FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Alonge, O
   Neel, AH
   Kalbarczyk, A
   Peters, MA
   Mahendradhata, Y
   Sarker, M
   Owoaje, E
   Deressa, W
   Kayembe, P
   Salehi, AS
   Gupta, SD
AF Alonge, Olakunle
   Neel, Abigail H.
   Kalbarczyk, Anna
   Peters, Michael A.
   Mahendradhata, Yodi
   Sarker, Malabika
   Owoaje, Eme
   Deressa, Wakgari
   Kayembe, Patrick
   Salehi, Ahmad Shah
   Gupta, S. D.
TI Synthesis and translation of research and innovations from polio
   eradication (STRIPE): initial findings from a global mixed methods study
SO BMC PUBLIC HEALTH
LA English
DT Article
DE Polio; Global Polio Eradication Initiative; GPEI; Implementation;
   Knowledge translation
ID LESSONS; CONTEXT; LEGACY
AB Background Lessons from polio eradication efforts and the Global Polio Eradication Initiative (GPEI) are useful for improving health service delivery and outcomes globally. The Synthesis and Translation of Research and Innovations from Polio Eradication (STRIPE) is a multi-phase project which aims to map, package and disseminate knowledge from polio eradication initiatives as academic and training programs. This paper discusses initial findings from the knowledge mapping around polio eradication activities across a multi-country context. Methods The knowledge mapping phase (January 2018 - December 2019) encompassed four research activities (scoping review, survey, key informant interviews (KIIs), health system analyses). This paper utilized a sequential mixed method design combining data from the survey and KIIs. The survey included individuals involved in polio eradication between 1988 and 2019, and described the contexts, implementation strategies, intended and unintended outcomes of polio eradication activities across levels. KIIs were conducted among a nested sample in seven countries (Afghanistan, Bangladesh, the Democratic Republic of Congo, Ethiopia, India, Indonesia, Nigeria) and at the global level to further explore these domains. Results The survey generated 3955 unique responses, mainly sub-national actors representing experience in over 74 countries; 194 KIIs were conducted. External factors including social, political, and economic factors were the most frequently cited barriers to eradication, followed by the process of implementing activities, including program execution, planning, monitoring, and stakeholder engagement. Key informants described common strategies for addressing these barriers, e.g. generating political will, engaging communities, capacity-building in planning and measurement, and adapting delivery strategies. The polio program positively affected health systems by investing in system structures and governance, however, long-term effects have been mixed as some countries have struggled to institutionalize program assets. Conclusion Understanding the implementing context is critical for identifying threats and opportunities to global health programs. Common implementation strategies emerged across countries; however, these strategies were only effective where organizational and individual capacity were sufficient, and where strategies were appropriately tailored to the sociopolitical context. To maximize gains, readiness assessments at different levels should predate future global health programs and initiatives should consider system integration earlier to ensure program institutionalization and minimize system distortions.
C1 [Alonge, Olakunle; Neel, Abigail H.; Kalbarczyk, Anna; Peters, Michael A.] Johns Hopkins Bloomberg Sch Publ Hlth, 615 N Wolfe St, Baltimore, MD 21205 USA.
   [Mahendradhata, Yodi] Univ Gadjah Mada, Fac Med Publ Hlth & Nursing, Yogyakarta, Indonesia.
   [Sarker, Malabika] BRAC Univ, BRAC James P Grant Sch Publ Hlth, Dhaka, Bangladesh.
   [Sarker, Malabika] Heidelberg Univ, Heidelberg Global Inst Hlth HIGH, Heidelberg, Germany.
   [Owoaje, Eme] Univ Ibadan, Coll Med, Ibadan, Nigeria.
   [Deressa, Wakgari] Addis Ababa Univ, Sch Publ Hlth, Addis Ababa, Ethiopia.
   [Kayembe, Patrick] Univ Kinshasa, Sch Publ Hlth, Kinshasa, DEM REP CONGO.
   [Salehi, Ahmad Shah] Global Innovat Consultancy, Kabul, Afghanistan.
   [Gupta, S. D.] Indian Inst Hlth Management Res, Jaipur, Rajasthan, India.
RP Alonge, O (通讯作者)，Johns Hopkins Bloomberg Sch Publ Hlth, 615 N Wolfe St, Baltimore, MD 21205 USA.
EM oalonge1@jhu.edu
RI GUPTA, SHIV DUTT/AAX-8923-2021; Mahendradhata, Yodi/AAQ-9044-2020
OI GUPTA, SHIV DUTT/0000-0002-8641-3724; Mahendradhata,
   Yodi/0000-0002-4522-1785; Kalbarczyk, Anna/0000-0002-6143-8634; Sarker,
   Malabika/0000-0001-7293-8805; Alonge, Olakunle/0000-0001-7642-2806
FU Bill and Melinda Gates Foundation
FX This study is funded by the Bill and Melinda Gates Foundation. The
   funder did not play any role in writing the protocol, interpreting the
   data, or in writing this manuscript. The funder provided and coordinated
   external peerreview for the study proposal.
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NR 30
TC 17
Z9 17
U1 0
U2 1
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1471-2458
J9 BMC PUBLIC HEALTH
JI BMC Public Health
PD AUG 12
PY 2020
VL 20
SU 2
SI SI
AR 1176
DI 10.1186/s12889-020-09156-9
PG 15
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA NE7KN
UT WOS:000562780600004
PM 32787949
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Basile, P
   Greco, C
   Suglia, A
   Semeraro, G
AF Basile, Pierpaolo
   Greco, Claudio
   Suglia, Alessandro
   Semeraro, Giovanni
TI Bridging the gap between linked open data-based recommender systems and
   distributed representations
SO INFORMATION SYSTEMS
LA English
DT Article
DE Recommender systems; Knowledge graph embedding; Linked data
ID KNOWLEDGE; DBPEDIA; WEB
AB Recently, several methods have been proposed for introducing Linked Open Data (LOD) into recommender systems. LOD can be used to enrich the representation of items by leveraging RDF statements and adopting graph-based methods to implement effective recommender systems. However, most of those methods do not exploit embeddings of entities and relations built on knowledge graphs, such as datasets coming from the LOD. In this paper, we propose a novel recommender system based on holographic embeddings of knowledge graphs built from Wikidata, a free and open knowledge base that can be read and edited by both humans and machines. The evaluation performed on three standard datasets such as Movielens 1M, Last.fm and LibraryThing shows promising results, which confirm the effectiveness of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Basile, Pierpaolo; Greco, Claudio; Suglia, Alessandro; Semeraro, Giovanni] Univ Bari Aldo Moro, Dept Comp Sci, Via E Orabona 4, I-70125 Bari, Italy.
RP Basile, P (通讯作者)，Univ Bari Aldo Moro, Dept Comp Sci, Via E Orabona 4, I-70125 Bari, Italy.
EM pierpaolo.basile@uniba.it; claudio.greco@unitn.it; as247@hw.ac.uk;
   giovanni.semeraro@uniba.it
RI Basile, Pierpaolo/AAE-5610-2020; Semeraro, Giovanni/AAC-2156-2020
OI Basile, Pierpaolo/0000-0002-0545-1105; Semeraro,
   Giovanni/0000-0001-6883-1853; Suglia, Alessandro/0000-0002-3177-5197
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   [No title captured]
NR 40
TC 2
Z9 2
U1 1
U2 48
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4379
EI 1873-6076
J9 INFORM SYST
JI Inf. Syst.
PD DEC
PY 2019
VL 86
BP 1
EP 8
DI 10.1016/j.is.2019.07.001
PG 8
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IS6FX
UT WOS:000482248800001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, DH
   Cui, ML
   Yang, Y
   Yang, P
   Xie, C
   Liu, D
   Yu, BB
   Chen, ZB
AF Zhang, Dehai
   Cui, Menglong
   Yang, Yun
   Yang, Po
   Xie, Cheng
   Liu, Di
   Yu, Beibei
   Chen, Zhibo
TI Knowledge Graph-Based Image Classification Refinement
SO IEEE ACCESS
LA English
DT Article
DE Biological vision; image classification; knowledge graph; convolutional
   neural network; semantic relationships
ID OBJECT
AB Biologically inspired ideas are important in image processing. Not only does more than 80% of the information received by humans comes from the visual system, but the human visual system also gives its fast, accurate, and efficient image processing capability. In the current image classification task, convolutional neural networks (CNNs) focus on processing pixels and often ignore the semantic relationships and human brain mechanisms. With the development of image analysis and processing techniques, the information in the image is becoming increasingly complicated. Humans can learn about the characteristics of objects and the relationships that occur between them to classify the images. It is a significant characteristic that sets humans apart from the modern learning-based computer vision algorithms. How to make full use of the semantic relationships in categories and how to apply the knowledge of biological vision to image classification are our main concerns. In this view, we propose the concept of the image knowledge graph (IKG) to incorporate the semantic association and the scene association to fully consider the relations between objects (external and internal). We take full advantage of the reasoning model of the knowledge graph that is closer to the biological visual information-processing model. We conduct extensive experiments on large-scale image datasets (ImageNet), demonstrating the effectiveness of our approach. Furthermore, our method participates in ILSVRC 2017 challenges and obtains the new state-of-the-art results on the ImageNet (82.43%).
C1 [Zhang, Dehai; Cui, Menglong; Yang, Yun; Yang, Po; Xie, Cheng; Liu, Di; Yu, Beibei; Chen, Zhibo] Yunnan Univ, Sch Software, Kunming, Yunnan, Peoples R China.
   [Yang, Po] Liverpool John Moores Univ, Dept Comp Sci, Liverpool, Merseyside, England.
RP Xie, C (通讯作者)，Yunnan Univ, Sch Software, Kunming, Yunnan, Peoples R China.
EM xiecheng@ynu.edu.cn
RI 迪, 刘/ABU-9202-2022; Yang, Po/C-9936-2011
OI 迪, 刘/0000-0002-4365-2768; Yang, Po/0000-0002-8553-7127; Yang,
   Yun/0000-0002-9893-3436
FU Science Foundation of Yunnan University [2017YDQN11]; Yunnan Provincial
   Science Research Project of the Department of Education [2018JS008];
   National Natural Science Foundation of China [61876166, 61663046]
FX This work was supported in part by the Science Foundation of Yunnan
   University under Grant 2017YDQN11, in part by the Yunnan Provincial
   Science Research Project of the Department of Education under Grant
   2018JS008, and in part by the National Natural Science Foundation of
   China under Grant 61876166 and Grant 61663046.
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NR 42
TC 7
Z9 7
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 57678
EP 57690
DI 10.1109/ACCESS.2019.2912627
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HY6DP
UT WOS:000468219600001
OA gold
DA 2022-08-23
ER

PT J
AU Chen, XJ
   Jia, SB
   Ding, L
   Shen, H
   Xiang, Y
AF Chen, Xiaojun
   Jia, Shengbin
   Ding, Ling
   Shen, Hong
   Xiang, Yang
TI SDT: An integrated model for open-world knowledge graph reasoning
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Open-world knowledge graph reasoning; Entity description; Hierarchical
   type; Knowledge representation
AB Knowledge graphs (KGs) have a wide range of applications, such as recommender systems, relation extraction, and intelligent question answering systems. However, existing KGs are far from complete. Knowledge graph reasoning (KGR) has been studied to complete KGs by inferring missing entities or relations. But most previous methods require that all entities should be seen during training, which is impractical for real-world KGs with new entities emerging daily. In this paper, we address the open-world KGR task: how to perform reasoning when entities are not observed at training time. The description-embodied knowledge representation learning (DKRL) model attempts to study the open-world KGR task. We find that DKRL does not consider hierarchical type information contained in entities when learning entities and relations representations, which results in poor performance. To address this problem, we propose a novel model, SDT, that incorporates the structural information, entity descriptions, and hierarchical type information of entities into a unified framework to learn more representative embeddings for KGs. Specifically, for entity descriptions, we explore continuous bag-of-words and convolutional neural networks models to encode the semantics of entity representations. For hierarchical types, we utilize a recursive hierarchy encoder and a weighted hierarchy encoder to construct the projection matrices of hierarchical types. We evaluate the SDT model on both open-world and closed-world reasoning tasks, including entity prediction and relation prediction. Experimental results on large-scale datasets show that SDT achieves a lower mean rank and higher Hits@10 than the baseline methods.
C1 [Chen, Xiaojun; Jia, Shengbin; Ding, Ling; Xiang, Yang] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.
   [Shen, Hong] Shanghai Univ Finance & Econ, Sch Publ Econ & Adm, Shanghai 200433, 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;
   dling@tongji.edu.cn; shcdshenhong@outlook.com; tjdxxiangyang@gmail.com
FU National Key R&D Program of China [2019YFB1704402]; National Natural
   Science Foundation of China [71571136]
FX We thank the anonymous reviewers for valuable comments. This work is
   supported by the National Key R&D Program of China under Grant No.
   2019YFB1704402 and the National Natural Science Foundation of China
   under Grant NO. 71571136.
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NR 30
TC 5
Z9 5
U1 6
U2 30
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 2020
VL 162
AR 113889
DI 10.1016/j.eswa.2020.113889
PG 9
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA OG8FU
UT WOS:000582113700048
DA 2022-08-23
ER

PT J
AU Liao, DL
AF Liao, Danlu
TI Construction of Knowledge Graph English Online Homework Evaluation
   System Based on Multimodal Neural Network Feature Extraction
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID TECHNOLOGY
AB This paper defines the data schema of the multimodal knowledge graph, that is, the definition of entity types and relationships between entities. The knowledge point entities are defined as three types of structures, algorithms, and related terms, speech is also defined as one type of entities, and six semantic relationships are defined between entities. This paper adopts a named entity recognition model that combines bidirectional long short-term memory network and convolutional neural network, combines local information and global information of text, uses conditional random field algorithm to label feature sequences, and combines domain dictionary. A knowledge evaluation method based on triplet context information is designed, which combines triplet context information (internal relationship path information in knowledge graph and external text information related to entities in triplet) through knowledge representation learning. The knowledge of triples is evaluated. The knowledge evaluation ability of the English online homework evaluation system was evaluated on the knowledge graph noise detection task, the knowledge graph completion task (entity link prediction task), and the triplet classification task. The experimental results show that the English online homework evaluation system has good noise processing ability and knowledge credibility calculation ability, and has a stronger evaluation ability for low-noise data. Using the online homework platform to implement personalized English homework is conducive to improving students' homework mood, and students' "happy" homework mood has been significantly improved. The implementation of English personalized homework based on the online homework platform is conducive to improving students' homework initiative. With the help of the online homework platform to implement personalized English homework, students' homework time has been reduced, and the homework has been completed well, achieving the purpose of "reducing burden and increasing efficiency."
C1 [Liao, Danlu] Chongqing Vocat Inst Tourism, Foreign Languages & Int Tourism Dept, Chongqing 409000, Peoples R China.
RP Liao, DL (通讯作者)，Chongqing Vocat Inst Tourism, Foreign Languages & Int Tourism Dept, Chongqing 409000, Peoples R China.
EM 18402189@masu.edu.cn
OI Liao, Danlu/0000-0002-3497-2962
FU Chongqing Education Committee Technology Project [Z212053]; China
   National Foreign Languages Guidance Committee of Ministry of Education
   [WYJZW-2021-105]; Chongqing Education Committee [KJQN2020004605]
FX This work was supported by Chongqing Education Committee Technology
   Project under Grant No. Z212053, China National Foreign Languages
   Guidance Committee of Ministry of Education under Grant
   No.WYJZW-2021-105, and Chongqing Education Committee under Grant
   No.KJQN2020004605.
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NR 30
TC 0
Z9 0
U1 5
U2 5
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD MAY 13
PY 2022
VL 2022
AR 7941414
DI 10.1155/2022/7941414
PG 12
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 1T3LN
UT WOS:000804634700005
PM 35602643
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Kliegr, T
   Zamazal, O
AF Kliegr, Tomas
   Zamazal, Ondrej
TI LHD 2.0: A text mining approach to typing entities in knowledge graphs
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Type inference; Support Vector Machines; Entity classification; DBpedia
ID WEB
AB The type of the entity being described is one of the key pieces of information in linked data knowledge graphs. In this article, we introduce a novel technique for type inference that extracts types from the free text description of the entity combining lexico-syntactic pattern analysis with supervised classification. For lexico-syntactic (Hearst) pattern-based extraction we use our previously published Linked Hypernyms Dataset Framework. Its output is mapped to the DBpedia Ontology with exact string matching complemented with a novel co-occurrence-based algorithm STI. This algorithm maps classes appearing in one knowledge graph to a different set of classes appearing in another knowledge graph provided that the two graphs contain common set of typed instances. The supervised results are obtained from a hierarchy of Support Vector Machines classifiers (hSVM) trained on the bag-of-words representation of short abstracts and categories of Wikipedia articles. The results of both approaches are probabilistically fused. For evaluation we created a gold-standard dataset covering over 2000 DBpedia entities using a commercial crowdsourcing service. The hierarchical precision of our hSVM and STI approaches is comparable to SDType, the current state-of-the-art type inference algorithm, while the set of applicable instances is largely complementary to SDType as our algorithms do not require semantic properties in the knowledge graph to type an instance. The paper also provides a comprehensive evaluation of type assignment in DBpedia in terms of hierarchical precision, recall and exact match with the gold standard. Dataset generated by a version of the presented approach is included in DBpedia 2015. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Kliegr, Tomas; Zamazal, Ondrej] Univ Econ, Fac Informat & Stat, Dept Informat & Knowledge Engn, W Churchilla 4, Prague 13067, Czech Republic.
   [Kliegr, Tomas] Queen Mary Univ London, Multimedia & Vis Res Grp, 327 Mile End Rd, London E1 4NS, England.
RP Kliegr, T (通讯作者)，Univ Econ, Fac Informat & Stat, Dept Informat & Knowledge Engn, W Churchilla 4, Prague 13067, Czech Republic.
EM tomas.kliegr@vse.cz; ondrej.zamazal@vse.cz
RI Kliegr, Tomáš/AAH-5577-2020
OI Kliegr, Tomáš/0000-0002-7261-0380
FU European Union [FP7-287911]; CSF, "COSOL-Categorization of Ontologies in
   Support of Ontology Life Cycle'' [14-14076P]
FX We wish to thank the three anonymous reviewers for their very helpful
   comments. Tomas Kliegr thanks Andre Melo and Heiko Paulheim for
   insightful comments that led to our adoption of the standard set of
   hierarchical evaluation metrics and the German DAAD grant agency for
   making this interaction possible. The development of the STI and hSVM
   algorithms was supported by the European Union's 7th Framework Programme
   via the LinkedTV project (FP7-287911). Ondrej Zamazal has been
   additionally supported by the CSF grant no. 14-14076P,
   "COSOL-Categorization of Ontologies in Support of Ontology Life Cycle''.
   This research is also partially supported by long term institutional
   support of research activities by Faculty of Informatics and Statistics,
   University of Economics, Prague.
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NR 31
TC 8
Z9 8
U1 0
U2 31
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
J9 J WEB SEMANT
JI J. Web Semant.
PD AUG
PY 2016
VL 39
BP 47
EP 61
DI 10.1016/j.websem.2016.05.001
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DS5SW
UT WOS:000380843600004
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Bresso, E
   Monnin, P
   Bousquet, C
   Calvier, FE
   Ndiaye, NC
   Petitpain, N
   Smail-Tabbone, M
   Coulet, A
AF Bresso, Emmanuel
   Monnin, Pierre
   Bousquet, Cedric
   Calvier, Francois-Elie
   Ndiaye, Ndeye-Coumba
   Petitpain, Nadine
   Smail-Tabbone, Malika
   Coulet, Adrien
TI Investigating ADR mechanisms with Explainable AI: a feasibility study
   with knowledge graph mining
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE Adverse drug reaction; Molecular mechanism; Mechanism of action;
   Explanation; Data mining; Machine learning; Knowledge graph; Explainable
   AI
ID SEMANTIC WEB; DRUG; PROTEINS
AB Background Adverse drug reactions (ADRs) are statistically characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. This is true even for hepatic or skin toxicities, which are classically monitored during drug design. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs, such as their properties, interactions, or involvements in pathways. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established. Methods We propose in this paper to mine knowledge graphs for identifying biomolecular features that may enable automatically reproducing expert classifications that distinguish drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models, which explain the classification itself, but may also provide elements of explanation for molecular mechanisms behind ADRs. In summary, (1) we mine a knowledge graph for features; (2) we train classifiers at distinguishing, on the basis of extracted features, drugs associated or not with two commonly monitored ADRs: drug-induced liver injuries (DILI) and severe cutaneous adverse reactions (SCAR); (3) we isolate features that are both efficient in reproducing expert classifications and interpretable by experts (i.e., Gene Ontology terms, drug targets, or pathway names); and (4) we manually evaluate in a mini-study how they may be explanatory. Results Extracted features reproduce with a good fidelity classifications of drugs causative or not for DILI and SCAR (Accuracy = 0.74 and 0.81, respectively). Experts fully agreed that 73% and 38% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 90% and 77% of them. Conclusion Knowledge graphs provide sufficiently diverse features to enable simple and explainable models to distinguish between drugs that are causative or not for ADRs. In addition to explaining classifications, most discriminative features appear to be good candidates for investigating ADR mechanisms further.
C1 [Bresso, Emmanuel; Monnin, Pierre; Smail-Tabbone, Malika; Coulet, Adrien] Univ Lorraine, LORIA, INRIA, CNRS, Nancy, France.
   [Bresso, Emmanuel] Univ Lorraine, CHRU Nancy, Inserm 1116, Ctr Invest Clin Plurithemat 1433, Nancy, France.
   [Monnin, Pierre] Orange, Belfort, France.
   [Bousquet, Cedric; Calvier, Francois-Elie] CHU St Etienne, Serv Sante Publ & Informat Med, St Etienne, France.
   [Bousquet, Cedric] Sorbonne Univ, Univ Paris 13, INSERM, LIMICS, Paris, France.
   [Ndiaye, Ndeye-Coumba] Univ Lorraine, Inserm U1256, NGERE, Nancy, France.
   [Petitpain, Nadine] CHRU Nancy, Ctr Reg Pharmacovigilance, Nancy, France.
   [Coulet, Adrien] Inria Paris, Paris, France.
   [Coulet, Adrien] Univ Paris, Sorbonne Univ, Ctr Rech Cordeliers, INSERM, Paris, France.
RP Coulet, A (通讯作者)，Univ Lorraine, LORIA, INRIA, CNRS, Nancy, France.; Coulet, A (通讯作者)，Inria Paris, Paris, France.; Coulet, A (通讯作者)，Univ Paris, Sorbonne Univ, Ctr Rech Cordeliers, INSERM, Paris, France.
EM adrien.coulet@inria.fr
OI Monnin, Pierre/0000-0002-2017-8426
FU French National Research Agency (ANR) [ANR-15-CE23-0028]; FIGHT-HF
   [15-RHUS-0004]
FX The authors acknowledge the French National Research Agency (ANR) for
   funding PractiKPharma (ANR-15-CE23-0028) and FIGHT-HF (15-RHUS-0004)
   projects. Funding body did not played any roles in the design of the
   study, in data collection, analysis, interpretation or in writing the
   manuscript.
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NR 48
TC 2
Z9 2
U1 6
U2 10
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 MAY 26
PY 2021
VL 21
IS 1
AR 171
DI 10.1186/s12911-021-01518-6
PG 14
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA SI9ZD
UT WOS:000655185900001
PM 34039343
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ludolph, R
   Allam, A
   Schulz, PJ
AF Ludolph, Ramona
   Allam, Ahmed
   Schulz, Peter J.
TI Manipulating Google's Knowledge Graph Box to Counter Biased Information
   Processing During an Online Search on Vaccination: Application of a
   Technological Debiasing Strategy
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE search engine; online health information search; vaccination; debiasing;
   search behavior; health communication; information processing;
   information seeking
ID WORLD-WIDE-WEB; HEALTH INFORMATION; COGNITIVE BIASES; IMPACT; INTERNET;
   SEEKING; MISINFORMATION; IMMUNIZATION; CRITICISM; HESITANCY
AB Background: One of people's major motives for going online is the search for health-related information. Most consumers start their search with a general search engine but are unaware of the fact that its sorting and ranking criteria do not mirror information quality. This misconception can lead to distorted search outcomes, especially when the information processing is characterized by heuristic principles and resulting cognitive biases instead of a systematic elaboration. As vaccination opponents are vocal on the Web, the chance of encountering their non. evidence-based views on immunization is high. Therefore, biased information processing in this context can cause subsequent impaired judgment and decision making. A technological debiasing strategy could counter this by changing people's search environment.
   Objective: This study aims at testing a technological debiasing strategy to reduce the negative effects of biased information processing when using a general search engine on people's vaccination-related knowledge and attitudes. This strategy is to manipulate the content of Google's knowledge graph box, which is integrated in the search interface and provides basic information about the search topic.
   Methods: A full 3x2 factorial, posttest-only design was employed with availability of basic factual information (comprehensible vs hardly comprehensible vs not present) as the first factor and a warning message as the second factor of experimental manipulation. Outcome variables were the evaluation of the knowledge graph box, vaccination-related knowledge, as well as beliefs and attitudes toward vaccination, as represented by three latent variables emerged from an exploratory factor analysis.
   Results: Two-way analysis of variance revealed a significant main effect of availability of basic information in the knowledge graph box on participants' vaccination knowledge scores (F-2,F-273=4.86, P=.01), skepticism/fear of vaccination side effects (F-2,F-273=3.5, P=.03), and perceived information quality (F-2,F-273=3.73, P=.02). More specifically, respondents receiving comprehensible information appeared to be more knowledgeable, less skeptical of vaccination, and more critical of information quality compared to participants exposed to hardly comprehensible information. Although, there was no significant interaction effect between the availability of information and the presence of the warning, there was a dominant pattern in which the presence of the warning appeared to have a positive influence on the group receiving comprehensible information while the opposite was true for the groups exposed to hardly comprehensible information and no information at all. Participants evaluated the knowledge graph box as moderately to highly useful, with no significant differences among the experimental groups.
   Conclusion: Overall, the results suggest that comprehensible information in the knowledge graph box positively affects participants' vaccination-related knowledge and attitudes. A small change in the content retrieval procedure currently used by Google could already make a valuable difference in the pursuit of an unbiased online information search. Further research is needed to gain insights into the knowledge graph box's entire potential.
C1 [Ludolph, Ramona; Schulz, Peter J.] Univ Svizzera Italiana, Univ Lugano, Fac Commun Sci, Inst Commun & Hlth, Via G Buffi 13, CH-6904 Lugano, Switzerland.
   [Allam, Ahmed] Yale Univ, Sch Med, Dept Pathol, New Haven, CT 06510 USA.
RP Ludolph, R (通讯作者)，Univ Svizzera Italiana, Univ Lugano, Fac Commun Sci, Inst Commun & Hlth, Via G Buffi 13, CH-6904 Lugano, Switzerland.
EM ramona.alexandra.ludolph@usi.ch
RI Schulz, Peter/AAM-9816-2020; Allam, Ahmed Aly/K-9931-2017
OI Schulz, Peter/0000-0003-4281-489X; Allam, Ahmed Aly/0000-0003-0702-8025
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NR 54
TC 14
Z9 14
U1 3
U2 35
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 59 WINNERS CIRCLE, TORONTO, ON M4L 3Y7, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD JUN
PY 2016
VL 18
IS 6
AR e137
DI 10.2196/jmir.5430
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 EC9WO
UT WOS:000388495200052
PM 27255736
OA gold, Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Pham, T
   Tao, XH
   Zhang, J
   Yong, JM
   Li, YF
   Xie, HR
AF Thuan Pham
   Tao, Xiaohui
   Zhang, Ji
   Yong, Jianming
   Li, Yuefeng
   Xie, Haoran
TI Graph-based multi-label disease prediction model learning from medical
   data and domain knowledge
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Multi-label classification; Knowledge graph; Medicine domain knowledge;
   Disease prediction; NHANES; MEDLINE
ID CLASSIFIER CHAINS; FEATURE-SELECTION; ENSEMBLE; NETWORK; BAG
AB In recent years, the means of disease diagnosis and treatment have been improved remarkably, along with the continuous development of technology and science. Researchers have spent tremendous time and effort to build models that aim to assist medical practitioners in decision-making support. However, one of the greatest challenges remains how to identify the connection between different diseases. This study aims to discover the relationship between diseases and symptoms to predict potential diseases for patients. Considering it a multi-label classification problem, the study proposed a new multi-disease prediction model learning from NHANES, an extensive health related dataset, and MEDLINE, a corpus with medical domain knowledge. A heterogeneous information graph is firstly constructed and then populated using medical domain knowledge discovered from MEDLINE. The knowledge graph is analysed for clarification of the relevancy within nodes in positive or negative space, helping to access to the correlation amongst multiple diseases and their symptoms. A -disease prediction model is then developed adopting the medical domain knowledge graph. Empirical experiments are conducted to evaluate the proposed model. The experimental results show that the performance of the proposed model surpassed state-of-the-art related works representing the mainstreams of multi-label classification. This study contributes to the medical community with a novel model for multi-disease prediction and represents a new endeavour on multi-label classification using knowledge graphs. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Thuan Pham; Tao, Xiaohui; Zhang, Ji] Univ Southern Queensland, Sch Sci, Toowoomba, Qld, Australia.
   [Yong, Jianming] Univ Southern Queensland, Sch Business, Toowoomba, Qld, Australia.
   [Li, Yuefeng] Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld, Australia.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
RP Pham, T; Tao, XH (通讯作者)，Univ Southern Queensland, Sch Sci, Toowoomba, Qld, Australia.
EM Thuan.Pham@usq.edu.au; Xiaohui.Tao@usq.edu.au
RI Xie, Haoran/AFS-3515-2022
OI PHAM, THUAN/0000-0001-7433-858X
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NR 68
TC 0
Z9 0
U1 23
U2 35
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JAN 10
PY 2022
VL 235
AR 107662
DI 10.1016/j.knosys.2021.107662
EA NOV 2021
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XB0OB
UT WOS:000721035100006
DA 2022-08-23
ER

PT J
AU Fan, HL
   Zhong, YC
   Zeng, GP
   Ge, CH
AF Fan, Huilian
   Zhong, Yuanchang
   Zeng, Guangpu
   Ge, Chenhao
TI Improving recommender system via knowledge graph based exploring user
   preference
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Recommender system; Knowledge graph; Item feature coefficients; User
   preference matrix
AB Knowledge graph(KG) has proven to improve recommendation performance. However, most efforts explore inter-entity relatedness by mining multi-hop relations on KG, thus failing to efficiently exploit these relations for enhanced user preference. To address this, we propose an end-to-end framework to improve the recommender system via a knowledge graph based on fusing entity relation(KGFER), which can sufficiently capture the users' preferences. The model samples from the 1-hop neighbors and relations in KG for the item with which the user interacts, and feeds them into TransR layer. Following this, CNN is employed to learn item features from entity-relations, and then aggregate item features with the interacting item by MLP. Finally, we apply user preference to project the refined item embeddings to the user latent space to predict the potential probability of the target item in which the user is interested in. Extensive experiments on four datasets about book, movie, music, and yelp2018 demonstrate that our approach outperforms state-of-the-art baselines. Also, further experiments show that the user preference matrix indeed makes a great contribution to our approach.
C1 [Fan, Huilian; Zeng, Guangpu] Yangtze Normal Univ, Sch Big Data & Intelligent Engn, Chongqing 408100, Peoples R China.
   [Zhong, Yuanchang; Ge, Chenhao] Chongqing Univ, Sch Elect Engn, Chongqing, Peoples R China.
   [Zhong, Yuanchang; Ge, Chenhao] China State Key Lab Power Transmiss Equipment & S, Chongqing 400044, Peoples R China.
RP Fan, HL (通讯作者)，Yangtze Normal Univ, Sch Big Data & Intelligent Engn, Chongqing 408100, Peoples R China.; Zhong, YC (通讯作者)，Chongqing Univ, Sch Elect Engn, Chongqing, Peoples R China.; Zhong, YC (通讯作者)，China State Key Lab Power Transmiss Equipment & S, Chongqing 400044, Peoples R China.
EM fanhl@yznu.edu.cn; zyc@cqu.edu.cn
FU Natural Science Foundation of Chongqing; Fundamental Research Funds for
   the Central Universities [2020CDCGTX055]; Major Natural Science Funds of
   Chongqing Education Commission
FX This work is supported by the Natural Science Foundation of Chongqing
   (No.cstc2019jscx-msxmX0349), the Fundamental Research Funds for the
   Central Universities (No.2020CDCGTX055) and the Major Natural Science
   Funds of Chongqing Education Commission(No.KJZD-M201901401).
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NR 53
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.
PD JUL
PY 2022
VL 52
IS 9
BP 10032
EP 10044
DI 10.1007/s10489-021-02872-8
EA JAN 2022
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2I2IM
UT WOS:000741637600002
DA 2022-08-23
ER

PT J
AU Jiang, YK
   Gao, X
   Su, WX
   Li, JR
AF Jiang, Yukun
   Gao, Xin
   Su, Wenxin
   Li, Jinrong
TI Systematic Knowledge Management of Construction Safety Standards Based
   on Knowledge Graphs: A Case Study in China
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE construction safety standards; knowledge graph; domain knowledge;
   knowledge management; standards planning
ID RISK-BASED MANAGEMENT; OCCUPATIONAL-SAFETY; LARGE-SCALE; PART 1;
   INDUSTRY; HEALTH; ONTOLOGY; PATTERNS; DESIGN; INJURY
AB Construction safety standards (CSS) have knowledge characteristics, but few studies have introduced knowledge graphs (KG) as a tool into CSS management. In order to improve CSS knowledge management, this paper first analyzed the knowledge structure of 218 standards and obtained three knowledge levels of CSS. Second, a concept layer was designed which consisted of five levels of concepts and eight types of relationships. Third, an entity layer containing 147 entities was constructed via entity identification, attribute extraction and entity extraction. Finally, 177 nodes and 11 types of attributes were collected and the construction of a knowledge graph of construction safety standard (KGCSS) was completed using knowledge storage. Furthermore, we implemented knowledge inference and obtained CSS planning, i.e., the list of standard work plans used to guide the development and revision of CSS. In addition, we conducted CSS knowledge retrieval; a process which supports interrogative input. The construction of KGCSS thus facilitates the analysis, querying, and sharing of safety standards knowledge.</p>
C1 [Jiang, Yukun; Gao, Xin; Su, Wenxin; Li, Jinrong] Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China.
RP Gao, X (通讯作者)，Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China.
EM 1930324@tongji.edu.cn; gaoxin@tongji.edu.cn; 1930325@tongji.edu.cn;
   1932758@tongji.edu.cn
FU National Natural Science Foundation of China [72174146]
FX This research was funded by the National Natural Science Foundation of
   China, grant number 72174146.
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NR 66
TC 2
Z9 2
U1 30
U2 56
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD OCT
PY 2021
VL 18
IS 20
AR 10692
DI 10.3390/ijerph182010692
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 WO0WP
UT WOS:000712184000001
PM 34682437
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Song, HJ
   Kim, AY
   Park, SB
AF Song, Hyun-Je
   Kim, A-Yeong
   Park, Seong-Bae
TI Learning Translation-Based Knowledge Graph Embeddings by N-Pair
   Translation Loss
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph embeddings; translation-based knowledge graph
   embeddings; N-pair translation loss; negative sampling; multiple
   negative triples
AB Translation-based knowledge graph embeddings learn vector representations of entities and relations by treating relations as translation operators over the entities in an embedding space. Since the translation is represented through a score function, translation-based embeddings are trained in general by minimizing a margin-based ranking loss, which assigns a low score to positive triples and a high score to negative triples. However, this type of embedding suffers from slow convergence and poor local optima because the loss adopts only one pair of a positive and a negative triple at a single update of learning parameters. Therefore, this paper proposes the N-pair translation loss that considers multiple negative triples at one update. The N-pair translation loss employs multiple negative triples as well as one positive triple and allows the positive triple to be compared against the multiple negative triples at each parameter update. As a result, it becomes possible to obtain better vector representations rapidly. The experimental results on link prediction prove that the proposed loss helps to quickly converge toward good optima at the early stage of training.
C1 [Song, Hyun-Je] Jeonbuk Natl Univ, Dept Informat Technol, Jeonju 54896, South Korea.
   [Kim, A-Yeong] Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, 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 hyunje.song@jbnu.ac.kr; aykim@sejong.knu.ac.kr; sbpark71@khu.ac.kr
OI Kim, A-Yeong/0000-0002-4033-334X
FU Jeonbuk National University; Institute for Information & communications
   Technology Planning & Evaluation(IITP) - Korea government(MSIT)
   [2013-0-00131]
FX This paper was supported by research funds for newly appointed
   professors of Jeonbuk National University in 2019 and Institute for
   Information & communications Technology Planning & Evaluation(IITP)
   grant funded by the Korea government(MSIT) (No. 2013-0-00131,
   Development of Knowledge Evolutionary WiseQA Platform Technology for
   Human Knowledge Augmented Services).
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NR 37
TC 3
Z9 3
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD JUN
PY 2020
VL 10
IS 11
AR 3964
DI 10.3390/app10113964
PG 11
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA MC6HQ
UT WOS:000543385900301
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Li, G
   He, B
   Wang, ZP
   Zhou, YM
AF Li, Gang
   He, Bin
   Wang, Zhipeng
   Zhou, Yanmin
TI A Data Agent Inspired by Interpersonal Interaction Behaviors for
   Wireless Sensor Networks
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Event detection; Monitoring; Feature extraction; Switches; Distributed
   databases; Correlation; Spatiotemporal phenomena; Data agent (DA); event
   monitoring; finite state machine (FSM); wireless sensor networks (WSNs)
ID EVENT DETECTION; DISTRIBUTED DETECTION; SCHEME; SYSTEM
AB Event monitoring is the main purpose of monitoring applications based on wireless sensor networks (WSNs). To realize the autonomous representation of tunnel disasters in WSNs, this work proposes a data agent (DA) inspired by interpersonal interaction behaviors, which bridges the gap between data and humans. The DA divides event monitoring into four states: 1) active perception; 2) understanding; 3) thinking; and 4) representation, to realize a kind of human-like event monitoring. First, a DA behavior model based on a finite-state machine is proposed, which can adaptively switch among these four states. Second, four methods, including active perception based on a dual neuron perception structure, understanding for forming the information granularity, thinking for forming the disaster knowledge graph, and representation using grayscale and knowledge graphs are proposed to realize the four states of the DA. The experimental results show that the DA inspired by interpersonal interaction can make WSNs not only achieve efficient and accurate disaster self-monitoring but also represent the disaster situation intuitively and quickly through grayscale and knowledge graphs.
C1 [Li, Gang; He, Bin; Wang, Zhipeng; Zhou, Yanmin] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.
   [Li, Gang; He, Bin; Wang, Zhipeng; Zhou, Yanmin] Shanghai Res Inst Intelligent Autonomous Syst, Shanghai 201210, Peoples R China.
RP He, B (通讯作者)，Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.
EM lig@tongji.edu.cn; hebin@tongji.edu.cn; wangzhipeng@tongji.edu.cn;
   yanmin.zhou@tongji.edu.cn
OI Li, Gang/0000-0003-3141-4428
FU National Natural Science Foundation of China [61801330, 61825303,
   62088101, U1713215]; Shanghai Science and Technology Commission Project
   [21ZR1466400, 2021SHZDZX0100]; Major Project of Special Development Fund
   for Shanghai Zhangjiang National Independent Innovation Demonstration
   Zone [ZJ2019-ZD-003]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61801330, Grant 61825303, Grant
   62088101, and Grant U1713215; in part by the Shanghai Science and
   Technology Commission Project under Grant 21ZR1466400 and Grant
   2021SHZDZX0100; and in part by the Major Project of Special Development
   Fund for Shanghai Zhangjiang National Independent Innovation
   Demonstration Zone under Grant ZJ2019-ZD-003.
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NR 38
TC 0
Z9 0
U1 2
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2327-4662
J9 IEEE INTERNET THINGS
JI IEEE Internet Things J.
PD JUN 1
PY 2022
VL 9
IS 11
BP 8397
EP 8411
DI 10.1109/JIOT.2021.3114259
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1M8KR
UT WOS:000800215600041
DA 2022-08-23
ER

PT J
AU Cremaschi, M
   De Paoli, F
   Rula, A
   Spahiu, B
AF Cremaschi, Marco
   Paoli, Flavio De
   Rula, Anisa
   Spahiu, Blerina
TI A fully automated approach to a complete Semantic Table Interpretation
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Semantic Web; Ontology; Linked Data; Knowledge Graph; Semantic Table
   Interpretation
ID STRUCTURED DATA
AB In recent years, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of text data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g. semantic search and dataset extension. Semantic Table Interpretation is the process of annotating elements in a table. Current approaches are mainly based on lexical matching algorithms that rely on metadata associated with tables or custom Knowledge Graphs. Their main limitations are due to the lack of metadata, the little use of contextual semantics, and the incompleteness of the proposed methods that do not include all the necessary steps. In this paper, we propose a comprehensive approach and a tool that provides an unsupervised method to annotate independent tables, possibly without header row or other external information. The approach is based on the definition of a context created from the elements within the table in order to discriminate among matching entities found in shared Knowledge Graphs and create high-quality annotations. The approach has achieved excellent results in an international challenge, thus proving its effectiveness. (c) 2020 Elsevier B.V. All rights reserved.
C1 [Cremaschi, Marco; Paoli, Flavio De; Rula, Anisa; Spahiu, Blerina] Univ Milano Bicocca, Viale Sarca 336, I-20126 Milan, Italy.
RP Cremaschi, M (通讯作者)，Univ Milano Bicocca, Viale Sarca 336, I-20126 Milan, Italy.
EM marco.cremaschi@unimib.it; flavio.depaoli@unimib.it;
   anisa.rula@unimib.it; blerina.spahiu@unimib.it
RI Spahiu, Blerina/ABF-8205-2020; Rula, Anisa/ABC-6214-2020
OI Spahiu, Blerina/0000-0002-6958-8215; Rula, Anisa/0000-0002-8046-7502;
   CREMASCHI, MARCO/0000-0001-7840-6228; de paoli,
   flavio/0000-0002-5047-7371
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NR 39
TC 4
Z9 4
U1 0
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD NOV
PY 2020
VL 112
BP 478
EP 500
DI 10.1016/j.future.2020.05.019
PG 23
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NM0WV
UT WOS:000567826400010
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Mendsaikhan, O
   Hasegawa, H
   Yamaguchi, Y
   Shimada, H
AF Mendsaikhan, Otgonpurev
   Hasegawa, Hirokazu
   Yamaguchi, Yukiko
   Shimada, Hajime
TI Quantifying the Significance and Relevance of Cyber-Security Text
   Through Textual Similarity and Cyber-Security Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Computer security; Data mining; Task analysis; Autonomous systems;
   Tagging; Computational modeling; Cyber-security knowledge graph; cyber
   threat; text analysis; textual similarity
AB In order to proactively mitigate cyber-security risks, security analysts have to continuously monitor sources of threat information. However, the sheer amount of textual information that needs to be processed is overwhelming, and it requires a great deal of mundane labor to separate the threats from the noise. We propose a novel approach to represent the relevance and significance of the cyber-security text in quantitative numbers. We trained custom Named Entity Recognition (NER) model and constructed a Cyber-security Knowledge Graph (CKG) to infer the subjective relevance of the cyber-security text to the user and to generate correlation features. In addition, the significance of the given text was analyzed in terms of its textual similarity with different repositories of pre-defined "significant" text and the maximum similarities were computed. These analysis results then act as features of the classifier to generate the significance score. The experimental result showed that the overall system could determine the significance and relevance of the text within a controlled environment with 88% accuracy.
C1 [Mendsaikhan, Otgonpurev] Nagoya Univ, Grad Sch Informat, Nagoya, Aichi 4648601, Japan.
   [Hasegawa, Hirokazu] Nagoya Univ, Informat Secur Off, Nagoya, Aichi 4648601, Japan.
   [Yamaguchi, Yukiko; Shimada, Hajime] Nagoya Univ, Informat Technol Ctr, Nagoya, Aichi 4648601, Japan.
RP Mendsaikhan, O (通讯作者)，Nagoya Univ, Grad Sch Informat, Nagoya, Aichi 4648601, Japan.
EM ogo@net.itc.nagoya-u.ac.jp
RI Mendsaikhan, Otgonpurev/AAZ-5058-2021
OI Mendsaikhan, Otgonpurev/0000-0003-1203-6652
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NR 33
TC 1
Z9 2
U1 0
U2 11
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 177041
EP 177052
DI 10.1109/ACCESS.2020.3027321
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NY2PV
UT WOS:000576238900001
OA gold
DA 2022-08-23
ER

PT J
AU Ban, TY
   Wang, XY
   Chen, L
   Wu, XY
   Chen, QJ
   Chen, HH
AF Ban, Taiyu
   Wang, Xiangyu
   Chen, Lyuzhou
   Wu, Xingyu
   Chen, Qiuju
   Chen, Huanhuan
TI Quality Evaluation of Triples in Knowledge Graph by Incorporating
   Internal With External Consistency
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article; Early Access
DE Integrated circuits; Measurement; Knowledge engineering; Iterative
   methods; Interference; Artificial neural networks; Statistical analysis;
   Consistency; knowledge graph (KG); knowledge quality (KQ); quality
   control
ID DISCOVERY
AB The evaluation of knowledge quality (KQ) in multisource knowledge graphs (KGs) is an essential step for many applications, such as fragmented knowledge fusion and knowledge base construction. Many existing quality evaluation methods for multisource knowledge are based on validation from high-quality knowledge bases or statistical analysis of knowledge related to a specific fact from multiple sources, named external consistency (EC)-based methods. However, high-quality KGs are difficult to obtain, and there might exist incorrect knowledge in multisource KGs interfering with KQ evaluation. To address the issue, this article refers to the internal structure of a KG to evaluate the degree to which the contained triples conform to the overall semantic pattern of the KG, such as KG embedding and logic inference-based approaches, defined as internal consistency (IC) evaluation. The IC is integrated with the EC to identify possible incorrect triples and reduce their influences on the KQ evaluation, thus alleviating the interference of incorrect knowledge. The proposed method is verified with multiple datasets, and the results demonstrate that the proposed method could significantly reduce wrong evaluations caused by incorrect knowledge and effectively improve the quality evaluation of triples.
C1 [Ban, Taiyu; Wang, Xiangyu; Chen, Lyuzhou; Wu, Xingyu; Chen, Huanhuan] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Peoples R China.
   [Chen, Qiuju] Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Peoples R China.
RP Chen, HH (通讯作者)，Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Peoples R China.
EM hchen@ustc.edu.cn
OI Wu, Xingyu/0000-0002-8204-6197
FU National Key Research and Development Program of China [2021ZD0111700];
   National Nature Science Foundation of China [62137002, 62176245]; Key
   Research and Development Program of Anhui Province [202104a05020011];
   Key Science and Technology Special Project of Anhui Province
   [202103a07020002]; Fundamental Research Funds for the Central
   Universities; Special Foundation for Science and Technology Innovation
   and Entrepreneurship of CCTEG [2020-2-TD-CXY006]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2021ZD0111700, in part by the
   National Nature Science Foundation of China under Grant 62137002 and
   Grant 62176245, in part by the Key Research and Development Program of
   Anhui Province under Grant 202104a05020011, in part by the Key Science
   and Technology Special Project of Anhui Province under Grant
   202103a07020002, in part by the Fundamental Research Funds for the
   Central Universities, and in part by the Special Foundation for Science
   and Technology Innovation and Entrepreneurship of CCTEG under Grant
   2020-2-TD-CXY006.
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NR 61
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 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
DI 10.1109/TNNLS.2022.3186033
EA JUL 2022
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 2W8KZ
UT WOS:000824768400001
PM 35786563
DA 2022-08-23
ER

PT J
AU Ren, H
   Chen, ZW
   Jiang, ZH
   Yang, CH
   Gui, WH
AF Ren, Hao
   Chen, Zhiwen
   Jiang, Zhaohui
   Yang, Chunhua
   Gui, Weihua
TI An Industrial Multilevel Knowledge Graph-Based Local-Global Monitoring
   for Plant-Wide Processes
SO IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
LA English
DT Article
DE Correlation; Process monitoring; Fault detection; Uncertainty; Fault
   location; Principal component analysis; Observers; Fault detection;
   fault location; multilevel knowledge graph (MLKG); plant-wide process
   monitoring
ID DATA-DRIVEN; FAULT-DETECTION
AB In order to satisfy safety requirements of modern plant-wide processes, multiblock-based distributed monitoring strategies are often used to obtain higher monitoring performance, and their two critical issues refer to suitable multiblock partition for reducing uncertainties and local-global fault interpret perception for practical physical meaning. To handle these problems, a novel multilevel knowledge-graph (MLKG) based on combining domain expert knowledge and monitoring data are constructed to describe characteristics of plant-wide processes. And then numerous monitoring variables of each node (block) can be used to calculate the node status which can be used to realize fault detection by exceeding corresponding thresholds. Creatively, numerous node statuses of multilevel can be aggregated into the top-level node status to globally characterize the system health to realize fault detection. Finally, methods such as variables contribute rate can be adopted to locally locate the fault to achieve fault location, which can be regarded as an attempt to interpret the fault detection results. Results of benchmark and practical-case-application can be used to demonstrate the effectiveness and applicability of this proposed method.
C1 [Ren, Hao; Chen, Zhiwen; Jiang, Zhaohui; Yang, Chunhua; Gui, Weihua] Peng Cheng Lab, Dept Math & Theories, Shenzhen 518055, Peoples R China.
   [Chen, Zhiwen; Jiang, Zhaohui; Yang, Chunhua; Gui, Weihua] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China.
RP Chen, ZW (通讯作者)，Peng Cheng Lab, Dept Math & Theories, Shenzhen 518055, Peoples R China.; Chen, ZW (通讯作者)，Cent South Univ, Sch Automat, Changsha 410083, Peoples R China.
EM renh@pcl.ac.cn; zhiwen.chen@csu.edu.cn; jzh0903@csu.edu.cn;
   ychh@csu.edu.cn; gwh@csu.edu.cn
OI Ren, Hao/0000-0001-8471-9132
FU Major Key Project of Peng Cheng Laboratory (PCL) [PCL2021A09]; National
   Natural Science Foundation of China [62103207, 61988101, 62173349]
FX This work was supported in part by the Major Key Project of Peng Cheng
   Laboratory (PCL) under Grant PCL2021A09 and in part by the National
   Natural Science Foundation of China under Grant 62103207, Grant
   61988101, and Grant 62173349.
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NR 34
TC 0
Z9 0
U1 18
U2 25
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9456
EI 1557-9662
J9 IEEE T INSTRUM MEAS
JI IEEE Trans. Instrum. Meas.
PY 2021
VL 70
AR 3526715
DI 10.1109/TIM.2021.3125110
PG 15
WC Engineering, Electrical & Electronic; Instruments & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Instruments & Instrumentation
GA WY8YP
UT WOS:000719563600006
DA 2022-08-23
ER

PT J
AU Santini, C
   Gesese, GA
   Peroni, S
   Gangemi, A
   Sack, H
   Alam, M
AF Santini, Cristian
   Gesese, Genet Asefa
   Peroni, Silvio
   Gangemi, Aldo
   Sack, Harald
   Alam, Mehwish
TI A knowledge graph embeddings based approach for author name
   disambiguation using literals
SO SCIENTOMETRICS
LA English
DT Article; Early Access
DE Author Name Disambiguation; Bibliographic data; Citation data;
   Clustering; Knowledge graph embeddings; Open citations
AB Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available in the form of Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc. This study more specifically targets the problem of Author Name Disambiguation (AND) on Scholarly KGs and presents a novel framework, Literally Author Name Disambiguation (LAND), which utilizes Knowledge Graph Embeddings (KGEs) using multimodal literal information generated from these KGs. This framework is based on three components: (1) multimodal KGEs, (2) a blocking procedure, and finally, (3) hierarchical Agglomerative Clustering. Extensive experiments have been conducted on two newly created KGs: (i) KG containing information from Scientometrics Journal from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known benchmark for AND provided by AMiner (AMiner-534K). The results show that our proposed architecture outperforms our baselines of 8-14% in terms of F-1 score and shows competitive performances on a challenging benchmark such as AMiner. The code and the datasets are publicly available through Github (https://github.com/sntcr istian/and-kge) and Zenodo (https://doi.org/10.5281/zenodo.6309855) respectively.
C1 [Santini, Cristian; Gesese, Genet Asefa; Sack, Harald; Alam, Mehwish] FIZ Karlsruhe Leibniz Inst Informat Infrastruct, Karlsruhe, Germany.
   [Santini, Cristian; Peroni, Silvio; Gangemi, Aldo] Univ Bologna, Bologna, Italy.
   [Santini, Cristian; Gesese, Genet Asefa; Sack, Harald; Alam, Mehwish] Karlsruhe Inst Technol, Inst AIFB, Karlsruhe, Germany.
RP Santini, C; Gesese, GA; Alam, M (通讯作者)，FIZ Karlsruhe Leibniz Inst Informat Infrastruct, Karlsruhe, Germany.; Santini, C (通讯作者)，Univ Bologna, Bologna, Italy.; Santini, C; Gesese, GA; Alam, M (通讯作者)，Karlsruhe Inst Technol, Inst AIFB, Karlsruhe, Germany.
EM cristian.santini@fiz-karlsruhe.de; genet-asefa.gesese@fiz-karlsruhe.de;
   mehwish.alam@kit.edu
FU Department of Classical Philology and Italian Studies, University of
   Bologna; Scholarship for research periods abroad aimed at the
   preparation of the master thesis
FX This study was partially funded by the "Scholarship for research periods
   abroad aimed at the preparation of the master thesis" by the Department
   of Classical Philology and Italian Studies, University of Bologna
   (https://ficlit.unibo.it/it).
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NR 53
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
DI 10.1007/s11192-022-04426-2
EA JUL 2022
PG 26
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 2Q6SD
UT WOS:000820549600006
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Chakraborty, N
   Lukovnikov, D
   Maheshwari, G
   Trivedi, P
   Lehmann, J
   Fischer, A
AF Chakraborty, Nilesh
   Lukovnikov, Denis
   Maheshwari, Gaurav
   Trivedi, Priyansh
   Lehmann, Jens
   Fischer, Asja
TI Introduction to neural network-based question answering over knowledge
   graphs
SO WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
LA English
DT Article
DE deep learning; knowledge graphs; question answering
ID WIKIDATA; DBPEDIA
AB Question answering has emerged as an intuitive way of querying structured data sources and has attracted significant advancements over the years. A large body of recent work on question answering over knowledge graphs (KGQA) employs neural network-based systems. In this article, we provide an overview of these neural network-based methods for KGQA. We introduce readers to the formalism and the challenges of the task, different paradigms and approaches, discuss notable advancements, and outline the emerging trends in the field. Through this article, we aim to provide newcomers to the field with a suitable entry point to semantic parsing for KGQA, and ease their process of making informed decisions while creating their own QA systems.
   This article is categorized under:
   Technologies > Machine Learning
   Technologies > Prediction
   Technologies > Artificial Intelligence
C1 [Chakraborty, Nilesh; Lehmann, Jens] Univ Bonn, Smart Data Analyt Grp, Bonn, Germany.
   [Lukovnikov, Denis; Fischer, Asja] Univ Bochum, Fac Math, Bochum, Germany.
   [Maheshwari, Gaurav; Trivedi, Priyansh; Lehmann, Jens] Fraunhofer IAIS, NetMedia Dept, Dresden, Germany.
RP Fischer, A (通讯作者)，Univ Bochum, Fac Math, Bochum, Germany.
EM jens.lehmann@iais.fraunhofer.de; asja.fischer@rub.de
OI Lehmann, Jens/0000-0001-9108-4278; Fischer, Asja/0000-0002-1916-7033
FU European Union [812997]; Cleopatra [812997]
FX European Union, Grant/Award Numbers: H2020 grant Cleopatra (GA no.
   812997), H2020; Cleopatra, Grant/Award Number: 812997
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   Yu M, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P571, DOI 10.18653/v1/P17-1053
   Yu T., 2018, P 2018 C EMP METH NA, P1653
   Zhang Y, 2016, P NAACL HLT
NR 117
TC 4
Z9 4
U1 9
U2 37
PU WILEY PERIODICALS, INC
PI SAN FRANCISCO
PA ONE MONTGOMERY ST, SUITE 1200, SAN FRANCISCO, CA 94104 USA
SN 1942-4787
EI 1942-4795
J9 WIRES DATA MIN KNOWL
JI Wiley Interdiscip. Rev.-Data Mining Knowl. Discov.
PD MAY
PY 2021
VL 11
IS 3
AR e1389
DI 10.1002/widm.1389
EA FEB 2021
PG 25
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RN9AL
UT WOS:000613554400001
OA hybrid
DA 2022-08-23
ER

PT J
AU FENG, X
   WEBER, TA
AF FENG, X
   WEBER, TA
TI KNOWLEDGE ACQUISITION ADVISER (KA2) - AN INTERACTIVE
   KNOWLEDGE-ACQUISITION TOOL FOR EXPERT-SYSTEM DEVELOPMENT
SO ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE KNOWLEDGE ACQUISITION; EXPERT SYSTEMS; KNOWLEDGE ENGINEERING; KNOWLEDGE
   GRAPH
AB A new interactive knowledge acquisition tool, called Knowledge Acquisition Advisor (KA2), is presented in this paper. The new tool will help knowledge engineers to conduct effective knowledge-elicitation interviews with domain experts through structured knowledge acquisition for both analytic and synthetic problems. A graphic modeling data structure, called Knowledge Graph is proposed, which allows knowledge engineers to model domain problems with their images and understanding. By using Knowledge Graph, knowledge engineers are able to decompose a domain problem into several components, to model the feature of each component, and to explore their relations by linking them with sets of questions. These questions can later be employed to guide the KA interview. Moreover, KA2 is particularly useful for interview through computer networks, so the knowledge acquisition can take place remotely.
C1 MARQUETTE UNIV,DEPT ELECT & COMP ENGN,MILWAUKEE,WI 53233.
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NR 17
TC 0
Z9 0
U1 1
U2 8
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB
SN 0952-1976
J9 ENG APPL ARTIF INTEL
JI Eng. Appl. Artif. Intell.
PD DEC
PY 1993
VL 6
IS 6
BP 507
EP 518
DI 10.1016/0952-1976(93)90048-3
PG 12
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Engineering, Multidisciplinary; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science; Engineering
GA MW554
UT WOS:A1993MW55400002
DA 2022-08-23
ER

PT J
AU Chen, L
   Tang, X
   Chen, WQ
   Qian, YT
   Li, YS
   Zhang, YJ
AF Chen, Ling
   Tang, Xing
   Chen, Weiqi
   Qian, Yuntao
   Li, Yansheng
   Zhang, Yongjun
TI DACHA: A Dual Graph Convolution Based Temporal Knowledge Graph
   Representation Learning Method Using Historical Relation
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE Temporal knowledge graph; representation learning; dual graph
   convolution
AB Temporal knowledge graph (TKG) representation learning embeds relations and entities into a continuous low-dimensional vector space by incorporating temporal information. Latest studies mainly aim at learning entity representations by modeling entity interactions from the neighbor structure of the graph. However, the interactions of relations from the neighbor structure of the graph are neglected, which are also of significance for learning informative representations. In addition, there still lacks an effective historical relation encoder tomodel themulti-range temporal dependencies. In this article, we propose a dual graph convolution network based TKG representation learning method using historical relations (DACHA). Specifically, we first construct the primal graph according to historical relations, as well as the edge graph by regarding historical relations as nodes. Then, we employ the dual graph convolution network to capture the interactions of both entities and historical relations from the neighbor structure of the graph. In addition, the temporal self-attentive historical relation encoder is proposed to explicitly model both local and global temporal dependencies. Extensive experiments on two event based TKG datasets demonstrate that DACHA achieves the state-of-the-art results.
C1 [Chen, Ling] Zhejiang Univ, Alibaba Zhejiang Univ Joint Res Inst Frontier Tec, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
   [Tang, Xing; Chen, Weiqi; Qian, Yuntao] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
   [Li, Yansheng; Zhang, Yongjun] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.
RP Chen, L (通讯作者)，Zhejiang Univ, Alibaba Zhejiang Univ Joint Res Inst Frontier Tec, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
EM lingchen@cs.zju.edu.cn; tangxing@cs.zju.edu.cn; jarvus@cs.zju.edu.cn;
   ytqian@cs.zju.edu.cn; yansheng.li@whu.edu.cn; zhangyj@whu.edu.cn
FU National Key Research and Development Program of China [2018YFB0505000];
   Fundamental Research Funds for the Central Universities [2020QNA5017]
FX This work was funded by the National Key Research and Development
   Program of China (No. 2018YFB0505000) and the Fundamental Research Funds
   for the Central Universities (No. 2020QNA5017).
CR Atzmueller M, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P1047, DOI 10.1145/3308560.3316707
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NR 42
TC 0
Z9 0
U1 6
U2 6
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1556-4681
EI 1556-472X
J9 ACM T KNOWL DISCOV D
JI ACM Trans. Knowl. Discov. Data
PD JUN
PY 2022
VL 16
IS 3
AR 46
DI 10.1145/3477051
PG 18
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1T8NW
UT WOS:000804983600006
DA 2022-08-23
ER

PT J
AU Guo, XT
   Gao, H
   Zou, ZN
AF Guo, Xintong
   Gao, Hong
   Zou, Zhaonian
TI WISE: Workload-Aware Partitioning for RDF Systems
SO BIG DATA RESEARCH
LA English
DT Article
DE Knowledge graph; RDF/SPARQL; Graph partitioning; Workload mining
ID SPARQL; DBPEDIA; ENGINE; GRAPHS; SCALE
AB Masses of large-scale knowledge graphs on various domains have sprung up in recent years. They are no longer able to be managed on a single machine. The distributed RDF systems intervene in the scalability issue using partitioning techniques. However, most of these systems are unaware of query workload and employ static partitioning. As diverse and dynamic workloads keep emerging in the knowledge graph applications, they cannot consistently provide good performance.
   To address the problem, we propose a workload-aware partitioning framework WISE, which could be deployed on any initial partitioning. It encodes the incoming SPARQL queries in a novel structure called query span and periodically examines the query span to identify the frequent query patterns. The triples of a frequent query pattern are moved to the same partition, aiming at improving the response time in the future. Our experiments on various RDF datasets and workloads indicate that WISE achieves dramatic communication reduction and considerable performance improvement over the baseline method. The migration overhead only accounts for a small portion of the total runtime. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Guo, Xintong; Gao, Hong; Zou, Zhaonian] Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China.
RP Gao, H (通讯作者)，Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China.
EM xintong.guo@hit.edu.cn; honggao@hit.edu.cn; znzou@hit.edu.cn
FU National Natural Science Foundation of China [61532015, 61672189,
   U19A2059]; National Key Research and Development Program of China
   [2019YFB2101902]
FX This work is supported by the Joint Funds of the National Natural
   Science Foundation of China No. U19A2059, the National Key Research and
   Development Program of China No. 2019YFB2101902, and National Natural
   Science Foundation of China No. 61532015 and No. 61672189.
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NR 38
TC 1
Z9 1
U1 1
U2 6
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 100161
DI 10.1016/j.bdr.2020.100161
PG 16
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OU4WS
UT WOS:000591531100003
DA 2022-08-23
ER

PT J
AU Huo, CG
   Ma, ST
   Liu, XZ
AF Huo, Chaoguang
   Ma, Shutian
   Liu, Xiaozhong
TI Hotness prediction of scientific topics based on a bibliographic
   knowledge graph
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Hotness prediction of scientific topics; Bibliographic Knowledge graph;
   Co-evolution; Node2vec; PageRank
ID CITATION NETWORKS; IMPACT; EVOLUTION; PROPAGATION; PAGERANK; SCIENCE
AB As a part of innovation in forecasting, scientific topic hotness prediction plays an essential role in dynamic scientific topic assessment and domain knowledge transformation modeling. To improve the topic hotness prediction performance, we propose an innovative model to estimate the co-evolution of scientific topic and bibliographic entities, which leverages a novel dynamic Bibliographic Knowledge Graph (BKG). Then, one can predict the topic hotness by using various kinds of topological entity information, i.e., TopicRank, PaperRank, AuthorRank, and VenueRank, along with pre-trained node embedding, i.e., node2vec embedding, and different pooling techniques. To validate the proposed method, we constructed a new BKG by using 4.5 million PubMed Central publications plus MeSH (Medical Subject Heading) thesaurus and witnessed the essential prediction improvement with extensive experiment outcomes over 10 years observations.
C1 [Huo, Chaoguang] Renmin Univ China, Sch Informat Resource Management, Beijing 100872, Peoples R China.
   [Huo, Chaoguang] Renmin Univ China, Res Ctr Digital Humanities, Beijing 100872, Peoples R China.
   [Ma, Shutian] Nanjing Univ Sci & Technol, Dept Informat Management, Nanjing 210094, Peoples R China.
   [Liu, Xiaozhong] Worcester Polytech Inst, Comp Sci, Worcester, MA 01609 USA.
RP Liu, XZ (通讯作者)，Worcester Polytech Inst, Worcester, MA 01609 USA.
EM huochaoguang@ruc.edu.cn; mashutian0608@hotmail.com; xliu14@wpi.edu
FU National Natural Science Foundation of China [72004221]
FX The authors acknowledge the Indiana University Pervasive Technology
   Institute for providing the High Performance Computing (Big Red 3,
   Karst, Carbonate) resources that have contributed to the research
   results reported within this article. This article was supported by the
   National Natural Science Foundation of China (Grant No. 72004221).
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NR 67
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD JUL
PY 2022
VL 59
IS 4
AR 102980
DI 10.1016/j.ipm.2022.102980
PG 21
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 3H9QN
UT WOS:000832363100010
DA 2022-08-23
ER

PT J
AU Chen, WJ
   Yang, SY
   Chang, JC
   Cheng, WC
   Lu, TP
   Wang, YN
   Juan, MH
   Hsu, RT
   Huang, SR
   Tu, JJ
   Wang, PC
   Feng, VWS
   Chang, PZ
AF Chen, Wei J.
   Yang, Shih-Yi
   Chang, Jung-Chih
   Cheng, Wei-Che
   Lu, Tzu-Pin
   Wang, Yu-Neng
   Juan, Ming-Hao
   Hsu, Ruey-Tzer
   Huang, Song-Ren
   Tu, Jia-Jang
   Wang, Pang-Chieh
   Feng, Vincent W-S
   Chang, Pei-Zen
TI Development of a semi-structured, multifaceted, computer-aided
   questionnaire for outbreak investigation: e-Outbreak Platform
SO BIOMEDICAL JOURNAL
LA English
DT Article
DE Outbreak; Epidemiological investigation; Platform; Knowledge graph;
   COVID-19; SARS-CoV-2
ID TRANSMISSION
AB Aggressive tracing of contacts of confirmed cases is crucial to Taiwan's successful control of the early spread of COVID-19. As the pandemic lingers, an epidemiological investigation that can be conducted efficiently in a timely manner can help decrease the burden on the health personnel and increase the usefulness of such information in decision making. To develop a new tool that can improve the current practice of epidemiological investigation by incorporating new technologies in digital platform and knowledge graphs. To meet the various needs of the epidemiological investigation, we decided to develop an e-Outbreak Platform that provides a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation. There are three major parts of the platform: (1) a graphic portal that allows users to have an at-glance grasp of the functions provided by the platform and then choose the one they need; (2) disease-specific questionnaires that can accommodate different formats of the information, including text typing, button selection, and pull-down menu; and (3) functions to utilize the stored information, including report generation, statistical analyses, and knowledge graphs displaying contact tracing. When the number of outbreak investigation increases, the knowledge graphs can be extended to encompass other persons appearing in the same location at the same time, i.e., constituting a potential contact cluster. The information extracted can also be used to display the tracing on a map in animation. Overall, this system can provide a basis for further refinement that can be generalized to a variety of outbreak investigations.
C1 [Chen, Wei J.] Natl Hlth Res Inst, Ctr Neuropsychiat Res, 35 Keyan Rd, Miaoli 35053, Taiwan.
   [Chen, Wei J.; Lu, Tzu-Pin] Natl Taiwan Univ, Coll Publ Hlth, Inst Epidemiol & Prevent Med, Taipei, Taiwan.
   [Chen, Wei J.; Lu, Tzu-Pin] Natl Taiwan Univ, Coll Publ Hlth, Dept Publ Hlth, Taipei, Taiwan.
   [Yang, Shih-Yi; Cheng, Wei-Che] Microsoft Taiwan, Publ Sect, Taipei, Taiwan.
   [Chang, Jung-Chih] Microsoft Taiwan, Solut Tech Unit, Taipei, Taiwan.
   [Wang, Yu-Neng; Juan, Ming-Hao; Hsu, Ruey-Tzer] Natl Taiwan Univ, Dept Elect Engn, Taipei, Taiwan.
   [Huang, Song-Ren] Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan.
   [Tu, Jia-Jang; Wang, Pang-Chieh; Feng, Vincent W-S] Ind Technol Res Inst, Computat Intelligence Technol Ctr, Hsinchu, Taiwan.
   [Chang, Pei-Zen] Ind Technol Res Inst, Hsinchu, Taiwan.
RP Chen, WJ (通讯作者)，Natl Hlth Res Inst, Ctr Neuropsychiat Res, 35 Keyan Rd, Miaoli 35053, Taiwan.
EM wjchen@nhri.edu.tw
RI Lu, Tzupin/ABC-2195-2021
OI Lu, Tzupin/0000-0003-3697-0386; Juan, Ming-Hao/0000-0003-0203-8750;
   Chen, Wei Jen/0000-0001-5899-5870
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NR 18
TC 3
Z9 3
U1 5
U2 14
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2319-4170
EI 2320-2890
J9 BIOMED J
JI Biomed. J.
PD AUG
PY 2020
VL 43
IS 4
BP 318
EP 324
DI 10.1016/j.bj.2020.06.007
PG 7
WC Biochemistry & Molecular Biology; Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Research & Experimental Medicine
GA OC8ZS
UT WOS:000579445300004
PM 32654885
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Teng, F
   Yang, W
   Chen, L
   Huang, LF
   Xu, Q
AF Teng, Fei
   Yang, Wei
   Chen, Li
   Huang, LuFei
   Xu, Qiang
TI Explainable Prediction of Medical Codes With Knowledge Graphs
SO FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
LA English
DT Article
DE automated ICD coding; knowledge graphs; explainable; medical records;
   natural language processing
ID ASSIGNMENT
AB International Classification of Diseases (ICD) is an authoritative health care classification system of different diseases. It is widely used for disease and health records, assisted medical reimbursement decisions, and collecting morbidity and mortality statistics. The most existing ICD coding models only translate the simple diagnosis descriptions into ICD codes. And it obscures the reasons and details behind specific diagnoses. Besides, the label (code) distribution is uneven. And there is a dependency between labels. Based on the above considerations, the knowledge graph and attention mechanism were expanded into medical code prediction to improve interpretability. In this study, a new method called G_Coder was presented, which mainly consists of Multi-CNN, graph presentation, attentional matching, and adversarial learning. The medical knowledge graph was constructed by extracting entities related to ICD-9 from freebase. Ontology contains 5 entity classes, which are disease, symptom, medicine, surgery, and examination. The result of G_Coder on the MIMIC-III dataset showed that the micro-F1 score is 69.2% surpassing the state of art. The following conclusions can be obtained through the experiment: G_Coder integrates information across medical records using Multi-CNN and embeds knowledge into ICD codes. Adversarial learning is used to generate the adversarial samples to reconcile the writing styles of doctor. With the knowledge graph and attention mechanism, most relevant segments of medical codes can be explained. This suggests that the knowledge graph significantly improves the precision of code prediction and reduces the working pressure of the human coders.
C1 [Teng, Fei; Yang, Wei; Huang, LuFei] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu, Peoples R China.
   [Chen, Li; Huang, LuFei] Third Peoples Hosp Chengdu, Chengdu, Peoples R China.
   [Xu, Qiang] Chengdu Univ Tradit Chinese Med, Sch Informat Engn, Chengdu, Peoples R China.
RP Teng, F (通讯作者)，Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu, Peoples R China.
EM fteng@swjtu.edu.cn
FU Sichuan Science and Technology Program [2017SZYZF0002]; National Key R&D
   Program of China [2019YFB2101802]; Sichuan Key RD project [2020YFG0035]
FX This work was supported by the Sichuan Science and Technology Program
   (No. 2017SZYZF0002), National Key R&D Program of China (No.
   2019YFB2101802), and Sichuan Key R&D project (No. 2020YFG0035).
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NR 44
TC 3
Z9 3
U1 10
U2 33
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-4185
J9 FRONT BIOENG BIOTECH
JI Front. Bioeng. Biotechnol.
PD AUG 14
PY 2020
VL 8
AR 867
DI 10.3389/fbioe.2020.00867
PG 11
WC Biotechnology & Applied Microbiology; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biotechnology & Applied Microbiology; Science & Technology - Other
   Topics
GA NJ7HB
UT WOS:000566213200001
PM 32923430
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhao, Y
   Gao, S
   Gallinari, P
   Guo, J
AF Zhao, Yu
   Gao, Sheng
   Gallinari, Patrick
   Guo, Jun
TI Zero-Shot Embedding for Unseen Entities in Knowledge Graph
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article
DE zero-shot learning; knowledge graph; embedding learning; relation
   prediction
AB Knowledge graph (KG) embedding aims at learning the latent semantic representations for entities and relations. However, most existing approaches can only be applied to KG completion, so cannot identify relations including unseen entities (or Out-of-KG entities). In this paper, motivated by the zero-shot learning, we propose a novel model, namely JointE, jointly learning KG and entity descriptions embedding, to extend KG by adding new relations with Out-of-KG entities. The JointE model is evaluated on entity prediction for zero-shot embedding. Empirical comparisons on benchmark datasets show that the proposed JointE model outperforms state-of-the-art approaches. The source code of JointE is available at https://github.com/yzur/JointE.
C1 [Zhao, Yu] BUPT, Sch Informat & Commun, Beijing 100876, Peoples R China.
   [Gao, Sheng; Guo, Jun] BUPT, Beijing 100876, Peoples R China.
   [Guo, Jun] BUPT, Sch Informat & Commun Engn, Beijing 100876, Peoples R China.
   [Gallinari, Patrick] Univ Paris 06, LIP6, Paris, France.
   [Zhao, Yu] Univ Rochester, Dept Comp Sci, Rochester, NY 14627 USA.
RP Gao, S (通讯作者)，BUPT, Beijing 100876, Peoples R China.
EM gaosheng@bupt.edu.cn
FU Natural Science Foundation of China [61300080, 61273217, 61671078]; 111
   Project [B08004]; MobileCloud Project [612212]; 360 Innovation; BUPT
   Excellent Ph.D. Students Foundation
FX This work was supported by the Natural Science Foundation of China under
   Grant No. 61300080, No. 61273217, No. 61671078, the 111 Project under
   Grant No. B08004 and FP7 MobileCloud Project under Grant No. 612212. The
   authors are partially supported by 360 Innovation, BUPT Excellent Ph.D.
   Students Foundation.
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NR 29
TC 2
Z9 2
U1 1
U2 11
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 JUL
PY 2017
VL E100D
IS 7
BP 1440
EP 1447
DI 10.1587/transinf.2016EDP7446
PG 8
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FC5FP
UT WOS:000406867600005
OA gold
DA 2022-08-23
ER

PT J
AU Liu, C
   Yang, SW
AF Liu, Chang
   Yang, Shiwu
TI Using text mining to establish knowledge graph from accident/incident
   reports in risk assessment
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Text mining; Knowledge graph; Named entity recognition; Machine
   learning; Risk assessment; Railway safety
ID INFORMATION; ACCIDENTS; ONTOLOGY; SYSTEM
AB To clarify the risk factors and propagation characteristics affecting railway safety, we learn from historical re-ports to build a connected network of hazards and accidents, forming a knowledge graph (KG), and apply it to railway hazard identification and risk assessment. First, the open source-British railway accident/incident reports are selected as the data source. The text augmentation algorithm in the text mining technology is introduced and optimized to achieve data enhancement. An ensemble model is constructed based on the hidden Markov model, conditional random field (CRF) algorithm, bidirectional long short-term memory (Bi-LSTM), and Bi-LSTM-CRF deep learning network, completing the named entity recognition of the reports. Then, using the random forest algorithm, the standardized classification of entities is accomplished, and the multi-dimensional knowledge graph network is established. Finally, after defining a series of safety-related feature parameters, the obtained KG is applied to the quantitative assessment of the corresponding risk level of the hazards. The results show that this approach realizes the visualization and quantitative description of the potential relationship among hazards, faults, and accidents by exploring the topological relationship of the railway accident network, further assisting the formulation of railway risk preventive measures.
C1 [Liu, Chang; Yang, Shiwu] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China.
RP Yang, SW (通讯作者)，Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China.
EM 19111061@bjtu.edu.cn; ysw@bjtu.edu.cn
FU Ministry of Science and Technology of the People?s Republic of China
   [W21B05300031, SJ2021-041]
FX Funding This work was supported in part by the Ministry of Science and
   Technology of the People?s Republic of China [grant number W21B05300031]
   and the National Railway Administration of the Peo- ple?s Republic of
   China [grant number SJ2021-041] .
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NR 68
TC 0
Z9 0
U1 4
U2 4
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD NOV 30
PY 2022
VL 207
AR 117991
DI 10.1016/j.eswa.2022.117991
PG 20
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 3M3CP
UT WOS:000835338800002
DA 2022-08-23
ER

PT J
AU Fell, M
   Cabrio, E
   Tikat, M
   Michel, F
   Buffa, M
   Gandon, F
AF Fell, Michael
   Cabrio, Elena
   Tikat, Maroua
   Michel, Franck
   Buffa, Michel
   Gandon, Fabien
TI The WASABI song corpus and knowledge graph for music lyrics analysis
SO LANGUAGE RESOURCES AND EVALUATION
LA English
DT Article; Early Access
DE Corpus (creation, annotation, etc.); Information extraction; Information
   retrieval; Knowledge graph; Music and song lyrics
AB We present the WASABI Song Corpus, a large corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis. More specifically, given that lyrics encode an important part of the semantics of a song, we focus here on the description of the methods we proposed to extract relevant information from the lyrics, such as their structure segmentation, their topics, the explicitness of the lyrics content, the salient passages of a song and the emotions conveyed. The corpus contains 1.73M songs with lyrics (1.41M unique lyrics) annotated at different levels with the output of the above mentioned methods. The corpus labels and the provided methods can be exploited by music search engines and music professionals (e.g. journalists, radio presenters) to better handle large collections of lyrics, allowing an intelligent browsing, categorization and recommendation of songs. We demonstrate the utility and versatility of the WASABI Song Corpus in three concrete application scenarios. Together with the work on the corpus, we present the work achieved to transition the dataset into a knowledge graph, the WASABI RDF Knowledge Graph, and we show how this will enable an even richer set of applications.
C1 [Fell, Michael] Univ Turin, Corso Svizzera 185, I-10149 Turin, Italy.
   [Cabrio, Elena; Tikat, Maroua; Michel, Franck; Buffa, Michel; Gandon, Fabien] Univ Cote dAzur, I3S, CNRS, 930 Route Colles, F-06903 Sophia Antipolis, France.
RP Fell, M (通讯作者)，Univ Turin, Corso Svizzera 185, I-10149 Turin, Italy.
EM mic.fell@gmail.com
FU French Research National Agency (ANR) under the WASABI project
   [ANR-16-CE23-0017-01]
FX This work is partly funded by the French Research National Agency (ANR)
   under the WASABI project (contract ANR-16-CE23-0017-01).
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NR 60
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1574-020X
EI 1574-0218
J9 LANG RESOUR EVAL
JI Lang. Resour. Eval.
DI 10.1007/s10579-022-09601-8
EA JUL 2022
PG 31
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3E1ZU
UT WOS:000829788900001
DA 2022-08-23
ER

PT J
AU Xiao, B
   Du, YJ
   Wu, QMJ
   Xu, QF
   Yan, LP
AF Xiao, Bo
   Du, Yujiao
   Wu, Q. M. Jonathan
   Xu, Qianfang
   Yan, Liping
TI A Fast Hybrid Model for Large-Scale Zero-Shot Image Recognition Based on
   Knowledge Graphs
SO IEEE ACCESS
LA English
DT Article
DE Zero-shot learning; WordNet; graph convolutional network; residual
   network; attention model
ID CLASSIFICATION
AB Zero-shot learning aims to recognize unseen categories by learning an embedding space between data samples and semantic representations. For the large-scale datasets with thousands of categories, embedding vectors of category labels are often used for semantic representation since it is difficult to define the semantic attributes of categories manually. Facing the problem of underutilization of prior knowledge during the construction of embedding vectors, this paper first constructs a novel knowledge graph as the supplement to the basic WordNet graph, and then proposes a fast hybrid model ARGCN-DKG, which means Attention based Residual Graph Convolutional Network on Different types of Knowledge Graphs. By introducing residual mechanism and attention mechanism, and integrating different knowledge graphs, the accuracy of knowledge transfer between different categories can be improved. Our model only use 2-layer GCN, the pretrained image features and category semantic features, so the training process could be done in minitues on single GPU, which could be one of the fastest training models for large-scale image recognition. Experiment results demonstrate that ARGCN-DKG model could get better results for large-scale datasets than the state-of-the-art model.
C1 [Xiao, Bo; Du, Yujiao; Xu, Qianfang] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China.
   [Xiao, Bo; Wu, Q. M. Jonathan; Yan, Liping] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada.
   [Yan, Liping] Beijing Inst Technol, Sch Automat, Key Lab Intelligent Control & Decis Complex Syst, Beijing 100081, Peoples R China.
RP Xiao, B (通讯作者)，Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China.; Xiao, B (通讯作者)，Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada.
EM xiaobo@bupt.edu.cn
OI xiao, bo/0000-0003-3392-3293; Wu, Q.M. Jonathan/0000-0002-5208-7975
FU National Natural Science Foundation Project of China [61773071]; Key
   Research and Development Program of Ministry of Science and Technology
   [2018YFB1003700]
FX This work was supported in part by the National Natural Science
   Foundation Project of China under Grant 61773071, and in part by the Key
   Research and Development Program of Ministry of Science and Technology
   under Grant 2018YFB1003700.
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NR 32
TC 0
Z9 0
U1 1
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 119309
EP 119318
DI 10.1109/ACCESS.2019.2935175
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA JP9CH
UT WOS:000498554500005
OA gold
DA 2022-08-23
ER

PT J
AU Silva, MC
   Eugenio, P
   Faria, D
   Pesquita, C
AF Silva, Marta Contreiras
   Eugenio, Patricia
   Faria, Daniel
   Pesquita, Catia
TI Ontologies and Knowledge Graphs in Oncology Research
SO CANCERS
LA English
DT Review
DE ontologies; semantic technologies; knowledge graph; cancer; oncology;
   review
ID STANDARDIZED DATA-COLLECTION; SQUAMOUS-CELL CARCINOMA; LONG NONCODING
   RNAS; EXPRESSION PROFILE; GENE ONTOLOGY; BIOINFORMATICS ANALYSIS;
   ENRICHMENT ANALYSIS; SIGNALING PATHWAYS; CANDIDATE GENES; BLADDER-CANCER
AB Simple Summary Cancer is a complex phenomenon and cancer research is increasingly data-rich. Representing this knowledge in a manner that is both human and computer-friendly can help manage and analyze the high volumes of complex cancer data that are created by scientific research and health care. This review looks at the last decade of works on using ontologies-computational representations of knowledge-in cancer, describing their contributions and achievements and charting a path for future research in this area. The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer-which is critical for precision medicine approaches-hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.
C1 [Silva, Marta Contreiras; Eugenio, Patricia; Faria, Daniel; Pesquita, Catia] Univ Lisbon, LASIGE, Fac Ciencias, P-1749016 Lisbon, Portugal.
RP Silva, MC (通讯作者)，Univ Lisbon, LASIGE, Fac Ciencias, P-1749016 Lisbon, Portugal.
EM mcdsilva@fc.ul.pt; peugenio@lasige.di.fc.ul.pt; dpfaria@fc.ul.pt;
   clpesquita@fc.ul.pt
OI Silva, Marta/0000-0003-1864-0105; Faria, Daniel/0000-0003-1511-277X
FU FCT through the LASIGE Research Unit [UIDB/00408/2020, UIDP/00408/2020];
   European Union [101017453]
FX This work was supported by FCT through the LASIGE Research Unit
   (UIDB/00408/2020 and UIDP/00408/2020). It was also partially supported
   by the KATY project which has received funding from the European Union's
   Horizon 2020 research and innovation program under grant agreement No
   101017453.
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NR 159
TC 0
Z9 0
U1 18
U2 18
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2072-6694
J9 CANCERS
JI Cancers
PD APR
PY 2022
VL 14
IS 8
AR 1906
DI 10.3390/cancers14081906
PG 27
WC Oncology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Oncology
GA 0Q8CQ
UT WOS:000785140000001
PM 35454813
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, TT
   Zhao, Y
   Zhang, CB
   Zhou, K
   Zhang, XJ
AF Li, Tingting
   Zhao, Yang
   Zhang, Chaobo
   Zhou, Kai
   Zhang, Xuejun
TI A semantic model-based fault detection approach for building energy
   systems
SO BUILDING AND ENVIRONMENT
LA English
DT Article
DE Semantic model; Ontology; Rule; Fault detection; Building energy system
ID DIAGNOSIS STRATEGY; VAV TERMINALS; HVAC SYSTEMS; ONTOLOGY; MANAGEMENT;
   INFORMATION; TOOL
AB In this paper, a semantic model-based approach is proposed for building energy systems fault detection. Its basic idea is to mimic the general intelligence of human experts in understanding massive amounts of operational data of various buildings, and further proposing customized fault detection solutions. A domain ontology is developed to allow computers to understand the prior knowledge of building energy systems fault detection. Classes and properties are developed to formalize all possible configurations in this domain. Semantic rules are proposed to detect the operation problems, control problems, equipment malfunction and sensor failure in building energy systems. These rules are written in abstract syntax. They can be reused in various building energy systems. For a target system, the building data are mapped to the ontology to generate a customized knowledge graph. The knowledge graph captures the physics underlying the system operations. The semantic rules are activated based on the knowledge graph to detect the faults. The proposed approach is demonstrated using the historical data from an industrial building located in Wuhan, China. The results show that the approach is powerful in providing the customized fault detection solutions for different situations. It has high levels of interpretability, reliability and automation. The knowledge graph is automatically updated with new data step by step. The semantic rules are activated if the conditions are satisfied based on the knowledge graph. The fault action mechanisms are captured based on the inference chains of the rules. Experts can find the fault reasons and take actions for commissioning.
C1 [Li, Tingting; Zhao, Yang; Zhang, Chaobo; Zhang, Xuejun] Zhejiang Univ, Inst Refrigerat & Cryogen, Hangzhou, Peoples R China.
   [Zhou, Kai] Zhejiang Energy Gas Grp Co Ltd, Hangzhou, Peoples R China.
RP Zhao, Y (通讯作者)，Zhejiang Univ, Inst Refrigerat & Cryogen, Hangzhou, Peoples R China.
EM youngzhao@zju.edu.cn
FU National Natural Science Foundation of China [51978601]; National Key
   Research and Development Program of China [2018YFE0116300]
FX This work was supported by the National Natural Science Foundation of
   China [51978601]; and the National Key Research and Development Program
   of China [2018YFE0116300].
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NR 49
TC 2
Z9 2
U1 1
U2 1
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0360-1323
EI 1873-684X
J9 BUILD ENVIRON
JI Build. Environ.
PD JAN
PY 2022
VL 207
AR 108548
DI 10.1016/j.buildenv.2021.108548
PN B
PG 16
WC Construction & Building Technology; Engineering, Environmental;
   Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA 2Q6DG
UT WOS:000820510100001
DA 2022-08-23
ER

PT J
AU Li, DF
   Madden, A
AF Li, Daifeng
   Madden, Andrew
TI Cascade embedding model for knowledge graph inference and retrieval
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Graph embedding; Knowledge embedding; Knowledge graph inference; Cascade
   ranking
AB Knowledge graphs are widely used in retrieval systems, question answering systems (QA), hypothesis generation systems, etc. Representation learning provides a way to mine knowledge graphs to detect missing relations; and translation-based embedding models are a popular form of representation model. Shortcomings of translation-based models however, limits their practicability as knowledge completion algorithms. The proposed model helps to address some of these shortcomings.
   The similarity between graph structural features of two entities was found to be correlated to the relations of those entities. This correlation can help to solve the problem caused by unbalanced relations and reciprocal relations. We used Node2vec, a graph embedding algorithm, to represent information related to an entity's graph structure, and we introduce a cascade model to incorporate graph embedding with knowledge embedding into a unified framework. The cascade model first refines feature representation in the first two stages (Local Optimization Stage), and then uses backward propagation to optimize parameters of all the stages (Global Optimization Stage). This helps to enhance the knowledge representation of existing translation-based algorithms by taking into account both semantic features and graph features and fusing them to extract more useful information. Besides, different cascade structures are designed to find the optimal solution to the problem of knowledge inference and retrieval.
   The proposed model was verified using three mainstream knowledge graphs: WIN18, FB15K and BioChem. Experimental results were validated using the hit@10 rate entity prediction task. The proposed model performed better than TransE, giving an average improvement of 2.7% on WN18, 2.3% on FB15k and 28% on BioChem. Improvements were particularly marked where there were problems with unbalanced relations and reciprocal relations. Furthermore, the step-wise-cascade structure is proved to be more effective and significantly outperforms other baselines.
C1 [Li, Daifeng; Madden, Andrew] Sun Yat Sen Univ, Sch Informat Management, Guangzhou, Guangdong, Peoples R China.
RP Li, DF (通讯作者)，Sun Yat Sen Univ, Sch Informat Management, Guangzhou, Guangdong, Peoples R China.
EM lidaifeng@mail.sysu.edu.cn
OI Madden, Andrew/0000-0003-2305-7790
FU Chinese National Natural Science Youth Foundation Research [61702564];
   Talent Scientific Research Foundation of Sun Yat-sen University
   [20000-18831102]
FX This research is supported by Chinese National Natural Science Youth
   Foundation Research (grant no: 61702564), Talent Scientific Research
   Foundation of Sun Yat-sen University (grant no: 20000-18831102).
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NR 47
TC 6
Z9 7
U1 13
U2 65
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD NOV
PY 2019
VL 56
IS 6
AR 102093
DI 10.1016/j.ipm.2019.102093
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 JA3YT
UT WOS:000487766200022
DA 2022-08-23
ER

PT J
AU Nassour, J
   Elhadad, M
   Sturm, A
   Yu, E
AF Nassour, Jumana
   Elhadad, Michael
   Sturm, Arnon
   Yu, Eric
TI Evaluating the comprehension of means-ends maps
SO SOFTWARE AND SYSTEMS MODELING
LA English
DT Article
DE Knowledge mapping; Literature review; ME-MAP; Evaluation; Controlled
   experiment
ID KNOWLEDGE; DESIGN
AB Information and software systems development is rapidly changing due to exponential technology development. This acceleration is also impacting other technology or engineering domains. Thus, there is a need to identify problems and their solutions, and to reason about new options so as to better arrive at the right decision of which technology or solution should be adopted among various alternatives. In this paper, we argue that such know-how information can be mapped, to ease such tasks. In particular, we examine the hypothesis that know-how mapping, using an approach we call Means-Ends Map (ME-MAP), facilitates analysis in technological domains. We design a controlled experiment to assess the comprehension of ME maps with that of textual summaries in two different domains. We find that subjects exploring a domain using ME maps were able to better identify solutions and better understand the tradeoffs among alternative solutions. Furthermore, these subjects gained that understanding faster compared to those using textual summaries.
C1 [Nassour, Jumana; Elhadad, Michael; Sturm, Arnon] Ben Gurion Univ Negev, Beer Sheva, Israel.
   [Sturm, Arnon; Yu, Eric] Univ Toronto, Toronto, ON, Canada.
RP Nassour, J (通讯作者)，Ben Gurion Univ Negev, Beer Sheva, Israel.
EM jumanan@post.bgu.ac.il; elhadad@cs.bgu.ac.il; sturm@bgu.ac.il;
   eric.yu@utoronto.ca
RI ELHADAD, MICHAEL/AIB-3614-2022
OI ELHADAD, MICHAEL/0000-0002-5629-2351; Sturm, Arnon/0000-0002-4021-7752
FU Israel Science Foundation [495/14]; Natural Sciences and Engineering
   Research Council of Canada
FX This research is partially supported by the Israel Science Foundation
   (Grant No. 495/14) and the Natural Sciences and Engineering Research
   Council of Canada
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NR 35
TC 0
Z9 0
U1 0
U2 2
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1619-1366
EI 1619-1374
J9 SOFTW SYST MODEL
JI Softw. Syst. Model.
PD JUN
PY 2019
VL 18
IS 3
BP 1885
EP 1903
DI 10.1007/s10270-018-0691-4
PG 19
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA HZ6XP
UT WOS:000468996100018
DA 2022-08-23
ER

PT J
AU Liao, SH
   Chub, PH
   Chen, YJ
   Chang, CC
AF Liao, Shu-hsien
   Chub, Pei-hui
   Chen, Yin-ju
   Chang, Chia-Chen
TI Mining customer knowledge for exploring online group buying behavior
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Data mining; Association rules; Cluster analysis; Online group buying;
   Online group buying behavior
AB Online group buying is an effective marketing method. By using online group buying, customers get unbelievable discounts on premium products and services. This not only meets customer demand, but also helps sellers to find new ways to sell products sales and open up new business models, all parties benefit in these transactions. During these bleak economic times, group buying has become extremely popular. Therefore, this study proposes a data mining approach for exploring online group buying behavior in Taiwan. Thus, this study uses the Apriori algorithm as an association rules approach, and clustering analysis for data mining, which is implemented for mining customer knowledge among online group buying customers in Taiwan. The results of knowledge extraction from data mining are illustrated as knowledge patterns, rules, and knowledge maps in order to propose suggestions and solutions to online group buying firms for future development. (C) 2011 Elsevier Ltd. All rights reserved.
C1 [Liao, Shu-hsien; Chen, Yin-ju; Chang, Chia-Chen] Tamkang Univ, Dept Management Sci, New Taipei City 251, Taiwan.
   [Chub, Pei-hui] Natl Taipei Coll Business, Dept Informat Management, Taipei 10051, Taiwan.
RP Liao, SH (通讯作者)，Tamkang Univ, Dept Management Sci, 151 Yingjuan Rd, New Taipei City 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
   100-2410-H-032-018-MY3]
FX This research was funded by the National Science Council, Taiwan,
   Republic of China, under Contract No. NSC 100-2410-H-032-018-MY3.
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NR 18
TC 51
Z9 52
U1 3
U2 63
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 FEB 15
PY 2012
VL 39
IS 3
BP 3708
EP 3716
DI 10.1016/j.eswa.2011.09.066
PG 9
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 858SP
UT WOS:000297823300155
DA 2022-08-23
ER

PT J
AU Leng, G
   Kuhn, KH
   Dunemann, L
   Idel, H
AF Leng, G
   Kuhn, KH
   Dunemann, L
   Idel, H
TI Gas chromatographic and mass spectrometric method for the determination
   of selected pyrethroid metabolites in urine
SO ZENTRALBLATT FUR HYGIENE UND UMWELTMEDIZIN
LA German
DT Article
ID CYPERMETHRIN
AB Pyrethroids are increasingly used indoors, because of their low mammalian toxicity but high insecticidal activity. Indoor application of pyrethroids, which is often carried out without adequate expert knowledge, map lead to high pesticide residues, often combined with health hazards for the people concerned. Objective of the present study is the development of a method for a biological monitoring to determine internal exposure to pyrethroids. Major pyrethroid metabolites were detected in urine by capillary gas chromatography in combination with mass selective detection (MSD). For the metabolites, the limits of determination range between 0.5 and 1 mu g/L urine. Our results so far demonstrate the detectability of cis- and trans- 3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropanecarboxylic acid (DVCA) and 3-phenoxybenzoic acid (3-PBA) in urine during a period of elimination of 48 hours after exposure to cypermethrin. In this paper the method is exemplary illustrated by one exposed person (pest control operator).
C1 UNIV DUSSELDORF,MED INST UMWELTHYG,D-40225 DUSSELDORF,GERMANY.
RP Leng, G (通讯作者)，UNIV DUSSELDORF,INST HYG,MOORENSTR 5,D-40225 DUSSELDORF,GERMANY.
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NR 9
TC 9
Z9 9
U1 0
U2 0
PU GUSTAV FISCHER VERLAG
PI STUTTGART
PA WOLLGRASWEG 49, D-70599 STUTTGART, GERMANY
SN 0934-8859
J9 ZBL HYG UMWELTMED
JI Zent.bl. Hyg. Umweltmed.
PD MAY
PY 1996
VL 198
IS 5
BP 443
EP 451
PG 9
WC Public, Environmental & Occupational Health; Infectious Diseases;
   Microbiology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Public, Environmental & Occupational Health; Infectious Diseases;
   Microbiology
GA UQ363
UT WOS:A1996UQ36300005
PM 9409900
DA 2022-08-23
ER

PT J
AU Linh, NV
   Bach, TX
   Than, K
AF Ngo Van Linh
   Tran Xuan Bach
   Khoat Than
TI A graph convolutional topic model for short and noisy text streams
SO NEUROCOMPUTING
LA English
DT Article
DE Topic models; Graph convolutional networks; Knowledge graph; Concept
   drift; Short texts
AB Learning hidden topics from data streams has become absolutely necessary but posed challenging problems such as concept drift as well as short and noisy data. Using prior knowledge to enrich a topic model is one of potential solutions to cope with these challenges. Prior knowledge that is derived from human knowledge (e.g. Wordnet) or a pre-trained model (e.g. Word2vec) is very valuable and useful to help topic models work better. However, in a streaming environment where data arrives continually and infinitely, existing studies are limited to exploiting these resources effectively. Especially, a knowledge graph, that contains meaningful word relations, is ignored. In this paper, to aim at exploiting a knowledge graph effectively, we propose a novel graph convolutional topic model (GCTM) which integrates graph convolutional networks (GCN) into a topic model and a learning method which learns the networks and the topic model simultaneously for data streams. In each minibatch, our method not only can exploit an external knowledge graph but also can balance the external and old knowledge to perform well on new data. We conduct extensive experiments to evaluate our method with both a human knowledge graph (Wordnet) and a graph built from pre-trained word embeddings (Word2vec). The experimental results show that our method achieves significantly better performances than state-of-the-art baselines in terms of probabilistic predictive measure and topic coherence. In particular, our method can work well when dealing with short texts as well as concept drift. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Ngo Van Linh; Tran Xuan Bach; Khoat Than] Hanoi Univ Sci & Technol, 1 Dai Co Viet Rd, Hanoi, Vietnam.
RP Linh, NV (通讯作者)，Hanoi Univ Sci & Technol, 1 Dai Co Viet Rd, Hanoi, Vietnam.
EM linhov@soict.hust.edu.vn; khoattq@soict.hust.edu.vn
FU Gia Lam Urban Development and Investment Company Limited; Vingroup
   Innovation Foundation (VINIF) [VINIF.2019.DA18]; Vingroup
FX This work was funded by Gia Lam Urban Development and Investment Company
   Limited, Vingroup and supported by Vingroup Innovation Foundation
   (VINIF) under project code VINIF.2019.DA18.
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NR 50
TC 0
Z9 0
U1 1
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JAN 11
PY 2022
VL 468
BP 345
EP 359
DI 10.1016/j.neucom.2021.10.047
EA OCT 2021
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WR8WX
UT WOS:000714776300008
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Wang, YQ
   Dong, LY
   Jiang, XQ
   Ma, XT
   Li, YL
   Zhang, H
AF Wang, YueQun
   Dong, LiYan
   Jiang, XiaoQuan
   Ma, XinTao
   Li, YongLi
   Zhang, Hao
TI KG2Vec: A node2vec-based vectorization model for knowledge graph
SO PLOS ONE
LA English
DT Article
AB Since the word2vec model was proposed, many researchers have vectorized the data in the research field based on it. In the field of social network, the Node2Vec model improved on the basis of word2vec can vectorize nodes and edges in social networks, so as to carry out relevant research on social networks, such as link prediction, and community division. However, social network is a network with homogeneous structure. When dealing with heterogeneous networks such as knowledge graph, Node2Vec will lead to inaccurate prediction and unreasonable vector quantization data. Specifically, in the Node2Vec model, the walk strategy for homogeneous networks is not suitable for heterogeneous networks, because the latter has distinguishing features for nodes and edges. In this paper, a Heterogeneous Network vector representation method is proposed based on random walks and Node2Vec, called KG2vec (Heterogeneous Network to Vector) that solves problems related to the inadequate consideration of the full-text semantics and the contextual relations that are encountered by the traditional vector representation of the knowledge graph. First, the knowledge graph is reconstructed and a new random walk strategy is applied. Then, two training models and optimizing strategies are proposed, so that the contextual environment between entities and relations is obtained, semantically providing a full vector representation of the Heterogeneous Network. The experimental results show that the KG2VEC model solves the problem of insufficient context consideration and unsatisfactory results of one-to-many relationship in the vectorization process of the traditional knowledge graph. Our experiments show that KG2vec achieves better performance with higher accuracy than traditional methods.
C1 [Wang, YueQun; Dong, LiYan; Jiang, XiaoQuan; Ma, XinTao; 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 32
TC 1
Z9 1
U1 4
U2 13
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 30
PY 2021
VL 16
IS 3
AR e0248552
DI 10.1371/journal.pone.0248552
PG 19
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA RH6XT
UT WOS:000636359600010
PM 33784319
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhao, GH
   Gu, WJ
   Cai, W
   Zhao, ZY
   Zhang, X
   Liu, JR
AF Zhao, Genghong
   Gu, Wenjian
   Cai, Wei
   Zhao, Zhiying
   Zhang, Xia
   Liu, Jiren
TI MLEE: A method for extracting object-level medical knowledge graph
   entities from Chinese clinical records
SO FRONTIERS IN GENETICS
LA English
DT Article
DE knowledge graph (KG); medical entity extraction; natural language
   processing (computer science); EMR data mining; Chinese clinical records
ID INFORMATION; CONSTRUCTION
AB As a typical knowledge-intensive industry, the medical field uses knowledge graph technology to construct causal inference calculations, such as "symptom-disease", "laboratory examination/imaging examination-disease", and "disease-treatment method". The continuous expansion of large electronic clinical records provides an opportunity to learn medical knowledge by machine learning. In this process, how to extract entities with a medical logic structure and how to make entity extraction more consistent with the logic of the text content in electronic clinical records are two issues that have become key in building a high-quality, medical knowledge graph. In this work, we describe a method for extracting medical entities using real Chinese clinical electronic clinical records. We define a computational architecture named MLEE to extract object-level entities with "object-attribute" dependencies. We conducted experiments based on randomly selected electronic clinical records of 1,000 patients from Shengjing Hospital of China Medical University to verify the effectiveness of the method.
C1 [Zhao, Genghong; Zhang, Xia; Liu, Jiren] Engn Northeastern Univ, Sch Comp Sci, Shenyang, Peoples R China.
   [Zhao, Genghong; Cai, Wei; Zhang, Xia] Neusoft Res Intelligent Healthcare Technol, Shenyang, Peoples R China.
   [Gu, Wenjian] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Peoples R China.
   [Zhao, Zhiying] China Med Univ, Dept Clin Epidemiol, Shengjing Hosp, Shenyang, Peoples R China.
   [Liu, Jiren] Neusoft Corp, Shenyang, Peoples R China.
RP Zhao, GH; Zhang, X; Liu, JR (通讯作者)，Engn Northeastern Univ, Sch Comp Sci, Shenyang, Peoples R China.; Zhao, GH; Zhang, X (通讯作者)，Neusoft Res Intelligent Healthcare Technol, Shenyang, Peoples R China.; Liu, JR (通讯作者)，Neusoft Corp, Shenyang, Peoples R China.
EM 1810626@stu.neu.edu.cn; zhangx@neusoft.com; liujr@neusoft.com
FU National Key Research and Development Program of China [2020AAA0109400];
   Shenyang Science and Technology Plan Fund [20-201-4-10]
FX Funding This research is supported by the National Key Research and
   Development Program of China No. 2020AAA0109400 and the Shenyang Science
   and Technology Plan Fund (No. 20-201-4-10).
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NR 27
TC 0
Z9 0
U1 0
U2 0
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1664-8021
J9 FRONT GENET
JI Front. Genet.
PD JUL 22
PY 2022
VL 13
AR 900242
DI 10.3389/fgene.2022.900242
PG 12
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA 3O8IG
UT WOS:000837077800001
PM 35938002
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU McCusker, JP
   Dumontier, M
   Yan, R
   He, S
   Dordick, JS
   McGuinness, DL
AF McCusker, James P.
   Dumontier, Michel
   Yan, Rui
   He, Sylvia
   Dordick, Jonathan S.
   McGuinness, Deborah L.
TI Finding melanoma drugs through a probabilistic knowledge graph
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Melanoma; Knowledge graphs; Drug repositioning; Uncertainty reasoning
ID PROTEIN-INTERACTION DATABASE; PHASE-II TRIAL; MALIGNANT-MELANOMA;
   COMPREHENSIVE RESOURCE; GENE-EXPRESSION; CANCER-CELLS; LUNG-CANCER;
   DISCOVERY; APOPTOSIS; METASTASES
AB Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.
C1 [McCusker, James P.; Yan, Rui; He, Sylvia; McGuinness, Deborah L.] Rensselaer Polytech Inst, Dept Comp Sci, Troy, NY 12180 USA.
   [Dumontier, Michel] Stanford Univ, Sch Med, Stanford Ctr Biomed Informat Res, Stanford, CA 94305 USA.
   [Dordick, Jonathan S.] Rensselaer Polytech Inst, Dept Chem & Biol Engn, Troy, NY USA.
   [Dordick, Jonathan S.; McGuinness, Deborah L.] Rensselaer Polytech Inst, Ctr Biotechnol & Interdisciplinary Studies, Troy, NY 12180 USA.
RP McCusker, JP; McGuinness, DL (通讯作者)，Rensselaer Polytech Inst, Dept Comp Sci, Troy, NY 12180 USA.; McGuinness, DL (通讯作者)，Rensselaer Polytech Inst, Ctr Biotechnol & Interdisciplinary Studies, Troy, NY 12180 USA.
EM mccusj@cs.rpi.edu; dlm@cs.rpi.edu
OI McCusker, Jamie/0000-0003-1085-6059; Dumontier,
   Michel/0000-0003-4727-9435; McGuinness, Deborah/0000-0001-7037-4567
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NR 81
TC 11
Z9 11
U1 5
U2 13
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 13
PY 2017
AR e106
DI 10.7717/peerj-cs.106
PG 20
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FW6FA
UT WOS:000425412400001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Wang, SD
   Du, ZZ
   Ding, M
   Rodriguez-Paton, A
   Song, T
AF Wang, Shudong
   Du, Zhenzhen
   Ding, Mao
   Rodriguez-Paton, Alfonso
   Song, Tao
TI KG-DTI: a knowledge graph based deep learning method for drug-target
   interaction predictions and Alzheimer's disease drug repositions
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Drug repositioning; Knowledge graph; Deep learning; Drug-target
   interaction
ID DISCOVERY
AB Drug repositioning, which recommends approved drugs to potential targets by predicting drug-target interactions (DTIs), can save the cost and shorten the period of drug development. In this work, we propose a novel knowledge graph based deep learning method, named KG-DTI, for DTIs predictions. Specifically, a knowledge graph of 29,607 positive drug-target pairs is constructed by DistMult embedding strategy. A Conv-Conv module is proposed to extract features of drug-target pairs (DTPs), which is followed by a fully connected neural network for DTIs calculation. Data experiments are conducted on randomly chosen 11,840 positive and negative samples. It is obtained that KG-DTI achieves average ACC by 88.0%, F1-Score by 87.7%, AUROC by 94.3% and AUPR by 95% in five-fold cross-validation. In practice, KG-DTI is applied to reposition drugs to Alzheimer's disease (AD) by Apolipoprotein E target. As results, it is found that seven of the top ten recommended drugs have been used in clinic practice or with literature supported useful to AD. Ligand-target docking results show that the top one recommended drug can dock with Apolipoprotein E significantly, which gives vital hints in repositioning potential drug to AD treatment.
C1 [Wang, Shudong; Du, Zhenzhen; Song, Tao] China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China.
   [Ding, Mao] Shandong Univ, Hosp 2, Cheeloo Coll Med, Dept Neurol Med, Jinan 250033, Peoples R China.
   [Rodriguez-Paton, Alfonso; Song, Tao] Univ Politecn Madrid, Fac Comp Sci, Dept Artificial Intelligence, Campus Montegancedo, Madrid 28660, Spain.
RP Song, T (通讯作者)，China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China.; Ding, M (通讯作者)，Shandong Univ, Hosp 2, Cheeloo Coll Med, Dept Neurol Med, Jinan 250033, Peoples R China.; Song, T (通讯作者)，Univ Politecn Madrid, Fac Comp Sci, Dept Artificial Intelligence, Campus Montegancedo, Madrid 28660, Spain.
EM 18264181312@163.com; t.song@upm.es
RI Song, Tao/T-7360-2018; Rodriguez-Paton, Alfonso/H-1003-2011
OI Song, Tao/0000-0002-0130-3340; Rodriguez-Paton,
   Alfonso/0000-0001-7289-2114
FU National Natural Science Foundation of China [61873280, 61972416];
   Taishan Scholarship [tsqn201812029]; Major projects of the National
   Natural Science Foundation of China [41890851]; Natural Science
   Foundation of Shandong Province [ZR2019MF012]; Fundamental Research
   Funds for the Central Universities [19CX05003A-6]
FX This work was supported by National Natural Science Foundation of China
   (Grant Nos. 61873280, 61972416), Taishan Scholarship (tsqn201812029),
   Major projects of the National Natural Science Foundation of China
   (Grant No. 41890851), Natural Science Foundation of Shandong Province
   (No. ZR2019MF012), Fundamental Research Funds for the Central
   Universities (19CX05003A-6).
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NR 31
TC 1
Z9 1
U1 15
U2 31
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 846
EP 857
DI 10.1007/s10489-021-02454-8
EA MAY 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YG8PL
UT WOS:000650122400002
DA 2022-08-23
ER

PT J
AU Gao, J
   Liu, S
   Li, ZJ
AF Gao, Jie
   Liu, Shu
   Li, Zhijian
TI Cooperative Evolution of China's Excellent Innovative Research Groups
   from the Perspective of Innovation Ecosystem: Taking an "Environmental
   Biogeochemistry" Research Innovation Group as a Case Study
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE innovative research group; innovation ecosystem; cooperation network;
   environmental biogeochemistry; scientometrics; knowledge map;
   exploratory case study
ID POLYCYCLIC AROMATIC-HYDROCARBONS; COMPETITION; TIANJIN; BIOMASS
AB Research, understanding, and prediction of complex systems is an important starting point for human beings to tackle major problems and emergencies such as global warming and COVID-19. Research on innovation ecosystem is an important part of research on complex systems. With the rapid development of sophisticated industries, the rise of innovative countries, and the newly developed innovation theory, innovation ecosystem has become a new explanation and new paradigm for adapting to today's global innovation cooperation network and the scientific development of complex systems, which is also in line with China's concept of building an innovative country and promoting comprehensive innovation and international cooperation with scientific and technological innovation as the core. The Innovative Research Group at Peking University is the most representative scientific and technological innovation team in the frontier field of basic research in China. The characteristics of its organization mechanism and dynamic evolution connotation are consistent with the characteristics and evolution of innovation ecosystem. An excellent innovative research group is regarded as a small innovation ecosystem. We selected the "Environmental Biogeochemistry" Innovation Research Group at Peking University as a typical case in order to understand and analyze the evolution of cooperation among scientific and technological innovation teams, improve the healthy development as well as internal and external governance of this special small innovation ecosystem, promote the expansion of an innovation team cooperation network and the improvement of cooperation quality, promote the linkage supports of funding and management departments, and improve their scientific and technological governance abilities. Through scientometrics, visual analysis of knowledge maps, and an exploratory case study, we study the evolution process and development law of team cooperation. It is found that the main node authors of the cooperation network maintain strong cooperation frequency and centrality, and gradually strengthen with the expansion of the cooperation network and the evolution of time. Driven by the internal cooperative governance of the team and the external governance of the funding and management departments, this group has gradually formed a healthy, orderly, open, and cooperative special innovation ecosystem, which is conducive to the stability and sustainable development of the national innovation ecosystem and the global innovation ecosystem.
C1 [Gao, Jie] Shandong Univ, Sch Innovat & Entrepreneurship, Qingdao 266237, Peoples R China.
   [Liu, Shu] Tsinghua Univ, Inst Intelligent Soc Governance, Beijing 100084, Peoples R China.
   [Li, Zhijian] Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China.
RP Liu, S (通讯作者)，Tsinghua Univ, Inst Intelligent Soc Governance, Beijing 100084, Peoples R China.; Li, ZJ (通讯作者)，Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China.
EM littlehope@sdu.edu.cn; shuliu@tsinghua.edu.cn; lizhijian@tsinghua.edu.cn
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NR 57
TC 0
Z9 0
U1 27
U2 30
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD DEC
PY 2021
VL 18
IS 23
AR 12584
DI 10.3390/ijerph182312584
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 XV6AF
UT WOS:000735022000001
PM 34886310
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Sinclair, M
   Siemieniuch, C
   Palmer, P
AF Sinclair, Murray
   Siemieniuch, Carys
   Palmer, Paul
TI The identification of knowledge gaps in the technologies of
   cyber-physical systems with recommendations for closing these gaps
SO SYSTEMS ENGINEERING
LA English
DT Article
DE architectural design; other systems engineering enablers; systems
   science
ID UNCERTAINTY
AB The paper reports some training, education, and operational findings from an EU Horizon 2020 project that included the production of technology road-maps for the domain of cyber-physical systems (CPS). The project reviewed Deliverables from 72 CPS projects, all within Framework Programme 7 and Horizon 2020, including 18 from the ARTEMIS and ECSEL subprograms. This analysis led to the production of a "Knowledge Map" containing 75 technologies identified within the 72 projects as nodes in this map, connected by interoperability links. Filtering this map for each node, in turn, has led, in combination with other parts of the project, to some 48 recommendations for future focus and funding of developments in these technologies to assist in the rapid adoption of CPS in all domains. While the focus has been limited to European Union research and innovation, it is believed that the recommendations are transferable to other regions of the world.
C1 [Sinclair, Murray; Siemieniuch, Carys; Palmer, Paul] Loughborough Univ, Wolfson Sch, Syst Engn Grp, Loughborough LE11 3TU, Leics, England.
RP Sinclair, M (通讯作者)，Loughborough Univ, Wolfson Sch, Syst Engn Grp, Loughborough LE11 3TU, Leics, England.
EM m.a.sinclair@lboro.ac.uk
RI Palmer, Paul/X-8964-2019
OI Palmer, Paul/0000-0002-1674-3728; Siemieniuch,
   Carys/0000-0002-4835-4006; Sinclair, murray/0000-0002-3709-7307
FU H2020 Leadership in Enabling and Industrial Technologies [644164];
   Horizon 2020 project: ROAD2CPS, EU [644164]
FX H2020 Leadership in Enabling and Industrial Technologies, Grant/Award
   Number: 644164 Horizon 2020 project: ROAD2CPS, EU Grant Number: 644164
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   [No title captured]
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NR 59
TC 3
Z9 3
U1 1
U2 6
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1098-1241
EI 1520-6858
J9 SYSTEMS ENG
JI Syst. Eng.
PD JAN
PY 2019
VL 22
IS 1
BP 3
EP 19
DI 10.1002/sys.21464
PG 17
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 HG4PB
UT WOS:000454956300001
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Zhang, W
   Zhang, QP
   Yu, B
   Zhao, LM
AF Zhang, Wei
   Zhang, Qingpu
   Yu, Bo
   Zhao, Limei
TI Knowledge map of creativity research based on keywords network and
   co-word analysis, 1992-2011
SO QUALITY & QUANTITY
LA English
DT Article
DE Creativity research paradigm; Co-word analysis; Mapping knowledge
   domains
ID RESEARCH-AND-DEVELOPMENT; EMPLOYEE CREATIVITY; PRODUCT DEVELOPMENT;
   ORIENTATION; LEADERSHIP; INNOVATION; COLLABORATION; PERFORMANCE;
   MOTIVATION; STRATEGY
AB To clarify the spatial structure of creativity research can let us clearly understand the specific distribution of research paradigms, thus contributing to better discover other topics we need to study. We picked out 163 top keywords from 4,575 papers on creativity research in the Web of Science Database during the period of 1992-2011, and drawn the keywords network and it spatial structure of major research topics and paradigms by employing K-core of SNA, Co-word analysis, and MDS and the functions of software UCINET 6.0 and NetDraw. The analysis results show that there are five main topics on creativity research: practice applications of creativity, pathology and physiology of creativity, individual-level creativity, organizational-level creativity, and the basic theories and methodologies of creativity, and the regular spatial structure.
C1 [Zhang, Wei; Zhang, Qingpu] Harbin Inst Technol, HIT UMKC Inst Innovat & Entrepreneurship, Sch Management, Harbin 150080, Peoples R China.
   [Yu, Bo] Harbin Inst Technol, Sch Management, Harbin 150080, Peoples R China.
   [Zhao, Limei] Heilongjiang Univ, Sch Informat Management, Harbin 150080, Peoples R China.
RP Zhang, W (通讯作者)，Harbin Inst Technol, HIT UMKC Inst Innovat & Entrepreneurship, Sch Management, Rm 337,Bldg 2H,2 Yikuang Str, Harbin 150080, Peoples R China.
EM hbue_wb@163.com; zzqp2000@126.com; yub@hit.edu.cn; hdzhaolimei@sina.com
RI Zhang, Wei/AAA-6273-2022
OI Zhang, Wei/0000-0001-7758-9675
FU National Natural Science Foundation of China [71273076]; National Social
   Science Foundation of China [12CTQ029]; Humanities and Social Sciences
   Youth Foundation of Chinese Ministry of Education [3YJC630166]
FX This research is supported by the National Natural Science Foundation of
   China (Grants Nos. 71273076), the National Social Science Foundation of
   China (Grant No. 12CTQ029) and Humanities and Social Sciences Youth
   Foundation of Chinese Ministry of Education (Grant No. 3YJC630166).
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   [No title captured]
NR 69
TC 51
Z9 54
U1 7
U2 142
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 MAY
PY 2015
VL 49
IS 3
BP 1023
EP 1038
DI 10.1007/s11135-014-0032-9
PG 16
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 CG3VX
UT WOS:000353208800014
DA 2022-08-23
ER

PT J
AU Garrido, R
   Cabeza, LF
   Falguera, V
AF Garrido, Ricard
   Cabeza, Luisa F.
   Falguera, Victor
TI An Overview of Bioplastic Research on Its Relation to National Policies
SO SUSTAINABILITY
LA English
DT Review
DE bioplastic; climate change; bibliometric analysis; knowledge mapping;
   trends; gaps; bioeconomy
ID BIO-BASED ECONOMY; CELLULOSE-ACETATE; GLASS-TRANSITION; WATER-CONTENT;
   STARCH; COMPOSITES; PLASTICS; BIODEGRADATION; NANOCOMPOSITES;
   OPPORTUNITIES
AB There is an increasing concern about fossil energy and products derived from it. The plastic industry depends on oil, and produced plastics cannot degrade naturally. An alternative to plastics from oil is the use of bioplastics, which can be degradable. The bibliometric study of the publications related to bioplastics carried out in this paper shows that research on this topic is growing both in developed (USA, European Union, and Canada) and in developing countries (China, India, Thailand, and Malaysia), mostly following the implementation of bioeconomy standards and labels by their governments. The main authors and the collaborations they have worldwide are also presented here. The research carried out in this paper is not only devoted to technological aspects but also to economic aspects and inhabitant ratios. Trends in publications, by country and authors, are analyzed. Finally, this paper also studies the countries governmental policies and how they impact the bioplastic field.
C1 [Garrido, Ricard; Cabeza, Luisa F.] Univ Lleida, GREiA Res Grp, Pere Cabrera S-N, Lleida 25001, Spain.
   [Falguera, Victor] AKIS Int, C Dr Robert 33, Albatarrec 25171, Spain.
RP Cabeza, LF (通讯作者)，Univ Lleida, GREiA Res Grp, Pere Cabrera S-N, Lleida 25001, Spain.
EM ricard.garrido@udl.cat; luisaf.cabeza@udl.cat;
   v.falguera@akisinternational.com
RI Cabeza, Luisa F./B-4587-2013
OI Cabeza, Luisa F./0000-0001-5086-872X; Falguera,
   Victor/0000-0002-6064-2393
FU ICREA under the ICREA Academia program
FX This work is partially supported by ICREA under the ICREA Academia
   program.
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NR 117
TC 1
Z9 1
U1 15
U2 29
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUL
PY 2021
VL 13
IS 14
AR 7848
DI 10.3390/su13147848
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 TO6BK
UT WOS:000676994300001
OA Green Published
DA 2022-08-23
ER

PT J
AU Lyu, X
   Peng, WL
   Yu, W
   Xin, ZF
   Niu, SD
   Qu, Y
AF Lyu, Xiao
   Peng, Wenlong
   Yu, Wei
   Xin, Zongfei
   Niu, Shandong
   Qu, Yi
TI Sustainable intensification to coordinate agricultural efficiency and
   environmental protection: a systematic review based on metrological
   visualization
SO JOURNAL OF LAND USE SCIENCE
LA English
DT Review
DE Sustainable intensification; literature review; citespace; biblioshiny;
   visualization; knowledge map
ID GLOBAL FOOD DEMAND; ECOLOGICAL INTENSIFICATION; ECOSYSTEM SERVICES;
   LAND-USE; AGROECOLOGICAL INTENSIFICATION; POLICY; BIODIVERSITY; IMPACTS;
   AGROBIODIVERSITY; OPPORTUNITIES
AB Sustainable intensification (SI) is a necessary approach to overcome the contradictions between food demands and resource and environmental constraints. We reviewed 2,475 papers on the topic 'Sustainable Intensification,' from Web of Science (WOS), with the help of visualizations drawn by CiteSpace and Biblioshiny. The objectives are: (i) to clarify the concept and connotations of SI, and (ii) to draw conclusions on the current situation, hotspots, and trends in SI research. The results show that: (1) Although SI has become one of the most frequently and widely used terms, its precise definition needs to be defined. (2) The number of papers on the topic of SI has been increasing, especially in the last five years. (3) A group of core authors, a representative journal cluster, and a cooperative research network have all been established. (4) Arable agriculture is the main field of SI research.
C1 [Lyu, Xiao; Peng, Wenlong; Niu, Shandong] Northeastern Univ, Sch Humanities & Law, Shenyang 110169, Peoples R China.
   [Lyu, Xiao; Peng, Wenlong; Yu, Wei; Xin, Zongfei; Qu, Yi] Qufu Normal Univ, Sch Geog & Tourism, Rizhao, Peoples R China.
RP Lyu, X (通讯作者)，Northeastern Univ, Sch Humanities & Law, Shenyang 110169, Peoples R China.
EM lvxiao@mail.neu.edu.cn
OI LYU, Xiao/0000-0002-7547-6885
FU National Natural Science Foundation of China [41671176, 42071226]; Liao
   Ning Revitalization Talents Program [XLYC1807060]; Fundamental Research
   Funds for the Central Universities [N2114006]
FX This work was supported by the National Natural Science Foundation of
   China [41671176,42071226], Liao Ning Revitalization Talents Program
   [XLYC1807060] and Fundamental Research Funds for the Central
   Universities [N2114006].
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NR 98
TC 6
Z9 7
U1 27
U2 66
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1747-423X
EI 1747-4248
J9 J LAND USE SCI
JI J. Land Use Sci.
PD MAY 4
PY 2021
VL 16
IS 3
BP 313
EP 338
DI 10.1080/1747423X.2021.1922524
EA MAY 2021
PG 26
WC Agriculture, Multidisciplinary; Ecology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Agriculture; Environmental Sciences & Ecology
GA SV8BV
UT WOS:000646948300001
DA 2022-08-23
ER

PT J
AU Sloep, M
   Kalendralis, P
   Choudhury, A
   Seyben, L
   Snel, J
   George, NM
   Veening, M
   Langendijk, JA
   Dekker, A
   van Soest, J
   Fijten, R
AF Sloep, Matthijs
   Kalendralis, Petros
   Choudhury, Ananya
   Seyben, Lerau
   Snel, Jasper
   George, Nibin Moni
   Veening, Martijn
   Langendijk, Johannes A.
   Dekker, Andre
   van Soest, Johan
   Fijten, Rianne
TI A knowledge graph representation of baseline characteristics for the
   Dutch proton therapy research registry
SO CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY
LA English
DT Article
ID RADIOTHERAPY
AB Cancer registries collect multisource data and provide valuable information that can lead to unique research opportunities. In the Netherlands, a registry and model-based approach (MBA) are used for the selection of patients that are eligible for proton therapy. We collected baseline characteristics including demographic, clinical, tumour and treatment information. These data were transformed into a machine readable format using the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and resulted in a knowledge graph with baseline characteristics of proton therapy patients. With this approach, we enable the possibility of linking external data sources and optimal flexibility to easily adapt the data structure of the existing knowledge graph to the needs of the clinic.
C1 [Sloep, Matthijs; Kalendralis, Petros; Dekker, Andre; van Soest, Johan; Fijten, Rianne] Maastricht Univ, Med Ctr, GROW Sch Oncol, Dept Radiat Oncol Maastro, Maastricht, Netherlands.
   [Choudhury, Ananya; Seyben, Lerau; Snel, Jasper; George, Nibin Moni; Veening, Martijn; Langendijk, Johannes A.] Univ Groningen, Univ Med Ctr Groningen, Dept Radiat Oncol, Groningen, Netherlands.
   [Dekker, Andre; van Soest, Johan] Maastricht Univ, Fac Sci & Engn, Brightlands Inst Smart Soc BISS, Heerlen, Netherlands.
RP Fijten, R (通讯作者)，Maastricht Univ, Med Ctr, GROW Sch Oncol, Dept Radiat Oncol Maastro, Maastricht, Netherlands.
EM m.sloep@maastrichtuniversity.nl; rianne.fijten@maastro.nl
OI van Soest, Johan/0000-0003-2548-0330; Langendijk, Johannes
   A./0000-0003-1083-372X; Kalendralis, Petros/0000-0002-4471-2021
FU Dutch Cancer Society [KWF 30943405N]
FX We want to thank the Dutch Cancer Society KWF 30943405N for funding the
   development of the ProTRAIT infrastructure and subsequent proton therapy
   research repository.
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NR 15
TC 0
Z9 0
U1 0
U2 1
PU ELSEVIER IRELAND LTD
PI CLARE
PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND
EI 2405-6308
J9 CLIN TRANSL RAD ONCO
JI Clin. Transl. Radiat. Oncol.
PD NOV
PY 2021
VL 31
BP 93
EP 96
DI 10.1016/j.ctro.2021.10.001
EA OCT 2021
PG 4
WC Oncology; Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Oncology; Radiology, Nuclear Medicine & Medical Imaging
GA WK9JA
UT WOS:000710033900003
PM 34667884
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Tan, Z
   Zhao, X
   Fang, Y
   Ge, B
   Xiao, WD
AF Tan, Zhen
   Zhao, Xiang
   Fang, Yang
   Ge, Bin
   Xiao, Weidong
TI Knowledge Graph Representation via Similarity-Based Embedding
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict the missing facts. Among various solutions, almost all embedding models have high time and memory-space complexities and, hence, are difficult to apply to large-scale knowledge graphs. Some other embedding models, such as TransE and DistMult, although with lower complexity, ignore inherent features and only use correlations between different entities to represent the features of each entity. To overcome these shortcomings, we present a novel low-complexity embedding model, namely, SimE-ER, to calculate the similarity of entities in independent and associated spaces. In Sim E- ER, each entity (relation) is described as two parts. The entity (relation) features in independent space are represented by the features entity (relation) intrinsically owns and, in associated space, the entity (relation) features are expressed by the entity (relation) features they connect. And the similarity between the embeddings of the same entities in different representation spaces is high. In experiments, we evaluate our model with two typical tasks: entity prediction and relation prediction. Compared with the stateof-the-art models, our experimental results demonstrate that SimE-ER outperforms existing competitors and has low time and memory-space complexities.
C1 [Tan, Zhen; Zhao, Xiang; Fang, Yang; Ge, Bin; Xiao, Weidong] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.
RP Tan, Z; Zhao, X (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.
EM tanzhen08a@nudt.edu.cn; xiangzhao@nudt.edu.cn
RI Zhao, Xiang/B-1303-2010
OI Zhao, Xiang/0000-0001-6339-0219
FU NSFC [71690233, 71331008]
FX This work was partially supported by NSFC under Grants nos. 71690233 and
   71331008.
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NR 54
TC 1
Z9 1
U1 1
U2 11
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PY 2018
VL 2018
AR 6325635
DI 10.1155/2018/6325635
PG 12
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GP0LI
UT WOS:000440500900001
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Ma, C
   Sun, HY
   Wang, SF
   Lu, SL
   Li, JY
AF Ma Chi
   Sun Hongyan
   Wang Shaofan
   Lu Shengliang
   Li Jingyan
TI Bond Default Prediction Based on Deep Learning and Knowledge Graph
   Technology
SO IEEE ACCESS
LA English
DT Article
DE Computational modeling; Licenses; Companies; Predictive models; Neural
   networks; Knowledge representation; Databases; Default prediction; deep
   learning; DeepFM; knowledge graph; knowledge representation learning
AB The traditional financial models used in bond default mainly focus on the analysis and prediction of bonds issued by listed companies, and they lack early warning abilities for a large number of bonds of nonlisted companies. At the same time, there is a great deal of relational data and category data in bond data. It is of great significance for bond default prediction to use these data reasonably, which may bring considerable revenue to companies in the near future. Therefore, this paper uses multisource information from bonds and issuers as well as macroeconomic data to predict bond defaults based on a knowledge graph and deep learning technology. On the basis of constructing a bond knowledge graph, knowledge representation learning technology is used to vectorize the knowledge in the graph, and the extracted vectors are inputted into the deep learning model as features to forecast bond default. The applied model is the deep factorization machine model, and good prediction results are obtained.
C1 [Ma Chi; Wang Shaofan; Lu Shengliang; Li Jingyan] Huizhou Univ, Sch Comp Sci & Engn, Huizhou 516007, Peoples R China.
   [Sun Hongyan; Wang Shaofan; Lu Shengliang] Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan 114051, Peoples R China.
RP Sun, HY (通讯作者)，Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan 114051, Peoples R China.
EM ustl_linux@126.com
OI Chi, Ma/0000-0003-1660-4258; Li, Jingyan/0000-0002-2705-5015; Wang,
   Shaofan/0000-0002-3549-6793; Lu, Shengliang/0000-0003-3447-7299; Sun,
   Hongyan/0000-0003-0381-4942
FU Foundation of Guangdong Educational Committee [2018KTSCX218];
   Professorial and Doctoral Scientific Research Foundation of Huizhou
   University [2018JB020]
FX This work was supported in the part by the Foundation of Guangdong
   Educational Committee under Grant 2018KTSCX218, and in the part by the
   Professorial and Doctoral Scientific Research Foundation of Huizhou
   University under Grant 2018JB020.
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NR 24
TC 1
Z9 1
U1 27
U2 60
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 12750
EP 12761
DI 10.1109/ACCESS.2021.3052054
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PY1KK
UT WOS:000611807100001
OA gold
DA 2022-08-23
ER

PT J
AU Phan, T
   Do, P
AF Phan, Trung
   Do, Phuc
TI Building a Vietnamese question answering system based on knowledge graph
   and distributed CNN
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE QAS; Deep learning; DM-Tree; Knowledge graph; Graph embedding
AB Question answering system (QAS) can be applied everywhere such as in schools, hospitals, banks, e-commerce websites. A smart QAS that can replace people is what people expect. Therefore, there are a lot of studies to build, develop, and improve QAS. However, QAS used for low-resource languages like Vietnamese is still very limited. So, in this paper, we present a method for building Vietnamese QAS. Except for specific Vietnamese language processes, most of our solutions can also be applied to other languages. We build QAS based on knowledge graph (KG) and convolutional neural network (CNN). KG provides knowledge and deducing ability for QAS. CNN is used to classify questions in the natural language to identify the correct answer to a given question. Moreover, we also use distributed architecture to train the CNN model. On the other hands, we also propose a solution to speed up searching for answers in a large KG by partitioning and indexing KG by using the DM-Tree structure. Besides, we also present experimental results and evaluation results of our model using common metrics to prove the effectiveness of our solution.
C1 [Phan, Trung; Do, Phuc] Vietnam Natl Univ, Univ Informat Technol, Ho Chi Minh City, Vietnam.
RP Do, P (通讯作者)，Vietnam Natl Univ, Univ Informat Technol, Ho Chi Minh City, Vietnam.
EM trungphansg@gmail.com; phucdo@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.
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NR 45
TC 4
Z9 4
U1 1
U2 4
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 NOV
PY 2021
VL 33
IS 21
BP 14887
EP 14907
DI 10.1007/s00521-021-06126-z
EA JUN 2021
PG 21
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WL7FY
UT WOS:000662912100003
DA 2022-08-23
ER

PT J
AU Gao, W
   Fang, Y
   Zhang, F
   Yang, Z
AF Gao, W.
   Fang, Y.
   Zhang, F.
   Yang, Z.
TI REPRESENTATION LEARNING OF KNOWLEDGE GRAPHS USING CONVOLUTIONAL NEURAL
   NETWORKS
SO NEURAL NETWORK WORLD
LA English
DT Article
DE convolutional neural network; knowledge representation learning;
   knowledge base; attention
AB Knowledge graphs have been playing an important role in many Artificial Intelligence (AI) applications such as entity linking, question answering and so forth. However, most of previous studies focused on the symbolic representation of knowledge graphs with structural information, which cannot deal well with new entities or rare entities with little relevant knowledge. In this paper, we propose a new deep knowledge representation architecture that jointly encodes both structure and textual information. We first propose a novel neural model to encode the text descriptions of entities based on Convolutional Neural Networks (CNN). Secondly, an attention mechanism is applied to capture the valuable information from these descriptions. Then we introduce position vectors as supplementary information. Finally, a gate mechanism is designed to integrate representations of structure and text into the joint representation. Experimental results on two datasets show that our models obtain state-of-the-art results on link prediction and triplet classification tasks, and achieve the best performance on the relation classification task.
C1 [Gao, W.] Jianghan Univ, Sch Artificial Intelligence, Wuhan, Peoples R China.
   [Gao, W.] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
   [Fang, Y.] Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.
   [Zhang, F.] Wuhan Donghu Univ, Coll Comp Sci, Wuhan, Peoples R China.
   [Yang, Z.] Wuhan Sports Univ, Coll Sports Sci & Technol, Wuhan, Peoples R China.
RP Gao, W (通讯作者)，Jianghan Univ, Sch Artificial Intelligence, Wuhan, Peoples R China.; Gao, W (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
EM gaowang2000@foxmail.com; fangyuan2000@foxmail.com; whzhangfan@126.com;
   yangzhif@foxmail.com
FU Doctoral Start-up Fund of Jianghan University and Scientific Research
   Planning and Guiding Program of Hubei Provincial Department of Education
FX We would like to thank the anonymous reviewers for their valuable
   comments. This work was funded by Doctoral Start-up Fund of Jianghan
   University and Scientific Research Planning and Guiding Program of Hubei
   Provincial Department of Education.
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PU ACAD SCIENCES CZECH REPUBLIC, INST COMPUTER SCIENCE
PI 182 07 PRAGUE 8
PA POD VODARENSKOU VEZI 2, 182 07 PRAGUE 8, 00000, CZECH REPUBLIC
SN 1210-0552
J9 NEURAL NETW WORLD
JI Neural Netw. World
PY 2020
VL 30
IS 3
BP 145
EP 160
DI 10.14311/NNW.2020.30.011
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NT3MT
UT WOS:000572850100001
OA Bronze
DA 2022-08-23
ER

PT J
AU Tubaishat, A
   Zia, T
   Faiz, R
   Al Obediat, F
   Shah, B
   Windridge, D
AF Tubaishat, Abdallah
   Zia, Tehseen
   Faiz, Rehana
   Al Obediat, Feras
   Shah, Babar
   Windridge, David
TI Discriminator-based adversarial networks for knowledge graph completion
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article; Early Access
DE Knowledge graph completion; Pre-trained language model; Transformer
   model
AB Knowledge graphs (KGs) inherently lack reasoning ability which limits their effectiveness for tasks such as question-answering and query expansion. KG embedding (KGE) is a predominant approach where proximity between relations and entities in the embedding space is used for reasoning over KGs. Most existing KGE approaches use structural information of triplets and disregard contextual information which could be crucial to learning long-term relations between entities. Moreover, KGE approaches mostly use discriminative models which require both positive and negative samples to learn a decision boundary. KGs, by contrast, contain only positive samples, necessitating that negative samples are generated by replacing the head/tail of predicates with randomly chosen entities. They are thus usually irrational and easily discriminable from positive samples, which can prevent the learning of sufficiently robust classifiers. To address the shortcomings, we propose to learn contextualized KGE using pre-trained adversarial networks. We assume multi-hop relational paths(mh-RPs) as textual sequences for competitively learning discriminator-based KGE against the negative mh-RP generator. We use a pre-trained ELECTRA model and feed it with relational paths. We employ a generator to corrupt randomly chosen entities with plausible alternatives and a discriminator to predict whether an entity is corrupted or not. We perform experiments on multiple benchmark knowledge graphs, and the results show that our proposed KG-ELECTRA model outperforms BERT in link prediction.
C1 [Tubaishat, Abdallah; Al Obediat, Feras; Shah, Babar] Zayed Univ, Coll Technol Innovat, Abu Dhabi, U Arab Emirates.
   [Zia, Tehseen; Faiz, Rehana] COMSATS Univ Islamabad, Dept Comp Sci, Islamabad, Pakistan.
   [Windridge, David] Middlesex Univ, Dept Comp Sci, London, England.
RP Zia, T (通讯作者)，COMSATS Univ Islamabad, Dept Comp Sci, Islamabad, Pakistan.
EM abdallah.tubaishat@zu.ac.ae; tehseen.zia@comsats.edu.pk;
   rehanafaiz8@gmail.com; feras.al-obeidat@zu.ac.ae; babar.shah@zu.ac.ae;
   d.windridge@mdx.ac.uk
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NR 39
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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/s00521-022-07680-w
EA AUG 2022
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3Q8OU
UT WOS:000838484500004
DA 2022-08-23
ER

PT J
AU Hu, WW
   He, L
   Ma, HH
   Wang, K
   Xiao, JF
AF Hu, Weiwei
   He, Liang
   Ma, Hanhan
   Wang, Kai
   Xiao, Jingfeng
TI KGNER: Improving Chinese Named Entity Recognition by BERT Infused with
   the Knowledge Graph
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE named-entity recognition; knowledge graph; conditional random field
AB Recently, the lexicon method has been proven to be effective for named entity recognition (NER). However, most existing lexicon-based methods cannot fully utilize common-sense knowledge in the knowledge graph. For example, the word embeddings pretrained by Word2vector or Glove lack better contextual semantic information usage. Hence, how to make the best of knowledge for the NER task has become a challenging and hot research topic. We propose a knowledge graph-inspired named-entity recognition (KGNER) featuring a masking and encoding method to incorporate common sense into bidirectional encoder representations from transformers (BERT). The proposed method not only preserves the original sentence semantic information but also takes advantage of the knowledge information in a more reasonable way. Subsequently, we model the temporal dependencies by taking the conditional random field (CRF) as the backend, and improve the overall performance. Experiments on four dominant datasets demonstrate that the KGNER outperforms other lexicon-based models in terms of performance.
C1 [Hu, Weiwei; He, Liang; Ma, Hanhan] Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Peoples R China.
   [He, Liang] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China.
   [Wang, Kai; Xiao, Jingfeng] State Grid Xinjiang Elect Power Co Ltd, Urumqi 830000, Peoples R China.
RP He, L (通讯作者)，Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Peoples R China.; He, L (通讯作者)，Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China.
EM h_inspire@stu.xju.edu.cn; heliang@mail.tsinghua.edu.cn;
   mhh0113@foxmail.com; wangkai@xj.sgcc.com.cn; xiaojingfeng@xj.sgcc.com.cn
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NR 56
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD AUG
PY 2022
VL 12
IS 15
AR 7702
DI 10.3390/app12157702
PG 16
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 3R3ZI
UT WOS:000838854800001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Kang, XJ
   Yao, H
   Li, QT
   Li, XC
   Liu, C
   Dong, LJ
AF Kang, Xiaojun
   Yao, Hong
   Li, Qingtao
   Li, Xinchuan
   Liu, Chao
   Dong, Lijun
TI TDN: An Integrated Representation Learning Model of Knowledge Graphs
SO IEEE ACCESS
LA English
DT Article
DE Artificial intelligence; embedding; knowledge graph; knowledge
   representation
AB Knowledge graph (KG) is playing an important role in many artificial intelligence applications. Representation learning of KGs aims to project both entities and relations into a continuous low-dimensional space. The representation learning technique based on embedding has been used to implement the KG completion, which aims to predict potential triples (head, relation, and tail) in KG. Most current methods concentrate on learning representations based on triple information while ignoring integrating the textual knowledge and network topology of KG. This leads to ambiguous completions. To address this problem and implement more accurate KG completion, we propose a new representation learning model, TDN model, which integratedly embeds the information of triples, text descriptions, and network structure of KG in a low-dimensional vector space. The framework of TDN is defined and the methodology of implementing TDN embedding is explored. To verify the effectiveness of the proposed model, we evaluate TDN via the experiments of link prediction on the real-world datasets. The experimental results confirm the above claims and show that TDN-based embedding significantly outperforms other baselines.
C1 [Kang, Xiaojun; Yao, Hong; Li, Qingtao; Li, Xinchuan; Liu, Chao; Dong, Lijun] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Hubei, Peoples R China.
   [Kang, Xiaojun; Yao, Hong; Li, Xinchuan; Liu, Chao; Dong, Lijun] China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430074, Hubei, Peoples R China.
RP Yao, H (通讯作者)，China Univ Geosci, Sch Comp Sci, Wuhan 430074, Hubei, Peoples R China.; Yao, H (通讯作者)，China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430074, Hubei, Peoples R China.
EM yaohong@cug.edu.cn
OI , Lijun/0000-0002-1409-9473
FU National Key R&D Program of China [2018YFB1004600]; National Science and
   Technology Major Project of China [2017ZX05036-001-010]; National
   Natural Science Foundation of China (NSFC) [61672474, 61501412,
   61673354]
FX This work was supported in part by the National Key R&D Program of China
   under Grant 2018YFB1004600, in part by the National Science and
   Technology Major Project of China under Grant 2017ZX05036-001-010, and
   in part by the National Natural Science Foundation of China (NSFC) under
   Grant 61672474, Grant 61501412, and Grant 61673354.
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NR 25
TC 2
Z9 2
U1 2
U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 55199
EP 55205
DI 10.1109/ACCESS.2019.2913086
PG 7
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HX8CP
UT WOS:000467633700001
OA gold
DA 2022-08-23
ER

PT J
AU Rinaldi, AM
   Russo, C
   Tommasino, C
AF Rinaldi, Antonio M.
   Russo, Cristiano
   Tommasino, Cristian
TI A semantic approach for document classification using deep neural
   networks and multimedia knowledge graph
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Multimedia topic detection; Document classification; Semantic analysis;
   Ontologies; Big data; Deep neural networks; Knowledge graph
ID MODEL
AB The amount of available multimedia data in different formats and from different sources increases everyday. From an information retrieval point of view, this high volume and heterogeneity of data involves several issues to be addressed related to information overload and lacks of well structured information. Even if modern information retrieval systems offer to the user manifold search options, it is still hard to find systems with optimal performances in the document seeking process starting from a given topic. In recent years, several frameworks have been proposed and developed to support this task based on different models and techniques. In this paper we propose a semantic approach to document classification using both textual and visual topic detection techniques based on deep neural networks and multimedia knowledge graph. A semantic multimedia knowledge base has been exploited and several experimental results show the effectiveness of our proposed approach.
C1 [Rinaldi, Antonio M.; Russo, Cristiano; Tommasino, Cristian] Univ Napoli Federico II, Dept Elect Engn & Informat Technol, Via Claudio 21, I-80125 Naples, Italy.
RP Rinaldi, AM (通讯作者)，Univ Napoli Federico II, Dept Elect Engn & Informat Technol, Via Claudio 21, I-80125 Naples, Italy.
EM antoniomaria.rinaldi@unina.it; cristiano.russo@unina.it;
   cristian.tommasino@unina.it
OI Tommasino, Cristian/0000-0001-9763-8745
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U1 1
U2 7
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD MAY 1
PY 2021
VL 169
AR 114320
DI 10.1016/j.eswa.2020.114320
EA FEB 2021
PG 13
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 SV3FJ
UT WOS:000663708000010
DA 2022-08-23
ER

PT J
AU Di Maria, A
   Alaimo, S
   Bellomo, L
   Billeci, F
   Ferragina, P
   Ferro, A
   Pulvirenti, A
AF Di Maria, Antonio
   Alaimo, Salvatore
   Bellomo, Lorenzo
   Billeci, Fabrizio
   Ferragina, Paolo
   Ferro, Alfredo
   Pulvirenti, Alfredo
TI BioTAGME: A Comprehensive Platform for Biological Knowledge Network
   Analysis
SO FRONTIERS IN GENETICS
LA English
DT Article
DE knowledge graph; text mining; annotation tools; TAGME; wikipedia;
   DT-hybrid
ID DATABASE
AB The inference of novel knowledge and new hypotheses from the current literature analysis is crucial in making new scientific discoveries. In bio-medicine, given the enormous amount of literature and knowledge bases available, the automatic gain of knowledge concerning relationships among biological elements, in the form of semantically related terms (or entities), is rising novel research challenges and corresponding applications. In this regard, we propose BioTAGME, a system that combines an entity-annotation framework based on Wikipedia corpus (i.e., TAGME tool) with a network-based inference methodology (i.e., DT-Hybrid). This integration aims to create an extensive Knowledge Graph modeling relations among biological terms and phrases extracted from titles and abstracts of papers available in PubMed. The framework consists of a back-end and a front-end. The back-end is entirely implemented in Scala and runs on top of a Spark cluster that distributes the computing effort among several machines. The front-end is released through the Laravel framework, connected with the Neo4j graph database to store the knowledge graph.
C1 [Di Maria, Antonio; Alaimo, Salvatore; Ferro, Alfredo; Pulvirenti, Alfredo] Univ Catania, Dept Clin & Expt Med, Catania, Italy.
   [Bellomo, Lorenzo] Scuola Normale Super Pisa, Pisa, Italy.
   [Billeci, Fabrizio] Univ Catania, Dept Maths & Comp Sci, Catania, Italy.
   [Ferragina, Paolo] Univ Pisa, Dept Comp Sci, Pisa, Italy.
RP Pulvirenti, A (通讯作者)，Univ Catania, Dept Clin & Expt Med, Catania, Italy.
EM alfredo.pulvirenti@unict.it
FU University of Catania-Piano di incentivi per la ricerca; EU [871042];
   Humane AI: Toward AI Systems That Augment and Empower Humans by
   Understanding Us, our Society and the World Around Us [820437];  [CUP
   G89J18000700007]
FX AP, SA, and AF, have been partially supported by the following research
   projects: PO-FESR Sicilia 2014-2020 "DiOncoGen: Innovative diagnostics"
   (CUP G89J18000700007). AP, has been also partially supported by the
   following research project: "PROMOTE: Identificazione di nuovi
   biomarcatori per la diagnosi precoce di mesotelioma maligno pleurico in
   soggetti ex esposti a fibre asbestiformi", University of Catania-Piano
   di incentivi per la ricerca 2020-2022. PF and LB have been supported by
   the EU H2020 programmes "SoBigData++: European Integrated Infrastructure
   for Social Mining and Big Data Analytics" (INFRAIA-01-2018-2019, grant
   #871042), and by "Humane AI: Toward AI Systems That Augment and Empower
   Humans by Understanding Us, our Society and the World Around Us" (grant
   #820437).
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NR 48
TC 0
Z9 0
U1 3
U2 3
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1664-8021
J9 FRONT GENET
JI Front. Genet.
PD APR 28
PY 2022
VL 13
AR 855739
DI 10.3389/fgene.2022.855739
PG 13
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA 1G4XL
UT WOS:000795851700001
PM 35571058
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, H
   Zhang, CX
   Xiao, ZW
   Chen, M
   Lu, DQ
   Liu, SH
AF Li, Heng
   Zhang, Chunxiao
   Xiao, Ziwei
   Chen, Min
   Lu, Dequan
   Liu, Shuhui
TI A Web-based geo-simulation approach integrating knowledge graph and
   model-services
SO ENVIRONMENTAL MODELLING & SOFTWARE
LA English
DT Article
DE Web-based geographic simulation; Geographic simulation knowledge;
   Knowledge graph; Model-services; Storm water management model(SWMM)
ID VIRTUAL GEOGRAPHIC ENVIRONMENT; FRAMEWORK; SYSTEM; CONSTRUCTION; DESIGN;
   CLIENT; TOOLS
AB Geographic models are becoming key methods for geographic environment research. In addition to the progress in model sharing and reusing, geographic simulation (geo-simulation) knowledge sharing and application are increasingly attracting much attention. This study aims to provide both models and geo-simulation knowledge simultaneously to support Web-based simulation. Firstly, we propose a geo-simulation approach consisting of three modules concerning data, model and knowledge with a conceptual framework. Secondly, taking the Storm Water Management Model as instance, a Web-based prototype system is implemented with geo-simulation knowledge graph constructed based on literatures using bilateral long shortterm memory - conditional random field (BiLSTM - CRF). Thirdly, application of the system in Nanchong, China is conducted with user experiences analysis. The results demonstrate that the proposed approach can effectively provide geo-simulation knowledge. Moreover, it can alleviate the difficulty of the geographic simulation process for diverse modelers, and further improve the use of the model simultaneously.
C1 [Li, Heng; Zhang, Chunxiao; Xiao, Ziwei] China Univ Geosci Beijing, Sch Informat Engn, Rm 216, 3 Teaching Bldg,29 Xueyuan Rd,Haidian Dis, Beijing 100083, Peoples R China.
   [Zhang, Chunxiao] Observat & Res Stn Beijing Fangshan Comprehens, Minist Nat Resources, Beijing 100083, Peoples R China.
   [Chen, Min] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China.
   [Lu, Dequan; Liu, Shuhui] Nanchong Meteorol Bur, Nanchong 637000, Peoples R China.
RP Zhang, CX (通讯作者)，China Univ Geosci Beijing, Sch Informat Engn, Rm 216, 3 Teaching Bldg,29 Xueyuan Rd,Haidian Dis, Beijing 100083, Peoples R China.
EM zcx@cugb.edu.cn
RI Chen, Min/A-8806-2016
OI Chen, Min/0000-0001-8922-8789; Xiao, Ziwei/0000-0003-3409-9363
FU Fundamental Research Funds for the Central Universities [2652018082];
   National Natural Sci-ence Foundation of China [72033005]
FX This work was supported by the Fundamental Research Funds for the
   Central Universities (No. 2652018082) , and the National Natural
   Sci-ence Foundation of China (No. 72033005) . The data support from
   Nanchong Meteorologic Bureau are gratefully acknowledged. Given their
   roles as Environmental Modelling & Software Editor, Min Chen was not
   involved in the peer-review of this article and has no ac-cess to
   information regarding its peer-review. Full responsibility for the
   editorial process for this article was delegated to journal Editor, Dr.
   Daniel P. Ames.
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NR 45
TC 2
Z9 2
U1 7
U2 20
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1364-8152
EI 1873-6726
J9 ENVIRON MODELL SOFTW
JI Environ. Modell. Softw.
PD OCT
PY 2021
VL 144
AR 105160
DI 10.1016/j.envsoft.2021.105160
EA AUG 2021
PG 15
WC Computer Science, Interdisciplinary Applications; Engineering,
   Environmental; Environmental Sciences; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Environmental Sciences & Ecology; Water
   Resources
GA UR5TL
UT WOS:000696811600004
DA 2022-08-23
ER

PT J
AU Zhang, YX
   Li, BH
   Gao, H
   Ji, Y
   Yang, H
   Wang, M
   Chen, WT
AF Zhang, Yuxin
   Li, Bohan
   Gao, Han
   Ji, Ye
   Yang, Han
   Wang, Meng
   Chen, Weitong
TI Fine-Grained Evaluation of Knowledge Graph Embedding Model in Knowledge
   Enhancement Downstream Tasks
SO BIG DATA RESEARCH
LA English
DT Article
DE Knowledge graph; Embedding model; Evaluation
AB Knowledge graph (KG) embedding models are proposed to encode entities and relations into a low dimensional vector space, in turn, can support various machine learning models on KG completion with good performance and robustness. However, the current entity ranking protocol about KG completion cannot adequately evaluate the impacts of KG embedding models in real-world applications. However, KG embeddings are not widely used as word embeddings. An asserted powerful KG embedding model may not be effective in downstream tasks. So in this paper, we commit to finding the answers by using downstream tasks instead of entity ranking protocol to evaluate the effectiveness of KG embeddings. Specifically, we conduct comprehensive experiments on different KG embedding models in KG based question answering, recommendation and natural language processing tasks. Utilizing different genre of downstream tasks, we try to mine the characteristics of each KGE model in actual application scenarios, and provide guidance for the research of KGE models and knowledge-enhanced downstream tasks. (C) 2021 Elsevier Inc. All rights reserved.
C1 [Zhang, Yuxin; Li, Bohan; Gao, Han; Ji, Ye] Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China.
   [Yang, Han] Peking Univ, Beijing, Peoples R China.
   [Wang, Meng] Southeast Univ, Nanjing, Peoples R China.
   [Chen, Weitong] Univ Queensland, Brisbane, Qld, Australia.
   [Li, Bohan] Lab Safety Crit Software, Nanjing, Jiangsu, Peoples R China.
   [Li, Bohan] Collaborat Innovat Ctr Novel Software Technol & I, Minist Ind & Informat Technol, Nanjing, Jiangsu, Peoples R China.
RP Wang, M (通讯作者)，Southeast Univ, Nanjing, Peoples R China.
EM meng.wang@seu.edu.cn
OI Chen, Tony Weitong/0000-0003-1001-7925; Yuxin, Zhang/0000-0001-6610-306X
FU National Natural Science Foundation of China [61772268, 61906037];
   Fundamental Research Funds for the Central Universities [NT2018028,
   NS201-8057]; China Degree and Graduate Education Fund, Guangzhou Science
   and Technology Program [202002030246]
FX We are very grateful to Professor Xue Li of the Neusoft for his help in
   this article. This work is supported in part by the Na-tional Natural
   Science Foundation of China (grants No. 61772268 and 61906037) ; the
   Fundamental Research Funds for the Central Universities (NT2018028,
   NS201-8057) ; China Degree and Gradu-ate Education Fund, Guangzhou
   Science and Technology Program (202002030246) .
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NR 27
TC 1
Z9 1
U1 5
U2 7
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-5796
J9 BIG DATA RES
JI Big Data Res.
PD JUL 15
PY 2021
VL 25
AR 100218
DI 10.1016/j.bdr.2021.100218
EA MAR 2021
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UH0YA
UT WOS:000689665100015
DA 2022-08-23
ER

PT J
AU Dong, W
   Anwar, MA
   Nawaz, MA
   Zhou, RT
   Asmi, F
   Sajjad, A
   Kazmi, SM
   Hammad, A
AF Dong, Wang
   Anwar, Muhammad Azfar
   Nawaz, Muhammad Asim
   Zhou Rongting
   Asmi, Fahad
   Sajjad, Aqsa
   Kazmi, Syed Mazahir
   Hammad, Ali
TI Knowledge mapping of GMO/GMF research in social sphere
SO COGENT FOOD & AGRICULTURE
LA English
DT Article
DE genetically modified food; genetically modified organisms; bibliometric;
   biotechnology
ID GENETICALLY-MODIFIED FOOD; WILLINGNESS-TO-PAY; BIBLIOMETRIC ANALYSIS;
   CONSUMER ACCEPTANCE; MORAL RESPONSIBILITY; MODIFIED ORGANISMS; AUTHOR
   COCITATION; UNITED-KINGDOM; GM FOODS; ATTITUDES
AB The document examined the intellectual development, evolution, and progress in the field of "Genetically Modified Food" (GMF) and "Genetically Modified Organisms" (GMO) by adopting the approach of bibliometric analysis. The total of 1,045 academic articles, 288 researchers, 204 journals and 15,744 references are covered during the current study. In term of identifying, defining, and bringing insight about the intellectual bases, research fronts, and the knowledge domain of GMO/GMF, the theoretical grounds are reviewed and future patterns are predicted. The current document studied articles, authors, keywords, institutions, countries, and journals who are actively participating in the knowledge area of GMO/GMF related studies under the sphere of WoS's Social Science Citation Index. Moreover, the current study identified cluster-level evolution of the knowledge domain of GMO/GMF. The study concluded by arguing about the urge to address hotspots, which support the emerging trends as the future directional work in the research area.
C1 [Dong, Wang; Zhou Rongting; Asmi, Fahad] Univ Sci & Technol China, Dept Sci & Technol Commun & Policy, Hefei, Anhui, Peoples R China.
   [Anwar, Muhammad Azfar; Kazmi, Syed Mazahir; Hammad, Ali] Univ Sci & Technol China, Sch Publ Affairs, Hefei, Anhui, Peoples R China.
   [Anwar, Muhammad Azfar; Sajjad, Aqsa] COMSATS Univ Islamabad, Dept Management Sci, Vehari Campus, Vehari, Pakistan.
   [Nawaz, Muhammad Asim] Univ Sci & Technol China, Sch Management, Hefei, Anhui, Peoples R China.
   [Sajjad, Aqsa] Univ Sci & Technol China, Dept Hist Sci & Technol, Hefei, Anhui, Peoples R China.
RP Zhou, RT (通讯作者)，Univ Sci & Technol China, Sci & Technol Commun & Policy, Hefei, Anhui, Peoples R China.
EM wang0817@mail.ustc.edu.cn; azfar@mail.ustc.edu.cn; rongting@ustc.edu.cn;
   fahadasmi@yahoo.com
RI dong, wang/T-9093-2019; kahlon, asim/AAD-1352-2020; Asmi,
   Fahad/B-3024-2018
OI Asmi, Fahad/0000-0002-3260-4277
FU National Science and Technology Major Project of the Ministry of Science
   and Technology of China [2016ZX08015002001]
FX This research work was supported by the National Science and Technology
   Major Project of the Ministry of Science and Technology of China -
   [Grand ID: 2016ZX08015002001]
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NR 94
TC 1
Z9 1
U1 6
U2 21
PU TAYLOR & FRANCIS AS
PI OSLO
PA KARL JOHANS GATE 5, NO-0154 OSLO, NORWAY
SN 2331-1932
J9 COGENT FOOD AGR
JI Cogent Food Agr.
PD APR 9
PY 2019
VL 5
IS 1
AR 1602103
DI 10.1080/23311932.2019.1602103
PG 25
WC Agriculture, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture
GA HX9UG
UT WOS:000467753400001
OA gold
DA 2022-08-23
ER

PT J
AU Li, DY
   Han, JW
   Shi, XM
   Chan, MC
AF Li, DY
   Han, JW
   Shi, XM
   Chan, MC
TI Knowledge representation and discovery based on linguistic atoms
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article; Proceedings Paper
CT 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining
CY FEB 22-23, 1997
CL SINGAPORE, SINGAPORE
DE qualitative representation; linguistic atom; compatibility cloud
ID FUZZY
AB An important issue in knowledge discovery in databases (KDD) is to allow the discovered knowledge to be as close as possible to natural languages to satisfy user needs with tractability on one hand, and to offer KDD systems robustness on the other. At this junction. this paper describes a new concept of linguistic atoms with three digital characteristics: expected value Ex entropy En, and deviation D. The mathematical description has effectively integrated the fuzziness and randomness of linguistic terms in a unified way. Based on this model, a method of knowledge representation in KDD is developed which bridges the gap between quantitative and qualitative knowledge. Mapping between quantities and qualities becomes much easier and interchangeable. In older to discover generalized knowledge from a database, we may use virtual linguistic terms and cloud transforms for the auto-generation of concept hierarchies to attributes. Predictive data mining with the cloud model is given for implementation. This further illustrates the advantages of this linguistic model in KDD. (C) 1998 Elsevier Science B.V. All rights reserved.
C1 Simon Fraser Univ, Sch Comp Sci, Burnaby, BC V5A 1S6, Canada.
   Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong.
RP Li, DY (通讯作者)，Simon Fraser Univ, Sch Comp Sci, Burnaby, BC V5A 1S6, Canada.
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NR 15
TC 82
Z9 123
U1 0
U2 11
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 MAY
PY 1998
VL 10
IS 7
BP 431
EP 440
DI 10.1016/S0950-7051(98)00038-0
PG 10
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZR154
UT WOS:000073942800005
DA 2022-08-23
ER

PT J
AU Xu, T
   Wang, ZH
   Zhang, XQ
AF Xu, Tao
   Wang, Zhi-hong
   Zhang, Xian-qi
TI Research on Intelligent Campus and Visual Teaching System Based on
   Internet of Things
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID SMART CAMPUS; ARCHITECTURE; MODEL; TECHNOLOGIES; DESIGN
AB The rapid development of Internet of things technology provides robust conditions for building a perfect intelligent campus. A visual teaching question answering system is essential for creating a smart campus, significantly improving education quality. However, the accuracy of the existing teaching question answering system is not high. To solve this problem, this paper proposes a visual teaching system based on a knowledge map. The system mainly includes two parts: problem processing and answer search. In the part of problem processing, combined with the pretraining language model, a new model framework is constructed to deal with the problem of entity reference recognition, entity link, and relationship extraction. By setting three kinds of classification labels, the problem is divided into simple, chain, and multientity problems. Different solutions are given to the above three classification problems in the answer search part. The experimental results show that the answer accuracy of this system is higher than other comparison methods.
C1 [Xu, Tao; Wang, Zhi-hong; Zhang, Xian-qi] Hebei Vocat Univ Technol & Engn, Xing Tai 054000, Hebei, Peoples R China.
RP Xu, T (通讯作者)，Hebei Vocat Univ Technol & Engn, Xing Tai 054000, Hebei, Peoples R China.
EM xutaoflyice@163.com
FU Research on the Construction of Civil Construction Specialty Group in
   Higher Vocational Colleges Based on Intelligent Construction [SZ2021034]
FX AcknowledgmentsThis work was funded by Research on the Construction of
   Civil Construction Specialty Group in Higher Vocational Colleges Based
   on Intelligent Construction (no. SZ2021034).
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NR 26
TC 1
Z9 1
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 APR 28
PY 2022
VL 2022
AR 4845978
DI 10.1155/2022/4845978
PG 10
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 1G2PZ
UT WOS:000795696400022
OA gold
DA 2022-08-23
ER

PT J
AU Karimi, F
   Khalilpour, R
AF Karimi, Faezeh
   Khalilpour, Rajab
TI Evolution of carbon capture and storage research: Trends of
   international collaborations and knowledge maps
SO INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL
LA English
DT Article
DE Carbon capture and storage; Carbon capture and sequestration; Climate
   change mitigation; Social network analysis
ID SCIENTIFIC COLLABORATION; NETWORK; COUNTRY; ENERGY; CO2
AB This study investigates the evolutionary trends of the international collaborations among the research community of carbon capture and storage (CCS) by looking at the collaboration network of countries publishing on CCS. The study also examines the evolution of major research topics in this field by exploring the co-occurrence network of keywords associated with CCS publications. The analysis is carried out based on the CCS related publications extracted from Scopus.com from 1980 to 2013. From the number of annual CCS publications, it seems that CCS research has closely followed the trend of international negotiations on climate change mitigation. The study elaborates how both international collaboration network and knowledge structure of the field have notably developed and interlinked over the years especially after 2005 during which almost 94% of the publications appeared. (C) 2015 Elsevier Ltd. All rights reserved.
C1 [Karimi, Faezeh] Univ Sydney, Complex Syst Res Grp, Project Management Program, Fac Engn & IT, Sydney, NSW 2006, Australia.
   [Khalilpour, Rajab] Univ Sydney, Sch Chem & Biomulecular Engn, Sydney, NSW 2006, Australia.
RP Karimi, F (通讯作者)，Univ Sydney, Complex Syst Res Grp, Project Management Program, Fac Engn & IT, Sydney, NSW 2006, Australia.
EM faezeh.karimi@sydney.edu.au; rajab.khalilpour@sydney.edu.au
RI Khalilpour, Kaveh Rajab/L-3858-2017
OI Khalilpour, Kaveh Rajab/0000-0003-4104-2488; Karimi,
   Faezeh/0000-0002-3343-4054
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NR 37
TC 30
Z9 31
U1 1
U2 42
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1750-5836
EI 1878-0148
J9 INT J GREENH GAS CON
JI Int. J. Greenh. Gas Control
PD JUN
PY 2015
VL 37
BP 362
EP 376
DI 10.1016/j.ijggc.2015.04.002
PG 15
WC Green & Sustainable Science & Technology; Energy & Fuels; Engineering,
   Environmental; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Energy & Fuels; Engineering
GA CL7IU
UT WOS:000357146500036
DA 2022-08-23
ER

PT J
AU Zhou, X
   Hui, B
   Zhang, LZ
   Ji, KX
AF Zhou, Xue
   Hui, Bei
   Zhang, Lizong
   Ji, Kexi
TI A structure distinguishable graph attention network for knowledge base
   completion
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Knowledge base completion; Smoothing problem; Graph attention network;
   Neighborhood aggregation scheme
AB A knowledge graph is a collection of triples, often represented in the form of "subject," "relation," "object." The task of knowledge graph completion (KGC) is to automatically predict missing links by reasoning over the information already present in the knowledge graph. Recent popularization of graph neural networks has also been spread to KGC. Typical techniques like SACN achieve dramatic achievements and beat previous state-of-the-art. However, those models still lack the ability to distinguish different local structures within a graph, which leads to the over smoothing problem. In this work, we propose SD-GAT, a graph attention network with a structure-distinguishable neighborhood aggregation scheme, which models the injective function to aggregate information from the neighborhood. The model is constituted of two modules. The encoder is a graph attention network that improved with our neighborhood aggregation scheme, which could be applied for a more distinct representation of entities and relations. The decoder is a convolutional neural network using 3 x 3 convolution filters. Our empirical research provides an effective solution to increase the discriminative power of graph attention networks, and we show significant improvement of the proposed SD-GAT compared to the state-of-the-art methods on standard FB15K-237 and WN18RR datasets.
C1 [Zhang, Lizong] 2006,Xiyuan Ave,West Hitech Zone, Chengdu 611731, Sichuan, Peoples R China.
   [Zhou, Xue; Ji, Kexi] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 611731, Peoples R China.
   [Hui, Bei; Zhang, Lizong] Univ Elect Sci & Technol China, Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 611731, Peoples R China.
RP Zhang, LZ (通讯作者)，2006,Xiyuan Ave,West Hitech Zone, Chengdu 611731, Sichuan, Peoples R China.; 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; 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 41
TC 1
Z9 1
U1 8
U2 20
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 2021
VL 33
IS 23
BP 16005
EP 16017
DI 10.1007/s00521-021-06221-1
EA JUN 2021
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WR3OP
UT WOS:000664435200003
DA 2022-08-23
ER

PT J
AU Niu, L
   Fu, CP
   Yang, Q
   Li, ZX
   Chen, ZG
   Liu, QS
   Zheng, K
AF Niu, Lei
   Fu, Chenpeng
   Yang, Qiang
   Li, Zhixu
   Chen, Zhigang
   Liu, Qingsheng
   Zheng, Kai
TI Open-world knowledge graph completion with multiple interaction
   attention
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph completion; Attention; Open-world
AB Knowledge Graph Completion (KGC) aims at complementing missing relationships between entities in a Knowledge Graph (KG). While closed-world KGC approaches utilizing the knowledge within KG could only complement very limited number of missing relations, more and more approaches tend to get knowledge from open-world resources such as online encyclopedias and newswire corpus. For instance, a recent proposed open-world KGC model called ConMask learns embeddings of the entity's name and parts of its text-description to connect unseen entities to the KGs. However, this model does not make full use of the rich feature information in the text descriptions, besides, the proposed relationship-dependent content masking method may easily miss to find the target-words. In this paper, we propose to use a Multiple Interaction Attention (MIA) mechanism to model the interactions between the head entity description, head entity name, the relationship name, and the candidate tail entity descriptions, to form the enriched representations. In addition, we try to use the additional textual features of head entity descriptions to enhance the head entity representation and apply the attention mechanism between candidate tail entities to enhance the representation of them. Besides, we try different scoring functions to increase the convergence of the model. Our empirical study conducted on three real-world data collections shows that our approach achieves significant improvements comparing to state-of-the-art KGC methods.
C1 [Niu, Lei; Fu, Chenpeng; Li, Zhixu] Soochow Univ, Sch Comp Sci & Technol, Inst Artificial Intelligence, Suzhou, Peoples R China.
   [Yang, Qiang] King Abdullah Univ Sci & Technol, Jeddah, Saudi Arabia.
   [Li, Zhixu] iFLYTEK Res, Suzhou, Peoples R China.
   [Chen, Zhigang] iFLYTEK, State Key Lab Cognit Intelligence, Suzhou, Peoples R China.
   [Liu, Qingsheng] Anhui Toycloud Technol, Hefei, Peoples R China.
   [Zheng, Kai] Univ Elect Sci & Technol, Sch Comp Sci & Engn, Chengdu, Peoples R China.
   [Zheng, Kai] Shandong Univ Technol, Sch Comp Sci & Technol, Zibo, Shandong, Peoples R China.
RP Zheng, K (通讯作者)，Univ Elect Sci & Technol, Sch Comp Sci & Engn, Chengdu, Peoples R China.; Zheng, K (通讯作者)，Shandong Univ Technol, Sch Comp Sci & Technol, Zibo, Shandong, Peoples R China.
EM lniu@stu.suda.edu.cn; cpfu@stu.suda.edu.cn; qiang.yang@kaustedu.sa;
   zhixuli@suda.edu.cn; zgchen@iflytek.com; qsliu@iflytek.com;
   zhengkai@uestc.edu.cn
OI li, zhixu/0000-0003-2355-288X
FU National Key R&D Program of China [2018AAA0101900]; Priority Academic
   Program Development of Jiangsu Higher Education Institutions; National
   Natural Science Foundation of China [62072323, 61632016, 61836007,
   61972069, 61832017, 61532018]; Natural Science Foundation of Jiangsu
   Province [BK20191420]; Natural Science Research Project of Jiangsu
   Higher Education Institution [17KJA520003]; Suda-Toycloud Data
   Intelligence Joint Laboratory
FX This research is partially supported by National Key R&D Program of
   China (No. 2018AAA0101900), the Priority Academic Program Development of
   Jiangsu Higher Education Institutions, National Natural Science
   Foundation of China (Grant No. 62072323, 61632016, 61836007, 61972069,
   61832017, 61532018), Natural Science Foundation of Jiangsu Province (No.
   BK20191420), Natural Science Research Project of Jiangsu Higher
   Education Institution (No. 17KJA520003), and the Suda-Toycloud Data
   Intelligence Joint Laboratory.
CR Balaevi I., 2019, ARXIV190109590
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NR 43
TC 2
Z9 2
U1 3
U2 7
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 2021
VL 24
IS 1
SI SI
BP 419
EP 439
DI 10.1007/s11280-020-00847-2
EA OCT 2020
PG 21
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PX7PL
UT WOS:000584891200001
DA 2022-08-23
ER

PT J
AU Shachnev, D
   Karpenko, D
AF Shachnev, Dmitry
   Karpenko, Dmitry
TI Using Subject Area Ontology for Automating Processes in Sphere of
   Scientific Investigation and Education
SO PROGRAMMING AND COMPUTER SOFTWARE
LA English
DT Article
DE scientometrics; ontology; subject area; thesaurus; taxonomy; learning
   process; tests generation; knowledge map
AB This paper presents methods of building ontologies, which are used in "ISTINA" current research information system (the name is the abbreviation for Russian equivalent of "Intellectual System for Thematic Investigation of Scientometrical Data"), used in Moscow State University and a number of other institutions. These methods include thematic analysis of scientific results present in the system; information search; context analysis of publications; a number of applications in the learning process, in particular, automatic generation of test suites and forming students' individual learning trajectories. In the framework of the suggested approach, ontology elements (concepts and relations) are added by users of the system, which work in different branches of science, using the web editor to be described in the paper. These elements are stored in a relational database. The editor works on the client side and communicates with the server using the SPARQL query language and specially developed protocol for modifying the ontology. The concepts and relations between them may be verified by experts in the corresponding areas.
C1 [Shachnev, Dmitry] Lomonosov Moscow State Univ, Dept Mech & Math, Moscow, Russia.
   [Karpenko, Dmitry] Lomonosov Moscow State Univ, Inst Complex Syst Math Res, Moscow, Russia.
RP Shachnev, D (通讯作者)，Lomonosov Moscow State Univ, Dept Mech & Math, Moscow, Russia.
EM mitya57@mitya57.me; dskarpenko@gmail.com
OI Shachnev, Dmitry/0000-0002-5940-9180
CR [Афонин С.А. Afonin S.A.], 2016, [Программная инженерия, Programmnaya inzheneriya], V7, P408, DOI 10.17587/prin.7.408-413
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   Sadovnichiy V., 2014, INTELLECTUAL SYSTEM
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   Vasenin V., 2013, INFORM SOC, V1-2, P21
   Zagorulko Y, 2015, INT C INTELLIGENT SO, P227
NR 8
TC 1
Z9 1
U1 0
U2 4
PU MAIK NAUKA/INTERPERIODICA/SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013-1578 USA
SN 0361-7688
EI 1608-3261
J9 PROGRAM COMPUT SOFT+
JI Program. Comput. Softw.
PD JAN
PY 2018
VL 44
IS 1
BP 15
EP 22
DI 10.1134/S0361768818010061
PG 8
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FY8TW
UT WOS:000427138800002
DA 2022-08-23
ER

PT J
AU Tsay, MY
   Shen, TM
   Liang, MH
AF Tsay, Ming-yueh
   Shen, Tung-mei
   Liang, Ming-hsin
TI A comparison of citation distributions of journals and books on the
   topic "information society"
SO SCIENTOMETRICS
LA English
DT Article
DE Information society; Knowledge map; Citation analysis; Co-citation
   analysis; Social network analysis
ID COCITATION ANALYSIS; AUTHORS
AB The purpose of this study is to explore and compare citations and discipline distribution in journal articles and books in the field of information society. By investigating citations, co-citation analysis and social network analysis, this study highlights the major disciplines in the information society field, identifies the highly-cited works and the relationships among them, and analyzes the multidisciplinary nature of the field. A total of 84 selective documents related to the study of information society were collected. The Web of Science, including Science Citation Index Expanded, Social Sciences Citation Index, Arts and Humanities Citation Index, and Book Citation Index, was selected to search for citation and co-citation data from 2005 to 2012. A co-citation matrix was built and subject clusters were determined. Moreover, co-citation data acquired from a social network analysis tool, UCINET, were put through centrality analysis to explore the influence of each document in the field of information society. Conclusions were made based on research results.
C1 [Tsay, Ming-yueh; Shen, Tung-mei; Liang, Ming-hsin] Natl Chengchi Univ, Grad Inst Lib Informat & Archival Studies, 64,Sec 2,Zhinan Rd, Taipei 116, Taiwan.
RP Tsay, MY (通讯作者)，Natl Chengchi Univ, Grad Inst Lib Informat & Archival Studies, 64,Sec 2,Zhinan Rd, Taipei 116, Taiwan.
EM mytsay@nccu.edu.tw; tabunmay@gmail.com; gary.liang@igrouptaiwan.com
RI Liang, Gary/AAT-4535-2020
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NR 23
TC 7
Z9 8
U1 4
U2 102
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD FEB
PY 2016
VL 106
IS 2
BP 475
EP 508
DI 10.1007/s11192-015-1791-3
PG 34
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 DC1ZP
UT WOS:000369017300001
DA 2022-08-23
ER

PT J
AU Blennow, K
   Persson, J
   Wallin, A
   Vareman, N
   Persson, E
AF Blennow, Kristina
   Persson, Johannes
   Wallin, Annika
   Vareman, Niklas
   Persson, Erik
TI Understanding risk in forest ecosystem services: implications for
   effective risk management, communication and planning
SO FORESTRY
LA English
DT Review
ID PUBLIC PREFERENCES; DECISION-MAKING; CLIMATE-CHANGE; ADVICE; JUDGMENT;
   INFORMATION; HEURISTICS; FREQUENCY; BENEFIT; POLICY
AB Uncertainty, insufficient information or information of poor quality, limited cognitive capacity and time, along with value conflicts and ethical considerations, are all aspects that make risk management and risk communication difficult. This paper provides a review of different risk concepts and describes how these influence risk management, communication and planning in relation to forest ecosystem services. Based on the review and results of empirical studies, we suggest that personal assessment of risk is decisive in the management of forest ecosystem services. The results are used together with a review of different principles of the distribution of risk to propose an approach to risk communication that is effective as well as ethically sound. Knowledge of heuristics and mutual information on both beliefs and desires are important in the proposed risk communication approach. Such knowledge provides an opportunity for relevant information exchange, so that gaps in personal knowledge maps can be filled in and effective risk communication can be promoted.
C1 [Blennow, Kristina; Persson, Erik] Swedish Univ Agr Sci SLU, Dept Landscape Architecture Planning & Management, SE-23053 Alnarp, Sweden.
   [Persson, Johannes; Vareman, Niklas] Lund Univ, Dept Philosophy, SE-22222 Lund, Sweden.
   [Wallin, Annika] Lund Univ, Cognit Sci Dept Philosophy, SE-22222 Lund, Sweden.
   [Wallin, Annika] Swedish Coll Adv Study, SE-75238 Uppsala, Sweden.
RP Blennow, K (通讯作者)，Swedish Univ Agr Sci SLU, Dept Landscape Architecture Planning & Management, SE-23053 Alnarp, Sweden.
EM kristina.blennow@slu.se
RI Blennow, Kristina/D-7388-2016; Persson, Erik/J-8155-2019
OI Blennow, Kristina/0000-0002-7602-5322; Persson,
   Erik/0000-0002-5000-948X; Persson, Johannes/0000-0003-4568-1850
FU EU project MOTIVE [226544]; Linnaeus programme LUCID ('Lund University
   Centre of Excellence for Integration of Social and Natural Dimensions of
   Sustainability', FORMAS]; foundation for strategic environmental
   research programme Mistra-SWECIA
FX This work was supported by the EU project MOTIVE ('Models for adaptive
   forest management', http://motive-project.net/) [grant number 226544 to
   K. B. and E. P.]; the Linnaeus programme LUCID ('Lund University Centre
   of Excellence for Integration of Social and Natural Dimensions of
   Sustainability' (http://www.lucid.lu.se/), FORMAS, 2008-2018) [to J.P.,
   A. W. and N.V.] and the foundation for strategic environmental research
   programme Mistra-SWECIA (www.mistra-swecia.se/) [to K.B.].
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NR 81
TC 38
Z9 39
U1 2
U2 42
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0015-752X
EI 1464-3626
J9 FORESTRY
JI Forestry
PD APR
PY 2014
VL 87
IS 2
SI SI
BP 219
EP 228
DI 10.1093/forestry/cpt032
PG 10
WC Forestry
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Forestry
GA AH1WO
UT WOS:000335912700004
OA hybrid, Green Published, Green Accepted
DA 2022-08-23
ER

PT J
AU Yang, S
   Yoo, S
   Jeong, O
AF Yang, SungMin
   Yoo, SoYeop
   Jeong, OkRan
TI DeNERT-KG: Named Entity and Relation Extraction Model Using DQN,
   Knowledge Graph, and BERT
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE named entity recognition; relation extraction; DQN; BERT; knowledge
   graph
AB Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people's language, in other words, natural language. For computers to learn on their own, the skill of understanding natural language is very important. There are a wide variety of tasks involved in the field of natural language processing, but we would like to focus on the named entity registration and relation extraction task, which is considered to be the most important in understanding sentences. We propose DeNERT-KG, a model that can extract subject, object, and relationships, to grasp the meaning inherent in a sentence. Based on the BERT language model and Deep Q-Network, the named entity recognition (NER) model for extracting subject and object is established, and a knowledge graph is applied for relation extraction. Using the DeNERT-KG model, it is possible to extract the subject, type of subject, object, type of object, and relationship from a sentence, and verify this model through experiments.
C1 [Yang, SungMin; Yoo, SoYeop; Jeong, OkRan] Gachon Univ, Dept Software, Seongnam Si 13120, South Korea.
RP Jeong, O (通讯作者)，Gachon Univ, Dept Software, Seongnam Si 13120, South Korea.
EM ysm0622@gc.gachon.ac.kr; bbusso@gc.gachon.ac.kr; 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 [NRF2019R1A2C1008412,
   2015-0-00932]
FX This research was funded 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 and Communications
   Technology Promotion) (Nos. NRF2019R1A2C1008412, 2015-0-00932).
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NR 41
TC 8
Z9 8
U1 7
U2 22
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 6429
DI 10.3390/app10186429
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 OF7PO
UT WOS:000581394800001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhu, ZY
   Xie, HM
AF Zhu, Zhuoyue
   Xie, Hongming
TI What Do We Know and What Do We Need to Know about COVID-19's
   Implications on Business Economics? From Bibliometric Analysis to a
   Conceptual Framework
SO SUSTAINABILITY
LA English
DT Article
DE COVID-19; business economics; bibliometric analysis; framework
ID INNOVATION; INSIGHTS; PRICES
AB This paper presents a bibliometric analysis of COVID-19-related research in business economics. The current status of research on economic management in COVID-19 is shown through descriptive statistics. The corresponding knowledge maps are obtained based on keyword clustering analysis, and research topics of interest to Chinese and foreign readers are identified. This paper finds that the impact of COVID-19 on business economics is mainly manifested in six major themes, namely COVID-19 and crisis management, COVID-19 and supply chain, COVID-19 and digitalization, COVID-19 and economic development, COVID-19 and organizational management, and COVID-19 and sustainable development. Based on these research foundations, this paper proposes a research framework for economic management under the influence of COVID-19. It describes the current research status, research directions, and future topics of six key research themes from macro, meso, and micro perspectives, to provide a knowledge base for research and practice in the field of economic management in the post-pandemic era.
C1 [Zhu, Zhuoyue] Zhejiang Univ Technol, Sch Management, Hangzhou 310023, Peoples R China.
   [Xie, Hongming] Guangzhou Univ, Sch Management, Guangzhou 510006, Peoples R China.
RP Xie, HM (通讯作者)，Guangzhou Univ, Sch Management, Guangzhou 510006, Peoples R China.
EM 1111904027@zjut.edu.cn; xiehm_gzhu@126.com
RI XIE, Hongming/P-6715-2014
OI XIE, Hongming/0000-0002-0772-7996
FU National Natural Science Foundation of China [71772163]; Guangzhou
   Social Science Planning Project [2020GZYB94]; Guangdong Provincial
   Philosophy and Social Science Planning Project [GD20CGL49]
FX This research was funded by the National Natural Science Foundation of
   China grant number 71772163, Guangzhou Social Science Planning Project
   grant number 2020GZYB94, and Guangdong Provincial Philosophy and Social
   Science Planning Project grant number GD20CGL49. And The APC was funded
   by the National Natural Science Foundation of China (71772163).
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NR 76
TC 0
Z9 0
U1 6
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUN
PY 2022
VL 14
IS 11
AR 6396
DI 10.3390/su14116396
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 1Z3QJ
UT WOS:000808742800001
OA gold
DA 2022-08-23
ER

PT J
AU Stirling, AC
   Scoones, I
AF Stirling, Andy C.
   Scoones, Ian
TI From Risk Assessment to Knowledge Mapping: Science, Precaution, and
   Participation in Disease Ecology
SO ECOLOGY AND SOCIETY
LA English
DT Article
DE ambiguity; ignorance; participation; precaution; risk; uncertainty
ID PRINCIPLE; POLICY; APPRAISAL
AB Governance of infectious disease risks requires understanding of often indeterminate interactions between diverse, complex, open, and dynamic human and natural systems. In the face of these challenges, worldwide policy making affords disproportionate status to "science-based" risk-assessment methods. These reduce multiple, complex dimensions to simple quantitative parameters of "outcomes" and "probabilities," and then re-aggregate across diverse metrics, contexts, and perspectives to yield a single ostensibly definitive picture of risk. In contrast, more precautionary or participatory approaches are routinely portrayed as less rigorous, complete, or robust. Yet, although conventional reductive-aggregative techniques provide powerful responses to a narrow state of risk, they are not applicable to less tractable conditions of uncertainty, ambiguity, and ignorance. Strong sensitivities to divergent framings can render results highly variable. Reductive aggregation can marginalize important perspectives and compound exposure to surprise. The value of more broad-based precautionary and participatory approaches may be appreciated. These offer ways to be more rigorous and complete in the mapping of different framings. They may also be more robust than reductive-aggregative appraisal methods, in "opening up" greater accountability for intrinsically normative judgements in decision making on threats like pandemic avian influenza.
C1 [Stirling, Andy C.; Scoones, Ian] Univ Sussex, Inst Dev Studies, Brighton BN1 9RH, E Sussex, England.
RP Stirling, AC (通讯作者)，Univ Sussex, Inst Dev Studies, Brighton BN1 9RH, E Sussex, England.
OI Stirling, Andy/0000-0002-0650-431X; Scoones, Ian/0000-0001-8547-4464
FU UK Economic and Social Research Council; Office of Health, Infectious
   Disease and Nutrition, Bureau for Global Health; U.S. Agency for
   International Development and Wildlife Conservation Society
   [LAG-A-00-99-00047-00, GHS-A-00-06-00005]; ESRC [ES/D004594/1] Funding
   Source: UKRI
FX The work reported here has been funded by the UK Economic and Social
   Research Council. This publication was made possible through support
   provided by the Office of Health, Infectious Disease and Nutrition,
   Bureau for Global Health, U.S. Agency for International Development and
   Wildlife Conservation Society, under the terms of Leader Award No.
   LAG-A-00-99-00047-00, Cooperative Agreement: GHS-A-00-06-00005.. The
   opinions expressed herein are those of the author(s) and do not
   necessarily reflect the views of the U.S. Agency for International
   Development or Wildlife Conservation Society.
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NR 108
TC 48
Z9 48
U1 1
U2 40
PU RESILIENCE ALLIANCE
PI WOLFVILLE
PA ACADIA UNIV, BIOLOGY DEPT, WOLFVILLE, NS B0P 1X0, CANADA
SN 1708-3087
J9 ECOL SOC
JI Ecol. Soc.
PY 2009
VL 14
IS 2
AR 14
PG 20
WC Ecology; Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA 576JY
UT WOS:000276141800009
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Del Mondo, G
   Peng, P
   Gensel, J
   Claramunt, C
   Lu, F
AF Del Mondo, Geraldine
   Peng, Peng
   Gensel, Jerome
   Claramunt, Christophe
   Lu, Feng
TI Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in
   the Field of Maritime Transportation
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE graphs; spatio-temporal; knowledge graphs; maritime transportation
ID COMPLEX NETWORK; SILK ROAD; TIME; PORTS; CENTRALITY; DYNAMICS; SPACE;
   VULNERABILITY; FLOWS; MODEL
AB This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena. While the integration of time within GIS has long been a domain of major interest, alternative modelling and data manipulation approaches derived from graph and knowledge-based principles provide many opportunities for many application domains. We first survey graph principles and how they have been applied to GIS and a few representative domains to date. A comprehensive analysis of the principles behind K-graphs, respective data representation and manipulation capabilities is discussed. The perspectives offered by a close integration of ST-graphs and K-graphs are explored. The whole approach is illustrated and discussed in the context of maritime transportation.
C1 [Del Mondo, Geraldine] Normandie Univ, INSA Rouen, Lab Comp Sci Informat Proc & Syst LITIS, F-76800 Rouen, France.
   [Peng, Peng; Claramunt, Christophe; Lu, Feng] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100864, Peoples R China.
   [Gensel, Jerome] Univ Grenoble Alpes, Grenoble Informatcs Lab LIG, F-38400 Grenoble, France.
   [Claramunt, Christophe] Naval Acad Res Inst, F-29240 Lanveoc, France.
RP Del Mondo, G (通讯作者)，Normandie Univ, INSA Rouen, Lab Comp Sci Informat Proc & Syst LITIS, F-76800 Rouen, France.
EM geraldine.del_mondo@insa-rouen.fr; pengp@lreis.ac.cn;
   Jerome.Gensel@univ-grenoble-alpes.fr;
   christophe.claramunt@ecole-navale.fr; luf@lreis.ac.cn
RI Gensel, Jérôme/AAD-8090-2022; Claramunt, Christophe/H-6121-2017
OI Claramunt, Christophe/0000-0002-5586-1997; Peng,
   Peng/0000-0003-2838-2111; Gensel, Jerome/0000-0003-1398-7118
FU French National Research Framework MAGIS [CNRS 2340]; National Natural
   Science Foundation of China [41631177, 42001391]; China Postdoctoral
   Science Foundation [2020T130644, 2019M660774]; Chinese Academy of
   Sciences President's International Fellowship Initiative [2021VTA0002]
FX Geraldine Del Mondo, Jerome Gensel and Christophe Claramunt are
   supported by the French National Research Framework MAGIS (CNRS 2340),
   Peng Peng and Feng Lu are supported by the National Natural Science
   Foundation of China (41631177, 42001391). Peng Peng is also supported by
   China Postdoctoral Science Foundation (2020T130644, 2019M660774).
   Christophe Claramunt is also supported by Chinese Academy of Sciences
   President's International Fellowship Initiative (2021VTA0002).
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NR 123
TC 3
Z9 3
U1 16
U2 36
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 AUG
PY 2021
VL 10
IS 8
AR 541
DI 10.3390/ijgi10080541
PG 25
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Physical Geography; Remote Sensing
GA UG3CT
UT WOS:000689135600001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Kim, J
   Sohn, M
AF Kim, Jongmo
   Sohn, Mye
TI Graph Representation Learning-Based Early Depression Detection Framework
   in Smart Home Environments
SO SENSORS
LA English
DT Article
DE smart home; early detection of depression (EDD); elderly; graph neural
   networks; graph representation learning; knowledge graph
ID HEAD POSE ESTIMATION; NEURAL-NETWORK; KNOWLEDGE
AB Although the diagnosis and treatment of depression is a medical field, ICTs and AI technologies are used widely to detect depression earlier in the elderly. These technologies are used to identify behavioral changes in the physical world or sentiment changes in cyberspace, known as symptoms of depression. However, although sentiment and physical changes, which are signs of depression in the elderly, are usually revealed simultaneously, there is no research on them at the same time. To solve the problem, this paper proposes knowledge graph-based cyber-physical view (CPV)-based activity pattern recognition for the early detection of depression, also known as KARE. In the KARE framework, the knowledge graph (KG) plays key roles in providing cross-domain knowledge as well as resolving issues of grammatical and semantic heterogeneity required in order to integrate cyberspace and the physical world. In addition, it can flexibly express the patterns of different activities for each elderly. To achieve this, the KARE framework implements a set of new machine learning techniques. The first is 1D-CNN for attribute representation in relation to learning to connect the attributes of physical and cyber worlds and the KG. The second is the entity alignment with embedding vectors extracted by the CNN and GNN. The third is a graph extraction method to construct the CPV from KG with the graph representation learning and wrapper-based feature selection in the unsupervised manner. The last one is a method of activity-pattern graph representation based on a Gaussian Mixture Model and KL divergence for training the GAT model to detect depression early. To demonstrate the superiority of the KARE framework, we performed the experiments using real-world datasets with five state-of-the-art models in knowledge graph entity alignment.
C1 [Kim, Jongmo; Sohn, Mye] Sungkyunkwan Univ, Dept Ind Engn, Suwon 16419, South Korea.
RP Sohn, M (通讯作者)，Sungkyunkwan Univ, Dept Ind Engn, Suwon 16419, South Korea.
EM dignityc@skku.edu; myesohn@skku.edu
OI Kim, Jongmo/0000-0002-4984-1674; Sohn, Mye/0000-0002-1951-3493
FU Basic Science Research Program through the National Research Foundation
   of Korea (NRF) - Ministry of Education, Science and Technology
   [NRF-2019R1A2C1004102]
FX FundingThis research was funded by the National Research Foundation of
   Korea (NRF) grant number (NRF-2019R1A2C1004102).
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NR 64
TC 0
Z9 0
U1 6
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD FEB
PY 2022
VL 22
IS 4
AR 1545
DI 10.3390/s22041545
PG 19
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry; Engineering; Instruments & Instrumentation
GA ZR7TY
UT WOS:000767983200001
PM 35214446
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Fernandez-Alvarez, D
   Labra-Gayo, JE
   Gayo-Avello, D
AF Fernandez-Alvarez, Daniel
   Labra-Gayo, Jose Emilio
   Gayo-Avello, Daniel
TI Automatic extraction of shapes using sheXer
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge Graph; RDF; ShEx; SHACL; Automatic extraction
ID KNOWLEDGE; WIKIDATA
AB There is an increasing number of projects based on Knowledge Graphs and SPARQL endpoints. These SPARQL endpoints are later queried by final users or used to feed many different kinds of applications. Shape languages, such as ShEx and SHACL, have emerged to guide the evolution of these graphs and to validate their expected topology. However, authoring shapes for an existing knowledge graph is a time-consuming task. The task gets more challenging when dealing with sources, possibly maintained by heterogeneous agents. In this paper, we present sheXer, a system that extracts shapes by mining the graph structure. We offer sheXer as a free Python library capable of producing both ShEx and SHACL content. Compared to other automatic shape extractors, sheXer includes some novel features such as shape inter-linkage and computation of big real-world datasets. We analyze the features and limitations w.r.t. performance with different experiments using the English chapter of DBpedia.& nbsp;(c) 2021 Elsevier B.V. All rights reserved.
C1 [Fernandez-Alvarez, Daniel; Labra-Gayo, Jose Emilio; Gayo-Avello, Daniel] Univ Oviedo, Dept Comp Sci, Oviedo, Spain.
RP Fernandez-Alvarez, D (通讯作者)，Univ Oviedo, Dept Comp Sci, Oviedo, Spain.
EM fernandezalvdaniel@uniovi.es; labra@uniovi.es; dani@uniovi.es
RI Gayo-Avello, Daniel/D-3791-2014; Labra-Gayo, Jose Emilio/L-9181-2014
OI Gayo-Avello, Daniel/0000-0002-4705-6891; Fernandez-Alvarez,
   Daniel/0000-0002-8666-7660; Labra-Gayo, Jose Emilio/0000-0001-8907-5348
FU Severo Ochoa Research Program [BP17-88]
FX Acknowledgment This work is supported by the Severo Ochoa Research
   Program (2017 call, exp. 1436 number BP17-88) .
CR Basse A., 2010, WEB SCI C 2010 WEBSC
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NR 25
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 FEB 28
PY 2022
VL 238
AR 107975
DI 10.1016/j.knosys.2021.107975
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0I1AX
UT WOS:000779159800009
DA 2022-08-23
ER

PT J
AU Yu, M
   Bai, C
   Yu, J
   Zhao, MK
   Xu, TY
   Liu, HW
   Li, XW
   Yu, RG
AF Yu, Mei
   Bai, Chen
   Yu, Jian
   Zhao, Mankun
   Xu, Tianyi
   Liu, Hongwei
   Li, Xuewei
   Yu, Ruiguo
TI Translation-Based Embeddings with Octonion for Knowledge Graph
   Completion
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph completion; octonion; hyperbolic geometry; Poincare
   space; link prediction
AB Knowledge representation learning achieves the automatic completion of knowledge graphs (KGs) by embedding entities into continuous low-dimensional vector space. In knowledge graph completion (KGC) tasks, the inter-dependencies and hierarchical information in KGs have gained attention. Existing methods do not well capture the latent dependencies between all components of entities and relations. To address this, we introduce the mathematical theories of octonion, a more expressive generalized form of complex number and quaternion, and propose a translation-based KGC model with octonion (TransO). TransO models entities as octonion coordinate vectors, relations as the combination of octonion component matrices and coordinate vectors, and uses specific grouping calculation rules to interact between entities and relations. In addition, since hyperbolic Poincare space in non-Euclidean mathematics can represent hierarchical data more accurately and effectively than traditional Euclidean space, we propose a Poincare-extended TransO model (PTransO). PTransO transforms octonion coordinate vectors into hyperbolic embeddings by exponential mapping, and integrates the Euclidean-based calculations into hyperbolic space by operations such as Mobius addition and hyperbolic distance. The experimental results of link prediction indicate that TransO outperforms other translation-based models on the WN18 benchmark, and PTransO further achieves state-of-the-art performance in low-dimensional space on the well-established WN18RR and FB15k-237 benchmarks.
C1 [Yu, Mei; Bai, Chen; Yu, Jian; Zhao, Mankun; Xu, Tianyi; Li, Xuewei; Yu, Ruiguo] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China.
   [Yu, Mei; Bai, Chen; Yu, Jian; Zhao, Mankun; Xu, Tianyi; Li, Xuewei; Yu, Ruiguo] Tianjin Univ, Tianjin Key Lab Adv Networking TANKLab, Tianjin 300350, Peoples R China.
   [Yu, Mei; Bai, Chen; Yu, Jian; Zhao, Mankun; Xu, Tianyi; Li, Xuewei; Yu, Ruiguo] Tianjin Univ, Tianjin Key Lab Cognit Comp & Applicat, Tianjin 300350, Peoples R China.
   [Liu, Hongwei] Tianjin Foreign Studies Univ, Foreign Language Literature & Culture Studies Ctr, Tianjin 300204, Peoples R China.
RP Li, XW (通讯作者)，Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China.; Li, XW (通讯作者)，Tianjin Univ, Tianjin Key Lab Adv Networking TANKLab, Tianjin 300350, Peoples R China.; Li, XW (通讯作者)，Tianjin Univ, Tianjin Key Lab Cognit Comp & Applicat, Tianjin 300350, Peoples R China.
EM yumei@tju.edu.cn; cbai@tju.edu.cn; yujian@tju.edu.cn; zmk@tju.edu.cn;
   tianyi.xu@tju.edu.cn; liuhongwei@tjfsu.edu.cn; lixuewei@tju.edu.cn;
   rgyu@tju.edu.cn
FU National Natural Science Foundation of China [61877044]
FX This research was funded by the National Natural Science Foundation of
   China (Grant No. 61877043) and the National Natural Science Foundation
   of China (Grant No. 61877044).
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NR 45
TC 0
Z9 0
U1 5
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD APR
PY 2022
VL 12
IS 8
AR 3935
DI 10.3390/app12083935
PG 24
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 0Q9YK
UT WOS:000785264200001
OA gold
DA 2022-08-23
ER

PT J
AU Dai, YF
   Wang, SP
   Chen, X
   Xu, CY
   Guo, WZ
AF Dai, Yuanfei
   Wang, Shiping
   Chen, Xing
   Xu, Chaoyang
   Guo, Wenzhong
TI Generative adversarial networks based on Wasserstein distance for
   knowledge graph embeddings
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph embedding; Generative adversarial networks; Wasserstein
   distance; Weak supervision information
AB Knowledge graph embedding aims to project entities and relations into low-dimensional and continuous semantic feature spaces, which has captured more attention in recent years. Most of the existing models roughly construct negative samples via a uniformly random mode, by which these corrupted samples are practically trivial for training the embedding model. Inspired by generative adversarial networks (GANs), the generator can be employed to sample more plausible negative triplets, that boosts the discriminator to improve its embedding performance further. However, vanishing gradient on discrete data is an inherent problem in traditional GANs. In this paper, we propose a generative adversarial network based knowledge graph representation learning model by introducing the Wasserstein distance to replace traditional divergence for settling this issue. Moreover, the additional weak supervision information is also absorbed to refine the performance of embedding model since these textual information contains detailed semantic description and offers abundant semantic relevance. In the experiments, we evaluate our method on the tasks of link prediction and triplet classification. The experimental results indicate that the Wasserstein distance is capable of solving the problem of vanishing gradient on discrete data and accelerating the convergence, additional weak supervision information also can significantly improve the performance of the model. (C) 2019 Published by Elsevier B.V.
C1 [Dai, Yuanfei; Wang, Shiping; Chen, Xing; Guo, Wenzhong] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Peoples R China.
   [Wang, Shiping; Chen, Xing; Guo, Wenzhong] Fuzhou Univ, Key Lab Network Comp & Intelligent Informat Proc, Fuzhou 350116, Peoples R China.
   [Xu, Chaoyang] Putian Univ, Sch Informat Engn, Putian 351100, Peoples R China.
RP Guo, WZ (通讯作者)，Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Peoples R China.
EM guowenzhong@fzu.edu.cn
FU National Key RAMP;D Program of China [2018YFB1004800]; National Natural
   Science Foundation of China [U1705262, 61672159]; Technology Innovation
   Platform Project of Fujian Province, China [2014H2005, 2009J1007];
   Fujian Collaborative Innovation Center for Big Data Application in
   Governments, China; Fujian Engineering Research Center of Big Data
   Analysis and Processing, China
FX This work is supported by the National Key R&D Program of China under
   Grant No. 2018YFB1004800, the National Natural Science Foundation of
   China (Nos. U1705262 and 61672159), the Technology Innovation Platform
   Project of Fujian Province, China under Grant (Nos. 2014H2005 and
   2009J1007), the Fujian Collaborative Innovation Center for Big Data
   Application in Governments, China, the Fujian Engineering Research
   Center of Big Data Analysis and Processing, China.
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NR 57
TC 14
Z9 14
U1 3
U2 14
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD FEB 29
PY 2020
VL 190
AR 105165
DI 10.1016/j.knosys.2019.105165
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KS7NC
UT WOS:000518492400012
DA 2022-08-23
ER

PT J
AU Yang, J
   Yang, LT
   Wang, H
   Gao, Y
   Liu, HZ
   Xie, X
AF Yang, Jing
   Yang, Laurence T.
   Wang, Hao
   Gao, Yuan
   Liu, Huazhong
   Xie, Xia
TI Tensor Graph Attention Network for Knowledge Reasoning in Internet of
   Things
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Tensors; Internet of Things; Knowledge engineering; Computer science;
   Semantics; Graph neural networks; Task analysis; Graph neural network;
   graph representation learning; Internet of Things (IoT); knowledge
   graph; knowledge reasoning; link prediction; tensor; tensor
   decomposition; tensor operations
AB Knowledge graph builds the bridge from massive data generated by the interaction and communication between various objects to intelligent applications and services in Internet of Things. The graph representation learning technology represented by graph neural networks plays an essential role in the understanding and reasoning of the knowledge graph with complicated internal structure. Although they are capable of assigning different attention weights to neighbors, the graph attention network (GAT) and its variants are inherently flawed and inadequate in modeling high-order knowledge graphs with high heterogeneity. Therefore, we propose a novel multirelational GAT framework in this article for knowledge reasoning over heterogeneous graphs by employing tensor and tensor operations. Specifically, we formulate the general high-order heterogeneous knowledge graph first. Then, the tensor GAT (TGAT), composed of three components: 1) heterogeneous information propagation; 2) multimodal semantic-aware attention; and 3) knowledge aggregation, is developed to simulate rich interactions between mixed triples, entities, and relationships when aggregating local information. What is more, we utilize the Tucker model to compress the parameters of TGAT and further reduce the storage and calculation consumption of the intermediate calculation process on the premise of maintaining the expressive power. We conduct extensive experiments to solve the link prediction task on four real-world heterogeneous graphs, and the results demonstrate that the TGAT model proposed in this article remarkably outperforms state-of-the-art competitors and improves the hits@1 accuracy by up to 7.6%.
C1 [Yang, Jing; Yang, Laurence T.; Wang, Hao; Gao, Yuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China.
   [Yang, Laurence T.] St Francis Xavier Univ, Dept Comp Sci, Antigonish, NS B2G 2W5, Canada.
   [Liu, Huazhong; Xie, Xia] Hainan Univ, Sch Comp Sci & Technol, Haikou 570000, Hainan, Peoples R China.
RP Liu, HZ (通讯作者)，Hainan Univ, Sch Comp Sci & Technol, Haikou 570000, Hainan, Peoples R China.
EM jing_yang@hust.edu.cn; ltyang@gmail.com; wanghao1999_cs@163.com;
   yuang@hust.edu.cn; sharp-shark_ding@163.com; shelicy@qq.com
OI Gao, Yuan/0000-0003-4096-9134; Yang, Laurence T./0000-0002-7986-4244
FU National Key Research and Development Program of China [2019YFB1705903];
   National Natural Science Foundation of China [61932010, 61867002,
   61802112]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2019YFB1705903, and in part by
   the National Natural Science Foundation of China under Grant 61932010,
   Grant 61867002, and Grant 61802112.
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NR 35
TC 0
Z9 0
U1 3
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2327-4662
J9 IEEE INTERNET THINGS
JI IEEE Internet Things J.
PD JUN 15
PY 2022
VL 9
IS 12
BP 9128
EP 9137
DI 10.1109/JIOT.2021.3092360
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1Y4FB
UT WOS:000808096100009
DA 2022-08-23
ER

PT J
AU Hitzler, P
AF Hitzler, Pascal
TI A Review of the Semantic Web Field Tracing the triumphs and challenges
   of two decades of Semantic Web research and applications
SO COMMUNICATIONS OF THE ACM
LA English
DT Review
ID KNOWLEDGE GRAPHS; LESSONS; SCALE
C1 [Hitzler, Pascal] Kansas State Univ, Dept Comp Sci, Ctr Artificial Intelligence & Data Sci, Manhattan, KS 66506 USA.
RP Hitzler, P (通讯作者)，Kansas State Univ, Dept Comp Sci, Ctr Artificial Intelligence & Data Sci, Manhattan, KS 66506 USA.
FU National Science Foundation [OIA-2033521]
FX This work was supported by the National Science Foundation under award
   OIA-2033521.
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NR 40
TC 32
Z9 32
U1 4
U2 18
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 FEB
PY 2021
VL 64
IS 2
BP 76
EP 83
DI 10.1145/3397512
PG 8
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QA8PZ
UT WOS:000613706000028
DA 2022-08-23
ER

PT J
AU WILLEMS, M
AF WILLEMS, M
TI SUBSUMPTION IN KNOWLEDGE GRAPHS
SO LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
LA English
DT Article
AB An important notion for representation formalisms of natural language semantics, is a subsumption hierarchy. Therefore a precise definition of subsumption is necessary. We shall argue that the usual solution of providing an extensional semantics and mapping subsumption onto set-inclusion, is not satisfactory. The problem is that extensions lose track of the structure. A better solution is used for conceptual graphs [13], where derivation rules define generalization.
   In this paper we shall introduce knowledge graphs, and give a definition of subsumption, that does keep track of the structure. Moreover, because structural subsumption can be tested with a tractable algorithm, the fundamental tradeoff between expressiveness and complexity of inferences [10] does not occur.
RP WILLEMS, M (通讯作者)，UNIV TWENTE, DEPT APPL MATH, POB 217, 7500 AE ENSCHEDE, NETHERLANDS.
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NR 13
TC 0
Z9 0
U1 2
U2 8
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 0302-9743
J9 LECT NOTES ARTIF INT
JI Lect. Notes Artif. Intell.
PY 1991
VL 567
BP 56
EP 66
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KV095
UT WOS:A1991KV09500006
DA 2022-08-23
ER

PT J
AU Monnin, P
   Raissi, C
   Napoli, A
   Coulet, A
AF Monnin, Pierre
   Raissi, Chedy
   Napoli, Amedeo
   Coulet, Adrien
TI Discovering alignment relations with Graph Convolutional Networks: A
   biomedical case study
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge graph; matching; embedding; Graph Convolutional Network;
   ontology; clustering
ID KNOWLEDGE; PHARMACOGENOMICS
AB Knowledge graphs are freely aggregated, published, and edited in the Web of data, and thus may overlap. Hence, a key task resides in aligning (or matching) their content. This task encompasses the identification, within an aggregated knowledge graph, of nodes that are equivalent, more specific, or weakly related. In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster. We conducted experiments with this approach on the real world application of aligning knowledge in the field of pharmacogenomics, which motivated our study. We particularly investigated the interplay between domain knowledge and GCN models with the two following focuses. First, we applied inference rules associated with domain knowledge, independently or combined, before learning node embeddings, and we measured the improvements in matching results. Second, while our GCN model is agnostic to the exact alignment relations (e.g., equivalence, weak similarity), we observed that distances in the embedding space are coherent with the "strength" of these different relations (e.g., smaller distances for equivalences), letting us considering clustering and distances in the embedding space as a means to suggest alignment relations in our case study.
C1 [Monnin, Pierre; Raissi, Chedy; Napoli, Amedeo; Coulet, Adrien] Univ Lorraine, LORIA, INRIA, CNRS, F-54000 Nancy, France.
   [Monnin, Pierre] Orange, Belfort, France.
   [Raissi, Chedy] Ubisoft, Singapore, Singapore.
   [Coulet, Adrien] Inria Paris, F-75012 Paris, France.
   [Coulet, Adrien] Sorbonne Univ, Univ Paris, Ctr Rech Cordeliers, INSERM,UMR1138, F-75006 Paris, France.
RP Monnin, P (通讯作者)，Univ Lorraine, LORIA, INRIA, CNRS, F-54000 Nancy, France.; Monnin, P (通讯作者)，Orange, Belfort, France.
EM pierre.monnin@loria.fr; chedy.raissi@inria.fr; amedeo.napoli@loria.fr;
   adrien.coulet@inria.fr
FU French National Research Agency (ANR) [ANR15-CE23-0028]; Snowball Inria
   Associate Team
FX This work was supported by the PractiKPharma project, founded by the
   French National Research Agency (ANR) under Grant ANR15-CE23-0028, and
   by the Snowball Inria Associate Team.
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NR 34
TC 1
Z9 1
U1 2
U2 2
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2022
VL 13
IS 3
BP 379
EP 398
DI 10.3233/SW-210452
PG 20
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:000780041500005
OA hybrid, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Kim, H
AF Kim, Haklae
TI Developing a Product Knowledge Graph of Consumer Electronics to Manage
   Sustainable Product Information
SO SUSTAINABILITY
LA English
DT Article
DE consumer electronics; product knowledge; knowledge graph; ontology
   model; formal concept analysis
AB Transformational computing paradigms, such as artificial intelligence, home automation, and the Internet of Things, are being rapidly applied to consumer electronics products, thus aiding in the development of integrated and innovative features. Hence, ubiquitous computing and electronic devices are increasingly becoming essential to everyday life. In this context, a wide gulf often exists between the capabilities and technical features of consumer electronic devices and the consumers' understanding of such devices and ability to operate them correctly and effectively. This study proposes a machine-readable knowledge model representing technical terms in product specifications along with a product knowledge graph to discover semantic relationships among various products. Formal concept analysis is applied to conceptually analyze the specification terms of heterogeneous electronic products and design a hierarchical knowledge structure of extracted concepts, to elaborate the proposed knowledge model.
C1 [Kim, Haklae] Chung Ang Univ, Dept Lib & Informat Sci, 84 Heukseok Ro, Seoul 06974, South Korea.
RP Kim, H (通讯作者)，Chung Ang Univ, Dept Lib & Informat Sci, 84 Heukseok Ro, Seoul 06974, South Korea.
EM haklaekim@cau.ac.kr
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NR 37
TC 1
Z9 1
U1 5
U2 15
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB
PY 2021
VL 13
IS 4
AR 1722
DI 10.3390/su13041722
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 QQ8LW
UT WOS:000624772100001
OA Green Published
DA 2022-08-23
ER

PT J
AU Ferre, S
   Cellier, P
AF Ferre, Sebastien
   Cellier, Peggy
TI Graph-FCA: An extension of formal concept analysis to knowledge graphs
SO DISCRETE APPLIED MATHEMATICS
LA English
DT Article
DE Formal concept analysis; Knowledge graph; Semantic web; Graph
   homomorphism
ID CONCEPT LATTICES
AB Knowledge graphs offer a versatile knowledge representation, and have been studied under different forms, such as conceptual graphs or RDF graphs in the Semantic Web. A challenge is to discover conceptual structures in those graphs, in the same way as Formal Concept Analysis (FCA) discovers conceptual structures in tables. FCA has been successful for analysing, mining, learning, and exploring tabular data, and our aim is to help transpose those results to graph-based data. Previous several FCA approaches have already addressed relational data, hence graphs, but with various limits. We propose Graph-FCA as an extension of FCA where a dataset is a hypergraph instead of a binary table. We show that it can be formalized simply by replacing objects by tuples of objects. This leads to the notion of "n-ary concept", whose extent is an n-ary relation of objects, and whose intent is a "projected graph pattern". In this paper, we formally reconstruct the fundamental results of FCA for knowledge graphs. We describe in detail the representation of hypergraphs, and the operations on them, as they are much more complex than the sets of attributes that they extend. We also propose an algorithm based on a notion of "pattern basis" to generate and display n-ary concepts in a more efficient and more compact way. We explore a few use cases, in order to study the feasibility and usefulness of Graph-FCA. We consider two use cases: workflow patterns in cooking recipes and linguistic structures from parse trees. In addition, we report on experiments about quantitative aspects of the approach. (C) 2019 Published by Elsevier B.V.
C1 [Ferre, Sebastien; Cellier, Peggy] Univ Rennes, INSA, CNRS, IRISA, Campus Beaulieu, F-35042 Rennes, France.
RP Ferre, S; Cellier, P (通讯作者)，Univ Rennes, INSA, CNRS, IRISA, Campus Beaulieu, F-35042 Rennes, France.
EM ferre@irisa.fr; cellier@irisa.fr
FU ANR project PEGASE [ANR-16-CE23-0011-08]
FX This research is supported by ANR project PEGASE (ANR-16-CE23-0011-08).
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NR 33
TC 5
Z9 6
U1 3
U2 10
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-218X
EI 1872-6771
J9 DISCRETE APPL MATH
JI Discret Appl. Math.
PD FEB 15
PY 2020
VL 273
SI SI
BP 81
EP 102
DI 10.1016/j.dam.2019.03.003
PG 22
WC Mathematics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA KK1BD
UT WOS:000512484200007
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Zhu, YC
   Zhuang, FZ
   Zhang, XL
   Qi, ZY
   Shi, ZP
   Cao, J
   He, Q
AF Zhu, Yongchun
   Zhuang, Fuzhen
   Zhang, Xiangliang
   Qi, Zhiyuan
   Shi, Zhiping
   Cao, Juan
   He, Qing
TI Combat data shift in few-shot learning with knowledge graph
SO FRONTIERS OF COMPUTER SCIENCE
LA English
DT Article
DE few-shot; data shift; knowledge graph
AB Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected performance in the scenario where all samples are drawn from the same distributions (i.i.d. observations). However, in real-world applications, few-shot learning paradigm often suffers from data shift, i.e., samples in different tasks, even in the same task, could be drawn from various data distributions. Most existing few-shot learning approaches are not designed with the consideration of data shift, and thus show downgraded performance when data distribution shifts. However, it is nontrivial to address the data shift problem in few-shot learning, due to the limited number of labeled samples in each task. Targeting at addressing this problem, we propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations with the help of knowledge graph. The data shift within/between tasks can thus be combated by the combination of task-shared and task-specific representations. The proposed model is evaluated on popular benchmarks and two constructed new challenging datasets. The evaluation results demonstrate its remarkable performance.
C1 [Zhu, Yongchun; Qi, Zhiyuan; Cao, Juan; He, Qing] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, CAS, Beijing 100190, Peoples R China.
   [Zhu, Yongchun; Cao, Juan; He, Qing] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Zhuang, Fuzhen] Beihang Univ, Inst Artificial Intelligence, Beijing 100191, Peoples R China.
   [Zhuang, Fuzhen] Xiamen Inst Data Intelligence, Xiamen 361021, Peoples R China.
   [Zhang, Xiangliang] Univ Notre Dame, Comp Sci & Engn, South Bend, IN 46556 USA.
   [Qi, Zhiyuan] Univ Calif San Diego, La Jolla, CA 92093 USA.
   [Shi, Zhiping] Capital Normal Univ, Coll Informat Engn, Beijing 100089, Peoples R China.
   [Shi, Zhiping] Capital Normal Univ, Acad Multidisciplinary Studies, Beijing 100089, Peoples R China.
RP Zhuang, FZ (通讯作者)，Beihang Univ, Inst Artificial Intelligence, Beijing 100191, Peoples R China.; Zhuang, FZ (通讯作者)，Xiamen Inst Data Intelligence, Xiamen 361021, Peoples R China.
EM zhuangfuzhen@buaa.edu.cn
FU National Natural Science Foundation of China [62176014, U1836206,
   61773361, U1811461]
FX The research work was supported by the National Natural Science
   Foundation of China (Grant Nos. 62176014, U1836206, 61773361, U1811461).
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NR 68
TC 0
Z9 0
U1 0
U2 0
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 2023
VL 17
IS 1
AR 171305
DI 10.1007/s11704-022-1339-7
PG 11
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3P7FN
UT WOS:000837701000002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Han, F
   Deng, YR
   Liu, QY
   Zhou, YZ
   Wang, J
   Huang, YJ
   Zhang, QL
   Bian, J
AF Han, Feng
   Deng, Yirong
   Liu, Qiyuan
   Zhou, Yongzhang
   Wang, Jun
   Huang, Yongjian
   Zhang, Qianlong
   Bian, Jing
TI Construction and application of the knowledge graph method in management
   of soil pollution in contaminated sites: A case study in South China
SO JOURNAL OF ENVIRONMENTAL MANAGEMENT
LA English
DT Article
DE Knowledge graph; Urban soil pollution; Contaminated site; Environmental
   management
AB Contaminated sites are a main cause of urban soil problems and have led to increasing pollution and public risk in China as a result of the rapid growth of industrial and urban land use. Because land pollution involves extensive multi-source heterogeneous information, identifying the risk of urban soil pollution efficiently and predicting pollution-related events are important for urban environmental management. Knowledge graphs (KGs) have unique advantages in dealing with massive amounts of information. This study attempts to construct a KG of contaminated sites in South China to explore its feasibility and effectiveness in urban soil environmental management. The results demonstrate that KGs have a favorable effect in information retrieval, knowledge reasoning, and visualization. Studied cases in this article demonstrate that the KG model can achieve many functions, including the display of global information of polluted sites, and discovery of regional distribution of characteristic pollutants and main pollutants of specific industries, based on special query syntax. However, this approach is limited by some technical difficulties, such as knowledge mining of natural resources, which must be overcome in future studies to improve the operability of KG technologies.
C1 [Han, Feng; Liu, Qiyuan; Zhou, Yongzhang; Zhang, Qianlong] Sun Yat Sen Univ, Sch Earth Sci & Engn, Zhuhai 519082, Peoples R China.
   [Deng, Yirong; Wang, Jun] Environm Acad Guangdong, Guangdong Prov Key Lab Geodynam & Geohazards, Guangzhou 510045, Peoples R China.
   [Liu, Qiyuan] Chinese Res Inst Environm Sci, Beijing 100012, Peoples R China.
   [Han, Feng; Zhou, Yongzhang; Zhang, Qianlong] Guangdong Prov Key Lab Geol Proc & Mineral Resourc, Zhuhai 519082, Peoples R China.
   [Huang, Yongjian] Guangzhou Xuanyuan Res Inst, Guangzhou 510006, Peoples R China.
   [Bian, Jing] Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China.
RP Zhou, YZ (通讯作者)，Sun Yat Sen Univ, Sch Earth Sci & Engn, Zhuhai 519082, Peoples R China.
EM zhouyz@mail.sysu.edu.cn
OI ZHOU, Yongzhang/0000-0002-8572-5849; Han, Feng/0000-0003-4602-4756
FU Environmental Academy of Guangdong, Sun Yat-Sen University - National
   Natural Science Foundation of China [U1911202]; Guangdong Provincial Key
   Research Program - Guangdong Provincial Department of Science and
   Technology [2020B111137001]
FX Funding: This work was supported by the Environmental Academy of
   Guangdong, Sun Yat-Sen University through the State Key Program funded
   by the National Natural Science Foundation of China [grant number
   U1911202) and Guangdong Provincial Key Research Program funded by
   Guangdong Provincial Department of Science and Technology [grant number
   2020B111137001] .
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NR 38
TC 0
Z9 0
U1 4
U2 4
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 0301-4797
EI 1095-8630
J9 J ENVIRON MANAGE
JI J. Environ. Manage.
PD OCT 1
PY 2022
VL 319
AR 115685
DI 10.1016/j.jenvman.2022.115685
PG 8
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 3D3VZ
UT WOS:000829234700007
PM 35816963
OA hybrid
DA 2022-08-23
ER

PT J
AU Hu, X
   Duan, JL
   Dang, DP
AF Hu, Xin
   Duan, Jiangli
   Dang, Depeng
TI Natural language question answering over knowledge graph: the marriage
   of SPARQL query and keyword search
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph; Question answering; Natural language question
   answering; SPARQL; Keyword search
AB Natural language question answering over knowledge graph has received widespread attention. However, the existing methods always aim to improve every phase of natural language question answering and neglect the defects; namely, not all query intentions can be identified and mapped to the correct SPARQL statement. In contrast, keyword search relies on the links among multiple keywords regardless of the exact logic relations in question. Therefore, we propose a framework (abbreviated as NLQSK for title of this paper) that introduces keyword search into natural language question answering to compensate for the defects mentioned above. First, we translate a natural language question into top-k SPARQL statements by using the existing methods. Second, we transform the valuable information that cannot be identified and mapped into keywords, and then, return the neighboring information in a knowledge graph by keyword index. Third, we combine the SPARQL block (i.e., the SPARQL statement and its result) and keyword search to produce the answer to the natural language question. Finally, the experiments on the benchmark dataset confirm that keyword search can compensate for the defects of natural language question answering and that NLQSK can answer more questions than the existing state-of-the-art question answering systems.
C1 [Hu, Xin; Duan, Jiangli] Yangtze Normal Univ, Coll Big Data & Intelligent Engn, Chongqing, Peoples R China.
   [Duan, Jiangli] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing, Peoples R China.
   [Dang, Depeng] Beijing Normal Univ, Sch Artificial Intelligence, Beijing, Peoples R China.
RP Duan, JL (通讯作者)，Yangtze Normal Univ, Coll Big Data & Intelligent Engn, Chongqing, Peoples R China.; Duan, JL (通讯作者)，Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing, Peoples R China.
EM d180201004@stu.cqupt.edu.cn
OI Duan, jiangli/0000-0002-0471-1147
FU youth Project of science and technology research program of Chongqing
   Education Commission of China [KJQN201901414, KJQN201901408]; Startup
   Foundation for Introducing Talent of Yangtze Normal University
   [0107/011160052]; PhD Candidate Talent Development Project [BYJS201908];
   Project of Chongqing Natural Science Foundation [cstc2019jcyj-msxmX0683,
   cstc2019jcyj-msxm1579]; major Project of science and technology research
   program of Chongqing Education Commission of China [KJZD-M201901401];
   National Natural Science Foundation of China [61672102, 61802244];
   Program for New Century Excellent Talents in University of Ministry of
   Education of China [NCET-10-0239]; Natural Science Basic Research Plan
   in Shaanxi Province of China [2019JQ-668]
FX This work was supported by the youth Project of science and technology
   research program of Chongqing Education Commission of China (No.
   KJQN201901414 and No. KJQN201901408), the Startup Foundation for
   Introducing Talent of Yangtze Normal University (No. 0107/011160052),
   the PhD Candidate Talent Development Project (No. BYJS201908), the
   Project of Chongqing Natural Science Foundation (No.
   cstc2019jcyj-msxmX0683 and No. cstc2019jcyj-msxm1579), major Project of
   science and technology research program of Chongqing Education
   Commission of China (No. KJZD-M201901401), the National Natural Science
   Foundation of China (Grant No. 61672102 and No. 61802244), the Program
   for New Century Excellent Talents in University of Ministry of Education
   of China (Grant No. NCET-10-0239) and the Natural Science Basic Research
   Plan in Shaanxi Province of China (No. 2019JQ-668).
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NR 45
TC 4
Z9 5
U1 8
U2 29
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0219-1377
EI 0219-3116
J9 KNOWL INF SYST
JI Knowl. Inf. Syst.
PD APR
PY 2021
VL 63
IS 4
BP 819
EP 844
DI 10.1007/s10115-020-01534-4
EA JAN 2021
PG 26
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RE4XH
UT WOS:000607996500001
DA 2022-08-23
ER

PT J
AU Schindler, D
   Bensmann, F
   Dietze, S
   Kruger, F
AF Schindler, David
   Bensmann, Felix
   Dietze, Stefan
   Krueger, Frank
TI The role of software in science: a knowledge graph-based analysis of
   software mentions in PubMed Central
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Knowledge graph; Software mention; Named entity recognition; Software
   citation
ID IMPACT; CITATION; USAGE
AB Science across all disciplines has become increasingly data-driven, leading to additional needs with respect to software for collecting, processing and analysing data. Thus, transparency about software used as part of the scientific process is crucial to understand provenance of individual research data and insights, is a prerequisite for reproducibility and can enable macro-analysis of the evolution of scientific methods over time. However, missing rigor in software citation practices renders the automated detection and disambiguation of software mentions a challenging problem. In this work, we provide a large-scale analysis of software usage and citation practices facilitated through an unprecedented knowledge graph of software mentions and affiliated metadata generated through supervised information extraction models trained on a unique gold standard corpus and applied to more than 3 million scientific articles. Our information extraction approach distinguishes different types of software and mentions, disambiguates mentions and outperforms the state-of-the-art significantly, leading to the most comprehensive corpus of 11.8 M software mentions that are described through a knowledge graph consisting of more than 300 M triples. Our analysis provides insights into the evolution of software usage and citation patterns across various fields, ranks of journals, and impact of publications. Whereas, to the best of our knowledge, this is the most comprehensive analysis of software use and citation at the time, all data and models are shared publicly to facilitate further research into scientific use and citation of software.
C1 [Schindler, David; Krueger, Frank] Univ Rostock, Inst Commun Engn, Rostock, Germany.
   [Bensmann, Felix; Dietze, Stefan] GESIS Leibniz Inst Social Sci, Cologne, Germany.
   [Dietze, Stefan] Heinrich Heine Univ, Dusseldorf, Germany.
   [Krueger, Frank] Univ Rostock, Dept Knowledge Culture & Transformat, Rostock, Germany.
RP Schindler, D; Kruger, F (通讯作者)，Univ Rostock, Inst Commun Engn, Rostock, Germany.; Kruger, F (通讯作者)，Univ Rostock, Dept Knowledge Culture & Transformat, Rostock, Germany.
EM david.schindler@uni-rostock.de; frank.krueger@uni-rostock.de
OI Kruger, Frank/0000-0002-7925-3363
FU Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [SFB
   1270/2: 299150580, 404417453, DFG: 440623123]
FX The following grant information was disclosed by the authors: Deutsche
   Forschungsgemeinschaft (DFG, German Research Foundation) SFB 1270/2:
   299150580. ScienceLinker: 404417453. DFG: 440623123.
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NR 53
TC 1
Z9 1
U1 5
U2 5
PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
EI 2376-5992
J9 PEERJ COMPUT SCI
JI PeerJ Comput. Sci.
PD JAN 14
PY 2022
VL 8
AR e835
DI 10.7717/peerj-cs.835
PG 47
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YV2ML
UT WOS:000752565200001
PM 35111920
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Hsu, PY
   Chen, CT
   Chou, C
   Huang, SH
AF Hsu, Pei-Ying
   Chen, Chiao-Ting
   Chou, Chin
   Huang, Szu-Hao
TI Explainable mutual fund recommendation system developed based on
   knowledge graph embeddings
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Explainable recommendation; Explainable AI; Knowledge graph; Fund
   recommendation
AB Because deep learning models have been used successfully in various fields during recent years, many recommendation systems have been developed using deep learning techniques. However, although deep learning-based recommendation systems have achieved high recommendation performance, their lack of interpretability may reduce users' trust and satisfaction. In this study, we aimed to predict and recommend the purchase of funds by customers in the next month while simultaneously providing relevant explanations. To achieve this goal, we employed a knowledge graph structure and deep learning techniques to embed features of customers and funds into a unified latent space. With the proposed structure, we learned some information that could not be learned using traditional deep learning models and obtained personalized recommendations and explanations simultaneously. Moreover, we obtained complex explanations by changing the training procedure of the model and developed a measure for rating the customized explanations according to their strength and uniqueness. Finally, we obtained some possible special recommendations based on the knowledge graph structure. By evaluating the data set of mutual fund transaction records, we verified the effectiveness of the developed model for providing precise recommendations. We also conducted some case studies of explanations to demonstrate the effectiveness of the developed model for providing usual explanations, complex explanations, and other special recommendations.
C1 [Hsu, Pei-Ying; Chou, Chin] Natl Chiao Tung Univ, Inst Informat Management, Hsinchu 30010, Taiwan.
   [Chen, Chiao-Ting] Natl Yang Ming Chiao Tung Univ, Dept Comp Sci, Hsinchu 30010, Taiwan.
   [Huang, Szu-Hao] Natl Yang Ming Chiao Tung Univ, Dept Informat Management & Finance, Hsinchu 30010, Taiwan.
RP Huang, SH (通讯作者)，Natl Yang Ming Chiao Tung Univ, Dept Informat Management & Finance, Hsinchu 30010, Taiwan.
EM szuhaohuang@nycu.edu.tw
OI Huang, Szu-Hao/0000-0002-4073-0652
FU Ministry of Science and Technology, Taiwan [MOST 1102221-E-A49-101, MOST
   110-2622-8-009-014-TM1]; Financial Technology (FinTech) Innovation
   Research Center, National Yang Ming Chiao Tung University
FX This work was supported in part by the Ministry of Science and
   Technology, Taiwan, under Contract MOST 1102221-E-A49-101 and Contract
   MOST 110-2622-8-009-014-TM1; and in part bythe Financial Technology
   (FinTech) Innovation Research Center,National Yang Ming Chiao Tung
   University
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NR 41
TC 0
Z9 0
U1 19
U2 23
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
PD JUL
PY 2022
VL 52
IS 9
BP 10779
EP 10804
DI 10.1007/s10489-021-03136-1
EA JAN 2022
PG 26
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2I2IM
UT WOS:000742989200001
DA 2022-08-23
ER

PT J
AU Lin, ZH
   Yang, D
   Yin, XC
AF Lin, Zhihuang
   Yang, Dan
   Yin, Xiaochun
TI Patient Similarity via Joint Embeddings of Medical Knowledge Graph and
   Medical Entity Descriptions
SO IEEE ACCESS
LA English
DT Article
DE Medical diagnostic imaging; Insulin; Diseases; Measurement; Licenses;
   Semantics; Patient similarity; medical knowledge graph embedding;
   medical entity descriptions; Siamese CNN with SPP
ID HEALTH; CLASSIFICATION; PERFORMANCE; QUALITY
AB With the prevalence and growing volume of Electronic Health Records (EHRs), there has been increasing interest in mining EHRs for improving clinical decision support. The accurate identification of patients with similar conditions based on EHRs is a key step in personalized healthcare. Existing studies model EHRs by medical knowledge graph embedding to learn the latent embeddings of medical entities (e.g., patients, medications, diagnoses and procedures). However, such precisely structured data is usually limited in quantity and in scope. Therefore, to enhance the quality of the embeddings it is important to consider more widely available medical information such as medical entity descriptions. In this paper we propose a novel framework, called Deep Patient Similarity (DeepPS). Specifically, DeepPS incorporates medical entity descriptions by augmenting the embeddings of medical entities and relations with the embeddings of words, which leverages both information from medical knowledge graph structures and the contexts of medical entity descriptions. Furthermore, DeepPS employs the embeddings to patient similarity learning by leveraging Siamese Convolutional Neural Network (CNN) with Spatial Pyramid Pooling (SPP). Extensive experiments on real datasets are conducted to show superior performance of our proposed framework.
C1 [Lin, Zhihuang; Yang, Dan] Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan 114051, Peoples R China.
   [Yin, Xiaochun] Weifang Univ Sci & Technol, Facil Hort Lab Univ Shandong, Shouguang 262700, Peoples R China.
RP Yang, D (通讯作者)，Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan 114051, Peoples R China.
EM asyangdan@163.com
OI yang, dan/0000-0002-3817-7333
FU National Natural Science Foundation of China [6167214]; General Scientic
   Research Projects of Liaoning Province [2019LNJC07]; University of
   Science and Technology Liaoning Talent Project [601011507-22]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 6167214, in part by the General Scientic
   Research Projects of Liaoning Province under Grant 2019LNJC07, and in
   part by the University of Science and Technology Liaoning Talent Project
   under Grant 601011507-22.
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NR 58
TC 2
Z9 2
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 2020
VL 8
BP 156663
EP 156676
DI 10.1109/ACCESS.2020.3019577
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NM6KD
UT WOS:000568203600001
OA gold
DA 2022-08-23
ER

PT J
AU Li, Y
   Ge, TJ
   Chen, C
AF Li, Yan
   Ge, Tingjian
   Chen, Cindy
TI Data Stream Event Prediction Based on Timing Knowledge and State
   Transitions
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB We study a practical problem of predicting the upcoming events in data streams using a novel approach. Treating event time orders as relationship types between event entities, we build a dynamic knowledge graph and use it to predict future event timing. A unique aspect of this knowledge graph embedding approach for prediction is that we enhance conventional knowledge graphs with the notion of "states"-in what we call the ephemeral state nodes-to characterize the state of a data stream over time. We devise a complete set of methods for learning relevant events, for building the event-order graph stream from the original data stream, for embedding and prediction, and for theoretically bounding the complexity. We evaluate our approach with four realworld stream datasets and find that our method results in high precision and recall values for event timing prediction, ranging between 0.7 and nearly 1, significantly outperforming baseline approaches. Moreover, due to our choice of efficient translation-based embedding, the overall throughput that the stream system can handle, including continuous graph building, training, and event predictions, is over one thousand to sixty thousand tuples per second even on a personal computer-which is especially important in resource constrained environments, including edge computing.
C1 [Li, Yan; Ge, Tingjian; Chen, Cindy] Univ Massachusetts, Lowell, MA 01854 USA.
RP Li, Y (通讯作者)，Univ Massachusetts, Lowell, MA 01854 USA.
EM yli2@cs.uml.edu; ge@cs.uml.edu; cchen@cs.uml.edu
FU NSF [IIS-1633271]
FX Ois work is supported in part by NSF grant IIS-1633271.
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U2 8
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
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JI Proc. VLDB Endow.
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PY 2020
VL 13
IS 10
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DI 10.14778/3401960.3401973
PG 14
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NU9NK
UT WOS:000573962300013
DA 2022-08-23
ER

PT J
AU Chen, Q
   Wang, W
   Huang, KZ
   Coenen, F
AF Chen, Qi
   Wang, Wei
   Huang, Kaizhu
   Coenen, Frans
TI Zero-Shot Text Classification via Knowledge Graph Embedding for Social
   Media Data
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Social networking (online); Data models; Task analysis; Sensors;
   Internet of Things; Bit error rate; Predictive models; Internet of
   Things (IoT); knowledge graph; natural language processing (NLP); social
   media data analysis; zero-shot learning
ID NETWORKS; INTERNET; THINGS
AB The idea of "citizen sensing" and "human as sensors" is crucial for social Internet of Things, an integral part of cyber-physical-social systems (CPSSs). Social media data, which can be easily collected from the social world, has become a valuable resource for research in many different disciplines, e.g., crisis/disaster assessment, social event detection, or the recent COVID-19 analysis. Useful information, or knowledge derived from social data, could better serve the public if it could be processed and analyzed in more efficient and reliable ways. Advances in deep neural networks have significantly improved the performance of many social media analysis tasks. However, deep learning models typically require a large amount of labeled data for model training, while most CPSS data is not labeled, making it impractical to build effective learning models using traditional approaches. In addition, the current state-of-the-art, pretrained natural language processing (NLP) models do not make use of existing knowledge graphs, thus often leading to unsatisfactory performance in real-world applications. To address the issues, we propose a new zero-shot learning method which makes effective use of existing knowledge graphs for the classification of very large amounts of social text data. Experiments were performed on a large, real-world tweet data set related to COVID-19, the evaluation results show that the proposed method significantly outperforms six baseline models implemented with state-of-the-art deep learning models for NLP.
C1 [Chen, Qi; Wang, Wei; Huang, Kaizhu] Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou 215123, Peoples R China.
   [Coenen, Frans] Univ Liverpool, Dept Comp Sci, Liverpool L69 3BX, Merseyside, England.
RP Wang, W (通讯作者)，Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou 215123, Peoples R China.
EM qi.chen@xjtlu.edu.cn; wei.wang03@xjtlu.edu.cn;
   kaizhu.huang@xjtlu.edu.cn; coenen@liverpool.ac.uk
RI ; Huang, Kaizhu/O-4721-2014
OI Coenen, Frans/0000-0003-1026-6649; Huang, Kaizhu/0000-0002-3034-9639
FU Research Development Fund at Xi'an Jiaotong-Liverpool University
   [RDF-16-01-34]; National Natural Science Foundation of China [61876155];
   Natural Science Foundation of Jiangsu Province [BK20181189,
   BE2020006-4B]; Key Program Special Fund in XJTLU [KSF-T-06, KSF-E-05]
FX This work was supported in part by the Research Development Fund at
   Xi'an Jiaotong-Liverpool University under Contract RDF-16-01-34; in part
   by the National Natural Science Foundation of China under Grant
   61876155; in part by the Natural Science Foundation of Jiangsu Province
   under Grant BK20181189 and Grant BE2020006-4B; and in part by the Key
   Program Special Fund in XJTLU under Grant KSF-T-06 and Grant KSF-E-05.
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NR 45
TC 0
Z9 0
U1 2
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2327-4662
J9 IEEE INTERNET THINGS
JI IEEE Internet Things J.
PD JUN 15
PY 2022
VL 9
IS 12
BP 9205
EP 9213
DI 10.1109/JIOT.2021.3093065
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1Y4FB
UT WOS:000808096100015
DA 2022-08-23
ER

PT J
AU Lei, ZF
   Ul Haq, A
   Zeb, A
   Suzauddola, M
   Zhang, DF
AF Lei, Zhenfeng
   Ul Haq, Anwar
   Zeb, Adnan
   Suzauddola, Md
   Zhang, Defu
TI Is the suggested food your desired?: Multi-modal recipe recommendation
   with demand-based knowledge graph
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Recipe recommender; Interpretation generation; Multi-modality; Knowledge
   graph; BERT
AB Personalized recipe recommender systems help users mine certain dishes they want to find and even really desire, which play a significant role in matching dishes, balancing nutrients, and preventing non-communicable diseases. Generally, customer's preferences or needs vary from person to person, and people are often reluctant to accept recommended food without reasonable explanation, especially when their demands are not explicitly addressed. In this paper, we are devoted to providing recipe suggestions accompanied by rational interpretations generated from images or videos. First, we construct a recipe knowledge graph (RcpKG) through the use of multi-modality and hierarchical thought, which focuses on the underlying demands of users and the consideration of multiple fine-grained factors. On this basis, a novel multi-modal recipe recommendation method via the knowledge graph (RcpMKR) is proposed, which represents nodes in multiple aspects and performs multi-relational graph structure extraction of the RcpKG. It not only takes into account local associations within the graph but also global information, and incorporates user concerns at different levels. Then, we adopt BERT-based multi-modal models and generative adversarial networks to generate interpretations. Additionally, dynamic convolution and random synthetic attention are utilized in our work to discriminate among features. Experimental results show that the proposed method and BERT-based fusion models improve recipe recommendation performance and explanation generation. Specifically, the precision of the RcpMKR method through RcpKG, user concerns and graph convolutional network improves by 7.82%, and the viExpCBTBERT method via 2D&3D convolutional neural networks for developing text interpretations enhances the F1-score by 10% compared with the baseline.
C1 [Lei, Zhenfeng; Ul Haq, Anwar; Zeb, Adnan; Suzauddola, Md; Zhang, Defu] Xiamen Univ, Sch Informat, Xiamen 361005, Peoples R China.
RP Zhang, DF (通讯作者)，Xiamen Univ, Sch Informat, Xiamen 361005, Peoples R China.
EM zflei621@foxmail.com; anwar@uom.edu.pk; adnanzeb@stu.xmu.edu.cn;
   suzau@stu.xmu.edu.cn; dfzhang@xmu.edu.cn
FU National Nature Science Foundation of China [61672439]
FX This work is partially supported by the National Nature Science
   Foundation of China under Grant 61672439.
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NR 60
TC 2
Z9 2
U1 14
U2 36
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 115708
DI 10.1016/j.eswa.2021.115708
EA AUG 2021
PG 14
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA WB9SP
UT WOS:000703905700002
DA 2022-08-23
ER

PT J
AU Jiang, W
   Liu, YN
   Deng, XY
AF Jiang, Wen
   Liu, Yuanna
   Deng, Xinyang
TI Fuzzy entity alignment via knowledge embedding with awareness of
   uncertainty measure
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Entity alignment; Intuitionistic fuzzy set;
   Uncertainty fusion
ID FRAMEWORK
AB Entity alignment refers to associate entities in different knowledge graphs if they are semantically identical. Embedding-based entity alignment approaches encode entities in a continuous embedding space where entities are aligned based on the similarity of learned embeddings. However, there exists ambiguity and uncertainty in entity alignment caused by single alignment metric. In this paper, a fuzzy entity alignment method FuzzyEA is proposed to model the uncertainty in alignment process based on intuitionistic fuzzy set (IFS). Iterative TransE model is designed to learn relational structure of knowledge graphs, where mutual selection and error correction mechanism is proposed to enhance the effect of iteration. The alignment results obtained by name/description embedding and structure embedding are fused based on Dempster's combination rule. Experiments on three benchmark datasets demonstrate that the proposed FuzzyEA consistently outperforms other entity alignment methods and contributes to promising improvement in alignment accuracy and discrimination ability. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Jiang, Wen; Liu, Yuanna; Deng, Xinyang] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China.
   [Jiang, Wen] Peng Cheng Lab, Shenzhen 518055, Peoples R China.
RP Jiang, W (通讯作者)，Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China.
EM jiangwen@nwpu.edu.cn
FU Shaanxi key research and development program [2021ZDLGY01-04]
FX The work is partially supported by Shaanxi key research and development
   program (No. 2021ZDLGY01-04).
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NR 66
TC 0
Z9 0
U1 8
U2 16
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JAN 11
PY 2022
VL 468
BP 97
EP 110
DI 10.1016/j.neucom.2021.10.026
EA OCT 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WR8WI
UT WOS:000714774800009
DA 2022-08-23
ER

PT J
AU Chaudhri, VK
   Baru, C
   Chittar, N
   Dong, XL
   Genesereth, M
   Hendler, J
   Kalyanpur, A
   Lenat, DB
   Sequeda, J
   Vrandecic, D
   Wang, KS
AF Chaudhri, Vinay K.
   Baru, Chaitanya
   Chittar, Naren
   Dong, Xin Luna
   Genesereth, Michael
   Hendler, James
   Kalyanpur, Aditya
   Lenat, Douglas B.
   Sequeda, Juan
   Vrandecic, Denny
   Wang, Kuansan
TI Knowledge graphs: Introduction, history, and perspectives
SO AI MAGAZINE
LA English
DT Article
ID WEB; AI
AB Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing information extracted by AI systems, and for improving the predictions of AI systems by giving them knowledge expressed in KGs as input. The goals of this article are to (a) introduce KGs and discuss important areas of application that have gained recent prominence; (b) situate KGs in the context of the prior work in AI; and (c) present a few contrasting perspectives that help in better understanding KGs in relation to related technologies.
C1 [Chaudhri, Vinay K.; Genesereth, Michael] Stanford Univ, Stanford, CA 94305 USA.
   [Baru, Chaitanya] Univ Calif San Diego, San Diego Supercomp Ctr, La Jolla, CA USA.
   [Chittar, Naren] JPMorgan Chase & Co, New York, NY USA.
   [Hendler, James] Renssalear Polytech Inst, Troy, NY USA.
   [Kalyanpur, Aditya] Elemental Cognit, New York, NY USA.
   [Lenat, Douglas B.] Cycorp, Austin, TX USA.
   [Sequeda, Juan] Dataworld, Austin, TX USA.
   [Vrandecic, Denny] Wikimedia Fdn, St Petersburg, FL USA.
   [Wang, Kuansan] Microsoft, Albuquerque, NM USA.
   [Chaudhri, Vinay K.] JPMorgan Chase & Co, 310 Univ Ave, Palo Alto, CA 94301 USA.
RP Chaudhri, VK (通讯作者)，JPMorgan Chase & Co, 310 Univ Ave, Palo Alto, CA 94301 USA.
EM Vinay.Chaudhri@jpmchase.com
FU National Science Foundation's Convergence Accelerator program
FX This work has been partially supported by National Science Foundation's
   Convergence Accelerator program. We sincerely thank Dr. RV Guha for his
   contributions and insightful comments on the paper.
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NR 56
TC 1
Z9 1
U1 3
U2 3
PU AMER ASSOC ARTIFICIAL INTELL
PI MENLO PK
PA 445 BURGESS DRIVE, MENLO PK, CA 94025-3496 USA
SN 0738-4602
EI 2371-9621
J9 AI MAG
JI AI Mag.
PD MAR
PY 2022
VL 43
IS 1
BP 17
EP 29
DI 10.1002/aaai.12033
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0J8FN
UT WOS:000780336400004
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Rasmussen, MH
   Lefrancois, M
   Pauwels, P
   Hviid, CA
   Karlshoj, J
AF Rasmussen, Mads Holten
   Lefrancois, Maxime
   Pauwels, Pieter
   Hviid, Christian Anker
   Karlshoj, Jan
TI Managing interrelated project information in AEC Knowledge Graphs
SO AUTOMATION IN CONSTRUCTION
LA English
DT Article
DE Linked data; Building information modelling; Complex design; Ontology;
   Inference; Information exchange; BIM; AEC Knowledge Graph; Linked
   building data
ID EXPRESS; IFCOWL; SYSTEM
AB In the architecture, engineering and construction (AEC) industry stakeholders from different companies and backgrounds collaborate in realising a common goal being some physical structure. The exact goal is typically not known from the beginning, and throughout all design stages, new decisions are made - similarly to other design industries [1]. As a result, the design must adapt and subsequent consequences follow. With working methods being predominantly document-centric, highly interrelated and rapidly changing design data in a complex network of decisions, requirements and product specifications is primarily captured in static documents. In this paper, we consider a purely data-driven approach based on semantic web technologies and an earlier proposed Ontology for Property Management (OPM). The main contribution of this work consists of extensions for OPM to account for new competency questions including the description of property reliability and the reasoning logic behind derived properties. The secondary contribution is the specification of a homogeneous way to generate parametric queries for managing an OPM-compliant AEC Knowledge Graph (AEC-KG). A software library for operating an OPM-compliant AEC-KG is further presented in the form of an OPM Query Generator (OPM-QG). The library generates SPARQL 1.1 queries to query and manipulate construction project Knowledge Graphs represented using OPM. The OPM ontology aligns with latest developments in the W3C Community Group on Linked Building Data and suggests an approach to working with design data in a distributed environment using separate graphs for explicit facts and for materialised, deduced data. Finally, we evaluate the suggested approach using an open-source software artefact developed using OPM and OPM-QG, demonstrated online with an actual building Knowledge Graph. The particular design task evaluated is performing heat loss calculations for spaces of a future building using an AEC-KG described using domain- and project specific extensions of the Building Topology Ontology (SOT) in combination with OPM. With this work, we demonstrate how a typical engineering task can be accomplished and managed in an evolving design environment, thereby providing the engineers with insights to support decision making as changes occur. The application uses a strict division between the client viewer and the actual data model holding design logic, and can easily be extended to support other design tasks.
C1 [Rasmussen, Mads Holten; Hviid, Christian Anker; Karlshoj, Jan] Tech Univ Denmark, Dept Civil Engn, Lyngby, Denmark.
   [Lefrancois, Maxime] Univ Jean Monnet, Univ Lyon, Mines St Etienne, IOGS,CNRS,Inst Henri Fayol,LHC,UMR 5516, F-42023 St Etienne, France.
   [Pauwels, Pieter] Eindhoven Univ Technol, Dept Built Environm, Eindhoven, Netherlands.
RP Rasmussen, MH (通讯作者)，Tech Univ Denmark, Dept Civil Engn, Lyngby, Denmark.
EM mhoras@byg.dtu.dk
RI Pauwels, Pieter/I-8256-2015; Karlshoj, Jan/H-9532-2017
OI Pauwels, Pieter/0000-0001-8020-4609; Karlshoj, Jan/0000-0001-5735-3032;
   Hviid, Christian Anker/0000-0002-8340-7222
FU NIRAS ALECTIA Foundation and Innovation Fund Denmark
FX Special thanks to the NIRAS ALECTIA Foundation and Innovation Fund
   Denmark for funding. Also thanks to Niras for allowing open distribution
   of the developed artifacts<SUP>13</SUP>. It is a fundamental necessity
   for the future growth of the proposed standards that they are adopted
   and further developed by the community.
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TC 19
Z9 19
U1 5
U2 48
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0926-5805
EI 1872-7891
J9 AUTOMAT CONSTR
JI Autom. Constr.
PD DEC
PY 2019
VL 108
AR 102956
DI 10.1016/j.autcon.2019.102956
PG 16
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA JS5YI
UT WOS:000500381900005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Xu, ZM
   Liu, HL
   Li, J
   Zhang, QQ
   Tang, Y
AF Xu, Zhuoming
   Liu, Hanlin
   Li, Jian
   Zhang, Qianqian
   Tang, Yan
TI CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N
   Recommendation
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph-based recommendation; top-N recommendation; user
   preference; heterogeneous propagation; graph attention network;
   attention aggregator
AB Knowledge graph-based recommendation methods are a hot research topic in the field of recommender systems in recent years. As a mainstream knowledge graph-based recommendation method, the propagation-based recommendation method captures users' potential interests in items by integrating the representations of entities and relations in the knowledge graph and the high-order connection patterns between entities to provide personalized recommendations. For example, the collaborative knowledge-aware attentive network (CKAN) is a typical state-of-the-art propagation-based recommendation method that combines user-item interactions and knowledge associations in the knowledge graph, and performs heterogeneous propagation in the knowledge graph to generate multi-hop ripple sets, thereby capturing users' potential interests. However, existing propagation-based recommendation methods, including CKAN, usually ignore the complex relations between entities in the multi-hop ripple sets and do not distinguish the importance of different ripple sets, resulting in inaccurate user potential interests being captured. Therefore, this paper proposes a top-N recommendation method named collaborative knowledge-aware graph attention network (CKGAT). Based on the heterogeneous propagation strategy, CKGAT uses the knowledge-aware graph attention network to extract the topological proximity structures of entities in the multi-hop ripple sets and then learn high-order entity representations, thereby generating refined ripple set embeddings. CKGAT further uses an attention aggregator to perform weighted aggregation on the ripple set embeddings, the user/item initial entity set embeddings, and the original representations of items to generate accurate user embeddings and item embeddings for the top-N recommendations. Experimental results show that CKGAT, overall, outperforms three baseline methods and six state-of-the-art propagation-based recommendation methods in terms of recommendation accuracy, and outperforms four representative propagation-based recommendation methods in terms of recommendation diversity.
C1 [Xu, Zhuoming; Liu, Hanlin; Li, Jian; Zhang, Qianqian; Tang, Yan] Hohai Univ, Coll Comp & Informat, Nanjing 210098, Peoples R China.
RP Xu, ZM (通讯作者)，Hohai Univ, Coll Comp & Informat, Nanjing 210098, Peoples R China.
EM zmxu@hhu.edu.cn; hanlin.liu@hhu.edu.cn; jli@hhu.edu.cn;
   qianqian.zhang@hhu.edu.cn; tangyan@hhu.edu.cn
OI Xu, Zhuoming/0000-0002-0080-6501
FU National Key Research and Development Program of China [2017YFC0405805]
FX FundingThis research was funded by the National Key Research and
   Development Program of China, grant number 2017YFC0405805 and the APC
   was funded by 2017YFC0405805.
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NR 42
TC 0
Z9 0
U1 29
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 FEB
PY 2022
VL 12
IS 3
AR 1669
DI 10.3390/app12031669
PG 23
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA ZA4OY
UT WOS:000756146100001
OA gold
DA 2022-08-23
ER

PT J
AU Purohit, H
   Shalin, VL
   Sheth, AP
AF Purohit, Hemant
   Shalin, Valerie L.
   Sheth, Amit P.
TI Knowledge Graphs to Empower Humanity-Inspired AI Systems
SO IEEE INTERNET COMPUTING
LA English
DT Article
DE Artificial intelligence; Cognition; Senior citizens; Next generation
   networking; Adaptive systems; Medical services; Knowledge management
AB We present a theoretically motivated design perspective, challenges, and applications of next-generation artificial intelligence (AI) systems. We envision systems with greater capabilities for meaningful human interaction, including socially adaptive behavior that incorporates personalization and sensitivity to social context and intentionality. Personalized knowledge graphs combining generic, common-sense, and domain-specific knowledge with both sociocultural values and norms and individual cognitive models provide a foundation for building humanity-inspired AI systems.
C1 [Purohit, Hemant] George Mason Univ, Informat Sci & Technol, Fairfax, VA 22030 USA.
   [Shalin, Valerie L.] Wright State Univ, Psychol, Dayton, OH 45435 USA.
   [Shalin, Valerie L.] Wright State Univ, Dayton, OH 45435 USA.
   [Sheth, Amit P.] Univ South Carolina, Campuswide Artificial Intelligence Inst, Columbia, SC 29208 USA.
RP Sheth, AP (通讯作者)，Univ South Carolina, Campuswide Artificial Intelligence Inst, Columbia, SC 29208 USA.
EM purohit@gmu.edu; valerie.shalin@wright.edu; amit@sc.edu
RI Sheth, Amit/ABC-4600-2020
OI Sheth, Amit/0000-0002-0021-5293
FU National Science Foundation [IIS-1815459, CNS-1513721]
FX This work was supported in part by the National Science Foundation under
   Grant IIS-1815459 and Grant CNS-1513721. Views expressed are those of
   the authors and not of the sponsors.
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NR 16
TC 6
Z9 6
U1 3
U2 15
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1089-7801
EI 1941-0131
J9 IEEE INTERNET COMPUT
JI IEEE Internet Comput.
PD JUL-AUG
PY 2020
VL 24
IS 4
BP 48
EP 54
DI 10.1109/MIC.2020.3013683
PG 7
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NR7EA
UT WOS:000571722800006
DA 2022-08-23
ER

PT J
AU Zhou, MX
   Xu, W
   Zhang, WP
   Jiang, QQ
AF Zhou, Mengxi
   Xu, Wei
   Zhang, Wenping
   Jiang, Qiqi
TI Leverage knowledge graph and GCN for fine-grained-level clickbait
   detection
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph; Graph convolutional network; Graph attention network;
   Clickbait detection
ID MODEL
AB Clickbait is the use of an enticing title as bait to deceive users to click. However, the corresponding content is often disappointing, infuriating or even deceitful. This practice has brought serious damage to our social trust, especially to online media, which is one of the most important channels for information acquisition in our daily life. Currently, clickbait is spreading on the internet and causing serious damage to society. However, research on clickbait detection has not yet been well performed. Almost all existing research treats clickbait detection as a binary classification task and only uses the title as the input. This shallow usage of information and detection technology not only suffers from low performance in real detection (e.g., it is easy to bypass) but is also difficult to use in further research (e.g., potential empirical studies). In this work, we proposed a novel clickbait detection model that incorporated a knowledge graph, a graph convolutional network and a graph attention network to conduct fine-grained-level clickbait detection. According to experiments using a real dataset, our novel proposed model outperformed classical and state-of-the-art baselines. In addition, certain explainability can also be achieved in our model through the graph attention network. Our fine-grained-level results can provide a measurement foundation for future empirical study. To the best of our knowledge, this is the first attempt to incorporate a knowledge graph and deep learning technique to detect clickbait and achieve explainability.
C1 [Zhou, Mengxi; Xu, Wei; Zhang, Wenping] Renmin Univ China, Sch Informat, Beijing, Peoples R China.
   [Jiang, Qiqi] Copenhagen Business Sch, Dept Digitalizat, Frederiksberg, Denmark.
RP Zhang, WP (通讯作者)，Renmin Univ China, Sch Informat, Beijing, Peoples R China.
EM zhoumx0808@ruc.edu.cn; weixu@ruc.edu.cn; wpzhang@ruc.edu.cn;
   qj.digi@cbs.dk
OI ZHANG, Wenping/0000-0002-0183-4504
FU Fundamental Research Funds for the Central Universities; Research Funds
   of Renmin University of China [21XNA035]
FX This work is supported by the Fundamental Research Funds for the Central
   Universities, and the Research Funds of Renmin University of China (No.
   21XNA035).
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NR 48
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 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD MAY
PY 2022
VL 25
IS 3
SI SI
BP 1243
EP 1258
DI 10.1007/s11280-022-01032-3
EA MAR 2022
PG 16
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1D5UO
UT WOS:000769845700002
PM 35308295
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Wang, X
   Liu, X
   Liu, J
   Chen, XM
   Wu, H
AF Wang, Xin
   Liu, Xiao
   Liu, Jin
   Chen, Xiaomei
   Wu, Hao
TI A novel knowledge graph embedding based API recommendation method for
   Mashup development
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Web API recommendation; Mashup development; Deep random walk; Knowledge
   graph; Graph embedding
ID WEB
AB Web API is an efficient and cost-effective method for service-oriented software development, and Mashup is a popular technology which combines multiple services to create more powerful services to address the increasing complexity of business requirements and speed up the software development process. Here, accurate and efficient API recommendation is vital for successful Mashup development. Currently, many existing methods combine various technologies and adopt diverse features, which results in complex models at the cost of higher computational overhead but with very limited improvement on recommendation accuracy. To address such an issue, in this paper, we propose an unsupervised API recommendation method based on deep random walks on knowledge graph. Specifically, we first construct a refined knowledge graph utilizing Mashup-API co-invocation patterns and service category attributes, and then we learn implicit low-dimensional embedding representations of entities from truncated random walks by treating walks as the equivalent of sentences. Meanwhile, to improve the recommendation accuracy, we design an entity bias procedure to reflect different entity preference (namely API-based neighborhood or Mashup-based neighborhood). Finally, we estimate the relevance between Mashup requirements and the existing services (Mashups and APIs) to obtain the API recommendation list. Since the API recommendation results can be obtained through unsupervised feature learning, automatic API recommendation can be provided for Mashup developers in real time. Comprehensive experimental results on a real-world dataset demonstrate that our proposed method can outperform several state-of-the-art methods in both recommendation accuracy and efficiency.
C1 [Wang, Xin; Liu, Jin] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
   [Liu, Xiao] Deakin Univ, Sch Informat Technol, Geelong, Vic, Australia.
   [Liu, Jin] Chinese Acad Sci, Inst Informat Engn, Key Lab Network Assessment Technol, Beijing, Peoples R China.
   [Chen, Xiaomei; Wu, Hao] Yunnan Univ, Sch Informat Sci & Engn, Kunming, Yunnan, Peoples R China.
RP Liu, J (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.; Liu, J (通讯作者)，Chinese Acad Sci, Inst Informat Engn, Key Lab Network Assessment Technol, Beijing, Peoples R China.; Wu, H (通讯作者)，Yunnan Univ, Sch Informat Sci & Engn, Kunming, Yunnan, Peoples R China.
EM xinwang0920@whu.edu.cn; jinliu@whu.edu.cn; haowu@ynu.edu.cn
RI Wu, Hao/T-5893-2019
OI Wu, Hao/0000-0002-3696-9281; Liu, Jin/0000-0003-0359-0248
FU National Natural Science Foundation of China [61972290, 61962061,
   61966037, 61463052, U1802271]; Open Fund of Key Laboratory of Network
   Assessment Technology from CAS; Yunnan Provincial Foundation for Leaders
   of Disciplines in Science and Technology [202005AC160005]; Top Young
   Talents of "Ten Thousand Plan" in Yunnan Province [YNWR-QNBJ-2019-188];
   Program for Excellent Young Talents of Yunnan University
FX This work was supported by the grands of the National Natural Science
   Foundation of China (61972290, 61962061, 61966037, 61463052, U1802271),
   supported by the Open Fund of Key Laboratory of Network Assessment
   Technology from CAS, partially supported by the Yunnan Provincial
   Foundation for Leaders of Disciplines in Science and
   Technology(202005AC160005), Top Young Talents of "Ten Thousand Plan" in
   Yunnan Province(YNWR-QNBJ-2019-188), the Program for Excellent Young
   Talents of Yunnan University.
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NR 49
TC 9
Z9 9
U1 11
U2 27
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD MAY
PY 2021
VL 24
IS 3
BP 869
EP 894
DI 10.1007/s11280-021-00894-3
EA MAY 2021
PG 26
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SF8YB
UT WOS:000651358900001
DA 2022-08-23
ER

PT J
AU Wang, T
   Shi, DQ
   Wang, ZD
   Xu, S
   Xu, H
AF Wang, Ting
   Shi, Daqian
   Wang, Zhaodan
   Xu, Shuai
   Xu, Hao
TI MRP2Rec: Exploring Multiple-Step Relation Path Semantics for Knowledge
   Graph-Based Recommendations
SO IEEE ACCESS
LA English
DT Article
DE Recommender systems; knowledge graph; semantic representation
AB Knowledge graphs (KGs) have been proven to be effective for improving the performance of recommender systems. KGs can store rich side information and relieve the data sparsity problem. There are many linked attributes between entity pairs (e.g., items and users) in KGs, which can be called multiplestep relation paths. Existing methods do not sufficiently exploit the information encoded in KGs. In this paper, we propose MRP2Rec to explore various semantic relations in multiple-step relation paths to improve recommendation performance. The knowledge representation learning approach is used in our method to learn and represent multiple-step relation paths, and they are further utilized to generate prediction lists by inner products in top-K recommendations. Experiments on two real-world datasets demonstrate that our model achieves higher performance compared with many state-of-the-art baselines.
C1 [Wang, Ting; Xu, Hao] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China.
   [Shi, Daqian] Univ Trento, ICT Int Doctoral Sch, I-38123 Trento, Italy.
   [Wang, Zhaodan] Aviat Univ Air Force, Coll Aviat Fdn, Changchun 130012, Peoples R China.
   [Xu, Shuai] Jilin Univ, Coll Elect Sci & Engn, Changchun 130012, Peoples R China.
RP Xu, H (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China.
EM xuhao@jlu.edu.cn
OI Wang, Ting/0000-0002-5869-5058; shi, daqian/0000-0003-2183-1957
FU Development Project of Jilin Province of China [20170203002GX,
   20170101006JC]; Ministry of Science and Technology of the People's
   Republic of China [2018YFC2002500]; Jilin Province Development and
   Reform Commission, China [2019C053-1]; Education Department of Jilin
   Province, China [JJKH20200993K]; Department of Science and Technology of
   Jilin Province, China [20200801002GH]
FX This work was supported in part by the Development Project of Jilin
   Province of China under Grant 20170203002GX and Grant 20170101006JC, in
   part by the Ministry of Science and Technology of the People's Republic
   of China under Grant 2018YFC2002500, in part by the Jilin Province
   Development and Reform Commission, China, under Grant 2019C053-1, in
   part by the Education Department of Jilin Province, China, under Grant
   JJKH20200993K, and in part by the Department of Science and Technology
   of Jilin Province, China, under Grant 20200801002GH.
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NR 35
TC 4
Z9 4
U1 3
U2 16
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 134817
EP 134825
DI 10.1109/ACCESS.2020.3011279
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MS6DA
UT WOS:000554362900001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, CB
   Ma, XG
   Chen, JG
   Chen, JW
AF Wang, Chengbin
   Ma, Xiaogang
   Chen, Jianguo
   Chen, Jingwen
TI Information extraction and knowledge graph construction from geoscience
   literature
SO COMPUTERS & GEOSCIENCES
LA English
DT Article
DE Geological corpus; Knowledge graph; Geoscience literature; Chinese word
   segmentation; Chord and bigram graphs
ID FREQUENCY
AB Geoscience literature published online is an important part of open data, and brings both challenges and opportunities for data analysis. Compared with studies of numerical geoscience data, there are limited works on information extraction and knowledge discovery from textual geoscience data. This paper presents a workflow and a few empirical case studies for that topic, with a focus on documents written in Chinese. First, we set up a hybrid corpus combining the generic and geology terms from geology dictionaries to train Chinese word segmentation rules of the Conditional Random Fields model. Second, we used the word segmentation rules to parse documents into individual words, and removed the stop-words from the segmentation results to get a corpus constituted of content-words. Third, we used a statistical method to analyze the semantic links between content words, and we selected the chord and bigram graphs to visualize the content-words and their links as nodes and edges in a knowledge graph, respectively. The resulting graph presents a clear overview of key information in an unstructured document. This study proves the usefulness of the designed workflow, and shows the potential of leveraging natural language processing and knowledge graph technologies for geoscience.
C1 [Wang, Chengbin; Chen, Jianguo; Chen, Jingwen] China Univ Geosci, State Key Lab Geol Proc & Mineral Resources, Wuhan 430074, Hubei, Peoples R China.
   [Wang, Chengbin; Chen, Jianguo; Chen, Jingwen] China Univ Geosci, Fac Earth Resources, Wuhan 430074, Hubei, Peoples R China.
   [Wang, Chengbin; Ma, Xiaogang] Univ Idaho, Dept Comp Sci, Moscow, ID 83844 USA.
RP Chen, JG (通讯作者)，China Univ Geosci, State Key Lab Geol Proc & Mineral Resources, Wuhan 430074, Hubei, Peoples R China.; Chen, JG (通讯作者)，China Univ Geosci, Fac Earth Resources, Wuhan 430074, Hubei, Peoples R China.; Ma, XG (通讯作者)，Univ Idaho, Dept Comp Sci, Moscow, ID 83844 USA.
EM max@uidaho.edu; jgchen@cug.edu.cn
RI Ma, Xiaogang/C-9286-2011; Wang, Chengbin/ABG-2166-2021
OI Ma, Xiaogang/0000-0002-9110-7369; Wang, Chengbin/0000-0001-5964-1556
FU National Key R&D Program of China [2017YFC0601500, 2017YFC0601504];
   China Geological Survey through the project of Research and Application
   of Big Data in geoscience [201511079-02]; National Science Foundation
   (NSF) through the NSF Idaho EPSCoR Program [IIA-1301792]; Computers and
   Geosciences Research Scholarships of IAMG; Office Of The Director
   [1301792] Funding Source: National Science Foundation
FX This work was supported by the National Key R&D Program of China
   (2017YFC0601500, 2017YFC0601504), the China Geological Survey through
   the project of Research and Application of Big Data in geoscience
   (201511079-02), the National Science Foundation (NSF) through the NSF
   Idaho EPSCoR Program (IIA-1301792), and the 2017 Computers and
   Geosciences Research Scholarships of IAMG.
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NR 45
TC 63
Z9 74
U1 23
U2 140
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 MAR
PY 2018
VL 112
BP 112
EP 120
DI 10.1016/j.cageo.2017.12.007
PG 9
WC Computer Science, Interdisciplinary Applications; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Geology
GA FU9OS
UT WOS:000424186000011
DA 2022-08-23
ER

PT J
AU Gruber, V
   Bossew, P
   De Cort, M
   Tollefsen, T
AF Gruber, V.
   Bossew, P.
   De Cort, M.
   Tollefsen, T.
TI The European map of the geogenic radon potential
SO JOURNAL OF RADIOLOGICAL PROTECTION
LA English
DT Article; Proceedings Paper
CT International Symposium on Natural Radiation Exposure and Low-dose
   Radiation Epidemiological Studies
CY FEB 29-MAR 03, 2012
CL Hirosaki Univ Inst Radiat Med, Hirosaki, JAPAN
SP Res Ctr BioMed Sci, Natl Inst Radiol Sci (NIRS), Japan Sci & Technol Agcy (JST)
HO Hirosaki Univ Inst Radiat Med
ID INDOOR RADON; RISK
AB As part of its projected European Atlas of Natural Radiation (EANR), the Joint Research Centre (JRC) of the European Commission, in cooperation with research institutions and radioprotection authorities all over Europe, is currently developing a map of the geogenic radon potential. In an accompanying report the state of knowledge, mapping approaches and problems are discussed. We explain the rationale and the legal situation in Europe and present an overview on the main problems stemming from the heterogeneity of input datasets between participating countries and from the definition of input variables and their differently implemented sampling procedures or protocols. Further topics are definition of the target variable which quantifies the geogenic radon potential and its estimation from heterogeneous input and proxy variables, as well as problems specific to mapping, such as choice of mapping support and resolution.
   The geogenic map was preceded by a European map of indoor radon concentrations, which is still growing as ever more countries decide to participate, and which served as training for harmonisation problems occurring in the European data realm. We shall also briefly discuss its main results and implications for the geogenic map.
C1 [Gruber, V.; De Cort, M.; Tollefsen, T.] Commiss European Communities, Joint Res Ctr, ITU Inst Transuranium Elements, I-10127 Ispra, VA, Italy.
   [Bossew, P.] Bundesamt Strahlenschutz German Fed Off Radiat Pr, D-10318 Berlin, Germany.
RP Gruber, V (通讯作者)，Commiss European Communities, Joint Res Ctr, ITU Inst Transuranium Elements, Via E Fermi 2749, I-10127 Ispra, VA, Italy.
EM valeria.gruber@jrc.ec.europa.eu; pbossew@bfs.de;
   marc.de-cort@jrc.ec.europa.eu
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   Kemski J, 2009, ENVIRON GEOL, V56, P1269, DOI 10.1007/s00254-008-1226-z
   Kemski J, 2001, SCI TOTAL ENVIRON, V272, P217, DOI 10.1016/S0048-9697(01)00696-9
   Neznal M., 2004, CZECH GEOLOGICAL SUR, V16
   Tollefsen T, 2011, RADIAT PROT DOSIM, V145, P110, DOI 10.1093/rpd/ncr072
NR 16
TC 55
Z9 61
U1 0
U2 18
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0952-4746
EI 1361-6498
J9 J RADIOL PROT
JI J. Radiol. Prot.
PD MAR
PY 2013
VL 33
IS 1
BP 51
EP 60
DI 10.1088/0952-4746/33/1/51
PG 10
WC Environmental Sciences; Public, Environmental & Occupational Health;
   Nuclear Science & Technology; Radiology, Nuclear Medicine & Medical
   Imaging
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health; Nuclear Science & Technology; Radiology, Nuclear Medicine &
   Medical Imaging
GA 066ZN
UT WOS:000313261200006
PM 23295644
DA 2022-08-23
ER

PT J
AU Zhao, YC
   Zhang, BK
   Gao, D
AF Zhao, Yuchao
   Zhang, Beike
   Gao, Dong
TI Construction of petrochemical knowledge graph based on deep learning
SO JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
LA English
DT Article
DE HAZOP; Knowledge; Graph; Deep learning; BI-GAT-CRF; Named entity
   recognition
AB Hazard and operability analysis (HAZOP) is a safety evaluation method that is vital in the chemical process. HAZOP analysis uses the form of "brainstorming " and "counterfactual reasoning " to determine the potential events for danger in the chemical process. However, this method relies too much on expert experience and a large amount of existing HAZOP information has not been shared and reused. Herein we prompt a semi-automated HAZOP knowledge graph to overcome the problem of information reuse and sharing. In the implementation of the knowledge graph, the construction of the HAZOP ontology is completed by the seven-step rule in the top down ontology construction. The named entity recognition task is one of the key tasks for the construction of knowledge graphs. The deep learning method is adopted in this paper and is based on the existing HAZOP data to recognize the named entity of HAZOP text. We adopt the deep neural network of BI-GAT-CRF, which has an accuracy of 90.75, a recall rate of 91.53, and an F1 score of 91.14 in the HAZOP Chinese text. BI-GAT-CRF aims to solve the problem of unclear entity boundary recognition and word ambiguity in named entity recognition tasks. The results show that the model performs well in HAZOP named entity recognition. BI-GAT-CRF can effectively determine entity boundaries and recognize named entity categories. BI-GAT-CRF also solves the problem of word ambiguity and improves the accuracy of named entity recognition tasks. Finally, a HAZOP analysis report of a certain oil equipment was used as an example to construct a HAZOP knowledge graph to verify the effectiveness of the construction method.
C1 [Zhao, Yuchao; Zhang, Beike; Gao, Dong] Beijing Univ Chem Technol, Chem Ind Syst Simulat Engn Technol Ctr, Sch Informat & Technol, Beijing 100029, Peoples R China.
RP Zhao, YC; Gao, D (通讯作者)，Beijing Univ Chem Technol, Chem Ind Syst Simulat Engn Technol Ctr, Sch Informat & Technol, Beijing 100029, Peoples R China.
EM zyc9817@163.com; gaodong@mail.buct.edu.cn
FU National Natural Science Foundation of China [61703026]
FX Acknowledgements This paper was funded by the National Natural Science
   Foundation of China: Research on Information Standardization and
   Analysis Methods for Online Safety Analysis of Petrochemical Processes
   (61703026) . Our special thanks go to Dong Gao, School of Beijing
   University of Chemical Technology, for his insightful suggestions and to
   FangGuo Li, Zhanjie Xia, and LanFei Peng for their help in named entity
   recognition.
CR Al-Zaidy RA, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P2551, DOI 10.1145/3308558.3313642
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   Zheng X., 2020, NEUROCOMPUTING
NR 30
TC 0
Z9 0
U1 12
U2 12
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0950-4230
EI 1873-3352
J9 J LOSS PREVENT PROC
JI J. Loss Prev. Process Ind.
PD MAY
PY 2022
VL 76
AR 104736
DI 10.1016/j.jlp.2022.104736
PG 13
WC Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 0U8FZ
UT WOS:000787884300002
DA 2022-08-23
ER

PT J
AU Xiong, WP
   Cao, J
   Zhou, X
   Du, JQ
   Nie, B
   Zeng, ZJ
   Li, TC
AF Xiong, Wangping
   Cao, Jun
   Zhou, Xian
   Du, Jianqiang
   Nie, Bin
   Zeng, Zhijun
   Li, Tianci
TI Design and Evaluation of a Prescription Drug Monitoring Program for
   Chinese Patent Medicine based on Knowledge Graph
SO EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE
LA English
DT Article
ID IMPACT
AB Background. Chinese patent medicines are increasingly used clinically, and the prescription drug monitoring program is an effective tool to promote drug safety and maintain health. Methods. We constructed a prescription drug monitoring program for Chinese patent medicines based on knowledge graphs. First, we extracted the key information of Chinese patent medicines, diseases, and symptoms from the domain-specific corpus by the information extraction. Second, based on the extracted entities and relationships, a knowledge graph was constructed to form a rule base for the monitoring of data. Then, the named entity recognition model extracted the key information from the electronic medical record to be monitored and matched the knowledge graph to realize the monitoring of the Chinese patent medicines in the prescription. Results. Named entity recognition based on the pretrained model achieved an F1 value of 83.3% on the Chinese patent medicines dataset. On the basis of entity recognition technology and knowledge graph, we implemented a prescription drug monitoring program for Chinese patent medicines. The accuracy rate of combined medication monitoring of three or more drugs of the program increased from 68% to 86.4%. The accuracy rate of drug control monitoring increased from 70% to 97%. The response time for conflicting prescriptions with two drugs was shortened from 1.3S to 0.8S. The response time for conflicting prescriptions with three or more drugs was shortened from 5.2S to 1.4S. Conclusions. The program constructed in this study can respond quickly and improve the efficiency of monitoring prescriptions. It is of great significance to ensure the safety of patients' medication.
C1 [Xiong, Wangping; Cao, Jun; Zhou, Xian; Du, Jianqiang; Nie, Bin; Li, Tianci] Jiangxi Univ Chinese Med, Sch Comp, Nanchang 330004, Jiangxi, Peoples R China.
   [Zeng, Zhijun] Jiangxi Univ Chinese Med, Jiangxi Prov Key Lab TCM Etiopathogenisis, Nanchang 330004, Jiangxi, Peoples R China.
RP Zhou, X (通讯作者)，Jiangxi Univ Chinese Med, Sch Comp, Nanchang 330004, Jiangxi, Peoples R China.
EM hoonber@foxmail.com; hoonber@126.com; corr2021@163.com;
   1476164560@qq.com; 1254692626@qq.com; tgsci21@sina.com; 670918214@qq.com
FU National Natural Science Foundation of China [61762051, 82060826];
   Jiangxi Natural Science Foundation [20192BAB205094, 20202BAB202019];
   National Key RD Plan [2019YFC1712301]; Science and Technology Research
   Project of Jiangxi Provincial Department of Education [GJJ190683];
   School Innovation Special Fund
FX The authors are grateful to the doctors of Jiangxi Hospital of Tradition
   Chinese Medicine for providing the test questions and part of the
   desensitized data. This research was supported by the National Natural
   Science Foundation of China (Nos. 61762051 and 82060826), the Jiangxi
   Natural Science Foundation (Nos. 20192BAB205094 and 20202BAB202019), the
   National Key R&D Plan (No. 2019YFC1712301), the Science and Technology
   Research Project of Jiangxi Provincial Department of Education (No.
   GJJ190683), and the School Innovation Special Fund.
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NR 33
TC 2
Z9 2
U1 18
U2 31
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1741-427X
EI 1741-4288
J9 EVID-BASED COMPL ALT
JI Evid.-based Complement Altern. Med.
PD JUL 17
PY 2021
VL 2021
AR 9970063
DI 10.1155/2021/9970063
PG 8
WC Integrative & Complementary Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Integrative & Complementary Medicine
GA TT7JI
UT WOS:000680520500003
PM 34326889
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Xia, YC
   Sun, N
   Wang, H
   Yuan, XH
   Gu, LC
   Wang, C
   Gao, QJ
AF Xia, Yingchun
   Sun, Ning
   Wang, Hui
   Yuan, Xiaohui
   Gu, Lichuan
   Wang, Chao
   Gao, Qijuan
TI RESEARCH ON KNOWLEDGE QUESTION ANSWERING SYSTEM FOR AGRICULTURE DISEASE
   AND PESTS BASED ON KNOWLEDGE GRAPH
SO JOURNAL OF NONLINEAR AND CONVEX ANALYSIS
LA English
DT Article; Proceedings Paper
CT 5th International Conference on Fuzzy Systems and Data Mining (FSDM)
   held in conjunction with the 9th International Conference on
   Electronics, Communications and Networks (CECNet)
CY OCT 18-21, 2019
CL Kitakyushu, JAPAN
DE Agricultural pests; knowledge graph; knowledge question answering
AB A large increase of agricultural data results in the emergence of redundant information on the Internet. Users can not meet the results which are search by matching keywords or analyzing simple semantic. This paper proposes a knowledge question answering (QA) method to answer questions directly and succinctly in the filed of agriculture disease and pests. The methods is based on entity linking, external knowledge and similarity calculation. It prunes inappropriate entities and relations through entity linking, and uses external knowledge to improve recall rate. In addition, questions are represented by the sum of word vectors. Entities in knowledge graph are trained into vectors. The final answer is obtained by computing similarity between question and answer. A Chinese knowledge QA system is designed and implemented by using the proposed method. In the experiment, the dataset is generated according to RDF triple and seed questions. The experimental results show that the proposed method outperforms other state-of-the-art algorithms.
C1 [Xia, Yingchun; Sun, Ning; Wang, Hui; Gu, Lichuan; Wang, Chao; Gao, Qijuan] Anhui Agr Univ, Sch Informat & Comp, Hefei 230036, Peoples R China.
   [Yuan, Xiaohui] Univ North Texas, Dept Comp Sci & Engn, Denton, TX 76203 USA.
RP Gu, LC (通讯作者)，Anhui Agr Univ, Sch Informat & Comp, Hefei 230036, Peoples R China.
EM 1415220245@qq.com; zodomain@163.com; 1835435117@qq.com;
   xiaohui.yuan@unt.edu; glc@ahau.edu.cn; warton_wang@qq.com;
   181510179@qq.com
OI Yuan, Xiaohui/0000-0001-6897-4563
FU National Natural Science Foundation of China [31771679, 31671589]; Anhui
   Foundation for Science and Technology Major Project, China [16030701092,
   18030901034, 201904e01020006]; Key Laboratory of Agricultural Electronic
   Commerce, Ministry of Agriculture of China [AEC2018003, AEC2018006];
   2019 Anhui University collaborative innovation project
   [sn:GXXT-2019-013]; Hefei Major Research Project of Key Technology
   [J2018G14]
FX This work is partially supported by the National Natural Science
   Foundation of China under Grant (31771679, 31671589), the Anhui
   Foundation for Science and Technology Major Project, China, under Grant
   (16030701092, 18030901034, 201904e01020006), the Key Laboratory of
   Agricultural Electronic Commerce, Ministry of Agriculture of China under
   Grant (AEC2018003, AEC2018006), the 2019 Anhui University collaborative
   innovation project (sn:GXXT-2019-013), the Hefei Major Research Project
   of Key Technology (J2018G14).
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NR 17
TC 0
Z9 0
U1 8
U2 29
PU YOKOHAMA PUBL
PI YOKOHAMA
PA 101, 6-27 SATSUKIGAOKA AOBA-KU, YOKOHAMA, 227-0053, JAPAN
SN 1345-4773
EI 1880-5221
J9 J NONLINEAR CONVEX A
JI J. Nonlinear Convex Anal.
PY 2020
VL 21
IS 7
SI SI
BP 1487
EP 1496
PG 10
WC Mathematics, Applied; Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Mathematics
GA NY1QN
UT WOS:000576173000006
DA 2022-08-23
ER

PT J
AU Alshahrani, M
   Almansour, A
   Alkhaldi, A
   Thafar, MA
   Uludag, M
   Essack, M
   Hoehndorf, R
AF Alshahrani, Mona
   Almansour, Abdullah
   Alkhaldi, Asma
   Thafar, Maha A.
   Uludag, Mahmut
   Essack, Magbubah
   Hoehndorf, Robert
TI Combining biomedical knowledge graphs and text to improve predictions
   for drug-target interactions and drug-indications
SO PEERJ
LA English
DT Article
DE Biomedical literature; Biomedical knowledge graphs; Drug-target
   interactions; Drug-indications; Multi-modal learning; Bio-ontologies;
   Linked Data
ID INTERACTION NETWORKS; DISEASE; INFORMATION; ONTOLOGY; WEB; TOOL
AB Biomedical knowledge is represented in structured databases and published in biomed-ical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical knowledge and published literature on the prediction of drug targets and drug indications. We demonstrate that our approach using datasets for drug-target interactions and drug indications is scalable to large graphs and can be used to improve the ranking of targets and indications by exploiting features from either structure or unstructured information alone.
C1 [Alshahrani, Mona; Almansour, Abdullah; Alkhaldi, Asma] Saudi Data & Artificial Intelligence Author SDAIA, Natl Ctr Artificial Intelligence NCAI, Riyadh, Saudi Arabia.
   [Thafar, Maha A.] Taif Univ, Coll Comp & Informat Technol, At Taif, Saudi Arabia.
   [Thafar, Maha A.; Uludag, Mahmut; Essack, Magbubah; Hoehndorf, Robert] King Abdullah Univ Sci & Technol, Computat Biosci Res Ctr CBRC, Comp Elect & Math Sci & Engn Div CEMSE, Thuwal, Saudi Arabia.
RP Alshahrani, M (通讯作者)，Saudi Data & Artificial Intelligence Author SDAIA, Natl Ctr Artificial Intelligence NCAI, Riyadh, Saudi Arabia.
EM mona.alshahrani@kaust.edu.sa
OI Hoehndorf, Robert/0000-0001-8149-5890; Uludag,
   Mahmut/0000-0001-8482-2172; Thafar, Maha/0000-0003-0539-7361
FU National Center of Artificial Intelligence (NCAI), Saudi Data and
   Artificial Intelligence Authority (SDAIA), Saudi Arabia; King Abdullah
   University of Science and Technology (KAUST) Office of Sponsored
   Research (OSR) [FCC/1/1976-20-01]
FX Mona Alshahrani, Abdullah Almansour and Asma Alkhaldi are supported by
   the National Center of Artificial Intelligence (NCAI) , Saudi Data and
   Artificial Intelligence Authority (SDAIA) , Saudi Arabia. Magbubah
   Essack has been supported by King Abdullah University of Science and
   Technology (KAUST) Office of Sponsored Research (OSR) grant no.
   FCC/1/1976-20-01.
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NR 87
TC 0
Z9 0
U1 7
U2 7
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 4
PY 2022
VL 10
AR e13061
DI 10.7717/peerj.13061
PG 24
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 0V0WH
UT WOS:000788066800002
PM 35402106
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Xie, B
   Shen, GW
   Guo, C
   Cui, YH
AF Xie, Bo
   Shen, Guowei
   Guo, Chun
   Cui, Yunhe
TI The Named Entity Recognition of Chinese Cybersecurity Using an Active
   Learning Strategy
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB In data-driven big data security analysis, knowledge graph-based multisource heterogeneous threat data organization, association mining, and inference analysis attach increasinginterest in the field of cybersecurity. Although the construction of knowledge graph based on deep learning has achieved great success, the construction of a largescale, high-quality, and domain-specific knowledge graph needs a manual annotation of large corpora, which means it is very difficult. To tackle this problem, we present a straightforward active learning strategy for cybersecurity entity recognition utilizing deep learning technology. BERT pre-trained model and residual dilation convolutional neural networks (RDCNN) are introduced to learn entity context features, and the conditional random field (CRF) layer is employed as a tag decoder. Then, taking advantages of the output results and distribution of cybersecurity entities, we propose an active learning strategy named TPCL that considers the uncertainty, confidence, and diversity. We evaluated TPCL on the general domain datasets and cybersecurity datasets, respectively. The experimental results show that TPCL performs better than the traditional strategies in terms of accuracy and F1. Moreover, compared with the general field, it has better performance in the cybersecurity field and is more suitable for the Chinese entity recognition task in this field.
C1 [Xie, Bo; Shen, Guowei; Guo, Chun; Cui, Yunhe] Guizhou Univ, Coll Comp Sci & Technol, Guiyang 550025, Peoples R China.
   [Xie, Bo; Shen, Guowei; Guo, Chun; Cui, Yunhe] Guizhou Prov Key Lab Publ Big Data, Guiyang 550025, Peoples R China.
RP Shen, GW (通讯作者)，Guizhou Univ, Coll Comp Sci & Technol, Guiyang 550025, Peoples R China.; Shen, GW (通讯作者)，Guizhou Prov Key Lab Publ Big Data, Guiyang 550025, Peoples R China.
EM xiebogzu@gmail.com; gwshen@gzu.edu.cn; gc_gzedu@163.com;
   yhcui@gzu.edu.cn
OI shen, guowei/0000-0002-1329-4058
FU National Natural Science Foundation of China [61802081]; Science and
   Technology Project of Guizhou Province [[2018]3001]
FX This work was supported by the National Natural Science Foundation of
   China (Grant no. 61802081) and the Science and Technology Project of
   Guizhou Province (Grant no. [2018]3001).
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NR 20
TC 0
Z9 0
U1 10
U2 16
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD APR 21
PY 2021
VL 2021
AR 6629591
DI 10.1155/2021/6629591
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA WG2VA
UT WOS:000706854000001
OA gold
DA 2022-08-23
ER

PT J
AU Cao, L
   Zhang, HJ
   Feng, L
AF Cao, Lei
   Zhang, Huijun
   Feng, Ling
TI Building and Using Personal Knowledge Graph to Improve Suicidal Ideation
   Detection on Social Media
SO IEEE TRANSACTIONS ON MULTIMEDIA
LA English
DT Article
DE Blogs; Social networking (online); Psychology; Stress; Statistics;
   Sociology; Knowledge engineering; Personal knowledge graph; social
   interaction; social media; suicidal ideation detection
ID PARENTING STYLE; NORMATIVE DATA; RISK-FACTORS; HEALTH-CARE; VALIDITY;
   ANXIETY; STRESS; ASSOCIATION; BEHAVIOR; TWITTER
AB A large number of individuals are suffering from suicidal ideation in the world. There are a number of causes behind why an individual might suffer from suicidal ideation. As the most popular platform for self-expression, emotion release, and personal interaction, individuals may exhibit a number of symptoms of suicidal ideation on social media. Nevertheless, challenges from both data and knowledge aspects remain as obstacles, constraining the social media-based detection performance. Data implicitness and sparsity make it difficult to discover the inner true intentions of individuals based on their posts. Inspired by psychological studies, we build and unify a high-level suicide-oriented knowledge graph with deep neural networks for suicidal ideation detection on social media. We further design a two-layered attention mechanism to explicitly reason and establish key risk factors to individual's suicidal ideation. The performance study on microblog and Reddit shows that: 1) with the constructed personal knowledge graph, the social media-based suicidal ideation detection can achieve over 93% accuracy; and 2) among the six categories of personal factors, post, personality, and experience are the top-3 key indicators. Under these categories, posted text, stress level, stress duration, posted image, and ruminant thinking contribute to one's suicidal ideation detection.
C1 [Cao, Lei; Zhang, Huijun; Feng, Ling] Tsinghua Univ, Ctr Computat Mental Healthcare, Dept Comp Sci & Technol, Res Inst Data Sci, Beijing 100084, Peoples R China.
RP Feng, L (通讯作者)，Tsinghua Univ, Ctr Computat Mental Healthcare, Dept Comp Sci & Technol, Res Inst Data Sci, Beijing 100084, Peoples R China.
EM cao-l17@mails.tsinghua.edu.cn; zhang-hj17@mails.tsinghua.edu.cn;
   fengling@mail.tsinghua.edu.cn
FU National Natural Science Foundation of China [61872214, 61532015,
   61521002]
FX This work was supported by the National Natural Science Foundation of
   China under Grants 61872214, 61532015, and 61521002. The associate
   editor coordinating the review of this manuscript and approving it for
   publication was Dr. ChuanWu.
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NR 81
TC 5
Z9 5
U1 10
U2 11
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1520-9210
EI 1941-0077
J9 IEEE T MULTIMEDIA
JI IEEE Trans. Multimedia
PY 2022
VL 24
BP 87
EP 102
DI 10.1109/TMM.2020.3046867
PG 16
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Telecommunications
GA YK9LD
UT WOS:000745524300007
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Liang, WZ
   Dai, B
   Zhao, GY
   Wu, H
AF Liang, Weizhang
   Dai, Bing
   Zhao, Guoyan
   Wu, Hao
TI A Scientometric Review on Rockburst in Hard Rock: Two Decades of Review
   from 2000 to 2019
SO GEOFLUIDS
LA English
DT Review
ID II HYDROPOWER STATION; NUMERICAL-SIMULATION; PREDICTOR VARIABLES; BURST
   PREDICTION; TUNNELS; FAILURE; ENERGY; INDEX; BEHAVIOR; SUPPORT
AB Considerable advances have been made from diverse perspectives in rockburst research to guarantee the safety of deep underground activities. However, to date, few reviews have been made to systematically analyze the overall research status of rockburst. This study is aimed at providing a scientometric review on rockburst research in hard rock from 2000 to 2019. First, a total of 430 papers were collected from the Web of Science Core Collection database. Then, the CiteSpace software was adopted to conduct a scientometric analysis from five main aspects. Finally, the existing knowledge domains and future directions were discussed. Based on the results of this study, main countries, institutions, authors, and journals of rockburst research were revealed, and some important papers, hot topics, and evolutionary trends were found out. This review contributes to the integrated knowledge map of rockburst research in hard rock and provides researchers a valuable and in-depth understanding in this domain.
C1 [Liang, Weizhang; Zhao, Guoyan; Wu, Hao] Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China.
   [Dai, Bing] Univ South China, Sch Resource Environm & Safety Engn, Hengyang 421001, Peoples R China.
   [Dai, Bing] Shandong Gold Grp Co Ltd, Deep Min Lab, Laizhou 261400, Peoples R China.
RP Zhao, GY (通讯作者)，Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China.; Dai, B (通讯作者)，Univ South China, Sch Resource Environm & Safety Engn, Hengyang 421001, Peoples R China.; Dai, B (通讯作者)，Shandong Gold Grp Co Ltd, Deep Min Lab, Laizhou 261400, Peoples R China.
EM wzlian@csu.edu.cn; daibingusc@usc.edu.cn; gyzhao@csu.edu.cn;
   hoekwu@csu.edu.cn
OI BING, DAI/0000-0002-4916-9208; Wu, Hao/0000-0001-6274-8493
FU National Key Research and Development Program of China [2018YFC0604606];
   National Natural Science Foundation of China [51774321, 51804163]; China
   Postdoctoral Science Foundation [2018M642678]
FX This work was supported by the National Key Research and Development
   Program of China (2018YFC0604606), National Natural Science Foundation
   of China (51774321 and 51804163), and China Postdoctoral Science
   Foundation (2018M642678).
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NR 55
TC 4
Z9 4
U1 16
U2 65
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1468-8115
EI 1468-8123
J9 GEOFLUIDS
JI Geofluids
PD APR 30
PY 2020
VL 2020
AR 8763283
DI 10.1155/2020/8763283
PG 17
WC Geochemistry & Geophysics; Geology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geochemistry & Geophysics; Geology
GA LO0PU
UT WOS:000533332800001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Kazemi, SM
   Goel, R
   Jain, K
   Kobyzev, I
   Sethi, A
   Forsyth, P
   Poupart, P
AF Kazemi, Seyed Mehran
   Goel, Rishab
   Jain, Kshitij
   Kobyzev, Ivan
   Sethi, Akshay
   Forsyth, Peter
   Poupart, Pascal
TI Representation Learning for Dynamic Graphs: A Survey
SO JOURNAL OF MACHINE LEARNING RESEARCH
LA English
DT Article
DE graph representation learning; dynamic graphs; knowledge graph
   embedding; heterogeneous information networks
ID LINK-PREDICTION-PROBLEM; NEURAL-NETWORKS; TIME-SERIES; MODELS; LONG
AB Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category. We also review several prominent applications and widely used datasets and highlight directions for future research.
C1 [Kazemi, Seyed Mehran; Goel, Rishab] Borealis AI, 310-6666 St Urbain, Montreal, PQ, Canada.
   [Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal] Borealis AI, 301-420 West Graham Way, Waterloo, ON, Canada.
RP Kazemi, SM (通讯作者)，Borealis AI, 310-6666 St Urbain, Montreal, PQ, Canada.
EM MEHRAN.KAZEMI@BOREALISAI.COM; RISHAB.GOEL@BOREALISAI.COM;
   KSHITIJ.JAIN@BOREALISAI.COM; IVAN.KOBYZEV@BOREALISAI.COM;
   AKSHAY.SETHI@BOREALISAI.COM; PETER.FORSYTH@BOREALISAI.COM;
   PASCAL.POUPART@BOREALISAI.COM
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NR 286
TC 39
Z9 39
U1 8
U2 20
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 2020
VL 21
PG 73
WC Automation & Control Systems; Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Automation & Control Systems; Computer Science
GA LI3SA
UT WOS:000529405000022
DA 2022-08-23
ER

PT J
AU Jin, JH
   Luo, JZ
   Khemmarat, S
   Gao, LX
AF Jin, Jiahui
   Luo, Junzhou
   Khemmarat, Samamon
   Gao, Lixin
TI Querying Web-Scale Knowledge Graphs Through Effective Pruning of Search
   Space
SO IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
LA English
DT Article
DE Subgraph similarity matching; knowledge graph; billion-node graph;
   index-free; top-k query; distributed system
ID FRAMEWORK; ALGORITHMS; ENGINE
AB Web-scale knowledge graphs containing billions of entities are common nowadays. Querying these graphs can be modeled as a subgraph matching problem. Since knowledge graphs are incomplete and noisy in nature, it is important to discover answers matching exactly as well as answers similar to queries. Existing graph matching algorithms usually use graph indices to accelerate query processing. For billion-node graphs, it may be infeasible to build the graph indices due to the amount of work and the memory/storage required. In this paper, we propose an efficient algorithm for finding the best k answers for a given query without precomputing graph indices. An answer's quality is measured by a matching score that is computed online. To accelerate query processing, we propose a novel technique for bounding the matching scores during the computation. By using bounds, the low quality answers can be efficiently pruned. The bounding technique can be implemented in a distributed environment, allowing our approach to efficiently query web-scale knowledge graphs. We evaluate the effectiveness and the efficiency of our approach on real-world datasets. The result shows that our bounding technique can reduce the running time up to two orders of magnitude comparing to an approach without using bounds.
C1 [Jin, Jiahui; Luo, Junzhou] Southeast Univ, Sch Comp Sci & Engn, Nanjing 210018, Jiangsu, Peoples R China.
   [Khemmarat, Samamon; Gao, Lixin] Univ Massachusetts, Dept Elect & Comp Engn, Amherst, MA 01003 USA.
RP Jin, JH (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing 210018, Jiangsu, Peoples R China.
EM jjin@seu.edu.cn; jluo@seu.edu.cn; khemmarat@ecs.umass.edu;
   lgao@ecs.umass.edu
FU National Natural Science Foundation of China [61632008, 61320106007,
   61572129, 61502097, 61370207, 61602112, 61532013, 61572128];
   International S&T Cooperation Program of China [2015DFA10490]; National
   Science Foundation of Jiangsu Province [BK20160695]; Jiangsu Provincial
   Key Laboratory of Network and Information Security [BM2003201]; Key
   Laboratory of Computer Network and Information Integration of Ministry
   of Education of China [93K-9]; Collaborative Innovation Center of Novel
   Software Technology and Industrialization; Collaborative Innovation
   Center of Wireless Communications Technology; US National Science
   Foundation [CNS-1217284, CCF-1018114]; China Scholarship Council
FX This work is supported by National Natural Science Foundation of China
   under Grants No. 61632008, No. 61320106007, No. 61572129, No. 61502097,
   No. 61370207, No. 61602112, No. 61532013, No. 61572128, International
   S&T Cooperation Program of China No. 2015DFA10490, National Science
   Foundation of Jiangsu Province No. BK20160695, Jiangsu Provincial Key
   Laboratory of Network and Information Security under Grants No.
   BM2003201, Key Laboratory of Computer Network and Information
   Integration of Ministry of Education of China under Grants No. 93K-9,
   and partially supported by Collaborative Innovation Center of Novel
   Software Technology and Industrialization and Collaborative Innovation
   Center of Wireless Communications Technology. This work is also
   partially supported by US National Science Foundation grants CNS-1217284
   and CCF-1018114. Jiahui Jin was a visiting student at UMass Amherst,
   supported by China Scholarship Council, when this work was performed.
   Any opinions, findings, conclusions or recommendations expressed in this
   paper are those of the authors and do not necessarily reflect the views
   of the sponsor. Preliminary version [1] appeared in Proceedings of the
   24th International Conference on World Wide Web (WWW 2015) as a regular
   research paper. Junzhou Luo is the corresponding author of this paper.
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NR 38
TC 10
Z9 10
U1 3
U2 29
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1045-9219
EI 1558-2183
J9 IEEE T PARALL DISTR
JI IEEE Trans. Parallel Distrib. Syst.
PD AUG 1
PY 2017
VL 28
IS 8
BP 2342
EP 2356
DI 10.1109/TPDS.2017.2665478
PG 15
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA FA9FA
UT WOS:000405750200017
OA hybrid
DA 2022-08-23
ER

PT J
AU Kumar, AJ
   Schmidt, C
   Kohler, J
AF Kumar, Ashwini Jaya
   Schmidt, Christoph
   Koehler, Joachim
TI A knowledge graph based speech interface for question answering systems
SO SPEECH COMMUNICATION
LA English
DT Article
DE Spoken question answering; Knowledge graphs; Automatic speech
   recognition; Spoken language understanding; Spoken interface; Linked
   data
AB Speech interfaces to conversational systems have been a focus in academia and industry for over a decade due to its applicability as a natural interface. Speech recognition and speech synthesis constitute the important input and output modules respectively for such spoken interface systems. In this paper, the speech recognition interface for question answering applications is reviewed, and existing limitations are discussed. The existing spoken question answering (QA) systems use an automatic speech recogniser by adapting acoustic and language models for the speech interface and off-the-shelf language processing systems for question interpretation. In the process, the impact of recognition errors and language processing inaccuracies is neglected. It is illustrated in the paper how a semantically rich knowledge graph can be used to solve automatic speech recognition and language processing specific problems. A simple concatenation of a speech recogniser and a natural language processing system is a shallow method for a speech interface. An effort beyond merely concatenating these two units is required to develop a successful spoken question answering system. It is illustrated in this paper how a knowledge graph based structured data can be used to build a unified system combining speech recognition and language understanding. This facilitates the use of a semantically rich data model for speech interface. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Kumar, Ashwini Jaya] Fraunhofer IAIS, Netmedia, St Augustin, Germany.
   Univ Bonn, Enterprise Informat Syst, Bonn, Germany.
RP Kumar, AJ (通讯作者)，Fraunhofer IAIS, Netmedia, St Augustin, Germany.
EM ashwini.jaya.kumar@iais.fraunhofer.de;
   christoph.andreas.schmidt@iais.fraunhofer.de;
   joachim.koehler@iais.fraunhofer.de
FU European Unions Horizon research and innovation program under the Marie
   Sklodowska-Curie grant [642795]
FX Parts of this work received funding from the European Unions Horizon
   2020 research and innovation program under the Marie Sklodowska-Curie
   grant agreement No. 642795 (WDAqua project).
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PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0167-6393
EI 1872-7182
J9 SPEECH COMMUN
JI Speech Commun.
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SC Acoustics; Computer Science
GA FD6SV
UT WOS:000407659100001
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ER

PT J
AU Cheng, JW
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AF Cheng, Jingwei
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TI Knowledge Graph Representation Learning With Multi-Scale Capsule-Based
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SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph representation learning; capsule network; link
   prediction; knowledge graph embedding
AB A Knowledge Graph (KG) is a directed graph with nodes as entities and edges as relations. KG representation learning (KGRL) aims to embed entities and relations in a KG into continuous low-dimensional vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. In this paper, we propose a KG embedding framework, namely MCapsEED (Multi-Scale Capsule-based Embedding Model Incorporating Entity Descriptions). MCapsEED employs a Transformer in combination with a relation attention mechanism to identify the relation-specific part of an entity description and obtain the description representation of an entity. The structured and description representations of an entity are integrated into a synthetic representation. A 3-column matrix with each column a synthetic representation of an element of a triple is fed into a Multi-Scale Capsule-based Embedding model to produce final representations of the head entity, the tail entity and the relation. Experiments show that MCapsEED achieves better performance than state-of-the-art embedding models for the task of link prediction on four benchmark datasets. Our code can be found at https://github.com/1780041410/McapsEED.
C1 [Cheng, Jingwei; Zhang, Fu; Yang, Zhi] Northeastern Univ, Coll Comp Sci & Engn, Shenyang 110169, Peoples R China.
RP Cheng, JW (通讯作者)，Northeastern Univ, Coll Comp Sci & Engn, Shenyang 110169, Peoples R China.
EM chengjingwei@mail.neu.edu.cn
OI Zhang, Fu/0000-0002-3880-8086; Cheng, Jingwei/0000-0003-1054-4952
FU National Natural Science Foundation of China [61672139]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 61672139.
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NR 49
TC 0
Z9 0
U1 0
U2 8
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 203028
EP 203038
DI 10.1109/ACCESS.2020.3035636
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA OR9EL
UT WOS:000589768600001
OA gold
DA 2022-08-23
ER

PT J
AU Li, YW
   Ma, YL
   Huo, X
   Wu, XK
AF Li, Yongwei
   Ma, Yalong
   Huo, Xiang
   Wu, Xinkai
TI Remote object navigation for service robots using hierarchical knowledge
   graph in human-centered environments
SO INTELLIGENT SERVICE ROBOTICS
LA English
DT Article; Early Access
DE Service robot; Remote object navigation; Scene knowledge graph;
   Probabilistic inference; Human-centered environments
ID SEMANTIC KNOWLEDGE; MOBILE; MAPS
AB Remote object navigation (RON), defined as navigating to a remote object that is invisible in the current view, is an inevitable and extremely challenging task for a service robot, particularly when facing unstructured or dynamic human-centered environments. How to apply object-level semantic knowledge about the scene (called scene knowledge graph, SKG) to assist robots in cognition of the environment has become a hot research topic in robot intelligence. In this paper, we propose a knowledge-based RON method to skillfully combine the hierarchical knowledge in SKG and the probability-based navigation strategy. In detail, we first develop an automated pipeline to construct a novel SKG from massive visual data in real indoor environments. Then we propose a reasoner to derive the probabilistic representation of the hierarchical knowledge contained in the SKG. Additionally, a two-stage navigator composed of global path planning and local search strategy is applied as a distance-aware task planner to reduce the navigation path cost. The experimental results in real-world scenarios indicate that the proposed method has efficient performance and robustness on RON task compared to other approaches.
C1 [Li, Yongwei; Huo, Xiang; Wu, Xinkai] Beihang Univ, Sch Transportat Sci & Engn, Xueyuan Rd, Beijing 100191, Peoples R China.
   [Ma, Yalong] Beijing Robint Technol Co Ltd, Xueyuan Rd, Beijing 100191, Peoples R China.
RP Wu, XK (通讯作者)，Beihang Univ, Sch Transportat Sci & Engn, Xueyuan Rd, Beijing 100191, Peoples R China.
EM yongweili@buaa.edu.cn; mayalong@buaa.edu.cn; huoxiang@buaa.edu.cn;
   xinkaiwu@buaa.edu.cn
FU National Natural Science Foundation of China [52172376]; Project Fund of
   the GENERAL ADMINISTRATION OF CUSTOMS.P.R.CHINA [2021HK261]; China
   Electronic Port Data Center Beijing Branch
FX This work was partially supported by the National Natural Science
   Foundation of China (52172376) and the Project Fund of the GENERAL
   ADMINISTRATION OF CUSTOMS.P.R.CHINA (2021HK261). Thanks for the support
   of the China Electronic Port Data Center Beijing Branch. The authors
   also gratefully acknowledge the helpful comments and suggestions of the
   reviewers, which have improved the presentation.
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NR 52
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1861-2776
EI 1861-2784
J9 INTEL SERV ROBOT
JI Intell. Serv. Robot.
DI 10.1007/s11370-022-00428-4
EA JUN 2022
PG 15
WC Robotics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Robotics
GA 2B7LE
UT WOS:000810365200001
DA 2022-08-23
ER

PT J
AU Rony, MRA
   Kumar, U
   Teucher, R
   Kovriguina, L
   Lehmann, J
AF Rony, Md Rashad Al Hasan
   Kumar, Uttam
   Teucher, Roman
   Kovriguina, Liubov
   Lehmann, Jens
TI SGPT: A Generative Approach for SPARQL Query Generation From Natural
   Language Questions
SO IEEE ACCESS
LA English
DT Article
DE Measurement; Linguistics; Syntactics; Resource description framework;
   Adaptation models; Standards; Training; Knowledge based systems;
   knowledge graph; information retrieval; query generation; language
   models
AB SPARQL query generation from natural language questions is complex because it requires an understanding of both the question and underlying knowledge graph (KG) patterns. Most SPARQL query generation approaches are template-based, tailored to a specific knowledge graph and require pipelines with multiple steps, including entity and relation linking. Template-based approaches are also difficult to adapt for new KGs and require manual efforts from domain experts to construct query templates. To overcome this hurdle, we propose a new approach, dubbed SGPT, that combines the benefits of end-to-end and modular systems and leverages recent advances in large-scale language models. Specifically, we devise a novel embedding technique that can encode linguistic features from the question which enables the system to learn complex question patterns. In addition, we propose training techniques that allow the system to implicitly employ the graph-specific information (i.e., entities and relations) into the language model's parameters and generate SPARQL queries accurately. Finally, we introduce a strategy to adapt standard automatic metrics for evaluating SPARQL query generation. A comprehensive evaluation demonstrates the effectiveness of SGPT over state-of-the-art methods across several benchmark datasets.
C1 [Rony, Md Rashad Al Hasan; Teucher, Roman; Kovriguina, Liubov; Lehmann, Jens] Fraunhofer Inst Intelligent Anal & Informat Syst, D-01069 Dresden, Germany.
   [Rony, Md Rashad Al Hasan; Lehmann, Jens] Univ Bonn, Smart Data Analyt Res Grp, D-53115 Bonn, Germany.
   [Kumar, Uttam] Univ Bonn, Data Sci & Intelligent Syst Grp, D-53115 Bonn, Germany.
RP Rony, MRA (通讯作者)，Fraunhofer Inst Intelligent Anal & Informat Syst, D-01069 Dresden, Germany.; Rony, MRA (通讯作者)，Univ Bonn, Smart Data Analyt Res Grp, D-53115 Bonn, Germany.
EM rashad.rony@iais.fraunhofer.de
OI Rony, Md Rashad Al Hasan/0000-0003-0665-389X; Lehmann,
   Jens/0000-0001-9108-4278
FU SPEAKERBMWi FKZ 01MK20011A; Fraunhofer Zukunftsstiftung; OpenGPT-X
   [BMWKFKZ 68GX21007A]; Excellence Clusters ML2R [BmBF FKZ01 15
   18038A/B/C]; ScaDS.AI [IS18026A-F]; TAILOR [EUGA 952215]
FX This work was supported in part by the SPEAKER under Grant BMWi FKZ
   01MK20011A, in part by the JOSEPH (Fraunhofer Zukunftsstiftung), in part
   by the OpenGPT-X under Grant BMWKFKZ 68GX21007A, in part by the
   Excellence Clusters ML2R under Grant BmBF FKZ01 15 18038A/B/C, in part
   by the ScaDS.AI under Grant IS18026A-F, and in part by the TAILOR under
   Grant EUGA 952215.
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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 70712
EP 70723
DI 10.1109/ACCESS.2022.3188714
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3Q8FC
UT WOS:000838459000001
OA gold
DA 2022-08-23
ER

PT J
AU McCarthy, I
   Ridgway, K
   Leseure, M
   Fieller, N
AF McCarthy, I
   Ridgway, K
   Leseure, M
   Fieller, N
TI Organisational diversity, evolution and cladistic classifications
SO OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
LA English
DT Review
DE cladistics; manufacturing; management; evolution; classification
ID SYSTEMATICS; STRATEGIES; PARSIMONY
AB This article presents a case for the construction of a formal classification of manufacturing systems using cladistics, a technique from the biological school of classification. A seven-stage framework for producing a manufacturing cladogram is presented, along with a pilot case study example. This article describes the role that classification plays in the pure and applied sciences, the social sciences and reviews the status of existing manufacturing classifications. If organisational diversity and organisational change processes are governed by evolutionary mechanisms, studies of organisations based on an evolutionary approach such as cladistics could have potential, because as March [March JG. The evolution of evolution. In: Baum JAC, Singh JV, editors. Evolutionary dynamics of organizations. Oxford University Press, 1994. p. 39-52], page 45, states "there is natural speculation that organisations, like species can be engineered by understanding the evolutionary processes well enough to intervene and produce competitive organisational effects". It is suggested that a cladistic study could provide organisations with a "knowledge map" of the ecosystem in which they exist and by using this phylogenetic and situational analysis, they could determine coherent and appropriate action for the specification of change. (C) 2000 Elsevier Science Ltd. All rights reserved.
C1 Univ Sheffield, Dept Engn Mech, Sheffield S1 3JD, S Yorkshire, England.
   Univ Sheffield, Sch Math & Stat, Sheffield S3 7RH, S Yorkshire, England.
RP McCarthy, I (通讯作者)，Univ Sheffield, Dept Engn Mech, Mappin St, Sheffield S1 3JD, S Yorkshire, England.
OI McCarthy, Ian/0000-0003-3330-0977
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   [No title captured]
   [No title captured]
   [No title captured]
   [No title captured]
NR 111
TC 34
Z9 34
U1 0
U2 14
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0305-0483
J9 OMEGA-INT J MANAGE S
JI Omega-Int. J. Manage. Sci.
PD FEB
PY 2000
VL 28
IS 1
BP 77
EP 95
DI 10.1016/S0305-0483(99)00030-4
PG 19
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 269GX
UT WOS:000084469300005
DA 2022-08-23
ER

PT J
AU Jiang, HI
   Wang, SL
   Yao, JR
AF Jiang, Huii
   Wang, Suli
   Yao, Jianrong
TI Structuration analysis of e-government studies: A bibliometric analysis
   based on knowledge maps
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article; Early Access
DE E-government; mapping knowledge domains; research paradigms; spatial
   structure
ID DIGITAL GOVERNMENT; ACCEPTANCE MODEL; INFORMATION; TECHNOLOGY;
   TRANSPARENCY; PARTICIPATION; TRUST; BIG
AB Considering the lack of systematic reviews on e-government research, this study includes research categories, spatial structure, research paradigms and noteworthy future topics in the domain of e-government. We collect 142 keywords from 2646 papers published in the Web of Science from 2000 to 2019 as the study object. Then, we identify four research categories: (1) technology and modelling in e-government, (2) drivers of e-government development, (3) public management and (4) governmental management. From public and government perspectives, we outline the spatial structure which includes theories and practices research and conclude four research paradigms: (1) theoretical modelling and application, (2) e-government development, (3) status of public management and (4) status of governmental management. Finally, we develop a 3D spatial map to analyse noteworthy topics and explore the well-studied themes: government-to-citizen, government-to-government and government-to-business, and the under-studied themes: government-to-civil society organisations and citizens-to-citizens, which helps scholars research e-government roundly.
C1 [Jiang, Huii; Wang, Suli; Yao, Jianrong] Zhejiang Univ Finance & Econ, Sch Informat Management & Artificial Intelligence, Hangzhou 310018, Peoples R China.
   [Jiang, Huii] Zhejiang Univ, Sch Management, Hangzhou, Peoples R China.
RP Yao, JR (通讯作者)，Zhejiang Univ Finance & Econ, Sch Informat Management & Artificial Intelligence, Hangzhou 310018, Peoples R China.
EM yjr@zufe.edu.cn
FU National Natural Science Foundation of China [71704153, 71701180,
   71704020, 71801187]; China Postdoctoral Science Foundation [2018M642472]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship and/or publication of this article: This work
   was supported by the National Natural Science Foundation of China (Grant
   Nos. 71704153, 71701180, 71704020 and 71801187) and China Postdoctoral
   Science Foundation (Grant No. 2018M642472).
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NR 48
TC 1
Z9 1
U1 9
U2 38
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
AR 0165551520978346
DI 10.1177/0165551520978346
EA MAY 2021
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 SE6XJ
UT WOS:000652214900001
DA 2022-08-23
ER

PT J
AU Zhang, JR
   Huang, J
   Gao, JL
   Han, RH
   Zhou, C
AF Zhang, Jiarui
   Huang, Jian
   Gao, Jialong
   Han, Runhai
   Zhou, Cong
TI Knowledge graph embedding by logical-default attention graph convolution
   neural network for link prediction
SO INFORMATION SCIENCES
LA English
DT Article
DE Link prediction; Default logic; GCN; Wasserstein distance; Iterative
ID DATABASE
AB The purpose of link prediction is to predict missing edges of the knowledge graph, or edges that may appear in the future. An abundance of data-driven methods exist, which rely on many labels and only consider the structural information about the graph. To address these problems, we have developed a logical-default attention graph convolution neural network model (DA-GCN). The model consists of three parts. First, the confidence of first-order logic inference with Default (DFLIN) was calculated by defining rules. Second, a local relational attention model was defined to represent self information, which includes first order, second order neighbor node information, and to train a graph convolution neural network based on iterative attention (IAGCN) by using centralized training distributed execution model. Third, iteratively update DFLIN and IAGCN using the Wasserstein distance to express the relation between entities. Finally, we conduct comparative experiments between the DA-GCN model and other models on a self-built dataset and three public data sets. The results show that the Mean Reciprocal Rank (MRR) value is about 36.48% higher than that of the R-GCN model. Our results show that DA-GCN achieves state-of-the-art performance compared to other specific or general integrated methods, especially on smallscale organized knowledge graphs.(c) 2022 Elsevier Inc. All rights reserved.
C1 [Zhang, Jiarui; Huang, Jian; Gao, Jialong; Han, Runhai; Zhou, Cong] Natl Univ Def Technol, Coll Intelligence Sci & Technol, Changsha 410073, Peoples R China.
RP Huang, J (通讯作者)，Natl Univ Def Technol, Coll Intelligence Sci & Technol, Changsha 410073, Peoples R China.
EM zjr888@stu.xjtu.edu.cn; nudtjhuang@hotmail.com; gaojialong@nudt.edu.cn;
   hanrunhai@qq.com; 18711327338@139.com
OI Zhang, Jiarui/0000-0002-9282-3770
FU 13th Five-Year equipment preresearch sharing technology [41412030301]
FX Acknowledgement This work was supported by the 13th Five-Year equipment
   pre-research sharing technology project No.41412030301.
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NR 46
TC 1
Z9 1
U1 15
U2 15
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD MAY
PY 2022
VL 593
BP 201
EP 215
DI 10.1016/j.ins.2022.01.076
PG 15
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZV7DO
UT WOS:000770686400011
DA 2022-08-23
ER

PT J
AU Assi, A
   Dhifli, W
AF Assi, Ali
   Dhifli, Wajdi
TI Instance Matching in Knowledge Graphs through random walks and semantics
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Affinity-preserving random walk; Data linking; Instance Matching;
   Knowledge Graph; Web of data
AB Instance Matching (IM) is the process of matching instances that refer to the same real-world object (e.g., the same person) across different independent Knowledge Bases (KBs). This process is considered as a key step, for instance, in the integration of different KBs. In this paper, we focus on the problem of IM across different KBs represented as Knowledge Graphs (KGs). We propose SBIGMat, a novel approach for the IM problem based on Markov random walks (RW). Our approach leverages both the local and global information mutually calculated from a pairwise similarity graph. Precisely, we first build an expanded association graph consisting of pairs of IM candidates. Then, we rank each candidate pair through the stationary distribution computed from the RW on the association graph. We propose semantic and bipartite graph-based post-processing strategies that operate on the obtained random walk ranks to optimize the final assignment of co-referents. We provide a scalable distributed implementation of our approach on top of the Spark framework and we evaluate it on benchmark datasets from the instance track of the Ontology Alignment Evaluation Initiative (OAEI). The experiments show the efficiency and scalability of SBIGMat compared to several state-of-the-art IM approaches. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Assi, Ali] Univ Quebec Montreal, 201 Av President Kennedy, Montreal, PQ H2X 3Y7, Canada.
   [Dhifli, Wajdi] Univ Lille, METRICS Evaluat Technol Sante & Prat Med, ULR 2694, F-59000 Lille, France.
RP Dhifli, W (通讯作者)，Univ Lille, METRICS Evaluat Technol Sante & Prat Med, ULR 2694, F-59000 Lille, France.
EM assi.ali@courrier.uqam.ca; wajdi.dhifli@univ-lille.fr
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NR 54
TC 1
Z9 1
U1 1
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 OCT
PY 2021
VL 123
BP 73
EP 84
DI 10.1016/j.future.2021.04.015
EA MAY 2021
PG 12
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SS6MD
UT WOS:000661867900007
DA 2022-08-23
ER

PT J
AU Shao, X
   Li, CY
   Yang, HH
   Lu, XY
   Liao, J
   Qian, JY
   Wang, K
   Cheng, JY
   Yang, PH
   Chen, HJ
   Xu, X
   Fan, XH
AF Shao, Xin
   Li, Chengyu
   Yang, Haihong
   Lu, Xiaoyan
   Liao, Jie
   Qian, Jingyang
   Wang, Kai
   Cheng, Junyun
   Yang, Penghui
   Chen, Huajun
   Xu, Xiao
   Fan, Xiaohui
TI Knowledge-graph-based cell-cell communication inference for spatially
   resolved transcriptomic data with SpaTalk
SO NATURE COMMUNICATIONS
LA English
DT Article
ID GENOME-WIDE EXPRESSION; CROSSTALK; CANCER
AB Cell-cell communication is a vital feature involving numerous biological processes. Here, the authors develop SpaTalk, a cell-cell communication inference method using knowledge graph for spatially resolved transcriptomic data, providing valuable insights into spatial intercellular tissue dynamics.
   Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.
C1 [Shao, Xin; Wang, Kai; Xu, Xiao; Fan, Xiaohui] Zhejiang Univ, Hangzhou Peoples Hosp 1, Key Lab Integrated Oncol & Intelligent Med Zheji, Dept Hepatobiliary & Pancreat Surg,Sch Med, Hangzhou 310006, Peoples R China.
   [Shao, Xin; Li, Chengyu; Lu, Xiaoyan; Liao, Jie; Qian, Jingyang; Cheng, Junyun; Yang, Penghui; Fan, Xiaohui] Zhejiang Univ, Pharmaceut Informat Inst, Coll Pharmaceut Sci, Hangzhou 310058, Peoples R China.
   [Yang, Haihong; Chen, Huajun] Zhejiang Univ, Hangzhou Innovat Ctr, Hangzhou 310058, Peoples R China.
   [Yang, Haihong; Chen, Huajun] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
   [Fan, Xiaohui] Zhejiang Univ, Innovat Ctr Yangtze River Delta, Future Hlth Lab, Jiaxia 314100, Peoples R China.
RP Xu, X; Fan, XH (通讯作者)，Zhejiang Univ, Hangzhou Peoples Hosp 1, Key Lab Integrated Oncol & Intelligent Med Zheji, Dept Hepatobiliary & Pancreat Surg,Sch Med, Hangzhou 310006, Peoples R China.; Fan, XH (通讯作者)，Zhejiang Univ, Pharmaceut Informat Inst, Coll Pharmaceut Sci, Hangzhou 310058, Peoples R China.; Chen, HJ (通讯作者)，Zhejiang Univ, Hangzhou Innovat Ctr, Hangzhou 310058, Peoples R China.; Chen, HJ (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.; Fan, XH (通讯作者)，Zhejiang Univ, Innovat Ctr Yangtze River Delta, Future Hlth Lab, Jiaxia 314100, Peoples R China.
EM huajunsir@zju.edu.cn; zjxu@zju.edu.cn; fanxh@zju.edu.cn
RI FAN, Xiaohui/F-6458-2010
OI FAN, Xiaohui/0000-0002-6336-3007; Shao, Xin/0000-0002-1928-3878; Li,
   Chengyu/0000-0003-3144-9460
FU National Natural Science Foundation of China [81930016, 81973701]; Key
   Research and Development Program of China [2021YFA1100500]; Major
   Research Plan of the National Natural Science Foundation of China
   [92159202]; Natural Science Foundation of Zhejiang Province
   [LZ20H290002]; China Postdoctoral Science Foundation [2021M702828];
   Alibaba Cloud
FX This work was supported by the National Natural Science Foundation of
   China (81973701, X.F.), the Key Program, National Natural Science
   Foundation of China (81930016, X.X.), the Key Research and Development
   Program of China (2021YFA1100500, X.X.), the Major Research Plan of the
   National Natural Science Foundation of China (No.92159202, X.X.), the
   Natural Science Foundation of Zhejiang Province (LZ20H290002, X.F.), the
   China Postdoctoral Science Foundation (2021M702828, X.S.) and Alibaba
   Cloud. The authors gratefully thank Giotto and NicheNet for their
   inspirations in the development of this project and thank Eric Xihui Lin
   for his implementation of NNLM package.
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NR 63
TC 0
Z9 0
U1 3
U2 3
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
EI 2041-1723
J9 NAT COMMUN
JI Nat. Commun.
PD JUL 30
PY 2022
VL 13
IS 1
AR 4429
DI 10.1038/s41467-022-32111-8
PG 15
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 3L3WB
UT WOS:000834693800003
PM 35908020
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Birchall, DW
   Tovstiga, G
AF Birchall, DW
   Tovstiga, G
TI Assessing the firm's strategic knowledge portfolio: a framework and
   methodology
SO INTERNATIONAL JOURNAL OF TECHNOLOGY MANAGEMENT
LA English
DT Article
DE knowledge mapping; knowledge portfolio; knowledge culture; process;
   content and infrastructure; strategic impact; competitive positioning
AB It is now widely acknowledged that knowledge rather than capital assets is the source of wealth in today's global economy in which firms increasingly are competing in knowledge-intensive markets. Consequently, firms are seeking to gain a better understanding of how their organisationally-embedded knowledge can be exploited to gain and maintain competitive edge in a turbulent marketplace. To be competitive and successful, firms are creating and sustaining a dynamic portfolio of knowledge repositories and processes. The authors contend that the outcomes can be much enhanced if this is based on a systematic approach to knowledge management.
   A consensus in leading edge firms about the importance of knowledge has resulted in considerable investment in the analysis of technology portfolios and such processes as technology mapping. Nevertheless many firms, particularly smaller and medium enterprises, have still not translated a concern for improved knowledge management into even the most rudimentary analysis. Consequently, many firms do not have the means for really understanding the strategic impact of their knowledge capital on their competitive performance. Practical tools and methodologies which assist in this process of assessing the breadth and depth of an organisation's knowledge portfolio can assist significantly in such cases in enabling firm's to get the key issues onto the board agenda.
   In this paper the authors present a conceptual framework for mapping a firm's knowledge portfolio in terms of four domains - content, process, infrastructure and culture. The mapping approach is designed to help guide the thinking process in the firm with respect to ways in which its knowledge is being created, stored and applied towards competitive advantage. The methodology begins with a mapping of the firm's business processes from a knowledge perspective. Core business processes are unbundled in terms of their specific knowledge domains. They are subsequently classified according to their degree of tacitness. Next, the competitive impact and competitive positioning of each area is evaluated and compared to the principal competition. Gaps in the firm's knowledge portfolio are identified; a strategic framework is provided for a proactive approach to strategic knowledge management.
   The methodology provides a systematic approach to building a strategic knowledge map. It represents a strategic tool for assessing a firm's knowledge portfolio - it is a map pointing to where knowledge resides in the firm; to key knowledge processes; and to the flow of knowledge between processes and between people.
C1 Henley Management Coll, Henley On Thames, England.
   ABB Business Serv Ltd, Business Consulting SBC, Baden, Switzerland.
RP Birchall, DW (通讯作者)，Henley Management Coll, Henley On Thames, England.
EM davidbi@henleymc.ac.uk; george.tovistiga@ch.abb.com
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TC 7
Z9 8
U1 0
U2 15
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 2002
VL 24
IS 4
BP 419
EP 434
DI 10.1504/IJTM.2002.003063
PG 16
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 582WQ
UT WOS:000177374500004
DA 2022-08-23
ER

PT J
AU Miao, F
   Wang, XT
   Feng, F
   Jin, C
   Jin, LB
AF Miao, Fang
   Wang, Xueting
   Feng, Feng
   Jin, Cong
   Jin, Libiao
TI A Renovated CNN-Based Model Enhances KGC Task Performance
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
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C1 [Miao, Fang; Jin, Cong; Jin, Libiao] Commun Univ China, Sch Informat & Commun Engn, Beijing 10024, Peoples R China.
   [Wang, Xueting] China United Network Commun Corp, Qingdao Branch, Qingdao, Peoples R China.
   [Feng, Feng] Univ Missouri, Dept Elect Engn & Comp Sci, Columbia, MO 65211 USA.
RP Miao, F (通讯作者)，Commun Univ China, Sch Informat & Commun Engn, Beijing 10024, Peoples R China.
EM miaof03@cuc.edu.cn
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TC 0
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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 MAR 16
PY 2022
VL 2022
AR 5968047
DI 10.1155/2022/5968047
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1X8WG
UT WOS:000807730100007
OA gold
DA 2022-08-23
ER

PT J
AU Shi, D
   Wang, N
   Zhang, FF
   Fang, W
AF Shi, Dan
   Wang, Nan
   Zhang, Fangfei
   Fang, Wei
TI Intelligent Electromagnetic Compatibility Diagnosis and Management With
   Collective Knowledge Graphs and Machine Learning
SO IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY
LA English
DT Article
DE Electromagnetic compatibility; Interference; Integrated circuit
   modeling; Machine learning; Knowledge based systems; Mathematical model;
   Collective knowledge graph; electromagnetic compatibility (EMC)
   management; interactive sessions; long short-term memory (LSTM) method;
   mathematical sets
ID NETWORK
AB The explosive growth of electronic devices brings a soaring demand for rapid electromagnetic compatibility (EMC) diagnosis. However, there is a significant learning curve for the electrical engineers to apply EMC knowledge. In this article, an efficient EMC diagnosis and management methodology was proposed, which provided a fast way for EMC analysis in seconds other than traditional simulation or calculation. The approach organized the EMC knowledges as knowledge graph composed by the interference/sensitive units, and mathematical set rules. The optimized graph structure is in form of rule, maxterm, basic unit, and entity layers. Based on the condensed relationships, it achieved high searching efficiency and graph expansibility. To facilitate the information retrieval from the knowledge graph, the interference/sensitive units and related parameters were acquired from interactive sessions, in which long short-term memory method was used to extract entities. The EMC specialized corpora were fed in training to enhance the accuracy of inference. Finally, the EMC diagnosis and management reports were automatically generated by knowledge graph searching application. The proposed method improved the calculation efficiency by three times. The storage of relationships and attributes of nodes was reduced by 76% and 60.7%. The identification accuracy was enhanced from 77.7% to 99.5%. The presented method is practically useful for EMC design in crosstalk analysis, radiated, and conducted interference diagnoses.
C1 [Shi, Dan] Beijing Univ Post & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China.
   [Wang, Nan] Baidu Netcom Sci Technol Co Ltd, Beijing 100083, Peoples R China.
   [Zhang, Fangfei] Koubei Shanghai Informat Technol Co Ltd, Shanghai 200136, Peoples R China.
   [Fang, Wei] Microsoft Res Asia, Beijing 100080, Peoples R China.
RP Shi, D (通讯作者)，Beijing Univ Post & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China.
EM shidan@bupt.edu.cn; wangnan@buptemc.com; zhangfangfei@buptemc.com;
   arthfa@microsoft.com
FU National Natural Science Foundation of China [61771069]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 61771069.
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NR 27
TC 2
Z9 2
U1 1
U2 14
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9375
EI 1558-187X
J9 IEEE T ELECTROMAGN C
JI IEEE Trans. Electromagn. Compat.
PD APR
PY 2021
VL 63
IS 2
BP 443
EP 453
DI 10.1109/TEMC.2020.3019801
PG 11
WC Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Telecommunications
GA RN9VB
UT WOS:000640699600013
DA 2022-08-23
ER

PT J
AU Varma, S
   Shivam, S
   Biswas, S
   Saha, P
   Jalan, K
AF Varma, Sandeep
   Shivam, Shivam
   Biswas, Snigdha
   Saha, Pritam
   Jalan, Khushi
TI Graph NLU enabled question answering system
SO HELIYON
LA English
DT Article
DE Conversational analytics; Graph traversal; Knowledge graph; Natural
   language query; Question answering; Structured data; Tabular data
AB Tabular data With a huge amount of information being stored as structured data, there is an increasing need for retrieving exact answers to questions from tables. Answering natural language questions on structured data usually involves semantic parsing of query to a machine understandable format which is then used to retrieve information from the database. Training semantic parsers for domain specific tasks is a tedious job and does not guarantee accurate results. In this paper, we used conversational analytics tool to create the user interface and to get the required entities and intents from the query thus avoiding the traditional semantic parsing approach. We then make use of Knowledge Graph for querying in structured data domain. Knowledge graphs can be easily leveraged for question answering systems, to use them as the database. We extract appropriate answers for different types of queries which have been illustrated in the Results section.
C1 [Varma, Sandeep; Shivam, Shivam; Biswas, Snigdha; Saha, Pritam; Jalan, Khushi] ZS Associates, World Trade Ctr, Tower 3, Pune 411014, Maharashtra, India.
RP Shivam, S; Biswas, S (通讯作者)，ZS Associates, World Trade Ctr, Tower 3, Pune 411014, Maharashtra, India.
EM shivam.shivam@zs.com; snigdhasawsib2000@gmail.com
OI , Shivam/0000-0003-2398-5474; Jalan, Khushi/0000-0002-9558-4509; Biswas,
   Snigdha/0000-0003-4033-4761
FU ZS Associates, Big Data PoD
FX This work has been done in and supported by ZS Associates, Big Data PoD.
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NR 13
TC 0
Z9 0
U1 1
U2 2
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
EI 2405-8440
J9 HELIYON
JI Heliyon
PD SEP
PY 2021
VL 7
IS 9
AR e08035
DI 10.1016/j.heliyon.2021.e08035
EA SEP 2021
PG 12
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA XM1KR
UT WOS:000728595200006
PM 34622048
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chaves-Fraga, D
   Priyatna, F
   Cimmino, A
   Toledo, J
   Ruckhaus, E
   Corcho, O
AF Chaves-Fraga, David
   Priyatna, Freddy
   Cimmino, Andrea
   Toledo, Jhon
   Ruckhaus, Edna
   Corcho, Oscar
TI GTFS-Madrid-Bench: A benchmark for virtual knowledge graph access in the
   transport domain
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Virtual knowledge graph; Benchmark; Query translation; Data integration;
   GTFS
ID SPARQL-TO-SQL
AB A large number of datasets are being made available on the Web using a variety of formats and according to diverse data models. Ontology Based Data Integration (OBDI) has been traditionally proposed as a mechanism to facilitate access to such heterogeneous datasets, providing a unified view over their data by means of ontologies. Recently, the term "Virtual Knowledge Graph Access"has begun to be used to refer to the mechanisms that provide query-based access to knowledge graphs virtually generated from heterogeneous data sources. Several OBDI engines exist in the state of the art, with overlapping capabilities but also clear differences among them (in terms of the data formats that they can deal with, mapping languages that they support, query expressivity that they allow, etc.). These engines have been evaluated with different testbeds and benchmarks. However, their heterogeneity has made it difficult to come up with a common comprehensive benchmark that allows for comparisons among them to facilitate their selection by practitioners, and more importantly, for their continuous improvement by the teams that maintain them. In this paper we present GTFS-Madrid-Bench, a benchmark to evaluate OBDI engines that can be used for the provision of access mechanisms to virtual knowledge graphs. Our proposal introduces several scenarios that aim at measuring the query capabilities, performance and scalability of all these engines, considering their heterogeneity. The data sources used in our benchmark are derived from the GTFS data files of the subway network of Madrid. They have been transformed into several formats (CSV, JSON, SQL and XML) and scaled up. The query set aims at addressing a representative number of SPARQL 1.1 features while covering usual queries that data consumers may be interested in. (C) 2020 The Authors. Published by Elsevier B.V.
C1 [Chaves-Fraga, David; Priyatna, Freddy; Cimmino, Andrea; Toledo, Jhon; Ruckhaus, Edna; Corcho, Oscar] Univ Politecn Madrid, Ontol Engn Grp, Boadilla Del Monte, Spain.
RP Chaves-Fraga, D (通讯作者)，Univ Politecn Madrid, Ontol Engn Grp, Boadilla Del Monte, Spain.
EM dchaves@fi.upm.es
RI Fraga, David Chaves/AAL-1548-2020; Cimmino, Andrea/AAT-7298-2021
OI Fraga, David Chaves/0000-0003-3236-2789; Cimmino,
   Andrea/0000-0002-1823-4484
FU project Semantics for PerfoRmant and scalable INteroperability of
   multimodal Transport [SPRINT H2020-826172]; Spanish Ministerio de
   Economia, Industria y Competitividad; EU FEDER funds under the DATOS
   4.0: RETOS Y SOLUCIONES -UPM Spanish national project
   [TIN2016-78011-C4-4-R]; FPI grant [BES-2017-082511]
FX The work presented in this paper is supported by the project Semantics
   for PerfoRmant and scalable INteroperability of multimodal Transport
   (SPRINT H2020-826172) and by the Spanish Ministerio de Economia,
   Industria y Competitividad and EU FEDER funds under the DATOS 4.0: RETOS
   Y SOLUCIONES -UPM Spanish national project (TIN2016-78011-C4-4-R) and by
   an FPI grant (BES-2017-082511).
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NR 44
TC 5
Z9 5
U1 3
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
EI 1873-7749
J9 J WEB SEMANT
JI J. Web Semant.
PD DEC
PY 2020
VL 65
AR 100596
DI 10.1016/j.websem.2020.100596
PG 28
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:000598338300008
OA hybrid
DA 2022-08-23
ER

PT J
AU Li, WD
   Zhang, XY
   Wang, YQ
   Yan, ZH
   Peng, R
AF Li, Weidong
   Zhang, Xinyu
   Wang, Yaqian
   Yan, Zhihuan
   Peng, Rong
TI Graph2Seq: Fusion Embedding Learning for Knowledge Graph Completion
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph completion; graph neural network; link prediction;
   information fusion
AB Knowledge Graph (KG) usually contains billions of facts about the real world, where a fact is represented as a triplet in the form of (head entity, relation, tail entity). KG is a complex network and consists of numerous nodes (entities) and edges (relations). Given that most KGs are noisy and far from being complete, KG analysis and completion methods are becoming more and more important. Knowledge graph embedding (KGE) aims to embed entities and relations in a low dimensional and continuous vector space, which is proven to be a quite efficient and effective method in knowledge graph completion tasks. KGE models devise various kinds of score functions to evaluate each fact in KG, which assign high points for true facts and low points for invalid ones. In a KG of the real world, some nodes may have hundreds of links with other nodes. There is a wealth of information around an entity, and the surrounding information (i.e., the sub-graph structure information) of one entity can make a significant contribution to predicting new facts. However, many previous works including, translational approaches such as Trans(E, H, R, and D), factorization approaches such as DistMult, ComplEx, and other deep learning approaches such as NTN, ConvE, concentrate on rating each fact in an isolated and separated way and lack a specially designed mechanism to learn the sub-graph structure information of the entity in KG. To conquer this challenge, we leverage the information fusion mechanism (Graph2Seq) used in graph neural network which is specially designed for graph-structured data, to learn fusion embeddings for entities in KG. And a novel fusion embedding learning KGE model (referred as G2SKGE) which aims to learn the sub-graph structure information of the entity in KG is proposed. With empirical experiments on four benchmark datasets, our proposed model achieves promising results and outperforms the state-of-the-art models.
C1 [Li, Weidong; Zhang, Xinyu; Wang, Yaqian; Yan, Zhihuan; Peng, Rong] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
RP Peng, R (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
EM rongpeng@whu.edu.cn
FU National Key Research and Development Plan of China [2017YFB0503702,
   2016YFB0501801]; National Natural Science Foundation of China [61170026]
FX This work was supported in part by the National Key Research and
   Development Plan of China under Grant 2017YFB0503702 and Grant
   2016YFB0501801, and in part by the National Natural Science Foundation
   of China under Grant 61170026.
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   Zheng H, 2016, BIOCHEM INSIGHTS, V9, P1, DOI 10.4137/BCI.S36141
   Zhou J., 2018, ARXIV181208434
NR 56
TC 5
Z9 7
U1 2
U2 14
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 157960
EP 157971
DI 10.1109/ACCESS.2019.2950230
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA KH1VE
UT WOS:000510433500001
OA gold
DA 2022-08-23
ER

PT J
AU Shi, DP
   Xie, CY
   Xiong, LC
AF Shi, Dongping
   Xie, Chengyu
   Xiong, Lichun
TI Changes in the Structures and Directions of Rock Excavation Research
   from 1999 to 2020: A Bibliometric Study
SO ADVANCES IN CIVIL ENGINEERING
LA English
DT Article
AB Rock excavation has been the hot spot and frontier of scientific research. Rock excavation research is in a period of rapid development. The bibliographies included in ISI Web of Knowledge database from 1999-2020 were used as data samples, and the collected data were analyzed by literature co-citation and cluster analysis using CiteSpace and VOSviewer information visualization techniques and dynamic network analysis tools. A knowledge map of the evolution of bibliometric research development is drawn to reveal the representative literature in the field of bibliometrics. The hot areas of bibliometric research are introduced. The development trend of bibliometrics is proposed. The results of the study show that the amount of the literature on rock excavation is growing rapidly. A large amount of the foreign literature is available in China, the United States, Australia, Canada, France, and other countries. The main included journals are Tunnelling and Underground Space Technology, International Journal of Rock Mechanics and Mining Sciences, "Rock Mechanics and Rock Engineering," "Engineering Geology," and "Bulletin of Engineering Geology and the Environment." Keyword co-occurrence analysis includes the following contents: rock damage constitutive model, excavation damage area, numerical simulation, stability analysis support, and prediction technology of rock.
C1 [Shi, Dongping] Xiangtan Univ, Coll Chem Engn, Postdoctoral Stn Chem Engn & Technol, Xiangtan 411105, Peoples R China.
   [Shi, Dongping; Xie, Chengyu] Xiangtan Univ, Sch Environm & Resources, Xiangtan 411105, Peoples R China.
   [Xiong, Lichun] Hunan Red Solar Photoelect Sci & Technol Co Ltd, Changsha 410002, Peoples R China.
RP Xie, CY (通讯作者)，Xiangtan Univ, Sch Environm & Resources, Xiangtan 411105, Peoples R China.
EM xiechengyu42@xtu.edu.cn
RI Xie, Chengyu/ABC-2277-2021; xie, chengyu/GPF-6540-2022
OI Xie, Chengyu/0000-0001-8821-3438; xie, chengyu/0000-0001-8821-3438; Shi,
   Dongping/0000-0001-8332-5289
FU Natural Science Foundation of Hunan Province [2021JJ40538, 2021JJ30679];
   Hunan Provincial Department of Education General Project [18C0129]
FX This research was funded by the Natural Science Foundation of Hunan
   Province (Grant nos. 2021JJ40538 and 2021JJ30679) and Hunan Provincial
   Department of Education General Project (Grant no. 18C0129).
CR Amar P, 2013, INT J MIN SCI TECHNO, V23, P33, DOI 10.1016/j.ijmst.2013.01.006
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NR 22
TC 0
Z9 0
U1 7
U2 27
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-8086
EI 1687-8094
J9 ADV CIV ENG
JI Adv. Civ. Eng.
PD SEP 13
PY 2021
VL 2021
AR 9274918
DI 10.1155/2021/9274918
PG 8
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA UW7UR
UT WOS:000700358600002
OA gold
DA 2022-08-23
ER

PT J
AU Chang, SH
   Chen, ML
   Kuo, YK
   Shen, YC
AF Chang, Shu-Hsuan
   Chen, Mei-Ling
   Kuo, Yen-Kuang
   Shen, Yung-Chi
TI A Simulation-Based LED Design Project in Photonics Instruction Based on
   Industry-University Collaboration
SO IEEE TRANSACTIONS ON EDUCATION
LA English
DT Article
DE Light-emitting diode (LED); peer assessment (PA); project-based learning
   (PBL); simulation-based learning (SBL)
ID ONLINE PEER ASSESSMENT; LIGHT-EMITTING-DIODES; LEARNING ENVIRONMENTS;
   SYSTEMS; PERCEPTIONS; STUDENTS; COURSES; TEACH; BLUE
AB In response to the growing industrial demand for light-emitting diode (LED) design professionals, based on industry-university collaboration in Taiwan, this paper develops a novel instructional approach: a simulation-based learning course with peer assessment to develop students' professional skills in LED design as required by industry as well as to enhance cognition and metacognition in students in higher education. The simulation-based learning course enables students to understand the influences on LED performance of the variation of different parameters and to seek the best design through comparing the effectiveness of different combinations of parameters. The evaluation results of pre- and post-test knowledge maps and a photonics scoreboard indicate that this project-based instruction may help students understand the operating principles of LEDs and enhance their skill in LED design. The Constructivist Project-based Learning Environment Survey is adopted to demonstrate that the proposed project-based learning environment is beneficial in cultivating student inquiry learning, reflective thinking, teamwork, and skills in creative problem solving.
C1 [Chang, Shu-Hsuan] Natl Changhua Univ Educ, Dept Ind Educ & Technol, Changhua, Taiwan.
   [Chen, Mei-Ling] Natl Changhua Univ Educ, Dept Phys, Changhua, Taiwan.
   [Kuo, Yen-Kuang] Natl Changhua Univ Educ, Dept Phys & Inst Photon, Changhua, Taiwan.
   [Shen, Yung-Chi] Natl Chiao Tung Univ, Grad Inst Technol Management, Hsinchu, Taiwan.
RP Chang, SH (通讯作者)，Natl Changhua Univ Educ, Dept Ind Educ & Technol, Changhua, Taiwan.
EM shc@cc.ncue.edu.tw
FU National Science Council (NSC) of Taiwan [NSC 95-2516-S-018-001-MY3, NSC
   98-2511-S-018-006-MY3]
FX This work was supported in part by the National Science Council (NSC) of
   Taiwan under Grants NSC 95-2516-S-018-001-MY3 and NSC
   98-2511-S-018-006-MY3.
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NR 40
TC 5
Z9 5
U1 1
U2 19
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 2011
VL 54
IS 4
BP 582
EP 589
DI 10.1109/TE.2010.2098877
PG 8
WC Education, Scientific Disciplines; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Engineering
GA 840YQ
UT WOS:000296478700010
OA Green Published
DA 2022-08-23
ER

PT J
AU Chen, RJ
   Liu, YQ
   Zhou, F
AF Chen, Ruijun
   Liu, Yingqi
   Zhou, Fei
TI Turning Danger into Safety: The Origin, Research Context and Theoretical
   Framework of Organizational Resilience
SO IEEE ACCESS
LA English
DT Article
DE Resilience; Organizations; Safety; Turning; Economics; Planning;
   Licenses; Organizational resilience; research context; theoretical
   framework; future prospects
ID DYNAMIC CAPABILITIES; MANAGEMENT; MODEL; TEAM; CAPACITY
AB The importance of organizational resilience as an effective means to cope with crises and achieve sustainable growth in crisis situations is gradually gaining attention from both theoretical and practical circles. Based on the review of existing research on organizational resilience, this study uses the scientific knowledge mapping method from the macro perspective and the literature analysis method from the micro level to review the development of organizational resilience research during 1984-2021, and analyzes 108 representative papers on organizational resilience. Finally, this study proposes future research directions based on the existing studies, such as enriching the influence factors of organizational resilience, strengthening the study of the mechanism of organizational resilience and strengthening the study of the effect of organizational resilience. This paper not only provides a reference for domestic scholars to construct an overall framework for organizational resilience research, but also provides theoretical and practical guidelines for enterprises to achieve rebound survival and then sustainable growth in the face of adversity.
C1 [Chen, Ruijun; Liu, Yingqi; Zhou, Fei] Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China.
RP Liu, YQ (通讯作者)，Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China.
EM liuyq2021@163.com; 19113079@bjtu.edu.cn
OI chen, ruijun/0000-0002-6713-8181
FU National Social Science Fund of China [16AGL004]; Research on Green
   Development of Electric Vehicles in Beijing, Tianjin and Hebei, through
   the Context of Platform Opening [19GLB030]
FX This work was supported in part by the National Social Science Fund of
   China, under Grant 16AGL004, and in part by the Research on Green
   Development of Electric Vehicles in Beijing, Tianjin and Hebei, through
   the Context of Platform Opening, under Grant 19GLB030.
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NR 82
TC 4
Z9 4
U1 33
U2 74
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 48899
EP 48913
DI 10.1109/ACCESS.2021.3069301
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA RJ3UO
UT WOS:000637523300001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, F
   Li, YS
   Zhang, YJ
   Wei, D
AF Wang, Fei
   Li, Yansheng
   Zhang, Yongjun
   Wei, Dong
TI KLGCN: Knowledge graph-aware Light Graph Convolutional Network for
   recommender systems
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Recommender systems; Knowledge graph; Graph convolutional network;
   Collaborative filtering
AB Most popular recommender systems learn the embedding of users and items through capturing valuable information from user-item interactions or item knowledge graph (KG) with Graph Convolutional Network. However, only a few methods capture information from both source data, and they introduce many trainable parameters that increase training difficulty. In this work, we aim to aggregate information from both the user-item interaction graph and the item KG in a light and effective manner. We first experimentally verify the effectiveness of removing feature transformation and nonlinear activation in KG-aware recommendation, which has been proven to greatly reduce parameters while improving performance in the collaborative filtering-based recommendation. Then we propose a new Knowledge graph-aware Light Graph Convolutional Network (KLGCN), which can learn partial embeddings of users and items by aggregating features on the source graphs for recommendation and introduces no extra parameters. Extensive experiments on three public datasets demonstrate that KLGCN achieves substantial improvement over several state-of-the-art models and maintains satisfactory performance on cold-start scenarios.
C1 [Wang, Fei; Li, Yansheng; Zhang, Yongjun; Wei, Dong] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan, Hubei, Peoples R China.
RP Li, YS; Zhang, YJ (通讯作者)，Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan, Hubei, Peoples R China.
EM flyking@whu.edu.cn; yansheng.li@whu.edu.cn; zhangyj@whu.edu.cn;
   weidong@whu.edu.cn
OI zhang, yong jun/0000-0001-9845-4251
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NR 58
TC 0
Z9 0
U1 8
U2 8
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 1
PY 2022
VL 195
AR 116513
DI 10.1016/j.eswa.2022.116513
PG 13
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 0T9KW
UT WOS:000787281000003
DA 2022-08-23
ER

PT J
AU Fang, YL
   Wang, H
   Wang, LT
   Di, RT
   Song, YQ
AF Fang, Youli
   Wang, Hong
   Wang, Lutong
   Di, Ruitong
   Song, Yongqiang
TI Diagnosis of COPD Based on a Knowledge Graph and Integrated Model
SO IEEE ACCESS
LA English
DT Article
DE COPD; knowledge graph; feature selection; integrated learning; dsa-svm
ID NEURAL-NETWORKS
AB Chronic obstructive pulmonary disease (COPD) is a chronic lung disease that causes a progressive decline in respiratory function. Diagnosing COPD in the early curable stages is very important and may even save the life of a patient. In this paper, we present an integrated model for diagnosing COPD based on a knowledge graph. First, we construct a knowledge graph of COPD to analyze the relationship between feature subsets and further discover the knowledge of implied diseases from the data. Second, we propose an algorithm for sorting features and an adaptive feature subset selection algorithm CMFS-eta, which selects an optimal subset of features from the original high-dimensional set. Finally, the DSA-SVM integrated model is suggested to build the classifier for the diagnosis and prediction of COPD. We performed extensive experiments on the dataset from the hospital outpatient electronic medical record database. The classification accuracy of our method was 95.1%. It is superior to some state-of-the-art classification methods for this problem.
C1 [Fang, Youli; Wang, Hong; Wang, Lutong; Di, Ruitong; Song, Yongqiang] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Shandong, Peoples R China.
   [Fang, Youli; Wang, Hong; Wang, Lutong; Di, Ruitong; Song, Yongqiang] Shandong Normal Univ, Shandong Prov Key Lab Distributed Comp Software N, Jinan 250358, Shandong, Peoples R China.
RP Wang, H (通讯作者)，Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Shandong, Peoples R China.
EM wanghong106@163.com
FU National Natural Science Foundation of China [61672329, 61373149,
   61472233, 61572300, 81273704]; Technology Program of Shandong Province
   [2014GGB01617]; Science and Technology Program of Shandong Colleges and
   Universities [J17KA070]; Excellent Course Project of Shandong Province
   [2012BK294, 2013BK399, 2013BK402]; Educational Science Planning Project
   of Shandong Province [ZK1437B010]; Shandong Province Science and
   Technology Plan Supported Project [2014GGX101026]; Taishan Scholar Fund
   of Shandong Province [TSHW201502038, 20110819]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61672329, Grant 61373149, Grant
   61472233, Grant 61572300, and Grant 81273704, in part by the Technology
   Program of Shandong Province under Grant 2014GGB01617, in part by the
   Science and Technology Program of Shandong Colleges and Universities
   under Grant J17KA070, in part by the Excellent Course Project of
   Shandong Province under Grant 2012BK294, Grant 2013BK399, and Grant
   2013BK402, in part by the Educational Science Planning Project of
   Shandong Province under Grant ZK1437B010, in part by the Shandong
   Province Science and Technology Plan Supported Project under Grant
   2014GGX101026, and in part by the Taishan Scholar Fund of Shandong
   Province under Grant TSHW201502038 and Grant 20110819.
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NR 32
TC 9
Z9 9
U1 4
U2 14
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 46004
EP 46013
DI 10.1109/ACCESS.2019.2909069
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HU9NR
UT WOS:000465621600001
OA gold
DA 2022-08-23
ER

PT J
AU Lin, QK
   Zhu, YF
   Lu, H
   Shi, KZ
   Niu, ZD
AF Lin, Qika
   Zhu, Yifan
   Lu, Hao
   Shi, Kaize
   Niu, Zhendong
TI Improving University Faculty Evaluations via multi-view Knowledge Graph
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE University faculty evaluation; Knowledge graph; Academic development
   prediction; E-learning
ID ALTMETRICS; IMPACT
AB University faculties generate a large amount of heterogeneous data in e-learning environments that online systems and toolkits have made widely available in all aspects of teaching and scientific researching activities. How to use the data efficiently and scientifically for faculty evaluations has recently become an important issue in university performance systems. However, it is still a challenge to comprehensively assess faculty members using multi-source and multi-modal data due to the lack of uniform representations and evaluation processes. To this end, this paper proposes a novel University Faculty Evaluation System based on a multi-view Knowledge Graph (UFES-KG) that integrates heterogeneous faculty data. Relevant data, collected both on the Internet and through university-administered internal systems, includes faculty information such as scientific research papers, patents, funds, monographs, awards, professional activities and teaching performance. Furthermore, we construct entity representations through knowledge graph embedding methods to retain their semantic information. In addition, by integrating the academic development status of scholars in the previous three years as well as student evaluation data, this paper proposes an academic development factor (ADF) for making predictions about faculty academic development. The experimental results show that this factor is closely related to the features of the knowledge graph and student evaluations. In a certain case study, this factor is superior to the traditional h-index, g-index, and RG score. Intuitively and scientifically, this multi-view approach can improve evaluations of university faculties. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Lin, Qika; Zhu, Yifan; Lu, Hao; Shi, Kaize; Niu, Zhendong] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.
   [Lu, Hao] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
   [Niu, Zhendong] Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA 15260 USA.
RP Niu, ZD (通讯作者)，Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.
EM linqika@bit.edu.cn; zhuyifan@bit.edu.cn; hao.lu@ia.ac.cn;
   kzshi@bit.edu.cn; zniu@bit.edu.cn
RI Zhu, Yifan/AAF-3987-2020
OI Zhu, Yifan/0000-0002-7695-1633; Lin, Qika/0000-0001-5650-0600
FU National Key R&D Program of China [2019YFB1406302]; National Natural
   Science Foundation of China [61370137]; Ministry of Education-China
   Mobile Research Foundation Project [2016/2-7]; Postgraduate Education
   Research Project of Beijing Institute of Technology, China
   [2017JYYJG-004]; China's National Strategic Basic Research Program (973
   Program) [2012CB720700]
FX This work is supported by the National Key R&D Program of China under
   Grant 2019YFB1406302, the National Natural Science Foundation of China
   under Grant 61370137, the Ministry of Education-China Mobile Research
   Foundation Project under Grant 2016/2-7, the Postgraduate Education
   Research Project of Beijing Institute of Technology, China under Grant
   2017JYYJG-004 and supported in part by China's National Strategic Basic
   Research Program (973 Program) under Grant 2012CB720700.
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NR 64
TC 6
Z9 7
U1 14
U2 67
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD APR
PY 2021
VL 117
BP 181
EP 192
DI 10.1016/j.future.2020.11.021
PG 12
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA PY5TN
UT WOS:000612106900015
DA 2022-08-23
ER

PT J
AU Witmer, BG
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AF Witmer, BG
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TI Virtual spaces and real world places: Transfer of route knowledge
SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES
LA English
DT Article
AB It has been widely suggested, but rarely demonstrated, that virtual environments (VEs) are effective training media. The purpose of this investigation was to evaluate how well a VE model of a complex office building trained individuals to navigate in the actual building. Sixty participants studied route directions and landmark photographs, then rehearsed the route using either the VE model, the actual building, or verbal directions and photographs. The VE model was presented in real time via a head-tracked display. Half of the participants in each rehearsal group also studied route maps. Everyone's route knowledge was then measured in the actual building. Building configuration knowledge was also measured. VE rehearsal produced more route knowledge than verbal rehearsal, but less than with rehearsal in the actual building. Type of rehearsal had no effect on configuration knowledge. Map study influenced neither route nor configuration knowledge. These results suggest that VEs that adequately represent real world complexity can be effective training media for learning complex routes in buildings, and should be considered whenever the real world site is unavailable for training. (C) 1996 Academic Press Limited
C1 UNIV CENT FLORIDA,INST SIMULAT & TRAINING,ORLANDO,FL 32826.
RP Witmer, BG (通讯作者)，USA,RES INST,SIMULATOR SYST RES UNIT,12350 RES PKWY,ORLANDO,FL 32826, USA.
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   [No title captured]
NR 25
TC 209
Z9 211
U1 0
U2 14
PU ACADEMIC PRESS LTD
PI LONDON
PA 24-28 OVAL RD, LONDON, ENGLAND NW1 7DX
SN 1071-5819
J9 INT J HUM-COMPUT ST
JI Int. J. Hum.-Comput. Stud.
PD OCT
PY 1996
VL 45
IS 4
BP 413
EP 428
DI 10.1006/ijhc.1996.0060
PG 16
WC Computer Science, Cybernetics; Ergonomics; Psychology, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Psychology
GA VL853
UT WOS:A1996VL85300003
DA 2022-08-23
ER

PT J
AU Ding, ZK
   Liu, RS
   Yuan, HP
AF Ding, Zhikun
   Liu, Rongsheng
   Yuan, Hongping
TI A text mining-based thematic model for analyzing construction and
   demolition waste management studies
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Review
DE Construction and demolition waste; Waste management; Latent Dirichlet
   allocation; Community detection analysis; Word2vec; Thematic network
ID LIFE-CYCLE ASSESSMENT; WETLAND SYSTEM; HONG-KONG; CONTRACTORS; BEHAVIOR;
   SCIENCE; DESIGN; GIS; CLASSIFICATION; MINIMIZATION
AB Over the years, numerous studies have been conducted to investigate construction and demolition waste (CDW) management problems. However, the massive amount of literature brings challenges to scholars because it is difficult and time-consuming to manually identify research emphasis from the literature. Therefore, a method that can informationize literature collection and automatically detect insights from the identified literature is worthy of exploration. This paper attempts to present a comprehensive thematic model by combining Latent Dirichlet Allocation, word2vec, and community detection algorithm on python to detect insights from CDW management literature. Based on the database of Web of Science, 641 articles published between 2000 and 2019 are retrieved and used as the sample for analysis. The comprehensive thematic results reveal a four-domain knowledge map in CDW management research, which covers (1) introducing current situation of CDW management, (2) quantifying CDW generation, (3) assessing CDW and by-products, and (4) facilitating waste diversion. Future research directions in CDW management research have also been discussed. The results prove that the comprehensive thematic model is useful in mining insights from CDW management literature.
C1 [Ding, Zhikun; Liu, Rongsheng] Shenzhen Univ, Dept Construct Management & Real Estate, Coll Civil & Transportat Engn, Shenzhen, Peoples R China.
   [Yuan, Hongping] Guangzhou Univ, Sch Management, Guangzhou 510006, Guangdong, Peoples R China.
RP Yuan, HP (通讯作者)，Guangzhou Univ, Sch Management, Guangzhou 510006, Guangdong, Peoples R China.
EM hpyuan@gzhu.edu.cn
FU National Nature Science Foundation of China [71974132]; Natural Science
   Foundation of Guangdong Province, China [2018A0303130037,
   2019A1515011794]; Shenzhen Government Nature Science Foundation
   [JCYJ20190808115809385]
FX This research was conducted with the support of the National Nature
   Science Foundation of China (Grant No.71974132), the Natural Science
   Foundation of Guangdong Province, China (Grant No. 2018A0303130037;
   2019A1515011794), and Shenzhen Government Nature Science Foundation
   (Grant No. JCYJ20190808115809385).
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NR 101
TC 4
Z9 4
U1 13
U2 58
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 JUN
PY 2021
VL 28
IS 24
BP 30499
EP 30527
DI 10.1007/s11356-021-13989-1
EA APR 2021
PG 29
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA TB2VK
UT WOS:000644789100006
PM 33905057
DA 2022-08-23
ER

PT J
AU Hegazy, T
   Ali, A
   Abdel-Monem, M
AF Hegazy, Tarek
   Ali, Abdelbaset
   Abdel-Monem, Mohamed
TI Prospects of Mind Maps for Better Visualization of Infrastructure
   Literature
SO JOURNAL OF PROFESSIONAL ISSUES IN ENGINEERING EDUCATION AND PRACTICE
LA English
DT Article
DE Mind maps; Concept maps; Literature; Education; Visualization;
   Infrastructure; Construction; Search; Research
ID MAINTENANCE; INFORMATION; INVESTMENT
AB Literature analysis is important for the identification of the state of existing knowledge and prevailing research gaps. Effective literature analysis, however, is a lengthy process and requires a large effort to consider the information from different viewpoints and to identify areas of cross benefits. This paper represents an approach to summarize the information related to the construction and infrastructure domains by using a category of visual tools referred to as mind maps. First, the capabilities of various knowledge mapping tools for graphically representing the hierarchical concepts (keywords) of a given domain of knowledge are discussed, and example mind maps are developed for the infrastructure asset management domain. Enhancements to mind maps are then proposed on the basis of an extensive literature analysis to visually show numerical scores of various publications associated with the concepts in the mind map. This facilitates the identification of the highly relevant and the most useful knowledge in the literature. Suggestions are then presented for the use of mind maps by publishers of large literature databases to facilitate better analysis and the visual access and retrieval of information from the literature. DOI: 10.1061/(ASCE)EI.1943-5541.0000072. (C) 2011 American Society of Civil Engineers.
C1 [Hegazy, Tarek; Ali, Abdelbaset; Abdel-Monem, Mohamed] Univ Waterloo, Waterloo, ON N2L 3G1, Canada.
RP Hegazy, T (通讯作者)，Univ Waterloo, Waterloo, ON N2L 3G1, Canada.
EM tarek@uwaterloo.ca; aiali@uwaterloo.ca; m2abdelm@engmail.uwaterloo.ca
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NR 43
TC 4
Z9 4
U1 1
U2 23
PU ASCE-AMER SOC CIVIL ENGINEERS
PI RESTON
PA 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA
SN 1052-3928
EI 1943-5541
J9 J PROF ISS ENG ED PR
JI J. Prof. Issues Eng. Educ. Pract.
PD OCT
PY 2011
VL 137
IS 4
BP 239
EP 247
DI 10.1061/(ASCE)EI.1943-5541.0000072
PG 9
WC Education, Scientific Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA 841LO
UT WOS:000296514000011
DA 2022-08-23
ER

PT J
AU Lv, WQ
   Liao, ZF
   Liu, SZ
   Zhang, Y
AF Lv, Wuqian
   Liao, Zhifang
   Liu, Shengzong
   Zhang, Yan
TI MEIM: A Multi-Source Software Knowledge Entity Extraction Integration
   Model
SO CMC-COMPUTERS MATERIALS & CONTINUA
LA English
DT Article
DE Entity extraction; software knowledge graph; software data
AB Entity recognition and extraction are the foundations of knowledge graph construction. Entity data in the field of software engineering come from different platforms and communities, and have different formats. This paper divides multi-source software knowledge entities into unstructured data, semi-structured data and code data. For these different types of data, Bi-directional Long Short-Term Memory (Bi-LSTM) with Conditional Random Field (CRF), template matching, and abstract syntax tree are used and integrated into a multi-source software knowledge entity extraction integration model (MEIM) to extract software entities. The model can be updated continuously based on user's feedbacks to improve the accuracy. To deal with the shortage of entity annotation datasets, keyword extraction methods based on Term Frequency-Inverse Document Frequency (TF-IDF), TextRank, and K-Means are applied to annotate tasks. The proposed MEIM model is applied to the Spring Boot framework, which demonstrates good adaptability. The extracted entities are used to construct a knowledge graph, which is applied to association retrieval and association visualization.
C1 [Lv, Wuqian; Liao, Zhifang] Cent South Univ, Sch Comp Sci & Engn, Changsha 410075, Peoples R China.
   [Liu, Shengzong] Hunan Univ Finance & Econ, Sch Informat Technol & Management, Changsha 410205, Peoples R China.
   [Zhang, Yan] Glasgow Caledonian Univ, Sch Comp Engn & Built Environm, Glasgow G4 0BA, Lanark, Scotland.
RP Liao, ZF (通讯作者)，Cent South Univ, Sch Comp Sci & Engn, Changsha 410075, Peoples R China.
EM zfliao@csu.edu.cn
FU Ministry of Science and Technology: Key Research and Development Project
   [2018YFB003800]; Hunan Provincial Key Laboratory of Finance & Economics
   Big Data Science and Technology (Hunan University of Finance and
   Economics) [2017TP1025, HNNSF 2018JJ2535];  [NSF61802120]
FX Zhifang Liao: Ministry of Science and Technology: Key Research and
   Development Project (2018YFB003800), Hunan Provincial Key Laboratory of
   Finance & Economics Big Data Science and Technology (Hunan University of
   Finance and Economics) 2017TP1025, HNNSF 2018JJ2535. Shengzong Liu:
   NSF61802120.
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NR 15
TC 3
Z9 3
U1 9
U2 22
PU TECH SCIENCE PRESS
PI HENDERSON
PA 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA
SN 1546-2218
EI 1546-2226
J9 CMC-COMPUT MATER CON
JI CMC-Comput. Mat. Contin.
PY 2021
VL 66
IS 1
BP 1027
EP 1042
DI 10.32604/cmc.2020.012478
PG 16
WC Computer Science, Information Systems; Materials Science,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Materials Science
GA OO7CT
UT WOS:000587534300066
OA gold
DA 2022-08-23
ER

PT J
AU Ali, M
   Hoyt, CT
   Domingo-Fernandez, D
   Lehmann, J
   Jabeen, H
AF Ali, Mehdi
   Hoyt, Charles Tapley
   Domingo-Fernandez, Daniel
   Lehmann, Jens
   Jabeen, Hajira
TI BioKEEN: a library for learning and evaluating biological knowledge
   graph embeddings
SO BIOINFORMATICS
LA English
DT Article
AB Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs' nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.
C1 [Ali, Mehdi; Lehmann, Jens; Jabeen, Hajira] Rheinische Friedrich Wilhelms Univ Bonn, Dept Comp Sci, D-53113 Bonn, Germany.
   [Hoyt, Charles Tapley; Domingo-Fernandez, Daniel] Rheinische Friedrich Wilhelms Univ Bonn, Bonn Aachen Int Ctr IT, Dept Life Sci Informat, D-53113 Bonn, Germany.
   [Hoyt, Charles Tapley; Domingo-Fernandez, Daniel] Fraunhofer Inst Algorithms & Sci Comp SCAI, Dept Bioinformat, D-53754 St Augustin, Germany.
   [Lehmann, Jens] Fraunhofer Inst Intelligent Anal & Informat Syst, Dept Enterprise Informat Syst, D-53754 St Augustin, Germany.
RP Ali, M (通讯作者)，Rheinische Friedrich Wilhelms Univ Bonn, Dept Comp Sci, D-53113 Bonn, Germany.
EM mehdi.ali@cs.uni-bonn.de
RI Domingo-Fernández, Daniel/AGH-3497-2022; Hoyt, Charles
   Tapley/N-6315-2019
OI Domingo-Fernández, Daniel/0000-0002-2046-6145; Hoyt, Charles
   Tapley/0000-0003-4423-4370; Jabeen, Hajira/0000-0003-1476-2121; Lehmann,
   Jens/0000-0001-9108-4278
FU Bio2Vec project [3454]; King Abdullah University of Science and
   Technology (KAUST)
FX This research was supported by Bio2Vec project (http://bio2vec.net/,
   CRG6) grant [3454] with funding from King Abdullah University of Science
   and Technology (KAUST).
CR Dettmers T, 2017, ARXIV170701476
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NR 10
TC 11
Z9 11
U1 1
U2 23
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD SEP 15
PY 2019
VL 35
IS 18
BP 3538
EP 3540
DI 10.1093/bioinformatics/btz117
PG 3
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA IZ8BR
UT WOS:000487327500056
PM 30768158
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Kochsiek, A
   Gemulla, R
AF Kochsiek, Adrian
   Gemulla, Rainer
TI Parallel Training of Knowledge Graph Embedding Models: A Comparison of
   Techniques
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB Knowledge graph embedding (KGE) models represent the entities and relations of a knowledge graph (KG) using dense continuous representations called embeddings. KGE methods have recently gained traction for tasks such as knowledge graph completion and reasoning as well as to provide suitable entity representations for downstream learning tasks. While a large part of the available literature focuses on small KGs, a number of frameworks that are able to train KGE models for large-scale KGs by parallelization across multiple GPUs or machines have recently been proposed. So far, the benefits and drawbacks of the various parallelization techniques have not been studied comprehensively. In this paper, we report on an experimental study in which we presented, re-implemented in a common computational framework, investigated, and improved the available techniques. We found that the evaluation methodologies used in prior work are often not comparable and can be misleading, and that most of currently implemented training methods tend to have a negative impact on embedding quality. We propose a simple but effective variation of the stratification technique used by PyTorch BigGraph for mitigation. Moreover, basic random partitioning can be an effective or even the best-performing choice when combined with suitable sampling techniques. Ultimately, we found that efficient and effective parallel training of large-scale KGE models is indeed achievable but requires a careful choice of techniques.
C1 [Kochsiek, Adrian; Gemulla, Rainer] Univ Mannheim, Mannheim, Germany.
RP Kochsiek, A (通讯作者)，Univ Mannheim, Mannheim, Germany.
EM adrian@informatik.uni-mannheim.de; rgemulla@uni-mannheim.de
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NR 37
TC 0
Z9 0
U1 1
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD NOV
PY 2021
VL 15
IS 3
BP 633
EP 645
DI 10.14778/3494124.3494144
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZB7QP
UT WOS:000757032600021
DA 2022-08-23
ER

PT J
AU Tran, QM
   Nguyen, HD
   Huynh, T
   Nguyen, KV
   Hoang, SN
   Pham, VT
AF Tran, Quan M.
   Nguyen, Hien D.
   Huynh, Tai
   Nguyen, Kha V.
   Hoang, Suong N.
   Pham, Vuong T.
TI Measuring the influence and amplification of users on social network
   with unsupervised behaviors learning and efficient interaction-based
   knowledge graph
SO JOURNAL OF COMBINATORIAL OPTIMIZATION
LA English
DT Article; Early Access
DE Knowledge graph; Node embedding; Social network analysis; Variational
   auto-encoder; Unsupervised learning; Graph convolutional network; Deep
   learning; Influencer marketing; Information propagation
ID MODEL
AB This study introduces a metric to measure the influence of users and communities on Social Media Networks. The proposed method is a combination of Knowledge Graph and Deep Learning approaches. Particularly, an effective Knowledge Graph is built to represent the interaction activities of users. Besides, an unsupervised deep learning model based on Variational Graph Autoencoder is also constructed to further learn and explore the behavior of users. This model is inspired by conventional Graph Convolutional layers. It is not only able to learn the attribute of users themselves but also enhanced to automatically extract and learn from the relationships among users. The model is robust to unseen data and takes no labeling effort. To ensure the state of the art and fashionable for this work, the dataset is collected by a designed crawling system. The experiments show significant performance and promising results which are competitive and outperforms some well-known Graph-convolutional-based. The proposed approach is applied to build a management system for an influencer marketing campaign, called ADVO system. The ADVO system can detect emerging influencers for a determined brand to run its campaign, and help the brand to manage its campaign. The proposed method is already applied in practice.
C1 [Tran, Quan M.] Kyanon Digital, Dept Res & Dev, Ho Chi Minh City, Vietnam.
   [Tran, Quan M.; Nguyen, Hien D.] Univ Informat Technol, Ho Chi Minh City, Vietnam.
   [Tran, Quan M.; Nguyen, Hien D.] Vietnam Natl Univ, Ho Chi Minh City, Vietnam.
   [Huynh, Tai] Kyanon Digital, Ho Chi Minh City, Vietnam.
   [Nguyen, Kha V.] Kyanon Digital, Dept Data Sci, Ho Chi Minh City, Vietnam.
   [Hoang, Suong N.] Olli Technol, Ho Chi Minh City, Vietnam.
   [Pham, Vuong T.] Sai Gon Univ, Ho Chi Minh City, Vietnam.
RP Nguyen, HD (通讯作者)，Univ Informat Technol, Ho Chi Minh City, Vietnam.; Nguyen, HD (通讯作者)，Vietnam Natl Univ, Ho Chi Minh City, Vietnam.
EM quan.tran@kyanon.digital; hiennd@uit.edu.vn; tai.huynh@kyanon.digital;
   kha.nguyen@kyanon.digital; suong@olli-ai.com; vuong.pham@sgu.edu.vn
OI Tran, Quan/0000-0002-7931-4525; Huynh, Tai/0000-0001-9934-8864; Nguyen,
   Hien/0000-0002-8527-0602
FU Gia Lam Urban Development and Investment Company Limited; Vingroup
   Innovation Foundation (VINIF) [DA13215062019]; Vingroup
FX This work was funded by Gia Lam Urban Development and Investment Company
   Limited, Vingroup and supported by Vingroup Innovation Foundation
   (VINIF) under project code DA13215062019.
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NR 57
TC 0
Z9 0
U1 4
U2 6
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.
DI 10.1007/s10878-021-00815-0
EA OCT 2021
PG 27
WC Computer Science, Interdisciplinary Applications; Mathematics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Mathematics
GA WF1AH
UT WOS:000706045600001
DA 2022-08-23
ER

PT J
AU Ma, C
   Morrison, SM
   Muscente, AD
   Wang, CB
   Ma, XG
AF Ma, Chao
   Morrison, Shaunna M.
   Muscente, A. Drew
   Wang, Chengbin
   Ma, Xiaogang
TI Incorporate temporal topology in a deep-time knowledge base to
   facilitate data-driven discovery in geoscience
SO GEOSCIENCE DATA JOURNAL
LA English
DT Article; Data Paper; Early Access
DE geologic time scale; knowledge graph; R package; semantics; workflow
   platforms
ID GEOLOGIC TIME; DATA INTEGRATION; SEMANTIC WEB; SCALE; EARTH;
   VISUALIZATION; INFORMATION
AB Data-driven discovery in geoscience requires an enormous amount of FAIR (findable, accessible, interoperable and reusable) data derived from a multitude of sources. Many geology resources include data based on the geologic time scale, a system of dating that relates layers of rock (strata) to times in Earth history. The terminology of this geologic time scale, including the names of the strata and time intervals, is heterogeneous across data resources, hindering effective and efficient data integration. To address that issue, we created a deep-time knowledge base that consists of knowledge graphs correlating international and regional geologic time scales, an online service of the knowledge graphs, and an R package to access the service. The knowledge base uses temporal topology to enable comparison and reasoning between various intervals and points in the geologic time scale. This work unifies and allows the querying of age-related geologic information across the entirety of Earth history, resulting in a platform from which researchers can address complex deep-time questions spanning numerous types of data and fields of study.
C1 [Ma, Chao; Ma, Xiaogang] Univ Idaho, Dept Comp Sci, 875 Perimeter Dr,MS 1010, Moscow, ID 83843 USA.
   [Morrison, Shaunna M.] Carnegie Inst Sci, Earth & Planets Lab, Washington, DC 20005 USA.
   [Muscente, A. Drew] Cornell Coll, Dept Geol, Mt Vernon, IA 52314 USA.
   [Wang, Chengbin] China Univ Geosci, State Key Lab Geol Proc & Mineral Resources, Wuhan, Peoples R China.
   [Ma, Chao] Chengdu Univ Technol, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu, Peoples R China.
RP Ma, XG (通讯作者)，Univ Idaho, Dept Comp Sci, 875 Perimeter Dr,MS 1010, Moscow, ID 83843 USA.
EM max@uidaho.edu
RI Ma, Xiaogang/C-9286-2011
OI Ma, Xiaogang/0000-0002-9110-7369
FU National Science Foundation [1835717, 2126315]; Carnegie Institution of
   Washington for Science; Alfred P. Sloan Foundation; Deep Carbon
   Observatory; IUGS Deep-time Digital Earth (DDE) Big Science Program
FX National Science Foundation, Grant/Award Number: 1835717 and 2126315;
   Carnegie Institution of Washington for Science; Alfred P. Sloan
   Foundation; Deep Carbon Observatory; IUGS Deep-time Digital Earth (DDE)
   Big Science Program
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Z9 0
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U2 4
PU WILEY
PI HOBOKEN
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J9 GEOSCI DATA J
JI Geosci. Data J.
DI 10.1002/gdj3.171
EA JUL 2022
PG 11
WC Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geology; Meteorology & Atmospheric Sciences
GA 2Y9YB
UT WOS:000826241800001
DA 2022-08-23
ER

PT J
AU Lu, XY
   Wang, LF
   Jiang, ZJ
   He, SC
   Liu, SZ
AF Lu, Xinyu
   Wang, Lifang
   Jiang, Zejun
   He, Shichang
   Liu, Shizhong
TI MMKRL: A robust embedding approach for multi-modal knowledge graph
   representation learning
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Knowledge graphs (KGs); Knowledge representation learning (KRL);
   Multi-modal knowledge representation learning (MMKRL)
AB Most knowledge representation learning (KRL) methods only use structured knowledge graphs (KGs); however, there is still much multi-modal (textual, visual) knowledge that has not been used. To address this challenge, we propose a novel solution called multi-modal knowledge representation learning (MMKRL) to take advantage of multi-source (structured, textual, and visual) knowledge. Instead of simply integrating multi-modal knowledge with structured knowledge in a unified space, we introduce a component alignment scheme and combine it with translation methods to accomplish multi-modal KRL. Specifically, MMKRL firstly reconstructs multi-source knowledge by summing different plausibility functions and then aligns multi-source knowledge using specific norm constraints to reduce reconstruction errors. We also select an adversarial training strategy to enhance the robustness of MMKRL, which is rarely considered in existing multi-modal KRL methods. Experimental results show that MMKRL can effectively utilize multi-modal knowledge to achieve better link prediction and triple classification than other baselines in two widely used datasets. Further, when relying on structured knowledge or limited multi-source knowledge, MMKRL still achieves competitive results in link prediction, demonstrating our model's superiority.
C1 [Lu, Xinyu; Wang, Lifang; Jiang, Zejun; He, Shichang; Liu, Shizhong] Northwestern Polytech Univ, Sch Comp Sci & Engn, Xian 710072, Peoples R China.
   [Wang, Lifang] Luoyang Normal Univ, Henan Key Lab Big Data Proc Analyt Elect Commerce, Luoyang 471934, Peoples R China.
RP Jiang, ZJ (通讯作者)，Northwestern Polytech Univ, Sch Comp Sci & Engn, Xian 710072, Peoples R China.
EM claud@nwpu.edu.cn
FU Henan key Laboratory for Big Data Processing & Analytics of Electronic
   Commerce [2020-KF-9]
FX This work was supported by the Henan key Laboratory for Big Data
   Processing & Analytics of Electronic Commerce(2020-KF-9).
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NR 43
TC 0
Z9 0
U1 21
U2 39
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 7480
EP 7497
DI 10.1007/s10489-021-02693-9
EA SEP 2021
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1A0YQ
UT WOS:000701364800001
DA 2022-08-23
ER

PT J
AU Ma, XG
AF Ma, Xiaogang
TI Knowledge graph construction and application in geosciences: A review
SO COMPUTERS & GEOSCIENCES
LA English
DT Review
DE Knowledge graph; Open data; Machine learning; Artificial intelligence;
   Data science
ID SEMANTIC WEB; INFORMATION EXTRACTION; IMAGE-ANALYSIS; DATA RESOURCES;
   ONTOLOGY; CLASSIFICATION; REPRESENTATION; VISUALIZATION; PROVENANCE;
   CHALLENGES
AB Knowledge graph (KG) is a topic of great interests to geoscientists as it can be deployed throughout the data life cycle in data-intensive geoscience studies. Nevertheless, comparing with the large amounts of publications on machine learning applications in geosciences, summaries and reviews of geoscience KGs are still limited. The aim of this paper is to present a comprehensive review of KG construction and implementation in geosciences. It consists of four major parts: 1) concepts relevant to KG and approaches for KG construction, 2) KG application in data collection, curation, and service, 3) KG application in data analysis, and 4) challenges and trends of geoscience KG creation and application in the near future. For each of the first three parts, a list of concepts, exemplar studies, and best practices are summarized. Those summaries are synthesized together in the challenge and trend analyses. As artificial intelligence and data science are thriving in geosciences, we hope this review of geoscience KGs can be of value to practitioners in data-intensive geoscience studies.
C1 [Ma, Xiaogang] Univ Idaho, Dept Comp Sci, 875 Perimeter Dr,MS 1010, Moscow, ID 83844 USA.
RP Ma, XG (通讯作者)，Univ Idaho, Dept Comp Sci, 875 Perimeter Dr,MS 1010, Moscow, ID 83844 USA.
EM max@uidaho.edu
RI Ma, Xiaogang/C-9286-2011
OI Ma, Xiaogang/0000-0002-9110-7369
FU National Science Foundation [2126315, 2019609, 1835717]; Na-tional
   Aeronautics and Space Administration [80NSSC21M0028]; Alfred P. Sloan
   Foundation [G-2018-10121]; Carnegie Institution for Science; Big Science
   Program; Deep Carbon Observatory
FX The author dedicates this paper to his late postdoctoral mentor Prof.
   Peter Fox at Rensselaer Polytechnic Institute, who passed away in 2021.
   Several topics presented here are derived from previous discussion with
   Prof. Fox. The work was supported by the National Science Foundation
   under Grants No. 2126315, No. 2019609, and No. 1835717, the Na-tional
   Aeronautics and Space Administration under Grant No. 80NSSC21M0028, and
   the Alfred P. Sloan Foundation under Grant No. G-2018-10121. The author
   thanks the Earth Science Information Part-ners, the Deep-time Data
   Driven Discovery (4D) initiative, the Carnegie Institution for Science,
   the IUGS Deep-time Digital Earth (DDE) Big Science Program, and the Deep
   Carbon Observatory for invitations to present relevant studies at
   several workshops and meetings in the past years. The author also thanks
   Prof. Mariana Belgiu at University of Twente, Netherlands and Prof. John
   Carranza at University of KwaZulu-Natal, South Africa for discussing
   knowledge graph application in geo-sciences. The author is grateful to
   three anonymous reviewers for their constructive comments on an original
   version of the manuscript, which led to improvement in the revision. Any
   remaining errors in the paper are the authors own responsibility.
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NR 196
TC 1
Z9 1
U1 35
U2 35
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 APR
PY 2022
VL 161
AR 105082
DI 10.1016/j.cageo.2022.105082
PG 15
WC Computer Science, Interdisciplinary Applications; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Geology
GA ZR8CD
UT WOS:000768004900002
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Bakhshi, M
   Nematbakhsh, M
   Mohsenzadeh, M
   Rahmani, AM
AF Bakhshi, Mahdi
   Nematbakhsh, Mohammadali
   Mohsenzadeh, Mehran
   Rahmani, Amir Masoud
TI SParseQA: Sequential word reordering and parsing for answering complex
   natural language questions over knowledge graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE RDF complex question answering; Uncertain question graph construction;
   Sequential word reordering and parsing; Relation pattern-based
   similarity
AB One of the effective approaches for answering natural language questions (NLQs) over knowledge graphs consists of two main stages. It first creates a query graph based on the NLQ and then matches this graph over the knowledge graph to construct a structured query. An obstacle in the first stage is the need to build question interpretations with candidate resources, even if some implicit phrases exist in the sentence. In the second stage, a serious problem is to map diverse NLQ relations to their corresponding predicates. To overcome these problems, in this paper, we propose a novel sequential word parsing-based method to construct and refine an uncertain question graph that is disambiguated directly over the knowledge graph. Instead of relying on the syntactic dependency relations and some predefined rules that recognize the relations and their arguments, we consider the identified entities and variables in the NLQ as well as their corresponding place in the structure of a query graph pattern to build question triples. First, by leveraging the ordered dependency tree of an NLQ, sentence words are reordered. Then the question graph structure is constructed by parsing the new sequence backward, starting from the identified items. Subsequently, the question graph is refined by eliminating the useless elements. Additionally, to improve the relation similarity measure in the graph similarity process, we exploit the knowledge hidden in a relation pattern taxonomy. Experimental studies over several benchmarks demonstrate that our proposed approach is effective as it achieves promising results in answering the complex NLQs. (C) 2021 Published by Elsevier B.V.
C1 [Bakhshi, Mahdi; Mohsenzadeh, Mehran] Islamic Azad Univ, Dept Comp Engn Sci & Res Branch, Tehran, Iran.
   [Nematbakhsh, Mohammadali] Univ Isfahan, Dept Comp Engn, Esfahan, Iran.
   [Rahmani, Amir Masoud] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan.
RP Nematbakhsh, M (通讯作者)，Univ Isfahan, Dept Comp Engn, Esfahan, Iran.
EM mbakhshi@srbiau.ac.ir; nematbakhsh@eng.ui.ac.ir;
   mohsenzadeh@srbiau.ac.ir; rahmania@yuntech.edu.tw
RI Mohsenzadeh, Mehran/AAA-4884-2022; Bakhshi/ABA-6044-2020; Rahmani, Amir
   Masoud/K-2702-2013
OI Mohsenzadeh, Mehran/0000-0001-6835-409X; Bakhshi/0000-0001-9110-0557;
   Nematbakhsh, Mohammadali/0000-0002-4374-9228; Rahmani, Amir
   Masoud/0000-0001-8641-6119
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NR 53
TC 2
Z9 2
U1 5
U2 13
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JAN 10
PY 2022
VL 235
AR 107626
DI 10.1016/j.knosys.2021.107626
EA NOV 2021
PG 20
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WW8BM
UT WOS:000718134800013
DA 2022-08-23
ER

PT J
AU Zhang, XM
   Liu, X
   Li, X
   Pan, DY
AF Zhang, Xiaoming
   Liu, Xin
   Li, Xin
   Pan, Dongyu
TI MMKG: An approach to generate metallic materials knowledge graph based
   on DBpedia and Wikipedia
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE DBpedia; Wikipedia; Metallic materials; Knowledge graph
AB The research and development of metallic materials are playing an important role in today's society, and in the meanwhile lots of metallic materials knowledge is generated and available on the Web (e.g., Wikipedia) for materials experts. However, due to the diversity and complexity of metallic materials knowledge, the knowledge utilization may encounter much inconvenience. The idea of knowledge graph (e.g., DBpedia) provides a good way to organize the knowledge into a comprehensive entity network. Therefore, the motivation of our work is to generate a metallic materials knowledge graph (MMKG) using available knowledge on the Web. In this paper, an approach is proposed to build MMKG based on DBpedia and Wikipedia. First, we use an algorithm based on directly linked sub-graph semantic distance (DLSSD) to preliminarily extract metallic materials entities from DBpedia according to some predefined seed entities; then based on the results of the preliminary extraction, we use an algorithm, which considers both semantic distance and string similarity (SDSS), to achieve the further extraction. Second, due to the absence of materials properties in DBpedia, we use an ontology-based method to extract properties knowledge from the HTML tables of corresponding Wikipedia Web pages for enriching MMKG. Materials ontology is used to locate materials properties tables as well as to identify the structure of the tables. The proposed approach is evaluated by precision, recall, Fl and time performance, and meanwhile the appropriate thresholds for the algorithms in our approach are determined through experiments. The experimental results show that our approach returns expected performance. A tool prototype is also designed to facilitate the process of building the MMKG as well as to demonstrate the effectiveness of our approach. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Zhang, Xiaoming; Li, Xin; Pan, Dongyu] Hebei Univ Sci & Technol, Sch Informat Sci & Engn, Shijiazhuang 050018, Hebei, Peoples R China.
   [Liu, Xin] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 10083, Peoples R China.
RP Liu, X (通讯作者)，Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 10083, Peoples R China.; Li, X (通讯作者)，26 Yuxiang St, Shijiazhuang 050018, Hebei, Peoples R China.
EM zxmhebust@163.com; liuxin_ustb@163.com; lixin19890906@163.com;
   pdyhebust@163.com
FU National Natural Science Foundation of China [51271033]; Natural Science
   Foundation of Hebei Province [F2013208107]
FX This work is supported by National Natural Science Foundation of China
   (No. 51271033), Natural Science Foundation of Hebei Province (No.
   F2013208107).
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TC 16
Z9 17
U1 6
U2 50
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD FEB
PY 2017
VL 211
SI SI
BP 98
EP 112
DI 10.1016/j.cpc.2016.07.005
PG 15
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physics
GA EF2UO
UT WOS:000390181300014
DA 2022-08-23
ER

PT J
AU Rawat, KS
   Sood, SK
AF Rawat, Keshav S.
   Sood, Sandeep K.
TI Knowledge mapping of computer applications in education using CiteSpace
SO COMPUTER APPLICATIONS IN ENGINEERING EDUCATION
LA English
DT Article
DE CiteSpace; higher education; information and communication technologies;
   scientometric; engineering education
AB The education field plays a vital role in the growth and progress of society. In recent years, with the evolution of technology, a significant development has been observed in the publication of this field. Henceforth, a scientific review is required to explore computer applications in education using internet communication and technology (ICT) technologies. This paper analyzes the scientometric features of documents published from 2011 to 2020 in the education field using ICT. The research literature is retrieved from Scopus and CiteSpace is used to visualize network analysis of country collaboration, co-citation, and keyword co-occurrence. Here, the field of higher education is identified as an important area with increasing research trends. Higher education, such as engineering education with the application of ICT, has experienced the latest research trend, and it involves designing of frameworks for improving student performance and the teaching-learning process. The outcome of this paper is the identification of the research frontiers, hotspots, and the latest areas in the field of education, which will help to understand the overall development and future research directions.
C1 [Rawat, Keshav S.] Cent Univ Himachal Pradesh, Dept Comp Sci & Informat, Dharamshala 176215, Himachal Prades, India.
   [Sood, Sandeep K.] Natl Inst Technol Kurukshetra, Dept Comp Applicat, Kurukshetra, Haryana, India.
RP Rawat, KS (通讯作者)，Cent Univ Himachal Pradesh, Dept Comp Sci & Informat, Dharamshala 176215, Himachal Prades, India.
EM keshav79699@gmail.com
OI Rawat, Dr. Keshav Singh/0000-0002-1497-985X; K. Sood,
   Sandeep/0000-0002-8196-5503
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NR 39
TC 9
Z9 11
U1 39
U2 137
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1061-3773
EI 1099-0542
J9 COMPUT APPL ENG EDUC
JI Comput. Appl. Eng. Educ.
PD SEP
PY 2021
VL 29
IS 5
BP 1324
EP 1339
DI 10.1002/cae.22388
EA JAN 2021
PG 16
WC Computer Science, Interdisciplinary Applications; Education, Scientific
   Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research; Engineering
GA UP2LU
UT WOS:000612383900001
DA 2022-08-23
ER

PT J
AU Sarhan, I
   Spruit, M
AF Sarhan, Injy
   Spruit, Marco
TI Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Cyber Threat Intelligence; Knowledge Graph; Named Entity Recognition;
   Open Information Extraction; Attention network
AB Instant analysis of cybersecurity reports is a fundamental challenge for security experts as an immeasurable amount of cyber information is generated on a daily basis, which necessitates automated information extraction tools to facilitate querying and retrieval of data. Hence, we present Open-CyKG: an Open Cyber Threat Intelligence (CTI) Knowledge Graph (KG) framework that is constructed using an attention-based neural Open Information Extraction (OIE) model to extract valuable cyber threat information from unstructured Advanced Persistent Threat (APT) reports. More specifically, we first identify relevant entities by developing a neural cybersecurity Named Entity Recognizer (NER) that aids in labeling relation triples generated by the OIE model. Afterwards, the extracted structured data is canonicalized to build the KG by employing fusion techniques using word embeddings. As a result, security professionals can execute queries to retrieve valuable information from the Open-CyKG framework. Experimental results demonstrate that our proposed components that build up Open-CyKG outperform state-of-the-art models.1 (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C1 [Sarhan, Injy] Arab Acad Sci Technol & Maritime Transport AAST, Dept Comp Engn, Alexandria, Egypt.
   [Sarhan, Injy; Spruit, Marco] Univ Utrecht, Dept Informat & Comp Sci, Utrecht, Netherlands.
   [Spruit, Marco] Leiden Univ Med Ctr LUMC, Dept Publ Hlth & Primary Care, Leiden, Netherlands.
   [Spruit, Marco] Leiden Univ, Leiden Inst Adv Comp Sci LIACS, Leiden, Netherlands.
RP Sarhan, I (通讯作者)，Univ Utrecht, Dept Informat & Comp Sci, Utrecht, Netherlands.
EM i.a.a.sarhan@uu.nl
OI Spruit, Marco/0000-0002-9237-221X
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NR 76
TC 3
Z9 3
U1 10
U2 24
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD DEC 5
PY 2021
VL 233
AR 107524
DI 10.1016/j.knosys.2021.107524
EA SEP 2021
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WK7QW
UT WOS:000709919000020
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Guo, XB
   Lin, WF
   Li, YR
   Liu, ZY
   Yang, L
   Zhao, SL
   Zhu, ZF
AF Guo, Xiaobo
   Lin, Wenfang
   Li, Youru
   Liu, Zhongyi
   Yang, Lin
   Zhao, Shuliang
   Zhu, Zhenfeng
TI DKEN: Deep knowledge-enhanced network for recommender systems
SO INFORMATION SCIENCES
LA English
DT Article
DE Recommender systems; Knowledge graph embedding; Ensemble learning
AB Despite that existing knowledge graphs embedding (KGE) based methods can achieve better recommendation performance compared with deep learning based ones, such improvement is limited due to lack of capturing the shared information between user-item interaction and item-item relation encoded in knowledge graph (KG) by fully leveraging the implicit and explicit relationship. To address this issue, in this paper, we propose a principled deep knowledge-enhanced network (DKEN) framework based on deep learning and KGE to model the semantics of entities and relations encoded in the KG. In particular, the DKEN utilizes deep neural networks (DNN) to learn higher-order feature interactions and ensembles KGE features with DNN features into an end-to-end learning process naturally to exploit implicit interaction and explicitt semantic features. Furthermore, a cross information sharing (CIS) layer is designed to facilitate information sharing between items and entities, and two aggregators are developed to improve the performance of the model. Extensive experiments on several public datasets, as well as online AB tests of an industrial recommendation scenario in the Ant Financial Service Group, demonstrate that DKEN achieves remarkably better performance than several state-of-the-art baselines. (c) 2020 Elsevier Inc. All rights reserved.
C1 [Guo, Xiaobo; Lin, Wenfang; Li, Youru; Liu, Zhongyi; Yang, Lin] Ant Financial Serv Grp, Beijing 100020, Peoples R China.
   [Li, Youru; Zhu, Zhenfeng] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China.
   [Li, Youru; Zhu, Zhenfeng] Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China.
   [Zhao, Shuliang] Hebei Normal Univ, Coll Comp & Cyber Secur, Shijiazhuang 050010, Hebei, Peoples R China.
RP Guo, XB (通讯作者)，Ant Financial Serv Grp, Beijing 100020, Peoples R China.
EM jefflittleguo.gxb@antfin.com
OI Li, Youru/0000-0002-9326-9863
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NR 41
TC 12
Z9 12
U1 6
U2 46
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD NOV
PY 2020
VL 540
BP 263
EP 277
DI 10.1016/j.ins.2020.06.041
PG 15
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NR3JP
UT WOS:000571459100009
DA 2022-08-23
ER

PT J
AU Zhang, GS
   Li, CK
AF Zhang, Gensheng
   Li, Chengkai
TI Maverick: A System for Discovering Exceptional Facts from Knowledge
   Graphs
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB This paper presents Maverick, a system for discovering exceptional facts about entities in knowledge graphs. Maverick is built upon a beam-search based algorithmic framework which we proposed in a research paper that is published in SIGMOD 2018. In this demonstration proposal, we showcase an end-to-end system that includes a user-facing portal and a cache server. In Maverick, an exceptional fact about an entity of interest is modeled as a context-subspace pair, in which the subspace is a set of attributes and the context is defined by a graph query pattern of which the entity is a match, together with other matching entities. The entity is exceptional among the entities in the context, with regard to the subspace. The portal allows users to search entities in a knowledge graph and explores exceptional facts about the entities of interest. It presents exceptional facts to users in forms of natural language sentences and illustration charts, for better interpretability of the discovered exceptional facts. The cache server stores intermediate computation results, such as pattern evaluations, exceptionality calculations, and candidate patterns. It is built for sharing computation across entities, such that repetitive computation across entities can be avoided.
C1 [Zhang, Gensheng] Google Inc, Mountain View, CA 94041 USA.
   [Li, Chengkai] Univ Texas Arlington, Arlington, TX 76019 USA.
RP Zhang, GS (通讯作者)，Google Inc, Mountain View, CA 94041 USA.
EM gezhang@google.com; cli@uta.edu
OI Li, Chengkai/0000-0002-1724-8278
FU NSF [IIS-1408928, IIS-1719054]
FX The work is partially supported by NSF grants IIS-1408928 and
   IIS-1719054. Any opinions, findings, and conclusions or recommendations
   expressed in this publication are those of the authors and do not
   necessarily reflect the views of the funding agencies.
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   Yang J., 2011, P CIDR, P148
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   Zhang G., 2014, TKDD, V8
   Zhang GS, 2018, INT CONF MANAGE DATA, P1317, DOI 10.1145/3183713.3183730
NR 7
TC 2
Z9 2
U1 0
U2 3
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD AUG
PY 2018
VL 11
IS 12
BP 1934
EP 1937
DI 10.14778/3229863.3236228
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HD5CA
UT WOS:000452544100021
DA 2022-08-23
ER

PT J
AU Zhang, YH
   Yuan, M
   Zhao, C
   Chen, M
   Liu, XY
AF Zhang, Yihao
   Yuan, Meng
   Zhao, Chu
   Chen, Mian
   Liu, Xiaoyang
TI Aggregating knowledge-aware graph neural network and adaptive relational
   attention for recommendation
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Recommender system; Knowledge graph; Relation embedding; Graph neural
   network
AB Currently, recommender systems based on knowledge graph (KG) consider various aspects of the item to provide accurate recommendations. Many studies have shown that exploiting the rich semantics of KG is effective to improve recommendation performance, and can solve data sparsity and provide interpretable recommendation. However, most existing KG-based recommender systems ignore the fact that users attach different degrees of importance to various relationships of items. To tackle this problem, we propose a knowledge graph recommender model based on adaptive relational attention (KGARA), which can capture the attention of various users to different relationships of items. Specifically, we introduce the relation embedding to model the semantic information of KG, and capture the user's attention on each relation of the targeted item with the attention mechanism. In addition, we introduce receptive fields to select neighbor nodes of the target node in the KG, which greatly alleviate computational burden. Extensive experiments on three real-world datasets demonstrate that the proposed algorithm has significant improvements over other state-of-the-art algorithms.
C1 [Zhang, Yihao; Yuan, Meng; Zhao, Chu; Chen, Mian] Chongqing Univ Technol, Sch Artificial Intelligence, Chongqing, Peoples R China.
   [Liu, Xiaoyang] Chongqing Univ Technol, Sch Comp Sci & Engn, Chongqing, Peoples R China.
RP Yuan, M (通讯作者)，Chongqing Univ Technol, Sch Artificial Intelligence, Chongqing, Peoples R China.
EM yhzhang@cqut.edu.cn; yuanmeng123@2019.cqut.edu.cn;
   zhaochu123@2019.cqut.edu.cn; marian@2019.cqut.edu.cn;
   lxy3103@cqut.edu.cn
RI , inner peace/AIE-2417-2022
FU National Natural Science Foundation of China [61702063]; Natural Science
   Foundation of Chongqing; Science and Technology Research Program of
   Chongqing Municipal Education Commission
FX The work is supported by the National Natural Science Foundation of
   China (No.61702063), the Natural Science Foundation of Chongqing
   (No.cstc2019jcyj-msxmX0544), the Science and Technology Research Program
   of Chongqing Municipal Education Commission (No.KJQN202001136).
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NR 35
TC 0
Z9 0
U1 8
U2 8
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/s10489-022-03359-w
EA APR 2022
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0H7MP
UT WOS:000778916100003
DA 2022-08-23
ER

PT J
AU Krinkin, K
   Vodyaho, A
   Kulikov, I
   Zhukova, N
AF Krinkin, Kirill
   Vodyaho, Alexander
   Kulikov, Igor
   Zhukova, Nataly
TI Method of Multilevel Adaptive Synthesis of Monitoring Object Knowledge
   Graphs
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE knowledge graph; deductive synthesis; adaptive synthesis; multilevel
   object model; inductive synthesis; object state prediction; statistical
   data; telecommunication network policy; monitoring data
ID CONSTRUCTION
AB The paper introduces a method for adaptive deductive synthesis of state models, of complex objects, with multilevel variable structures. The method makes it possible to predict the state of objects using the data coming from them. The data from the objects are collected with sensors installed on them. Multilevel knowledge graphs (KG) are used to describe the observed objects. The new adaptive synthesis method develops previously proposed inductive and deductive synthesis methods, allowing the context to be taken into account when predicting the states of the monitored objects based on the data obtained from them. The article proposes the algorithm for the suggested method and presents its computational complexity analysis. The software system, based on the proposed method, and the algorithm for multilevel adaptive synthesis of the object models developed, are described in the article. The effectiveness of the proposed method is shown in the results from modeling the states of telecommunication networks of cable television operators.
C1 [Krinkin, Kirill; Vodyaho, Alexander; Kulikov, Igor] St Petersburg Electrotech Univ LETI, Fac Comp Sci & Technol, St Petersburg 197376, Russia.
   [Zhukova, Nataly] Russian Acad Sci SPCRAS, St Petersburg Fed Res Ctr, St Petersburg 199178, Russia.
RP Kulikov, I (通讯作者)，St Petersburg Electrotech Univ LETI, Fac Comp Sci & Technol, St Petersburg 197376, Russia.
EM kirill@krinkin.com; aivodyaho@mail.ru; i.a.kulikov@gmail.com;
   nazhukova@mail.ru
RI Kulikov, Igor/AAE-9550-2021; Krinkin, Kirill/D-8725-2015
OI Kulikov, Igor/0000-0002-2532-5579; Krinkin, Kirill/0000-0001-5949-7830
CR Bellini P, 2018, J VISUAL LANG COMPUT, V45, P24, DOI 10.1016/j.jvlc.2018.03.002
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   Brezillon P., P AAAI WORKSH MOD CO, P16
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   Krinkin K, 2021, PROC CONF OPEN INNOV, P200, DOI 10.23919/FRUCT50888.2021.9347588
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NR 30
TC 1
Z9 1
U1 1
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 JUL
PY 2021
VL 11
IS 14
AR 6251
DI 10.3390/app11146251
PG 19
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA TN1BP
UT WOS:000675978400001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Yang, ZJ
   Wang, Y
   Gan, JH
   Li, H
   Lei, N
AF Yang, Zhijun
   Wang, Yang
   Gan, Jianhou
   Li, Hang
   Lei, Ning
TI Design and Research of Intelligent Question-Answering(Q&A) System Based
   on High School Course Knowledge Graph
SO MOBILE NETWORKS & APPLICATIONS
LA English
DT Article
DE High school; Knowledge graph; Intelligent question answering; Big data
AB Question answering is an indispensable link in high school teaching. Through question answering, on the one hand, it can solve students' learning doubts, on the other hand, it can provide teachers with teaching feedback. However, through the investigation and research, it is found that with the expansion of student size, the effect of question answering in high school is not satisfactory. This paper analyzes the current situation of question answering in high school, and designs an intelligent question answering system for high school teaching based on constructivism learning theory and cognitive structure learning theory. The system, which is the first innovative application in the field of high school teaching, integrates knowledge graph technology and intelligent question answering technology, introduces big data technology. It can solve students' questions in time and accurately, link the knowledge points related to the questions to help students construct knowledge network graph, and the big data technology is used to analyze the students' questioning behavior and to predict students' learning behavior in order to feedback the teaching effect.
C1 [Yang, Zhijun] Yunnan Univ, Acad Educ Sci Res, Educ Dept Yunnan Prov, Kunming, Yunnan, Peoples R China.
   [Wang, Yang; Lei, Ning] Yunnan Univ, Coll Vocat & Continuing Educ, Kunming, Yunnan, Peoples R China.
   [Gan, Jianhou] Yunnan Normal Univ, Key Lab Educ Informalizat Nationalities Minist Ed, Kunming, Yunnan, Peoples R China.
   [Li, Hang] Univ Queensland, Brisbane, Qld, Australia.
RP Gan, JH (通讯作者)，Yunnan Normal Univ, Key Lab Educ Informalizat Nationalities Minist Ed, Kunming, Yunnan, Peoples R China.
EM 353738698@qq.com; 1358661928@qq.com; ganjh@ynnu.edu.cn;
   hang.li4@uq.net.au; 839259966@qq.com
FU Program for Yunnan Key Laboratory of Smart Education; Program for
   innovative research team(in Science and Technology) in University of
   Yunnan Province
FX The research is supported by Program for Yunnan Key Laboratory of Smart
   Education, and Program for innovative research team(in Science and
   Technology) in University of Yunnan Province.
CR Chen PH, 2018, IEEE ACCESS, V6, P31553, DOI 10.1109/ACCESS.2018.2839607
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   Liu Y, 2019, FDN TRENDS SMART LEA
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NR 19
TC 2
Z9 2
U1 17
U2 69
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1383-469X
EI 1572-8153
J9 MOBILE NETW APPL
JI Mobile Netw. Appl.
PD OCT
PY 2021
VL 26
IS 5
SI SI
BP 1884
EP 1890
DI 10.1007/s11036-020-01726-w
EA JAN 2021
PG 7
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA XM8PJ
UT WOS:000604818900001
DA 2022-08-23
ER

PT J
AU Liu, ZY
   Li, Y
   Zhao, LX
   Guo, PT
AF Liu, Ziyu
   Li, Ying
   Zhao, Lixia
   Guo, Pengtao
TI Construction of intelligent query system for metro electromechanical
   equipment faults based on the knowledge graph
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Intelligent inquiry; the knowledge graph; mechanical and electrical
   equipment fault; system construction; Neo4j
ID QUESTION ANSWERING SYSTEM
AB The intelligent inquiry system for metro electro-mechanical equipment faults based on the knowledge graph can effectively consolidate various semi-structured failure messages, and can provide users with quick, accurate and high-quality intelligent inquiry services such as equipment fault causes-researching and solutions-delivering, which could be really relevant to this research field and application areas. The recorded date which related to metro electromechanical equipment failures were in this research collected, consolidated and converted, so that these failures could be stored in our databases. In this context, various functions of the intelligent inquiry system have been implemented, including: natural language question analysis, language Cypher-based question and answer design, Naive Bayesian classification based on characteristic core words, and user interaction interface realization. The experimental results show that the system can effectively solve the problems related to fault handling in metro mechanical and electrical equipment, thus improving the efficiency of equipment fault maintenance.
C1 [Liu, Ziyu; Zhao, Lixia; Guo, Pengtao] Hebei Univ Sci & Technol, Sch Econ & Management, Shijiazhuang, Hebei, Peoples R China.
   [Li, Ying] Hebei Univ Sci & Technol, Sch Informat & Engn, Shijiazhuang, Hebei, Peoples R China.
RP Guo, PT (通讯作者)，Hebei Univ Sci & Technol, Sch Econ & Management, Shijiazhuang, Hebei, Peoples R China.; Li, Y (通讯作者)，Hebei Univ Sci & Technol, Sch Informat & Engn, Shijiazhuang, Hebei, Peoples R China.
EM 532560881@qq.com; 1003938608@qq.com
FU 100 Outstanding Innovative Talents Support Project of Hebei Province
   [SLRC2019005]; Social Science Foundation Project of Hebei Province
   [HB20GL011]; Soft science project of Hebei Science and Technology
   Department [20554702D]
FX This paper was financially supported by the 100 Outstanding Innovative
   Talents Support Project of Hebei Province (SLRC2019005), Social Science
   Foundation Project of Hebei Province (HB20GL011), and Soft science
   project of Hebei Science and Technology Department (20554702D) which
   made the present work possible.
CR Abdi A, 2018, SOFT COMPUT, V22, P213, DOI 10.1007/s00500-016-2328-2
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NR 21
TC 0
Z9 0
U1 21
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 2021
VL 41
IS 3
BP 4351
EP 4368
DI 10.3233/JIFS-189695
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WK4EC
UT WOS:000709679300014
DA 2022-08-23
ER

PT J
AU Liu, CY
   Liu, ZH
   Zhang, ZT
   Li, YN
   Fang, RY
   Li, F
   Zhang, JD
AF Liu, Chaoyang
   Liu, Zehua
   Zhang, Zhentao
   Li, Yanan
   Fang, Ruying
   Li, Fei
   Zhang, Jingdong
TI A Scientometric Analysis and Visualization of Research on Parkinson's
   Disease Associated With Pesticide Exposure
SO FRONTIERS IN PUBLIC HEALTH
LA English
DT Article
DE Parkinson's disease; pesticide exposure; knowledge mapping;
   bibliometric; citespace
ID EMERGING TRENDS; ALPHA-SYNUCLEIN; MOTOR DEFICITS; NITRIC-OXIDE;
   ROTENONE; PARAQUAT; MANEB; MODEL; NEUROINFLAMMATION; DEGENERATION
AB The etiology and pathogenesis of Parkinson's disease (PD) have not yet been clearly described. Both genetic and environmental factors contribute to the onset and progression of PD. Some pesticides have been demonstrated to be associated with PD by many previous studies and experiments, and an increasing number of researchers have paid attention to this area in recent years. This paper aims to explore the knowledge structure, analyze the current research hot spots, and discuss the research trend through screening and summarizing the present literature. Based on 1767 articles from the Web of Science Core Collection and PubMed database, this study carried out the analysis from the keywords, cited references, countries, authors, and some other aspects by using Citespace. The hot topics, valuable articles, and productive authors in this research field could be found after that. To the best of our knowledge, this is the first study to specifically visualize the relationship between pesticide exposure and PD, and forecast research tendency in the future.
C1 [Liu, Chaoyang; Liu, Zehua; Li, Yanan; Fang, Ruying; Li, Fei; Zhang, Jingdong] Zhongnan Univ Econ & Law, Res Ctr Environm & Hlth, Wuhan, Peoples R China.
   [Liu, Chaoyang; Zhang, Zhentao] Wuhan Univ, Renmin Hosp, Dept Neurol, Wuhan, Peoples R China.
   [Liu, Chaoyang; Liu, Zehua; Li, Yanan; Fang, Ruying; Li, Fei; Zhang, Jingdong] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.
RP Liu, CY (通讯作者)，Zhongnan Univ Econ & Law, Res Ctr Environm & Hlth, Wuhan, Peoples R China.; Liu, CY (通讯作者)，Wuhan Univ, Renmin Hosp, Dept Neurol, Wuhan, Peoples R China.; Liu, CY (通讯作者)，Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.
EM lcy@zuel.edu.cn
FU National Social Science Foundation of China [19FJYY002]; Social Science
   Foundation of Hubei Province [2017161]; Hubei Special Technology
   Innovation (Soft Science Research) [2018ADC074]; Fundamental Research
   Funds for the Central Universities, Zhongnan University of Economics and
   Law [2722020JCG067]
FX This study was financially supported by the National Social Science
   Foundation of China (19FJYY002), the Social Science Foundation of Hubei
   Province (2017161), the Hubei Special Technology Innovation (Soft
   Science Research) (2018ADC074), and the Fundamental Research Funds for
   the Central Universities, Zhongnan University of Economics and Law
   (2722020JCG067).
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NR 72
TC 10
Z9 10
U1 12
U2 32
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 APR 7
PY 2020
VL 8
AR 91
DI 10.3389/fpubh.2020.00091
PG 14
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA LH8NI
UT WOS:000529039000001
PM 32318533
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ford, N
AF Ford, N
TI Improving the 'darkness to light' ratio in user-related information
   retrieval research
SO JOURNAL OF DOCUMENTATION
LA English
DT Article
AB Research into information retrieval (IR) cannot yet answer the basic question of how we can design IR systems to help people search for information with optimal levels of effectiveness. In relation to human-system interaction, we have failed to develop any valid and at the same time robust user models capable of driving practical system development. If we strip away assumptions and over-optimism relating to the generalisability of what are essentially sporadic and fragmented research efforts, the great 'darkness to light' ratio characterising our knowledge of human aspects of IR becomes apparent. From a more critical and pessimistic (but by no means less realistic) perspective, we are getting nowhere fast. A range of strategies is proposed to improve the situation by supporting relatively 'horizontal' as well as 'vertical' knowledge integration. These consist of: greater use of pluralistic research approaches; enhanced access to research data; more multidisciplinary and multi-perspective integrative reviews and conceptual mappings; and establishing a greater critical mass of published research findings sufficient to support the generation of a less sparse and fragmented evidence-based knowledge map. The potential of electronic publishing and data access for helping achieve these goals is discussed.
C1 Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England.
RP Ford, N (通讯作者)，Univ Sheffield, Dept Informat Studies, Western Bank, Sheffield S10 2TN, S Yorkshire, England.
EM n.ford@sheffield.ac.uk
OI Ford, Nigel/0000-0002-5546-7332
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   [No title captured]
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NR 59
TC 10
Z9 10
U1 0
U2 6
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0022-0418
EI 1758-7379
J9 J DOC
JI J. Doc.
PY 2000
VL 56
IS 6
BP 624
EP 643
DI 10.1108/EUM0000000007134
PG 20
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA V3329
UT WOS:000181362300002
DA 2022-08-23
ER

PT J
AU Varitimiadis, S
   Kotis, K
   Pittou, D
   Konstantakis, G
AF Varitimiadis, Savvas
   Kotis, Konstantinos
   Pittou, Dimitra
   Konstantakis, Georgios
TI Graph-Based Conversational AI: Towards a Distributed and Collaborative
   Multi-Chatbot Approach for Museums
SO APPLIED SCIENCES-BASEL
LA English
DT Review
DE museum chatbot; conversational AI; knowledge graphs; distributed AI;
   collaborative AI; multi-agent systems
ID ANALYTIC HIERARCHY PROCESS
AB Nowadays, museums are developing chatbots to assist their visitors and to provide an enhanced visiting experience. Most of these chatbots do not provide a human-like conversation and fail to deliver the complete requested knowledge by the visitors. There are plenty of stand-alone museum chatbots, developed using a chatbot platform, that provide predefined dialog routes. However, as chatbot platforms are evolving and AI technologies mature, new architectural approaches arise. Museums are already designing chatbots that are trained using machine learning techniques or chatbots connected to knowledge graphs, delivering more intelligent chatbots. This paper is surveying a representative set of developed museum chatbots and platforms for implementing them. More importantly, this paper presents the result of a systematic evaluation approach for evaluating both chatbots and platforms. Furthermore, the paper is introducing a novel approach in developing intelligent chatbots for museums. This approach emphasizes graph-based, distributed, and collaborative multi-chatbot conversational AI systems for museums. The paper accentuates the use of knowledge graphs as the key technology for potentially providing unlimited knowledge to chatbot users, satisfying conversational AI's need for rich machine-understandable content. In addition, the proposed architecture is designed to deliver an efficient deployment solution where knowledge can be distributed (distributed knowledge graphs) and shared among different chatbots that collaborate when is needed.
C1 [Varitimiadis, Savvas; Kotis, Konstantinos] Univ Aegean, Dept Cultural Technol & Commun, I Lab, Mitilini 81100, Greece.
   [Varitimiadis, Savvas; Pittou, Dimitra; Konstantakis, Georgios] Aegean Solut SA, Iraklion 71202, Greece.
RP Kotis, K (通讯作者)，Univ Aegean, Dept Cultural Technol & Commun, I Lab, Mitilini 81100, Greece.
EM svaritimiadis@aegean.gr; kotis@aegean.gr; pittou@aegeansolutions.com;
   gkonstantakis@aegeansolutions.com
RI Kotis, Konstantinos/B-1883-2009
OI Kotis, Konstantinos/0000-0001-7838-9691
FU project: "MEMOBOT, talking with Kazantzakis: Intelligent Mixed Reality
   Experiences"part of the single RTDI State Aid Action
   "RESEARCH-CREATE-INNOVATE" by the Greek Operational Programme
   Competitiveness, Entrepreneurship and Innovation 2014-2020 (EPAnEK)
   [T2EDK-03334]; project: "MuBot, a smart guide for museums", which is
   part of Action 1.b.3 "Research and development by SMEs in the Sector of
   RIS3Crete" by European Fund of Regional Development (ERDF)
   [KPHP3-0026737]
FX This research was funded by the following two projects: "MEMOBOT,
   talking with Kazantzakis: Intelligent Mixed Reality Experiences", which
   is part of the single RTDI State Aid Action "RESEARCH-CREATE-INNOVATE"
   funded by the Greek Operational Programme Competitiveness,
   Entrepreneurship and Innovation 2014-2020 (EPAnEK). Project number:
   T2EDK-03334. "MuBot, a smart guide for museums", which is part of Action
   1.b.3 "Research and development by SMEs in the Sector of RIS3Crete"
   funded by the European Fund of Regional Development (ERDF) in the
   framework of the Operational Program CRETE 2014-2020 of the NSRF
   2014-2020. Project number: KPHP3-0026737.
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NR 116
TC 0
Z9 0
U1 13
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 OCT
PY 2021
VL 11
IS 19
AR 9160
DI 10.3390/app11199160
PG 33
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA WH5UW
UT WOS:000707743400001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Bharadwaj, AG
   Starly, B
AF Bharadwaj, Akshay G.
   Starly, Binil
TI Knowledge graph construction for product designs from large CAD model
   repositories
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Digital manufacturing; 3D shape search; 3D recommendation; Deep learning
ID COMMUNITY DETECTION; NETWORKS; REPRESENTATION; INFORMATION; GENERATION;
   CENTRALITY
AB Product Design based Knowledge graphs (KG) aid the representation of product assemblies through heterogeneous relationships that link entities obtained from multiple structured and unstructured sources. This study describes an approach to constructing a multi-relational and multi-hierarchical knowledge graph that extracts information contained within the 3D product model data to construct Assembly-Subassembly-Part and Shape Similarity relationships. This approach builds on a combination of utilizing 3D model meta-data and structuring the graph using the Assembly-Part hierarchy alongside 3D Shape-based Clustering. To demonstrate our approach, from a dataset consisting of 110,770 CAD models, 92,715 models were organized into 7,651 groups of varying sizes containing highly similar shapes, demonstrating the varied nature of design repositories, but inevitably also containing a significant number of repetitive and unique designs. Using the Product Design Knowledge Graph, we demonstrate the effectiveness of 3D shape retrieval using Approximate Nearest Neighbor search. Finally, we illustrate the use of the KG for Design Reuse of co-occurring components, Rule-Based Inference for Assembly Similarity and Collaborative Filtering for Multi-Modal Search of manufacturing process conditions. Future work aims to expand the KG to include downstream data within product manufacturing and towards improved reasoning methods to provide actionable suggestions for design bot assistants and manufacturing automation.
C1 [Bharadwaj, Akshay G.; Starly, Binil] North Carolina State Univ, Dept Ind & Syst Engn, Raleigh, NC 27695 USA.
RP Starly, B (通讯作者)，North Carolina State Univ, Dept Ind & Syst Engn, Raleigh, NC 27695 USA.
EM bstarly@ncsu.edu
OI Starly, Binil/0000-0002-8527-1269
FU NSF [1937043]
FX Acknowledgements This research was supported in part by NSF
   Grant#1937043. The authors do not have any competing interests with
   respect to the work described in this paper.
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NR 102
TC 0
Z9 0
U1 4
U2 4
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1474-0346
EI 1873-5320
J9 ADV ENG INFORM
JI Adv. Eng. Inform.
PD AUG
PY 2022
VL 53
AR 101680
DI 10.1016/j.aei.2022.101680
PG 19
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 2X7GD
UT WOS:000825367100005
DA 2022-08-23
ER

PT J
AU Bharadhwaj, VS
   Ali, M
   Birkenbihl, C
   Mubeen, S
   Lehmann, J
   Hofmann-Apitius, M
   Hoyt, CT
   Domingo-Fernandez, D
AF Bharadhwaj, Vinay Srinivas
   Ali, Mehdi
   Birkenbihl, Colin
   Mubeen, Sarah
   Lehmann, Jens
   Hofmann-Apitius, Martin
   Hoyt, Charles Tapley
   Domingo-Fernandez, Daniel
TI CLEP: a hybrid data- and knowledge-driven framework for generating
   patient representations
SO BIOINFORMATICS
LA English
DT Article
ID BETA
AB A Summary: As machine learning and artificial intelligence increasingly attain a larger number of applications in the biomedical domain, at their core, their utility depends on the data used to train them. Due to the complexity and high dimensionality of biomedical data, there is a need for approaches that combine prior knowledge around known biological interactions with patient data. Here, we present CLinical Embedding of Patients (CLEP), a novel approach that generates new patient representations by leveraging both prior knowledge and patient-level data. First, given a patient-level dataset and a knowledge graph containing relations across features that can be mapped to the dataset, CLEP incorporates patients into the knowledge graph as new nodes connected to their most characteristic features. Next, CLEP employs knowledge graph embedding models to generate new patient representations that can ultimately be used for a variety of downstream tasks, ranging from clustering to classification. We demonstrate how using new patient representations generated by CLEP significantly improves performance in classifying between patients and healthy controls for a variety of machine learning models, as compared to the use of the original transcriptomics data. Furthermore, we also show how incorporating patients into a knowledge graph can foster the interpretation and identification of biological features characteristic of a specific disease or patient subgroup. Finally, we released CLEP as an open source Python package together with examples and documentation.
C1 [Bharadhwaj, Vinay Srinivas; Birkenbihl, Colin; Mubeen, Sarah; Hofmann-Apitius, Martin; Hoyt, Charles Tapley; Domingo-Fernandez, Daniel] Fraunhofer Inst Algorithms & Sci Comp, Dept Bioinformat, D-53757 St Augustin, Germany.
   [Bharadhwaj, Vinay Srinivas; Birkenbihl, Colin; Mubeen, Sarah; Hofmann-Apitius, Martin] Univ Bonn, Bonn Aachen Int Ctr Informat Technol B IT, D-53115 Bonn, Germany.
   [Ali, Mehdi; Lehmann, Jens; Hoyt, Charles Tapley; Domingo-Fernandez, Daniel] Rheinische Friedrich Wilhelms Univ Bonn, D-53113 Bonn, Germany.
   [Ali, Mehdi; Lehmann, Jens] Fraunhofer Inst Intelligent Anal & Informat Syst, Dresden, Germany.
   [Ali, Mehdi; Lehmann, Jens] Fraunhofer Inst Intelligent Anal & Informat Syst, St Augustin, Germany.
   [Mubeen, Sarah; Hoyt, Charles Tapley; Domingo-Fernandez, Daniel] Fraunhofer Ctr Machine Learning, Bonn, Germany.
RP Domingo-Fernandez, D (通讯作者)，Fraunhofer Inst Algorithms & Sci Comp, Dept Bioinformat, D-53757 St Augustin, Germany.; Domingo-Fernandez, D (通讯作者)，Rheinische Friedrich Wilhelms Univ Bonn, D-53113 Bonn, Germany.; Domingo-Fernandez, D (通讯作者)，Fraunhofer Ctr Machine Learning, Bonn, Germany.
EM daniel.domingo.fernandez@scai.fraunhofer.de
RI Domingo-Fernández, Daniel/AGH-3497-2022; Hoyt, Charles/B-5720-2018
OI Domingo-Fernández, Daniel/0000-0002-2046-6145; Ali,
   Mehdi/0000-0003-1653-3920; Bharadhwaj, Vinay/0000-0002-9901-9494; Hoyt,
   Charles/0000-0003-4423-4370
FU German Federal Ministry of Education and Research (BMBF) [01IS18050D];
   Fraunhofer Cluster of Excellence 'Cognitive Internet Technologies'
FX This work was funded by the Fraunhofer Cluster of Excellence `Cognitive
   Internet Technologies' as well as by the German Federal Ministry of
   Education and Research (BMBF) [01IS18050D] (project `MLWin').
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NR 52
TC 1
Z9 1
U1 4
U2 5
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD SEP 15
PY 2021
VL 37
IS 19
BP 3311
EP 3318
DI 10.1093/bioinformatics/btab340
EA MAY 2021
PG 8
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA XT8JM
UT WOS:000733827400029
PM 33964127
OA Green Published, hybrid, Green Submitted
DA 2022-08-23
ER

PT J
AU Xu, ZG
   Dang, YZ
AF Xu, Zhaoguang
   Dang, Yanzhong
TI Data-driven causal knowledge graph construction for root cause analysis
   in quality problem solving
SO INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
LA English
DT Article; Early Access
DE Root cause analysis; causal knowledge graph; quality management; data
   mining; automotive industry
ID FAULT-DIAGNOSIS; NETWORK CONSTRUCTION; EXTRACTION; MODEL; SYSTEM;
   MAINTENANCE; IMPROVEMENT; PREDICTION; DISCOVERY; MACHINE
AB Root cause analysis (RCA) plays an essential role in quality problem solving (QPS). Due to the difficulty of obtaining causal knowledge of quality problems, companies often rely on expert experience and conventional RCA tools when conducting RCA. Rich QPS data have remained mostly untapped but provide the potential for causal knowledge mining, while the semistructured nature of these data poses enormous challenges to this task. Thus, we propose a data-driven framework to mine large-scale causalities between quality problems and production factors from QPS data and exploit a causal knowledge graph for quality problems (QPCKG) to express these causalities. We first classify QPS data to identify the data containing causality. The causal linguistic patterns are then employed to extract cause slots and effect slots from these data. Subsequently, we apply the BiLSTM-CRF to extract the core content of problems. A vertex fusion method is last proposed to integrate discrete causalities into QPCKG. The approach is validated in a real-world application at a leading automotive company. Three potential applications of the QPCKG are demonstrated for quality diagnosis and prediction. The QPCKG reveals a grand picture of the core interaction mechanism of product quality and production factors and provides decision-making support for RCA.
C1 [Xu, Zhaoguang; Dang, Yanzhong] Dalian Univ Technol, Inst Syst Engn, Dalian, Peoples R China.
RP Xu, ZG (通讯作者)，Dalian Univ Technol, Inst Syst Engn, Dalian, Peoples R China.
EM zhgxu@dlut.edu.cn
RI Xu, Zhaoguang/N-2688-2014
OI Xu, Zhaoguang/0000-0001-9965-2583
FU National Natural Science Foundation of China [72001034,71871041]
FX This work was supported by National Natural Science Foundation of China:
   [Grant Number 72001034,71871041].
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NR 87
TC 0
Z9 0
U1 14
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.
DI 10.1080/00207543.2022.2078748
EA MAY 2022
PG 19
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA 1O1LQ
UT WOS:000801103400001
DA 2022-08-23
ER

PT J
AU Liu, YF
   Wang, G
   Zhou, Y
   Liu, YH
AF Liu, Yufei
   Wang, Guan
   Zhou, Yuan
   Liu, Yuhan
TI Advanced Technology Evolution Pathways of Nanogenerators: A Novel
   Framework Based on Multi-Source Data and Knowledge Graph
SO NANOMATERIALS
LA English
DT Article
DE nanogenerator; technology evolution pathway; knowledge graph;
   representation learning; multi-source data
ID PATENT ANALYSIS; THIN-FILM; CHINA; AUGMENTATION; NETWORKS
AB As an emerging nano energy technology, nanogenerators have been developed rapidly, which makes it crucial to analyze the evolutionary pathways of advanced technology in this field to help estimate the development trend and direction. However, some limitations existed in previous studies. On the one hand, previous studies generally made use of the explicit correlation of data such as citation and cooperation between patents and papers, which ignored the rich semantic information contained in them. On the other hand, the progressive evolutionary process from scientific grants to academic papers and then to patents was not considered. Therefore, this paper proposes a novel framework based on a separated three-layer knowledge graph with several time slices using grant data, paper data, and patent data. Firstly, by the representation learning method and clustering algorithm, several clusters representing specific technologies in different layers and different time slices can be obtained. Then, by calculating the similarity between clusters of different layers, the evolutionary pathways of advanced technology from grants to papers and then to patents is drawn. Finally, this paper monitors the pathways of some developed technologies, which evolve from grants to papers and then to patents, and finds some emerging technologies under research.
C1 [Liu, Yufei] Chinese Acad Engn, Ctr Strateg Studies, Beijing 100088, Peoples R China.
   [Wang, Guan; Liu, Yuhan] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Natl Numer Control Syst Engn Res Ctr, Wuhan 430074, Peoples R China.
   [Zhou, Yuan] Tsinghua Univ, Sch Publ Policy & Management, Beijing 100084, Peoples R China.
RP Zhou, Y (通讯作者)，Tsinghua Univ, Sch Publ Policy & Management, Beijing 100084, Peoples R China.
EM liuyf@cae.cn; liuyf@cae.cn; zhou_yuan@mail.tsinghua.edu.cn; liuyf@cae.cn
OI Zhou, Yuan/0000-0002-9198-6586
FU National Natural Science Foundation of China [72104224, 71974107,
   L2124002, 91646102, L1924058]; MOE (Ministry of Education in China)
   Project of Humanities and Social Sciences [16JDGC011]; Construction
   Project of China Knowledge Center for Engineering Sciences and
   Technology [CKCEST-2021-2-7]; Tsinghua University Initiative Scientific
   Research Program [2019Z02CAU]; Tsinghua University Project of
   Volvo-Supported Green Economy and Sustainable Development [20183910020]
FX This research was funded by the National Natural Science Foundation of
   China [grant numbers 72104224, 71974107, L2124002, 91646102, L1924058];
   the MOE (Ministry of Education in China) Project of Humanities and
   Social Sciences [grant number 16JDGC011]; the Construction Project of
   China Knowledge Center for Engineering Sciences and Technology [grant
   number CKCEST-2021-2-7]; the Tsinghua University Initiative Scientific
   Research Program [grant number 2019Z02CAU]; and the Tsinghua University
   Project of Volvo-Supported Green Economy and Sustainable Development
   [grant number 20183910020].
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NR 43
TC 1
Z9 1
U1 33
U2 33
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-4991
J9 NANOMATERIALS-BASEL
JI Nanomaterials
PD MAR
PY 2022
VL 12
IS 5
AR 838
DI 10.3390/nano12050838
PG 14
WC Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry; Science & Technology - Other Topics; Materials Science;
   Physics
GA ZW5JS
UT WOS:000771249600001
PM 35269326
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU He, SF
   Sun, DQ
   Wang, Z
AF He, Shufeng
   Sun, Dianqi
   Wang, Zhao
TI Named entity recognition for Chinese marine text with knowledge-based
   self-attention
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Chinese named entity recognition; Self-attention mechanism; Knowledge
   graph
AB Chinese named entity recognition has been widely used in many fields, such as species recognition in marine information, and so on. Compared with the standard named entity recognition (NER), the performance of the Chinese marine named entity recognition is low, which is mainly limited by the normative nature of the text and the scale of the tagged corpus. In recent years, the research of named entity recognition primarily focuses on the small scale of the tagged corpus. It tends to use external knowledge or use joint training to improve final recognition performance. However, there is little research on the problem that the accuracy of NER will be suboptimal if the model is trained inadequately. To this end, in order to improve the accuracy of naming entity recognition and recognize more entities in corpus, this paper proposes a named entity recognition method that combines knowledge graph embedding with a self-attention mechanism. The entity embeddings of the marine knowledge graph (KG) are empolyed for the hidden units of NER model with attention mechanism in an end-to-end way. Therefore, the model can get additional auxiliary information to improve performance. Lastly, we conduct extensive experiments on marine corpus and other public datasets. The experimental results verify the effectiveness of our proposed method.
C1 [He, Shufeng; Sun, Dianqi; Wang, Zhao] China Geol Survey, Qingdao Inst Marine Geol, Qingdao, Peoples R China.
RP He, SF (通讯作者)，China Geol Survey, Qingdao Inst Marine Geol, Qingdao, Peoples R China.
EM hshufeng@mail.cgs.gov.cn; sdianqi@mail.cgs.gov.cn;
   wzhao_qds@mail.cgs.gov.cn
FU Second Level Research Project of China Geological Survey [DD20191008]
FX This work is supported by the Second Level Research Project of China
   Geological Survey (Grant No. DD20191008).
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NR 57
TC 2
Z9 2
U1 28
U2 72
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD JUN
PY 2022
VL 81
IS 14
BP 19135
EP 19149
DI 10.1007/s11042-020-10089-z
EA JAN 2021
PG 15
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1L8ZN
UT WOS:000607369100007
DA 2022-08-23
ER

PT J
AU Farazi, F
   Krdzavac, NB
   Akroyd, J
   Mosbach, S
   Menon, A
   Nurkowski, D
   Kraft, M
AF Farazi, Feroz
   Krdzavac, Nenad B.
   Akroyd, Jethro
   Mosbach, Sebastian
   Menon, Angiras
   Nurkowski, Daniel
   Kraft, Markus
TI Linking reaction mechanisms and quantum chemistry: An ontological
   approach
SO COMPUTERS & CHEMICAL ENGINEERING
LA English
DT Article
DE Chemical kinetic reaction mechanism; Quantum chemistry; Linked data;
   Chemical ontology; Thermodynamic data; Knowledge-graph; J-Park Simulator
ID INJECTION ENGINE; SOOT FORMATION; LARGE-SCALE; DATABASE; MANAGEMENT
AB In this paper, a linked-data framework for connecting species in chemical kinetic reaction mechanisms with quantum calculations is presented. A mechanism can be constructed from thermodynamic, reaction rate, and transport data that has been obtained either experimentally, computationally, or by a combination of both. This process in practice requires multiple sources of data, which raises, inter alia, species naming and data inconsistency issues. A linked data-centric knowledge-graph approach is taken in this work to address these challenges. In order to implement this approach, two existing ontologies, namely OntoKin, for representing chemical kinetic reaction mechanisms, and OntoCompChem, for representing quantum chemistry calculations, are extended. In addition, a new ontology, which we call OntoSpecies, is developed for uniquely representing chemical species. The framework also includes agents to populate and link knowledge-bases created through the instantiation of these ontologies. In addition, the developed knowledge-graph and agents naturally form a part of the J-Park Simulator (JPS) - an Industry 4.0 platform which combines linked data and an eco-system of autonomous agents for cross-domain applications. The functionality of the framework is demonstrated via a use-case based on a hydrogen combustion mechanism. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Farazi, Feroz; Krdzavac, Nenad B.; Akroyd, Jethro; Mosbach, Sebastian; Menon, Angiras; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, Philippa Fawcett Dr, Cambridge CB3 0AS, England.
   [Krdzavac, Nenad B.; Akroyd, Jethro; Mosbach, Sebastian; Menon, Angiras; Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore CARES, CREATE Tower,1 Create Way, Singapore 138602, Singapore.
   [Nurkowski, Daniel] CMCL Innovat, Sheraton House,Castle Pk,Castle St, Cambridge CB3 0AX, England.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, 62 Nanyang Dr, Singapore 637459, Singapore.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, Philippa Fawcett Dr, Cambridge CB3 0AS, England.; Kraft, M (通讯作者)，Cambridge Ctr Adv Res & Educ Singapore CARES, CREATE Tower,1 Create Way, Singapore 138602, Singapore.; 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 Akroyd, Jethro/A-8698-2010; Kraft, Markus/D-7243-2016
OI Akroyd, Jethro/0000-0002-2143-8656; Kraft, Markus/0000-0002-4293-8924;
   Menon, Angiras/0000-0002-6810-2797
FU National Research Foundation (NRF), Prime Minister's Office, Singapore
   under its Campus for Research Excellence and Technological Enterprise
   (CREATE) programme; European Union [724145]; Alexander von Humboldt
   foundation
FX This work was partly funded by the National Research Foundation (NRF),
   Prime Minister's Office, Singapore under its Campus for Research
   Excellence and Technological Enterprise (CREATE) programme, and by the
   European Union Horizon 2020 Research and Innovation Programme under
   grant agreement 724145. Markus Kraft gratefully acknowledges the support
   of the Alexander von Humboldt foundation.
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TC 9
Z9 9
U1 4
U2 15
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0098-1354
EI 1873-4375
J9 COMPUT CHEM ENG
JI Comput. Chem. Eng.
PD JUN 9
PY 2020
VL 137
AR 106813
DI 10.1016/j.compchemeng.2020.106813
PG 12
WC Computer Science, Interdisciplinary Applications; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA LP5GG
UT WOS:000534345300010
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Yan, HH
   Yang, J
   Wan, JF
AF Yan, Hehua
   Yang, Jun
   Wan, Jiafu
TI KnowIME: A System to Construct a Knowledge Graph for Intelligent
   Manufacturing Equipment
SO IEEE ACCESS
LA English
DT Article
DE Data mining; Databases; Semantics; Information systems; Data
   visualization; Smart manufacturing; Intelligent manufacturing equipment;
   knowledge graph; Neo4j; CRF; syntactic analysis
ID ARCHITECTURE
AB With the development of a new generation of information technology, such as big data and cognitive intelligence, we are in the postmodern era of artificial intelligence. Currently, the manufacturing industry is in the critical period of transitioning to smart manufacturing, but the cognitive capabilities of devices in smart factories are still scarce. Knowledge Graph (KG) is one of the key technologies of cognitive intelligence, which opens a new path for the horizontal integration of intelligent manufacturing. Therefore, this paper proposes and builds a manufacturing equipment information query system based on KG. Firstly, a large amount of heterogeneous data that contains vast devices information is obtained from the network. Secondly, the conditional random fields (CRF) algorithm is used to extract the entity name, product place, and company name of the device, and then the relationship between the device entities is identified by calculating the similarity and Chinese syntax analysis. In the validation section, we use to the map of Neo4j graph database, when we input a name of a device in the search box, the system can return a relational graph node. In addition, the shortest path optimization algorithm is used to calculate the similarity between nodes in the search process to achieve the recommendation of similar node information.
C1 [Yan, Hehua] Guangdong Mech & Elect Polytech, Sch Elect Technol, Guangzhou 510515, Peoples R China.
   [Yang, Jun; Wan, Jiafu] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Peoples R China.
RP Wan, JF (通讯作者)，South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Peoples R China.
EM jiafuwan_76@163.com
RI Wan, Jiafu/I-3059-2016
OI Wan, Jiafu/0000-0001-9188-4179
FU National Key Research and Development Program of China [2017YFE0101000];
   Key Program of National Natural Science Foundation of China [U1801264];
   Key Areas Research and Development Program of Guangdong Province, China
   [2019B010150002, 2019B090919002]; Key Program of Natural Science
   Foundation of Guangdong Province, China [2017B030311008]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2017YFE0101000, in part by the
   Key Program of National Natural Science Foundation of China under Grant
   U1801264, in part by the Key Areas Research and Development Program of
   Guangdong Province, China, under Grant 2019B010150002 and Grant
   2019B090919002, and in part by the Key Program of Natural Science
   Foundation of Guangdong Province, China, under Grant 2017B030311008.
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NR 43
TC 12
Z9 13
U1 19
U2 44
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 41805
EP 41813
DI 10.1109/ACCESS.2020.2977136
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LC5RU
UT WOS:000525388000002
OA gold
DA 2022-08-23
ER

PT J
AU Jiang, TW
   Zeng, QK
   Zhao, T
   Qin, B
   Liu, T
   Chawla, NV
   Jiang, M
AF Jiang, Tianwen
   Zeng, Qingkai
   Zhao, Tong
   Qin, Bing
   Liu, Ting
   Chawla, Nitesh, V
   Jiang, Meng
TI Biomedical Knowledge Graphs Construction From Conditional Statements
SO IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
LA English
DT Article
DE Biological system modeling; Labeling; Tagging; Feeds; MIMO
   communication; Task analysis; Biomedical knowledge graph; information
   extraction; conditional statement; sequence labeling
AB Conditions play an essential role in biomedical statements. However, existing biomedical knowledge graphs (BioKGs) only focus on factual knowledge, organized as a flat relational network of biomedical concepts. These BioKGs ignore the conditions of the facts being valid, which loses essential contexts for knowledge exploration and inference. We consider both facts and their conditions in biomedical statements and proposed a three-layered information-lossless representation of BioKG. The first layer has biomedical concept nodes, attribute nodes. The second layer represents both biomedical fact and condition tuples by nodes of the relation phrases, connecting to the subject and object in the first layer. The third layer has nodes of statements connecting to a set of fact tuples and/or condition tuples in the second layer. We transform the BioKG construction problem into a sequence labeling problem based on a novel designed tag schema. We design a Multi-Input Multi-Output sequence labeling model (MIMO) that learns from multiple input signals and generates proper number of multiple output sequences for tuple extraction. Experiments on a newly constructed dataset show that MIMO outperforms the existing methods. Further case study demonstrates that the BioKGs constructed provide a good understanding of the biomedical statements.
C1 [Jiang, Tianwen; Qin, Bing; Liu, Ting] Harbin Inst Technol, Res Ctr Social Comp & Informat Retrieval, Harbin 150001, Peoples R China.
   [Zeng, Qingkai; Zhao, Tong; Chawla, Nitesh, V; Jiang, Meng] Univ Notre Dame, Dept Comp Sci & Engn, Notre Dame, IN 46556 USA.
RP Qin, B (通讯作者)，Harbin Inst Technol, Res Ctr Social Comp & Informat Retrieval, Harbin 150001, Peoples R China.
EM twjiang@ir.hit.edu.cn; qzheng@nd.edu; tzhao2@nd.edu; bqin@ir.hit.edu.cn;
   tliu@ir.hit.edu.cn; nchawla@nd.edu; mjiang2@nd.edu
RI ; Chawla, Nitesh/F-2690-2016
OI Zhao, Tong/0000-0001-7660-1732; Chawla, Nitesh/0000-0003-3932-5956;
   Jiang, Meng/0000-0002-3009-519X
FU National Key Research and Development Program of China [2018YFB1005103];
   National Natural Science Foundation of China (NSFC) [61632011]; NSF
   [IIS-1849816, CCF-1901059]
FX The authors would like to thank the anonymous reviewers for their
   valuable comments and insightful suggestions. This work was supported in
   part by National Key Research and Development Program of China No.
   2018YFB1005103, National Natural Science Foundation of China (NSFC) No.
   61632011, and NSF Grants IIS-1849816 and CCF-1901059.
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NR 38
TC 1
Z9 1
U1 3
U2 9
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1545-5963
EI 1557-9964
J9 IEEE ACM T COMPUT BI
JI IEEE-ACM Trans. Comput. Biol. Bioinform.
PD MAY-JUN
PY 2021
VL 18
IS 3
BP 823
EP 835
DI 10.1109/TCBB.2020.2979959
PG 13
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 SN5QC
UT WOS:000658343000003
PM 32167907
DA 2022-08-23
ER

PT J
AU Boroujeni, GA
   Faili, H
   Yaghoobzadeh, Y
AF Boroujeni, Golshan Afzali
   Faili, Heshaam
   Yaghoobzadeh, Yadollah
TI Answer selection in community question answering exploiting knowledge
   graph and context information
SO SEMANTIC WEB
LA English
DT Article
DE Community question answering; knowledge graph; context;
   convolutional-deconvolutional; variational autoencoder
ID NETWORK
AB With the increasing popularity of knowledge graph (KG), many applications such as sentiment analysis, trend prediction, and question answering use KG for better performance. Despite the obvious usefulness of commonsense and factual information in the KGs, to the best of our knowledge, KGs have been rarely integrated into the task of answer selection in community question answering (CQA). In this paper, we propose a novel answer selection method in CQA by using the knowledge embedded in KGs. We also learn a latent-variable model for learning the representations of the question and answer, jointly optimizing generative and discriminative objectives. It also uses the question category for producing context-aware representations for questions and answers. Moreover, the model uses variational autoencoders (VAE) in a multi-task learning process with a classifier to produce class-specific representations for answers. The experimental results on three widely used datasets demonstrate that our proposed method is effective and outperforms the existing baselines significantly.
C1 [Boroujeni, Golshan Afzali; Faili, Heshaam; Yaghoobzadeh, Yadollah] Univ Tehran, Coll Engn, Sch Elect & Comp Engn, Tehran, Iran.
   [Faili, Heshaam] Inst Res Fundamental Sci IPM, Sch Comp Sci, Tehran, Iran.
RP Faili, H (通讯作者)，Univ Tehran, Coll Engn, Sch Elect & Comp Engn, Tehran, Iran.; Faili, H (通讯作者)，Inst Res Fundamental Sci IPM, Sch Comp Sci, Tehran, Iran.
EM golshan.afzali@ut.ac.ir; hfaili@ut.ac.ir; y.yaghoobzadeh@ut.ac.ir
FU Iran National Science Foundation (INSF) [4002438]
FX This work is based upon research funded by Iran National Science
   Foundation (INSF) under project No. 4002438.
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NR 65
TC 1
Z9 1
U1 2
U2 2
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2022
VL 13
IS 3
BP 339
EP 356
DI 10.3233/SW-222970
PG 18
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0J3YH
UT WOS:000780041500003
OA hybrid
DA 2022-08-23
ER

PT J
AU Kalender, M
   Eren, MT
   Wu, ZH
   Cirakman, O
   Kutluk, S
   Gultekin, G
   Korkmaz, EE
AF Kalender, Murat
   Eren, M. Tolga
   Wu, Zonghuan
   Cirakman, Ozgun
   Kutluk, Sezer
   Gultekin, Gunay
   Korkmaz, Emin Erkan
TI Videolization: knowledge graph based automated video generation from web
   content
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Semantic web; Computer graphics; Text-to-video; Entity linking;
   Knowledge graph; DBpedia
AB Web content nowadays can also be accessed through new generation of Internet connected TVs. However, these products failed to change users' behavior when consuming online content. Users still prefer personal computers to access Web content. Certainly, most of the online content is still designed to be accessed by personal computers or mobile devices. In order to overcome the usability problem of Web content consumption on TVs, this paper presents a knowledge graph based video generation system that automatically converts textual Web content into videos using semantic Web and computer graphics based technologies. As a use case, Wikipedia articles are automatically converted into videos. The effectiveness of the proposed system is validated empirically via opinion surveys. Fifty percent of survey users indicated that they found generated videos enjoyable and 42 % of them indicated that they would like to use our system to consume Web content on their TVs.
C1 [Kalender, Murat; Korkmaz, Emin Erkan] Yeditepe Univ, Dept Comp Engn, Fac Engn, Istanbul, Turkey.
   [Kalender, Murat; Eren, M. Tolga; Cirakman, Ozgun; Kutluk, Sezer; Gultekin, Gunay] Huawei Technol, Huawei Turkey R&D Ctr, Istanbul, Turkey.
   [Wu, Zonghuan] Huawei Technol, Software Lab, Santa Clara, CA USA.
RP Kalender, M (通讯作者)，Yeditepe Univ, Dept Comp Engn, Fac Engn, Istanbul, Turkey.; Kalender, M (通讯作者)，Huawei Technol, Huawei Turkey R&D Ctr, Istanbul, Turkey.
EM murat.kalender@huawei.com; zonghuanwu@huawei.com;
   ekorkmaz@cse.yeditepe.edu.tr
RI Kutluk, Sezer/AAB-3214-2020; Kalender, Murat/ABH-1321-2020
OI Kutluk, Sezer/0000-0002-3048-5526; Cirakman, Ozgun/0000-0001-6649-3280;
   Korkmaz, Emin Erkan/0000-0002-7842-7667
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NR 30
TC 2
Z9 2
U1 2
U2 18
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 JAN
PY 2018
VL 77
IS 1
BP 567
EP 595
DI 10.1007/s11042-016-4275-4
PG 29
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA FS7RL
UT WOS:000419995400024
DA 2022-08-23
ER

PT J
AU Ramezani, M
   Feizi-Derakhshi, MR
   Balafar, MA
AF Ramezani, Majid
   Feizi-Derakhshi, Mohammad-Reza
   Balafar, Mohammad-Ali
TI Knowledge Graph-Enabled Text-Based Automatic Personality Prediction
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID RECOGNITION
AB How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications takes place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text, a knowledge graph, which is a set of interlinked descriptions of concepts, was built by matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving a more powerful representation, the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph, which is now a knowledgeable alternative for the input text, was embedded to yield an embedding matrix. Finally, to perform personality predictions, the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers.
C1 [Ramezani, Majid; Feizi-Derakhshi, Mohammad-Reza] Univ Tabriz, Fac Elect & Comp Engn, Dept Comp Engn, Computerized Intelligence Syst Lab, Tabriz, Iran.
   [Balafar, Mohammad-Ali] Univ Tabriz, Fac Elect & Comp Engn, Dept Comp Engn, Tabriz, Iran.
RP Ramezani, M; Feizi-Derakhshi, MR (通讯作者)，Univ Tabriz, Fac Elect & Comp Engn, Dept Comp Engn, Computerized Intelligence Syst Lab, Tabriz, Iran.
EM m_ramezani@tabrizu.ac.ir; mfeizi@tabrizu.ac.ir; balafarila@tabrizu.ac.ir
RI Feizi Derakhshi, Mohammad Reza/AAK-1687-2020; Balafar, Prof
   M.A/D-1865-2010
OI Feizi Derakhshi, Mohammad Reza/0000-0002-8548-976X; Balafar, Prof
   M.A/0000-0001-5898-0871
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NR 61
TC 0
Z9 0
U1 1
U2 1
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD JUN 20
PY 2022
VL 2022
AR 3732351
DI 10.1155/2022/3732351
PG 18
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 3K6AB
UT WOS:000834155800015
PM 35769270
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Chi, Y
   Yu, CC
   Qi, XH
   Xu, H
AF Chi, Yang
   Yu, Congcong
   Qi, Xiaohui
   Xu, Hao
TI Knowledge Management in Healthcare Sustainability: A Smart Healthy Diet
   Assistant in Traditional Chinese Medicine Culture
SO SUSTAINABILITY
LA English
DT Article
DE healthcare sustainability; healthcare education; healthy diet; knowledge
   graph; traditional Chinese medicine
ID SYSTEM; INTERVENTION; ENVIRONMENT; PLANTS; FOOD
AB In the past 40 years, with the changes to dietary structure and the dramatic increase in the consumption of meat products in developing countries, especially in China, encouraging populations to maintain their previous healthy eating patterns will have health, environmental, and economic co-benefits. Healthy diet education plays an important role in the promotion of people's healthy behavior. However, in the modern age, the data regarding healthy diets available on the internet is increasing rapidly and is distributed on multiple sources. It is time-consuming for users to learn about healthy diets on the internet: they need to search data on multiple platforms, choose and integrate information, and then understand what they have learned. To help people retrieve and learn healthy diet knowledge more efficiently and comprehensively, this paper designs a knowledge graph to integrate healthy diet information on the internet and provides a semantic retrieval system. In the knowledge graph, five main concepts are defined, including food material, dish, nutritional element, symptom, and crowd, as well as the relationships among them. In addition, Chinese dietary culture elements and traditional Chinese medicine (TCM) theory are also contained in the knowledge graph. The preliminary results show that by using the system, users learn healthy diet knowledge more quickly and comprehensively and they are more inclined to have balanced diets. This work could be regarded as a retrieval and education tool, which can assist healthcare and national sustainable development.
C1 [Chi, Yang; Yu, Congcong; Qi, Xiaohui; Xu, Hao] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China.
   [Xu, Hao] Jilin Univ, Sch Management, Changchun 130012, Jilin, Peoples R China.
   [Xu, Hao] Jilin Univ, Zhuhai Coll, Dept Comp Sci & Technol, Zhuhai 519041, Peoples R China.
   [Xu, Hao] Jilin Univ, Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Jilin, Peoples R China.
RP Xu, H (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China.; Xu, H (通讯作者)，Jilin Univ, Sch Management, Changchun 130012, Jilin, Peoples R China.; Xu, H (通讯作者)，Jilin Univ, Zhuhai Coll, Dept Comp Sci & Technol, Zhuhai 519041, Peoples R China.; Xu, H (通讯作者)，Jilin Univ, Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Jilin, Peoples R China.
EM chiyang16@mails.jlu.edu.cn; ccyu17@mails.jlu.edu.cn;
   qixh17@mails.jlu.edu.cn; xuhao@jlu.edu.cn
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NR 44
TC 3
Z9 3
U1 10
U2 44
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD NOV
PY 2018
VL 10
IS 11
AR 4197
DI 10.3390/su10114197
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 HC1AQ
UT WOS:000451531700371
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Sun, YW
   Zhai, Y
AF Sun, Yaowu
   Zhai, Yi
TI Mapping the knowledge domain and the theme evolution of appropriability
   research between 1986 and 2016: a scientometric review
SO SCIENTOMETRICS
LA English
DT Review
DE Appropriability; Knowledge map; Cluster analysis; Open innovation;
   Platform governance
ID RESEARCH-AND-DEVELOPMENT; INTENSIVE BUSINESS SERVICES; OPEN INNOVATION;
   APPROPRIATION MECHANISMS; ABSORPTIVE-CAPACITY; INTELLECTUAL PROPERTY;
   INTERNAL CAPABILITIES; STRATEGIC ALLIANCES; EMPIRICAL-ANALYSIS; EMERGING
   TRENDS
AB The scholars in the research domains of innovation and strategic management concerned about the appropriability for about 30 years or more. They focused on appropriability research and constantly evolving. In this paper, we analyze 30 years (1986-2016) literature on appropriability studies from Web of Science Core Collection database. A cited reference clustering map of different periods and terms co-occurrence map have been generated using bibliometric analysis and content analysis. Based on this, we study the evolutionary trajectory, mechanisms and theoretical architecture of appropriability research and explore further research directions. The results indicate that the essence of the appropriability research evolution is the perception changes in opening and sharing, value creation and value growth, and future research is focusing on role of appropriability in the platform governance, generative appropriability and the evolution of the problem-solving mechanisms.
C1 [Sun, Yaowu; Zhai, Yi] Hunan Univ, Business Sch, Changsha 410082, Hunan, Peoples R China.
RP Sun, YW (通讯作者)，Hunan Univ, Business Sch, Changsha 410082, Hunan, Peoples R China.
EM sunyaowu@hnu.edu.cn
FU National Natural Science Foundation of China [71472061, 71172193];
   Specialized Research Fund for the Doctoral Program of Higher Education
   of China [20130161110032]
FX The authors gratefully acknowledge financial support from several
   sources: the National Natural Science Foundation of China (No. 71472061;
   No. 71172193) and Specialized Research Fund for the Doctoral Program of
   Higher Education of China (No. 20130161110032).
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NR 87
TC 10
Z9 13
U1 15
U2 121
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 2018
VL 116
IS 1
BP 203
EP 230
DI 10.1007/s11192-018-2748-0
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 GM4WR
UT WOS:000438126800009
DA 2022-08-23
ER

PT J
AU Wang, YM
   Ren, SJ
   Song, L
   Zhang, J
AF Wang, Yongmei
   Ren, Shujun
   Song, Li
   Zhang, Jiang
TI Construction and Knowledge Mining of Traditional Chinese Medicine
   Ancient Books Bibliographic Abstracts Database Based on Genetic
   Algorithm and BP Neural Network
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
AB With the rapid development of modern science technology and Internet technology, the establishment of a unified and standardized bibliographic summary database to realize the exchange and resource sharing of ancient Chinese medicine bibliography, is the inevitable trend of ancient Chinese medicine bibliography digital service. Firstly, this paper formulates the bibliographic metadata specification of traditional Chinese medicine ancient books (TCMAB), extracts each cataloging file into an XML document in line with the bibliographic metadata specification of TCMAB. Secondly, this paper realizes the unified description of ancient book resources in the database system of TCMAB, uses the native XML database eXist to store and manage the XML documents of all traditional Chinese medicine ancient book resources, and integrates the multimedia data with XML data. Finally, genetic algorithm and BP neural network are used for knowledge mining and discovery, the overall model design of TCMAB bibliographic abstracts database system is proposed based on the construction process of knowledge map. The system platform adopts B/S mode, eXist database management system, PowerSSP streaming media and video server for audio video processing.
C1 [Wang, Yongmei; Song, Li; Zhang, Jiang] Hefei Normal Univ, Sch Comp Sci & Technol, Hefei 230031, Peoples R China.
   [Ren, Shujun] Lib Anhui Univ Tradit Chinese Med, Hefei 230038, Peoples R China.
RP Wang, YM (通讯作者)，Hefei Normal Univ, Sch Comp Sci & Technol, Hefei 230031, Peoples R China.
EM wangymfddy@hfnu.edu.cn; ren0312@163.com; lilyu20032003@163.com;
   99034871@qq.com
FU Scientific Research Project of the Education Department of Anhui
   Province [KJ2021A0903]
FX AcknowledgmentsThis work was supported by the Scientific Research
   Project of the Education Department of Anhui Province (Grant no.
   KJ2021A0903), And The authors wish to thank the anonymous reviewers who
   helped to improve the quality of the paper.
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U1 6
U2 6
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD APR 6
PY 2022
VL 2022
AR 6838714
DI 10.1155/2022/6838714
PG 15
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 0Z6NF
UT WOS:000791192100005
OA gold
DA 2022-08-23
ER

PT J
AU Wan, SS
   Liu, Y
AF Wan, Shanshan
   Liu, Ying
TI A security detection approach based on autonomy-oriented user sensor in
   social recommendation network
SO INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
LA English
DT Article
DE Autonomous sensor; social recommendation network; dynamic knowledge
   graph; shilling attack; graph community detection
ID MODEL; ATTACKS
AB User social network-based recommender system has achieved significant performance in current recommendation fields. However, the characteristic of openness brings great hidden dangers to the security of recommender systems. Shilling attackers can change the recommendations by foraging user relationships. Most shilling attack detection approaches depend on the explicit user historical data to locate shilling attackers. Some important features such as information propagation and social feedback of users in social networks have not been noticed. We propose a security detection method based on autonomy-oriented user sensor (AOUSD) to identify shilling attackers. Specifically, (1) the user is simulated as a social sensor with autonomous capabilities, (2) the user interaction model is built based on information propagation, information feedback and information disappearance mechanisms of social sensors, and a user dynamic knowledge graph is formed by considering the variable time function, (3) hierarchical clustering method is used to generate preliminary suspicious candidate groups and graph community detection clustering method is applied on the dynamic knowledge graph to detect the attackers. Then, AOUSD is first simulated on NetLogo and it is compared with other algorithms based on the Amazon data. The results prove the advantages of AOUSD in the efficiency and accuracy on shilling attack detection.
C1 [Wan, Shanshan; Liu, Ying] Beijing Univ Civil Engn & Architecture, Sch Elect & Informat Engn, Beijing 100044, Peoples R China.
RP Wan, SS (通讯作者)，Beijing Univ Civil Engn & Architecture, Sch Elect & Informat Engn, Beijing 100044, Peoples R China.
EM wss@bucea.edu.cn
OI Wan, Shanshan/0000-0002-3421-4387
FU National Natural Science Foundation of China [61902016, 61871020];
   Beijing Municipal High-Level Innovation Group Construction Project
   [IDHT20190506]; Natural Science Foundation of Beijing Municipality
   [4204093]; National Key R&D Program of China [2018YFC0807806-2]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: The
   authors wish to acknowledge the support of the National Natural Science
   Foundation of China (no. 61902016, no. 61871020, no. 61871020), the
   Beijing Municipal High-Level Innovation Group Construction Project (no.
   IDHT20190506), the Natural Science Foundation of Beijing Municipality
   (no. 4204093), and the National Key R&D Program of China (no.
   2018YFC0807806-2).
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PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1550-1477
J9 INT J DISTRIB SENS N
JI Int. J. Distrib. Sens. Netw.
PD MAR
PY 2022
VL 18
IS 3
AR 15501329221082415
DI 10.1177/15501329221082415
PG 21
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 0F6SN
UT WOS:000777487200001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, M
   Wang, HF
   Liu, X
   Ma, XY
   Wang, BL
AF Wang, Meng
   Wang, Haofen
   Liu, Xing
   Ma, Xinyu
   Wang, Beilun
TI Drug-Drug Interaction Predictions via Knowledge Graph and Text
   Embedding: Instrument Validation Study
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE drug-drug interactions; knowledge graph; natural language processing
ID INTERACTION EXTRACTION
AB Background: Minimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discovering various DDIs. However, these data contain a huge amount of noise and provide knowledge bases that are far from being complete or used with reliability. Most existing studies focus on predicting binary DDIs between drug pairs and ignore other interactions.
   Objective: Leveraging both drug knowledge graphs and biomedical text is a promising pathway for rich and comprehensive DDI prediction, but it is not without issues. Our proposed model seeks to address the following challenges: data noise and incompleteness, data sparsity, and computational complexity.
   Methods: We propose a novel framework, Predicting Rich DDI, to predict DDIs. The framework uses graph embedding to overcome data incompleteness and sparsity issues to make multiple DDI label predictions. First, a large-scale drug knowledge graph is generated from different sources. The knowledge graph is then embedded with comprehensive biomedical text into a common low-dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process.
   Results: To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world data sets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy.
   Conclusions: We propose a novel framework, Predicting Rich DDI, to predict DDIs. Using rich DDI information, it can competently predict multiple labels for a pair of drugs across numerous domains, ranging from pharmacological mechanisms to side effects. To the best of our knowledge, this framework is the first to provide a joint translation-based embedding model that learns DDIs by integrating drug knowledge graphs and biomedical text simultaneously in a common low-dimensional space. The model also predicts DDIs using multiple labels rather than single or binary labels. Extensive experiments were conducted on real-world data sets to demonstrate the effectiveness and efficiency of the model. The results show our proposed framework outperforms several state-of-the-art baselines.
C1 [Wang, Meng; Ma, Xinyu; Wang, Beilun] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Wang, Meng] Southeast Univ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.
   [Wang, Haofen] Tongji Univ, Coll Design & Innovat, 281 Fuxin Rd, Shanghai 200092, Peoples R China.
   [Liu, Xing] Cent South Univ, Xiangya Hosp 3, Changsha, Peoples R China.
RP Wang, HF (通讯作者)，Tongji Univ, Coll Design & Innovat, 281 Fuxin Rd, Shanghai 200092, Peoples R China.
EM carter.whfcarter@gmail.com
FU National Natural Science Foundation of China [61906037]; Fundamental
   Research Funds for the Central Universities [4309002159, 22120210109];
   CCF-Baidu Open Fund
FX Grant support was received from the National Natural Science Foundation
   of China with (grant 61906037), the Fundamental Research Funds for the
   Central Universities with (grants 4309002159 and 22120210109), and the
   CCF-Baidu Open Fund.
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TC 0
Z9 0
U1 3
U2 12
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 JUN
PY 2021
VL 9
IS 6
AR e28277
DI 10.2196/28277
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA TB7AK
UT WOS:000668096200013
PM 34185011
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Osorio, M
   Buil-Aranda, C
   Santana-Perez, I
   Garijo, D
AF Osorio, Maximiliano
   Buil-Aranda, Carlos
   Santana-Perez, Idafen
   Garijo, Daniel
TI DockerPedia: A Knowledge Graph of Software Images and Their Metadata
SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
LA English
DT Article
DE Software container; software image; knowledge graph; software metadata;
   Docker; experiment reproducibility; ontology
ID FRAMEWORK; REPRODUCIBILITY; WORKFLOWS
AB An increasing amount of researchers use software images to capture the requirements and code dependencies needed to carry out computational experiments. Software images preserve the computational environment required to execute a scientific experiment and have become a crucial asset for reproducibility. However, software images are usually not properly documented and described, making it challenging for scientists to find, reuse and understand them. In this paper, we propose a framework for automatically describing software images in a machine-readable manner by (i) creating a vocabulary to describe software images; (ii) developing an annotation framework designed to automatically document the underlying environment of software images and (iii) creating DockerPedia, a Knowledge Graph with over 150,000 annotated software images, automatically described using our framework. We illustrate the usefulness of our approach in finding images with specific software dependencies, comparing similar software images, addressing versioning problems when running computational experiments; and flagging problems with vulnerable software dependencies.
C1 [Osorio, Maximiliano; Buil-Aranda, Carlos] Univ Tecn Federico Santa Maria, Dept Informat, Ave Espana 1680, Valparaiso, Chile.
   [Santana-Perez, Idafen] Univ Las Palmas Gran Canaria, Dept Senales & Comunicac, Las Palmas Gran Canaria, Spain.
   [Garijo, Daniel] Univ Politecn Madrid, Ontol Engn Grp, Madrid, Spain.
RP Garijo, D (通讯作者)，Univ Politecn Madrid, Ontol Engn Grp, Madrid, Spain.
EM mosorio@inf.utfsm.cl; cbuil@inf.utfsm.cl; idafen.santana@ulpgc.es;
   daniel.garijo@upm.es
RI Santana-Perez, IDAFEN/J-4513-2012
OI Santana-Perez, IDAFEN/0000-0001-8296-8629
FU Fondecyt [11170714]; Insitituto Milenio de Fundamentos de los Datos,
   IMFD; DGIIP from Universidad Tecnica Federico Santa Maria; Madrid
   Government (Comunidad de Madrid-Spain); Universidad Politecnica de
   Madrid
FX Maximiliano Osorio and Carlos Buil-Aranda have been funded by Fondecyt
   Iniciacion project 11170714 and by Insitituto Milenio de Fundamentos de
   los Datos, IMFD. Maximiliano Osorio has also been funded by DGIIP from
   Universidad T~ecnica Federico Santa Maria.; Daniel Garijo has been
   supported by the Madrid Government (Comunidad de Madrid-Spain) under the
   Multiannual Agreement with Universidad Polit~ecnica de Madrid in the
   line Support for R&D projects for Beatriz Galindo researchers, in the
   context of the V PRICIT (Regional Programme of Research and
   Technological Innovation).
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PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-1940
EI 1793-6403
J9 INT J SOFTW ENG KNOW
JI Int. J. Softw. Eng. Knowl. Eng.
PD JAN
PY 2022
VL 32
IS 01
BP 71
EP 89
DI 10.1142/S0218194022500036
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 ZG4ZX
UT WOS:000760269000003
DA 2022-08-23
ER

PT J
AU Pan, ZL
   Su, C
   Deng, YC
   Cheng, J
AF Pan, Zaolin
   Su, Cheng
   Deng, Yichuan
   Cheng, Jack
TI Video2Entities: A computer vision-based entity extraction framework for
   updating the architecture, engineering and construction industry
   knowledge graphs
SO AUTOMATION IN CONSTRUCTION
LA English
DT Article
DE Zero-shot learning; Computer vision; Entity extraction; Knowledge graph;
   Knowledge update
AB Due to the decentralisation and complexity of knowledge in the architecture, engineering and construction (AEC) industry, the research on knowledge graphs (KGs) is still insufficient, and most of the research focuses on textbased KG structuring or updating. Entity extraction, a sub-task of knowledge extraction, is critical in general KG update approaches. While the mainstream approach for this task generally uses textual data, visual data is more readily available, more accurate and has a shorter update cycle than textual data. Therefore, this paper integrates zero-shot learning techniques with general KGs to present a novel framework called ?video2entities? that can extract entities from videos to update the AEC KG. The framework combines the perceptual capabilities of computer vision with the cognitive capabilities of KG to improve the accuracy and timeliness of KG updates. Experimental results demonstrate that the framework can extract ?new entities? from architectural decoration videos for AEC KG updates.
C1 [Pan, Zaolin; Su, Cheng; Deng, Yichuan] South China Univ Technol, Sch Civil Engn & Transportat, Guangzhou 510641, Peoples R China.
   [Pan, Zaolin] Sino Singapore Int Joint Res Inst, Guangzhou 510555, Peoples R China.
   [Su, Cheng; Deng, Yichuan] State Key Lab Subtrop Bldg Sci, Guangzhou 510641, Peoples R China.
   [Cheng, Jack] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China.
RP Deng, YC (通讯作者)，South China Univ Technol, Sch Civil Engn & Transportat, Guangzhou 510641, Peoples R China.
EM ctycdeng@scut.edu.cn
OI PAN, Zaolin/0000-0002-1549-554X; Deng, Yichuan/0000-0003-1492-4370
FU Guangdong Science Foundation [2018A030310363]; Science and Technology
   Program of Guangzhou [201804020069]; Sino Singapore International Joint
   Research Institute [205D119001]
FX The authors would like to acknowledge the support of the Guangdong
   Science Foundation, Grant No. 2018A030310363, the support ofthe Science
   and Technology Program of Guangzhou, Grant No. 201804020069 and the
   support of the SinoSingapore International Joint Research Institute,
   Project No. 205D119001.
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PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0926-5805
EI 1872-7891
J9 AUTOMAT CONSTR
JI Autom. Constr.
PD MAY
PY 2021
VL 125
AR 103617
DI 10.1016/j.autcon.2021.103617
EA FEB 2021
PG 14
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA SB0HL
UT WOS:000649683700004
DA 2022-08-23
ER

PT J
AU Borrego, A
   Ayala, D
   Hernandez, I
   Rivero, CR
   Ruiz, D
AF Borrego, Agustin
   Ayala, Daniel
   Hernandez, Inma
   Rivero, Carlos R.
   Ruiz, David
TI CAFE: Knowledge graph completion using neighborhood-aware features
SO ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Knowledge Graphs; Knowledge graph completion; Link prediction; Machine
   learning
AB Knowledge Graphs (KGs) currently contain a vast amount of structured information in the form of entities and relations. Because KGs are often constructed automatically by means of information extraction processes, they may miss information that was either not present in the original source or not successfully extracted. As a result, KGs might lack useful and valuable information. Current approaches that aim to complete missing information in KGs have two main drawbacks. First, some have a dependence on embedded representations, which impose a very expensive preprocessing step and need to be recomputed again as the KG grows. Second, others are based on long random paths that may not cover all relevant information, whereas exhaustively analyzing all possible paths between entities is very time-consuming. In this paper, we present an approach to complete KGs based on evaluating candidate triples using a set of neighborhood-based features. Our approach exploits the highly connected nature of KGs by analyzing the entities and relations surrounding any given pair of entities, while avoiding full recomputations as new entities are added. Our results indicate that our proposal is able to identify correct triples with a higher effectiveness than other state-of-the-art approaches, achieving higher average F1 scores in all tested datasets. Therefore, we conclude that the information present in the vicinities of the two entities within a candidate triple can be leveraged to determine whether that triple is missing from the KG or not.
C1 [Borrego, Agustin; Ayala, Daniel; Hernandez, Inma; Ruiz, David] Univ Seville, ETS Ingn Informat, Avda Reina Mercedes S-N, Seville, Spain.
   [Rivero, Carlos R.] Rochester Inst Technol, 92 Lomb Mem Dr, Rochester, NY 14623 USA.
RP Borrego, A (通讯作者)，Univ Seville, ETS Ingn Informat, Avda Reina Mercedes S-N, Seville, Spain.
EM borrego@us.es; dayala1@us.es; inmahernandez@us.es; crr@cs.rit.edu;
   druiz@us.es
RI Borrego, Agustín/AAC-5407-2021; Ayala, Daniel/X-1748-2018; Rivero,
   Carlos R./F-3283-2016
OI Ayala, Daniel/0000-0003-2095-1009; Borrego, Agustin/0000-0003-3750-8484;
   Rivero, Carlos R./0000-0002-5770-950X; Ruiz, David/0000-0003-4460-5493
FU Spanish Ministry of Science and Innovation [TIN2016-75394-R,
   PID2019105471RB-I00]
FX This work was supported by the Spanish Ministry of Science and
   Innovation under grants TIN2016-75394-R and PID2019105471RB-I00.
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U2 11
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0952-1976
EI 1873-6769
J9 ENG APPL ARTIF INTEL
JI Eng. Appl. Artif. Intell.
PD AUG
PY 2021
VL 103
AR 104302
DI 10.1016/j.engappai.2021.104302
EA MAY 2021
PG 10
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Engineering, Multidisciplinary; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science; Engineering
GA SP7MK
UT WOS:000659848800002
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Malik, KM
   Krishnamurthy, M
   Alobaidi, M
   Hussain, M
   Alam, F
   Malik, G
AF Malik, Khalid Mahmood
   Krishnamurthy, Madan
   Alobaidi, Mazen
   Hussain, Maqbool
   Alam, Fakhare
   Malik, Ghaus
TI Automated domain-specific healthcare knowledge graph curation framework:
   Subarachnoid hemorrhage as phenotype
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge Graph; Ontology; Electronic Health Records; Intracranial
   Aneurysm; Association Rules; Ensemble Learning; Subarachnoid Hemorrhage
   Stroke
ID BASE
AB To derive meaningful insights from voluminous healthcare data, it is essential to convert it into machine understandable knowledge. Currently, machine understandable domain specific healthcare knowledge curation framework does not exist for complex neurological diseases such as subarachnoid hemorrhage stroke. We envisage futuristic clinical decision support systems and tools backed with such knowledge will aide in complex neurological disease prognosis, diagnosis, and treatment. Existing knowledge graphs (KGs) only contain concepts and relationships between them and offer this knowledge to information extraction and knowledge management applications. However, the proposed domain-specific automated KG curation framework enables extraction of concepts, relationships, individual and cohort graphs, and predictive knowledge. By employing ontology-based information extraction, ensemble learning and word embedding based on skip-gram techniques on structured and unstructured data from electronic health records of 1025 patients with an intracranial aneurysm, this paper proposes a novel fully automated framework to curate knowledge graph, consisting of concepts, different hierarchical and non-hierarchical relationships, and predictive rules for prediction of subarachnoid hemorrhage. The evaluation shows that proposed framework achieves 78% precision and 71% recall respectively, for concept extraction from clinical text. Taxonomic relationships evaluation had precision and recall of 68%, and 95%, respectively. Evaluation of knowledge to predict unruptured status using validation dataset shows accuracy, precision, recall, of 73%, 76%, and 90% respectively. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Malik, Khalid Mahmood; Krishnamurthy, Madan; Alobaidi, Mazen; Alam, Fakhare] Oakland Univ, Dept Comp Sci & Engn, 115 Lib Dr, Rochester, MI 48309 USA.
   [Hussain, Maqbool] Sejong Univ, Dept Software, Seoul, South Korea.
   [Malik, Ghaus] Henry Ford Hosp, Dept Neurosurg, 2799 West Grand Blvd, Detroit, MI 48202 USA.
RP Malik, KM (通讯作者)，Oakland Univ, Dept Comp Sci & Engn, 115 Lib Dr, Rochester, MI 48309 USA.; Hussain, M (通讯作者)，Sejong Univ, Dept Software, Seoul, South Korea.
EM mahmood@oakland.edu; mkrishna@oakland.edu; malobaid@oakland.edu;
   maqbool.hussain@sejong.ac.kr; fakharealam@oakland.edu; gmalik1@hfhs.org
RI Alobaidi, Mazen/AAM-5365-2020; Alobaidi, Mazen/AAE-7295-2022
OI HUSSAIN, MAQBOOL/0000-0002-1663-1718; Mahmood Malik,
   Khalid/0000-0002-7927-3436
FU Brain Aneurysm Foundation; Oakland University
FX This research was funded partially by the Brain Aneurysm Foundation,
   Oakland University, and graciously supported by the invaluable data and
   domain expert assistance rendered by Neuroscience Institute, Henry Ford
   Health System, Michigan.
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   Song M, 2015, J BIOMED INFORM, V57, P320, DOI 10.1016/j.jbi.2015.08.008
   Steiner T., 2012, 11 INT SEMANTIC WEB, V914, P65
   Vrandecic D, 2014, COMMUN ACM, V57, P78, DOI 10.1145/2629489
   Weng H, 2017, LECT NOTES COMPUT SC, V10594, P170, DOI 10.1007/978-3-319-69182-4_18
   Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
   Yu T, 2017, ARTIF INTELL MED, V77, P48, DOI 10.1016/j.artmed.2017.04.001
   Yuan JB, 2020, KNOWL INF SYST, V62, P317, DOI 10.1007/s10115-019-01351-4
   Zhao Q, 2018, COMPUT BIOL MED, V101, P39, DOI 10.1016/j.compbiomed.2018.08.009
NR 42
TC 14
Z9 14
U1 2
U2 38
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD MAY 1
PY 2020
VL 145
AR 113120
DI 10.1016/j.eswa.2019.113120
PG 15
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 KT0HV
UT WOS:000518692500014
DA 2022-08-23
ER

PT J
AU Li, CC
   Chang, TC
AF Li, Chi-Chen
   Chang, Tin-Chang
TI Applying the GIS to Discuss the Traffic Accidents under Environmental
   Analysis - Case on Taichung City
SO EKOLOJI
LA English
DT Article
DE traffic accident; geographic information system; knowledge map
ID MOTORCYCLISTS
AB Traffic accidents are frequently caused by complicated connection among people, vehicles, roads and the environment, and surely those accidents increase great expenditure on social cost; therefore, the "traveling security" becomes the most important issue that is obviously taking the place on the top priority of the traffic aspect. Because the geographic location and environmental distribution have been the attributes of traffic accidents, using geographic information system to build traffic accidents data will make the analysis even more complete. First of all, this research has used the geographic information system to build the database of traffic accidents happened in Taichung City, and next, through the interviews with the senior superiors of Taichung City's Traffic Police Brigade and from their professional and experiential point of view, the discussion about the ten accident-prone road segments for situation factors and accident prevention strategies has been done. Furthermore, the knowledge map of traffic accidents in Taichung City has been built. Through pairing individual intersection with incident location and causes of incidents, the study of individual case has been done. And because of doing so, this study can be used to help the traffic management unit to do the best arrangement for traffic service and establishing the best prevention strategy. The major causes of the traffic accidents happened in Taichung City were mostly because of illegal overtaking, race for the road, lane change, or improper driving directions, drunk driving and not maintaining a safe distance. Furthermore, according to the Traffic Accident Prevention Strategy in Taichung City and the interview based on it, the result of analysis shows that the traffic accidents mostly can be divided into the categories of speeding, race for the road, changing lane at will, not following the traffic sign, traffic lanes and drunk driving. Therefore, the causes of traffic accidents are mainly personal factors, and most of the accidents happened especially at the intersections, places near the intersections, U-turn lanes, fast lanes and cross border areas. If the police and the city government can make a good use of geographic information system to analyze the major causes of accidents, keep banning violation of traffic rules and doing routine spot check, fulfill the education of traffic security for the citizens to help them cultivate the good habit of following traffic rules and regulations, the occurrence probability of traffic accidents will surely be effectively reduced.
C1 [Li, Chi-Chen] Natl Taichung Univ Sci & Technol, Taichung, Taiwan.
   [Chang, Tin-Chang] Asia Univ, Dept Business Adm, Dept Digital Media Design, Taichung, Taiwan.
RP Chang, TC (通讯作者)，Asia Univ, Dept Business Adm, Dept Digital Media Design, Taichung, Taiwan.
EM ervine@asia.edu.tw
CR Bjornskau T, 2012, ACCIDENT ANAL PREV, V49, P50, DOI 10.1016/j.aap.2011.09.051
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   Zhang GN, 2016, ACCIDENT ANAL PREV, V87, P34, DOI 10.1016/j.aap.2015.10.033
NR 14
TC 1
Z9 1
U1 0
U2 4
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 3805
EP 3816
AR UNSP e107424
PG 12
WC Ecology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA HP4WZ
UT WOS:000461678300423
DA 2022-08-23
ER

PT J
AU Zhu, Q
   Nguyen, DT
   Sheils, T
   Alyea, G
   Sid, E
   Xu, YJ
   Dickens, J
   Mathe, EA
   Pariser, A
AF Zhu, Qian
   Dac-Trung Nguyen
   Sheils, Timothy
   Alyea, Gioconda
   Sid, Eric
   Xu, Yanji
   Dickens, James
   Mathe, Ewy A.
   Pariser, Anne
TI Scientific evidence based rare disease research discovery with research
   funding data in knowledge graph
SO ORPHANET JOURNAL OF RARE DISEASES
LA English
DT Article
ID INSTITUTES; HEALTH
AB Background: Limited knowledge and unclear underlying biology of many rare diseases pose significant challenges to patients, clinicians, and scientists. To address these challenges, there is an urgent need to inspire and encourage scientists to propose and pursue innovative research studies that aim to uncover the genetic and molecular causes of more rare diseases and ultimately to identify effective therapeutic solutions. A clear understanding of current research efforts, knowledge/research gaps, and funding patterns as scientific evidence is crucial to systematically accelerate the pace of research discovery in rare diseases, which is an overarching goal of this study.
   Methods: To semantically represent NIH funding data for rare diseases and advance its use of effectively promoting rare disease research, we identified NIH funded projects for rare diseases by mapping GARD diseases to the project based on project titles; subsequently we presented and managed those identified projects in a knowledge graph using Neo4j software, hosted at NCATS, based on a pre-defined data model that captures semantics among the data. With this developed knowledge graph, we were able to perform several case studies to demonstrate scientific evidence generation for supporting rare disease research discovery.
   Results: Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting amongst mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research.
   Conclusion: We developed an integrative knowledge graph with rare disease funding data and demonstrated its use as a source from where we can effectively identify and generate scientific evidence to support rare disease research. With the success of this preliminary study, we plan to implement advanced computational approaches for analyzing more funding related data, e.g., project abstracts and PubMed article abstracts, and linking to other types of biomedical data to perform more sophisticated research gap analysis and identify opportunities for future research in rare diseases.
C1 [Zhu, Qian; Dac-Trung Nguyen; Sheils, Timothy; Mathe, Ewy A.] NIH, Div Preclin Innovat, Natl Ctr Adv Translat Sci NCATS, Rockville, MD 20850 USA.
   [Alyea, Gioconda] ICF Int Inc, Rockville, MD USA.
   [Sid, Eric; Xu, Yanji; Dickens, James; Pariser, Anne] NIH, Off Rare Dis Res, Natl Ctr Adv Translat Sci NCATS, Bethesda, MD 20892 USA.
RP Zhu, Q (通讯作者)，NIH, Div Preclin Innovat, Natl Ctr Adv Translat Sci NCATS, Rockville, MD 20850 USA.
EM qian.zhu@nih.gov
OI Zhu, Qian/0000-0002-4858-6333
FU National Institutes of Health (NIH); NCATS, NIH
FX Open Access funding provided by the National Institutes of Health (NIH).
   This research was supported by the Intramural and Extramural research
   program of the NCATS, NIH.
CR [Anonymous], BATTEN DIS FACT SHEE
   [Anonymous], NIH FUNDING BOLSTERS
   [Anonymous], NIH EXPORTER
   [Anonymous], MONDO DIS ONTOLOGY
   [Anonymous], ENZYME REPLACEMENT T
   [Anonymous], AHRQS HEALTHCARE COS
   [Anonymous], **NON-TRADITIONAL**
   [Anonymous], NCATS INTEGRATED DIS
   [Anonymous], 2019, WE HAVE BUDGET FY 20
   [Anonymous], MONDO DIS ONTOLOGY
   [Anonymous], WHY IS RARE DIS FUND
   [Anonymous], FDA APPROVES TWICE Y
   [Anonymous], CYPHER QUERY LANGUAG
   [Anonymous], POLIO ENDGAME STRATE
   [Anonymous], NIH REPORT
   Franceschi AM, 2017, ACAD RADIOL, V24, P1162, DOI 10.1016/j.acra.2017.02.018
   Gross CP, 1999, NEW ENGL J MED, V340, P1881, DOI 10.1056/NEJM199906173402406
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NR 27
TC 0
Z9 0
U1 3
U2 8
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1750-1172
J9 ORPHANET J RARE DIS
JI Orphanet J. Rare Dis.
PD NOV 18
PY 2021
VL 16
IS 1
AR 483
DI 10.1186/s13023-021-02120-9
PG 12
WC Genetics & Heredity; Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity; Research & Experimental Medicine
GA XA1RZ
UT WOS:000720434600008
PM 34794473
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, WA
   Luo, DS
   Fu, FY
   He, HY
   Zhang, K
AF Chen, WenGang
   Luo, DianSheng
   Fu, FangYu
   He, HongYing
   Zhang, Ke
TI Research on the Transformer Intelligent Operation and Maintenance System
   Based on the Graph Neural Network
SO FRONTIERS IN ENERGY RESEARCH
LA English
DT Article
DE power transformer; intelligent operation and maintenance system; graph
   neural network; data management technology; knowledge graph
AB With the development of the smart grid and energy Internet, the power industry generates huge, multi-source, heterogeneous, and highly coupled data, which are difficult to utilize. The intelligent operation and maintenance system of the power transformer based on the knowledge graph and graph neural network is developed in this article. The multi-source heterogeneous data are structured and modeled by the constructed knowledge graph, and it presents the correlation among data more intuitively. On this basis, the graph neural network is designed to achieve the prediction and excavate the deep information hidden in the data. The testing results show that the system has fully used the multi-dimensional and interrelated heterogeneous data, achieving a deep information mine. It benefits the management and strategy implementation for the system scientifically and guides the operation and maintenance of the transformer. The system is of great significance on improving the efficiency of the transformer maintenance and safe operation.
C1 [Chen, WenGang; Zhang, Ke] State Grid Shanxi Elect Power Co, Jincheng Power Supply Co, Jincheng, Peoples R China.
   [Luo, DianSheng; Fu, FangYu; He, HongYing] Hunan Univ, Coll Elect & Informat Engn, Changsha, Peoples R China.
RP Luo, DS; He, HY (通讯作者)，Hunan Univ, Coll Elect & Informat Engn, Changsha, Peoples R China.
EM lhx20070322@hnu.edu.cn; lhx20070322@sina.com
FU Science and Technology Project of State Grid Shanxi Electric Power Co.,
   Ltd., China [SGSXJC00YCJS2100679]
FX Funding This work was supported by the Science and Technology Project of
   State Grid Shanxi Electric Power Co., Ltd., China (SGSXJC00YCJS2100679).
CR Fan Zhang, 2020, 2020 International Signal Processing, Communications and Engineering Management Conference (ISPCEM), P177, DOI 10.1109/ISPCEM52197.2020.00042
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   Kedong Zhu, 2021, 2021 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), P431, DOI 10.1109/ICPICS52425.2021.9524131
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   Suwnansri T, 2014, 2014 INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON)
   Takahashi T, 2016, IEEE T DIELECT EL IN, V23, P1643, DOI 10.1109/TDEI.2016.005574
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   Xie BQ, 2020, FRONT ENERGY RES, V8, DOI 10.3389/fenrg.2020.00150
   Xu QiangChao, 2020, 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), P29, DOI 10.1109/IFEEA51475.2020.00014
   Zhang JF, 2021, FRONT ENERGY RES, V9, DOI 10.3389/fenrg.2021.779587
   Zhou B, 2021, J MOD POWER SYST CLE, V9, P463, DOI 10.35833/MPCE.2019.000237
NR 15
TC 0
Z9 0
U1 3
U2 3
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-598X
J9 FRONT ENERGY RES
JI Front. Energy Res.
PD JUN 28
PY 2022
VL 10
AR 935359
DI 10.3389/fenrg.2022.935359
PG 7
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA 2V9BO
UT WOS:000824132300001
OA gold
DA 2022-08-23
ER

PT J
AU Krause, S
   Hennig, L
   Moro, A
   Weissenborn, D
   Xu, FY
   Uszkoreit, H
   Navigli, R
AF Krause, Sebastian
   Hennig, Leonhard
   Moro, Andrea
   Weissenborn, Dirk
   Xu, Feiyu
   Uszkoreit, Hans
   Navigli, Roberto
TI Sar-graphs: A language resource connecting linguistic knowledge with
   semantic relations from knowledge graphs
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge graphs; Language resources; Linguistic patterns; Relation
   extraction
ID WIKIPEDIA; ONTOLOGY
AB Recent years have seen a significant growth and increased usage of large-scale knowledge resources in both academic research and industry. We can distinguish two main types of knowledge resources: those that store factual information about entities in the form of semantic relations (e.g., Freebase), namely so-called knowledge graphs, and those that represent general linguistic knowledge (e.g., WordNet or UWN). In this article, we present a third type of knowledge resource which completes the picture by connecting the two first types. Instances of this resource are graphs of semantically-associated relations (sar-graphs), whose purpose is to link semantic relations from factual knowledge graphs with their linguistic representations in human language.
   We present a general method for constructing sar-graphs using a language-and relation-independent, distantly supervised approach which, apart from generic language processing tools, relies solely on the availability of a lexical semantic resource, providing sense information for words, as well as a knowledge base containing seed relation instances. Using these seeds, our method extracts, validates and merges relation-specific linguistic patterns from text to create sar-graphs. To cope with the noisily labeled data arising in a distantly supervised setting, we propose several automatic pattern confidence estimation strategies, and also show how manual supervision can be used to improve the quality of sar-graph instances. We demonstrate the applicability of our method by constructing sar-graphs for 25 semantic relations, of which we make a subset publicly available at http://sargraph.dfki.de.
   We believe sar-graphs will prove to be useful linguistic resources for a wide variety of natural language processing tasks, and in particular for information extraction and knowledge base population. We illustrate their usefulness with experiments in relation extraction and in computer assisted language learning. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Krause, Sebastian; Hennig, Leonhard; Weissenborn, Dirk; Xu, Feiyu; Uszkoreit, Hans] DFKI Language Technol Lab, Alt Moabit 91c, D-10559 Berlin, Germany.
   [Moro, Andrea; Navigli, Roberto] Univ Roma La Sapienza, Dipartimento Informat, Viale Regina Elena 295, I-00161 Rome, Italy.
RP Krause, S (通讯作者)，DFKI Language Technol Lab, Alt Moabit 91c, D-10559 Berlin, Germany.
EM skrause@dfki.de
OI Navigli, Roberto/0000-0003-3831-9706
FU German Federal Ministry of Education and Research (BMBF) through the
   project Deependance [01IW11003]; German Federal Ministry of Education
   and Research (BMBF) through the project ALL SIDES [01IW14002]; German
   Federal Ministry of Education and Research (BMBF) through the project
   BBDC [01IS14013E]; ERC Starting Grant MultiJEDI [259234]; Google Focused
   Research Award
FX This research was supported by the German Federal Ministry of Education
   and Research (BMBF) through the projects Deependance (contract
   01IW11003), ALL SIDES (contract 01IW14002) and BBDC (contract
   01IS14013E), as well as by the ERC Starting Grant MultiJEDI No. 259234,
   and a Google Focused Research Award granted in July 2013.
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   DODDINGTON G, 2004, P LREC
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   FINKEL JR, 2005, P ACL
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   Gardner Matt, 2014, P EMNLP
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   [No title captured]
   [No title captured]
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NR 65
TC 8
Z9 8
U1 0
U2 24
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 112
EP 131
DI 10.1016/j.websem.2016.03.004
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:000376457600008
DA 2022-08-23
ER

PT J
AU Wang, ZK
   Li, LJ
   Zeng, D
AF Wang, Zikang
   Li, Linjing
   Zeng, Daniel
TI SRGCN: Graph-based multi-hop reasoning on knowledge graphs
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph; Multi-hop reasoning; Graph convolutional network
AB Learning to infer missing links is one of the fundamental tasks in the knowledge graph. Instead of reasoning based on separate paths in the existing methods, in this paper, we propose a new model, Sequential Relational Graph Convolutional Network (SRGCN), which treats the multiple paths between an entity pair as a sequence of subgraphs. Specifically, to reason the relationship between two entities, we first construct a graph for the entities based on the knowledge graph and serialize the graph to a sequence. For each hop in the sequence, Relational Graph Convolutional Network (R-GCN) is then applied to update the embeddings of the entities. The updated embedding of the tail entity contains information of the entire graph, hence the relationship between two entities can be inferred from it. Compared to the existing approaches that deal with paths separately, SRGCN treats the graph as a whole, which can encode structural information and interactions between paths better. Experiments show that SRGCN outperforms path-based baselines on both link and fact prediction tasks. We also show that SRGCN is highly efficient in the sense that only one epoch of training is enough to achieve high accuracy, and even partial datasets can lead to competitive performance. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Wang, Zikang; Li, Linjing; Zeng, Daniel] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
   [Wang, Zikang; Li, Linjing; Zeng, Daniel] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China.
   [Li, Linjing; Zeng, Daniel] Shenzhen Artificial Intelligence & Data Sci Inst, Shenzhen, Peoples R China.
RP Li, LJ (通讯作者)，Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
EM wangzikang2016@ia.ac.cn; linjing.li@ia.ac.cn; dajun.zeng@ia.ac.cn
RI Wang, Zikang/ABD-3193-2021
OI Wang, Zikang/0000-0003-1406-1237
FU National Key Research and Development Program of China [2020AAA0103405];
   National Natural Science Foundation of China [71621002]; Strategic
   Priority Research Program of Chinese Academy of Sciences [XDA27030100]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2020AAA0103405, the National
   Natural Science Foundation of China under Grants 71621002, as well as
   the Strategic Priority Research Program of Chinese Academy of Sciences
   under Grant XDA27030100.
CR Battaglia P. W., 2018, ARXIV PREPRINT ARXIV
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   Das R., 2018, ICLR
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   Deng Zhi-Hong, 2019, ICLR
   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
   Hamilton Will, 2017, NEURIPS
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NR 35
TC 2
Z9 2
U1 14
U2 27
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD SEP 24
PY 2021
VL 454
BP 280
EP 290
DI 10.1016/j.neucom.2021.05.016
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TI0KA
UT WOS:000672468800003
DA 2022-08-23
ER

PT J
AU Cui, H
   Peng, T
   Feng, LZ
   Bao, T
   Liu, L
AF Cui, Hai
   Peng, Tao
   Feng, Lizhou
   Bao, Tie
   Liu, Lu
TI Simple Question Answering over Knowledge Graph Enhanced by Question
   Pattern Classification
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Knowledge graph; Question answering; Simple questions; Entity detection;
   Entity linking; Relation detection
ID MODEL
AB Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn significant attention in recent years. In this paper, we focus on single-relation questions, which can be answered through a single fact in KG. This task is a non-trivial problem since capturing the meaning of questions and selecting the golden fact from billions of facts in KG are both challengeable. We propose a pipeline framework for KGQA, which consists of three cascaded components: (1) an entity detection model, which can label the entity mention in the question; (2) a novel entity linking model, which considers the contextual information of candidate entities in KG and builds a question pattern classifier according to the correlations between question patterns and relation types to mitigate entity ambiguity problem; and (3) a simple yet effective relation detection model, which is used to match the semantic similarity between the question and relation candidates. Substantial experiments on the SimpleQuestions benchmark dataset show that our proposed method could achieve better performance than many existing state-of-the-art methods on accuracy, top-N recall and other evaluation metrics.
C1 [Cui, Hai; Peng, Tao; Bao, Tie; Liu, Lu] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China.
   [Peng, Tao; Bao, Tie; Liu, Lu] Jilin Univ, Coll Software, Changchun 130012, Jilin, Peoples R China.
   [Peng, Tao; Bao, Tie; Liu, Lu] Minist Educ, Key Lab Symbol Computat & Knowledge Engineer, Changchun 130012, Jilin, Peoples R China.
   [Feng, Lizhou] Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA.
RP Liu, L (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China.; Liu, L (通讯作者)，Jilin Univ, Coll Software, Changchun 130012, Jilin, Peoples R China.; Liu, L (通讯作者)，Minist Educ, Key Lab Symbol Computat & Knowledge Engineer, Changchun 130012, Jilin, Peoples R China.
EM liulu@jlu.edu.cn
FU National Natural Science Foundation of China [61872163, 61806084]; Jilin
   Provincial Education Department project [JJKH20190160KJ]
FX This work was supported by the National Natural Science Foundation of
   China under Grant Nos. 61872163 and 61806084 and Jilin Provincial
   Education Department project under Grant No. JJKH20190160KJ.
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NR 44
TC 1
Z9 1
U1 3
U2 11
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0219-1377
EI 0219-3116
J9 KNOWL INF SYST
JI Knowl. Inf. Syst.
PD OCT
PY 2021
VL 63
IS 10
BP 2741
EP 2761
DI 10.1007/s10115-021-01609-w
EA SEP 2021
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UW8OC
UT WOS:000692297700001
DA 2022-08-23
ER

PT J
AU Yin, C
   Zhang, BY
   Liu, WZ
   Du, MY
   Luo, NN
   Zhai, X
   Ba, T
AF Yin, Chuan
   Zhang, Binyu
   Liu, Wanzeng
   Du, Mingyi
   Luo, Nana
   Zhai, Xi
   Ba, Tu
TI Geographic Knowledge Graph Attribute Normalization: Improving the
   Accuracy by Fusing Optimal Granularity Clustering and Co-Occurrence
   Analysis
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE attribute normalization; geographic knowledge graph; co-occurrence
   analysis; optimal clustering granularity
AB Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet's encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attributes. Specifically: (1) We design a classification system for geographic attributes, that is, using a community discovery algorithm to classify the attribute names. The optimal clustering granularity is identified by the marker target detection algorithm. (2) We complete the fine-grained identification of attribute relations by analyzing co-occurrence relations of the attributes and rule inference. (3) Finally, the performance of the system is verified by manual discrimination using the case of "landscape, forest, field, lake and grass". The results show the following: (1) The average precision of spatial relations was 0.974 and the average recall was 0.937; the average precision of data relations was 0.977 and the average recall was 0.998. (2) The average F1 for similarity results is 0.473; the average F1 for co-occurrence analysis results is 0.735; the average F1 for rule-based modification results is 0.934; the results show that the accuracy is greater than 90%. Compared to traditional methods only focusing on similarity, the accuracy of synonymous attribute recognition improves the system and we are capable of identifying near-sense attributes. Integration of our system and attribute normalization can greatly improve both the processing efficiency and accuracy.
C1 [Yin, Chuan; Zhang, Binyu; Du, Mingyi; Luo, Nana; Ba, Tu] Beijing Univ Civil Engn & Architecture, Sch Geomat & Urban Spatial Informat, Beijing 100044, Peoples R China.
   [Yin, Chuan] Beijing Key Lab Urban Spatial Informat Engn, Beijing 100038, Peoples R China.
   [Liu, Wanzeng; Zhai, Xi] Natl Geomat Ctr China, Informat Serv Dept, Beijing 100830, Peoples R China.
RP Liu, WZ (通讯作者)，Natl Geomat Ctr China, Informat Serv Dept, Beijing 100830, Peoples R China.
EM yinchuan@bucea.edu.cn; 2108160220006@stu.bucea.edu.cn; lwz@ngcc.cn;
   dumingyi@bucea.edu.cn; luonana@bucea.edu.cn; zhaixi@ngcc.cn;
   2108570020108@stu.bucea.edu.cn
FU National Natural Science Foundation (NSFC) of China [41930650]; Beijing
   Key Laboratory of Urban Spatial Information Engineering [2020202];
   Program (Surface Projects) [KM202110016003]
FX This research was supported by the National Natural Science Foundation
   (NSFC) of China (Key Project #41930650); Beijing Key Laboratory of Urban
   Spatial Information Engineering (No. 2020202); and Scientific Research
   Projects of Beijing Municipal Education Commission-General Projects of
   Science and Technology Program (Surface Projects) (KM202110016003).
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NR 37
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 JUL
PY 2022
VL 11
IS 7
AR 360
DI 10.3390/ijgi11070360
PG 17
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA 3J3GV
UT WOS:000833287800001
OA gold
DA 2022-08-23
ER

PT J
AU Orogat, A
   Liu, I
   El-Roby, A
AF Orogat, Abdelghny
   Liu, Isabelle
   El-Roby, Ahmed
TI CBench: Towards Better Evaluation of Question Answering Over Knowledge
   Graphs
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB Recently, there has been an increase in the number of knowledge graphs that can be only queried by experts. However, describing questions using structured queries is not straightforward for non-expert users who need to have sufficient knowledge about both the vocabulary and the structure of the queried knowledge graph, as well as the syntax of the structured query language used to describe the user's information needs. The most popular approach introduced to overcome the aforementioned challenges is to use natural language to query these knowledge graphs. Although several question answering benchmarks can be used to evaluate question-answering systems over a number of popular knowledge graphs, choosing a benchmark to accurately assess the quality of a question answering system is a challenging task.
   In this paper, we introduce CBench, an extensible, and more informative benchmarking suite for analyzing benchmarks and evaluating question answering systems. CBench can be used to analyze existing benchmarks with respect to several fine-grained linguistic, syntactic, and structural properties of the questions and queries in the benchmark. We show that existing benchmarks vary significantly with respect to these properties deeming choosing a small subset of them unreliable in evaluating QA systems. Until further research improves the quality and comprehensiveness of benchmarks, CBench can be used to facilitate this evaluation using a set of popular benchmarks that can be augmented with other user-provided benchmarks. CBench not only evaluates a question answering system based on popular single-number metrics but also gives a detailed analysis of the linguistic, syntactic, and structural properties of answered and unanswered questions to better help the developers of question answering systems to better understand where their system excels and where it struggles.
C1 [Orogat, Abdelghny; Liu, Isabelle; El-Roby, Ahmed] Carleton Univ, Ottawa, ON, Canada.
RP Orogat, A (通讯作者)，Carleton Univ, Ottawa, ON, Canada.
EM abdelghny.orogat@carleton.ca; isabelle.liu@carleton.ca;
   ahmed.elroby@carleton.ca
FU Google Cloud
FX This material is based upon work supported by Google Cloud.
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NR 53
TC 0
Z9 0
U1 0
U2 4
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD APR
PY 2021
VL 14
IS 8
BP 1325
EP 1337
DI 10.14778/3457390.3457398
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SN7WX
UT WOS:000658499200008
DA 2022-08-23
ER

PT J
AU Potoniec, J
AF Potoniec, J.
TI Learning OWL 2 Property Characteristics as an Explanation for an RNN
SO BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES
LA English
DT Article
DE recurrent neural networks; ontology learning; property characteristics;
   knowledge graphs; semantic web; deep learning
AB We propose an approach to indirectly learn the Web Ontology Language OWL 2 property characteristics as an explanation for a deep recurrent neural network (RNN). The input is a knowledge graph represented in Resource Description Framework (RDF) and the output are scored axioms representing the characteristics. The proposed method is capable of learning all the characteristics included in OWL 2: functional, inverse functional, reflexive and irreflexive, symmetric and asymmetric, transitive. We report and discuss experimental evaluation on DBpedia 2016-10, showing that the proposed approach has advantages over a simple counting baseline.
C1 [Potoniec, J.] Poznan Univ Tech, Inst Comp, Ul Piotrowo 2, PL-60965 Poznan, Poland.
   [Potoniec, J.] Poznan Univ Tech, Ctr Artificial Intelligence & Machine Learning, Ul Piotrowo 2, PL-60965 Poznan, Poland.
RP Potoniec, J (通讯作者)，Poznan Univ Tech, Inst Comp, Ul Piotrowo 2, PL-60965 Poznan, Poland.; Potoniec, J (通讯作者)，Poznan Univ Tech, Ctr Artificial Intelligence & Machine Learning, Ul Piotrowo 2, PL-60965 Poznan, Poland.
EM jpotoniec@cs.put.poznan.pl
RI Potoniec, Jędrzej/C-2531-2013
OI Potoniec, Jędrzej/0000-0002-6115-6485
FU National Centre for Research and Development, Poland
   [LIDER/14/0086/L10/18/NCBR/2019];  [09/91/DSMK/0659]
FX We acknowledge support from the grant 09/91/DSMK/0659 and by the
   National Centre for Research and Development, Poland, grant no.
   LIDER/14/0086/L10/18/NCBR/2019. We thank Aleksandra Walkiewicz of
   Nicolaus Copernicus University in Torun and Patrycja Potoniec of The
   Institute of Literary Research of the Polish Academy of Sciences for
   invaluable linguistic consultations.
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NR 35
TC 1
Z9 1
U1 0
U2 1
PU POLSKA AKAD NAUK, POLISH ACAD SCI, DIV IV TECHNICAL SCIENCES PAS
PI WARSZAWA
PA PL DEFILAD 1, WARSZAWA, 00-901, POLAND
SN 0239-7528
EI 2300-1917
J9 B POL ACAD SCI-TECH
JI Bull. Pol. Acad. Sci.-Tech. Sci.
PD DEC
PY 2020
VL 68
IS 6
BP 1481
EP 1490
DI 10.24425/bpasts.2020.134625
PG 10
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA PN6PZ
UT WOS:000604600200023
DA 2022-08-23
ER

PT J
AU Niu, BH
   Huang, YF
AF Niu, Bohan
   Huang, Yongfeng
TI An Improved Method for Web Text Affective Cognition Computing Based on
   Knowledge Graph
SO CMC-COMPUTERS MATERIALS & CONTINUA
LA English
DT Article
DE Affective cognition; fine-grained; knowledge representation; knowledge
   graph
AB The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions, emotions, evaluations and attitudes that people hold about the entities and their attributes from the text. The word level affective cognition becomes an important topic in sentiment analysis. Extracting the (attribute, opinion word) binary relationship by word segmentation and dependency parsing, and labeling those by existing emotional dictionary combined with webpage information and manual annotation, this paper constitutes a binary relationship knowledge base. By using knowledge embedding method, embedding each element in (attribute, opinion, opinion word) as a word vector into the Knowledge Graph by TransG, and defining an algorithm to distinguish the opinion between the attribute word vector and the opinion word vector. Compared with traditional method, this engine has the advantages of high processing speed and low occupancy, which makes up the time-costing and high calculating complexity in the former methods.
C1 [Niu, Bohan] Columbia Univ, Dept Elect Engn, New York, NY 10027 USA.
   [Huang, Yongfeng] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China.
RP Niu, BH (通讯作者)，Columbia Univ, Dept Elect Engn, New York, NY 10027 USA.
EM bn2300@Columbia.edu
OI Huang, Yongfeng/0000-0003-3825-2230
FU Key Program of National Natural Science Foundation of China [U1536201,
   U1405254]; National Natural Science Foundation of China [61472092]
FX This research is supported by the Key Program of National Natural
   Science Foundation of China (Grant Nos. U1536201 and U1405254), the
   National Natural Science Foundation of China (Grant No. 61472092).
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TC 9
Z9 9
U1 2
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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 2019
VL 59
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EP 14
DI 10.32604/cmc.2019.06032
PG 14
WC Computer Science, Information Systems; Materials Science,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Materials Science
GA HR5HT
UT WOS:000463178400001
OA gold
DA 2022-08-23
ER

PT J
AU Kwak, JH
   Lee, J
   Whang, JJ
   Jo, S
AF Kwak, Ji Ho
   Lee, Jaejun
   Whang, Joyce Jiyoung
   Jo, Sungho
TI Semantic Grasping Via a Knowledge Graph of Robotic Manipulation: A Graph
   Representation Learning Approach
SO IEEE ROBOTICS AND AUTOMATION LETTERS
LA English
DT Article
DE AI-enabled robotics; representation learning; grasping
ID DATASET
AB Semantic grasping aims to make stable robotic grasps suitable for specific object manipulation tasks. While existing semantic grasping models focus only on the grasping regions of objects based on their affordances, reasoning about which gripper to use for grasping, e.g., a rigid parallel-jaw gripper or a soft gripper, and how strongly to grasp the target object allows more sophisticated robotic manipulation. In this letter, we create a knowledge graph of robotic manipulation named roboKG to represent information about objects (e.g., the material and the components of an object), tasks, and appropriate robotic manipulation such as which component of an object to grasp, which gripper to use, and how strongly to grasp. Using knowledge graph embedding, we generate semantic representations of the entities and relations in roboKG, enabling us to make predictions on robotic manipulation. Based on the predicted gripper type, grasping component, and grasping force, a real robot performs seven different real-world tasks on 42 household objects, achieving an accuracy of 95.21%.
C1 [Kwak, Ji Ho; Lee, Jaejun; Whang, Joyce Jiyoung; Jo, Sungho] Korea Adv Inst Sci & Technol, Sch Comp, Daejeon 305701, South Korea.
RP Whang, JJ; Jo, S (通讯作者)，Korea Adv Inst Sci & Technol, Sch Comp, Daejeon 305701, South Korea.
EM jim9611@kaist.ac.kr; jjlee98@kaist.ac.kr; jjwhang@kaist.ac.kr;
   shjo@kaist.ac.kr
FU IITP, Korean government (MSIT) [2017-0-00432, 2022-0-00369,
   2020-0-00153]; NRF of Korea, Korean Government (MSIT) [2016R1A5A1938472,
   2018R1A5A1059921, 2022R1A2C4001594]
FX This work was supported in part by IITP, Korean government (MSIT) under
   Grants 2017-0-00432, 2022-0-00369, and 2020-0-00153 (Penetration
   Security Testing of ML Model Vulnerabilities and Defense), and in part
   by the NRF of Korea, Korean Government (MSIT) under Grants
   2016R1A5A1938472, 2018R1A5A1059921, and 2022R1A2C4001594 [DOI:
   10.1109/LRA.2022.3191194]. (Ji Ho Kwak and Jaejun Lee equally
   contributed to this work.)
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NR 40
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 2377-3766
J9 IEEE ROBOT AUTOM LET
JI IEEE Robot. Autom. Lett.
PD OCT
PY 2022
VL 7
IS 4
BP 9397
EP 9404
DI 10.1109/LRA.2022.3191194
PG 8
WC Robotics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Robotics
GA 3G2JU
UT WOS:000831182500014
DA 2022-08-23
ER

PT J
AU Wang, SR
   Zhou, WA
   Zhou, Q
AF Wang, Shirui
   Zhou, Wen'an
   Zhou, Qiang
TI Talking-heads attention-based knowledge representation for link
   prediction
SO COMPUTER SPEECH AND LANGUAGE
LA English
DT Article
DE Knowledge representation; Link prediction; Talking-heads attention
AB State-of-the-art methods for link prediction, also known as knowledge graph embedding, aim to represent both entities and relations in the given knowledge graphs (KGs) into a continuous low-dimensional vector space and thus could be used to fill the missing facts or identify the spurious facts in KGs, where a fact is represented as a triple in the form of (head entity, relation, tail entity). Most previous attempts solely learn triples independently and thus fail to utilize the rich hidden inference and semantic information in the local neighbourhood existed surrounding each triple in KGs. To this effect, this paper proposes a talking-heads attention-based knowledge representation method, a novel graph attention networks-based method for link prediction which learns the knowledge graph embedding with talking-heads attention guidance from multi hop neighbourhood triples. We evaluate our model in Freebase, WordNet and Kinship datasets on link prediction, experimental results demonstrate that the injection of talking-heads attention mechanism could better capture the semantic relationship of the neighbourhood surrounding triples and indeed achieve promising performance on link prediction.
C1 [Wang, Shirui; Zhou, Wen'an; Zhou, Qiang] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China.
RP Zhou, WA (通讯作者)，Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China.
EM zhouwa@bupt.edu.cn
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NR 19
TC 0
Z9 0
U1 0
U2 0
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 0885-2308
EI 1095-8363
J9 COMPUT SPEECH LANG
JI Comput. Speech Lang.
PD JUL
PY 2022
VL 74
AR 101340
DI 10.1016/j.csl.2021.101340
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Q1QM
UT WOS:000820203600004
DA 2022-08-23
ER

PT J
AU Zhao, XJ
   Jia, Y
   Li, AP
   Jiang, R
   Chen, K
   Wang, Y
AF Zhao, Xiaojuan
   Jia, Yan
   Li, Aiping
   Jiang, Rong
   Chen, Kai
   Wang, Ye
TI Target relational attention-oriented knowledge graph reasoning
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph reasoning; Information propagation; Attention mechanism
AB In knowledge graph reasoning, the existing graph attention mechanisms tend to distribute attention to certain high-frequency relations. In this work, we design a target relational attention-oriented reasoning model, which focuses more on the relations that match the target relation. We propose a hierarchical (node-level and relational subgraph-level) attention mechanism to aggregate the information of multi hop neighbors, and to thereby obtain a better node-embedding representation, (with high-order propagation characteristics). The mechanism also relieves over-smoothing to a certain extent. Node-level information aggregation uses the classical graph-attention mechanism, and the distribution of attention in the subgraph-level information aggregation is determined according to the relation in the reasoning task. In other words, we must give these relations different attentions according to the reasoning relation in the task. Experiments show that our model significantly outperforms current state-of-the-art methods. We further study the influence of encoder parameters on the model performance; increasing the number of attention-heads, layers, or hidden out-dimensions can effectively improve the performance. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Zhao, Xiaojuan; Jia, Yan; Li, Aiping; Jiang, Rong; Chen, Kai; Wang, Ye] Natl Univ Def Technol, Coll Comp Sci & Technol, Changsha, Peoples R China.
   [Zhao, Xiaojuan] Hunan Univ Technol, Coll Business, Zhuzhou, Peoples R China.
   [Wang, Ye] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen, Peoples R China.
RP Wang, Y (通讯作者)，Natl Univ Def Technol, Coll Comp Sci & Technol, Changsha, Peoples R China.
EM ye.wang@nudt.edu.cn
FU National Key R&D Pro-gram of China [2016QY03D0603,
   2016QY03D0601,2019QY1406, 2017YFB0802204, 2017YFB0803303]; Key R&D
   Program of Guangdong Province [2019B010136003]; Hunan Provincial Key RD
   program [2018GK2056]; National Natural Science Foundation of China
   [61732022, 61732004, 62072131, 61672020]; Scientific Research Found of
   Hunan Provincial Education Department [19C0597]
FX This work was partially supported by the National Key R&D Pro-gram of
   China (No. 2016QY03D0603, 2016QY03D0601,2019QY1406, 2017YFB0802204,
   2017YFB0803303) , the Key R&D Program of Guangdong Province (No.
   2019B010136003) , the Hunan Provincial Key R&D program (2018GK2056) and
   the National Natural Science Foundation of China (No. 61732022,
   61732004, 62072131, 61672020) , Scientific Research Found of Hunan
   Provincial Education Department (No. 19C0597).
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NR 40
TC 1
Z9 1
U1 4
U2 12
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 577
EP 586
DI 10.1016/j.neucom.2021.03.135
EA SEP 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WC2IT
UT WOS:000704086300008
DA 2022-08-23
ER

PT J
AU Xie, LJ
   Hu, ZM
   Cai, XJ
   Zhang, WS
   Chen, JJ
AF Xie, Lijie
   Hu, Zhaoming
   Cai, Xingjuan
   Zhang, Wensheng
   Chen, Jinjun
TI Explainable recommendation based on knowledge graph and multi-objective
   optimization
SO COMPLEX & INTELLIGENT SYSTEMS
LA English
DT Article
DE Recommendation system; Knowledge graph; Multi-objective optimization;
   Explainability
ID GENETIC ALGORITHM; SYSTEM
AB Recommendation system is a technology that can mine user's preference for items. Explainable recommendation is to produce recommendations for target users and give reasons at the same time to reveal reasons for recommendations. The explainability of recommendations that can improve the transparency of recommendations and the probability of users choosing the recommended items. The merits about explainability of recommendations are obvious, but it is not enough to focus solely on explainability of recommendations in field of explainable recommendations. Therefore, it is essential to construct an explainable recommendation framework to improve the explainability of recommended items while maintaining accuracy and diversity. An explainable recommendation framework based on knowledge graph and multi-objective optimization is proposed that can optimize the precision, diversity and explainability about recommendations at the same time. Knowledge graph connects users and items through different relationships to obtain an explainable candidate list for target user, and the path between target user and recommended item is used as an explanation basis. The explainable candidate list is optimized through multi-objective optimization algorithm to obtain the final recommendation list. It is concluded from the results about experiments that presented explainable recommendation framework provides high-quality recommendations that contains high accuracy, diversity and explainability.
C1 [Xie, Lijie; Hu, Zhaoming; Cai, Xingjuan] Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan, Peoples R China.
   [Zhang, Wensheng] Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing, Peoples R China.
   [Chen, Jinjun] Swinburne Univ Technol, Melbourne, Vic, Australia.
RP Cai, XJ (通讯作者)，Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan, Peoples R China.
EM lijiexie11@163.com; hzming11@163.com; xingjuancai@163.com;
   wensheng.zhang@ia.ac.cn; jinjun.chen@gmail.com
RI 钊鸣, 胡/ABG-9149-2021
OI 钊鸣, 胡/0000-0002-1870-4167
FU National Key Research and Development Program of China [2018YFC1604000];
   National Natural Science Foundation of China [61806138, U1636220,
   61961160707, 61976212]; Key R&D program of Shanxi Province
   (International Cooperation) [201903D421048]; Australian Research Council
   (ARC) [DP190101893, DP170100136, LP180100758]
FX This work is supported by the National Key Research and Development
   Program of China under Grant No. 2018YFC1604000, the National Natural
   Science Foundation of China under Grant No. 61806138, No. U1636220, No.
   61961160707 and No. 61976212, Key R&D program of Shanxi Province
   (International Cooperation) under Grant No. 201903D421048. Australian
   Research Council (ARC) projects DP190101893, DP170100136, and
   LP180100758.
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NR 40
TC 13
Z9 13
U1 30
U2 68
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 2199-4536
EI 2198-6053
J9 COMPLEX INTELL SYST
JI COMPLEX INTELL. SYST.
PD JUN
PY 2021
VL 7
IS 3
BP 1241
EP 1252
DI 10.1007/s40747-021-00315-y
EA MAR 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SI9ZV
UT WOS:000625746400001
OA gold
DA 2022-08-23
ER

PT J
AU Liu, JN
   Liu, HY
   Chen, XH
   Guo, X
   Zhao, QB
   Li, J
   Kang, L
   Liu, JX
AF Liu, Junnan
   Liu, Haiyan
   Chen, Xiaohui
   Guo, Xuan
   Zhao, Qingbo
   Li, Jia
   Kang, Lei
   Liu, Jianxiang
TI A Heterogeneous Geospatial Data Retrieval Method Using Knowledge Graph
SO SUSTAINABILITY
LA English
DT Article
DE data retrieval; knowledge graph; geospatial data; semantics; data
   integration; ontology
AB Information resources have increased rapidly in the big data era. Geospatial data plays an indispensable role in spatially informed analyses, while data in different areas are relatively isolated. Therefore, it is inadequate to use relational data in handling many semantic intricacies and retrieving geospatial data. In light of this, a heterogeneous retrieval method based on knowledge graph is proposed in this paper. There are three advantages of this method: (1) the semantic knowledge of geospatial data is considered; (2) more information required by users could be obtained; (3) data retrieval speed can be improved. Firstly, implicit semantic knowledge is studied and applied to construct a knowledge graph, integrating semantics in multi-source heterogeneous geospatial data. Then, the query expansion rules and the mappings between knowledge and database are designed to construct retrieval statements and obtain related spatial entities. Finally, the effectiveness and efficiency are verified through comparative analysis and practices. The experiment indicates that the method could automatically construct database retrieval statements and retrieve more relevant data. Additionally, users could reduce the dependence on data storage mode and database Structured Query Language syntax. This paper would facilitate the sharing and outreach of geospatial knowledge for various spatial studies.
C1 [Liu, Junnan; Liu, Haiyan; Chen, Xiaohui; Zhao, Qingbo; Li, Jia; Kang, Lei; Liu, Jianxiang] Strateg Support Force Informat Engn Univ, Sch Data & Target Engn, Zhengzhou 450001, Peoples R China.
   [Guo, Xuan] Strateg Support Force Informat Engn Univ, Inst Geospatial Informat, Zhengzhou 450001, Peoples R China.
RP Liu, HY (通讯作者)，Strateg Support Force Informat Engn Univ, Sch Data & Target Engn, Zhengzhou 450001, Peoples R China.
EM lpjunnan.love@163.com; liuharry2020@163.com; cxh_vrlab@163.com;
   gx930707@163.com; q13526647215@163.com; lijia_kk@163.com;
   xxgcdxkl@163.com; liu_2012111@163.com
OI Guo, Xuan/0000-0001-6678-4168; Liu, Junnan/0000-0002-3211-5342
FU National Natural Science Foundation of China [41801313]; Natural Science
   Foundation of Henan Province [182300410005]
FX This research was funded by the National Natural Science Foundation of
   China, grant number 41801313 and the Natural Science Foundation of Henan
   Province, grant number 182300410005.
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NR 63
TC 1
Z9 2
U1 15
U2 24
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB
PY 2021
VL 13
IS 4
AR 2005
DI 10.3390/su13042005
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 QQ8UI
UT WOS:000624794100001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU El Fadel, M
   Rachid, G
   El-Samra, R
   Boutros, GB
   Hashisho, J
AF El Fadel, M.
   Rachid, G.
   El-Samra, R.
   Boutros, G. Bou
   Hashisho, J.
TI Knowledge management mapping and gap analysis in renewable energy:
   Towards a sustainable framework in developing countries
SO RENEWABLE & SUSTAINABLE ENERGY REVIEWS
LA English
DT Review
DE Renewable energy knowledge; Barriers; Gaps; SWOT analysis
ID WIND ENERGY; BARRIERS; TECHNOLOGIES
AB This paper presents a mapping of knowledge management in renewable energy (RE) promoted through international and regional organizations with emphasis on gap analysis for the purpose of increasing RE deployment in developing countries. The knowledge mapping showed that most efforts are focused on RE information sharing and awareness raising, followed by policy assistance and technology transfer. Priorities seem vague with minimal close implementation, coordination, and evaluation whereby technology transfer and capacity building efforts do not always cater to the needs of benefiting countries with a lack of specialized RE financial mechanisms that provide incentives for countries to invest in RE. Equally significant, limited efforts are discerned about joint research initiatives with a slow progress towards standardization and certification of RE technologies. A general framework is proposed with a definition of short, medium and long-term undertakings towards increased RE penetration in developing countries. The profile of well-positioned organizations to adopt such a framework is identified on the basis of a SWOT analysis. (C) 2012 Elsevier Ltd. All rights reserved.
C1 [El Fadel, M.; Rachid, G.; El-Samra, R.; Boutros, G. Bou; Hashisho, J.] Amer Univ Beirut, Fac Engn & Architecture, Beirut, Lebanon.
RP El Fadel, M (通讯作者)，Amer Univ Beirut, Fac Engn & Architecture, Bliss St,POB 11-0236, Beirut, Lebanon.
EM mfadel@aub.edu.lb
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NR 61
TC 28
Z9 28
U1 2
U2 48
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1364-0321
J9 RENEW SUST ENERG REV
JI Renew. Sust. Energ. Rev.
PD APR
PY 2013
VL 20
BP 576
EP 584
DI 10.1016/j.rser.2012.11.071
PG 9
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 115QZ
UT WOS:000316828300046
DA 2022-08-23
ER

PT J
AU Behera, B
   Selvam, SM
   Paramasivan, B
AF Behera, Bunushree
   Selvam, Mari S.
   Paramasivan, Balasubramanian
TI Research trends and market opportunities of microalgal biorefinery
   technologies from circular bioeconomy perspectives
SO BIORESOURCE TECHNOLOGY
LA English
DT Article
DE Biorefinery; Circular bio-economy; Microalgae; Market; Research trend;
   Enviro-economic feasibility
ID LIFE-CYCLE ASSESSMENT; TECHNOECONOMIC ANALYSIS; ALGAL BIOREFINERY;
   NUTRIENT REMOVAL; OPTIMIZATION; CULTIVATION; BIODIESEL; ENERGY; COST;
   CO2
AB Microalgae as an alternative feedstock for sustainable bio-products have gained significant interest over years. Even though scientific productivity related to microalgae-based research has increased in recent decades, translation to industrial scale is still lacking. Therefore, it is essential to understand the current state-of-art and, identify research gaps and hotspots driving industrial scale up. The present review through scientometric analysis attempted to delineate the research evolution contributing to this emerging field. The research trends were analysed over the last decade globally highlighting the collaborative network between the countries. The comprehensive knowledge map generated confirmed microalgal biorefinery as a scientifically active field, where the present research interest is focussed on synergistically integrating the unit processes involved to make it enviro-economically feasible. Market opportunities and regulatory policy requirements along with the consensus need to adopt circular bio-economy perspectives were highlighted to facilitate real-time implementation of microalgal biorefinery.
C1 [Behera, Bunushree; Selvam, Mari S.; Paramasivan, Balasubramanian] Natl Inst Technol Rourkela, Dept Biotechnol & Med Engn, Agr & Environm Biotechnol Grp, Rourkela 769008, Odisha, India.
RP Behera, B (通讯作者)，Natl Inst Technol Rourkela, Dept Biotechnol & Med Engn, Agr & Environm Biotechnol Grp, Rourkela 769008, Odisha, India.
EM bunushree1991@gmail.com
RI Behera, Bunushree/AAZ-4001-2021
OI S, Mari Selvam/0000-0002-6716-702X; Behera,
   Bunushree/0000-0003-3326-6158
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NR 79
TC 0
Z9 0
U1 3
U2 3
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0960-8524
EI 1873-2976
J9 BIORESOURCE TECHNOL
JI Bioresour. Technol.
PD MAY
PY 2022
VL 351
AR 127038
DI 10.1016/j.biortech.2022.127038
PG 13
WC Agricultural Engineering; Biotechnology & Applied Microbiology; Energy &
   Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture; Biotechnology & Applied Microbiology; Energy & Fuels
GA 1C8YX
UT WOS:000793399500014
PM 35331886
DA 2022-08-23
ER

PT J
AU Zhao, GM
   Geng, Y
   Sun, HP
   Tian, X
   Chen, W
   Wu, D
AF Zhao, Guimei
   Geng, Yong
   Sun, Huaping
   Tian, Xu
   Chen, Wei
   Wu, Dong
TI Mapping the knowledge of green consumption: a meta-analysis
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Review
DE Sustainable development; Green consumption; Meta-analysis; Knowledge
   mapping; Environmental economics
ID CO-WORD; BIBLIOMETRIC ANALYSIS; MANAGEMENT; IMPACT; INTERDISCIPLINARY;
   COMMUNICATION; COOCCURRENCE; BEHAVIOR; LAW
AB Green consumption can facilitate sustainable industrial development and improve the overall efficiency of resource utilization. In response to rapid economic development and increasing environmental emissions, it is critical to promote green consumption so that the whole society can move toward sustainable development. This study aims to systematically review studies on green consumption by means of meta-analysis, bibliometric analysis, and social network analysis. The results show that green consumption is an interdisciplinary research field, involving environmental science, social science, medical science, economics, and other disciplines. Most productive countries, institutions, authors are identified so that the new researchers in this field can find their research partners. Keywords analysis results help identify the research hotpots in this field. It is suggested that future green consumption research should focus on behavior mechanism, stakeholder coordination, and policy evaluation. In general, the results obtained from this study provide valuable information for researchers and practitioners to promote green consumption research.
C1 [Zhao, Guimei; Wu, Dong] Shanghai Jiao Tong Univ, Sch Environm Sci & Engn, Shanghai 200240, Peoples R China.
   [Zhao, Guimei; Sun, Huaping] Jiangsu Univ, Sch Finance & Econ, Zhenjiang 212013, Jiangsu, Peoples R China.
   [Geng, Yong; Tian, Xu] Shanghai Jiao Tong Univ, Sch Int & Publ Affairs, Shanghai 200030, Peoples R China.
   [Geng, Yong] Shanghai Jiao Tong Univ, China Inst Urban Governance, Shanghai 200030, Peoples R China.
   [Geng, Yong] China Univ Min & Technol, Sch Management, Xuzhou 221116, Jiangsu, Peoples R China.
   [Chen, Wei] Shandong Normal Univ, Sch Geog & Environm, Jinan 250358, Peoples R China.
RP Geng, Y (通讯作者)，Shanghai Jiao Tong Univ, Sch Int & Publ Affairs, Shanghai 200030, Peoples R China.
EM ygeng@sjtu.edu.cn
RI Geng, Yong/B-6310-2018
OI Geng, Yong/0000-0002-2284-1375; Wu, Dong/0000-0003-4398-0208
FU National Key RAMP;D Program of China [2019YFC1908501]; National Natural
   Science Foundation of China [71810107001, 71690241, 71774071];
   Postdoctoral Science Foundation of China [2018 M641989]; Jiangsu
   Province' Philosophy and Social Science Foundation of China
   [2019SJA1887]; China Scholarship Council Program [202008320101]
FX This research was supported by the National Key R&D Program of China
   (no. 2019YFC1908501), the National Natural Science Foundation of China
   (no.71810107001, no. 71690241, and no.71774071), the Postdoctoral
   Science Foundation of China (no. 2018 M641989), the Jiangsu Province'
   Philosophy and Social Science Foundation of China (no. 2019SJA1887), and
   the China Scholarship Council Program (no. 202008320101).
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NR 76
TC 8
Z9 8
U1 21
U2 74
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
PD DEC
PY 2020
VL 27
IS 36
BP 44937
EP 44950
DI 10.1007/s11356-020-11029-y
EA OCT 2020
PG 14
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA PP6OD
UT WOS:000574360200011
PM 33001398
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Qi, P
   Sun, Y
   Luo, H
   Guizani, M
AF Qi, Peng
   Sun, Yan
   Luo, Hong
   Guizani, Mohsen
TI Scratch-DKG: A Framework for Constructing Scratch Domain Knowledge Graph
SO IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
LA English
DT Article
DE Data mining; Feature extraction; Labeling; Programming profession;
   Tools; Visualization; Scratch; Knowledge Graph; DeepDive; Secondary
   Labeling Algorithm; S-TextRank; programming knowledge points
ID EXTRACTION
AB With the rapid development of programming platforms, how to utilize the tremendous amount of data produced by the platforms, such as Scatch, has been a big challenge to researchers. The growing data is not only huge, but also heterogeneous and diverse, leading that the existing tools cannot effectively extract valuable information. In this article, considering particular features of Scratch data, we propose an effective framework about constructing a Scratch Domain Knowledge Graph (Scratch-DKG). Our framework includes four modules which are designed to process the semi-structured data, users profile data, projects data and programming knowledge points, respectively. For webpages, we design a template-based wrapper method to extract triples from the semi-structured data. As for users profile data, we improve DeepDive, which is a useful tool to extract information but with the problem of wrong labeling, to extract knowledge triples by the proposed Secondary Labeling Algorithm. For projects data, we propose an advanced keywords extraction method (S-TextRank) to extract keywords triples. For programming knowledge points, we develop a frequently contiguous block combinations mining algorithm to extract the potential domain information of Scratch. Finally, extensive experiments are carried out to evaluate the performance of our proposed methods. The experimental results show that, compared to other competing methods, our proposal can extract more correct and comprehensive Scratch triples.
C1 [Qi, Peng; Sun, Yan; Luo, Hong] Beijing Univ Posts & Telecommun, Dept Comp Sci, Beijing 100876, Peoples R China.
   [Guizani, Mohsen] Qatar Univ, Dept Comp Sci & Engn, Doha, Qatar.
RP Guizani, M (通讯作者)，Qatar Univ, Dept Comp Sci & Engn, Doha, Qatar.
EM mguizani@ieee.org
OI Qi, Peng/0000-0003-0390-5449
FU National Key Research and Development Program of China [2018YFB2100300];
   National Natural Science Foundation of China [61877005, 61672109,
   61772085]; BUPT Excellent Ph.D.; Students Foundation [CX2018001]
FX This work is partly supported by the National Key Research and
   Development Program of China 2018YFB2100300, partly supported by the
   National Natural Science Foundation of China under Grant 61877005,
   61672109, 61772085, and partly supported by BUPT Excellent Ph.D.
   Students Foundation CX2018001.
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NR 38
TC 4
Z9 4
U1 7
U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2168-6750
J9 IEEE T EMERG TOP COM
JI IEEE Trans. Emerg. Top. Comput.
PD JAN 1
PY 2022
VL 10
IS 1
BP 170
EP 185
DI 10.1109/TETC.2020.2996710
PG 16
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA ZN1US
UT WOS:000764828500019
DA 2022-08-23
ER

PT J
AU Du, YJ
   Li, CX
   Hu, Q
   Li, XL
   Chen, XL
AF Du, YaJun
   Li, ChenXing
   Hu, Qiang
   Li, XiaoLei
   Chen, XiaoLiang
TI Ranking webpages using a path trust knowledge graph
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph; Language model; Path trust degree; Topic-specific
   crawlers; Context graph
ID WEB; ONTOLOGY; INFORMATION; ALGORITHM; CRAWLER; LINK
AB The number of webpages on the Internet continues to increase rapidly. Countless webpages focus on user related issues. Finding useful information on such webpages constitutes a real challenge in the realms of information retrieval and search engine management. Based on the Link Context Graph (LCG), Relevancy Context Graph (RCG) and Concept Context Graph (CCG), we propose and construct a Path Trust Knowledge Graph (PTKG) model for assigning priority values to unvisited webpages. The PTKG model guides a focused crawler to retrieve more relevant webpages with respect to user-specific topics. A given user-specific topic t includes a PTKG that involves six steps: (1) building context graph G(t) = (V, E), where V is the crawled history webpage set and E is the hyperlink set within the history webpages, (2) retrieving the knowledge implied in the paths among these webpages and finding the path lengths, (3) computing the trust degrees of webpages, (4) constructing topic-specific language models using the trust degrees, (5) establishing general language models, and (6) assigning priority values to webpages for ranking purposes. A framework of topic-specific web crawlers based on a PTKG is constructed to collect webpages on user specific topics. Finally, we draw experimental comparisons between our proposed PTKG approach and the classic LCG and RCG approaches. We show that our method outperforms the LCG and RCG approaches. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Du, YaJun; Li, ChenXing; Hu, Qiang; Li, XiaoLei; Chen, XiaoLiang] Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Sichuan, Peoples R China.
RP Du, YJ (通讯作者)，Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Sichuan, Peoples R China.
EM duyajun@mail.xhu.edu.cn
FU National Nature Science Foundation [61472329, 61532009]
FX This work was supported by the National Nature Science Foundation (Grant
   No. 61472329 and 61532009). We especially thank Prof. Xiaofei He at
   Zhejiang University for many helpful comments and suggestions.
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NR 46
TC 4
Z9 6
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PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD DEC 20
PY 2017
VL 269
SI SI
BP 58
EP 72
DI 10.1016/j.neucom.2016.08.142
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FI8QA
UT WOS:000412266000008
DA 2022-08-23
ER

PT J
AU Zhan, HJ
   Lin, J
   Ak, KE
   Shi, BX
   Duan, LY
   Kot, AC
AF Zhan, Huijing
   Lin, Jie
   Ak, Kenan Emir
   Shi, Boxin
   Duan, Ling-Yu
   Kot, Alex C.
TI A(3)-FKG: Attentive Attribute-Aware Fashion Knowledge Graph for Outfit
   Preference Prediction
SO IEEE TRANSACTIONS ON MULTIMEDIA
LA English
DT Article
DE Attention; attribute-aware; knowledge graph; multi-modal; personalized
   preference prediction
ID HETEROGENEOUS INFORMATION
AB With the booming development of the online fashion industry, effective personalized recommender systems have become indispensable for the convenience they brought to the customers and the profits to the e-commercial platforms. Estimating the user's preference towards the outfit is at the core of a personalized recommendation system. Existing works on fashion recommendation are largely centering on modelling the clothing compatibility without considering the user factor or characterizing the user's preference over the single item. However, how to effectively model the outfits with either few or even none interactions, is yet under-explored. In this paper, we address the task of personalized outfit preference prediction via a novel Attentive Attribute-Aware Fashion Knowledge Graph (A(3)-FKG), which is incorporated to build the association between different outfits with both outfit- and item- level attributes. Additionally, a two-level attention mechanism is developed to capture the user's preference: 1) User-specific relation-aware attention layer, which captures the user's fine-grained preferences with different focus on relations for learning outfit representation; 2) Target-aware attention layer, which characterizes the user's latent diverse interests from his/her behavior sequences for learning user representation. Extensive experiments conducted on a large-scale fashion outfit dataset demonstrate significant improvements over other methods, which verify the excellence of our proposed framework.
C1 [Zhan, Huijing; Lin, Jie; Ak, Kenan Emir] Inst Infocomm Res I2R, Singapore 138634, Singapore.
   [Shi, Boxin; Duan, Ling-Yu] Peking Univ, Dept Comp Sci & Technol, Natl Engn Lab Video Technol, Beijing 100871, Peoples R China.
   [Kot, Alex C.] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore.
RP Lin, J (通讯作者)，Inst Infocomm Res I2R, Singapore 138634, Singapore.
EM zhan_huijing@i2r.a-star.edu.sg; lin-j@i2r.a-star.edu.sg;
   kenan_emir_ak@i2r.a-star.edu.sg; boxin.shi@gmail.com; lingyu@pku.edu.cn;
   eackot@ntu.edu.sg
OI Kot, Alex/0000-0001-6262-8125
FU Agency for Science, Technology and Research (A*STAR) under its AME
   Programmatic Funds Project [A1892b0026]
FX This research is supported by the Agency for Science, Technology and
   Research (A*STAR) under its AME Programmatic Funds Project A1892b0026.
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NR 59
TC 2
Z9 2
U1 9
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1520-9210
EI 1941-0077
J9 IEEE T MULTIMEDIA
JI IEEE Trans. Multimedia
PY 2022
VL 24
BP 819
EP 831
DI 10.1109/TMM.2021.3059514
PG 13
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA YW5XZ
UT WOS:000753488100024
DA 2022-08-23
ER

PT J
AU Smith, DP
   Oechsle, O
   Rawling, MJ
   Savory, E
   Lacoste, AMB
   Richardson, PJ
AF Smith, Daniel P.
   Oechsle, Olly
   Rawling, Michael J.
   Savory, Ed
   Lacoste, Alix M. B.
   Richardson, Peter John
TI Expert-Augmented Computational Drug Repurposing Identified Baricitinib
   as a Treatment for COVID-19
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE drug repurposing; knowledge graph; human computer interaction;
   SARS-CoV-2; COVID-19; knowledge discovery and data mining
ID CLINICAL PHARMACOKINETICS; ANTITUMOR-ACTIVITY; INHIBITOR;
   PHARMACODYNAMICS; CORONAVIRUS; PROTEIN; JAK2
AB The onset of the 2019 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated the identification of approved drugs to treat the disease, before the development, approval and widespread administration of suitable vaccines. To identify such a drug, we used a visual analytics workflow where computational tools applied over an AI-enhanced biomedical knowledge graph were combined with human expertise. The workflow comprised rapid augmentation of knowledge graph information from recent literature using machine learning (ML) based extraction, with human-guided iterative queries of the graph. Using this workflow, we identified the rheumatoid arthritis drug baricitinib as both an antiviral and anti-inflammatory therapy. The effectiveness of baricitinib was substantiated by the recent publication of the data from the ACTT-2 randomised Phase 3 trial, followed by emergency approval for use by the FDA, and a report from the CoV-BARRIER trial confirming significant reductions in mortality with baricitinib compared to standard of care. Such methods that iteratively combine computational tools with human expertise hold promise for the identification of treatments for rare and neglected diseases and, beyond drug repurposing, in areas of biological research where relevant data may be lacking or hidden in the mass of available biomedical literature.
C1 [Smith, Daniel P.; Oechsle, Olly; Rawling, Michael J.; Savory, Ed; Richardson, Peter John] BenevolentAI, London, England.
   [Lacoste, Alix M. B.] BenevolentAI, Brooklyn, NY USA.
RP Richardson, PJ (通讯作者)，BenevolentAI, London, England.
EM peter.richardson@benevolent.ai
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NR 47
TC 3
Z9 3
U1 2
U2 7
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1663-9812
J9 FRONT PHARMACOL
JI Front. Pharmacol.
PD JUL 28
PY 2021
VL 12
AR 709856
DI 10.3389/fphar.2021.709856
PG 14
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA TY5UD
UT WOS:000683848400001
PM 34393789
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Georgiadou, MC
   Hacking, T
   Guthrie, P
AF Georgiadou, Maria Christina
   Hacking, Theophilus
   Guthrie, Peter
TI A conceptual framework for future-proofing the energy performance of
   buildings
SO ENERGY POLICY
LA English
DT Article
DE Energy performance; Future-proofing; Low-energy buildings
ID LIFE-CYCLE ASSESSMENT; SUSTAINABILITY; AGENDA; RISK
AB This paper presents a review undertaken to understand the concept of 'future-proofing' the energy performance of buildings. The long lifecycles of the building stock, the impacts of climate change and the requirements for low carbon development underline the need for long-term thinking from the early design stages. 'Future-proofing' is an emerging research agenda with currently no widely accepted definition amongst scholars and building professionals. In this paper, it refers to design processes that accommodate explicitly full lifecycle perspectives and energy trends and drivers by at least 2050, when selecting energy efficient measures and low carbon technologies. A knowledge map is introduced, which explores the key axes (or attributes) for achieving a 'future-proofed' energy design: namely, coverage of sustainability issues, lifecycle thinking, and accommodating risks and uncertainties that affect the energy consumption. It is concluded that further research is needed so that established building energy assessment methods are refined to better incorporate future-proofing. The study follows an interdisciplinary approach and is targeted at design teams with aspirations to achieve resilient and flexible low-energy buildings over the long-term. (C). 2012 Elsevier Ltd. All rights reserved.
C1 [Georgiadou, Maria Christina; Guthrie, Peter] Univ Cambridge, Dept Engn, Ctr Sustainable Dev, Cambridge CB2 1PZ, England.
   [Hacking, Theophilus] Cambridge Programme Sustainabil Leadership, Cambridge CB2 1QA, England.
RP Georgiadou, MC (通讯作者)，Univ Cambridge, Dept Engn, Ctr Sustainable Dev, Cambridge CB2 1PZ, England.
EM mcg36@cam.ac.uk; theo.hacking@cpsl.cam.ac.uk; pmg31@cam.ac.uk
OI Hacking, Theo/0000-0003-0205-0610; Guthrie, Peter/0000-0002-9523-3733
FU Engineering Physical Sciences Research Council (EPSRC); Alexander S.
   Onassis Public Benefit Foundation in Greece
FX This PhD research is funded by the Engineering Physical Sciences
   Research Council (EPSRC) Doctoral Training Account of the Cambridge
   University Engineering Department and the Alexander S. Onassis Public
   Benefit Foundation in Greece. The authors would also like to thank three
   anonymous reviewers for their constructive feedback.
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   [No title captured]
   [No title captured]
   [No title captured]
NR 109
TC 33
Z9 33
U1 0
U2 30
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0301-4215
EI 1873-6777
J9 ENERG POLICY
JI Energy Policy
PD AUG
PY 2012
VL 47
BP 145
EP 155
DI 10.1016/j.enpol.2012.04.039
PG 11
WC Economics; Energy & Fuels; Environmental Sciences; Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Energy & Fuels; Environmental Sciences & Ecology
GA 971JL
UT WOS:000306200100015
DA 2022-08-23
ER

PT J
AU Yan, CR
   Chen, YZ
   Zhou, LJ
AF Yan, Cairong
   Chen, Yizhou
   Zhou, Lingjie
TI Differentiated Fashion Recommendation Using Knowledge Graph and Data
   Augmentation
SO IEEE ACCESS
LA English
DT Article
DE Recommender systems; differentiated recommendation framework; data
   augmentation; knowledge graph; factorization machine; cold start problem
AB E-commerce recommender systems (RSs) can help users quickly find what they need or new products they might be interested in. To continuously enhance user trust in the website, improve page visits and dwell time, and most importantly, increase gross merchandise value (GMV), it is crucial to understand and capture the important information hidden in the data, which has a great impact on user choice. The fashion e-commerce websites can collect the attributes of items and users as well as the user purchase behaviors, but lack the fine-grained classification of the items and the implicit relationship between items and users. This paper focuses on Amazon fashion dataset, one of the most widely used datasets in the fashion field. A differentiated recommendation framework is proposed that provides different recommendation paths for active and inactive users to improve the overall recommendation quality. In the framework, a data augmentation algorithm based on transfer learning is proposed to filter out the irrelevant items and label items with fine-grained tags, and a user-item knowledge graph is built to discover the potential relationship between items and users. Finally, a differentiated recommendation strategy is put forward to make different recommendations for users with different characteristics. The experimental results show that through data augmentation algorithm to improve data quality, factorization machine model produces higher recommendation accuracy, the constructed knowledge graph can alleviate the cold start problem for recommendation, and the differentiated recommendation strategy has achieved better recommendations for both active and inactive users.
C1 [Yan, Cairong; Chen, Yizhou; Zhou, Lingjie] Donghua Univ, Sch Comp Sci & Technol, Shanghai 201620, Peoples R China.
RP Yan, CR (通讯作者)，Donghua Univ, Sch Comp Sci & Technol, Shanghai 201620, Peoples R China.
EM cryan@dhu.edu.cn
OI Yan, Cairong/0000-0003-0313-8833
FU National Natural Science Foundation of China [61402100, 61602109];
   Fundamental Research Funds for the Central Universities of China
   [16D111210]; Natural Science Foundation of Shanghai [19ZR1401900]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61402100 and Grant 61602109, in part by
   the Fundamental Research Funds for the Central Universities of China
   under Grant 16D111210, and in part by the Natural Science Foundation of
   Shanghai under Grant 19ZR1401900.
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NR 31
TC 7
Z9 7
U1 2
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 2019
VL 7
BP 102239
EP 102248
DI 10.1109/ACCESS.2019.2928848
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IR8JN
UT WOS:000481688500143
OA gold
DA 2022-08-23
ER

PT J
AU Liu, PS
   Tian, B
   Liu, XB
   Gu, SJ
   Yan, L
   Bullock, L
   Ma, C
   Liu, Y
   Zhang, WB
AF Liu, Peishun
   Tian, Bing
   Liu, Xiaobao
   Gu, Shijing
   Yan, Li
   Bullock, Leon
   Ma, Chao
   Liu, Yin
   Zhang, Wenbin
TI Construction of Power Fault Knowledge Graph Based on Deep Learning
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE power failure; knowledge graph; attention mechanism; GRU; BERT
ID SECTION ESTIMATION; EXPERT-SYSTEM; DIAGNOSIS; MECHANISM; ATTENTION;
   NETWORK; MODEL
AB A knowledge graph can structure heterogeneous knowledge in the field of power faults, construct the correlation between different pieces of knowledge, and solve the diversification, complexity, and island of fault data. There are many kinds of entities in power fault defect text, the relationship between entities is complex, and the data are often mixed with noise. It is necessary to research how to effectively mine the target data and separate the salient knowledge from the noise. Moreover, the traditional entity and relationship extraction methods used in the construction of a power fault knowledge graph cannot fully understand the text semantics, and the response accuracy is low. The Log system usually contains all kinds of information related to faults and a log analysis helps us collect fault information and perform association analysis. Therefore, a Bidirectional Sliced GRU with Gated Attention mechanism (BiSGRU-GA) model is proposed to detect the anomalous logs in the power system, this enriches the fault knowledge base and provides a good data resource for the construction of the knowledge graph. A new Bidirectional GRU with Gated Attention mechanism and Conditional Random Fields and a BERT input layer (BBiGRU-GA-CRF) model is proposed by introducing a BERT layer and Attention Mechanism into the Bidirectional GRU (BiGRU) model to more fully understand the context information of fault sentences and improve the accuracy of entity recognition of fault sentences. Aiming to solve the problems of large calculation cost and propagation error which occur in the traditional relationship extraction model, an improved Bidirectional Gated Recurrent Unit neural network with fewer parameters and the Gated Attention Mechanism (BiGRU-GA) model is proposed. This new model introduces an improved Gated Attention Mechanism to achieve better effects in relationship extraction. Compared with Bidirectional Long Short-Term Memory with Attention Mechanism (BiLSTM-Attention), the accuracy, recall, and F-measure of the model were improved by 1.79%, 13.83%, and 0.30% respectively, and the time cost is reduced by about 16%. The experimental results show that the BiGRU-GA model can capture local features, reduce the training time cost, and improve the model recognition effect.
C1 [Liu, Peishun; Gu, Shijing; Bullock, Leon] Ocean Univ China, Coll Informat Sci & Technol, Qingdao 266100, Peoples R China.
   [Tian, Bing; Liu, Xiaobao; Yan, Li; Ma, Chao; Liu, Yin; Zhang, Wenbin] State Grid Shandong Elect Power Co, Informat & Telecommun Co, Jinan 250001, Peoples R China.
RP Gu, SJ; Bullock, L (通讯作者)，Ocean Univ China, Coll Informat Sci & Technol, Qingdao 266100, Peoples R China.
EM liups@ouc.edu.cn; tianbing@sd.sgcc.com.cn; liuxiaobao@sd.sgcc.com.cn;
   gushijing@stu.ouc.edu.cn; yanli@sd.sgcc.com.cn;
   leonbevanbullock@ouc.edu.cn; machao@sd.sgcc.com.cn;
   liuyin@sd.sgcc.com.cn; wbzhang@sd.sgcc.com.cn
FU China's national key RD plan; project name: research and application
   demonstration of key technologies of integrated emergency communication
   based on radio and television system [2017YFC0806200]
FX This work is supported by China's national key R&D plan and supported by
   the project name: research and application demonstration of key
   technologies of integrated emergency communication based on radio and
   television system, subject No. 2017YFC0806200.
CR Abdel-Salam S, 2022, INFORMATION, V13, DOI 10.3390/info13020067
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NR 35
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD JUL
PY 2022
VL 12
IS 14
AR 6993
DI 10.3390/app12146993
PG 28
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 3H0ZV
UT WOS:000831772800001
OA gold
DA 2022-08-23
ER

PT J
AU Chi, H
   Wang, BY
   Ge, QB
   Huo, GY
AF Chi, Hainan
   Wang, Boyue
   Ge, Qibin
   Huo, Guangyu
TI Knowledge Graph-Based Enhanced Transformer for Metro Individual Travel
   Destination Prediction
SO JOURNAL OF ADVANCED TRANSPORTATION
LA English
DT Article
AB Accurate and timely destination prediction of subway passengers is of great significance in improving urban residents' travel efficiency, alleviating urban traffic pressure, and recommending the proper location-based service. Although some individual travel destination prediction methods have been proposed, the prediction performance is poor due to the large difference in travel locations of different individuals, the difficulty of evaluating the individual travel intention, the sparsity of individual travel trajectory data, and other problems. To solve these problems, this paper proposes a knowledge graph-based enhanced Transformer method (KG-Trans) for the metro individual travel destination prediction task (MITD-Pre), which contains three main modules: (1) the knowledge graph (KG) module constructs a multilayer individual travel KG from top to bottom, which accurately describes the travel individuals and their travel intentions. By analyzing the association relationship between nodes in the KG, the relationship between travel individuals can be naturally established. The learned similar travel regularity can solve the problem of sparse travel trajectories of some individuals. (2) The enhanced Transformer module extracts the dynamic and hierarchical features from the long-term sequential travel trajectory data. (3) The classifier module introduces the cross-entropy loss to constrain the uniqueness of the predicted subway travel station. The experimental results show that the proposed method obtains a higher destination prediction accuracy than the previous individual travel destination prediction methods.
C1 [Chi, Hainan; Wang, Boyue; Huo, Guangyu] Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China.
   [Chi, Hainan; Wang, Boyue; Huo, Guangyu] Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing 100124, Peoples R China.
   [Ge, Qibin] Beijing Intelligent Transportat Dev Ctr, Beijing 102208, Peoples R China.
RP Wang, BY (通讯作者)，Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China.; Wang, BY (通讯作者)，Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing 100124, Peoples R China.
EM chihainan@emails.bjut.edu.cn; wby@bjut.edu.cn;
   geqibin@jtw.beijing.gov.cn; gyhuo@emails.bjut.edu.cn
OI Huo, Guangyu/0000-0002-5759-1185
FU National Natural Science Foundation of China [62072015, U19B2039,
   U1811463]; National Key R&D Program of China [2018YFB1600903]
FX THe research project was partially supported by the National Natural
   Science Foundation of China under Grant nos. 62072015, U19B2039, and
   U1811463 and National Key R&D Program of China under Grant no.
   2018YFB1600903.
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NR 36
TC 0
Z9 0
U1 6
U2 6
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 0197-6729
EI 2042-3195
J9 J ADV TRANSPORT
JI J. Adv. Transp.
PD APR 4
PY 2022
VL 2022
AR 8030690
DI 10.1155/2022/8030690
PG 9
WC Engineering, Civil; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Transportation
GA 0Z6LT
UT WOS:000791188300002
OA gold
DA 2022-08-23
ER

PT J
AU Tseng, CH
   Lin, JR
AF Tseng, Chun-Hsiung
   Lin, Jia-Rou
TI A semi-hierarchical clustering method for constructing knowledge trees
   from stackoverflow
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Clustering; folksonomy; k-means; knowledge tree; tags
AB To help students learn how to programme, we have to give them a clear knowledge map and sufficient materials. Question-based websites, such as stackoverflow, are excellent information sources for this goal. However, for beginners, the process can be a little tricky since they may not know how to ask correct questions if they do not have sufficient background knowledge, and a knowledge tree is usually considered more helpful in such a scenario. In this research, a method to infer a knowledge tree automatically from the type of websites and to group documents based on the resulting knowledge tree is proposed. The proposed method mainly addresses two issues: first, the quality of tags cannot be guaranteed, and second, clustering-based methods usually generate the flat schema. The occurrence count and the co-occurrence ratio were used together to identify important tags. Then, an algorithm was developed to infer the hierarchical relationship between tags. Using these tags as centres, the clustering performance is better than applyingk-means alone.
C1 [Tseng, Chun-Hsiung; Lin, Jia-Rou] Yuan Ze Univ, Dept Elect Engn, 135 Yuan Tung Rd, Chungli 32003, Taiwan.
RP Tseng, CH (通讯作者)，Yuan Ze Univ, Dept Elect Engn, 135 Yuan Tung Rd, Chungli 32003, Taiwan.
EM lendle.tseng.archive@gmail.com
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NR 21
TC 0
Z9 0
U1 2
U2 9
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 JUN
PY 2022
VL 48
IS 3
BP 393
EP 405
AR 0165551520961035
DI 10.1177/0165551520961035
EA SEP 2020
PG 13
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 1K3IJ
UT WOS:000571799200001
DA 2022-08-23
ER

PT J
AU Dong, C
   Wang, F
   Li, H
   Ding, LY
   Luo, HB
AF Dong, Chao
   Wang, Fan
   Li, Heng
   Ding, Lieyun
   Luo, Hanbin
TI Knowledge dynamics-integrated map as a blueprint for system development:
   Applications to safety risk management in Wuhan metro project
SO AUTOMATION IN CONSTRUCTION
LA English
DT Article
DE Safety knowledge; Knowledge map; Knowledge flow dynamics; Knowledge
   management; Tunnel construction project
ID CONSTRUCTION SAFETY; IDENTIFICATION SYSTEM; WARNING SYSTEM; ONTOLOGY;
   MODEL; REPRESENTATION; DESIGN; HEALTH; FLOW; COMMUNITIES
AB Safety has always been a persistent problem in the construction industry, particularly for tunnel construction projects due to the inherent uncertainty in geotechnical conditions and the complexity of the tunnel construction process. Literatures and practices highlight the importance of safety knowledge to the construction safety. However, safety knowledge is not always provided in a convenient and timely manner. This paper develops a Knowledge-dynamics Integrated Map (KIM) to visualize safety knowledge flow in tunnel construction safety risk management. The KIM highlights the what, the who, and the why of knowledge flow by portraying its dynamics associated to the working processes. With the use of KIM, the safety knowledge flow barriers are identified so that knowledge flow facilitators can be designed. The KIM has been applied in Wuhan metro project as a blueprint for systematically developing effective safety knowledge management systems.
C1 [Dong, Chao; Wang, Fan; Ding, Lieyun; Luo, Hanbin] Huazhong Univ Sci & Technol, Sch Civil Engn & Mech, Wuhan, Hubei, Peoples R China.
   [Dong, Chao; Ding, Lieyun; Luo, Hanbin] HUST, Hubei Engn Res Ctr Virtual Safe & Automated Const, Wuhan, Hubei, Peoples R China.
   [Dong, Chao; Wang, Fan; Li, Heng] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Hong Kong, Peoples R China.
   [Wang, Fan] Wuhan Inst Technol, Sch Civil Engn & Architecture, Wuhan, Hubei, Peoples R China.
RP Wang, F (通讯作者)，Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Hong Kong, Peoples R China.
EM chao.dong@polyu.edu.hk; wang_fan@hust.edu.cn; heng.li@polyu.edu.hk;
   dly@hust.edu.cn; luohbcem@hust.edu.cn
RI li, chengqian/AAH-7343-2019; Hanbin, Luo/AAH-7441-2019; Li,
   Heng/B-2821-2015
OI Li, Heng/0000-0002-3187-9041; Dong, Chao/0000-0002-0435-9470; Ding,
   Lieyun/0000-0002-9873-3776
FU National Natural Science Foundation of China (NSFC) [51608399,
   51678265]; National 12th Five -Year Plan Major Scientific and
   Technological Issues (NFYPMSTI) [2015BAK33B04]; Research Grants Council
   of Hong Kong [PolyU 152093/14E]; Fundamental Research Funds for the
   Central Universities [HUST: cx15-011]
FX The presented work has been supported by the National Natural Science
   Foundation of China (NSFC) under Grant No. 51608399 and 51678265, the
   National 12th Five -Year Plan Major Scientific and Technological Issues
   (NFYPMSTI) under Grant No. 2015BAK33B04, the Research Grants Council of
   Hong Kong Grant No. PolyU 152093/14E, and the Fundamental Research Funds
   for the Central Universities, HUST: cx15-011.
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NR 79
TC 24
Z9 26
U1 19
U2 130
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 2018
VL 93
BP 112
EP 122
DI 10.1016/j.autcon.2018.05.014
PG 11
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Construction & Building Technology; Engineering
GA GQ5AH
UT WOS:000441687500009
DA 2022-08-23
ER

PT J
AU Chen, HH
   Chen, YJ
   Chen, KJ
AF Chen, Hsuan-Hung
   Chen, Yau-Jane
   Chen, Kim-Joan
TI The Design and Effect of a Scaffolded Concept Mapping Strategy on
   Learning Performance in an Undergraduate Database Course
SO IEEE TRANSACTIONS ON EDUCATION
LA English
DT Article
DE Computer-aided instruction; computer science education; concept mapping;
   database systems; scaffolding
ID CONCEPT MAPS; KNOWLEDGE MAPS
AB This paper proposes a scaffolded concept mapping strategy, which considers a student's (high or low) prior knowledge, and provides flexible learning aids (scaffolding and fading) for reading and drawing a concept map. The goal is that the whole class should progress in synchronized fashion through this strategy, with each student achieving meaningful learning. The scaffolding is tailored to the student's needs, with various levels of support. The increasing demand for scaffolding, and its constant evolution, means that software aids are needed. The authors adopted a systematic instructional design, with associated software, to create a learning environment that supports the execution of the strategy. The paper also investigates whether this strategy improved student learning for 54 undergraduates in a database management system course. The results indicate that the students who used the strategy had better learning achievements than those who only experienced traditional lectures. Moreover, the implementation of the strategy received positive student feedback.
C1 [Chen, Hsuan-Hung; Chen, Kim-Joan] Natl Chung Cheng Univ, Grad Inst Commun Engn, Chiayi 62102, Taiwan.
   [Chen, Yau-Jane] Natl Chung Cheng Univ, Grad Inst Curriculum & Instruct, Chiayi 62102, Taiwan.
RP Chen, HH (通讯作者)，Natl Chung Cheng Univ, Grad Inst Commun Engn, Chiayi 62102, Taiwan.
EM hsuanhung@gmail.com; grcyjc@ccu.edu.tw; ieekjc@ccu.edu.tw
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NR 29
TC 8
Z9 8
U1 0
U2 31
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 AUG
PY 2013
VL 56
IS 3
BP 300
EP 307
DI 10.1109/TE.2012.2217747
PG 8
WC Education, Scientific Disciplines; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Engineering
GA 194UC
UT WOS:000322657200008
DA 2022-08-23
ER

PT J
AU Allen, B
AF Allen, B
TI Designing information systems for user abilities and tasks: An
   experimental study
SO ONLINE & CDROM REVIEW
LA English
DT Article
ID INDIVIDUAL-DIFFERENCES; KNOWLEDGE MAPS; TEXT; MEMORY
AB With the many choices that can be built into information systems, it is possible to customize such systems for users. This customization may be based on the tasks that users are accomplishing, or on the personal characteristics of users, or a combination of these factors. An experiment is reported in which detailed logging of use of experimental information systems was used to determine the optimal configuration of the systems for each user. Tasks were varied, and the cognitive abilities of users were tested to assess one important personal characteristic. The results showed that it was possible to create an optimal configuration to match the cognitive abilities of users, but that it was more difficult to assess which configuration was the best match for specific tasks. The person-in-task interaction proved to be the least powerful indicator of design configurations. These results suggest that usable information systems can be created for users by careful analysis of the interaction of design features with personal characteristics such as cognitive abilities.
C1 Univ Missouri, Sch Informat Sci & Learning Technol, Columbia, MO 65211 USA.
RP Allen, B (通讯作者)，Univ Missouri, Sch Informat Sci & Learning Technol, 217 Townsend Hall, Columbia, MO 65211 USA.
EM isbryce@showme.missouri.edu
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NR 23
TC 5
Z9 5
U1 0
U2 3
PU LEARNED INFORMATION LTD
PI OXFORD
PA WOODSIDE, HINKSEY HILL, OXFORD OX1 5AU, ENGLAND
SN 1353-2642
J9 ONLINE CDROM REV
JI Online Cdrom Rev.
PD JUN
PY 1998
VL 22
IS 3
BP 139
EP 153
DI 10.1108/eb024663
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 ZW579
UT WOS:000074425600001
DA 2022-08-23
ER

PT J
AU Chen, CM
   Ross, KE
   Gavali, S
   Cowart, JE
   Wu, CH
AF Chen, Chuming
   Ross, Karen E.
   Gavali, Sachin
   Cowart, Julie E.
   Wu, Cathy H.
TI COVID-19 Knowledge Graph from semantic integration of biomedical
   literature and databases
SO BIOINFORMATICS
LA English
DT Article
AB A Summary: The global response to the COVID-19 pandemic has led to a rapid increase of scientific literature on this deadly disease. Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis and treatment. We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator and SemRep with relevant biological databases and formalized the knowledge in a standardized and computable COVID-19 Knowledge Graph (KG). We published the COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a knowledge portal with browsing and searching interfaces. We also developed a RESTful API to support programmatic access and provided RDF dumps for download.
C1 [Chen, Chuming; Gavali, Sachin; Cowart, Julie E.; Wu, Cathy H.] Univ Delaware, Dept Comp & Informat Sci, Newark, DE 19716 USA.
   [Ross, Karen E.; Wu, Cathy H.] Georgetown Univ, Dept Biochem & Mol & Cellular Biol, Med Ctr, Washington, DC 20007 USA.
RP Chen, CM (通讯作者)，Univ Delaware, Dept Comp & Informat Sci, Newark, DE 19716 USA.
EM chenc@udel.edu
RI Chen, Chuming/A-7585-2009
OI Chen, Chuming/0000-0002-7287-9013; Gavali, Sachin/0000-0001-6118-9503
FU National Institutes of Health [U24HG007822, R35GM141873]
FX This work was partially supported by the National Institutes of Health
   [U24HG007822 and R35GM141873] and institutional resources at the
   University of Delaware.
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NR 21
TC 2
Z9 2
U1 8
U2 18
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD DEC 1
PY 2021
VL 37
IS 23
BP 4597
EP 4598
DI 10.1093/bioinformatics/btab694
EA OCT 2021
PG 2
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA XT1SD
UT WOS:000733374500049
PM 34613368
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Zhang, LP
   Xia, XQ
   Lu, MY
AF Zhang Lipai
   Xia Xiqiang
   Lu Mengyuan
TI Corporate governance reform in the era of artificial intelligence:
   research overview and prospects based on knowledge graph
SO ANNALS OF OPERATIONS RESEARCH
LA English
DT Article; Early Access
DE Information disclosure; Big data; Artificial intelligence; Financial
   upgradings; Corporate governance
ID ENHANCE
AB As an emerging concept, artificial intelligence has continuously impacted traditional corporate organizational structures and business models in the past ten years. Traditional corporate governance needs to continuously adapt to the needs of the changing environment. Combining the intelligent analysis method of knowledge graph, this paper combs the literature of corporate governance reform in the era of artificial intelligence during 2010-2020, analyzes the trend of corporate digital governance, how companies should change, and how Chinese companies should be in line with the world trend.
C1 [Zhang Lipai] Shanghai Int Studies Univ, Sch Business & Management, Shanghai, Peoples R China.
   [Xia Xiqiang; Lu Mengyuan] Zhengzhou Univ, Sch Business, Zhengzhou, Peoples R China.
RP Zhang, LP (通讯作者)，Shanghai Int Studies Univ, Sch Business & Management, Shanghai, Peoples R China.
EM pzyz0907@126.com
FU National Natural Science Foundation of China [71832006]
FX The author received Fund Project: the key project of the National
   Natural Science Foundation of China, "Research on Organizational Change
   and Management in a Changing Environment" (71832006).
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NR 45
TC 0
Z9 0
U1 33
U2 46
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0254-5330
EI 1572-9338
J9 ANN OPER RES
JI Ann. Oper. Res.
DI 10.1007/s10479-021-04416-2
EA NOV 2021
PG 16
WC Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Operations Research & Management Science
GA XA4DY
UT WOS:000720600900001
DA 2022-08-23
ER

PT J
AU Lu, HN
   Hu, HL
   Lin, XD
AF Lu, Haonan
   Hu, Hailin
   Lin, Xiaodong
TI DensE: An enhanced non-commutative representation for knowledge graph
   embedding with adaptive semantic hierarchy
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph embedding; Link prediction; Semantic hierarchy;
   Non-commutative composite relations; Geometric interpretation
AB Capturing the composition patterns of relations is a vital task in knowledge graph completion. It also serves as a fundamental step towards multi-hop reasoning over learned knowledge. Previously, several rotation-based translational methods have been developed to model composite relations using the product of a series of complex-valued diagonal matrices. However, these methods tend to make several oversimplified assumptions on the composite relations, e.g., forcing them to be commutative, independent from entities and lacking semantic hierarchy. To systematically tackle these problems, we have developed a novel knowledge graph embedding method, named DensE, to provide an improved modeling scheme for the complex composition patterns of relations. In particular, our method decomposes each relation into an SO(3) group-based rotation operator and a scaling operator in the three dimensional (3-D) Euclidean space. This design principle leads to several advantages of our method: (1) For composite relations, the corresponding diagonal relation matrices can be non-commutative, reflecting a predominant scenario in real world applications; (2) Our model preserves the natural interaction between relational operations and entity embeddings; (3) The scaling operation provides the modeling power for the intrinsic semantic hierarchical structure of entities; (4) The enhanced expressiveness of DensE is achieved with high computational efficiency in terms of both parameter size and training time; and (5) Modeling entities in Euclidean space instead of quaternion space keeps the direct geometrical interpretations of relational patterns. Experimental results on multiple benchmark knowledge graphs show that DensE is comparable to the current state-of-the-art models for missing link prediction, especially on composite relations. In addition, the interpretations generated by DensE also reveal how relations with distinct patterns (i.e., symmetry/anti-symmetry, inversion and composition) are modeled, which suggests several important directions of future studies. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Lu, Haonan] OPPO Res, Topol Lab, Data Intelligence Dept, Shenzhen, Peoples R China.
   [Hu, Hailin] Huawei Technol, AI Applicat Res Ctr, Shenzhen, Peoples R China.
   [Lin, Xiaodong] Rutgers State Univ, Dept Management Sci & Informat Syst, Piscataway, NJ 08854 USA.
RP Lu, HN (通讯作者)，OPPO Res, Topol Lab, Data Intelligence Dept, Shenzhen, Peoples R China.
EM luhaonan@oppo.com; huhailin2@huawei.com; lin@business.rutgers.edu
RI Hu, Hailin/AFV-3910-2022
OI Hu, Hailin/0000-0002-5768-4437
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NR 29
TC 1
Z9 1
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD MAR 1
PY 2022
VL 476
BP 115
EP 125
DI 10.1016/j.neucom.2021.12.079
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZI5HR
UT WOS:000761652400009
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Jin, YR
   Ji, SF
AF Jin, Yuran
   Ji, Shoufeng
TI Mapping hotspots and emerging trends of business model innovation under
   networking in Internet of Things
SO EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
LA English
DT Article
DE Business model innovation; Visualization; Networking; Internet of
   things; Scientometrics
ID VALUE CREATION; ELECTRIC VEHICLES; TECHNOLOGIES; PERSPECTIVE; CHALLENGES
AB Networking in Internet of Things (IoT) has had an immeasurable impact on the existing business models. In this context, exploring the hotspots and trends of business model innovation has become particularly necessary. For the topic literature over the past 20 years retrieved from Science Citation Index and Social Sciences Citation Index databases, scientometrics with information visualization technology was used to carry on the knowledge mapping with the following indicator: the co-cited reference networks, reference bursts, keyword bursts, and keyword co-occurrence networks. The results show (1) "e-commerce," "open source," "performance," "entrepreneurship," etc. are the main hotpots, and "value creation," "open innovation," "small business," "networks," etc. are the new hotpots; (2) the trends of hotspots transited from early "information technology" to later "self-service," "mass customization," and "biotechnology" and to present "cloud manufacturing," "telemedicine," "climate change," and "sustainable development,". etc.; (3) "intelligent robot," "3D printing," and the methodology of business mode innovation may be the future hotspots. This is the first paper visualizing the hotspots and emerging trends of business model innovation specially through scientometrics from a global perspective.
C1 [Jin, Yuran] Univ Sci & Technol Liaoning, Sch Business Adm, Anshan 114051, Peoples R China.
   [Jin, Yuran; Ji, Shoufeng] Northeastern Univ, Sch Business Adm, Shenyang 110167, Liaoning, Peoples R China.
RP Jin, YR (通讯作者)，Univ Sci & Technol Liaoning, Sch Business Adm, Anshan 114051, Peoples R China.; Jin, YR (通讯作者)，Northeastern Univ, Sch Business Adm, Shenyang 110167, Liaoning, Peoples R China.
EM jinyuran@163.com
RI Jin, Yuran/A-3671-2012
OI Jin, Yuran/0000-0002-6277-6475
FU National Natural Science Foundation of China [71572031, 71372121]
FX The study is supported by the National Natural Science Foundation of
   China (grant 71572031) and the National Natural Science Foundation of
   China (grant 71372121).
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NR 56
TC 9
Z9 9
U1 1
U2 75
PU SPRINGEROPEN
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 1687-1472
EI 1687-1499
J9 EURASIP J WIREL COMM
JI EURASIP J. Wirel. Commun. Netw.
PD MAY 2
PY 2018
AR 96
DI 10.1186/s13638-018-1115-4
PG 12
WC Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Telecommunications
GA GG2WZ
UT WOS:000432554900007
OA gold
DA 2022-08-23
ER

PT J
AU Lopez, JCP
   Barriga, JFR
AF Perales Lopez, Jose Cesar
   Romero Barriga, Juan Francisco
TI Language-image integrative processing in instructional contexts: A
   cognitivist perspective
SO ANALES DE PSICOLOGIA
LA Spanish
DT Article
DE Didactical image; didactical graph design; dual coding; language
   comprehension; multimedia learning; executive processes
ID SPATIAL WORKING-MEMORY; SHORT-TERM-MEMORY; DUAL-CODING THEORY; KNOWLEDGE
   MAPS; VISUOSPATIAL SKETCHPAD; TASK PERFORMANCE; SPLIT-ATTENTION; SPEECH
   RATE; PICTORIAL; COMPREHENSION
AB Didactical images are powerful tools to complement written and oral language comprehension in instructional situations. The present work summarizes the most relevant studies on conjoint processing of language and visual displays, focusing on those situations in which the image complements a narration (multimedia presentations, oral communications...) or a text (textbooks, online training materials...).
   On the base of the well-established ground of human perception, attention and memory research, a cognitive model of text-image integrative processing is proposed -also applicable, taking into account certain considerations, to narration-image integrated presentations-. The model over-comes some of the limitations of previous approaches, and stresses the importance of executive processing in language and image interpretation, and in establishing referential links between their correspondent mental codes. The model specifies the executive functions involved in the elaboration of unitary-distributed representations that include visuospatial and propositional information (stabilization), and in sequencing the series of actions that the learner carries out in a real-time fashion during integration tasks (sequencing).
C1 [Perales Lopez, Jose Cesar] Univ Granada, Dept Psicol Expt Secretaria, Fac Psicol, E-18071 Granada, Spain.
RP Lopez, JCP (通讯作者)，Univ Granada, Dept Psicol Expt Secretaria, Fac Psicol, Campus Cartuja S-N, E-18071 Granada, Spain.
EM jcesar@ugr.es
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   [No title captured]
NR 149
TC 0
Z9 0
U1 0
U2 4
PU UNIV MURCIA
PI MURCIA
PA SOC ESPANOLA HISTORIA AGRARIA, CAMPUS ESPINARDO, MURCIA, 30100, SPAIN
SN 0212-9728
EI 1695-2294
J9 AN PSICOL-SPAIN
JI An. Psicol.
PD JUN
PY 2005
VL 21
IS 1
BP 129
EP 148
PG 20
WC Psychology; Psychology, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Psychology
GA V22DZ
UT WOS:000208257200014
DA 2022-08-23
ER

PT J
AU Li, XL
   Li, ZY
   Tian, YH
AF Li, Xiaolin
   Li, Zhiyi
   Tian, Yahe
TI Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on
   the Official Account of Chinese Universities
SO ELECTRONICS
LA English
DT Article
DE knowledge graph; sentimental analysis; public opinion analysis;
   university official account; COVID-19; opinion mining
ID LEARNING-MODEL
AB With the advent of the new media mobile Internet era, the network public opinion in colleges and universities, as an extension of social network public opinion, is also facing a crisis in the prevention, control, and governance system. In this paper, the Fiddler was used to collect the comments and other relevant data of the COVID-19 topic articles on the WeChat Official Accounts of China's top ten universities in 2020. The BILSTM_LSTM sentiment analysis model was used to analyze the sentiment tendency of the comments, and the LDA topic model was used to mine the topics of the comments with different emotional attributes at different stages of COVID-19. Based on sentiment analysis and text mining, entities and relationships in the theme graph of public opinion events in colleges and universities were identified, and the Neo4j graph database was established to construct the sentimental knowledge graph of the pandemic theme of university public accounts. People's attitudes in university public opinion are easily influenced by a variety of factors, and the degree of emotional disposition changes over time, with the stage the pandemic is in, and with different commentators; official account opinion topics change with the development of the time stage of the pandemic, and students' positive and negative comment topics show a diverse trend. By incorporating topic mining into the sentimental knowledge graph, the graph can realize functions such as the emotion retrieval of comments on university public numbers, a source search of security threats in university social networks, and monitoring of comments on public opinion under the theme of the pandemic, which provides new ideas for further exploring the research and governance system of university network public opinion and is conducive to preventing and resolving campus public opinion crises.
C1 [Li, Xiaolin; Li, Zhiyi] South China Normal Univ, Sch Econ & Management, Guangzhou 510006, Peoples R China.
   [Li, Zhiyi] Macau Univ Sci & Technol, Fac Informat Technol, Macau 999078, Peoples R China.
   [Tian, Yahe] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China.
RP Li, ZY (通讯作者)，South China Normal Univ, Sch Econ & Management, Guangzhou 510006, Peoples R China.; Li, ZY (通讯作者)，Macau Univ Sci & Technol, Fac Informat Technol, Macau 999078, Peoples R China.
EM cervusnippon_lea@foxmail.com; leeds@scnu.edu.cn; 220205329@seu.edu.cn
OI li, zhi yi/0000-0001-6407-2554; li, xiao lin/0000-0001-7612-7612
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NR 34
TC 1
Z9 1
U1 24
U2 27
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD DEC
PY 2021
VL 10
IS 23
AR 2921
DI 10.3390/electronics10232921
PG 24
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA YE9MW
UT WOS:000741443300001
OA gold
DA 2022-08-23
ER

PT J
AU He, W
   Liu, XH
   Zhao, L
AF He, Wei
   Liu, Xiaohong
   Zhao, Li
TI Visualization Analysis of Media Literacy From 2010-2019 Based on
   CiteSpace
SO JOURNAL OF INTERNET TECHNOLOGY
LA English
DT Article
DE Media literacy; CiteSpace; Visualization analysis; Knowledge mapping
AB In order to explore the similarities and differences in media literacy research between Chinese and international publications in the last decade, this study used CiteSpace to arrive at a comparative analysis of 957 articles from China National Knowledge Infrastructure (CNKI) and 1,779 articles from Web of Science (WOS). High impact research scholars and institutions, hotspots, trends, and frontiers of media literacy research were visualized and analyzed. The findings showed that: (1) the number of international publications was three times that of Chinese; (2) Chinese and international publications presented different cooperative maps of authors and institutions; (3) the main research subjects in China were teenagers and college students, while the research from international publications focused on children; (4) the hotspots in China focused on were media literacy education and online media; while the international focus was hotspots like films, fiction and other media content and the media industry; (5) in both China and other countries, media literacy could be summarized into four categories; and (6) both in Chinese and international publications, new media and self-media (independent, individually-operated accounts producing content and posting it on social media platforms) were the frontier fields.
C1 [He, Wei; Liu, Xiaohong; Zhao, Li] Nanjing Normal Univ, Sch Educ Sci, Nanjing, Peoples R China.
RP Zhao, L (通讯作者)，Nanjing Normal Univ, Sch Educ Sci, Nanjing, Peoples R China.
EM 1324122949@qq.com; 2093242714@qq.com; li.zhao@njnu.edu.cn
RI Zhao, Li/AGN-1808-2022
OI Zhao, Li/0000-0003-1000-1296; Xiaohong, Liu/0000-0003-4931-8370
FU National Social Science Foundation of China [BCA200093]; Priority
   Academic Program Development of Jiangsu Higher Education Institutions
FX The research was supported by National Social Science Foundation of
   China (Grant No. BCA200093), and Priority Academic Program Development
   of Jiangsu Higher Education Institutions.
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NR 17
TC 0
Z9 0
U1 26
U2 52
PU LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV
PI HUALIEN
PA LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV, HUALIEN, 00000, TAIWAN
SN 1607-9264
EI 2079-4029
J9 J INTERNET TECHNOL
JI J. Internet Technol.
PY 2021
VL 22
IS 3
BP 533
EP 541
DI 10.3966/160792642021052203004
PG 9
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA SN5AG
UT WOS:000658301700004
DA 2022-08-23
ER

PT J
AU Chen, ZY
   Zhao, X
   Liao, JZ
   Li, XY
   Kanoulas, E
AF Chen, Ziyang
   Zhao, Xiang
   Liao, Jinzhi
   Li, Xinyi
   Kanoulas, Evangelos
TI Temporal knowledge graph question answering via subgraph reasoning
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Questionanswering; Subgraphreasoning; Temporalknowledgegraph
AB Knowledge graph question answering (KGQA) has recently received a lot of attention and many innovative methods have been proposed in this area, but few have been developed for temporal KGQA. Most of the existing temporal KGQA methods focus on semantic or temporal level matching and lack the ability to reason about time constraints. In this paper we propose a subgraph-based model for answering complex questions over temporal knowledge graphs (TKG), inspired by human cognition. Our method, called SubGraph Temporal Reasoning (SubGTR), consists of three main modules: implicit knowledge extraction, relevant facts search, and subgraph logic reasoning. First, the question is reformulated using background knowledge stored in the temporal knowledge graph to acquire explicit time constraints. Then, the TKG is being searched to identify relevant entities and obtain an initial scoring of them. Finally the time constraints are quantified and applied using temporal logic to reach to the final answer. To evaluate our model we experiment against temporal QA benchmarks. We observe that existing benchmarks contain many pseudo-temporal questions, and we propose Complex-CronQuestions, which a filtered version of CronQuestions and which can better demonstrate the model's inference ability for complex temporal questions. Experimental results show that SubGTR achieves state-of-the-art performance on both CronQuestions and Complex-CronQuestions. Moreover, our model shows better performance in handling the entity cold-start problem compared to existing temporal KGQA methods. (C) 2022 The Authors. Published by Elsevier B.V.
C1 [Chen, Ziyang; Zhao, Xiang; Liao, Jinzhi; Li, Xinyi] Natl Univ Def Technol, Lab Big Data & Decis, Changsha, Hunan, Peoples R China.
   [Kanoulas, Evangelos] Univ Amsterdam, Amsterdam, Netherlands.
RP Zhao, X (通讯作者)，Natl Univ Def Technol, Lab Big Data & Decis, Changsha, Hunan, Peoples R China.
EM xiangzhao@nudt.edu.cn
FU NSFC [61872446, 71971212, 62002373]; Science and Technology Inno-vation
   Program of Hunan Province [2020RC4046]; NWO Innovational Research
   Incentives Scheme Vidi [016, Vidi.189.039]; NWO Smart Culture-Big
   Data/Digital Humanities [314-99-301, H2020-EU.3.4, 814961]
FX The authors would like to thank the anonymous reviewers for their
   insightful and constructive comments, which greatly contributed to
   improving the quality of the paper. This work was partially supported by
   NSFC under grants Nos. 61872446, 71971212, and 62002373, and The Science
   and Technology Inno-vation Program of Hunan Province under grant No.
   2020RC4046, and The NWO Innovational Research Incentives Scheme Vidi
   un-der grant No. 016.Vidi.189.039, and the NWO Smart Culture-Big
   Data/Digital Humanities under grant No. 314-99-301, and the
   H2020-EU.3.4.-SOCIETAL CHALLENGES-Smart, Green And Inte-grated Transport
   under grant No. 814961. All content represents the opinion of the
   authors, which is not necessarily shared or endorsed by their respective
   employers and/or sponsors.
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   Xu K., 2019, LONG SHORT PAPERS, V1, P2937
   Xu Y., 2021, P 2021 C N AM CHAPTE, P5671
   Yang B., 2015, ICLR
   Zheng WG, 2018, PROC VLDB ENDOW, V11, P1373, DOI 10.14778/3236187.3236192
   Zou L, 2014, SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P313, DOI 10.1145/2588555.2610525
NR 52
TC 0
Z9 0
U1 3
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD SEP 5
PY 2022
VL 251
SI SI
AR 109134
DI 10.1016/j.knosys.2022.109134
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Z9PH
UT WOS:000826899900003
OA hybrid
DA 2022-08-23
ER

PT J
AU Yao, JP
   Wang, YJ
   Li, XJ
   Yuan, C
   Cheng, KY
AF Yao, Junping
   Wang, Yijing
   Li, Xiaojun
   Yuan, Cong
   Cheng, Kaiyuan
TI TERQA: Question answering over knowledge graph considering precise
   dependencies of temporal information on vectors
SO DISPLAYS
LA English
DT Article
DE Temporal knowledge graph; Knowledge graph question answering; Temporal
   information enhancement; Precise dependency
AB Time questions involve explicit and implicit constraints as well as complex time interval interactions, making them critical criteria for measuring the effect of knowledge base question answering. Although attention to temporal questions has spurred the development of temporal knowledge graphs, existing studies have focused on the simple splicing and fusion of temporal information with question or knowledge base embeddings, losing sight of the hidden interaction features between temporal information and embedded vectors. In this paper, we proposed TERQA, a temporal knowledge base question-answering approach to explore precise spatial de-pendencies between temporal information and embedded vectors. The exploration of the deep dependency be-tween time and embedded vectors was divided into two stages. In the first stage, the Transformer model of depth extraction was employed to extract richer features from questions and the representation was enhanced with temporal information; in the second stage, high-level capsules were adopted to extract the low-level vector features for detailed pose determination, allowing a more precise deep dependency of temporal facts on embedded vectors. We conducted an experiment using two temporal question answering datasets, TempQues-tions and CronQuestions, and the results showed that accuracy for TERQA improved 11.3% from baseline on the dataset TempQuestions with higher annotated information. Additionally, the adapted TERQA also showed varying degrees of improvements over the baseline in the larger but simply annotated dataset CronQuestions.
C1 [Yao, Junping; Wang, Yijing; Li, Xiaojun; Yuan, Cong; Cheng, Kaiyuan] Xian Res Inst High Tech, Xian 710025, Shaanxi, Peoples R China.
RP Wang, YJ (通讯作者)，Xian Res Inst High Tech, Xian 710025, Shaanxi, Peoples R China.
EM xi_anwyj@163.com
CR Bai LY, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3443687
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   Shao PP, 2022, KNOWL-BASED SYST, V238, DOI 10.1016/j.knosys.2021.107841
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NR 31
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0141-9382
EI 1872-7387
J9 DISPLAYS
JI Displays
PD SEP
PY 2022
VL 74
AR 102269
DI 10.1016/j.displa.2022.102269
PG 10
WC Computer Science, Hardware & Architecture; Engineering, Electrical &
   Electronic; Instruments & Instrumentation; Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Instruments & Instrumentation; Optics
GA 3K2PN
UT WOS:000833923500001
OA hybrid
DA 2022-08-23
ER

PT J
AU Liang, SQ
   Stockinger, K
   de Farias, TM
   Anisimova, M
   Gil, M
AF Liang, Shiqi
   Stockinger, Kurt
   de Farias, Tarcisio Mendes
   Anisimova, Maria
   Gil, Manuel
TI Querying knowledge graphs in natural language
SO JOURNAL OF BIG DATA
LA English
DT Article
DE Natural language processing; Query processing; Knowledge graphs; SPARQL
ID INTERFACE
AB Knowledge graphs are a powerful concept for querying large amounts of data. These knowledge graphs are typically enormous and are often not easily accessible to end-users because they require specialized knowledge in query languages such as SPARQL. Moreover, end-users need a deep understanding of the structure of the underlying data models often based on the Resource Description Framework (RDF). This drawback has led to the development of Question-Answering (QA) systems that enable end-users to express their information needs in natural language. While existing systems simplify user access, there is still room for improvement in the accuracy of these systems. In this paper we propose a new QA system for translating natural language questions into SPARQL queries. The key idea is to break up the translation process into 5 smaller, more manageable sub-tasks and use ensemble machine learning methods as well as Tree-LSTM-based neural network models to automatically learn and translate a natural language question into a SPARQL query. The performance of our proposed QA system is empirically evaluated using the two renowned benchmarks-the 7th Question Answering over Linked Data Challenge (QALD-7) and the Large-Scale Complex Question Answering Dataset (LC-QuAD). Experimental results show that our QA system outperforms the state-of-art systems by 15% on the QALD-7 dataset and by 48% on the LC-QuAD dataset, respectively. In addition, we make our source code available.
C1 [Liang, Shiqi] ETH Swiss Fed Inst Technol, Ramistr 101, CH-8092 Zurich, Switzerland.
   [Stockinger, Kurt; Anisimova, Maria; Gil, Manuel] Zurich Univ Appl Sci, Obere Kirchgasse 2, CH-8400 Winterthur, Switzerland.
   [de Farias, Tarcisio Mendes; Anisimova, Maria; Gil, Manuel] SIB Swiss Inst Bioinformat, Batiment Amphipole, CH-1015 Lausanne, Switzerland.
   [de Farias, Tarcisio Mendes] Univ Lausanne, Dept Ecol & Evolut, Batiment Biophore, CH-1015 Lausanne, Switzerland.
RP Liang, SQ (通讯作者)，ETH Swiss Fed Inst Technol, Ramistr 101, CH-8092 Zurich, Switzerland.
EM shiqi9352@gmail.com
OI Mendes de Farias, Tarcisio/0000-0002-3175-5372
FU Swiss National Science foundation [NRP 75, 407540_167149]
FX We thank the Swiss National Science foundation for funding (NRP 75,
   grant 407540_167149).
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NR 41
TC 9
Z9 9
U1 7
U2 14
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
EI 2196-1115
J9 J BIG DATA-GER
JI J. Big Data
PD JAN 6
PY 2021
VL 8
IS 1
AR 3
DI 10.1186/s40537-020-00383-w
PG 23
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PW1BK
UT WOS:000610410900005
PM 33489717
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, DH
   Xia, XQ
   Yang, Y
   Yang, P
   Xie, C
   Cui, ML
   Liu, Q
AF Zhang, Dehai
   Xia, Xiaoqiang
   Yang, Yun
   Yang, Po
   Xie, Cheng
   Cui, Menglong
   Liu, Qing
TI A novel word similarity measure method for IoT-enabled Healthcare
   applications
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Word similarity; Knowledge graph; Word embedding; Entropy; Internet of
   Things; Healthcare
ID SEMANTIC SIMILARITY; CONTEXT; MODELS
AB With the development of the Internet of Things (IoT), Natural Language Processing(NLP) has become a key part of IoT applications in Healthcare. NLP is bringing a revolutionary shift to Healthcare, powered by rapid progress of NLP analytics techniques and increasing availability of Healthcare data. Therefore, using NLP solution for IoT enable Healthcare application is an urgent and valuable task. Word similarity measurement is the basis of semantic analysis, which can be applied to translation and disambiguation of medical terms, prescription analysis, medical question and answer systems, diagnostic assistance, etc. Previous similarity measures have mainly focused on either knowledge-graph-based or word-embedding-based methods, which suffer from two problems: (1) word-embedding-based methods have difficulty discriminating words with approximately the same surrounding context; and (2) knowledge-graph-based methods do not contain multiexpression words or named entities and cannot generally converge for large-scale and updated words. To solve these two problems, this paper proposes a novel method that combines knowledge-graph-based and word-embedding-based similarity measures via word entropy. An experiment is conducted on five public datasets (R&G, M&C, WS353, WS353-Sim and SimLex). The experimental results show that the proposed method achieves significant improvements over other word similarity measures in terms of the correlation coefficient. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Zhang, Dehai; Xia, Xiaoqiang; Yang, Yun; Xie, Cheng; Cui, Menglong; Liu, Qing] Yunnan Univ, Sch Software, Kunming, Yunnan, Peoples R China.
   [Zhang, Dehai; Xia, Xiaoqiang; Yang, Yun; Xie, Cheng; Cui, Menglong; Liu, Qing] Key Lab Software Engn Yunnan Prov, Kunming, Yunnan, Peoples R China.
   [Zhang, Dehai; Yang, Yun; Xie, Cheng; Liu, Qing] Kunming Key Lab Data Sci & Intelligent Comp, Kunming, Yunnan, Peoples R China.
   [Yang, Po] Univ Sheffield, Dept Comp Sci, Sheffield, S Yorkshire, England.
RP Yang, Y (通讯作者)，Yunnan Univ, Sch Software, Kunming, Yunnan, Peoples R China.
EM yangyun@ynu.edu.cn
FU Natural Science Foundation China (NSFC) [61402397, 61263043, 61562093,
   61663046]; Yunnan Provincial Young Academic and Technical Leaders
   Reserve Talents, China [2017HB005]; Yunnan Provincial Innovation Team,
   China [2017HC012]; Youth Talent Project of China Association for Science
   and Technology [W8193209]; Science Foundation of Yunnan University,
   China [2017YDQN11]; Yunnan Provincial Science Research Project, China of
   the Department of Education [2018JS008]
FX This work is supported 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, China
   under Grant No. 2017HB005; (iii) Yunnan Provincial Innovation Team,
   China 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, China under Grant No.
   2017YDQN11; and (vi) Yunnan Provincial Science Research Project, China
   of the Department of Education under the Grant No. 2018JS008.
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NR 39
TC 3
Z9 3
U1 5
U2 50
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD JAN
PY 2021
VL 114
BP 209
EP 218
DI 10.1016/j.future.2020.07.053
PG 10
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA OC5HG
UT WOS:000579186700017
DA 2022-08-23
ER

PT J
AU Li, XY
   Zhang, XG
   Wang, PP
   Cao, ZY
AF Li, Xiaoyu
   Zhang, Xiuguo
   Wang, Peipeng
   Cao, Zhiying
TI Web services recommendation based on Metapath-guided graph attention
   network
SO JOURNAL OF SUPERCOMPUTING
LA English
DT Article
DE Web service recommendation; Knowledge graph; Graph attention network;
   Meta-path
ID QOS PREDICTION
AB Existing Web services recommendation models suffer from matrix sparsity and cold-start problems due to the limited number of user-service interactions and too little contextual information, so this paper introduces knowledge graph as auxiliary information to Web service recommendation to alleviate matrix sparsity and cold-start problems. And we propose the Web services recommendation based on Metapath-guided Graph Attention Network Model (WSR-MGAT) to fully exploit the structural information of the knowledge graph to improve the recommendation accuracy. Specifically, WSR-MGAT uses graph embedding method to obtain the initial embedding of entities and relationships. In order to obtain more closely related neighbors, we propose to use a distance-aware path sampling method to extract meta-path instances with closer relationships. Previous knowledge graph-based Web service recommendations do not make full use of the rich interaction information, which may lead to limited performance. To address this problem, this paper uses meta-paths to guide nodes to recursively aggregate higher-order neighbor information and use an attention mechanism to distinguish the importance of neighbors. Meanwhile, the semantic information of nodes under different meta-paths is fused to obtain a more comprehensive embedding of nodes. To verify the performance of the model, we use the real data crawled from the ProgrammableWeb platform to conduct multiple groups of experiments. The experimental results show that the WSR-MGAT improves over the strongest baselines w.r.t. Prec@10 by 4.6%; Recall@10 by 3.8%; NDCG@10 by 4.2% and F1@10 by 4.9%.
C1 [Li, Xiaoyu; Zhang, Xiuguo; Wang, Peipeng; Cao, Zhiying] Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian 116026, Peoples R China.
RP Zhang, XG; Cao, ZY (通讯作者)，Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian 116026, Peoples R China.
EM zhangxg@dlmu.edu.cn; czysophy@dlmu.edu.cn
FU National Key R&D Program of China [2018YFB1601502]; LiaoNing
   Revitalization Talents Program [XLYC1902071]
FX This work is supported by the National Key R&D Program of China (Grant
   No. 2018YFB1601502) and the LiaoNing Revitalization Talents Program
   (Grant No. XLYC1902071).
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NR 44
TC 0
Z9 0
U1 14
U2 14
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-8542
EI 1573-0484
J9 J SUPERCOMPUT
JI J. Supercomput.
PD JUL
PY 2022
VL 78
IS 10
BP 12621
EP 12647
DI 10.1007/s11227-022-04369-8
EA MAR 2022
PG 27
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1Z9XJ
UT WOS:000766083000001
DA 2022-08-23
ER

PT J
AU Ponza, M
   Ferragina, P
   Chakrabarti, S
AF Ponza, Marco
   Ferragina, Paolo
   Chakrabarti, Soumen
TI On Computing Entity Relatedness in Wikipedia, with Applications
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Entity relatedness; Wikipedia; Knowledge graph
AB Many text mining tasks, such as clustering, classification, retrieval, and named entity linking, benefit from a measure of relatedness between entities in a knowledge graph. We present a thorough study of all entity relatedness measures in recent literature based on Wikipedia as the knowledge graph. To facilitate this study, we introduce a new dataset with human judgments of entity relatedness. No clear dominance is seen between measures based on textual similarity and graph proximity. Some of the better measures involve expensive global graph computations. We propose a new, space-efficient, computationally lightweight, two-stage framework for relatedness computation. In the first stage, a small weighted subgraph is dynamically grown around the two query entities; in the second stage, relatedness is derived based on computations on this subgraph. Our system shows better agreement with human judgment than existing proposals both on the new dataset and on an established one. Our framework also shows improvements with respect to the state-of-the-art on three different extrinsic evaluations in the domains of ranking entity pairs, entity linking, and synonym extraction. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Ponza, Marco; Ferragina, Paolo] Univ Pisa, Pisa, Italy.
   [Chakrabarti, Soumen] Indian Inst Technol, Mumbai, Maharashtra, India.
RP Ponza, M (通讯作者)，Univ Pisa, Pisa, Italy.
EM marco.ponza@di.unipi.it
RI Ferragina, Paolo/AAZ-2580-2020
OI Ferragina, Paolo/0000-0003-1353-360X
FU Bloomberg Data Science Research Grant (2017); EU [654024]; Amazon; IBM
FX We warmly thank Francesco Piccinno for fruitful discussions and help in
   constructing the WiRe dataset and for kindly providing us access to
   several resources he generated during his PhD. Part of the work of the
   first two authors has been supported by a Bloomberg Data Science
   Research Grant (2017), and by the EU grant for the Research
   Infrastructure "SoBigData: Social Mining & Big Data Ecosystem"
   (INFRAIA-1-2014-2015, agreement #654024). Part of the work of the third
   author has been supported by an Amazon and IBM research grant.
CR [Anonymous], 2009, STUDY SIMILARITY REL
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NR 49
TC 4
Z9 4
U1 1
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JAN 5
PY 2020
VL 188
AR 105051
DI 10.1016/j.knosys.2019.105051
PG 22
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KL2YR
UT WOS:000513295000029
DA 2022-08-23
ER

PT J
AU Wang, LJ
   Zhang, GM
   Wang, ZY
   Liu, JG
   Shang, JL
   Liang, L
AF Wang, Lijuan
   Zhang, Guimin
   Wang, Ziyi
   Liu, Jiangui
   Shang, Jiali
   Liang, Liang
TI Bibliometric Analysis of Remote Sensing Research Trend in Crop Growth
   Monitoring: A Case Study in China
SO REMOTE SENSING
LA English
DT Article
DE crop growth monitoring; remote sensing; CiteSpace; bibliometric analysis
ID VEGETATION DYNAMICS; MODEL; WHEAT; INDEXES; SERIES
AB Remote sensing of crop growth monitoring is an important technique to guide agricultural production. To gain a comprehensive understanding of historical progression and current status, and future trend of remote sensing researches and applications in the field of crop growth monitoring in China, a study was carried out based on the publications from the past 20 years by Chinese scholars. Using the knowledge mapping software CiteSpace, a quantitative and qualitative analysis of research development, current hotspots, and future directions of crop growth monitoring using remote sensing technology in China was conducted. Furthermore, the relationship between high-frequency keywords and the emerging hot topics were visually analyzed. The results revealed that Chinese researchers paid more attention on keywords such as vegetation index, crop growth, winter wheat, leaf area index (LAI), and model in the field of crop growth monitoring, and LAI and unmanned aerial vehicle (UAV), appeared increasingly in frontier research of this discipline. Overall, bibliometric results from this CiteSpace-aided study provide a quantitative visualization to enrich our understanding on the historical development, current status, and future trend of crop growth monitoring in China.
C1 [Wang, Lijuan; Wang, Ziyi; Liang, Liang] Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Jiangsu, Peoples R China.
   [Zhang, Guimin] China Univ Min & Technol, Sch Mech & Civil Engn, Xuzhou 221116, Jiangsu, Peoples R China.
   [Liu, Jiangui; Shang, Jiali] Agr & Agri Food Canada, Ottawa Res & Dev Ctr, Ottawa, ON K1A 0C6, Canada.
RP Shang, JL (通讯作者)，Agr & Agri Food Canada, Ottawa Res & Dev Ctr, Ottawa, ON K1A 0C6, Canada.
EM wanglj2013@jsnu.edu.cn; gmzhang@cumt.edu.cn; 6020130068@jsnu.edu.cn;
   jiangui.liu@canada.ca; jiali.shang@canada.ca; liang_rs@jsnu.edu.cn
FU National Natural Science Funds of China [41401397]; Natural Science
   Funds of Jiangsu Province [BK20140237]; Jiangsu Undergraduate Training
   Programs for Innovation and Entrepreneurship [201810320078Z]
FX This research was supported by the National Natural Science Funds of
   China (grant No. 41401397), the Natural Science Funds of Jiangsu
   Province (grant No. BK20140237), and the Jiangsu Undergraduate Training
   Programs for Innovation and Entrepreneurship (grant No. 201810320078Z).
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NR 49
TC 14
Z9 17
U1 32
U2 93
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2072-4292
J9 REMOTE SENS-BASEL
JI Remote Sens.
PD APR 1
PY 2019
VL 11
IS 7
AR 809
DI 10.3390/rs11070809
PG 11
WC Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science & Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging
   Science & Photographic Technology
GA HU8OS
UT WOS:000465549300074
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Inauen, A
   Rettke, H
   Fridrich, A
   Spirig, R
   Bauer, GF
AF Inauen, Alice
   Rettke, Horst
   Fridrich, Annemarie
   Spirig, Rebecca
   Bauer, Georg F.
TI Critical factors for optimising skill-grade-mix based on principles of
   Lean Management. A qualitative substudy
SO PFLEGE
LA German
DT Article
DE lean healthcare; health resources; organisational innovation; focus
   groups; knowledge mapping
ID HEALTH-CARE
AB Background: Due to scarce resources in health care, staff deployment has to meet the demands. To optimise skill-grade-mix, a Swiss University Hospital initiated a project based on principles of Lean Management. The project team accompanied each participating nursing department and scientifically evaluated the results of the project.
   Aim: The aim of this qualitative sub-study was to identify critical success factors of this project.
   Method: In four focus groups, participants discussed their experience of the project. Recruitment was performed from departments assessing the impact of the project retrospectively either positive or critical. In addition, the degree of direct involvement in the project served as a distinguishing criterion.
   Results: While the degree of direct involvement in the project was not decisive, conflicting opinions and experiences appeared in the groups with more positive or critical project evaluation. Transparency, context and attitude proved critical for the project's success.
   Conclusions: Project managers should ensure transparency of the project's progress and matching of the project structure with local conditions in order to support participants in their critical or positive attitude towards the project.
C1 [Inauen, Alice; Fridrich, Annemarie; Bauer, Georg F.] Univ Zurich, Inst Epidemiol Biostat & Pravent, CH-8006 Zurich, Switzerland.
   [Rettke, Horst] Univ Spital Zurich, Zentrum Klin Pflegewissensch, Zurich, Switzerland.
   [Spirig, Rebecca] Univ Spital zurich, Direkt Pflege & MTTB, Bern, Switzerland.
   [Spirig, Rebecca] Univ Basel, Inst Pflegewissensch, CH-4003 Basel, Switzerland.
RP Inauen, A (通讯作者)，Univ Zurich, Inst Epidemiol Biostat & Pravent, Abt Publ & Org Hlth, Hirschengraben 84, CH-8001 Zurich, Switzerland.
EM aliceinauen@gmx.net
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NR 29
TC 1
Z9 1
U1 0
U2 18
PU VERLAG HANS HUBER
PI BERN 9
PA LANGGASS-STRASSE 76, CH-3000 BERN 9, SWITZERLAND
SN 1012-5302
EI 1664-283X
J9 PFLEGE
JI Pflege
PY 2017
VL 30
IS 1
BP 29
EP 38
DI 10.1024/1012-5302/a000511
PG 10
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA EO1PL
UT WOS:000396470300004
PM 27808659
DA 2022-08-23
ER

PT J
AU Guo, YX
AF Guo, Yixuan
TI Financial Market Sentiment Prediction Technology and Application Based
   on Deep Learning Model
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID STOCK RETURNS; VOLATILITY
AB In the real world, there are a variety of situations that require strategy control, that is reinforcement learning, as a method for studying the decision-making and behavioral strategies of intelligence. It has received a lot of research and empirical evidence on its functions and roles and is also a method recognized by scholars. Among them, combining reinforcement learning with sentiment analysis is an important theoretical research direction, but so far there is still relatively little research work about it, and it still has the problems of poor application effect and low accuracy rate. Therefore, in this study, we use the features related to sentiment analysis and deep reinforcement learning and use various algorithms for optimization to deal with the above problems. In this study, a sentiment analysis method incorporating knowledge graphs is designed using the characteristics of the stock trading market. A deep reinforcement learning investment trading strategy algorithm for sentiment analysis combined with knowledge graphs from this study is used in the subsequent experiments. The deep reinforcement learning system combining sentiment analysis and knowledge graph implemented in this study not only analyzes the algorithm from the theoretical aspect but also simulates data from the stock exchange market for experimental comparison and analysis. The experimental results illustrate that the deep reinforcement learning algorithm combining sentiment analysis and knowledge graphs used in this study can achieve better gains than the existing traditional reinforcement learning algorithms and has better practical application value.
C1 [Guo, Yixuan] Shanghai Lixin Univ Accounting & Finance, Dept Business Adm, Shanghai 201209, Peoples R China.
RP Guo, YX (通讯作者)，Shanghai Lixin Univ Accounting & Finance, Dept Business Adm, Shanghai 201209, Peoples R China.
EM 20099994@lixin.edu.cn
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NR 28
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 MAR 4
PY 2022
VL 2022
AR 1988396
DI 10.1155/2022/1988396
PG 10
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 0J2VG
UT WOS:000779963300003
PM 35281197
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ma, JT
   Li, DY
   Chen, YG
   Qiao, YQ
   Zhu, HD
   Zhang, XC
AF Ma, Jiangtao
   Li, Duanyang
   Chen, Yonggang
   Qiao, Yaqiong
   Zhu, Haodong
   Zhang, Xuncai
TI A Knowledge Graph Entity Disambiguation Method Based on
   Entity-Relationship Embedding and Graph Structure Embedding
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
AB The purpose of knowledge graph entity disambiguation is to match the ambiguous entities to the corresponding entities in the knowledge graph. Current entity ambiguity elimination methods usually use the context information of the entity and its attributes to obtain the mention embedding vector, compare it with the candidate entity embedding vector for similarity, and perform entity matching through the similarity. The disadvantage of this type of method is that it ignores the structural characteristics of the knowledge graph where the entity is located, that is, the connection between the entity and the entity, and therefore cannot obtain the global semantic features of the entity. To improve the Precision and Recall of entity disambiguation problems, we propose the EDEGE (Entity Disambiguation based on Entity and Graph Embedding) method, which utilizes the semantic embedding vector of entity relationship and the embedding vector of subgraph structure feature. EDEGE first trains the semantic vector of the entity relationship, then trains the graph structure vector of the subgraph where the entity is located, and balances the weights of these two vectors through the entity similarity function. Finally, the balanced vector is input into the graph neural network, and the matching between the entities is output to achieve entity disambiguation. Extensive experimental results proved the effectiveness of the proposed method. Among them, on the ACE2004 data set, the Precision, Recall, and F1 values of EDEGE are 9.2%, 7%, and 11.2% higher than baseline methods.
C1 [Ma, Jiangtao; Li, Duanyang; Zhu, Haodong] Zhengzhou Univ Light Ind, Coll Comp & Commun Engn, Zhengzhou 450002, Peoples R China.
   [Chen, Yonggang] State Informat Ctr, Beijing 100045, Peoples R China.
   [Qiao, Yaqiong] North China Univ Water Resources & Elect Power, Coll Informat Engn, Zhengzhou 450046, Peoples R China.
   [Zhang, Xuncai] Zhengzhou Univ Light Ind, Coll Elect & Informat Engn, Zhengzhou 450002, Peoples R China.
RP Zhang, XC (通讯作者)，Zhengzhou Univ Light Ind, Coll Elect & Informat Engn, Zhengzhou 450002, Peoples R China.
EM zhangxuncai@163.com
OI , Jiangtao/0000-0001-5181-4045; Zhang, Xuncai/0000-0002-2190-7651; Li,
   Duanyang/0000-0002-7299-6332
FU Henan Province Science and Technology Department Foundation
   [202102310237, 202102310295, 192102210133, 192102310021]; Science and
   Technology Planning Project of the Administration for Market Regulation
   Henan Province [2020SJ19]; Doctoral Research Fund of Zhengzhou
   University of Light Industry [2018BSJJ039]
FX This work was supported by the Henan Province Science and Technology
   Department Foundation (Grants nos. 202102310237, 202102310295,
   192102210133, and 192102310021); the Science and Technology Planning
   Project of the Administration for Market Regulation Henan Province
   (2020SJ19); and the Doctoral Research Fund of Zhengzhou University of
   Light Industry (Grant no. 2018BSJJ039).
CR Adjali O., P ADV INF RETR 42 EU, P463
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NR 36
TC 0
Z9 0
U1 3
U2 8
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD SEP 24
PY 2021
VL 2021
AR 2878189
DI 10.1155/2021/2878189
PG 11
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA WB1BD
UT WOS:000703313800007
PM 34603428
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Zhang, L
   Li, Z
   Ren, HL
   Yu, X
   Ma, YX
   Zhang, QX
AF Zhang, Li
   Li, Zhao
   Ren, Huali
   Yu, Xiao
   Ma, Yuxi
   Zhang, Quanxin
TI Knowledge graph and behavior portrait of intelligent attack against path
   planning
SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
LA English
DT Article; Early Access
DE attack behavior portrait; knowledge graph; time-series clustering;
   unmanned vehicle path planning
AB The broad application of artificial intelligence (AI) shows more and more vulnerabilities. Adversaries have more opportunities to attack AI systems. For example, unmanned vehicles may be interfered with by adversaries in path planning, resulting in unmanned vehicles being unable to move according to the planned route, and even serious safety problems. On the other side, the portrait technology can extract highly refined characteristics of different attack strategies, so that unmanned vehicles can defend themselves based on the characteristics of each attack. Existing research lacks intelligent attack research on path planning in the field of unmanned vehicles, and lacks portraits of attack behaviors in this scenario. This paper combines multiagent reinforcement learning technology, time-series segmentation clustering technology, and knowledge graph technology to study the portrait technology of adversary intelligent attack behavior in the field of unmanned vehicle path planning. First, the simulation results of unmanned vehicle path planning are obtained, and the steps of adversary attack behavior are extracted by using Toeplitz inverse covariance-based clustering time-series segmentation cluster technology. Second, the knowledge graph is used to save the attack strategy, so as to form the attack behavior portrait of unmanned vehicle path planning. The test on the Neo4j platform shows that our method is universal, can effectively describe the attack steps for unmanned vehicle path planning, and provides the basis for attack detection to establish the defense system of unmanned vehicles.
C1 [Zhang, Li] Commun Univ Zhejiang, Dept Media Engn, Hangzhou, Peoples R China.
   [Li, Zhao; Ma, Yuxi; Zhang, Quanxin] Beijing Inst Technol, Sch Comp Sci, Beijing 100081, Peoples R China.
   [Ren, Huali] Guangzhou Univ, Inst Artificial Intelligence & Blockchain, Guangzhou, Peoples R China.
   [Yu, Xiao] Shandong Univ Technol, Dept Comp Sci & Technol, Zibo, Peoples R China.
RP Zhang, QX (通讯作者)，Beijing Inst Technol, Sch Comp Sci, Beijing 100081, Peoples R China.; Ren, HL (通讯作者)，Guangzhou Univ, Inst Artificial Intelligence & Blockchain, Guangzhou, Peoples R China.
EM rhl717445@163.com; zhangqx@bit.edu.cn
FU National Natural Science Foundation of China [61876019]
FX National Natural Science Foundation of China, Grant/Award Number:
   61876019
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NR 34
TC 0
Z9 0
U1 8
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0884-8173
EI 1098-111X
J9 INT J INTELL SYST
JI Int. J. Intell. Syst.
DI 10.1002/int.22874
EA MAR 2022
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZV9WH
UT WOS:000770873400001
DA 2022-08-23
ER

PT J
AU Su, XR
   Hu, L
   You, ZH
   Hu, PW
   Zhao, BW
AF Su, Xiaorui
   Hu, Lun
   You, Zhuhong
   Hu, Pengwei
   Zhao, Bowei
TI Attention-based Knowledge Graph Representation Learning for Predicting
   Drug-drug Interactions
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article
DE drug-drug interactions; graph neural network; knowledge graph;
   attention-based representation learning
AB Drug-drug interactions (DDIs) are known as the main cause of life-threatening adverse events, and their identification is a key task in drug development. Existing computational algorithms mainly solve this problem by using advanced representation learning techniques. Though effective, few of them are capable of performing their tasks on biomedical knowledge graphs (KGs) that provide more detailed information about drug attributes and drug-related triple facts. In this work, an attention-based KG representation learning framework, namely DDKG, is proposed to fully utilize the information of KGs for improved performance of DDI prediction. In particular, DDKG first initializes the representations of drugs with their embeddings derived from drug attributes with an encoder-decoder layer, and then learns the representations of drugs by recursively propagating and aggregating first-order neighboring information along top-ranked network paths determined by neighboring node embeddings and triple facts. Last, DDKG estimates the probability of being interacting for pairwise drugs with their representations in an end-to-end manner. To evaluate the effectiveness of DDKG, extensive experiments have been conducted on two practical datasets with different sizes, and the results demonstrate that DDKG is superior to state-of-the-art algorithms on the DDI prediction task in terms of different evaluation metrics across all datasets.
C1 [Su, Xiaorui; Hu, Lun; Hu, Pengwei; Zhao, Bowei] Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China.
   [You, Zhuhong] Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Peoples R China.
RP Hu, L (通讯作者)，Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China.
EM hulun@ms.xjb.ac.cn
FU Natural Science Foundation of Xinjiang Uygur Autonomous Region
   [2021D01D05]; Pioneer Hundred Talents Program of Chinese Academy of
   Sciences; National Natural Science Foundation of China [62172355]; NSFC
   [61722212]; Science and Technology Innovation 2030-New Generation
   Artificial Intelligence Major Project [2018AAA0100100]; Tianshan youth -
   Excellent Youth [2019Q029]
FX The Natural Science Foundation of Xinjiang Uygur Autonomous Region under
   grant 2021D01D05, in part by the Pioneer Hundred Talents Program of
   Chinese Academy of Sciences, in part by the National Natural Science
   Foundation of China under grant 62172355, in part by the Awardee of the
   NSFC Excellent Young Scholars Program under grant 61722212, in part by
   the Science and Technology Innovation 2030-New Generation Artificial
   Intelligence Major Project, under grant 2018AAA0100100 and the Tianshan
   youth - Excellent Youth under grant 2019Q029.
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NR 71
TC 1
Z9 1
U1 10
U2 10
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1467-5463
EI 1477-4054
J9 BRIEF BIOINFORM
JI Brief. Bioinform.
PD MAY 13
PY 2022
VL 23
IS 3
DI 10.1093/bib/bbac140
EA APR 2022
PG 13
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA 1Z0DH
UT WOS:000785004800001
PM 35453147
DA 2022-08-23
ER

PT J
AU Wang, S
   Zhang, XY
   Ye, P
   Du, M
   Lu, YX
   Xue, HN
AF Wang, Shu
   Zhang, Xueying
   Ye, Peng
   Du, Mi
   Lu, Yanxu
   Xue, Haonan
TI Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge
   Representation
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE geographic knowledge representation; geographic knowledge graph;
   formalization; GeoKG
ID ONTOLOGIES; BASE
AB Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm {9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-.... The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO's with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation.
C1 [Wang, Shu; Zhang, Xueying; Ye, Peng; Du, Mi; Lu, Yanxu; Xue, Haonan] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Jiangsu, Peoples R China.
   [Wang, Shu; Zhang, Xueying; Ye, Peng; Du, Mi; Lu, Yanxu; Xue, Haonan] State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Jiangsu, Peoples R China.
   [Wang, Shu; Zhang, Xueying; Ye, Peng; Du, Mi; Lu, Yanxu; Xue, Haonan] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China.
RP Zhang, XY (通讯作者)，Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Jiangsu, Peoples R China.; Zhang, XY (通讯作者)，State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Jiangsu, Peoples R China.; Zhang, XY (通讯作者)，Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China.
EM 141301020@stu.njnu.edu.cn; zhangxueying@njnu.edu.cn;
   161301027@stu.njnu.edu.cn; 171301021@stu.njnu.edu.cn;
   171302083@stu.njnu.edu.cn; 181302106@stu.njnu.edu.cn
RI Wang, Shu/P-7465-2019; YE, Peng/ABA-7360-2021
OI Wang, Shu/0000-0002-5355-2015; YE, Peng/0000-0003-2149-8269
FU National Natural Science Foundation of China [41631177, 41671393];
   National Key Research and Development Program of China [2017YFB0503602]
FX The authors thank Mingguang Wu, Junzhi Liu and Jie Zhu for their
   critical reviews and constructive comments. This research is supported
   by the National Natural Science Foundation of China grants no. 41631177
   and no. 41671393 and the National Key Research and Development Program
   of China, no. 2017YFB0503602.
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NR 32
TC 13
Z9 17
U1 18
U2 59
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 APR
PY 2019
VL 8
IS 4
AR 184
DI 10.3390/ijgi8040184
PG 24
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Physical Geography; Remote Sensing
GA HX6FK
UT WOS:000467499300025
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Yang, ZH
   Bi, YX
   Wang, LB
   Cao, DW
   Li, RX
   Li, QQ
AF Yang, Zhihao
   Bi, Yingxin
   Wang, Linbing
   Cao, Dongwei
   Li, Rongxu
   Li, Qianqian
TI Development and application of a field knowledge graph and search engine
   for pavement engineering
SO SCIENTIFIC REPORTS
LA English
DT Article
ID EXTRACTION; FRAMEWORK
AB Integrated, timely data about pavement structures, materials and performance information are crucial for the continuous improvement and optimization of pavement design by the engineering research community. However, at present, pavement structures, materials and performance information in China are relatively isolated and cannot be integrated and managed. This results in a waste of a large amount of effective information. One of the significant development trends of pavement engineering is to collect, analyze, and manage the knowledge assets of pavement information to realize intelligent decision-making. To address these challenges, a knowledge graph (KG) is adopted, which is a novel and effective knowledge management technology and provides an ideal technical method to realize the integration of information in pavement engineering. First, a neural network model is used based on the principle of deep learning to obtain knowledge. On this basis, the relationship between knowledge is built from siloed databases, data in textual format and networks, and the knowledge base. Second, KG-Pavement is presented, which is a flexible framework that can integrate and ingest heterogeneous pavement engineering data to generate knowledge graphs. Furthermore, the index and unique constraints on attributes for knowledge entities are proposed in KG-Pavement, which can improve the efficiency of internal retrieval in the system. Finally, a pavement information search engine based on a knowledge graph is constructed to realize information interaction and target information matching between a webpage server and graph database. This is the first successful application of knowledge graphs in pavement engineering. This will greatly promote knowledge integration and intelligent decision-making in the domain of pavement engineering.
C1 [Yang, Zhihao] Univ Sci & Technol Beijing USTB, Natl Ctr Mat Serv Safety, Beijing 100083, Peoples R China.
   [Yang, Zhihao; Cao, Dongwei; Li, Rongxu; Li, Qianqian] Minist Transport, Res Inst Highway, Beijing 100088, Peoples R China.
   [Bi, Yingxin] Univ Sci & Technol Beijing USTB, Sch Econ & Management, Beijing 100083, Peoples R China.
   [Wang, Linbing] VA Tech, USTB, Dept Civil & Environm Engn, Joint USTB Virginia Tech Lab Multifunct Mat, Blacksburg, VA 24061 USA.
   [Cao, Dongwei] Changan Univ, Sch Mat Sci & Engn, Xian 710018, Peoples R China.
RP Cao, DW (通讯作者)，Minist Transport, Res Inst Highway, Beijing 100088, Peoples R China.; Cao, DW (通讯作者)，Changan Univ, Sch Mat Sci & Engn, Xian 710018, Peoples R China.
EM dw.cao@rioh.cn
FU  [0221KF06SC0048]
FX This research was funded by the research and development project of
   Hebei Xiong'an Jingde Expressway Co., Ltd., grantnumber 0221KF06SC0048.
CR Aliyu I., 2020, INT J ED MANAGE ENG, V10, P1, DOI 10.5815/ijeme.2020.02.01
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NR 30
TC 0
Z9 0
U1 9
U2 9
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD MAY 12
PY 2022
VL 12
IS 1
AR 7796
DI 10.1038/s41598-022-11604-y
PG 12
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 1F4TU
UT WOS:000795163100082
PM 35550555
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Tran, TK
   Ta, CDC
   Phan, TT
AF Thien Khai Tran
   Chien D C Ta
   Tuoi Thi Phan
TI Simplified effective method for identifying semantic relations from a
   knowledge graph
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Semantic relations; knowledge graph; information extraction
AB Semantic relations have been adopted in many research fields, including the semantic web, information retrieval, and Q&A systems. The aim of the semantic relations is to remove conceptual and terminological confusion. This is achieved by specifying a set of general concepts that characterize domains and their definitions and interrelationships. This research describes how to detect semantic relations, including synonyms, hyponyms, and hypernym s based on WordNet and entities of a knowledge graph (KG). This KG was built from two resources: ACM Digital Library and Wikipedia. We used natural language processing and the deep learning approach for processing data before generating the KG with an effective algorithm. We chose five of 245 categories in the ACM Digital Library to evaluate the proposed method. The generated results show that our system has excellent performance.
C1 [Thien Khai Tran] Ho Chi Minh City Univ Foreign Languages Informat, Ho Chi Minh City, Vietnam.
   [Chien D C Ta] Ind Univ Ho Chi Minh City IUH, Ho Chi Minh City 70000, Vietnam.
   [Tuoi Thi Phan] Ho Chi Minh City Univ Technol HCMUT, Ho Chi Minh City, Vietnam.
   [Tuoi Thi Phan] Vietnam Natl Univ Ho Chi Minh City, Ho Chi Minh City, Vietnam.
RP Ta, CDC (通讯作者)，Ind Univ Ho Chi Minh City IUH, Ho Chi Minh City 70000, Vietnam.
EM taduycongchien@iuh.edu.vn
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NR 13
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 43
IS 2
SI SI
BP 1871
EP 1876
DI 10.3233/JIFS-219288
PG 6
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2D3EP
UT WOS:000811435100020
DA 2022-08-23
ER

PT J
AU Celebi, R
   Uyar, H
   Yasar, E
   Gumus, O
   Dikenelli, O
   Dumontier, M
AF Celebi, Remzi
   Uyar, Huseyin
   Yasar, Erkan
   Gumus, Ozgur
   Dikenelli, Oguz
   Dumontier, Michel
TI Evaluation of knowledge graph embedding approaches for drug-drug
   interaction prediction in realistic settings
SO BMC BIOINFORMATICS
LA English
DT Article
DE Drug-drug interaction; Paired data; Disjoint cross-validation; Realistic
   evaluation
ID SIMILARITY; WEB
AB Background: Current approaches to identifying drug-drug interactions (DDIs), include safety studies during drug development and post-marketing surveillance after approval, offer important opportunities to identify potential safety issues, but are unable to provide complete set of all possible DDIs. Thus, the drug discovery researchers and healthcare professionals might not be fully aware of potentially dangerous DDIs. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. In this work, we aimed to present realistic evaluation settings to predict DDIs using knowledge graph embeddings. We propose a simple disjoint cross-validation scheme to evaluate drug-drug interaction predictions for the scenarios where the drugs have no known DDIs.
   Results: We designed different evaluation settings to accurately assess the performance for predicting DDIs. The settings for disjoint cross-validation produced lower performance scores, as expected, but still were good at predicting the drug interactions. We have applied Logistic Regression, Naive Bayes and Random Forest on DrugBank knowledge graph with the 10-fold traditional cross validation using RDF2Vec, TransE and TransD. RDF2Vec with Skip-Gram generally surpasses other embedding methods. We also tested RDF2Vec on various drug knowledge graphs such as DrugBank, PharmGKB and KEGG to predict unknown drug-drug interactions. The performance was not enhanced significantly when an integrated knowledge graph including these three datasets was used.
   Conclusion: We showed that the knowledge embeddings are powerful predictors and comparable to current state-of-the-art methods for inferring new DDIs. We addressed the evaluation biases by introducing drug-wise and pairwise disjoint test classes. Although the performance scores for drug-wise and pairwise disjoint seem to be low, the results can be considered to be realistic in predicting the interactions for drugs with limited interaction information.
C1 [Celebi, Remzi; Dumontier, Michel] Maastricht Univ, Inst Data Sci, NL-6200 Maastricht, Netherlands.
   [Uyar, Huseyin; Yasar, Erkan; Gumus, Ozgur; Dikenelli, Oguz] Ege Univ, Comp Engn Dept, TR-35100 Izmir, Turkey.
RP Celebi, R (通讯作者)，Maastricht Univ, Inst Data Sci, NL-6200 Maastricht, Netherlands.
EM remzi.celebi@maastrichtuniversity.nl
RI yasar, erkan/AAM-4876-2020
OI Celebi, Remzi/0000-0001-7769-4272; Uyar, Huseyin/0000-0001-9801-1709
FU King Abdullah University of Science and Technology (KAUST) Office of
   Sponsored Research (OSR) [URF/1/3454-01-01]; Ege University Research
   Fund [16-MUH-095 BAP]
FX This work was supported by funding from King Abdullah University of
   Science and Technology (KAUST) Office of Sponsored Research (OSR) under
   Award No. URF/1/3454-01-01. This work was also supported by Ege
   University Research Fund through the 16-MUH-095 BAP project. These
   funding sources had no role in the design of the study, collection,
   analysis, interpretation of data, and in writing the manuscript.
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NR 57
TC 21
Z9 21
U1 6
U2 18
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD DEC 18
PY 2019
VL 20
IS 1
AR 726
DI 10.1186/s12859-019-3284-5
PG 14
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 KI8LO
UT WOS:000511609600003
PM 31852427
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Bilo, V
   Fanelli, A
   Flammini, M
   Moscardelli, L
AF Bilo, Vittorio
   Fanelli, Angelo
   Flammini, Michele
   Moscardelli, Luca
TI When ignorance helps: Graphical multicast cost sharing games
SO THEORETICAL COMPUTER SCIENCE
LA English
DT Article
DE Algorithmic game theory; Nash equilibrium; Social networks; Price of
   anarchy
ID TRANSMISSIONS
AB In non-cooperative games played on highly decentralized networks the assumption that each player knows the strategy adopted by any other player may be too optimistic or even infeasible. In such situations, the set of players of which each player knows the chosen strategy can be modeled by means of a social knowledge graph in which nodes represent players and there is an edge from i to j if i knows j. Following the framework introduced in 171, we study the impact of social knowledge graphs on the fundamental multicast cost sharing game in which all the players want to receive the same communication from a given source in an undirected network. In the classical complete information case, such a game is known to be highly inefficient, since its price of anarchy can be as high as the total number of players rho. We first show that, under our incomplete information setting, pure Nash equilibria always exist only if the social knowledge graph is directed acyclic (DAG). We then prove that the price of stability of any DAG is at least 1/2 log rho and provide a DAG lowering the classical price of anarchy to a value between 1/2 log rho and log(2) rho. If specific instances of the game are concerned, that is if the social knowledge graph can be selected as a function of the instance, we show that the price of stability is at least 4 rho/rho+3, and that the same bound holds also for the price of anarchy of any social knowledge graph (not only DAGs). Moreover, we provide a nearly matching upper bound by proving that, for any fixed instance, there always exists a DAG yielding a price of anarchy less than 4. Our results open a new window on how the performances of non-cooperative systems may benefit from the lack of total knowledge among players. (C) 2009 Elsevier B.V. All rights reserved.
C1 [Moscardelli, Luca] Univ G dAnnunzio, Dipartimento Sci, I-65127 Pescara, Italy.
   [Bilo, Vittorio] Univ Salento, Dipartimento Matemat, I-73100 Lecce, Italy.
   [Fanelli, Angelo] Univ Aachen, Rhein Westfal TH Aachen, Dept Comp Sci, Aachen, Germany.
   [Flammini, Michele] Univ Aquila, Dipartimento Informat, I-67100 Laquila, Italy.
RP Moscardelli, L (通讯作者)，Univ G dAnnunzio, Dipartimento Sci, Viale Pindaro 42, I-65127 Pescara, Italy.
EM vittorio.bilo@unile.it; fanelli@cs.rwth-aachen.de;
   flammini@di.univaq.it; moscardelli@sci.unich.it
RI Bilò, Vittorio/P-1913-2014
OI Fanelli, Angelo/0000-0002-4896-6889; BILO',
   Vittorio/0000-0001-7848-4904; FLAMMINI, Michele/0000-0003-0327-3728
CR Albers S, 2009, SIAM J COMPUT, V38, P2273, DOI 10.1137/070701376
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NR 36
TC 14
Z9 14
U1 0
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0304-3975
EI 1879-2294
J9 THEOR COMPUT SCI
JI Theor. Comput. Sci.
PD JAN 6
PY 2010
VL 411
IS 3
BP 660
EP 671
DI 10.1016/j.tcs.2009.10.007
PG 12
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 549SM
UT WOS:000274074600006
OA Bronze
DA 2022-08-23
ER

PT J
AU Zhou, ZB
   Wen, JF
   Wang, YS
   Xue, X
   Hung, PCK
   Nguyen, LD
AF Zhou, Zhangbing
   Wen, Jinfeng
   Wang, Yasha
   Xue, Xiao
   Hung, Patrick C. K.
   Nguyen, Long D.
TI Topic-based crossing-workflow fragment discovery
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Crossing-workflow fragments; Activity knowledge graph; Topic discovery;
   Scientific workflows
AB Along with the large and increasing number of scientific workflows publicly accessible on Web repositories, the discovery of workflow fragments is significant to promote the reuse or repurposing of best-practices evidenced in legacy workflows, when novel scientific experiments are to be conducted. This paper proposes a novel crossing-workflow fragment discovery mechanism, where an activity knowledge graph is constructed to capture flat invocation relations between activities, and hierarchical parent-child relations specified upon sub-workflows and their corresponding activities. Semantic relevance of activities and sub-workflows is calculated based on their representative topics, where these topics are generated by applying the biterm topic model. Given a requirement specified in terms of a workflow fragment template, individual candidate activities or sub-workflows are discovered when considering their semantic relevance and short-document descriptions. Candidate fragments are constructed through discovering the relations in activity knowledge graph specified upon candidate activities or sub-workflows. These fragments are evaluated by balancing their structural and semantic similarities. Evaluation results show that our approach is accurate in discovering appropriate crossing-workflow fragments in comparison with the state of art's techniques. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Zhou, Zhangbing; Wen, Jinfeng] China Univ Geosci Beijing, Sch Informat Engn, Beijing, Peoples R China.
   [Wang, Yasha] Peking Univ, Natl Res & Engn Ctr Software Engn, Beijing, Peoples R China.
   [Xue, Xiao] Tianjin Univ, Sch Software Engn, Tianjin, Peoples R China.
   [Hung, Patrick C. K.] Ontario Tech Univ, Fac Business & Informat Technol, Oshawa, ON, Canada.
   [Nguyen, Long D.] Duy Tan Univ, Da Nang, Vietnam.
   [Zhou, Zhangbing] TELECOM SudParis, Comp Sci Dept, Evry, France.
RP Zhou, ZB (通讯作者)，China Univ Geosci Beijing, Sch Informat Engn, Beijing, Peoples R China.
EM zbzhou@cugb.edu.cn; wenjinfeng@cugb.edu.cn; wangyasha@pku.edu.cn;
   jzxuexiao@tju.edu.cn; patrick.hung@uoit.ca; dinhlonghcmut@gmail.com
FU National Key Research and Development Program of China
FX This work was supported by the National Key Research and Development
   Program of China. The authors thank anonymous reviewers very much for
   their insight comments and suggestions.
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NR 40
TC 1
Z9 1
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD NOV
PY 2020
VL 112
BP 1141
EP 1155
DI 10.1016/j.future.2020.05.029
PG 15
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NM0XY
UT WOS:000567829300006
DA 2022-08-23
ER

PT J
AU Zhang, YJ
   Zhang, SS
   Huang, R
   Huang, B
   Liang, JC
   Zhang, H
   Wang, Z
AF Zhang, Yajun
   Zhang, Shusheng
   Huang, Rui
   Huang, Bo
   Liang, Jiachen
   Zhang, Hang
   Wang, Zheng
TI Combining deep learning with knowledge graph for macro process planning
SO COMPUTERS IN INDUSTRY
LA English
DT Article
DE Available online xxxx; Macro process planning; Deep learning; Knowledge
   graph; Stacked auto-encoders
ID PROCESS REUSE; SELECTION
AB The macro process, as the core process content of the whole machining process for a design part, serves as a guide for the complete machining process planning. It determines the final machining quality and machining cost of the part to a large extent. However, due to the various bottlenecks in machining process knowledge representation, matching, and inference, macro process planning still depends heavily on the knowledge and experience of process designers. In this paper, a macro process decision-making approach combining knowledge graph and deep learning technology is proposed. Firstly, based on deep learning, the macro process reasoning function is learned to model the mapping relationship between the process elements of the design part and macro process analysis rules. Then the process elements of the design part are fed into the reasoning function to activate the applicable process analysis rules of the process knowledge graph. Next, according to the association relationship between the activated process analysis rules and the predefined machining methods of the macro process graph, a series of working steps nodes and the directed edges are activated, which constitutes the feasible solution space for the macro process. Finally, the swarm intelligence algorithm is applied to search for an effective and low-cost macro process scheme from the feasible macro process solution space. In experimental studies, the slot cavity parts are taken as examples to verify the feasibility and effectiveness of the proposed approach.
C1 [Zhang, Yajun; Zhang, Shusheng; Liang, Jiachen; Zhang, Hang; Wang, Zheng] Northwestern Polytech Univ, Sch Mech Engn, Xian 710072, Peoples R China.
   [Huang, Rui] HoHai Univ, Coll IOT Engn, Changzhou 213022, Peoples R China.
   [Huang, Bo] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China.
   [Huang, Bo] Shenzhen Key Lab Precis Engn, Shenzhen 518055, Peoples R China.
RP Zhang, SS (通讯作者)，Northwestern Polytech Univ, 127 West Youyi Rd, Xian 710072, Shaanxi, Peoples R China.
EM zssnwpu@163.com
FU National Natural Science Foundation of China [51875474, 52075148];
   National Science Foundation for Young Scientists of China [52105518]
FX Acknowledgments The authors are grateful to the financial support from
   the National Natural Science Foundation of China (No.51875474, 52075148)
   . The authors are also grateful to financial support from the National
   Science Foundation for Young Scientists of China (No.52105518) .
CR [Anonymous], 2004, 1464910 ISO
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NR 36
TC 0
Z9 0
U1 10
U2 10
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-3615
EI 1872-6194
J9 COMPUT IND
JI Comput. Ind.
PD SEP
PY 2022
VL 140
AR 103668
DI 10.1016/j.compind.2022.103668
PG 21
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1X5DT
UT WOS:000807475000001
DA 2022-08-23
ER

PT J
AU Yin, GS
   Chen, FK
   Dong, YX
   Li, GS
AF Yin, Guisheng
   Chen, Fukun
   Dong, Yuxin
   Li, Gesu
TI Knowledge-aware recommendation model with dynamic co-attention and
   attribute regularize
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Knowledge-aware; Recommender system; Dynamic co-attention; Attribute
   regularizer
AB As important information provided by recommender systems, knowledge graphs are widely applied in computer science and many other fields. The recommender system performance can be significantly improved by leveraging the knowledge graph between the user and item. Various recommendation approaches have been proposed based on the knowledge graph in recent years; however, most of the existing models only apply item-level user representations or attention mechanisms to users and items in the same way and ignore the fact that user and item attributes are significantly different. Hence, these models are not an effectively exploited attribute information and circumscribe the further improvement of recommender performance. In this paper, a novel approach of dynamic co-attention with an attribute regularizer (DCAR) for a knowledge-aware recommender system is proposed to explore the latent connections between the user level and item level. The model dynamically adjusts the dynamic co-attention mechanism through the attribute similarity between the target user and the candidate item. Specifically, an attribute regularizer between user and item is designed to improve the quality of attribute embedding. Experimental results on two realistic datasets show that our proposed model can significantly improve recommender system effectiveness and represents an advancement beyond the compared deep models.
C1 [Yin, Guisheng; Chen, Fukun; Dong, Yuxin; Li, Gesu] Harbin Engn Univ, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China.
RP Chen, FK (通讯作者)，Harbin Engn Univ, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China.
EM yinguisheng@hrbeu.edu.cn; fukun@hrbeu.edu.cn; dongyuxin@hrbeu.edu.cn;
   lgs7788@hrbeu.edu.cn
FU National Natural Science Foundation of China [61472096, 61272186,
   61472095, 61502410]; Fundamental Research Funds for the Central
   Universities [3072021CF0609]
FX This work is supported by the National Natural Science Foundation of
   China under Grants No. 61472096, No. 61272186, No. 61472095, No.
   61502410, the Fundamental Research Funds for the Central
   Universities(No. 3072021CF0609).
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NR 35
TC 0
Z9 0
U1 6
U2 14
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
PD MAR
PY 2022
VL 52
IS 4
BP 3807
EP 3824
DI 10.1007/s10489-021-02598-7
EA JUL 2021
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZF7EO
UT WOS:000671766600001
DA 2022-08-23
ER

PT J
AU Ai, QY
   Zhang, YF
   Bi, KP
   Croft, WB
AF Ai, Qingyao
   Zhang, Yongfeng
   Bi, Keping
   Croft, W. Bruce
TI Explainable Product Search with a Dynamic Relation Embedding Model
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE Product search; explainable model; knowledge graph; relation embedding
AB Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on developing effective retrieval models that rank items by their likelihood to be purchased. However, they ignore the problem that there is a gap between how systems and customers perceive the relevance of items. Without explanations, users may not understand why product search engines retrieve certain items for them, which consequentially leads to imperfect user experience and suboptimal system performance in practice. In this work, we tackle this problem by constructing explainable retrieval models for product search. Specifically, we propose to model the "search and purchase" behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session. Ranking is conducted based on the relationship between users and items in the latent space, and explanations are generated with logic inferences and entity soft matching on the knowledge graph. Empirical experiments show that our model, which we refer to as the Dynamic Relation Embedding Model (DREM), significantly outperforms the state-of-the-art baselines and has the ability to produce reasonable explanations for search results.
C1 [Ai, Qingyao] Univ Utah, Sch Comp, Salt Lake City, UT 84112 USA.
   [Zhang, Yongfeng] Rutgers State Univ, Dept Comp Sci, Piscataway, NJ 08854 USA.
   [Bi, Keping; Croft, W. Bruce] Univ Massachusetts, Coll Informat & Comp Sci, Amherst, MA 01003 USA.
RP Ai, QY (通讯作者)，Univ Utah, Sch Comp, Salt Lake City, UT 84112 USA.
EM aiqy@cs.utah.edu; yongfeng.zhang@rutgers.edu; kbi@cs.umass.edu;
   croft@cs.umass.edu
OI Ai, Qingyao/0000-0002-5030-709X
FU Center for Intelligent Information Retrieval; Amazon.com
FX This work was supported in part by the Center for Intelligent
   Information Retrieval and in part by an award from Amazon.com. Any
   opinions, findings, and conclusions or recommendations expressed in this
   material are those of the authors and do not necessarily reflect those
   of the sponsor.
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NR 76
TC 13
Z9 13
U1 2
U2 9
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1046-8188
EI 1558-2868
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
PD FEB
PY 2020
VL 38
IS 1
AR 4
DI 10.1145/3361738
PG 29
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LO7IO
UT WOS:000533801900004
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Guo, S
   Wang, Q
   Wang, B
   Wang, LH
   Guo, L
AF Guo, Shu
   Wang, Quan
   Wang, Bin
   Wang, Lihong
   Guo, Li
TI SSE: Semantically Smooth Embedding for Knowledge Graphs
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Knowledge graph embedding; semantic smoothness; Laplacian eigenmaps;
   locally linear embedding
ID NONLINEAR DIMENSIONALITY REDUCTION; DECOMPOSITIONS
AB This paper considers the problem of embedding Knowledge Graphs (KGs) consisting of entities and relations into low-dimensional vector spaces. Most of the existing methods perform this task based solely on observed facts. The only requirement is that the learned embeddings should be compatible within each individual fact. In this paper, aiming at further discovering the intrinsic geometric structure of the embedding space, we propose Semantically Smooth Embedding (SSE). The key idea of SSE is to take full advantage of additional semantic information and enforce the embedding space to be semantically smooth, i.e., entities belonging to the same semantic category will lie close to each other in the embedding space. Two manifold learning algorithms Laplacian Eigenmaps and Locally Linear Embedding are used to model the smoothness assumption. Both are formulated as geometrically based regularization terms to constrain the embedding task. Two lines of embedding strategies are tested, i.e., strategies based on latent distance models and strategies based on tensor factorization techniques. We empirically evaluate SSE on two benchmark tasks of link prediction and triple classification, and achieve significant and consistent improvements over state-of-the-art methods. The results demonstrate the superiority and generality of SSE.
C1 [Guo, Shu; Wang, Quan; Wang, Bin; Guo, Li] Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China.
   [Guo, Shu; Wang, Quan; Wang, Bin; Guo, Li] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Wang, Lihong] Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China.
RP Guo, S (通讯作者)，Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China.; Guo, S (通讯作者)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
EM guoshu@iie.ac.cn; wangquan@iie.ac.cn; wangbin@iie.ac.cn; wlh@isc.org.cn;
   guoli@iie.ac.cn
FU National Natural Science Foundation of China [61402465]; Strategic
   Priority Research Program of the Chinese Academy of Sciences
   [XDA06030200]
FX This work is supported by the National Natural Science Foundation of
   China (grant No. 61402465) and the Strategic Priority Research Program
   of the Chinese Academy of Sciences (grant No. XDA06030200). Quan Wang is
   the corresponding author of this paper.
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SC Computer Science; Engineering
GA EP7TV
UT WOS:000397581000013
DA 2022-08-23
ER

PT J
AU Tiddi, I
   Schlobach, S
AF Tiddi, Ilaria
   Schlobach, Stefan
TI Knowledge graphs as tools for explainable machine learning: A survey
SO ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Explainable systems; Knowledge graphs; Explanations; Symbolic AI;
   Subsymbolic AI; Neuro-symbolic integration; Explainable AI
ID EXPLANATIONS; SYSTEMS
AB This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI has become a very active field of research by addressing the limitations of the latest machine learning solutions that often provide highly accurate, but hardly scrutable and interpretable decisions. An increasing interest has also been shown in the integration of Knowledge Representation techniques in Machine Learning applications, mostly motivated by the complementary strengths and weaknesses that could lead to a new generation of hybrid intelligent systems. Following this idea, we hypothesise that knowledge graphs, which naturally provide domain background knowledge in a machine-readable format, could be integrated in Explainable Machine Learning approaches to help them provide more meaningful, insightful and trustworthy explanations. Using a systematic literature review methodology we designed an analytical framework to explore the current landscape of Explainable Machine Learning. We focus particularly on the integration with structured knowledge at large scale, and use our framework to analyse a variety of Machine Learning domains, identifying the main characteristics of such knowledge-based, explainable systems from different perspectives. We then summarise the strengths of such hybrid systems, such as improved understandability, reactivity, and accuracy, as well as their limitations, e.g. in handling noise or extracting knowledge efficiently. We conclude by discussing a list of open challenges left for future research. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C1 [Tiddi, Ilaria; Schlobach, Stefan] Vrije Univ Amsterdam, Boelelaan 1111, NL-1081 HV Amsterdam, Netherlands.
RP Tiddi, I (通讯作者)，Vrije Univ Amsterdam, Boelelaan 1111, NL-1081 HV Amsterdam, Netherlands.
EM i.tiddi@vu.nl; k.s.schlobach@vu.nl
RI ; Tiddi, Ilaria/I-6946-2018
OI Schlobach, Stefan/0000-0002-3282-1597; Tiddi, Ilaria/0000-0001-7116-9338
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NR 106
TC 2
Z9 2
U1 22
U2 33
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0004-3702
EI 1872-7921
J9 ARTIF INTELL
JI Artif. Intell.
PD JAN
PY 2022
VL 302
AR 103627
DI 10.1016/j.artint.2021.103627
EA NOV 2021
PG 21
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA XM0PR
UT WOS:000728539800001
OA hybrid
DA 2022-08-23
ER

PT J
AU Li, Y
   Qian, BY
   Zhang, XL
   Liu, H
AF Li, Yang
   Qian, Buyue
   Zhang, Xianli
   Liu, Hui
TI Graph Neural Network-Based Diagnosis Prediction
SO BIG DATA
LA English
DT Article; Proceedings Paper
CT SIAM International Conference on Data Mining (SDM)
CY MAY 07-09, 2020
CL Cincinnati, OH
SP SIAM
DE deep learning; health care informatics; medical knowledge graph
ID ELECTRONIC HEALTH RECORDS
AB Diagnosis prediction is an important predictive task in health care that aims to predict the patient future diagnosis based on their historical medical records. A crucial requirement for this task is to effectively model the high-dimensional, noisy, and temporal electronic health record (EHR) data. Existing studies fulfill this requirement by applying recurrent neural networks with attention mechanisms, but facing data insufficiency and noise problem. Recently, more accurate and robust medical knowledge-guided methods have been proposed and have achieved superior performance. These methods inject the knowledge from a graph structure medical ontology into deep models via attention mechanisms to provide supplementary information of the input data. However, these methods only partially leverage the knowledge graph and neglect the global structure information, which is an important feature. To address this problem, we propose an end-to-end robust solution, namely Graph Neural Network-Based Diagnosis Prediction (GNDP). First, we propose to utilize the medical knowledge graph as an internal information of a patient by constructing sequential patient graphs. These graphs not only carry the historical information from the EHR but also infuse with domain knowledge. Then we design a robust diagnosis prediction model based on a spatial-temporal graph convolutional network. The proposed model extracts meaningful features from sequential graph EHR data effectively through multiple spatial-temporal graph convolution units to generate robust patients' representations for accurate diagnosis predictions. We evaluate the performance of GNDP against a set of state-of-the-art methods on two real-world medical data sets, the results demonstrate that our methods can achieve a better utilization of knowledge graph and improve the accuracy on diagnosis prediction tasks.
C1 [Li, Yang; Zhang, Xianli] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Natl Engn Lab Big Data Analyt, Xian 710049, Shaanxi, Peoples R China.
   [Qian, Buyue; Liu, Hui] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Peoples R China.
RP Li, Y (通讯作者)，Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Natl Engn Lab Big Data Analyt, Xian 710049, Shaanxi, Peoples R China.
EM vigilee@stu.xjtu.edu.cn
RI Xianli, Zhang/AAW-5416-2021
FU National Key Research and Development Program of China [2018YFC130078];
   National Natural Science Foun-dation of China [61672420]; Key Project of
   Natural Science Foundation of China [61532015]; Innovation Research Team
   of Ministry of Education [IRT_17R86]; Project of China Knowledge Center
   for Engineering Science and Technology, National Natural Science
   Foundation of China Innovation Research Team [61721002]
FX This work was supported by the National Key Research and Development
   Program of China under grant No. 2018YFC130078, the National Natural
   Science Foun-dation of China General Program under grant No. 61672420,
   the Key Project of Natural Science Foundation of China under grant No.
   61532015, the Project of China Knowledge Center for Engineering Science
   and Technology, National Natural Science Foundation of China Innovation
   Research Team No. 61721002, Innovation Research Team of Ministry of
   Education (IRT_17R86).
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Z9 7
U1 13
U2 38
PU MARY ANN LIEBERT, INC
PI NEW ROCHELLE
PA 140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA
SN 2167-6461
EI 2167-647X
J9 BIG DATA-US
JI Big Data
PD OCT 1
PY 2020
VL 8
IS 5
BP 379
EP 390
DI 10.1089/big.2020.0070
EA AUG 2020
PG 12
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA OF1CW
UT WOS:000581830600001
PM 32783631
DA 2022-08-23
ER

PT J
AU Guo, Q
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AF Guo, Quan
   Cao, Shuai
   Yi, Zhang
TI A medical question answering system using large language models and
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SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
LA English
DT Article; Early Access
DE knowledge graph; medical question answering; named entity recognition;
   pretrained language model; Siamese network
ID EXTRACTION; ONTOLOGY
AB Question answering systems have become prominent in all areas, while in the medical domain it has been challenging because of the abundant domain knowledge. Retrieval based approach has become promising as large pretrained language models come forth. This study focuses on building a retrieval-based medical question answering system, tackling the challenge with large language models and knowledge extensions via graphs. We first retrieve an extensive but coarse set of answers via Elasticsearch efficiently. Then, we utilize semantic matching with pretrained language models to achieve a fine-grained ranking enhanced with named entity recognition and knowledge graphs to exploit the relation of the entities in question and answer. A new architecture based on siamese structures for answer selection is proposed. To evaluate the approach, we train and test the model on two Chinese data sets, NLPCC2017 and cMedQA. We also conduct experiments on two English data sets, TREC-QA and WikiQA. Our model achieves consistent improvement as compared to strong baselines on all data sets. Qualification studies with cMedQA and our in-house data set show that our system gains highly competitive performance. The proposed medical question answering system outperforms baseline models and systems in quantification and qualification evaluations.
C1 [Guo, Quan; Cao, Shuai; Yi, Zhang] Sichuan Univ, Coll Comp Sci, Machine Intelligence Lab, Chengdu 610065, Sichuan, Peoples R China.
RP Yi, Z (通讯作者)，Sichuan Univ, Coll Comp Sci, Machine Intelligence Lab, Chengdu 610065, Sichuan, Peoples R China.
EM zhangyi@scu.edu.cn
FU National Key Research and Development Program of China [2018AAA0100201]
FX This study is supported by the National Key Research and Development
   Program of China, Grant Number 2018AAA0100201.
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NR 42
TC 0
Z9 0
U1 5
U2 5
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0884-8173
EI 1098-111X
J9 INT J INTELL SYST
JI Int. J. Intell. Syst.
DI 10.1002/int.22955
EA JUL 2022
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2T9BH
UT WOS:000822761000001
DA 2022-08-23
ER

PT J
AU Ye, Q
   Hsieh, CY
   Yang, ZY
   Kang, Y
   Chen, JM
   Cao, DS
   He, SB
   Hou, TJ
AF Ye, Qing
   Hsieh, Chang-Yu
   Yang, Ziyi
   Kang, Yu
   Chen, Jiming
   Cao, Dongsheng
   He, Shibo
   Hou, Tingjun
TI A unified drug-target interaction prediction framework based on
   knowledge graph and recommendation system
SO NATURE COMMUNICATIONS
LA English
DT Article
ID LARGE-SCALE PREDICTION; NETWORK; POLYPHARMACOLOGY; IDENTIFICATION;
   FINGERPRINT; ALGORITHM; MECHANISM; DISCOVERY
AB Prediction of drug-target interactions (DTI) plays a vital role in drug development through applications in various areas, such as virtual screening for lead discovery, drug repurposing and identification of potential drug side effects. Here, the authors develop a unified framework for DTI prediction by combining a knowledge graph and a recommendation system.
   Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.
C1 [Ye, Qing; Kang, Yu; Hou, Tingjun] Zhejiang Univ, Coll Pharmaceut Sci, Innovat Inst Artificial Intelligence Med, Hangzhou 310058, Zhejiang, Peoples R China.
   [Ye, Qing; Chen, Jiming; He, Shibo] Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Zhejiang, Peoples R China.
   [Ye, Qing; Hou, Tingjun] Zhejiang Univ, State Key Lab CAD&CG, Hangzhou 310058, Zhejiang, Peoples R China.
   [Hsieh, Chang-Yu; Yang, Ziyi] Tencent Quantum Lab, Shenzhen 518057, Guangdong, Peoples R China.
   [Cao, Dongsheng] Cent South Univ, Xiangya Sch Pharmaceut Sci, Changsha 410013, Hunan, Peoples R China.
RP Hou, TJ (通讯作者)，Zhejiang Univ, Coll Pharmaceut Sci, Innovat Inst Artificial Intelligence Med, Hangzhou 310058, Zhejiang, Peoples R China.; He, SB (通讯作者)，Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Zhejiang, Peoples R China.; Hou, TJ (通讯作者)，Zhejiang Univ, State Key Lab CAD&CG, Hangzhou 310058, Zhejiang, Peoples R China.; Cao, DS (通讯作者)，Cent South Univ, Xiangya Sch Pharmaceut Sci, Changsha 410013, Hunan, Peoples R China.
EM oriental-cds@163.com; s18he@zju.edu.cn; tingjunhou@zju.edu.cn
RI Hsieh, Chang-Yu/AIB-4437-2022; Kang, Yu/L-2912-2013; Hou,
   Tingjun/C-7492-2011
OI Hsieh, Chang-Yu/0000-0002-6242-4218; Kang, Yu/0000-0002-0999-8802; Ye,
   Qing/0000-0003-3927-1919; Hou, Tingjun/0000-0001-7227-2580
FU Natural Science Foundation of China of Zhejiang Province [LZ19H300001];
   Key R&D Program of Zhejiang Province [2020C03010]; Fundamental Research
   Funds for the Central Universities [2020QNA7003]; National Natural
   Science Foundation of China [62088101]
FX T.H. was financially supported by Natural Science Foundation of China of
   Zhejiang Province (LZ19H300001), Key R&D Program of Zhejiang Province
   (2020C03010), and Fundamental Research Funds for the Central
   Universities (2020QNA7003). S.H. acknowledges support from National
   Natural Science Foundation of China (62088101).
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NR 70
TC 4
Z9 4
U1 27
U2 56
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
EI 2041-1723
J9 NAT COMMUN
JI Nat. Commun.
PD NOV 22
PY 2021
VL 12
IS 1
AR 6775
DI 10.1038/s41467-021-27137-3
PG 12
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA XC4TK
UT WOS:000722007200009
PM 34811351
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Qiu, CC
   Li, B
   Liu, HQ
   He, SP
   Hao, CH
AF Qiu, Chaochao
   Li, Bin
   Liu, Hongqi
   He, Songping
   Hao, Caihua
TI A novel method for machine tool structure condition monitoring based on
   knowledge graph
SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Machine tool; Structure health monitoring; Knowledge graph
ID FAULT-DIAGNOSIS; MODAL-ANALYSIS; ONTOLOGY; BRIDGE; IDENTIFICATION;
   DESIGN; REPRESENTATION; SYSTEM; SIGNAL
AB During a long-term structural health monitoring (SHM), large-scale machine tool structural response observed from various sensors show obvious big data characteristics. However, serious "data island" issues existing in traditional SHM inevitably limit the efficiencies of sensor data analysis. Besides, most existing identification methods for these diverse and complex signal data are manual methods, which are extremely time-consuming and inefficient. In this paper, a novel method based on knowledge graph (KG) was proposed to deal with the fine-grained domain knowledge modeling and multi-source sensor data integration problems in the field of machine tool SHM. With the help of KG-based querying and reasoning, it is more intelligent and convenient to retrieve and analyze sensor data. To verify the effectiveness of the proposed method, a long-term condition monitoring experiment was conducted on a CNC machine tool in an automobile factory. The dynamic properties of the machine tool structure were automatically identified based on KG, and a condition monitoring indicator based on the similarity of dynamic properties was applied to monitor the health condition of the machine tool. The final result showed the effectiveness of the KG for health monitoring of the machine tool structure.
C1 [Qiu, Chaochao; Li, Bin; He, Songping] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China.
   [Qiu, Chaochao; Li, Bin; Liu, Hongqi] Huazhong Univ Sci & Technol, Natl NC Syst Engn Res Ctr, Sch Mech Sci & Engn, 1037 Luoyu Rd, Wuhan 430074, Peoples R China.
   [Hao, Caihua] Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430074, Hubei, Peoples R China.
RP Qiu, CC (通讯作者)，Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China.; Qiu, CC (通讯作者)，Huazhong Univ Sci & Technol, Natl NC Syst Engn Res Ctr, Sch Mech Sci & Engn, 1037 Luoyu Rd, Wuhan 430074, Peoples R China.
EM qiucc@hust.edu.cn
FU National Natural Science Foundation of China [51875224, 51705174]; major
   scientific and technological innovation projects in Shandong Province
   [2019JZZY010442]
FX This research is supported by the National Natural Science Foundation of
   China under Grant nos. 51875224 and 51705174, major scientific and
   technological innovation projects in Shandong Province, 2019JZZY010442.
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NR 50
TC 1
Z9 1
U1 18
U2 20
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0268-3768
EI 1433-3015
J9 INT J ADV MANUF TECH
JI Int. J. Adv. Manuf. Technol.
PD MAY
PY 2022
VL 120
IS 1-2
BP 563
EP 582
DI 10.1007/s00170-022-08757-5
EA FEB 2022
PG 20
WC Automation & Control Systems; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering
GA 0M9QK
UT WOS:000749983500005
DA 2022-08-23
ER

PT J
AU Vogt, L
AF Vogt, Lars
TI FAIR data representation in times of eScience: a comparison of
   instance-based and class-based semantic representations of empirical
   data using phenotype descriptions as example
SO JOURNAL OF BIOMEDICAL SEMANTICS
LA English
DT Article
DE Phenotype data; Phenotype knowledge graph; Semantic phenotype; Ontology;
   Knowledge management; Semantic graph; Data representation; FAIR data;
   ABox expression; TBox expression
ID WILD-TYPE; DATA STANDARDS; LOGICAL BASIS; ONTOLOGIES; MORPHOLOGY;
   HOMOLOGY; MODEL; NEED; INFORMATICS; HYMENOPTERA
AB Background The size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). This can be achieved by using ontologies and through representing them as semantic graphs. Here, we discuss two different semantic graph approaches of representing empirical data and metadata in a knowledge graph, with phenotype descriptions as an example. Almost all phenotype descriptions are still being published as unstructured natural language texts, with far-reaching consequences for their FAIRness, substantially impeding their overall usability within the life sciences. However, with an increasing amount of anatomy ontologies becoming available and semantic applications emerging, a solution to this problem becomes available. Researchers are starting to document and communicate phenotype descriptions through the Web in the form of highly formalized and structured semantic graphs that use ontology terms and Uniform Resource Identifiers (URIs) to circumvent the problems connected with unstructured texts. Results Using phenotype descriptions as an example, we compare and evaluate two basic representations of empirical data and their accompanying metadata in the form of semantic graphs: the class-based TBox semantic graph approach called Semantic Phenotype and the instance-based ABox semantic graph approach called Phenotype Knowledge Graph. Their main difference is that only the ABox approach allows for identifying every individual part and property mentioned in the description in a knowledge graph. This technical difference results in substantial practical consequences that significantly affect the overall usability of empirical data. The consequences affect findability, accessibility, and explorability of empirical data as well as their comparability, expandability, universal usability and reusability, and overall machine-actionability. Moreover, TBox semantic graphs often require querying under entailment regimes, which is computationally more complex. Conclusions We conclude that, from a conceptual point of view, the advantages of the instance-based ABox semantic graph approach outweigh its shortcomings and outweigh the advantages of the class-based TBox semantic graph approach. Therefore, we recommend the instance-based ABox approach as a FAIR approach for documenting and communicating empirical data and metadata in a knowledge graph.
C1 [Vogt, Lars] TIB Leibniz Informat Ctr Sci & Technol, Welfengarten 1B, D-30167 Hannover, Germany.
RP Vogt, L (通讯作者)，TIB Leibniz Informat Ctr Sci & Technol, Welfengarten 1B, D-30167 Hannover, Germany.
EM lars.m.vogt@gmail.com
OI Vogt, Lars/0000-0002-8280-0487
FU German Research Foundation DFG [VO 1244/8-1]; Leibniz Competition
   [SAW-2016-SGN-2]; ERC H2020 Project 'ScienceGraph' [819536]
FX This work was supported by the grant VO 1244/8-1 from the German
   Research Foundation DFG, grant SAW-2016-SGN-2 of the Leibniz
   Competition, and the ERC H2020 Project `ScienceGraph' (819536). Lars
   Vogt is also grateful to the taxpayers of Germany.
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NR 103
TC 1
Z9 1
U1 1
U2 2
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 2041-1480
J9 J BIOMED SEMANT
JI J. Biomed. Semant.
PD NOV 25
PY 2021
VL 12
IS 1
AR 20
DI 10.1186/s13326-021-00254-0
PG 25
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA XD3MN
UT WOS:000722615100001
PM 34823588
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Schneider, JM
   Rehm, G
   Montiel-Ponsoda, E
   Rodriguez-Doncel, V
   Martin-Chozas, P
   Navas-Loro, M
   Kaltenbock, M
   Revenko, A
   Karampatakis, S
   Sageder, C
   Gracia, J
   Maganza, F
   Kernerman, I
   Lonke, D
   Lagzdins, A
   Gil, JB
   Verhoeven, P
   Diaz, EG
   Ballesteros, PB
AF Schneider, Julian Moreno
   Rehm, Georg
   Montiel-Ponsoda, Elena
   Rodriguez-Doncel, Victor
   Martin-Chozas, Patricia
   Navas-Loro, Maria
   Kaltenbock, Martin
   Revenko, Artem
   Karampatakis, Sotirios
   Sageder, Christian
   Gracia, Jorge
   Maganza, Filippo
   Kernerman, Ilan
   Lonke, Dorielle
   Lagzdins, Andis
   Gil, Julia Bosque
   Verhoeven, Pieter
   Diaz, Elsa Gomez
   Ballesteros, Pascual Boil
TI Lynx: A knowledge-based AI service platform for content processing,
   enrichment and analysis for the legal domain
SO INFORMATION SYSTEMS
LA English
DT Article
DE Text analytics; Tools; Systems; Applications; Knowledge discovery;
   representation
ID CASE LAW
AB The EU-funded project Lynx focuses on the creation of a knowledge graph for the legal domain (Legal Knowledge Graph, LKG) and its use for the semantic processing, analysis and enrichment of documents from the legal domain. This article describes the use cases covered in the project, the entire developed platform and the semantic analysis services that operate on the documents. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C1 [Schneider, Julian Moreno; Rehm, Georg] DFKI GmbH, Gottingen, Germany.
   [Montiel-Ponsoda, Elena; Rodriguez-Doncel, Victor; Martin-Chozas, Patricia; Navas-Loro, Maria] Univ Politecn Madrid, Madrid, Spain.
   [Kaltenbock, Martin; Revenko, Artem; Karampatakis, Sotirios] Semant Web Co GmbH, Vienna, Austria.
   [Sageder, Christian] Cybly GmbH, Vienna, Austria.
   [Gracia, Jorge] Univ Zaragoza, Zaragoza, Spain.
   [Maganza, Filippo] Alpenite, Milan, Italy.
   [Kernerman, Ilan; Lonke, Dorielle] KDictionaries, Tel Aviv, Israel.
   [Lagzdins, Andis] Tilde, Riga, Latvia.
   [Diaz, Elsa Gomez; Ballesteros, Pascual Boil] Cuatrecasas, Madrid, Spain.
   [Verhoeven, Pieter] DNV GL, Groningen, Netherlands.
RP Schneider, JM (通讯作者)，DFKI GmbH, Gottingen, Germany.
EM julian.moreno_schneider@dfki.de
OI Revenko, Artem/0000-0001-6681-3328
FU project Lynx from the EU's Horizon 2020 research and innovation
   programme [780602]
FX This work has been partially funded by the project Lynx, which has
   received funding from the EU's Horizon 2020 research and innovation
   programme under grant agreement no. 780602, see
   http://www.lynx-project.eu.
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NR 44
TC 0
Z9 0
U1 6
U2 6
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4379
EI 1873-6076
J9 INFORM SYST
JI Inf. Syst.
PD MAY
PY 2022
VL 106
AR 101966
DI 10.1016/j.is.2021.101966
PG 18
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0O9QY
UT WOS:000783859500003
OA hybrid
DA 2022-08-23
ER

PT J
AU Wang, J
   Wu, M
   Huang, XH
   Wang, L
   Zhang, S
   Liu, H
   Zheng, J
AF Wang, Jie
   Wu, Min
   Huang, Xuhui
   Wang, Li
   Zhang, Sophia
   Liu, Hui
   Zheng, Jie
TI SynLethDB 2.0: a web-based knowledge graph database on synthetic
   lethality for novel anticancer drug discovery
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Article
ID SCREENS
AB Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival.The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs).
C1 [Wang, Jie; Wang, Li; Zheng, Jie] ShanghaiTech Univ, Sch Informat Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China.
   [Wu, Min] ASTAR, Inst Infocomm Res, 1 Fusionopolis Way, Singapore 138632, Singapore.
   [Huang, Xuhui] Natl Univ Singapore, Sch Comp, Comp 1,13 Comp Dr, Singapore 117417, Singapore.
   [Zhang, Sophia] Cornell Univ, Coll Agr & Life Sci, 260 Roberts Hall, Ithaca, NY 14853 USA.
   [Liu, Hui] Nanjing Tech Univ, Sch Comp Sci & Technol, 30 Puzhu Rd, Nanjing 211816, Peoples R China.
   [Zheng, Jie] Shanghai Engn Res Ctr Intelligent Vis & Imaging, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China.
RP Zheng, J (通讯作者)，ShanghaiTech Univ, Sch Informat Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China.; Zheng, J (通讯作者)，Shanghai Engn Res Ctr Intelligent Vis & Imaging, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China.
EM zhengjie@shanghaitech.edu.cn
RI Zheng, Jie/C-1356-2011
OI Zheng, Jie/0000-0001-6774-9786; Wu, Min/0000-0003-0977-3600
FU ShanghaiTech university startup grant
FX ShanghaiTech university startup grant.
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NR 48
TC 0
Z9 0
U1 3
U2 3
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD MAY 14
PY 2022
VL 2022
AR baac030
DI 10.1093/database/baac030
PG 10
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA 1F5IQ
UT WOS:000795203200001
PM 35562840
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, L
   Cui, J
   Tang, X
   Qian, YT
   Li, YS
   Zhang, YJ
AF Chen, Ling
   Cui, Jun
   Tang, Xing
   Qian, Yuntao
   Li, Yansheng
   Zhang, Yongjun
TI RLPath: a knowledge graph link prediction method using reinforcement
   learning based attentive relation path searching and representation
   learning
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Knowledge graph link prediction; Representation learning; Reinforcement
   learning; Path searching
AB Due to containing rich patterns between entities, relation paths have been widely used in knowledge graph link prediction. The state-of-the-art link prediction methods considering relation paths obtain relation paths by reinforcement learning with an untrainable reward setting, and realize link prediction by path-ranking algorithm (PRA), which ignores information in entities. In this paper, we propose a new link prediction method RLPath to employ information in both relation paths and entities, which alternately trains a reinforcement learning model with a trainable reward setting to search high-quality relation paths, and a translation-based model to realize link prediction. Simultaneously, we propose a novel reward setting for the reinforcement learning model, which shares the parameters with the attention of the translation-based model, so that these parameters can not only measure the contributions of relation paths, but also guide agents to search relation paths that have high contributions for link prediction, forming mutual promotion. In experiments, we compare RLPath with the state-of-the-art link prediction methods. The results show that RLPath has competitive performance.
C1 [Chen, Ling; Cui, Jun; Tang, Xing; Qian, Yuntao] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
   [Li, Yansheng; Zhang, Yongjun] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.
RP Chen, L (通讯作者)，Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China.
EM lingchen@cs.zju.edu.cn; cuijun@cs.zju.edu.cn; tangxing@cs.zju.edu.cn;
   ytqian@zju.edu.cn; yansheng.li@whu.edu.cn; zhangyj@whu.edu.cn
FU National Key Research and Development Program of China [2018YFB0505000];
   Fundamental Research Funds for the Central Universities [2020QNA5017]
FX This work was funded by the National Key Research and Development
   Program of China (No. 2018YFB0505000) and the Fundamental Research Funds
   for the Central Universities (No.2020QNA5017).
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Z9 1
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PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
PD MAR
PY 2022
VL 52
IS 4
BP 4715
EP 4726
DI 10.1007/s10489-021-02672-0
EA JUL 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZF7EO
UT WOS:000677972500004
DA 2022-08-23
ER

PT J
AU Qiu, J
   Du, QF
   Yin, KL
   Zhang, SL
   Qian, CS
AF Qiu, Juan
   Du, Qingfeng
   Yin, Kanglin
   Zhang, Shuang-Li
   Qian, Chongshu
TI A Causality Mining and Knowledge Graph Based Method of Root Cause
   Diagnosis for Performance Anomaly in Cloud Applications
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE causality graph; knowledge graph; microservice system; operation and
   maintenance (O&M); root cause diagnosis
AB With the development of cloud computing technology, the microservice architecture (MSA) has become a prevailing application architecture in cloud-native applications. Many user-oriented services are supported by many microservices, and the dependencies between services are more complicated than those of a traditional monolithic architecture application. In such a situation, if an anomalous change happens in the performance metric of a microservice, it will cause other related services to be downgraded or even to fail, which would probably cause large losses to dependent businesses. Therefore, in the operation and maintenance job of cloud applications, it is critical to mine the causality of the problem and find its root cause as soon as possible. In this paper, we propose an approach for mining causality and diagnosing the root cause that uses knowledge graph technology and a causal search algorithm. We verified the proposed method on a classic cloud-native application and found that the method is effective. After applying our method on most of the services of a cloud-native application, both precision and recall were over 80%.
C1 [Qiu, Juan; Du, Qingfeng; Yin, Kanglin; Zhang, Shuang-Li] Tongji Univ, Sch Software Engn, Shanghai 201804, Peoples R China.
   [Qian, Chongshu] Univ Calif Irvine, Donald Bren Sch Informat & Comp Sci, 6210 Donald Bren Hall, Irvine, CA 92697 USA.
RP Qiu, J; Du, QF (通讯作者)，Tongji Univ, Sch Software Engn, Shanghai 201804, Peoples R China.
EM juan_qiu@tongji.edu.cn; du_cloud@tongji.edu.cn; 14_ykl@tongji.edu.cn;
   1931551@tongji.edu.cn; chongshq@uci.edu
FU National Natural Science Foundation of China [61672384]
FX This work has been supported by the National Natural Science Foundation
   of China (Grant No. 61672384).
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PD MAR
PY 2020
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AR 2166
DI 10.3390/app10062166
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WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
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WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA LI1NZ
UT WOS:000529252800267
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Sui, Y
   Feng, SS
   Zhang, HX
   Cao, J
   Hu, L
   Zhu, NJ
AF Sui, Yuan
   Feng, Shanshan
   Zhang, Huaxiang
   Cao, Jian
   Hu, Liang
   Zhu, Nengjun
TI Causality-aware Enhanced Model for Multi-hop Question Answering over
   Knowledge Graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph-based question&nbsp; answering; Causal representation
   learning; Constraint pairwise-based clustering; Knowledge graph
   embedding; Confounding bias
AB To improve the performance of knowledge graph-based question answering system (KGQA), several approaches have been developed to construct a semantic parser based on entity linking, relation identification and logical/numerical structure identification. However, existing methods arrive at answers only by maximizing the data likelihood only on the sparse or imbalanced explicit relations, ignoring the potentially large number of latent relations. It makes KGQA suffer from a high level of spurious entity relations and missing link challenge. In this paper, we propose a causal filter (CF) model for KGQA (CF-KGQA), which performs causal interference on the relation representation space to reduce the spurious relation representation in a data-driven manner, i.e., the goal of this work is to comprehensively discover disentangled latent factors to alleviate the spurious correlation problem in KGQA. The model comprises a causal pairwise aggregator (A(P)) and a disentangled latent factor aggregator (A(C)). The former filters out most spurious entity relations inconsistent to their dense groups' neighborhood, and generates a causal pairwise matrix among all the candidate relations. The latter learns the latent relation representation via an encoder-decoder on the causal pairwise matrix. It disconnects the latent factor and the causal confounder beneath the knowledge embedding space by causal intervention. To prove the effectiveness and efficiency of the proposed approach, we test CFKGQA and other state-of-the-art methods on four public real-world datasets. The experiments indicate that our approach outperforms the recent methods and is also less sensitive to the spurious correlation problem, thus demonstrating the robustness of CF-KGQA. (C) 2022 Published by Elsevier B.V.
C1 [Sui, Yuan; Feng, Shanshan; Zhang, Huaxiang] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China.
   [Zhang, Huaxiang] Shandong JiaoTong Univ, Jinan, Peoples R China.
   [Cao, Jian] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai, Peoples R China.
   [Hu, Liang] Tongji Univ, DeepBlue Acad Sci, Sch Elect & Informat Engn, Shanghai, Peoples R China.
   [Hu, Liang] DeepBlue Acad Sci, Shanghai, Peoples R China.
   [Zhu, Nengjun] Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China.
RP Feng, SS (通讯作者)，Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China.
EM yuansui08@gmail.com; fswl6869@foxmail.com; huaxzhang@163.com;
   cao-jian@sjtu.edu.cn; rainmilk@gmail.com; zhu_nj@shu.edu.cn
OI zhang, hua xiang/0000-0001-6259-7533
FU Taishan Scholar Project of Shandong of China [U1836216]; National
   Natural Science Foundation of China [ZR2019ZD03]; Major Fundamental
   Research Project of Shandong of China [22YF1413700]; Shanghai Youth
   Science and Technology Talents Sailing Program
FX Acknowledgments This work was supported in part by Taishan Scholar
   Project of Shandong of China, the National Natural Science Foundation of
   China under Grant U1836216, the Major Fundamental Research Project of
   Shandong of China under Grant ZR2019ZD03, and the Shanghai Youth Science
   and Technology Talents Sailing Program under Grant 22YF1413700.
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NR 56
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD AUG 17
PY 2022
VL 250
AR 108943
DI 10.1016/j.knosys.2022.108943
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1W5KI
UT WOS:000806811900010
DA 2022-08-23
ER

PT J
AU Enkhsaikhan, M
   Holden, EJ
   Duuring, P
   Liu, W
AF Enkhsaikhan, Majigsuren
   Holden, Eun-Jung
   Duuring, Paul
   Liu, Wei
TI Understanding ore-forming conditions using machine reading of text
SO ORE GEOLOGY REVIEWS
LA English
DT Article
DE Machine reading of text; Ore-forming conditions; Geological information
   extraction
ID INFORMATION EXTRACTION
AB Mineral exploration reports assist present day exploration by providing valuable observations about the geological environments in which mineral deposits form. Querying and aggregating historical data can assist in reducing future exploration risks and costs. However, since the reports are written in unstructured text, it is a challenging task to derive meaningful geological information without manually reading through a large collection of reports, which is a formidable task for geologists. In this study, geological information relevant to mineralisation and ore-forming conditions is automatically extracted from such under-utilised exploration reports. This is achieved by constructing knowledge graphs that describe geological entities and their relations as they appear in exploration reports. Natural language processing and deep learning methods are used to automatically extract and label geological terms with the correct entity types and establish the relationships between these entities. In this research, six dominant entity types are considered, namely, geographical location, geological timescale, stratigraphic unit, rock type, ore and deposit type, and contained minerals. Two knowledge graphs are constructed for two high-quality mineral exploration reports, one for iron ore and the other for gold deposit, to illustrate the effectiveness of our methodology. The knowledge graphs are then assessed by determining whether the contents of the source reports were depicted accurately, specifically the labelled geological terms in the nodes and their associations in node connections. The results show that the structured information stored in the knowledge graphs faithfully represent the contents of the source reports, matching well with the domain knowledge. The proposed methods are capable of rapidly and robustly transforming text data into a structured form, the untapped area towards geological knowledge mining.
C1 [Enkhsaikhan, Majigsuren; Holden, Eun-Jung; Liu, Wei] Univ Western Australia, Perth, WA, Australia.
   [Duuring, Paul] Dept Mines Ind Regulat & Safety, Perth, WA, Australia.
RP Enkhsaikhan, M (通讯作者)，Univ Western Australia, Perth, WA, Australia.
EM majigsuren.enkhsaikhan@research.uwa.edu.au
OI Liu, Wei/0000-0002-7409-0948; Holden, Eun-Jung/0000-0002-8752-1639;
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PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-1368
EI 1872-7360
J9 ORE GEOL REV
JI Ore Geol. Rev.
PD AUG
PY 2021
VL 135
AR 104200
DI 10.1016/j.oregeorev.2021.104200
EA MAY 2021
PG 15
WC Geology; Mineralogy; Mining & Mineral Processing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geology; Mineralogy; Mining & Mineral Processing
GA TD6NY
UT WOS:000669442400002
DA 2022-08-23
ER

PT J
AU Kosasih, EE
   Margaroli, F
   Gelli, S
   Aziz, A
   Wildgoose, N
   Brintrup, A
AF Kosasih, Edward Elson
   Margaroli, Fabrizio
   Gelli, Simone
   Aziz, Ajmal
   Wildgoose, Nick
   Brintrup, Alexandra
TI Towards knowledge graph reasoning for supply chain risk management using
   graph neural networks
SO INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
LA English
DT Article; Early Access
DE supply chain; knowledge graph; neurosymbolic; link prediction; machine
   learning; artificial intelligence
ID LINK-PREDICTION
AB Modern supply chains are complex, interconnected systems that contain emergent, invisible dependencies. Lack of visibility often hinders effective risk planning and results in delayed discovery of supply chain problems, with examples ranging from product contamination, unsustainable production practices, or exposure to suppliers clustered in geographical areas prone to natural or man-made disasters. Initiatives that rely on manual collection of data often fail due to supply chain complexity and unwillingness of suppliers to share data. In this paper, we propose a neurosymbolic machine learning technique to proactively uncover hidden risks in supply chains and discover new information. Our method uses a combination of graph neural networks and knowledge graph reasoning. Unlike existing research our model is able to infer multiple types of hidden relationship risks, presenting a step change in automated supply chain surveillance. The approach has been tested on two empirical datasets from the automotive and energy industries, illustrating that it can provide inference in multiple types of links such as companies, products, production capabilities, certifications; thereby facilitating complex queries that go beyond who-supplies-whom. As such, additional risk insights can emerge from graph structure, providing practitioners with new knowledge.
C1 [Kosasih, Edward Elson; Aziz, Ajmal; Brintrup, Alexandra] Univ Cambridge, Inst Mfg, Cambridge, England.
   [Margaroli, Fabrizio; Gelli, Simone] Achilles Informat Ltd, Abingdon, Oxon, England.
   [Wildgoose, Nick] Supplien Consulting Ltd, London, England.
   [Aziz, Ajmal] Inst Mfg, Cambridge, England.
RP Kosasih, EE (通讯作者)，Univ Cambridge, Inst Mfg, Cambridge, England.
EM eek31@cam.ac.uk
OI Brintrup, Alexandra/0000-0002-4189-2434
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NR 70
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 0020-7543
EI 1366-588X
J9 INT J PROD RES
JI Int. J. Prod. Res.
DI 10.1080/00207543.2022.2100841
EA JUL 2022
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 3A1ST
UT WOS:000827045000001
OA hybrid
DA 2022-08-23
ER

PT J
AU Wu, ZW
   Chen, CT
   Huang, SH
AF Wu, Zih-Wun
   Chen, Chiao-Ting
   Huang, Szu-Hao
TI Poisoning attacks against knowledge graph-based recommendation systems
   using deep reinforcement learning
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Poisoning attacks; Adversarial attacks; Knowledge graph-based
   recommendation; Deep reinforcement learning
ID USER
AB In recent years, studies have revealed that introducing knowledge graphs (KGs) into recommendation systems as auxiliary information can improve recommendation accuracy. However, KGs are usually based on third-party data that may be manipulated by malicious individuals. In this study, we developed a poisoning attack strategy applied on a KG-based recommendation system to analyze the influence of fake links. The aim of an attacker is to recommend specific products to improve their visibility. Most related studies have focused on adversarial attacks on graph data; KG-based recommendation systems have rarely been discussed. We propose an attack model corresponding to recommendations. In the model, the current recommended status and a specified item are analyzed to estimate the effects of different attack decisions (addition or deletion of facts), thereby generating the optimal attack combination. Finally, the KG is contaminated by the attack combination so that the trained recommendation model recommends a specific item to as many people as possible. We formulated the process into a deep reinforcement learning method. Conducting experiments on the movie and the fund data sets enabled us to systematically analyze our poisoning attack strategy. The experimental results proved that the proposed strategy can effectively improve an item's ranking in a recommendation list.
C1 [Wu, Zih-Wun] Natl Yang Ming Chiao Tung Univ, Inst Informat Management, Hsinchu 30010, Taiwan.
   [Chen, Chiao-Ting] Natl Yang Ming Chiao Tung Univ, Dept Comp Sci, Hsinchu 30010, Taiwan.
   [Huang, Szu-Hao] Natl Yang Ming Chiao Tung Univ, Dept Informat Management & Finance, Hsinchu 30010, Taiwan.
RP Huang, SH (通讯作者)，Natl Yang Ming Chiao Tung Univ, Dept Informat Management & Finance, Hsinchu 30010, Taiwan.
EM zihwun07534.iim07g@nctu.edu.tw; rsps971130.cs09@nycu.edu.tw;
   szuhaohuang@nycu.edu.tw
OI Huang, Szu-Hao/0000-0002-4073-0652
FU Ministry of Science and Technology, Taiwan [MOST 110-2221-E-A49 -101,
   MOST 110-2622-8-009 -014 - TM1]; Financial Technology (FinTech)
   Innovation Research Center, National Yang Ming Chiao Tung University
FX This work was supported in part by the Ministry of Science and
   Technology, Taiwan, under Contract MOST 110-2221-E-A49 -101 and Contract
   MOST 110-2622-8-009 -014 - TM1; and in part by the Financial Technology
   (FinTech) Innovation Research Center, National Yang Ming Chiao Tung
   University.
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NR 54
TC 1
Z9 1
U1 9
U2 20
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 3097
EP 3115
DI 10.1007/s00521-021-06573-8
EA OCT 2021
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YV1BC
UT WOS:000705810600001
DA 2022-08-23
ER

PT J
AU Lee, K
   Jung, H
   Hong, JS
   Kim, W
AF Lee, Kwangyon
   Jung, Haemin
   Hong, June Seok
   Kim, Wooju
TI Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph
   Data
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE frequent subgraph mining; knowledge graph; semantic web; ontology;
   rating prediction; recommendation
AB In many areas, vast amounts of information are rapidly accumulating in the form of ontology-based knowledge graphs, and the use of information in these forms of knowledge graphs is becoming increasingly important. This study proposes a novel method for efficiently learning frequent subgraphs (i.e., knowledge) from ontology-based graph data. An ontology-based large-scale graph is decomposed into small unit subgraphs, which are used as the unit to calculate the frequency of the subgraph. The frequent subgraphs are extracted through candidate generation and chunking processes. To verify the usefulness of the extracted frequent subgraphs, the methodology was applied to movie rating prediction. Using the frequent subgraphs as user profiles, the graph similarity between the rating graph and new item graph was calculated to predict the rating. The MovieLens dataset was used for the experiment, and a comparison showed that the proposed method outperformed other widely used recommendation methods. This study is meaningful in that it proposed an efficient method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. Furthermore, the proposed method can provide results that include semantic information to serve as a logical basis for rating prediction or recommendation, which existing methods are unable to provide.
C1 [Lee, Kwangyon; Jung, Haemin; Kim, Wooju] Yonsei Univ, Dept Ind Engn, Seoul 03722, South Korea.
   [Hong, June Seok] Kyonggi Univ, Dept Management Informat Syst, Gyeonggi Do 16227, South Korea.
RP Kim, W (通讯作者)，Yonsei Univ, Dept Ind Engn, Seoul 03722, South Korea.
EM michael17@yonsei.ac.kr; hmjung@yonsei.ac.kr; junehong@kyonggi.ac.kr;
   wkim@yonsei.ac.kr
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NR 25
TC 0
Z9 0
U1 3
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD FEB
PY 2021
VL 11
IS 3
AR 932
DI 10.3390/app11030932
PG 28
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA QC7BU
UT WOS:000614989700001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Lu, FY
   Cong, PJ
   Huang, XL
AF Lu, Fengyuan
   Cong, Peijin
   Huang, Xinli
TI Utilizing Textual Information in Knowledge Graph Embedding: A Survey of
   Methods and Applications
SO IEEE ACCESS
LA English
DT Article
DE Task analysis; Semantics; Encoding; Earth; Moon; Knowledge
   representation; Training; Knowledge graph embedding; textual
   information; text-based embedding; text-improved embedding;
   embedding-based applications
ID LARGE-SCALE
AB Techniques that map the entities and relations of the knowledge graph (KG) into a low-dimensional continuous space are called KG embedding or knowledge representation learning. However, most existing techniques learn the embeddings based on the facts in KG alone, suffering from the issues of imperfection and spareness of KG. Recently, the research on textual information in KG embedding has attracted much attention due to the rich semantic information supplied by the texts. Thus, in this paper, a survey of techniques for textual information based KG embedding is proposed. Firstly, we introduce the techniques for encoding the textual information to represent the entities and relations from perspectives of encoding models and scoring functions, respectively. Secondly, methods for incorporating the textual information in the existing embedding techniques are summarized. Thirdly, we discuss the training procedure of textual information based KG embedding techniques. Finally, applications of KG embedding with textual information in the specific tasks such as KG completion in zero-shot scenario, multilingual entity alignment, relation extraction and recommender system are explored. We hope that this survey will give insights to researchers into textual information based KG embedding.
C1 [Lu, Fengyuan; Cong, Peijin; Huang, Xinli] East China Normal Univ, Sch Comp Sci & Technol, Shanghai 20062, Peoples R China.
RP Huang, XL (通讯作者)，East China Normal Univ, Sch Comp Sci & Technol, Shanghai 20062, Peoples R China.
EM xlhuang@cs.ecnu.edu.cn
FU National Key Research and Development Plan of China [2019YFB2012803];
   Key Project of Shanghai Science and Technology Innovation Action Plan
   [19DZ1100400, 18511103302]; Key Program of Shanghai Arti~cial
   Intelligence Innovation Development Plan [2018-RGZN-02060]
FX This work was supported in part by the National Key Research and
   Development Plan of China under Grant 2019YFB2012803, in part by the Key
   Project of Shanghai Science and Technology Innovation Action Plan under
   Grant 19DZ1100400 and Grant 18511103302, and in part by the Key Program
   of Shanghai Arti~cial Intelligence Innovation Development Plan under
   Grant 2018-RGZN-02060.
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   Xiao H., 2016, P 25 INT JOINT C ART, P1315
   Xie R., 2016, P 25 INT JOINT C ART, P2965
   Xu JC, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1318
   Yao H, 2015, INT J WEB SERV RES, V12, P1, DOI 10.4018/IJWSR.2015100101
   Zeng D., 2014, P COLING, P2335
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NR 76
TC 6
Z9 6
U1 3
U2 12
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 92072
EP 92088
DI 10.1109/ACCESS.2020.2995074
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LW3JW
UT WOS:000539042100007
OA gold
DA 2022-08-23
ER

PT J
AU Zhao, D
   Wang, J
   Sang, ST
   Lin, HF
   Wen, JB
   Yang, CM
AF Zhao, Di
   Wang, Jian
   Sang, Shengtian
   Lin, Hongfei
   Wen, Jiabin
   Yang, Chunmei
TI Relation path feature embedding based convolutional neural network
   method for drug discovery
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article; Proceedings Paper
CT 4th China Health Information Processing Conference (CHIP)
CY DEC 01-02, 2018
CL Shenzhen, PEOPLES R CHINA
DE Literature-based discovery; Drug discovery; Knowledge graph; Path
   ranking algorithm; Convolutional neural network
ID FISH-OIL
AB BackgroundDrug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discovery of drugs.MethodsHere, we propose a relation path features embedding based convolutional neural network model with attention mechanism for drug discovery from literature, which we denote as PACNN. First, we use predications from biomedical abstracts to construct a biomedical knowledge graph, and then apply a path ranking algorithm to extract drug-disease relation path features on the biomedical knowledge graph. After that, we use these drug-disease relation features to train a convolutional neural network model which combined with the attention mechanism. Finally, we employ the trained models to mine drugs for treating diseases.ResultsThe experiment shows that the proposed model achieved promising results, comparing to several random walk algorithms.ConclusionsIn this paper, we propose a relation path features embedding based convolutional neural network with attention mechanism for discovering potential drugs from literature. Our method could be an auxiliary method for drug discovery, which can speed up the discovery of new drugs for the incurable diseases.
C1 [Zhao, Di; Wang, Jian; Sang, Shengtian; Lin, Hongfei] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian, Peoples R China.
   [Wen, Jiabin; Yang, Chunmei] Dalian Med Univ, Hosp 2, Dept VIP, Dalian, Peoples R China.
RP Wang, J; Sang, ST (通讯作者)，Dalian Univ Technol, Sch Comp Sci & Technol, Dalian, Peoples R China.
EM wangjian@dlut.edu.cn; sangst@mail.dlut.edu.cn
FU National Key Research Development Program of China [2016YFB1001103];
   National Natural Science Foundation of China [61572098, 61572102]
FX Publication costs are funded by the National Key Research Development
   Program of China (No. 2016YFB1001103), the National Natural Science
   Foundation of China (Nos. 61572098, 61572102).
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NR 35
TC 3
Z9 3
U1 5
U2 17
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD APR 9
PY 2019
VL 19
SU 2
AR 59
DI 10.1186/s12911-019-0764-5
PG 10
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Medical Informatics
GA HS8NN
UT WOS:000464126500012
PM 30961599
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Pechsiri, C
   Piriyakul, R
AF Pechsiri, Chaveevan
   Piriyakul, Rapepun
TI Explanation Knowledge Graph Construction Through Causality Extraction
   from Texts
SO JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
LA English
DT Article
DE elementary discourse unit; explanation knowledge graph; causality
   boundary; effect-event pattern
AB Explanation knowledge expressed by a graph, especially in the graphical model, is essential to comprehend clearly all paths of effect events in causality for basic diagnosis. This research focuses on determining the effect boundary using a statistical based approach and patterns of effect events in the graph whether they are consequence or concurrence without temporal markers. All necessary causality events from texts for the graph construction are extracted on multiple clauses/EDUs (Elementary Discourse Units) which assist in determining effect-event patterns from written event sequences in documents. To extract the causality events from documents, it has to face the effect-boundary determination problems after applying verb pair rules (a causative verb and an effect verb) to identify the causality. Therefore, we propose Bayesian Network and Maximum entropy to determine the boundary of the effect EDUs. We also propose learning the effect-verb order pairs from the adjacent effect EDUs to solve the effect-event patterns for representing the extracted causality by the graph construction. The accuracy result of the explanation knowledge graph construction is 90% based on expert judgments where as the average accuracy results from the effect boundary determination by Bayesian Network and Maximum entropy are 90% and 93%, respectively.
C1 [Pechsiri, Chaveevan] Dhurakij Pundit Univ, Dept Informat Technol, Bangkok, Thailand.
   [Piriyakul, Rapepun] Ramkumheang Univ, Dept Comp Sci, Bangkok, Thailand.
RP Pechsiri, C (通讯作者)，Dhurakij Pundit Univ, Dept Informat Technol, Bangkok, Thailand.
OI Pechsiri, Chaveevan/0000-0001-6133-2285
FU Thai Research Fund [MRG5280094]
FX Supported by the Thai Research Fund under Grant No. MRG5280094.
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NR 25
TC 16
Z9 16
U1 2
U2 45
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 SEP
PY 2010
VL 25
IS 5
BP 1055
EP 1070
DI 10.1007/s11390-010-9387-0
PG 16
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 651HH
UT WOS:000281918000014
DA 2022-08-23
ER

PT J
AU Meng, LC
   Wen, KH
   Brewin, R
   Wu, Q
AF Meng, Lingchao
   Wen, Kuo-Hsun
   Brewin, Richard
   Wu, Qiong
TI Knowledge Atlas on the Relationship between Urban Street Space and
   Residents' Health-A Bibliometric Analysis Based on VOSviewer and
   CiteSpace
SO SUSTAINABILITY
LA English
DT Article
DE urban street space; residents' health; knowledge map; quantitative
   analysis
ID OUTDOOR THERMAL COMFORT; GREEN SPACE; PHYSICAL-ACTIVITY; ASPECT RATIO;
   DESIGN; HEAT; ENVIRONMENTS; ORIENTATION; CENTRALITY; NETWORK
AB In order to draw and analyze the current status of the research on the relationship between international urban street space (public space, public open space) and residents' health, we explore the hotspots and frontiers of this research field and the overall evolution path from 1999 to 2019. This study uses 4552 related research articles included in the core database of Web of Science as the basis for data analysis, and uses the advantages of VOSviewer and CiteSpace's bibliometric visualization software to study countries, institutions, literature keywords, and literature co-citation networks, combined with the Alluvial Generator analysis of landmark literature. In this research area, the research content is constantly enriching, including urban street networks, health systems, green spaces, road traffic, thermal comfort, and vulnerable people. The research methods are also constantly enriching. By analyzing and sorting out the current research status, research hotspots and possible future trends in this field of research, the paper hopes to provide more reference for research implementation.
C1 [Meng, Lingchao] Northwest Minzu Univ, Sch Fine Arts, 1 Northwest New Village, Lanzhou 730030, Peoples R China.
   [Meng, Lingchao] Macau Univ Sci & Technol, Fac Humanities & Arts, Ave Wai Long, Taipa 999078, Macau, Peoples R China.
   [Wen, Kuo-Hsun] Macao polytech Inst, Sch Arts, Rua Luis Gonzaga Gomes, Macau 999078, Peoples R China.
   [Brewin, Richard] Beijing Normal Univ, 18 Jinfeng Rd, Zhuhai 519000, Peoples R China.
   [Wu, Qiong] City Univ Macau, Fac Business, Ave Padre Tomas Pereira, Taipa 999078, Macau, Peoples R China.
RP Meng, LC (通讯作者)，Northwest Minzu Univ, Sch Fine Arts, 1 Northwest New Village, Lanzhou 730030, Peoples R China.; Meng, LC (通讯作者)，Macau Univ Sci & Technol, Fac Humanities & Arts, Ave Wai Long, Taipa 999078, Macau, Peoples R China.; Wen, KH (通讯作者)，Macao polytech Inst, Sch Arts, Rua Luis Gonzaga Gomes, Macau 999078, Peoples R China.
EM lcmeng87@gmail.com; khwen@ipm.edu.mo
RI WEN, Kuohsun/AAH-3733-2020
OI WEN, Kuohsun/0000-0001-7441-8854
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NR 78
TC 15
Z9 16
U1 42
U2 105
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 2384
DI 10.3390/su12062384
PG 20
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA LA1YW
UT WOS:000523751400231
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Bell, C
   Simmons, A
   Martin, E
   McKenzie, C
   McLeod, J
   McCoombe, S
AF Bell, Colin
   Simmons, Annie
   Martin, Erik
   McKenzie, Candice
   McLeod, Janet
   McCoombe, Scott
TI Competent with patients and populations: integrating public health into
   a medical program
SO BMC MEDICAL EDUCATION
LA English
DT Article
ID EDUCATION; STUDENTS
AB BackgroundAs the global burden of chronic disease grows, and infectious disease threats loom large, the need for medical graduates with expertise in public health medicine (PHM) is growing. A recurrent challenge is integrating this broad knowledge into crowded medical curricula and making PHM relevant. This study describes the process of integrating public health content into an Australian graduate entry medical course.MethodsA redesign of the PHM curriculum at Deakin University School of Medicine was conducted in 2014 to make the curriculum practice-based and solution-oriented. Central to the redesign was the development of a curriculum map.ResultsPublic health is now taught from a practice-based framework adapted from the World Health Organization emphasizing skills aligned with the Australasian Faculty of Public Health Medicine domains that prepare students for specialisation. Learning outcomes are structured to build depth and application in student knowledge. Mapping the curriculum provided the ability to measure alignment of learning outcomes with course, university and accrediting body outcomes. Regular feedback from students indicates engagement has improved along with perceived relevance to future careers.ConclusionsDoctors with public health skills are increasingly sought after in Australia, particularly in rural areas. Deakin graduates are well placed to meet this demand.
C1 [Bell, Colin; Simmons, Annie; Martin, Erik; McKenzie, Candice; McLeod, Janet; McCoombe, Scott] Deakin Univ, Sch Med, Locked Bag 2000, Geelong, Vic 3220, Australia.
RP Bell, C (通讯作者)，Deakin Univ, Sch Med, Locked Bag 2000, Geelong, Vic 3220, Australia.
EM colin.bell@deakin.edu.au
RI Bell, Colin/X-9902-2019
OI Bell, Colin/0000-0003-2731-9858; McCoombe, Scott/0000-0001-6717-7511;
   McLeod, Janet/0000-0002-0027-0225
FU School of Medicine, Deakin University
FX This study was supported by the School of Medicine, Deakin University.
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TC 1
Z9 1
U1 1
U2 4
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1472-6920
J9 BMC MED EDUC
JI BMC Med. Educ.
PD MAY 31
PY 2019
VL 19
AR 179
DI 10.1186/s12909-019-1635-6
PG 9
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA IA7YM
UT WOS:000469774200002
PM 31151448
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Potnis, AV
   Durbha, SS
   Shinde, RC
AF Potnis, Abhishek V.
   Durbha, Surya S.
   Shinde, Rajat C.
TI Semantics-Driven Remote Sensing Scene Understanding Framework for
   Grounded Spatio-Contextual Scene Descriptions
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE remote sensing scene understanding; semantics-driven; grounded natural
   language scene descriptions; spatio-contextual; Scene Knowledge Graphs;
   flood ontology; semantic web; GeoSPARQL; Resource Description Framework
   (RDF); Semantic Web Rule Language (SWRL)
ID INFORMATION-RETRIEVAL; ENABLED FRAMEWORK; IMAGE ARCHIVES; PRINCIPLES;
   DISCOVERY
AB Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a Remote Sensing Scene Ontology (RSSO)-a core ontology for an inclusive remote-sensing-scene data product. The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes. The Sem-RSSU, at its core, constitutes novel Ontology-driven Spatio-Contextual Triple Aggregation and realization algorithms to transform KGs to render grounded natural language scene descriptions. Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework. In that regard, a contextual domain knowledge encompassing Flood Scene Ontology (FSO) has been developed. Extensive experimental evaluations show promising results, further validating the efficacy of this framework.
C1 [Potnis, Abhishek V.; Durbha, Surya S.; Shinde, Rajat C.] Indian Inst Technol, Ctr Studies Resources Engn, Mumbai 400076, Maharashtra, India.
RP Potnis, AV (通讯作者)，Indian Inst Technol, Ctr Studies Resources Engn, Mumbai 400076, Maharashtra, India.
EM abhishekvpotnis@iitb.ac.in; sdurbha@iitb.ac.in; rajatshinde@iitb.ac.in
RI Potnis, Abhishek/AAG-5180-2019
OI Potnis, Abhishek/0000-0001-8168-857X; Shinde, Rajat/0000-0002-9505-6204
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NR 42
TC 1
Z9 1
U1 14
U2 20
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2220-9964
J9 ISPRS INT J GEO-INF
JI ISPRS Int. J. Geo-Inf.
PD JAN
PY 2021
VL 10
IS 1
AR 32
DI 10.3390/ijgi10010032
PG 32
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA PV8XL
UT WOS:000610264100001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Munoz, E
   Novacek, V
   Vandenbussche, PY
AF Munoz, Emir
   Novacek, Vit
   Vandenbussche, Pierre-Yves
TI Facilitating prediction of adverse drug reactions by using knowledge
   graphs and multi-label learning models
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article
DE adverse drug reactions (ADR); drug similarity; knowledge graphs;
   multi-label learning
ID IDENTIFICATION; ATTRITION; PROFILES; NETWORKS; SAFETY
AB Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect on patient lives and also make drug development pipelines more robust and efficient. Reliable in silico prediction of ADRs can be helpful in this context, and thus, it has been intensely studied. Recent works achieved promising results using machine learning. The presented work focuses on machine learning methods that use drug profiles for making predictions and use features from multiple data sources. We argue that despite promising results, existing works have limitations, especially regarding flexibility in experimenting with different data sets and/or predictive models. We suggest to address these limitations by generalization of the key principles used by the state of the art. Namely, we explore effects of: (1) using knowledge graphs-machine-readable interlinked representations of biomedical knowledge-as a convenient uniform representation of heterogeneous data; and (2) casting ADR prediction as a multi-label ranking problem. We present a specific way of using knowledge graphs to generate different feature sets and demonstrate favourable performance of selected off-the-shelf multi-label learning models in comparison with existing works. Our experiments suggest better suitability of certain multi-label learning methods for applications where ranking is preferred. The presented approach can be easily extended to other feature sources or machine learning methods, making it flexible for experiments tuned toward specific requirements of end users. Our work also provides a clearly defined and reproducible baseline for any future related experiments.
C1 [Munoz, Emir] Natl Univ Ireland Galway, Insight Ctr Data Analyt, Galway, Ireland.
   [Munoz, Emir] Fujitsu Ireland Ltd, Insight Bldg,IDA Business Pk, Galway, Ireland.
   [Novacek, Vit] Insight Ctr Data Analyt, Galway, Ireland.
   [Vandenbussche, Pierre-Yves] Fujitsu Ireland, Knowledge Engn & Discovery Res Team, Galway, Ireland.
   [Vandenbussche, Pierre-Yves] Insight Ctr, Galway, Ireland.
RP Munoz, E (通讯作者)，Fujitsu Ireland Ltd, Insight Bldg,IDA Business Pk, Galway, Ireland.
EM emir.mu-noz@ie.fujitsu.com
RI Muñoz, Emir/J-5126-2017; Novacek, Vit/ABD-3387-2020; Vandenbussche,
   Pierre-Yves/G-1496-2019
OI Muñoz, Emir/0000-0002-0089-8135; Vandenbussche,
   Pierre-Yves/0000-0003-0591-6109
FU Fujitsu Laboratories Ltd., Japan; Insight Centre for Data Analytics at
   National University of Ireland Galway (Science Foundation Ireland (SFI))
   [SFI/12/RC/2289]
FX The TOMOE project funded by Fujitsu Laboratories Ltd., Japan and Insight
   Centre for Data Analytics at National University of Ireland Galway
   (supported by the Science Foundation Ireland (SFI) under Grant Number
   SFI/12/RC/2289).
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NR 55
TC 24
Z9 25
U1 4
U2 46
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1467-5463
EI 1477-4054
J9 BRIEF BIOINFORM
JI Brief. Bioinform.
PD JAN
PY 2019
VL 20
IS 1
BP 190
EP 202
DI 10.1093/bib/bbx099
PG 13
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA HI8WI
UT WOS:000456736200017
PM 28968655
OA Green Published
DA 2022-08-23
ER

PT J
AU Xiong, HB
   Wang, ST
   Tang, MR
   Wang, LP
   Lin, XM
AF Xiong, Haobo
   Wang, Shuting
   Tang, Mingrong
   Wang, Liping
   Lin, Xuemin
TI Knowledge Graph Question Answering with semantic oriented fusion model
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph question answering; Topic entity recognition; Path
   generation; Model fusion
AB Knowledge Graph Question Answering (KGQA) is a major branch of question answering tasks, which can answer fact questions effectively by using the reasonable characteristics of the knowledge graph. Currently, lots of related works combined with a variety of deep learning models are presented for the KGQA task. However, there are still some challenges, such as topic entity recognition under ambiguity expression, semantic level representation of natural language, efficient construction of searching space for answers, etc. In this paper, we propose a comprehensive approach for complex question answering over KG. Firstly, during the stage of topic entity recognition, a deep transition model is constructed to extract topic entities, and an efficient entity linking strategy is presented, which combines character matching and entity disambiguation model. Secondly, for candidate path ranking, a dynamic candidate path generation algorithm is proposed to efficiently create the candidate answer set. And four dedicated similarity calculation models are designed to handle the intricate condition of complex questions with long sequence and diversity expression. Moreover, a fusion policy is proposed to make decision for the final correct answer. We evaluate our approach on CKBQA, a Chinese knowledge base question answering dataset, from CCKS2019 competition. Experimental results demonstrate that the improvements in each process are effective and our approach achieves better performance than the best team in CCKS2019 competition. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Xiong, Haobo; Tang, Mingrong; Wang, Liping; Lin, Xuemin] East China Normal Univ, Shanghai Key Lab Trustworthy Comp, Shanghai, Peoples R China.
   [Wang, Shuting] Zhejiang Gongshang Univ, Sch Comp & Informat Engn, Hangzhou, Zhejiang, Peoples R China.
   [Lin, Xuemin] Univ New South Wales, Sydney, NSW, Australia.
RP Wang, LP (通讯作者)，East China Normal Univ, Shanghai Key Lab Trustworthy Comp, Shanghai, Peoples R China.
EM lipingwang@sei.ecnu.edu.cn
FU National Key R&D Program of China [2018AAA0102502]; GuangDong Basic and
   Applied Basic Research Foundation [2019B1515120048]
FX This work is supported by the National Key R&D Program of China under
   grant 2018AAA0102502 and the GuangDong Basic and Applied Basic Research
   Foundation 2019B1515120048.
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NR 35
TC 3
Z9 3
U1 5
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 JUN 7
PY 2021
VL 221
AR 106954
DI 10.1016/j.knosys.2021.106954
EA MAR 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RO9BB
UT WOS:000641333500008
DA 2022-08-23
ER

PT J
AU Li, WL
   Zhu, J
   Zhang, YH
   Fu, L
   Gong, YH
   Hu, Y
   Cao, YG
AF Li, Weilian
   Zhu, Jun
   Zhang, Yunhao
   Fu, Lin
   Gong, Yuhang
   Hu, Ya
   Cao, Yungang
TI An on-demand construction method of disaster scenes for multilevel users
SO NATURAL HAZARDS
LA English
DT Article
DE Disasters; Multilevel users; Knowledge graph; 3D scenes; On-demand
   construction
ID VIRTUAL GEOGRAPHIC ENVIRONMENT; EMERGENCY DECISION-MAKING; FLOOD HAZARD
   MAPS; NATURAL DISASTERS; RISK-ASSESSMENT; INFORMATION; MANAGEMENT; VGES;
   COMMUNICATION; OPTIMIZATION
AB Disaster scenes can effectively transmit disaster information and help people make sensible decisions. However, the current 3D scenes of disasters still have certain limitations. First, related studies have focused on the construction of 3D scene technology itself and lacked a detailed semantic description of the disaster scene, which is not conducive to standardizing the process of scene construction and supporting efficient analysis. Second, the 3D scene is generally fixed, preventing full consideration of the different needs of multilevel users involved in disaster management. This paper proposes an on-demand construction method of disaster scenes for multilevel users. The creation of a knowledge graph for disasters, calculation of semantic relevance and optimal selection of scene contents are discussed in detail. Finally, taking a debris flow disaster as an example, a prototype system is developed to implement experimental analysis. The experimental results show that the constructed knowledge graph can normalize the semantic relationships among multilevel users, scene objects and visualization methods in a formal way and accurately describe the different needs of multilevel users. The 3D scenes of debris flow disasters driven by the knowledge graph can reduce the complexity and difficulty of the modeling process while satisfying the diverse needs of multilevel users.
C1 [Li, Weilian; Zhu, Jun; Zhang, Yunhao; Fu, Lin; Hu, Ya; Cao, Yungang] Southwest Jiaotong Univ, Fac Geosc Ences & Environm Engn, Chengdu, Sichuan, Peoples R China.
   [Gong, Yuhang] Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China.
RP Cao, YG (通讯作者)，Southwest Jiaotong Univ, Fac Geosc Ences & Environm Engn, Chengdu, Sichuan, Peoples R China.
EM yungang@swjtu.cn
RI Cao, Yungang/GOK-0565-2022
FU National Key Research and Development Program of China [2016YFC0803105];
   National Natural Science Foundation of China [41801297, 41871289];
   Fundamental Research Funds for the Central Universities [2682018CX35];
   Doctoral Innovation Fund Program of Southwest Jiaotong University
FX This paper was supported by the National Key Research and Development
   Program of China (Grant No. 2016YFC0803105), the National Natural
   Science Foundation of China (Grant Nos. 41801297 and 41871289), the
   Fundamental Research Funds for the Central Universities (Grant No.
   2682018CX35), and the Doctoral Innovation Fund Program of Southwest
   Jiaotong University.
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NR 54
TC 9
Z9 9
U1 8
U2 38
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 MAR
PY 2020
VL 101
IS 2
BP 409
EP 428
DI 10.1007/s11069-020-03879-z
EA FEB 2020
PG 20
WC Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences;
   Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Geology; Meteorology & Atmospheric Sciences; Water Resources
GA KW4ZI
UT WOS:000516429800001
DA 2022-08-23
ER

PT J
AU Malas, TB
   Vlietstra, WJ
   Kudrin, R
   Starikov, S
   Charrout, M
   Roos, M
   Peters, DJM
   Kors, JA
   Vos, R
   't Hoen, PAC
   van Mulligen, EM
   Hettne, KM
AF Malas, Tareq B.
   Vlietstra, Wytze J.
   Kudrin, Roman
   Starikov, Sergey
   Charrout, Mohammed
   Roos, Marco
   Peters, Dorien J. M.
   Kors, Jan A.
   Vos, Rein
   't Hoen, Peter A. C.
   van Mulligen, Erik M.
   Hettne, Kristina M.
TI Drug prioritization using the semantic properties of a knowledge graph
SO SCIENTIFIC REPORTS
LA English
DT Article
ID POLYCYSTIC KIDNEY-DISEASE; PROLIFERATION; GROWTH
AB Compounds that are candidates for drug repurposing can be ranked by leveraging knowledge available in the biomedical literature and databases. This knowledge, spread across a variety of sources, can be integrated within a knowledge graph, which thereby comprehensively describes known relationships between biomedical concepts, such as drugs, diseases, genes, etc. Our work uses the semantic information between drug and disease concepts as features, which are extracted from an existing knowledge graph that integrates 200 different biological knowledge sources. RepoDB, a standard drug repurposing database which describes drug-disease combinations that were approved or that failed in clinical trials, is used to train a random forest classifier. The 10-times repeated 10-fold cross-validation performance of the classifier achieves a mean area under the receiver operating characteristic curve (AUC) of 92.2%. We apply the classifier to prioritize 21 preclinical drug repurposing candidates that have been suggested for Autosomal Dominant Polycystic Kidney Disease (ADPKD). Mozavaptan, a vasopressin V2 receptor antagonist is predicted to be the drug most likely to be approved after a clinical trial, and belongs to the same drug class as tolvaptan, the only treatment for ADPKD that is currently approved. We conclude that semantic properties of concepts in a knowledge graph can be exploited to prioritize drug repurposing candidates for testing in clinical trials.
C1 [Malas, Tareq B.; Charrout, Mohammed; Roos, Marco; Peters, Dorien J. M.; 't Hoen, Peter A. C.; Hettne, Kristina M.] Leiden Univ, Med Ctr, Dept Human Genet, NL-2300 RC Leiden, Netherlands.
   [Vlietstra, Wytze J.; Kors, Jan A.; Vos, Rein; van Mulligen, Erik M.] Erasmus MC, Univ Med Ctr Rotterdam, Dept Med Informat, NL-3000 CA Rotterdam, Netherlands.
   [Kudrin, Roman; Starikov, Sergey] Moscow MV Lomonosov State Univ, Fac Bioengn & Bioinformat, Moscow 119234, Russia.
   [Vos, Rein] Maastricht Univ, Dept Methodol & Stat, NL-6200 MD Maastricht, Netherlands.
   ['t Hoen, Peter A. C.] Radboud Univ Nijmegen, Med Ctr, Radboud Inst Mol Life Sci, Ctr Mol & Biomol Informat, NL-6500 HB Nijmegen, Netherlands.
RP Hettne, KM (通讯作者)，Leiden Univ, Med Ctr, Dept Human Genet, NL-2300 RC Leiden, Netherlands.
EM k.m.hettne.2@umail.leidenuniv.nl
RI Peters, Dorien/AFH-8507-2022; Hoen, Peter A 't/D-3950-2018; Hettne,
   Kristina/J-6252-2014
OI Peters, Dorien/0000-0001-6974-7143; Hoen, Peter A
   't/0000-0003-4450-3112; Hettne, Kristina/0000-0002-4182-7560; Vlietstra,
   Wytze/0000-0002-1096-8563
FU European Community [305444]; People Program (Marie Curie Actions) under
   Research Executive Agency [317246]
FX We acknowledge the support by the European Community's Seventh Framework
   Programme (FP7/2007-2013) under grant agreement 305444 'RD-Connect' and
   People Program (Marie Curie Actions) under Research Executive Agency
   Grant Agreement 317246 'TranCYST'.
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NR 30
TC 14
Z9 14
U1 0
U2 11
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 APR 18
PY 2019
VL 9
AR 6281
DI 10.1038/s41598-019-42806-6
PG 10
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA HU0ZQ
UT WOS:000465001600035
PM 31000794
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Liu, DR
   Lai, CH
AF Liu, Duen-Ren
   Lai, Chin-Hui
TI Mining group-based knowledge flows for sharing task knowledge
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE Knowledge flow; Group-based knowledge flow; Knowledge graph; Knowledge
   sharing; Data mining; Topic; Task
ID DISCOVERY; WORKFLOW; MODEL
AB In an organization, knowledge is the most important resource in the creation of core competitive advantages. It is circulated and accumulated by knowledge flows (KFs) in the organization to support workers' task needs. Because workers accumulate knowledge of different domains, they may cooperate and participate in several task-based groups to satisfy their needs. In this paper, we propose algorithms that integrate information retrieval and data mining techniques to mine and construct group-based KFs (GKFs) for task-based groups. A GKF is expressed as a directed knowledge graph which represents the knowledge referencing behavior, or knowledge flow, of a group of workers with similar task needs. Task-related knowledge topics and their relationships (flows) can be identified from the knowledge graph so as to fulfill workers' task needs and promote knowledge sharing for collaboration of group members. Moreover, the frequent knowledge referencing path can be identified from the knowledge graph to indicate the frequent knowledge flow of the workers. To demonstrate the efficacy of the proposed methods, we implement a prototype of the GKF mining system. Our GKF mining methods can enhance organizational learning and facilitate knowledge management, sharing, and reuse in an environment where collaboration and teamwork are essential. (C) 2010 Elsevier B.V. All rights reserved.
C1 [Liu, Duen-Ren; Lai, Chin-Hui] Natl Chiao Tung Univ, Inst Informat Management, Hsinchu, Taiwan.
RP Liu, DR (通讯作者)，Natl Chiao Tung Univ, Inst Informat Management, Hsinchu, Taiwan.
EM dliu@mail.nctu.edu.tw
FU National Science Council of the Taiwan [NSC 96-2416-H-009-007-MY3]
FX This research was supported by the National Science Council of the
   Taiwan under grant NSC 96-2416-H-009-007-MY3.
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NR 41
TC 19
Z9 23
U1 5
U2 44
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 370
EP 386
DI 10.1016/j.dss.2010.09.004
PG 17
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 714ZX
UT WOS:000286851300002
DA 2022-08-23
ER

PT J
AU Bellomarini, L
   Sallinger, E
   Gottlob, G
AF Bellomarini, Luigi
   Sallinger, Emanuel
   Gottlob, Georg
TI The Vadalog System: Datalog-based Reasoning for Knowledge Graphs
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
AB Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowledge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog +/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford's contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog +/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation.
C1 [Bellomarini, Luigi; Sallinger, Emanuel; Gottlob, Georg] Univ Oxford, Oxford, England.
   [Bellomarini, Luigi] Banca Italia, Rome, Italy.
   [Bellomarini, Luigi] Univ Roma Tre, Rome, Italy.
   [Gottlob, Georg] TU Wien, Vienna, Austria.
RP Bellomarini, L (通讯作者)，Univ Oxford, Oxford, England.; Bellomarini, L (通讯作者)，Banca Italia, Rome, Italy.; Bellomarini, L (通讯作者)，Univ Roma Tre, Rome, Italy.
FU EPSRC [EP/M025268/1 VADA]; EPSRC [EP/M025268/1] Funding Source: UKRI
FX The work on this paper was supported by EPSRC programme grant
   EP/M025268/1 VADA. The Vadalog system as presented here is IP of the
   University of Oxford.
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NR 35
TC 35
Z9 35
U1 3
U2 9
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD MAY
PY 2018
VL 11
IS 9
BP 975
EP 987
DI 10.14778/3213880.3213888
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HD4XX
UT WOS:000452532800005
DA 2022-08-23
ER

PT J
AU Zhang, YQ
   Yao, QM
   Chen, L
AF Zhang, Yongqi
   Yao, Quanming
   Chen, Lei
TI Simple and automated negative sampling for knowledge graph embedding
SO VLDB JOURNAL
LA English
DT Article
DE Knowledge Graph; Graph Embedding; Negative Sampling; Automated Machine
   Learning
AB Negative sampling, which samples negative triplets from non-observed ones in knowledge graph (KG), is an essential step in KG embedding. Recently, generative adversarial network (GAN) has been introduced in negative sampling. By sampling negative triplets with large gradients, these methods avoid the problem of vanishing gradient and thus obtain better performance. However, they make the original model more complex and harder to train. In this paper, motivated by the observation that negative triplets with large gradients are important but rare, we propose to directly keep track of them with the cache. In this way, our method acts as a "distilled" version of previous GAN-based methods, which does not waste training time on additional parameters to fit the full distribution of negative triplets. However, how to sample from and update the cache are two critical questions. We propose to solve these issues by automated machine learning techniques. The automated version also covers GAN-based methods as special cases. Theoretical explanation of NSCaching is also provided, justifying the superior over fixed sampling scheme. Besides, we further extend NSCaching with skip-gram model for graph embedding. Finally, extensive experiments show that our method can gain significant improvements on various KG embedding models and the skip-gram model and outperforms the state-of-the-art negative sampling methods.
C1 [Zhang, Yongqi; Yao, Quanming] 4Paradigm Inc, Beijing, Peoples R China.
   [Zhang, Yongqi] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China.
   [Chen, Lei] Hong Kong Univ & Technol, Dept Sci & Engn, Hong Kong, Peoples R China.
RP Yao, QM (通讯作者)，4Paradigm Inc, Beijing, Peoples R China.
EM zhangyongqi@4paradigm.com; yaoquanming@4paradigm.com; leichen@cse.ust.hk
OI Yao, Quanming/0000-0001-8944-8618
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NR 69
TC 0
Z9 0
U1 0
U2 13
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 2021
VL 30
IS 2
BP 259
EP 285
DI 10.1007/s00778-020-00640-7
EA JAN 2021
PG 27
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QZ5JS
UT WOS:000612582900001
DA 2022-08-23
ER

PT J
AU Lv, P
   Wang, XX
   Xu, J
   Wang, JB
AF Lv, Pin
   Wang, Xiaoxin
   Xu, Jia
   Wang, Junbin
TI Intelligent personalised exercise recommendation: A weighted knowledge
   graph-based approach
SO COMPUTER APPLICATIONS IN ENGINEERING EDUCATION
LA English
DT Article
DE competence to knowledge point; intelligent tutoring system; knowledge
   graph; personalised recommendation; student&#8208; testing behaviour
AB As a critical function for intelligent tutoring system services, personalised exercise recommendation plays an important role in boosting the study performance of students. However, recent studies on personalised exercise recommendations have only considered the ability of a student during recommendation and have failed to include the essential relationships between knowledge points, which provide a suitable learning sequence of these knowledge points during a study procedure. In this study, we propose an intelligent exercise recommendation method (weighted knowledge graph-based recommendation [WKG-R]) for students, based on weighted knowledge graphs, wherein each node represents a knowledge point weighted by the ability of a student and an arrowed edge between two knowledge points indicates their prerequisite relationship. The novelty of WKG-R can be summarised as follows: (1) It makes a leading attempt to quantify the ability of a student based on various testing behaviours and (2) it attempts to employ the ability of a student and the prerequisite dependencies between knowledge points for enhancing the effectiveness of personalised exercise recommendation. A real classroom teaching practice was conducted to evaluate the effectiveness of the proposed WKG-R method. The experimental results demonstrated the distinct advantage of WKG-R in improving the testing scores of students as compared with contemporary solutions. The average score improvement ratio of students for WKG-R is up to 33%, whereas for the state-of-the-art solution, it is only 22%. The questionnaires collected from the students also reflected a higher level of satisfaction towards WKG-R than with the contemporary solutions.
C1 [Lv, Pin; Wang, Xiaoxin; Xu, Jia; Wang, Junbin] Guangxi Univ, Sch Comp Elect & Informat, Dept Cpmputer Sci & Technol, Nanning, Peoples R China.
   [Lv, Pin; Xu, Jia] Guangxi Key Lab Multimedia Commun & Network Techn, Nanning, Peoples R China.
   [Wang, Xiaoxin] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, MIIT Key Lab Pattern Anal & Machine Intelligence, Nanjing, Peoples R China.
RP Xu, J (通讯作者)，Guangxi Univ, Sch Comp Elect & Informat, Nanning, Peoples R China.
EM xujia@gxu.edu.cn
FU National Natural Science Foundation of China [62067001]; Higher
   Education Undergraduate Teaching Reform Project in Guangxi [2020JGA116,
   2017JGZ103, 2018JGA106, 2019JGB106]; Natural Science Foundation of
   Guangxi Province [2019JJA170045, 2018JJA170028]
FX National Natural Science Foundation of China, Grant/Award Number:
   62067001; Higher Education Undergraduate Teaching Reform Project in
   Guangxi, Grant/Award Numbers: 2020JGA116, 2017JGZ103, 2018JGA106,
   2019JGB106; Natural Science Foundation of Guangxi Province, Grant/Award
   Numbers: 2019JJA170045, 2018JJA170028; Guangxi BaGui Scholars
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NR 40
TC 1
Z9 1
U1 19
U2 42
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1061-3773
EI 1099-0542
J9 COMPUT APPL ENG EDUC
JI Comput. Appl. Eng. Educ.
PD SEP
PY 2021
VL 29
IS 5
BP 1403
EP 1419
DI 10.1002/cae.22395
EA FEB 2021
PG 17
WC Computer Science, Interdisciplinary Applications; Education, Scientific
   Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research; Engineering
GA UP2LU
UT WOS:000617901500001
DA 2022-08-23
ER

PT J
AU Norton, SB
   Schofield, KA
AF Norton, Susan B.
   Schofield, Kate A.
TI Conceptual model diagrams as evidence scaffolds for environmental
   assessment and management
SO FRESHWATER SCIENCE
LA English
DT Article
DE conceptual models; evidence; environmental assessment; evidence
   synthesis; literature-based; mountaintop; removal mining.
ID KNOWLEDGE MAPS; ECOLOGICAL RISK; DISPLAYS; OHIO
AB Sound environmental management relies on scientific evidence to indicate whether action is warranted and, if so, which actions will be most effective. We discuss conceptual model diagrams that schematically describe how sources or human activities lead to specific stressors and ultimately to biotic effects. They are useful for describing how undesirable effects are produced and identifying where management actions may be most effective in preventing unwanted effects or improving existing conditions. We illustrate the use of conceptual model diagrams in a literature -based assessment of the impacts of mountaintop removal mining on downstream aquatic systems. The diagram-development process combined extraction and evaluation of research results with iterative refinement of diagrams depicting source-to-stressor-to-effect causal pathways. Conceptual -model diagrams provided a useful scaffold for organizing and synthesizing multiple individual pieces of evidence extracted from different studies and an effective way to summarize and present the evidentiary foundation of the assessment's conclusions. Key words: conceptual models, evidence, environmental assessment, evidence synthesis, literature -based, mountaintop removal mining.
C1 [Norton, Susan B.; Schofield, Kate A.] US EPA, Off Res & Dev, Natl Ctr Environm Assessment, Washington, DC 20460 USA.
RP Norton, SB (通讯作者)，US EPA, Off Res & Dev, Natl Ctr Environm Assessment, Washington, DC 20460 USA.
EM norton.susan@epa.gov; schofield.kate@epa.gov
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NR 39
TC 9
Z9 9
U1 0
U2 8
PU UNIV CHICAGO PRESS
PI CHICAGO
PA 1427 E 60TH ST, CHICAGO, IL 60637-2954 USA
SN 2161-9549
EI 2161-9565
J9 FRESHW SCI
JI Freshw. Sci.
PD MAR
PY 2017
VL 36
IS 1
BP 231
EP 239
DI 10.1086/690296
PG 9
WC Ecology; Marine & Freshwater Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Marine & Freshwater Biology
GA EL5AJ
UT WOS:000394633500019
DA 2022-08-23
ER

PT J
AU Liu, XW
   Liu, HJ
AF Liu, Xiaowei
   Liu, Hongjin
TI Design and Application of English Grammar Intelligent Question Bank
   System
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB The English grammar question bank can reserve a large number of special knowledge points and special categories of questions, which is helpful for English learners. Therefore, we design and verify the application effect of an intelligent question bank system. We construct an English grammar knowledge map, which is used to design the system architecture, including basic layer, data layer, service layer, business layer, and user layer. On the basis of the system structure, the index system of the item bank is established, and the functional modules of the system are designed comprehensively. Finally, we design a system database based on MySQL and give the table of knowledge points of English grammar. In order to verify the performance of the designed English grammar intelligent test bank system, a verification experiment is carried out. The experiment selects the accuracy rate of English grammatical feature extraction, recall rate, convergence, and system configuration accuracy. The experimental results show that the proposed system can extract English grammatical features with higher accuracy, and the recall rate is more satisfactory. Compared with the reference systems, the designed system configuration accuracy is higher.
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RP Liu, XW (通讯作者)，Wuxi Vocat Coll Sci & Technol, Sch Culture & Tourism, Wuxi 214000, Peoples R China.
EM 3101015@wxsc.edu.cn; wangxing.114@163.com
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NR 18
TC 0
Z9 0
U1 10
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 OCT 29
PY 2021
VL 2021
AR 9483719
DI 10.1155/2021/9483719
PG 10
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XM9ZN
UT WOS:000729175400001
OA gold
DA 2022-08-23
ER

PT J
AU Jahara, RD
   Lins, MPE
AF Jahara, Rafael da Costa
   Estellita Lins, Marcos Pereira
TI Multimethodology for diagnosis and intervention in a prosthetics and
   orthotics factory in Brazil
SO INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
LA English
DT Article
DE multimethodology; hybrid modeling; health care; discrete-event
   simulation; concept maps
ID HEALTH-CARE; SYSTEM DYNAMICS; SIMULATION; FRAMEWORK; SOFT; METHODOLOGY;
   MANAGEMENT; MODELS; HARD
AB This paper develops an approach that articulates methods characterized as soft and hard Operational Research. This approach is known in the literature as Multimethodology, Hybrid Modeling, or Mixing Methods. As a Multi-Paradigm Hybrid Study, the present research provides an interface between the qualitative and quantitative modeling of the problem addressed. In this way, it facilitates the engagement of managers with analysts in the search for solutions to problems. The applied objective is to improve the performance of production processes at a prosthetics and orthotics factory located in Rio de Janeiro, Brazil. It applies a multimethodology to support evaluation and intervention using knowledge mapping and a discrete-event simulation model. Lessons learned from integrated workshops enabled the devising of an alternative improvement scenario for orthotics manufacturing. Theoretical results are validated through effective intervention, providing an increase in production capacity and reduction of the manufacturing lead time. This was made possible through the understanding and engagement of stakeholders during the modeling process.
C1 [Jahara, Rafael da Costa; Estellita Lins, Marcos Pereira] Fed Univ Rio de Janeiro UFRJ, Prod Engn Program, COPPE, Rio De Janeiro, Brazil.
   [Estellita Lins, Marcos Pereira] Fed Univ Rio de Janeiro UFRJ, Prod Engn Dept, CCET, Rio De Janeiro, Brazil.
RP Jahara, RD (通讯作者)，Fed Univ Rio de Janeiro UFRJ, Prod Engn Program, COPPE, Rio De Janeiro, Brazil.
EM rdcjahara@pep.ufrj.br; marcos.estellita.lins@gmail.com
RI Lins, Marcos Estellita/AAF-1092-2019
OI Lins, Marcos Estellita/0000-0003-1980-0764
FU CNPq [303346/2017-5]
FX The second author gratefully acknowledges the grant from CNPq
   303346/2017-5 in support to this study.
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NR 44
TC 0
Z9 0
U1 3
U2 3
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0969-6016
EI 1475-3995
J9 INT T OPER RES
JI Int. Trans. Oper. Res.
PD JAN
PY 2023
VL 30
IS 1
SI SI
BP 619
EP 639
DI 10.1111/itor.13074
EA OCT 2021
PG 21
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 3M5IG
UT WOS:000711739700001
DA 2022-08-23
ER

PT J
AU Liu, HY
   Ma, RZ
   Li, DY
   Yan, L
   Ma, ZM
AF Liu, Haiying
   Ma, Ruizhe
   Li, Daiyi
   Yan, Li
   Ma, Zongmin
TI Machinery Fault Diagnosis Based on Deep Learning for Time Series
   Analysis and Knowledge Graphs
SO JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO
   TECHNOLOGY
LA English
DT Article
DE Fault diagnosis; Rolling bearing; Time series; CNN; GRU; Attention
   mechanism; Knowledge graph
ID DECOMPOSITION; ONTOLOGY; NETWORK; CNN
AB With the rapid development of modern industrial production, mechanical equipment plays an increasingly important role, and its fault diagnosis is therefore critical. Traditional fault diagnosis generally needs a manual selection of features and a large amount of labeled data for training. In addition, diagnosis results are often too isolated and cannot provide a complete fault diagnosis process. In this paper, we propose a fault diagnosis approach for mechanical equipment, which combines one-dimensional CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit), attention mechanism, and KG (knowledge graph). We first construct an ATT-1D CNN-GRU model by the serial merger of the one-dimensional CNN and GRU. The CNN model is used for feature extraction, and GRU and attention mechanism are applied for more accurate feature extraction. To prevent overfitting, we add the BN (Batch Normalization) before each pooling layer input. Then, we train the ATT-1D CNN-GRU model according to the existing fault data and obtain a precise prediction of the fault diagnosis results. Furthermore, we match the prediction results by searching through the knowledge graph and obtaining more relevant information about this fault. We verify our attention-based 1D CNN-GRU model over rolling bearing datasets under different loads. The results show that the accuracy of our proposed model is up to 99%.
C1 [Liu, Haiying; Li, Daiyi; Yan, Li; Ma, Zongmin] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China.
   [Ma, Ruizhe] Univ Massachusetts, Dept Comp Sci, Lowell, MA 01854 USA.
RP Ma, RZ (通讯作者)，Univ Massachusetts, Dept Comp Sci, Lowell, MA 01854 USA.
EM ruizhe_ma@uml.edu
OI Ma, Ruizhe/0000-0003-2749-3063
FU National Natural Science Foundation of China [U1931207]
FX The authors wish to thank the anonymous referees for their valuable
   comments and suggestions, which greatly improved the technical content
   and the presentation of the paper. The work was supported in part by the
   National Natural Science Foundation of China (U1931207).
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NR 50
TC 4
Z9 4
U1 38
U2 61
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 DEC
PY 2021
VL 93
IS 12
BP 1433
EP 1455
DI 10.1007/s11265-021-01718-3
EA NOV 2021
PG 23
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA XN9FY
UT WOS:000719264700001
DA 2022-08-23
ER

PT J
AU Hossayni, H
   Khan, I
   Aazam, M
   Taleghani-Isfahani, A
   Crespi, N
AF Hossayni, Hicham
   Khan, Imran
   Aazam, Mohammad
   Taleghani-Isfahani, Amin
   Crespi, Noel
TI SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic
   Knowledge Graphs
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE failure diagnostics; industry 4; 0; industrial internet of things
   (IIoT); knowledge graph; machine maintenance; semantic web
ID SYSTEM; WEB
AB The recent focus on sustainability and improved efficiency requires innovative approaches in industrial automation. We present SemKoRe, a knowledge graph developed to improve machine maintenance in the industrial domain. SemKoRe is vendor-agnostic, it helps Original Equipment Manufacturers (OEMs) to capture, share and exploit the failure knowledge generated by their customers machines located around the world. Based on our interactions with actual customers, it usually takes several hours to days to fix a machine-related issue. During this time, production stops and incurs cost in terms of lost production. SemKoRe significantly enhances the maintenance process by reducing the failure diagnostic time, and by centralizing machine maintenance knowledge fed by the experts and technicians around the world. We developed flexible architecture to cover our customers' varying needs, along with failure and machine domain ontologies. To demonstrate the feasibility of SemKoRe, a proof-of-concept is developed. SemKoRe gathers all failure related data in the knowledge graph, and shares it among all connected customers in order to easily solve future failures of the same type. SemKoRe received the approval of several substantial clients located in USA, UK, France, Germany, Italy and China, associated with various segments such as pharmaceutical, automotive, HVAC and food and beverage.
C1 [Hossayni, Hicham; Khan, Imran; Taleghani-Isfahani, Amin] Schneider Elect, F-38000 Grenoble, France.
   [Aazam, Mohammad] Univ Nottingham, Sch Comp Sci, Semenyih 43500, Malaysia.
   [Crespi, Noel] Telecom SudParis, Inst Polytech Paris, IMT, F-91011 Evry, France.
RP Hossayni, H (通讯作者)，Schneider Elect, F-38000 Grenoble, France.
EM hicham.hossayni@se.com; imran2.khan@se.com; aazam@ieee.org;
   amin.taleghani@se.com; noel.crespi@it-sudparis.eu
RI Crespi, Noel/ABE-7052-2020; Aazam, Mohammad/T-3898-2017
OI Crespi, Noel/0000-0003-2962-192X; Hossayni, Hicham/0000-0002-9097-2895;
   Khan, Imran/0000-0001-5415-1785; Aazam, Mohammad/0000-0002-5473-6684
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PU MDPI
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PD SEP
PY 2020
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IS 18
AR 6325
DI 10.3390/app10186325
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WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
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WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA OE2MJ
UT WOS:000580370900001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Shao, BL
   Li, XJ
   Bian, GQ
AF Shao, Bilin
   Li, Xiaojun
   Bian, Genqing
TI A survey of research hotspots and frontier trends of recommendation
   systems from the perspective of knowledge graph
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Review
DE Recommendation system; Knowledge graph; Collaborative filtering;
   Personalization; User modeling
ID COLD-START PROBLEM; OF-THE-ART; DIVERSITY
AB With the advent of the era of big data, the recommendation system has become an effective solution to the problem of information overload. This paper takes the literature data related to the recommendation system theme from 2009 to 2018 and included in the core collection of Web of Science database as the research object, and utilizes bibliometric methods to analyze the theme of recommendation system. To this end, firstly, classify statistics and feature analysis of valid literature data. Secondly, use VOSviewer software to construct various different scientific knowledge graph to discover valuable knowledge. Thirdly, according to keyword co-concurrence graph conclude five main hotspots of current research about recommendation system and discover five main directions that have potential value in research field of recommendation system. Finally, deeply explore five main key issues and propose corresponding solutions. (C) 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
C1 [Shao, Bilin; Li, Xiaojun; Bian, Genqing] Xian Univ Architecture & Technol, Sch Management, Xian 710055, Shaanxi, Peoples R China.
   [Bian, Genqing] Xian Univ Architecture & Technol, Sch Informat & Control Engn, Xian 710055, Shaanxi, Peoples R China.
RP Li, XJ (通讯作者)，Xian Univ Architecture & Technol, Sch Management, Xian 710055, Shaanxi, Peoples R China.
EM sblin0462@sina.com; xiaojunlee@foxmail.com; bgq_003@163.com
OI LI, Xiaojun/0000-0002-6275-8808
FU National Natural Science Foundation of China [61672416, 61872284]
FX This work is supported in part by the National Natural Science
   Foundation of China under Grant Nos. 61672416 and 61872284.
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NR 116
TC 16
Z9 16
U1 37
U2 115
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 2021
VL 165
AR 113764
DI 10.1016/j.eswa.2020.113764
PG 20
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 PT2WR
UT WOS:000608479600005
OA hybrid
DA 2022-08-23
ER

PT J
AU Neumaier, S
   Polleres, A
AF Neumaier, Sebastian
   Polleres, Axel
TI Enabling Spatio-Temporal Search in Open Data
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Open data; Spatio-temporal labelling; Spatio-temporal knowledge graph
AB Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet - to the best of our knowledge - no working solution exists. To this end, we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at data.wu.ac.at/odgraphsearch/. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Neumaier, Sebastian; Polleres, Axel] Vienna Univ Econ & Business, Vienna, Austria.
   [Neumaier, Sebastian] Vienna Univ Technol, Vienna, Austria.
   [Polleres, Axel] Complex Sci Hub Vienna, Vienna, Austria.
   [Polleres, Axel] Stanford Univ, Stanford, CA 94305 USA.
RP Neumaier, S (通讯作者)，Vienna Univ Econ & Business, Vienna, Austria.; Neumaier, S (通讯作者)，Vienna Univ Technol, Vienna, Austria.
EM sebastian.neumaier@wu.ac.at; axel.polleres@wu.ac.at
OI Neumaier, Sebastian/0000-0002-9804-4882
FU Austrian Federal Ministry of Transport, Innovation and Technology
   (BMVIT) through the program "ICT of the Future'' by the Austrian
   Research Promotion Agency (FFG) under the project CommuniData;
   Distinguished Visiting Austrian Chair Professors program
FX Sebastian Neumaier's work was funded by the Austrian Federal Ministry of
   Transport, Innovation and Technology (BMVIT) through the program "ICT of
   the Future'' by the Austrian Research Promotion Agency (FFG) under the
   project CommuniData.; Axel Polleres' work was supported under the
   Distinguished Visiting Austrian Chair Professors program hosted by The
   Europe Center of Stanford University, United States.
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NR 35
TC 7
Z9 7
U1 0
U2 7
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 2019
VL 55
BP 21
EP 36
DI 10.1016/j.websem.2018.12.007
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 HQ1OB
UT WOS:000462166300002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU He, Y
   Tian, L
   Zhang, LZ
   Zeng, X
AF He, Yang
   Tian, Ling
   Zhang, Lizong
   Zeng, Xi
TI Knowledge Graph Representation Fusion Framework for Fine-Grained Object
   Recognition in Smart Cities
SO COMPLEXITY
LA English
DT Article
AB Autonomous object detection powered by cutting-edge artificial intelligent techniques has been an essential component for sustaining complex smart city systems. Fine-grained image classification focuses on recognizing subcategories of specific levels of images. As a result of the high similarity between images in the same category and the high dissimilarity in the same subcategories, it has always been a challenging problem in computer vision. Traditional approaches usually rely on exploring only the visual information in images. Therefore, this paper proposes a novel Knowledge Graph Representation Fusion (KGRF) framework to introduce prior knowledge into fine-grained image classification task. Specifically, the Graph Attention Network (GAT) is employed to learn the knowledge representation from the constructed knowledge graph modeling the categories-subcategories and subcategories-attributes associations. By introducing the Multimodal Compact Bilinear (MCB) module, the framework can fully integrate the knowledge representation and visual features for learning the high-level image features. Extensive experiments on the Caltech-UCSD Birds-200-2011 dataset verify the superiority of our proposed framework over several existing state-of-the-art methods.
C1 [He, Yang; Tian, Ling; Zhang, Lizong] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China.
   [Tian, Ling; Zhang, Lizong] Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 610000, Peoples R China.
   [Zeng, Xi] China Elect Technol Cyber Secur Co Ltd, Chengdu, Peoples R China.
RP Tian, L (通讯作者)，Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China.; Tian, L (通讯作者)，Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 610000, Peoples R China.
EM yhe@std.uestc.edu.cn; lingtian@uestc.edu.cn; l.zhang@uestc.edu.cn;
   xizeng6@163.com
RI Zhang, Lizong/AAU-5095-2021
OI Zhang, Lizong/0000-0002-0719-9556; He, Yang/0000-0001-8740-876X
FU Sichuan Science and Technology Program [2019YFG0507, 2020YFG0328];
   National Natural Science Foundation of China (NSFC) [U19A2059]; Young
   Scientists Fund of the National Natural Science Foundation of China
   [61802050]
FX This research work was partly supported by Sichuan Science and
   Technology Program (2019YFG0507 and 2020YFG0328), the National Natural
   Science Foundation of China (NSFC) (U19A2059), and Young Scientists Fund
   of the National Natural Science Foundation of China (61802050).
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NR 60
TC 0
Z9 0
U1 8
U2 14
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 JUL 14
PY 2021
VL 2021
AR 8041029
DI 10.1155/2021/8041029
PG 9
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA TT7ID
UT WOS:000680517400009
OA gold
DA 2022-08-23
ER

PT J
AU Wang, HR
   Su, M
   Li, WJ
   Han, M
AF Wang, Hairong
   Su, Meng
   Li, Weijun
   Han, Meng
TI AN APPROACH FOR APPROXIMATE SUBGRAPH MATCHING IN DOMAIN KNOWLEDGE GRAPH
SO JOURNAL OF NONLINEAR AND CONVEX ANALYSIS
LA English
DT Article; Proceedings Paper
CT 5th International Conference on Fuzzy Systems and Data Mining (FSDM)
   held in conjunction with the 9th International Conference on
   Electronics, Communications and Networks (CECNet)
CY OCT 18-21, 2019
CL Kitakyushu, JAPAN
DE Subgraph matching; node filtering; matching cost; sorting mechanism
ID QUERIES
AB To solve the validity problem of knowledge acquisition in vertical field, a query method of approximate subgraph matching based on knowledge graph is proposed in this paper. Firstly, the node filtering rules are defined by using the structural information and semantic information of the nodes in the knowledge graph to filter the noise data and form the candidate node set of the query graph. Then, the matching costs of query nodes and candidate nodes are computed, and the node weights are set to weight the matching costs of neighbor vectors, which makes the matching costs of the nodes better reflect structural properties of query graph nodes. Finally, the sorting mechanism is called to further deal with the query results and return the first k query results which satisfies the user's query intention. In order to verify the method proposed in this paper, we built an experiment platform and conducted many experiments with different algorithms and on general search engines, of which the results show the effectiveness and availability of our method.
C1 [Wang, Hairong; Su, Meng; Li, Weijun; Han, Meng] North Minzu Univ, Sch Comp Sci & Engn, Yinchuan 750021, Ningxia, Peoples R China.
RP Su, M (通讯作者)，North Minzu Univ, Sch Comp Sci & Engn, Yinchuan 750021, Ningxia, Peoples R China.
EM bmdwhr@163.com; 1290094552@qq.com; liweijun51@163.com;
   2003051@nun.edu.cn
FU Ningxia key research and development program (Special Talents)
   [2018BEB04002]; Ningxia first-class discipline and scientific research
   projects (electronic science and technology) [NXYLXK2017A07]; North
   Minzu University Graduate Innovation Project [YCX18050]
FX This work has been support by the Ningxia key research and development
   program (Special Talents) (No.2018BEB04002), Ningxia first-class
   discipline and scientific research projects (electronic science and
   technology), NO. NXYLXK2017A07 and the North Minzu University Graduate
   Innovation Project (No.YCX18050).
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NR 23
TC 0
Z9 0
U1 0
U2 2
PU YOKOHAMA PUBL
PI YOKOHAMA
PA 101, 6-27 SATSUKIGAOKA AOBA-KU, YOKOHAMA, 227-0053, JAPAN
SN 1345-4773
EI 1880-5221
J9 J NONLINEAR CONVEX A
JI J. Nonlinear Convex Anal.
PY 2020
VL 21
IS 8
SI SI
BP 1665
EP 1676
PG 12
WC Mathematics, Applied; Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Mathematics
GA OD2KP
UT WOS:000579682400003
DA 2022-08-23
ER

PT J
AU Li, XT
   Li, H
AF Li, Xiaotong
   Li, Hua
TI A Visual Analysis of Research on Information Security Risk by Using
   CiteSpace
SO IEEE ACCESS
LA English
DT Article
DE Visualization; information security risk; knowledge mapping; Web of
   Science; CiteSpace
ID MODELS; RETURNS; SYSTEMS
AB The security of information issue has become a global problem and has risen the concerns of both researchers and practitioners. When security incidents occur, there is a risk that national military secrets or confidential information of corporations will leak out and cause serious damages to the collective. This paper is aimed to explore the knowledge structure, development, and the future trend of information security area by providing a comprehensive review of the present information security risk (ISR) literature. The visualization analysis was conducted on journal literatures from the Web of Science, IEEE, ACM, and Scopus database, and the results were mapped into the I-model. According to 2748 articles, evaluation methods, e.g., frequency statistics, clustering coefficient, and centrality calculation, are employed to analyze all of the interrelated matrixes that are supported by CiteSpace. Some useful outcomes of a variety of objectives are shown under a significant level, such as author, country/territory, cluster, institute, as well as reference. A synthetical analysis has demonstrated the future research trend on ISR. As for researchers and practitioners, this paper suggests an analysis of integrated visualization in terms of the knowledge and innovation based on the area of ISR.
C1 [Li, Hua] Xidian Univ, Sch Econ & Management, Xian, Shaanxi, Peoples R China.
   Xidian Univ, Shaanxi Soft Sci Inst Informatizat & Digital Econ, Xian, Shaanxi, Peoples R China.
RP Li, H (通讯作者)，Xidian Univ, Sch Econ & Management, Xian, Shaanxi, Peoples R China.
EM lihua@xidian.edu.cn
FU Fundamental Research Funds for the Central Universities [JB180608]
FX This work was supported in part by the Fundamental Research Funds for
   the Central Universities (No. JB180608). The authors are grateful for
   the help in writing this article by Fang Wang and Ke Ai. They also thank
   the editor and the anonymous reviewers for their valuable comments.
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NR 38
TC 17
Z9 17
U1 18
U2 76
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 63243
EP 63257
DI 10.1109/ACCESS.2018.2873696
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA HA7QH
UT WOS:000450477300001
OA gold
DA 2022-08-23
ER

PT J
AU Shi, C
   Ding, JY
   Cao, XH
   Hu, LM
   Wu, B
   Li, XL
AF Shi, Chuan
   Ding, Jiayu
   Cao, Xiaohuan
   Hu, Linmei
   Wu, Bin
   Li, Xiaoli
TI Entity set expansion in knowledge graph: a heterogeneous information
   network perspective
SO FRONTIERS OF COMPUTER SCIENCE
LA English
DT Article
DE entity set expansion; knowledge graph; heterogeneous information
   network; multi-type meta path
ID COMBINATION
AB Entity set expansion (ESE) aims to expand an entity seed set to obtain more entities which have common properties. ESE is important for many applications such as dictionary construction and query suggestion. Traditional ESE methods relied heavily on the text and Web information of entities. Recently, some ESE methods employed knowledge graphs (KGs) to extend entities. However, they failed to effectively and efficiently utilize the rich semantics contained in a KG and ignored the text information of entities in Wikipedia. In this paper, we model a KG as a heterogeneous information network (HIN) containing multiple types of objects and relations. Fine-grained multi-type meta paths are proposed to capture the hidden relation among seed entities in a KG and thus to retrieve candidate entities. Then we rank the entities according to the meta path based structural similarity. Furthermore, to utilize the text description of entities in Wikipedia, we propose an extended model CoMeSE++ which combines both structural information revealed by a KG and text information in Wikipedia for ESE. Extensive experiments on real-world datasets demonstrate that our model achieves better performance by combining structural and textual information of entities.
C1 [Shi, Chuan; Ding, Jiayu; Cao, Xiaohuan; Hu, Linmei; Wu, Bin] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China.
   [Li, Xiaoli] Agcy Sci Technol & Res, Inst Infocomm Res, Singapore 138632, Singapore.
RP Hu, LM (通讯作者)，Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China.
EM hulinmei@bupt.edu.cn
RI Li, Xiaoli/C-9739-2012
OI Li, Xiaoli/0000-0002-0762-6562
FU National Natural Science Foundation of China [61806020, 61772082,
   61972047, 61702296]; National Key Research and Development Program of
   China [2017YFB0803304]; Beijing Municipal Natural Science Foundation
   [4182043]; CCF-Tencent Open Fund; Fundamental Research Funds for the
   Central Universities
FX This work was supported by the National Natural Science Foundation of
   China (Grant Nos. 61806020, 61772082, 61972047, 61702296), the National
   Key Research and Development Program of China (2017YFB0803304), the
   Beijing Municipal Natural Science Foundation (4182043), the CCF-Tencent
   Open Fund, and the Fundamental Research Funds for the Central
   Universities.
CR [Anonymous], 2018, IEEE T BIG DATA
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NR 38
TC 4
Z9 4
U1 6
U2 14
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 2021
VL 15
IS 1
AR 151307
DI 10.1007/s11704-020-9240-8
PG 12
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NZ3JM
UT WOS:000576993400004
DA 2022-08-23
ER

PT J
AU Fang, YS
   Lee, LS
AF Fang, Yu-Shen
   Lee, Lung-Sheng
TI Research front and evolution of technology education in Taiwan and
   abroad: bibliometric co-citation analysis and maps
SO INTERNATIONAL JOURNAL OF TECHNOLOGY AND DESIGN EDUCATION
LA English
DT Article
DE Discipline infiltration; Discipline structure; Knowledge evolution;
   Research front; Technological literacy education
ID SCIENCE; TRENDS
AB In Taiwan, issues in technology education (TE), such as indeterminate core mission and unclear role, could be addressed through an objective knowledge map of research to discern directions of development and prospects. This study explores sub-fields, genres, characteristics, and trends in TE research in Chinese and English over 24 years through bibliometric co-citation analysis using two databases: Web of Science and Airiti Library. As per the findings: (1) TE research has been progressing slowly in Taiwan, while it is developing rapidly elsewhere; Disciplines integrated with TE include education and educational research, engineering, computer science, social sciences, and medicine; (2) Research topics have evolved from industrial arts, technical and vocational education, technological literacy education, and curriculum development, to pedagogical skills of teachers of technology; Other countries focus on "professional development of technology teachers," while Taiwan emphasizes "curriculum and teaching materials;" (3) Clusters from different periods are complementary, and research topics and fronts reflect continuity. (4) "Assessment," as a research dimension, has received insufficient attention. Insights into the evolution of research topics and research fronts are offered for technology educators.
C1 [Fang, Yu-Shen] Zhaoqing Univ, Zhaoqing, Guangdong, Peoples R China.
   [Lee, Lung-Sheng] Natl Taiwan Normal Univ, Taipei, Taiwan.
RP Lee, LS (通讯作者)，Natl Taiwan Normal Univ, Taipei, Taiwan.
EM lungshenglee@gmail.com
OI Fang, Yu-Shen/0000-0002-8248-8585
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NR 40
TC 4
Z9 4
U1 3
U2 23
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 APR
PY 2022
VL 32
IS 2
BP 1337
EP 1368
DI 10.1007/s10798-020-09649-z
EA JAN 2021
PG 32
WC Education & Educational Research; Education, Scientific Disciplines;
   Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Engineering
GA 0B8ML
UT WOS:000604829800006
DA 2022-08-23
ER

PT J
AU Zhang, K
   Zhao, W
   Wang, J
   Chen, L
   Guo, X
AF Zhang, Kai
   Zhao, Wu
   Wang, Jie
   Chen, Ling
   Guo, Xin
TI Knowledge push technology based on quality function knowledge deployment
SO PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF
   MECHANICAL ENGINEERING SCIENCE
LA English
DT Article
DE Product innovation design; knowledge push; knowledge mapping; quality
   function knowledge deployment; space robot gripper
ID DESIGN; METHODOLOGY; INTEGRATION; INNOVATION; FRAMEWORK; MODEL
AB In order to help designers to acquire and apply knowledge, a kind of knowledge push technologies based on quality function knowledge deployment was proposed. During the whole lifecycle of product design, the knowledge requirement house of quality model could link up the required knowledge with design activities to help designers to track the required knowledge for realizing product innovation design. The hierarchical relationship between design tasks, designers, design activities, and required knowledge was analyzed, and the knowledge management system structure for product innovation design based on designers was put forward. Furthermore, knowledge acquisition method was analyzed. In view of the different requirements for knowledge in the different design stages, this paper proposed the mapping model between the process of product innovation design and required knowledge. Then the knowledge push model for product innovation design based on quality function knowledge deployment was established. Finally, a prototype system of computer-aided innovation design platform was developed to implement this knowledge push model, and through its application on the design case of space robot gripper, the practicability and validity of the prototype system were demonstrated.
C1 [Zhang, Kai; Zhao, Wu; Wang, Jie; Chen, Ling; Guo, Xin] Sichuan Univ, Sch Mfg Sci & Engn, 24,South Sect 1,Yihuan Rd, Chengdu 610065, Sichuan, Peoples R China.
RP Zhao, W (通讯作者)，Sichuan Univ, Sch Mfg Sci & Engn, 24,South Sect 1,Yihuan Rd, Chengdu 610065, Sichuan, Peoples R China.
EM zhaowu@scu.edu.cn
OI Guo, Xin/0000-0001-8494-4516
FU National Natural Science Foundation of China [51435011]; Research Fund
   for the Doctoral Program of Higher Education of China [20130181130011]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This work
   has been supported by the National Natural Science Foundation of China
   (Grant No. 51435011) and Research Fund for the Doctoral Program of
   Higher Education of China (Grant No. 20130181130011).
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NR 45
TC 2
Z9 2
U1 9
U2 54
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0954-4062
EI 2041-2983
J9 P I MECH ENG C-J MEC
JI Proc. Inst. Mech. Eng. Part C-J. Eng. Mech. Eng. Sci.
PD FEB
PY 2019
VL 233
IS 4
BP 1119
EP 1138
DI 10.1177/0954406218768843
PG 20
WC Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA HL6LJ
UT WOS:000458845400002
DA 2022-08-23
ER

PT J
AU Chen, Z
   Zuo, XJ
   Hou, BT
   Dong, N
   Chang, J
AF Chen, Ze
   Zuo, Xiaojun
   Hou, Botao
   Dong, Na
   Chang, Jie
TI Research on Automatic Vulnerability Mining Model Based on Knowledge
   Graph
SO INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
LA English
DT Article
DE Power grid control; system management; vulnerability mining; knowledge
   graph; knowledge model
ID INTERNET; SCHEME
AB In the information extraction, information sources can be screened according to the characteristics of the target network at the present stage, and the knowledge graph generated thereby can play a role in assisting the security analysis of the general network or power grid control network, mobile Internet and other special networks. In the method proposed in this paper, knowledge reasoning is mainly based on the attack conditions and attack methods to reason about the success rate and return of the attack. Through the obtained quality information, map construction information extraction and reasoning are performed to realize the correlation analysis of the information, and the information processing results are stored in the graphic structure. When analyzing the alerts generated by IDS, it is necessary to solve the multi-source alarm format generated by various devices produced by different suppliers. The attack diagram constructs the attack mode to guide the defense side to take targeted defense measures, and the attack success rate is used to judge the defense priority of all network nodes. After completing the construction of the graph, the attack graph is generated for the specific network environment under the guidance of the knowledge graph. In the process of attack graph generation, attack method and attack condition of attack instance can be used to guide the match of pre-condition and post-condition, so as to find the attack path. Attack success rate and attack profit attribute can be used to assist subsequent risk analysis. After simulation tests, the timeliness and availability of the system are verified, and this makes a contribution to the grid network management.
C1 [Chen, Ze; Zuo, Xiaojun; Hou, Botao; Dong, Na; Chang, Jie] State Grid Hebei Elect Power Res Inst, Shijiazhuang 50021, Hebei, Peoples R China.
RP Chen, Z (通讯作者)，State Grid Hebei Elect Power Res Inst, Shijiazhuang 50021, Hebei, Peoples R China.
EM dyy_czdiy@163.com; hbszxj01@163.com; 568453349@qq.com;
   longcaochuan@126.com; 15230111698@126.com
FU State Grid Hebei Electric Power Co. Ltd., China; project of Research and
   Application of Network Security Vulnerability Mining Technology based on
   Artificial Intelligence [kj2019-062]
FX The study was supported by State Grid Hebei Electric Power Co. Ltd.,
   China and the project of Research and Application of Network Security
   Vulnerability Mining Technology based on Artificial Intelligence, which
   number is Grant No. kj2019-062.
CR Da Y. S., 2019, CHINA FINANCE, V7, P212
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NR 17
TC 1
Z9 1
U1 5
U2 14
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-2130
EI 1793-6349
J9 INT J ARTIF INTELL T
JI Int. J. Artif. Intell. Tools
PD DEC
PY 2020
VL 29
IS 7-8
SI SI
AR 2040024
DI 10.1142/S0218213020400242
PG 16
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA PZ6PD
UT WOS:000612860900020
DA 2022-08-23
ER

PT J
AU Mongiovi, M
   Recupero, DR
   Gangemi, A
   Presutti, V
   Consoli, S
AF Mongiovi, Misael
   Recupero, Diego Reforgiato
   Gangemi, Aldo
   Presutti, Valentina
   Consoli, Sergio
TI Merging open knowledge extracted from text with MERGILO
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge reconciliation; Coreference resolution; Knowledge base
   integration; Graph alignment
AB This paper presents MERGILO, a method for reconciling knowledge extracted from multiple natural language sources, and for delivering it as a knowledge graph. The underlying problem is relevant in many application scenarios requiring the creation and dynamic evolution of a knowledge base, e.g. automatic news summarization, human-robot dialoguing, etc. After providing a formal definition of the problem, we propose our holistic approach to handle natural language input - typically independent texts as in news from different sources - and we output a knowledge graph representing their reconciled knowledge. MERGILO is evaluated on its ability to identify corresponding entities and events across documents against a manually annotated corpus of news, showing promising results. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Mongiovi, Misael; Recupero, Diego Reforgiato; Gangemi, Aldo; Presutti, Valentina; Consoli, Sergio] ISTC CNR, Semant Technol Lab, Rome, Italy.
   [Mongiovi, Misael; Recupero, Diego Reforgiato; Gangemi, Aldo; Presutti, Valentina; Consoli, Sergio] ISTC CNR, Semant Technol Lab, Catania, Italy.
   [Gangemi, Aldo] Univ Paris 13, Sorbonne Cite, CNRS UMR7030, F-93430 Villetaneuse, France.
   [Recupero, Diego Reforgiato] Univ Cagliari, Via Osped 72, I-09124 Cagliari, Italy.
   [Consoli, Sergio] Philips Res, Data Sci Grp, High Tech Campus 36, NL-5656 AE Eindhoven, Netherlands.
RP Mongiovi, M (通讯作者)，ISTC CNR, Semant Technol Lab, Rome, Italy.; Mongiovi, M (通讯作者)，ISTC CNR, Semant Technol Lab, Catania, Italy.
EM misael.mongiovi@istc.cnr.it
RI Mongiovì, Misael/GNP-0059-2022; Gangemi, Aldo/C-7420-2013; Consoli,
   Sergio/ABF-3293-2020
OI Mongiovì, Misael/0000-0003-0528-5490; Gangemi, Aldo/0000-0001-5568-2684;
   Consoli, Sergio/0000-0001-7357-5858; Reforgiato Recupero,
   Diego/0000-0001-8646-6183
FU European Union [643808]
FX The research leading to these results has received funding from the
   European Union Horizons 2020 the Framework Programme for Research and
   Innovation (2014-2020) under Grant agreement 643808 Project MARIO
   Managing active and healthy aging with use of caring service robots.
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NR 44
TC 5
Z9 5
U1 0
U2 8
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 SEP 15
PY 2016
VL 108
SI SI
BP 155
EP 167
DI 10.1016/j.knosys.2016.05.014
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DV0FE
UT WOS:000382592600016
OA Green Published
DA 2022-08-23
ER

PT J
AU Berven, A
   Christensen, OA
   Moldeklev, S
   Opdahl, AL
   Villanger, KJ
AF Berven, Arne
   Christensen, Ole A.
   Moldeklev, Sindre
   Opdahl, Andreas L.
   Villanger, Kjetil J.
TI A knowledge-graph platform for newsrooms
SO COMPUTERS IN INDUSTRY
LA English
DT Article
DE Computational journalism; Journalistic knowledge platforms; Newsroom
   systems; Knowledge graphs; Semantic technologies; RDF; OWL; Ontology;
   Natural-language processing (NLP); Machine learning (ML)
ID SEMANTIC WEB; JOURNALISTS; MACHINE; DESIGN
AB Journalism is challenged by digitalisation and social media, resulting in lower subscription numbers and reduced advertising income. Information and communication techniques (ICT) offer new opportunities. Our research group is collaborating with a software developer of news production tools for the inter-national market to explore how social, open, and other data sources can be leveraged for journalistic purposes. We have developed an architecture and prototype called News Hunter that uses knowledge graphs, natural-language processing (NLP), and machine learning (ML) together to support journalists. Our focus is on combining existing data sources and computation and storage techniques into a flexible architecture for news journalism. The paper presents News Hunter along with plans and possibilities for future work. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C1 [Berven, Arne] Wolftech Broadcast Solut, Nostegaten 72, N-5011 Bergen, Norway.
   [Christensen, Ole A.; Moldeklev, Sindre; Opdahl, Andreas L.; Villanger, Kjetil J.] Univ Bergen, POB 7802, N-5020 Bergen, Norway.
RP Opdahl, AL (通讯作者)，Univ Bergen, POB 7802, N-5020 Bergen, Norway.
EM andreas.opdahl@uib.no
FU Norwegian Center of Expertise
FX Early development of News Hunter was supported by NCEMedia, a Norwegian
   Center of Expertise.
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NR 41
TC 4
Z9 4
U1 4
U2 7
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-3615
EI 1872-6194
J9 COMPUT IND
JI Comput. Ind.
PD DEC
PY 2020
VL 123
AR 103321
DI 10.1016/j.compind.2020.103321
PG 10
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SV5AE
UT WOS:000663832400007
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Fei, H
   Ren, YF
   Zhang, Y
   Ji, DH
   Liang, XH
AF Fei, Hao
   Ren, Yafeng
   Zhang, Yue
   Ji, Donghong
   Liang, Xiaohui
TI Enriching contextualized language model from knowledge graph for
   biomedical information extraction
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Review
DE biomedical information extraction; language model; neural network;
   knowledge graph
AB Biomedical information extraction (BioIE) is an important task. The aim is to analyze biomedical texts and extract structured information such as named entities and semantic relations between them. In recent years, pre-trained language models have largely improved the performance of BioIE. However, they neglect to incorporate external structural knowledge, which can provide rich factual information to support the underlying understanding and reasoning for biomedical information extraction. In this paper, we first evaluate current extraction methods, including vanilla neural networks, general language models and pre-trained contextualized language models on biomedical information extraction tasks, including named entity recognition, relation extraction and event extraction. We then propose to enrich a contextualized language model by integrating a large scale of biomedical knowledge graphs (namely, BioKGLM). In order to effectively encode knowledge, we explore a three-stage training procedure and introduce different fusion strategies to facilitate knowledge injection. Experimental results on multiple tasks show that BioKGLM consistently outperforms state-of-the-art extraction models. A further analysis proves that BioKGLM can capture the underlying relations between biomedical knowledge concepts, which are crucial for BioIE.
C1 [Fei, Hao] Wuhan Univ, Inst Big Data Anal & Artificial Intelligence, Sch Cyber Sci & Engn, Wuhan, Peoples R China.
   [Ren, Yafeng] Guangdong Univ Foreign Studies, Guangzhou, Peoples R China.
   [Zhang, Yue] Singapore Univ Technol & Design, Singapore, Singapore.
   [Zhang, Yue] Westlake Univ, Hangzhou, Peoples R China.
   [Ji, Donghong] Inst Infocomm Res, Singapore, Singapore.
   [Ji, Donghong] Wuhan Univ, Wuhan, Peoples R China.
   [Liang, Xiaohui] Wuhan Univ, Sch Hlth Sci, Wuhan, Peoples R China.
RP Ren, YF (通讯作者)，Guangdong Univ Foreign Studies, Lab Language & Artificial Intelligence, Guangzhou, Peoples R China.
EM renyafeng@whu.edu.cn
FU National Natural Science Foundation of China [61702121, 61772378];
   Research Foundation of Ministry of Education of China [18JZD015]; Key
   Project of State Language Commission of China [ZDI135-112]; Guangdong
   Basic and Applied Basic Research Foundation [2020A151501705]
FX National Natural Science Foundation of China (61702121 and 61772378);
   Research Foundation of Ministry of Education of China (18JZD015); Key
   Project of State Language Commission of China (ZDI135-112); Guangdong
   Basic and Applied Basic Research Foundation (2020A151501705).
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NR 67
TC 0
Z9 0
U1 1
U2 1
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1467-5463
EI 1477-4054
J9 BRIEF BIOINFORM
JI Brief. Bioinform.
PD MAY
PY 2021
VL 22
IS 3
AR bbaa110
DI 10.1093/bib/bbaa110
PG 14
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA WK0YS
UT WOS:000709461300056
DA 2022-08-23
ER

PT J
AU Garcia, N
   Renoust, B
   Nakashima, Y
AF Garcia, Noa
   Renoust, Benjamin
   Nakashima, Yuta
TI ContextNet: representation and exploration for painting classification
   and retrieval in context
SO INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
LA English
DT Article
DE Art classification; Multi-modal retrieval; Knowledge graphs;
   Visualisation; Multitask learning
ID IMAGE; KNOWLEDGE
AB In automatic art analysis, models that besides the visual elements of an artwork represent the relationships between the different artistic attributes could be very informative. Those kinds of relationships, however, usually appear in a very subtle way, being extremely difficult to detect with standard convolutional neural networks. In this work, we propose to capture contextual artistic information from fine-art paintings with a specific ContextNet network. As context can be obtained from multiple sources, we explore two modalities of ContextNets: one based on multitask learning and another one based on knowledge graphs. Once the contextual information is obtained, we use it to enhance visual representations computed with a neural network. In this way, we are able to (1) capture information about the content and the style with the visual representations and (2) encode relationships between different artistic attributes with the ContextNet. We evaluate our models on both painting classification and retrieval, and by visualising the resulting embeddings on a knowledge graph, we can confirm that our models represent specific stylistic aspects present in the data.
C1 [Garcia, Noa; Renoust, Benjamin; Nakashima, Yuta] Osaka Univ, Inst Databil Sci, Osaka, Japan.
RP Garcia, N (通讯作者)，Osaka Univ, Inst Databil Sci, Osaka, Japan.
EM noagarcia@ids.osaka-u.ac.jp
OI Garcia, Noa/0000-0002-9200-6359
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NR 60
TC 7
Z9 8
U1 1
U2 4
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2192-6611
EI 2192-662X
J9 INT J MULTIMED INF R
JI Int. J. Multimed. Inf. Retr.
PD MAR
PY 2020
VL 9
IS 1
SI SI
BP 17
EP 30
DI 10.1007/s13735-019-00189-4
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KQ6ZX
UT WOS:000517070300003
OA hybrid
DA 2022-08-23
ER

PT J
AU Luo, J
   Wang, YF
   Xu, Y
AF Luo, Jie
   Wang, Yifei
   Xu, Yi
TI Incremental Theory Closure Reasoning for Large Scale Knowledge Graphs
SO IEEE ACCESS
LA English
DT Article
DE Cybersecurity; incremental reasoning; knowledge graph; knowledge
   reasoning; OWL2 RL
AB Knowledge graph(KG)s have become more and more important for the field of cybersecurity. However, they usually contain much implicit semantic information, which needs to be further mined through semantic inference to be more useful for both analysts and automated systems. In this paper, we propose an incremental reasoning algorithm KGRL-Incre for the scenario that the instances of a KG is expanded with only a small set of triples, which can perform an incremental update to the previous reasoning result effectively to avoid a full re-reasoning over the expanded KG. The main contributions of our approach are the irrelevant triple filtering algorithms which reduce the scale of data that need to be processed and a delay reasoning strategy which limits the number of time-consuming iterations while still preserves relative completeness of the final result. The extensive experiments and comprehensive evaluations are conducted and the experimental results show that the KGRL-Incre can significantly reduce time consumption compared with the expanding and reasoning approach in the target scenario.
C1 [Luo, Jie; Wang, Yifei; Xu, Yi] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.
RP Luo, J (通讯作者)，Beihang Univ, Sch Comp Sci & Engn, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.
EM luojie@nlsde.buaa.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 in part by the National Natural Science
   Foundation of China under Grant 61502022, and in part by the State Key
   Laboratory of Software Development Environment under Grant
   SKLSDE-2017ZX-17.
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NR 22
TC 2
Z9 3
U1 2
U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 24593
EP 24601
DI 10.1109/ACCESS.2019.2900297
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HO5QH
UT WOS:000460982300001
OA gold
DA 2022-08-23
ER

PT J
AU de Bono, B
   Gillespie, T
   Surles-Zeigler, MC
   Kokash, N
   Grethe, JS
   Martone, M
AF de Bono, Bernard
   Gillespie, Tom
   Surles-Zeigler, Monique C.
   Kokash, Natallia
   Grethe, Jeff S.
   Martone, Maryann
TI Representing Normal and Abnormal Physiology as Routes of Flow in
   ApiNATOMY
SO FRONTIERS IN PHYSIOLOGY
LA English
DT Article
DE anatomy; physiology; pathology; knowledge management; knowledge
   representation; ontologies; visualization
ID ONTOLOGY; COMMON; MODEL
AB We present (i) the ApiNATOMY workflow to build knowledge models of biological connectivity, as well as (ii) the ApiNATOMY TOO map, a topological scaffold to organize and visually inspect these connectivity models in the context of a canonical architecture of body compartments. In this work, we outline the implementation of ApiNATOMY's knowledge representation in the context of a large-scale effort, SPARC, to map the autonomic nervous system. Within SPARC, the ApiNATOMY modeling effort has generated the SCKAN knowledge graph that combines connectivity models and TOO map. This knowledge graph models flow routes for a number of normal and disease scenarios in physiology. Calculations over SCKAN to infer routes are being leveraged to classify, navigate and search for semantically-linked metadata of multimodal experimental datasets for a number of cross-scale, cross-disciplinary projects.
C1 [de Bono, Bernard] Univ Auckland, Auckland Bioengn Inst, Auckland, New Zealand.
   [Gillespie, Tom; Surles-Zeigler, Monique C.; Grethe, Jeff S.; Martone, Maryann] Univ Calif, Dept Neurosci, San Diego, CA USA.
   [Kokash, Natallia] Univ Amsterdam, Fac Humanities, Amsterdam, Netherlands.
RP de Bono, B (通讯作者)，Univ Auckland, Auckland Bioengn Inst, Auckland, New Zealand.
EM b.debono@auckland.ac.nz
OI Surles-Zeigler, Monique/0000-0002-2308-8813
FU NIH [SPARC OT2OD030541, CFDE 1OT2OD030164-01]
FX We gratefully acknowledge the NIH grants SPARC OT2OD030541 and CFDE
   1OT2OD030164-01 in support of this work.
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NR 49
TC 0
Z9 0
U1 0
U2 0
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1664-042X
J9 FRONT PHYSIOL
JI Front. Physiol.
PD APR 25
PY 2022
VL 13
AR 795303
DI 10.3389/fphys.2022.795303
PG 18
WC Physiology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physiology
GA 1E1MF
UT WOS:000794259200001
PM 35547570
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Qian, FL
   Zhu, YH
   Chen, H
   Chen, J
   Zhao, S
   Zhang, YP
AF Qian, Fulan
   Zhu, Yuhui
   Chen, Hai
   Chen, Jie
   Zhao, Shu
   Zhang, Yanping
TI Reduce unrelated Knowledge through Attribute Collaborative signal for
   knowledge graph recommendation
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Recommender system; Knowledge graph; Collaborative signal; Attention
   mechanism
AB Knowledge graph (KG), as an auxiliary information, plays an important role in the recommendation system, which effectively solves the sparsity and cold start problems of collaborative filtering algorithms. The recommendation algorithm that introduces the propagation mechanism on the KG has been a great success, it enriches the representation of users and items by aggregating multi-hop neighbors. However, the existing KG-based propagation recommendation algorithm aggregating all entity information cannot guarantee the improvement of recommendation results, because entity information in KG is not all helpful to recommend appropriate items to users. Indiscriminately aggregating the entity information in the neighborhood allows the learned embedding representation to be influenced by its unrelated entities.
   In this paper, we propose a new model named Reduce unrelated Knowledge through Attribute Collaborative signal (RKAC). Compared to other KG-based propagation methods, RKAC offers a new concept of combining item attributes with collaborative signals to reduce information about unrelated entities. Specifically, the initial entity set of users was obtained by collaborative signals and the initial entity set of items was obtained by filtering redundant collaborative signals based on item attributes, and then they were propagated on KG as seeds to acquire multi-hop neighbor entities. Finally, domain entities of different importance were gathered through attention mechanism to obtain more accurate embedding representation of entities. Experimental results on four benchmark datasets of music, book, movie and restaurant show that the AUC of RKAC on CTR prediction increases by 1.4%, 1.3%, 0.8% and 0.5% respectively, compared with the state-of-the-art existing approaches.
C1 Anhui Univ, Key Lab Intelligent Comp & Signal Proc, Minist Educ, Hefei 230039, Anhui, Peoples R China.
   Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Peoples R China.
   Informat Mat & Intelligent Sensing Lab Anhui Prov, Hefei, Peoples R China.
RP Qian, FL (通讯作者)，Anhui Univ, Qingyuan Campus,111 Jiulong Rd, Hefei, Anhui, Peoples R China.
EM qianfulan@hotmail.com; zhuyuhuilz@163.com; chber_ahu@hotmail.com;
   chenjie200398@163.com; zhaoshuzs2002@hotmail.com; zhangyp2@gmail.com
FU General Project of the National Natural Science Foundation of China
   [61876001]; Natural Science Foundation for the Higher Education
   Institutions of Anhui Province of China [KJ2021A0039]
FX T This work is supported by the General Project of the National Natural
   Science Foundation of China (61876001), and the the Natural Science
   Foundation for the Higher Education Institutions of Anhui Province of
   China (KJ2021A0039). In addition, we acknowledge the High performance
   Computing Platform of Anhui University for providing computing
   resources.
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NR 41
TC 0
Z9 0
U1 1
U2 1
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 117078
DI 10.1016/j.eswa.2022.117078
PG 9
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 3E7OD
UT WOS:000830169300008
DA 2022-08-23
ER

PT J
AU Lovera, FA
   Cardinale, YC
   Homsi, MN
AF Lovera, Fernando Andres
   Cardinale, Yudith Coromoto
   Homsi, Masun Nabhan
TI Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning
   Classification
SO ELECTRONICS
LA English
DT Article
DE sentiment analysis; knowledge graph; Long-Short Term Memory (LSTM);
   model interpretability
ID SOCIAL MEDIA; WORDS; NETWORKS; MODEL; BAG
AB The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative) in short documents, such as posts on Twitter. In this proposal, tweets are represented as graphs; then, graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and interpretability of the classification results, thanks to the integration of the Local Interpretable Model-agnostic Explanations (LIME) model at the end of the pipeline. LIME allows raising trust in predictive models, since the model is not a black box anymore. Uncovering the black box allows understanding and interpreting how the network could distinguish between sentiment polarities. Each phase of the proposed approach conformed by pre-processing, graph construction, dimensionality reduction, graph similarity, sentiment prediction, and interpretability steps is described. The proposal is compared with character n-gram embeddings-based Deep Learning models to perform Sentiment Analysis. Results show that the proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.
C1 [Lovera, Fernando Andres; Cardinale, Yudith Coromoto; Homsi, Masun Nabhan] Univ Simon Bolivar, Dept Comp Sci, Caracas 1080, Venezuela.
   [Homsi, Masun Nabhan] Helmholtz Dept Mol Syst Biol, Environm Res Ctr, D-04318 Leipzig, Germany.
RP Lovera, FA; Cardinale, YC; Homsi, MN (通讯作者)，Univ Simon Bolivar, Dept Comp Sci, Caracas 1080, Venezuela.; Homsi, MN (通讯作者)，Helmholtz Dept Mol Syst Biol, Environm Res Ctr, D-04318 Leipzig, Germany.
EM flovera@usb.ve; ycardinale@usb.ve; masun.homsi@ufz.de
RI ; Nabhan Homsi, Masun/I-9151-2016
OI Cardinale, Yudith/0000-0002-5966-0113; Nabhan Homsi,
   Masun/0000-0001-7427-6198
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NR 72
TC 1
Z9 1
U1 4
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD NOV
PY 2021
VL 10
IS 22
AR 2739
DI 10.3390/electronics10222739
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 XH1EY
UT WOS:000725185700001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Hu, W
   Li, CH
   Ye, C
   Wang, J
   Wei, WW
   Deng, Y
AF Hu, Wen
   Li, Chun-hua
   Ye, Chun
   Wang, Ji
   Wei, Wei-wei
   Deng, Yong
TI Research progress on ecological models in the field of water
   eutrophication: CiteSpace analysis based on data from the ISI web of
   science database
SO ECOLOGICAL MODELLING
LA English
DT Review
DE Eutrophication; Ecological models; CiteSpace; Knowledge mapping
ID RISK-ASSESSMENT; CYANOBACTERIAL BLOOMS; FRESH-WATER; LAKE TAIHU;
   COMMUNITY STRUCTURE; ECOSYSTEM MODEL; RESIDENCE TIME; BALTIC SEA; PART
   I; PHYTOPLANKTON
AB Eco-models have shown great potential in the field of water eutrophication research. We use the information visualization technology of CiteSpace to present a systematic review of published literature on the application of eco-models to eutrophication from 1968-2018. In total, 2099 references from the Web of Science (WoS) Core Collection were analyzed using the CiteSpace software, bibliometric methods, and documents combing methods to determine the hotspots, frontiers, and development trends in the application of eco-models to eutrophication. According to our analysis, ecological models in the field of water eutrophication are applied to the Baltic Sea, agricultural phosphorus use, particulate nitrogen input, risk assessment, net ecosystem production, competition patterns, biological factors, semi-enclosed marine systems, chlorophyll fluorescence parameters, evolving conceptual models, and nutrient transfer. The eco-models range from dimension-models to time-dependent dynamic models. Moreover, the recent trend of close coupling between modeling and the acquisition of new types of experimental data (i.e., remote sensing, high-frequency field sensors) provides a higher prediction ability of ecological models.
C1 [Hu, Wen; Li, Chun-hua; Ye, Chun; Wei, Wei-wei] Chinese Res Inst Environm Sci, Natl Engn Lab Lake Pollut Control & Ecol Restorat, Beijing 100012, Peoples R China.
   [Hu, Wen; Wang, Ji] Guizhou Normal Univ, Sch Geog & Environm Sci, Guiyang 550025, Guizhou, Peoples R China.
   [Deng, Yong] Guizhou Res Inst Chem Ind, Guiyang 550002, Guizhou, Peoples R China.
RP Ye, C (通讯作者)，Chinese Res Inst Environm Sci, Natl Engn Lab Lake Pollut Control & Ecol Restorat, Beijing 100012, Peoples R China.; Wang, J (通讯作者)，Guizhou Normal Univ, Sch Geog & Environm Sci, Guiyang 550025, Guizhou, Peoples R China.
EM yechbj@163.com; wangji@gznu.edu.cn
FU 13th-five National Water Pollution Control and Treatment Science and
   Technology Major Project of China [2017ZX07203-005]; Project of Science
   and Technology Supporting Plan, Guizhou Province [2580]
FX This work was jointly supported by the 13th-five National Water
   Pollution Control and Treatment Science and Technology Major Project of
   China (2017ZX07203-005) and the Project of Science and Technology
   Supporting Plan, Guizhou Province (Qian Sci. Co.[2017], No. 2580)
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NR 96
TC 34
Z9 35
U1 49
U2 338
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0304-3800
EI 1872-7026
J9 ECOL MODEL
JI Ecol. Model.
PD OCT 15
PY 2019
VL 410
AR 108779
DI 10.1016/j.ecolmodel.2019.108779
PG 11
WC Ecology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA IZ6FZ
UT WOS:000487178500004
DA 2022-08-23
ER

PT J
AU Xiao, D
   Wang, QY
   Cai, M
   Zhu, ZH
   Zhao, WM
AF Xiao, Ding
   Wang, Qianyu
   Cai, Ming
   Zhu, Zhaohui
   Zhao, Weiming
TI A3ID: An Automatic and Interpretable Implicit Interference Detection
   Method for Smart Home via Knowledge Graph
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Interference detection; knowledge graph; natural language processing
   (NLP); smart home
ID VERIFICATION; SYSTEM
AB The smart home brings together devices, the cloud, data, and people to make home living more comfortable and safer. Trigger-action programming enables users to connect smart devices using if-this-then-that (IFTTT)-style rules. With the increasing number of devices in smart home systems, multiple running rules that act on actuators in contradictory ways may cause unexpected and unpredictable interference problems, which can put residents and their belongings at risk. Previous studies have considered explicit interference problems related to multiple rules targeting a single actuator, whereas implicit interference (interference across different actuators) detection is still challenging and not yet well studied owing to the effort-intensive and time-consuming annotation work of obtaining device information. The lack of knowledge about devices is a critical reason that affects the accuracy and efficiency in implicit interference detection. In this article, we propose A3ID, an automatic detection method for implicit interference based on knowledge graphs. Using natural language processing (NLP) techniques and a lexical database, A3ID can extract knowledge of devices from a knowledge graph, including functionality, effect, and scope. Then, it analyzes and detects interferences among the different devices semantically in three steps, without human intervention. Furthermore, it provides user-friendly explanations in a well-designed structure to specify possible reasons for the implicit interference problems. Our experiment on 11 859 IFTTT-style rules shows that A3ID outperforms state-of-the-art methods by more than 33% in the F1-score for the detection of implicit interference. Moreover, evaluations on an extended data set for devices from ConceptNet (a knowledge graph) and five smart home systems suggest that A3ID also has favorable performance with other devices not limited to the smart home domain.
C1 [Xiao, Ding; Wang, Qianyu; Cai, Ming; Zhu, Zhaohui; Zhao, Weiming] Zhejiang Univ, Dept Comp Sci & Technol, Hangzhou 310027, Peoples R China.
RP Cai, M (通讯作者)，Zhejiang Univ, Dept Comp Sci & Technol, Hangzhou 310027, Peoples R China.
EM derll.xiao@gmail.com; w_morpheus@zju.edu.cn; cm@zju.edu.cn;
   21621138@zju.edu.cn; 21621305@zju.edu.cn
OI Xiao, Ding/0000-0002-6632-8856
FU National Natural Science Foundation of China [51775496, 51875503]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 51775496 and Grant 51875503.
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NR 33
TC 1
Z9 1
U1 2
U2 14
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 MAR
PY 2020
VL 7
IS 3
BP 2197
EP 2211
DI 10.1109/JIOT.2019.2959063
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA KY0LN
UT WOS:000522265900052
DA 2022-08-23
ER

PT J
AU Rahman, MM
   Takasu, A
AF Rahman, Md Mostafizur
   Takasu, Atsuhiro
TI Leveraging Entity-Type Properties in the Relational Context for
   Knowledge Graph Embedding
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article
DE knowledge representations; entity types; embedding models
AB Knowledge graph embedding aims to embed entities and relations of multi-relational data in low dimensional vector spaces. Knowledge graphs are useful for numerous artificial intelligence (AI) applications. However, they (KGs) are far from completeness and hence KG embedding models have quickly gained massive attention. Nevertheless, the state-of-the-art KG embedding models ignore the category specific projection of entities and the impact of entity types in relational aspect. For example, the entity "Washington" could belong to the person or location category depending on its appearance in a specific relation. In a KG, an entity usually holds many type properties. It leads us to a very interesting question: are all the type properties of an entity are meaningful for a specific relation? In this paper, we propose a KG embedding model TPRC that leverages entity-type properties in the relational context. To show the effectiveness of our model, we apply our idea to the TransE, TransR and TransD. Our approach outperforms other state-of-the-art approaches as TransE, TransD, DistMult and ComplEx. Another, important observation is: introducing entity type properties in the relational context can improve the performances of the original translation distance based models.
C1 [Rahman, Md Mostafizur; Takasu, Atsuhiro] Grad Univ Adv Studies SOKENDAI, Tokyo 1018430, Japan.
   [Rahman, Md Mostafizur; Takasu, Atsuhiro] Natl Inst Informat, Tokyo 1018430, Japan.
RP Rahman, MM (通讯作者)，Grad Univ Adv Studies SOKENDAI, Tokyo 1018430, Japan.; Rahman, MM (通讯作者)，Natl Inst Informat, Tokyo 1018430, Japan.
EM rahman@nii.ac.jp
FU Cross-ministerial Strategic Innovation Promotion Program (SIP) Second
   Phase, "Bigdata and AI-enabled Cyberspace Technologies" by New Energy
   and Industrial Technol-ogy Development Organization (NEDO)
FX This work was supported by Cross-ministerial Strategic Innovation
   Promotion Program (SIP) Second Phase, "Bigdata and AI-enabled Cyberspace
   Technologies" by New Energy and Industrial Technol-ogy Development
   Organization (NEDO).
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NR 35
TC 2
Z9 2
U1 3
U2 6
PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
PI TOKYO
PA KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011,
   JAPAN
SN 1745-1361
J9 IEICE T INF SYST
JI IEICE Trans. Inf. Syst.
PD MAY
PY 2020
VL E103D
IS 5
BP 958
EP 968
DI 10.1587/transinf.2019DAP0007
PG 11
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LK2DK
UT WOS:000530668200006
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, JS
   Liang, S
   Sheng, YP
   Shao, J
AF Zhang, Jiasheng
   Liang, Shuang
   Sheng, Yongpan
   Shao, Jie
TI Temporal knowledge graph representation learning with local and global
   evolutions
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Temporal evolution; Representation learning
AB Temporal knowledge graph (TKG) representation learning aims to project entities and relations in TKGs to a low-dimensional vector space while preserving the evolutionary nature of TKGs. Most existing methods treat knowledge that happens at different times separately, which fails to explore how temporal knowledge graphs evolve over time. Actually, TKGs should evolve both on the local and global structures. The local structure evolution describes the formation process of the graph structure in a detailed manner, while the global structure evolution refers to the dynamic topology (e.g., community partition) of the graph, which is derived from the continuous formation process. Both are key factors for understanding the evolutionary nature of the TKGs. Unfortunately, little attention has been given to this area of research. In this paper, we propose a new TKG representation learning framework with local and global structure evolutions, named EvoExplore. Specifically, we define the local structure evolution as an establishment process of the relations between the entities, and propose a hierarchical-attention-based temporal point process to capture the formation process of the graph structure in a fine-grained manner. For global structure evolution, we propose a novel soft modularity parameterized by the entity representations to capture the dynamic community partition of the TKGs. Finally, we employ a multi-task loss function to jointly optimize the above two parts, which allows EvoExplore to learn the mutual influences of the local and global structure evolutions. Experimental results on three real-world datasets demonstrate the superiority of EvoExplore compared with the baseline methods. (C) 2022 Elsevier B.V. All rights reserved.
C1 [Zhang, Jiasheng; Liang, Shuang; Shao, Jie] Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China.
   [Sheng, Yongpan] Chongqing Univ, Chongqing 401331, Peoples R China.
   [Shao, Jie] UESTC, Shenzhen Inst Adv Study, Shenzhen 518110, Peoples R China.
RP Shao, J (通讯作者)，UESTC, Shenzhen Inst Adv Study, Shenzhen 518110, Peoples R China.
EM shaojie@uestc.edu.cn
FU Shenzhen Science and Technology Program [JCYJ20210324121213037];
   CAAI-Huawei MindSpore Open Fund; National Natural Science Foundation of
   China [61832001]
FX This work is supported by the National Natural Science Foundation of
   China (No. 61832001) , Shenzhen Science and Technology Program (No.
   JCYJ20210324121213037) and CAAI-Huawei Mind Spore Open Fund. We
   gratefully acknowledge the support of Mind Spore, CANN (Compute
   Architecture for Neural Networks) and Ascend AI Processor used for this
   research.
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NR 48
TC 0
Z9 0
U1 3
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD SEP 5
PY 2022
VL 251
SI SI
AR 109234
DI 10.1016/j.knosys.2022.109234
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Z9PH
UT WOS:000826899900002
DA 2022-08-23
ER

PT J
AU Liu, ZG
   Chen, XR
AF Liu, Zuoguo
   Chen, Xiaorong
TI Research on relation extraction of named entity on social media in smart
   cities
SO SOFT COMPUTING
LA English
DT Article
DE Smart cities; Social media; Named entity relation extraction;
   Interaction scenario; Distant supervision
ID KRILL HERD ALGORITHM
AB Social media make significant contribution to the evolution of smart cities. The key issue of smart cities is to develop a series of automatic methods to support smart applications. As one of the basic techniques of smart cities, the task of relation extraction of named entities on social media provides an indispensable means to construct and expand knowledge map and contributes to the utilization of information resources in smart cities. What's more, it is conducive to improve the efficiency of network supervision. This paper proposes an automatic method to extract entity relations via deep learning techniques on a two-level neural network named Bi-CLSTM. The research conquers some challenges of relation extraction on social media. To extract entity relations on conversation scenarios, Bi-CLSTM represents texts with the strategy of "word embedding + position embedding + shortest dependency path" and extracts relations via a hybrid model of LSTM and PCNN. The nodes and networks of Bi-CLSTM are designed to adapt to the scenarios of conversation and over-sentence. To reduce the dependency on training data, distant supervised strategy is employed and a two-level attention mechanism is used to prevent noise signals. Experiments are carried out on Sina Microblog corpus, and the results show that Bi-CLSTM model makes outstanding performance.
C1 [Liu, Zuoguo; Chen, Xiaorong] Guizhou Univ, Coll Comp Sci & Technol, Guiyang, Guizhou, Peoples R China.
RP Liu, ZG (通讯作者)，Guizhou Univ, Coll Comp Sci & Technol, Guiyang, Guizhou, Peoples R China.
EM 412769371@qq.com; xrchengz@163.com
FU National Natural Science Foundation of China [61363028]
FX This work is supported by the National Natural Science Foundation of
   China (No. 61363028). Thanks are also given to Guizhou University and
   NLP lab of Guizhou University for great supports. Last but not least,
   the HowNet knowledge database which developed by Mr. Dong makes great
   significance for our research.
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NR 41
TC 2
Z9 2
U1 1
U2 9
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 AUG
PY 2020
VL 24
IS 15
SI SI
BP 11135
EP 11147
DI 10.1007/s00500-020-04742-w
EA FEB 2020
PG 13
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MI8VP
UT WOS:000516083100003
DA 2022-08-23
ER

PT J
AU Zhang, X
   Vogel, DR
   Zhou, ZY
AF Zhang, Xi
   Vogel, Douglas R.
   Zhou, Zhongyun
TI Effects of information technologies, department characteristics and
   individual roles on improving knowledge sharing visibility: a
   qualitative case study
SO BEHAVIOUR & INFORMATION TECHNOLOGY
LA English
DT Article
DE IT functions; knowledge sharing visibility; department characteristics;
   knowledge management systems; social loafing; case study
ID MANAGING KNOWLEDGE; COMMUNITIES; MANAGEMENT; SYSTEMS; MODEL
AB Knowledge sharing visibility (KSV) is a critical environmental factor which can reduce social loafing in knowledge sharing (KS). This is especially true in ICT-based KS in learning organisations. As such, it is imperative that we better understand how to design technology enabled knowledge management systems (KMS) to support high KSV. This article examines the impact of knowledge management technology functions (e.g. tracking, knowledge storing) on KSV through qualitative analysis of 16 semi-structured interviews with participants in a Chinese company. Impact and implications of use for their existing KMS are examined. This article also examined the effects of department characteristics (i.e. group size and task characteristics) and individual roles (i.e. employee positions) on the IT-KSV relationship. Results encourage applied statistical, tracking, knowledge distribution and knowledge storing functions for monitoring explicit KS, and suggest integration of visualised knowledge maps with communication tools (e.g. Instant Messenger (IM)) to support visibility for implicit KS. Findings also suggest that KM technologies are more salient on improving KSV in large department with routine tasks, and that low-level employees may have more positive attitude on accepting communication tools on sharing knowledge. Extension to use of Web 2.0 technologies (e.g. weblogs) in KMS is also explored.
C1 [Zhang, Xi] Chinese Acad Sci, Ctr Innovat & Dev, Inst Policy & Management, Beijing, Peoples R China.
   [Vogel, Douglas R.] City Univ Hong Kong, Dept Informat Syst, Hong Kong, Hong Kong, Peoples R China.
   [Zhou, Zhongyun] Tongji Univ, Sch Econ & Management, Dept Management Sci & Engn, Shanghai 200092, Peoples R China.
RP Zhang, X (通讯作者)，Chinese Acad Sci, Ctr Innovat & Dev, Inst Policy & Management, Beijing, Peoples R China.
EM xizhang@casipm.ac.cn
OI Zhang, Xi/0000-0002-1105-9417
FU National Science Foundation of China [71201155]
FX The work described in this paper was part of a Project (No. 71201155)
   supported by National Science Foundation of China.
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NR 38
TC 41
Z9 42
U1 0
U2 56
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0144-929X
EI 1362-3001
J9 BEHAV INFORM TECHNOL
JI Behav. Inf. Technol.
PY 2012
VL 31
IS 11
SI SI
BP 1117
EP 1131
DI 10.1080/0144929X.2012.687770
PG 15
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA 033ZO
UT WOS:000310840100006
DA 2022-08-23
ER

PT J
AU Huang, JY
   Du, YK
   Tao, ST
   Xu, K
   Xie, PT
AF Huang, Jiayuan
   Du, Yangkai
   Tao, Shuting
   Xu, Kun
   Xie, Pengtao
TI Structured Self-Supervised Pretraining for Commonsense Knowledge Graph
   Completion
SO TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
LA English
DT Article
AB To develop commonsense-grounded NLP applications, a comprehensive and accurate commonsense knowledge graph (CKG) is needed. It is time-consuming to manually construct CKGs andmany research efforts have been devoted to the automatic construction of CKGs. Previous approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an capability to generate unobvious concepts. In this work, we aim to bridge this gap. We propose a general graph-to-paths pretraining framework that leverages high-order structures in CKGs to capture high-order relationships between concepts. We instantiate this general framework to four special cases: long path, path-to-path, router, and graph-node-path. Experiments on two datasets demonstrate the effectiveness of our methods. The code will be released via the public GitHub repository.
C1 [Huang, Jiayuan; Du, Yangkai; Tao, Shuting] Zhejiang Univ, Hangzhou, Peoples R China.
   [Xu, Kun] Tencent AI Lab, Bellevue, WA USA.
   [Xie, Pengtao] Univ Calif San Diego, La Jolla, CA 92093 USA.
RP Xie, PT (通讯作者)，Univ Calif San Diego, La Jolla, CA 92093 USA.
EM plxie@eng.ucsd.edu
FU Tencent AI Lab and Amazon AWS
FX This work was supported by gift funds from Tencent AI Lab and Amazon
   AWS.
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NR 47
TC 0
Z9 0
U1 2
U2 2
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2307-387X
J9 T ASSOC COMPUT LING
JI Trans. Assoc. Comput. Linguist.
PY 2021
VL 9
BP 1268
EP 1284
DI 10.1162/tacl_a_00426
PG 17
WC Computer Science, Artificial Intelligence; Linguistics; Language &
   Linguistics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Computer Science; Linguistics
GA YU3MT
UT WOS:000751952200075
OA gold
DA 2022-08-23
ER

PT J
AU Metzger, S
   Schenkel, R
   Sydow, M
AF Metzger, Steffen
   Schenkel, Ralf
   Sydow, Marcin
TI QBEES: query-by-example entity search in semantic knowledge graphs based
   on maximal aspects, diversity-awareness and relaxation
SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
LA English
DT Article
DE Semantic knowledge graph; Similar entity search; Aspect model;
   Diversity; Experimental evaluation
ID WEB SEARCH; SIMILARITY; ALGORITHM
AB Structured knowledge bases are an increasingly important way for storing and retrieving information. Within such knowledge bases, an important search task is finding similar entities based on one or more example entities. We present QBEES, a novel framework for defining entity similarity based on structural features, so-called aspects and maximal aspects of the entities, that naturally model potential interest profiles of a user submitting an ambiguous query. Our approach based on maximal aspects provides natural diversity awareness and includes query-dependent and query-independent entity ranking components. We present evaluation results with a number of existing entity list completion benchmarks, comparing to several state-of-the-art baselines.
C1 [Metzger, Steffen] Max Planck Inst Informat, Saarbrucken, Germany.
   [Schenkel, Ralf] Univ Trier, Trier, Germany.
   [Sydow, Marcin] Polish Acad Sci, Inst Comp Sci, Warsaw, Poland.
   [Sydow, Marcin] Polish Japanese Acad Informat Technol, Warsaw, Poland.
RP Sydow, M (通讯作者)，Polish Acad Sci, Inst Comp Sci, Warsaw, Poland.; Sydow, M (通讯作者)，Polish Japanese Acad Informat Technol, Warsaw, Poland.
EM smetzger@mpi-inf.mpg.de; schenkel@uni-trier.de; msyd@ipipan.waw.pl
OI Sydow, Marcin/0000-0001-9566-2688
FU Polish National Science Centre [2012/07/B/ST6/01239]; European Union
   under the European Social Fund [UDA-POKL.04.01.01-00-051/10-00]
FX This work was partially supported by Polish National Science Centre
   grant "DISQUSS" 2012/07/B/ST6/01239. We would like to thank Grzegorz
   Sobczak for providing additional experimental evaluation results of
   QBEES on the QALD3 and QALD4 data sets, his work was supported by the
   European Union under the European Social Fund Project PO KL "Information
   technologies: Research and their interdisciplinary applications",
   Agreement UDA-POKL.04.01.01-00-051/10-00.
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NR 38
TC 6
Z9 6
U1 0
U2 18
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 2017
VL 49
IS 3
BP 333
EP 366
DI 10.1007/s10844-017-0443-x
PG 34
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FL8LX
UT WOS:000414502400002
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Zhang, S
   Tan, QY
   Xu, GC
   Li, JH
AF Zhang, Shang
   Tan, Quanyin
   Xu, Guochang
   Li, Jinhui
TI Unveiling characteristics and trend of zero waste research: a
   scientometric perspective
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article
DE Zero waste strategy; Waste management; CiteSpace; Knowledge mapping;
   Network analysis; Scientometric analysis
ID FOOD WASTE; MANAGEMENT; INDEX
AB Solid waste generation has been significantly accelerated by the rapid growth of economy and population worldwide. The traditional waste management focusing on waste utilization and disposal is unable to unravel the continuous depletion of finite natural resources. "Zero waste" as an integrated waste management method to promote waste reduction through a pack of strategies such as cleaner production and green consumption has emerged. Here, we systematically reviewed the researches on zero waste from a scientometric perspective, and visually unveiled the most productive countries, evolution process, main authors, major research areas, and documents of zero waste research domain. The results show that with increased publications, zero waste has become a multidisciplinary study area and food waste reduction is the biggest sub-network in zero waste research. The current research frontiers are mainly regarding "lean production," "consumer behavior," "productivity." Accordingly, this study proposed a pathway for realizing zero waste cities with three lanes: (1) research and development, (2) management method, and (3) policy and implementation. The findings are also expected to be beneficial to latecomer researches in the waste management field.
C1 [Zhang, Shang; Tan, Quanyin; Xu, Guochang; Li, Jinhui] Tsinghua Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Sino Italian Environm & Energy Efficient Bldg, Beijing 100084, Peoples R China.
RP Li, JH (通讯作者)，Tsinghua Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Sino Italian Environm & Energy Efficient Bldg, Beijing 100084, Peoples R China.
EM jinhui@tsinghua.edu.cn
FU National Key R&D Program of China [2019YFC1908504]; major project of
   "The Beijing Social Science Fund" [17ZDA27]; Tsinghua University
   Initiative Scientific Research Program [2016THZWYX12]
FX This research is supported by the National Key R&D Program of China
   (2019YFC1908504), the major project of "The Beijing Social Science Fund"
   (17ZDA27), and the Tsinghua University Initiative Scientific Research
   Program (2016THZWYX12).
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NR 58
TC 0
Z9 0
U1 40
U2 41
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 JUN
PY 2022
VL 29
IS 29
BP 44391
EP 44403
DI 10.1007/s11356-021-18048-3
EA FEB 2022
PG 13
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 2D8EE
UT WOS:000752216000004
PM 35129751
DA 2022-08-23
ER

PT J
AU Hosseini, H
   Mansouri, M
   Bagheri, E
AF Hosseini, Hawre
   Mansouri, Mehran
   Bagheri, Ebrahim
TI A systemic functional linguistics approach to implicit entity
   recognition in tweets
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Named entity recognition; Tweet analytics; Knowledge graphs;
   Computational linguistics
AB The identification of knowledge graph entity mentions in textual content has already attracted much attention. The major assumption of existing work is that entities are explicitly mentioned in text and would only need to be disambiguated and linked. However, this assumption does not necessarily hold for social content where a significant portion of information is implied. The focus of our work in this paper is to identify whether textual social content include implicit mentions of knowledge graph entities or not, hence forming a two-class classification problem. To this end, we adopt the systemic functional linguistic framework that allows for capturing meaning expressed through language. Based on this theoretical framework we systematically introduce two classes of features, namely syntagmatic and paradigmatic features, for implicit entity recognition. In our experiments, we show the utility of these features for the task, report on ablation studies, measure the impact of each feature subset on each other and also provide a detailed error analysis of our technique.
C1 [Hosseini, Hawre; Bagheri, Ebrahim] Ryerson Univ, Lab Syst Software & Semant LS 3, Toronto, ON, Canada.
   [Mansouri, Mehran] Allameh Tabatabai Univ, Tehran, Iran.
RP Bagheri, E (通讯作者)，Ryerson Univ, Lab Syst Software & Semant LS 3, Toronto, ON, Canada.
EM hawre.hosseini@ryerson.ca; bagheri@ryerson.ca
OI Bagheri, Ebrahim/0000-0002-5148-6237
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NR 51
TC 0
Z9 0
U1 2
U2 2
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 2022
VL 59
IS 4
AR 102957
DI 10.1016/j.ipm.2022.102957
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 1T6KS
UT WOS:000804837900001
DA 2022-08-23
ER

PT J
AU Tao, XH
   Pham, T
   Zhang, J
   Yong, JM
   Goh, WP
   Zhang, WP
   Cai, Y
AF Tao, Xiaohui
   Pham, Thuan
   Zhang, Ji
   Yong, Jianming
   Goh, Wee Pheng
   Zhang, Wenping
   Cai, Yi
TI Mining health knowledge graph for health risk prediction
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Health knowledge graph; Classification; Healthcare; Electronic health
   data
ID CLASSIFICATION; MODEL
AB Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients' personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients' health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health.
C1 [Tao, Xiaohui; Pham, Thuan; Zhang, Ji; Yong, Jianming; Goh, Wee Pheng] Univ Southern Queensland, Toowoomba, Qld, Australia.
   [Zhang, Wenping] Renmin Univ China, Beijing, Peoples R China.
   [Cai, Yi] South China Univ Technol, Guangzhou, Guangdong, Peoples R China.
RP Tao, XH (通讯作者)，Univ Southern Queensland, Toowoomba, Qld, Australia.
EM Xiaohui.Tao@usq.edu.au
OI PHAM, THUAN/0000-0001-7433-858X
FU National Natural Science Foundation of China [NSFC 71801217]
FX The work is partially supported by the National Natural Science
   Foundation of China (NSFC 71801217). The authors also like to
   acknowledge the use of National Health and Nutrition Examination Survey
   (NHAMES) in the study and specifically, thank Centers for Disease
   Control and Prevention of the Department of Health and Human Services,
   United States for making the data set publicly available for research
   purpose.
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NR 50
TC 6
Z9 6
U1 9
U2 48
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 JUL
PY 2020
VL 23
IS 4
SI SI
BP 2341
EP 2362
DI 10.1007/s11280-020-00810-1
EA MAR 2020
PG 22
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LZ2LD
UT WOS:000520911200001
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Sacenti, JAP
   Fileto, R
   Willrich, R
AF Sacenti, Juarez A. P.
   Fileto, Renato
   Willrich, Roberto
TI Knowledge graph summarization impacts on movie recommendations
SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
LA English
DT Article
DE Recommender systems; Knowledge graphs; Graph summarization; Entity
   clustering; Knowledge graph embeddings; Multi-view clustering
ID TRUST
AB A classical problem that frequently compromises Recommender System (RS) accuracy is the sparsity of the data about the interactions of the users with the items to be recommended. The use of side information (e.g. movie domain information) from a Knowledge Graph (KG) has proven effective to circumvent this problem. However, KG growth in terms of size and complexity gives rise to many challenges, including the demand for high-cost algorithms to handle large amounts of partially irrelevant and noisy data. Meanwhile, though Graph Summarization (GS) has become popular to support tasks such as KG visualization and search, it is still relatively unexplored in the KG-based RS domain. In this work, we investigate the potential of GS as a preprocessing step to condense side information in a KG and consequently reduce computational costs of using this information. We propose a GS method that combines embedding based on latent semantics (ComplEx) with nodes clustering (K-Means) in single-view and multi-view approaches for KG summarization, i.e. which act on the whole KG at once or on a separated KG view at a time, respectively. Then, we evaluate the impacts of these alternative GS approaches on several state-of-the-art KG-based RSs, in experiments using the MovieLens 1M dataset and side information gathered from IMDb and DBpedia. Our experimental results show that KG summarization can speed up the recommendation process without significant changes in movie recommendation quality, which vary in accordance with the GS approach, the summarization ratio, and the recommendation method.
C1 [Sacenti, Juarez A. P.; Fileto, Renato; Willrich, Roberto] Fed Univ Santa Catarina UFSC, Grad Program Comp Sci PPGCC, Florianopolis, SC, Brazil.
RP Sacenti, JAP (通讯作者)，Fed Univ Santa Catarina UFSC, Grad Program Comp Sci PPGCC, Florianopolis, SC, Brazil.
EM juarez.sacenti@posgrad.ufsc.br; r.fileto@ufsc.br;
   roberto.willrich@ufsc.br
RI Willrich, Roberto/U-9378-2018
OI Willrich, Roberto/0000-0001-5067-0406; Sacenti,
   Juarez/0000-0002-8963-5787
FU Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil
   (CAPES) [001]; Print CAPES-UFSC "Automation 4.0"
FX This study was financed in part by the Coordenacao de Aperfeicoamento de
   Pessoal de N ' ivel Superior -Brasil (CAPES) -Finance Code 001 and by
   the Print CAPES-UFSC "Automation 4.0".
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NR 62
TC 4
Z9 4
U1 2
U2 16
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 FEB
PY 2022
VL 58
IS 1
BP 43
EP 66
DI 10.1007/s10844-021-00650-z
EA JUN 2021
PG 24
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YX6LS
UT WOS:000664425000001
DA 2022-08-23
ER

PT J
AU Pechsiri, C
   Piriyakul, R
AF Pechsiri, Chaveevan
   Piriyakul, Rapepun
TI Construction of Disease-Symptom Knowledge Graph from Web-Board Documents
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE cause-effect relation; disease-symptom knowledge graph; word
   co-occurrence pattern
AB Featured Application Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. The research aim is to construct a disease-symptom knowledge graph (DSKG) as a cause-effect knowledge graph containing disease-symptom relations as a cause-effect relation type determined from downloaded documents on medical web-board resources. Each disease-symptom relation connects a disease-name concept node (a causative-concept node) to a corresponding node having a group of correlated symptom-concept/effect-concept features as common symptom-concept/effect-concept features among some disease-name concepts. The DSKG benefits non-professionals in preliminary diagnosis through a recommender web-board. There are three main problems: how to determine symptom concepts from sentences without annotation on the documents having disease-name concepts as the documents' topic-names; how to determine the disease-symptom relations from the documents with/without complications; and how to construct the DSKG involving high dimensional symptom-concept features after union of the correlated symptom-concept groups. Therefore, we apply a word co-occurrence pattern including medical-symptom expressions from Wikipedia including MeSH and the Lexitron Dictionary to determine the symptom concepts. The Cartesian product is applied for automatic-supervised machine learning to determine the disease-symptom relation. We propose using Principal Component Analysis for constructing the DSKG by dimensionality reduction in the symptom-concept features with minimized information loss. In contrast to previous works, the proposed approach enables the DSKG construction with precise and concise representation scores of 7.8 and 9, respectively.
C1 [Pechsiri, Chaveevan] Dhurakij Pundit Univ, Coll Innovat Technol & Engn, Bangkok 10210, Thailand.
   [Piriyakul, Rapepun] Ramkhamhang Univ, Dept Comp Sci, Bangkok 10240, Thailand.
RP Pechsiri, C (通讯作者)，Dhurakij Pundit Univ, Coll Innovat Technol & Engn, Bangkok 10210, Thailand.
EM chaveevan.pec@dpu.ac.th; rapepunnight@yahoo.com
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NR 41
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD JUL
PY 2022
VL 12
IS 13
AR 6615
DI 10.3390/app12136615
PG 28
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 2X9YG
UT WOS:000825551500001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Tao, SH
   Qiu, RH
   Ping, Y
   Ma, H
AF Tao, Shaohua
   Qiu, Runhe
   Ping, Yuan
   Ma, Hui
TI Multi-modal Knowledge-aware Reinforcement Learning Network for
   Explainable Recommendation
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Multi-modal knowledge graph; Knowledge graph; Image; Deep reinforcement
   learning; Recommendation
AB Knowledge graphs (KGs) can provide rich, structured information for recommendation systems as well as increase accuracy and perform explicit reasoning. Deep reinforcement learning (RL) has also sparked great interest in personalized recommendations. The combination of the two holds promise in carrying out interpretable causal inference procedures and improving the performance of graph-structured recommendation. However, most KG-based recommendation focus on rich semantic relationships between entities in a heterogeneous knowledge graph, and thus fail to fully make use of the image information corresponding to an entity. In order to address these issues, we proposed a novel Multimodal Knowledge-aware Reinforcement Learning Network (MKRLN), which couples recommendation and interpretability by providing actual paths in multi-modal KG (MKG). The MKRLN can generate path representation by composing the structural and visual information of entities, and infers the underlying rational of agent-MKG interactions by leveraging the sequential dependencies within a path from the MKG. In addition, as KGs have too many attributes and entities, their combination with RL leads to too many action spaces and states in the reinforcement learning space, which complicates the search of action spaces. Furthermore, in order to solve this problem, we proposed a new hierarchical attention-path, which makes users focus their attention on the items they are interested in. This reduces the relations and entities in the KGs, which in turn reduces the action space and state in RL, shortens the path to the target entity, and improves the accuracy of recommendation. Our model has explicit explanation ability in knowledge and images. Finally, we extensively evaluated our model on several large-scale real-world benchmark datasets, and it yielded favorable results compared with state-of-the-art methods. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Tao, Shaohua; Qiu, Runhe] Donghua Univ, Coll Informat Sci & Technol, Shanghai, Peoples R China.
   [Tao, Shaohua; Ping, Yuan; Ma, Hui] XuChang Univ, Sch Informat Engn, Xuchang, Henan, Peoples R China.
   [Tao, Shaohua] Donghua Univ, Minist Educ, Engn Res Ctr Digitized Text & Fash Technol, Shanghai, Peoples R China.
RP Tao, SH; Qiu, RH (通讯作者)，Donghua Univ, Coll Informat Sci & Technol, Shanghai, Peoples R China.
EM 1179211@mail.dhu.edu.cn; qiurh@dhu.edu.cn; pyuan.lhn@xcu.edu.cn;
   mahuixc@xcu.edu.cn
FU National Key R&D Program of China [2017YFB0802000]; National Natural
   Science Foundation of China [U1736111]; Plan For Scientific Innovation
   Talent of Hen'an Province, China [184100510012]; Key Technologies R&D
   Program of He'nan Province, China [212102210084]; Innovation Scientists
   and Technicians Troop Construction Projects of He'nan Province Key
   Technologies R&D Program, China of Henan Province [192102210295]
FX This work is supported by the National Key R&D Program of China under
   Grant 2017YFB0802000, the National Natural Science Foundation of China
   under Grant U1736111, the Plan For Scientific Innovation Talent of
   Hen'an Province, China under Grant 184100510012, Key Technologies R&D
   Program of He'nan Province, China under Grant 212102210084, and
   Innovation Scientists and Technicians Troop Construction Projects of
   He'nan Province Key Technologies R&D Program, China of Henan Province
   under Grant NO. 192102210295.
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NR 27
TC 3
Z9 3
U1 22
U2 51
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD SEP 5
PY 2021
VL 227
AR 107217
DI 10.1016/j.knosys.2021.107217
EA JUN 2021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TS0WX
UT WOS:000679379400007
DA 2022-08-23
ER

PT J
AU Vlietstra, WJ
   Zielman, R
   van Dongen, RM
   Schultes, EA
   Wiesman, F
   Vos, R
   van Mulligen, EM
   Kors, JA
AF Vlietstra, Wytze J.
   Zielman, Ronald
   van Dongen, Robin M.
   Schultes, Erik A.
   Wiesman, Floris
   Vos, Rein
   van Mulligen, Erik M.
   Kors, Jan A.
TI Automated extraction of potential migraine biomarkers using a semantic
   graph
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Knowledge graph; Graph semantics; Biomarker identification; Migraine
   biomarkers; Semantic subgraph
ID NUCLEIC-ACIDS-RESEARCH; RESEARCH DATABASE ISSUE; PLASMA VASOPRESSIN;
   DISCOVERY; KNOWLEDGE; DISEASE; UMLS
AB Problem: Biomedical literature and databases contain important clues for the identification of potential disease biomarkers. However, searching these enormous knowledge reservoirs and integrating findings across heterogeneous sources is costly and difficult. Here we demonstrate how semantically integrated knowledge, extracted from biomedical literature and structured databases, can be used to automatically identify potential migraine biomarkers.
   Method: We used a knowledge graph containing more than 3.5 million biomedical concepts and 68.4 million relationships. Biochemical compound concepts were filtered and ranked by their potential as biomarkers based on their connections to a subgraph of migraine-related concepts. The ranked results were evaluated against the results of a systematic literature review that was performed manually by migraine researchers. Weight points were assigned to these reference compounds to indicate their relative importance.
   Results: Ranked results automatically generated by the knowledge graph were highly consistent with results from the manual literature review. Out of 222 reference compounds, 163 (73%) ranked in the top 2000, with 547 out of the 644 (85%) weight points assigned to the reference compounds. For reference compounds that were not in the top of the list, an extensive error analysis has been performed. When evaluating the overall performance, we obtained a ROC-AUC of 0.974.
   Discussion: Semantic knowledge graphs composed of information integrated from multiple and varying sources can assist researchers in identifying potential disease biomarkers. (C) 2017 The Author(s). Published by Elsevier Inc.
C1 [Vlietstra, Wytze J.; Vos, Rein; van Mulligen, Erik M.; Kors, Jan A.] Erasmus MC, Dept Med Informat, Rotterdam, Netherlands.
   [Zielman, Ronald; van Dongen, Robin M.] Leiden Univ, Med Ctr, Dept Neurol, Leiden, Netherlands.
   [Schultes, Erik A.] Leiden Univ, Med Ctr, Dept Human Genet, Leiden, Netherlands.
   [Wiesman, Floris] Acad Med Ctr, Dept Med Informat, Amsterdam, Netherlands.
   [Vos, Rein] Maastricht Univ, Dept Methodol & Stat, Maastricht, Netherlands.
RP Vlietstra, WJ (通讯作者)，Erasmus MC, Dept Med Informat, Rotterdam, Netherlands.
EM w.vlietstra@erasmusmc.nl
RI van Dongen, Robin/Y-5488-2018; Schultes, Erik/B-6035-2012
OI van Dongen, Robin/0000-0002-7946-0485; Schultes,
   Erik/0000-0001-8888-635X; Vlietstra, Wytze/0000-0002-1096-8563; van
   Mulligen, Erik/0000-0003-1377-9386
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NR 57
TC 18
Z9 18
U1 2
U2 16
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD JUL
PY 2017
VL 71
BP 178
EP 189
DI 10.1016/j.jbi.2017.05.018
PG 12
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA FG0FM
UT WOS:000409394600015
PM 28579531
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Aguiar, JG
   Correia, PRM
AF Aguiar, Joana G.
   Correia, Paulo R. M.
TI Using concept maps as instructional materials to foster the
   understanding of the atomic model and matter-energy interaction
SO CHEMISTRY EDUCATION RESEARCH AND PRACTICE
LA English
DT Article
ID COGNITIVE LOAD THEORY; GENERAL-CHEMISTRY; KNOWLEDGE MAPS; STUDENTS;
   SCIENCE; TOOL; REPRESENTATIONS; MISCONCEPTIONS; PHILOSOPHY; PRINCIPLES
AB In this paper, we explore the use of concept maps (Cmaps) as instructional materials prepared by teachers, to foster the understanding of chemistry. We choose fireworks as a macroscopic event to teach basic chemical principles related to the Bohr atomic model and matter-energy interaction. During teachers' Cmap navigation, students can experience a sense of disorientation, which is detrimental to the learning process. Two graphical cues were tested as Cmap navigation guidance: (1) colour-coded concepts, to group similar content and (2) numbered propositions to offer a reading sequence. A quasi-experimental pre-test-post-test design combined with mental effort was utilized to measure the efficiency of Cmaps in learning. First-year undergraduate students (n = 85) were randomly assigned to study one of four possible Cmaps. The results showed that all students were able to increase their level of factual knowledge, despite the Cmap being used as an instructional material. The lack of cues impaired conceptual understanding. Signalling similar content using colours was critical to reduce the invested mental effort and foster understanding about chemical concepts.
C1 [Aguiar, Joana G.] Univ Sao Paulo, Programa Posgrad Interunidades Ensino Ciencias, BR-05508900 Sao Paulo, SP, Brazil.
   [Correia, Paulo R. M.] Univ Sao Paulo, Escola Artes Ciencias & Humanidades, BR-03820000 Sao Paulo, SP, Brazil.
RP Correia, PRM (通讯作者)，Univ Sao Paulo, Escola Artes Ciencias & Humanidades, BR-03820000 Sao Paulo, SP, Brazil.
EM prmc@usp.br
RI Aguiar, Joana G/J-3508-2012; Aguiar, Joana/AAS-8585-2021
OI Aguiar, Joana G/0000-0002-4111-3058; Aguiar, Joana/0000-0002-4111-3058;
   Correia, Paulo/0000-0003-2419-7103
FU FAPESP (Sao Paulo Research Foundation) [2012/22693-5]; CAPES
   (Coordination for the Improvement of Higher Education Personnel)
FX The authors thank FAPESP (grant #2012/22693-5, Sao Paulo Research
   Foundation) for funding our research group. J.G.A. thanks CAPES
   (Coordination for the Improvement of Higher Education Personnel) for her
   scholarship.
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NR 66
TC 7
Z9 7
U1 2
U2 30
PU ROYAL SOC CHEMISTRY
PI CAMBRIDGE
PA THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
   ENGLAND
SN 1109-4028
EI 1756-1108
J9 CHEM EDUC RES PRACT
JI Chem. Educ. Res. Pract.
PY 2016
VL 17
IS 4
BP 756
EP 765
DI 10.1039/c6rp00069j
PG 10
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA EA5XM
UT WOS:000386698500011
DA 2022-08-23
ER

PT J
AU Zhao, FK
   Du, FK
   Shi, D
   Zhou, WK
   Jiang, YH
   Ma, LW
AF Zhao, Fangkun
   Du, Fengkun
   Shi, Dong
   Zhou, Wenkai
   Jiang, Youhong
   Ma, Liwei
TI Mapping research trends of retinal vein occlusion from 2009 to 2018: a
   bibliometric analysis
SO PEERJ
LA English
DT Article
DE VOSviewer; Bibliometric analysis; Retinal vein occlusion
ID DEXAMETHASONE INTRAVITREAL IMPLANT; MACULAR EDEMA; INFLAMMATORY FACTORS;
   RISK-FACTORS; THERAPY; PATHOGENESIS; RANIBIZUMAB; CYTOKINES; OUTCOMES;
   SAFETY
AB Objectives. To map publication trends and explore research hotspots of retinal vein occlusion (RVO) study.
   Methods. Based on Web of Science Core Collection (WoSCC), a bibliometric analysis was carried out. The knowledge map was constructed by VOSviewer v.1.6.10 to visualize the annual publication number, the distribution of countries, international collaborations, author productivity, source journals, cited reference and keywords in this field.
   Results. A total of 2,135 peer-reviewed papers were retrieved on RVO from 2009 to 2018. The United States ranks highest among countries with the most publications and the most active institution was Kyoto University. Noma H contributed the most publications in this field. Retina-The Journal of Retinal and Vitreous Disease was the most prolific journal in RVO research. The top cited references mainly presented anti-VEGF medications on the management of RVO. The keywords formed six clusters: (1) Risk factors and pathogenesis of RVO; (2) Metabolismof RVO; (3) Therapeutic use of corticosteroids on RVO; (4) Diagnostic methodsof RVO; (5) Management of macular edema secondary to RVO (6) Anti-VEGFtreatment of RVO.
   Conclusions. The six major research hotspots could provide an insight into RVO research and valuable information for researchers to identify potential collaborators and partner institutions.
C1 [Zhao, Fangkun; Shi, Dong; Zhou, Wenkai; Ma, Liwei] China Med Univ, Eye Hosp, Dept Ophthalmol, Key Lens Lab Liaoning Prov,Affiliated Hosp 4, Shenyang, Liaoning, Peoples R China.
   [Du, Fengkun] Xavier Univ Louisiana, Dept Biol, New Orleans, LA USA.
   [Jiang, Youhong] China Med Univ, Canc Res Inst, Dept Mol Oncol, Affiliated Hosp 1, Shenyang, Liaoning, Peoples R China.
RP Ma, LW (通讯作者)，China Med Univ, Eye Hosp, Dept Ophthalmol, Key Lens Lab Liaoning Prov,Affiliated Hosp 4, Shenyang, Liaoning, Peoples R China.; Jiang, YH (通讯作者)，China Med Univ, Canc Res Inst, Dept Mol Oncol, Affiliated Hosp 1, Shenyang, Liaoning, Peoples R China.
EM jiangyouhong2000@aliyun.com; lwma@cmu.edu.cn
RI shi, dongquan/ABH-3078-2021
OI shi, dongquan/0000-0002-4769-7816
FU China Postdoctoral Science Foundation [2019M650057]; National Science
   Foundation for Young Scientists of China [81600777]; Basic Research Fund
   for Young Instructors of Higher Education in Liaoning Province
   [LQNK201717]
FX The work was supported by the China Postdoctoral Science Foundation
   (Grant No. 2019M650057), the National Science Foundation for Young
   Scientists of China (Grant No.81600777), and the Basic Research Fund for
   Young Instructors of Higher Education in Liaoning Province (Grant No.
   LQNK201717). 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 29
TC 8
Z9 8
U1 2
U2 16
PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
SN 2167-8359
J9 PEERJ
JI PeerJ
PD AUG 29
PY 2019
VL 7
AR e7603
DI 10.7717/peerj.7603
PG 17
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA IT6XP
UT WOS:000483019900005
PM 31528507
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Xiao, LP
   Lei, PR
   Peng, WC
AF Xiao, Li-Pin
   Lei, Po-Ruey
   Peng, Wen-Chih
TI Hybrid Embedding of Multi-Behavior Network and Product-Content Knowledge
   Graph for Tourism Product Recommendation
SO JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
LA English
DT Article
DE tourism product recommendation; hybrid embedding system;
   multi-interaction behaviors; knowledge graph; network embedding
ID SYSTEMS
AB In the last decade, recommendation systems have gradually become the most important service for online business, which serve as sales assistants for e-commerce business increasing their profits. However, the conventional recommendation systems are usually confronted with two challenges. First, in online shopping contexts, users often browse products that they do not go on to order. The majority of action sequences are browsing-browsing rather than browsing-order. As a result, user actions are not a direct reflection of user preferences. Second, the popularity of sold products creates a skewed distribution that results in the problem of cold-start product for recommendation. In this paper, we present our research on developing a two stage framework of hybrid recommendation system to tackle these two challenges for tourism product recommendation. In order to extract knowledge from users??? implicit feedback, we develop the neighborhood structure of users and products in the multi-behavior interaction network that simultaneously incorporates the browsing and order behaviors. To ensure the coverage of cold products, we considered the metadata associated with products and extracted more features from the textual content to form a product-content knowledge graph. By embedding the multi-behavior network and product content knowledge graph within the recommendation system, we were able to capture user preferences from implicit feedback and the relationships among products. To evaluate the proposed model, we conducted experiments on a real-world dataset. Experimental results indicate that the proposed approach outperforms several widely-used recommendation systems.
C1 [Xiao, Li-Pin] KKday, Taipei, Taiwan.
   [Lei, Po-Ruey] ROC Naval Acad, Dept Elect Engn, Kaohsiung 813, Taiwan.
   [Peng, Wen-Chih] Natl Yang Ming Chiao Tang Univ, Dept Comp Sci, Hsinchu 300, Taiwan.
RP Lei, PR (通讯作者)，ROC Naval Acad, Dept Elect Engn, Kaohsiung 813, Taiwan.
EM felix.xiao@kkday.com; barry@cna.edu.tw; wcpeng@cs.nctu.edu.tw
FU Ministry of Science and Technology of Taiwan [MOST
   109-2221-E-012-004-MY2]
FX + Corresponding author was supported in part by the Ministry of Science
   and Technology of Taiwan, Project MOST 109-2221-E-012-004-MY2.
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NR 48
TC 0
Z9 0
U1 3
U2 3
PU INST INFORMATION SCIENCE
PI TAIPEI
PA ACADEMIA SINICA, TAIPEI 115, TAIWAN
SN 1016-2364
J9 J INF SCI ENG
JI J. Inf. Sci. Eng.
PD MAY
PY 2022
VL 38
IS 3
BP 547
EP 570
DI 10.6688/JISE.20220538(3).0004
PG 24
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1X8ZV
UT WOS:000807739800001
DA 2022-08-23
ER

PT J
AU Yao, KM
   Wang, HY
   Li, YL
   Rodrigues, JJPC
   de Albuquerque, VHC
AF Yao, Kaiming
   Wang, Haiyan
   Li, Yuliang
   Rodrigues, Joel J. P. C.
   de Albuquerque, Victor Hugo C.
TI A Group Discovery Method Based on Collaborative Filtering and Knowledge
   Graph for IoT Scenarios
SO IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
LA English
DT Article
DE Collaboration; Feature extraction; Knowledge engineering; Internet of
   Things; Prediction algorithms; Electronic mail; Semantics; Collaborative
   filtering; group discovery; Internet of Things (IoT); knowledge graph
ID RECOMMENDATION MODEL
AB With the massive growth of Internet-of-Things (IoT) devices, how to provide users with recommendation services in the IoT environment has become a research hotspot. Group discovery, as a prerequisite step of group recommendation that can be used to assist groups of users to select services in IoT-enriched environments, has an important impact on recommendation performance. However, existing group recommendation solutions assume that a user belongs to a specific group and ignore the possible correlation between the user's preferences and other groups' preferences. In addition, existing solutions treat group members as equal individuals and assign them equal weights, which makes it hard to meet the user's accurate recommendation requirements. Furthermore, these methods focus on group members' explicit preference information while ignoring implicit preferences. To address these problems, we propose a group discovery method based on collaborative filtering and knowledge graph (GD-CFKG). This method first uses the attention mechanism to learn the embedding of service entities from knowledge graphs and interaction between users and services to achieve users' own preferences embedding. Considering that the preferences of similar users will help to attain accurate target user's preferences, we then train users' final preferences embedding by collaborative filtering and word2vec method. We conduct experiments to evaluate our approach using the MovieLens and Douban data sets. Experimental results show that our proposed method has better group recommendation performance than those baseline methods.
C1 [Yao, Kaiming; Wang, Haiyan] Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing 210023, Peoples R China.
   [Li, Yuliang] Qihoo 360 Technol Co Ltd, Shanghai 201203, Peoples R China.
   [Rodrigues, Joel J. P. C.] Inst Telecomunicacoes, P-1049001 Lisbon, Portugal.
   [Rodrigues, Joel J. P. C.] Univ Fed Piaui, Res & Dev Ctr, BR-64049550 Teresina, Brazil.
   [de Albuquerque, Victor Hugo C.] ARMTEC Tecnol Robot, BR-60811341 Fortaleza, Ceara, Brazil.
RP Wang, HY (通讯作者)，Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing 210023, Peoples R China.
EM cn_kai@163.com; wanghy@njupt.edu.cn; lee_yl@foxmail.com;
   joeljr@ieee.org; victor.albuquerque@ieee.org
OI Rodrigues, Joel/0000-0001-8657-3800; Yao, Kaiming/0000-0002-7635-883X
FU National Natural Science Foundation of China [61772285]; Jiangsu High
   Technology Research Key Laboratory for Wireless Sensor Networks;
   Brazilian National Council for Research and Development (CNPq)
   [304315/2017-6, 430274/2018-1]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61772285; in part by the Jiangsu High
   Technology Research Key Laboratory for Wireless Sensor Networks; and in
   part by the Brazilian National Council for Research and Development
   (CNPq) under Grant #304315/2017-6 and Grant #430274/2018-1.
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NR 38
TC 1
Z9 1
U1 5
U2 6
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2329-924X
J9 IEEE T COMPUT SOC SY
JI IEEE Trans. Comput. Soc. Syst.
PD FEB
PY 2022
VL 9
IS 1
BP 279
EP 290
DI 10.1109/TCSS.2021.3050622
EA JAN 2021
PG 12
WC Computer Science, Cybernetics; Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YR6AC
UT WOS:000732880100001
DA 2022-08-23
ER

PT J
AU Liu, YY
AF Liu, Yingying
TI DKG-PIPD: A Novel Method About Building Deep Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Insects; Diseases; Data mining; Knowledge based systems; Deep learning;
   Internet; Databases; DKG-PIPD; semi-structured knowledge; unstructured
   knowledge; end-to-end model
ID NEURAL-NETWORK
AB In this study, a novel method about building Deep Knowledge Graph for the Plant Insect Pest and Disease, namely DKG-PIPD, was proposed. Specifically, the semi-automatic extraction of semi-structured and unstructured knowledge was carried out on the basis of domain ontology, and the knowledge graph was stored in the third-party knowledge database according to the corpus characteristic of the plant insect pest and disease, to realize the visual display of entity interactive relationship and knowledge inference. Furthermore, DKG-PIPD performed joint extraction about the entity and the relationship in unstructured knowledge in a corpus tagging method that is suitable for domain data. In this way, the entity and the relationship were annotated synchronically, and the triplet can be obtained directly through label matching and label mapping, which not only effectively improved the annotation efficiency, but also solved the problem of one-versus-many overlapping relation extraction. In addition, DKG-PIPD used a novel end-to-end model to train and predict on the crawled dataset. The experimental contrast results with other classical benchmark methods demonstrated the effectiveness of the proposed method. Moreover, the related work in this paper first introduced the general architecture required for the building of knowledge graph, and then summarized its key points, that is, named entity recognition, entity relationship extraction and knowledge inference using deep learning are emphatically introduced. Finally, the improvement direction of this paper was also introduced in the discussion section.
C1 [Liu, Yingying] Informat Engn Univ, Data & Target Engn Coll, Zhengzhou 450001, Peoples R China.
   [Liu, Yingying] Henan Univ Anim Husb & Econ, Informat Engn Coll, Zhengzhou 450046, Peoples R China.
RP Liu, YY (通讯作者)，Informat Engn Univ, Data & Target Engn Coll, Zhengzhou 450001, Peoples R China.; Liu, YY (通讯作者)，Henan Univ Anim Husb & Econ, Informat Engn Coll, Zhengzhou 450046, Peoples R China.
EM conslexanve1985@gmx.com
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NR 50
TC 0
Z9 0
U1 7
U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 137295
EP 137308
DI 10.1109/ACCESS.2021.3116467
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA WG2IN
UT WOS:000706820900001
OA gold
DA 2022-08-23
ER

PT J
AU Long, JW
   Chen, ZP
   He, WB
   Wu, TY
   Ren, JT
AF Long, Jiawei
   Chen, Zhaopeng
   He, Weibing
   Wu, Taiyu
   Ren, Jiangtao
TI An integrated framework of deep learning and knowledge graph for
   prediction of stock price trend: An application in Chinese stock
   exchange market
SO APPLIED SOFT COMPUTING
LA English
DT Article
DE Stock trend prediction; Deep neural network; Knowledge graph
ID NEURAL-NETWORK; MOVEMENT PREDICTION; MODE DECOMPOSITION; MACHINE;
   SUPPORT
AB Many studies have been carried out on stock price trend prediction, but most of them focused on the public market data and did not utilize the trading behaviors owing to the unavailability of real transaction records data. In fact, trading behaviors can better reflect the market movements, and the fusion of trading information and market information can further improve the prediction accuracy. In this paper, we propose a deep neural network model using the desensitized transaction records and public market information to predict stock price trend. Considering the correlation between stocks, our method utilizes the knowledge graph and graph embeddings techniques to select the relevant stocks of the target for constructing the market and trading information. Given the considerable number of investors and the complexity of transaction records data, the investors are clustered to reduce the dimensions of the trading feature matrices, and then the matrices are fed into the convolutional neural network to unearth the investment patterns. Eventually, the attention-based bidirectional long short-term memory network can predict the stock price trends for financial decision support. The experiments on the price movement direction and trend prediction show that our method achieves the best performance in comparison with other prediction baselines. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Long, Jiawei; Chen, Zhaopeng; Ren, Jiangtao] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangdong Prov Key Lab Computat Sci, Guangzhou 510275, Guangdong, Peoples R China.
   [He, Weibing; Wu, Taiyu] GF Secur, 26 Machang Rd, Guangzhou 510627, Peoples R China.
RP Ren, JT (通讯作者)，Sun Yat Sen Univ, Sch Data & Comp Sci, Guangdong Prov Key Lab Computat Sci, Guangzhou 510275, Guangdong, Peoples R China.
EM issrjt@mail.sysu.edu.cn
OI Long, Jiawei/0000-0001-8273-3280; Ren, Jiangtao/0000-0003-2827-8322
FU National Natural Science Foundation of China [U1811462]
FX This research is partially supported by National Natural Science
   Foundation of China (No. U1811462).
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NR 32
TC 43
Z9 43
U1 22
U2 101
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1568-4946
EI 1872-9681
J9 APPL SOFT COMPUT
JI Appl. Soft. Comput.
PD JUN
PY 2020
VL 91
AR 106205
DI 10.1016/j.asoc.2020.106205
PG 13
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LR1TN
UT WOS:000535478600016
DA 2022-08-23
ER

PT J
AU Abdelaziz, I
   Fokoue, A
   Hassanzadeh, O
   Zhang, P
   Sadoghi, M
AF Abdelaziz, Ibrahim
   Fokoue, Achille
   Hassanzadeh, Oktie
   Zhang, Ping
   Sadoghi, Mohammad
TI Large-scale structural and textual similarity-based mining of knowledge
   graph to predict drug-drug interactions
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Drug interaction; Similarity-based; Link prediction
ID SOURCE JAVA LIBRARY; DEVELOPMENT KIT CDK; DATABASE
AB Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients' health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs. (c) 2017 Elsevier B.V. All rights reserved.
C1 [Abdelaziz, Ibrahim] King Abdullah Univ Sci & Technol, Thuwal, Saudi Arabia.
   [Fokoue, Achille; Hassanzadeh, Oktie; Zhang, Ping] IBM TJ Watson Res Ctr, Yorktown Hts, NY USA.
   [Sadoghi, Mohammad] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA.
RP Abdelaziz, I (通讯作者)，King Abdullah Univ Sci & Technol, Thuwal, Saudi Arabia.
EM ibrahim.abdelaziz@kaust.edu.sa; achille@us.ibm.com;
   hassanzadeh@us.ibm.com; pzhang@us.ibm.com; msadoghi@purdue.edu
RI Abdelaziz, Ibrahim/AAE-2152-2021
OI Abdelaziz, Ibrahim/0000-0003-1449-5115; Hassanzadeh,
   Oktie/0000-0001-5307-9857; Zhang, Ping/0000-0002-4601-0779
CR Apweiler R, 2004, NUCLEIC ACIDS RES, V32, pD115, DOI 10.1093/nar/gkh131
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NR 34
TC 36
Z9 36
U1 3
U2 37
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
J9 J WEB SEMANT
JI J. Web Semant.
PD MAY
PY 2017
VL 44
BP 104
EP 117
DI 10.1016/j.websem.2017.06.002
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FQ6LQ
UT WOS:000418476400007
OA Green Published
DA 2022-08-23
ER

PT J
AU Bao, BH
   Ke, MH
   Guo, JQ
   Pan, ZK
   Huang, HN
   Ke, ZH
   Zhou, XF
   Liu, BX
AF Bao, Binghao
   Ke, Minghui
   Guo, Jianqiang
   Pan, Zhengkun
   Huang, Haonan
   Ke, Zhenghao
   Zhou, Xiaofeng
   Liu, Baoxing
TI Bibliometrics and visualisation analysis of literature on varicocele:
   From 2002 to 2021
SO ANDROLOGIA
LA English
DT Article; Early Access
DE andrology; bibliometrics; infertility; varicocele
ID INTRACYTOPLASMIC SPERM INJECTION; OXIDATIVE STRESS; MEN; INFERTILITY;
   OUTCOMES; ASSOCIATION; GUIDELINES
AB Varicocele is a common disease in men, with a global incidence of approximately 25%. A comprehensive and systematic analysis of the knowledge map on it will help in assessing frontier research and identify knowledge gaps. In total, 4103 articles published from 2002 to 2021 in 1066 journals were included. They represent the current research status worldwide, potential hotspots and future research directions. In the past decades, the number of publications and citations of varicocele-related studies have increased steadily. Academic institutions in the United States played a leading role in varicocele research. The country, institution, journal and author with the most publications were the United States (779), Cleveland Clinic Foundation (132), Andrologia (246) and Agarwal A (106), respectively. The most frequently used keywords were Varicocele (1620), Male Infertility (944), Varicocelectomy (288), Testis (245), Sperm (166), Oxidative Stress (144), Azoospermia (119), Semen Analysis (118), Laparoscopy (116) and Adolescent (97). Currently, the main focus of current varicocele research is its surgical treatment method and effect on sperm quality. The frontier research hotspot is the specific mechanism of varicocele-induced decrease in sperm quality.
C1 [Bao, Binghao; Guo, Jianqiang; Pan, Zhengkun; Huang, Haonan; Ke, Zhenghao] Beijing Univ Chinese Med, Grad Sch, Beijing, Peoples R China.
   [Bao, Binghao; Ke, Minghui; Guo, Jianqiang; Pan, Zhengkun; Huang, Haonan; Ke, Zhenghao; Liu, Baoxing] China Japan Friendship Hosp, Dept Androl, 2 Yinghuayuan East St, Beijing, Peoples R China.
   [Zhou, Xiaofeng] China Japan Friendship Hosp, Dept Urol, 2 Yinghuayuan East St, Beijing, Peoples R China.
RP Liu, BX (通讯作者)，China Japan Friendship Hosp, Dept Androl, 2 Yinghuayuan East St, Beijing, Peoples R China.; Zhou, XF (通讯作者)，China Japan Friendship Hosp, Dept Urol, 2 Yinghuayuan East St, Beijing, Peoples R China.
EM doctorzxf@126.com; liubaoxing@zryhyy.com.cn
FU National Natural Science Foundation of China [81873307]
FX This study was supported by the National Natural Science Foundation of
   China (No. 81873307). The authors are particularly grateful to Dr.
   Shenglou Ni (Beijing University of Chinese Medicine, China) for his
   critical comments on paper preparation.
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NR 39
TC 0
Z9 0
U1 0
U2 0
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0303-4569
EI 1439-0272
J9 ANDROLOGIA
JI Andrologia
AR e14537
DI 10.1111/and.14537
EA AUG 2022
PG 13
WC Andrology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Endocrinology & Metabolism
GA 3M1HF
UT WOS:000835208200001
PM 35920088
DA 2022-08-23
ER

PT J
AU Sun, J
   Zhou, Y
   Zong, CQ
AF Sun, Jian
   Zhou, Yu
   Zong, Chengqing
TI One-Shot Relation Learning for Knowledge Graphs via Neighborhood
   Aggregation and Paths Encoding
SO ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION
   PROCESSING
LA English
DT Article
DE One-shot learning; relation learning; knowledge graph completion
AB The relation learning between two entities is an essential task in knowledge graph (KG) completion that has received much attention recently. Previous work almost exclusively focused on relations widely seen in the original KGs, which means that enough training data are available for modeling. However, long-tail relations that only show in a few triples are actually much more common in practical KGs. Without sufficiently large training data, the performance of existing models on predicting long-tail relations drops impressively. This work aims to predict the relation under a challenging setting where only one instance is available for training. We propose a path-based one-shot relation prediction framework, which can extract neighborhood information of an entity based on the relation query attention mechanism to learn transferable knowledge among the same relation. Simultaneously, to reduce the impact of long-tail entities on relation prediction, we selectively fuse path information between entity pairs as auxiliary information of relation features. Experiments in three one-shot relation learning datasets show that our proposed framework substantially outperforms existing models on one-shot link prediction and relation prediction.
C1 [Sun, Jian; Zhou, Yu; Zong, Chengqing] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China.
   [Sun, Jian; Zhou, Yu; Zong, Chengqing] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100049, Peoples R China.
   [Zhou, Yu] Beijing Fanyu Echnol Co Ltd, Beijing, Peoples R China.
RP Sun, J (通讯作者)，Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China.; Sun, J (通讯作者)，Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100049, Peoples R China.
EM jian.sun@nlpr.ia.ac.cn; yzhou@nlpr.ia.ac.cn; cqzong@nlpr.ia.ac.cn
OI Zong, Chengqing/0000-0002-9864-3818
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NR 42
TC 0
Z9 0
U1 0
U2 0
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 2375-4699
EI 2375-4702
J9 ACM T ASIAN LOW-RESO
JI ACM Trans. Asian Low-Resour. Lang. Inf. Process.
PD MAY
PY 2022
VL 21
IS 3
AR 52
DI 10.1145/3484729
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0H0SQ
UT WOS:000778450600010
DA 2022-08-23
ER

PT J
AU Yin, QY
   Wu, S
   Wang, L
AF Yin, Qiyue
   Wu, Shu
   Wang, Liang
TI Multiview Clustering via Unified and View-Specific Embeddings Learning
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article
DE Incomplete multiview data; knowledge graph embedding; multiview
   learning; subspace learning
ID DIMENSIONALITY REDUCTION
AB Multiview clustering, which aims at using multiple distinct feature sets to boost clustering performance, has a wide range of applications. A subspace-based approach, a type of widely used methods, learns unified embedding from multiple sources of information and gives a relatively good performance. However, these methods usually ignore data similarity rankings; for example, example A may be more similar to B than C, and such similarity triplets may be more effective in revealing the data cluster structure. Motivated by recent embedding methods for modeling knowledge graph in natural-language processing, this paper proposes to mimic different views as different relations in a knowledge graph for unified and view-specific embedding learning. Moreover, in real applications, it happens so often that some views suffer from missing information, leading to incomplete multiview data. Under such a scenario, the performance of conventional multiview clustering degenerates notably, whereas the method we propose here can be naturally extended for incomplete multiview clustering, which enables full use of examples with incomplete feature sets for model promotion. Finally, we demonstrate through extensive experiments that our method performs better than the state-of-the-art clustering methods.
C1 [Yin, Qiyue; Wu, Shu; Wang, Liang] Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China.
   [Yin, Qiyue; Wu, Shu; Wang, Liang] Univ Chinese Acad Sci, Beijing, Peoples R China.
   [Wang, Liang] Chinese Acad Sci, Inst Automat, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China.
RP Wang, L (通讯作者)，Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China.
EM qyyin@nlpr.ia.ac.cn; shu.wu@nlpr.ia.ac.cn; wangliang@nlpr.ia.ac.cn
FU National Key Research and Development Program of China [2016YFB1001000];
   National Natural Science Foundation of China [61772528, 61525306,
   61633021, 61572504, 61420106015]; Strategic Priority Research Program of
   the CAS [XDB02070001]; Beijing Natural Science Foundation [4162058]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2016YFB1001000, in part by the
   National Natural Science Foundation of China under Grant 61772528, Grant
   61525306, Grant 61633021, Grant 61572504, and Grant 61420106015, in part
   by the Strategic Priority Research Program of the CAS under Grant
   XDB02070001, and in part by the Beijing Natural Science Foundation under
   Grant 4162058.
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NR 64
TC 25
Z9 28
U1 3
U2 19
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
PD NOV
PY 2018
VL 29
IS 11
BP 5541
EP 5553
DI 10.1109/TNNLS.2017.2786743
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA GX5YO
UT WOS:000447832200029
PM 29993610
DA 2022-08-23
ER

PT J
AU Shetty, M
   Bansal, C
   Kumar, S
   Rao, N
   Nagappan, N
AF Shetty, Manish
   Bansal, Chetan
   Kumar, Sumit
   Rao, Nikitha
   Nagappan, Nachiappan
TI SoftNER: Mining knowledge graphs from cloud incidents
SO EMPIRICAL SOFTWARE ENGINEERING
LA English
DT Article
DE Cloud services; Service incidents; Knowledge extraction; Knowledge
   graphs; Deep learning; Machine learning
AB The move from boxed products to services and the widespread adoption of cloud computing has had a huge impact on the software development life cycle and DevOps processes. Particularly, incident management has become critical for developing and operating large-scale services. Prior work on incident management has heavily focused on the challenges with incident triaging and de-duplication. In this work, we address the fundamental problem of structured knowledge extraction from service incidents. We have built SoftNER, a framework for mining Knowledge Graphs from incident reports. First, we build a novel multi-task learning based BiLSTM-CRF model which leverages not just the semantic context but also the data-types for extracting factual information in the form of named entities. Next, we present an approach to mine relations between the named entities for automatically constructing knowledge graphs. We have deployed SoftNER at Microsoft, a major cloud service provider and have evaluated it on more than 2 months of cloud incidents. We show that SoftNER's unsupervised pipeline learns the software entity types from unstructured incident data with high precision of 0.96 (at rank 50) and 0.77 (at rank 100). We also evaluate and show that SoftNER's unsupervised pipeline accurately labels data with a precision of 0.94. Further, our multi-task learning based deep learning model also outperforms the state-of-the-art NER models with an average F1 of 0.96. Lastly, using the knowledge extracted by SoftNER, we are able to build accurate models for tasks such as incident triaging and recommending entities based on their relevance to incident titles.
C1 [Shetty, Manish; Rao, Nikitha] Microsoft Res, Vigyan, 9 Lavelle Rd, Bengaluru 560001, Karnataka, India.
   [Bansal, Chetan; Nagappan, Nachiappan] Res Microsoft, Microsoft, One Microsoft Way, Redmond, WA 98052 USA.
   [Kumar, Sumit] Microsoft, One Microsoft Way, Redmond, WA 98052 USA.
RP Shetty, M (通讯作者)，Microsoft Res, Vigyan, 9 Lavelle Rd, Bengaluru 560001, Karnataka, India.
EM manish.shetty.m@outlook.com; chetanb@microsoft.com; sumitku@gmail.com;
   nikithar@andrew.cmu.edu; nnagappan@acm.org
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NR 58
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1382-3256
EI 1573-7616
J9 EMPIR SOFTW ENG
JI Empir. Softw. Eng.
PD JUL
PY 2022
VL 27
IS 4
AR 93
DI 10.1007/s10664-022-10159-w
PG 34
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V8JM
UT WOS:000788586200001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Tonon, A
   Catasta, M
   Prokofyev, R
   Demartini, G
   Aberer, K
   Cudre-Mauroux, P
AF Tonon, Alberto
   Catasta, Michele
   Prokofyev, Roman
   Demartini, Gianluca
   Aberer, Karl
   Cudre-Mauroux, Philippe
TI Contextualized ranking of entity types based on knowledge graphs
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Entity typing; Ranking; Context; Crowdsourcing; Knowledge graphs
ID WEB
AB A large fraction of online queries targets entities. For this reason, Search Engine Result Pages (SERPs) increasingly contain information about the searched entities such as pictures, short summaries, related entities, and factual information. A key facet that is often displayed on the SERPs and that is instrumental for many applications is the entity type. However, an entity is usually not associated to a single generic type in the background knowledge graph but rather to a set of more specific types, which may be relevant or not given the document context. For example, one can find on the Linked Open Data cloud the fact that Tom Hanks is a person, an actor, and a person from Concord, California. All these types are correct but some may be too general to be interesting (e.g., person), while other may be interesting but already known to the user (e.g., actor), or may be irrelevant given the current browsing context (e.g., person from Concord, California). In this paper, we define the new task of ranking entity types given an entity and its context. We propose and evaluate new methods to find the most relevant entity type based on collection statistics and on the knowledge graph structure interconnecting entities and types. An extensive experimental evaluation over several document collections at different levels of granularity (e.g., sentences, paragraphs) and different type hierarchies (including DBpedia, Freebase, and schema.org) shows that hierarchy-based approaches provide more accurate results when picking entity types to be displayed to the end-user. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Tonon, Alberto; Prokofyev, Roman; Cudre-Mauroux, Philippe] Univ Fribourg, eXascale Infolab, CH-1700 Fribourg, Switzerland.
   [Catasta, Michele; Aberer, Karl] Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland.
   [Demartini, Gianluca] Univ Sheffield, Informat Sch, Sheffield S10 2TN, S Yorkshire, England.
RP Tonon, A (通讯作者)，Univ Fribourg, eXascale Infolab, CH-1700 Fribourg, Switzerland.
EM alberto.tonon@unifr.ch; michele.catasta@epfl.ch;
   roman.prokofyev@unifr.ch; g.demartini@sheffield.ac.uk;
   karl.aberer@epfl.ch; philippe.cudre-mauroux@unifr.ch
RI Demartini, Gianluca/G-4635-2012
OI Demartini, Gianluca/0000-0002-7311-3693; Tonon,
   Alberto/0000-0002-1118-3186; Cudre-Mauroux, Philippe/0000-0003-2588-4212
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NR 43
TC 15
Z9 16
U1 0
U2 24
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 170
EP 183
DI 10.1016/j.websem.2015.12.005
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DM6JK
UT WOS:000376457600011
OA Green Accepted, Green Submitted
DA 2022-08-23
ER

PT J
AU Espinoza-Arias, P
   Garijo, D
   Corcho, O
AF Espinoza-Arias, Paola
   Garijo, Daniel
   Corcho, Oscar
TI Crossing the chasm between ontology engineering and application
   development: A survey
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Ontology; OWL; Ontology engineering; Web API; Application development;
   Knowledge graph
ID GRAPHS; SCALE; API; EVOLUTION; LESSONS
AB Y The adoption of Knowledge Graphs (KGs) by public and private organizations to integrate and publish data has increased in recent years. Ontologies play a crucial role in providing the structure for KGs, but are usually disregarded when designing Application Programming Interfaces (APIs) to enable browsing KGs in a developer-friendly manner. In this paper we provide a systematic review of the state of the art on existing approaches to ease access to ontology-based KG data by application developers. We propose two comparison frameworks to understand specifications, technologies and tools responsible for providing APIs for KGs. Our results reveal several limitations on existing API-based specifications, technologies and tools for KG consumption, which outline exciting research challenges including automatic API generation, API resource path prediction, ontology-based API versioning, and API validation and testing. (C) 2021 The Authors. Published by Elsevier B.V.
C1 [Espinoza-Arias, Paola; Corcho, Oscar] Univ Politecn Madrid, Ontol Engn Grp, Madrid, Spain.
   [Garijo, Daniel] Univ Southern Calif, Informat Sci Inst, Los Angeles, CA 90007 USA.
RP Espinoza-Arias, P (通讯作者)，Univ Politecn Madrid, Ontol Engn Grp, Madrid, Spain.
EM pespinoza@fi.upm.es; dgarijo@isi.edu; ocorcho@fi.upm.es
OI Garijo, Daniel/0000-0003-0454-7145; Espinoza-Arias,
   Paola/0000-0002-3938-2064
FU I+D+i program of the Universidad Politecnica de Madrid; Spanish project
   DATOS 4.0: RETOS Y SOLUCIONES [TIN201678011-C4-4-R]; Defense Advanced
   Research Projects Agency (DARPA), United States [W911NF-18-1-0027];
   National Institutes of Health (NIH), United States [1R01AG059874-01]
FX This work has been funded by a Predoctoral grant from the I+D+i program
   of the Universidad Politecnica de Madrid, the Spanish project DATOS 4.0:
   RETOS Y SOLUCIONES (TIN201678011-C4-4-R), by the Defense Advanced
   Research Projects Agency (DARPA), United States with award
   W911NF-18-1-0027 and the National Institutes of Health (NIH), United
   States with Award number 1R01AG059874-01. The authors would like to
   thank Carlos Badenes Olmedo and Jose Luis Redondo Garcia for all their
   valuable comments and feedback.
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NR 103
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
EI 1873-7749
J9 J WEB SEMANT
JI J. Web Semant.
PD JUL
PY 2021
VL 70
AR 100655
DI 10.1016/j.websem.2021.100655
EA JUL 2021
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YZ0ME
UT WOS:000755176800002
OA hybrid
DA 2022-08-23
ER

PT J
AU Bounhas, I
   Soudani, N
   Slimani, Y
AF Bounhas, Ibrahim
   Soudani, Nadia
   Slimani, Yahya
TI Building a morpho-semantic knowledge graph for Arabic information
   retrieval
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Morpho-semantic knowledge extraction; Classical Arabic text mining;
   Arabic information retrieval; Graph-based knowledge representation
ID MODEL
AB In this paper, we propose to build a morpho-semantic knowledge graph from Arabic vocalized corpora. Our work focuses on classical Arabic as it has not been deeply investigated in related works. We use a tool suite which allows analyzing and disambiguating Arabic texts, taking into account short diacritics to reduce ambiguities. At the morphological level, we combine Ghwanmeh stemmer and MADAMIRA which are adapted to extract a multi-level lexicon from Arabic vocalized corpora. At the semantic level, we infer semantic dependencies between tokens by exploiting contextual knowledge extracted by a concordancer. Both morphological and semantic links are represented through compressed graphs, which are accessed through lazy methods. These graphs are mined using a measure inspired from BM25 to compute one-to-many similarity. Indeed, we propose to evaluate the morpho-semantic Knowledge Graph in the context of Arabic Information Retrieval (IR). Several scenarios of document indexing and query expansion are assessed. That is, we vary indexing units for Arabic IR based on different levels of morphological knowledge, a challenging issue which is not yet resolved in previous research. We also experiment several combinations of morpho-semantic query expansion. This permits to validate our resource and to study its impact on IR based on state-of-the art evaluation metrics.
C1 [Bounhas, Ibrahim; Soudani, Nadia; Slimani, Yahya] Carthage Univ, LISI Lab Comp Sci Ind Syst, Tunis, Tunisia.
   [Soudani, Nadia] La Manouba Univ, Natl Sch Comp Sci, Manouba, Tunisia.
   [Slimani, Yahya] La Manouba Univ, Higher Inst Multimedia Arts Manouba, Manouba, Tunisia.
RP Bounhas, I (通讯作者)，Carthage Univ, LISI Lab Comp Sci Ind Syst, Tunis, Tunisia.
EM bounhas.ibrahim@gmail.com
RI Bounhas, Ibrahim/AAG-8419-2020
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NR 103
TC 14
Z9 14
U1 3
U2 13
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 102124
DI 10.1016/j.ipm.2019.102124
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 OG9PN
UT WOS:000582206800011
DA 2022-08-23
ER

PT J
AU Stavropoulos, TG
   Meditskos, G
   Lazarou, I
   Mpaltadoros, L
   Papagiannopoulos, S
   Tsolaki, M
   Kompatsiaris, I
AF Stavropoulos, Thanos G.
   Meditskos, Georgios
   Lazarou, Ioulietta
   Mpaltadoros, Lampros
   Papagiannopoulos, Sotirios
   Tsolaki, Magda
   Kompatsiaris, Ioannis
TI Detection of Health-Related Events and Behaviours from Wearable Sensor
   Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept
   Application in the Care of Multiple Sclerosis
SO SENSORS
LA English
DT Article
DE wearables; sensors; ontologies; symbolic reasoning; knowledge graphs;
   ehealth; multiple sclerosis
ID ACTIVITY RECOGNITION; OWL; SYSTEM
AB In this paper, we demonstrate the potential of a knowledge-driven framework to improve the efficiency and effectiveness of care through remote and intelligent assessment. More specifically, we present a rule-based approach to detect health related problems from wearable lifestyle sensor data that add clinical value to take informed decisions on follow-up and intervention. We use OWL 2 ontologies as the underlying knowledge representation formalism for modelling contextual information and high-level concepts and relations among them. The conceptual model of our framework is defined on top of existing modelling standards, such as SOSA and WADM, promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of SHACL constraints and rules to recognise patterns, anomalies and situations of interest based on the predefined and stored rules and conditions. A dashboard visualizes both sensor data and detected events to facilitate clinical supervision and decision making. Preliminary results on the performance and scalability are presented, while a focus group of clinicians involved in an exploratory research study revealed their preferences and perspectives to shape future clinical research using the framework.
C1 [Stavropoulos, Thanos G.; Meditskos, Georgios; Lazarou, Ioulietta; Mpaltadoros, Lampros; Kompatsiaris, Ioannis] Inst Informat Technol, Ctr Res Technol Hellas, 6th Km Charilaou Thermi, Thessaloniki 57001, Greece.
   [Meditskos, Georgios] Aristotle Univ Thessaloniki, Sch Informat, Thessaloniki 54124, Greece.
   [Papagiannopoulos, Sotirios] Aristotle Univ Thessaloniki, Sch Med, Dept Neurol 3, Thessaloniki 54124, Greece.
   [Tsolaki, Magda] Aristotle Univ Thessaloniki, Sch Med, Dept Neurol 1, Thessaloniki 54124, Greece.
RP Meditskos, G (通讯作者)，Inst Informat Technol, Ctr Res Technol Hellas, 6th Km Charilaou Thermi, Thessaloniki 57001, Greece.; Meditskos, G (通讯作者)，Aristotle Univ Thessaloniki, Sch Informat, Thessaloniki 54124, Greece.
EM athstavr@iti.gr; gmeditsk@csd.auth.gr; iouliettalaz@iti.gr;
   lamprosmpalt@iti.gr; spapagia@auth.gr; tsolakim1@gmail.com; ikom@iti.gr
RI Meditskos, Georgios/AGO-3230-2022; Kompatsiaris, Ioannis
   (Yiannis)/P-8594-2015
OI Meditskos, Georgios/0000-0003-4242-5245; Tsolaki,
   Magda/0000-0002-2072-8010; Lazarou, Ioulietta/0000-0001-5113-8366;
   Kompatsiaris, Ioannis (Yiannis)/0000-0001-6447-9020; Mpaltadoros,
   Lampros/0000-0001-8652-7628; Stavropoulos, Thanos G./0000-0003-2389-4329
FU Greece and the European Union (European Social Fund- ESF) through the
   Operational Programme "Human Resources Development, Education and
   Lifelong Learning 2014-2020" [MIS 5050689]
FX This research is co-financed by Greece and the European Union (European
   Social Fund- ESF) through the Operational Programme "Human Resources
   Development, Education and Lifelong Learning 2014-2020" in the context
   of the project "eHealth4MS: Electronic Health for the Care of Multiple
   Sclerosis" (MIS 5050689).
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NR 60
TC 1
Z9 1
U1 2
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD SEP
PY 2021
VL 21
IS 18
AR 6230
DI 10.3390/s21186230
PG 28
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA UW1WA
UT WOS:000699953900001
PM 34577437
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Badawy, A
   Fisteus, JA
   Mahmoud, TM
   Abd El-Hafeez, T
AF Badawy, Ahmed
   Fisteus, Jesus A.
   Mahmoud, Tarek M.
   Abd El-Hafeez, Tarek
TI Topic Extraction and Interactive Knowledge Graphs for Learning Resources
SO SUSTAINABILITY
LA English
DT Article
DE education; e-learning; topic identification; natural language
   processing; interactive knowledge graph
ID SEMANTIC WEB; IDENTIFICATION; REVOLUTION
AB Humanity development through education is an important method of sustainable development. This guarantees community development at present time without any negative effects in the future and also provides prosperity for future generations. E-learning is a natural development of the educational tools in this era and current circumstances. Thanks to the rapid development of computer sciences and telecommunication technologies, this has evolved impressively. In spite of facilitating the educational process, this development has also provided a massive amount of learning resources, which makes the task of searching and extracting useful learning resources difficult. Therefore, new tools need to be advanced to facilitate this development. In this paper we present a new algorithm that has the ability to extract the main topics from textual learning resources, link related resources and generate interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks no matter how big or small the texts are. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm's accuracy was evaluated against Gensim, largely improving its accuracy. This could be a step towards strengthening self-learning and supporting the sustainable development of communities, and more broadly of humanity, across different generations.
C1 [Badawy, Ahmed; Fisteus, Jesus A.] Univ Carlos III Madrid, Telemat Engn Dept, Legane 28911, Spain.
   [Badawy, Ahmed; Mahmoud, Tarek M.; Abd El-Hafeez, Tarek] Minia Univ, Comp Sci Dept, Fac Sci, El Minia 61519, Egypt.
   [Mahmoud, Tarek M.] Sadat City Univ, Fac Comp & Artificial Intelligence, Sadat City 32897, Egypt.
   [Abd El-Hafeez, Tarek] Deraya Univ, Comp Sci Unit, El Minia 61765, Egypt.
RP Badawy, A (通讯作者)，Univ Carlos III Madrid, Telemat Engn Dept, Legane 28911, Spain.; Badawy, A (通讯作者)，Minia Univ, Comp Sci Dept, Fac Sci, El Minia 61519, Egypt.
EM ahmed.badawy2000@mu.edu.eg; jesus.arias@uc3m.es; d.tarek@mu.edu.eg;
   tarek@mu.edu.eg
RI El-Hafeez, Tarek Abd/L-5316-2013; Arias Fisteus, Jesus/H-6230-2012
OI El-Hafeez, Tarek Abd/0000-0003-1785-1058; M Mahmoud,
   Tarek/0000-0002-0309-8088; Arias Fisteus, Jesus/0000-0002-4381-2071
CR Abasi AK, 2021, MULTIMED TOOLS APPL, V80, P37, DOI 10.1007/s11042-020-09504-2
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NR 45
TC 0
Z9 0
U1 6
U2 8
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 226
DI 10.3390/su14010226
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 YT1HT
UT WOS:000751119300001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Choi, W
   Lee, H
AF Choi, Wonjun
   Lee, Hyunju
TI Identifying disease-gene associations using a convolutional neural
   network-based model by embedding a biological knowledge graph with
   entity descriptions
SO PLOS ONE
LA English
DT Article
ID DATABASE; CANCER
AB Understanding the role of genes in human disease is of high importance. However, identifying genes associated with human diseases requires laborious experiments that involve considerable effort and time. Therefore, a computational approach to predict candidate genes related to complex diseases including cancer has been extensively studied. In this study, we propose a convolutional neural network-based knowledge graph-embedding model (KGED), which is based on a biological knowledge graph with entity descriptions to infer relationships between biological entities. As an application demonstration, we generated gene-interaction networks for each cancer type using gene-gene relationships inferred by KGED. We then analyzed the constructed gene networks using network centrality measures, including betweenness, closeness, degree, and eigenvector centrality metrics, to rank the central genes of the network and identify highly correlated cancer genes. Furthermore, we evaluated our proposed approach for prostate, breast, and lung cancers by comparing the performance with that of existing approaches. The KGED model showed improved performance in predicting cancer-related genes using the inferred gene-gene interactions. Thus, we conclude that gene-gene interactions inferred by KGED can be helpful for future research, such as that aimed at future research on pathogenic mechanisms of human diseases, and contribute to the field of disease treatment discovery.
C1 [Choi, Wonjun; Lee, Hyunju] Gwangju Inst Sci & Technol, Sch Elect Engn & Comp Sci, Gwangju, South Korea.
RP Lee, H (通讯作者)，Gwangju Inst Sci & Technol, Sch Elect Engn & Comp Sci, Gwangju, South Korea.
EM hyunjulee@gist.ac.kr
FU Ministry of Science and ICT through the National Research Foundation of
   Korea (NRF) [NRF2016M3A9C4939665]; NRF - Korean government (MSIT)
   [2021R1A2C2006268]
FX 7 This research was supported by the Bio-Synergy Research Project
   (NRF2016M3A9C4939665) of the Ministry of Science and ICT through the
   National Research Foundation of Korea (NRF), and by the NRF grant funded
   by the Korean government (MSIT) (2021R1A2C2006268).
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NR 44
TC 0
Z9 0
U1 5
U2 6
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 15
PY 2021
VL 16
IS 10
AR e0258626
DI 10.1371/journal.pone.0258626
PG 27
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA YZ7YW
UT WOS:000755689200040
PM 34653225
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU He, GQ
   Huo, YC
   He, MY
   Zhang, HX
   Fan, JP
AF He, Guiqing
   Huo, Yincheng
   He, Mingyao
   Zhang, Haixi
   Fan, Jianping
TI A Novel Orthogonality Loss for Deep Hierarchical Multi-Task Learning
SO IEEE ACCESS
LA English
DT Article
DE Task analysis; Feature extraction; Convolutional neural networks;
   Vegetation; Object recognition; Birds; Loss measurement; Orthogonality
   loss; multi-task learning; orthogonal distribution regularization
ID FEATURE-EXTRACTION; IMAGE; ATTENTION; FEATURES
AB In this paper, a novel loss function is proposed to measure the correlation among different learning tasks and select useful feature components for each classification task. Firstly, the knowledge map we proposed is used for organizing the affiliation relationship between objects in natural world. Secondly, a novel loss function & x2013;orthogonality loss is proposed to make the deep features more discriminative by removing useless feature components. Furthermore, in order to prevent the extracted feature maps from being too divergent and causing over-fitting which will reduce network performance, this paper also added the orthogonal distribution regularization term to constrain the distribution of network parameters. Finally, the proposed orthogonality loss is applied in a multi-task network structure to learn more discriminative deep feature, and also to evaluate the validity of the proposed loss function.The results show that compared with the traditional deep convolutional neural network and a multi-task network without orthogonality loss, the multi -task based orthogonality loss is significantly better than the other two types of networks on image classification.
C1 [He, Guiqing; Huo, Yincheng; He, Mingyao; Zhang, Haixi] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China.
   [Fan, Jianping] Univ North Carolina Charlotte, Dept Comp Sci, Charlotte, NC 28223 USA.
RP He, GQ (通讯作者)，Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China.
EM guiqing_he@nwpu.edu.cn
FU National Natural Science Foundation of China [61402368]; Aerospace
   Science and Technology Innovation Foundation of China [2017ZD53047];
   Aerospace Support Foundation of China [2019-HT-XGD]; Seed Foundation of
   Innovation and Creation for Graduate Students in Northwestern
   Polytechnical University [ZZ2019166]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61402368, in part by the Aerospace
   Science and Technology Innovation Foundation of China under Grant
   2017ZD53047, in part by the Aerospace Support Foundation of China under
   Grant 2019-HT-XGD, and in part by the Seed Foundation of Innovation and
   Creation for Graduate Students in Northwestern Polytechnical University
   under Grant ZZ2019166.
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NR 59
TC 1
Z9 1
U1 7
U2 13
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 67735
EP 67744
DI 10.1109/ACCESS.2020.2985991
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LK4KJ
UT WOS:000530829400001
OA gold
DA 2022-08-23
ER

PT J
AU Hsiao, YT
   Lee, WP
   Yang, W
   Muller, S
   Flamm, C
   Hofacker, I
   Kugler, P
AF Hsiao, Yu-Ting
   Lee, Wei-Po
   Yang, Wei
   Mueller, Stefan
   Flamm, Christoph
   Hofacker, Ivo
   Kuegler, Philipp
TI Practical Guidelines for Incorporating Knowledge-Based and Data-Driven
   Strategies into the Inference of Gene Regulatory Networks
SO IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
LA English
DT Article; Proceedings Paper
CT 10th International Conference on Intelligent Computing (ICIC)
CY AUG 03-06, 2014
CL Taiyuan, PEOPLES R CHINA
SP IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn China, Tongji Univ, N Univ China, Taiyuan Normal Univ, Taiyuan Univ Sci & Technol
DE Prior knowledge-mapping; S-system; gene regulatory network modeling;
   evolutionary algorithms
ID S-SYSTEM MODELS; EXPRESSION; TUMOR; YEAST; SENSITIVITY; EVOLUTION;
   REPAIR
AB Modeling gene regulatory networks (GRNs) is essential for conceptualizing how genes are expressed and how they influence each other. Typically, a reverse engineering approach is employed; this strategy is effective in reproducing possible fitting models of GRNs. To use this strategy, however, two daunting tasks must be undertaken: one task is to optimize the accuracy of inferred network behaviors; and the other task is to designate valid biological topologies for target networks. Although existing studies have addressed these two tasks for years, few of the studies can satisfy both of the requirements simultaneously. To address these difficulties, we propose an integrative modeling framework that combines knowledge-based and data-driven input sources to construct biological topologies with their corresponding network behaviors. To validate the proposed approach, a real dataset collected from the cell cycle of the yeast S. cerevisiae is used. The results show that the proposed framework can successfully infer solutions that meet the requirements of both the network behaviors and biological structures. Therefore, the outcomes are exploitable for future in vivo experimental design.
C1 [Hsiao, Yu-Ting; Lee, Wei-Po] Natl Sun Yat Sen Univ, Dept Informat Management, Kaohsiung 80424, Taiwan.
   [Hsiao, Yu-Ting] Acad Sinica, Genom Res Ctr, Taipei 115, Taiwan.
   [Yang, Wei] Fudan Univ, Shanghai Ctr Math Sci, Shanghai 200433, Peoples R China.
   [Mueller, Stefan; Kuegler, Philipp] Austrian Acad Sci, Johann Radon Inst Computat & Appl Math, Linz, Austria.
   [Flamm, Christoph; Hofacker, Ivo] Univ Vienna, Inst Theoret Chem, Vienna, Austria.
   [Hofacker, Ivo] Univ Vienna, Fac Comp Sci, Res Grp BCB, Vienna, Austria.
   [Kuegler, Philipp] Univ Hohenheim, Inst Appl Math & Stat, Stuttgart, Germany.
RP Hsiao, YT; Lee, WP (通讯作者)，Natl Sun Yat Sen Univ, Dept Informat Management, Kaohsiung 80424, Taiwan.; Hsiao, YT (通讯作者)，Acad Sinica, Genom Res Ctr, Taipei 115, Taiwan.; Yang, W (通讯作者)，Fudan Univ, Shanghai Ctr Math Sci, Shanghai 200433, Peoples R China.; Kugler, P (通讯作者)，Austrian Acad Sci, Johann Radon Inst Computat & Appl Math, Linz, Austria.; Flamm, C; Hofacker, I (通讯作者)，Univ Vienna, Inst Theoret Chem, Vienna, Austria.; Hofacker, I (通讯作者)，Univ Vienna, Fac Comp Sci, Res Grp BCB, Vienna, Austria.; Kugler, P (通讯作者)，Univ Hohenheim, Inst Appl Math & Stat, Stuttgart, Germany.
EM tin915x@gmail.com; wplee@mail.nsysu.edu.tw; weiyangfd@gmail.com;
   xtof@tbi.univie.ac.at; ivo@tbi.univie.ac.at;
   philipp.kuegler@uni-hohenheim.de
RI Hofacker, Ivo L/A-2378-2013
OI Hofacker, Ivo L/0000-0001-7132-0800; Muller, Stefan/0000-0002-3541-7856;
   Flamm, Christoph/0000-0001-5500-2415
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NR 58
TC 2
Z9 3
U1 0
U2 10
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 JAN-FEB
PY 2016
VL 13
IS 1
BP 64
EP 75
DI 10.1109/TCBB.2015.2465954
PG 12
WC Biochemical Research Methods; Computer Science, Interdisciplinary
   Applications; Mathematics, Interdisciplinary Applications; Statistics &
   Probability
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Biochemistry & Molecular Biology; Computer Science; Mathematics
GA DJ3HB
UT WOS:000374094300008
PM 26441429
DA 2022-08-23
ER

PT J
AU Sun, K
   Hu, YJ
   Song, J
   Zhu, YQ
AF Sun, Kai
   Hu, Yingjie
   Song, Jia
   Zhu, Yunqiang
TI Aligning geographic entities from historical maps for building knowledge
   graphs
SO INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
LA English
DT Article
DE Historical map; geographic knowledge graph; geographic entity alignment;
   geospatial data matching; map conflation
ID SEMANTIC SIMILARITY; FOREST COVER; CONFLATION; EXTRACTION;
   REPRESENTATION; APPROXIMATE; INTEGRATION; NETWORKS; AREA
AB Historical maps contain rich geographic information about the past of a region. They are sometimes the only source of information before the availability of digital maps. Despite their valuable content, it is often challenging to access and use the information in historical maps, due to their forms of paper-based maps or scanned images. It is even more time-consuming and labor-intensive to conduct an analysis that requires a synthesis of the information from multiple historical maps. To facilitate the use of the geographic information contained in historical maps, one way is to build a geographic knowledge graph (GKG) from them. This paper proposes a general workflow for completing one important step of building such a GKG, namely aligning the same geographic entities from different maps. We present this workflow and the related methods for implementation, and systematically evaluate their performances using two different datasets of historical maps. The evaluation results show that machine learning and deep learning models for matching place names are sensitive to the thresholds learned from the training data, and a combination of measures based on string similarity, spatial distance, and approximate topological relation achieves the best performance with an average F-score of 0.89.
C1 [Sun, Kai; Song, Jia; Zhu, Yunqiang] Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China.
   [Sun, Kai] Univ Chinese Acad Sci, Beijing, Peoples R China.
   [Sun, Kai; Hu, Yingjie] SUNY Buffalo, Dept Geog, GeoAI Lab, Buffalo, NY USA.
   [Song, Jia; Zhu, Yunqiang] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China.
RP Zhu, YQ (通讯作者)，Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China.; Zhu, YQ (通讯作者)，Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China.
EM zhuyq@igsnrr.ac.cn
RI Song, Jia/A-3577-2017
OI Song, Jia/0000-0002-9051-1925; Hu, Yingjie/0000-0002-5515-4125; Sun,
   Kai/0000-0002-0343-6032
FU National Natural Science Foundation of China [41771430, 41631177];
   University at Buffalo Research Foundation [38159749]; Strategic Priority
   Research Program of the Chinese Academy of Sciences [XDA23100100]; China
   Scholarship Council [201804910732]
FX This work was supported by the National Natural Science Foundation of
   China [41771430, 41631177]; University at Buffalo Research Foundation
   [38159749]; the Strategic Priority Research Program of the Chinese
   Academy of Sciences [XDA23100100]; the China Scholarship Council
   [201804910732].
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NR 89
TC 4
Z9 4
U1 13
U2 49
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1365-8816
EI 1362-3087
J9 INT J GEOGR INF SCI
JI Int. J. Geogr. Inf. Sci.
PD OCT 3
PY 2021
VL 35
IS 10
BP 2078
EP 2107
DI 10.1080/13658816.2020.1845702
EA NOV 2020
PG 30
WC Computer Science, Information Systems; Geography; Geography, Physical;
   Information Science & Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Geography; Physical Geography; Information Science &
   Library Science
GA UO5YU
UT WOS:000588532600001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Chatterjee, A
   Nardi, C
   Oberije, C
   Lambin, P
AF Chatterjee, Avishek
   Nardi, Cosimo
   Oberije, Cary
   Lambin, Philippe
TI Knowledge Graphs for COVID-19: An Exploratory Review of the Current
   Landscape
SO JOURNAL OF PERSONALIZED MEDICINE
LA English
DT Review
DE COVID-19; knowledge graph; natural language processing; drug repurposing
AB Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was "covid-19 knowledge graph". In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.
C1 [Chatterjee, Avishek; Oberije, Cary; Lambin, Philippe] Maastricht Univ, Dept Precis Med, Lab D, GROW Sch Oncol, NL-6229 ER Maastricht, Netherlands.
   [Nardi, Cosimo] Univ Florence, Dept Expt & Clin Biomed Sci, I-50134 Florence, Italy.
RP Chatterjee, A (通讯作者)，Maastricht Univ, Dept Precis Med, Lab D, GROW Sch Oncol, NL-6229 ER Maastricht, Netherlands.
EM a.chatterjee@maastrichtuniversity.nl; cosimo.nardi@unifi.it;
   c.oberije@maastrichtuniversity.nl;
   philippe.lambin@maastrichtuniversity.nl
RI Oberije, Cary/ABA-6178-2020; Nardi, Cosimo/M-5298-2018
OI Oberije, Cary/0000-0003-0749-5117; Nardi, Cosimo/0000-0002-5489-7824;
   Chatterjee, Avishek/0000-0003-3536-670X; Lambin,
   Philippe/0000-0001-7961-0191
FU European Commission's Horizon 2020 Research and Innovation program
   [101016131]; Innovative Medicines Initiative 2 Joint Undertaking
   [101005122]
FX Authors acknowledge financial support from the European Commission's
   Horizon 2020 Research and Innovation program under grant agreement
   101016131 (ICOVID) and the Innovative Medicines Initiative 2 Joint
   Undertaking under grant agreement No 101005122 (DRAGON).
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NR 36
TC 1
Z9 1
U1 3
U2 10
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-4426
J9 J PERS MED
JI J. Pers. Med.
PD APR
PY 2021
VL 11
IS 4
AR 300
DI 10.3390/jpm11040300
PG 21
WC Health Care Sciences & Services; Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; General & Internal Medicine
GA RR5HQ
UT WOS:000643129400001
PM 33919882
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU AlMousa, M
   Benlamri, R
   Khoury, R
AF AlMousa, Mohannad
   Benlamri, Rachid
   Khoury, Richard
TI Exploiting non-taxonomic relations for measuring semantic similarity and
   relatedness in WordNet
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Semantic similarity and relatedness; Knowledge graph; Information
   content; WordNet
ID KNOWLEDGE; MODELS
AB Various applications in computational linguistics and artificial intelligence employ semantic similarity to solve challenging tasks, such as word sense disambiguation, text classification, information retrieval, machine translation, and document clustering. To our knowledge, research to date rely solely on the taxonomic relation "ISA'' to evaluate semantic similarity and relatedness between terms. This paper explores the benefits of using all types of non-taxonomic relations in large linked data, such as WordNet knowledge graph, to enhance existing semantic similarity and relatedness measures. We propose a holistic poly-relational approach based on a new relation-based information content and non-taxonomic-based weighted paths to devise a comprehensive semantic similarity and relatedness measure. To demonstrate the benefits of exploiting non-taxonomic relations in a knowledge graph, we used three strategies to deploy non-taxonomic relations at different granularity levels. We conduct experiments on four well-known gold standard datasets. The results of our proposed method demonstrate an improvement over the benchmark semantic similarity methods, including the state-of-the-art knowledge graph embedding techniques, that ranged from 3.8%-23.8%, 1.3%-18.3%, 31.8%-117.2%, and 19.1%-111.1%, on all gold standard datasets MC, RG, WordSim, and Mturk, respectively. These results demonstrate the robustness and scalability of the proposed semantic similarity and relatedness measure, significantly improving existing similarity measures. (C) 2020 Elsevier B.V. All rights reserved.
C1 [AlMousa, Mohannad; Benlamri, Rachid] Lakehead Univ, Dept Software Engn, Thunder Bay, ON P7B 5E1, Canada.
   [Khoury, Richard] Univ Laval, Dept Comp Sci & Software Engn, Quebec City, PQ G1V 0A6, Canada.
RP AlMousa, M (通讯作者)，Lakehead Univ, Dept Software Engn, Thunder Bay, ON P7B 5E1, Canada.
EM malmous@lakeheadu.ca; rbenlamr@lakeheadu.ca;
   richard.khoury@ift.ulaval.ca
RI Almousa, Mohannad/AAI-5036-2021
OI Almousa, Mohannad/0000-0003-0087-1941
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NR 42
TC 7
Z9 7
U1 2
U2 16
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 106565
DI 10.1016/j.knosys.2020.106565
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA PN5NK
UT WOS:000604525200014
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Vlietstra, WJ
   Vos, R
   van den Akker, M
   van Mulligen, EM
   Kors, JA
AF Vlietstra, Wytze J.
   Vos, Rein
   van den Akker, Marjan
   van Mulligen, Erik M.
   Kors, Jan A.
TI Identifying disease trajectories with predicate information from a
   knowledge graph
SO JOURNAL OF BIOMEDICAL SEMANTICS
LA English
DT Article
DE Knowledge graph; Disease trajectories; Predicates; Temporal
   relationships; Directionality of predicates; Protein-protein
   interactions
ID NETWORK ANALYSIS; SEMANTIC WEB; ASSOCIATIONS; SEPSIS; GENES; RISK
AB Background Knowledge graphs can represent the contents of biomedical literature and databases as subject-predicate-object triples, thereby enabling comprehensive analyses that identify e.g. relationships between diseases. Some diseases are often diagnosed in patients in specific temporal sequences, which are referred to as disease trajectories. Here, we determine whether a sequence of two diseases forms a trajectory by leveraging the predicate information from paths between (disease) proteins in a knowledge graph. Furthermore, we determine the added value of directional information of predicates for this task. To do so, we create four feature sets, based on two methods for representing indirect paths, and both with and without directional information of predicates (i.e., which protein is considered subject and which object). The added value of the directional information of predicates is quantified by comparing the classification performance of the feature sets that include or exclude it. Results Our method achieved a maximum area under the ROC curve of 89.8% and 74.5% when evaluated with two different reference sets. Use of directional information of predicates significantly improved performance by 6.5 and 2.0 percentage points respectively. Conclusions Our work demonstrates that predicates between proteins can be used to identify disease trajectories. Using the directional information of predicates significantly improved performance over not using this information.
C1 [Vlietstra, Wytze J.; Vos, Rein; van Mulligen, Erik M.; Kors, Jan A.] Erasmus Univ, Med Ctr, Dept Med Informat, Dr Molewaterpl 50, NL-3015 GE Rotterdam, Netherlands.
   [Vos, Rein] Maastricht Univ, Dept Methodol & Stat, POB 616, NL-6200 MD Maastricht, Netherlands.
   [van den Akker, Marjan] Goethe Univ Frankfurt, Inst Gen Practice, Theodor Stern Kai 7, D-60590 Frankfurt, Germany.
   [van den Akker, Marjan] Maastricht Univ, Dept Family Med, POB 616, NL-6200 MD Maastricht, Netherlands.
RP Vlietstra, WJ (通讯作者)，Erasmus Univ, Med Ctr, Dept Med Informat, Dr Molewaterpl 50, NL-3015 GE Rotterdam, Netherlands.
EM w.vlietstra@erasmusmc.nl
OI Vlietstra, Wytze/0000-0002-1096-8563
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NR 60
TC 3
Z9 3
U1 2
U2 8
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 2041-1480
J9 J BIOMED SEMANT
JI J. Biomed. Semant.
PD AUG 20
PY 2020
VL 11
IS 1
AR 9
DI 10.1186/s13326-020-00228-8
PG 11
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA NK9WS
UT WOS:000567079300002
PM 32819419
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Yu, DJ
   Xu, ZS
   Wang, XZ
AF Yu, Dejian
   Xu, Zeshui
   Wang, Xizhao
TI Bibliometric analysis of support vector machines research trend: a case
   study in China
SO INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
LA English
DT Article
DE Bibliometric analysis; Support vector machines; Co-citation;
   Co-occurrence; China
ID EXTREME LEARNING-MACHINE; CLASSIFICATION; INFORMATION; DOMAIN; SVM;
   OPTIMIZATION; SPECTROSCOPY; HUMANITIES; PREDICTION; MANAGEMENT
AB Support vector machine (SVM) is a widely used algorithm in the field of machine learning, and it is a research hotspot in the field of data mining. In order to fully understand the historical progress and current situation of SVM researches, as well as its future development trend in China, this paper conducts a comprehensive bibliometric study based on the publications from web of science database by Chinese scholars in this field. First, this paper focuses on some of the basic characteristics of the research publications of SVM in China, including important journals, research institutions and countries/regions, most cited publications, and so on. Then, based on the knowledge mapping software VOSviewer, the cooperation between other countries and China as well as the cooperation between research institutions in China are explored. Finally, VOSviewer based bibliometric visualization graphics are used to identify the changes of the research hotspots in the SVM field. This paper provides a relatively broad perspective for the evaluation of SVM scientific researches, and reveals the development trend in this field.
C1 [Yu, Dejian] Nanjing Audit Univ, Business Sch, Nanjing 211815, Jiangsu, Peoples R China.
   [Xu, Zeshui] Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China.
   [Wang, Xizhao] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China.
RP Xu, ZS (通讯作者)，Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China.
EM yudejian62@126.com; xuzeshui@263.net; xizhaowang@ieee.org
RI Wang, Xizhao/ABG-7225-2020; Xu, Zeshui/N-8908-2013
OI Xu, Zeshui/0000-0003-3547-2908; Yu, Dejian/0000-0003-2796-9148
FU Ministry of Education of Humanities and Social Science project
   [19YJC630208]; Qinglan Project of Jiangsu Province (2019); National
   Natural Science Foundation of China [71771155, 71571123]; Natural
   Science Research Project of Jiangsu Higher Education Institutions
   [19KJB120008]
FX This manuscript was supported by the Ministry of Education of Humanities
   and Social Science project (No. 19YJC630208), the Qinglan Project of
   Jiangsu Province (2019), the National Natural Science Foundation of
   China (Nos. 71771155, 71571123), and the Natural Science Research
   Project of Jiangsu Higher Education Institutions (19KJB120008).
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NR 65
TC 44
Z9 45
U1 7
U2 66
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 MAR
PY 2020
VL 11
IS 3
BP 715
EP 728
DI 10.1007/s13042-019-01028-y
EA NOV 2019
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA KL2UG
UT WOS:000493931300001
DA 2022-08-23
ER

PT J
AU Wu, SB
   Feng, J
   Liu, CJ
   Wu, H
   Qiu, ZK
   Ge, JJ
   Sun, SY
   Hong, X
   Li, YK
   Wang, XN
   Yang, AD
   Guo, F
   Qiao, JJ
AF Wu, Shengbo
   Feng, Jie
   Liu, Chunjiang
   Wu, Hao
   Qiu, Zekai
   Ge, Jianjun
   Sun, Shuyang
   Hong, Xia
   Li, Yukun
   Wang, Xiaona
   Yang, Aidong
   Guo, Fei
   Qiao, Jianjun
TI Machine learning aided construction of the quorum sensing communication
   network for human gut microbiota
SO NATURE COMMUNICATIONS
LA English
DT Article
ID PSEUDOMONAS-AERUGINOSA; PROTEIN-STRUCTURE; BACTERIA; DATABASE; SYSTEMS;
   INDOLE; SIGNAL; REGULATORS; DIVERSITY; MOTIFS
AB Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database (http://www.qshgm.lbci.net/) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases.
C1 [Wu, Shengbo; Liu, Chunjiang; Qiu, Zekai; Ge, Jianjun; Sun, Shuyang; Hong, Xia; Li, Yukun; Wang, Xiaona; Qiao, Jianjun] Tianjin Univ, Sch Chem Engn & Technol, Tianjin 300072, Peoples R China.
   [Wu, Shengbo; Liu, Chunjiang] Tianjin Univ, State Key Lab Chem Engn, Tianjin 300072, Peoples R China.
   [Feng, Jie] Tianjin Univ, Coll Intelligence & Comp, Sch Comp Sci & Technol, Tianjin 300350, Peoples R China.
   [Wu, Hao; Qiao, Jianjun] Tianjin Univ, Zhejiang Shaoxing Res Inst, Shaoxing 312300, Peoples R China.
   [Yang, Aidong] Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England.
   [Guo, Fei] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
   [Qiao, Jianjun] Tianjin Univ, Key Lab Syst Bioengn, Minist Educ, Tianjin 300072, Peoples R China.
RP Qiao, JJ (通讯作者)，Tianjin Univ, Sch Chem Engn & Technol, Tianjin 300072, Peoples R China.; Qiao, JJ (通讯作者)，Tianjin Univ, Zhejiang Shaoxing Res Inst, Shaoxing 312300, Peoples R China.; Yang, AD (通讯作者)，Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England.; Guo, F (通讯作者)，Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.; Qiao, JJ (通讯作者)，Tianjin Univ, Key Lab Syst Bioengn, Minist Educ, Tianjin 300072, Peoples R China.
EM aidong.yang@eng.ox.ac.uk; guofei@csu.edu.cn; jianjunq@tju.edu.cn
OI Wu, Shengbo/0000-0001-7497-2935
FU National Key Research and Development Project of China [2019YFA0905600];
   National Natural Science Foundation of China [62172296]; Funds for
   Creative Research Groups of China [21621004]; National Key Research and
   Development Program of China [2020YFA0907900]
FX This study was supported by the National Key Research and Development
   Project of China (No.2019YFA0905600, J.Q.), the National Natural Science
   Foundation of China (62172296, F.G.), the Funds for Creative Research
   Groups of China (21621004, J.Q.), and National Key Research and
   Development Program of China (No. 2020YFA0907900, J.Q.).
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NR 91
TC 0
Z9 0
U1 19
U2 19
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
EI 2041-1723
J9 NAT COMMUN
JI Nat. Commun.
PD JUN 2
PY 2022
VL 13
IS 1
AR 3079
DI 10.1038/s41467-022-30741-6
PG 13
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 1U1UC
UT WOS:000805202900013
PM 35654892
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Lee, JH
   Suh, HW
AF Lee, Jae-Hyun
   Suh, Hyo-Won
TI Ontology-based Multi-layered Knowledge Framework for Product Lifecycle
   Management
SO CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS
LA English
DT Article
DE ontology; knowledge framework; knowledge management system; product
   lifecycle management
ID REPRESENTATION; INFORMATION
AB This paper proposes an ontology-based multi-layered knowledge framework for a product lifecycle management. Participants in a product life cycle want to share comprehensive product knowledge without ambiguity or heterogeneity. However, previous knowledge management approaches are limited in providing these aspects. Our knowledge framework has a systematic lattice structure with three product knowledge types and four layers. The three knowledge types are axioms, knowledge maps, and specialized knowledge for a domain. The four layers consist of a product context model, a product specific model, a product planning model, and a product manufacturing model. These layers represent different abstraction levels of the product knowledge conceptualization. Each layer includes the aforementioned three knowledge types in order to accommodate comprehensive knowledge and is represented with first order logic for a uniform representation. We provide formal definitions of the framework to manage comprehensive knowledge according to the proposed knowledge framework. In addition, we develop a prototype system for realization of the framework. The system will help knowledge engineers create, edit, and visualize product knowledge according to the framework and infer new knowledge with a prolog inference engine.
C1 [Lee, Jae-Hyun; Suh, Hyo-Won] Korea Adv Inst Sci & Technol, Dept Ind Engn, Taejon 305701, South Korea.
RP Lee, JH (通讯作者)，Korea Adv Inst Sci & Technol, Dept Ind Engn, 373-1 Guseong Dong, Taejon 305701, South Korea.
EM knight@kaist.ac.kr
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NR 25
TC 13
Z9 13
U1 2
U2 17
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1063-293X
EI 1531-2003
J9 CONCURRENT ENG-RES A
JI Concurrent Eng.-Res. Appl.
PD DEC
PY 2008
VL 16
IS 4
BP 301
EP 311
DI 10.1177/1063293X08100030
PG 11
WC Computer Science, Interdisciplinary Applications; Engineering,
   Manufacturing; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 386FV
UT WOS:000261868600008
DA 2022-08-23
ER

PT J
AU Zehra, S
   Mohsin, SFM
   Wasi, S
   Jami, SI
   Siddiqui, MS
   Syed, MKURR
AF Zehra, Samreen
   Mohsin, Syed Farhan Mohsin
   Wasi, Shaukat
   Jami, Syed Imran
   Siddiqui, Muhammad Shoaib
   Syed, Muhammad Khaliq-Ur-Rahman Raazi
TI Financial Knowledge Graph Based Financial Report Query System
SO IEEE ACCESS
LA English
DT Article
DE Information retrieval; Ontologies; Data mining; Databases; Companies;
   Semantics; Task analysis; Ontology; financial knowledge graph;
   information extraction
AB Annual Financial Reports are the core in the Banking Sector to publish its financial statistics. Extracting useful information from these complex and lengthy reports involves manual process to resolve the financial queries, resulting in delays and ambiguity in investment decisions. One of the major reasons is the lack of any standardization in the format and vocabulary used in the reports. An automated system for resolution of intelligent financial queries is therefore difficult to design. Several works have been proposed to overcome these problems using Information Extraction; however, they do not address the semantic interoperability of the reports across different institutions. This work proposed an automated querying engine to answer the financial queries using Ontology based Information Extraction. For Semantic modeling of financial reports, a Financial Knowledge Graph, assisted by Financial Ontology, has been proposed. The nodes are populated with entities, while links are populated with relationships using Information Extraction applied on annual reports. Two benefits have been provided by this system to stakeholders through automation: decision making through queries and generation of custom financial stories. The work can further be extended to other domains including healthcare and academia where physical reports are used for communication.
C1 [Zehra, Samreen; Mohsin, Syed Farhan Mohsin; Wasi, Shaukat; Jami, Syed Imran; Syed, Muhammad Khaliq-Ur-Rahman Raazi] Mohammad Ali Jinnah Univ, Dept Comp Sci, Karachi 75400, Pakistan.
   [Siddiqui, Muhammad Shoaib] Islamic Univ Madinah, Fac Comp & Informat Syst, Medina 42351, Saudi Arabia.
RP Mohsin, SFM; Wasi, S (通讯作者)，Mohammad Ali Jinnah Univ, Dept Comp Sci, Karachi 75400, Pakistan.
EM farhan.mohsin@duhs.edu.pk; shaukat.wasi@jinnah.edu
OI Jami, Syed Imran/0000-0002-9490-7943; Mohsin, Syed
   Farhan/0000-0003-3527-6544
FU Mohammed Ali Jinnah University, Karachi; Islamic University of Madinah
FX This work was supported in part by the Mohammed Ali Jinnah University,
   Karachi, and in part by the Islamic University of Madinah.
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NR 52
TC 0
Z9 0
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 2021
VL 9
BP 69766
EP 69782
DI 10.1109/ACCESS.2021.3077916
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA SC1PK
UT WOS:000650452200001
OA gold
DA 2022-08-23
ER

PT J
AU Xu, XL
   Hong, QF
   Wang, YX
   Jin, JH
   Xuan, XL
   Fu, T
AF Xu, Xiaoliang
   Hong, Qifan
   Wang, Yuxiang
   Jin, Jiahui
   Xuan, Xinle
   Fu, Tao
TI A random walk sampling on knowledge graphs for semantic-oriented
   statistical tasks
SO DATA & KNOWLEDGE ENGINEERING
LA English
DT Article
DE Knowledge graph; Random walk sampling; Approximate estimation
ID SEARCH
AB A knowledge graph (KG) manages large-scale and real-world facts as a big graph in a schema flexible manner, which has recently attracted considerable attention . It is very common that users deploy some statistical tasks on a KG to achieve the latent information of interest. There are two types of the statistical tasks, that are, topology-oriented and semantic-oriented statistical tasks. Many efforts have been made for the former one (e.g., finding the average degree of a KG). The basic idea is concluded as: estimating an approximate statistical result based on a random sample collected through a topology-aware KG sampling approach. Unfortunately, this method cannot be directly deployed to support semantic-oriented statistical tasks (e.g., achieving the average fuel economy of cars produced in Germany), because the topology-aware sampling does not consider the semantics of a KG (or we say the sample is collected only based on the topological information of a KG, while excluding the semantics of a KG), hence leading to a low-quality random sample and would significantly affect the accuracy. In this paper, we propose a semantic-aware random walk sampling on KGs to quickly and accurately collect samples that match the semantic constraint of the semantic-oriented statistical task, and obtain an approximate statistical result by well-designed unbiased estimators. Moreover, we propose an optimization on our semantic-aware sampling to improve the sampling efficiency. Finally, extensive experiments were conducted on our method, which confirmed the effectiveness and efficiency of our approach.
C1 [Xu, Xiaoliang; Hong, Qifan; Wang, Yuxiang; Fu, Tao] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou, Peoples R China.
   [Xu, Xiaoliang] Zhejiang Lab Zhejiang Prov, Hangzhou, Peoples R China.
   [Jin, Jiahui] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Xuan, Xinle] Hangzhou Sanhui Digital Informat Technol Co Ltd, Hangzhou, Peoples R China.
RP Wang, YX (通讯作者)，Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou, Peoples R China.
EM singloxuan@163.com; qfhong@hdu.edu.cn; lsswyx@hdu.edu.cn;
   jjin@seu.edu.cn; singloxuan@163.com; ft@hdu.edu.cn
FU Primary Research & Development Plan of Zhejiang Province, China
   [2021C03156, 2021C02004]; National NSF of China [62072149]; Natural
   Science Foundation of Zhejiang [LGG19F020017, 2021KG0AC01]; Primary
   Research & Development Plan of Zhejiang Province, China [2021C03156,
   2021C02004]; National NSF of China [62072149]; Natural Science
   Foundation of Zhejiang [LGG19F020017]; Center-initiated Research Project
   of Zhejiang Lab [2021KG0AC01]
FX This work was supported by the Primary Research & Development Plan of
   Zhejiang Province, China under grants 2021C03156 and 2021C02004, the
   National NSF of China under grant 62072149, the Natural Science
   Foundation of Zhejiang under grant LGG19F020017, and the
   Center-initiated Research Project of Zhejiang Lab under grant
   2021KG0AC01.
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   Zlatic V.., TOPOLOGICALLY BIASED
NR 48
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-023X
EI 1872-6933
J9 DATA KNOWL ENG
JI Data Knowl. Eng.
PD JUL
PY 2022
VL 140
AR 102024
DI 10.1016/j.datak.2022.102024
PG 17
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1U2GH
UT WOS:000805235100001
DA 2022-08-23
ER

PT J
AU Bone, J
   Dias, M
   Ferreira, JC
   Ribeiro, R
AF Bone, Joao
   Dias, Mariana
   Ferreira, Joao C.
   Ribeiro, Ricardo
TI DisKnow: A Social-Driven Disaster Support Knowledge Extraction System
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE disaster management; natural language processing; information
   extraction; crowdsourcing; automatic knowledge base construction;
   knowledge graphs
ID TWITTER; MEDIA
AB This research is aimed at creating and presenting DisKnow, a data extraction system with the capability of filtering and abstracting tweets, to improve community resilience and decision-making in disaster scenarios. Nowadays most people act as human sensors, exposing detailed information regarding occurring disasters, in social media. Through a pipeline of natural language processing (NLP) tools for text processing, convolutional neural networks (CNNs) for classifying and extracting disasters, and knowledge graphs (KG) for presenting connected insights, it is possible to generate real-time visual information about such disasters and affected stakeholders, to better the crisis management process, by disseminating such information to both relevant authorities and population alike. DisKnow has proved to be on par with the state-of-the-art Disaster Extraction systems, and it contributes with a way to easily manage and present such happenings.
C1 [Bone, Joao; Dias, Mariana; Ferreira, Joao C.; Ribeiro, Ricardo] Inst Univ Lisboa ISCTE IUL, P-1649026 Lisbon, Portugal.
   [Bone, Joao; Dias, Mariana; Ferreira, Joao C.] Inst Novas Tecnol, INOV INESC Inovacao, P-1000029 Lisbon, Portugal.
   [Ferreira, Joao C.] Informat Sci Technol & Architecture Res Ctr ISTAR, P-1649026 Lisbon, Portugal.
   [Ribeiro, Ricardo] INESC ID Lisboa, P-1000029 Lisbon, Portugal.
RP Bone, J (通讯作者)，Inst Univ Lisboa ISCTE IUL, P-1649026 Lisbon, Portugal.; Bone, J (通讯作者)，Inst Novas Tecnol, INOV INESC Inovacao, P-1000029 Lisbon, Portugal.
EM Joao_Bone@iscte-iul.pt; Mariana_Rebelo@iscte-iul.pt; jcafa@iscte-iul.pt;
   ricardo.ribeiro@iscte-iul.pt
RI Ferreira, João Carlos Amaro/B-5351-2009
OI Ferreira, João Carlos Amaro/0000-0002-6662-0806; Ribeiro,
   Ricardo/0000-0002-2058-693X; Bone, Joao/0000-0002-3359-9780; Dias,
   Mariana/0000-0001-6563-9858
FU FCT, Fundacao para a Ciencia e a Tecnologia [UIDB/50021/2020];
   Infrastress Project
FX This work was supported in part by the Infrastress Project-The
   Infrastress project is a H2020 financed project, aiming to address
   cyber-physical security of Sensitive Industrial Plants and Sites (SIPS),
   improve resilience and protection capabilities of SIPS exposed to large
   scale, combined, cyber-physical threats and hazards, and guarantee
   continuity of operations, while mitigating effects in the infrastructure
   itself, the environment, and citizens in vicinity, at reasonable cost.
   This work was also supported by national funds through FCT, Fundacao
   para a Ciencia e a Tecnologia, under project UIDB/50021/2020.
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NR 43
TC 1
Z9 1
U1 5
U2 13
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 17
AR 6083
DI 10.3390/app10176083
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 NP1XL
UT WOS:000569973900001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Xue, XS
   Zhu, H
AF Xue, Xingsi
   Zhu, Hai
TI Matching Knowledge Graphs with Compact Niching Evolutionary Algorithm
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge Graph matching; Compact Niching Evolutionary Algorithm;
   Compact evolutionary mechanism; Niching strategy
ID MEMETIC ALGORITHM; ONTOLOGIES
AB To address the Knowledge Graph (KG) heterogeneity issue, we need to determine a set of entity correspondences, which requires aggregating several complementary similarity measures to improve the confidence of the results. How to determine the suitable aggregating weights for the similarity measures to improve the KG alignment's quality is called the KG meta-matching problem, whose challenge of scalability remains significant in the Semantic Web (SW) domain. To face this challenge, this work proposes a Compact Niching Evolutionary Algorithm (CNEA) based matching technique. We first propose an approximate evaluation metric on the alignment's quality, and on this basis, a multi-modal optimization model is constructed to formally define the KG meta-matching problem. Then, a niching strategy is combined with EA's evolutionary paradigm to address it, which is able to effectively locate and maintain multiple global optimal solutions. Moreover, a serial matching framework and the compact evolutionary mechanism are presented to improve CNEA's efficiency. In particular, the former reduces the algorithm's search space of the instance matching phase with schema level alignments, and the latter uses the probability representation on the population to reduce the algorithm's memory consumption and run time. The experiment utilizes Ontology Alignment Evaluation Initiative (OAEI)'s KG track to test our proposal's performance, and experimental results show that CNEA-based KG matching technique is both effective and efficient.
C1 [Xue, Xingsi] Fujian Univ Technol, Fujian Prov Key Lab Big Data Min & Applicat, Fuzhou 350118, Fujian, Peoples R China.
   [Zhu, Hai] Zhoukou Normal Univ, Sch Network Engn, Zhoukou 466001, Henan, Peoples R China.
RP Xue, XS (通讯作者)，Fujian Univ Technol, Fujian Prov Key Lab Big Data Min & Applicat, Fuzhou 350118, Fujian, Peoples R China.
EM jack8357@gmail.com; zhu_sea@163.com
OI Xue, Xingsi/0000-0002-3008-8782
FU National Natural Science Foundation of China [62172095, 62172457];
   Natural Science Founda-tion of Fujian Province [2020J01875]; Scientific
   Research Foundation of Fujian University of Technology [GY-Z17162]
FX This work is supported by the National Natural Science Foundation of
   China (Nos. 62172095 and 62172457) , the Natural Science Founda-tion of
   Fujian Province (No. 2020J01875) and the Scientific Research Foundation
   of Fujian University of Technology (No. GY-Z17162) .
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NR 40
TC 0
Z9 0
U1 3
U2 3
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD OCT 1
PY 2022
VL 203
AR 117371
DI 10.1016/j.eswa.2022.117371
PG 9
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 1T7RV
UT WOS:000804926200003
DA 2022-08-23
ER

PT J
AU Zhao, D
   Wang, J
   Zhang, YJ
   Wang, X
   Lin, HF
   Yang, ZH
AF Zhao, Di
   Wang, Jian
   Zhang, Yijia
   Wang, Xin
   Lin, Hongfei
   Yang, Zhihao
TI Incorporating representation learning and multihead attention to improve
   biomedical cross-sentence n-ary relation extraction
SO BMC BIOINFORMATICS
LA English
DT Article
DE Biomedical n-ary relation; Multihead attention; Representation learning
AB BackgroundMost biomedical information extraction focuses on binary relations within single sentences. However, extracting n-ary relations that span multiple sentences is in huge demand. At present, in the cross-sentence n-ary relation extraction task, the mainstream method not only relies heavily on syntactic parsing but also ignores prior knowledge.ResultsIn this paper, we propose a novel cross-sentence n-ary relation extraction method that utilizes the multihead attention and knowledge representation that is learned from the knowledge graph. Our model is built on self-attention, which can directly capture the relations between two words regardless of their syntactic relation. In addition, our method makes use of entity and relation information from the knowledge base to impose assistance while predicting the relation. Experiments on n-ary relation extraction show that combining context and knowledge representations can significantly improve the n-ary relation extraction performance. Meanwhile, we achieve comparable results with state-of-the-art methods.ConclusionsWe explored a novel method for cross-sentence n-ary relation extraction. Unlike previous approaches, our methods operate directly on the sequence and learn how to model the internal structures of sentences. In addition, we introduce the knowledge representations learned from the knowledge graph into the cross-sentence n-ary relation extraction. Experiments based on knowledge representation learning show that entities and relations can be extracted in the knowledge graph, and coding this knowledge can provide consistent benefits.
C1 [Zhao, Di; Wang, Jian; Zhang, Yijia; Wang, Xin; Lin, Hongfei; Yang, Zhihao] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian, Peoples R China.
RP Wang, J (通讯作者)，Dalian Univ Technol, Sch Comp Sci & Technol, Dalian, Peoples R China.
EM wangjian@dlut.edu.cn
FU National Natural Science Foundation of China [61572098, 61572102]
FX This work is supported by the National Natural Science Foundation of
   China (Nos. 61572098, 61572102). The funding bodies did not play any
   role in the design of the study, data collection and analysis, or
   preparation of the manuscript.
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NR 31
TC 0
Z9 0
U1 4
U2 26
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD JUL 16
PY 2020
VL 21
IS 1
AR 312
DI 10.1186/s12859-020-03629-9
PG 17
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 MP7OI
UT WOS:000552390300002
PM 32677883
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, QY
   Tang, XL
   Wang, TY
   Yang, HZ
   Song, HJ
AF Li, Qianyu
   Tang, Xiaoli
   Wang, Tengyun
   Yang, Haizhi
   Song, Hengjie
TI Unifying Task-Oriented Knowledge Graph Learning and Recommendation
SO IEEE ACCESS
LA English
DT Article
DE Recommender systems; knowledge representation; sampling methods
AB Incorporating knowledge graphs (KGs) into recommender systems (knowledge-aware recommendation) to improve the recommendation accuracy and explainability has attracted considerable research efforts. However, existing methods largely assume that KGs are complete when transferring knowledge from them, which may lead to suboptimal performance for those KGs, can be hardly complete in real-life scenarios. In this paper, we present a robustly co-learning model (RCoLM) that takes the incompleteness nature of KGs into consideration when incorporating them into recommendation. The RCoLM aims at transferring knowledge between recommendation task and knowledge graph completion (KG completion) task by utilizing a transfer learning model. An earlier version of this paper appeared in KDD 2019. This version is an extension of the previous submission and two major innovations are presented here. At first, distinct from previous knowledge-aware recommendation methods, which mainly focus on transferring knowledge from KGs to item recommendations, the RCoLM attempts to exploit user-item interactions from recommendations for KG completion, and unifis the two tasks in a joint model for mutual enhancements. Second, the RCoLM provides a general task-oriented negative sampling strategy on KG completion task, which further improves the adaptive ability of the proposed algorithm and plays an essential role for obtaining superior performance in various sub-tasks of the KG completion. The extensive experiments on two real-world public datasets demonstrate that RCoLM outperforms not only state-of-the-art knowledge-aware recommendation methods but also existing KG completion methods.
C1 [Li, Qianyu; Tang, Xiaoli; Wang, Tengyun; Yang, Haizhi; Song, Hengjie] South China Univ Technol, Sch Software Engn, Guangzhou 510006, Guangdong, Peoples R China.
RP Song, HJ (通讯作者)，South China Univ Technol, Sch Software Engn, Guangzhou 510006, Guangdong, Peoples R China.
EM sehjsong@scut.edu.cn
OI Tang, Xiaoli/0000-0002-1967-2953; Wang, Tengyun/0000-0001-8160-9392
FU National Key Research and Development Program of China [2018YFC0830900];
   National Natural Science Foundation of China [71671069]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018YFC0830900, and in part by
   the National Natural Science Foundation of China under Grant 71671069.
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NR 41
TC 7
Z9 8
U1 1
U2 13
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 115816
EP 115828
DI 10.1109/ACCESS.2019.2932466
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IV4FY
UT WOS:000484230200019
OA gold
DA 2022-08-23
ER

PT J
AU Wu, YL
   Zhao, SL
AF Wu, Yongliang
   Zhao, Shuliang
TI Community answer generation based on knowledge graph
SO INFORMATION SCIENCES
LA English
DT Article
DE Community question answering; Answer generation; Phrase embedding;
   Knowledge graph; Phrase mining
ID QUESTIONS; SYSTEMS
AB Community Question Answering (CQA) has become an indispensable way for modern people to share and acquire knowledge. It allows users to ask questions, which will be answered by experienced users enthusiastically. By recording user operation logs, CQA has accumulated a large amount of valuable and complex data. However, askers must wait (usually for a long time) until other expert users answer their questions on social platforms. This will seriously affect the user experience. In this paper, we propose a Community Answer Generation method based on the Knowledge Graph, called CAGKG, to generate natural language answers automatically. Firstly, we extract the core phrases of posts to represent their semantics relations. Then, we model the user's knowledge background based on their action records. Finally, we query knowledge entities in a knowledge graph based on user background and question semantics, then convert them into natural language answers. Besides, we proposed a Phrase-based Answers Semantic Similarity Evaluation indicator, called PASSE, which focuses on the semantic similarity between texts instead of literal matching. To the best of our knowledge, it is the first work that utilizes the user knowledge and text semantics to improve the performance of CQA. Experiments on four real datasets (Stack Overflow, Super User, Mathematics, and Quora) show that CAGKG is superior to the state-of-the-art question answering frameworks. Compared with other answer evaluation indicators, PASSE is a promising indicator for evaluating semantic similarity. (C) 2020 Elsevier Inc. All rights reserved.
C1 [Wu, Yongliang] Hebei Normal Univ, Sch Math Sci, Shijiazhuang 050024, Hebei, Peoples R China.
   [Zhao, Shuliang] Hebei Normal Univ, Coll Comp & Cyber Secur, Shijiazhuang 050024, Hebei, Peoples R China.
   [Zhao, Shuliang] Hebei Prov Key Lab Network & Informat Secur, Shijiazhuang 050024, Hebei, Peoples R China.
   [Zhao, Shuliang] Hebei Prov Engn Res Ctr Supply Chain Big Data Ana, Shijiazhuang 050024, Hebei, Peoples R China.
RP Zhao, SL (通讯作者)，Hebei Normal Univ, Coll Comp & Cyber Secur, Shijiazhuang 050024, Hebei, Peoples R China.
EM shuliangzhao@hebtu.edu.cn
FU National Social Science Fund of China [18ZDA200, 13ZD091]; Key R&D
   project of Hebei Province [20370301D]
FX This research is partially supported by The National Social Science Fund
   of China No. 18ZDA200, No. 13&ZD091 and The Key R&D project of Hebei
   Province No. 20370301D. Shuliang Zhao is the corresponding author.
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NR 50
TC 2
Z9 3
U1 11
U2 73
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 FEB 4
PY 2021
VL 545
BP 132
EP 152
DI 10.1016/j.ins.2020.07.077
PG 21
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA OV7SZ
UT WOS:000592406000008
DA 2022-08-23
ER

PT J
AU Nizzoli, L
   Avvenuti, M
   Tesconi, M
   Cresci, S
AF Nizzoli, Leonardo
   Avvenuti, Marco
   Tesconi, Maurizio
   Cresci, Stefano
TI Geo-semantic-parsing: AI-powered geoparsing by traversing semantic
   knowledge graphs
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE Geoparsing; Geotagging; Artificial intelligence; Knowledge graphs;
   Twitter
ID DECISION-SUPPORT-SYSTEMS; SOCIAL MEDIA; LOCATION PREDICTION; REAL-TIME;
   TWITTER; GEOLOCATION; CLASSIFICATION; TWEETS
AB Online social networks convey rich information about geospatial facets of reality. However in most cases, geographic information is not explicit and structured, thus preventing its exploitation in real-time applications. We address this limitation by introducing a novel geoparsing and geotagging technique called Geo-Semantic-Parsing (Gs P). GSP identifies location references in free text and extracts the corresponding geographic coordinates. To reach this goal, we employ a semantic annotator to identify relevant portions of the input text and to link them to the corresponding entity in a knowledge graph. Then, we devise and experiment with several efficient strategies for traversing the knowledge graph, thus expanding the available set of information for the geoparsing task. Finally, we exploit all available information for learning a regression model that selects the best entity with which to geotag the input text. We evaluate GSP on a well-known reference dataset including almost 10 k event-related tweets, achieving F1 = 0.66. We extensively compare our results with those of 2 baselines and 3 state-of-the-art geoparsing techniques, achieving the best performance. On the same dataset, competitors obtain F1 <= 0.55. We conclude by providing in-depth analyses of our results, showing that the overall superior performance of GSP is mainly due to a large improvement in recall, with respect to existing techniques.
C1 [Nizzoli, Leonardo; Avvenuti, Marco] Univ Pisa, Dept Informat Engn, Pisa, Italy.
   [Nizzoli, Leonardo; Tesconi, Maurizio; Cresci, Stefano] Natl Res Council IIT CNR, Inst Informat & Telemat, Pisa, Italy.
RP Cresci, S (通讯作者)，Natl Res Council IIT CNR, Inst Informat & Telemat, Pisa, Italy.
EM l.nizzoli@iit.cnr.it; marco.avvenuti@unipi.it; m.tesconi@iit.cnr.it;
   s.cresci@iit.cnr.it
RI TESCONI, MAURIZIO/P-2441-2016; Cresci, Stefano/Q-4031-2018; Nizzoli,
   Leonardo/ABE-5096-2020
OI TESCONI, MAURIZIO/0000-0001-8228-7807; Cresci,
   Stefano/0000-0003-0170-2445; Nizzoli, Leonardo/0000-0002-9168-8992
FU EU H2020 Program under the scheme INFRAIA-01-2018-2019: Research and
   Innovation action [871042]
FX The authors would like to thank Franco Maria Nardini for his insightful
   suggestions. This research is supported in part by the EU H2020 Program
   under the scheme INFRAIA-01-2018-2019: Research and Innovation action
   grant agreement #871042 SoBigData++: European Integrated Infrastructure
   for Social Mining and Big Data Analytics.
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NR 58
TC 7
Z9 7
U1 11
U2 35
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-9236
EI 1873-5797
J9 DECIS SUPPORT SYST
JI Decis. Support Syst.
PD SEP
PY 2020
VL 136
AR 113346
DI 10.1016/j.dss.2020.113346
PG 16
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 MU5LY
UT WOS:000555716000005
DA 2022-08-23
ER

PT J
AU Weikum, G
AF Weikum, Gerhard
TI Knowledge Graphs 2021: A Data Odyssey
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article; Proceedings Paper
CT 47th International Conference on Very Large Data Bases (VLDB)
CY AUG 16-20, 2021
CL Copenhagen, DENMARK
AB Providing machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing vision and challenge for AI. Over the last 15 years, huge knowledge bases, also known as knowledge graphs, have been automatically constructed from web data, and have become a key asset for search engines and other use cases. Machine knowledge can be harnessed to semantically interpret texts in news, social media and web tables, contributing to question answering, natural language processing and data analytics. This position paper reviews these advances and discusses lessons learned. It highlights the role of "DB thinking" in building and maintaining high-quality knowledge bases from web contents. Moreover, the paper identifies open challenges and new research opportunities. In particular, extracting quantitative measures of entities (e.g., height of buildings or energy efficiency of cars), from text and web tables, presents an opportunity to further enhance the scope and value of knowledge bases.
C1 [Weikum, Gerhard] Max Planck Inst Informat, Saarland Informat Campus E1-4, D-66123 Saarbrucken, Germany.
RP Weikum, G (通讯作者)，Max Planck Inst Informat, Saarland Informat Campus E1-4, D-66123 Saarbrucken, Germany.
EM weikum@mpi-inf.mpg.de
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NR 41
TC 0
Z9 0
U1 2
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD AUG
PY 2021
VL 14
IS 12
BP 3233
EP 3238
DI 10.14778/3476311.3476393
PG 6
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA YH1AX
UT WOS:000742908500091
DA 2022-08-23
ER

PT J
AU Anelli, VW
   Di Noia, T
   Di Sciascio, E
   Ragone, A
   Trotta, J
AF Anelli, Vito Walter
   Di Noia, Tommaso
   Di Sciascio, Eugenio
   Ragone, Azzura
   Trotta, Joseph
TI Semantic Interpretation of Top-N Recommendations
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Semantics; Recommender systems; Computational modeling; Data models;
   Robustness; Motion pictures; Resource description framework; Recommender
   systems; knowledge graph; interpretable models; factorization machines
ID MATRIX FACTORIZATION
AB Over the years, model-based approaches have shown their effectiveness in computing recommendation lists in different domains and settings. By relying on the computation of latent factors, they can recommend items with a very high level of accuracy. Unfortunately, when moving to the latent space, even if the model embeds content-based information, we miss references to the actual semantics of the recommended item. It makes the interpretation of the recommendation process non-trivial. In this paper, we show how to initialize latent factors in Factorization Machines by using semantic features coming from knowledge graphs to train an interpretable model, which is, in turn, able to provide recommendations with a high level of accuracy. In the presented approach, semantic features are injected into the learning process to retain the original informativeness of the items available in the dataset. By relying on the information encoded in the original knowledge graph, we also propose two metrics to evaluate the semantic accuracy and robustness of knowledge-aware interpretability. An extensive experimental evaluation on six different datasets shows the effectiveness of the interpretable model in terms of both accuracy and diversity of recommendation results and interpretability robustness.
C1 [Anelli, Vito Walter; Di Noia, Tommaso; Di Sciascio, Eugenio; Trotta, Joseph] Polytech Univ Bari, Dept Elect & Informat Engn, I-70126 Bari, Italy.
   [Ragone, Azzura] EY, London, England.
RP Di Noia, T (通讯作者)，Polytech Univ Bari, Dept Elect & Informat Engn, I-70126 Bari, Italy.
EM vitowalter.anelli@poliba.it; tommaso.dinoia@poliba.it;
   eugenio.disciascio@poliba.it; azzurra.ragone@gmail.com;
   joseph.trotta@poliba.it
OI Anelli, Vito Walter/0000-0002-5567-4307; di sciascio,
   eugenio/0000-0002-5484-9945; Di Noia, Tommaso/0000-0002-0939-5462
FU Polytechnic University of Bari [PON ARS01_00821 FLET4.0]
FX The authors with Polytechnic University of Bari acknowledge partial
   support of the following projects: Innonetwork CONTACT, PON ARS01_00821
   FLET4.0, FINCONS Smart Digital Solutions for the Creative Industry, PON
   OKINSAID.
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NR 64
TC 4
Z9 4
U1 8
U2 8
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 2416
EP 2428
DI 10.1109/TKDE.2020.3010215
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:000777332000030
DA 2022-08-23
ER

PT J
AU Yang, XZ
   Peng, GJ
   Zhang, DN
   Lv, YQ
AF Yang, Xiuzhang
   Peng, Guojun
   Zhang, Dongni
   Lv, Yangqi
TI An Enhanced Intrusion Detection System for IoT Networks Based on Deep
   Learning and Knowledge Graph
SO SECURITY AND COMMUNICATION NETWORKS
LA English
DT Article
AB Nowadays, the intrusion detection system (IDS) plays a crucial role in the Internet of Things (IoT) networks, which could effectively protect sensitive data from various attacks. However, the existing works have not considered multiview features fusion and failed to capture the semantic relationships among the anomalous requests. They are not robust and cannot detect the attack types in real-time. This paper proposes a lightweight intrusion detection system based on deep learning and knowledge graph. First, our system extracts semantic relationships and key features by knowledge graph and statistical analysis. Then, IoT network requests are converted into word vectors through multiview feature fusion and feature alignment. Finally, an attention-based CNN-BiLSTM model is designed to identify malicious request attacks, which can capture long-distance dependence and contextual semantic information. Experiment results show that the proposed model significantly outperforms the existing solution in the robustness of the model. Moreover, it can select more critical features for IDS to achieve better accuracy and lower the false alarm rate. Compared with the state-of-the-art systems, the proposed IDS achieves a higher detection accuracy of 90.01%. In addition, our system can detect various stealthy attack types (including DoS, Probe, R2L, and U2L) and extract semantic relationships among features.
C1 [Yang, Xiuzhang; Peng, Guojun; Zhang, Dongni; Lv, Yangqi] Minist Educ, Key Lab Aerosp Informat Secur & Trusted Comp, Wuhan 430072, Peoples R China.
   [Yang, Xiuzhang; Peng, Guojun; Zhang, Dongni; Lv, Yangqi] Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China.
RP Peng, GJ (通讯作者)，Minist Educ, Key Lab Aerosp Informat Secur & Trusted Comp, Wuhan 430072, Peoples R China.; Peng, GJ (通讯作者)，Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China.
EM guojpeng@whu.edu.cn
FU National Natural Science Foundation of China [62172308, U1626107,
   61972297, 62172144]
FX This research was in part supported by the National Natural Science
   Foundation of China under Grant Nos. 62172308, U1626107, 61972297, and
   62172144.
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NR 45
TC 0
Z9 0
U1 2
U2 2
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1939-0114
EI 1939-0122
J9 SECUR COMMUN NETW
JI Secur. Commun. Netw.
PD APR 26
PY 2022
VL 2022
AR 4748528
DI 10.1155/2022/4748528
PG 21
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 3K7LI
UT WOS:000834256200002
OA gold
DA 2022-08-23
ER

PT J
AU Shi, HL
   Yang, ML
   Jiang, PY
AF Shi, Haoliang
   Yang, Maolin
   Jiang, Pingyu
TI Social Production System: A Three-Layer Smart Framework for Implementing
   Autonomous Human-Machine Collaborations in a Shop Floor
SO IEEE ACCESS
LA English
DT Article
DE Manufacturing; Production; Collaboration; Production systems;
   Man-machine systems; Data models; Task analysis; Social production
   system (Social-PS); smart resources; knowledge graph; production-event;
   human-machine collaboration
ID CYBER-PHYSICAL SYSTEMS; KNOWLEDGE; ARCHITECTURE; ENTERPRISES
AB The continuous fast-changing of production requirements and the development of advanced information technology have promoted the development of the production system in a more knowledge-intensive, resources-decentralized, and autonomous collaborative manner. In order to provide guidelines for the smart transformation of the production shop floor, this paper proposes a smart production system with a three-layer framework defined as the social production system (Social-PS) by integrating cyber-physical system (CPS), knowledge graph technology, and production-event driven model. The concept of smart resources (include smart operator, smart machine, smart workpiece, and smart gateway), social sensor, the unified information model is clarified to construct the physical carrier of Social-PS. The unified knowledge repository's overall construction method based on the knowledge graph is elaborated to provide task-resource-associated heterogeneous production data sharing and management for human-machine collaboration. Subsequently, to support the production dynamic monitoring and interaction, a production-event and active knowledge indexing model is proposed. Furthermore, to validate the proposed Social-PS framework, a software and hardware integrated prototype system is implemented to demonstrate the knowledge-driven human-machine collaboration of the production process, which provides a basis for realizing smart and autonomous production collaboration.
C1 [Shi, Haoliang; Yang, Maolin; Jiang, Pingyu] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Peoples R China.
RP Jiang, PY (通讯作者)，Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Peoples R China.
EM pjiang@mail.xjtu.edu.cn
FU National Natural Science Foundation of China (NSFC) [51975464]; China
   Ministry of Science and Technologies under Innovation Method Fund
   [2016IM010100]
FX This work was supported in part by the National Natural Science
   Foundation of China (NSFC) under Grant 51975464, and in part by the
   China Ministry of Science and Technologies under Innovation Method Fund
   under Grant 2016IM010100.
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NR 44
TC 1
Z9 2
U1 6
U2 22
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 26696
EP 26711
DI 10.1109/ACCESS.2021.3050236
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA QJ1DC
UT WOS:000619428800001
OA gold
DA 2022-08-23
ER

PT J
AU Zheng, CY
AF Zheng, Chenyu
TI Comparisons of the City Brand Influence of Global Cities: Word-Embedding
   Based Semantic Mining and Clustering Analysis on the Big Data of GDELT
   Global News Knowledge Graph
SO SUSTAINABILITY
LA English
DT Article
DE global city; city brand influence; knowledge graph; semantic big data
   mining; GDELT
ID PLACE; CULTURE; SCALE; IMAGE
AB Global cities act as influential hubs in the networked world. Their city brands, which are projected by the global news media, are becoming sustainable resources in various global competitions and cooperations. This study adopts the research paradigm of computational social science to assess and compare the city brand attention, positivity, and influence of ten Globalization and World Cities Research Network (GaWC) Alpha+ global cities, along with their dimensional structures, based on combining the cognitive and affective theoretical perspectives on the frameworks of the Anholt global city brand dimension system, the big data of global news knowledge graph in Google's Global Database of Events, Language, and Tone (GDELT), and the technologies of word-embedding semantic mining and clustering analysis. The empirical results show that the overall values and dimensional structures of city brand influence of global cities form distinct levels and clusters, respectively. Although global cities share a common structural characteristic of city brand influence of the dimensions of presence and potential being most prominent, Western and Eastern global cities differentiate in the clustering of dimensional structures of city brand attention, positivity, and influence. City brand attention is more important than city brand positivity in improving the city brand influence of global cities. The preferences of the global news media over global city brands fits the nature of global cities.
C1 [Zheng, Chenyu] Fudan Univ, Sch Journalism, Shanghai 200433, Peoples R China.
RP Zheng, CY (通讯作者)，Fudan Univ, Sch Journalism, Shanghai 200433, Peoples R China.
EM chenyuzheng@fudan.edu.cn
FU Shanghai Philosophy and Social Science Research Project "A Study on
   Shanghai's Global City Brand Competitiveness, Evolution, and Improvement
   Strategies based on Word-Embedding Semantic Mining on the Big Data of
   the GDELT Global News" [2019EXW007]
FX This research was funded by Shanghai Philosophy and Social Science
   Research Project "A Study on Shanghai's Global City Brand
   Competitiveness, Evolution, and Improvement Strategies based on
   Word-Embedding Semantic Mining on the Big Data of the GDELT Global
   News", grant number 2019EXW007.
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NR 51
TC 1
Z9 1
U1 13
U2 32
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD AUG
PY 2020
VL 12
IS 16
AR 6294
DI 10.3390/su12166294
PG 18
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA OC0RV
UT WOS:000578871800001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Liang, K
   Zhou, BX
   Zhang, YY
   Li, YP
   Zhang, B
   Zhang, XK
AF Liang, Kun
   Zhou, Baoxian
   Zhang, Yiying
   Li, Yiping
   Zhang, Bo
   Zhang, Xiankun
TI PF2RM: A Power Fault Retrieval and Recommendation Model Based on
   Knowledge Graph
SO ENERGIES
LA English
DT Article
DE knowledge graph; fault research and judgment; search recommendation;
   smart grid
AB Energy saving and emission reduction have become common concerns in countries around the world. In China, with the implementation of the new strategy of "carbon peak and neutrality" and the rapid development of the new smart grid infrastructure, the amount of data of actual power grid dispatching and fault analysis show exponential growth, which has led to phenomena such as poor supervision effectiveness and difficulty in handling faults in the process of grid operation and maintenance. Existing research on retrieval recommendation methods has had a lower accuracy rate at cold-start due to a small sample of user interactions. In addition, the cumulative learning of user personalization during general retrieval results in a poor perception of potential interest. By constructing a power knowledge graph, this paper presents a power fault retrieval and recommendation model (PF2RM) based on user-polymorphic perception. This model includes two methods: the power fault retrieval method (PFR) and the user-polymorphic retrieval recommendation method (UPRR). First, we take the power grid fault dispatching business as the core and reconstruct the ontology layer of the power knowledge graph. The PFR method is used to design the graph-neighbor fault entity cluster to enhance the polymerization degree of a fault implementation scenario. This method can solve the search cold-start recommendation problem. At the same time, the UPRR method aims to form user retrieval subgraphs of the past-state and current-state and make a feature matching for the graph-neighbor fault entity cluster, and then realize the accurate prediction of the user's general search intention. The model is compared with other current classical models through the evaluation of multiple recommendation evaluation metrics, and the experimental results show that the model has a 3-8% improvement in the cold-start recommendation effect and 2-10% improvement in regular retrieval. The model has the best average recommendation performance in multiple metrics and has good results in fault analysis and retrieval recommendation. It plays a helpful role in intelligent operation and maintenance of the power grid and auxiliary decision-making, and effectively improves the reliability of the power grid.
C1 [Liang, Kun; Zhou, Baoxian; Zhang, Yiying; Li, Yiping; Zhang, Xiankun] Tianjin Univ Sci & Technol, Coll Artificial Intelligence, Tianjin 300457, Peoples R China.
   [Zhang, Bo] State Grid Smart Grid Res Inst Co Ltd, Nanjing 210003, Peoples R China.
RP Zhang, YY (通讯作者)，Tianjin Univ Sci & Technol, Coll Artificial Intelligence, Tianjin 300457, Peoples R China.
EM liangkun@tust.edu.cn; zhoubaoxian@yeah.net; yiyingzhang@tust.edu.cn;
   liyiping20010919@mail.tust.edu.cn; zhangbo_bx@sina.com; zhxk@tust.edu.cn
OI Zhou, Baoxian/0000-0003-4207-9178
FU National Natural Science Foundation of China [61807024]
FX FundingThis research was funded by the National Natural Science
   Foundation of China, grant number 61807024.
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NR 28
TC 1
Z9 1
U1 7
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1073
J9 ENERGIES
JI Energies
PD MAR
PY 2022
VL 15
IS 5
AR 1810
DI 10.3390/en15051810
PG 18
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA ZT2EP
UT WOS:000768966900001
OA gold
DA 2022-08-23
ER

PT J
AU Mouromtsev, D
AF Mouromtsev, Dmitry
TI Semantic Reference Model for Individualization of Information Processes
   in IoT Heterogeneous Environment
SO ELECTRONICS
LA English
DT Article
DE semantic reference model; knowledge graphs; information processes
   individualization; IoT process
AB The individualization of information processes based on artificial intelligence (AI), especially in the context of industrial tasks, requires new, hybrid approaches to process modeling that take into account the novel methods and technologies both in the field of semantic representation of knowledge and machine learning. The combination of both AI techniques imposes several requirements and restrictions on the types of data and object properties and the structure of ontologies for data and knowledge representation about processes. The conceptual reference model for effective individualization of information processes (IIP CRM) proposed in this work considers these requirements and restrictions. This model is based on such well-known standard upper ontologies as BFO, GFO and MASON. Evaluation of the proposed model is done on a practical use case in the field of precise agriculture where IoT-enabled processes are widely used. It is shown that IIP CRM allows the construction of a knowledge graph about processes that are surrounded by unstructured data in soft and heterogeneous domains. CRM also provides the ability to answer specific questions in the domain using queries written with the CRM vocabulary, which makes it easier to develop applications based on knowledge graphs.
C1 [Mouromtsev, Dmitry] ITMO Univ, Fac Software Engn & Comp Syst, St Petersburg 197101, Russia.
RP Mouromtsev, D (通讯作者)，ITMO Univ, Fac Software Engn & Comp Syst, St Petersburg 197101, Russia.
EM mouromtsev@itmo.ru
RI Mouromtsev, Dmitry/N-6485-2016
OI Mouromtsev, Dmitry/0000-0002-0644-9242
FU Ministry of Education and Science of Russia [075-15-2020-805]
FX This research was partially funded by the Ministry of Education and
   Science of Russia No. 075-15-2020-805 2 October 2020.
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NR 31
TC 2
Z9 2
U1 1
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD OCT
PY 2021
VL 10
IS 20
AR 2523
DI 10.3390/electronics10202523
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA WQ7PE
UT WOS:000714003800001
OA gold
DA 2022-08-23
ER

PT J
AU Chi, Y
   Qin, Y
   Song, R
   Xu, H
AF Chi, Yang
   Qin, Yue
   Song, Rui
   Xu, Hao
TI Knowledge Graph in Smart Education: A Case Study of Entrepreneurship
   Scientific Publication Management
SO SUSTAINABILITY
LA English
DT Article
DE smart education; entrepreneurship; knowledge graph; knowledgemanagement;
   datamining
AB In recent years, with the rapid growth of science and innovation, plenty of constantly-updated scientific achievements containing innovative knowledge can be acquired and used to solve problems. However, most undergraduate students and non-researchers cannot use them efficiently. In traditional teacher-centric education, education for sustainability is often marginalized and the interdisciplinary demand is neglected. Additionally, it fails to provide education for learners to connect abstract knowledge with actual world problems. This paper presents the design of a scientific publication management model to integrate scientific metadata based on the knowledge graph and data analysis technologies. Based on this model, an interdisciplinary transregional multiple application platform could be realized for scientific resource retrieval and analysis, the purpose of which is to enhance scientific retrieval efficiency and reduce learning difficulty in the scientific domains and encourage non-researchers to utilize scientific resources in their study and work. Finally, to evaluate this model, the use of the case of an entrepreneurship scientific publication management prototype system was implemented. This work not only favors student's learning for sustainability through analysis and knowledge management functions, but also promotes their awareness, comprehensive thinking, and the skills to deal with the issues of sustainability in their future work.
C1 [Chi, Yang; Qin, Yue; Xu, Hao] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China.
   [Song, Rui] Jilin Univ, Coll Software Engn, Changchun 130012, Jilin, Peoples R China.
   [Xu, Hao] Jilin Univ, Sch Management, Changchun 130012, Jilin, Peoples R China.
   [Xu, Hao] Jilin Univ, Zhuhai Coll, Dept Comp Sci & Technol, Zhuhai 519041, Peoples R China.
   [Xu, Hao] Jilin Univ, Symbol Computat & Knowledge Engineer Minist Educ, Changchun 130012, Jilin, Peoples R China.
RP Xu, H (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China.; Xu, H (通讯作者)，Jilin Univ, Sch Management, Changchun 130012, Jilin, Peoples R China.; Xu, H (通讯作者)，Jilin Univ, Zhuhai Coll, Dept Comp Sci & Technol, Zhuhai 519041, Peoples R China.; Xu, H (通讯作者)，Jilin Univ, Symbol Computat & Knowledge Engineer Minist Educ, Changchun 130012, Jilin, Peoples R China.
EM chiyang16@mails.jlu.edu.cn; qinyue15@mails.jlu.edu.cn;
   songrui5514@mails.jlu.edu.cn; xuhao@jlu.edu.cn
FU Development Project of Jilin Province of China [20170203002GX,
   20160414009GH, 20170101006JC, 20160204022GX]; National Natural Science
   Foundation of China [61472159, 71620107001, 71232011]; China
   Postdoctoral Science Foundation [2016M601379]; MOE Research Center for
   Online Education Quantong Education Foundation [2017YB131]; Zhuhai
   Government; Premier Key-Discipline Enhancement Scheme - Guangdong
   Government Funds
FX The authors thank Li Cai for her thoughtful advice and suggestions on
   research design and implementation. This work is supported by the
   Development Project of Jilin Province of China (Nos. 20170203002GX,
   20160414009GH, 20170101006JC, 20160204022GX), the National Natural
   Science Foundation of China (Nos. 61472159, 71620107001, 71232011),
   China Postdoctoral Science Foundation (No. 2016M601379) and MOE Research
   Center for Online Education Quantong Education Foundation (No.
   2017YB131). Premier-Discipline Enhancement Scheme supported by the
   Zhuhai Government and the Premier Key-Discipline Enhancement Scheme
   supported Guangdong Government Funds.
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   Zhu Y., 2017, P 6 INT C INF ENG DA
NR 31
TC 17
Z9 20
U1 12
U2 75
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD APR
PY 2018
VL 10
IS 4
AR 995
DI 10.3390/su10040995
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 GJ3IY
UT WOS:000435188000099
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Zhou, YC
   An, N
   Yao, JW
AF Zhou, Yichen
   An, Na
   Yao, Jiawei
TI Characteristics, Progress and Trends of Urban Microclimate Research: A
   Systematic Literature Review and Bibliometric Analysis
SO BUILDINGS
LA English
DT Review
DE bibliometrics; citespace; development stages; distribution
   characteristics; research trends; urban microclimate; vosviewer
ID ISLAND MITIGATION STRATEGIES; OUTDOOR THERMAL COMFORT; HEAT-ISLAND;
   CITY; CLIMATE; IMPACT; PATTERNS; ENERGY; ENVIRONMENTS; PERFORMANCE
AB Climate change has been a hot topic in recent years. However, the urban microclimate is more valuable for research because it directly affects people's living environments and can be adjusted by technological means to enhance the resilience of cities in the face of climate change and disasters. This paper analyses the literature distribution characteristics, development stages, and research trends of urban microclimate research based on the literature on "urban microclimate" collected in the Web of Science core database since 1990, using CiteSpace and VOSviewer bibliometric software. It is found that the literature distribution of the urban microclimate is characterized by continuous growth, is interdisciplinary, and can be divided into four stages: nascent exploration, model quantification, diversified development and ecological synergy. Based on the knowledge mapping analysis of keyword clustering, annual overlap, and keyword highlighting, it can be predicted that the research on foreign urban land patch development has three hot trends-multi-scale modelling, multi-factor impact, and multi-policy guidance. The study's findings help recognize the literature distribution characteristics and evolutionary lineage of urban microclimate research and provide suggestions for future urban microclimate research.
C1 [Zhou, Yichen; An, Na; Yao, Jiawei] Tongji Univ, Coll Architecture & Urban Planning, Shanghai 200092, Peoples R China.
   [Yao, Jiawei] Minist Nat Resources, Key Lab Spatial Intelligent Planning Technol, Shanghai 200092, Peoples R China.
RP Yao, JW (通讯作者)，Tongji Univ, Coll Architecture & Urban Planning, Shanghai 200092, Peoples R China.; Yao, JW (通讯作者)，Minist Nat Resources, Key Lab Spatial Intelligent Planning Technol, Shanghai 200092, Peoples R China.
EM 1954034@tongji.edu.cn; anna2010255@tongji.edu.cn;
   jiawei.yao@tongji.edu.cn
FU National Natural Science Foundation of China [51908410]; Shanghai
   Municipal Science and Technology Major Project [2021SHZDZX0100];
   Fundamental Research Funds for the Central Universities
FX National Natural Science Foundation of China under Grant NO. 51908410;
   the Shanghai Municipal Science and Technology Major Project under Grant
   NO. 2021SHZDZX0100; the Fundamental Research Funds for the Central
   Universities.
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NR 64
TC 0
Z9 0
U1 5
U2 5
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 877
DI 10.3390/buildings12070877
PG 17
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA 3J4KO
UT WOS:000833365700001
OA gold
DA 2022-08-23
ER

PT J
AU Li, X
   Wu, P
   Shen, GQP
   Wang, XY
   Teng, Y
AF Li, Xiao
   Wu, Peng
   Shen, Geoffrey Qiping
   Wang, Xiangyu
   Teng, Yue
TI Mapping the knowledge domains of Building Information Modeling (BIM): A
   bibliometric approach
SO AUTOMATION IN CONSTRUCTION
LA English
DT Review
DE Building Information Modeling (BIM); Bibliometrics; Literature review;
   Knowledge map
ID ONTOLOGY-BASED APPROACH; AUGMENTED REALITY; SOFTWARE TOOLS;
   CONSTRUCTION; DESIGN; FRAMEWORK; ADOPTION; SUPPORT; SYSTEM; 3D
AB Building Information Modeling (BIM) has been recognized as an emerging technological innovation which can help transform the construction industry and it has been adopted broadly in the field of built environment. Due to the rapid development of BIM research, various stakeholders require a state-of-the-art review of the BIM research and implementation. The purpose of this paper is to provide an objective and accurate summary of BIM knowledge using 1874 published BIM-related papers. The results show that 60 key research areas, such as information systems, 3D modeling, design and sustainability and 10 key research clusters, such as architecture design studio, building information and lean construction, are extremely important for the development of BIM knowledge. The results are useful for the identification of research clusters and topics in the BIM community. More importantly, these results can help highlight how BIM-related research evolves over time, thus greatly contributing to understanding the underlying structure of BIM. This study offers useful and new insights to summarize the status quo of BIM knowledge and can be used as a dynamic platform to integrate future BIM developments.
C1 [Li, Xiao; Shen, Geoffrey Qiping] Hong Kong Polytech Univ, Dept Build & Real Estate, Hong Kong, Hong Kong, Peoples R China.
   [Wu, Peng] Curtin Univ, Dept Construct Management, Perth, WA, Australia.
   [Wang, Xiangyu] Curtin Univ, Sch Built Environm, Australasian Joint Res Ctr Bldg Informat Modellin, Perth, WA, Australia.
   [Teng, Yue] Univ Hong Kong, Dept Civil Engn, Hong Kong, Hong Kong, Peoples R China.
RP Wu, P (通讯作者)，Curtin Univ, Dept Construct Management, Perth, WA, Australia.
EM shell.x.li@connect.polyu.hk; peng.wu@curtin.edu.au;
   geoffrey.shen@polyu.edu.hk; Xiangyu.Wang@curtin.edu.au;
   tengyue@connect.hku.hk
RI Wang, Xiangyu/B-6232-2013; Shen, Geoffrey Qiping/A-1250-2014; Li,
   Xiao/AAG-9786-2021
OI Wang, Xiangyu/0000-0001-8718-6941; Shen, Geoffrey
   Qiping/0000-0002-3111-2019; Li, Xiao/0000-0001-9702-4153; Teng,
   Yue/0000-0002-0333-639X; Wu, Wei/0000-0002-7024-3812; wu,
   peng/0000-0002-3793-0653
FU Ministry of Science and Technology of the People's Republic of China,
   The Thirteenth Five-Year National Key Research Projects
   [2016YFC0702005-04]
FX This research was undertaken with the benefit of a grant from the
   Ministry of Science and Technology of the People's Republic of China,
   The Thirteenth Five-Year National Key Research Projects (Project No.
   2016YFC0702005-04).
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NR 94
TC 117
Z9 121
U1 40
U2 414
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 DEC
PY 2017
VL 84
BP 195
EP 206
DI 10.1016/j.autcon.2017.09.011
PG 12
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Construction & Building Technology; Engineering
GA FK6IP
UT WOS:000413607600017
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Youn, J
   Rai, N
   Tagkopoulos, I
AF Youn, Jason
   Rai, Navneet
   Tagkopoulos, Ilias
TI Knowledge integration and decision support for accelerated discovery of
   antibiotic resistance genes
SO NATURE COMMUNICATIONS
LA English
DT Article
ID ESCHERICHIA-COLI; METRONIDAZOLE; EMBEDDINGS; MUTANTS; PROTEIN; FAMILY;
   WAAP
AB We present a machine learning framework to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction. By incorporating knowledge from 10 publicly available sources, we construct an Escherichia coli antibiotic resistance knowledge graph with 651,758 triples from 23 triple types after resolving 236 sets of inconsistencies. Iteratively applying link prediction to this graph and wet-lab validation of the generated hypotheses reveal 15 antibiotic resistant E. coli genes, with 6 of them never associated with antibiotic resistance for any microbe. Iterative link prediction leads to a performance improvement and more findings. The probability of positive findings highly correlates with experimentally validated findings (R-2 = 0.94). We also identify 5 homologs in Salmonella enterica that are all validated to confer resistance to antibiotics. This work demonstrates how evidence-driven decisions are a step toward automating knowledge discovery with high confidence and accelerated pace, thereby substituting traditional time-consuming and expensive methods.
   Here the authors present KIDS, a knowledge graph integration and phenotypic prediction framework. When applied on antibiotic data, it identifies 6 novel antibiotic resistant E. coli genes that the authors subsequently validate.
C1 [Youn, Jason; Rai, Navneet; Tagkopoulos, Ilias] Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 USA.
   [Youn, Jason; Rai, Navneet; Tagkopoulos, Ilias] Univ Calif Davis, Genome Ctr, Davis, CA 95616 USA.
   [Youn, Jason; Rai, Navneet; Tagkopoulos, Ilias] Univ Calif Davis, USDA, NSF AI Inst Next Generat Food Syst AIFS, Davis, CA 95616 USA.
RP Tagkopoulos, I (通讯作者)，Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 USA.; Tagkopoulos, I (通讯作者)，Univ Calif Davis, Genome Ctr, Davis, CA 95616 USA.; Tagkopoulos, I (通讯作者)，Univ Calif Davis, USDA, NSF AI Inst Next Generat Food Syst AIFS, Davis, CA 95616 USA.
EM itagkopoulos@ucdavis.edu
FU USDA-NIFA AI Institute for Next Generation Food Systems (AIFS);
   USDA-NIFA [2020-67021-32855]; NIEHS [P42ES004699]
FX We would like to thank the members of the Tagkopoulos lab and the
   reviewers for their suggestions, Nick Joodi and Minseung Kim for their
   help in the initial discussions, and Ameen Eetemadi for his comments on
   creating the figures. This work was supported by the USDA-NIFA AI
   Institute for Next Generation Food Systems (AIFS), USDA-NIFA award
   number 2020-67021-32855, and the NIEHS grant P42ES004699 to I.T. All
   code and instructions on how to reproduce the results can be found in
   https://github.com/IBPA/KIDS.
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NR 112
TC 0
Z9 0
U1 10
U2 10
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
EI 2041-1723
J9 NAT COMMUN
JI Nat. Commun.
PD APR 29
PY 2022
VL 13
IS 1
AR 2360
DI 10.1038/s41467-022-29993-z
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 0W4SJ
UT WOS:000789018200004
PM 35487919
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhao, Y
   Feng, HL
   Gallinari, P
AF Zhao, Yu
   Feng, Huali
   Gallinari, Patrick
TI Embedding Learning with Triple Trustiness on Noisy Knowledge Graph
SO ENTROPY
LA English
DT Article
DE knowledge graph; embedding learning; cross entropy; noise detection;
   triple trustiness
AB Embedding learning on knowledge graphs (KGs) aims to encode all entities and relationships into a continuous vector space, which provides an effective and flexible method to implement downstream knowledge-driven artificial intelligence (AI) and natural language processing (NLP) tasks. Since KG construction usually involves automatic mechanisms with less human supervision, it inevitably brings in plenty of noises to KGs. However, most conventional KG embedding approaches inappropriately assume that all facts in existing KGs are completely correct and ignore noise issues, which brings about potentially serious errors. To address this issue, in this paper we propose a novel approach to learn embeddings with triple trustiness on KGs, which takes possible noises into consideration. Specifically, we calculate the trustiness value of triples according to the rich and relatively reliable information from large amounts of entity type instances and entity descriptions in KGs. In addition, we present a cross-entropy based loss function for model optimization. In experiments, we evaluate our models on KG noise detection, KG completion and classification. Through extensive experiments on three datasets, we demonstrate that our proposed model can learn better embeddings than all baselines on noisy KGs.
C1 [Zhao, Yu; Feng, Huali] Southwestern Univ Finance & Econ, Sch Econ Informat Engn, Financial Intelligence & Financial Engn Key Lab S, Chengdu 611130, Sichuan, Peoples R China.
   [Gallinari, Patrick] Univ Paris 06, LIP6, F-75252 Paris, France.
RP Zhao, Y; Feng, HL (通讯作者)，Southwestern Univ Finance & Econ, Sch Econ Informat Engn, Financial Intelligence & Financial Engn Key Lab S, Chengdu 611130, Sichuan, Peoples R China.
EM zhaoyu@swufe.edu.cn; fenghuali0723@163.com; patrick.gallinari@lip6.fr
FU National Natural Science Foundation of China [61906159]; Sichuan Science
   and Technology Program [2018JY0607]; Fundamental Research Funds for the
   Central Universities [JBK1901030, JBK1801080]
FX This research was funded in part by the National Natural Science
   Foundation of China under Grant No. 61906159, in part by the Sichuan
   Science and Technology Program under Grant No. 2018JY0607, in part by
   the Fundamental Research Funds for the Central Universities under Grant
   No. JBK1901030, No. JBK1801080.
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NR 56
TC 4
Z9 5
U1 0
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD NOV
PY 2019
VL 21
IS 11
AR 1083
DI 10.3390/e21111083
PG 16
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA JV1QZ
UT WOS:000502145000056
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Wang, Q
   Mao, ZD
   Wang, B
   Guo, L
AF Wang, Quan
   Mao, Zhendong
   Wang, Bin
   Guo, Li
TI Knowledge Graph Embedding: A Survey of Approaches and Applications
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Statistical relational learning; knowledge graph embedding; latent
   factor models; tensor/matrix factorization models
AB Knowledge graph (KG) embedding is to embed components of a KG including entities and relations into continuous vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. It can benefit a variety of downstream tasks such as KG completion and relation extraction, and hence has quickly gained massive attention. In this article, we provide a systematic review of existing techniques, including not only the state-of-the-arts but also those with latest trends. Particularly, we make the review based on the type of information used in the embedding task. Techniques that conduct embedding using only facts observed in the KG are first introduced. We describe the overall framework, specific model design, typical training procedures, as well as pros and cons of such techniques. After that, we discuss techniques that further incorporate additional information besides facts. We focus specifically on the use of entity types, relation paths, textual descriptions, and logical rules. Finally, we briefly introduce how KG embedding can be applied to and benefit a wide variety of downstream tasks such as KG completion, relation extraction, question answering, and so forth.
C1 [Wang, Quan; Mao, Zhendong; Wang, Bin; Guo, Li] Chinese Acad Sci, Inst Informat Engn, Beijing 100049, Peoples R China.
   [Wang, Quan; Mao, Zhendong; Wang, Bin; Guo, Li] Univ CAS, Sch Cyber Secur, Beijing 100049, Peoples R China.
   [Wang, Quan] Chinese Acad Sci, State Key Lab Informat Secur, Beijing 100093, Peoples R China.
RP Wang, Q (通讯作者)，Chinese Acad Sci, Inst Informat Engn, Beijing 100049, Peoples R China.; Wang, Q (通讯作者)，Univ CAS, Sch Cyber Secur, Beijing 100049, Peoples R China.; Wang, Q (通讯作者)，Chinese Acad Sci, State Key Lab Informat Secur, Beijing 100093, Peoples R China.
EM wangquan@iie.ac.cn; maozhendong@iie.ac.cn; wangbin@iie.ac.cn;
   guoli@iie.ac.cn
FU National Key Research and Development Program of China [2016QY03D0503,
   2016QY03D0505]; National Natural Science Foundation of China [61402465,
   61502477]; Fundamental Theory and Cutting Edge Technology Research
   Program of the Institute of Information Engineering, Chinese Academy of
   Sciences [Y7Z0261101]
FX The authors would like to thank all the reviewers for their insightful
   and valuable suggestions, which significantly improve the quality of
   this survey. This work is supported by the National Key Research and
   Development Program of China (grants No. 2016QY03D0503 and
   2016QY03D0505) and the National Natural Science Foundation of China
   (grants No. 61402465 and 61502477) and the Fundamental Theory and
   Cutting Edge Technology Research Program of the Institute of Information
   Engineering, Chinese Academy of Sciences (grant No. Y7Z0261101).
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NR 132
TC 631
Z9 689
U1 84
U2 496
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD DEC
PY 2017
VL 29
IS 12
BP 2724
EP 2743
DI 10.1109/TKDE.2017.2754499
PG 20
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA FM1DF
UT WOS:000414712700008
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Hilman, D
   Serban, O
AF Hilman, David
   Serban, Ovidiu
TI A unified Link Prediction architecture applied on a novel heterogenous
   Knowledge Base
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Heterogeneous Knowledge Base; Link Prediction; Financial entity
   prediction
AB Link Prediction (LP) aims at addressing incompleteness of Knowledge Graph (KG). The goal of LP is to capture the distribution of entities and relations present in a KG and utilise these to predict probability of missing information. State-of-the-art LP approaches rely on latent feature models for this purpose. The research focus has predominantly been on application of LP to triple based datasets (e.g. Freebase, YAGO). However, with growing adoption of KGs, it is common to see more heterogeneous property graphs being used, examples of common properties are temporal and weight data. The contributions of the following work are two fold. First, we introduce a novel framework which is the first to provide support for latent feature model LP on heterogeneous Knowledge Bases (KBs). Second, we utilise a novel KB - Refinitiv Knowledge Graph, to produce a heterogeneous dataset with which capabilities of the framework are examined. Crown Copyright (c) 2022 Published by Elsevier B.V. All rights reserved.
C1 [Hilman, David; Serban, Ovidiu] Imperial Coll London, Dept Comp, South Kensington Campus, London SW7 2AZ, England.
   [Serban, Ovidiu] Data Sci Inst, Imperial Coll London, South Kensington Campus, London SW7 2AZ, England.
RP Serban, O (通讯作者)，Imperial Coll London, Dept Comp, South Kensington Campus, London SW7 2AZ, England.; Serban, O (通讯作者)，Data Sci Inst, Imperial Coll London, South Kensington Campus, London SW7 2AZ, England.
EM o.serban@imperial.ac.uk
OI Serban, Ovidiu/0000-0001-5359-3661
FU Refinitiv-Imperial College London, United Kingdom collaboration grant
FX We would like to thank the team at Refinitiv, an LSEG business, for
   their support and help throughout the development of this project,
   including access to the Refinitiv Workspace and the Knowledge Graph. We
   thank Jochen Leidner, Hanna Helin (Refinitiv) and Geoffrey Horrell
   (Refinitiv) for their extensive feedback offered throughout the project.
   We also thank Ben Chu (Refinitiv) for his thorough review and feedback
   on the paper. Ovidiu Serban's Fellowship is funded by a
   Refinitiv-Imperial College London, United Kingdom collaboration grant.
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NR 40
TC 0
Z9 0
U1 5
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD APR 6
PY 2022
VL 241
AR 108228
DI 10.1016/j.knosys.2022.108228
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V2EK
UT WOS:000788156700015
OA hybrid
DA 2022-08-23
ER

PT J
AU Liu, ZY
   Zhao, LX
AF Liu Ziyu
   Zhao Lixia
TI Influencing Factors of Academic Entrepreneurship-Based Knowledge
   Mapping: A Comparative Analysis of Chinese and International Literature
SO DISCRETE DYNAMICS IN NATURE AND SOCIETY
LA English
DT Review
ID UK
AB In order to analyze the hot frontier of academic entrepreneurship and study the influencing factors of academic entrepreneurship, By using citespace V information visualization software and content analysis, 32 papers in CNKI (China National Knowledge Infrastructure) database are retrieved which are indexed by CSSCI (Chinese Social Sciences Citation Index), and 66 papers are retrieved in Web of Science database which are indexed by SSCI (Social Sciences Citation Index); the Chinese and international sample papers were compared and analyzed, and a comparative analysis between Chinese and international papers was made. The results show that the influencing factors of academic entrepreneurship are divided into personal, environmental, and organizational factors. There are many researches on personal factors in China and external factors such as environment, organization, and system in international countries. Chinese research tends to the macrolevel, while international research pays more attention to the microlevel. The research frontier at home and abroad is the research of academic entrepreneurial process at microlevel. Finally, some suggestions are given to academic entrepreneurship: academic exchanges should be strengthened, more support platforms should be set up in university institutions, and the state should introduce incentive policies to enhance the strength and level of academic entrepreneurship.
C1 [Liu Ziyu; Zhao Lixia] Hebei Univ Sci & Technol, Sch Econ & Management, Shijiazhuang, Hebei, Peoples R China.
RP Liu, ZY (通讯作者)，Hebei Univ Sci & Technol, Sch Econ & Management, Shijiazhuang, Hebei, Peoples R China.
EM purpleyuliu@163.com; 1540892068@qq.com
OI Zhao, Lixia/0000-0001-8858-5098
FU 100 Outstanding Innovative Talents Support Project of Hebei Province
   [SLRC2019005]; Research and Practice Project of Higher Education
   Teaching Reform in Hebei Province [2019GJJG602]
FX AcknowledgmentsThis paper was financially supported by the 100
   Outstanding Innovative Talents Support Project of Hebei Province
   (SLRC2019005) and the Research and Practice Project of Higher Education
   Teaching Reform in Hebei Province (2019GJJG602) which made the present
   work possible.
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NR 33
TC 0
Z9 0
U1 14
U2 14
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 NOV 29
PY 2021
VL 2021
AR 6187564
DI 10.1155/2021/6187564
PG 16
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 ZY9KY
UT WOS:000772899300006
OA gold
DA 2022-08-23
ER

PT J
AU Chiang, CT
   Kou, TC
   Koo, TL
AF Chiang, Chang-Tang
   Kou, Tun-Chih
   Koo, Tian-Lih
TI A Systematic Literature Review of the IT-Based Supply Chain Management
   System: Towards a Sustainable Supply Chain Management Model
SO SUSTAINABILITY
LA English
DT Review
DE information technology; sustainable supply chain management (SSCM);
   keyword analysis; IT innovation capability; supply chain integration;
   risk management; collaboration
ID INFORMATION-TECHNOLOGY; FIRM PERFORMANCE; DYNAMIC-CAPABILITIES; PROCESS
   INTEGRATION; RISK-MANAGEMENT; LOGISTICS; COLLABORATION; IMPACT;
   EXPLOITATION; EXPLORATION
AB The concept of sustainability has been highly valued by all aspects of life, while the supply chain plays a critical role in production and logistics. Supply chain management (SCM) is continuously transformed by information technology (IT). The purpose of this study is to review and discuss the effect of information technology-based SCM on sustainability. This paper conducted a systematic literature review by collecting author-anchored keywords from peer-reviewed articles on IT-based supply chain management. A total of 1264 articles and 2575 keywords from eleven supply chain-related journals were analysed with social network analysis. A knowledge map with ten research hot topics was identified. Additionally, a sustainable supply chain management (SSCM) model is developed not only to guide researchers to further understand IT-based SCM topical and structural meanings but also to contribute to enlightening a coherent and rigorous body of theories relevant to academics and supply chain and logistics managers interested in SSCM.
C1 [Chiang, Chang-Tang] Chinese Culture Univ, Dept Informat Management, Taipei 111, Taiwan.
   [Kou, Tun-Chih] Ming Chuan Univ, Dept Int Business, Taipei 111, Taiwan.
   [Koo, Tian-Lih] Shih Chien Univ, Dept Accounting, Taipei 104, Taiwan.
RP Kou, TC (通讯作者)，Ming Chuan Univ, Dept Int Business, Taipei 111, Taiwan.
EM arthurctchiang@gmail.com; fredonef@hotmail.com; kootl@g2.usc.edu.tw
FU Ministry of Science and Technology, Taiwan, R.O.C.; MOST
   [108-2410-H-034-005]
FX This research was partially funded by the Ministry of Science and
   Technology, Taiwan, R.O.C. under Project No. MOST 108-2410-H-034-005.
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NR 74
TC 4
Z9 4
U1 24
U2 48
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAR
PY 2021
VL 13
IS 5
AR 2547
DI 10.3390/su13052547
PG 18
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA QW4SD
UT WOS:000628640300001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Li, MN
   Zhou, Y
AF Li, Munan
   Zhou, Yuan
TI Visualizing the knowledge profile on self-powered technology
SO NANO ENERGY
LA English
DT Article
DE Self-powered technology; Knowledge profile; Nanogenerator; Knowledge
   base; Research fronts
ID ENERGY HARVESTING DEVICE; TRIBOELECTRIC NANOGENERATOR; EMERGING TRENDS;
   BODY; PERFORMANCE; GENERATOR; NANOWIRES; RELEVANT; SENSORS; DRIVEN
AB Since the 1990s, self-powered technology (SPT) for micro devices has gained more attention in niche academic circles; however, breakthrough research was very rare until the piezoelectric nanogenerator (PENG) was invented around 2007. Recently, with substantial development of pervasive/ubiquitous computing technology in recent decades, the relevant applications in areas such as cloud computing, the Internet of Things, remote sensing, and remote healthcare have entered people's lives and gradually become prevalent. Furthermore, determining how to continuously supply power to those micro devices or components embedded into vehicles, mini-sensors, or heart pacemakers has also become a critical issue. Self-powered technology based on nano-generators appears to be a promising development opportunity, particularly after another better nanogenerator; namely, the triboelectric nanogenerator, was invented around 2011, at which point the relevant studies of SPT appeared to enter a new era. To further visualize the knowledge profile of SPT, an integrated approach is proposed in this paper, and knowledge-mapping of SPT is also illustrated. Accompanying the SPT knowledge profiling, new phenomena and implications are revealed.
C1 [Li, Munan] South China Univ Technol, Sch Business Adm, Guangzhou, Guangdong, Peoples R China.
   [Zhou, Yuan] Tsinghua Univ, Sch Publ Policy & Management, Beijing 100084, Peoples R China.
RP Li, MN (通讯作者)，South China Univ Technol, Sch Business Adm, Guangzhou, Guangdong, Peoples R China.; Zhou, Y (通讯作者)，Tsinghua Univ, Sch Publ Policy & Management, Beijing 100084, Peoples R China.
EM limn@scut.edu.cn; zhou_yuan@tsinghua.edu.cn
FU National Natural Science Foundation of China [71673088, 91646102];
   Foundation of Guangdong Soft Science [2017A070706003]
FX This material is based on work supported by the National Natural Science
   Foundation of China (No. 71673088, 91646102) and the Foundation of
   Guangdong Soft Science (No. 2017A070706003).
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NR 63
TC 11
Z9 11
U1 5
U2 71
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2211-2855
EI 2211-3282
J9 NANO ENERGY
JI Nano Energy
PD SEP
PY 2018
VL 51
BP 250
EP 259
DI 10.1016/j.nanoen.2018.06.068
PG 10
WC Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science,
   Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry; Science & Technology - Other Topics; Materials Science;
   Physics
GA GP2QY
UT WOS:000440682100028
DA 2022-08-23
ER

PT J
AU Li, YX
   Wang, Y
   Rui, X
   Li, YX
   Li, Y
   Wang, HZ
   Zuo, J
   Tong, YD
AF Li, Yixi
   Wang, Yuan
   Rui, Xue
   Li, Yaxiu
   Li, Yang
   Wang, Huanzhi
   Zuo, Jian
   Tong, Yindong
TI Sources of atmospheric pollution: a bibliometric analysis
SO SCIENTOMETRICS
LA English
DT Article
DE Bibliometric analysis; Particulate matters; Source apportionment;
   Knowledge mapping; Social network analysis
ID AIR-POLLUTION; CHEMICAL CHARACTERISTICS; SOURCE APPORTIONMENT; BURNING
   EMISSIONS; PM2.5; IMPACT; FIELD; POLLUTANTS; TRANSPORT; AEROSOLS
AB A bibliometric analysis based on Web of Science was carried out to provide a critical analysis of the literature on atmospheric pollution sources during 2006-2015. The result showed that particulate matter represented the core of this research field, and methods of source apportionment such as positive matrix factorization were the mainstream techniques. Five clusters were identified from the keywords network with central node of particulate matter, traffic, heavy metal, elemental carbon and renewable energy respectively. The hotspots and their relationships were illustrated to describe the characteristics of this research field. The USA and China took the leading position, however, their research emphasis were health effect and characteristics of pollutants respectively. International collaboration was mostly conducted within Asia-Pacific countries, and EU countries. For journals, Atmospheric Environment was most productive during the study period while Environmental Science and Technology had highest impact factor in 2015. This study provides an effective approach to obtain a general knowledge of the atmospheric pollution sources and supports a deeper understanding of research directions in the future.
C1 [Li, Yixi; Wang, Yuan; Rui, Xue; Li, Yaxiu; Li, Yang; Wang, Huanzhi; Tong, Yindong] Tianjin Univ, Sch Environm Sci & Engn, Tianjin, Peoples R China.
   [Zuo, Jian] Univ Adelaide, Sch Architecture & Built Environm, ECIC, Adelaide, SA 5005, Australia.
RP Wang, Y (通讯作者)，Tianjin Univ, Sch Environm Sci & Engn, Tianjin, Peoples R China.
EM wyuan@tju.edu.cn
OI Zuo, Jian/0000-0002-8279-9666; Rui, Xue/0000-0002-6652-9527
FU Natural Science Foundation of China [41571522, 71673198]
FX This research has been supported by the Natural Science Foundation of
   China (Grant Nos. 41571522 and 71673198).
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NR 52
TC 21
Z9 22
U1 12
U2 143
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD AUG
PY 2017
VL 112
IS 2
BP 1025
EP 1045
DI 10.1007/s11192-017-2421-z
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 FA2MM
UT WOS:000405275500015
DA 2022-08-23
ER

PT J
AU Leyer, M
   Schneider, C
   Claus, N
AF Leyer, Michael
   Schneider, Christian
   Claus, Nina
TI Would you like to know who knows? Connecting employees based on
   process-oriented knowledge mapping
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE Knowledge management; Business processes; Information system; System
   evaluation
ID PERCEIVED ORGANIZATIONAL SUPPORT; INFORMATION-SYSTEMS SUCCESS; PROCESS
   ORIENTATION; MANAGEMENT-SYSTEMS; DESIGN SCIENCE; CREATION; TECHNOLOGY;
   CONTEXT; ISSUES; FLOOR
AB Employees' knowledge as the guarantor of companies' success is an important asset of the enterprises' value. However, this knowledge is often not optimally used or even visible and available for employees and management within companies. This intangible knowledge leads to continuously reinvented wheels within organisations and employees misspend time learning processes in a cumbersome way on their own. To overcome this problem, we present a new approach aimed at connecting employees socially. The main novelty is the use of the organisation's business processes as basis for connecting employees by indicating their process-related areas of expertise. The goal is to enable enduring sharing and distribution of knowledge within an organisation to support decision making in process execution. Our evaluation results from two financial services companies show that employees perceive such a network as very helpful. Our approach leads to easier access, better search results and more opportunities to connect knowledge with other employees. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Leyer, Michael] Univ Rostock, Ulmenstr 69, D-18057 Rostock, Germany.
   [Schneider, Christian] Frankfurt Sch Finance & Management, Sonnemannstr 9-11, D-60314 Frankfurt, Germany.
   [Claus, Nina] Peggy Diggledey GmbH, Osdorfer Landstr 131, D-22609 Hamburg, Germany.
RP Leyer, M (通讯作者)，Univ Rostock, Ulmenstr 69, D-18057 Rostock, Germany.
EM michael.leyer@uni-rostock.de; c.d.schneider@gmx.de;
   nina.claus@peggydiggledey.net
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   [No title captured]
   [No title captured]
NR 71
TC 12
Z9 12
U1 0
U2 22
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 JUL
PY 2016
VL 87
BP 94
EP 104
DI 10.1016/j.dss.2016.05.003
PG 11
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 DQ3JA
UT WOS:000379097400009
DA 2022-08-23
ER

PT J
AU Yao, SL
   Pi, DC
   Chen, JF
AF Yao, Shuanglong
   Pi, Dechang
   Chen, Junfu
TI Knowledge embedding via hyperbolic skipped graph convolutional networks
SO NEUROCOMPUTING
LA English
DT Article
DE Hyperbolic space; Poincare ball; Knowledge graphs; Knowledge embedding;
   Knowledge representation; Link prediction
AB With the aim of constructing a low-dimensional representation space, Hyperbolic Knowledge Embeddings have gradually become a hot spot in various information retrieval and machine learning tasks. However, most of the existing Hyperbolic knowledge embedding methods focus on the shallow embedding, and often ignore the network structure characteristics (e.g., hierarchy) of Knowledge Graphs. Therefore, this paper designs a novel Hyperbolic Skipped Knowledge Graph Convolutional Network, HSKGCN, to improve link prediction accuracy with low embedding dimension requirements. Firstly, the model is designed based on the hyperbolic geometric operations on the Poincare ball, which can effectively utilize the characteristics of the hyperbolic geometry (e.g., Poincare ball) to capture the hierarchy of Knowledge Graphs; Secondly, each single-layer convolutional layer introduces the feature aggregation weight, which ensures the reasonable distribution of node features during the aggregation process; In addition, the skip-connection mechanism is applied to HSKGCN to weaken the information loss caused by the stacked of the graph convolutional layers; Finally, we evaluate HSKGCN on benchmark datasets, WN18RR and FB15k-237. Experiments show that HSKGCN achieves substantial improvements against state-of-the-art models on the 32-dimensional embedding task, and the results of different relations on WN18RR show graphs similar with tree topology can performer better. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Yao, Shuanglong; Pi, Dechang; Chen, Junfu] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China.
RP Pi, DC (通讯作者)，Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China.
EM dc.pi@nuaa.edu.cn; cjf@nuaa.edu.cn
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   Dettmers T, 2018, AAAI CONF ARTIF INTE, P1811
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   Ji GL, 2016, AAAI CONF ARTIF INTE, P985
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   Nickel M., 2017, ADV NEUR IN, V30
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   Sun Z., 2019, 7 INT C LEARN REPR
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   Vashishth S, 2020, AAAI CONF ARTIF INTE, V34, P3009
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   Yang B., 2015, P INT C LEARN REPR I
   Yang SH, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1935
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   Zheng X, 2021, NEUROCOMPUTING, V430, P104, DOI 10.1016/j.neucom.2020.10.095
NR 30
TC 0
Z9 0
U1 11
U2 11
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD APR 1
PY 2022
VL 480
BP 119
EP 130
DI 10.1016/j.neucom.2022.01.037
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZI7KX
UT WOS:000761796800010
DA 2022-08-23
ER

PT J
AU Shen, Y
   Li, DG
   Nan, D
AF Shen, Ying
   Li, Dagang
   Nan, Du
TI Modeling path information for knowledge graph completion
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Knowledge graph completion; Relation prediction; Representation
   learning; Path denoising; Information propagation
AB Knowledge graphs (KGs) store real-world information in the form of graphs consisting of relationships between entities and have been widely used in the Semantic Web community since it is readable by machines. However, most KGs are known to be very incomplete. The issues of structure sparseness and noise paths in large-scale KGs create a substantial barrier to representation learning. In this paper, we propose an Attribute-embodied neural Relation Path Prediction (ARPP) model to predict missing relations between entities in a KG. The ARPP framework leverages both structural information and textual information from the KG to enrich the representation learning and aid in learning more valuable information from noise paths for relation prediction. To handle the overlooked equal path weight distribution issue which hinders the performance of KG completion, our method evaluates the information propagation for the path by mining neighboring nodes. In order to verify the benefits of incorporating structural information and textual information and the effectiveness of path weight re-distribution, we conduct experiments from various aspects to evaluate the quantitative results for link prediction and entity prediction task, the accuracy change caused by the ablation studies, the effectiveness of the entity attribute and entity/sequence attention, the applicability of the proposed method on Knowledge Graph Completion task, and case study. Results demonstrate that the ARPP model significantly outperforms the state-of-the-art methods.
C1 [Shen, Ying; Nan, Du] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China.
   [Li, Dagang] Macau Univ Sci & Technol, Next Generat Internet Res Inst, Macau, Peoples R China.
RP Shen, Y (通讯作者)，Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China.
EM sheny76@mail.sysu.edu.cn; dgli@must.edu.mo; nan@dawnlight.com
OI SHEN (chen ying), Ying/0000-0002-3220-904X
FU National Natural Science Foundation of China [61602013]; Shenzhen
   General Research Project [JCYJ20190808182805919]
FX This work was financially supported by the National Natural Science
   Foundation of China (No.61602013) and the Shenzhen General Research
   Project (No. JCYJ20190808182805919).
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NR 35
TC 0
Z9 0
U1 4
U2 7
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 3
SI SI
BP 1951
EP 1961
DI 10.1007/s00521-021-06460-2
EA SEP 2021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YT6TZ
UT WOS:000697077800001
DA 2022-08-23
ER

PT J
AU Li, XY
   Chen, CH
   Zheng, P
   Wang, ZX
   Jiang, ZH
   Jiang, ZX
AF Li, Xinyu
   Chen, Chun-Hsien
   Zheng, Pai
   Wang, Zuoxu
   Jiang, Zuhua
   Jiang, Zhixing
TI A Knowledge Graph-Aided Concept-Knowledge Approach for Evolutionary
   Smart Product-Service System Development
SO JOURNAL OF MECHANICAL DESIGN
LA English
DT Article
DE knowledge graph; concept-knowledge model; smart product-service system;
   knowledge evolution; conceptual design; creativity and concept
   generation
ID DESIGN APPROACH; DATA ANALYTICS; INNOVATION; FRAMEWORK; MANAGEMENT;
   FIRMS
AB In order to meet user expectations and to optimize user experience with a higher degree of flexibility and sustainability, the Smart product-service system (Smart PSS), as a novel value proposition paradigm considering both online and offline smartness, was proposed. However, conventional manners for developing PSS require many professional consultations and still cannot meet with the new features of Smart PSS, such as user context-awareness and ever-evolving knowledge management. Therefore, aiming to assist Smart PSS development cost-effectively, this paper adopted the knowledge graph (KG) technique and concept-knowledge (C-K) model to propose an evolutionary design approach. Two knowledge graphs are firstly established with open-source knowledge, prototype specifications, and user-generated textual data. Then, triggered by personalized requirements, four KG-aided C-K operators are conducted based on graph-based query patterns and computational linguistics algorithms, thus generating innovative solutions for evolving Smart PSS. To validate the performance of the proposed approach, a case study of a smart nursing bed fulfilling multiple personalized requirements is conducted, and the evaluation result of its knowledge evolution is acceptable. It hopes that this work can offer insightful guidance to industrial organizations in their development of Smart PSS.
C1 [Li, Xinyu] Nanyang Technol Univ, Delta NTU Corp Lab Cyber Phys Syst, Sch Elect & Engn, Singapore 639798, Singapore.
   [Li, Xinyu; Chen, Chun-Hsien; Wang, Zuoxu] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore.
   [Li, Xinyu; Jiang, Zuhua] Shanghai Jiao Tong Univ, Dept Ind Engn & Management, Shanghai 200240, Peoples R China.
   [Zheng, Pai] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hung Hom, Hong Kong 999077, Peoples R China.
   [Jiang, Zhixing] Fudan Univ, Div Rheumatol, Huashan Hosp, Inst Rheumatol Immunol & Allergy, Shanghai 200040, Peoples R China.
RP Li, XY (通讯作者)，Nanyang Technol Univ, Delta NTU Corp Lab Cyber Phys Syst, Sch Elect & Engn, Singapore 639798, Singapore.; Li, XY (通讯作者)，Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore.; Li, XY (通讯作者)，Shanghai Jiao Tong Univ, Dept Ind Engn & Management, Shanghai 200240, Peoples R China.
EM cash.li@ntu.edu.sg; MCHchen@ntu.edu.sg; pai.zheng@polyu.edu.hk;
   zuoxu001@e.ntu.edu.sg; zhjiang@sjtu.edu.cn; 15211220008@fudan.edu.cn
RI LI, XINYU/S-5783-2019; ZHENG, PAI/K-7989-2012; wang, zuoxu/AAZ-2850-2020
OI LI, XINYU/0000-0002-4117-9698; ZHENG, PAI/0000-0002-2329-8634; wang,
   zuoxu/0000-0003-2524-1217
FU Delta Electronics Inc; National Research Foundation (NRF) Singapore
   under the Corporate Laboratory @ University Scheme at Nanyang
   Technological University, Singapore [RCA-16/434, SCO-RP1]; National
   Natural Science Foundation of China [71671113]
FX This work is conducted within the Delta-NTU Corporate Lab for
   Cyber-Physical Systems with funding support from Delta Electronics Inc
   and the National Research Foundation (NRF) Singapore under the Corporate
   Laboratory @ University Scheme (Ref. RCA-16/434; SCO-RP1) at Nanyang
   Technological University, Singapore, and National Natural Science
   Foundation of China (No. 71671113). The authors are also grateful for
   the contributions from members of the nursing bed project at Shanghai
   Jiao Tong University and the medical suggestions from physicians in
   Huashan Hospital, Shanghai.
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NR 71
TC 33
Z9 35
U1 21
U2 87
PU ASME
PI NEW YORK
PA TWO PARK AVE, NEW YORK, NY 10016-5990 USA
SN 1050-0472
EI 1528-9001
J9 J MECH DESIGN
JI J. Mech. Des.
PD OCT 1
PY 2020
VL 142
IS 10
AR 101403
DI 10.1115/1.4046807
PG 19
WC Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering
GA NN5AU
UT WOS:000568802200004
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Lang, Q
   Liu, XD
   Jia, WJ
AF Lang, Qi
   Liu, Xiaodong
   Jia, Wenjuan
TI AFS Graph: Multidimensional Axiomatic Fuzzy Set Knowledge Graph for
   Open-Domain Question Answering
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article; Early Access
DE Cognition; Semantics; Predictive models; Task analysis; Transformers;
   Internet; Fuzzy sets; Axiomatic fuzzy set (AFS); knowledge reasoning;
   question answering (QA); unsupervised learning
AB Open-domain question answering (QA) tasks require a model to retrieve inference chains associated with the answer from massive documents. The core of a QA model is the information filtering ability and reasoning ability. This article proposes a semantic knowledge reasoning graph model based on the multidimensional axiomatic fuzzy set (AFS), which can generate the knowledge graph (KG) and build reasoning paths for reading comprehension tasks through unsupervised learning. Moreover, taking advantage of the interpretable AFS framework enables the proposed model to have the ability to learn and analyze the semantic relationships between candidate documents. Meanwhile, the utilization of the multidimensional AFS acquires semantic descriptions of candidate documents more concise and flexible. The similarity degree between paragraphs is calculated according to the AFS description to generate the graph. Interpretable chains of reasoning provided by the AFS knowledge graph (AFS Graph) will serve as the basis for the answer prediction. Compared with the previous methods, the AFS Graph model presented in this article improves interpretability and reasoning ability. Experimental results show that the proposed model can achieve the state-of-the-art performance on datasets of HotpotQA, SQuAD, and Natural Questions Open.
C1 [Lang, Qi; Liu, Xiaodong] Dalian Univ Technol, Fac Elect Informat & Elect Engn, Sch Control Sci & Engn, Dalian 116024, Peoples R China.
   [Jia, Wenjuan] Dongbei Univ Finance & Econ, Sch Data Sci & Artificial Intelligence, Dalian 116025, Peoples R China.
RP Liu, XD (通讯作者)，Dalian Univ Technol, Fac Elect Informat & Elect Engn, Sch Control Sci & Engn, Dalian 116024, Peoples R China.
EM xdliuros@dlut.edu.cn
OI Lang, Qi/0000-0003-2252-7738
FU National Key Research and Development Program of China [2018YFB1702200];
   National Natural Science Foundation of China [62076049]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018YFB1702200 and in part by
   the National Natural Science Foundation of China under Grant 62076049.
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NR 71
TC 0
Z9 0
U1 9
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
DI 10.1109/TNNLS.2022.3171677
EA MAY 2022
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1G1BF
UT WOS:000795589700001
PM 35544488
DA 2022-08-23
ER

PT J
AU Hou, XC
AF Hou, Xuechen
TI Design and Application of Intelligent Financial Accounting Model Based
   on Knowledge Graph
SO MOBILE INFORMATION SYSTEMS
LA English
DT Article
ID CONVOLUTIONAL NEURAL-NETWORKS
AB With the continuous progress of science and technology, economic globalization has become an important direction for the development of enterprises. In the process of enterprise development, artificial intelligence and machine learning techniques have greatly improved the efficiency of enterprise accounting and financial management and have gradually shifted traditional financial accounting to modern financial management and accounting. Owing to the existing problems of inefficiency, large consumption of time and resources, and low degree of intelligence in the existing computerized financial data prediction systems, this study proposes an intelligent financial accounting and a financial risk monitoring and early warning model based on knowledge graph and deep learning techniques. To validate its performance, the model is trained using financial data of 120 listed companies, and the model is applied to establish the prediction of whether the listed companies are facing a financial crisis, using another 60 companies as the test sample. Results show that the use of deep learning and knowledge graph techniques can significantly improve the regulatory model, enhance regulatory penetration, and alleviate regulatory time lag, thus improving the ability of regulation to detect enterprise problems and prevent risks.
C1 [Hou, Xuechen] Xian Peihua Univ, Sch Accounting & Finance, Xian 710000, Shaanxi, Peoples R China.
RP Hou, XC (通讯作者)，Xian Peihua Univ, Sch Accounting & Finance, Xian 710000, Shaanxi, Peoples R China.
EM hxc@peihua.edu.cn
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NR 30
TC 0
Z9 0
U1 10
U2 10
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 MAR 10
PY 2022
VL 2022
AR 8353937
DI 10.1155/2022/8353937
PG 9
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 0A1DX
UT WOS:000773702900002
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Garay-Ruiz, D
   Bo, C
AF Garay-Ruiz, Diego
   Bo, Carles
TI Chemical reaction network knowledge graphs: the OntoRXN ontology
SO JOURNAL OF CHEMINFORMATICS
LA English
DT Article
DE Ontologies; Reaction networks; Semantics; Reactivity
ID WORLD-WIDE-WEB; SPAN MODEL; MARKUP; XML; ENTITIES
AB The organization and management of large amounts of data has become a major point in almost all areas of human knowledge. In this context, semantic approaches propose a structure for the target data, defining ontologies that state the types of entities on a certain field and how these entities are interrelated. In this work, we introduce OntoRXN, a novel ontology describing the reaction networks constructed from computational chemistry calculations. Under our paradigm, these networks are handled as undirected graphs, without assuming any traversal direction. From there, we propose a core class structure including reaction steps, network stages, chemical species, and the lower-level entities for the individual computational calculations. These individual calculations are founded on the OntoCompChem ontology and on the ioChem-BD database, where information is parsed and stored in CML format. OntoRXN is introduced through several examples in which knowledge graphs based on the ontology are generated for different chemical systems available on ioChem-BD. Finally, the resulting knowledge graphs are explored through SPARQL queries, illustrating the power of the semantic approach to standardize the analysis of intricate datasets and to simplify the development of complex workflows.
C1 [Garay-Ruiz, Diego; Bo, Carles] Barcelona Inst Sci & Technol, Inst Chem Res Catalonia ICIQ, Av Paisos Catalans 16, Tarragona 43007, Spain.
   [Garay-Ruiz, Diego; Bo, Carles] Univ Rovira & Virgili, Dept Quim Fis & Inorgan, Marcelli Domingo S-N, Tarragona 43007, Spain.
RP Bo, C (通讯作者)，Barcelona Inst Sci & Technol, Inst Chem Res Catalonia ICIQ, Av Paisos Catalans 16, Tarragona 43007, Spain.; Bo, C (通讯作者)，Univ Rovira & Virgili, Dept Quim Fis & Inorgan, Marcelli Domingo S-N, Tarragona 43007, Spain.
EM cbo@iciq.cat
OI Garay Ruiz, Diego/0000-0003-0744-0562
FU ICIQ Foundation; CERCA program; Spanish Ministerio de Ciencia e
   Innovacion [PID2020-112806RBI00]; Severo Ochoa Excellence Accreditation
   2020-2023, MCI/AEI [CEX2019-000925-S]; AGAUR; Secretaria d'Universitats
   i Recerca of the Generalitat de Catalunya; European Social Fund
FX The authors thank the ICIQ Foundation, the CERCA program and the Spanish
   Ministerio de Ciencia e Innovacion, through the project
   PID2020-112806RBI00 and the Severo Ochoa Excellence Accreditation
   2020-2023 (CEX2019-000925-S, MCI/AEI. D.G.R. does also thank AGAUR, the
   Secretaria d'Universitats i Recerca of the Generalitat de Catalunya and
   the European Social Fund for a FI predoctoral grant.
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NR 55
TC 0
Z9 0
U1 1
U2 1
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1758-2946
J9 J CHEMINFORMATICS
JI J. Cheminformatics
PD MAY 30
PY 2022
VL 14
IS 1
AR 29
DI 10.1186/s13321-022-00610-x
PG 12
WC Chemistry, Multidisciplinary; Computer Science, Information Systems;
   Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Computer Science
GA 1U7DK
UT WOS:000805568400001
PM 35637523
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Djenouri, Y
   Belhadi, A
   Srivastava, G
   Lin, JCW
AF Djenouri, Youcef
   Belhadi, Asma
   Srivastava, Gautam
   Lin, Jerry Chun-Wei
TI Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data
   Analysis
SO IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
LA English
DT Article; Early Access
DE Social networking (online); Deep learning; Blogs; Semantics;
   Correlation; Feature extraction; COVID-19; Cognitive computing; deep
   learning; hashtag recommendation; semantic analysis; social network
AB This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitter platform's user communities. Each community is trained with the embedded deep learning model. To recommend hashtags for the new user in the social network, the correlation between the tweets of such user and the knowledge graph of each community is explored to set the relevant communities of such user. The models of the relevant communities are used to infer the hashtags of the tweets of such users. We conducted extensive testing to demonstrate the usefulness of our methods on a variety of tweet collections. Experimental results show that the proposed approach is more efficient than the baseline approaches in terms of both runtime and accuracy.
C1 [Djenouri, Youcef] SINTEF Digital, N-7465 Oslo, Norway.
   [Belhadi, Asma] Kristiania Univ Coll, Sch Econ Innovat & Technol, N-0107 Oslo, Norway.
   [Srivastava, Gautam] Brandon Univ, Dept Math & Comp Sci, Brandon, MB R7A 6A9, Canada.
   [Srivastava, Gautam] China Med Univ, Res Ctr Interneural Comp, Taichung 40402, Taiwan.
   [Lin, Jerry Chun-Wei] Western Norway Univ Appl Sci, Dept Comp Sci Elect Engn & Math Sci, N-5063 Bergen, Norway.
RP Lin, JCW (通讯作者)，Western Norway Univ Appl Sci, Dept Comp Sci Elect Engn & Math Sci, N-5063 Bergen, Norway.
EM youcef.djenouri@sintef.no; asma.belhadi@kristiania.no;
   srivastavag@brandonu.ca; jerrylin@ieee.org
OI Djenouri, Youcef/0000-0003-0135-7450; Lin, Jerry
   Chun-Wei/0000-0001-8768-9709; Srivastava, Gautam/0000-0001-9851-4103
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NR 41
TC 0
Z9 0
U1 5
U2 5
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2329-924X
J9 IEEE T COMPUT SOC SY
JI IEEE Trans. Comput. Soc. Syst.
DI 10.1109/TCSS.2022.3169838
EA MAY 2022
PG 10
WC Computer Science, Cybernetics; Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1F4DD
UT WOS:000795118500001
DA 2022-08-23
ER

PT J
AU Huang, HY
   Shang, YM
   Sun, X
   Wei, W
   Mao, XL
AF Huang, Heyan
   Shang, Yu-Ming
   Sun, Xin
   Wei, Wei
   Mao, Xianling
TI Three birds, one stone: A novel translation based framework for joint
   entity and relation extraction
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Entity and relation extraction; Overlapping triples; Redundant
   predictions; Relation connections; Knowledge graph embedding
AB Joint entity and relation extraction is an important task in natural language processing and knowledge graph construction. Existing studies mainly focus on three issues: redundant predictions, overlapping triples and relation connections. However, as far as we know, none of them is able to solve the three problems simultaneously in a unified architecture. To address this issue, in this paper, we propose a novel translation based unified framework. Specifically, the proposed framework contains two components: an entity tagger and a relation extractor. The former is used to recognize all candidate head entities and tail entities respectively. The latter predicts relations for every entity pair dynamically through ranking with translation mechanism. To show the superiority of the proposed framework, we instantiate it through the simplest binary entity tagger and TransE algorithm. Extensive experiments over two widely used datasets demonstrate that, even with the simplest components, the proposed framework can still achieve competitive performance with most previous baselines. Moreover, the framework is flexible. It enjoys further performance boost when employing more powerful entity tagger and knowledge graph embedding algorithm. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Huang, Heyan; Shang, Yu-Ming; Sun, Xin; Mao, Xianling] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.
   [Huang, Heyan] Beijing Engn Res Ctr High Volume Language Informa, Beijing 100081, Peoples R China.
   [Wei, Wei] Huazhong Univ Sci & Technol, Sch Comp Sci, Wuhan 430074, Peoples R China.
RP Mao, XL (通讯作者)，Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.
EM hhy63@bit.edu.cn; ymshang@63.bit.edu.cn; sunxin@bit.edu.cn;
   weiw@hust.edu.cn; maoxl@bit.edu.cn
OI Wei, Wei/0000-0003-4488-0102
FU National Key RD Plan, China [2018YFB1005100]; Natu-ral Science
   Foundation of China [61772076, 61751201, 61602197]; Natural Science
   Foundation of Beijing Municipality, China [Z181100008918002]
FX Acknowledgments We would like to thank Jia Liu and Zishuo Zang for their
   insightful comments and suggestions. This work is supported by National
   Key R&D Plan, China (No. 2018YFB1005100) , Natu-ral Science Foundation
   of China (No. 61772076, 61751201 and 61602197) , Natural Science
   Foundation of Beijing Municipality, China (No. Z181100008918002) .
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NR 36
TC 0
Z9 0
U1 4
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JAN 25
PY 2022
VL 236
AR 107677
DI 10.1016/j.knosys.2021.107677
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V9JF
UT WOS:000788654600014
DA 2022-08-23
ER

PT J
AU Zheng, WB
   Yan, L
   Gou, C
   Zhang, ZC
   Zhang, JJ
   Hu, M
   Wang, FY
AF Zheng, Wenbo
   Yan, Lan
   Gou, Chao
   Zhang, Zhi-Cheng
   Zhang, Jun Jason
   Hu, Ming
   Wang, Fei-Yue
TI Pay attention to doctor-patient dialogues: Multi-modal knowledge graph
   attention image-text embedding for COVID-19 diagnosis
SO INFORMATION FUSION
LA English
DT Article
DE COVID-19 diagnose; Knowledge attention mechanism; Knowledge-based
   representation learning; Knowledge embedding
ID PREDICTING COVID-19; FUSION; CLASSIFICATION; LOCALIZATION; FRAMEWORK;
   ACCURATE; NETWORK; SYSTEM; CANCER
AB The sudden increase in coronavirus disease 2019 (COVID-19) cases puts high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. In general, there are two issues to overcome: (1) Current deep learning-based works suffer from multimodal data adequacy issues; (2) In this scenario, multimodal (e.g., text, image) information should be taken into account together to make accurate inferences. To address these challenges, we propose a multi-modal knowledge graph attention embedding for COVID-19 diagnosis. Our method not only learns the relational embedding from nodes in a constituted knowledge graph but also has access to medical knowledge, aiming at improving the performance of the classifier through the mechanism of medical knowledge attention. The experimental results show that our approach significantly improves classification performance compared to other state-of-the-art techniques and possesses robustness for each modality from multi-modal data. Moreover, we construct a new COVID-19 multi-modal dataset based on text mining, consisting of 1393 doctor-patient dialogues and their 3706 images (347 X-ray + 2598 CT + 761 ultrasound) about COVID-19 patients and 607 non-COVID-19 patient dialogues and their 10754 images (9658 X-ray + 494 CT + 761 ultrasound), and the fine-grained labels of all. We hope this work can provide insights to the researchers working in this area to shift the attention from only medical images to the doctor-patient dialogue and its corresponding medical images.
C1 [Zheng, Wenbo] Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China.
   [Zheng, Wenbo; Yan, Lan; Wang, Fei-Yue] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
   [Yan, Lan] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China.
   [Gou, Chao] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Peoples R China.
   [Zhang, Zhi-Cheng] Gen Hosp Peoples Liberat Army, Med Ctr 7, Beijing 100700, Peoples R China.
   [Zhang, Jun Jason] Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China.
   [Hu, Ming] Wuhan Pulm Hosp, Intens Care Unit, Wuhan 430030, Peoples R China.
RP Wang, FY (通讯作者)，Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
EM zwb2017@stu.xjtu.edu.cn; Yanlan2017@ia.ac.cn; gouchao@mail.sysu.edu.cn;
   dr_zhangzhicheng@126.com; jun.zhang.ee@whu.edu.cn; huming74@163.com;
   feiyue.wang@ia.ac.cn
OI Yan, Lan/0000-0001-6452-9649; Gou, Chao/0000-0002-4128-886X
FU National Key RAMP;D Program of China [2020YFB1600400]; National Natural
   Science Foundation of China [61806198, 61533019, U1811463]; Key
   Technologies Research and Development Program of Guangzhou, China
   [202007050002]; National Key Research and Development Program of China
   [2018AAA0101502]
FX We would like to thank the General Hospital of the People's Liberation
   Army and Wuhan Pulmonary Hospital for medical data and helpful advice on
   this research. This work is supported in part by the National Key R&D
   Program of China (2020YFB1600400), in part by the National Natural
   Science Foundation of China (61806198, 61533019, U1811463), in part by
   the Key Technologies Research and Development Program of Guangzhou,
   China (202007050002), and in part by the National Key Research and
   Development Program of China (No. 2018AAA0101502).
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NR 114
TC 6
Z9 6
U1 19
U2 82
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1566-2535
EI 1872-6305
J9 INFORM FUSION
JI Inf. Fusion
PD NOV
PY 2021
VL 75
BP 168
EP 185
DI 10.1016/j.inffus.2021.05.015
EA JUN 2021
PG 18
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TF9EV
UT WOS:000671018300014
PM 34093095
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Hoegele, W
   Loeschel, R
   Dobler, B
   Koelbl, O
   Zygmanski, P
AF Hoegele, Wolfgang
   Loeschel, Rainer
   Dobler, Barbara
   Koelbl, Oliver
   Zygmanski, Piotr
TI Bayesian Estimation Applied to Stochastic Localization with Constraints
   due to Interfaces and Boundaries
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID RECONSTRUCTION
AB Purpose. We present a systematic Bayesian formulation of the stochastic localization/triangulation problem close to constraining interfaces. Methods. For this purpose, the terminology of Bayesian estimation is summarized suitably for applied researchers including the presentation of Maximum Likelihood (ML), Maximum A Posteriori (MAP), and Minimum Mean Square Error (MMSE) estimation. Explicit estimators for triangulation are presented for the linear 2D parallel beam and the nonlinear 3D cone beam model. The priors in MAP and MMSE optionally incorporate (A) the hard constraints about the interface and (B) knowledge about the probability of the object with respect to the interface. All presented estimators are compared in several simulation studies for live acquisition scenarios with 10,000 samples each. Results. First, the presented application shows that MAP and MMSE perform considerably better, leading to lower Root Mean Square Errors (RMSEs) in the simulation studies compared to the ML approach by typically introducing a bias. Second, utilizing priors including (A) and (B) is very beneficial compared to just including (A). Third, typically MMSE leads to better results than MAP, by the cost of significantly higher computational effort. Conclusion. Depending on the specific application and prior knowledge, MAP and MMSE estimators strongly increase the estimation accuracy for localization close to interfaces.
C1 [Hoegele, Wolfgang; Dobler, Barbara; Koelbl, Oliver] Univ Regensburg, Med Ctr, Dept Radiat Oncol, D-93053 Regensburg, Germany.
   [Hoegele, Wolfgang; Loeschel, Rainer] Univ Appl Sci, Dept Comp Sci & Math, D-93053 Regensburg, Germany.
   [Zygmanski, Piotr] Brigham & Womens Hosp, Dept Radiat Oncol, Boston, MA 02115 USA.
   [Zygmanski, Piotr] Harvard Univ, Sch Med, Boston, MA 02115 USA.
RP Hoegele, W (通讯作者)，Univ Regensburg, Med Ctr, Dept Radiat Oncol, Franz Josef Str Allee 11, D-93053 Regensburg, Germany.
EM wolfgang@hoegele.de
RI Zygmanski, Piotr/L-9104-2019; Hoegele, Wolfgang/A-2575-2013
OI Hoegele, Wolfgang/0000-0002-5303-9334
FU German Research Foundation (DFG)
FX This work was supported by the German Research Foundation (DFG) within
   the funding programme Open Access Publishing.
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NR 19
TC 1
Z9 1
U1 0
U2 3
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PY 2013
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AR 960421
DI 10.1155/2013/960421
PG 17
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 153MK
UT WOS:000319606000001
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU MAJOR, JS
AF MAJOR, JS
TI CHALLENGES OF HIGH-THROUGHPUT SCREENING AGAINST CELL-SURFACE RECEPTORS
SO JOURNAL OF RECEPTOR AND SIGNAL TRANSDUCTION RESEARCH
LA English
DT Article; Proceedings Paper
CT 6th Swiss Workshop of Methodology in Receptor Research
CY APR 10-14, 1994
CL VERBIER, SWITZERLAND
AB Excessive or inappropriate activation of cell surface receptors can mediate the development of disease. Receptors, therefore, are a focus for drug discovery activities. Empirical screening is important in the search for novel compounds acting at receptors. Technical developments and the application of molecular biology have facilitated access to receptors of interest and have provided efficient screening methods capable of very high throughput. Reliability in high throughput screening requires the use of appropriate methodology, good screen design and effective validation and quality control processes. Validation should aim to establish that the basic experimental design is sound. In developing software to handle high throughput screening data, a fundamental requirement is to provide performance monitoring and error trapping facilities. Additional requirements are automatic data capture from instruments, on-line data reduction and analysis and transfer of results to central databases. As data volumes increase through effective high throughput screening, conventional interrogation methods become less appropriate and are being augmented by newer computing techniques referred to as knowledge mapping or database mining. Targeting cell surface receptors has been very successful as an approach to drug discovery. If the challenges of high throughput empirical screening are addressed effectively, cell surface receptors will provide new opportunities for improved therapy in the coming years.
RP MAJOR, JS (通讯作者)，ZENECA PHARMACEUT,ALDERLY PK,MACCLESFIELD,CHESHIRE,ENGLAND.
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PI NEW YORK
PA 270 MADISON AVE, NEW YORK, NY 10016
SN 1079-9893
J9 J RECEPT SIGNAL TR R
JI J. Recept. Signal Transduct. Res.
PY 1995
VL 15
IS 1-4
BP 595
EP 607
DI 10.3109/10799899509045242
PG 13
WC Biochemistry & Molecular Biology; Cell Biology
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Cell Biology
GA QQ945
UT WOS:A1995QQ94500043
PM 8903966
DA 2022-08-23
ER

PT J
AU Du, J
   Li, XY
AF Du, Jian
   Li, Xiaoying
TI A Knowledge Graph of Combined Drug Therapies Using Semantic Predications
   From Biomedical Literature: Algorithm Development
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE combined drug therapy; knowledge graph; knowledge discovery; semantic
   predications
ID COMBINATION THERAPY
AB Background: Combination therapy plays an important role in the effective treatment of malignant neoplasms and precision medicine. Numerous clinical studies have been carried out to investigate combination drug therapies. Automated knowledge discovery of these combinations and their graphic representation in knowledge graphs will enable pattern recognition and identification of drug combinations used to treat a specific type of cancer, improve drug efficacy and treatment of human disorders.
   Objective: This paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical trial reports and clinical practice guidelines.
   Methods: Based on semantic predications, which consist of a triple structure of subject-predicate-object (SPO), we proposed an automated algorithm to discover knowledge of combination drug therapies using the following rules: 1) two or more semantic predications (S-1-P-O and S-i-P-O, i = 2, 3...) can be extracted from one conclusive claim (sentence) in the abstract of a given publication, and 2) these predications have an identical predicate (that closely relates to human disease treatment, eg, "treat") and object (eg, disease name) but different subjects (eg, drug names). A customized knowledge graph organizes and visualizes these combinations, improving the traditional semantic triples. After automatic filtering of broad concepts such as "pharmacologic actions" and generic disease names, a set of combination drug therapies were identified and characterized through manual interpretation.
   Results: We retrieved 22,263 clinical trial reports and 31 clinical practice guidelines from PubMed abstracts by searching "antineoplastic agents" for drug restriction (published between Jan 2009 and Oct 2019). There were 15,603 conclusive claims locally parsed using the search terms "conclusion*" and "conclude*" ready for semantic predications extraction by SemRep, and 325 candidate groups of semantic predications about combined medications were automatically discovered within 316 conclusive claims Based on manual analysis, we determined that 255/316 claims (78.46%) were accurately identified as describing combination therapies and adopted these to construct the customized knowledge graph. We also identified two categories (and 4 subcategories) to characterize the inaccurate results: limitations of SemRep and limitations of proposal. We further learned the predominant patterns of drug combinations based on mechanism of action for new combined medication studies and discovered 4 obvious markers ("combin*," "coadministration," "co-administered," and "regimen") to identify potential combination therapies to enable development of a machine learning algorithm.
   Conclusions: Semantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. A machine learning approach is warranted to take full advantage of the identified markers and other contextual features.
C1 [Du, Jian] Peking Univ, Natl Inst Hlth Data Sci, Beijing, Peoples R China.
   [Li, Xiaoying] Chinese Acad Med Sci, Inst Med Informat, 69 Dongdan North St, Beijing 100005, Peoples R China.
RP Li, XY (通讯作者)，Chinese Acad Med Sci, Inst Med Informat, 69 Dongdan North St, Beijing 100005, Peoples R China.
EM lixiaoying@imicams.ac.cn
OI Du, Jian/0000-0001-8436-778X
FU National Natural Science Foundation of China [71603280]; Young Elite
   Scientists Sponsorship Program by China Association for Science and
   Technology [2017QNRC001]
FX This work was funded by the National Natural Science Foundation of
   China, grant number 71603280 and the Young Elite Scientists Sponsorship
   Program by China Association for Science and Technology, grant number
   2017QNRC001.
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NR 31
TC 3
Z9 4
U1 12
U2 30
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 APR
PY 2020
VL 8
IS 4
AR e18323
DI 10.2196/18323
PG 12
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA LK8CX
UT WOS:000531089000022
PM 32343247
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Pakparvar, M
   Cornelis, W
   Pereira, LS
   Gabriels, D
   Hosseinimarandi, H
   Edraki, M
   Kowsar, SA
AF Pakparvar, M.
   Cornelis, W.
   Pereira, L. S.
   Gabriels, D.
   Hosseinimarandi, H.
   Edraki, M.
   Kowsar, S. A.
TI Remote sensing estimation of actual evapotranspiration and crop
   coefficients for a multiple land use arid landscape of southern Iran
   with limited available data
SO JOURNAL OF HYDROINFORMATICS
LA English
DT Article
DE GRASS GIS; Landsat; reference evapotranspiration; SEBS model; surface
   energy balance; wind speed adjustments
ID BALANCE SYSTEM SEBS; ENERGY-BALANCE; ZAGROS MOUNTAINS; ESTIMATING SOIL;
   SPRING WHEAT; WINTER-WHEAT; WATER-USE; SURFACE; VEGETATION; AREA
AB The Gareh Bygone Plain is an arid area, south of Zagros Mountains, in Southern Iran, where a floodwater spreading project has been implemented for artificial recharge of groundwater. Knowledge/mapping of actual evapotranspiration for the mainland uses (natural pasture, irrigated crops and tree plantations) is of major importance for water management in this remote area. The Surface Energy Balance System (SEBS) model was used to estimate actual evapotranspiration (ET) using non-cloudy images for 32 dates of Landsat 5 TM from May 2009 to October 2010. Various improvements were required for ET computations, including relative to the very high wind speed observed. Reference ET was computed with observed weather data and SEBS products. Thus, crop coefficients (K-c) were obtained as the ratios of actual to reference ET relative to the main types of vegetation. The mid-season K-c generated with SEBS were compared with those previously obtained in the region and with those published in literature. Consumed water by cultivated crops based on SEBS compared well with applied water measurements. Coherent results were obtained which allow validating the SEBS approach for conditions of limited available data.
C1 [Pakparvar, M.; Cornelis, W.; Gabriels, D.] Univ Ghent, Dept Soil Management, Fac Biosci Engn, B-9000 Ghent, Belgium.
   [Pakparvar, M.] Charles Sturt Univ, Sch Environm Sci, Wagga Wagga, NSW, Australia.
   [Pereira, L. S.] Univ Lisbon, CEER Biosyst Engn, Inst Agron, P-1699 Lisbon, Portugal.
   [Edraki, M.] Bur Meteorol, Canberra, ACT, Australia.
   [Hosseinimarandi, H.; Kowsar, S. A.] Fars Res Ctr Agr & Nat Resources, Shiraz, Iran.
RP Pakparvar, M (通讯作者)，Univ Ghent, Dept Soil Management, Fac Biosci Engn, 653 Coupure Links, B-9000 Ghent, Belgium.
EM Mojtaba.Pakparvar@UGent.be
RI Pakparvar, Mojtaba/S-1963-2016; Kowsar, Sayyed Ahang/AFM-6737-2022;
   Hosseinimarandi, Hamid/N-4990-2017; Pereira, Luis S/F-8799-2010
OI Pakparvar, Mojtaba/0000-0002-9345-6247; Kowsar, Sayyed
   Ahang/0000-0001-7962-0154; Pereira, Luis S/0000-0003-4425-3408;
   Cornelis, Wim/0000-0002-6605-6723
FU SUMAMAD project - Flemish Government of Belgium; UNESCO; Charles Sturt
   University, Australia
FX Part of the study is supported by SUMAMAD project financed by Flemish
   Government of Belgium and UNESCO which are cordially acknowledged. The
   former International Centre of Water for Food Security (under auspices
   of Charles Sturt University, Australia), and Fars Research Center for
   Agriculture and Natural Resources in Iran are appreciated for providing
   the working environment and necessary facilities to conduct this
   research. Thanks are also due to Dr Yan Chemin who bestowed valuable
   guidance in both the theory and application of ET mapping based on RS.
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NR 70
TC 12
Z9 12
U1 0
U2 19
PU IWA PUBLISHING
PI LONDON
PA ALLIANCE HOUSE, 12 CAXTON ST, LONDON SW1H0QS, ENGLAND
SN 1464-7141
EI 1465-1734
J9 J HYDROINFORM
JI J. Hydroinform.
PY 2014
VL 16
IS 6
BP 1441
EP 1460
DI 10.2166/hydro.2014.140
PG 20
WC Computer Science, Interdisciplinary Applications; Engineering, Civil;
   Environmental Sciences; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Environmental Sciences & Ecology; Water
   Resources
GA AZ9FX
UT WOS:000348519900015
OA Bronze
DA 2022-08-23
ER

PT J
AU Shi, HB
   Huang, D
   Wang, L
   Wu, MY
   Xu, YC
   Zeng, BE
   Pang, C
AF Shi, Hui-Bin
   Huang, Dan
   Wang, Li
   Wu, Mei-Yu
   Xu, Ying-Cheng
   Zeng, Bao-Er
   Pang, Chen
TI An information integration approach to spacecraft fault diagnosis
SO ENTERPRISE INFORMATION SYSTEMS
LA English
DT Article
DE Spacecraft faults; information integration; whole life circle; knowledge
   graph; Function-Behaviour-Structure theory; human-machine interface
ID HUMAN-MACHINE INTERFACE; WAVELET TRANSFORM; KNOWLEDGE; DESIGN; SYSTEMS;
   OPERATION; DEFECTS
AB An important part of a rapid response to spacecraft faults is obtaining information efficiently and accurately determining the cause of the faults. The cause may originate from any stage in the life cycle. This study aims to integrate spacecraft information using fusion technologies to construct a whole life cycle knowledge graph. A human-computer interface for spacecraft rolling bearing fault diagnosis is designed according to Function-Behaviour-Structure theory. Thus, the whole process of fault diagnosis for spacecraft rolling bearings is demonstrated. This research realises the application of information integration in spacecraft fault diagnosis and improves fault response capabilities, thus ensuring spacecraft integrity.
C1 [Shi, Hui-Bin; Huang, Dan] Shenyang Univ Technol, Sch Management, Shenyang, Liaoning, Peoples R China.
   [Wang, Li] Beihang Univ, Sch Econ & Management, Beijing, Peoples R China.
   [Wu, Mei-Yu] Shangdong Normal Univ, Sch Business, Jinan, Shandong, Peoples R China.
   [Xu, Ying-Cheng] Natl Inst Standardizat, Qual Management Branch, Beijing, Peoples R China.
   [Zeng, Bao-Er; Pang, Chen] Beijing C Stellar Sci&Technol Inst Co Ltd, Beijing, Peoples R China.
RP Wang, L (通讯作者)，Beihang Univ, Sch Econ & Management, Beijing, Peoples R China.
EM wlbh@vip.163.com
FU National Key RD Plan of China [2017YFF0209604, 2017YFB1400200]; National
   Natural Science Foundation of China (NSFC) [91646122, 91746202]; Social
   Science Planning Fund of Liaoning Province [L19BGL028]
FX This work was supported by The National Key R&D Plan of China [No.
   2017YFF0209604, 2017YFB1400200, 2017YFB1400200], National Natural
   Science Foundation of China (NSFC) [No. 91646122, 91746202] and Social
   Science Planning Fund of Liaoning Province [L19BGL028].
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NR 120
TC 4
Z9 4
U1 15
U2 52
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1751-7575
EI 1751-7583
J9 ENTERP INF SYST-UK
JI Enterp. Inf. Syst.
PD SEP 14
PY 2021
VL 15
IS 8
SI SI
BP 1128
EP 1161
DI 10.1080/17517575.2019.1709663
EA JAN 2020
PG 34
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA UU1RI
UT WOS:000504943900001
DA 2022-08-23
ER

PT J
AU Jeschke, JM
   Heger, T
   Kraker, P
   Schramm, M
   Kittel, C
   Mietchen, D
AF Jeschke, Jonathan M.
   Heger, Tina
   Kraker, Peter
   Schramm, Maxi
   Kittel, Christopher
   Mietchen, Daniel
TI Towards an open, zoomable atlas for invasion science and beyond
SO NEOBIOTA
LA English
DT Article
DE hierarchy-of-hypotheses (HoH) approach; hypothesis networks; interactive
   knowledge discovery; invasive alien species; invasion biology; &nbsp;
   Open Knowledge Maps; open science; Wikidata
AB Biological invasions are on the rise, and their global impacts on ecosystems, economies and human health are a major challenge. Invasion science is critical to mitigate invader impacts, yet due to the strong increase of data and information in this area, it has become difficult to acquire and maintain an overview of the field. As a result, existing evidence is often not found, knowledge is too rarely transferred to practice, and research is sometimes conducted in pursuit of dead ends. We propose to address these challenges by developing an interactive atlas of invasion science that can be extended to other disciplines in the future. This online portal, which we aim to create in the course of the project described here, will be an evolving knowledge resource and open for anyone to use, including researchers, citizen scientists, practitioners and policy makers. Users will be able to zoom into the major research questions and hypotheses of invasion science, which are connected to the relevant studies published in the field and, if available, the underly-ing raw data. The portal will apply cutting-edge visualization techniques, artificial intelligence and novel methods for knowledge synthesis.
C1 [Jeschke, Jonathan M.] Leibniz Inst Freshwater Ecol & Inland Fisheries I, Berlin, Germany.
   [Jeschke, Jonathan M.] Free Univ Berlin, Inst Biol, Berlin, Germany.
   [Jeschke, Jonathan M.; Heger, Tina] Berlin Brandenburg Inst Adv Biodivers Res BBIB, Berlin, Germany.
   [Heger, Tina] Univ Potsdam, Biodivers Res Systemat Bot, Potsdam, Germany.
   [Heger, Tina] Tech Univ Munich, Restorat Ecol, Freising Weihenstephan, Germany.
   [Kraker, Peter; Schramm, Maxi; Kittel, Christopher] Open Knowledge Maps Verein Forderung Sichtbarkeit, Vienna, Austria.
   [Mietchen, Daniel] Univ Virginia, Sch Data Sci, Charlottesville, VA USA.
RP Jeschke, JM (通讯作者)，Leibniz Inst Freshwater Ecol & Inland Fisheries I, Berlin, Germany.; Jeschke, JM (通讯作者)，Free Univ Berlin, Inst Biol, Berlin, Germany.; Jeschke, JM (通讯作者)，Berlin Brandenburg Inst Adv Biodivers Res BBIB, Berlin, Germany.
EM jonathan.jeschke@gmx.net
RI Jeschke, Jonathan M/A-2707-2008; Heger, Tina/AAH-4802-2019; Mietchen,
   Daniel/A-7748-2009
OI Jeschke, Jonathan M/0000-0003-3328-4217; Heger,
   Tina/0000-0002-5522-5632; Mietchen, Daniel/0000-0001-9488-1870; Kraker,
   Peter/0000-0002-5238-4195
FU VolkswagenStiftung [97 863]
FX We are grateful to Christopher Schwarzkopf and Sarah Behrens from
   Wikimedia Deutschland for bringing us together as a team and providing
   valuable suggestions, to the VolkswagenStiftung for funding this project
   (grant number 97 863) and to Eliza-bete Marchante and anonymous
   reviewers for providing helpful comments.
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NR 41
TC 3
Z9 3
U1 5
U2 11
PU PENSOFT PUBLISHERS
PI SOFIA
PA 12 PROF GEORGI ZLATARSKI ST, SOFIA, 1700, BULGARIA
SN 1619-0033
EI 1314-2488
J9 NEOBIOTA
JI NeoBiota
PD AUG 18
PY 2021
VL 68
BP 5
EP 18
DI 10.3897/neobiota.68.66685
PG 14
WC Biodiversity Conservation; Ecology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biodiversity & Conservation; Environmental Sciences & Ecology
GA UL5XU
UT WOS:000692724900001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Restrepo, L
   Aguilar, J
   Toro, M
   Suescun, E
AF Restrepo, Luisa
   Aguilar, Jose
   Toro, Mauricio
   Suescun, Elizabeth
TI A sustainable-development approach for self-adaptive cyber-physical
   system's life cycle: A systematic mapping study
SO JOURNAL OF SYSTEMS AND SOFTWARE
LA English
DT Article
DE Self-adaptive systems; Sustainability; Cyber-physical systems;
   Systems-development life-cycle
ID RESEARCH DIRECTIONS; TECHNICAL DEBT; INTERNET
AB Cyber-Physical Systems (CPS) refer to a new generation of systems where the cyber and physical layers are -strongly- interconnected. The development of these systems requires two fundamental parts. First, the design of sustainable architectures-centered on adaptation, throughout a System-Development Life-Cycle (SDLC)- to develop robust and economically profitable products. Second, the use of self-adaptive techniques to adjust CPSs to the evolving circumstances of their operation context. This work presents a systematic mapping study (SMS) that discusses different approaches used to develop self-adaptive CPSs (SA-CPSs) at each stage of the SDLC, focused on sustainability. The results show trends such as (i) Designs are not limited to particular application domains, (ii) Performance was the most commonly used attribute, and (iii) Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K) is the predominant feedback loop applied in the cyber layer. The results also raise challenges such as (i) How to design and evaluate sustainable SA-CPSs, (ii) How to apply unit and integration testing in the development of SA-CPSs, and (iii) How to develop feedback loops on SA-CPSs with the integration of machine-learning techniques. (C) 2021 Elsevier Inc. All rights reserved.
C1 [Restrepo, Luisa; Aguilar, Jose; Toro, Mauricio; Suescun, Elizabeth] Univ EAFIT, RID Informat Technol & Commun Res Grp, Medellin, Colombia.
   [Aguilar, Jose] Univ Los Andes, CEMISID, Merida, Venezuela.
   [Aguilar, Jose] Univ Alcala De Henares, Dept Automat, Alcala De Henares, Spain.
RP Aguilar, J (通讯作者)，Univ Los Andes, CEMISID, Merida, Venezuela.
EM lrestr61@eafit.edu.co; jlaguilarc@eafit.edu.co; mtorobe@eafit.edu.co;
   esuescu1@eafit.edu.co
RI Toro, Mauricio/AAR-1195-2020; Suescun Monsalve, Elizabeth/T-3654-2018;
   Aguilar, Jose/H-3017-2015
OI Toro, Mauricio/0000-0002-7280-8231; Restrepo Gutierrez, Luisa
   Fernanda/0000-0002-4448-9309; Suescun Monsalve,
   Elizabeth/0000-0001-7872-7638; Aguilar, Jose/0000-0003-4194-6882
FU Universidad EAFIT, Colombia
FX The authors would like to thank Vicerectoria de Descubrimiento y
   Creacion from Universidad EAFIT. This research was supported by
   Universidad EAFIT, Colombia. The authors would also like to thank David
   Velasquez for his early comments and suggestions on this research. All
   authors approved the version of the manuscript to be published.
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NR 63
TC 2
Z9 2
U1 6
U2 6
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0164-1212
EI 1873-1228
J9 J SYST SOFTWARE
JI J. Syst. Softw.
PD OCT
PY 2021
VL 180
AR 111010
DI 10.1016/j.jss.2021.111010
EA JUN 2021
PG 17
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TT0TS
UT WOS:000680064900003
DA 2022-08-23
ER

PT J
AU Eskandari, F
   Shafieian, M
   Aghdam, MM
   Laksari, K
AF Eskandari, Faezeh
   Shafieian, Mehdi
   Aghdam, Mohammad M.
   Laksari, Kaveh
TI A knowledge map analysis of brain biomechanics: Current evidence and
   future directions
SO CLINICAL BIOMECHANICS
LA English
DT Article
DE Brain biomechanics; Mechanical properties; Neurological diseases;
   Traumatic injuries; Research trends
ID MAGNETIC-RESONANCE ELASTOGRAPHY; TISSUE IN-VIVO; MECHANICAL-PROPERTIES;
   FINITE-ELEMENT; CONSTITUTIVE MODEL; PORCINE BRAIN; WHITE-MATTER;
   VISCOELASTIC PROPERTIES; MR ELASTOGRAPHY; LARGE-STRAIN
AB Although brain, one of the most complex organs in the mammalian body, has been subjected to many studies from physiological and pathological points of view, there remain significant gaps in the available knowledge regarding its biomechanics. This article reviews the research trends in brain biomechanics with a focus on injury. We used published scientific articles indexed by Web of Science database over the past 40 years and tried to address the gaps that still exist in this field. We analyzed the data using VOSviewer, which is a software tool designed for scientometric studies.
   The results of this study showed that the response of brain tissue to external forces has been one of the significant research topics among biomechanicians. These studies have addressed the effects of mechanical forces on the brain and mechanisms of traumatic brain injury, as well as characterized changes in tissue behavior under trauma and other neurological diseases to provide new diagnostic and monitoring methods. In this study, some challenges in the field of brain injury biomechanics have been identified and new directions toward understanding the gaps in this field are suggested.
C1 [Eskandari, Faezeh; Shafieian, Mehdi] Amirkabir Univ Technol, Dept Biomed Engn, Tehran Polytech, Tehran, Iran.
   [Aghdam, Mohammad M.] Amirkabir Univ Technol, Dept Mech Engn, Tehran Polytech, Tehran, Iran.
   [Laksari, Kaveh] Univ Arizona, Dept Biomed Engn, Tucson, AZ USA.
RP Shafieian, M (通讯作者)，Amirkabir Univ Technol, Tehran Polytech, 424 Hafez Ave, Tehran 1591634311, Iran.
EM shafieian@aut.ac.ir
RI Eskandari, Faezeh/GMX-3256-2022
OI Eskandari, Faezeh/0000-0003-1221-6733; Laksari,
   Kaveh/0000-0002-7570-4796
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NR 134
TC 3
Z9 3
U1 1
U2 7
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0268-0033
EI 1879-1271
J9 CLIN BIOMECH
JI Clin. Biomech.
PD MAY
PY 2020
VL 75
AR 105000
DI 10.1016/j.clinbiomech.2020.105000
PG 11
WC Engineering, Biomedical; Orthopedics; Sport Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Orthopedics; Sport Sciences
GA LN1BE
UT WOS:000532679000001
PM 32361083
DA 2022-08-23
ER

PT J
AU Oliveira-Barros, EG
   Nicolau-Neto, P
   Da Costa, NM
   Pinto, LFR
   Palumbo, A
   Nasciutti, LE
AF Oliveira-Barros, Eliane Gouvea
   Nicolau-Neto, Pedro
   Da Costa, Nathalia Meireles
   Ribeiro Pinto, Luis Felipe
   Palumbo, Antonio, Jr.
   Nasciutti, Luiz Eurico
TI Prostate cancer molecular profiling: the Achilles heel for the
   implementation of precision medicine
SO CELL BIOLOGY INTERNATIONAL
LA English
DT Review
DE DNA copy-number variations; gene fusions; mutations; precision medicine;
   prostate cancer; rearrangements
ID EXPRESSION; GENOMICS; FOXA1
AB Cancer has been mainly treated by traditional therapeutic approaches which do not consider the human genetic diversity and present limitations, probably as a consequence of a poor knowledge of both patient's genetic background and tumor biology. Due to genome project conclusion and large-scale gene analyses emergence, the therapeutic management of several prevalent and aggressive tumors has dramatically improved and represents the closest examples of a precision medicine intervention in this field. Nonetheless, prostate cancer (PCa) remains as a challenge to personalized medicine implementation, probably due to its notorious heterogeneous molecular profile. Cancer treatment personalized approaches rely on the premise that a welldefined panorama of tumor molecular alterations can help selecting new and specific therapeutic targets for its treatment and potentially discriminate tumors which behave differentially. Lately, molecular and genetic studies have been investigating PCa basis, revealing multiple recurrent genomic alterations that include mutations, DNA copy-number variations, rearrangements, and gene fusions, among others. In addition to the increment on PCa molecular biology knowledge, mapping the molecular alterations pattern of this neoplasia, especially the differences existent between tumors displaying distinct behaviors, could represent a great improvement concerning the identification of new targets, personalized medicine, and patients' management and prognosis.
C1 [Oliveira-Barros, Eliane Gouvea; Palumbo, Antonio, Jr.; Nasciutti, Luiz Eurico] Univ Fed Rio de Janeiro, Inst Ciencias Biomed, Programa Pesquisa Biol Celular & Desenvolvimento, BR-21941902 Rio De Janeiro, Brazil.
   [Nicolau-Neto, Pedro; Da Costa, Nathalia Meireles; Ribeiro Pinto, Luis Felipe; Palumbo, Antonio, Jr.] Inst Nacl Canc INCA, Ctr Pesquisas CPQ, Programa Carcinogenese Mol, Rua Andre Cavalcanti,37 Ctr, BR-20231050 Rio De Janeiro, Brazil.
RP Oliveira-Barros, EG (通讯作者)，Univ Fed Rio de Janeiro, Inst Ciencias Biomed, Programa Pesquisa Biol Celular & Desenvolvimento, BR-21941902 Rio De Janeiro, Brazil.
EM eligouveab@gmail.com
RI Palumbo, Antonio/AAX-9572-2020; Nicolau, Pedro/AAC-9740-2021
OI Nicolau, Pedro/0000-0003-3322-2120; Gouvea de Oliveira Barros,
   Eliane/0000-0002-0020-6653; Ribeiro Pinto, Luis
   Felipe/0000-0001-5509-1008
FU Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq);
   Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES);
   Fundacao de Amparoa Pesquisa Carlos Chagas Filho (FAPERJ)
FX The authors would like to acknowledge Conselho Nacional de
   Desenvolvimento Cientifico e Tecnologico (CNPq), Coordenacao de
   Aperfeicoamento de Pessoal de Nivel Superior (CAPES) and Fundacao de
   Amparoa Pesquisa Carlos Chagas Filho (FAPERJ) for financial support.
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U2 7
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1065-6995
EI 1095-8355
J9 CELL BIOL INT
JI Cell Biol. Int.
PD NOV
PY 2017
VL 41
IS 11
SI SI
BP 1239
EP 1245
DI 10.1002/cbin.10785
PG 7
WC Cell Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Cell Biology
GA FO2OG
UT WOS:000416618000009
PM 28477422
DA 2022-08-23
ER

PT J
AU Zeb, AN
   Ul Haq, A
   Chen, JD
   Lei, ZF
   Zhang, DF
AF Zeb, Adnan
   Ul Haq, Anwar
   Chen, Junde
   Lei, Zhenfeng
   Zhang, Defu
TI Learning hyperbolic attention-based embeddings for link prediction in
   knowledge graphs
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graph; Link prediction; Graph neural network; Hyperbolic graph
   neural network; Poincare ball model
AB Knowledge graph (KG) embedding methods aim to learn low-dimensional representations of entities and relations to predict new valid triples for KG completion. Most of the existing KG embedding models learn embeddings in Euclidean space, which cannot accurately capture hierarchical structures and complex properties of relations found in KGs. To this effect, a recent model MuRP, as a first hyperbolic method, learns the KG embeddings in hyperbolic space and outperforms existing Euclidean embedding models. However, MuRP treats the KG triples individually, and hence fails to capture the complex structural information inherent in the local vicinity of a node, leading to low-quality node embeddings. On the other hand, the recent hyperbolic graph neural network (HGNN) provides a way of learning high-quality hyperbolic node embeddings by capturing information from each node's neighborhood. However, HGNN ignores the relation features and treats all neighboring nodes with equal importance. To this end, we propose RHGNN, which extends HGNN by including the relation features and performing a hyperbolic attention-based neighborhood aggregation. We then combine RHGNN with MuRP into a novel encoder-decoder hyperbolic embedding learning framework (which we call HyperGEL) for KG completion. RHGNN gathers information from the neighboring nodes to generate rich hyperbolic node embeddings, and MuRP uses these embeddings to predict new triples. Experimental results show the proposed framework's effectiveness that consistently marks performance gains over several previous models on recent standard KG completion datasets. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Zeb, Adnan; Ul Haq, Anwar; Chen, Junde; Lei, Zhenfeng; Zhang, Defu] Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China.
   [Ul Haq, Anwar] Univ Malakand, Dept Comp Sci & IT, Chakdara 18800, KP, Pakistan.
RP Zhang, DF (通讯作者)，Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China.
EM adnanzeb@stu.xmu.edu.cn; anwar@uom.edu.pk; chen2wo@126.com;
   zflei621@foxmail.com; dfzhang@xmu.edu.cn
OI Zeb, Adnan/0000-0001-6105-3796; Haq, Anwar Ul/0000-0003-2319-5624; ,
   xtu502/0000-0003-1748-4374
FU National Natural Science Foundation of China [61672439]
FX This work is partially supported by the National Natural Science
   Foundation of China (Grant no. 61672439).
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NR 57
TC 3
Z9 3
U1 8
U2 13
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD OCT 11
PY 2021
VL 229
AR 107369
DI 10.1016/j.knosys.2021.107369
EA AUG 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UU3DZ
UT WOS:000698681300002
DA 2022-08-23
ER

PT J
AU Wang, T
   Ali, AS
   Au-Yong, CP
AF Wang, Tao
   Ali, Azlan Shah
   Au-Yong, Cheong Peng
TI Exploring a body of knowledge for promoting the building information
   model for facility management
SO AIN SHAMS ENGINEERING JOURNAL
LA English
DT Article
DE BIM; Facility management; Knowledge body; Scientific analysis
ID BIM-BASED FRAMEWORK; AUGMENTED REALITY; LIFE-CYCLE; IMPLEMENTATION;
   FUTURE; FM; MAINTENANCE; OPERATION; BENEFITS; SYSTEM
AB The aim of this research is to explore the body of knowledge on BIM-enabled facility management (BIM-FM). This study proposes a method for knowledge mapping analysis through scientometric analysis, which uses two software, VOSviewer, and CiteSpace, to analyze the results of literature data related to BIM-FM. 319 journal articles were collected for collaborative network analysis, co-occurrence network analysis, and co-citation network analysis. The findings of the study conclude that 1) the knowledge structure of BIM-FM was divided into five significant clusters; 2) the key research topics of BIM-FM show an evolutionary trend in different periods, from model construction, framework, and gradually expanding to implementation, system integration, and smart building; 3) future research foresees three research directions, including enhancing the application of BIM in multiple facility management; advanced applications in facility management; health and safety for facility managers; and improving regulations and risk awareness. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
C1 [Wang, Tao; Ali, Azlan Shah; Au-Yong, Cheong Peng] Univ Malaya, Fac Built Environm, Dept Bldg Surveying, Ctr Bldg Construct & Trop Architecture BuCTA, Kuala Lumpur 50603, Malaysia.
RP Au-Yong, CP (通讯作者)，Univ Malaya, Fac Built Environm, Dept Bldg Surveying, Ctr Bldg Construct & Trop Architecture BuCTA, Kuala Lumpur 50603, Malaysia.
EM 17221416@siswa.um.edu.my; asafab@um.edu.my; auyongcp@um.edu.my
RI Au-Yong, Cheong Peng/K-7599-2012
OI Au-Yong, Cheong Peng/0000-0003-1306-9595
FU Fundamental Research Grant Scheme (FRGS) by the Ministry of Education,
   Malaysia [FRGS/1/2019/SSI11/UM/02/6, FP077-2019A]; Natural Science
   Foundation of Liaoning Province [2021-BS-072]
FX The authors gratefully acknowledge the financial support from the
   Fundamental Research Grant Scheme (FRGS) FRGS/1/2019/SSI11/UM/02/6, No.
   FP077-2019A, established by the Ministry of Education, Malaysia and
   Natural Science Foundation of Liaoning Province (No. 2021-BS-072). The
   authors thank the editor and anonymous reviewers for their numerous
   constructive comments and encouragement that have improved the paper
   greatly.
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NR 78
TC 1
Z9 1
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2090-4479
EI 2090-4495
J9 AIN SHAMS ENG J
JI Ain Shams Eng. J.
PD JUN
PY 2022
VL 13
IS 4
AR 101717
DI 10.1016/j.asej.2022.101717
PG 14
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 3G3XG
UT WOS:000831288700009
OA gold
DA 2022-08-23
ER

PT J
AU De Leon, SS
   Selva, D
   Way, DW
AF De Leon, Samalis Santini
   Selva, Daniel
   Way, David W.
TI Improving Rule Mining for Entry, Descent, and Landing Simulations Using
   Knowledge Graphs
SO JOURNAL OF AEROSPACE INFORMATION SYSTEMS
LA English
DT Article; Early Access
ID INTERESTINGNESS MEASURES; ASSOCIATION RULES; LINK-PREDICTION; LOGIC
AB Designing planetary entry, descent, and landing (EDL) systems requires analyzing large datasets containing tens of thousands of parameters. These datasets are generally manually analyzed by subject-matter experts trying to find interesting correlations and couplings between parameters that explain the behaviors observed. A popular approach to automate the extraction of explanation rules is association rule mining, in which rules with high statistical strength are mined from the dataset. However, current rule mining algorithms generate too many rules that are redundant, too complex, too obvious, or do not make sense to the user. In this paper, we propose a new approach to improve the comprehensibility, insightfulness, and usefulness of the association rules generated during the analysis of an EDL dataset by leveraging a user-provided knowledge graph. The knowledge graph captures the user knowledge about EDL and the specific problem at hand. We then use a statistical relational learning framework based on probabilistic soft logic to assess the degree of consistency of the rule with the user's knowledge of the system. The method is validated in a small study with N=6 subject-matter experts. The results of the study also show interesting relationships between comprehensibility, usefulness, and insightfulness of the extracted rules.
C1 [De Leon, Samalis Santini; Selva, Daniel] Texas A&M Univ, Dept Aerosp Engn, College Stn, TX 77843 USA.
   [Way, David W.] NASA Langley Res Ctr, Atmospher Flight & Entry Syst Branch D205, Hampton, VA 23666 USA.
RP Way, DW (通讯作者)，NASA Langley Res Ctr, Atmospher Flight & Entry Syst Branch D205, Hampton, VA 23666 USA.
EM david.w.way@nasa.gov
FU NASA Science and Technology Research Fellowship Program [80NSSC18~K1635]
FX This work was funded by the NASA Science and Technology Research
   Fellowship Program under grant number 80NSSC18~K1635. The authors would
   like to thank all of the subject-matter experts who volunteered and took
   the time to respond to the survey. Their input and feedback were
   extremely valuable for this research.
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NR 54
TC 0
Z9 0
U1 0
U2 0
PU AMER INST AERONAUTICS  ASTRONAUTICS
PI RESTON
PA 1801 ALEXANDER BELL DRIVE, STE 500, RESTON, VA 22091-4344 USA
EI 2327-3097
J9 J AEROSP INFORM SYST
JI J. Aerosp. Inf. Syst.
DI 10.2514/1.I011063
EA JUN 2022
PG 15
WC Engineering, Aerospace
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 2P9BB
UT WOS:000820025700001
DA 2022-08-23
ER

PT J
AU Zhu, Y
   Liang, XW
   Lin, BQ
   Ye, QX
   Jiao, JB
   Lin, L
   Liang, XD
AF Zhu, Yi
   Liang, Xiwen
   Lin, Bingqian
   Ye, Qixiang
   Jiao, Jianbin
   Lin, Liang
   Liang, Xiaodan
TI Configurable Graph Reasoning for Visual Relationship Detection
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article
DE Graph learning; scene graph generation; visual reasoning; visual
   relationship detection (VRD)
AB Visual commonsense knowledge has received growing attention in the reasoning of long-tailed visual relationships biased in terms of object and relation labels. Most current methods typically collect and utilize external knowledge for visual relationships by following the fixed reasoning path of {subject, object -> predicate} to facilitate the recognition of infrequent relationships. However, the knowledge incorporation for such fixed multidependent path suffers from the data set biased and exponentially grown combinations of object and relation labels and ignores the semantic gap between commonsense knowledge and real scenes. To alleviate this, we propose configurable graph reasoning (CGR) to decompose the reasoning path of visual relationships and the incorporation of external knowledge, achieving configurable knowledge selection and personalized graph reasoning for each relation type in each image. Given a commonsense knowledge graph, CGR learns to match and retrieve knowledge for different subpaths and selectively compose the knowledge routed path. CGR adaptively configures the reasoning path based on the knowledge graph, bridges the semantic gap between the commonsense knowledge, and the real-world scenes and achieves better knowledge generalization. Extensive experiments show that CGR consistently outperforms previous state-of-the-art methods on several popular benchmarks and works well with different knowledge graphs. Detailed analyses demonstrated that CGR learned explainable and compelling configurations of reasoning paths.
C1 [Zhu, Yi; Ye, Qixiang; Jiao, Jianbin] Univ Chinese Acad Sci UCAS, Beijing, Peoples R China.
   [Liang, Xiwen; Lin, Bingqian; Lin, Liang; Liang, Xiaodan] Sun Yat Sen Univ, Guangzhou, Peoples R China.
RP Jiao, JB (通讯作者)，Univ Chinese Acad Sci UCAS, Beijing, Peoples R China.
EM zhuyi215@mails.ucas.ac.cn; liangcici5@gmail.com; bingqianlin@126.com;
   qxye@ucas.ac.cn; jiaojb@ucas.ac.cn; linliang@ieee.org;
   xdliang328@gmail.com
OI Liang, Lin/0000-0003-2248-3755; ye, qi xiang/0000-0003-1215-6259; Liang,
   Xiwen/0000-0002-2484-6962
FU National Key Research and Development Program of China [2018AAA0100300];
   National Natural Science Foundation of China [61836012, U19A2073,
   61976233]; Guangdong Province Basic and Applied Basic Research (Regional
   Joint Fund-Key) [2019B1515120039]; Nature Science Foundation of Shenzhen
   [2019191361]; Zhijiang Laboratory's Open Fund [2020AA3AB14]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018AAA0100300, in part by the
   National Natural Science Foundation of China under Grant 61836012, Grant
   U19A2073, and Grant 61976233; in part by the Guangdong Province Basic
   and Applied Basic Research (Regional Joint Fund-Key) under Grant
   2019B1515120039; in part by the Nature Science Foundation of Shenzhen
   under Grant 2019191361; and in part by the Zhijiang Laboratory's Open
   Fund under Grant 2020AA3AB14.
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NR 51
TC 2
Z9 2
U1 10
U2 12
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
PD JAN
PY 2022
VL 33
IS 1
BP 117
EP 129
DI 10.1109/TNNLS.2020.3027575
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA YC4AK
UT WOS:000739635300015
PM 33119512
DA 2022-08-23
ER

PT J
AU Tang, JL
AF Tang, Jiali
TI Optimization of English Learning Platform Based on a Collaborative
   Filtering Algorithm
SO COMPLEXITY
LA English
DT Article
ID TEACHING MODE; TECHNOLOGIES; INTERNET
AB This paper provides a detailed description of the recommendation system and collaborative filtering algorithm to optimize the English learning platform through the collaborative filtering algorithm and analyses the algorithmic principles and specific techniques of collaborative filtering. After introducing the recommendation system and collaborative filtering algorithm, this paper elaborates on the theoretical basis and technical principles of the recommendation algorithm based on cognitive ability and difficulty and provides an in-depth analysis of the design and implementation of the recommendation algorithm by combining cognitive diagnosis theory, readability formula, and English knowledge map, which provides a comprehensive and solid theoretical guidance and support for the application development of the online English learning platform. The system is tested by building a Spring Cloud platform, importing actual business data, focusing on the validation of the recommendation model, and connecting the recommendation system to the formal production system to analyse the recommendation effect. Compared with the original recommendation method, the online English learning platform designed and implemented in this paper based on the cognitive ability and difficulty collaborative filtering recommendation algorithm has a better recommendation effect. The system is proved to be well designed and has certain reference and guiding value for the whole web-based online learning platform and has a broader application prospect nowadays and in the future.
C1 [Tang, Jiali] Zunyi Med Univ, Sch Foreign Languages, Zunyi 563000, Guizhou, Peoples R China.
RP Tang, JL (通讯作者)，Zunyi Med Univ, Sch Foreign Languages, Zunyi 563000, Guizhou, Peoples R China.
EM tjl@zmu.edu.cn
CR Alam A., INT J ADV SCI TECHNO, V29, P5077
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NR 25
TC 0
Z9 0
U1 3
U2 11
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1076-2787
EI 1099-0526
J9 COMPLEXITY
JI Complexity
PD APR 30
PY 2021
VL 2021
AR 6624012
DI 10.1155/2021/6624012
PG 14
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA SX2CV
UT WOS:000665019200004
OA gold
DA 2022-08-23
ER

PT J
AU Nie, ZW
   Zheng, SS
   Liu, YJ
   Chen, ZF
   Li, SN
   Lei, K
   Pan, F
AF Nie, Zhiwei
   Zheng, Shisheng
   Liu, Yuanji
   Chen, Zhefeng
   Li, Shunning
   Lei, Kai
   Pan, Feng
TI Automating Materials Exploration with a Semantic Knowledge Graph for
   Li-Ion Battery Cathodes
SO ADVANCED FUNCTIONAL MATERIALS
LA English
DT Article
DE data-driven knowledge discovery; knowledge graph; Li-ion battery
   cathodes; semantics representation; text mining
ID LITHIUM; NACL
AB The recent marriage of materials science and artificial intelligence has created the need to extract and collate materials information from the tremendous backlog of academic publications. However, this is notoriously hard to achieve in sophisticated application domains, such as Li-ion battery (LIB) cathodes, which require multiple variables for materials selection, making it challenging to automatically identify the critical terms in the text. Herein, a semantics representation framework, featuring a dual-attention module that refines word embeddings through multi-source information fusion, is proposed for literature mining of LIB cathodes. The word embeddings thus produced are biased toward domain-specific knowledge and can enable the detection of deep-seated associations among materials for targeted applications. Based on this framework, we establish a semantic knowledge graph dedicated to LIB cathodes, which allows us to unravel the latent materials relationships from scientific literature and even to discover candidate materials not yet exploited as cathodes before. This work provides a long-sought path to the realization of text-mining-based knowledge management for complicated materials systems with little dependence on domain expertise.
C1 [Nie, Zhiwei; Zheng, Shisheng; Liu, Yuanji; Chen, Zhefeng; Li, Shunning; Pan, Feng] Peking Univ, Shenzhen Grad Sch, Sch Adv Mat, Shenzhen 518055, Peoples R China.
   [Lei, Kai] Peking Univ, Shenzhen Grad Sch, Shenzhen Key Lab Informat Centr Networking & Bloc, Shenzhen 518055, Peoples R China.
RP Li, SN; Pan, F (通讯作者)，Peking Univ, Shenzhen Grad Sch, Sch Adv Mat, Shenzhen 518055, Peoples R China.; Lei, K (通讯作者)，Peking Univ, Shenzhen Grad Sch, Shenzhen Key Lab Informat Centr Networking & Bloc, Shenzhen 518055, Peoples R China.
EM lisn@pku.edu.cn; leik@pkusz.edu.cn; panfeng@pkusz.edu.cn
RI Li, Shunning/ABB-4187-2021
OI Li, Shunning/0000-0002-5381-6025
FU Soft Science Research Project of Guangdong Province [2017B030301013];
   Shenzhen Science and Technology Research Grant [JCYJ20200109140416788];
   Chemistry and Chemical Engineering Guangdong Laboratory [1922018];
   National Natural Science Foundation of China [62072012, 22109003];
   Key-Area Research and Development Program of Guangdong Province
   [2020B0101090003]
FX This work was financially supported by Soft Science Research Project of
   Guangdong Province (No. 2017B030301013), The Shenzhen Science and
   Technology Research Grant (No. JCYJ20200109140416788), the Chemistry and
   Chemical Engineering Guangdong Laboratory (Grant No. 1922018), the
   National Natural Science Foundation of China (62072012 and 22109003),
   and Key-Area Research and Development Program of Guangdong Province
   (2020B0101090003).
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NR 58
TC 0
Z9 0
U1 26
U2 26
PU WILEY-V C H VERLAG GMBH
PI WEINHEIM
PA POSTFACH 101161, 69451 WEINHEIM, GERMANY
SN 1616-301X
EI 1616-3028
J9 ADV FUNCT MATER
JI Adv. Funct. Mater.
PD JUN
PY 2022
VL 32
IS 26
AR 2201437
DI 10.1002/adfm.202201437
EA MAR 2022
PG 7
WC Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience &
   Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied;
   Physics, Condensed Matter
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Science & Technology - Other Topics; Materials Science;
   Physics
GA 2J0GM
UT WOS:000773565600001
DA 2022-08-23
ER

PT J
AU Cui, P
   Liu, SW
   Zhu, WW
AF Cui, Peng
   Liu, Shaowei
   Zhu, Wenwu
TI General Knowledge Embedded Image Representation Learning
SO IEEE TRANSACTIONS ON MULTIMEDIA
LA English
DT Article
DE Image representation learning; knowledge base; multirelational graph
   embedding
AB Image representation learning is a fundamental problem in understanding semantics of images. However, traditional classification-based representation learning methods face the noisy and incomplete problem of the supervisory labels. In this paper, we propose a general knowledge base embedded image representation learning approach, which uses general knowledge graph, which is a multitype relational knowledge graph consisting of human commonsense beyond image space, as external semantic resource to capture the relations of concepts in image representation learning. A relational regularized regression CNN (R-3 CNN) model is designed to jointly optimize the image representation learning problem and knowledge graph embedding problem. In this manner, the learnt representation can capture not only labeled tags but also related concepts of images, which involves more precise and complete semantics. Comprehensive experiments are conducted to investigate the effectiveness and transferability of our approach in tag prediction task, zero-shot tag inference task, and content-based image retrieval task. The experimental results demonstrate that the proposed approach performs significantly better than the existing representation learning methods. Finally, observation of the learnt relations show that our approach can somehow refine the knowledge base to describe images and label the images with structured tags.
C1 [Cui, Peng; Liu, Shaowei; Zhu, Wenwu] Tsinghua Univ, Dept Comp Sci, Beijing 100084, Peoples R China.
RP Cui, P (通讯作者)，Tsinghua Univ, Dept Comp Sci, Beijing 100084, Peoples R China.
EM cuip@tsinghua.edu.cn; liushaowei@mails.tsinghua.edu.cn;
   wwzhu@tsinghua.edu.cn
FU research fund of Tsinghua-Tencent Joint Laboratory for Internet
   Innovation Technology; Young Elite Scientist Sponsorship Program by CAST
FX The authors would like to thank the research fund of Tsinghua-Tencent
   Joint Laboratory for Internet Innovation Technology and the Young Elite
   Scientist Sponsorship Program by CAST.
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NR 32
TC 39
Z9 41
U1 4
U2 30
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1520-9210
EI 1941-0077
J9 IEEE T MULTIMEDIA
JI IEEE Trans. Multimedia
PD JAN
PY 2018
VL 20
IS 1
BP 198
EP 207
DI 10.1109/TMM.2017.2724843
PG 10
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA FQ3WS
UT WOS:000418289700016
DA 2022-08-23
ER

PT J
AU Chaudhari, S
   Azaria, A
   Mitchell, T
AF Chaudhari, Sneha
   Azaria, Amos
   Mitchell, Tom
TI An entity graph based Recommender System
SO AI COMMUNICATIONS
LA English
DT Article
DE Recommender Systems; knowledge-graphs; PageRank
AB Recommender Systems have become increasingly important and are applied in an increasing number of domains. While common collaborative methods measure similarity between different users, common content based methods measure similarity between different content. We propose a privacy aware recommender system that exploits relations present between entities appearing in content from user's history and entities appearing in candidate content. In order to identify such relations, we use the knowledge graph of NELL, which encodes entities and their relations. We present a novel normalized version of Personalized PageRank, to rank candidate content. We test our approach on the movie recommendation domain and show that the proposed method outperforms other baseline methods, including the standard Personalized PageRank. We intend to deploy our recommender system as a news recommendation app for mobile devices.
C1 [Chaudhari, Sneha; Mitchell, Tom] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA.
   [Azaria, Amos] Ariel Univ, Comp Sci Dept, Ariel, Israel.
RP Azaria, A (通讯作者)，Ariel Univ, Comp Sci Dept, Ariel, Israel.
EM sschaudh@andrew.cmu.edu; amos.azaria@ariel.ac.il;
   tom.mitchell@cs.cmu.edu
RI Azaria, Amos/Y-6302-2019
OI Azaria, Amos/0000-0002-5057-1309
FU Yahoo! as part of the InMind project [4]; Samsung
FX This research was supported by Yahoo! as part of the InMind project [4],
   and by Samsung.
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NR 33
TC 12
Z9 12
U1 3
U2 38
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 0921-7126
EI 1875-8452
J9 AI COMMUN
JI AI Commun.
PY 2017
VL 30
IS 2
BP 141
EP 149
DI 10.3233/AIC-170728
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EV2BQ
UT WOS:000401559000003
DA 2022-08-23
ER

PT J
AU Zhang, HZ
   Wang, YL
   Chen, C
   Liu, RX
   Zhou, SW
   Gao, TL
AF Zhang, Hanzhong
   Wang, Yinglong
   Chen, Chao
   Liu, Ruixia
   Zhou, Shuwang
   Gao, Tianlei
TI Enhancing Knowledge of Propagation-Perception-Based Attention
   Recommender Systems
SO ELECTRONICS
LA English
DT Article
DE recommender systems; knowledge graph; attention mechanism; heterogeneous
   propagation
AB Researchers have introduced side information such as social networks or knowledge graphs to alleviate the problems of data sparsity and cold starts in recommendation systems. However, most of the methods ignore the exploration of feature differentiation aspects in the knowledge propagation process. To solve the above problem, we propose a new attention recommendation method based on an enhanced knowledge propagation perception. Specifically, to capture user preferences in a fine-grained manner in a knowledge graph, an asymmetric semantic attention mechanism is adopted. It identifies the influence of propagation neighbors on user preferences through a more precise representation of the preference semantics for head and tail entities. Furthermore, in consideration of the memory and generalization of different propagation depth features and adaptively adjusting the propagation weights, a new propagation feature exploration framework is designed. The performance of the proposed model is validated by two real-world datasets. The baseline model averagely increases by 9.65% and 9.15% for the Area Under Curve (AUC) and Accuracy (ACC) indicators, which proves the effectiveness of the model.
C1 [Zhang, Hanzhong; Zhou, Shuwang; Gao, Tianlei] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao 266590, Peoples R China.
   [Zhang, Hanzhong; Chen, Chao; Liu, Ruixia; Zhou, Shuwang; Gao, Tianlei] Qilu Univ Technol, Shandong Artificial Intelligence Inst, Shandong Acad Sci, Jinan 266590, Peoples R China.
   [Wang, Yinglong] Qilu Univ Technol, Shandong Acad Sci, Shandong Comp Sci Ctr, Natl Supercomp Ctr Jinan, Jinan 266590, Peoples R China.
RP Wang, YL (通讯作者)，Qilu Univ Technol, Shandong Acad Sci, Shandong Comp Sci Ctr, Natl Supercomp Ctr Jinan, Jinan 266590, Peoples R China.
EM zhanghz178@163.com; wangylscsc@126.com; chench@sdas.org; liurx@sdas.org;
   zhoushw@sdas.org; gaotl@sdas.org
OI liu, ruixia/0000-0002-4044-5384; Zhang, Hanzhong/0000-0002-5145-5166
FU National Key R&D Program (Demonstration of R&D and Application of
   Integrated Science and Technology Service Platform for Central Plains
   Urban Agglomeration) [2018YFB1404500]
FX FundingThis research was funded by National Key R&D Program
   (Demonstration of R&D and Application of Integrated Science and
   Technology Service Platform for Central Plains Urban Agglomeration),
   grant number 2018YFB1404500.
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NR 40
TC 0
Z9 0
U1 6
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD FEB
PY 2022
VL 11
IS 4
AR 547
DI 10.3390/electronics11040547
PG 20
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA ZQ5PS
UT WOS:000767157200001
OA gold
DA 2022-08-23
ER

PT J
AU Xu, XR
   Hou, GM
   Wang, JP
AF Xu, Xiurui
   Hou, Guangming
   Wang, Junpeng
TI Research on Digital Transformation Based on Complex Systems:
   Visualization of Knowledge Maps and Construction of a Theoretical
   Framework
SO SUSTAINABILITY
LA English
DT Article
DE digital transformation; organization management system; bibliometrics;
   visual analysis; sustainable competitive advantage
ID DYNAMIC CAPABILITIES; READINESS; EXPLORATION; TECHNOLOGY; CITESPACE;
   IMPACT; PATHS; SMART; IOT
AB In the digital age, the exploration of digital transformation has made remarkable progress in many fields. However, the existing theories related to digital transformation at the organizational level are relatively scattered, which makes it difficult to support the practical exploration of organizational change in the digital context. Through quantitative and visual analysis of the literature in the field of digital transformation, this study analyzes the research situation in this field from the aspects of the paper publishing trend, node literature, key scholars and regional cooperation. Through the analysis of the keywords co-occurrence network, the research frontier of digital transformation is identified, and, based on complex systems, this study discusses the research frontier from three aspects: organizational symbiosis oriented by environmental coordination, ability remodeling oriented by structural optimization and value creation oriented by functional realization. Further, based on the analysis framework and principle of organizational management systematics, this study constructs the theoretical framework of digital transformation from three aspects: core dimension, implementation mechanism and action mechanism. The systematic theoretical framework can provide reference for the development of relevant theories of digital transformation and better support the management practice of digital transformation.
C1 [Xu, Xiurui; Hou, Guangming; Wang, Junpeng] Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China.
RP Wang, JP (通讯作者)，Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China.
EM xuxiurui@foxmail.com; gmhou@bit.edu.cn; abwjp@bit.edu.cn
OI Xu, Xiurui/0000-0001-9707-410X
FU Major Project of Beijing Social Science Fund of China [18ZDA02]
FX FundingThis research was funded by the Major Project of Beijing Social
   Science Fund of China under Grant No.18ZDA02.
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NR 64
TC 0
Z9 0
U1 53
U2 53
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAR
PY 2022
VL 14
IS 5
AR 2683
DI 10.3390/su14052683
PG 19
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA ZR5FO
UT WOS:000767808800001
OA gold
DA 2022-08-23
ER

PT J
AU Li, WB
   Li, XL
   Wu, WW
AF Li, Weibao
   Li, Xiaoli
   Wu, Weiwei
TI Knowledge Innovation Mechanism Based on Linkages between Core Knowledge
   and Periphery Knowledge: The Case of R&D Cooperation between Latecomers
   and Forerunners
SO COMPLEXITY
LA English
DT Article
ID SPILLOVERS; MANAGEMENT; CAPABILITY; CREATION; TACIT
AB With rapid changes in technology, knowledge innovation has become more and more complex. Complex partners facilitate knowledge creation and innovation in complex problem-solving. Relying on R&D cooperation with forerunners, latecomers can acquire new knowledge factors and new ideas and use them to create new knowledge. This study demonstrated the knowledge innovation mechanism based on linkages between periphery knowledge and core knowledge with knowledge map theory and set methodology. A case of R&D cooperation between latecomers and forerunners was introduced to verify this mechanism. The results show that latecomers could share forerunners' periphery knowledge in R&D cooperation through formal and informal interaction and acquire the core knowledge factors in periphery knowledge based on the linkages between them. With these core knowledge factors, latecomers integrate them into their internal knowledge system and create new knowledge. By analyzing the linkages between periphery and core knowledge factors, this study creatively demonstrates the knowledge innovation mechanism in knowledge linkage perspective. It deepens the understanding of R&D cooperation between latecomers and forerunners and expands the focus of knowledge innovation theory. This study provides a new insight into latecomers effectively activating internal and external knowledge resources through R&D cooperation.
C1 [Li, Weibao] North China Inst Aerosp Engn, Sch Econ & Management, Langfang 065000, Peoples R China.
   [Li, Xiaoli] Harbin Univ Sci & Technol, Sch Econ & Management, Harbin 150001, Peoples R China.
   [Wu, Weiwei] Harbin Inst Technol, Sch Econ & Management, Harbin 150001, Peoples R China.
RP Li, XL (通讯作者)，Harbin Univ Sci & Technol, Sch Econ & Management, Harbin 150001, Peoples R China.
EM lixiaoli@hrbust.edu.cn
FU National Natural Science Foundation of China [72072047]; Heilongjiang
   Philosophy and Social Science Research Project [19GLB087, 20JYB033];
   Science and Technology Program of Hebei Province [20557688D]; Doctor
   Research Foundation of North China Institute of Aerospace Engineering
   [BKY-2018-31]
FX This research was funded by the National Natural Science Foundation of
   China (Grant no. 72072047), Heilongjiang Philosophy and Social Science
   Research Project (Grant nos. 19GLB087 and 20JYB033), Science and
   Technology Program of Hebei Province (Grant no. 20557688D), and Doctor
   Research Foundation of North China Institute of Aerospace Engineering
   (Grant no. BKY-2018-31).
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NR 51
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 1076-2787
EI 1099-0526
J9 COMPLEXITY
JI Complexity
PD FEB 18
PY 2022
VL 2022
AR 8417784
DI 10.1155/2022/8417784
PG 13
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA 3D1UU
UT WOS:000829096300001
OA gold
DA 2022-08-23
ER

PT J
AU Padilla-Rivera, A
   Russo-Garrido, S
   Merveille, N
AF Padilla-Rivera, Alejandro
   Russo-Garrido, Sara
   Merveille, Nicolas
TI Addressing the Social Aspects of a Circular Economy: A Systematic
   Literature Review
SO SUSTAINABILITY
LA English
DT Review
DE circular economy; social aspects; systematic review; sustainable
   development
ID GREEN ECONOMY; WASTE MANAGEMENT; SUSTAINABILITY; INDICATORS;
   PERFORMANCE; RESOURCES; FRAMEWORK; NETWORKS; NIMBY; TOOL
AB Circular Economy (CE) is a growing topic among scholars, industries, and governments, and is aimed at decoupling economic growth and development from the consumption of finite resources. CE incorporates different meanings, from reduce, reuse, and recycle activities, to environmental degradation or resource scarcity, and is supported by specific indicators to attain sustainable development. However, so far, there has been no agreement to measure how effective an industry/product is in making the transition from linear to circular approaches, particularly those that affect society. This research work aims to perform a systematic literature review (n = 60) to analyze and discuss how social aspects have been considered and integrated in CE research so far. Moreover, this review provides an overview of the literature on social impact within the CE, which results in three main outputs: a knowledge map of the CE, an analysis of social aspects within CE, and the theories/frameworks used to evaluate social impact of CE. Finally, this study brings to light how CE implementation can affect society and highlights the importance of social dimension in the domains of CE and a policy-making community, which could help move CE towards a sustainable development.
C1 [Padilla-Rivera, Alejandro] Polytech Montreal, CIRAIG, Dept Math & Ind Engn, Montreal, PQ H3T 1J4, Canada.
   [Russo-Garrido, Sara; Merveille, Nicolas] Univ Quebec Montreal, Dept Strategy & Corp Social Responsibil, CIRAIG, ESG, Montreal, PQ H3C 3P8, Canada.
RP Padilla-Rivera, A (通讯作者)，Polytech Montreal, CIRAIG, Dept Math & Ind Engn, Montreal, PQ H3T 1J4, Canada.
EM alejandro-de-jesus.padilla-rivera@polymtl.ca;
   russo_garrido.sara@uqam.ca; Merveille.nicolas@uqam.ca
OI Padilla-Rivera, Alejandro/0000-0002-8290-6804; Yanet,
   Niko/0000-0002-6758-4851; Merveille, Nicolas/0000-0002-8026-8394
FU Consejo Nacional de Ciencia y Tecnologia (Mexico) [740519]; UQAM
FX This research was funded by Consejo Nacional de Ciencia y Tecnologia
   (Mexico) grant number: 740519. And the APC was funded by UQAM.
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NR 86
TC 44
Z9 44
U1 13
U2 40
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD OCT
PY 2020
VL 12
IS 19
AR 7912
DI 10.3390/su12197912
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 ON3EP
UT WOS:000586589000001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Xu, YH
   Sun, SJ
   Zhang, HG
   Yi, CA
   Miao, Y
   Yang, D
   Meng, XN
   Hu, Y
   Wang, K
   Min, HQ
   Song, HJ
   Miao, CAY
AF Xu, Yonghui
   Sun, Shengjie
   Zhang, Huiguo
   Yi, Chang'an
   Miao, Yuan
   Yang, Dong
   Meng, Xiaonan
   Hu, Yi
   Wang, Ke
   Min, Huaqing
   Song, Hengjie
   Miao, Chuanyan
TI Time-Aware Graph Embedding: A Temporal Smoothness and Task-Oriented
   Approach
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE Knowledge graph; graph embedding; temporal smoothness
ID DATABASE
AB Knowledge graph embedding, which aims at learning the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, most knowledge graph embedding methods focus on the structural relationships in fixed triples while ignoring the temporal information. Currently, existing time-aware graph embedding methods only focus on the factual plausibility, while ignoring the temporal smoothness, which models the interactions between a fact and its contexts, and thus can capture fine-granularity temporal relationships. This leads to the limited performance of embedding related applications. To solve this problem, this article presents a Robustly Time-aware Graph Embedding (RTGE) method by incorporating temporal smoothness. Two major innovations of our article are presented here. At first, RTGE integrates a measure of temporal smoothness in the learning process of the time-aware graph embedding. Via the proposed additional smoothing factor, RTGE can preserve both structural information and evolutionary patterns of a given graph. Secondly, RTGE provides a general task-oriented negative sampling strategy associated with temporally aware information, which further improves the adaptive ability of the proposed algorithm and plays an essential role in obtaining superior performance in various tasks. Extensive experiments conducted on multiple benchmark tasks show that RTGE can increase performance in entity/relationship/temporal scoping prediction tasks.
C1 [Xu, Yonghui] Shandong Univ, Joint SDU NTU Ctr Artificial Intelligence Res, Jinan 250100, Peoples R China.
   [Sun, Shengjie; Yang, Dong; Meng, Xiaonan; Hu, Yi] Alibaba Grp, Hangzhou 310052, Peoples R China.
   [Zhang, Huiguo] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore.
   [Yi, Chang'an] Foshan Univ, Sch Elect & Informat Engn, Foshan 528000, Peoples R China.
   [Miao, Yuan] Victoria Univ, Melbourne, Vic 2052, Australia.
   [Wang, Ke] Simon Fraser Univ, Burnaby, BC V5A 1S6, Canada.
   [Min, Huaqing; Song, Hengjie] South China Univ Technol, Guangzhou 510006, Peoples R China.
   [Miao, Chuanyan] Nanyang Technol Univ, Singapore 639798, Singapore.
RP Song, HJ (通讯作者)，South China Univ Technol, Guangzhou 510006, Peoples R China.; Miao, CAY (通讯作者)，Nanyang Technol Univ, Singapore 639798, Singapore.
EM xu.yonghui@hotmail.com; shengjie.ssj@alibaba-inc.com;
   HUIGU0001@e.ntu.edu.sg; yi.changan@fosu.edu.cn; yuan.miao@vu.edu.au;
   trovato@corporation.com; xiaonan.mengxn@alibaba-inc.com;
   erwin.huy@alibaba-inc.com; wangk@cs.sfu.ca; hqmin@scut.edu.cn;
   sehjsong@scut.edu.cn; ascymiao@ntu.edu.sg
FU Alibaba-NTU Joint Research Project; Fundamental Research Funds of
   Shandong University; Humanities and Social Science Youth Foundation of
   Ministry of Education of China [18YJCZH226]
FX This work is supported by Alibaba-NTU Joint Research Project, the
   Fundamental Research Funds of Shandong University. This work is partly
   supported by the Humanities and Social Science Youth Foundation of
   Ministry of Education of China (18YJCZH226).
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NR 50
TC 0
Z9 0
U1 1
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1556-4681
EI 1556-472X
J9 ACM T KNOWL DISCOV D
JI ACM Trans. Knowl. Discov. Data
PD JUN
PY 2022
VL 16
IS 3
AR 56
DI 10.1145/3480243
PG 23
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1T8NW
UT WOS:000804983600016
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Li, ZT
   Zhao, YL
   Li, YJ
   Rahman, S
   Wang, F
   Xin, XJ
   Zhang, J
AF Li, Zhuotong
   Zhao, Yongli
   Li, Yajie
   Rahman, Sabidur
   Wang, Feng
   Xin, Xiangjun
   Zhang, Jie
TI Fault Localization based on Knowledge Graph in Software-Defined Optical
   Networks
SO JOURNAL OF LIGHTWAVE TECHNOLOGY
LA English
DT Article
DE Location awareness; Optical fiber networks; Knowledge engineering;
   Cognition; Fault diagnosis; Artificial intelligence; Data models; Alarm
   analysis; fault localization; graph neural network; knowledge graph;
   optical networks; SDON platform
ID NEURAL-NETWORK; LOCATION
AB In the era of the fifth-generation fixed network (F5G), optical networks must be developed to support large bandwidth, low latency, high reliability, and intelligent management. Studies have shown that software-defined optical networks (SDON) and artificial intelligence can help improve the performance and management capabilities of optical networks. Inside a large-scale optical network, many types of alarms are reported that indicate network anomalies. Relationships between the alarms are complicated, making it difficult to accurately locate the source of the fault(s). In this work, we propose a knowledge-guided fault localization method, using network alarm knowledge to analyze network abnormalities. Our method introduces knowledge graphs (KGs) into the alarm analysis process. We also propose a reasoning model based on graph neural network (GNN), to perform relational reasoning on alarm KGs and locate the network faults. We develop an ONOS-based SDON platform for experimental verification, which includes a set of processes for the construction and application of alarm KGs. The experimental results show the proposed method has high accuracy and provide motivation for the industry-scale use of KGs for alarm analysis and fault localization.
C1 [Li, Zhuotong; Zhao, Yongli; Li, Yajie; Xin, Xiangjun; Zhang, Jie] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China.
   [Rahman, Sabidur] Sonoma State Univ, Rohnert Pk, CA 94928 USA.
   [Wang, Feng] Ningxia Grid Informat & Telecommun Co, Yinchuan 750001, Ningxia, Peoples R China.
RP Zhao, YL (通讯作者)，Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China.
EM lizhuotong@bupt.edu.cn; yonglizhao@bupt.edu.cn; yajieli@bupt.edu.cn;
   rahmank@sonoma.edu; 371673703@qq.com; xjxin@bupt.edu.cn;
   lgr24@bupt.edu.cn
OI Rahman, K M Sabidur/0000-0001-9957-8336; Li,
   Zhuotong/0000-0001-5182-0453
FU National Key Research and Development Program of China [2020YFB1805602];
   National Natural Science Foundation of China (NSFC) [62021005, 61822105,
   61901053]; Fundamental Research Funds for the Central Universities
   [2019XD-A05]
FX Manuscript received September 17, 2020; revised January 9, 2021 andMarch
   29, 2021; accepted April 4, 2021. Date of publication April 8, 2021;
   date of current version July 2, 2021. Thisworkwas supported in part by
   theNationalKey Research andDevelopment Program of China underGrant
   2020YFB1805602, in part by the National Natural Science Foundation of
   China (NSFC) under Grants 62021005, 61822105, and 61901053, and in part
   by the Fundamental Research Funds for the Central Universities under
   Grant 2019XD-A05. (Corresponding author: Yongli Zhao.)
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NR 37
TC 4
Z9 4
U1 23
U2 38
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0733-8724
EI 1558-2213
J9 J LIGHTWAVE TECHNOL
JI J. Lightwave Technol.
PD JUL 1
PY 2021
VL 39
IS 13
BP 4236
EP 4246
DI 10.1109/JLT.2021.3071868
PG 11
WC Engineering, Electrical & Electronic; Optics; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Optics; Telecommunications
GA SX2CC
UT WOS:000665017300001
DA 2022-08-23
ER

PT J
AU Wang, YX
   Ge, ZP
   Yan, HJ
   Xu, XL
   Xia, YX
AF Wang, Yuxiang
   Ge, Zhangpeng
   Yan, Haijiang
   Xu, Xiaoliang
   Xia, Yixing
TI Semantic locality-based approximate knowledge graph query
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article; Proceedings Paper
CT 6th International Conference on Advanced Cloud and BigData (CBD)
CY AUG 12-14, 2018
CL Lanzhou, PEOPLES R CHINA
DE approximate query; corpus generation; RDF knowledge graph; semantic
   locality; text embedding
ID SIMILARITY SEARCH; SCALE
AB RDF knowledge graphs (KG) usually contain billions of labeled entities, and how to obtain the desired results efficiently on RDF KG for given SPARQL queries have attracted increasing attentions recently. However, it is difficult for users to write a complex SPARQL query without full knowledge of the underlying KG schema due to the "schema-free" feature of RDF KG. In this paper, we study the problem of acquiring semantic approximate results for a simple SPARQL query instead of the complex expression. The basic idea behind is to use the real knowledge semantics to extend the query intention of given simplified SPARQL query, and then a semantic based greedy search over is designed to return top-k similar results. To obtain the knowledge semantics efficiently, we define type similarity to reorganize the original KG as a corpus with semantic locality and then a context aware text embedding model is adopted to achieve the semantic vectors of existing knowledge. Afterwards, an approximate query method over RDF KG is designed to obtain top-k similar results based on the knowledge semantics above. Extensive experiments over DBpedia dataset and QALD-4 benchmark confirm our solution's superiority on both effectiveness and efficiency.
C1 [Wang, Yuxiang; Ge, Zhangpeng; Yan, Haijiang; Xu, Xiaoliang; Xia, Yixing] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China.
RP Xu, XL (通讯作者)，Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China.
EM xxl@hdu.edu.cn
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NR 32
TC 0
Z9 0
U1 0
U2 26
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-0626
EI 1532-0634
J9 CONCURR COMP-PRACT E
JI Concurr. Comput.-Pract. Exp.
PD DEC 25
PY 2019
VL 31
IS 24
SI SI
AR e5345
DI 10.1002/cpe.5345
PG 13
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 JP3VT
UT WOS:000498195900006
DA 2022-08-23
ER

PT J
AU Zhu, L
   Li, N
   Bai, LY
   Gong, YQ
   Xing, YZ
AF Zhu, Lin
   Li, Nan
   Bai, Luyi
   Gong, Yunqing
   Xing, Yizong
TI stRDFS: Spatiotemporal Knowledge Graph Modeling
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph; spatiotemporal data; stRDFS
ID BASE; WEB
AB In Semantic Web, modeling knowledge graph based on RDF becomes more and more popular. There is quite a lot of spatiotemporal information in Semantic Web, and recent works focus on not only general data but also spatiotemporal data. Existing efforts are mainly to add spatiotemporal labels to RDF, which expand RDF triple into quad or quintuple. However, extra labels often cause additional overhead for the system and lead to inefficient information organization management. In order to overcome this limitation, we propose an stRDFS model by labeling properties with spatiotemporal features and the corresponding determination methods of topological relations among different spatiotemporal entities. stRDFS considers spatiotemporal attribute as a part of the RDF model, which can record spatiotemporal information without changing the current RDF standard. Our approach improves the ability of recording and linking spatiotemporal data. More importantly, depending on formatting of spatiotemporal attributes in stRDFS, it will improve the semantic inferring ability, and the users are not required to be familiar with the underlying representations of spatiotemporal data.
C1 [Zhu, Lin; Li, Nan; Bai, Luyi; Gong, Yunqing; Xing, Yizong] Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China.
RP Bai, LY (通讯作者)，Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China.
EM baily@neuq.edu.cn
OI Bai, Luyi/0000-0001-9546-3208; , Nan/0000-0002-7588-8118
FU National Natural Science Foundation of China [61402087]; Natural Science
   Foundation of Hebei Province [F2019501030]; Natural Science Foundation
   of Liaoning Province [2019-MS-130]; Key Project of Scienti~c Research
   Funds in Colleges and Universities of the Hebei Education Department
   [ZD2020402]; Fundamental Research Funds for the Central Universities
   [N2023019]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61402087, in part by the Natural Science
   Foundation of Hebei Province under Grant F2019501030, in part by the
   Natural Science Foundation of Liaoning Province under Grant 2019-MS-130,
   in part by the Key Project of Scienti~c Research Funds in Colleges and
   Universities of the Hebei Education Department under Grant ZD2020402,
   and in part by the Fundamental Research Funds for the Central
   Universities under Grant N2023019.
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NR 56
TC 0
Z9 1
U1 7
U2 16
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 129043
EP 129057
DI 10.1109/ACCESS.2020.3008688
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MO9XS
UT WOS:000551869500001
OA gold
DA 2022-08-23
ER

PT J
AU Marzano, A
   Miranda, S
AF Marzano, Antonio
   Miranda, Sergio
TI The DynaMap Remediation Approach (DMRA) in online learning environments
SO COMPUTERS & EDUCATION
LA English
DT Article
DE Online learning; Teaching/learning strategies; Dynamic concept maps;
   Remediation; Knowledge representation
ID FORMATIVE ASSESSMENT; SELF-REGULATION; KNOWLEDGE MAPS; STUDENTS;
   COMPUTERS; TEXT
AB The aim of this paper is to present an empirical study with the intention of answering the question: can an intentionally structured online environment stimulate and enhance the processes of mutual interaction and hybridization between digital artefacts and analogic artefacts (books) so as to encourage the development of significant learning in students?
   This research, carried out in the academic year 2018/2019, involved 183 Italian students enrolled on the bachelor's degree in Primary Education Sciences at the University of Salerno attending the course "School experimentation and educational planning". The data were collected by working out and using an information retrieval questionnaire and four different tests. The virtual interaction space was e-LENA, the Learning Management System of the RIMEDI@ research lab.
   The experimental results highlight how this methodology, called DynaMap Remediation Approach (DMRA) by the authors, has had effects in terms of reducing study time and improving learning outcomes. The online learning environment stimulated to study in greater detail with a book, with the learner being able to restructure and systematize his own knowledge. The DMRA valorises the active role of the learner during his process of knowledge construction and may have significant implications for educators who would like to use innovative and effective online learning environments to enhance student learning.
C1 [Marzano, Antonio; Miranda, Sergio] Univ Salerno, Dept Humanities Philosophy & Educ, Via Giovanni Paolo 2 132, Fisciano, SA, Italy.
RP Miranda, S (通讯作者)，Univ Salerno, Dept Human Philosophy & Educ Sci DISUFF, Via Giovanni Paolo 2 132, Fisciano, SA, Italy.
EM semiranda@unisa.it
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NR 62
TC 2
Z9 2
U1 4
U2 30
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 MAR
PY 2021
VL 162
AR 104079
DI 10.1016/j.compedu.2020.104079
PG 18
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA PQ6RQ
UT WOS:000606672100002
DA 2022-08-23
ER

PT J
AU Alam, M
   Recupero, DR
   Mongiovi, M
   Gangemi, A
   Ristoski, P
AF Alam, Mehwish
   Recupero, Diego Reforgiato
   Mongiovi, Misael
   Gangemi, Aldo
   Ristoski, Petar
TI Event-based knowledge reconciliation using frame embeddings and frame
   similarity
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge reconciliation; Frame semantics; Frame embeddings; Frame
   similarity; Role similarity; Role embeddings; FrameNet; Framester
ID SEMANTICS
AB This paper proposes an evolution over MERGILO, a tool for reconciling knowledge graphs extracted from text, using graph alignment and word similarity. The reconciled knowledge graphs are typically used for multi-document summarization, or to detect knowledge evolution across document series. The main point of improvement focuses on event reconciliation i.e., reconciling knowledge graphs generated by text about two similar events described differently. In order to gather a complete semantic representation of events, we use FRED semantic web machine reader, jointly with Framester, a linguistic linked data hub represented using a novel formal semantics for frames. Framester is used to enhance the extracted event knowledge with semantic frames. We extend MERGILO with similarities based on the graph structure of semantic frames and the subsumption hierarchy of semantic roles as defined in Framester. With an effective evaluation strategy similarly as used for MERGILO, we show the improvement of the new approach (MERGILO plus semantic frame/role similarities) over the baseline. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Alam, Mehwish; Gangemi, Aldo] Univ Paris 13, 99 Ave JB Clement, F-93430 Paris, France.
   [Recupero, Diego Reforgiato] Univ Cagliari, Dept Math & Comp Sci, Via Osped 72, I-09124 Cagliari, Italy.
   [Mongiovi, Misael] CNR, ISTC, Catania, Italy.
   [Recupero, Diego Reforgiato; Gangemi, Aldo] CNR, ISTC, Via S Martino Battaglia 44, Rome, Italy.
   [Ristoski, Petar] Univ Mannheim, Mannheim, Germany.
RP Recupero, DR (通讯作者)，Via Simeto 5, San Gregorio, Italy.
EM alam@lipn.univ-paris13.fr; diego.reforgiato@unica.it;
   misael.mongiovi@istc.cnr.it; aldo.gangemi@lipn.univ-paris13.fr;
   petar.ristoski@informatik.uni-mannheim.de
RI Mongiovì, Misael/GNP-0059-2022; Gangemi, Aldo/C-7420-2013
OI Mongiovì, Misael/0000-0003-0528-5490; Gangemi, Aldo/0000-0001-5568-2684;
   Reforgiato Recupero, Diego/0000-0001-8646-6183; Alam,
   Mehwish/0000-0002-7867-6612
FU European Union Horizon the Framework Programme for Research and
   Innovation [643808]; French National Research Agency (ANR) as part of
   the program "Investissements d'Avenir" [ANR-10-LABX-0083]; Sardinia
   Regional Government [7/2007, 2016 DGR 28/21, CUP: F72F16003030002]
FX The research leading to these results has received funding from the
   European Union Horizon 2020 the Framework Programme for Research and
   Innovation (2014-2020) under grant agreement 643808 Project MARIO
   Managing active and healthy aging with use of caring service robots as
   well as by a public grant overseen by the French National Research
   Agency (ANR) as part of the program "Investissements d'Avenir"
   (reference: ANR-10-LABX-0083). Moreover, the authors gratefully
   acknowledge Sardinia Regional Government for the financial support
   (Convenzione triennale tra la Fondazione di Sardegna e gli Atenei Sardi
   Regione Sardegna L.R. 7/2007 annualit 2016 DGR 28/21 del 17.05.201, CUP:
   F72F16003030002).
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NR 62
TC 10
Z9 10
U1 2
U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD NOV 1
PY 2017
VL 135
BP 192
EP 203
DI 10.1016/j.knosys.2017.08.014
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA FJ9AB
UT WOS:000413058700017
DA 2022-08-23
ER

PT J
AU Lyu, MT
   Li, XY
   Chen, CH
AF Lyu, Mengtao
   Li, Xinyu
   Chen, Chun-Hsien
TI Achieving Knowledge-as-a-Service in IIoT-driven smart manufacturing: A
   crowdsourcing-based continuous enrichment method for Industrial
   Knowledge Graph
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Knowledge-as-a-Service; Industrial Knowledge Graph; Crowdsourcing; Smart
   Manufacturing Service; Industrial Internet of Things
ID INTERNET; THINGS; CONSTRUCTION; ONTOLOGY; PLATFORM; CONTEXT; SYSTEMS;
   DESIGN
AB The rapid development of the industrial internet of things (IIoT) enlightened more tools and techniques to optimize the smart manufacturing service (SMS) paradigm, emphasizing the services based on pervasive IIoT products. However, the serviceability in the smart manufacturing environment is still limited since most of the conventional services are conducted in a passively reactive mode. The knowledge as a service (KaaS) model is hence to be introduced to emphasize the cognitive capability by actively utilizing massive knowledge, where industrial knowledge graph (IKG) plays as the core to generate context-awareness and proactive services for the optimization of serviceability and productivity. Meanwhile, as building IKGs for specific industrial cases is still plagued by the small scale and the lack of continuous enrichment, ensuring the quality and availability of the IKG are still challenging. Aiming to fill this gap with a practical and systematic approach, this paper proposes a generic crowdsourcing approach for continuously evolving the IKG. Through the IKG continuous enrichment approach, IKG-enabled systems indicate the higher value creation ability to utilize knowledge as a kind of service, rather than just a kind of resource. To further illustrate the proposed approach, a case study of a printed circuit board (PCB) processing machine is given with discussions. As an explorative study, future perspectives are also discussed to attract more open and in-depth studies for more robust applications of KaaS and IKG in the IIoTdriven smart manufacturing environment.
C1 [Lyu, Mengtao; Li, Xinyu] Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China.
   [Lyu, Mengtao; Chen, Chun-Hsien] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore.
   [Lyu, Mengtao] Hong Kong Polytech Univ, Dept Aeronaut & Aviat Engn, Hong Kong 999077, Peoples R China.
RP Li, XY (通讯作者)，Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China.
EM M200019@e.ntu.edu.sg; lixinyu@dhu.edu.cn; mchchen@ntu.edu.sg
RI Mengtao, Lyu/ABG-2369-2021
OI Mengtao, Lyu/0000-0003-1505-1970
FU Delta-NTU Corporate Lab for Cyber-Physical Systems; Delta Electronics
   Inc; National Research Foundation (NRF) Singapore under the Corporate
   Laboratory @ University Scheme at (SCO-RP1) at Nanyang Technological
   University, Singapore [RCA-16/434, SCO-RP1]; (SCO-RP1) at Nanyang
   Technological University, Singapore
FX This work is conducted within the Delta-NTU Corporate Lab for
   Cyber-Physical Systems with funding support from Delta Electronics Inc
   and the National Research Foundation (NRF) Singapore under the Corporate
   Laboratory @ University Scheme (Ref. RCA-16/434; SCO-RP1) at Nanyang
   Technological University, Singapore.
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NR 73
TC 3
Z9 3
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 JAN
PY 2022
VL 51
AR 101494
DI 10.1016/j.aei.2021.101494
PG 14
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1K4GL
UT WOS:000798561200004
DA 2022-08-23
ER

PT J
AU Tosi, MDL
   dos Reis, JC
AF Lucca Tosi, Mauro Dalle
   dos Reis, Julio Cesar
TI Understanding the evolution of a scientific field by clustering and
   visualizing knowledge graphs
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Knowledge graphs; knowledge representation; scientific evolution
ID COMMUNITIES; NETWORKS
AB The process of tracking the evolution of a scientific field is arduous. It allows researchers to understand trends in areas of science and predict how they may evolve. Nowadays, most of the automated mechanisms developed to assist researchers in this process do not consider the content of articles to identify changes in its structure, only the articles metadata. These methods are not suited to easily assist researchers to study the concepts that compose an area and its evolution. In this article, we propose a method to track the evolution of a scientific field at a concept level. Our method structures a scientific field using two knowledge graphs, representing distinct periods of the studied field. Then, it clusters them and identifies correspondent clusters between the knowledge graphs, representing the same subareas in distinct time periods. Our solution enables to compare the corresponding clusters, tracking their evolution. We apply and experiment our method in two case studies concerning the artificial intelligence (AI) and the biotechnology (BIO) fields. Findings indicate befitting results regarding the way their evolution can be assessed with our implemented software tool. From our analyses, we perceived evolution in broader subareas of a scientific field, as the growth of the 'Convolutional Neural Network' area from 2006; to specific ones, as the decrease of research works using mice to studyBRAF-mutation lung cancer from 2018. This work contributes with the development of a web application with interactive user interfaces to assist researchers in representing, analysing and tracking the evolution of scientific fields at a concept level.
C1 [Lucca Tosi, Mauro Dalle; dos Reis, Julio Cesar] Univ Estadual Campinas, Inst Comp, Av Albert Einstein 1251,Cidade Univ, BR-13083852 Campinas, SP, Brazil.
RP Tosi, MDL (通讯作者)，Univ Estadual Campinas, Inst Comp, Av Albert Einstein 1251,Cidade Univ, BR-13083852 Campinas, SP, Brazil.
EM maurodlt@hotmail.com
RI Dalle Lucca Tosi, Mauro/ADO-8638-2022; Tosi, Mauro Dalle
   Lucca/AAE-9511-2022
OI Dalle Lucca Tosi, Mauro/0000-0002-0218-2413; Tosi, Mauro Dalle
   Lucca/0000-0002-0218-2413
FU Sao Paulo Research Foundation (FAPESP) [2017/02325-5, 2013/08293-7];
   Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil
   (CAPES) [001]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship and/or publication of this article: This work
   was financially supported by the Sa~o Paulo Research Foundation (FAPESP)
   (grant #2017/02325-5 and #2013/08293-7) and the Coordenacxa~ o de
   Aperfeicxoamento de Pessoal de Ni ' vel Superior -Brasil (CAPES)
   -Finance Code 001. The opinions expressed in here are not necessarily
   shared by the financial support agency.
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NR 24
TC 3
Z9 3
U1 12
U2 33
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD FEB
PY 2022
VL 48
IS 1
BP 71
EP 89
AR 0165551520937915
DI 10.1177/0165551520937915
EA JUL 2020
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 ZD6PU
UT WOS:000548570300001
DA 2022-08-23
ER

PT J
AU Yang, CM
   Xiao, D
   Luo, YY
   Li, B
   Zhao, XJ
   Zhang, H
AF Yang, Chunming
   Xiao, Dan
   Luo, Yuanyuan
   Li, Bo
   Zhao, Xujian
   Zhang, Hui
TI A hybrid method based on semi-supervised learning for relation
   extraction in Chinese EMRs
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE Semi-supervised learning; Relation extraction; Medical knowledge graphs;
   Residual network; Bootstrapping
ID FRAMEWORK
AB Background Building a large-scale medical knowledge graphs needs to automatically extract the relations between entities from electronic medical records (EMRs) . The main challenges are the scarcity of available labeled corpus and the identification of complexity semantic relations in text of Chinese EMRs. A hybrid method based on semi-supervised learning is proposed to extract the medical entity relations from small-scale complex Chinese EMRs. Methods The semantic features of sentences are extracted by a residual network and the long dependent information is captured by bidirectional gated recurrent unit. Then the attention mechanism is used to assign weights for the extracted features respectively, and the output of two attention mechanisms is integrated for relation prediction. We adjusted the training process with manually annotated small-scale relational corpus and bootstrapping semi-supervised learning algorithm, and continuously expanded the datasets during the training process. Results We constructed a small corpus of Chinese EMRs relation extraction based on the EMR datasets released at the China Conference on Knowledge Graph and Semantic Computing. The experimental results show that the best F1-score of the proposed method on the overall relation categories reaches 89.78%, which is 13.07% higher than the baseline CNN.
C1 [Yang, Chunming; Xiao, Dan; Luo, Yuanyuan; Li, Bo; Zhao, Xujian] Southwest Univ Sci & Technol, Sch Comp Sci & Technol, Mianyang 621010, Sichuan, Peoples R China.
   [Zhang, Hui] Southwest Univ Sci & Technol, Sch Sci, Mianyang 621010, Sichuan, Peoples R China.
   [Yang, Chunming] Sichuan Big Data & Intelligent Syst Engn Technol, Mianyang 621010, Sichuan, Peoples R China.
RP Yang, CM (通讯作者)，Southwest Univ Sci & Technol, Sch Comp Sci & Technol, Mianyang 621010, Sichuan, Peoples R China.
EM yangchunming@swust.edu.cn
FU Sichuan Science and Technology Program [2020YFS0057,2021YFG0031]
FX This study is supported by the Sichuan Science and Technology Program
   (NO. 2020YFS0057,2021YFG0031)
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NR 31
TC 0
Z9 0
U1 7
U2 7
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD JUN 27
PY 2022
VL 22
IS 1
AR 169
DI 10.1186/s12911-022-01908-4
PG 13
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA 2L8YL
UT WOS:000817300800002
PM 35761319
DA 2022-08-23
ER

PT J
AU Bai, JR
   Geeson, R
   Farazi, F
   Mosbach, S
   Akroyd, J
   Bringley, EJ
   Kraft, M
AF Bai, Jiaru
   Geeson, Rory
   Farazi, Feroz
   Mosbach, Sebastian
   Akroyd, Jethro
   Bringley, Eric J.
   Kraft, Markus
TI Automated Calibration of a Poly(oxymethylene) Dimethyl Ether Oxidation
   Mechanism Using the Knowledge Graph Technology
SO JOURNAL OF CHEMICAL INFORMATION AND MODELING
LA English
DT Article
ID LARGE-SCALE; COMBUSTION; ONTOLOGY; MODEL; OPTIMIZATION; IMPROVEMENT;
   MANAGEMENT; KINETICS; PARK
AB In this paper, we develop a knowledge graph-based framework for the automated calibration of combustion reaction mechanisms and demonstrate its effectiveness on a case study of poly(oxymethylene)dimethyl ether (PODEn, where n = 3) oxidation. We develop an ontological representation for combustion experiments, OntoChemExp, that allows for the semantic enrichment of experiments within the J-Park simulator (JPS, theworldavatancom), an existing cross-domain knowledge graph. OntoChe-mExp is fully capable of supporting experimental results in the Process Informatics Model (PrIMe) database. Following this, a set of software agents are developed to perform experimental result retrieval, sensitivity analysis, and calibration tasks. The sensitivity analysis agent is used for both generic sensitivity analyses and reaction selection for subsequent calibration. The calibration process is performed as a sampling task, followed by an optimization task. The agents are designed for use with generic models but are demonstrated with ignition delay time and laminar flame speed simulations. We find that calibration times are reduced, while accuracy is increased compared to manual calibration, achieving a 79% decrease in the objective function value, as defined in this study. Further, we demonstrate how this workflow is implemented as an extension of the JPS.
C1 [Bai, Jiaru; Geeson, Rory; Farazi, Feroz; Mosbach, Sebastian; Akroyd, Jethro; Bringley, Eric J.; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.
   [Geeson, Rory] Univ Cambridge, Dept Comp Sci & Technol, Cambridge CB3 0AS, England.
   [Mosbach, Sebastian; Akroyd, Jethro; Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore CARES, Singapore 138602, Singapore.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.; Kraft, M (通讯作者)，Cambridge Ctr Adv Res & Educ Singapore CARES, Singapore 138602, Singapore.; Kraft, M (通讯作者)，Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.
EM mk306@cam.ac.uk
RI Kraft, Markus/D-7243-2016; Akroyd, Jethro/A-8698-2010
OI Kraft, Markus/0000-0002-4293-8924; Bringley, Eric/0000-0003-4101-4874;
   Akroyd, Jethro/0000-0002-2143-8656; Bai, Jiaru/0000-0002-1246-1993;
   Geeson, Rory/0000-0003-4766-8588; Mosbach,
   Sebastian/0000-0001-7018-9433; Farazi, Feroz/0000-0002-6786-7309
FU National Research Foundation, Prime Minister's Office, Singapore, under
   its Campus for Research Excellence and Technological Enterprise (CREATE)
   program; UK Engineering and Physical Sciences Research Council (EPSRC)
   [EP/R029369/1]; ARCHER; CSC Cambridge International Scholarship from
   Cambridge Trust; China Scholarship Council; EPSRC for SynTech Centre for
   Doctoral Training, University of Cambridge [EP/S024220/1]; Gates
   Cambridge Scholarship [OPP1144]; Alexander von Humboldt Foundation
FX This research was supported by the National Research Foundation, Prime
   Minister's Office, Singapore, under its Campus for Research Excellence
   and Technological Enterprise (CREATE) program. The authors are grateful
   to the UK Engineering and Physical Sciences Research Council (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).The authors express their gratitude to
   Qinjie Lin and Prof Wenming Yang from the Green and Sustainable
   Transportation and Power Generation Laboratory of the National
   University of Singapore for their correspondence and sharing of the
   PODE3 experiment file. J.B. acknowledges financial support provided by
   the CSC Cambridge International Scholarship from Cambridge Trust and
   China Scholarship Council. R.G. acknowledges financial support EPSRC
   (grant EP/S024220/1) for SynTech Centre for Doctoral Training,
   University of Cambridge. E.J.B. was funded by a Gates Cambridge
   Scholarship (OPP1144). M.K. gratefully acknowledges the support of the
   Alexander von Humboldt Foundation.
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NR 55
TC 4
Z9 4
U1 3
U2 8
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 1549-9596
EI 1549-960X
J9 J CHEM INF MODEL
JI J. Chem Inf. Model.
PD APR 26
PY 2021
VL 61
IS 4
BP 1701
EP 1717
DI 10.1021/acs.jcim.0c01322
EA APR 2021
PG 17
WC Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science,
   Information Systems; Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy; Chemistry; Computer Science
GA RT8TP
UT WOS:000644728600020
PM 33825473
OA Green Published, Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Lei, JP
   Ouyang, DT
   Liu, Y
AF Lei, Jingpei
   Ouyang, Dantong
   Liu, Ying
TI Adversarial Knowledge Representation Learning Without External Model
SO IEEE ACCESS
LA English
DT Article
DE Knowledge graph; knowledge representation learning; link prediction;
   adversarial learning; negative sampling
AB Knowledge representation learning, which embeds entities and relations of knowledge graph into low-dimensional vectors, is effficient for predicting missing facts. Knowledge graph datasets only store positive triplets. Nevertheless, negative cases are similarly crucial in knowledge representation learning. Conventionally, corrupted triplets are uniformly generated as negative cases, but actually, these corrupted triplets are heterogeneous. The majority of corrupted triplets are trivial, and they have limited influence on learning. Regarding the large number of corrupted triplet candidates, it is not effficient to train the model by uniformly generated corrupted triplets. Generative adversarial network (GAN)-inspired approaches are proposed to remit easily discriminated negative training examples, enabling faster and better convergence of the embedding models. Pre-trained external sampling models are required in these approaches. In this paper, we introduce a simple but strong negative sampling approach for adversarial knowledge representation learning, named loss adaptive sampling mechanism, which is effficient without an external sampling model. Furthermore, false negative cases are always over-trained in the training stage with effficient negative sampling approaches. We propose a push-up mechanism and verify whether it is feasible to alleviate these over-trained false negative cases. The experimental results show that our adversarial knowledge representation learning approach outperforms the GAN-based sampling method-KBGAN.
C1 [Ouyang, Dantong] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China.
   [Lei, Jingpei; Ouyang, Dantong; Liu, Ying] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Jilin, Peoples R China.
RP Ouyang, DT (通讯作者)，Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China.
EM ouyd@jlu.edu.cn
OI Lei, Jingpei/0000-0001-7788-2329
FU National Natural Science Foundation of China [61872159, 61672261,
   61502199]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61872159, Grant 61672261, and Grant
   61502199.
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NR 39
TC 1
Z9 1
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 2019
VL 7
BP 3512
EP 3524
DI 10.1109/ACCESS.2018.2889481
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HI3PV
UT WOS:000456364200001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, T
   Gu, HZ
   Wu, Z
   Gao, J
AF Wang, Ting
   Gu, Hanzhe
   Wu, Zhuang
   Gao, Jing
TI Multi-source knowledge integration based on machine learning algorithms
   for domain ontology
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Domain ontology; Thesaurus; Online encyclopedia; Similarity computing
ID EXTRACTION
AB In this paper, a new approach of automatic building for domain ontology based on machine learning algorithm is proposed, and by which the large-scale e-Gov ontology is built automatically. The advent of the knowledge graph era puts forward higher requirements for semantic search and analysis. Since traditional manual ontology construction requires the participation of domain experts in large-scale ontology construction, which will take time and considerable resources, and the ontology scale is also limited. The approach proposed in this paper not only makes up for the shortage of thesaurus description of the semantic relation between terms, but also takes advantage of the massive online encyclopedia knowledge and typical similarity algorithm in machine learning to fill the domain ontology automatically, so that the advantages of the two different knowledge sources are fully utilized and the system as a whole is gained. Ultimately, this may provide the foundation and support for the construction of knowledge graph and the semantic-oriented applications.
C1 [Wang, Ting; Gu, Hanzhe; Wu, Zhuang; Gao, Jing] Capital Univ Econ & Business, Informat Sch, Beijing 100070, Peoples R China.
   [Wu, Zhuang] Capital Univ Econ & Business, Informat Sch, CTSC Ctr, Beijing 100070, Peoples R China.
RP Wang, T (通讯作者)，Capital Univ Econ & Business, Informat Sch, Beijing 100070, Peoples R China.
EM wangting@cueb.edu.cn
RI WANG, TING/ACE-5023-2022
OI WANG, TING/0000-0003-2481-2890
FU Scientific Research Project of Beijing Municipal Education Commission
   (General Social Science Project); Youth Excellent Teachers Grant of
   Capital University of Economics and Business [23491854840429]
FX This work was supported by the Scientific Research Project of Beijing
   Municipal Education Commission (General Social Science Project) and the
   Youth Excellent Teachers Grant of Capital University of Economics and
   Business (No. 23491854840429).
CR Aleksander P, 2005, AUTOMATIC ONTOLOGY G
   [Anonymous], [No title captured]
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   Yoshida M, 2001, P PAC ASS COMP LING, P332
NR 23
TC 10
Z9 10
U1 11
U2 31
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
PD JAN
PY 2020
VL 32
IS 1
SI SI
BP 235
EP 245
DI 10.1007/s00521-018-3806-5
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KJ2CA
UT WOS:000511864500024
DA 2022-08-23
ER

PT J
AU Li, JH
   Luo, ZY
   Huang, HY
   Ding, ZH
AF Li, Jiahui
   Luo, Zhiyi
   Huang, Hongyun
   Ding, Zuohua
TI Towards Knowledge-Based Tourism Chinese Question Answering System
SO MATHEMATICS
LA English
DT Article
DE knowledge graph; tourism question answering system; intent recognition;
   language model
AB With the rapid development of the tourism industry, various travel websites are emerging. The tourism question answering system explores a large amount of information from these travel websites to answer tourism questions, which is critical for providing a competitive travel experience. In this paper, we propose a framework that automatically constructs a tourism knowledge graph from a series of travel websites with regard to tourist attractions in Zhejiang province, China. Backed by this domain-specific knowledge base, we developed a tourism question answering system that also incorporates the underlying knowledge from a large-scale language model such as BERT. Experiments on real-world datasets demonstrate that the proposed method outperforms the baseline on various metrics. We also show the effectiveness of each of the question answering components in detail, including the query intent recognition and the answer generation.
C1 [Li, Jiahui; Luo, Zhiyi; Ding, Zuohua] Zhejiang Sci Tech Univ, Sch Informat Sci & Technol, Hangzhou 310018, Peoples R China.
   [Huang, Hongyun] Zhejiang Sci Tech Univ, Ctr Multimedia Data Anal, Hangzhou 310018, Peoples R China.
RP Huang, HY (通讯作者)，Zhejiang Sci Tech Univ, Ctr Multimedia Data Anal, Hangzhou 310018, Peoples R China.
EM 201920603004@mails.zstu.edu.cn; luozhiyi@zstu.edu.cn;
   zs0040@zstu.edu.cn; zuohuading@zstu.edu.cn
FU NSFC [62132014]
FX FundingThis research was funded by NSFC grant number 62132014.
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   Zhou B., 2017, INTELL COMPUT APPL, V7, P150
NR 24
TC 0
Z9 0
U1 12
U2 12
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD FEB
PY 2022
VL 10
IS 4
AR 664
DI 10.3390/math10040664
PG 18
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA ZT3XY
UT WOS:000769094300001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, ZD
   Pan, JS
   Chen, QY
   Yang, SY
AF Wang, Zhengdi
   Pan, Jeng-Shyang
   Chen, Qinyin
   Yang, Shuangyuan
TI BiLSTM-CRF-KG: A Construction Method of Software Requirements
   Specification Graph
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE software requirement specifications; BiLSTM-CRF-KG; knowledge graph
AB A requirement analysis is the basis and source of software system development, and its accuracy, consistency and completeness are the keys to determining software quality. However, at present, most software requirements specifications are prepared manually, which has some problems, such as inconsistency with business description, low preparation efficiency, being error prone and difficulty communicating effectively with business personnel. Aiming at the above problems, this paper realizes a construction model of the software requirements specification graph BiSLTM-CRF-KG by using natural language processing and knowledge graph technology. Simulation experiments on 150 real software system business requirements description corpora show that the BiSLTM-CRF-KG model can obtain 96.31% functional entity recognition accuracy directly from the original corpus, which is better than the classical BiSLTM-CRF, IDCNN-CRF and CRF++ models, and has good performance on different kinds of data sets.
C1 [Wang, Zhengdi; Yang, Shuangyuan] Xiamen Univ, Dept Software Engn, Xiamen 361005, Peoples R China.
   [Pan, Jeng-Shyang] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao 266590, Peoples R China.
   [Chen, Qinyin] Xiamen City Univ, Xiamen 361005, Peoples R China.
RP Yang, SY (通讯作者)，Xiamen Univ, Dept Software Engn, Xiamen 361005, Peoples R China.
EM 24320191152534@stu.xmu.edu.cn; jspan@ieee.org; chen.qin.yin@hotmail.com;
   yangshuangyuan@xmu.edu.cn
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NR 22
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 JUN
PY 2022
VL 12
IS 12
AR 6016
DI 10.3390/app12126016
PG 23
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 2N1GX
UT WOS:000818137800001
OA gold
DA 2022-08-23
ER

PT J
AU Heger, T
   Jeschke, JM
   Febria, C
   Kollmann, J
   Murphy, S
   Rochefort, L
   Shackelford, N
   Temperton, VM
   Higgs, E
AF Heger, Tina
   Jeschke, Jonathan M.
   Febria, Catherine
   Kollmann, Johannes
   Murphy, Stephen
   Rochefort, Line
   Shackelford, Nancy
   Temperton, Vicky M.
   Higgs, Eric
TI Mapping and assessing the knowledge base of ecological restoration
SO RESTORATION ECOLOGY
LA English
DT Article; Early Access
DE conceptual overview; evidence-based restoration; interactive online
   platform; knowledge mapping; research synthesis; science-practice links
ID HYPOTHESES; TRAITS
AB Information on restoration science and practice is dispersed across large numbers of scientific papers, reports, books, and other resources, and there is a lack of synthetic approaches and of linkages between ecological theory and practice. With recent calls for scaling up ecological restoration, there is an urgent need for improving the effectiveness of restoration ecology by presenting existing knowledge in an organized and accessible form. Practitioners benefit from knowing which theories explain patterns and processes in a specific ecosystem, and scientists need an overview of empirical evidence supporting current theories. Strengthening links between restoration practice and science benefits both areas. Based on a new approach used for organizing and assessing hypotheses in invasion biology, we suggest the development of an interactive online platform that promotes the integration of restoration science and practice by (1) presenting an overview of restoration ecology; (2) mapping theoretical work relevant for ecological restoration; (3) displaying direct links to relevant publications; and (4) providing summaries of empirical evidence for ecological theories in specific settings. This online knowledge base should be developed in an open process, bringing together the restoration community with experts in semantic web and natural language processing, library scientists, web designers, and other specialists. The platform should become an evolving, searchable, openly accessible, and intuitively organized tool for future ecological restoration.
C1 [Heger, Tina] Univ Potsdam, Biodivers Res Bot, Maulbeerallee 2a, D-14469 Potsdam, Germany.
   [Heger, Tina; Jeschke, Jonathan M.] Berlin Brandenburg Inst Adv Biodivers Res BBIB, Konigin Luise Str 2-4, D-14195 Berlin, Germany.
   [Heger, Tina; Kollmann, Johannes] Tech Univ Munich, Restorat Ecol, Emil Ramann Str 6, D-85350 Freising Weihenstephan, Germany.
   [Heger, Tina; Jeschke, Jonathan M.] Leibniz Inst Freshwater Ecol & Inland Fisheries I, Evolutionary & Integrat Ecol, Muggelseedamm 310, D-12587 Berlin, Germany.
   [Heger, Tina; Jeschke, Jonathan M.] Free Univ Berlin, Inst Biol, Dept Biol, Chem,Pharm, Konigin Luise Str 1-3, D-14195 Berlin, Germany.
   [Febria, Catherine] Univ Windsor, Great Lakes Inst Environm Res, Dept Integrat Biol, Windsor, ON N9B 3P4, Canada.
   [Kollmann, Johannes] Norwegian Inst Bioecon Res NIBIO, POB 115, N-1431 As, Norway.
   [Murphy, Stephen] Univ Waterloo, Sch Environm Resources & Sustainabil, Waterloo, ON N2L 3G1, Canada.
   [Rochefort, Line] Univ Laval, Ctr Northern Studies, Peatland Ecol Res Grp, Quebec City, PQ G1V 0A6, Canada.
   [Shackelford, Nancy; Higgs, Eric] Univ Victoria, Sch Environm Studies, Victoria, BC V8W 2Y2, Canada.
   [Temperton, Vicky M.] Leuphana Univ Luneburg, Fac Sustainabil, Inst Ecol, Univ Allee 1, D-21335 Luneburg, Germany.
RP Heger, T (通讯作者)，Univ Potsdam, Biodivers Res Bot, Maulbeerallee 2a, D-14469 Potsdam, Germany.; Heger, T (通讯作者)，Berlin Brandenburg Inst Adv Biodivers Res BBIB, Konigin Luise Str 2-4, D-14195 Berlin, Germany.; Heger, T (通讯作者)，Tech Univ Munich, Restorat Ecol, Emil Ramann Str 6, D-85350 Freising Weihenstephan, Germany.; Heger, T (通讯作者)，Leibniz Inst Freshwater Ecol & Inland Fisheries I, Evolutionary & Integrat Ecol, Muggelseedamm 310, D-12587 Berlin, Germany.; Heger, T (通讯作者)，Free Univ Berlin, Inst Biol, Dept Biol, Chem,Pharm, Konigin Luise Str 1-3, D-14195 Berlin, Germany.
EM t.heger@wzw.tum.de
RI Kollmann, Johannes/B-4255-2012; Heger, Tina/AAH-4802-2019
OI Kollmann, Johannes/0000-0002-4990-3636; Heger, Tina/0000-0002-5522-5632;
   Rochefort, Line/0000-0001-8450-0376; Temperton,
   Vicky/0000-0003-0543-4521; Febria, Catherine/0000-0002-3570-3588; Higgs,
   Eric/0000-0003-4298-0134
FU Deutsche Forschungsgemeinschaft (DFG) [HE 5893/7-1]; German Federal
   Ministry of Education and Research (BMBF) within the Collaborative
   Project "Bridging in Biodiversity Science (BIBS)" [01LC1501]; Volkswagen
   Foundation [97 863]; DFG [HE 5893/8-1]; Canada Research Chairs Program,
   NSERC Canada; State of Lower Saxony; Natural Sciences and Engineering
   Research Council of Canada (NSERC) [138097-2012]; Projekt DEAL
FX The ideas presented in this manuscript were triggered by a research
   visit of T.H. with E.H., funded by the Deutsche Forschungsgemeinschaft
   (DFG, HE 5893/7-1). T.H. and J.M.J. additionally received funding from
   the German Federal Ministry of Education and Research (BMBF) within the
   Collaborative Project "Bridging in Biodiversity Science (BIBS)" (funding
   number 01LC1501) and from the Volkswagen Foundation (funding number 97
   863). DFG also funds TH's current position (HE 5893/8-1). C.F. is funded
   by Canada Research Chairs Program, NSERC Canada, V.M.T. by the State of
   Lower Saxony, and L.R. by Natural Sciences and Engineering Research
   Council of Canada (NSERC Discovery grant, no. 138097-2012). The authors
   thank four anonymous reviewers for their helpful comments on earlier
   versions of the manuscript. Open Access funding enabled and organized by
   Projekt DEAL.
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NR 40
TC 0
Z9 0
U1 8
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1061-2971
EI 1526-100X
J9 RESTOR ECOL
JI Restor. Ecol.
AR e13676
DI 10.1111/rec.13676
EA APR 2022
PG 10
WC Ecology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 0G8LF
UT WOS:000778290300001
OA hybrid
DA 2022-08-23
ER

PT J
AU Reid, EF
   Chen, HC
AF Reid, Edna F.
   Chen, Hsinchun
TI Mapping the contemporary terrorism research domain
SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES
LA English
DT Article; Proceedings Paper
CT Symposium on Information Security in the Knowledge Economy
CY SEP 07-08, 2005
CL Tempe, AZ
SP Arizona State Univ, Ctr Advancing Business Informat Technol
DE terrorism; visualization; bibliometrics; co-citation analysis;
   intellectual structure
ID AUTHOR COCITATION ANALYSIS
AB A systematic view of terrorism research to reveal the intellectual structure of the field and empirically discern the distinct set of core researchers, institutional affiliations, publications, and conceptual areas can help us gain a deeper understanding of approaches to terrorism. This paper responds to this need by using an integrated knowledge-mapping framework that we developed to identify the core researchers and knowledge creation approaches in terrorism. The framework uses three types of analysis: (a) basic analysis of scientific output using citation, bibliometric, and social network analyses, (b) content map analysis of large corpora of literature, and (c) cocitation analysis to analyse linkages among pairs of researchers. We applied domain visualization techniques such as content map analysis, block-modeling, and co-citation analysis to the literature and author citation data from the years 1965 to 2003. The data were gathered from ten databases such as the ISI Web of Science. The results reveal: (1) the names of the top 42 core terrorism researchers (e.g., Brian Jenkins, Bruce Hoffman, and Paul Wilkinson) as well as their institutional affiliations; (2) their influential publications; (3) clusters of terrorism researchers who work in similar areas; and (4) that the research focus has shifted from terrorism as a low-intensity conflict to a strategic threat to world powers with increased focus on Osama Bin Laden. (c) 2006 Elsevier Ltd. All rights reserved.
C1 Univ Arizona, MIS Dept, Artificial Intelligence Lab, Tucson, AZ 85721 USA.
RP Reid, EF (通讯作者)，Clarion Univ Pennsylvania, Dept Lib Sci, 840 Wood St, Clarion, PA 16214 USA.
EM ednareid@eller.arizona.edu
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NR 57
TC 45
Z9 46
U1 3
U2 39
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 1071-5819
EI 1095-9300
J9 INT J HUM-COMPUT ST
JI Int. J. Hum.-Comput. Stud.
PD JAN
PY 2007
VL 65
IS 1
BP 42
EP 56
DI 10.1016/j.ijhcs.2006.08.006
PG 15
WC Computer Science, Cybernetics; Ergonomics; Psychology, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Engineering; Psychology
GA 117SK
UT WOS:000242893400005
DA 2022-08-23
ER

PT J
AU Zheng, P
   Xia, LQ
   Li, CX
   Li, XY
   Liu, BF
AF Zheng, Pai
   Xia, Liqiao
   Li, Chengxi
   Li, Xinyu
   Liu, Bufan
TI Towards Self-X cognitive manufacturing network: An industrial knowledge
   graph-based multi-agent reinforcement learning approach
SO JOURNAL OF MANUFACTURING SYSTEMS
LA English
DT Article
DE Industrial knowledge graph; Graph embedding; Cognitive manufacturing;
   Graph neural network; Reinforcement learning
ID DESIGN; SYSTEM
AB Empowered by the advanced cognitive computing, industrial Internet-of-Things, and data analytics techniques, today's smart manufacturing systems are ever-increasingly equipped with cognitive capabilities, towards an emerging Self-X cognitive manufacturing network with higher level of automation. Nevertheless, to our best knowledge, the readiness of 'Self-X' levels (e.g., self-configuration, self-optimization, and self-adjust/adaptive/healing) is still in the infant stage. To pave its way, this work stepwise introduces an industrial knowledge graph (IKG)-based multi-agent reinforcement learning (MARL) method for achieving the Self-X cognitive manufacturing network. Firstly, an IKG should be formulated based on the extracted empirical knowledge and recognized patterns in the manufacturing process, by exploiting the massive human-generated and machine-sensed multimodal data. Then, a proposed graph neural network-based embedding algorithm can be performed based on a comprehensive understanding of the established IKG, to achieve semantic-based self-configurable solution searching and task decomposition. Moreover, a MARL-enabled decentralized system is presented to self-optimize the manufacturing process, and to further complement the IKG towards Self-X cognitive manufacturing network. An illustrative example of multi-robot reaching task is conducted lastly to validate the feasibility of the proposed approach. As an explorative study, limitations and future perspectives are also highlighted to attract more open discussions and in-depth research for ever smarter manufacturing.
C1 [Zheng, Pai; Xia, Liqiao; Li, Chengxi] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hung Hom, Hong Kong, Peoples R China.
   [Li, Chengxi] Hong Kong Sci Pk, Lab Artificial Intelligence Design, Hong Kong, Peoples R China.
   [Li, Xinyu] Donghua Univ, Coll Mech Engn, Shanghai, Peoples R China.
   [Li, Xinyu; Liu, Bufan] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore, Singapore.
RP Zheng, P (通讯作者)，Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hung Hom, Hong Kong, Peoples R China.; Li, XY (通讯作者)，Donghua Univ, Coll Mech Engn, Shanghai, Peoples R China.
EM pai.zheng@polyu.edu.hk; cash.li@ntu.edu.sg
RI ZHENG, PAI/K-7989-2012
OI ZHENG, PAI/0000-0002-2329-8634; Li, Xinyu/0000-0002-4117-9698; Liu,
   Bufan/0000-0002-2683-0139
FU National Natural Research Foundation of China [52005424]; Innovation and
   Technology Commission, HKSAR, China; National Key R&D Programs of
   Cooperation on Scientific and Technological Innovation in Hong Kong,
   Macao and Taiwan [SQ2020YFE020182]; Jiangsu Provincial Policy Guidance
   Program (Hong Kong/Macau/Taiwan Science and Technology Cooperation)
   [BZ2020049]; Mainland-Hong Kong Joint Funding Scheme [MHX/001/20]
FX The authors wish to acknowledge the funding support from the National
   Natural Research Foundation of China (No. 52005424), Mainland-Hong Kong
   Joint Funding Scheme (MHX/001/20), Innovation and Technology Commission,
   HKSAR, China, National Key R&D Programs of Cooperation on Scientific and
   Technological Innovation in Hong Kong, Macao and Taiwan
   (SQ2020YFE020182), and Jiangsu Provincial Policy Guidance Program (Hong
   Kong/Macau/Taiwan Science and Technology Cooperation, BZ2020049).
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NR 75
TC 13
Z9 13
U1 26
U2 47
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0278-6125
EI 1878-6642
J9 J MANUF SYST
JI J. Manuf. Syst.
PD OCT
PY 2021
VL 61
BP 16
EP 26
DI 10.1016/j.jmsy.2021.08.002
EA AUG 2021
PG 11
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA WE9XB
UT WOS:000705969500002
DA 2022-08-23
ER

PT J
AU Yang, G
   Cao, J
   Chen, ZN
   Guo, JB
   Li, JT
AF Yang, Guang
   Cao, Juan
   Chen, Zhineng
   Guo, Junbo
   Li, Jintao
TI Graph-based neural networks for explainable image privacy inference
SO PATTERN RECOGNITION
LA English
DT Article
DE Image privacy protection; Graph neural networks; Image classification
AB With the development of social media and smartphones, people share their daily lives via a large number of images, but the convince also raises a problem of privacy leakage. Therefore, effective methods are needed to infer the privacy risk of images and identify images that may disclose privacy. Several works have tried to solve this problem with deep learning models. However, we know little about how the models infer the privacy label of an image, thus it is not easy to understand why the image may disclose privacy. Inspired by recent research on graph neural networks, we introduce prior knowledge to the deep models to make the inference more explainable. We propose the Graph-based neural networks for Image Privacy (GIP) to infer the privacy risk of images. The GIP mainly focuses on objects in an image, and the knowledge graph is extracted from the objects in the dataset without reliance on extra knowledge. Experimental results show that the GIP achieves higher performance compared with the object-based methods and comparable performance even compared with the multi-modal fusion method. The results show that the introduction of the knowledge graph not only makes the deep model more explainable but also makes better use of the information of objects provided by the images. Combing the knowledge graph with deep learning is a promising way to help protect image privacy that is worth exploring. (C) 2020 Elsevier Ltd. All rights reserved.
C1 [Yang, Guang; Cao, Juan; Guo, Junbo; Li, Jintao] Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100086, Peoples R China.
   [Yang, Guang; Cao, Juan] Univ Chinese Acad Sci, Beijing, Peoples R China.
   [Chen, Zhineng] Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China.
RP Cao, J (通讯作者)，Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100086, Peoples R China.; Chen, ZN (通讯作者)，Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China.
EM gyang@ict.ac.cn; caojuan@ict.ac.cn; zhineng.chen@ia.ac.cn;
   guojunbo@ict.ac.cn; jtli@ict.ac.cn
FU National Key Research and Development Program [2016YFB0800403]; National
   Nature Science Foundation of China [U1703261]
FX This work was supported by the National Key Research and Development
   Program (2016YFB0800403) and the National Nature Science Foundation of
   China (U1703261).
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Z9 4
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PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0031-3203
EI 1873-5142
J9 PATTERN RECOGN
JI Pattern Recognit.
PD SEP
PY 2020
VL 105
AR 107360
DI 10.1016/j.patcog.2020.107360
PG 12
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA LW9JE
UT WOS:000539457100011
DA 2022-08-23
ER

PT J
AU Zhao, XJ
   Jia, Y
   Li, AP
   Jiang, R
   Song, YC
AF Zhao, Xiaojuan
   Jia, Yan
   Li, Aiping
   Jiang, Rong
   Song, Yichen
TI Multi-source knowledge fusion: a survey
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE multi-source knowledge fusion; knowledge graph; knowledge
   representation; entity alignment; knowledge reasoning
ID ENTITY ALIGNMENT
AB Multi-source knowledge fusion is one of the important research topics in the fields of artificial intelligence, natural language processing, and so on. The research results of multi-source knowledge fusion can help computer to better understand human intelligence, human language and human thinking, effectively promote the Big Search in Cyberspace, effectively promote the construction of domain knowledge graphs (KGs), and bring enormous social and economic benefits. Due to the uncertainty of knowledge acquisition, the reliability and confidence of KG based on entity recognition and relationship extraction technology need to be evaluated. On the one hand, the process of multi-source knowledge reasoning can detect conflicts and provide help for knowledge evaluation and verification; on the other hand, the new knowledge acquired by knowledge reasoning is also uncertain and needs to be evaluated and verified. Collaborative reasoning of multi-source knowledge includes not only inferring new knowledge from multi-source knowledge, but also conflict detection, i.e. identifying erroneous knowledge or conflicts between knowledges. Starting from several related concepts of multi-source knowledge fusion, this paper comprehensively introduces the latest research progress of open-source knowledge fusion, multi-knowledge graphs fusion, information fusion within KGs, multi-modal knowledge fusion and multi-source knowledge collaborative reasoning. On this basis, the challenges and future research directions of multi-source knowledge fusion in a large-scale knowledge base environment are discussed.
C1 [Zhao, Xiaojuan; Jia, Yan; Li, Aiping; Jiang, Rong; Song, Yichen] Natl Univ Def Technol, Coll Comp, Changsha, Peoples R China.
RP Li, AP (通讯作者)，Natl Univ Def Technol, Coll Comp, Changsha, Peoples R China.
EM zhaoxiaojuan18@nudt.edu.cn; jiayanjy@vip.sina.com; liaiping@nudt.edu.cn;
   jiangrong@nudt.edu.cn; songyichen14@nudt.edu.cn
FU National Key Research and Development Program of China [2019QY1406,
   2016QY03D0603, 2017YFB0802204,2016QY01W0101, 2016QY03D0602,
   2017YFB0803301]; Key RAMP;D Program of Guangdong Province
   [2019B010136003]; Hunan Provincial Key R D program [2018GK2056];
   National Natural Science Foundation of China [61732004, 61732022,
   61502517, 61472433, 61672020]
FX The work described in this paper is partially supported by the National
   Key Research and Development Program of China (No. 2019QY1406, No.
   2016QY03D0603, 2017YFB0802204,2016QY01W0101, 2016QY03D0602,
   2017YFB0803301), the Key R&D Program of Guangdong Province
   (No.2019B010136003), the Hunan Provincial Key R & D program (2018GK2056)
   and the National Natural Science Foundation of China (No. 61732004,
   61732022, 61502517, 61472433, 61672020).
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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 JUL
PY 2020
VL 23
IS 4
SI SI
BP 2567
EP 2592
DI 10.1007/s11280-020-00811-0
EA APR 2020
PG 26
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LZ2LD
UT WOS:000525250800001
OA hybrid
DA 2022-08-23
ER

PT J
AU Zhou, CB
   Song, WY
AF Zhou, Caibo
   Song, Wenyan
TI Digitalization as a way forward: A bibliometric analysis of 20 Years of
   servitization research
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Article
DE Product-service systems; Servitization; Collaboration network analysis;
   Co-citation network analysis; Research trends
ID PRODUCT-SERVICE SYSTEMS; OF-THE-ART; INTEGRATED PRODUCT; DESIGN;
   FRAMEWORK; FUTURE; FIRMS; PSS; SUSTAINABILITY; OPPORTUNITIES
AB This paper conducts a bibliometric analysis of servitization and PSS by examining 690 selected papers from Web of Science database. Knowledge mapping techniques including collaboration analysis, co citation analysis and keywords co-occurrence analysis methods are adopted in the bibliometric analysis. Our findings show that China and England are the top two countries publishing more papers, and Sweden, Germany, Switzerland and Denmark keep tight collaboration relationships with other regions. We also find that some active researchers play critical roles as interfaces to connect different research groups in the servitization and PSS fields. Some authors of highly cited papers in earlier years gradually turned to other research directions. In addition, 'Digital PSS and servitization', and 'territorial servitization' are found to be emerging trends in recent years. Although the earlier sustainability topics in the servitization field are more associated with eco-design and lifecycle extension, they gradually shift to sustainable business models in recent years. Compared with most of the literature reviews in the field, this study provides insights by revealing the distribution structure of literature, the collaboration relationship and the dynamic change of research trends with a transparent, repeatable and objective procedure.
   (c) 2021 Elsevier Ltd. All rights reserved.
C1 [Zhou, Caibo; Song, Wenyan] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China.
   [Song, Wenyan] Beihang Univ, Minist Educ, Key Lab Complex Syst Anal Management & Decis, Beijing 100191, Peoples R China.
RP Song, WY (通讯作者)，Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China.
EM songwenyan@buaa.edu.cn
OI Song, Wenyan/0000-0003-1339-0717
FU National Key Research and Development Program of China [2019YFB1405502];
   National Natural Science Foundation of China [71971012, 71501006,
   71632003]; Fundamental Research Funds for the Central Universities
FX This work is supported by the National Key Research and Development
   Program of China (No. 2019YFB1405502) . This research is also supported
   by the National Natural Science Foundation of China (No. 71971012,
   71501006, 71632003) ,and the Fundamental Research Funds for the Central
   Universities (No.YWF21BJJ428) . The authors would like to thank the
   editor and the anonymous reviewers for their helpful and constructive
   comments and suggestions on the drafts of this paper.
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NR 93
TC 6
Z9 6
U1 16
U2 63
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 JUN 1
PY 2021
VL 300
AR 126943
DI 10.1016/j.jclepro.2021.126943
EA APR 2021
PG 14
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 RV9HK
UT WOS:000646136300014
DA 2022-08-23
ER

PT J
AU Kim, J
   Kim, K
   Sohn, M
   Park, G
AF Kim, Jongmo
   Kim, Kunyoung
   Sohn, Mye
   Park, Gyudong
TI Deep Model-Based Security-Aware Entity Alignment Method for
   Edge-Specific Knowledge Graphs
SO SUSTAINABILITY
LA English
DT Article
DE edge computing; data privacy and security; entity alignment; knowledge
   graph; deep model
ID CENTRALITY; NETWORKS; INTERNET
AB This paper proposes a deep model-based entity alignment method for the edge-specific knowledge graphs (KGs) to resolve the semantic heterogeneity between the edge systems' data. To do so, this paper first analyzes the edge-specific knowledge graphs (KGs) to find unique characteristics. The deep model-based entity alignment method is developed based on their unique characteristics. The proposed method performs the entity alignment using a graph which is not topological but data-centric, to reflect the characteristics of the edge-specific KGs, which are mainly composed of the instance entities rather than the conceptual entities. In addition, two deep models, namely BERT (bidirectional encoder representations from transformers) for the concept entities and GAN (generative adversarial networks) for the instance entities, are applied to model learning. By utilizing the deep models, neural network models that humans cannot interpret, it is possible to secure data on the edge systems. The two learning models trained separately are integrated using a graph-based deep learning model GCN (graph convolution network). Finally, the integrated deep model is utilized to align the entities in the edge-specific KGs. To demonstrate the superiority of the proposed method, we perform the experiment and evaluation compared to the state-of-the-art entity alignment methods with the two experimental datasets from DBpedia, YAGO, and wikidata. In the evaluation metrics of Hits@k, mean rank (MR), and mean reciprocal rank (MRR), the proposed method shows the best predictive and generalization performance for the KG entity alignment.
C1 [Kim, Jongmo; Kim, Kunyoung; Sohn, Mye] Sungkyunkwan Univ, Dept Ind Engn, Suwon 16419, South Korea.
   [Park, Gyudong] Agcy Def Dev, R&D Inst 2, Seoul 05771, South Korea.
RP Sohn, M (通讯作者)，Sungkyunkwan Univ, Dept Ind Engn, Suwon 16419, South Korea.
EM dignityc@skku.edu; kimkun0@skku.edu; myesohn@skku.edu; iobject@add.re.kr
FU Agency for Defense Development in Korea [UE201114ED]
FX This research was funded by the Agency for Defense Development in Korea
   grant number UE201114ED.
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NR 59
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUL
PY 2022
VL 14
IS 14
AR 8877
DI 10.3390/su14148877
PG 22
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 3K0QP
UT WOS:000833791100001
OA gold
DA 2022-08-23
ER

PT J
AU Do, P
   Pham, P
AF Do, Phuc
   Pham, Phu
TI W-KG2Vec: a weighted text-enhanced meta-path-based knowledge graph
   embedding for similarity search
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Meta-path; Similarity search; Knowledge graph embedding; Document
   similarity; BERT; Bi-LSTM encoder
AB Recently, similar entity searching over knowledge graph (KG) has gained much attentions by researchers. However, in rich-semantic KGs with multi-typed entities and relations, also known as heterogeneous information network, relevant entity search is considered as a challenging task due to the ambiguity as well as complexity of user's queries in realistic applications, such as QA chatbot and KG-based information retrieval. In this paper, we propose a novel approach, called W-KG2Vec which enables to automatically learn the semantic representations of entities in KG by applying the meta-path. The proposed W-KG2Vec is a meta-path-specific model which supports to evaluate both semantic relations as well as the text-based similarity between entities. The combination of text- and structure-based embedding mechanism of W-KG2Vec is promising to achieve better representations of entities in given KGs for handling complex user's queries. To effectively learn the sequential textual representations of entities' descriptions, we propose a combination of BERT pre-trained model with LTSM encoder, called BERT-Text2Vec. Then, the text-based similarity between entities is used to leverage our weighted meta-path-based random walk mechanism in W-KG2Vec model. Extensive experiences on real-world KGs (YAGO and Freebase) demonstrate the effectiveness of our proposed model against recent state-of-the-art KG embedding baselines.
C1 [Do, Phuc; Pham, Phu] VNU HCM, Univ Informat Technol UIT, Ho Chi Minh City, Vietnam.
RP Do, P (通讯作者)，VNU HCM, Univ Informat Technol UIT, Ho Chi Minh City, Vietnam.
EM phucdo@uit.edu.vn; phamtheanhphu@gmail.com
FU Vietnam National University HoChiMinh City (VNU-HCM) [DS2020-26-01]
FX This research is funded by Vietnam National University HoChiMinh City
   (VNU-HCM) under grant number DS2020-26-01.
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NR 42
TC 1
Z9 1
U1 3
U2 9
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
PD DEC
PY 2021
VL 33
IS 23
BP 16533
EP 16555
DI 10.1007/s00521-021-06252-8
EA JUL 2021
PG 23
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WR3OP
UT WOS:000669168600001
DA 2022-08-23
ER

PT J
AU Karidi, DP
   Stavrakas, Y
   Vassiliou, Y
AF Karidi, Danae Pla
   Stavrakas, Yannis
   Vassiliou, Yannis
TI Tweet and followee personalized recommendations based on knowledge
   graphs
SO JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
LA English
DT Article
DE Social recommendation; Content based recommender systems; Knowledge
   graph; Tweet recommendation; User recommendation
ID TOPOLOGY-BASED RECOMMENDATION; REAL-TIME; USERS
AB Twitter users get the latest tweets of their followees on their timeline. However, they are often overwhelmed by the large number of tweets, which makes it difficult for them to find interesting information among them. In this work, we present an efficient semantic recommendation method that helps users filter the Twitter stream for interesting content. The foundation of this method is a knowledge graph (KG) that can represent all user topics of interest as a variety of concepts, objects, events, persons, entities, locations and the relations between them. Our method uses the KG and graph theory algorithms not yet applied in social network analysis in order to construct user interest profiles by retrieving semantic information from tweets. Next, it produces ranked tweet recommendations. In addition, we use the KG to calculate interest similarity between users, and we present a followee recommender based on the same underlying principles. An important advantage of our method is that it reduces the effects of problems such as over-recommendation and over-specialization. As another advantage, our method is not impaired by the limitations posed by Twitter on the availability of the user graph data. We implemented from scratch the best-known state-of-the-art approaches in order to compare with them and assess our method. Moreover, we evaluate the efficiency and runtime scalability of our method.
C1 [Karidi, Danae Pla; Stavrakas, Yannis] Athena RC, IMIS, Athens, Greece.
   [Vassiliou, Yannis] NTUA, Athens, Greece.
RP Karidi, DP (通讯作者)，Athena RC, IMIS, Athens, Greece.
EM danae@imis.athena-innovation.gr; yannis@imis.athena-innovation.gr;
   yv@cs.ntua.gr
OI Pla Karidi, Danae/0000-0002-3154-6212
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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 NOV
PY 2018
VL 9
IS 6
SI SI
BP 2035
EP 2049
DI 10.1007/s12652-017-0491-7
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA GW2QT
UT WOS:000446730900021
DA 2022-08-23
ER

PT J
AU Shimizu, R
   Matsutani, M
   Goto, M
AF Shimizu, Ryotaro
   Matsutani, Megumi
   Goto, Masayuki
TI An explainable recommendation framework based on an improved knowledge
   graph attention network with massive volumes of side information
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Explainable artificial intelligence; Explainable recommendation;
   Model-intrinsic approach; Knowledge graph attention network; Knowledge
   graph embedding
AB In recent years, explainable recommendation has been a topic of active study. This is because the branch of the machine learning field related to methodologies is enabling human understanding of the reasons for the outputs of recommender systems. The realization of explainable recommendation is widely expected to increase both user satisfaction and the demand for explainable recommendation systems. Explainable recommendation utilizes a wealth of side information (such as sellers, brands, user ages and genders, and bookmark information, among others) to expound the decision-making reasoning applied by recommendation models. In explainable recommendation, although learning side information containing numerous variables leads to rich interpretability, learning too many variables presents a challenge because decreases the amount of learning that a given computational resource can perform, and the accuracy of the recommendation model may be degraded. However, numerous and diverse variables are included in the side information stored by the actual companies operating massive real-world services. Hence, to realize practical applications of this valuable information, it is necessary to resolve problems such as computational cost. In this study, we propose a new framework for explainable recommendation based on an improved knowledge graph attention network model, which utilizes the side information of items and realizes high recommendation accuracy. The proposed framework enables direct interpretation by visualizing the reasons for the recommendations provided. Experimental results show that the proposed framework reduced computational time requirements by approximately 80%, while maintaining recommendation accuracy by enabling the model to learn the probabilistically given edges included in the graph structure. Moreover, the results show that the proposed framework exhibited richer interpretability than the conventional model. Finally, a multifaceted analysis suggests that the proposed framework is not only effective as an explainable recommendation model but also provides a powerful tool for planning various marketing strategies. (c) 2021 The Author(s). 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 [Shimizu, Ryotaro] Waseda Univ, Grad Sch Creat Sci & Engn, 3-4-1 Ohkubo,Shinjuku Ku, Tokyo 1698555, Japan.
   [Shimizu, Ryotaro; Matsutani, Megumi] ZOZO Res, Kioi Tower 22F,1-3 Kioicho,Chiyoda ku, Tokyo 1020094, Japan.
   [Goto, Masayuki] Waseda Univ, Sch Creat Sci & Engn, 3-4-1 Ohkubo,Shinjuku Ku, Tokyo 1698555, Japan.
RP Shimizu, R (通讯作者)，Waseda Univ, Grad Sch Creat Sci & Engn, 3-4-1 Ohkubo,Shinjuku Ku, Tokyo 1698555, Japan.
EM shi3mizu8-r@fuji.waseda.jp
OI Shimizu, Ryotaro/0000-0002-4841-1824
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NR 53
TC 2
Z9 2
U1 13
U2 13
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD MAR 5
PY 2022
VL 239
AR 107970
DI 10.1016/j.knosys.2021.107970
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0V7BG
UT WOS:000788495000004
OA hybrid
DA 2022-08-23
ER

PT J
AU Janowicz, K
   Hitzler, P
   Li, WW
   Rehberger, D
   Schildhauer, M
   Zhu, R
   Shimizu, C
   Fisher, CK
   Cai, L
   Mai, GC
   Zalewski, J
   Zhou, L
   Stephen, S
   Gonzalez, S
   Mecum, B
   Lopez-Carr, A
   Schroeder, A
   Smith, D
   Wright, D
   Wang, SZ
   Tian, YY
   Liu, ZL
   Shi, ML
   D'Onofrio, A
   Gu, ZN
   Currier, K
AF Janowicz, Krzysztof
   Hitzler, Pascal
   Li, Wenwen
   Rehberger, Dean
   Schildhauer, Mark
   Zhu, Rui
   Shimizu, Cogan
   Fisher, Colby K.
   Cai, Ling
   Mai, Gengchen
   Zalewski, Joseph
   Zhou, Lu
   Stephen, Shirly
   Gonzalez, Seila
   Mecum, Bryce
   Lopez-Carr, Anna
   Schroeder, Andrew
   Smith, David
   Wright, Dawn
   Wang, Sizhe
   Tian, Yuanyuan
   Liu, Zilong
   Shi, Meilin
   D'Onofrio, Anthony
   Gu, Zhining
   Currier, Kitty
TI Know, Know Where, KnowWhereGraph: A densely connected, cross-domain
   knowledge graph and geo-enrichment service stack for applications in
   environmental intelligence
SO AI MAGAZINE
LA English
DT Article
ID SCALE
AB Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologies have become a core component of modern search engines, intelligent personal assistants, business intelligence, and so on. Interestingly, despite large-scale data availability, they have yet to be as successful in the realm of environmental data and environmental intelligence. In this paper, we will explain why spatial data require special treatment, and how and when to semantically lift environmental data to a KG. We will present our KnowWhereGraph that contains a wide range of integrated datasets at the human-environment interface, introduce our application areas, and discuss geospatial enrichment services on top of our graph. Jointly, the graph and services will provide answers to questions such as "what is here," "what happened here before," and "how does this region compare to horizontal ellipsis " for any region on earth within seconds.
C1 [Janowicz, Krzysztof; Schildhauer, Mark; Zhu, Rui; Cai, Ling; Stephen, Shirly; Smith, David; Liu, Zilong; Shi, Meilin; Currier, Kitty] Univ Calif Santa Barbara, Santa Barbara, CA 93106 USA.
   [Hitzler, Pascal; Shimizu, Cogan; Zalewski, Joseph; Zhou, Lu] Kansas State Univ, Manhattan, KS 66506 USA.
   [Li, Wenwen; Wang, Sizhe; Tian, Yuanyuan; Gu, Zhining] Arizona State Univ, Tempe, AZ USA.
   [Rehberger, Dean; Gonzalez, Seila; D'Onofrio, Anthony] Michigan State Univ, Matrix, E Lansing, MI 48824 USA.
   [Schildhauer, Mark; Mecum, Bryce] Natl Ctr Ecol Anal & Synth, Santa Barbara, CA USA.
   [Fisher, Colby K.] Hydronos Labs, Princeton, NJ USA.
   [Mai, Gengchen] Stanford Univ, Stanford, CA 94305 USA.
   [Lopez-Carr, Anna; Schroeder, Andrew] Direct Relief, Santa Barbara, CA USA.
   [Wright, Dawn] Esri, Redlands, CA USA.
RP Janowicz, K (通讯作者)，Univ Calif Santa Barbara, Santa Barbara, CA 93106 USA.
EM janowicz@ucsb.edu
FU National Science Foundation [2033521 A1]
FX The authors acknowledge support by the National Science Foundation under
   Grant 2033521 A1: KnowWhereGraph: Enriching and Linking Cross-Domain
   Knowledge Graphs using Spatially-Explicit AI Technologies. 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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Z9 1
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PU AMER ASSOC ARTIFICIAL INTELL
PI MENLO PK
PA 445 BURGESS DRIVE, MENLO PK, CA 94025-3496 USA
SN 0738-4602
EI 2371-9621
J9 AI MAG
JI AI Mag.
PD MAR
PY 2022
VL 43
IS 1
BP 30
EP 39
DI 10.1002/aaai.12043
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0J8FN
UT WOS:000780336400005
OA Green Published
DA 2022-08-23
ER

PT J
AU Pedro, A
   Pham-Hang, AT
   Nguyen, PT
   Pham, HC
AF Pedro, Akeem
   Pham-Hang, Anh-Tuan
   Nguyen, Phong Thanh
   Pham, Hai Chien
TI Data-Driven Construction Safety Information Sharing System Based on
   Linked Data, Ontologies, and Knowledge Graph Technologies
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE construction safety; information sharing; knowledge graph; linked data;
   ontology; semantic web; data-driven; knowledge engineering; knowledge
   management; accident prevention
ID HEALTH; MANAGEMENT; INDUSTRY
AB Accident, injury, and fatality rates remain disproportionately high in the construction industry. Information from past mishaps provides an opportunity to acquire insights, gather lessons learned, and systematically improve safety outcomes. Advances in data science and industry 4.0 present new unprecedented opportunities for the industry to leverage, share, and reuse safety information more efficiently. However, potential benefits of information sharing are missed due to accident data being inconsistently formatted, non-machine-readable, and inaccessible. Hence, learning opportunities and insights cannot be captured and disseminated to proactively prevent accidents. To address these issues, a novel information sharing system is proposed utilizing linked data, ontologies, and knowledge graph technologies. An ontological approach is developed to semantically model safety information and formalize knowledge pertaining to accident cases. A multi-algorithmic approach is developed for automatically processing and converting accident case data to a resource description framework (RDF), and the SPARQL protocol is deployed to enable query functionalities. Trials and test scenarios utilizing a dataset of 200 real accident cases confirm the effectiveness and efficiency of the system in improving information access, retrieval, and reusability. The proposed development facilitates a new "open" information sharing paradigm with major implications for industry 4.0 and data-driven applications in construction safety management.
C1 [Pedro, Akeem] Imperial Coll London, Ctr Syst Engn & Innovat, London SW7 2BX, England.
   [Pham-Hang, Anh-Tuan] Int Univ, Sch Comp Sci & Engn, Ho Chi Minh City 700000, Vietnam.
   [Nguyen, Phong Thanh] Ho Chi Minh City Open Univ, Dept Project Management, Ho Chi Minh City 700000, Vietnam.
   [Pham, Hai Chien] Ton Duc Thang Univ, Fac Civil Engn, Appl Computat Civil & Struct Engn Res Grp, Ho Chi Minh City 700000, Vietnam.
RP Pham, HC (通讯作者)，Ton Duc Thang Univ, Fac Civil Engn, Appl Computat Civil & Struct Engn Res Grp, Ho Chi Minh City 700000, Vietnam.
EM lanrepedro3@gmail.com; anhtuanphamhang@gmail.com; phong.nt@ou.edu.vn;
   phamhaichien@tdtu.edu.vn
RI Pham, Hai Chien/AAD-1248-2019; Pham-Hang, Anh-Tuan/AAF-8634-2020
OI Pham, Hai Chien/0000-0001-7830-7967; Pham-Hang,
   Anh-Tuan/0000-0002-3950-5220; Nguyen, Phong/0000-0002-6180-2520
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NR 46
TC 0
Z9 0
U1 33
U2 33
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD JAN
PY 2022
VL 19
IS 2
AR 794
DI 10.3390/ijerph19020794
PG 18
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA YS3ER
UT WOS:000750564100001
PM 35055616
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Jin, YC
   Liu, JZ
   Wang, XH
   Li, PP
   Wang, JZ
AF Jin, Yucheng
   Liu, Jizhan
   Wang, Xiuhong
   Li, Pingping
   Wang, Jizhang
TI Technology Recommendations for an Innovative Agricultural Robot Design
   Based on Technology Knowledge Graphs
SO PROCESSES
LA English
DT Article
DE knowledge graph; agricultural robot; technology recommendation;
   innovative design
ID PATENT; MANAGEMENT; ONTOLOGY; SYSTEM
AB The process of agricultural robot design is a complex system requiring the cooperation and integration of agricultural, machinery, automation, and information technology. These demands create great challenges for the innovative design of agricultural robots. Meanwhile, more than 95% of the latest inventions and creations in the world are recorded in the patent literature. In order to make effective use of the information and data resources of patents, shorten the design cycle, and provide knowledge for the designers, according to the operation's objectives, an agricultural robot technology knowledge graph (TKG) was established for innovative designs. By analyzing the patent information, a patent IPC co-classification network (IPCNet) for adaptive design process recognition was put forward to meet the requirements of the different operation objectives and operation links. Through the extraction of the technology keywords and efficacy keywords, based on the word co-occurrence network (WCONet), a technology-efficacy map (TEM) was constructed. Through the integration of the adaptive design process and the TEM, the agricultural robot design TKG was constructed for determining technological recommendations for agricultural robot design. The case of the citrus picking robot design was realized to implement the design process. With the technology recommendation results, the moving system, body, and end-effector for the citrus picking robot were designed to verify the results of the recommendation.
C1 [Jin, Yucheng; Liu, Jizhan; Wang, Jizhang] Jiangsu Univ, Minist Educ, Key Lab Modern Agr Equipment & Technol, Zhenjiang 212013, Jiangsu, Peoples R China.
   [Wang, Xiuhong] Jiangsu Univ, Inst Sci & Tech Informat, Zhenjiang 212013, Jiangsu, Peoples R China.
   [Li, Pingping] Nanjing Forestry Univ, Coll Biol & Environm, Nanjing 210037, Peoples R China.
RP Wang, JZ (通讯作者)，Jiangsu Univ, Minist Educ, Key Lab Modern Agr Equipment & Technol, Zhenjiang 212013, Jiangsu, Peoples R China.
EM 1000003026@ujs.edu.cn; liujizhan@ujs.edu.cn; xiuhongwang@ujs.edu.cn;
   ppli@njfu.edu.cn; whxh@ujs.edu.cn
OI Wang, Jizhang/0000-0002-8658-8575
FU National Natural Science Foundation of China [31971795]; Priority
   Academic Program Development of Jiangsu Higher Education Institutions
   [PAPD-2018-87]
FX FundingThe research was supported by grants from the National Natural
   Science Foundation of China (Grant No. 31971795) and a project funded by
   the Priority Academic Program Development of Jiangsu Higher Education
   Institutions (No. PAPD-2018-87).
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NR 62
TC 0
Z9 0
U1 15
U2 27
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-9717
J9 PROCESSES
JI Processes
PD NOV
PY 2021
VL 9
IS 11
AR 1905
DI 10.3390/pr9111905
PG 21
WC Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA XG6QL
UT WOS:000724875100001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Guo, H
   Tang, JY
   Zeng, WX
   Zhao, X
   Liu, L
AF Guo, Hao
   Tang, Jiuyang
   Zeng, Weixin
   Zhao, Xiang
   Liu, Li
TI Multi-modal entity alignment in hyperbolic space
SO NEUROCOMPUTING
LA English
DT Article
DE Multi-modal knowledge graphs; Entity alignment; Hyperbolic Graph
   Convolutional Networks; Hyperboloid manifold
AB Many AI-related tasks involve the interactions of data in multiple modalities. It has been a new trend to merge multi-modal information into knowledge graph (KG), resulting in multi-modal knowledge graphs (MMKG). However, MMKGs usually suffer from low coverage and incompleteness. To mitigate this prob-lem, a viable approach is to integrate complementary knowledge from other MMKGs. To this end, although existing entity alignment approaches could be adopted, they operate in the Euclidean space, and the resulting Euclidean entity representations can lead to large distortion of KG's hierarchical struc-ture. Besides, the visual information has yet not been well exploited. In response to these issues, in this work, we propose a novel multi-modal entity alignment approach, Hyperbolic multi-modal entity alignment (HMEA), which extends the Euclidean representation to hyper-boloid manifold. We first adopt the Hyperbolic Graph Convolutional Networks (HGCNs) to learn struc-tural representations of entities. Regarding the visual information, we generate image embeddings using the densenet model, which are also projected into the hyperbolic space using HGCNs. Finally, we combine the structure and visual representations in the hyperbolic space and use the aggregated embed-dings to predict potential alignment results. Extensive experiments and ablation studies demonstrate the effectiveness of our proposed model and its components. CO 2021 Published by Elsevier B.V.
C1 [Guo, Hao; Tang, Jiuyang; Zeng, Weixin; Zhao, Xiang; Liu, Li] Natl Univ Def Technol, Changsha, Peoples R China.
RP Zhao, X (通讯作者)，Natl Univ Def Technol, Changsha, Peoples R China.
OI Zeng, Weixin/0000-0002-2690-3190
FU NSFC [61872446, 71971212]; NSF of Hunan Province [2019JJ20024]
FX This work was partially supported by NSFC under Grant Nos. 61872446 and
   71971212, and NSF of Hunan Province under Grant No. 2019JJ20024.
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NR 48
TC 2
Z9 2
U1 8
U2 13
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 598
EP 607
DI 10.1016/j.neucom.2021.03.132
EA SEP 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WC2IT
UT WOS:000704086300010
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, ZC
   Liu, Y
   Han, ZQ
   Liao, XC
AF Zhang, Zuocheng
   Liu, Yang
   Han, Zongqi
   Liao, Xianchun
TI Green Finance and Carbon Emission Reduction: A Bibliometric Analysis and
   Systematic Review
SO FRONTIERS IN ENVIRONMENTAL SCIENCE
LA English
DT Review
DE green finance; carbon emission; citespace; knowledge map; bibliometric
   analysis; DPSIR framework
ID FOOD-WATER NEXUS; REDD PLUS; MACROECONOMIC DETERMINANTS; CLIMATE
   FINANCE; CRUDE-OIL; CHINA; MARKET; INVESTMENT; IMPACT; SCHEME
AB Green finance is an emerging topic which is broadly discussed in context of adapting and mitigating environmental deterioration due to climate change. As an effective incentive mechanism, it provides strong support for carbon emission reduction. However, a limited review articles investigate the specific combination of green finance and carbon emission reduction. Here, we apply a bibliometric analysis to review research on green finance and carbon emission reduction based on the literature from 2010 to 2021 in the Web of Science core database. The results indicate that countries with the most publications were those with high economic development, salient environmental problems, and a strong demand for ecological protection. Top publishing journals include Climate Policy, Journal of Cleaner Production, and Energy Policy. The author collaboration is fragmented, mostly less than three researchers. Based on analyses of keyword frequency and centrality, deforestation, carbon markets, and financial development were the most significant research topics. The research hotspots included clean development mechanism, adaptation, carbon market, and sequestration. Finally, the DPSIR framework is applied to explore driving forces, state, pressure, impact and response of current research. We hope our work provides a systematic review of green finance for carbon emission reduction to boost the research in this field.
C1 [Zhang, Zuocheng; Liu, Yang] Univ Jinan, Business Sch, Jinan, Peoples R China.
   [Han, Zongqi] Jinan Foreign Language Sch, Jinan, Peoples R China.
   [Liao, Xianchun] Univ Jinan, Inst Green Dev, Jinan, Peoples R China.
RP Liu, Y (通讯作者)，Univ Jinan, Business Sch, Jinan, Peoples R China.
EM liuyang0531py@126.com
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NR 137
TC 0
Z9 0
U1 8
U2 8
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2296-665X
J9 FRONT ENV SCI-SWITZ
JI Front. Environ. Sci.
PD JUN 30
PY 2022
VL 10
AR 929250
DI 10.3389/fenvs.2022.929250
PG 17
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 3H2RR
UT WOS:000831887800001
OA gold
DA 2022-08-23
ER

PT J
AU Chen, P
   Tian, Y
AF Chen, Peng
   Tian, Yang
TI Analysis of Research Hot Spot and Trends of Bioinformatics Based on Cite
   Space
SO NANOSCIENCE AND NANOTECHNOLOGY LETTERS
LA English
DT Article
DE Bioinformatics; Hot Spot and Progress; Bibliometric Analysis; Cite
   Space; Knowledge Mapping
ID INTERACTION NETWORKS; EMERGING TRENDS; TRANSFORMATION; DIVERSITY;
   WORKFLOWS; VIRUS
AB Bioinformatics is an interdisciplinary science that consists of acquisition, processing, storage, distribution, analysis, interpretation and other aspects of biological information. It has become one of the important frontiers of today's life sciences and natural sciences. As research in these areas advances rapidly, it is critical to keep abreast of emerging trends and critical turns in development of collective knowledge. The current study collected 2916 samples of journal articles and proceeding papers on bioinformatics research that were included in the ISI Web of Science from 2008 to 2018. The study adopted bibliometric analysis method and visual knowledge network analysis by using Cite Space to carry out in-depth exploration of aspects such as development status, developing trend, and hot subjects on bioinformatics research. The study identified major intellectual cooperation network, co-occurrence keywords, research clusters and landmark articles. Our results showed that: (1) In terms of contributing countries, institutions, and authors, the dominant driving force of bioinformatics research was from China, USA and England. (2) Bioinformatics, expression, database, protein, identification were the top 5 keywords with high co-occurrence frequency in the whole period from 2008 to 2018; (3) The thematic clusters of mash up, microRNA, and next-generation sequencing reflected the most significant knowledge base clusters of bioinformatics research.
C1 [Chen, Peng] Capital Normal Univ, Dept Educ Technol, Beijing 100048, Peoples R China.
   [Chen, Peng; Tian, Yang] Beijing Normal Univ, Smart Learning Inst, Beijing 100875, Peoples R China.
   [Tian, Yang] Jiangsu Open Univ, Inst Acad Off, Nanjing 210036, Jiangsu, Peoples R China.
RP Chen, P (通讯作者)，Capital Normal Univ, Dept Educ Technol, Beijing 100048, Peoples R China.; Chen, P (通讯作者)，Beijing Normal Univ, Smart Learning Inst, Beijing 100875, Peoples R China.
FU Research on Co-creation, Sharing, Promotion and Implementation of Open
   Educational Resources along the Belt and Road Initiative, A national
   general project in educational science in "the 13th Five-year Plan"
   [BCA170073]
FX We gratefully acknowledge the financial supports from Research on
   Co-creation, Sharing, Promotion and Implementation of Open Educational
   Resources along the Belt and Road Initiative, A national general project
   in educational science in "the 13th Five-year Plan" (BCA170073).
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NR 45
TC 0
Z9 0
U1 26
U2 83
PU AMER SCIENTIFIC PUBLISHERS
PI VALENCIA
PA 26650 THE OLD RD, STE 208, VALENCIA, CA 91381-0751 USA
SN 1941-4900
EI 1941-4919
J9 NANOSCI NANOTECH LET
JI Nanosci. Nanotechnol. Lett.
PD NOV
PY 2018
VL 10
IS 11
BP 1532
EP 1540
DI 10.1166/nnl.2018.2815
PG 9
WC Nanoscience & Nanotechnology; Materials Science, Multidisciplinary;
   Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Materials Science; Physics
GA HB5ET
UT WOS:000451082200008
DA 2022-08-23
ER

PT J
AU Dragoni, M
   Tettamanzi, AGB
   Pereira, CD
AF Dragoni, Mauro
   Tettamanzi, Andrea G. B.
   Pereira, Celia da Costa
TI Propagating and Aggregating Fuzzy Polarities for Concept-Level Sentiment
   Analysis
SO COGNITIVE COMPUTATION
LA English
DT Article
DE Sentiment analysis; Multi-domain learning; Fuzzy logic
AB An emerging field within sentiment analysis concerns the investigation about how sentiment polarities associated with concepts have to be adapted with respect to the different domains in which they are used. In this paper, we explore the use of fuzzy logic for modeling concept polarities, and the uncertainty associated with them, with respect to different domains. The approach is based on the use of a knowledge graph built by combining two linguistic resources, namely WordNet and SenticNet. Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets. The system implementing the proposed approach has been evaluated on the Blitzer dataset. The results demonstrate its viability in real-world cases.
C1 [Dragoni, Mauro] FBK IRST, Trento, Italy.
   [Tettamanzi, Andrea G. B.; Pereira, Celia da Costa] Univ Nice Sophia Antipolis, UMR 7271, I3S, F-06189 Nice, France.
RP Dragoni, M (通讯作者)，FBK IRST, Trento, Italy.
EM dragoni@fbk.eu; andrea.tettamanzi@unice.fr; celia.pereira@unice.fr
RI Tettamanzi, Andrea G. B./H-5544-2019
OI Tettamanzi, Andrea G. B./0000-0002-8877-4654; da Costa Pereira,
   Celia/0000-0001-6278-7740; Dragoni, Mauro/0000-0003-0380-6571
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NR 60
TC 36
Z9 37
U1 0
U2 30
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1866-9956
EI 1866-9964
J9 COGN COMPUT
JI Cogn. Comput.
PD APR
PY 2015
VL 7
IS 2
SI SI
BP 186
EP 197
DI 10.1007/s12559-014-9308-6
PG 12
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA CF0CQ
UT WOS:000352210700002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhao, F
   Li, YG
   Hou, J
   Bai, L
AF Zhao, Fen
   Li, Yinguo
   Hou, Jie
   Bai, Ling
TI Improving question answering over incomplete knowledge graphs with
   relation prediction
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Question answering over knowledge graphs (KGs-QA); Relation prediction;
   Attention-based graph embedding; Entity importance estimation
ID RETRIEVAL
AB Large-scale knowledge graphs (KGs) play a critical role in question answering over KGs (KGs-QA). Despite of large scale, KGs suffer from incompleteness, which has fueled a lot of research on relation prediction. Since existing researches of relation prediction process each triple independently, the hidden relations which are inherently present can not be captured. Complementarily, to simultaneously capture both entity features and relation features in a given entity's neighborhood, an entity importance estimation network of attention-based graph embedding is proposed, which consists of the attention-based graph embedding module and the entity importance estimation module. Firstly, the new embedding of an entity from its n-hop neighbor is learned by an attention-based graph embedding module. Then, the learned new embedding is integrated into the entity importance estimation module to find entities of high importance in n-hop neighbors of the central entity. Finally, multi-hop relations are encapsulated and an auxiliary edge of n-hop neighbors is introduced, which realizes the relation prediction task. To the best our knowledge, we are the first to realize KGs-QA while realizing relation prediction, which alleviates the phenomenon of missing relations and the low-precision problem of KGs-QA. On the SQ datasets, the proposed method obtains a high F1 score (49.3%) in 10% missing relation, compared to QASE and MCCNNs with F1 scores of 44.2% and 46.3%, respectively.
C1 [Zhao, Fen; Li, Yinguo; Bai, Ling] Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China.
   [Li, Yinguo; Hou, Jie] Chongqing Univ Posts & Telecommun, Sch Automat, Chongqing 400065, Peoples R China.
   [Bai, Ling] McMaster Univ, Dept Elect & Comp Engn, Hamilton, ON L8S 4L8, Canada.
RP Zhao, F (通讯作者)，Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China.
EM d180201019@stu.cqupt.edu.cn; liyg@cqupt.edu.cn; houjie@cqupt.edu.cn;
   d170201001@stu.cqupt.edu.cn
OI Bai, Ling/0000-0002-2135-5404
FU Chongqing Postgraduate Research and Innovation Project [CYB20175];
   National Natural Science Foundation of China [61903057]; Science and
   Technology Reasearch Program of Chongqing Municipal Education Commission
   [KJQN202000602]; Chongqing Natural Science Foundation
   [cstc2019jcyj-msxmX0129]; Artificial Intelligence Technology Innovation
   Significant Theme Special Project of Chongqing Science and Technology
   Commission [cstc2019jscx-zdztzxX0027]
FX This work is supported by Chongqing Postgraduate Research and Innovation
   Project under Grant CYB20175;National Natural Science Foundation of
   China under Grants 61903057; Science and Technology Reasearch Program of
   Chongqing Municipal Education Commission Grants KJQN202000602; the
   Chongqing Natural Science Foundation under Grant cstc2019jcyj-msxmX0129;
   the Artificial Intelligence Technology Innovation Significant Theme
   Special Project of Chongqing Science and Technology Commission
   cstc2019jscx-zdztzxX0027.
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NR 41
TC 0
Z9 0
U1 7
U2 7
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 APR
PY 2022
VL 34
IS 8
BP 6331
EP 6348
DI 10.1007/s00521-021-06736-7
EA JAN 2022
PG 18
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZU8BS
UT WOS:000742823300001
DA 2022-08-23
ER

PT J
AU Zhao, XJ
   Chen, HH
   Xing, ZC
   Miao, CY
AF Zhao, Xuejiao
   Chen, Huanhuan
   Xing, Zhenchang
   Miao, Chunyan
TI Brain-Inspired Search Engine Assistant Based on Knowledge Graph
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article; Early Access
DE Brain-inspired system; cognitive process; knowledge graph; question and
   answering (Q&A) system
ID MEMORY; INFORMATION
AB Search engines can quickly respond to a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine search keywords and open a large number of web pages to find and summarize answers. Many research works of question and answering (Q&A) system attempt to assist search engines by providing simple, accurate, and understandable answers. However, without original semantic contexts, these answers lack explainability, making them difficult for users to trust and adopt. In this article, a brain-inspired search engine assistant named DeveloperBot based on knowledge graph is proposed, which aligns to the cognitive process of humans and has the capacity to answer complex queries with explainability. Specifically, DeveloperBot first constructs a multilayer query graph by splitting a complex multiconstraint query into several ordered constraints. Then, it models a constraint reasoning process as a subgraph search process inspired by a spreading activation model of cognitive science. In the end, novel features of the subgraph are extracted for decision-making. The corresponding reasoning subgraph and answer confidence are derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate answers and answer confidences with high accuracy. We implement a prototype and conduct a user study to evaluate whether and how the direct answers and the explanations provided by DeveloperBot can assist developers' information needs.
C1 [Zhao, Xuejiao; Miao, Chunyan] Nanyang Technol Univ, Joint NTU UBC, Res Ctr Excellence Act Living Elderly LILY, Singapore 639798, Singapore.
   [Zhao, Xuejiao; Miao, Chunyan] Nanyang Technol Univ, Alibaba NTU Singapore Joint Res Inst, Singapore 639798, Singapore.
   [Zhao, Xuejiao; Miao, Chunyan] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore.
   [Chen, Huanhuan] Univ Sci & Technol China USTC, Sch Comp Sci & Technol, Hefei 230026, Peoples R China.
   [Xing, Zhenchang] Australian Natl Univ, Res Sch Comp Sci, Canberra, ACT 0200, Australia.
RP Miao, CY (通讯作者)，Nanyang Technol Univ, Joint NTU UBC, Res Ctr Excellence Act Living Elderly LILY, Singapore 639798, Singapore.; Miao, CY (通讯作者)，Nanyang Technol Univ, Alibaba NTU Singapore Joint Res Inst, Singapore 639798, Singapore.; Miao, CY (通讯作者)，Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore.
EM zhenchang.xing@anu.edu.au; hchen@ustc.edu.cn; xjzhao@ntu.edu.sg;
   ascymiao@ntu.edu.sg
OI Xing, Zhenchang/0000-0001-7663-1421
FU National Research Foundation, Prime Minister's Office, Singapore,
   through its AI Singapore Programme (AISG) [AISG-GC-2019-003]; National
   Research Foundation, Prime Minister's Office, Singapore, through its NRF
   Investigatorship Programme (NRFI) [NRF-NRFI05-2019-0002]; Alibaba Group
   through the Alibaba Innovative Research (AIR) Program; Alibaba-NTU
   Singapore Joint Research Institute (JRI), Nanyang Technological
   University, Singapore; NSFC [62176245]; Fundamental Research Funds for
   the Central Universities, Anhui [202104a05020011, 202103a07020002]
FX This work was supported in part by the National Research Foundation,
   Prime Minister's Office, Singapore, through its AI Singapore Programme
   (AISG) under Award AISG-GC-2019-003 and its NRF Investigatorship
   Programme (NRFI) under Award NRF-NRFI05-2019-0002, in part by the
   Alibaba Group through the Alibaba Innovative Research (AIR) Program, in
   part by the Alibaba-NTU Singapore Joint Research Institute (JRI),
   Nanyang Technological University, Singapore, in part by NSFC under Grant
   62176245, and in part by the Fundamental Research Funds for the Central
   Universities, Anhui, under Grant 202104a05020011 and Grant
   202103a07020002.
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NR 73
TC 3
Z9 3
U1 2
U2 8
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
DI 10.1109/TNNLS.2021.3113026
EA OCT 2021
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA XR7CI
UT WOS:000732382000001
PM 34609944
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Yang, N
   Pun, SH
   Vai, MI
   Yang, YF
   Miao, QL
AF Yang, Ning
   Pun, Sio Hang
   Vai, Mang, I
   Yang, Yifan
   Miao, Qingliang
TI A Unified Knowledge Extraction Method Based on BERT and Handshaking
   Tagging Scheme
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE named entity recognition; fixed-domain relation extraction; open-domain
   relation extraction; knowledge graph; handshaking tagging scheme; BERT
AB Featured Application Knowledge extraction technology can be applied to many scenarios, such as factual knowledge graph construction. By using knowledge extraction technology, we can extract named entities, their relationships, attributes and concepts from massive unstructured text data. These factual knowledge graphs can be used in search engines and recommendation systems. Meanwhile, knowledge extraction technology can also be used to build a vertical domain knowledge graph, such as medical knowledge graph, which extracts symptoms, diseases, drugs, surgery, treatment methods, etc. Through knowledge extraction technology, a large amount of medical knowledge can be extracted from the medical literature and electronic medical records to assist doctors in disease diagnosis in the CDSS system. Knowledge extraction technology can also be used to analyze the dialogue between patients and doctors, extract the dialogue information of patients from medical dialogue, provide intelligent pre-consultation and guidance services and generate electronic medical records. In the dialogue system, knowledge extraction technology can be used to understand the user's query intention and slot extraction, such as ticket booking scenario; knowledge extraction technology can extract customer booking information, such as departure city, arrival city, time, preference, etc. In the actual knowledge extraction system, different applications have different entity classes and relationship schema, so the generalization and migration ability of knowledge extraction are very important. By training a knowledge extraction model in the source domain and applying the model to an arbitrary target domain directly, open domain knowledge extraction technology becomes crucial to mitigate the generalization and migration ability issues. Traditional knowledge extraction models cannot be directly transferred to new domains and also cannot extract undefined relation types. In order to deal with the above issues, in this paper, we proposed an end-to-end Chinese open-domain knowledge extraction model, TPORE (Extract Open-domain Relations through Token Pair linking), which combined BERT with a handshaking tagging scheme. TPORE can alleviate the nested entities and nested relations issues. Additionally, a new loss function that conducts a pairwise comparison of target category score and non-target category score to automatically balance the weight was adopted, and the experiment results indicate that the loss function can bring speed and performance improvements. The extensive experiments demonstrate that the proposed method can significantly surpass strong baselines. Specifically, our approach can achieve new state-of-the-art Chinese open Relation Extraction (ORE) benchmarks (COER and SAOKE). In the COER dataset, F1 increased from 66.36% to 79.63%, and in the SpanSAOKE dataset, F1 increased from 46.0% to 54.91%. In the medical domain, our method can obtain close performance compared with the SOTA method in the CMeIE and CMeEE datasets.
C1 [Yang, Ning; Vai, Mang, I] Univ Macau, Fac Sci & Technol, Dept Elect & Comp Engn, Macau 999078, Peoples R China.
   [Yang, Ning; Pun, Sio Hang; Vai, Mang, I] Univ Maca, State Key Lab Analog & Mixed Signal VLSI, Macau 999078, Peoples R China.
   [Vai, Mang, I] Key Lab Med Instrumentat & Pharmaceut Technol Fuj, Fuzhou 350116, Peoples R China.
   [Yang, Yifan; Miao, Qingliang] AI Speech Co Ltd, Bldg 14,Tengfei Sci & Technol Pk,388 Xinping St, Suzhou 215000, Peoples R China.
RP Pun, SH (通讯作者)，Univ Maca, State Key Lab Analog & Mixed Signal VLSI, Macau 999078, Peoples R China.
EM yb77534@umac.mo; lodgepun@um.edu.mo; fstmiv@um.edu.mo;
   yifan.yang@aispeech.com; qingliang.miao@aispeech.com
FU National Key R&D Program of China [2020YFB1313502]; Shenzhen-Hong
   Kong-Macau S&T Program (Category C) of SZSTI [SGDX20201103 094002009];
   University of Macau [MYRG2019-00056-AMSV, MYRG2020-00098-FST]; Science
   and Technology Development Fund, Macau SAR [0144/2019/A3,
   0022/2020/AFJ]; Science and Technology Development Fund, Macau SAR
   (SKL-AMSV (FDCT)); Science and Technology Development Fund, Macau SAR
   (SKL-AMSV-ADDITIONAL FUND); Science and Technology Development Fund,
   Macau SAR (SKL-AMSV(UM)-2020-2022)
FX This work was funded by the National Key R&D Program of China (No.
   2020YFB1313502) and funded by the Shenzhen-Hong Kong-Macau S&T Program
   (Category C) of SZSTI (SGDX20201103 094002009) and funded by the
   University of Macau (File no. MYRG2019-00056-AMSV, MYRG2020-00098-FST)
   and funded by The Science and Technology Development Fund, Macau SAR
   (File no. 0144/2019/A3, 0022/2020/AFJ, SKL-AMSV (FDCT-funded),
   SKL-AMSV-ADDITIONAL FUND, SKL-AMSV(UM)-2020-2022).
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NR 41
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD JUL
PY 2022
VL 12
IS 13
AR 6543
DI 10.3390/app12136543
PG 14
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 2Y0NJ
UT WOS:000825591100001
OA gold
DA 2022-08-23
ER

PT J
AU Chen, ZQ
   Peng, B
   Ioannidis, VN
   Li, MF
   Karypis, G
   Ning, X
AF Chen, Ziqi
   Peng, Bo
   Ioannidis, Vassilis N.
   Li, Mufei
   Karypis, George
   Ning, Xia
TI A knowledge graph of clinical trials (CTKG)
SO SCIENTIFIC REPORTS
LA English
DT Article
ID MEDICAL DICTIONARY; PREVENTION; GUIDELINES; MANAGEMENT; HEPATITIS; DRUG
AB Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as CTKG. CTKG includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of CTKG in various applications such as drug repurposing and similarity search, among others.
C1 [Chen, Ziqi; Peng, Bo; Ning, Xia] Ohio State Univ, Columbus, OH 43210 USA.
   [Ioannidis, Vassilis N.; Karypis, George] Amazon Web Serv AI, Palo Alto, CA USA.
   [Li, Mufei] Shanghai AI Lab, Amazon Web Serv, Shanghai, Peoples R China.
RP Ning, X (通讯作者)，Ohio State Univ, Columbus, OH 43210 USA.
EM ning.104@osu.edu
FU National Science Foundation [IIS-1855501]
FX This project was made possible, in part, by support from the National
   Science Foundation under Grant Number IIS-1855501. 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
   funding agencies.
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NR 62
TC 0
Z9 0
U1 7
U2 7
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD MAR 18
PY 2022
VL 12
IS 1
AR 4724
DI 10.1038/s41598-022-08454-z
PG 14
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA ZV7IC
UT WOS:000770698200028
PM 35304504
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Xu, FF
   Wang, XP
   Zhou, SL
AF Xu, Feifei
   Wang, Xinpeng
   Zhou, Shanlin
TI Story Generation Using Knowledge Graph under Psychological States
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB Story generation, aiming to generate a story that people could understand easily, captures increasing researchers' attention in recent years. However, a good story usually requires interesting and emotional plots. Previous works only consider a specific or binary emotion like positive or negative. In our work, we propose a Knowledge-Aware Generation framework under Controllable CondItions (K-GuCCI). The model assigns a change line of psychological states to story characters, which makes the story develop following the setting. Besides, we incorporate the knowledge graph into the model to facilitate the coherence of the story. Moreover, we investigate a metric AGPS to evaluate the accuracy of generated stories' psychological states. Experiments exhibit that the proposed model improves over standard benchmarks, while also generating stories reliable and valid.
C1 [Xu, Feifei; Wang, Xinpeng; Zhou, Shanlin] Shanghai Univ Elect Power, Coll Comp Sci & Technol, Shanghai, Peoples R China.
RP Wang, XP (通讯作者)，Shanghai Univ Elect Power, Coll Comp Sci & Technol, Shanghai, Peoples R China.
EM xufeifei@shiep.edu.cn; wangxinpeng@mail.shiep.edu.cn;
   zhoushanlin@mail.shiep.edu.cn
OI Zhou, Shanlin/0000-0003-1016-1281; Wang, Xinpeng/0000-0003-1857-0346
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NR 42
TC 0
Z9 0
U1 2
U2 7
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD APR 28
PY 2021
VL 2021
AR 5530618
DI 10.1155/2021/5530618
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA TK6SC
UT WOS:000674284800005
OA gold
DA 2022-08-23
ER

PT J
AU Canongia, C
   Antunes, A
   Pereira, MDF
AF Canongia, C
   Antunes, A
   Pereira, MDF
TI Technological foresight - the use of biotechnology in the development of
   new drugs against breast cancer
SO TECHNOVATION
LA English
DT Article
DE knowledge and information management; health sector; technological
   foresight; competitive intelligence; breast cancer
AB The aim of the study is to demonstrate knowledge and information management as a mechanism for developing technological foresight regarding the use of biotechnology in drugs for breast cancer. The methodology applies competitive intelligence (CI) tools to identify international trends concerning drugs for treatment and/or diagnosis, and to identify leading institutions. The study was performed by collecting, treating and analyzing information extracted from specialized databases and patent databases. Subsequently, knowledge maps were generated, which could serve to guide the development of the health sector that works in the area of breast cancer, supplying a basis for decision-making and for the construction of a vision of the future. The article shows the results of data mining in specialized medical and patent databases with regard to the use of biotechnology in the treatment of breast cancer, identifying the most frequently cited new drugs and drug combinations, as well as the authors of research (articles) and the creators of new technology (patents) at the beginning of the 21st century. (C) 2003 Elsevier Ltd. All rights reserved.
C1 UFRJ, EQ, SIQUIM, Ctr Tecnol,IBICT, Rio De Janeiro, RJ, Brazil.
RP Canongia, C (通讯作者)，UFRJ, EQ, SIQUIM, Ctr Tecnol,IBICT, Bloco E,1222, Rio De Janeiro, RJ, Brazil.
EM claudia@ibict.br
RI Antunes, Adelaide M S/C-6328-2017
OI Antunes, Adelaide/0000-0002-2245-7517
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NR 24
TC 13
Z9 15
U1 1
U2 34
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0166-4972
EI 1879-2383
J9 TECHNOVATION
JI Technovation
PD APR
PY 2004
VL 24
IS 4
BP 299
EP 309
DI 10.1016/j.technovation.2003.10.001
PG 11
WC Engineering, Industrial; Management; Operations Research & Management
   Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Business & Economics; Operations Research & Management
   Science
GA 810WO
UT WOS:000220734600003
DA 2022-08-23
ER

PT J
AU Kraak, MJ
AF Kraak, MJ
TI Geovisualization illustrated
SO ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
LA English
DT Article; Proceedings Paper
CT Joint Workshop on Challenges in Geospatial Analysis and Visualization
CY OCT 29-31, 2001
CL ATHENS, GEORGIA
SP ISPRS Commiss IV Working Grp
DE geovisualization; alternative views; maps; Minard
ID INFORMATION
AB In recent years, the visualization of geospatial data has undergone dramatic and important developments. Next to static maps, nowadays, immersive and highly interactive virtual environments can be used to explore and present dynamic geospatial data. Additionally, the World Wide Web has developed into a prominent medium to disseminate geospatial data and maps. In visualizing geospatial data, methods and techniques from fields, such as scientific visualization and information visualization, are applied because of the large volumes of data at hand. This has accumulated in what is known as geovisualization-the use of visual geospatial displays to explore data and through that exploration to generate hypotheses, develop problem solutions and construct knowledge. Maps and other linked graphics play a key role in this process. The objective of this paper is to demonstrate the usefulness of geovisualization and, in particular, how alternative graphic representations can stimulate the visual thought process. This is demonstrated by applying geovisualization techniques to Minard's well-known map of Napoleon's 1812 campaign into Russia, the "Carte figurative des pertes successives en hommes de l'Armee Francaise dans la campagne de Russie 1812-1813" (http://www.itc.nl/personal/kraak/1812/). (C) 2003 Elsevier Science B.V. All rights reserved.
C1 ITC, Int Inst Geoinformat Sci & Earth Observ, NL-7500 AA Enschede, Netherlands.
RP Kraak, MJ (通讯作者)，ITC, Int Inst Geoinformat Sci & Earth Observ, POB 6, NL-7500 AA Enschede, Netherlands.
RI Kraak, Menno-Jan/A-2809-2012
OI Kraak, Menno-Jan/0000-0002-8605-0484
CR AUSTIN PB, 2000, NAPOLEONS INVASION R
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   [No title captured]
NR 24
TC 60
Z9 61
U1 3
U2 24
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0924-2716
J9 ISPRS J PHOTOGRAMM
JI ISPRS-J. Photogramm. Remote Sens.
PD APR
PY 2003
VL 57
IS 5-6
BP 390
EP 399
DI 10.1016/S0924-2716(02)00167-3
PG 10
WC Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science & Photographic Technology
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Physical Geography; Geology; Remote Sensing; Imaging Science &
   Photographic Technology
GA 663LD
UT WOS:000182006300009
DA 2022-08-23
ER

PT J
AU Yang, L
   Fang, X
   Zhu, JQ
AF Yang, Li
   Fang, Xin
   Zhu, Junqi
TI Knowledge Mapping Analysis of Public Health Emergency Management
   Research Based on Web of Science
SO FRONTIERS IN PUBLIC HEALTH
LA English
DT Article
DE public health; emergency management; bibliometric analysis; CiteSpace;
   VOSviewer
ID CORE COMPETENCES; ISCHEMIC-STROKE; UNITED-STATES; CHRONIC PAIN;
   PREPAREDNESS; OUTBREAK; EPIDEMIC; SYSTEM; INFLUENZA; FRAMEWORK
AB At present, major public health emergencies frequently occur worldwide, and it is of great significance to analyze the research status and latest developments in this field to improve the ability of public health emergency management in various countries. This paper took 5,143 related studies from 2007 to 2020 from the Web of Science as research object and used CiteSpace, VOSviewer, and other software to perform co-word analysis, social network analysis, and cluster analysis. The results and conclusions were as follows: (1) the related research identified three periods: the exploration, growth, and outbreak period; (2) chronologically: the relevant research evolved from medical and health care for major diseases to emergency management and risk assessment of public health emergencies and then researched the novel coronavirus (COVID-19) pneumonia epidemic; (3) clustering analysis of high-frequency keywords, identifying three research hotspots: "disaster prevention and emergency medical services," "outbreak and management of infectious diseases in Africa," and "emergency management under the COVID-19 pneumonia epidemic." Finally, this study combined the data and literature analysis to point out possible future research directions: from the research of the COVID-19 pneumonia epidemic to the research of general major public health emergencies, thinking and remodeling of the national public health emergency management system, and exploring the establishment of an efficient international emergency management cooperation mechanism.
C1 [Yang, Li; Fang, Xin; Zhu, Junqi] Anhui Univ Sci & Technol, Sch Econ & Management, Huainan, Peoples R China.
RP Fang, X (通讯作者)，Anhui Univ Sci & Technol, Sch Econ & Management, Huainan, Peoples R China.
EM f470930@163.com
OI Zhu, Junqi/0000-0002-5247-9805
FU General Project of the National Natural Science Foundation of China
   [71971003]; Major Project of the National Social Science Funding of
   China [20ZDA084]
FX Funding This study was supported by General Project of the National
   Natural Science Foundation of China (grant no. 71971003) and the Major
   Project of the National Social Science Funding of China (grant no.
   20ZDA084).
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NR 64
TC 0
Z9 0
U1 39
U2 39
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 MAR 9
PY 2022
VL 10
AR 755201
DI 10.3389/fpubh.2022.755201
PG 14
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA 1K6PC
UT WOS:000798719200001
PM 35356021
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Wilson-Lopez, A
   Mejia, JA
   Hasbun, IM
   Kasun, GS
AF Wilson-Lopez, Amy
   Mejia, Joel Alejandro
   Hasbun, Indhira Maria
   Kasun, G. Sue
TI Latina/o Adolescents' Funds of Knowledge Related to Engineering
SO JOURNAL OF ENGINEERING EDUCATION
LA English
DT Article
DE design practice; student background; race/ethnicity
ID CULTURALLY SUSTAINING PEDAGOGY; SCIENCE-EDUCATION; MATHEMATICS;
   LITERACY; STUDENTS; EXPERIENCES; DISCOURSES; COMMUNITY; THINKING;
   FACULTY
AB Background According to a growing body of research, many Latinas/os experience dissonance between their everyday cultural practices and the cultural practices prevalent in engineering. This dissonance contributes to many Latinas/os' sense that engineering is not for me.
   Purpose This study sought to explore the meaning in the relationship between engineering cultural practices and the funds of knowledge found in Latina/o adolescents' familial, community, and recreational settings.
   Design/Method This ethnographic study followed seven groups of Latina/o adolescents as they identified problems in their communities and solved them through engineering design processes. Using a modified form of constant comparative analysis, we analyzed three data sources: individual interviews, observations of group meetings, and concurrent or retrospective protocols. We developed a coding scheme that categorized the participants' funds of knowledge as they related to engineering.
   Results The participants' everyday skills and bodies of knowledge aligned with engineering practices. Specifically, their familial, community, and recreational funds of knowledge mapped onto the application of engineering design processes, systems thinking, ethical and empathetic reasoning, knowledge of production and processing, use of communication and construction tools, scientific and mathematical knowledge, and teamwork.
   Conclusions Engineering instruction for Latina/o adolescents can be reconceptualized as a third space of learning and knowing where adolescents' everyday familial, community, and recreational practices are actively solicited and connected with the cultural practices of engineering.
C1 [Wilson-Lopez, Amy] Utah State Univ, Sch Teacher Educ & Leadership, 2805 Old Main Hill, Logan, UT 84322 USA.
   [Mejia, Joel Alejandro] Angelo State Univ, Dept Civil Engn, 2601 W Ave N,ASU Stn 11056, San Angelo, TX 76909 USA.
   [Hasbun, Indhira Maria] Virginia Polytech Inst & State Univ, Dept Engn Educ, 645 Prices Fork Rd, Blacksburg, VA 24061 USA.
   [Kasun, G. Sue] Georgia State Univ, Coll Educ & Human Dev, Dept Middle & Secondary Educ, POB 3978, Atlanta, GA 30302 USA.
RP Wilson-Lopez, A (通讯作者)，Utah State Univ, Sch Teacher Educ & Leadership, 2805 Old Main Hill, Logan, UT 84322 USA.; Mejia, JA (通讯作者)，Angelo State Univ, Dept Civil Engn, 2601 W Ave N,ASU Stn 11056, San Angelo, TX 76909 USA.; Hasbun, IM (通讯作者)，Virginia Polytech Inst & State Univ, Dept Engn Educ, 645 Prices Fork Rd, Blacksburg, VA 24061 USA.; Hasbun, IM; Kasun, GS (通讯作者)，Georgia State Univ, Coll Educ & Human Dev, Dept Middle & Secondary Educ, POB 3978, Atlanta, GA 30302 USA.
EM amyalexandra.wilson@usu.edu; alex.mejia@angelo.edu; imhasbun@vt.edu;
   gskasun@gmail.com
OI Mejia, Joel/0000-0003-3908-9930
FU National Science Foundation [1222566]; Direct For Education and Human
   Resources [1222566] Funding Source: National Science Foundation
FX This material is based upon work supported by the National Science
   Foundation under Grant 1222566. 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. Wilson-Lopez and colleagues (Wilson, Mejia, Hasbun,
   Householder, & Hailey, 2013) and Mejia, Wilson, Hasbun, and Householder
   (2014) presented earlier versions of this work at conferences.
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WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Engineering
GA DK1AQ
UT WOS:000374644700004
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Al-Moslmi, T
   Ocana, MG
   Opdahl, AL
   Veres, C
AF Al-Moslmi, Tareq
   Ocana, Marc Gallofre
   Opdahl, Andreas L.
   Veres, Csaba
TI Named Entity Extraction for Knowledge Graphs: A Literature Overview
SO IEEE ACCESS
LA English
DT Article
DE Task analysis; Natural language processing; Semantics; Standards; Hidden
   Markov models; Iris recognition; Data mining; Knowledge graphs;
   natural-language processing; named-entity extraction; named-entity
   recognition; named-entity disambiguation; named-entity linking
ID SEMANTIC SIMILARITY; SOCIAL MEDIA; RECOGNITION; LINKING; DISAMBIGUATION;
   WEB; FRAMEWORK; MACHINE; MODEL
AB An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other & x2019;s context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.
C1 [Al-Moslmi, Tareq; Ocana, Marc Gallofre; Opdahl, Andreas L.; Veres, Csaba] Univ Bergen, Dept Informat Sci & Media Studies, N-5007 Bergen, Norway.
RP Al-Moslmi, T (通讯作者)，Univ Bergen, Dept Informat Sci & Media Studies, N-5007 Bergen, Norway.
EM tareq.al-moslmi@uib.no
RI Ocaña, Marc Gallofré/G-9273-2019
OI Ocaña, Marc Gallofré/0000-0001-7637-3303; Al-Moslmi,
   Tareq/0000-0002-5296-2709
FU News Angler Project through the Norwegian Research Council [275872]
FX This work was supported by the News Angler Project through the Norwegian
   Research Council under Project 275872.
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NR 146
TC 19
Z9 22
U1 13
U2 44
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 32862
EP 32881
DI 10.1109/ACCESS.2020.2973928
PG 20
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LC6DE
UT WOS:000525420200003
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Ren, ZH
   Yu, CQ
   Li, LP
   You, ZH
   Guan, YJ
   Wang, XF
   Pan, J
AF Ren, Zhong-Hao
   Yu, Chang-Qing
   Li, Li-Ping
   You, Zhu-Hong
   Guan, Yong-Jian
   Wang, Xin-Fei
   Pan, Jie
TI BioDKG-DDI: predicting drug-drug interactions based on drug knowledge
   graph fusing biochemical information
SO BRIEFINGS IN FUNCTIONAL GENOMICS
LA English
DT Article
DE DDIs drug-drug interactions; representation attention; deep learning;
   multi-feature integration; drug knowledge graph
AB The way of co-administration of drugs is a sensible strategy for treating complex diseases efficiently. Because of existing massive unknown interactions among drugs, predicting potential adverse drug-drug interactions (DDIs) accurately is promotive to prevent unanticipated interactions, which may cause significant harm to patients. Currently, numerous computational studies are focusing on potential DDIs prediction on account of traditional experiments in wet lab being time-consuming, labor-consuming, costly and inaccurate. These approaches performed well; however, many approaches did not consider multi-scale features and have the limitation that they cannot predict interactions among novel drugs. In this paper, we proposed a model of BioDKG-DDI, which integrates multi-feature with biochemical information to predict potential DDIs through an attention machine with superior performance. Molecular structure features, representation of drug global association using drug knowledge graph (DKG) and drug functional similarity features are fused by attention machine and predicted through deep neural network. A novel negative selecting method is proposed to certify the robustness and stability of our method. Then, three datasets with different sizes are used to test BioDKG-DDI. Furthermore, the comparison experiments and case studies can demonstrate the reliability of our method. Upon our finding, BioDKG-DDI is a robust, yet simple method and can be used as a benefic supplement to the experimental process.
C1 [Ren, Zhong-Hao; Yu, Chang-Qing; Li, Li-Ping; You, Zhu-Hong; Guan, Yong-Jian; Wang, Xin-Fei; Pan, Jie] Xijing Univ, Sch Informat Engn, Xian 710100, Peoples R China.
RP Yu, CQ; Li, LP (通讯作者)，Xijing Univ, Sch Informat Engn, Xian 710100, Peoples R China.
EM cs2bioinformatics@gmail.com; y.akune@imperial.ac.uk
OI ren, zhonghao/0000-0002-1614-8988
FU National Natural Science Foundation of China [61722212]
FX This research was funded by National Natural Science Foundation of
   China, grant number62002297and 61722212.
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NR 54
TC 1
Z9 1
U1 6
U2 6
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 2041-2649
EI 2041-2657
J9 BRIEF FUNCT GENOMICS
JI Brief. Funct. Genomics
PD MAY 21
PY 2022
VL 21
IS 3
BP 216
EP 229
DI 10.1093/bfgp/elac004
EA APR 2022
PG 14
WC Biotechnology & Applied Microbiology; Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biotechnology & Applied Microbiology; Genetics & Heredity
GA 1L9TR
UT WOS:000785554300001
PM 35368060
DA 2022-08-23
ER

PT J
AU Liu, YD
   Chen, W
AF Liu, Yudong
   Chen, Wen
TI Recommendation Model Based on Semantic Features and a Knowledge Graph
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
ID INFORMATION; DBPEDIA
AB In the field of information science, how to help users quickly and accurately find the information they need from a tremendous amount of short texts has become an urgent problem. The recommendation model is an important way to find such information. However, existing recommendation models have some limitations in case of short text recommendation. To address these issues, this paper proposes a recommendation model based on semantic features and a knowledge graph. More specifically, we first select DBpedia as a knowledge graph to extend short text features of items and get the semantic features of the items based on the extended text. And then, we calculate the item vector and further obtain the semantic similarity degrees of the users. Finally, based on the semantic features of the items and the semantic similarity of the users, we apply the collaborative filtering technology to calculate prediction rating. A series of experiments are conducted, demonstrating the effectiveness of our model in the evaluation metrics of mean absolute error (MAE) and root mean square error (RMSE) compared with those of some recommendation algorithms. The optimal MAE for the model proposed in this paper is 0.6723, and RMSE is 0.8442. The promising results show that the recommendation effect of the model on the movie field is significantly better than those of these existing algorithms.
C1 [Liu, Yudong; Chen, Wen] South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Peoples R China.
RP Liu, YD (通讯作者)，South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Peoples R China.
EM liuyudong@scnu.edu.cn
FU project of the Guangzhou Science and Technology Bureau, China
   [202007040006]
FX We wish to express our appreciation to the reviewers for their helpful
   suggestions which greatly improved the presentation of this paper. This
   work was supported by the project of the Guangzhou Science and
   Technology Bureau, China, under Grant no. 202007040006.
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NR 31
TC 0
Z9 0
U1 4
U2 9
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 JUL 20
PY 2021
VL 2021
AR 2382892
DI 10.1155/2021/2382892
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA TX6AO
UT WOS:000683171600001
OA gold
DA 2022-08-23
ER

PT J
AU Lan, CZ
   Lu, WJ
   Xu, Q
   Zhou, Y
   Shi, QS
   Lyu, L
AF Lan, Chaozhen
   Lu, Wanjie
   Xu, Qing
   Zhou, Yang
   Shi, Qunshan
   Lyu, Liang
TI Construction of Space Object Situation Information Service Based on
   Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Space object situation; information service; knowledge graph; semantic
   relation parsing; knowledge representation; dynamic spatial-temporal
   environment
ID ONTOLOGY
AB To effectively guarantee the safety and efficiency of space activities, and to meet the needs of different levels of users for space object situation (SOS) knowledge, formalized and standardized SOS knowledge is needed to assist cognition. A knowledge graph (KG), as a suitable technology that can formally express knowledge and construct a standardized knowledge base, can provide knowledge generation, representation and intelligent service of SOS. In this paper, an SOS information service based on the KG (SOSKG) was constructed. Aimed at different scales of components in SOS, a multi-granularity knowledge structure was constructed, and a formalized expression of SOSKG was realized. To incorporate the complex relations into the SOSKG, a multi-level semantic relation parsing model and formalized representation were proposed from the three aspects of basic relations, spatial relations, and temporal relations. Additionally, a multi-element knowledge construction model was constructed to address the dynamic characteristics of various relations in SOSKG under a complex spatio-temporal environment. This study constructed different cases and scenarios to test the service, and the results show that the proposed service can effectively organize and express SOS knowledge in a complex spatio-temporal environment, provide an intermediate bridge between users and SOS knowledge, and promote users' cognition of SOS knowledge.
C1 [Lan, Chaozhen; Lu, Wanjie; Xu, Qing; Zhou, Yang; Shi, Qunshan; Lyu, Liang] Strateg Support Force Informat Engn Univ, Inst Geospatial Informat, Zhengzhou 450052, Peoples R China.
RP Lu, WJ (通讯作者)，Strateg Support Force Informat Engn Univ, Inst Geospatial Informat, Zhengzhou 450052, Peoples R China.
EM lwj285149763@163.com
OI Lu, Wanjie/0000-0002-7210-7670; LYU, Liang/0000-0003-1168-210X
FU National Natural Science Foundation of China (NSFC) [41701463]
FX This work was supported in part by the National Natural Science
   Foundation of China (NSFC) under Grant 41701463.
CR [Anonymous], [No title captured]
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   [Anonymous], [No title captured]
   [Anonymous], [No title captured]
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NR 48
TC 2
Z9 2
U1 4
U2 14
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 22625
EP 22640
DI 10.1109/ACCESS.2020.2969704
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LC5TQ
UT WOS:000525393900071
OA gold
DA 2022-08-23
ER

PT J
AU Niu, GL
   Li, B
   Zhang, YF
   Sheng, YP
   Shi, C
   Li, JY
   Pu, SL
AF Niu, Guanglin
   Li, Bo
   Zhang, Yongfei
   Sheng, Yongpan
   Shi, Chuan
   Li, Jingyang
   Pu, Shiliang
TI Joint semantics and data-driven path representation for knowledge graph
   reasoning
SO NEUROCOMPUTING
LA English
DT Article
DE Knowledge graph reasoning; Path representation; Horn rules; Entity
   converting; Joint semantics and data-driven
AB Reasoning on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering. The path-based reasoning models can leverage much information over paths other than pure triples in the KG but face several challenges. Firstly, all the existing path-based methods are data-driven, lacking explainability, namely how the path representations and the reasoning results are obtained with human-understandable explanations. Besides, some approaches either consider only relational paths or ignore the heterogeneity between entities and relations both in paths, which cannot capture the rich semantics of paths well. To address the above challenges, in this work, we propose a novel joint semantics and data-driven path representation that balances explainability and generalization in the framework of KG embedding. Specifically, we inject horn rules to obtain the condensed paths through a transparent and explainable path composition procedure. The entity converter is designed to transform entities along paths into the representations in the semantic level similar to relations for reducing the heterogeneity between entities and relations. The KGs, both with and without type information, are considered. Our proposed model is evaluated on two classes of tasks: link prediction and path query answering. The experimental results show that our model obtains significant performance gains over several state-ofthe-art baselines. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Niu, Guanglin; Li, Bo; Zhang, Yongfei] Beihang Univ, Sch Comp Sci & Engn, Beijing Key Lab Digital Media, Beijing, Peoples R China.
   [Li, Bo; Zhang, Yongfei; Li, Jingyang] Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China.
   [Zhang, Yongfei] Pengcheng Lab, Shenzhen, Peoples R China.
   [Sheng, Yongpan] Chongqing Univ, Sch Big Data & Software Engn, Chongqing, Peoples R China.
   [Shi, Chuan] Beijing Univ Posts & Telecommun, Beijing, Peoples R China.
   [Pu, Shiliang] Hikvision Res Inst, Hangzhou, Peoples R China.
RP Zhang, YF (通讯作者)，Beihang Univ, Sch Comp Sci & Engn, Beijing Key Lab Digital Media, Beijing, Peoples R China.
EM yfzhang@buaa.edu.cn
OI Zhang, Yongfei/0000-0002-5080-1733
FU National Natural Science Foundation of China [61772054]; NSFC Key
   Project [61632001]; Fundamental Research Fund for the Central
   Universities of China; National Key Research and Development Project
   [2018YFB2101200]
FX This work was partially funded by the National Natural Science
   Foundation of China (No. 61772054), the NSFC Key Project (No. 61632001),
   a Fundamental Research Fund for the Central Universities of China, and
   the National Key Research and Development Project (No. 2018YFB2101200).
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NR 51
TC 0
Z9 0
U1 11
U2 11
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD APR 28
PY 2022
VL 483
BP 249
EP 261
DI 10.1016/j.neucom.2022.02.011
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZI7NP
UT WOS:000761803800005
DA 2022-08-23
ER

PT J
AU Phoobane, P
   Masinde, M
   Mabhaudhi, T
AF Phoobane, Paulina
   Masinde, Muthoni
   Mabhaudhi, Tafadzwanashe
TI Predicting Infectious Diseases: A Bibliometric Review on Africa
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE infectious diseases; prediction; bibliometric review; Africa; research
   hotspots
ID MALARIA; SYSTEMS; TRENDS
AB Africa has a long history of novel and re-emerging infectious disease outbreaks. This reality has attracted the attention of researchers interested in the general research theme of predicting infectious diseases. However, a knowledge mapping analysis of literature to reveal the research trends, gaps, and hotspots in predicting Africa's infectious diseases using bibliometric tools has not been conducted. A bibliometric analysis of 247 published papers on predicting infectious diseases in Africa, published in the Web of Science core collection databases, is presented in this study. The results indicate that the severe outbreaks of infectious diseases in Africa have increased scientific publications during the past decade. The results also reveal that African researchers are highly underrepresented in these publications and that the United States of America (USA) is the most productive and collaborative country. The relevant hotspots in this research field include malaria, models, classification, associations, COVID-19, and cost-effectiveness. Furthermore, weather-based prediction using meteorological factors is an emerging theme, and very few studies have used the fourth industrial revolution (4IR) technologies. Therefore, there is a need to explore 4IR predicting tools such as machine learning and consider integrated approaches that are pivotal to developing robust prediction systems for infectious diseases, especially in Africa. This review paper provides a useful resource for researchers, practitioners, and research funding agencies interested in the research theme-the prediction of infectious diseases in Africa-by capturing the current research hotspots and trends.
C1 [Phoobane, Paulina; Masinde, Muthoni; Mabhaudhi, Tafadzwanashe] Cent Univ Technol, Dept Informat Technol, Private Bag X200539, ZA-9300 Bloemfontein, South Africa.
   [Mabhaudhi, Tafadzwanashe] Univ KwaZulu Natal, Sch Agr Earth & Environm Sci, Ctr Transformat Agr & Food Syst, Private Bag X01, ZA-3201 Pietermaritzburg, South Africa.
   [Mabhaudhi, Tafadzwanashe] West Africa Off, Int Water Management Inst IWMI GH, PMB CT 112 Cantonments, GA-015 Accra, Ghana.
RP Phoobane, P (通讯作者)，Cent Univ Technol, Dept Informat Technol, Private Bag X200539, ZA-9300 Bloemfontein, South Africa.
EM mphoobane@cut.ac.za; emasinde@cut.ac.za; Mabhaudhi@ukzn.ac.za
RI Mabhaudhi, Tafadzwanashe/AAF-2418-2019; Masinde, Muthoni/P-7374-2015
OI Mabhaudhi, Tafadzwanashe/0000-0002-9323-8127; Masinde,
   Muthoni/0000-0002-8914-0055
FU South Africa Research Foundation (NRF) [117800]
FX The project is funded by the South Africa Research Foundation (NRF)
   grant for 2021: Thuthuka Funding Instrument (Unique Grant No: 117800).
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NR 70
TC 0
Z9 0
U1 10
U2 10
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD FEB
PY 2022
VL 19
IS 3
AR 1893
DI 10.3390/ijerph19031893
PG 20
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA YY5FB
UT WOS:000754813800001
PM 35162917
OA gold, Green Published, Green Submitted
DA 2022-08-23
ER

PT J
AU Mottin, D
   Lissandrini, M
   Velegrakis, Y
   Palpanas, T
AF Mottin, Davide
   Lissandrini, Matteo
   Velegrakis, Yannis
   Palpanas, Themis
TI Exemplar queries: a new way of searching
SO VLDB JOURNAL
LA English
DT Article
DE Exemplar query; Query answering; Knowledge graph; Knowledge base
ID GRAPH; OPTIMIZATION
AB Modern search engines employ advanced techniques that go beyond the structures that strictly satisfy the query conditions in an effort to better capture the user intentions. In this work, we introduce a novel query paradigm that considers a user query as an example of the data in which the user is interested. We call these queries exemplar queries. We provide a formal specification of their semantics and show that they are fundamentally different from notions like queries by example, approximate queries and related queries. We provide an implementation of these semantics for knowledge graphs and present an exact solution with a number of optimizations that improve performance without compromising the result quality. We study two different congruence relations, isomorphism and strong simulation, for identifying the answers to an exemplar query. We also provide an approximate solution that prunes the search space and achieves considerably better time performance with minimal or no impact on effectiveness. The effectiveness and efficiency of these solutions with synthetic and real datasets are experimentally evaluated, and the importance of exemplar queries in practice is illustrated.
C1 [Mottin, Davide] Hasso Plattner Inst, Potsdam, Germany.
   [Lissandrini, Matteo; Velegrakis, Yannis] Univ Trento, Trento, Italy.
   [Palpanas, Themis] Paris Descartes Univ, Paris, France.
RP Lissandrini, M (通讯作者)，Univ Trento, Trento, Italy.
EM davide.mottin@hpi.de; ml@disi.unitn.eu; velgias@disi.unitn.eu;
   themis@mi.parisdescartes.fr
RI Lissandrini, Matteo/ABD-9472-2021
OI Lissandrini, Matteo/0000-0001-7922-5998; Mottin,
   Davide/0000-0001-8256-2258
FU Trento RISE Big Data Project; Keystone COST action [IC1302]
FX This work was partially supported by the Trento RISE Big Data Project
   [4] and the Keystone COST action IC1302. We would like to thank the
   authors of [10], NeMa [29] and strong simulation [32] for kindly
   providing us their code. We thank Paola Quaglia for the valuable
   discussion and suggestions about simulation.
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   [No title captured]
NR 52
TC 20
Z9 20
U1 0
U2 6
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 DEC
PY 2016
VL 25
IS 6
BP 741
EP 765
DI 10.1007/s00778-016-0429-2
PG 25
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EB6NN
UT WOS:000387501000001
DA 2022-08-23
ER

PT J
AU LIU, GQ
   GONG, RB
   SHI, YJ
   WANG, ZZ
   MI, L
   CHAO, Y
   ZHONG, JB
AF LIU Guoqiang
   GONG Renbin
   SHI Yujiang
   WANG Zhenzhen
   MI Lan
   YUAN Chao
   ZHONG Jibin
TI Construction of well logging knowledge graph and intelligent
   identification method of hydrocarbon-bearing formation
SO PETROLEUM EXPLORATION AND DEVELOPMENT
LA English
DT Article
DE well logging; hydrocarbon bearing formation identification; knowledge
   graph; graph embedding technique; in-telligent identification; neural
   network
AB Based on the well logging knowledge graph of hydrocarbon-bearing formation (HBF), a Knowledge-Powered Neural Network Formation Evaluation model (KPNFE) has been proposed. It has the following functions: (1) extracting characteristic parameters describing HBF in multiple dimensions and multiple scales; (2) showing the characteristic parameter-related entities, relationships, and attributes as vectors via graph embedding technique; (3) intelligently identifying HBF; (4) seamlessly integrating expertise into the intelligent computing to establish the assessment system and ranking algorithm for potential pay recommendation. Taking 547 wells encountered the low porosity and low permeability Chang 6 Member of Triassic in the Jiyuan Block of Ordos Basin, NW China as objects, 80% of the wells were randomly selected as the training dataset and the remainder as the validation dataset. The KPNFE prediction results on the validation dataset had a coincidence rate of 94.43% with the expert interpretation results and a coincidence rate of 84.38% for all the oil testing layers, which is 13 percentage points higher in accuracy and over 100 times faster than the primary conventional interpretation. In addition, a number of potential pays likely to produce industrial oil were recommended. The KPNFE model effectively inherits, carries forward and improves the expert knowledge, nicely solving the robustness problem in HBF identification. The KPNFE, with good interpretability and high accuracy of computation results, is a powerful technical means for efficient and high-quality well logging re-evaluation of old wells in mature oilfields.
C1 [LIU Guoqiang] PetroChina Explorat & Prod Co, Beijing 100007, Peoples R China.
   [GONG Renbin; WANG Zhenzhen; MI Lan; YUAN Chao] PetroChina Res Inst Petr Explorat & Dev, Beijing 100083, Peoples R China.
   [SHI Yujiang] CNPC Logging Co, Xian 710077, Peoples R China.
   [ZHONG Jibin] PetroChina Changqing Oilfield Co, Xian 710018, Peoples R China.
RP LIU, GQ (通讯作者)，PetroChina Explorat & Prod Co, Beijing 100007, Peoples R China.
EM lgqi@petrochina.com.cn
FU National Science and Technology Major Project [2016ZX05007-004]
FX Supported by the National Science and Technology Major Project
   (2016ZX05007-004).
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   [石玉江 Shi Yujiang], 2016, [测井技术, Well Logging Technology], V40, P493
   [孙歧峰 Sun Qifeng], 2021, [石油勘探与开发, Petroleum Exploration and Development], V48, P843
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   XIA L, 2020, SHENYANG 2020 IEEE I
   [张钹 Zhang Bo], 2020, [中国科学. 信息科学, Scientia Sinica Informationis], V50, P1281
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NR 30
TC 0
Z9 0
U1 2
U2 2
PU KEAI PUBLISHING LTD
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING, DONGCHENG DISTRICT 100717,
   PEOPLES R CHINA
SN 1000-0747
J9 PETROL EXPLOR DEV+
JI Petroleum Explor. Dev.
PD JUN
PY 2022
VL 49
IS 3
BP 572
EP 585
DI 10.1016/S1876-3804(22)60047-8
PG 14
WC Energy & Fuels; Engineering, Petroleum; Geosciences, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels; Engineering; Geology
GA 2H7ZA
UT WOS:000814507500007
OA gold
DA 2022-08-23
ER

PT J
AU Tian, LG
   Yang, BL
   Yin, XL
   Kang, K
   Wu, J
AF Tian, Luogeng
   Yang, Bailong
   Yin, Xinli
   Kang, Kai
   Wu, Jing
TI Multipath Cross Graph Convolution for Knowledge Representation Learning
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
AB In the past, most of the entity prediction methods based on embedding lacked the training of local core relationships, resulting in a deficiency in the end-to-end training. Aiming at this problem, we propose an end-to-end knowledge graph embedding representation method. It involves local graph convolution and global cross learning in this paper, which is called the TransC graph convolutional network (TransC-GCN). Firstly, multiple local semantic spaces are divided according to the largest neighbor. Secondly, a translation model is used to map the local entities and relationships into a cross vector, which serves as the input of GCN. Thirdly, through training and learning of local semantic relations, the best entities and strongest relations are found. The optimal entity relation combination ranking is obtained by evaluating the posterior loss function based on the mutual information entropy. Experiments show that this paper can obtain local entity feature information more accurately through the convolution operation of the lightweight convolutional neural network. Also, the maximum pooling operation helps to grasp the strong signal on the local feature, thereby avoiding the globally redundant feature. Compared with the mainstream triad prediction baseline model, the proposed algorithm can effectively reduce the computational complexity while achieving strong robustness. It also increases the inference accuracy of entities and relations by 8.1% and 4.4%, respectively. In short, this new method can not only effectively extract the local nodes and relationship features of the knowledge graph but also satisfy the requirements of multilayer penetration and relationship derivation of a knowledge graph.
C1 [Tian, Luogeng; Yang, Bailong; Kang, Kai; Wu, Jing] High Tech Inst Xian, Xian 710025, Peoples R China.
   [Tian, Luogeng] Natl Univ Def Technol, Informat & Commun Coll, Xian 710106, Peoples R China.
   [Yin, Xinli] Natl Innovat Inst Def Technol, Beijing 100071, Peoples R China.
RP Tian, LG (通讯作者)，High Tech Inst Xian, Xian 710025, Peoples R China.; Tian, LG (通讯作者)，Natl Univ Def Technol, Informat & Commun Coll, Xian 710106, Peoples R China.
EM tianlg82@163.com; yangbl_68@163.com; xinli27@163.com;
   kaikang_htixa@163.com; sdfdggdc@sina.cn
OI yin, xinli/0000-0002-8086-986X
FU National Natural Science Foundation of China [61501469]
FX AcknowledgmentsThis study was supported by the National Natural Science
   Foundation of China (61501469).
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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 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD DEC 28
PY 2021
VL 2021
AR 2547905
DI 10.1155/2021/2547905
PG 13
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA YE5YO
UT WOS:000741201100007
PM 34992642
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Wang, M
   Chen, WT
   Wang, S
   Jiang, YL
   Yao, LN
   Qi, GL
AF Wang, Meng
   Chen, Weitong
   Wang, Sen
   Jiang, Yinlin
   Yao, Lina
   Qi, Guilin
TI Efficient search over incomplete knowledge graphs in binarized embedding
   space
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Knowledge graph embedding; Learning to hashing; Graph query
AB Knowledge graph (KG) embedding techniques represent entities and relations as low-dimensional and continuous vectors. This enables KG machine learning models to be easily adapted for KG reasoning, completion, and querying tasks. However, learned dense vectors are inefficient for large-scale similarity computations. Learning-to-hash is to a method that learns compact binary codes from high-dimensional input data and provides a promising way to accelerate efficiency by measuring the Hamming distance instead of Euclidean distance. Alternatively, a dot-product is used in a continuous vector space. Unfortunately, most learning-to-hash methods cannot be directly applied to KG structure encoding because they focus on similarity preservation between images. In this paper, we introduce a novel end-to-end learning-to-hash framework for encoding incomplete KGs and graph queries in a Hamming space. To preserve KG structure information, from embeddings to hash codes, and address the ill-posed gradient issue in the optimization, we utilize a continuation method (with convergence guarantees) to jointly encode queries and KG entities using geometric operations. The hashed embedding of a query can be utilized to discover target entities from incomplete KGs whilst the efficiency has been greatly improved. To evaluate the proposed framework, we have compared our model to state-of-the-art methods commonly used in real-world KGs. Extensive experimental results show that our framework not only significantly speeds up the search process, but also provides good results when unanswerable queries are caused by incomplete information.(1) (C) 2021 Elsevier B.V. All rights reserved.
C1 [Wang, Meng; Jiang, Yinlin; Qi, Guilin] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Wang, Meng; Qi, Guilin] MOE Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.
   [Chen, Weitong] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld, Australia.
   [Wang, Sen] Univ Queensland, Sch Informat Technol & Elect Engn, Comp Sci, Brisbane, Qld, Australia.
   [Yao, Lina] Univ New South Wales, Sydney, NSW, Australia.
RP Wang, M (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
EM meng.wang@seu.edu.cn
OI Wang, Sen/0000-0002-5414-8276; Chen, Tony Weitong/0000-0003-1001-7925
FU National Natural Science Foundation of China [61906037, U1736204];
   National Key Research and Development Program of China [2018YFC0830201,
   2017YFB1002801]; Fundamental Research Funds for the Central
   Universities; CCFBaidu Open Fund
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 Fundamental Research Funds for the Central
   Universities; CCFBaidu Open Fund.
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NR 43
TC 1
Z9 1
U1 5
U2 23
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD OCT
PY 2021
VL 123
BP 24
EP 34
DI 10.1016/j.future.2021.04.006
EA APR 2021
PG 11
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SS6MD
UT WOS:000661867900003
DA 2022-08-23
ER

PT J
AU Valera-Gran, D
   Prieto-Botella, D
   Peral-Gomez, P
   Hurtado-Pomares, M
   Sanchez-Perez, A
   Navarrete-Munoz, EM
AF Valera-Gran, Desiree
   Prieto-Botella, Daniel
   Peral-Gomez, Paula
   Hurtado-Pomares, Miriam
   Sanchez-Perez, Alicia
   Navarrete-Munoz, Eva-Maria
TI Bibliometric Analysis of Research on Telomere Length in Children: A
   Review of Scientific Literature
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Review
DE telomere; childhood; scientometrics; scientific publication; genomic
   research; epidemiological research
ID SCIENCE; BIOLOGY; IMPACT
AB Telomere length in early life has been recently associated with biological aging and development of negative consequences in later adult life. A relevant area of research has emerged to understand the factors that impact telomere length in children. We conducted a bibliometric analysis to track research output and identify global trends and gaps in the knowledge of telomere length in children. Bibliographic data were retrieved from the Web of Science database and then analyzed by using Bibliometrix R package. A total of 840 publications were yielded from 1991 to 2019. The references were prominently published in journals, with 20 high ranked journals contributing to 30% of literature on telomere length in children. The USA was the most productive country (35.7%), followed by Europe (12.1%), and Asia (11.9%). A knowledge map of telomere length in children through keyword analyses revealed that there were two potential main lines of research based on two different approaches: genomic research and epidemiological research. This study shows that telomere length in children is a topic of research that has gained significant relevance in the last decade. This bibliometric study may be helpful in identifying research trends and finding research hot spots and gaps in this research field.
C1 [Valera-Gran, Desiree; Prieto-Botella, Daniel; Peral-Gomez, Paula; Hurtado-Pomares, Miriam; Sanchez-Perez, Alicia; Navarrete-Munoz, Eva-Maria] Miguel Hernandez Univ, Dept Surg & Pathol, Alicante 03550, Spain.
   [Valera-Gran, Desiree; Peral-Gomez, Paula; Hurtado-Pomares, Miriam; Sanchez-Perez, Alicia; Navarrete-Munoz, Eva-Maria] Miguel Hernandez Univ, Grp Invest Terapia Ocupac InTeO, Alicante 03550, Spain.
RP Valera-Gran, D; Navarrete-Munoz, EM (通讯作者)，Miguel Hernandez Univ, Dept Surg & Pathol, Alicante 03550, Spain.; Valera-Gran, D; Navarrete-Munoz, EM (通讯作者)，Miguel Hernandez Univ, Grp Invest Terapia Ocupac InTeO, Alicante 03550, Spain.
EM dvalera@umh.es; daniel.prieto@alu.umh.es; pperal@umh.es;
   mhurtado@umh.es; alicia.sanchez@umh.es; enavarrete@umh.es
RI Valera-Gran, Desirée/Q-6427-2017; Botella, Daniel Prieto/AAS-8726-2020
OI Valera-Gran, Desirée/0000-0001-6388-127X; Botella, Daniel
   Prieto/0000-0003-0973-4492; Peral-Gomez, Paula/0000-0002-0390-0894;
   Navarrete-Munoz, Eva Maria/0000-0002-1494-5676; Sanchez-Perez,
   Alicia/0000-0001-9444-8516; HURTADO-POMARES, MIRIAM/0000-0003-2186-8821
FU Instituto de Salud Carlos III/Agencia Estatal de Investigacion
   [PI18/00825]; Union Europea (FEDER) "Una manera de hacer Europa"
FX This research was funded by Instituto de Salud Carlos III/Agencia
   Estatal de Investigacion, grant number PI18/00825 Project: Dieta y
   actividad fisica en embarazo y tras el nacimiento y longitud del
   telomero en ninos y adolescentes: Proyecto TeloDiPA"and Union Europea
   (FEDER) "Una manera de hacer Europa". The funders had no role in the
   design or conduct of the study; collection, management, analysis, and
   interpretation of the data; or preparation, review, and approval of the
   manuscript.
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NR 25
TC 3
Z9 3
U1 2
U2 15
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 2020
VL 17
IS 12
AR 4593
DI 10.3390/ijerph17124593
PG 17
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA MR3TU
UT WOS:000553513600001
PM 32604805
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Wu, YN
   Wang, HJ
   Wang, ZX
   Zhang, B
   Meyer, BC
AF Wu, Yunong
   Wang, Huijie
   Wang, Zhexiao
   Zhang, Bin
   Meyer, Burghard C.
TI Knowledge Mapping Analysis of Rural Landscape Using CiteSpace
SO SUSTAINABILITY
LA English
DT Review
DE bibliometrics; mapping knowledge domains; rural landscape research;
   frontier paths; trending topics
ID LAND-USE CHANGE; ECOSYSTEM SERVICES; BIODIVERSITY CONSERVATION; MOUNTAIN
   LANDSCAPES; SPECIES RICHNESS; PLACE ATTACHMENT; NEW-ENGLAND; MANAGEMENT;
   DIVERSITY; PATTERNS
AB This study visualizes and quantifies extant publications of rural landscape research (RLR) in Web of Science using CiteSpace for a wide range of research topics, from a multi-angle analysis of the overall research profile, while providing a method and approach for quantitative analysis of massive literature data. First, it presents the number of papers published, subject distribution, author network, the fundamental condition of countries, and research organizations involved in RLR through network analysis. Second, it identifies the high-frequency and high betweenness-centrality values of the basic research content of RLR through keyword co-occurrence analysis and keyword time zones. Finally, it identifies research fronts and trending topics of RLR in the decade from 2009 to 2018 by using co-citation clustering, and noun-term burst detection. The results show that basic research content involves protection, management, biodiversity, and land use. Five clearer research frontier pathways and top 20 research trending topics are extracted to show diversified research branch development. All this provides the reader with a general preliminary grasp of RLR, showing that cooperation and analysis involving multiple disciplines, specialties, and angles will become a dominant trend in the field.
C1 [Wu, Yunong; Wang, Zhexiao; Zhang, Bin] Huazhong Agr Univ, Dept Landscape Architecture, Coll Hort & Forestry, Wuhan 430070, Peoples R China.
   [Wu, Yunong; Wang, Zhexiao; Zhang, Bin] Minist Agr & Rural ffairs, Key Lab Urban Agr Cent China, Wuhan 430000, Peoples R China.
   [Wang, Huijie] Wuhan Landscape Construct Dev Grp CO LTD, Wuhan 430000, Peoples R China.
   [Meyer, Burghard C.] Univ Leipzig, Inst Geog, Fac Phys & Earth Sci, D-04103 Leipzig, Germany.
RP Zhang, B (通讯作者)，Huazhong Agr Univ, Dept Landscape Architecture, Coll Hort & Forestry, Wuhan 430070, Peoples R China.; Zhang, B (通讯作者)，Minist Agr & Rural ffairs, Key Lab Urban Agr Cent China, Wuhan 430000, Peoples R China.; Meyer, BC (通讯作者)，Univ Leipzig, Inst Geog, Fac Phys & Earth Sci, D-04103 Leipzig, Germany.
EM yunong@webmail.hzau.edu.cn; huijie@webmail.hzau.edu.cn;
   welixchina@gmail.com; zhangbin@mail.hzau.edu.cn;
   burghard.meyer@uni-leipzig.de
OI wu, yunong/0000-0002-6694-0842
FU National Natural Science Foundation of China [51678269]; Chinese
   Fundamental Research Funds for the Central Universities [HZAU:
   2662018YJ017]
FX This research was funded by the National Natural Science Foundation of
   China, grant number 51678269, and the Chinese Fundamental Research Funds
   for the Central Universities (HZAU: 2662018YJ017).
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   [No title captured]
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   [No title captured], DOI DOI 10.3390/su10041159
   [No title captured]
NR 94
TC 16
Z9 18
U1 65
U2 154
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JAN
PY 2020
VL 12
IS 1
AR 66
DI 10.3390/su12010066
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 KX5YC
UT WOS:000521955600066
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Binsahaq, A
   Sheltami, TR
   Salah, K
AF Binsahaq, Ahmed
   Sheltami, Tarek R.
   Salah, Khaled
TI A Survey on Autonomic Provisioning and Management of QoS in SDN Networks
SO IEEE ACCESS
LA English
DT Article
DE Autonomic computing; autonomous systems; automation; autonomic
   networking; SDN; QoS
ID SOFTWARE-DEFINED NETWORKING; RESOURCE-ALLOCATION; MONITORING SCHEME;
   SYSTEM; VIDEO; LATENCY; FRAMEWORK; DELAY; OPTIMIZATION; ARCHITECTURE
AB In today's Internet, killer network services and applications, such as video and audio streaming, network storage, and online video games, are pushing the network infrastructure resources to the edge. By design and for the most part, the Internet is the best offer delivery ecosystem with little or no end-to-end quality-of-service (QoS) guarantees. Even, frameworks, such as IntServ and DiffServ that were designed and implemented to provide QoS guarantees, still fail to solve this problem at a wide scale. Software-defined networking (SDN) is a fast emerging networking paradigm that promises to provide end-to-end QoS guaranteeing by offering greater network flexibility, abstraction, control, and programmability to network resources. In this paper, we review, survey, and discuss the current state of the art on QoS provisioning in the area of SDN, with respect to applying the concept of autonomic computing (AC) to automatically support, provision, monitor, and maintain QoS requirements. This paper includes in-depth classification, taxonomy, and comparative analysis for the autonomic-based QoS provisioning in accordance with the famous influential and widely adopted the monitor-analyze-plan-execute-knowledge (MAPE-K) IBM architectural model for autonomic computing.
C1 [Binsahaq, Ahmed; Sheltami, Tarek R.] King Fahd Univ Petr & Minerals, Comp Engn Dept, Dhahran 31261, Saudi Arabia.
   [Salah, Khaled] Khalifa Univ, Elect & Comp Engn Dept, Abu Dhabi, U Arab Emirates.
RP Binsahaq, A (通讯作者)，King Fahd Univ Petr & Minerals, Comp Engn Dept, Dhahran 31261, Saudi Arabia.
EM ishaq1900@gmail.com
RI BinSahaq, Ahmed/ABF-6477-2021; Sheltami, Tarek R/B-6019-2015
OI Sheltami, Tarek R/0000-0002-7879-1469; BinSahaq,
   Ahmed/0000-0002-2506-6255
FU King Fahd University of Petroleum and Minerals for this Research
FX This work was supported by the King Fahd University of Petroleum and
   Minerals for this Research.
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NR 271
TC 19
Z9 19
U1 5
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 2019
VL 7
BP 73384
EP 73435
DI 10.1109/ACCESS.2019.2919957
PG 52
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IE2GU
UT WOS:000472203400001
OA gold
DA 2022-08-23
ER

PT J
AU Pouliot, Y
   Gao, J
   Su, QJJ
   Liu, GZG
   Ling, XFB
AF Pouliot, Y
   Gao, J
   Su, QJJ
   Liu, GZG
   Ling, XFB
TI DIAN: A novel algorithm for genome ontological classification
SO GENOME RESEARCH
LA English
DT Article
ID PROTEIN-SEQUENCE DATABASE; WISKOTT-ALDRICH-SYNDROME; RESOURCES;
   APOPTOSIS; PROSITE
AB Faced with the determination of many completely sequenced genomes, computational biology is now faced with the challenge of interpreting the significance of these data sets. A multiplicity of data-related problems impedes this goal: Biological annotations associated with raw data are often not normalized, and the data themselves are often poorly interrelated and their interpretation unclear. All of these problems make interpretation of genomic databases increasingly difficult. With the current explosion of sequences now available from the human genome as well as from model organisms, the importance of sorting this vast amount of conceptually unstructured source data into a limited universe of genes, proteins, functions, structures, and pathways has become a bottleneck for the field. To address this problem, we have developed a method of interrelating data sources by applying a novel method of associating biological objects to ontologies. We have developed an intelligent knowledge-based algorithm, DIAN, to support biological knowledge mapping, and, in particular, to facilitate the interpretation of genomic data. In this respect, the method makes it possible to inventory genomes by collapsing multiple types of annotations and normalizing them to various ontologies. By relying on a conceptual view of the genome, researchers can now easily navigate the human genome in a biologically intuitive, scientifically accurate manner.
C1 DoubleTwist Inc, Oakland, CA 94612 USA.
RP Ling, XFB (通讯作者)，Tularik Inc, 2 Corp Dr, San Francisco, CA 94080 USA.
EM XLING@tularik.com
OI Pouliot, Yannick/0000-0002-0344-1923
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NR 38
TC 17
Z9 17
U1 0
U2 1
PU COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT
PI COLD SPRING HARBOR
PA 1 BUNGTOWN RD, COLD SPRING HARBOR, NY 11724 USA
SN 1088-9051
EI 1549-5469
J9 GENOME RES
JI Genome Res.
PD OCT
PY 2001
VL 11
IS 10
BP 1766
EP 1779
DI 10.1101/gr.183301
PG 14
WC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Genetics & Heredity
GA 480HM
UT WOS:000171456000019
PM 11591654
OA Bronze, Green Published
DA 2022-08-23
ER

PT J
AU Ong, L
   Karmakar, G
   Atherton, J
   Zhou, XC
   Lim, MQ
   Chadzynski, A
   Li, LY
   Wang, XN
   Kraft, M
AF Ong, Laura
   Karmakar, Gourab
   Atherton, John
   Zhou, Xiaochi
   Lim, Mei Qi
   Chadzynski, Arkadiusz
   Li, Lanyu
   Wang, Xiaonan
   Kraft, Markus
TI Embedding Energy Storage Systems into a Dynamic Knowledge Graph
SO INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
LA English
DT Article
ID POWER-PLANTS; TRANSITION
AB This Article illustrates how a dynamic knowledge graph approach in the context of The World Avatar (TWA) project can support the decarbonization of energy systems by leveraging the existing energy storage system (ESS) selection framework to assist in the selection and optimal placement of the ESS. TWA is a dynamic knowledge graph based on the Semantic Web and its associated technologies, with intelligent agents operating on it. The agents act autonomously to update and extend TWA, and thus it evolves in time. TWA also provides the ability to consider different scenarios, referred to as parallel worlds, allowing for scenario analysis without mutual interference. A use case-the addition of a battery energy storage system to the Singapore Jurong Island electrical network-is introduced to demonstrate the application of this approach. The domain ontology, OntoPowSys, was extended to describe and instantiate the relevant ESSs considered in the use case. This extension is described in the Article using the description logic syntax. The Article also outlines the details of how the various agents involved in the use case are being integrated into TWA. The use case also highlights how the parallel world framework can facilitate scenario analysis by considering different scenarios without affecting the real-world representation.
C1 [Ong, Laura; Karmakar, Gourab; Lim, Mei Qi; Chadzynski, Arkadiusz; Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore, Singapore 138602, Singapore.
   [Atherton, John; Zhou, Xiaochi; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.
   [Li, Lanyu; Wang, Xiaonan] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.
RP Kraft, M (通讯作者)，Cambridge Ctr Adv Res & Educ Singapore, Singapore 138602, Singapore.; Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England.; Kraft, M (通讯作者)，Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore.
EM mk306@cam.ac.uk
RI Wang, Xiaonan/T-1102-2017; Kraft, Markus/D-7243-2016
OI Wang, Xiaonan/0000-0001-9775-2417; Kraft, Markus/0000-0002-4293-8924
FU National Research Foundation (NRF) , Prime Minister's Office, Singapore
   under its Campus for Research Excellence and Technological Enterprise
   (CREATE) programme; EPSRC [EP/R029369/1]; ARCHER; Alexander von Humboldt
   Foundation
FX This research is supported by the National Research Foundation (NRF) ,
   Prime Minister's Office, Singapore under its Campus for Research
   Excellence and Technological Enterprise (CREATE) programme. We 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 ) . M.K.
   gratefully acknowledges the support of the Alexander von Humboldt
   Foundation. We thank Andrew Charles Breeson for content editing. Maps
   throughout this Article were created using ArcGIS software by Esri.
   ArcGIS and ArcMap are the intellectual property of Esri and are used
   herein under license. Copyright Esri. All rights reserved. For more
   information about Esri software, please visit www.esri.com .
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NR 39
TC 2
Z9 2
U1 6
U2 7
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0888-5885
J9 IND ENG CHEM RES
JI Ind. Eng. Chem. Res.
PD JAN 6
PY 2022
VL 61
IS 24
BP 8390
EP 8398
DI 10.1021/acs.iecr.1c03838
EA JAN 2022
PG 9
WC Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 2J5SE
UT WOS:000742178600001
DA 2022-08-23
ER

PT J
AU Wang, HY
   Yao, KM
   Luo, J
   Lin, Y
AF Wang, Haiyan
   Yao, Kaiming
   Luo, Jian
   Lin, Yi
TI An Implicit Preference-Aware Sequential Recommendation Method Based on
   Knowledge Graph
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB Sequential recommendation system has received widespread attention due to its good performance in solving data overload. However, most of the sequential recommendation methods assume that user's preferences only depend on specific items in the current sequence and do not consider user's implicit interests. In addition, most of the previous works mainly focus on exploiting relationships between items in the sequence and seldom consider quantifying the degree of preferences for items implied by user's different behaviors. In order to address these above two problems, we propose an implicit preference-aware sequential recommendation method based on knowledge graph (IPAKG). Firstly, this method introduces knowledge graph to exploit user's implicit preference representations. Secondly, we integrate recurrent neural network and attention mechanism to capture user's evolving interests and relationships between different items in the sequence. Thirdly, we introduce the concept of behavior intensity and design a behavior activation unit to exploit the degree of preferences for items implied by a user's different behaviors. Through the activation unit, the user's preferences on different items are further quantified. Finally, we conduct experiments on an Amazon electronics dataset and Tmall dataset to evaluate the performance of our method. Experimental results demonstrate that our proposed method has better performance than those baseline methods.
C1 [Wang, Haiyan; Yao, Kaiming; Luo, Jian; Lin, Yi] Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing 210023, Peoples R China.
   [Wang, Haiyan; Yao, Kaiming; Lin, Yi] Jiangsu Key Lab Big Data Secur & Intelligent Proc, Nanjing 210023, Peoples R China.
RP Wang, HY (通讯作者)，Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing 210023, Peoples R China.; Wang, HY (通讯作者)，Jiangsu Key Lab Big Data Secur & Intelligent Proc, Nanjing 210023, Peoples R China.
EM wanghy@njupt.edu.cn
OI Yao, Kaiming/0000-0002-7635-883X
FU National Natural Science Foundation of China [61772285]; Jiangsu Key
   Laboratory of Big Data Security and Intelligent Processing
FX This work is supported partly by the National Natural Science Foundation
   of China under Grant No. 61772285 and the Jiangsu Key Laboratory of Big
   Data Security and Intelligent Processing.
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NR 39
TC 0
Z9 0
U1 3
U2 8
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD AUG 16
PY 2021
VL 2021
AR 5206228
DI 10.1155/2021/5206228
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA UJ2JU
UT WOS:000691119100002
OA gold
DA 2022-08-23
ER

PT J
AU Lin, QK
   Liu, J
   Pan, YD
   Zhang, LL
   Hu, X
   Ma, J
AF Lin, Qika
   Liu, Jun
   Pan, Yudai
   Zhang, Lingling
   Hu, Xin
   Ma, Jie
TI Rule-enhanced iterative complementation for knowledge graph
SO INFORMATION SCIENCES
LA English
DT Article
DE Logic rules; Knowledge graph; Reasoning; Graph embedding; Graph
   convolutional network
AB Knowledge graph (KG) reasoning aims to infer missing valid triples from observed triples, thereby improving the semantics of the whole KG. The general KG reasoning involves rule based and embedding-based methods. The former can provide an interpretable reasoning process but has low efficiency, while the latter is the converse. Therefore, some hybrid methods have been proposed, but there are still two challenges: the completeness of rule learning and the determination of hidden triples. To address these challenges, this paper proposes a rule-enhanced iterative complementation (Rule-IC) method, which involves three components: rule learning, an embedding learner and a triple discriminator. Such an iterative process enriches the semantics of KG and further increases the completeness of rule learning to generate hidden triples. In order to precisely determine the validity of hidden triples, a multi-relational graph convolutional network (GCN) with attentive message passing is introduced as a triple discriminator. The embedding learner for KG reasoning and the GCN discriminator complement each other by valid hidden triples. In addition, the performance of these three components improves overall during the iterative process. Experimental results show that most evaluation metrics of Rule-IC are better than those of several baselines on four common KGs. Furthermore, it is scalable and can be extended to all KG embedding models theoretically. (c) 2021 Elsevier Inc. All rights reserved.
C1 [Lin, Qika; Liu, Jun; Pan, Yudai; Zhang, Lingling; Hu, Xin; Ma, Jie] Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xian 710049, Shaanxi, Peoples R China.
   [Lin, Qika; Liu, Jun; Pan, Yudai] Xi An Jiao Tong Univ, Natl Engn Lab Big Data Analyt, Xian 710049, Shaanxi, Peoples R China.
   [Zhang, Lingling; Hu, Xin; Ma, Jie] Xi An Jiao Tong Univ, Shaanxi Prov Key Lab Satellite & Terr Network Tec, Xian 710049, Shaanxi, Peoples R China.
RP Liu, J (通讯作者)，Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xian 710049, Shaanxi, Peoples R China.
EM qikalin@foxmail.com; liukeen@xjtu.edu.cn; pyd418@foxmail.com;
   zhanglling@xjtu.edu.cn; dr.huxin711@foxmail.com; dr.majie@foxmail.com
FU National Key Research and Development Program of China [2020AAA0108800];
   National Natural Science Foundation of China [61877050, 61937001,
   62020194]; 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, MoE-CMCC "Artifical Intelligence"Project
   [MCM20190701]
FX This work was supported by National Key Research and Development Program
   of China (2020AAA0108800), National Natural Science Foundation of China
   (61877050, 61937001, and 62020194), 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, MoE-CMCC
   ``Artifical Intelligence"Project (MCM20190701).
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NR 49
TC 3
Z9 3
U1 4
U2 10
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 OCT
PY 2021
VL 575
BP 66
EP 79
DI 10.1016/j.ins.2021.06.040
EA JUN 2021
PG 14
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UU0OI
UT WOS:000698504400004
DA 2022-08-23
ER

PT J
AU Ma, YX
   Shen, M
   Zhao, YH
   Li, Z
   Tong, XY
   Zhang, QX
   Wang, Z
AF Ma, Yuxi
   Shen, Meng
   Zhao, Yuhang
   Li, Zhao
   Tong, Xiaoyao
   Zhang, Quanxin
   Wang, Zhi
TI Opponent portrait for multiagent reinforcement learning in competitive
   environment
SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
LA English
DT Article
DE deep reinforcement learning; intention inference; knowledge graph;
   multiagent system; opponent modeling
AB Existing investigations of opponent modeling and intention inferencing cannot make clear descriptions and practical explanations of the opponent's behaviors and intentions, which may inevitably limit the applicability of them. In this work, we propose a novel approach for opponent's policy explanation and intention inference based on the behavioral portrait of opponent. Specifically, we use the multiagent deep deterministic policy gradients (MADDPG) algorithm to train the agent and opponent in the competitive environment, and collect the behavioral data of opponent based on agent's observations. Then we perform pattern segmentation and extract the opponent's behavior events via Toeplitz inverse covariance-based clustering (TICC) algorithm; hence the opponent's behavior data can be encoded into a knowledge graph, named opponent's behavior knowledge graph (OKG). Based on this, we built a question-answer system (QA system) to query and match opponent historical information in OKG, so that the agent can obtain additional experience and gradually infer the intention of opponent with the episodes of iteration. We evaluate the proposed method on the competitive scenario in multiagent particle environment (MPE). Simulation results show that the agents are able to learn better policies with opponent portrait in competitive settings.
C1 [Ma, Yuxi; Zhao, Yuhang; Li, Zhao; Tong, Xiaoyao; Zhang, Quanxin] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.
   [Shen, Meng] Beijing Inst Technol, Sch Cyberspace Sci & Technol, Beijing, Peoples R China.
   [Wang, Zhi] Nankai Univ, Coll Cyber Sci, Tianjin 300071, Peoples R China.
RP Zhang, QX (通讯作者)，Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.; Wang, Z (通讯作者)，Nankai Univ, Coll Cyber Sci, Tianjin 300071, Peoples R China.
EM zhangqx@bit.edu.cn; zwang@nankai.edu.cn
OI Tong, Xiaoyao/0000-0003-1698-8693; Ma, Yuxi/0000-0001-5639-5214; Li,
   Zhao/0000-0001-6918-0204; Wang, Zhi/0000-0002-3252-9254
FU National Natural Science Foundation of China [61876019, U1936218,
   62072037]
FX This work was supported by the National Natural Science Foundation of
   China (nos. 61876019, U1936218, and 62072037).
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NR 35
TC 6
Z9 6
U1 11
U2 21
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0884-8173
EI 1098-111X
J9 INT J INTELL SYST
JI Int. J. Intell. Syst.
PD DEC
PY 2021
VL 36
IS 12
BP 7461
EP 7474
DI 10.1002/int.22594
EA AUG 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WM4RK
UT WOS:000685429600001
DA 2022-08-23
ER

PT J
AU Zhao, YM
   Yun, J
   Gao, S
   Liu, LM
AF Zhao Yumeng
   Yun Jing
   Gao Shuo
   Liu Limin
TI News Image-Text Matching With News Knowledge Graph
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Task analysis; Licenses; Buildings; Visualization; Knowledge
   engineering; Image edge detection; Image-text matching; named entity;
   indirect relations of named entities
AB Image-text matching using the image caption method has made a great progress. However, there are many named entities in news text, and existing approaches are unable to directly generate named entities in the news image caption. It leads to a semantic gap between text and news image caption. Moreover, the existing methods lack the analysis of indirect relations between named entities. Therefore those approaches easily leads to relations error when generating news image caption. To generate the news image caption with named entities by analyzing the indirect relations between named entities. We propose a novel model. In details, we propose the TopNews dataset with related news, which aims to construct the relations between named entities as widely as possible. Then we develop the news knowledge graph by extracting named entities from TopNews dataset. Furthermore, we propose News Knowledge Driven Graph Neural Network (NKD-GNN). We utilize NKD-GNN to analyzing the whole relations of entities in news knowledge graph. In this way, we generate the news image caption with named entities. The results of extensive experiments based on TopNews dataset and common dataset demonstrate that our approach is effective in detecting the consistency of news images and text.
C1 [Zhao Yumeng; Yun Jing; Gao Shuo; Liu Limin] Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 010080, Peoples R China.
   [Zhao Yumeng; Gao Shuo; Liu Limin] Inner Mongolia Autonomous Reg Engn & Technol Res, Hohhot 010080, Peoples R China.
RP Yun, J (通讯作者)，Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 010080, Peoples R China.
EM yunjing_zoe@163.com
OI zhao, yu meng/0000-0003-4205-718X
FU National Natural Science Foundation of China [62062055]; Natural Science
   Foundation of Inner Mongolia [2020MS06025]; Department of Science and
   Technology of Inner Mongolia [2019GG372]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grants 62062055, in part by the Natural
   Science Foundation of Inner Mongolia under Grant 2020MS06025, and in
   part by the Department of Science and Technology of Inner Mongolia under
   Grant 2019GG372.
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NR 45
TC 0
Z9 0
U1 7
U2 11
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 108017
EP 108027
DI 10.1109/ACCESS.2021.3093650
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA TW0KX
UT WOS:000682101700001
OA gold
DA 2022-08-23
ER

PT J
AU Chen, MX
   Yang, H
   Song, ZD
   Gu, YH
   Zheng, YH
   Zhu, JW
   Wang, AW
   Fu, L
AF Chen, Meixiang
   Yang, Hong
   Song, Zhengda
   Gu, Yonghua
   Zheng, Yuhong
   Zhu, Jiangwei
   Wang, Aiwu
   Fu, Li
TI Knowledge Mapping of Opuntia Milpa Alta Since 1998: A Scientometric
   Analysis
SO PHYTON-INTERNATIONAL JOURNAL OF EXPERIMENTAL BOTANY
LA English
DT Article
DE Opuntia Milpa Alta; China; bibliometric analysis; planting technique;
   biomedical uses
ID EMERGING TRENDS; POLYSACCHARIDES
AB Opuntia Milpa Alta is a cactus cultivated, domesticated, hybridized and selected from the plant Opuntia ficusindica by Mexican agricultural experts, which can be used as fruit and vegetable. Opuntia Milpa Alta leaves and fruit are superior to wild varieties and suitable for storage and transportation. In 1998, Opuntia Milpa Alta was introduced to China from Mexico by the Quality Product Development Center of the Ministry of Agriculture of China. Up to now, the Opuntia Milpa Alta has been cultivated on a certain scale in China. This study aims to identify the research progress and development trends of Opuntia Milpa Alta in China. Papers published between 1998 to 2019 from two major Chinese academic databases (CNKI and Wangfang) with a topic search related to Opuntia Milpa Alta were collected. The research progress and development trends were analyzed based on CiteSpace software of text mining and visualization. The analysis found that Opuntia Milpa Alta has gone through three obvious research phases after being introduced to China. In the first phase, the researchers paid attention to its cultivation method. Subsequently, researchers began to use extraction methods to extract some of its components, such as polysaccharides and flavonoids. Finally, these extracted ingredients began to be used in some biomedical research.
C1 [Chen, Meixiang; Yang, Hong; Song, Zhengda; Gu, Yonghua; Zheng, Yuhong] Jiangsu Prov & Chinese Acad Sci, Inst Bot, Nanjing Bot GardenMem Sun Yat Sen, Nanjing 210014, Peoples R China.
   [Zheng, Yuhong] Jiangsu Prov Platform Conservat & Utilizat Agr Ge, Nanjing 210014, Peoples R China.
   [Zhu, Jiangwei] Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Peoples R China.
   [Wang, Aiwu] Shenzhen Technol Univ, Ctr Adv Mat Diagnost Technol, Shenzhen 518118, Peoples R China.
   [Fu, Li] Hangzhou Dianzi Univ, Coll Mat & Environm Engn, Key Lab Novel Mat Sensor Zhejiang Prov, Hangzhou 310018, Peoples R China.
RP Gu, YH (通讯作者)，Jiangsu Prov & Chinese Acad Sci, Inst Bot, Nanjing Bot GardenMem Sun Yat Sen, Nanjing 210014, Peoples R China.; Fu, L (通讯作者)，Hangzhou Dianzi Univ, Coll Mat & Environm Engn, Key Lab Novel Mat Sensor Zhejiang Prov, Hangzhou 310018, Peoples R China.
EM g_yhua@aliyun.com; fuli@hdu.edu.cn
RI Fu, Li/AAH-4689-2020
OI Fu, Li/0000-0002-5957-7790
FU National Natural Science Foundation of China [22004026]; Guangdong
   Provincial Department of Education Young Innovative Talents Project
   [2019KQNCX179]; North Jiangsu Science and Technology Special Project
   [SZ-SQ2017006]
FX This research was supported by the National Natural Science Foundation
   of China (22004026), Guangdong Provincial Department of Education Young
   Innovative Talents Project (2019KQNCX179) and North Jiangsu Science and
   Technology Special Project (SZ-SQ2017006).
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NR 55
TC 8
Z9 8
U1 16
U2 77
PU TECH SCIENCE PRESS
PI HENDERSON
PA 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA
SN 0031-9457
EI 1851-5657
J9 PHYTON-INT J EXP BOT
JI Phyton-Int. J. Exp. Bot.
PD NOV 5
PY 2021
VL 90
IS 5
BP 1507
EP 1518
DI 10.32604/phyton.2021.015723
PG 12
WC Plant Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Plant Sciences
GA SK9DV
UT WOS:000656519900008
OA gold
DA 2022-08-23
ER

PT J
AU Wang, MX
   Liu, P
   Zhang, R
   Li, Z
   Li, X
AF Wang, Minxi
   Liu, Ping
   Zhang, Rui
   Li, Zhi
   Li, Xin
TI A Scientometric Analysis of Global Health Research
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE global health; public health; scientometric study; knowledge map;
   visualization analysis; CiteSpace
ID SYSTEMATIC ANALYSIS; GOOGLE-SCHOLAR; RISK-FACTORS; SCIENCE; DISEASE;
   BURDEN; PERSPECTIVES; COCITATION; EDUCATION; CLUSTERS
AB With the development and deepening of the process of global integration, global health is gaining increasing attention. An increasing number of studies have examined global health from diverse perspectives to promote the realization of global public health. The purpose of this research is to systematically and comprehensively evaluate the knowledge structure, knowledge domain, and evolution trend in the field of global health research. Based on the 14,692 document data retrieved from Web of Science Core Collection from 1996 to 2019, this article carried out a visual analysis of global health research from the perspective of scientific output characteristics, scientific research cooperation networks, keywords, and highly cited literature. The results show that scholars' interest in global health research is increasing, especially after the outbreak of SARS. USA, England, Canada, Australia, and China have the most prominent contributions to global health research. Significant authors, high impact journals and core institutions also identified. The study found that "global health governance", "global health diplomacy", "medical education", "global health education" and "antimicrobial resistance" are the research frontiers and hot spots. This study provides an overview and valuable guidance for researchers and related personnel to find the research direction and practice of global health.
C1 [Wang, Minxi; Liu, Ping; Zhang, Rui; Li, Zhi; Li, Xin] Chengdu Univ Technol, Coll Management Sci, Chengdu 60059, Peoples R China.
RP Li, X (通讯作者)，Chengdu Univ Technol, Coll Management Sci, Chengdu 60059, Peoples R China.
EM wangminxi@mail.cdut.edu.cn; liupingcdu@163.com; ruizhang033@163.com;
   leannlee0618@gmail.com; lixin2012@cdut.cn
OI li, xin/0000-0002-2939-4811
FU Special Projects of Public Health Emergency Management for Chengdu
   University of Technology
FX This work was supported by the Special Projects of Public Health
   Emergency Management for Chengdu University of Technology in 2020.
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NR 56
TC 14
Z9 14
U1 12
U2 33
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD APR
PY 2020
VL 17
IS 8
AR 2963
DI 10.3390/ijerph17082963
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 LR5OL
UT WOS:000535744100348
PM 32344668
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Cheng, B
   Zhu, J
   Guo, MM
AF Cheng, Bo
   Zhu, Jia
   Guo, Meimei
TI MultiJAF: Multi-modal joint entity alignment framework for multi-modal
   knowledge graph
SO NEUROCOMPUTING
LA English
DT Article
DE Entity alignment; Multi-modal fusion; Multi-modal knowledge graph
ID NATURAL-LANGUAGE GENERATION; FEATURES
AB Entity Alignment (EA) is a crucial task in knowledge fusion, which aims to link entities with the same real-world identity from different Knowledge Graphs (KGs). Existing methods have achieved satisfactory performance, however, they mainly focus on single modal KG, which is difficult to be effectively applied to multi-modal scenes. In this paper, we propose a Multi-modal Joint entity Alignment Framework (MultijAF), which can effectively utilize the knowledge of various modalities. Concretely, we first learn the embeddings of different modalities, i.e., structure, attribute and image modalities. Next, we adopt an attention-based multi-modal fusion network to integrate these embeddings and use obtained joint embeddings to compute a joint embedding-based similarity matrix S-J. Moreover, we design a Numerical Process Module (NPM) to infer a similarity matrix S-N according to the numerical information of entities. In the end, we utilize a simple late fusion method to ensemble two similarity matrices for the final alignment. In addition, to reduce the cost of labeling data, we propose a novel NPM-based unsupervised multi-modal EA method. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed MultijAF. (C) 2022 Elsevier B.V. All rights reserved.
C1 [Cheng, Bo; Guo, Meimei] South China Normal Univ, Sch Comp Sci, Shipai Campus, Guangzhou 510631, Guangdong, Peoples R China.
   [Zhu, Jia] Zhejiang Normal Univ, Key Lab Intelligent Educ Technol & Applicat Zheji, Jinhua 321004, Zhejiang, Peoples R China.
RP Zhu, J (通讯作者)，Zhejiang Normal Univ, Key Lab Intelligent Educ Technol & Applicat Zheji, Jinhua 321004, Zhejiang, Peoples R China.
EM jiazhu@zjnu.edu.cn
FU Key Laboratory of Intelligent Education Technology and Application of
   Zhejiang Province [62077015]; National Natural Science Foundation of
   China
FX This work was supported by the Key Laboratory of Intelligent Education
   Technology and Application of Zhejiang Province and the National Natural
   Science Foundation of China under Grant 62077015.
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NR 62
TC 0
Z9 0
U1 12
U2 12
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD AUG 21
PY 2022
VL 500
BP 581
EP 591
DI 10.1016/j.neucom.2022.05.058
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2B2EG
UT WOS:000810004900015
DA 2022-08-23
ER

PT J
AU Huang, WT
   Mao, YY
   Yang, L
   Yang, Z
   Long, J
AF Huang, Wenti
   Mao, Yiyu
   Yang, Liu
   Yang, Zhan
   Long, Jun
TI Local-to-global GCN with knowledge-aware representation for distantly
   supervised relation extraction
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Relation extraction; Knowledge graph; Self-attention; Graph
   convolutional network
AB Distantly supervised relation extraction is a prevalent technique for identifying semantic relations between two entities. Most prior models cannot distinguish the local and global information in the long-range dependency among words effectively. The latent semantic information presented in existing knowledge graphs is completely neglected, as the knowledge graph information is simply used as a label to specify the class of relations instead of being treated as a graph. Moreover, previous studies only utilized a selective attention mechanism over sentences to alleviate the impact of noise; they did not consider the implicit interaction between sentences in a sentence bag. In this paper, (1) we propose a knowledge-aware framework to enhance word representations, which can highlight the importance of key words and relation clues; (2) we adopt a piecewise self-attention mechanism to model long-range dependency among words; (3) we design a heterogeneous graph structure for each sentence bag and introduce a heterogeneous graph convolutional network with knowledge attention to aggregate the implicit interaction among sentences from a local-to-global perspective. Experimental results on two popular relation extraction datasets demonstrate that our model obtains more discriminative relation representations and outperforms most of the state-of-the-art models. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Huang, Wenti] Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan 411100, Peoples R China.
   [Yang, Liu] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
   [Yang, Zhan] Cent South Univ, Big Data Inst, Changsha 410083, Peoples R China.
   [Huang, Wenti; Long, Jun] Cent South Univ, Network Resources Management & Trust Evaluat Key, Changsha 410083, Peoples R China.
   [Mao, Yiyu] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China.
RP Long, J (通讯作者)，Cent South Univ, Big Data Inst, Changsha 410083, Peoples R China.; Long, J (通讯作者)，Cent South Univ, Network Resources Management & Trust Evaluat Key, Changsha 410083, Peoples R China.
EM huangwenti@csu.edu.cn; yiyumao@csu.edu.cn; yangliu@csu.edu.cn;
   zyang22@csu.edu.cn; junlong@csu.edu.cn
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NR 51
TC 4
Z9 4
U1 7
U2 18
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD DEC 25
PY 2021
VL 234
AR 107565
DI 10.1016/j.knosys.2021.107565
EA OCT 2021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WO5AI
UT WOS:000712466600021
DA 2022-08-23
ER

PT J
AU Shi, MY
AF Shi, Miaoyuan
TI Knowledge Graph Question and Answer System for Mechanical Intelligent
   Manufacturing Based on Deep Learning
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID PERFORMANCE
AB With the development of deep learning and its wide application in the field of natural language, the question and answer research of knowledge graph based on deep learning has gradually become the focus of attention. After that, the natural language query is converted into a structured query sentence to identify the entities and attributes in the user's natural language query and the specified entities and attributes are used to retrieve answers to the knowledge graph. Using the advantage of deep learning in capturing sentence information, it incorporates the attention mechanism to obtain the semantic vector of the relevant attributes in the query and uses the parameter sharing mechanism to insert candidate attributes into the triple in the same model to obtain the semantic vector of typical candidates. The experiment measured that under the 100,000 RDF dataset, the single entity query of the MIQE model does not exceed 3 seconds, and the connection query does not exceed 5 seconds. Under the one-million RDF dataset, the single entity query of the MIQE model does not exceed 8 seconds, and the connection query will not be more than 10 seconds. Experimental data show that the system of knowledge-answering questions of engineering of intelligent construction based on deep learning has good horizontal scalability.
C1 [Shi, Miaoyuan] Suzhou AI Speech Co Ltd, R&D Dept, Suzhou 215009, Jiangsu, Peoples R China.
RP Shi, MY (通讯作者)，Suzhou AI Speech Co Ltd, R&D Dept, Suzhou 215009, Jiangsu, Peoples R China.
EM goldbook@163.com
FU Scientific Project Program of Suzhou City [SYN201511]
FX This work was supported by the Scientific Project Program of Suzhou City
   (no. SYN201511).
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NR 29
TC 6
Z9 6
U1 6
U2 20
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD FEB 16
PY 2021
VL 2021
AR 6627114
DI 10.1155/2021/6627114
PG 8
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA QR7DO
UT WOS:000625375700004
OA gold
DA 2022-08-23
ER

PT J
AU He, M
   Wang, B
   Du, XK
AF He, Ming
   Wang, Bo
   Du, Xiangkun
TI HI2Rec: Exploring Knowledge in Heterogeneous Information for Movie
   Recommendation
SO IEEE ACCESS
LA English
DT Article
DE Recommender systems; knowledge graph; knowledge representation learning
AB Online movies' recommender systems aim to address the information explosion of movies and make the personalized recommendation for users. Recently, knowledge graphs have been proven to be highly effective to recommender systems, because they are able to fuse various recommendation models and can handle the issues of data sparsity and cold start to improve recommendation performance. However, less consideration is given to the information about the user's properties than the item's properties in the existing knowledge graph recommendation methods, which leads to some limitations in the recommendation results. In this paper, we propose HI2Rec, which integrates multiple information to learn the user's and item's vector representations for top-N recommendation to address the above-mentioned issues. We extract the movie-related information from the Linked Open Data and then leverage the knowledge representation learning approach to embed this information as well as real-world datasets' information of recommender systems to a unified vector space. These vector representations are further calculated to generate a preliminary recommendation list. Finally, we utilize a collaborative filter approach to generate a precision recommendation list. The experimental results on the real-world datasets demonstrate that HI2Rec gives substantial performance improvements against the state-of-the-art recommendation models.
C1 [He, Ming; Wang, Bo; Du, Xiangkun] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China.
RP He, M (通讯作者)，Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China.
EM heming@bjut.edu.cn
OI wang, bo/0000-0001-8826-9968; Du, Xiangkun/0000-0002-6495-3893
FU Beijing Natural Science Foundation [4192008]; General Project of the
   Beijing Municipal Education Commission [KM201710005023]
FX This work was supported in part by the Beijing Natural Science
   Foundation under Grant 4192008, and in part by the General Project of
   the Beijing Municipal Education Commission under Grant KM201710005023.
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NR 30
TC 7
Z9 9
U1 1
U2 13
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 30276
EP 30284
DI 10.1109/ACCESS.2019.2902398
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HQ5SS
UT WOS:000462473500001
OA gold
DA 2022-08-23
ER

PT J
AU Yan, ZH
   Peng, R
   Wang, YQ
   Li, WD
AF Yan, Zhihuan
   Peng, Rong
   Wang, Yaqian
   Li, Weidong
TI Soft-self and Hard-cross Graph Attention Network for Knowledge Graph
   Entity Alignment
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Entity alignment; Four-channels graph attention layer; Soft-self
   attention; Hard-cross attention
AB Knowledge Graph (KG) entity alignment aims to identify entities across different KGs that refer to the same real world object, and it is the key step towards KG integration and KG complement. Recently, Graph Attention Network (GAT) based models become a popular paradigm in entity alignment community owing to its ability in modeling structural data. But current GAT based models either ignore relation semantics and edge directions when learning entity neighbor representations or make no distinction between incoming neighbors and outgoing neighbors when calculating their attention scores. Furthermore, softmax functions utilized in soft attention mechanisms of current models always assign small but nonzero probabilities to trivial elements, which is unsuitable for learning alignment oriented entity embeddings. Taking these issues into account, this paper proposes a novel GAT based entity alignment model SHEA (Soft-self and Hard-cross Graph Attention Networks for Knowledge Graph Entity Alignment), which takes both relation semantics and edge directions into consideration when modeling single KG, and distinguishes prior aligned neighbors from the general ones to take full advantage of prior aligned information. Specifically, a type of four-channels graph attention layer is conceived to aggregate information from entity neighbors in different cases. The first two channels teach entities to aggregate information from their neighbors with soft-self attention, where both neighboring entities and the linked relations are used to obtain attention values. The other two channels teach entities to aggregate information from their neighbors with hard-cross graph attention, where tf _idf(1) is utilized to measure the importance of entity neighbors. Extensive experiments on five publicly available datasets demonstrate our superior performances. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Yan, Zhihuan; Peng, Rong; Wang, Yaqian; Li, Weidong] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
RP Peng, R (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
EM zhihuanyan@whu.edu.cn; rongpeng@whu.edu.cn; yaqian@whu.edu.cn;
   weidonghappy@whu.edu.cn
OI Peng, Rong/0000-0003-2330-9409
FU National Key Research and Development Plan of China [2017YFB0503702,
   2016YFB0501801]; National Natural Science Foundation of China [61170026]
FX This work is supported by the National Key Research and Development Plan
   of China under Grant No. 2017YFB0503702, 2016YFB0501801, and National
   Natural Science Foundation of China under Grant No. 61170026.
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U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD NOV 14
PY 2021
VL 231
AR 107415
DI 10.1016/j.knosys.2021.107415
EA SEP 2021
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WC1EB
UT WOS:000704005800001
DA 2022-08-23
ER

PT J
AU Guo, L
   Yan, F
   Li, T
   Yang, T
   Lu, YQ
AF Guo, Liang
   Yan, Fu
   Li, Tian
   Yang, Tao
   Lu, Yuqian
TI An automatic method for constructing machining process knowledge base
   from knowledge graph
SO ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
LA English
DT Article
DE Process Knowledge Base; Knowledge Graph; Fuzzy Evaluation; NLP
ID DESIGN RATIONALE; ONTOLOGY; BEHAVIOR; INTEGRATION; MODEL; REUSE
AB The process knowledge base is the key module in intelligent process design, it determines the intelligence degree of the design system and affects the quality of product design. However, traditional process knowledge base construction is non-automated, time consuming and requires much manual work, which is not sufficient to meet the demands of the modern manufacturing mode. Moreover, the knowledge base often adopts a single knowledge representation, and this may lead to ambiguity in the meaning of some knowledge, which will affect the quality of the process knowledge base. To overcome the above problems, an automatic construction framework for the process knowledge base in the field of machining based on knowledge graph (KG) is introduced. First, the knowledge is classified and annotated based on the function-behavior-states (FBS) design method. Second, a knowledge extraction framework based on BERT-BiLSTM-CRF is established to perform the automatic knowledge extraction of process text. Third, a knowledge representation method based on fuzzy comprehensive evaluation is established, forming three types of knowledge representation with the KG as the main, production rules and twodimensional data linked list as a supplement. In addition, to overcome the redundancy in the knowledge fusion stage, a hybrid algorithm based on an improved edit distance and attribute weighting is built. Finally, a prototype system is developed, and quality analysis is carried out. Compared with the F values of BiLSTM-CRF and CNN-BiLSTM-CRF, that of the proposed extraction method in the machining domain is increased by 7.35% and 3.87%, respectively.
C1 [Guo, Liang; Yan, Fu; Yang, Tao] Southwest Petr Univ, Sch Mechatron Engn, Chengdu, Peoples R China.
   [Li, Tian] AECC Chengdu Engine Co Ltd, Chengdu 610503, Peoples R China.
   [Lu, Yuqian] Univ Auckland, Dept Mech Engn, Auckland, New Zealand.
RP Guo, L (通讯作者)，Southwest Petr Univ, Sch Mechatron Engn, Chengdu, Peoples R China.
EM gl@swpu.edu.cn
RI Lu, Yuqian/U-6820-2019
OI Lu, Yuqian/0000-0001-5954-0421
FU Youth Science Foundation of the National Natural Science Foundation
   [51705438]; Sichuan Science and Technology Project [2018JY0366]; Young
   Science and Technology Innovation Team of SWPU [2019CXTD02]; Independent
   Innovation Special Fund Project, AECC [ZZCX-2017-039]; Chengdu
   International Science and Technology Cooperation Project
   [2020-GH02-00040-HZ]
FX We would like to thank the reviewers and the editor for their
   constructive comments and suggestions regarding this paper. We also
   acknowledge the Youth Science Foundation of the National Natural Science
   Foundation (No. 51705438) , the Sichuan Science and Technology Project
   (No. 2018JY0366) , the Young Science and Technology Innovation Team of
   SWPU (No. 2019CXTD02) , the Independent Innovation Special Fund Project,
   AECC (No. ZZCX-2017-039) and Chengdu International Science and
   Technology Cooperation Project (No:2020-GH02-00040-HZ) .
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EI 1879-2537
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GA WA3AX
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ER

PT J
AU Tiwari, S
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   Gaurav, D
AF Tiwari, Sanju
   Al-Aswadi, Fatima N.
   Gaurav, Devottam
TI Recent trends in knowledge graphs: theory and practice
SO SOFT COMPUTING
LA English
DT Article
DE Knowledge graphs; Knowledge extraction; Learning techniques; Reasoning
AB With the extensive growth of data that has been joined with the thriving development of the Internet in this century, finding or getting valuable information and knowledge from these huge noisy data became harder. The Concept of Knowledge Graph (KG) is one of the concepts that has come into the public view as a result of this development. In addition, with that thriving development especially in the last two decades, the need to process and extract valuable information in a more efficient way is increased. KG presents a common framework for knowledge representation, based on the analysis and extraction of entities and relationships. Techniques for KG construction can extract information from either structured, unstructured or even semi-structured data sources, and finally organize the information into knowledge, represented in a graph. This paper presents a characterization of different types of KGs along with their construction approaches. It reviews the existing academia, industry and expert KG systems and discusses in detail about the features of it. A systematic review methodology has been followed to conduct the review. Several databases (Scopus, GS, WoS) and journals (SWJ, Applied Ontology, JWS) are analysed to collect the relevant study and filtered by using inclusion and exclusion criteria. This review includes the state-of-the-art, literature review, characterization of KGs, and the knowledge extraction techniques of KGs. In addition, this paper overviews the current KG applications, problems, and challenges as well as discuss the perspective of future research. The main aim of this paper is to analyse all existing KGs with their features, techniques, applications, problems, and challenges. To the best of our knowledge, such a characterization table among these most commonly used KGs has not been presented earlier.
C1 [Tiwari, Sanju] Univ Autonoma Tamaulipas, Ciudad Victoria, Tamaulipas, Mexico.
   [Al-Aswadi, Fatima N.] Hodeidah Univ, Fac Comp Sci & Engn, Hodeidah, Yemen.
   [Gaurav, Devottam] Indian Inst Technol, Dept Comp Sci & Engn, Delhi, India.
RP Tiwari, S (通讯作者)，Univ Autonoma Tamaulipas, Ciudad Victoria, Tamaulipas, Mexico.
EM tiwarisanju18@ieee.org; fatima7aswadi@gmail.com; gauravpurusho@gmail.com
RI Al-Aswadi, Fatima/AAG-8319-2020
OI Al-Aswadi, Fatima/0000-0001-5413-1207
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TC 6
Z9 7
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U2 55
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 JUL
PY 2021
VL 25
IS 13
BP 8337
EP 8355
DI 10.1007/s00500-021-05756-8
EA APR 2021
PG 19
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SS3AJ
UT WOS:000640730500003
DA 2022-08-23
ER

PT J
AU Jin, GM
   He, LF
   Tsai, SB
AF Jin, Gaimin
   He, Lifang
   Tsai, Sang-Bing
TI An Empirical Study on Virtual English Teaching System Based on the
   Microservice Architecture with Wireless Internet Sensor Network
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID DESIGN
AB We design in this paper a virtual English teaching system based on a wireless sensor network using the features of microservice architecture such as low coupling, lightweight, and high autonomy. This paper explains the characteristics of microservice architecture, uses the wireless sensor network as a carrier, uses the technology provided by Spring Cloud ecology to practice microservices, and makes a specific design and implementation of microservice architecture. The system allows teachers and students to upload and download English course resources, adopts the knowledge map to classify and manage course resources, realizes the diversified search and sorting function of course resources, greatly facilitates users to query and manage course resources, and improves the utilization rate. The internal functional modules of the platform are reasonably divided with the microservice design principle and are developed and deployed independently. For the security of the system, the authentication mechanism of the device and the authentication and authentication strategy of the application interface are designed, respectively. Finally, functional and nonfunctional tests were conducted on the system through the built test environment. The test results show that the virtual English teaching system designed and implemented in this paper has good feasibility and scalability. Through the research of this paper, it brings great help to the wireless sensor network virtual English teaching system and also improves the learning efficiency of students.
C1 [Jin, Gaimin; He, Lifang] Shi Jia Zhuang Univ Appl Technol, Shijiazhuang 050081, Hebei, Peoples R China.
   [Tsai, Sang-Bing] Wuyi Univ, Reg Green Econ Dev Res Ctr, Sch Business, Nanping, Peoples R China.
RP Jin, GM (通讯作者)，Shi Jia Zhuang Univ Appl Technol, Shijiazhuang 050081, Hebei, Peoples R China.; Tsai, SB (通讯作者)，Wuyi Univ, Reg Green Econ Dev Res Ctr, Sch Business, Nanping, Peoples R China.
EM schoen_jin@126.com; sangbing@hotmail.com
OI Tsai, Sang-Bing/0000-0001-6988-5829
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NR 23
TC 0
Z9 0
U1 1
U2 5
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD SEP 2
PY 2021
VL 2021
AR 8494410
DI 10.1155/2021/8494410
PG 10
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA UP4ZW
UT WOS:000695391200004
OA gold
DA 2022-08-23
ER

PT J
AU Amiri, R
   Elkeelany, O
AF Amiri, Rami
   Elkeelany, Omar
TI FPGA Design of Elliptic Curve Cryptosystem (ECC) for Isomorphic
   Transformation and EC ElGamal Encryption
SO IEEE EMBEDDED SYSTEMS LETTERS
LA English
DT Article
DE ELGamal; cryptography; isomorphic transformation; Koblitz curve
AB Attacking or tampering with sensitive data continues to increase risks to economic processes or human activities. These risks are significant key factors to improve the development and implementation of security systems. Therefore, improving cryptography is essentially needed to enhance the security of critical data. For example, elliptic curve cryptography (ECC) over the Galois field GF(2163) is one of the public-key (asymmetric) cryptographic techniques, in which demands mapping a message (163-bit) to a point in the prime subgroup of the elliptic curve. To the best of our knowledge, mapping methods are not yet available on Field-Programmable Gate Arrays (FPGAs). Also, asymmetric encryption schemes often do not consider encrypting/decrypting data packets because of their computation complexity and performance limitations. In this letter, we propose and develop a concurrent reconfigurable cryptosystem to encrypt and decrypt stream of data using ECC on FPGA. First, we present hardware design and implementation to map a plain message on the elliptic curve based on isomorphic transformation, then second, we architect the elliptic curve ElGamal public-key encryption method by using point addition and multiplication on Koblitz elliptic curve on FPGA. Our proposed cryptosystem is synthesized and implemented on Intel Cyclone 10 GX and Xilinx Kintex-7 FPGAs to evaluate throughput, and it achieves 25.73-57.1 Mbps.
C1 [Amiri, Rami] St Cloud State Univ, Elect & Comp Engn Dept, St Cloud, MN 56304 USA.
   [Elkeelany, Omar] Tennessee Technol Univ, ECE Dept, Cookeville, TN 38505 USA.
RP Amiri, R (通讯作者)，St Cloud State Univ, Elect & Comp Engn Dept, St Cloud, MN 56304 USA.
EM ralami9791@gmail.com
OI Alkelany, Omer/0000-0002-6366-4352; Amiri, Rami/0000-0003-2404-5003
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PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1943-0663
EI 1943-0671
J9 IEEE EMBED SYST LETT
JI IEEE Embed. Syst. Lett.
PD JUN
PY 2021
VL 13
IS 2
BP 65
EP 68
DI 10.1109/LES.2020.3003978
PG 4
WC Computer Science, Hardware & Architecture; Computer Science, Software
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WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA SJ0UJ
UT WOS:000655243600009
DA 2022-08-23
ER

PT J
AU Zhou, XC
   Kraft, M
AF Zhou, Xiaochi
   Kraft, Markus
TI Blockchain Technology in the Chemical Industry
SO ANNUAL REVIEW OF CHEMICAL AND BIOMOLECULAR ENGINEERING
LA English
DT Review
DE blockchain; smart contract; chemical industry; Internet of Things;
   knowledge graph
ID OF-THE-ART; SMART CONTRACT; KNOWLEDGE GRAPH; ENERGY; SYSTEM; MANAGEMENT;
   STATE; POWER; CITY; MICROGRIDS
AB This article presents a review of the application of blockchain and blockchain-based smart contracts in the chemical and related industries. We introduce the basic concepts of blockchain and smart contracts and explain how some of their features are enabled. We review several typical or novel applications of blockchain and smart contract technologies and their enabling concepts and underlying technologies. We classify the selected literature into five categories and discuss their motivations and technical designs. We recognize that the trend of decentralization creates a need to use blockchain and smart contracts to implement trust and distributed control mechanisms. We also speculate on future applications of blockchain and smart contracts. We believe that, in the future, blockchains with different consensus mechanisms will be studied and applied to achieve more efficient and practical decentralized systems. Also, blockchain-based smart contracts will be more widely applied to enhance autonomous distributed controls in decentralized systems.
C1 [Zhou, Xiaochi; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge, England.
   [Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore CARES, Singapore, Singapore.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore, Singapore.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge, England.; Kraft, M (通讯作者)，Cambridge Ctr Adv Res & Educ Singapore CARES, Singapore, Singapore.; Kraft, M (通讯作者)，Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore, Singapore.
EM mk306@cam.ac.uk
FU CMCL Innovations; National Research Foundation, Prime Minister's Office,
   Singapore, under its Campus for Research Excellence and Technological
   Enterprise (CREATE) program; Alexander von Humboldt Foundation
FX The writing of this review was supported by CMCL Innovations and the
   National Research Foundation, Prime Minister's Office, Singapore, under
   its Campus for Research Excellence and Technological Enterprise (CREATE)
   program. M.K. acknowledges the support of the Alexander von Humboldt
   Foundation. The authors also thank Andrew Charles Breeson for
   proofreading and figure preparation.
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NR 133
TC 0
Z9 0
U1 3
U2 3
PU ANNUAL REVIEWS
PI PALO ALTO
PA 4139 EL CAMINO WAY, PO BOX 10139, PALO ALTO, CA 94303-0139 USA
SN 1947-5438
EI 1947-5446
J9 ANNU REV CHEM BIOMOL
JI Annu. Rev. Chem. Biomol. Engineer.
PY 2022
VL 13
BP 347
EP 371
DI 10.1146/annurev-chembioeng-092120-022935
PG 25
WC Chemistry, Applied; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering
GA 2O6MV
UT WOS:000819171900017
PM 35363506
DA 2022-08-23
ER

PT J
AU Khan, N
   Ma, ZM
   Ullah, A
   Polat, K
AF Khan, Nasrullah
   Ma, Zongmin
   Ullah, Aman
   Polat, Kemal
TI Categorization of knowledge graph based recommendation methods and
   benchmark datasets from the perspectives of application scenarios: A
   comprehensive survey
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Review
DE Categorization; Knowledge graph; Side information; Recommendation
   methods; Benchmark datasets; Knowledge repositories
ID LARGE-SCALE; DBPEDIA; SYSTEMS; EXTRACTION; ENTITY
AB Recommender Systems (RS) are established to deal with the preferences of users to enhance their experience and interest in innumerable online applications by streamlining the stress persuaded by the reception of excessive information through the recommendation methods. Although researches have put a lot of efforts in making recommendation processes accurate, specific, and personalized; different issues like cold start, data sparsity or gray sheep etc., still pop up in one or the other form of challenges. Recently, exploitation of Knowledge Graph (KG)-based data as Side Information in recommendation methods has revealed as a sign of resolution to the corresponding challenges; and thus, acquired incredible focus, applicability, and popularity. The incorporation of KG in recommendation has not only effectively alleviated the contrasting challenges, but also has provided specific, accurate, personalized and explainable recommendations about the target items to the end users. In this paper, we explore well-known RSs, popular knowledge repositories, benchmark datasets, recommendation methods, and future research dimensions about the current research. Intuitively, we investigate recommendation methods and associated datasets with respect to the corresponding application scenarios in a categorical way.
C1 [Khan, Nasrullah; Ma, Zongmin] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China.
   [Ma, Zongmin] Collaborat Innovat Ctr Novel Software Technol & In, Nanjing 210000, Peoples R China.
   [Ullah, Aman] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
   [Polat, Kemal] Abant Izzet Baysal Univ, Dept Elect & Elect Engn, TR-14280 Bolu, Turkey.
   [Khan, Nasrullah] COMSATS Univ Islamabad, Dept Comp Sci, Vehari Campus, Vehari 61100, Pakistan.
   [Ma, Zongmin] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing, Peoples R China.
RP Ma, ZM (通讯作者)，Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing, Peoples R China.
EM nasrullah@nuaa.edu.cn; zongminma@nuaa.edu.cn; dr.aman@csu.edu.cn
FU Basic Research Program of Jiangsu Province [BK20191274]; National
   Natural Science Foundation of China [62176121, 61772269]
FX Acknowledgements The work was supported in part by the Basic Research
   Program of Jiangsu Province (BK20191274) and the National Natural
   Science Foundation of China (62176121 and 61772269) .
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NR 242
TC 0
Z9 0
U1 1
U2 1
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 2022
VL 206
AR 117737
DI 10.1016/j.eswa.2022.117737
PG 28
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 3M0GP
UT WOS:000835136700004
DA 2022-08-23
ER

PT J
AU Fu, C
   Liu, JH
   Wang, SD
AF Fu, Chao
   Liu, Jihong
   Wang, Shude
TI Building SysML Model Graph to Support the System Model Reuse
SO IEEE ACCESS
LA English
DT Article
DE Modeling; Unified modeling language; Object oriented modeling;
   Analytical models; Systems engineering and theory; Knowledge
   engineering; Semantics; Model based system engineering; SysML; system
   model reuse; knowledge graph
ID KNOWLEDGE
AB With the implementation and application of MBSE (Model Based System Engineering) in enterprises, a vast number of system model files is constantly and increasingly generated. The problem of how to acquire the knowledge carried by the system model and reuse the system model is urgently required to be solved. Therefore, a cross-products system model graph construction method supporting system model reuse is proposed. System model graph is constructed by defining the SysML metamodel ontology and the construction strategy is given. The entity alignment based on the relationship set and the rule-based reasoning is applied to achieve the fusion of multiple subgraphs, and the semantic extended retrieval of design requirements is realized based on this graph. Finally, the case studies of the Automatic Climate Control Integrated System of car and satellite design verify the effectiveness and practicability of the method, supporting system model reuse. In conclusion, the paper indicates that it is feasible to express the system model with knowledge graph and the constructed system model knowledge graph can well support the retrieval and reuse of cross- products system models.
C1 [Fu, Chao; Liu, Jihong; Wang, Shude] Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China.
RP Liu, JH (通讯作者)，Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China.
EM ryukeiko@buaa.edu.cn
FU National Key Research and Development Program of China [2018YFB1701702];
   Basic Research Program for National Defense of China [JCKY2018204B010]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2018YFB1701702, and in part by
   the Basic Research Program for National Defense of China under Grant
   JCKY2018204B010.
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NR 29
TC 0
Z9 0
U1 3
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 132374
EP 132389
DI 10.1109/ACCESS.2021.3115165
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA UZ9TA
UT WOS:000702539800001
OA gold
DA 2022-08-23
ER

PT J
AU Zhou, J
   Huang, JX
   Hu, QV
   He, L
AF Zhou, Jie
   Huang, Jimmy Xiangji
   Hu, Qinmin Vivian
   He, Liang
TI SK-GCN: Modeling Syntax and Knowledge via Graph Convolutional Network
   for aspect-level sentiment classification
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Aspect-level; Sentiment analysis; Graph Convolutional Network (GCN);
   Commonsense knowledge graph
AB Aspect-level sentiment classification is a fundamental subtask of fine-grained sentiment analysis. The syntactic information and commonsense knowledge are important and useful for aspect-level sentiment classification, while only a limited number of studies have explored to incorporate them via flexible graph convolutional neural networks (GCN) for this task. In this paper, we propose a new Syntax- and Knowledge-based Graph Convolutional Network (SK-GCN) model for aspect-level sentiment classification, which leverages the syntactic dependency tree and commonsense knowledge via GCN. In particular, to enhance the representation of the sentence toward the given aspect, we develop two strategies to model the syntactic dependency tree and commonsense knowledge graph, namely SK-GCN(1) and SK-GCN(2) respectively. SK-GCN(1) models the dependency tree and knowledge graph via Syntax-based GCN (S-GCN) and Knowledge-based GCN (K-GCN) independently, and SK-GCN(2) models them jointly. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Extensive experiments on five benchmark datasets demonstrate that our approach can effectively improve the performance of aspect-level sentiment classification compared with the state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Zhou, Jie; He, Liang] East China Normal Univ, Shanghai Key Lab Multidimens Informat Proc, Shanghai 200241, Peoples R China.
   [Zhou, Jie; He, Liang] East China Normal Univ, Dept Comp Sci & Technol, Shanghai 200241, Peoples R China.
   [Huang, Jimmy Xiangji] York Univ, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON M3J 1P3, Canada.
   [Hu, Qinmin Vivian] Ryerson Univ, Sch Comp Sci, Toronto, ON M5B 2K3, Canada.
RP He, L (通讯作者)，East China Normal Univ, Dept Comp Sci & Technol, Shanghai 200241, Peoples R China.
EM jzhou@ica.stc.sh.cn; jhuang@yorku.ca; vivian@ryerson.ca;
   lhe@cs.ecnu.edu.cn
RI He, Liang/CAF-0477-2022; Zhou, Jie/AAL-4491-2021
OI Huang, Jimmy Xiangji/0000-0003-1292-1491
FU Natural Sciences and Engineering Research Council (NSERC) of Canada;
   NSERC; York Research Chairs (YRC) program; ORF-RE (Ontario Research
   Fund-Research Excellence) award in BRAIN Alliance; Science and
   Technology Commission of Shanghai Municipality [19511120200]
FX We greatly appreciate anonymous reviewers and the associate editor for
   their valuable and high-quality comments that greatly helped to improve
   the quality of this article. This research is supported by the Natural
   Sciences and Engineering Research Council (NSERC) of Canada, an NSERC
   CREATE award in ADERSIM,8 the York Research Chairs (YRC) program and an
   ORF-RE (Ontario Research Fund-Research Excellence) award in BRAIN
   Alliance.9 This research is also funded by the Science and Technology
   Commission of Shanghai Municipality (19511120200). The computation is
   performed in ECNU Multifunctional Platform for Innovation(001).
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TC 26
Z9 29
U1 27
U2 116
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD OCT 12
PY 2020
VL 205
AR 106292
DI 10.1016/j.knosys.2020.106292
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NK4SB
UT WOS:000566720700005
DA 2022-08-23
ER

PT J
AU Liu, JJ
   Luo, HB
   Liu, HY
AF Liu, Jiajing
   Luo, Hanbin
   Liu, Henry
TI Deep learning-based data analytics for safety in construction
SO AUTOMATION IN CONSTRUCTION
LA English
DT Review
DE Safety; Deep learning; Computer vision; Natural language processing;
   Knowledge graph
ID CONVOLUTIONAL NEURAL-NETWORKS; BEHAVIOR; SYSTEM; CLASSIFICATION;
   MANAGEMENT; INTERNET; THINGS; FALLS
AB Deep learning has been acknowledged as being robust in managing and controlling the performance of construction safety. However, there is an absence of state-of-the-art review that examines its developments and applications from the perspective of data utilization. Our review aims to fill this void and addresses the following research question: what developments in deep learning for data mining have been made to manage safety in construction? We systematically review the extant literature of deep-learning-based data analytics for construction safety management, including: (1) image/video-based; (2) text-based; (3) non-visual sensor-based; and (4) multimodal-based. The review revealed three challenges of existing research in the construction industry: (1) lack of high-quality database; (2) inadequate ability of deep learning models; and (3) limited application scenarios. Based on our observations for the prevailing literature and practice, we identify that future research on safety management is needed and focused on the: (1) development of dynamic multi-modal knowledge graph; and (2) knowledge graph-based decision-making for safety. The application of deep learning is an emerging line of inquiry in construction, and this study not only identifies new research opportunities to support safety management, but also facilitates practicing deep learning for construction projects.
C1 [Liu, Jiajing; Luo, Hanbin] Huazhong Univ Sci & Technol, Natl Ctr Technol Innovat Digital Construction, Wuhan 430074, Peoples R China.
   [Liu, Jiajing; Luo, Hanbin] Huazhong Univ Sci & Technol, Sch Civil Engn & Hydraul Engn, Wuhan 430074, Peoples R China.
   [Liu, Henry] Univ Canberra, Sch Design & Built Environm, 11 Kirinari St, Bruce, ACT 2617, Australia.
RP Luo, HB (通讯作者)，Huazhong Univ Sci & Technol, Natl Ctr Technol Innovat Digital Construction, Wuhan 430074, Peoples R China.; Luo, HB (通讯作者)，Huazhong Univ Sci & Technol, Sch Civil Engn & Hydraul Engn, Wuhan 430074, Peoples R China.
EM luohbcem@hust.edu.cn
FU National Natural Science Foundation of China [51978302, U21A20151,
   71732001]; China Scholarship Council
FX Acknowledgement The authors would like to acknowledge the financial
   support pro-vided by the National Natural Science Foundation of China
   (Grant No. 51978302, U21A20151, 71732001) and the China Scholarship
   Council.
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NR 129
TC 1
Z9 1
U1 27
U2 27
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0926-5805
EI 1872-7891
J9 AUTOMAT CONSTR
JI Autom. Constr.
PD AUG
PY 2022
VL 140
AR 104302
DI 10.1016/j.autcon.2022.104302
PG 12
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA 1S5US
UT WOS:000804117000004
DA 2022-08-23
ER

PT J
AU Chun, S
   Jung, J
   Jin, XN
   Seo, S
   Lee, KH
AF Chun, Sejin
   Jung, Jooik
   Jin, Xiongnan
   Seo, Seungmin
   Lee, Kyong-Ho
TI Designing an integrated knowledge graph for smart energy services
SO JOURNAL OF SUPERCOMPUTING
LA English
DT Article
DE Knowledge graph; Ontology; Microgrid; Smart grid; Decentralized energy
   resource
ID ONTOLOGY
AB The sharp growth of distributed energy-related resources requires an efficient energy management for future grids. The traditional power grid that highly depends on information model standards collects energy data depending on them and creates energy services. Nowadays, decentralized grids utilize information schema generated by reusing standards as well as existing schemas. The schema helps implement smart energy services in the future grids. To meet such requirements, domain experts proposed upper-level schemas that manage a wide range of energy-related knowledge resources. However, their schemas could not conduct an effective reuse of energy-related knowledge resources due to their unsuitable schema development methodologies. Moreover, there is a lack of vocabularies that satisfy critical requirements for decentralized grids. To cope with these problems, we propose an energy knowledge graph (EKG) as an upper schema for the integration of knowledge resources in energy systems. First, we utilize the existing methodology that offers guidelines for reusing existing knowledge resources. Second, EKG supports concepts of energy trading and communities to satisfy the requirements of decentralized grids. Third, EKG presents compliant concepts that are compatible with existing schemas. Fourth, we modeled the use cases using the EKG and evaluated them according to the scenario specification of energy services. Last, to demonstrate the benefits of the EKG, we implemented key components such as semantic mashup and complex event processing.
C1 [Chun, Sejin; Jung, Jooik; Jin, Xiongnan; Seo, Seungmin; Lee, Kyong-Ho] Yonsei Univ, Dept Comp Sci, Seoul, South Korea.
RP Lee, KH (通讯作者)，Yonsei Univ, Dept Comp Sci, Seoul, South Korea.
EM sjchun@icl.yonsei.ac.kr; jijung@icl.yonsei.ac.kr;
   wnkim@icl.yonsei.ac.kr; smseo@icl.yonsei.ac.kr; khlee89@yonsei.ac.kr
OI Jin, Xiongnan/0000-0001-5080-1883
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NR 35
TC 6
Z9 6
U1 6
U2 33
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-8542
EI 1573-0484
J9 J SUPERCOMPUT
JI J. Supercomput.
PD OCT
PY 2020
VL 76
IS 10
SI SI
BP 8058
EP 8085
DI 10.1007/s11227-018-2672-3
PG 28
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA NO0BK
UT WOS:000569152500026
DA 2022-08-23
ER

PT J
AU Han, X
   Zhang, CH
   Guo, CC
   Ji, Y
   Hu, Z
AF Han, Xiao
   Zhang, Chunhong
   Guo, Chenchen
   Ji, Yang
   Hu, Zheng
TI Distributed representation of knowledge graphs with subgraph-aware
   proximity
SO THEORETICAL COMPUTER SCIENCE
LA English
DT Article
DE Knowledge graph; Distributed representation; Subgraph-aware proximity
AB The distributed representation of Knowledge graphs (KGs) aims to embed the original KG into a low-dimensional embedding vector space, so as to facilitate the completion of KGs as well as the application of KGs in other Al fields. Most existing models preserve certain proximity property of KGs in the embedding space, such as the first/second-order proximity and the sequence-aware higher-order proximity. However, the ubiquitous similarity relationship between different sequences has rarely been discussed. In this paper, we propose a unified framework to preserve the subgraph-aware proximity in the embedding space, holding that the sequences within a subgraph generally imply a similar pattern. Especially, according to the composition and structure of KG sequences, we provide three methods for computing the embeddings of KG sequences: 1) Simply adding the involved relations of the KG sequences in a relation subgraph; 2) Recurrent neural network for the KG sequences in a complete subgraph; 3) Dilated recurrent neural network to match the special structure of the KG sequences in a complete subgraph. Empirically, we evaluate the proposed framework on the KG completion tasks of link prediction and entity classification. The results show that our framework outperforms the baselines by preserving the subgraph-aware proximity. Especially, exploring the special structure of KG sequences can further improve the performance. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Han, Xiao; Zhang, Chunhong; Guo, Chenchen; Ji, Yang] Beijing Univ Posts & Telecommun, Key Lab Universal Wireless Commun, Minist Educ, Beijing, Peoples R China.
   [Hu, Zheng] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China.
RP Han, X (通讯作者)，Beijing Univ Posts & Telecommun, Key Lab Universal Wireless Commun, Minist Educ, Beijing, Peoples R China.
EM hanxiao1007@bupt.edu.cn; zhangch@bupt.edu.cn; orangegcc@bupt.edu.cn;
   jiyang@bupt.edu.cn
FU National Natural Science Foundation of China [61602048, 61601046,
   61520106007, 61421016]; BUPT-SICE Excellent Graduate Students Innovation
   Funds; Major Science and Technology Plan Project of Hainan Province
   [ZDKJ201808]
FX This work is supported by National Natural Science Foundation of China,
   Grant 61602048, 61601046, 61520106007, and 61421016. BUPT-SICE Excellent
   Graduate Students Innovation Funds, 2016. Major Science and Technology
   Plan Project of Hainan Province, ZDKJ201808.
CR [Anonymous], [No title captured]
   [Anonymous], ARXIV160307704
   [Anonymous], [No title captured]
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NR 29
TC 3
Z9 5
U1 2
U2 16
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0304-3975
EI 1879-2294
J9 THEOR COMPUT SCI
JI Theor. Comput. Sci.
PD JAN 10
PY 2020
VL 803
BP 48
EP 56
DI 10.1016/j.tcs.2019.03.033
PG 9
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KH0BB
UT WOS:000510312300005
DA 2022-08-23
ER

PT J
AU Duan, Y
   Hou, L
   Leng, S
AF Duan, Yang
   Hou, Li
   Leng, Song
TI A novel cutting tool selection approach based on a metal cutting process
   knowledge graph
SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Metal cutting; Tool selection; Knowledge graph; Personalized PageRank
AB With the wide and deep application of computer technologies in the field of metal cutting, many manufacturing enterprises have accumulated a large volume of data including workpieces, cutting tools, machine tools, processes, and materials. These data are very valuable resources for enterprises. In this paper, a novel tool selection approach is proposed to deeply mine the relationships hidden in these data. First, an ontology model of the metal cutting process is established with OWL (Web Ontology Language), and a metal cutting process knowledge graph (MCPKG) is constructed based on the ontology model. Then, a data model describing the relationship "structural feature-material-cutting tool" is designed to build a subgraph based on the MCPKG. Moreover, the personalized PageRank (PPR) algorithm is employed with the data model to recommend cutting tools for process planning, and the result of an illustrative example is discussed in detail to verify the algorithm. Finally, a cutting tool selection system with B/S (browser/server) structure based on .NET MVC (model view control) is developed. To recommend cutting tools, the presented approach utilizes the connectivity of the MCPKG to score and rank the usage effect of each tool, which is universally applicable to manufacturing enterprises and provides valuable insights into intelligent cutting tool selection.
C1 [Duan, Yang; Hou, Li; Leng, Song] Sichuan Univ, Sch Mech Engn, 24 South Sect 1,Yihuan Rd, Chengdu 610065, Peoples R China.
RP Duan, Y (通讯作者)，Sichuan Univ, Sch Mech Engn, 24 South Sect 1,Yihuan Rd, Chengdu 610065, Peoples R China.
EM duan_yang71@126.com; houli4@163.com; lengsong@scu.edu.cn
OI Duan, Yang/0000-0002-9592-0015
FU Sichuan Province Science and Technology Support Program (CN)
   [2018GZ0117]
FX This study was funded by the Sichuan Province Science and Technology
   Support Program (CN) (grant number 2018GZ0117).
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NR 28
TC 2
Z9 2
U1 12
U2 35
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0268-3768
EI 1433-3015
J9 INT J ADV MANUF TECH
JI Int. J. Adv. Manuf. Technol.
PD FEB
PY 2021
VL 112
IS 11-12
BP 3201
EP 3214
DI 10.1007/s00170-021-06606-5
EA JAN 2021
PG 14
WC Automation & Control Systems; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering
GA QE0GH
UT WOS:000607772700001
DA 2022-08-23
ER

PT J
AU Wu, ZZ
   Deng, KW
   Chen, CH
   Li, H
   Antwi-Afari, MF
   Wang, Y
AF Wu, Zezhou
   Deng, Kaiwen
   Chen, Changhong
   Li, Heng
   Antwi-Afari, Maxwell Fordjour
   Wang, Ying
TI STATUS QUO AND FUTURE TRENDS OF BIM-BASED COORDINATION RESEARCH: A
   CRITICAL REVIEW
SO JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
LA English
DT Review
DE Building Information Modeling (BIM); construction management;
   coordination; network analysis; knowledge map
ID SCIENTOMETRIC ANALYSIS; CONSTRUCTION NETWORKS; COLLABORATIVE DESIGN;
   INFORMATION MODEL; LIFE-CYCLE; MANAGEMENT; IMPLEMENTATION; FRAMEWORK;
   INTERNET; SYSTEM
AB Enhancing coordination between stakeholders is a critical function of Building Information Modeling (BIM). Recently, there have been numerous studies investigating BIM-based coordination; however, no explicit attempt has been made to investigate the current status of relevant research and determine the future directions. Hence, this study examines the BIM-based coordination literature published from 2006 to 2020 through bibliometric literature searching, scientific mapping, and in-depth critical analysis to fill the research gap. After a comprehensive filtering process, 656 pieces of literature were collected from Scopus. To map the representative information in the BIM-based coordination research, the determination and visualization of the most influential scholars, journals, countries/regions, and articles, as well as their importance and relationships, were performed through VOSviewer. Four emerging research topics were further discussed according to the determined scientific map. Moreover, a framework that presents the existing research gaps and future research directions was proposed. This research provides a clear picture of the leading information and future research trends of the BIM-based coordination, contributing to the body of knowledge theoretically and offering references for related stakeholders practically.
C1 [Wu, Zezhou; Deng, Kaiwen; Chen, Changhong] Shenzhen Univ, Sino Australia Joint Res Ctr BIM & Smart Construc, Shenzhen, Peoples R China.
   [Deng, Kaiwen; Li, Heng; Wang, Ying] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Peoples R China.
   [Antwi-Afari, Maxwell Fordjour] Aston Univ, Coll Engn & Phys Sci, Dept Civil Engn, Birmingham, W Midlands, England.
RP Wang, Y (通讯作者)，Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Peoples R China.
EM misty-ying.wang@connect.polyu.hk
RI Wang, Ying/AAO-3771-2021; Li, Heng/B-2821-2015
OI Wang, Ying/0000-0003-2073-9884; Li, Heng/0000-0002-3187-9041;
   Antwi-Afari, Maxwell Fordjour/0000-0002-6812-7839
FU Public Policy Research Funding Scheme from the Policy Innovation and
   Co-ordination Office of The Government of the Hong Kong Special
   Administrative Region [2019.A6.143.19D]
FX This study is funded by the Public Policy Research Funding Scheme
   (Project Number: 2019.A6.143.19D) from the Policy Innovation and
   Co-ordination Office of The Government of the Hong Kong Special
   Administrative Region.
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NR 106
TC 0
Z9 0
U1 4
U2 4
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 2022
VL 28
IS 6
BP 469
EP 484
DI 10.3846/jcem.2022.16928
PG 16
WC Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 2D9DJ
UT WOS:000811838200003
OA Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Ayala-Gomez, F
   Daroczy, B
   Benczur, A
   Mathioudakis, M
   Gionis, A
AF Ayala-Gomez, Frederick
   Daroczy, Balint
   Benczur, Andras
   Mathioudakis, Michael
   Gionis, Aristides
TI Global citation recommendation using knowledge graphs
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Citation recommendations; knowledge graphs; recommender systems
AB Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation, i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs - and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10.
C1 [Ayala-Gomez, Frederick] Eotvos Lorand Univ, Fac Informat, H-1117 Budapest, Hungary.
   [Daroczy, Balint; Benczur, Andras] Hungarian Acad Sci, MTA SZTAKI, Inst Comp Sci & Control, Budapest, Hungary.
   [Mathioudakis, Michael] Univ Lyon, CNRS, INSA Lyon, LIRIS,UMR5205, Lyon, France.
   [Gionis, Aristides] Aalto Univ, Dept Comp Sci, Espoo, Finland.
RP Ayala-Gomez, F (通讯作者)，Eotvos Lorand Univ, Fac Informat, H-1117 Budapest, Hungary.
EM fayala@caesar.elte.hu
RI Daroczy, Balint/AAB-9663-2022; Gionis, Aristides/G-2225-2013
OI Gionis, Aristides/0000-0002-5211-112X; Ayala-Gomez,
   Frederick/0000-0001-8748-173X; Mathioudakis,
   Michael/0000-0003-0074-3966; Andras, Benczur/0000-0002-9392-0986
FU Mexican National Council for Science and Technology (Consejo Nacional de
   Ciencia y Tecnologia-CONACYT); European Institute of Innovation and
   Technology (EIT) Digital Doctoral School; EU H2020 research and
   innovation program [688191]; Hungarian Academy of Sciences; Academy of
   Finland [286211]; EC H2020 RIA project "SoBigData" [654024]
FX Frederick Ayala-Gomez was supported by the Mexican National Council for
   Science and Technology (Consejo Nacional de Ciencia y
   Tecnologia-CONACYT), and by the European Institute of Innovation and
   Technology (EIT) Digital Doctoral School. This publication was also
   supported from the EU H2020 research and innovation program under grant
   agreement Streamline No 688191 and the "Big Data-Momentum" grant of the
   Hungarian Academy of Sciences. Aristides Gionis is supported by the
   Academy of Finland project "Nestor" (286211) and the EC H2020 RIA
   project "SoBigData" (654024).
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TC 11
Z9 11
U1 5
U2 53
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 34
IS 5
BP 3089
EP 3100
DI 10.3233/JIFS-169493
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GH2AV
UT WOS:000433204800024
DA 2022-08-23
ER

PT J
AU Do, P
   Phan, THV
AF Phuc Do
   Phan, Truong H., V
TI Developing a BERT based triple classification model using knowledge
   graph embedding for question answering system
SO APPLIED INTELLIGENCE
LA English
DT Article
DE BERT based triple classification model; Knowledge graph embedding;
   Meta-path; Motif finding
AB The current BERT-based question answering systems use a question and a contextual text to find the answer. This causes the systems to return wrong answers or nothing if the text contains irrelevant contents with the input question. Besides, the systems haven't answered yes-no and aggregate questions yet. Besides that, the systems only concentrate on the contents of text regardless of the relationship between entities in the corpus. This systems cannot validate the answer. In this paper, we presented a solution to solve these issues by using the BERT model and the knowledge graph to enhance a question answering system. We combined content-based and linked-based information for knowledge graph representation learning and classified triples into one of three classes such as base class, derived class, or non-existent class. We then used the BERT model to build two classifiers: BERT-based text classification for content information and BERT-based triple classification for link information. The former was able to make a contextual embedding vector for representing triples that were used to classify into the three above classes. The latter generated all path instances from all meta paths of a large heterogeneous information network by running the Motif Search method of Apache Spark on a distributed environment. After creating the path instances, we produced triples from these path instances. We made content-based information by converting triples into natural language text with labels and considered them as a text classification problem. Our proposed solution outperformed other embedding methods with an average accuracy of 92.34% on benchmark datasets and the Motif Finding algorithm with an average executive time improvement of 37% on the distributed environment.
C1 [Phuc Do; Phan, Truong H., V] Vietnam Natl Univ, Univ Informat Technol, Ho Chi Minh City, Vietnam.
   [Phan, Truong H., V] Van Lang Univ, Ho Chi Minh City, Vietnam.
RP Do, P (通讯作者)，Vietnam Natl Univ, Univ Informat Technol, Ho Chi Minh City, Vietnam.
EM phucdo@uit.edu.vn; truongphv.ncs@grad.uit.edu.vn
OI H. V Phan, Truong/0000-0003-1773-6703
FU Vietnam National University Ho Chi Minh City (VNU-HCMC) [DS2020-26-01]
FX This research is funded by Vietnam National University Ho Chi Minh City
   (VNU-HCMC) under grant number DS2020-26-01.
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NR 37
TC 5
Z9 5
U1 14
U2 34
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 636
EP 651
DI 10.1007/s10489-021-02460-w
EA MAY 2021
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YG8PL
UT WOS:000648365100002
DA 2022-08-23
ER

PT J
AU Bu, CY
   Yu, XC
   Hong, Y
   Jiang, TT
AF Bu, Chenyang
   Yu, Xingchen
   Hong, Yan
   Jiang, Tingting
TI Low-Quality Error Detection for Noisy Knowledge Graphs
SO JOURNAL OF DATABASE MANAGEMENT
LA English
DT Article
DE HAO Intelligence; Knowledge Graph Denoising; Noise Detection
ID BIG; MACHINE
AB The automatic construction of knowledge graphs (KGs) from multiple data sources has received increasing attention. The automatic construction process inevitably brings considerable noise, especially in the construction of KGs from unstructured text. The noise in a KG can be divided into two categories: factual noise and low-quality noise. Factual noise refers to plausible triples that meet the requirements of ontology constraints. For example, the plausible triple <New_York, IsCapitalOf, America> satisfies the constraints that the head entity "New_York" is a city and the tail entity "America" belongs to a country. Low-quality noise denotes the obvious errors commonly created in information extraction processes. This study focuses on entity type errors. Most existing approaches concentrate on refining an existing KG, assuming that the type information of most entities or the ontology information in the KG is known in advance. However, such methods may not be suitable at the start of a KG's construction. Therefore, the authors propose an effective framework to eliminate entity type errors. The experimental results demonstrate the effectiveness of the proposed method.
C1 [Bu, Chenyang] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei, Peoples R China.
   [Yu, Xingchen; Hong, Yan; Jiang, Tingting] Hefei Univ Technol, Hefei, Peoples R China.
RP Bu, CY (通讯作者)，Hefei Univ Technol, Sch Comp Sci & Informat Engn, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei, Peoples R China.
RI Bu, Chenyang/T-5939-2019
OI Bu, Chenyang/0000-0001-8203-0956
FU National Natural Science Foundation of China [61806065]; Fundamental
   Research Funds for the Central Universities [JZ2020HGQA0186]
FX This work was supported by the National Natural Science Foundation of
   China (61806065), and the Fundamental Research Funds for the Central
   Universities (JZ2020HGQA0186).
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NR 52
TC 0
Z9 0
U1 4
U2 5
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1063-8016
EI 1533-8010
J9 J DATABASE MANAGE
JI J. Database Manage.
PD OCT-DEC
PY 2021
VL 32
IS 4
BP 48
EP 64
DI 10.4018/JDM.2021100104
PG 17
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YL0VS
UT WOS:000745619700004
DA 2022-08-23
ER

PT J
AU Chang, DS
   Cho, GH
   Choi, YS
AF Chang, Doo Soo
   Cho, Gun Hee
   Choi, Yong Suk
TI Zero-Shot Recognition Enhancement by Distance-Weighted Contextual
   Inference
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE zero-shot recognition; similarity measures; distance-weighting;
   knowledge graph; semantic embedding
ID OBJECT DETECTION
AB Zero-shot recognition (ZSR) aims to perform visual classification by category in the absence of training samples. The focus in most traditional ZSR models is using semantic knowledge about familiar categories to represent unfamiliar categories with only the visual appearance of an unseen object. In this research, we consider not only visual information but context to enhance the classifier's cognitive ability in a multi-object scene. We propose a novel method, contextual inference, that uses external resources such as knowledge graphs and semantic embedding spaces to obtain similarity measures between an unseen object and its surrounding objects. Using the intuition that close contexts involve more related associations than distant ones, distance weighting is applied to each piece of surrounding information with a newly defined distance calculation formula. We integrated contextual inference into traditional ZSR models to calibrate their visual predictions, and performed extensive experiments on two different datasets for comparative evaluations. The experimental results demonstrate the effectiveness of our method through significant enhancements in performance.
C1 [Chang, Doo Soo; Cho, Gun Hee; Choi, Yong Suk] Hanyang Univ, Artificial Intelligence Lab, Seoul 04763, South Korea.
RP Choi, YS (通讯作者)，Hanyang Univ, Artificial Intelligence Lab, Seoul 04763, South Korea.
EM tim0225@hanyang.ac.kr; gunneng@hanyang.ac.kr; cys@hanyang.ac.kr
OI Chang, Doo Soo/0000-0003-4208-5922; Choi, Yong Suk/0000-0002-9042-0599
FU Technology Innovation Program - Ministry of Trade, Industry & Energy
   (MOTIE, Korea) [10077553]; Institute of Information & communications
   Technology Planning & Evaluation(IITP) - Korea government(MSIT)
   [2020-0-01373]; National Research Foundation of Korea (NRF) - Korea
   government (MSIT) [2020R1A2C1014037]
FX This work was supported by the Technology Innovation Program (10077553,
   Development of Social Robot Intelligence for Social Human-Robot
   Interaction of Service Robots) funded By the Ministry of Trade, Industry
   & Energy (MOTIE, Korea), supported by Institute of Information &
   communications Technology Planning & Evaluation(IITP) grant funded by
   the Korea government(MSIT) (No. 2020-0-01373, Artificial Intelligence
   Graduate School Program (Hanyang University)), and the National Research
   Foundation of Korea (NRF) grant funded by the Korea government (MSIT)
   (No. 2020R1A2C1014037).
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NR 60
TC 1
Z9 1
U1 0
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 OCT
PY 2020
VL 10
IS 20
AR 7234
DI 10.3390/app10207234
PG 19
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA OH8MZ
UT WOS:000582847200001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Fan, T
   Wang, H
AF Fan, Tao
   Wang, Hao
TI Research of Chinese intangible cultural heritage knowledge graph
   construction and attribute value extraction with graph attention network
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Digital humanity; Intangible cultural heritage; Knowledge graph;
   Attribute value extraction
AB The development of digital technology promotes the construction of the Intangible cultural heritage (ICH) database but the data is still unorganized and not linked well, which makes the public hard to master the overall knowledge of the ICH. An ICH knowledge graph (KG) can help the public to understand the ICH and facilitate the protection of the ICH. However, a general framework of ICH KG construction is lacking now. In this study, we take the Chinese ICH (nation-level) as an example and propose a framework to build a Chinese ICH KG combining multiple data sources from Baike and the official website, which can extend the scale of the KG. Besides, the data of ICH grows daily, requiring us to design an efficient model to extract the knowledge from the data to update the KG in time. The built KG is based on the triple < entity, attribute, attribute value > and we introduce the attribute value extraction (AVE) task. However, the public Chinese ICH annotated AVE corpus is lacking. To solve that, we construct a Chinese ICH AVE corpus based on the Distant Supervision (DS) automatically rather than employing traditional manual annotation. Currently, AVE is usually seen as the sequence tagging task. In this paper, we take the ICH AVE as a node classification task and propose an AVE model BGC, combining the BiLSTM and graph attention network, which can fuse and utilize the word-level and character-level information by means of the ICH lexicon generated from the KG. We conduct extensive experiments and compare the proposed model with other state-of-the-art models. Experimental results show that the proposed model is of superiority.
C1 [Fan, Tao; Wang, Hao] Nanjing Univ, Sch Informat Management, Nanjing 210023, Peoples R China.
   [Fan, Tao; Wang, Hao] Jiangsu Key Lab Data Engn & Knowledge Serv, Nanjing 210023, Peoples R China.
RP Wang, H (通讯作者)，Nanjing Univ, Sch Informat Management, Nanjing 210023, Peoples R China.
EM fantao@smail.nju.edu.cn; ywhaowang@nju.edu.cn
OI Fan, Tao/0000-0002-6846-2901
FU National Natural Science Foundation of China [72074108]
FX This paper is supported by the National Natural Science Foundation of
   China under contract no. 72074108.
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NR 48
TC 1
Z9 1
U1 40
U2 68
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD JAN
PY 2022
VL 59
IS 1
AR 102753
DI 10.1016/j.ipm.2021.102753
EA SEP 2021
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 WV2UG
UT WOS:000717092800013
OA Bronze
DA 2022-08-23
ER

PT J
AU Yan, SY
   Zhang, ZQ
   Sun, X
   Xu, GL
   Jin, L
   Li, SC
AF Yan, Shiyao
   Zhang, Zequn
   Sun, Xian
   Xu, Guangluan
   Jin, Li
   Li, Shuchao
TI HYPER2: Hyperbolic embedding for hyper-relational link prediction
SO NEUROCOMPUTING
LA English
DT Article
DE Link prediction; Knowledge graph embedding; Hyperbolic space;
   Hyper-relational knowledge graph; Hyper-relational link prediction
AB Knowledge graphs (KGs) Embedding has been broadly studied in recent years. However, less light is shed on the ubiquitous hyper-relational KGs. Most existing hyper-relational KG embedding methods decompose n-ary facts into smaller tuples, undermining the structure of n-ary facts. Moreover, these models always suffer from low expressiveness and high complexity. In this work, to tackle the indecomposability issue, we represent n-ary fact as a hyperedge, keeping the integrity of fact and maintaining the vital role that primary triple plays. To address the expressiveness and complexity issue, we propose HYPER2 where we generalize hyperbolic Poincare embedding from binary to arbitrary arity data, and we design an information aggregation module to capture the interaction between entities within and beyond triple. Extensive experiments demonstrate HYPER2 is superior to its translational and deep analogues, improving MRR and other metrics by a large margin with relatively few dimensions. Moreover, we study the side effect of literals, and we theoretically and experimentally compare the computational complexity of HYPER2 against several best-performing baselines. HYPER2 is much quicker than its counterparts. (C) 2022 Published by Elsevier B.V.
C1 [Yan, Shiyao; Zhang, Zequn; Sun, Xian; Xu, Guangluan; Jin, Li; Li, Shuchao] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China.
   [Yan, Shiyao; Zhang, Zequn; Sun, Xian; Xu, Guangluan; Jin, Li; Li, Shuchao] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Network Informat Syst Technol NIST, Beijing 100190, Peoples R China.
   [Yan, Shiyao; Sun, Xian] Univ Chinese Acad Sci, Beijing 100190, Peoples R China.
   [Yan, Shiyao] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100190, Peoples R China.
RP Zhang, ZQ (通讯作者)，Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China.
EM zqzhang1@mail.ie.ac.cn
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NR 48
TC 0
Z9 0
U1 6
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JUL 1
PY 2022
VL 492
BP 440
EP 451
DI 10.1016/j.neucom.2022.04.026
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1H2CQ
UT WOS:000796353500004
DA 2022-08-23
ER

PT J
AU Fordson, H
   Xing, XF
   Guo, KL
   Xu, XM
AF Fordson, Hayford
   Xing, Xiaofen
   Guo, Kailing
   Xu, Xiangmin
TI Emotion Recognition With Knowledge Graph Based on Electrodermal Activity
SO FRONTIERS IN NEUROSCIENCE
LA English
DT Article
DE affective computing; electrodermal activity; knowledge graph; emotion
   recognition; MLP
ID SELECTION
AB Electrodermal activity (EDA) sensor is emerging non-invasive equipment in affect detection research, which is used to measure electrical activities of the skin. Knowledge graphs are an effective way to learn representation from data. However, few studies analyzed the effect of knowledge-related graph features with physiological signals when subjects are in non-similar mental states. In this paper, we propose a model using deep learning techniques to classify the emotional responses of individuals acquired from physiological datasets. We aim to improve the execution of emotion recognition based on EDA signals. The proposed framework is based on observed gender and age information as embedding feature vectors. We also extract time and frequency EDA features in line with cognitive studies. We then introduce a sophisticated weighted feature fusion method that combines knowledge embedding feature vectors and statistical feature (SF) vectors for emotional state classification. We finally utilize deep neural networks to optimize our approach. Results obtained indicated that the correct combination of Gender-Age Relation Graph (GARG) and SF vectors improve the performance of the valence-arousal emotion recognition system by 4 and 5% on PAFEW and 3 and 2% on DEAP datasets.
C1 [Fordson, Hayford; Xing, Xiaofen; Guo, Kailing; Xu, Xiangmin] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou, Peoples R China.
   [Xu, Xiangmin] South China Univ Technol, Sch Future Technol, Guangzhou, Peoples R China.
RP Guo, KL; Xu, XM (通讯作者)，South China Univ Technol, Sch Elect & Informat Engn, Guangzhou, Peoples R China.; Xu, XM (通讯作者)，South China Univ Technol, Sch Future Technol, Guangzhou, Peoples R China.
EM guokl@scut.edu.cn; xmxu@scut.edu.cn
FU National Natural Science Foundation of China [U1801262]; Guangdong
   Provincial Key Laboratory of Human Digital Twin [2022B1212010004];
   Key-Area Research and Development Program of Guangdong Province, China
   [2019B010154003]; Natural Science Foundation of Guangdong Province,
   China [2019B010154003, 2020A1515010781]; Guangzhou key Laboratory of
   Body Data Science [201605030011]; Science and Technology Project of
   Zhongshan [2019AG024]; Fundamental Research Funds for Central
   Universities, SCUT [2019PY21, 2019MS028, XYMS202006]; Guangdong
   Philosophy and Social Sciences Planning Project [GD20CYS33]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant U1801262, Guangdong Provincial Key
   Laboratory of Human Digital Twin (2022B1212010004), in part by the
   Key-Area Research and Development Program of Guangdong Province, China,
   under Grant 2019B010154003, in part by Natural Science Foundation of
   Guangdong Province, China, under Grant 2020A1515010781, and Grant
   2019B010154003, in part by the Guangzhou key Laboratory of Body Data
   Science, under Grant 201605030011, in part by Science and Technology
   Project of Zhongshan, under Grant 2019AG024, in part by the Fundamental
   Research Funds for Central Universities, SCUT, under Grant 2019PY21,
   Grant 2019MS028, and Grant XYMS202006, and in part by Guangdong
   Philosophy and Social Sciences Planning Project, under Grant GD20CYS33.
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NR 46
TC 0
Z9 0
U1 0
U2 0
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1662-453X
J9 FRONT NEUROSCI-SWITZ
JI Front. Neurosci.
PD JUN 9
PY 2022
VL 16
AR 911767
DI 10.3389/fnins.2022.911767
PG 12
WC Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA 2I5SW
UT WOS:000815038800001
PM 35757534
DA 2022-08-23
ER

PT J
AU Altuner, AB
   Kilimci, ZH
AF Altuner, Anil Berk
   Kilimci, Zeynep Hilal
TI A novel deep reinforcement learning based stock price prediction
   usingknowledge graph and community aware sentiments
SO TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
LA English
DT Article
DE Deep reinforcement learning; knowledge graphs; sentiment analysis;
   social graphs; stock prediction; q-learning
ID MARKET
AB Stock market prediction has been an important topic for investors, researchers, and analysts. Because it is affected by too many factors, stock market prediction is a difficult task to handle. In this study, we propose a novel method that is based on deep reinforcement learning methodologies for the prediction of stock prices using sentiments of community and knowledge graph. For this purpose, we firstly construct a social knowledge graph of users by analyzing relations between connections. After that, time series analysis of related stock and sentiment analysis is blended with deep reinforcement methodology. Turkish version of Bidirectional Encoder Representations from Transformers (BerTurk) is employed to analyze the sentiments of the users while deep Q-learning methodology is used for the deep reinforcement learning side of the proposed model to construct the deep Q-network. In order to demonstrate the effectiveness of the proposed model, Garanti Bank (GARAN), Akbank (AKBNK), T??rkiye ???? Bankas?? (ISCTR) stocks in Borsa ??stanbul are used as a case study. Experiment results show that the proposed novel model achieves remarkable results for stock market prediction task.
C1 [Altuner, Anil Berk; Kilimci, Zeynep Hilal] Kocaeli Univ, Fac Technol, Dept Informat Syst Engn, Izmit, Turkey.
RP Kilimci, ZH (通讯作者)，Kocaeli Univ, Fac Technol, Dept Informat Syst Engn, Izmit, Turkey.
EM zeynep.kilimci@kocaeli.edu.tr
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NR 23
TC 0
Z9 0
U1 2
U2 2
PU TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY
PI ANKARA
PA ATATURK BULVARI NO 221, KAVAKLIDERE, ANKARA, 00000, TURKEY
SN 1300-0632
EI 1303-6203
J9 TURK J ELECTR ENG CO
JI Turk. J. Electr. Eng. Comput. Sci.
PY 2022
VL 30
IS 4
BP 1506
EP 1524
DI 10.55730/1300-0632.3862
PG 20
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1W5GT
UT WOS:000806802400013
OA Bronze
DA 2022-08-23
ER

PT J
AU Cardoso, SD
   Da Silveira, M
   Pruski, C
AF Cardoso, Silvio Domingos
   Da Silveira, Marcos
   Pruski, Cedric
TI Construction and exploitation of an historical knowledge graph to deal
   with the evolution of ontologies
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Knowledge graphs; Ontology evolution; Biomedical ontology; Versioning
ID MAPPINGS; SYSTEM
AB With the advances of Artificial Intelligence, the need for annotated data increases. However, the quality of these annotations can be impacted by the evolution of domain knowledge since the relations between successive versions of ontologies are rarely described and the history of concepts is not kept at the ontology level. As a consequence, using datasets annotated at different times becomes a real challenge for data- and knowledge-intensive systems. This work presents a way to address this problem. We introduce a Historical Knowledge Graph (HKG), where information from previous versions of an ontology can be found inside a single graph, reducing storage space (no need for versioning) and data treatment time (no need for laborious analysis of each version of the ontology). The HKG proposed in this work represents the evolutionary aspects of the knowledge in a structural way. Examples of the applicability of an HKG for information retrieval and the maintenance of semantic annotations show the capability of our approach for improving the quality of existing techniques. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Cardoso, Silvio Domingos; Da Silveira, Marcos; Pruski, Cedric] Luxembourg Inst Sci & Technol, LIST, 5 Ave Hauts Fourneaux, L-4362 Esch Sur Alzette, Luxembourg.
RP Pruski, C (通讯作者)，Luxembourg Inst Sci & Technol, LIST, 5 Ave Hauts Fourneaux, L-4362 Esch Sur Alzette, Luxembourg.
EM silvio.cardoso@list.lu; marcos.dasilveira@list.lu; cedric.pruski@list.lu
OI Pruski, Cedric/0000-0002-2103-0431; Da Silveira,
   Marcos/0000-0002-2604-3645
FU Luxembourg National Research Fund (FNR) through the ELISA project
   [C14/IS/824 9162]
FX This work was funded by the Luxembourg National Research Fund (FNR)
   through the ELISA project [grant number C14/IS/824 9162].
CR [Anonymous], INFORM SCI
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NR 31
TC 12
Z9 12
U1 2
U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD APR 22
PY 2020
VL 194
AR 105508
DI 10.1016/j.knosys.2020.105508
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LP8QD
UT WOS:000534581300010
DA 2022-08-23
ER

PT J
AU Fuji, M
   Morita, H
   Goto, K
   Maruhashi, K
   Anai, H
   Igata, N
AF Fuji, Masaru
   Morita, Hajime
   Goto, Keisuke
   Maruhashi, Koji
   Anai, Hirokazu
   Igata, Nobuyuki
TI Explainable Al Through Combination of Deep Tensor and Knowledge Graph
SO FUJITSU SCIENTIFIC & TECHNICAL JOURNAL
LA English
DT Article
AB One of the most significant advancements made in Al in recent years is the greatly enhanced accuracy of machine learning through deep learning. However, because deep learning deals with huge volumes of data and involves vast neural networks in the learning process, it is often difficult to explain how or why an output was reached even if the inference was correct. This issue impedes the application of Al technology to such business areas as finance and medicine, which demand absolute reliability. To address this issue, we have developed an Al technology that combines Deep Tensor, Fujitsu's original machine-learning technology based on enhanced deep learning, and another Fujitsu-developed machine-learning technology based on knowledge graph, a knowledge base represented by graph data taken from past documents and databases. This has enabled us to logically explain the reasons and bases in which Deep Tensor reaches its inference output. This paper explains the technology that makes Explainable Al possible in terms of application cases in network intrusion detection and genomic medicine.
C1 [Fuji, Masaru; Morita, Hajime; Goto, Keisuke; Maruhashi, Koji; Anai, Hirokazu; Igata, Nobuyuki] Fujitsu Labs Ltd, Akashi, Hyogo, Japan.
RP Fuji, M (通讯作者)，Fujitsu Labs Ltd, Akashi, Hyogo, Japan.
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NR 6
TC 6
Z9 6
U1 0
U2 11
PU FUJITSU LTD
PI KAWASAKI
PA 1015 KAMIKODANAKU NAKAHARA-KU, KAWASAKI, 211, JAPAN
SN 0016-2523
J9 FUJITSU SCI TECH J
JI Fujitsu Sci. Tech. J.
PY 2019
VL 55
IS 2
BP 58
EP 64
PG 7
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA HP5OQ
UT WOS:000461726800010
DA 2022-08-23
ER

PT J
AU Joseph, S
   Curtis, MA
AF Joseph, Susan
   Curtis, Michael A.
TI Microbial transitions from health to disease
SO PERIODONTOLOGY 2000
LA English
DT Review
DE dysbiosis; oral health; oral microbiome; pathogenesis; periodontal
   disease; stability
ID ORAL MICROBIOME; SUBGINGIVAL MICROBIOME; PERIODONTAL-DISEASE; DIVERSITY;
   COMMUNITY; GUT; PATHOGENESIS; RESILIENCE; PLAQUE; BIOGEOGRAPHY
AB Recent advances in our understanding of the microbial populations that colonize the human mouth, their acquisition, interdependency, and coevolution with the host, bring a different perspective to the mechanisms underpinning the maintenance of periodontal health and the development of disease. In this work we suggest that our knowledge map of the etiology of periodontal health and disease can be viewed as a broad, highly connected, and integrated system that spans the entire spectrum of microbe/host/clinical interactions. The overall concept of present Periodontology 2000, that the microbial biofilm can be considered a human tissue of bacteriological origin, is entirely consistent with this integrated system view. The health-associated community structure of microbial biofilms can be considered a system that is normally resilient to perturbation. Equally, there is evidence to suggest that the dysbiotic community structure in disease may share similar resilience properties. In both instances, the resilience may be governed by the precise makeup of the acquired microbiome and by the genetics of the host. Understanding the mechanisms that enable the resistance to change of healthy and dysbiotic microbial populations may be important in the development of approaches to prevent the progression of disease and to restore health in diseased individuals.
C1 [Joseph, Susan; Curtis, Michael A.] Kings Coll London, Ctr Host Microbiome Interact, Fac Dent Oral & Craniofacial Sci, London, England.
RP Curtis, MA (通讯作者)，Kings Coll London, Ctr Host Microbiome Interact, Fac Dent Oral & Craniofacial Sci, Guys Hosp, Tower Wing, London SE1 9RT, England.
EM Mike.Curtis@kcl.ac.uk
RI Joseph, Susan/I-8936-2019
OI Joseph, Susan/0000-0002-0297-5405; Curtis, Mike A/0000-0002-1563-9953
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NR 62
TC 30
Z9 30
U1 3
U2 6
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0906-6713
EI 1600-0757
J9 PERIODONTOL 2000
JI Periodontol. 2000
PD JUN
PY 2021
VL 86
IS 1
BP 201
EP 209
DI 10.1111/prd.12377
EA MAR 2021
PG 9
WC Dentistry, Oral Surgery & Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Dentistry, Oral Surgery & Medicine
GA RP8YC
UT WOS:000627068100001
PM 33690926
OA hybrid
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Wang, Y
   Gao, Y
   Deng, YL
   Yang, L
AF Wang, Ye
   Gao, Yan
   Deng, Yaling
   Yang, Lei
TI Modeling of Brain-Like Concept Coding with Adulthood Neurogenesis in the
   Dentate Gyrus
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID PATTERN SEPARATION; GRANULE CELLS; NEURONS; REPRESENTATION; HIPPOCAMPUS;
   ASSOCIATIONS; DOPAMINE; MEMORIES; TERM
AB Mammalian brains respond to new concepts via a type of neural coding termed "concept coding." During concept coding, the dentate gyrus (DG) plays a vital role in pattern separation and pattern integration of concepts because it is a brain region with substantial neurogenesis in adult mammals. Although concept coding properties of the brain have been extensively studied by experimental work, modeling of the process to guide both further experimental studies and applications such as natural language processing is scarce. To model brain-like concept coding, we built a spiking neural network inspired by adulthood neurogenesis in the DG. Our model suggests that neurogenesis may facilitate integration of closely related concepts and separation of less relevant concepts. Such pattern agrees with the previous experimental observations in classification tasks and place cells in the hippocampus. Therefore, our simulation provides insight for future experimental studies on the neural coding difference between perception and cognition. By presenting 14 contexts each containing 4 concepts to the network, we found that neural responses of the DG changed dynamically as the context repetition time increased and were eventually consistent with the category organization of humans. Thus, our work provides a new framework of word representation for the construction of brain-like knowledge map.
C1 [Wang, Ye; Gao, Yan; Deng, Yaling] Commun Univ China, Neurosci & Intelligent Media Inst, Beijing 100024, Peoples R China.
   [Wang, Ye] State Key Lab Math Engn & Adv Comp, Wuxi 214125, Jiangsu, Peoples R China.
   [Yang, Lei] Pacific Northwest Res Inst, Seattle, WA 98122 USA.
RP Wang, Y (通讯作者)，Commun Univ China, Neurosci & Intelligent Media Inst, Beijing 100024, Peoples R China.; Wang, Y (通讯作者)，State Key Lab Math Engn & Adv Comp, Wuxi 214125, Jiangsu, Peoples R China.
EM yewang@cuc.edu.cn
OI Wang, Ye/0000-0001-6073-4688; Deng, Yaling/0000-0003-2764-5645; Yang,
   Lei/0000-0001-9284-1744
FU Fundamental Research Funds for the Central Universities [CUC2019B079,
   2018CUCTJ031, 2018CUCTJ032, CUC18A001]; State Key Laboratory of
   Mathematical Engineering and Advanced Computing [2018A08]; High-Quality
   and Cutting-Edge Disciplines Construction Project for Universities in
   Beijing (Internet Information, Communication University of China)
FX This study was supported by the Fundamental Research Funds for the
   Central Universities (grant nos. CUC2019B079, 2018CUCTJ031,
   2018CUCTJ032, and CUC18A001), the Open Project Program of the State Key
   Laboratory of Mathematical Engineering and Advanced Computing (grant no.
   2018A08), and the High-Quality and Cutting-Edge Disciplines Construction
   Project for Universities in Beijing (Internet Information, Communication
   University of China).
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NR 37
TC 1
Z9 2
U1 1
U2 1
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD NOV 3
PY 2019
VL 2019
AR 2367075
DI 10.1155/2019/2367075
PG 13
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA JP8VO
UT WOS:000498537000003
PM 31814816
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Yang, R
   Ye, Q
   Cheng, CL
   Zhang, SH
   Lan, Y
   Zou, J
AF Yang, Rui
   Ye, Qing
   Cheng, Chunlei
   Zhang, Suhua
   Lan, Yong
   Zou, Jing
TI Decision-Making System for the Diagnosis of Syndrome Based on
   Traditional Chinese Medicine Knowledge Graph
SO EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE
LA English
DT Article
ID AUXILIARY DIAGNOSIS
AB The clinical informatization of traditional Chinese medicine (TCM) focuses on serving users and assisting in diagnosis. The rules of clinical knowledge play an important role in improving the TCM informatization service. However, many rules are difficult to find because of the complexity of the data in the current TCM syndrome prediction. Therefore, we proposed an end-to-end model, called Decision-making System for the Diagnosis of Syndrome (DSDS), which is based on the knowledge graph (KG) of TCM. This paper introduces the link prediction for the diagnosis of syndrome by dismantling medical records into multiple symptoms. In addition, based on the symptoms and predicted syndromes, the most relevant syndrome could be determined by the scoring and voting method in this paper. The results show that the accuracy of DSDS is 80.6%.
C1 [Yang, Rui; Ye, Qing; Cheng, Chunlei; Zhang, Suhua; Lan, Yong; Zou, Jing] Jiangxi Univ Chinese Med, Sch Comp, Nanchang 330004, Jiangxi, Peoples R China.
RP Ye, Q; Cheng, CL (通讯作者)，Jiangxi Univ Chinese Med, Sch Comp, Nanchang 330004, Jiangxi, Peoples R China.
EM 1031812593@qq.com; 78466490@qq.com; chunlei_cheng@163.com;
   1689932845@qq.com; jxgaly290@163.com; 1985116519@qq.com
OI Yang, Rui/0000-0003-3803-6722
FU National Key Research and Development Plan of China [2019YFC1712301];
   Jiangxi Provincial Department of Education Science and Technology
   Research Key Project [GJJ201204]; Jiangxi Provincial Department of
   Education Science and Technology Research Project [GJJ170727]
FX AcknowledgmentsThis research was funded by the National Key Research and
   Development Plan of China (2019YFC1712301), Jiangxi Provincial
   Department of Education Science and Technology Research Key Project
   (GJJ201204), and Jiangxi Provincial Department of Education Science and
   Technology Research Project (GJJ170727).
CR [Anonymous], 2018, P 2018 2 INT C ART I, DOI DOI 10.2991/ICAITA-18.2018.49
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NR 29
TC 0
Z9 0
U1 10
U2 10
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1741-427X
EI 1741-4288
J9 EVID-BASED COMPL ALT
JI Evid.-based Complement Altern. Med.
PD FEB 10
PY 2022
VL 2022
AR 8693937
DI 10.1155/2022/8693937
PG 9
WC Integrative & Complementary Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Integrative & Complementary Medicine
GA 0B9CP
UT WOS:000774924200004
PM 35186106
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chen, HN
   Luo, XW
AF Chen, Hainan
   Luo, Xiaowei
TI An automatic literature knowledge graph and reasoning network modeling
   framework based on ontology and natural language processing
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Representation ontology; Natural language processing; Knowledge graph;
   Knowledge reasoning
ID CONSTRUCTION SAFETY; MANAGEMENT; CLASSIFICATION; SOFTWARE; CITATION;
   SYSTEM
AB With the advancement of scientific and engineering research, a huge number of academic literature are accumulated. Manually reviewing the existing literature is the main way to explore embedded knowledge, and the process is quite time-consuming and labor intensive. As the quantity of literature is increasing exponentially, it would be more difficult to cover all aspects of the literature using the traditional manual review approach. To overcome this drawback, bibliometric analysis is used to analyze the current situation and trend of a specific research field. In the bibliometric analysis, only a few key phrases (e.g., authors, publishers, journals, and citations) are usually used as the inputs for analysis. Information other than those phrases is not extracted for analysis, while that neglected information (e.g., abstract) might provide more detailed knowledge in the article. To tackle with this problem, this study proposed an automatic literature knowledge graph and reasoning network modeling framework based on ontology and Natural Language Processing (NLP), to facilitate the efficient knowledge exploration from literature abstract. In this framework, a representation ontology is proposed to characterize the literature abstract data into four knowledge elements (background, objectives, solutions, and findings), and NLP technology is used to extract the ontology instances from the abstract automatically. Based on the representation ontology, a four-space integrated knowledge graph is built using NLP technology. Then, reasoning network is generated according to the reasoning mechanism defined in the proposed ontology model. To validate the proposed framework, a case study is conducted to analyze the literature in the field of construction management. The case study proves that the proposed ontology model can be used to represent the knowledge embedded in the literatures' abstracts, and the ontology elements can be automatically extracted by NLP models. The proposed framework can be an enhancement for the bibliometric analysis to explore more knowledge from the literature.
C1 [Chen, Hainan] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Guangdong, Peoples R China.
   [Chen, Hainan; Luo, Xiaowei] City Univ Hong Kong, Architecture & Civil Engn Res Ctr, Shenzhen Res Inst, Hong Kong, Peoples R China.
   [Chen, Hainan; Luo, Xiaowei] City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Peoples R China.
RP Luo, XW (通讯作者)，City Univ Hong Kong, Architecture & Civil Engn Res Ctr, Shenzhen Res Inst, Hong Kong, Peoples R China.; Luo, XW (通讯作者)，City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Peoples R China.
EM xiaowluo@cityu.edu.hk
RI CHEN, Hainan/L-9003-2019; luo, xiaowei/GON-4131-2022
OI CHEN, Hainan/0000-0002-6864-6268; luo, xiaowei/0000-0002-9360-7308
FU Shenzhen Science and Technology Innovation Committee
   [JCYJ20180507181647320]; National Science Foundation of China [51408519]
FX This work was jointly supported by Shenzhen Science and Technology
   Innovation Committee Grant #JCYJ20180507181647320 and National Science
   Foundation of China Grant #51408519. The conclusions herein are those of
   the authors and do not necessarily reflect the views of the sponsoring
   agencies.
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NR 76
TC 26
Z9 27
U1 12
U2 63
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 2019
VL 42
AR 100959
DI 10.1016/j.aei.2019.100959
PG 17
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA JU0SH
UT WOS:000501389000007
DA 2022-08-23
ER

PT J
AU Wongchaisuwat, P
   Klabjan, D
AF Wongchaisuwat, Papis
   Klabjan, Diego
TI Truth validation with evidence
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Truth validation; Knowledge Graph; Ontologies; Evidence-extracting
   algorithm
AB In the modern era, abundant information is easily accessible from various sources, however, only a few of these sources are reliable as they mostly contain unverified contents. We develop a system to validate the truthfulness of a given statement together with underlying evidence. The proposed system provides supporting evidence when the statement is tagged as false. Our work relies on an inference method on a knowledge graph (KG) to identify the truthfulness of statements. In order to extract the evidence of falseness, the proposed algorithm takes into account combined knowledge from KG and ontologies. The system shows very good results as it provides valid and concise evidence. The quality of KG plays a role in the performance of the inference method which explicitly affects the performance of our evidence-extracting algorithm.
C1 [Wongchaisuwat, Papis; Klabjan, Diego] Northwestern Univ, Dept Ind Engn & Management Sci, 2145 Sheridan Rd, Evanston, IL 60208 USA.
RP Wongchaisuwat, P (通讯作者)，Northwestern Univ, Dept Ind Engn & Management Sci, 2145 Sheridan Rd, Evanston, IL 60208 USA.
EM papiswongchaisuwat2013@u.northwestern.edu
OI Wongchaisuwat, Papis/0000-0002-4438-7601
CR [Anonymous], 2016, 13 AM DONT USE INTER
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NR 29
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0219-1377
EI 0219-3116
J9 KNOWL INF SYST
JI Knowl. Inf. Syst.
PD MAY
PY 2022
VL 64
IS 5
BP 1187
EP 1209
DI 10.1007/s10115-022-01663-y
EA MAR 2022
PG 23
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1F4PH
UT WOS:000769286300001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Sheth, A
   Padhee, S
   Gyrard, A
AF Sheth, Amit
   Padhee, Swati
   Gyrard, Amelie
TI Knowledge Graphs and Knowledge Networks: The Story in Brief
SO IEEE INTERNET COMPUTING
LA English
DT Article
DE Photonic crystal fibers; Optical fiber losses; Propagation losses;
   Optical polarization; Network theory (graphs)
ID EXTRACTION; THINGS
AB Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.
C1 [Sheth, Amit] Univ South Carolina, AI Inst, Columbia, SC 29208 USA.
   [Padhee, Swati; Gyrard, Amelie] Wright State Univ, Dayton, OH 45435 USA.
RP Sheth, A (通讯作者)，Univ South Carolina, AI Inst, Columbia, SC 29208 USA.
EM amit@sc.edu; swati@knoesis.org; amelie@knoesis.org
RI Sheth, Amit/ABC-4600-2020
OI Sheth, Amit/0000-0002-0021-5293
FU kHealth NIH [1 R01 HD087132-01]; Hazards SEES NSF [EAR 1520870]
FX The authors would like to thank U. Kursuncu and M. Gaur for their
   contributions in Figure 1 and overall feedback. This work was supported
   in part by kHealth NIH under Grant 1 R01 HD087132-01 and in part by
   Hazards SEES NSF under Award EAR 1520870. The opinions expressed are
   those of the authors and do not reflect those of the sponsors.
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Z9 14
U1 2
U2 31
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1089-7801
EI 1941-0131
J9 IEEE INTERNET COMPUT
JI IEEE Internet Comput.
PD JUL-AUG
PY 2019
VL 23
IS 4
BP 67
EP 75
DI 10.1109/MIC.2019.2928449
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JV0RR
UT WOS:000502075600010
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Dai, YF
   Guo, CH
   Guo, WZ
   Eickhoff, C
AF Dai, Yuanfei
   Guo, Chenhao
   Guo, Wenzhong
   Eickhoff, Carsten
TI Drug-drug interaction prediction with Wasserstein Adversarial
   Autoencoder-based knowledge graph embeddings
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article
DE drug-drug interaction; knowledge graph embedding; adversarial learning;
   Wasserstein distance
AB An interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, several knowledge graph (KG) embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets. However, existing methods only apply a uniformly random mode to construct negative samples. As a consequence, these samples are often too simplistic to train an effective model. In this paper, we propose a new KG embedding framework by introducing adversarial autoencoders (AAEs) based on Wasserstein distances and Gumbel-Softmax relaxation for DDI tasks. In our framework, the autoencoder is employed to generate high-quality negative samples and the hidden vector of the autoencoder is regarded as a plausible drug candidate. Afterwards, the discriminator learns the embeddings of drugs and interactions based on both positive and negative triplets. Meanwhile, in order to solve vanishing gradient problems on the discrete representation-an inherent flaw in traditional generative models-we utilize the Gumbel-Softmax relaxation and the Wasserstein distance to train the embedding model steadily. We empirically evaluate our method on two tasks: link prediction and DDI classification. The experimental results show that our framework can attain significant improvements and noticeably outperform competitive baselines. Supplementary information: Supplementary data and code are available at https://github.com/dyf0631/AAE_FOR_KG.
C1 [Dai, Yuanfei; Guo, Wenzhong] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou, Fujian, Peoples R China.
   [Dai, Yuanfei; Eickhoff, Carsten] Brown Univ, Brown Ctr Biomed Informat, Providence, RI 02912 USA.
   [Guo, Chenhao] Fuzhou Univ, Fuzhou, Peoples R China.
RP Guo, WZ (通讯作者)，Fuzhou Univ, Coll Math & Comp Sci, Fuzhou, Fujian, Peoples R China.; Eickhoff, C (通讯作者)，Brown Univ, Ctr Biomed Informat, Providence, RI 02912 USA.
EM guowenzhong@fzu.edu.cn; carsten@brown.edu
FU China Scholarship Council [201906650004]; National Natural Science
   Foundation of China [U1705262, 61672159]
FX This work is supported by the China Scholarship Council (grant no.
   201906650004) and the National Natural Science Foundation of China (nos.
   U1705262 and 61672159).
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NR 45
TC 11
Z9 11
U1 8
U2 25
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1467-5463
EI 1477-4054
J9 BRIEF BIOINFORM
JI Brief. Bioinform.
PD JUL
PY 2021
VL 22
IS 4
AR bbaa256
DI 10.1093/bib/bbaa256
PG 16
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA WK1AV
UT WOS:000709466800033
PM 33126246
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Lackner, A
   Fathalla, S
   Nayyeri, M
   Behrend, A
   Manthey, R
   Auer, S
   Lehmann, J
   Vahdati, S
AF Lackner, Arthur
   Fathalla, Said
   Nayyeri, Mojtaba
   Behrend, Andreas
   Manthey, Rainer
   Auer, Soeren
   Lehmann, Jens
   Vahdati, Sahar
TI Analysing the evolution of computer science events leveraging a
   scholarly knowledge graph: a scientometrics study of top-ranked events
   in the past decade
SO SCIENTOMETRICS
LA English
DT Article
DE Scientific Events; Ontology; Metadata Analysis; Scholarly Communication;
   Metric Suite
AB The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics.
C1 [Lackner, Arthur; Fathalla, Said; Nayyeri, Mojtaba; Manthey, Rainer; Lehmann, Jens] Univ Bonn, Bonn, Germany.
   [Fathalla, Said] Univ Alexandria, Fac Sci, Alexandria, Egypt.
   [Nayyeri, Mojtaba; Vahdati, Sahar] Inst Appl Informat InfAI, Nat Inspired Machine Intelligence, Dresden, Germany.
   [Behrend, Andreas] TH Koln, Inst Telecommun INT, Cologne, Germany.
   [Lehmann, Jens] Fraunhofer IAIS, NetMedia Dept, Dresden, Germany.
   [Auer, Soeren] Leibniz Univ Hannover, Res Ctr L3S, Hannover, Germany.
   [Auer, Soeren] TIB Leibniz Informat Ctr Sci & Technol, Hannover, Germany.
RP Fathalla, S (通讯作者)，Univ Bonn, Bonn, Germany.; Fathalla, S (通讯作者)，Univ Alexandria, Fac Sci, Alexandria, Egypt.
EM sm_fathalla@alex-sci.edu.eg
OI Fathalla, Said/0000-0002-2818-5890
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
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NR 23
TC 3
Z9 3
U1 28
U2 83
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD SEP
PY 2021
VL 126
IS 9
BP 8129
EP 8151
DI 10.1007/s11192-021-04072-0
EA JUL 2021
PG 23
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 UB2BQ
UT WOS:000671640200005
PM 34276109
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Xie, P
AF Xie, Ping
TI Study of international anticancer research trends via co-word and
   document co-citation visualization analysis
SO SCIENTOMETRICS
LA English
DT Article
DE Anticancer drug; Co-word; Document co-citation; Citespace; Visualization
   analysis
ID TUMOR-CELL-LINES; MACROMOLECULAR THERAPEUTICS; DRUGS; CANCER; ASSAY
AB The aim of this work is to make a bibliometric analysis of anticancer research literature based on the data from the Web of Science. Anticancer drug research references published from 2000 to 2014 were used. Citespace software was employed to generate the knowledge maps of country/institution, cited authors, cited journals, co-words and cited references related with anticancer drug research. Results of this analysis indicated that the USA is the most productive country and the Chinese Acad. Sci. is the most productive institution in this field. Maeda H is the most influential author, leading the highest citation author group. "CANCER RES" is the most cited journal in which the most influential anticancer drug research articles were published. Mosmann's (1983) paper is a representative and symbolic reference with the highest co-citation of number of 146 (centrality 0.29). The five hot anticancer drug research topics were also disclosed; they are: (1) chemotherapy drugs, (2) drug delivery, (3) bioscreening, (4) drug resistance research, and (5) enzyme inhibitor studies. Research frontiers identified included drug delivery with nanoparticles, controlled release, metabolism and so on. These analyses will be valuable for the reader to grasp an overall picture of anticancer research and research trends during these years.
C1 Jiangsu Univ Lib, Zhenjiang City 212013, Jiangsu, Peoples R China.
RP Xie, P (通讯作者)，Jiangsu Univ Lib, Xuefu Rd 301, Zhenjiang City 212013, Jiangsu, Peoples R China.
EM xieping@ujs.edu.cn
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NR 21
TC 78
Z9 87
U1 25
U2 313
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD OCT
PY 2015
VL 105
IS 1
BP 611
EP 622
DI 10.1007/s11192-015-1689-0
PG 12
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA CS3RP
UT WOS:000361992800040
DA 2022-08-23
ER

PT J
AU Cashman, D
   Xu, SY
   Das, S
   Heimerl, F
   Liu, C
   Humayoun, SR
   Gleicher, M
   Endert, A
   Chang, R
AF Cashman, Dylan
   Xu, Shenyu
   Das, Subhajit
   Heimerl, Florian
   Liu, Cong
   Humayoun, Shah Rukh
   Gleicher, Michael
   Endert, Alex
   Chang, Remco
TI CAVA: A Visual Analytics System for Exploratory Columnar Data
   Augmentation Using Knowledge Graphs
SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
LA English
DT Article
DE Task analysis; Visual analytics; Machine learning; Data models; Data
   visualization; Google; Training; Visual Analytics; Information Foraging;
   Data Augmentation
ID SEARCH
AB Most visual analytics systems assume that all foraging for data happens before the analytics process; once analysis begins, the set of data attributes considered is fixed. Such separation of data construction from analysis precludes iteration that can enable foraging informed by the needs that arise in-situ during the analysis. The separation of the foraging loop from the data analysis tasks can limit the pace and scope of analysis. In this paper, we present CAVA, a system that integrates data curation and data augmentation with the traditional data exploration and analysis tasks, enabling information foraging in-situ during analysis. Identifying attributes to add to the dataset is difficult because it requires human knowledge to determine which available attributes will be helpful for the ensuing analytical tasks. CAVA crawls knowledge graphs to provide users with a a broad set of attributes drawn from external data to choose from. Users can then specify complex operations on knowledge graphs to construct additional attributes. CAVA shows how visual analytics can help users forage for attributes by letting users visually explore the set of available data, and by serving as an interface for query construction. It also provides visualizations of the knowledge graph itself to help users understand complex joins such as multi-hop aggregations. We assess the ability of our system to enable users to perform complex data combinations without programming in a user study over two datasets. We then demonstrate the generalizability of CAVA through two additional usage scenarios. The results of the evaluation confirm that CAVA is effective in helping the user perform data foraging that leads to improved analysis outcomes, and offer evidence in support of integrating data augmentation as a part of the visual analytics pipeline.
C1 [Cashman, Dylan; Liu, Cong; Chang, Remco] Tufts Univ, Medford, MA 02155 USA.
   [Xu, Shenyu; Das, Subhajit; Endert, Alex] Georgia Tech, Atlanta, GA USA.
   [Heimerl, Florian; Gleicher, Michael] Univ Wisconsin, Madison, WI USA.
   [Humayoun, Shah Rukh] San Francisco State Univ, San Francisco, CA 94132 USA.
RP Cashman, D (通讯作者)，Tufts Univ, Medford, MA 02155 USA.
EM dcashm01@cs.tufts.edu; shenyuxu@gatech.edu; das@gatech.edu;
   heimerl@cs.wisc.edu; cong@cs.tufts.edu; humayoun@sfsu.edu;
   gleicher@cs.wisc.edu; endert@gatech.edu; remco@cs.tufts.edu
RI Cashman, Dylan/AAD-1909-2022; Cashman, Dylan/ABC-6776-2021
OI Cashman, Dylan/0000-0003-4853-5701; Chang, Remco/0000-0002-6484-6430
FU National Science Foundation [IIS-1452977, OAC-1940175, OAC-1939945,
   DGE-1855886, 1841349]; DARPA [FA8750-17-2-0107]
FX We thank our collaborators in DARPA's Data Driven Discovery of Models
   (d3m) program, as well as the reviewers for their helpful feedback. This
   work was supported by National Science Foundation grants IIS1452977,
   OAC-1940175, OAC-1939945, DGE-1855886, and 1841349, as well as DARPA
   grant FA8750-17-2-0107.
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NR 76
TC 1
Z9 1
U1 3
U2 5
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 FEB
PY 2021
VL 27
IS 2
BP 1731
EP 1741
DI 10.1109/TVCG.2020.3030443
PG 11
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WF5FO
UT WOS:000706330100149
PM 33048737
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Li, R
   Yin, CC
   Yang, S
   Qian, BY
   Zhang, P
AF Li, Rui
   Yin, Changchang
   Yang, Samuel
   Qian, Buyue
   Zhang, Ping
TI Marrying Medical Domain Knowledge With Deep Learning on Electronic
   Health Records: A Deep Visual Analytics Approach
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE electronic health records; interpretable deep learning; knowledge graph;
   visual analytics
AB Background: Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly.
   Objective: The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation.
   Methods: A domain-knowledge-guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation.
   Results: We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz.
   Conclusions: In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN-based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.
C1 [Li, Rui; Yin, Changchang; Yang, Samuel; Zhang, Ping] Ohio State Univ, Lincoln Tower 310A,1800 Cannon Dr, Columbus, OH 43210 USA.
   [Yang, Samuel] Nationwide Childrens Hosp, Columbus, OH USA.
   [Qian, Buyue] Xi An Jiao Tong Univ, Xian, Peoples R China.
RP Zhang, P (通讯作者)，Ohio State Univ, Lincoln Tower 310A,1800 Cannon Dr, Columbus, OH 43210 USA.
EM zhang.10631@osu.edu
OI LI, RUI/0000-0002-8166-9667; Zhang, Ping/0000-0002-4601-0779; Qian,
   Buyue/0000-0003-4780-5677; Yin, Changchang/0000-0002-6540-6365
FU Lyntek Medical Technologies Inc.
FX This project was funded in part under a grant with Lyntek Medical
   Technologies Inc. The authors would like to thank Dr Jian Chen for her
   insightful discussions and suggestions on our visualization design. The
   authors also thank the reviewers for their thorough review and
   constructive comments on our manuscript.
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NR 32
TC 7
Z9 7
U1 6
U2 21
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD SEP 28
PY 2020
VL 22
IS 9
AR e20645
DI 10.2196/20645
PG 17
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA PF8WW
UT WOS:000599328800005
PM 32985996
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Balabin, H
   Hoyt, CT
   Birkenbihl, C
   Gyori, BM
   Bachman, J
   Kodamullil, AT
   Ploger, PG
   Hofmann-Apitius, M
   Domingo-Fernandez, D
AF Balabin, Helena
   Hoyt, Charles Tapley
   Birkenbihl, Colin
   Gyori, Benjamin M.
   Bachman, John
   Kodamullil, Alpha Tom
   Ploeger, Paul G.
   Hofmann-Apitius, Martin
   Domingo-Fernandez, Daniel
TI STonKGs: a sophisticated transformer trained on biomedical text and
   knowledge graphs
SO BIOINFORMATICS
LA English
DT Article
ID WEB
AB Motivation: The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models. However, representations based on a single modality are inherently limited.
   Results: To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs (KGs). This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations in a shared embedding space. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against three baseline models trained on either one of the modalities (i.e. text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.084 (i.e. from 0.881 to 0.965). Finally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications.
C1 [Balabin, Helena; Birkenbihl, Colin; Kodamullil, Alpha Tom; Hofmann-Apitius, Martin; Domingo-Fernandez, Daniel] Fraunhofer Inst Algorithms & Sci Comp, Dept Bioinformat, D-53757 St Augustin, Germany.
   [Balabin, Helena; Ploeger, Paul G.] Univ Appl Sci, Dept Bonn Rhein Sieg, D-53757 St Augustin, Germany.
   [Hoyt, Charles Tapley; Gyori, Benjamin M.; Bachman, John] Harvard Med Sch, Lab Syst Pharmacol, Boston, MA 02115 USA.
   [Domingo-Fernandez, Daniel] Fraunhofer Ctr Machine Learning, St Augustin, Germany.
   [Domingo-Fernandez, Daniel] Enveda Biosci, Boulder, CO 80301 USA.
RP Balabin, H; Domingo-Fernandez, D (通讯作者)，Fraunhofer Inst Algorithms & Sci Comp, Dept Bioinformat, D-53757 St Augustin, Germany.; Balabin, H (通讯作者)，Univ Appl Sci, Dept Bonn Rhein Sieg, D-53757 St Augustin, Germany.; Domingo-Fernandez, D (通讯作者)，Fraunhofer Ctr Machine Learning, St Augustin, Germany.; Domingo-Fernandez, D (通讯作者)，Enveda Biosci, Boulder, CO 80301 USA.
EM helena.balabin@gmail.com; daniel.domingo.fernandez@scai.fraunhofer.de
RI Domingo-Fernández, Daniel/AGH-3497-2022; Hofmann-Apitius,
   Martin/E-6123-2018; Hoyt, Charles/B-5720-2018
OI Domingo-Fernández, Daniel/0000-0002-2046-6145; Hofmann-Apitius,
   Martin/0000-0001-9012-6720; Hoyt, Charles/0000-0003-4423-4370; Gyori,
   Benjamin/0000-0001-9439-5346; Tom Kodamullil, Alpha/0000-0001-9896-3531;
   Ploger, Paul/0000-0001-5563-5458; Balabin, Helena/0000-0002-6392-9306
FU Fraunhofer Cluster of Excellence 'Cognitive Internet Technologies';
   Defense Advanced Research Projects Agency (DARPA) Automating Scientific
   Knowledge Extraction (ASKE) program [HR00111990009]
FX This work was supported by the Fraunhofer Cluster of Excellence
   `Cognitive Internet Technologies' and the Defense Advanced Research
   Projects Agency (DARPA) Automating Scientific Knowledge Extraction
   (ASKE) program under award HR00111990009.
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NR 33
TC 2
Z9 2
U1 8
U2 8
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD MAR 4
PY 2022
VL 38
IS 6
BP 1648
EP 1656
DI 10.1093/bioinformatics/btac001
PG 9
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA ZN2SI
UT WOS:000764890000021
PM 34986221
OA Green Submitted, Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Moon, CS
   Jin, CM
   Dong, XL
   Abrar, S
   Zheng, WF
   Chirkova, RY
   Tropsha, A
AF Moon, Changsung
   Jin, Chunming
   Dong, Xialan
   Abrar, Saad
   Zheng, Weifan
   Chirkova, Rada Y.
   Tropsha, Alexander
TI Original Learning Drug-Disease-Target Embedding (DDTE) from knowledge
   graphs to inform drug repurposing hypotheses
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Data mining; Graph embedding; Knowledge graph; Drug repurposing
ID PREDICTION
AB We aimed to develop and validate a new graph embedding algorithm for embedding drug-disease-target networks to generate novel drug repurposing hypotheses. Our model denotes drugs, diseases and targets as subjects, predicates and objects, respectively. Each entity is represented by a multidimensional vector and the predicate is regarded as a translation vector from a subject to an object vectors. These vectors are optimized so that when a subject -predicate -object triple represents a known drug-disease-target relationship, the summed vector between the subject and the predicate is to be close to that of the object; otherwise, the summed vector is distant from the object. The DTINet dataset was utilized to test this algorithm and discover unknown links between drugs and diseases. In cross-validation experiments, this new algorithm outperformed the original DTINet model. The MRR (Mean Reciprocal Rank) values of our models were around 0.80 while those of the original model were about 0.70. In addition, we have identified and verified several pairs of new therapeutic relations as well as adverse effect relations that were not recorded in the original DTINet dataset. This approach showed excellent performance, and the predicted drug-disease and drug-side-effect relationships were found to be consistent with literature reports. This novel method can be used to analyze diverse types of emerging biomedical and healthcare-related knowledge graphs (KG).
C1 [Moon, Changsung; Abrar, Saad; Chirkova, Rada Y.] North Carolina State Univ, Dept Comp Sci, Raleigh, NC 27695 USA.
   [Jin, Chunming; Dong, Xialan; Zheng, Weifan] North Carolina Cent Univ, Coll Hlth & Sci, BRITE Inst, Durham, NC 27707 USA.
   [Jin, Chunming; Dong, Xialan; Zheng, Weifan] North Carolina Cent Univ, Coll Hlth & Sci, Dept Pharmaceut Sci, Durham, NC 27707 USA.
   [Zheng, Weifan; Tropsha, Alexander] Univ North Carolina Chapel Hill, UNC Eshelman Sch Pharm, Chapel Hill, NC 27599 USA.
RP Chirkova, RY (通讯作者)，North Carolina State Univ, Dept Comp Sci, Raleigh, NC 27695 USA.; Zheng, WF (通讯作者)，North Carolina Cent Univ, Coll Hlth & Sci, BRITE Inst, Durham, NC 27707 USA.; Zheng, WF (通讯作者)，North Carolina Cent Univ, Coll Hlth & Sci, Dept Pharmaceut Sci, Durham, NC 27707 USA.; Tropsha, A (通讯作者)，Univ North Carolina Chapel Hill, UNC Eshelman Sch Pharm, Chapel Hill, NC 27599 USA.
EM wzheng@nccu.cdu; rychirko@ncsu.edu; alex_tropsha@unc.edu
FU NIH [1OT2TR003441-01, U01CA207160]; University of North Carolina System
FX This study was supported in part by NIH award to AT and WZ (U01CA207160)
   , NIH award to AT (1OT2TR003441-01) and a joint ROI grant from the
   University of North Carolina System to AT, RYC and WZ.
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NR 36
TC 1
Z9 2
U1 5
U2 10
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD JUL
PY 2021
VL 119
AR 103838
DI 10.1016/j.jbi.2021.103838
EA JUN 2021
PG 8
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA TK9YX
UT WOS:000674512900012
PM 34119691
OA Bronze
DA 2022-08-23
ER

PT J
AU Sawant, U
   Garg, S
   Chakrabarti, S
   Ramakrishnan, G
AF Sawant, Uma
   Garg, Saurabh
   Chakrabarti, Soumen
   Ramakrishnan, Ganesh
TI Neural architecture for question answering using a knowledge graph and
   web corpus
SO INFORMATION RETRIEVAL JOURNAL
LA English
DT Article; Proceedings Paper
CT 2nd Workshop on Knowledge Graphs and Semantics for Text Retrieval,
   Analysis, and Understanding (KG4IR) / International Workshop on Data
   Search (DATA SEARCH) Colocated with SIGIR Conference
CY JUL 12, 2018
CL Ann Arbor, MI
SP SIGIR
DE Question answering; Knowledge graph; Neural network; Convolutional
   network; Entity ranking
AB In Web search, entity-seeking queries often trigger a special question answering (QA) system. It may use a parser to interpret the question to a structured query, execute that on a knowledge graph (KG), and return direct entity responses. QA systems based on precise parsing tend to be brittle: minor syntax variations may dramatically change the response. Moreover, KG coverage is patchy. At the other extreme, a large corpus may provide broader coverage, but in an unstructured, unreliable form. We present AQQUCN, a QA system that gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of query syntax, between well-formed questions to short telegraphic keyword sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals from KGs and large corpora to directly rank KG entities, rather than commit to one semantic interpretation of the query. AQQUCN models the ideal interpretation as an unobservable or latent variable. Interpretations and candidate entity responses are scored as pairs, by combining signals from multiple convolutional networks that operate collectively on the query, KG and corpus. On four public query workloads, amounting to over 8000 queries with diverse query syntax, we see 5-16% absolute improvement in mean average precision (MAP), compared to the entity ranking performance of recent systems. Our system is also competitive at entity set retrieval, almost doubling F1 scores for challenging short queries.
C1 [Sawant, Uma; Garg, Saurabh; Chakrabarti, Soumen; Ramakrishnan, Ganesh] Indian Inst Technol, Mumbai 400076, Maharashtra, India.
RP Chakrabarti, S (通讯作者)，Indian Inst Technol, Mumbai 400076, Maharashtra, India.
EM soumen@cse.iitb.ac.in
FU IBM; nVidia
FX Thanks to the reviewers for their constructive suggestions. Thanks to
   Elmar Haussmann for generous help with AQQU. Thanks to Doug Oard for
   advice on set versus ranked retrieval. Thanks to Saurabh Sarda for
   migrating the code of Joshi et al. (2014) to use AQQU. Partly supported
   by grants from IBM and nVidia.
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NR 67
TC 14
Z9 15
U1 1
U2 54
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1386-4564
EI 1573-7659
J9 INFORM RETRIEVAL J
JI Inf. Retr. J.
PD AUG
PY 2019
VL 22
IS 3-4
SI SI
BP 324
EP 349
DI 10.1007/s10791-018-9348-8
PG 26
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA HZ5UO
UT WOS:000468918400005
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Sydow, M
   Pikula, M
   Schenkel, R
AF Sydow, Marcin
   Pikula, Mariusz
   Schenkel, Ralf
TI The notion of diversity in graphical entity summarisation on semantic
   knowledge graphs
SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
LA English
DT Article
DE Diversity; Semantic knowledge graph; Graphical entity summarisation;
   Summarisation; Diversity measure; Algorithm; Experiment; Crowdsourcing
AB Given an entity represented by a single node q in semantic knowledge graph D, the Graphical Entity Summarisation problem (GES) consists in selecting out of D a very small surrounding graph S that constitutes a generic summary of the information concerning the entity q with given limit on size of S. This article concerns the role of diversity in this quite novel problem. It gives an overview of the diversity concept in information retrieval, and proposes how to adapt it to GES. A measure of diversity for GES, called ALC, is defined and two algorithms presented, baseline, diversity-oblivious PRECIS and diversity-aware DIVERSUM. A reported experiment shows that DIVERSUM actually achieves higher values of the ALC diversity measure than PRECIS. Next, an objective evaluation experiment demonstrates that diversity-aware algorithm is superior to the diversity-oblivious one in terms of fact selection. More precisely, DIVERSUM clearly achieves higher recall than PRECIS on ground truth reference entity summaries extracted from Wikipedia. We also report another intrinsic experiment, in which the output of diversity-aware algorithm is significantly preferred by human expert evaluators. Importantly, the user feedback clearly indicates that the notion of diversity is the key reason for the preference. In addition, the experiment is repeated twice on an anonymous sample of broad population of Internet users by means of a crowd-sourcing platform, that further confirms the results mentioned above.
C1 [Sydow, Marcin; Pikula, Mariusz] Polish Japanese Inst Informat Technol, Web Min Lab, Warsaw, Poland.
   [Sydow, Marcin] Polish Acad Sci, Inst Comp Sci, PL-00901 Warsaw, Poland.
   [Schenkel, Ralf] Univ Saarland, D-66123 Saarbrucken, Germany.
   [Schenkel, Ralf] MPI Informat, Saarbrucken, Germany.
RP Sydow, M (通讯作者)，Polish Japanese Inst Informat Technol, Web Min Lab, Warsaw, Poland.
EM msyd@poljap.edu.pl; mariusz.pikula@poljap.edu.pl;
   schenkel@mmci.uni-saarland.de
OI Schenkel, Ralf/0000-0001-5379-5191
FU Polish Ministry of Science and Higher Education [N N516 481940]; DAAD
FX The work is supported by the N N516 481940 grant of the Polish Ministry
   of Science and Higher Education. The first author was supported by the
   DAAD Prize in 2010 for visiting MPII, Saarbruecken, where early part of
   this work was done. The authors would like to thank all the volunteers
   from the MPI for Informatics that participated in the expert-based
   experimental evaluation.
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NR 50
TC 31
Z9 32
U1 0
U2 49
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 OCT
PY 2013
VL 41
IS 2
BP 109
EP 149
DI 10.1007/s10844-013-0239-6
PG 41
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 214FR
UT WOS:000324118000001
OA hybrid
DA 2022-08-23
ER

PT J
AU Zhang, M
   Wang, JL
AF Zhang, Min
   Wang, Juanle
TI Global Flood Disaster Research Graph Analysis Based on Literature Mining
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE flood disaster; research hotspot; literature mining; natural language
   processing; knowledge graph
AB Floods are the most frequent and highest-impact among the natural disasters caused by global climate change. A large number of flood disaster knowledge were buried in the scientific literature. This study mines research trends and hotspots on flood disasters and identifies their quantitative and spatial distribution features using natural language process technology. The abstracts of 14,076 studies related to flood disasters from 1990 to 2020 were used for text mining. The study used logistic regression to classify themes, adopted the dictionary matching method to analyze flood disaster subcategories, analyzed the spatial distribution characteristics of research institutions, and used Stanford named entity recognition to identify hot research areas. Finally, the disaster information was integrated and visualized as a knowledge graph. The main findings are as follows. (1) The research hotspots are concentrated on flood disaster risks and prediction. Rainfall, coastal floods, and flash floods are the most-studied flood disaster sub-categories. (2) There are some connections and differences between the physical occurrence and research frequency of flood disasters. Occurrence frequency and research frequency of flood disasters are correlated. However, the spatial distribution at the global and intercontinental scales is geographically imbalanced. (3) The study's flood disaster knowledge graph contains 39,679 nodes and 64,908 edges, reflecting the literature distribution and field information on the research themes. Future research will extract more disaster information from the full texts of the studies to enrich the flood disaster knowledge graph and obtain more knowledge on flood disaster risk and reduction.
C1 [Zhang, Min; Wang, Juanle] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.
   [Zhang, Min] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China.
   [Wang, Juanle] China Pakistan Joint Res Ctr Earth Sci, Islamabad 45320, Pakistan.
   [Wang, Juanle] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.
RP Wang, JL (通讯作者)，Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.; Wang, JL (通讯作者)，China Pakistan Joint Res Ctr Earth Sci, Islamabad 45320, Pakistan.; Wang, JL (通讯作者)，Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.
EM zhangmin@lreis.ac.cn; wangjl@igsnrr.ac.cn
RI , Juanle/R-8881-2016
OI , Juanle/0000-0002-5641-0813
FU National Natural Science Foundation of China [42050105]; Chinese Academy
   of Sciences [ZDRW-XH-2021-3]; Construction Project of the China
   Knowledge Center for Engineering Sciences and Technology
   [CKCEST-2021-2-18]
FX This research was funded by the National Natural Science Foundation of
   China, grant number 42050105; the Chinese Academy of Sciences, grant
   number ZDRW-XH-2021-3; the Construction Project of the China Knowledge
   Center for Engineering Sciences and Technology, grant number
   CKCEST-2021-2-18.
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NR 36
TC 1
Z9 1
U1 8
U2 8
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD MAR
PY 2022
VL 12
IS 6
AR 3066
DI 10.3390/app12063066
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 0D4UA
UT WOS:000775991000001
OA gold
DA 2022-08-23
ER

PT J
AU Kalayci, TE
   Bricelj, B
   Lah, M
   Pichler, F
   Scharrer, MK
   Rubesa-Zrim, J
AF Kalayci, Tahir Emre
   Bricelj, Bor
   Lah, Marko
   Pichler, Franz
   Scharrer, Matthias K.
   Rubesa-Zrim, Jelena
TI A Knowledge Graph-Based Data Integration Framework Applied to Battery
   Data Management
SO SUSTAINABILITY
LA English
DT Article
DE anomaly detection; battery data management; data integration; data
   analysis; intelligent transport systems; knowledge graphs; machine
   learning techniques
AB Today, the automotive and transportation sector is undergoing a transformation process to meet the requirements of sustainable and efficient operations. This transformation mainly reveals itself by electric vehicles, hybrid electric vehicles, and electric vehicle sharing. One significant, and the most expensive, component in electric vehicles is the batteries, and the management of batteries is crucial. It is essential to perform constant monitoring of behavior changes for operational purposes and quickly adjust components and operations to these changes. Thus, to address these challenges, we propose a knowledge graph-based data integration framework for simplifying access and analysis of data accumulated through the operations of vehicles and related transportation systems. The proposed framework aims to enable the effortless analysis and navigation of integrated knowledge and the creation of additional data sets from this knowledge to use during the application of data analysis and machine learning. The knowledge graph serves as a significant component to simplify the extraction, enrichment, exploration, and generation of data in this framework. We have developed it according to the human-centered design, and various roles of the data science and machine learning life cycle can use it. Its main objective is to streamline the exploration and interaction with the integrated data to maximize human productivity. Finally, we present a battery use case to show the feasibility and benefits of the proposed framework. The use case illustrates the usage of the framework to extract knowledge from raw data, navigate and enrich it with additional knowledge, and generate data sets.
C1 [Kalayci, Tahir Emre; Bricelj, Bor; Lah, Marko; Pichler, Franz; Scharrer, Matthias K.; Rubesa-Zrim, Jelena] Virtual Vehicle Res GmbH, Inffeldgasse 21A, A-8010 Graz, Austria.
RP Kalayci, TE (通讯作者)，Virtual Vehicle Res GmbH, Inffeldgasse 21A, A-8010 Graz, Austria.
EM emre.kalayci@v2c2.at; bor.bricelj@v2c2.at; marko.lah@v2c2.at;
   franz.pichler@v2c2.at; matthias.scharrer@v2c2.at;
   jelena.rubesa-zrim@v2c2.at
RI Kalayci, T. E./B-2365-2012
OI Kalayci, T. E./0000-0001-6228-1221; Scharrer,
   Matthias/0000-0003-3675-4878
FU COMET Competence Centers for Excellent Technologies from the Austrian
   Federal Ministry for Climate Action; Austrian Federal Ministry for
   Digital and Economic Affairs; Province of Styria (Dept. 12); Styrian
   Business Promotion Agency (SFG)
FX Virtual Vehicle Research GmbH has received funding within COMET
   Competence Centers for Excellent Technologies from the Austrian Federal
   Ministry for Climate Action, the Austrian Federal Ministry for Digital
   and Economic Affairs, the Province of Styria (Dept. 12) and the Styrian
   Business Promotion Agency (SFG). The Austrian Research Promotion Agency
   (FFG) has been authorized for the program management.
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NR 60
TC 2
Z9 2
U1 6
U2 16
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB
PY 2021
VL 13
IS 3
AR 1583
DI 10.3390/su13031583
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 QD6EL
UT WOS:000615608800001
OA Green Published
DA 2022-08-23
ER

PT J
AU Lu, Y
   Chen, YR
   Zhao, DB
   Li, D
AF Lu, Yi
   Chen, Yaran
   Zhao, Dongbin
   Li, Dong
TI MGRL: Graph neural network based inference in a Markov network with
   reinforcement learning for visual navigation
SO NEUROCOMPUTING
LA English
DT Article
DE Visual navigation; Graph neural network; Markov network; Reinforcement
   learning; Probabilistic graph model; Knowledge graph
AB Visual navigation is an essential task for indoor robots and usually uses the map as assistance to providing global information for the agent. Because the traditional maps match the environments, the map based and map-building-based navigation methods are limited in the new environments for obtaining maps. Although the deep reinforcement learning navigation method, utilizing the non-map-based navigation technique, achieves satisfactory performance, it lacks the interpretability and the global view of the environment. Therefore, we propose a novel abstract map for the deep reinforcement learning navigation method with better global relative position information and more reasonable interpretability. The abstract map is modeled as a Markov network which is used for explicitly representing the regularity of objects arrangement, influenced by people activities in different environments. Besides, a knowledge graph is utilized to initialize the structure of the Markov network, as providing the prior structure for the model and reducing the difficulty of model learning. Then, a graph neural network is adopted for probability inference in the Markov network. Furthermore, the update of the abstract map, including the knowledge graph structure and the parameters of the graph neural network, are combined into an end-to-end learning process trained by a reinforcement learning method. Finally, experiments in the AI2THOR framework and the physical environment indicate that our algorithm greatly improves the success rate of navigation in case of new environments, thus confirming the good generalization. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Lu, Yi; Chen, Yaran; Zhao, Dongbin; Li, Dong] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
   [Lu, Yi; Chen, Yaran; Zhao, Dongbin; Li, Dong] Univ Chinese Acad Sci, Beijing 101408, Peoples R China.
RP Zhao, DB (通讯作者)，Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China.
EM luyi2017@ia.ac.cn; chenyaran2013@ia.ac.cn; dongbin.zhao@ia.ac.cn;
   lidong2014@ia.ac.cn
FU National Natural Science Foundation of China (NSFC) [61803371]; Beijing
   Science and Technology Plan [Z191100007419002, GJHZ1849]; International
   Partnership Program of Chinese Academy of Science
FX This work is supported partly by the National Natural Science Foundation
   of China (NSFC) under Grants No. 61803371, the Beijing Science and
   Technology Plan under Grants Z191100007419002, and No. GJHZ1849
   International Partnership Program of Chinese Academy of Science.
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NR 46
TC 5
Z9 5
U1 4
U2 31
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JAN 15
PY 2021
VL 421
BP 140
EP 150
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OW8AJ
UT WOS:000593102100012
DA 2022-08-23
ER

PT J
AU Zhong, D
   Zhu, YA
   Wang, LQ
   Duan, JH
   He, JX
AF Zhong, Dong
   Zhu, Yi-An
   Wang, Lanqing
   Duan, Junhua
   He, Jiaxuan
TI A Cognition Knowledge Representation Model Based on Multidimensional
   Heterogeneous Data
SO COMPLEXITY
LA English
DT Article
AB The information in the working environment of industrial Internet is characterized by diversity, semantics, hierarchy, and relevance. However, the existing representation methods of environmental information mostly emphasize the concepts and relationships in the environment and have an insufficient understanding of the items and relationships at the instance level. There are also some problems such as low visualization of knowledge representation, poor human-machine interaction ability, insufficient knowledge reasoning ability, and slow knowledge search speed, which cannot meet the needs of intelligent and personalized service. Based on this, this paper designs a cognitive information representation model based on a knowledge graph, which combines the perceptual information of industrial robot ontology with semantic description information such as functional attributes obtained from the Internet to form a structured and logically reasoned cognitive knowledge graph including perception layer and cognition layer. Aiming at the problem that the data sources of the knowledge base for constructing the cognitive knowledge graph are wide and heterogeneous, and there are entity semantic differences and knowledge system differences among different data sources, a multimodal entity semantic fusion model based on vector features and a system fusion framework based on HowNet are designed, and the environment description information such as object semantics, attributes, relations, spatial location, and context acquired by industrial robots and their own state information are unified and standardized. The automatic representation of robot perceived information is realized, and the universality, systematicness, and intuition of robot cognitive information representation are enhanced, so that the cognition reasoning ability and knowledge retrieval efficiency of robots in the industrial Internet environment can be effectively improved.
C1 [Zhong, Dong; Zhu, Yi-An; Wang, Lanqing; Duan, Junhua] Univ Northwestern Polytech China, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China.
   [He, Jiaxuan] Univ Northwestern Polytech China, Sch Software, Xian 710129, Shaanxi, Peoples R China.
RP Zhong, D (通讯作者)，Univ Northwestern Polytech China, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China.
EM dzhong@nwpu.edu.cn; zhuya@nwpu.edu.cn; 757813916@qq.com;
   duanjh@nwpu.edu.cn; hejiaxuan@mail.nwpu.edu.cn
OI Zhu (zhu yi an), Yian/0000-0001-6564-5756
FU Key R & D Program of Shaanxi Province [2019ZDLGY03-04]; Xi'an Science
   and Technology Plan Project [GXYD19.8, GXYD19.7]; National Natural
   Science Foundation of China [61303225]; Space Science and Technology
   Fund [D5120200106]
FX This work was supported in part by the Key R & D Program of Shaanxi
   Province (no. 2019ZDLGY03-04), Xi'an Science and Technology Plan Project
   (Program nos. GXYD19.8 and GXYD19.7), and the National Natural Science
   Foundation of China (no. 61303225), and the Space Science and Technology
   Fund (Program no. D5120200106).
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NR 38
TC 2
Z9 2
U1 4
U2 13
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1076-2787
EI 1099-0526
J9 COMPLEXITY
JI Complexity
PD DEC 29
PY 2020
VL 2020
AR 8812459
DI 10.1155/2020/8812459
PG 17
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA PU7SA
UT WOS:000609499800003
OA gold
DA 2022-08-23
ER

PT J
AU Kertkeidkachorn, N
   Ichise, R
AF Kertkeidkachorn, Natthawut
   Ichise, Ryutaro
TI An Automatic Knowledge Graph Creation Framework from Natural Language
   Text
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article; Proceedings Paper
CT 15th International Semantic Web Conference (ISWC)
CY OCT 17-21, 2016
CL Kobe, JAPAN
SP Semant Web Sci Assoc, IBM, Semant Software, Oracle, IOS Press, Recruit Technologies Co Ltd, Fujitsu, NTT Resonant, SYSTAP Metaphacts, Hitachi, Rakuten Inst Technol, Yahoo Japan, Google
DE knowledge graph; knowledge discovery; knowledge extraction; linked data
AB Knowledge graphs (KG) play a crucial role in many modern applications. However, constructing a KG from natural language text is challenging due to the complex structure of the text. Recently, many approaches have been proposed to transform natural language text to triples to obtain KGs. Such approaches have not yet provided efficient results for mapping extracted elements of triples, especially the predicate, to their equivalent elements in a KG. Predicate mapping is essential because it can reduce the heterogeneity of the data and increase the searchability over a KG. In this article, we propose T2KG, an automatic KG creation framework for natural language text, to more effectively map natural language text to predicates. In our framework, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its corresponding predicate in a KG. Based on experimental results, the hybrid approach can identify more similar predicate pairs than a baseline method in the predicate mapping task. An experiment on KG creation is also conducted to investigate the performance of the T2KG. The experimental results show that the T2KG also outperforms the baseline in KG creation. Although KG creation is conducted in open domains, in which prior knowledge is not provided, the T2KG still achieves an F1 score of approximately 50% when generating triples in the KG creation task. In addition, an empirical study on knowledge population using various text sources is conducted, and the results indicate the T2KG could be used to obtain knowledge that is not currently available from DBpedia.
C1 [Kertkeidkachorn, Natthawut; Ichise, Ryutaro] Grad Univ Adv Studies, SOKENDAI, Tokyo 1018430, Japan.
   [Ichise, Ryutaro] Natl Inst Informat, Informat Res Div, Tokyo 1018430, Japan.
   [Ichise, Ryutaro] Natl Inst Adv Ind Sci & Technol, Tokyo 1008921, Japan.
RP Kertkeidkachorn, N (通讯作者)，Grad Univ Adv Studies, SOKENDAI, Tokyo 1018430, Japan.
EM natthawut@nii.ac.jp
RI Kertkeidkachorn, Natthawut/M-7453-2018
OI Kertkeidkachorn, Natthawut/0000-0003-4527-776X
FU NEDO (New Energy and Industrial Technology Development Organization)
FX This work was partially supported by NEDO (New Energy and Industrial
   Technology Development Organization).
CR Abedjan Ziawasch, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P140
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NR 23
TC 13
Z9 14
U1 3
U2 14
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 JAN
PY 2018
VL E101D
IS 1
BP 90
EP 98
DI 10.1587/transinf.2017SWP0006
PG 9
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA GF2IA
UT WOS:000431760600014
OA gold
DA 2022-08-23
ER

PT J
AU Ruan, T
   Huang, YQ
   Liu, XL
   Xia, YH
   Gao, J
AF Ruan, Tong
   Huang, Yueqi
   Liu, Xuli
   Xia, Yuhang
   Gao, Ju
TI QAnalysis: a question-answer driven analytic tool on knowledge graphs
   for leveraging electronic medical records for clinical research
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE Electronic medical record; Statistical question answering; Graph
   database; Context-free grammar
ID NAVIGATION; SEARCH; FACET
AB BackgroundWhile doctors should analyze a large amount of electronic medical record (EMR) data to conduct clinical research, the analyzing process requires information technology (IT) skills, which is difficult for most doctors in China.MethodsIn this paper, we build a novel tool QAnalysis, where doctors enter their analytic requirements in their natural language and then the tool returns charts and tables to the doctors. For a given question from a user, we first segment the sentence, and then we use grammar parser to analyze the structure of the sentence. After linking the segmentations to concepts and predicates in knowledge graphs, we convert the question into a set of triples connected with different kinds of operators. These triples are converted to queries in Cypher, the query language for Neo4j. Finally, the query is executed on Neo4j, and the results shown in terms of tables and charts are returned to the user.ResultsThe tool supports top 50 questions we gathered from two hospital departments with the Delphi method. We also gathered 161 questions from clinical research papers with statistical requirements on EMR data. Experimental results show that our tool can directly cover 78.20% of these statistical questions and the precision is as high as 96.36%. Such extension is easy to achieve with the help of knowledge-graph technology we have adopted. The recorded demo can be accessed from https://github.com/NLP-BigDataLab/QAnalysis-project.ConclusionOur tool shows great flexibility in processing different kinds of statistic questions, which provides a convenient way for doctors to get statistical results directly in natural language.
C1 [Ruan, Tong; Huang, Yueqi; Liu, Xuli; Xia, Yuhang] East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China.
   [Gao, Ju] Shanghai Shuguang Hosp, Shanghai 200021, Peoples R China.
RP Ruan, T (通讯作者)，East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China.
EM ruantong@ecust.edu.cn
FU National Natural Science Foundation of China [61772201]; 863 plan of
   China Ministry of Science and Technology [2015AA020107]; Science and
   Technology Innovation Project of Shanghai Science and Technology
   Commission [16511101000]; National Science and Technology Support
   Program of China [2015BAH12 F01-05]
FX This work is supported by National Natural Science Foundation of China
   (No. 61772201), The 863 plan of China Ministry of Science and Technology
   (No. 2015AA020107), The Science and Technology Innovation Project of
   Shanghai Science and Technology Commission (No. 16511101000) and
   National Science and Technology Support Program of China (No. 2015BAH12
   F01-05). The funders had no role in the design of the study, collection,
   analysis and interpretation of data, or writing the manuscript.
CR [Anonymous], 1971, EXPT ASSESSMENT DELP
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NR 29
TC 6
Z9 6
U1 0
U2 18
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD APR 1
PY 2019
VL 19
AR 82
DI 10.1186/s12911-019-0798-8
PG 13
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA HR3SC
UT WOS:000463058800001
PM 30935389
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Lin, WW
   Luo, YY
   Liu, F
   Li, HT
   Wang, Q
   Dong, ZY
   Chen, XM
AF Lin, Wenwen
   Luo, Yayong
   Liu, Fang
   Li, Hangtian
   Wang, Qian
   Dong, Zheyi
   Chen, Xiangmei
TI Status and Trends of the Association Between Diabetic Nephropathy and
   Diabetic Retinopathy From 2000 to 2021: Bibliometric and Visual Analysis
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE diabetic nephropathy; diabetic retinopathy; diabetes; bibliometrics;
   citespace
ID RISK-FACTORS; MICROVASCULAR COMPLICATIONS; KIDNEY-DISEASE; GLYCEMIC
   CONTROL; MICROALBUMINURIA; ALBUMINURIA; PROGRESSION; PREVALENCE;
   PREVENTION; OUTCOMES
AB Background: Diabetic nephropathy (DN) and diabetic retinopathy (DR) are microvascular complications of diabetes that share a similar pathogenesis and clinical relevance. The study aimed to visually analyze the research status and development trend of the relationship between DN and DR by means of bibliometrics and knowledge mapping.Methods: Publications were collected from the Science Citation Index-Expanded of the Web of Science Core Collection between 2000 and 2021. CiteSpace, Alluvial Generator, and Microsoft Excel were used to analyze and present the data.Results: A total of 3,348 publications were retrieved and 3,285 were included in the analysis after deduplication. The publications demonstrated an annually increasing trend. The results of the collaborative network analysis showed that the United States, Steno Diabetes Center, and Tien Y. Wong were the most influential country, institution and author, in this field of research, respectively. The analysis of references and keywords showed that the pathogenesis of DN and DR and their relationship with cardiovascular disease are research hotspots. The clinical relevance and drug therapy for DN and DR will become frontiers of future research in this field.Conclusion: This study is the first to visualize the correlation between DN and DR using a bibliometric approach. This study provides a reference of research trends for scholars.
C1 [Lin, Wenwen; Luo, Yayong; Liu, Fang; Li, Hangtian; Chen, Xiangmei] Guangdong Pharmaceut Univ, Sch Clin Med, Guangzhou, Peoples R China.
   [Lin, Wenwen; Luo, Yayong; Liu, Fang; Li, Hangtian; Wang, Qian; Dong, Zheyi; Chen, Xiangmei] Chinese Peoples Liberat Army Gen Hosp, Nephrol Inst Chinese Peoples Liberat Army, Natl Clin Res Ctr Kidney Dis, State Key Lab Kidney Dis,Med Ctr 1,Beijing Key Lab, Beijing, Peoples R China.
RP Chen, XM (通讯作者)，Guangdong Pharmaceut Univ, Sch Clin Med, Guangzhou, Peoples R China.; Dong, ZY; Chen, XM (通讯作者)，Chinese Peoples Liberat Army Gen Hosp, Nephrol Inst Chinese Peoples Liberat Army, Natl Clin Res Ctr Kidney Dis, State Key Lab Kidney Dis,Med Ctr 1,Beijing Key Lab, Beijing, Peoples R China.
EM shengdai26@163.com; xmchen301@126.com
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NR 79
TC 0
Z9 0
U1 8
U2 8
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 JUN 20
PY 2022
VL 13
AR 937759
DI 10.3389/fphar.2022.937759
PG 13
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA 2Q2NG
UT WOS:000820264300001
PM 35795563
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhao, W
   Liu, J
AF Zhao, Wei
   Liu, Juan
TI Application of Knowledge Map Based on BiLSTM-CRF Algorithm Model in
   Ideological and Political Education Question Answering System
SO MOBILE INFORMATION SYSTEMS
LA English
DT Article
ID ENTITY RECOGNITION
AB Ideological and political education plays an important role in university education and is an important way to realize the function of educating people. It is of great significance to establish a perfect automatic question answering system for ideological and political education. Traditional automatic question answering methods usually rely on predicates and other prior information to achieve knowledge base question answering, which requires a lot of manpower and poor generalization ability. In order to solve this problem, this paper designs a question answering system for ideological and political education based on BiLSTM-CRF algorithm model (BiLSTM: Bidirectional Long Short-Term Memory and CRF: Conditional Random Fields). For the knowledge base question answering method with weak-dependent information, this paper combines BERT (Bidirectional Encoder Representation from Transformers) and BiLSTM-CRF network to extract the named entity in the questions and locate the triplet information related to the entity in the knowledge base. Through the answer matching network, the similarity score is marked for the answers in the triplet set, and the threshold selection strategy is used to select the answers that meet the requirements. And according to the similarity score from high to paper, it is presented to the user. The experimental results show that the method weakens the dependence on prior information, reduces manual intervention and ensures the quality of question answering, and completes the validity verification of the question answering system of ideological and political education.
C1 [Zhao, Wei] Yellow River Conservancy Tech Inst, Sch Art & Design, Kaifeng City 475000, Henan Province, Peoples R China.
   [Liu, Juan] Student Affairs Off, Yellow River Conservancy Tech Inst, Kaifeng City 475000, Henan Province, Peoples R China.
RP Zhao, W (通讯作者)，Yellow River Conservancy Tech Inst, Sch Art & Design, Kaifeng City 475000, Henan Province, Peoples R China.
EM 2010840672@yrcti.edu.cn; 2017910950@yrcti.edu.cn
FU Yellow River Conservancy Technical Institute
FX This work is supported and helped by Yellow River Conservancy Technical
   Institute.
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NR 30
TC 0
Z9 0
U1 0
U2 0
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1574-017X
EI 1875-905X
J9 MOB INF SYST
JI Mob. Inf. Syst.
PD MAY 13
PY 2022
VL 2022
AR 4139323
DI 10.1155/2022/4139323
PG 9
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 2J7EO
UT WOS:000815815700003
OA gold
DA 2022-08-23
ER

PT J
AU Yang, X
   Tan, LP
AF Yang, Xia
   Tan, Leping
TI The Construction of Accurate Recommendation Model of Learning Resources
   of Knowledge Graph under Deep Learning
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB With the rapid development of science and technology and the continuous progress of teaching, it is now flooded with rich learning resources. Massive learning resources provide learners with a good learning foundation. At the same time, learners want to be precise from many learning resources. Second, it becomes more and more difficult to quickly obtain the learning resources you want. Therefore, it is very important to accurately and quickly recommend learning resources to learners. During the last two decades, a large number of different types of recommendation systems were adopted that present the users with contents of their choice, such as videos, products, and educational content recommendation systems. The knowledge graph has been fully applied in this process. The application of deep learning in the recommendation systems has further enhanced their performance. This article proposes a learning resource accurate recommendation model based on the knowledge graph under deep learning. We build a recommendation system based on deep learning that is comprised of a learner knowledge representation (KR) model and a learning resource KR model. Information such as learner's basic information, learning resource information, and other data is used by the recommendation engine to calculate the target learner's score based on the learner KR and the learning resource KR and generate a recommendation list for the target learner. We use mean absolute error (MAE) as the evaluation indicator. The experimental results show that the proposed recommendation system achieves better results as compared to the traditional systems.
C1 [Yang, Xia] Changsha Social Work Coll, Changsha 410004, Hunan, Peoples R China.
   [Tan, Leping] Hunan Vocat Coll Sci & Technol, Changsha 410004, Hunan, Peoples R China.
RP Yang, X (通讯作者)，Changsha Social Work Coll, Changsha 410004, Hunan, Peoples R China.
EM 20200090302048@email.sxu.edu.cn; tlp678@bitc.edu.cn
FU Hunan Provincial Natural Science Foundation [2019JJ70062]
FX This study was supported by the Hunan Provincial Natural Science
   Foundation Project: Construction of Accurate Learning Resource
   Recommendation Model Based on Knowledge Graphs (no. 2019JJ70062).
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NR 15
TC 0
Z9 0
U1 9
U2 9
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD JAN 25
PY 2022
VL 2022
AR 1010122
DI 10.1155/2022/1010122
PG 11
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZR1FF
UT WOS:000767537400014
OA gold
DA 2022-08-23
ER

PT J
AU Ko, H
   Witherell, P
   Lu, Y
   Kim, S
   Rosen, DW
AF Ko, Hyunwoong
   Witherell, Paul
   Lu, Yan
   Kim, Samyeon
   Rosen, David W.
TI Machine learning and knowledge graph based design rule construction for
   additive manufacturing
SO ADDITIVE MANUFACTURING
LA English
DT Article
DE Additive manufacturability; Data; Design for additive manufacturing;
   Design rule; Machine learning; Knowledge
AB Additive Manufacturing (AM) is becoming data-intensive while increasingly generating newly available data. The availability of AM data provides Design for AM (DfAM) with a newfound opportunity to construct AM design rules with improved understanding of AM's influence on part qualities. To seize the opportunity, this paper proposes a novel approach for AM design rule construction based on machine learning and knowledge graph. First, this paper presents a framework that enables i) deploying machine learning for extracting knowledge on predictive additive manufacturability from data, ii) adopting ontology with knowledge graphs as a knowledge base for storing both a priori and newfound AM knowledge, and iii) reasoning with knowledge for deriving data-driven prescriptive AM design rules. Second, this paper presents a methodology that constructs knowledge on predictive additive manufacturability and prescriptive AM design rules. In the methodology, we formalize knowledge representations, extractions, and reasoning, which enhances automated and autonomous construction and improvements of AM design rules. The methodology then employs a machine learning algorithm of Classification and Regression Tree on measurement data from National Institute of Standards and Technology for construction of a Laser Powder Bed Fusion-specific design rule for overhang features. This work supports AI related decision-making in additive manufacturability analysis and (re-)design for AM and guides machine learning to addressing problems related to AM design rules. This work is also meaningful as it provides sharable AM design rule knowledge with the AM society.
C1 [Ko, Hyunwoong; Witherell, Paul; Lu, Yan] NIST, Syst Integrat Div, 100 Bur Dr, Gaithersburg, MD 20899 USA.
   [Ko, Hyunwoong] Nanyang Technol Univ, Sch Mech & Aerosp Engn, 50 Nanyang Ave, Singapore 639798, Singapore.
   [Kim, Samyeon; Rosen, David W.] Singapore Univ Technol & Design, Digital Mfg & Design Ctr, 8 Somapah Rd, Singapore 487372, Singapore.
   [Kim, Samyeon] Jeonju Univ, Dept Mech Syst Engn, 303 Cheonjam Ro, Jeonju Si 55069, Jeollabuk Do, South Korea.
   [Rosen, David W.] Georgia Inst Technol, GW Woodruff Sch Mech Engn, Atlanta, GA 30332 USA.
RP Ko, H (通讯作者)，NIST, Syst Integrat Div, 100 Bur Dr, Gaithersburg, MD 20899 USA.
EM hyunwoong.ko@nist.gov; paul.witherell@nist.gov; yan.lu@nist.gov;
   samyeon.kim@jj.ac.kr; david.rosen@me.gatech.edu
FU National Institute of Standards and Technology, Gaithersburg, MD, USA
FX This work was supported by the National Institute of Standards and
   Technology, Gaithersburg, MD, USA.
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NR 54
TC 20
Z9 20
U1 26
U2 48
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-8604
EI 2214-7810
J9 ADDIT MANUF
JI Addit. Manuf.
PD JAN
PY 2021
VL 37
AR 101620
DI 10.1016/j.addma.2020.101620
PG 16
WC Engineering, Manufacturing; Materials Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Materials Science
GA PU3KH
UT WOS:000609203100016
OA Bronze
DA 2022-08-23
ER

PT J
AU Xiong, Y
   Peng, H
   Xiang, Y
   Wong, KC
   Chen, QC
   Yan, J
   Tang, BZ
AF Xiong, Ying
   Peng, Hao
   Xiang, Yang
   Wong, Ka-Chun
   Chen, Qingcai
   Yan, Jun
   Tang, Buzhou
TI Leveraging Multi-source knowledge for Chinese clinical named entity
   recognition via relational graph convolutional network
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Clinical named entity recognition; Multi-source knowledge; Graph neural
   network
ID NEURAL-NETWORKS
AB Objective: External knowledge, such as lexicon of words in Chinese and domain knowledge graph (KG) of concepts, has been recently adopted to improve the performance of machine learning methods for named entity recognition (NER) as it can provide additional information beyond context. However, most existing studies only consider knowledge from one source (i.e., either lexicon or knowledge graph) in different ways and consider lexicon words or KG concepts independently with their boundaries. In this paper, we focus on leveraging multi source knowledge in a unified manner where lexicon words or KG concepts are well combined with their boundaries for Chinese Clinical NER (CNER). Material and Methods: We propose a novel method based on relational graph convolutional network (RGCN), called MKRGCN, to utilize multi-source knowledge in a unified manner for CNER. For any sentence, a relational graph based on words or concepts in each knowledge source is constructed, where lexicon words or KG concepts appearing in the sentence are linked to the containing tokens with the boundary information of the lexicon words or KG concepts. RGCN is used to model all relational graphs constructed from multi-source knowledge, and the representations of tokens from multi-source knowledge are integrated into the context representations of tokens via an attention mechanism. Based on the knowledge-enhanced representations of tokens, we deploy a conditional random field (CRF) layer for named entity label prediction. In this study, a lexicon of words and a medical knowledge graph are used as knowledge sources for Chinese CNER. Results: Our proposed method achieves the best performance on CCKS2017 and CCKS2018 in Chinese with F1 scores of 91.88% and 89.91%, respectively, significantly outperforming existing methods. The extended experiments on NCBI-Disease and BC2GM in English also prove the effectiveness of our method when only considering one knowledge source via RGCN. Conclusion: The MKRGCN model can integrate knowledge from the external lexicon and knowledge graph effectively for Chinese CNER and has the potential to be applied to English NER.
C1 [Xiong, Ying; Peng, Hao; Chen, Qingcai; Tang, Buzhou] Harbin Inst Technol, Dept Comp Sci, Shenzhen, Peoples R China.
   [Xiong, Ying; Xiang, Yang; Chen, Qingcai; Tang, Buzhou] Peng Cheng Lab, Shenzhen, Peoples R China.
   [Wong, Ka-Chun] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China.
   [Yan, Jun] Yidu Cloud Beijing Technol Co Ltd, Beijing, Peoples R China.
RP Tang, BZ (通讯作者)，Harbin Inst Technol, Dept Comp Sci, Shenzhen, Peoples R China.
EM tangbuzhou@gmail.com
OI WONG, Ka Chun/0000-0001-6062-733X
FU National Natural Science Foundations of China [61876052, U1813215];
   National Natural Science Foundations of Guangdong, China
   [2019A1515011158]; Stra-tegic Emerging Industry Development Special
   Funds of Shenzhen [20200821174109001]; Pilot Project in 5G + Health
   Application of Ministry of Industry and Information Technology &
   National Health Commission (5G + Luohu Hospital Group: an Attempt to New
   Health Management Styles of Residents)
FX This paper is supported in part by grants: National Natural Science
   Foundations of China (61876052 and U1813215) , National Natural Science
   Foundations of Guangdong, China (2019A1515011158) , Stra-tegic Emerging
   Industry Development Special Funds of Shenzhen (20200821174109001) and
   the Pilot Project in 5G + Health Application of Ministry of Industry and
   Information Technology & National Health Commission (5G + Luohu Hospital
   Group: an Attempt to New Health Management Styles of Residents) .
CR Alsentzer E., 2019, P 2 CLIN NAT LANG PR
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NR 46
TC 0
Z9 0
U1 24
U2 24
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD APR
PY 2022
VL 128
AR 104035
DI 10.1016/j.jbi.2022.104035
PG 9
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA ZR6FZ
UT WOS:000767877600005
PM 35217186
DA 2022-08-23
ER

PT J
AU Taheriyan, M
   Knoblock, CA
   Szekely, P
   Ambite, JL
AF Taheriyan, Mohsen
   Knoblock, Craig A.
   Szekely, Pedro
   Ambite, Jose Luis
TI Learning the semantics of structured data sources
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge graph; Semantic model; Semantic labeling; Semantic web;
   Ontology; Linked data
ID STEINER PROBLEM; ALGORITHM; TABLES
AB Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.
   We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs. (C) 2015 Elsevier B.V. All rights reserved.
C1 [Taheriyan, Mohsen; Knoblock, Craig A.; Szekely, Pedro; Ambite, Jose Luis] Univ So Calif, Inst Informat Sci, 4676 Admiralty Way, Marina Del Rey, CA 90292 USA.
RP Taheriyan, M (通讯作者)，Univ So Calif, Inst Informat Sci, 4676 Admiralty Way, Marina Del Rey, CA 90292 USA.
EM mohsen@isi.edu; knoblock@isi.edu; pszekely@isi.edu; ambite@isi.edu
OI Szekely, Pedro/0000-0002-4621-2266; Ambite, Jose
   Luis/0000-0003-0087-080X
FU National Science Foundation [1117913]; Defense Advanced Research
   Projects Agency (DARPA) via AFRL [FA8750-14-C-0240, FA8750-16-C-0045];
   Div Of Information & Intelligent Systems [1117913] Funding Source:
   National Science Foundation
FX This research was supported in part by the National Science Foundation
   under Grant No. 1117913 and in part by Defense Advanced Research
   Projects Agency (DARPA) via AFRL contract numbers FA8750-14-C-0240 and
   FA8750-16-C-0045. The U.S. Government is authorized to reproduce and
   distribute reprints for Governmental purposes notwithstanding any
   copyright annotation thereon. The views and conclusions contained herein
   are those of the authors and should not be interpreted as necessarily
   representing the official policies or endorsements, either expressed or
   implied, of NSF, DARPA, AFRL, or the U.S. Government. We would like to
   thank the anonymous reviewers for their valuable comments and
   suggestions to improve the paper. We are also grateful to Yinyi Chen for
   her help in creating the gold standard models for our evaluation.
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   [No title captured]
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NR 50
TC 29
Z9 29
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 MAR
PY 2016
VL 37-38
BP 152
EP 169
DI 10.1016/j.websem.2015.12.003
PG 18
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:000376457600010
OA Green Submitted, Bronze
DA 2022-08-23
ER

PT J
AU Lin, YC
AF Lin, Yu-Cheng
TI CONSTRUCTION 3D BIM-BASED KNOWLEDGE MANAGEMENT SYSTEM: A CASE STUDY
SO JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
LA English
DT Article
DE construction knowledge management; BIM; building information modeling;
   project management; web-based information system
ID DESIGN; MODEL; PROJECT; 4D; DOCUMENTATION; VISUALIZATION; SIMULATION
AB Knowledge management (KM) is the organization, creation, sharing and flow of knowledge within organizations. Knowledge can be shared and reused among involved engineers and experts to improve a construction process and reduce the time and cost of solving problems. This paper proposes a new and practical methodology to capture and represent construction project knowledge by using a Building Information Modeling (BIM) approach. Using BIM approach, users can make visual knowledge management in the 3D Computer-aided Design (CAD) environment. This study addresses the application of knowledge management in the construction phase of construction projects and proposes a Construction BIM-based Knowledge Management (CBIMKM) system for general contractors. The CBIMKM is then applied in selected case studies of a construction building project in Taiwan to verify our proposed methodology and demonstrate the effectiveness of sharing knowledge in the 3D environment. By applying the BIM approach, all participants in a project can share and reuse explicit and tacit knowledge through the 3D CAD-based knowledge map. The combined results demonstrate that the CBIMKM system can be used as a visual 3D-based knowledge management platform by utilizing the BIM approach and web technology.
C1 Natl Taipei Univ Technol, Dept Civil Engn, Taipei 106, Taiwan.
RP Lin, YC (通讯作者)，Natl Taipei Univ Technol, Dept Civil Engn, 1 Chung Hsiao E Rd,Sec 3, Taipei 106, Taiwan.
EM yclin@ntut.edu.tw
FU National Science Council, Taiwan [NSC 99-2221-E-027-093]
FX The authors would like to acknowledge the National Science Council,
   Taiwan, for financially supporting this work under contract number NSC
   99-2221-E-027-093 and express our appreciation to the F-D Construction
   Inc for assistance in the system design and interviews, and to the
   experts and engineers of the owner of the project for providing useful
   data, valuable information, and helpful comments during system design
   and development.
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NR 65
TC 46
Z9 46
U1 9
U2 91
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 2014
VL 20
IS 2
BP 186
EP 200
DI 10.3846/13923730.2013.801887
PG 15
WC Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA AF8EZ
UT WOS:000334949600005
OA hybrid
DA 2022-08-23
ER

PT J
AU Chiu, WS
   Fan, TCM
   Nam, SB
   Sun, PH
AF Chiu, Weisheng
   Fan, Thomas Chun Man
   Nam, Sang-Back
   Sun, Ping-Hung
TI Knowledge Mapping and Sustainable Development of eSports Research: A
   Bibliometric and Visualized Analysis
SO SUSTAINABILITY
LA English
DT Review
DE eSports; competitive gaming; bibliometric analysis; Scopus; VOSviewer
ID GOOGLE-SCHOLAR; RESEARCH TRENDS; VIDEO GAMES; E-SPORTS; SCIENCE; SCOPUS;
   WEB; MOTIVATIONS; ACCEPTANCE; MANAGEMENT
AB The rapid expansion of the eSports industry has attracted scholars' attention in recent years. However, little research has investigated the evolution of the extant eSports literature. This study aimed to explore the existing knowledge base of eSports and its research networks across authors, journals, institutions, and countries by performing a bibliometric analysis. A total of 260 studies published between 2010 and 2021 were extracted from the Scopus database, which is one of the largest abstract and citation databases. Then, they were analyzed using VOSviewer. Specifically, a series of analyses were conducted: (1) citation analysis, (2) co-occurrence analysis of keywords, and (3) co-citation analysis. The findings revealed that the existing eSports literature mainly revolves around eSports games and activities closely related to eSports. Moreover, the most influential authors and publications were identified. In addition, the studies have been published in journals of various disciplines (e.g., technology and psychology), and the concepts and theories in sport-related fields (e.g., sports management) have been extensively applied in eSports research. This study's findings contribute to a better understanding of eSports research, which can further provide directions for the sustainable development of eSports research.
C1 [Chiu, Weisheng; Fan, Thomas Chun Man] Hong Kong Metropolitan Univ, Lee Shau Kee Sch Business & Adm, Hong Kong, Peoples R China.
   [Nam, Sang-Back] Hanyang Univ, Div Sport Sci, ERICA Campus, Ansan 15588, South Korea.
   [Sun, Ping-Hung] Natl Chengchi Univ, Phys Educ Off, Taipei 11605, Taiwan.
RP Fan, TCM (通讯作者)，Hong Kong Metropolitan Univ, Lee Shau Kee Sch Business & Adm, Hong Kong, Peoples R China.; Nam, SB (通讯作者)，Hanyang Univ, Div Sport Sci, ERICA Campus, Ansan 15588, South Korea.; Sun, PH (通讯作者)，Natl Chengchi Univ, Phys Educ Off, Taipei 11605, Taiwan.
EM chiuws@hkmu.edu.hk; cmfan@hkmu.edu.hk; nsb300@gmail.com;
   stansun@nccu.edu.tw
RI Chiu, Weisheng/D-3512-2018
OI Chiu, Weisheng/0000-0002-8090-5082; FAN, Thomas, Chun
   Man/0000-0002-6729-3392
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NR 81
TC 3
Z9 3
U1 23
U2 46
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD SEP
PY 2021
VL 13
IS 18
AR 10354
DI 10.3390/su131810354
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 UZ2UL
UT WOS:000702065300001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhou, B
   Bao, JS
   Li, J
   Lu, YQ
   Liu, TY
   Zhang, QW
AF Zhou, Bin
   Bao, Jinsong
   Li, Jie
   Lu, Yuqian
   Liu, Tianyuan
   Zhang, Qiwan
TI A novel knowledge graph-based optimization approach for resource
   allocation in discrete manufacturing workshops
SO ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
LA English
DT Article
DE Discrete manufacturing; Knowledge graph; Ontology; Mixed-model
   production; Resource allocation; Community-based device
ID OPTIMAL CONFIGURATION; SERVICE COMPOSITION; MATHEMATICAL-MODEL;
   AUTOMATIC DESIGN; CELL-FORMATION; ONTOLOGY; ROBUST; DECISION; SYSTEM;
   RULES
AB Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.
C1 [Zhou, Bin; Bao, Jinsong; Li, Jie; Liu, Tianyuan; Zhang, Qiwan] Donghua Univ, Coll Mech Engn, Shanghai 201620, 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 201620, Peoples R China.
EM bao@dhu.edu.cn
RI Lu, Yuqian/U-6820-2019
OI Lu, Yuqian/0000-0001-5954-0421
FU National Key Research and Development Program of China [2019YFB1706300];
   Fundamental Research Funds for the Central Universities [2232019D332];
   Shanghai Sailing Program [19YF1401600]
FX Research in this paper is partially supported by the National Key
   Research and Development Program of China (Grant No. 2019YFB1706300) ;
   and in part by the Fundamental Research Funds for the Central
   Universities (Grant No. 2232019D332) ; and in part by Shanghai Sailing
   Program (Grant No. 19YF1401600) .
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NR 56
TC 13
Z9 13
U1 37
U2 105
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0736-5845
EI 1879-2537
J9 ROBOT CIM-INT MANUF
JI Robot. Comput.-Integr. Manuf.
PD OCT
PY 2021
VL 71
AR 102160
DI 10.1016/j.rcim.2021.102160
EA MAR 2021
PG 14
WC Computer Science, Interdisciplinary Applications; Engineering,
   Manufacturing; Robotics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Robotics
GA SU7UJ
UT WOS:000663337200007
DA 2022-08-23
ER

PT J
AU He, LL
   Dong, B
   Jiang, PY
AF He, Longlong
   Dong, Bo
   Jiang, Pingyu
TI A Heuristic Grafting Strategy for Manufacturing Knowledge Graph
   Extending and Completion Based on Nature Language Processing: KnowTree
SO IEEE ACCESS
LA English
DT Article
DE Knowledge engineering; Manufacturing; Ontologies; Cognition; Semantics;
   Data mining; Biomimetics; Knowledge graph extending and completion;
   heuristic grafting strategy (HGS); NLP
AB Applied to search, question answering, and semantic web of close-or-open domain, knowledge graph (KG) is known for its incompleteness subject to the rapid knowledge growing pace. Inspired by the agricultural grafting technology to fruit variety, this paper proposes a heuristic knowledge grafting strategy (HGS) for manufacturing knowledge graph (MKG) named KnowTree extending and completion with natural language processing (NLP) mining engineering cases document. Based on similarity analysis, firstly the grafting related definitions and mechanisms (completeness, relatedness, connectivity and reutilization) are defined. Then, focused on the four mechanisms, HGS takes a pair same engineering documents as input. KnowWords is built as a collection of KnowScion, and each scion is mined from engineering documents based on the SAO structure network, whose importance is evaluated by SAORank counting the in-out degree of centrality. On another hand, the KnowRoot system is designed based on the extended P & S ontology model to characterize the structure of abstract document into four sub-space of knowledge: know-what (problem), know-why (context), know-how (solution) and know-with (result), where a pre-trained language representation model K-BERT is used to classify the KnowScion candidates into the designed KnowRoot system with a fine-tuning classification task. In the knowledge grafting process, the connection unit is constructed based on the extracted domain knowledge triples of the K-BERT model, where the head element of a triple is from the KnowScion candidate set KnowWords satisfying the threshold value, the tail element is from the domain MKG to be extended, and a connection factor is used to evaluate the relationship of union combination. To the goal of knowledge reuse, the path based reasoning rules are designed for KnowTree reutilization. Finally, take the latest engineering case abstract (ECA) in whitegoods domain as resources, a case study is conducted to validate the proposed HGS strategy.
C1 [He, Longlong; Jiang, Pingyu] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Peoples R China.
   [Dong, Bo] Univ Texas Dallas, Dept Comp Sci, Richardson, TX 75080 USA.
RP Jiang, PY (通讯作者)，Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Peoples R China.
EM pjiang@mail.xjtu.edu.cn
FU National Natural Science Foundation of China [51975464]; Ministry of
   Science and Technologies through the Innovation Method Fund of China
   [2016IM010100]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 51975464, and in part by the Ministry of
   Science and Technologies through the Innovation Method Fund of China
   under Grant 2016IM010100.
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NR 39
TC 1
Z9 1
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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 90847
EP 90862
DI 10.1109/ACCESS.2021.3092019
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA TJ7IH
UT WOS:000673650200001
OA gold
DA 2022-08-23
ER

EF