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

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.