Optimizing Inconsistency-tolerant Description Logic Reasoning

The study of inconsistency-tolerant description logic reasoning is of growing importance for the Semantic Web since knowledge within it may not be logically consistent. The recently developed quasi-classical description logic has proved successful in handling inconsistency in description logic. To achieve a high level of performance when using tableau-based algorithms requires the incorporation of a wide range of optimizations. In our previous work, we developed a naive inconsistency-tolerant reasoner that can handle inconsistency directly. Here, we investigate a set of well known, state-ofthe-art optimization techniques for inconsistency-tolerant reasoning. Our experimental results show significant performance improvements over our naive reasoner for several well-known ontologies. To the best of our knowledge, this is the first attempt to apply optimizations for inconsistency-tolerant tableau-based reasoning.