A SHACL-based Data Consistency Solution for Contract Compliance Verification
David, Robert, Ahmeti, Albin, Bushati, Geni, Tauqeer, Amar, Fensel, Anna · Socio-Environmental Systems Modeling · 2025
In recent years, there have been many developments for GDPR-compliant data access and sharing based on consent. For more complex data sharing scenarios, where consent might not be sufficient, many parties rely on contracts. Before a contract is signed, it must undergo the process of contract negotiation within the contract lifecycle, which consists of negotiating the obligations associated with the contract. Contract compliance verification (CCV) provides a means to verify whether a contract is GDPR-compliant, i.e., adheres to legal obligations and there are no violations. The rise of knowledge graph (KG) adoption, enabling semantic interoperability using well-defined semantics, allows CCV to be applied on KGs. In the scenario of different participants negotiating obligations, there is a need for data consistency to ensure that CCV is done correctly. Recent work introduced the automated contracting tool (ACT), a KG-based and ODRL-employing tool for GDPR CCV, which was developed in the Horizon 2020 project smashHit (https://smashhit.eu). In this work, we propose a novel approach to overcome some limitations of ACT. We semi-automatically resolve CCV inconsistencies by providing repair strategies, which automatically propose (optimal) solutions to the user to re-establish data consistency and thereby support them in managing GDPR-compliant contract lifecycle data. We have implemented the approach, integrated it into ACT and tested its correctness and performance against basic CCV consistency requirements.