Knowledge Graphs have become fundamental infrastructure for applications such as intelligent question answering and recommender systems due to their expressive representation. Nevertheless, real-world knowledge is heterogeneous, leading to a pronounced long-tailed distribution over relations. Previous studies mainly based on metric matching or meta learning. However, they often overlook the distributional characteristics of positive and negative triple samples. In this paper, we propose a few-shot knowledge graph completion framework that integrates two-stage attention triple enhancer with U-KAN based diffusion model. Extensive experiments on two public datasets show significant advantages of our methods.

翻译：知识图谱因其表达能力强，已成为智能问答和推荐系统等应用的基础设施。然而，现实世界中的知识具有异质性，导致关系呈现出显著的长尾分布。先前的研究主要基于度量匹配或元学习，但它们往往忽略了正负三元组样本的分布特性。本文提出了一种少样本知识图谱补全框架，该框架将两阶段注意力三元组增强器与基于U-KAN的扩散模型相结合。在两个公开数据集上进行的大量实验表明，我们的方法具有显著优势。