On the use of hierarchical information in sequential mining-based XML document similarity computation
Measuring the structural similarity among XML documents is the task of finding their semantic correspondence and is fundamental to many web-based applications. While there exist several methods to address the problem, the data mining approach seems to be a novel, interesting and promising one. It explores the idea of extracting paths from XML documents, encoding them as sequences and finding the maximal frequent sequences using the sequential pattern mining algorithms. In view of the deficiencies encountered by ignoring the hierarchical information in encoding the paths for mining, a new sequential pattern mining scheme for XML document similarity computation is proposed in this paper. It makes use of a preorder tree representation (PTR) to encode the XML tree's paths so that both the semantics of the elements and the hierarchical structure of the document can be taken into account when computing the structural similarity among documents. In addition, it proposes a postprocessing step to reuse the mined patterns to estimate the similarity of unmatched elements so that another metric to qualify the similarity between XML documents can be introduced. Encouraging experimental results were obtained and reported.
information retrieval sequential mining xml structural similarity