Commenced in January 2007
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FCA-based Conceptual Knowledge Discovery in Folksonomy

Authors: Yu-Kyung Kang, Suk-Hyung Hwang, Kyoung-Mo Yang


The tagging data of (users, tags and resources) constitutes a folksonomy that is the user-driven and bottom-up approach to organizing and classifying information on the Web. Tagging data stored in the folksonomy include a lot of very useful information and knowledge. However, appropriate approach for analyzing tagging data and discovering hidden knowledge from them still remains one of the main problems on the folksonomy mining researches. In this paper, we have proposed a folksonomy data mining approach based on FCA for discovering hidden knowledge easily from folksonomy. Also we have demonstrated how our proposed approach can be applied in the collaborative tagging system through our experiment. Our proposed approach can be applied to some interesting areas such as social network analysis, semantic web mining and so on.

Keywords: classification, Formal Concept Analysis, Folksonomy data mining, collaborative tagging, conceptual knowledge discovery

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[1] Ching-man Au Yeung, Nicholas Gibbins, and Nigel Shadbolt, "Understanding the Semantics of Ambiguous Tags in Folksonomies", The International Workshop on Emergent Semantics and Ontology Evolution(ESOE2007) at ISWC/ASWC 2007, pp. 108~121, 2007.
[2] Scott A. Golder and Bernardo A. Huberman, " Usage patterns of collaborative tagging systems ", Journal of Information Science, 32 (2), pp. 198-208, 2006.
[3] Suk-Hyung Hwang, Yu-Kyung Kang, "Applying Hierarchical Classes Analysis to Triadic context for Folksonomy Mining", 2007 International Conference on Convergence Information Technology (ICCIT'07), pp.103-109, 2007.
[4] Christoph Schmitz, Andreas Hotho, Robert Jaschke, Gerd Stumme, "Mining Association Rules in Folksonomies", In: Data Science and Classification: Proc. of the 10th IFCS Conf.Berlin, Heidelberg: Springer (2006) , p. 261--270..
[5] B. Ganter, R. Wille, Formal Concept Analysis : Mathematical Foundations., Springer, 1999.D
[6] C. Carpineto, G. Romano, Concept Data Analysis: Theory and Applications., Wiley, September, 2004.
[7] B. A. Davey, H. A. Priestley, Introduction to Lattices and Order., Cambridge University Press, 2002.
[8] D. Doubois, W. Ostasiewicz and H. Prade, Fuzzy sets: History and Basic Notions., Kluwer Academic Boston, 1999.
[9] B. Walczak and D. L. Massart,"Tutorial Rough sets theory", Chemometrics and Intelligent Laboratory Systems., vol.47, pp. 1-16, 1999.