Specialty crop industries generate heterogeneous knowledge across cultivation, pest and disease management, post-harvest handling, quality control, and marketing, yet this knowledge is often dispersed across technical documents, web resources, relational datasets, and expert experience. Taking the jujube (Ziziphus jujuba Mill.) industry as a case study, this study develops a reproducible ontology-constrained knowledge-engineering framework. A lifecycle-oriented domain ontology and a curated knowledge graph prototype were constructed from verified ontology-aligned records, while unstructured texts were used to evaluate prospective information-extraction components. Four BERT-based named entity recognition models were compared under leakage-controlled five-fold cross-validation, and versioned ontology-constrained trigger rules were evaluated under a gold-entity setting. The final ontology contained 457 RDF triples, 38 classes, 13 object properties, and 71 data properties, and logical consistency checks detected no inconsistent named classes. BERT-Softmax obtained the highest mean NER F1-score (0.328 ± 0.146) in this small corpus, but the fold variability does not support a general claim of superiority over CRF-based architectures. Under gold entities, the refined trigger rules achieved an F1-score of 0.683 ± 0.142; in a stricter diagnostic using automatically predicted fine-grained entities, end-to-end relation extraction declined to 0.035 ± 0.054. On a 93-question benchmark generated from verified graph triples, the rule-graph QA engine achieved a token-level F1-score of 0.973 and a tail-set exact accuracy of 98.9%, primarily reflecting relation mapping and graph retrieval correctness. A separate 20-question human-authored challenge audit found three partially supported questions and no fully supported question, illustrating the boundary beyond graph-grounded retrieval. The results provide case-specific, exploratory evidence that explicit ontology constraints can support reproducible knowledge organization and verifiable retrieval under limited domain data; they do not establish deployment-level or unrestricted agricultural QA performance.

Data classification and grading is an information governance mechanism through which information resources are categorized, assigned responsibilities, linked to differentiated controls, and updated over time. Using China as the main case, this review examines how classification and grading has been operationalized across regulatory, local public data, and sectoral settings, and where its implementation remains incomplete. This study adopts a structured analytical review design, synthesizing academic studies, national laws and regulatory documents, standards and technical guidance, local public data normative documents, and sectoral implementation instruments, with a focused comparison of governance approaches in China, the United States, and the European Union. The review develops a five-dimensional information governance framework covering information stewardship roles, protected information assets and governance concerns, information categorization criteria and risk logics, differentiated information control mechanisms, and assessment and adaptive updating. The findings show that China has rapidly expanded regulatory and sector-specific arrangements for data classification and grading, especially in public data governance and in sectors such as telecommunications, finance, healthcare, and industry. However, significant operationalization gaps remain, including fragmented standards, weak cross-sector interoperability, uneven information stewardship capacity, limited translation of categories into access, handling, sharing, lifecycle, and audit controls, and underdeveloped feedback and updating mechanisms. By repositioning data classification and grading as information governance rather than only regulatory compliance, this review contributes to research on differentiated data management, information stewardship, and adaptive governance practice.

In recent years, various agricultural research institutions in China have begun to increase the investment in informatization construction, so informatization brings great convenience to scientific research and management, but corresponding cybersecurity issues have become more and more important. Relying on the cybersecurity classified protection work, in-depth analysis of each weak link in the cyber system construction of agricultural scientific research institutions, to improve the information system security and avoid information security incidents has been placed in the prominent position of agricultural research institutions.[Method/Process] This article discusses the content of strengthening the cybersecurity management of agricultural scientific research institutions based on National Cybersecurity Classified Protection 2.0. First of all, this article introduces the importance of cybersecurity classified protection. Secondly, the difference between National Cybersecurity Classified Protection 2.0 standard and National Cybersecurity Classified Protection 1.0 standard is analyzed. Thirdly, the system of cybersecurity management and the content of cybersecurity management technology of Chinese Academy of Agricultural Sciences were analyzed in detail. Finally, it discusses and summarizes the effective strategies to strengthen the information security construction of agricultural research institutions. [Results/Conclusions] Taking the Chinese Academy of Agricultural Sciences as an example, this paper discusses its related practices in detail, hope to provide reference for agricultural research institutions to carry out cybersecurity level protection and security construction.

By analyzing the progresses of pomology information retrieval which are driven by tasks, the author put forward an idea of expressing the tasks in pomology information retrieval. An interview test was used to validate the idea and based on analyzing the data of the test, a method of expressing the tasks in pomology information retrieval, in which the difference of searchers’ acknowledgement is considered, was put forward.