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Second China Conference, CCKS 2017, Chengdu, China, August 26–29, 2017, ... in Computer and Information Science)

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The cover of “Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence” - Open Library.

"Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence" is published by Springer in Feb 24, 2018 - Singapore and it has 188 pages.


“Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence” Metadata:

  • Title: ➤  Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence
  • Authors: ➤  
  • Number of Pages: 188
  • Publisher: Springer
  • Publish Date:
  • Publish Location: Singapore

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Edition Specifications:

  • Format: paperback

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"Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence" Description:

Open Data:

Intro -- Preface -- Organization -- Contents -- Knowledge Base Completion by Learning to Rank Model -- 1 Introduction -- 2 Learning to Rank Model -- 3 Experiments -- 4 Related Work -- 5 Conclusion -- References -- Path-Based Learning for Plant Domain Knowledge Graph -- Abstract -- 1 Introduction -- 2 Approach -- 2.1 TransE Model -- 2.2 PTA Model -- 2.3 PTA (Path-Based TransE for Attributes) -- 2.4 Model Formulation -- 3 Experiments -- 3.1 Setup -- 3.2 Evaluation -- 4 Conclusion -- References -- A Graph-Based Approach to Incremental Classification in OWL 2 QL Ontology -- 1 Introduction -- 2 Preliminaries -- 2.1 OWL 2 QL -- 2.2 Graph Theory Notions -- 2.3 Digraph Representation of OWL QL 2 Ontologies -- 3 Mapping Ontologies Direct Graphs -- 4 Identifying Affected Paths and Updating Transitive Closure -- 5 Optimization -- 6 Implementation and Evaluation -- 7 Discussions -- References -- Tensor-Based Representation and Reasoning of Horn-SHOIQ Ontologies -- 1 Introduction -- 2 Ontology and Tensor Operations -- 3 A Tensor-Based Representation for Ontologies -- 4 Materialization via Tensor Operations -- 5 Conclusions and Future Work -- References -- Attention-Based Event Relevance Model for Stock Price Movement Prediction -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Attention-Based Event Relevance Model -- 3.3 Model Training -- 4 Experiments and Results -- 4.1 Dataset Construction -- 4.2 Implementation and Hyper-Parameter Setting -- 4.3 Stock Price Movement Prediction Experiments -- 4.4 Short-Term, Medium-Term, Long-Term Influence -- 5 Conclusion and Future Work -- References -- A Survey on Relation Extraction -- Abstract -- 1 Introduction -- 2 Datasets for Relation Extraction -- 3 Mainstream Methods -- 3.1 Rule-Based Approaches -- 3.2 Statistic-Based Approaches -- 4 Open Information Extraction (OIE)

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