Maximum Mutual Information Estimation based Graph Attention Network for Knowledge Graph Completion

Abstract

Knowledge graphs often face the issue of missing links. Addressing the problem of reasoning about and completing these missing entities or relations has become a key research focus. However, existing graph attention networks rely on connections within the graph for information propagation and aggregation, limiting their ability to effectively utilize disconnected graph structures, which leads to a poor performance. In this paper, we propose a maximum Mutual Information Estimation based Graph Attention Network (MIEGAT). This model aims to capture both local connected information and global non-connected information from knowledge graphs, enabling unsupervised extraction of relational graph structural information through mutual information maximization. Experimental results demonstrate that the MIEGAT model achieves the state-of-the-art performance across four datasets, effectively extracting non-connected information and representing sparse entities.