SemLytics: Semantic Enrichment for FAIR Analytics Architectures
Resumo
A key component of FAIR Analytics Architectures is the Semantic Enrichment Layer, which provides advantages such as enhancing interpretability and exploring complex relationships. However, developing this layer while addressing architectural specification, FAIR compliance, and semantic representation quality remains an unexplored challenge. In this paper, we propose SemLytics, a Semantic Enrichment Layer designed to address these issues. We present its logical level and demonstrate its implementation in a state-of-the-art FAIR analytics architecture. The results show that SemLytics improves FAIR compliance and semantic representation quality.
Palavras-chave:
Semantic Enrichment, FAIR Principles, Analytics Architectures, Knowledge Graphs, Data Warehouses, QB4OLAP, R2RML, Relational-to-RDF Mapping
Referências
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Castro, J. P. C., Romero, L. M., Carniel, A. C., and Aguiar, C. D. (2022). Open science in the cloud: The CloudFAIR architecture for FAIR-compliant repositories. In Proc. ADBIS, pages 56–66.
Castro, J. P. C., Romero, L. M. F., Chaves, A. C., and Aguiar, C. D. (2025a). The BigFAIR architecture: Enabling big data analytics in FAIR-compliant repositories. J. Data Inf. Qual., 17(4).
Castro, J. P. C., Vasconcelos, G. F. X., Vargas-Solar, G., and Aguiar, C. D. (2025b). Building FAIR-compliant Lakehouses with FLAMI. In Proc. CAiSE, pages 113–129.
Chen, Z., Chamorro-Padial, J., et al. (2025). Tab2KGWiz: Interactive assistant for mapping tabular data to knowledge graphs. SoftwareX, 31:102221.
D’Aquin, M., Kirstein, F., Oliveira, D., Schimmler, S., and Urbanek, S. (2022). FAIREST: A framework for assessing research repositories. Data Intell., 5:1–48.
Etcheverry, L. and Vaisman, A. (2012). QB4OLAP: A new vocabulary for OLAP cubes on the semantic web. In CEUR Workshop Proceedings, volume 905.
Janssen, N., Ilayperuma, T., et al. (2024). The evolution of data storage architectures: Examining the secure value of the data lakehouse. J. Data Inf. Manag., 6:309–334.
Kimball, R. and Ross, M. (2013). The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. Wiley, 3rd edition.
Mathis, C. (2017). Data lakes. Datenbank-Spektrum, 17(3):289–293.
Medeiros, C. B., Darboux, B. R., et al. (2020). IAP input into the UNESCO Open Science Recommendation. Available at [link].
Nath, R. P. D., Hose, K., Pedersen, T. B., and Romero, O. (2017). SETL: A programmable semantic extract-transform-load framework for semantic data warehouses. Inf. Syst., 68:17–43.
Stvilia, B., Pang, Y., Lee, D. J., and Gunaydin, F. (2024). Data quality assurance practices in research data repositories: A systematic literature review. An annual review of information science and technology (arist) paper. J. Assoc. Inf. Sci. Technol., 76:238–261.
Tarasowa, D., Lange, C., and Auer, S. (2015). Measuring the quality of relational-to-rdf mappings. In Proc. KESW, pages 210–224.
Vazquez, P., Hirayama-Shoji, K., Novik, S., Krauss, S., and Rayner, S. (2022). Globally accessible distributed data sharing (GADDS): A decentralized FAIR platform to facilitate data sharing in the life sciences. Bioinformatics, 38:3812–3817.
Wilkinson, M. D., Dumontier, M., et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Sci. Data, 3(1):1–9.
Bimonte, S., Gallinucci, E., Marcel, P., and Rizzi, S. (2023). Logical design of multi-model data warehouses. Knowl. Inf. Syst., 65(3):1067–1103.
Castro, J. P. C., Romero, L. M., Carniel, A. C., and Aguiar, C. D. (2022). Open science in the cloud: The CloudFAIR architecture for FAIR-compliant repositories. In Proc. ADBIS, pages 56–66.
Castro, J. P. C., Romero, L. M. F., Chaves, A. C., and Aguiar, C. D. (2025a). The BigFAIR architecture: Enabling big data analytics in FAIR-compliant repositories. J. Data Inf. Qual., 17(4).
Castro, J. P. C., Vasconcelos, G. F. X., Vargas-Solar, G., and Aguiar, C. D. (2025b). Building FAIR-compliant Lakehouses with FLAMI. In Proc. CAiSE, pages 113–129.
Chen, Z., Chamorro-Padial, J., et al. (2025). Tab2KGWiz: Interactive assistant for mapping tabular data to knowledge graphs. SoftwareX, 31:102221.
D’Aquin, M., Kirstein, F., Oliveira, D., Schimmler, S., and Urbanek, S. (2022). FAIREST: A framework for assessing research repositories. Data Intell., 5:1–48.
Etcheverry, L. and Vaisman, A. (2012). QB4OLAP: A new vocabulary for OLAP cubes on the semantic web. In CEUR Workshop Proceedings, volume 905.
Janssen, N., Ilayperuma, T., et al. (2024). The evolution of data storage architectures: Examining the secure value of the data lakehouse. J. Data Inf. Manag., 6:309–334.
Kimball, R. and Ross, M. (2013). The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. Wiley, 3rd edition.
Mathis, C. (2017). Data lakes. Datenbank-Spektrum, 17(3):289–293.
Medeiros, C. B., Darboux, B. R., et al. (2020). IAP input into the UNESCO Open Science Recommendation. Available at [link].
Nath, R. P. D., Hose, K., Pedersen, T. B., and Romero, O. (2017). SETL: A programmable semantic extract-transform-load framework for semantic data warehouses. Inf. Syst., 68:17–43.
Stvilia, B., Pang, Y., Lee, D. J., and Gunaydin, F. (2024). Data quality assurance practices in research data repositories: A systematic literature review. An annual review of information science and technology (arist) paper. J. Assoc. Inf. Sci. Technol., 76:238–261.
Tarasowa, D., Lange, C., and Auer, S. (2015). Measuring the quality of relational-to-rdf mappings. In Proc. KESW, pages 210–224.
Vazquez, P., Hirayama-Shoji, K., Novik, S., Krauss, S., and Rayner, S. (2022). Globally accessible distributed data sharing (GADDS): A decentralized FAIR platform to facilitate data sharing in the life sciences. Bioinformatics, 38:3812–3817.
Wilkinson, M. D., Dumontier, M., et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Sci. Data, 3(1):1–9.
Publicado
08/09/2026
Como Citar
ARAUJO, Thiago Zero; SEDENHO, Cecília Nunes; CASTRO, João Pedro de Carvalho; SANTOS, Bruno Jesus dos; AGUIAR, Cristina Dutra.
SemLytics: Semantic Enrichment for FAIR Analytics Architectures. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 987-993.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2026.249647.
