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Updated: Jan 11, 2026

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Knowledge Graph Embedding Model Based on Spiking Neural-like Graph Attention Network for Relation Prediction.

Yu Cao1, Bing Li1, Hong Peng1

  • 1School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China.

International Journal of Neural Systems
|November 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces GEGS, a novel framework for knowledge graph (KG) embedding, enhancing relation prediction by integrating spiking neural P (SNP) mechanisms. GEGS achieves state-of-the-art results in KG completion tasks.

Keywords:
Knowledge graphgraph attention networknonlinear spiking neural P systemsrelation prediction

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Natural Language Processing

Background:

  • Knowledge graphs (KGs) are crucial for NLP but suffer from incompleteness, limiting their utility.
  • Predicting missing relations in KGs is a significant research challenge.

Purpose of the Study:

  • To propose GEGS, a novel KG embedding framework for enhanced scalability and expressiveness in relation prediction.
  • To address the limitations of incomplete knowledge graphs.

Main Methods:

  • GEGS integrates GAT-SNP (Graph Attention Network with Spiking Neural P mechanisms) to capture complex relational structures.
  • A BiLSTM-SNP component is incorporated to mitigate information loss in long-range and sequential path features.

Main Results:

  • GEGS achieved superior performance in link prediction tasks on benchmark datasets (Kinship, FB15k-237, WN18RR).
  • The model demonstrated state-of-the-art results across multiple evaluation metrics, including Hits@10 and MRR.

Conclusions:

  • GEGS offers an effective solution for knowledge graph completion by improving relation prediction.
  • The framework's enhanced scalability and expressiveness pave the way for large-scale knowledge base applications.