Abstract
In this paper, we introduce a new approach for constructing complex mappings between ontologies by transforming it to a rule learning process. Derived from the classical Inductive Logic Programming, our approach uses instance mappings as training data and employs tailoring heuristics to improve the learning efficiency. Empirical evaluation shows that our generated Horn-rule mappings are meaningful.
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Hu, W., Chen, J., Zhang, H., Qu, Y. (2012). Learning Complex Mappings between Ontologies. In: Pan, J.Z., et al. The Semantic Web. JIST 2011. Lecture Notes in Computer Science, vol 7185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29923-0_24
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DOI: https://doi.org/10.1007/978-3-642-29923-0_24
Publisher Name: Springer, Berlin, Heidelberg
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