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EBOOK
Author Kejriwal, Mayank,
Title Populating a linked data entity name system : a big data solution to unsupervised instance matching / Mayank Kejriwal.
Imprint Amsterdam, Netherlands : IOS Press, 2017.

Author Kejriwal, Mayank,
Series Studies on the semantic web ; vol. 027
Studies on the Semantic Web ; http://id.loc.gov/authorities/names/no2009156151 v. 027.
Subject RDF (Document markup language)
Linked data.
Big data.
Author Kejriwal, Mayank,
Series Studies on the semantic web ; vol. 027
Studies on the Semantic Web ; http://id.loc.gov/authorities/names/no2009156151 v. 027.
Subject RDF (Document markup language)
Linked data.
Big data.
Description 1 online resource.
polychrome rdacc http://rdaregistry.info/termList/RDAColourContent/1003
Bibliography Note Includes bibliographical references.
Contents 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.
Note Online resource; title from PDF title page (IOS Press, viewed January 26, 2017).
ISBN 9781614996927 (electronic bk.)
161499692X (electronic bk.)
9781614996910 (print)
OCLC # 970041843