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On Computing Similarity in Academic Literature Data: Methods and Evaluation

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Web-Age Information Management (WAIM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8597))

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Abstract

Similarity computation for academic literature data is one of the interesting topics that have been discussed recently in information retrieval and data mining. Consequently, a variety of methods has been proposed to compute the similarity of scientific papers. In this paper, we present various similarity methods and evaluate their effectiveness via extensive experiments on a real-world dataset of scientific papers.

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Notes

  1. 1.

    http://www.informatik.uni-trier.de.

  2. 2.

    http://academic.research.microsoft.com/.

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Acknowledgments

This work was supported by (1) Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2013 (Grants No. C0006278), (2) the MSIP (Ministry of Science, ICT, and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-H0301-13-4009) supervised by the NIPA (National IT Industry Promotion Agency), and (3) the Seoul Creative Human Development Program (HM120006).

Author information

Authors and Affiliations

  1. Department of Computer Software, Hanyang Uiversity, Seoul, Korea

    Masoud Reyhani Hamedani & Sang-Wook Kim

Authors
  1. Masoud Reyhani Hamedani
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  2. Sang-Wook Kim
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Corresponding author

Correspondence to Masoud Reyhani Hamedani .

Editor information

Editors and Affiliations

  1. DEKE Lab., Renmin University of China, Bei**g, China

    Yueguo Chen

  2. Institute for Information Systems, Technical University Braunschweig, Braunschweig, Germany

    Wolf-Tilo Balke

  3. Hong Kong Baptist University Dept. Computer Science, Kowloon Tong, Hong Kong SAR

    Jianliang Xu

  4. School of Information, Renmin University of China, Bei**g, China

    Wei Xu

  5. School of Computer Science and Technology, Hefei, China

    Peiquan **

  6. Department of Computer Science, East China Normal University, Shanghai, China

    **n Lin

  7. Department of Computer Science, Kean University, Wenzhou, China

    Tiffany Tang

  8. School of Electrical Engineering, Korea University, Seoul, Korea, Republic of (South Korea)

    Eenjun Hwang

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Hamedani, M.R., Kim, SW. (2014). On Computing Similarity in Academic Literature Data: Methods and Evaluation. In: Chen, Y., et al. Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science(), vol 8597. Springer, Cham. https://doi.org/10.1007/978-3-319-11538-2_37

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  • DOI: https://doi.org/10.1007/978-3-319-11538-2_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11537-5

  • Online ISBN: 978-3-319-11538-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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