基于BP神经网络的Deep Web实体识别方法

Method of Deep Web entities identification based on BP neural network

针对现有实体识别方法自动化水平不高、适应性差等不足,提出一种基于反向传播(BP)神经网络的Deep Web实体识别方法。该方法将实体分块后利用反向传播神经网络的自主学习特性,将语义块相似度值作为反向传播神经网络的输入,通过训练得到正确的实体识别模型,从而实现对异构数据源的自动化实体识别。实验结果表明,所提方法的应用不仅能够减少实体识别中的人工干预,而且能够提高实体识别的效率和准确率。

To solve the problems such as low level automation and poor adaptability of current entity recognition methods, a Deep Web entity recognition method based on Back Propagation （BP） neural network was proposed in this paper. The method divided the entities into blocks first, then used the similarity of semantic blocks as the input of BY＇ neural network, lastly obtained a correct entity recognition model by training which was based on the autonomic learning ability of BP neural network. It can achieve entity recognition automation in heterogeneous data sources. The experimental results show that the application of the method can not only reduce manual interventions, but also improve the efficiency and the accuracy rate of entity recognition.