Temporal knowledge bases associate relational (s,r,o) triples with a set of
times (or a single time instant) when the relation is valid. While
time-agnostic KB completion (KBC) has witnessed significant research, temporal
KB completion (TKBC) is in its early days. In this paper, we consider
predicting missing entities (link prediction) and missing time intervals (time
prediction) as joint TKBC tasks where entities, relations, and time are all
embedded in a uniform, compatible space. We present TIMEPLEX, a novel
time-aware KBC method, that also automatically exploits the recurrent nature of
some relations and temporal interactions between pairs of relations. TIMEPLEX
achieves state-of-the-art performance on both prediction tasks.
We also find that existing TKBC models heavily overestimate link prediction
performance due to imperfect evaluation mechanisms. In response, we propose
improved TKBC evaluation protocols for both link and time prediction tasks,
dealing with subtle issues that arise from the partial overlap of time
intervals in gold instances and system predictions.

本文介绍了 TIMEPLEX（一种新颖的基于时间感知的知识库补全方法），用于联合实体、关系、时间的预测任务中，以及改进的 TKBC 评估协议。实验证明，TIMEPLEX 在预测任务中表现出色，并且既考虑了某些关系的可重复性，又考虑了关系对之间的时间交互作用。