Populating a linked data entity name system : a big data solution to unsupervised instance matching /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kejriwal, Mayank (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands : IOS Press, 2017.
Colección:Studies on the Semantic Web ; v. 027.
Temas:
RDF (Document markup language)
Linked data.
Big data.
Semantic Web
RDF (Langage de balisage)
Données liées.
Données volumineuses.
COMPUTERS > Intelligence (AI) & Semantics.
Big data
Linked data
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note: ch. 1 Introduction
  • 1.1. Linked Data
  • 1.2. Entity Name System
  • 1.3. Research Question and Thesis
  • 1.4. Dissertation
  • 1.5. Contributions
  • ch. 2 Background
  • 2.1. Structured Data Models
  • 2.1.1. Resource Description Framework (RDF)
  • 2.1.2. Relational Database (RDB) Model
  • 2.1.3. Serializing RDF Data
  • 2.2. Instance Matching
  • 2.2.1. Blocking Step
  • 2.2.2. Similarity Step
  • 2.2.3. Evaluating Instance Matching
  • 2.3. Heterogeneity
  • 2.3.1. Type Heterogeneity
  • 2.3.2. Property Heterogeneity
  • 2.3.3. Extending the Two-Step Workflow
  • 2.4. Scalability
  • 2.4.1. Motivation
  • 2.4.2. Implementation
  • ch. 3 Related Work
  • 3.1. Existing Domain-Independent Systems
  • 3.1.1. Systems Addressing Automation
  • 3.1.2. Systems Addressing Heterogeneity
  • 3.1.3. Systems Addressing Scalability
  • 3.1.4. Other Systems
  • 3.2. Discussion
  • 3.2.1. Automation vs. Scalability
  • 3.2.2. Issues of Structural Heterogeneity
  • 3.3.3. Issues of Unsupervised Blocking
  • ch. 4 Type Alignment
  • 4.1. Motivating Example and Preliminaries: A Review
  • 4.2. Applications of Type Alignment
  • 4.3. Approach
  • 4.3.1. Possible Strategy Implementations
  • 4.4. Evaluations
  • 4.4.1. Test Cases
  • 4.4.2. Metrics and Methodology
  • 4.4.3. Results and Discussion
  • ch. 5 Training Set Generation
  • 5.1. Intuition
  • 5.2. Approach
  • 5.3. Evaluations
  • 5.3.1. Test Suite
  • 5.3.2. Metrics
  • 5.3.3. Setup
  • 5.3.4. Results and Discussion
  • ch. 6 Property Alignment
  • 6.1. Approach
  • 6.2. Evaluations
  • 6.2.1. Setup
  • 6.2.2. Results and Discussion
  • ch. 7 Blocking and Classification
  • 7.1. Approach
  • 7.1.1. Feature Generator
  • 7.1.2. Learning Procedures
  • 7.2. Evaluations
  • 7.2.1. Blocking
  • 7.2.2. Similarity (non-iterative run)
  • 7.2.3. Similarity (iterative run)
  • ch. 8 Scalability
  • 8.1. Summary of Algorithms
  • 8.2. Motivation and Use-Cases
  • 8.3. MapReduce Implementations
  • 8.3.1. Type Alignment
  • 8.3.2. Training Set Generator
  • 8.3.3. Property Alignment and Learning Procedures
  • 8.3.4. Blocking and Similarity
  • ch. 9 Conclusion
  • 9.1. Summary
  • 9.2. Future Work
  • 9.2.1. Linked Data Quality
  • 9.2.2. Schema-Free Approaches
  • 9.2.3. Transfer Learning.

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