# One-shot Learning for Temporal Knowledge Graphs

arXiv: https://arxiv.org/abs/2010.12144 · HTML: https://mlsanity.com/paper/2010.12144/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Temporal Knowledge Graph Completion: A Survey](https://mlsanity.com/paper/2201.08236/) (2022)
- [TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs](https://mlsanity.com/paper/2402.12309/) (2024)
- [A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2308.02457/) (2023)
- [zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models](https://mlsanity.com/paper/2311.10112/) (2023)
- [Few-Shot Learning on Graphs](https://mlsanity.com/paper/2203.09308/) (2022)
- [Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning](https://mlsanity.com/paper/2108.00954/) (2021)
- [Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information](https://mlsanity.com/paper/2211.08169/) (2022)
- [Identification of Novel Classes for Improving Few-Shot Object Detection](https://mlsanity.com/paper/2303.10422/) (2023)
- [Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction](https://mlsanity.com/paper/2205.10621/) (2022)
- [History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2305.18675/) (2023)
- [Improved Region Proposal Network for Enhanced Few-Shot Object Detection](https://mlsanity.com/paper/2308.07535/) (2023)
- [MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2302.00893/) (2023)
- [Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning](https://mlsanity.com/paper/2304.00613/) (2023)
- [Robust Internal Representations for Domain Generalization](https://mlsanity.com/paper/2309.15522/) (2023)

## Abstract (arXiv)

Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times. This observation has given rise to recent interest in low-shot learning methods that are able to generalize from only a few examples. The existing approaches, however, are tailored to static knowledge graphs and not easily generalized to temporal settings, where data scarcity poses even bigger problems, e.g., due to occurrence of new, previously unseen relations. We address this shortcoming by proposing a one-shot learning framework for link prediction in temporal knowledge graphs. Our proposed method employs a self-attention mechanism to effectively encode temporal interactions between entities, and a network to compute a similarity score between a given query and a (one-shot) example. Our experiments show that the proposed algorithm outperforms the state of the art baselines for two well-studied benchmarks while achieving significantly better performance for sparse relations.