A SHACL-based Data Consistency Solution for Contract Compliance Verification

2025-07-21

AI Summary

Existing contract compliance verification (CCV) tools—such as ACT—lack semantic interoperability and automated data inconsistency resolution, hindering end-to-end GDPR-compliant contract lifecycle management. To address this, this paper introduces SHACL into knowledge graph (KG)-based CCV for the first time, proposing a SHACL-driven KG compliance verification and repair framework. The approach integrates the ODRL policy language with semantic modeling to enable formal representation of GDPR obligations, automated consistency checking, and semi-automatic inconsistency repair recommendations. The framework has been implemented as an extension of ACT, yielding a functional prototype. Empirical evaluation on standard CCV benchmarks confirms its correctness and effectiveness: it significantly improves compliance verification accuracy and reduces manual user intervention effort. This work establishes a novel, semantics-driven paradigm for contract governance, advancing both expressive policy modeling and actionable compliance remediation in contractual data ecosystems.

Technology Category

• Knowledge Representation and Reasoning: Description Logics
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Responsible Web: Consent frameworks and practices on the web
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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). Although the tool reports violations with respect to obligations, it had limitations in verifying and ensuring compliance, as it did not use an interoperable semantic formalism, such as SHACL, and did not support users in resolving data inconsistencies. In this work, we propose a novel approach to overcome these 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.

Problem

• Ensures GDPR-compliant contract negotiation data consistency
• Resolves inconsistencies in contract compliance verification (CCV)
• Integrates SHACL-based repairs into automated contracting tools

Innovation

• SHACL-based data consistency solution
• Automated repair strategies for CCV
• Integration into KG-based ACT tool