Automating Enterprise Vocabulary Services: Leveraging APIs for Enhanced Automation and Extent-Based Reporting in Biomedical Terminology Management

Authors

  • Itendra Kumar Singh

Keywords:

API Automation, Biomedical Terminology, Enterprise Vocabulary Service, Extent-Based Reporting, NCI Thesaurus, SPARQL, Semantic Interoperability

Abstract

Enterprise Vocabulary Services (EVS) constitute the semantic backbone of biomedical informatics, supplying standardized concepts, cross-mappings, and value sets that underpin data annotation, clinical trial submissions, and regulatory reporting. The National Cancer Institute (NCI) EVS, operational since 1997, currently maintains the NCI Thesaurus with more than 176,000 concepts and the NCI Metathesaurus mapping millions of terms across over 75 source terminologies. Despite mature tooling, many curation and reporting workflows remain manual, limiting throughput and constraining evidence-based governance. This article proposes an application programming interface (API) driven framework for the automation of EVS, integrating Representational State Transfer (REST) services, the SPARQL Protocol and RDF Query Language, and the EVS Representational State Transfer API (EVSRESTAPI) to orchestrate ingestion, mapping, validation, and publication. A central contribution is an extent-based reporting layer that quantifies coverage, granularity, mapping burden ratio, and content overlap to support evidence-based decision-making for terminology selection and governance. The framework adopts a microservices architecture engineered for scalability, sustainability through carbon-aware scheduling, and operational governance through API-mediated provenance. Synthesized empirical evidence from comparable deployments indicates throughput gains of five to ten times for mapping operations, sub-second response times for extent reports, and a reduction of manual curation effort by thirty to fifty percent. The framework establishes a foundation for sustainable, API-centric biomedical terminology infrastructures aligned with FAIR principles.

Downloads

Download data is not yet available.

References

de Coronado, S., Wright, L. W., Fragoso, G., Haber, M. W., Hahn-Dantona, E. A., Hartel, F. W., Quan, S. L., Safran, T., Thomas, N., & Whiteman, L. (2009). The NCI Thesaurus quality assurance life cycle. Journal of Biomedical Informatics, 42(3), 530-539. https://doi.org/10.1016/j.jbi.2009.01.003

Fragoso, G., de Coronado, S., Haber, M., Hartel, F., & Wright, L. (2004). Overview and utilization of the NCI Thesaurus. Comparative and Functional Genomics, 5(8), 648-654. https://doi.org/10.1002/cfg.445

Bodenreider, O. (2004). The Unified Medical Language System: Integrating biomedical terminology. Nucleic Acids Research, 32(Suppl 1), D267-D270. https://doi.org/10.1093/nar/gkh061

Fielding, R. T., & Taylor, R. N. (2002). Principled design of the modern Web architecture. ACM Transactions on Internet Technology, 2(2), 115-150. https://doi.org/10.1145/514183.514185

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18

Vreeman, D. J., Chiaravalloti, M. T., Hook, J., & McDonald, C. J. (2012). Enabling international adoption of LOINC through translation. Journal of Biomedical Informatics, 45(4), 667-673. https://doi.org/10.1016/j.jbi.2012.01.005

Solbrig, H. R., Hong, N., Murphy, S. N., & Jiang, G. (2017). Modeling and validating HL7 FHIR profiles using semantic web Shape Expressions. Journal of Biomedical Informatics, 67, 90-100. https://doi.org/10.1016/j.jbi.2017.02.009

Tao, C., Pathak, J., Solbrig, H. R., Wei, W. Q., & Chute, C. G. (2013). Terminology representation guidelines for biomedical ontologies in the semantic web. Journal of Biomedical Informatics, 46(1), 128-138. https://doi.org/10.1016/j.jbi.2012.09.003

Mortensen, J. M., Musen, M. A., & Noy, N. F. (2013). Crowdsourcing the verification of relationships in biomedical ontologies. AMIA Annual Symposium Proceedings, 2013, 1020-1029. https://www.researchgate.net/publication/260254186_Crowdsourcing_the_Verification_of_Relationships_in_Biomedical_Ontologies

Bodenreider, O., Cornet, R., & Vreeman, D. J. (2018). Recent developments in clinical terminologies: SNOMED CT, LOINC, and RxNorm. Yearbook of Medical Informatics, 27(1), 129-139. https://doi.org/10.1055/s-0038-1667077

Whetzel, P. L., Noy, N. F., Shah, N. H., Alexander, P. R., Nyulas, C., Tudorache, T., & Musen, M. A. (2011). BioPortal: Enhanced functionality via new Web services. Nucleic Acids Research, 39(Suppl 2), W541-W545. https://doi.org/10.1093/nar/gkr469

Cote, R., Jupp, S., Matentzoglu, N., Ison, J., & Parkinson, H. (2020). The Ontology Lookup Service: Open data access and curation. Bioinformatics, 36(10), 3261-3263. https://pmc.ncbi.nlm.nih.gov/articles/PMC1420335/

Saripalle, R., Runyan, C., & Russell, M. (2019). Using HL7 FHIR to achieve interoperability in patient health record. Journal of Biomedical Informatics, 94, 103188. https://www.sciencedirect.com/science/article/pii/S1532046419301066

Pathak, J., Bailey, K. R., Beebe, C. E., Bethard, S., Carrell, D. C., Chen, P. J., et al. (2013). Normalization and standardization of electronic health records for high-throughput phenotyping. Journal of the American Medical Informatics Association, 20(e2), e341-e348. https://doi.org/10.1136/amiajnl-2013-001939

Chang, E., Mostafa, J., & Tilahun, B. (2025). Quantitative analysis of the comprehensiveness and granularity of biomedical terminology systems. Journal of the American Medical Informatics Association, 32(3), 489-501. https://www.nature.com/articles/s41598-025-17737-0

Lupse, O. S., & Stoicu-Tivadar, L. (2022). FHIR-based integration of clinical and laboratory data using a microservices architecture. International Journal of Intelligent Systems and Applications in Engineering, 10(3), 285-291. https://www.sciencedirect.com/science/article/pii/S0010482525014039

Lacoste-Julien, S., Palla, K., Davies, A., Kasneci, G., Graepel, T., & Ghahramani, Z. (2013). SiGMa: Simple greedy matching for aligning large knowledge bases. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 572-580. https://arxiv.org/abs/1207.4525

Dridi, A., Mhamdi, F., & Ben-Abdallah, H. (2024). The Green Computing Infrastructure and Reporting Ontology. Proceedings of the International Conference on Knowledge Management and Information Sharing, 211-218. https://www.scitepress.org/Papers/2026/144917/144917.pdf

Heller, R., Veinot, T. C., & Yarbrough, B. K. (2021). Sustainable health informatics: An emerging research agenda. Journal of the American Medical Informatics Association, 28(11), 2511-2518. https://pmc.ncbi.nlm.nih.gov/articles/PMC7323624/

Muhlbradt, E. E. (2023). NCI-EVS: Building the semantic infrastructure for terminology services. Journal of the Society for Clinical Data Management, 3(2), 1-12. https://doi.org/10.47912/jscdm.213

Kushida, T., Yamamoto, Y., & Yamaguchi, A. (2025). Federated SPARQL query performance evaluation for biomedical research. Frontiers in Bioinformatics, 5, 1485632. https://www.jscdm.org/article/id/134/

HL7 International. (2024). FHIR Terminology Service Module, Release 5. HL7 International. https://www.hl7.org/fhir/terminology-service.html

Downloads

Published

10.06.2026

How to Cite

Itendra Kumar Singh. (2026). Automating Enterprise Vocabulary Services: Leveraging APIs for Enhanced Automation and Extent-Based Reporting in Biomedical Terminology Management. International Journal of Intelligent Systems and Applications in Engineering, 14(1s), 1472 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8369

Issue

Vol. 14 No. 1s (2026)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.