Knowledge Graph Unlearning with Schema

AbstractGraph unlearning emerges as a crucial step to eliminate the impact of deleted elements from a trained model. However, unlearning on the knowledge graph (KG) has not yet been extensively studied. We remark that KG unlearning is non-trivial because KG is distinctive from general graphs. In this paper, we first propose a new unlearning method based on schema for KG. Specifically, we update the representation of the deleted element’s neighborhood with an unlearning object that regulates the affinity between the affected neighborhood and the instances within the same schema. Second, we raise a new task: schema unlearning. Given a schema graph to be deleted, we remove all instances matching the pattern and make the trained model forget the removed instances. Last, we evaluate the proposed unlearning method on various KG embedding models with benchmark datasets. Our codes are available at https://github.com/NKUShaw/KGUnlearningBySchema.