Conflict Resolution in Partially Ordered OWL DL Ontologies. Ji, Q., Gao, Z., & Huang, Z. *Frontiers in Artificial Intelligence and Applications*, 263:471–476, IOS Press, 2014. Proceedings title: Proceedings of 21st European Conference on Artificial Intelligence (ECAI2014) Publisher: IOS Pressdoi abstract bibtex Inconsistency handling in OWL DL ontologies is an important problem because an ontology can easily be inconsistent when it is generated or modified. Current approaches to dealing with inconsistent ontologies often assume that there exists a total order over axioms and use such an order to select axioms to remove. However, in some cases, such as ontology merging, a total order may not be available and we only have a partial order over axioms. In this paper, we consider a general notion of logical inconsistency and define the notion of conflict of an inconsistent ontology. We then propose a general approach to resolving inconsistency of a partially ordered ontology. We instantiate this approach by proposing two algorithms to calculate prioritized hitting sets for a set of conflicts. We implement the algorithms and provide evaluation results on the efficiency and effectiveness by considering both artificial and real-life data sets.

@article{104f96ec6d1b4495a7fe2d6887d1b81f,
title = "Conflict Resolution in Partially Ordered OWL DL Ontologies",
abstract = "Inconsistency handling in OWL DL ontologies is an important problem because an ontology can easily be inconsistent when it is generated or modified. Current approaches to dealing with inconsistent ontologies often assume that there exists a total order over axioms and use such an order to select axioms to remove. However, in some cases, such as ontology merging, a total order may not be available and we only have a partial order over axioms. In this paper, we consider a general notion of logical inconsistency and define the notion of conflict of an inconsistent ontology. We then propose a general approach to resolving inconsistency of a partially ordered ontology. We instantiate this approach by proposing two algorithms to calculate prioritized hitting sets for a set of conflicts. We implement the algorithms and provide evaluation results on the efficiency and effectiveness by considering both artificial and real-life data sets.",
author = "Q. Ji and Z. Gao and Z. Huang",
note = "Proceedings title: Proceedings of 21st European Conference on Artificial Intelligence (ECAI2014) Publisher: IOS Press",
year = "2014",
doi = "10.3233/978-1-61499-419-0-471",
volume = "263",
pages = "471--476",
journal = "Frontiers in Artificial Intelligence and Applications",
issn = "0922-6389",
publisher = "IOS Press",
}

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