小麦纹枯病是我国麦区常发的土传真菌病害，在小麦整个生育期均可发生，对我国小麦产量和品质影响极大。通过构建小麦纹枯病防治领域本体，对领域知识进行建模，以期整合和共享小麦纹枯病防治领域的知识，为农业决策、病害防治等方面提供重要的支持和指导。本研究以小麦纹枯病防治领域文献为数据源，采用KeyBERT关键词提取算法挖掘本体核心概念，通过层次聚类提取本体概念间层次关系。最后利用Protégé对本体概念、概念间关系进行可视化表达。本研究提出了小麦纹枯病防治领域本体构建方法，阐述了通过构建小麦纹枯病语料库来构建本体的基本方法，给出了领域本体构建的流程框架，详细阐述了构建中所使用的算法和构建工具。本研究数据源主要为科技文献，未来可进一步扩充数据源，对本体进行扩展。本体评估部分目前主要依靠领域专家评估，未来可增加量化评估。本研究所构建的小麦纹枯病防治领域本体包含了较完整的小麦纹枯病概念体系，符合本体评价标准和本体构建需求，可为领域本体的构建提供借鉴与参考，为小麦纹枯病防治领域的知识发现与智能问答、智能推荐等下游应用提供有力支持。

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.