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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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Hierarchical feature-guided prototypical network for few-shot knowledge graph completion.

Yuling Li1, Kui Yu2, Fei Yang1

  • 1School of Biomedical Engineering, Anhui Medical University, Hefei, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

Few-shot knowledge graph completion (FKGC) improves predictions for new relations by using a hierarchical approach. This method captures richer entity features and focuses on important dimensions for better accuracy.

Keywords:
Few-shot knowledge graph completionFew-shot learningKnowledge graph completionPrototype learning

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Machine Learning

Background:

  • Few-shot knowledge graph completion (FKGC) predicts missing information in knowledge graphs for relations with limited examples.
  • Existing FKGC methods rely on direct entity neighborhoods, potentially missing crucial features and leading to inaccurate relation prototypes.
  • Current approaches often treat all entity features equally, overlooking their varying importance for different relations.

Purpose of the Study:

  • To propose a novel Hierarchical Feature-guided Prototypical Network (HPNet) to address limitations in current FKGC methods.
  • To enhance the reliability of relation prototypes by incorporating both direct and distant entity neighborhood information.
  • To improve the accuracy of FKGC by considering the differential importance of entity features for specific relations.

Main Methods:

  • HPNet utilizes a hierarchical neighbor encoder to capture comprehensive entity features from both immediate and extended neighborhoods.
  • A feature-guided prototype learner is employed to compare query triples with relation prototypes, focusing on task-relevant feature dimensions.
  • The model dynamically weights entity features based on their importance for specific relations during comparison.

Main Results:

  • The proposed HPNet demonstrates superior performance compared to existing methods in few-shot knowledge graph completion tasks.
  • The hierarchical encoding effectively captures more representative entity features, leading to more robust relation prototypes.
  • The feature-guided comparison mechanism enhances the accuracy of predicting missing triples for unseen relations.

Conclusions:

  • HPNet offers a more effective and reliable approach to few-shot knowledge graph completion by addressing key limitations of prior methods.
  • The integration of hierarchical neighborhood information and feature-guided learning significantly improves prediction accuracy.
  • The proposed model provides a promising direction for advancing research in knowledge graph completion.