• Journal of the Korean Society for Library and Information Science
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• v.42 no.4
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• pp.95-112
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• 2008

In contrast to traditional taxonomy, folksonomy is generated not only by experts but also by creators and consumers of the content. Folksonomy is the practice and method of collaboratively creating and managing tags to annotate and categorize content. It is also known as collaborative tagging or social indexing. Folksonomy is also used to link to create social network that connect people to people who share same interest. Folksonomy users can generally discover the contents by which the tag sets of another user who tends to interpret contents in a way that makes sense to them. Firstly, this study consider the significance and some critical issues about folksonomy. Secondly, analyze special features of Korean academic site's folksonomy, which is managed by academic information site. Accordingly consider the directions of development about folksonomy system.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.345-349
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• 2009

Social bookmarking systems are a typical web 2.0 service based on folksonomy, providing the platform for storing and sharing bookmarking information. Spammers in social bookmarking systems denote the users who abuse the system for their own interests in an improper way. They can make the entire resources in social bookmarking systems useless by posting lots of wrong information. Hence, it is important to detect spammers as early as possible and protect social bookmarking systems from their attack. In this paper, we applied a diverse set of machine learning approaches, i.e., decision tables, decision trees (ID3), $na{\ddot{i}}ve$ Bayes classifiers, TAN (tree-augment $na{\ddot{i}}ve$ Bayes) classifiers, and artificial neural networks to this task. In our experiments, $na{\ddot{i}}ve$ Bayes classifiers performed significantly better than other methods with respect to the AUC (area under the ROC curve) score as veil as the model building time. Plausible explanations for this result are as follows. First, $na{\ddot{i}}ve$> Bayes classifiers art known to usually perform better than decision trees in terms of the AUC score. Second, the spammer detection problem in our experiments is likely to be linearly separable.