001438907 000__ 08721cam\a2200877\i\4500 001438907 001__ 1438907 001438907 003__ OCoLC 001438907 005__ 20230309004400.0 001438907 006__ m\\\\\o\\d\\\\\\\\ 001438907 007__ cr\cn\nnnunnun 001438907 008__ 210814s2021\\\\sz\a\\\\o\\\\\101\0\eng\d 001438907 019__ $$a1263339850$$a1263341138$$a1263341482$$a1268574334 001438907 020__ $$a9783030821364$$q(electronic bk.) 001438907 020__ $$a3030821366$$q(electronic bk.) 001438907 020__ $$a9783030821531$$q(electronic bk.) 001438907 020__ $$a3030821536$$q(electronic bk.) 001438907 020__ $$a9783030821470$$q(electronic bk.) 001438907 020__ $$a3030821471$$q(electronic bk.) 001438907 020__ $$z9783030821357 001438907 020__ $$z3030821358 001438907 020__ $$z3030821463 001438907 020__ $$z9783030821463 001438907 020__ $$z3030821528 001438907 020__ $$z9783030821524 001438907 0247_ $$a10.1007/978-3-030-82136-4$$2doi 001438907 035__ $$aSP(OCoLC)1263874892 001438907 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dGW5XE$$dYDX$$dOCLCO$$dOCLCQ$$dDKU$$dOCLCF$$dSFB$$dOCLCO$$dOCLCQ$$dCOM$$dOCLCO$$dOCLCQ 001438907 049__ $$aISEA 001438907 050_4 $$aQA76.76.E95$$bK77 2021 001438907 08204 $$a006.3/31$$223 001438907 1112_ $$aKSEM (Conference)$$n(14th :$$d2021 :$$cTokyo, Japan) 001438907 24510 $$aKnowledge science, engineering and management :$$b14th international conference, KSEM 2021, Tokyo, Japan, August 14-16, 2021, proceedings.$$nPart I /$$cHan Qiu, Cheng Zhang, Zongming Fei, Meikang Qiu, Sun-Yuan Kung (eds.). 001438907 24630 $$aKSEM 2021 001438907 264_1 $$aCham :$$bSpringer,$$c[2021] 001438907 264_4 $$c©2021 001438907 300__ $$a1 online resource (710 pages) :$$billustrations (chiefly color) 001438907 336__ $$atext$$btxt$$2rdacontent 001438907 337__ $$acomputer$$bc$$2rdamedia 001438907 338__ $$aonline resource$$bcr$$2rdacarrier 001438907 347__ $$atext file 001438907 347__ $$bPDF 001438907 4901_ $$aLecture notes in computer science. Lecture notes in artificial intelligence ;$$v12815 001438907 4901_ $$aLNCS sublibrary: SL7 - Artificial intelligence 001438907 500__ $$aIncludes author index. 001438907 5050_ $$aKnowledge Science with Learning and AI (KSLA) -- Research on Innovation Trends of AI Applied to Medical Instruments Using Informetrics Based on Multi-Sourse Information -- Extracting Prerequisite Relations among Wikipedia Concepts using the Clickstream Data -- Clustering Massive-categories and Complex Documents via Graph Convolutional Network -- Structure-enhanced Graph Representation Learning for Link Prediction in Signed Networks -- A Property-based Method for Acquiring Commonsense Knowledge -- Multi-hop Learning promote Cooperation in Multi-agent Systems -- FedPS: Model Aggregation with Pseudo Samples -- Dense Incremental Extreme Learning Machine with Accelerating -- Amount and Proportional Integral Differential -- Knowledge-based Diverse Feature Transformation For Few-shot Relation Classification -- Community Detection In Dynamic Networks: A Novel Deep Learning Method -- Additive Noise Model Structure Learning Based on Rank Statistics -- A MOOCs Recommender System Based on User's Knowledge Background -- TEBC-Net: An effective relation extraction approach for simple question answering over knowledge graphs -- Representing Knowledge Graphs with Gaussian Mixture Embedding -- A Semi-supervised Multi-objective Evolutionary Algorithm for Multi-layer Network Community Detection -- Named Entity Recognition Based on Reinforcement Learning and Adversarial Training -- Improved Partitioning Graph Embedding Framework for Small Cluster -- A Framework of Data Fusion through Spatio-temporal Knowledge Graph -- SEGAR: Knowledge Graph Augmented Session-based Recommendation -- Symbiosis: A Novel Framework for Integrating Hierarchies from Knowledge Graph into Recommendation System -- An Ensemble Fuzziness-based Online Sequential Learning Approach and Its Application -- GASKT: A Graph-based Attentive Knowledge-Search Model for Knowledge Tracing -- Fragile Neural Network Watermarking with Trigger Image Set -- Introducing Graph Neural Networks for Few-Shot Relation Prediction in Knowledge Graph Completion Task -- A Research Study on Running Machine Learning Algorithms on Big Data with Spark -- Attentional Neural Factorization Machines for Knowledge Tracing -- Node-Image CAEïơA Novel Embedding Method via Convolutional Auto-Encoder and High-Order Proximities -- EN-DIVINE: An Enhanced Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning -- Knowledge Distillation via Channel Correlation Structure -- Feature Interaction Convolutional Network for Knowledge Graph Embedding -- Towards a Modular Ontology for Cloud Consumer Review Mining -- Identification of Critical Nodes in Urban Transportation Network through Network Topology and Server Routes -- Graph Ensemble Networks for Semi-Supervised Embedding Learning -- Rethinking the Information inside Documents for Sentiment Classification -- Dependency Parsing Representation Learning for Open Information Extraction -- Hierarchical Policy Network with Multi-Agent for Knowledge Graph Reasoning Based on Reinforcement Learning -- Inducing Bilingual Word Representations for Non-Isomorphic Spaces by an Unsupervised Way -- A Deep Learning Model Based on Neural Bag-of-words Attention for Sentiment Analysis -- Graph Attention Mechanism with Cardinality Preservation for Knowledge Graph Completion -- Event Relation Reasoning Based on Event Knowledge Graph -- PEN4Rec: Preference Evolution Networks for Session-based Recommendation -- HyperspherE: An Embedding Method for Knowledge Graph Completion Based on Hypersphere -- TroBo: A Novel Deep Transfer Model for Enhancing Cross-project Bug Localization -- A Neural Language Understanding for Dialogue State Tracking -- Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer -- Knowledge Tracing with Exercise-Enhanced Key-Value Memory Networks -- Entity Alignment between Knowledge Graphs Using Entity Type Matching -- Text-Aware Recommendation Model Based on Multi-Attention Neural Network -- Chinese Named Entity Recognition Based on Gated Graph Neural Network -- Learning a Similarity Metric Discriminatively with Application to Ancient Character Recognition -- Incorporating Global Context into Multi-task Learning for Session-based Recommendation -- Exploring Sequential and Collaborative Contexts for Next Point-of-Interest Recommendation -- Predicting User Preferences via Heterogeneous Information Network and Metric Learning -- An IoT Ontology Class Recommendation Method Based on Knowledge Graph -- Ride-Sharing Matching of Commuting Private Car using Reinforcement Learning -- Optimization of Remote Desktop with CNN Based Image Compression Model. 001438907 506__ $$aAccess limited to authorized users. 001438907 520__ $$aThis three-volume set constitutes the refereed proceedings of the 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021, held in Tokyo, Japan, in August 2021. The 164 revised full papers were carefully reviewed and selected from 492 submissions. The contributions are organized in the following topical sections: knowledge science with learning and AI; knowledge engineering research and applications; knowledge management with optimization and security. 001438907 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 17, 2021). 001438907 650_0 $$aKnowledge acquisition (Expert systems)$$vCongresses. 001438907 650_0 $$aKnowledge management$$vCongresses. 001438907 650_0 $$aDecision making$$xData processing$$vCongresses. 001438907 650_0 $$aProblem solving$$xData processing$$vCongresses. 001438907 650_0 $$aInformation technology$$vCongresses. 001438907 650_0 $$aData mining$$vCongresses. 001438907 650_6 $$aGestion des connaissances$$vCongrès. 001438907 650_6 $$aPrise de décision$$xInformatique$$vCongrès. 001438907 650_6 $$aTechnologie de l'information$$vCongrès. 001438907 650_6 $$aExploration de données (Informatique)$$vCongrès. 001438907 655_7 $$aConference papers and proceedings.$$2lcgft 001438907 655_7 $$aActes de congrès.$$2rvmgf 001438907 655_0 $$aElectronic books. 001438907 7001_ $$aQiu, Han,$$eeditor. 001438907 7001_ $$aZhang, Cheng,$$eeditor. 001438907 7001_ $$aFei, Zongming,$$eeditor. 001438907 7001_ $$aQiu, Meikang,$$eeditor. 001438907 7001_ $$aKung, S. Y.$$q(Sun Yuan),$$eeditor. 001438907 77608 $$iPrint version:$$aQiu, Han.$$tKnowledge Science, Engineering and Management.$$dCham : Springer International Publishing AG, ©2021$$z9783030821357 001438907 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence. 001438907 830_0 $$aLecture notes in computer science ;$$v12815. 001438907 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence. 001438907 852__ $$bebk 001438907 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-82136-4$$zOnline Access$$91397441.1 001438907 909CO $$ooai:library.usi.edu:1438907$$pGLOBAL_SET 001438907 980__ $$aBIB 001438907 980__ $$aEBOOK 001438907 982__ $$aEbook 001438907 983__ $$aOnline 001438907 994__ $$a92$$bISE