Handling qualitative conditional preferencequeries in SPARQL: Possibilistic logic approach

Abstract

Because of the rise in data volume of knowledge bases that are being published as a result of Open Data initiatives, new approaches are required to assist users in locating the items that most closely matches their preference criteria. In many approaches, the user is called to supply quantitative weights that may not be known in advance to manage the ranking of results.Contrary to the quantitative technique, preference criteria are sometimes more intuitive and can be conveyed more readily under the qualitative approach. We are interested in this paper to the problem of evaluating SPARQL qualitative preference queries over user preferences in SPARQL. Many approaches address this problem based on different frameworks as CP-net, skyline, fuzzy set and top-k. This article outlines a novel approach for dealing with SPARQL preference queries, where preferences are represented through symbolic weights using the possibilistic logic framework. It is possible to manage symbolic weights without using numerical values where a partial ordering is used instead. This approach is compared to numerous other approaches, including those based on skylines, fuzzy sets, and CP-nets.