Abstract: Schema matching is an important research in the field of data integration．The uncertainty of column names and data values is a common situation．The common method at present dealing with schema matching problem is based on mutual information and Euclidean distance．But this method does not solve the mistaken matching problem caused by the identity or the high similarity of the attributes．To solve this problem,this paper proposed multiple iterative screening method,which firstly,in two relation models,fixes some of the corrects attribute pairs in one time and then selects the best optimized attribute pair．Secondly,this paper lodged the method based on conditional mutual information,which utilizes the best optimized attribute pair to calculate the conditional mutual information of un-matched attributes and further calculates the Euclidean distance between each attribute．Finally,the matching result was acquired．The wrong matching problem was solved．The experiment result indicates the given algorithm is correct and effective.

Key words: Uncertainty,Schema matching,Conditional mutual information