AgentODRL: A Large Language Model-based Multi-agent System for ODRL Generation
Abstract: The Open Digital Rights Language (ODRL) is a pivotal standard for automating data rights management. However, the inherent logical complexity of authorization policies, combined with the scarcity of high-quality "Natural Language-to-ODRL" training datasets, impedes the ability of current methods to efficiently and accurately translate complex rules from natural language into the ODRL format. To address this challenge, this research leverages the potent comprehension and generation capabilities of LLMs to achieve both automation and high fidelity in this translation process. We introduce AgentODRL, a multi-agent system based on an Orchestrator-Workers architecture. The architecture consists of specialized Workers, including a Generator for ODRL policy creation, a Decomposer for breaking down complex use cases, and a Rewriter for simplifying nested logical relationships. The Orchestrator agent dynamically coordinates these Workers, assembling an optimal pathway based on the complexity of the input use case. Specifically, we enhance the ODRL Generator by incorporating a validator-based syntax strategy and a semantic reflection mechanism powered by a LoRA-finetuned model, significantly elevating the quality of the generated policies. Extensive experiments were conducted on a newly constructed dataset comprising 770 use cases of varying complexity, all situated within the context of data spaces. The results, evaluated using ODRL syntax and semantic scores, demonstrate that our proposed Orchestrator-Workers system, enhanced with these strategies, achieves superior performance on the ODRL generation task.
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What is this paper about?
This paper is about teaching AI to turn plain‑English rules (like “who can use which data, when, and how”) into a precise, computer-readable format called ODRL. Think of ODRL as a strict, universal rulebook that computers can read to enforce data permissions. The authors build a team of AI “agents” (like a manager and specialists) that work together to do this translation more accurately, especially for tricky, real-world rules.
What questions did the researchers ask?
- Can a team of AI agents (each with a specific job) translate complex, real-life rules into ODRL better than a single AI model doing everything?
- How can we keep the translation both:
- grammatically correct (follows ODRL’s strict format), and
- semantically correct (captures the original meaning completely)?
- How do we handle complicated rule structures, like multiple rules in one paragraph or rules that depend on other rules?
- Can we build a good test set of examples to fairly measure progress on this task?
How did they do it?
The authors created a multi‑agent system called AgentODRL. Imagine a small company:
- The Orchestrator is the project manager. It reads the input rule and decides which specialists should handle it.
- The Worker agents are specialists:
- Rewriter: When a rule depends on another rule (like “see rule 35”), it rewrites things so the meaning is self-contained and clear.
- Splitter (also called Decomposer): When a text contains several separate rules (for different users or cases), it splits them into clean, single-purpose pieces.
- Generator: It turns each cleaned-up piece into ODRL code.
To boost quality, the Generator uses two helpers:
- A syntax checker loop (like a strict grammar teacher for code):
- The Generator writes ODRL.
- A validator tool (based on SHACL) checks it against official ODRL rules.
- If it’s wrong, the tool explains what broke; the Generator fixes it and tries again—until it passes or hits a retry limit.
- A meaning checker (like a checklist buddy):
- A small, fine‑tuned helper model (trained with a light method called LoRA) pulls out key facts from the original text—who, what data, what actions, what limits—into a simple “semantic checklist.”
- The Generator must ensure its ODRL covers every item on that checklist so nothing important is lost.
They also built a new dataset to test AgentODRL:
- 770 examples of rules about data use (“data space” scenarios).
- Three types:
- Simple (one clean rule),
- Parallel (several rules side-by-side for different cases),
- Recursive (rules that depend on other rules).
To measure success, they used:
- Grammar Score: Does the ODRL follow the official format perfectly?
- Semantic Score: Does it capture all the important meaning from the original text? (Checked with a careful, LLM-based reviewing process.)
What did they find, and why is it important?
Here are the main results in plain terms:
- Much better accuracy than older methods:
- Grammar improved on average by about 5 percentage points (and often reached near-perfect compliance).
- Meaning accuracy improved on average by about 15 percentage points compared to a strong previous approach (called SCR-Enhanced).
- Works especially well on hard cases:
- Complex rules with multiple parts or cross-references stayed accurate and didn’t fall apart under pressure.
- Even small AI models got much better when using the team approach plus the two quality checks.
- Smart routing saves effort:
- The Orchestrator (manager) usually picks a path that’s almost as good as the absolute best fixed path for that case, but uses fewer compute resources (tokens). In short: near-best results at lower cost.
Why this matters:
- People who aren’t tech experts (like policy writers or lawyers) can describe rules in plain language, and the system produces high-quality, enforceable ODRL automatically.
- This helps organizations share data safely and fairly—only the right people can do the right things, under the right conditions.
What’s the bigger impact?
- Faster, safer data sharing: Clear, machine-readable rules make it easier to share data across companies and countries while following laws and agreements.
- Lower barrier for policy authors: Domain experts won’t need to learn complex web standards to create enforceable policies.
- A general blueprint: The “manager + specialists + double-checkers” approach could be used for other tasks that need both perfect format and faithful meaning (for example, turning legal or scientific text into precise, structured rules).
In short, AgentODRL shows that a well-coordinated team of AI agents—guided by a manager, using checklists for meaning and strict validators for format—can reliably turn human-friendly rules into computer-friendly policies. This could make real-world data sharing more trustworthy and efficient.
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