  Knowledge graphs (KGs) have garnered significant attention for their vast
potential across diverse domains. However, the issue of outdated facts poses a
challenge to KGs, affecting their overall quality as real-world information
evolves. Existing solutions for outdated fact detection often rely on manual
recognition. In response, this paper presents DEAN (Deep outdatEd fAct
detectioN), a novel deep learning-based framework designed to identify outdated
facts within KGs. DEAN distinguishes itself by capturing implicit structural
information among facts through comprehensive modeling of both entities and
relations. To effectively uncover latent out-of-date information, DEAN employs
a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph,
weighted by the number of entities. Experimental results demonstrate the
effectiveness and superiority of DEAN over state-of-the-art baseline methods.
