Towards Global-Topology Relation Graph for Inductive Knowledge Graph Completion

Ling Ding, Lei Huang, Zhizhi Yu, Di Jin, Dongxiao He

Abstract: Knowledge Graphs (KGs) are structured data presented as directed graphs. Due to the common issues of incompleteness and inaccuracy encountered during construction and maintenance, completing KGs becomes a critical task. Inductive Knowledge Graph Completion (KGC) excels at inferring patterns or models from seen data to be applied to unseen data. However, existing methods mainly focus on new entities, while relations are usually randomly initialized. To this end, we propose TARGI, a simple yet effective inductive method for KGC. Specifically, we first construct a global relation graph for each topology from a global graph perspective, thus leveraging the in-variance of relation structures. We then utilize this graph to aggregate the rich embeddings of new relations and new entities, thereby performing KGC robustly in inductive scenarios. This successfully addresses the excessive reliance on the degree of relations and resolves the high complexity and limited scope of enclosing subgraph sampling in existing fully inductive algorithms. We conduct KGC experiments on six inductive datasets using inference data where entities are entirely new and new relations at 100 percent, 50 percent, and 0 percent radios. Extensive results demonstrate that our model accurately learns the topological structures and embeddings of new relations, and guides the embedding learning of new entities. Notably, our model outperforms 15 SOTA methods, especially in two fully inductive datasets.