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Learning element similarity matrix for semi-structured document analysis

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Abstract

Capturing latent structural and semantic properties in semi-structured documents (e.g., XML documents) is crucial for improving the performance of related document analysis tasks. Structured Link Vector Mode (SLVM) is a representation recently proposed for modeling semi-structured documents. It uses an element similarity matrix to capture the latent relationships between XML elements—the constructing components of an XML document. In this paper, instead of applying heuristics to define the element similarity matrix, we propose to compute the matrix using the machine learning approach. In addition, we incorporate term semantics into SLVM using latent semantic indexing to enhance the model accuracy, with the element similarity learnability property preserved. For performance evaluation, we applied the similarity learning to k-nearest neighbors search and similarity-based clustering, and tested the performance using two different XML document collections. The SLVM obtained via learning was found to outperform significantly the conventional Vector Space Model and the edit-distance-based methods. Also, the similarity matrix, obtained as a by-product, can provide higher-level knowledge on the semantic relationships between the XML elements.

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Authors and Affiliations

  1. Institute of Computer Science and Technology, Peking University, Beijing, 100871, China

    Jianwu Yang & Xiaoou Chen

  2. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    William K. Cheung

Authors
  1. Jianwu Yang
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  2. William K. Cheung
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  3. Xiaoou Chen
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Correspondence to Jianwu Yang.

Additional information

An abridged version of this manuscript has appeared in the Proceedings of the 2005 IEEE/WIC/ACM international conference on web intelligence, Compeigne, France, September, 2005.

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Yang, J., Cheung, W.K. & Chen, X. Learning element similarity matrix for semi-structured document analysis. Knowl Inf Syst 19, 53–78 (2009). https://doi.org/10.1007/s10115-008-0138-2

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