种子质量标准文件知识图的构建和应用
Zhenwei Yang1,2, Qiong He3,4, Jian Zhang1,2
1College of Management Science and Engineering, Beijing Information Science and Technology University, Beijing, 100192, China.
Scientific reports
|January 22, 2026
概括
本研究引入了种子质量标准的知识图 (KG),改善了信息检索. 该框架使用了本体学和混合提取,增强了种植园行业的数字管理.
科学领域:
- 农业科学 农业科学
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
背景情况:
- 种子质量标准对于作物种植监督和农业发展至关重要.
- 中国种子质量标准文件的数量不断增加,由于其非结构性,在高效查询和语义关联方面存在挑战.
- 现有的知识表示方法对于种子领域是不够的,阻碍了有效的数据利用.
研究的目的:
- 使用知识图 (KG) 开发种子质量标准的结构化知识表示框架.
- 解决种子质量标准中非结构化数据的局限性,以改善信息检索和管理.
- 为种植园行业内智能质量管理提供数字基础.
主要方法:
- 构建一个具有7个核心类和12个关系类型的域特定本体,以定义语义结构.
- 实施混合知识提取策略,将半结构化数据的正则表达式和非结构化文本的BERT-BiLSTM-CRF模型结合起来.
- 开发一个包含2436个节点和3011个关系的知识图 (KG),存储在Neo4j中,用于多维检索和可视化.
主要成果:
- 拟议的BERT-BiLSTM-CRF模型在命名实体识别 (NER) 中获得了91.61%的高F1得分,超过了其他模型.
- 构建的KG有效地组织了种子质量标准信息,实现了高效的多维检索和可视化.
- 标准信息检索的准确性得到显著改善.
结论:
- 拟议的知识图构建框架为结构化和查询种子质量标准提供了有效的解决方案.
- 混合知识提取策略提高了农业领域数据处理的准确性和效率.
- 开发的KG作为一个数字基础,在种植园行业推进智能质量管理.
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