Ontology-Constrained Knowledge Graph Construction and Relation-Grounded Question Answering for Specialty Crop Knowledge Services: A Jujube Industry Case Study

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.