The vision of the Semantic Web is to make use of semantic representations on the largest possible scale -the Web. Large knowledge bases such as DBpedia, OpenCyc, GovTrack, and others are emerging and are freely available as Linked Data and SPARQL endpoints. Exploring and analyzing such knowledge bases is a significant hurdle for Semantic Web research and practice. As one possible direction for tackling this problem, we present an approach for obtaining complex class descriptions from objects in knowledge bases by using Machine Learning techniques. We describe how we leverage existing techniques to achieve scalability on large knowledge bases available as SPARQL endpoints or Linked Data. Our algorithms are made available in the open source DL-Learner project and can be used in real-life scenarios by Semantic Web applications.
REF uses machine learning to find OWL class descriptions.
Learning of OWL Class Descriptions on Very Large Knowledge Bases
International Semantic Web Conference
Computer Science