A clustering-based approach for large-scale ontology matching
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Neighbour based structural proximity measures for ontology matching systems
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Many systems in sciences, engineering and nature can be modeled as networks. Examples include the internet, WWW and social networks. Finding hidden structures is important for making sense of complex networked data. In this paper we present a new network clustering method that can find clusters in an agglomerative fashion using structural similarity of vertices in the given network. Experiments conducted on real datasets demonstrate promising performance of the new method.