Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning

Knowledge Graphs (KGs) often suffer from incomplete knowledge, which restricts their utility. Recently, Contrastive Learning (CL) has been introduced to Knowledge Graph Completion (KGC), significantly improving the discriminative capabilities of KGC models and setting new benchmarks in performance. However, existing contrastive methods primarily focus on individual triples, overlooking the broader structural connectivities of KGs. This narrow focus hampers a more comprehensive understanding of the graph's structural knowledge. To address this gap, we propose StructKGC, a novel contrastive learning framework designed to flexibly accommodate the diverse topologies inherent in KGs. We introduce four contrastive tasks tailored to KG data: Vertex-level CL, Neighbor-level CL, Path-level CL, and Relation composition level CL. These tasks are trained synergistically during the fine-tuning of pretrained language models (PLMs), allowing for a more nuanced capture of subgraph semantics. To validate the effectiveness of our method, we perform a comprehensive set of experiments on several real-world datasets. The experimental results demonstrate that our approach achieves SOTA performance under standard supervised and low-resource settings. Furthermore, we observe that the various structure-aware tasks introduced can mutually reinforce each other, resulting in consistent performance enhancements.