Temporal knowledge graph completion (TKGC) has become a popular approach for
reasoning over the event and temporal knowledge graphs, targeting the
completion of knowledge with accurate but missing information. In this context,
tensor decomposition has successfully modeled interactions between entities and
relations. Their effectiveness in static knowledge graph completion motivates
us to introduce Time-LowFER, a family of parameter-efficient and time-aware
extensions of the low-rank tensor factorization model LowFER. Noting several
limitations in current approaches to represent time, we propose a cycle-aware
time-encoding scheme for time features, which is model-agnostic and offers a
more generalized representation of time. We implement our methods in a unified
temporal knowledge graph embedding framework, focusing on time-sensitive data
processing. The experiments show that our proposed methods perform on par or
better than the state-of-the-art semantic matching models on two benchmarks.

介绍了一种基于张量分解的时间知识图谱补全方法 (Time-LowFER)，并提出了一种模型无关的、更广义的时间特征表示方法，实验表明该方法的表现与当前先进的语义匹配模型相当或更好。