Dbpedia Based Factoid Question Answering System

Abstract

Question Answering (QA) is an area of natural language processing research aimed at providing human users with a convenient and natural interface for accessing information.Nowadays, the need to develop accurate systems gains more importance due to available structured knowledge-bases and the continuous demand to access information rapidly and efficiently.In This paper we propose a new architecture to develop a factoid question answering system based on the DBpedia ontology and the DBpedia extraction framework.We use the SVM learning machine algorithm to train the systems question classifier to achieve a high accuracy ratio.The design and implementation steps are covered with sufficient details.In addition, tests and experiment results of the developed system are presented with a short discussion about the systems efficiency.