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Using Link-Based Content Analysis to Measure Document Similarity Effectively

  • Conference paper
Advances in Data and Web Management (APWeb 2009, WAIM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5446))

Abstract

Along with a massive amount of information being placed online, it is a challenge to exploit the internal and external information of documents when assessing similarity between them. A variety of approaches have been proposed to model the document similarity based on different foundations, but usually they are not applicable for combining internal and external information. In this paper, we introduce a link-based method into content analysis, which is based on random walk on graphs. By defining similarity as the meeting probability of two random surfers, we propose a computational model for content analysis, which can also be integrated with external information of documents. Empirical study shows that our method achieves good accuracy, acceptable performance and fast convergent rate in multi-relational document similarity measuring.

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Author information

Authors and Affiliations

  1. Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China

    Pei Li, Zhixu Li, Jun He & Xiaoyong Du

  2. School of Information, Renmin University of China, Beijing, China

    Pei Li, Zhixu Li, Jun He & Xiaoyong Du

  3. Department of Management Science and Engineering, Tsinghua University, Beijing, China

    Hongyan Liu

Authors
  1. Pei Li
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  2. Zhixu Li
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  3. Hongyan Liu
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  4. Jun He
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  5. Xiaoyong Du
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Editor information

Editors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Qing Li

  2. Department of Computer Science & Technology, Tsinghua University, Beijing, China

    Ling Feng

  3. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby BC, Canada

    Jian Pei

  4. Department of Computer Science, University of Vermont, VT 05405, Burlington, USA

    Sean X. Wang

  5. School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Brisbane, Australia

    Xiaofang Zhou

  6. Jiangsu Provincial Key Lab of Computer Information Processing Technology School of Computer Science & Technology, Soochow University China, 1 shizi Street Suzhou, 215006, Jiangsu, China

    Qiao-Ming Zhu

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© 2009 Springer-Verlag Berlin Heidelberg

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Cite this paper

Li, P., Li, Z., Liu, H., He, J., Du, X. (2009). Using Link-Based Content Analysis to Measure Document Similarity Effectively. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_40

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  • DOI: https://doi.org/10.1007/978-3-642-00672-2_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00671-5

  • Online ISBN: 978-3-642-00672-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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