  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.
