Nonmonotonic inferences are not yet supported by Description Logic technology, although their potential usefulness is widely recognized. Lack of support to nonmonotonic reasoning is due to a number of issues related to expressiveness, computational complexity, and optimizations. This work contributes to the practical support of nonmono-tonic reasoning in description logics by introducing a new semantics designed to address knowledge engineering needs. The formalism is validated through extensive comparison with the other non-monotonic DLs, and systematic scalability tests.

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Abstract. We introduce optimization techniques for reasoning in DL N -a recently introduced family of nonmonotonic description logics whose characterizing features appear well-suited to model the applicative examples naturally arising in biomedical domains and semantic web access control policies. Such optimizations are validated experimentally on large KBs with more than 30K axioms. Speedups exceed 1 order of magnitude. For the first time, response times compatible with real-time reasoning are obtained with nonmonotonic KBs of this size.

DL N is a recent approach that extends description logics with defeasible reasoning capabilities. In this paper we provide an overview on DL N , illustrating the underlying knowledge engineering requirements as well as the characteristic features that preserve DL N from some recurrent semantic and computational drawbacks. We also compare DL N with some alternative nonmonotonic semantics, enlightening the relationships between the KLM postulates and DL N .