A New Approach for a Dynamic Enterprise Architecture Model using

Ontology and Case-based Reasoning

Imane Ettahiri

, Karim Doumi

and Noureddine Falih

ENSIAS, Mohamed V University in Rabat, Rabat, Morocco

LIMATI Laboratory, Sultan Moulay Slimane University, BeniMellal, Morocco

Keywords: Dynamic, Ontology, Enterprise Architecture, Case-based Reasoning, Enterprise Ontology.

Abstract: To meet the demands of a dynamic and constantly changing environment, (DRP) Disaster Recovery Plans,

(BCP) Business Continuity Plans, change management, agile activities, and best practice guides are developed

with the ultimate objective of providing enterprises with the tools to deal with change rapidly and flexibly.

Starting from the premise that Enterprise architecture remains the instrument ensuring this alignment

Strategy//business//IT, dynamic aspects should be present in the EA representation but also should be

perceived in the reaction of enterprises managing the change. On the other hand, ontologies offer a formal

and a shared representation of the domain studied; EA in our case. Once formalized, the representation became

computable so, all the EA reactions became dynamic towards the triggers of change. To benefit from the

previous experiences, Case-based reasoning is introduced in our approach allowing a problem resolution via

similarity and adaptation of knowledge to the current context.

1 INTRODUCTION

Today, companies are aware that their need for

business/IT alignment must be in perpetual

readjustment to follow the rapid changes impacting

the internal and the external of the organizations.

According to Gartner, organizations must have the

necessary dynamism to adapt quickly to meet the

need for business/IT alignment by bringing together

the external and internal capacities of the company,

or even any line-up couplet. Using the Enterprise

Architecture to control their evolution, by

maintaining the alignment between their business and

IT (Doumi et al.,2011) The keywords will thus be

speed of interception of the change factor and

effectiveness of implementation of the resulting

change process on the EA to ensure its consistency

and continuity after the implementation of this change

while keeping the enterprise IT alignment in a fluid

and flexible way. To deal with this great challenge, an

approach is proposed based on a key element: the

notion of Enterprise Ontologies, to ensure a

specification that is formally compliant and that can

be used as a basis for machine language thereafter,

thus gaining in speed and dynamic support

consistency of intercepted changes.

In addition to ontologies, our model combines the

advantages of the case-based reasoning too, to gain

adaptability, reuse and evolution via learning new

cases enriching the case base. This combination has

been already proposed to deal with other problematic

and has shown its advantages. In our paper, we tackle

the research question related to what extent could

ontologies and CBR respond to the problematic of

dynamic aspect in enterprise architecture.

The paper is structured as follows: Section 2

presents fundamentals definitions of the concepts

used in our work: Ontology, Case-based Reasoning

(CBR), and dynamic aspect in EA. An overview of

the relationship between EA and ontology is

presented in Section 3. Section 4 exposes the

architectural principles to which the proposed model

has to comply. Some related works are presented in

Section 5, as a ground for projection in our research

question: can the ontologies give an answer to the

dynamic aspect problematic in EA? In Section 6, we

explain our proposed approach based on CRB and EA

ontology. Section 7 illustrates the proposal through its

instantiation on a concrete scenario. Finally, Section

8 concludes the paper and provides directions for

future work.

Ettahiri, I., Doumi, K. and Falih, N.

A New Approach for a Dynamic Enterprise Architecture Model using Ontology and Case-based Reasoning.

DOI: 10.5220/0011079400003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 553-560

ISBN: 978-989-758-569-2; ISSN: 2184-4992

553

2 FUNDAMENTALS

2.1 Ontology Definition

According to (Studer et al., 1998), an ontology is

defined as “an explicit and formal specification of a

shared conceptualization, which is based on the well-

known definitions of (Gruber, 1993) and

(Borst,1997). According to (Guarino et al., 2009) and

(Genesereth et al., 1987), conceptualization refers to

"an abstract simplified view of the world", containing

"objects, concepts, and other entities that are assumed

to exist in a domain of interest and the relationships

that exist between them ". (Studer et al., 1998) links

"explicit" to the definition of "types of concepts used,

and the constraints of use", formal, it is the fact that

the conceptualization must be readable by the

machine. Finally, "shared" means that the ontology

"captures consensus knowledge".

2.2 Case-based Reasoning

According to (Leake,1996), CBR is ‘reasoning by

remembering’. It is a technology independent

methodology (Watson, 1999) for humans and

information systems. (Kolodner,1993) describes

CBR in two ways: ‘Case-based reasoning is both: the

ways people use cases to solve problems and the ways

we can make machines use them’. CBR can utilize the

specific experience of previously solved, concrete

problem situations (cases). A new problem is solved

by finding a similar past case and reusing the solution

in the new problem situation (Aamodt and Plaza

1994). As it explained, the Case-based Reasoning life

cycle consisting of the following steps: (1) Retrieve

the most similar case(s) from the case base, which

contains historical cases, based on the

characterization of the current situation used as query,

using a similarity mechanism. (2) Reuse the lesson

from the retrieved case(s) as the suggested solution

for the new situation; adapt the retrieved lesson to the

new situation, which becomes part of a new case. (3)

Revise the new case after evaluating it in the new

situation.

2.3 Dynamic Aspect in EA

The word dynamic is defined in the English

dictionary oxford learner’s as the characteristic (of a

process, relationship or system) always changing and

making progress. It is the opposite of static, and it is

widely studied in different domains such as:

mechanic, statistic, geophysics, hydrology,

sociology, bacteriology and sociology….

As dynamic remains a complex paradigm (Saat et

al., 2009), we tried in our previous study, to explore

some facets of the dynamics in enterprise architecture

(Ettahiri et al., 2021). As can be seen, the dynamic

aspect in enterprise architecture differs depending on

the prism of decomposition adopted: service view,

perspective view (Zachman layer), dynamic design

layer view, dynamic capabilities view, view zooming

on the dynamic component, agility-centric view…

This dynamic aspect is omnipresent across the

different scales (inter-enterprise, intra-enterprise,

holistic…EA vision, dynamic components…) And

through the different action phases of the EA

(Planning, analysis, modeling / Design,

Implementation, or measurement). The advantages of

each of our approaches oscillate between the

consistency of the stability of the static aspect in EA

and the agility and flexibility of the dynamic aspect.

Explanatory approaches help to bring a better

understanding: of complex EA reality for reliable

representation, and a deep understanding of Dynamic

EA capabilities to bring organizational benefits.

In our current study, we tried to constitute a model

as comprehensive as possible, trying to resemble the

maximum of advantages of the last work.

3 EA AND ONTOLOGY

3.1 Enterprise Ontology: EO

The development of an enterprise ontology has been

initiated since the 1990s, especially in Canada and the

United States (Jabloun, 2013). An ontology for

business engineering was proposed by the University

of Edinburgh to improve business modeling tools

(Uschold et al., 1998). It is described both verbatim

and in a semi-formal language (ontolingua). An

activity ontology to support the model-driven

business engineering approach has also been

proposed by the University of Toronto (Tham et al.,

1994). An open model (the Open Information Model

of the OMG Group) has also been proposed by IT

standardization organizations and is described in the

company's technical and business metadata using

UML (Prothman, 2000). After a comparison between

the different proposals, a global ontology for the

company is proposed in a hierarchical approach

(Bertolazzi, 2001) which defines the "Core enterprise

Ontology” .Other ontologies exist, such as:

"Enterprise Process Handbook" developed by MIT,

"TOVE" (Toronto Virtual Enterprise) developed by

the University of Toronto, There are also works

centered on knowledge and process modeling (KIF,

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PIF/PSL), with major contributions from Stanford

University and "SymEnterprise," as well as research

conducted by LEKS IASI-CNR. Recently, ontologies

are increasingly seen as complementary to the use of

EA meta-models. Indeed, the large size of the modern

information system and the underlying

multidisciplinary profession make it impossible to

produce a single ontology for the multiplicity of fields

covered. Thus, research has been directed towards the

question of the alignment between the points of view

(views) and the consistency between the models

(Millet, 2008)

3.2 EA Issues Resolved via Ontologies

According to the literature review conducted by

(Bakhshandeh, 2016) to constitute an idea about the

application of ontologies on EA, we can note that

ontologies since their primary role in terms of

formalization and allowing human-machine

communication, a step towards automation,

ontologies are also used to solve problems of EA

analysis and EA integration.

Ontologies are also considered as a modeling

support allowing the sharing of knowledge

represented by the ontology. A greater awareness of

the importance of ontology in the field of Enterprise

Engineering. Indeed, ontology has evolved from a

simple knowledge arrangement model to a

complement to the use of EA meta-models to support

alignment. Additionally, the importance of ontology

engineering techniques has become evident,

considering the increasing number of use cases

including software engineering (Happel et al., 2006).

Among the analysis categories:

 Integration: Ontology provides an integrated

environment, an interlingua, for information

repositories or software tools.

 Semantic search (reasoning): In this scenario,

ontologies are used to refine common (keyword-

based) search algorithms using domain

knowledge in the form of subsumption relations

or logical constraints.

 Semantic Annotation: In this scenario, the

purpose of ontology is to provide a controlled

vocabulary, as well as a clearly defined

classification and navigation structure for

information items in a repository.

 Knowledge Representation: Ontology is used to

formalize the type of objects related to a system

or context.

 Semantic Rules: Ontology is used to express

rules and logic and to add more expressiveness

to the ontology.

4 ARCHITECTURAL

PRINCIPALES FOR A

COMPREHENSIVE MODEL OF

DYNAMIC ASPECT IN EA

In this section, we describe and analyze the

problematic addressed in our work. To embed our

proposal in good practices, we have defined a list of

architectural principles with which a solution to our

problematic must comply. An architectural principle

can be described as "a statement that prescriptively

prescribes a property of an artifact's design that is

necessary to ensure that an artifact meets its essential

requirements" (Greefhorst et al., 2011). These

principles are identified based on (Lumor et al, 2021)

and (Antunes et al, 2014) to describe the architecture

of our system. For the selection phase, we matched

those principles with the list of advantages identified

in our previous works about dynamic aspect in

enterprise architecture. (Ettahiri et al., 2021). A list

combining the advantages of the different approaches

and studies related to dynamic aspect in EA was

established, such as : Low coupling, Highly-cohesive,

Coherence, Flexibility, Agility, Pragmatic, Semantic

rigor for successful communication and

documentation, Reactivity, Innovation, Tools to

direct the transformation effort towards predictable

and beneficial results, Deep understanding to

delineate Dynamic EA capabilities to bring

organizational benefits….and we tried to cover the

maximum by matching them onto principles to ensure

having the most comprehensive model as possible.

4.1 Architectural Principle N°1 –

Flexibility and Adaptability

The architecture of the solution must be able to adapt

to changing conditions and flexible to allow making

the right decisions about the problems and

opportunities. It is to highlight that architectures that

are created with too much detail will often result in

inflexible designs and implementations resulting in

systems that cannot adapt to changing circumstances

and environments.

4.2 Architectural Principle N°2 –

Expressiveness

The architecture of the solution should be able to

represent the concepts of the domain without

ambiguity to ensure a clear communication. This

implies the definition of a set of types and

relationships to describe a domain. Although the need

A New Approach for a Dynamic Enterprise Architecture Model using Ontology and Case-based Reasoning

555

for multiple views of the system is recognized by the

standard, the truth is that it is difficult to maintain

these relationships when multiple meta-models and

independent models are involved (Lankhorst, 2006).

As such, some enterprise architecture modeling

approaches attempt to be as comprehensive as

possible up to a certain level of abstraction, providing

a meta-model that addresses the different layers of an

organization (Fischer et al.,2007) But the fact is that,

many times, the integration of many meta-models is

imperative in order to provide project-or domain-

specific solutions to many problems (Zivkovic et

al.,2007)

4.3 Architectural Principle N°3 –

Extensibility

The architecture of the solution should be able to

respond to the extensions as the modelling of a

context implies the usage of multi perspectives for the

same problem. This stems from the ability to respond

to multiple concerns. Therefore, domain-specific and

domain-independent models must coexist, and the

overall architecture must cope with the

transformation and integration of multiple models. A

specific concern is that the architecture is extensible

to new application domains.

4.4 Architectural Principle N°4 –

Modularity and Reuse

The architecture of the solution must follow the

principles of high cohesion and low coupling.

Compliance with these principles contributes to the

expressiveness and extensibility of the architecture. It

is especially important that adding new domain-

specific aspects to the model does not interfere with

concepts already present in the model.

Considering this, the creation of computable

representations for enterprise architecture models

emerges as a relevant need (Martin et al., 2004). The

combination of computable models with the

application of dependencies brings benefits for

enterprise architecture, such as information retrieval,

management and processing. An example of these

benefits is dependency analysis, which can be used to

assess the alignment between business and IT

concepts.

4.5 Principe Architectural N° 5 –

Durability and Prediction

The durability of the architectures and resilience to

different changes that might occur over the lifetime

of the architectures, are a very important criterion of

our model, that should preempt as much as possible

the future conditions and environments.

4.6 Principe Architectural N° 6 -

Viewpoint-Orientation

The architecture of the solution should support

different views of its concepts. To facilitate

communication and management of models. Views

will make it easier to address multiple concerns and

can improve decision-making by isolating certain

aspects of the architecture in views as needed by

decision makers. In this sense, viewpoint

specifications can be as simple as a filter applied to

the overall constellation of enterprise architecture

models, or as complex as an algorithm that uses the

information contained on the models to perform a

calculation determined. (Antunes et al.,2014)

5 RELATED WORKS

In this section, we explore through a literature review,

the related works that have already dealt with

ontologies and CBR, separately or combined.

Thereafter, we focus on the EA domain, with a

purpose of identifying the advantages of this

combination, followed by a projection in our research

question about the dynamic aspect in EA: can the

ontology and CBR respond to our architectural

principles predefined for dynamic EA.

Several works have already combined ontologies

and CBR; (Daz-Agudo et al.,2001) and (Wang et

al.,2003). Especially, in medical and clinical domain

that has been prominent in the recent past in the field

of OBCBR: Ontology-based Case Based Reasoning

(martin et al.,2016). we list here some examples:

(Shen et al.,2015) propose an OBCBR and multi-

agent-based clinical decision support system. The

used ontology employs the domain knowledge to ease

the extraction of similar clinical cases and provide

treatment suggestions to patients and physicians.

(Sene, et al.,2015) propose an OBCBR approach

based on taxonomic reasoning for telemedicine in the

oncology domain with the inclusion of natural

language processing (NLP). (Delir Haghighi et

al.,2013) introduce a development and evaluation of

an OBCBR system in medical emergency

management.

If we move to other fields, (Amailef et al.,2013)

introduce an OBCBR implementation for intelligent

m-Government emergency response services. It is

notable that this implementation gives end users the

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possibility to adjust extempore certain similarity

weights during retrieval phase and allows them to

evaluate the proposed solution (outcome) during

retaining phase.

In the EA field, and as presented in section (3.2),

many EA issues have been resolved via ontologies

such: analysis, integration, sematic search, semantic

annotation, knowledge representation. (Ding et al.,

2021), proposed an ontology-based technology to

mine the core knowledge of successful projects with

the purpose of improving the quality of application

project and making an enhancement for development

efficiency, through building a common library to

extract knowledge from the process of project

building with standard pattern for high-quality

software application delivery. From precious

experiences of each successful projects, he defines

two ways of ontology-based domain knowledge

pattern, first is for application project management,

and second is for software engineering process. To

decompose the project knowledge from the same

application domain with tree structures. The results of

this paper reduce development and requirement cost,

and user satisfaction is better than without ontology,

we don’t need an experienced project leader so often,

because the ontology model will teach us how to face

difficulty.

The combination of ontologies with the CBR has

been also tackled in (martin et al.,2016) according to

him ontologies and CBR are used in EA with the aim

of enriching the knowledge bases of projects and

improving the results of CBR by an ontological

representation allowing a better calculation of

similarities, this varies depending on the different

viewpoints and the company's stockholders. He used

the structure given by the enterprise ontology named

ArchiMEO, that is a partial realization of the

Enterprise Architecture Framework (EAF)

Archimate. And to apply the new ORCBR approach

to different viewpoints, he used the case viewpoint

model derived from the ISO/IEC/IEEE 42010

standards.

In the light of what precedes, we can note that a

correlation is possible between our predefined

architectural principles in one hand, and the coverage

ensured by a hybrid use of the two concepts

ontologies and CBR in the other hand.

Expressiveness (principle 2) is the main advantage of

ontologies, ensuring a formal representation for a

clear communication between the different

stockholders even with different viewpoints

(principle 5). This ability to respond to multiple

concerns (viewpoints, domain, new concepts, …) is

allowed by the distinction between domain-specific

and domain-independent or upper ontologies that

should co-exist to ensure extensibility (principle 3),

flexibility and adaptability (principle 1), as well as

modularity and reuse (principle 4) .Those two last

principles (1,4) are enhanced by the CBR through the

Case base, that is filled and enriched by the couplets

(problem, solution), but also adapted while applying

the algorithm and requesting for similar cases. Thus,

allowing the maintain of a rich, durable and a

dynamic case base (principle 6) ensuring a rapid

response to a given new case.

6 PROPOSED APPROACH:

REPRESENTING DYNAMIC

ASPECT IN EA USING

ONTOLOGIES AND CBR

To meet the need for a representation that allows the

modelling of the dynamic aspect in the different

stages of enterprise architecture, and that meets with

our architectural principals, our proposal assumes that

ontologies can represent, extend, and enrich the

dynamic aspect in the models of enterprise

architectures in order to allow a dynamic and fluid

reaction of the enterprise in front of a change. We

propose to start from existing ontologies in terms of

enterprise architecture and enrich them with new

concepts relating to the dynamic aspect and the

response to changes.

According to (Dongwoo et al.,2010) the

enterprise architecture is made up of the components

of the EA and the relationships between them. The

figure below, Fig. 1, demonstrates this with a

simplified model.

The Enterprise Architecture is modeled into three

components: Strategy, Business, and Application.

Figure 1: Simplified EA model (Dongwoo, 2010).

A New Approach for a Dynamic Enterprise Architecture Model using Ontology and Case-based Reasoning

557

To represent the ontology of each components and

their relations, we choose to use ArchiMEO in our

study that is an ontological representation resulting

from the transformation of ArchiMate concepts and

relations. Archimate is an enterprise architecture

framework, providing a modelling notation which

intentionally resembles the UML notation (The Open

Group, 2012). We add the ontological representation

of three fundamental concepts to our approach:

change, EA version and transformation plan.

The proposed approach presents the variation

between micro versions of enterprise architecture,

symbolized δEA

reproducing the states at the

strategy, business, and application levels to follow up

on transient changes whose summation represents the

transformation plan for taking charge of the change

factor until the final state of δEA

Targe

. is obtained.

Figure 2: Evolution of EA versions after a change.

The interception of change is the entry point of

our model (either by detection or prediction from

information sources, social networks, government

sources, which saves time in the study and decision-

making before we are faced with a fait accompli…),

it is the (new case) in our logic of CBR.

The second phase is the identification and

categorization based on the case characterization,

formalized by an ontological representation. So,

according to the internal case base of the factors of

change, and their association to the successions of EA

versions to achieve the target EA, it is at this point

that a process of reasoning by case begins to bring

together via ontological techniques the

similarities

between the current case and the cases already

recorded in the database to determine the part of the

EA affected by this change, the target state desired by

this change and identifying the EA intermediaries and

the transformation plan to achieve it. The steps of the

proposed approach are represented in the flowchart

above (Fig.3).

Figure 3: Steps proposed for the model.

7 CASE STUDY

To illustrate our proposed approach, we take the

example of the dynamic behavior of a Moroccan

enterprise “enterprise A” in the face of covid-19

variants and its impact on the mode of work: office

working, remote working or hybrid mode.

In our case base, we consider that we have

accumulated behavioral experiences since the

apparition of the pandemic in December 2019, the

reactions facing the delta variant, arriving today at the

omicron variant. We can consider that for an

instantiation of our model in a Moroccan context, the

interception of the variant omicron was made via

prediction, as long as on the news, the variant has

already appeared in other countries, which gives more

time to react. To describe the application of the

proposed method on the case studied, we will do it by

steps: In the beginning, the enterprise “A” has to

represent the new case in accordance with the

standardised representation based-ontology, taking

into account: the characteristics of the trigger of the

change: omicron variant in our case: such as

(spreading rate, transmissibility, the tag given by the

OMS, severity, risk factors for company staff,...). The

variation on EA resulting from this change, is

deconstructed to micro variations from δEA

(with i=

initial), δEA

i+1

, δEA

i+2

…until δEA

target

, in each

iteration, we emphasis a micro variation of one level:

strategy, Business for example (alternatives for

Business Processes not completely automatized…)

application: (laptop availability, VPN configuration,

…) the summation represents the transformation plan.

The second step is to retrieve the most similar cases

from the case base, which contains historical cases,

based on the characterization of the current situation

used as query, using a similarity mechanism. The next

step is reusing the lesson from the retrieved cases as

the suggested solution for the new situation; adapt the

retrieved lesson to the new situation, which becomes

part of a new case. And finally, revise the new case

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after evaluating it in the new situation, and enrich the

case base with the new results.

8 CONCLUSIONS

In this paper, we explore the opportunity of using

ontologies as an approach giving a dynamic push to

enterprise architecture models. Coupled with case-

based reasoning, to retrieve and maintain existing

knowledge. A set of architectural principales was

proposed as a requirement of the proposed model.

Ontologies and Case based reasoning were combined

with the Enterprise architecture to respond to our

architectural principles, finally a use case is presented

to illustrate our proposal.

As future work we plan to implement semi-

automatic reuse (Adaptation OWL/Rule reasoning

/inferencing and machine learning) and enhanced

automatic retention (case learning and ontology

learning, adding to elements to domain ontology,

OWL/rule reasoning). Additionally, we plan to

enhance this model with natural language-processing

technology to overcome incomplete case descriptions

and add a new change prediction component.

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