Ontology-based Reasoning

Ontology-based Reasoning (OBR) is a knowledge-intensive technique that uses formal ontologies to perform automated inference over structured data. Unlike traditional rule-based systems, OBR leverages semantic relationships, inheritance, and consistency checking to derive new knowledge from existing facts. In the context of the EU AI Act (AIA) and the EU Cyber Resilience Act (CRA), OBR enables the transformation of legal requirements into machine-executable rules. This ensures that AI system compliance is not just a manual checklist but a logically verifiable process. According to ISO/IEC 42001, AI systems must be transparent and accountable; OBR provides the formal mechanism to achieve this by providing a traceable reasoning path for every compliance decision, which is critical for regulatory audits and stakeholder trust.

OBR application in enterprise risk management (ERM) follows a three-stage process. First, Knowledge Engineering: Mapping regulatory frameworks like the EU AI Act, GDPR, and Taiwan's Personal Data Protection Act into a unified ontology. Second, Automated Inference: Using reasoners to check the AI system's operational state against the ontology's rules, identifying non-compliance in real-time. Third, Explainable Reporting: Generating human-readable justifications for each compliance determination. For example, a Taiwan-based electronics manufacturer deploying AI-driven quality control can use OBR to ensure the AI model meets the EU CRA's security-by-design requirements. This automation can reduce compliance-related operational costs by up to 40% and decrease the risk of regulatory fines by 70% within the first year of implementation.

Taiwan enterprises face three primary challenges. First, the shortage of interdisciplinary talent—combining AI engineering with legal expertise. The solution is to partner with specialized consultants like Winners Consulting who provide turnkey solutions. Second, the difficulty of converting unstructured regulatory documents into machine-readable formats. This can be addressed by investing in NLP-based knowledge extraction tools. Third, the initial cost-benefit uncertainty. To overcome this, companies should start with a pilot project focusing on a single high-risk AI application (as defined by the EU AI Act), demonstrate the reduction in manual audit hours (typically 50-70%), and then scale the solution across the organization. This phased approach ensures measurable ROI and smoother adoption by the leadership team.

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