https://w3id.org/np/RADcN-KY8U1XKFIuW73fcMhnAl9IU1NhaMfY3p6ayB3nI/Head https://w3id.org/np/RADcN-KY8U1XKFIuW73fcMhnAl9IU1NhaMfY3p6ayB3nI http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RADcN-KY8U1XKFIuW73fcMhnAl9IU1NhaMfY3p6ayB3nI/assertion https://w3id.org/np/RADcN-KY8U1XKFIuW73fcMhnAl9IU1NhaMfY3p6ayB3nI http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RADcN-KY8U1XKFIuW73fcMhnAl9IU1NhaMfY3p6ayB3nI/provenance https://w3id.org/np/RADcN-KY8U1XKFIuW73fcMhnAl9IU1NhaMfY3p6ayB3nI http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RADcN-KY8U1XKFIuW73fcMhnAl9IU1NhaMfY3p6ayB3nI/pubinfo https://w3id.org/np/RADcN-KY8U1XKFIuW73fcMhnAl9IU1NhaMfY3p6ayB3nI http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RADcN-KY8U1XKFIuW73fcMhnAl9IU1NhaMfY3p6ayB3nI/assertion https://archive-ouverte.unige.ch/unige:196525 http://id.loc.gov/vocabulary/relators/ths https://orcid.org/0000-0001-5048-5251 https://archive-ouverte.unige.ch/unige:196525 http://id.loc.gov/vocabulary/relators/ths https://orcid.org/0000-0002-1401-5110 https://archive-ouverte.unige.ch/unige:196525 http://purl.org/dc/terms/abstract Organizations across various industries face numerous legal and regulatory constraints related to their data and processes. Ensuring compliance with these constraints is a critical and continuous challenge. These legal constraints are typically defined in natural language, which, along with their increasing number, complicates the automation of regulatory compliance processes, making them time- and resource-consuming. Therefore, adopting approaches and technologies that support the automation and streamlining of compliance checking is essential. Various industries have developed methodologies and tools to manage regulatory knowledge and ensure compliance. These solutions are often either newly implemented systems or adaptations of existing solutions to meet specific needs. However, they are frequently application- and domain-specific, lacking a generic, scalable, and maintainable approach for representing and managing regulatory knowledge. In recent years, Semantic Web has proven effective for representing and managing knowledge in a unified and interoperable manner. Those technologies allow semantic enrichment of data and support automation across domains. Widely adopted in sectors such as healthcare, finance, and architecture, these technologies facilitate the development of FAIR-compliant knowledge bases, enhancing data integration, interoperability, and reusability across systems. This research proposes an approach to regulatory compliance automation based on Semantic Web technologies. The proposed approach can be applied across various industries, providing a domain-agnostic solution for several regulatory contexts. To this end, we propose a comprehensive framework that offers tools for domain experts and practitioners to achieve automated compliance checking. This includes a unified regulatory knowledge formalization model, concretely designed as an ontology-based DSL for modeling the regulatory statements structure at a meta-level, while keeping the domain-specific business logics separate from the regulatory knowledge representation. Additionally, the framework proposes an abstract workflow that, when implemented, leads to automated regulatory compliance. It is flexible and adaptable to different regulatory compliance processes, providing a solution for both compliance checking and compliance by design approaches. To validate the proposed approach, we present a proof-of-concept (PoC) implementation leveraging knowledge graphs and SHACL. The PoC is tested against real-world data and regulatory statements taken from diverse domains. It shows the flexibility and efficacy of the proposed approach, despite the different nature of these domains. In conclusion, this research shows the potential of semantic web technologies to enable automated regulatory compliance across domains. The proposed domain-agnostic framework and its PoC implementation demonstrate a scalable and efficient solution for managing regulatory knowledge in accordance with FAIR principles. The framework supports organizations in ensuring compliance while also promoting interoperability and knowledge integration. Moreover, it provides tools for domain experts to define and manage regulatory knowledge, supporting them in automating compliance processes. Future work will focus on refining and extending the framework, as well as expanding its application to additional regulatory contexts. https://archive-ouverte.unige.ch/unige:196525 http://purl.org/dc/terms/issued 2025-07-07 https://archive-ouverte.unige.ch/unige:196525 http://purl.org/dc/terms/title A Domain-Agnostic Semantic Framework for Automating Regulatory Compliance https://archive-ouverte.unige.ch/unige:196525 http://purl.org/ontology/bibo/authorList https://w3id.org/np/RADcN-KY8U1XKFIuW73fcMhnAl9IU1NhaMfY3p6ayB3nI/author-list https://archive-ouverte.unige.ch/unige:196525 http://purl.org/spar/fabio/hasPublicationYear 2025 https://archive-ouverte.unige.ch/unige:196525 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/DoctoralThesis https://archive-ouverte.unige.ch/unige:196525 http://www.wikidata.org/entity/P4101 http://www.wikidata.org/entity/Q503473 https://archive-ouverte.unige.ch/unige:196525 https://schema.org/about 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