RTA: A reinforcement learning-based temporal knowledge graph question answering model

Abstract

Temporal Knowledge Graph Question Answering (TKGQA) is crucial research, focusing on finding an entity or a timestamp to answer temporal questions in the corresponding temporal knowledge graph. Currently, the main challenge in the temporal KGQA task is answering complex temporal questions, often necessitating complex multi-hop temporal reasoning in the TKG. In this paper, we propose a method for the TKGQA task called Reinforcement learning Temporal knowledge graph question Answering (RTA). First, in the question understanding stage, our model extracts context information to select topic entities of the given question, which can effectively deal with scenarios involving multiple entities in complex temporal questions. Furthermore, reasoning complexity escalates significantly with complex temporal questions, as varying timestamps alter the relations between entities. Therefore, we introduce reinforcement learning into the reasoning process. In the policy network, a dynamic path-matching module is specifically included to aggregate the features of relational paths to effectively capture the dynamic changes of the relations between entities on the reasoning paths. At the same time, the weights are calculated to obtain the degree of attention of each candidate action. Then the score of each candidate action is obtained through a weighted summation mechanism which helps the agent learn the optimal path reasoning policy for effective exploration. Finally, we evaluate our method on the CRONQUESTIONS dataset and validate its superiority over all baseline methods. Specifically, our approach proves effective in handling complex temporal questions.