# Interpreting OWL Complex Classes in AutomationML based on Bidirectional Translation

## TL;DR

This paper presents a bidirectional translation method between OWL complex classes and AutomationML concept models, making OWL ontologies more accessible for engineers without expert knowledge.

## Contribution

It introduces AML concept models for OWL complex classes and algorithms for their translation, simplifying ontology handling for nonexperts.

## Key findings

- Efficient translation algorithms for OWL and AML.
- Enhanced visualization and editing of OWL classes in AML.
- Improved accessibility of OWL ontologies for engineers.

## Abstract

The World Wide Web Consortium (W3C) has published several recommendations for building and storing ontologies, including the most recent OWL 2 Web Ontology Language (OWL). These initiatives have been followed by practical implementations that popularize OWL in various domains. For example, OWL has been used for conceptual modeling in industrial engineering, and its reasoning facilities are used to provide a wealth of services, e.g. model diagnosis, automated code generation, and semantic integration. More specifically, recent studies have shown that OWL is well suited for harmonizing information of engineering tools stored as AutomationML (AML) files. However, OWL and its tools can be cumbersome for direct use by engineers such that an ontology expert is often required in practice. Although much attention has been paid in the literature to overcome this issue by transforming OWL ontologies from/to AML models automatically, dealing with OWL complex classes remains an open research question. In this paper, we introduce the AML concept models for representing OWL complex classes in AutomationML, and present algorithms for the bidirectional translation between OWL complex classes and their corresponding AML concept models. We show that this approach provides an efficient and intuitive interface for nonexperts to visualize, modify, and create OWL complex classes.