An Overview of Similarity Measures for Clustering XML Documents

The large amount and heterogeneity of XML documents on the Web require the development of clustering techniques to group together similar documents. Documents can be grouped together according to their content, their structure, and links inside and among documents. For instance, grouping together documents with similar structures has interesting applications in the context of information extraction, of heterogeneous data integration, of personalized content delivery, of access control definition, of web site structural analysis, of comparison of RNA secondary structures. Many approaches have been proposed for evaluating the structural and content similarity between tree-based and vector-based representations of XML documents. Link-based similarity approaches developed for Web data clustering have been adapted for XML documents. This chapter discusses and compares the most relevant similarity measures and their employment for XML document clustering. INTRODUCTION XML is a markup language introduced by W3C (1998) that allows one to structure documents by means of nested tagged elements. The element tag allows the annotation of the semantic description of the element content and can be exploited in order to effectively retrieve only relevant documents. Thus, the document structure can be exploited for document retrieval. Moreover, through the Xlink language (W3C, 2001), different types of links can be specified among XML documents. In Xlink, a link is a relationship among two or more resources that can be described inside an XML document. These relationships can be exploited as well to improve document retrieval. The exponential growing of XML structured data available on the Web has raised the need of developing clustering techniques for XML documents. Web data clustering (Vakali et al., 2004) is the process of grouping Web data into clusters so that similar data belong to the same cluster and dissimilar data to different clusters. The goal of organizing data in such a way is to improve data availability and to fasten data access, so that Web information retrieval and content delivery on the Web are improved. Moreover, clustering together similar documents allows the development of homogeneous indexing structures and schemas that are more representative of such documents. XML documents can also be used for annotating Web resources (like articles, images, movies, and also Web Services). For example, an image can be coupled with an XML 2 document representing the image author and the date in which it has been shot as well as a textual description of its content or theme. A search engine can …