KCAM: Concentrating on Structural Similarity for XML Fragments (original) (raw)

Abstract

This paper proposes a new method, KCAM, to measure the structural similarity of XML fragments satisfying given keywords. Its name is derived directly after the key structure in this method, Keyword Common Ancestor Matrix. One KCAM for one XML fragment is a k × k upper triangle matrix. Each element a i, j stores the level information of the SLCA (Smallest Lowest Common Ancestor) node corresponding to the keywords k i , k j . The matrix distance between KCAMs, denoted as KDist(,), can be used as the approximate structural similarity. KCAM is independent of label information in fragments. It is powerful to distinguish the structural difference between XML fragments.

Supported by Project 2005AA4Z307 under the National High-tech Research and Development of China, Project 60503037 under National Natural Science Foundation of China (NSFC), Project 4062018 under Beijing Natural Science Foundation (BNSF).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Peking University, Beijing, 100871, China
    Lingbo Kong, Shiwei Tang, Dongqing Yang, Tengjiao Wang & Jun Gao
  2. National Laboratory on Machine Perception, Peking University, Beijing, 100871, China
    Shiwei Tang

Authors

  1. Lingbo Kong
  2. Shiwei Tang
  3. Dongqing Yang
  4. Tengjiao Wang
  5. Jun Gao

Editor information

Editors and Affiliations

  1. Chinese University of Hong Kong, Hong Kong, China
    Jeffrey Xu Yu
  2. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8505, Tokyo, Japan
    Masaru Kitsuregawa
  3. Department of Computing, Hong Kong Polytechnic University, Hong Kong
    Hong Va Leong

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© 2006 Springer-Verlag Berlin Heidelberg

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Kong, L., Tang, S., Yang, D., Wang, T., Gao, J. (2006). KCAM: Concentrating on Structural Similarity for XML Fragments. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300\_4

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Keywords

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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