针对知识图谱关系长尾分布，提出双阶段注意力增强与U-KAN扩散模型。
TA-KAND: Two-stage Attention Triple Enhancement and U-KAN based Diffusion For Few-shot Knowledge Graph Completion
- 分两阶段用注意力机制强化正负三元组表示
- 在两个公开数据集上显著优于现有方法
- 适合少样本知识图谱补全研究者参考
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。然而现实世界知识具有异质性，导致关系呈现明显的长尾分布。以往研究多基于度量匹配或元学习，但常忽略正负三元组的分布特性。本文提出一种少样本知识图谱补全框架，结合双阶段注意力三元组增强器与基于U-KAN的扩散模型。在两个公开数据集上的大量实验表明，该方法具有显著优势。
原文摘要 · Abstract (English)
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.
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