# Few-shot Continual Relation Extraction via Open Information Extraction

arXiv: https://arxiv.org/abs/2502.16648 · HTML: https://mlsanity.com/paper/2502.16648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [Open-world Learning and Application to Product Classification](https://mlsanity.com/paper/1809.06004/) (2018)
- [Revisiting the Negative Data of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.10158/) (2021)
- [Improving Continual Relation Extraction through Prototypical Contrastive Learning](https://mlsanity.com/paper/2210.04513/) (2022)
- [RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction](https://mlsanity.com/paper/2310.15743/) (2023)
- [Preserving Generalization of Language models in Few-shot Continual Relation Extraction](https://mlsanity.com/paper/2410.00334/) (2024)
- [Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction](https://mlsanity.com/paper/2305.06616/) (2023)
- [Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors](https://mlsanity.com/paper/2402.15713/) (2024)
- [Open Set Relation Extraction via Unknown-Aware Training](https://mlsanity.com/paper/2306.04950/) (2023)

## Abstract (arXiv)

Typically, Few-shot Continual Relation Extraction (FCRE) models must balance retaining prior knowledge while adapting to new tasks with extremely limited data. However, real-world scenarios may also involve unseen or undetermined relations that existing methods still struggle to handle. To address these challenges, we propose a novel approach that leverages the Open Information Extraction concept of Knowledge Graph Construction (KGC). Our method not only exposes models to all possible pairs of relations, including determined and undetermined labels not available in the training set, but also enriches model knowledge with diverse relation descriptions, thereby enhancing knowledge retention and adaptability in this challenging scenario. In the perspective of KGC, this is the first work explored in the setting of Continual Learning, allowing efficient expansion of the graph as the data evolves. Experimental results demonstrate our superior performance compared to other state-of-the-art FCRE baselines, as well as the efficiency in handling dynamic graph construction in this setting.