一种有效的深网入口识别方法

深网入口自动识别是深网数据集成的前提和基础。由于表单在设计上具有较大的随意性，使得深网入口缺 乏统一的构建标准，难以利用确定性的规则对其进行判断。首先基于统计特征，抽取了部分表单属性作为深网入口与 非深网入口的可区分特征，在此基础上，提出了一种利用神经网络进行深网入口自动识别的方法。不同于基于规则的 判断方法，神经网络是被训练的，不需要任何先验知识，这种特性使其非常适合于对具有复杂表现形式的深网入口进 行判定。实验结果表明了该方法的有效性。

Effective Approach to Deep Web Entries Identification

Automatic identification of deep Web entries is the basis of deep Web data integration. Owing to the subjec- tivity of form design,deep Web entries lack unified standard and it is difficult to judge whether the form is a deep Web entry by the definite rules. Based on the statistics, this paper first chose several form attributes as the defining features, which can distinguish searchable forms from non-searchable forms. Then, an entry identification algorithm was proposed by using neural network. Unlike previous approaches, neural network can be trained, which is very suitable for entry i- dentification of the deep Web. I}he experimental results show that our proposed algorithm can be an effective way in au- tomatic identification of the deep Web.