Ontology Mapping And Research Of Data Integration Based On The Ontoly

With the rapid development of science and technology, Managing data with information technology is applied in more and more fields, which simplifies the operations and improves the working efficiency. However, as the development of information technology, the decentralization and diversification of data deposition cause the phenomenon of Information Silo which hampers the progress of construction of information technology. Because transactions in each enterprise are independent and there is no standard to follow, each ontology has different description of the same field. To make it possible to understand each other while exchanging the information, the ontology mapping technology is proposed. Ontology mapping technology is oriented from the schema matching between (?)eterogeneous databases. After decades'research, great progress has been made in this area. But it still lies the problems as lack of heuristic information, low precision and low automation level.Based on the analysis of the relevant research background of the current heterogeneous data integration and ontologies, This dissertation proposed a solution which base on ontology mapping method for data integration to solve the heterogeneity and semantic conflict. In this method, using hybrid-mode ontology approach to built the correspondence between each other to achieve heterogeneous data sharing. This study mostly including:(1) We proposed attribute mapping method and the integration of attribute mapping and concept mapping to improve the integrality of ontology mapping.(2) We proposed a semi-automatic method for creating global ontology, which can detect and resolve the semantic conflicts in local ontologies and thus build the mapping bridge between global ontology and local ontologies.(3) ontology-based mapping of heterogeneous data integration framework successfully applied to the integration of modern digital community information system.According to the study of this article, we find that with the integration of different ontology mapping methods and using of some machine learning techniques, we can improve the accuracy of the automatic ontology mapping greatly. At the same time, with the study of knowledge processing workflow, we can help the enterprise construct their knowledge management system more effectively.