ArchiMEO: A Standardized Enterprise Ontology based on the

ArchiMate Conceptual Model

Knut Hinkelmann

, Emanuele Laurenzi

, Andreas Martin

, Devid Montecchiari

Maja Spahic

and Barbara Th

onssen

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-

https://orcid.org/0000-0002-1746-6945

https://orcid.org/0000-0001-9142-7488

https://orcid.org/0000-0002-7909-7663

https://orcid.org/0000-0002-8969-1973

https://orcid.org/0000-0003-1625-0162

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

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

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

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

. Contracted

partners are monitored by easyMonitoring and in case

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

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

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

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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