基于关键词聚类和节点距离的网页信息抽取

大部分网页信息抽取方法都针对特定的网站，例如基于网站抽取规则和基于训练网页样例的方法。这些方法在某一个网站上可以很好地应用。但当遇到新的网站时，必须人为地增加抽取规则或者提供新的训练网页集。而且，当网站的模版改变时，也要重新设计这些规则或重新输入训练网页集。这些方法难以维护，因此不能应用到从大量不同的网站上进行信息抽取。本文提出了一种新的网页信息抽取方法，该方法基于特定主题的关键词组和节点距离，能够不加区分地对不同的网站页面信息自动抽取。对大量网站的网页进行信息抽取的实验显示，该方法能够不依赖网页的来源而正确和自动地抽取相关信息，并且已经成功应用到电子商务智能搜索和挖掘系统中。

Web Pages Information Retrieval Based on Keywords Cluster and Node Instance

Many Web information retrieval methods are related to special Web sites, for example, the method based on extracting rules and the one based on training page samples. These methods can do well in a Web site but fail in the others without adding new rules or inputting new training pages manually. Furthermore, if the template of the Web site is changed, it has to redesign the extracting rules or re-inputting the training pages. It is hard to be maintained and used to extract information from large number of different Web sites. In the paper, there is a new method which can extract the useful information from the different sites automatically based on the keywords of a certain topic and the distance of the nodes. Experimental evaluation on a large of Web pages from different Web sites indicates that this method correctly and automatically extracts the information ignoring which Web sites the pages come from. This method has been applied to the system of intelligent searching and mining of electronic business successfully.