Reasoning with DL-based CP-Nets

Preference representation and reasoning is a key issue in many realworld scenarios where a personalized access to information is needed. Currently, there are many approaches allowing a system to assess preferences in a qualitative or quantitative way, and among the qualitative ones the most prominent are CP-nets. Their clear graphical structure unifies an easy representation of user desires with nice 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 have an ontological structure or, in other words, variable values are DL formulas constrained relative to an underlying domain ontology. We also show how the computation of Pareto-optimal outcomes for an ontological CP-net can be reduced to the solution of constraint satisfaction problems.