Print version: Balog, Krisztian Entity-Oriented Search Cham : Springer International Publishing AG,c2018 ISBN 9783319939339
Intro -- Preface -- Website -- Contents -- Acronyms -- Notation -- 1 Introduction -- 1.1 What Is an Entity? -- 1.1.1 Named Entities vs. Concepts -- 1.1.2 Properties of Entities -- 1.1.3 Representing Properties of Entities -- 1.2 A Brief Historical Outlook -- 1.2.1 Information Retrieval -- 1.2.2 Databases -- 1.2.3 Natural Language Processing -- 1.2.4 Semantic Web -- 1.3 Entity-Oriented Search -- 1.3.1 A Bird’s-Eye View -- 1.3.1.1 Users and Information Needs -- 1.3.1.2 Search Engine -- 1.3.1.3 Data -- 1.3.2 Tasks and Challenges -- 1.3.2.1 Entities as the Unit of Retrieval -- 1.3.2.2 Entities for Knowledge Representation -- 1.3.2.3 Entities for an Enhanced User Experience -- 1.3.3 Entity-Oriented vs. Semantic Search -- 1.3.4 Application Areas -- 1.4 About the Book -- 1.4.1 Focus -- 1.4.2 Audience and Prerequisites -- 1.4.3 Organization -- 1.4.4 Terminology and Notation -- References -- 2 Meet the Data -- 2.1 The Web -- 2.1.1 Datasets and Resources -- 2.2 Wikipedia -- 2.2.1 The Anatomy of a Wikipedia Article -- 2.2.1.1 Title -- 2.2.1.2 Infobox -- 2.2.1.3 Introductory Text -- 2.2.2 Links -- 2.2.3 Special-Purpose Pages -- 2.2.3.1 Redirect Pages -- 2.2.3.2 Disambiguation Pages -- 2.2.4 Categories, Lists, and Navigation Templates -- 2.2.4.1 Categories -- 2.2.4.2 Lists -- 2.2.4.3 Navigation Templates -- 2.2.5 Resources -- 2.3 Knowledge Bases -- 2.3.1 A Knowledge Base Primer -- 2.3.1.1 Knowledge Bases vs. Ontologies -- 2.3.1.2 RDF -- 2.3.2 DBpedia -- 2.3.2.1 Ontology -- 2.3.2.2 Extraction -- 2.3.2.3 Datasets and Resources -- 2.3.3 YAGO -- 2.3.3.1 Taxonomy -- 2.3.3.2 Extensions -- 2.3.3.3 Resources -- 2.3.4 Freebase -- 2.3.5 Wikidata -- 2.3.6 The Web of Data -- 2.3.6.1 Datasets and Resources -- 2.3.7 Standards and Resources -- 2.4 Summary -- References -- Part I Entity Ranking -- 3 Term-Based Models for Entity Ranking -- 3.1 The Ad Hoc Entity Retrieval Task.
3.2 Constructing Term-Based Entity Representations -- 3.2.1 Representations from Unstructured Document Corpora -- 3.2.1.1 Document-Level Annotations -- 3.2.1.2 Mention-Level Annotations -- 3.2.2 Representations from Semi-structured Documents -- 3.2.3 Representations from Structured Knowledge Bases -- 3.2.3.1 Predicate Folding -- 3.2.3.2 From Triples to Text -- 3.2.3.3 Multiple Knowledge Bases -- 3.3 Ranking Term-Based Entity Representations -- 3.3.1 Unstructured Retrieval Models -- 3.3.1.1 Language Models -- 3.3.1.2 BM25 -- 3.3.1.3 Sequential Dependence Models -- 3.3.2 Fielded Retrieval Models -- 3.3.2.1 Mixture of Language Models -- 3.3.2.2 Probabilistic Retrieval Model for Semi-Structured Data -- 3.3.2.3 BM25F -- 3.3.2.4 Fielded Sequential Dependence Models -- 3.3.3 Learning-to-Rank -- 3.3.3.1 Features -- 3.3.3.2 Learning Algorithms -- 3.3.3.3 Practical Considerations -- 3.4 Ranking Entities Without Direct Representations -- 3.5 Evaluation -- 3.5.1 Evaluation Measures -- 3.5.2 Test Collections -- 3.5.2.1 TREC Enterprise -- 3.5.2.2 INEX Entity Ranking -- 3.5.2.3 TREC Entity -- 3.5.2.4 Semantic Search Challenge -- 3.5.2.5 INEX Linked Data -- 3.5.2.6 Question Answering over Linked Data -- 3.5.2.7 The DBpedia-Entity Test Collection -- 3.6 Summary -- 3.7 Further Reading -- References -- 4 Semantically Enriched Models for Entity Ranking -- 4.1 Semantics Means Structure -- 4.2 Preserving Structure -- 4.2.1 Multi-Valued Predicates -- 4.2.1.1 Parameter Settings -- 4.2.2 References to Entities -- 4.3 Entity Types -- 4.3.1 Type Taxonomies and Challenges -- 4.3.2 Type-Aware Entity Ranking -- 4.3.3 Estimating Type-Based Similarity -- 4.4 Entity Relationships -- 4.4.1 Ad Hoc Entity Retrieval -- 4.4.2 List Search -- 4.4.3 Related Entity Finding -- 4.4.3.1 Candidate Selection -- 4.4.3.2 Type Filtering -- 4.4.3.3 Entity Relevance -- 4.5 Similar Entity Search.