Abstract: As the volume of information in the Deep Web grows, a Deep Web data source classification algorithm based on query interface context is presented. Two methods are combined to get the search interfaces similarity. One is based on the vector space. The classical TF-IDF statistics are used to gain the similarity between search interfaces. The other is to compute the two pages semantic similarity by the use of HowNet. Based on the K-NN algorithm, a WDB classifaction algorithm is presented. Experimental results show this algorithm generates high-quality clusters, measuring with both in terms of entropy and F-measure. It has the practical value of application.

Key words: Deep Web, data source classification, HowNet, K-NN algorithm, semantic classification

摘要： 根据Deep Web数量的爆炸性增长特点，提出一种基于查询接口文本的Deep Web数据源分类算法，对于分类的查询接口，采用 2种方法：基于向量空间的TF-IDF方法和基于知网的语义相似度方法。综合2种方法获得接口之间的相似度。借鉴K-NN算法，提出WDB分类算法，从而实现Deep Web数据源的分类。实验结果表明，该算法在熵和F-measure 2种评价标准上均能获得较高质量，具有一定实用价值。

关键词: 深层网, 数据源分类, 知网, K-NN算法, 语义分类

CLC Number: 

• TP311.52