Pre-trained model-driven construction and application of a swine disease knowledge graph

WEN Jiwei; YANG Zhe; WANG Cheng; JIN Zhe; DENG Bing; LI Guoliang; FANG Yaping; LIU Feng; DU Xiaoyong

doi:10.13300/j.cnki.hnlkxb.2026.03.011

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Pre-trained model-driven construction and application of a swine disease knowledge graph

Agricultural artificial intelligence | 更新时间：2026-11-05

- Pre-trained model-driven construction and application of a swine disease knowledge graph
- Journal of Huazhong Agricultural University Vol. 45, Issue 3, Pages: 127-136(2026)
- 作者机构：
  
  1.华中农业大学信息学院/农业农村部智慧养殖技术重点实验室/农业智能技术教育部工程研究中心/ 湖北省农业大数据工程技术研究中心，武汉430070
  2.农业动物遗传育种与繁殖教育部重点实验室，武汉430070
  3.武汉市农业科学院畜牧兽医研究所，武汉430200
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2026.03.011
  CLC： TP391.4;S858.28
- Received：30 September 2025，
  
  Published：25 May 2026
- 稿件说明：
移动端阅览
文纪威，杨喆，王成，等.预训练模型驱动的猪病知识图谱构建与应用［J］.华中农业大学学报，2026，45（3）：127‐136.

WEN Jiwei,YANG Zhe,WANG Cheng,et al.Pre-trained model-driven construction and application of a swine disease knowledge graph[J].Journal of Huazhong Agricultural University,2026,45(03):127-136.
文纪威，杨喆，王成，等.预训练模型驱动的猪病知识图谱构建与应用［J］.华中农业大学学报，2026，45（3）：127‐136. DOI： 10.13300/j.cnki.hnlkxb.2026.03.011.

WEN Jiwei,YANG Zhe,WANG Cheng,et al.Pre-trained model-driven construction and application of a swine disease knowledge graph[J].Journal of Huazhong Agricultural University,2026,45(03):127-136. DOI： 10.13300/j.cnki.hnlkxb.2026.03.011.

摘要

为提高猪病防治知识的获取效率，缓解基层畜牧兽医服务资源匮乏的问题，本研究构建了面向猪病防治的垂直领域知识图谱及智能问答系统。针对多源异构数据描述存在冲突的问题，研究依据兽医病理学建立领域本体，引入BERT语义相似度融合策略，构建专业标注语料库。针对兽医术语复合性强且长尾分布的特点，提出ERNIE-Bi-LSTM-CRF实体识别模型。命名实体识别试验及消融实验结果显示，ERNIE独特的实体级掩码机制能有效克服传统字级掩码对长难术语的语义割裂问题，其

值达89.63%，显著优于Bi-LSTM+CRF及BERT等基线模型。基于此，集成Neo4j图数据库与意图识别技术开发了微信端问答机器人，测试结果显示该机器人能够准确理解专业性的兽医诊疗问题，并能提供全面、科学的检测指导，有效辅助养殖人员进行疾病确诊。结果表明，所构建系统能够实现从自然语言问题到图谱知识检索的闭环处理，可为猪病辅助诊断与防控知识服务提供技术支持。

Abstract

To enhance the efficiency of acquiring knowledge on swine disease prevention and control and to alleviate the shortage of veterinary service resources at the grassroots level，this study develops a vertic

al domain knowledge graph and an intelligent question-answering system specifically for swine disease prevention and control.To resolve descriptive conflicts in multi-source heterogeneous data，the study establishes a domain ontology based on veterinary pathology and introduces a BERT-based semantic similarity fusion strategy to construct a professionally annotated corpus.Given the complex and long-tail distribution of veterinary terms，we propose an ERNIE-Bi-LSTM-CRF entity recognition model.Named entity recognition experiments and ablation tests show that ERNIE’s unique entity-level masking mechanism effectively overcomes the semantic fragmentation problem caused by traditional character-level masking of complex terms.The

score reaches 89.63%，significantly outperforming baseline models such as Bi-LSTM+CRF and BERT.Based on this，the study integrates the Neo4j graph database and intent recognition technology to develop a WeChat-based question-answering chatbot.Tests show that the chatbot can accurately understand professional veterinary diagnosis and treatment issues，providing comprehensive，and scientifically sound diagnostic guidance.This effectively assists farmers in disease diagnosis.The results indicate that the developed system can achieve a closed-loop process，from natural language queries to knowledge retrieval within the knowledge graph，thereby offering technical support for swine disease diagnosis and prevention knowledge services.

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references

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