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A SVM-based Method for Classifying Tagged Web Resources using Tag Stability of Folksonomy in Categories  

Koh, Byung-Gul (서울대학교 컴퓨터공학부)
Lee, Kang-Pyo (서울대학교 컴퓨터공학부)
Kim, Hyoung-Joo (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.15, no.6, 2009 , pp. 414-423 More about this Journal
Abstract
Folksonomy, which is collaborative classification created by freely selected keywords, is one of the driving factors of the web 2.0. Folksonomy has advantage of being built at low cost while its weakness is lack of hierarchical or systematic structure in comparison with taxonomy. If we can build classifier that is able to classify web resources from collective intelligence in taxonomy, we can build taxonomy at low cost. In this paper, targeting folksonomy in Slashdot.org, we define a general model and show that collective intelligence, which can build classifier, really exists in folksonomy using a stability value. We suggest method that builds SVM classifier using stability that is result from this collective intelligence. The experiment shows that our proposed method managed to build taxonomy from folksonomy with high accuracy.
Keywords
Folksonomy; Tag; Collective intelligence; Taxonomy; Classification; SVM;
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