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）是识别知识图谱中新事实的有效方法之一。除少数基于图网络的方法外，多数KGC方法倾向于基于独立三元组进行训练，难以充分利用知识网络中包含的全局网络连接信息。为解决这一问题，本研究提出一种简单有效的基于网络的预训练框架（NetPeace），该框架兼顾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，在FB15k-237上，TuckER模型的Hits@1提升36.45%，MRR提升27.40%）均取得一致且显著的性能提升，尤其在稠密知识图谱上表现突出。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优性能（MRR最高提升104.03%，Hit@1最高提升143.89%）。