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Translating expressive ontology mappings into rewriting rules to implement query rewriting

Translating expressive ontology mappings into rewriting rules to implement query rewriting
Translating expressive ontology mappings into rewriting rules to implement query rewriting
The increasing amount of structured RDF data published by the Linked Data community poses a great challenge when it comes to reconcile heterogeneous schemas adopted by data publishers. For several years, the Semantic Web community has been developing algorithms for aligning data models (ontologies). Nevertheless, exploiting such ontology alignments for achieving data integration is still an under supported research topic. The semantics of ontology alignments, often defined over a logical framework, implies a reasoning step over huge amounts of data. This is often hard to implement and rarely scales on Web dimensions. This paper presents our approach for translating DL-like ontology alignments into graph patterns that can be used to implement ontological mediation in the form of SPARQL query rewriting and generation. This approach backs up a previous work for achieving SPARQL query rewriting where syntactical transformations of basic graph patterns are used. Supporting a rich ontology alignment language into our system is important for two reasons. Firstly the users can express rich alignments focusing on their semantic soundness; secondly more verbose correspondences of RDF patterns can be generated by the translation process providing a denotational semantics to the alignment language itself. The approach has been implemented into an open source Java API freely available to the community.
sparql query rewriting ontology alignments edoal
Correndo, Gianluca
fea0843a-6d4a-4136-8784-0d023fcde3e2
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
Correndo, Gianluca
fea0843a-6d4a-4136-8784-0d023fcde3e2
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7

Correndo, Gianluca and Shadbolt, Nigel (2011) Translating expressive ontology mappings into rewriting rules to implement query rewriting. Ontology Matching.

Record type: Conference or Workshop Item (Paper)

Abstract

The increasing amount of structured RDF data published by the Linked Data community poses a great challenge when it comes to reconcile heterogeneous schemas adopted by data publishers. For several years, the Semantic Web community has been developing algorithms for aligning data models (ontologies). Nevertheless, exploiting such ontology alignments for achieving data integration is still an under supported research topic. The semantics of ontology alignments, often defined over a logical framework, implies a reasoning step over huge amounts of data. This is often hard to implement and rarely scales on Web dimensions. This paper presents our approach for translating DL-like ontology alignments into graph patterns that can be used to implement ontological mediation in the form of SPARQL query rewriting and generation. This approach backs up a previous work for achieving SPARQL query rewriting where syntactical transformations of basic graph patterns are used. Supporting a rich ontology alignment language into our system is important for two reasons. Firstly the users can express rich alignments focusing on their semantic soundness; secondly more verbose correspondences of RDF patterns can be generated by the translation process providing a denotational semantics to the alignment language itself. The approach has been implemented into an open source Java API freely available to the community.

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More information

Published date: 23 October 2011
Additional Information: Event Dates: 23/10/2011
Venue - Dates: Ontology Matching, 2011-10-23
Keywords: sparql query rewriting ontology alignments edoal
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 272774
URI: http://eprints.soton.ac.uk/id/eprint/272774
PURE UUID: 8a09fc82-e2c2-4ca5-aa1c-b2ab38cfeccc
ORCID for Gianluca Correndo: ORCID iD orcid.org/0000-0003-3335-5759

Catalogue record

Date deposited: 14 Sep 2011 14:50
Last modified: 20 Jul 2019 00:53

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