Learning ontologies for geographic entity matching and multi-sources data fusion

Geographic information integration and fusion from multi-sources geospatial data is still a challenge because of semantic heterogeneity of geospatial entity data. These will delay geospatial cooperation decision support and integrated assessment application for natural resources and environmental problem. Ontology as a conceptualization and knowledge representation of application domain have provided a potential and aided support for entity matching and information integration of multiple sources and heterogeneous data. However, many data sources may not provided ontology definition and formalization representation. This paper will provide a method of graph model based ontologies representation and ontologies learning from data sources schema. Entity matching and fusion with ontology support is also discussed. This paper has classified unary properties and binary properties in ontology representation. Binary semantic relations in ontologies are analyzed. Based on semantic relations, the Tree model and Network model of ontology representation are defined. Through analyzing entity data schema, semantic relations extraction and learning are also discussed in constitution of entity ontologies. The framework of entity fusion with ontology support has been designed. Finally entity matching and fusion methods are discussed.