Abstract
Representing and reasoning about preferences is a key issue in many real-world scenarios in which personalized access to information is required. Many approaches have been proposed and studied in the literature that allow a system to work with qualitative or quantitative preferences; among the qualitative models, one of the most prominent are CP-nets. Their clear graphical structure unifies an easy representation of user preferences with good computational properties when computing the best outcome. In this paper, we show how to reason with CP-nets when the attributes modeling the knowledge domain are structured via an underlying domain ontology. We show how the computation of all undominated feasible outcomes of an ontological CP-net can be reduced to the solution of a constraint satisfaction problem, and study the computational complexity of the basic reasoning problems in ontological CP-nets.
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Acknowledgements
This work was supported by the UK EPSRC grant EP/J008346/1 “PrOQAW: Probabilistic Ontological Query Answering on the Web”, by a Google European Doctoral Fellowship, by the ERC (FP7/2007-2013) grant 246858 (“DIADEM”), by a Yahoo! Research Fellowship, and by PON02_00563_3470993 (“VINCENTE”). This paper is a significantly extended and revised version of papers that appeared in Proceedings URSW-2013 [10] and Proceedings SUM-2013 [11].
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Di Noia, T., Lukasiewicz, T., Martinez, M.V., Simari, G.I., Tifrea-Marciuska, O. (2014). Ontological CP-Nets. In: Bobillo, F., et al. Uncertainty Reasoning for the Semantic Web III. URSW URSW URSW 2012 2011 2013. Lecture Notes in Computer Science(), vol 8816. Springer, Cham. https://doi.org/10.1007/978-3-319-13413-0_15
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DOI: https://doi.org/10.1007/978-3-319-13413-0_15
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