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Updated: Aug 4, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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K-RET: knowledgeable biomedical relation extraction system.

Diana F Sousa1, Francisco M Couto1

  • 1Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa 1749-016, Portugal.

Bioinformatics (Oxford, England)
|April 5, 2023
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Summary
This summary is machine-generated.

K-RET enhances biomedical relation extraction by integrating knowledge from ontologies. This novel system significantly improves the prediction of biomedical associations, advancing text mining capabilities.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Text Mining

Background:

  • Relation extraction (RE) is vital for uncovering associations in large text volumes.
  • Current state-of-the-art RE methods, like BERT, can be limited by insufficient external knowledge integration.
  • Biomedical ontologies offer high-quality knowledge crucial for improving RE in the biomedical domain.

Purpose of the Study:

  • To develop K-RET, a novel system for biomedical relation extraction that injects external knowledge.
  • To enhance the prediction of explainable biomedical associations by leveraging ontologies.
  • To address limitations in current RE by handling diverse association types, multiple knowledge sources, and multi-token entities.

Main Methods:

  • Developed K-RET, a knowledge-infused biomedical relation extraction system.
  • Integrated multiple biomedical ontologies to inject external knowledge.
  • Handled various association types, knowledge sources, and multi-token entities within the RE process.

Main Results:

  • K-RET achieved an average improvement of 2.68% over state-of-the-art results across three corpora (DDI, BC5CDR, PGR).
  • The DDI Corpus showed a significant performance boost, increasing F-measure from 79.30% to 87.19% (P-value: 2.91×10-12).
  • The system demonstrated enhanced prediction of biomedical associations through knowledge injection.

Conclusions:

  • K-RET represents a significant advancement in biomedical relation extraction through effective knowledge injection.
  • The system's ability to utilize diverse ontologies and entity types leads to improved performance.
  • K-RET provides a valuable tool for discovering and understanding biomedical associations from text.