Semantic search for novel information [electronic resource]

• 作者： Farber, Michael, author.
• 其他題名：
  • Studies on the Semantic Web ;
• 出版： Amsterdam, Netherlands : IOS Press
• 叢書名： Studies on the semantic web ;v. 031
• 主題： Semantic computing. , Semantic Web.
• ISBN： 9781614997757 (e-book) 、 9781614997740 (Paperback)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Includes bibliographical references. Title Page ; Abstract; Acknowledgements; Contents; List of Figures; List of Tables; List of Listings; Introduction; Motivation; Problem Statement; Research Questions; Contribution of the Thesis; Published Results; Readers' Guide; Foundations; Semantic Web Technologies; The Vision of the Semantic Web; RDF and SPARQL; Knowledge Graph; Information Extraction, Machine Learning, Information Retrieval, and Data Quality; Information Extraction; Machine Learning; Information Retrieval; Data Quality; State-of-the-Art; Statistical Search for Relevant Information; Temporal Information Retrieval Trend DetectionSemantic Search for Relevant Information; Semantic Search for Relevant Entities; Semantic Search for Relevant Statements; Semantic Search for Relevant Events; Statistical Search for Relevant, Novel Information; Characteristics of Statistical Search for Relevant, Novel Information; Evaluations and Data Sets; Approaches to the Statistical Search for Relevant, Novel Information; Semantic Search for Relevant, Novel Information; Semantic Search for Novel Entities; Semantic Search for Novel Statements; Semantic Search for Novel Events The Suitability of Knowledge Graphs for Semantic Novelty DetectionSelection of Knowledge Graphs; Key Statistics of Selected Knowledge Graphs; Related Work; Number of Triples and Statements; Classes and Domains; Relations and Predicates; Instances and Entities; Subjects and Objects; Summary of Key Statistics; Completeness and Timeliness of Selected Knowledge Graphs; Gold Standard; Completeness; Timeliness; Discussion; Conclusions; Emerging Entity Detection; Motivation; Entity Linking Challenges Arising from Missing Entities and Missing Surface Forms; Overview of Entity Linking Challenges Challenges in the WildSummary of Findings; Approach: Emerging Entity Detection; The Approach; Evaluation Results; Related Work; Challenge 1: Linking to in-KG Entities via Known Surface Forms; Challenge 2: Linking to in-KG Entities via Unknown Surface Forms; Challenge 3: Linking to Out-of-KG Entities via Known Surface Forms; Challenge 4: Linking to Out-of-KG Entities via Unkown Surface Forms; Conclusions; Novel Statement Extraction; Motivation; Measuring Semantic Novelty of Statements; The Novel Statement Extraction System; Textual Triple Extraction; KG Linking; Novelty Detection Evaluation 1: CrunchBaseData Used; Evaluation Setting; Evaluation Results; Evaluation 2: DBpedia; Data Used; The Baseline Approach and its Evaluation Results; Evaluation Results of Our Approach; Discussion; Related Work; Conclusions; Conclusions; Summary; Limitations; Outlook; Appendix; Supplementary Material; Emerging Entity Detection; Bibliography
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