Few-shot Knowledge Graph Completion (FKGC) infers missing triples from limited support samples, tackling long-tail distribution challenges. Existing methods, however, struggle to capture complex relational patterns and mitigate data sparsity. To address these challenges, we propose a novel FKGC framework for conjugate relation modeling (CR-FKGC). Specifically, it employs a neighborhood aggregation encoder to integrate higher-order neighbor information, a conjugate relation learner combining an implicit conditional diffusion relation module with a stable relation module to capture stable semantics and uncertainty offsets, and a manifold conjugate decoder for efficient evaluation and inference of missing triples in manifold space. Experiments on three benchmarks demonstrate that our method achieves superior performance over state-of-the-art methods.

翻译：少样本知识图谱补全（FKGC）旨在从有限的支持样本中推断缺失的三元组，以应对长尾分布带来的挑战。然而，现有方法在捕捉复杂关系模式及缓解数据稀疏性方面存在不足。为解决这些问题，我们提出了一种新颖的用于共轭关系建模的FKGC框架（CR-FKGC）。具体而言，该框架采用邻域聚合编码器整合高阶邻居信息，通过结合隐式条件扩散关系模块与稳定关系模块的共轭关系学习器来捕捉稳定语义与不确定性偏移，并利用流形共轭解码器在流形空间中高效评估与推断缺失三元组。在三个基准数据集上的实验表明，我们的方法取得了优于现有先进方法的性能。