A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
On the relative expressiveness of description logics and predicate logics
Artificial Intelligence
A complete anytime algorithm for number partitioning
Artificial Intelligence
Model-based diagnosis of hardware designs
Artificial Intelligence
Algorithmic Program DeBugging
Machine Learning
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
Appendix: description logic terminology
The description logic handbook
Consistency-based diagnosis of configuration knowledge bases
Artificial Intelligence
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
Debugging Incoherent Terminologies
Journal of Automated Reasoning
Web Semantics: Science, Services and Agents on the World Wide Web
Laconic and Precise Justifications in OWL
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A catalogue of OWL ontology antipatterns
Proceedings of the fifth international conference on Knowledge capture
Hypertableau reasoning for description logics
Journal of Artificial Intelligence Research
Finding all justifications of OWL DL entailments
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Query strategy for sequential ontology debugging
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
A general diagnosis method for ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Repairing unsatisfiable concepts in OWL ontologies
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
RIO: minimizing user interaction in ontology debugging
RR'13 Proceedings of the 7th international conference on Web Reasoning and Rule Systems
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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 apriori 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.