2010/04/29 by Kostyantyn Shchekotykhin, Gerhard Friedrich, Shchekotykhin, Kostyantyn +6 · 1 citation
Computer Science · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Semantic Web and Ontologies #Service-Oriented Architecture and Web Services #cs.AI #cs.LO
paper · pdf · doi:10.48550/arxiv.1004.5339
Preprint submitted to Web Semantics: Science, Services and Agents on the World Wide Web
openalex publication_date 2010/04/29 · arxiv created 2011/07/21 · arxiv updated 2015/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.