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

IF 4.6 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-03-12 DOI:10.1109/OJIES.2026.3672995

Tom Westermann;Malte Ramonat;Johannes Hujer;Felix Gehlhoff;Alexander Fay

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引用次数: 0

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, such as querying and data validation. This article demonstrates how the transformation of AutomationML into web ontology language (OWL) enables new use cases in querying with SPARQL and validation with shapes constraint language. To support this, it provides practitioners with, first, an up-to-date ontology of the concepts defined in the AutomationML standard and, second, a declarative mapping to automatically transform any AutomationML model into resource description framework triples. A study on examples from the automation domain concludes that transforming AutomationML to OWL opens up new powerful ways for querying and validation that would have been impossible without this transformation.

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用于图查询和验证的AutomationML文件到本体的自动映射

AutomationML作为自动化领域的开放数据交换格式已经被广泛采用。它是基于可扩展标记语言（XML）的开放且与供应商无关的标准。然而，AutomationML用额外的语义扩展了XML，这些语义限制了通用XML工具对应用程序的适用性，比如查询和数据验证。本文演示了AutomationML到web本体语言（OWL）的转换如何支持使用SPARQL查询和使用形状约束语言验证的新用例。为了支持这一点，它为从业者提供了，首先，在AutomationML标准中定义的概念的最新本体，其次，一个声明性映射，将任何AutomationML模型自动转换为资源描述框架三元组。对自动化领域示例的研究得出结论，将AutomationML转换为OWL为查询和验证开辟了新的强大方法，如果没有这种转换，这些方法是不可能实现的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Open Journal of the Industrial Electronics Society ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

10.80

自引率

2.40%

发文量

审稿时长

12 weeks

期刊介绍： The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments. Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.