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Lecture Notes in Computer Science
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 ofdoi:10.1007/978-3-642-23737-9_30 fatcat:zku6egpckbdwtbev227rj7lc7y