ODRL Landscape update after OPAL

Hey everybody,

On Tuesday 12 May at 10, there was a hybrid meeting on the Formal 
Semantics of ODRL.

Part of the discussion was also to recap future steps over the great 
work that has been presented at OPAL[1] the day prior, as well as the 
discussions held during the ODRL Tutorial [2], co-located with ESWC 2026 
[3].

I went through all papers and explored several of the ideas raised 
during the discussions at ESWC, incorporating the results into the ODRL 
Landscape document [4].

Summary of the additions:
* bpmn2odrl: Converts Business Process Model and Notation (BPMN) 
workflows into ODRL policies.
* BAMBON: A learning-based negotiator that automatically negotiates ODRL 
based on the constraints imposed by the involved parties.
* ODRL Atomization: Transforms compact ODRL policies with composite 
rules into policies with atomic rules.
* ODRL Validator: Validates ODRL policies using SHACL and detects policy 
conflicts with Notation3 reasoning.
* Risk Detector Application (risk-e-tos): Explores ODRL policy risk 
assessment and mitigation suggestions using Notation3 reasoning.
* O-prime: Framework for transparent monitoring of personal data usage, 
policy violation detection, and provenance tracking in decentralized 
Solid ecosystems.
* ODRL Engine: Python-based ODRL evaluator evaluating ODRL policies 
against a defined state of the world.

If I missed anything, feel free to let me know or edit yourselves!

Kind regards,
Wout Slabbinck

[1] ODRL and beyond: practical applications and challenges for 
policy-base access and usage control (OPAL 2026): 
https://opal-workshop.github.io/2026-2nd-edition/
[2] Policy Evaluation and Enforcement on the Web with ODRL: 
https://potr-knows.github.io/odrl-tutorial/
[3] ESWC 26: https://2026.eswc-conferences.org/
[4] ODRL Landscape: https://github.com/w3c/odrl/tree/master/landscape

Received on Monday, 18 May 2026 09:14:01 UTC