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.
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Ratcliffe, D., Taylor, K. (2014). Closed-World Concept Induction for Learning in OWL Knowledge Bases. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds) Knowledge Engineering and Knowledge Management. EKAW 2014. Lecture Notes in Computer Science(), vol 8876. Springer, Cham. https://doi.org/10.1007/978-3-319-13704-9_33
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DOI: https://doi.org/10.1007/978-3-319-13704-9_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13703-2
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