Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions
Damai Dai, Hua Zheng, Fuli Luo, Pengcheng Yang, Tianyu Liu, Zhifang Sui, Baobao Chang · 2021
Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training.However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with outof-knowledge-graph (OOKG) entities added frequently, and we need to efficiently represent these entities.Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG.To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions.Moreover, given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method even needs no additional learning.Experimental results on two KGC tasks with OOKG entities show that our method outperforms the previous methods by a large margin with higher efficiency. 1