001451811 000__ 05944cam\a2200637\a\4500
001451811 001__ 1451811
001451811 003__ OCoLC
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001451811 019__ $$a1353100828
001451811 020__ $$a9789811983009$$q(electronic bk.)
001451811 020__ $$a9811983003$$q(electronic bk.)
001451811 020__ $$z9811982996
001451811 020__ $$z9789811982996
001451811 0247_ $$a10.1007/978-981-19-8300-9$$2doi
001451811 035__ $$aSP(OCoLC)1354206110
001451811 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dUKAHL$$dOCLCF
001451811 049__ $$aISEA
001451811 050_4 $$aQA76.5913$$b.C35 2022eb
001451811 08204 $$a006.3/32$$223/eng/20221212
001451811 1112_ $$aCCKS (Conference)$$n(7th :$$d2022 :$$cQinhuangdao Shi, China)
001451811 24510 $$aCCKS 2022 -- Evaluation track :$$b7th China Conference on Knowledge Graph and Semantic Computing Evaluations, CCKS 2022, Qinhuangdao, China, August 24-27, 2022, Revised selected papers /$$cNingyu Zhang, Meng Wang, Tianxing Wu, Wei Hu, Shumin Deng (eds.).
001451811 2463_ $$aEvaluation track
001451811 260__ $$aSingapore :$$bSpringer,$$c2022.
001451811 300__ $$a1 online resource (249 p.).
001451811 4901_ $$aCommunications in Computer and Information Science ;$$v1711
001451811 500__ $$a4.3 Model Parameters and Result
001451811 500__ $$aIncludes author index.
001451811 504__ $$aReferences -- A Translation Model-Based Question Answering Approach over Cross-Lingual Knowledge Graphs -- 1 Introduction -- 2 Approach -- 2.1 Overview -- 2.2 Design of Stages -- 2.3 Our Strategies -- 3 Experiments -- 3.1 Data Set -- 3.2 Implementation -- 3.3 Experiment Results -- 3.4 Competition Results -- 4 Conclusion -- References -- Cascaded Solution for Multi-domain Conditional Question Answering with Multiple-Span Answers -- 1 Background and Task Introduction -- 2 Technical Solution -- 2.1 Data Analysis and Processing -- 2.2 Condition-Answer Extraction -- 2.3 Post-extraction Processing
001451811 5050_ $$aIntro -- Preface -- Organization -- Contents -- A Chemical Domain Knowledge-Aware Framework for Multi-view Molecular Property Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Supervised MRL -- 2.2 Self-supervised MRL -- 2.3 Domain Knowledge Based MRL -- 3 Our Approach -- 3.1 KPGT -- 3.2 Functional Group Embedding -- 3.3 Knowledge Graph Embedding -- 4 Experiments -- 4.1 Dataset -- 4.2 Parameter Settings -- 4.3 Results -- 4.4 Discussion -- 5 Conclusion -- References -- A Coarse Pipeline to Solve Hierarchical Multi-answer Questions with Conditions -- 1 Introduction -- 2 Method
001451811 5058_ $$a2.1 Answer Span Detection -- 2.2 Relation Classification -- 2.3 Additional Strategies -- 3 Experiments -- 3.1 Data Processing -- 3.2 Experiments of Answer Span Detection -- 3.3 Experiments of Relation Classification -- 3.4 Online Result -- 4 Discussion -- 4.1 First Attempt -- 4.2 Second Attempt -- 4.3 Third Attempt -- 4.4 Fourth Attempt -- 4.5 Future Work -- References -- A Pipeline-Based Multimodal Military Event Argument Extraction Framework -- 1 Introduction -- 2 Method -- 2.1 Global Pointer Model for Named Entity Recognition -- 2.2 Yolo Model for Object Detection -- 2.3 Multimodal Matcher
001451811 5058_ $$a3 Experiment -- 3.1 Dataset -- 3.2 Implementation -- 3.3 Main Result -- 4 Conclusion -- References -- A Search-Enhanced Path Mining and Ranking Method for Cross-lingual Knowledge Base Question Answering -- 1 Introduction -- 2 Task Description -- 3 Method -- 3.1 Question Classification -- 3.2 Principal Entity Extraction -- 3.3 Search-Enhanced Candidate Path Mining -- 3.4 Path Ranking -- 4 Experiment Result -- 4.1 Question Classification -- 4.2 Principal Entity Extraction -- 4.3 Search-Enhanced Candidate Path Mining -- 4.4 Path Ranking -- 4.5 End-To-End Evaluation Result -- 5 Conclusion
001451811 5058_ $$a2.4 Condition-Answer Relation Classification -- 2.5 Post-classification Processing -- 3 Experiment -- 3.1 Model Effect Evaluation -- 3.2 End-To-End Effect Evaluation -- 4 Conclusion -- References -- Compound Property Prediction Based on Multiple Different Molecular Features and Ensemble Learning -- 1 Introduction -- 2 Related Work -- 2.1 Molecular Descriptor -- 2.2 SMILES -- 2.3 Molecular Graph Representation -- 3 Method -- 3.1 Molecular Vector Representation -- 3.2 AutoEncoder Model -- 3.3 Ensemble Model -- 4 Experiment -- 4.1 Data Introduction -- 4.2 Experimental Setup
001451811 506__ $$aAccess limited to authorized users.
001451811 520__ $$aThis book constitutes the refereed proceedings of the 7th China Conference on Knowledge Graph and Semantic Computing Evaluations, CCKS 2022, which took place in Qinhuangdao, China, in August 2022. The 25 full papers presented in this volume were carefully reviewed and selected from 42 submissions. CCKS technology evaluation track aims to provide researchers with platforms and resources for testing knowledge and semantic computing technologies, algorithms and systems, promote the technical development in the field of domestic knowledge, and the integration of academic achievements and industrial needs.
001451811 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 12, 2022).
001451811 650_0 $$aSemantic computing$$vCongresses.
001451811 650_0 $$aKnowledge representation (Information theory)$$vCongresses.
001451811 650_0 $$aBig data$$vCongresses.
001451811 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001451811 655_0 $$aElectronic books.
001451811 7001_ $$aZhang, Ningyu.
001451811 7001_ $$aWang, Meng.
001451811 7001_ $$aWu, Tianxing.
001451811 7001_ $$aHu, Wei.
001451811 7001_ $$aDeng, Shumin.
001451811 77608 $$iPrint version:$$aZhang, Ningyu$$tCCKS 2022 - Evaluation Track$$dSingapore : Springer,c2023$$z9789811982996
001451811 830_0 $$aCommunications in computer and information science ;$$v1711.
001451811 852__ $$bebk
001451811 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-8300-9$$zOnline Access$$91397441.1
001451811 909CO $$ooai:library.usi.edu:1451811$$pGLOBAL_SET
001451811 980__ $$aBIB
001451811 980__ $$aEBOOK
001451811 982__ $$aEbook
001451811 983__ $$aOnline
001451811 994__ $$a92$$bISE