An improving reasoning network for complex question answering over temporal knowledge graphs

Songlin Jiao, Zhenfang Zhu, Wenqing Wu, Zicheng Zuo, Jiangtao Qi, Wenling Wang, Guangyuan Zhang, Peiyu Liu

Abstract: Question Answering Over Temporal Knowledge Graphs is an important topic in question answering, which aims to find an entity or timestamp to answer temporal reasoning questions from temporal knowledge graphs. Answering complex questions remains a major challenge for question answering over temporal knowledge graphs because it is associated with complex temporal reasoning. The performance of the existing state-of-the-art model falls short when the question contains constraints (e.g., ‘before/after’, ‘first/last’ and ‘during’) that require complex temporal reasoning based on multiple relevant facts. In this paper, we propose an improving reasoning method called the Complex Temporal Reasoning Network, which improves the complex temporal reasoning for temporal reasoning questions. For each question, we capture implicit temporal features and relation representation and then integrate them to generate implicit temporal relation representation. The experimental results on the CRONQUESTIONS dataset demonstrate that our method significantly outperforms all baselines. In particular, we demonstrate the effectiveness of our method on complex questions. The source code of CTRN will be available at https://github.com/2399240664/CTRN.

External IDs:dblp:journals/apin/JiaoZWZQWZL23