Completion through the embedding representation of the knowledge graph (KGE) has been a research hotspot in recent years. Realistic knowledge graphs are mostly related to time, while most of the existing KGE algorithms ignore the time information. A few existing methods directly or indirectly encode the time information, ignoring the balance of timestamp distribution, which greatly limits the performance of temporal knowledge graph completion (KGC). In this paper, a temporal KGC method is proposed based on the direct encoding time information framework, and a given time slice is treated as the finest granularity for balanced timestamp distribution. A large number of experiments on temporal knowledge graph datasets extracted from the real world demonstrate the effectiveness of our method.

翻译：近些年来,通过嵌入知识图(KGE)完成过程是一个研究热点。现实知识图大多与时间有关,而现有的大多数KGE算法忽略了时间信息。一些现有的方法直接或间接地编码时间信息,忽略了时间图分布的平衡,这极大地限制了时间知识图完成的性能。在本文中,根据直接编码时间信息框架提出了时间KGC方法,而一个特定的时间切片被视为平衡时间戳分布的最佳颗粒。从真实世界中提取的关于时间知识图数据集的大量实验证明了我们的方法的有效性。