The many initiatives on trustworthy AI result in a confusing and multipolar landscape that organizations are operating within the fluid and complex international value chains must navigate in pursuing trustworthy AI. The EU’s proposed AI Act will now shift the focus of these organizations towards the normative requirements for regulatory compliance. Understanding to what extent standards compliance will deliver regulatory compliance for AI remains a complex challenge. This paper introduces the Trustworthy AI Requirements (TAIR) ontology, a simple and replicable method for extracting and sharing relevant terms and concepts from legal regulations and standard texts into open-knowledge graphs. The TAIR ontology is vital for evaluating the sufficiency of standards conformance for regulatory compliance, providing a foundation for identifying areas where further development of technical consensus in trustworthy AI value chains will be indispensable to achieve regulatory compliance. The evaluation of the TAIR ontology aims to detect errors or inconsistencies based on best practices for ontology design.