  Debugging of ontologies is an important prerequisite for their wide-spread
application, especially in areas that rely upon everyday users to create and
maintain knowledge bases, as in the case of the Semantic Web. Recent approaches
use diagnosis methods to identify causes of inconsistent or incoherent
ontologies. However, in most debugging scenarios these methods return many
alternative diagnoses, thus placing the burden of fault localization on the
user. This paper demonstrates how the target diagnosis can be identified by
performing a sequence of observations, that is, by querying an oracle about
entailments of the target ontology. We exploit a-priori probabilities of
typical user errors to formulate information-theoretic concepts for query
selection. Our evaluation showed that the proposed method significantly reduces
the number of required queries compared to myopic strategies. We experimented
with different probability distributions of user errors and different qualities
of the a-priori probabilities. Our measurements showed the advantageousness of
information-theoretic approach to query selection even in cases where only a
rough estimate of the priors is available.
