细粒度苹果病虫害知识图谱构建研究

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。

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

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.