Design and evaluation of a web classification system for educational resources

Abstract

As the web continues to grow, it has become increasingly difficult to retrieve appropriate educational information using traditional search engines. Topic-specific search engines provide an alternative way to support efficient information retrieval in the educational domain. However, developers of topic-specific search engines need to address an issue: how to filter out irrelevant documents from a set of documents collected from the web. This paper proposes a progressive classification approach that combines web content analysis and web structure analysis. In this approach, each web page is represented by a set of content-based and link-based features, which can be used as the input for the classifiers. Three experiments were conducted to compare the proposed progressive approach with two existing web page classification methods. The experimental results showed that the proposed approach in general performed better than the other approaches, and got better precision and recall rates. This proposed approach can be applied in topic-specific search engine development and other web applications such as web content management.