Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deployed as static artifacts, making them difficult to modify post-deployment without re-training after deployment. To address this issue, we propose a new task of editing language model-based KG embeddings in this paper. This task is designed to facilitate rapid, data-efficient updates to KG embeddings without compromising the performance of other aspects. We build four new datasets: E-FB15k237, A-FB15k237, E-WN18RR, and A-WN18RR, and evaluate several knowledge editing baselines demonstrating the limited ability of previous models to handle the proposed challenging task. We further propose a simple yet strong baseline dubbed KGEditor, which utilizes additional parametric layers of the hyper network to edit/add facts. Our comprehensive experimental results reveal that KGEditor excels in updating specific facts without impacting the overall performance, even when faced with limited training resources. Code and datasets are available in https://github.com/zjunlp/PromptKG/tree/main/deltaKG.

翻译：近几十年来，语言模型在知识图谱嵌入框架中的经验成功已得到实证验证。然而，基于语言模型的知识图谱嵌入通常被部署为静态模块，导致其部署后难以在不重新训练的情况下进行修改。为解决这一问题，本文提出一项新任务：编辑基于语言模型的知识图谱嵌入。该任务旨在在不影响其他方面性能的前提下，实现对知识图谱嵌入的快速、数据高效更新。我们构建了四个新数据集：E-FB15k237、A-FB15k237、E-WN18RR和A-WN18RR，并评估了多种知识编辑基线方法，结果表明现有模型在处理这一具有挑战性的新任务时能力有限。我们进一步提出了一种简单而强大的基线方法KGEditor，该方法利用超网络的附加参数层来编辑/添加事实。综合实验结果表明，即使在训练资源有限的情况下，KGEditor在更新特定事实时仍能保持整体性能不受影响。代码与数据集可在https://github.com/zjunlp/PromptKG/tree/main/deltaKG获取。