CDM-Core: A Manufacturing Domain Ontology in OWL2 for
Production and Maintenance
Luca Mazzola
, Patrick Kapahnke, Marko Vujic and Matthias Klusch
I2S - Intelligent Information Systems Research Team, ASR- Agents and Simulated Reality Department,
German Research Center for Artificial Intelligence (DFKI) Stuhlsatzenhausweg 3, D-66123 Saarbruecken, Germany
Keywords:
CDM-Core, Applied Ontology, Semantic Annotation, Knowledge Engineering, CREMA H2020 RIA Project,
Ontology Quality Measurement.
Abstract:
Ontology engineering is known to be a complex, time-consuming, and costly process, in particular, if an on-
tology has to be developed from scratch, and respective domain knowledge has to be formally encoded. This
paper presents the largest publicly available manufacturing ontology CDM-Core in the standard formal on-
tology language OWL2
1
. The CDM-Core ontology has been developed within the European research project
CREMA in close collaboration with the user partners in order to sufficiently cover the CREMA use case do-
mains of metal press maintenance and automative exhaust production. CDM-Core makes use of many relevant
standard vocabularies and ontologies, with only about one fifth of its size being CREMA use case specific.
The practical applicability of CDM-Core for semantic annotation of domain-related process models, sensor
data and services has been approved by the user partners, and its quality according to selected common cri-
teria of verification and validation was successfully evaluated. From the public release of the CDM-Core, we
expect to cover the lack of a base common ontology for the manufacturing domain, thanks to feedbacks from
industrial reuse and improvements from the community.
1 INTRODUCTION
Developing ontologies is considered nowadays a stan-
dard activity in research project dealing with seman-
tics. Unfortunately, this is not a common result of
applied projects, where the effort and knowledge re-
quired to develop an ontology from scratch is con-
sidered not sustainable, in respect of the expected
benefits. For this reason, in the context of the EU-
founded Horizon2020 CREMA
2
project, we decided
to develop the ”CREMA Data Model, Core mod-
ule” (CDM-Core), as a manufacturing ontology tak-
ing into account both the general manufacturing do-
main applicability and the specific project use cases
coverage. As a result, this is the first publicly avail-
able applied manufacturing ontology (Mazzola et al.,
2016), composed by three different parts: a gen-
eral manufacturing-related flat layer, a set of domain-
specific or standard-based vertical slices such as
”Conditional Monitoring” or ”Semantic Sensor Net-
Corresponding author, mazzola.luca@gmail.com
1
Available at: http://sourceforge.net/projects/cdm-core/
2
CREMA is ”Cloud-based Rapid Elastic MAnufactur-
ing” and its website is http://www.crema-project.eu/
work”, and some use cases specialized segments (au-
tomotive exhaust production and metallic press main-
tenance), that can be a guidance for developing other
specific applications.
The rest of the paper is organized as follows: In
section 2 the CDM-Core requirements are presented,
together with the obtained results from the distributed
process for the ontology creation; some of the CDM-
Core usages for semantic annotation of process mod-
els, services and data streams are showcased in Sec-
tion 3; whether Section 4 closes the paper with an
analysis of the quality measures for the developed on-
tology.
2 THE CDM-Core ONTOLOGY
From the use cases description and the user partner
inputs, a set of high level requirements was designed,
and subsequently validated.
The elicited requirements include multifaceted as-
pects, such as the CDM-Core capability to represent
domain knowledge for both use cases, in order to al-
low annotation of process model, services and sensors
136
Mazzola, L., Kapahnke, P., Vujic, M. and Klusch, M.
CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance.
DOI: 10.5220/0006056301360143
In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 2: KEOD, pages 136-143
ISBN: 978-989-758-203-5
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
data; its expected (logical) consistency; the adoption
of selected relevant freely available standards; and the
usage of a standard W3C modelling language. Based
on these specifications, the development started with
the engineering phase, as briefly described in the next
section.
2.1 Ontology Engineering
The CDM-Core is modeled in the standard W3C on-
tology language OWL2-DL (Consortium et al., 2012).
The CDM-Core has been developed by the task part-
ners according to the distributed ontology engineer-
ing (OE) methodology DILIGENT (Tempich et al.,
2005). Coherently with the literature (Sure et al.,
2009), (Simperl et al., 2010) there are some common
steps, that were followed also in this case.
Initially, the Domain analysis concerns the elici-
tation of requirements from use cases, the identifica-
tion of relevant information sources and the ranking
and adoption of relevant semantic data models which
are available for public reuse. Then there is the Con-
ceptualisation of knowledge, where these inputs are
tranformed into of a semi-formal conceptual model
of objects and concepts with taxonomic relationships.
Following there is the Formalisation of the concep-
tual model where the conceptual model is translated
into a knowledge representation language with formal
semantics. Eventually, the Evaluation of the formal
ontology phase analyzes the sufficient coverage and
description of the domains by users and domain ex-
perts and the syntactic correctness, consistency and
normalisation of the ontology by the ontology engi-
neer. At the end of each phase, an iteration loop can
happen, if some of the involved professionals ask for a
refinement based on the reached results. In our partic-
ular case, we observed multiple iteration, in particular
triggered by the user partners.
As result, the requirement that CDM-Core repre-
sents knowledge of the CREMA use cases was even-
tually achieved to a satisfactory degree. In fact, the
semantics of the given process models and sensor data
can be basically described using the ontology.
The distributed engineering of the shared CDM-
Core ontology has been performed by task part-
ners according to the DILIGENT methodology (Pinto
et al., 2004).
The role of ontology engineer is manifold and in-
cludes (a) the support of domain analysis and concep-
tualisation of knowledge, (b) the formalisation of the
conceptual model in OWL2, and (c) the technological
evaluation of the CDM-Core. A set of selected part-
ners plays the role respectively of domain experts and
users of the CDM-Core. All task partners are mem-
bers of the control board for ontology analysis, revi-
sion and evaluation.
The CDM-Core ontology engineering process is
cyclic. It is based on four main steps with controlled
iterations: the first step is Build where the ontology
engineering team builds a very small and basic con-
sensual version of the CDM-Core ontology. These
initial activities are carried out by the domain experts
intensively supported by the ontology engineer. The
second operation is a Local refinement where each
domain expert performs an in-depth refinement of the
shared CDM-Core version at the local site, towards
a refinement of the conceptual model per use case.
These activities are carried out concurrently and at ge-
ographically dispersed sites. Every local ontologies
is evaluated by domain experts and ontology engi-
neer, and then formalised. As third step, the Analysis
and revision requires that the control board analyses
the locally refined ontologies and revises the shared
CDM-Core ontology accordingly, by means of identi-
fication of similarities and their respective alignment.
Eventually, after a new release of the ontology, a Lo-
cal update is performed by domain experts before ini-
tiating further local refinements (i.e. 2
nd
operation).
The user partners informed that for the considered
processes and sensor data in the use cases currently
no standard data models were used at their sites.
The result of the initial search and assessment of
relevant non-semantic standard data models carried
out by the task partners is shown in Table 1. In par-
ticular, the main domains of listed data models ac-
cording to their public description are shown in the
column Domain Coverage; in column Public Reuse
the availability of these data models for their transla-
tion (e.g. to OWL2) and inclusion into the publicly
available semantic data model CDM-Core is shown.
Instead, in Table 2 are presented the initial set of con-
sidered semantic data models and extensions in the
OWL2 language.
2.2 Result
The distributed development based on the presented
requirements guided the creation of the ontology. In
Table 3 the actually reused public ontologies in CDM-
Core are stated, together with their role and their
characteristics (numbers of classes, properties and ax-
ioms). The main result, beside the ontology itself, is
its usage for covering the identified requirements, ex-
amples of which are presented in the next section.
CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance
137
Table 1: Selected non-semantic standard data models for CREMA use cases.
Data Model Modeling Lan-
guage
Domain Coverage Public
Reuse
ISO 13372:2012 UML Txt/Tab Condition monitoring and diagnostics of machines, pre-
dictive maintenance
(Y)
*
ISO 10303 (STEP)
APs
UML EXPRESS
EXPRESS-G
AP214 in AP242:2104 - Core data for automotive me-
chanical design processes
AP239 - Product Life Cycle Support
AP224 - Mechanical product definition for process plans
AP240 - Process plans for machined products
N
ISO STEP PDM
Schema V1.2
Graphical notation,
Txt/Tab
Product data management (common subset extracted
from STEP APs 214, 203, 212, 232)
(Y)
UN/CEFACT CCL,
UNTDED-ISO 7372
Graphical notation,
Txt/Tab, XML(S)
Supply chain and cross-border trading transaction mes-
sages for buy, ship and pay business processes
Y
ASD S-2000M V6.0 UML Txt/Tab Material management incl. spare parts, focus on
aerospace industry
N
ISA-88 UML Txt/Tab
B2MML
Batch control configuration and communication between
components in batch manufacturing plants
N
ISA-95 UML Txt/Tab
B2MML
Business logistics and manufacturing control incl. pro-
duction scheduling, maintenance management - at the
level of enterprise, site, area
N
ISO 3166 , ISO 4217 Txt/Tab, XML Country and currency codes Y
*
: only the Informative sections
Table 2: Selected semantic data models and extensions in OWL2 language for CREMA use cases.
Ontology Type Relevant Domain Coverage Standard Public
Reuse
SSN (extends DUL) Domain Sensory, Sensor Networks W3C Y
MASON Upper Manufacturing Y
DUL (DOLCE+DnS
Ultralite)
Upper Concepts of physical and social context, tem-
poral and spatial relations
W3C Y
GeoNames Domain Geolocation W3C Y
ONTO-PDM
[PDC12]
Upper Manufacturing product data IEC 62264,
ISO 10303
N
SCORVoc Domain Supply chain operations reference (SCOR) APICS (Y)
**
CM (extends SSN) Domain Condition Monitoring, Machinery Mainte-
nance
ISO/IEC
13372
Y
**
: the original N answer is overrided by the written permission received by APICS
Table 3: Publicly available ontologies (or non-semantic standards translated) used inside CDM-Core release.
Ontology Type
Characteristics
Classes Properties Axioms
MASON (Lemaignan et al., 2006) Upper Ontology 224 40 370
SSN (Compton et al., 2012) Domain 52 55 127
ConditionMonitoring (G
¨
unel et al., 2013) Domain 182 41 363
vCard (Iannella and McKinney, 2013) Domain 62 83 679
Org (Reynolds, 2014) Domain 15 37 662
Time (Hobbs and Pan, 2006) Upper Ontology 13 41 181
TimeLine (Raimond and Abdallah, 2006) Upper Ontology 26 60 350
DUL (Gangemi, 2012) Upper Ontology 76 109 517
SCORvoc (Petersen et al., a) (Petersen et al., b) Domain 279 297 7657
KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development
138
3 USAGES
This section presents examples of semantic annota-
tions of process models, services, and sensor data of
the CREMA use cases based on the CDM-Core. This
is complemented with examples of how these seman-
tic annotations can be used by other CREMA compo-
nents in the use cases, for example for the automatic
implementation and optimization of process model
with services (SOA paradigm).
3.1 Process Model Annotation
In CREMA, the process models are described in the
standard BPMN (Business Process Model Notation).
The formal semantics of a process model in BPMN
can be described by means of its annotation with el-
ements of formal ontologies. Such annotation al-
lows applications to semantically reason on them in
general, and assist process managers in their seman-
tic service-based implementation in particular. Ser-
vices can be more precisely selected and automat-
ically composed for implementing a given process
model based on its semantic annotation. This is in
line with the semantic SOA paradigm
3
and reference
model (MacKenzie et al., 2006).
In CREMA, a component will perform an optimal
service-based implementation of given process mod-
els with advanced means of semantic service selection
and planning. That requires the semantic annotation
of both process models and available services. The
semantic annotation of process models based on the
CDM-Core is manually done by the process manager
with the help of some specialized interfaces.
There is no standard format for the semantic an-
notation of process models in BPMN yet (Boissel-
Dallier et al., 2015). On an abstract level, the se-
mantics of processes and services can be basically de-
scribed in terms of their input (I), output (O), precon-
dition (P) and effect (E) of their execution. In particu-
lar, the task partners first informally described the ba-
sic IOPE semantics of all process tasks of given pro-
cess models in the CREMA use cases. These infor-
mal semantic description consists of text, including
relevant main concepts which were either identified
in or newly added to the CDM-Core for this purpose.
Next, these semi-formal descriptions were manually
transformed into the formal semantic IOPE-based an-
notations with CDM-Core.
3
Semantic SOA is considered key to the development
of semantics-empowered intelligent applications for the fu-
ture Internet in various domains including manufacturing
4.0, and supported by an increasing number of industrial
stakeholders such as Software AG, SAP, IBM, Siemens.
Figure 1 shows an example of a semantic IOPE-
based annotation of one process task of one process
model. In particular, this process task is concerned
with the resource allocation of a suitable robot. Given
a production schedule containing a list of orders to
be fulfilled, this task identifies a robot cell and corre-
sponding robot, and leases it for the schedule. A robot
must be able to perform welding operations and has to
be equipped with particular clamps to hold a certain
type of exhaust as specified in the tasks of the sched-
ule. The requirements described above are specified
through different parameters, including the produc-
tion schedule input, the robot cell and robot outputs,
and a range of internal variables. These semantic an-
notations (such as the ones in Fig. 1) were made rely-
ing on domain-specific and upper ontologies together
with the use case specific part of CDM-Core.
The following Listing provides an example of the
semantic annotation of the process task in part A of
the Figure 1 as extension in the BPMN XML source
code. Additional attributes named inputs, outputs,
preconditions and effects are used to attach the seman-
tic annotations to the standard BPMN definitions.
<?xml version="1.0" encoding="UTF-8"?>
<bpmn:definitions ...
xmlns:tco=http://<URI1>/wp8/tco.owl#
xmlns:mas="http://<URI2>/mason.owl#">
...
<bpmn:task id="Task_1w5d3zt"
name="Select Robot Cell"
inputs="tco:ProductionSchedule :PS"
preconditions="tco:includes(:PS,:T)
and tco:ProductionTask(:T)
and tco:includes(:T,:OP)
and mas:requiresTool(:OP,:TOOL)
and tco:Welder(:TOOL)
and tco:produces(:OP,:EX)
and tco:Exhaust(:EX)"
outputs="tco:RobotCell :CELL
(tco:Robot
and tco:supports some tco:ExhaustClamp
and tco:supports some tco:Welder) :R"
effects="tco:isAllocatedFor(:CELL,:PS)
and tco:includes(:CELL,:R)
and tco:supports(:R,:TOOL)">
...
</bpmn:task>
<bpmn:incoming>SeqFlow_0egvn5w</bpmn:incoming>
<bpmn:outgoing>SeqFlow_03haiqx</bpmn:outgoing>
</bpmn:definitions>
3.2 Service Annotation
Semantic services are services whose functional and
non-functional semantics are described with formal
ontology-based annotations. On an abstract level,
a semantic service description includes a semantic
IOPE-based profile and a semantic process model that
CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance
139
describe what this service does and how it actually
works (Klusch, 2008a), (Klusch, 2008b).
According to Semantic SOA, process models can
be implemented with executable services which se-
mantics are described with a shared formal ontology.
The implementation of each step or task of a process
model with a relevant single or composite service by a
process designer can be supported by means of auto-
mated high-precision semantic selection and planning
of annotated services, either in fully automatically or
in semi-automatically (with user interaction).
Since the CDM-Core is defined in OWL2, one nat-
ural choice of the semantic service description format
would be OWL-S (Web Ontology Language for Web
Services (Martin et al., 2004)) which allows a ground-
ing of semantic services in WSDL or REST services
(Lathem et al., 2007). As mentioned above, seman-
tic service descriptions will be used to determine a
functionally optimal assignment of services to given
annotated process models.
3.3 Data Stream Annotation
In CREMA, CDM-Core can also be used to semanti-
cally annotate sensor data of the use cases. The user
partners provided general information about the metal
press system, the robots, their components and the
attached sensors, as well as the relevant sensor data
schema. The given data schema for the stream of
time-stamped and sequentially ordered data buckets
can be in different format, such as CSV, TSV (Tab
Separated Values) or JSON (Gray et al., 2011).
In particular, each sensor observes or measures
one property such as pressure or temperature. The se-
mantic annotation of streamed sensor data will be au-
tomatically done usign the CDM-Core concepts. That
requires the mapping of the sensor measurement la-
bel to the concept in the ontology, which defines the
formal semantics of this label in XML-RDF encoded
OWL2. As a result, the data item is described by a
set of RDF triples as an instance of the corresponding
concept in the ontology. This mapping table and re-
spective naming of sensor classes and measurements
is given by the partner generating the stream itself.
Figure 2 presents an example of semantic anno-
tation of multi-variate sensor data from multiple sen-
sors attached to the hydraulic drive system of a metal
press. The semantic annotation of sensor data with
the CDM-Core allows for a qualitative, that is domain
knowledge-based, rather than quantitative statistics-
based data interpretation.
Figure 1: Usage of the CDM-Core ontology for (A) Process
Task inside the BPMN semantic annotation, (B) Service se-
mantic Annotation, and (C) a matching using the plug-in
approach (Paolucci et al., 2002) between the semantic an-
notation on top and middle of this example.
KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development
140
Figure 2: Example of annotation for a data stream schema, in the context of a CREMA project use case.
4 QUALITY MEASURES
Ontology evaluation is the task of measuring the qual-
ity of an ontology according to given criteria. It is ”a
technical judgment of the content of the ontology with
respect to a frame of reference during every phase and
between phases of their lifecycle and can be classi-
fied into ontology verification and validation (G
´
omez-
P
´
erez et al., 2003). There are several approaches,
methods and tools for ontology evaluation available,
but no best practices and guideline for the selection of
measures for ontology quality criteria.
The result has been evaluated according to a se-
lected subset of ontology quality criteria and mea-
sures defined in (Vrandecic, 2010). In particular, the
selected criteria subsume the given requirements for
the CDM-Core. Since there is no publicly available
ontology for the manufacturing domain, our CDM-
Core could not be evaluated against some gold stan-
dard as a reference. For this reason it was performed
by all task partners with user partners as stakeholders.
In summary, all requirements for the CDM-Core
are satisfied. The individual results are described in
the following in the context of the selected criteria.
Verification was concerned with evaluating if CDM-
Core specification is formally correct and meaningful
in terms of syntactic validity, and logical consistency.
Syntactic validity refers to the syntactically correct
encoding of the ontology specification, which can be
tested with appropriate validation tools such as the
OWL validator
4
, SWOOP
5
, CityPulse Ontology Val-
idator
6
, Eyeball
7
, OBO-Edit
8
, and OOPS!
9
.
Logical Consistency requires that the ontology speci-
fication does not include or allow for any logical con-
tradiction. In other words, an ontology is inconsistent
10
if it does not allow any formal model to satisfy its
axioms. Checking of consistency can be done with
a classical ontological reasoner such as Pellet (Sirin
et al., 2007). The above mentioned validation tools
support consistency checking, but they differ in the
extent they check for common problems or pitfalls.
Results: The CDM-Core is syntactically valid and
consistent. The XML-RDF encoded specification of
the CDM-Core ontology in OWL2 was successfully
tested with the tool OOPS! (also, advanced evaluation
option): it does not contain critical problems such as
circular class hierarchies, redundant axioms, incon-
4
http://mowl-power.cs.man.ac.uk:8080/validator/
5
http://semanticweb.org/wiki/Swoop
6
http://iot.ee.surrey.ac.uk/SSNValidation/
7
https://www.w3.org/2001/sw/wiki/Eyeball
8
http://oboedit.org/docs/index.html
9
http://oops.linkeddata.es/
10
It is is a kind of inconsistency (Flouris et al., 2006).
CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance
141
sistent naming schemes, or other logically inconsis-
tent definitions of concepts and relations.
Validation was concerned with evaluating if the on-
tology is practically useful for the targeted stakehold-
ers in terms of its accuracy and completeness of both
covering the use case domains and supporting the
tasks for which the ontology has been designed, its
computational efficiency, and its adaptability to man-
ufacturing domains and tasks of other stakeholders
(additional criterion that the task partners identified
later).
Accuracy checks if the specification complies with the
knowledge of the stakeholders. Correctness in this
case means compliance to the gold standards” of use
case descriptions and respective conceptual models.
Completeness verify that the use case domains are
not only accurately but fully covered by the ontology,
and that semantic annotations of given use case pro-
cess models, services, and sensor data are sufficiently
supported. It also covers the structural quality of the
ontology for which measurement the following mea-
sures were selected:
(TD) maximal class Taxonomy Depth (Lozano-Tello
and G
´
omez-P
´
erez, 2004), i.e. the maximum sub-
sumption path length of the CDM-Core. It covers the
intuition that a high TD reflects a more detailed con-
cept knowledge represented by the ontology.
(RR) Relationship Richness (Vrandecic, 2010), i.e.
the ratio between the number of property names and
the number of class names and property names of the
CDM-Core. It reflects the diversity of relations in the
ontology and cover the intuition that detailing of ex-
isting classes would increase the relational richness of
the ontology.
(AR) Attribute Richness (Tartir et al., 2005), i.e. the
average number of properties (attributes) per class. It
suggests the intuition that high attribute richness indi-
cates more information about each class on average.
Computational efficiency is the reasoning complexity
(RC) of CDM-Core, i.e. the complexity that applies
to the common reasoning tasks for the OWL2 frag-
ment that is actually being used in the CDM-Core.
Adaptability represents an indicator for the effort ex-
pected for effectively reusing the developed ontology
in different cases inside the same domain.
Results: CDM-Core is accurate: as a consequence
of the approval of its sufficient matching with the un-
derlying conceptual model by the stakeholder, defini-
tions and descriptions in CDM-Core are correct.
It is also complete according to the stakeholders. As
a result of its joint engineering, the CDM-Core was
eventually approved by the stakeholders to represent
all relevant instances, concepts and relations of the
conceptual model. Moreover, it allowed the annota-
tion of each of the given process models with con-
cepts and properties; all sensor measurements of the
given data stream schema were semantically mapped
to corresponding elements in it; and every used sen-
sors, robot cells and metal press is represented by an
appropriately designed individual in CDM-Core.
The structural quality factors of the CDM-Core are:
TD = 7, RR = 0.3993, AR = 0.8156. Our interpre-
tation of these values is that the developed CDM-
Core features a very high number of domain-specific
classes with very high attribute richness (AR), which
indicates a high amount of detailed information about
each class on average. Its class hierarchy is of the
same moderate maximal depth (TD) as, for example,
the generic manufacturing ontology MASON and the
standard W3C SSN ontology, it partly builds upon.
The worst case reasoning complexity (RC) is com-
puted as the intersection of the complexity of the dif-
ferent OWL2 fragments in the CDM-Core, and is
equivalent to SROIQ(D). Anyway no definition of
CDM-Core covers jointly all the operators of this
complexity, which indicates that the reasoning com-
plexity in practice might be of some magnitude lower.
Adaptability is limited due to its focus on covering the
use case domains described (CREMA
consortium,
2016a), (CREMA consortium, 2016b) and allow-
ing the tasks of annotating the given process mod-
els, services and sensor data. However, the CDM-
Core in particular builds on and includes generic and
standard-based ontologies. These generic parts can
serve other stakeholders to model knowledge of dif-
ferent manufacturing domains and tasks. The normal-
ized proportion of the generic to the CREMA use case
domain-specific parts is 21.09%.
The CDM-Core is specified in OWL2-DL and can
be in principle extended and specialized monotoni-
cally, i.e. without the need to remove axioms.
5 CONCLUSIONS
In this paper we presented the first publicly available
ontology for the manufacturing domain, together with
the process for its development. It is one intermediate
result of a shared effort of different organizations in
the context of a collaborative project. The validation
showed its capability for covering the requirements
elicited as prerequisite for the ontology engineering
phase. Additionally, some measures for the struc-
tural quality of the produced CDM-Core ontology are
presented, together with its applications for process
model, services, and data stream annotations. The re-
sulting ontology is released for public reuse and we
expect that industries can reuse it, provide feedbacks
KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development
142
and ask for improvement to the community.
This work was partially supported by the Com-
mission of the European Union within the CREMA
H2020-RIA project (Grant agreement no. 637066).
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