- Title
- Deep outdated fact detection in knowledge graphs
- Creator
- Tu, Huiling; Yu, Shuo; Saikrishna, Vidya; Xia, Feng; Verspoor, Karin
- Date
- 2023
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/199917
- Identifier
- vital:19295
- Identifier
-
https://doi.org/10.1109/ICDMW60847.2023.00184
- Identifier
- ISBN:2375-9232 (ISSN); 9798350381641 (ISBN)
- 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 However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs.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 To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities.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. © 2023 IEEE.
- Publisher
- IEEE Computer Society
- Relation
- 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023, Shanghai, China, 1-4 December 2023, 23rd IEEE International Conference on Data Mining Workshops Proceedings p. 1443-1452
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 IEEE
- Subject
- Contrastive learning; Graph learning; Knowledge graphs; Outdated fact detection
- Full Text
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