通过共轭关系建模，提升少样本知识图谱补全的准确性。
Conjugate Relation Modeling for Few-Shot Knowledge Graph Completion
- 引入邻居聚合编码器融合高阶邻域信息
- 共轭关系学习器有效捕捉语义稳定性和不确定性偏移
- 流形空间解码器加速推理，适合小样本场景
少样本知识图谱补全（FKGC）旨在从少量支持样本中推断缺失三元组，以应对长尾分布问题。现有方法难以捕捉复杂关系模式且易受数据稀疏性影响。为此，我们提出一种新的共轭关系建模框架（CR-FKGC）。该框架包含：1）邻居聚合编码器，用于整合高阶邻域信息；2）共轭关系学习器，结合隐式条件扩散关系模块与稳定关系模块，以捕捉语义稳定性与不确定性偏移；3）流形共轭解码器，在流形空间中高效评估和推断缺失三元组。在三个基准数据集上的实验表明，本方法显著优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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