=Paper=
{{Paper
|id=Vol-3214/WS1Paper1
|storemode=property
|title=The Use of Digital Twins to Overcome Semantic Barriers in Hyperconnected Ecosystems for Industry
|pdfUrl=https://ceur-ws.org/Vol-3214/WS1Paper1.pdf
|volume=Vol-3214
|authors=Frank-Walter Jaekel,Patrick Gering,Thomas Knothe
|dblpUrl=https://dblp.org/rec/conf/iesa/JaekelGK22
}}
==The Use of Digital Twins to Overcome Semantic Barriers in Hyperconnected Ecosystems for Industry==
https://ceur-ws.org/Vol-3214/WS1Paper1.pdf
The Use of Digital Twins to Overcome Semantic Barriers in
Hyperconnected Ecosystems for Industry
Frank-Walter Jaekel1, Patrick Gering1 and Thomas Knothe1
1
Fraunhofer Institute for Production Systems and Design Technology IPK, Pascalstr. 8-9, D-10587 Berlin,
Germany
Abstract
To establish business networks a match between businesses demands and potential partner
information is required. Publicly available information on the Internet about companies,
products and services usually don’t follow a common standard. The concept of a digital twin
could be used to organise the different information and, in the future, to harmonise the way
company data is made available on the web. Every company usually has a web presence,
related documents, web pages and a trace on the web, which can be used for an initial
structure of the digital twin. Hereby, first services for the correlations between partner
companies and requirements can be designed. But it requires the management of legal aspects
e.g. the access of bots to the public available information. The paper provides initial ideas
and feasibility checks and it propose an evolution of the current heterogeneous content and
structure of the data into a well-structured digital twin including content related ontologies to
describe the company characteristics.
Keywords 1
Hyperconnected, ecosystems, digital twin, ontology, supply chain, semantic
1. Introduction
Supply chains suspended, the availability of raw materials becomes more difficult, new laws
demand high transparency across the entire supply chain. The need for high flexibility of sourcing and
reaction on demands from the market requires not only agile, adapting processes within
manufacturing, but also cross-functional cooperating manufacturing networks in particular.
The Hyperconnected Ecosystems for industrial networks should enable the networking of any
relevant information and its accessibility at anytime from anywhere (hyperconnected). Barriers
between network partners are reduced and aligned with current requirements. The evolution of the
network will be enabled by its configurability and flexible integration of services. Ultimately, any
required information should be immediately available in a usable form and without effort at any
location. In this way, the network has similarities to social networks in which partners find each other
and exchange data and services as needed.
This blurs the boundaries between the partners in the network and creates the need for the use of
data from machines, products and processes across the network without delay. At the same time, the
range for tracing the paths in the network increases, as the supplier's connections can also be
identified and mapped as a network. This incorporates services providers for logistic, IT and finance
as well as legal services. Assessments can be carried out quickly in this way, for example to identify
deviations from the legal framework. However, the industrial requirements for security, sovereignty
and transparency must be met.
Proceedings of the Workshop of I-ESA’22, March 23–24, 2022, Valencia, Spain
EMAIL: frank-walter.jaekel@ipk.fraunhofer.de (F.-W. Jaekel); patrick.gering@ipk.fraunhofer.de (P. Gering);
thomas.knothe@ipk.fraunhofer.de (T. Knothe)
ORCID: 0000-0003-4846-005X (F.-W. Jaekel); 0000-0002-3055-7155 (T. Knothe)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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The construction of the "hyperconnected ecosystem for industry" faces, among other things, the
challenge of the existence of a difficult-to-manage number of platforms, digital services, cloud
solutions and services, which also include enterprise platforms for products and services.
Interoperability usually only takes place within a solution and can only be realized across the board
with a high translation effort. This makes the targeted search for and networking with new providers
time-consuming and difficult. Solutions for specific markets already exists and uses smart methods to
interrelate demands and offers of their partners [1, 2].
The “hyperconnected ecosystems for industrial networks” aim at fast communication and flexible
data use in dynamic enterprise networks and thus in particular at transparent and flexible supply
relationships. The technological basis for this is provided by approaches from the field of social
networks but requires effective tendering mechanism and interoperability [3]. Especially the
correlation between demand and offer needs to be supported as well as the understanding of the
provided data within communication along the network. Even if there is no consensus of digital twin
definitions, the formalisation in terms of ontologies, models related data sources and managing
bidirectional changes of data might improve of interoperability between the network partners.
This incorporates not only digital twins of products and services but also digital twins of the
factory or company in general. The twin might be represented in a first attempt by a semantical
enriched webpage like considered by semantic web approaches [4]. In the future it should be evolve
in terms of more behaviour details and cross references to experiences and evaluations of the partner
e.g. by others references and sources describing the detailed work of the company from their customer
side. This way would allow an evolution from a very simple twin provided by the company to a more
and more enriched twin.
The Hyperconnected Ecosystem for industrial networks assumes a networking of independent
companies. This creates semi-automatic connections between the network partners. For example, a
partner can find other partners via defined properties and then address them specifically. The retention
of sovereignty over the data to be transmitted (data ownership) must be ensured. In addition, services
can be provided as well as searched for and embedded in one's own environment. Here the role of the
digital twin can support the clear data ownership on the partner company side in case the company
provides a digital twin. This includes the update of the data and control about the data published
through the network.
Approaches such as the Open Platform Communications Unified Architecture (OPC-UA) [5], the
international data space (IDS) [6], Asset Administration Shell (AAS) [7] and BaSys/BaSyx [8]
provides already interface definitions and related information model structures. In fact in BaSyx the
AAS implementation is addressed as a digital twin [9]. However specific semantic or ontologies
covering behaviour, reaction times, laws, business indicators as well as product information needs to
be correlated across different sources such as standards and quasi standards e.g. ECLASS (eCl@ss)
[10], OPC-UA companion specification.
2. The digital twin within the network
To date, no consensus definition of a digital twin has been established in scientific publications.
While digital twins are defined on the one hand for final products [11, 12] or entire product life cycles
[13, 14]. Gartner coined the term Digital Twin of an Organization (DTO) and no longer limits digital
twins to physical objects. The vision is to create a digital image of an entire organization, its processes
including infrastructure, roles and responsibilities, products, etc. [15, 16].
Following the idea, we refer to van der Aalst's definition of the digital twin and the classification
of the digital shadow and master, which replaces the physical object with reality and removes the
restriction to physical objects [16].
According to the Fraunhofer Institute for Production Systems and Design Technology (IPK), the
digital master is described by reference models and information of products and process originating
from development phases. The digital shadow results in the continuously recording, storing and
provisioning of operational data. Only the intelligent linking of the digital master with the digital
shadow results in the digital twin [17].
In the paper we see the “partner company twin” as a digital representation of the available
information of a legal entity usually a company or parts of a legal entity such as factories of a group.
According to a digital twin within the paper, it consists of a digital master and the related digital
shadow of one or more real objects and its relationship. This includes their products, legal status,
business activities etc. (Figure 1) and comes close to the idea of the DTO. In this case the master
provides a structure of the twin in terms of references to data sources, models, ontologies, etc.. The
digital twin represents the digital representation of the company by providing the data requested by
the digital master and linking it to the transaction data and traces on the web in the sense of a digital
shadow. The twin should represent the current status of the company. The digital representation of the
company can evolve from a simple web page to a more comprehensive twin. A challenge is the
bidirectional interaction between company data and the digital twin, because changes in the real world
should directly trigger a feedback in the digital world.
Partner
Company
Twin
Enterprise models
Web references
Infrastructure models
- Quality
- Evaluations
Ontology
- Project references
- Products
- Customers
- Services
- Customer feedbacks
- Business criteria
- Behavior
Figure 1: Concept of company twin representing network partner
The twin should allow the interrelation of this information. In an initial attempt this is just a profile
covering characteristics of a network partner. The characteristics are just defined terms with structure,
synonyms and relations, which might evolve to ontology but also consider different existing
ontologies.
The profile can be filled in manually, but in discussions with stakeholders, especially small and
medium-sized enterprises, they did not agree with the effort involved. Therefore, we also considered
an automatic search and data retrieval mechanism that is described in the technical part of the paper.
The “partner company twin” is now composed of instantiated characteristics and references to data
sources. This creates a library of potential partners of a business network. They can be located in
different web or cloud infrastructures. Network relationships and connections can be established by
business demands or by data requests.
This should be supported by a tendering service to find appropriate business partners. A demand
needs to be described e.g. a required part of a product or a required service. Afterwards a search takes
place to find correlation between the different partners on the characteristics described. In the future
this might be automatically if a network partner disappears or is not able to deliver in the timeframe.
Also new partners can be considered easily and reconfigured into the network (Figure 2).
WEB
Twin
Twin Twin
Twin
Twin
Twin
Figure 2: Rough concept of network of twins across platforms and cloud
3. Technologies and current work
Available technologies to realize the describe concept of “hyperconnected ecosystems for
industry” using “partner company twins” are communication technologies such as OPC-UA, MQTT
or just REST protocols, cloud infrastructures, concepts such as international data space, asset
administration shell, FIWARE[18], I4Q [19], GAIAX [20], BASYx which we analysed. They provide
a very good basis and also initial and open toolsets such as BASYx [9]. However, we did not find “the
approach” because of lots of technical dependency related to programming languages, developing
environments and platforms. A good connectivity between machines and enterprise application can be
achieved if OPC-UA is used and a common companion specification but every system needs these
OPC-UA interface. BASYx allows different interface options such as OPC-UA, MQTT etc. but you
need a BASYx implementation following the AAS concept. Therefore, it is difficult to decide which
technology will be persistent in the future.
So, we followed a more generic approach with small exchangeable services and connecting
different “partner company twins”. The feasibility is still a challenge and we work currently on related
feasibility tests. In parallel with the user interfaces being developed with industry partners, we have
identified the following services for initial feasibility testing:
1. Matching of demand and partner characteristics
2. Semi-automatic filling of partner profiles
3. Identification of requested data in text
The matcher correlates simply a demand with different characteristics across a set of profiles. The
design of the matcher relies on REST interfaces. The demand is based on logical statements but also
the characteristics within the profiles can have logical interdependencies. The matcher is configured
by properties and the related logic. It takes a parameter set combined with logical equations and check
it against a set of profiles. A future extension of the matcher is the use of ontologies for the
correlation. This is especially related to different descriptions of products and materials.
The service to fill the profiles relays currently on technologies such as web crawler to analyses
web addresses and scraping. After initial tests we encounter that most of the supplier pages are
permitted concerning automatic data analysis (robots.txt). Therefore, we have requested a special
approval for the scraping tool before the analysis proceed. The crawler was finally less needed
because we will get specifically the web address of the partner. The interest of the partner companies
is the reduction of work about maintaining a profile. This supports the decision to take the actual data
directly from their web pages.
The work with the scraper and the different web pages provides initial results. However, formats
are quite different on the web pages, semantic web guidelines are less considered and additional
information are provided in text form such as product specifications. Therefore, the text analysis with
the Industrial-Strength Natural Language Processing (spaCy) [21] has been considered. It provides a
grammatical analysis of the text to identify for example relationships between criteria and related
values. The example in Figure 3 illustrates the usage and the language independency. The sentence on
the left side “The diameter is only 20 mm despite its great performance” is analyzed using the profile
template. The result is a row of the profile with the requested data.
The diameter is only 20 mm A ttrib u te Va lu e Un it
despite its g reat
diameter 20 mm
performance.
D er D urchmesser beträg t A ttrib u t We rt E in h e it
trotz g roßer Leistung nur
D urchmesser 20 mm
20 mm.
Figure 3: Text analysis
The tool chain realized by the described services above are expressed in Figure 4. The Crawler
might be used if the partner urls are not available. Otherwise the target page is already selected by the
url of the considered partner company. The scraper uses the target page to extract the detailed
information of the partner company. In case of text documents spaCy will be in place to support
detailed analysis regarding missing values for the profile. The data are finally introduced into the
profile. However, if data is missing further activities might be initialized otherwise they will be kept
empty. In case of further characteristics which might be encounter because of property tables they will
be added to the profile for further use. The analysis requires a minimum structure of the text on the
web page in terms of property tables. In terms of text the grammar needs to be correct and the text
should be easy to read. The provided information should be consistent because currently no constancy
check is in place but in the future it should be implemented. This tool chain will fill the profiles and
also keep the profiles actual as far as the provided information will act like a digital twin of the
partner company and actualizes the data accordantly to changes in the real work of the company.
Having the profiles available they can be used for tendering or communication access between the
partners. This is supported by the matcher which can be configured with actual business rules and
parameter types. The matcher takes the demand and find matches to create partnerships. Logical
relationships and use of the characteristics are defined in the configuration of the matcher.
The described technology is one possible usage of “partner company digital twins”. It requires a
clear description of company information as well as the connection between real world company
business and its digital representation. This can be done through specific processes for regularly
updating company information on the web in the form of manual activities or interfaces to the
company's IT systems.
Text spaCy
WEB
Target
(Crawler) Scraper
Pages
Configuration
Property Value Unit Logic Ontology
Property Value Unit Logic Ontology
Property Value Unit Logic Ontology Property Value Unit Logic Ontology
Demand Matcher Property Value
Profiles
Unit Logic Ontology
Matches Library of potential partners (registry)
Figure 4: Tool chain about data collection and matching
4. Conclusion and outlook
Digital twins can provide a formal description in terms of ontologies, models and data
relationships describing the company and its products as well as in term of its shadow e.g. evaluations
and project results in the web. An example of the usage of these Digital Twins has been described in
chapter 3 in terms of a feasibility study for data collection and match making. This is an important
feature within a hyperconnected ecosystems to find partners and establish networks.
Currently only few organisations provide the data in a form of the digital twin. Therefore, also
unstructured and incomplete data has to be considered for the correlation between demands and
providers. The unstructured data appears as one of the barriers for automatic data processing because
finally it cannot process full automatically. It still requires a final check by experts if the data is
correctly identified.
The described work in this paper represents concepts and feasibility checks related to a
hyperconnected ecosystem for industry starting with initial services such as matching between
demand and partner characteristics. The next steps will focus on the real usage of the technology in
terms of getting information from supplier web pages demanded by customers. We already start
cooperation with companies in terms of the related analysis of their web pages. Also, profiles on the
customer side has been drafted. The target is to archive a technical readiness level which will allow an
initial distribution of the services.
However, a presumption still needs further consideration, the “partner company digital twin”
currently the available digital twins are poor. So, it will need more work on developing technologies
to simplify the provision of partner company digital twins.
5. Acknowledgements
Foundations of the described work has been developed in the scope of a project of the Werner von
Siemens Centre (https://wvsc.berlin/) WvSC.EA “Electric motors 2.0” supported by the European
Regional Development Fund (EFRE). The described services benefit in terms of usage partially from
discussions within WvSC.EA.DI05 with the following partners Siemens AG, pi4_robotics GmbH,
Chair of Service-centric Networking (SNET) from the Technische Universität Berlin, TresCom
Technology, 5thIndustry and FhG IPK.
6. References
[1] Globality, Smart sourcing, 2022. URL: https://www.globality.com/en-us.
[2] Facturee, Online Manufacturing, 2022. URL: https://www.facturee.de/en/.
[3] ASSURANT, Enabling Interoperability the key to a Connected Ecosystem, 2020. URL:
https://www.assurant.co.uk/newsroom-detail/Features/2020/September/enabling-interoperability-
the-key-to-a-connected-ecosystem.
[4] W3C, Semantic Web, 2022. URL: https://www.w3.org/standards/semanticweb/.
[5] OPC-UA, Welcome to the World of OPC, 2022. URL: https://opcfoundation.org/.
[6] International Data Space Association, Reference Architecture Model, Version 3.0, 2019. URL:
https://internationaldataspaces.org/wp-content/uploads/IDS-Reference-Architecture-Model-3.0-
2019.pdf.
[7] BMWI, Details of the Asset Administration Shell, 2022. URL: https://www.plattform-
i40.de/IP/Redaktion/EN/Downloads/Publikation/Details_of_the_Asset_Administration_Shell_Pa
rt1_V3.html.
[8] BASYS4.0, Basissystem Industrie 4.0: Eine offene Plattform fur die vierte industrielle
Revolution, 2022. URL: https://www.basys40.de/.
[9] The Eclipse Foundation, BASYSx, 2021. URL: https://www.eclipse.org/basyx/?target
[10] eCl@ass, Enable your global business and digitalization, 2022. URL:
https://www.eclass.eu/en/index.html.
[11] G. N. Schroeder, C. Steinmetz, C. E. Pereira, D. B. Espindola, Digital Twin Data Modeling with
AutomationML and a Communication Methodology for Data Exchange, IFAC-PapersOnLine 49
(2016) 12-17. doi: 10.1016/j.ifacol.2016.11.115.
[12] M. Abramovici, J. C. Göbel, H. B. Dang, Semantic data management for the development and
continuous reconfiguration of smart products and systems, CIRP Annals 65 (2016) 185-188. doi:
10.1016/j.cirp.2016.04.051.
[13] R. Rosen, G. von Wichert, G. Lo, K. D. Bettenhausen, About The Importance of Autonomy and
Digital Twins for the Future of Manufacturing, IFAC-PapersOnLine 48 (2015) 567-572. doi:
10.1016/j.ifacol.2015.06.141.
[14] T. Gabor, L. Belzner, M. Kiermeier, M. T. Beck, A. Neitz, A Simulation-Based Architecture for
Smart Cyber-Physical Systems, in: 2016 IEEE International Conference on Autonomic
Computing (ICAC), IEEE, New York, 2016, 374-379. doi: 10.1109/ICAC.2016.29.
[15] M. Kerremans, J. Kopcho, Create a Digital Twin of Your Organization to Optimize Your Digital
Transformation Program, 2019. URL: https://www.gartner.com/en/documents/3901491/create-a-
digital-twin-of-your-organization-to-optimize-y.
[16] W. M. P. van der Aalst, Concurrency and Objects Matter! Disentangling the Fabric of Real
Operational Processes to Create Digital Twins, in: A. Cerone, P. C. Ölveczky (Eds.), Lecture
Notes in Computer Science, Theoretical Aspects of Computing, Springer, Cham, 2021, pp. 3-17.
doi: 10.1007/978-3-030-85315-0_1.
[17] T. Knothe, K. Lindow, C. Geisert, Gut vernetzt ist halb gewonnen. Um Digitale Zwillinge
gewinnbringend einzusetzen, müssen produzierende Unternehmen wissen, wie sie diese sinnvoll
vernetzen können, 2021. URL: https://www.ipk.fraunhofer.de/de/publikationen/futur/futur-2021-
2/gut-vernetzt-ist-halb-gewonnen.html.
[18] FIWARE, FIWARE: The Open Source Platform for Our Smart Digital Future, 2021. URL:
https://www.fiware.org/.
[19] I4Q, Industrial Data Services for Quality Control in Smart Manufacturing, 2021. URL:
https://www.i4q-project.eu/.
[20] GAIAX, About Gaia-X, 2021. URL: https://www.gaia-x.eu/what-is-gaia-x.
[21] SpaCy, Industrial-Strength Natural Language Processing, 2022. URL: https://spacy.io/.