RakSOR: Ranking of Ontology Reasoners Based on Predicted Performances

Abstract

Over the last decade, several ontology reasoners have been proposed to overcome the computational complexity of inference tasks on expressive ontology languages. Nevertheless, it is well-accepted that there is no outstanding reasoner that can outperform in all input ontologies. Thus, an algorithm selection problem have emerged in this field of study. In this paper, we describe first steps to develop a new system to provide user support when looking for guidance over ontology reasoners. Our main goal is to be able to automatically rank a set of candidate reasoners for any given ontology. Robustness standing for the ability of reasoner to correctly achieve a reasoning task within a fixed time limit is our primary ranking criterion. Our ranking method follows a meta-learning approach and applies bucket order rules. An extensive experiments covering over 2500 well selected real-world ontologies and six state-of-the-art of the most performing reasoners was carried out to provide enough data for the study. Our prediction and ranking results are encouraging, witnessing the potential benefits of the proposed approach.