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Learning Element Similarity for XML Document Clustering

This paper presents advancements in ontology-enabled web search by focusing on the integration of element similarity for XML document clustering. Authored by John and presented at the Web Intelligence conference, it discusses the structured link vector model (SLVM) and the iterative similarity learning algorithm, building on previous work by Jianwu Yang and William K. Cheung. The research emphasizes using element kernel and term semantics to achieve improved document similarity assessment, thus enhancing the efficiency of web search processes.

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Learning Element Similarity for XML Document Clustering

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  1. <article> <title>Ontology Enabled Web Search</title> <author>John</author> <conference>Web Intelligence<conference> </article> title author conference Ontology Enabled Web Search John Intelligence Learning Element Similarity for XML Document Clustering Jianwu Yang (from Founder R&D) and William K. Cheung XML Document Similarity Element Similarity Matrix Structured Link Vector Model (SLVM) Document Similarity Matrix The Iterative Similarity Learning Algorithm J. Yang, W. K. Cheung, Xiaoou Chen, " Integrating Element Kernel and Term Semantics for Similarity-Based XML Document Clustering," Proceedings of WI, Sept2005

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