Towards answering geography questions in Gaokao: A hybrid approach

Abstract

Answering geography questions in a university’s entrance exam (e.g., Gaokao in China) is a new AI challenge. In this paper, we analyze its difficulties in problem understanding and solving, which suggest the necessity of developing novel methods. We present a pipeline approach that mixes information retrieval techniques with knowledge engineering and exhibits an interpretable problem solving process. Our implementation integrates question parsing, semantic matching, and spreading activation over a knowledge graph to generate answers. We report its promising performance on a representative sample of 1,863 questions used in real exams. Our analysis of failures reveals a number of open problems to be addressed in the future.