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Fast and Semantic Measurements on Collaborative Tagging Quality

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Advances in Knowledge Discovery and Data Mining (PAKDD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9652))

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Abstract

This paper focuses on the problem of tagging quality evaluation in collaborative tagging systems. By investigating the dynamics of tagging process, we find that high frequency tags almost cover the main aspects of a resource content and can be determined stable much earlier than a whole tag set. Motivated by this finding, we design the swapping index and smart moving index on tagging quality. We also study the correlations in tag usage and propose the semantic measurement on tagging quality. The proposed methods are evaluated against real datasets and the results show that they are more efficient than previous methods, which are appropriate for a large number of web resources. The effectiveness is justified by the results in tag based applications. The light weight metrics bring a little loss on the performance, while the semantic metric is better than current methods.

This work is supported by NSF China (61173140), SAICT Experts Program, Independent Innovation & Achievements Transformation Program (2014ZZCX03301) and Science & Technology Development Program of Shandong Province (2014GGX101046).

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, China

    Yuqing Sun

  2. Engineering Research Center of Digital Media Technology, MOE, Jinan, China

    Yuqing Sun

  3. Software College, Shandong University, Jinan, China

    Haiqi Sun

  4. Department of Computer Science, The University of Hong Kong, Hong Kong, China

    Reynold Cheng

Authors
  1. Yuqing Sun
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  2. Haiqi Sun
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  3. Reynold Cheng
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Corresponding author

Correspondence to Yuqing Sun .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  2. The University of Texas at Dallas, Richardson, Texas, USA

    Latifur Khan

  3. Osaka University, Osaka, Japan

    Takashi Washio

  4. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  5. Shenzhen University, Shenzhen, China

    Joshua Zhexue Huang

  6. Massey University, Auckland, New Zealand

    Ruili Wang

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© 2016 Springer International Publishing Switzerland

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Sun, Y., Sun, H., Cheng, R. (2016). Fast and Semantic Measurements on Collaborative Tagging Quality. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_29

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  • DOI: https://doi.org/10.1007/978-3-319-31750-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31749-6

  • Online ISBN: 978-3-319-31750-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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