BGAT-CCRF: A novel end-to-end model for knowledge graph noise correction

Abstract

Knowledge graph (KG) noise correction aims to select suitable candidates to correct the noises in KGs. Most of the existing studies have limited performance in repairing the noisy triple that contains more than one incorrect entity or relation, which significantly constrains their implementation in real-world KGs. To overcome this challenge, we propose a novel end-to-end model (BGAT-CCRF) that achieves better noise correction results. Specifically, we construct a balanced-based graph attention model (BGAT) to learn the features of nodes in triples’ neighborhoods and capture the correlation between nodes based on their position and frequency. Additionally, we design a constrained conditional random field model (CCRF) to select suitable candidates guided by three constraints for correcting one or more noises in the triple. In this way, BGAT-CCRF can select multiple candidates from a smaller domain to repair multiple noises in triples simultaneously, rather than selecting candidates from the whole KG to repair noisy triples as traditional methods do, which can only repair one noise in the triple at a time. The effectiveness of BGAT-CCRF is validated by the KG noise correction experiment. Compared with the state-of-the-art models, BGAT-CCRF improves the fMRR metric by 3.58% on the FB15K dataset. Hence, it has the potential to facilitate the implementation of KGs in the real world.