Building knowledge base for Vietnamese information retrieval

Abstract

At present, Vietnamese knowledge base (vnKB) is one of the most important focuses of Vietnamese researchers because of its applications in wide areas such as Information Retrieval (IR), Machine Translation (MT) etc. There have been several separate projects developing vnKB in various domains. The training in vnBK is the most difficulty because of quantity and quality of training data, and lacking of available Vietnamese corpus with acceptable quality. This paper introduces an approach, which first extracts semantic information from Vietnamese Wikipedia (vnWK), then trains the proposed vnKB by applying support vector machine (SVM) technique. The experimentation of the proposed approach shows that it is a potential solution because of its good results and proves that it can provide more valuable benefits when applying to our Vietnamese Semantic Information Retrieval system. © 2010 ACM.