Abstract
The roles of ontologies in KDD are potentially manifold. We track them through different phases of the KDD process, from data understanding through task setting to mining result interpretation and sharing over the semantic web. The underlying KDD paradigm is association mining tailored to our 4ft-Miner tool. Experience from two different application domains—medicine and sociology—is presented throughout the paper. Envisaged software support for prior knowledge exploitation via customisation of an existing user-oriented KDD tool is also discussed.
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The Rule Markup Initiative, http://www.ruleml.org/
Unified Medical Language System, http://www.nlm.nih.gov/research/umls/
Almuallim, H., Akiba, Y.A., Kaneda, S.: On Handling Tree-Structured Attributes in Decision Tree Learning. In: Proc. ICML 2005, pp. 12–20. Morgan Kaufmann, San Francisco (2005)
Aronis, J.M., Provost, F.J., Buchanan, B.G.: Exploiting Background Knowledge in Automated Discovery. In: Proc. SIGKDD 1996 (1996)
Berendt, B., Hotho, A., Stumme, G.: 2nd Workshop on Semantic Web Mining, held at ECML/PKDD-2002, Helsinki (2002), http://km.aifb.uni-karlsruhe.de/semwebmine2002
Cannataro, M., Guzzi, P.H., Mazza, T., Tradigo, G., Veltri, P.: Using Ontologies in PROTEUS for Modeling Proteomics Data Mining Applications. In: From Grid to Healthgrid: Proceedings of Healthgrid 2005, pp. 17–26. IOS Press, Amsterdam (2005)
Brisson, L., Collard, M., Le Brigant, K., Barbry, P.: KTA: A Framework for Integrating Expert Knowledge and Experiment Memory in Transcriptome Analysis. In: International Workshop on Knowledge Discovery and Ontologies, held with ECML/PKDD 2004, Pisa, pp. 85–90 (2004)
Buitelaar, P., Cimiano, P., Magnini, B. (eds.): Ontology Learning and Population. IOS Press, Amsterdam (2005)
Češpivová, H., Rauch, J., Svátek, V., Kejkula, M., Tomečková, M.: Roles of Medical Ontology in Association Mining CRISP-DM Cycle. In: ECML/PKDD04 Workshop on Knowledge Discovery and Ontologies (KDO 2004), Pisa (2004)
Clark, P., Matwin, S.: Using Qualitative Models to Guide Inductive Learning. In: Proceedings of the 1993 International Conference on Machine Learning, pp. 49–56 (1993)
Domingues, M.A., Rezende, S.A.: Using Taxonomies to Facilitate the Analysis of the Association Rules. In: The 2nd International Workshop on Knowledge Discovery and Ontologies, held with ECML/PKDD 2005, Porto, pp. 59–66 (2005)
Gómez-Perez, A., Fernández-Lopez, M., Corcho, O.: Ontological Engineering. Springer, Heidelberg (2004)
Hájek, P., Havránek, T.: Mechanising Hypothesis Formation - Mathematical Foundations for a General Theory. Springer, Heidelberg (1978)
Kováč, M., Kuchař, T., Kuzmin, A.: Ferda, New Visual Environment for Data Mining (in Czech). In: Znalosti 2006, Czecho-Slovak Knowledge Technology Conference, Hradec Králové, pp. 118–129 (2006)
Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: A Semantic Web Rule Language Combining OWL and RuleML. W3C Submission, May 21 (2004), Online: http://www.w3.org/Submission/SWRL
Lín, V., Rauch, J., Svátek, V.: Content-based Retrieval of Analytic Reports. In: Schroeder, M., Wagner, G. (eds.) Rule Markup Languages for Business Rules on the Semantic Web, Sardinia, pp. 219–224 (2002)
Maedche, A.: Ontology Learning for the Semantic Web. Kluwer, Dordrecht (2002)
Núñez, M.: The Use of Background Knowledge in Decision Tree Induction. Machine Learning 6, 231–250 (1991)
Phillips, J., Buchanan, B.G.: Ontology-guided knowledge discovery in databases. In: International Conf. Knowledge Capture, Victoria, Canada (2001)
Ralbovský, M.: Usage of Domain Knowledge for Applications of GUHA Procedures (in Czech), Master thesis, Faculty of Mathematics and Physics, Charles University, Prague (2006)
Rauch, J.: Logical Calculi for Knowledge Discovery in Databases. In: Principles of Data Mining and Knowledge Discovery (PKDD-97). Springer, Heidelberg (1997)
Rauch, J.: Logic of Association Rules. Applied Intelligence 22, 9–28 (2005)
Rauch, J., Šimunek, M.: An Alternative Approach to Mining Association Rules. In: Lin, T.Y., Ohsuga, S., Liau, C.J., Tsumoto, S. (eds.) Data Mining: Foundations, Methods, and Applications, pp. 211–232. Springer, Heidelberg (2005)
Strossa, P., Černý, Z., Rauch, J.: Reporting Data Mining Results in a Natural Language. In: Lin, T.Y., Ohsuga, S., Liau, C.J., Hu, X. (eds.) Foundations of Data Mining and Knowledge Discovery, pp. 347–362. Springer, Berlin (2005)
Svátek, V.: Exploiting Value Hierarchies in Rule Learning. In: van Someren, M., Widmer, G. (eds.) ECML 1997. LNCS, vol. 1224, pp. 108–117. Springer, Heidelberg (1997)
Svátek, V., Rauch, J., Flek, M.: Ontology-Based Explanation of Discovered Associations in the Domain of Social Reality. In: The 2nd ECML/PKDD Workshop on Knowledge Discovery and Ontologies, Porto, pp. 75–86 (2005)
Thomas, J., Laublet, P., Ganascia, J.G.: A Machine Learning Tool Designed for a Model-Based Knowledge Acquisition Approach. In: Aussenac, N., Boy, G.A., Ganascia, J.-G., Kodratoff, Y., Linster, M., Gaines, B.R. (eds.) EKAW 1993. LNCS, vol. 723, pp. 123–138. Springer, Heidelberg (1993)
van Dompseler, H.J.H., van Someren, M.W.: Using Models of Problem Solving as Bias in Automated Knowledge Acquisition. In: ECAI 1994 - European Conference on Artificial Intelligence, Amsterdam, pp. 503–507 (1994)
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Svátek, V., Rauch, J., Ralbovský, M. (2006). Ontology-Enhanced Association Mining. In: Ackermann, M., et al. Semantics, Web and Mining. EWMF KDO 2005 2005. Lecture Notes in Computer Science(), vol 4289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908678_11
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DOI: https://doi.org/10.1007/11908678_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47697-9
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