NDLI: Integrating element and term semantics for similarity-based XML document clustering

Content Provider	IEEE Xplore Digital Library
Author	Jianwu Yang Cheung, W.K. Xiaoou Chen
Copyright Year	2005
Description	Author affiliation: Inst. of Comput. Sci. & Tech., Peking Univ., Beijing, China (Jianwu Yang)
Abstract	Structured link vector model (SLVM) is a recently proposed document representation that takes into account both structural and semantic information for measuring XML document similarity. Its formulation includes an element similarity matrix for capturing the semantic similarity between XML elements - the structural components of XML documents. In this paper, instead of applying heuristics to define the similarity matrix, we proposed to learn the matrix using pair wise similar training data in an iterative manner. In addition, we extended SLVM to SLVM-LSI by incorporating term semantics into SLVM using latent semantic indexing, with the element similarity related properties of the original SLVM preserved. For performance evaluation, we applied SLVM-LSI to similarity-based clustering of two XML datasets and the proposed SLVM-LSI was found to significantly outperform the conventional vector space model and the edit-distance based methods. The similarity matrix, obtained as a byproduct via the learning, can provide higher level knowledge about the semantic relationship between the XML elements.
Sponsorship	IEEE Comput. Soc. Web Intelligence Consortium (WIC) Assoc. for Comput. Machinery (ACM)
Starting Page	222
Ending Page	228
File Size	2525858
Page Count	7
File Format	PDF
ISBN	076952415X
DOI	10.1109/WI.2005.80
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-19
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tree data structures Text analysis Fourier transforms XML Training data Virtual manufacturing Kernel Indexing Information analysis
Content Type	Text
Resource Type	Article

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