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Updated: Jul 28, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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BuB: a builder-booster model for link prediction on knowledge graphs.

Mohammad Ali Soltanshahi1, Babak Teimourpour1, Hadi Zare2

  • 1Department of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

Applied Network Science
|May 30, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the BuB model for link prediction (LP), addressing challenges in one-to-many and many-to-many relationships using discriminative fine-tuning (DFT). The BuB model enhances relationship building and strengthening, outperforming existing methods.

Keywords:
BuBDiscriminative fine-tuningKnowledge graph completionLink predictionRelationship builder and booster

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Area of Science:

  • Graph Neural Networks
  • Machine Learning
  • Network Science

Background:

  • Link prediction (LP) is crucial in various domains, but existing models struggle with one-to-many and many-to-many relationships.
  • Discriminative fine-tuning (DFT), adjusting learning rates for model parts, has not been explored for LP.
  • Handling complex relationship structures remains a significant challenge in link prediction.

Purpose of the Study:

  • To introduce a novel model, BuB, designed to effectively handle one-to-many and many-to-many relationships in link prediction.
  • To explore the application of discriminative fine-tuning (DFT) in link prediction for the first time.
  • To enhance the solution space and improve the performance of link prediction models.

Main Methods:

  • The proposed BuB model consists of two components: a relationship Builder and a relationship Booster.
  • The ranking function is reformulated in polar coordinates with an nth root to manage complex relationships.
  • Discriminative fine-tuning (DFT) is employed to adjust learning rates, emphasizing the Builder component.

Main Results:

  • The BuB model successfully addresses one-to-many and many-to-many relationship challenges in link prediction.
  • The use of polar coordinates and nth root expands the optimal solution space.
  • Experimental results demonstrate that the BuB model surpasses state-of-the-art methods on benchmark datasets.

Conclusions:

  • The BuB model, incorporating DFT and a novel ranking function, offers a significant advancement in link prediction.
  • The method effectively handles complex relational structures, improving prediction accuracy.
  • This research opens new avenues for applying DFT in graph-based machine learning tasks.