  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 hypernetwork 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.
