An ontology-based webpage classification approach for the knowledge grid environment

With the rapid growth of the amount of information available in the Web, webpage classification technologies are widely employed by many search engines in order to formulate user queries and make users' search tasks easier. Knowledge Grid is a new form of Web environment, in which a Resource Space Model is employed in order to classify available semantic documents within the Web environment. However, it is well known that the semantic documents are proportionally small in relation to the whole Web documents, and the Resource Space Model cannot process these Web documents without semantic supports. In order to solve the above issue, in this paper, we present a novel ontology-based webpage classification method for the Knowledge Grid environment, which utilizes generated metadata from webpages as the intermedium to classify the webpages by ontology concepts. We design a conceptual model of a Webpage Classification Agent and build the prototype in a chosen domain. A series of experiments have been conducted using the prototype in order to evaluate the conceptual model. Conclusions about the evaluation are drawn in the final section.