# Automated Validation of Textual Constraints Against AutomationML via LLMs and SHACL

arXiv: https://arxiv.org/abs/2506.10678 · HTML: https://mlsanity.com/paper/2506.10678/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 1 of 1)

- [Automatic Mapping of AutomationML Files to Ontologies for Graph Queries and Validation](https://mlsanity.com/paper/2504.21694/) (2025)

## Built on by (most cited first, 2 of 2)

- [Stemming Hallucination in Language Models Using a Licensing Oracle](https://mlsanity.com/paper/2511.06073/) (2025)
- [NL2SHACL-Bench: A Benchmark Suite for Natural Language to SHACL Translation](https://mlsanity.com/paper/2608.07530/) (2026)

## Abstract (arXiv)

AutomationML (AML) enables standardized data exchange in engineering, yet existing recommendations for proper AML modeling are typically formulated as informal and textual constraints. These constraints cannot be validated automatically within AML itself. This work-in-progress paper introduces a pipeline to formalize and verify such constraints. First, AML models are mapped to OWL ontologies via RML and SPARQL. In addition, a Large Language Model translates textual rules into SHACL constraints, which are then validated against the previously generated AML ontology. Finally, SHACL validation results are automatically interpreted in natural language. The approach is demonstrated on a sample AML recommendation. Results show that even complex modeling rules can be semi-automatically checked -- without requiring users to understand formal methods or ontology technologies.