An intelligent information sharing method of heterogeneous geographic data based on unified metamodel and entity matching

Abstract

Integration and sharing of multiple heterogeneous geographic information and data sources are important for spatial analysis and decision making. However, the sharing and exchange of the geographic information are difficult because of semantic and schema heterogeneity of geographic information in different data sources. This paper presents an intelligent information sharing method of heterogeneous geographic data sources based on metamodel and domain entity matching. A graph based unified metamodel (GUM) method is proposed. Based on GUM, the models of heterogeneous data sources are transformed into GUM based entity models by model transformation. The information sharing are implemented by entity matching between the transformed models. The intelligent entity matching methods are also proposed.