基于证据理论的不确定模式匹配方法

Uncertainty schema matching approach based on evidence theory

由于数据源数据模式的自治性、异构性,不确定性是模式匹配过程固有的本质特性。提出了一种基于证据理论的不确定性匹配方法,首先根据属性类型把模式空间分成若干模式子空间;然后将不同的匹配器结果看作不同的证据源,利用不同的匹配器的结果生成了多个基本概率分配函数,采用改进的Dempster组合规则把多个匹配器结果自动组合,减少人工干预,并解决了不同的匹配器结果组合时证据间冲突的问题;最后利用Kuhn-Munkres算法获取模式映射。实验结果表明了方法的可行性和有效性。

Due to autonomy and heterogeneity data sources,uncertainty is an inherent character of schema matching.In order to improve the performance of schema matching,an uncertainty matching approach based on evidence theory is proposed.Firstly,the schema space is divided into several schema subspaces according to attributes types.Secondly,different matchers are viewed as different sources of evidence,and mass distributions are defined on the basis of the match results from these matchers.Thirdly,an improved evidence theory is used to automatically combine multiple matchers,which reduces human involvement and solves the situations with high conflict results from different matchers.Finally the mapping is generated by the improved Kuhn Munkres algorithm.The experiments show that the proposed method is highly accurate and effective.