Abstract
Temporal knowledge graph completion (TKGC) aims to predict the missing links among the entities in a temporal knowledge graph (TKG). Most previous TKGC methods only consider predicting the missing links among the entities seen in the training set, while they are unable to achieve great performance in link prediction concerning newly-emerged unseen entities. Recently, a new task, i.e., TKG few-shot out-of-graph (OOG) link prediction, is proposed, where TKGC models are required to achieve great link prediction performance concerning newly-emerged entities that only have few-shot observed examples. In this work, we propose a TKGC method FITCARL that combines few-shot learning with reinforcement learning to solve this task. In FITCARL, an agent traverses through the whole TKG to search for the prediction answer. A policy network is designed to guide the search process based on the traversed path. To better address the data scarcity problem in the few-shot setting, we introduce a module that computes the confidence of each candidate action and integrate it into the policy for action selection. We also exploit the entity concept information with a novel concept regularizer to boost model performance. Experimental results show that FITCARL achieves stat-of-the-art performance on TKG few-shot OOG link prediction. Code and supplementary appendices are provided (https://github.com/ZifengDing/FITCARL/tree/main).
Z. Ding and J. Wu—Equal contribution.
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Notes
- 1.
TITer can model unseen entities, but it is not designed for few-shot setting and requires a substantial number of associated facts. Besides, both TITer and CluSTeR are TKG forecasting methods, where models are asked to predict future links given the past TKG information (different from TKGC, see Appendix B for discussion).
- 2.
For each query quadruple in the form of \((\tilde{e}_q, r_q, e', t_q)\), we derive its LP query as \((e', r_q^{-1}, ?, t_q)\). \(r_q^{-1}\) is \(r_q\)’s inverse relation. The agent always starts from \((e', t_q)\).
- 3.
Both original and inverse relations are trained in pre-training.
- 4.
All LP queries are transformed into object prediction in TKG few-shot OOG LP.
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Ding, Z., Wu, J., Li, Z., Ma, Y., Tresp, V. (2023). Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs Using Confidence-Augmented Reinforcement Learning. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14171. Springer, Cham. https://doi.org/10.1007/978-3-031-43418-1_33
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