<figure><img src="image_1.png" /></figure>

# Towards Linked Data for Ecosystems of Digital Twins

Samuele Burattini

Department of Computer Science and Engineering, Alma
Mater Studiorum University of Bologna
Cesena, Italy
samuele.burattini@unibo.it

Marco Picone

marco.picone@unimore.it

Department of Sciences and Methods for Engineering,
University of Modena and Reggio Emilia
Reggio nell'Emilia, Emilia Romagna, Italy

## ABSTRACT

Due to either the inherent complexity of the domain or the evolving nature of systems, we can envision solutions that digitalize assets in a complex domain using an *ecosystem* of distributed Digital Twins instead of a single monolithic one. To effectively tackle interoperability in such ecosystems, this paper advocates for the introduction of a representation based on Semantic Web technologies enabling the discovery of both Digital Twin structure – i.e. the static information about the asset model and offered services – and state – i.e. the data and metrics collected at runtime – to support the management of ecosystems and the creation of application mashups. A review of the state of the art suggests that currently investigated ways to describe a Digital Twin are not sufficient to achieve this objective. A proposal of key requirements for a Digital Twin representation is outlined leading to the proposal of a core ontology and a Linked Data approach for state management.

## CCS CONCEPTS

* Computer systems organization → Embedded and cyber-physical systems; * Information systems → Semantic web description languages; *Mashups*; Ontologies.

## KEYWORDS

Digital Twins, Semantic Web, Interoperability, Ontologies

**ACM Reference Format:**

Samuele Burattini, Antoine Zimmermann, Marco Picone, and Alessandro Ricci. 2024. Towards Linked Data for Ecosystems of Digital Twins. In *ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems (MODELS Companion '24)*, September 22-27, 2024, Linz, Austria. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3652620.3688245

<figure><img src="image_2.png" /></figure>

This work is licensed under a Creative Commons Attribution International 4.0 License.
MODELS Companion '24, September 22-27, 2024, Linz, Austria

© 2024 Copyright held by the owner/author(s).

ACM ISBN 979-8-4007-0622-6/24/09

https://doi.org/10.1145/3652620.3688245

Antoine Zimmermann

Mines Saint-Étienne, Univ Clermont Auvergne, INP
Clermont Auvergne, CNRS, UMR 6158 LIMOS
Saint-Étienne, France
antoine.zimmermann@emse.fr

Alessandro Ricci

Department of Computer Science and Engineering, Alma
Mater Studiorum University of Bologna
Cesena, Italy
a.ricci@unibo.it

## 1 INTRODUCTION

Digital Twins (DTs) have risen from a concept tied to the product lifecycle management [13] to be a generic paradigm for the digitalization of physical assets (PAs) in many application domains [27]. Still, DT solutions mostly either focus on the mirroring of individual assets, failing to capture the relationships that exist among them, or adopt a monolithic approach in the digitalization of large-scale entities (e.g. a full city [8]) leading to issues concerning the scale and quality of the model and its evolvability.

Using multiple DTs to create a modular digital representation might be the key to effectively model large-scale scenarios – e.g., a nationwide Digital Twin [6] – and interoperability is considered the key challenge to achieving full maturity of the DT approach [20]. So-called *ecosystems* of Digital Twins [26] would then be composed of several entities – possibly connected by relationships – each mirroring crucial assets of an application domain and together achieving the digital representation of a complex system.

On the one hand, this would allow stakeholders to incrementally build the Digital Twin layer following their needs. The evolution of the DTs would be easier as they would be essentially independent from both a modelling and technological standpoint. The quality of the overall model would also improve, as it would be possible to keep track of the relationship between PAs and to model them at different relative scales for different application purposes, instead of relying on a single monolithic view. Moreover, this would open the way towards a vision of mashup applications that can use DT features as-a-service [26], even potentially leveraging DTs made available by different stakeholders and organizations belonging to different application domains.

On the other hand, managing the complex network of DTs opens up several challenges on how to integrate them effectively: with issues concerning data and service discoverability, deployment management, data and knowledge integration etc.

In this paper, following the trends from academia, industry and standardization bodies [9], we argue that a common representation model for DTs could address some of these issues. We investigate an approach based on Linked Data [17] principles and Semantic Web technologies to foster the adoption of a layer allowing Digital Twins to be represented as Web resources through a rich semantic model that could address interoperability concerns for the discoverability of DTs, their data and their exposed services. We ground
our contribution with respect to well-known conceptual models for
Digital Twins and present the main features and technologies tied
to Linked Data in Section 2. We then survey the state-of-the-art
Digital Twin representations in Section 3 to identify gaps that we
relate to the core problem of interoperability in DT ecosystems.
Finally, we propose two directions to handle the representation of
Digital Twins – a core ontology and DT state management based
on the Linked Data Platform – in Section 4 and highlight the main
open challenges towards the achievement of the overall goal of in-
teroperable ecosystems of DTs in Section 5 that we plan to explore
in future works.

## 2 BACKGROUND

In this section, we will discuss the conceptual models of DTs that we
take as reference to identify requirements for their representation.
We further introduce the principles and technologies tied to the
(Semantic) Web and Linked Data [17] highlighting their role in
enabling interoperability in heterogeneous distributed systems.

### 2.1 Digital Twin Conceptual Models

Despite being a well-recognized concept, Digital Twins are still in a
very dynamic evolution phase, with new proposals for conceptual
models emerging from different domains and areas of application,
ranging from computer science to manufacturing, medicine, etc.

Among those proposals, the five-dimensional model presented in [25] is becoming one of the most relevant in the area as it captures the essential features of a DT in a domain-agnostic fashion. It is composed by a *Physical Dimension* concerning the actual PA being mirrored, a *Connection Dimension* concerning the data flows from the real world to the DT (and vice versa) and towards external services, a *Data Dimension* that stores and process the data generated by the DT, a *Model Dimension* which is a virtual model of the properties and behaviour of the PA and a *Service Dimension* which enables external applications to access the data of the DT and exposes services such as prediction and simulation of the DT behaviour. This model already gives an intuition on the inherent complexity of capturing the full nature of a DT.

The need for a common meta-model for Digital Twins is an
essential feature in the vision for DT ecosystems. In the proposal for
a Web of Digital Twins [26] this is identified with the possibility of
conceptualizing DTs as a set of *properties* the DT measures, *actions*
the DT exposes to change the state of the corresponding asset, *events*
that can notify observers of changes and *relationships* with other
DTs. This very simple model is generally sufficient to represent
the features of any kind of PA, enabling its representation in the
digital world. Very similar models are adopted in the Internet of
Things domain such as the W3C<fnref n="1" /> Web of Things (WoT) with its
*Thing Description* [22] and also in the data model [23] designed by
Microsoft for its Azure Digital Twins cloud-based platform. This
proves, empirically, its effectiveness in providing a general model
to represent assets across different domains.

Despite such general models exist, for a DT to be interopera-
ble in complex ecosystems an integrated representation of all its
dimensions is needed.

## 2.2 Interoperability in the Web and Linked Data

The vision of the Semantic Web [4] consists of introducing new
technologies, standards, and best practices to the Web to ease the
exploitation of information at large online. In particular, Semantic
Web technologies should enable machines to automatically pro-
cess information from arbitrary sources to perform tasks on our
behalf. A first step towards this vision is to build a Web of data
that complements the Web of documents that people consume.
While multimedia documents serve humans well, accompanying
structured data simplifies information consumption by software
systems. To improve the exploitation of data worldwide, standard
data formats should be used across the Web. Explicit schemas fa-
cilitate processing further. Finally, ontologies help pinpoint the
exact meaning of the data structures. These roughly outline the
building blocks of the Semantic Web. As information on the Web
often describes physical reality this enables machines to use the
Semantic Web to understand reality and take action in it. In many
ways, this goal aligns with the very core idea of DTs, making their
overlap interesting to study.

We take the Web as inspiration because it is grounded on princi-
ples of interoperability between heterogeneous components, that
are summarized in the REPresentational State Transfer (REST)
constraints derived from the Web architecture [10]. REST sug-
gests that interoperability is achieved through a uniform interface,
which on the Web is built leveraging URIs to commonly identify
resources and the manipulation of resources through the exchange
of representations—using the semantics encoded in the HTTP pro-
tocol methods and negotiated serialization formats.

The Semantic Web enriches this idea by encoding semantics
in the representation through the general model of the Resource
Description Framework (RDF) [7]. This allows representations of
resources to be expressed in machine-readable triples that state
facts using a common vocabulary encoded within ontologies, them-
selves encoded in the Web Ontology Language (OWL) [24]. A set
of triples defining relationships between resources and data consti-
tutes a Knowledge Graph [18]. Linked Data [17], further completes
the idea by applying a hypermedia-based design to RDF, using
dereferenceable URIs as names for anything, and pointing those
URIs at useful resources that describe the concept they are identi-
fying. This essentially allows clients to follow them and discover
additional information, by browsing it. In this vision, the whole
Semantic Web becomes a distributed knowledge graph.

To further streamline how to manage Linked Data, the W3C
produced a recommendation in the form of the Linked Data Plat-
form (LDP) [29] that provides patterns to handle Linked Data using
RESTful interactions and defines the concept of a *container* of RDF
resources. LDP Containers can be used to group resources and guide
clients in their manipulation with standard behaviour as when an
LDP Container is discovered, it is then possible to get the list of
contained resources, request their representations and (optionally)
modify them or create new ones.

## 3 DIGITAL TWIN REPRESENTATIONS

In this section, we first define what are the desirable features a DT
representation should offer drawing from the conceptual models

<footnote n="1">¹World Wide Web Consortium</footnote>
of DTs and with regard to the problem of interoperability in DT ecosystems that we here summarize as:

Given an ecosystem of DTs we want to allow consumers to easily discover what DTs are present in it, what assets they mirror and how they are mirroring them, the services that the DTs expose and the real-time and past data the DTs are gathering, regardless of the underlying implementing technology.

We then analyze some notable works in the area to highlight how the idea of introducing a level of representation of DTs is well-recognized in the literature and industry, but at the same time, no existing proposal makes it possible to capture both the structure and the state of a DT in a coherent framework.

## 3.1 Requirements for Digital Twins Representations

When adopting a vision for ecosystems of Digital Twins where several entities of the target domain are digitalized, each through individual DTs, the need to have suitable representations for such DTs emerges quite naturally to achieve several objectives. This is reflected by DT standardization efforts [9] that are advocating for descriptions and representations to improve interoperability among different DTs. In this section we will analyze key features that make such a level of representation desirable and will use them as requirements for an ideal representation model for a Digital Twin, to later compare with existing related works.

Two main perspectives are adopted, on one hand, the representation is useful for DT consumers i.e., users and application developers that may wish to access the DT data, act on the physical world through the DT or leverage the services offered by the DT. On the other hand, the representation can be useful for DT operators i.e. DT developers and maintainers that need to manage a complex ecosystem of running instances of DTs, update their models, verify their state of operation and so on. For both kinds of users, the representations offer a uniform interface to access information about the DT, regardless of the underlying implementing technology.

*Identification.* The need to identify uniquely a DT is essential when considering open ecosystems. Moreover, the DT should clearly identify also the unique PA it is representing, as more DTs of the same asset may exist with potentially very different models.

*Physical Asset Modelling.* As different DTs for the same asset can be built, the representation of a unique DT should also include information about the models it is offering for the asset. An example of this could be in terms of which properties of the asset are measured and what temporal scale the DT is considered to update their values. Different models of the same room could very well in fact monitor the current temperature, the average temperature of the last hour or the energy consumption.

*Physical Asset State.* As DT digitalize PAs, one of the core objectives in their representation is making the real-time and past state of the mirrored entity available to consumers. This allows querying for assets that are in a given state at any given time (e.g. shutting off all devices that are consuming energy over a given threshold).

*Service Description.* DTs offer a bi-directional interaction with PAs which allows both to monitor and control the physical reality. Often, their models can also be used to predict or simulate the behaviour of the asset, and in some cases, visualization of the PA state is offered to allow human operators to understand the behaviour of the asset in real time. In open ecosystems of DTs, all of these features can be exposed as *services* for consumers. The description of these services is then essential to discovering the capabilities of a DT and using it accordingly, enabling building mashup applications on top of newly discovered DTs, leveraging their services.

*Deployment Context.* Technological details concerning how the DT is developed, alongside its version, authors and deployment requirements (e.g. dependency on MQTT brokers, GPU usage) give additional information to system administrators needing to update the DT models, or deploy instances of DTs. Consumers may also choose among different replicas of the same DT the ones that are deployed *closer* to either the source of data or the application depending on their needs.

*Metrics and State of Operation.* Metrics concerning its coupling with regard to the PA (e.g. [3]) allow for assessing the ability of the DT to effectively represent the PA in real time. Moreover, the lifecycle of a DT can be fairly complex, and a DT could sometimes be disconnected from its physical counterpart. The state of operation of the DT is essential for operators who may need to investigate bugs and consumers who may need to understand that the DT is offline and cannot be used to control the PA.

*Data Sources and Storage.* Describing how data is obtained – i.e., by means of which sources – allows keeping track of provenance and eases the management of the systems supporting the DT. Additionally, a DT may point to dedicated storage which allows consumers to perform data-intensive operations.

## 3.2 State of the Art Analysis

Having identified the requirements for Digital Twin representations, we believe a complete model should capture all of them in a coherent representation. We then compare relevant works in the state of the art for DT representations with regard to the support of those requirements. We selected them based on their relevance to the topic of creating descriptions for ecosystems of Digital Twins picking from well-known ontologies and data models used in the context of Digital Twins from both industry and academia and selecting among recent works with the keywords “Digital Twins” and “Ontology”. Table 1 reports the main analysed works. We do not claim this collection to be exhaustive, but we believe it is a sufficient selection to highlight the open gaps in the area. Additionally, our interest was specifically in works aiming to achieve a description that makes the DT easier to operate with for external consumers. We refer interested readers to a recent survey [19] for a more general analysis of the use of ontologies in Digital Twins.

The Digital Twin Definition Language (DTDL) [23] is an RDF-based model for the Azure Digital Twins<fnref n="2" /> platform by Microsoft. Its model allows to define models for DTs in terms of their properties, commands and relationships with other twins and to instantiate

<footnote n="2">²https://azure.microsoft.com/en-us/products/digital-twins</footnote>
<table><caption>Table 1: Surveyed works in the state of the art on DT representations, with the respective features they support: (✓) means the feature is directly supported, (~) means the feature can be represented within the framework but not explicitly addressed or not completely supported, (x) means the feature is not supported.</caption><thead><tr><th>Representation</th><th>Identification</th><th>PA Modelling</th><th>PA State</th><th>Services</th><th>Deployment</th><th>Metrics</th><th>Data</th></tr></thead><tbody><tr><td>Azure DTDL [23]</td><td>~</td><td>✓</td><td>✓</td><td>x</td><td>x</td><td>~</td><td>x</td></tr><tr><td>WoT Thing Description [22]</td><td>~</td><td>✓</td><td>x</td><td>✓</td><td>x</td><td>~</td><td>x</td></tr><tr><td>ETSI SAREF [11]</td><td>x</td><td>✓</td><td>✓</td><td>~</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Barros et al. [1]</td><td>✓</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Barth et al. [2]</td><td>✓</td><td>x</td><td>x</td><td>~</td><td>~</td><td>x</td><td>✓</td></tr><tr><td>Singh et al. [28]</td><td>✓</td><td>✓</td><td>✓</td><td>x</td><td>x</td><td>x</td><td>✓</td></tr><tr><td>Steinmetz et al. [30]</td><td>✓</td><td>✓</td><td>x</td><td>✓</td><td>x</td><td>x</td><td>✓</td></tr><tr><td>Gonzalez et al. [12]</td><td>✓</td><td>~</td><td>~</td><td>✓</td><td>x</td><td>x</td><td>✓</td></tr></tbody></table>

them within the platform, storing the last updated values of the properties in a so-called *twin graph*. Even though each instance has an identifier, the represented PA is not identified explicitly. Azure DT keeps track of the timestamps of the updates of each properties automatically, but does not represent any other metric explicitly, nor it allows to indicate data provenance.

The W3C WoT Thing Description (TD) [22] is a model to describe connected devices under the abstraction of *things* offering interaction affordances to read properties, execute actions or register to events. A TD identifies ambiguously both a device or a software entity implementing the *thing*—that as per the WoT architecture [21] could also be a Digital Twin. Interaction affordances in a TD are equipped with forms to detail how to invoke them, using a specific communication protocol, they thus allow new consumers to understand how to act on the thing and can represent services to interact with the DT.

The Smart Application Reference (SAREF) ontology [11] by the European Telecommunications Standards Institute (ETSI), is an ontology for IoT applications, that promotes interoperability across several domains. It suits the purpose of representing DTs and a dedicated group is investigating how to adapt it better for the purpose [9]. SAREF includes concepts such as services, properties and observations to collect the value of specific properties making it a good candidate to represent the model and state of a PA. It is not clear though how it would be possible to identify the software counterpart of the DT, nor how to decorate it with information about the deployment context, metrics or data sources.

Other proposals from the academia [1, 2, 28, 30] have different objectives with regard to the level of description of a DT. While [1] attempts to link the concept of Digital Twins with foundational ontologies [16] achieving a very high-level description, [2] attempts a systematization with respect to the data sources that compose a DT and its value on the target context. Differently [28] and [30] focus on the IoT domain, with the former focusing on data, whereas the latter focusing on the services and APIs that a DT offers, extending the IoTLite<fnref n="3" /> ontology to explicitly identify DTs.

A notable work is the recent model proposed by Gonzalez et al. [12] that extended the WoT TD ontology to represent Digital Twins, explicitly modelling the five-dimensions of DTs [25]. With this extension, they can represent the relationship between a DT and

its physical counterpart, despite not providing detailed instructions on describing its model or state. Services are represented through TD interaction affordances, and data sources and repositories linked to the DT are represented explicitly through the DCAT<fnref n="4" /> ontology, allowing consumers to access also non-semantic data.

# 4 PROPOSING SEMANTIC DIGITAL TWIN REPRESENTATIONS

Having identified requirements for interoperability through DT representations that capture the DT structure and state and recognized that existing models generally fall short in representing DTs in their entirety, we here propose an approach based on Linked Data to represent DTs for interoperable ecosystems.

The approach is based on two main elements: the first is a draft of a core ontology to explicitly identify concepts that are required to represent a DT, the second is an approach to handle browsable DT history using a Linked Data approach.

## 4.1 Towards a Core Ontology for Digital Twins

From the requirements outlined in the previous section, we here sketch a model for a core ontology that can support the definition of DT representations (Figure 1). Developing an ontology is a long iterative process and as the goal is to support interoperability among the most possible types of DTs we just show here a tentative model that we plan to continue refining in future works.

First, we distinguish clearly the DT from the PA it is mirroring. Each has a state (and a history, see section 4.2). The state of the PA includes data about the asset, whereas the state of the DT includes its state of operation, metrics and deployment context. The DT also has a model, that is described in terms of the features that compose it (properties, actions and events). Finally, the DT will expose Services, that can be related to other components of the DT – e.g. the service for subscribing to an event or invoking an action that could be described with a WoT Thing Description [22] – or not – e.g. a simulation service that is generic for the whole DT.

We believe this simple model to be a good start towards the definition of a core ontology, although it will need further refinement and testing with different kinds of DTs. Also, we present this as a core model, since we expect the development of extensions

<footnote n="3">³https://www.w3.org/submissions/iot-lite/</footnote>

<footnote n="4">⁴Data Catalog Vocabulary: https://www.w3.org/TR/vocab-dcat-3/</footnote>
for specific kinds of DTs. Still, having a core root that can already address most requirements can drive interoperability to build DT ecosystems.

<figure><img src="image_3.png" /><figcaption>Figure 1: A schema of the entities for a draft core ontology for DT representations. Relationships are not necessarily 1:1 (i.e., a DT can have multiple Models).</figcaption></figure>

## 4.2 A Linked Data Approach for State History

Digital Twins by definition evolve over time to reflect the changes of their physical counterparts. As such, their representation must evolve as well. As can be seen from Table 1, most existing approaches present a level of description that does not include the state of the PA. Nevertheless, this is valuable as it allows clients to directly interrogate the ecosystem of DTs with queries on the current state of a system of DTs. This is supported, for instance, in the Azure Digital Twins platform that offers a representation of a DT in terms of the latest values of its properties, but not its history which is delegated to a separate data storage and as such needs to be accessed with a different tool and query language.

A different approach is instead adopted in other kinds of descriptions such as the one that can be built with the SAREF ontology [11] that includes an Observation as a first-class concept to represent the operation that leads to collecting a measurement of some property value at a given time. Managing the evolving state of the asset would then mean adding new observations and property values to the same description. This would make the representation more complete, but also heavier: better for querying but less effective when simply browsing the ecosystem of DTs.

In general, handling the evolution of knowledge graphs over time is a complex subject, but necessary if one wants to keep the representation of a DT up to date. At any given time the representation of the DT should give a consistent and updated representation of its state, while at the same time offering the possibility to browse back in time. For this reason, we envision supporting such a mechanism using the rules of the Linked Data Platform [29] to host and allow access to the data of a DT.

With every state update, the DT will produce a snapshot graph that describes its state at a given time. The DT will then be granted authorized access to POST it as a resource in an LDP Container. If intended as a named graph [5] each snapshot of the DT state can further be described in terms of the time frame it represents

<caption>**Listing 1:** An example of how the history of the states of the DT can be represented using LDP containers</caption>

```yaml
@base <http://localhost:8080/>.
@prefix ldp: <http://www.w3.org/ns/ldp#>.
@prefix dto: <http://example.org/dtOntology#>.
@prefix time: <http://www.w3.org/2006/time#>.
@prefix dcterms: <http://purl.org/dc/terms/>.

<myDT> a dto:DigitalTwin;
       dto:hasStateHistory <myDT/stateContainer>.

##Container Description##

<myDT/stateContainer> a dto:StateHistory;
                     a ldp:BasicContainer;
                     dcterms: title "The state of the DT";
                     ldp:contains <s1>, <s2>, <s3>.

<s1> time:hasTime _:t1.
<s2> time:hasTime _:t2.
<s3> time:hasTime _:t3.

.:t1 time:before _:t2.
.:t2 time:before _:t3.
```

and with an ordering relationship with the other ones, creating a chain of immutable representations that consumers can browse (Listing 1). The advantage of using the LDP standard would offer a uniform interface for browsing the DT state history.

It must be noted that the semantics of named graphs that we adopted in this paper are still an object of debate in the Semantic Web community<fnref n="5" /> We consider this an interesting use case that motivates the need to allow the attribution of names to a graph to further describe it as a whole.

Of course, this practice is not meant to replace more efficient data storage solutions (e.g. time series databases) that might still be more performing when requiring data in bulk for analytics purposes. This would allow though to have uniform, Linked Data-compliant access to all the features of a DT, enabling browsing through time with pure RESTful interactions.

# 5 OPEN CHALLENGES AND FUTURE WORKS

In this paper, we have outlined the requirements for representations of DTs while showing how existing works fail to capture all of them. We then suggested how representations based on Linked Data principles can address such requirements: We advocate for a core ontology for DTs and propose a Linked Data approach to manage DT history under a uniform interface that can be browsed by consumers.

The vision of interoperable ecosystems of DTs comes with several challenges, both at the technical and societal levels. Here we highlight the main ones and comment on future works.

At the societal level, stakeholders may not be willing to open the data and services of DTs to other consumers, limiting interoperability and preferring the creation of closed systems, especially when DTs are built by companies that sell both the hardware and software counterparts. At the same time, institutions have already shown interest in the realization of DTs for the management of the public infrastructure and open ecosystems of DTs might improve transparency in public settings. Of course, for this framework to

<footnote n="5">⁵see https://www.w3.org/TR/2014/NOTE-rdf11-datasets-20140225/ for an in-depth discussion</footnote>
be effective, it must be possible to allow fine-grained access con-
trol to data and guarantee security and privacy. We believe the
Solid<fnref n="6" /> project to be an interesting direction to achieve this goal in
decentralized Linked Data ecosystems.

On the technical level, the main challenges concern the dis-
tributed nature of ecosystems of DTs which makes it difficult to
create links between DTs, especially when such links are dynamic
relationships. Managing the huge amount of data DTs produce, also
challenges the possibility of storing them and interrogating them
effectively with knowledge graphs. We believe a hybrid approach
to be a plausible solution with DTs' representations pointing to
dedicated data storages that use more suitable formats for bulk
access. Finally, generating the representations for DTs could be a
daunting task for developers, and limit their applicability in prac-
tice. We believe development tools should ease this task. A basic
core ontology could provide shared structure across different DT
development frameworks, leaving only the more domain-oriented
aspects to consider for DT developers.

Addressing interoperability is a multi-faceted problem. On one
hand, using semantic technologies does not grant interoperability
on its own [15] and at the same time, we acknowledge that a core
ontology may create a false-agreement problem [14]. Nevertheless,
this direction can be a step forward: in future works, we plan to
continue developing such core ontology testing the representation
of DTs in practical use cases, paired with the Linked Data Platform
container approach of snapshot graphs for DT history to evaluate
its effectiveness. Finally, an essential step will be the creation of
alignments with other existing ontologies, to better support repre-
sentations for DTs across different domains.

ACKNOWLEDGMENTS

This work is partially supported by the project "Digital Twins
Ecosystems for the clinical, strategic and process governance in
Healthcare" funded by the European Union - NextGenerationEU and
AUSL della Romagna (PNRR M4C2, Investment 3.3 DM 352/2022) -
CUP J33C22001400009

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<footnote n="6">⁶https://solidproject.org/</footnote>