---
title: "Formalizing the DATASUS RTS — Pires 2026"
description: "Brazil's public health platform DataSUS publishes its national Health Terminology Repository (RTS) as relational tables. 16 references."
authors: ["Vitor Pires", "Dalvan Griebler", "Felipe Meneguzzi"]
year: 2026
venue: "Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1"
source: "https://aclanthology.org/2026.propor-1.90/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/pires-2026-formalizing-datasus-rts-ontological-model/"
---
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# Formalizing the DATASUS RTS: An Ontological Model for a Resource Description Framework Knowledge Graph

**Authors:** Vitor Pires, Dalvan Griebler and Felipe Meneguzzi · **Year:** 2026 · **Venue:** Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.propor-1.90/) · [PDF](https://aclanthology.org/2026.propor-1.90.pdf)

## Summary
Brazil's public health platform DataSUS publishes its national Health Terminology Repository (RTS) as relational tables. Tables serve routine operations well, but they make it hard to link systems or find implicit associations between concepts. The authors convert the RTS into a formal RDF knowledge graph based on W3C standards (RDF, OWL, SKOS). They follow Scenario 2 of the NeOn methodology, which covers re-engineering non-ontological resources into ontologies. Their pipeline has three stages:
- recovering the hidden conceptual model from the source files and their layout descriptions;
- defining mapping rules from columns to SKOS concepts and OWL classes;
- generating the graph and applying OWL 2 RL reasoning in GraphDB.

The graph is organized around the SUS Procedure class. The result exceeds 1.4 million triples, and about half of them come from logical inference alone. Parameterized SPARQL templates support flexible querying in Portuguese over both official terms and lay synonyms. The resource matters for Portuguese clinical NLP. It can support entity linking and acronym expansion, and it can act as a factual anchor that reduces LLM hallucinations in health question answering.

## Key points
- **Graph size:** over 1.4M triples in total, with roughly 700,000 derived purely by OWL 2 RL inference in the GraphDB triplestore.
- **Source structure:** the RTS has three levels (terminology groups, terminologies, sublevels), and its registries fall into ten thematic groups.
- **Pipeline details:**
  - a custom LayoutParser extracts the relational schema;
  - regex-based normalization (removing accents and case differences) produces stable URIs;
  - Python configuration dictionaries drive the mappings;
  - ontology design patterns include N-ary relations.
- **Portuguese querying:** SPARQL templates use `@pt` language tags, `skos:prefLabel`/`skos:altLabel`, and a `{{NLP_EXTRACTED_TERM}}` placeholder that an entity recognizer fills in (e.g., "obesidade"). This replaces the complex SQL UNION/LIKE queries the tables would otherwise need.
- **Limitations and future work:**
  - the graph relies on a single data source, and errors in the source coding propagate into it;
  - planned work includes linking to international ontologies and a RAG setup for health chatbots, with bariatric procedures suggested as a focused sub-domain.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [🕸️ Knowledge graphs & ontologies](https://md.research.elamur.ai/free/en/topics/knowledge-graphs/)

## Cite

Cite as: Vitor Pires, Dalvan Griebler and Felipe Meneguzzi. 2026. Formalizing the DATASUS RTS: An Ontological Model for a Resource Description Framework Knowledge Graph. Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{pires2026formalizing,
  title = {{Formalizing the DATASUS RTS: An Ontological Model for a Resource Description Framework Knowledge Graph}},
  author = {Vitor Pires, Dalvan Griebler and Felipe Meneguzzi},
  booktitle = {Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1},
  year = {2026},
  url = {https://aclanthology.org/2026.propor-1.90/}
}
```

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## References

16 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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