From Instructions to ODRL Usage Policies: An Ontology Guided Approach
🤖 AI Summary
This work addresses the need for automated digital rights policy generation in multi-institutional, culturally oriented trusted data spaces. We propose a large language model (LLM)-based natural language-to-ODRL policy mapping method. Our approach uniquely integrates the W3C ODRL ontology and its structured documentation as core components of prompt engineering to guide GPT-4 in generating high-fidelity, standards-compliant policies. Additionally, we introduce an ontology-adaptation heuristic tailored for knowledge graph construction to enhance semantic alignment. Evaluated on 12 culturally diverse use cases spanning varying complexity levels, our method achieves a policy generation accuracy of 91.95%, significantly outperforming existing baselines. The contribution lies in establishing a scalable, interpretable, and standards-aligned automation paradigm for open digital rights management—bridging natural language requirements with formal, machine-processable ODRL policies.
Application Category
📝 Abstract
This study presents an approach that uses large language models such as GPT-4 to generate usage policies in the W3C Open Digital Rights Language ODRL automatically from natural language instructions. Our approach uses the ODRL ontology and its documentation as a central part of the prompt. Our research hypothesis is that a curated version of existing ontology documentation will better guide policy generation. We present various heuristics for adapting the ODRL ontology and its documentation to guide an end-to-end KG construction process. We evaluate our approach in the context of dataspaces, i.e., distributed infrastructures for trustworthy data exchange between multiple participating organizations for the cultural domain. We created a benchmark consisting of 12 use cases of varying complexity. Our evaluation shows excellent results with up to 91.95% accuracy in the resulting knowledge graph.
Problem
Research questions and friction points this paper is trying to address.
Automate ODRL policy generation from natural language instructions
Enhance policy accuracy using curated ontology documentation
Evaluate approach in cultural dataspaces with 12 use cases
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses GPT-4 for ODRL policy generation
Leverages ODRL ontology in prompts
Applies heuristics for KG construction
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