Efficient Non-Sequential Relational Modeling for Temporal Knowledge Graph Link Predictions

Abstract

Temporal Knowledge Graphs (TKGs) are being widely explored to predict the future for they record multi-relational knowledge and the happening time of real-life facts. Existing works learn sequential patterns to infer the future from past facts in TKGs for predictions. Although achieving promising results, they are restricted by the sequential modeling in both efficiency and effectiveness. To resolve these limitations, we propose our efficient and effective non-sequential relational modeling (NoSeq). NoSeq works non-sequentially for temporal patterns where it transforms the happening time into time intervals. Time intervals are the period of time between happening time and prediction time which state the temporal distance clearly. Both time intervals and relations are represented using embeddings and merged non-sequentially into entity embeddings for future predictions. We evaluate NoSeq on four datasets from the perspective of effectiveness, efficiency, sensitivity, and the ability to transfer. Consistent better performances verify our idea.