Multi-label Based Learning for Better Multi-criteria Ranking of Ontology Reasoners

A growing number of highly optimized reasoning algorithms have been developed to allow inference tasks on expressive ontology languages such as OWL(DL). Nevertheless, there is broad agreement that a reasoner could be optimized for some, but not all the ontologies. This particular fact makes it hard to select the best performing reasoner to handle a given ontology, especially for novice users. In this paper, we present a novel method to support the selection ontology reasoners. Our method generates a recommendation in the form of reasoner ranking. The efficiency as well as the correctness are our main ranking criteria. Our solution combines and adjusts multi-label classification and multi-target regression techniques. A large collection of ontologies and 10 well-known reasoners are studied. The experimental results show that the proposed method performs significantly better than several state-of-the-art ranking solutions. Furthermore, it proves that our introduced ranking method could effectively be evolved to a competitive meta-reasoner.