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In the last few years we have observed a proliferation of approaches for clustering XML documents and schemas based on their structure and content. The presence of such a huge amount of approaches is due to the different applications requiring the clustering of XML data. These applications need data in the form of similar contents, tags, paths, structures, and semantics. In this article, we first outline the application contexts in which clustering is useful, then we survey approaches so far proposed relying on the abstract representation of data (instances or schema), on the identified similarity measure, and on the clustering algorithm. In this presentation, we aim to draw a taxonomy in which the current approaches can be classified and compared. We aim at introducing an integrated view that is useful when comparing XML data clustering approaches, when developing a new clustering algorithm, and when implementing an XML clustering component. Finally, the article moves into the description of future trends and research issues that still need to be faced.

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Abstract. Schema and ontology matching have attracted a great deal of interest among researchers. Despite the advances achieved, the large matching problem still presents a real challenge, such as it is a timeconsuming and memory-intensive process. We therefore propose a scalable, clustering-based matching approach that breaks up the large matching problem into smaller matching problems. In particular, we first introduce a structure-based clustering approach to partition each schema graph into a set of disjoint subgraphs (clusters). Then, we propose a new measure that efficiently determines similar clusters between every two sets of clusters to obtain a set of small matching tasks. Finally, we adopt the matching prototype COMA++ to solve individual matching tasks and combine their results. The experimental analysis reveals that the proposed method permits encouraging and significant improvements.

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