Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star

2025-10-06

AI Summary

In the digital era, cross-domain data flows are intensifying, yet conventional obligation monitoring approaches lack formal temporal support, rendering them inadequate for modeling the dynamic evolution of obligation states (e.g., fulfilled, violated, expired). Method: This paper introduces, for the first time in the GUCON framework, formal temporal semantics for cross-domain data usage scenarios. We propose a temporal obligation model grounded in RDF-star/SPARQL-star, explicitly encoding obligation start/end times and state-transition logic. Furthermore, we design an Obligation State Manager that leverages a temporal knowledge graph—built from data usage traces and policy rules—to perform graph-pattern-based state inference and continuous compliance monitoring. Contribution/Results: Evaluation via a prototype implementation demonstrates significant improvements in expressive power for obligation modeling and accuracy in regulatory compliance assessment, enabling fine-grained, time-aware governance of cross-domain data use.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Responsible Web: Consent frameworks and practices on the web

Abstract

In the digital age, data frequently crosses organizational and jurisdictional boundaries, making effective governance essential. Usage control policies have emerged as a key paradigm for regulating data usage, safeguarding privacy, protecting intellectual property, and ensuring compliance with regulations. A central mechanism for usage control is the handling of obligations, which arise as a side effect of using and sharing data. Effective monitoring of obligations requires capturing usage traces and accounting for temporal aspects such as start times and deadlines, as obligations may evolve over times into different states, such as fulfilled, violated, or expired. While several solutions have been proposed for obligation monitoring, they often lack formal semantics or provide limited support for reasoning over obligation states. To address these limitations, we extend GUCON, a policy framework grounded in the formal semantics of SPAQRL graph patterns, to explicitly model the temporal aspects of an obligation. This extension enables the expressing of temporal obligations and supports continuous monitoring of their evolving states based on usage traces stored in temporal knowledge graphs. We demonstrate how this extended model can be represented using RDF-star and SPARQL-star and propose an Obligation State Manager that monitors obligation states and assess their compliance with respect to usage traces. Finally, we evaluate both the extended model and its prototype implementation.

Problem

• Modeling temporal obligations in usage control policies
• Monitoring evolving obligation states using RDF-star
• Managing temporal aspects like deadlines and fulfillment states

Innovation

• Extended GUCON framework with temporal obligation modeling
• Used RDF-star and SPARQL-star for representation and monitoring
• Implemented Obligation State Manager for compliance assessment