Applying Semantic Links for Classifying Web Pages

Automatic hypertext classification is an essential technique for organizing vast amount of Internet Web pages or HTML documents. One the of problems in classifying Web pages is that Web pages are usually short and contain insufficient text to clearly identify its category. Text classification mechanisms, by analyzing only the contents of the document itself, are relatively ineffective in classifying short Web pages. This paper proposes a new hypertext classification mechanism to address the problem by analyzing not only the Web page itself but also its linked Web pages referred by the URLs contained within the page. The URLs are treated as semantic links. The hypothesis is that the linked Web pages contain related information to help identifying the category of the Web page. Experimental results show that the proposed approach could increase the accuracy by 35% over the approach of analyzing only the Web page itself.