An Open Knowledge Graph-Based Approach for Mapping Concepts and Requirements between the EU AI Act and International Standards

The EU AI Act creates compliance complexity for organizations: Companies operating across international supply chains now face a "confusing and multipolar landscape" of trustworthy AI initiatives, with the EU AI Act requiring conformance to technical requirements through Harmonized Standards—a process that remains "a complex challenge" to verify.

Terminology gaps between regulations and standards threaten consistency: The paper identifies that "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," creating potential regulatory uncertainty.

Small organizations and public sector bodies are particularly vulnerable: SMEs and public sector organizations that rely on standards conformance (rather than developing proprietary compliance solutions) face heightened risk due to unclear alignment between standards and regulatory requirements for high-risk AI systems.

Current mapping approaches are inadequate: Simply creating "high-level mapping" between the AI Act and standards is insufficient; the paper proposes that a more systematic approach using knowledge graphs is needed to properly assess whether standards conformity actually delivers regulatory compliance.

A repeatable technical solution is proposed: The paper offers an "open knowledge graph-based approach" as a mechanism to map terms and requirements from regulations and standards, providing organizations with a basis for identifying compliance gaps and areas needing further development.