Ontology-Driven Digital Twins

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:

In the domain of smart healthcare, we are currently investigating how to provide a systematic way of improving IoT interoperability for e-Health in scenarios where different ontologies are used. In particular, we are extending SAREF4ehaw with medical terms from the SNOMED CT ontology [3]. As output of this research, we identified that the SAREF4ehaw ontology requires further work, as empirically validating it against other ontologies such as the FHIR RDF, which is quite relevant towards higher semantic interoperability within e-Health. Besides validating, new ontology alignments between SAREF4ehaw and FHIR RDF standards are required, evaluating them in scenarios where these semantic artefacts play different roles. Furthermore, an ontological analysis of FHIR standard based on UFO, perhaps grounding the FHIR RDF version in gUFO/OWL (similar to SAREF) can improve FHIR semantic expressivity and correctness, besides helping to identify the mappings for the semantic translations’ rules [4].

In general, this kind of approach can actually be applied over the main domain-specific standards that have a close relation to IoT, as the FIWARE Smart Data Models (https://www.fiware.org/smart-data-models/), and even if the standard is not based on RDF/OWL. For example, for the logistics domain, an ontological analysis of the OTM can result in improved interoperability for logistics systems. The semantic mappings and their respective translations can be realized with RDF Mapping Language (RML), which enables the development of transformation mappings from the JSON model of the OTM API (https://otm5.opentripmodel.org/) to the OTM operational ontology in gUFO/OWL. Within this context, alignments of OTM with other traditional data models, such as EDIFACT (https://unece.org/trade/uncefact/introducing-unedifact), is quite relevant to demonstrate how data exchange can happen with legacy systems.

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
de Souza, P.L., et al.: Ontology-driven IoT system for monitoring hypertension. In: Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, pp. 757–767. INSTICC, SciTePress (2023). ISBN 978-989-758-648-4. https://doi.org/10.5220/0011989100003467
Trojahn, C., Vieira, R., Schmidt, D., Pease, A., Guizzardi, G.: Foundational ontologies meet ontology matching: a survey. Semant. Web 13(4), 685–704 (2022). https://doi.org/10.3233/SW-210447