Abstract
DL-Lite is a powerful and tractable family of description logics specifically tailored for applications that use huge volumes of data. In many real world applications, data are often provided by several and potentially conflicting sources of information having different levels of priority. Possibility theory offers a very natural framework to deal with ordinal and qualitative uncertain beliefs or prioritized preferences. Thus, to encode prioritized assertional facts, a possibility DL-Lite logic is more suited.
We propose in this paper a min-based assertional merging operator for possibilistic DL-Lite knowledge bases. We investigate in particular the situation where the sources share the same terminological base. We present a syntactic method based on conflict resolution which has a meaningful semantic counterpart when merging possibility distributions. We finally provide an analysis in the light of a new set of postulates dedicated to uncertain DL-Lite merging.
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Benferhat, S., Bouraoui, Z., Lagrue, S., Rossit, J. (2014). Min-based Assertional Merging Approach for Prioritized DL-Lite Knowledge Bases. In: Straccia, U., Calì, A. (eds) Scalable Uncertainty Management. SUM 2014. Lecture Notes in Computer Science(), vol 8720. Springer, Cham. https://doi.org/10.1007/978-3-319-11508-5_2
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DOI: https://doi.org/10.1007/978-3-319-11508-5_2
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