Continuous Validation and Governance of Enterprise Ontologies Using SPARQL-Based Automation Pipelines

Abstract

Enterprise ontologies constitute foundational knowledge infrastructure for biomedical research, clinical informatics, regulatory science, and large-scale data integration, yet the governance of these assets has remained stubbornly dependent on manual, periodic review processes that cannot keep pace with contemporary ontology engineering demands. The National Cancer Institute Enterprise Vocabulary Services (NCI EVS) comprising 170,247 active concepts and approximately 14.8 million RDF triples epitomizes both the ambition and the operational strain of maintaining production-grade controlled vocabularies at the intersection of regulatory accountability and high-frequency scientific update cycles. This paper presents a complete framework for continuous validation and governance of enterprise ontologies through automation pipelines that couple SPARQL 1.1 constraint rules with Shapes Constraint Language (SHACL) shape graphs within a four-layer reference architecture: Ontology, Validation, Pipeline, and Governance. A hybrid validation engine executes an extensible catalogue of constraint rules covering cardinality, referential integrity, annotation completeness, cycle detection, and cross-ontology alignment, dispatched through a Jenkins-orchestrated CI/CD pipeline with Docker containerization for reproducibility. Empirical evaluation against the NCI EVS production environment demonstrates full validation cycle time reductions of 58-62% relative to prior practice, a throughput improvement from 0.32 to 4.9 validations per hour, and an anomaly detection recall of 94% representing a 33-percentage-point improvement over manual scripting. The governance layer implements tiered policy enforcement, W3C PROV-O-compliant provenance recording, semantic versioning, and a real-time stewardship dashboard, establishing a reproducible and auditable model for enterprise-grade ontology quality assurance applicable across biomedical and knowledge management domains.