Interactive debugging of ontologies

Date: September 18, 2015

Speaker: Kostyantyn Shchekotykhin

Abstract

Effective debugging of ontologies is an important prerequisite for their broad application, especially in the areas that rely on everyday users which create and maintain ontologies such as the Semantic Web. In many cases users have different views of a domain, i.e. which knowledge about the domain has to be captured in an ontology and how it should be represented. Inappropriate usage of concepts together with natural complications when formulating and understanding logical descriptions may result in faulty ontologies.

Recent approaches to debugging of ontologies use diagnosis methods to identify causes of the faults. In most debugging scenarios there are multiple explanations for a fault and, therefore, many diagnoses can be found by a debugger. In this case, localization of a fault, i.e. selection of the target diagnosis, must be done manually.

In this talk we present an interactive debugging approach which automatically identifies the target diagnosis by performing a sequence of queries to a user. That is, the system uses discrepancies between different fault explanations to generate a query to a user: whether or not some logical sentence must be implied by an ontology a user is going to create. Due to an impossibility of a user to provide answers to all existing queries, the debugger can use a variety of query selection strategies applied in active learning and select the most informative ones. This allows to minimize the number of queries required to localize a fault and simplifies debugging of ontologies in general.

References

• Friedrich, G., & Shchekotykhin, K. (2005). A General Diagnosis Method for Ontologies. Proceedings of the 4th International Semantic Web Conference (ISWC-05) (pp. 232–246).
• Shchekotykhin, K., Friedrich, G., Fleiss, P., & Rodler, P. (2012). Interactive ontology debugging : two query strategies for efficient fault localization. Web Semantics: Science, Services and Agents on the World Wide Web, 12-13, 88–103.
• Shchekotykhin, K., Friedrich, G., Rodler, P., & Fleiss, P. (2014). Sequential diagnosis of high cardinality faults in knowledge-bases by direct diagnosis generation. Proceedings of the 21st European Conference on Artificial Intelligence (ECAI 2014).