摘要
Web信息抽取中需要对目标网站的网页进行聚类分析,以检测并生成信息抽取所需的模板。传统的基于DOM树编辑距离的网页聚类算法不适合文档对象模型(DOM)树结构复杂的动态模板网页,提出了一种基于局部标签树匹配的改进网页聚类算法,利用标签树中模板节点和非模板节点的层次差异性,根据节点对布局影响的大小赋予节点不同的匹配权值,使用局部树匹配完成对网页结构相似性的有效计算。实验结果表明,改进的算法较传统的基于DOM树编辑距离的网页聚类算法,在对采用模板生成的动态网页进行聚类分析时具有更高的准确率,且时间复杂度低。
In the process of Web information extraction,Web pages on the target websites should be clustered in order to detect and generate templates that are used to extract required information.Traditional page clustering algorithm based on DOM tree edit distance is not suitable for the complex Document Object Model（DOM）tree structure pages created from dynamic templates.In this paper,an improved Web page clustering algorithm was proposed based on partial tag tree matching.In the proposed algorithm,the appropriate weights were assigned to the nodes according to their effects on the layout of Web pages and the level difference between template nodes and non-template nodes.After that,the structure similarity between Web pages was computed efficiently based on partial tree matching approach.Compared with the traditional algorithms,the experimental results show that the proposed algorithm is of higher accuracy in clustering dynamic Web pages and lower computing complexity.
出处
《计算机应用》
CSCD
北大核心
2010年第3期818-820,共3页
journal of Computer Applications
基金
湖南省自然科学基金资助项目(09JJ3123)
关键词
WEB信息抽取
网页聚类
树编辑距离
局部标签树匹配
Web information extraction
Web page clustering
tree edit distance
partial tag tree matching
