Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction

Understand

Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly been studied.

• Compared to KGs, TKGs contain rich temporal information, thus requiring temporal reasoning techniques for modeling.
• This poses a greater challenge in learning few-shot relations in the temporal context.
• In this paper, we follow the previous work that focuses on few-shot relational learning on static KGs and extend two fundamental TKG reasoning tasks, i.e., interpolated and extrapolated link prediction, to the one-shot setting.