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.

翻译：知识图谱（KGs）通常包含各种错误。以往基于图结构的三元组嵌入进行错误检测的研究，往往难以区分语义相似的正确三元组与噪声。本文提出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与图结构信息，以更好区分语义。我们设计交互式对比学习来捕捉文本模式与结构模式之间的差异。此外，构建包含语义相似噪声与对抗性噪声的真实数据集。实验结果表明，CCA在检测语义相似噪声和对抗性噪声方面显著优于现有最优基线模型。