Well-Founded Digital Twin Core Ontology

MAster assignment

Type: Master CS

Period: Start date: as soon as possible

Student: Unassigned

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Background:
The concept of Digital Twin (DT) has gained popularity as a digital representation of physical entities that interact with their real world counterparts in (near) real-time through sensors and actuators. DTs can be applied across different sectors, offering benefits like simulation, remote monitoring, and predictive maintenance, which are relevant capabilities of smart systems. However, achieving the full potential of DTs requires addressing interoperability challenges posed by the complex networks of devices and systems that play different roles in DTs. Recently, we published a paper [1] that presents a research agenda aimed at enhancing DT interoperability grounded in four computer/information science disciplines: architecture of distributed systems, model-based system engineering, ontology-driven conceptual modeling, and linked data with semantic web. This paper highlights how leveraging on existing standards, such as modelling languages and ontologies, is important for improved DT interoperability.

Interoperability refers to the capability of multiple systems or components to exchange and effectively utilize shared information. Therefore, interoperability defines the way of interconnection between sensors, devices, manufacturing systems, and people, including exchange of products and materials among facilities. In particular, semantic interoperability is the most challenging because it is about the “interpretation of shared data in an unambiguously way, ensuring that the understanding of the information is the same for senders and receivers”. Establishing automatic semantic interoperability for seamless systems’ integration is an arduous task.

The core function of a DT relies on merging the virtual model with sensor data that are collected with support of IoT technologies. DT data are formalized in diverse ways, gathered from various sensors and must be integrated with other data that rely on different languages and their serialization syntax. These can vary according to the different domains and purposes, and this complexity elevates integration and interoperability challenges at both syntactic and semantic aspects, and in all interoperability levels: legal, organizational, semantic and technical. We have been working with the concepts of digital thread, digital model and digital shadow within the DT research [2], which involves various representations of a target system adapted for specific purposes. These representations can include digital models for static analysis or simulation of different system versions. Advances in IoT technology enable the creation of digital shadows, using real-time data for visualization. DTs take this further through a bidirectional connection to the real system, utilizing real-time data to mimic and influence the actual behavior of the system, facilitating analysis, prediction, and rapid corrective actions by integrating models from the digital thread with sensor data and system actuators. In this context, the specification artifacts covered by the architecture of distributed systems play an important role for digital threads, since they prescribe the structural and behavioral elements of the systems, such as components, data sources, and services.

Master Project assignment:

This assignment focuses on an ontology for representing the main concepts surrounding DTs. An open issue is the lack of a reference (core) ontology grounded in a foundational ontology (such as UFO) of the DT concept, exploiting the main structural and behavioral aspects, and therefore, describing precisely what is (and what is not) an DT. Since IoT is a fundamental concept of DTs, this core ontology should leverage on the many existing IoT ontologies, and in particular the ones that are standardized, such as SAREF. Our previous research showed that a complete ontological analysis of SAREF is required to improve its semantics [8]. Grounding SAREF in UFO can be performed through gUFO and executed through the systematized approach on inferring ontological categories from OWL [21]. Besides grounding SAREF in gUFO, it is relevant to provide ontology alignments with the current version of SAREF and its extensions, as well as covering other standardized IoT ontologies such as SSN/SOSA, making sure to cover the foundational elements such as actuators (saref:Actuator) and their costs through profiles (saref:Profile). These alignments can be implemented as semantic translations and their validation should cover their semantic completeness in different application domains.

References:

Moreira, João. 2024. The Role of Interoperability for Digital Twins. EDOC.
Pessoa, M.V.P., Pires, L.F., Moreira, J.L.R., Wu, C.: Model-based digital threads for socio-technical systems. In: Marques, G., Gonzalez-Briones, A., Molina Lopez, J.M. (eds.) Machine Learning for Smart Environments/Cities. Intelligent Systems Reference Library, vol. 121, pp. 27–52. Springer, Cham (2022). ISBN 978-3-030-97516-6. https://doi.org/10.1007/978-3-030-97516-6 2