question answering system with recommendation using fuzzy relational product operator
When a user finds the answer using question answering system, sometimes the user may not be satisfied with answers. This is because it is difficult to deliver the intention of a user question to the system due to the lack of expression ability and polysemy and etc. So, the system must provide additional ways to search the correct answers. In this paper, we propose a new method to recommend questions that can satisfy the purpose of user question with high probability. We define the supplementary similarity to calculate the similarity between sentences which have a few common terms. And we define the fuzzy relation product operator to find the questions that are recommended when user is not satisfied with the answers. User can select the question among the list of recommended questions and has more chance to find the answers that the user can be satisfied with. We experiment and show that the performance of the system gets high as the recommendation is repeated.
question answering system fuzzy relational product operator supplementary similarity question recommendation