Services for connecting and integrating big numbers of linked datasets

Services for connecting and integrating big numbers of linked datasets /

Saved in:
Bibliographic Details
Main Author: Mountantonakis, Michalis
Corporate Author: ProQuest (Firm)
Format: eBook
Language:English
Published: Amsterdam : IOS Press, 2021.
Series:Studies on the Semantic Web ; v. 50.
Subjects:
Linked data.
Big data.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of Contents:
  • Machine generated contents note: ch. 1 Introduction
  • 1.1. Context and Motivation
  • 1.2. Related Problems
  • 1.3. Analysis of the Problems and Related Challenges
  • 1.4. Contributions of this Dissertation
  • 1.5. Publications
  • 1.6. Outline of Dissertation
  • ch. 2 Large Scale Semantic Integration Of Linked Data: A Survey
  • 2.1. Background and Context
  • 2.2. Why Data Integration is Difficult
  • 2.3. Data Integration Landscape
  • 2.4. Surveying the Integration Methods
  • 2.5. Processes for Integration
  • 2.6. Evaluation of Integration
  • 2.7. Semantic Integration On a Large Scale
  • 2.8. Discussion
  • ch. 3 Research Gaps & Motivating Scenarios
  • 3.1. Placement of Dissertation
  • 3.2. Task A. Object Coreference & All Facts
  • 3.3. Task B. Connectivity Analytics
  • 3.4. Task C. Dataset Search, Discovery & Selection
  • 3.5. Task D. Data Enrichment
  • 3.6. Task E. Data Quality Assessment
  • 3.7. Proposed Process
  • ch. 4 Cross-Dataset Identity Reasoning & Semantics-Aware Indexes At Global Scale
  • 4.1. Background & Notations
  • 4.2. Requirements
  • 4.3. Problem Statement & Process
  • 4.4. Cross-Dataset Identity Reasoning at Global Scale
  • 4.5. Set of Semantics-Aware Indexes
  • 4.6. Comparison of Parallel Algorithms
  • 4.7. Experimental Evaluation - Efficiency
  • 4.8. Epilogue
  • ch. 5 Content-Based Intersection, Union And Complement Metrics Among Several Linked Datasets
  • 5.1. Problem Statement
  • 5.2. Why Plain SPARQL Implementations are Not Enough
  • 5.3. Lattice of Measurements by Using Indexes
  • 5.4. How to Compute Content-Based Metrics
  • 5.5. Incremental Computation of Metrics
  • 5.6. Computing Lattice Measurements in Parallel
  • 5.7. Experimental Evaluation - Efficiency
  • 5.8. Connectivity Analytics over LOD Cloud Datasets
  • 5.9. Epilogue
  • ch. 6 Lodsyndesis Suite Of Services
  • 6.1. LODsyndesis Services for Tasks A-E
  • 6.2. LODsyndesisML. Linked Data & Machine Learning
  • 6.3. LODVec. Knowledge Graph Embeddings
  • 6.4. LODQA. Linked Data Question Answering
  • 6.5. LODsyndesisIE: Entity Extraction and Enrichment
  • 6.6. Epilogue
  • ch. 7 Conclusion
  • 7.1. Synopsis of Contributions
  • 7.2. Directions for Future Work and Research.

Similar Items