Answering questions in Gaokao (the national college entrance examination in China) brings a great challenge for recent AI systems, where the difficulty of questions and the lack of formal knowledge are two main obstacles, among others. In this paper, we focus on answering multiple-choice questions in geographical Gaokao. Specifically, a concept graph is automatically constructed from textbook tables and Chinese wild encyclopedia, to capture core concepts and relations in geography. Based on this concept graph, a graph search based question answering approach is designed to find explainable inference paths between questions and options. We developed an online system called CGQA and conducted experiments on two real datasets created from the last ten year geographical Gaokao. Our experimental results demonstrated that CGQA can generate accurate judgments and provide explainable solving procedures. Additionally, CGQA showed promising improvement by combining with existing approaches.
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