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 (SWSW) systems are gaining importance. Current RDFRDF benchmarking efforts either consider schema-less RDFRDF datasets or rely on fixed RDFSRDFS schemas. In this paper, we present the first RDFSRDFS schema generator, termed PoweRGen  , which takes into account the features exhibited by real SWSW schemas. It considers the power-lawpower-law 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 (TCTC) of the subsumption   graph (which essentially captures the class hierarchy). The synthetic schemas generated by PoweRGen respect the power-lawpower-law 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.

► We present the first RDFS schema generator, termed PoweRGen.
► It considers power-law property and subsumption graphs.
► Linear Programming reductions for graph generation.