Statistical Schema Induction

Presented at: 8th Extended Semantic Web Conference (ESWC2011)

by Johanna Voelker, Mathias Niepert

While the realization of the Semantic Web as once envisioned by Tim Berners-Lee remains in a distant future, the Web of Data has already become a reality. Billions of RDF statements out there on the Internet, facts about a variety of different domains, are ready to be used by semantic applications. Some of these applications, however, crucially hinge on the availability of expressive schemas suitable for logical inference that yields non-trivial conclusions. In this paper, we present a statistical approach to the induction of expressive schemas from large RDF repositories. We describe in detail the implementation of this approach and report on an evaluation that we conducted using several data sets including DBpedia.

Keywords: data mining, linked data, ontology learning


Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2011/paper/linked-open-data/8


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