Hypermedia-Based Discovery for Source Selection Using Low-Cost Linked Data Interfaces

Miel Vander Sande, Ruben Verborgh, Anastasia Dimou, Pieter Colpaert and Erik Mannens
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Miel Vander Sande: Data Science Lab, Ghent University - iMinds, Ghent, Belgium
Ruben Verborgh: Data Science Lab, Ghent University - iMinds, Ghent, Belgium
Anastasia Dimou: Data Science Lab, Ghent University - iMinds, Ghent, Belgium
Pieter Colpaert: Data Science Lab, Ghent University - iMinds, Ghent, Belgium
Erik Mannens: Data Science Lab, Ghent University - iMinds, Ghent, Belgium

International Journal on Semantic Web and Information Systems (IJSWIS), 2016, vol. 12, issue 3, 79-110

Abstract: Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed—even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness.

Date: 2016
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