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KCAM: Concentrating on Structural Similarity for XML Fragments

  • Lingbo Kong
  • Shiwei Tang
  • Dongqing Yang
  • Tengjiao Wang
  • Jun Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4016)

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( Open image in new window , Open image in new window ), 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.

Keywords

Edit Distance Node Position Very Large Data Base Precision Ratio Triangle Matrix 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lingbo Kong
    • 1
  • Shiwei Tang
    • 1
    • 2
  • Dongqing Yang
    • 1
  • Tengjiao Wang
    • 1
  • Jun Gao
    • 1
  1. 1.Department of Computer Science and TechnologyPeking UniversityBeijingChina
  2. 2.National Laboratory on Machine PerceptionPeking UniversityBeijingChina

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