Deep Outdated Fact Detection in Knowledge Graphs
This addresses the challenge of maintaining knowledge graph quality for users relying on up-to-date information, representing an incremental improvement over existing manual or less advanced methods.
The paper tackles the problem of outdated facts in knowledge graphs, which degrade quality as real-world information changes, by introducing DEAN, a deep learning framework that identifies such facts through implicit structural modeling, achieving superior performance over state-of-the-art baselines.
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 However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs.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 To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities.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.