Abstract
XML documents have both structural and semantic information, bringing data integration and deep utilization based on XML more precise description and versatile expression. But in the meanwhile traditional NLP and DM methods can't be applied directly. Feature dimension reduction and general similarity of XML based on tensor analysis are discussed. Considering the correlation between XML's structure and content, a tensor based model for describing XML documents and an MMI method to XML's dimension reduction is presented. Since structure and content are not independent with each other, a tensor based algorithm to calculate general similarity from a non-linear angle is designed to show their relationships and effects to its performance.
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