Closed-world concept induction for learning in OWL knowledge bases

Abstract

We present a general-purpose method for inducing OWL class descriptions over data and knowledge captured with RDF and OWL in a closed-world way. We combine our approach with a top-down refinement-based search with Description Logic (DL) expressions which incorporates OWL background knowledge. Our methods are designed for speed and scalability to support analysis tasks like data mining over large knowledge-rich data sets. We compare our methods to a state-of the-art DL learning tool with respect to a large benchmark problem to demonstrate the speed and effectiveness of our approach.