农业大数据学报2024，Vol.6Issue(4)：485-496,12.DOI:10.19788/j.issn.2096-6369.000011
小麦纹枯病防治领域本体构建
Ontology Construction in the Field of Wheat Sharp Eyespot Control
摘要
Abstract
Wheat Sharp Eyespot is a soil-borne fungal disease commonly found in China's wheat areas,which can occur throughout the entire reproductive period of wheat and has a great impact on the yield and quality of wheat in China.By constructing a Wheat Sharp Eyespot control domain ontology and modeling domain knowledge,we aim to integrate and share the knowledge in the field of Wheat Sharp Eyespot control to provide important support and guidance for agricultural decision-making and disease control.The ontology construction process for Wheat Sharp Eyespot control is proposed to meet the actual needs of Wheat Sharp Eyespot control.For the problems of low efficiency and limited expert knowledge in constructing ontologies by manual methods,this study will explore new methods for ontology construction.Special attention will be paid to the methodology of mining core concepts of the ontology to reduce the subjectivity and limitations in the construction process,so that the ontology will have a wider application potential.In this study,used the literature in the field of Wheat Sharp Eyespot control as a data source,KeyBERT keyword extraction algorithm was used to mine the core concepts of ontology,and BERT embedding and cosine similarity were used to find out the subphrases in the document that were most similar to the document itself.Hierarchical relationships between ontology concepts were extracted by hierarchical clustering,topic modeling was performed using BERTopic,Transformer and c-TF-IDF were used to create dense clusters.Finally,Protégé was used to visualize and express the ontology concepts and inter-concept relationships.In this study,the results of thematic and hierarchical clustering were analyzed and condensed to classify the ontology of Wheat Sharp Eyespot control into eight parent concepts,which were pathogenicity pattern,wheat growth period,etiology of the disease,disease area,disease extent,symptoms and control measures.According to the characteristics of the Wheat Sharp Eyespot control domain,11 object attributes,16 first-level data attributes,and 8 second-level data attributes were defined for the Wheat Sharp Eyespot control ontology by organizing and analyzing the associations among the parent concepts.Finally,Protégé was used to visualize and express the ontology concepts and inter-concept relationships.This study proposed a method for constructing a domain ontology for Wheat Sharp Eyespot control,described the basic method for constructing an ontology by building a corpus of Wheat Sharp Eyespot,gived a process framework for constructing a domain ontology,and described in detail the algorithms and construction tools used in the construction.The data source of this study was mainly scientific and technical literature,and the ontology can be extended in the future by further expanding the data source.The assessment part of the ontology mainly relied on the assessment of domain experts at present,and quantitative assessment can be added in the future.The Wheat Sharp Eyespot control domain ontology constructed in this study contained a more complete conceptual system of Wheat Sharp Eyespot,meeting the ontology evaluation criteria and ontology construction requirements,and can provide reference for the construction of domain ontology,and provide powerful support for knowledge discovery and downstream applications in the field of Wheat Sharp Eyespot prevention and control,such as intelligent Q&A,intelligent recommendation,and so on.关键词
小麦纹枯病/防治/领域本体/本体构建/关键词提取/层次聚类Key words
wheat sharp eyespot/prevention and treatment/domain ontology/ontology construction/keywords extraction/hierarchical clustering引用本文复制引用
刘珂艺,崔运鹏,谷钢,王末..小麦纹枯病防治领域本体构建[J].农业大数据学报,2024,6(4):485-496,12.基金项目
国家重点研发计划项目子课题"农业及资源环境领域知识对象深度挖掘技术研究"(2022YFF0711902-01) （2022YFF0711902-01）
现代农业产业技术体系北京市创新团队建设项目(BAIC10-2023-E10) （BAIC10-2023-E10）
NSTL下一代开放知识服务平台关键技术优化集成与系统研发"词表工具优化及智能参考咨询优化对接"(2023XM42-04). （2023XM42-04）
