Knowledge Graph Error Detection with Contrastive Confidence Adaption
DOI:
https://doi.org/10.1609/aaai.v38i8.28729Keywords:
DMKM: Linked Open Data, Knowledge Graphs & KB CompletioAbstract
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.Downloads
Published
2024-03-24
How to Cite
Liu, X., Liu, Y., & Hu, W. (2024). Knowledge Graph Error Detection with Contrastive Confidence Adaption. Proceedings of the AAAI Conference on Artificial Intelligence, 38(8), 8824-8831. https://doi.org/10.1609/aaai.v38i8.28729
Issue
Section
AAAI Technical Track on Data Mining & Knowledge Management