Abstract
Uncertainty reasoning and inconsistency handling are two important problems that often occur in the applications of the Semantic Web. Possibilistic description logics provide a flexible framework for representing and reasoning with ontologies where uncertain and/or inconsistent information is available. Although possibilistic logic has become a popular logical framework for uncertainty reasoning and inconsistency handling, its role in the Semantic Web is underestimated. One of the challenging problems is to provide a practical algorithm for reasoning in possibilistic description logics. In this paper, we propose a tableau algorithm for possibilistic description logic \(\mathcal{ALC}\). We show how inference services in possibilistic \(\mathcal{ALC}\) can be reduced to the problem of computing the inconsistency degree of the knowledge base. We then give tableau expansion rules for computing the inconsistency degree of a possibilistic \(\mathcal{ALC}\) knowledge. We show that our algorithm is sound and complete. The computational complexity of our algorithm is analyzed. Since our tableau algorithm is an extension of a tableau algorithm for \(\mathcal{ALC}\), we can reuse many optimization techniques for tableau algorithms of \(\mathcal{ALC}\) to improve the performance of our algorithm so that it can be applied in practice.
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Qi, G., Pan, J.Z. (2008). A Tableau Algorithm for Possibilistic Description Logic \(\mathcal{ALC}\) . In: Domingue, J., Anutariya, C. (eds) The Semantic Web. ASWC 2008. Lecture Notes in Computer Science, vol 5367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89704-0_5
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DOI: https://doi.org/10.1007/978-3-540-89704-0_5
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