# Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs

arXiv: https://arxiv.org/abs/1909.01515 · HTML: https://mlsanity.com/paper/1909.01515/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

## Built on by (most cited first, 30 of 73)

- [PRODIGY: Enabling In-context Learning Over Graphs](https://mlsanity.com/paper/2305.12600/) (2023)
- [Towards Foundation Models for Knowledge Graph Reasoning](https://mlsanity.com/paper/2310.04562/) (2023)
- [NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs](https://mlsanity.com/paper/2106.12144/) (2021)
- [OntoZSL: Ontology-enhanced Zero-shot Learning](https://mlsanity.com/paper/2102.07339/) (2021)
- [KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion](https://mlsanity.com/paper/2402.02389/) (2024)
- [Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2104.13095/) (2021)
- [Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding](https://mlsanity.com/paper/2110.14170/) (2021)
- [Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion](https://mlsanity.com/paper/2209.07299/) (2022)
- [Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2105.03953/) (2021)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Graph Few-shot Learning with Task-specific Structures](https://mlsanity.com/paper/2210.12130/) (2022)
- [APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP](https://mlsanity.com/paper/2304.05995/) (2023)
- [A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes](https://mlsanity.com/paper/2307.06046/) (2023)
- [RAGraph: A General Retrieval-Augmented Graph Learning Framework](https://mlsanity.com/paper/2410.23855/) (2024)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [The Neural Process Family: Survey, Applications and Perspectives](https://mlsanity.com/paper/2209.00517/) (2022)
- [Decomposed Meta-Learning for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2204.05751/) (2022)
- [Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs](https://mlsanity.com/paper/2210.08654/) (2022)
- [Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types](https://mlsanity.com/paper/2302.01313/) (2023)
- [Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey](https://mlsanity.com/paper/2302.07200/) (2023)
- [Cross-Domain Few-Shot Graph Classification](https://mlsanity.com/paper/2201.08265/) (2022)
- [Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2304.08183/) (2023)
- [Few-shot Relational Reasoning via Connection Subgraph Pretraining](https://mlsanity.com/paper/2210.06722/) (2022)
- [MKGL: Mastery of a Three-Word Language](https://mlsanity.com/paper/2410.07526/) (2024)
- [Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting](https://mlsanity.com/paper/2205.04692/) (2022)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [Multilingual and cross-lingual document classification: A meta-learning approach](https://mlsanity.com/paper/2101.11302/) (2021)
- [Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs](https://mlsanity.com/paper/2302.01859/) (2023)
- [Trans4E: Link Prediction on Scholarly Knowledge Graphs](https://mlsanity.com/paper/2107.03297/) (2021)
- [Few-Shot Learning on Graphs](https://mlsanity.com/paper/2203.09308/) (2022)

## Abstract (arXiv)

Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples. We solve few-shot link prediction by focusing on transferring relation-specific meta information to make model learn the most important knowledge and learn faster, corresponding to relation meta and gradient meta respectively in MetaR. Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.