PoweRGen: A power-law based generator of RDFS schemas

As the amount of RDF datasets available on the Web has grown significantly over the last years, scalability and performance of Semantic Web (image) systems are gaining importance. Current image benchmarking efforts either consider schema-less image datasets or rely on fixed image schemas. In this paper, we present the first image schema generator, termed PoweRGen, which takes into account the features exhibited by real image schemas. It considers the image functions involved in (a) the combined in- and out-degree distribution of the property graph (which captures the domains and ranges of the properties defined in a schema) and (b) the out-degree distribution of the transitive closure (image) of the subsumption graph (which essentially captures the class hierarchy). The synthetic schemas generated by PoweRGen respect the image functions given as input with an accuracy ranging between 89 and 96%, as well as, various morphological characteristics regarding the subsumption hierarchy depth, structure, etc.