OWL Reasoning: Subsumption Test Hardness and Modularity

Reasoning with $$ , the logic that underpins the popular Web Ontology Language (OWL), has a high worst case complexity (N2Exptime). Decomposing the ontology into modules prior to classification, and then classifying the composites one-by-one, has been suggested as a way to mitigate this complexity in practice. Modular reasoning is currently motivated by the potential for reducing the hardness of subsumption tests, reducing the number of necessary subsumption tests and integrating efficient delegate reasoners. To date, we have only a limited idea of what we can expect from modularity as an optimisation technique. We present sound evidence that, while the impact of subsumption testing is significant only for a small number of ontologies across a popular collection of 330 ontologies (BioPortal), modularity has a generally positive effect on subsumption test hardness (2-fold mean reduction in our sample). More than 50% of the tests did not change in hardness at all, however, and we observed large differences across reasoners. We conclude (1) that, in general, optimisations targeting subsumption test hardness need to be well motivated because of their comparatively modest overall impact on classification time and (2) that employing modularity for optimisation should not be motivated by beneficial effects on subsumption test hardness alone.