Large-scale ontology-mediated query answering over OWL 2 RL ontologies

Abstract

Ontology-mediated query answering (OMQA) is an emerging paradigm at the basis of many semantic-centric applications. In this setting, a conjunctive query has to be evaluated against a logical theory (knowledge base) consisting of an extensional database paired with an ontology, which provides a semantic conceptual view of the data. Among the formalisms that are capable to express such a conceptual layer, the Web Ontology Language OWL is certainly the most popular one. Reasoning over OWL is a very expensive task, in general. For that reason, expressive yet decidable fragments of OWL have been identi ed. Among them, we focus on OWL 2 RL, which o ers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources|such as DBpedia|fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting SPARQL queries; (iii) properly applying the sameAs property without adopting the unique name assumption; (iv) dealing with concrete datatypes. This thesis aims to provide a contribution in this setting. Primarily, we present DaRLing: an open-source Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. We describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability. Then, to reduce memory consumption and possibly optimize execution times of Datalog queries over large databases, we introduce novel techniques to determine an optimal indexing schema together with suitable body-orderings for Datalog rules, based on the concept of optimal evaluation plan. The ASP encoding of a planner for the computation of such plans is provided and explained in detail. The new approach is then compared with the standard execution plans implemented in stat-of-the-art Datalog systems over widely used ontological benchmarks.