What Makes Ontology Reasoning so Arduous?
Nourhène Alaya, Sadok Ben Yahia, Myriam Lamolle · 2015
Reasoning with ontologies is one of the core fields of research in Description Logics. A variety of efficient reasoner with highly optimized algorithms have been developed to allow inference tasks on expressive ontology languages such as OWL(DL). However, reasoner reported computing times have exceeded and sometimes fall behind the expected theoretical values. From an empirical perspective, it is not yet well understood, which particular aspects in the ontology are reasoner performance degrading factors. In this paper, we conducted an investigation about state of art works that attempted to portray potential correlation between reasoner empirical behaviour and particular ontological features. These works were analysed and then broken down into categories. Further, we proposed a set of ontology features covering a broad range of structural and syntactic ontology characteristics. We claim that these features are good indicators of the ontology hardness level against reasoning tasks. In order to assess the worthiness of our proposals, we adopted a supervised machine learning approach. Features served as the bases to learn predictive models of reasoners robustness. These models was trained for 6 well known reasoners and using their evaluation results during the ORE'2014 competition. Our prediction models showed a high accuracy level which witness the effectiveness of our set of features.