Inductive Learning on Commonsense Knowledge Graph Completion
Bin Wang, Guangtao Wang, Jing Huang, Jiaxuan You, Jure Leskovec, C.‐C. Jay Kuo
Abstract
Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. Existing CKG completion methods focus on transductive learning setting, where all the entities are present during training. Here, we propose the first inductive learning setting for CKG completion, where unseen entities may appear at test time. We emphasize that the inductive learning setting is crucial for CKGs, because unseen entities are frequently introduced due to the fact that CKGs are dynamic and highly sparse. We propose InductivE as the first framework targeted at the inductive CKG completion task. InductivE first ensures the inductive learning capability by directly computing entity embeddings from raw entity attributes. Second, a graph neural network with novel densification process is proposed to further enhance unseen entity representation with neighboring structural information. Experimental results show that InductivE performs especially well on inductive scenarios where it achieves above 48% improvement over previous methods while also outperforms state-of-the-art baselines in transductive settings.