Text Classification Based on Domain Ontology

With the quick increase of information and knowledge, automatically classifying text documents is becoming a hotspot of knowledge management. A critical capability of knowledge management systems is to classify the text documents into different categories, which are meaningful to users. In this paper, a text topic classification model based on domain ontology by using Vector Space Model is proposed. Eigenvectors as the input to the vector space model are constructed by utilizing concepts and hierarchical structure of ontology, which also provides the domain knowledge. However, a limited vocabulary problem is encountered while mapping keywords to their corresponding ontology concepts. A synonymy lexicon is utilized to extend the ontology and compress the eigenvector. The problem that eigenvectors are too large and complex to be calculated in traditional methods can be solved. At last, combing the concept＇s supporting, a top-down method according to the ontology structure is used to complete topic classification. An experimental system is implemented and the model is applied to this practical system. Test results show that this model is feasible.

Text Classification Based on Domain Ontology