What Can Argumentation Do for Inconsistent Ontology Query Answering?

Madalina Croitoru 1 Srdjan Vesic 2
1 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The area of inconsistent ontological knowledge base query answering studies the problem of inferring from an inconsistent ontology. To deal with such a situation, different semantics have been defined in the literature (e.g. AR, IAR, ICR). Argumentation theory can also be used to draw conclusions under inconsistency. Given a set of arguments and attacks between them, one applies a particular semantics (e.g. stable, preferred, grounded) to calculate the sets of accepted arguments and conclusions. However, it is not clear what are the similarities and differences of semantics from ontological knowledge base query answering and semantics from argumentation theory. This paper provides the answer to that question. Namely, we prove that: (1) sceptical acceptance under stable and preferred semantics corresponds to ICR semantics; (2) universal acceptance under stable and preferred semantics corresponds to AR semantics; (3) acceptance under grounded semantics corresponds to IAR semantics. We also prove that the argumentation framework we define satisfies the rationality postulates (e.g. consistency, closure).
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SUM: Scalable Uncertainty Management, Sep 2013, Washington, DC, United States. Springer, 7th International Conference on Scalable Uncertainty Management, LNCS (8078), pp.15-29, 2013, Scalable Uncertainty Management. 〈10.1007/978-3-642-40381-1_2〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00936486
Contributeur : Madalina Croitoru <>
Soumis le : dimanche 26 janvier 2014 - 14:17:58
Dernière modification le : jeudi 24 mai 2018 - 15:59:22

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Madalina Croitoru, Srdjan Vesic. What Can Argumentation Do for Inconsistent Ontology Query Answering?. SUM: Scalable Uncertainty Management, Sep 2013, Washington, DC, United States. Springer, 7th International Conference on Scalable Uncertainty Management, LNCS (8078), pp.15-29, 2013, Scalable Uncertainty Management. 〈10.1007/978-3-642-40381-1_2〉. 〈lirmm-00936486〉

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