Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems

Computer Science > Artificial Intelligence [Submitted on 22 May 2026] Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems Aasish Kumar Sharma, Julian M. Kunkel AI-enabled services deployed in critical digital infrastructure are subject to governance obligations spanning transparency, accountability, fairness, and traceability. Compliance today remains documentation-centric: obligations are described in prose, audits rely on static checklists, and verification depends on manual review. Such approaches do not scale to automated AI systems. This paper introduces Ontological Knowledge Blocks (OKBs), a programmable governance infrastructure that compiles regulatory obligations into machine-checkable constraints over structured evidence graphs. We formalize an OKB as a 5-tuple that binds normative obligations to an RDF/OWL concept schema, executable SHACL validation rules, explicit evidence requirements, and PROV-O provenance links. A deterministic regulatory compiler translates structured Intermediate Representation (IR) records into composable KB modules, enabling profile-based governance reconfiguration without modifying service code. We implement two prototypes and evaluate them in an AI-assisted HPC resource allocation scenario across 24 validation runs and four governance profiles. Results demonstrate profile-sensitive validation, strictly additive violation accumulation, SHACL validation latency between 12.6 ms and 100.3 ms, and profile equivalence testing confirming Combined as the strictly most comprehensive profile. All artefacts are released as open source. Comments: 6 pages, 3 figures. Accepted at the Security, Trust and Privacy for Software and Applications (STPSA) Workshop, IEEE COMPSAC 2026, Madrid, Spain, July 7-10, 2026 Subjects: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC) ACM classes: K.5.2; I.2.4; H.4 Cite as: arXiv:2605.23297 [cs.AI]   (or arXiv:2605.23297v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.23297 Focus to learn more Submission history From: Aasish Kumar Sharma [view email] [v1] Fri, 22 May 2026 07:14:31 UTC (203 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.DC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)