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Entity-Oriented Search.

Bibliographic Details
Main Author: Balog, Krisztian.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2018.
Edition:1st ed.
Series:The Information Retrieval Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • 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.
  • 4.5.1 Pairwise Entity Similarity
  • 4.5.1.1 Term-Based Similarity
  • 4.5.1.2 Corpus-Based Similarity
  • 4.5.1.3 Distributional Similarity
  • 4.5.1.4 Graph-Based Similarity
  • 4.5.1.5 Property-Specific Similarity
  • 4.5.2 Collective Entity Similarity
  • 4.5.2.1 Structure-Based Method
  • 4.5.2.2 Aspect-Based Method
  • 4.6 Query-Independent Ranking
  • 4.6.1 Popularity
  • 4.6.2 Centrality
  • 4.6.2.1 PageRank
  • 4.6.2.2 PageRank for Entities
  • 4.6.2.3 A Two-Layered Extension of PageRank for the Web of Data
  • 4.6.3 Other Methods
  • 4.7 Summary
  • 4.8 Further Reading
  • References
  • Part II Bridging Text and Structure
  • 5 Entity Linking
  • 5.1 From Named Entity Recognition Toward Entity Linking
  • 5.1.1 Named Entity Recognition
  • 5.1.2 Named Entity Disambiguation
  • 5.1.3 Entity Coreference Resolution
  • 5.2 The Entity Linking Task
  • 5.3 The Anatomy of an Entity Linking System
  • 5.4 Mention Detection
  • 5.4.1 Surface Form Dictionary Construction
  • 5.4.2 Filtering Mentions
  • 5.4.3 Overlapping Mentions
  • 5.5 Candidate Selection
  • 5.6 Disambiguation
  • 5.6.1 Features
  • 5.6.1.1 Prior Importance Features
  • 5.6.1.2 Contextual Features
  • 5.6.1.3 Entity-Relatedness Features
  • 5.6.2 Approaches
  • 5.6.2.1 Individual Local Disambiguation
  • 5.6.2.2 Individual Global Disambiguation
  • 5.6.2.3 Collective Disambiguation
  • 5.6.3 Pruning
  • 5.7 Entity Linking Systems
  • 5.8 Evaluation
  • 5.8.1 Evaluation Measures
  • 5.8.2 Test Collections
  • 5.8.2.1 Individual Researchers
  • 5.8.2.2 INEX Link-the-Wiki
  • 5.8.2.3 TAC Entity Linking
  • 5.8.2.4 Entity Recognition and Disambiguation Challenge
  • 5.8.3 Component-Based Evaluation
  • 5.9 Resources
  • 5.9.1 A Cross-Lingual Dictionary for English Wikipedia Concepts
  • 5.9.2 Freebase Annotations of the ClueWeb Corpora
  • 5.10 Summary
  • 5.11 Further Reading
  • References
  • 6 Populating Knowledge Bases.
  • 6.1 Harvesting Knowledge from Text
  • 6.1.1 Class-Instance Acquisition
  • 6.1.1.1 Obtaining Instances of Semantic Classes
  • 6.1.1.2 Obtaining Semantic Classes of Instances
  • 6.1.2 Class-Attribute Acquisition
  • 6.1.3 Relation Extraction
  • 6.2 Entity-Centric Document Filtering
  • 6.2.1 Overview
  • 6.2.2 Mention Detection
  • 6.2.3 Document Scoring
  • 6.2.3.1 Mention-Based Scoring
  • 6.2.3.2 Boolean Queries
  • 6.2.3.3 Supervised Learning
  • 6.2.4 Features
  • 6.2.4.1 Document Features
  • 6.2.4.2 Entity Features
  • 6.2.4.3 Document-Entity Features
  • 6.2.4.4 Temporal Features
  • 6.2.5 Evaluation
  • 6.2.5.1 Test Collections
  • 6.2.5.2 Annotations
  • 6.2.5.3 Evaluation Methodology
  • 6.2.5.4 Evaluation Methodology Revisited
  • 6.3 Slot Filling
  • 6.3.1 Approaches
  • 6.3.2 Evaluation
  • 6.4 Summary
  • 6.5 Further Reading
  • References
  • Part III Semantic Search
  • 7 Understanding Information Needs
  • 7.1 Semantic Query Analysis
  • 7.1.1 Query Classification
  • 7.1.1.1 Query Intent Classification
  • 7.1.1.2 Query Topic Classification
  • 7.1.2 Query Annotation
  • 7.1.2.1 Query Segmentation
  • 7.1.2.2 Query Tagging
  • 7.1.3 Query Interpretation
  • 7.2 Identifying Target Entity Types
  • 7.2.1 Problem Definition
  • 7.2.2 Unsupervised Approaches
  • 7.2.2.1 Type-Centric Model
  • 7.2.2.2 Entity-Centric Model
  • 7.2.3 Supervised Approach
  • 7.2.4 Evaluation
  • 7.2.4.1 Evaluation Measures
  • 7.2.4.2 Test Collections
  • 7.3 Entity Linking in Queries
  • 7.3.1 Entity Annotation Tasks
  • 7.3.1.1 Named Entity Recognition
  • 7.3.1.2 Semantic Linking
  • 7.3.1.3 Interpretation Finding
  • 7.3.2 Pipeline Architecture for Interpretation Finding
  • 7.3.3 Candidate Entity Ranking
  • 7.3.3.1 Unsupervised Approach
  • 7.3.3.2 Supervised Approach
  • 7.3.3.3 Gathering Additional Context
  • 7.3.3.4 Evaluation and Test Collections
  • 7.3.4 Producing Interpretations.
  • 7.3.4.1 Unsupervised Approach
  • 7.3.4.2 Supervised Approach
  • 7.3.4.3 Evaluation Measures
  • 7.3.4.4 Test Collections
  • 7.4 Query Templates
  • 7.4.1 Concepts and Definitions
  • 7.4.2 Template Discovery Methods
  • 7.4.2.1 Classify&amp
  • Match
  • 7.4.2.2 QueST
  • 7.5 Summary
  • 7.6 Further Reading
  • References
  • 8 Leveraging Entities in Document Retrieval
  • 8.1 Mapping Queries to Entities
  • 8.2 Leveraging Entities for Query Expansion
  • 8.2.1 Document-Based Query Expansion
  • 8.2.2 Entity-Centric Query Expansion
  • 8.2.3 Unsupervised Term Selection
  • 8.2.4 Supervised Term Selection
  • 8.2.4.1 Features
  • 8.2.4.2 Training
  • 8.3 Projection-Based Methods
  • 8.3.1 Explicit Semantic Analysis
  • 8.3.1.1 ESA Concept-Based Indexing
  • 8.3.1.2 ESA Concept-Based Retrieval
  • 8.3.2 Latent Entity Space Model
  • 8.3.3 EsdRank
  • 8.3.3.1 Features
  • 8.3.3.2 Learning-to-Rank Model
  • 8.4 Entity-Based Representations
  • 8.4.1 Entity-Based Document Language Models
  • 8.4.2 Bag-of-Entities Representation
  • 8.4.2.1 Basic Ranking Models
  • 8.4.2.2 Explicit Semantic Ranking
  • 8.4.2.3 Word-Entity Duet Framework
  • 8.4.2.4 Attention-Based Ranking Model
  • 8.5 Practical Considerations
  • 8.6 Resources and Test Collections
  • 8.7 Summary
  • 8.8 Further Reading
  • References
  • 9 Utilizing Entities for an Enhanced Search Experience
  • 9.1 Query Assistance
  • 9.1.1 Query Auto-completion
  • 9.1.1.1 Leveraging Entity Types
  • 9.1.2 Query Recommendations
  • 9.1.2.1 Query-Flow Graph
  • 9.1.2.2 Exploiting Entity Aspects
  • 9.1.2.3 Entity Types
  • 9.1.2.4 Entity Relationships
  • 9.1.3 Query Building Interfaces
  • 9.2 Entity Cards
  • 9.2.1 The Anatomy of an Entity Card
  • 9.2.2 Factual Entity Summaries
  • 9.2.2.1 Fact Ranking
  • 9.2.2.2 Summary Generation
  • 9.3 Entity Recommendations
  • 9.3.1 Recommendations Given an Entity
  • 9.3.2 Personalized Recommendations.
  • 9.3.2.1 Entity-Based Method.

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