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.
Ontological CP-Nets / Di Noia, Tommaso; Lukasiewicz, Thomas; Vanina Martinez, Maria; Simari, Gerardo I.; Tifrea-Marciuska, Oana (LECTURE NOTES IN COMPUTER SCIENCE). - In: Uncertainty Reasoning for the Semantic Web III : ISWC International Workshops, URSW 2011-2013. Revised Selected Papers / [a cura di] Fernando Bobillo, Rommel N. Carvalho, Paulo C.G. Costa, Claudia d'Amato, Nicola Fanizzi, Kathryn B. Laskey, Kenneth J. Laskey, Thomas Lukasiewicz, Matthias Nickles, Michael Pool. - STAMPA. - Cham, CH : Spinger, 2014. - ISBN 978-3-319-13412-3. - pp. 289-308 [10.1007/978-3-319-13413-0_15]
Ontological CP-Nets
Tommaso Di Noia;
2014-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
