  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.
