Home > Archive>Volume 42, Issue 3, 2023 >63-70. DOI:10.13300/j.cnki.hnlkxb.2023.03.008
PDF HTML XML Export Cite reminder
Construction and application of knowledge graph of sheep & goat disease
CSTR:
[cstr]
Author:
  • YANG Zhe 1,2

    YANG Zhe

    College of Informatics， Huazhong Agricultural University/ Key Laboratory of Smart Farming for Agricultural Animals， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Agricultural Intelligent Technology， the Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data， Wuhan 430070， China;College of Animal Sciences & Technology， Huazhong Agricultural University/Key Laboratory of Agricultural Animal Genetics，Breeding and Reproduction of Ministry of Education，Wuhan 430070，China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
  • XU Tian 1

    XU Tian

    College of Informatics， Huazhong Agricultural University/ Key Laboratory of Smart Farming for Agricultural Animals， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Agricultural Intelligent Technology， the Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data， Wuhan 430070， China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
  • JIN Zhe 1

    JIN Zhe

    College of Informatics， Huazhong Agricultural University/ Key Laboratory of Smart Farming for Agricultural Animals， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Agricultural Intelligent Technology， the Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data， Wuhan 430070， China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
  • KONG Mei 1

    KONG Mei

    College of Informatics， Huazhong Agricultural University/ Key Laboratory of Smart Farming for Agricultural Animals， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Agricultural Intelligent Technology， the Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data， Wuhan 430070， China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
  • LI Guoliang 1

    LI Guoliang

    College of Informatics， Huazhong Agricultural University/ Key Laboratory of Smart Farming for Agricultural Animals， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Agricultural Intelligent Technology， the Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data， Wuhan 430070， China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
  • DU Xiaoyong 1,2

    DU Xiaoyong

    College of Informatics， Huazhong Agricultural University/ Key Laboratory of Smart Farming for Agricultural Animals， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Agricultural Intelligent Technology， the Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data， Wuhan 430070， China;College of Animal Sciences & Technology， Huazhong Agricultural University/Key Laboratory of Agricultural Animal Genetics，Breeding and Reproduction of Ministry of Education，Wuhan 430070，China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
Affiliation:

1.College of Informatics， Huazhong Agricultural University/ Key Laboratory of Smart Farming for Agricultural Animals， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Agricultural Intelligent Technology， the Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data， Wuhan 430070， China;2.College of Animal Sciences & Technology， Huazhong Agricultural University/Key Laboratory of Agricultural Animal Genetics，Breeding and Reproduction of Ministry of Education，Wuhan 430070，China

Clc Number:

TP391

  • Article
    • |
  • Figures
    • |
  • Metrics
    • |
  • Reference
    • |
  • Related
    • |
  • Cited by
    • | |
  • Comments
    Abstract:

    In order to solve the problem of a large amount of redundant data in the retrieval process of sheep disease and the waste of resources caused by manual selection of accurate answers after retrieval， this study constructed a question-and-answer system based on the knowledge graph of sheep & goat disease through the following three steps： （1） The data was obtained through web crawlers and some is manually extracted， automated information extraction was carried out using the bidirectional long short-term memory recurrent neural network （Bi-LSTM） model with an attention mechanism for improved recognition efficiency in the named entity recognition task. The entity annotation was performed using the BIO rule to complete the information extraction. The data was then integrated and stored in the Neo4j graph database . （2） For the attribute mapping， we constructed the Bert-softmax model； according to the user’s question， the Bert model was used to calculate the semantic similarity between the question and the attribute to determine the user’s intention， then the softmax algorithm was used to for normalization， finally， the most suitable answer was found and fed back to the system. （3） We built a sheep & goat disease diagnosis platform using Bootstrap， Echarts， and Vue components to visualize the sheep disease question-and-answer system.We used flask framework included in the Python language to build a backend， encapsulate disease information， present it to users through the web frontend， and establish a connection on the backend to enable data interaction. The results in the study show that the F1 value of entity recognition based on Bi-LSTM + Attention + CRF model is 83.16%， and the constructed knowledge graph contains 4 576 entities and more than 13 000 entity relationships. The pre-trained model Bert was added to the question answering system， and the F1 value of problem recognition was 85.24%. The results indicated that the system can quickly retrieve and accurately answer various types of questions such as the prevention and control measures of sheep diseases， and assist the farmers to make production decisions when faced with sheep diseases.

    Table 1 Evaluation of two different named entity recognition
    Fig.1 Named entity recognition flowchart
    Fig.2 Knowledge graph construction steps
    Fig.3 Bi-LSTM model structure diagram
    Fig.4 Flow chart of overall design of question answering algorithm for herd disease
    Fig.5 Neo4j entity and relationship visualization diagram of sheep disease knowledge graph
    Fig.6 A case diagram of sample dataset
    Fig.7 Q & A example of sheep disease knowledge graph
    Table 2 Sheep disease knowledge graph entity type
    Table 3 Sheep disease entity relationship types
    Reference
    Related
    Cited by
Get Citation

杨喆,许甜,靳哲,孔玫,李国亮,杜小勇. Construction and application of knowledge graph of sheep & goat disease[J]. Jorunal of Huazhong Agricultural University,2023,42(3):63-70.

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:December 05,2022
  • Online: June 20,2023
Article QR Code

Contact Us

Tel: 027-87287364
Postcode: 430070
E-mail：hnlkxb@mail.hzau.edu.cn
Address: No.1 Shizishan Street,Hongshan District,Wuhan, Hubei,China

Journal Subscription Email Alert ISSN:1000-2421 FPostal Code:BM 3816 CN:42-1181/S Domestic Code:38-120

Jorunal of Huazhong Agricultural University ® 2025 All Rights Reserved   Supported by:Beijing E-Tiller Technology Development Co., Ltd.