Mining RDF Data for Property Axioms

  • Daniel Fleischhacker
  • Johanna Völker
  • Heiner Stuckenschmidt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7566)


The Linked Data cloud grows rapidly as more and more knowledge bases become available as Linked Data. Knowledge-based applications have to rely on efficient implementations of query languages like SPARQL, in order to access the information which is contained in large datasets such as DBpedia, Freebase or one of the many domain-specific RDF repositories. However, the retrieval of specific facts from an RDF dataset is often hindered by the lack of schema knowledge, that would allow for query-time inference or the materialization of implicit facts. For example, if an RDF graph contains information about films and actors, but only Titanic starring Leonardo_DiCaprio is stated explicitly, a query for all movies Leonardo DiCaprio acted in might not yield the expected answer. Only if the two properties starring and actedIn are declared inverse by a suitable schema, the missing link between the RDF entites can be derived. In this work, we present an approach to enriching the schema of any RDF dataset with property axioms by means of statistical schema induction. The scalability of our implementation, which is based on association rule mining, as well as the quality of the automatically acquired property axioms are demonstrated by an evaluation on DBpedia.


Linked Data Ontology Learning OWL2 Property Characteristics 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniel Fleischhacker
    • 1
  • Johanna Völker
    • 1
  • Heiner Stuckenschmidt
    • 1
  1. 1.KR & KM Research GroupUniversity of MannheimGermany

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