Knowledge graph completion (KGC) is one of the effective methods to identify new facts in knowledge graph. Except for a few methods based on graph network, most of KGC methods trend to be trained based on independent triples, while are difficult to take a full account of the information of global network connection contained in knowledge network. To address these issues, in this study, we propose a simple and effective Network-based Pre-training framework for knowledge graph completion (termed NetPeace), which takes into account the information of global network connection and local triple relationships in knowledge graph. Experiments show that in NetPeace framework, multiple KGC models yields consistent and significant improvements on benchmarks (e.g., 36.45% Hits@1 and 27.40% MRR improvements for TuckER on FB15k-237), especially dense knowledge graph. On the challenging low-resource task, NetPeace that benefits from the global features of KG achieves higher performance (104.03% MRR and 143.89% Hit@1 improvements at most) than original models.

翻译：知识图谱补全（KGC）是识别知识图谱中未知事实的有效方法之一。除少数基于图网络的方法外，大多数KGC方法倾向于基于独立三元组进行训练，难以充分考虑知识图谱中全局网络连接的信息。为解决这一问题，本文提出一种简单高效的基于网络的预训练框架（称为NetPeace），该框架兼顾了知识图谱中的全局网络连接信息与局部三元组关系。实验表明，在NetPeace框架下，多种KGC模型在基准数据集上（例如TuckER在FB15k-237上获得36.45%的Hits@1和27.40%的MRR提升）取得了持续且显著的改进，尤其在密集知识图谱中表现突出。在具有挑战性的低资源任务中，受益于知识图谱全局特征的NetPeace相比原始模型实现了更高性能（最高可达104.03%的MRR和143.89%的Hit@1提升）。