Semantic extraction is essential for semantic interoperability in multi-enterprise business collaboration environments. Although many studies on semantic extraction have been carried out, few have focused on how to pr...Semantic extraction is essential for semantic interoperability in multi-enterprise business collaboration environments. Although many studies on semantic extraction have been carried out, few have focused on how to precisely and effectively extract semantics from multiple heterogeneous data schemas. This paper presents a semi-automatic semantic extraction method based on a neutral representation format （NRF） for acquiring semantics from heterogeneous data schemas. As a unified syntax-independent model, NRF removes all the contingencies of heterogeneous data schemas from the original data environment. Conceptual extraction and keyword extraction are used to acquire the semantics from the NRF. Conceptual extraction entails constructing a conceptual model, while keyword extraction seeks to obtain the metadata. An industrial case is given to validate the approach. This method has good extensibility and flexibility. The results show that the method provides simple, accurate, and effective semantic interoperability in multi-enterprise business collaboration environments.展开更多
基金Supported by the National Natural Science Foundation of China(No.60674080)the Europe Union Project of Software for Ambient Semantic Interoperable Services(FP6-2005-IST-5-034980)the National High-Tech Research and Development(863) Program of China(Nos.2006AA04Z166 and 2007AA04Z150)
文摘Semantic extraction is essential for semantic interoperability in multi-enterprise business collaboration environments. Although many studies on semantic extraction have been carried out, few have focused on how to precisely and effectively extract semantics from multiple heterogeneous data schemas. This paper presents a semi-automatic semantic extraction method based on a neutral representation format （NRF） for acquiring semantics from heterogeneous data schemas. As a unified syntax-independent model, NRF removes all the contingencies of heterogeneous data schemas from the original data environment. Conceptual extraction and keyword extraction are used to acquire the semantics from the NRF. Conceptual extraction entails constructing a conceptual model, while keyword extraction seeks to obtain the metadata. An industrial case is given to validate the approach. This method has good extensibility and flexibility. The results show that the method provides simple, accurate, and effective semantic interoperability in multi-enterprise business collaboration environments.
