面向农作物种质资源智能化管控与应用的本体构建  

Ontology Construction for Intelligent Control and Application of Crop Germplasm Resources

作　　者：范可昕 鲜国建[1,4] 赵瑞雪 黄永文[1,3] 孙坦 FAN Kexin;XIAN Guojian;ZHAO Ruixue;HUANG Yongwen;SUN Tan(Agricultural Information Institute,Chinese Academy of Agricultural Sciences,Beijing 100081;The Chinese Academy of Agricultural Sciences,Beijing 100081;Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing,National Press and Publication Administration,Beijing 100081;Key Laboratory of Agricultural Big Data,Ministry of Agriculture and Rural Affairs,Beijing 100081)

机构地区：[1]中国农业科学院农业信息研究所,北京100081 [2]中国农业科学院,北京100081 [3]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 [4]农业农村部农业大数据重点实验室,北京100081

出　　处：《农业图书情报学报》2024年第3期92-107,共16页Journal of Library and Information Science in Agriculture

基　　金：国家社会科学基金一般项目“多模态科技资源的语义组织与关联发现服务研究”(22BTQ079)。

摘　　要：[目的/意义]以“生物技术+人工智能+大数据信息技术”为特征的育种4.0对种质资源的数字化管控与智能利用提出了新需求。为满足智能背景下对知识服务形态多样化的支持需求,文章旨在提出一种有效知识化组织、深度语义关联的方法。[方法/过程]通过分析领域数据描述及组织现状,参考作物本体、达尔文核心,融合《农作物种质资源技术规范》和实例数据,构建了覆盖粮食、经济等五大类农作物的本体模型,定义表型、基因型等11个核心类、10个对象属性和56个数据?属性。[结果/结论]基于该本体模型,文章提出农作物种质资源知识图谱构建思路,以及知识图谱驱动的智能问答、知识计算等新型智能化知识服务场景设计展望,以期为计算育种工作提供更加准确和高效的支持,为新质生产力的创新提供参考。[Purpose/Significance]Breeding 4.0,characterized by"biotechnology+artificial intelligence+big data information technology,"has brought new requirements for the digital management and intelligent utilization of germplasm resources.In order to meet the diverse support needs for knowledge service forms under an intelligent background,this article aims to propose an effective method for knowledge organization and deep semantic association.This is essential to address the inconveniences that discrete germplasm resource data bring to researchers when collaborating across regions and institutions.Therefore,the article presents a method that integrates fragmented domain data into a systematic knowledge system,which is particularly important.[Method/Process]By analyzing the domain data descriptions and the current organizational status,the ontology construction was performed using the seven-step method developed by Stanford University Hospital.First,existing ontologies such as the Crop Ontology,Gene Ontology,and Darwin Core were referenced and reused,and then integrated with the knowledge framework from the"Technical Specifications for Crop Germplasm Resources"series and example datasets.Consequently,an ontology model was successfully constructed,which covers five major categories of crops:cereals,cash crops,vegetables,fruit trees,and forage and green manure crops.This model defines 11 core classes including phenotypes and genotypes,as well as identification methods and evaluation standards,along with 10 object properties and 56 data properties.[Results/Conclusions]Based on the ontology model,the article proposes a methodology for constructing a knowledge graph of crop germplasm resources.Using rice as an example,a domain-specific fine-grained knowledge graph is developed to facilitate semantic association and querying across multiple knowledge dimensions.The article also outlines prospective designs for new intelligent knowledge service scenarios driven by the knowledge graph,such as intelligent question and answer and k

关 键 词：种质资源 本体模型 知识图谱 新质生产力 计算育种 

分 类 号：G252[文化科学—图书馆学] G253