Intelligent Automation of Ontology Validation Using SPARQL and AI-Driven Rule Engines

Abstract

Ontology-driven systems increasingly underpin enterprise and biomedical data ecosystems, where semantic interoperability and standardized knowledge representation are essential for scientific discovery, regulatory reporting, and decision support. Yet maintaining ontological consistency and correctness at the scale of contemporary knowledge graphs, which routinely span hundreds of thousands to billions of triples, remains a labor-intensive endeavor that strains the limits of conventional Description Logic reasoners. This article proposes a hybrid validation framework that combines deterministic query-based validation expressed in the SPARQL Protocol and RDF Query Language with adaptive, AI-driven rule engines based on neuro-symbolic reasoning. The framework decomposes validation into two complementary layers: a symbolic layer that executes Shapes Constraint Language and SPARQL-based axioms for verifiable structural compliance, and a learning layer that identifies anomalies, infers candidate constraints, and ranks rule relevance using machine-learning models trained on curated ontology repair corpora. The framework is articulated as a microservices architecture engineered for scalability, sustainability, and operational governance. Empirical synthesis from comparable deployments suggests speedups of five to ten times over baseline reasoners, precision above ninety-five percent in rule recommendation, and carbon footprint reductions of up to forty percent through carbon-aware scheduling and infrastructure profiling. By embedding both verifiability and adaptability into a unified pipeline, the proposed approach establishes a practical foundation for continuous, sustainable, and high-assurance ontology governance in enterprise environments.