ArchiMEO: A Standardized Enterprise Ontology based on the
ArchiMate Conceptual Model
Knut Hinkelmann
a
, Emanuele Laurenzi
b
, Andreas Martin
c
, Devid Montecchiari
d
,
Maja Spahic
e
and Barbara Th
¨
onssen
f
School of Business, FHNW University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland
Keywords:
Enterprise Ontology, Enterprise Architecture, ArchiMate, Enterprise Modeling.
Abstract:
Many enterprises face the increasing challenge of sharing and exchanging data from multiple heterogeneous
sources. Enterprise Ontologies can be used to effectively address such challenge. In this paper, we present
an Enterprise Ontology called ArchiMEO, which is based on an ontological representation of the ArchiMate
standard for modeling Enterprise Architectures. ArchiMEO has been extended to cover various application
domains such as supply risk management, experience management, workplace learning and business process
as a service. Such extensions have successfully proven that our Enterprise Ontology is beneficial for enterprise
applications integration purposes.
1 INTRODUCTION
The advent of digitalization has led to the generation
of an increasing amount of data in enterprises. Data
that originates from multiple heterogeneous sources
might share the same meaning but have a differ-
ent structure. Data elements might have different
names and can be processed by different applications.
For example, different software applications for Sup-
ply Chain Management (SCM), Enterprise Resource
Planning (ERP) or Customer Relationship Manage-
ment (CRM) offer different functionalities, but they
all deal with data about suppliers, products or cus-
tomers. Different data models require a quite high
engineering effort to be integrated. The integration
is commonly done by designing integration adapters,
which also include the development of several unit
test cases (Ritter and Holzleitner, 2015). If the data
presents a shared conceptualization, the integration
effort can be avoided and also lead to additional ben-
efits. Data can be correctly and uniformly interpreted
so to enable building intelligent information systems
(Emmenegger et al., 2012, 2017). Moreover, ana-
a
https://orcid.org/0000-0002-1746-6945
b
https://orcid.org/0000-0001-9142-7488
c
https://orcid.org/0000-0002-7909-7663
d
https://orcid.org/0000-0002-8969-1973
e
https://orcid.org/0000-0003-1625-0162
f
https://orcid.org/0000-0002-1825-5234
lytic power can be exploited to obtain valuable in-
sights. For example, Business Intelligence applica-
tions can use data from different applications to sup-
port decision-making and prediction.
Ontologies have been successfully used to effec-
tively combine data from multiple sources (Wache
et al., 2001). They address the issue of semantic het-
erogeneity of data from different sources. Ontolo-
gies are formal models with explicitly defined con-
cepts and named relationships. Enterprise Ontologies
(Dietz, 2006) contain definitions of business concepts
and relationships among them. Although various En-
terprise Ontologies (Fox et al., 1996; Uschold et al.,
1998; Dietz, 2006; Lepp
¨
anen, 2005; Bertolazzi et al.,
2001) exist, they have not yet achieved a standardiza-
tion.
The objective of this paper is to present an Enter-
prise Ontology that can be used as a standardized ref-
erence. The ontology serves as a basis for cases where
a shared conceptualization and interchangeability of
data is required. The ontology is called ArchiMEO
and can be extended to address a specific application
domain.
The remainder of the paper is structured as fol-
lows: section 2 provides the relevant information con-
cerning Enterprise Architecture Descriptions (EAD)
and Enterprise Ontologies. Next, section 3 describes
the ArchiMEO development approach. An overview
of the ArchiMEO structure and the description of the
chosen ontology representation language is given in
Hinkelmann, K., Laurenzi, E., Martin, A., Montecchiari, D., Spahic, M. and Thönssen, B.
ArchiMEO: A Standardized Enterprise Ontology based on the ArchiMate Conceptual Model.
DOI: 10.5220/0009000204170424
In Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2020), pages 417-424
ISBN: 978-989-758-400-8; ISSN: 2184-4348
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
417
section 4. Finally, section 5 presents the manner
ArchiMEO was used and extended to address specific
application domains.
2 STATE OF THE ART ON
CONCEPTUALIZATION OF
ENTERPRISE KNOWLEDGE
Enterprise Ontologies contain a shared conceptual-
ization of enterprise aspects. There exist several
Enterprise Ontologies such as the Toronto Virtual
Enterprise (TOVE) (Fox et al., 1996); The Enter-
prise Ontology (Uschold et al., 1998); Context-Based
Enterprise Ontology (Lepp
¨
anen, 2005); Core Enter-
prise Ontology (CEO) (Bertolazzi et al., 2001); and
Resource-Event-Agent (REA) (Geerts and McCarthy,
2000, 2002).
Enterprise Architectures also describe concepts
and relations of an enterprise. A definition of
Enterprise Architecture that is in line with the
ISO/IEC/IEEE 42010 standard
1
defines an enterprise
architecture as “fundamental concepts or properties of
an enterprise in its environment embodied in its ele-
ments, relationships, and in the principles of its de-
sign and evolution. This means that the definition of
concepts and relations for describing an enterprise ar-
chitecture can be regarded as an enterprise ontology.
Enterprise Architecture modeling (and descrip-
tion) and ontology modeling originally stem from
two different application domains and recently started
to be merged. According to Dietz and Hooger-
vorst (2008) “the terms ‘Enterprise Ontology’ and
‘Enterprise Architecture’ [now] belong to the stan-
dard vocabulary of those professionals who are con-
cerned with re-designing and re-engineering enter-
prises”. The term ‘ontology’ emerged in the context
of Artificial Intelligence and the Word Wide Web, par-
ticularly of the Semantic Web (Dietz, 2006). The term
‘Enterprise Architecture’ became generally known as
a management topic in the end of the 1980ies, for ex-
ample through the Zachman Enterprise Architecture
Framework (Zachman, 1987). Zachman (2009) has
renamed his Enterprise Architecture Framework as an
“Enterprise Ontology”.
There are two different perceptions of Enterprise
Architecture. One perception is as a high level ab-
straction (of reality) with the purpose of reducing
complexity and increasing stakeholder’s understand-
ing and communication (amongst others by Chen
1
The ISO/IEC/IEEE 42010 (DSCI, 2016) is an Interna-
tional Standard entitled, Systems and software engineering
— Architecture description.
et al. (2008) and Dietz (2006). According to Di-
etz (2006) the most dominant problem, stated in sci-
entific as well as in popular science on enterprise
management, is complexity and how it can be man-
aged. He claims that because of the complexity of
enterprises a conceptual model is needed that “only
shows the essence of the operation of an enterprise”
and therefore “the model abstracts from all realiza-
tion and implementation”. Dietz (2006) also stated
that it is enough to have a conceptual model of En-
terprise Architecture, which is independent from any
ICT implementation. The other more recent percep-
tion of Enterprise Architecture focuses on integrating
the graphical models with ontologies (Woitsch et al.,
2009; Hinkelmann et al., 2016a; Valtonen et al., 2011)
so to provide machine interpretation to models.
There is a huge variety of Enterprise Architec-
ture frameworks like The Open Group Architecture
Framework TOGAF (The Open Group, 2018), the
Zachmann Framework (Zachman, 2009), the Archi-
tecture for Information Systems ARIS (Scheer, 2012),
the Best Practice Enterprise Architecture (Hanschke,
2009) and the Enterprise Architecture framework de-
veloped in the Plug-IT project (Wache et al., 2010). In
his compendium Matthes (2008) gives a detailed de-
scription of 34 Enterprise Architecture frameworks,
based on clearly structured and well defined criteria.
Schelp and Winter (2009) provide an overview
of research on languages for Enterprise Architecture
Descriptions. Amongst others they mention Archi-
Mate (The Open Group, 2017). ArchiMate is a Enter-
prise Architecture modeling language that was devel-
oped by a broad consortium of companies and knowl-
edge institutes (Lankhorst, 2017) and later adopted
as a standard by The Open Group. Ettema and Di-
etz (2009) show ArchiMate is semantically vaguely
defined; they demonstrate the benefits of their own
ontology for representing Enterprise Architecture De-
scriptions.
3 ONTOLOGY DEVELOPMENT
To develop our standardized Enterprise Ontology, we
applied the widely used development approach de-
scribed in Noy et al. (2001).
The requirements of business partners were de-
rived first. As suggested in Noy et al. (2001) and
Gruninger and Fox (1995), a list of competency ques-
tions was created as benchmark. That is, the “ontol-
ogy is necessary and sufficient to represent the tasks
specified by the competency questions and their so-
lution” (Fox et al., 1996). Competency questions
are formulated in natural language to determine the
MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development
418
scope and evaluate the appropriateness of an ontol-
ogy. From the questions and answers the required
concepts, properties and axioms of the ontology are
extracted. Then, competency questions are written
formally using first-order logic to specify terminol-
ogy and axioms (Gruninger and Fox, 1995). The
complete list of competency questions is described in
(Th
¨
onssen, 2013).
The extracted concepts and properties were fur-
ther refined by interviewing practitioners from differ-
ent companies. The interviewees proposed to provide
a holistic Enterprise Ontology which is able to cover
the needed enterprise aspects. The ontology should
include different standards and architectures on a gen-
eral level. The interviewees additionally suggested to
consider existing work, from which a basic structure
can be derived. The basic structure can then be ex-
tended for each company to create domain-specific
concepts and relations.
As a second step of the methodology Noy et al.
(2001), the interviewees recommendations were ad-
dressed as detailed below.
All the Enteprise Ontologies mentioned in Section
2 were analysed. The analysis led to consider them
as best practice, guidelines and templates for the de-
velopment of our standardized Enterprise Ontologies.
However, they are proprietary ontologies and there is
no evidence for their practical use. The lack of practi-
cal usage hinders their wide acceptance, which makes
these ontologies unsuitable to be used as standards.
Therefore, they were not considered as core of our
Enterprise Ontology.
Enterprise Architecture modeling was identified
as an additional source for the reuse of already ex-
isting concepts. The architecture frameworks listed
in Section 2 were examined to identify the main con-
stituents to be represented in an Enterprise Ontology.
We identified the concepts and relations that these
frameworks provide in order to describe the enterprise
architectures. It turned out that the Enterprise Archi-
tecture descriptions are not well defined. This is in
line with Chen et al. (2008), who were among the first
to stress the lack of sound scientific principles for de-
veloping Enterprise Architecture descriptions. Given
its broad acceptance in practice, the ArchiMate (The
Open Group, 2017) standard was finally chosen as the
most suitable Enterprise Architecture framework for
our standardized Enterprise Ontology.
The ontological representation of ArchiMate con-
stitutes an Enterprise Upper Ontology. However,
ArchiMate does not provide general concepts, like
location or time. Therefore, we introduced a Top-
Level Ontology following (Bertolazzi et al., 2001).
Together, the Top-Level Ontology and the ontologi-
cal representation of ArchiMate build the ArchiMEO
ontology (see Figure 1).
As to follow the remaining steps of Noy et al.
(2001)’s methdology, (i) the important terms of the
ontology were enumerated; (ii) the classes and the
class hierarchy were defined; (iii) the properties of
classes—slots were defined; (iv) the facets of the slots
were defined and finally (v) the instances were cre-
ated.
4 STRUCTURE OF ArchiMEO
The name ArchiMEO is chosen to indicate its foun-
dation in ArchiMate (“Archi”) plus its adaptation
and enhancements to serve as a metamodel (“MEO”:
Meta Enterprise Ontology).
Figure 1: ArchiMEO structure (Th
¨
onssen, 2013).
As shown in Figure 1, our semantically enriched En-
terprise Architecture Description (seEAD) is logically
structured into four parts:
a Top-level Ontology comprising generic con-
cepts of the world like time, location and event;
an Enterprise Upper Ontology comprising the
ArchiMate concepts represented in an ontology
language;
a meta Enterprise Ontology (ArchiMEO) adapting
and enhancing the ArchiMate standard by addi-
tional concepts and relations. For example, busi-
ness processes and business representations such
as documents;
an application-specific ontology, comprising spe-
cific concepts of a certain enterprise or domain.
ArchiMEO: A Standardized Enterprise Ontology based on the ArchiMate Conceptual Model
419
ArchiMEO includes the Top-Level Ontology and the
Enterprise Upper Ontology; it can be extended by the
Enterprise Application Ontology.
The concepts and relations of seEAD reside in
the metamodel (B
´
ezivin, 2004). This is conform to
the Meta Object Facility specification (Object Man-
agement Group, 2016). Domain-specific enhance-
ments targeting an enterprise can therefore be per-
formed. This allows modelling an enterprise-specific
semantically-enriched Enterprise Architecture.
With this approach, an Enterprise Ontology of
high quality can be developed and completeness can
be ensured. Furthermore, it provides the basis for cre-
ating specific views on an Enterprise Architecture De-
scription: for specific viewpoints, model kinds and ar-
chitecture description languages.
Breaking down the holistic view into top-level
concepts leads to structure the semantically-enriched
Enterprise Architecture Description. The top-level
concepts reflect (a) multiple Enterprise Architecture
Frameworks with different aspects and perspectives,
(b) different modelling languages that represent one
or more of these dimensions and (c) general concepts
in enterprise environments such as “Time” or “Loca-
tion”. ArchiMate concepts are not considered in the
top-level hierarchy.
The top-level concepts are detailed as follows:
The Location and Time concepts consist of spatial
and temporal information, respectively.
The Event concept describes events that are ex-
ternal to a business context (for example environ-
mental disasters that may affect obligations to de-
liver in a supply chain).
The ModelType concept consist of notations or
modelling languages, which may represent parts
of an Enterprise Architecture, e.g. BPMN process
models.
The Perspective concept contains viewpoints ac-
cording to which the enterprise objects or models
can be categorized. For example, the perspectives
of the Zachman Framework.
The Modelling Construct concept depicts the syn-
tax of a modelling language and used to create
models. For example, in BPMN a Swimlane is
a modelling construct, which may refer to a busi-
ness entity or role. Thus, a Swimlane represents a
modelling construct rather than a distinct concept
of an enterprise. It then may be used to represent
an enterprise object in a model.
NCO describes a collection of constructs, which
cannot reasonably be assigned to other top-level
concepts (e.g. Languages or Specification Stan-
dards). NCO stands for ‘non-categorized objects’.
The Aspect concept categorizes an enterprise ob-
ject having a certain perspective. In this sense, the
Aspect concepts supplements the Perspective con-
cepts. The relationships of enterprise objects with
Aspect concepts allow identifying the relevant as-
pects for certain stakeholders or for the organiza-
tion.
The core of ArchiMEO is subordinated to the En-
terpriseObject concept. The latter contains all
types of objects that occur in an organization. As
shown in Figure 2, EnterpriseObject consists of
the ArchiMate core concepts along with the rela-
tions.
All ArchiMate concepts and relations are sub-
concepts of the ArchiMEO concept EnterpriseObject.
For example, Figure 2 contains the BehaviourElement
as a sub-class of EnterpriseObject. Further on, the
BusinessBehaviourElement is a specification of the
BehaviourElement related to the ArchiMate business
perspective and behaviour aspect.
5 THE USE AND EXTENSION OF
ArchiMEO
Over the years, the core ontology of ArchiMEO de-
scribed in Chapter 4 was extended and validated to
address specific application domains. An ArchiMEO-
based prototype was created in each of the following
described use cases.
5.1 Contract Management
Within the DokLife project, a prototype for auto-
matic meta-data creation for contracts was devel-
oped (Th
¨
onssen and Lutz, 2012). The project goal
was to automate and improve a contract’s lifecycle
management. Information like contract begin, con-
tract end, contract parties, obligations were automat-
ically extracted using automated metadata generation
(Th
¨
onssen, 2010). The project used an application-
specific extension of the ArchiMEO ontology. This
extension defined classes related to contract lifecycle
management. For example, specific business events
like bankruptcy were defined that can trigger actions
for contract management.
ArchiMEO was directly linked to external sources
as the easyMonitoring business database
2
. Contracted
partners are monitored by easyMonitoring and in case
2
The company Easymonitoring AG offers services for
monitoring business partners concerning financial inci-
dents. URL: https://www.easymonitoring.ch/ (retrieved:
19.05.2019)
MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development
420
Figure 2: Enterprise Objects Related to the ArchiMate Representation (Th
¨
onssen, 2013).
a contract-relevant event happens (e.g. bankruptcy of
a partner), automatically an alert is sent and affected
obligations and the contract documents in which they
are represented are identified based on ArchiMEO
reasoning.
5.2 Supply-risk Management
In (Emmenegger et al., 2012), an Early Warning Sys-
tem was developed integrating semantic technologies
for the assessment of procurement risks.
ArchiMEO was extended with a Semantic Risk
Model. For this, the methodology described in
(Gruninger and Fox, 1995) was adopted. Thus,
the new concepts and relations were elicited from
competency questions. The core of the Seman-
tic Risk Model is based on ontology concepts like
RiskEvent, RiskIndicator, CrisisPhase, WarningSig-
nal and Top10ProcurementRisk.
As risks evolve from external events, concepts
from the Top-Level Ontology were integrated to
ArchiMEO, i.e., time, event, and location as well as
concepts of general interest.
Additionally, semantic rules were developed to be
fired against the extended ArchiMEO concepts with
the ultimate purpose of inferencing the final level of
each top ten procurement risk. Hence, a risk assess-
ment procedure was created embracing a bottom-up
approach with values aggregations starting from the
detection of atomic risk indicators, e.g. a single-
source supplier that is about to go bankrupt or sup-
pliers located in places affected by natural disasters
or by political turmoils.
From a technical perspective, the automation of
the procurement risk calculation was implemented by
integrating an inference engine in the final prototype
of the Early Warning System.
5.3 Experience Management
The accessibility of experiential knowledge obtained
from previous cases or projects is an important and
crucial matter in enterprises and organisations. Expe-
riential knowledge is usually and can be captured in
cases, which can be managed and referred to as expe-
rience management. Case-based reasoning (CBR) has
its roots in cognitive science, machine learning and
knowledge-based systems (Martin and Hinkelmann,
2018) and can be used for manage experiences in the
form of cases, by supporting the main four R’s of
CBR retrieve, reuse, revise and retain as main phases
to manage cases. To manage enterprise-specific cases
with CBR already available generic or specific enter-
prise knowledge is beneficial.
Martin (2016) developed a new ontology-based
case-based reasoning (OBCBR) approach called ICE-
BERG, which stands for interlinked case-based rea-
soning, that use the Enterprise Ontology ArchiMEO.
The use of ArchiMEO improves the ICEBERG CBR
system through the systematic inclusion of enterprise-
specific knowledge. Further, the information need
about experiential knowledge can be different from
one person to another depending on the different roles
someone has. To support the different information
needs of different stakeholders, ICEBERG was built
in such a way that different views, viewpoints, con-
cerns and stakeholders can be considered, which has
been derived from the ISO/IEC/IEEE 42010
1
stan-
dard likewise as ArchiMEO itself.
The OBCBR foundation of the ICEBERG ap-
proach has been laid in the applied research project
[sic!] (Martin et al., 2013; Witschel et al., 2015; Mar-
tin et al., 2016), which stands for software integration
using ontology-based case-based reasoning
3
The ICEBERG approach has been introduced and
described by Martin (2016), and finally been eval-
uated using a further application scenario (Martin,
2016; Martin and Hinkelmann, 2018). The appli-
cation scenario was the admission process for mas-
ter’s students at the FHNW School of Business. The
triangulated evaluation provided evidence that the
3
The [sic!] project was funded by the Swiss Commis-
sion for Technology and Innovation (CTI).
ArchiMEO: A Standardized Enterprise Ontology based on the ArchiMate Conceptual Model
421
ontology-based CBR approach support knowledge
workers.
5.4 Workplace Learning
ArchiMEO has been used to develop an ontology-
based workplace learning approach. The approach
aims to support inexperienced employees in public
administrations by suggesting historical cases and
providing recommendations of experts and learning
resources. For this, users’ workplace environment is
taken into account such as learning preferences as
well as required and acquired competencies. Com-
petency questions were created from which both a
Domain Ontology (DOMO) and Case-based Reason-
ing (CBR) Ontology were developed as extensions
of ArchiMEO. For instance, the following triples
are an excerpt of the new concepts and relations
necessary to infer the suitable learning materials:
CompetencyProfile-isAcquiredBy-Worker; Compe-
tencyProfile-containsAcquired-Competency; Compe-
tency-has-CompetencyLevel; Worker-hasPreferred-
LearningStyle.
The approach was implemented in the form of a
recommendation system, which was integrated to fit
the overall architecture of the LearnPAd system plat-
form (De Angelis et al., 2016). The latter comprises
mainly the modelling environments, the transforma-
tion component, the learning platform’s Wiki front-
end and the ontology recommender component (in-
cluding the CBR component). In a first step, enter-
prise models are transformed into ontology instances
conformed to extended ArchiMEO classes and incor-
porated with the latter. This transformation is called
semantic lifting (Kappel et al., 2006). Next, semantic
rules are fired against the ontology for recommenda-
tion purposes.
5.5 Business Process as a Service
(BPaaS)
The Business Process as a Service (BPaaS) (Woitsch
and Utz, 2015) use case consists of hybrid usage of
models and ontology aiming to align business require-
ments with cloud offerings. The ultimate goal of
BPaaS is to support entrepreneurs in identifying the
most appropriate cloud solutions for their business by
expressing requirements in a business language rather
than an IT language. For this, semantic technologies
are used to enable the smart Business-IT alignment.
Business requirements are expressed in the form of
a business process while Cloud IT-specifications in
the form of deployable workflows bundles. Both are
modelled employing the BPMN standard and Ser-
vice Description Model (SDM). The latter extends
BPMN to further specify both functional and non-
functional (1) business process requirements and (2)
IT-specifications.
The existing ArchiMEO concepts representing the
BPMN standards were re-used as well as extended
to model the class structure of the Service Descrip-
tion Model. The extension work is described in
(Hinkelmann et al., 2016b). As an example, non-
functional requirements in business language would
be NumberOfProcessExecutionPerYear and the File-
Type a cloud service should support. From the IT per-
spective, the values of these two requirements can be
used to calculate the minimum amount of Available-
DataStorage expected to be offered by a cloud ser-
vice, i.e. NumberOfProcessExecutionPerYear * size
of the chosen FileType. The new concepts and rela-
tions conceived the BPaaS Ontology as an extension
of ArchiMEO. In a second phase of the project, two
additional sets of ontology representing the functional
requirements were created and included in the BPaaS
Ontology, i.e. APQC Ontology (reflecting the APQC
Process Classification Framework) and the FBPD On-
tology (containing the combination between ‘verbs’
and ‘nouns’, e.g. Send Invoice).
In a third phase of the project, the BPaaS On-
tology was re-used and further extended into the so-
called Questionnaire Ontology to create an ontology-
based context-adaptive questionnaire (Kritikos et al.,
2017). The main advantage of it consists of the sig-
nificantly reduced time in specifying business require-
ments, compared to the aforementioned model-based
approach. Hence, the questionnaire allows identify-
ing the needed cloud services by answering the least
number of questions. For this, a prioritization al-
gorithm was developed, which incorporates the en-
tropy calculation and includes the user preferences,
i.e. Data Security, Payment, Performance, Service
support and or Target Market.
6 CONCLUSION
Many companies have to deal with highly complex
enterprise-wide IT systems, and lots of intercon-
nected systems need to be managed (Lindstr
¨
om et al.,
2006). We developed the ArchiMEO enterprise on-
tology to deal with the challenge of integrating appli-
cations and sharing data in a heterogeneous IT land-
scape.
Additionally, the representation of the ArchiMate
metamodel as an ontology allows for semantic lift-
ing of ArchiMate models (Kappel et al., 2006). The
ArchiMEO Enterprise Ontology defines the machine-
MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development
422
interpretable semantics of the modeling languages.
Because of this 1:1 correspondence of language con-
structs and ontology concepts, an architecture model
can be transformed into an ontology. This so-called
semantic lifting Kappel et al. (2006) allows for reuse
of knowledge represented in the models for auto-
mated reasoning. It has been implemented for work-
place learning and BPaaS (see sections 5.4 and 5.5).
Ontology-based metamodeling (Hinkelmann
et al., 2018) is an advancement of semantic lifting
that seamlessly integrates modeling languages and
ontologies. It has been recently implemented in
the agile modeling approach described in (Laurenzi
et al., 2018), which allows the ontology to evolve
over time and to be easily re-used and adapted for
new application domains.
In future projects, more model types, resp. lan-
guages for models, will be developed and ArchiMEO
will be further extended gradually for several other
application domains. In parallel the ArchiMEO pro-
totype will be advanced with respect to implement-
ing the link between the ontology and an application
database.
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