An Improved Concept Vector Space Model for Ontology Based Classification

This paper proposes an improved concept vector space (ICVS) model which takes into account the importance of ontology concepts. Concept importance shows how important a concept is in an ontology. This is reflected by the number of relations a concept has to other concepts. Concept importance is computed automatically by converting the ontology into a graph initially and then employing one of the Markov based algorithms. Concept importance is then aggregated with concept relevance which is computed using the frequency of concept occurrences in the dataset. In order to demonstrate the applicability of our proposed model and to validate its efficacy, we conducted experiments on document classification using concept based vector space model. The dataset used in this paper consists of 348 documents from the funding domain. The results show that the proposed model yields higher classification accuracy comparing to traditional concept vector space (CVS) model, ultimately giving better document classification performance. We also used different classifiers in order to check for the classification accuracy. We tested CVS and ICVS on Naive Bayes and Decision Tree classifiers and the results show that the classification performance in terms of F1 measure is improved when ICVS is used on both classifiers.