Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (5): 270-280.DOI: 10.3778/j.issn.1002-8331.2205-0556

• Engineering and Applications • Previous Articles     Next Articles

Research on Construction of Fine-Grained Knowledge Graph of Apple Diseases and Pests

ZHANG Jiayu, GUO Mei, ZHANG Yongliang, LI Mei, GENG Nan, GENG Yaojun   

  1. 1.College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
    2.Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Yangling, Shaanxi 712100, China
    3.Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A&F University, Yangling, Shaanxi 712100, China
  • Online:2023-03-01 Published:2023-03-01



  1. 1.西北农林科技大学 信息工程学院,陕西 杨凌 712100
    2.西北农林科技大学 农业农村部农业物联网重点实验室,陕西 杨凌 712100
    3.西北农林科技大学 陕西省农业信息感知与智能服务重点实验室,陕西 杨凌 712100

Abstract: In view of the problem that existing agricultural knowledge graphs do not portray entities and relationships related to disease and pest control in sufficient detail, this paper takes the construction of a knowledge graph of apple diseases and pests as an example to study the construction method of fine-grained agricultural knowledge graphs. Firstly, the entity types and relationship types of apple disease and pest knowledge are defined at a fine-grained level, and a total of 19 entity categories and 22 entity relationships are classified, based on which the apple disease and pest knowledge graph dataset AppleKG is annotated and constructed. Then, the APD-CA model is used to identify named entities in the apple disease and pest field, and the ED-ARE model is used to extract the relationships between entities. The experimental results show that the F1-score of the models in this paper reaches 93.08% and 94.73% in the subtasks of named entity recognition and relationship extraction, respectively. Finally, the knowledge graph is stored and visualised using the Neo4j database, and a discussion is held on how fine-grained apple disease and pest knowledge graphs can provide the underlying technical support for downstream tasks such as accurate disease and pest information query and intelligent assisted diagnosis.

Key words: apple disease and pest control, knowledge graph, deep learning, recurrent neural network, knowledge extraction

摘要: 鉴于现有农业知识图谱对病虫害防治相关实体、关系刻画不够细致的问题,以苹果病虫害知识图谱构建为例,研究细粒度农业知识图谱的构建方法。对苹果病虫害知识的实体类型和关系种类进行细粒度定义,共划分出19种实体类别和22种实体关系,以此为基础标注并构建了苹果病虫害知识图谱数据集AppleKG。使用APD-CA模型对苹果病虫害领域命名实体进行识别,使用ED-ARE模型对实体关系进行抽取。实验结果表明,该文模型在命名实体识别和关系抽取两项子任务中的F1值分别达到了93.08%和94.73%。使用Neo4j数据库对知识图谱进行了存储和可视化,并就细粒度苹果病虫害知识图谱可以为精准病虫害信息查询、智能辅助诊断等下游任务提供底层技术支撑进行了讨论。

关键词: 苹果病虫害防治, 知识图谱, 深度学习, 循环神经网络, 知识抽取