Semantic Analytical Reports: A Framework for Post-processing Data Mining Results

Authors:
Tomáš Kliegr;Martin Ralbovský;Vojtěch Svátek;Milan Šimůnek;Vojtěch Jirkovský;Jan Nemrava;Jan Zemánek
Affiliations:
Faculty of Informatics and Statistics, University of Economics, Prague, Praha 3, Czech Republic 130 67;Faculty of Informatics and Statistics, University of Economics, Prague, Praha 3, Czech Republic 130 67;Faculty of Informatics and Statistics, University of Economics, Prague, Praha 3, Czech Republic 130 67;Faculty of Informatics and Statistics, University of Economics, Prague, Praha 3, Czech Republic 130 67;Dept. of Computer Science and Engineering, Czech Technical University, Praha 2, Czech Republic 121 35;Faculty of Informatics and Statistics, University of Economics, Prague, Praha 3, Czech Republic 130 67;Faculty of Informatics and Statistics, University of Economics, Prague, Praha 3, Czech Republic 130 67
Venue:
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Year:
2009

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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.