SeSICL: Semantic and Structural Integrated Contrastive Learning for Knowledge Graph Error Detection
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3384543
Abstract: As knowledge graphs (KGs) become more widely used in various applications, error detection for KGs has received more attention, which can reduce quality issues such as errors and inconsistencies. With the development of representation learning,… read more here.
Keywords: detection; structural integrated; error detection; semantic structural ... See more keywords