Development research of highly accurate Question Answering system doing precise matching of meaning

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We have developed Metis, a question-answering system that finds an answer by matching a question graph with the knowledge graphs. The question graph is obtained as a result of semantic analysis of a question sentence, the knowledge graphs are similarly analyzed from knowledge sentences retrieved from a database using keywords extracted from the question sentence. In retrieving such knowledge sentences, the system searches for and collects them using Lucene, a search engine, based on search keywords extracted from the question graph. To extract the answer, Metis calculates the degrees of similarity between the question and knowledge graphs to conduct precise matching. In this matching, the system calculates the degrees of similarity, which is the relative size of the similarity co-occurrence graph to the question graphs with respect to all combinations of nodes in the knowledge graph corresponding to those in the question graph. The system then chooses the knowledge graph with the highest degree of similarity and extracts from it the portion that corresponds to the given interrogative word. The system presents this portion as the answer. The evaluation experiment was done by using 100 quiz millionaire questions. The precision to obtain the correct answer within the first three answers was 74%. Moreover, it participated in the evaluation contest in NTCIR, and the precision was 18% in QAC which asked factoid question, the precision was 22% in CLQA which asked the reasons, the method and etc.