Modeling and Managing Temporal Obligations in GUCON Using RDF 1.2 and SPARQL 1.2

Submitted by Ines Akaichi on 05/20/2026 - 06:30

Tracking #: 4084-5298

Authors: 
Ines Akaichi
Giorgos Flouris
Irini Fundulaki
Sabrina Kirrane

Responsible editor: 
Cogan Shimizu

Submission type: 
Full Paper
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 time 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 SPARQL 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 1.2 and SPARQL 1.2 and propose an Obligation State Manager that monitors obligation states and assesses their compliance with respect to usage traces. Finally, we evaluate both the extended model and its prototype implementation. Results show a linear performance trend with respect to increasing numbers of obligations and larger knowledge graph sizes.
Full PDF Version: 
PDF icon swj4084.pdf
Previous Version: 
Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
Tags: 
Reviewed
Long-term Stable Link to Resources: 
https://github.com/Ines-Akaichi/Temporal-GUCON/tree/main/results

Decision/Status: 
Accept

Solicited Reviews:
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Review #1
By Ruben Taelman submitted on 06/Jun/2026
Suggestion:
Accept
Review Comment:

This is a revision of the article "Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star".
In my previous review, I was generally positive about the work, but mainly had concerns about the explanation of experiments and results discussion, reliance on RDF/SPARQL-star, and missing related work.
After going through the new version, I am happy to see that all concerns have been resolved.
First, the policy and dataset sizes have been clarified.
Second, the authors have reworked the paper to build upon RDF/SPARQL 1.2 instead of RDF/SPARQL-star.
Third, discussion has been added to relate the work to RDF stream processing.
As such, I recommend this work to be accepted.

## Minor issues

Page 12, line 4: "≪ (spo) ≫" I recommend adding spaces between "s p o".
Page 20 (and later): I suggest adding comma's or dots as separators within large numbers to improve readability. For example "2545342 triples" -> "2,545,342 triples".
- References 12 and 13 are identical

Review #2
By Julián Arenas-Guerrero submitted on 18/Jun/2026
Suggestion:
Accept
Review Comment:

I thank the authors for carefully considering and addressing my comments. The revised version is substantially improved wrt the original one. Most notably, the effort to update to RDF 1.2 and SPARQL 1.2 is really valuable. Also, the addition of running examples suggested by other reviewer has improved the paper and it is now easier to follow.

# Minor comment:
- The version of Apache Jeva used in 5.1.0, but the first verison of Jena supporting RDF 1.2 is 5.4.0. I assume the autors forgot to update the version

Review #3
By Sebastián Ferrada submitted on 25/Jun/2026
Suggestion:
Accept
Review Comment:

This manuscript was submitted as 'full paper' and should be reviewed along the usual dimensions for research contributions which include (1) originality, (2) significance of the results, and (3) quality of writing. Please also assess the data file provided by the authors under “Long-term stable URL for resources”. In particular, assess (A) whether the data file is well organized and in particular contains a README file which makes it easy for you to assess the data, (B) whether the provided resources appear to be complete for replication of experiments, and if not, why, (C) whether the chosen repository, if it is not GitHub, Figshare or Zenodo, is appropriate for long-term repository discoverability, and (4) whether the provided data artifacts are complete. Please refer to the reviewer instructions and the FAQ for further information.

I thank the authors for thoroughly addressing all of my comments. In particular, I believe that adopting RDF 1.2, which is now more stable, will increase the impact of GUCON. Similarly, the examples now make it much easier to follow the more dense definitions and to demonstrate the practicality and usability of the tool.

I am still a bit on the fence w.r.t. Thorn's Criteria, but I think that the discussion is now more transparent with the limitations of the evaluation.