XML Document Classification Using Extended VSM
Focused Access to XML Documents
Semantic clustering of XML documents
ACM Transactions on Information Systems (TOIS)
Semantics-guided clustering of heterogeneous XML schemas
Journal on data semantics IX
Structure and content similarity for clustering XML documents
WAIM'10 Proceedings of the 2010 international conference on Web-age information management
A semantic matching of information segments for tolerating error chinese words
WISE'06 Proceedings of the 7th international conference on Web Information Systems
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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 SL VM 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 af two XMZ. datasets and the proposed SLVM-LSI was found to significant(y outpeform 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.