---
title: "FS-KEN: Few-shot Knowledge Graph Reasoning by Adversarial Negative Enhancing"
authors:
  - "Lingyuan Meng"
  - "Ke Liang"
  - "Zeyu Zhu"
  - "Xinwang Liu"
  - "Wenpeng Lu"
doi: "10.24963/ijcai.2025/512"
url: "https://ijcai.org/proceedings/2025/512"
pdf: "https://ijcai.org/proceedings/2025/0512.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "4597-4605"
track: "Main Track"
keywords:
  - "Computer Vision: CV: Structural and model-based approaches, knowledge representation and reasoning"
  - "Knowledge Representation and Reasoning: KRR: Learning and reasoning"
  - "Machine Learning: ML: Few-shot learning"
---

# FS-KEN: Few-shot Knowledge Graph Reasoning by Adversarial Negative Enhancing

Lingyuan Meng, Ke Liang, Zeyu Zhu, Xinwang Liu, Wenpeng Lu

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 4597-4605. DOI: [10.24963/ijcai.2025/512](https://doi.org/10.24963/ijcai.2025/512)

## Abstract

Few-shot knowledge graph reasoning (FS-KGR) try to infer missing facts in a knowledge graphs using limited data (such as only 3/5 samples).Existing strategies have shown good performance by mining more supervised information for few-shot learning through meta-learning and self-supervised learning. However, the problem of insufficient samples has not been fundamentally solved. In this paper, we propose a novel algorithm based on adversarial learning for Enhancing Negative samples in few-shot scenarios of FS-KGR, termed FS-KEN. Specifically, we are the first to use GAN to conduct data augmentation on FS-KGR scenario. FS-KEN uses policy gradient GANs for negative sample augmentation, solving the gradient back-propagation issue in traditional GANs. The generator aims to produce high-quality negative entities. while the objective of the discriminator is to distinguish between generated entities and real entities. Comprehensive experiments conducted on two few-shot knowledge graph completion datasets reveal that FS-KEN surpasses other baseline models, achieving state-of-the-art results.