Abstract
Available domain ontologies are increasing over the time. However there is a huge amount of data stored and managed with RDBMS. We propose a method for learning association rules from both sources of knowledge in an integrated way. The extracted patterns can be used for performing: data analysis, knowledge completion, ontology refinement.
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d’Amato, C., Bryl, V., Serafini, L. (2012). Semantic Knowledge Discovery from Heterogeneous Data Sources. In: ten Teije, A., et al. Knowledge Engineering and Knowledge Management. EKAW 2012. Lecture Notes in Computer Science(), vol 7603. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33876-2_5
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DOI: https://doi.org/10.1007/978-3-642-33876-2_5
Publisher Name: Springer, Berlin, Heidelberg
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