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Conference Papers Year : 2016

Summarizing Linked Data RDF Graphs Using Approximate Graph Pattern Mining

Abstract

The Linked Open Data (LOD) cloud brings together information described in RDF and stored on the web in (possibly distributed) RDF Knowledge Bases (KBs). The data in these KBs are not necessarily described by a known schema and many times it is extremely time consuming to query all the interlinked KBs in order to acquire the necessary information. To tackle this problem, we propose a method of summarizing large RDF KBs using approximate RDF graph patterns and calculating the number of instances covered by each pattern. Then we transform the patterns to an RDF schema that describes the contents of the KB. Thus we can then query the RDF graph summary to identify whether the necessary information is present and if so its size, before deciding to include it in a federated query result.
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Dates and versions

hal-01417795 , version 1 (16-12-2016)

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Mussab Zneika, Claudio Lucchese, Dan Vodislav, Dimitris Kotzinos. Summarizing Linked Data RDF Graphs Using Approximate Graph Pattern Mining. EDBT 2016, 2016, Bordeaux, France. pp.684-685, ⟨10.5441/002/edbt.2016.86⟩. ⟨hal-01417795⟩
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