Skyline Modeling and Computing over Trust RDF Data

Resource Description Framework (RDF) data come from various sources whose reliability is sometimes questionable. Therefore, several researchers enriched the basic RDF data model with trust information. New methods to represent and reason with trust RDF data are introduced. In this paper, we are interested in querying trust RDF data. We particularly tackle the skyline problem, which consists in extracting the most interesting trusted resources according to user-defined criteria. To this end, we first redefined the dominance relationship in the context of trust RDF data. Then, we proposed an appropriate semantics of the trust-skyline; the set of most interesting resources in a trust RDF dataset. Efficient methods to compute the trust-skyline are provided and compared to some existing approaches as well. Experiments led on the algorithms' implementations showed promising results.