An Efficient Matching Algorithm for Fuzzy RDF Graph

The rapid growth of RDF (Resource Description Framework) data shows a steady trendy of decentralization. Identify correspondences among these data sources is an important task. Concentrating on the RDF data with fuzzy information, in this paper, we propose a unified framework combined multiple measures of similarity for automatically solving the problem of fuzzy RDF data matching. For this purpose, syntactic and semantic similarities are calculated based on element labels of RDF graph. Motivated by the structural characteristics of fuzzy RDF graph, we introduce an effective similarity measure approach by interactively utilizing structural information, in which we take fuzzy edge values and edge similarities into consideration. It is proved that the iterative computation of the proposed similarity approach converges. Finally, we combine these results of the individual metrics and extract the correspondences on the basis of the total similarities. The experimental results show that our method can effectively identify correspondences among the fuzzy RDF graphs.