Complex Question Answering Over Temporal Knowledge Graphs

Shaonan Long, Jinzhi Liao, Shiyu Yang, Xiang Zhao, Xuemin Lin

Abstract: A temporal knowledge graph (TKG) comprises facts aligned with timestamps. Question answering over TKGs (TKGQA) finds an entity or timestamp to answer a question with certain temporal constraints. Current studies assume that the questions are fully annotated before being fed into the system, and treat question answering as a link prediction task. Moreover, the process of choosing answers is not interpretable due to the implicit reasoning in the latent space. In this paper, we propose a semantic parsing based method, namely AE-TQ, which leverages abstract meaning representation (AMR) for understanding complex questions, and produces question-oriented semantic information for explicit and effective temporal reasoning. We evaluate our method on CronQuestions, the largest known TKGQA dataset, and the experiment results demonstrate that AE-TQ empirically outperforms several competing methods in various settings.