The Impact of Ontology on the Performance of Information Retrieval : A Case of

The large amount and heterogeneity of XML documents on the Web requires the development of clustering techniques to group together similar documents. Documents can be grouped together according to their content, their structure, and the links inside and among the documents. For instance, grouping together documents with similar structure has interesting applications in the context of information extraction, heterogeneous data integration, personalized content delivery, access-control definition, Web site structural analysis, and the comparison of RNA secondary structures. Many approaches have been proposed for evaluating the structural and content similarity between tree-based and vector-based repIDEA GROUP PUBLISHING This paper appears in the publication, Web Data Management Practices: Emerging Techniques and Technologies edited by Athena Vakali and George Pallis © 2007, Idea Group Inc. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB13451 An Overview of Similarity Measures for Clustering XML Documents 57 Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. resentations 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.