Fusing Relation Graph and Mutual Information for Inductive Link Prediction in Knowledge Graphs

Inductive link prediction in knowledge graphs refers to the task of inferring known relations between entities unseen during training. Most existing approach-es are limited to predicting only known relations and struggle to generalize to un-seen relations, which restricts their utility in dynamic settings. To address this challenge, we propose a novel inductive link prediction approach named RGIILP. Specifically, we construct a relation graph from the source knowledge graph and design a neural network model that enables interactive feature propagation be-tween entities and relations. Furthermore, we introduce the mutual information maximization mechanism between global and local representations to capture the global structural information of the graph. Experiments on several benchmark da-tasets demonstrate that RGIILP outperforms existing state-of-the-art methods for inductive link prediction task.