Two-Stage Query Graph Selection for Knowledge Base Question Answering

Yonghui Jia, Chuanyuan Tan, Yuehe Chen, Muhua Zhu, Pingfu Chao, Wenliang Chen

Abstract: Finding the best answer to a question in Knowledge Base Question Answering (KBQA) is always challenging due to its enormous searching space and the interactive performance requirement. A typical solution is to retrieve the answer by finding the optimal query graph, which is a sub-graph of the knowledge graph. However, existing methods usually generate a considerable number of sub-graph candidates, then fail to find the optimal one effectively, resulting in a significant gap between top-1 performance and the oracle score of all the graph candidates. To address this issue, this paper presents a novel two-stage method based on the idea of first reducing the candidates to form a shortlist, and then selecting the optimal one from them. Before the selection, we generate many, often hundreds of, candidates for each question. In the first selection stage, we sort the candidates and select a small set of query graphs (top-k), while in the second stage we propose to rerank them to select the final answer. We evaluate our system on both English and Chinese data, and the results show that our proposed two-stage method achieves competitive performance on all datasets (Our code is publicly available at https://github.com/EnernityTwinkle/KBQA-QueryGraphSelection .).