Knowledge Graph Embedding Model Based on Spiking Neural-like Graph Attention Network for Relation Prediction.

Abstract

Knowledge graphs (KGs) which represent entities and their relations in a structured manner, have become a fundamental resource for various natural language processing tasks. However, the incompleteness of KGs significantly hinders their effectiveness, thereby reducing their practical utility. The challenge of predicting missing relations between entities and performing these predictions efficiently has become a focal point of research. To address the challenge of incomplete KGs, we propose GEGS, a novel KG embedding framework that enhances scalability and expressiveness for relation prediction. GEGS introduces GAT-SNP, a graph attention network that, for the first time, integrates nonlinear spiking neural P (SNP) mechanisms into graph attention models and applies them to the KG domain, effectively capturing complex relational structures. The GAT-SNP network assigns distinct attention weights to each node, enabling the model to focus on the most relevant nodes in the graph. To mitigate information loss in long-range and sequential path features, we incorporate a BiLSTM-SNP component, which alleviates long-term dependency issues while preserving global path information. By leveraging GAT-SNP and BiLSTM-SNP, GEGS achieves superior performance in link prediction tasks, paving the way for applications in large-scale knowledge base completion. Kinship, FB15k-237, and WN18RR are used to evaluate the proposed GEGS model. The experimental results indicate that the proposed GEGS model has achieved state-of-the-art results in multiple evaluation metrics(e.g. Hits@10 and MRR).