Automated Validation of Textual Constraints Against AutomationML via LLMs and SHACL
This work addresses the challenge of automating constraint validation in engineering data exchange for AML users, though it is incremental as it builds on existing technologies like LLMs and SHACL.
The paper tackles the problem of automatically validating informal textual constraints in AutomationML (AML) engineering models by introducing a pipeline that uses Large Language Models (LLMs) to translate these rules into SHACL constraints and validate them against AML ontologies, enabling semi-automatic checking without requiring expertise in formal methods.
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.