A Method of Deep Web Classification

The research on deep Web classification is an important area in large-scale deep Web integration, which is still at its early stage. Many deep Web sources are structured by providing structured query interfaces and results. Classifying such structured sources into domains is one of the critical steps toward the integration of heterogeneous Web sources. In this paper, we present an ontology-based deep Web classification, which includes a category ontology model and a deep Web vector space model (VSM). The experimental results show that we can get a good performance with average precision 91.6% and average recall 92.4%.