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Updated: May 12, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

A method for constructing a multi-modal knowledge graph of sheep disease based on a pre-trained model.

Liu Jiahao¹, Wang Fushun^2,3, Yuan Wanzhe^4,5

¹College of Information Science and Technology, Hebei Agricultural University, Baoding, China. 20237060902@pgs.hebau.edu.cn.

Veterinary Research Communications

|May 11, 2026

Summary

This study introduces a novel method for constructing sheep disease multi-modal knowledge graphs, significantly improving diagnostic accuracy and model efficiency in animal husbandry. The approach enhances semantic representation for intelligent disease diagnosis with limited data.

Keywords:

Knowledge Graph LoRA fine-tuning Multi-modal Pre-trained Model Sheep Disease

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Last Updated: May 12, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

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

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

Published on: June 13, 2025

Area of Science:

Veterinary Science
Artificial Intelligence
Data Science

Background:

The increasing complexity of animal husbandry knowledge highlights the need for advanced tools like multi-modal knowledge graphs (MMKG) for intelligent animal disease diagnosis.
Current MMKG construction faces challenges including limited professional knowledge, scarce multi-modal data, and insufficient labeled samples, particularly in specialized fields like sheep disease diagnosis.

Purpose of the Study:

To propose and validate a novel method for constructing a sheep disease multi-modal knowledge graph (MMKG).
To address the challenges of data scarcity and improve the efficiency and accuracy of intelligent sheep disease diagnosis.

Main Methods:

Construction of a text knowledge graph using RoBERTa+BiLSTM+CRF sequence labeling for multi-modal knowledge alignment.
Cross-modal representation learning via a vision-language pre-training two-stream model with LoRA fine-tuning and Bayesian optimization for domain adaptation.
Multi-modal knowledge alignment, storage, and visualization using the GraphXR tool and proposed rules.

Main Results:

Achieved high image-text matching accuracy: 81.82% on a self-built sheep disease dataset and 93.11% on the EuroSAT dataset.
Demonstrated significant improvements over pre-fine-tuning performance, with accuracy increases of 40.91% and 38.04%, respectively.
Validated the effectiveness of fusing pre-trained models with fine-tuning for MMKG construction in small-sample, vertical domains.

Conclusions:

The proposed method effectively constructs sheep disease multi-modal knowledge graphs, enhancing semantic representation and diagnostic capabilities.
This approach significantly improves model efficiency and generalization ability, especially in data-scarce environments.
Provides a robust knowledge base and technical support for intelligent sheep disease diagnosis applications.