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Semantic extraction for multi-enterprise business collaboration

机译:用于多企业业务协作的语义提取

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摘要

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.
机译:语义提取对于多企业业务协作环境中的语义互操作性至关重要。尽管已经进行了很多关于语义提取的研究,但是很少有研究集中在如何从多个异构数据模式中准确有效地提取语义。本文提出了一种基于中性表示格式(NRF)的半自动语义提取方法,用于从异构数据模式中获取语义。作为统一的语法独立模型,NRF从原始数据环境中删除了异构数据模式的所有意外情况。概念提取和关键字提取用于从NRF获取语义。概念提取需要构建概念模型,而关键字提取则寻求获得元数据。给出了一个工业案例来验证该方法。该方法具有良好的扩展性和灵活性。结果表明,该方法在多企业业务协作环境中提供了简单,准确,有效的语义互操作性。

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