Enhancing Heterogeneous Knowledge Graph Completion with a Novel GAT-based Approach

Abstract

Knowledge graphs (KGs) play a vital role in enhancing search results and recommendation systems. With the rapid increase in the size of KGs, they are becoming inaccurate and incomplete. This problem can be solved by the KG completion methods, of which graph attention network (GAT)-based methods stand out because of their superior performance. However, existing GAT-based KG completion methods often suffer from overfitting issues when dealing with heterogeneous KGs, primarily due to the unbalanced number of samples. Additionally, these methods demonstrate poor performance in predicting the tail (head) entity that shares the same relation and head (tail) entity with others. To solve these problems, we propose GATH, a novel GAT-based method designed for Heterogeneous KGs. GATH incorporates two separate attention network modules that work synergistically to predict the missing entities. We also introduce novel encoding and feature transformation approaches, enabling the robust performance of GATH in scenarios with imbalanced samples. Comprehensive experiments are conducted to evaluate GATH's performance. Compared with the existing state-of-the-art GAT-based model on Hits@10 and MRR metrics, our model improves performance by 5.2% and 5.2% on the FB15K-237 dataset and by 4.5% and 14.6% on the WN18RR dataset, respectively.