  Effective debugging of ontologies is an important prerequisite for their
broad application, especially in areas that rely on everyday users to create
and maintain knowledge bases, such as the Semantic Web. In such systems
ontologies capture formalized vocabularies of terms shared by its users.
However in many cases users have different local views of the domain, i.e. of
the context in which a given term is used. Inappropriate usage of terms
together with natural complications when formulating and understanding logical
descriptions may result in faulty ontologies. Recent ontology debugging
approaches use diagnosis methods to identify causes of the faults. 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. To identify the best query we propose two query selection strategies:
a simple "split-in-half" strategy and an entropy-based strategy. The latter
allows knowledge about typical user errors to be exploited to minimize the
number of queries. Our evaluation showed that the entropy-based method
significantly reduces the number of required queries compared to the
"split-in-half" approach. We experimented with different probability
distributions of user errors and different qualities of the a-priori
probabilities. Our measurements demonstrated the superiority of entropy-based
query selection even in cases where all fault probabilities are equal, i.e.
where no information about typical user errors is available.
