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Updated: Jan 7, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Entity relationship extraction method based on dependency parsing and graph neural networks.

Fupeng Wei1, Xing Liu2, Limin Pan3

  • 1School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.

Scientific Reports
|December 30, 2025
PubMed
Summary

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This summary is machine-generated.

The MGRel model improves entity relationship extraction for campus security by integrating syntactic analysis and graph neural networks. This enhances accuracy and handles complex relationships, boosting knowledge acquisition for traffic safety.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Current knowledge graph ternary extraction methods struggle with semantic ambiguity and overlapping relationships in text.
  • Entity separation and weak associations between distant entities in campus security data reduce extraction accuracy and recall.
  • Existing techniques lack the flexibility to manage complex, overlapping relationships crucial for campus traffic safety management.

Purpose of the Study:

  • To introduce the MGRel model for enhanced entity relationship extraction in campus security contexts.
  • To address limitations in current methods regarding ambiguity, overlapping relationships, and entity separation.
  • To improve knowledge acquisition for campus security governance, particularly traffic safety.

Main Methods:

Keywords:
Dependency parsingEntity Relationship extractionGraph neural networksSmart campus securityTriple classification

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  • Integration of dependent syntactic analysis with a graph neural network (GNN) in the MGRel model.
  • Utilizing a dual analysis mechanism (global semantic dependency and syntactic dependency) for long-distance association capture.
  • Development of a hierarchical semantic graph convolutional neural network and an attention-driven multi-feature fusion module for refined feature extraction and classification.

Main Results:

  • The MGRel model demonstrated improved F1 scores on benchmark datasets: NYT (+1.3%), WebNLG (+0.4%), and DuIE (+3.2%) compared to optimal models.
  • Effective capture of long-distance semantic associations and fine-grained extraction of implied semantic features.
  • Enhanced discriminative capacity of the ternary classifier through noise filtering.

Conclusions:

  • The MGRel model significantly outperforms existing techniques in entity relationship extraction.
  • The model shows considerable advantage and potential value for campus security applications, especially campus traffic safety.
  • The integration of syntactic analysis and GNNs offers a robust solution for complex relationship extraction challenges.