Query strategy for sequential ontology debugging

TL;DR

This paper introduces an information-theoretic query strategy for sequential ontology debugging that efficiently identifies the target diagnosis by minimizing user queries, outperforming traditional methods.

Contribution

It proposes a novel query selection method based on a-priori error probabilities and information theory, reducing user effort in ontology debugging.

Findings

Significantly fewer queries needed compared to myopic strategies.

Effective even with rough estimates of user error probabilities.

Demonstrated robustness across different probability distributions.

Abstract

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.