Knowledge Graph Error Detection with Contrastive Confidence Adaption

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 on semantically-similar noise and adversarial noise.

Authors

• Xiangyu Liu State Key Laboratory for Novel Software Technology, Nanjing University, China
• Yang Liu College of Computer Science, Sichuan University Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education
• Wei Hu State Key Laboratory for Novel Software Technology, Nanjing University, China National Institute of Healthcare Data Science, Nanjing University, China

Keywords

• DMKM: Linked Open Data, Knowledge Graphs & KB Completio