SMM: An Effective Schema Matching Model for Data Grids

Data Grids primarily deal with providing services and infrastructure for distributed data-intensive applications, in which schema matching is a key technology for supporting dynamic data integration. Against the limitations of existed schema matching methods and with the aim of reducing the amount of user effort as much as possible, a novel approach to schema matching called SMM is proposed based on schema structure information and known matching knowledge, in which neural network influence procedure is imitated to realize semantic matching and reasoning, known matching knowledge is reused to supplement the matching and reasoning. At last, the experiment results show that SMM is feasible.