Abstract
This paper investigates the concept approximation problem using ontology as an domain knowledge representation model and rough set theory. In [7] [8], we have presented a rough set based multi-layered learning framework for approximation of complex concepts assuming the existence of a simple concept hierarchy. The proposed methodology utilizes the ontology structure to learn compound concepts using the rough approximations of the primitive concepts as input attributes. In this paper we consider the extended model for knowledge representation where the concept hierarchies are embedded with additional knowledge in a form of relations or constrains among sub-concepts. We present an extended multi-layered learning scheme that can incorporate the additional knowledge and propose some classes of such relations that assure an improvement of the learning algorithm as well as a convenience of the knowledge modeling process. We illustrate the proposed method and present some results of experiment with data from sunspot recognition problem.
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Bazan, J., Nguyen, H.S., Skowron, A., Szczuka, M.: A view on rough set concept approximation. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 181–188. Springer-Verlag, Heidelberg (2003)
Bazan, J.G., Szczuka, M.S.: RSES and RSESlib - A Collection of Tools for Rough Set Computations. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, p. 106. Springer, Heidelberg (2001)
Davies, J., Fensel, D., van Harmelen, F. (eds.): Towards the Semantic Web – Ontology-Driven Knowledge Management. Wiley, London (2002)
Gomez-Perez, A., Corcho, O., Fernandez-Lopez, M.: Ontological Engineering. Springer, London (2002)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)
Kloesgen, W., Żytkow, J. (eds.): Handbook of Knowledge Discovery and Data Mining. Oxford University Press, Oxford (2002)
Nguyen, S.H., Bazan, J.G., Skowron, A., Nguyen, H.S.: Layered learning for concept synthesis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187–208. Springer, Heidelberg (2004)
Nguyen, S.H., Nguyen, T.T., Nguyen, H.S.: Rough Set Approach to Sunspot Classification Problem. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS, vol. 3642, pp. 263–272. Springer, Heidelberg (2005)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z., Skowron, A.: A rough set approach for decision rules generation. In: Proc. of IJCAI 1993, Chambéry, France, pp. 114–119. Morgan Kaufmann, San Francisco (1993)
Skowron, A.: Approximation spaces in rough neurocomputing. In: Inuiguchi, M., Tsumoto, S., Hirano, S. (eds.) Rough Set Theory and Granular Computing, pp. 13–22. Springer, Heidelberg (2003)
Sowa, J.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks Cole Publishing Co., Pacific Grove (2000)
Stone, P.: Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer. The MIT Press, Cambridge (2000)
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46, 39–59 (1993)
Zupan, B., Bohanec, M., Bratko, I., Demsar, J.: Machine learning by function decomposition. In: Proc. Fourteenth International Conference on Machine Learning, San Mateo, CA, pp. 421–429. Morgan Kaufmann, San Francisco (1997)
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Nguyen, S.H., Nguyen, T.T., Nguyen, H.S. (2006). Ontology Driven Concept Approximation. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_57
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DOI: https://doi.org/10.1007/11908029_57
Publisher Name: Springer, Berlin, Heidelberg
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