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This paper proposes a conceptual ontology-driven governance layer for AIOps systems, addressing the Semantic Governance Gap — the structural absence of machine-processable semantic traceability in current AIOps architectures.

Building upon principles from ontology engineering, temporal-semantic modeling in knowledge graphs, and recent work on Knowledge Graph integration for log anomaly detection, the paper introduces a formal semantic framework that structures the lifecycle of operational decisions — from incident detection through recommendation and feedback.

The proposed model enables explainability by design, transforming AIOps from systems with post-hoc statistical interpretations into structurally transparent decision architectures, with structural alignment to EU AI Act requirements (Articles 11–17). Empirical validation is identified as future work.