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Semantic Knowledge Augmented Hypergraph Contrastive Representation Learning for Zero-Shot Biomedical Text

Ratri Mukherjee1, Kishlay Jha1

  • 1University of Iowa, Iowa City, Iowa, USA.

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|December 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hypergraph approach for zero-shot biomedical text classification, improving how scientific articles are labeled with unseen concepts like new diseases and drugs.

Keywords:
biomedical multi-label text classificationcontrastive learningsemantic hypergraphzero-shot learning

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Zero-shot biomedical text classification is crucial for labeling scientific articles with novel concepts.
  • Existing methods struggle to capture complex semantic relationships between biomedical entities.
  • New diseases, genes, and drugs constantly emerge, necessitating adaptable classification systems.

Purpose of the Study:

  • To develop an advanced approach for zero-shot biomedical text classification.
  • To effectively leverage high-order semantic relationships between biomedical entities.
  • To improve generalization performance on unseen labels in biomedical text.

Main Methods:

  • Proposed a novel approach utilizing a hypergraph structure to model high-order semantic relationships.
  • Introduced an augmentation strategy using biomedical domain knowledge to generate enhanced hypergraph views.
  • Developed robust feature representations for biomedical entities.

Main Results:

  • The proposed hypergraph approach significantly improved zero-shot classification performance.
  • Augmented hypergraph views enhanced the model's ability to capture complex semantic information.
  • Experiments on a large biomedical corpus validated the approach's effectiveness.

Conclusions:

  • The hypergraph-based method offers a powerful solution for zero-shot biomedical text classification.
  • Leveraging semantic knowledge through hypergraph augmentation leads to better generalization.
  • This approach addresses the challenge of classifying emerging biomedical concepts effectively.