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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.

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View abstract on PubMed

Summary
This summary is machine-generated.

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.

Keywords:
Agricultural economicsAgricultural productsPriceRDFSPARQLSemantic webTime series

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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.