Abstract
Intelligent post-processing of data mining results can provide valuable knowledge. In this paper we present the first systematic solution to post-processing that is based on semantic web technologies. The framework input is constituted by PMML and description of background knowledge. Using the Topic Maps formalism, a generic Data Mining ontology and Association Rule Mining ontology were designed. Through combination of a content management system and a semantic knowledge base, the analyst can enter new pieces of information or interlink existing ones. The information is accessible either via semi-automatically authored textual analytical reports or via semantic querying. A prototype implementation of the framework for generalized association rules is demonstrated on the PKDD’99 Financial Data Set.
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Kliegr, T. et al. (2009). Semantic Analytical Reports: A Framework for Post-processing Data Mining Results. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_12
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DOI: https://doi.org/10.1007/978-3-642-04125-9_12
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