Extracting Information Content from Web Pages Using Block Clustering Method

Abstract

The World Wide Web is the main “all kind of information” repository and has been so far very successful in disseminating to humans. As web sites are getting more complicated, the construction of web information extraction systems becomes more difficult and timeconsuming. Therefore we need to mine the main content of web page in order to extract information from such web pages. In this paper, we study the problem of automatically extracting the web information (unsupervised IE) without any learning examples or other similar human input. Firstly, web pages are segment into several raw chunks. Then remove the noisy blocks based on product features. Data region identification is based on the observation that appearance similarity of the data record in web document. Therefore block clustering method is proposed based on this observation. This approach requires no human intervention and experimental results have shown its accuracy to be promising.