Objective This study proposes a metadata-ontology fusion method from the perspective of agricultural science and technology management in China, which aims to provide a method and case for cross-departmental and cross-domain scientific data sharing and fusion in China's agricultural science and technology management. Methods/Approach Firstly, the study presents the connotation and methods of the correlation between scientific data and Science and Technology literature （S&T literature）, and analyzes unstructured data. Secondly, the data characteristics of agricultural science and technology management are explored. Thirdly, the ontology of agricultural science and technology management is constructed to support the integration of multi-source heterogeneous data. In the empirical part, the data requirements of agricultural science and technology management in Sichuan are targeted, and the industrial chain and data chain are integrated to propose 20 data requirements. Finally, the ontology is established and revised, and the linkage between scientific data and S&T literature is realized on a demonstration platform. Conclusion The ontology of agricultural industry management is realized, and the correlation and fusion of multi-language (Chinese/English) data and unstructured data are achieved. The study builds two demonstration platforms, integrates 24, 200 data, including 2, 119 expert data, and realizes the correlation of 16 types of agricultural science data and 4 types of S&T literature, verifying the feasibility of the ontology. © 2023 Dublin Core metadata initiative. All rights reserved.
Miaoling et al. (2023) studied this question.