Mining RDF Data for Property Axioms

Abstract

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.

Parallel mining of OWL 2 EL ontology from large linked datasets

“…However, the process of constructing association tables is very time consuming. Fleischhacker et al [17] later worked on mining RDF data for various types of property axioms. Töpper et al [18] followed similar idea to learn property domains and ranges as well as class disjointness.…”

Detecting hidden errors in an ontology using contextual knowledge

“…The ontology used in this experiment is learned automatically by analyzing statistical schema induction on DBpedia instances (see Fleischhacker et al (2012); Völker & Niepert (2011)) and generated by GoldMiner (Fleischhacker & Völker, 2011) tool. The learned axioms mostly include Disjointness axioms between concepts of DBpedia ontology and have ALCH expressiveness.…”

Statistical Induction of Coupled Domain/Range Restrictions from RDF Knowledge Bases

“…Besides inducing domain and range restrictions of properties, in their work further axioms are induced such as subsumption axioms (e.g., (ex:A, rdfs:subClassOf, ex:B)) and transitivity axioms (e.g., (ex:P, rdf:type, owl:TransitiveProperty)). This work was subsequently extended in [4] and [5] with further types of axioms. The main difference to this work is that we induce independent domain and range restrictions as well as coupled domain/range restrictions whereas in these works only independent domain and range restrictions are induced.…”