# Enabling Reasoning with LegalRuleML

arXiv: https://arxiv.org/abs/1711.06128 · HTML: https://mlsanity.com/paper/1711.06128/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2017.

## Built on by (most cited first, 8 of 8)

- [Bridging between LegalRuleML and TPTP for Automated Normative Reasoning (extended version)](https://mlsanity.com/paper/2209.05090/) (2022)
- [DPCL: a Language Template for Normative Specifications](https://mlsanity.com/paper/2201.04477/) (2022)
- [Can Legislation Be Made Machine-Readable in PROLEG?](https://mlsanity.com/paper/2601.01477/) (2026)
- [Compliance checking in reified IO logic via SHACL](https://mlsanity.com/paper/2110.07033/) (2021)
- [Code-Driven Law NO, Normware SI!](https://mlsanity.com/paper/2410.17257/) (2024)
- [Representing Normative Regulations in OWL DL for Automated Compliance Checking Supported by Text Annotation](https://mlsanity.com/paper/2504.05951/) (2025)
- [GDPR Auto-Formalization with AI Agents and Human Verification](https://mlsanity.com/paper/2604.14607/) (2026)
- [A decision-support system applied to Law: Reasoning and explainability of the decision](https://mlsanity.com/paper/2609.35370/) (2026)

## Abstract (arXiv)

In order to automate verification process, regulatory rules written in natural language need to be translated into a format that machines can understand. However, none of the existing formalisms can fully represent the elements that appear in legal norms. For instance, most of these formalisms do not provide features to capture the behavior of deontic effects, which is an important aspect in automated compliance checking. This paper presents an approach for transforming legal norms represented using LegalRuleML to a variant of Modal Defeasible Logic (and vice versa) such that a legal statement represented using LegalRuleML can be transformed into a machine-readable format that can be understood and reasoned about depending upon the client's preferences.