A Deep Web Query Interfaces Classification Method Based on RBF Neural Network

摘要

This paper proposes a new approach for classification for query interfaces of Deep Web,which extracts features from the form's text data on the query interfaces,assisted with the synonym library,and uses radial basic function neural network(RBFNN) algorithm to classify the query interfaces. The applied RBFNN is a kind of effective feed-forward artificial neural network,which has a simple networking structure but features with strength of excel-lent nonlinear approximation,fast convergence and global con-vergence. A TEL_8 query interfaces' data set from UIUC on-line database is used in our experiments,which consists of 477 query interfaces in 8 typical domains. Experimental results proved that the proposed approach can efficiently classify the query interfaces with an accuracy of 95.67%.