mlsanityData to 7 Oct 2026

From Instructions to ODRL Usage Policies: An Ontology Guided Approach

arXiv 2506.03301 ↗First posted 3 Jun 2025cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 3 Jun 2025.

Builds on

9 of 17 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  3. 2020KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoningcited by 106
  4. 2023LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunitiescited by 97
  5. 2022Generative Knowledge Graph Construction: A Reviewcited by 49
  6. 2020Neural Entity Linking: A Survey of Models Based on Deep Learningcited by 46
  7. 2023PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMscited by 44
  8. 2023Construction of Knowledge Graphs: State and Challengescited by 12
  9. 2023Policy Patterns for Usage Control in Data Spacescited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.