Temporal Knowledge Graph Question Answering Models Enhanced with GAT

Temporal Knowledge Graph Question Answering (TKGQA) task aims to find an entity or timestamp from a temporal knowledge graph to answer temporal reasoning questions. However, most existing models fail to capture the implicit temporal information in the questions, resulting in weak performance when handling complex temporal reasoning tasks. To address this issue, this paper proposes a novel TKGQA model called GATQR, which integrates graph attention mechanism. The model utilizes a pre-trained temporal knowledge base in the form of quadruples and introduces Graph Attention Network (GAT) to effectively capture the implicit temporal information in the questions. By integrating with relation representations trained by the RoBERTa, it further enhances the temporal relationship representation in the queries. Finally, this representation is combined with the pre-trained TKG embeddings to predict the entity or timestamp with the highest score as the answer. Experimental results on the largest benchmark dataset CronQuestion demonstrate that compared to baseline models such as CronKGQA, EntityQR, and TempoQR-Soft, the GATQR achieves significant improvements in Hits@l results for handling complex and temporal question types, with increases of 35% and 13%, 18% and 9%, and 9% and 3%, respectively. These results validate the effectiveness and superiority of the GATQR model in capturing implicit temporal information and enhancing complex reasoning capabilities.