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. But even when the KB schema is known, we need actually to know which parts of the schema are used. We solve this problem by summarizing large RDF KBs using top-K approximate RDF graph patterns, which we transform to an RDF schema that describes the contents of the KB. This schema describes accurately the KB, even more accurately than an existing schema because it describes the actually used schema, which corresponds to the existing data. We add information on the number of various instances of the patterns, thus allowing the query to estimate the expected results. That way we can then query the RDF graph summary to identify whether the necessary information is present and if it is present in significant numbers whether to be included in a federated query result.

RDF Graph Summarization Based on Approximate Patterns

LUCCHESE, Claudio;
2016-01-01

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. But even when the KB schema is known, we need actually to know which parts of the schema are used. We solve this problem by summarizing large RDF KBs using top-K approximate RDF graph patterns, which we transform to an RDF schema that describes the contents of the KB. This schema describes accurately the KB, even more accurately than an existing schema because it describes the actually used schema, which corresponds to the existing data. We add information on the number of various instances of the patterns, thus allowing the query to estimate the expected results. That way we can then query the RDF graph summary to identify whether the necessary information is present and if it is present in significant numbers whether to be included in a federated query result.
2016
Communications in Computer and Information Science
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3692229
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