农业图书情报学报 ›› 2024, Vol. 36 ›› Issue (11): 79-91.doi: 10.13998/j.cnki.issn1002-1248.24-0764

所属专题: 知识服务

• 研究论文 • 上一篇    下一篇

大豆育种知识模型与构建研究

关陟昊1, 单治易2,3, 李甜1, 赵瑞雪1,4()   

  1. 1. 中国农业科学院农业信息研究所,北京 100081
    2. 中国科学院文献情报中心,北京 100190
    3. 中国科学院大学 经济与管理学院信息资源管理系,北京 100190
    4. 国家新闻出版署 农业融合出版知识挖掘与知识服务重点实验室,北京 100081
  • 收稿日期:2024-09-27 出版日期:2024-11-05 发布日期:2025-04-09
  • 通讯作者: 赵瑞雪
  • 作者简介:

    关陟昊(1997- ),女,博士研究生,研究方向为计算育种

    单治易(1995- ),男,博士研究生,研究方向为专利分析

    李甜(1992- ),女,博士,助理研究员,研究方向为数字图书馆

  • 基金资助:
    科技创新2030—新一代人工智能重大项目资助项目“农业智能知识服务平台研发与应用示范”(2021ZD0113700)

Knowledge Model and Construction of Soybean Breeding

Zhihao GUAN1, Zhiyi SHAN2,3, Tian LI1, Ruixue ZHAO1,4()   

  1. 1. Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081
    2. National Science Library, Chinese Academy of Sciences, Beijing 100190
    3. Department of Information Resources Management, School of Economics and Management, University of ChineseAcademy ofSclences, Beijing 100190
    4. Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, National Press and Publication Administration, Beijing 100081
  • Received:2024-09-27 Online:2024-11-05 Published:2025-04-09
  • Contact: Ruixue ZHAO

摘要:

[目的/意义] 针对语义歧义问题和亟待深度揭示的大豆育种知识,通过建立结构化的知识模型,深入探讨育种过程中涉及的关键概念及其相互作用关系的定义,对大豆育种知识进行规范的定义和组织,促进知识的统一化表达。 [方法/过程] 通过分析大豆分子育种领域的知识结构特点,依据斯坦福本体构建七步法,利用本体构建工具Protégé 5.6.3,建立大豆育种领域的语义模型。大豆育种概念本体共构建了48个类,明确了性状、化合物、富集通路和生长分类下的概念、概念之间的层级关联,定义了表达、相互作用和正向调节等7类因果关系以及结合、位于和存在于3类状态关系。 [结果/结论] 本研究整合了已有知识库和本体中大豆育种相关知识,建立了大豆育种领域生物分子水平上的知识模型,能够规范化描述生物分子在特定发育阶段或组织中的调控作用,减少知识表达的语义歧义现象,为大豆育种领域的专家和研究人员提供统一的知识框架,助力大豆育种研究的创新发展。

关键词: 语义模型, 本体构建, 大豆育种, 知识组织

Abstract:

[Purpose/Significance] To address the problem of semantic ambiguity and soybean breeding knowledge that needs to be revealed in depth, a structured knowledge model was established to thoroughly discuss the definition of key concepts and their interactions involved in the breeding process, standardize the definition and organization of soybean breeding knowledge, and promote the unified expression of knowledge. [Method/Process] By analyzing the characteristics of knowledge structure in the field of soybean molecular breeding, according to the seven-step method of Stanford ontology construction, the semantic model of soybean molecular breeding was established by using the ontology construction tool protege 5.6.3. A total of 48 classes were constructed in the soybean breeding concept ontology, which clarified the concepts and hierarchical associations among concepts under traits, compounds, enrichment pathways and growth classification. Seven types of causal relationships and three types of static relationships were defined. Finally, the ontology-based knowledge graph was presented based on a PubMed literature, and the knowledge unit with Dt1 gene as the central node was queried. [Results/Conclusions] This study integrated the existing knowledge base and ontology related to soybean breeding, established a knowledge model at the biomolecular level in the field of soybean breeding, and provided a certain reference for knowledge sharing and semantic integration in this field. Compared with the existing knowledge models, this study analyzed the characteristics of knowledge structure in soybean breeding, extracted the key entity types and relationship types in the process of hypothesis generation, and constructed an ontology model based on this, which could describe gene expression patterns in soybean growth and development more comprehensively. This is of great significance for discovering the key genes associated with specific traits and analyzing the molecular regulatory networks formed by traits, which will help to accurately design and optimize breeding strategies. The knowledge model constructed in this study could be applied to knowledge discovery, causal reasoning and other scenarios in soybean breeding, supporting experimental design and promoting interdisciplinary communication. The limitation of this study is that the ontology was constructed manually and no automated natural language processing method was used. In addition, in the subsequent use of soybean breeding knowledge model, it is necessary to keep up with the frontier of development in soybean breeding, expand new concept types, add new concept names and relationship names in time according to the knowledge description needs of field scientists, and regularly maintain and expand soybean breeding knowledge model.

Key words: semantic model, ontology construction, soybean breeding, knowledge organization

中图分类号:  G250,G358

引用本文

关陟昊, 单治易, 李甜, 赵瑞雪. 大豆育种知识模型与构建研究[J]. 农业图书情报学报, 2024, 36(11): 79-91.

Zhihao GUAN, Zhiyi SHAN, Tian LI, Ruixue ZHAO. Knowledge Model and Construction of Soybean Breeding[J]. Journal of Library and Information Science in Agriculture, 2024, 36(11): 79-91.