Using an integrated ontology database to categorize web pages

As we know, current classification methods are mostly based on the vector space model, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. We have proposed a system that uses integrated ontologies and natural language processing techniques to index texts. The traditional words matrix is replaced by a concepts-based matrix. For this purpose, we have developed fully automated methods for mapping keywords to their corresponding ontology concepts. Support vector machine, a successful machine learning technique, is used for classification. Experimental results show that the proposed method improves text classification performance significantly.