A theory of diagnosis from first principles
Artificial Intelligence
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Debugging Incoherent Terminologies
Journal of Automated Reasoning
Laconic and Precise Justifications in OWL
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Reasoning Support for Mapping Revision
Journal of Logic and Computation
An Efficient Method for Computing Alignment Diagnoses
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
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
A general diagnosis method for ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Interactive ontology debugging: Two query strategies for efficient fault localization
Web Semantics: Science, Services and Agents on the World Wide Web
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Efficient ontology debugging is a cornerstone for many activities in the context of the Semantic Web, especially when automatic tools produce (parts of) ontologies such as in the field of ontology matching. The best currently known interactive debugging systems rely upon some meta information in terms of fault probabilities, which can speed up the debugging procedure in the good case, but can also have negative impact on the performance in the bad case. The problem is that assessment of the meta information is only possible a-posteriori. Consequently, as long as the actual fault is unknown, there is always some risk of suboptimal interactive diagnoses discrimination. As an alternative, one might prefer to rely on a tool which pursues a no-risk strategy. In this case, however, possibly well-chosen meta information cannot be exploited, resulting again in inefficient debugging actions. In this work we present a reinforcement learning strategy that continuously adapts its behavior depending on the performance achieved and minimizes the risk of using low-quality meta information. Therefore, this method is suitable for application scenarios where reliable a-priori fault estimates are difficult to obtain. Using a corpus of incoherent real-world ontologies from the field of ontology matching, we show that the proposed risk-aware query strategy outperforms both meta information based approaches and no-risk strategies on average in terms of required amount of user interaction.