Formalizing the DATASUS RTS: An Ontological Model for a Resource Description Framework Knowledge Graph

Vitor Pires, Dalvan Griebler, Felipe Meneguzzi


Abstract
The Brazilian DataSUS platform provides vast health databases in relational formats that, while operationally efficient, lack the robust representation needed for advanced scientific data management, restricting interoperability. In this paper, we develop a knowledge engineering pipeline using Scenario 2 of the NeOn methodology to extract, process, and transform knowledge from the DataSUS Health Terminology Repository into a formal knowledge graph that adheres to World Wide Web Consortium standards.We illustrate the potential of this formalization by showing how the graph captures the domain’s complex relationships.The resulting graph comprises over 1.4 million triples, with approximately 700,000 associations generated solely through logical inference. Our pipeline provides a foundational resource that enables advanced structural and semantic querying in Portuguese.
Anthology ID:
2026.propor-1.90
Volume:
Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1
Month:
April
Year:
2026
Address:
Salvador, Brazil
Editors:
Marlo Souza, Iria de-Dios-Flores, Diana Santos, Larissa Freitas, Jackson Wilke da Cruz Souza, Eugénio Ribeiro
Venue:
PROPOR
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
908–916
Language:
URL:
https://aclanthology.org/2026.propor-1.90/
DOI:
Bibkey:
Cite (ACL):
Vitor Pires, Dalvan Griebler, and Felipe Meneguzzi. 2026. Formalizing the DATASUS RTS: An Ontological Model for a Resource Description Framework Knowledge Graph. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 908–916, Salvador, Brazil. Association for Computational Linguistics.
Cite (Informal):
Formalizing the DATASUS RTS: An Ontological Model for a Resource Description Framework Knowledge Graph (Pires et al., PROPOR 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.propor-1.90.pdf