s11430-020-9750-4
EA MAY 2021
PG 10
WC Geosciences, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geology
GA TG7IB
UT WOS:000654827800002
DA 2022-08-23
ER

PT J
AU ALAM, MM
   RONY, MRA
   NAYYERI, M
   MOHIUDDIN, K
   AKTER, MSTM
   VAHDATI, S
   LEHMANN, J
AF ALAM, M. I. R. Z. A. M. O. H. T. A. S. H. I. M.
   RONY, M. D. R. A. S. H. A. D. A. L. H. A. S. A. N.
   NAYYERI, M. O. J. T. A. B. A.
   MOHIUDDIN, K. A. R. I. S. H. M. A.
   AKTER, M. S. T. M. A. H. F. U. J. A.
   VAHDATI, S. A. H. A. R.
   LEHMANN, J. E. N. S.
TI Language Model Guided Knowledge Graph Embeddings
SO IEEE ACCESS
LA English
DT Article
DE Predictive models; Computational modeling; Task analysis; Electronic
   mail; Adaptation models; Unified modeling language; Licenses; Knowledge
   graph; knowledge graph embeddings; language models; link prediction
AB Knowledge graph embedding models have become a popular approach for knowledge graph completion through predicting the plausibility of (potential) triples. This is performed by transforming the entities and relations of the knowledge graph into an embedding space. However, knowledge graphs often include further textual information stored in literal, which is ignored by such embedding models. As a consequence, the learning process stays limited to the structure and the connections between the entities, which has the potential to negatively influence the performance. We bridge this gap by leveraging the capabilities of pre-trained language models to include textual knowledge in the learning process of embedding models. This is achieved by introducing a new loss function that guides embedding models in measuring the likelihood of triples by taking such complementary knowledge into consideration. The proposed solution is a model-independent loss function that can be plugged into any knowledge graph embedding model. In this paper, Sentence-BERT and fastText are used as pre-trained language models from which the embeddings of the textual knowledge are obtained and injected into the loss function. The loss function contains a trainable slack variable that determines the degree to which the language models influence the plausibility of triples. Our experimental evaluation on six benchmarks, namely Nations, UMLS, WordNet, and three versions of CodEx confirms the advantage of using pre-trained language models for boosting the accuracy of knowledge graph embedding models. We showcase this by performing evaluations on top of the five well-known knowledge graph embedding models such as TransE, RotatE, ComplEx, DistMult, and QuatE. The results show an improvement in accuracy up to 9% on UMLS dataset for the Distmult model and 4.2% on the Nations dataset for the ComplEx model when they are guided by pre-trained language models. We additionally studied the effect of multiple factors such as the structure of the knowledge graphs and training steps and presented them as ablation studies.
C1 [ALAM, M. I. R. Z. A. M. O. H. T. A. S. H. I. M.; VAHDATI, S. A. H. A. R.; LEHMANN, J. E. N. S.] Inst Appl Informat Asociat eV InfAI, D-01069 Dresden, Germany.
   [RONY, M. D. R. A. S. H. A. D. A. L. H. A. S. A. N.] Fraunhofer IAIS, D-01069 Dresden, Germany.
   [RONY, M. D. R. A. S. H. A. D. A. L. H. A. S. A. N.; NAYYERI, M. O. J. T. A. B. A.; MOHIUDDIN, K. A. R. I. S. H. M. A.; AKTER, M. S. T. M. A. H. F. U. J. A.] Univ Bonn, Inst Comp Sci, D-53113 Bonn, Germany.
   [LEHMANN, J. E. N. S.] Amazon, D-01097 Dresden, Germany.
RP ALAM, MM (通讯作者)，Inst Appl Informat Asociat eV InfAI, D-01069 Dresden, Germany.
EM turzo.mohtasim@gmail.com
OI Rony, Md Rashad Al Hasan/0000-0003-0665-389X; Lehmann,
   Jens/0000-0001-9108-4278; Akter, Mst. Mahfuja/0000-0002-9439-2820
FU European Union (EU) [EU GA 952215, 101004152, GA 101016453];
   Bundesministerium fur Bildung und Forschung (BMBF) [01IS18050, BMWi FKZ
   01MK20011A]; Center for Scalable Data Analytics and Artificial
   Intelligence (ScaDS.AI) [IS18026A-F]; Bundesministerium fur Bildung und
   Forschung (BMBF) through Project JOSEPH (Fraunhofer Zukunftsstiftung)
FX This work was supported in part by the European Union (EU) through the
   Project TAILOR under Grant EU GA 952215, through the Project CALLISTO
   under Grant 101004152, and through the Project e-Vita under Grant GA
   101016453; in part by the Bundesministerium fur Bildung und Forschung
   (BMBF) through the Project MLwin under Grant 01IS18050, through the
   Project SPEAKER under Grant BMWi FKZ 01MK20011A, and through the Project
   JOSEPH (Fraunhofer Zukunftsstiftung); and in part by the Center for
   Scalable Data Analytics and Arti~cial Intelligence (ScaDS.AI) under
   Grant IS18026A-F.
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NR 45
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 76008
EP 76020
DI 10.1109/ACCESS.2022.3191666
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3G0WI
UT WOS:000831077300001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, K
   Liu, Y
   Xu, XJ
   Sheng, QZ
AF Wang, Kai
   Liu, Yu
   Xu, Xiujuan
   Sheng, Quan Z.
TI Enhancing knowledge graph embedding by composite neighbors for link
   prediction
SO COMPUTING
LA English
DT Article
DE Knowledge graph embedding; Link prediction; Graph memory networks;
   Knowledge graphs
AB Knowledge graph embedding (KGE) aims to represent entities and relations in a low-dimensional continuous vector space. Recent KGE works focus on incorporating additional information, such as local neighbors and textual descriptions, to learn valuable representations. However, the non-uniformity and redundancy hinder the effectiveness of entity features from those information sources. In this paper, we propose a novel end-to-end framework, called composite neighborhood embedding (CoNE), utilizing composite neighbors to enhance the existing KGE methods. To ease past problems, the new composite neighbors are gathered from both entity descriptions and local neighbors. We design a novel Graph Memory Networks to extract entity features from composite neighbors, and fulfill the entity representation in the target KGE method. The experimental results show that CoNE effectively enhances three different KGE methods, TransE, ConvE, and RotatE, and achieves the state-of-the-art results on four real-world large datasets. Furthermore, our approach outperforms the recent text-enhanced models with fewer parameters and calculation. The source code of our work can be obtained from.
C1 [Wang, Kai; Liu, Yu; Xu, Xiujuan] Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116023, Peoples R China.
   [Sheng, Quan Z.] Macquarie Univ, Dept Comp, Sydney, NSW 2109, Australia.
RP Wang, K (通讯作者)，Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116023, Peoples R China.
EM kai_wang@mail.dlut.edu.cn; yuliu@dlut.edu.cn; xjxu@dlut.edu.cn;
   michael.sheng@mq.edu.au
RI Wang, Kai/AFE-0761-2022; Wang, Kai/AAE-9502-2022; Sheng, Quan
   Z./B-8169-2008; Liu, Yu/ABD-6335-2021
OI Wang, Kai/0000-0002-8828-2887; Wang, Kai/0000-0002-8828-2887; Sheng,
   Quan Z./0000-0002-3326-4147; 
FU National Natural Science Foundation in China [61672128]; Fundamental
   Research Fund for Central University [DUT20TD107]
FX This research is supported by the National Natural Science Foundation in
   China (Grant: 61672128) and the Fundamental Research Fund for Central
   University (Grant: DUT20TD107).
CR [Anonymous], 2018, ADV NEURAL INFORM PR
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NR 33
TC 2
Z9 2
U1 1
U2 20
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 DEC
PY 2020
VL 102
IS 12
BP 2587
EP 2606
DI 10.1007/s00607-020-00842-5
EA OCT 2020
PG 20
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OE4NY
UT WOS:000575694100001
DA 2022-08-23
ER

PT J
AU Guan, SP
   Jin, XL
   Wang, YZ
   Jia, YT
   Shen, HW
   Li, ZX
   Cheng, XQ
AF Guan, Saiping
   Jin, Xiaolong
   Wang, Yuanzhuo
   Jia, Yantao
   Shen, Huawei
   Li, Zixuan
   Cheng, Xueqi
TI Self-learning and embedding based entity alignment
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Entity alignment; Knowledge graph; Self-learning; Embedding
AB Entity alignment aims to identify semantical matchings between entities from different groups. Traditional methods (e.g., attribute comparison-based methods, graph operation-based methods and active learning ones) are usually supervised by labeled data as prior knowledge. Since it is not trivial to label data for training, researchers have then turned to unsupervised methods, and have thus developed similarity-based methods, probabilistic methods, graphical model-based methods, etc. In addition, structure or class information is further explored. As an important part of a knowledge graph, entities contain rich semantical information that can be well learned by knowledge graph embedding methods in low-dimensional vector spaces. However, existing methods for entity alignment have paid little attention to knowledge graph embedding. In this paper, we propose a self-learning and embedding based method for entity alignment, thus called SEEA, to iteratively find semantically aligned entity pairs, which makes full use of semantical information contained in the attributes of entities. Experiments on three realistic datasets and comparison with a few baseline methods validate the effectiveness and merits of the proposed method.
C1 [Guan, Saiping; Jin, Xiaolong; Wang, Yuanzhuo; Jia, Yantao; Shen, Huawei; Li, Zixuan; Cheng, Xueqi] Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China.
   [Guan, Saiping; Jin, Xiaolong; Wang, Yuanzhuo; Jia, Yantao; Shen, Huawei; Li, Zixuan; Cheng, Xueqi] Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China.
RP Jin, XL (通讯作者)，Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China.; Jin, XL (通讯作者)，Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China.
EM jinxiaolong@ict.ac.cn
RI Shen, Hua-Wei/E-8028-2010
OI Shen, Hua-Wei/0000-0002-1081-8119
FU National Key Research and Development Program of China [2016YFB1000902,
   2017YFC0820404]; National Natural Science Foundation of China [61772501,
   61572473, 61572469, 91646120]
FX This work is supported by National Key Research and Development Program
   of China under Grants 2016YFB1000902 and 2017YFC0820404, and National
   Natural Science Foundation of China under Grants 61772501, 61572473,
   61572469, and 91646120.
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TC 5
Z9 11
U1 2
U2 23
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0219-1377
EI 0219-3116
J9 KNOWL INF SYST
JI Knowl. Inf. Syst.
PD MAY
PY 2019
VL 59
IS 2
BP 361
EP 386
DI 10.1007/s10115-018-1191-0
PG 26
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HP3JI
UT WOS:000461572500005
DA 2022-08-23
ER

PT J
AU Jia, Y
   Qi, YL
   Shang, HJ
   Jiang, R
   Li, AP
AF Jia, Yan
   Qi, Yulu
   Shang, Huaijun
   Jiang, Rong
   Li, Aiping
TI A Practical Approach to Constructing a Knowledge Graph for Cybersecurity
SO ENGINEERING
LA English
DT Article
DE Cybersecurity; Knowledge graph; Knowledge deduction
ID ONTOLOGY
AB Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity recognizer (NER) is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a recognizer in the cybersecurity domain in preparation for future work. (C) 2018 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.
C1 [Jia, Yan; Qi, Yulu; Shang, Huaijun; Jiang, Rong; Li, Aiping] Natl Univ Def Technol, Sch Comp Sci, Changsha 410073, Hunan, Peoples R China.
RP Li, AP (通讯作者)，Natl Univ Def Technol, Sch Comp Sci, Changsha 410073, Hunan, Peoples R China.
EM liaiping@nudt.edu.cn
FU National Natural Science Foundation of China [U163215, 61472433,
   61732022, 61732004, 61672020, 61502517]; National Key Research and
   Development Program [2016YFB0800802, 2016YFB0800803, 2016YFB0800804,
   2017YFB0802204, 2016QY03D0601, 2016QY03D0603, 2016YFB0800303]
FX We are grateful for the support of the National Natural Science
   Foundation of China (U163215, 61472433, 61732022, 61732004, 61672020,
   and 61502517) and the National Key Research and Development Program
   (2016YFB0800802, 2016YFB0800803, 2016YFB0800804, 2017YFB0802204,
   2016QY03D0601, 2016QY03D0603, and 2016YFB0800303).
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NR 25
TC 50
Z9 55
U1 4
U2 45
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 2095-8099
EI 2096-0026
J9 ENGINEERING-PRC
JI Engineering
PD FEB
PY 2018
VL 4
IS 1
SI SI
BP 53
EP 60
DI 10.1016/j.eng.2018.01.004
PG 8
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA GC4VD
UT WOS:000429782000010
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, ZW
   Cao, L
   Chen, XL
   Tang, W
   Xu, ZX
   Meng, YY
AF Zhang, Zhongwei
   Cao, Lei
   Chen, Xiliang
   Tang, Wei
   Xu, Zhixiong
   Meng, Yangyang
TI Representation Learning of Knowledge Graphs With Entity Attributes
SO IEEE ACCESS
LA English
DT Article
DE Attribute; knowledge graph; representation learning
AB Most of the existing knowledge representation learning methods project the entities and relations represented by symbols in the knowledge graph into the low-dimensional vector space from the perspective of the structure and semantics of triples, and express the complex relations between entities and relations with dense low-dimensional vectors. However, triples in the knowledge graph not only contain relation triples, but also contain a large number of attribute triples. Existing knowledge representation methods often confuse these two kinds of triples and pay little attention to the semantic information contained in attributes and attribute values. In this paper, a novel representation learning method which makes use of the attribute information of entities is proposed. Specifically, deep convolutional neural network model is used to encode attribute information of entities, and both attribute information and triple structure information are utilized to learn knowledge representation, and then generate attribute-based representation of entities. The knowledge graph completion task was used to evaluate this method, and the experimental results on open data sets FB15K and FB24k showed that the attribute-embodied knowledge representation learning model outperforms the other baselines.
C1 [Zhang, Zhongwei; Cao, Lei; Chen, Xiliang; Tang, Wei; Xu, Zhixiong] Army Engn Univ PLA, Command & Control Engn Coll, Nanjing 210000, Peoples R China.
   [Zhang, Zhongwei] PLA Unit 73671, Luan 237000, Peoples R China.
   [Meng, Yangyang] PLA Unit 31121, Nanjing 210000, Peoples R China.
RP Chen, XL; Tang, W (通讯作者)，Army Engn Univ PLA, Command & Control Engn Coll, Nanjing 210000, Peoples R China.
EM 383618393@qq.com; 120641566@qq.com
OI XU, ZHIXIONG/0000-0003-0996-436X; Zhang, Zhongwei/0000-0001-8892-2146
FU National Natural Science Foundation of China [61806221]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61806221.
CR [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
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NR 20
TC 5
Z9 6
U1 7
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 2020
VL 8
BP 7435
EP 7441
DI 10.1109/ACCESS.2020.2963990
PG 7
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LC6ED
UT WOS:000525422700057
OA gold
DA 2022-08-23
ER

PT J
AU Zheng, WG
   Cheng, H
   Yu, JX
   Zou, L
   Zhao, KF
AF Zheng, Weiguo
   Cheng, Hong
   Yu, Jeffrey Xu
   Zou, Lei
   Zhao, Kangfei
TI Interactive natural language question answering over knowledge graphs
SO INFORMATION SCIENCES
LA English
DT Article
DE Interactive query; Natural language question and answering; Knowledge
   graph; Question understanding
ID INTERFACE; WORDNET; QUERIES
AB As many real-world data are constructed into knowledge graphs, providing effective and convenient query techniques for end users is an urgent and important task. Although structured query languages, such as SPARQL, offer a powerful expression ability to query RDF datasets, they are difficult to use. Keywords are simple but have a very limited expression ability. Natural language question (NLQ) is promising for querying knowledge graphs. A huge challenge is how to understand the question clearly so as to translate the unstructured question into a structured query. In this paper, we present a data + oracle approach to answer NLQs over knowledge graphs. We let users verify the ambiguities during the query understanding. To reduce the interaction cost, we formalize an interaction problem and design an efficient strategy to solve the problem. We also propose a query prefetching technique by exploiting the latency in the interactions with users. Moreover, we devise a hybrid approach that incorporates NLP-based, data-driven, and interaction techniques together to complete the question understanding. Extensive experiments over real datasets demonstrate that our proposed approach is effective as it outperforms state-of-the-art methods significantly. (C) 2018 Elsevier Inc. All rights reserved.
C1 [Zheng, Weiguo; Cheng, Hong; Yu, Jeffrey Xu; Zhao, Kangfei] Chinese Univ Hong Kong, Shatin, Hong Kong, Peoples R China.
   [Zou, Lei] Peking Univ, Beijing, Peoples R China.
   [Zou, Lei] PKU, Natl Engn Lab Big Data Anal Technol & Applicat, Beijing, Peoples R China.
RP Zheng, WG (通讯作者)，Chinese Univ Hong Kong, Shatin, Hong Kong, Peoples R China.
EM wgzheng@se.cuhk.edu.hk; hcheng@se.cuhk.edu.hk; yu@se.cuhk.edu.hk;
   zoulei@pku.edu.cn; kfzhao@se.cuhk.edu.hk
FU Research Grant Council of the Hong Kong Special Administrative Region,
   China [CUHK 14205617, CUHK 14221716]; National Key Research and
   Development Program of China [2018YFB1003504]; NSFC [61622201, 61532010]
FX The work described in this papaer was substantially supported by grants
   from the Research Grant Council of the Hong Kong Special Administrative
   Region, China [Project No.: CUHK 14205617] and [Project No.: CUHK
   14221716]. Lei Zou was supported by the National Key Research and
   Development Program of China under grant 2018YFB1003504 and NSFC under
   grant 61622201 and 61532010.
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NR 41
TC 23
Z9 25
U1 4
U2 99
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD MAY
PY 2019
VL 481
BP 141
EP 159
DI 10.1016/j.ins.2018.12.032
PG 19
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HN0AO
UT WOS:000459846300009
DA 2022-08-23
ER

PT J
AU Wu, QY
   Fu, DK
   Shen, BJ
   Chen, YT
AF Wu, Qinyue
   Fu, Duankang
   Shen, Beijun
   Chen, Yuting
TI Semantic Service Search in IT Crowdsourcing Platform: A Knowledge
   Graph-Based Approach
SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
LA English
DT Article
DE Semantic search; IT service crowdsourcing; knowledge graph; query
   expansion; learning to rank
AB Understanding user's search intent in vertical websites like IT service crowdsourcing platform relies heavily on domain knowledge. Meanwhile, searching for services accurately on crowdsourcing platforms is still difficult, because these platforms do not contain enough information to support high-performance search. To solve these problems, we build and leverage a knowledge graph named ITServiceKG to enhance search performance of crowdsourcing IT services. The main ideas are to (1) build an IT service knowledge graph from Wikipedia, Baidupedia, CN-DBpedia, StuQ and data in IT service crowdsourcing platforms, (2) use properties and relations of entities in the knowledge graph to expand user query and service information, and (3) apply a listwise approach with relevance features and topic features to re-rank the search results. The results of our experiments indicate that our approach outperforms the traditional search approaches.
C1 [Wu, Qinyue; Fu, Duankang; Shen, Beijun; Chen, Yuting] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China.
RP Shen, BJ (通讯作者)，Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China.
EM bjshen@sjtu.edu.cn
FU 973 Program in China [2015CB352203]; National Nature Science Foundation
   of China [61472242, 61572312]; Shanghai Municipal Commission of Economy
   and Informatization [201701052]
FX This research is supported by the 973 Program in China (Grant No.
   2015CB352203), National Nature Science Foundation of China (Grant Nos.
   61472242 and 61572312), and Shanghai Municipal Commission of Economy and
   Informatization (No. 201701052). Thanks JointForce for providing the
   experimental dataset.
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NR 26
TC 5
Z9 5
U1 4
U2 12
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 765
EP 783
DI 10.1142/S0218194020400069
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:000558101900003
DA 2022-08-23
ER

PT J
AU Lee, JY
   Ahn, S
   Kim, D
AF Lee, June Young
   Ahn, Sejung
   Kim, Dohyun
TI Deep learning-based prediction of future growth potential of
   technologies
SO PLOS ONE
LA English
DT Article
ID FORECASTING EMERGING TECHNOLOGIES; EMERGENCE; MODEL
AB Research papers are a repository of information on the various elements that make up science and technology R&D activities. Generating knowledge maps based on research papers enables identification of specific areas of scientific and technical research as well as understanding of the flow of knowledge between those areas. Recently, as the number of electronic publishing and informatics archives along with the amount of accumulated knowledge related to science and technology has proliferated, the need to utilize the meta-knowledge obtainable from research papers has increased. Therefore, this study devised a model based on meta-knowledge (i.e., text information including citations, abstracts, area codes) for prediction of future growth potential using deep learning algorithms and investigated the applicability of the various forms of meta-knowledge to the prediction of future growth potential. It also proposes how to select the promising technology clusters based on the proposed model.
C1 [Lee, June Young; Ahn, Sejung] Korea Inst Sci & Technol Informat, Future Technol Anal Ctr, Seoul, South Korea.
   [Kim, Dohyun] Myongji Univ, Dept Ind & Management Engn, Yongin, South Korea.
RP Kim, D (通讯作者)，Myongji Univ, Dept Ind & Management Engn, Yongin, South Korea.
EM ftgog@mju.ac.kr
OI Lee, June Young/0000-0001-8606-2429; Ahn, Sejung/0000-0002-7286-4698
FU Korea Institute of Science and Technology Information (KISTI)
   [K-21-L03-C01-S01]; Basic Science Research Program through the National
   Research Foundation of Korea (NRF) - Ministry of Science, ICT and Future
   Planning [NRF-2017R1E1A1A01077375]
FX This work was supported by Korea Institute of Science and Technology
   Information (KISTI) (K-21-L03-C01-S01) and the Basic Science Research
   Program through the National Research Foundation of Korea (NRF) funded
   by the Ministry of Science, ICT and Future Planning
   (NRF-2017R1E1A1A01077375).
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NR 34
TC 0
Z9 0
U1 4
U2 7
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 4
PY 2021
VL 16
IS 6
AR e0252753
DI 10.1371/journal.pone.0252753
PG 16
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA SW6RB
UT WOS:000664640100127
PM 34086769
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Prieto-Sandoval, V
   Jaca, C
   Ormazabal, M
AF Prieto-Sandoval, Vanessa
   Jaca, Carmen
   Ormazabal, Marta
TI Towards a consensus on the circular economy
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Article
DE Circular economy; Eco-innovation; Systematic literature review;
   Industrial ecology; Paradigm shift
ID CRADLE-TO-CRADLE; ECO-INDUSTRIAL DEVELOPMENT; ECOLOGY; CHINA;
   SUSTAINABILITY; MANAGEMENT; WASTE; OPPORTUNITIES; CONSUMPTION;
   EFFICIENCY
AB The growing importance of the concept of the circular economy as a way to attain sustainable development has encouraged scholars to propose different ways to understand it. Given the large number of studies done on the circular economy, their differing approaches and their multiple applications, this paper attempts to propose a consensus view of the basic notions of the circular economy framework and highlight its relationship with eco-innovation. To that end, this study carried out a systematic literature review that resulted in four main outputs: a knowledge map of the circular economy, an analysis of the main notions of the concept, principles, and determinants of a circular economy. Finally, this study brings to light some remarkable examples of eco-innovations developed for implementation in the circular economy. (C) 2017 Elsevier Ltd. All rights reserved.
C1 [Prieto-Sandoval, Vanessa; Jaca, Carmen; Ormazabal, Marta] Univ Navarra, TECNUN, Ind Management Dept, San Sebastian, Spain.
RP Prieto-Sandoval, V (通讯作者)，Univ Navarra, TECNUN, Ind Management Dept, San Sebastian, Spain.
EM vprieto@tecnun.es
RI Sandoval, Julieth Vanessa Prieto/ABH-7920-2020; Jaca,
   Carmen/A-2667-2013; Ormazabal, Marta/M-2395-2014
OI Sandoval, Julieth Vanessa Prieto/0000-0001-9937-7710; Jaca,
   Carmen/0000-0002-8438-5387; Ormazabal, Marta/0000-0002-3602-3912
FU Spanish National Program for Fostering Excellence in Scientific and
   Technical Research and The European Regional Development Fund
   [DPI2015-70832-R]
FX This work has been sponsored by the Spanish National Program for
   Fostering Excellence in Scientific and Technical Research and The
   European Regional Development Fund: DPI2015-70832-R (MINECO/FEDER). The
   authors would like to thank the International Sustainable Development
   Research Society (ISDRS) and the journal anonymous reviewers for their
   recommendations for improving the paper.
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NR 101
TC 334
Z9 339
U1 37
U2 239
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0959-6526
EI 1879-1786
J9 J CLEAN PROD
JI J. Clean Prod.
PD APR 1
PY 2018
VL 179
BP 605
EP 615
DI 10.1016/j.jclepro.2017.12.224
PG 11
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 FW8GM
UT WOS:000425568700050
OA hybrid
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Chen, XJ
   Ding, L
   Xiang, Y
AF Chen, Xiaojun
   Ding, Ling
   Xiang, Yang
TI Neighborhood aggregation based graph attention networks for open-world
   knowledge graph reasoning
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Open-world knowledge graph reasoning; neighborhood information; graph
   attention networks; knowledge representation learning
AB Knowledge graph reasoning or completion aims at inferring missing facts based on existing ones in a knowledge graph. In this work, we focus on the problem of open-world knowledge graph reasoning-a task that reasons about entities which are absent from KG at training time (unseen entities). Unfortunately, the performance of most existing reasoning methods on this problem turns out to be unsatisfactory. Recently, some works use graph convolutional networks to obtain the embeddings of unseen entities for prediction tasks. Graph convolutional networks gather information from the entity's neighborhood, however, they neglect the unequal natures of neighboring nodes. To resolve this issue, we present an attention-based method named as NAKGR, which leverages neighborhood information to generate entities and relations representations. The proposed model is an encoder-decoder architecture. Specifically, the encoder devises an graph attention mechanism to aggregate neighboring nodes' information with a weighted combination. The decoder employs an energy function to predict the plausibility for each triplets. Benchmark experiments show that NAKGR achieves significant improvements on the open-world reasoning tasks. In addition, our model also performs well on the closed-world reasoning tasks.
C1 [Chen, Xiaojun; Ding, Ling; Xiang, Yang] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.
RP Xiang, Y (通讯作者)，Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.
EM tjdxxiangyang@gmail.com
FU National Natural Science Foundation of China [72071145]; National Key
   R&D Program of China [2019YFB1704402]
FX This work is supported by the National Natural Science Foundation of
   China under Grant No.72071145, the National Key R&D Program of China
   under Grant No.2019YFB1704402.
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NR 50
TC 0
Z9 0
U1 13
U2 16
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2021
VL 41
IS 2
BP 3797
EP 3808
DI 10.3233/JIFS-211889
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA US3NM
UT WOS:000697340200085
DA 2022-08-23
ER

EF