A New Search Engine Filtering Scheme Based on Improved Neural Network and Ontology

Current search engines are not very effective in filtering out harmful information since the technology they use for filtering is often based on traditional text classification in which texts are often classified according to feature words. To improve the effectiveness of filtering, in this paper, we propose a new filtering scheme in which we combine the neural network and ontology categorization techniques to improve the accuracy of classification. We show that, by using the new categorization techniques, the accuracy of filtering in search engines can be greatly enhanced and many of the common problems can also be resolved.