{"type":"ScholarlyArticle","title":"FS-KEN: Few-shot Knowledge Graph Reasoning by Adversarial Negative Enhancing","authors":[{"name":"Lingyuan Meng","slug":"lingyuan-meng","given":"Lingyuan","family":"Meng","affiliation":""},{"name":"Ke Liang","slug":"ke-liang","given":"Ke","family":"Liang","affiliation":""},{"name":"Zeyu Zhu","slug":"zeyu-zhu","given":"Zeyu","family":"Zhu","affiliation":""},{"name":"Xinwang Liu","slug":"xinwang-liu","given":"Xinwang","family":"Liu","affiliation":""},{"name":"Wenpeng Lu","slug":"wenpeng-lu","given":"Wenpeng","family":"Lu","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/512","url":"https://ijcai.org/proceedings/2025/512","pdf":"https://ijcai.org/proceedings/2025/0512.pdf","markdown":"https://ijcai.org/proceedings/2025/512.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/512","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":512,"pages":{"first":4597,"last":4605},"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"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}