Empirical Investigation of Subsumption Test Hardness in Description Logic Classification

Recently, modular techniques have been employed for optimising Description Logic reasoning, specifically to enable incremental reasoning and improve overall classification time. Classifying a module of an ontology should be significantly easier than reasoning in the whole ontology. However, we observed in previous work that neither it is generally true that modular reasoning techniques have a reliable positive effect, nor even that the classification time of a module is less than or equal to the classification time of the whole ontology. One possible explanation for the latter could be that counter-productive optimisations are triggered within the reasoner when dealing with the sub-module, and thus individual subsumption tests get harder when parts of the ontology are missing. The goal of this paper is to understand the contribution of subsumption tests to the hardness of classification. The contribution is twofold: (1) We analyse the impact of subsumption test hardness on DL classification by analysing a well known corpus of ontologies, and (2) we present a novel approach based on modularity to robustly detecting subsumption tests that are too hard.