SMGM: One Schema Matching Model Based on Schema Structures and Known Matching Knowledge
SMGM：一种基于模式结构和已有匹配知识的模式匹配模型

Schema matching is the task of finding semantic correspondences between elements of two schemas. It is critical in many applications, such as data integration, data warehouse loading and XML message mapping, etc. Against the limitations of existed schema matching methods,with the aim of reducing the amount of user effort as much as possible to automatic schema matching, based on the schema structure information and known matching knowledge, we propose a novel approach to schema matching method called SMGM. It imitates the influence procedure between neurons to realize the semantic matching reasoning. By reusing the known matching knowledge to supplement and dive the matching knowledge and curtail the uncertain threshold interval automatically, and presented a self learning schema matching model which can mine and dive the known matching knowledge adaptively and iterately. The result of our experiment shows that the SMGM is feasible.