基于全知熵的模式集成不确定性度量模型

Uncertainty Measure Model of Schema Integration Based on All Known Entropy

不确定性是模式集成的一个固有性质,不确定性度量对模式集成具有重大影响。本文提出一种度量模型,在该模型中模式对象及其属性清洗模块使该模型免受规模影响。根据模式集成多属性分阶段决策的特点,本文基于粗糙集理论的全知熵不确定率进行各阶段的不确定性度量,并把过程模型的不确定性度量引入到总体不确定性的度量中,最后给出了合成多不确定率的方法。实例分析证实所设计模型是可行、有效的。

Uncertainty is intrinsic in schema integration. An uncertainty measure model of schema integration system （SIS） is presented. Schema object cleanout module and its attribute cleanout module can make the model measure uncertainty of large-scale SIS. Schema integration is a process with multi-attribute and decision-making. Uncertainty ratio based on all known entropy of rough set is adopted for measuring uncertainty of submodels of SIS. Uncertainty measure of process model is used in whole uncertainty measure. The method for synthesizing the uncertainty ratio is provided. Experimental results show that the presented model is feasible and effectual.