# Representing Normative Regulations in OWL DL for Automated Compliance Checking Supported by Text Annotation

arXiv: https://arxiv.org/abs/2504.05951 · HTML: https://mlsanity.com/paper/2504.05951/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Enabling Reasoning with LegalRuleML](https://mlsanity.com/paper/1711.06128/) (2017)
- [Deciding SHACL Shape Containment through Description Logics Reasoning (Extended Version)](https://mlsanity.com/paper/2008.13603/) (2020)
- [SHACL: A Description Logic in Disguise](https://mlsanity.com/paper/2108.06096/) (2021)
- [CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking](https://mlsanity.com/paper/2403.02231/) (2024)

## Abstract (arXiv)

Compliance checking is the process of determining whether a regulated entity adheres to these regulations. Currently, compliance checking is predominantly manual, requiring significant time and highly skilled experts, while still being prone to errors caused by the human factor. Various approaches have been explored to automate compliance checking, however, representing regulations in OWL DL language which enables compliance checking through OWL reasoning has not been adopted. In this work, we propose an annotation schema and an algorithm that transforms text annotations into machine-interpretable OWL DL code. The proposed approach is validated through a proof-of-concept implementation applied to examples from the building construction domain.