KGAP:农业大宗商品价格的RDF知识图
Filipi Miranda Soares1,2,3, Luís Ferreira Pires1, Fernando Elias Corrêa4
1Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, Overijssel, the Netherlands.
Data in brief
|March 9, 2026
概括
农业价格知识图 (KGAP) 集成了巴西各种农业商品数据. 这种语义方法提高了数据的一致性,并使经济和政策的高级分析成为可能.
科学领域:
- 农业经济学 农业经济学
- 数据科学数据科学数据科学
- 信息科学 信息科学 信息科学
背景情况:
- 巴西的农业大宗商品价格数据在各机构 (Cepea,Conab,Ipea) 中是分散的.
- 现有的数据集采用异质格式,阻碍了互操作性和分析.
- 缺乏统一的,语义上一致的数据资源限制了对市场动态的洞察力.
研究的目的:
- 开发农业价格知识图 (KGAP),整合来自巴西主要机构的数据.
- 确保农业价格数据的语义一致性和遵守FAIR数据原则.
- 为农业经济学,政策分析和数据科学应用提供可查询的资源.
主要方法:
- 来自Cepea,Conab和Ipea的综合农产品价格数据.
- 协调并将异质数据集转换为RDF/Turtle格式,使用Almes Core元数据模式.
- 使用农产品类型实体学 (APTO) 进行分类的农产品,并与地理名称标识符对齐的地理引用.
主要成果:
- 创建了KGAP,这是巴西农产品价格的综合知识图.
- 通过标准化建模,确保语义一致性和FAIR数据原则.
- 建立了一个公共的SPARQL终点,用于查询跨机构,区域和时间段的综合价格数据.
结论:
- KGAP为分析农业商品价格提供了一个有语义意识的资源.
- 知识图方便机构间数据比较,并防止分析错误.
- KGAP支持各种应用,包括政策分析,新闻和农业中的机器学习.
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