Deep Outdated Fact Detection in Knowledge Graphs

• URL: http://arxiv.org/abs/2402.03732v1
• Date: Tue, 6 Feb 2024 05:58:15 GMT
• Title: Deep Outdated Fact Detection in Knowledge Graphs
• Authors: Huiling Tu, Shuo Yu, Vidya Saikrishna, Feng Xia, Karin Verspoor
• Abstract summary: This paper presents DEAN, a novel deep learning-based framework designed to identify outdated facts within Knowledge Graphs (KGs) DEAN distinguishes itself by capturing implicit structural information among facts through comprehensive modeling of both entities and relations. Experimental results demonstrate the effectiveness and superiority of DEAN over state-of-the-art baseline methods.
• Score: 13.711099395945988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs.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 To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities.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.

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