mlsanityData to 7 Oct 2026

Deep Outdated Fact Detection in Knowledge Graphs

arXiv 2402.03732 ↗First posted 6 Feb 2024cs.AI cs.CL cs.DLcited by 6

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Abstract (arXiv)

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 However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs.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 To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities.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

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 21 of its 35 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

21 of 35 references.

References · 21

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
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  4. 2018Deep Graph Infomaxcited by 1,101
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9 more
  1. 2018End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completioncited by 136
  2. 2019Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphscited by 125
  3. 2023Knowledge Graphs: Opportunities and Challengescited by 70
  4. 2020PairRE: Knowledge Graph Embeddings via Paired Relation Vectorscited by 67
  5. 2022SelfKG: Self-Supervised Entity Alignment in Knowledge Graphscited by 32
  6. 2022Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challengescited by 15
  7. 2017Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidencecited by 14
  8. 2022CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphscited by 14
  9. 2021Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Predictioncited by 12

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Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learningcited by 12
  2. 2024FUGNN: Harmonizing Fairness and Utility in Graph Neural Networkscited by 11
  3. 2024RobGC: Towards Robust Graph Condensationcited by 8
  4. 2024Brain-inspired Artificial Intelligence: A Comprehensive Reviewcited by 8
  5. 2025Factor Graph-based Interpretable Neural Networkscited by 2
  6. 2025ED-Filter: Dynamic Feature Filtering for Eating Disorder Classificationcited by 0