A novel web pages classification model based on integrated ontology

Abstract

The main existed problem in the traditional text classification methods is can't use the rich semantic information in training data set. This paper proposed a new text classification model based SUMO (The Suggested Upper Merged Ontology) and WordNet ontology integration. This model utilizes the mapping relations between WordNet synsets and SUMO ontology concepts to map terms in document-words vector space into the corresponding concepts in ontology, forming document-concepts vector space, based this, we carry out a text classification experiment. Experiment results show that the proposed method can greatly decrease the dimensionality of vector space and improve the text classification performance. © 2011 Springer-Verlag.