Abstract. 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. Keywords: Linked Data, Ontology Learning, OWL2, Property Characteristics Currently, the amount of information which is available as Linked Data on the Web is growing rapidly. Large repositories like DBpedia or Freebase already contain data on many different topics, and thus serve as crystallization points for the inclusion of new data and domain-specific knowledge repositories. Its tremendous growth in terms of both size and coverage makes Linked Data more and more appealing for various types of applications. However, a problem of many linked data sets remains the lack of an appropriate schema that could be used to infer implicit information and to validate the consistency of newly added data. The semantic web architecture foresees formal ontologies as a basis for deriving implicit information and data verification. The creation of such ontologies, however, is a complex and time-consuming process and the size and heterogeneity of linked data sources renders the manual creation of formal ontologies an unrealistic scenario. There are some efforts for creating schema information for linked data. Examples are Johanna Völker is financed by a Margarete-von-Wrangell scholarship of the European Social Fund (ESF) and the Ministry of Science, Research and the Arts Baden-Württemberg.
Fleischhacker and colleagues, for example, use statistical schema induction in the approach they proposed to enrich the schema of any RDF dataset with property axioms REF .
Mining RDF Data for Property Axioms
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