A semantic weighting method for document classification based on Markov logic networks

This paper proposes a semantic weighting method to classify textural documents. Human lives in the world where web documents have a great potential and the amount of valuable information has been consistently growing over the year. There is a problem that finding relevant web documents corresponding to what users want is more difficult due to the huge amount of web size. For this reason, there have been many researchers overcome this problem. The most important thing is document classification. All documents are composed of numerous words. Many classification methods have been extracted keywords from documents and then analyzed keywords pattern or frequency. In this paper, we propose Category Term Weight (CTW) using keywords from documents in order to enhance performance in document classification. CTW combines keywords frequency with semantic information. The frequency and semantic information have a great potential to find similarities between documents. That is why we calculates CTW from collection of training documents. After this step, CTW from unknown document and CTW in previous Category Term Database will be applied by designed Markov Logic Networks Model. Our designed MLNs Model and existing Naive-bayse Model will be compared by applied CTW. The experimental results shows the improvement of precision compare with the existing model.