{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:53Z","timestamp":1758672893435,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/512","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"4597-4605","source":"Crossref","is-referenced-by-count":0,"title":["FS-KEN: Few-shot Knowledge Graph Reasoning by Adversarial Negative Enhancing"],"prefix":"10.24963","author":[{"given":"Lingyuan","family":"Meng","sequence":"first","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Liang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinwang","family":"Liu","sequence":"additional","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenpeng","family":"Lu","sequence":"additional","affiliation":[{"name":"Shandong Computer Science Center(National Supercomputer Center in Jinan)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:34:14Z","timestamp":1758627254000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/512"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/512","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}