Automated Validation of Textual Constraints Against AutomationML via LLMs and SHACL

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 (LLM) translates textual rules into SHACL constraints, which are then validated against the previously generated AML ontology. Finally, SHACL validation results are automatically translated to natural language by an LLM. 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 ontologies.