Grr: Generating Random RDF

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Abstract

This paper presents Grr, a powerful system for generating random RDF data, which can be used to test Semantic Web applications. Grr has a sparql-like syntax, which allows the system to be both powerful and convenient. It is shown that Grr can easily be used to produce intricate datasets, such as the LUBM benchmark. Optimization techniques are employed, which make the generation process efficient and scalable.

This work was partially supported by the GIF (Grant 2201-1880.6/2008) and the ISF (Grant 143/09).