The many initiatives on trustworthy AI result in a confusing and multipolar landscape that organizations operating within the fluid and complex international value chains must navigate in pursuing trustworthy AI. The EU's AI Act will now shift the focus of such organizations toward conformance with the technical requirements for regulatory compliance, for which the Act relies on Harmonized Standards. Though a high-level mapping to the Act's requirements will be part of such harmonization, determining the degree to which standards conformity delivers regulatory compliance with the AI Act remains a complex challenge. Variance and gaps in the definitions of concepts and how they are used in requirements between the Act and harmonized standards may impact the consistency of compliance claims across organizations, sectors, and applications. This may present regulatory uncertainty, especially for SMEs and public sector bodies relying on standards conformance rather than proprietary equivalents for developing and deploying compliant high-risk AI systems. To address this challenge, this paper offers a simple and repeatable mechanism for mapping the terms and requirements relevant to normative statements in regulations and standards, e.g., AI Act and ISO management system standards, texts into open knowledge graphs. This representation is used to assess the adequacy of standards conformance to regulatory compliance and thereby provide a basis for identifying areas where further technical consensus development in trustworthy AI value chains is required to achieve regulatory compliance.

本研究解决了在多元复杂的国际价值链中，组织在实现可信人工智能过程中面临的合规挑战，尤其是在欧盟人工智能法案与和谐标准之间的概念和要求的差异。论文提出了一种简单且可重复的机制，通过开放知识图谱映射相关术语和要求，旨在评估标准合规性是否满足法规要求并确定需要进一步技术共识发展的领域，以增强可信人工智能的合规性。

基于开放知识图谱的方法，映射欧盟人工智能法案与国际标准之间的概念和要求