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