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arXiv · 2205.10621

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

Zifeng Ding·Bailan He·Yunpu Ma·Zhen Han·Volker Tresp
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Abstract

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. We propose four new large-scale benchmark datasets and develop a TKG reasoning model for learning one-shot relations in TKGs. Experimental results show that our model can achieve superior performance on all datasets in both TKG link prediction tasks.

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Zifeng Ding, Bailan He, Yunpu Ma, Zhen Han, Volker Tresp. 2023-05-24. Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction. https://arxiv.org/abs/2205.10621

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