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. © 2012 Springer-Verlag.
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CITATION STYLE
D’Amato, C., Bryl, V., & Serafini, L. (2012). Semantic knowledge discovery from heterogeneous data sources. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7603 LNAI, pp. 26–31). https://doi.org/10.1007/978-3-642-33876-2_5
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