BiRKGC: Bidirectional Relation and Graph-Aware Model for Knowledge Graph Completion

In recent years, many studies have focused on improving knowledge graph completion (KGC) using pre-trained language models (PLMs). Most PLM-based methods focus on encoding textual triples but fail to account for relation directionality and structural context, which are crucial for capturing complex relational semantics. To address this limitation, we propose BiRKGC, a Bidirectional Relation and Graph-Aware model for KGC. The framework consists of three collaborative components: a Bidirectional Relation Encoder (BiR) for explicit direction modeling, a Graph-Aware Sequence Encoder (GaS) for integrating subgraph-level structure, and a Masked Token Modeling (MTM) task for semantic pre-training. Together, these components enhance the model’s ability to fuse textual semantics with structural information for more effective reasoning. In addition, BiRKGC introduces a dynamic subgraph sampling mechanism to improve structural adaptability. Experiments on WN18RR and FB15k-237 show that BiRKGC consistently improves MRR, Hits@1, and other metrics over strong baselines, while also narrowing the performance gap between forward and backward reasoning. Ablation studies validate the effectiveness of each module, demonstrating BiRKGC’s potential and competitiveness in KGC tasks.