# Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

arXiv: https://arxiv.org/abs/2007.02387 · HTML: https://mlsanity.com/paper/2007.02387/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 30 of 39)

- [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021)
- [Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning](https://mlsanity.com/paper/2203.09064/) (2022)
- [Exploring Task Difficulty for Few-Shot Relation Extraction](https://mlsanity.com/paper/2109.05473/) (2021)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Uncovering Main Causalities for Long-tailed Information Extraction](https://mlsanity.com/paper/2109.05213/) (2021)
- [Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning](https://mlsanity.com/paper/2311.03748/) (2023)
- [Unified Semantic Typing with Meaningful Label Inference](https://mlsanity.com/paper/2205.01826/) (2022)
- [MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction](https://mlsanity.com/paper/2109.04108/) (2021)
- [Entity Concept-enhanced Few-shot Relation Extraction](https://mlsanity.com/paper/2106.02401/) (2021)
- [Creating Training Sets via Weak Indirect Supervision](https://mlsanity.com/paper/2110.03484/) (2021)
- [Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection](https://mlsanity.com/paper/2105.09509/) (2021)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [Knowledge-Aware Meta-learning for Low-Resource Text Classification](https://mlsanity.com/paper/2109.04707/) (2021)
- [Information Extraction in Low-Resource Scenarios: Survey and Perspective](https://mlsanity.com/paper/2202.08063/) (2022)
- [SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues](https://mlsanity.com/paper/2106.01006/) (2021)
- [A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction](https://mlsanity.com/paper/2205.09536/) (2022)
- [Better Few-Shot Relation Extraction with Label Prompt Dropout](https://mlsanity.com/paper/2210.13733/) (2022)
- [Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph](https://mlsanity.com/paper/2302.05640/) (2023)
- [Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification](https://mlsanity.com/paper/2204.12111/) (2022)
- [Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction](https://mlsanity.com/paper/2312.12021/) (2023)
- [Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation](https://mlsanity.com/paper/2510.08962/) (2025)
- [Ensemble Making Few-Shot Learning Stronger](https://mlsanity.com/paper/2105.11904/) (2021)
- [Linguistic representations for fewer-shot relation extraction across domains](https://mlsanity.com/paper/2307.03823/) (2023)
- [Few-shot Classification with Hypersphere Modeling of Prototypes](https://mlsanity.com/paper/2211.05319/) (2022)
- [Retrieval-Augmented Meta Learning for Low-Resource Text Classification](https://mlsanity.com/paper/2309.04979/) (2023)
- [Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2109.05182/) (2021)
- [Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning](https://mlsanity.com/paper/2110.08254/) (2021)
- [Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning](https://mlsanity.com/paper/2403.16543/) (2024)
- [Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model](https://mlsanity.com/paper/2410.14225/) (2024)

## Abstract (arXiv)

This paper studies few-shot relation extraction, which aims at predicting the relation for a pair of entities in a sentence by training with a few labeled examples in each relation. To more effectively generalize to new relations, in this paper we study the relationships between different relations and propose to leverage a global relation graph. We propose a novel Bayesian meta-learning approach to effectively learn the posterior distribution of the prototype vectors of relations, where the initial prior of the prototype vectors is parameterized with a graph neural network on the global relation graph. Moreover, to effectively optimize the posterior distribution of the prototype vectors, we propose to use the stochastic gradient Langevin dynamics, which is related to the MAML algorithm but is able to handle the uncertainty of the prototype vectors. The whole framework can be effectively and efficiently optimized in an end-to-end fashion. Experiments on two benchmark datasets prove the effectiveness of our proposed approach against competitive baselines in both the few-shot and zero-shot settings.