关键词: 种质资源, 本体模型, 知识图谱, 新质生产力, 计算育种

Abstract: [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 knowledge computation, aiming to meet the knowledge service needs of researchers, breeding companies, and the general public. This is intended to provide more accurate and efficient support for computational breeding efforts. Currently, the research focuses only on rice as an example of a cereal crop, with economic crops, vegetables, and other types of crop germplasm resources not yet included in the study. Future work will expand the scope of the study and add new classes and properties specific to different germplasm resources to better address the diverse and personalized knowledge needs of users in the eraa of big data. This approach aims to promote the contextualization, ubiquity, and intelligence of knowledge services, and to further integrate them into different academic disciplines related to the development of new quality digital productivity.

Key words: germplasm resources, ontology model, knowledge graph, new quality digital productivity, computational breeding