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 However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs.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 To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities.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.

翻译：知识图谱因其在各领域中的巨大潜力而备受关注。然而，过时事实问题对知识图谱构成挑战，影响了其整体质量，因为现实世界信息在不断演变。现有的过时事实检测解决方案往往依赖人工识别。为此，本文提出DEAN（深度过时事实检测），一种新颖的基于深度学习的框架，旨在识别知识图谱中的过时事实。DEAN通过全面建模实体和关系，捕捉事实间隐含的结构信息，从而脱颖而出。为了有效揭示潜在的过时信息，DEAN采用了一种基于预定义的、按实体数量加权的“关系到节点”（R2N）图的对比方法。实验结果表明，DEAN相较于最先进的基线方法具有有效性和优越性。