Skyline queries over possibilistic RDF data

Abstract 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.

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