Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently. Lots of works have been made to model the historical structural and temporal characteristics for the reasoning task. Most existing works model the graph structure mainly depending on entity representation. However, the magnitude of TKG entities in real-world scenarios is considerable, and an increasing number of new entities will arise as time goes on. Therefore, we propose a novel architecture modeling with relation feature of TKG, namely aDAptivE path-MemOry Network (DaeMon), which adaptively models the temporal path information between query subject and each object candidate across history time. It models the historical information without depending on entity representation. Specifically, DaeMon uses path memory to record the temporal path information derived from path aggregation unit across timeline considering the memory passing strategy between adjacent timestamps. Extensive experiments conducted on four real-world TKG datasets demonstrate that our proposed model obtains substantial performance improvement and outperforms the state-of-the-art up to 4.8% absolute in MRR.

翻译：时序知识图谱推理旨在基于历史信息预测未来缺失的事实，近年来受到越来越多的研究关注。许多工作致力于对推理任务中的历史结构和时序特征进行建模。现有的大多数工作主要依赖实体表示来建模图结构。然而，实际场景中时序知识图谱实体的规模相当庞大，并且随着时间推移将出现越来越多新实体。因此，我们提出一种利用时序知识图谱关系特征的新型架构，即自适应路径记忆网络（DaeMon），该网络可自适应地建模查询主语与各候选宾语之间跨越历史时间的时序路径信息。它不依赖实体表示即可对历史信息进行建模。具体而言，DaeMon使用路径记忆记录来自路径聚合单元跨时间线的时序路径信息，同时考虑相邻时间戳之间的记忆传递策略。在四个真实时序知识图谱数据集上进行的广泛实验表明，我们提出的模型获得了显著的性能提升，在MRR指标上比现有最优方法高出绝对4.8%。