A New Sequential Mining Approach to XML Document Similarity Computation

1 Manuscript submitted to Postgraduate Research Day 2 Corresponding author Abstract 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 works on 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 takes use of a preorder tree representation (PTR) to encode the XML tree’s paths so that the element’s semantic and the hierarchical structure of document can be taken into accounts when computing the structural similarity among documents. In addition, it includes a post-processing 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.