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

Jianwu Yang*, Kwok Wai CHEUNG, Xiaoou Chen

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

13 Citations (Scopus)

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 by-product via the learning, can provide higher-level knowledge about the semantic relationship between the XML elements.

Original languageEnglish
Title of host publicationProceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005
Pages222-228
Number of pages7
DOIs
Publication statusPublished - 2005
Event2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005 - Compiegne Cedex, France
Duration: 19 Sep 200522 Sep 2005

Publication series

NameProceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005
Volume2005

Conference

Conference2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005
Country/TerritoryFrance
CityCompiegne Cedex,
Period19/09/0522/09/05

Scopus Subject Areas

  • Engineering(all)

Fingerprint

Dive into the research topics of 'Integrating element and term semantics for similarity-based XML document clustering'. Together they form a unique fingerprint.

Cite this