Re-Temp: Relation-Aware Temporal Representation Learning for Temporal Knowledge Graph Completion
This work addresses a key challenge in predicting future facts in temporal knowledge graphs, which is incremental as it builds on existing methods by enhancing temporal and relational awareness.
The paper tackles the problem of Temporal Knowledge Graph Completion under extrapolation by proposing Re-Temp, a model that uses explicit temporal embeddings and skip information flow to improve prediction accuracy, achieving significant performance gains over eight state-of-the-art models on six datasets.
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.