R2O2: An Efficient Ranking-Based Reasoner for OWL Ontologies

Abstract. It has been shown, both theoretically and empirically, that performing core reasoning tasks on large and expressive ontologies in OWL 1 and OWL 2 is time-consuming and resource-intensive. Moreover, due to the different reasoning algorithms and optimisation techniques employed, each reasoner may be efficient for ontologies with different characteristics. In this paper, we present R2O2, a meta-reasoner that automatically combines, ranks and selects from a number of state-of-the-art OWL 2 DL reasoners to achieve high efficiency, making use of performance prediction models and ranking models. Our comprehensive evaluation on a large ontology corpus shows that R2O2 significantly and consistently outperforms 6 state-of-the-art OWL 2 DL reasoners on average performance, with an average speedup of up to 14x. R2O2 also shows a 1.4x speedup over Konclude, the current dominant OWL 2 DL reasoner.