A Pre-training Framework for Knowledge Graph Completion

TL;DR

This paper introduces NetPeace, a pre-training framework that leverages global network information to significantly improve knowledge graph completion models, especially in dense and low-resource scenarios.

Contribution

It proposes a novel network-based pre-training framework that incorporates global network features into KGC models, enhancing their performance.

Findings

Significant improvements on benchmark datasets, e.g., 36.45% Hits@1 for TuckER.

Enhanced performance in low-resource knowledge graph tasks.

Effective utilization of global network information improves KGC accuracy.

Abstract

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.