Few-Shot Knowledge Graph Completion with Structure-Aware Graph Attention Network

Few-shot knowledge graph completion refers to inferring missing entity using limited instances. A key challenge lies in entity representation, which is complicated by diverse neighbor attributes. Although the entity’s neighborhood topology holds potential to address this, its significance is overlooked in current research. In this paper, we propose a structure-aware graph attention network for few-shot knowledge graph completion. Firstly, to enhance entity representations, we design a structure-aware graph attention encoder to capture the graph’s structural features of nodes, generating embedding for entity pairs. Secondly, a semantic prototype matching network is employed to compute the prediction score. Experiments on the NELL-One and Wiki-One datasets show that our proposed model outperforms the best baseline models by 0.021, 0.026, 0.039, 0.032 and 0.016, 0.064, 0.043, 0.040 in terms of MRR, Hits@10, Hits@5, and Hits@1 metrics, respectively. This demonstrates that our model can effectively leverage neighborhood topological information to improve the accuracy of knowledge completion, and achieve a better generalization.