A new graph-based flooding matching method for ontology integration

Ontology integration is a well-known problem, a crucial mechanism for semantic interoperability and knowledge reusing, and a backbone of Semantic Web. In this paper, a graph-based method, which combines similarity flooding and concept classification for ontology integration, is proposed. This method consists of three main steps: model ontologies into directed labeled graph, concept classification, and similarity flooding for computing fix-points of pairwise connectivity graph. The main issue presented here is how to shrink spreading scale before we use similarity flooding. Experimental results demonstrate that our method is more effective and obtain better results than original similarity flooding algorithm.