Temporal Knowledge Graph Completion (TKGC) under the extrapolation setting aims to predict the missing entity from a fact in the future, posing a challenge that aligns more closely with real-world prediction problems. Existing research mostly encodes entities and relations using sequential graph neural networks applied to recent snapshots. However, these approaches tend to overlook the ability to skip irrelevant snapshots according to entity-related relations in the query and disregard the importance of explicit temporal information. To address this, we propose our model, Re-Temp (Relation-Aware Temporal Representation Learning), which leverages explicit temporal embedding as input and incorporates skip information flow after each timestamp to skip unnecessary information for prediction. Additionally, we introduce a two-phase forward propagation method to prevent information leakage. Through the evaluation on six TKGC (extrapolation) datasets, we demonstrate that our model outperforms all eight recent state-of-the-art models by a significant margin.

翻译：外推设定下的时序知识图谱补全（TKGC）旨在预测未来事实中的缺失实体，这一挑战更贴近现实中的预测问题。现有研究主要利用序列图神经网络对近期快照中的实体和关系进行编码，然而这些方法往往忽略了根据查询中实体相关关系跳过无关快照的能力，且未充分考虑显式时序信息的重要性。针对此问题，我们提出Re-Temp（面向关系的时序表示学习）模型，该模型将显式时序嵌入作为输入，并在每个时间戳后引入信息跳过流以剔除预测所需的无关信息。此外，我们提出两阶段前向传播方法以防止信息泄漏。通过在六个TKGC（外推）数据集上的评估，我们证明模型性能显著优于所有八个最新基准模型。