用元学习实现知识图谱嵌入的精准删除，保护隐私同时保持模型性能。
Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning
- 通过元学习动态调整嵌入表示，实现对特定数据的定向清除
- 在多个基准数据集上验证了删除效果，保留其余数据性能稳定
- 适合需要数据合规与隐私保护的知识图谱应用
知识图谱嵌入方法将实体和关系映射到连续向量空间，提升链接预测和问答等任务表现。随着隐私问题日益突出，机器可遗忘（MU）作为关键AI技术应运而生，使模型能够消除特定数据的影响。现有方法多依赖数据混淆和损失函数调整，但缺乏跨任务泛化能力。本文提出基于元学习的知识图谱嵌入可遗忘框架MetaEU，利用元学习机制来消除特定嵌入的影响，从而减轻其对模型的干扰，同时保持对剩余数据的性能。在多个基准数据集上的实验表明，该方法在知识图谱嵌入可遗忘任务中具有显著有效性。
原文摘要 · Abstract (English)
Knowledge graph (KG) embedding methods map entities and relations into continuous vector spaces, improving performance in tasks like link prediction and question answering. With rising privacy concerns, machine unlearning (MU) has emerged as a critical AI technology, enabling models to eliminate the influence of specific data. Existing MU approaches often rely on data obfuscation and adjustments to training loss but lack generalization across unlearning tasks. This paper introduces MetaEU, a Meta-Learning-Based Knowledge Graph Embedding Unlearning framework. MetaEU leverages meta-learning to unlearn specific embeddings, mitigating their impact while preserving model performance on remaining data. Experiments on benchmark datasets demonstrate its effectiveness in KG embedding unlearning.
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