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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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KG-MFEND: an efficient knowledge graph-based model for multi-domain fake news detection.

Lifang Fu1, Huanxin Peng2, Shuai Liu2

  • 1Northeast Agricultural University, Harbin, 150030 China.

The Journal of Supercomputing
|June 26, 2023
PubMed
Summary

This study introduces KG-MFEND, a novel framework for multi-domain fake news detection using knowledge graphs (KG). It effectively identifies fake news across various topics, outperforming existing methods with enhanced generalization capabilities.

Keywords:
Fake news detectionKnowledge graphKnowledge noiseMulti-domain learning

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Social Science

Background:

  • Fake news on social media poses significant societal risks.
  • Existing fake news detection models lack cross-domain applicability due to domain-specific language variations.
  • A robust, multi-domain fake news detection system is crucial for real-world social media environments.

Discussion:

  • The proposed KG-MFEND framework leverages knowledge graphs (KG) to integrate external, multi-domain knowledge, enhancing BERT's capabilities.
  • It constructs a novel KG with entity triples to create sentence trees, enriching news context and mitigating domain differences at the word level.
  • Techniques like soft position, visible matrix in knowledge embedding, and label smoothing are employed to address embedding space, knowledge noise, and label noise.

Key Insights:

  • KG-MFEND demonstrates strong generalization across single, mixed, and multiple domains.
  • The framework effectively alleviates domain differences by incorporating external knowledge.
  • Experimental results on Chinese datasets confirm KG-MFEND's superior performance compared to state-of-the-art methods.

Outlook:

  • Future research could explore dynamic KG updates for evolving fake news tactics.
  • Investigating the framework's performance on multilingual datasets is a promising direction.
  • Further refinement of knowledge integration techniques can enhance robustness against adversarial attacks.