小麦品种知识图谱构建与可视化研究

为探索知识图谱技术在农业智能生产中应用与落地, 解决复杂多样的农业生产数据的精准查询与可视化问题, 本研究以小麦品种知识为例, 利用爬虫技术, 爬取1852个小麦品种信息、735个微百科、102349个词条; 基于知识图谱技术, 设计品种知识图谱实体与关系, 对抓取数据进行清洗、抽取与融合, 经过实体识别、关系构造等处理, 构建实体258484个, 关系328933个. 在此基础上, 设计了小麦品种知识存储方式, 结构化数据存储在MySQL中, 非结构化数据存储在MongoDB中, 使用Neo4j图数据库存储知识图谱来提高知识的查询性能, 在此基础上实现小麦品种关系查询与实体识别, 提供品种数据精确表达与可视化, 表明利用知识图谱技术实现品种等信息的可视化是可行的, 该研究可以为知识图谱在农业中的应用提供技术参考和理论支撑.

Construction and Visualization of Knowledge Map of Wheat Varieties

In order to explore the application and implementation of knowledge mapping technology in intelligent agricultural production and realize the accurate query and visualization of complex and diverse agricultural production data, this study took wheat varieties as an example and collected the information of 1852 wheat varieties, 735 micro encyclopedias, and 102 349 entries by a crawler. Through knowledge mapping technology, this study designed the entities of variety knowledge graphs and their relationships, with data cleaned, extracted, and fused. A total of 258 484 entities were recognized and 328 933 relationships built. On this basis, the approach to storing wheat variety knowledge was worked out, with structured data stored in a MySQL, unstructured data in the MongoDB. Neo4j was employed to optimize knowledge query. In this way, the query about relationships between wheat varieties and entity recognition was made possible with variety data expressed accurately and visualized, proving the feasibility of knowledge mapping in visualization of information such as variety. This research can provide technical reference and theoretical support for the application of knowledge mapping in agriculture.