Knowledge Base Question Answering with a Matching-Aggregation Model and Question-Specific Contextual Relations

Making use of knowledge bases to answer questions (KBQA) is a key direction in question answering systems. Researchers have developed a diverse range of methods to address this problem, but there are still some limitations with the existing methods. Specifically, the existing neural network-based methods for KBQA have not taken advantage of the recent 'matching-Aggregation' framework for the sequence matching, and when representing a candidate answer entity, they may not choose the most useful context of the candidate for matching. In this paper, we explore the use of a 'matching-Aggregation' framework to match candidate answers with questions. We further make use of question-specific contextual relations to enhance the representations of candidate answer entities. Our complete method is able to achieve state-of-The-Art performance on two benchmark datasets: WebQuestions and SimpleQuestions.