A Pre-training Framework for Knowledge Graph Completion
This work addresses the challenge of leveraging global network information in knowledge graph completion, which is incremental but offers strong performance gains for tasks involving dense and low-resource knowledge graphs.
The paper tackles the problem of knowledge graph completion by proposing a pre-training framework that incorporates global network connections and local triple relationships, resulting in significant improvements such as 36.45% Hits@1 and 27.40% MRR gains for TuckER on FB15k-237.
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.