Cesar Bernabe

Cesar's research investigates how ontologies and well-founded semantic models influence machine learning outcomes, aiming to improve biomedical data quality and stewardship in line with the FAIR principles of Findability, Accessibility, Interoperability, and Reusability.

• Student travel grant: Simonsfonds Foundation, 2023
• FOIS 2023 distinguished paper award 

Bernabé, C. et al. (2025). Restructuring Knowledge Graphs with Conceptual Models: Implications for Machine Learning Predictions in Drug Repurposing. In: Krogstie, J., Rinderle-Ma, S., Kappel, G., Proper, H.A. (eds) Advanced Information Systems Engineering. CAiSE 2025. Lecture Notes in Computer Science, vol 15702. Springer, Cham. https://doi.org/10.1007/978-3-031-94571-7_17

Bernabé C, Valle I, Prince Sales T, et al. GO-Plan: A goal-oriented method for FAIRification planning. Information Services and Use. 2025;45(1-2):100-124. doi:10.1177/18758789251324008

Bernabé, C. H., Thielemans, L., Kaliyaperumal, R., Carta, C., Zhang, S., van Gelder, C. W., ... & Jacobsen, A. (2024). Building expertise on FAIR through evolving Bring Your Own Data (BYOD) workshops: describing the data, software, and management-focused approaches and their evolution. Data Intelligence, 6(2), 429-456.

Bernabé, C.H., Queralt-Rosinach, N., Silva Souza, V.E. et al. The use of foundational ontologies in biomedical research. J Biomed Semant 14, 21 (2023). https://doi.org/10.1186/s13326-023-00300-z

Sales, T. P., Barcelos, P. P. F., Fonseca, C. M., Souza, I. V., Romanenko, E., Bernabé, C. H., ... & Guizzardi, G. (2023). A FAIR catalog of ontology-driven conceptual models. Data & Knowledge Engineering, 147, 102210.

Van Damme, P., Moreno, P. A., Bernabé, C. H., Ballesteros, A. C., Le Cornec, C. M., Vieira, B. D. S., ... & Benis, N. (2023). A resource for guiding data stewards to make European rare disease patient registries FAIR. Data Science Journal, 22, 12.

Aussenac-Gilles, N. (2024, January). A method to improve alignments between domain and foundational ontologies. In Formal Ontology in Information Systems: Proceedings of the 13th International Conference (FOIS 2023) (Vol. 377, p. 125). SAGE Publications Limited.

dos Santos Vieira, B., Bernabé, C.H., Zhang, S. et al. Towards FAIRification of sensitive and fragmented rare disease patient data: challenges and solutions in European reference network registries. Orphanet J Rare Dis 17, 436 (2022). https://doi.org/10.1186/s13023-022-02558-5

Bernabé, C.H., Silva Souza, V.E., Almeida Falbo, R.d., Guizzardi, R.S.S., Silva, C. (2019). GORO 2.0: Evolving an Ontology for Goal-Oriented Requirements Engineering. In: Guizzardi, G., Gailly, F., Suzana Pitangueira Maciel, R. (eds) Advances in Conceptual Modeling. ER 2019. Lecture Notes in Computer Science(), vol 11787. Springer, Cham. https://doi.org/10.1007/978-3-030-34146-6_15

CaiSE 2025 paper presentation: “Restructuring Knowledge Graphs with Conceptual Models: Implications for Machine Learning Predictions in Drug Repurposing” 

ERDERA’s FAIR Training Program: “Making Research Data Interoperable and Reusable in Rare Diseases” (2025)

Helios FAIR Principles Lecture Series: “FAIR principles and conceptual models” (2025)

International Summer School on Rare Disease and Orphan Drug Registries: “FAIR principles and conceptual models” (2022, 2023, 2024)

ISCB Academy Webinars: “FAIRification Pain-Points: Support in Identifying FAIRification Objectives and Semantic (Meta)Data Modeling of FAIR Resources.” (2024)

ER2023 Workshops paper presentation: “A goal-oriented method for FAIRification planning”