关联数据的自动语义融合方法研究

Research on Automatic Semantic Integration Approach of Linked Data

研究关联数据的自动语义融合方法,提高关联数据的语义融合程度。利用传统算法进行数据的自动语义融合,往往只能针对同一知识单元的数据进行融合,假设待融合数据为不同知识单元、不同属性的关联数据,则数据间的语义融合很难实现。为此,提出基于概念关联性和重要性度量算法的关联数据的自动语义融合方法。针对关联数据的自动语义进行关联度计算,为避免对高端语义判别错误,采用语义概念相关性进行语义筛选,获取概念的被选择概率,根据改进方法步骤实现关联数据自动语义的进一步融合。实验结果表明,利用改进算法进行关联数据的自动语义融合,能够有效的获取语义关联度值以及相关性自动语义概念选择,从而实现关联数据的自动语义融合,提高了自动语义的融合程度,具有极大的优越性。

Study automatic semantic correlation data fusion method, increase the degree of the semantic integration of associated data. Using the traditional algorithm for automatic semantic integration of data, often only on the same data fusion of knowledge unit, assume that for different knowledge unit, data fusion for different attribute correlation data, the data between the semantic integration difficult to achieve. Therefore, based on concepts of relevance and importance of measurement algorithm of automatic semantic correlation data fusion method. In view of the automatic semantic correlation calculation correlation data, in order to avoid for high-end semantic discfiminant error, using semantic concept semantic association screening, access to the concept of probability, selected according to the improved algorithm steps to realize the further integration of automatic semantic correlation data. Experimental results show that the improved algorithm for automatic semantic of associated data fusion, could obtain the effective choice correlation automatic semantic concepts and semantic correlation value, so as to realize the automatic semantic of associated data fusion, improve the dearee of automatic semantic integration, has a great advantage.