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KGAP: An RDF knowledge graph of agricultural commodity prices
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
Summary
The Knowledge Graph for Agricultural Prices (KGAP) integrates diverse Brazilian agricultural commodity data. This semantic approach enhances data consistency and enables advanced analysis for economics and policy.
Area of Science:
- Agricultural Economics
- Data Science
- Information Science
Background:
- Agricultural commodity price data in Brazil is fragmented across institutions (Cepea, Conab, Ipea).
- Existing datasets are in heterogeneous formats, hindering interoperability and analysis.
- Lack of a unified, semantically consistent data resource limits insights into market dynamics.
Purpose of the Study:
- To develop a Knowledge Graph for Agricultural Prices (KGAP) integrating data from major Brazilian institutions.
- To ensure semantic consistency and adherence to FAIR data principles for agricultural price data.
- To provide a queryable resource for agricultural economics, policy analysis, and data science applications.
Main Methods:
- Integrated agricultural commodity price data from Cepea, Conab, and Ipea.
- Harmonized and converted heterogeneous datasets into RDF/Turtle format using the Almes Core metadata schema.
- Classified agricultural products using the Agricultural Product Types Ontology (APTO) and aligned geographic references with GeoNames identifiers.
Main Results:
- Created KGAP, a comprehensive knowledge graph of Brazilian agricultural prices.
- Ensured semantic consistency and FAIR data principles through standardized modeling.
- Established a public SPARQL endpoint for querying integrated price data across institutions, regions, and time periods.
Conclusions:
- KGAP provides a semantically-aware resource for analyzing agricultural commodity prices.
- The knowledge graph facilitates inter-institutional data comparison and prevents analytical errors.
- KGAP supports diverse applications, including policy analysis, journalism, and machine learning in agriculture.
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