Efficient ontology-based data management

Ontology-mediated query answering (OMQA) consists in asking database queries on knowledge bases (KBs); a KB is a set of facts called a database, which is described by a  domain knowledge called an ontology. A main OMQA technique is FO-rewriting, which reformulates a query asked on a KB \wrt to the KB's ontology; query answers are then computed through the relational evaluation of the query reformulation on the KB's database. Essentially, because FO-rewriting compiles the domain knowledge relevant to queries into their reformulations, query reformulations may be complex and their optimization is the crux of efficiency. We devise a novel optimization framework for a large set of OMQA settings that enjoy FO-rewriting: conjunctive queries, i.e., the core select-project-join queries, asked on KBs expressed in datalog$\pm$ and existential rules, description logic and OWL, or RDF/S. We optimize the query reformulations produced by any state-of-the-art algorithm for FO-rewriting by computing rapidly, using a KB's database summary, simpler queries with same answers that can be evaluated faster by DBMSs. We show on a well-established OMQA benchmark that time performance is significantly improved by our optimization framework in general, up to three orders of magnitude.

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