THE TWO PHASE CHECKING ALGORITHM BASED ON BP NEURAL NETWORK FOR ATTRIBUTES MATCHING
In order to realize data sharing, identifying corresponding attributes is an important issue in heterogeneous databases. The main methods at present are evaluating the similarity of attributes by comparing all attributes. But these methods can’t present correct results 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, decrease the size of BP neural network, and realize the parallel computation of attributes matching. The experimental results show it can improve the system performance, the precision ratio and recall ratio of attributes matching obviously.
BP neural network attributes matching two phase checking algorithm heterogeneous databases
Ge JiKe Qiu YuHui Yu Bo
Faculty of Computer & Information Science, Southwest University, Chongqing, China School of Management & Economics, Beijing Institute of Technology, Beijing, China
国际会议
北京
英文
2007-11-01（万方平台首次上网日期，不代表论文的发表时间）