Federated Architecture Based on Knowledge Graphs and Blockchain for Semantic Integration of Public Health Data

• Nacles B. P. Gomes UFBA
• Laís do Nascimento Salvador UFBA
• Paulo Caetano da Silva UNIFACS

DOI: https://doi.org/10.5753/sbcas.2026.20358

Resumo

The integration of heterogeneous data from the Brazilian Unified Health System (SUS) for epidemiological surveillance remains challenging. Legacy systems such as SIM (mortality) and SINASC (live births) operate independently, without shared identifiers. Existing approaches typically address these dimensions separately, lacking an integrated treatment of semantic mediation, decentralized custody, provenance auditing, and uncertain entity reconciliation over SUS legacy data. This paper presents the Distributed Federated Semantic Architecture (DFSA), combining knowledge graphs, permissioned blockchain, distributed encrypted storage, and fuzzy logic. The solution was validated with real-world data from the municipality of Camaçari, Bahia, Brazil, comprising 5.4 million RDF triples. It achieved a federated latency of less than 10 ms, with an estimated throughput of 1.6 million queries per hour, along with full auditability. The solution also received positive feedback from managers due to the automated generation of indicators.

Referências

Acosta, M., Vidal, M., Lampo, T., Castillo, J., and Ruckhaus, E. (2011). ANAPSID: an adaptive query processing engine for SPARQL endpoints. The Semantic Web ISWC 2011, 7031:18–34.

Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., Caro, A. D., Enyeart, D., Ferris, C., Laventman, G., Manevich, Y., Muralidharan, S., Murthy, C., Nguyen, B., Sethi, M., Singh, G., Smith, K., Sorniotti, A., Stathakopoulou, C., Vukolić, M., Cocco, S. W., and Yellick, J. (2018). Hyperledger fabric: A distributed operating system for permissioned blockchains. In Proceedings of the Thirteenth EuroSys Conference, EuroSys ’18, pages 30:1–30:15. ACM.

Bainomugisha, E., Carreton, A. L., van Cutsem, T., Mostinckx, S., and Meuter, W. D. (2013). A survey on reactive programming. ACM Computing Surveys, 45(4):52:1–52:34.

Benet, J. (2014). IPFS - content addressed, versioned, P2P file system. CoRR, abs/1407.3561.

Bizer, C. and Schultz, A. (2009). The berlin SPARQL benchmark. International Journal on Semantic Web and Information Systems, 5(2):1–24.

Calvanese, D., Cogrel, B., Komla-Ebri, S., Kontchakov, R., Lanti, D., Rezk, M., Rodríguez-Muro, M., and Xiao, G. (2017). Ontop: Answering SPARQL queries over relational databases. Semantic Web, 8(3):471–487.

Calvanese, D., Giacomo, G. D., Lembo, D., Lenzerini, M., and Rosati, R. (2007). Tractable reasoning and efficient query answering in description logics: The DL-Lite family. Journal of Automated Reasoning, 39(3):385–429.

Christen, P. (2012). Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer.

da Silva Costa, M. V., Camargos, M. C. S., Viana, S. M. N., and de Souza Mendes, U. V. (2025). Avanços e desafios da interoperabilidade no Sistema Único de Saúde. Journal of Health Informatics, 17(1):1112.

Doan, A., Halevy, A. Y., and Ives, Z. G. (2012). Principles of Data Integration. Morgan Kaufmann, 1st edition.

Ferreira, A., Redaelli, E., Maldaner, L., and Rigo, S. (2025). Inovação e transformação no ecossistema de saúde auditiva: Inteligência artificial e interoperabilidade. In Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde, pages 985–990, Porto Alegre, RS, Brasil. SBC.

Fischer, G., Righi, R., Costa, C., and Roehrs, A. (2024). Combinando elasticidade proativa e reativa para gestão multi-hospitalar de recursos humanos no contexto das cidades inteligentes. In Anais do XXIV Simpósio Brasileiro de Computação Aplicada à Saúde, pages 249–260, Porto Alegre, RS, Brasil. SBC.

Franco, S. d. C., Gomes, N. B. P., and Salvador, L. d. N. (2025). Integração de dados de saúde pública para apoio à tomada de decisão: Uma abordagem baseada em grafo de conhecimento semântico. In Proceedings of the 18th Seminar on Ontology Research in Brazil (ONTOBRAS 2025), volume 4177 of CEUR Workshop Proceedings, pages 63–76. CEUR-WS.org.

Gamma, E., Helm, R., Johnson, R., and Vlissides, J. (1995). Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley.

Gomes, N. B. P., Franco, S. d. C., and Salvador, L. d. N. (2022). ONTOVID – uma abordagem para construção de grafos de conhecimento semântico com enfoque em notificações e óbitos relacionados ao novo coronavírus (COVID-19). In Anais do XXII Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), pages 425–436, Porto Alegre, RS, Brasil. SBC.

Halevy, A., Rajaraman, A., and Ordille, J. (2006). Data integration: The teenage years. In Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB), pages 9–16. VLDB Endowment.

Han, R., John, L. K., and Zhan, J. (2018). Benchmarking big data systems: A review. IEEE Transactions on Services Computing, 11(3):580–597.

Hasselgren, A., Kralevska, K., Gligoroski, D., Pedersen, S. A., and Faxvaag, A. (2020). Blockchain in healthcare and health sciences—A scoping review. International Journal of Medical Informatics, 134:104040.

Hevner, A. R., March, S. T., Park, J., and Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1):75–105.

Hoffmann, J., Renner, J., Real, R., and Barbosa, J. (2025). Ontodemer: Uma ontologia para identificação de riscos de doenças com base nos comportamentos de cadeirantes. In Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde, pages 164–175, Porto Alegre, RS, Brasil. SBC.

Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., and Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4):71:1–71:37.

Mello, B. H., Rigo, S. J., da Costa, C. A., da Rosa Righi, R., Aguiar, B. D., Bez, M. R., and Schunke, L. C. (2022). Semantic interoperability in health records standards: a systematic literature review. Health and Technology, 12:255–272.

Ministério da Saúde do Brasil (2020). Portaria GM/MS no 2.049, de 24 de setembro de 2020. institui a rede nacional de dados em saúde (rnds). Accessed: 2025-03-13.

Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3):45–77.

Ribeiro, F., Sinigaglia, M., Ávila, A., and Silva, I. (2025). Investigação de padrões epidemiológicos em oncologia pediátrica a partir de integração e visualização de dados. In Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde, pages 677–688, Porto Alegre, RS, Brasil. SBC.

Schwarte, A., Haase, P., Hose, K., Schenkel, R., and Schmidt, M. (2011). FedX: optimization techniques for federated query processing on linked data. In The Semantic Web - ISWC 2011, volume 7031 of Lecture Notes in Computer Science, pages 601–616. Springer.

Silva, D., Mello, B., Fröhlich, W., Rigo, S., Schwertner, M., and Costa, C. (2022). Avaliação de modelos para extração de dados não estruturados de um sistema ehr para atender a estrutura final de uma ontologia. In Anais do XXII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 437–448, Porto Alegre, RS, Brasil. SBC.

Tanenbaum, A. S. and van Steen, M. (2015). Distributed Systems: Principles and Paradigms. Pearson, 3rd edition.

Todorov, K., Hudelot, C., and Geibel, P. (2014). Fuzzy ontology alignment using background knowledge. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), pages 134–145. AAAI Press.

Wiederhold, G. (1992). Mediators in the architecture of future information systems. Computer, 25(3):38–49.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3):338–353.