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

2026-04-12 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

The Brazilian DataSUS platform, which offers extensive health databases in relational formats, faces limitations in interoperability and advanced scientific data management due to its current data representation. To address this, a knowledge engineering pipeline was developed using Scenario 2 of the NeOn methodology. This pipeline extracts, processes, and transforms knowledge from the DataSUS Health Terminology Repository into a formal knowledge graph, adhering to World Wide Web Consortium (W3C) standards. The resulting graph contains over 1.4 million triples, with approximately 700,000 associations generated through logical inference. This formalization enhances the representation of complex domain relationships and provides a foundational resource for advanced structural and semantic querying in Portuguese.

Key takeaway

For data engineers and health informaticians managing large relational health datasets, formalizing data into a knowledge graph, as demonstrated with DataSUS, can significantly improve interoperability and enable more sophisticated semantic querying. Consider adopting W3C standards and knowledge engineering methodologies like NeOn to build robust, inferentially enriched data structures that support advanced analytical capabilities.

Key insights

Transforming relational health data into a W3C-compliant knowledge graph improves interoperability and semantic querying.

Principles

• Formal knowledge graphs enhance data interoperability.
• Logical inference can significantly expand graph associations.

Method

The NeOn methodology (Scenario 2) guides extracting, processing, and transforming relational data into a W3C-standard Resource Description Framework (RDF) knowledge graph.

In practice

• Use NeOn methodology for knowledge graph development.
• Apply logical inference to enrich semantic relationships.

Topics

• DataSUS
• Knowledge Graph
• NeOn Methodology
• Health Terminology
• Resource Description Framework

Best for: AI Scientist, Research Scientist, Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.