KAU 方法应对知识图谱嵌入遗忘后事实复活问题
When Forgotten Facts Return: Relearning-Aware Unlearning of Knowledge Graph Embeddings via Tangent Kernels
删掉的知识图谱事实会在继续训练时悄悄回来，这篇论文解释了为什么，还给出 KAU 方法，泄漏最多降八成，做知识图谱的可以看看。
arXiv 论文研究了知识图谱嵌入近似遗忘后的事实复活现象：删除请求通过编辑实体嵌入完成，但随后继续训练会让被删事实比精确遗忘基线更容易恢复。作者将这一复活归因于被删事实与新事实共享实体嵌入，并提出 Kernel-Anticipated Unlearning（KAU），用切向核以闭式解建模继续训练对被删事实分数的一阶影响，并在遗忘损失中对其求导。在相同继续训练条件下，KAU 在 FB15k-237 上将最强基线的泄漏降低 46-63%，在 CoDEx-L 上降低 50-82%，且保留效用更高。
When Forgotten Facts Return: Relearning-Aware Unlearning of Knowledge Graph Embeddings via Tangent Kernels
Approximate unlearning answers a deletion request on a knowledge-graph embedding by editing entity embeddings instead of retraining from scratch. These embeddings keep being updated as new facts arrive. Under this ordinary continued training, forgotten facts recover more than in the exact-unlearning baseline, a model retrained without them and given the same continued training. We trace this relearning to entity embeddings shared by forgotten and new facts. To our knowledge, this is the first study of relearning in knowledge-graph embeddings. Relearning-robust methods developed for deep networks do not account for the graph structure underlying this recovery. We propose Kernel-Anticipated Unlearning (KAU), which uses the tangent kernel to model how continued training changes forgotten facts' scores through these shared parameters. KAU expresses this first-order response in closed form and differentiates through it inside the forgetting loss. The objective steers edits toward directions that continued training reverses less. We compare KAU with state-of-the-art knowledge-graph unlearners and relearning-robust methods under identical continued training, measuring leakage beyond the exact-unlearning baseline. Across three backbones, KAU reduces the strongest knowledge-graph unlearner's leakage by 46-63% on FB15k-237 and 50-82% on CoDEx-L, at higher retained utility.
