TA-KAND: Two-stage Attention Triple Enhancement and U-KAN based Diffusion For Few-shot Knowledge Graph Completion
This work addresses the challenge of long-tailed relation distributions in knowledge graphs for applications like intelligent question answering and recommender systems, representing an incremental improvement.
The paper tackles the problem of few-shot knowledge graph completion by integrating a two-stage attention triple enhancer with a U-KAN based diffusion model, achieving significant advantages in experiments on two public datasets.
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.