A armadilha da validade artificial: auditoria da perda semântica no crosswalk de metadados
Resumo
A interoperabilidade em Ciência Aberta enfrenta o desafio da degradação informacional em mapeamentos de metadados. Este trabalho propõe um framework para avaliação de interoperabilidade semântica em crosswalks, fundamentado na distinção formal entre validade estrutural e fidelidade informacional, operacionalizado pela tupla ⟨S, T, C, V⟩ e pela Métrica de Lossiness Ponderada (Lw). A metodologia aplica o algoritmo de auditoria a 150 conjuntos de dados dos repositórios UFSCar, Embrapa e GBIF. Os resultados indicam que mapeamentos para padrões planos (Dublin Core) resultam em perdas de até 73,24%, concentradas em conflitos de Tipo e Semântica. Conclui-se que a validade sintática mascara falhas de reusabilidade, sendo o modelo uma ferramenta eficaz para curadores auditarem a fidelidade semântica em migrações.
Palavras-chave:
Metadados, Crosswalk de metadados, Interoperabilidade, Dados científicos
Referências
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Euzenat, J. and Shvaiko, P. (2013). Ontology Matching. Springer, 2nd edition.
Gradmann, S. (2010). Knowledge = information in context: on the importance of semantic contextualisation in europeana. White Paper 1, Europeana, The Hague.
Haslhofer, B. and Klas, W. (2010). A survey of techniques for managing metadata evolution. ACM Computing Surveys, 42(3):1–37.
Lenzerini, M. (2002). Data integration: A theoretical perspective. In Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pages 233–246.
Obrst, L. (2003). Ontologies for semantically interoperable systems. In Proceedings of the 12th ACM International Conference on Information and Knowledge Management (CIKM ’03), pages 366–369, New Orleans, Louisiana, USA. ACM.
Park, J.-R. (2009). Metadata quality in digital repositories: A survey of the current state of the art. Cataloging & Classification Quarterly, 47(3-4):213–228.
Pipino, L. L., Lee, Y. W., and Wang, R. Y. (2002). Data quality assessment. Communications of the ACM, 45(4):211–218.
Rahm, E. and Bernstein, P. A. (2001). A survey of approaches to automatic schema matching. The VLDB Journal, 10(4):334–350.
RDA FAIR Data Maturity Model Working Group (2020). FAIR data maturity model: Specification and guidelines. Technical report, Research Data Alliance.
Wilkinson, M. D. et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3.
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Zeng, M. L. and Chan, L. M. (2006). Metadata interoperability and standardization: A study of methodologies and best practices at the system level. D-Lib Magazine, 12(6).
Bernstein, P. A. and Melnik, S. (2007). Model management 2.0: manipulating richer mappings. In Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, SIGMOD ’07, page 1–12, New York, NY, USA. Association for Computing Machinery.
Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press, Cambridge, MA.
Bruce, T. R. and Hillmann, D. I. (2004). The continuum of metadata quality: Defining, expressing, exploiting. In Metadata in Practice. ALA Editions, Chicago.
Candela, L., Mangione, D., and Pavone, G. (2024). The FAIR assessment conundrum: Reflections on tools and metrics. Data Science Journal, 23:33.
Devaraju, A., Huber, R., Mokrane, M., et al. (2022). FAIRsFAIR data object assessment metrics (0.5). Zenodo.
Doerr, M. (2003). The cidoc conceptual reference model: An ontological approach to semantic interoperability of metadata. AI Magazine, 24(3):75–92.
Ehrig, M. (2007). Ontology Alignment: Bridging the Semantic Gap. Number 4 in Semantic Web and Beyond. Springer, New York.
EOSC Association – FAIR Metrics and Data Quality Task Force (2022). Towards a data quality framework for EOSC. Technical report, EOSC Association.
EOSC Association – Semantic Interoperability Task Force (2023). Converging on a semantic interoperability framework for EOSC. Technical report, EOSC Association.
Euzenat, J. and Shvaiko, P. (2013). Ontology Matching. Springer, 2nd edition.
Gradmann, S. (2010). Knowledge = information in context: on the importance of semantic contextualisation in europeana. White Paper 1, Europeana, The Hague.
Haslhofer, B. and Klas, W. (2010). A survey of techniques for managing metadata evolution. ACM Computing Surveys, 42(3):1–37.
Lenzerini, M. (2002). Data integration: A theoretical perspective. In Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pages 233–246.
Obrst, L. (2003). Ontologies for semantically interoperable systems. In Proceedings of the 12th ACM International Conference on Information and Knowledge Management (CIKM ’03), pages 366–369, New Orleans, Louisiana, USA. ACM.
Park, J.-R. (2009). Metadata quality in digital repositories: A survey of the current state of the art. Cataloging & Classification Quarterly, 47(3-4):213–228.
Pipino, L. L., Lee, Y. W., and Wang, R. Y. (2002). Data quality assessment. Communications of the ACM, 45(4):211–218.
Rahm, E. and Bernstein, P. A. (2001). A survey of approaches to automatic schema matching. The VLDB Journal, 10(4):334–350.
RDA FAIR Data Maturity Model Working Group (2020). FAIR data maturity model: Specification and guidelines. Technical report, Research Data Alliance.
Wilkinson, M. D. et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3.
Wilkinson, M. D., Sansone, S.-A., Schultes, E., Doorn, P., Bonino da Silva Santos, L. O., and Dumontier, M. (2018). A design framework and exemplar metrics for fairness. Scientific Data, 5(1):180118.
Zeng, M. L. and Chan, L. M. (2006). Metadata interoperability and standardization: A study of methodologies and best practices at the system level. D-Lib Magazine, 12(6).
Publicado
08/09/2026
Como Citar
LIMA, Karolayne C. R. de; SUNYE, Marcos S..
A armadilha da validade artificial: auditoria da perda semântica no crosswalk de metadados. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 261-274.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2026.249205.
