用扩散模型动态生成难易度可调的负样本，提升知识图谱补全效果。
DANS-KGC: Diffusion Based Adaptive Negative Sampling for Knowledge Graph Completion
- 基于难度评估与条件扩散模型生成不同难易度的负样本
- 在六大数据集上实现最优性能，尤其在UMLS和YAGO3-10上领先
- 适合需要高精度知识图谱补全的研究者与工业应用
负采样策略在知识图谱表示中至关重要。为克服现有方法对假负例敏感、泛化能力弱及难以控制样本难度的问题，我们提出DANS-KGC（基于扩散的自适应负采样用于知识图谱补全）。该方法包含三个核心模块：难度评估模块（DAM）通过融合语义与结构特征评估实体学习难度；自适应负采样模块（ANS）采用难度感知噪声调度的条件扩散模型，在去噪阶段结合语义与邻域信息生成多样难度的负样本；动态训练机制（DTM）则在训练过程中动态调整负样本难度分布，实现从易到难的课程式学习。在六个基准数据集上的大量实验表明，DANS-KGC具有优异的有效性与泛化能力，在所有三项评估指标上均取得当前最优结果，尤其在UMLS和YAGO3-10数据集上表现突出。
原文摘要 · Abstract (English)
Negative sampling (NS) strategies play a crucial role in knowledge graph representation. In order to overcome the limitations of existing negative sampling strategies, such as vulnerability to false negatives, limited generalization, and lack of control over sample hardness, we propose DANS-KGC (Diffusion-based Adaptive Negative Sampling for Knowledge Graph Completion). DANS-KGC comprises three key components: the Difficulty Assessment Module (DAM), the Adaptive Negative Sampling Module (ANS), and the Dynamic Training Mechanism (DTM). DAM evaluates the learning difficulty of entities by integrating semantic and structural features. Based on this assessment, ANS employs a conditional diffusion model with difficulty-aware noise scheduling, leveraging semantic and neighborhood information during the denoising phase to generate negative samples of diverse hardness. DTM further enhances learning by dynamically adjusting the hardness distribution of negative samples throughout training, enabling a curriculum-style progression from easy to hard examples. Extensive experiments on six benchmark datasets demonstrate the effectiveness and generalization ability of DANS-KGC, with the method achieving state-of-the-art results on all three evaluation metrics for the UMLS and YAGO3-10 datasets.
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