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Ontological CP−Nets

Tommaso Di Noia‚ Thomas Lukasiewicz‚ Maria Vanina Martinez‚ Gerardo I. Simari and Oana Tifrea−Marciuska


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.

Book Title
Uncertainty Reasoning for the Semantic Web III‚ International Workshops URSW 2011−2013‚ Held at ISWC‚ Revised Selected Papers
Fernando Bobillo and Rommel Carvalho and Paulo C. G. Costa and Claudia d'Amato and Nicola Fanizzi and Kathryn B. Laskey and Kenneth J. Laskey and Thomas Lukasiewicz and Matthias Nickles and Michael Pool
Lecture Notes in Computer Science