DTO 2024 Abstracts


Area 1 - Ontologies for Digital Twin

Full Papers
Paper Nr: 7
Title:

Linking Digital Twin Design and Ontologies with Model-Driven Engineering: Application to Railway Infrastructure

Authors:

Alexis Chartrain, Gilles Dessagne, Noël Haddad and David R. C. Hill

Abstract: In this position paper, we argue that ontologies, combined with Model-Driven Engineering (MDE) approach and the object-oriented approach, could be leveraged to produce model-driven Digital Twins. This paper presents our framework for the production of model-driven Digital Twins using the aforementioned approach. Despite the interest in existing frameworks, this paper offers a new perspective that could help pave the way towards a future standardized, generic framework and shows insights of application at the French Railway Infrastructure Manager, SNCF RÉSEAU.
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Short Papers
Paper Nr: 5
Title:

Towards Developing an Ontology for a Digital Twin in Battery Testing

Authors:

Nuno Marques, Marco Rodrigues, Mannin Himanshu and Foad Gandoman

Abstract: Ontologies are of great importance in organizing and structuring domain knowledge towards interoperability and data integration in various fields, as provide a standarised vocabulary and a formal representation of relationships between concepts, which is essential for advancing data-driven applications. This paper presents the development of an OWL (Web Ontology Language) ontology for the battery testing field, creating the foundations for the development of a Digital Twin that will virtualize tests to be performed on battery cells and modules. It emphasizes the significance of the battery domain characterization and ontology definition as critical components in developing an effective Digital Twin for battery testing. An investigation of prior studies available in the literature was conducted, highlighting examples of ontologies such as SSN (Semantic Sensor Network) and SOSA (Sensor, Observation, Sample, and Actuator), which targets integration into the digital twin environments to enhance sensor data management and interoperability. The research also found hybrid ontologies, combining elements from existing ones and battery-specific Digital Twin architectures. The developed ontology was validated through a practical use-case by integration with cloud platform Microsoft Azure Digital Twins, converting the ontology from OWL to DTDL (Digital Twins Definition Language). This step completes the cycle as the proposed framework aims to create a robust and scalable Digital Twin environment that can be adapted to various battery testing scenarios, providing actionable insights from tests.
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Paper Nr: 6
Title:

Extracting API Structures from Documentation to Create Virtual Knowledge Graphs

Authors:

Maximilian Weigand, Felix Gehlhoff and Alexander Fay

Abstract: Semantic Web technologies and standards have emerged as effective solutions for data exchange, also in engineering contexts. They provide a standardized way to exchange data between different software and facilitate interoperability. Within this work, we introduce a workflow to systematically analyze the structure of application programming interfaces (APIs) of software, enabling the efficient transformation of information available from the API into information models that are structured according to Semantic Web standards. Our goal is to create a reusable interface for engineering software on top of its API. The approach leverages shared concepts between object-oriented programming and knowledge graphs to abstract components of the API into a knowledge graph. The workflow allows to selectively extract relevant API components and automates the generation of necessary code. To demonstrate the approach, we created an application that implements the workflow and use it for a Java-based API for a modeling software, showcasing the reduction of manual effort.
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