Automated Validation of Textual Constraints Against AutomationML via LLMs and SHACL

TL;DR

This paper presents a semi-automated pipeline that formalizes and validates textual constraints in AutomationML models using LLMs and SHACL, enabling automatic validation without deep formal methods knowledge.

Contribution

It introduces a novel pipeline combining LLMs, RML, OWL, and SHACL to formalize and verify textual AML constraints automatically.

Findings

Complex AML modeling rules can be semi-automatically validated.

The approach does not require users to understand formal ontology methods.

Validation results are interpretable in natural language.

Abstract

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.