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Related Experiment Video

Updated: Sep 19, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Relation prediction in knowledge graphs: A self-organizing neural network approach.

Budhitama Subagdja1, D Shanthoshigaa1, Ah-Hwee Tan1

  • 1School of Computing and Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore.

Neural Networks : the Official Journal of the International Neural Network Society
|June 18, 2025
PubMed
Summary

KG2ART, a novel neural network, enhances knowledge graph completion by performing parallel inference without representation learning. This approach achieves superior accuracy and speed in relation prediction across diverse datasets.

Keywords:
Adaptive Resonance TheoryKnowledge graphsRelation prediction

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Graph Neural Networks

Background:

  • Specialized knowledge graphs (KGs) often contain incomplete information, hindering their utility.
  • Current KG completion methods predominantly use neural network-based representation learning.

Purpose of the Study:

  • To introduce KG2ART, a novel self-organizing neural network for knowledge graph completion.
  • To demonstrate KG2ART's effectiveness in relation prediction without relying on representation learning.

Main Methods:

  • KG2ART employs parallel inference through bidirectional interactions between bottom-up activations and top-down pattern matching.
  • The model operates directly on the graph structure, bypassing traditional representation learning.

Main Results:

  • KG2ART consistently outperforms state-of-the-art baselines (TuckER, ComplEX, RESCAL, ConvE, CompGCN) in prediction accuracy across five diverse KGs.
  • Achieved Hits@1 scores exceeding 90% for Nations and 60% for CoDEx-M.
  • Demonstrated superior training and prediction speeds compared to existing models.

Conclusions:

  • KG2ART offers a fundamentally different and highly effective approach to knowledge graph completion.
  • The model provides a significant advancement in both accuracy and efficiency for relation prediction tasks.