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Analyzing Tag Distributions in Folksonomies for Resource Classification

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7091)

Abstract

Recent research has shown the usefulness of social tags as a data source to feed resource classification. Little is known about the effect of settings on folksonomies created on social tagging systems. In this work, we consider the settings of social tagging systems to further understand tag distributions in folksonomies. We analyze in depth the tag distributions on three large-scale social tagging datasets, and analyze the effect on a resource classification task. To this end, we study the appropriateness of applying weighting schemes based on the well-known TF-IDF for resource classification. We show the great importance of settings as to altering tag distributions. Among those settings, tag suggestions produce very different folksonomies, which condition the success of the employed weighting schemes. Our findings and analyses are relevant for researchers studying tag-based resource classification, user behavior in social networks, the structure of folksonomies and tag distributions, as well as for developers of social tagging systems in search of an appropriate setting.

Keywords

  • Weighting Scheme
  • Resource Classification
  • Social Bookmark
  • Social Annotation
  • Open Directory Project

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Zubiaga, A., Martínez, R., Fresno, V. (2011). Analyzing Tag Distributions in Folksonomies for Resource Classification. In: Xiong, H., Lee, W.B. (eds) Knowledge Science, Engineering and Management. KSEM 2011. Lecture Notes in Computer Science(), vol 7091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25975-3_9

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  • DOI: https://doi.org/10.1007/978-3-642-25975-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25974-6

  • Online ISBN: 978-3-642-25975-3

  • eBook Packages: Computer ScienceComputer Science (R0)