An intelligent matcher for schema mapping problem

Data exchange between companies needs to fulfill the requirements of common data format and data representation. The core technique for solving schema conflict in data exchange is matching imported XML documents into internal schemas. There are two major methods in schema mapping: linguistic matching and structural matching. Based on previous research results, one single method can not effectively solve the schema mapping problems. Therefore, this paper proposes an intelligent schema matcher based on the similarity flooding method to solve the schema matching problem in one-to-one case. This intelligent matcher uses linguistic similarity values to simplify the directed graph to increase the effectiveness of schema mapping. This matcher reduces the computational effort due to the simplification of the graph, and it increases the matching performances compared to the original similarity flooding method in many indices.