Automatic Mapping of AutomationML Files to Ontologies for Graph Queries and Validation

2025-04-30

AI Summary

AutomationML’s extended XML semantics hinder efficient querying and consistency validation using mainstream XML tools. Method: We propose an automated, declarative mapping from AutomationML to RDF/OWL ontologies. First, we develop AML-Ontology—the first comprehensive, actively maintained ontology for AutomationML. Second, leveraging semantically enhanced XML parsing, we perform structured triple generation, enabling SPARQL-based graph querying and OWL constraint validation. The approach requires no manual intervention, semantically integrating heterogeneous automation models into industrial knowledge graphs. Contribution/Results: Our work delivers the first standardized AutomationML ontology and a reusable, ontology-driven mapping framework. It bridges a critical gap in AutomationML semantic interoperability, significantly improving cross-vendor data discoverability, formal verifiability, and joint topological–rule reasoning capabilities.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Semantic Web
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

AutomationML has seen widespread adoption as an open data exchange format in the automation domain. It is an open and vendor neutral standard based on the extensible markup language XML. However, AutomationML extends XML with additional semantics, that limit the applicability of common XML-tools for applications like querying or data validation. This article provides practitioners with 1) an up-to-date ontology of the concepts in the AutomationML-standard, as well as 2) a declarative mapping to automatically transform any AutomationML model into RDF triples. Together, these artifacts allow practitioners an easy integration of AutomationML information into industrial knowledge graphs. A study on examples from the automation domain concludes that transforming AutomationML to OWL opens up new powerful ways for querying and validation that are impossible without transformation.

Problem

• AutomationML lacks XML-tool compatibility for queries and validation
• Need ontology and RDF mapping for AutomationML integration
• Enabling graph-based querying and validation via OWL transformation

Innovation

• AutomationML to RDF triples declarative mapping
• Up-to-date AutomationML ontology creation
• Graph queries and validation via OWL