Skyline queries over possibilistic RDF data

Volume and veracity of data on the Web are two main issues in managing information. In this paper, we tackle these two issues, with a particular interest to Resource Description Framework (RDF) data. For veracity management, we rely on a powerful uncertainty theory, namely possibility theory. Therefore, we propose a model for representing and managing possibilistic RDF data. Alongside, to filter the massive amount of RDF data, we use the skyline operator to find out a small set of resources that satisfy predefined user preferences. To this aim, we also propose a skyline operator to extract possibilistic RDF resources that are possibly dominated by no other resources according to Pareto dominance definition. We introduce a dominance operator and a skyline model adopted to the aforementioned kind of data. In addition, we propose an efficient algorithm to compute the skyline with a reasonable performance. Experiments led on the skyline computation showed satisfying results.