Knowledge graphs (KGs) often contain various errors. Previous works on
detecting errors in KGs mainly rely on triplet embedding from graph structure.
We conduct an empirical study and find that these works struggle to
discriminate noise from semantically-similar correct triplets. In this paper,
we propose a KG error detection model CCA to integrate both textual and graph
structural information from triplet reconstruction for better distinguishing
semantics. We design interactive contrastive learning to capture the
differences between textual and structural patterns. Furthermore, we construct
realistic datasets with semantically-similar noise and adversarial noise.
Experimental results demonstrate that CCA outperforms state-of-the-art
baselines, especially in detecting semantically-similar noise and adversarial
noise.

我们提出了一种知识图谱错误检测模型 CCA，通过整合三元组重构中的文本信息和图结构信息，以更好地区分语义，设计交互式对比学习来捕捉文本和结构模式之间的差异，并通过构建包含语义相似噪声和对抗噪声的现实数据集来验证实验结果，表明 CCA 在检测语义相似噪声和对抗噪声方面优于现有基线模型。