Research on Attribute Matching Approach Based on Neural Network
基于BP神经网络的属性匹配方法研究

In order to realize data sharing, identifying corresponding attributes is an important issue in heterogeneous databases. The main approaches at present use the characteristics describing attributes to evaluate the similarity of attributes by comparing all attributes. But these approaches can not present correct results due to the obvious difference of metadata and value information describing attributes when the same attribute is expressed using different data types, and result in incorrect attributes matching for the interference among attributes with different data types. So two phase checking algorithm based on BP neural network is presented to realize attributes matching, in which attributes are required to be categorized according to data types, and the BP neural networks are trained several times respectively using the categorized attributes, and the final attributes matching results are the intersection of every time matching results. This algorithm can resolve the interference among attributes with different data types, and decrease the size of BP neural network, and realize the parallel computation of attributes matching. The experimental results show our approach can improve the system performance, the precision ratio and recall ratio of attributes matching obviously.