模式匹配不确定性的多因素集结度量  

Uncertain measure for schema matching based on the aggregation of uncertain factors

作　　者：胡文彬[1] 潘祝山 纪兆辉[1] 

机构地区：[1]淮海工学院计算机工程学院,江苏连云港222005

出　　处：《智能系统学报》2015年第2期286-292,共7页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金资助项目(60903027);江苏省自然科学重大研究项目资助项目(BK2011023);江苏省自然科学基金资助项目(BK2011370)

摘　　要：为了能够有效度量模式匹配的不确定性,提出了一个模式匹配不确定性的度量模型,根据不确定性因素间的关系提出了一个集结算子。使用全知熵度量语义匹配和属性匹配的不确定性,引入过程不确定性的度量方法度量匹配决策过程的不确定性。使用多因素集结算子判断各因素的影响程度,并可合成各度量结果。实验证明,所提模型和方法能够有效度量模式匹配的不确定性,且具有高效性和可扩展性。To measure efficiently uncertainty of schema matching,a measure model based on all uncertain factors was proposed and an aggregation operator was given according to the relations of uncertain factors. A measure method of semantic matching and attribute matching based on all known entropy uncertain ratio was designed. A measure algorithm of process uncertainty was introduced to measure uncertainty of a decision making process. The aggregation operator based on relationships between uncertain factors was proposed to determine influence degree of uncertain factors and merge all measure values in the measure process. The real world examples illustrate that the proposed model and methods can completely reflect three factors of uncertainty and can measure efficiently uncertainty for schema matching. The proposed methods are efficient and scalable.

关 键 词：模式定义 模式分析 模式匹配 不确定性分析 数据不确定性度量 度量方法 决策分析 熵 集结评估方法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391[自动化与计算机技术—控制科学与工程]