A semantic approach for question answering using DBpedia and WordNet

Semantic Question Answering (SQA) was concerned about the natural language processing. The purpose of this study was to help facilitate the users to access the information through the natural language and to obtain the concise and needed information. As considered the current studies, it was found that this processing still encountered the problems of flexibility and accuracy, particularly those of the question processing, which was a very important processing for developing question answering system. Thus, this study proposed a semantic approach for question answering using DBpedia and WordNet. For this paper, the techniques for solving the problems were proposed consisting of (1) extracting named entities from the question and solving the problems of similarities of named entities, (2) extracting properties from the question and solving the problems of similarities of properties, and (3) evaluating the accurate capability of the answer of question. This approach evaluated the test dataset from TREC question collections, DBpedia and achieved an F-measure score of 93.43%, an average precision of 92.73%, and an average recall of 94.15% over 500 questions.