Complete Classification of Complex ALCHO Ontologies Using a Hybrid Reasoning Approach

Consequence-based reasoners are typically significantly faster than tableau-based reasoners for ontology classification. However, for more expressive DL languages like ALCHO, consequence-based reasoners are not applicable, but tableau-based reasoners can sometimes require an unacceptably long time for large and complex ontologies. This paper presents a weakening and strengthening approach for classification ofALCHO ontologies, using a hybrid of consequenceand tableau-based reasoning. We approximate the original ontologyOo by a weakened versionOw and a strengthened versionOs, both are in a less expressive DLALCH and classified by a consequence-based main reasoner. The classification from Ow is sound but possibly incomplete with respect to Oo, while that from Os is complete but possibly unsound. The additional subsumptions derived from Os may be unsound so are further verified by a tableau-based assistant reasoner. A prototype classifier called WSClassifier is implemented based on this hybrid approach. The experiments results show that for classifying many large and complex ALCHO ontologies, WSClassifier’s performance is significantly faster than tableau-based reasoners.