FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Zhao, YF
   Smidts, C
AF Zhao, Yunfei
   Smidts, Carol
TI A method for systematically developing the knowledge base of reactor
   operators in nuclear power plants to support cognitive modeling of
   operator performance
SO RELIABILITY ENGINEERING & SYSTEM SAFETY
LA English
DT Article
DE Knowledge base development; Cognitive modeling; Human reliability
   analysis; Nuclear power plant; Severe accident
ID HUMAN RELIABILITY-ANALYSIS; DYNAMIC PROBABILISTIC SIMULATION; COMPLEX
   SYSTEM ACCIDENTS; SAFETY ASSESSMENT; CREW RESPONSE
AB Methods based on cognitive modeling have attracted increasing attention in the human reliability analysis community. In such methods, the operator knowledge base plays a central role. This paper proposes a method for systematically developing the knowledge base of nuclear power plant operators. The method starts with a systematic literature review of a predefined topic. Then, the many collected publications are reduced to summaries. Relevant knowledge is then extracted from the summaries using an improved qualitative content analysis method to generate a large number of pieces of knowledge. Lastly, the pieces of knowledge are integrated in a systematic way to generate a knowledge graph consisting of nodes and links. As a case study, the proposed method is applied to develop the knowledge base of reactor operators pertaining to severe accidents in nuclear power plants. The results show that the proposed method exhibits advantages over conventional methods, including reduced reliance on expert knowledge and improved traceability of the process. Generalization of the proposed method to other sources of materials and application of the knowledge base are also discussed. Although this paper is focused on nuclear applications, the proposed method may be extended to other industrial sectors with little additional effort.
C1 [Zhao, Yunfei; Smidts, Carol] Ohio State Univ, Nucl Engn Program, Dept Mech & Aerosp Engn, 201 W 19th Ave, Columbus, OH 43210 USA.
RP Zhao, YF (通讯作者)，Ohio State Univ, Nucl Engn Program, Dept Mech & Aerosp Engn, 201 W 19th Ave, Columbus, OH 43210 USA.
EM zhao.2263@osu.edu
OI Zhao, Yunfei/0000-0003-1314-4081
FU U.S. Nuclear Regulatory Commission [NRC-HQ-60-15-G-0002]
FX The authors would like to thank the undergraduate students, Muhammad
   Anandika, Kreteeka Chaudhury, Keith Altman, and Charles Cummings, for
   their support in the research. The authors also would like to thank the
   three anonymous reviewers for their valuable comments on the manuscript.
   This research was performed under the award NRC-HQ-60-15-G-0002 from the
   U.S. Nuclear Regulatory Commission. The statements, findings,
   conclusions, and recommendations are those of the authors and do not
   necessarily reflect the view of the US Nuclear Regulatory Commission.
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NR 40
TC 12
Z9 12
U1 5
U2 24
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0951-8320
EI 1879-0836
J9 RELIAB ENG SYST SAFE
JI Reliab. Eng. Syst. Saf.
PD JUN
PY 2019
VL 186
BP 64
EP 77
DI 10.1016/j.ress.2019.02.014
PG 14
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 HU1WK
UT WOS:000465062300006
DA 2022-08-23
ER

PT J
AU Gharibshah, Z
   Zhu, XQ
AF Gharibshah, Zhabiz
   Zhu, Xingquan
TI User Response Prediction in Online Advertising
SO ACM COMPUTING SURVEYS
LA English
DT Article
DE Click; conversion; impression; landing page; demand side platform;
   supplier side platform; data management platform; dwell time; bounce
   rate; user engagement; factorization machines; deep learning; knowledge
   graph; graph neural network; convolutional neural network; recurrent
   neural network
ID CLICK; ASSOCIATION
AB Online advertising, as a vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps. The prosperity of online campaigns is a challenge in online marketing and is usually evaluated by user response through different metrics, such as clicks on advertisement (ad) creatives, subscriptions to products, purchases of items, or explicit user feedback through online surveys. Recent years have witnessed a significant increase in the number of studies using computational approaches, including machine learning methods, for user response prediction. However, existing literature mainly focuses on algorithmic-driven designs to solve specific challenges, and no comprehensive review exists to answer many important questions. What are the parties involved in the online digital advertising eco-systems? What type of data are available for user response prediction? How do we predict user response in a reliable and/or transparent way? In this survey, we provide a comprehensive review of user response prediction in online advertising and related recommender applications. Our essential goal is to provide a thorough understanding of online advertising platforms, stakeholders, data availability, and typical ways of user response prediction. We propose a taxonomy to categorize state-of-the-art user response prediction methods, primarily focusing on the current progress of machine learning methods used in different online platforms. In addition, we also review applications of user response prediction, benchmark datasets, and open source codes in the field.
C1 [Gharibshah, Zhabiz; Zhu, Xingquan] Florida Atlantic Univ, Dept Comp & Elect Engn & Comp Sci, 777 Glades Rd, Boca Raton, FL 33431 USA.
RP Gharibshah, Z (通讯作者)，Florida Atlantic Univ, Dept Comp & Elect Engn & Comp Sci, 777 Glades Rd, Boca Raton, FL 33431 USA.
EM zgharibshah2017@fau.edu; xzhu3@fau.edu
RI ZOU, Fengcai/ABE-4598-2021
OI ZOU, Fengcai/0000-0002-9613-3734
FU U.S. National Science Foundation [IIS-1763452, CNS-1828181]
FX This work is partially sponsored by the U.S. National Science Foundation
   through Grant Nos. IIS-1763452 and CNS-1828181 and by the Bidtellect
   Inc. through a sponsorship agreement.
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NR 194
TC 3
Z9 3
U1 17
U2 25
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 0360-0300
EI 1557-7341
J9 ACM COMPUT SURV
JI ACM Comput. Surv.
PD APR
PY 2022
VL 54
IS 3
AR 64
DI 10.1145/3446662
PG 43
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA SR6CQ
UT WOS:000661130600018
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Khatami, SG
   Domingo-Fernandez, D
   Mubeen, S
   Hoyt, CT
   Robinson, C
   Karki, R
   Iyappan, A
   Kodamullil, AT
   Hofmann-Apitius, M
AF Khatami, Sepehr Golriz
   Domingo-Fernandez, Daniel
   Mubeen, Sarah
   Hoyt, Charles Tapley
   Robinson, Christine
   Karki, Reagon
   Iyappan, Anandhi
   Kodamullil, Alpha Tom
   Hofmann-Apitius, Martin
TI A Systems Biology Approach for Hypothesizing the Effect of Genetic
   Variants on Neuroimaging Features in Alzheimer's Disease
SO JOURNAL OF ALZHEIMERS DISEASE
LA English
DT Article
DE Alzheimer's disease; genetic variants; knowledge graph; neuroimaging;
   systems biology
ID SYNAPTIC PLASTICITY; OXIDATIVE STRESS; NEURODEGENERATIVE DISEASES;
   HISTONE ACETYLATION; CHOLESTEROL; EXPRESSION; BTG2; MOF; ANNOTATION;
   MECHANISMS
AB Background: Neuroimaging markers provide quantitative insight into brain structure and function in neurodegenerative diseases, such as Alzheimer's disease, where we lack mechanistic insights to explain pathophysiology. These mechanisms are often mediated by genes and genetic variations and are often studied through the lens of genome-wide association studies. Linking these two disparate layers (i.e., imaging and genetic variation) through causal relationships between biological entities involved in the disease's etiology would pave the way to large-scale mechanistic reasoning and interpretation.
   Objective: We explore how genetic variants may lead to functional alterations of intermediate molecular traits, which can further impact neuroimaging hallmarks over a series of biological processes across multiple scales.
   Methods: We present an approach in which knowledge pertaining to single nucleotide polymorphisms and imaging readouts is extracted from the literature, encoded in Biological Expression Language, and used in a novel workflow to assist in the functional interpretation of SNPs in a clinical context.
   Results: We demonstrate our approach in a case scenario which proposes KANSL1 as a candidate gene that accounts for the clinically reported correlation between the incidence of the genetic variants and hippocampal atrophy. We find that the workflow prioritizes multiple mechanisms reported in the literature through which KANSL1 may have an impact on hippocampal atrophy such as through the dysregulation of cell proliferation, synaptic plasticity, and metabolic processes.
   Conclusion: We have presented an approach that enables pinpointing relevant genetic variants as well as investigating their functional role in biological processes spanning across several, diverse biological scales.
C1 [Khatami, Sepehr Golriz; Domingo-Fernandez, Daniel; Mubeen, Sarah; Hoyt, Charles Tapley; Robinson, Christine; Karki, Reagon; Iyappan, Anandhi; Kodamullil, Alpha Tom; Hofmann-Apitius, Martin] Fraunhofer Inst Algorithms & Sci Comp Fraunhofer, Dept Bioinformat, D-53757 St Augustin, Germany.
   [Khatami, Sepehr Golriz; Mubeen, Sarah; Robinson, Christine; Karki, Reagon; Iyappan, Anandhi; Kodamullil, Alpha Tom; Hofmann-Apitius, Martin] Rhein Friedrich Wilhelms Univ Bonn, Bonn Aachen Int Ctr IT, Bonn, Germany.
RP Khatami, SG (通讯作者)，Fraunhofer Inst Algorithms & Sci Comp Fraunhofer, Dept Bioinformat, D-53757 St Augustin, Germany.
EM sepehr.golriz.khatami@scai.fraunhofer.de
RI Domingo-Fernández, Daniel/AGH-3497-2022; Hoyt, Charles/B-5720-2018
OI Domingo-Fernández, Daniel/0000-0002-2046-6145; Golriz Khatami,
   Sepehr/0000-0002-7606-5228; Hoyt, Charles/0000-0003-4423-4370
FU B-IT foundation
FX The authors would like to acknowledge the financial support from the
   B-IT foundation.
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NR 77
TC 1
Z9 1
U1 1
U2 2
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1387-2877
EI 1875-8908
J9 J ALZHEIMERS DIS
JI J. Alzheimers Dis.
PY 2021
VL 80
IS 2
BP 831
EP 840
DI 10.3233/JAD-201397
PG 10
WC Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA RF0DQ
UT WOS:000634519100029
PM 33554913
OA Green Published
DA 2022-08-23
ER

PT J
AU Chen, YQ
   Qian, TY
AF Chen, Yiqi
   Qian, Tieyun
TI Relation constrained attributed network embedding
SO INFORMATION SCIENCES
LA English
DT Article
DE Attributed network embedding; Basic relation; Composite relation; Social
   network analysis
AB Network embedding aims at learning a low-dimensional dense vector for each node in the network. In recent years, it has attracted great research attention due to its wide applications. Most existing studies model the graph structure only and neglect the attribute information. Although several attributed network embedding methods take the node attribute into account, they mainly focus on the basic relations between the nodes and their attributes like a user and his/her interests (attributes). The composite relations between two nodes, and two nodes attributes, and the related nodes and their attributes, contain rich information and can enhance the performance of many network analysis tasks. For example, two scholars having the common interests as "nature language processing" and "knowledge graph" may collaborate in the future and there will be a new edge in the network. However, such important information is still under-exploited.
   To address this limitation, we propose a novel framework to exploit the abundant relation information to enhance attributed network embedding. The main idea is to employ the multiple types of relations in attributed networks as the constraints to improve the network representation. To this end, we first construct the composite relations between two nodes and their attributes in addition to the commonly used basic relations. We then develop a relation constrained attributed network (RCAN) framework to learn the node representations by constraining them with these relations. We conduct extensive experiments on three real-world datasets to show the effectiveness of our proposed RCAN as an attributed network embedding method for modeling various social networks. The results demonstrate that our method achieves significantly better performance than the state-of-the-art baselines in both the link prediction and node clustering tasks. (C) 2019 Elsevier Inc. All rights reserved.
C1 [Chen, Yiqi; Qian, Tieyun] Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
RP Qian, TY (通讯作者)，Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
EM yiqic16@whu.edu.cn; qty@whu.edu.cn
FU NSFC [61572376, 91646206]; 111 project [B07037]
FX The work described in this paper is supported by the NSFC projects
   (61572376, 91646206), and the 111 project (B07037). The authors are very
   thankful to Prof. Huan Liu for his valuable comments and suggestions in
   improving the quality of the paper.
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NR 36
TC 5
Z9 5
U1 3
U2 12
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD APR
PY 2020
VL 515
BP 341
EP 351
DI 10.1016/j.ins.2019.12.033
PG 11
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KL2XZ
UT WOS:000513293200021
DA 2022-08-23
ER

PT J
AU Wang, YY
   Xia, CH
   Si, CX
   Yao, BT
   Wang, TB
AF Wang, Yongyue
   Xia, Chunhe
   Si, Chengxiang
   Yao, Beitong
   Wang, Tianbo
TI Robust Reasoning Over Heterogeneous Textual Information for Fact
   Verification
SO IEEE ACCESS
LA English
DT Article
DE Cognition; Semantics; Task analysis; Robustness; Internet; Neural
   networks; Aggregates; Fact verification; heterogeneous information;
   graph neural network; robust reasoning
AB Automatic fact verification (FV) based on artificial intelligence is considered as a promising approach which can be used to identify misinformation distributed on the web. Even though previous FV using deep learning have made great achievements in single dataset (e.g., FEVER), the trained systems are unlikely to be capable of extracting evidence from heterogeneous web-sources and validating claims in accordance with evidence found on the Internet. Nevertheless, the heterogeneity covers abundant semantic information, which will help FV system identify misinformation in a more accurate way. The current work is the first attempt to make the combination of knowledge graph (KG) and graph neural network (GNN) to enhance the robustness of FV systems for heterogeneous information. As a result, it can be generalized to multi-domain datasets after training on a sufficient single one. To make information update and aggregate well on the collaborative graph, the present study proposes a double graph attention network (DGAT) framework which recursively propagates the embeddings from a node's neighbors to refine the node's embedding as well as applies an attention mechanism to classify the importance of the neighbors. We train and evaluate our system on FEVER, a single and benchmark dataset for FV, and then re-evaluate our system on UKP Snopes Corpus, a new richly annotated corpus for FV tasks on the basis of heterogeneous web sources. According to experimental results, although DGAT has no excellent advantages in a single dataset, it shows outstanding performance in more realistic and multi-domain datasets. Moreover, the current study also provides a feasible method for deep learning to have the ability to infer heterogeneous information robustly.
C1 [Wang, Yongyue; Xia, Chunhe; Yao, Beitong] Beihang Univ, Beijing Key Lab Network Technol, Sch Comp Sci & Engn, Beijing 100191, Peoples R China.
   [Xia, Chunhe] Guangxi Normal Univ, Sch Comp Sci & Informat Technol, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China.
   [Si, Chengxiang] Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China.
   [Wang, Tianbo] Beihang Univ, Sch Cyber Sci & Technol, Beijing 100191, Peoples R China.
RP Wang, TB (通讯作者)，Beihang Univ, Sch Cyber Sci & Technol, Beijing 100191, Peoples R China.
EM wangtb@buaa.edu.cn
FU National Natural Science Foundation of China [U1636208, 61862008,
   61902013]; Beihang Youth Top Talent Support Program [YWF-20-BJ-J-1038]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant U1636208, Grant 61862008, and Grant
   61902013; and in part by the Beihang Youth Top Talent Support Program
   under Grant YWF-20-BJ-J-1038.
CR Ai QY, 2018, ALGORITHMS, V11, DOI 10.3390/a11090137
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NR 46
TC 2
Z9 2
U1 4
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 157140
EP 157150
DI 10.1109/ACCESS.2020.3019586
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NM6VU
UT WOS:000568234400001
OA gold
DA 2022-08-23
ER

PT J
AU Li, WW
   Song, MM
   Tian, YY
AF Li, Wenwen
   Song, Miaomiao
   Tian, Yuanyuan
TI An Ontology-Driven Cyberinfrastructure for Intelligent Spatiotemporal
   Question Answering and Open Knowledge Discovery
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE spatial decision support; scientific workflow; provenance;
   reproducibility; open knowledge discovery; cyberinfrastructure;
   knowledge graphs
ID SPATIAL-ANALYSIS; BIG DATA; SERVICES; WORKFLOWS; SCIENCE; TOOLS
AB The proliferation of geospatial data from diverse sources, such as Earth observation satellites, social media, and unmanned aerial vehicles (UAVs), has created a pressing demand for cross-platform data integration, interoperation, and intelligent data analysis. To address this big data challenge, this paper reports our research in developing a rule-based, semantic-enabled service chain model to support intelligent question answering for leveraging the abundant data and processing resources available online. Four key techniques were developed to achieve this goal: (1) A spatial and temporal reasoner resolves the spatial and temporal information in a given scientific question and enables place-name disambiguation based on support from a gazetteer; (2) a spatial operation ontology categorizes important spatial analysis operations, data types, and data themes, which will be used in automated chain generation; (3) a language-independent chaining rule defines the template for input, spatial operation, and output as well as rules for embedding multiple spatial operations for solving a complex problem; and (4) a recursive algorithm facilitates the generation of executive workflow metadata according to the chaining rules. We implement this service chain model in a cyberinfrastructure for online and reproducible spatial analysis and question answering. Moving the problem-solving environment from a desktop-based environment onto a geospatial cyberinfrastructure (GeoCI) offers better support to collaborative spatial decision-making and ensures science replicability. We expect this work to contribute significantly to the advancement of a reproducible spatial data science and to building the next-generation open knowledge network.
C1 [Li, Wenwen; Tian, Yuanyuan] Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ 85287 USA.
   [Song, Miaomiao] Qilu Univ Technol, Shandong Acad Sci, Inst Oceanog Instrumentat, Qingdao 266061, Shandong, Peoples R China.
RP Li, WW (通讯作者)，Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ 85287 USA.
EM Wenwen@asu.edu; mmsong@qlu.edu.cn; ytian72@asu.edu
RI Tian, Yuanyuan/ABE-1913-2020
OI Tian, Yuanyuan/0000-0002-5137-1296
FU National Science Foundation [1455349, 1937908, 1936677]
FX This research was funded by National Science Foundation, grant number
   1455349, 1937908, 1936677
CR Akram A, 2006, SIXTH IEEE INTERNATIONAL SYMPOSIUM ON CLUSTER COMPUTING AND THE GRID, P269
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NR 44
TC 8
Z9 8
U1 2
U2 10
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2220-9964
J9 ISPRS INT GEO-INF
JI ISPRS Int. Geo-Inf.
PD NOV
PY 2019
VL 8
IS 11
AR 496
DI 10.3390/ijgi8110496
PG 18
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA JV3NI
UT WOS:000502272600030
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Meng, XY
   Jiang, TH
   Zhou, X
   Ma, B
   Wang, Y
   Zhao, F
AF Meng, Xiaoyan
   Jiang, Tonghai
   Zhou, Xi
   Ma, Bo
   Wang, Yi
   Zhao, Fan
TI Improving Distant Supervised Relation Extraction with Noise Detection
   Strategy
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE relation extraction; distant supervision; noise detection
AB Distant supervised relation extraction (DSRE) is widely used to extract novel relational facts from plain text, so as to improve the knowledge graph. However, distant supervision inevitably suffers from the noisy labeling problem that will severely damage the performance of relation extraction. Currently, most DSRE methods are mainly focused on reducing the weights of noisy sentences, ignoring the bag-level noise where all sentences in a bag are wrongly labeled. In this paper, we present a novel noise detection-based relation extraction approach (NDRE) to automatically detect noisy labels with entity information and dynamically correct them, which can alleviate both instance-level and bag-level noisy problems. By this means, we can extend the dataset from the Web tables without introducing more noise. In this approach, to embed the semantics of sentences from corpus and web tables, we firstly propose a powerful sentence coder that employs an internal multi-head self-attention mechanism between the piecewise max-pooling convolutional neural network. Second, we adopt a noise detection strategy, which is expected to dynamically detect and correct the original noisy label according to the similarity between sentence representation and entity-aware embeddings. Then, we aggregate the information from corpus and web tables to make the final relation prediction. Experimental results on a public benchmark dataset demonstrate that our proposed approach achieves significant improvements over the state-of-the-art baselines and can effectively reduce the noisy labeling problem.
C1 [Meng, Xiaoyan; Jiang, Tonghai; Zhou, Xi; Ma, Bo; Wang, Yi; Zhao, Fan] Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China.
   [Meng, Xiaoyan; Zhou, Xi; Ma, Bo; Wang, Yi; Zhao, Fan] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
   [Meng, Xiaoyan; Jiang, Tonghai; Zhou, Xi; Ma, Bo; Wang, Yi; Zhao, Fan] Chinese Acad Sci, Xinjiang Lab Minor Speech & Language Informat Pro, Urumqi 830011, Peoples R China.
   [Meng, Xiaoyan] Xinjiang Agr Univ, Coll Comp & Informat Engn, Urumqi 830052, Peoples R China.
RP Jiang, TH (通讯作者)，Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China.; Jiang, TH (通讯作者)，Chinese Acad Sci, Xinjiang Lab Minor Speech & Language Informat Pro, Urumqi 830011, Peoples R China.
EM mxy@xjau.edu.cn; jth@ms.xjb.ac.cn; zhouxi@ms.xjb.ac.cn;
   mabo@ms.xjb.ac.cn; wangyi@ms.xjb.ac.cn; zhaofan@ms.xjb.ac.cn
FU West Light Foundation of The Chinese Academy of Sciences
   [2019-XBQNXZ-A-004]; National Key Research and Development, Program of
   China [2017YFC0820704]; Tianshan Excellent Young Scholars of Xinjiang
   [2018Q032]
FX This research was funded by the West Light Foundation of The Chinese
   Academy of Sciences, Grant No. 2019-XBQNXZ-A-004; the National Key
   Research and Development, Program of China under Grant No.
   2017YFC0820704; and the Tianshan Excellent Young Scholars of Xinjiang,
   Grant No. 2018Q032.
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NR 25
TC 0
Z9 0
U1 4
U2 12
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 2021
VL 11
IS 5
AR 2046
DI 10.3390/app11052046
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 QV4OQ
UT WOS:000627953500001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhao, JJ
   Xu, GL
   Xu, JH
AF Zhao, Jingjie
   Xu, Guanglei
   Xu, Jiahui
TI RESEARCH STATUS QUO AND TREND OF RESEARCH ON DYNAMIC CAPABILITY BASED ON
   THE CONCEPT OF ECOLOGICAL SUSTAINABILITY
SO FRESENIUS ENVIRONMENTAL BULLETIN
LA English
DT Article
DE Ecological sustainability; dynamic capability; CiteSpace; knowledge
   graph; research status quo
ID INNOVATION
AB Nowadays, the competitive environment of enterprises is becoming increasingly fierce, and environmental regulations have put forward higher requirements and standards for enterprises. Driven by ecological concepts and sustainable development concepts, the acquisition and promotion of dynamic capabilities has become the key. The dynamic capability literature included in the Web of Science database is the research data. Firstly, the quantitative analysis and influence analysis of dynamic capability research are carried out by using the bibliometric method. It is found that the number of literatures published in this field has been increasing exponentially in recent years, and the literatures are mainly in developed countries (regions). The research fields belong to strategic management with tendency of cross-discipline and fusion. The research method is based on quantitative empirical research, and the richness and universality need to be strengthened. Secondly, clustering shows that the content layout of dynamic capability research can be roughly summarized as four aspects: dynamic capability research on improving the enterprises' internationalization strategy, dynamic capability research on building the micro-organization level of enterprises, research on building dynamic capability to continuously improve competitiveness of enterprise through enterprise's resources innovation, and research on building dynamic capability from the perspective of enterprise cooperative network. Finally, with the development of economic globalization, research is expended gradually from single enterprise's dynamic capacity building, to inter-enterprise cooperation to form unique dynamic capacity, enhancing the competitive advantage of enterprises.
C1 [Zhao, Jingjie; Xu, Guanglei] Jilin Univ, Sch Management, Changchun 130000, Peoples R China.
   [Xu, Jiahui] Guangdong Univ Finance & Econ, Sch Cultural Creat & Tourism, Guangzhou 510320, Peoples R China.
RP Xu, JH (通讯作者)，Guangdong Univ Finance & Econ, Sch Cultural Creat & Tourism, Guangzhou 510320, Peoples R China.
EM jiahui1991yang@126.com
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NR 20
TC 0
Z9 0
U1 3
U2 9
PU PARLAR SCIENTIFIC PUBLICATIONS (P S P)
PI FREISING
PA ANGERSTR. 12, 85354 FREISING, GERMANY
SN 1018-4619
EI 1610-2304
J9 FRESEN ENVIRON BULL
JI Fresenius Environ. Bull.
PY 2021
VL 30
IS 1
BP 521
EP 531
PG 11
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA QX2ME
UT WOS:000629181200058
DA 2022-08-23
ER

PT J
AU Bilidas, D
   Koubarakis, M
AF Bilidas, Dimitris
   Koubarakis, Manolis
TI In-memory parallelization of join queries over large ontological
   hierarchies
SO DISTRIBUTED AND PARALLEL DATABASES
LA English
DT Article
DE RDF; SPARQL; OWL; Join processing
ID KNOWLEDGE-BASE; RDF; SPARQL; WEB
AB The Resource Description Framework (RDF) data model enables the construction of knowledge graphs over various domains, using ontologies in order to encode information about the domain, and simple statements in the form of subject-predicate-object triples for data representation, facilitating the interlinking and exchange of Web data. However, this simplicity comes with the cost of having to execute a large number of joins in order to get the desirable query results, while at the same time large ontological hierarchies complicate the query answering process even more, for systems that provide complete answers with respect to such ontological axioms. In this work we present PARJ, an in-memory RDF store which takes into consideration ontological hierarchies during join processing with very low performance overhead, avoiding expensive preprocessing and materialization of implications, and is also amenable to straightforward parallelization. Specifically, we present a join implementation that allows to achieve any desired degree of parallelism on arbitrary join queries and RDF graphs stored in memory using compact vertical partitioning. We use an adaptive join processing approach, such that we take advantage of complete or even partial ordering of RDF data, which is compactly stored in order to increase spatial locality and keep memory consumption low, coupled with an ID-to-Position vector index used when ordering does not allow for efficient scanning of the input relation. Finally, we experimentally show the efficiency and scalability of our proposal.
C1 [Bilidas, Dimitris; Koubarakis, Manolis] Natl & Kapodistrian Univ Athens, Athens, Greece.
RP Bilidas, D (通讯作者)，Natl & Kapodistrian Univ Athens, Athens, Greece.
EM d.bilidas@di.uoa.gr; koubarak@di.uoa.gr
FU European Union [825258]
FX The present work was funded by the European Union's Horizon 2020
   research and innovation programme under Grant Agreement No. 825258.
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NR 58
TC 1
Z9 1
U1 0
U2 4
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0926-8782
EI 1573-7578
J9 DISTRIB PARALLEL DAT
JI Distrib. Parallel Databases
PD SEP
PY 2021
VL 39
IS 3
BP 545
EP 582
DI 10.1007/s10619-020-07305-y
EA JUN 2020
PG 38
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UT8UX
UT WOS:000544134400001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, H
   Li, L
   Hu, W
   Yao, WJ
   Zhu, HH
AF Zhang, Hang
   Li, Lin
   Hu, Wei
   Yao, Wenjing
   Zhu, Haihong
TI Visualization of Location-Referenced Web Textual Information Based on
   Map Mashups
SO IEEE ACCESS
LA English
DT Article
DE Geotagging; information visualization; location-referenced information;
   map mashups; web textual information
AB Space is the most fundamental organizing dimension for information that forms the basic spatial understanding around which all other temporal and semantic details are situated. Various types of web information are present in our daily lives, and spatial content can facilitate the understanding of that information. Hence, many studies have been conducted to extract the implicit spatial location from different web resources, and they have mainly investigated web textual information. However, the existing studies mainly focus on the extraction or simple presentation of the location of the information, while further exploration and visualization of the information through the comprehensive consideration of its spatial, temporal, and semantic dimensions have rarely been performed. Thus, this paper proposes a novel modeling and visualization framework for location-referenced web textual information. The framework applies an information model to structurally extract and resolve spatial content along with the temporal and semantic elements from the location-referenced information items. Based on the constructed information model, the information items are hierarchically clustered and visualized by combining map mashups with cartographic processes and methods. This framework enables users to interactively index and browses individual information items. Furthermore, the association relationships involved in the information set are explored in our work. With knowledge graphs that are automatically generated based on the information model, a high-level understanding of the information set can be obtained by users.
C1 [Zhang, Hang; Li, Lin; Hu, Wei; Zhu, Haihong] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.
   [Li, Lin] Wuhan Univ, Geospatial Informat Sci Collaborat Innovat Ctr, Wuhan 430079, Hubei, Peoples R China.
   [Li, Lin] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Hubei, Peoples R China.
   [Yao, Wenjing] Wuhan Winsse Secur Technol Co Ltd, Wuhan 430079, Hubei, Peoples R China.
RP Li, L (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.; Li, L (通讯作者)，Wuhan Univ, Geospatial Informat Sci Collaborat Innovat Ctr, Wuhan 430079, Hubei, Peoples R China.; Li, L (通讯作者)，Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Hubei, Peoples R China.
EM lilin@whu.edu.cn
OI Hu, Wei/0000-0002-3744-8357
FU National Key Research and Development Program of China [2017YFB0503701]
FX This work was supported by the National Key Research and Development
   Program of China under Grant 2017YFB0503701.
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NR 45
TC 2
Z9 3
U1 1
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 40475
EP 40487
DI 10.1109/ACCESS.2019.2907570
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HT0OX
UT WOS:000464265600002
OA gold
DA 2022-08-23
ER

PT J
AU He, LR
   Liu, B
   Li, GX
   Sheng, YP
   Wang, YF
   Xu, ZL
AF He, Lirong
   Liu, Bin
   Li, Guangxi
   Sheng, Yongpan
   Wang, Yafang
   Xu, Zenglin
TI Knowledge Base Completion by Variational Bayesian Neural Tensor
   Decomposition
SO COGNITIVE COMPUTATION
LA English
DT Article
DE Knowledge base completion; Variational Bayesian; Neural networks
AB Knowledge base completion is an important research problem in knowledge bases, which play important roles in question answering, information retrieval, and other applications. A number of relational learning algorithms have been proposed to solve this problem. However, despite their success in modeling the entity relations, they are not well founded in a Bayesian manner and thus are hard to model the prior information of the entity and relation factors. Furthermore, they under-represent the interaction between entity and relation factors. In order to avoid these disadvantages, we provide a neural-inspired approach, namely Bayesian Neural Tensor Decomposition approach for knowledge base completion based on the Stochastic Gradient Variational Bayesian framework. We employ a multivariate Bernoulli likelihood function to represent the existence of facts in knowledge graphs. We further employ a Multi-layered Perceptrons to represent more complex interactions between the latent subject, predicate, and object factors. The SGVB framework can enable us to make efficient approximate variational inference for the proposed nonlinear probabilistic tensor decomposition by a novel local reparameterization trick. This way avoids the need of expensive iterative inference schemes such as MCMC and does not make any over-simplified assumptions about the posterior distributions, in contrary to the common variational inference. In order to evaluate the proposed model, we have conducted experiments on real-world knowledge bases, i.e., FreeBase and WordNet. Experimental results have indicated the promising performance of the proposed method.
C1 [He, Lirong; Liu, Bin; Li, Guangxi; Sheng, Yongpan; Xu, Zenglin] Univ Elect Sci & Technol China, SMILE Lab, Sch Comp Sci & Engn, Chengdu, Sichuan, Peoples R China.
   [Wang, Yafang] Ant Financial Serv Co, Hangzhou, Zhejiang, Peoples R China.
RP Xu, ZL (通讯作者)，Univ Elect Sci & Technol China, SMILE Lab, Sch Comp Sci & Engn, Chengdu, Sichuan, Peoples R China.
EM zlxu@uestc.edu.cn
OI Sheng, Yongpan/0000-0002-9606-2999
FU Natural Science Foundation of China [61572111]; 1000-Talent Startup
   Funds [G05QNQR004, A1098531023601041]; Fundamental Research Fund for the
   Central Universities of China [ZYGX2016Z003]
FX All the authors except Yafang Wang were supported by the Natural Science
   Foundation of China (No. 61572111), 1000-Talent Startup Funds (Nos.
   G05QNQR004, A1098531023601041) and a Fundamental Research Fund for the
   Central Universities of China (No. ZYGX2016Z003).
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NR 37
TC 12
Z9 13
U1 3
U2 15
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1866-9956
EI 1866-9964
J9 COGN COMPUT
JI Cogn. Comput.
PD DEC
PY 2018
VL 10
IS 6
BP 1075
EP 1084
DI 10.1007/s12559-018-9565-x
PG 10
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Neurosciences & Neurology
GA HE4NX
UT WOS:000453344800015
DA 2022-08-23
ER

PT J
AU Callahan, A
   Polony, V
   Posada, JD
   Banda, JM
   Gombar, S
   Shah, NH
AF Callahan, Alison
   Polony, Vladimir
   Posada, Jose D.
   Banda, Juan M.
   Gombar, Saurabh
   Shah, Nigam H.
TI ACE: the Advanced Cohort Engine for searching longitudinal patient
   records
SO JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
LA English
DT Article
DE electronic health records; in-memory datastore; query language; search
   engine; data science
ID CLINICAL-DATA; WEAK SUPERVISION; DATA WAREHOUSE; DESIGN; SYSTEM; CARE;
   IMPLEMENTATION; ARCHITECTURE; INFORMATICS; RECRUITMENT
AB Objective: To propose a paradigm for a scalable time-aware clinical data search, and to describe the design, implementation and use of a search engine realizing this paradigm.
   Materials and Methods: The Advanced Cohort Engine (ACE) uses a temporal query language and in-memory datastore of patient objects to provide a fast, scalable, and expressive time-aware search. ACE accepts data in the Observational Medicine Outcomes Partnership Common Data Model, and is configurable to balance performance with compute cost. ACE's temporal query language supports automatic query expansion using clinical knowledge graphs. The ACE API can be used with R, Python, Java, HTTP, and a Web UI.
   Results: ACE offers an expressive query language for complex temporal search across many clinical data types with multiple output options. ACE enables electronic phenotyping and cohort-building with subsecond response times in searching the data of millions of patients for a variety of use cases.
   Discussion: ACE enables fast, time-aware search using a patient object-centric datastore, thereby overcoming many technical and design shortcomings of relational algebra-based querying. Integrating electronic phenotype development with cohort-building enables a variety of high-value uses for a learning health system. Tradeoffs include the need to learn a new query language and the technical setup burden.
   Conclusion: ACE is a tool that combines a unique query language for time-aware search of longitudinal patient records with a patient object datastore for rapid electronic phenotyping, cohort extraction, and exploratory data analyses.
C1 [Callahan, Alison; Polony, Vladimir; Posada, Jose D.; Shah, Nigam H.] Stanford Univ, Ctr Biomed Informat Res, Sch Med, Room X231,Med Sch Off Bldg,1265 Welch Rd, Stanford, CA 94305 USA.
   [Banda, Juan M.] Georgia State Univ, Dept Comp Sci, Atlanta, GA USA.
   [Gombar, Saurabh] Stanford Univ, Sch Med, Dept Pathol, Stanford, CA USA.
RP Callahan, A (通讯作者)，Stanford Univ, Ctr Biomed Informat Res, Sch Med, Room X231,Med Sch Off Bldg,1265 Welch Rd, Stanford, CA 94305 USA.
EM acallaha@stanford.edu
RI Posada, Jose/AAB-5284-2022
OI Posada, Jose/0000-0003-3864-0241; Banda, Juan/0000-0001-8499-824X
FU National Institutes of Health [R01LM011369-06]
FX This work was supported by the National Institutes of Health, grant
   number R01LM011369-06.
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NR 83
TC 1
Z9 1
U1 1
U2 1
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1067-5027
EI 1527-974X
J9 J AM MED INFORM ASSN
JI J. Am. Med. Inf. Assoc.
PD JUL
PY 2021
VL 28
IS 7
BP 1468
EP 1479
DI 10.1093/jamia/ocab027
EA MAR 2021
PG 12
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Health Care Sciences & Services;
   Information Science & Library Science; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Health Care Sciences & Services; Information Science &
   Library Science; Medical Informatics
GA UA5NT
UT WOS:000685209200013
PM 33712854
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Fallucchi, F
   Di Stabile, R
   Purificato, E
   Giuliano, R
   De Luca, EW
AF Fallucchi, Francesca
   Di Stabile, Rosario
   Purificato, Erasmo
   Giuliano, Romeo
   De Luca, Ernesto William
TI Enriching videos with automatic place recognition in google maps
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Named entity recognition; Video retrieval; Enriching video; Geographic
   information retrieval; Education with media
ID NAMED ENTITY RECOGNITION
AB The availability of videos has grown rapidly in recent years. Finding and browsing relevant information to be automatically extracted from videos is not an easy task, but today it is an indispensable feature due to the immense number of digital products available. In this paper, we present a system which provides a process to automatically extract information from videos. We describe a system solution that uses a re-trained OpenNLP model to locate all the places and famous people included in a specific video. The system obtains information from the Google Knowledge Graph related to relevant named entities such as places or famous people. In this paper we will also present the Automatic Georeferencing Video (AGV) system developed by RAI (Radiotelevisione italiana, which is the national public broadcasting company of Italy, owned by the Ministry of Economy and Finance) Teche for the European Project "La Citta Educante" (The Educating City: teaching and learning processes in cross-media ecosystem) Our system contributes to The Educating City project by providing the technological environment to create statistical models for automatic named entity recognition (NER), and has been implemented in the field of education, in Italian initially. The system has been applied to the learning challenges facing the world of educational media and has demonstrated how beneficial combining topical news content with scientific content can be in education.
C1 [Fallucchi, Francesca; Giuliano, Romeo; De Luca, Ernesto William] Guglielmo Marconi Univ, Via Plinio 44, Rome, Italy.
   [Di Stabile, Rosario; De Luca, Ernesto William] RAI SpA, Viale Giuseppe Mazzini 14, Rome, Italy.
   [Purificato, Erasmo] Otto von Guericke Univ, Univ Pl 2, Magdeburg, Germany.
   [Fallucchi, Francesca; Purificato, Erasmo; De Luca, Ernesto William] Georg Eckert Inst, Celler Str 3, Braunschweig, Germany.
RP Fallucchi, F (通讯作者)，Guglielmo Marconi Univ, Via Plinio 44, Rome, Italy.; Fallucchi, F (通讯作者)，Georg Eckert Inst, Celler Str 3, Braunschweig, Germany.
EM f.fallucchi@unimarconi.it
OI Fallucchi, Francesca/0000-0002-3288-044X; Purificato,
   Erasmo/0000-0002-5506-3020
CR Anita HB, 2017, INT J APPL ENG RES, V12, P14750
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NR 38
TC 0
Z9 0
U1 3
U2 4
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD JUL
PY 2022
VL 81
IS 16
BP 23105
EP 23121
DI 10.1007/s11042-021-11253-9
EA JUL 2021
PG 17
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 2I0NV
UT WOS:000679293200001
DA 2022-08-23
ER

PT J
AU Tian, JR
   Tu, ZY
   Li, N
   Su, TH
   Xu, XF
   Wang, ZJ
AF Tian, Junrui
   Tu, Zhiying
   Li, Nan
   Su, Tonghua
   Xu, Xiaofei
   Wang, Zhongjie
TI Intention model based multi-round dialogue strategies for conversational
   AI bots
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Intention recognition; Requirement rewriting; Requirement pattern;
   Multi-round dialogue strategy
AB Conversational AI (CoAI) bot, such as customer service bots, navigation bots, chat bots and etc., is a new form of software application. How to accurately identify user requirements and provide appropriate services is one of the biggest challenges that hinder its widespread use. This requires CoAI to get rid of the template and standard knowledge structure, enhance the reasoning ability of service and the knowledge involved. Furthermore, It have the ability to guide users to express complete requirements through multiple rounds of dialogue. To achieve this, firstly, this study purposes a new intention model, and Knowledge Graph of Requirements (KGR) to expand the requirement knowledge scope of conversational AI bots. Secondly, this study purposes a knowledge processing method based on the theory of granular computing. This method could help CoAI bot to determine the next round of inquiry content automatically before the dialogue begins. At last, considering the commonality of the requirement pattern (RP) repository and the personalization of the KGR, this study proposes three optimization methods to improve the dialogue strategy based on two intention models for scenarios with different characteristics. The experimental result shows that, compared with the existing methods, the proposed method can reduce redundancy in dialogues, and integrate the commonality and individuality of user requirements successfully. Meanwhile, this method effectively improves the efficiency and performance of requirement elicitation, and takes the user experience into account as well.
C1 [Tian, Junrui; Tu, Zhiying; Li, Nan; Su, Tonghua; Xu, Xiaofei; Wang, Zhongjie] Harbin Inst Technol, Res Ctr Intelligent Comp Enterprises & Serv, Harbin, Peoples R China.
RP Wang, ZJ (通讯作者)，Harbin Inst Technol, Res Ctr Intelligent Comp Enterprises & Serv, Harbin, Peoples R China.
EM junruit97@gmail.com; tzy_hit@hit.edu.cn; li_nann@outlook.com;
   thsu@hit.edu.cn; xiaofei@hit.edu.cn; rainy@hit.edu.cn
FU National Key Research and Development Program of China [2018YFB1402500];
   National Science Foundation of China [61802089, 61832004, 61772155,
   61832014]
FX Research in this paper is partially supported by the National Key
   Research and Development Program of China (No 2018YFB1402500), the
   National Science Foundation of China (61802089, 61832004, 61772155,
   61832014).
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NR 39
TC 0
Z9 0
U1 5
U2 5
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/s10489-022-03288-8
EA MAR 2022
PG 25
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZK3IG
UT WOS:000762885000004
DA 2022-08-23
ER

PT J
AU Zhang, J
   Tao, DC
AF Zhang, Jing
   Tao, Dacheng
TI Empowering Things With Intelligence: A Survey of the Progress,
   Challenges, and Opportunities in Artificial Intelligence of Things
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Review
DE Internet of Things; Sensors; Edge computing; Deep learning; Computer
   architecture; Cloud computing; Artificial intelligence; 3-D; aged care;
   artificial intelligence (AI); biometric recognition; causal reasoning;
   cloud; fog; edge computing; deep learning; human&#8211; machine
   interaction; Internet of Things (IoT); machine translation (MT);
   privacy; security; sensors; smart agriculture; smart city; smart grids;
   speech recognition
ID KNOWLEDGE GRAPH; DEEP; MANAGEMENT; INTERNET; MODELS; SYSTEM
AB In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the Internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas, including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of AI of things (AIoT). This article presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: 1) perceiving; 2) learning; 3) reasoning; and 4) behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.
C1 [Zhang, Jing; Tao, Dacheng] Univ Sydney, Fac Engn, Sch Comp Sci, Darlington, NSW 2008, Australia.
RP Tao, DC (通讯作者)，Univ Sydney, Fac Engn, Sch Comp Sci, Darlington, NSW 2008, Australia.
EM jing.zhang1@sydney.edu.au; dacheng.tao@sydney.edu.au
FU Australian Research Council [FL-170100117, DP-180103424, IH-180100002]
FX This work was supported by the Australian Research Council Projects
   under Grant FL-170100117, Grant DP-180103424, and Grant IH-180100002.
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NR 58
TC 71
Z9 71
U1 108
U2 178
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2327-4662
J9 IEEE INTERNET THINGS
JI IEEE Internet Things J.
PD MAY 15
PY 2021
VL 8
IS 10
BP 7789
EP 7817
DI 10.1109/JIOT.2020.3039359
PG 29
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA RY9ES
UT WOS:000648206800003
OA Green Submitted
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Shi, YL
   Liu, XP
AF Shi, Yingling
   Liu, Xinping
TI Research on the Literature of Green Building Based on the Web of
   Science: A Scientometric Analysis in CiteSpace (2002-2018)
SO SUSTAINABILITY
LA English
DT Review
DE literature review; WoS; green building; visualized analysis; CiteSpace
ID LIFE-CYCLE ASSESSMENT; ENVIRONMENTAL ASSESSMENT; ENERGY-CONSUMPTION;
   DECOMPOSITION ANALYSIS; TECHNOLOGIES ADOPTION; DEVELOPING-COUNTRIES;
   PROJECT-MANAGEMENT; THERMAL COMFORT; EMERGING TRENDS; DESIGN
AB Since the 21st century, the concept of green building has been gradually popularized and implemented in more countries, which has become a popular direction in the area of sustainability in the building industry. Over the past few decades, many scholars and experts have done extensive research on green building. The purpose of this paper is to systematically analyze and visualize the status quo of green building. Therefore, based on Web of Science (WoS), this paper analyzed the existing knowledge system of green building using CiteSpace, identified keywords related to green building and their frequency of occurrence using the function of keyword co-occurrence analysis, recognized five clusters using the function of cluster analysis, and explored the knowledge evolution pattern of green building using citation bursts analysis in order to reveal how research related to green building has evolved over time. On the basis of aforementioned keywords, clusters, and citation bursts analysis, this paper has built a knowledge graph for green building. This paper can help readers to better understand the status quo and development trend of green building and to easier recognize the shortcomings in the development of green building, so as to provide a promising direction for future research.
C1 [Shi, Yingling; Liu, Xinping] North China Elect Power Univ, Sch Econ & Management, Beijing 102200, Peoples R China.
RP Shi, YL; Liu, XP (通讯作者)，North China Elect Power Univ, Sch Econ & Management, Beijing 102200, Peoples R China.
EM 50600633@ncepu.edu.cn; 1182206253@ncepu.edu.cn
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NR 106
TC 45
Z9 45
U1 87
U2 259
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUL 1
PY 2019
VL 11
IS 13
AR 3716
DI 10.3390/su11133716
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 IL1IB
UT WOS:000477051900206
OA Green Published
DA 2022-08-23
ER

PT J
AU Murase, Y
   Koichiro, Y
   Nakamura, S
AF Murase, Yukitoshi
   Koichiro, Yoshino
   Nakamura, Satoshi
TI Associative knowledge feature vector inferred on external knowledge base
   for dialog state tracking
SO COMPUTER SPEECH AND LANGUAGE
LA English
DT Article
DE Dialog state tracking; Knowledge base; Knowledge graph; Associative
   knowledge inference
AB The dialog state tracker is one of the most important modules on task-oriented dialog systems, its accuracy strongly affects the quality of the system response. The architecture of the tracker has been changed from pipeline processing to an end-to-end approach that directly estimates a user's intention from each current utterance and a dialog history because of the growth in the use of the neural-network-based classifier. However, tracking appropriate slot-value pairs of dialog states that are not explicitly mentioned in user utterances is still a difficult problem. In this research, we propose creating feature vectors by using inference results on an external knowledge base. This inference process predicts associative entities in the knowledge base, which contribute to the dialog state tracker for unseen entities of utterances. We extracted a part of a graph structure from an external knowledge base (Wikidata). Label propagation was used for inferring associative nodes (entities) on the graph structure to produce feature vectors. We used the vectors for the input of a fully connected neural network (FCNN) based tracker. We also introduce a convolutional neural network (CNN) tracker as a state-of-the-art tracker and ensemble models of FCNN and CNN trackers. We used a common test bed, Dialog State Tracking Challenge 4 for experiments. We confirmed the effectiveness of the associative knowledge feature vector, and one ensemble model outperformed other models. (C) 2018 Elsevier Ltd. All rights reserved.
C1 [Murase, Yukitoshi; Koichiro, Yoshino; Nakamura, Satoshi] Nara Inst Sci & Technol, Takayamacho 8916-5, Ikoma, Nara 6300192, Japan.
   [Koichiro, Yoshino] RIKEN AIP, Takayamacho 8916-5, Ikoma, Nara 6300192, Japan.
   [Koichiro, Yoshino] JST PRESTO, Honcho 4-1-8, Kawaguchi, Saitama 3320012, Japan.
RP Murase, Y (通讯作者)，Nara Inst Sci & Technol, Takayamacho 8916-5, Ikoma, Nara 6300192, Japan.
EM y-murase@is.naist.jp
FU JST PRESTO [JPMJPR165B]
FX Part of this research was supported by JST PRESTO (JPMJPR165B).
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TC 1
Z9 1
U1 0
U2 19
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 MAR
PY 2019
VL 54
BP 1
EP 16
DI 10.1016/j.csl.2018.08.003
PG 16
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HB4SM
UT WOS:000451046000001
DA 2022-08-23
ER

PT J
AU Yuan, WJ
   Yang, L
   Yang, Q
   Sheng, YH
   Wang, ZY
AF Yuan, Wenjing
   Yang, Lin
   Yang, Qing
   Sheng, Yehua
   Wang, Ziyang
TI Extracting Spatio-Temporal Information from Chinese Archaeological Site
   Text
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE archaeological site; Chinese archaeological text; information
   extraction; deep learning
ID ENTITY RECOGNITION
AB Archaeological site text is the main carrier of archaeological data at present, which contains rich information. How to efficiently extract useful knowledge from the massive unstructured archaeological site texts is of great significance for the mining and reuse of archaeological information. According to the site information (such as name, location, cultural type, dynasty, etc.) recorded in the Chinese archaeological site text, this paper combines deep learning and natural language processing techniques to study the information extraction method for automatically obtaining the spatio-temporal information of sites. The initial construction of the corpus of Chinese archaeological site text is completed for the first time, and the corpus is input into the Bidirectional Long Short-Term Memory with Conditional Random Fields (BiLSTM-CRF) entity recognition model and Bidirectional Gated Recurrent Units with Dual Attention (BiGRU-Dual Attention) relationship extraction model for training. The F1 values of BiLSTM-CRF model and BiGRU-Dual Attention model on the test set reach 87.87% and 88.05%, respectively. The study demonstrates that the information extraction method proposed in this paper is feasible for the Chinese archaeological site texts, which promotes the establishment of knowledge graphs in archaeology and provides new methods and ideas for the development of information mining technology in archaeology.
C1 [Yuan, Wenjing; Yang, Lin; Yang, Qing; Sheng, Yehua; Wang, Ziyang] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China.
   [Yang, Lin; Sheng, Yehua] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.
   [Yang, Lin] Nanjing Normal Univ, Inst Environm Archaeol, Nanjing 210023, Peoples R China.
RP Yang, L (通讯作者)，Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China.; Yang, L (通讯作者)，Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.; Yang, L (通讯作者)，Nanjing Normal Univ, Inst Environm Archaeol, Nanjing 210023, Peoples R China.
EM 201302143@njnu.edu.cn; 09298@njnu.edu.cn; 191335001@njnu.edu.cn;
   shengyehua@njnu.edu.cn; 191335025@njnu.edu.cn
FU National Natural Science Foundation of China [41631175, 41471102]; Major
   Research Project of the China National Social Science Fund [15ZDB054]
FX This research was funded by the National Natural Science Foundation of
   China (Grant Nos. 41631175, 41471102) and the Major Research Project of
   the China National Social Science Fund (Grant No. 15ZDB054).
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NR 28
TC 0
Z9 0
U1 8
U2 8
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 MAR
PY 2022
VL 11
IS 3
AR 175
DI 10.3390/ijgi11030175
PG 18
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA 0C4YM
UT WOS:000775320600001
OA gold
DA 2022-08-23
ER

PT J
AU He, Y
   Duan, GD
   Luo, GC
   Liu, X
AF He, Yang
   Duan, Guiduo
   Luo, Guangchun
   Liu, Xin
TI Robust Visual Relationship Detection towards Sparse Images in
   Internet-of-Things
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
ID CLASSIFICATION; SEGMENTATION
AB Visual relationship can capture essential information for images, like the interactions between pairs of objects. Such relationships have become one prominent component of knowledge within sparse image data collected by multimedia sensing devices. Both the latent information and potential privacy can be included in the relationships. However, due to the high combinatorial complexity in modeling all potential relation triplets, previous studies on visual relationship detection have used the mixed visual and semantic features separately for each object, which is incapable for sparse data in IoT systems. Therefore, this paper proposes a new deep learning model for visual relationship detection, which is a novel attempt for cooperating computational intelligence (CI) methods with IoTs. The model imports the knowledge graph and adopts features for both entities and connections among them as extra information. It maps the visual features extracted from images into the knowledge-based embedding vector space, so as to benefit from information in the background knowledge domain and alleviate the impacts of data sparsity. This is the first time that visual features are projected and combined with prior knowledge for visual relationship detection. Moreover, the complexity of the network is reduced by avoiding the learning of redundant features from images. Finally, we show the superiority of our model by evaluating on two datasets.
C1 [He, Yang; Duan, Guiduo] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China.
   [Duan, Guiduo; Luo, Guangchun] Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 610000, Peoples R China.
   [Luo, Guangchun; Liu, Xin] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 611731, Peoples R China.
RP Duan, GD (通讯作者)，Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China.; Duan, GD (通讯作者)，Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 610000, Peoples R China.
EM duanguiduo@163.com
OI 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 the Sichuan Science and
   Technology Program (2019YFG0507 and 2020YFG0328) and the National
   Natural Science Foundation of China (NSFC) (U19A2059). The work was also
   supported in part by the Young Scientists Fund of the National Natural
   Science Foundation of China under Grant No. 61802050.
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NR 65
TC 0
Z9 0
U1 3
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 JUL 20
PY 2021
VL 2021
AR 6383646
DI 10.1155/2021/6383646
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA TX7EP
UT WOS:000683252000001
OA gold
DA 2022-08-23
ER

PT J
AU Song, X
   Zhang, HR
   Akerkar, R
   Huang, HW
   Guo, S
   Zhong, L
   Ji, YS
   Opdahl, AL
   Purohit, H
   Skupin, A
   Pottathil, A
   Culotta, A
AF Song, Xuan
   Zhang, Haoran
   Akerkar, Rajendra
   Huang, Huawei
   Guo, Song
   Zhong, Lei
   Ji, Yusheng
   Opdahl, Andreas L.
   Purohit, Hemant
   Skupin, Andre
   Pottathil, Akshay
   Culotta, Aron
TI Big Data and Emergency Management: Concepts, Methodologies, and
   Applications
SO IEEE TRANSACTIONS ON BIG DATA
LA English
DT Article
DE Emergency services; Sensors; Big Data; Social networking (online);
   Mathematical model; Communication networks; Remote sensing; Disaster
   informatics; urban computing; smart city; emergency management
ID VOLUNTEERED GEOGRAPHIC INFORMATION; SEMANTIC WEB; LARGE-SCALE;
   PREPLANNED RECOVERY; DISASTER RESPONSE; NETWORKS; SYSTEM; ARCHITECTURE;
   BEHAVIOR; PREDICTABILITY
AB Recent decades have seen a significant increase in the frequency, intensity, and impact of natural disasters and other emergencies, forcing the governments around the world to make emergency response and disaster management national priorities. The growth of extremely large and complex datasets-commonly referred to as big data-and various advances in information and communications technology and computing now support more effective approaches to humanitarian relief, logistical coordination, overall disaster management, and long-term recovery in connection with natural disasters and emergency events. Leveraging big data and technological advances for emergency management has attracted considerable attention in the research community. However, the desired merging of big data and emergency management (BDEM) requires coordinated efforts to align and define interdisciplinary terminologies and methodologies. To date, the key concepts and technologies in this emerging research area have not been coherently discussed in a sufficiently broad and multidisciplinary manner. In this article, an international team presents an overview of the BDEM domain, highlighting a general framework and discussing key challenges from several perspectives. We introduce and summarize typical technologies and applications, organized into the six broad categories of remote sensing, resilient communication networks, mobile communication networks, human mobility and urban sensing, social network analysis, and knowledge graphs. Finally, we outline several directions of future research.
C1 [Song, Xuan] Univ Sci & Technol SUS Tech, Shenzhen 518055, Guangdong, Peoples R China.
   [Song, Xuan; Zhang, Haoran] Univ Tokyo, Bunkyo City, Tokyo 1138654, Japan.
   [Akerkar, Rajendra] Western Norway Res Inst, N-6856 Sogndal, Norway.
   [Huang, Huawei; Guo, Song] Hong Kong Polytech Univ, Kowloon, Hong Kong, Peoples R China.
   [Zhong, Lei; Ji, Yusheng] Natl Inst Informat, Tokyo, Tokyo 1018430, Japan.
   [Opdahl, Andreas L.] Univ Bergen, N-5007 Bergen, Norway.
   [Purohit, Hemant] George Mason Univ, Fairfax, VA 22030 USA.
   [Skupin, Andre; Pottathil, Akshay] San Diego State Univ, San Diego, CA 92182 USA.
   [Culotta, Aron] IIT, Chicago, IL 60616 USA.
RP Song, X (通讯作者)，Univ Sci & Technol SUS Tech, Shenzhen 518055, Guangdong, Peoples R China.; Song, X (通讯作者)，Univ Tokyo, Bunkyo City, Tokyo 1138654, Japan.
EM songxuan@csis.u-tokyo.ac.jp; zhang_ronan@csis.u-tokyo.ac.jp;
   rak@vestforsk.no; davyhwang.cug@gmail.com; song.guo@polyu.edu.hk;
   zhong@nii.ac.jp; kei@nii.ac.jp; andreas.opdahl@uib.no; hpurohit@gmu.edu;
   skupin@mail.sdsu.edu; akshay.pottathil@sdsu.edu; culotta@cs.iit.edu
RI Guo, Song/AAZ-4542-2020
OI Guo, Song/0000-0001-9831-2202
FU Transnational Partnership for Excellent Research and Education in Big
   Data and Emergency Management Project - Research Council of Norway
   (RCN); Norwegian Centre for International Cooperation in Education (SiU)
   through the INTPART programme; Japan Ministry of Education, Culture,
   Sports, Science, and Technology (MEXT) [19K15260]
FX This work was supported by the Transnational Partnership for Excellent
   Research and Education in Big Data and Emergency Management Project
   funded by the Research Council of Norway (RCN), the Norwegian Centre for
   International Cooperation in Education (SiU) through the INTPART
   programme, and the Grant-in-Aid for Early-Career Scientists (19K15260)
   of the Japan Ministry of Education, Culture, Sports, Science, and
   Technology (MEXT).
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NR 197
TC 4
Z9 4
U1 26
U2 26
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2332-7790
J9 IEEE T BIG DATA
JI IEEE Trans. Big Data
PD APR 1
PY 2022
VL 8
IS 2
BP 397
EP 419
DI 10.1109/TBDATA.2020.2972871
PG 23
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZR5UU
UT WOS:000767848400008
DA 2022-08-23
ER

PT J
AU Li, XS
   Fu, CC
   Zhong, R
   Zhong, D
   He, TT
   Jiang, XP
AF Li, Xusheng
   Fu, Chengcheng
   Zhong, Ran
   Zhong, Duo
   He, Tingting
   Jiang, Xingpeng
TI A hybrid deep learning framework for bacterial named entity recognition
   with domain features
SO BMC BIOINFORMATICS
LA English
DT Article; Proceedings Paper
CT IEEE International Conference on Bioinformatics and Biomedicine (BIBM) -
   Bioinformatics and Systems Biology
CY DEC 03-06, 2018
CL Madrid, SPAIN
SP IEEE
DE Named entity recognition; Biomedical text mining; Conditional random
   field; Deep learning
ID GUT MICROBIOTA
AB Background Microbes have been shown to play a crucial role in various ecosystems. Many human diseases have been proved to be associated with bacteria, so it is essential to extract the interaction between bacteria for medical research and application. At the same time, many bacterial interactions with certain experimental evidences have been reported in biomedical literature. Integrating this knowledge into a database or knowledge graph could accelerate the progress of biomedical research. A crucial and necessary step in interaction extraction (IE) is named entity recognition (NER). However, due to the specificity of bacterial naming, there are still challenges in bacterial named entity recognition. Results In this paper, we propose a novel method for bacterial named entity recognition, which integrates domain features into a deep learning framework combining bidirectional long short-term memory network and convolutional neural network. When domain features are not added, F1-measure of the model achieves 89.14%. After part-of-speech (POS) features and dictionary features are added, F1-measure of the model achieves 89.7%. Hence, our model achieves an advanced performance in bacterial NER with the domain features. Conclusions We propose an efficient method for bacterial named entity recognition which combines domain features and deep learning models. Compared with the previous methods, the effect of our model has been improved. At the same time, the process of complex manual extraction and feature design are significantly reduced.
C1 [Li, Xusheng; Fu, Chengcheng; Zhong, Duo; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
   [Li, Xusheng; Fu, Chengcheng; Zhong, Duo; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.
   [Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
RP Jiang, XP (通讯作者)，Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.; Jiang, XP (通讯作者)，Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.
EM xpjiang@mail.ccnu.edu.cn
FU National Key Research and Development Program of China [2017YFC0909502];
   National Natural Science Foundation of China [61532008, 61872157]
FX The research was supported by the National Key Research and Development
   Program of China (2017YFC0909502), the National Natural Science
   Foundation of China (61532008 and 61872157).; Specifically, the
   publication costs are funded by the National Key Research and
   Development Program of China (2017YFC0909502).
CR [Anonymous], [No title captured]
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PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD DEC 2
PY 2019
VL 20
SU 16
SI SI
AR 583
DI 10.1186/s12859-019-3071-3
PG 9
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA JR9KT
UT WOS:000499935900002
PM 31787075
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Lazrak, N
   Ouarzazi, J
   Zahir, J
   Mousannif, H
AF Lazrak, Noussair
   Ouarzazi, Jamal
   Zahir, Jihad
   Mousannif, Hajar
TI Enabling distributed intelligence in Internet of Things: an air quality
   monitoring use case
SO PERSONAL AND UBIQUITOUS COMPUTING
LA English
DT Article; Early Access
DE Distributed learning; Distributed intelligence; Shared knowledge;
   Distributed machine learning; Internet of Things; Air quality
   monitoring; Air quality prediction
ID EXTREME LEARNING-MACHINE; AIRBORNE POLLEN; MORTALITY
AB Air pollution is worsening almost everywhere in the world. According to the Health Effects Institute (HEI), more than 95% of the world population breathe polluted air, toxic to their cardiovascular and respiratory health, which caused the death of 4.2 million people worldwide in 2016. As a result, the air pollution has become one of the leading causes of death worldwide. Therefore, an early cost-efficient warning system based on precise forecasting tools must be put in place to measure and avoid the harmful effects of exposure to the main air pollutants. Thus, it is essential to obtain reliable analytical information on air quality in a specific time and place. This paper focuses on monitoring air quality using a distributed intelligence which is a cost-efficient solution that enables a flexible prediction process distributed within a network of nodes and devices using a cross-platform solution. The suggested architecture enables collaborative learning along with collective knowledge graph building and knowledge sharing using the state of the art in Internet of Things, distributed machine learning, and ontologies. The proposed architecture suggests a flexible prediction system personalized for each node based on its need of information. Similar nodes get together for collective learning which allows for resource optimization, knowledge reusability, and device interoperability. The paper describes the modeling framework of distributed intelligence monitoring and analysis system designed for urban regions.
C1 [Lazrak, Noussair; Ouarzazi, Jamal; Zahir, Jihad; Mousannif, Hajar] Cadi Ayyad Univ, Marrakech, Morocco.
RP Lazrak, N (通讯作者)，Cadi Ayyad Univ, Marrakech, Morocco.
EM lazrak.noussair@gmail.com
OI LAZRAK, Noussair/0000-0002-7459-146X
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NR 37
TC 1
Z9 1
U1 1
U2 3
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 1617-4909
EI 1617-4917
J9 PERS UBIQUIT COMPUT
JI Pers. Ubiquitous Comput.
DI 10.1007/s00779-020-01483-3
EA NOV 2020
PG 11
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA OV0WT
UT WOS:000591942000001
DA 2022-08-23
ER

PT J
AU Zhang, MQ
   Ma, L
   Ren, YZ
   Zhang, GG
   Liu, XL
AF Zhang, Mengqi
   Ma, Lei
   Ren, Yanzhao
   Zhang, Ganggang
   Liu, Xinliang
TI Span-based model for overlapping entity recognition and multi-relations
   classification in the food domain
SO MATHEMATICAL BIOSCIENCES AND ENGINEERING
LA English
DT Article
DE information extraction; span-based approach; overlapping entity
   recognition; category marker; multi-relations classification; entity
   attributes
AB Information extraction (IE) is an important part of the entire knowledge graph lifecycle. In the food domain, extracting information such as ingredient and cooking method from Chinese recipes is crucial to safety risk analysis and identification of ingredient. In comparison with English, due to the complex structure, the richness of information in word combination, and lack of tense, Chinese IE is much more challenging. This dilemma is particularly prominent in the food domain with high-density knowledge, imprecise syntactic structure. However, existing IE methods focus only on the features of entities in a sentence, such as context and position, and ignore features of the entity itself and the influence of self attributes on prediction of inter entity relationship. To solve the problems of overlapping entity recognition and multi-relations classification in the food domain, we propose a span-based model known as SpIE for IE. The SpIE uses the span representation for each possible candidate entity to capture span-level features, which transforms named entity recognition (NER) into a classification mission. Besides, SpIE feeds extra information about the entity into the relation classification (RC) model by considering the effect of entity's attributes (both the entity mention and entity type) on the relationship between entity pairs. We apply SpIE on two datasets and observe that SpIE significantly outperforms the previous neural approaches due to capture the feature of overlapping entity and entity attributes, and it remains very competitive in general IE.
C1 [Zhang, Mengqi; Ma, Lei; Liu, Xinliang] Beijing Technol & Business Univ, Sch E Business & Logist, Beijing 100048, Peoples R China.
   [Zhang, Mengqi; Ma, Lei; Liu, Xinliang] Beijing Technol & Business Univ, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100048, Peoples R China.
   [Ren, Yanzhao] Beijing Technol & Business Univ, Sch Comp Sci & Engn, Beijing 100048, Peoples R China.
   [Zhang, Ganggang] Capital Normal Univ, Digital Campus Construct Ctr, Beijing 100048, Peoples R China.
RP Liu, XL (通讯作者)，Beijing Technol & Business Univ, Sch E Business & Logist, Beijing 100048, Peoples R China.; Liu, XL (通讯作者)，Beijing Technol & Business Univ, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100048, Peoples R China.
EM liuxinl@btbu.edu.cn
FU Beijing Science and Technology Planning Project [Z191100008619007]
FX This project was supported by Beijing Science and Technology Planning
   Project [Grant Numbers Z191100008619007] .
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NR 39
TC 0
Z9 0
U1 14
U2 14
PU AMER INST MATHEMATICAL SCIENCES-AIMS
PI SPRINGFIELD
PA PO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES
SN 1547-1063
EI 1551-0018
J9 MATH BIOSCI ENG
JI Math. Biosci. Eng.
PY 2022
VL 19
IS 5
BP 5134
EP 5152
DI 10.3934/mbe.2022240
PG 19
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA ZZ6HI
UT WOS:000773367000003
PM 35430857
OA gold
DA 2022-08-23
ER

PT J
AU Feng, FL
   He, XN
   Tang, J
   Chua, TS
AF Feng, Fuli
   He, Xiangnan
   Tang, Jie
   Chua, Tat-Seng
TI Graph Adversarial Training: Dynamically Regularizing Based on Graph
   Structure
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Perturbation methods; Training; Neural networks; Robustness; Predictive
   models; Task analysis; Standards; Adversarial training; graph-based
   learning; graph neural networks
AB Recent efforts show that neural networks are vulnerable to small but intentional perturbations on input features in visual classification tasks. Due to the additional consideration of connections between examples (e.g., articles with citation link tend to be in the same class), graph neural networks could be more sensitive to the perturbations, since the perturbations from connected examples exacerbate the impact on a target example. Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization. However, existing AT methods focus on standard classification, being less effective when training models on graph since it does not model the impact from connected examples. In this work, we explore adversarial training on graph, aiming to improve the robustness and generalization of models learned on graph. We propose Graph Adversarial Training (GraphAT), which takes the impact from connected examples into account when learning to construct and resist perturbations. We give a general formulation of GraphAT, which can be seen as a dynamic regularization scheme based on the graph structure. To demonstrate the utility of GraphAT, we employ it on a state-of-the-art graph neural network model - Graph Convolutional Network (GCN). We conduct experiments on two citation graphs (Citeseer and Cora) and a knowledge graph (NELL), verifying the effectiveness of GraphAT which outperforms normal training on GCN by 4.51 percent in node classification accuracy. Codes are available via: https://github.com/fulifeng/GraphAT.
C1 [Feng, Fuli; Chua, Tat-Seng] Natl Univ Singapore Comp 1, Sch Comp, Comp Dr, Singapore 117417, Singapore.
   [He, Xiangnan] Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Peoples R China.
   [Tang, Jie] Tsinghua Univ, Beijing 100084, Peoples R China.
RP He, XN (通讯作者)，Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Peoples R China.
EM fulifeng93@gmail.com; xiangnanhe@gmail.com; jietang@tsinghua.edu.cn;
   dcscts@nus.edu.sg
FU National Research Foundation Singapore under its AI Singapore Programme
   [AISG-100E-2018-012]; Prime Minister's Office under its IRC@SG Funding
   Initiative; National Natural Science Foundation of China [61972372]
FX This research was part of NExT++ reserach, supported by the National
   Research Foundation Singapore under its AI Singapore Programme
   (AISG-100E-2018-012), Prime Minister's Office under its IRC@SG Funding
   Initiative, and National Natural Science Foundation of China (61972372).
   We thank the anonymous reviewers and the associated editor for their
   reviewing efforts.
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NR 53
TC 25
Z9 27
U1 9
U2 21
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD JUN 1
PY 2021
VL 33
IS 6
BP 2493
EP 2504
DI 10.1109/TKDE.2019.2957786
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA SE4KV
UT WOS:000652042700001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Yuan, MS
   Chen, L
   Deng, MH
AF Yuan, Musu
   Chen, Liang
   Deng, Minghua
TI scMRA: a robust deep learning method to annotate scRNA-seq data with
   multiple reference datasets
SO BIOINFORMATICS
LA English
DT Article
ID CELL RNA-SEQ
AB Motivation: Single-cell RNA-seq (scRNA-seq) has been widely used to resolve cellular heterogeneity. After collecting scRNA-seq data, the natural next step is to integrate the accumulated data to achieve a common ontology of cell types and states. Thus, an effective and efficient cell-type identification method is urgently needed. Meanwhile, high-quality reference data remain a necessity for precise annotation. However, such tailored reference data are always lacking in practice. To address this, we aggregated multiple datasets into a meta-dataset on which annotation is conducted. Existing supervised or semi-supervised annotation methods suffer from batch effects caused by different sequencing platforms, the effect of which increases in severity with multiple reference datasets.
   Results: Herein, a robust deep learning-based single-cell Multiple Reference Annotator (scMRA) is introduced. In scMRA, a knowledge graph is constructed to represent the characteristics of cell types in different datasets, and a graphic convolutional network serves as a discriminator based on this graph. scMRA keeps intra-cell-type closeness and the relative position of cell types across datasets. scMRA is remarkably powerful at transferring knowledge from multiple reference datasets, to the unlabeled target domain, thereby gaining an advantage over other state-of-theart annotation methods in multi-reference data experiments. Furthermore, scMRA can remove batch effects. To the best of our knowledge, this is the first attempt to use multiple insufficient reference datasets to annotate target data, and it is, comparatively, the best annotation method for multiple scRNA-seq datasets.
C1 [Yuan, Musu; Chen, Liang; Deng, Minghua] Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China.
   [Yuan, Musu; Deng, Minghua] Peking Univ, Ctr Quantitat Biol, Beijing 100871, Peoples R China.
   [Deng, Minghua] Peking Univ, Ctr Stat Sci, Beijing 100871, Peoples R China.
RP Deng, MH (通讯作者)，Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China.; Deng, MH (通讯作者)，Peking Univ, Ctr Quantitat Biol, Beijing 100871, Peoples R China.; Deng, MH (通讯作者)，Peking Univ, Ctr Stat Sci, Beijing 100871, Peoples R China.
EM dengmh@pku.edu.cn
RI Deng, Minghua/B-5430-2012
OI Deng, Minghua/0000-0002-9143-1898
FU National Key Research and Development Program of China [2016YFA0502303];
   National Key Basic Research Project of China [2015CB910303]; National
   Natural Science Foundation of China [31871342]
FX This work was supported by the National Key Research and Development
   Program of China [2016YFA0502303], the National Key Basic Research
   Project of China [2015CB910303] and the National Natural Science
   Foundation of China [31871342].
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NR 28
TC 2
Z9 2
U1 11
U2 15
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1460-2059
J9 BIOINFORMATICS
JI Bioinformatics
PD FEB 1
PY 2022
VL 38
IS 3
BP 738
EP 745
DI 10.1093/bioinformatics/btab700
EA OCT 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 YH8BG
UT WOS:000743386000017
PM 34623390
DA 2022-08-23
ER

PT J
AU Wu, DS
   Song, JX
   Bian, Y
   Zheng, XL
   Zhang, Z
AF Wu, Desheng
   Song, Jingxiu
   Bian, Yuan
   Zheng, Xiaolong
   Zhang, Zhu
TI Risk perception and intelligent decision in complex social information
   network
SO INDUSTRIAL MANAGEMENT & DATA SYSTEMS
LA English
DT Article
DE Complex information network; Risk perception; Intelligent decision
   making; Integrated platform
AB Purpose The increase of turbulence sources and risk points under the complex social information network has brought severe challenges. This paper discusses risk perception and intelligent decision-making under the complex social information network to maintain social security and financial security. Design/methodology/approach Cross-modal semantic fusion and social risk perception, temporal knowledge graph and analysis, complex social network intelligent decision-making methods have been studied. A big data computing platform of software and hardware integration for security combat is constructed based on the technical support. Findings The software and hardware integration platform driven by big data can realize joint identification of significant risks, intelligent analysis and large-scale group decision-making. Practical implications The integrated platform can monitor the abnormal operation and potential associated risks of Listed Companies in real-time, reduce information asymmetry and accounting costs and improve the capital market's ability to serve the real economy. It can also provide critical technical support and decision support in necessary public opinion monitoring and control business. Originality/value In this paper, the theory of knowledge-enhanced multi-modal multi-granularity dynamic risk analysis and intelligent group decision-making and the idea of an inference think tank (I-aid-S) is proposed. New technologies and methods, such as association analysis, time series evolution and super large-scale group decision-making, have been established. It's also applied in behavior and situation deduction, public opinion and finance and provides real-time, dynamic, fast and high-quality think tank services.
C1 [Wu, Desheng; Song, Jingxiu; Bian, Yuan] Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China.
   [Zheng, Xiaolong; Zhang, Zhu] Chinese Acad Sci, Inst Automat, Beijing, Peoples R China.
RP Wu, DS (通讯作者)，Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China.
EM dwu@ucas.ac.cn
FU Ministry of Science and Technology of China [2020AAA0108400,
   2020AAA0108402]; National Natural Science Foundation of China
   [71825007]; Chinese Academy of Sciences [QYZDB-SSW-SYS021,
   211211KYSB20180042]; CAS [XDA2302020]
FX This work was supported in part by the Ministry of Science and
   Technology of China under Grant 2020AAA0108400 and Grant 2020AAA0108402,
   in part by the National Natural Science Foundation of China under Grant
   71825007, in part by the Chinese Academy of Sciences Frontier Scientific
   Research Key Project under Grant QYZDB-SSW-SYS021, and supported by the
   International Partnership Program of Chinese Academy of Sciences, Grant
   No. 211211KYSB20180042 and the Strategic Priority Research Program of
   CAS under Grant XDA2302020.
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NR 39
TC 0
Z9 0
U1 16
U2 73
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0263-5577
EI 1758-5783
J9 IND MANAGE DATA SYST
JI Ind. Manage. Data Syst.
PD FEB 4
PY 2021
VL 121
IS 1
SI SI
BP 99
EP 110
DI 10.1108/IMDS-10-2020-0566
EA DEC 2020
PG 12
WC Computer Science, Interdisciplinary Applications; Engineering,
   Industrial
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA QG1EV
UT WOS:000600828100001
DA 2022-08-23
ER

PT J
AU Singh, G
   Kuzniar, A
   Brouwer, M
   Martinez-Ortiz, C
   Bachem, CWB
   Tikunov, YM
   Bovy, AG
   Visser, RGF
   Finkers, R
AF Singh, Gurnoor
   Kuzniar, Arnold
   Brouwer, Matthijs
   Martinez-Ortiz, Carlos
   Bachem, Christian W. B.
   Tikunov, Yury M.
   Bovy, Arnaud G.
   Visser, Richard G. F.
   Finkers, Richard
TI Linked Data Platform for Solanaceae Species
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE prioritization of candidate genes; plant breeding; Solanaceae; QTLs;
   semantic web; linked data
ID GENOME SEQUENCE; TOMATO; LYCOPENE; IDENTIFICATION; QTL; INTROGRESSION;
   CAROTENOIDS; ONTOLOGIES; RESOURCE; SYNTHASE
AB Genetics research is increasingly focusing on mining fully sequenced genomes and their annotations to identify the causal genes associated with traits (phenotypes) of interest. However, a complex trait is typically associated with multiple quantitative trait loci (QTLs), each comprising many genes, that can positively or negatively affect the trait of interest. To help breeders in ranking candidate genes, we developed an analytical platform called pbg-ld that provides semantically integrated geno- and phenotypic data on Solanaceae species. This platform combines both unstructured data from scientific literature and structured data from publicly available biological databases using the Linked Data approach. In particular, QTLs were extracted from tables of full-text articles from the Europe PubMed Central (PMC) repository using QTLTableMiner(++) (QTM), while the genomic annotations were obtained from the Sol Genomics Network (SGN), UniProt and Ensembl Plants databases. These datasets were transformed into Linked Data graphs, which include cross-references to many other relevant databases such as Gramene, Plant Reactome, InterPro and KEGG Orthology (KO). Users can query and analyze the integrated data through a web interface or programmatically via the SPARQL and RESTful services (APIs). We illustrate the usability of pbg-ld by querying genome annotations, by comparing genome graphs, and by two biological use cases in Jupyter Notebooks. In the first use case, we performed a comparative genomics study using pbg-ld to compare the difference in the genetic mechanism underlying tomato fruit shape and potato tuber shape. In the second use case, we developed a seamlessly integrated workflow that uses genomic data from pbg-ld knowledge graphs and prioritization pipelines to predict candidate genes within QTL regions for metabolic traits of tomato.
C1 [Singh, Gurnoor; Brouwer, Matthijs; Bachem, Christian W. B.; Tikunov, Yury M.; Bovy, Arnaud G.; Visser, Richard G. F.; Finkers, Richard] Wageningen Univ & Res, Plant Breeding, NL-6708 PB Wageningen, Netherlands.
   [Kuzniar, Arnold; Martinez-Ortiz, Carlos] Netherlands eSci Ctr, NL-1098 XG Amsterdam, Netherlands.
   [Singh, Gurnoor] Radboud Univ Nijmegen, Med Ctr, Ctr Mol & Biomol Informat CMBI, NL-6525 GA Nijmegen, Netherlands.
RP Finkers, R (通讯作者)，Wageningen Univ & Res, Plant Breeding, NL-6708 PB Wageningen, Netherlands.; Kuzniar, A (通讯作者)，Netherlands eSci Ctr, NL-1098 XG Amsterdam, Netherlands.
EM gurnoor1990@gmail.com; a.kuzniar@esciencecenter.nl;
   matthijs.brouwer@wur.nl; c.martinez@esciencecenter.nl;
   Christian.Bachem@wur.nl; Yury.tikunov@wur.nl; arnaud.bovy@wur.nl;
   richard.visser@wur.nl; richard.finkers@wur.nl
RI ; Finkers, Richard/G-1706-2010
OI Bachem, Christian/0000-0002-3077-6833; Singh,
   Gurnoor/0000-0003-1615-4197; Bovy, Arnaud/0000-0003-2643-7377;
   Martinez-Ortiz, Carlos/0000-0001-5565-7577; Kuzniar,
   Arnold/0000-0003-1711-7961; Finkers, Richard/0000-0002-4368-8058
FU Netherlands Organisation for Scientific Research (NWO) via the
   Netherlands eScience Center [27014204]
FX This work was supported by the Netherlands Organisation for Scientific
   Research (NWO) via the Netherlands eScience Center (grant number:
   27014204).
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NR 55
TC 2
Z9 2
U1 1
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD OCT
PY 2020
VL 10
IS 19
AR 6813
DI 10.3390/app10196813
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 ON1FQ
UT WOS:000586456500001
OA Green Submitted, gold, Green Accepted
DA 2022-08-23
ER

PT J
AU Yumusak, S
   Dogdu, E
   Kodaz, H
   Kamilaris, A
   Vandenbussche, PY
AF Yumusak, Semih
   Dogdu, Erdogan
   Kodaz, Halife
   Kamilaris, Andreas
   Vandenbussche, Pierre-Yves
TI SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines
SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
LA English
DT Article; Proceedings Paper
CT 8th Forum on Data Engineering and Information Management (DEIM)
CY MAR, 2016
CL Fukuoka, JAPAN
DE linked data; semantic Web; SPARQL endpoint; endpoint discovery;
   metasearch; knowledge graph
ID WEB; FRAMEWORK; CRAWLER
AB Linked data endpoints are online query gateways to semantically annotated linked data sources. In order to query these data sources, SPARQL query language is used as a standard. Although a linked data endpoint (i.e. SPARQL endpoint) is a basic Web service, it provides a platform for federated online querying and data linking methods. For linked data consumers, SPARQL endpoint availability and discovery are crucial for live querying and semantic information retrieval. Current studies show that availability of linked datasets is very low, while the locations of linked data endpoints change frequently. There are linked data respsitories that collect and list the available linked data endpoints or resources. It is observed that around half of the endpoints listed in existing repositories are not accessible (temporarily or permanently offline). These endpoint URLs are shared through repository websites, such as Datahub. io, however, they are weakly maintained and revised only by their publishers. In this study, a novel metacrawling method is proposed for discovering and monitoring linked data sources on the Web. We implemented the method in a prototype system, named SPARQL Endpoints Discovery (SpEnD). SpEnD starts with a "search keyword" discovery process for finding relevant keywords for the linked data domain and specifically SPARQL endpoints. Then, the collected search keywords are utilized to find linked data sources via popular search engines (Google, Bing, Yahoo, Yandex). By using this method, most of the currently listed SPARQL endpoints in existing endpoint repositories, as well as a significant number of new SPARQL endpoints, have been discovered. We analyze our findings in comparison to Datahub collection in detail.
C1 [Yumusak, Semih] KTO Karatay Univ, Comp Engn Dept, Konya, Turkey.
   [Dogdu, Erdogan] Cankaya Univ, Comp Engn Dept, Ankara, Turkey.
   [Kodaz, Halife] Selcuk Univ, Comp Engn Dept, Konya, Turkey.
   [Kamilaris, Andreas] Insight Res Ctr Data Analyt, Istanbul, Turkey.
   [Vandenbussche, Pierre-Yves] Fujitsu Ireland Ltd, Swords, Ireland.
RP Yumusak, S (通讯作者)，KTO Karatay Univ, Comp Engn Dept, Konya, Turkey.
EM semih.yumusak@karatay.edu.tr
RI Kamilaris, Andreas/H-8744-2019; Yumusak, Semih/AAZ-8158-2020;
   Vandenbussche, Pierre-Yves/G-1496-2019; KODAZ, Halife/ABG-2951-2020;
   Yumusak, Semih/Y-1134-2019
OI Kamilaris, Andreas/0000-0002-8484-4256; Vandenbussche,
   Pierre-Yves/0000-0003-0591-6109; KODAZ, Halife/0000-0001-8602-4262;
   Yumusak, Semih/0000-0002-8878-4991; Dogdu, Erdogan/0000-0001-5987-0164
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NR 36
TC 12
Z9 12
U1 0
U2 16
PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATION ENGINEERS
PI TOKYO
PA KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011,
   JAPAN
SN 0916-8532
EI 1745-1361
J9 IEICE T INF SYST
JI IEICE Trans. Inf. Syst.
PD APR
PY 2017
VL E100D
IS 4
BP 758
EP 767
DI 10.1587/transinf.2016DAP0025
PG 10
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 ES2PT
UT WOS:000399371100018
OA gold
DA 2022-08-23
ER

PT J
AU Pham, T
   Tao, XH
   Zhang, J
   Yong, JM
AF Pham Thuan
   Tao, Xiaohui
   Zhang, Ji
   Yong, Jianming
TI Constructing a knowledge-based heterogeneous information graph for
   medical health status classification
SO HEALTH INFORMATION SCIENCE AND SYSTEMS
LA English
DT Article
DE Knowledge graph; Electronic health data; Classification; Healthcare
ID DISEASE; NETWORK; PREDICTION; EXTRACTION; MEDLINE
AB Applying Pearson correlation and semantic relations in building a heterogeneous information graph (HIG) to develop a classification model has achieved a notable performance in improving the accuracy of predicting the status of health risks. In this study, the approach that was used, integrated knowledge of the medical domain as well as taking advantage of applying Pearson correlation and semantic relations in building a classification model for diagnosis. The research mined knowledge which was extracted from titles and abstracts of MEDLINE to discover how to assess the links between objects relating to medical concepts. A knowledge-base HIG model then was developed for the prediction of a patient's health status. The results of the experiment showed that the knowledge-base model was superior to the baseline model and has demonstrated that the knowledge-base could help improve the performance of the classification model. The contribution of this study has been to provide a framework for applying a knowledge-base in the classification model which helps these models achieve the best performance of predictions. This study has also contributed a model to medical practice to help practitioners become more confident in making final decisions in diagnosing illness. Moreover, this study affirmed that biomedical literature could assist in building a classification model. This contribution will be advantageous for future researchers in mining the knowledge-base to develop different kinds of classification models.
C1 [Pham Thuan; Tao, Xiaohui; Zhang, Ji; Yong, Jianming] Univ Southern Queensland, Toowoomba, Qld, Australia.
RP Pham, T (通讯作者)，Univ Southern Queensland, Toowoomba, Qld, Australia.
EM Thuan.Pham@usq.edu.au
OI PHAM, THUAN/0000-0001-7433-858X
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NR 45
TC 7
Z9 7
U1 2
U2 19
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2047-2501
J9 HEALTH INF SCI SYST
JI Health Inf. Sci. Syst.
PD FEB 14
PY 2020
VL 8
IS 1
AR 10
DI 10.1007/s13755-020-0100-6
PG 14
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA KL3TV
UT WOS:000513350600001
PM 32117570
OA Green Published
DA 2022-08-23
ER

PT J
AU Guo, SL
   Yang, WT
   Han, LN
   Song, XW
   Wang, GW
AF Guo, Shuli
   Yang, Wentao
   Han, Lina
   Song, Xiaowei
   Wang, Guowei
TI A multi-layer soft lattice based model for Chinese clinical named entity
   recognition
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE Clinical named entity recognition; Clinical text mining; Fine-tuning
   BERT; Medical information processing; Transformer; Word-character
   lattice
AB Objective Named entity recognition (NER) is a key and fundamental part of many medical and clinical tasks, including the establishment of a medical knowledge graph, decision-making support, and question answering systems. When extracting entities from electronic health records (EHRs), NER models mostly apply long short-term memory (LSTM) and have surprising performance in clinical NER. However, increasing the depth of the network is often required by these LSTM-based models to capture long-distance dependencies. Therefore, these LSTM-based models that have achieved high accuracy generally require long training times and extensive training data, which has obstructed the adoption of LSTM-based models in clinical scenarios with limited training time. Method Inspired by Transformer, we combine Transformer with Soft Term Position Lattice to form soft lattice structure Transformer, which models long-distance dependencies similarly to LSTM. Our model consists of four components: the WordPiece module, the BERT module, the soft lattice structure Transformer module, and the CRF module. Result Our experiments demonstrated that this approach increased the F1 by 1-5% in the CCKS NER task compared to other models based on LSTM with CRF and consumed less training time. Additional evaluations showed that lattice structure transformer shows good performance for recognizing long medical terms, abbreviations, and numbers. The proposed model achieve 91.6% f-measure in recognizing long medical terms and 90.36% f-measure in abbreviations, and numbers. Conclusions By using soft lattice structure Transformer, the method proposed in this paper captured Chinese words to lattice information, making our model suitable for Chinese clinical medical records. Transformers with Mutilayer soft lattice Chinese word construction can capture potential interactions between Chinese characters and words.
C1 [Guo, Shuli; Yang, Wentao; Song, Xiaowei; Wang, Guowei] Beijing Inst Technol, Sch Automat, State Key Lab Intelligent Control & Decis Complex, Beijing, Peoples R China.
   [Han, Lina] Chinese Peoples Liberat Army Gen Hosp, Dept Cardiol, Med Ctr 2, Natl Clin Res Ctr Geriatr Dis, Beijing, Peoples R China.
RP Han, LN (通讯作者)，Chinese Peoples Liberat Army Gen Hosp, Dept Cardiol, Med Ctr 2, Natl Clin Res Ctr Geriatr Dis, Beijing, Peoples R China.
EM 2438381279@qq.com
FU Beijing Natural Science Foundation [L192064, M21018]; Key Research and
   Development Project of Hainan Province [ZDYF2021GXJS205]
FX This research was supported by the Beijing Natural Science Foundation
   (L192064 and M21018) and Key Research and Development Project of Hainan
   Province (ZDYF2021GXJS205).
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NR 34
TC 0
Z9 0
U1 1
U2 1
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD JUL 30
PY 2022
VL 22
IS 1
AR 201
DI 10.1186/s12911-022-01924-4
PG 12
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA 3J6KB
UT WOS:000833501300001
PM 35908055
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Fu, GH
   Song, CW
   Li, JQ
   Ma, Y
   Chen, P
   Wang, RQ
   Yang, BX
   Huang, ZS
AF Fu, Guanghui
   Song, Changwei
   Li, Jianqiang
   Ma, Yue
   Chen, Pan
   Wang, Ruiqian
   Yang, Bing Xiang
   Huang, Zhisheng
TI Distant Supervision for Mental Health Management in Social Media:
   Suicide Risk Classification System Development Study
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE deep learning; distant supervision; mental health; crisis prevention
AB Background: Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder.
   Objective: We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk.
   Methods: To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts).
   Results: Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data.
   Conclusions: In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide.
C1 [Fu, Guanghui; Song, Changwei; Li, Jianqiang; Wang, Ruiqian] Beijing Univ Technol, Sch Software Engn, Beijing, Peoples R China.
   [Ma, Yue] Univ Paris Saclay, Ctr Natl Rech Sci, Interdisciplinary Lab Digital Sci, Orsay, France.
   [Chen, Pan; Yang, Bing Xiang] Wuhan Univ, Sch Hlth Sci, Wuhan 430072, Peoples R China.
   [Huang, Zhisheng] Vrije Univ Amsterdam, Dept Artificial Intelligence, Amsterdam, Netherlands.
RP Yang, BX (通讯作者)，Wuhan Univ, Sch Hlth Sci, Wuhan 430072, Peoples R China.
EM 00009312@whu.wdu.cn
RI Fu, Guanghui/ABA-7593-2021; Huang, Zhisheng/P-3284-2014
OI Huang, Zhisheng/0000-0003-3794-9829; Ma, Yue/0000-0002-2805-2473
FU National Natural Science Foundation of China [72174152]; Project of
   Humanities and Social Sciences of the Ministry of Education in China
   [20YJCZH204]
FX This study is supported by the National Natural Science Foundation of
   China (No. 72174152) and the Project of Humanities and Social Sciences
   of the Ministry of Education in China (The Proactive Levelled
   Intervention for Social Network Users' Emotional Crisis-an Automatic
   Crisis Balance Analysis Model, 20YJCZH204).
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NR 33
TC 1
Z9 1
U1 12
U2 15
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD AUG 26
PY 2021
VL 23
IS 8
AR e26119
DI 10.2196/26119
PG 13
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 UH1WU
UT WOS:000689731100004
PM 34435964
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Yi, HC
   You, ZH
   Huang, DS
   Kwoh, CK
AF Yi, Hai-Cheng
   You, Zhu-Hong
   Huang, De-Shuang
   Kwoh, Chee Keong
TI Graph representation learning in bioinformatics: trends, methods and
   applications
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Review
DE graph representation learning; deep learning; graph neural network;
   graph embedding; knowledge graph; healthcare
ID DIMENSIONALITY REDUCTION; NETWORK; KNOWLEDGEBASE; MEDICINE; TOOL
AB Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeeds in vast bioinformatics scenarios with data represented in Euclidean domain. However, rich relational information between biological elements is retained in the non-Euclidean biomedical graphs, which is not learning friendly to classic machine learning methods. Graph representation learning aims to embed graph into a low-dimensional space while preserving graph topology and node properties. It bridges biomedical graphs and modern machine learning methods and has recently raised widespread interest in both machine learning and bioinformatics communities. In this work, we summarize the advances of graph representation learning and its representative applications in bioinformatics. To provide a comprehensive and structured analysis and perspective, we first categorize and analyze both graph embedding methods (homogeneous graph embedding, heterogeneous graph embedding, attribute graph embedding) and graph neural networks. Furthermore, we summarize their representative applications from molecular level to genomics, pharmaceutical and healthcare systems level. Moreover, we provide open resource platforms and libraries for implementing these graph representation learning methods and discuss the challenges and opportunities of graph representation learning in bioinformatics. This work provides a comprehensive survey of emerging graph representation learning algorithms and their applications in bioinformatics. It is anticipated that it could bring valuable insights for researchers to contribute their knowledge to graph representation learning and future-oriented bioinformatics studies.
C1 [Yi, Hai-Cheng] Univ Chinese Acad Sci, Beijing, Peoples R China.
   [Yi, Hai-Cheng] Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Beijing, Peoples R China.
   [You, Zhu-Hong] Northwestern Polytech Univ, Xian, Peoples R China.
   [Huang, De-Shuang] Tongji Univ, Inst Machines Learning & Syst Biol, Shanghai, Peoples R China.
   [Kwoh, Chee Keong] Nanyang Technol Univ NTU, Sch Comp Engn, Singapore, Singapore.
   [Kwoh, Chee Keong] Nanyang Technol Univ, MSc Bioinformat Program, Singapore, Singapore.
RP You, ZH (通讯作者)，Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Peoples R China.
EM zhuhongyou@nwpu.edu.cn
RI Yi, Hai-Cheng/S-3695-2019
OI Yi, Hai-Cheng/0000-0001-8339-396X
FU National Natural Science Foundation of China [61873212, 61732012];
   National Outstanding Youth Science Foundation of National Natural
   Science Foundation of China [61722212]
FX This work was supported by the National Natural Science Foundation of
   China (under grants 61873212, 61732012), in part by the National
   Outstanding Youth Science Foundation of National Natural Science
   Foundation of China (under grant 61722212).
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PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1467-5463
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J9 BRIEF BIOINFORM
JI Brief. Bioinform.
PD JAN 17
PY 2022
VL 23
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AR bbab340
DI 10.1093/bib/bbab340
PG 16
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA ZK5AS
UT WOS:000763000800143
PM 34471921
DA 2022-08-23
ER

PT J
AU Trappey, AJC
   Trappey, CV
   Liang, CP
   Lin, HJ
AF Trappey, Amy J. C.
   Trappey, Charles V.
   Liang, Chih-Ping
   Lin, Hsin-Jung
TI IP Analytics and Machine Learning Applied to Create Process
   Visualization Graphs for Chemical Utility Patents
SO PROCESSES
LA English
DT Article
DE chemical utility patents; knowledge graph visualization; text mining;
   machine learning; bidirectional encoder representations (ALBERT);
   chemical manufacturing process visualization; IP analytics
AB Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual property or IP) that provide detailed descriptions of the processes used to create a new chemical or a new process to manufacture a known chemical. The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. This research uses text mining, integrated machine learning, and knowledge visualization techniques to effectively and accurately support the extraction and graphical presentation of chemical processes disclosed in patent documents. The computer framework trains a machine learning model called ALBERT for automatic paragraph text classification. ALBERT separates chemical and non-chemical descriptive paragraphs from a patent for effective chemical term extraction. The ChemDataExtractor is used to classify chemical terms, such as inputs, units, and reactions from the chemical paragraphs. A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. The computer-supported chemical knowledge visualization approach helps researchers to quickly understand the innovative and unique chemical or processes of any chemical patent of interest.
C1 [Trappey, Amy J. C.; Liang, Chih-Ping; Lin, Hsin-Jung] Natl Tsing Hua Univ, Dept Ind Engn & Engn Management, Hsinchu 300, Taiwan.
   [Liang, Chih-Ping] Natl Yang Ming Chiao Tung Univ, Dept Management Sci, Hsinchu 300, Taiwan.
RP Trappey, AJC (通讯作者)，Natl Tsing Hua Univ, Dept Ind Engn & Engn Management, Hsinchu 300, Taiwan.
EM trappey@ie.nthu.edu.tw; trappey@nycu.edu.tw;
   s109034534@m109.nthu.edu.tw; tinairislin@gmail.com
OI Trappey, Charles/0000-0002-3069-6702
FU Ministry of Science and Technology, Taiwan [MOST-108-2221-E-007-075-MY3,
   MOST-108-2410-H-009-025-MY2]; National Tsing Hua University
   multi-disciplinary research grant
FX The research is partially supported by the research grants of Ministry
   of Science and Technology, Taiwan (Grant numbers:
   MOST-108-2221-E-007-075-MY3 and MOST-108-2410-H-009-025-MY2) and
   National Tsing Hua University multi-disciplinary research grant.
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PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
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JI Processes
PD AUG
PY 2021
VL 9
IS 8
AR 1342
DI 10.3390/pr9081342
PG 16
WC Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA UH5QA
UT WOS:000689984000001
OA gold
DA 2022-08-23
ER

PT J
AU Curry, E
   Salwala, D
   Dhingra, P
   Pontes, FA
   Yadav, P
AF Curry, Edward
   Salwala, Dhaval
   Dhingra, Praneet
   Pontes, Felipe Arruda
   Yadav, Piyush
TI Multimodal Event Processing: A Neural-Symbolic Paradigm for the Internet
   of Multimedia Things
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Data management and analytics; event processing; Internet of Multimedia
   Things (IoMT); service middleware and platform
ID VIDEO; QUERIES
AB With the Internet of Multimedia Things (IoMT) becoming a reality, new approaches are needed to process real-time multimodal event streams. Existing approaches to event processing have limited consideration for the challenges of multimodal events, including the need for complex content extraction, and increased computational and memory costs. This article explores event processing as a basis for processing real-time IoMT data. This article introduces the multimodal event processing (MEP) paradigm, which provides a formal basis for native approaches to neural multimodal content analysis (i.e., computer vision, linguistics, and audio) with symbolic event processing rules to support real-time queries over multimodal data streams using the multimodal event processing language to express single, primitive multimodal, and complex multimodal event patterns. The content of multimodal streams is represented using multimodal event knowledge graphs to capture the semantic, spatial, and temporal content of the multimodal streams. The approach is implemented and evaluated within a MEP engine using single and multimodal queries achieving near real-time performance with a throughput of frames processed per second (fps) and subsec- ond latency of 0.075-0.30 s for video streams of similar to 30 fps input rate. Support for high input stream rates (45 fps) is achieved through content-aware load-shedding techniques with a similar to 127X latency improvement resulting in only a minor decrease in accuracy.
C1 [Curry, Edward; Salwala, Dhaval; Dhingra, Praneet; Pontes, Felipe Arruda; Yadav, Piyush] Natl Univ Ireland Galway, Insight SFI Res Ctr Data Analyt, Data Sci Inst, Galway H91 AEX4, Ireland.
RP Curry, E (通讯作者)，Natl Univ Ireland Galway, Insight SFI Res Ctr Data Analyt, Data Sci Inst, Galway H91 AEX4, Ireland.
EM edward.curry@nuigalway.ie
FU Science Foundation Ireland - European Regional Development Fund
   [SFI/12/RC/2289_P2]
FX This work was supported by the Science Foundation Ireland, co-funded by
   the European Regional Development Fund under Grant SFI/12/RC/2289_P2.
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NR 61
TC 1
Z9 1
U1 0
U2 0
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 AUG 1
PY 2022
VL 9
IS 15
BP 13705
EP 13724
DI 10.1109/JIOT.2022.3143171
PG 20
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3G2WW
UT WOS:000831217100067
OA hybrid
DA 2022-08-23
ER

PT J
AU Asadifar, S
   Kahani, M
   Shekarpour, S
AF Asadifar, Somayeh
   Kahani, Mohsen
   Shekarpour, Saeedeh
TI Schema and content aware classification for predicting the sources
   containing an answer over corpus and knowledge graphs
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Question answering; Hybrid search; Data integration; Knowledge-based;
   Corpus; Source prediction
ID LINKED DATA; WEB
AB Today, several attempts to manage question answering (QA) have been made in three separate areas: (1) knowledge-based (KB), (2) text-based and (3) hybrid, which takes advantage of both prior areas in extracting the response. On the other hand, in question answering on a large number of sources, source prediction to ensure scalability is very important. In this paper, a method for source prediction is presented in hybrid QA, involving several KB sources and a text source. In a few hybrid methods for source selection, including only one KB source in addition to the textual source, prioritization or heuristics have been used that have not been evaluated so far. Most methods available in source selection services are based on general metadata or triple instances. These methods are not suitable due to the unstructured source in hybrid QA. In this research, we need data details to predict the source. In addition, unlike KB federated methods that are based on triple instances, we use the behind idea of mediated schema to ensure data integration and scalability. Results from evaluations that consider word, triple, and question level information, show that the proposed approach performs well against a few benchmarks. In addition, the comparison of the proposed method with the existing approaches in hybrid and KB source prediction and also QA tasks has shown a significant reduction in response time and increased accuracy.
C1 [Asadifar, Somayeh; Kahani, Mohsen] Ferdowsi Univ Mashhad, Fac Engn, Mashhad, Razavi Khorasan, Iran.
   [Shekarpour, Saeedeh] Univ Dayton, Coll Arts & Sci Comp Sci, Dayton, OH 45469 USA.
RP Kahani, M (通讯作者)，Ferdowsi Univ Mashhad, Fac Engn, Mashhad, Razavi Khorasan, Iran.
EM kahani@um.ac.ir
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NR 53
TC 0
Z9 0
U1 1
U2 1
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 MAR 3
PY 2022
VL 8
AR e846
DI 10.7717/peerj-cs.846
PG 29
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZQ8XB
UT WOS:000767379800003
PM 35494835
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Qi, T
   Qiu, S
   Shen, XQ
   Chen, HP
   Yang, S
   Wen, H
   Zhang, Y
   Wu, YQ
   Huang, YF
AF Qi, Tao
   Qiu, Shan
   Shen, Xiaoqian
   Chen, Haopu
   Yang, Shuai
   Wen, Hao
   Zhang, Ya
   Wu, Yuanqing
   Huang, Yongfeng
TI KeMRE: Knowledge-enhanced medical relation extraction for Chinese
   medicine instructions
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Medical RE; Knowledge-enhanced model; Medical knowledge; Chinese
   medicine instructions
AB Medicine instructions usually contain rich medical relations, and extracting them is very helpful for many downstream tasks such as medicine knowledge graph construction and medicine side-effect prediction. Existing relation extraction (RE) methods usually predict relations between entities from their contexts and do not consider medical knowledge. However, understanding a part of medical relations may need some expert knowledge in the medical field, making it challenging for existing methods to achieve satisfying performances of medical RE. In this paper, we propose a knowledge-enhanced framework for medical RE, which can exploit medical knowledge of medicines to better conduct medical RE on Chinese medicine instructions. We first propose a BERT-CNN-LSTM based framework for text modeling and learn representations of characters from their contexts. Then we learn representations of each entity by aggregating representations of their characters. Besides, we propose a CNN-LSTM based framework for entity modeling and learn entity representations from their relatedness. In addition, there are usually many different instructions for the same medicine, which usually share general knowledge on this medicine. Thus, to obtain medical knowledge of medicines, we annotate relations on a randomly-sampled instruction of each medicine. Then we build knowledge embeddings to represent potential relations between entities from knowledge of medicines. Finally, we use an MLP network to predict relations between entities from their representations and knowledge embeddings. Extensive experiments on a real-world dataset show that our method can significantly outperform existing methods.
C1 [Qi, Tao; Qiu, Shan; Huang, Yongfeng] Tsinghua Univ, Dept Elect Engn & BNRist, Beijing 100084, Peoples R China.
   [Yang, Shuai; Wen, Hao; Zhang, Ya; Wu, Yuanqing] Beijing Jingdong Jiankang Co Ltd, Beijing 101111, Peoples R China.
   [Shen, Xiaoqian] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China.
   [Chen, Haopu] Beijing Univ Posts & Telecommun, Int Sch, Beijing 100876, Peoples R China.
RP Qi, T (通讯作者)，Tsinghua Univ, Dept Elect Engn & BNRist, Beijing 100084, Peoples R China.
EM qt20@mails.tsinghua.edu.cn; qs19@mails.tsinghua.edu.cn;
   dealexce@bupt.edu.cn; yangshuai15@jd.com; wenhao71@jd.com;
   zhangya973@jd.com; wuyuanqing@jd.com; yfhuang@tsinghua.edu.cn
RI Zhang, Ya/CAF-2605-2022; Wen, Hao/AAC-8935-2022
OI Wen, Hao/0000-0002-8856-0883; Huang, Yongfeng/0000-0003-3825-2230; Zhang
   (zhang ya), Ya/0000-0001-6970-0767
FU National Natural Science Foundation of China [82090053, U1936208]
FX This work was supported by the National Natural Science Foundation of
   China under Grant Nos. 82090053, U1936208.
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NR 41
TC 1
Z9 1
U1 12
U2 23
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 AUG
PY 2021
VL 120
AR 103834
DI 10.1016/j.jbi.2021.103834
EA JUL 2021
PG 9
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA TY1ER
UT WOS:000683527500006
PM 34119692
OA Bronze
DA 2022-08-23
ER

PT J
AU Zhou, Y
   Zhou, TF
   Zhou, T
   Fu, HZ
   Liu, JC
   Shao, L
AF Zhou, Yi
   Zhou, Tianfei
   Zhou, Tao
   Fu, Huazhu
   Liu, Jiacheng
   Shao, Ling
TI Contrast-Attentive Thoracic Disease Recognition With Dual-Weighting
   Graph Reasoning
SO IEEE TRANSACTIONS ON MEDICAL IMAGING
LA English
DT Article
DE Lung; Diseases; X-rays; Visualization; Radiology; Medical diagnosis;
   Lesions; Thoracic diseases; intra; and inter-contrastive attention;
   dual-weighting graph reasoning
ID CONVOLUTIONAL NEURAL-NETWORKS; DEEP
AB Automatic thoracic disease diagnosis is a rising research topic in the medical imaging community, with many potential applications. However, the inconsistent appearances and high complexities of various lesions in chest X-rays currently hinder the development of a reliable and robust intelligent diagnosis system. Attending to the high-probability abnormal regions and exploiting the priori of a related knowledge graph offers one promising route to addressing these issues. As such, in this paper, we propose two contrastive abnormal attention models and a dual-weighting graph convolution to improve the performance of thoracic multi-disease recognition. First, a left-right lung contrastive network is designed to learn intra-attentive abnormal features to better identify the most common thoracic diseases, whose lesions rarely appear in both sides symmetrically. Moreover, an inter-contrastive abnormal attention model aims to compare the query scan with multiple anchor scans without lesions to compute the abnormal attention map. Once the intra- and inter-contrastive attentions are weighted over the features, in addition to the basic visual spatial convolution, a chest radiology graph is constructed for dual-weighting graph reasoning. Extensive experiments on the public NIH ChestX-ray and CheXpert datasets show that our model achieves consistent improvements over the state-of-the-art methods both on thoracic disease identification and localization.
C1 [Zhou, Yi] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
   [Zhou, Tianfei] Swiss Fed Inst Technol, Comp Vis Lab, CH-8092 Zurich, Switzerland.
   [Zhou, Tao] Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Peoples R China.
   [Fu, Huazhu; Shao, Ling] Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates.
   [Liu, Jiacheng] Southeast Univ, Zhongda Hosp, Nanjing 210009, Peoples R China.
RP Zhou, Y (通讯作者)，Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China.
EM yi.zhou@ieee.org; ztfei.debug@gmail.com; taozhou.dreams@gmail.com;
   hz.fu@ieee.org; jiachengliu@seu.edu.cn; ling.shao@ieee.org
RI Fu, Huazhu/A-1411-2014; Zhou, Tianfei/AAC-6115-2022
OI Fu, Huazhu/0000-0002-9702-5524; Zhou, Tianfei/0000-0001-5475-1473
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NR 49
TC 10
Z9 10
U1 0
U2 0
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0278-0062
EI 1558-254X
J9 IEEE T MED IMAGING
JI IEEE Trans. Med. Imaging
PD APR
PY 2021
VL 40
IS 4
BP 1196
EP 1206
DI 10.1109/TMI.2021.3049498
PG 11
WC Computer Science, Interdisciplinary Applications; Engineering,
   Biomedical; Engineering, Electrical & Electronic; Imaging Science &
   Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Imaging Science & Photographic
   Technology; Radiology, Nuclear Medicine & Medical Imaging
GA RJ3XU
UT WOS:000637532800009
PM 33406037
DA 2022-08-23
ER

PT J
AU Bean, DM
   Al-Chalabi, A
   Dobson, RJB
   Iacoangeli, A
AF Bean, Daniel M.
   Al-Chalabi, Ammar
   Dobson, Richard J. B.
   Iacoangeli, Alfredo
TI A Knowledge-Based Machine Learning Approach to Gene Prioritisation in
   Amyotrophic Lateral Sclerosis
SO GENES
LA English
DT Article
DE gene prioritisation; machine learning; gene discovery; amyotrophic
   lateral sclerosis; motor neurone disease; knowledge graph
ID HEXANUCLEOTIDE REPEAT; ANALYSES IDENTIFY; ALS; ASSOCIATION; COMMON;
   INTERACTOME; VARIANTS; PLATFORM; C9ORF72; INJURY
AB Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two to five years of first symptoms. Several rare disruptive gene variants have been associated with ALS and are responsible for about 15% of all cases. Although our knowledge of the genetic landscape of this disease is improving, it remains limited. Machine learning models trained on the available protein-protein interaction and phenotype-genotype association data can use our current knowledge of the disease genetics for the prediction of novel candidate genes. Here, we describe a knowledge-based machine learning method for this purpose. We trained our model on protein-protein interaction data from IntAct, gene function annotation from Gene Ontology, and known disease-gene associations from DisGeNet. Using several sets of known ALS genes from public databases and a manual review as input, we generated a list of new candidate genes for each input set. We investigated the relevance of the predicted genes in ALS by using the available summary statistics from the largest ALS genome-wide association study and by performing functional and phenotype enrichment analysis. The predicted sets were enriched for genes associated with other neurodegenerative diseases known to overlap with ALS genetically and phenotypically, as well as for biological processes associated with the disease. Moreover, using ALS genes from ClinVar and our manual review as input, the predicted sets were enriched for ALS-associated genes (ClinVarp= 0.038 and manual reviewp= 0.060) when used for gene prioritisation in a genome-wide association study.
C1 [Bean, Daniel M.; Dobson, Richard J. B.; Iacoangeli, Alfredo] Kings Coll London, Dept Biostat & Hlth Informat, 16 De Crespigny Pk, London SE5 8AF, England.
   [Bean, Daniel M.; Dobson, Richard J. B.] UCL, Hlth Data Res UK London, 16 De Crespigny Pk, London SE5 8AF, England.
   [Al-Chalabi, Ammar] Kings Coll Hosp London, Bessemer Rd,Denmark Hill, London SE5 9RS, England.
   [Al-Chalabi, Ammar; Iacoangeli, Alfredo] Kings Coll London, Maurice Wohl Clin Neurosci Inst, Dept Basic & Clin Neurosci, 5 Cutcombe Rd, London SE5 9RT, England.
   [Dobson, Richard J. B.] UCL, Inst Hlth Informat, 222 Euston Rd, London NW1 2DA, England.
RP Bean, DM; Iacoangeli, A (通讯作者)，Kings Coll London, Dept Biostat & Hlth Informat, 16 De Crespigny Pk, London SE5 8AF, England.; Bean, DM (通讯作者)，UCL, Hlth Data Res UK London, 16 De Crespigny Pk, London SE5 8AF, England.; Iacoangeli, A (通讯作者)，Kings Coll London, Maurice Wohl Clin Neurosci Inst, Dept Basic & Clin Neurosci, 5 Cutcombe Rd, London SE5 9RT, England.
EM daniel.bean@kcl.ac.uk; ammar.al-chalabi@kcl.ac.uk;
   richard.j.dobson@kcl.ac.uk; alfredo.iacoangeli@kcl.ac.uk
RI iacoangeli, alfredo/ABI-1861-2020; Bean, Daniel/AFQ-4601-2022;
   Al-Chalabi, Ammar/E-5361-2010; dobson, richard/C-9269-2011
OI iacoangeli, alfredo/0000-0002-5280-5017; Al-Chalabi,
   Ammar/0000-0002-4924-7712; dobson, richard/0000-0003-4224-9245; Bean,
   Daniel/0000-0002-8594-7804
FU UKRI Innovation Fellowship as part of Health Data Research UK
   [MR/S00310X/1]; UK Medical Research Council; Engineering and Physical
   Sciences Research Council; Economic and Social Research Council;
   Department of Health and Social Care (England); Health and Social Care
   Research and Development Division (Welsh Government); Public Health
   Agency (Northern Ireland); British Heart Foundation; Wellcome Trust;
   BigData@Heart Consortium - Innovative Medicines Initiative-2 Joint
   Undertaking [116074]; European Union; EFPIA; National Institute for
   Health Research University College London Hospitals 20 Biomedical
   Research Centre; National Institute for Health Research (NIHR)
   Biomedical Research Centre at South London and Maudsley NHS Foundation
   Trust and King's College London; Motor Neurone Disease Association;
   United Kingdom, Medical Research Council [MR/L501529/1, MR/R024804/1];
   Economic and Social Research Council [ES/L008238/1]; European Community
   [633413]; Health Data Research UK; Chief Scientist Office of the
   Scottish Government Health and Social Care Directorates; ESRC
   [ES/L008238/1] Funding Source: UKRI; MRC [MC_PC_17214, MR/S00310X/1,
   MR/R024804/1] Funding Source: UKRI
FX D.M.B. is funded by a UKRI Innovation Fellowship as part of Health Data
   Research UK. MR/S00310X/1 (https://www.hdruk.ac.uk).R.J.B.D.is supported
   by: 1. Health Data Research UK, which is funded by the UK Medical
   Research Council, Engineering and Physical Sciences Research Council,
   Economic and Social Research Council, Department of Health and Social
   Care (England), Chief Scientist Office of the Scottish Government Health
   and Social Care Directorates, Health and Social Care Research and
   Development Division (Welsh Government), Public Health Agency (Northern
   Ireland), British Heart Foundation and Wellcome Trust. 2. The
   BigData@Heart Consortium, funded by the Innovative Medicines
   Initiative-2 Joint Undertaking under grant agreement No. 116074. This
   Joint Undertaking receives support from the European Union0 s Horizon
   2020 research and innovation programme and EFPIA; it is chaired, by DE
   Grobbee and SD Anker, partnering with 20 academic and industry partners
   and ESC. 3. The National Institute for Health Research University
   College London Hospitals 20 Biomedical Research Centre. RJBD, AAC, and
   AI are supported by the National Institute for Health Research (NIHR)
   Biomedical Research Centre at South London and Maudsley NHS Foundation
   Trust and King0 s College London. The Motor Neurone Disease Association
   supports AI. This is an EU Joint Programme-Neurodegenerative Disease
   Research (JPND) project. The project is supported through the following
   funding organisations under the aegis of JPND-www.jpnd.eu (United
   Kingdom, Medical Research Council (MR/L501529/1, AACPI; MR/R024804/1,
   AAC PI) and Economic and Social Research Council (ES/L008238/1, AAC
   co-PI)) and through the Motor Neurone Disease Association. The work
   leading up to this publication was funded by the European Community 0s
   Horizon 2020 Programme (H2020-PHC-2014-two-stage; grant agreement number
   633413). Sequence data used in this research were, in part, obtained
   from the UK National DNA Bank for MND Research, funded by the Motor
   Neurone Disease Association and the Wellcome Trust.
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NR 70
TC 9
Z9 9
U1 0
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4425
J9 GENES-BASEL
JI Genes
PD JUN
PY 2020
VL 11
IS 6
AR 668
DI 10.3390/genes11060668
PG 17
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA MO5LW
UT WOS:000551568300001
PM 32575372
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ji, B
   Li, SS
   Yu, J
   Ma, J
   Tang, JT
   Wu, QB
   Tan, YS
   Liu, HJ
   Ji, Y
AF Ji, Bin
   Li, Shasha
   Yu, Jie
   Ma, Jun
   Tang, Jintao
   Wu, Qingbo
   Tan, Yusong
   Liu, Huijun
   Ji, Yun
TI Research on Chinese medical named entity recognition based on
   collaborative cooperation of multiple neural network models
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Deep learning; Medical named entity recognition; Transfer learning;
   Chinese electronic medical record; Cancer
AB Medical named entity recognition (NER) in Chinese electronic medical records (CEMRs) has drawn much research attention, and plays a vital prerequisite role for extracting high-value medical information. In 2018, China Health Information Processing Conference (CHIP2018) organized a medical NER academic competition aiming to extract three types of malignant tumor entity from CEMRs. Since the three types of entity are highly domain-specific and interdependency, extraction of them cannot be achieved with a single neural network model. Based on comprehensive study of the three types of entity and the entity interdependencies, we propose a collaborative cooperation of multiple neural network models based approach, which consists of two BiLSTM-CRF models and a CNN model. In order to tackle the problem that target scene dataset is small and entity distributions are sparse, we introduce non-target scene datasets and propose sentence-level neural network model transfer learning. Based on 30,000 real-world CEMRs, we pre-train medical domain-specific Chinese character embeddings with word2vec, GloVe and ELMo, and apply them to our approach respectively to validate effects of pre-trained language models in Chinese medical NER. Also, as control experiments, we apply Gated Recurrent Unit to our approach. Finally, our approach achieves an overall F1-score of 87.60%, which is the state-of-the-art performance to the best of our knowledge. In addition, our approach has won the champion of the medical NER academic competition organized by 2019 China Conference on Knowledge Graph and Semantic Computing, which proves the outstanding generalization ability of our approach.
C1 [Ji, Bin; Li, Shasha; Yu, Jie; Ma, Jun; Tang, Jintao; Wu, Qingbo; Tan, Yusong] Natl Univ Def Technol, Coll Comp, Changsha, Peoples R China.
   [Liu, Huijun] China Acad Engn Phys, Inst Comp Applicat, Mianyang, Sichuan, Peoples R China.
   [Ji, Yun] Shandong Univ Tradit Chinese Med, Dermatol Dept, Affiliated Hosp, Jinan, Peoples R China.
RP Yu, J (通讯作者)，Natl Univ Def Technol, Coll Comp, Changsha, Peoples R China.; Liu, HJ (通讯作者)，China Acad Engn Phys, Inst Comp Applicat, Mianyang, Sichuan, Peoples R China.; Ji, Y (通讯作者)，Shandong Univ Tradit Chinese Med, Dermatol Dept, Affiliated Hosp, Jinan, Peoples R China.
EM yj@nudt.edu.cn; lhj12uestc@163.com; jiyun1983@163.com
OI Ji, Bin/0000-0002-5508-5051
FU National Key Research and Development Program of China [2018YFB0204301]
FX This work is supported by National Key Research and Development Program
   of China (No. 2018YFB0204301).
CR [Anonymous], 2019, ERNIE ENHANCED LANGU
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NR 40
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Z9 13
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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 2020
VL 104
AR 103395
DI 10.1016/j.jbi.2020.103395
PG 10
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA LD0PU
UT WOS:000525736200008
PM 32109551
OA Bronze
DA 2022-08-23
ER

PT J
AU Liu, J
   Zhang, X
   Li, YH
   Wang, J
   Kim, HJ
AF Liu, Jin
   Zhang, Xin
   Li, Yunhui
   Wang, Jin
   Kim, Hye-Jin
TI Deep Learning-Based Reasoning With Multi-Ontology for IoT Applications
SO IEEE ACCESS
LA English
DT Article
DE Deep learning; ontology based reasoning; IoT; sensor ontology; data
   mining
ID INTERNET
AB In the era of mobile big data, data driven intelligent Internet of Things (IoT) applications are becoming widespread, and knowledge-based reasoning is one of the essential tasks of these applications. While most knowledge-based reasoning work is conducted with knowledge graph, ontology-based reasoning method can inherently achieve higher level intelligence by leveraging both explicit and tacit knowledge in specific domains, and its performance is determined by precise refinement of the inference rules. However, most ontology-based reasoning work concentrates on semantic reasoning in a single ontology, and fail to utilize association of multiple ontologies in various domains to extend reasoning capacity. This is even the case for the IoT applications where knowledge from multiple domains needs to be utilized. To overcome this issue, we propose a deep learning-based method to associate multiple ontology rule bases, thereby discover new inference rules. In our method, we first use a regression tree model to determine the threshold value for parameters in inference rules that constitute the ontology rule base, avoiding the influence of uncertainty factors on knowledge reasoning results. Then, a two-way GRU (Gated Recurrent Unit) neural network with attention mechanism is used to discover semantic relations among the rule bases of ontologies. Therefore, the association of multiple ontology rule bases is realized, and the rule base of knowledge reasoning is expanded by acquiring some unspecified rules. To the best our knowledge, this work is the first one to leverage deep learning in reasoning with multiple ontologies. In order to verify the effectiveness of our method, we apply it in a real traffic safety monitoring application by relating rule bases of a vehicle ontology and a traffic management ontology, and achieve effective knowledge reasoning.
C1 [Liu, Jin; Zhang, Xin] Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China.
   [Li, Yunhui] CFETS Informat Technol Shanghai Co Ltd, Shanghai, Peoples R China.
   [Wang, Jin] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Hunan, Peoples R China.
   [Wang, Jin] Fujian Univ Technol, Sch Informat Sci & Engn, Fuzhou 350108, Fujian, Peoples R China.
   [Kim, Hye-Jin] Sungshin Womens Univ, Business Adm Res Inst, Seoul 02844, South Korea.
RP Wang, J (通讯作者)，Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Hunan, Peoples R China.; Wang, J (通讯作者)，Fujian Univ Technol, Sch Informat Sci & Engn, Fuzhou 350108, Fujian, Peoples R China.
EM jinwang@csust.edu.cn
RI Kim, Hye-jin/AAM-5806-2020
OI Kim, Hye-jin/0000-0003-0015-1645; Liu, Jin/0000-0001-7249-698X
FU National Natural Science Foundation of China [61872231, 61701297,
   61772454, 61811530332, 61811540410]
FX This work was supported by the National Natural Science Foundation of
   China (61872231, 61701297, 61772454, 61811530332, 61811540410).
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NR 67
TC 4
Z9 4
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 2019
VL 7
BP 124688
EP 124701
DI 10.1109/ACCESS.2019.2937353
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA JA4TS
UT WOS:000487829700002
OA gold
DA 2022-08-23
ER

PT J
AU Liu, Y
   Wu, YH
   Wen, PS
   Shi, YJ
   Qiu, Y
   Cheng, MM
AF Liu, Yun
   Wu, Yu-Huan
   Wen, Peisong
   Shi, Yujun
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   Cheng, Ming-Ming
TI Leveraging Instance-, Image- and Dataset-Level Information for Weakly
   Supervised Instance Segmentation
SO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
LA English
DT Article
DE Semantics; Proposals; Image segmentation; Training; Probability
   distribution; Feature extraction; Noise measurement; Weakly supervised
   learning; instance segmentation; semantic segmentation; multiple
   instance learning; multi-way cut
AB Weakly supervised semantic instance segmentation with only image-level supervision, instead of relying on expensive pixel-wise masks or bounding box annotations, is an important problem to alleviate the data-hungry nature of deep learning. In this article, we tackle this challenging problem by aggregating the image-level information of all training images into a large knowledge graph and exploiting semantic relationships from this graph. Specifically, our effort starts with some generic segment-based object proposals (SOP) without category priors. We propose a multiple instance learning (MIL) framework, which can be trained in an end-to-end manner using training images with image-level labels. For each proposal, this MIL framework can simultaneously compute probability distributions and category-aware semantic features, with which we can formulate a large undirected graph. The category of background is also included in this graph to remove the massive noisy object proposals. An optimal multi-way cut of this graph can thus assign a reliable category label to each proposal. The denoised SOP with assigned category labels can be viewed as pseudo instance segmentation of training images, which are used to train fully supervised models. The proposed approach achieves state-of-the-art performance for both weakly supervised instance segmentation and semantic segmentation. The code is available at https://github.com/yun-liu/LIID.
C1 [Liu, Yun; Wu, Yu-Huan; Wen, Peisong; Shi, Yujun; Cheng, Ming-Ming] Nankai Univ, Coll Comp Sci, TKLNDST, Tianjin 300350, Peoples R China.
   [Qiu, Yu] Nankai Univ, Coll Artificial Intelligence, Tianjin 300350, Peoples R China.
RP Cheng, MM (通讯作者)，Nankai Univ, Coll Comp Sci, TKLNDST, Tianjin 300350, Peoples R China.
EM nk12csly@mail.nankai.edu.cn; wuyuhuan@mail.nankai.edu.cn;
   wps_@mail.nankai.edu.cn; 1511201@mail.nankai.edu.cn;
   yqiu@mail.nankai.edu.cn; cmm@nankai.edu.cn
RI Cheng, Ming-Ming/A-2527-2009; Wu, Yu-Huan/ABG-4219-2020
OI Cheng, Ming-Ming/0000-0001-5550-8758; Wu, Yu-Huan/0000-0001-8666-3435;
   Qiu, Yu/0000-0001-6722-3039; Reis, AlessanRSS/0000-0001-8486-7469; Liu,
   Yun/0000-0001-6143-0264
FU Major Project for New Generation of AI [2018AAA0100400]; NSFC
   [61922046]; Tianjin Natural Science Foundation [18ZXZNGX00110]; S&T
   innovation project from Chinese Ministry of Education
FX This research was supported by the Major Project for New Generation of
   AI under Grant No. 2018AAA0100400, NSFC (61922046), Tianjin Natural
   Science Foundation (18ZXZNGX00110), and S&T innovation project from
   Chinese Ministry of Education. Yun Liu and Yu-HuanWucontributed equally
   to this work.
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NR 93
TC 22
Z9 22
U1 28
U2 45
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0162-8828
EI 1939-3539
J9 IEEE T PATTERN ANAL
JI IEEE Trans. Pattern Anal. Mach. Intell.
PD MAR 1
PY 2022
VL 44
IS 3
BP 1415
EP 1428
DI 10.1109/TPAMI.2020.3023152
PG 14
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA YU4MA
UT WOS:000752018000025
PM 32915726
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Jin, H
   Luo, Y
   Gao, CJ
   Tang, XZ
   Yuan, PP
AF Jin, Hai
   Luo, Yi
   Gao, Chenjing
   Tang, Xunzhu
   Yuan, Pingpeng
TI ComQA: Question Answering Over Knowledge Base via Semantic Matching
SO IEEE ACCESS
LA English
DT Article
DE Question answering; knowledge graph; semantic matching
ID LARGE-SCALE; DBPEDIA
AB Question answering over knowledge base (KBQA) is a powerful tool to extract answers from graph-like knowledge bases. Here, we present ComQA-a three-phase KBQA framework by which end-users can ask complex questions and get answers in a natural way. In ComQA, a complex question is decomposed into several triple patterns. Then, ComQA retrieves candidate subgraphs matching the triple patterns from the knowledge base and evaluates the semantic similarity between the subgraphs and the triple patterns to find the answer. It is a long-standing problem to evaluate the semantic similarity between the question and the heterogeneous subgraph containing the answer. To handle this problem, first, a semantic-based extension method is proposed to identify entities and relations in the question while considering the underlying knowledge base. The precision of identifying entities and relations determines the correctness of successive steps. Second, by exploiting the syntactic pattern in the question, ComQA constructs the query graphs for natural language questions so that it can filter out topology-mismatch subgraphs and narrow down the search space in knowledge bases. Finally, by incorporating the information from the underlying knowledge base, we fine-tune general word vectors, making them more specific to ranking possible answers in KBQA task. Extensive experiments over a series of QALD challenges confirm that the performance of ComQA is comparable to those state-of-the-art approaches with respect to precision, recall, and F1-score.
C1 [Jin, Hai; Luo, Yi; Gao, Chenjing; Tang, Xunzhu; Yuan, Pingpeng] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Natl Engn Res Ctr Big Data Technol & Syst, Serv Comp Technol & Syst Lab,Cluster & Grid Comp, Wuhan 430074, Hubei, Peoples R China.
RP Yuan, PP (通讯作者)，Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Natl Engn Res Ctr Big Data Technol & Syst, Serv Comp Technol & Syst Lab,Cluster & Grid Comp, Wuhan 430074, Hubei, Peoples R China.
EM ppyuan@hust.edu.cn
FU National Key Research & Development Program of China [2018YFB1004002];
   NSFC [61672255]; Science and Technology Planning Project of Guangdong
   Province, China [2016B030306003, 2016B030305002]; Fundamental Research
   Funds for the Central Universities, HUST
FX This research was supported by The National Key Research & Development
   Program of China (No. 2018YFB1004002), NSFC (No. 61672255), Science and
   Technology Planning Project of Guangdong Province, China(No.
   2016B030306003 and 2016B030305002), and the Fundamental Research Funds
   for the Central Universities, HUST.
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TC 8
Z9 8
U1 3
U2 21
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 75235
EP 75246
DI 10.1109/ACCESS.2019.2918675
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IE9TF
UT WOS:000472718800001
OA gold
DA 2022-08-23
ER

PT J
AU Zhao, MX
   Wu, QY
   Ma, EZ
   Shen, BJ
   Chen, YT
AF Zhao, Mingxin
   Wu, Qinyue
   Ma, Enze
   Shen, Beijun
   Chen, Yuting
TI Context-Aware Conversational Recommendation of Trigger-Action Rules in
   IoT Programming
SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
LA English
DT Article
DE Trigger-action programming; context-aware; conversational recommendation
AB Trigger-action (TA) programming is a programming paradigm that allows end-users to automate and connect IoT devices and online services using if-trigger-then-action rules. Early studies have demonstrated this paradigms usability, but more recent work has also highlighted complexities that arise in realistic scenarios. To facilitate end-users in TA programming, we propose AutoTAR, a context-aware conversational recommendation technique for recommending TA rules. AutoTAR leverages a TA knowledge graph to encode semantic features and abstract functionalities of rules, and then takes a two-phase method to recommend TA rules to end-users: during the context-aware recommendation phase, it elicits user preferences from programming context and recommends the top-N rules using a mixed content and collaborative technique; during the conversational recommendation phase, it justifies recommendations by iteratively raising questions and collecting feedback from end-users. We evaluate AutoTAR on Mturk and real data collected from the IFTTT community. The results show that our method outperforms state-of-the-arts significantly - its context-aware recommendation outperforms RecRules by 26% on R@5 and 21% on NDCG@5; its conversational recommendation outperforms LARecommender (a conversational recommender with the LA model) by 67.64% on accuracy. In addition, AutoTAR is effective in solving three problems frequently occurring in TA rule recommendations, i.e., the cold-start problem, the repeat-consumption problem, and the incomplete-intent problem.
C1 [Zhao, Mingxin; Wu, Qinyue; Shen, Beijun; Chen, Yuting] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai, Peoples R China.
   [Ma, Enze] Beijing Forestry Univ, Beijing, Peoples R China.
RP Zhao, MX (通讯作者)，Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai, Peoples R China.
EM zhaomingxin@sjtu.edu.cn; wuqinyue@sjtu.edu.cn;
   joseph9morgan@bifu.edu.cn; bjshen@sjtu.edu.cn; chenyt@sjtu.edu.cn
FU National Natural Science Foundation of China [62032004]
FX We would like to thank the anonymous reviewers for their constructive
   feedback. This research is supported by National Natural Science
   Foundation of China (Grant No. 62032004). This submission is an
   extension to our early work in ICSR 2020 [41].
CR Brackenbury W, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300782
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NR 32
TC 0
Z9 0
U1 2
U2 6
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-1940
EI 1793-6403
J9 INT J SOFTW ENG KNOW
JI Int. J. Softw. Eng. Knowl. Eng.
PD OCT
PY 2021
VL 31
IS 10
BP 1517
EP 1538
DI 10.1142/S0218194021500510
PG 22
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA WX0ZH
UT WOS:000718333000007
DA 2022-08-23
ER

PT J
AU Bi, ZQ
   Zhang, TC
   Zhou, P
   Li, YB
AF Bi, Zhongqin
   Zhang, Tianchen
   Zhou, Ping
   Li, Yongbin
TI Knowledge Transfer for Out-of-Knowledge-Base Entities: Improving
   Graph-Neural-Network-Based Embedding Using Convolutional Layers
SO IEEE ACCESS
LA English
DT Article
DE Task analysis; Knowledge based systems; Computational modeling;
   Adaptation models; Training; Convolution; Predictive models;
   Convolutional neural network; graph neural network; knowledge base
   completion; out-of-knowledge-base entities
AB Knowledge base completion (KBC) aims to predict missing information in a knowledge base. Most existing embedding-based KBC models assume that all test entities are available at training time. Thus, a question arises-that is, how to answer queries concerning test entities not observed at training time, which is called the out-of-knowledge-base (OOKB) entity problem. In this article, we propose a parameter-efficient embedding model that combines the benefits of a graph neural network (GNN) and a convolutional neural network (CNN) to solve the KBC task with OOKB entities. First, we apply the GNN architecture to learn the information between nodes in the graph. Second, convolution layers are used as a transition matrix in GNN to learn more expressive embeddings with fewer parameters. Finally, we use a transition-based knowledge graph embedding model to solve the KBC task. The model has learnable weights that adapt based on information from neighbors and can exploit auxiliary knowledge for OOKB entities to compute their embedding while remaining parameter efficient. We demonstrate the effectiveness of the proposed model on OOKB datasets, and the code is available at https://github.com/Tianchen627/Knowledge-Transfer-for-Out-of-Knowledge-Base-Entities.
C1 [Bi, Zhongqin; Zhang, Tianchen; Zhou, Ping; Li, Yongbin] Shanghai Univ Elect Power, Coll Comp Sci & Technol, Shanghai 200090, Peoples R China.
RP Zhang, TC (通讯作者)，Shanghai Univ Elect Power, Coll Comp Sci & Technol, Shanghai 200090, Peoples R China.
EM zhangtianchen@gmail.com
OI zhang, tianchen/0000-0002-5044-6065; Bi, Zhongqin/0000-0001-8815-4845
FU National Nature Science Foundation of China [61972357, 61672337]
FX This work was supported by the National Nature Science Foundation of
   China under Grant 61972357 and Grant 61672337.
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NR 45
TC 5
Z9 6
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 2020
VL 8
BP 159039
EP 159049
DI 10.1109/ACCESS.2020.3019592
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NM7GD
UT WOS:000568261900001
OA gold
DA 2022-08-23
ER

PT J
AU Dong, ZA
   Lu, JH
   Ling, TW
   Fan, J
   Chen, YG
AF Dong, Zhaoan
   Lu, Jiaheng
   Ling, Tok Wang
   Fan, Ju
   Chen, Yueguo
TI Using hybrid algorithmic-crowdsourcing methods for academic knowledge
   acquisition
SO CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND
   APPLICATIONS
LA English
DT Article
DE Knowledge acquisition; Crowdsourcing; Knowledge cells; Academic
   knowledge graph
ID SYSTEMS; ISSUES
AB Scientific literature contains a lot of meaningful objects such as Figures, Tables, Definitions, Algorithms, etc., which are called Knowledge Cells hereafter. An advanced academic search engine which could take advantage of Knowledge Cells and their various relationships to obtain more accurate search results is expected. Further, it's expected to provide a fine-grained search regarding to Knowledge Cells for deep-level information discovery and exploration. Therefore, it is important to identify and extract the Knowledge Cells and their various relationships which are often intrinsic and implicit in articles. With the exponential growth of scientific publications, discovery and acquisition of such useful academic knowledge impose some practical challenges For example, existing algorithmic methods can hardly extend to handle diverse layouts of journals, nor to scale up to process massive documents. As crowdsourcing has become a powerful paradigm for large scale problem-solving especially for tasks that are difficult for computers but easy for human, we consider the problem of academic knowledge discovery and acquisition as a crowd-sourced database problem and show a hybrid framework to integrate the accuracy of crowdsourcing workers and the speed of automatic algorithms. In this paper, we introduce our current system implementation, a platform for academic knowledge discovery and acquisition (PANDA), as well as some interesting observations and promising future directions.
C1 [Lu, Jiaheng] Univ Helsinki, Dept Comp Sci, Helsinki, Finland.
   [Dong, Zhaoan; Lu, Jiaheng; Fan, Ju; Chen, Yueguo] Renmin Univ China, MOE, DEKE, Beijing, Peoples R China.
   [Dong, Zhaoan; Lu, Jiaheng; Fan, Ju; Chen, Yueguo] Renmin Univ China, Sch Informat, Beijing, Peoples R China.
   [Ling, Tok Wang] Natl Univ Singapore, Sch Comp, Dept Comp Sci, Singapore, Singapore.
RP Lu, JH (通讯作者)，Univ Helsinki, Dept Comp Sci, Helsinki, Finland.; Lu, JH (通讯作者)，Renmin Univ China, MOE, DEKE, Beijing, Peoples R China.; Lu, JH (通讯作者)，Renmin Univ China, Sch Informat, Beijing, Peoples R China.
EM dongzhaoan@163.com; jiahenglu@gmail.com; lingtw@comp.nus.edu.sg;
   fanju1984@gmail.com; chenyueguo@gmail.com
RI Lu, Jiaheng/T-7826-2019
OI Lu, Jiaheng/0000-0003-2067-454X
FU National Natural Science Foundation of China [61472427]; Renmin
   University of China [11XNJ003]
FX This study was funded by the National Natural Science Foundation of
   China (Grant No. 61472427) and the Research Funds of Renmin University
   of China (Grant No. 11XNJ003).
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NR 35
TC 7
Z9 7
U1 1
U2 15
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-7857
EI 1573-7543
J9 CLUSTER COMPUT
JI Cluster Comput.
PD DEC
PY 2017
VL 20
IS 4
SI SI
BP 3629
EP 3641
DI 10.1007/s10586-017-1089-8
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FM1YV
UT WOS:000414780400066
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Wang, EN
   Wang, F
   Yang, ZH
   Wang, L
   Zhang, Y
   Lin, HF
   Wang, J
AF Wang, Erniu
   Wang, Fan
   Yang, Zhihao
   Wang, Lei
   Zhang, Yin
   Lin, Hongfei
   Wang, Jian
TI A Graph Convolutional Network-Based Method for Chemical-Protein
   Interaction Extraction: Algorithm Development
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE chemical-protein interaction; graph convolutional network; long-range
   syntactic; dependency structure
AB Background: Extracting the interactions between chemicals and proteins from the biomedical literature is important for many biomedical tasks such as drug discovery, medicine precision, and knowledge graph construction. Several computational methods have been proposed for automatic chemical-protein interaction (CPI) extraction. However, the majority of these proposed models cannot effectively learn semantic and syntactic information from complex sentences in biomedical texts.
   Objective: To relieve this problem, we propose a method to effectively encode syntactic information from long text for CPI extraction.
   Methods: Since syntactic information can be captured from dependency graphs, graph convolutional networks (GCNs) have recently drawn increasing attention in natural language processing. To investigate the performance of a GCN on CPI extraction, this paper proposes a novel GCN-based model. The model can effectively capture sequential information and long-range syntactic relations between words by using the dependency structure of input sentences.
   Results: We evaluated our model on the ChemProt corpus released by BioCreative VI; it achieved an F-score of 65.17%, which is 1.07% higher than that of the state-of-the-art system proposed by Peng et al. As indicated by the significance test (P<.001), the improvement is significant. It indicates that our model is effective in extracting CPIs. The GCN-based model can better capture the semantic and syntactic information of the sentence compared to other models, therefore alleviating the problems associated with the complexity of biomedical literature.
   Conclusions: Our model can obtain more information from the dependency graph than previously proposed models. Experimental results suggest that it is competitive to state-of-the-art methods and significantly outperforms other methods on the ChemProt corpus, which is the benchmark data set for CPI extraction.
C1 [Wang, Erniu; Wang, Fan; Yang, Zhihao; Lin, Hongfei; Wang, Jian] Dalian Univ Technol, Coll Comp Sci & Technol, 2 Linggong Rd, Dalian, Peoples R China.
   [Wang, Lei; Zhang, Yin] Beijing Inst Hlth Adm & Med Informat, Beijing, Peoples R China.
RP Yang, ZH (通讯作者)，Dalian Univ Technol, Coll Comp Sci & Technol, 2 Linggong Rd, Dalian, Peoples R China.
EM yangzh@dlut.edu.cn
OI Lin, hongfei/0000-0003-0872-7688; Wang, Erniu/0000-0001-7233-8869
FU National Key Research and Development Program of China [2016YFC0901902]
FX We appreciate the valuable feedback provided by three anonymous
   reviewers. EW carried out the overall algorithm design and experiments
   as well as the writing of the manuscript. FW, ZY, LW, YZ, HL, and JW
   contributed to the algorithm design and the writing of the manuscript.
   All authors read and approved the final manuscript. This work was
   supported by a grant from the National Key Research and Development
   Program of China (#2016YFC0901902).
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NR 30
TC 2
Z9 2
U1 1
U2 6
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
EI 2291-9694
J9 JMIR MED INF
JI JMIR Med. Inf.
PD MAY
PY 2020
VL 8
IS 5
AR e17643
DI 10.2196/17643
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA MH0CX
UT WOS:000546399900009
PM 32348257
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Chen, CY
   Xing, ZC
   Liu, Y
AF Chen, Chunyang
   Xing, Zhenchang
   Liu, Yang
TI What's Spain's Paris? Mining analogical libraries from Q&A discussions
SO EMPIRICAL SOFTWARE ENGINEERING
LA English
DT Article
DE Analogical libraries; Word embedding; Knowledge graph; Relational
   knowledge; Categorical knowledge
AB Third-party libraries are an integral part of many software projects. It often happens that developers need to find analogical libraries that can provide comparable features to the libraries they are already familiar with for different programming languages or different mobile platforms. Existing methods to find analogical libraries are limited by the community-curated list of libraries, blogs, or Q&A posts, which often contain overwhelming or out-of-date information. In this paper, we present a new approach to recommend analogical libraries based on a knowledge base of analogical libraries mined from tags of millions of Stack Overflow questions. The novelty of our approach is to solve analogical-library questions by combining state-of-the-art word embedding technique and domain-specific relational and categorical knowledge mined from Stack Overflow. Given a library and a recommended analogical library, our approach further extracts questions and answer snippets in Stack Overflow about comparison of analogical libraries, which can potentially offer useful information scents for developers to further their investigation of the recommended analogical libraries. We implement our approach in a proof-of-concept web application and more than 34.8 thousands of users visited our website from November 2015 to August 2017. Our evaluation shows that our approach can make accurate recommendation of analogical libraries. We also demonstrate the usefulness of our analogical-library recommendations by using them to answer analogical-library questions in Stack Overflow. Google Analytics of our website traffic and analysis of the visitors' interaction with website contents provide the insights into the usage patterns and the system design of our web application.
C1 [Chen, Chunyang] Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia.
   [Xing, Zhenchang] Australian Natl Univ, Coll Engn & Comp Sci, Canberra, ACT, Australia.
   [Liu, Yang] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore.
RP Chen, CY (通讯作者)，Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia.
EM chunyang.chen@monash.edu; zhenchang.xing@anu.edu.au; yangliu@ntu.edu.sg
RI Liu, Yang/D-2306-2013
OI Liu, Yang/0000-0001-7300-9215; Chen, Chunyang/0000-0003-2011-9618
FU Monash University
FX We'd like to appreciate the valuable review from reviewers. This work is
   partially supported by the seed grant from Monash University.
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NR 57
TC 9
Z9 9
U1 0
U2 5
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 JUN
PY 2019
VL 24
IS 3
BP 1155
EP 1194
DI 10.1007/s10664-018-9657-y
PG 40
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ID9YT
UT WOS:000472043800004
DA 2022-08-23
ER

PT J
AU Gottron, T
   Knauf, M
   Scherp, A
AF Gottron, Thomas
   Knauf, Malte
   Scherp, Ansgar
TI Analysis of schema structures in the Linked Open Data graph based on
   unique subject URIs, pay-level domains, and vocabulary usage
SO DISTRIBUTED AND PARALLEL DATABASES
LA English
DT Article
DE Linked Open Data; Schema analysis; Information; Entropy
ID WEB
AB The Linked Open Data (LOD) graph represents a web-scale distributed knowledge graph interlinking information about entities across various domains. A core concept is the lack of pre-defined schema which actually allows for flexibly modelling data from all kinds of domains. However, Linked Data does exhibit schema information in a twofold way: by explicitly attaching RDF types to the entities and implicitly by using domain-specific properties to describe the entities. In this paper, we present and apply different techniques for investigating the schematic information encoded in the LOD graph at different levels of granularity. We investigate different information theoretic properties of so-called Unique Subject URIs (USUs) and measure the correlation between the properties and types that can be observed for USUs on a large-scale semantic graph data set. Our analysis provides insights into the information encoded in the different schema characteristics. Two major findings are that implicit schema information is far more discriminative and that applications involving schema information based on either types or properties alone will only capture between 63.5 and 88.1 % of the schema information contained in the data. As the level of discrimination depends on how data providers model and publish their data, we have conducted in a second step an investigation based on pay-level domains (PLDs) as well as the semantic level of vocabularies. Overall, we observe that most data providers combine up to 10 vocabularies to model their data and that every fifth PLD uses a highly structured schema.
C1 [Gottron, Thomas; Knauf, Malte] Univ Koblenz Landau, Inst Web Sci & Technol WeST, D-56070 Koblenz, Germany.
   [Scherp, Ansgar] Univ Kiel, Kiel, Germany.
   [Scherp, Ansgar] Leibniz Informat Ctr Econ, Kiel, Germany.
RP Scherp, A (通讯作者)，Univ Kiel, Kiel, Germany.
EM gottron@uni-koblenz.de; mknauf@uni-koblenz.de;
   asc@informatik.uni-kiel.de
RI Scherp, Ansgar/Q-2315-2016
OI Scherp, Ansgar/0000-0002-2653-9245
FU European Community's Seventh Framework Programme;  [610928]
FX The research leading to these results has received funding from the
   European Community's Seventh Framework Programme (FP7/2007-2013), REVEAL
   (Grant agree number 610928). Parts of the computations and experiments
   were conducted on resources provided by the HPI Future SOC Lab.
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NR 30
TC 4
Z9 4
U1 0
U2 25
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0926-8782
EI 1573-7578
J9 DISTRIB PARALLEL DAT
JI Distrib. Parallel Databases
PD DEC
PY 2015
VL 33
IS 4
SI SI
BP 515
EP 553
DI 10.1007/s10619-014-7143-0
PG 39
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA CQ4DE
UT WOS:000360553700003
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Fenza, G
   Gallo, M
   Loia, V
   Volpe, A
AF Fenza, Giuseppe
   Gallo, Mariacristina
   Loia, Vincenzo
   Volpe, Alberto
TI Cognitive name-face association through context-aware Graph Neural
   Network
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Graph neural networks; Name-face association; Context aware; OSINT
ID MULTILAYER PERCEPTRON
AB The extraction of valuable insights from unstructured content has attracted much attention in the last decades. Main results lie in the area of text mining, while the understanding of multimedia contents, thanks to the improvements in computer vision, mainly relies on adopting emerging deep learning models. About image understanding, people's name association in images is still an open issue. The approaches at the state of the art mainly use facial features and find the corresponding names by extracting the most recurring entities in the attached captions. These methods are experimented for celebrities and often fail when few labeled samples are available or there are particular poses. The proposed solution tries to improve the name-face association in such cases by defining a cognitive layer for a deep learning architecture embedding the surrounding context of the entities in the caption or the image. The method mainly focuses on name-face association as enabling technology for people recognition in open-source intelligence frameworks that mostly investigate not popular (or unknown) people. Given a face, the proposed system predicts the most likely corresponding name leveraging image features, caption, and context. The learning model embeds the knowledge graph of the context elicited in the open sources through a graph neural network. The experimentation highlights that the context improves the results of the name-face association, especially when other methods fail.
C1 [Fenza, Giuseppe; Gallo, Mariacristina; Loia, Vincenzo; Volpe, Alberto] Univ Salerno, Dept Management & Innovat Syst, Fisciano, Italy.
RP Loia, V (通讯作者)，Univ Salerno, Dept Management & Innovat Syst, Fisciano, Italy.
EM loia@unisa.it
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NR 60
TC 2
Z9 2
U1 2
U2 2
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
PD JUL
PY 2022
VL 34
IS 13
SI SI
BP 10279
EP 10293
DI 10.1007/s00521-021-06617-z
EA NOV 2021
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2J4QQ
UT WOS:000714314600006
DA 2022-08-23
ER

PT J
AU Sangeetha, M
   Thiagarajan, MD
AF Sangeetha, M.
   Thiagarajan, Meera Devi
TI An Enhanced Neural Graph based Collaborative Filtering with Item
   Knowledge Graph
SO INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
LA English
DT Article
DE filtering techniques; neural network; graph convolution neural network
ID CLASSIFICATION
AB Recommendation system is a process of filtering information to retain buyers on e-commerce sites or applications. It is used on all e-commerce sites, social media platform and multimedia platform. This recommendation is based on their own experience or experience between users. In recent days, the graph-based filtering techniques are used for the recommendation to improve the suggestions and for easy analysing. Neural graph based collaborative filtering is also one of the techniques used for recommendation system. It is implemented on the benchmark datasets like Yelp, Gowalla and Amazon books. This technique can suggest better recommendations as compared to the existing graph based or convolutional based networks. However, it requires higher processing time for convolutional neural network for performing limited suggestions. Hence, in this paper, an improved neural graph collaborative filtering is proposed. Here, the content-based filtering is performed before the collaborative filtering process. Then, the embedding layer will process on both the recommendations to provide a higher order relation between the users and items. As the suggestion is based on hybrid recommendation, the processing time of Convolutional neural network is reduced by reducing the number of epochs. Due to this, the final recommendation is not affected by the smaller number of epochs and also able to reduce its computational time. The whole process is realized in Python 3.6 under windows 10 environment on benchmark datasets Go Walla and Amazon books. Based on the comparison of recall and NDCG metric, the proposed neural graph-based filtering outperforms the collaborative filtering based on graph convolution neural network.
C1 [Sangeetha, M.] Kongu Engn Coll, Dept Comp Sci & Engn, Erode, Tamil Nadu, India.
   [Thiagarajan, Meera Devi] Kongu Engn Coll, Dept Elect & Commun Engn, Erode, Tamil Nadu, India.
RP Sangeetha, M (通讯作者)，Kongu Engn Coll, Dept Comp Sci & Engn, Erode, Tamil Nadu, India.
EM sangeethcse@gmail.com; tmeeradevi@gmail.com
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NR 15
TC 0
Z9 0
U1 0
U2 0
PU CCC PUBL-AGORA UNIV
PI BIHOR
PA PIATA TINERETULUI 8, ORADEA, JUD, BIHOR, 410526, ROMANIA
SN 1841-9836
EI 1841-9844
J9 INT J COMPUT COMMUN
JI Int. J. Comput. Commun. Control
PD AUG
PY 2022
VL 17
IS 4
AR 4568
DI 10.15837/ijccc.2022.4.4568
PG 11
WC Automation & Control Systems; Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA 3D6JP
UT WOS:000829406100001
OA gold
DA 2022-08-23
ER

PT J
AU Chantamunee, S
   Wong, KW
   Fung, CC
AF Chantamunee, Siripinyo
   Wong, Kok Wai
   Fung, Chun Che
TI Relation-aware collaborative autoencoder for personalized multiple facet
   selection
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Multiple facets; Personalized facet selection; Collaborative-based
   personalization; Autoencoder; Deep neural network; Knowledge graph
   embedding
AB Collaborative-based personalization has been one of the most successful techniques used in building personalization for recommender systems and facet selection. The technique predicts users' interests based on the preferences of similar people or items. The prediction is usually made on one single group of users or items/facets. However, multiple facet selection creates a different challenge where the prediction needs to be based on the similarity among different groups of users and facets. In conventional collaborative approach, user-facet representation is created from the concatenation of user preferences on each facet. This creates a spared representation which affects the accuracy of the personalized model. It is essential to develop a more suitable representation that effectively represents the collaborative preferences given across multiple facets and a predictive model to estimate the possible preferences across those groups. Multiple facets appear to be correlated to each other and this can be useful for associating the existing preferences. None of the previous works has addressed the issue due to the association of facet relationships. Hence, this paper aims to examine the effectiveness of a new approach that utilizes multiple-facet relationships to associate the collaborative interests across different facets. This study proposes a new collaborative-based personalization model for multiple facet selection, called Relation-aware Collaborative Autoencoder (RCAE) Model. A new embedding methodology was introduced for incorporating multiple facet relationships into user-facet interaction. Evaluations based on four real-world datasets demonstrated that the proposed model utilizing facet relationships has achieved significant improvement over the conventional collaborative approach. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
C1 [Chantamunee, Siripinyo; Wong, Kok Wai; Fung, Chun Che] Murdoch Univ, Discipline Informat Technol, Perth, Australia.
   [Chantamunee, Siripinyo] Walailak Univ, Sch Engn & Technol, Nakhon Si Thammarat, Thailand.
   [Chantamunee, Siripinyo] Walailak Univ, Informat Innovat Ctr Excellence, Nakhon Si Thammarat, Thailand.
RP Chantamunee, S (通讯作者)，Murdoch Univ, Discipline Informat Technol, Perth, Australia.
EM csiripin@wu.ac.th; k.wong@murdoch.edu.au; l.fung@murdoch.edu.au
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NR 38
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JUN 21
PY 2022
VL 246
AR 108683
DI 10.1016/j.knosys.2022.108683
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1G1AQ
UT WOS:000795588200002
DA 2022-08-23
ER

PT J
AU Hassani-Pak, K
   Singh, A
   Brandizi, M
   Hearnshaw, J
   Parsons, JD
   Amberkar, S
   Phillips, AL
   Doonan, JH
   Rawlings, C
AF Hassani-Pak, Keywan
   Singh, Ajit
   Brandizi, Marco
   Hearnshaw, Joseph
   Parsons, Jeremy D.
   Amberkar, Sandeep
   Phillips, Andrew L.
   Doonan, John H.
   Rawlings, Chris
TI KnetMiner: a comprehensive approach for supporting evidence-based gene
   discovery and complex trait analysis across species
SO PLANT BIOTECHNOLOGY JOURNAL
LA English
DT Article
DE gene discovery; gene network; knowledge graph; knowledge discovery;
   exploratory data mining; data integration; candidate gene
   prioritization; information visualization; systems biology;
   bioinformatics
ID KNOWLEDGE NETWORKS; WHEAT; PHENOTYPES; GRAPHS
AB The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: .
C1 [Hassani-Pak, Keywan; Singh, Ajit; Brandizi, Marco; Hearnshaw, Joseph; Parsons, Jeremy D.; Amberkar, Sandeep; Phillips, Andrew L.; Rawlings, Chris] Rothamsted Res, Harpenden, Herts, England.
   [Doonan, John H.] Aberystwyth Univ, IBERS, Aberystwyth, Dyfed, Wales.
RP Hassani-Pak, K (通讯作者)，Rothamsted Res, Harpenden, Herts, England.
EM keywan.hassani-pak@rothamsted.ac.uk
OI Hearnshaw, Joseph/0000-0002-0438-9266; Singh, Ajit
   Pratap/0000-0001-6239-9967; Hassani-Pak, Keywan/0000-0001-9625-0511;
   Brandizi, Marco/0000-0002-5427-2496
FU UKRI Biotechnology and Biological Sciences Research Council (BBSRC)
   through the Designing Future Wheat ISP [BB/P016855/1]; UKRI
   Biotechnology and Biological Sciences Research Council (BBSRC) through
   the DiseaseNetMiner TRDF [BB/N022874/1]; UKRI Biotechnology and
   Biological Sciences Research Council (BBSRC) through the ONDEX SABR
   funding [BB/F006039/1]; UKRI Biotechnology and Biological Sciences
   Research Council (BBSRC) through the National Capability in Crop
   Phenotyping [BB/J004464/1]; BBSRC; National Science Foundation (cROP
   project) [1340112]; BBSRC [BBS/E/C/000I0220] Funding Source: UKRI
FX This work was supported by the UKRI Biotechnology and Biological
   Sciences Research Council (BBSRC) through the Designing Future Wheat ISP
   (BB/P016855/1), DiseaseNetMiner TRDF (BB/N022874/1), ONDEX SABR funding
   (BB/F006039/1) and National Capability in Crop Phenotyping
   (BB/J004464/1). CR and KHP are additionally supported by strategic
   funding to Rothamsted Research from BBSRC. JHD also acknowledges support
   from the National Science Foundation (cROP project 1340112). We
   acknowledge all the past and present members of the KnetMiner
   Bioinformatics team at Rothamsted for their scientific inputs, software
   testing and technical support: Emma Bailey, Dan Smith, Robert King,
   David Hughes, Monika Mistry, Minja Zorc, Fengyuan Hu, Jan Taubert and
   William Brown. We acknowledge all our collaborators who contributed to
   the development of the KnetMiner resources and software in the past
   including Martin Castellote, Maria Esch, Vasiliki Koutra, Haolin Li,
   Philipp Bayer, Ramil Mauleon, Cristobal Uauy, Jean-Luc Jannink, Clay
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NR 56
TC 11
Z9 11
U1 0
U2 6
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1467-7644
EI 1467-7652
J9 PLANT BIOTECHNOL J
JI Plant Biotechnol. J.
PD AUG
PY 2021
VL 19
IS 8
BP 1670
EP 1678
DI 10.1111/pbi.13583
EA APR 2021
PG 9
WC Biotechnology & Applied Microbiology; Plant Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biotechnology & Applied Microbiology; Plant Sciences
GA UE3YK
UT WOS:000636801300001
PM 33750020
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Yin, D
   Gao, H
   Zou, ZN
   Li, JZ
AF Yin, Dan
   Gao, Hong
   Zou, Zhaonian
   Li, Jianzhong
TI Minimized-cost cube query on heterogeneous information networks
SO JOURNAL OF COMBINATORIAL OPTIMIZATION
LA English
DT Article
DE Heterogeneous information network; OLAP; Aggregate graph; Cube;
   Materialization
AB Data cube is the foundation of on-line analytical processing (OLAP), which can provide users with data views from different perspectives and granularities. Heterogeneous information networks consist of multiple types of nodes and edges which represent different semantic relations. With the rapid development of social networks and knowledge graphs, heterogeneous information networks have become increasingly popular. In heterogeneous information networks, cube is the set of aggregate graphs and cube query is required for supporting OLAP. The existing research mainly studies aggregate graph query on homogeneous networks, but only considers the attributes of nodes. To overcome these challenges, this paper investigates cube query problem on heterogeneous information networks. (1) A novel cube model for heterogeneous information networks is proposed, which captures both the attribute and structure semantics. (2) Because the total number of aggregate graphs is huge, computing and storing them cost plenty of time and storage. The problem of partial cube materialization on heterogeneous information networks is investigated. Given a fixed size of memory space, select a subset of aggregate graphs in cube, to minimize the computing cost of the whole cube. This optimization problem is proved to be NP-complete and there is no n(1-epsilon) approximation algorithm unless P = NP. (3) A greedy algorithm is proposed for partial cube materialization based on two interesting dependencies between aggregate graphs, attribute dependence and path dependence. (4) Experiments on real world data sets show the cube definition is meaningful, and the partial cube materialization algorithm is efficient.
C1 [Yin, Dan; Gao, Hong; Zou, Zhaonian; Li, Jianzhong] Harbin Inst Technol, Dept Comp Sci & Technol, Harbin, Peoples R China.
RP Yin, D (通讯作者)，Harbin Inst Technol, Dept Comp Sci & Technol, Harbin, Peoples R China.
EM yindan630@163.com; honggao@hit.edu.cn; znzou@hit.edu.cn;
   lijzh@hit.edu.cn
RI Li, Jianzhong/AAJ-2912-2021
FU National Grand Fundamental Research 973 Program of China [2012CB316200];
   National Natural Science Foundation of China [61190115, 61173023,
   61532015]
FX This work is supported by the National Grand Fundamental Research 973
   Program of China under Grant 2012CB316200, the National Natural Science
   Foundation of China under Grant 61190115, 61173023 and 61532015.
CR [Anonymous], [No title captured]
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NR 19
TC 0
Z9 0
U1 0
U2 11
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1382-6905
EI 1573-2886
J9 J COMB OPTIM
JI J. Comb. Optim.
PD JAN
PY 2017
VL 33
IS 1
BP 339
EP 364
DI 10.1007/s10878-015-9967-6
PG 26
WC Computer Science, Interdisciplinary Applications; Mathematics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Mathematics
GA EI9HA
UT WOS:000392817300025
DA 2022-08-23
ER

PT J
AU Shehnepoor, S
   Togneri, R
   Liu, W
   Bennamoun, M
AF Shehnepoor, Saeedreza
   Togneri, Roberto
   Liu, Wei
   Bennamoun, Mohammed
TI DFraud(3): Multi-Component Fraud Detection Free of Cold-Start
SO IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
LA English
DT Article
DE Feature extraction; Australia; Writing; Training; Surges; Performance
   gain; Linear programming; Social media; fraud; cold-start; inductive
   learning; multi-component classification; camouflage
ID SPAM DETECTION
AB Fraud review detection is a hot research topic in recent years. The Cold-start is a particularly new but significant problem referring to the failure of a detection system to recognize the authenticity of a new user. State-of-the-art solutions employ a translational knowledge graph embedding approach (TransE) to model the interaction of the components of a review system. However, these approaches suffer from the limitation of TransE in handling N-1 relations and the narrow scope of a single classification task, i.e., detecting fraudsters only. In this paper, we model a review system as a Heterogeneous Information Network (HIN) which enables a unique representation to every component and performs graph inductive learning on the review data through aggregating features of nearby nodes. HIN with graph induction helps to address the camouflage issue (fraudsters with genuine reviews) which has shown to be more severe when it is coupled with cold-start, i.e., new fraudsters with genuine first reviews. In this research, instead of focusing only on one component, detecting either fraud reviews or fraud users (fraudsters), vector representations are learned for each component, enabling multi-component classification. In other words, we can detect fraud reviews, fraudsters, and fraud-targeted items, thus the name of our approach DFraud(3). DFraud(3) demonstrates a significant accuracy increase of 13% over the state of the art on Yelp.
C1 [Shehnepoor, Saeedreza; Togneri, Roberto] Univ Western Australia, Sch Elect Elect & Comp Engn, Perth, WA 6009, Australia.
   [Liu, Wei; Bennamoun, Mohammed] Univ Western Australia, Dept Comp Sci & Software Engn, Perth, WA 6009, Australia.
RP Shehnepoor, S (通讯作者)，Univ Western Australia, Sch Elect Elect & Comp Engn, Perth, WA 6009, Australia.
EM shehnepoor@research.uwa.edu.au; roberto.togneri@uwa.edu.au;
   wei.liu@uwa.edu.au; mohammed.bennamoun@uwa.edu.au
RI Bennamoun, Mohammed/C-2789-2013
OI Bennamoun, Mohammed/0000-0002-6603-3257; Togneri,
   Roberto/0000-0002-3778-4633; Liu, Wei/0000-0002-7409-0948; Shehnepoor,
   Saeedreza/0000-0001-6760-4501
FU Research Training Program (RTP) Scholarship - Australian Government
FX This work was supported by the Research Training Program (RTP)
   Scholarship funded by the Australian Government.
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NR 38
TC 3
Z9 3
U1 2
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1556-6013
EI 1556-6021
J9 IEEE T INF FOREN SEC
JI IEEE Trans. Inf. Forensic Secur.
PY 2021
VL 16
BP 3456
EP 3468
DI 10.1109/TIFS.2021.3081258
PG 13
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA SN5LX
UT WOS:000658332100007
DA 2022-08-23
ER

PT J
AU Chen, WX
   Helu, XH
   Jin, CJ
   Zhang, M
   Lu, H
   Sun, YB
   Tian, ZH
AF Chen, Weixiang
   Helu, Xiaohan
   Jin, Chengjie
   Zhang, Man
   Lu, Hui
   Sun, Yanbin
   Tian, Zhihong
TI Advanced persistent threat organization identification based on software
   gene of malware
SO TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES
LA English
DT Article
ID SENSOR-CLOUD
AB Since the concept of IoT (Internet of Things) was proposed, it has digitized the real world and has a wide range of applications. However, with tremendous evolution in data acquisition and transfer, a new type of attack represented by advanced persistent threat (APT) has attracted wide attention. APT organization identification for malware is a method to detect APT attacks. However, most of malware is tailored to the goal, it is complex and changeable, or can be updated very quickly. The traditional analysis method is difficult to obtain the source information of APT organization from the malware in the IoT. To this end, we propose a software genes method to solve this problem. Software gene is binary fragment of specific function or information in the software body. In this paper, different from traditional data flow and instruction flow, a new gene model is proposed which combine with knowledge graph of malware behavior. We fill the processed malware information into the gene model to obtain the APT organization gene pool. Of course, the gene pool should be optimized to include the genetic characteristics of APT. In theory, there genetic characteristics can help us identify malware and APT accurately in the IoT. However, biological genetic similarity algorithms cannot be used directly. A genetic similarity algorithm for APT organization identification of malware will be designed instead. Simulations on real-world dataset corroborate theoretical analysis and reveal the possibility of using genes for malware traceability.
C1 [Chen, Weixiang; Helu, Xiaohan; Jin, Chengjie; Lu, Hui; Sun, Yanbin; Tian, Zhihong] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China.
   [Zhang, Man] Peng Cheng Lab, Shenzhen, Peoples R China.
RP Lu, H; Sun, YB (通讯作者)，Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China.
EM luhui@gzhu.edu.cn; sunyanbin@gzhu.edu.cn
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NR 29
TC 3
Z9 3
U1 3
U2 10
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2161-3915
J9 T EMERG TELECOMMUN T
JI Trans. Emerg. Telecommun. Technol.
PD DEC
PY 2020
VL 31
IS 12
SI SI
AR e3884
DI 10.1002/ett.3884
PG 14
WC Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Telecommunications
GA PK8EJ
UT WOS:000602670700035
DA 2022-08-23
ER

PT J
AU Lavrac, N
   Skrlj, B
   Robnik-Sikonja, M
AF Lavrac, Nada
   Skrlj, Blaz
   Robnik-Sikonja, Marko
TI Propositionalization and embeddings: two sides of the same coin
SO MACHINE LEARNING
LA English
DT Article
DE Inductive logic programming; Relational learning; Propositionalization;
   Embeddings; Knowledge graphs
ID RELATIONAL DATA; EVIDENCE CONTRARY; STATISTICAL VIEW; DISCOVERY
AB Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches. While both approaches aim at transforming data into tabular data format, they use different terminology and task definitions, are perceived to address different goals, and are used in different contexts. This paper contributes a unifying framework that allows for improved understanding of these two data transformation techniques by presenting their unified definitions, and by explaining the similarities and differences between the two approaches as variants of a unified complex data transformation task. In addition to the unifying framework, the novelty of this paper is a unifying methodology combining propositionalization and embeddings, which benefits from the advantages of both in solving complex data transformation and learning tasks. We present two efficient implementations of the unifying methodology: an instance-based PropDRM approach, and a feature-based PropStar approach to data transformation and learning, together with their empirical evaluation on several relational problems. The results show that the new algorithms can outperform existing relational learners and can solve much larger problems.
C1 [Lavrac, Nada] Jozef Stefan Inst, Jamova 39, Ljubljana 1000, Slovenia.
   [Lavrac, Nada] Univ Nova Gorica, Glavni Trg 8, Vipava 5271, Slovenia.
   [Skrlj, Blaz] Jozef Stefan Inst, Int Postgrad Sch Jozef Stefan, Jamova 39, Ljubljana 1000, Slovenia.
   [Robnik-Sikonja, Marko] Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, Ljubljana 1000, Slovenia.
RP Skrlj, B (通讯作者)，Jozef Stefan Inst, Int Postgrad Sch Jozef Stefan, Jamova 39, Ljubljana 1000, Slovenia.
EM nada.lavrac@ijs.si; blaz.skrlj@ijs.si; marko.robnik@fri.uni-lj.si
OI Lavrac, Nada/0000-0002-9995-7093; Skrlj, Blaz/0000-0002-9916-8756
FU Slovenian Research Agency [P2-0103, P6-0411]; ERC [N2-0078]; European
   Union's Horizon 2020 research and innovation programme [825153]
FX We acknowledge the financial support of the Slovenian Research Agency
   through core research programmes P2-0103 and P6-0411 and project
   Semantic Data Mining for Linked Open Data (financed under the ERC
   Complementary Scheme, N2-0078). The authors have received funding also
   from the European Union's Horizon 2020 research and innovation programme
   under grant agreement No 825153 (EMBEDDIA). The work of the second
   author was funded by the Slovenian Research Agency through a young
   researcher grant. We wish to thank Jan Kralj for his insightful comments
   on the formulation of the proposed framework and for mathematical
   proofreading. Further, we are grateful to Vid Podpean and Nika Eren for
   their help with the implementation of the new version of the PyRDM
   library. Finally, we would like to thank the anonymous reviewers for
   careful reading, many insightful observations, and the encouragements to
   expand the initial work.
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NR 95
TC 7
Z9 7
U1 1
U2 1
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0885-6125
EI 1573-0565
J9 MACH LEARN
JI Mach. Learn.
PD JUL
PY 2020
VL 109
IS 7
SI SI
BP 1465
EP 1507
DI 10.1007/s10994-020-05890-8
EA JUN 2020
PG 43
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ML6TT
UT WOS:000545237900001
PM 32704202
OA Green Submitted, hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Bonifati, A
   Martens, W
   Timm, T
AF Bonifati, Angela
   Martens, Wim
   Timm, Thomas
TI An analytical study of large SPARQL query logs
SO VLDB JOURNAL
LA English
DT Article
DE Query log analysis; Structural analysis; Query shapes; Streak analysis;
   SPARQL endpoint; Knowledge graph
AB With the adoption of RDF as the data model for Linked Data and the Semantic Web, query specification from end users has become more and more common in SPARQL endpoints. In this paper, we conduct an in-depth analytical study of the queries formulated by end users and harvested from large and up-to-date structured query logs from a wide variety of RDF data sources. As opposed to previous studies, ours is the first assessment on a voluminous query corpus, spanning over several years and covering many representative SPARQL endpoints. Apart from the syntactical structure of the queries that exhibits already interesting results on this generalized corpus, we drill deeper in the structural characteristics related to the graph and hypergraph representation of queries. We outline the most common shapes of queries when visually displayed as undirected graphs and characterize their treewidth, length of their cycles, maximal degree of nodes, and more. For queries that cannot be adequately represented as graphs, we investigate their hypergraphs and hypertreewidth. Moreover, we analyze the evolution of queries over time, by introducing the novel concept of a streak, i.e., a sequence of queries that appear as subsequent modifications of a seed query. Our study offers several fresh insights on the already rich query features of real SPARQL queries formulated by real users and brings us to draw a number of conclusions and pinpoint future directions for SPARQL query evaluation, query optimization, tuning, and benchmarking.
C1 [Bonifati, Angela] Lyon 1 Univ, Lyon, France.
   [Martens, Wim; Timm, Thomas] Univ Bayreuth, Bayreuth, Germany.
RP Bonifati, A (通讯作者)，Lyon 1 Univ, Lyon, France.
EM Angela.Bonifati@univ-lyon1.fr
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NR 43
TC 12
Z9 13
U1 0
U2 4
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1066-8888
EI 0949-877X
J9 VLDB J
JI VLDB J.
PD MAY
PY 2020
VL 29
IS 2-3
BP 655
EP 679
DI 10.1007/s00778-019-00558-9
PG 25
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KU7XZ
UT WOS:000519927700004
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Lee, B
   Zhang, S
   Poleksic, A
   Xie, L
AF Lee, Bohyun
   Zhang, Shuo
   Poleksic, Aleksandar
   Xie, Lei
TI Heterogeneous Multi-Layered Network Model for Omics Data Integration and
   Analysis
SO FRONTIERS IN GENETICS
LA English
DT Review
DE data mining and knowledge discovery; machine learning; biological data
   analysis; biological network; link prediction; relation inference; deep
   learning
ID INFORMATION; SYSTEMS
AB Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-of-the-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models.
C1 [Lee, Bohyun; Zhang, Shuo; Xie, Lei] CUNY, PhD Program Comp Sci, New York, NY 10021 USA.
   [Poleksic, Aleksandar] Univ Northern Iowa, Dept Comp Sci, Cedar Falls, IA USA.
   [Xie, Lei] CUNY, PhD Program Biochem & Biol, New York, NY 10021 USA.
   [Xie, Lei] CUNY Hunter Coll, Dept Comp Sci, New York, NY 10021 USA.
   [Xie, Lei] Cornell Univ, Helen & Robert Appel Alzheimers Dis Res Inst, Feil Family Brain & Mind Res Inst, Weill Cornell Med, Ithaca, NY 14850 USA.
RP Xie, L (通讯作者)，CUNY, PhD Program Comp Sci, New York, NY 10021 USA.; Xie, L (通讯作者)，CUNY, PhD Program Biochem & Biol, New York, NY 10021 USA.; Xie, L (通讯作者)，CUNY Hunter Coll, Dept Comp Sci, New York, NY 10021 USA.; Xie, L (通讯作者)，Cornell Univ, Helen & Robert Appel Alzheimers Dis Res Inst, Feil Family Brain & Mind Res Inst, Weill Cornell Med, Ithaca, NY 14850 USA.
EM lxie@iscb.org
RI Zhang, Shuo/AAV-3379-2020
OI Zhang, Shuo/0000-0001-9497-6263; Poleksic,
   Aleksandar/0000-0002-6444-0350
FU National Library of Medicine (NLM) [R01LM011986]; National Institute of
   General Medical Sciences (NIGMS) [R01GM122845]; National Institute on
   Aging on the National Institute of Health (NIH) [R01AD057555]; NATIONAL
   INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM122845] Funding Source: NIH
   RePORTER; NATIONAL INSTITUTE ON AGING [R01AG057555] Funding Source: NIH
   RePORTER; NATIONAL LIBRARY OF MEDICINE [R01LM011986] Funding Source: NIH
   RePORTER
FX This work was supported by Grant Number R01LM011986 from the National
   Library of Medicine (NLM), Grant Number R01GM122845 from the National
   Institute of General Medical Sciences (NIGMS), and Grand Number
   R01AD057555 of National Institute on Aging on the National Institute of
   Health (NIH). The funding agencies do not play roles in writing this
   manuscript.
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Z9 24
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U2 21
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1664-8021
J9 FRONT GENET
JI Front. Genet.
PD JAN 28
PY 2020
VL 10
AR 1381
DI 10.3389/fgene.2019.01381
PG 11
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA KM7BM
UT WOS:000514294500001
PM 32063919
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Mumtaz, R
   Qadir, MA
AF Mumtaz, Raabia
   Qadir, Muhammad Abdul
TI CustRE: a rule based system for family relations extraction from english
   text
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Semantic Computing; Relation Extraction; Knowledge Extraction; Relation
   Classification; NLP; Information Extraction
AB Relation extraction is an important information extraction task that must be solved in order to transform data into Knowledge Graph (KG), as semantic relations between entities form KG edges of the graph. Although much effort has been devoted to solve this task during the last three decades, but the results achieved are not as good yet. For instance, winner at Text Analysis Conference's (TAC) Knowledge Base Population (KBP) 2015 slot filling task, the Stanford's system, achieves F1 score of 60.5% on standard Relation Extraction (RE) dataset (Zhang et al., in: Position-aware attention and supervised data improve slot_lling. In: EMNLP 2017-Conference on Empirical Methods in Natural Language Processing, Proceedings, (2017). https://doi.org/10.18653/v1/d17-1004). The RE task therefore needs better solutions. This paper presents our system, CustRE, for better identification and classification of family relations from English text. CustRE is a rule based system, that uses regular expressions for pattern matching to extract family relations explicitly mentioned in text, and uses co-reference and propagation rules to extract family relations implicitly implied in the text. The proposed system, its implementation and the results obtained are presented in this paper. The results show that our approach makes a great improvement over existing methods by achieving F1 scores of 79.7% and 76.6% on TACRED family relations and CustFRE datasets respectively, which are 6.3 and 18.5 points higher than LUKE, the best score reporter on TACRED.
C1 [Mumtaz, Raabia; Qadir, Muhammad Abdul] Capital Univ Sci & Technol, Dept Comp Sci, Islamabad, Pakistan.
RP Mumtaz, R (通讯作者)，Capital Univ Sci & Technol, Dept Comp Sci, Islamabad, Pakistan.
EM raabia.asifsaeed@gmail.com; aqadir@cust.edu.pk
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PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
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J9 KNOWL INF SYST
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PD JUL
PY 2022
VL 64
IS 7
BP 1817
EP 1844
DI 10.1007/s10115-022-01687-4
EA JUN 2022
PG 28
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Y6NM
UT WOS:000810306200001
DA 2022-08-23
ER

PT J
AU Pang, N
   Tan, Z
   Zhao, X
   Zeng, WX
   Xiao, WD
AF Pang, Ning
   Tan, Zhen
   Zhao, Xiang
   Zeng, Weixin
   Xiao, Weidong
TI Domain relation extraction from noisy Chinese texts
SO NEUROCOMPUTING
LA English
DT Article
DE Relation extraction; Text sparsity; Distant supervision; Attentional MIL
AB Relation extraction is a typical method to extend the knowledge graph (KG); nevertheless, when it is applied in a particular domain, the issue of text sparsity becomes noteworthy. As a remedy, distant super-vision is introduced to mitigate the issue, which, however, brings about noise simultaneously. The two issues have been notorious in Chinese domain, since Chinese KGs are less developed in comparison with popular languages like English. To tackle the challenge, we propose a complementary convolution neural network (com-CNN) with attentional multiple instance learning (MIL) to obtain highly comprehensive features for extracting relations, and alleviate the negative effect caused by sentence-level noise. Our model com-CNN fully captures information from two different representations of a relation instance, raw word sequence (RWS) and multiple dependency path (MDP), and enables them to complement each other. To achieve better combination of RWS and MDP, we design a flexible feature fusion method. To mitigate the over-fitting problem of attention mechanism, which is caused by sparse texts, entity information is employed to guide the computation of attention scores for multiple instances in a bag to alleviate the impact of the wrongly labelled data. Experiments on Chinese relation extraction show that our proposal outperforms state-of-the-art approaches, and that the combination of RWS and MDP can generate more representative features for relation extraction. Besides, the empirical results validate our intention that entity-integrated attentional MIL offers the best performance in denoising for sparse domain texts compared with alternatives. (C) 2020 Published by Elsevier B.V.
C1 [Pang, Ning; Tan, Zhen; Zhao, Xiang; Zeng, Weixin; Xiao, Weidong] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Hunan, Peoples R China.
   [Zhao, Xiang; Xiao, Weidong] Collaborat Innovat Ctr Geospatial Technol, Wuhan, Peoples R China.
RP Tan, Z; Xiao, WD (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Hunan, Peoples R China.
EM tanzhen08a@nudt.edu.cn; wdxiao@nudt.edu.cn
RI Zeng, Weixin/AAX-2993-2021
OI Zeng, Weixin/0000-0002-2690-3190
FU NSFC [61872446, 61902417, 71971212, 71690233]; NSF of Hunan province
   [2019JJ20024]
FX This work was partially supported by NSFC under Grant Nos. 61872446,
   61902417, 71971212 and 71690233, and NSF of Hunan province under Grant
   No. 2019JJ20024.
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TC 2
Z9 2
U1 11
U2 27
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
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J9 NEUROCOMPUTING
JI Neurocomputing
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PY 2020
VL 418
BP 21
EP 35
DI 10.1016/j.neucom.2020.07.077
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OS1FZ
UT WOS:000589911100003
DA 2022-08-23
ER

PT J
AU Li, KJ
   Ye, WJ
AF Li, Kangjie
   Ye, Wenjing
TI Semi-supervised node classification via graph learning convolutional
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SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Graph learning; Semi-supervised; Graph convolutional neural networks
AB Graph convolutional neural networks (GCNs) have become increasingly popular in recent times due to the emerging graph data in scenes such as social networks and recommendation systems. However, engineering graph data are often noisy and incomplete or even unavailable, making it challenging or impossible to implement the de facto GCNs method directly on them. Current efforts for tackling this issue either require an overparameterized model that is hard to scale, or simply re-weight the existing edges for different downward tasks. In this work, we tackle this problem through introducing a graph learning convolutional neural network (GLCNN), which can be employed on both Euclidean space data and non-Euclidean space data. The similarity matrix is learned by a supervised method in the graph learning layer of the GLCNN. Moreover, graph pooling and distilling operations are utilized to reduce over-fitting. Comparative experiments are done on three different datasets: citation dataset, knowledge graph dataset, and image dataset. Results demonstrate that the GLCNN can improve the accuracy of the semi-supervised node classification by mining useful relationships among nodes. The performance is more obvious especially on datasets of Euclidean space. Specifically, GLCNN outperforms the best baseline by 3.1% and 1.1% on MNIST and SVHN datasets. Moreover, the robustness is explored by adding noises on the edge of the graph. Sensitive analysis and visualizations are performed to demonstrate effects of some key parameters.
C1 [Li, Kangjie; Ye, Wenjing] Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Kowloon, Clear Water Bay, Hong Kong, Peoples R China.
   [Li, Kangjie] Zhejiang Univ, State Key Lab Fluid Power & Mechatron Syst, Hangzhou, Peoples R China.
RP Ye, WJ (通讯作者)，Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Kowloon, Clear Water Bay, Hong Kong, Peoples R China.
EM kliba@connect.ust.hk; mewye@ust.hk
FU Hong Kong Research Grants under Competitive Earmarked Research Grant
   [16206320]
FX This work is supported by the Hong Kong Research Grants under
   Competitive Earmarked Research Grant No. 16206320.
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EA FEB 2022
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YW9SY
UT WOS:000753751800005
DA 2022-08-23
ER

PT J
AU He, CH
   Tan, Z
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   Zhang, C
   Hu, YL
   Ge, B
AF He, Chunhui
   Tan, Zhen
   Wang, Haoran
   Zhang, Chong
   Hu, Yanli
   Ge, Bin
TI Open Domain Chinese Triples Hierarchical Extraction Method
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE named entity recognition; open relation prediction; information
   extraction; CTHE
AB Featured Application This method can be applied to the task of automatic extraction of triples in unstructured text. Open domain relation prediction is an important task in triples extraction. When faced with the task of constructing large-scale knowledge graph systems, with the exception of structured data, it is necessary to automatically extract triples from a large amount of unstructured text to expand entities and relations. Although a large number of English open relation prediction methods have achieved good performance, the high-performance system for open domain Chinese triples extraction remains undeveloped due to the lack of large-scale Chinese annotation corpora and the difficulty of Chinese language processing. In this paper, we propose an integrated open domain Chinese triples hierarchical extraction method (CTHE) to solve this problem, considering the advantages of Bi-LSTM-CRF and Att-Bi-GRU models based on the pre-trained BERT encoding model. This method can recognize the named entities from Chinese sentences to establish entity pairs, and implement hierarchical extraction of specific and open relations based on the user-defined schema library and attention mechanism. The experimental results demonstrate the effectiveness of this method, which achieved stable performance on the test dataset, and better precision and F1-score in comparison with state-of-the-art Chinese open domain triples extraction methods. Furthermore, a large-scale annotated dataset for a Chinese named entity recognition (NER) task is established, which provides support for research on Chinese NER tasks.
C1 [He, Chunhui; Tan, Zhen; Wang, Haoran; Zhang, Chong; Hu, Yanli; Ge, Bin] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
RP Wang, HR (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Peoples R China.
EM xtuhch@163.com; tanzhen@nudt.edu.cn; 15697312525@163.com;
   chongzhang@nudt.edu.cn; huyanli@nudt.edu.cn; gebin@nudt.edu.cn
OI He, Chunhui/0000-0003-1505-1620; Tan, Zhen/0000-0001-8643-4683
FU National Natural Science Foundation of China (NSFC) [61902417,
   61872446]; Basic Foundation [2019-JCJQ-JJ-231]
FX This research received the support of the National Natural Science
   Foundation of China (NSFC) via grant No. 61902417 and No. 61872446, and
   it is also funded by Basic Foundation via grant No. 2019-JCJQ-JJ-231.
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NR 28
TC 3
Z9 3
U1 4
U2 14
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD JUL
PY 2020
VL 10
IS 14
AR 4819
DI 10.3390/app10144819
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 MS4AG
UT WOS:000554220500001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ge, K
   Zhao, JQ
   Zhao, YY
AF Ge, Kao
   Zhao, Jian-Qiang
   Zhao, Yan-Yong
TI GR-GNN: Gated Recursion-Based Graph Neural Network Algorithm
SO MATHEMATICS
LA English
DT Article
DE GR-GNN; graph neural network; bias random walks; GRU
ID REPRESENTATION
AB Under an internet background involving artificial intelligence and big data-unstructured, materialized, network graph-structured data, such as social networks, knowledge graphs, and compound molecules, have gradually entered into various specific business scenarios. One problem that urgently needs to be solved in the industry involves how to perform feature extractions, transformations, and operations in graph-structured data to solve downstream tasks, such as node classifications and graph classifications in actual business scenarios. Therefore, this paper proposes a gated recursion-based graph neural network (GR-GNN) algorithm to solve tasks such as node depth-dependent feature extractions and node classifications for graph-structured data. The GRU neural network unit was used to complete the node classification task and, thereby, construct the GR-GNN model. In order to verify the accuracy, effectiveness, and superiority of the algorithm on the open datasets Cora, CiteseerX, and PubMed, the algorithm was used to compare the operation results with the classical graph neural network baseline algorithms GCN, GAT, and GraphSAGE, respectively. The experimental results show that, on the validation set, the accuracy and target loss of the GR-GNN algorithm are better than or equal to other baseline algorithms; in terms of algorithm convergence speed, the performance of the GR-GNN algorithm is comparable to that of the GCN algorithm, which is higher than other algorithms. The research results show that the GR-GNN algorithm proposed in this paper has high accuracy and computational efficiency, and very wide application significance.
C1 [Ge, Kao] Chinese Acad Sci, Nanjing Inst Software Technol, Inst Software, Nanjing 211135, Peoples R China.
   [Zhao, Jian-Qiang] Xuzhou Univ Technol, Sch Math & Stat, Xuzhou 221018, Jiangsu, Peoples R China.
   [Zhao, Yan-Yong] Nanjing Audit Univ, Dept Stat, Nanjing 211815, Peoples R China.
RP Zhao, YY (通讯作者)，Nanjing Audit Univ, Dept Stat, Nanjing 211815, Peoples R China.
EM gekao@nj.iscas.ac.cn; zjqghh@xzit.edu.cn; yyzhao@nau.edu.cn
OI Zhao, Yan-Yong/0000-0003-2354-8469
FU National Social Science Foundation of China [21BTJ070]; sixth "333
   project" in Jiangsu Province; Social Science Foundation of Jiangsu
   Province [20EYC008]; National Statistical Research Project of China
   [2020LZ35]; Open Project of Jiangsu Key Laboratory of Financial
   Engineering [NSK2021-12]; third level training object of the sixth "333
   project" in Jiangsu Province
FX Jian-Qiang Zhao's research is supported by the National Social Science
   Foundation of China under grant no. 21BTJ070, and the third training
   object of the sixth "333 project" in Jiangsu Province. Yan-Yong Zhao's
   research is supported by the Social Science Foundation of Jiangsu
   Province under grant no.20EYC008, the National Statistical Research
   Project of China under grant no.2020LZ35, the Open Project of Jiangsu
   Key Laboratory of Financial Engineering under grant no.NSK2021-12, and
   the third level training object of the sixth "333 project" in Jiangsu
   Province.
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PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
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J9 MATHEMATICS-BASEL
JI Mathematics
PD APR
PY 2022
VL 10
IS 7
AR 1171
DI 10.3390/math10071171
PG 13
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA 0K4UH
UT WOS:000780784600001
OA gold
DA 2022-08-23
ER

PT J
AU Yang, H
   Yuan, JQ
   Li, C
   Zhao, GL
   Sun, ZJ
   Yao, QY
   Bao, BW
   Vasilakos, AV
   Zhang, J
AF Yang, Hui
   Yuan, Jiaqi
   Li, Chao
   Zhao, Guanliang
   Sun, Zhengjie
   Yao, Qiuyan
   Bao, Bowen
   Vasilakos, Athanasios V.
   Zhang, Jie
TI BrainIoT: Brain-Like Productive Services Provisioning With Federated
   Learning in Industrial IoT
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Production; Prediction algorithms; Industrial Internet of Things;
   Collaborative work; Delays; Artificial intelligence; Production
   facilities; Artificial intelligence (AI); brain; federated learning
   (FL); Industrial Internet of Things (IIoT); services provisioning
ID OPTIMIZATION; NETWORKS; CORE
AB The Industrial Internet of Things (IIoT) accommodates a huge number of heterogeneous devices to bring vast services under a distributed computing scenarios. Most productive services in IIoT are closely related to production control and require distributed network support with low delay. However, the resource reservation based on gross traffic prediction ignores the importance of productive services and treats them as ordinary services, so it is difficult to provide stable low delay support for large amounts of productive service requests. For many productions, unexpected communication delays are unacceptable, and the delay may lead to serious production accidents causing great losses, especially when the productive service is security related. In this article, we propose a brain-like productive service provisioning scheme with federated learning (BrainIoT) for IIoT. The BrainIoT scheme is composed of three algorithms, including industrial knowledge graph-based relation mining, federated learning-based service prediction, and globally optimized resource reservation. BrainIoT combines production information into network optimization, and utilizes the interfactory and intrafactory relations to enhance the accuracy of service prediction. The globally optimized resource reservation algorithm suitably reserves resources for predicted services considering various resources. The numerical results show that the BrainIoT scheme utilizes interfactory relation and intrafactory relation to make an accurate service prediction, which achieves 96% accuracy, and improves the quality of service.
C1 [Yang, Hui; Yuan, Jiaqi; Li, Chao; Zhao, Guanliang; Sun, Zhengjie; Yao, Qiuyan; Bao, Bowen; Zhang, Jie] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China.
   [Yang, Hui; Yuan, Jiaqi] Sci & Technol Commun Networks Lab, Shijiazhuang 050081, Hebei, Peoples R China.
   [Vasilakos, Athanasios V.] Univ Technol Sydney, Sch Elect & Data Engn, Ultimo, NSW 2007, Australia.
   [Vasilakos, Athanasios V.] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China.
   [Vasilakos, Athanasios V.] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, S-14671 Lulea, Sweden.
RP Yang, H (通讯作者)，Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China.
EM yanghui@bupt.edu.cn; th.vasilakos@gmail.com
OI Vasilakos, Athanasios/0000-0003-1902-9877
FU NSFC [61871056]; Beijing Natural Science Foundation [4202050]; Key
   Laboratory Fund [6142104190412]; Fund of SKL of IPOC (BUPT)
   [IPOC2020A004, IPOC2018A001]
FX This work was supported in part by NSFC under Project 61871056; in part
   by Beijing Natural Science Foundation under Grant 4202050; in part by
   Key Laboratory Fund under Grant 6142104190412; and in part by the Fund
   of SKL of IPOC (BUPT) under Grant IPOC2020A004 and Grant IPOC2018A001.
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U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2327-4662
J9 IEEE INTERNET THINGS
JI IEEE Internet Things J.
PD FEB 1
PY 2022
VL 9
IS 3
BP 2014
EP 2024
DI 10.1109/JIOT.2021.3089334
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
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WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA YN7VN
UT WOS:000747462100034
DA 2022-08-23
ER

PT J
AU Gonzalez, LP
   Fensel, A
   Berbis, JMG
   Popa, A
   Seco, AD
AF Prieto Gonzalez, Lisardo
   Fensel, Anna
   Gomez Berbis, Juan Miguel
   Popa, Angela
   de Amescua Seco, Antonio
TI A Survey on Energy Efficiency in Smart Homes and Smart Grids
SO ENERGIES
LA English
DT Review
DE energy efficiency; smart home; semantic technology; knowledge graphs;
   security; fraud; detection; encryption; smart grid; communication;
   microgrid
ID INTEROPERABILITY; SECURITY; MACHINE; SYSTEMS; SCHEME
AB Empowered by the emergence of novel information and communication technologies (ICTs) such as sensors and high-performance digital communication systems, Europe has adapted its electricity distribution network into a modern infrastructure known as a smart grid (SG). The benefits of this new infrastructure include precise and real-time capacity for measuring and monitoring the different energy-relevant parameters on the various points of the grid and for the remote operation and optimization of distribution. Furthermore, a new user profile is derived from this novel infrastructure, known as a prosumer (a user that can produce and consume energy to/from the grid), who can benefit from the features derived from applying advanced analytics and semantic technologies in the rich amount of big data generated by the different subsystems. However, this novel, highly interconnected infrastructure also presents some significant drawbacks, like those related to information security (IS). We provide a systematic literature survey of the ICT-empowered environments that comprise SGs and homes, and the application of modern artificial intelligence (AI) related technologies with sensor fusion systems and actuators, ensuring energy efficiency in such systems. Furthermore, we outline the current challenges and outlook for this field. These address new developments on microgrids, and data-driven energy efficiency that leads to better knowledge representation and decision-making for smart homes and SGs.
C1 [Prieto Gonzalez, Lisardo; Gomez Berbis, Juan Miguel; de Amescua Seco, Antonio] Carlos III Univ Madrid, Dept Comp Sci, Software Architect Grp, Leganes 28911, Spain.
   [Fensel, Anna] Wageningen Univ & Res, Wageningen Data Competence Ctr WDCC, NL-6708 PB Wageningen, Netherlands.
   [Fensel, Anna] Wageningen Univ & Res, Consumpt & Hlth Lifestyles Grp, NL-6708 PB Wageningen, Netherlands.
   [Fensel, Anna; Popa, Angela] Univ Innsbruck, STI Semant Technol Inst Innsbruck, Dept Comp Sci, A-6020 Innsbruck, Austria.
RP Fensel, A (通讯作者)，Wageningen Univ & Res, Wageningen Data Competence Ctr WDCC, NL-6708 PB Wageningen, Netherlands.; Fensel, A (通讯作者)，Wageningen Univ & Res, Consumpt & Hlth Lifestyles Grp, NL-6708 PB Wageningen, Netherlands.; Fensel, A (通讯作者)，Univ Innsbruck, STI Semant Technol Inst Innsbruck, Dept Comp Sci, A-6020 Innsbruck, Austria.
EM lisardo.prieto@uc3m.es; anna.fensel@wur.nl; juanmiguel.gomez@uc3m.es;
   popa.angela@gmail.com; antonio.amescua@uc3m.es
RI Prieto Gonzalez, Lisardo/G-9526-2016
OI Prieto Gonzalez, Lisardo/0000-0001-9799-3106; Amescua,
   Antonio/0000-0003-4355-6896
FU Interreg Osterreich-Bayern 2014-2020 programme project KI-Net: Bausteine
   fur KI-basierte Optimierungen in der industriellen Fertigung [AB 292];
   ITEA3 OPTIMUM project - Centro Tecnologico de Desarrollo Industrial
   (CDTI), Spain; ITEA3 SCRATCH project - Centro Tecnologico de Desarrollo
   Industrial (CDTI), Spain
FX This research was co-funded by Interreg Osterreich-Bayern 2014-2020
   programme project KI-Net: Bausteine fur KI-basierte Optimierungen in der
   industriellen Fertigung (AB 292). This work is also supported by the
   ITEA3 OPTIMUM project and ITEA3 SCRATCH project, all of them funded by
   the Centro Tecnologico de Desarrollo Industrial (CDTI), Spain.
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NR 67
TC 4
Z9 4
U1 9
U2 13
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1073
J9 ENERGIES
JI Energies
PD NOV
PY 2021
VL 14
IS 21
AR 7273
DI 10.3390/en14217273
PG 16
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA XJ1GS
UT WOS:000726546200001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Lofrano, FC
   Morita, DM
   Kurokawa, FA
   de Souza, PA
AF Lofrano, Fabio Cunha
   Morita, Dione Mari
   Kurokawa, Fernando Akira
   de Souza, Podalyro Amaral
TI New General Maximum Entropy Model for Flow Through Porous Media
SO TRANSPORT IN POROUS MEDIA
LA English
DT Article
DE Porous media; Fluid flow; Principle of maximum entropy; Analytical
   model; Velocity distribution within the pore space
ID NON-DARCY FLOW; VELOCITY DISTRIBUTION; HEAD LOSS; INFORMATION-THEORY;
   WATER-MOVEMENT; FLUID-FLOW; PRE-DARCY; PROBABILITY; COEFFICIENT; BED
AB New experimental and numerical techniques constitute the major recent advancements in the study of flow through porous media. However, a model that duly links the micro- and macroscales of this phenomenon is still lacking. Therefore, the present work describes a new, analytical model suitable for both Darcian and post-Darcian flow. Unlike its predecessors, most of which are based on empirical assessments or on some derivation of the Navier-Stokes equations, the presented model employed the principle of maximum entropy, along with a reduced number of premises. Nevertheless, it is compatible with classic expressions, such as Darcy's and Forchheimer's laws. Also, great adherence to previously published experimental results was observed. Moreover, the developed model allows for the delimitation of Darcian and post-Darcian regimes. It enabled the determination of a probabilistic distribution function of flow velocities within the pore space. Further, it bestowed richer interpretations of the physical meanings of principal flow parameters. Finally, through a new quantity called the entropy parameter, the proposed model may serve as a bridge between experimental and numerical findings both at the micro- and macroscales. Therefore, the present research yielded an analytical, entropy-based model for flow through porous media that is sufficiently general and robust to be applied in several fields of knowledge. Graphic
C1 [Lofrano, Fabio Cunha; Morita, Dione Mari; de Souza, Podalyro Amaral] Univ Sao Paulo, Polytech Sch, Dept Hydraul & Environm Engn, Sao Paulo, Brazil.
   [Kurokawa, Fernando Akira] Univ Sao Paulo, Polytech Sch, Dept Construct Engn, Sao Paulo, Brazil.
RP Lofrano, FC (通讯作者)，Univ Sao Paulo, Polytech Sch, Dept Hydraul & Environm Engn, Sao Paulo, Brazil.
EM fabio.lofrano@usp.br; dmmorita@usp.br; fernando.kurokawa@usp.br;
   podalyro@usp.br
RI Kurokawa, Fernando Akira A/C-9108-2014; Lofrano, Fabio/P-7198-2018;
   Morita, Dione/C-9746-2013
OI Kurokawa, Fernando Akira A/0000-0002-5597-4764; Lofrano,
   Fabio/0000-0003-4111-5472; Morita, Dione/0000-0002-0506-0972
FU CoordenacAo de Aperfeicoamento de Pessoal de Nivel Superior-Brasil
   (Capes) [001]
FX This study was financed in part by the CoordenacAo de Aperfeicoamento de
   Pessoal de Nivel Superior-Brasil (Capes)-Finance Code 001.
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NR 104
TC 3
Z9 3
U1 1
U2 4
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0169-3913
EI 1573-1634
J9 TRANSPORT POROUS MED
JI Transp. Porous Media
PD JAN
PY 2020
VL 131
IS 2
BP 681
EP 703
DI 10.1007/s11242-019-01362-3
PG 23
WC Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA KV1HT
UT WOS:000520175600015
DA 2022-08-23
ER

PT J
AU Li, R
   Mo, TJ
   Yang, JX
   Li, D
   Jiang, SX
   Wang, D
AF Li, Ren
   Mo, Tianjin
   Yang, Jianxi
   Li, Dong
   Jiang, Shixin
   Wang, Di
TI Bridge inspection named entity recognition via BERT and lexicon
   augmented machine reading comprehension neural model
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Bridge inspection; Named entity recognition; Machine reading
   comprehension; BERT
ID CONDITIONAL RANDOM-FIELDS
AB As an important data source in the field of bridge management, bridge inspection reports contain large-scale fine-grained data, including information on bridge members and structural defects. However, due to insufficient research on automatic information extraction in this field, valuable bridge inspection information has not been fully utilized. Particularly, for Chinese bridge inspection entities, which involve domain-specific vocabularies and have obvious nesting characteristics, most of the existing named entity recognition (NER) solutions are not suitable. To address this problem, this paper proposes a novel lexicon augmented machine reading comprehension-based NER neural model for identifying flat and nested entities from Chinese bridge inspection text. The proposed model uses the bridge inspection text and predefined question queries as input to enhance the ability of contextual feature representation and to integrate prior knowledge. Based on the character-level fea-tures encoded by the pre-trained BERT model, bigram embeddings and weighted lexicon features are further combined into a context representation. Then, the bidirectional long short-term memory neural network is used to extract sequence features before predicting the spans of named entities. The proposed model is verified by the Chinese bridge inspection named entity corpus. The experimental results show that the proposed model out-performs other mainstream NER models on the bridge inspection corpus. The proposed model not only provides a basis for automatic bridge inspection information extraction but also supports the downstream tasks such as knowledge graph construction and question answering systems.
C1 [Li, Ren; Mo, Tianjin; Yang, Jianxi; Li, Dong; Jiang, Shixin; Wang, Di] Chongqing Jiaotong Univ, Sch Informat Sci & Engn, Chongqing 400074, Peoples R China.
   [Mo, Tianjin] Chongqing Coll Elect Engn, Chongqing 401331, Peoples R China.
RP Li, R (通讯作者)，Chongqing Jiaotong Univ, Sch Informat Sci & Engn, Chongqing 400074, Peoples R China.
EM renli@cqjtu.edu.cn; mtj@mails.cqjtu.edu.cn; yjx@cqjtu.edu.cn;
   lidong@mails.cqjtu.edu.cn; shixinjiang@cqjtu.edu.cn; diwang@cqjtu.edu.cn
FU National Natural Science Foundation of China [62003063]; Natural Science
   Foundation of Chongqing, China [cstc2020jcyj-msxmX0047]; Science and
   Technology Research Program of Chongqing Municipal Education Commission
   of China [KJZD-M20200702, KJQN201800705, KJQN201900726, KJQN202000726];
   Research and Innovation Project of Graduate Student in Chongqing
   Jiaotong University [2020S0044]
FX This work was joint supported by the National Natural Science Foundation
   of China under Grant 62003063, the Natural Science Foundation of
   Chongqing, China under Grant cstc2020jcyj-msxmX0047, the Science and
   Technology Research Program of Chongqing Municipal Education Commission
   of China under Grant KJZD-M20200702, KJQN201800705, KJQN201900726 and
   KJQN202000726, and the Research and Innovation Project of Graduate
   Student in Chongqing Jiaotong University under Grant 2020S0044.
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TC 6
Z9 6
U1 28
U2 47
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1474-0346
EI 1873-5320
J9 ADV ENG INFORM
JI Adv. Eng. Inform.
PD OCT
PY 2021
VL 50
AR 101416
DI 10.1016/j.aei.2021.101416
EA SEP 2021
PG 11
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA UY8XY
UT WOS:000701801400001
DA 2022-08-23
ER

PT J
AU Huo, YJ
   Wong, DF
   Ni, LM
   Chao, LS
   Zhang, J
   Zuo, X
AF Huo, Yujia
   Wong, Derek F.
   Ni, Lionel M.
   Chao, Lidia S.
   Zhang, Jing
   Zuo, Xin
TI Learning cognitive embedding using signed knowledge interaction graph
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Signed interaction graph; Representation learning; Knowledge
   representation
AB Measuring learner cognition based on their problem-solving performance is a joint discipline of cognitive psychology and machine learning. In the case of learner problem-solving, the interaction between learner and knowledge forms a typical type of signed interaction graph. Interaction graphs are a widely used and effective solution to model the relationships between interacting entities. However, most of previous interaction graph methods are inclined to the observed interactions as positive links but they often fail to consider unobserved and negative links, which leads to an insufficiency in capturing the complete cognitionimis-cognition proximity information. To address this limitation, we propose a knowledge graph representation learning method that is based on signed knowledge interaction network (SKIN). We explicitly model the correct/incorrect cognitive performance as the positively (+)/negatively (-) signed links in the graph. The model simultaneously measures the nodes' local and global proximity , and then preserves them in the learned knowledge embedding. We architect a pairwise neural network that is based on a tri-sampling strategy and a sign-driven distance measuring objective function. The network generates knowledge representations by maximizing the knowledge distance between oppositely-signed pairs and minimizing the distance between identically-signed pairs. Our experimental results show the learned knowledge embedding demonstrates a desired Euclidean property and can be visualized with clear classification boundaries. We also show it can power downstream tasks such as learner-performance-prediction. The learned embeddings generate promising prediction scores on this task when compared to several methods in network sign prediction and learner-performance-prediction. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Huo, Yujia] Guizhou Minzu Univ, Sch Data Sci & Informat Engn, Guiyang 550025, Guizhou, Peoples R China.
   [Huo, Yujia; Wong, Derek F.; Chao, Lidia S.] Univ Macau, Dept Comp & Informat Sci, NLP2CT Lab, Taipa, Macao, Peoples R China.
   [Ni, Lionel M.] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China.
   [Huo, Yujia; Zuo, Xin] Special Key Lab Artificial Intelligence & Intelli, Guiyang, Peoples R China.
   [Zuo, Xin] Guizhou Educ Univ, Sch Math & Big Data, Guiyang, Peoples R China.
   [Zhang, Jing] Univ Macau, Dept Portuguese, Taipa, Macao, Peoples R China.
RP Huo, YJ (通讯作者)，Guizhou Minzu Univ, Sch Data Sci & Informat Engn, Guiyang 550025, Guizhou, Peoples R China.
EM huo.yujia@connect.um.edu.mo
RI Wong, Derek F/CAI-7740-2022
OI Zhang, Jing/0000-0003-4801-6354
FU Science and Technology Development Fund, Macau SAR [0101/2019/A2];
   University of Macau [MYRG2020-00054-FST]; Special Key Laboratory of
   Artificial Intelligence and Intelligent Control of Guizhou Province,
   China [KY[2020]001]; Guizhou Key Laboratory of Big Data Statistics
   Analysis, China [[2019]5103]; Higher Education Fund of the Government of
   Macao SAR
FX The authors acknowledge support from the Science and Technology
   Development Fund, Macau SAR (Grant Nos. 0101/2019/A2), the Multi-year
   Research Grant from the University of Macau (Grant No.
   MYRG2020-00054-FST), the Special Key Laboratory of Artificial
   Intelligence and Intelligent Control of Guizhou Province, China (Grant
   No. KY[2020]001), the Guizhou Key Laboratory of Big Data Statistics
   Analysis, China (Grant No. [2019]5103) and the Higher Education Fund of
   the Government of Macao SAR. We thank the anonymous reviewers for their
   insightful comments.
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NR 53
TC 1
Z9 1
U1 7
U2 11
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD OCT 11
PY 2021
VL 229
AR 107327
DI 10.1016/j.knosys.2021.107327
EA AUG 2021
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA UO7YW
UT WOS:000694909500005
DA 2022-08-23
ER

PT J
AU Zhang, L
   Huang, Y
   Yang, JQ
   Lu, W
AF Zhang, Li
   Huang, Yong
   Yang, Jinqing
   Lu, Wei
TI Aggregating large-scale databases for PubMed author name disambiguation
SO JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
LA English
DT Article
DE PubMed; author name disambiguation; bibliographic database; digital
   library
AB Objective: PubMed has suffered from the author ambiguity problem for many years. Existing studies on author name disambiguation (AND) for PubMed only used internal metadata for development. However, some of them are incomplete (eg, a large number of names are only abbreviated and their full names are not available) or less discriminative. To this end, we present a new disambiguation method, namely AggAND, by aggregating information from external databases.
   Materials and Methods: We address this issue by exploring Microsoft Academic Graph, Semantic Scholar, and PubMed Knowledge Graph to enhance the built-in name metadata, and extend the internal metadata with some external and more discriminative metadata.
   Results: Experimental results on enhanced name metadata demonstrate comparable performance to 3 author identifier systems, as well as show superiority over the original name metadata. More importantly, our method, AggAND, incorporating both enhanced name and extended metadata, yields F1 scores of 95.80% and 93.71% on 2 datasets and outperforms the state-of-the-art method by a large margin (3.61% and 6.55%, respectively).
   Conclusions: The feasibility and good performance of our methods not only help better understand the importance of external databases for disambiguation, but also point to a promising direction for future AND studies in which information aggregated from multiple bibliographic databases can be effective in improving disambiguation performance. The methodology shown here can be generalized to broader bibliographic databases beyond PubMed. Our code and data are available online (https://github.com/carmanzhang/PubMed-AND-method).
C1 [Zhang, Li; Huang, Yong; Yang, Jinqing; Lu, Wei] Wuhan Univ, Sch Informat Management, 299 Bayi St, Wuhan, Peoples R China.
RP Lu, W (通讯作者)，Wuhan Univ, Sch Informat Management, 299 Bayi St, Wuhan, Peoples R China.
EM weilu@whu.edu.cn
OI /0000-0003-2104-0194
FU Major Project of the National Social Science Foundation of China
   [17ZDA292]
FX This work was supported by the Major Project of the National Social
   Science Foundation of China grant number 17ZDA292.
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NR 31
TC 0
Z9 1
U1 8
U2 17
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1067-5027
EI 1527-974X
J9 J AM MED INFORM ASSN
JI J. Am. Med. Inf. Assoc.
PD SEP
PY 2021
VL 28
IS 9
BP 1919
EP 1927
DI 10.1093/jamia/ocab095
EA JUN 2021
PG 9
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Health Care Sciences & Services;
   Information Science & Library Science; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Health Care Sciences & Services; Information Science &
   Library Science; Medical Informatics
GA UL3TG
UT WOS:000692577000014
PM 34180522
OA Green Published
DA 2022-08-23
ER

PT J
AU Hou, JQ
   Li, X
   Yao, HP
   Sun, HC
   Mai, TL
   Zhu, RC
AF Hou, Jiaqi
   Li, Xin
   Yao, Haipeng
   Sun, Haichun
   Mai, Tianle
   Zhu, Rongchen
TI BERT-Based Chinese Relation Extraction for Public Security
SO IEEE ACCESS
LA English
DT Article
DE Bit error rate; Feature extraction; Security; Task analysis; Training;
   Semantics; Data mining; BERT; relationship extraction; public security;
   MaxPooler; ReLU
AB The past few years have witnessed some public safety incidents occurring around the world. With the advent of the big data era, effectively extracting public security information from the internet has become of great significance. Up to hundreds of TBs of data are injected into the network every second, and thus it is impossible to process them manually. Natural Language Processing (NLP) is dedicated to the development of an intelligent system for effective text information mining. By analysing the text and quickly extracting the relationships between the relevant entities, NLP can establish the knowledge graph (KG) of public security, which lays the foundation for safety case analysis, information monitoring, and activity tracking and locating. One of the current pre-training relation extraction models is the Word2Vec model. The Word2vec model is single mapped, and it produces a static, single representation of the words in sentences. Then, the BERT model considers contextual information and provides more dynamic, richer vector representations of generated words. Therefore, in this paper, we propose a Bidirectional Encoder Representation from Transformers (BERT) based on the Chinese relation extraction algorithm for public security, which can effectively mine security information. The BERT model is obtained by training the Masked Language Model and predicting the next sentence task, which is based on the Transformer Encoder and the main model structure is the stacked Transformers. Extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.
C1 [Hou, Jiaqi; Li, Xin; Sun, Haichun; Zhu, Rongchen] Peoples Publ Secur Univ China, Sch Informat Technol & Cyber Secur, Beijing 100038, Peoples R China.
   [Yao, Haipeng; Mai, Tianle] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China.
RP Li, X (通讯作者)，Peoples Publ Secur Univ China, Sch Informat Technol & Cyber Secur, Beijing 100038, Peoples R China.
EM lixin@ppsuc.edu.cn
RI Zhu, Rongchen/AAZ-2501-2020; Yao, haipeng/O-7366-2019; ,
   厚佳琪Yoona/ABC-3740-2020
OI Yao, haipeng/0000-0003-1391-7363; Zhu, Rongchen/0000-0002-8379-5579;
   Mai, Tianle/0000-0002-8500-1461
FU National Key Research and Development Program of China [2017YFC0803700];
   People's Public Security University of China 2019 Basic Research
   Operating Expenses New Teacher Research Startup Fund Project
   [2019JKF424]; Natural Science Foundation of China [41971367]
FX This work was supported in part by the National Key Research and
   Development Program of China under Grant 2017YFC0803700, in part by the
   People's Public Security University of China 2019 Basic Research
   Operating Expenses New Teacher Research Startup Fund Project under Grant
   2019JKF424, and in part by the Natural Science Foundation of China under
   Grant 41971367.
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NR 29
TC 5
Z9 6
U1 8
U2 31
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 132367
EP 132375
DI 10.1109/ACCESS.2020.3002863
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MU3FZ
UT WOS:000555559600001
OA gold
DA 2022-08-23
ER

PT J
AU Wang, YS
   Zhang, HH
   Liu, ZR
   Zhou, Q
AF Wang, Yashen
   Zhang, Huanhuan
   Liu, Zhirun
   Zhou, Qiang
TI Hierarchical Concept-Driven Language Model
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE Language modeling; text generation; concept semantic information;
   interpretation; recurrent conceptualization-enhanced gamma belief
   network; hierarchical language modeling; representation learning
AB For guiding natural language generation, many semantic-driven methods have been proposed. While clearly improving the performance of the end-to-end training task, these existing semantic-driven methods still have clear limitations: for example, (i) they only utilize shallow semantic signals (e.g., from topic models) with only a single stochastic hidden layer in their data generation process, which suffer easily from noise (especially adapted for short-text etc.) and lack of interpretation; (ii) they ignore the sentence order and document context, as they treat each document as a bag of sentences, and fail to capture the long-distance dependencies and global semantic meaning of a document. To overcome these problems, we propose a novel semantic-driven language modeling framework, which is a method to learn a Hierarchical Language Model and a Recurrent Conceptualization-enhanced Gamma Belief Network, simultaneously. For scalable inference, we develop the auto-encoding Variational Recurrent Inference, allowing efficient end-to-end training and simultaneously capturing global semantics from a text corpus. Especially, this article introduces concept information derived from high-quality lexical knowledge graph Probase, which leverages strong interpretability and anti-nose capability for the proposed model. Moreover, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence concept dependence. Experiments conducted on several NLP tasks validate the superiority of the proposed approach, which could effectively infer meaningful hierarchical concept structure of document and hierarchical multi-scale structures of sequences, even compared with latest state-of-the-art Transformer-based models.
C1 [Wang, Yashen; Zhang, Huanhuan] China Acad Elect & Informat Technol CETC, Natl Engn Lab Risk Percept & Prevent RPP, Shuangyuan St 11, Beijing 100041, Peoples R China.
   [Liu, Zhirun; Zhou, Qiang] Beijing Inst Technol, Sch Comp, Beijing Engn Res Ctr High Volume Language Informa, Zhongguancun South St 5, Beijing 100081, Peoples R China.
RP Wang, YS (通讯作者)，China Acad Elect & Informat Technol CETC, Natl Engn Lab Risk Percept & Prevent RPP, Shuangyuan St 11, Beijing 100041, Peoples R China.
EM yswang@bit.edu.cn; huanhuanz_bit@126.com; zrliu@bit.edu.cn;
   qzhou@bit.edu.cn
FU National Natural Science Foundation of China [U19B2026]; New Generation
   of Artificial Intelligence Special Action Project [AI20191125008];
   National Key Research and Development Project [2017YFC0820503]; National
   Integrated Big Data Center Pilot Project [20500908, 17111001, 17111002];
   Joint Advanced Research Foundation of China Electronics Technology Group
   Corporation (CETC) [6141B08010102]
FX This work is funded by: (i) the National Natural Science Foundation of
   China (No. U19B2026); (ii) the New Generation of Artificial Intelligence
   Special Action Project (No. AI20191125008); (iii) the National Key
   Research and Development Project (No. 2017YFC0820503); (iv) the National
   Integrated Big Data Center Pilot Project (No. 20500908, 17111001, and
   17111002); and (v) the Joint Advanced Research Foundation of China
   Electronics Technology Group Corporation (CETC) (No. 6141B08010102).
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NR 86
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 1556-4681
EI 1556-472X
J9 ACM T KNOWL DISCOV D
JI ACM Trans. Knowl. Discov. Data
PD DEC
PY 2021
VL 15
IS 6
AR 204
DI 10.1145/3451167
PG 22
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZP1SE
UT WOS:000766204500011
DA 2022-08-23
ER

PT J
AU Zablith, F
   Azad, B
AF Zablith, Fouad
   Azad, Bijan
TI Reconciling Instructors' and Students' Course Overlap Perspectives via
   Linked Data Visualization
SO IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
LA English
DT Article
DE Course overlap; curriculum mapping; knowledge graphs; linked data;
   ontologies; Semantic Web; visualization
ID SEMANTIC WEB TECHNOLOGIES; RESOURCES
AB Research on the use of modeling and mapping tools in curriculum management is thriving, often focusing on the perspectives of the faculty alone. However, scholarly works that also incorporate the students' curriculum concerns are rare. A recurring theme in students' curriculum concerns is the perceived overlap among courses, usually expressed at the level of common concepts across courses. This common "concept" emphasis imposes a challenge for modelers, who often focus on the course-level comparisons because they usually lack tools with subcourse (concept)-level granularity. This article investigates how to model and represent curriculum information to help in reconciling the gap between instructors' and students' views of cross-course overlap. The proposed approach involves the design and development of a digital environment to model a curriculum via linked data through an ontology representing concept-level granularity, offer instructors aid in populating course content, and facilitate the visualization and manipulation of data. The visualization tools are designed to offer functions for perceived common concept overlap identification and rectification. This digital environment was deployed and evaluated in the context of a curriculum review process, in which 25 course instructors employed the visualization tools to address a perceived course overlap problem. The preliminary results demonstrate, first, the usefulness of the approach in reconciling the views of instructors and students regarding perceived course overlap. Second, the results highlight that the approach contributes to transforming course overlap from a fuzzy notion to a more concrete and actionable construct defined as either repetition or reinforcement.
C1 [Zablith, Fouad; Azad, Bijan] Amer Univ Beirut, Olayan Sch Business, Beirut 110236, Lebanon.
RP Zablith, F (通讯作者)，Amer Univ Beirut, Olayan Sch Business, Beirut 110236, Lebanon.
EM fouad.zablith@aub.edu.lb; bijan.azad@aub.edu.lb
RI Azad, Bijan/AAF-1675-2019
OI Azad, Bijan/0000-0003-4127-1037; Zablith, Fouad/0000-0002-8978-9911
FU University Research Board of the American University of Beirut [102721,
   102853]; Olayan Fund for Enhancing Research [630089-9060]
FX This work was supported in part by the University Research Board of the
   American University of Beirut under Grant 102721 and Grant 102853, and
   in part by the Olayan Fund for Enhancing Research under Grant
   630089-9060. (Fouad Zablith and Bijan Azad contributed equally to this
   work.)
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NR 38
TC 0
Z9 0
U1 3
U2 3
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1939-1382
J9 IEEE T LEARN TECHNOL
JI IEEE Trans. Learn. Technol.
PD OCT 1
PY 2021
VL 14
IS 5
BP 680
EP 694
DI 10.1109/TLT.2021.3118902
PG 15
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 YJ4UT
UT WOS:000744529600010
DA 2022-08-23
ER

PT J
AU Honti, G
   Abonyi, J
AF Honti, Gergely
   Abonyi, Janos
TI Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF
   Databases
SO MATHEMATICS
LA English
DT Article
DE multi-layer network; RDF store; DataToKnowledgeToNetwork; linked data
ID SEMANTIC WEB
AB Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis. The results are used to form layers in an analysable multidimensional network. The methodology enables a consistent, transparent, multi-aspect-oriented knowledge extraction from the linked dataset. To demonstrate the usability and effectiveness of the methodology, we analyse how the science of sustainability and climate change are structured using the Microsoft Academic Knowledge Graph. In the case study, the FIM forms networks of disciplines to reveal the significant interdisciplinary science communities in sustainability and climate change. The constructed multilayer network then enables an analysis of the significant disciplines and interdisciplinary scientific areas. To demonstrate the proposed knowledge extraction process, we search for interdisciplinary science communities and then measure and rank their multidisciplinary effects. The analysis identifies discipline similarities, pinpointing the similarity between atmospheric science and meteorology as well as between geomorphology and oceanography. The results confirm that frequent itemset mining provides an informative sampled subsets of RDF databases which can be simultaneously analysed as layers of a multilayer network.
C1 [Honti, Gergely; Abonyi, Janos] Univ Pannonia, MTA PE Complex Syst Monitoring Res Grp, H-8200 Veszprem, Hungary.
RP Abonyi, J (通讯作者)，Univ Pannonia, MTA PE Complex Syst Monitoring Res Grp, H-8200 Veszprem, Hungary.
EM geri@honti.us; janos@abonyilab.com
RI Abonyi, Janos/O-2832-2013
OI Abonyi, Janos/0000-0001-8593-1493; Honti, Gergely
   Marcell/0000-0002-4893-1838
FU National Laboratory for Climate Change [NKFIH-872, TKP2020-NKA-10,
   2020-4.1.1-TKP2020]; National Research, Development and Innovation Fund
   of Hungary
FX This research was funded by the National Laboratory for Climate Change
   (NKFIH-872 project) and supported by the TKP2020-NKA-10 project financed
   under the 2020-4.1.1-TKP2020 Thematic Excellence Programme by the
   National Research, Development and Innovation Fund of Hungary.
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NR 56
TC 1
Z9 1
U1 1
U2 9
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD FEB
PY 2021
VL 9
IS 4
AR 450
DI 10.3390/math9040450
PG 17
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA QP9ZN
UT WOS:000624187000001
OA gold
DA 2022-08-23
ER

PT J
AU Lu, YQ
   Asghar, MR
AF Lu, Yuqian
   Asghar, Muhammad Rizwan
TI Semantic communications between distributed cyber-physical systems
   towards collaborative automation for smart manufacturing
SO JOURNAL OF MANUFACTURING SYSTEMS
LA English
DT Article
DE Machine-to-machine communication; Cyber-physical system; Smart
   manufacturing; Semantic web; Industrial Internet of Things (IIoT);
   Semantic-aware cyber-physical system; Knowledge graph
ID OPC-UA; INFORMATION MODEL; ONTOLOGY; WEB; METHODOLOGY; TOOLS
AB Machine-to-machine (M2M) communication is a crucial technology for collaborative manufacturing automation in the Industrial Internet of Things (IIoT)-empowered industrial networks. The new decentralized manufacturing automation paradigm features ubiquitous communication and interoperable interactions between machines. However, peer-to-peer (P2P) interoperable communications at the semantic level between industrial machines is a challenge. To address this challenge, we introduce a concept of Semantic-aware Cyber-Physical Systems (SCPSs) based on which manufacturing devices can establish semantic M2M communications. In this work, we propose a generic system architecture of SCPS and its enabling technologies. Our proposed system architecture adds a semantic layer and a communication layer to the conventional cyber-physical system (CPS) in order to maximize compatibility with the diverse CPS implementation architecture. With Semantic Web technologies as the backbone of the semantic layer, SCPSs can exchange semantic messages with maximum interoperability following the same understanding of the manufacturing context. A pilot implementation of the presented work is illustrated with a proof-of-concept case study between two semantic-aware cyber-physical machine tools. The semantic communication provided by the SCPS architecture makes ubiquitous M2M communication in a network of manufacturing devices environment possible, laying the foundation for collaborative manufacturing automation for achieving smart manufacturing. Another case study focusing on decentralized production control between machines in a workshop also proved the merits of semantic-aware M2M communication technologies.
C1 [Lu, Yuqian] Univ Auckland, Dept Mech Engn, Auckland, New Zealand.
   [Asghar, Muhammad Rizwan] Univ Auckland, Sch Comp Sci, Auckland, New Zealand.
RP Lu, YQ (通讯作者)，Univ Auckland, Dept Mech Engn, Auckland, New Zealand.
EM yuqian.lu@auckland.ac.nz; r.asghar@auckland.ac.nz
RI Lu, Yuqian/U-6820-2019
OI Lu, Yuqian/0000-0001-5954-0421
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NR 65
TC 21
Z9 24
U1 14
U2 55
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0278-6125
EI 1878-6642
J9 J MANUF SYST
JI J. Manuf. Syst.
PD APR
PY 2020
VL 55
BP 348
EP 359
DI 10.1016/j.jmsy.2020.05.001
PG 12
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA LZ3IL
UT WOS:000541121300027
DA 2022-08-23
ER

PT J
AU Bach, SH
   Broecheler, M
   Huang, B
   Getoor, L
AF Bach, Stephen H.
   Broecheler, Matthias
   Huang, Bert
   Getoor, Lise
TI Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
SO JOURNAL OF MACHINE LEARNING RESEARCH
LA English
DT Article
DE Probabilistic graphical models; statistical relational learning;
   structured prediction
ID APPROXIMATION ALGORITHMS; BELIEF NETWORKS; CLASSIFICATION; OPTIMIZATION;
   SEARCH; MAPS; SAT
AB A fundamental challenge in developing high-impact machine learning technologies is balancing the need to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured and large scale, from social and biological networks, to knowledge graphs and the Web, to images, video, and natural language. In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data. The first, hinge-loss Markov random fields (HL-MRFs), is a new kind of probabilistic graphical model that generalizes different approaches to convex inference. We unite three approaches from the randomized algorithms, probabilistic graphical models, and fuzzy logic communities, showing that all three lead to the same inference objective. We then define HL-MRFs by generalizing this unified objective. The second new formalism, probabilistic soft logic (PSL), is a probabilistic programming language that makes HL-MRFs easy to define using a syntax based on first-order logic. We introduce an algorithm for inferring most-probable variable assignments (MAP inference) that is much more scalable than general-purpose convex optimization methods, because it uses message passing to take advantage of sparse dependency structures. We then show how to learn the parameters of HL-MRFs. The learned HL-MRFs are as accurate as analogous discrete models, but much more scalable. Together, these algorithms enable HL-MRFs and PSL to model rich, structured data at scales not previously possible.
C1 [Bach, Stephen H.] Stanford Univ, Comp Sci Dept, Stanford, CA 94305 USA.
   [Broecheler, Matthias] DataStax, Santa Clara, CA USA.
   [Huang, Bert] Virginia Tech, Comp Sci Dept, Blacksburg, VA 24061 USA.
   [Getoor, Lise] Univ Calif Santa Cruz, Comp Sci Dept, Santa Cruz, CA 95064 USA.
RP Bach, SH (通讯作者)，Stanford Univ, Comp Sci Dept, Stanford, CA 94305 USA.
EM BACH@CS.STANFORD.EDU; MATTHIAS@DATASTAX.COM; BHUANG@VT.EDU;
   GETOOR@SOE.UCSC.EDU
FU IARPA via DoI/NBC [D12PC00337]; NSF [CCF0937094, IIS1218488]
FX We acknowledge the many people who have contributed to the development
   of HL-MRFs and PSL. Contributors include Eriq Augustine, Shobeir
   Fakhraei, James Foulds, Angelika Kimmig, Stanley Kok, Ben London, Hui
   Miao, Lilyana Mihalkova, Dianne P. O'Leary, Jay Pujara, Arti Ramesh,
   Theodoros Rekatsinas, and V.S. Subrahmanian. This work was supported by
   NSF grants CCF0937094 and IIS1218488, and IARPA via DoI/NBC contract
   number D12PC00337. The U.S. Government is authorized to reproduce and
   distribute reprints for governmental purposes notwithstanding any
   copyright annotation thereon.
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NR 141
TC 69
Z9 69
U1 0
U2 3
PU MICROTOME PUBL
PI BROOKLINE
PA 31 GIBBS ST, BROOKLINE, MA 02446 USA
SN 1532-4435
J9 J MACH LEARN RES
JI J. Mach. Learn. Res.
PY 2017
VL 18
AR 109
PG 67
WC Automation & Control Systems; Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA FV4IY
UT WOS:000424538000001
DA 2022-08-23
ER

PT J
AU Guo, XC
   Hao, X
   Tang, Z
   Diao, L
   Bai, Z
   Lu, SH
   Li, L
AF Guo, Xuchao
   Hao, Xia
   Tang, Zhan
   Diao, Lei
   Bai, Zhao
   Lu, Shuhan
   Li, Lin
TI ACE-ADP: Adversarial Contextual Embeddings Based Named Entity
   Recognition for Agricultural Diseases and Pests
SO AGRICULTURE-BASEL
LA English
DT Article
DE digital agriculture; Chinese agricultural diseases and pests; named
   entity recognition; adversarial training; semantic enhancement
AB Entity recognition tasks, which aim to utilize the deep learning-based models to identify the agricultural diseases and pests-related nouns such as the names of diseases, pests, and drugs from the texts collected on the internet or input by users, are a fundamental component for agricultural knowledge graph construction and question-answering, which will be implemented as a web application and provide the general public with solutions for agricultural diseases and pest control. Nonetheless, there are still challenges: (1) the polysemous problem needs to be further solved, (2) the quality of the text representation needs to be further enhanced, (3) the performance for rare entities needs to be further improved. We proposed an adversarial contextual embeddings-based model named ACE-ADP for named entity recognition in Chinese agricultural diseases and pests domain (CNER-ADP). First, we enhanced the text representation and overcame the polysemy problem by using the fine-tuned BERT model to generate the contextual character-level embedded representation with the specific knowledge. Second, adversarial training was also introduced to enhance the generalization and robustness in terms of identifying the rare entities. The experimental results showed that our model achieved an F-1 of 98.31% with 4.23% relative improvement compared to the baseline model (i.e., word2vec-based BiLSTM-CRF) on the self-annotated corpus named Chinese named entity recognition dataset for agricultural diseases and pests (AgCNER). Besides, the ablation study and discussion demonstrated that ACE-ADP could not only effectively extract rare entities but also maintain a powerful ability to predict new entities in new datasets with high accuracy. It could be used as a basis for further research on other domain-specific named entity recognition.</p>
C1 [Guo, Xuchao; Tang, Zhan; Diao, Lei; Bai, Zhao; Li, Lin] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China.
   [Hao, Xia] Shandong Agr Univ, Coll Informat Sci & Engn, Tai An 271000, Shandong, Peoples R China.
   [Lu, Shuhan] Univ Michigan, Sch Informat, Ann Arbor, MI 48104 USA.
RP Li, L (通讯作者)，China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China.
EM gxc@cau.edu.cn; haoxia@sdau.edu.cn; styx_tang@cau.edu.cn;
   S20193081368@cau.edu.cn; s20193081367@cau.edu.cn; shuhanlu@umich.edu;
   lilinlsl@cau.edu.cn
OI Guo, Xuchao/0000-0002-6457-0534
FU National Key Research and Development Program [2016YFD0300710]
FX FundingThis research was funded by the National Key Research and
   Development Program, grant number 2016YFD0300710.
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NR 57
TC 0
Z9 0
U1 16
U2 25
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2077-0472
J9 AGRICULTURE-BASEL
JI Agriculture-Basel
PD OCT
PY 2021
VL 11
IS 10
AR 912
DI 10.3390/agriculture11100912
PG 20
WC Agronomy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture
GA WO8HR
UT WOS:000712689000001
OA gold
DA 2022-08-23
ER

PT J
AU Hofmeister, M
   Mosbach, S
   Hammacher, J
   Blum, M
   Rohrig, G
   Dorr, C
   Flegel, V
   Bhave, A
   Kraft, M
AF Hofmeister, Markus
   Mosbach, Sebastian
   Hammacher, Jorg
   Blum, Martin
   Roehrig, Gerd
   Doerr, Christoph
   Flegel, Volker
   Bhave, Amit
   Kraft, Markus
TI Resource-optimised generation dispatch strategy for district heating
   systems using dynamic hierarchical optimisation
SO APPLIED ENERGY
LA English
DT Article
DE District heating; Generation optimisation; Load dispatch; Model
   predictive control; Load forecasting
ID PREDICTIVE CONTROL; OPTIMAL OPERATION; NEURAL-NETWORKS; MODEL; WEB
AB District heating is expected to play an essential role in the cost-effective decarbonisation strategy of many countries. Resource-optimised management of district heating networks depends on a wide range of factors, including demand forecasting, operational flexibility, and increasingly volatile market conditions. However, traditional operations often still rely on static models and rather simple heuristics, while holistic optimisation requires dynamic cross-domain interoperability to allow the consideration of all these factors. This paper demonstrates a proof-of-concept for a knowledge graph based optimisation problem to minimise total heat generation cost for a district heating provider. The optimisation follows a hierarchical approach based on a merit-order principle and is embedded in a model predictive control framework to allow the system to incorporate most recent information and react to disturbances promptly. A detailed sensitivity study is conducted to identify key model parameters and assess the impact of anticipated changes in regulation and market conditions. Simulation-based optimisation is used to determine the short-term heat generation mix based on data-driven gas consumption models and day-ahead forecasts for the network's energy demand and grid temperatures. Seasonal autoregressive integrated moving average models with exogenous predictor variables are found to be sufficiently accurate and precise. The effectiveness of the approach is demonstrated for a case study of an existing heating network of a midsize town in Germany, where a reduction of approximately 20% and 40% compared to baseline operational data is obtained for operating cost and CO2 emissions, respectively.
C1 [Hofmeister, Markus; Mosbach, Sebastian; Kraft, Markus] Univ Cambridge, Dept Chem Engn & Biotechnol, West Cambridge Site,Philippa Fawcett Dr, Cambridge CB3 0AS, England.
   [Mosbach, Sebastian; Kraft, Markus] Cambridge Ctr Adv Res & Educ Singapore, CARES, 1 Create Way,CREATE Tower 05-05, Singapore 138602, Singapore.
   [Kraft, Markus] Nanyang Technol Univ, Sch Chem & Biomed Engn, 62 Nanyang Dr, Singapore 637459, Singapore.
   [Hofmeister, Markus; Mosbach, Sebastian; Bhave, Amit; Kraft, Markus] CMCL Innovat, Sheraton House, Cambridge CB3 0AX, England.
   [Hammacher, Jorg; Blum, Martin; Roehrig, Gerd; Doerr, Christoph] Stadtwerke Pirmasens Versorgungs GmbH, Streckbrucke 4, D-66954 Pirmasens, Germany.
   [Flegel, Volker] Celron GmbH, Ludwigstr 8, D-80539 Munich, Germany.
RP Kraft, M (通讯作者)，Univ Cambridge, Dept Chem Engn & Biotechnol, West Cambridge Site,Philippa Fawcett Dr, Cambridge CB3 0AS, England.
EM mk306@cam.ac.uk
RI Kraft, Markus/D-7243-2016
OI Kraft, Markus/0000-0002-4293-8924
FU National Research Foundation, Prime Minister's Office, Singapore under
   its Campus for Research Excellence and Technological Enterprise (CREATE)
   programme; Cambridge Trust; CMCL; 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) programme. M. Hofmeister
   acknowledges financial support provided by the Cambridge Trust and CMCL.
   M. Kraft gratefully acknowledges the support of the Alexander von
   Humboldt Foundation.
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NR 59
TC 0
Z9 0
U1 4
U2 12
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-2619
EI 1872-9118
J9 APPL ENERG
JI Appl. Energy
PD JAN 1
PY 2022
VL 305
AR 117877
DI 10.1016/j.apenergy.2021.117877
EA SEP 2021
PG 20
WC Energy & Fuels; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels; Engineering
GA UY9ZS
UT WOS:000701873700006
DA 2022-08-23
ER

PT J
AU Wang, HB
   Jiang, SC
   Yu, ZT
AF Wang, Hongbin
   Jiang, Shengchen
   Yu, Zhengtao
TI Modeling of complex internal logic for knowledge base completion
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Knowledge base completion; Semantic gap; Knowledge subgraph; Multi-hop
   attention mechanism; Reinforcement learning
AB Knowledge base completion has been an active research topic for knowledge graph. However, existing methods are of low learning and generalization abilities, and neglect the rich internal logic between entities and relationships. To solve the above problems, this paper proposes the modeling of complex internal logic for knowledge base completion. This method first integrates the semantic information into the knowledge representation model and strengthens the credibility scores of the positive and negative triples with the semantic gap, which not only makes the model converge faster, but also can obtain the knowledge representation of the fusion semantic information; and then we put forward the concept of knowledge subgraph, through the memory network and multi-hop attention mechanism, the knowledge information in the knowledge subgraph and the to-be-complemented triple are merged. In the process of model training, we have different training methods from the classical memory network, and added reinforcement learning. The reciprocal of the correct reasoning knowledge information in the model output is used as the reward value, and the final training model complements the triple information. The high computing capability of knowledge representation, the high learning and generalization abilities of the memory network and the multi-hop attention mechanism are also utilized in the method. The experimental results on data sets FB15k and WN18 show that the present method performs well in knowledge base completion and can effectively improve Hits@10 and MRR values. We also verified the practicability of the proposed method in the recommendation system and question answering system base on knowledge base, and have achieved good results.
C1 [Wang, Hongbin; Jiang, Shengchen; Yu, Zhengtao] Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming 650500, Yunnan, Peoples R China.
   [Wang, Hongbin; Jiang, Shengchen; Yu, Zhengtao] Kunming Univ Sci & Technol, Yunnan Key Lab Artificial Intelligence, Kunming 650500, Yunnan, Peoples R China.
RP Yu, ZT (通讯作者)，Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming 650500, Yunnan, Peoples R China.; Yu, ZT (通讯作者)，Kunming Univ Sci & Technol, Yunnan Key Lab Artificial Intelligence, Kunming 650500, Yunnan, Peoples R China.
EM whbin2007@126.com; 2287453848@qq.com; ztyu@hotmail.com
FU National Key R&D Program of China [2018YFB1402900]; Nature and Science
   Foundation of China [61966020]
FX This work was supported by the National Key R&D Program of China
   (2018YFB1402900) and the Nature and Science Foundation of China
   (61966020).
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NR 27
TC 5
Z9 6
U1 3
U2 18
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
PD OCT
PY 2020
VL 50
IS 10
BP 3336
EP 3349
DI 10.1007/s10489-020-01734-z
EA JUN 2020
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NI2ZM
UT WOS:000538239800002
DA 2022-08-23
ER

PT J
AU Liu, Y
   Zhang, Y
   Hu, HD
   Liu, XD
   Zhang, L
   Liu, RJ
AF Liu, Yi
   Zhang, Yue
   Hu, Haidong
   Liu, Xiaodong
   Zhang, Lun
   Liu, Ruijun
TI An Extended Text Combination Classification Model for Short Video Based
   on Albert
SO JOURNAL OF SENSORS
LA English
DT Article
AB With the rise and rapid development of short video sharing websites, the number of short videos on the Internet has been growing explosively. The organization and classification of short videos have become the basis for the effective use of short videos, which is also a problem faced by major short video platforms. Aiming at the characteristics of complex short video content categories and rich extended text information, this paper uses methods in the text classification field to solve the short video classification problem. Compared with the traditional way of classifying and understanding short video key frames, this method has the characteristics of lower computational cost, more accurate classification results, and easier application. This paper proposes a text classification model based on the attention mechanism of multitext embedding short video extension. The experiment first uses the training language model Albert to extract sentence-level vectors and then uses the attention mechanism to study the text information in various short video extensions in a short video classification weight factor. And this research applied Google's unsupervised data augmentation (UDA) method based on unsupervised data, creatively combining it with the Chinese knowledge graph, and realized TF-IDF word replacement. During the training process, we introduced a large amount of unlabeled data, which significantly improved the accuracy of model classification. The final series of related experiments is aimed at comparing with the existing short video title classification methods, classification methods based on video key frames, and hybrid methods, and proving that the method proposed in this article is more accurate and robust on the test set.
C1 [Liu, Yi; Liu, Xiaodong] Beijing Res Inst Automat Machinery Ind Co Ltd, Beijing, Peoples R China.
   [Zhang, Yue; Hu, Haidong; Zhang, Lun; Liu, Ruijun] Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing, Peoples R China.
RP Liu, RJ (通讯作者)，Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing, Peoples R China.
EM liuruijun@btbu.edu.cn
OI Liu, Ruijun/0000-0003-4871-8989
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NR 19
TC 1
Z9 1
U1 9
U2 9
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-725X
EI 1687-7268
J9 J SENSORS
JI J. Sens.
PD OCT 16
PY 2021
VL 2021
AR 8013337
DI 10.1155/2021/8013337
PG 7
WC Engineering, Electrical & Electronic; Instruments & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Instruments & Instrumentation
GA ZK5YK
UT WOS:000763063700003
OA gold
DA 2022-08-23
ER

PT J
AU Yu, H
   Dong, WM
   Shi, JY
AF Yu, Hui
   Dong, WenMin
   Shi, JianYu
TI RANEDDI: Relation-aware network embedding for drug-drug interaction
   prediction
SO INFORMATION SCIENCES
LA English
DT Article
DE Network embedding; Bioinformatics; Drug-drug interactions;
   Relation-aware learning; Prediction
ID KNOWLEDGE GRAPH
AB Many embedding approaches of drugs have been proposed for the downstream task of drug-drug interaction (DDI) prediction in a DDI-derived network where drugs are considered nodes, and interactions are represented as edges. One of the most popular approaches is learning the representation of a drug from the DDI network by aggregating the features or information of its neighboring drugs. However, existing methods do not consider the specific type of the relation between the drugs, leading to an incomplete embedding learning process. Given that different relations between drugs may have different effects on drug embedding, the combination of multirelational embedding and relation-aware network structure embedding of drugs can be helpful to improve the prediction of DDIs. Therefore, in this paper, a relation-aware network embedding model for the prediction of drug-drug interactions (RANEDDI) is proposed. RANEDDI not only considers the multirelational information between drugs but also integrates the relation-aware network structure information in the topology of a multirelational DDI network to obtain the drug embedding. Under evaluation metrics such as AUC, AUPR and F1, the experimental results show that RANEDDI is superior to several state-of-the-art methods and can be used in the prediction of binary and multirelational DDIs. We also perform ablation studies that demonstrate that RANEDDI is effective and that it is robust in the task of binary DDI prediction, even in the case of a scarcity of labeled DDIs. The source code is freely available at https://github.com/DongWenMin/RANEDDI. (c) 2021 Elsevier Inc. All rights reserved.
C1 [Yu, Hui; Dong, WenMin] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.
   [Shi, JianYu] Northwestern Polytech Univ, Sch Life Sci, Xian 710072, Peoples R China.
RP Yu, H (通讯作者)，Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.; Shi, JY (通讯作者)，Northwestern Polytech Univ, Sch Life Sci, Xian 710072, Peoples R China.
RI Shi, Jian-Yu/S-4485-2019; Yu, Hui/ADG-1933-2022
OI Shi, Jian-Yu/0000-0002-2303-273X; Yu, Hui/0000-0002-5314-8127
FU National Nature Science Foundation of China [61872297]; Shaanxi
   Provincial Key Research & Development Program, China [2020KW-063]
FX This work was supported by National Nature Science Foundation of China
   (Grant No. 61872297) and Shaanxi Provincial Key Research & Development
   Program, China (Grand No. 2020KW-063) . The authors would like to thank
   editors and the anonymous reviewers for their valuable comments and
   suggestions, and Arnold K. Nyamabo for article polishing.
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TC 2
Z9 2
U1 8
U2 21
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD JAN
PY 2022
VL 582
BP 167
EP 180
DI 10.1016/j.ins.2021.09.008
EA SEP 2021
PG 14
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UY0HG
UT WOS:000701214100008
DA 2022-08-23
ER

PT J
AU Cirillo, F
   Wu, FJ
   Solmaz, G
   Kovacs, E
AF Cirillo, Flavio
   Wu, Fang-Jing
   Solmaz, Guerkan
   Kovacs, Ernoe
TI Embracing the Future Internet of Things
SO SENSORS
LA English
DT Article
DE Internet-of-Things; smart cities; hyper-connected IoT; context
   management; linked data; semantic interoperability; knowledge graph
AB All of the objects in the real world are envisioned to be connected and/or represented, through an infrastructure layer, in the virtual world of the Internet, becoming Things with status information. Services are then using the available data from this Internet-of-Things (IoT) for various social and economical benefits which explain its extreme broad usage in very heterogeneous fields. Domain administrations of diverse areas of application developed and deployed their own IoT systems and services following disparate standards and architecture approaches that created a fragmentation of things, infrastructures and services in vertical IoT silos. Coordination and cooperation among IoT systems are the keys to build smarter IoT services boosting the benefits magnitude. This article analyses the technical trends of the future IoT world based on the current limitations of the IoT systems and the capability requirements. We propose a hyper-connected IoT framework in which things are connected to multiple interdependent services and describe how this framework enables the development of future applications. Moreover, we discuss the major limitations in today's IoT and highlight the required capabilities in the future. We illustrate this global vision with the help of two concrete instances of the hyper-connected IoT in smart cities and autonomous driving scenarios. Finally, we analyse the trends in the number of connected things and point out open issues and future challenges. The proposed hyper-connected IoT framework is meant to scale the benefits of IoT from local to global.
C1 [Cirillo, Flavio; Solmaz, Guerkan; Kovacs, Ernoe] NEC Labs Europe, D-69115 Heidelberg, Germany.
   [Cirillo, Flavio] Univ Naples Federico II, Dept Elect Engn & Informat Technol, I-80138 Naples, Italy.
   [Wu, Fang-Jing] TU Dortmund Univ, Dept Elect Engn & Informat Technol, D-44227 Dortmund, Germany.
RP Cirillo, F; Solmaz, G (通讯作者)，NEC Labs Europe, D-69115 Heidelberg, Germany.; Cirillo, F (通讯作者)，Univ Naples Federico II, Dept Elect Engn & Informat Technol, I-80138 Naples, Italy.; Wu, FJ (通讯作者)，TU Dortmund Univ, Dept Elect Engn & Informat Technol, D-44227 Dortmund, Germany.
EM flavio.cirillo@neclab.eu; fang-jing.wu@tu-dortmund.de;
   gurkan.solmaz@neclab.eu; ernoe.kovacs@neclab.eu
RI Cirillo, Flavio/ABG-8419-2020
FU European Union [731993]; NEC Laboratories Europe GmbH Heidelberg,
   Germany; Technische Universitat Dortmund, Germany
FX This work was funded by the joint project collaborations between NEC
   Laboratories Europe GmbH Heidelberg, Germany and Technische Universitat
   Dortmund, Germany, and has been partially funded by the European Union's
   Horizon 2020 Programme under Grant Agreement No. 731993 AUTOPILOT:
   AUTOmated driving Progressed by the Internet Of Things. The content of
   this paper does not reflect the official opinion of the European Union.
   Responsibility for the information and views expressed therein lies
   entirely with the authors.
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NR 50
TC 11
Z9 11
U1 4
U2 24
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD JAN 2
PY 2019
VL 19
IS 2
AR 351
DI 10.3390/s19020351
PG 20
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA HL2WI
UT WOS:000458569300134
PM 30654571
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Saloot, MA
   Idris, N
   Mahmud, R
   Ja'afar, S
   Thorleuchter, D
   Gani, A
AF Saloot, Mohammad Arshi
   Idris, Norisma
   Mahmud, Rohana
   Ja'afar, Salinah
   Thorleuchter, Dirk
   Gani, Abdullah
TI Hadith data mining and classification: a comparative analysis
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE Review; Comparison; Islamic knowledge; Hadith; Classification; Data
   mining
AB Hadiths are important textual sources of law, tradition, and teaching in the Islamic world. Analyzing the unique linguistic features of Hadiths (e.g. ancient Arabic language and story-like text) results to compile and utilize specific natural language processing methods. In the literature, no study is solely focused on Hadith from artificial intelligence perspective, while many new developments have been overlooked and need to be highlighted. Therefore, this review analyze all academic journal and conference publications that using two main methods of artificial intelligence for Hadith text: Hadith classification and mining. All Hadith relevant methods and algorithms from the literature are discussed and analyzed in terms of functionality, simplicity, F-score and accuracy. Using various different Hadith datasets makes a direct comparison between the evaluation results impossible. Therefore, we have re-implemented and evaluated the methods using a single dataset (i.e. 3150 Hadiths from Sahih Al-Bukhari book). The result of evaluation on the classification method reveals that neural networks classify the Hadith with 94% accuracy. This is because neural networks are capable of handling complex (high dimensional) input data. The Hadith mining method that combines vector space model, Cosine similarity, and enriched queries obtains the best accuracy result (i.e. 88%) among other re-evaluated Hadith mining methods. The most important aspect in Hadith mining methods is query expansion since the query must be fitted to the Hadith lingo. The lack of knowledge based methods is evident in Hadith classification and mining approaches and this absence can be covered in future works using knowledge graphs.
C1 [Saloot, Mohammad Arshi; Idris, Norisma; Mahmud, Rohana; Ja'afar, Salinah; Gani, Abdullah] Univ Malaya, Kuala Lumpur 50603, Malaysia.
   [Thorleuchter, Dirk] Inst Fraunhofer INT, Appelsgarten 2, D-53879 Euskirchen, Germany.
RP Saloot, MA (通讯作者)，Univ Malaya, Kuala Lumpur 50603, Malaysia.
EM phd_siamak@yahoo.com; norisma@um.edu.my; rohanamahmud@um.edu.my;
   b1salina@um.edu.my; dirk.thorleuchter@int.fraunhofer.de;
   abdullah@um.edu.my
RI Gani, Abdullah/C-2888-2009; Mahmud, Rohana/B-9588-2010; IDRIS,
   NORISMA/B-9373-2010; Ja'afar, Salinah/B-3138-2010
OI Gani, Abdullah/0000-0002-4388-020X; Mahmud, Rohana/0000-0002-8286-3891;
   Ja'afar, Salinah/0000-0002-1369-094X
FU University of Malaya UMRG Grant [RP003C-14HNE]
FX The research for this paper was financially supported by the University
   of Malaya UMRG Grant (RP003C-14HNE).
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BP 113
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GA DQ2IP
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DA 2022-08-23
ER

PT J
AU Kojima, R
   Sato, T
AF Kojima, Ryosuke
   Sato, Taisuke
TI Learning to rank in PRISM
SO INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
LA English
DT Article
DE PRISM; Probabilistic logic programming language; Learning to rank;
   Ranking; Preference learning
AB Learning parameters associated with propositions is one of the main tasks of probabilistic logic programming (PLP), and learning algorithms for PLP have been primarily developed based on maximum likelihood estimation or the optimization of discriminative criteria. This paper explores yet another innovative approach to parameter learning, learning to rank or rank learning, that has been studied mainly in the field of preference learning. We combine learning to rank with techniques developed in PLP to make the latter applicable to a variety of ranking problems such as information retrieval. We implement our approach in PRISM, a PLP system based on the distribution semantics. It supports many parameter learning algorithms such as the expectation maximization algorithm, the variational Bayes algorithm and an algorithm for Viterbi training efficiently by mapping them onto a single data structure called explanation graph. To ensure the same efficiency for parameter learning by learning to rank as in the current PRISM, we introduce a gradient-based learning method that takes advantage of dynamic programming on the explanation graph. This paper also presents three experimental results. The first one is with synthetic data to check the learning behaviors of the proposed approach. The second one uses a knowledge base (knowledge graph) and apply rank learning to a DistMult model for the task of deciding whether relations over entities exist or not. The last one tackles the problem of parsing by a probabilistic context free grammar whose parameters are learned from a tree corpus by rank learning. These experiments successfully demonstrated the potential and effectiveness of learning to rank in PLP. We plan to release a new version of PRISM augmented with the ability of learning to rank in the near future. (C) 2017 Elsevier Inc. All rights reserved.
C1 [Kojima, Ryosuke] Kyoto Univ, Grad Sch Med, Dept Biomed Data Intelligence, Kyoto, Japan.
   [Sato, Taisuke] Natl Inst Adv Ind Sci & Technol, AI Res Ctr AIRC, Tokyo, Japan.
RP Kojima, R (通讯作者)，Kyoto Univ, Grad Sch Med, Dept Biomed Data Intelligence, Kyoto, Japan.
EM kojima.ryosuke.8e@kyoto-u.ac.jp
RI Sato, Taisuke/Q-3245-2016
OI Sato, Taisuke/0000-0001-9062-0729
FU New Energy and Industrial Technology Development Organization (NEDO)
FX We would like to thank Prof. Naoaki Okazaki for data conversion of
   FB15K. This research is supported in part by a project commissioned by
   the New Energy and Industrial Technology Development Organization
   (NEDO).
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NR 35
TC 0
Z9 0
U1 0
U2 2
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0888-613X
EI 1873-4731
J9 INT J APPROX REASON
JI Int. J. Approx. Reasoning
PD FEB
PY 2018
VL 93
BP 561
EP 577
DI 10.1016/j.ijar.2017.11.011
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FV6VY
UT WOS:000424721400028
OA Bronze
DA 2022-08-23
ER

PT J
AU Senderov, V
   Simov, K
   Franz, N
   Stoev, P
   Catapano, T
   Agosti, D
   Sautter, G
   Morris, RA
   Penev, L
AF Senderov, Viktor
   Simov, Kiril
   Franz, Nico
   Stoev, Pavel
   Catapano, Terry
   Agosti, Donat
   Sautter, Guido
   Morris, Robert A.
   Penev, Lyubomir
TI OpenBiodiv-O: ontology of the OpenBiodiv knowledge management system
SO JOURNAL OF BIOMEDICAL SEMANTICS
LA English
DT Article
DE Biodiversity; Biodiversity informatics; Semantic web; Semantic
   publishing; Ontology; Knowledge management; Linked open data; RDF; OWL;
   Taxonomy; Concept taxonomy; Biological systematics; Data modeling
ID TAXONOMY; NAMES; TAXA; BIG
AB Background: The biodiversity domain, and in particular biological taxonomy, is moving in the direction of semantization of its research outputs. The present work introduces OpenBiodiv-O, the ontology that serves as the basis of the OpenBiodiv Knowledge Management System. Our intent is to provide an ontology that fills the gaps between ontologies for biodiversity resources, such as DarwinCore-based ontologies, and semantic publishing ontologies, such as the SPAR Ontologies. We bridge this gap by providing an ontology focusing on biological taxonomy.
   Results: OpenBiodiv-O introduces classes, properties, and axioms in the domains of scholarly biodiversity publishing and biological taxonomy and aligns them with several important domain ontologies (FaBiO, DoCO, DwC, Darwin-SW, NOMEN, ENVO). By doing so, it bridges the ontological gap across scholarly biodiversity publishing and biological taxonomy and allows for the creation of a Linked Open Dataset (LOD) of biodiversity information (a biodiversity knowledge graph) and enables the creation of the OpenBiodiv Knowledge Management System. A key feature of the ontology is that it is an ontology of the scientific process of biological taxonomy and not of any particular state of knowledge. This feature allows it to express a multiplicity of scientific opinions. The resulting OpenBiodiv knowledge system may gain a high level of trust in the scientific community as it does not force a scientific opinion on its users (e.g. practicing taxonomists, library researchers, etc.), but rather provides the tools for experts to encode different views as science progresses.
   Conclusions: OpenBiodiv-O provides a conceptual model of the structure of a biodiversity publication and the development of related taxonomic concepts. It also serves as the basis for the OpenBiodiv Knowledge Management System.
C1 [Senderov, Viktor; Stoev, Pavel; Penev, Lyubomir] Pensoft Publishers, Prof Georgi Zlatarski 12, Sofia 1700, Bulgaria.
   [Senderov, Viktor; Penev, Lyubomir] Bulgarian Acad Sci, Inst Biodivers & Ecosyst Res, Sofia, Bulgaria.
   [Simov, Kiril] Bulgarian Acad Sci, Inst Informat & Commun Technol, Sofia, Bulgaria.
   [Franz, Nico] Arizona State Univ, Sch Life Sci, Tempe Campus 4501, Tempe, AZ USA.
   [Catapano, Terry; Agosti, Donat; Sautter, Guido] Plazi, Bern, Switzerland.
   [Morris, Robert A.] Univ Massachusetts, Boston, MA 02125 USA.
   [Stoev, Pavel] Natl Museum Nat Hist, 1 Tsar Osvoboditel Blvd, Sofia 1000, Bulgaria.
RP Senderov, V (通讯作者)，Pensoft Publishers, Prof Georgi Zlatarski 12, Sofia 1700, Bulgaria.; Senderov, V (通讯作者)，Bulgarian Acad Sci, Inst Biodivers & Ecosyst Res, Sofia, Bulgaria.
EM vsenderov@gmail.com
RI Penev, Lyubomir/O-9982-2019; Stoev, Pavel/H-5112-2019; Simov,
   Kiril/AAB-3393-2019; Agosti, Donat/M-3468-2018
OI Stoev, Pavel/0000-0002-5702-5677; Agosti, Donat/0000-0001-9286-1200;
   Senderov, Viktor/0000-0003-3340-5963; Catapano,
   Terry/0000-0002-6857-0021
FU European Union's Horizon research and innovation program under the Marie
   Sklodowska-Curie grant [642241]
FX Research financed through the European Union's Horizon 2020 research and
   innovation program under the Marie Sklodowska-Curie grant agreement No.
   642241.
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NR 66
TC 17
Z9 17
U1 0
U2 17
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 JAN 18
PY 2018
VL 9
AR 5
DI 10.1186/s13326-017-0174-5
PG 15
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA FT4BB
UT WOS:000423095700001
PM 29347997
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 Graphical Congestion Games
SO ALGORITHMICA
LA English
DT Article
DE Algorithmic game theory; Nash equilibrium; Price of anarchy; Price of
   stability; Congestion games; Social knowledge
ID SELFISH; EQUILIBRIA; INEFFICIENCY; ANARCHY; PRICE
AB We consider congestion games with linear latency functions in which each player is aware only of a subset of all the other players. This is modeled by means of a social knowledge graph G in which nodes represent players and there is an edge from i to j if i knows j. Under the assumption that the payoff of each player is affected only by the strategies of the adjacent ones, we first give a complete characterization of the games possessing pure Nash equilibria. Namely, if the social graph G is undirected, the game is an exact potential game and thus isomorphic to a classical congestion game. As a consequence, it always converges and possesses Nash equilibria. On the other hand, if G is directed an equilibrium is not guaranteed to exist, but the game is always convergent and an equilibrium can be found in polynomial time if G is acyclic, even if finding the best equilibrium remains an intractable problem.
   We then investigate the impact of the limited knowledge of the players on the performance of the game. More precisely, given a bound on the maximum degree of G, for the convergent cases we provide tight lower and upper bounds on the price of stability and asymptotically tight bounds on the price of anarchy. Such results are determined for four natural social cost functions: total and maximum presumed latencies, that is the ones the players believe to pay due to the fact that they are only aware of the existence of their neighbors, and total and maximum perceived latencies, i.e. actually experienced due to all (and not only the known) players using the same facilities.
   All the results are then extended to singleton congestion games.
C1 [Moscardelli, Luca] Univ G dAnnunzio, Dipartimento Sci, I-65127 Pescara, Italy.
   [Bilo, Vittorio] Univ Salento, Dipartimento Matemat, I-73100 Lecce, Italy.
   [Fanelli, Angelo] Nanyang Technol Univ, Div Math Sci, Sch Phys & Math Sci, Singapore, Singapore.
   [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; angelo.fanelli@ntu.edu.sg;
   flammini@di.univaq.it; moscardelli@sci.unich.it
RI Bilò, Vittorio/P-1913-2014
OI Fanelli, Angelo/0000-0002-4896-6889; FLAMMINI,
   Michele/0000-0003-0327-3728; BILO', Vittorio/0000-0001-7848-4904
FU Italian Ministry of University and Research [NRF-RF2009-08]
FX This research was partially supported by the grant NRF-RF2009-08
   "Algorithmic aspects of coalitional games" and by the PRIN 2008 research
   project "COGENT COmputational and GamE-theoretic aspects of
   uncoordinated NeTworks", funded by the Italian Ministry of University
   and Research.
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NR 40
TC 17
Z9 17
U1 0
U2 6
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0178-4617
EI 1432-0541
J9 ALGORITHMICA
JI Algorithmica
PD OCT
PY 2011
VL 61
IS 2
BP 274
EP 297
DI 10.1007/s00453-010-9417-x
PG 24
WC Computer Science, Software Engineering; Mathematics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Mathematics
GA 798TJ
UT WOS:000293234800003
DA 2022-08-23
ER

PT J
AU Xu, J
   Lei, ZY
   Wang, HF
   Niu, ZY
   Wu, H
   Che, WX
   Huang, JZ
   Liu, T
AF Xu, Jun
   Lei, Zeyang
   Wang, Haifeng
   Niu, Zheng-Yu
   Wu, Hua
   Che, Wanxiang
   Huang, Jizhou
   Liu, Ting
TI Coherent Dialog Generation with Query Graph
SO ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION
   PROCESSING
LA English
DT Article
DE Open-domain dialog policy; hierarchical knowledge sequence planning;
   query graph; heterogeneous graph neural network and reinforcement
   learning
AB Learning to generate coherent and informative dialogs is an enduring challenge for open-domain conversation generation. Previous work leverage knowledge graph or documents to facilitate informative dialog generation, with little attention on dialog coherence. In this article, to enhance multi-turn open-domain dialog coherence, we propose to leverage a new knowledge source, web search session data, to facilitate hierarchical knowledge sequence planning, which determines a sketch of a multi-turn dialog. Specifically, we formulate knowledge sequence planning or dialog policy learning as a graph grounded Reinforcement Learning (RL) problem. To this end, we first build a two-level query graph with queries as utterance-level vertices and their topics (entities in queries) as topic-level vertices. We then present a two-level dialog policy model that plans a high-level topic sequence and a low-level query sequence over the query graph to guide a knowledge aware response generator. In particular, to foster forward-looking knowledge planning decisions for better dialog coherence, we devise a heterogeneous graph neural network to incorporate neighbouring vertex information, or possible future RL action information, into each vertex (as an RL action) representation. Experiment results on two benchmark dialog datasets demonstrate that our framework can outperform strong baselines in terms of dialog coherence, informativeness, and engagingness.
C1 [Xu, Jun; Che, Wanxiang; Liu, Ting] Harbin Inst Technol, 92 West Dazhi St, Harbin 150001, Heilongjiang, Peoples R China.
   [Lei, Zeyang; Wang, Haifeng; Niu, Zheng-Yu; Wu, Hua; Huang, Jizhou] Baidu Inc, 10 Xibeiwang East Rd, Beijing 100193, Peoples R China.
RP Che, WX (通讯作者)，Harbin Inst Technol, 92 West Dazhi St, Harbin 150001, Heilongjiang, Peoples R China.
EM jxu@ir.hit.edu.cn; leizeyang@baidu.com; wanghaifeng@baidu.com;
   niuzhengyu@baidu.com; wu_hua@baidu.com; car@ir.hit.edu.cn;
   huangjizhou01@baidu.com; tliu@ir.hit.edu.cn
OI Huang, Jizhou/0000-0003-1022-0309
CR Asghar Nabiha, 2016, ARXIV1612039293
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NR 79
TC 0
Z9 0
U1 1
U2 2
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 NOV
PY 2021
VL 20
IS 6
AR 101
DI 10.1145/3462551
PG 23
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XB8QE
UT WOS:000721586800010
DA 2022-08-23
ER

PT J
AU Fang, Y
   Zhao, X
   Tan, Z
   Xiao, WD
AF Fang, Yang
   Zhao, Xiang
   Tan, Zhen
   Xiao, Weidong
TI Relational Knowledge Prediction via Dynamic Bi-Mode Embedding
SO IEEE ACCESS
LA English
DT Article
DE Knowledge embedding; representation learning
AB Knowledge graphs are a crucial concept in artificial intelligence with a wide spectrum of real-life applications. Nonetheless, they are currently suffering from the incompleteness issue, i.e., relational knowledge in the graphs may not yet meet the practical needs. To address this issue, mainstream solutions propose to predict links by using compositional models or translation models. However, the prediction accuracy is still of particular concern. In this paper, we propose a new method, namely, Bi-Mult, which combines the advantages of compositional models and translation models. Bi-Mult is based on the compositional model, such that an entity (resp. relation) embedding is decomposed into two parts, one is to represent intraentity (resp. relation) state and the other is for inter-entity (resp. relation) state, and we call such embedding as bi-mode embedding. In addition, the bi-mode relation embedding enhances relation's interaction with entities, resulting its improvement on handling antisymmetric relations. Moreover, we incorporate mapping matrices in translation models through bi-mode entity embedding to construct dynamic embeddings for expressing complex relations, such as "1-to-N", "N-to-1," and "N-to-N" relations. In experiments, we evaluate our method on the benchmark data sets and the task of link prediction, and our method is demonstrated to outperform state-of-the-art methods consistently and significantly.
C1 [Fang, Yang; Zhao, Xiang; Tan, Zhen; Xiao, Weidong] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.
   [Zhao, Xiang; Xiao, Weidong] Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.
RP Zhao, X (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China.; Zhao, X (通讯作者)，Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.
EM xiangzhao@nudt.edu.cn
RI Zhao, Xiang/B-1303-2010
OI Zhao, Xiang/0000-0001-6339-0219
FU NSFC [71331008, 71690233]
FX This work was supported by NSFC under Grant 71331008 and Grant 71690233.
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NR 19
TC 0
Z9 0
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 2018
VL 6
BP 25715
EP 25723
DI 10.1109/ACCESS.2018.2832165
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA GI8WE
UT WOS:000434805700001
OA gold
DA 2022-08-23
ER

PT J
AU Sun, TT
   Zhang, CH
   Ji, Y
   Hu, Z
AF Sun, Tingting
   Zhang, Chunhong
   Ji, Yang
   Hu, Zheng
TI MSnet: Multi-Head Self-Attention Network for Distantly Supervised
   Relation Extraction
SO IEEE ACCESS
LA English
DT Article
DE Relation extraction; distant supervision; multi-head self-attention;
   label denoising
AB Distant supervision for relation extraction is a task of recognizing semantic relations between entities in a large amount of plain text weakly supervised by external knowledge bases, which can benefit many NLP applications, such as knowledge graph completion and question answering. While it significantly alleviates the expensive cost for data labeling, it severely suffers from noisy labels. In this paper, we propose a Multi-head Self-attention Network (MSNet)-based label denoising method for relation extraction. More specifically, we encode the words, entities and their positions information into contextual embeddings via a multi-head self-attention mechanism, then extract the discriminative sentence features with max pooling operation. MSNet can capture the inherent structure of a sentence and model the relatedness between two words without regard to their distance. Moreover, we adopt a novel label confidence learning method to correct the noisy labels. A latent label is predicted step by step during training as the ground-truth according to a curriculum function of label confidence. This label denoising mechanism gradually incorporates the obtained latent label of easy relation patterns into later latent label prediction of hard patterns, which makes latent label consistent learning more reliable. To verify the effectiveness of our proposed method, in addition to the widely used PCNN-based architecture, we also perform the experiment on BiLSTM model as a comparison. The results demonstrate that our approach can outperform the state-of-the-art systems on the popular evaluation dataset.
C1 [Sun, Tingting; Zhang, Chunhong; Ji, Yang; Hu, Zheng] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China.
RP Zhang, CH (通讯作者)，Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China.
EM zhangch@bupt.edu.cn
OI Sun, Tingting/0000-0002-7383-1847
FU National Natural Science Foundation of China [61421061, 61602048,
   61601046, 61520106007]; Project of Hainan Passenger Behavior
   Intelligence Analysis Platform and Precise Service Mining Prediction
   [ZDKJ201808]; Beijing University of Posts and Telecommunications, School
   of Information and Communication Engineering, Excellent Graduate
   Students Innovation Fund in 2016
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61421061, Grant 61602048, Grant
   61601046, and Grant 61520106007, in part by the Project of Hainan
   Passenger Behavior Intelligence Analysis Platform and Precise Service
   Mining Prediction under Grant ZDKJ201808, and in part by the Beijing
   University of Posts and Telecommunications, School of Information and
   Communication Engineering, Excellent Graduate Students Innovation Fund
   in 2016.
CR Ba L. J., 2016, LAYER NORMALIZATION
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NR 34
TC 6
Z9 6
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 54472
EP 54482
DI 10.1109/ACCESS.2019.2913316
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA HX7QD
UT WOS:000467599600001
OA gold
DA 2022-08-23
ER

PT J
AU Qian, TY
   Liu, B
   Hong, L
   You, ZN
AF Qian, Tie-Yun
   Liu, Bei
   Hong, Liang
   You, Zhen-Ni
TI Time and Location Aware Points of Interest Recommendation in
   Location-Based Social Networks
SO JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
LA English
DT Article
DE point of interest (POI) recommendation; location-based social network
   (LBSN); time and location aware
AB The wide spread of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Recent advances on distributed representation shed light on learning low dimensional dense vectors to alleviate the data sparsity problem. Current studies on representation learning for POI recommendation embed both users and POIs in a common latent space, and users' preference is inferred based on the distance/similarity between a user and a POI. Such an approach is not in accordance with the semantics of users and POIs as they are inherently different objects. In this paper, we present a novel translation-based, time and location aware (TransTL) representation, which models the spatial and temporal information as a relationship connecting users and POIs. Our model generalizes the recent advances in knowledge graph embedding. The basic idea is that the embedding of a <time, location> pair corresponds to a translation from embeddings of users to POIs. Since the POI embedding should be close to the user embedding plus the relationship vector, the recommendation can be performed by selecting the top-k POIs similar to the translated POI, which are all of the same type of objects. We conduct extensive experiments on two real-world datasets. The results demonstrate that our TransTL model achieves the state-of-the-art performance. It is also much more robust to data sparsity than the baselines.
C1 [Qian, Tie-Yun; Liu, Bei; You, Zhen-Ni] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China.
   [Hong, Liang] Wuhan Univ, Sch Informat Management, Wuhan 430072, Hubei, Peoples R China.
RP Hong, L (通讯作者)，Wuhan Univ, Sch Informat Management, Wuhan 430072, Hubei, Peoples R China.
EM qty@whu.edu.cn; liubei@whu.edu.cn; hong@whu.edu.cn; znyou@whu.edu.cn
FU National Natural Science Foundation of China [61572376, 91646206];
   National Key Research and Development Program of China [2016YFB1000603]
FX This work was supported by the National Natural Science Foundation of
   China under Grant Nos. 61572376 and 91646206, and the National Key
   Research and Development Program of China under Grant No.
   2016YFB1000603.
CR Bordes A, 2014, MACH LEARN, V94, P233, DOI 10.1007/s10994-013-5363-6
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NR 29
TC 9
Z9 9
U1 0
U2 39
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 NOV
PY 2018
VL 33
IS 6
BP 1219
EP 1230
DI 10.1007/s11390-018-1883-7
PG 12
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HA9UA
UT WOS:000450651400009
DA 2022-08-23
ER

PT J
AU Di Porto, F
AF Di Porto, Fabiana
TI Algorithmic disclosure rules
SO ARTIFICIAL INTELLIGENCE AND LAW
LA English
DT Article; Early Access
DE Disclosure regulation; Failure; Consumers; Law and technology;
   Information duties; Machine learning; Algorithms; Natural language
   processing; Regulatory sandboxes; Knowledge graph; Due process
ID MARKET; QUALITY; LAW
AB During the past decade, a small but rapidly growing number of Law&Tech scholars have been applying algorithmic methods in their legal research. This Article does it too, for the sake of saving disclosure regulation failure: a normative strategy that has long been considered dead by legal scholars, but conspicuously abused by rule-makers. Existing proposals to revive disclosure duties, however, either focus on the industry policies (e.g. seeking to reduce consumers' costs of reading) or on rulemaking (e.g. by simplifying linguistic intricacies). But failure may well depend on both. Therefore, this Article develops a `comprehensive approach', suggesting to use computational tools to cope with linguistic and behavioral failures at both the enactment and implementation phases of disclosure duties, thus filling a void in the Law & Tech scholarship. Specifically, it outlines how algorithmic tools can be used in a holistic manner to address the many failures of disclosures from the rulemaking in parliament to consumer screens. It suggests a multi-layered design where lawmakers deploy three tools in order to produce optimal disclosure rules: machine learning, natural language processing, and behavioral experimentation through regulatory sandboxes. To clarify how and why these tasks should be performed, disclosures in the contexts of online contract terms and privacy online are taken as examples. Because algorithmic rulemaking is frequently met with well-justified skepticism, problems of its compatibility with legitimacy, efficacy and proportionality are also discussed.
C1 [Di Porto, Fabiana] Univ Salento, Dept Econ Sci, Lecce, Italy.
   [Di Porto, Fabiana] LUISS, Law Fac, Rome, Italy.
   [Di Porto, Fabiana] Hebrew Univ Jerusalem, Law Fac, Jerusalem, Israel.
RP Di Porto, F (通讯作者)，Univ Salento, Dept Econ Sci, Lecce, Italy.; Di Porto, F (通讯作者)，LUISS, Law Fac, Rome, Italy.; Di Porto, F (通讯作者)，Hebrew Univ Jerusalem, Law Fac, Jerusalem, Israel.
EM fabiana.diporto@unisalento.it
OI DI PORTO, Fabiana/0000-0002-4420-9741
FU Lady Davis Fellowship Trust, Hebrew University of Jerusalem
FX Funding was provided by Lady Davis Fellowship Trust, Hebrew University
   of Jerusalem.
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NR 76
TC 1
Z9 1
U1 8
U2 11
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-8463
EI 1572-8382
J9 ARTIF INTELL LAW
JI Artif. Intell. Law
DI 10.1007/s10506-021-09302-7
EA NOV 2021
PG 39
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Law
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Government & Law
GA WT2HP
UT WOS:000715690300001
OA hybrid
DA 2022-08-23
ER

PT J
AU Alshammari, M
   Nasraoui, O
   Sanders, S
AF Alshammari, Mohammed
   Nasraoui, Olfa
   Sanders, Scott
TI Mining Semantic Knowledge Graphs to Add Explainability to Black Box
   Recommender Systems
SO IEEE ACCESS
LA English
DT Article
DE Artificial intelligence; recommender systems; collaborative filtering;
   matrix factorization; explanations; semantic web
ID EXPLANATIONS
AB Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload. In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered state-of-the-art in recommendation systems. Unfortunately, these black box systems lack transparency, as they provide little information about the reasoning behind their predictions. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of big data and machine learning methods to a mass audience without compromising trust. Explanations can take a variety of formats, depending on the recommendation domain and the machine learning model used to make predictions. The objective of this work is to build a recommender system that can generate both accurate predictions and semantically rich explanations that justify the predictions. We propose a novel approach to build an explanation generation mechanism into a latent factor-based black box recommendation model. The designed model is trained to learn to make predictions that are accompanied by explanations that are automatically mined from the semantic web. Our evaluation experiments, which carefully study the trade-offs between the quality of predictions and explanations, show that our proposed approach succeeds in producing explainable predictions without a significant sacrifice in prediction accuracy.
C1 [Alshammari, Mohammed; Nasraoui, Olfa] Univ Louisville, Dept Comp Engn & Comp Sci, Knowledge Discovery & Web Min Lab, Louisville, KY 40292 USA.
   [Alshammari, Mohammed] Northern Border Univ, Comp Sci Dept, Rafha 76313, Saudi Arabia.
   [Sanders, Scott] Univ Louisville, Dept Commun, Louisville, KY 40292 USA.
RP Alshammari, M (通讯作者)，Univ Louisville, Dept Comp Engn & Comp Sci, Knowledge Discovery & Web Min Lab, Louisville, KY 40292 USA.
EM msalsh03@louisville.edu
RI Alshammari, Mohammed/AAE-3010-2021
OI Alshammari, Mohammed/0000-0002-5859-7490
FU Northern Border University, Saudi Arabia
FX This work was supported by the Northern Border University, Saudi Arabia
   through a scholarship.
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NR 44
TC 10
Z9 10
U1 2
U2 20
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 110563
EP 110579
DI 10.1109/ACCESS.2019.2934633
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IV7HA
UT WOS:000484436000002
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Rinaldi, AM
   Russo, C
AF Rinaldi, Antonio M.
   Russo, Cristiano
TI Using a multimedia semantic graph for web document visualization and
   summarization
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Multimedia topic detection; Document classification; Semantic analysis;
   Ontologies; Big data; Deep neural networks; Knowledge graph
ID AGGREGATION; DESCRIPTOR; COLOR; MODEL
AB The synthesis process of document content and its visualization play a basic role in the context of knowledge representation and retrieval. Existing methods for tag-clouds generations are mostly based on text content of documents, others also consider statistical or semantic information to enrich the document summary, while precious information deriving from multimedia content is often neglected. In this paper we present a document summarization and visualization technique based on both statistical and semantic analysis of textual and visual contents. The result of our framework is a Visual Semantic Tag Cloud based on the highlighting of relevant terms in a document using some features (font size, color, etc.) showing the importance of a term compared to other ones. The semantic information is derived from a knowledge base where concepts are represented through several multimedia items. The Visual Semantic Tag Cloud can be used not only to synthesize a document but also to represent a set of documents grouped by categories using a topic detection technique based on textual and visual analysis of multimedia features. Our work aims at demonstrating that with the help of semantic analysis and the combination of textual and visual features it is possible to improve the user knowledge acquisition by means of a synthesized visualization. The whole strategy has been evaluated by means of a ground truth and compared with similar approaches. Experimental results show the effectiveness of our approach, which outperforms state-of-art algorithms in topic detection combining both visual and semantic information.
C1 [Rinaldi, Antonio M.; Russo, Cristiano] Univ Naples Federico II, Dept Elect Engn & Informat Technol, Naples, Italy.
   [Rinaldi, Antonio M.] IKNOS LAB Intelligent & Knowledge Syst LUPT, Naples, Italy.
RP Rinaldi, AM (通讯作者)，Univ Naples Federico II, Dept Elect Engn & Informat Technol, Naples, Italy.; Rinaldi, AM (通讯作者)，IKNOS LAB Intelligent & Knowledge Syst LUPT, Naples, Italy.
EM antoniomaria.rinaldi@unina.it; cristiano.russo@unina.it
OI Russo, Cristiano/0000-0002-8732-1733
FU Universit`a degli Studi di Napoli Federico II within the CRUICARE
   Agreement
FX D Open access funding provided by Universit`a degli Studi di Napoli
   Federico II within the CRUICARE Agreement.
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NR 76
TC 10
Z9 10
U1 0
U2 7
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 2021
VL 80
IS 3
BP 3885
EP 3925
DI 10.1007/s11042-020-09761-1
EA SEP 2020
PG 41
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA PS9RK
UT WOS:000572601100005
OA hybrid
DA 2022-08-23
ER

PT J
AU Gupta, M
   Mishra, R
AF Gupta, Mukul
   Mishra, Rajhans
TI Network projection-based edge classification framework for signed
   networks
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE Signed network; Edge classification; Spanning subgraph projection
ID BALANCE
AB Many real-world networks have signed relationships between the nodes. Identification of these relationships is an important aspect of decision making. The existing signed relationships in a network may impact the relationships between the other nodes, hence learning from the existing signed relationships in a network can be used for decision making in various mining tasks. These signed networks are getting attention in recent years due to their relevance to many applications such as categorization, recommendation, and relationship discovery in various domains for decision support such as biological, social network analysis, communication and making knowledge graphs. In this work, we focus on edge classification (sign/label prediction for edges) in unweighted and undirected signed networks where the task is to predict the label of the unlabeled edges. Edge classification is a challenging problem as in real-world signed networks, edges are scarcely labeled. In our work, we are using labeled edges to predict the sign of unlabeled edges (classification) with the help of structural information. In this work, we have proposed a novel framework named NPECF for the classification of unlabeled edges. The proposed framework is novel in its way of utilizing the existing information in the signed network to predict the label of unlabeled edges. The utilization of the unlabeled edges in NPECF using three spanning subgraph projections of the given network minimizes the information loss. The experiments have been performed on four realworld datasets from different domains to demonstrate the effectiveness of the proposed framework.
C1 [Gupta, Mukul; Mishra, Rajhans] Indian Inst Management Indore, Informat Syst Area, Indore, India.
RP Mishra, R (通讯作者)，Indian Inst Management Indore, Informat Syst Area, Indore, India.
EM mukulg@iimidr.ac.in; rajhansm@iimidr.ac.in
OI mishra, rajhans/0000-0001-9413-8075
CR Aggarwal C, 2016, PROC INT CONF DATA, P1038, DOI 10.1109/ICDE.2016.7498311
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NR 26
TC 3
Z9 3
U1 3
U2 12
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 AUG
PY 2020
VL 135
AR 113321
DI 10.1016/j.dss.2020.113321
PG 12
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 MF4HF
UT WOS:000545304800002
DA 2022-08-23
ER

PT J
AU Han, C
   Chen, J
   Tan, MK
   Ng, MK
   Wu, QY
AF Han, Chao
   Chen, Jian
   Tan, Mingkui
   Ng, Michael K.
   Wu, Qingyao
TI A Tensor-Based Markov Chain Model for Heterogeneous Information Network
   Collective Classification
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Tensors; Task analysis; Probability distribution; Classification
   algorithms; Arrays; Visualization; Training data; Heterogeneous
   information network; node classification; relative importance of links;
   tensor; Markov chain; iterative algorithm
ID KNOWLEDGE GRAPH
AB Heterogeneous Information Network (HIN) collecitve classification studies the problem of predicting labels for one type of nodes in a HIN which contains multiple types of nodes multiple types of links among them. Previous studies have revealed that exploiting relative importance of links is quite useful to improve node classification performance as connected nodes tend to have similar labels. Most existing approaches exploit the relative importance of links either by directly counting the number of connections among nodes or by learning the weight of each type of link from labeled data only. However, these approaches either neglect the importance of types of links to the class labels or may lead to overfitting problem. We propose a Tensor-based Markov chain (T-Mark) approach, which is able to automatically and simultaneously predict the labels for unlabeled nodes and give the relative importance of types of links that actually improve the classification accuracy. Specifically, we build two tensor equations by using the HIN and features of nodes from both labeled and unlabeled data. A Markov chain-based model is proposed and it is solved by an iterative process to obtain the stationary distributions. Theoretical analyses of the existence and uniqueness of such probability distributions are given. Extensive experimental results demonstrate that T-Mark is able to achieve superior performance in the comparison and obtain reasonable relative importance of links.
C1 [Han, Chao] Guangxi Univ, Sch Elect Engn, Nanning 530004, Guangxi, Peoples R China.
   [Chen, Jian; Tan, Mingkui; Wu, Qingyao] South China Univ Technol, Sch Software Engn, Minist Educ, Guangzhou 510641, Peoples R China.
   [Chen, Jian; Tan, Mingkui; Wu, Qingyao] South China Univ Technol, Key Lab Big Data & Intelligent Robot, Minist Educ, Guangzhou 510641, Peoples R China.
   [Ng, Michael K.] Univ Hong Kong, Dept Math, Hong Kong, Peoples R China.
RP Wu, QY (通讯作者)，South China Univ Technol, Sch Software Engn, Minist Educ, Guangzhou 510641, Peoples R China.; Wu, QY (通讯作者)，South China Univ Technol, Key Lab Big Data & Intelligent Robot, Minist Educ, Guangzhou 510641, Peoples R China.
EM hanchaoc@gmail.com; ellachen@scut.edu.cn; mingkuitan@scut.edu.cn;
   mng@maths.hku.hk; qyw@scut.edu.cn
OI NG, Michael/0000-0001-6833-5227
FU National Natural Science Foundation of China [62072186, 61876208]; Hong
   Kong Research Grant Council [.12200317, 12300218, 12300519, 17201020];
   Key-Area Research and Development Program of Guangdong Province
   [2018B010108002]; Guangdong Basic and Applied Basic Research Foundation
   [2019 B1515130001]; Guangdong Special Branch Plans Young Talent with
   Scientific and Technological Innovation [2016TQ03X445]; Pearl River ST
   Nova Program of Guangzhou [201806010081]; Guangzhou Science and
   Technology Planning Project [201904010197]; Opening Project of Guangdong
   Key Laboratory of Big Data Analysis and Processing; Support Program for
   Postdoctoral Innovative Talents of Guangxi
FX This work was supported in part by the National Natural Science
   Foundation of China Projects No. 62072186, No. 61876208, the Hong Kong
   Research Grant Council General Research FundNo.12200317, No. 12300218,
   No. 12300519, No. 17201020, the Key-Area Research and Development
   Program of Guangdong Province No. 2018B010108002, the Guangdong Basic
   and Applied Basic Research Foundation No. 2019 B1515130001, the
   Guangdong Special Branch Plans Young Talent with Scientific and
   Technological Innovation No. 2016TQ03X445, the Pearl River ST Nova
   Program of Guangzhou No. 201806010081, the Guangzhou Science and
   Technology Planning Project No. 201904010197, the Opening Project of
   Guangdong Key Laboratory of Big Data Analysis and Processing, the
   Support Program for Postdoctoral Innovative Talents of Guangxi.
CR Bordes A., 2013, ADV NEURAL INFORM PR, P2787
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NR 38
TC 0
Z9 0
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD SEPT 1
PY 2022
VL 34
IS 9
BP 4063
EP 4076
DI 10.1109/TKDE.2020.3039533
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 3O1VG
UT WOS:000836626800002
DA 2022-08-23
ER

PT J
AU Xia, LQ
   Zheng, P
   Li, XY
   Gao, RB
   Wang, LH
AF Xia, Liqiao
   Zheng, Pai
   Li, Xinyu
   Gao, Robert. X.
   Wang, Lihui
TI Toward cognitive predictive maintenance: A survey of graph-based
   approaches
SO JOURNAL OF MANUFACTURING SYSTEMS
LA English
DT Review
DE Predictive maintenance; Graph neural network; Knowledge graph; Bayesian
   network; Cognitive computing
ID FAULT-DIAGNOSIS; FEATURE-EXTRACTION; BAYESIAN NETWORK; CAUSALITY GRAPH;
   SYSTEM; MODEL; MANAGEMENT; INFORMATION; ONTOLOGY
AB Predictive Maintenance (PdM) has continually attracted interest from the manufacturing community due to its significant potential in reducing unexpected machine downtime and related cost. Much attention to existing PdM research has been paid to perceiving the fault, while the identification and estimation processes are affected by many factors. Many existing approaches have not been able to manage the existing knowledge effectively for reasoning the causal relationship of fault. Meanwhile, complete correlation analysis of identified faults and the corresponding root causes is often missing. To address this problem, graph-based approaches (GbA) with cognitive intelligence are proposed, because the GbA are superior in semantic causal inference, heterogeneous association, and visualized explanation. In addition, GbA can achieve promising performance on PdM's perception tasks by revealing the dependency relationship among parts/components of the equipment. However, despite its advantages, few papers discuss cognitive inference in PdM, let alone GbA. Aiming to fill this gap, this paper concentrates on GbA, and carries out a comprehensive survey organized by the sequential stages in PdM, i. e., anomaly detection, diagnosis, prognosis, and maintenance decision-making. Firstly, GbA and their corresponding graph construction methods are introduced. Secondly, the implementation strategies and instances of GbA in PdM are presented. Finally, challenges and future works toward cognitive PdM are proposed. It is hoped that this work can provide a fundamental basis for researchers and industrial practitioners in adopting GbAbased PdM, and initiate several future research directions to achieve the cognitive PdM.
C1 [Xia, Liqiao; Zheng, Pai; Li, Xinyu] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China.
   [Zheng, Pai] Ctr Adv Reliabil & Safety CAiRS, Hong Kong, Peoples R China.
   [Li, Xinyu] Donghua Univ, Coll Mech Engn, Shanghai, Peoples R China.
   [Gao, Robert. X.] Case Western Reserve Univ, Dept Mech & Aerosp Engn, Cleveland, OH USA.
   [Wang, Lihui] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden.
RP Zheng, P (通讯作者)，Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China.
EM pai.zheng@polyu.edu.hk
RI Wang, Lihui/O-3907-2014; ZHENG, PAI/K-7989-2012
OI Wang, Lihui/0000-0001-8679-8049; ZHENG, PAI/0000-0002-2329-8634
FU Innovation and Technology Commis-sion (ITC) , Hong Kong Special
   Administration Region, National Key R&D Programs of Cooperation on
   Science and Technology Innovation with Hong Kong, Macao and Taiwan
   [MHX/001/20]; Mainland-Hong Kong Joint Funding Scheme [MHX/001/20];
   Innovation and Technology Commis-sion (ITC) , Hong Kong Special
   Administration Region; National Key R&D Programs of Cooperation on
   Science and Technology Innovation with Hong Kong, Macao and Taiwan (
   [22YF1400200]; Ministry of Science and Technology (MOST) of the People's
   Republic of China; Centre for Advances in Reliability and Safety
   (CAiRS); Science and Technology Commission of Shanghai Municipality
   [MHX/001/20];  [SQ2020YFE020182]
FX This research is partially funded by the Mainland-Hong Kong Joint
   Funding Scheme (MHX/001/20) , Innovation and Technology Commis-sion
   (ITC) , Hong Kong Special Administration Region, National Key R & D
   Programs of Cooperation on Science and Technology Innovation with Hong
   Kong, Macao and Taiwan (SQ2020YFE020182) , Ministry of Science and
   Technology (MOST) of the People's Republic of China, Centre for Advances
   in Reliability and Safety (CAiRS) admitted under AIR@InnoHK Research
   Cluster, and the Shanghai Rising-Star Plan (Yangfan Program) from the
   Science and Technology Commission of Shanghai Municipality (22YF1400200)
   .
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NR 126
TC 0
Z9 0
U1 8
U2 8
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0278-6125
EI 1878-6642
J9 J MANUF SYST
JI J. Manuf. Syst.
PD JUL
PY 2022
VL 64
BP 107
EP 120
DI 10.1016/j.jmsy.2022.06.002
PG 14
WC Engineering, Industrial; Engineering, Manufacturing; Operations Research
   & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA 2F3MD
UT WOS:000812815800005
DA 2022-08-23
ER

PT J
AU Li, ZY
   Wang, HF
   Zhang, WQ
AF Li, Ziyan
   Wang, Haofen
   Zhang, Wenqiang
TI Translational relation embeddings for multi-hop knowledge base question
   answering
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
DE Knowledge base question answering; Knowledge graph embedding; Multi-hop
   relation extraction
AB Multi-hop Knowledge Base Question Answering (KBQA) aims to predict answers that require multi-hop reasoning from the topic entity in the question over the Knowledge Base (KB). Relation extraction is a core step in KBQA, which extracts the relation path from the topic entity to the answer entity. Compared with single-hop questions, multi-hop ones have more complex syntactic structures to understand, and multi-hop relation paths lead to a larger search space, which makes it much more challenging to extract the correct relation paths. To tackle the above challenges, most existing relation extraction approaches focus on the semantic similarity between questions and relation paths. However, those approaches only consider the word semantics of the relation names but ignore the graph semantics inside the knowledge base. As a result, their generalization ability relying on the naming rules of the relations, making it more difficult to generalize over large knowledge bases. To address the current limitations and take advantage of the graph semantics of relations, we propose a novel translational embedding-based relation extractor that utilizes pretrained embeddings from TransE. In particular, we treat the multi-hop relation path as a translation from the first relation to the last one in the semantic space of TransE. Then we map the question into this space under the supervision of the path embeddings. To take full advantage of the pretrained graph semantics in TransE, we propose a KBQA framework that leverages pretrained relation semantics in relation extraction and pretrained entity semantics in answer selection. Our approach achieves state-of-the-art performance on two benchmark datasets, WebQuestionSP and MetaQA, demonstrating its effectiveness on the multi-hop KBQA task. (C) 2022 Elsevier B.V. All rights reserved.
C1 [Li, Ziyan; Zhang, Wenqiang] Fudan Univ, Acad Engn & Technol, Shanghai, Peoples R China.
   [Wang, Haofen] Tongji Univ, Coll Design & Innovat, Shanghai, Peoples R China.
RP Zhang, WQ (通讯作者)，Fudan Univ, Acad Engn & Technol, Shanghai, Peoples R China.; Wang, HF (通讯作者)，Tongji Univ, Coll Design & Innovat, Shanghai, Peoples R China.
EM ziyanli20@fudan.edu.cn; carter.whfcarter@gmail.com; wqzhang@fudan.edu.cn
OI Li, Ziyan/0000-0002-7100-539X
FU National Natural Science Foundation of China [62176185]
FX This work is supported by National Natural Science Foundation of China
   (No. 62176185) .
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NR 47
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
EI 1873-7749
J9 J WEB SEMANT
JI J. Web Semant.
PD OCT
PY 2022
VL 74
AR 100723
DI 10.1016/j.websem.2022.100723
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 2D4DM
UT WOS:000811499800003
DA 2022-08-23
ER

PT J
AU Liu, ZH
   Hou, WY
   Zhang, JY
   Cao, CY
   Wu, B
AF Liu, Zihe
   Hou, Weiying
   Zhang, Jiayi
   Cao, Chenyu
   Wu, Bin
TI A Multimodal Approach for Multiple-Relation Extraction in Videos
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Video understanding; Multiple-relation extraction; Social relationship
   recognition; Multimodal fusion
AB Automatically interpreting social relations, e.g., friendship, kinship, etc., from visual scenes has huge potential application value in areas such as knowledge graphs construction, person behavior and emotion analysis, entertainment ecology, etc. Great progress has been made in social analysis based on structured data. However, existing video-based methods consider social relationship extraction as a general classification task and categorize videos into only predefined types. Such methods are unable to recognize multiple relations in multi-person videos, which is obviously not consistent with the actual application scenarios. At the same time, videos are inherently multimodal. Subtitles in the video also provide abundant cues for relationship recognition that is often ignored by researchers. In this paper, we introduce and define a new task named "Multiple-Relation Extraction in Videos (MREV)". To solve the MREV task, we propose the Visual-Textual Fusion (VTF) framework for jointly modeling visual and textual information. For the spatial representation, we not only adopt a SlowFast network to learn global action and scene information, but also exploit the unique cues of face, body and dialogue between characters. For the temporal domain, we propose a Temporal Feature Aggregation module to perform temporal reasoning, which assesses the quality of different frames adaptively. After that, we use a Multi-Conv Attention module to capture the inter-modal correlation and map the features of different modes to a coordinated feature space. By this means, our VTF framework comprehensively exploits abundant multimodal cues for the MREV task and achieves 49.2% and 50.4% average accuracy on a self-constructed Video Multiple-Relation(VMR) dataset and ViSR dataset, respectively. Extensive experiments on VMR dataset and ViSR dataset demonstrate the effectiveness of the proposed framework.
C1 [Liu, Zihe; Hou, Weiying; Zhang, Jiayi; Cao, Chenyu; Wu, Bin] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, 10 Xitucheng Rd, Beijing 100876, Peoples R China.
RP Wu, B (通讯作者)，Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, 10 Xitucheng Rd, Beijing 100876, Peoples R China.
EM ziheliu@bupt.edu.cn; weiying.hou@bupt.edu.cn; brokenarrow@bupt.edu.cn;
   ccyu@bupt.edu.cn; wubin@bupt.edu.cn
FU National Natural Science Foundation of China [61972047]; National Key
   Research and Development Program of China [2018YFC0831500]; NSFC-General
   Technology Basic Research Joint Funds [U1936220]
FX This study was supported by the National Natural Science Foundation of
   China (grant no. 61972047), the National Key Research and Development
   Program of China (2018YFC0831500), and the NSFC-General Technology Basic
   Research Joint Funds (grant no. U1936220).
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   Zhu ZH, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1097
NR 63
TC 0
Z9 0
U1 7
U2 15
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD FEB
PY 2022
VL 81
IS 4
BP 4909
EP 4934
DI 10.1007/s11042-021-11466-y
EA SEP 2021
PG 26
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 ZF7SH
UT WOS:000695787500001
DA 2022-08-23
ER

PT J
AU Mai, SJ
   Zheng, SJ
   Sun, Y
   Zeng, Y
   Yang, YD
   Hu, HF
AF Mai, Sijie
   Zheng, Shuangjia
   Sun, Ya
   Zeng, Ying
   Yang, Yuedong
   Hu, Haifeng
TI Dynamic graph dropout for subgraph-based relation prediction
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Inductive relation prediction; Interpretability; Graph neural network;
   Explainability
AB Relation prediction for knowledge graphs aims at predicting missing relationships between entities. Inductive relation prediction methods have received increasing attention because of their capability of handling unseen entities. Among the inductive methods, the subgraph-based algorithms have emerged, which inductively predict relations using the subgraph surrounding the target entities. Despite the effectiveness, prior subgraph-based studies rarely focus on the explainability of subgraph reasoning, with which is critical for humans to understand and trust the prediction from GNNs. One of the reasons for the weak explainability of subgraph-based methods is the existence of noisy nodes and edges, which also hinders having higher performance of the model. In this paper, we present a dynamic graph dropout algorithm that prunes irrelevant nodes and edges to find the minimum sufficient subgraph for relation prediction. By this means, the proposed algorithm provides an explanation for which parts of the subgraph the model derives its results from. Specifically, we design an estimation function to evaluate the importance of the nodes and edges. Two dropout functions, i.e., soft and hard dropouts, are elaborately designed to filter out noisy nodes and edges based on their importance. Moreover, multiple restriction losses, including topological loss and penalty loss, are proposed to regularize the generation of the pruned subgraph. In this way, our model seeks to preserve the topology information in the subgraph and meanwhile maximally eliminate the redundant information. By removing noisy information, the proposed algorithm outperforms state-of-the-art models on eight inductive datasets. (c) 2022 Elsevier B.V. All rights reserved.
C1 [Mai, Sijie; Zheng, Shuangjia; Sun, Ya; Zeng, Ying; Yang, Yuedong; Hu, Haifeng] Sun Yat sen Univ, Guangzhou 510006, Guangdong, Peoples R China.
RP Hu, HF (通讯作者)，Sun Yat sen Univ, Guangzhou 510006, Guangdong, Peoples R China.
EM huhaif@mail.sysu.edu.cn
OI Hu, Haifeng/0000-0002-4884-323X
FU National Natural Science Foundation of China [62076262]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 62076262.
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NR 54
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 AUG 17
PY 2022
VL 250
AR 109172
DI 10.1016/j.knosys.2022.109172
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2D1SE
UT WOS:000811334800005
DA 2022-08-23
ER

PT J
AU Du, ZH
   Wu, YH
   Huang, YA
   Chen, J
   Pan, GQ
   Hu, L
   You, ZH
   Li, JQ
AF Du, Zhi-Hua
   Wu, Yang-Han
   Huang, Yu-An
   Chen, Jie
   Pan, Gui-Qing
   Hu, Lun
   You, Zhu-Hong
   Li, Jian-Qiang
TI GraphTGI: an attention-based graph embedding model for predicting
   TF-target gene interactions
SO BRIEFINGS IN BIOINFORMATICS
LA English
DT Article
DE transcriptional regulatory network; transcription factor; graph neural
   network; chemical similarity; graph auto-encoder
ID DNA; DATABASE
AB Motivation: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network.
   Results: In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale.
C1 [Du, Zhi-Hua; Wu, Yang-Han; Chen, Jie; Pan, Gui-Qing; Li, Jian-Qiang] ShenZhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China.
   [Huang, Yu-An; You, Zhu-Hong] Northwestern Polytech Univ, Sch Comp Sci, Xian, Peoples R China.
   [Hu, Lun] Chinese Acad Sci, Xinjiang Tech Phys & Chem, Urumqi, Peoples R China.
RP Huang, YA (通讯作者)，Northwestern Polytech Univ, Xian, Shaanxi, Peoples R China.; Li, JQ (通讯作者)，Shenzhen Univ, Shenzhen, Guangdong, Peoples R China.
EM yahuang1991@gmail.com; lijq@szu.edu.cn
FU National Key R&D Program of China [2020YFA0908700]; National Nature
   Science Foundation of China [62176164]; Shen-zhen Scientific Research
   and Development Funding Program [GGFW2018020518310863]
FX The National Key R&D Program of China under Grant 2020YFA0908700,
   National Nature Science Foundation of China Grant 62176164. Shen-zhen
   Scientific Research and Development Funding Program
   (GGFW2018020518310863).
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NR 32
TC 0
Z9 0
U1 5
U2 5
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/bbac148
EA APR 2022
PG 11
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:000790909600001
PM 35511108
DA 2022-08-23
ER

PT J
AU Sun, YH
   Xiao, XK
   Cui, B
   Halgamuge, S
   Lappas, T
   Luo, J
AF Sun, Yahui
   Xiao, Xiaokui
   Cui, Bin
   Halgamuge, Saman
   Lappas, Theodoros
   Luo, Jun
TI Finding Group Steiner Trees in Graphs with both Vertex and Edge Weights
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
ID APPROXIMATION ALGORITHM; KEYWORD SEARCH
AB Given an undirected graph and a number of vertex groups, the group Steiner tree problem is to find a tree such that (i) this tree contains at least one vertex in each vertex group; and (ii) the sum of vertex and edge weights in this tree is minimized. Solving this problem is useful in various scenarios, ranging from social networks to knowledge graphs. Most existing work focuses on solving this problem in vertex-unweighted graphs, and not enough work has been done to solve this problem in graphs with both vertex and edge weights. Here, we develop several algorithms to address this issue. Initially, we extend two algorithms from vertex-unweighted graphs to vertex- and edge-weighted graphs. The first one has no approximation guarantee, but often produces good solutions in practice. The second one has an approximation guarantee of vertical bar Gamma vertical bar - 1, where vertical bar Gamma vertical bar is the number of vertex groups. Since the extended (vertical bar Gamma vertical bar - 1)-approximation algorithm is too slow when all vertex groups are large, we develop two new (vertical bar Gamma vertical bar - 1)-approximation algorithms that overcome this weakness. Furthermore, by employing a dynamic programming approach, we develop another (vertical bar Gamma vertical bar h+1)-approximation algorithm, where h is a parameter between 2 and vertical bar Gamma vertical bar. Experiments show that, while no algorithm is the best in all cases, our algorithms considerably outperform the state of the art in many scenarios.
C1 [Sun, Yahui; Xiao, Xiaokui] Natl Univ Singapore, Sch Comp, Singapore, Singapore.
   [Cui, Bin] Peking Univ, Dept Comp Sci, Beijing, Peoples R China.
   [Halgamuge, Saman] Univ Melbourne, Sch Elect Mech & Infrastruct Engn, Melbourne, Vic, Australia.
   [Lappas, Theodoros] Stevens Inst Technol, Sch Business, Hoboken, NJ 07030 USA.
   [Luo, Jun] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore.
RP Sun, YH (通讯作者)，Natl Univ Singapore, Sch Comp, Singapore, Singapore.
EM yahuisun@outlook.com; xkxiao@nus.edu.sg; bin.cui@pku.edu.cn;
   saman@unimelb.edu.au; tlappas@stevens.edu; junluo@ntu.edu.sg
RI Lappas, Theodoros/AAB-7388-2022; Cui, Bin/A-4554-2012
OI Lappas, Theodoros/0000-0002-4669-4170; Luo, Jun/0000-0002-7036-5158;
   CUI, Bin/0000-0003-1681-4677
FU Singapore Ministry of Education [MOE2016-T2-2-022]; National University
   of Singapore
FX We sincerely thank Dr. Bolin Ding [14] and Dr. Ronghua Li [31] for
   sharing codes of their algorithms. This work is sequentially funded by
   MOE2016-T2-2-022 (2019-2020) from the Singapore Ministry of Education,
   and a Start Up Grant (SUG) (2020-2021) from the National University of
   Singapore.
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NR 42
TC 0
Z9 0
U1 1
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 MAR
PY 2021
VL 14
IS 7
BP 1137
EP 1149
DI 10.14778/3450980.3450982
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SN7WE
UT WOS:000658497300003
DA 2022-08-23
ER

PT J
AU Lakshmanan, LVS
   Simpson, M
   Thirumuruganathan, S
AF Lakshmanan, Laks V. S.
   Simpson, Michael
   Thirumuruganathan, Saravanan
TI Combating Fake News: A Data Management and Mining Perspective
SO PROCEEDINGS OF THE VLDB ENDOWMENT
LA English
DT Article
ID COMPETITIVE INFLUENCE
AB Fake news is a major threat to global democracy resulting in diminished trust in government, journalism and civil society. The public popularity of social media and social networks has caused a contagion of fake news where conspiracy theories, disinformation and extreme views flourish. Detection and mitigation of fake news is one of the fundamental problems of our times and has attracted widespread attention. While fact checking websites such as snopes, politifact and major companies such as Google, Facebook, and Twitter have taken preliminary steps towards addressing fake news, much more remains to be done. As an interdisciplinary topic, various facets of fake news have been studied by communities as diverse as machine learning, databases, journalism, political science and many more.
   The objective of this tutorial is two-fold. First, we wish to familiarize the database community with the efforts by other communities on combating fake news. We provide a panoramic view of the state-of-the-art of research on various aspects including detection, propagation, mitigation, and intervention of fake news. Next, we provide a concise and intuitive summary of prior research by the database community and discuss how it could be used to counteract fake news. The tutorial covers research from areas such as data integration, truth discovery and fusion, probabilistic databases, knowledge graphs and crowdsourcing from the lens of fake news. Effective tools for addressing fake news could only be built by leveraging the synergistic relationship between database and other research communities. We hope that our tutorial provides an impetus towards such synthesis of ideas and the creation of new ones.
C1 [Lakshmanan, Laks V. S.; Simpson, Michael] Univ British Columbia, Dept Comp Sci, Vancouver, BC, Canada.
   [Thirumuruganathan, Saravanan] HBKU, QCRI, Data Analyt Grp, Doha, Qatar.
RP Lakshmanan, LVS (通讯作者)，Univ British Columbia, Dept Comp Sci, Vancouver, BC, Canada.
EM laks@cs.ubc.ca; mesimp@cs.ubc.ca; sthirumuruganathan@hbku.edu.qa
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NR 33
TC 4
Z9 5
U1 3
U2 22
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2150-8097
J9 PROC VLDB ENDOW
JI Proc. VLDB Endow.
PD AUG
PY 2019
VL 12
IS 12
BP 1990
EP 1993
DI 10.14778/3352063.3352117
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JO5VL
UT WOS:000497646000054
DA 2022-08-23
ER

PT J
AU Zhang, D
   Kabuka, M
AF Zhang, Da
   Kabuka, Mansur
TI Multimodal deep representation learning for protein interaction
   identification and protein family classification
SO BMC BIOINFORMATICS
LA English
DT Article; Proceedings Paper
CT IEEE International Conference on Bioinformatics and Biomedicine (BIBM) -
   Bioinformatics and Systems Biology
CY DEC 03-06, 2018
CL Madrid, SPAIN
SP IEEE
DE Protein-protein interaction network; Multimodal deep neural network;
   Knowledge graph representation learning
ID PREDICTION
AB Background Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge. Results In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods. Conclusion To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.
C1 [Zhang, Da; Kabuka, Mansur] Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33124 USA.
RP Zhang, D (通讯作者)，Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33124 USA.
EM zhang.1855@miami.edu
RI ARSLAN, Okan/AAA-3232-2020
FU National Cancer Institute, part of the U.S. National Institutes of
   Health (NIH) [R44CA206782]
FX Publication costs were funded by grant R44CA206782 from the National
   Cancer Institute, part of the U.S. National Institutes of Health (NIH).
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NR 33
TC 9
Z9 9
U1 5
U2 12
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD DEC 2
PY 2019
VL 20
SU 16
SI SI
AR 531
DI 10.1186/s12859-019-3084-y
PG 14
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA JR9KT
UT WOS:000499935900006
PM 31787089
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Tomeo, P
   Fernandez-Tobias, I
   Cantador, I
   Di Noia, T
AF Tomeo, Paolo
   Fernandez-Tobias, Ignacio
   Cantador, Ivan
   Di Noia, Tommaso
TI Addressing the Cold Start with Positive-Only Feedback Through
   Semantic-Based Recommendations
SO INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED
   SYSTEMS
LA English
DT Article
DE Recommender systems; cold start; positive-only feedback; semantic-based
   recommendations; matrix factorization; graph-based algorithms
AB Recommender systems aim to provide users with accurate item suggestions in a personalized fashion, but struggle in the case of cold start users, for whom there is a scarcity of preference data. User preferences can be either explicitly stated by the users often by means of ratings , or implicitly acquired by a system for instance by mining text reviews, search queries, and purchase records. Recommendation methods have been mostly designed to deal with numerical ratings. However, real scenarios with user preferences expressed in the form of binary and unary (positive-only) feedback, e.g. the thumbs up/down in YouTube, and the likes in Facebook, are increasingly popular, and make the user cold start problem even more challenging. To address the cold start with positive-only feedback situations, we propose to exploit data additional to user preferences by means of specialized hybrid recommendation methods. In particular, we investigate a number of graph-based and matrix factorization recommendation models that jointly exploit user preferences and item semantic metadata automatically extracted from the well-known knowledge graph of DBpedia. Following a rigorous evaluation methodology for cold start, we empirically compare the above hybrid recommendation models on a Facebook dataset containing users likes for items in three different domains, namely books, movies and music. The achieved experimental results show that the semantics-aware hybrid approaches we propose outperform content-based and collaborative filtering baselines. In addition to recommendation accuracy, in our evaluation we also consider individual and aggregate diversity of recommendations as key quality factors in the users satisfaction.
C1 [Tomeo, Paolo; Di Noia, Tommaso] Politecn Bari, Via Orabona 4, I-70125 Bari, Italy.
   [Fernandez-Tobias, Ignacio; Cantador, Ivan] Univ Autonoma Madrid, Calle Francisco Tomas & Valiente 11, E-28049 Madrid, Spain.
RP Tomeo, P (通讯作者)，Politecn Bari, Via Orabona 4, I-70125 Bari, Italy.
EM paolo.tomeo@poliba.it; ignacio.fernandezt@uam.es; ivan.cantador@uam.es;
   tommaso.dinoia@poliba.it
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NR 38
TC 5
Z9 5
U1 1
U2 29
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-4885
EI 1793-6411
J9 INT J UNCERTAIN FUZZ
JI Int. J. Uncertainty Fuzziness Knowl.-Based Syst.
PD DEC
PY 2017
VL 25
SU 2
BP 57
EP 78
DI 10.1142/S0218488517400116
PG 22
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FQ6HB
UT WOS:000418463300005
DA 2022-08-23
ER

PT J
AU Li, MT
   Liu, FF
   Zhu, JA
   Zhang, R
   Qin, Y
   Gao, DP
AF Li, Meiting
   Liu, Feifei
   Zhu, Jia'an
   Zhang, Ran
   Qin, Yi
   Gao, Dongping
TI Model-based clinical note entity recognition for rheumatoid arthritis
   using bidirectional encoder representation from transformers
SO QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
LA English
DT Article
DE Named entity recognition; rheumatoid arthritis (RA); artificial
   intelligence; bidirectional encoder representation from transformers
   (BERT); clinical notes
ID INFORMATION
AB Background: Rheumatoid arthritis (RA) is a disease of the immune system with a high rate of disability and there are a large amount of valuable disease diagnosis and treatment information in the clinical note of the electronic medical record. Artificial intelligence methods can be used to mine useful information in clinical notes effectively. This study aimed to develop an effective method to identify and classify medical entities in the clinical notes relating to RA and use the entity identification results in subsequent studies.
   Methods: In this paper, we introduced the bidirectional encoder representation from transformers (BERT) pre-training model to enhance the semantic representation of word vectors. The generated word vectors were then inputted into the model, which is composed of traditional bidirectional long short-term memory neural networks and conditional random field machine learning algorithms for the named entity recognition of clinical notes to improve the model's effectiveness. The BERT method takes the combination of token embeddings, segment embeddings, and position embeddings as the model input and fine-tunes the model during training.
   Results: Compared with the traditional Word2vec word vector model, the performance of the BERT pre-training model to obtain a word vector as model input was significantly improved. The best F1-score of the named entity recognition task after training using many rheumatoid arthritis clinical notes was 0.936.
   Conclusions: This paper confirms the effectiveness of using an advanced artificial intelligence method to carry out named entity recognition tasks on a corpus of a large number of clinical notes; this application is promising in the medical setting. Moreover, the extraction of results in this study provides a lot of basic data for subsequent tasks, including relation extraction, medical knowledge graph construction, and disease reasoning.
C1 [Li, Meiting; Zhang, Ran; Qin, Yi; Gao, Dongping] Chinese Acad Med Sci, Inst Med Informat, 3 Yabao Rd, Beijing, Peoples R China.
   [Liu, Feifei; Zhu, Jia'an] Peking Univ Peoples Hosp, Dept Ultrasound, Beijing, Peoples R China.
RP Gao, DP (通讯作者)，Chinese Acad Med Sci, Inst Med Informat, 3 Yabao Rd, Beijing, Peoples R China.
EM gaodp_gaodp@126.com
FU National Key Research and Development Program of China [2020AAA0104905];
   Major National Social Science Project [19ZDA041]; National Natural
   Science Foundation of China [82071930]
FX This research was supported by the National Key Research and Development
   Program of China, grant ID 2020AAA0104905; the Major National Social
   Science Project, grant ID 19ZDA041; and the National Natural Science
   Foundation of China, grant 82071930.
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NR 28
TC 0
Z9 0
U1 14
U2 37
PU AME PUBL CO
PI SHATIN
PA FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG
   00000, PEOPLES R CHINA
SN 2223-4292
EI 2223-4306
J9 QUANT IMAG MED SURG
JI Quant. Imaging Med. Surg.
PD JAN
PY 2022
VL 12
IS 1
BP 184
EP 195
DI 10.21037/qims-21-90
EA JUN 2021
PG 12
WC Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Radiology, Nuclear Medicine & Medical Imaging
GA YJ0HU
UT WOS:000687124700001
PM 34993070
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Gu, Q
   Cao, J
   Liu, YC
AF Gu, Qi
   Cao, Jian
   Liu, Yancen
TI Entity resolution for media metadata based on structural clustering
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Entity resolution; Structural clustering; Iterative propagation; Graph
   structure
AB An increasing amount of media metadata are published by different organizations on the Web which leads to a fragmented dataset landscape. Identifying media metadata from disparate datasets and integrating heterogeneous datasets have many applications but also pose significant challenges. To tackle this problem, entity resolution methods are commonly used as an essential prerequisite for integrating media information from different sources and effectively foster the re-use of existing data sources. While the amount of media metadata published on the Web grows steadily, how to scale it well to large media knowledge bases while maintaining a high matching quality is a critical challenge. This article investigates the relationships between media entities. To that end, the media database is formulated as a knowledge graph with entities as nodes and the associations between related entities as edges. Thus, media entities are grouped into communities by how they share neighbors. Then, a structural clustering-based model is proposed to detect communities and discover anchor vertices as well as isolated vertices. Specifically, an initial seed set of matched anchor vertex pairs is obtained. Furthermore, an iterative propagation approach for identifying the matched entities in the whole graph is developed, where community similarity is introduced into the measure function to control the total measurement of candidate pairs. Therefore, starting with the elements of the initial seed set, the entity resolution algorithm updates the matching information over the whole network along with the neighbor relationships iteratively. Extensive experiments are conducted on real datasets to evaluate how the seed set impacts the matching process and performance. The experiment results show this model can achieve an excellent balance between accuracy and efficiency and is a clear improvement compared to state-of-the-art methods.
C1 [Gu, Qi; Cao, Jian; Liu, Yancen] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Sch Elect Informat & Elect Engn, 800 Dongchuan Rd, Shanghai 200240, Peoples R China.
   [Gu, Qi] Nantong Univ, Sch Informat Sci & Technol, Dept Comp Sci, 9 Seyuan Rd, Nantong 226019, Peoples R China.
RP Cao, J (通讯作者)，Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Sch Elect Informat & Elect Engn, 800 Dongchuan Rd, Shanghai 200240, Peoples R China.
EM guqi@sjtu.edu.cn; cao-jian@sjtu.edu.cn; LiuYancen@sjtu.edu.cn
FU National Key Research and Development Plan [2018YFB1003800]
FX This work is partially supported by National Key Research and
   Development Plan (No. 2018YFB1003800).
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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 2020
VL 79
IS 1-2
BP 219
EP 242
DI 10.1007/s11042-019-08062-6
PG 24
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 KS0FL
UT WOS:000517987600010
DA 2022-08-23
ER

PT J
AU Cifariello, P
   Ferragina, P
   Ponza, M
AF Cifariello, Paolo
   Ferragina, Paolo
   Ponza, Marco
TI WISER: A semantic approach for expert finding in academia based on
   entity linking
SO INFORMATION SYSTEMS
LA English
DT Article
DE Expert finding; Expert profiling; Expertise retrieval; Entity linking;
   Information retrieval; Wikipedia
AB We present WISER, a new semantic search engine for expert finding in academia. Our system is unsupervised and it jointly combines classical language modeling techniques, based on text evidences, with the Wikipedia Knowledge Graph, via entity linking.
   WISER indexes each academic author through a novel profiling technique which models her expertise with a small, labeled and weighted graph drawn from Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the author's publications, whereas the weighted edges express the semantic relatedness among these entities computed via textual and graph-based relatedness functions. Every node is also labeled with a relevance score which models the pertinence of the corresponding entity to author's expertise, and is computed by means of a proper random-walk calculation over that graph; and with a latent vector representation which is learned via entity and other kinds of structural embeddings derived from Wikipedia.
   At query time, experts are retrieved by combining classic document-centric approaches, which exploit the occurrences of query terms in the author's documents, with a novel set of profile-centric scoring strategies, which compute the semantic relatedness between the author's expertise and the query topic via the above graph-based profiles.
   The effectiveness of our system is established over a large-scale experimental test on a standard dataset for this task. We show that WISER achieves better performance than all the other competitors, thus proving the effectiveness of modeling author's profile via our "semantic" graph of entities. Finally, we comment on the use of WISER for indexing and profiling the whole research community within the University of Pisa, and its application to technology transfer in our University. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Cifariello, Paolo; Ferragina, Paolo; Ponza, Marco] Univ Pisa, Dipartimento Informat, Pisa, Italy.
RP Ponza, M (通讯作者)，Univ Pisa, Dipartimento Informat, 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, Italy Grant (2017); EU [654024]; MIUR
   project FFO 2016 [552, 11]
FX We warmly thank Marco Cornolti for the preliminary processing of the
   dataset and for his invaluable suggestions in the deployment of
   ELASTICSEARCH. We also thank the anonymous reviewers for their careful
   reading of the manuscript and their insightful comments that allowed us
   to significantly improve the quality of the paper. Part of this work has
   been supported by a Bloomberg Data Science Research, Italy Grant (2017),
   by the EU grant for the Research Infrastructure "SoBigData: Social
   Mining & Big Data Ecosystem" (INFRAIA-1-2014-2015, agreement #654024)
   and by the MIUR project FFO 2016 (DM 6 July 2016, n. 552, art. 11).
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TC 19
Z9 20
U1 1
U2 12
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 2019
VL 82
BP 1
EP 16
DI 10.1016/j.is.2018.12.003
PG 16
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HS2JP
UT WOS:000463687200001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Kaushal, M
   Oni-Orisan, A
   Chen, G
   Li, WJ
   Leschke, J
   Ward, BD
   Kalinosky, B
   Budde, MD
   Schmit, BD
   Li, SJ
   Muqeet, V
   Kurpad, SN
AF Kaushal, Mayank
   Oni-Orisan, Akinwunmi
   Chen, Gang
   Li, Wenjun
   Leschke, Jack
   Ward, B. Douglas
   Kalinosky, Benjamin
   Budde, Matthew D.
   Schmit, Brian D.
   Li, Shi-Jiang
   Muqeet, Vaishnavi
   Kurpad, Shekar N.
TI Evaluation of Whole-Brain Resting-State Functional Connectivity in
   Spinal Cord Injury: A Large-Scale Network Analysis Using Network-Based
   Statistic
SO JOURNAL OF NEUROTRAUMA
LA English
DT Article
DE neural injury; neuroplasticity; traumatic SCI
ID REORGANIZATION; MRI; CORTEX; RECOVERY; MONKEYS; STROKE; PAIN
AB Large-scale network analysis characterizes the brain as a complex network of nodes and edges to evaluate functional connectivity patterns. The utility of graph-based techniques has been demonstrated in an increasing number of restingstate functional MRI (rs-fMRI) studies in the normal and diseased brain. However, to our knowledge, graph theory has not been used to study the reorganization pattern of resting-state brain networks in patients with traumatic complete spinal cord injury (SCI). In the present analysis, we applied a graph-theoretical approach to explore changes to global brain network architecture as a result of SCI. Fifteen subjects with chronic (> 2 years) complete (American Spinal Injury Association [ASIA] A) cervical SCI and 15 neurologically intact controls were scanned using rs-fMRI. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI) or nodes. The average time series was extracted at each node, and correlation analysis was performed between every pair of nodes. A functional connectivity matrix for each subject was then generated. Subsequently, the matrices were averaged across groups, and network changes were evaluated between groups using the network-based statistic (NBS) method. Our results showed decreased connectivity in a subnetwork of the whole brain in SCI compared with control subjects. Upon further examination, increased connectivity was observed in a subnetwork of the sensorimotor cortex and cerebellum network in SCI. In conclusion, our findings emphasize the applicability of NBS to study functional connectivity architecture in diseased brain states. Further, we show reorganization of large-scale resting-state brain networks in traumatic SCI, with potential prognostic and therapeutic implications.
C1 [Kaushal, Mayank; Kalinosky, Benjamin; Schmit, Brian D.] Marquette Univ, Dept Biomed Engn, Milwaukee, WI 53233 USA.
   [Oni-Orisan, Akinwunmi; Kurpad, Shekar N.] Med Coll Wisconsin, Dept Neurosurg, Milwaukee, WI 53226 USA.
   [Chen, Gang; Li, Wenjun; Ward, B. Douglas; Budde, Matthew D.; Li, Shi-Jiang] Med Coll Wisconsin, Dept Biophys, Milwaukee, WI 53226 USA.
   [Leschke, Jack] Med Coll Wisconsin, Dept Neurol, Milwaukee, WI 53226 USA.
   [Muqeet, Vaishnavi] Clement J Zablocki Vet Affairs Med Ctr, Dept Phys Med & Rehabil, Milwaukee, WI USA.
RP Kaushal, M (通讯作者)，Marquette Univ, Dept Biomed Engn, Milwaukee, WI 53233 USA.; Oni-Orisan, A (通讯作者)，Med Coll Wisconsin, Dept Neurosurg, Milwaukee, WI 53226 USA.
EM mayank.kaushal@marquette.edu
RI Kaushal, Mayank/AAK-7667-2020; Kurpad, Shekar/V-2188-2019; Li,
   Shi-Jiang/M-2534-2017
OI Kaushal, Mayank/0000-0002-4121-6751; 
FU Marquette University; Bryon Riesch Paralysis Foundation; Falk Foundation
FX The authors thank Moriah Iverson, MS, and Judeen Richlen, RN, (research
   coordination); Dana Seslija, MD, MS, William Waring, MD, and Merle Orr,
   MD, (patient recruitment support); and Yu Liu, MS (MRI technical
   support). This study was funded by the Research Administration Committee
   of the Department of Physical Medicine and Rehabilitation at the Medical
   College of Wisconsin, Marquette University; Bryon Riesch Paralysis
   Foundation; and The Falk Foundation.
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PU MARY ANN LIEBERT, INC
PI NEW ROCHELLE
PA 140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA
SN 0897-7151
EI 1557-9042
J9 J NEUROTRAUM
JI J. Neurotrauma
PD MAR
PY 2017
VL 34
IS 6
BP 1278
EP 1282
DI 10.1089/neu.2016.4649
PG 5
WC Critical Care Medicine; Clinical Neurology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine; Neurosciences & Neurology
GA EN7ZR
UT WOS:000396221200015
PM 27937140
OA Green Published
DA 2022-08-23
ER

PT J
AU Fu, WJ
   Wang, M
   Hao, SJ
   Mu, TT
AF Fu, Weijie
   Wang, Meng
   Hao, Shijie
   Mu, Tingting
TI FLAG: Faster Learning on Anchor Graph with Label Predictor Optimization
SO IEEE TRANSACTIONS ON BIG DATA
LA English
DT Article
DE Optimization; Computational modeling; Big Data; Sparse matrices;
   Computational efficiency; Approximation algorithms; Complexity theory;
   Semi-supervised learning; graph-based learning; machine learning
ID VECTOR MACHINES; CONSTRUCTION
AB Knowledge graphs have received intensive research interests. When the labels of most nodes or datapoints are missing, anchor graph and hierarchical anchor graph models can be employed. With an anchor graph or hierarchical anchor graph, we only need to optimize the labels of the coarsest anchors, and the labels of datapoints can be inferred from these anchors in a coarse-to-fine manner. The complexity of optimization is therefore reduced to a cubic cost with respect to the number of the coarsest anchors. However, to obtain a high accuracy when a data distribution is complex, the scale of this anchor set still needs to be large, which thus inevitably incurs an expensive computational burden. As such, a challenge in scaling up these models is how to efficiently estimate the labels of these anchors while keeping classification performance. To address this problem, we propose a novel approach that adds an anchor label predictor in the conventional anchor graph and hierarchical anchor graph models. In the proposed approach, the labels of the coarsest anchors are not directly optimized, and instead, we learn a label predictor which estimates the labels of these anchors with their spectral representations. The predictor is optimized with a regularization on all datapoints based on a hierarchical anchor graph, and we show that its solution only involves the inversion of a small-size matrix. Built upon the anchor hierarchy, we design a sparse intra-layer adjacency matrix over these anchors, which can simultaneously accelerate spectral embedding and enhance effectiveness. Our approach is named Faster Learning on Anchor Graph (FLAG) as it improves conventional anchor-graph-based methods in terms of efficiency. Experiments on a variety of publicly available datasets with sizes varying from thousands to millions of samples demonstrate the effectiveness of our approach.
C1 [Fu, Weijie; Wang, Meng; Hao, Shijie] Hefei Univ Technol, Sch Comp & Informat, Hefei 230009, Anhui Sheng, Peoples R China.
   [Mu, Tingting] Univ Manchester, Sch Comp Sci, Manchester M13 9PL, Lancs, England.
RP Mu, TT (通讯作者)，Univ Manchester, Sch Comp Sci, Manchester M13 9PL, Lancs, England.
EM fwj.edu@gmail.com; eric.mengwang@gmail.com; hfut.hsj@gmail.com;
   tingting.mu@manchester.ac.uk
OI hao, shijie/0000-0003-3181-1220
FU National 973 Program of China [2014CB347600]; National Nature Science
   Foundation of China [61432019, 71490725, 61772171]
FX The authors sincerely appreciate the useful comments and suggestions
   from the anonymous reviewers. This work was partially supported by the
   National 973 Program of China under grant 2014CB347600, and the National
   Nature Science Foundation of China under grant 61432019, 71490725, and
   61772171.
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   Zhu Xiaojin, 2003, P INT C MACH LEARN I, P912
NR 54
TC 4
Z9 4
U1 0
U2 0
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2332-7790
J9 IEEE T BIG DATA
JI IEEE Trans. Big Data
PD JUN 1
PY 2022
VL 8
IS 3
BP 579
EP 591
DI 10.1109/TBDATA.2017.2757522
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1F3YX
UT WOS:000795107500001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Piekos, SN
   Gaddam, S
   Bhardwaj, P
   Radhakrishnan, P
   Guha, RV
   Oro, AE
AF Piekos, Samantha N.
   Gaddam, Sadhana
   Bhardwaj, Pranav
   Radhakrishnan, Prashanth
   Guha, Ramanathan V.
   Oro, Anthony E.
TI Biomedical Data Commons (BMDC) prioritizes B-lymphocyte non-coding
   genetic variants in Type 1 Diabetes
SO PLOS COMPUTATIONAL BIOLOGY
LA English
DT Article
ID GENOME-WIDE ASSOCIATION; MHC CLASS-I; RISK-FACTORS; ONSET; DISCOVERY;
   CHROMATIN; PREVENTS; REGIONS
AB The repurposing of biomedical data is inhibited by its fragmented and multi-formatted nature that requires redundant investment of time and resources by data scientists. This is particularly true for Type 1 Diabetes (T1D), one of the most intensely studied common childhood diseases. Intense investigation of the contribution of pancreatic beta-islet and T-lymphocytes in T1D has been made. However, genetic contributions from B-lymphocytes, which are known to play a role in a subset of T1D patients, remain relatively understudied. We have addressed this issue through the creation of Biomedical Data Commons (BMDC), a knowledge graph that integrates data from multiple sources into a single queryable format. This increases the speed of analysis by multiple orders of magnitude. We develop a pipeline using B-lymphocyte multi-dimensional epigenome and connectome data and deploy BMDC to assess genetic variants in the context of Type 1 Diabetes (T1D). Pipeline-identified variants are primarily common, non-coding, poorly conserved, and are of unknown clinical significance. While variants and their chromatin connectivity are cell-type specific, they are associated with well-studied disease genes in T-lymphocytes. Candidates include established variants in the HLA-DQB1 and HLA-DRB1 and IL2RA loci that have previously been demonstrated to protect against T1D in humans and mice providing validation for this method. Others are included in the well-established T1D GRS2 genetic risk scoring method. More intriguingly, other prioritized variants are completely novel and form the basis for future mechanistic and clinical validation studies The BMDC community-based platform can be expanded and repurposed to increase the accessibility, reproducibility, and productivity of biomedical information for diverse applications including the prioritization of cell type-specific disease alleles from complex phenotypes.
C1 [Piekos, Samantha N.; Gaddam, Sadhana; Oro, Anthony E.] Stanford Univ, Program Epithelial Biol, Stanford, CA 94305 USA.
   [Piekos, Samantha N.; Radhakrishnan, Prashanth; Guha, Ramanathan V.] Google Data Commons, Mountain View, CA 94039 USA.
   [Bhardwaj, Pranav] Stanford Univ, Dept Stat, Stanford, CA 94305 USA.
RP Piekos, SN (通讯作者)，Stanford Univ, Program Epithelial Biol, Stanford, CA 94305 USA.; Piekos, SN (通讯作者)，Google Data Commons, Mountain View, CA 94039 USA.
EM Samantha.Piekos@isbscience.org; oro@stanford.edu
RI Gaddam, Sadhana/AAS-2252-2021
OI Gaddam, Sadhana/0000-0001-6477-0483; Guha,
   Ramanathan/0000-0003-4428-129X; Oro, Anthony/0000-0002-6261-138X;
   Piekos, Samantha/0000-0002-8119-6724
FU NIH [R01 ARO73170]; Bio-X Stanford Interdisciplinary Graduate Fellowship
   through the Stanford University School of Medicine; Google Data Commons
FX This work was funded by NIH R01 ARO73170 (A.E.O.;
   https://www.niams.nih.gov/), the Bio-X Stanford Interdisciplinary
   Graduate Fellowship awarded through the Stanford University School of
   Medicine (S.N.P.; https://biox.stanford.edu/research/phd-fellows), and
   Google Data Commons (R.V.G.; https://datacommons.org/).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 63
TC 0
Z9 0
U1 1
U2 3
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1553-734X
EI 1553-7358
J9 PLOS COMPUT BIOL
JI PLoS Comput. Biol.
PD SEP
PY 2021
VL 17
IS 9
AR e1009382
DI 10.1371/journal.pcbi.1009382
PG 29
WC Biochemical Research Methods; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA UT0VL
UT WOS:000697843100002
PM 34543288
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Hosseini, MB
   Breaux, TD
   Slavin, R
   Niu, JW
   Wang, XY
AF Hosseini, Mitra Bokaei
   Breaux, Travis D.
   Slavin, Rocky
   Niu, Jianwei
   Wang, Xiaoyin
TI Analyzing privacy policies through syntax-driven semantic analysis of
   information types
SO INFORMATION AND SOFTWARE TECHNOLOGY
LA English
DT Article
DE Privacy policy; Ambiguity; Generality; Ontology
ID REQUIREMENTS; FRAMEWORK; AMBIGUITY; LANGUAGE
AB Context: Several government laws and app markets, such as Google Play, require the disclosure of app data practices to users. These data practices constitute critical privacy requirements statements, since they underpin the app's functionality while describing how various personal information types are collected, used, and with whom they are shared.
   Objective: Abstract and ambiguous terminology in requirements statements concerning information types (e.g., "we collect your device information"), can reduce shared understanding among app developers, policy writers, and users.
   Method: To address this challenge, we propose a syntax-driven method that first parses a given information type phrase (e.g. mobile device identifier) into its constituents using a context-free grammar and second infers semantic relationships between constituents using semantic rules. The inferred semantic relationships between a given phrase and its constituents generate a hierarchy that models the generality and ambiguity of phrases. Through this method, we infer relations from a lexicon consisting of a set of information type phrases to populate a partial ontology. The resulting ontology is a knowledge graph that can be used to guide requirements authors in the selection of the most appropriate information type terms.
   Results: We evaluate the method's performance using two criteria: (1) expert assessment of relations between information types; and (2) non-expert preferences for relations between information types. The results suggest performance improvement when compared to a previously proposed method. We also evaluate the reliability of the method considering the information types extracted from different data practices (e.g., collection, usage, sharing, etc.) in privacy policies for mobile or web-based apps in various app domains.
   Contributions: The method achieves average of 89% precision and 87% recall considering information types from various app domains and data practices. Due to these results, we conclude that the method can be generalized reliably in inferring relations and reducing the ambiguity and abstraction in privacy policies.
C1 [Hosseini, Mitra Bokaei] St Marys Univ, 1 Camino Santa Maria, San Antonio, TX 78228 USA.
   [Breaux, Travis D.] Carnegie Mellon Univ, 5000 Forbes Ave, Pittsburgh, PA 15213 USA.
   [Slavin, Rocky; Niu, Jianwei; Wang, Xiaoyin] Univ Texas San Antonio, 1 UTSA Circle, San Antonio, TX USA.
RP Hosseini, MB (通讯作者)，St Marys Univ, 1 Camino Santa Maria, San Antonio, TX 78228 USA.
EM mbokaeihossein@stmarytx.edu; breaux@cs.cmu.edu; Rocky.Slavin@utsa.edu;
   Jianwei.Niu@utsa.edu; xiaoyin.wang@utsa.edu
FU NSF, United State [1736209, 1748109, 2007718, 1453139, 1948244]
FX We thank Xue Qin for her participation as an expert in experiment
   E<INF>1.2</INF>. This research was supported by NSF, United State awards
   #1736209, #1748109, #2007718, #1453139, and #1948244.
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U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-5849
EI 1873-6025
J9 INFORM SOFTWARE TECH
JI Inf. Softw. Technol.
PD OCT
PY 2021
VL 138
AR 106608
DI 10.1016/j.infsof.2021.106608
EA MAY 2021
PG 18
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA TI1GV
UT WOS:000672531500013
DA 2022-08-23
ER

PT J
AU Sun, B
   Kong, DH
   Wang, SF
   Li, JH
   Yin, BC
   Luo, XN
AF Sun, Bin
   Kong, Dehui
   Wang, Shaofan
   Li, Jinghua
   Yin, Baocai
   Luo, Xiaonan
TI GAN for vision, KG for relation: A two-stage network for zero-shot
   action recognition
SO PATTERN RECOGNITION
LA English
DT Article
DE Action recognition; Zero-shot learning; Generative adversarial networks;
   Graph convolution network
AB Zero-shot action recognition can recognize samples of unseen classes that are unavailable in training by exploring common latent semantic representation in samples. However, most methods neglected the connotative relation and extensional relation between the action classes, which leads to the poor generalization ability of the zero-shot learning. Furthermore, the learned classifier inclines to predict the samples of seen class, which leads to poor classification performance. To solve the above problems, we propose a two-stage deep neural network for zero-shot action recognition, which consists of a feature generation sub-network serving as the sampling stage and a graph attention sub-network serving as the classification stage. In the sampling stage, we utilize generative adversarial networks (GAN) trained by action features and word vectors of seen classes to synthesize the action features of unseen classes, which can balance the training sample data of seen classes and unseen classes. In the classification stage, we construct a knowledge graph (KG) based on the relationship between word vectors of action classes and related objects, and propose a graph convolution network (GCN) based on attention mechanism, which dynamically updates the relationship between action classes and objects, and enhances the generalization ability of zero-shot learning. In both stages, we all use word vectors as bridges for feature generation and classifier generalization from seen classes to unseen classes. We compare our method with state-of-theart methods on UCF101 and HMDB51 datasets. Experimental results show that our proposed method improves the classification performance of the trained classifier and achieves higher accuracy. (c) 2022 Elsevier Ltd. All rights reserved.
C1 [Sun, Bin; Kong, Dehui; Wang, Shaofan; Li, Jinghua; Yin, Baocai; Luo, Xiaonan] Beijing Univ Technol, Fac Informat Technol, Beijing Inst Artificial Intelligence, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China.
RP Wang, SF (通讯作者)，Beijing Univ Technol, Fac Informat Technol, Beijing Inst Artificial Intelligence, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China.
EM wangshaofan@bjut.edu.cn
FU National Key R&D Program of China [2021ZD0111900]; National Science
   Foundation of China [62172022, U21B2038, 61772049]; Beijing Natural
   Science Foundation [4202003]
FX Project supported by National Key R&D Program of China (2021ZD0111900) ,
   National Science Foundation of China (62172022 , U21B2038, 61772049) ,
   and Beijing Natural Science Foundation (4202003) .
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NR 40
TC 0
Z9 0
U1 10
U2 10
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 JUN
PY 2022
VL 126
AR 108563
DI 10.1016/j.patcog.2022.108563
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA ZH7AK
UT WOS:000761086800004
DA 2022-08-23
ER

PT J
AU Narayanasamy, SK
   Srinivasan, K
   Hu, YC
   Masilamani, SK
   Huang, KY
AF Narayanasamy, Senthil Kumar
   Srinivasan, Kathiravan
   Hu, Yuh-Chung
   Masilamani, Satish Kumar
   Huang, Kuo-Yi
TI A Contemporary Review on Utilizing Semantic Web Technologies in
   Healthcare, Virtual Communities, and Ontology-Based Information
   Processing Systems
SO ELECTRONICS
LA English
DT Article
DE semantic web; ontology; Web 2; 0; information retrieval; virtual
   communities; e-learning; healthcare
ID LINKED DATA; FEATURE-SELECTION; KNOWLEDGE GRAPHS; REPRESENTATIONS;
   DISCOVERY
AB The semantic web is an emerging technology that helps to connect different users to create their content and also facilitates the way of representing information in a manner that can be made understandable for computers. As the world is heading towards the fourth industrial revolution, the implicit utilization of artificial-intelligence-enabled semantic web technologies paves the way for many real-time application developments. The fundamental building blocks for the overwhelming utilization of semantic web technologies are ontologies, and it allows sharing as well as reusing the concepts in a standardized way so that the data gathered from heterogeneous sources receive a common nomenclature, and it paves the way for disambiguating the duplicates very easily. In this context, the right utilization of ontology capabilities would further strengthen its presence in many web-based applications such as e-learning, virtual communities, social media sites, healthcare, agriculture, etc. In this paper, we have given the comprehensive review of using the semantic web in the domain of healthcare, some virtual communities, and other information retrieval projects. As the role of semantic web is becoming pervasive in many domains, the demand for the semantic web in healthcare, virtual communities, and information retrieval has been gaining huge momentum in recent years. To obtain the correct sense of the meaning of the words or terms given in the textual content, it is deemed necessary to apply the right ontology to fix the ambiguity and shun any deviations that persist on the concepts. In this review paper, we have highlighted all the necessary information for a good understanding of the semantic web and its ontological frameworks.
C1 [Narayanasamy, Senthil Kumar] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India.
   [Srinivasan, Kathiravan] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore 632014, Tamil Nadu, India.
   [Hu, Yuh-Chung] Natl Ilan Univ, Dept Mech & Electromech Engn, Yilan 26047, Taiwan.
   [Masilamani, Satish Kumar] Sri Venkateshwara Coll Engn & Technol, Chittoor 517127, India.
   [Huang, Kuo-Yi] Natl Chung Hsing Univ, Dept Bioind Mechatron Engn, Taichung 402, Taiwan.
RP Huang, KY (通讯作者)，Natl Chung Hsing Univ, Dept Bioind Mechatron Engn, Taichung 402, Taiwan.
EM senthilkumar.n@vit.ac.in; kathiravan.srinivasan@vit.ac.in;
   ychu@niu.edu.tw; m.satishkumar@svcet.in; kuoyi@dragon.nchu.edu.tw
RI Srinivasan, Kathiravan/P-6125-2014
OI Srinivasan, Kathiravan/0000-0002-9352-0237; Hu,
   Yuh-Chung/0000-0003-0183-8182
FU Ministry of Science and Technology, Taiwan [MOST 110-2221-E-005-067-]
FX FundingThe authors thank the Ministry of Science and Technology, Taiwan,
   for financially supporting this research under Contract MOST
   110-2221-E-005-067-.
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NR 121
TC 0
Z9 0
U1 5
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD FEB
PY 2022
VL 11
IS 3
AR 453
DI 10.3390/electronics11030453
PG 28
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   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA YZ3IR
UT WOS:000755373400001
OA gold
DA 2022-08-23
ER

PT J
AU Wu, X
   Hong, DF
   Zhao, D
AF Wu, Xin
   Hong, Danfeng
   Zhao, Di
TI Hyper-Embedder: Learning a Deep Embedder for Self-Supervised
   Hyperspectral Dimensionality Reduction
SO IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
LA English
DT Article
DE Hyperspectral imaging; Manifolds; Dimensionality reduction; Training;
   Testing; Vegetation; Redundancy; Deep learning; dimensionality reduction
   (DR); hyperspectral data; manifold embedding; regression; remote
   sensing; self-supervised
ID MANIFOLD ALIGNMENT; LAND-COVER; FRAMEWORK
AB Hyperspectral imaging has attracted growing interest among researchers from the geoscience and remote sensing fields owing to its very rich spectral information. However, the high spectral dimensionality of hyperspectral images (HSI) tends to suffer from information redundancy. Manifold embedding is a mainstream strategy of nonlinear hyperspectral dimensionality reduction (DR). The sensitivity to the noise and the inflexibility to the out-of-sample problem (i.e., new samples) are the main drawbacks of the manifold embedding-based methods. To this end, we propose to learn a deep embedder in a self-supervised fashion for hyperspectral DR, called hyper-embedder. Hyper-embedder effectively reduces the computational complexity and storage-costing compared to conventional embedding models and improves the robustness against various noises, e.g., spectral variabilities. More significantly, hyper-embedder is capable of learning an explicit nonlinear mapping to make a one-to-one match between each original pixel (spectral signature) in the HSI and its dimension-reduced representation. These low-dimensional representations can be generated and given by existing and classic nonlinear manifold embedding methods. In this letter, we attempt to learn the correspondence or mapping by optimizing a deep regression network. The to-be-developed network cannot only capture the local topological knowledge graph of all spectral signatures of hyperspectral data but be applicable to fast prediction and inference of samples from other hyperspectral scenes. The proposed hyper-embedder outperforms existing state-of-the-art hyperspectral DR algorithms on two commonly used hyperspectral datasets, i.e., Indian pines and Augsburg scenes.
C1 [Wu, Xin] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China.
   [Wu, Xin] Beijing Key Lab Fract Signals & Syst, Beijing 100081, Peoples R China.
   [Hong, Danfeng] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China.
   [Zhao, Di] Yulin Normal Univ, Guangxi Coll & Univ, Key Lab Complex Syst Optimizat & Big Data Proc, Yulin 537000, Peoples R China.
   [Zhao, Di] Yulin Normal Univ, Sch Phys & Telecommun Engn, Yulin 537000, Peoples R China.
RP Hong, DF (通讯作者)，Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China.
EM 040251522wuxin@163.com; hongdf@aircas.ac.cn; zhaodi@ylu.edu.cn
FU China Postdoctoral Science Foundation [2021M690385]; National Natural
   Science Foundation of China [62101045]; Project of Yulin Normal
   University [G2019ZK03]
FX This work was supported in part by the China Postdoctoral Science
   Foundation under Project 2021M690385, in part by the National Natural
   Science Foundation of China under Grant 62101045, and in part by the
   Project of Yulin Normal University under Grant G2019ZK03. This article
   was presented in part at IGARSS2020 [1].
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NR 29
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 1545-598X
EI 1558-0571
J9 IEEE GEOSCI REMOTE S
JI IEEE Geosci. Remote Sens. Lett.
PY 2022
VL 19
AR 5510605
DI 10.1109/LGRS.2021.3119339
PG 5
WC Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote
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WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science
   & Photographic Technology
GA YG0ZJ
UT WOS:000742225800025
DA 2022-08-23
ER

PT J
AU Yu, LY
   Cheng, ML
   Qiu, WR
   Xiao, X
   Lin, WZ
AF Yu, Liyi
   Cheng, Meiling
   Qiu, Wangren
   Xiao, Xuan
   Lin, Weizhong
TI idse-HE: Hybrid embedding graph neural network for drug side effects
   prediction
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Drug side effect; Graph neural network; Drug molecular structure; Matrix
   reconstruction
ID INTEGRATION; SYSTEM; SMILES
AB In drug development, unexpected side effects are the main reason for the failure of candidate drug trials. Discovering potential side effects of drugs in silico can improve the success rate of drug screening. However, most previous works extracted and utilized an effective representation of drugs from a single perspective. These methods merely considered the topological information of drug in the biological entity network, or combined the association information (e.g. knowledge graph KG) between drug and other biomarkers, or only used the chemical structure or sequence information of drug.
   Consequently, to jointly learn drug features from both the macroscopic biological network and the microscopic drug molecules. We propose a hybrid embedding graph neural network model named idse-HE, which integrates graph embedding module and node embedding module. idse-HE can fuse the drug chemical structure information, the drug substructure sequence information and the drug network topology information. Our model deems the final representation of drugs and side effects as two implicit factors to reconstruct the original matrix and predicts the potential side effects of drugs. In the robustness experiment, idse-HE shows stable performance in all indicators. We reproduce the baselines under the same conditions, and the experimental results indicate that idse-HE is superior to other advanced methods. Finally, we also collect evidence to confirm several real drug side effect pairs in the predicted results, which were previously regarded as negative samples. More detailed information, scientific researchers can access the user-friendly web-server of idse-HE at http://bioinfo.jcu.edu.cn/idse-HE. In this server, users can obtain the original data and source code, and will be guided to reproduce the model results.
C1 [Yu, Liyi; Cheng, Meiling; Qiu, Wangren; Xiao, Xuan; Lin, Weizhong] Jingdezhen Ceram Inst, Sch Informat Engn, Jingdezhen 333403, Peoples R China.
RP Qiu, WR; Xiao, X (通讯作者)，Jingdezhen Ceram Inst, Sch Informat Engn, Jingdezhen 333403, Peoples R China.
EM qiuone@163.com; jdzxiaoxuan@163.com
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NR 52
TC 0
Z9 0
U1 3
U2 3
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD JUL
PY 2022
VL 131
AR 104098
DI 10.1016/j.jbi.2022.104098
PG 8
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA 2D5TA
UT WOS:000811608000001
PM 35636720
OA hybrid
DA 2022-08-23
ER

PT J
AU Kong, DH
   Li, XL
   Wang, SF
   Li, JH
   Yin, BC
AF Kong, Dehui
   Li, Xiliang
   Wang, Shaofan
   Li, Jinghua
   Yin, Baocai
TI Learning visual-and-semantic knowledge embedding for zero-shot image
   classification
SO APPLIED INTELLIGENCE
LA English
DT Article; Early Access
DE Zero-shot learning; Graph convolutional neural network; Image
   classification; Knowledge assistance
AB Recently, several classifier prediction methods have emerged exploiting knowledge graphs and Graph Convolutional Neural Network (GCN), achieving excellent results in the field of Zero-Shot Learning (ZSL). However, existing methods only rely on pre-trained seen class classifier parameters to guide the model's training, prohibiting the discriminative visual features from being mined and not guaranteeing the effective use of semantic features. Therefore, this work presents a novel Knowledge-Assisted ZSL Model (KAZSLM), which improves the classification ability by embedding visual information and semantic information into the classifier space. In this work, GCN classifier prediction network promoted by word embedding and inter-class relationships is employed as the Basic Framework (BF), which is then combined with a Visual Knowledge Assistant (VKA) module and a Semantic Knowledge Assistant (SKA) module to form KAZSLM. In the VKA module, the average visual feature of all the samples in each seen class and its corresponding class label are used to guide the model to refine the classifier at a lower computational cost. Regarding the SKA module, the samples' semantic features per class are applied to refine the classifier through a GCN with a loss function related to reconstruct each classes' semantic features from the corresponding classifier parameters. These two assistant modules allow visual knowledge and semantic knowledge to force the whole model to acquire more precise classifier. Moreover, a simple convolutional residual network is taken to further reinforce the performance of the model on the AWA2 dataset. Experimental results on the AWA2 and ImageNet datasets demonstrate that KAZSLM achieves a better image classification performance than current ZSL classification methods.
C1 [Kong, Dehui; Li, Xiliang; Wang, Shaofan; Li, Jinghua; Yin, Baocai] Beijing Univ Technol, Fac Informat Technol, Beijing Inst Artificial Intelligence, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China.
RP Kong, DH; Li, XL (通讯作者)，Beijing Univ Technol, Fac Informat Technol, Beijing Inst Artificial Intelligence, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China.
EM kdh@bjut.edu.cn; 1005839841@qq.com; wangshaofan@bjut.edu.cn;
   lijinghua@bjut.edu.cn; ybc@bjut.edu.cn
FU National Natural Science Foundation of China [61772049]; Beijing Natural
   Science Foundation [4202003]
FX National Natural Science Foundation of China(No. 61772049), Beijing
   Natural Science Foundation (4202003).
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NR 45
TC 0
Z9 0
U1 15
U2 15
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/s10489-022-03443-1
EA MAY 2022
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0Z4WS
UT WOS:000791080800001
DA 2022-08-23
ER

PT J
AU Yang, R
   Duan, ZW
   Du, MJ
   Miao, X
AF Yang, Rui
   Duan, Ziwei
   Du, Mengjin
   Miao, Xin
TI A Comprehensive Knowledge Pedigree on Environmental Transparency
SO POLISH JOURNAL OF ENVIRONMENTAL STUDIES
LA English
DT Review
DE environmental transparency; CiteSpace; knowledge pedigree; comprehensive
   review
ID CORPORATE SOCIAL-RESPONSIBILITY; INFORMATION DISCLOSURE; SCIENTOMETRIC
   ANALYSIS; EMERGING TRENDS; CHINA; CHAIN; DETERMINANTS; PERFORMANCE;
   LEGITIMACY; MANAGEMENT
AB Environmental transparency issue has drawn increasing concern, and the related research has shown explosive growth. A comprehensive knowledge pedigree of environmental transparency can help understand the spatial and temporal distribution of themes, research categories, cutting-edge research topics and development trends of environmental transparency research, providing research opportunities for the future. Based on the: Social Science Citation Index database in the Web of Science, this article retrieves 930 papers with the theme of "environment transparency" in all years, and analyzes the its knowledge system via the software of CiteSpace. Keywords co-occurrence analysis function is applied to identify keywords and their occurrence frequency in environmental transparency literature, and a keyword co-occurrence network was generated on the basis of high-frequency keywords to explore the knowledge base in the field of environmental transparency research. Then, through co-citation analysis and cluster analysis, the high-frequency co-citation literature and four hot research paradigms of environmental transparency are identified to present the knowledge domain of its research. Next, the paper uses the time zone and strong citation burst analysis to detect the situation where the reference volume changed significantly during a certain period of time, in order to discover the decline or rise of a key keyword in the beginning, development, and cutting-edge period in environmental transparency research field, which contributes to unearth the knowledge evolution path of environmental transparency and reveal how researches related to environmental transparency have evolved over time. Finally, according to the knowledge base, knowledge domain, and knowledge evolution, a visual knowledge graph is constructed to provide a comprehensive review of existing research in the field of environmental transparency.
C1 [Yang, Rui; Du, Mengjin; Miao, Xin] Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
   [Duan, Ziwei] Harbin Inst Technol, Sch Humanities Social Sci & Law, Harbin 15001, Peoples R China.
RP Miao, X (通讯作者)，Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
EM miaoxin@hit.edu.cn
FU National Natural Science Foundation of China [71471047]
FX This work is supported by the National Natural Science Foundation of
   China (Grant No. 71471047).
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NR 96
TC 2
Z9 2
U1 18
U2 55
PU HARD
PI OLSZTYN 5
PA POST-OFFICE BOX, 10-718 OLSZTYN 5, POLAND
SN 1230-1485
EI 2083-5906
J9 POL J ENVIRON STUD
JI Pol. J. Environ. Stud.
PY 2021
VL 30
IS 1
BP 535
EP 554
DI 10.15244/pjoes/123290
PG 20
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA OW1DN
UT WOS:000592636800004
OA gold
DA 2022-08-23
ER

PT J
AU McLamore, ES
   Datta, SPA
   Morgan, V
   Cavallaro, N
   Kiker, G
   Jenkins, DM
   Rong, Y
   Gomes, C
   Claussen, J
   Vanegas, D
   Alocilja, EC
AF McLamore, Eric S.
   Datta, Shoumen Palit Austin
   Morgan, Victoria
   Cavallaro, Nicholas
   Kiker, Greg
   Jenkins, Daniel M.
   Rong, Yue
   Gomes, Carmen
   Claussen, Jonathan
   Vanegas, Diana
   Alocilja, Evangelyn C.
TI SNAPS: Sensor Analytics Point Solutions for Detection and Decision
   Support Systems
SO SENSORS
LA English
DT Review
DE sensor; smart systems; data analytics; cyber-physical systems;
   artificial reasoning tools; ART; drag and drop analytics; DADA;
   sensor-analytics point solutions; SNAPS; sense-analyze-respond-actuate;
   SARA; machine-assisted tools; MAT; machine-assisted platform; MAP;
   knowledge graphs; trans-disciplinary convergence
ID HEALTH-RISK ASSESSMENT; PLANETARY HEALTH; RAPID DETECTION;
   WATER-QUALITY; BIOSENSORS; GRAPHENE; FUTURE; ALGORITHM; NANOPARTICLES;
   IMPEDANCE
AB In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools.
C1 [McLamore, Eric S.; Datta, Shoumen Palit Austin; Morgan, Victoria; Cavallaro, Nicholas; Kiker, Greg; Rong, Yue] Univ Florida, Inst Food & Agr Sci, Agr & Biol Engn, Gainesville, FL 32611 USA.
   [Datta, Shoumen Palit Austin] MIT, MIT Auto ID Labs, Dept Mech Engn, Cambridge, MA 02139 USA.
   [Datta, Shoumen Palit Austin] Harvard Med Sch, Massachusetts Gen Hosp, Dept Anesthesiol, MDPnP Labs,Biomed Engn Program, 65 Landsdowne St, Cambridge, MA 02139 USA.
   [Jenkins, Daniel M.] Univ Hawaii Manoa, Mol Biosci & Bioengn, Honolulu, HI 96822 USA.
   [Gomes, Carmen] Iowa State Univ, Mech Engn, Ames, IA 50011 USA.
   [Claussen, Jonathan] Iowa State Univ, Mech Engn Dept, Ames, IA 50011 USA.
   [Claussen, Jonathan] Ames Lab, Ames, IA 50011 USA.
   [Vanegas, Diana] Clemson Univ, Environm Engn & Earth Sci, Clemson, SC 29634 USA.
   [Alocilja, Evangelyn C.] Michigan State Univ, Global Alliance Rapid Diagnost, E Lansing, MI 48824 USA.
   [Alocilja, Evangelyn C.] Michigan State Univ, Nanobiosensors Lab, E Lansing, MI 48824 USA.
RP McLamore, ES (通讯作者)，Univ Florida, Inst Food & Agr Sci, Agr & Biol Engn, Gainesville, FL 32611 USA.
EM emclamore@ufl.edu; shoumendatta@ufl.edu; tvlmorgan@ufl.edu;
   ncavallaro@ufl.edu; gkiker@ufl.edu; danielje@hawaii.edu;
   yuerong@ufl.edu; carmen@iastate.edu; jcclauss@iastate.edu;
   dvanega@clemson.edu; alocilja@msu.edu
RI Claussen, Jonathan C/N-5559-2018; Vanegas, Diana/K-9187-2019; Rong,
   Yue/ABA-5348-2020; McLamore, Eric S/H-3441-2011; Kiker,
   Gregory/K-6149-2017; Gomes, Carmen/J-5091-2015
OI Vanegas, Diana/0000-0001-9858-0960; McLamore, Eric
   S/0000-0002-1662-7372; Kiker, Gregory/0000-0001-6215-0686; Rong,
   Yue/0000-0003-2456-8584; Jenkins, Daniel/0000-0003-1616-0845; Gomes,
   Carmen/0000-0003-0095-6478
FU Agriculture and Food Research Initiative Competitive from the USDA
   National Institute of Food and Agriculture [2018-67016-27578]; NSF
   [1805512, 1511953]
FX This project was supported by Agriculture and Food Research Initiative
   Competitive Grant no. 2018-67016-27578 awarded as a Center of Excellence
   from the USDA National Institute of Food and Agriculture. The authors
   also acknowledge NSF project 1805512 (Biosensing) and NSF project
   1511953 (Nanobiosensing) for financial support.
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NR 116
TC 7
Z9 7
U1 2
U2 9
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD NOV
PY 2019
VL 19
IS 22
AR 4935
DI 10.3390/s19224935
PG 22
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA JW9QY
UT WOS:000503381500114
PM 31766116
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ahmed, U
   Srivastava, G
   Djenouri, Y
   Lin, JCW
AF Ahmed, Usman
   Srivastava, Gautam
   Djenouri, Youcef
   Lin, Jerry Chun-Wei
TI Deviation Point Curriculum Learning for Trajectory Outlier Detection in
   Cooperative Intelligent Transport Systems
SO IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
LA English
DT Article; Early Access
DE Trajectory; Public transportation; Anomaly detection; Roads; Monitoring;
   Intelligent sensors; Databases; Trajectory analysis; outlier detection;
   data mining; road traffic management; smart city application
ID ANOMALY DETECTION
AB Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes' neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increased F-measure by 5.06% and 2.4% while compared with generic Node2Vec and Struct2Vec methods.
C1 [Ahmed, Usman; Lin, Jerry Chun-Wei] Western Norway Univ Appl Sci, Dept Comp Sci Elect Engn & Math Sci, N-5063 Bergen, Norway.
   [Srivastava, Gautam] Brandon Univ, Dept Math & Comp Sci, Brandon, MB R7A 6A9, Canada.
   [Srivastava, Gautam] China Med Univ, Res Ctr Interneural Comp, Taichung 406040, Taiwan.
   [Djenouri, Youcef] SINTEF Digital, Dept Math & Cybernet, N-0373 Oslo, 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 usman.ahmed@hvl.no; srivastavag@brandonu.ca; youcef.djenouri@sintef.no;
   jerrylin@ieee.org
OI Srivastava, Gautam/0000-0001-9851-4103; Djenouri,
   Youcef/0000-0003-0135-7450; Lin, Jerry Chun-Wei/0000-0001-8768-9709
FU National Centre for Research and Development
   [NOR/POLNOR/CoBotAGV/0027/2019-00]
FX This work was supported in part by the National Centre for Research and
   Development under the project Automated Guided Vehicles Integrated With
   Collaborative Robots for Smart Industry Perspective under Contract
   NOR/POLNOR/CoBotAGV/0027/2019-00.
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NR 35
TC 3
Z9 3
U1 10
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1524-9050
EI 1558-0016
J9 IEEE T INTELL TRANSP
JI IEEE Trans. Intell. Transp. Syst.
DI 10.1109/TITS.2021.3131793
EA DEC 2021
PG 10
WC Engineering, Civil; Engineering, Electrical & Electronic; Transportation
   Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Transportation
GA XR4GH
UT WOS:000732189200001
DA 2022-08-23
ER

PT J
AU Shang, JY
   Sun, YN
AF Shang, Jiayu
   Sun, Yanni
TI Predicting the hosts of prokaryotic viruses using GCN-based
   semi-supervised learning
SO BMC BIOLOGY
LA English
DT Article
DE Prediction of virus-host interactions; Deep learning; Graph
   convolutional neural network
ID PHAGE
AB Background Prokaryotic viruses, which infect bacteria and archaea, are the most abundant and diverse biological entities in the biosphere. To understand their regulatory roles in various ecosystems and to harness the potential of bacteriophages for use in therapy, more knowledge of viral-host relationships is required. High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect. However, there are two main challenges for computational host prediction. First, the empirically known virus-host relationships are very limited. Second, although sequence similarity between viruses and their prokaryote hosts have been used as a major feature for host prediction, the alignment is either missing or ambiguous in many cases. Thus, there is still a need to improve the accuracy of host prediction. Results In this work, we present a semi-supervised learning model, named HostG, to conduct host prediction for novel viruses. We construct a knowledge graph by utilizing both virus-virus protein similarity and virus-host DNA sequence similarity. Then graph convolutional network (GCN) is adopted to exploit viruses with or without known hosts in training to enhance the learning ability. During the GCN training, we minimize the expected calibrated error (ECE) to ensure the confidence of the predictions. We tested HostG on both simulated and real sequencing data and compared its performance with other state-of-the-art methods specifically designed for virus host classification (VHM-net, WIsH, PHP, HoPhage, RaFAH, vHULK, and VPF-Class). Conclusion HostG outperforms other popular methods, demonstrating the efficacy of using a GCN-based semi-supervised learning approach. A particular advantage of HostG is its ability to predict hosts from new taxa.
C1 [Shang, Jiayu; Sun, Yanni] City Univ Hong Kong, Elect Engn, Hong Kong, Peoples R China.
RP Sun, YN (通讯作者)，City Univ Hong Kong, Elect Engn, Hong Kong, Peoples R China.
EM yannisun@cityu.edu.hk
RI Shang, Jiayu/AAS-1130-2021; SUN, Yanni/AHC-4163-2022
OI Shang, Jiayu/0000-0001-5974-4985; SUN, Yanni/0000-0003-1373-8023
FU Hong Kong Innovation and Technology Commission and City University of
   Hong Kong [7005453]; HKIDS [9360163]
FX This work was supported by the Hong Kong Innovation and Technology
   Commission and City University of Hong Kong (Project 7005453) and HKIDS
   (9360163).
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TC 1
Z9 1
U1 10
U2 15
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1741-7007
J9 BMC BIOL
JI BMC Biol.
PD NOV 24
PY 2021
VL 19
IS 1
AR 250
DI 10.1186/s12915-021-01180-4
PG 15
WC Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics
GA XC8DK
UT WOS:000722239500003
PM 34819064
OA Green Published, gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Zhang, KL
   Huang, W
   Hou, XY
   Xu, JH
   Su, RD
   Xu, HY
AF Zhang, Kunlin
   Huang, Wei
   Hou, Xiaoyu
   Xu, Jihui
   Su, Ruidan
   Xu, Huaiyu
TI A Fault Diagnosis and Visualization Method for High-Speed Train Based on
   Edge and Cloud Collaboration
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE edge intelligence; fault diagnosis; deep learning; edge cloud
   collaboration; visual analysis
ID NEURAL-NETWORKS; BEARING; ALGORITHM; SYSTEM; OPTIMIZATION; MOTOR
AB Safety is the most important aspect of railway transportation. To ensure the safety of high-speed trains, various train components are equipped with sensor devices for real-time monitoring. Sensor monitoring data can be used for fast intelligent diagnosis and accurate positioning of train faults. However, existing train fault diagnosis technology based on cloud computing has disadvantages of long processing times and high consumption of computing resources, which conflict with the real-time response requirements of fault diagnosis. Aiming at the problems of train fault diagnosis in the cloud environment, this paper proposes a train fault diagnosis model based on edge and cloud collaboration. The model first utilizes a SAES-DNN (stacked auto-encoders deep neural network) fault recognition method, which can integrate automatic feature extraction and type recognition and complete fault classification over deep hidden features in high-dimensional data, so as to quickly locate faults. Next, to adapt to the characteristics of edge computing, the model applies a SAES-DNN model trained in the cloud and deployed in the edge via the transfer learning strategy and carries out real-time fault diagnosis on the vehicle sensor monitoring data. Using a motor fault as an example, when compared with a similar intelligent learning model, the proposed intelligent fault diagnosis model can greatly improve diagnosis accuracy and significantly reduce training time. Through the transfer learning approach, adaptability of the fault diagnosis algorithm for personalized applications and real-time performance of the fault diagnosis is enhanced. This paper also proposes a visual analysis method of train fault data based on knowledge graphs, which can effectively analyze fault causes and fault correlation.
C1 [Zhang, Kunlin; Huang, Wei; Hou, Xiaoyu; Xu, Jihui; Su, Ruidan; Xu, Huaiyu] Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai 201210, Peoples R China.
   [Zhang, Kunlin; Huang, Wei; Hou, Xiaoyu; Xu, Jihui] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
RP Su, RD; Xu, HY (通讯作者)，Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai 201210, Peoples R China.
EM zhangkl@sari.ac.cn; huangw@sari.ac.cn; houxiaoyu@sari.ac.cn;
   xujh@sari.ac.cn; surd@sari.ac.cn; xuhuaiyu@sari.ac.cn
OI xu, ji hui/0000-0003-3647-9765; SU, RUIDAN/0000-0003-0058-4088; zhang,
   kun lin/0000-0003-0284-1666
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NR 52
TC 1
Z9 1
U1 13
U2 41
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 1251
DI 10.3390/app11031251
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 QC6MG
UT WOS:000614949200001
OA Green Published
DA 2022-08-23
ER

PT J
AU Liu, Z
   Wang, XN
   Yu, H
   Shi, JY
   Dong, WM
AF Liu, Zun
   Wang, Xing-Nan
   Yu, Hui
   Shi, Jian-Yu
   Dong, Wen-Min
TI Predict multi-type drug-drug interactions in cold start scenario
SO BMC BIOINFORMATICS
LA English
DT Article
DE Machine learning; Drug-drug interactions; Multi-type interactions;
   Prediction; Cold start
ID KNOWLEDGE GRAPH
AB Background: Prediction of drug-drug interactions (DDIs) can reveal potential adverse pharmacological reactions between drugs in co-medication. Various methods have been proposed to address this issue. Most of them focus on the traditional link prediction between drugs, however, they ignore the cold-start scenario, which requires the prediction between known drugs having approved DDIs and new drugs having no DDI. Moreover, they're restricted to infer whether DDIs occur, but are not able to deduce diverse DDI types, which are important in clinics.
   Results: In this paper, we propose a cold start prediction model for both single-type and multiple-type drug-drug interactions, referred to as CSMDDI. CSMDDI predict not only whether two drugs trigger pharmacological reactions but also what reaction types they induce in the cold start scenario. We implement several embedding methods in CSMDDI, including SVD, GAE,TransE, RESCAL and compare it with the state-of-the-art multi-type DDI prediction method DeepDDl and DDIMDL to verify the performance. The comparison shows that CSMDDI achieves a good performance of DDI prediction in the case of both the occurrence prediction and the multi-type reaction prediction in cold start scenario.
   Conclusions: Our approach is able to predict not only conventional binary DDIs but also what reaction types they induce in the cold start scenario. More importantly, it learns a mapping function who can bridge the drugs attributes to their network embeddings to predict DDIs. The main contribution of CSMDDI contains the development of a generalized framework to predict the single-type and multi-type of DDIs in the cold start scenario, as well as the implementations of several embedding models for both single-type and multi-type of DDIs. The dataset and source code can be accessed at https://github.com/itsosy/csmddi.
C1 [Liu, Zun; Wang, Xing-Nan; Yu, Hui; Dong, Wen-Min] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.
   [Shi, Jian-Yu] Northwestern Polytech Univ, Sch Life Sci, Xian 710072, Peoples R China.
RP Yu, H (通讯作者)，Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.; Shi, JY (通讯作者)，Northwestern Polytech Univ, Sch Life Sci, Xian 710072, Peoples R China.
EM huiyu@nwpu.edu.cn; jianyushi@nwpu.edu.cn
RI Yu, Hui/ADG-1933-2022; Shi, Jian-Yu/S-4485-2019
OI Yu, Hui/0000-0002-5314-8127; Shi, Jian-Yu/0000-0002-2303-273X
FU National Nature Science Foundation of China [61872297]; Shaanxi
   Provincial Key Research and Development Program, China [2020KW-063]
FX This work was supported by National Nature Science Foundation of China
   (Grant No. 61872297) and Shaanxi Provincial Key Research and Development
   Program, China (Grand No. 2020KW-063).
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NR 26
TC 0
Z9 0
U1 12
U2 12
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD FEB 16
PY 2022
VL 23
IS 1
AR 75
DI 10.1186/s12859-022-04610-4
PG 13
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 ZB4NS
UT WOS:000756821200001
PM 35172712
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Berger, G
   Gottlob, G
   Pieris, A
   Sallinger, E
AF Berger, Gerald
   Gottlob, Georg
   Pieris, Andreas
   Sallinger, Emanuel
TI The Space-Efficient Core of Vadalog
SO ACM TRANSACTIONS ON DATABASE SYSTEMS
LA English
DT Article
DE Reasoning; query answering; Datalog; tuple-generating dependencies;
   complexity; expressive power
AB Vadalog is a system for performing complex reasoning tasks such as those required in advanced knowledge graphs. The logical core of the underlying Vadalog language is the warded fragment of tuple-generating dependencies (TGDs). This formalism ensures tractable reasoning in data complexity, while a recent analysis focusing on a practical implementation led to the reasoning algorithm around which the Vadalog system is built. A fundamental question that has emerged in the context of Vadalog is whether we can limit the recursion allowed by wardedness in order to obtain a formalism that provides a convenient syntax for expressing useful recursive statements, and at the same time achieves space-efficiency. After analyzing several real-life examples of warded sets of TGDs provided by our industrial partners, as well as recent benchmarks, we observed that recursion is often used in a restricted way: the body of a TGD contains at most one atom whose predicate is mutually recursive with a predicate in the head. We show that this type of recursion, known as piece-wise linear in the Datalog literature, is the answer to our main question. We further show that piece-wise linear recursion alone, without the wardedness condition, is not enough as it leads to undecidability. We also study the relative expressiveness of the query languages based on (piece-wise linear) warded sets of TGDs. Finally, we give preliminary experimental evidence for the practical effect of piece-wise linearity on Vadalog.
C1 [Berger, Gerald; Gottlob, Georg; Sallinger, Emanuel] TU Wien, Favoritenstr 9-11, A-1040 Vienna, Austria.
   [Gottlob, Georg; Sallinger, Emanuel] Univ Oxford, Wolfson Bldg,Parks Rd, Oxford OX1 3QD, England.
   [Pieris, Andreas] Univ Edinburgh, Crichton St, Edinburgh EH8 9AB, Midlothian, Scotland.
   [Pieris, Andreas] Univ Cyprus, Informat Forum, Nicosia, Cyprus.
RP Berger, G (通讯作者)，TU Wien, Favoritenstr 9-11, A-1040 Vienna, Austria.
EM georg.gottlob@cs.ox.ac.uk; georg.gottlob@cs.ox.ac.uk;
   apieris@inf.ed.ac.uk; emanuel.sallinger@cs.ox.ac.uk
FU Royal Society Research Professorship [RP/R1/201074]; EPSRC
   [EP/S003800/1]; Vienna Science and Technology Fund (WWTF) [VRG18-013];
   EPSRC Programme Grant [EP/M025268/1]
FX Georg Gottlob was supported by his Royal Society Research Professorship,
   grant "RAISON DATA" RP/R1/201074. Andreas Pieris was supported by the
   EPSRC grant EP/S003800/1 "EQUID". Emanuel Sallinger was supported by the
   Vienna Science and Technology Fund (WWTF) grant VRG18-013. This work was
   also supported by the EPSRC Programme Grant EP/M025268/1 "VADA".
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NR 24
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 0362-5915
EI 1557-4644
J9 ACM T DATABASE SYST
JI ACM Trans. Database Syst.
PD MAR
PY 2022
VL 47
IS 1
AR 1
DI 10.1145/3488720
PG 46
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0Z7HI
UT WOS:000791244500001
OA Green Published, Green Submitted, Green Accepted
DA 2022-08-23
ER

PT J
AU Geng, ZQ
   Chen, GF
   Han, YM
   Lu, G
   Li, F
AF Geng, ZhiQiang
   Chen, GuoFei
   Han, YongMing
   Lu, Gang
   Li, Fang
TI Semantic relation extraction using sequential and tree-structured LSTM
   with attention
SO INFORMATION SCIENCES
LA English
DT Article
DE Semantic relation extraction; Dependency syntax; Tree-structured LSTM;
   Attention
ID JOINT ENTITY; IMAGE
AB Semantic relation extraction is crucial to automatically constructing a knowledge graph (KG), and it supports a variety of downstream natural language processing (NLP) tasks such as query answering (QA), semantic search and textual entailment. In addition, the semantic relation extraction task is mainly responsible for identifying entity pairs from raw texts and extracting the semantic relations between the extracted entity pairs. Existing methods consider only lexical-level features and often ignore syntactic features, resulting in poor relation extraction performance. By analyzing the necessity of the syntactic dependency and the contributions of words in a sentence to relation extraction, this paper proposes an end-to-end method that uses bidirectional tree-structured long short-term memory (LSTM) to extract structural features based on the dependency tree of a sentence. To enhance the performance of the relation extraction, the bidirectional sequential LSTM with attention is used to identify word-based features including the positional information of entity pairs and the contribution of words. Then, structural features and word-based features are concatenated to optimize the relation extraction performance. Finally, the proposed method is used on the SemEval 2010 task 8 and the CoNLL04 datasets to validate its performance. The experimental results show that the proposed method achieves state-of-the-art results on the SemEval 2010 task 8 and the CoNLL04 datasets. (C) 2019 Elsevier Inc. All rights reserved.
C1 [Geng, ZhiQiang; Chen, GuoFei; Han, YongMing; Lu, Gang; Li, Fang] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing, Peoples R China.
   [Geng, ZhiQiang; Chen, GuoFei; Han, YongMing; Lu, Gang; Li, Fang] Minist Educ China, Engn Res Ctr Intelligent PSE, Beijing 100029, Peoples R China.
RP Han, YM (通讯作者)，Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing, Peoples R China.; Han, YM (通讯作者)，Minist Educ China, Engn Res Ctr Intelligent PSE, Beijing 100029, Peoples R China.
EM hanym@mail.buct.edu.cn
FU National Key Research and Development Program of China [2017YFC1601800];
   National Natural Science Foundation of China [61673046, 61603025];
   Science and Technology Major Project of Guizhou Province (Guizhou
   Branch) [[2018]3002]; Fundamental Research Funds for the Central
   Universities [XK1802-4]
FX This work is partly financial supported by the National Key Research and
   Development Program of China (2017YFC1601800), the National Natural
   Science Foundation of China (61673046 and 61603025), Science and
   Technology Major Project of Guizhou Province (Guizhou Branch [2018]3002)
   and the Fundamental Research Funds for the Central Universities
   (XK1802-4).
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NR 46
TC 70
Z9 73
U1 17
U2 103
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0020-0255
EI 1872-6291
J9 INFORM SCIENCES
JI Inf. Sci.
PD JAN
PY 2020
VL 509
BP 183
EP 192
DI 10.1016/j.ins.2019.09.006
PG 10
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JK5LA
UT WOS:000494883700012
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Gavriilidis, GI
   Dimitriadis, VK
   Jaulent, MC
   Natsiavas, P
AF Gavriilidis, George I.
   Dimitriadis, Vlasios K.
   Jaulent, Marie-Christine
   Natsiavas, Pantelis
TI Identifying Actionability as a Key Factor for the Adoption of
   'Intelligent' Systems for Drug Safety: Lessons Learned from a
   User-Centred Design Approach
SO DRUG SAFETY
LA English
DT Article
ID HEALTH-CARE DATABASES; CHANGE-POINT ANALYSIS; BIG DATA;
   ARTIFICIAL-INTELLIGENCE; SIGNAL-DETECTION; PHARMACOVIGILANCE; HYPE
AB Introduction Information technology (IT) plays an important role in the healthcare landscape via the increasing digitization of medical data and the use of modern computational paradigms such as machine learning (ML) and knowledge graphs (KGs). These 'intelligent' technical paradigms provide a new digital 'toolkit' supporting drug safety and healthcare processes, including 'active pharmacovigilance'. While these technical paradigms are promising, intelligent systems (ISs) are not yet widely adopted by pharmacovigilance (PV) stakeholders, namely the pharma industry, academia/research community, drug safety monitoring organizations, regulatory authorities, and healthcare institutions. The limitations obscuring the integration of ISs into PV activities are multifaceted, involving technical, legal and medical hurdles, and thus require further elucidation. Objective We dissect the abovementioned limitations by describing the lessons learned during the design and implementation of the PVClinical platform, a web platform aiming to support the investigation of potential adverse drug reactions (ADRs), emphasizing the use of knowledge engineering (KE) as its main technical paradigm. Results To this end, we elaborate on the related 'business processes' (i.e. operational processes) and 'user goals' identified as part of the PVClinical platform design process based on Design Thinking principles. We also elaborate on key challenges restricting the adoption of such ISs and their integration in the clinical setting and beyond. Conclusions We highlight the fact that beyond providing analytics and useful statistics to the end user, 'actionability' has emerged as the operational priority identified through the whole process. Furthermore, we focus on the needs for valid, reproducible, explainable and human-interpretable results, stressing the need to emphasize on usability.
C1 [Gavriilidis, George I.; Dimitriadis, Vlasios K.; Natsiavas, Pantelis] Ctr Res & Technol Hellas, Inst Appl Biosci, 6th Km Charilaou,Thermi Rd,POB 60361, Thessaloniki, Greece.
   [Jaulent, Marie-Christine; Natsiavas, Pantelis] Univ Paris 13, Sorbonne Univ, Lab Informat Med & Ingn Connaissances & Sante, LIMICS,INSERM, F-75006 Paris, France.
RP Natsiavas, P (通讯作者)，Ctr Res & Technol Hellas, Inst Appl Biosci, 6th Km Charilaou,Thermi Rd,POB 60361, Thessaloniki, Greece.; Natsiavas, P (通讯作者)，Univ Paris 13, Sorbonne Univ, Lab Informat Med & Ingn Connaissances & Sante, LIMICS,INSERM, F-75006 Paris, France.
EM ggeorav@certh.gr; blasisd@certh.gr; marie-christine.jaulent@inserm.fr;
   pnatsiavas@certh.gr
RI Natsiavas, Pantelis/P-1629-2018; Jaulent, Marie-Christine/G-1065-2013
OI Natsiavas, Pantelis/0000-0002-4061-9815; Jaulent,
   Marie-Christine/0000-0003-4445-7494
FU European Union; Greek national funds through the Operational Program
   Competitiveness, Entrepreneurship and Innovation [1EDK-03789]
FX This research has been co-financed by the European Union and Greek
   national funds through the Operational Program Competitiveness,
   Entrepreneurship and Innovation, under the call RESEARCH -CREATE
   -INNOVATE (project code:.1EDK-03789).
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NR 43
TC 0
Z9 0
U1 0
U2 2
PU ADIS INT LTD
PI NORTHCOTE
PA 5 THE WAREHOUSE WAY, NORTHCOTE 0627, AUCKLAND, NEW ZEALAND
SN 0114-5916
EI 1179-1942
J9 DRUG SAFETY
JI Drug Saf.
PD NOV
PY 2021
VL 44
IS 11
BP 1165
EP 1178
DI 10.1007/s40264-021-01103-w
EA OCT 2021
PG 14
WC Public, Environmental & Occupational Health; Pharmacology & Pharmacy;
   Toxicology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Public, Environmental & Occupational Health; Pharmacology & Pharmacy;
   Toxicology
GA WO4BU
UT WOS:000709766300001
PM 34674190
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Wang, Y
   Sun, YN
   Ma, ZC
   Gao, LS
   Xu, Y
AF Wang, Yu
   Sun, Yining
   Ma, Zuchang
   Gao, Lisheng
   Xu, Yang
TI An ERNIE-Based Joint Model for Chinese Named Entity Recognition
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE joint training; named entity recognition; pre-training models; ERNIE;
   BERT
AB Named Entity Recognition (NER) is the fundamental task for Natural Language Processing (NLP) and the initial step in building a Knowledge Graph (KG). Recently, BERT (Bidirectional Encoder Representations from Transformers), which is a pre-training model, has achieved state-of-the-art (SOTA) results in various NLP tasks, including the NER. However, Chinese NER is still a more challenging task for BERT because there are no physical separations between Chinese words, and BERT can only obtain the representations of Chinese characters. Nevertheless, the Chinese NER cannot be well handled with character-level representations, because the meaning of a Chinese word is quite different from that of the characters, which make up the word. ERNIE (Enhanced Representation through kNowledge IntEgration), which is an improved pre-training model of BERT, is more suitable for Chinese NER because it is designed to learn language representations enhanced by the knowledge masking strategy. However, the potential of ERNIE has not been fully explored. ERNIE only utilizes the token-level features and ignores the sentence-level feature when performing the NER task. In this paper, we propose the ERNIE-Joint, which is a joint model based on ERNIE. The ERNIE-Joint can utilize both the sentence-level and token-level features by joint training the NER and text classification tasks. In order to use the raw NER datasets for joint training and avoid additional annotations, we perform the text classification task according to the number of entities in the sentences. The experiments are conducted on two datasets: MSRA-NER and Weibo. These datasets contain Chinese news data and Chinese social media data, respectively. The results demonstrate that the ERNIE-Joint not only outperforms BERT and ERNIE but also achieves the SOTA results on both datasets.
C1 [Wang, Yu; Sun, Yining; Ma, Zuchang; Gao, Lisheng; Xu, Yang] Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, AnHui Prov Key Lab Med Phys & Technol, Hefei 230031, Peoples R China.
   [Wang, Yu; Sun, Yining] Univ Sci & Technol China, Grad Sch, Sci Isl Branch, Hefei 230026, Peoples R China.
RP Sun, YN (通讯作者)，Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, AnHui Prov Key Lab Med Phys & Technol, Hefei 230031, Peoples R China.; Sun, YN (通讯作者)，Univ Sci & Technol China, Grad Sch, Sci Isl Branch, Hefei 230026, Peoples R China.
EM briskyu@mail.ustc.edu.cn; ynsun@iim.ac.cn; zcma@iim.ac.cn;
   lsgao@iim.ac.cn; yxu@hfcas.ac.cn
OI gao, li sheng/0000-0002-2510-1663; Wang, Yu/0000-0003-3096-2481
FU major special project of Anhui Science and Technology Department
   [18030801133]; Science and Technology Service Network Initiative
   [KFJ-STS-ZDTP-079]
FX This research was funded by the major special project of Anhui Science
   and Technology Department grant number 18030801133, and Science and
   Technology Service Network Initiative grant number KFJ-STS-ZDTP-079.
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NR 33
TC 7
Z9 9
U1 26
U2 74
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD AUG
PY 2020
VL 10
IS 16
AR 5711
DI 10.3390/app10165711
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 NL0VI
UT WOS:000567143300001
OA gold, Green Submitted
DA 2022-08-23
ER

PT J
AU Yang, J
   Ma, WZ
   Zhang, M
   Zhou, X
   Liu, YQ
   Ma, SP
AF Yang, Jun
   Ma, Weizhi
   Zhang, Min
   Zhou, Xin
   Liu, Yiqun
   Ma, Shaoping
TI LegalGNN: Legal Information Enhanced Graph Neural Network for
   Recommendation
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE Legal information recommendation; heterogeneous environments;
   heterogeneous information network; graph neural network
AB Recommendation in legal scenario (Legal-Rec) is a specialized recommendation task that aims to provide potential helpful legal documents for users. While there are mainly three differences compared with traditional recommendation: (1) Both the structural connections and textual contents of legal information are important in the Legal-Rec scenario, which means feature fusion is very important here. (2) Legal-Rec users prefer the newest legal cases (the latest legal interpretation and legal practice), which leads to a severe new-item problem. (3) Different from users in other scenarios, most Legal-Rec users are expert and domain-related users. They often concentrate on several topics and have more stable information needs. So it is important to accurately model user interests here. To the best of our knowledge, existing recommendation work cannot handle these challenges simultaneously.
   To address these challenges, we propose a legal information enhanced graph neural network-based recommendation framework (LegalGNN). First, a unified legal content and structure representation model is designed for feature fusion, where the Heterogeneous Legal Information Network (HLIN) is constructed to connect the structural features (e.g., knowledge graph) and contextual features (e.g., the content of legal documents) for training. Second, to model user interests, we incorporate the queries users issued in legal systems into the HLIN and link them with both retrieved documents and inquired users. This extra information is not only helpful for estimating user preferences, but also valuable for cold users/items (with less interaction history) in this scenario. Third, a graph neural network with relational attention mechanism is applied to make use of high-order connections in HLIN for Legal-Rec. Experimental results on a real-world legal dataset verify that LegalGNN outperforms several state-of-the-art methods significantly. As far as we know, LegalGNN is the first graph neural model for legal recommendation.
C1 [Yang, Jun; Ma, Weizhi; Zhang, Min; Liu, Yiqun; Ma, Shaoping] Tsinghua Univ, 30 Shuangqing Rd, Beijing, Peoples R China.
   [Zhou, Xin] IBM Res, Beijing, Peoples R China.
RP Zhang, M (通讯作者)，Tsinghua Univ, 30 Shuangqing Rd, Beijing, Peoples R China.
EM yangjun18@mails.tsinghua.edu.cn; mawz@tsinghua.edu.cn;
   z-m@tsinghua.edu.cn; zhouxin@cn.ibm.com; yiqunliu@tsinghua.edu.cn;
   msp@tsinghua.edu.cn
FU National Key Research and Development Program of China [2018YFC0831700,
   2018YFC0831900]; Natural Science Foundation of China [62002191,
   61672311, 61532011]; IBM Global Academic Award - China Postdoctoral
   Science Foundation [2020M670339]; Shuimu Tsinghua Scholar Program
FX This work is partly supported by the National Key Research and
   Development Program of China (2018YFC0831700, 2018YFC0831900), Natural
   Science Foundation of China (Grant Nos. 62002191, 61672311, 61532011)
   and Tsinghua University Guoqiang Research Institute. This article is
   based upon work supported by an IBM Global Academic Award and this
   project is also funded by China Postdoctoral Science Foundation
   (2020M670339). Dr. Weizhi Ma has been supported by Shuimu Tsinghua
   Scholar Program.
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NR 61
TC 1
Z9 1
U1 9
U2 9
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1046-8188
EI 1558-2868
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
PD APR
PY 2022
VL 40
IS 2
AR 33
DI 10.1145/3469887
PG 29
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YV0FL
UT WOS:000752408800013
DA 2022-08-23
ER

PT J
AU Chaves-Fraga, D
   Ruckhaus, E
   Priyatna, F
   Vidal, ME
   Corcho, O
AF Chaves-Fraga, David
   Ruckhaus, Edna
   Priyatna, Freddy
   Vidal, Maria-Esther
   Corcho, Oscar
TI Enhancing virtual ontology based access over tabular data with Morph-CSV
SO SEMANTIC WEB
LA English
DT Article
DE Knowledge graphs; tabular data; mapping languages; constraints
AB Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets (e.g., relational databases, CSV and JSON files), either by materializing integrated data into RDF or by performing on-thefly querying via SPARQL query translation. In the specific case of tabular datasets represented as several CSV or Excel files, query translation approaches have been applied by considering each source as a single table that can be loaded into a relational database management system (RDBMS). Nevertheless, constraints over these tables are not represented (e.g., referential integrity among sources, datatypes, or data integrity); thus, neither consistency among attributes nor indexes over tables are enforced. As a consequence, efficiency of the SPARQL-to-SQL translation process may be affected, as well as the completeness of the answers produced during the evaluation of the generated SQL query. Our work is focused on applying implicit constraints on the OBDA query translation process over tabular data. We propose Morph-CSV, a framework for querying tabular data that exploits information from typical OBDA inputs (e.g., mappings, queries) to enforce constraints that can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV relies on both a constraint component and a set of constraint operators. For a given set of constraints, the operators are applied to each type of constraint with the aim of enhancing query completeness and performance. We evaluate Morph-CSV in several domains: e-commerce with the BSBM benchmark; transportation with the GTFS-Madrid benchmark; and biology with a use case extracted from the Bio2RDF project. We compare and report the performance of two SPARQL-to-SQL OBDA engines, without and with the incorporation of Morph-CSV. The observed results suggest that Morph-CSV is able to speed up the total query execution time by up to two orders of magnitude, while it is able to produce all the query answers.
C1 [Chaves-Fraga, David; Ruckhaus, Edna; Priyatna, Freddy; Corcho, Oscar] Univ Politecn Madrid, Ontol Engn Grp, Madrid, Spain.
   [Vidal, Maria-Esther] TIB Leibniz Informat Ctr Sci & Technol, Hannover, Germany.
   [Vidal, Maria-Esther] L3S Leibniz Univ Hannover, Hannover, Germany.
RP Chaves-Fraga, D (通讯作者)，Univ Politecn Madrid, Ontol Engn Grp, Madrid, Spain.
EM dchaves@fi.upm.es; eruckhaus@fi.upm.es; fpriyatna@fi.upm.es;
   maria.vidal@tib.eu; ocorcho@fi.upm.es
FU Spanish Ministerio de Economia, Industria y Competitividad; EU FEDER
   [TIN2016-78011-C4-4-R]; FPI grant [BES-2017-082511]
FX We are very thankful to Anastasia Dimou, Ben de Meester and Pieter
   Heyvaert (the RML team), who helped us in the initial discussions about
   the main contributions of our approach and in the creation of (YARR)RML
   mappings. We are also very thankful to the developers of Morph-CSV: Jhon
   Toledo and Luis Pozo-Gilo. The work presented in this paper is supported
   by the Spanish Ministerio de Economia, Industria y Competitividad and EU
   FEDER funds under the DATOS 4.0: RETOS Y SOLUCIONES -UPM Spanish
   national project (TIN2016-78011-C4-4-R) and by an FPI grant
   (BES-2017-082511).
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NR 55
TC 0
Z9 0
U1 1
U2 1
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2021
VL 12
IS 6
BP 869
EP 902
DI 10.3233/SW-210432
PG 34
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WF0CZ
UT WOS:000705985000002
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Lin, D
   Lin, JZ
   Zhao, L
   Wang, ZJ
   Chen, ZK
AF Lin, Dan
   Lin, Jianzhe
   Zhao, Liang
   Wang, Z. Jane
   Chen, Zhikui
TI Multilabel Aerial Image Classification With a Concept Attention Graph
   Neural Network
SO IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
LA English
DT Article
DE Correlation; Feature extraction; Task analysis; Semantics; Deep
   learning; Graph neural networks; Software; Aerial images; graph neural
   network; multilabel image classification
AB Compared with natural images, aerial images collected by satellite sensors/aerial cameras can provide a much larger field of view and often contain multiple objects of interest (multiple labels). There are certain limitations of existing multilabel aerial image classification methods. First, label correlations were often ignored in previous MAIC work, and thus, multilabel classifiers failed to be self-adapted. Second, existing multilabeled data sets for aerial images only cover limited images with fixed labels. Therefore, the underlying semantic correlations of labels cannot be fully included, while such correlation information is implicitly used as common knowledge by human beings. To tackle these concerns, we propose a novel multilabel classification method for aerial images. Our contributions are twofold. First, as the first attempt, label correlations are inferred from both the specific data set and ConceptNet (a popular knowledge graph for common sense). Second, based on graph neural network (GNN), we propose a novel end-to-end aerial image classification model, named the multiple label concept graph (ML-CG). ML-CG builds a concept graph to describe the semantic correlations from both the label set and the ConceptNet. We also incorporate both semantic attention and label attention in the GNN to better extract meaningful information of image labels. Compared with state-of-the-art methods, the effectiveness of the proposed method is demonstrated on both the commonly used UCM data set and a recently proposed DFC15 data set with high image resolution.
C1 [Lin, Dan; Zhao, Liang; Chen, Zhikui] Dalian Univ Technol, Sch Software Technol, Key Lab Ubiquitous Network & Serv Softwar, Dalian 116621, Peoples R China.
   [Lin, Jianzhe; Wang, Z. Jane] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada.
RP Chen, ZK (通讯作者)，Dalian Univ Technol, Sch Software Technol, Key Lab Ubiquitous Network & Serv Softwar, Dalian 116621, Peoples R China.
EM lindan0823@mail.dlut.edu.cn; jianzhelin@ece.ubc.ca;
   liangzhao@dlut.edu.cn; zjanew@ece.ubc.ca; zkchen@dlut.edu.cn
FU China Scholarship Council; National Natural Science Foundation of China
   [62076047, 62006035, 61906030]; Fundamental Research Funds for the
   Central Universities [DUT20LAB136, DUT20TD107]
FX Manuscript received April 6, 2020; revised October 5, 2020; accepted
   November 15, 2020. Date of publication March 2, 2021; date of current
   version December 6, 2021. This work was supported in part by the China
   Scholarship Council; in part by the National Natural Science Foundation
   of China under Grant 62076047, Grant 62006035, and Grant 61906030, and
   in part by the Fundamental Research Funds for the Central Universities
   under Grant DUT20LAB136, and Grant DUT20TD107. (Corresponding author:
   Zhikui Chen.)
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NR 33
TC 3
Z9 3
U1 12
U2 13
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0196-2892
EI 1558-0644
J9 IEEE T GEOSCI REMOTE
JI IEEE Trans. Geosci. Remote Sensing
PY 2022
VL 60
AR 5602112
DI 10.1109/TGRS.2020.3041461
PG 12
WC Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote
   Sensing; Imaging Science & Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science
   & Photographic Technology
GA XL6PS
UT WOS:000728266600055
DA 2022-08-23
ER

PT J
AU Chen, BZ
   Zhang, Z
   Lu, Y
   Chen, FL
   Lu, GM
   Zhang, D
AF Chen, Bingzhi
   Zhang, Zheng
   Lu, Yao
   Chen, Fanglin
   Lu, Guangming
   Zhang, David
TI Semantic-Interactive Graph Convolutional Network for Multilabel Image
   Recognition
SO IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
LA English
DT Article; Early Access
DE Semantics; Image recognition; Visualization; Correlation; Feature
   extraction; Task analysis; Training; Graph convolutional network (GCN);
   label co-occurrence; multilabel image recognition; semantic interactive;
   semantic similarity
AB Multilabel image recognition, a critically practical task in computer vision, aims to predict multiple objects present in each image. The existing studies mainly focus on conceptual visual cues but fail to reconcile the visual information with their semantic guidance. Intuitively, humans can not only associate extra topological concepts but also imagine other approximate scenes based on a semantic description. Inspired by such semantic-interactive capability, two different types of semantic priors, i.e., the concept correlations of the same scene and semantic similarities among different scenes, should be further explored for the recognition decisions. To efficiently interact with these semantic relationships, in this article, we propose a novel semantic-interactive graph convolutional network (SI-GCN), which can leverage the topological information learned from knowledge graphs to boost the performance of multilabel recognition. Specifically, the proposed SI-GCN framework consists of two different GCN-based branches in parallel, i.e., concept correlations learning (CCL) branch and semantic similarity learning (SSL) branch. Inputting the semantic-embedding vectors of all the concepts, the CCL branch maps the label co-occurrence graph into a set of interdependent concept classifiers. Recalibrating the image feature embedding with the standardized supervision of the semantic similarity graph, the SSL branch learns the semantically consistent in-batch visual representations. Finally, a well-established interactive learning scheme is formulated to concurrently optimize the obtained concept classifiers and the visual representation learning in an end-to-end manner. Extensive experiments on the MS-COCO and Pascal VOC 2007 & 2012 benchmarks demonstrate the superiorities of the proposed SI-GCN method compared to the state-of-the-art baselines.
C1 [Chen, Bingzhi; Zhang, Zheng; Lu, Yao; Chen, Fanglin; Lu, Guangming] Harbin Inst Technol, Shenzhen Med Biometr Percept & Anal Engn Lab, Shenzhen 518055, Peoples R China.
   [Zhang, David] Chinese Univ Hong Kong, Sch Data Sci, Shenzhen 518055, Peoples R China.
RP Zhang, Z; Lu, Y (通讯作者)，Harbin Inst Technol, Shenzhen Med Biometr Percept & Anal Engn Lab, Shenzhen 518055, Peoples R China.
EM chenbingzhi@stu.hit.edu.cn; darrenzz219@gmail.com; yaolu_1992@126.com;
   chenfanglin@hit.edu.cn; luguangm@hit.edu.cn; davidzhang@cuhk.edu.cn
RI Chen, Fanglin/I-1527-2013
OI Chen, Fanglin/0000-0002-9193-5412; Chen, Bingzhi/0000-0002-2497-6214;
   /0000-0003-1470-6998; Lu, Guangming/0000-0003-1578-2634
FU Guangdong Basic and Applied Basic Research Foundation [2019Bl515120055,
   2021A1515012528]; Shenzhen Key Technical Project [2020N046]; Shenzhen
   Institute of Artificial Intelligence and Robotics for Society
   [AC01202005018, AC01202005017]; Shenzhen Fundamental Research Fund
   [GXWD20201230155427003-20200824103320001]; National Natural Science
   Foundation of China Fund [61906162]; Shenzhen Science and Technology
   Program [RCBS20200714114910193]; Medical Biometrics Perception and
   Analysis Engineering Laboratory, Shenzhen, China
FX This work was supported in part by the Guangdong Basic and Applied Basic
   Research Foundation under Grant 2019Bl515120055 and Grant
   2021A1515012528; in part by the Shenzhen Key Technical Project under
   Grant 2020N046; in part by the Open Project Fund from Shenzhen Institute
   of Artificial Intelligence and Robotics for Society under Grant
   AC01202005018 and Grant AC01202005017; in part by the Shenzhen
   Fundamental Research Fund under Grant
   GXWD20201230155427003-20200824103320001; in part by the National Natural
   Science Foundation of China Fund under Grant 61906162; in part by the
   Shenzhen Science and Technology Program under Grant
   RCBS20200714114910193; and in part by the Medical Biometrics Perception
   and Analysis Engineering Laboratory, Shenzhen, China.
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NR 48
TC 2
Z9 2
U1 8
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2168-2216
EI 2168-2232
J9 IEEE T SYST MAN CY-S
JI IEEE Trans. Syst. Man Cybern. -Syst.
DI 10.1109/TSMC.2021.3103842
EA AUG 2021
PG 13
WC Automation & Control Systems; Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA XT3RO
UT WOS:000733509300001
DA 2022-08-23
ER

PT J
AU Kotis, KI
   Vouros, GA
   Spiliotopoulos, D
AF Kotis, Konstantinos, I
   Vouros, George A.
   Spiliotopoulos, Dimitris
TI Ontology engineering methodologies for the evolution of living and
   reused ontologies: status, trends, findings and recommendations
SO KNOWLEDGE ENGINEERING REVIEW
LA English
DT Review
ID SEMANTIC WEB; SYSTEM; EARTH
AB The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies 'in track'. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered 'shared, commonly agreed and continuously evolved "live" conceptualizations' of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.
C1 [Kotis, Konstantinos, I] Univ Aegean, Dept Cultural Technol & Commun, Intelligent Syst Lab, Univ Hill, Lesvos 81100, Greece.
   [Kotis, Konstantinos, I; Vouros, George A.] Univ Piraeus, Dept Digital Syst, AI Lab, Gr Lampraki 126, Piraeus, Greece.
   [Spiliotopoulos, Dimitris] Univ Peloponnese, Dept Informat & Telecommun, Tripoli, Greece.
RP Kotis, KI (通讯作者)，Univ Aegean, Dept Cultural Technol & Commun, Intelligent Syst Lab, Univ Hill, Lesvos 81100, Greece.; Kotis, KI (通讯作者)，Univ Piraeus, Dept Digital Syst, AI Lab, Gr Lampraki 126, Piraeus, Greece.
EM kotis@aegean.gr; georgev@unipi.gr; dspiliot@uop.gr
RI Vouros, George/AAF-3229-2020; Spiliotopoulos, Dimitris/AAH-8178-2020;
   Vouros, George/AAO-8415-2021; Kotis, Konstantinos/B-1883-2009
OI Spiliotopoulos, Dimitris/0000-0003-3646-1362; Vouros,
   George/0000-0001-5451-622X; Kotis, Konstantinos/0000-0001-7838-9691
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NR 91
TC 17
Z9 17
U1 0
U2 7
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA
SN 0269-8889
EI 1469-8005
J9 KNOWL ENG REV
JI Knowl. Eng. Rev.
PY 2020
VL 35
AR e4
DI 10.1017/S0269888920000065
PG 34
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KH0BC
UT WOS:000510312400001
OA hybrid
DA 2022-08-23
ER

PT J
AU Mao, YQ
   Fung, KW
AF Mao, Yuqing
   Fung, Kin Wah
TI Use of word and graph embedding to measure semantic relatedness between
   Unified Medical Language System concepts
SO JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
LA English
DT Article
DE UMLS; semantic relatedness; medical terminologies; deep learning; word
   embedding; graph embedding
ID SIMILARITY
AB Objective: The study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts.
   Materials and Methods: Concept sentence embeddings were generated for UMLS concepts by applying the word embedding models BioWordVec and various flavors of BERT to concept sentences formed by concatenating UMLS terms. Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS hierarchical relations. Semantic relatedness was measured by the cosine between the concepts' embedding vectors. Performance was compared with 2 traditional path-based (shortest path and Leacock-Chodorow) measurements and the publicly available concept embeddings, cui2vec, generated from large biomedical corpora. The concept sentence embeddings were also evaluated on a word sense disambiguation (WSD) task. Reference standards used included the semantic relatedness and semantic similarity datasets from the University of Minnesota, concept pairs generated from the Standardized MedDRA Queries and the MeSH (Medical Subject Headings) WSD corpus.
   Results: Sentence embeddings generated by BioWordVec outperformed all other methods used individually in semantic relatedness measurements. Graph convolutional network graph embedding uniformly outperformed path-based measurements and was better than some word embeddings for the Standardized MedDRA Queries dataset. When used together, combined word and graph embedding achieved the best performance in all datasets. For WSD, the enhanced versions of BERT outperformed BioWordVec.
   Conclusions: Word and graph embedding techniques can be used to harness terms and relations in the UMLS to measure semantic relatedness between concepts. Concept sentence embedding outperforms path-based measurements and cui2vec, and can be further enhanced by combining with graph embedding.
C1 [Mao, Yuqing; Fung, Kin Wah] NIH, Natl Lib Med, Bldg 10, Bethesda, MD 20892 USA.
RP Fung, KW (通讯作者)，Natl Lib Med, Bldg 38A,Rm9S918,MSC 3826,8600 Rockville Pike, Bethesda, MD 20894 USA.
EM kwfung@nlm.nih.gov
RI Mao, Yuqing/AAD-2720-2021
OI Mao, Yuqing/0000-0003-0098-8525
FU Intramural Research Program of the National Institutes of Health,
   National Library of Medicine
FX This research was supported in part by the Intramural Research Program
   of the National Institutes of Health, National Library of Medicine.
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NR 48
TC 4
Z9 4
U1 5
U2 16
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1067-5027
EI 1527-974X
J9 J AM MED INFORM ASSN
JI J. Am. Med. Inf. Assoc.
PD OCT
PY 2020
VL 27
IS 10
BP 1538
EP 1546
DI 10.1093/jamia/ocaa136
PG 9
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Health Care Sciences & Services;
   Information Science & Library Science; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Health Care Sciences & Services; Information Science &
   Library Science; Medical Informatics
GA OW8EL
UT WOS:000593112700006
PM 33029614
OA Green Published, hybrid
DA 2022-08-23
ER

PT J
AU Lara-Navarra, P
   Falciani, H
   Sanchez-Perez, EA
   Ferrer-Sapena, A
AF Lara-Navarra, Pablo
   Falciani, Herve
   Sanchez-Perez, Enrique A.
   Ferrer-Sapena, Antonia
TI Information Management in Healthcare and Environment: Towards an
   Automatic System for Fake News Detection
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE healthcare; environment; fake news; reinforcement learning; graph
AB Comments and information appearing on the internet and on different social media sway opinion concerning potential remedies for diagnosing and curing diseases. In many cases, this has an impact on citizens' health and affects medical professionals, who find themselves having to defend their diagnoses as well as the treatments they propose against ill-informed patients. The propagation of these opinions follows the same pattern as the dissemination of fake news about other important topics, such as the environment, via social media networks, which we use as a testing ground for checking our procedure. In this article, we present an algorithm to analyse the behaviour of users of Twitter, the most important social network with respect to this issue, as well as a dynamic knowledge graph construction method based on information gathered from Twitter and other open data sources such as web pages. To show our methodology, we present a concrete example of how the associated graph structure of the tweets related to World Environment Day 2019 is used to develop a heuristic analysis of the validity of the information. The proposed analytical scheme is based on the interaction between the computer tool-a database implemented with Neo4j-and the analyst, who must ask the right questions to the tool, allowing to follow the line of any doubtful data. We also show how this method can be used. We also present some methodological guidelines on how our system could allow, in the future, an automation of the procedures for the construction of an autonomous algorithm for the detection of false news on the internet related to health.
C1 [Lara-Navarra, Pablo] Univ Oberta Catalunya, Ciencias Informac & Comunicac, Barcelona 08035, Spain.
   [Falciani, Herve] Tact Whistleblower Assoc, Valencia 46022, Spain.
   [Sanchez-Perez, Enrique A.; Ferrer-Sapena, Antonia] Univ Politecn Valencia, Inst Univ Matemat Pura & Aplicada, E-46022 Valencia, Spain.
RP Ferrer-Sapena, A (通讯作者)，Univ Politecn Valencia, Inst Univ Matemat Pura & Aplicada, E-46022 Valencia, Spain.
EM plara@uoc.edu; herfal@upvnet.upv.es; easancpe@mat.upv.es; anfersa@upv.es
RI Lara-Navarra, Pablo/I-1019-2015; Perez, Enrique A. Sanchez/H-1618-2015;
   Ferrer-Sapena, Antonia/AAM-3528-2020; Lara-Navarra, Pablo/AAZ-2815-2021
OI Lara-Navarra, Pablo/0000-0003-0595-3161; Perez, Enrique A.
   Sanchez/0000-0001-8854-3154; Ferrer-Sapena, Antonia/0000-0001-6432-917X;
   Lara-Navarra, Pablo/0000-0003-0595-3161
FU Spanish Ministry for Science, Innovation and Universities; Spanish State
   Research Agency; European Regional Development Fund
   [MTM2016-77054-C2-1-P, SO2015-65594-C2-1R Y 2R]
FX The first-named and the forth-named authors were supported by the
   Spanish Ministry for Science, Innovation and Universities, the Spanish
   State Research Agency and the European Regional Development Fund under
   Research Grant SO2015-65594-C2-1R Y 2R, and to the Catedra de
   Transparencia y Gestion de Datos UPV/GVA. The third-named author was
   supported by the Spanish Ministry for Science, Innovation and
   Universities, the Spanish State Research Agency and the European
   Regional Development Fund under Research Grant MTM2016-77054-C2-1-P.
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NR 14
TC 13
Z9 16
U1 3
U2 26
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 1
PY 2020
VL 17
IS 3
AR 1066
DI 10.3390/ijerph17031066
PG 12
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA KR7GF
UT WOS:000517783300391
PM 32046238
OA Green Accepted, Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Gim, J
   Lee, S
   Joo, W
AF Gim, Jangwon
   Lee, Sukhoon
   Joo, Wonkyun
TI A Study of Prescriptive Analysis Framework for Human Care Services Based
   On CKAN Cloud
SO JOURNAL OF SENSORS
LA English
DT Article
ID HEALTH
AB A number of sensor devices are widely distributed and used today owing to the accelerated development of IoT technology. In particular, this technological advancement has allowed users to carry IoT devices with more convenience and efficiency. Based on the IoT sensor data, studies are being actively carried out to recognize the current situation or to analyze and predict future events. However, research for existing smart healthcare services is focused on analyzing users' behavior from single sensor data and is also focused on analyzing and diagnosing the current situation of the users. Therefore, a method for effectively managing and integrating a large amount of IoT sensor data has become necessary, and a framework considering data interoperability has become necessary. In addition, an analysis framework is needed not only to provide the analysis of the users' environment and situation from the integrated data, but also to provide guide information to predict future events and to take appropriate action by users. In this paper, we propose a prescriptive analysis framework using a 5W1H method based on CKAN cloud. Through the CKAN cloud environment, IoT sensor data stored in individual CKANs can be integrated based on common concepts. As a result, it is possible to generate an integrated knowledge graph considering interoperability of data, and the underlying data is used as the base data for prescriptive analysis. In addition, the proposed prescriptive analysis framework can diagnose the situation of the users through analysis of user environment information and supports users' decision making by recommending the possible behavior according to the coming situation of the users. We have verified the applicability of the 5W1H prescriptive analysis framework based on the use case of collecting and analyzing data obtained from various IoT sensors.
C1 [Gim, Jangwon; Lee, Sukhoon] Kunsan Natl Univ, Dept Software Convergence Engn, Gunsan Si, South Korea.
   [Joo, Wonkyun] KISTI, Biomed HPC Technol Res Ctr, Daejeon, South Korea.
RP Joo, W (通讯作者)，KISTI, Biomed HPC Technol Res Ctr, Daejeon, South Korea.
EM joo@kisti.re.kr
RI gim, jangwon/CAF-5544-2022
OI gim, jangwon/0000-0002-4480-7944; Joo, Wonkyun/0000-0002-2567-3311; Lee,
   Sukhoon/0000-0002-3390-5602
FU Development of biomedical data network analysis technology for dementia
   researches - Korea Institute of Science and Technology Information
   [K-18-L03-C02]; National Research Foundation of Korea (NRF) - Korea
   government (MSIT) [NRF-2017R1C1B2012065]
FX This work was supported by the "Development of biomedical data network
   analysis technology for dementia researches (K-18-L03-C02)" funded by
   Korea Institute of Science and Technology Information. This work was
   supported by the National Research Foundation of Korea (NRF) grant
   funded by the Korea government (MSIT) (NRF-2017R1C1B2012065).
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NR 33
TC 2
Z9 2
U1 0
U2 5
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-725X
EI 1687-7268
J9 J SENSORS
JI J. Sens.
PY 2018
VL 2018
AR 6167385
DI 10.1155/2018/6167385
PG 10
WC Engineering, Electrical & Electronic; Instruments & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Instruments & Instrumentation
GA GB2WP
UT WOS:000428916900001
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Shen, XH
   Wu, YZ
   Chen, SH
   Luo, XM
AF Shen, Xuehao
   Wu, Yuezhong
   Chen, Shuhong
   Luo, Xueming
TI An Intelligent Garbage Sorting System Based on Edge Computing and Visual
   Understanding of Social Internet of Vehicles
SO MOBILE INFORMATION SYSTEMS
LA English
DT Article
AB In order to enable Social Internet of Vehicles devices to achieve the purpose of intelligent and autonomous garbage classification in a public environment, while avoiding network congestion caused by a large amount of data accessing the cloud at the same time, it is therefore considered to combine mobile edge computing with Social Internet of Vehicles to give full play to mobile edge computing features of high bandwidth and low latency. At the same time, based on cutting-edge technologies such as deep learning, knowledge graph, and 5G transmission, the paper builds an intelligent garbage sorting system based on edge computing and visual understanding of Social Internet of Vehicles. First of all, for the massive multisource heterogeneous Social Internet of Vehicles big data in the public environment, different item modal data adopts different processing methods, aiming to obtain a visual understanding model. Secondly, using the 5G network, the model is deployed on the edge device and the cloud for cloud-side collaborative management, aiming to avoid the waste of edge node resources, while ensuring the data privacy of the edge node. Finally, the Social Internet of Vehicles devices is used to make intelligent decision-making on the big data of the items. First, the items are judged as garbage, and then the category is judged, and finally the task of grabbing and sorting is realized. The experimental results show that the system proposed in this paper can efficiently process the big data of Social Internet of Vehicles and make valuable intelligent decisions. At the same time, it also has a certain role in promoting the promotion of Social Internet of Vehicles devices.
C1 [Shen, Xuehao; Wu, Yuezhong] Hunan Univ Technol, Coll Railway Transportat, Zhuzhou 412007, Peoples R China.
   [Chen, Shuhong] Guangzhou Univ, Sch Comp Sci & Cyber Engn, Guangzhou 510006, Peoples R China.
   [Luo, Xueming] Hunan Univ Technol, Sch Comp Sci, Zhuzhou 412007, Peoples R China.
RP Wu, YZ (通讯作者)，Hunan Univ Technol, Coll Railway Transportat, Zhuzhou 412007, Peoples R China.; Chen, SH (通讯作者)，Guangzhou Univ, Sch Comp Sci & Cyber Engn, Guangzhou 510006, Peoples R China.
EM 2515847126@qq.com; wuyuezhong@hut.edu.cn; shuhongchen@gzhu.edu.cn;
   42835673@qq.com
OI Chen, Shuhong/0000-0002-6120-6358; Wu, Yuezhong/0000-0002-3618-6068
FU National Key R&D Program of China [2019YFE0122600, 2018YFB1700200];
   Hunan Provincial Key Research and Development Project of China
   [2019GK2133]; Natural Science Foundation of Hunan Province
   [2021JJ50050]; Scientific Research Project of Hunan Provincial
   Department of Education [19B147]
FX This work was supported in part by the National Key R&D Program of China
   under Grant nos. 2019YFE0122600 and 2018YFB1700200, in part by the Hunan
   Provincial Key Research and Development Project of China under Grant no.
   2019GK2133, in part by the Natural Science Foundation of Hunan Province
   under Grant no. 2021JJ50050, and in part by the Scientific Research
   Project of Hunan Provincial Department of Education under Grant no.
   19B147.
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NR 33
TC 1
Z9 1
U1 19
U2 33
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 AUG 31
PY 2021
VL 2021
AR 5231092
DI 10.1155/2021/5231092
PG 12
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA UP3XU
UT WOS:000695317300003
OA gold
DA 2022-08-23
ER

PT J
AU Waagmeester, A
   Willighagen, EL
   Su, AI
   Kutmon, M
   Gayo, JEL
   Fernandez-Alvarez, D
   Groom, Q
   Schaap, PJ
   Verhagen, LM
   Koehorst, JJ
AF Waagmeester, Andra
   Willighagen, Egon L.
   Su, Andrew, I
   Kutmon, Martina
   Gayo, Jose Emilio Labra
   Fernandez-Alvarez, Daniel
   Groom, Quentin
   Schaap, Peter J.
   Verhagen, Lisa M.
   Koehorst, Jasper J.
TI A protocol for adding knowledge to Wikidata: aligning resources on human
   coronaviruses
SO BMC BIOLOGY
LA English
DT Article
DE COVID-19; Wikidata; Linked data; ShEx; Open Science
ID GENE; PROTEIN
AB Background: Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a "commons." Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions.
   Results: As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates.
   Conclusions: Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).
C1 [Waagmeester, Andra] Micelio, Antwerp, Belgium.
   [Willighagen, Egon L.; Kutmon, Martina] Maastricht Univ, NUTRIM, Dept Bioinformat BiGCaT, Maastricht, Netherlands.
   [Su, Andrew, I] Scripps Res Inst, Dept Integrat Struct & Computat Biol, La Jolla, CA USA.
   [Kutmon, Martina] Maastricht Univ, Maastricht Ctr Syst Biol MaCSBio, Maastricht, Netherlands.
   [Gayo, Jose Emilio Labra; Fernandez-Alvarez, Daniel] Univ Oviedo, WESO Res Grp, Oviedo, Spain.
   [Groom, Quentin] Meise Bot Garden, Meise, Belgium.
   [Schaap, Peter J.; Koehorst, Jasper J.] Wageningen Univ & Res, Dept Agrotechnol & Food Sci, Lab Syst & Synthet Biol, Wageningen, Netherlands.
   [Verhagen, Lisa M.] Intravacc, POB 450, NL-3720 AL Bilthoven, Netherlands.
RP Koehorst, JJ (通讯作者)，Wageningen Univ & Res, Dept Agrotechnol & Food Sci, Lab Syst & Synthet Biol, Wageningen, Netherlands.
EM jasper.koehorst@wur.nl
RI Gayo, José Emilio Labra/L-9181-2014; Kutmon, Martina/AAT-2718-2021;
   Koehorst, Jasper Jan/G-3595-2013
OI Gayo, José Emilio Labra/0000-0001-8907-5348; Kutmon,
   Martina/0000-0002-7699-8191; Su, Andrew I./0000-0002-9859-4104;
   Willighagen, Egon/0000-0001-7542-0286; Koehorst, Jasper
   Jan/0000-0001-8172-8981; Fernandez-Alvarez, Daniel/0000-0002-8666-7660;
   Schaap, Peter/0000-0002-4346-6084; Groom, Quentin/0000-0002-0596-5376
FU National Institute of General Medical Sciences [R01 GM089820]; Alfred P.
   Sloan Foundation [G-2019-11458]; Spanish Ministry of Economy and
   Competitiveness [TIN2017-88877-R]; Netherlands Organisation for
   Scientific Research [NRGWI.obrug.2018.005]; SYNTHESYS+ a Research and
   Innovation action under H2020-EU.1.4.1.2 [823827]; ZonMw
   [10430012010015]
FX National Institute of General Medical Sciences (R01 GM089820); Andra
   Waagmeester, Andrew I. Su Alfred P. Sloan Foundation (grant number
   G-2019-11458); Egon Willighagen Spanish Ministry of Economy and
   Competitiveness (Society challenges: TIN2017-88877-R); Jose Emilio Labra
   Gayo, Daniel Fernandez Alvarez Netherlands Organisation for Scientific
   Research funded UNLOCK project (NRGWI.obrug.2018.005); Jasper Koehorst,
   Peter Schaap; SYNTHESYS+ a Research and Innovation action funded under
   H2020-EU.1.4.1.2. Grant agreement ID: 823827.; Quentin Groom ZonMw
   (grant number: 10430012010015); Martina Kutmon, Egon Willighagen
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NR 69
TC 5
Z9 5
U1 2
U2 6
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1741-7007
J9 BMC BIOL
JI BMC Biol.
PD JAN 22
PY 2021
VL 19
IS 1
AR 12
DI 10.1186/s12915-020-00940-y
PG 14
WC Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics
GA QA3LF
UT WOS:000613348100001
PM 33482803
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Rospocher, M
   Corcoglioniti, F
AF Rospocher, Marco
   Corcoglioniti, Francesco
TI Knowledge-driven joint posterior revision of named entity classification
   and linking
SO JOURNAL OF WEB SEMANTICS
LA English
DT Article
ID BASE; RECOGNITION
AB In this work we address the problem of extracting quality entity knowledge from natural language text, an important task for the automatic construction of knowledge graphs from unstructured content.
   More in details, we investigate the benefit of performing a joint posterior revision, driven by ontological background knowledge, of the annotations resulting from natural language processing (NLP) entity analyses such as named entity recognition and classification (NERC) and entity linking (EL). The revision is performed via a probabilistic model, called jpO, that given the candidate annotations independently identified by NERC and EL tools on the same textual entity mention, reconsiders the best annotation choice performed by the tools in light of the coherence of the candidate annotations with the ontological knowledge. The model can be explicitly instructed to handle the information that an entity can potentially be NIL (i.e., lacking a corresponding referent in the target linking knowledge base), exploiting it for predicting the best NERC and EL annotation combination.
   We present a comprehensive evaluation of jpOalong various dimensions, comparing its performances with and without exploiting NIL information, as well as the usage of three different background knowledge resources (YAGO, DBpedia, and Wikidata) to build the model. The evaluation, conducted using different tools (the popular Stanford NER and DBpedia Spotlight, as well as the more recent Flair NER and End-to-End Neural EL) with three reference datasets (AIDA, MEANTIME, and TAC-KBP), empirically confirms the capability of the model to improve the quality of the annotations of the given tools, and thus their performances on the tasks they are designed for. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Rospocher, Marco] Univ Verona, Lungadige Porta Vittoria 41, I-37129 Verona, Italy.
   [Corcoglioniti, Francesco] Free Univ Bozen Bolzano, Piazza Univ 1, I-39100 Bolzano, Italy.
RP Rospocher, M (通讯作者)，Univ Verona, Lungadige Porta Vittoria 41, I-37129 Verona, Italy.
EM marco.rospocher@univr.it; francesco.corcoglioniti@unibz.it
RI Rospocher, Marco/AGW-8382-2022; Rospocher, Marco/AAD-9108-2022
OI Rospocher, Marco/0000-0001-9391-3201
CR Akbik A., 2019, LONG SHORT PAPERS, V1, P724
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NR 42
TC 0
Z9 0
U1 1
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1570-8268
EI 1873-7749
J9 J WEB SEMANT
JI J. Web Semant.
PD DEC
PY 2020
VL 65
AR 100617
DI 10.1016/j.websem.2020.100617
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 PE4MD
UT WOS:000598338300006
DA 2022-08-23
ER

PT J
AU Molgora, JME
   Sedda, L
   Atkinson, PM
AF Molgora, Juan M. Escamilla
   Sedda, Luigi
   Atkinson, Peter M.
TI Biospytial: spatial graph-based computing for ecological Big Data
SO GIGASCIENCE
LA English
DT Article
DE spatial data infrastructure; biodiversity informatics; ecological
   knowledge engine; big ecological data; open science
ID VEGETATION MODELS; BIODIVERSITY; CONSERVATION; DEFINITION; JAGUARS;
   NETWORK
AB Background: The exponential accumulation of environmental and ecological data together with the adoption of open data initiatives bring opportunities and challenges for integrating and synthesising relevant knowledge that need to be addressed, given the ongoing environmental crises. Findings: Here we present Biospytial, a modular open source knowledge engine designed to import, organise, analyse and visualise big spatial ecological datasets using the power of graph theory. The engine uses a hybrid graph-relational approach to store and access information. A graph data structure uses linkage relationships to build semantic structures represented as complex data structures stored in a graph database, while tabular and geospatial data are stored in an efficient spatial relational database system. We provide an application using information on species occurrences, their taxonomic classification and climatic datasets. We built a knowledge graph of the Tree of Life embedded in an environmental and geographical grid to perform an analysis on threatened species co-occurring with jaguars (Panthera onca). Conclusions: The Biospytial approach reduces the complexity of joining datasets using multiple tabular relations, while its scalable design eases the problem of merging datasets from different sources. Its modular design makes it possible to distribute several instances simultaneously, allowing fast and efficient handling of big ecological datasets. The provided example demonstrates the engine's capabilities in performing basic graph manipulation, analysis and visualizations of taxonomic groups co-occurring in space. The example shows potential avenues for performing novel ecological analyses, biodiversity syntheses and species distribution models aided by a network of taxonomic and spatial relationships.
C1 [Molgora, Juan M. Escamilla] Univ Lancaster, Lancaster Environm Ctr, Lib Ave, Lancaster LA1 4YQ, England.
   [Molgora, Juan M. Escamilla] Univ Lancaster, Fac Hlth & Med, Lancaster Med Sch, Ctr Hlth Informat Comp & Stat CHICAS, Furness Bldg, Lancaster LA1 4YQ, England.
   [Sedda, Luigi] Univ Lancaster, Fac Hlth & Med, Lancaster Med Sch, Furness Bldg, Lancaster LA1 4YQ, England.
   [Atkinson, Peter M.] Univ Lancaster, Fac Sci & Technol, Old Engn Bldg, Lancaster LA1 4YQ, England.
RP Molgora, JME (通讯作者)，Univ Lancaster, Lancaster LA1 4YQ, England.
EM j.escamillamolgora@lancaster.ac.uk
RI Sedda, Luigi/ABD-8784-2020
OI Sedda, Luigi/0000-0002-9271-6596; Escamilla Molgora,
   Juan/0000-0002-3682-9828; Atkinson, Peter/0000-0002-5489-6880
FU Mexican Science and Technology Council (CONACYT, Becas al Extranjero);
   Faculty of Science and Technology from Lancaster University (FST-LU);
   GBIF Consortium; Engineering and Physical Sciences Research Council
   [EP/R01860X/1]
FX J.M.E.M and the project were jointly sponsored by the Doctoral
   Scholarships Program from the Mexican Science and Technology Council
   (CONACYT, Becas al Extranjero), the Faculty of Science and Technology
   from Lancaster University (FST-LU) and the GBIF Consortium through the
   GBIF Young Researchers Award (2016). L.S and P.M.A are supported by the
   Engineering and Physical Sciences Research Council grant number
   EP/R01860X/1 "Data Science of the Natural Environment"
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NR 92
TC 2
Z9 2
U1 7
U2 15
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 2047-217X
J9 GIGASCIENCE
JI GigaScience
PD MAY
PY 2020
VL 9
IS 5
AR giaa039
DI 10.1093/gigascience/giaa039
PG 25
WC Biology; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics; Science & Technology - Other
   Topics
GA LT9OJ
UT WOS:000537394700004
PM 32391910
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Gao, N
   Zhu, ZY
   Weng, ZQ
   Chen, GL
   Zhang, M
AF Gao, Nan
   Zhu, Zhenyang
   Weng, Zhengqiu
   Chen, Guolang
   Zhang, Min
TI A Supervised Named Entity Recognition Method Based on Pattern Matching
   and Semantic Verification
SO JOURNAL OF INTERNET TECHNOLOGY
LA English
DT Article
DE Named entity recognition; Weakly supervised learning; Deep learning;
   Pattern matching
AB Named entity recognition is a basic task in the field of natural language processing and plays a pivotal role in tasks such as information extraction, machine translation, and knowledge graph construction. It has also received widespread attention in financial, biological and pharmaceutical industries. This paper proposes a method of weakly supervised learning to recognize the complex named entities (commonly composed of multiple small entity sequences, hereinafter referred to as CNEs) in the corpus, which makes it difficult to determine the boundaries of such entities. To improve the recognition accuracy, our method Masked-BiLSTM-CRF is proposed to separate the context semantic relationship determination from the entity boundary confirmation. This method is based on two aspects to solve the above problems: (1) Semantic model based on CNEs mask processing. Before training, the CNEs in the corpus will be masked, and then use the masked corpus training the semantic model through BiLSTM-CRF, which can verify whether the context semantics of the corresponding location entities are correct. (2) A weakly supervised CNEs boundary confirmation model based on sequential patterns. In the small sample data set, the target CNE candidate set is found by sliding window combined with sequence pattern matching, and then it is effectively screened and judged by the semantic understanding model obtained in (1). The experimental results show that compared with the named entity recognition method based directly on BiLSTMCRF on the weakly-supervised named entity recognition in financial field, our proposed method improves F1Score in the small data training sample set by nearly 9%, and it has some generalization ability.
C1 [Gao, Nan; Zhu, Zhenyang; Weng, Zhengqiu; Zhang, Min] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou, Peoples R China.
   [Weng, Zhengqiu; Chen, Guolang] Wenzhou Polytech, Dept Informat Technol, Wenzhou, Peoples R China.
   [Chen, Guolang] Zhejiang Univ, Sch Management, Hangzhou, Peoples R China.
RP Chen, GL (通讯作者)，Wenzhou Polytech, Dept Informat Technol, Wenzhou, Peoples R China.; Chen, GL (通讯作者)，Zhejiang Univ, Sch Management, Hangzhou, Peoples R China.
EM gaonan@zjut.edu.cn; sivan.zhu@qq.com; derisweng@163.com;
   2019f098@zju.edu.cn; larainezm@qq.com
RI Zhu, Sivan/AAC-2579-2022
FU Major Scientific Research Projects of Wenzhou Polytechnics [WZY2020001];
   First Batch of Teaching Reform Research Projects in the 13th Five Year
   Plan of Higher Education in Zhejiang Province [jg20180585]; Zhejiang
   Province "the 13th Five-Year Plan" for Collaborative Education Project
   between Industry and University; Wenzhou Scientific Research Projects
   for Underdeveloped Areas [5]
FX This research was sponsored by Major Scientific Research Projects of
   Wenzhou Polytechnics (No. WZY2020001), funded by Wenzhou Scientific
   Research Projects for Underdeveloped Areas (WenRenSheFa [2020] 61 (No.
   5)), sponsored by the First Batch of Teaching Reform Research Projects
   in the 13th Five Year Plan of Higher Education in Zhejiang Province (No.
   jg20180585), supported by Zhejiang Province "the 13th Five-Year Plan"
   for Collaborative Education Project between Industry and University.(The
   ECommerce "1+X" Curriculum Design based on Univerisity and Industry
   Co-cultivation).
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NR 36
TC 1
Z9 1
U1 5
U2 10
PU LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV
PI HUALIEN
PA LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV, HUALIEN, 00000, TAIWAN
SN 1607-9264
EI 2079-4029
J9 J INTERNET TECHNOL
JI J. Internet Technol.
PY 2020
VL 21
IS 7
BP 1917
EP 1928
DI 10.3966/160792642020122107006
PG 12
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA PR3BA
UT WOS:000607113300006
DA 2022-08-23
ER

PT J
AU Li, YB
   Wang, XH
   Hui, LH
   Zou, LP
   Li, HJ
   Xu, L
   Liu, WH
AF Li, Yongbin
   Wang, Xiaohua
   Hui, Linhu
   Zou, Liping
   Li, Hongjin
   Xu, Luo
   Liu, Weihai
TI Chinese Clinical Named Entity Recognition in Electronic Medical Records:
   Development of a Lattice Long Short-Term Memory Model With
   Contextualized Character Representations
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE clinical named entity recognition; ELMo; lattice LSTM; deep learning;
   neural network; sequence tagging
ID WORD EMBEDDINGS
AB Background: Clinical named entity recognition (CNER), whose goal is to automatically identify clinical entities in electronic medical records (EMRs), is an important research direction of clinical text data mining and information extraction. The promotion of CNER can provide support for clinical decision making and medical knowledge base construction, which could then improve overall medical quality. Compared with English CNER, and due to the complexity of Chinese word segmentation and grammar, Chinese CNER was implemented later and is more challenging.
   Objective: With the development of distributed representation and deep learning, a series of models have been applied in Chinese CNER. Different from the English version, Chinese CNER is mainly divided into character-based and word-based methods that cannot make comprehensive use of EMR information and cannot solve the problem of ambiguity in word representation.
   Methods: In this paper, we propose a lattice long short-term memory (LSTM) model combined with a variant contextualized character representation and a conditional random field (CRF) layer for Chinese CNER: the Embeddings from Language Models (ELMo)-lattice-LSTM-CRF model. The lattice LSTM model can effectively utilize the information from characters and words in Chinese EMRs; in addition, the variant ELMo model uses Chinese characters as input instead of the character-encoding layer of the ELMo model, so as to learn domain-specific contextualized character embeddings.
   Results: We evaluated our method using two Chinese CNER datasets from the China Conference on Knowledge Graph and Semantic Computing (CCKS): the CCKS-2017 CNER dataset and the CCKS-2019 CNER dataset We obtained F1 scores of 90.13% and 85.02% on the test sets of these two datasets, respectively.
   Conclusions: Our results show that our proposed method is effective in Chinese CNER. In addition, the results of our experiments show that variant contextualized character representations can significantly improve the performance of the model.
C1 [Li, Yongbin; Wang, Xiaohua; Hui, Linhu; Zou, Liping; Li, Hongjin; Xu, Luo] Zunyi Med Univ, Sch Med Informat Engn, 6 Xuefu Rd West, Zunyi 563000, Guizhou, Peoples R China.
   [Liu, Weihai] Beilun Dist Peoples Hosp, Radiol Dept, Ningbo, Peoples R China.
RP Li, YB (通讯作者)，Zunyi Med Univ, Sch Med Informat Engn, 6 Xuefu Rd West, Zunyi 563000, Guizhou, Peoples R China.
EM bynn456@126.com
OI Wang, Xiaohua/0000-0003-1367-6264; Li, Hongjin/0000-0001-5717-3787; Li,
   Yongbin/0000-0003-3163-8448
FU Zunyi Science and Technology Program; Huichuan District Science and
   Technology Program; Master's Fund of Zunyi Medical University [F-974]
FX This work was supported by the Zunyi Science and Technology Program
   (Nos.....HZ.[2019]1.,....HZ. [2019]22., and....HZ.[2020]43.), the
   Huichuan District Science and Technology Program (....201711), and the
   Master's Fund of Zunyi Medical University (F-974).
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NR 40
TC 8
Z9 8
U1 6
U2 23
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 SEP
PY 2020
VL 8
IS 9
AR e19848
DI 10.2196/19848
PG 16
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Medical Informatics
GA NZ8ZE
UT WOS:000577388800014
PM 32885786
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Han, JB
   Wang, HZ
AF Han, Jiabao
   Wang, Hongzhi
TI Graph matching based reasoner: A symbolic approach to question answering
SO ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Question answering; Subgraph matching; Symbolic reasoning
ID KNOWLEDGE GRAPH; NEURAL-NETWORK; HYBRID
AB Text understanding and reasoning are among the core areas of artificial intelligence. Even a total solution to automatic text understanding and reasoning is still beyond the current techniques, thus it is time to build the stepping stones to solve a more straightforward problem set like question-answering (QA) without ambiguous utterances in the contexts or questions. The reported state-of-the-art approaches to this kind of problem are nearly all connectionist models based on neural networks. Significant progress has even been made in this direction. It is still hard for the pure connectionist models to handle logical reasoning. They generally suffer from the longstanding drawback of poor explainability and sensitivity to data noise and distribution. In this paper, we propose a complementary symbolic approach, GMR (Graph Matching based Reasoner) to QA - it automatically generates reasoning rules in the form of graphs from the training set and uses the generated rules to infer answers to the questions in the test set via graph matching. By employing this symbolic approach, 20 tasks in bAbI are solved with an average accuracy of 99.38%, and it outperforms the state-of-the-art for a real-life QA dataset WikiTableQuestions. After analyzing the accuracy of the basic evaluation indicators, we studied the generalization ability of the model in the paper, including the anti-noise ability, the convergence of the model, the stability of the model, the complexity of the algorithm, and the uncertainty of the parameters. Through comprehensive analysis and comparison, our model is stronger than the neural network model regarding anti-noise interference. Compared with the neural network model, our model performs very well in multi-tasking, and the stability of the model is quite high. The diversity of tasks did not reduce the stability of the model. We have conducted a comprehensive analysis and comparison of the parameter uncertainties. Our model can optimally select parameter configurations and will not cause a sharp drop in performance due to the parameter uncertainties. Finally, we describe the complexity of the GMR method and the optimal configuration of its parameters.
C1 [Han, Jiabao; Wang, Hongzhi] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China.
RP Wang, HZ (通讯作者)，Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China.
EM jiabaohan@hit.edu.cn; wangzh@hit.edu.cn
FU NSFC grant, China [U1866602, 61772157]
FX This paper was partially supported by NSFC grant, China U1866602,
   61772157.
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NR 77
TC 0
Z9 0
U1 3
U2 6
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 OCT
PY 2021
VL 105
AR 104425
DI 10.1016/j.engappai.2021.104425
EA AUG 2021
PG 14
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 WD0PX
UT WOS:000704650900009
DA 2022-08-23
ER

PT J
AU Chenya, L
   Aminudin, E
   Mohd, S
   Yap, LS
AF Chenya, Liao
   Aminudin, Eeydzah
   Mohd, Suzila
   Yap, Loh Seng
TI Intelligent Risk Management in Construction Projects: Systematic
   Literature Review
SO IEEE ACCESS
LA English
DT Review
DE Risk management; Construction industry; Systematics; Bibliographies;
   Market research; Software; Text mining; Intelligent risk management;
   construction projects; CiteSpace; bibliometric analysis
ID SAFETY; RECOGNITION; BEHAVIOR; DESIGN; INDUSTRY; AHP
AB As digitalization leads to the development of the global economy and artificial intelligence stimulates technological innovation, integrating smart management methods into project management is becoming more and more critical to the development of the construction industry. As an influential branch of engineering management, intelligent risk management in the construction field will be a future research direction. Hence, the aim of this study is to find the gaps and future research trends in intelligent risk management field by Systematic Literature Review. In order to achieve these objectives, 436 articles were selected from the WOS and Scopus databases to be analyzed by CiteSpace scientometric software, and the results were classified into collaborative network analysis, co-citation analysis, and public network analysis. From the analysis, China had the highest number of publications, while the United States was the most influential country in this field. The researchers at different institutions in this field have formed research teams despite the lack of collaboration between the authors and their institutions. In co-cited references, the emphasis was on traditional methods and applications of risk analysis, with fewer citations of novel methods. According to the keywords, the clusters of keywords, and the temporal evolution of the keywords, and combining the conceptual model, 5 research gaps areas were discovered. In order to fulfill these challenges and barriers, this study found the future research trends, including: developing digital management platform for intelligent construction management and risk management; building a decision-making system for risk management to find an optimum solution; refining building digital models which would be the basis of digital management platform; identifying and category the characteristic construction risk factors by using machine learning techniques, just like text mining or knowledge graph; building an API to embed decision making system into digital management platform, and the intelligent risk management system in real-time projects to verify its possibility.
C1 [Chenya, Liao; Aminudin, Eeydzah; Mohd, Suzila; Yap, Loh Seng] Univ Teknol Malaysia, Sch Civil Engn, Johor Baharu 81310, Malaysia.
   [Chenya, Liao] Sichuan Water Conservancy Coll, Sch Civil Engn, Chengdu 611830, Peoples R China.
   [Aminudin, Eeydzah] Univ Teknol Malaysia, UTM Construct Res Ctr UTM CRC, Johor Baharu 81310, Malaysia.
RP Chenya, L (通讯作者)，Univ Teknol Malaysia, Sch Civil Engn, Johor Baharu 81310, Malaysia.; Chenya, L (通讯作者)，Sichuan Water Conservancy Coll, Sch Civil Engn, Chengdu 611830, Peoples R China.
EM icyliaocy@qq.com
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NR 84
TC 0
Z9 0
U1 1
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 72936
EP 72954
DI 10.1109/ACCESS.2022.3189157
PG 19
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3Q8WP
UT WOS:000838505000001
OA gold
DA 2022-08-23
ER

PT J
AU Zhang, J
   Luo, YK
   Tao, ZL
   You, J
AF Zhang, Jun
   Luo, Yukun
   Tao, Zilong
   You, Jie
TI Graphic-processable deep neural network for the efficient prediction of
   2D diffractive chiral metamaterials
SO APPLIED OPTICS
LA English
DT Article
ID SPECTROSCOPY; MANIPULATION; POLARIZATION; FIELD
AB We propose a novel, to the best of our knowledge, graphic-processable deep neural network (DNN) to automatically predict and elucidate the optical chirality of two-dimensional (2D) diffractive chiral metamaterials. Four classes of 2D chiral metamaterials are studied here, with material components changing among Au, Ag, Al, and Cu. The graphic-processable DNN algorithm can not only handle arbitrary 2D images representing any metamaterials that may even go beyond human intuition, but also capture the influence of other parameters such as thickness and material composition, which are rarely explored in the field of metamaterials, laying the groundwork for future research into more complicated nanostructures and nonlinear optical devices. Notably, the rigorous coupled wave analysis (RCWA) algorithm is first deployed to calculate circular dichroism (CD) in the higher-order diffraction beams and simultaneously promote the training of DNN. For the first time we creatively encode the material component and thickness of the metamaterials into the color images serving as input of the graphic-processable DNN, in addition to arbitrary graphical parameters. Especially, the smallest intensity is found in the third-order diffraction beams of E-like metamaterials, whose CD response turns out to be the largest. A comprehensive study is conducted to capture the influence of shape, unit period, thickness, and material component of arrays on chiroptical response. As expected, a satisfied precision and an accelerated computing speed that is 4 orders of magnitude quicker than RCWA are both achieved using DNN. This work belongs to one of the first attempts to thoroughly examine the generalization ability of the graphic-processable DNN for the study of arbitrary-shaped nanostructures and hypersensitive nanodevioes. (C) 2021 Optical Society of America
C1 [Zhang, Jun; Tao, Zilong] Natl Univ Def Technol, Coll Comp Sci & Technol, Changsha 410073, Hunan, Peoples R China.
   [Luo, Yukun; You, Jie] Acad Mil Sci PLA China, Def Innovadson Inst, Beijing 100071, Peoples R China.
RP You, J (通讯作者)，Acad Mil Sci PLA China, Def Innovadson Inst, Beijing 100071, Peoples R China.
EM jieyou1991@hotmail.com
RI You, Jie/Q-4674-2016
OI You, Jie/0000-0003-3735-0592
FU National Natural Science Foundation of China [62075240, 11902358,
   41904167]; National Key Research and Development Program of China
   [2020YFB2205804]
FX National Natural Science Foundation of China (62075240, 11902358,
   41904167); National Key Research and Development Program of China
   (2020YFB2205804).
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NR 43
TC 2
Z9 2
U1 2
U2 12
PU OPTICAL SOC AMER
PI WASHINGTON
PA 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA
SN 1559-128X
EI 2155-3165
J9 APPL OPTICS
JI Appl. Optics
PD JUL 1
PY 2021
VL 60
IS 19
BP 5691
EP 5698
DI 10.1364/AO.428581
PG 8
WC Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Optics
GA TC9VW
UT WOS:000668986500043
PM 34263863
DA 2022-08-23
ER

PT J
AU Jiang, TT
   Bu, CY
   Zhu, Y
   Wu, XD
AF Jiang, Tingting
   Bu, Chenyang
   Zhu, Yi
   Wu, Xindong
TI Combining embedding-based and symbol-based methods for entity alignment
SO PATTERN RECOGNITION
LA English
DT Article
DE Entity alignment; Knowledge graph embedding; String Similarity
AB The objective of entity alignment is to judge whether entities refer to the same object in the real world. Methods for entity alignment can be grossly divided into two groups: conventional symbol-based entity alignment methods and embedding-based entity alignment methods. Both groups of methods have advantages and disadvantages (which are detailed in Section 1). Therefore, combining the advantages of both methods might be a promising strategy. However, to the best of our knowledge, only the RTEA algorithm that was proposed in our previous conference paper (Proceeding of Pacific Rim International Conference on Artificial Intelligence, pp. 162-175, 2019) utilizes this strategy for entity alignment. This manuscript is an extended version of that conference paper, in which an improved algorithm, namely, ESEA (combining embedding-based and symbol-based methods for entity alignment), is proposed based on the following steps. First, a novel method for combining embedding models with symbol-based models is proposed. Entities with high vector similarities are obtained through a hybrid embedding model, and the final aligned entity pairs are calculated via symbol-based methods. Second, a series of symbol based methods, instead of only the edit distance method in the original version, are combined with embedding-based methods for relation alignment. Third, we combine symbol-based and embedding based methods in a more complicated framework with the objective of better exploiting the advantages of both methods. The experimental results on real-world datasets demonstrate that the proposed method outperformed several state-of-the-art embedding-based entity alignment approaches and outperformed our previous RTEA method.(c) 2021 Elsevier Ltd. All rights reserved.
C1 [Jiang, Tingting; Bu, Chenyang; Zhu, Yi; Wu, Xindong] Hefei Univ Technol, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei, Peoples R China.
   [Jiang, Tingting; Bu, Chenyang; Zhu, Yi] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei, Peoples R China.
   [Jiang, Tingting; Bu, Chenyang; Wu, Xindong] Hefei Univ Technol, Inst Big Knowledge Sci, Hefei, Peoples R China.
   [Zhu, Yi] Yangzhou Univ, Sch Informat Engn, Yangzhou, Jiangsu, Peoples R China.
   [Wu, Xindong] Mininglamp Technol, Mininglamp Acad Sci, Beijing, Peoples R China.
RP Wu, XD (通讯作者)，Hefei Univ Technol, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei, Peoples R China.; Wu, XD (通讯作者)，Hefei Univ Technol, Inst Big Knowledge Sci, Hefei, Peoples R China.; Wu, XD (通讯作者)，Mininglamp Technol, Mininglamp Acad Sci, Beijing, Peoples R China.
EM jiangtt@mail.hfut.edu.cn; chenyangbu@hfut.edu.cn; xwu@hfut.edu.cn
FU National Key Research and Development Program of China [2016YFB1000901];
   National Natural Science Foundation of China [6180 60 65, 91746209];
   funds for International Cooperation and Exchange of the National Natural
   Science Foundation of China [62120106008]; Fundamental Research Funds
   for the Central Universities [JZ2020HGQA0186]
FX This work was partly supported by the National Key Research and
   Development Program of China (No. 2016YFB1000901), the National Natural
   Science Foundation of China (No. 6180 60 65 and No. 91746209), the funds
   for International Cooperation and Exchange of the National Natural
   Science Foundation of China (No. 62120106008), and the Fundamental
   Research Funds for the Central Universities (No. JZ2020HGQA0186).
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NR 43
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 0031-3203
EI 1873-5142
J9 PATTERN RECOGN
JI Pattern Recognit.
PD APR
PY 2022
VL 124
AR 108433
DI 10.1016/j.patcog.2021.108433
PG 14
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 0E5BS
UT WOS:000776697500004
DA 2022-08-23
ER

PT J
AU Zhang, F
   Li, ZY
   Peng, DH
   Cheng, JW
AF Zhang, Fu
   Li, Zhiyin
   Peng, Dunhong
   Cheng, Jingwei
TI RDF for temporal data management - a survey
SO EARTH SCIENCE INFORMATICS
LA English
DT Review
DE Resource description framework (RDF); Temporal data management; Temporal
   RDF; Survey
ID KNOWLEDGE; QUERY; FRAMEWORK; LANGUAGE; TIME
AB Over the years, a large amount of temporal data needs to be shared and exchanged on the Web. Resource Description Framework (RDF) has been widely accepted and has rapidly gained popularity to al RDF) model as mentioned in represent and share data in many application domains (e.g., the Data of Web, Linked Data, and Knowledge Graph). Accordingly, efficient management of temporal data based on RDF is of increasing importance. Much work has been devoted to the representation, querying, storage, and management of temporal RDF data. Therefore, to investigate and more importantly serve as helping researchers grasp the main ideas and results of temporal RDF data management, and to highlight an ongoing research on them, in this paper, we provide a full up-to-date overview of the current state of the art in temporal RDF data management, including representation, querying, storage, and other directions. In detail, regarding to the representation of temporal RDF models, we first classify the existing temporal RDF models into two main categories according to their focuses (including the new RDF extension models and the original RDF models based on timestamp). We summarize and compare each model from the perspectives of syntax, semantics, and etc. Then, we further summarize the temporal RDF querying and storage techniques in detail. Moreover, to well introduce the main idea of each approach, we use some examples to explain each approach. In addition, the directions for future research and some comparisons and analyses are discussed in our whole survey. This survey will help readers understand and catch some key techniques about the issue and also identify some future research directions.
C1 [Zhang, Fu; Li, Zhiyin; Peng, Dunhong; Cheng, Jingwei] Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Peoples R China.
RP Zhang, F (通讯作者)，Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Peoples R China.
EM zhangfu@cse.neu.edu.cn
OI Zhang, Fu/0000-0002-3880-8086
FU Natural Science Foundation of Ningxia Province [2020AAC03212]; National
   Natural Science Foundation of China [61672139]
FX The authors really appreciate the hard work and the time that the
   reviewers for reviewing this paper. The work is supported by the Natural
   Science Foundation of Ningxia Province (NO. 2020AAC03212) and the
   National Natural Science Foundation of China (61672139).
CR Al-Dhaheri S, SURVEY TEMPORAL KNOW
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TC 2
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U2 10
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1865-0473
EI 1865-0481
J9 EARTH SCI INFORM
JI Earth Sci. Inform.
PD JUN
PY 2021
VL 14
IS 2
BP 563
EP 599
DI 10.1007/s12145-021-00574-w
EA MAR 2021
PG 37
WC Computer Science, Interdisciplinary Applications; Geosciences,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Geology
GA RZ3ZR
UT WOS:000626367100001
DA 2022-08-23
ER

PT J
AU Holzinger, A
   Malle, B
   Saranti, A
   Pfeifer, B
AF Holzinger, Andreas
   Malle, Bernd
   Saranti, Anna
   Pfeifer, Bastian
TI Towards multi-modal causability with Graph Neural Networks enabling
   information fusion for explainable AI
SO INFORMATION FUSION
LA English
DT Article
DE Information fusion; Explainable AI; xAI; Graph Neural Networks;
   Multi-modal causability; Knowledge graphs; Counterfactuals
ID CLASSIFICATION; CANCER
AB AI is remarkably successful and outperforms human experts in certain tasks, even in complex domains such as medicine. Humans on the other hand are experts at multi-modal thinking and can embed new inputs almost instantly into a conceptual knowledge space shaped by experience. In many fields the aim is to build systems capable of explaining themselves, engaging in interactive what-if questions. Such questions, called counterfactuals, are becoming important in the rising field of explainable AI (xAI). Our central hypothesis is that using conceptual knowledge as a guiding model of reality will help to train more explainable, more robust and less biased machine learning models, ideally able to learn from fewer data. One important aspect in the medical domain is that various modalities contribute to one single result. Our main question is ?How can we construct a multi-modal feature representation space (spanning images, text, genomics data) using knowledge bases as an initial connector for the development of novel explanation interface techniques???. In this paper we argue for using Graph Neural Networks as a method-of-choice, enabling information fusion for multi-modal causability (causability ? not to confuse with causality ? is the measurable extent to which an explanation to a human expert achieves a specified level of causal understanding). The aim of this paper is to motivate the international xAI community to further work into the fields of multi-modal embeddings and interactive explainability, to lay the foundations for effective future human?AI interfaces. We emphasize that Graph Neural Networks play a major role for multi-modal causability, since causal links between features can be defined directly using graph structures.
C1 [Holzinger, Andreas; Malle, Bernd; Saranti, Anna; Pfeifer, Bastian] Med Univ Graz, Graz, Austria.
   [Holzinger, Andreas] Alberta Machine Intelligence Inst, Edmonton, AB, Canada.
RP Holzinger, A (通讯作者)，Med Univ Graz, Graz, Austria.
EM andreas.holzinger@medunigraz.at
RI Holzinger, Andreas/E-9530-2010
OI Holzinger, Andreas/0000-0002-6786-5194; Saranti,
   Anna/0000-0002-1085-8428; Malle, Bernd/0000-0002-0354-5132
FU EU Project FeatureCloud; European Union [826078]; Austrian Science Fund
   (FWF) [P-32554]
FX We are grateful for the valuable comments of the anonymous reviewers.
   Parts of this work have received funding from the EU Project
   FeatureCloud. The FeatureCloud project has received funding from the
   European Union's Horizon 2020 research and innovation programme under
   grant agreement No. 826078. This publication reflects only the author's
   view and the European Commission is not responsible for any use that may
   be made of the information it contains. Parts of this work have been
   funded by the Austrian Science Fund (FWF), Project: P-32554 "explainable
   Artificial Intelligence".
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NR 91
TC 89
Z9 89
U1 78
U2 201
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1566-2535
EI 1872-6305
J9 INFORM FUSION
JI Inf. Fusion
PD JUL
PY 2021
VL 71
BP 28
EP 37
DI 10.1016/j.inffus.2021.01.008
EA FEB 2021
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RB8SV
UT WOS:000632377000004
OA hybrid
HC Y
HP N
DA 2022-08-23
ER

PT J
AU Wang, Y
   Sun, YN
   Ma, ZC
   Gao, LS
   Xu, Y
AF Wang, Yu
   Sun, Yining
   Ma, Zuchang
   Gao, Lisheng
   Xu, Yang
TI Named Entity Recognition in Chinese Medical Literature Using Pretraining
   Models
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB The medical literature contains valuable knowledge, such as the clinical symptoms, diagnosis, and treatments of a particular disease. Named Entity Recognition (NER) is the initial step in extracting this knowledge from unstructured text and presenting it as a Knowledge Graph (KG). However, the previous approaches of NER have often suffered from small-scale human-labelled training data. Furthermore, extracting knowledge from Chinese medical literature is a more complex task because there is no segmentation between Chinese characters. Recently, the pretraining models, which obtain representations with the prior semantic knowledge on large-scale unlabelled corpora, have achieved state-of-the-art results for a wide variety of Natural Language Processing (NLP) tasks. However, the capabilities of pretraining models have not been fully exploited, and applications of other pretraining models except BERT in specific domains, such as NER in Chinese medical literature, are also of interest. In this paper, we enhance the performance of NER in Chinese medical literature using pretraining models. First, we propose a method of data augmentation by replacing the words in the training set with synonyms through the Mask Language Model (MLM), which is a pretraining task. Then, we consider NER as the downstream task of the pretraining model and transfer the prior semantic knowledge obtained during pretraining to it. Finally, we conduct experiments to compare the performances of six pretraining models (BERT, BERT-WWM, BERT-WWM-EXT, ERNIE, ERNIE-tiny, and RoBERTa) in recognizing named entities from Chinese medical literature. The effects of feature extraction and fine-tuning, as well as different downstream model structures, are also explored. Experimental results demonstrate that the method of data augmentation we proposed can obtain meaningful improvements in the performance of recognition. Besides, RoBERTa-CRF achieves the highestF1-score compared with the previous methods and other pretraining models.
C1 [Wang, Yu; Sun, Yining; Ma, Zuchang; Gao, Lisheng; Xu, Yang] Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Anhui Prov Key Lab Med Phys & Technol, Hefei 230031, Peoples R China.
   [Wang, Yu; Sun, Yining] Univ Sci & Technol China, Hefei 230026, Peoples R China.
RP Sun, YN (通讯作者)，Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Anhui Prov Key Lab Med Phys & Technol, Hefei 230031, Peoples R China.; Sun, YN (通讯作者)，Univ Sci & Technol China, Hefei 230026, Peoples R China.
EM briskyu@mail.ustc.edu.cn; ynsun@iim.ac.cn; zcma@iim.ac.cn;
   1609885693@qq.com; 625076229@qq.com
OI Wang, Yu/0000-0003-3096-2481
FU major special project of Anhui Science and Technology Department
   [18030801133]; Science and Technology Service Network Initiative
   [KFJ-STS-ZDTP-079]
FX The authors would like to thank the AIStudio platform for providing free
   computing resources. This work was partially supported by the major
   special project of Anhui Science and Technology Department under Grant
   18030801133 and Science and Technology Service Network Initiative under
   Grant KFJ-STS-ZDTP-079.
CR [Anonymous], 2015, P 2015 C EMP METH NA
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NR 26
TC 7
Z9 8
U1 9
U2 34
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 SEP 9
PY 2020
VL 2020
AR 8812754
DI 10.1155/2020/8812754
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA NV6BH
UT WOS:000574404000001
OA gold
DA 2022-08-23
ER

PT J
AU Dumitru, CO
   Schwarz, G
   Datcu, M
AF Dumitru, Corneliu Octavian
   Schwarz, Gottfried
   Datcu, Mihai
TI Semantic Labeling of Globally Distributed Urban and Nonurban Satellite
   Images Using High-Resolution SAR Data
SO IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE
   SENSING
LA English
DT Article
DE Semantics; Synthetic aperture radar; Remote sensing; Annotations; Urban
   areas; Optical sensors; Image resolution; Active learning (AL);
   datasets; high-resolution satellite images; knowledge extraction;
   ontologies; semantic classes; synthetic aperture radar (SAR); TerraSAR-X
ID CLASSIFICATION; EXPLORATION; ANNOTATION; SYSTEM
AB While the analysis and understanding of multispectral (i.e., optical) remote sensing images have made considerable progress during the last decades, the automated analysis of synthetic aperture radar (SAR) satellite images still needs some innovative techniques to support nonexpert users in the handling and interpretation of these big and complex data. In this article, we present a survey of existing multispectral and SAR land cover image datasets. To this end, we demonstrate how an advanced SAR image analysis system can be designed, implemented, and verified that is capable of generating semantically annotated classification results (e.g., maps) as well as local and regional statistical analytics such as graphical charts. The initial classification is made based on Gabor features and followed by class assignments (labeling). This is followed by inclusion. This can be accomplished by the inclusion of expert knowledge via active learning with selected examples, and the extraction of additional knowledge from public databases to refine the classification results. Then, based on the generated semantics, we can create new topic models, find typical country-specific phenomena and distributions, visualize them interactively, and present significant examples including confusion matrices. This semiautomated and flexible methodology allows several annotation strategies, the inclusion of dedicated analytics procedures, and can generate broad as well as detailed semantic (multi-)labels for all continents, and statistics or models for selected countries and cities. Here, we employ knowledge graphs and exploit ontologies. These components could already be validated successfully. The proposed methodology can also be adapted to other SAR instruments with different resolutions as well as to multispectral images.
C1 [Dumitru, Corneliu Octavian; Schwarz, Gottfried; Datcu, Mihai] German Aerosp Ctr, Remote Sensing Technol Inst, D-82234 Wessling, Germany.
RP Dumitru, CO (通讯作者)，German Aerosp Ctr, Remote Sensing Technol Inst, D-82234 Wessling, Germany.
EM corneliu.dumitru@dlr.de; gottfried.schwarz@dlr.de; mihai.datcu@dlr.de
FU European Space Agency; European Commission
FX This work was supported by different projects funded by the European
   Space Agency and by the European Commission under the FP7 and H2020
   Programmes.
CR [Anonymous], TenGeoP-SARwv: Labelled SAR imagery dataset of ten geophysical phenomena from Sentinel-1 wave mode
   Ao DY, 2018, REMOTE SENS-BASEL, V10, DOI 10.3390/rs10101597
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   KIM System, KIM SYSTEM KNOWLEDGE KIM SYSTEM KNOWLEDGE
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   Wikipedia, POPULATION DENSITY S
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NR 21
TC 0
Z9 0
U1 0
U2 0
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1939-1404
EI 2151-1535
J9 IEEE J-STARS
JI IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.
PY 2021
VL 14
BP 6009
EP 6068
DI 10.1109/JSTARS.2021.3084314
PG 60
WC Engineering, Electrical & Electronic; Geography, Physical; Remote
   Sensing; Imaging Science & Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Physical Geography; Remote Sensing; Imaging Science &
   Photographic Technology
GA TC8IR
UT WOS:000668882800004
OA Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Zhu, Q
   Nguyen, DT
   Sid, E
   Pariser, A
AF Zhu, Qian
   Nguyen, Dac-Trung
   Sid, Eric
   Pariser, Anne
TI Leveraging the UMLS As a Data Standard for Rare Disease Data
   Normalization and Harmonization
SO METHODS OF INFORMATION IN MEDICINE
LA English
DT Article
DE UMLS; rare diseases; data normalization; data harmonization
AB Objective In this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease.
   Methods We analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO). Two types of disease mappings were assessed, (1) curated mappings extracted from those four resources; and (2) established mappings generated by querying the rare disease-based integrative knowledge graph developed in the previous study.
   Results We found that 100% of OMIM concepts, and over 50% of concepts from GARD, MONDO, and Orphanet were normalized by the UMLS and accurately categorized into the appropriate UMLS semantic groups. We analyzed 58,636 UMLS mappings, which resulted in 3,876 UMLS concepts across these resources. Manual evaluation of a random set of 500 UMLS mappings demonstrated a high level of accuracy (99%) of developing those mappings, which consisted of 414 mappings of synonyms (82.8%), 76 are subtypes (15.2%), and five are siblings (1%).
   Conclusion The mapping results illustrated in this study that the UMLS was able to accurately represent rare disease concepts, and their associated information, such as genes and phenotypes, and can effectively be used to support data harmonization across existing resources developed on collecting rare disease data. We recommend the adoption of the UMLS as a data standard for rare disease to enable the existing rare disease datasets to support future applications in a clinical and community settings.
C1 [Zhu, Qian; Nguyen, Dac-Trung] Natl Ctr Adv Translat Sci NCATS, Div Preclin Innovat, NIH, 9800 Med Ctr Dr, Rockville, MD 20850 USA.
   [Sid, Eric; Pariser, Anne] Natl Ctr Adv Translat Sci NCATS, Off Rare Dis Res, NIH, Bethesda, MD USA.
RP Zhu, Q (通讯作者)，Natl Ctr Adv Translat Sci NCATS, Div Preclin Innovat, NIH, 9800 Med Ctr Dr, Rockville, MD 20850 USA.
EM Qian.Zhu@nih.gov
OI Sid, Eric/0000-0001-7697-3026
FU Intramural Research Program of the National Institutes of Health,
   National Center for Advancing Translational Sciences
FX This research was supported by the Intramural Research Program of the
   National Institutes of Health, National Center for Advancing
   Translational Sciences.
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NR 21
TC 1
Z9 1
U1 2
U2 11
PU GEORG THIEME VERLAG KG
PI STUTTGART
PA RUDIGERSTR 14, D-70469 STUTTGART, GERMANY
SN 0026-1270
EI 2511-705X
J9 METHOD INFORM MED
JI Methods Inf. Med.
PD AUG
PY 2020
VL 59
IS 04/05
BP 131
EP 139
DI 10.1055/s-0040-1718940
EA NOV 2020
PG 9
WC Computer Science, Information Systems; Health Care Sciences & Services;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Health Care Sciences & Services; Medical Informatics
GA QK4FR
UT WOS:000585182700001
PM 33147635
DA 2022-08-23
ER

PT J
AU Xie, X
   Zhou, XR
   Li, JZ
   Dai, WJ
AF Xie, Xiao
   Zhou, Xiran
   Li, Jingzhong
   Dai, Weijiang
TI An Ontology-Based Framework for Complex Urban Object Recognition through
   Integrating Visual Features and Interpretable Semantics
SO COMPLEXITY
LA English
DT Article
ID CLASSIFICATION; KNOWLEDGE; EXPLORATION; PERFORMANCE; ENVIRONMENT;
   EXTRACTION; INFERENCE; PATTERNS; DATABASE; SYSTEMS
AB Although previous works have proposed sophisticatedly probabilistic models that has strong capability of extracting features from remote sensing data (e.g., convolutional neural networks, CNN), the efforts that focus on exploring the human's semantics on the object to be recognized are required more explorations. Moreover, interpretability of feature extraction becomes a major disadvantage of the state-of-the-art CNN. Especially for the complex urban objects, which varies in geometrical shapes, functional structures, environmental contexts, etc, due to the heterogeneity between low-level data features and high-level semantics, the features derived from remote sensing data alone are limited to facilitate an accurate recognition. In this paper, we present an ontology-based methodology framework for enabling object recognition through rules extracted from the high-level semantics, rather than unexplainable features extracted from a CNN. Firstly, we semantically organize the descriptions and definitions of the object as semantics (RDF-triple rules) through our developed domain ontology. Secondly, we exploit semantic web rule language to propose an encoder model for decomposing the RDF-triple rules based on a multilayer strategy. Then, we map the low-level data features, which are defined from optical satellite image and LiDAR height, to the decomposed parts of RDF-triple rules. Eventually, we apply a probabilistic belief network (PBN) to probabilistically represent the relationships between low-level data features and high-level semantics, as well as a modified TanH function is used to optimize the recognition result. The experimental results on lacking of the training process based on data samples show that our proposed approach can reach an accurate recognition with high-level semantics. This work is conducive to the development of complex urban object recognition toward the fields including multilayer learning algorithms and knowledge graph-based relational reinforcement learning.
C1 [Xie, Xiao; Zhou, Xiran] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China.
   [Xie, Xiao; Li, Jingzhong] Chinese Acad Sci, Inst Appl Ecol, Key Lab Environm Computat & Sustainabil Liaoning, Shenyang 110016, Peoples R China.
   [Zhou, Xiran] China Univ Min & Technol, Artificial Intelligence Res Inst, Xuzhou 221116, Jiangsu, Peoples R China.
   [Li, Jingzhong] Xuchang Univ, Coll Urban & Environm Sci, Xuchang 461000, Peoples R China.
   [Dai, Weijiang] Nucl Ind Huzhou Engn Invest Inst, Huzhou 313000, Peoples R China.
RP Zhou, XR (通讯作者)，China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China.; Zhou, XR (通讯作者)，China Univ Min & Technol, Artificial Intelligence Res Inst, Xuzhou 221116, Jiangsu, Peoples R China.
EM xiexiao@iae.ac.cn; xrzhou@cumt.edu.cn; zhong_lij@163.com;
   hgyhzkcy@163.com
FU Fundamental Research Funds for the Central Universities [2020QN28]
FX This research was supported by "the Fundamental Research Funds for the
   Central Universities (Grant no. 2020QN28)."
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NR 63
TC 0
Z9 0
U1 4
U2 16
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 SEP 10
PY 2020
VL 2020
AR 5125891
DI 10.1155/2020/5125891
PG 15
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA NV4YB
UT WOS:000574328000002
OA gold
DA 2022-08-23
ER

PT J
AU Fernandez-Tobias, I
   Cantador, I
   Tomeo, P
   Anelli, VW
   Di Noia, T
AF Fernandez-Tobias, Ignacio
   Cantador, Ivan
   Tomeo, Paolo
   Anelli, Vito Walter
   Di Noia, Tommaso
TI Addressing the user cold start with cross-domain collaborative
   filtering: exploiting item metadata in matrix factorization
SO USER MODELING AND USER-ADAPTED INTERACTION
LA English
DT Article
DE Cross-domain recommender systems; User cold start; Item metadata; Linked
   data
ID RECOMMENDATION
AB Providing relevant personalized recommendations for new users is one of the major challenges in recommender systems. This problem, known as the user cold start has been approached from different perspectives. In particular, cross-domain recommendation methods exploit data from source domains to address the lack of user preferences in a target domain. Most of the cross-domain approaches proposed so far follow the paradigm of collaborative filtering, and avoid analyzing the contents of the items, which are usually highly heterogeneous in the cross-domain setting. Content-based filtering, however, has been successfully applied in domains where item content and metadata play a key role. Such domains are not limited to scenarios where items do have text contents (e.g., books, news articles, scientific papers, and web pages), and where text mining and information retrieval techniques are often used. Potential application domains include those where items have associated metadata, e.g., genres, directors and actors for movies, and music styles, composers and themes for songs. With the advent of the Semantic Web, and its reference implementation Linked Data, a plethora of structured, interlinked metadata is available on the Web. These metadata represent a potential source of information to be exploited by content-based and hybrid filtering approaches. Motivated by the use of Linked Data for recommendation purposes, in this paper we present and evaluate a number of matrix factorization models for cross-domain collaborative filtering that leverage metadata as a bridge between items liked by users in different domains. We show that in case the underlying knowledge graph connects items from different domains and then in situations that benefit from cross-domain information, our models can provide better recommendations to new users while keeping a good trade-off between recommendation accuracy and diversity.
C1 [Fernandez-Tobias, Ignacio; Cantador, Ivan] Univ Autonoma Madrid, Dept Ingn Informat, Madrid, Spain.
   [Tomeo, Paolo; Anelli, Vito Walter; Di Noia, Tommaso] Politecn Bari, Dipartimento Ingn Elettr & Informaz, Bari, Italy.
RP Fernandez-Tobias, I (通讯作者)，Univ Autonoma Madrid, Dept Ingn Informat, Madrid, Spain.
EM ignacio.fernandezt@uam.es; ivan.cantador@uam.es; paolo.tomeo@poliba.it;
   vitowalter.anelli@poliba.it; tommaso.dinoia@poliba.it
RI Anelli, Vito Walter/AAT-5985-2020
OI Anelli, Vito Walter/0000-0002-5567-4307
FU Spanish Ministry of Economy, Industry and Competitiveness
   [TIN2016-80630-P]
FX This work was supported by the Spanish Ministry of Economy, Industry and
   Competitiveness (TIN2016-80630-P). The authors thank the reviewers for
   their thoughtful comments and suggestions.
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NR 76
TC 36
Z9 39
U1 3
U2 38
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-1868
EI 1573-1391
J9 USER MODEL USER-ADAP
JI User Model. User-Adapt. Interact.
PD APR
PY 2019
VL 29
IS 2
SI SI
BP 443
EP 486
DI 10.1007/s11257-018-9217-6
PG 44
WC Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HX8UF
UT WOS:000467680900006
DA 2022-08-23
ER

PT J
AU Mammen, MJ
   Tu, CJ
   Morris, MC
   Richman, S
   Mangione, W
   Falls, Z
   Qu, J
   Broderick, G
   Sethi, S
   Samudrala, R
AF Mammen, Manoj J.
   Tu, Chengjian
   Morris, Matthew C.
   Richman, Spencer
   Mangione, William
   Falls, Zackary
   Qu, Jun
   Broderick, Gordon
   Sethi, Sanjay
   Samudrala, Ram
TI Proteomic Network Analysis of Bronchoalveolar Lavage Fluid in Ex-Smokers
   to Discover Implicated Protein Targets and Novel Drug Treatments for
   Chronic Obstructive Pulmonary Disease
SO PHARMACEUTICALS
LA English
DT Article
DE proteomics; drug repurposing; translational bioinformatics; interaction
   signature; bronchoalveolar lavage fluid; chronic obstructive pulmonary
   disease; label-free quantitation; plasma; serum; biomarker
ID FLUOCINOLONE ACETONIDE; OXIDATIVE STRESS; UNITED-STATES; COPD; LUNG;
   INFLAMMATION; INFECTION; CANDO; PATHOPHYSIOLOGY; PATHOGENESIS
AB Bronchoalveolar lavage of the epithelial lining fluid (BALF) can sample the profound changes in the airway lumen milieu prevalent in chronic obstructive pulmonary disease (COPD). We compared the BALF proteome of ex-smokers with moderate COPD who are not in exacerbation status to non-smoking healthy control subjects and applied proteome-scale translational bioinformatics approaches to identify potential therapeutic protein targets and drugs that modulate these proteins for the treatment of COPD. Proteomic profiles of BALF were obtained from (1) never-smoker control subjects with normal lung function (n = 10) or (2) individuals with stable moderate (GOLD stage 2, FEV1 50-80% predicted, FEV1 /FVC < 0.70) COPD who were ex-smokers for at least 1 year (n = 10). After identifying potential crucial hub proteins, drug-proteome interaction signatures were ranked by the computational analysis of novel drug opportunities (CANDO) platform for multiscale therapeutic discovery to identify potentially repurposable drugs. Subsequently, a literature-based knowledge graph was utilized to rank combinations of drugs that most likely ameliorate inflammatory processes. Proteomic network analysis demonstrated that 233 of the >1800 proteins identified in the BALF were significantly differentially expressed in COPD versus control. Functional annotation of the differentially expressed proteins was used to detail canonical pathways containing the differential expressed proteins. Topological network analysis demonstrated that four putative proteins act as central node proteins in COPD. The drugs with the most similar interaction signatures to approved COPD drugs were extracted with the CANDO platform. The drugs identified using CANDO were subsequently analyzed using a knowledge-based technique to determine an optimal two-drug combination that had the most appropriate effect on the central node proteins. Network analysis of the BALF proteome identified critical targets that have critical roles in modulating COPD pathogenesis, for which we identified several drugs that could be repurposed to treat COPD using a multiscale shotgun drug discovery approach.
C1 [Mammen, Manoj J.] Univ Rochester, Sch Med & Dent, Dept Med, Rochester, NY 14642 USA.
   [Mammen, Manoj J.; Mangione, William; Falls, Zackary; Samudrala, Ram] SUNY Buffalo, Jacobs Sch Med & Biol Sci, Dept Biomed Informat, Buffalo, NY 14214 USA.
   [Tu, Chengjian; Qu, Jun] SUNY Buffalo, Dept Pharmaceut Sci, Buffalo, NY 14260 USA.
   [Tu, Chengjian; Qu, Jun] New York State Ctr Excellence Bioinformat & Life, 701 Ellicott St, Buffalo, NY 14203 USA.
   [Morris, Matthew C.; Richman, Spencer; Broderick, Gordon] Rochester Gen Hosp, Ctr Clin Syst Biol, Rochester, NY 14621 USA.
   [Sethi, Sanjay] WNY VA Healthcare Syst, Buffalo, NY 14215 USA.
   [Sethi, Sanjay] SUNY Buffalo, Jacobs Sch Med & Biol Sci, Dept Med, Buffalo, NY 14214 USA.
RP Mammen, MJ (通讯作者)，Univ Rochester, Sch Med & Dent, Dept Med, Rochester, NY 14642 USA.; Mammen, MJ; Samudrala, R (通讯作者)，SUNY Buffalo, Jacobs Sch Med & Biol Sci, Dept Biomed Informat, Buffalo, NY 14214 USA.
EM manoj_mammen@urmc.rochester.edu; ctu3@buffalo.edu;
   matthew.morris@rochesterregional.org; scr6045@rit.edu;
   wmangion@buffalo.edu; zmfalls@buffalo.edu; junqu@buffalo.edu;
   gordon.broderick@rochesterrregional.org; ssethi@buffalo.edu;
   ram@compbio.org
RI Falls, Zackary/N-5493-2019
OI Falls, Zackary/0000-0003-4116-0441; Mangione,
   William/0000-0003-0582-4247; Mammen, Manoj/0000-0003-0343-3234; Morris,
   Matthew/0000-0002-1236-1255; Samudrala, Ram/0000-0001-9069-8497
FU University at Buffalo; National Heart, Lung, and Blood Institute;
   National Institute Health (NIH) [U54HD071594]; American Heart
   Association (AHA) [12SDG9450036]; Rochester Regional Health; US
   Department of Defense Congressionally Directed Medical Research Programs
   (CDMRP) under the Peer Reviewed Medical Research Program (PRMRP)
   [W81XWH1910804]; Elsevier AB (Amsterdam); NIH Director's Pioneer Award
   [DP1OD006779]; NIH Clinical and Translational Sciences Award
   [UL1TR001412]; NIH Buffalo Research Innovation in Genomic and Healthcare
   Technology Education Award [T15LM012495]; NIH NCATS ASPIRE
   Reduction-to-Practice Award; Department of Biomedical Informatics at the
   University at Buffalo; NIH NCATS ASPIRE Design Challenge Award
FX Mammen was supported by a research grant from the University at Buffalo.
   A National Heart, Lung, and Blood Institute grant to Sethi supported the
   collection of bronchoalveolar lavage specimens. This work is also
   supported, in part, by National Institute Health (NIH) awards
   U54HD071594 (JQ) and by American Heart Association (AHA) award
   12SDG9450036 (JQ). Rochester Regional Health also supported this work in
   conjunction with the US Department of Defense Congressionally Directed
   Medical Research Programs (CDMRP) under the Peer Reviewed Medical
   Research Program (PRMRP) award W81XWH1910804 (Broderick-PI;
   Sethi-Partnering PI, JQ-CoI), as well as by Elsevier AB (Amsterdam). The
   development of the CANDO platform was supported in part by a NIH
   Director's Pioneer Award (DP1OD006779), NIH Clinical and Translational
   Sciences Award (UL1TR001412), NIH Buffalo Research Innovation in Genomic
   and Healthcare Technology Education Award (T15LM012495), NIH NCATS
   ASPIRE Design Challenge Award, NIH NCATS ASPIRE Reduction-to-Practice
   Award, and startup funds from the Department of Biomedical Informatics
   at the University at Buffalo.
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NR 80
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8247
J9 PHARMACEUTICALS-BASE
JI Pharmaceuticals
PD MAY
PY 2022
VL 15
IS 5
AR 566
DI 10.3390/ph15050566
PG 18
WC Chemistry, Medicinal; Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA 1R5GS
UT WOS:000803397500001
PM 35631392
OA gold, Green Submitted, Green Published
DA 2022-08-23
ER

PT J
AU Xiong, J
   Qi, WC
   Liu, JC
   Zhang, ZQ
   Wang, ZW
   Bao, JK
   Wu, CF
   Liang, FR
AF Xiong, Jian
   Qi, Wenchuan
   Liu, Jiacheng
   Zhang, Zhenqing
   Wang, Ziwen
   Bao, Jinku
   Wu, Chuanfang
   Liang, Fanrong
TI Research Progress of Ferroptosis: A Bibliometrics and Visual Analysis
   Study
SO JOURNAL OF HEALTHCARE ENGINEERING
LA English
DT Article
ID CELL-DEATH; PROMOTES FERROPTOSIS; LIPID-PEROXIDATION; P53;
   FERROSTATIN-1; METABOLISM; MECHANISM; TOXICITY; PROTECTS; DISEASE
AB Background. Ferroptosis is a type of cell death with major topic of debate under current research and plays an important role in disease regulation. Objective. In this study, the literature management software Bibexcel and knowledge graph tool VOSviewer were used to summarize and analyze the international research trends and hotspots about ferroptosis in recent years, which highlight the disease mechanism, diagnosis, and treatment related to ferroptosis. Material/Methods. The core collection database of Web of Science was used for retrieving ferroptosis research literature. The information such as the amount of text, the country, the period, the institution, the fund, and the keywords was extracted by the bibliometric tool Bibexcel. The cooccurrence and clustering function of VOSviewer were used to analyze the high-frequency keywords and the cooperative network of the author, institution, and country. Results. The research of ferroptosis started late and was formally proposed in 2012. It has developed rapidly and presented an "exponential" growth trend. China, the United States, Germany, Japan, and France are the main national forces of ferroptosis research development. The United States and China have a relatively high degree of support and attention to ferroptosis. Exploring oxidative stress, inducers/inhibitors, synergistic antitumor effect, relationships with other cell death types, GSH/GPX4 and iron metabolism imbalance related mechanisms of ferroptosis, and ferroptosis in the nervous system disease, ischemia-reperfusion injury, tumor, inflammation, and age-related diseases are the hot research directions. Conclusion. Ferroptosis has been a research hotspot in the field of biomedicine in recent years and has attracted the attention of scholars all over the world. The occurrence mechanism of ferroptosis and its application in neurological diseases, ischemia and reperfusion injury, tumors, inflammation, and aging are the hot directions of current research. In the future, ferroptosis can be appropriately considered for strengthening new approaches, new diseases, new inductors, new inhibitors, clinical transformation, and traditional medicine research.
C1 [Xiong, Jian; Qi, Wenchuan; Wang, Ziwen; Liang, Fanrong] Chengdu Univ Tradit Chinese Med, Chengdu 610075, Peoples R China.
   [Liu, Jiacheng; Zhang, Zhenqing] Guangxi Univ Tradit Chinese Med, Nanning 530001, Peoples R China.
   [Bao, Jinku; Wu, Chuanfang] Sichuan Univ, Coll Life Sci, Key Lab Bioresource & Ecoenvironm, Minist Educ, Chengdu 610041, Peoples R China.
RP Liang, FR (通讯作者)，Chengdu Univ Tradit Chinese Med, Chengdu 610075, Peoples R China.
EM 19821001@cdutcm.edu.cn
OI Xiong, Jian/0000-0002-8346-178X; Qi, Wenchuan/0000-0002-3294-8870
FU National Natural Science Foundation of China [81590951, 82074556]; China
   Postdoctoral Science Foundation [260350]; Support Fund for National "Ten
   Thousand Talents Plan" Famous Teachers [6 [2018]]; National
   Administration of Traditional Chinese Medicine "Hundreds of Millions"
   Talent Project for TCM Inheritance and Innovation (Qhuang Project)
   Qhuang Scholar Support Fund (National Letter of Education of TCM
   Scholars) [288]; Special Project of "Central Government Guides Local
   Science and Technology Development" in Sichuan Provincial Department of
   Science and Technology [2020ZYD046]
FX The authors thank all the authors of the original studies included in
   this analysis. This paper was supported by the National Natural Science
   Foundation of China (nos. 81590951 and 82074556), China Postdoctoral
   Science Foundation (no. 260350), Support Fund for National "Ten Thousand
   Talents Plan" Famous Teachers (no. 6 [2018] of Group Office), National
   Administration of Traditional Chinese Medicine "Hundreds of Millions"
   Talent Project for TCM Inheritance and Innovation (Qhuang Project)
   Qhuang Scholar Support Fund (National Letter of Education of TCM
   Scholars [2018] no. 288), and Special Project of "Central Government
   Guides Local Science and Technology Development" in Sichuan Provincial
   Department of Science and Technology (2020ZYD046).
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NR 75
TC 4
Z9 4
U1 7
U2 16
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 2040-2295
EI 2040-2309
J9 J HEALTHC ENG
JI J. Healthc. Eng.
PD AUG 10
PY 2021
VL 2021
AR 2178281
DI 10.1155/2021/2178281
PG 13
WC Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services
GA UD8LB
UT WOS:000687453300010
PM 34413966
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Kesavareddigari, H
   Eryilmaz, A
AF Kesavareddigari, Himaja
   Eryilmaz, Atilla
TI Counter-Intuitive Characteristics of Rational Decision-Making Using
   Biased Inputs in Information Networks
SO IEEE-ACM TRANSACTIONS ON NETWORKING
LA English
DT Article
DE Social networking (online); Knowledge engineering; IEEE transactions;
   Error probability; Decision making; Sensors; Random variables; Rational
   decision-making; information networks; statistical decision theory;
   cognitive bias; data fusion
ID CHANNELS; FUSION
AB We consider an information network comprised of nodes that are: rational-information-consumers (RICs) and/or biased-information-providers (BIPs). Making the reasonable abstraction that any external event is reported as an answer to a logical statement, we model each node's information-sharing behavior as a binary channel. For various reasons, malicious or otherwise, BIPs might share incorrect reports of the event regardless of their private beliefs. In doing so, a BIP might favor one of the two outcomes, exhibiting intentional or unintentional bias (e.g. human cognitive biases). Inspired by the limitations of humans and low-memory devices in information networks, we previously investigated a graph-blind rational-information-consumer interested in identifying the ground truth. We concluded that to minimize its error probability, graph-blind RIC follows a counter-intuitive but tractable rule. In this work, we build on this foundational knowledge: "graph-blind RICs prefer the combination of information-providers that are all fully-biased against the a-priori likely input, over all other combinations." Upon studying RICs with partial knowledge of the network graph, we find that they act similar to graph-blind RICs when their BIPs "listen to" sufficiently many information-providers of their own. Furthermore, if a common node is informing/influencing all n BIPs of a partially-aware RIC, that RIC anticipates its discovery of the "influential node" to diminish the average error probability by a factor that increases exponentially with n . However, from the partially-aware RIC's perspective, choosing n fully-, similarly-biased BIPs outweighs the discovery of influential nodes among its BIPs' sources. These insights might inform the design of consumer-centric information networks.
C1 [Kesavareddigari, Himaja; Eryilmaz, Atilla] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA.
   [Kesavareddigari, Himaja] Qualcomm Inc, Bridgewater Township, NJ 08807 USA.
RP Kesavareddigari, H (通讯作者)，Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA.; Kesavareddigari, H (通讯作者)，Qualcomm Inc, Bridgewater Township, NJ 08807 USA.
EM kesavareddigari.1@osu.edu; eryilmaz.2@osu.edu
RI Kesavareddigari, Himaja/AEZ-6992-2022
OI Kesavareddigari, Himaja/0000-0003-3737-1043
FU NSF [CNS-NeTS-1514260, CNS-NeTS-1717045, CMMI-SMOR-1562065,
   CNS-ICN-WEN-1719371, CNS-SpecEES-1824337, CNS-NeTS-2007231]; Office of
   Naval Research (ONR) [N00014-19-1-2621]; Defense Threat Reduction Agency
   (DTRA) [HDTRA1-15-1-0003, HDTRA1-18-1-0050]
FX This work was supported in part by the NSF under Grant CNS-NeTS-1514260,
   Grant CNS-NeTS-1717045, Grant CMMI-SMOR-1562065, Grant
   CNS-ICN-WEN-1719371, Grant CNS-SpecEES-1824337, and Grant
   CNS-NeTS-2007231; in part by the Office of Naval Research (ONR) under
   Grant N00014-19-1-2621; and in part by the Defense Threat Reduction
   Agency (DTRA) under Grant HDTRA1-15-1-0003 and Grant HDTRA1-18-1-0050.
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NR 22
TC 0
Z9 0
U1 0
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1063-6692
EI 1558-2566
J9 IEEE ACM T NETWORK
JI IEEE-ACM Trans. Netw.
PD AUG
PY 2021
VL 29
IS 4
BP 1774
EP 1785
DI 10.1109/TNET.2021.3075430
PG 12
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA UB5NE
UT WOS:000685891800028
DA 2022-08-23
ER

PT J
AU Hosseini, H
   Nguyen, TT
   Wu, J
   Bagheri, E
AF Hosseini, Hawre
   Nguyen, Tam T.
   Wu, Jimmy
   Bagheri, Ebrahim
TI Implicit entity linking in tweets: An ad-hoc retrieval approach
SO APPLIED ONTOLOGY
LA English
DT Article
DE Implicit entity linking; Semantic retrieval; DBpedia; Knowledge graph
AB Within the context of Twitter analytics, the notion of implicit entity linking has recently been introduced to refer to the identification of a named entity, which is central to the topic of the tweet, but whose surface form is not present in the tweet itself. Compared to traditional forms of entity linking where the linking process revolves around an identified surface form of a potential entity, implicit entity linking relies on contextual clues to determine whether an implicit entity is present within a given tweet and if so, which entity is being referenced. The objective of this paper, while introducing and publicly sharing a comprehensive gold standard dataset for implicit entity linking, is to perform the task of implicit entity linking The dataset consists of 7,870 tweets, which are classified as either containing implicit entities, explicit entities, both, or neither. The implicit entities are then linked to three levels of entities on Wikipedia, namely coarse-grained level, e.g., Person, Fine-grained level, e.g., Comedian, and the actual entity, e.g., Seinfeld. The proposed model in this work formulates the problem of implicit entity linking as an ad-hoc document retrieval process where the input query is the tweet, which needs to be implicitly linked and the document space is the set of textual descriptions of entities in the knowledge base. The novel contributions of our work include: 1) designing and collecting a gold standard dataset for the task of implicit entity linking; 2) defining the implicit entity linking process as an ad-hoc document retrieval task; and 3) proposing a neural embedding-based feature function that is interpolated with prior term dependency and entity-based feature functions to enhance implicit entity linking We systematically compare our work with existing work in this area and show that our method is able to provide improvements on a number of retrieval measures.
C1 [Hosseini, Hawre; Nguyen, Tam T.; Wu, Jimmy; Bagheri, Ebrahim] Ryerson Univ, LS3, Toronto, ON, Canada.
RP Hosseini, H (通讯作者)，Ryerson Univ, LS3, Toronto, ON, Canada.
EM hawre.hosseini@ryerson.ca; nthanhtam@gmail.com; jimmy1.wu@ryerson.ca;
   bagheri@ryerson.ca
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NR 44
TC 4
Z9 4
U1 1
U2 6
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-5838
EI 1875-8533
J9 APPL ONTOL
JI Appl. Ontol.
PY 2019
VL 14
IS 4
BP 451
EP 477
DI 10.3233/AO-190215
PG 27
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KH6DS
UT WOS:000510741300005
DA 2022-08-23
ER

PT J
AU Lawan, A
   Leung, A
   Battie, MC
AF Lawan, Aliyu
   Leung, Andrew
   Battie, Michele C.
TI Vertebral endplate defects: nomenclature, classification and measurement
   methods: a scoping review
SO EUROPEAN SPINE JOURNAL
LA English
DT Review
DE Endplate; Nomenclature; Measurement; Classification; Psychometric
   properties; Vertebrae
ID INTERVERTEBRAL DISC DEGENERATION; RESONANCE-IMAGING ANALYSIS; LUMBAR
   SPINE; SCHMORL NODES; BACK-PAIN; MODIC CHANGES; ASSOCIATION; DISEASE;
   PREVALENCE; POPULATION
AB Purpose To summarize the scope of nomenclature and measurement methods used to document endplate defects in the health sciences literature. Methods The scoping review followed the York framework and was reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. The databases of PubMed, Scopus, Embase and CINAHL were searched using key terms. Screening and selection were conducted by two independent reviewers. A standardized, pilot-tested form was used for extracting data, which were analyzed descriptively. Results The review included 211 studies, originating from 29 countries, with the USA (18.8%) and China (12.26%) as leading contributors. Thirty-four different terms for structural endplate defects were reported, but were never defined in most studies (65%). Of the 34 different terms used, some appeared to represent the same phenomenon, while the same terms were occasionally defined differently between studies. Schmorl's nodes were most commonly investigated (n = 99 studies) and defined similarly across studies, with the main difference relating to whether the indentation (node) was required to have a sclerotic margin. There were also similarities in definitions for endplate sclerosis. However, there was great variability in the definitions of other terms, such as lesions, irregularities, abnormalities, erosions and changes. Conclusion With the possible exception of Schmorl's nodes, we lack a common language for effectively communicating structural endplate findings. This review provides a foundation and impetus for standardizing terminology and core measures to improve communication and synthesis of the growing body of endplate research to advance related knowledge. Graphic abstract These slides can be retrieved under Electronic Supplementary Material (paragraph). Then process the ppt slide as graphical image.
   [GRAPHICS]
   .
C1 [Lawan, Aliyu; Battie, Michele C.] Western Univ, Elborn Coll, Fac Hlth & Rehabil Sci, Room 1445, London, ON N6G 1H1, Canada.
   [Lawan, Aliyu; Battie, Michele C.] Western Univ, Elborn Coll, Westerns Bone & Joint Inst, Room 1445, London, ON N6G 1H1, Canada.
   [Leung, Andrew] Victoria Hosp, London Hlth Sci Ctr, Dept Med Imaging, Room C1-649,800 Commissioners Rd East, London, ON N6A 5W9, Canada.
RP Battie, MC (通讯作者)，Western Univ, Elborn Coll, Fac Hlth & Rehabil Sci, Room 1445, London, ON N6G 1H1, Canada.; Battie, MC (通讯作者)，Western Univ, Elborn Coll, Westerns Bone & Joint Inst, Room 1445, London, ON N6G 1H1, Canada.
EM mbattie@uwo.ca
RI Leung, Andrew/AAJ-6850-2020
OI Leung, Andrew/0000-0003-3349-1574
FU Faculty of Health Sciences; Transdisciplinary Training Award from the
   Bone and Joint Institute at Western University
FX No specific funding was received for this project. However, Aliyu Lawan,
   as a graduate student, receives support from the Faculty of Health
   Sciences and a Transdisciplinary Training Award from the Bone and Joint
   Institute at Western University.
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NR 68
TC 6
Z9 6
U1 0
U2 4
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0940-6719
EI 1432-0932
J9 EUR SPINE J
JI Eur. Spine J.
PD JUN
PY 2020
VL 29
IS 6
BP 1397
EP 1409
DI 10.1007/s00586-020-06378-8
EA MAR 2020
PG 13
WC Clinical Neurology; Orthopedics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology; Orthopedics
GA LU4UA
UT WOS:000521912400004
PM 32219597
DA 2022-08-23
ER

PT J
AU Zhang, F
   Fan, HB
   Wang, KJ
   Zhao, YJ
   Zhang, XX
   Ma, Y
AF Zhang, Feng
   Fan, Huibing
   Wang, Keju
   Zhao, Yongjin
   Zhang, Xiaoxi
   Ma, Yang
TI Research on Intelligent Target Recognition Integrated With Knowledge
SO IEEE ACCESS
LA English
DT Article
DE Feature extraction; Convolution; Object detection; Military aircraft;
   Licenses; Semantics; Deep learning; Prior knowledge; NVIDIA JetsonTX2;
   YOLOv5; intelligent weapon system; knowledge graph
AB With the development of artificial intelligence technology, intelligent weapon systems that can automatically identify, lock on and strike targets have gradually appeared and can replace humans in executing simple decision-making commands. Target detection is a key part of intelligent weapons. At present, large-scale target detection has serious challenges such as long-tail data distributions, severe occlusion, and category ambiguity. The main detection algorithms only detect each independent area without considering the key semantic dependencies between objects. It has become a hot trend to apply deep learning to prior knowledge to form a model. This article uses both internal and external knowledge to instill a target detection system with human reasoning capabilities. Commonly used external embedded knowledge includes geometric relations, attributes, locations, etc. They have a common shortcoming in that they require large amounts of labeled data, and the integration costs are huge. The purpose of this article is to construct a general external prior knowledge module to guide network learning. By paying attention to the characteristics of each object in different semantic contexts, the characteristics of each object are adaptively enhanced, and the high-level semantics of all categories evolve on a global scale. Internal knowledge uses a convolutional attention module that can learn spatial and channel information at multiple scales. The experimental results show the superiority of our knowledge-YOLOv5. The proposed method achieved 1.7%, 2.2%, 1.1%, and 0.7% improvements over YOLOv5s, YOLOv5m, VOLOv51, and YOLOv5x, respectively, on the COCO data sets; and the proposed method also achieves a 0.9% improvement on the self-built data set. The trained lightweight model Knowledge-YOLOv5s is deployed on an NVIDIA Jetson TX2 through TensorRT acceleration, and the real-time detection frame is 20 ms, which meets the real-time detection requirements. This system can also be used as a module of an intelligent weapon system, which has certain referential significance for autonomous weapons and unmanned combat systems.
C1 [Zhang, Feng; Wang, Keju; Zhao, Yongjin; Zhang, Xiaoxi; Ma, Yang] 208 Res Inst China Ordnance Ind, Beijing 102202, Peoples R China.
   [Fan, Huibing] Beijing Mech & Elect Res Inst, Beijing 102202, Peoples R China.
RP Fan, HB (通讯作者)，Beijing Mech & Elect Res Inst, Beijing 102202, Peoples R China.
EM ayrh18811592868@163.com
RI feng, zhang/ABC-8109-2021
OI zhang, feng/0000-0003-1719-9645
CR Akata Z, 2013, PROC CVPR IEEE, P819, DOI 10.1109/CVPR.2013.111
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NR 26
TC 0
Z9 0
U1 11
U2 23
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 137107
EP 137115
DI 10.1109/ACCESS.2021.3116866
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA WG2GU
UT WOS:000706816300001
OA gold
DA 2022-08-23
ER

PT J
AU Delgoshaei, P
   Heidarinejad, M
   Austin, MA
AF Delgoshaei, Parastoo
   Heidarinejad, Mohammad
   Austin, Mark A.
TI A Semantic Approach for Building System Operations: Knowledge
   Representation and Reasoning
SO SUSTAINABILITY
LA English
DT Article
DE building management system; context-aware control; knowledge
   representation and reasoning; intelligent buildings; Web Ontology
   Language
AB Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for building automation, integration and control applications. This article explored the use of semantic models, a subfield of artificial intelligence, for knowledge representation and reasoning (KRR) across a wide variety of applications in building control, automation and analytics. These KRR-enabled applications include context-aware control of mechanical systems, building energy auditing and commissioning, indoor air monitoring, fault detection and diagnostics (FDD) of mechanical equipment and systems and building-to-grid integration. To this end, this work employed the Apache Jena Application Programming Interface (API) to develop KRR and integrate it with some domain-specific ontologies expressed in the Resource Description Framework (RDF) and Web Ontology Language (OWL). The ontology-driven rules were represented using Jena rule formalisms to enable the inference of implicit information from data asserted in the ontologies. Moreover, SPARQL (SPARQL Query Language for RDF) was used to query the knowledge graph and obtain useful information for a variety of building applications. This approach enhances building analytics through multi-domain knowledge integration; spatial and temporal reasoning for monitoring building operations, and control systems and devices; and the performance of compliance checking. We show that existing studies have not leveraged state-of-the-art ontologies to infer information from different domains. While the proposed semantic infrastructure and methods in this study demonstrated benefits for different building applications applicable to mechanical systems, the approach also has great potential for lighting, shading and security applications. Multi-domain knowledge integration that includes spatial and temporal reasoning allows the optimization of the performance of building equipment and systems.
C1 [Delgoshaei, Parastoo] NIST, Mech Syst & Controls Grp, Engn Lab, Gaithersburg, MD 20899 USA.
   [Heidarinejad, Mohammad] IIT, Dept Civil Architectural & Environm Engn, Chicago, IL 60616 USA.
   [Austin, Mark A.] Univ Maryland, Dept Civil & Environm Engn, College Pk, MD 20742 USA.
RP Delgoshaei, P (通讯作者)，NIST, Mech Syst & Controls Grp, Engn Lab, Gaithersburg, MD 20899 USA.
EM parastoo.delgoshaei@nist.gov; muh182@iit.edu; austin@umd.edu
OI Heidarinejad, Mohammad/0000-0002-3064-825X
FU ASHRAE New Investigator Award
FX Mohammad Heidarinejad was supported, in part, by an ASHRAE New
   Investigator Award.
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NR 56
TC 0
Z9 0
U1 3
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAY
PY 2022
VL 14
IS 10
AR 5810
DI 10.3390/su14105810
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 1R3QX
UT WOS:000803288700001
OA gold
DA 2022-08-23
ER

PT J
AU Pan, Y
   Deng, XY
   Zhuang, Y
   Li, JY
AF Pan, Yun
   Deng, Xianyu
   Zhuang, Ying
   Li, Jiyu
TI Research Trends around Exercise Rehabilitation among Cancer Patients: A
   Bibliometrics and Visualized Knowledge Graph Analysis
SO BIOMED RESEARCH INTERNATIONAL
LA English
DT Article
ID OPTIMAL FORAGING THEORY; MEDICINE
AB This study analyzed the research hotspots and frontiers of exercise rehabilitation among cancer patients via CiteSpace. Relevant literature published in the core collection of the Web of Science (WoS) database from January 1, 2000, to February 6, 2022, was searched. Further, we used CiteSpace5.8R1 to generate a network map and identified top authors, institutions, countries, keywords, and research trends. A total of 2706 related literature were retrieved. The most prolific writer was found to be Kathryn H Schmitz (21 articles). The University of Toronto (64 articles) was found to be the leading institution, with the United States being the leading country. Further, "rehabilitation," "exercise," "quality of life," "cancer," and "physical activity" were the top 5 keywords based on frequency; next, "disability," "survival," "fatigue," "cancer," and "rehabilitation" were the top 5 keywords based on centrality. The keyword "fatigue" was ranked at the top of the most cited list. Finally, "rehabilitation medicine," "activities of daily living," "lung neoplasm," "implementation," "hospice," "exercise oncology," "mental health," "telemedicine," and "multidisciplinary" are potential topics for future research. Our results show that the research hotspots have changed from "quality of life," "survival," "rehabilitation," "exercise," "cancer," "physical therapy," "fatigue," and "breast cancer" to "exercise oncology," "COVID-19," "rehabilitation medicine," "inpatient rehabilitation," "implementation," "telemedicine," "lung neoplasm," "telehealth," "multidisciplinary," "psycho-oncology," "hospice," "adapted physical activity," "cancer-related symptom," "cognitive function," and "behavior maintenance." Future research should explore the recommended dosage and intensity of exercise in cancer patients. Further, following promotion of the concept of multidisciplinary cooperation and the rapid development of Internet medical care, a large amount of patient data has been accumulated; thus, how to effectively use this data to generate results of high clinical value is a question for future researchers.
C1 [Pan, Yun; Li, Jiyu] Tongji Univ, Peoples Hosp 10, Sch Med, Dept Gen Surg, Shanghai 200072, Peoples R China.
   [Deng, Xianyu] Tongji Univ, Peoples Hosp 10, Sch Med, Dept Neurosurg, Shanghai 200072, Peoples R China.
   [Zhuang, Ying] Shanghai Tenth Peoples Hosp, Colorectal Canc Ctr, Shanghai 200072, Peoples R China.
   [Li, Jiyu] Fudan Univ, Huadong Hosp, Geriatr Canc Ctr, Shanghai 200040, Peoples R China.
RP Li, JY (通讯作者)，Tongji Univ, Peoples Hosp 10, Sch Med, Dept Gen Surg, Shanghai 200072, Peoples R China.; Zhuang, Y (通讯作者)，Shanghai Tenth Peoples Hosp, Colorectal Canc Ctr, Shanghai 200072, Peoples R China.; Li, JY (通讯作者)，Fudan Univ, Huadong Hosp, Geriatr Canc Ctr, Shanghai 200040, Peoples R China.
EM lemon526@163.com; lijiyu@fudan.edu.cn
OI Li, Jiyu/0000-0002-2529-2914
FU National Natural Science Foundation of China [82072647]
FX This study was supported by grants from the National Natural Science
   Foundation of China (No. 82072647).
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NR 42
TC 0
Z9 0
U1 2
U2 2
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 2314-6133
EI 2314-6141
J9 BIOMED RES INT
JI Biomed Res. Int.
PD MAY 27
PY 2022
VL 2022
AR 3755460
DI 10.1155/2022/3755460
PG 11
WC Biotechnology & Applied Microbiology; Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biotechnology & Applied Microbiology; Research & Experimental Medicine
GA 3O7UD
UT WOS:000837038100006
PM 35669723
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Buranasing, W
   Lilakiataskun, W
AF Buranasing, Watchira
   Lilakiataskun, Woraphon
TI Semantic Relation Extraction from Cultural Heritage Archives
SO JOURNAL OF WEB ENGINEERING
LA English
DT Article
DE Digital archive; relation extraction; cultural archive; word vector
   representation; information extraction
AB Digital preservation technologies are now being increasingly adopted by cultural heritage organizations. This cultural heritage data is often disseminated in the form of digital text through a variety of channels such as Wikipedia, cultural heritage archives, etc. To acquire knowledge from digital data, the extraction technique becomes an important part. However, in the case of digital text, which has characteristics such as ambiguity, complex grammar structures such as the Thai language, and others, it makes it more challenging to extract information with a high level of accuracy. We thus propose a method for improving the performance of data extraction techniques based on word features, multiple instance learning, and unseen word mapping. Word features are used to improve the quality of word definition by concatenating parts of speech (POS) and word position is used to establish the accurate definition of a word and convert all of this into a vector. In addition, we use multiple instance learning to solve issues where words do not fully express the meaning of the triple. We also cluster the particular word to find the predicate word by removing words that are irrelevant between the subject and the object. The difficulty of having a new set of words that have never been trained before can be overcome by using unseen word mapping with sub-word and nearest neighbor word mapping. We conducted several experiments on a cultural heritage knowledge graph to show the efficacy of the proposed method. The results demonstrated that our proposed technique outperforms existing models currently utilized in relation to extraction systems. It can achieve excellent accuracy since its precision, recall, and F1 score are 0.89, 0.88, and 0.89, respectively. Furthermore, it also performed well in terms of unseen word prediction, precision, recall, and F1 score, which were 0.81, 0.87, and 0.84, respectively.
C1 [Buranasing, Watchira; Lilakiataskun, Woraphon] Mahanakorn Univ Technol, Fac Informat Sci & Technol, Bangkok 10530, Thailand.
   [Buranasing, Watchira] Natl Elect & Comp Technol Ctr, Pathum Thani 12120, Thailand.
RP Buranasing, W (通讯作者)，Mahanakorn Univ Technol, Fac Informat Sci & Technol, Bangkok 10530, Thailand.; Buranasing, W (通讯作者)，Natl Elect & Comp Technol Ctr, Pathum Thani 12120, Thailand.
EM watchira.buranasing@nectec.or.th
CR Bouraoui B., 2018, P 27 INT C COMP LING
   Bouraoui Zied, 2017, PROBABILISTIC RELATI
   Buranasing W., 2018, INT C COMPL INT SOFT
   Choi D., 2008, 16 INT C KNOWL ENG K
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NR 14
TC 0
Z9 0
U1 0
U2 0
PU RIVER PUBLISHERS
PI GISTRUP
PA ALSBJERGVEJ 10, GISTRUP, 9260, DENMARK
SN 1540-9589
EI 1544-5976
J9 J WEB ENG
JI J. Web Eng.
PY 2022
VL 21
IS 4
BP 1081
EP 1102
DI 10.13052/jwe1540-9589.2145
PG 22
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0O8DM
UT WOS:000783753900006
DA 2022-08-23
ER

PT J
AU Mehmood, Q
   Saleem, M
   Jha, A
   D'Aquin, M
AF Mehmood, Qaiser
   Saleem, Muhammad
   Jha, Alokkumar
   D'Aquin, Mathieu
TI Efficient distributed path computation on RDF knowledge graphs using
   partial evaluation
SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
LA English
DT Article
DE Distributed graphs; Reachability; Federated paths; Graph traversal; Path
   caching; Path query; RDF; SPARQL 1; 1 Property path
ID SPARQL; QUERIES
AB A key property of Linked Data is the representation and publication of data as interconnected labelled graphs where different resources linked to each other form a network of meaningful information. Searching these important relationships between resources - within single or distributed graphs - can be reduced to a pathfinding or navigation problem, i.e., looking for chains of intermediate nodes. SPARQL1.1, the current standard query language for RDF-based Linked Data defines a construct - called Property Paths (PPs) - to navigate between entities within a single graph. Since Linked Data technologies are naturally aimed at decentralised scenarios, there are many cases where centralising this data is not feasible or even not possible for querying purposes. To address these problems, we propose a SPARQL PP-based graph processing approach - dubbed DpcLD - where users can execute SPARQL PP queries and find paths distributed across multiple, connected graphs exposed as SPARQL endpoints. To execute the distributed path queries we implemented an index-free, cache-based query engine that communicates with a shared algorithm running on each remote endpoint, and computes the distributed paths. In this paper, we highlight the way in which this approach exploits and aggregates partial paths, within a distributed environment, to produce complete results. We perform extensive experiments to demonstrate the performance of our approach on two datasets: One representing 10 million triples from the DBPedia SPARQL benchmark, and another full benchmark dataset corresponding to 124 million triples. We also perform a scalability test of our approach using real-world genomics datasets distributed across multiple endpoints. We compare our distributed approach with other distributed and centralized pathfinding approaches, showing that it outperforms other distributed approaches by orders of magnitude, and provides a good trade-off for cases when the data cannot be centralised.
C1 [Mehmood, Qaiser; D'Aquin, Mathieu] NUI Galway, Data Sci Inst, Galway, Ireland.
   [Saleem, Muhammad] AKSW, Leipzig, Germany.
   [Jha, Alokkumar] Stanford Univ, Stanford, CA 94305 USA.
RP Mehmood, Q (通讯作者)，NUI Galway, Data Sci Inst, Galway, Ireland.
EM qaiser.mehmood@insight-centre.org;
   muhammad.saleem@informatik.uni-leipzig.de; alokjha@stanford.edu;
   mathieu.daquin@insight-centre.org
RI Jha, Alokkumar/ABH-8075-2022; Jha, Alokkumar/AAB-7162-2019
OI Jha, Alokkumar/0000-0002-8024-5854; Mehmood, Qaiser/0000-0002-1380-1750
FU Science Foundation Ireland; European Regional Development Fund
   [SFI/12/RC/2289_P2]; project LIMBO [19F2029I]; OPAL [19F2028A];
   KnowGraphs [860801]; SOLIDE [13N14456]
FX This work is partly supported by a research grant from Science
   Foundation Ireland, co-funded by the European Regional Development Fund,
   for the Insight SFI Research Centre for Data Analytics under Grant
   Number SFI/12/RC/2289_P2. The work conducted in the University of
   Leipzig has been supported by the project LIMBO (Grant no. 19F2029I),
   OPAL (no. 19F2028A), KnowGraphs (no. 860801), and SOLIDE (no. 13N14456).
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NR 72
TC 0
Z9 0
U1 1
U2 2
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1386-145X
EI 1573-1413
J9 WORLD WIDE WEB
JI World Wide Web
PD MAR
PY 2022
VL 25
IS 2
SI SI
BP 1005
EP 1036
DI 10.1007/s11280-021-00965-5
EA NOV 2021
PG 32
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZK2AM
UT WOS:000714529800001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Cui, H
   Zhang, LM
   Ford, B
   Cheng, HL
   Macklin, JA
   Reznicek, A
   Starr, J
AF Cui, Hong
   Zhang, Limin
   Ford, Bruce
   Cheng, Hsin-liang
   Macklin, James A.
   Reznicek, Anton
   Starr, Julian
TI Measurement Recorder: developing a useful tool for making species
   descriptions that produces computable phenotypes
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Article
ID MEASUREMENT ONTOLOGY; EVOLUTION; DIVERSIFICATION; SYSTEMATICS; SPACE;
   GENUS; CAREX
AB To use published phenotype information in computational analyses, there have been efforts to convert descriptions of phenotype characters from human languages to ontologized statements. This postpublication curation process is not only slow and costly, it is also burdened with significant intercurator variation (including curator-author variation), due to different interpretations of a character by various individuals. This problem is inherent in any human-based intellectual activity. To address this problem, making scientific publications semantically clear (i.e. computable) by the authors at the time of publication is a critical step if we are to avoid postpublication curation. To help authors efficiently produce species phenotypes while producing computable data, we are experimenting with an author-driven ontology development approach and developing and evaluating a series of ontology-aware software modules that would create publishable species descriptions that are readily useable in scientific computations. The first software module prototype called Measurement Recorder has been developed to assist authors in defining continuous measurements and reported in this paper. Two usability studies of the software were conducted with 22 undergraduate students majoring in information science and 32 in biology. Results suggest that participants can use Measurement Recorder without training and they find it easy to use after limited practice. Participants also appreciate the semantic enhancement features. Measurement Recorder's character reuse features facilitate character convergence among participants by 48% and have the potential to further reduce user errors in defining characters. A set of software design issues have also been identified and then corrected. Measurement Recorder enables authors to record measurements in a semantically clear manner and enriches phenotype ontology along the way. Future work includes representing the semantic data as Resource Description Framework (RDF) knowledge graphs and characterizing the division of work between authors as domain knowledge providers and ontology engineers as knowledge formalizers in this new author-driven ontology development approach.
C1 [Cui, Hong; Zhang, Limin] Univ Arizona, Sch Informat, Tucson, AZ 85705 USA.
   [Ford, Bruce] Univ Manitoba, Dept Biol Sci, Winnipeg, MB R3T 2N2, Canada.
   [Cheng, Hsin-liang] Missouri Univ Sci & Technol, Curtis Laws Wilson Lib, Rolla, MO 65409 USA.
   [Macklin, James A.] Agr & Agri Food Canada, Ottawa Res & Dev Ctr, Ottawa, ON K1A 0C6, Canada.
   [Reznicek, Anton] Univ Michigan, LSA Herbarium, Ann Arbor, MI 48019 USA.
   [Starr, Julian] Univ Ottawa, Dept Biol, Ottawa, ON K1N 6N5, Canada.
RP Cui, H (通讯作者)，Univ Arizona, Sch Informat, Tucson, AZ 85705 USA.
OI ZHANG, LIMIN/0000-0002-5578-3271
FU US National Science Foundation [DBI-1661485]
FX This work was supported by US National Science Foundation (DBI-1661485).
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NR 50
TC 2
Z9 2
U1 0
U2 0
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD NOV 20
PY 2020
AR baaa079
DI 10.1093/database/baaa079
PG 35
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA PC8DQ
UT WOS:000597225400001
PM 33216896
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Chen, XYK
   Na, JC
   Tan, LKW
   Chong, M
   Choy, M
AF Chen, Xingyu Ken
   Na, Jin-Cheon
   Tan, Luke Kien-Weng
   Chong, Mark
   Choy, Murphy
TI Exploring how online responses change in response to debunking messages
   about COVID-19 on WhatsApp
SO ONLINE INFORMATION REVIEW
LA English
DT Article; Early Access
DE COVID-19; Debunking; Aspect-based sentiment analysis; Information
   distortion; Source credibility; Deep learning
ID SENTIMENT ANALYSIS; AMPLIFICATION
AB Purpose The COVID-19 pandemic has spurred a concurrent outbreak of false information online. Debunking false information about a health crisis is critical as misinformation can trigger protests or panic, which necessitates a better understanding of it. This exploratory study examined the effects of debunking messages on a COVID-19-related public chat on WhatsApp in Singapore. Design/methodology/approach To understand the effects of debunking messages about COVID-19 on WhatsApp conversations, the following was studied. The relationship between source credibility (i.e. characteristics of a communicator that affect the receiver's acceptance of the message) of different debunking message types and their effects on the length of the conversation, sentiments towards various aspects of a crisis, and the information distortions in a message thread were studied. Deep learning techniques, knowledge graphs (KG), and content analyses were used to perform aspect-based sentiment analysis (ABSA) of the messages and measure information distortion. Findings Debunking messages with higher source credibility (e.g. providing evidence from authoritative sources like health authorities) help close a discussion thread earlier. Shifts in sentiments towards some aspects of the crisis highlight the value of ABSA in monitoring the effectiveness of debunking messages. Finally, debunking messages with lower source credibility (e.g. stating that the information is false without any substantiation) are likely to increase information distortion in conversation threads. Originality/value The study supports the importance of source credibility in debunking and an ABSA approach in analysing the effect of debunking messages during a health crisis, which have practical value for public agencies during a health crisis. Studying differences in the source credibility of debunking messages on WhatsApp is a novel shift from the existing approaches. Additionally, a novel approach to measuring information distortion using KGs was used to shed insights on how debunking can reduce information distortions.
C1 [Chen, Xingyu Ken; Na, Jin-Cheon; Tan, Luke Kien-Weng] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Singapore, Singapore.
   [Chong, Mark] Singapore Management Univ, Lee Kong Chian Sch Business, Singapore, Singapore.
   [Choy, Murphy] Alionova Consulting Private Ltd, Singapore, Singapore.
RP Chen, XYK (通讯作者)，Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Singapore, Singapore.
EM xychen1991@gmail.com
OI Chen, Xingyu Ken/0000-0003-3733-4015; Na, Jin-Cheon/0000-0002-2211-9382
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NR 54
TC 0
Z9 0
U1 16
U2 17
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1468-4527
EI 1468-4535
J9 ONLINE INFORM REV
JI Online Inf. Rev.
DI 10.1108/OIR-08-2021-0422
EA JAN 2022
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 YT5SK
UT WOS:000751419200001
OA Green Accepted
DA 2022-08-23
ER

PT J
AU Gao, M
   He, XN
   Chen, LH
   Liu, TT
   Zhang, JL
   Zhou, AY
AF Gao, Ming
   He, Xiangnan
   Chen, Leihui
   Liu, Tingting
   Zhang, Jinglin
   Zhou, Aoying
TI Learning Vertex Representations for Bipartite Networks
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Bipartite networks; network representation learning; matrix
   factorization; link prediction; recommendation
AB Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages. This work addresses the research gap of learning vertex representations for bipartite networks. We present a new solution BiNE, short for Bipartite Network Embedding, which accounts for two special properties of bipartite networks: long-tail distribution of vertex degrees and implicit connectivity relations between vertices of the same type. Technically speaking, we make three contributions: (1) We design a biased random walk generator to generate vertex sequences that preserve the long-tail distribution of vertices; (2) We propose a new optimization framework by simultaneously modeling the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved but transitive links); (3) We explore the theoretical foundations of BiNE to shed light on how it works, proving that BiNE can be interpreted as factorizing multiple matrices. We perform extensive experiments on five real datasets covering the tasks of link prediction (classification) and recommendation (ranking), empirically verifying the effectiveness and rationality of BiNE. Our experiment codes are available at: https://github.com/clhchtcjj/BiNE.
C1 [Gao, Ming] East China Normal Univ, Sch Data Sci & Engn, Sch Stat, Shanghai 200062, Peoples R China.
   [Gao, Ming] East China Normal Univ, KLATASDS MOE, Sch Stat, Shanghai 200062, Peoples R China.
   [Chen, Leihui; Liu, Tingting; Zhou, Aoying] East China Normal Univ, Sch Data Sci & Engn, Shanghai 200062, Peoples R China.
   [He, Xiangnan] Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230052, Peoples R China.
   [Zhang, Jinglin] Nanjing Univ Informat Sci & Technol, Key Lab Meteorol Disaster, Minist Educ, Nanjing 210044, Peoples R China.
RP Zhou, AY (通讯作者)，East China Normal Univ, Sch Data Sci & Engn, Shanghai 200062, Peoples R China.; Zhang, JL (通讯作者)，Nanjing Univ Informat Sci & Technol, Key Lab Meteorol Disaster, Minist Educ, Nanjing 210044, Peoples R China.
EM mgao@dase.ecnu.edu.cn; xiangnanhe@gmail.com; leihuichen@gmail.com;
   tingtingliu223@gmail.com; linglin.zhang@nuist.edu.cn;
   ayzhou@dase.ecnu.edu.cn
FU National Key Research and Development Program of China [2016YFB1000905];
   National Natural Science Foundation of China [U1811264, U1911203,
   61877018, 61672234, 61672384, 41775008, 61702275]; Shanghai Agriculture
   Applied Technology Development Program, China [T20170303]
FX This work has been supported by the National Key Research and
   Development Program of China under grant 2016YFB1000905, and the
   National Natural Science Foundation of China under Grant No. U1811264,
   U1911203, 61877018, 61672234, 61672384, 41775008, 61702275, and the
   Shanghai Agriculture Applied Technology Development Program, China
   (GrantNo.T20170303). This paper is an extended version of the SIGIR '18
   conference paper [1].
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NR 52
TC 3
Z9 3
U1 12
U2 13
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD JAN 1
PY 2022
VL 34
IS 1
BP 379
EP 393
DI 10.1109/TKDE.2020.2979980
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA XM1DS
UT WOS:000728576400026
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Li, Z
   Xu, C
   Fu, J
   Zulipikaer, M
   Deng, T
   Chen, JY
AF Li, Zhuo
   Xu, Chi
   Fu, Jun
   Zulipikaer, Maimaiti
   Deng, Tao
   Chen, Jiying
TI Scientific Knowledge Graph and Trend Analysis of Central Sensitization:
   A Bibliometric Analysis
SO JOURNAL OF PAIN RESEARCH
LA English
DT Article
DE central sensitization; bibliometric analysis; pain; co-word;
   co-citation; collaboration network
ID QUALITY-OF-LIFE; DORSAL-HORN NEURONS; CHRONIC PAIN; INTELLECTUAL
   STRUCTURE; BACK-PAIN; ARTICLES; MODEL
AB Background: Central sensitization refers to a state of hypersensitivity in the central nervous system and is associated with the development and maintenance of chronic pain. Central sensitization plays an essential role in various diseases. Nevertheless, there has been no bibliometric analysis before in this field. The purpose of this study was to provide critical themes and trends in the area of central sensitization, to build a network of knowledge, and to facilitate the future development of relevant basic and clinical research. Methods: Publications on central sensitization were extracted from the Science Citation Index-Expanded. We used R software to systematically analyze the countries, institutions, authors, journals, references, and keywords of the publications. Besides, conceptual structure, intellectual structure, and social structure were constructed. Results: A total of 4466 publications were included. Research in the field of central sensitization generally showed a steady upward trend. The three structural networks showed that the United States is the leading country in this field. Arendt-Nielsen L and Woolf CJ were the most productive and influential authors, respectively. "Pain" was the journal with the most studies. Most journals that published and cited articles about central sensitization were academically influential. Cluster analysis revealed that research in central sensitization contains three main conceptual clusters, and the themes of research evolve frequently. Current research focuses on the pathogenesis of central sensitization in neuropathic pain, the role of central sensitization in different diseases, and related clinical double-blind trials. Conclusion: Central sensitization received widespread attention. The United States led the way in academic activity. In this field, the current situation of cooperation and communication between different countries and institutions is positive. The present research hotspots were the pathogenesis of central sensitization in neuropathic pain, the role of central sensitization in different diseases, and related clinical double-blind trials.
C1 [Li, Zhuo] Nankai Univ, Sch Med, Tianjin, Peoples R China.
   [Li, Zhuo; Xu, Chi; Fu, Jun; Zulipikaer, Maimaiti; Deng, Tao; Chen, Jiying] Chinese Peoples Liberat Army Gen Hosp, Dept Orthoped Surg, 28 Fuxing Rd, Beijing 100853, Peoples R China.
RP Chen, JY (通讯作者)，Chinese Peoples Liberat Army Gen Hosp, Dept Orthoped Surg, 28 Fuxing Rd, Beijing 100853, Peoples R China.
EM jiyingchen_301@163.com
RI Maimaiti, Zulipikaer/AAE-9329-2022
OI Maimaiti, Zulipikaer/0000-0002-8684-5885; Li, Zhuo/0000-0002-9296-2738;
   Xu, Chi/0000-0003-4211-0657
FU National Key Research and Development Program of China [2020YFC2004900]
FX This work was supported by National Key Research and Development Program
   of China (No. 2020YFC2004900).
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NR 63
TC 1
Z9 1
U1 12
U2 12
PU DOVE MEDICAL PRESS LTD
PI ALBANY
PA PO BOX 300-008, ALBANY, AUCKLAND 0752, NEW ZEALAND
SN 1178-7090
J9 J PAIN RES
JI J. Pain Res.
PY 2022
VL 15
BP 561
EP 575
DI 10.2147/JPR.S348946
PG 15
WC Clinical Neurology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA ZQ8ND
UT WOS:000767354000001
PM 35237073
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zeng, WX
   Zhao, X
   Li, XY
   Tang, JY
   Wang, W
AF Zeng, Weixin
   Zhao, Xiang
   Li, Xinyi
   Tang, Jiuyang
   Wang, Wei
TI On entity alignment at scale
SO VLDB JOURNAL
LA English
DT Article; Early Access
DE Entity alignment; Scalable entity alignment; Reciprocal inference
ID SCHEME
AB Knowledge graph (KG), as an effective approach of organizing and storing data, has received growing attention over the last decade. A KG can hardly reach completeness since there are always a large amount of new data emerging. To increase the scale and coverage of KGs, a possible solution is to incorporate data from other KGs, and entity alignment (EA) plays a vital role during this process. EA is the task of detecting the entities that refer to the same real-world object but come from different KGs. Although a pile of approaches have been put forward to tackle this task, they are mostly evaluated on datasets in small size and cannot deal with large-scale data in practice. In this work, we study the task of EA at scale and put forward a novel solution that can manage large-scale KG pairs and meanwhile achieve promising alignment performance. First, we devise seed-oriented graph partition strategies to divide large-scale KG pairs into smaller subgraph pairs. Next, within each subgraph pair, we learn the unified entity representations using existing methods and conceive a novel reciprocal alignment inference strategy to model the bi-directional alignment interactions, which can lead to more accurate alignment results. To further improve the scalability of reciprocal alignment inference, we put forward two variant strategies that can significantly reduce the memory and time costs at the expense of a small drop of effectiveness. Our proposal is generic and can be applied to existing representation learning-based EA models to improve their capability of dealing with large-scale KG pairs. Finally, we build a new EA dataset with millions of entities and conduct detailed experiments to validate that our proposed model can effectively cope with EA at scale. We also evaluate our proposed model against state-of-the-art baselines on popular EA datasets, and the extensive experiments demonstrate its effectiveness and superiority.
C1 [Zeng, Weixin; Zhao, Xiang; Li, Xinyi; Tang, Jiuyang] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410072, Hunan, Peoples R China.
   [Wang, Wei] Hong Kong Univ Sci & Technol Guangzhou, Guangzhou 511453, Guangdong, Peoples R China.
RP Zhao, X (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410072, Hunan, Peoples R China.
EM zengweixin13@nudt.edu.cn; xiagnzhao@nudt.edu.cn; xinyili@nudt.edu.cn;
   jiuyang_tang@nudt.edu.cn; weiwcs@ust.hk
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 Grants Nos. 61872446 and
   71971212 and NSF of Hunan Province under Grant No. 2019JJ20024.
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NR 67
TC 1
Z9 1
U1 8
U2 8
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1066-8888
EI 0949-877X
J9 VLDB J
JI VLDB J.
DI 10.1007/s00778-021-00703-3
EA JAN 2022
PG 25
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YC5YQ
UT WOS:000739767200001
DA 2022-08-23
ER

PT J
AU Xu, F
   Guo, GG
   Zhu, FD
   Tan, XJ
   Fan, LQ
AF Xu, Fang
   Guo, Ganggang
   Zhu, Feida
   Tan, Xiaojun
   Fan, Liqing
TI Protein deep profile and model predictions for identifying the causal
   genes of male infertility based on deep learning
SO INFORMATION FUSION
LA English
DT Article
DE Data integration; Disease phenotype; Male infertility; Causal gene;
   Knowledge representation; Convolutional neural network; Manifold
   learning; Deep learning
ID GENOMIC DATA; DISEASE ASSOCIATIONS; CLINICAL VALIDITY; INTEGRATING DATA;
   NETWORK; FUSION; CLASSIFICATION; SEQUENCE; HEALTH; OMICS
AB A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. Millions of genes have been sequenced in data-driven genomics era, but their causal relationships with disease phenotypes remain limited, due to the difficulty of elucidating underlying causal genes by laboratory based strategies. Here, we proposed an innovative deep learning computational modeling alternative (DPPCG framework) for identifying causal (coding) genes for a specific disease phenotype. In terms of male infertility, we introduced proteins as intermediate cell variables, leveraging integrated deep knowledge representations (Word2vec, ProtVec, Node2vec, and Space2vec) quantitatively represented as 'protein deep profiles'. We adopted deep convolutional neural network (CNN) classifier to model protein deep profiles relationships with male infertility, creatively training deep CNN models of single-label binary classification and multi label eight classification. We demonstrate the capabilities of DPPCG framework by integrating and fully harnessing the utility of heterogeneous biomedical big data, including literature, protein sequences, protein-protein interactions, gene expressions, and gene-phenotype relationships, and effective indirect prediction of 794 causal genes of male infertility and associated pathological processes. We present this research in an interactive 'Smart Protein' intelligent (demo) system (http://www.smartprotein.cloud/public/home). Researchers can benefit from our intelligent system by (i) accessing a shallow gene/protein-radar service involving research status and a knowledge graph-based vertical search; (ii) querying and downloading protein deep profile matrices; (iii) accessing intelligent recommendations for causal genes of male infertility and associated pathological processes, and references for model architectures, parameter settings, and training outputs; and (iv) carrying out personalized analysis such as online K-Means clustering.
C1 [Xu, Fang; Fan, Liqing] Cent South Univ, Sch Basic Med Sci, Inst Reprod & Stem Cell Engn, Changsha 410000, Peoples R China.
   [Guo, Ganggang] Cent South Univ, Sch Business, Changsha 410000, Peoples R China.
   [Zhu, Feida] Singapore Management Univ, Sch Informat Syst, Stamford Rd, Singapore 178902, Singapore.
   [Xu, Fang; Tan, Xiaojun] Cent South Univ, Clin Practice Base, Xiangtan Cent Hosp, Xiangtan 411100, Peoples R China.
RP Fan, LQ (通讯作者)，Cent South Univ, Sch Basic Med Sci, Inst Reprod & Stem Cell Engn, Changsha 410000, Peoples R China.
EM liqingfan@csu.edu.cn
OI GUO, Ganggang/0000-0002-5580-241X
FU National Natural Science Foundation of China [31472054]; National Key
   Research and Development Program of China [2016YFC1000600]
FX The authors would like to thank WebShop for its linguistic assistance
   with an earlier version of the manuscript. This work was supported by
   the National Natural Science Foundation of China (No. 31472054) and the
   National Key Research and Development Program of China (2016YFC1000600).
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NR 177
TC 0
Z9 0
U1 15
U2 37
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 70
EP 89
DI 10.1016/j.inffus.2021.04.012
EA APR 2021
PG 20
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TF9EV
UT WOS:000671018300006
DA 2022-08-23
ER

PT J
AU Makarov, I
   Kiselev, D
   Nikitinsky, N
   Subelj, L
AF Makarov, Ilya
   Kiselev, Dmitrii
   Nikitinsky, Nikita
   Subelj, Lovro
TI Survey on graph embeddings and their applications to machine learning
   problems on graphs
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Graph embedding; Knowledge representation; Machine learning; Network
   science; Geometric deep learning; Graph neural networks; Node
   classification; Link prediction; Node clustering; Graph visualization
ID TARGET INTERACTION PREDICTION; LINK-PREDICTION; KNOWLEDGE GRAPH;
   NETWORK; REPRESENTATION; SIMILARITY; ATTENTION; SECURITY; SEQUENCE;
   DYNAMICS
AB Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.
C1 [Makarov, Ilya; Kiselev, Dmitrii] HSE Univ, Moscow, Russia.
   [Makarov, Ilya; Subelj, Lovro] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana, Slovenia.
   [Nikitinsky, Nikita] Natl Univ Sci & Technol MISIS, Big Data Res Ctr, Moscow, Russia.
RP Makarov, I; Kiselev, D (通讯作者)，HSE Univ, Moscow, Russia.; Makarov, I (通讯作者)，Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana, Slovenia.
EM iamakarov@hse.ru; dkiseljov@hse.ru
RI Makarov, Ilya/G-9195-2015; Nikitinsky, Nikita/AAE-4248-2022; Kiselev,
   Dmitrii/AAL-7231-2021; Subelj, Lovro/V-9106-2018
OI Makarov, Ilya/0000-0002-3308-8825; Kiselev, Dmitrii/0000-0001-6190-7587;
   Subelj, Lovro/0000-0002-4161-2260
FU Russian Science Foundation [19-11-00281]; Faculty of Computer Science,
   HSE University
FX The work of Nikita Nikitinsky on Section 6 was supported by the Russian
   Science Foundation grant 19-11-00281. The OA fee was covered under
   support of Faculty of Computer Science, HSE University. 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 401
TC 13
Z9 13
U1 11
U2 29
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 FEB 4
PY 2021
AR e357
DI 10.7717/peerj-cs.357
PG 62
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA QE3NX
UT WOS:000616117000001
PM 33817007
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Li, S
   Chen, JP
   Xiang, J
AF Li Shi
   Chen Jianping
   Xiang Jie
TI Prospecting Information Extraction by Text Mining Based on Convolutional
   Neural Networks-A case study of the Lala Copper Deposit, China
SO IEEE ACCESS
LA English
DT Article
DE Prospecting information; convolution neural networks; text mining;
   textual geoscience data; visual analysis
ID NATURAL-LANGUAGE; KNOWLEDGE
AB With geological big data becoming a focus of geoscience research, the vast amount of textual geoscience data provides both opportunities and challenges for data analysis and data mining. In fact, it does not seem possible to meet the demands of the big data age through the traditional manual reading for information extraction and gaining knowledge. In this paper, a workflow is proposed to extract prospecting information by text mining based on convolutional neural networks (CNNs). The aim is to classify the text data and extract the prospecting information automatically. The procedure involves three parts: 1) text data acquisition; 2) text classification based on CNN; and 3) statistics and visualization. First, the large amount of available text data was acquired based on geoscience big data acquisition methodologies. After text preprocessing, the CNN was used to classify the geoscience text data into four categories (geology, geophysics, geochemistry, and remote sensing), with each category consisting of three levels of text scales (word, sentence, and paragraph). Second, the word frequency statistics, co-occurrence matrix statistics, and term frequency-inverse document frequency (TF-IDF) statistics were for words, sentences, and paragraphs, respectively, which aimed to obtain the key nodes and links derived from the content-words. Finally, the deep semantic information of the big data mining of relevant geoscience texts was visualized by word clouds, knowledge graphs (e.g., the chord and bigram graphs), and TF-IDF statistical graphs. The Lala copper deposit in Sichuan province was taken as a test case, for which the prospecting information was extracted successfully by the developed text mining methodologies. This paper provides a strong basis for research into establishing mineral deposits prospecting models based on logical knowledge trees. In addition, it shows the great potential of this method for intelligent information extraction within geoscience big data.
C1 [Li Shi; Chen Jianping] China Univ Geosci, Sch Earth Sci & Resources, Beijing 100083, Peoples R China.
   [Li Shi; Chen Jianping] China Univ Geosci, Land Resources Informat Dev & Res Key Lab Beijing, Beijing 100083, Peoples R China.
   [Xiang Jie] CAGS, Inst Mineral Resources, MNR Key Lab Metallogeny & Mineral Assessment, Beijing 10037, Peoples R China.
RP Chen, JP (通讯作者)，China Univ Geosci, Sch Earth Sci & Resources, Beijing 100083, Peoples R China.; Chen, JP (通讯作者)，China Univ Geosci, Land Resources Informat Dev & Res Key Lab Beijing, Beijing 100083, Peoples R China.
EM 3s@cugb.edu.cn
FU Chinese MOST Methods and Models for Quantitative Prediction of Deep
   Metallogenic Geological Anomalies Project [2017YFC0601502]
FX This work was supported by the Chinese MOST Methods and Models for
   Quantitative Prediction of Deep Metallogenic Geological Anomalies
   Project under Grant 2017YFC0601502.
CR Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
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NR 41
TC 24
Z9 25
U1 10
U2 55
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 52286
EP 52297
DI 10.1109/ACCESS.2018.2870203
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA GX4MN
UT WOS:000447707600001
OA gold
DA 2022-08-23
ER

PT J
AU Vermeulen, B
   Pyka, A
AF Vermeulen, Ben
   Pyka, Andreas
TI The Role of Network Topology and the Spatial Distribution and Structure
   of Knowledge in Regional Innovation Policy: A Calibrated Agent-Based
   Model Study
SO COMPUTATIONAL ECONOMICS
LA English
DT Article
DE Regional innovativeness; Innovation network; Specialization;
   Diversification; Related variety; Gatekeeper; Smart specialization;
   Branching; Agent-based model; Patent; Social planner
ID AGGLOMERATION EXTERNALITIES; CLUSTER EVOLUTION; TACIT KNOWLEDGE;
   LIFE-CYCLE; ALLIANCES; CITIES; COMPETITIVENESS; PERFORMANCE;
   GATEKEEPERS; TECHNOLOGY
AB Recently, new regional innovation policy paradigms emerged that transcend a long-lived dispute on whether either regional specialization, diversification, or rather related variety is most conducive to regional innovativeness. This includes 'smart specialization' in which regions are deliberately specialized and connected following technological relatedness, 'gatekeepers' in which there are pipelines between dense regional networks, and a 'regional mix' of knowledge that allows sustained 'branching'. We develop and use an agent-based model to study the conjecture that, to stimulate (supra)regional innovativeness, social planners need to consider both, in conjunction, the mix of technological knowledge in regions and the (inter)regional innovation network topology. We use this agent-based model to evaluate the performance of and study the internal mechanisms of these newpolicy paradigms in a variety of scenarios. To increase the external validity of our findings, we calibrate the knowledge graph searched by the agents in our model to the OECD patent database. We confirm that access to related variety is important, yet that, on top of that, access to incidentally related knowledge is crucial to prevent high-level lock-in and ensure long term technological progress. Moreover, we find that networks with regional gatekeepers are particularly innovative, because these gatekeepers form 'knowledge hubs', create short paths to potential partners, and enlarge the total pool of knowledge. In case agents have few relationships, we find exceptionally high performance for the gatekeeper network in combination with regional diversification. The smart specialization network is a solid second option, although it lacks access to incidentally related knowledge and thus will ultimately fall behind whenever agents have relatively few relationships. The study elaborates on specific scenarios to reveal intricacies in the relationship between knowledge distribution, network topology, and the structure of interrelationships between knowledge fields.
C1 [Vermeulen, Ben; Pyka, Andreas] Hohenheim Univ, Inst Econ, Chair Innovat Econ, Wollgrasweg 23, D-70599 Stuttgart, Germany.
   [Vermeulen, Ben] Eindhoven Univ Technol, Dept Ind Engn & Innovat Sci, Den Dolech 2,POB 513, NL-5600 MB Eindhoven, Netherlands.
RP Vermeulen, B (通讯作者)，Hohenheim Univ, Inst Econ, Chair Innovat Econ, Wollgrasweg 23, D-70599 Stuttgart, Germany.; Vermeulen, B (通讯作者)，Eindhoven Univ Technol, Dept Ind Engn & Innovat Sci, Den Dolech 2,POB 513, NL-5600 MB Eindhoven, Netherlands.
EM b.vermeulen@uni-hohenheim.de
OI Vermeulen, Ben/0000-0001-6563-2568; Pyka, Andreas/0000-0001-6207-6690
FU DACH research program Innovation Networks for Regional Development -
   German science foundation DFG [PY 70/8-1]; Dutch science foundation NWO
   [458-03-112]; MOST-DAAD Germany-Taiwan PPP [57219050]
FX This work was realized with financial support of the DACH research
   program Innovation Networks for Regional Development funded by the
   German science foundation DFG, Grant PY 70/8-1, extending work done
   within the Netwerken van netwerken program funded by the Dutch science
   foundation NWO, Grant 458-03-112. Moreover, part of the writing was
   finished during an extended stay at the AI-ECON Research Center of the
   National Chengchi University (NCCU) funded by the MOST-DAAD
   Germany-Taiwan PPP, Project 57219050. We are grateful for helpful and
   inspiring feedback from Bin-Tzong Chie, Shu-Heng Chen, Giuseppe Vizzari,
   Holger Graf, Ingrid Ott, Matthias Muller, Tobias Buchmann, several
   anonymous reviewers, and participants at EMAEE 2017, ERSA 2014, WEHIA
   2014, and IEEE-CIFEr 2014.
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NR 72
TC 15
Z9 16
U1 3
U2 26
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0927-7099
EI 1572-9974
J9 COMPUT ECON
JI Comput. Econ.
PD OCT
PY 2018
VL 52
IS 3
SI SI
BP 773
EP 808
DI 10.1007/s10614-017-9776-3
PG 36
WC Economics; Management; Mathematics, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Mathematics
GA GV0IJ
UT WOS:000445742700004
OA hybrid, Green Published
DA 2022-08-23
ER

PT J
AU Yang, N
AF Yang, Ning
TI Financial Big Data Management and Control and Artificial Intelligence
   Analysis Method Based on Data Mining Technology
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
ID QUALITY
AB Driven by capital and Internet information (IT) technology, the operating scale and capital scale of modern industrial and commercial enterprises and various organizations have increased exponentially. At present, the manual-based financial work model has been unable to adapt to the changing speed of the modern business environment and the business rhythm of enterprises. All kinds of enterprises and organizations, especially large enterprises, urgently need to improve the operational efficiency of financial systems. By enhancing the integrity, timeliness, and synergy of financial information, it improves the comprehensiveness and ability of analyzing complex problems in financial analysis. It can cope with such rapid changes and help improve the financial management capabilities of enterprises. It provides more valuable decision-making guidance for business operations and reduces business risks. In recent years, the vigorous development of artificial intelligence technology has provided a feasible solution to meet the urgent needs of enterprises. Combining data mining, deep learning, image recognition, natural language processing, knowledge graph, human-computer interaction, intelligent decision-making, and other artificial intelligence technologies with IT technology to transform financial processes, it can significantly reduce the processing time of repetitive basic financial processes, reduce the dependence on manual accounting processing, and improve the work efficiency of the financial department. Through the autonomous analysis and decision-making of artificial intelligence, the intelligentization of financial management is realized, and more accurate and effective financial decision-making support is provided for enterprises. This paper studies the company's intelligent financial reengineering process, so as to provide reference and reference for other enterprises to upgrade similar financial systems. The results of the analysis showed that at the level of alpha =0.05, there was a significant difference in the mean between the two populations. When the r value is in the range of -1 and 1, the linear relationship between the x and y variables is more obvious. This paper proposes decision-making suggestions and risk control early warning to the group decision-making body, or evaluates the financial impact of the group's decision-making, and opens the road to financial intelligence.
C1 [Yang, Ning] Yellow River Conservancy Tech Inst, Coll Finance & Accounting, Kaifeng 475004, Henan, Peoples R China.
RP Yang, N (通讯作者)，Yellow River Conservancy Tech Inst, Coll Finance & Accounting, Kaifeng 475004, Henan, Peoples R China.
EM 2007820373@yrcti.edu.cn
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NR 30
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 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD MAY 29
PY 2022
VL 2022
AR 7596094
DI 10.1155/2022/7596094
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 2S0WW
UT WOS:000821523000008
OA gold
DA 2022-08-23
ER

PT J
AU Louarn, M
   Chatonnet, F
   Garnier, X
   Fest, T
   Siegel, A
   Faron, C
   Dameron, O
AF Louarn, Marine
   Chatonnet, Fabrice
   Garnier, Xavier
   Fest, Thierry
   Siegel, Anne
   Faron, Catherine
   Dameron, Olivier
TI Improving reusability along the data life cycle: a regulatory circuits
   case study
SO JOURNAL OF BIOMEDICAL SEMANTICS
LA English
DT Article
DE Dataset architecture; Bioinformatics; RDF named graphs; SPARQL;
   Reusability; Linked Open Data
ID SEMANTIC-WEB
AB Background: In life sciences, there has been a long-standing effort of standardization and integration of reference datasets and databases. Despite these efforts, many studies data are provided using specific and non-standard formats. This hampers the capacity to reuse the studies data in other pipelines, the capacity to reuse the pipelines results in other studies, and the capacity to enrich the data with additional information. The Regulatory Circuits project is one of the largest efforts for integrating human cell genomics data to predict tissue-specific transcription factor-genes interaction networks. In spite of its success, it exhibits the usual shortcomings limiting its update, its reuse (as a whole or partially), and its extension with new data samples. To address these limitations, the resource has previously been integrated in an RDF triplestore so that TF-gene interaction networks could be generated with two SPARQL queries. However, this triplestore did not store the computed networks and did not integrate metadata about tissues and samples, therefore limiting the reuse of this dataset. In particular, it does not enable to reuse only a portion of Regulatory Circuits if a study focuses on a subset of the tissues, nor to combine the samples described in the datasets with samples from other studies. Overall, these limitations advocate for the design of a complete, flexible and reusable representation of the Regulatory Circuits dataset based on Semantic Web technologies.
   Results: We provide a modular RDF representation of the Regulatory Circuits, called Linked Extended Regulatory Circuits (LERC). It consists in (i) descriptions of biological and experimental context mapped to the references databases, (ii) annotations about TF-gene interactions at the sample level for 808 samples, (iii) annotations about TF-gene interactions at the tissue level for 394 tissues, (iv) metadata connecting the knowledge graphs cited above. LERC is based on a modular organisation into 1,205 RDF named graphs for representing the biological data, the sample-specific and the tissue-specific networks, and the corresponding metadata. In total it contains 3,910,794,050 triples and is available as a SPARQL endpoint.
   Conclusion: The flexible and modular architecture of LERC supports biologically-relevant SPARQL queries. It allows an easy and fast querying of the resources related to the initial Regulatory Circuits datasets and facilitates its reuse in other studies.
C1 [Louarn, Marine; Garnier, Xavier; Siegel, Anne; Dameron, Olivier] Univ Rennes, CNRS, IRISA, INRIA,UMR 6074, F-35000 Rennes, France.
   [Louarn, Marine; Chatonnet, Fabrice; Fest, Thierry] Univ Rennes 1, Etab Francais Sang, INSERM, UMR S1236, F-35000 Rennes, France.
   [Chatonnet, Fabrice; Fest, Thierry] Ctr Hosp Univ Rennes, Lab Hematol, Pole Biol, F-35033 Rennes, France.
   [Faron, Catherine] Univ Cote dAzur, INRIA, I3S, CNRS, Sophia Antipolis, France.
RP Louarn, M; Dameron, O (通讯作者)，Univ Rennes, CNRS, IRISA, INRIA,UMR 6074, F-35000 Rennes, France.; Louarn, M (通讯作者)，Univ Rennes 1, Etab Francais Sang, INSERM, UMR S1236, F-35000 Rennes, France.
EM marine.louarn@inria.fr; olivier.dameron@univ-rennes1.fr
RI Chatonnet, Fabrice/AHI-8250-2022
OI Chatonnet, Fabrice/0000-0003-3639-6775; Dameron,
   Olivier/0000-0001-8959-7189
FU joint INSERM-INRIA "Digital Health" PhD grant
FX Marine Louarn is supported by a joint INSERM-INRIA "Digital Health" PhD
   grant.
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NR 25
TC 0
Z9 0
U1 2
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 MAR 28
PY 2022
VL 13
IS 1
AR 11
DI 10.1186/s13326-022-00266-4
PG 15
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA 0A4LJ
UT WOS:000773927500001
PM 35346379
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Lv, X
   Xie, Z
   Xu, DX
   Jin, XG
   Ma, K
   Tao, LF
   Qiu, QJ
   Pan, YS
AF Lv, Xia
   Xie, Zhong
   Xu, Dexin
   Jin, Xiangguo
   Ma, Kai
   Tao, Liufeng
   Qiu, Qinjun
   Pan, Yongsheng
TI Chinese Named Entity Recognition in the Geoscience Domain Based on BERT
SO EARTH AND SPACE SCIENCE
LA English
DT Article
ID BILSTM-CRF
AB Geological reports are frequently used by geologists involved in geological surveys and scientific research to record the results and outcomes of geological surveys. With such a rich data source, a substantial amount of knowledge has yet to be mined and analyzed. This paper focuses on automatically information extraction from geological reports, namely, geological named entity recognition. Geological named entity recognition has an important role in data mining, knowledge discovery and Knowledge graph construction. Existing general named entity recognition models/tools are limited in the domain of geoscience due to the various language irregularities associated with geological text, such as informal sentence structures, several domain-geoscience words, large character lengths and multiple combinations of independent words. We present Bidirectional encoder representations from transformers (BERT)-(Bidirectional gated recurrent unit network) BiGRU- (Conditional random field) CRF, which is a deep learning-based geological named entity recognition model that is designed specifically with these linguistic irregularities in mind. Based on the pretrained language model, an integrated deep learning model incorporating BERT, BiGRU and CRF is constructed to obtain character vectors rich in semantic information through the BERT pretrained language model to alleviate for the lack of specificity of static word vectors (e.g., word2vec) and to improve the extraction capability of complex geological entities. We demonstrate our proposed model by applying it to four test datasets, including a geoscience NER data set from regional geological reports, and by comparing its performance with those of five baseline models.
   Plain Language Summary Geological named entity recognition has an important role in information extraction and knowledge discovery. This paper presents BERT-BiGRU-CRF, which is a deep learning-based geological named entity recognition model that is designed specifically with these linguistic irregularities in mind. We hope that our approach will serve as an alternative method that deserves further study.
C1 [Lv, Xia] Natl Engn Res Ctr Geog Informat Syst, Wuhan, Peoples R China.
   [Lv, Xia] Natl Geol Arch China, Dev & Res Ctr, Beijing, Peoples R China.
   [Xie, Zhong; Jin, Xiangguo; Tao, Liufeng; Qiu, Qinjun] China Univ Geosci, Sch Comp Sci, Wuhan, Peoples R China.
   [Xu, Dexin] Wuhan Geomat Inst, Wuhan, Peoples R China.
   [Ma, Kai] China Three Gorges Univ, Coll Comp & Informat Technol, Yichang, Peoples R China.
   [Qiu, Qinjun] Hubei Key Lab Intelligent Geoinformat Proc, Wuhan, Peoples R China.
   [Pan, Yongsheng] Wuhan Zondy Cyber Sci & Technol Co Ltd, Wuhan, Peoples R China.
RP Qiu, QJ (通讯作者)，China Univ Geosci, Sch Comp Sci, Wuhan, Peoples R China.; Qiu, QJ (通讯作者)，Hubei Key Lab Intelligent Geoinformat Proc, Wuhan, Peoples R China.
EM qiuqinjun@cug.edu.cn
FU National Natural Science Foundation of China [42050101, 41871311,
   41871305]; China Postdoctoral Science Foundation [2021M702991]; Open
   Research Project of The Hubei Key Laboratory of Intelligent
   Geo-Information Processing [KLIGIP-2021A01]; Fundamental Research Funds
   for the Central Universities, China University of Geosciences (Wuhan)
   [CUG2106116]; Wuhan Multi-Element Urban Geological Survey Demonstration
   Project [WHDYS-2020-004]
FX This study was financially supported by the National Natural Science
   Foundation of China (42050101, 41871311, 41871305), the China
   Postdoctoral Science Foundation (No.2021M702991), Wuhan Multi-Element
   Urban Geological Survey Demonstration Project (WHDYS-2020-004),Open
   Research Project of The Hubei Key Laboratory of Intelligent
   Geo-Information Processing (No. KLIGIP-2021A01), and the Fundamental
   Research Funds for the Central Universities, China University of
   Geosciences (Wuhan; No. CUG2106116)).
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NR 42
TC 0
Z9 0
U1 16
U2 16
PU AMER GEOPHYSICAL UNION
PI WASHINGTON
PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA
EI 2333-5084
J9 EARTH SPACE SCI
JI Earth Space Sci.
PD MAR
PY 2022
VL 9
IS 3
AR e2021EA002166
DI 10.1029/2021EA002166
PG 15
WC Astronomy & Astrophysics; Geosciences, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Astronomy & Astrophysics; Geology
GA 0F8MM
UT WOS:000777609100020
OA gold
DA 2022-08-23
ER

PT J
AU Li, XY
   Chen, YF
   Pettit, B
   De Rijke, M
AF Li, Xinyi
   Chen, Yifan
   Pettit, Benjamin
   De Rijke, Maarten
TI Personalised Reranking of Paper Recommendations Using Paper Content and
   User Behavior
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE Academic search; paper recommendation; reranking
AB Academic search engines have been widely used to access academic papers, where users' information needs are explicitly represented as search queries. Some modern recommender systems have taken one step further by predicting users' information needs without the presence of an explicit query. In this article, we examine an academic paper recommender that sends out paper recommendations in email newsletters, based on the users' browsing history on the academic search engine. Specifically, we look at users who regularly browse papers on the search engine, and we sign up for the recommendation newsletters for the first time. We address the task of reranking the recommendation candidates that are generated by a production system for such users.
   We face the challenge that the users on whom we focus have not interacted with the recommender system before, which is a common scenario that every recommender system encounters when new users sign up. We propose an approach to reranking candidate recommendations that utilizes both paper content and user behavior. The approach is designed to suit the characteristics unique to our academic recommendation setting. For instance, content similarity measures can be used to find the closest match between candidate recommendations and the papers previously browsed by the user. To this end, we use a knowledge graph derived from paper metadata to compare entity similarities (papers, authors, and journals) in the embedding space. Since the users on whom we focus have no prior interactions with the recommender system, we propose a model to learn a mapping from users' browsed articles to user clicks on the recommendations. We combine both content and behavior into a hybrid reranking model that outperforms the production baseline significantly, providing a relative 13% increase in Mean Average Precision and 28% in Precision@1.
   Moreover, we provide a detailed analysis of the model components, highlighting where the performance boost comes from. The obtained insights reveal useful components for the reranking process and can be generalized to other academic recommendation settings as well, such as the utility of graph embedding similarity. Also, recent papers browsed by users provide stronger evidence for recommendation than historical ones.
C1 [Li, Xinyi; Chen, Yifan] Univ Amsterdam, Amsterdam, Netherlands.
   [Li, Xinyi; Chen, Yifan] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Hunan, Peoples R China.
   [Pettit, Benjamin] Elsevier, London, England.
   [De Rijke, Maarten] Univ Amsterdam, Informat Inst, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.
RP Li, XY (通讯作者)，Univ Amsterdam, Amsterdam, Netherlands.; Li, XY (通讯作者)，Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha, Hunan, Peoples R China.
EM lixinyimichael@gmail.com; y.chen4@uva.nl; b.pettit@elsevier.com;
   derijke@uva.nl
OI de Rijke, Maarten/0000-0002-1086-0202
FU Ahold Delhaize; China Scholarship Council; Innovation Center for
   Artificial Intelligence (ICAI)
FX This research was partially supported by Ahold Delhaize, the China
   Scholarship Council, and the Innovation Center for Artificial
   Intelligence (ICAI). 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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   [No title captured]
NR 70
TC 19
Z9 19
U1 6
U2 34
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1046-8188
EI 1558-2868
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
PD JUL
PY 2019
VL 37
IS 3
AR 31
DI 10.1145/3312528
PG 23
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA JL3FT
UT WOS:000495417300004
DA 2022-08-23
ER

PT J
AU Siebenhuhner, F
   Wang, SH
   Arnulfo, G
   Lampinen, A
   Nobili, L
   Palva, JM
   Palva, S
AF Siebenhuhner, Felix
   Wang, Sheng H.
   Arnulfo, Gabriele
   Lampinen, Anna
   Nobili, Lino
   Palva, J. Matias
   Palva, Satu
TI Genuine cross-frequency coupling networks in human resting-state
   electrophysiological recordings
SO PLOS BIOLOGY
LA English
DT Article
ID CORTICAL CORRELATION STRUCTURE; NEURONAL OSCILLATIONS;
   PHASE-SYNCHRONIZATION; GAMMA OSCILLATIONS; WORKING-MEMORY; BAND
   OSCILLATIONS; BRAIN NETWORKS; MEG; CORTEX; THETA
AB Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase-amplitude coupling (PAC) or by n:m-cross-frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high-frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.
C1 [Siebenhuhner, Felix; Wang, Sheng H.; Arnulfo, Gabriele; Lampinen, Anna; Palva, J. Matias; Palva, Satu] Univ Helsinki, Neurosci Ctr, HiLIFE Helsinki Inst Life Sci, Helsinki, Finland.
   [Siebenhuhner, Felix; Wang, Sheng H.] Univ Helsinki, Doctoral Program Brain & Mind, Helsinki, Finland.
   [Siebenhuhner, Felix] HUS Med Imaging Ctr, BioMag Lab, Helsinki, Finland.
   [Arnulfo, Gabriele] Univ Genoa, Dept Informat Bioengn Robot & Syst Engn, Genoa, Italy.
   [Nobili, Lino] Univ Genoa, Dept Neurosci DINOGMI, Child Neuropsychiat Unit, Ist G Gaslini,IRCCS, Genoa, Italy.
   [Nobili, Lino] Osped Niguarda Ca Granda, Claudio Munari Epilepsy Surg Ctr, Milan, Italy.
   [Palva, J. Matias] Aalto Univ, Dept Neurosci & Biomed Engn, Espoo, Finland.
   [Palva, J. Matias; Palva, Satu] Univ Glasgow, Ctr Cognit Neuroimaging, Inst Neurosci & Psychol, Glasgow, Lanark, Scotland.
RP Palva, JM; Palva, S (通讯作者)，Univ Helsinki, Neurosci Ctr, HiLIFE Helsinki Inst Life Sci, Helsinki, Finland.; Palva, JM (通讯作者)，Aalto Univ, Dept Neurosci & Biomed Engn, Espoo, Finland.
EM matias.palva@helsinki.fi; satu.palva@helsinki.fi
RI Palva, Satu/D-7400-2013; Palva, Satu/AAH-6938-2020; ARNULFO,
   GABRIELE/J-6812-2018; Siebenhuhner, Felix/A-4788-2019; Nobili,
   Lino/K-7738-2016
OI Palva, Satu/0000-0001-9496-7391; Palva, J. Matias/0000-0003-1885-2749;
   Lampinen, Anna/0000-0002-1272-7747; ARNULFO,
   GABRIELE/0000-0002-1140-0320; Siebenhuhner, Felix/0000-0002-2621-9145;
   Nobili, Lino/0000-0001-9317-5405; Wang, Sheng H/0000-0002-6226-6213
FU Academy of Finland [SA 266402, 303933, SA 325404, SA 253130, 296304];
   Sigrid Juselius Foundation
FX This work was supported by the Academy of Finland (SA 266402, 303933,
   and SA 325404 to SP and SA 253130 and 296304 to JMP) and Sigrid Juselius
   Foundation to SP & JMP. 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 114
TC 22
Z9 22
U1 5
U2 10
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1544-9173
EI 1545-7885
J9 PLOS BIOL
JI PLoS. Biol.
PD MAY
PY 2020
VL 18
IS 5
AR e3000685
DI 10.1371/journal.pbio.3000685
PG 40
WC Biochemistry & Molecular Biology; Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Life Sciences & Biomedicine - Other
   Topics
GA LU9GM
UT WOS:000538055100016
PM 32374723
OA Green Published, Green Accepted, gold
DA 2022-08-23
ER

PT J
AU Natarajan, S
   Vairavasundaram, S
   Kotecha, K
   Indragandhi, V
   Palani, S
   Saini, JR
   Ravi, L
AF Natarajan, Senthilselvan
   Vairavasundaram, Subramaniyaswamy
   Kotecha, Ketan
   Indragandhi, V
   Palani, Saravanan
   Saini, Jatinderkumar R.
   Ravi, Logesh
TI CD-SemMF: Cross-Domain Semantic Relatedness Based Matrix Factorization
   Model Enabled With Linked Open Data for User Cold Start Issue
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Metadata; Recommender systems; Computational modeling; Motion
   pictures; Matrix decomposition; Symbiosis; Linked open data;
   cross-domain recommender system; semantic relatedness; item metadata;
   user cold start
AB Personalized recommendations to cold start user is one of the significant challenges in information filtering systems. Most of the existing systems inherited the idea of collaborative filtering (CF) and avoids the item metadata. For instance, consider a book domain whose metadata are author, publisher, language horizontal ellipsis etc. The elimination of item metadata is due to the diverse nature of item attributes in a cross-domain environment. Because of this ideology, it is hard for the filtering systems to provide better recommendations to cold start users. Cold start users are people who are new to a domain whose preferences are unknown. A novel model is proposed in this research work called Cross-Domain Semantic Relatedness based Matrix Factorization model (CD-SemMF) resolves the user cold start issue in collaborative filtering recommender system by exploiting Linked Open Data (LOD). "DBpedia" is a widely used LOD resource that contains semantic information about different domains, which is used in this research work to resolve the above-said problem. Here, the metadata available in LOD connects the items preferred by the target user from various domains. The fundamental knowledge graph links items from various domains and also benefits from cross-domain information. "LOD Semantic-Relatedness Measure" a new measure is proposed which calculates the closeness of items across domains. Semantic relatedness is measured instead of similarity in this research because the cross-domain item attributes are diverse. The Alternating Least Square method is applied here to learn the user preferences. The proposed model provides relevant, personalized recommendations for the target new user with the user preferences gained from the source domain and by exploiting item semantic relatedness. Experimental evaluation is done on Facebook and Amazon datasets. It is observed from the result that the proposed CD-SemMF model gives better recommendations in target domain for new users than the baseline methods.
C1 [Natarajan, Senthilselvan; Vairavasundaram, Subramaniyaswamy; Palani, Saravanan] SASTRA Deemed Univ, Sch Comp, Thanjavur 613401, India.
   [Kotecha, Ketan] Symbiosis Int Deemed Univ, Symbiosis Ctr Appl Articial Intelligence, Pune 412115, Maharashtra, India.
   [Indragandhi, V] Vellore Inst Technol, Sch Elect Engn, Vellore 632014, Tamil Nadu, India.
   [Saini, Jatinderkumar R.] Symbiosis Int Deemed Univ, Symbiosis Inst Comp Studies & Res, Pune 412115, Maharashtra, India.
   [Ravi, Logesh] Vellore Inst Technol, Sch Elect Engn SENSE, Chennai 600127, Tamil Nadu, India.
RP Vairavasundaram, S (通讯作者)，SASTRA Deemed Univ, Sch Comp, Thanjavur 613401, India.; Kotecha, K (通讯作者)，Symbiosis Int Deemed Univ, Symbiosis Ctr Appl Articial Intelligence, Pune 412115, Maharashtra, India.
EM vsubramaniyaswamy@gmail.com; head@scaai.siu.edu.in
RI Saini, Jatinderkumar R./M-5584-2013; Kotecha, K/U-3927-2017
OI Saini, Jatinderkumar R./0000-0001-5205-5263; Palani,
   Saravanan/0000-0002-3679-2113; , Subramaniyaswamy/0000-0001-5328-7672;
   Kotecha, K/0000-0003-2653-3780; v, indragandhi/0000-0003-0351-7761
FU Science and Engineering Research Board (SERB), Department of Science and
   Technology, India, through the Mathematical Research Impact Centric
   Support (MATRICS) Scheme [MTR/2019/000542]; SASTRA Deemed University,
   Thanjavur; Symbiosis International University
FX This work was supported in part by the Science and Engineering Research
   Board (SERB), Department of Science and Technology, India, through the
   Mathematical Research Impact Centric Support (MATRICS) Scheme under
   Grant MTR/2019/000542, in part by SASTRA Deemed University, Thanjavur,
   and in part by Symbiosis International University.
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NR 48
TC 0
Z9 0
U1 1
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 52955
EP 52970
DI 10.1109/ACCESS.2022.3175566
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1R4IQ
UT WOS:000803334800001
OA gold
DA 2022-08-23
ER

PT J
AU Chen, W
   Chen, JQ
   Xian, YT
AF Chen, Wei
   Chen, Junqiu
   Xian, Yantuan
TI Prototype Network for Text Entity Relationship Recognition in
   Metallurgical Field Based on Integrated Multi-class Loss Functions
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Syntactic analysis; integration learning; prototype network; entity
   relationship recognition
AB It is of great significance to recognize the metallurgical entity relations in order to construct the Knowledge graph of Metallurgical Literature and to further understand the metallurgical literature. However, there are few researches on the textual entity relations in metallurgical fields either few marked Corpora. The syntactic structure of the same entity relationship category is relatively simple and has strong domain characteristics. The traditional entity relationship model can not identify the domain entity relationship well. Meanwhile the syntactic structure of the same entity relations class is relatively simple, and the syntactic structure is relatively simple in the recognition of entity relations in metallurgy field. Furthermore, the entities with similar syntactic structure often have the same entity relations and the different words in the sentence have different contribution to the entity relations. In order to solve the mentioned problems, this paper will combine the algorithm that can highlight the syntactic structure in sentences and improve the accuracy of the model with the Algorithm that can highlight the contribution of words in sentences and the loss function level integration is carried out in the framework of small sample prototype network, so as to maximize the advantages of each algorithm and improve the accuracy - firstly, in the coding layer of the prototype network, we use the CNN algorithm which can highlight the important words in the sentences and the TreeLSTM algorithm which can parse the sentences in the text so that the syntactic relations between the words in the sentences can be acted on in the relation recognition, the sentences are coded together by two algorithms, then, the EUCLIDEAN distance loss is calculated by using this high quality coding and the prototype coding, finally, the traditional entity relation recognition model with Attention Mechanism is integrated into the loss function, further highlighting the decisive role of important words in text sentences in relation recognition and improving the generalization of the model. The results showed that compared with the traditional methods such as CNN, RNN, PCNN and Bi-LSTM, the proposed method in this paper has better performance in the case of small sample data set.
C1 [Chen, Wei; Xian, Yantuan] Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming, Yunnan, Peoples R China.
   [Chen, Wei; Xian, Yantuan] Kunming Univ Sci & Technol, Yunnan Key Lab Artificial Intelligence, Kunming, Yunnan, Peoples R China.
   [Chen, Junqiu] Yunnan Univ, Sch Foreign Languages, Kunming, Yunnan, Peoples R China.
RP Chen, W (通讯作者)，Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming, Yunnan, Peoples R China.; Chen, W (通讯作者)，Kunming Univ Sci & Technol, Yunnan Key Lab Artificial Intelligence, Kunming, Yunnan, Peoples R China.
EM weichen198309@gmail.com
FU Open Fund of Yunnan Key Laboratory of computer technology application in
   China [140520200151]
FX This work is supported by the Open Fund of Yunnan Key Laboratory of
   computer technology application in China(No.140520200151).
CR Dietterich TG, 1997, ARTIF INTELL, V89, P31, DOI 10.1016/S0004-3702(96)00034-3
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NR 33
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 2021
VL 40
IS 6
BP 12061
EP 12073
DI 10.3233/JIFS-210163
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TA8PR
UT WOS:000667508800116
DA 2022-08-23
ER

PT J
AU Huet, A
   Segonds, F
   Pinquie, R
   Veron, P
   Guegan, J
   Mallet, A
AF Huet, Armand
   Segonds, Frederic
   Pinquie, Romain
   Veron, Philippe
   Guegan, Jerome
   Mallet, Antoine
TI Context-aware cognitive design assistant: Implementation and study of
   design rules recommendations
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Design rule; Computer-aided design; Knowledge graph; Knowledge
   management; Context-Awareness; Cognitive assistant; Recommendation
   system; CACDA
ID SYSTEM; MODEL
AB Design rules are an essential interface to facilitate the information exchange between designers and experts. Despite many innovations in Knowledge-Based Engineering and Knowledge Management, unstructured design rules documents are still widely used in the manufacturing industry. Due to the complexity of the design process, these documents often contain hundreds of design rules, applicable in varying design contexts. Searching for the right rules according to a design context is demanding in time and cognitive resources. In this paper, we propose a Context-Aware Cognitive Design Assistant (CACDA) to capture the design context and perform tasks such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Contrary to existing works in model quality testing, the CACDA uses a proactive approach of design rules application and helps designers to provide error-free designs on first attempt. In this paper, we present the design rules recommendation system of the CACDA, its capabilities and its implementation. Then, to measure the impact of design rules recommendations on the design process, we compare our approach with the use of traditional design rules documents. Results show that the use of the CACDA's design rules recommendations lower the perceived difficulty of design rules retrieval from 75 to 43.5 on a scale of 100. On average, participants that used the demonstrator successfully applied 8.6 design rules on the 25 applicable design rules of our set. Participants that used unstructured documentation correctly applied 4.3 design rules. The global cognitive weight of the design activity as well as the design rules retrieval performances appear to be unchanged. These results demonstrate the usability of the demonstrator and show a positive impact on the design process and on the quality of CAD models. Future works will focus on the overcome of the main limitations of our current experiments, with a panel of professional designers, a lager design rules set and the implementation of several lacking features of the CACDA into the demonstrator.
C1 [Huet, Armand; Segonds, Frederic] HESAM Univ, Arts & Metiers Inst Technol, LCPI, F-75013 Paris, France.
   [Pinquie, Romain] Univ Grenoble Alpes, CNRS, Grenoble INP, G SCOP, Grenoble, France.
   [Veron, Philippe] HESAM Univ, Arts & Metiers Inst Technol, LISPEN, F-13617 Aix En Provence, France.
   [Mallet, Antoine] Capgemini DEMS, Toulouse, France.
   [Guegan, Jerome] Univ Paris, LaPEA, Boulogne, France.
   [Guegan, Jerome] Univ Gustave Eiffel, Boulogne, France.
RP Huet, A (通讯作者)，HESAM Univ, Arts & Metiers Inst Technol, LCPI, F-75013 Paris, France.
EM armand.huet@ensam.eu
OI HUET, Armand/0000-0002-4761-8866
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NR 60
TC 1
Z9 1
U1 4
U2 6
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1474-0346
EI 1873-5320
J9 ADV ENG INFORM
JI Adv. Eng. Inform.
PD OCT
PY 2021
VL 50
AR 101419
DI 10.1016/j.aei.2021.101419
EA OCT 2021
PG 14
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA WN2VE
UT WOS:000711630700009
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Hu, WF
   Ma, BS
   Li, ZQ
   Li, YJ
   Wang, Y
AF Hu, Weifeng
   Ma, Baosen
   Li, Zeqiang
   Li, Yujun
   Wang, Yue
TI A Cross-Media Deep Relationship Classification Method Using
   Discrimination Information
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Cross-Media Retrieval; Recurrent Neural Networks; Relation
   classification; Entity direction; Discrimination Information
AB Relation classification is one of the most fundamental tasks in the area of cross-media, which is essential for many practical applications such as information extraction, question&answer system, and knowledge base construction. In the cross-media semantic retrieval task, in order to meet the needs of cross-media uniform representation and semantic analysis, it is necessary to analyze the semantic potential relationship and construct semantic-related cross-media knowledge graph. The relationship classification technology is an important part of solving semantic correlation classification. Most of existing methods regard relation classification as a multi-classification task, without considering the correlation between different relationships. However, two relationships in the opposite directions are usually not independent of each other. Hence, this kind of relationships are easily confused in the traditional way. In order to solve the problem of confusing the relationships of the same semantic with different entity directions, this paper proposes a neural network fusing discrimination information for relation classification. In the proposed model, discrimination information is used to distinguish the relationship of the same semantic with different entity directions, the direction of entity in space is transformed into the direction of vector in mathematics by the method of entity vector subtraction, and the result of entity vector subtraction is used as discrimination information. The model consists of three modules: sentence representation module, relation discrimination module and discrimination fusion module. Moreover, two fusion methods are used for feature fusion. One is a Cascade-based feature fusion method, and another is a feature fusion method based on convolution neural network. In addition, this paper uses the new function added by cross-entropy function and deformed Max-Margin function as the loss function of the model. The experimental results show that the proposed discriminant feature is effective in distinguishing confusing relationships, and the proposed loss function can improve the performance of the model to a certain extent. Finally, the proposed model achieves 84.8% of the F1 value without any additional features or NLP analysis tools. Hence, the proposed method has a promising prospect of being incorporated in various cross-media systems.
C1 [Hu, Weifeng; Ma, Baosen; Li, Zeqiang; Li, Yujun; Wang, Yue] Shandong Univ, Sch Informat Sci & Engn, Qingdao, Peoples R China.
RP Li, YJ (通讯作者)，Shandong Univ, Sch Informat Sci & Engn, Qingdao, Peoples R China.
EM liyujun@sdu.edu.cn
FU National Key Research and Development Program of China [2017YFC080 3400,
   2018YFC0831000]
FX This work is supported by the National Key Research and Development
   Program of China (Grant No. 2017YFC080 3400 and No. 2018YFC0831000).
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NR 28
TC 2
Z9 2
U1 0
U2 10
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 102344
DI 10.1016/j.ipm.2020.102344
PG 14
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA OG9PN
UT WOS:000582206800058
DA 2022-08-23
ER

PT J
AU Wang, X
   Wu, C
   Lu, YT
   Tian, M
AF Wang, Xi
   Wu, Cong
   Lu, Yutian
   Tian, Miao
TI The Synergy of Metadata and Metamodel through Algorithm Modeling-Case
   Study of the Roof Tiles in Yangxindian Palace (Beijing, China)
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE HBIM; Grasshopper; Neo4j; graph database; algorithm modeling; metalevel;
   ontology; asset management; heritage conservation; Yangxindian Palace
ID HBIM; GIS; BIM
AB Featured Application This study purposes a synergy approach to generate the IFC model of very large quantities of data along with its graph database. Giving the identifier to each tile, following database syntax, the historic information and metadata are merged into the graph database. This synergy approach is developed on Grasshopper and Neo4j but is also applicable to other platforms. The investigation of cultural heritage is a continuous process, in which a large scale of metadata is gradually accumulated, i.e., condition data, intervention data, and the comprehension of building techniques. We take the roof tile of Yangxindian Palace as an example. The total number of roof tiles is 17,154. Their positions are various, as they are equally distributed on double-curved irregular surfaces. Their metadata are multisource and heterogeneous. Therefore, the integration between the model and the data can be complex. This paper aims to generate the M1 (metalevel) model with its graph database based on the M3 (metalevel) model through algorithm modeling. This synergy approach consists of three parts. First is the data collection. The unique identifier is used as the index while investigating onsite, which eventually forms the metadata repository. Second is the algorithm modeling. All the roof tiles are rebuilt in Grasshopper with identifiers. Their metadata are merged through Cypher syntax, which can generate the graph database in Neo4j. Third is the delivery. The IFC model and knowledge graph are connected in real time during the whole process. The significance of this paper is to provide a synergy approach and discuss its efficiency for the integration between a model and data of very large quantities. The results show that it is efficient to express the semantic relationships of both conditions and interventions. It is practicable to lightweight the IFC model and to associate metadata into the graph database, enabling online browsing and querying.
C1 [Wang, Xi; Wu, Cong; Lu, Yutian; Tian, Miao] Tianjin Univ, Sch Architecture, Tianjin 300072, Peoples R China.
RP Wang, X; Wu, C (通讯作者)，Tianjin Univ, Sch Architecture, Tianjin 300072, Peoples R China.
EM wangxi881ove@timedu.cn; wucong@tju.edu.cn; akilu6677@tju.edu.cn;
   tianmiao206082@tju.edu.cn
FU National Natural Science Foundation of China [51878440]
FX This research was funded by the National Natural Science Foundation of
   China (project NO. 51878440).
CR Abubakar M, 2018, ADV INTELL SYST, V700, P455, DOI 10.1007/978-3-319-72550-5_44
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NR 35
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 JUL
PY 2022
VL 12
IS 14
AR 7031
DI 10.3390/app12147031
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 3L0UT
UT WOS:000834486500001
OA gold
DA 2022-08-23
ER

PT J
AU Kilicoglu, H
   Rosemblat, G
   Fiszman, M
   Shin, D
AF Kilicoglu, Halil
   Rosemblat, Graciela
   Fiszman, Marcelo
   Shin, Dongwook
TI Broad-coverage biomedical relation extraction with SemRep
SO BMC BIOINFORMATICS
LA English
DT Article
DE Natural language processing; Biomedical relation extraction; Semantic
   interpretation; Scientific publications
ID EVENT EXTRACTION; INFORMATION; UMLS; TERMINOLOGY; ONTOLOGIES; SENTENCES;
   KNOWLEDGE; METAMAP; SYSTEM; GENE
AB Background In the era of information overload, natural language processing (NLP) techniques are increasingly needed to support advanced biomedical information management and discovery applications. In this paper, we present an in-depth description of SemRep, an NLP system that extracts semantic relations from PubMed abstracts using linguistic principles and UMLS domain knowledge. We also evaluate SemRep on two datasets. In one evaluation, we use a manually annotated test collection and perform a comprehensive error analysis. In another evaluation, we assess SemRep's performance on the CDR dataset, a standard benchmark corpus annotated with causal chemical-disease relationships. Results A strict evaluation of SemRep on our manually annotated dataset yields 0.55 precision, 0.34 recall, and 0.42 F (1) score. A relaxed evaluation, which more accurately characterizes SemRep performance, yields 0.69 precision, 0.42 recall, and 0.52 F (1) score. An error analysis reveals named entity recognition/normalization as the largest source of errors (26.9%), followed by argument identification (14%) and trigger detection errors (12.5%). The evaluation on the CDR corpus yields 0.90 precision, 0.24 recall, and 0.38 F (1) score. The recall and the F (1) score increase to 0.35 and 0.50, respectively, when the evaluation on this corpus is limited to sentence-bound relationships, which represents a fairer evaluation, as SemRep operates at the sentence level. Conclusions SemRep is a broad-coverage, interpretable, strong baseline system for extracting semantic relations from biomedical text. It also underpins SemMedDB, a literature-scale knowledge graph based on semantic relations. Through SemMedDB, SemRep has had significant impact in the scientific community, supporting a variety of clinical and translational applications, including clinical decision making, medical diagnosis, drug repurposing, literature-based discovery and hypothesis generation, and contributing to improved health outcomes. In ongoing development, we are redesigning SemRep to increase its modularity and flexibility, and addressing weaknesses identified in the error analysis.
C1 [Kilicoglu, Halil; Rosemblat, Graciela; Shin, Dongwook] Natl Lib Med, Lister Hill Natl Ctr Biomed Commun, 8600 Rockville Pike, Bethesda, MD 20894 USA.
   [Kilicoglu, Halil] Univ Illinois, Sch Informat Sci, 501 E Daniel St, Champaign, IL 61820 USA.
RP Kilicoglu, H (通讯作者)，Natl Lib Med, Lister Hill Natl Ctr Biomed Commun, 8600 Rockville Pike, Bethesda, MD 20894 USA.; Kilicoglu, H (通讯作者)，Univ Illinois, Sch Informat Sci, 501 E Daniel St, Champaign, IL 61820 USA.
EM halil@illinois.edu
FU intramural research program at the U.S. National Library of Medicine,
   National Institutes of Health
FX This work was supported by the intramural research program at the U.S.
   National Library of Medicine, National Institutes of Health.
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NR 129
TC 9
Z9 11
U1 6
U2 21
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD MAY 14
PY 2020
VL 21
IS 1
AR 188
DI 10.1186/s12859-020-3517-7
PG 28
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA LS2PQ
UT WOS:000536231700003
PM 32410573
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhang, HM
   Liu, X
   Pan, HJ
   Ke, HW
   Ou, JF
   Fang, TQ
   Song, YQ
AF Zhang, Hongming
   Liu, Xin
   Pan, Haojie
   Ke, Haowen
   Ou, Jiefu
   Fang, Tianqing
   Song, Yangqiu
TI ASER: Towards large-scale commonsense knowledge acquisition via
   higher-order selectional preference over eventualities
SO ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Commonsense acquisition; Selectional preference; Eventualities
AB Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect commonsense knowledge. In this paper, we propose to develop principles for collecting commonsense knowledge based on selectional preference, which is a common phenomenon in human languages that has been shown to be related to semantics. We generalize the definition of selectional preference from one-hop linguistic syntactic relations to higher-order relations over linguistic graphs. Unlike previous commonsense knowledge definitions (e.g., ConceptNet), the selectional preference (SP) knowledge only relies on statistical distributions over linguistic graphs, which can be efficiently and accurately acquired from the unlabeled corpora with modern tools, rather than human-defined relations. As a result, acquiring SP knowledge is a much more scalable way of acquiring commonsense knowledge. Following this principle, we develop a large-scale eventuality (a linguistic term covering activity, state, and event)-based knowledge graph ASER, where each eventuality is represented as a dependency graph, and the relation between them is a discourse relation defined in shallow discourse parsing. The higher-order selectional preference over collected linguistic graphs reflects various kinds of commonsense knowledge. For example, dogs are more likely to bark than cats as the eventuality "dog barks " appears 14,998 times in ASER while "cat barks " only appears 6 times. "Be hungry " is more likely to be the reason rather than result of "eat food " as the edge ( "be hungry, " Cause, "eat food ") appears in ASER while ( "eat food, " Cause, "be hungry ") does not. Moreover, motivated by the observation that humans understand events by abstracting the observed events to a higher level and can thus transfer their knowledge to new events, we propose a conceptualization module on top of the collected knowledge to significantly boost the coverage of ASER. In total, ASER contains 648 million edges between 438 million eventualities. After conceptualization with Probase, a selectional preference based concept-instance relational knowledge base, our concept graph contains 15 million conceptualized eventualities and 224 million edges between them. Detailed analysis is provided to demonstrate its quality. (C) 2022 Elsevier B.V. All rights reserved.
C1 [Zhang, Hongming; Liu, Xin; Pan, Haojie; Ke, Haowen; Ou, Jiefu; Fang, Tianqing; Song, Yangqiu] HKUST, CSE, Hong Kong, Peoples R China.
RP Zhang, HM (通讯作者)，HKUST, CSE, Hong Kong, Peoples R China.
EM hzhangal@cse.ust.hk; xliucr@cse.ust.hk; hpanad@cse.ust.hk;
   hkeaa@cse.ust.hk; jouaa@connect.ust.hk; tfangaa@cse.ust.hk;
   yqsong@cse.ust.hk
OI Liu, Xin/0000-0001-9610-9526
FU RGC of Hong Kong [16211520, R602019, R602120]; NSFC of China [U20B2053];
   ITC of Hong Kong [MHP/001/19]; National Key R&D Program of China
   [2019YFE0198200]; UGC [RMGS20EG01-D, RMGS20CR11, RMGS20CR12, RMGS20EG19,
   RMGS20EG21]
FX This paper was supported by the GRF (16211520) and the RIF (R602019 and
   R602120) from RGC of Hong Kong, the NSFC Fund (U20B2053) from the NSFC
   of China, the MHKJFS (MHP/001/19) from ITC of Hong Kong and the National
   Key R & D Program of China (2019YFE0198200) with special thanks to
   HKMAAC and CUSBLT. We also thank the UGC Research Matching Grants
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NR 108
TC 0
Z9 0
U1 3
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0004-3702
EI 1872-7921
J9 ARTIF INTELL
JI Artif. Intell.
PD AUG
PY 2022
VL 309
AR 103740
DI 10.1016/j.artint.2022.103740
PG 42
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2F6HP
UT WOS:000813008800001
OA Green Submitted
DA 2022-08-23
ER

PT J
AU Long, H
   Zhu, Y
   Jia, LR
   Gao, B
   Liu, J
   Liu, LH
   Herre, H
AF Long, Hai
   Zhu, Yan
   Jia, Lirong
   Gao, Bo
   Liu, Jing
   Liu, Lihong
   Herre, Heinrich
TI An ontological framework for the formalization, organization and usage
   of TCM-Knowledge
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 Ontology; TCM; TCMLS; GFO; Semantic retrieval; TCM clinical decision
   support
AB BackgroundThe traditional Chinese Medicine Language System (TCMLS) is a large-scale terminology system, developed from 2002 on by the Institute of Information of Traditional Chinese Medicine (IITCM). Until now, more than 120,000 concepts, 300,000 terms and 1.27 million semantic relational links are included. Its top-level framework, called TCMLS-semantic network (SN), provides an important basis for the standardization and mapping of traditional Chinese Medicine (TCM) terminology systems. Though, many data produced and stored in TCMLS have poor quality for historical reasons or because of human factors. There is a large number of classification errors or inconsistent expressions of terms remained in the current TCMLS- SN, which hamper an efficient utilization of the data stored in TCMLS in practical applications.MethodsWe start with analyzing the technical specification based on TCMLS, considering some obvious classification errors and problems of ambiguity of semantic expressions in TCMLS-SN, followed with using a top-down approach for building a middle level ontology which is based on the framework General Formal Ontology (GFO), take into account the compatibility with TCM related concepts, turn out the results of a modification of the current TCMLS-SN, called GFO-TCM.ResultsThrough comparison with TCMLS-SN, according to viewpoints of GFO, some semantic types and relations were reconstructed within GFO-TCM. We propose a middle level ontology for TCMLS which may support entailment and ensure coherence, we also draw out a mapping which possess a more reasonable framework with a unified semantic criterion, it is application scenarios oriented and can be further updated and extended.ConclusionsThe goal is to construct a formal middle-level ontology that is compatible with both the traditional medical terminology system and modern medical terminology standards. it is intended to satisfy functional requirements which are relevant for natural language processing, information extraction, semantic retrieval, clinical decision support in the field of traditional Chinese medicine. It also provides a foundation and methodology for building a large-scale, unified semantic and extensible knowledge graph platform.
C1 [Zhu, Yan; Jia, Lirong; Gao, Bo; Liu, Jing; Liu, Lihong] China Acad Chinese Med Sci, Inst Informat Tradit Chinese Med, Beijing, Peoples R China.
   [Long, Hai; Herre, Heinrich] Univ Leipzig, IMISE, Leipzig, Germany.
RP Zhu, Y (通讯作者)，China Acad Chinese Med Sci, Inst Informat Tradit Chinese Med, Beijing, Peoples R China.; Herre, H (通讯作者)，Univ Leipzig, IMISE, Leipzig, Germany.
EM longhail2008@yahoo.de; zhuyan166@126.com;
   heinrich.herre@imise.uni-leipzig.de
FU National Natural Science Foundation of China [81403398]; Beijing Natural
   Science Foundation [7174328]; Basic Scientific Research Fund of Chinese
   Academy of Chinese Medical Science [zz110318]
FX This study was supported by National Natural Science Foundation of China
   (Grant No. 61701546), The work was partially supported by National
   Natural Science Foundation of China (Grant No. 81403398), Beijing
   Natural Science Foundation (Grant No. 7174328). The publishing costs for
   this manuscript were provided by Independent Subject Project Funded by
   Basic Scientific Research Fund of Chinese Academy of Chinese Medical
   Science (Grant No. zz110318).
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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 53
DI 10.1186/s12911-019-0760-9
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Medical Informatics
GA HS8NN
UT WOS:000464126500008
PM 30961578
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Logette, E
   Lorin, C
   Favreau, C
   Oshurko, E
   Coggan, JS
   Casalegno, F
   Sy, MF
   Monney, C
   Bertschy, M
   Delattre, E
   Fonta, PA
   Krepl, J
   Schmidt, S
   Keller, D
   Kerrien, S
   Scantamburlo, E
   Kaufmann, AK
   Markram, H
AF Logette, Emmanuelle
   Lorin, Charlotte
   Favreau, Cyrille
   Oshurko, Eugenia
   Coggan, Jay S.
   Casalegno, Francesco
   Sy, Mohameth Francois
   Monney, Caitlin
   Bertschy, Marine
   Delattre, Emilie
   Fonta, Pierre-Alexandre
   Krepl, Jan
   Schmidt, Stanislav
   Keller, Daniel
   Kerrien, Samuel
   Scantamburlo, Enrico
   Kaufmann, Anna-Kristin
   Markram, Henry
TI A Machine-Generated View of the Role of Blood Glucose Levels in the
   Severity of COVID-19
SO FRONTIERS IN PUBLIC HEALTH
LA English
DT Article
DE carbohydrates; COVID-19; glucose; glycolysis; glycosylation;
   hyperglycemia; ketogenic diet; SARS-CoV-2
ID SURFACTANT PROTEIN-D; ADVANCED GLYCATION ENDPRODUCTS; ORCHESTRATE INNATE
   IMMUNITY; INTENSIVE INSULIN THERAPY; DENDRITIC CELL SUBSETS; BODY-MASS
   INDEX; HOST-DEFENSE; SARS CORONAVIRUS; INFLUENZA-VIRUS; END-PRODUCTS
AB SARS-CoV-2 started spreading toward the end of 2019 causing COVID-19, a disease that reached pandemic proportions among the human population within months. The reasons for the spectrum of differences in the severity of the disease across the population, and in particular why the disease affects more severely the aging population and those with specific preconditions are unclear. We developed machine learning models to mine 240,000 scientific articles openly accessible in the CORD-19 database, and constructed knowledge graphs to synthesize the extracted information and navigate the collective knowledge in an attempt to search for a potential common underlying reason for disease severity. The machine-driven framework we developed repeatedly pointed to elevated blood glucose as a key facilitator in the progression of COVID-19. Indeed, when we systematically retraced the steps of the SARS-CoV-2 infection, we found evidence linking elevated glucose to each major step of the life-cycle of the virus, progression of the disease, and presentation of symptoms. Specifically, elevations of glucose provide ideal conditions for the virus to evade and weaken the first level of the immune defense system in the lungs, gain access to deep alveolar cells, bind to the ACE2 receptor and enter the pulmonary cells, accelerate replication of the virus within cells increasing cell death and inducing an pulmonary inflammatory response, which overwhelms an already weakened innate immune system to trigger an avalanche of systemic infections, inflammation and cell damage, a cytokine storm and thrombotic events. We tested the feasibility of the hypothesis by manually reviewing the literature referenced by the machine-generated synthesis, reconstructing atomistically the virus at the surface of the pulmonary airways, and performing quantitative computational modeling of the effects of glucose levels on the infection process. We conclude that elevation in glucose levels can facilitate the progression of the disease through multiple mechanisms and can explain much of the differences in disease severity seen across the population. The study provides diagnostic considerations, new areas of research and potential treatments, and cautions on treatment strategies and critical care conditions that induce elevations in blood glucose levels.
C1 [Logette, Emmanuelle; Lorin, Charlotte; Favreau, Cyrille; Oshurko, Eugenia; Coggan, Jay S.; Casalegno, Francesco; Sy, Mohameth Francois; Monney, Caitlin; Bertschy, Marine; Delattre, Emilie; Fonta, Pierre-Alexandre; Krepl, Jan; Schmidt, Stanislav; Keller, Daniel; Kerrien, Samuel; Scantamburlo, Enrico; Kaufmann, Anna-Kristin; Markram, Henry] Ecole Polytech Fed Lausanne EPFL, Blue Brain Project, Geneva, Switzerland.
RP Logette, E; Markram, H (通讯作者)，Ecole Polytech Fed Lausanne EPFL, Blue Brain Project, Geneva, Switzerland.
EM emmanuelle.logette@epfl.ch; henry.markram@epfl.ch
OI Kaufmann, Anna-Kristin/0000-0002-6084-418X; Scantamburlo,
   Enrico/0000-0003-4598-3998
FU Swiss government's ETH Board of the Swiss Federal Institutes of
   Technology
FX This study was supported by funding to the Blue Brain Project, a
   research center of the Ecole polytechnique federale de Lausanne (EPFL),
   from the Swiss government's ETH Board of the Swiss Federal Institutes of
   Technology.
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NR 442
TC 13
Z9 13
U1 3
U2 5
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2296-2565
J9 FRONT PUBLIC HEALTH
JI Front. Public Health
PD JUL 28
PY 2021
VL 9
AR 695139
DI 10.3389/fpubh.2021.695139
PG 53
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA TY6CU
UT WOS:000683871000001
PM 34395368
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Folschette, M
   Legagneux, V
   Poret, A
   Chebouba, L
   Guziolowski, C
   Theret, N
AF Folschette, Maxime
   Legagneux, Vincent
   Poret, Arnaud
   Chebouba, Lokmane
   Guziolowski, Carito
   Theret, Nathalie
TI A pipeline to create predictive functional networks: application to the
   tumor progression of hepatocellular carcinoma
SO BMC BIOINFORMATICS
LA English
DT Article
DE Data and network integration; Discrete modeling; Hepatocellular
   carcinoma; Signaling and regulatory knowledge; KEGG
ID NF-KAPPA-B; TO-MESENCHYMAL TRANSITION; GENE-EXPRESSION; TRANSCRIPTION;
   SURVIVAL; MODELS
AB Background Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this possibility. Results The presented work proposes an automatic pipeline to extract automatically regulatory knowledge from pathway databases and generate novel computational predictions related to the state of expression or activity of biological molecules. We applied it in the context of hepatocellular carcinoma (HCC) progression, and evaluate the precision and the stability of these computational predictions. Our working base is a graph of 3383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems. Conclusion This new pipeline allows biologists to develop their own predictive models based on a list of genes. It facilitates the identification of new regulatory biomolecules using knowledge graphs and predictive computational methods. Our workflow is implemented in an automatic python pipeline which is publicly available at and contains as testing data all the data used in this paper.
C1 [Folschette, Maxime; Theret, Nathalie] Univ Rennes, INRIA, CNRS, IRISA,UMR 6074, Rennes, France.
   [Folschette, Maxime; Legagneux, Vincent; Theret, Nathalie] Univ Rennes, INSERM, EHESP, Irset,UMR S1085, Rennes, France.
   [Folschette, Maxime] IFB CORE, Inst Francais Bioinformat, UMS CNRS 3601, Evry, France.
   [Folschette, Maxime; Poret, Arnaud; Chebouba, Lokmane; Guziolowski, Carito] LS2N, UMR 6004, Nantes, France.
   [Folschette, Maxime] Univ Lille, CNRS, Cent Lille, CRIStAL,UMR 9189, F-59000 Lille, France.
   [Chebouba, Lokmane; Guziolowski, Carito] Ecole Cent Nantes, Nantes, France.
   [Chebouba, Lokmane] USTHB, Elect Engn & Comp Sci Fac, LRIA Lab, Dept Comp Sci, Algiers, Algeria.
RP Theret, N (通讯作者)，Univ Rennes, INRIA, CNRS, IRISA,UMR 6074, Rennes, France.; Guziolowski, C (通讯作者)，LS2N, UMR 6004, Nantes, France.
EM carito.guziolowski@ls2n.fr; nathalie.theret@univ-rennes1.fr
RI Theret, Nathalie/I-2871-2015; Legagneux, Vincent/I-3409-2015; theret,
   nathalie/ABG-6780-2020
OI Theret, Nathalie/0000-0002-5857-7828; Legagneux,
   Vincent/0000-0002-1417-6678; theret, nathalie/0000-0002-5857-7828;
   Folschette, Maxime/0000-0002-3727-2320
FU Universite Bretagne Loire; GenOuest bioinformatics core facility
FX This work was supported by the Universite Bretagne Loire and the
   GenOuest bioinformatics core facility<SUP>10</SUP>. The funding bodies
   played no role in the design of the study and collection, analysis and
   interpretation of data and in writing the manuscript.
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NR 44
TC 1
Z9 1
U1 0
U2 2
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD JAN 14
PY 2020
VL 21
IS 1
AR 18
DI 10.1186/s12859-019-3316-1
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 KL7ZA
UT WOS:000513635900001
PM 31937236
OA Green Submitted, Green Published, gold
DA 2022-08-23
ER

PT J
AU Liu, ZJ
AF Liu, Zijie
TI CURRENT SITUATION AND DEVELOPMENT TREND OF IDEOLOGICAL AND CULTURAL
   PSYCHOLOGY RESEARCH IN THE CONTEXT OF THE COVID-19 EPIDEMIC-KNOWLEDGE
   GRAPH ANALYSIS BASED ON CITESPACE
SO PSYCHIATRIA DANUBINA
LA English
DT Article
DE COVID-19 epidemic; ideology; cultural psychology; CiteSpace
ID EMERGING TRENDS
AB Background: In order to fully understand the frontier hotspots and development trends of ideological and cultural psychology research in the context of the COVID-19 epidemic.
   Subjects and methods: We used the software CiteSpace to analyze the literature on ideological and cultural psychology research from the core database in Web of Science under the COVID-19 context.
   Results: The country/institution visualization network graph includes 58 nodes and 85 connections; The visual network graph of the research institution includes 109 nodes and 61 connections; 156 nodes and 552 connections are obtained, as shown in keyword co-occurrence graph and keyword cluster graph analysis; Keyword time-zone graph and keyword timeline graph are obtained.
   Conclusions: Relevant conclusions were drawn. First, the research on ideological and cultural psychology in the context of the COVID-19 epidemic began in 2020. The number of relevant literatures in 2021 has grown substantially. In 2022, the academic community still pays much attention to this field. Second, countries like the United States, the United Kingdom, and China are at the forefront in terms of the number and influence of documents issued. Research institutions such as Duke Univ, Univ Gothenburg, and Univ British Columbia ranks top in terms of documents number issued. Third, the research literature in this field can be divided into 11 clusters with relatively concentrated themes. Fourth, after the research hotspots explosively increased in this field in 2021, there have been fewer new hotspots in 2022. Future hotspots are likely to emerge on the basis of conservatism-prejudice. The current shortcomings of this research field are also discussed in this paper, namely, lack of innovation and less generation of new research hotspots. The above conclusions comprehensively demonstrate the current situation and development trend of ideological and cultural psychology research in the context of the COVID-19 epidemic and provide a basis for further related research.
C1 [Liu, Zijie] Shanghai Univ, Sch Marxism, Shanghai 200444, Peoples R China.
RP Liu, ZJ (通讯作者)，Shanghai Univ, Sch Marxism, Shanghai 200444, Peoples R China.
EM hanjiangyu@shu.edu.can
CR Bantimaroudis P., 2021, INT J COMMUN-US, V15, P24
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NR 39
TC 0
Z9 0
U1 1
U2 1
PU MEDICINSKA NAKLADA
PI ZAGREB
PA VLASKA 69, HR-10000 ZAGREB, CROATIA
SN 0353-5053
EI 1849-0867
J9 PSYCHIAT DANUB
JI Psychiatr. Danub.
PY 2022
VL 34
SU 4
BP S878
EP S890
PG 13
WC Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Psychiatry
GA 3F7FH
UT WOS:000830830000629
DA 2022-08-23
ER

PT J
AU Xiong, Y
   Chen, S
   Tang, BZ
   Chen, QC
   Wang, XL
   Yan, J
   Zhou, Y
AF Xiong, Ying
   Chen, Shuai
   Tang, Buzhou
   Chen, Qingcai
   Wang, Xiaolong
   Yan, Jun
   Zhou, Yi
TI Improving deep learning method for biomedical named entity recognition
   by using entity definition information
SO BMC BIOINFORMATICS
LA English
DT Article; Proceedings Paper
CT 5th Workshop on BioNLP Open Shared Tasks
CY NOV 04, 2019
CL Hong Kong, HONG KONG
DE Biomedical named entity recognition; Entity definition information;
   Machine reading comprehension; Span-level one-pass method
AB Background Biomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type. To accelerate the development of biomedical NER techniques in Spanish, the PharmaCoNER organizers launched a competition to recognize pharmacological substances, compounds, and proteins. Biomedical NER is usually recognized as a sequence labeling task, and almost all state-of-the-art sequence labeling methods ignore the meaning of different entity types. In this paper, we investigate some methods to introduce the meaning of entity types in deep learning methods for biomedical NER and apply them to the PharmaCoNER 2019 challenge. The meaning of each entity type is represented by its definition information. Material and method We investigate how to use entity definition information in the following two methods: (1) SQuad-style machine reading comprehension (MRC) methods that treat entity definition information as query and biomedical text as context and predict answer spans as entities. (2) Span-level one-pass (SOne) methods that predict entity spans of one type by one type and introduce entity type meaning, which is represented by entity definition information. All models are trained and tested on the PharmaCoNER 2019 corpus, and their performance is evaluated by strict micro-average precision, recall, and F1-score. Results Entity definition information brings improvements to both SQuad-style MRC and SOne methods by about 0.003 in micro-averaged F1-score. The SQuad-style MRC model using entity definition information as query achieves the best performance with a micro-averaged precision of 0.9225, a recall of 0.9050, and an F1-score of 0.9137, respectively. It outperforms the best model of the PharmaCoNER 2019 challenge by 0.0032 in F1-score. Compared with the state-of-the-art model without using manually-crafted features, our model obtains a 1% improvement in F1-score, which is significant. These results indicate that entity definition information is useful for deep learning methods on biomedical NER. Conclusion Our entity definition information enhanced models achieve the state-of-the-art micro-average F1 score of 0.9137, which implies that entity definition information has a positive impact on biomedical NER detection. In the future, we will explore more entity definition information from knowledge graph.
C1 [Xiong, Ying; Chen, Shuai; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong] Harbin Inst Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China.
   [Tang, Buzhou; Chen, Qingcai] Peng Cheng Lab, Shenzhen, Peoples R China.
   [Yan, Jun] Yidu Cloud Beijing Technol Co Ltd, Beijing, Peoples R China.
   [Zhou, Yi] Sun Yat Sen Univ, Zhongshan Sch Med, Guangzhou 510080, Peoples R China.
RP Tang, BZ (通讯作者)，Harbin Inst Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China.; Zhou, Y (通讯作者)，Sun Yat Sen Univ, Zhongshan Sch Med, Guangzhou 510080, Peoples R China.
EM tangbuzhou@gmail.com; zhouyi@sysu.edu.cn
OI Tang, Buzhou/0000-0003-0271-8246
FU National Natural Science Foundations of China [U1813215, 61876052,
   61573118]; Special Foundation for Technology Research Program of
   Guangdong Province [2015B010131010]; National Natural Science
   Foundations of Guangdong, China [2019A1515011158]; Guangdong Province
   Covid-19 Pandemic Control Research Fund [2020KZDZX1222]; Strategic
   Emerging Industry Development Special Funds of Shenzhen
   [JCYJ20180306172232154, JCYJ20170307150528934]; Innovation Fund of
   Harbin Institute of Technology [HIT. NSRIF.2017052]
FX This paper is supported in part by grants: National Natural Science
   Foundations of China (U1813215, 61876052 and 61573118), Special
   Foundation for Technology Research Program of Guangdong Province
   (2015B010131010), National Natural Science Foundations of Guangdong,
   China (2019A1515011158), Guangdong Province Covid-19 Pandemic Control
   Research Fund (2020KZDZX1222), Strategic Emerging Industry Development
   Special Funds of Shenzhen (JCYJ20180306172232154 and
   JCYJ20170307150528934) and Innovation Fund of Harbin Institute of
   Technology (HIT. NSRIF.2017052).
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NR 48
TC 0
Z9 0
U1 10
U2 11
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD DEC 17
PY 2021
VL 22
IS SUPPL 1
SU 1
SI SI
AR 600
DI 10.1186/s12859-021-04236-y
PG 12
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Science (CPCI-S)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA XQ0PK
UT WOS:000731257100001
PM 34920699
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Ribeiro, RS
   Silva, AMT
   Pastrana-Martinez, LM
   Figueiredo, JL
   Faria, JL
   Gomes, HT
AF Ribeiro, Rui S.
   Silva, Adrian M. T.
   Pastrana-Martinez, Luisa M.
   Figueiredo, Jose L.
   Faria, Joaquim L.
   Gomes, Helder T.
TI Graphene-based materials for the catalytic wet peroxide oxidation of
   highly concentrated 4-nitrophenol solutions
SO CATALYSIS TODAY
LA English
DT Article; Proceedings Paper
CT CarboCat 6 Symposium
CY JUN 22-25, 2014
CL Trondheim, NORWAY
DE Graphene-based materials; Reduced graphene oxide; Catalytic wet peroxide
   oxidation (CWPO); Highly polluted waters; 4-Nitrophenol
ID ACTIVATED-CARBON; SURFACE-CHEMISTRY; AIR OXIDATION; OXALIC-ACID;
   ORANGE-II; ADSORPTION; GRAPHITE; REDUCTION; REMOVAL; DECOMPOSITION
AB Reduced graphene oxide (rGO) samples were prepared from graphene oxide (GO) using different reducing agents (i.e., glucose, hydrazine and vitamin C, resulting in rGOG, rGOH and rGOV, respectively). These samples were tested in the catalytic wet peroxide oxidation (CWPO) of highly concentrated 4-nitrophenol (4-NP) solutions (5 g L-1) at mild operating conditions (i.e., atmospheric pressure, T = 323 K, pH = 3, [catalyst] = 2.5 g L-1 and [H2O2] 0= 17.8 g L-1). The highest catalytic activity was found for the rGOV sample, reaching a 4-NP removal of 1294 mg g(-1) and a TOC removal of 241 mg g(-1) after 24 h runs (corresponding to 4-NP and TOC removals of 65% and 23%, respectively).
   The TOC removal per unit of H2O2 decomposed decreases with the increase of the surface oxygen content of the samples, as well as with the increase of their amounts of structural defects. This was ascribed, respectively, to the electron withdrawal capacity of oxygen-containing functionalities and to reduced adsorption of 4-NP in the samples with higher amounts of defects, which diminish the efficiency of the reaction between hydroxyl radicals resulting from the decomposition of H2O2 and the organic compounds.
   In a series of three consecutive CWPO runs, rGOH was the most stable material (4-NP removal slightly decreasing from 980 mg g(-1), in the first and second runs, to 841 mg g(-1) in the third run, corresponding to 4-NP removals of 49% and 42%, respectively). The development of oxygen functionalities at the surface of the rGO samples during the CWPO runs was found to be the major cause of catalyst deactivation.
   4-Nitrocatechol was the main aromatic intermediate detected in the CWPO of 4-NP. Hydroquinone, benzoquinone, catechol and several low molecular weight carboxylic acids (e.g., malonic, malic, maleic and acetic acids) were also identified as reaction intermediates.
   To the best of our knowledge, graphene-based materials are reported for the first time as active and stable catalysts in the CWPO process, even when applied to highly polluted waters at mild conditions. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Ribeiro, Rui S.; Gomes, Helder T.] Polytech Inst Braganca, Sch Technol Management, Dept Chem & Biol Technol, Lab Catalysis & Mat,Associate Lab LSRE LCM, P-5300857 Braganca, Portugal.
   [Silva, Adrian M. T.; Pastrana-Martinez, Luisa M.; Figueiredo, Jose L.; Faria, Joaquim L.] Univ Porto, Fac Engn, Dept Engn Quim, LCM Lab Catalysis & Mat,Associate Lab LSRE LCM, P-4200465 Oporto, Portugal.
RP Silva, AMT (通讯作者)，Univ Porto, Fac Engn, Dept Engn Quim, LCM Lab Catalysis & Mat,Associate Lab LSRE LCM, Rua Dr Roberto Frias S-N, P-4200465 Oporto, Portugal.
EM adrian@fe.up.pt; htgomes@ipb.pt
RI Faria, Joaquim L/G-6272-2010; Pastrana-Martinez, Luisa M./AAT-5806-2020;
   Ribeiro, Rui S./O-1151-2019; Silva, Adrián M.T./J-3346-2013; Gomes,
   Helder/ABC-4508-2020; Martinez, Luisa M Pastrana/H-7798-2015
OI Faria, Joaquim L/0000-0002-6531-3978; Pastrana-Martinez, Luisa
   M./0000-0001-7037-3417; Ribeiro, Rui S./0000-0003-2314-0447; Silva,
   Adrián M.T./0000-0001-8551-6353; Martinez, Luisa M
   Pastrana/0000-0001-7037-3417; Gomes, Helder/0000-0001-6898-2408;
   Figueiredo, Jose/0000-0002-0395-8199
FU FEDER through COMPETE [PEst-C/EQB/LA0020/2013,
   NORTE-07-0162-FEDER-000050, NORTE-07-0124-FEDER-000015]; ON2; FCT -
   Fundacao para a Ciencia e a Tecnologia; FCT [SFRH/BD/94177/2013,
   SFRH/BPD/88964/2012]; Human Potential Operational Programme; QREN; FCT
   Investigator Programme [IF/01501/2013]; European Social Fund; 
   [PTDC/AAC-AMB/110088/2009]
FX Work supported by project PTDC/AAC-AMB/110088/2009 and partially by
   projects PEst-C/EQB/LA0020/2013, NORTE-07-0162-FEDER-000050 and
   NORTE-07-0124-FEDER-000015, co-financed by FEDER through COMPETE, QREN
   and ON2, and by FCT - Fundacao para a Ciencia e a Tecnologia. RSR and
   LMPM acknowledge financial support from the FCT grants
   SFRH/BD/94177/2013 and SFRH/BPD/88964/2012, respectively. A.M.T. Silva
   acknowledges the FCT Investigator 2013 Programme (IF/01501/2013), with
   financing from the European Social Fund and the Human Potential
   Operational Programme.
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NR 43
TC 48
Z9 50
U1 4
U2 112
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0920-5861
EI 1873-4308
J9 CATAL TODAY
JI Catal. Today
PD JUL 1
PY 2015
VL 249
BP 204
EP 212
DI 10.1016/j.cattod.2014.10.004
PG 9
WC Chemistry, Applied; Chemistry, Physical; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Chemistry; Engineering
GA CG0LC
UT WOS:000352957100028
OA Bronze
DA 2022-08-23
ER

PT J
AU Zhao, KF
   Su, J
   Yu, JX
   Zhang, H
AF Zhao, Kangfei
   Su, Jiao
   Yu, Jeffrey Xu
   Zhang, Hao
TI SQL-G: Efficient Graph Analytics by SQL
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Structured Query Language; Sparks; Algebra; Engines; Programming;
   Distributed databases; Data processing; Graph analytics; distributed
   graph processing; SQL recursive query; spark
ID COMPUTATION; SOCIALITE
AB Querying graphs and conducting graph analytics become important in data processing since many real applications are dealing with massive graphs, such as online social networks, Semantic Web, knowledge graphs, etc. Over the years, many distributed graph processing systems have been developed to support graph analytics using various programming models, and many graph querying languages have been proposed. A natural question that arises is how to integrate graph data and traditional non-graph data in a distributed system for users to conduct analytics. There are two issues. One issue is related to expressiveness on how to specify graph analytics as well as data analytics by a querying language. The other issue is related to efficiency on how to process analytics in a distributed system. For the first issue, SQL is a best candidate, since SQL is a well-accepted language for data processing. We concentrate on SQL for graph analytics. Our early work shows that graph analytics can be supported by SQL in a way from "semiring + while" to "relational algebra + while" via the enhanced recursive SQL queries. In this article, we focus on the second issue on how to process such enhanced recursive SQL queries based on the GAS (Gather-Apply-Scatter) model under which efficient graph processing systems can be developed. To demonstrate the efficiency, we implemented a system by tightly coupling Spark SQL and GraphX on Spark which is one of the most popular in-memory data-flow processing platforms. First, we enhance Spark SQL by adding the capability of supporting the enhanced recursive SQL queries for graph analytics. In this regard, graph analytics can be processed using a distributed SQL engine alone. Second, we further propose new transformation rules to optimize/translate the operations for recursive SQL queries to the operations by GraphX. In this regard, graph analytics by SQL can be processed in a similar way as done by a distributed graph processing system using the APIs provided by the system. We conduct extensive performance studies to test graph analytics using large real graphs. We show that our approach can achieve similar or even higher efficiency, in comparison to the built-in graph algorithms in the existing graph processing systems.
C1 [Zhao, Kangfei; Su, Jiao; Yu, Jeffrey Xu; Zhang, Hao] Chinese Univ Hong Kong, Sha Tin, Hong Kong, Peoples R China.
RP Yu, JX (通讯作者)，Chinese Univ Hong Kong, Sha Tin, Hong Kong, Peoples R China.
EM kfzhao@se.cuhk.edu.hk; jiaosu@se.cuhk.edu.hk; yu@se.cuhk.edu.hk;
   hzhang@se.cuhk.edu.hk
FU Research Grants Council of the Hong Kong SAR, China [14203618, 14202919]
FX This work was supported by the Research Grants Council of the Hong Kong
   SAR, China, No. 14203618 and No. 14202919.
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NR 61
TC 0
Z9 0
U1 0
U2 7
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD MAY 1
PY 2021
VL 33
IS 5
BP 2237
EP 2251
DI 10.1109/TKDE.2019.2950620
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA RJ3XT
UT WOS:000637532700032
DA 2022-08-23
ER

PT J
AU Glavaski, M
   Velicki, L
AF Glavaski, Mila
   Velicki, Lazar
TI Humans and machines in biomedical knowledge curation: hypertrophic
   cardiomyopathy molecular mechanisms' representation
SO BIODATA MINING
LA English
DT Article
DE Data mining; Curation; Automated curation; Hypertrophic cardiomyopathy;
   Signaling pathways; Knowledge graphs; Disease maps
ID ANNOTATION; ASTHMAMAP; GENETICS; DATABASE; DISEASE; MODELS; NDEX
AB Background Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype-phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms' models and examined the performance of different machine approaches for that task. Results We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models' differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems' performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. Conclusions Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.
C1 [Glavaski, Mila; Velicki, Lazar] Univ Novi Sad, Fac Med, Novi Sad, Serbia.
   [Velicki, Lazar] Inst Cardiovasc Dis Vojvodina, Sremska Kamenica, Serbia.
RP Glavaski, M (通讯作者)，Univ Novi Sad, Fac Med, Novi Sad, Serbia.
EM milaglavaski@yahoo.com
OI Glavaski, Mila/0000-0003-4709-9872
FU European Union [777204]
FX This work was supported by the European Union's Horizon 2020 Research
   and Innovation Programme as part of the SILICOFCM project
   (www.silicofcm.eu) [Grant Agreement No 777204]. This article only
   reflects the author's view. The Commission is not responsible for any
   use that may be made of the information it contains.
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PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1756-0381
J9 BIODATA MIN
JI BioData Min.
PD OCT 2
PY 2021
VL 14
IS 1
AR 45
DI 10.1186/s13040-021-00279-2
PG 25
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA WA3IY
UT WOS:000702784000001
PM 34600580
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Perez-Perez, M
   Igrejas, G
   Fdez-Riverola, F
   Lourenco, A
AF Perez-Perez, Martin
   Igrejas, Gilberto
   Fdez-Riverola, Florentino
   Lourenco, Analia
TI A framework to extract biomedical knowledge from gluten-related tweets:
   The case of dietary concerns in digital era
SO ARTIFICIAL INTELLIGENCE IN MEDICINE
LA English
DT Article
DE Social media; Sociome profiling; Text mining; Graph mining; Machine
   learning; Health for informatics
ID CELIAC-DISEASE; SOCIAL MEDIA; HEALTH; TWITTER; PREVALENCE; TEXT;
   IDENTIFICATION; SENTIMENT; SYMPTOMS; FUTURE
AB Big data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from >400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.
C1 [Perez-Perez, Martin; Fdez-Riverola, Florentino; Lourenco, Analia] Univ Vigo, CINBIO, Dept Comp Sci, ESEI Escuela Super Ingn Informat, Orense 32004, Spain.
   [Perez-Perez, Martin; Fdez-Riverola, Florentino; Lourenco, Analia] SERGAS UVIGO, Galicia Hlth Res Inst IIS Galicia Sur, SING Res Grp, Vigo, Spain.
   [Igrejas, Gilberto] Univ Tras Os Montes & Alto Douro, Dept Genet & Biotechnol, Vila Real, Portugal.
   [Igrejas, Gilberto] Univ Tras Os Montes & Alto Douro, Funct Genom & Prote Unit, Vila Real, Portugal.
   [Igrejas, Gilberto] Nova Univ Lisbon, Fac Sci & Technol, LAQV REQUIMTE, Lisbon, Portugal.
   [Lourenco, Analia] Univ Minho, Ctr Biol Engn, CEB, Campus Gualtar, P-4710057 Braga, Portugal.
RP Perez-Perez, M (通讯作者)，Univ Vigo, CINBIO, Dept Comp Sci, ESEI Escuela Super Ingn Informat, Orense 32004, Spain.
EM martiperez@uvigo.es; gigrejas@utad.pt
RI Fdez-Riverola, Florentino/G-1411-2011; Lourenço, Anália/Y-9447-2019;
   Igrejas, Gilberto P P/A-9308-2010
OI Fdez-Riverola, Florentino/0000-0002-3943-8013; Lourenço,
   Anália/0000-0001-8401-5362; Igrejas, Gilberto P P/0000-0002-6365-0735;
   Perez-Perez, Martin/0000-0003-1349-6562
FU Associate Laboratory for Green Chemistry-LAQV; Portuguese Foundation for
   Science and Technology (FCT [UIDB/50006/2020, UIDB/04469/2020];
   BioTecNorte operation [NORTE010145FEDER000004]; European Regional
   Development Fund under the scope of Norte2020Programa Operacional
   Regional do Norte; Xunta de Galicia (Centro singular de investigacion de
   Galicia accreditation 2019-2022); European Union (European Regional
   Development Fund - ERDF)- Ref [ED431G2019/06]; Conselleria de Educacion,
   Universidades e Formacion Profesional (Xunta de Galicia)
   [ED431C2018/55GRC]; Competitive Reference Group; post-doctoral
   fellowship [ED481B2019032]; Xunta de Galicia; Universidade de Vigo/CISUG
FX SING group thanks CITI (Centro de Investigacion, Transferencia e
   Innovacion) from the University of Vigo for hosting its IT
   infrastructure. This work was supported by: the Associate Laboratory for
   Green Chemistry-LAQV, which is financed by national funds from and the
   Portuguese Foundation for Science and Technology (FCT) under the scope
   of the strategic funding of [UIDB/50006/2020] and [UIDB/04469/2020]
   units, and BioTecNorte operation [NORTE010145FEDER000004] funded by the
   European Regional Development Fund under the scope of Norte2020Programa
   Operacional Regional do Norte, the Xunta de Galicia (Centro singular de
   investigacion de Galicia accreditation 2019-2022) and the European Union
   (European Regional Development Fund - ERDF)- Ref. [ED431G2019/06] , and
   Conselleria de Educacion, Universidades e Formacion Profesional (Xunta
   de Galicia) under the scope of the strategic funding of
   [ED431C2018/55GRC] Competitive Reference Group. The authors also
   acknowledge the post-doctoral fellowship [ED481B2019032] of Martin
   PerezPerez, funded by the Xunta de Galicia. Funding for open access
   charge: Universidade de Vigo/CISUG
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NR 108
TC 1
Z9 1
U1 2
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0933-3657
EI 1873-2860
J9 ARTIF INTELL MED
JI Artif. Intell. Med.
PD AUG
PY 2021
VL 118
AR 102131
DI 10.1016/j.artmed.2021.102131
EA JUL 2021
PG 17
WC Computer Science, Artificial Intelligence; Engineering, Biomedical;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Medical Informatics
GA UB0JC
UT WOS:000685538400009
PM 34412847
OA Green Submitted, hybrid
DA 2022-08-23
ER

PT J
AU Wang, CG
   Song, YQ
   Roth, D
   Zhang, M
   Han, JW
AF Wang, Chenguang
   Song, Yangqiu
   Roth, Dan
   Zhang, Ming
   Han, Jiawei
TI World Knowledge as Indirect Supervision for Document Clustering
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE World knowledge; heterogeneous information network; document clustering;
   knowledge base; knowledge graph
AB One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then, the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this article, we provide an example of using world knowledge for domain-dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then, we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.
   A preliminary version of this work appeared in the proceedings of KDD 2015 [Wang et al. 2015a]. This journal version has made several major improvements. First, we have proposed a new and general learning framework for machine learning with world knowledge as indirect supervision, where document clustering is a special case in the original paper. Second, in order to make our unsupervised semantic parsing method more understandable, we add several real cases from the original sentences to the resulting logic forms with all the necessary information. Third, we add details of the three semantic filtering methods and conduct deep analysis of the three semantic filters, by using case studies to show why the conceptualization-based semantic filter can produce more accurate indirect supervision. Finally, in addition to the experiment on 20 newsgroup data and Freebase, we have extended the experiments on clustering results by using all the combinations of text (20 newsgroup, MCAT, CCAT, ECAT) and world knowledge sources (Freebase, YAGO2).
C1 [Wang, Chenguang; Zhang, Ming] Peking Univ, Sch EECS, Beijing 100871, Peoples R China.
   [Song, Yangqiu] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China.
   [Roth, Dan; Han, Jiawei] Univ Illinois, Dept Comp Sci, Champaign, IL 61820 USA.
   [Wang, Chenguang] IBM Res Almaden, 650 Harry Rd, San Jose, CA 95120 USA.
RP Zhang, M (通讯作者)，Peking Univ, Sch EECS, Beijing 100871, Peoples R China.
EM wangchenguang@pku.edu.cn; yqsong@cse.ust.hk; danr@illinois.edu;
   mzhang_cs@pku.edu.cn; hanj@illinois.edu
RI Zhang, Ming/L-8193-2019
FU National Natural Science Foundation of China (NSFC) [61472006]; National
   Basic Research Program (973 Program) [2014CB340405]; Army Research
   Laboratory (ARL) [W911NF-09-20053]; DARPA [FA8750-13-2-0008]; China
   National 973 project [2014CB340304]; U.S. National Science Foundation -
   NIGMS through trans-NIH Big Data to Knowledge (BD2K) initiative
   [IIS-1320617, IIS-1354329, IIS 16-18481, HDTRA1-10-1-0120,
   1U54GM114838]; MIAS, a DHS-IDS Center for Multimodal Information Access
   and Synthesis at UIUC; Div Of Information & Intelligent Systems
   [1320617, 1618481] Funding Source: National Science Foundation
FX C. Wang gratefully acknowledges the support by the National Natural
   Science Foundation of China (NSFC Grant Number 61472006) and the
   National Basic Research Program (973 Program No. 2014CB340405). The
   research is also partially supported by the Army Research Laboratory
   (ARL) under agreement W911NF-09-20053, and by DARPA under agreement
   number FA8750-13-2-0008. The research is also partially sponsored by
   China National 973 project 2014CB340304, U.S. National Science
   Foundation IIS-1320617, IIS-1354329 and IIS 16-18481, HDTRA1-10-1-0120,
   and grant 1U54GM114838 awarded by NIGMS through funds provided by the
   trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov),
   and MIAS, a DHS-IDS Center for Multimodal Information Access and
   Synthesis at UIUC. 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 by
   these agencies or the U.S. Government.
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NR 104
TC 4
Z9 4
U1 2
U2 12
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1556-4681
EI 1556-472X
J9 ACM T KNOWL DISCOV D
JI ACM Trans. Knowl. Discov. Data
PD DEC
PY 2016
VL 11
IS 2
AR 13
DI 10.1145/2953881
PG 36
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EJ4JX
UT WOS:000393184000002
OA Green Submitted, Bronze
DA 2022-08-23
ER

PT J
AU Kung, HY
   Yu, RW
   Chen, CH
   Tsai, CW
   Lin, CY
AF Kung, Hsu-Yang
   Yu, Ren-Wu
   Chen, Chi-Hua
   Tsai, Chan-Wei
   Lin, Chia-Yu
TI Intelligent pig-raising knowledge question-answering system based on
   neural network schemes
SO AGRONOMY JOURNAL
LA English
DT Article
AB The most important parts of Taiwan's agriculture are animal husbandry and the pig industry, of which the output value reached NT$75.6 billion in 2017. Taiwan has a high technical level of pig raising. However, practical pig-raising skills rely mainly on the inheritance of mentoring experience. The livestock and pig breeding industry in Taiwan has no relevant pig breeding knowledge management information system and no intelligent knowledge question-answering system. Therefore, this study designs and implements an intelligent knowledge question-and-answer system for pig farming. To identify intelligent questions and answers for raising livestock pigs, this study addresses the following issues: (a) to determine the semantic meaning of a sentence, the system needs to accurately interpret the meaning of a question and to identify the expression of a knowledge entity. Therefore, this study applies the lattice long-short-term memory (LSTM) and structured perceptron methods to parse sentences accurately and correctly perform word segmentation. A set of stop words for pig raising in Taiwan is initially established to ensure accurate sentence parsing; (b) to understand the intent of each sentence, the bidirectional gated recurrent init (bi-GRU) method is adopted to realize the knowledge extraction of the livestock in the question and complete the intent detection and slot filling. The bi-GRU extracts the correct livestock knowledge and classifies it into suitable topics; (c) the wide range of knowledge sources in questions often leads to unrelated vocabulary, repeated words, and structural loss in potential answers. To establish an effective knowledge search graph, the method infers implicit knowledge from the existing knowledge and conducts related knowledge retrieval based on the inference results. The knowledge data model is defined in the open standard resource description framework Knowledge filtering and knowledge reasoning strategies are presented to produce candidate answers to the livestock problem; (d) to obtain the final answer, most related works search established knowledge graphs, often producing multiple ambiguous candidate answers. The Siamese neural network (SNN) is adopted to obtain accurate answers. An SNN compares candidate answers to a livestock problem as two input values, previously trained by the bidirectional LSTM neural network. The final answer is determined from the cosine similarity value, which represents the highest relevance to the question. Finally, this study implements the intelligent pig-raising knowledge question-answering system based on the proposed methodology. The evaluation results reveal that the proposed system is accurate and practicable.
C1 [Kung, Hsu-Yang; Yu, Ren-Wu] Natl Pingtung Univ Sci & Technol, Dept Management Informat Syst, Pingtung, Taiwan.
   [Chen, Chi-Hua] Southwest Univ, Business Coll, Chongqing, Peoples R China.
   [Chen, Chi-Hua] Fuzhou Univ, Key Lab Intelligent Metro Univ Fujian, Fuzhou, Peoples R China.
   [Tsai, Chan-Wei] Natl Pingtung Univ Sci & Technol, Dept Business Adm, Pingtung, Taiwan.
   [Lin, Chia-Yu] Yuan Ze Univ, Dept Comp Sci & Engn, Taoyuan, Taiwan.
RP Chen, CH (通讯作者)，Southwest Univ, Business Coll, Chongqing, Peoples R China.
EM chihua0826@gmail.com
RI Chen, Chi-Hua/F-1067-2010
OI Chen, Chi-Hua/0000-0001-7668-7425
FU National Natural Science Foundation of China [61906043, 11501114,
   11901100, 61877010, 61902313, 62002063, 61902072]; Fundamental Research
   Funds for the Central Universities of China [SWU2009107]; Chongqing
   Science and Technology Bureau [cstc2020ngzx0010]; Funds of Education
   Department of Fujian Province [JAT190026]; Ministry of Science and
   Technology, Taiwan [MOST108-2637-E-020-00, MOST109-2637-E-020-004];
   Fuzhou University [0330/50016703, 0330/50009113, 510649/XRC-18049,
   510650/XRC-18050, 510730/XRC-18075, 510872/GXRC-20016,
   510930/XRC-20060]; Fujian Natural Science Funds [2019J01243, 2020J01498,
   2020J05111, 2020J05112]
FX National Natural Science Foundation of China, Grant/Award Numbers:
   61906043, 11501114, 11901100, 61877010, 61902313, 62002063, 61902072,
   11901100; Fundamental Research Funds for the Central Universities of
   China, Grant/Award Number: SWU2009107; Chongqing Science and Technology
   Bureau, Grant/AwardNumber: cstc2020ngzx0010; Funds of Education
   Department of Fujian Province, Grant/AwardNumber: JAT190026; Ministry of
   Science and Technology, Taiwan, Grant/Award Numbers:
   MOST108-2637-E-020-00, MOST109-2637-E-020-004; Fuzhou University,
   Grant/Award Numbers: 0330/50016703, 0330/50009113, 510649/XRC-18049,
   510650/XRC-18050, 510730/XRC-18075, 510872/GXRC-20016, 510930/XRC-20060;
   Fujian Natural Science Funds, Grant/Award Numbers: 2019J01243,
   2020J01498, 2020J05111, 2020J05112
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TC 1
Z9 1
U1 11
U2 26
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0002-1962
EI 1435-0645
J9 AGRON J
JI Agron. J.
PD MAR
PY 2021
VL 113
IS 2
BP 906
EP 922
DI 10.1002/agj2.20622
EA MAR 2021
PG 17
WC Agronomy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture
GA SD3WV
UT WOS:000634152200001
DA 2022-08-23
ER

PT J
AU Fomin, FV
   Golovach, PA
   Lokshtanov, D
   Saurabh, S
   Zehavi, M
AF Fomin, Fedor, V
   Golovach, Petr A.
   Lokshtanov, Daniel
   Saurabh, Saket
   Zehavi, Meirav
TI Clique-width III: Hamiltonian Cycle and the Odd Case of Graph Coloring
SO ACM TRANSACTIONS ON ALGORITHMS
LA English
DT Article
DE Coloring; Hamiltonian cycle; fine-grained complexity; exponential time
   hypothesis
ID MONADIC 2ND-ORDER LOGIC; PARTITIONING PROBLEMS; LOWER BOUNDS;
   COMPLEXITY; ALGORITHMS; DECOMPOSITIONS; MINORS
AB MAX-CUT, EDGE DOMINATING SET, GRAPH COLORING, and HAMILTONIAN CYCLE on graphs of bounded clique-width have received significant attention as they can be formulated in MSO2 (and, therefore, have linear-time algorithms on bounded treewidth graphs by the celebrated Courcelle's theorem), but cannot be formulated in MSO1 (which would have yielded linear-time algorithms on bounded clique-width graphs by a well-known theorem of Courcelle, Makowsky, and Rotics). Each of these problems can be solved in time g(k)n(f(k)) on graphs of clique-width k. Fomin et al. (2010) showed that the running times cannot be improved to g(k)n(O(1) )assuming W[1]not equal FPT. However, this does not rule out non-trivial improvements to the exponent f (k) in the running times. In a follow-up paper, Fomin et al. (2014) improved the running times for EDGE DOMINATING SET and MAx-CUT to n(O(k)), and proved that these problems cannot be solved in time g(k)n(o(k)) unless ETH fails. Thus, prior to this work, EDGE DOMINATING SET and MAx-CUT were known to have tight n(Theta(k)) algorithmic upper and lower bounds.
   In this article, we provide lower bounds for HAMILTONIAN CYCLE and GRAPH COLORING. For HAMILTONIAN CYCLE, our lower bound g(k)n(o(k)) matches asymptotically the recent upper bound n(O(k)) due to Bergougnoux, Kante and Kwon (2017).
   As opposed to the asymptotically tight n(Theta(k)) bounds for EDGE DOMINATING SET, MAX-CUT, and HAMILTONIAN CYCLE, the GRAPH COLORING problem has an upper bound of n(O()(2k)()) and a lower bound of merely n(o(4 root k)) (implicit from the W[1]-hardness proof). In this article, we close the gap for GRAPH COLORING by proving a lower bound of n(2o(k)). This shows that GRAPH COLORING behaves qualitatively different from the other three problems. To the best of our knowledge, GRAPH COLORING is the first natural problem known to require exponential dependence on the parameter in the exponent of n.
C1 [Fomin, Fedor, V; Golovach, Petr A.; Lokshtanov, Daniel; Saurabh, Saket] Univ Bergen, Dept Informat, PB 7803, N-5020 Bergen, Norway.
   [Saurabh, Saket] Inst Math Sci, HBNI, 4th Cross St,CIT Campus, Madras 600113, Tamil Nadu, India.
   [Zehavi, Meirav] Ben Gurion Univ Negev, Comp Sci Dept, Alon High Tech Bldg, Beer Sheva, Israel.
RP Fomin, FV (通讯作者)，Univ Bergen, Dept Informat, PB 7803, N-5020 Bergen, Norway.
EM fedor.fomin@uib.no; petr.golovach@uib.no; daniello@iluib.no;
   saket@imsc.res.in; meiravze@bgu.ac.il
RI Saurabh, Saket/AAT-4427-2020
OI Fomin, Fedor V./0000-0003-1955-4612
FU Research Council of Norway via the project "CLASSIS"; Research Council
   of Norway via the project "MULTIVAL"; Israel Science Foundation
   [1176/18]; European Research Council under ERC Starting Grant [715744]
FX This work is supported by the Research Council of Norway via the
   projects "CLASSIS" and "MULTIVAL", the Israel Science Foundation under
   Grant No. 1176/18, and the European Research Council under ERC Starting
   Grant No. 715744 "Pareto-Optimal Parameterized Algorithms".
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NR 46
TC 11
Z9 11
U1 0
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1549-6325
EI 1549-6333
J9 ACM T ALGORITHMS
JI ACM Trans. Algorithms
PD JAN
PY 2019
VL 15
IS 1
AR 9
DI 10.1145/3280824
PG 27
WC Computer Science, Theory & Methods; Mathematics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Mathematics
GA HJ4HZ
UT WOS:000457136400009
DA 2022-08-23
ER

PT J
AU Zhang, ZC
   Zhu, L
   Yu, PL
AF Zhang, Zhichang
   Zhu, Lin
   Yu, Peilin
TI Multi-Level Representation Learning for Chinese Medical Entity
   Recognition: Model Development and Validation
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE medical entity recognition; multi-level representation learning;
   Chinese; natural language processing; electronic medical records;
   multi-head attention mechanism
AB Background: Medical entity recognition is a key technology that supports the development of smart medicine. Existing methods on English medical entity recognition have undergone great development, but their progress in the Chinese language has been slow. Because of limitations due to the complexity of the Chinese language and annotated corpora, these methods are based on simple neural networks, which cannot effectively extract the deep semantic representations of electronic medical records (EMRs) and be used on the scarce medical corpora. We thus developed a new Chinese EMR (CEMR) dataset with six types of entities and proposed a multi-level representation learning model based on Bidirectional Encoder Representation from Transformers (BERT) for Chinese medical entity recognition.
   Objective: This study aimed to improve the performance of the language model by having it learn multi-level representation and recognize Chinese medical entities.
   Methods: In this paper, the pretraining language representation model was investigated; utilizing information not only from the final layer but from intermediate layers was found to affect the performance of the Chinese medical entity recognition task. Therefore, we proposed a multi-level representation learning model for entity recognition in Chinese EMRs. Specifically, we first used the BERT language model to extract semantic representations. Then, the multi-head attention mechanism was leveraged to automatically extract deeper semantic information from each layer. Finally, semantic representations from multi-level representation extraction were utilized as the final semantic context embedding for each token and we used softmax to predict the entity tags.
   Results: The best F1 score reached by the experiment was 82.11% when using the CEMR dataset, and the F1 score when using the CCKS (China Conference on Knowledge Graph and Semantic Computing) 2018 benchmark dataset further increased to 83.18%. Various comparative experiments showed that our proposed method outperforms methods from previous work and performs as a new state-of-the-art method.
   Conclusions: The multi-level representation learning model is proposed as a method to perform the Chinese EMRs entity recognition task. Experiments on two clinical datasets demonstrate the usefulness of using the multi-head attention mechanism to extract multi-level representation as part of the language model.
C1 [Zhang, Zhichang; Zhu, Lin; Yu, Peilin] Univ Northwest Normal, Coll Comp Sci & Engn, 967 Arming East Rd, Lanzhou, Peoples R China.
RP Zhang, ZC (通讯作者)，Univ Northwest Normal, Coll Comp Sci & Engn, 967 Arming East Rd, Lanzhou, Peoples R China.
EM zzc@nwnu.edu.cn
OI Zhang, Zhichang/0000-0003-3306-8493; Zhu, Lin/0000-0002-7902-4819
FU National Natural Science Foundation of China [61762081, 61662067,
   61662068]; Key Research and Development Project of Gansu Province
   [17YF1GA016]
FX This work is supported by the National Natural Science Foundation of
   China (No. 61762081, No. 61662067, and No. 61662068) and the Key
   Research and Development Project of Gansu Province (No. 17YF1GA016). The
   datasets used and analyzed during this study are available from the
   first author upon reasonable request. The CCKS 2018 dataset that
   supports the findings of this study were adopted from the Chinese EMR
   named ER task from the CCKS 2018, but restrictions apply to the
   availability of these data, which were used under license for this study
   and are not publicly available.
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PY 2020
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IS 5
AR e17637
DI 10.2196/17637
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WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA LK8CZ
UT WOS:000531089200008
PM 32364514
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Saini, R
   Mussbacher, G
   Guo, JLC
   Kienzle, J
AF Saini, Rijul
   Mussbacher, Gunter
   Guo, Jin L. C.
   Kienzle, Jorg
TI Automated, interactive, and traceable domain modelling empowered by
   artificial intelligence
SO SOFTWARE AND SYSTEMS MODELING
LA English
DT Article
DE Domain model; Natural language (NL); Natural language processing (NLP);
   Machine learning (ML); Neural networks; Descriptive model; Predictive
   model; Bot-Modeller interactions; Traceability information model;
   Traceability knowledge graph
ID REQUIREMENTS; GENERATION
AB Model-Based Software Engineering provides various modelling formalisms for capturing the structural, behavioral, configuration, and intentional aspects of software systems. One of the most widely used kinds of models-domain models-are used during requirements analysis or the early stages of design to capture the domain concepts and relationships in the form of class diagrams. Modellers perform domain modelling to transform the problem descriptions that express informal requirements in natural language to domain models, which are more concise and analyzable. However, this manual practice of domain modelling is laborious and time-consuming. Existing approaches, which aim to assist modellers by automating or semi-automating the construction of domain models from problem descriptions, fail to address three non-trivial aspects of automated domain modelling. First, automatically extracted domain models from existing approaches are not accurate enough to be used directly or with minor modifications for software development or teaching purposes. Second, existing approaches do not support modeller-system interactions beyond providing recommendations. Finally, existing approaches do not facilitate the modellers to learn the rationale behind the modelling decisions taken by an extractor system. Therefore, in this paper, we extend our previous work to facilitate bot-modeller interactions. We propose an algorithm to discover alternative configurations during bot-modeller interactions. Our bot uses this algorithm to find alternative configurations and then present these configurations in the form of suggestions to modellers. Our bot then updates the domain model in response to the acceptance of these suggestions by a modeller. Furthermore, we evaluate the bot for its effectiveness and performance for the test problem descriptions. Our bot achieves median F1 scores of 86%, 91%, and 90% in the Found Configurations, Offered Suggestions, and Updated Domain Models categories, respectively. We also show that the median time taken by our bot to find alternative configurations is 55.5ms for the problem descriptions which are similar to the test problem descriptions in terms of model size and complexity. Finally, we conduct a pilot user study to assess the benefits and limitations of our bot and present the lessons learned from our study in preparation for a large-scale user study.
C1 [Saini, Rijul; Mussbacher, Gunter] McGill Univ, Dept Elect & Comp Engn, Montreal, PQ, Canada.
   [Guo, Jin L. C.; Kienzle, Jorg] McGill Univ, Sch Comp Sci, Montreal, PQ, Canada.
RP Saini, R (通讯作者)，McGill Univ, Dept Elect & Comp Engn, Montreal, PQ, Canada.
EM rijul.saini@mail.mcgill.ca; gunter.mussbacher@mcgill.ca;
   jguo@cs.mcgill.ca; joerg.kienzle@cs.mcgill.ca
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PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
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J9 SOFTW SYST MODEL
JI Softw. Syst. Model.
PD JUN
PY 2022
VL 21
IS 3
BP 1015
EP 1045
DI 10.1007/s10270-021-00942-6
EA JAN 2022
PG 31
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0U0DM
UT WOS:000740147900001
DA 2022-08-23
ER

PT J
AU Chereda, H
   Bleckmann, A
   Menck, K
   Perera-Bel, J
   Stegmaier, P
   Auer, F
   Kramer, F
   Leha, A
   Beissbarth, T
AF Chereda, Hryhorii
   Bleckmann, Annalen
   Menck, Kerstin
   Perera-Bel, Julia
   Stegmaier, Philip
   Auer, Florian
   Kramer, Frank
   Leha, Andreas
   Beissbarth, Tim
TI Explaining decisions of graph convolutional neural networks:
   patient-specific molecular subnetworks responsible for metastasis
   prediction in breast cancer
SO GENOME MEDICINE
LA English
DT Article
DE Gene expression data; Explainable AI; Personalized medicine; Precision
   medicine; Classification of cancer; Deep learning; Prior knowledge;
   Molecular networks
ID MESSENGER-RNA; GENE; CELLS; PROGRESSION; CARCINOMA; PATHWAY
AB Background Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. However, the machine learning community made recent elaborations on interpretability methods explaining data point-specific decisions of deep learning techniques. We believe that such explanations can assist the need in personalized precision medicine decisions via explaining patient-specific predictions. Methods Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g., distant metastasis in cancer, for each individual patient. Results We extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset and then apply the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. Conclusions The developed method could be potentially highly useful on interpreting classification results in the context of different omics data and prior knowledge molecular networks on the individual patient level, as for example in precision medicine approaches or a molecular tumor board.
C1 [Chereda, Hryhorii; Beissbarth, Tim] Univ Med Ctr Gottingen, Med Bioinformat, Gottingen, Germany.
   [Bleckmann, Annalen; Menck, Kerstin] Univ Hosp Munster, Dept Med Hematol Oncol Hemostaseol & Pulmonol A, Munster, Germany.
   [Perera-Bel, Julia] Hosp Mar Med Res Inst IMIM, Barcelona, Spain.
   [Stegmaier, Philip] GeneXplain GmbH, Wolfenbuttel, Germany.
   [Auer, Florian; Kramer, Frank] Univ Augsburg, IT Infrastruct Translat Med Res, Augsburg, Germany.
   [Leha, Andreas] Univ Med Ctr Gottingen, Med Stat, Gottingen, Germany.
   [Beissbarth, Tim] Univ Gottingen, Campus Inst Data Sci CIDAS, Gottingen, Germany.
RP Beissbarth, T (通讯作者)，Univ Med Ctr Gottingen, Med Bioinformat, Gottingen, Germany.; Beissbarth, T (通讯作者)，Univ Gottingen, Campus Inst Data Sci CIDAS, Gottingen, Germany.
EM tim.beissbarth@bioinf.med.uni-goettingen.de
RI Menck, Kerstin/AAP-9066-2021
OI Auer, Florian/0000-0002-5320-8900; Perera-Bel,
   Julia/0000-0002-6809-132X; Kramer, Frank/0000-0002-2857-7122;
   Beissbarth, Tim/0000-0001-6509-2143
FU German Ministry of Education and Research (BMBF) e:Med project MyPathSem
   [031L0024]; German Research Foundation (DFG) [424252458]; Gottingen
   University; the project MTB-Report by the big data initiative of the
   Volkswagenstiftung
FX This work was funded by the German Ministry of Education and Research
   (BMBF) e:Med project MyPathSem (031L0024) and the project MTB-Report by
   the big data initiative of the Volkswagenstiftung. KM was supported by
   German Research Foundation (DFG) project 424252458. We acknowledge
   support by the Open Access Publication Funds of the Gottingen
   University.
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NR 72
TC 10
Z9 10
U1 12
U2 21
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1756-994X
J9 GENOME MED
JI Genome Med.
PD MAR 11
PY 2021
VL 13
IS 1
AR 42
DI 10.1186/s13073-021-00845-7
PG 16
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA QV1ZF
UT WOS:000627775700001
PM 33706810
OA Green Published, Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Tian, S
   An, YX
   Zhang, RY
   Wang, LM
   Wang, YY
AF Tian, Shuo
   An, Yuxin
   Zhang, Ruyue
   Wang, Liming
   Wang, Yuyu
TI Bibliometric Analyses of Web of Science Illuminate Research Advances of
   Neuropterida
SO INSECTS
LA English
DT Article
DE Neuropterida; Chrysopa pallens; Neuroptera; CiteSpace; knowledge graph;
   bibliometric
ID CHRYSOPERLA CARNEA NEUROPTERA; GREEN LACEWINGS NEUROPTERA; MITOCHONDRIAL
   GENOME; SCHNEIDER NEUROPTERA; GENE ORGANIZATION; NATURAL ENEMIES;
   SUGARCANE APHID; FED PREY; PHYLOGENY; INSECTA
AB Simple Summary Neuropterida is a relatively primitive group of Holometabola including about 6500 extant species. Many species of this group are natural enemies and can prey on a variety of agricultural pests. This research analyzed the relevant literature in the core database of Web of Science by using CiteSpace, and then summarized the beginning and development of the research in the field of Neuropterida. The results showed that the United States and China had the most publications on the Neuropterida and were the main countries for research. These two countries have had the most productive authors and institutions. Representative institutions for research on natural enemies of Neuropterida included the China Agricultural University and the Chinese Academy of Agricultural Sciences. Due to the late start of Neuropterida research in China, the early research focused on practical applications. In addition, there is more cross-research in the fields of agriculture and biochemistry, molecular biology, chemistry, etc., indicating that the research of Neuropterida can be covered from the macroscopic study to the microscopic study. In addition, it was found that the early research focused on the biological control of Neuropterida by analyzing the keyword burst, whereas the current research has focused on the phylogeny of Neuropterida more. Neuropterida is a relatively primitive group of Holometabola. There are about 6500 extant species. Many species of this group are natural enemies and can prey on a variety of agricultural pests. In order to understand the leading research institutions, researchers and research contents, and to predict the future research directions of Neuropterida, the Web of Science core database, from January 1995 to September 2021, was searched with the theme of "Neuropterida or Neuroptera or Megaloptera or Raphidioptera or Lacewing". The results showed that the United States and China published relatively more publications than other countries. In addition, researchers from these two countries had more cooperation with other countries. China Agricultural University ranked the highest in the number of publications and centrality in this field. In addition, it was found that the early research focused on the biological control of Neuropterida by analyzing the keyword burst, whereas the more recent research focused on the phylogeny of Neuropterida. As the first representative chromosome-level genome of Neuropterida has been published, the future research of Neuropterida will focus on the genomic studies and molecular mechanisms of their morphological characters, behavior, historical evolution and so on.
C1 [Tian, Shuo; An, Yuxin; Zhang, Ruyue; Wang, Liming; Wang, Yuyu] Hebei Agr Univ, Coll Plant Protect, Baoding 071001, Peoples R China.
RP Wang, LM; Wang, YY (通讯作者)，Hebei Agr Univ, Coll Plant Protect, Baoding 071001, Peoples R China.
EM shuotian139@126.com; aaanyuxin@126.com; ruyue_zhang@126.com;
   wanglm1990@126.com; wangyy_amy@126.com
OI Wang, Yuyu/0000-0002-8645-303X; Wang, Liming/0000-0003-1606-4642
FU National Natural Science Foundation of China [31900341, 32170451]; Hebei
   Natural Science Foundation for Excellent Young Scholars [C2019204149];
   Hebei Youth Talent Support Program [BJ2019039]; Hebei Importing Overseas
   Students Support Program [C20190341]; Starting Scientific Research
   Foundation for the Introduced Talents of Hebei Agricultural University
   [YJ201817]
FX This work was supported by the National Natural Science Foundation of
   China (31900341, 32170451), the Hebei Natural Science Foundation for
   Excellent Young Scholars (C2019204149), the Hebei Youth Talent Support
   Program (BJ2019039), the Hebei Importing Overseas Students Support
   Program (C20190341) and the Starting Scientific Research Foundation for
   the Introduced Talents of Hebei Agricultural University (YJ201817).
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NR 72
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-4450
J9 INSECTS
JI Insects
PD MAY
PY 2022
VL 13
IS 5
AR 464
DI 10.3390/insects13050464
PG 20
WC Entomology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Entomology
GA 1O8RB
UT WOS:000801590900001
PM 35621799
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Afzal, M
   Hussain, M
   Hussain, J
   Bang, J
   Lee, S
AF Afzal, Muhammad
   Hussain, Maqbool
   Hussain, Jamil
   Bang, Jaehun
   Lee, Sungyoung
TI COVID-19 Knowledge Resource Categorization and Tracking: Conceptual
   Framework Study
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE information organization; resource management; knowledge graphs;
   interactive dashboard; dependency tracking; COVID-19; pandemic;
   information technology; tracing information; dashboards; digital health
AB Background: Since the declaration of COVID-19 as a global pandemic by the World Health Organization, the disease has gained momentum with every passing day. Various private and government sectors of different countries allocated funding for research in multiple capacities. A significant portion of efforts has been devoted to information technology and service infrastructure development, including research on developing intelligent models and techniques for alerts, monitoring, early diagnosis, prevention, and other relevant services. As a result, many information resources have been created globally and are available for use. However, a defined structure to organize these resources into categories based on the nature and origin of the data are lacking.
   Objective: This study aims to organize COVID-19 information resources into a well-defined structure to facilitate the easy identification of a resource, tracking information workflows, and to provide a guide for a contextual dashboard design and development.
   Methods: A sequence of action research was performed that involved a review of COVID-19 efforts and initiatives on a global scale during the year 2020. Data were collected according to the defined structure of primary, secondary, and tertiary categories. Various techniques for descriptive statistical analysis were employed to gain insights into the data to help develop a conceptual framework to organize resources and track interactions between different resources.
   Results: Investigating diverse information at the primary, secondary, and tertiary levels enabled us to develop a conceptual framework for COVID-19-related efforts and initiatives. The framework of resource categorization provides a gateway to access global initiatives with enriched metadata, and assists users in tracking the workflow of tertiary, secondary, and primary resources with relationships between various fragments of information. The results demonstrated mapping initiatives at the tertiary level to secondary level and then to the primary level to reach firsthand data, research, and trials.
   Conclusions: Adopting the proposed three-level structure allows for a consistent organization and management of existing COVID-19 knowledge resources and provides a roadmap for classifying future resources. This study is one of the earliest studies to introduce an infrastructure for locating and placing the right information at the right place. By implementing the proposed framework according to the stated guidelines, this study allows for the development of applications such as interactive dashboards to facilitate the contextual identification and tracking of interdependent COVID-19 knowledge resources.
C1 [Afzal, Muhammad; Hussain, Maqbool] Sejong Univ, Dept Software, 209 Neungdong Ro,Innovat Ctr Off 422, Seoul 05006, South Korea.
   [Hussain, Jamil] Sejong Univ, Dept Data Sci, Seoul, South Korea.
   [Bang, Jaehun; Lee, Sungyoung] Kyung Hee Univ, Dept Comp Sci & Engn, Yongin, South Korea.
RP Afzal, M (通讯作者)，Sejong Univ, Dept Software, 209 Neungdong Ro,Innovat Ctr Off 422, Seoul 05006, South Korea.
EM mafzal@sejong.ac.kr
RI Afzal, Muhammad/D-3741-2019
OI Afzal, Muhammad/0000-0002-7851-2327; HUSSAIN,
   MAQBOOL/0000-0002-1663-1718
FU Ministry of Science and ICT (MSIT), South Korea, through the Information
   Technology Research Center (ITRC) Support Program
   [IITP-2021-2017-0-01629]; Institute for Information & Communications
   Technology Promotion (IITP), through the Korea Government (MSIT)
   [2017-0-00655, IITP-2021-2020-0-01489, NRF-2019R1A2C2090504]
FX This work was supported in part by the Ministry of Science and ICT
   (MSIT), South Korea, through the Information Technology Research Center
   (ITRC) Support Program (IITP-2021-2017-0-01629), and in part by the
   Institute for Information & Communications Technology Promotion (IITP),
   through the Korea Government (MSIT) (2017-0-00655,
   IITP-2021-2020-0-01489, and NRF-2019R1A2C2090504).
CR Afzal M, M AFZAL COVID 19 RES
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NR 64
TC 0
Z9 0
U1 7
U2 13
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD JUN 1
PY 2021
VL 23
IS 6
AR e29730
DI 10.2196/29730
PG 22
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA SL5NR
UT WOS:000656965400003
PM 33999833
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Sun, ZH
   Lu, XD
   Duan, HL
   Li, HM
AF Sun, Zhaohong
   Lu, Xudong
   Duan, Huilong
   Li, Haomin
TI Deep Dynamic Patient Similarity Analysis: Model Development and
   Validation in ICU
SO COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
LA English
DT Article
DE Data Heterogeneity; Deep Learning; Dynamic Patient Similarity Analysis;
   Personalized Medicine; Sequential Complexity
AB Background: Personalized medicine requires the patient similarity analysis for providing specific treat-ments tailed for each patient. However, the patient similarity analysis in personalized clinical scenarios encounters challenges, which are twofold. First, heterogeneous and multi-type data are usually recorded to Electronic Health Records (EHRs) during the course of admissions, which makes it difficult to mea-sure the patient similarity. Second, disease progression manifests diverse disease states at different times, which brings sequential complexity to dynamically retrieve similar patients' sequences.Materials and methods: To overcome the above-mentioned challenges, we propose a novel dynamic pa-tient similarity analysis model based on deep learning. Specifically, the proposed model embeds the multi-type and heterogeneous data into hidden representations with a specially designed embedding and attention module. Thereafter, the proposed model retrieves similar patients' sequences based on these hidden representations in a dynamic manner. More importantly, we adopt two clinical tasks, i.e., diagno-sis prediction and medication recommendation, to validate the effectiveness of the proposed model. It is worth noticing that the proposed model integrates a drug-drug interaction (DDI) knowledge graph in the medication recommendation task to reduce adverse reactions caused by combinational treatments, such that a more rational strategy can be realized. We evaluate our proposed model using the critical care database MIMIC-III, which includes 5,430 patients covering 14,096 clinical visits.Results: The proposed model outperforms several state-of-the-art methods. For diagnosis prediction, the average PR-AUC score of the proposed model reaches 0.6200, which is significantly higher than that of the baseline models (0.2497 similar to 0.5407). Meanwhile, for medication recommendation, the average PR-AUC of the proposed model is 0.6682 (Jaccard: 0.4070; F1: 0.5672; Recall: 0.7832) whereas the K-nearest model can only reach 0.3805 (Jaccard: 0.3911; F1: 0.5465; Recall: 0.5705). In addition, our proposed model achieves a lower DDI rate.Conclusion: We propose a novel dynamic patient similarity analysis model, which can be implemented into a decision support system for clinical tasks including diagnosis prediction, surgical procedure se-lection, medication recommendation, etc. Also, the proposed model serves as an explainable protocol in clinical practice thanks to its analogy to real clinical reasoning where a doctor diagnoses diseases and prescribes medications according to the previous cured patients empirically.(c) 2022 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 [Sun, Zhaohong; Lu, Xudong; Duan, Huilong] Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Hangzhou 310027, Peoples R China.
   [Li, Haomin] Zhejiang Univ, Childrens Hosp, Sch Med, Hangzhou 310052, Peoples R China.
   [Li, Haomin] Zhejiang Univ, Sch Med, Hangzhou, Peoples R China.
RP Li, HM (通讯作者)，Zhejiang Univ, Sch Med, Hangzhou, Peoples R China.
EM zhaohongsun@zju.edu.cn; lvxd@zju.edu.cn; duanhl@zju.edu.cn;
   hmli@zju.edu.cn
OI Li, Haomin/0000-0002-6420-7719
FU National Natural Sci- ence Foundation of China [81871456]
FX This work was partially supported by the National Natural Sci- ence
   Foundation of China (81871456) .
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NR 31
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER IRELAND LTD
PI CLARE
PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND
SN 0169-2607
EI 1872-7565
J9 COMPUT METH PROG BIO
JI Comput. Meth. Programs Biomed.
PD OCT
PY 2022
VL 225
AR 107033
DI 10.1016/j.cmpb.2022.107033
PG 8
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods; Engineering, Biomedical; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Medical Informatics
GA 3R4VP
UT WOS:000838912700005
PM 35905698
OA hybrid
DA 2022-08-23
ER

PT J
AU Zhang, MY
AF Zhang, Maoyue
TI Educational Psychology Analysis Method for Extracting Students' Facial
   Information Based on Image Big Data
SO OCCUPATIONAL THERAPY INTERNATIONAL
LA English
DT Article
ID KNOWLEDGE GRAPH; ENGAGEMENT; SYSTEM; SIGNAL; RECOGNITION; PERSONALITY;
   SICKNESS; FORCE
AB At present, most of the research on academic emotions focuses on the concept, current situation, and relevance. There are not many researches on the application of artificial intelligence-based neural network facial expression recognition technology in practical teaching. With reference to image-based big data, this research integrates the application of artificial intelligence facial expression recognition technology with the research on educational theory and applies information technology to the actual teaching process, in order to promote the optimization of the teaching process and improve the learning effect. Method. A Hadoop cluster consisting of 3 nodes is built on the Linux system, and the environment required for Opencv execution is compiled for each node, which provides support for subsequent parallel optimization, feature extraction, feature fusion, and recognition of student facial images. The image data type and input and output format based on MapReduce framework are designed, and the image data is optimized by means of serialized files. The color features, texture features, and Sift features of students' facial images and common distractors were analyzed. A parallel extraction framework of student facial image features is designed, and based on this, the student facial image feature extraction under Hadoop platform is implemented. This paper proposes a dynamic sequential facial expression recognition method that combines shallow and deep features with an attention mechanism. The relative position of facial landmarks and local area texture features based on FACS represent shallow-level features. At the same time, the structure of ALexNet is improved to extract the deep features of sequence images to express high-level semantic features. The effectiveness of the facial expression recognition system is improved by introducing three attention mechanisms: self-attention, weight-attention, and convolutional attention. Results/Discussion. Through the analysis of the teaching effect, we found that when teachers can obtain the correct student's academic mood, they can intervene on the students' positive academic mood. The purpose of the intervention is to improve the positive academic emotions of students. After the students receive the intervention, their academic emotions are also improved and are positively correlated with their academic performance. Through the analysis of teaching effect, the research can achieve the predetermined goal. From the specific teaching effect of this study, it is concluded that in classroom teaching, teachers should devote energy to intervene in students' positive academic emotions, in order to improve students' positive academic emotions, which will improve students' academic performance and teaching.
C1 [Zhang, Maoyue] Tianjin Normal Univ, Sch Law, Tianjin 300387, Peoples R China.
RP Zhang, MY (通讯作者)，Tianjin Normal Univ, Sch Law, Tianjin 300387, Peoples R China.
EM maoyuezhang@tjnu.edu.cn
FU School of Law, Tianjin Normal University
FX This work was supported by School of Law, Tianjin Normal University.
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NR 49
TC 0
Z9 0
U1 5
U2 5
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 0966-7903
EI 1557-0703
J9 OCCUP THER INT
JI Occup. Ther. Int.
PD MAY 11
PY 2022
VL 2022
AR 8709591
DI 10.1155/2022/8709591
PG 11
WC Rehabilitation
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Rehabilitation
GA 1P8PM
UT WOS:000802265700001
PM 35645653
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhang, Y
   Wang, XW
   Hou, Z
   Li, J
AF Zhang, Yu
   Wang, Xuwen
   Hou, Zhen
   Li, Jiao
TI Clinical Named Entity Recognition From Chinese Electronic Health Records
   via Machine Learning Methods
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE clinical named entity recognition; machine learning; treatment;
   diagnosis; physical examination; human body; syndrome; electronic health
   records; bidirectional LSTM-CRF
ID TEXT
AB Background: Electronic health records (EHRs) are important data resources for clinical studies and applications. Physicians or clinicians describe patients' disorders or treatment procedures in EHRs using free text (unstructured) clinical notes. The narrative information plays an important role in patient treatment and clinical research. However, it is challenging to make machines understand the clinical narratives.
   Objective: This study aimed to automatically identify Chinese clinical entities from free text in EHRs and make machines semantically understand diagnoses, tests, body parts, symptoms, treatments, and so on.
   Methods: The dataset we used for this study is the benchmark dataset with human annotated Chinese EHRs, released by the China Conference on Knowledge Graph and Semantic Computing 2017 clinical named entity recognition challenge task. Overall, 2 machine learning models, the conditional random fields (CRF) method and bidirectional long short-term memory (LSTM)-CRF, were applied to recognize clinical entities from Chinese EHR data. To train the CRF-based model, we selected features such as bag of Chinese characters, part-of-speech tags, character types, and the position of characters. For the bidirectional LSTM-CRF-based model, character embeddings and segmentation information were used as features. In addition, we also employed a dictionary-based approach as the baseline for the purpose of performance evaluation. Precision, recall, and the harmonic average of precision and recall (F1 score) were used to evaluate the performance of the methods.
   Results: Experiments on the test set showed that our methods were able to automatically identify types of Chinese clinical entities such as diagnosis, test, symptom, body part, and treatment simultaneously. With regard to overall performance, CRF and bidirectional LSTM-CRF achieved a precision of 0.9203 and 0.9112, recall of 0.8709 and 0.8974, and F1 score of 0.8949 and 0.9043, respectively. The results also indicated that our methods performed well in recognizing each type of clinical entity, in which the "symptom" type achieved the best F1 score of over 0.96. Moreover, as the number of features increased, the F1 score of the CRF model increased from 0.8547 to 0.8949.
   Conclusions: In this study, we employed two computational methods to simultaneously identify types of Chinese clinical entities from free text in EHRs. With training, these methods can effectively identify various types of clinical entities (eg, symptom and treatment) with high accuracy. The deep learning model, bidirectional LSTM-CRF, can achieve better performance than the CRF model with little feature engineering. This study contributed to translating human-readable health information into machine-readable information.
C1 [Zhang, Yu; Wang, Xuwen; Hou, Zhen; Li, Jiao] Chinese Acad Med Sci, Peking Union Med Coll, Inst Med Informat & Lib, 3rd Yabao Rd, Beijing, Peoples R China.
RP Li, J (通讯作者)，Chinese Acad Med Sci, Peking Union Med Coll, Inst Med Informat & Lib, 3rd Yabao Rd, Beijing, Peoples R China.
EM li.jiao@imicams.ac.cn
OI Li, Jiao/0000-0001-6391-8343; Zhang, Yu/0000-0002-4277-3234
FU National Natural Science Foundation of China [81601573]; National Key
   Research and Development Program of China [2016YFC0901901]; National
   Population and Health Scientific Data Sharing Program of China;
   Knowledge Centre for Engineering Sciences and Technology (Medical
   Centre); Key Laboratory of Medical Information Intelligent Technology
   Chinese Academy of Medical Sciences; Key Laboratory of Knowledge
   Technology for Medical Integrative Publishing; Chinese Academy of
   Medical Sciences [2016RC330005]; Fundamental Research Funds for the
   Central Universities [3332018153]
FX The authors would like to thank the CCKS 2017 CNER challenge organizers
   for providing the training, development, and test corpora. The authors
   would also like to thank Kesheng Liu for the help with program
   development. This research is funded by the National Natural Science
   Foundation of China (Grant No. 81601573), the National Key Research and
   Development Program of China (Grant No. 2016YFC0901901), the National
   Population and Health Scientific Data Sharing Program of China, the
   National Population and Health Scientific Data Sharing Program of China,
   the Knowledge Centre for Engineering Sciences and Technology (Medical
   Centre), the Key Laboratory of Medical Information Intelligent
   Technology Chinese Academy of Medical Sciences, the Key Laboratory of
   Knowledge Technology for Medical Integrative Publishing, and the Chinese
   Academy of Medical Sciences (Grant No. 2016RC330005) and supported by
   the Fundamental Research Funds for the Central Universities (Grant No.
   3332018153).
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NR 32
TC 18
Z9 21
U1 2
U2 13
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 59 WINNERS CIRCLE, TORONTO, ON M4L 3Y7, CANADA
EI 2291-9694
J9 JMIR MED INF
JI JMIR Med. Inf.
PD OCT-DEC
PY 2018
VL 6
IS 4
BP 242
EP 254
AR e50
DI 10.2196/medinform.9965
PG 13
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA HF3VM
UT WOS:000454162600020
PM 30559093
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ernst, P
   Siu, A
   Weikum, G
AF Ernst, Patrick
   Siu, Amy
   Weikum, Gerhard
TI KnowLife: a versatile approach for constructing a large knowledge graph
   for biomedical sciences
SO BMC BIOINFORMATICS
LA English
DT Article
DE Biomedical text mining; Knowledge base; Relation extraction
ID EVENT EXTRACTION; DISAMBIGUATION; DOMAIN; BASE; RICH
AB Background: Biomedical knowledge bases (KB's) have become important assets in life sciences. Prior work on KB construction has three major limitations. First, most biomedical KBs are manually built and curated, and cannot keep up with the rate at which new findings are published. Second, for automatic information extraction (IE), the text genre of choice has been scientific publications, neglecting sources like health portals and online communities. Third, most prior work on IE has focused on the molecular level or chemogenomics only, like protein-protein interactions or gene-drug relationships, or solely address highly specific topics such as drug effects.
   Results: We address these three limitations by a versatile and scalable approach to automatic KB construction. Using a small number of seed facts for distant supervision of pattern-based extraction, we harvest a huge number of facts in an automated manner without requiring any explicit training. We extend previous techniques for pattern-based IE with confidence statistics, and we combine this recall-oriented stage with logical reasoning for consistency constraint checking to achieve high precision. To our knowledge, this is the first method that uses consistency checking for biomedical relations. Our approach can be easily extended to incorporate additional relations and constraints. We ran extensive experiments not only for scientific publications, but also for encyclopedic health portals and online communities, creating different KB's based on different configurations. We assess the size and quality of each KB, in terms of number of facts and precision. The best configured KB, KnowLife, contains more than 500,000 facts at a precision of 93% for 13 relations covering genes, organs, diseases, symptoms, treatments, as well as environmental and lifestyle risk factors.
   Conclusion: KnowLife is a large knowledge base for health and life sciences, automatically constructed from different Web sources. As a unique feature, KnowLife is harvested from different text genres such as scientific publications, health portals, and online communities. Thus, it has the potential to serve as one-stop portal for a wide range of relations and use cases. To showcase the breadth and usefulness, we make the KnowLife KB accessible through the health portal ( http://knowlife.mpi-inf.mpg.de).
C1 [Ernst, Patrick; Siu, Amy; Weikum, Gerhard] Max Planck Inst Informat, D-66123 Saarbrucken, Germany.
RP Ernst, P (通讯作者)，Max Planck Inst Informat, Campus E1 4, D-66123 Saarbrucken, Germany.
EM pernst@mpi-inf.mpg.de
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NR 54
TC 64
Z9 65
U1 4
U2 47
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1471-2105
J9 BMC BIOINFORMATICS
JI BMC Bioinformatics
PD MAY 14
PY 2015
VL 16
AR 157
DI 10.1186/s12859-015-0549-5
PG 13
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Mathematical & Computational Biology
GA CJ2BX
UT WOS:000355290300001
PM 25971816
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Dandala, B
   Joopudi, V
   Tsou, CH
   Liang, JJ
   Suryanarayanan, P
AF Dandala, Bharath
   Joopudi, Venkata
   Tsou, Ching-Huei
   Liang, Jennifer J.
   Suryanarayanan, Parthasarathy
TI Extraction of Information Related to Drug Safety Surveillance From
   Electronic Health Record Notes: Joint Modeling of Entities and Relations
   Using Knowledge-Aware Neural Attentive Models
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE electronic health records; adverse drug events; natural language
   processing; deep learning; information extraction; adverse drug reaction
   reporting systems; named entity recognition; relation extraction
ID EVENTS; PHARMACOVIGILANCE; CHALLENGES; COSTS
AB Background: An adverse drug event (ADE) is commonly defined as ' an injury resulting from medical intervention related to a drug." Providing information related to ADEs and alerting caregivers at the point of care can reduce the risk of prescription and diagnostic errors and improve health outcomes. ADEs captured in structured data in electronic health records (EHRs) as either coded problems or allergies are often incomplete, leading to underreporting. Therefore, it is important to develop capabilities to process unstructured EHR data in the form of clinical notes, which contain a richer documentation of a patient's ADE. Several natural language processing (NLP) systems have been proposed to automatically extract information related to ADEs. However, the results from these systems showed that significant improvement is still required for the automatic extraction of ADEs from clinical notes.
   Objective: This study aims to improve the automatic extraction of ADEs and related information such as drugs, their attributes, and reason for administration from the clinical notes of patients.
   Methods: This research was conducted using discharge summaries from the Medical Information Mart for Intensive Care III (MIMIC-III) database obtained through the 2018 National NLP Clinical Challenges (n2c2) annotated with drugs, drug attributes (ie, strength, form, frequency, route, dosage, duration), ADEs, reasons, and relations between drugs and other entities. We developed a deep learning-based system for extracting these drug-centric concepts and relations simultaneously using a joint method enhanced with contextualized embeddings, a position-attention mechanism, and knowledge representations. The joint method generated different sentence representations for each drug, which were then used to extract related concepts and relations simultaneously. Contextualized representations trained on the MIMIC-III database were used to capture context-sensitive meanings of words. The position-attention mechanism amplified the benefits of the joint method by generating sentence representations that capture long-distance relations. Knowledge representations were obtained from graph embeddings created using the US Food and Drug Administration Adverse Event Reporting System database to improve relation extraction, especially when contextual clues were insufficient.
   Results: Our system achieved new state-of-the-art results on the n2c2 data set, with significant improvements in recognizing crucial drug-reason (F1=0.650 versus F1=0.579) and drug-ADE (F1=0.490 versus F1=0.476) relations.
   Conclusions: This study presents a system for extracting drug-centric concepts and relations that outperformed current state-of-the-art results and shows that contextualized embeddings, position-attention mechanisms, and knowledge graph embeddings effectively improve deep learning-based concepts and relation extraction. This study demonstrates the potential for deep learning-based methods to help extract real-world evidence from unstructured patient data for drug safety surveillance.
C1 [Dandala, Bharath; Joopudi, Venkata; Tsou, Ching-Huei; Liang, Jennifer J.; Suryanarayanan, Parthasarathy] IBM Res, 1101 Kitchawan Rd, Yorktown Hts, NY 10598 USA.
RP Dandala, B (通讯作者)，IBM Res, 1101 Kitchawan Rd, Yorktown Hts, NY 10598 USA.
EM bdand@us.ibm.com
OI Joopudi, Venkata/0000-0001-8426-3219; Liang,
   Jennifer/0000-0002-5197-1590; Dandala, Bharath/0000-0002-9643-991X;
   Tsou, Ching-Huei/0000-0003-1273-5904; Suryanarayanan,
   Parthasarathy/0000-0002-8619-2976
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NR 87
TC 8
Z9 8
U1 4
U2 24
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
EI 2291-9694
J9 JMIR MED INF
JI JMIR Med. Inf.
PD JUL
PY 2020
VL 8
IS 7
AR e18417
DI 10.2196/18417
PG 19
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Medical Informatics
GA MX1AM
UT WOS:000557459300004
PM 32459650
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Chang, D
   Lin, E
   Brandt, C
   Taylor, RA
AF Chang, David
   Lin, Eric
   Brandt, Cynthia
   Taylor, Richard Andrew
TI Incorporating Domain Knowledge Into Language Models by Using Graph
   Convolutional Networks for Assessing Semantic Textual Similarity: Model
   Development and Performance Comparison
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE natural language processing; graph neural networks; National NLP
   Clinical Challenges; bidirectional encoder representation from
   transformers
ID UMLS
AB Background: Although electronic health record systems have facilitated clinical documentation in health care, they have also introduced new challenges, such as the proliferation of redundant information through the use of copy and paste commands or templates. One approach to trimming down bloated clinical documentation and improving clinical summarization is to identify highly similar text snippets with the goal of removing such text. Objective: We developed a natural language processing system for the task of assessing clinical semantic textual similarity. The system assigns scores to pairs of clinical text snippets based on their clinical semantic similarity. Methods: We leveraged recent advances in natural language processing and graph representation learning to create a model that combines linguistic and domain knowledge information from the MedSTS data set to assess clinical semantic textual similarity. We used bidirectional encoder representation from transformers (BERT)-based models as text encoders for the sentence pairs in the data set and graph convolutional networks (GCNs) as graph encoders for corresponding concept graphs that were constructed based on the sentences. We also explored techniques, including data augmentation, ensembling, and knowledge distillation, to improve the model's performance, as measured by the Pearson correlation coefficient (r). Results: Fine-tuning the BERT_base and ClinicalBERT models on the MedSTS data set provided a strong baseline (Pearson correlation coefficients: 0.842 and 0.848, respectively) compared to those of the previous year's submissions. Our data augmentation techniques yielded moderate gains in performance, and adding a GCN-based graph encoder to incorporate the concept graphs also boosted performance, especially when the node features were initialized with pretrained knowledge graph embeddings of the concepts (r=0.868). As expected, ensembling improved performance, and performing multisource ensembling by using different language model variants, conducting knowledge distillation with the multisource ensemble model, and taking a final ensemble of the distilled models further improved the system's performance (Pearson correlation coefficients: 0.875, 0.878, and 0.882, respectively). Conclusions: This study presents a system for the MedSTS clinical semantic textual similarity benchmark task, which was created by combining BERT-based text encoders and GCN-based graph encoders in order to incorporate domain knowledge into the natural language processing pipeline. We also experimented with other techniques involving data augmentation, pretrained concept embeddings, ensembling, and knowledge distillation to further increase our system's performance. Although the task and its benchmark data set are in the early stages of development, this study, as well as the results of the competition, demonstrates the potential of modern language model-based systems to detect redundant information in clinical notes.
C1 [Chang, David; Brandt, Cynthia; Taylor, Richard Andrew] Yale Univ, Yale Ctr Med Informat, Suite 501 300 George St, New Haven, CT 06511 USA.
   [Lin, Eric] Yale Univ, Sch Med, Dept Psychiat, New Haven, CT USA.
   [Brandt, Cynthia; Taylor, Richard Andrew] Yale Univ, Sch Med, Dept Emergency Med, New Haven, CT USA.
   [Brandt, Cynthia] Vet Affairs Connecticut Healthcare Syst, West Haven Campus, West Haven, CT USA.
RP Taylor, RA (通讯作者)，Yale Univ, Yale Ctr Med Informat, Suite 501 300 George St, New Haven, CT 06511 USA.
EM richard.taylor@yale.edu
OI brandt, cynthia/0000-0001-8179-1796; /0000-0002-9082-6644; Lin,
   Eric/0000-0002-1061-7704
FU National Institutes of Health Training Grant [5T15LM007056-33]; National
   Institute of Mental Health Training Grant [2R25MH071584-12]; Veterans
   Affairs Boston Medical Informatics Fellowship
FX This project was supported by the National Institutes of Health Training
   Grant (grant 5T15LM007056-33) , the National Institute of Mental Health
   Training Grant (grant 2R25MH071584-12) , and the Veterans Affairs Boston
   Medical Informatics Fellowship.
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NR 37
TC 0
Z9 0
U1 3
U2 5
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
EI 2291-9694
J9 JMIR MED INF
JI JMIR Med. Inf.
PD NOV
PY 2021
VL 9
IS 11
AR e23101
DI 10.2196/23101
PG 10
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA YA8MN
UT WOS:000738580700009
PM 34842531
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Khare, R
   Li, J
   Lu, ZY
AF Khare, Ritu
   Li, Jiao
   Lu, Zhiyong
TI LabeledIn: Cataloging labeled indications for human drugs
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Corpus annotation; Drug labels; Drug indications; Natural language
   processing
ID KNOWLEDGE; ERRORS
AB Drug-disease treatment relationships, i.e., which drug(s) are indicated to treat which disease(s), are among the most frequently sought information in PubMed (R). Such information is useful for feeding the Google Knowledge Graph, designing computational methods to predict novel drug indications, and validating clinical information in EMRs. Given the importance and utility of this information, there have been several efforts to create repositories of drugs and their indications. However, existing resources are incomplete. Furthermore, they neither label indications in a structured way nor differentiate them by drug-specific properties such as dosage form, and thus do not support computer processing or semantic interoperability. More recently, several studies have proposed automatic methods to extract structured indications from drug descriptions: however, their performance is limited by natural language challenges in disease named entity recognition and indication selection.
   In response, we report Labeledln: a human-reviewed, machine-readable and source-linked catalog of labeled indications for human drugs. More specifically, we describe our semi-automatic approach to derive Labeledln from drug descriptions through human annotations with aids from automatic methods. As the data source, we use the drug labels (or package inserts) submitted to the FDA by drug manufacturers and made available in DailyMed. Our machine-assisted human annotation workflow comprises: (i) a grouping method to remove redundancy and identify representative drug labels to be used for human annotation, (ii) an automatic method to recognize and normalize mentions of diseases in drug labels as candidate indications, and (iii) a two-round annotation workflow for human experts to judge the precomputed candidates and deliver the final gold standard.
   In this study, we focused on 250 highly accessed drugs in PubMed Health, a newly developed public web resource for consumers and clinicians on prevention and treatment of diseases. These 250 drugs corresponded to more than 8000 drug labels (500 unique) in DailyMed in which 2950 candidate indications were pre-tagged by an automatic tool. After being reviewed independently by two experts, 1618 indications were selected, and additional 97 (missed by computer) were manually added, with an inter-annotator agreement of 88.35% as measured by the Kappa coefficient. Our final annotation results in Labeledln consist of 7805 drug-disease treatment relationships where drugs are represented as a triplet of ingredient, dose form, and strength.
   A systematic comparison of Labeledln with an existing computer-derived resource revealed significant discrepancies, confirming the need to involve humans in the creation of such a resource. In addition, Labeledln is unique in that it contains detailed textual context of the selected indications in drug labels, making it suitable for the development of advanced computational methods for the automatic extraction of indications from free text. Finally, motivated by the studies on drug nomenclature and medication errors in EMRs, we adopted a fine-grained drug representation scheme, which enables the automatic identification of drugs with indications specific to certain dose forms or strengths. Future work includes expanding our coverage to more drugs and integration with other resources.
   The Labeledln dataset and the annotation guidelines are available at http://ftp.ncbi.nlm.nih.gov/pub/lu/LabeledIn/. Published by Elsevier Inc.
C1 [Khare, Ritu; Lu, Zhiyong] US Natl Inst Hlth, Natl Ctr Biotechnol Informat NCBI, Bethesda, MD 20892 USA.
   [Li, Jiao] Chinese Acad Med Sci, Inst Med Informat, Beijing 100730, Peoples R China.
RP Lu, ZY (通讯作者)，US Natl Inst Hlth, Natl Ctr Biotechnol Informat NCBI, 8600 Rockville Pike, Bethesda, MD 20892 USA.
EM ritu.khare@nih.gov; li.jiao@imicams.ac.cn; zhiyong.lu@nih.gov
RI Lu, Zhiyong/H-3622-2016
OI Lu, Zhiyong/0000-0001-9998-916X; Li, Jiao/0000-0001-6391-8343
FU Intramural Research Program of the NIH - National Library of Medicine;
   National Key Technology R&D Program of China [2013BA106B01]; Fundamental
   Research Funds for the Central Universities [13R0101]; NATIONAL LIBRARY
   OF MEDICINE [ZIALM091813, ZIHLM200888] Funding Source: NIH RePORTER
FX This research was supported by the Intramural Research Program of the
   NIH - National Library of Medicine, the National Key Technology R&D
   Program of China (Grant No. 2013BA106B01), and the Fundamental Research
   Funds for the Central Universities (No. 13R0101).
CR Administration USFaD, DRUG APPR DAT DRUGS
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NR 39
TC 28
Z9 31
U1 1
U2 15
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD DEC
PY 2014
VL 52
BP 448
EP 456
DI 10.1016/j.jbi.2014.08.004
PG 9
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA AY0HC
UT WOS:000347277000045
PM 25220766
OA Green Accepted, hybrid
DA 2022-08-23
ER

PT J
AU Zhu, L
   Liu, XT
   He, S
   Shi, J
   Pang, M
AF Zhu, Lin
   Liu, Xiantao
   He, Sha
   Shi, Jun
   Pang, Ming
TI Keywords co-occurrence mapping knowledge domain research base on the
   theory of Big Data in oil and gas industry
SO SCIENTOMETRICS
LA English
DT Article
DE Oil and gas industry; Keyword co-occurrence; Mapping knowledge domain;
   Big Data; Data mining
ID VISUALIZATION
AB Taking the theses' keywords in China from 1986 to 2014 as the research materials, use the basis concept of the Big Data Theory to further study the keywords which related to oil and gas industry. Analyze the keywords frequency of the theses in oil and gas industry and its co-occurrence frequency pair, and then use the theory of mapping knowledge domain to visualize the keywords co-occurrence network in petroleum industry so as to make further research of the heated issues that mapping knowledge domain has shown. According to the research we can see that the application technology R&D (research and development) predominate the oil and gas industry, featuring a high concentration and long tail phenomenon (which means various researches focus on different kinds of things, the scale of the research is large).
C1 [Zhu, Lin; Liu, Xiantao; He, Sha; Shi, Jun; Pang, Ming] Southwest Petr Univ, Sch Econ & Management Sci, Chengdu, Peoples R China.
RP Zhu, L (通讯作者)，Southwest Petr Univ, Sch Econ & Management Sci, Chengdu, Peoples R China.
EM messagescn@gmail.com
OI zhu, lin/0000-0002-5454-8544
CR Bertschi S, 2011, IEEE INT CONF INF VI, P329, DOI 10.1109/IV.2011.58
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NR 25
TC 17
Z9 17
U1 6
U2 126
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD OCT
PY 2015
VL 105
IS 1
BP 249
EP 260
DI 10.1007/s11192-015-1658-7
PG 12
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA CS3RP
UT WOS:000361992800016
DA 2022-08-23
ER

PT J
AU Shi, JG
   Miao, W
   Si, HY
AF Shi, Jian-Gang
   Miao, Wei
   Si, Hongyun
TI Visualization and Analysis of Mapping Knowledge Domain of Urban Vitality
   Research
SO SUSTAINABILITY
LA English
DT Article
DE urban vitality; bibliometric analysis; VOSviewer; visualization; mapping
   knowledge domain
ID PHYSICAL-ACTIVITY; BIBLIOMETRIC ANALYSIS; BUILT ENVIRONMENT; LAND-USE;
   GROWTH; CITIES; SPACE; NEIGHBORHOODS; RENAISSANCE; WALKABILITY
AB Mapping knowledge domain (MKD) is an important research technology in bibliometrics. It provides a visual perspective for researchers and helps researchers to clearly understand the general situations of specific research fields. Presently, MKD has been widely used in fields such as ecology and transportation safety. In this paper, we conduct a systematically bibliometric analysis of the development trend on urban vitality research by using VOSviewer software, and this work is based on the related articles published in the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) between 1998 and 2018. In the implementation, we firstly introduce the annual numbers of articles, source journals, highly cited papers, and production countries relating to urban vitality research. Then, we adopt a keywords co-occurrence analysis to explore the topic distribution, research frontiers, and research trends of urban vitality research intuitively. Overall, the Mapped Knowledge Domain method has proved to be an effective way to assess research trends in specific disciplines and to contribute to researchers and governments with respect to management and decision-making on issues of science.
C1 [Shi, Jian-Gang; Miao, Wei; Si, Hongyun] Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China.
RP Miao, W (通讯作者)，Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China.
EM sjg126com@126.com; miaowei@tongji.edu.cn; sihongyun@tongji.edu.cn
RI Si, Hongyun/AAY-2908-2021
OI si, hong yun/0000-0003-2367-4756
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NR 106
TC 14
Z9 15
U1 13
U2 60
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB 2
PY 2019
VL 11
IS 4
AR 988
DI 10.3390/su11040988
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 HO3JV
UT WOS:000460819100048
OA Green Submitted, gold, Green Published
DA 2022-08-23
ER

PT J
AU Qin, Y
   Zhang, Q
   Liu, YR
AF Qin, Yi
   Zhang, Qing
   Liu, Yaru
TI Analysis of knowledge bases and research focuses of cerebral
   ischemia-reperfusion from the perspective of mapping knowledge domain
SO BRAIN RESEARCH BULLETIN
LA English
DT Review
DE Cerebral ischemia-reperfusion; Bibliometrics; Mapping knowledge domain;
   Visualization; VOSviewer; CiteSpace
ID NEURONAL DAMAGE; OXIDATIVE STRESS; PYRAMIDAL NEURONS; RAT-BRAIN; INJURY;
   HIPPOCAMPUS; PRETREATMENT; PROTECTS; GLIOSIS; IMMUNOREACTIVITY
AB Cerebral ischemia-reperfusion (IR) has attracted wide attention as a serious clinical problem. So far, the field has accumulated a large amount of scientific research literature. To clarify the temporal and spatial distribution characteristics of research resources, knowledge bases and research focuses, a visual analysis was performed on 5814 articles cited in the WoS databases from 2004 to 2019. This analysis was based on bibliometrics and mapping knowledge domain (MKD) analysis with VOSviewer, and CiteSpace 5.4.R4. The results can be elaborated from four aspects. First, the volume of publications in this area is on the rise. Second, the United States and China are the active regions. The USA is the central region of cerebral ischemia-reperfusion research. Third, the knowledge bases of IR have focused on five major areas of "Suitable small-animal models", "A framework with further study", "Molecular signaling targets by oxidative stress", "Finding new potential targets for therapy" and "Protective effect of multiple transient ischemia". Fourth, the research focuses consist of three representative areas: "Oxidative stress closelyd with cerebral ischemia-reperfusion", "Neuronal apoptosis and neuronal protection", and "Neuroprotective effect of the blood-brain barrier".
C1 [Qin, Yi; Liu, Yaru] Beijing Univ Chinese Med, Sch Acupuncture Moxibust & Tuina, Beijing 100029, Peoples R China.
   [Qin, Yi] China Acad Chinese Med Sci, Xiyuan Hosp, Beijing 100091, Peoples R China.
   [Zhang, Qing] Beijing Univ Chinese Med, Hosp 4, Zaozhuang 277000, Shandong, Peoples R China.
RP Liu, YR (通讯作者)，Beijing Univ Chinese Med, Sch Acupuncture Moxibust & Tuina, Beijing 100029, Peoples R China.
EM liuyaru@bucm.edu.cn
FU Beijing University of Chinese Medicine New Teacher Startup Fund
   [2019-JYB-XJSJJ014]
FX This work was supported by the Beijing University of Chinese Medicine
   New Teacher Startup Fund (No. 2019-JYB-XJSJJ014).
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NR 64
TC 22
Z9 22
U1 11
U2 34
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0361-9230
EI 1873-2747
J9 BRAIN RES BULL
JI Brain Res. Bull.
PD MAR
PY 2020
VL 156
BP 15
EP 24
DI 10.1016/j.brainresbull.2019.12.004
PG 10
WC Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA KO0AE
UT WOS:000515207600003
PM 31843561
OA hybrid
DA 2022-08-23
ER

PT J
AU He, Y
   Yang, S
   Chan, CY
   Chen, L
   Wu, CZ
AF He, Yi
   Yang, Shuo
   Chan, Ching-Yao
   Chen, Long
   Wu, Chaozhong
TI Visualization Analysis of Intelligent Vehicles Research Field Based on
   Mapping Knowledge Domain
SO IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
LA English
DT Article
DE Intelligent vehicles; Bibliometrics; Safety; Visualization; Data
   visualization; CiteSpace; intelligent vehicles; mapping knowledge
   domain; visualization analysis; VOSviewer
ID ADAPTIVE CRUISE CONTROL; AUTONOMOUS VEHICLES; CONNECTED VEHICLE;
   TRACKING; ROAD; PREFERENCES; TECHNOLOGY; INTERNET; BEHAVIOR; SYSTEMS
AB This study combines applied mathematics, visual analysis technology, information science with an approach of Scientometrics to systematically analyze the development status, research distribution and future trend of intelligent vehicles research. A total number of 3933 published paper index by SCIE and SSCI from 2000 to 2019 are researched based on Mapping Knowledge Domain (MKD) and Scientometrics approaches. Firstly, this paper analyzes the literature content in the field of intelligent vehicles by including the literature number, literature productive countries, research organization, co-authorship of main research groups and the journals from which the articles are mainly sourced. Then, co-citation analysis is used to obtain five major research directions in the field of intelligent vehicles, which include "system framework", "internet of vehicles", "intersection control algorithms", "influence on traffic flow", and "policies and barriers", respectively. The keyword co-occurrence analysis is applied to identify four dominant clusters: "planning and control system", "autonomous vehicle questionnaire", "sensor and vision", and "connected vehicles". Finally, we divide burst keywords into three phases according to the publication date to show more clearly the change of research focus and direction over time.
C1 [He, Yi; Yang, Shuo; Wu, Chaozhong] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China.
   [He, Yi; Yang, Shuo; Wu, Chaozhong] Wuhan Univ Technol, Engn Res Ctr Transportat Safety, Minist Educ, Wuhan 430063, Peoples R China.
   [He, Yi] Natl Engn Lab Transportat Safety & Emergency Info, Beijing 100011, Peoples R China.
   [Yang, Shuo] CATARC Automot Informat Consulting Co Ltd, Tianjin 300300, Peoples R China.
   [Chan, Ching-Yao] Univ Calif Berkeley, Calif PATH, Berkeley, CA 94804 USA.
   [Chen, Long] Sun Yat Sen Univ, Sch Elect & Informat Technol, Zhuhai 519082, Peoples R China.
RP Chen, L (通讯作者)，Sun Yat Sen Univ, Sch Elect & Informat Technol, Zhuhai 519082, Peoples R China.
EM chenl46@mail.sysu.edu.cn
FU National Key Research and Development Project [2019YFB1600800]; National
   Natural Science Foundation of China [51605350, 51775396, U1764262]; Key
   Consulting Project of Chinese Academy of Engineering [2018-XZ-010];
   National Engineering Laboratory for Transportation Safety and Emergency
   Informatics, China [YW170301-09]; Wuhan Science and Technology Bureau
   Project [2019010702011301]
FX This work was supported in part by the National Key Research and
   Development Project under Grant 2019YFB1600800, in part by the National
   Natural Science Foundation of China under Grant 51605350, Grant
   51775396, and Grant U1764262, in part by the Key Consulting Project of
   Chinese Academy of Engineering under Grant 2018-XZ-010, in part by the
   National Engineering Laboratory for Transportation Safety and Emergency
   Informatics, China, under Grant YW170301-09, and in part by the Wuhan
   Science and Technology Bureau Project under Grant 2019010702011301. The
   Associate Editor for this article was L. Li.
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NR 54
TC 16
Z9 16
U1 21
U2 31
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1524-9050
EI 1558-0016
J9 IEEE T INTELL TRANSP
JI IEEE Trans. Intell. Transp. Syst.
PD SEP
PY 2021
VL 22
IS 9
BP 5721
EP 5736
DI 10.1109/TITS.2020.2991642
PG 16
WC Engineering, Civil; Engineering, Electrical & Electronic; Transportation
   Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Transportation
GA UK8IS
UT WOS:000692209100026
DA 2022-08-23
ER

PT J
AU Chen, C
   Ping, S
   Zhang, XM
   Yi, YN
AF Chen, Chen
   Ping, Shuo
   Zhang, Xinmei
   Yi, Yanan
TI Transfer study of safety training based on mapping knowledge
   domain-Overview, factors and future
SO SAFETY SCIENCE
LA English
DT Article
DE Safety training; Training transfer; Bibliometric method; Mapping
   knowledge domain
ID MANAGEMENT-SYSTEMS; SELF-EFFICACY; MODEL; CLIMATE; QUALITY; ROLES; JOB
AB Safety training is an important means to reduce unsafe behaviors and improve safety management efficiency. However, the contradiction between "high frequency and wide coverage" and "low transfer efficiency" in safety training at this stage is prominent. To explore the factors that affect the transfer of safety training, this study selects three academic literature groups, training transfer, safety training, and safety training transfer, and introduces the bibliometric method and the mapping knowledge domain to analyze the co-occurrence of the subject keywords of each literature group, evolution process, and knowledge structure. When these results are combined with the characteristics of safety training and based on the classic training transfer theory and safety training theory, it can be seen that the characteristics of the trainees, the safety training design, and the working environment affect the safety training transfer. Based on the functions and characteristics of safety training, it is shown that the driving mechanism between the influencing factors of safety transfer and the multiangle systematic improvement of safety training transfer long-term mechanism will become two important directions for improving the efficiency of safety training transfer in the future.
C1 [Chen, Chen; Ping, Shuo] China Univ Petr, Sch Econ & Management, 66 Changjiang West Rd, Qingdao 266580, Shandong, Peoples R China.
   [Zhang, Xinmei; Yi, Yanan] China Univ Petr, Coll Mech & Elect Engn, 66 Changjiang West Rd, Qingdao 266580, Shandong, Peoples R China.
RP Zhang, XM (通讯作者)，China Univ Petr, Coll Mech & Elect Engn, 66 Changjiang West Rd, Qingdao 266580, Shandong, Peoples R China.
EM chenchen@upc.edu.cn; s19080016@s.upc.edu.cn; zhangxm@upc.edu.cn;
   z19040013@s.upc.edu.cn
FU Natural Science Foundation of Shandong Province [ZR2019MG023,
   ZR2020MB126]; People's Livelihood Science and Technology Project of
   Qingdao [19-6-1-84-nsh]; Fundamental Research Funds for the Central
   Universities [18CX04012B]; National Natural Science Foundation of China
   [71403293, 51104174]
FX The paper was supported by Natural Science Foundation of Shandong
   Province (ZR2019MG023, ZR2020MB126), People's Livelihood Science and
   Technology Project of Qingdao (19-6-1-84-nsh), the Fundamental Research
   Funds for the Central Universities (18CX04012B), National Natural
   Science Foundation of China (71403293, 51104174).
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NR 66
TC 2
Z9 2
U1 9
U2 9
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-7535
EI 1879-1042
J9 SAFETY SCI
JI Saf. Sci.
PD APR
PY 2022
VL 148
AR 105678
DI 10.1016/j.ssci.2022.105678
PG 16
WC Engineering, Industrial; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Operations Research & Management Science
GA 0I4SH
UT WOS:000779410600006
DA 2022-08-23
ER

PT J
AU Bai, W
   Yan, L
   Liang, JB
   Zhang, L
AF Bai, Wuliyasu
   Yan, Liang
   Liang, Jingbo
   Zhang, Long
TI Mapping Knowledge Domain on Economic Growth and Water Sustainability: A
   Scientometric Analysis
SO WATER RESOURCES MANAGEMENT
LA English
DT Article; Early Access
DE Water sustainability; Economic growth; Mapping Knowledge Domain; Cluster
   analysis
ID ENVIRONMENTAL KUZNETS CURVE; RESOURCES MANAGEMENT; DEVELOPMENT GOALS;
   ENERGY; FOOD; CHALLENGES; FUTURE; SIMULATION; INDICATORS; NEXUS
AB With the extensive growth of economy, the water system of the globe is under unprecedented pressure. So, the relationship between economic growth and water sustainability has been a common concern for the academia. The purpose of this paper is to use the bibliometric method to sort out the research progresses on economic growth and water sustainability during the past 30 years. More importantly, it tries to recognize the research hotspots and future trends in this field, to provide valuable directions for later research. A systemic review and bibliometric analysis are conducted by retrieving 1287 papers on water sustainability and economic growth. The results indicate that the number of articles related to the field of economic growth and water sustainability has increased steadily. The 1287 articles are published in 581 different journals, where the top 20 publishing journals have published 31.31% of them, and Sustainability and Journal of Cleaner Production have published the most retrieved articles. By introducing the Mapping Knowledge Domain with the combination of the software of COOC and VOSviewer, five clusters of research hotspots are summarized: economic growth and water consumption; water-energy-food-economy nexus and circular economy; climate change, water scarcity and water quality; water resources management and water footprint; wastewater management and valorization.
C1 [Bai, Wuliyasu; Yan, Liang] China Univ Geosci, Sch Econ & Management, Wuhan 430078, Peoples R China.
   [Liang, Jingbo; Zhang, Long] Xinyang Normal Univ, Sch Business, Xinyang 464000, Peoples R China.
RP Yan, L (通讯作者)，China Univ Geosci, Sch Econ & Management, Wuhan 430078, Peoples R China.; Zhang, L (通讯作者)，Xinyang Normal Univ, Sch Business, Xinyang 464000, Peoples R China.
EM ylyzb@cug.edu.cn; hbzhanglong876@163.com
FU Humanities and Social Sciences Planning Fund of Ministry of Education in
   China [19YJAZH050]; Philosophy and Social Science Planning Program in
   Henan Province, China [2020CJJ094]; Key R&D and Promotion Project (Soft
   Science Research) in Henan Province, China [222400410122]; Nanhu
   Scholars Program for Young Scholars of XYNU, China
FX This work was supported by the Humanities and Social Sciences Planning
   Fund of Ministry of Education in China (19YJAZH050), the Philosophy and
   Social Science Planning Program in Henan Province, China (2020CJJ094),
   the Key R&D and Promotion Project (Soft Science Research) in Henan
   Province, China (222400410122), and the Nanhu Scholars Program for Young
   Scholars of XYNU, China.
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NR 107
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-4741
EI 1573-1650
J9 WATER RESOUR MANAG
JI Water Resour. Manag.
DI 10.1007/s11269-022-03245-7
EA AUG 2022
PG 23
WC Engineering, Civil; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Water Resources
GA 3L4KP
UT WOS:000834731600002
DA 2022-08-23
ER

PT J
AU Li, XW
   Du, JG
   Long, HY
AF Li, Xingwei
   Du, Jianguo
   Long, Hongyu
TI Dynamic analysis of international green behavior from the perspective of
   the mapping knowledge domain
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article
DE Mapping knowledge domain; Green behavior; CiteSpace; Bibliometrics;
   Visualization
ID SUSTAINABLE CONSUMPTION; CONSUMER-BEHAVIOR; PURCHASE; MODEL; SKEPTICISM;
   DIFFUSION; IMPACT; BELIEF
AB Green behavior is an important part of the study of green development. To promote green development, it is necessary to engage in further innovation on the basis of lessons from international advancements in green behavior. To clarify the spatial and temporal distribution characteristics of international researchers in the study of green behavior, the research focus, and the research frontiers, a visual analysis was performed on 22,114 articles cited in the SSCI and SCI-EXPANDED databases from 2000 to 2017. This analysis was based on bibliometrics and mapping knowledge domain (MKD) analysis with CiteSpace 5.3.R4, a scientific and technological text-mining and visualization software. The results show that international green behavior research has the following main characteristics. First, a positive correlation was found between the number of papers and time. Second, research is highly valued by researchers in the natural sciences, such as ecology. The USA is involved in major research efforts and maintains a high level of cooperation with other countries and regions. International cooperation among institutions needs to be strengthened. The co-cited journals are primarily academic journals in the natural sciences. Third, research hotspots mainly focus on the theory of planned behavior (TPB), consumers, attitudes, performance, and environment, all of which evolve according to the phase path of germination-growth. Fourth, the research frontier issues are largely related to management science and environmental science, and the research objects and methods will become increasingly complex and interdisciplinary over time.
C1 [Li, Xingwei; Du, Jianguo] Jiangsu Univ, Sch Management, Zhenjiang 212013, Jiangsu, Peoples R China.
   [Long, Hongyu] Southwest Petr Univ, Sch Civil Engn & Architecture, Chengdu 610500, Sichuan, Peoples R China.
RP Du, JG (通讯作者)，Jiangsu Univ, Sch Management, Zhenjiang 212013, Jiangsu, Peoples R China.
EM djg@ujs.edu.cn
RI Long, Hongyu/V-5811-2019; Li, Xingwei/M-8450-2018; Du,
   Jianguo/V-5788-2019
OI Long, Hongyu/0000-0003-3826-619X; Li, Xingwei/0000-0002-8853-356X; Du,
   Jianguo/0000-0001-9628-7496
FU Special Funds of the National Social Science Fund of China [18VSJ038]
FX This work was also supported by the Special Funds of the National Social
   Science Fund of China (18VSJ038).
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NR 54
TC 42
Z9 42
U1 18
U2 129
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 FEB
PY 2019
VL 26
IS 6
BP 6087
EP 6098
DI 10.1007/s11356-018-04090-1
PG 12
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA HO3OA
UT WOS:000460831600074
PM 30617886
DA 2022-08-23
ER

PT J
AU Guo, XR
   Li, X
   Guo, YM
AF Guo, Xing-Rong
   Li, Xiang
   Guo, Yi-Ming
TI Mapping Knowledge Domain Analysis in Smart Education Research
SO SUSTAINABILITY
LA English
DT Article
DE smart education; mapping knowledge domain; bibliometric analysis;
   VOSviewer; CiteSpace
ID BIBLIOMETRIC ANALYSIS; TECHNOLOGY; CITIES; TRENDS
AB Smart education is considered an inevitable choice and an important educational trend development in the Information Age. Smart education development can promote learning opportunities to master high-quality education and lifelong learning. It vigorously promotes sustainable development. A bibliometric analysis was performed to better understand smart education research field knowledge structures and help researchers understand smart education research field characteristics. VOSviewer and CiteSpace were used to analyze 2358 Web of Science core collection articles related to smart education. The articles were dated 2000-2021. This study visually presents a systematic overview of smart education literature research, including the publication outputs, main categories, most influential countries, organizations, journals, and authors, important documents, and research academic trends. Using a series of cooperation analyses, this paper looked at research cooperation among countries and regions, organizations, and authors to build a smart education knowledge structure map. A smart education theoretical framework was proposed based on the literature review. A bibliometric analysis of the existing research results provided useful and innovative suggestions for researchers and practitioners in the field of smart education.
C1 [Guo, Xing-Rong; Li, Xiang] Shanghai Maritime Univ, Coll Foreign Languages, Shanghai 201306, Peoples R China.
   [Guo, Yi-Ming] Shanghai Maritime Univ, Sch Econ & Management, Shanghai 201306, Peoples R China.
RP Guo, XR (通讯作者)，Shanghai Maritime Univ, Coll Foreign Languages, Shanghai 201306, Peoples R China.
EM ymguo@shmtu.edu.cn
OI guo, xingrong/0000-0001-8672-2108
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NR 63
TC 5
Z9 5
U1 37
U2 51
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD DEC
PY 2021
VL 13
IS 23
AR 13234
DI 10.3390/su132313234
PG 28
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA XU9QQ
UT WOS:000734590500001
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Liu, H
   Chen, HL
   Hong, R
   Liu, HG
   You, WJ
AF Liu, Hui
   Chen, Hailun
   Hong, Rui
   Liu, Huiguang
   You, Wenjiao
TI Mapping knowledge structure and research trends of emergency evacuation
   studies
SO SAFETY SCIENCE
LA English
DT Article
DE Emergency evacuation; Bibliometrics; Visualization; Mapping knowledge
   domain; Evolutionary trend
ID CELLULAR-AUTOMATON MODEL; BIBLIOMETRIC ANALYSIS; NATURAL DISASTERS;
   CROWD EVACUATION; SIMULATION; NETWORK; BEHAVIORS; CHOICE; SAFETY
AB To understand the current development and status of research on emergency evacuation, the published literature in the Web of Science core database from 2008 to 2018 were taken as the research objects, and the knowledge structure and research trends in the field of emergency evacuation were visualized and analyzed by using the scientific mapping knowledge domain. It is found that research on emergency evacuations throughout the world has a basic theoretical framework, but the various research branches have not yet formed a perfect knowledge network. The results show that China's publishing volume ranks first among countries/regions, and Tsinghua University has the largest number of publications among related institutions; traffic evacuation modeling, group behavior and crowd evacuation are the main knowledge groups for emergency evacuation research; the research fields mainly include disaster emergency management, personnel evacuation simulation and emergency traffic evacuation; and the research topic of emergency evacuation mainly evolves into three core paths, namely, "disaster - management - preparation - response - information", "evacuation - behavior - model - cellular automaton - time - design" and "evacuation - behavior - simulation - social force model - exit choice". Crowd evacuation and exit choice have become the frontiers of current emergency evacuation research.
C1 [Liu, Hui; Chen, Hailun; Hong, Rui; You, Wenjiao] China Jiliang Univ, Coll Qual & Safety Engn, 258 Xueyuan St, Hangzhou, Zhejiang, Peoples R China.
   [Liu, Huiguang] HangZhou Econ & Technol Dev Area, Construct Qual & Safety Supervis Stn, Hangzhou, Zhejiang, Peoples R China.
RP Liu, H (通讯作者)，China Jiliang Univ, Coll Qual & Safety Engn, 258 Xueyuan St, Hangzhou, Zhejiang, Peoples R China.
EM liuhui2003@126.com
RI liu, hui/K-2142-2015
OI liu, hui/0000-0002-4265-7241
FU National Key R&D Program of China [2017YFC0804900]; Zhejiang Provincial
   Natural Science Foundation of China [LY14E040001]
FX This work was supported in part by the National Key R&D Program of China
   (No. 2017YFC0804900), the Zhejiang Provincial Natural Science Foundation
   of China (No. LY14E040001).
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Z9 36
U1 22
U2 71
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-7535
EI 1879-1042
J9 SAFETY SCI
JI Saf. Sci.
PD JAN
PY 2020
VL 121
BP 348
EP 361
DI 10.1016/j.ssci.2019.09.020
PG 14
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 JN0OM
UT WOS:000496603900029
DA 2022-08-23
ER

PT J
AU He, Y
   Feng, Q
   Yan, LX
   Lu, XY
AF He, Yi
   Feng, Qi
   Yan, Lixin
   Lu, Xiao-Yun
TI Visualization and Analysis of Mapping Knowledge Domain of Heterogeneous
   Traffic Flow
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID ADAPTIVE CRUISE CONTROL; AUTONOMOUS VEHICLES; MODEL; IMPACT; STABILITY;
   COCITATION; POLICY
AB Mapping knowledge domain (MKD) is an important application in bibliometrics, which is a method of visually presenting and explaining newly developed interdisciplinary scientific fields using data mining, information analysis, scientific measurement, and graphic rendering. This study combines applied mathematics, visual analysis technology, information science, and scientometrics to systematically analyze the development status, research distribution, and future trend of the heterogeneous traffic flow by using the MKD software tools VOSviewer and CiteSpace. Based on the MKD and Bibliometrics approaches, 4709 articles have been studied, which were published by Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) from 2004 to 2021 in the field of heterogeneous traffic flows. Firstly, this paper presents the annual numbers of articles, origin countries, main research organizations, and groups as well as the source journals on heterogeneous traffic flow studies. Then, cocitation analysis is used to divide heterogeneous traffic flow into three main research directions, which include "heterogeneous traffic flow model," "traffic flow capacity analysis," and "traffic flow stability analysis." The keyword cooccurrence analysis is applied to identify five dominant clusters: "modeling and optimization methods," "traffic flow characteristics analysis," "driving behavior analysis," "simulation experiment," and "policies and barriers." Finally, burst keywords were studied according to the publication date to present more clearly the change of research focus and direction over time.
C1 [He, Yi; Feng, Qi] Wuhan Univ Technol, Intelligent Transport Syst Res Ctr, Wuhan 430063, Peoples R China.
   [Yan, Lixin] East China Jiaotong Univ, Sch Transportat & Logist, Nanchang 430063, Peoples R China.
   [Lu, Xiao-Yun] Univ Calif Berkeley, Calif PATH, Berkeley, CA 94804 USA.
RP Yan, LX (通讯作者)，East China Jiaotong Univ, Sch Transportat & Logist, Nanchang 430063, Peoples R China.
EM heyi@whut.edu.cn; 261527@whut.edu.cn; yanlixinits@163.com;
   xylu@path.berkeley.edu
OI HE, Yi/0000-0002-3005-6772
FU National Key Research and Development Project [2020YFE0201200]; National
   Natural Science Foundation of China [52072292]
FX AcknowledgmentsThis study was sponsored by the National Key Research and
   Development Project (2020YFE0201200) and the National Natural Science
   Foundation of China (52072292).
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NR 60
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PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD FEB 4
PY 2022
VL 2022
AR 7754961
DI 10.1155/2022/7754961
PG 15
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA ZT8QD
UT WOS:000769414000001
PM 35154305
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Ding, L
   Yu, S
   Li, L
AF Ding, Ling
   Yu, Shui
   Li, Li
TI Research trends of cultural identity of mongol nationality based on
   mapping knowledge domain and visualization
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Bibliometric analysis; mapping knowledge domain and visualization; the
   mongol nationality; cultural identity; research trends
AB Bibliometric analysis and information visualization software (Cite Space) were used to analyze and organize related research on whether people with the surname Yu descended from Temujin (Genghis Khan) of the Mongol nationality since 1981. Data were collected from the Chinese National Knowledge Infrastructure database, and analytic dimensionalities included basic knowledge of researcher, research institute, sampling of subjects, release time of research results, keywords and hotspot issues, critical cultural symbols, so on. Colleges, universities, and professors in Guizhou Province, China participated in the research, and few journalists also participated in many related surveys. The kernel keywords and critical symbols embodied in the research were "Mongol Nationality," "Genghis Khan," "Tiegaiyu," "Nine Sons, Ten Jinshi (successful candidates in imperial examinations)," among others. Related studies since 1981 are divided into four development phases and continue to present an expanding trend. Related mainstream viewpoints are divided into three types, namely, the Mongol nationality, Tangut people in the Western Xia Dynasty, and the Han nationality. Research arguments focus on whether people with surname Yu in southwestern China are descendants of Genghis Khan in Mongolia. Future research emphasis and difficulty lies in identifying if people with surname Yu in southwestern China are descendants of Genghis Khan in Mongolia, combining historical truths by processing and analyzing the enormous family trees with the surname Yu. Future research should focus on the emic approach. In the current study, paternal Y chromogene detection and related articles on migrants into Sichuan are predicted to facilitate further research development.
C1 [Ding, Ling] Guizhou Univ, Sch Publ Adm, Guiyang, Guizhou, Peoples R China.
   [Yu, Shui] Guiyang Univ, Sch Educ Sci, Guiyang, Guizhou, Peoples R China.
   [Li, Li] Wuhan Univ Technol, Sch Law Humanities & Sociol, Wuhan, Peoples R China.
RP Yu, S (通讯作者)，Guiyang Univ, Sch Educ Sci, Guiyang, Guizhou, Peoples R China.
EM 1411178517@qq.com
FU Fund program: 2018 National Social Science Project "A Psychological and
   Behavioral Study of Cultural Identity of the Same Surname in Southwest
   Region" [18XMZ058]
FX The work is supported by Fund program: 2018 National Social Science
   Project "A Psychological and Behavioral Study of Cultural Identity of
   the Same Surname in Southwest Region" (project number: 18XMZ058).
CR [Anonymous], CHONGQING TIMES
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NR 16
TC 0
Z9 0
U1 1
U2 4
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2020
VL 38
IS 6
SI SI
BP 6873
EP 6882
DI 10.3233/JIFS-179765
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MY7XE
UT WOS:000558628900015
DA 2022-08-23
ER

PT J
AU Jiang, YY
   Gao, Y
   Song, WQ
   Li, Y
   Quan, Q
AF Jiang Yangyang
   Gao Yan
   Song Wenqi
   Li Yue
   Quan Quan
TI Bibliometric analysis of UAV swarms
SO JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS
LA English
DT Article
DE unmanned aerial vehicle (UAV) swarm; bibliometric; mapping knowledge
   domain
ID MOBILE ROBOTS; SPECIAL-ISSUE; MULTIPLE; COMMUNICATION; TELEOPERATION;
   FLOCKING; SYSTEMS; SEARCH; CAMERA; EYES
AB Projects on unmanned aerial vehicle (UAV) swarms have been initiated in a big way in the last few years, especially from 2015 to 2016. As a result, the number of related works on UAV swarms has been on the rise, with the rate of growth dramatically accelerating since 2017. This research conducts a bibliometric analysis of robotics swarms and UAV swarms to answer the following questions: (i) Disciplines mentioned in the UAV swarms research. (ii) The future development trends and hotspots in the UAV swarms research. (iii) Tracking related outcomes in the UAV swarms research.
C1 [Jiang Yangyang] Beihang Univ Lib, Beijing 100191, Peoples R China.
   [Gao Yan; Song Wenqi; Li Yue; Quan Quan] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China.
RP Quan, Q (通讯作者)，Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China.
EM yangyang@buaa.edu.cn; buaa_gaoyan@buaa.edu.cn; jnswqno1@163.com;
   liyue9556@buaa.edu.cn; qq_buaa@buaa.edu.cn
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NR 74
TC 0
Z9 0
U1 15
U2 15
PU SYSTEMS ENGINEERING & ELECTRONICS, EDITORIAL DEPT
PI BEIJING
PA PO BOX 142-32, BEIJING, 100854, PEOPLES R CHINA
SN 1004-4132
J9 J SYST ENG ELECTRON
JI J. Syst. Eng. Electron.
PD APR
PY 2022
VL 33
IS 2
BP 406
EP 425
DI 10.23919/JSEE.2022.000042
PG 20
WC Automation & Control Systems; Engineering, Electrical & Electronic;
   Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering; Operations Research &
   Management Science
GA 1G0YQ
UT WOS:000795583000020
OA gold
DA 2022-08-23
ER

PT J
AU Concari, A
   Kok, G
   Martens, P
AF Concari, Alessandro
   Kok, Gerjo
   Martens, Pim
TI Recycling behaviour: Mapping knowledge domain through bibliometrics and
   text mining
SO JOURNAL OF ENVIRONMENTAL MANAGEMENT
LA English
DT Article
DE Recycling behaviour; Mapping knowledge domain (MKD); Bibliometric
   analysis; Systematic literature review (SLR); Waste management; Text
   mining
ID PRO-ENVIRONMENTAL BEHAVIOR; WASTE MANAGEMENT; SUSTAINABLE CONSUMPTION;
   HOUSEHOLD; CONSERVATION; SCIENCE; FUTURE; COLLECTION; CHALLENGES;
   PREDICTION
AB In recent years, the research on human behaviour in relation to waste management has increased at an exponential rate. At the same time, the expanding academic literature on this topic makes it more difficult to understand the main areas of interest, the leading institutions and authors, the possible interconnections among different disciplines, and the gaps. This paper maps knowledge domain on recycling behaviour through bibliometric analysis and text mining in order to identify current trends, research networks and hot topics. 2061 articles between 1975 and 2020 from three different databases are examined with an interdisciplinary approach. The findings reveal that 60% of papers have been published between 2015 and 2020, and this topic is of global interest. Leading countries are mainly located in Europe, North America and Commonwealth; however, China and Malaysia are also assuming a driving role. Bibliometrics and text mining provide the intellectual configuration of the knowledge on recycling behaviour; co-word analysis individuates conceptual sub-domains in food waste, determinants of recycling behaviour, waste management system, waste electrical and electronic equipment (WEEE), higher-level education, plastic bags, and local government. Overall, waste management and related human behaviour represent a universal challenge requiring a structured and interdisciplinary approach at all levels (individual, institutions, industry, academia). Lastly, this paper offers some suggestions for future research such as smart city design, sensor network system, consumer responsibilisation, the adoption of a more comprehensive view of the areas of investigation through the holistic analysis of all stakeholders.
C1 [Concari, Alessandro] Maastricht Univ, Maastricht Sustainabil Inst, POB 616, NL-6200 MD Maastricht, Netherlands.
   [Kok, Gerjo] Maastricht Univ, Fac Psychol & Neurosci, Dept Work & Social Psychol, POB 616, NL-6200 MD Maastricht, Netherlands.
   [Martens, Pim] Maastricht Univ, Univ Coll Venlo, NL-5911 BV Venlo, Netherlands.
RP Concari, A (通讯作者)，Maastricht Univ, Maastricht Sustainabil Inst, POB 616, NL-6200 MD Maastricht, Netherlands.
EM alessandro.concari@maastrichtuniversity.nl;
   g.kok@maastrichtuniversity.nl; p.martens@maastrichtuniversity.nl
OI Kok, Gerjo/0000-0002-3501-4096; Concari, Alessandro/0000-0003-2004-146X
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NR 101
TC 0
Z9 0
U1 34
U2 45
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 0301-4797
EI 1095-8630
J9 J ENVIRON MANAGE
JI J. Environ. Manage.
PD FEB 1
PY 2022
VL 303
AR 114160
DI 10.1016/j.jenvman.2021.114160
EA DEC 2021
PG 15
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA XS6DG
UT WOS:000732996500003
PM 34906834
OA hybrid
DA 2022-08-23
ER

PT J
AU Hong, R
   Liu, H
   Xiang, CL
   Song, YM
   Lv, C
AF Hong, Rui
   Liu, Hui
   Xiang, Chenglang
   Song, Yimeng
   Lv, Chen
TI Visualization and analysis of mapping knowledge domain of oxidation
   studies of sulfide ores
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article
DE Mapping knowledge domain; Sulfide ore; Oxidation; Knowledge structure;
   Research trends
ID SPONTANEOUS COMBUSTION TENDENCY; BIBLIOMETRIC ANALYSIS; MECHANICAL
   ACTIVATION; MINE DRAINAGE; PART I; PYRITE; KINETICS; SULFUR; TRENDS;
   DISSOLUTION
AB The oxidation of sulfide ores is a common phenomenon. To better understand the current development and status of oxidation studies of sulfide ores (OSSO), a bibliometric analysis of OSSO was conducted by mapping the spatiotemporal distribution of the knowledge domain and the research focus using VOSviewer and Citespace tools. The data were derived from theWeb of Science (WOS) core collection database from 2000 to 2018. Study emphases covered publication outputs, countries/regions, organizations, top journals, research focus and keyword co-occurrence network, and theme development. The results include the following findings: (1) The line of the 3-year moving average of publications (3-yearMAP), h-index (3-yearMAH), and authors (3-year MAA) increased from 2001 to 2018. Conversely, the h-index continuously declined. (2) Asia had the most publications, with 1052, followed by Europe, with 923, and America, with 767. China, the USA, and Australia are the most active countries. (3) The top 10 organizations with the most publications are five Chinese organizations and one organization from each of the following countries: Russia, Australia, France, and India. (4) Hydrometallurgy, Minerals Engineering, Ore Geology Reviews, Geochimica Et Cosmochimica Acta, and Economic Geology are among the top 10 journals that researchers are most concerned about. (5) Cooperation among different organization or different countries is the most effective way to produce the most influential papers. (6) The OSSO is still focused on the process of oxidation by using different methods and techniques. In future work, it is necessary to progress new methods to understand the process of self-heating and prevent spontaneous combustion disaster of sulfide ore which result from OSSO.
C1 [Hong, Rui; Liu, Hui; Xiang, Chenglang; Lv, Chen] China Jiliang Univ, Coll Qual & Safety Engn, Hangzhou 310014, Peoples R China.
   [Song, Yimeng] Natl Inst Occupat Safety, Beijing, Peoples R China.
RP Liu, H (通讯作者)，China Jiliang Univ, Coll Qual & Safety Engn, Hangzhou 310014, Peoples R China.
EM liuhui2003@126.com
RI liu, hui/K-2142-2015
OI liu, hui/0000-0002-4265-7241
FU Key Laboratory of Safety Engineering and Technology Research of Zhejiang
   Province [201904]; Zhejiang Provincial Natural Science Foundation of
   China [LY14E040001, LQ20E040005]; Science and Technology Project of
   Department of Education of Zhejiang Province [Y201840583, Y201942504]
FX This work was supported in part by the Key Laboratory of Safety
   Engineering and Technology Research of Zhejiang Province (201904), the
   Zhejiang Provincial Natural Science Foundation of China (No.
   LY14E040001, LQ20E040005), the Science and Technology Project of
   Department of Education of Zhejiang Province (Y201840583, Y201942504).
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NR 72
TC 14
Z9 14
U1 6
U2 38
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 FEB
PY 2020
VL 27
IS 6
BP 5809
EP 5824
DI 10.1007/s11356-019-07226-z
PG 16
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA KU3UD
UT WOS:000519635800014
PM 31858411
DA 2022-08-23
ER

PT J
AU Zhang, W
   Zhao, L
AF Zhang, Wei
   Zhao, Liang
TI Mapping the Global Knowledge Domain for Building Information Models
SO INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE BIM; mapping knowledge domain; centrality; cluster analysis; co-citation
ID BIM
AB This paper presents the method and findings of a visual knowledge domain map used to explore the development and evolution of Building information modeling (BIM) technology. Metadata taken from 806 literature records is used to visualize the state of BIM research. The records were published between 1998 and 2017, and Citespace software is employed to visualize key data about the research contained on the Web of Science platform, including nationality of research institutions, research themes, co-citations of periodicals, authorship, literature sources, and the development of the discipline over time. The distribution of power in this field is also examined by interrogating the BIM knowledge base, identifying research hotspots to provide a reference for assessing the current state-of-the-art.
C1 [Zhang, Wei] Jiangsu Vocat Inst Architectural Technol, Inst Bldg Intelligence, Xuzhou 221000, Jiangsu, Peoples R China.
   [Zhao, Liang] Jiangsu Vocat Inst Architectural Technol, Sch Informat & Elect Engn, Xuzhou 221000, Jiangsu, Peoples R China.
RP Zhao, L (通讯作者)，Jiangsu Vocat Inst Architectural Technol, Sch Informat & Elect Engn, Xuzhou 221000, Jiangsu, Peoples R China.
EM godswish2006@126.com; 22605181@qq.com
OI zhao, liang/0000-0002-9658-245X
FU Jiangsu Province Teaching Reform project [2017JSJG284]; Jiangsu
   Provincial Department of Housing and Urban Rural Construction Project
   [2018ZD328, 2018ZD295]; China Institute of Higher Education Project
   [GZYYB2017109, GZYYB2018135]; Jiangsu Education Informationization
   Research Project [20180022]; Jiangsu Natural Science Foundation Project
   [17KJA560002]
FX This research was financially supported by the Jiangsu Province Teaching
   Reform project (Grant No. 2017JSJG284), the Jiangsu Provincial
   Department of Housing and Urban Rural Construction Project (Grant No.
   2018ZD295), the China Institute of Higher Education Project (Grant No.
   GZYYB2017109, GZYYB2018135), the Jiangsu Education Informationization
   Research Project (Grant No. 20180022), the Jiangsu Natural Science
   Foundation Project (Grant No. 17KJA560002), and the Jiangsu Provincial
   Department of Housing and Urban Rural Construction Project (No.
   2018ZD328).
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NR 28
TC 1
Z9 1
U1 5
U2 23
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-0014
EI 1793-6381
J9 INT J PATTERN RECOGN
JI Int. J. Pattern Recognit. Artif. Intell.
PD DEC 31
PY 2019
VL 33
IS 14
AR 1959044
DI 10.1142/S0218001419590444
PG 23
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KB2KQ
UT WOS:000506328700012
DA 2022-08-23
ER

PT J
AU Liu, S
   Sun, YP
   Gao, XL
   Sui, Y
AF Liu, Shuo
   Sun, Ya-Ping
   Gao, Xu-Ling
   Sui, Yi
TI Knowledge domain and emerging trends in Alzheimer's disease: a
   scientometric review based on CiteSpace analysis
SO NEURAL REGENERATION RESEARCH
LA English
DT Review
DE nerve regeneration; Alzheimer's disease; neuroprotection; mapping
   knowledge domain; Web of Science; CiteSpace; neural regeneration
ID MILD COGNITIVE IMPAIRMENT; AMYLOID PRECURSOR PROTEIN; GENOME-WIDE
   ASSOCIATION; DIAGNOSTIC GUIDELINES; NATIONAL INSTITUTE; IDENTIFIES
   VARIANTS; RECOMMENDATIONS; WORKGROUPS; DEMENTIA; CHALLENGES
AB Alzheimer's disease is the most common cause of dementia. It is an increasingly serious global health problem and has a significant impact on individuals and society. However, the precise cause of Alzheimer's disease is still unknown. In this study, 11,748 Web-of-Science-indexed manuscripts regarding Alzheimer's disease, all published from 2015 to 2019, and their 693,938 references were analyzed. A document co-citation network map was drawn using CiteSpace software. Research frontiers and development trends were determined by retrieving subject headings with apparent changing word frequency trends, which can be used to forecast future research developments in Alzheimer's disease.
C1 [Liu, Shuo; Sun, Ya-Ping; Gao, Xu-Ling] Fourth Peoples Hosp Shenyang, Shenyang, Liaoning, Peoples R China.
   [Sui, Yi] First Peoples Hosp Shenyang, Shenyang, Liaoning, Peoples R China.
RP Gao, XL (通讯作者)，Fourth Peoples Hosp Shenyang, Shenyang, Liaoning, Peoples R China.; Sui, Y (通讯作者)，First Peoples Hosp Shenyang, Shenyang, Liaoning, Peoples R China.
EM gxl590603@163.com; jakeyisui@icloud.com
RI Sui, Yi/I-5278-2019
OI Sui, Yi/0000-0002-2898-7877
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NR 41
TC 33
Z9 36
U1 88
U2 248
PU WOLTERS KLUWER MEDKNOW PUBLICATIONS
PI MUMBAI
PA WOLTERS KLUWER INDIA PVT LTD , A-202, 2ND FLR, QUBE, C T S  NO 1498A-2
   VILLAGE MAROL, ANDHERI EAST, MUMBAI, 400059, INDIA
SN 1673-5374
EI 1876-7958
J9 NEURAL REGEN RES
JI Neural Regen. Res.
PD SEP
PY 2019
VL 14
IS 9
BP 1643
EP 1650
DI 10.4103/1673-5374.255995
PG 8
WC Cell Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Cell Biology; Neurosciences & Neurology
GA HY3XE
UT WOS:000468060200031
PM 31089065
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Zhao, PY
   Liu, JG
   Wu, CC
AF Zhao PengYuan
   Liu JinGuo
   Wu ChenChen
TI Survey on research and development of on-orbit active debris removal
   methods
SO SCIENCE CHINA-TECHNOLOGICAL SCIENCES
LA English
DT Review
DE space debris; active debris removal; on-orbit servicing; space robotics;
   capture method
ID ION-BEAM SHEPHERD; SPACE-DEBRIS; ATTITUDE-CONTROL; ELECTRODYNAMIC
   TETHERS; ELECTRIC PROPULSION; CONTACT DYNAMICS; MISSION DESIGN; SLIDING
   MODE; SOLAR SAIL; SYSTEM
AB Space debris is growing dramatically with the rapid pace of human exploration of space, which seriously threatens the safety of artificial spacecraft in orbit. Therefore, the active debris removal (ADR) is important. This review aims to review the ADR methods and to advance related research in the future. The current research and development status are clearly demonstrated by mapping knowledge domain and charts. In this paper, the latest research results are classified and summarized in detail from two aspects of space debris capture and removal. The scheme comparison and evaluation of all ADR methods are performed, and the applicable scopes of various methods are summarized. Each ADR method is scored using a cobweb evaluation model based on six indicators. Future development of ADR is discussed to promote further research interest.
C1 [Zhao PengYuan; Liu JinGuo; Wu ChenChen] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China.
   [Zhao PengYuan; Liu JinGuo; Wu ChenChen] Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110169, Peoples R China.
   [Zhao PengYuan] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
RP Liu, JG (通讯作者)，Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China.; Liu, JG (通讯作者)，Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110169, Peoples R China.
EM liujinguo@sia.cn
RI Zhao, Pengyuan/AIE-6474-2022
OI Zhao, Pengyuan/0000-0002-1645-191X; Liu, Jinguo/0000-0002-6790-6582
FU National Key RAMP;D Program of China [2018YFB1304600]; National Natural
   Science Foundation of China [51775541]; CAS Interdisciplinary Innovation
   Team [JCTD-2018-11]; State Key Laboratory of Robotics Foundation
   [Y91Z0303]; Hundred-Talent Program (Chinese Academy of Sciences)
   [Y8A3210304]
FX This work was supported by the National Key R&D Program of China (Grant
   No. 2018YFB1304600), the National Natural Science Foundation of China
   (Grant No. 51775541), the CAS Interdisciplinary Innovation Team (Grant
   No. JCTD-2018-11), the State Key Laboratory of Robotics Foundation
   (Grant No. Y91Z0303) and the Hundred-Talent Program (Chinese Academy of
   Sciences) (Grant No. Y8A3210304).
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NR 184
TC 17
Z9 18
U1 11
U2 57
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1674-7321
EI 1869-1900
J9 SCI CHINA TECHNOL SC
JI Sci. China-Technol. Sci.
PD NOV
PY 2020
VL 63
IS 11
BP 2188
EP 2210
DI 10.1007/s11431-020-1661-7
EA SEP 2020
PG 23
WC Engineering, Multidisciplinary; Materials Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Materials Science
GA OQ4DJ
UT WOS:000573754000002
DA 2022-08-23
ER

PT J
AU Wan, M
   Liang, Y
   Yan, LX
   Zhou, TQ
AF Wan, Ming
   Liang, Ying
   Yan, Lixin
   Zhou, Tuqiang
TI Bibliometric analysis of human factors in aviation accident using MKD
SO IET IMAGE PROCESSING
LA English
DT Article; Early Access
ID HUMAN ERROR; CLASSIFICATION-SYSTEM; MAINTENANCE; MODEL; STRATEGIES;
   FREQUENCY; FAILURE
AB This study aims to provide a better understanding of human factors and human performance mechanism in aviation accident analysis by visualisation and taxonomy analysis using mapping knowledge domain (MKD). An overview of aviation accident analysis involving human factors is first conducted, followed by an introduction of several conceptual models and human reliability analysis methods. Finally, a specific framework for risk assessment of human factors in aviation is proposed. The human factors analysis and classification system structure combined with the quantitative method is the most frequently used mode in aviation accident analysis. From a macro ergonomic perspective, systematic consideration has been emphasised frequently in existing research. Pilots or individuals are thought to be associated with the organisations and environment they located in. On the other hand, the micro ergonomic aspect prefers a quantitative and probabilistic way to reveal the individual's failure mode or error rate. Based on previous studies, the proposed framework provides a systematic view to help with the evaluation of human performance in aviation, taking into account human issues as well as management and environmental factors.
C1 [Wan, Ming; Liang, Ying; Yan, Lixin; Zhou, Tuqiang] East China Jiaotong Univ, Sch Transportat & Logist, Nanchang 330013, Jiangxi, Peoples R China.
RP Yan, LX (通讯作者)，East China Jiaotong Univ, Sch Transportat & Logist, Nanchang 330013, Jiangxi, Peoples R China.
EM yanlixinits@163.com
FU China Scholarship Council [201606950010]; National Natural Science
   Foundation of China (NSFC) [51805169, 51708218]; Science and Technology
   Project of Transportation Department of Jiangxi Province [2014R0009]
FX The authors thank the China Scholarship Council for their financial
   support under grant no. 201606950010, and the National Natural Science
   Foundation of China (NSFC) under grant nos. 51805169 and 51708218 for
   this research. This work was also supported by the Science and
   Technology Project of Transportation Department of Jiangxi Province
   under grant no. 2014R0009.
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NR 37
TC 0
Z9 0
U1 3
U2 12
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1751-9659
EI 1751-9667
J9 IET IMAGE PROCESS
JI IET Image Process.
DI 10.1049/ipr2.12167
EA MAR 2021
PG 9
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Imaging Science & Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Imaging Science & Photographic Technology
GA QT4TE
UT WOS:000626580800001
OA gold
DA 2022-08-23
ER

PT J
AU Liu, ZQ
   Jin, P
   Yin, YQ
   Yin, FP
AF Liu, ZhiQun
   Jin, Ping
   Yin, YiQiu
   Yin, FangPing
TI Mapping the knowledge domains of smart textile: visualization
   analysis-based studies
SO JOURNAL OF THE TEXTILE INSTITUTE
LA English
DT Article; Early Access
DE Smart textile; mapping knowledge domains; visualization analysis;
   CiteSpace
AB To better understand the current status and development of smart textile studies and explore the research hotspots and directions, 2647 papers related to smart textile studies from 1996 to 2021 were collected from the Web of Science core collection database as data samples. The mapping knowledge domain technology was used to visually analyze the knowledge structure and research trends in the field of smart textile studies. The study found that smart textile studies have recorded a rapid growth between 2010 and 2021. The disciplines of smart textile research mainly focus on materials science and engineering, chemistry, physics, computer science, biomedicine, optics, etc. The 'ADVANCED MATERIALS', 'ACS APPLIED MATERIALS & INTERFACES', 'ADVANCED FUNCTIONAL MATERIALS' are the highly cited journals in the field, and 'STOPPA M' as the highly cited author has the highest number of citations. Publications in China, the USA and Germany rank top 3 among countries/regions. Donghua University and the Chinese Academy of Sciences have the most active cooperation with other research institutions. Research on smart textile has basically formed a theoretical research framework, and the research topics are concentrated, but the research hotpots have changed continuously. The current research hotspots and frontiers are how to explore new technologies and applications of smart textile to promote the development in this area.
C1 [Liu, ZhiQun; Jin, Ping; Yin, YiQiu; Yin, FangPing] Lib XiAn Polytech Univ, Xian 710048, Peoples R China.
RP Liu, ZQ (通讯作者)，Lib XiAn Polytech Univ, Xian 710048, Peoples R China.
EM 20130507@xpu.edu.cn
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NR 22
TC 0
Z9 0
U1 3
U2 9
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0040-5000
EI 1754-2340
J9 J TEXT I
JI J. Text. Inst.
DI 10.1080/00405000.2021.2005278
EA NOV 2021
PG 9
WC Materials Science, Textiles
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Materials Science
GA WZ1LS
UT WOS:000719735300001
DA 2022-08-23
ER

PT J
AU Wu, Y
   Xue, YZ
   Xue, ZL
AF Wu, Ying
   Xue, Yunzhen
   Xue, Zhanling
TI The study on the core personality trait words of Chinese medical
   university students based on social network analysis
SO MEDICINE
LA English
DT Article
DE Chinese medical university students; personality trait words; SNA
ID SCIENTIFIC COLLABORATION
AB The medical university students in China whose school work is relatively heavy and educational system is long are a special professional group. Many students have psychological problems more or less. So, to understand their personality characteristics will provide a scientific basis for the intervention of psychological health.
   We selected top 30 personality trait words according to the order of frequency. Additionally, some methods such as social network analysis (SNA) and visualization technology of mapping knowledge domain were used in this study.
   Among these core personality trait words Family conscious had the 3 highest centralities and possessed the largest core status and influence. From the analysis of core- peripheral structure, we can see polarized core-perpheral structure was quite obvious. From the analysis of K-plex, there were in total 588 "K-2" K-plexs. From the analysis of Principal Components, we selected the 11 principal components.
   This study of personality not only can prevent disease, but also provide a scientific basis for students' psychological healthy education. In addition, we have adopted SNA to pay more attention to the relationship between personality trait words and the connection among personality dimensions. This study may provide the new ideas and methods for the research of personality structure.
C1 [Wu, Ying; Xue, Yunzhen; Xue, Zhanling] Shanxi Med Univ, Sch Humanities & Social Sci, 56 South Xinjian Rd, Taiyuan 030001, Shanxi, Peoples R China.
RP Wu, Y (通讯作者)，Shanxi Med Univ, Sch Humanities & Social Sci, 56 South Xinjian Rd, Taiyuan 030001, Shanxi, Peoples R China.
EM wuguaiguai9896@126.com
FU Analysis on Scientific Collaboration and Trends Prediction of Research
   Fronts in Psychiatry Field - National Natural Science Foundation of
   China [71503152]; Program for the Outstanding Innovative Teams of Higher
   Learning Institutions of Shanxi (OIT)
FX The authors thank the participants for their time and willingness to
   participate in this study. The authors also acknowledge the support for
   this study from 'Analysis on Scientific Collaboration and Trends
   Prediction of Research Fronts in Psychiatry Field (No. 71503152),'
   awarded by National Natural Science Foundation of China. This study was
   also supported by Program for the Outstanding Innovative Teams of Higher
   Learning Institutions of Shanxi (OIT).
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NR 12
TC 1
Z9 2
U1 1
U2 17
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0025-7974
EI 1536-5964
J9 MEDICINE
JI Medicine (Baltimore)
PD SEP
PY 2017
VL 96
IS 37
AR e8078
DI 10.1097/MD.0000000000008078
PG 5
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA FH1GH
UT WOS:000410887400061
PM 28906409
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Xue, ZH
   Liu, JG
   Wu, CC
   Tong, YC
AF Xue, Zhihui
   Liu, Jinguo
   Wu, Chenchen
   Tong, Yuchuang
TI Review of in-space assembly technologies
SO CHINESE JOURNAL OF AERONAUTICS
LA English
DT Review
DE In-situ resource utilization; In-space assembly; Modular; On-orbit
   servicing; Space robots
ID FLEXIBLE SPACECRAFT; COLLISION-AVOIDANCE; SHAPE OPTIMIZATION; OBSTACLE
   AVOIDANCE; VIBRATION CONTROL; ROBOT; SATELLITE; TELEOPERATION; DESIGN;
   IDENTIFICATION
AB With the rapid development of space technology and the increasing demand for space missions, the traditional spacecraft manufacturing, deployment and launch methods have been unable to meet existing needs. In-space assembly (ISA) technologies can effectively adapt to the assembly of large space structures, improve spacecraft performance, and reduce operating costs. In this paper, the development and technologies for ISA are reviewed. ISA is classified from multiple angles, and the research status of ISA is shown clearly through the visual mapping knowledge domain. Then the development status of autonomous robot assembly in the United States, Europe, Japan, Canada and China is reviewed. Furthermore, the key technologies of ISA are analyzed from three aspects: assembly structure design, robot technologies and integrated management technologies. ISA technologies are still facing major challenges and need to be further explored to promote future development. Finally, future development trends and potential applications of ISA are given, which show that ISA will play a vital role in human space exploration in the future. (c) 2020 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
C1 [Xue, Zhihui; Liu, Jinguo; Wu, Chenchen; Tong, Yuchuang] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China.
   [Xue, Zhihui; Liu, Jinguo; Wu, Chenchen; Tong, Yuchuang] Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110169, Peoples R China.
   [Xue, Zhihui; Tong, Yuchuang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
RP Liu, JG (通讯作者)，Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China.
EM liujinguo@sia.cn
OI Liu, Jinguo/0000-0002-6790-6582
FU National Key RAMP;D Pro-gram of China [2018YFB1304600]; Natural Science
   Foundation of China [51775541]; CAS Interdisciplinary Innovation Team
   [JCTD-2018-11]
FX This work was supported in part by National Key R&D Pro-gram of China
   (No. 2018YFB1304600) , the Natural Science Foundation of China (No.
   51775541) , CAS Interdisciplinary Innovation Team (No. JCTD-2018-11) .
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NR 218
TC 12
Z9 13
U1 19
U2 28
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 1000-9361
EI 2588-9230
J9 CHINESE J AERONAUT
JI Chin. J. Aeronaut.
PD NOV
PY 2021
VL 34
IS 11
BP 21
EP 47
DI 10.1016/j.cja.2020.09.043
EA SEP 2021
PG 27
WC Engineering, Aerospace
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA UO5AE
UT WOS:000694706100001
OA hybrid
DA 2022-08-23
ER

PT J
AU Yan, TT
   Xue, JH
   Zhou, ZD
   Wu, YB
AF Yan, Taotao
   Xue, Jianhui
   Zhou, Zhidong
   Wu, Yongbo
TI The Trends in Research on the Effects of Biochar on Soil
SO SUSTAINABILITY
LA English
DT Article
DE biochar; soil; mapping knowledge domain; CiteSpace; popular research
   topics; research trends
ID GREENHOUSE-GAS EMISSIONS; BLACK CARBON; MICROBIAL BIOMASS; PHOSPHORUS
   SORPTION; NITROGEN-RETENTION; CONTAMINATED SOIL; CLIMATE-CHANGE;
   AVAILABILITY; AMENDMENTS; PYROLYSIS
AB The present study used bibliometric methods to analyze the literature regarding the biochar effects on soil that are included in the Web of Science Core Collection database and quantified the annual number of publications in the field and distribution of publications. Using CiteSpace as a visual analytic software for the literature, the distribution of the subject categories, author collaborations, institution collaborations, international (regional) collaborations, and cocitation and keyword clustering were analyzed. The results showed the basic characteristics of the literature related to the effects of biochar on soil. Furthermore, the main research powers in this field were identified. Then, we recognized the main intellectual base in the domain of biochar effects on soil. Meanwhile, this paper revealed the research hotspots and trends of this field. Furthermore, focuses of future research in this field are discussed. The present study quantitatively and objectively describes the research status and trends of biochar effects on soil from the bibliometric perspective to promote in-depth research in this field and provide reference information for scholars in the relevant fields to refine their research directions, address specific scientific issues, and help scholars to seek/establish relevant collaborations in their fields of interests.
C1 [Yan, Taotao; Xue, Jianhui; Wu, Yongbo] Nanjing Forestry Univ, Coll Biol & Environm, Nanjing 210037, Peoples R China.
   [Yan, Taotao; Xue, Jianhui; Zhou, Zhidong; Wu, Yongbo] Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Peoples R China.
   [Xue, Jianhui; Zhou, Zhidong] Inst Bot, Nanjing, Jiangsu, Peoples R China.
   [Xue, Jianhui; Zhou, Zhidong] Chinese Acad Sci, Nanjing 210014, Peoples R China.
RP Xue, JH (通讯作者)，Nanjing Forestry Univ, Coll Biol & Environm, Nanjing 210037, Peoples R China.; Xue, JH (通讯作者)，Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Peoples R China.; Xue, JH (通讯作者)，Inst Bot, Nanjing, Jiangsu, Peoples R China.; Xue, JH (通讯作者)，Chinese Acad Sci, Nanjing 210014, Peoples R China.
EM ecologyan@njfu.edu.cn; jhxue@njfu.edu.cn; zhidongzhou1209@cnbg.net;
   yongbowu@njfu.edu.cn
OI Yan, Taotao/0000-0002-3027-1093
FU Major State Basic Research Development Program of China
   [2016YFC0502605]; National Science and Technology Program during the
   Twelfth Five-Year Plan Period [2015BAD07B0404]; Priority Academic
   Program Development of Jiangsu Higher Education Institutions [PAPD]
FX This research was supported by Major State Basic Research Development
   Program of China (grant no. 2016YFC0502605); National Science and
   Technology Program during the Twelfth Five-Year Plan Period (grant no.
   2015BAD07B0404); and was a project funded by Priority Academic Program
   Development of Jiangsu Higher Education Institutions (grant no. PAPD).
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NR 85
TC 6
Z9 6
U1 17
U2 53
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD SEP
PY 2020
VL 12
IS 18
AR 7810
DI 10.3390/su12187810
PG 23
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA OJ9NG
UT WOS:000584280500001
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Banales-Seguel, C
   Maulen, WR
   Alvez, A
   Habit, E
AF Banales-Seguel, Camila
   Riquelme Maulen, Wladimir
   Alvez, Amaya
   Habit, Evelyn
TI Scientific Landscape Related to Mapuche Indigenous Peoples and Wallmapu
   Territory
SO SUSTAINABILITY
LA English
DT Review
DE indigenous knowledge; Latin America; Chile; Argentina; mapping knowledge
   domain; bibliometric; indigenous movements; environmental defense
ID KNOWLEDGE; COMMUNITY; PERSPECTIVE; GEOGRAPHY; REGION; WOMEN;
   MULTICULTURALISM; PERCEPTION; COCITATION; PATAGONIA
AB Recent international recognition of indigenous peoples, their rights and contributions to solving current challenges has directed academic attention to the way research is conducted in relation to them, what topics are studied, by whom, and using which methodologies. The Mapuche are the most numerous indigenous peoples in South America and have ancestrally inhabited a territory denominated Wallmapu. This study undertook a comprehensive revision and meta-analysis of the scientific literature related to the Mapuche and Wallmapu. Through quantification and visualization of the scientific landscape, the goal was to enhance the understanding of the themes and patterns that have guided research in this field of study. We found 1611 documents in Web of Science and SciELO collections published between 1975 and 2020. Using CiteSpace software, we analyzed the main conceptual hotspots, identified central authors and pivotal works, among other aspects of the scientific knowledge domain. The main research themes found revolve around political and historical aspects of the relationship between Mapuche and the State; ethnobotanical research including plant properties and cultural knowledge transmission; and the indisputable connection between indigenous resistance and environmental depredation. We argue that valuing cultural diversity paves the road for reclaiming indigenous knowledge as a contribution to our understanding of the world.
C1 [Banales-Seguel, Camila] Univ Concepcion, Fac Ciencias Ambientales, Concepcion 4070386, Region Del Biob, Chile.
   [Riquelme Maulen, Wladimir] Pontificia Univ CatOlica Chile, Ctr Desarrollo Local, Campus Villarrica, Villarrica 4930000, Region De La Ar, Chile.
   [Riquelme Maulen, Wladimir] Univ Los Lagos, Ctr Estudios Desarrollo Reg & Polit Publ, Osorno 100213, Region De Los L, Chile.
   [Alvez, Amaya] Univ Concepcion, Fac Ciencias Jurid & Sociales, Dept Derecho Publ, Concepcion 4070386, Region Del Biob, Chile.
   [Habit, Evelyn] Univ Concepcion, Fac Ciencias Ambientales, Dept Sistemas Acuat, Concepcion 4070386, Region Del Biob, Chile.
RP Banales-Seguel, C (通讯作者)，Univ Concepcion, Fac Ciencias Ambientales, Concepcion 4070386, Region Del Biob, Chile.
EM caspicamila@gmail.com; weriquelme@uc.cl; aalvez@udec.cl; ehabit@udec.cl
OI Banales-Seguel, Camila/0000-0002-5206-8141; Habit,
   Evelyn/0000-0002-5113-5496; Alvez Marin, Amaya/0000-0002-1510-7770;
   Riquelme Maulen, Wladimir Esteban/0000-0002-4586-3980
FU Agencia Nacional de Investigacion (ANID) [2018-21190264]; ANID
   [2020-21202417]; Project Fondecyt Regular [1190647]; Project Fondap
   [15130015]
FX C.B.-S. was supported by the Agencia Nacional de Investigacion (ANID)
   PhD grant 2018-21190264. W.R.M. was supported by ANID PhD grant
   2020-21202417. Project Fondecyt Regular 1190647 to E.H. supported
   travels and expenses. Project Fondap 15130015 supported work done by
   A.A.
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NR 149
TC 4
Z9 5
U1 3
U2 8
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 7895
DI 10.3390/su12197895
PG 30
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA ON2XH
UT WOS:000586570000001
OA Green Submitted, gold
DA 2022-08-23
ER

PT J
AU Qi, RM
   Xue, Z
   Liu, YR
AF Qi, Rongming
   Xue, Zhe
   Liu, Yaru
TI Visualization and Analysis of the Mapping Knowledge Domain of
   Acupuncture and Central Nervous System Cell Apoptosis
SO EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE
LA English
DT Review
ID ISCHEMIA-REPERFUSION INJURY; FOCAL CEREBRAL-ISCHEMIA; ELECTROACUPUNCTURE
   PRETREATMENT; COGNITIVE IMPAIRMENT; OXIDATIVE STRESS; EXPRESSION; RATS;
   ACTIVATION; INHIBITION; RECEPTOR
AB Chinese acupuncture therapy has demonstrated good clinical effects on neurological diseases and is widely used internationally. In the past 20 years, an increasing number of researchers around the world have devoted themselves to the study of the effect and mechanism of acupuncture for the treatment of central nervous system cell apoptosis. To discover the current research status of acupuncture-induced antiapoptosis in the central nervous system, we used the method of scientometric research and data visualization software to visually analyse 155 articles. The findings are as follows. First, the antiapoptosis effects of acupuncture in the central nervous system have received increasing attention overseas and domestically. China and the United States have leading positions in this research field. Second, 5 stable and high-yielding research teams have been formed in the field of acupuncture-induced antiapoptosis. The main research directions of these teams are electroacupuncture (EA) pretreatment for the central nervous system cell apoptosis, acupuncture for antineuronal apoptosis in vascular dementia, EA regulation of related signalling pathways, EA regulation of nerve cell apoptosis and autophagy after stroke, and EA regulation of the MAPK signalling pathway. Researchers on teams with more extensive cooperation have more research results and better research continuity. Third, there are diversified research hotspots. The original research hotspots are still receiving attention, and new hotspots have emerged in recent years.
C1 [Qi, Rongming; Liu, Yaru] Beijing Univ Chinese Med, Sch Acupuncture Moxibust & Tuina, Beijing 100029, Peoples R China.
   [Xue, Zhe] Beijing Univ Chinese Med, Sch Tradit Chinese Med, Beijing 100029, Peoples R China.
RP Liu, YR (通讯作者)，Beijing Univ Chinese Med, Sch Acupuncture Moxibust & Tuina, Beijing 100029, Peoples R China.
EM 1559134790@qq.com; xuezhesanctity@163.com; liuyaru@bucm.edu.cn
FU Beijing University of Chinese Medicine New Teacher Startup Fund
   [2019-JYB-XJSJJ014]
FX AcknowledgmentsThis work was supported by the Beijing University of
   Chinese Medicine New Teacher Startup Fund (no. 2019-JYB-XJSJJ014).
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NR 50
TC 0
Z9 0
U1 3
U2 3
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1741-427X
EI 1741-4288
J9 EVID-BASED COMPL ALT
JI Evid.-based Complement Altern. Med.
PD APR 14
PY 2022
VL 2022
AR 1751702
DI 10.1155/2022/1751702
PG 12
WC Integrative & Complementary Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Integrative & Complementary Medicine
GA 1V7ES
UT WOS:000806248900003
PM 35463084
OA gold, Green Published
DA 2022-08-23
ER

PT J
AU Li, XK
   Hu, YP
   Sun, YQ
   Hu, J
   Zhang, JY
   Qu, MX
AF Li, Xunkai
   Hu, Youpeng
   Sun, Yaoqi
   Hu, Ji
   Zhang, Jiyong
   Qu, Meixia
TI A Deep Graph Structured Clustering Network
SO IEEE ACCESS
LA English
DT Article
DE Topology; Task analysis; Convolution; Clustering methods; Network
   topology; Feature extraction; Mathematical model; Autoencoder; deep
   graph convolutional network; deep graph clustering; unsupervised
   learning
AB Graph clustering is a fundamental task in data analysis and has attracted considerable attention in recommendation systems, mapping knowledge domain, and biological science. Because graph convolution is very effective in combining the feature information and topology information of graph data, some graph clustering methods based on graph convolution have achieved superior performance. However, current methods lack the consideration of structured information and the process of graph convolution. Specifically, most of existing methods ignore the implicit interaction between topology information and feature information, and the stacking of a small number of graph convolutional layers leads to insufficient learning of complex information. Inspired by graph convolutional network and auto-encoder, we propose a deep graph structured clustering network that applies a deep clustering method to graph structured data processing. Deep graph convolution is employed in the backbone network, and evaluates the result of each iteration with node feature and topology information. In order to optimize the network without supervision, a triple self-supervised module is designed to help update parameters for overall network. In our model, we exploit all information of the graph structured data and perform self-supervised learning. Furthermore, improved graph convolution layers significantly alleviate the problem of clustering performance degradation caused by over-smoothing. Our model is designed to perform on representative and indirect graph datasets, and experimental results demonstrate that our model achieves superior performance over state-of-the-art models.
C1 [Li, Xunkai; Hu, Youpeng; Qu, Meixia] Shandong Univ, Sch Mech & Informat Engn, Weihai 264209, Peoples R China.
   [Sun, Yaoqi; Zhang, Jiyong] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Peoples R China.
   [Hu, Ji] Hangzhou Dianzi Univ, Sch Elect Informat, Hangzhou 310018, Peoples R China.
RP Qu, MX (通讯作者)，Shandong Univ, Sch Mech & Informat Engn, Weihai 264209, Peoples R China.
EM mxqu@sdu.edu.cn
RI Li, Xunkai/ABC-2432-2020
OI Hu, Youpeng/0000-0003-2097-5879; Zhang, Jiyong/0000-0001-9600-8477; Sun,
   Yaoqi/0000-0001-8874-241X; Li, Xunkai/0000-0002-1230-7603
FU Natural Science Foundation, Shandong [ZR2018MA032]
FX This work was supported by the Natural Science Foundation, Shandong,
   under Grant ZR2018MA032.
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NR 37
TC 4
Z9 4
U1 3
U2 21
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 161727
EP 161738
DI 10.1109/ACCESS.2020.3020192
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NP4CR
UT WOS:000570125600001
OA gold
DA 2022-08-23
ER

PT J
AU Sun, LW
   Wu, LF
   Qi, PX
AF Sun, Liwen
   Wu, Linfei
   Qi, Peixiao
TI Global characteristics and trends of research on industrial structure
   and carbon emissions: a bibliometric analysis
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Review
DE Bibliometrics analysis; Industrial structure; Carbon emissions; Mapping
   knowledge domain; Global characteristics; Research hotspots
ID DIOXIDE EMISSIONS; CO2 EMISSION; EMERGING TRENDS; DRIVING FORCES; CHINA;
   ENERGY; DECOMPOSITION; IMPACTS; NETWORK; REGIONS
AB The relationship between industrial structure and carbon emissions has been widely identified as a critical research topic by international organizations and academics. Using bibliometrics analysis, this study aimed at dissecting the global characteristics and trends of research on industrial structure and carbon emissions. Based on the 806 documents from 2004 to 2019 in Web of Science, this work was implemented from four aspects, including basic characteristics analysis, country/territory and institution analysis, category and journal analysis, and reference and keyword analysis. The results of this study showed rapid growth trends of research on industrial structure and carbon emissions from 2015 to 2019. The collaborations among countries and institutions were extensive worldwide with China, the USA, and the UK as the main participants. Furthermore, the corresponding research topics, research priorities, and research paths were summarized according to the references co-citation analysis and keywords cluster analysis, which from the perspective of the correlation between different types of industry with carbon emissions. Finally, the timezone view of the top 100 keywords indicated that the emerging trends in the research on industrial structure and carbon emissions were regional analysis, industrialization, and environmental efficiency, and prediction of carbon emissions peak and the spatial distribution in different types of industries were the hotspots in recent years. The findings provide a better understanding of global characteristics and trends that have emerged in this field, which can also offer reference for future research.
C1 [Sun, Liwen; Wu, Linfei] Hebei Univ Technol, Sch Econ & Management, Tianjin 300401, Peoples R China.
   [Qi, Peixiao] China Res Inst Sci Popularizat, Beijing 100081, Peoples R China.
RP Wu, LF (通讯作者)，Hebei Univ Technol, Sch Econ & Management, Tianjin 300401, Peoples R China.
EM wulinfei1988@126.com
OI Wu, Linfei/0000-0002-7557-9710
FU Major Bidding Projects of National Social Science Foundation of China
   [18ZD044]
FX This work was financially supported by the Major Bidding Projects of
   National Social Science Foundation of China "Study on theConstruction
   Mechanism and path of Innovation ecosystem in Xiongan New area"(Grant
   no. 18ZD044).
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NR 55
TC 4
Z9 4
U1 31
U2 88
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 44892
EP 44905
DI 10.1007/s11356-020-10915-9
EA SEP 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:000573786700007
PM 32996091
DA 2022-08-23
ER

PT J
AU Liu, YW
   Cao, XS
   Li, T
AF Liu, Yongwei
   Cao, Xiaoshu
   Li, Tao
TI Influence of Accessibility on Land Use and Landscape Pattern Based on
   Mapping Knowledge Domains: Review and Implications
SO JOURNAL OF ADVANCED TRANSPORTATION
LA English
DT Review
ID URBAN-GROWTH; ROAD NETWORK; DRIVING FORCES; GRADIENT ANALYSIS; YUNNAN
   PROVINCE; SPATIAL-PATTERN; CHINESE CITIES; HANGZHOU BAY; FRAGMENTATION;
   IMPACTS
AB Research on the influence of accessibility on land use and landscape patterns is one of the most important subfields in landscape ecology and transportation geography. In this review article, we use CiteSpace and VOSviewer to analyze relevant information, including the number of published papers, highly cited literature, high-frequency keywords, periodicals, and the leading countries conducting research on this particular field. Based on the mapping knowledge domain theory and summarizing method, this research, using an extensive review of the existing literature to analyze the influence of accessibility on land use and landscape patterns, the following conclusions have been reached: first, most of the relevant studies are conducted by applying theories on landscape ecology rather than on transportation geography, and the measure index of accessibility is relatively simple. Second, while accessibility has played a key role in analyzing the interactions between transportation, land use, and landscape patterns, studies on the long-term effect of transportation on land use and land patterns are extremely important. Also, different road types have been found to impose different effects. Third, research on the functional landscape in inner cities has become a significant research focus, particularly with the progress in big data. And fourth, improvements in data acquisition and processing have greatly benefited the field, specifically with recent advancements in GIS and RS technology. However, studies on landscape patterns with regional perspectives have largely been insufficient, especially those conducted over long time scales.
C1 [Liu, Yongwei] Ludong Univ, Sch Business, Yantai, Shandong, Peoples R China.
   [Liu, Yongwei; Cao, Xiaoshu] Sun Yat Sen Univ, Sch Geog Sci & Planning, Guangzhou, Guangdong, Peoples R China.
   [Cao, Xiaoshu; Li, Tao] Shaanxi Normal Univ, Inst Transport Geog & Spatial Planning, Xian, Shaanxi, Peoples R China.
RP Liu, YW (通讯作者)，Ludong Univ, Sch Business, Yantai, Shandong, Peoples R China.; Liu, YW (通讯作者)，Sun Yat Sen Univ, Sch Geog Sci & Planning, Guangzhou, Guangdong, Peoples R China.
EM 3629@ldu.edu.cn
OI Liu, Yongwei/0000-0002-6804-1875
FU Humanity and Social Science Foundation of Ministry of Education of China
   [20YJCZH104, 19YJCZH229]
FX The authors would like to acknowledge the research support received from
   the Humanity and Social Science Foundation of Ministry of Education of
   China (20YJCZH104 and 19YJCZH229) and thanks Yongwei Liu and Peng Yin
   for the foundation support.
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NR 100
TC 7
Z9 7
U1 15
U2 50
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 JUL 18
PY 2020
VL 2020
AR 7985719
DI 10.1155/2020/7985719
PG 12
WC Engineering, Civil; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Transportation
GA MV4PG
UT WOS:000556341500001
OA Green Published
DA 2022-08-23
ER

PT J
AU Li, JP
   Weng, GM
   Pan, Y
   Li, CH
   Wang, N
AF Li, Jianpu
   Weng, Gangmin
   Pan, Yue
   Li, Conghui
   Wang, Ning
TI A scientometric review of tourism carrying capacity research:
   Cooperation, hotspots, and prospect
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Review
DE Tourism carrying capacity; CiteSpace; Scientometric analysis; Mapping
   knowledge domain
ID SUSTAINABLE DEVELOPMENT; NATIONAL-PARK; ESTUARINE BEACHES; EMERGING
   TRENDS; PROTECTED AREA; MANAGEMENT; ECOTOURISM; PERSPECTIVE;
   ENVIRONMENT; FRAMEWORK
AB With the emergence of a series of problems in the tourism sustainable development, such as environmental pollution, landscape destruction, ecological imbalance, and decreased satisfaction of tourists and residents, it is very urgent to research the tourism carrying capacity. Numerous studies have investigated the tourism carrying capacity but none of them provides a comprehensive analysis of the whole field. The purpose of this study was to identify the hot topics, knowledge gaps and future research directions of the tourism carrying capacity research. Based on the bibliometric records of Web of Science core collection database in 2001-2020, the CiteSpace software and the analysis methods of co-authorship analysis, co-citation analysis and co-occurrence analysis were combined to present a holistic review of the tourism carrying capacity research. The results showed that China, Spain and the United States were the major countries in the research of tourism carrying capacity. Five hot research topics were identified: 1) concept and connotation of tourism carrying capacity, 2) index system and performance model of tourism carrying capacity, 3) sustainable management and practical application, 4) social tourism carrying capacity, and 5) sustainable tourism and ecotourism carrying capacity. In addition, four knowledge gaps were identified, and future research directions for bridging these knowledge gaps were also proposed. This study benefits academics, industry practitioners and governments by providing a comprehension of status quo, hot research topics, knowledge gaps and future research directions in the domain of tourism carrying capacity.
C1 [Li, Jianpu; Weng, Gangmin; Pan, Yue; Li, Conghui; Wang, Ning] Yanshan Univ, Sch Econ & Management, 438 Hebei St, Qinhuangdao 066004, Hebei, Peoples R China.
   [Weng, Gangmin] Yanshan Univ, Res Ctr Reg Econ Dev, 438 Hebei St, Qinhuangdao 066004, Hebei, Peoples R China.
RP Weng, GM (通讯作者)，Yanshan Univ, Sch Econ & Management, 438 Hebei St, Qinhuangdao 066004, Hebei, Peoples R China.
EM lijianpuysu@163.com; wenggangmin@126.com; panyue_e@163.com;
   liconghui1996@126.com; n1842960407@163.com
OI li, jianpu/0000-0002-6490-1487; PAN, Yue/0000-0001-5674-5958
FU Chinese National Funding of Social Sciences [19FGLB001]; Hebei
   Provincial Natural Science Foundation [G2020203023]
FX We thank Ms. Xiangna Gu for her help in revising this manuscript, and
   the five anonymous reviewers who significantly improved this manuscript.
   This work was supported by the [Chinese National Funding of Social
   Sciences] under Grant [number 19FGLB001] and [Hebei Pro-vincial Natural
   Science Foundation] under Grant [number G2020203023] .
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NR 103
TC 4
Z9 4
U1 63
U2 80
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0959-6526
EI 1879-1786
J9 J CLEAN PROD
JI J. Clean Prod.
PD NOV 20
PY 2021
VL 325
AR 129278
DI 10.1016/j.jclepro.2021.129278
EA OCT 2021
PG 13
WC Green & Sustainable Science & Technology; Engineering, Environmental;
   Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Engineering; Environmental Sciences
   & Ecology
GA XM9OG
UT WOS:000729146100006
DA 2022-08-23
ER

PT J
AU Wu, J
   Wang, F
   Wang, ZW
   Hu, HM
   Yang, LN
   Fu, HZ
AF Wu, Jing
   Wang, Fan
   Wang, Ziwei
   Hu, Huimin
   Yang, Lina
   Fu, Huizhen
TI Global performance and trends of research on per- and polyfluoroalkyl
   substances (PFASs) between 2001 and 2018 using bibliometric analysis
SO CHEMOSPHERE
LA English
DT Article
DE Bibliometrics; Review; VOSviewer; Sci2
ID POTASSIUM PERFLUOROOCTANESULFONATE K+PFOS; PERFLUOROALKYL ACIDS PFAAS;
   DRINKING-WATER; PERFLUOROCARBOXYLIC ACIDS; PERFLUORINATED COMPOUNDS;
   FLUOROTELOMER ALCOHOL; ATMOSPHERIC CHEMISTRY; PPAR-ALPHA; PHOTOCHEMICAL
   DECOMPOSITION; LACTATIONAL EXPOSURE
AB Per- and polyfluoroalkyl substances (PFASs) are widely used in food packaging, non-stick pots, and surfactants. However, their persistence in the environment, hazardous nature, and potential for bioaccumulation and longrange transport have alarmed an increasing number of scholars and research institutions. Although several literature reviews on PFASs research exist, only a few of them have considered bibliometric indices. In this study, 3,373 PFASs-related articles published between 2001 and 2018 were analyzed using a bibliometric analysis method based on the Science Citation Index (SCI) Expanded. The software tools for mapping knowledge domain (MKD) (VOSviewer and Science of Science (Sci2)) were used to analyze the performance of contributors and PFASs research topics, hotspots, and trends. Our results reveal that the number of PFASs-related articles published annually has increased significantly, with most originating from the United States (followed by those from China). The Chinese Academy of Sciences has published the most articles. A comprehensive analysis of title, keywords, and keywords plus showed that PFASs research hotspots include humans, precursors, and detection methods, with the main focuses being environmental science, toxicology, and environmental engineering. The four main research topics of PFASs were identified, and a literature review was carried out for each one. Overall, this study can supply researchers with a deeper understanding of the development of PFASs studies and provide a comprehensive data reference for researchers to further grasp the research direction in this field.
C1 [Wu, Jing; Wang, Fan; Wang, Ziwei; Hu, Huimin; Yang, Lina] North China Elect Power Univ, Coll Environm Sci & Engn, MOE Key Lab Resource & Environm Syst Optimizat, Beijing 102206, Peoples R China.
   [Fu, Huizhen] Zhejiang Univ, Sch Publ Affairs, Dept Informat Resources Management, Hangzhou 310058, Peoples R China.
RP Fu, HZ (通讯作者)，Zhejiang Univ, Sch Publ Affairs, Dept Informat Resources Management, Hangzhou 310058, Peoples R China.
EM fuhuizhen@zju.edu.cn
OI Hu, Huimin/0000-0002-9089-150X
FU Second Tibetan Plateau Scientific Expedition and Research Program (STEP)
   [2019QZKK0103]; National Natural Science Foundation of China (NSFC)
   [21976053, 71804163]; Fundamental Research Funds for the Central
   Universities [2019MS042]
FX This work was supported by the Second Tibetan Plateau Scientific
   Expedition and Research Program (STEP) (Grant No. 2019QZKK0103), the
   National Natural Science Foundation of China (NSFC) (Grant No. 21976053
   & 71804163) and the Fundamental Research Funds for the Central
   Universities (Grant No. 2019MS042). The authors thank all individuals
   involved in this work.
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NR 107
TC 0
Z9 0
U1 68
U2 71
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0045-6535
EI 1879-1298
J9 CHEMOSPHERE
JI Chemosphere
PD MAY
PY 2022
VL 295
AR 133853
DI 10.1016/j.chemosphere.2022.133853
PG 11
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA ZD7WR
UT WOS:000758407500002
PM 35122817
DA 2022-08-23
ER

PT J
AU Chen, J
   Meng, S
   Zhou, W
AF Chen, Jin
   Meng, Sun
   Zhou, Wei
TI The exploration of fuzzy linguistic research: A scientometric review
   based on CiteSpace
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Fuzzy logic; linguistics; decision making; CiteSpace; scientometric
ID GROUP DECISION-MAKING; INFORMATION-RETRIEVAL SYSTEM; AGGREGATION
   OPERATORS; PREFERENCE RELATIONS; EMERGING TRENDS; CONSENSUS MODEL; TERM
   SETS; LOGIC; VISUALIZATION; CONSISTENCY
AB In today's society, many decision-making problems cannot be merely solved based on quantitative data. Even though some issues are able to be addressed by quantitative data, researchers may face the difficulty of obtaining accurate and sufficient numbers. Some qualitative evaluations given by experts or decision makers are usually linguistic expressions. Therefore, fuzzy linguistic research has been broadly studied to address the above issues. This research has also been attracting increasing attention from researchers and decision makers in the world. It is believed that analyzing the status quo and emerging trends in this research area is of great necessity, especially for the beginners who are interested in fuzzy linguistic research. To do so, this paper provides the mapping knowledge domain of fuzzy linguistic research based on 648 papers on Web of Science from 1975 to 2018 by using CiteSpace which is an effective tool for scientometric studies. The visualization analyses of cited reference clusters, collaborations networks, author co-citation networks, burst detection and time zone view are presented in this study to show the research streams and the papers that made significant theoretical contributions. Also, the active counties, institutions, journals and authors in this research area are analyzed in detail. Besides, the specific hot spots and emerging trends can be known. There are two contributions in this study. Firstly, we give a comprehensive investigation about the status quo and emerging trends of fuzzy linguistic research in the recent 43 years. Secondly, we make the development of fuzzy linguistic research easier and direct to learn for beginners.
C1 [Chen, Jin; Meng, Sun; Zhou, Wei] Yunnan Univ Finance & Econ, Business Sch, Kunming 650221, Yunnan, Peoples R China.
RP Meng, S (通讯作者)，Yunnan Univ Finance & Econ, Business Sch, Kunming 650221, Yunnan, Peoples R China.
EM mengsun_2004@163.com
FU Natural Science Foundation of China [71561026, 71840001]; Social Science
   Foundation of Ministry of Education of China [18YJC790118]
FX This work was supported by Natural Science Foundation of China (Nos.
   71561026 and 71840001) and Social Science Foundation of Ministry of
   Education of China (No. 18YJC790118).
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NR 80
TC 7
Z9 7
U1 2
U2 44
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2019
VL 37
IS 3
BP 3655
EP 3669
DI 10.3233/JIFS-182737
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA JD4IV
UT WOS:000489941200059
DA 2022-08-23
ER

PT J
AU Jia, QL
   Shi, SQ
   Yuan, GZ
   Shi, JJ
   Shi, S
   Hu, YH
AF Jia, Qiulei
   Shi, Shuqing
   Yuan, Guozhen
   Shi, Jingjing
   Shi, Shuai
   Hu, Yuanhui
TI Analysis of knowledge bases and research hotspots of coronavirus from
   the perspective of mapping knowledge domain
SO MEDICINE
LA English
DT Review
DE bibliometric analysis; CiteSpace; coronavirus; research hotspots
ID RESPIRATORY SYNDROME CORONAVIRUS; SYNDROME SARS CORONAVIRUS;
   NEUTRALIZING ANTIBODY; NUCLEOCAPSID PROTEIN; FUNCTIONAL RECEPTOR;
   MERS-CORONAVIRUS; GENOME SEQUENCE; SPIKE PROTEIN; SAUDI-ARABIA; S
   PROTEIN
AB Background: Coronaviruses have drawn attention since the beginning of the 21st century. Over the past 17 years, coronaviruses have triggered several outbreaks of epidemic in people, which brought great threats to global public health security. We analyzed the publications on coronavirus with bibliometrics software and qualitatively and quantitatively evaluated the knowledge base and hot topics of coronavirus research from 2003 to 2020. Methods: We explored the publications on coronavirus in the Web of Science core collection (WOSCC) from 2003 to 2020. Bibliometric analysis, evaluating knowledge base, and research hotspots were performed based on CiteSpace V (Drexel University, Chaomei Chen). Results: There were a total of 8433 publications of coronavirus. The research on coronavirus boomed when a novel coronavirus triggered outbreaks in people. The leading country was the United States, and the leading institution was the University of Hong Kong. The most productive researchers were: Yuen KY, Drosten C, Baric RS. The keywords analysis showed that SARS-CoV, infection, acute respiratory syndrome, antibody, receptor, and spike protein were research hotspots. The research categories analysis showed that virology, microbiology, veterinary sciences, infectious diseases, and biochemistry and molecular biology were hot research categories. Conclusions: Bibliometric analysis of the literature shows the research on coronavirus boomed when a novel coronavirus triggered outbreaks in people. With the end of the epidemic, the research tended to be cooling. Virus identification, pathogenesis, and coronavirus-mediated diseases attracted much attention. We must continue studying the viruses after an outbreak ended.
C1 [Jia, Qiulei; Shi, Shuqing; Yuan, Guozhen; Shi, Jingjing; Shi, Shuai; Hu, Yuanhui] China Acad Chinese Med Sci, Cardiovasc Dept, Guanganmen Hosp, Beijing 100053, Peoples R China.
   [Jia, Qiulei; Shi, Shuqing] Beijing Univ Chinese Med, Grad Sch, Beijing, Peoples R China.
RP Hu, YH (通讯作者)，China Acad Chinese Med Sci, Cardiovasc Dept, Guanganmen Hosp, Beijing 100053, Peoples R China.
EM huiyuhui55@sohu.com
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Z9 9
U1 4
U2 15
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0025-7974
EI 1536-5964
J9 MEDICINE
JI Medicine (Baltimore)
PD MAY 29
PY 2020
VL 99
IS 22
AR 20378
DI 10.1097/MD.0000000000020378
PG 14
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA MO4PW
UT WOS:000551511100068
PM 32481423
OA gold
DA 2022-08-23
ER

PT J
AU Huang, L
   Chen, K
   Zhou, M
AF Huang, Li
   Chen, Ke
   Zhou, Mi
TI Climate change and carbon sink: a bibliometric analysis
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article
DE Bibliometric analysis; Climate change; Carbon sink; CiteSpace; Mapping
   knowledge domain
ID FOREST BIOMASS CARBON; LAND-USE CHANGE; CHANGE IMPACTS; ATMOSPHERIC CO2;
   TERRESTRIAL ECOSYSTEMS; KNOWLEDGE DOMAIN; EMERGING TRENDS; BALANCE;
   WATER; SEQUESTRATION
AB In recent years, climate change and carbon sinks have been widely studied by the academic community, and relevant research results have emerged in abundance. In this paper, a scientometric analysis of 747 academic works published between 1991 and 2018 related to climate change and carbon sinks is presented to characterize the intellectual landscape by identifying and revealing the basic characteristics, research power, intellectual base, research topic evolution, and research hotspots in this field. The results show that & x2460; the number of publications in this field has increased rapidly and the field has become increasingly interdisciplinary; & x2461; the most productive authors and institutions in this subject area are in the USA, China, Canada, Australia, and European countries, and the cooperation between these researchers is closer than other researchers in the field; & x2462; 11 of the 747 papers analyzed in this study have played a key role in the evolution of the field; and & x2463; in this paper, we divide research hotspots into three decade-long phases (1991-1999, 2000-2010, and 2011-present). Drought problems have attracted more and more attention from scholars. In the end, given the current trend of the studies, we conclude a list of research potentials of climate change and carbon sinks in the future. This paper presents an in-depth analysis of climate change and carbon sink research to better understand the global trends and directions that have emerged in this field over the past 28 years, which can also provide reference for future research in this field.
C1 [Huang, Li; Chen, Ke; Zhou, Mi] Shenyang Agr Univ, Coll Econom, Management, Shenyang, Liaoning, Peoples R China.
RP Zhou, M (通讯作者)，Shenyang Agr Univ, Coll Econom, Management, Shenyang, Liaoning, Peoples R China.
EM zhoumi2011@syau.edu.cn
RI Huang, Li/AAY-8487-2020
OI huang, li/0000-0001-6048-7277
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NR 115
TC 21
Z9 22
U1 39
U2 146
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 MAR
PY 2020
VL 27
IS 8
BP 8740
EP 8758
DI 10.1007/s11356-019-07489-6
PG 19
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA KQ7FI
UT WOS:000517084600083
PM 31912388
DA 2022-08-23
ER

PT J
AU Wang, H
   Liu, H
   Yao, JY
   Ye, D
   Lang, ZH
   Glowacz, A
AF Wang, Han
   Liu, Hui
   Yao, Jieyu
   Ye, Dong
   Lang, Zhihui
   Glowacz, Adam
TI Mapping the knowledge domains of new energy vehicle safety: Informetrics
   analysis-based studies
SO JOURNAL OF ENERGY STORAGE
LA English
DT Article
DE New energy vehicle (nev); Safety; Research hotspot; Bibliometrics;
   Mapping knowledge domains
ID HYDROGEN REFUELING STATION; LI-ION BATTERIES; OF-THE-ART; ELECTRIC
   VEHICLE; LITHIUM BATTERIES; PHOSPHO-OLIVINES; STORAGE DEVICES;
   FUEL-CELLS; DESIGN; HYBRID
AB To better understand the current status and development of new energy vehicle (NEV) safety studies and explore the knowledge base and research hotspots, 1,007 papers related to NEV safety studies from 2000-2020 were collected from the Web of Science Core Collection database as data samples. The bibliometric method and mapping knowledge domain technology were used to visually analyze the knowledge structure and research trends in the field of NEV safety studies. The study found that publications in the United States rank first among countries/regions. Tsinghua University has the most publications in related research; the disciplines of NEV safety research mainly focus on energy and fuel, chemistry and electrochemistry; and the Journal of Power Sources, International Journal of Hydrogen Energy and International Journal of Crashworthiness are the core journals in the field. New energy exploration and application as well as performance improvement/lifetime optimization/ use safety of lithium-ion batteries are the research knowledge bases in the field. Lithium-ion batteries, fuel cell and hydrogen energy safety, autonomous driving technology, computer simulation, and vehicle crashworthiness are the five main research directions. Research on the safety of NEVs has basically formed a theoretical research framework, but the system construction of various research directions has not yet been fully formed. The research topics are concentrated, but a clear evolutionary path has not been formed. The current research hotspots and frontiers are how to improve the performance of lithium-ion batteries, the safety of fuel cells, the autonomous driving technology and the crashworthiness of NEVs.
C1 [Wang, Han; Liu, Hui; Yao, Jieyu; Ye, Dong; Lang, Zhihui] China Jiliang Univ, Coll Qual & Safety Engn, Hangzhou 310018, Peoples R China.
   [Glowacz, Adam] AGH Univ Sci & Technol, Fac Elect Engn Automat Comp Sci & Biomed Engn, Al A Mickiewicza 30, PL-30059 Krakow, Poland.
RP Liu, H (通讯作者)，China Jiliang Univ, Coll Qual & Safety Engn, Hangzhou 310018, Peoples R China.
EM liuhui2003@126.com
RI Glowacz, Adam/N-6462-2013; Glowacz, Adam/I-1024-2019; liu,
   hui/K-2142-2015
OI Glowacz, Adam/0000-0003-0546-7083; Glowacz, Adam/0000-0003-0546-7083;
   liu, hui/0000-0002-4265-7241
FU Zhejiang Provincial Natural Science Foundation of China [LY14E040001];
   Science and Technology Project of Department of Education of Zhejiang
   Province [Y202045438]; Fundamental Research Funds for the Provincial
   Universities of Zhejiang [2020YW55]
FX This work was supported in part by the Zhejiang Provincial Natural
   Science Foundation of China (No. LY14E040001) , the Science and
   Technology Project of Department of Education of Zhejiang Province (No.
   Y202045438) and the Fundamental Research Funds for the Provincial
   Universities of Zhejiang (No. 2020YW55) .
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NR 105
TC 8
Z9 8
U1 6
U2 40
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
EI 2352-152X
J9 J ENERGY STORAGE
JI J. Energy Storage
PD MAR
PY 2021
VL 35
AR 102275
DI 10.1016/j.est.2021.102275
EA JAN 2021
PG 19
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Energy & Fuels
GA QT6HY
UT WOS:000626688000006
DA 2022-08-23
ER

PT J
AU Dong, HL
   Wu, QH
   Pang, Y
   Wu, BY
AF Dong, Huiling
   Wu, Qunhong
   Pang, Yue
   Wu, Bingyi
TI A comparative analysis on risk communication between international and
   Chinese literature from the perspective of knowledge domain
   visualization
SO ENVIRONMENTAL HEALTH AND PREVENTIVE MEDICINE
LA English
DT Article
DE Risk communication; Risk perception; Research hotspot; Knowledge domains
ID EMERGING TRENDS; HEALTH; CITESPACE
AB Background The outbreak of coronavirus disease (COVID-19) severely damaged and endangered people's lives at the end of 2019. Risk communication plays an important role in the response to it successfully, which has been appreciated by the World Health Organization. Therefore, a comprehensive analysis of risk communication research is necessary, which can understand current research hotspots and reveal new trends. Methods In this study, we collected 1134 international articles from the Web of Science database and 3983 Chinese articles from the China National Knowledge Infrastructure database. Bibliometric and mapping knowledge domain analysis methods were used for temporal distribution analysis, cooperation network analysis, co-word network analysis, and burst detection analysis. Results The first article in this field was published by western scholars earlier, while the first Chinese article in 2002. Research institutions mainly come from universities. The USA plays a key role in this field. Chinese scholars had a closer cooperation network, but there was less cooperation among domestic institutions. Risk perception, trust, risk management, and risk information had always been the research hotspots in this academic. Trust, sentiment research, and public risk events were essential directions for the future. There are 25 burst words for international articles, while 11 burst words for Chinese articles from 2000 to 2020. Conclusions In summary, both domestic and international researchers are concerned about risk communication, risk perception, trust, and risk information. International research on risk communication is systematic and comprehensive relatively. However, Chinese scholars take severe acute respiratory syndrome as the research background and reviewing foreign knowledge as the research starting point. With the purpose of practical and applied research based on a public emergency, the risk communication research lacks continuity in Chinese academy in the past years.
C1 [Dong, Huiling] Weifang Med Univ, Dept Publ Hlth, 7166 Baotong West St, Weifang 261053, Shandong, Peoples R China.
   [Wu, Qunhong] Harbin Med Univ, Dept Hlth Management, 157 Hlth Rd, Harbin 150081, Peoples R China.
   [Pang, Yue; Wu, Bingyi] Weifang Med Univ, Dept Management, 7166 Baotong West St, Weifang 261053, Shandong, Peoples R China.
RP Wu, QH (通讯作者)，Harbin Med Univ, Dept Hlth Management, 157 Hlth Rd, Harbin 150081, Peoples R China.
EM 15965787355@163.com
FU National Social Science Foundation of China [:18BRK013]; Shandong
   Education Science Plan [:2020YB050]
FX The study was supported by the National Social Science Foundation of
   China (Project approval No:18BRK013) and Shandong Education Science Plan
   (Project approval No:2020YB050).
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NR 44
TC 1
Z9 1
U1 25
U2 38
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1342-078X
EI 1347-4715
J9 ENVIRON HEALTH PREV
JI Environ. Health Prev.
PD MAY 28
PY 2021
VL 26
IS 1
AR 60
DI 10.1186/s12199-021-00981-x
PG 11
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA SJ9PO
UT WOS:000655857800001
PM 34049481
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Lang, ZH
   Liu, H
   Meng, N
   Wang, HN
   Wang, H
   Kong, FY
AF Lang, Zhihui
   Liu, Hui
   Meng, Na
   Wang, Haining
   Wang, Han
   Kong, Fanyu
TI Mapping the knowledge domains of research on fire safety - an
   informetrics analysis
SO TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
LA English
DT Article
DE Fire safety; Informetrics; Knowledge base; Research hotspots; Mapping
   knowledge domain
ID HEAT RELEASE RATES; FLAME RETARDANCY; RESEARCH TRENDS; TEMPERATURE
   PROFILE; SMOKE FLOW; GRAPHENE; VENTILATION; PERFORMANCE; SIMULATION;
   SUPPRESSION
AB To understand the current status and the development of fire safety studies, explore the knowledge bases and research frontiers, 2027 fire safety-related papers from 2000 to 2019 were retrieved from the SCIE and SSCI databases of the Web of Science platform as data samples. Bibliometric analysis methods for informetrics, such as co-occurrence analysis, co-citation analysis, and burst analysis of keywords were adopted for comprehensive visualization analysis of fire safety studies by means of two tools of mapping knowledge domains. The results show that China has the highest number of publications in the country/region and that the University of Science and Technology of China has the highest number of publications of relevant institutions. Analysis of flame retardance, thermodynamics and flame retardant polymer development of graphite and other nanomaterials are the knowledge bases in the field of fire safety studies. Fire Safety Journal, Fire Technology, Fire and Materials, Journal of Fire Sciences, and Building and Environment are the core journals in this field. Tunnelling and Underground Space Technology, Fire Safety Journal and Fire Technology are the top 3 journals related to fire safety studies of tunnel and underground space. The research topics were aggregated into three core paths: the "fire safety of building and tunnel" path, the "combustion behavior-simulation analysis" path, and the "flame-retardant material" path. Building and tunnel fires, forest fires, and materials science engineering have been hot research areas in recent years. At present, the basic theory and research system of fire safety studies are essentially completed, and there are many future research directions with extensive branches. The fire safety of tunnel and underground space, intelligent firefighting and rescue, the basic theories and research systems in the field of fire and flame retardant materials represent the current frontiers of fire safety studies.
C1 [Lang, Zhihui; Liu, Hui; Wang, Haining; Wang, Han; Kong, Fanyu] China Jiliang Univ, Coll Qual & Safety Engn, Hangzhou 310018, Peoples R China.
   [Meng, Na] Shandong Univ Sci & Technol, Coll Safety & Environm Engn, Qingdao 266590, Shandong, Peoples R China.
RP Liu, H (通讯作者)，China Jiliang Univ, Coll Qual & Safety Engn, Hangzhou 310018, Peoples R China.
EM liuhui2003@126.com
RI liu, hui/K-2142-2015
OI liu, hui/0000-0002-4265-7241
FU Key Laboratory of Safety Engineering and Technology Research of Zhejiang
   Province [201904]; Natural Science Foundation of China [51974175];
   Zhejiang Provincial Natural Science Foundation of China [LY14E040001];
   Science and Technology Project of Department of Education of Zhejiang
   Province [Y201942504]; Key Research and Development Program of Zhejiang
   Province [2018C01031-1]
FX This work was supported in part by the Key Laboratory of Safety
   Engineering and Technology Research of Zhejiang Province (No. 201904),
   the Natural Science Foundation of China (No. 51974175), the Zhejiang
   Provincial Natural Science Foundation of China (No. LY14E040001), the
   Science and Technology Project of Department of Education of Zhejiang
   Province (Y201942504) and Key Research and Development Program of
   Zhejiang Province (2018C01031-1).
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NR 75
TC 5
Z9 5
U1 9
U2 57
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0886-7798
EI 1878-4364
J9 TUNN UNDERGR SP TECH
JI Tunn. Undergr. Space Technol.
PD FEB
PY 2021
VL 108
AR 103676
DI 10.1016/j.tust.2020.103676
PG 18
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Construction & Building Technology; Engineering
GA PQ3RX
UT WOS:000606465500004
DA 2022-08-23
ER

PT J
AU Xu, JW
   Zhao, FK
   Fang, JX
   Shi, MP
   Pan, JC
   Sun, W
   Du, CX
AF Xu, Jiawei
   Zhao, Fangkun
   Fang, Jianxia
   Shi, Meipan
   Pan, Junchao
   Sun, Wen
   Du, Chixin
TI Mapping research trends of chronic ocular graft-versus-host disease from
   2009 to 2020: a bibliometric analysis
SO INTERNATIONAL OPHTHALMOLOGY
LA English
DT Article; Early Access
DE Ocular chronic graft versus host disease; Bibliometric analysis; Mapping
   knowledge domain; VOSviewer; Hematopoietic stem cell transplantation
ID SERUM EYE DROPS; CONSENSUS DEVELOPMENT PROJECT; SEVERE DRY EYE;
   CLINICAL-TRIALS; DIAGNOSIS; CRITERIA; CITATION
AB Purpose To evaluate the global scientific output of research on ocular chronic Graft versus host disease (cGVHD) and explore the current status and trends in this field over the past decade by bibliometric analysis. Methods The bibliometric search was performed in the Web of Science Core Collection (WoSCC) database. VOSviewer v.1.6.16 was used to map the knowledge domain. The annual number of publications and citations, distribution of countries and organizations, productivity of authors and journals, international collaborations, cited references, and keywords in the field of ocular cGVHD were visualized. Results In total, 398 peer-reviewed publications from 2009 to 2020 on ocular cGVHD were retrieved. The United States among all countries had the highest number of publications and citations, and Keio University was the most effective institution. Dana, R ranks the highest regarding the number of publications and citations on ocular cGVHD. Cornea and Biology of Blood and Marrow Transplantation were the most-cited journals in ocular cGVHD studies from ophthalmology and hematology, respectively. The top-cited references were primarily centered on dry eye. The keywords constituted three clusters: (1) consensus criteria and epidemiology of ocular cGVHD, (2) preclinical medical research of ocular cGVHD, and (3) treatment. Conclusion Based on the data retrieved from WoSCC, a comparative analysis of the quantity and quality of papers on ocular cGVHD was conducted through bibliometric methods. This may contribute to better understanding of the status of ocular cGVHD study. The three major research topics shed new light on the ocular cGVHD study as well as meaningful materials for scholars to identify potential collaborators and promising partner institutions.
C1 [Xu, Jiawei; Fang, Jianxia; Shi, Meipan; Pan, Junchao; Sun, Wen; Du, Chixin] Zhejiang Univ, Dept Ophthalmol, Affiliated Hosp 1, Coll Med, 79 Qingchun Rd, Hangzhou 310003, Peoples R China.
   [Zhao, Fangkun] China Med Univ, Dept Ophthalmol, Affiliated Hosp 4, Hosp Eye, Shenyang, Liaoning, Peoples R China.
RP Du, CX (通讯作者)，Zhejiang Univ, Dept Ophthalmol, Affiliated Hosp 1, Coll Med, 79 Qingchun Rd, Hangzhou 310003, Peoples R China.
EM duchixin@zju.edu.cn
FU Higher Education Institutions Scientific Research Project of Liaoning
   Province [LJKZ0773]; Medical Science and Technology Project of Zhejiang
   Province [2021KY157]
FX This study was funded by Higher Education Institutions Scientific
   Research Project of Liaoning Province (Grant No. LJKZ0773) and Medical
   Science and Technology Project of Zhejiang Province (Grant No.
   2021KY157).
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Z9 0
U1 4
U2 4
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0165-5701
EI 1573-2630
J9 INT OPHTHALMOL
JI Int. Ophthalmol.
DI 10.1007/s10792-022-02380-9
EA JUL 2022
PG 14
WC Ophthalmology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Ophthalmology
GA 2X7FP
UT WOS:000825365700001
PM 35799074
DA 2022-08-23
ER

PT J
AU Zhou, XY
   Li, TC
   Ma, XQ
AF Zhou, Xinyu
   Li, Tuochen
   Ma, Xiaoqi
TI A bibliometric analysis of comparative research on the evolution of
   international and Chinese green supply chain research hotspots and
   frontiers
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Review
DE Green supply chain; Bibliometrics; Mapping knowledge domain;
   Visualization; Research hotspots; Evolution of frontiers
ID OF-THE-ART; MANAGEMENT-PRACTICES; REVERSE LOGISTICS; EMERGING TRENDS;
   PERFORMANCE; DESIGN; OPTIMIZATION; FRAMEWORK; SUSTAINABILITY;
   COORDINATION
AB Green supply chain (GSC), one of the most vital sub-topics of sustainable development, indicates people provoking on the rationality of business practices and resource consumption patterns. Under the background of economy globalization, developing countries, especially China, severely affected by green barriers became the global focus. A systematic review of articles about GSC which published in leading journals of Web of Science Core Collection (WoSCC) and China Knowledge Resource Infrastructure (CNKI) is proposed for exploring publishing trends, the distribution of authors and journals, research topics, and hotspots and predicting frontiers by utilizing VOSviewer, Sci2, and CiteSpace. The results show that (1) there are differences in the attention of GSC between international and Chinese academia. (2) "Green" is referred to environmental friendly practices in international academia. Scholars advocate to promote management to strengthen cooperation among GSC members and boost technology investment to improve the comprehensive performance; however, specific practices such as "low-carbon," "emission reduction," "recycling," and "remanufacture" are referred to environmental friendly behaviors in Chinese academia. Scholars expect to avoid enterprises' short-term profit compression relying on government subsidies and make contracts to share environment protection cost equally out for ensuring GSC stable operation. (3) Exploring collaboration among GSC members using complex operation research and artificial intelligence will be international research frontier. Relevant papers are to provide Chinese research with merely innovation in methodology. Besides, the "government-enterprise-university-research institute-customer-economy" management mode proposed by development countries like China will enrich the international GSC research scope, leading international GSC knowledge structure to change. The contribution of this study is to afford reference for future research on GSC.
C1 [Zhou, Xinyu; Li, Tuochen] Harbin Engn Univ, Sch Econ & Management, Harbin 150001, Heilongjiang, Peoples R China.
   [Ma, Xiaoqi] Heilongjiang Univ, Sch Informat Management, Harbin 150008, Heilongjiang, Peoples R China.
RP Zhou, XY (通讯作者)，Harbin Engn Univ, Sch Econ & Management, Harbin 150001, Heilongjiang, Peoples R China.
EM zhouxinyu0824@foxmail.com
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NR 148
TC 11
Z9 11
U1 84
U2 206
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 FEB
PY 2021
VL 28
IS 6
BP 6302
EP 6323
DI 10.1007/s11356-020-11947-x
EA JAN 2021
PG 22
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA PZ0ZK
UT WOS:000604210000003
PM 33389578
DA 2022-08-23
ER

PT J
AU Li, J
   Zhu, YH
   Feng, JN
   Meng, WJ
   Begma, K
   Zhu, GP
   Wang, XX
   Wu, D
   Shi, FY
   Wang, SZ
AF Li, Juan
   Zhu, Yuhang
   Feng, Jianing
   Meng, Weijing
   Begma, Kseniia
   Zhu, Gaopei
   Wang, Xiaoxuan
   Wu, Di
   Shi, Fuyan
   Wang, Suzhen
TI A comparative study of international and Chinese public health emergency
   management from the perspective of knowledge domains mapping
SO ENVIRONMENTAL HEALTH AND PREVENTIVE MEDICINE
LA English
DT Article
DE Public health; Emergency management; Knowledge domains
ID BIBLIOMETRIC ANALYSIS; TRENDS; PREPAREDNESS; CITESPACE; EVOLUTION
AB Background At the end of 2019, the outbreak of coronavirus disease 2019 (COVID-19) severely damaged and endangered people's lives. The public health emergency management system in China has played an essential role in handling the response to the outbreak, which has been appreciated by the World Health Organization and some countries. Hence, it is necessary to conduct an overall analysis of the development of the health emergency management system in China. This can provide a reference for scholars to aid in understanding the current situation and to reveal new research topics. Methods We collected 2247 international articles from the Web of Science database and 959 Chinese articles from the China National Knowledge Infrastructure database. Bibliometric and mapping knowledge domain analysis methods were used in this study for temporal distribution analysis, cooperation network analysis, and co-word network analysis. Results The first international article in this field was published in 1991, while the first Chinese article was published in 2005. The research institutions producing these studies mainly existed in universities and health organizations. Developed countries and European countries published the most articles overall, while eastern China published the most articles within China. There were 52 burst words for international articles published from 1999-2018 and 18 burst words for Chinese articles published from 2003-2018. International top-ranked articles according to the number of citations appeared in 2005, 2007, 2009, 2014, 2015, and 2016, while the corresponding Chinese articles appeared in 2003, 2004, 2009, and 2011. Conclusions There are differences in the regional and economic distribution of international and Chinese cooperation networks. International research is often related to timely issues mainly by focusing on emergency preparedness and monitoring of public health events, while China has focused on public health emergencies and their disposition. International research began on terrorism and bioterrorism, followed by disaster planning and emergency preparedness, epidemics, and infectious diseases. China considered severe acute respiratory syndrome as the starting research background and the legal system construction as the research starting point, which was followed by the mechanism, structure, system, and training abroad for public health emergency management.
C1 [Li, Juan; Zhu, Yuhang; Feng, Jianing; Zhu, Gaopei; Wang, Xiaoxuan; Shi, Fuyan; Wang, Suzhen] Weifang Med Univ, Sch Publ Hlth, Weifang 261053, Peoples R China.
   [Zhu, Yuhang] Univ Med Ctr Hamburg Eppendorf, Ctr Psychosocial Med, D-20246 Hamburg, Germany.
   [Meng, Weijing] Weifang Med Univ, Sch Life Sci & Technol, Weifang 261053, Peoples R China.
   [Begma, Kseniia] Weifang Med Univ, Sch Foreign Languages, Weifang 261053, Peoples R China.
   [Wu, Di] Weifang Med Univ, Dept Teaching Qual Monitoring & Evaluat, Weifang 261053, Peoples R China.
RP Shi, FY; Wang, SZ (通讯作者)，Weifang Med Univ, Sch Publ Hlth, Weifang 261053, Peoples R China.
EM shifuyan@126.com; wangsz@wfmc.edu.cn
RI Zhu, Yuhang/ABD-3257-2021
OI Zhu, Yuhang/0000-0002-9236-2812; Zhu, Gaopei/0000-0002-2085-9336; Shi,
   Fuyan/0000-0002-0972-8335; Li, Juan/0000-0001-8710-4854
FU National Natural Science Foundation of China [81872719, 81803337];
   Natural Science Foundation of Shandong Province [ZR2019MH034]
FX This research was supported by two National Natural Science Foundation
   of China (Project approval No.: 81872719 and 81803337) and the Natural
   Science Foundation of Shandong Province (Project approval No.:
   ZR2019MH034).
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NR 64
TC 3
Z9 3
U1 13
U2 54
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1342-078X
EI 1347-4715
J9 ENVIRON HEALTH PREV
JI Environ. Health Prev.
PD OCT 2
PY 2020
VL 25
IS 1
AR 57
DI 10.1186/s12199-020-00896-z
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 NV6VL
UT WOS:000574456400001
PM 33008319
OA Green Published, gold
DA 2022-08-23
ER

PT J
AU Shen, JK
   Li, JH
   Yu, PM
   Du, GJ
AF Shen, Junkai
   Li, Jiahuan
   Yu, Peiming
   Du, Gangjun
TI Research Status and Hotspots of Anticancer Natural Products Based on the
   Patent Literature and Scientific Articles
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE cancer; natural product; patent analysis; mapping knowledge domain;
   traditional Chinese medicine
ID CANCER STATISTICS; INHIBITORS; MECHANISM; DISCOVERY; AGENT; MODEL
AB Background: The patent literature contains a large amount of information on the internal state of current industrial technologies that are not available in other literature studies. Scientific articles are the direct achievements of theoretical research in this field and can reveal how current theories in basic research have developed. In this study, the progress and status of natural anticancer products in this field were summarized, and the research hotspots were explored through the analysis of the relevant patent literature and scientific articles. Methods: Patent data were retrieved from the incoPat patent retrieval database, and paper data were retrieved from the Web of Science core set and PubMed. GraphPad Prism 8, Microsoft Excel 2010, and CiteSpace 5.8.R3 were used to perform visual processing. The analyzed patent literature includes the patent applicant type, country (or region), and technical subject. The analyzed scientific article includes academic groups, subject areas, keyword clustering, and burst detection. Results: A total of 20,435 patent families and 38,746 articles were collected by 4 January 2022. At present, antitumor drugs derived from natural products mainly include 1) apoptosis inducers such as curcumin, gallic acid, resveratrol, Theranekron D6, and gaillardin; 2) topoisomerase inhibitors such as camptothecins, scaffold-hopped flavones, podophyllotoxin, oxocrebanine, and evodiamine derivatives; 3) telomerase inhibitors such as camptothecin and isoquinoline alkaloids of Chelidonium majus, amentoflavone, and emodin; 4) microtubule inhibitors such as kolaflavanone, tanshinone IIA analog, eugenol, and millepachine; 5) immunomodulators such as fucoidan, myricetin, bergapten, and atractylenolide I; 6) tumor microenvironment regulators such as betaescin and icaritin; 7) multidrug resistance reversal agents such as berberine, quercetin, and dihydromyricetin; and 8) antiangiogenic and antimetastatic agents such as epigallocatechin-3-gallate, lupeol, ononin, and saikosaponin A. Conclusion: Anticancer natural product technology was introduced earlier, but the later development momentum was insufficient. In addition, scientific research activities are relatively closed, and technical exchanges need to be strengthened. Currently, the development of medicinal plants and the research on the anticancer mechanism of natural active products are still research hotspots, especially those related to immune checkpoints, essential oils, and metastatic cancer. Theories of traditional Chinese medicine (TCM), such as "restraining excessiveness to acquire harmony, " "same treatment for different diseases, " "Meridian induction theory, " and "Fuzheng Quxie, " have important guiding significance to the research of anticancer mechanisms and the development of new drugs and can provide new ideas for this process.
C1 [Shen, Junkai; Li, Jiahuan; Yu, Peiming; Du, Gangjun] Henan Univ, Sch Pharm, Kaifeng, Peoples R China.
RP Li, JH; Du, GJ (通讯作者)，Henan Univ, Sch Pharm, Kaifeng, Peoples R China.
EM jiahuan1020@126.com; 10200029@vip.henu.edu.cn
FU Advanced Education Reform Research and Practice Project of Henan
   Province; Graduate Education Innovation and Quality Improvement Program
   of Henan University [SYL20040116]; Undergraduate Education Reform
   Research and Practice Project of Henan University 2020 [20200174];
   Graduate Education Reform Research and Practice Project of Henan
   University 2022 [YJSJG2022XJ036]
FX The research was supported by the Advanced Education Reform Research and
   Practice Project of Henan Province, the Graduate Education Innovation
   and Quality Improvement Program of Henan University (SYL20040116), the
   Undergraduate Education Reform Research and Practice Project of Henan
   University 2020 (20200174), and the Graduate Education Reform Research
   and Practice Project of Henan University 2022 (YJSJG2022XJ036).
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NR 74
TC 0
Z9 0
U1 4
U2 4
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1663-9812
J9 FRONT PHARMACOL
JI Front. Pharmacol.
PD JUN 17
PY 2022
VL 13
AR 903239
DI 10.3389/fphar.2022.903239
PG 18
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA 2P0TW
UT WOS:000819465300001
PM 35784720
OA gold, Green Published
DA 2022-08-23
ER

EF