Knowledge Engineering for Business Process Modeling

Sonya Ouali, Mohamed Mhiri and Faiez Gargouri

University of Sfax, Tunisia

MIR@CL Laboratory, Technopark of Sfax: Tunis Road Km 10 BP. 242, 3021 Sfax, Tunisia

Keywords:

Business Process, BPM, Ontology, Multidimensional Business Knowledge, Semantic Relationships, Breast

Cancer.

Abstract:

The process is the pivot of the business modeling. Thus, the goal of modeling is to present the main ﬂows

exchanged with the internal and external environment. Indeed, there are several pieces of information that take

place throughout the process life cycle, from design to execution. Behind these pieces of information, there

is a lot of business knowledge that should be acquired to improve the quality of such a modeling. The aim of

this paper is to manipulate the business knowledge when developing modeling perspectives. For this reason,

our solution consists in proposing an ontological approach to create a Multidimensional Business Knowledge

base (MBK BASE) to help the designers of the business process with their tasks. In this way, on the one hand,

we outline an overview of our proposed solution by relating it to other research. In fact, we deﬁne the main

business concepts and we describe some semantic relationships which are expressed with the descriptive logic.

On the other hand, we give an illustrative case study related to the treatment choice process of a patient with

breast cancer in order to demonstrate the applicability of our solution.

1 INTRODUCTION

Research Context. As it is already known, the busi-

ness domain is one of the most complex domains to

model. For this reason, organizations attach more

importance to their Business Process Management

(BPM), which plays a crucial role in the improve-

ment of their performance (G

abor and Szab

o, 2013).

Therefore, a clear business process modeling is at the

heart of the major challenges of the BPM, given that

this modeling enables to describe the sequence of dif-

ferent activities and the way they are connected in

giving a complete description of a model called: Pro-

cess Model (PM). In fact, this description is not an

easy task because it requires a better understanding

and an effective management of the business process.

However, the description of the same sequence of ac-

tivities, more than one PM is required since this de-

scription is strongly related to the different views of

the designers and the used modeling language. In-

deed, in the literature, there are several methods and

techniques which support the modeling of business

process such as UML (Uniﬁed Modeling Language),

BPMN (Business Process Modeling Notation) and

many others (Recker et al., 2009). Thanks to these

standards, business processes are more understood by

a large public, but, what about knowledge?

Knowledge is gravitated in the memory of the design-

ers and ﬁgures in their habits and their daily tasks. For

this reason, the use of knowledge can be considered as

the most important thing when modeling a business

process because it deals with many different pieces

of information that differ from one sector to another

and from one participant to another. Furthermore, it

is necessary to express knowledge and make it in the

disposal of the designers.

The problem being arisen in this paper is ”how we

can present this knowledge and enable the designers

to beneﬁt from their use at an early stage?”

Our solution is to propose an ontological approach

to construct a Multidimensional Business Knowledge

BASE (MBK BASE) which is an original way to de-

compose business knowledge by the perspectives of

the business process. Besides, such a base provides

a clear presentation and an easy use of exactly the

needed dimension of knowledge.

Organization of the Paper. The remainder of this

manuscript is organized as follows. Section 2 pro-

vides some preliminaries which revolve around the

business process modeling and knowledge engineer-

ing. Section 3 highlights the studies and projects re-

lated to our positioning. Section 4 exposes the basics

of our proposed approach. Section 5 presents an il-

lustrative case study so as to explain the utility of our

Ouali, S., Mhiri, M. and Gargouri, F.

Knowledge Engineering for Business Process Modeling.

DOI: 10.5220/0006323200810090

In Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2017), pages 81-90

ISBN: 978-989-758-250-9

proposed solution. Finally, section 6 concludes the

paper and suggests some future research studies.

2 MOTIVATIONS

Research in the business process modeling does not

stop due to the high speed of technological develop-

ment and the already increasing customers’ demands

for efﬁcient improvements of products and services.

Indeed, one of the most important research objectives

is to support a business process with semantic level.

Such a level has a crucial impact on the improvement

of the business process BP through a better under-

standing. In what follows, we present two prelimi-

naries in order to put our work in its research context.

2.1 Business Process Modeling

In recent years, organizations have been increas-

ingly competitive using great dynamics and com-

plex processes. Furthermore, to respond to their

customers’ requirements, organizations think more

seriously about their business process management

(BPM). In this context, BPM is deﬁned in (Dumas

et al., 2013) as: ”the art and science of overseeing how

work is performed in an organization to ensure consis-

tent outcomes and take advantage of the improvement

opportunities”. Moreover, the crucial role of the BPM

is to manage the chains of events, activities and deci-

sions so as to add value to the organization. Chains of

events, activities and decision are simply represented

in a business process.

The business process is not a new term. In fact, it

has several deﬁnitions in literature, such the one of

(Harrington, 1991), (Hammer and Champy, 1993),

(Weske, 2007) and (Gernert and K

oppen, 2006).

However, the most popular deﬁnition is that of (Coali-

tion, 1996) which states that a business process is

”a set of one or more linked procedures or activities

which collectively realize a business objective or pol-

icy goal, normally within the context of an organi-

zational structure deﬁning functional roles and rela-

tionships”. Indeed, business process modeling lies in

the heart of the BPM given that its objective of the

business process modeling is to give a clear graphical

presentation taking into account the smallest details

so as to facilitate its understanding. However, mod-

eling is not a simple task in a business domain char-

acterized by a diversity of concepts and used terms.

Therefore, several process modeling languages and

notations emerged with the aim of assisting enter-

prises with the documentation and presentation of

their processes. These notations describe the busi-

ness process and take into account the functional, or-

ganizational and informational ways (Rosemann and

vom Brocke, 2015),(van der Aalst, 2013) (Indulska

et al., 2009). In this context, we can mention the

most prominent notations, such as the BPMN (Busi-

ness Process Modeling and Notation), the UML (Uni-

ﬁed Modeling Language) especially through the ac-

tivity diagram, the Petri net, the EPC (Event driven

Process Chain) (La Rosa et al., 2013) and (van der

Aalst, 2013). However, the BPMN is considered as

the de facto standard approved by ISO/OSI (Model,

2011). BPMN is deﬁned by OMG in order to make

the understanding of the business processes easier for

business analysts and technical developers. At the

semantic level of modeling the business processes,

PMs are as understood as their graphical presentations

using these standards and notations. This semantic

level aims at limiting the ambiguities using different

terms. In fact, several studies had been carried out

in this context, more precisely in expressing knowl-

edge, which results in the appearance of knowledge

engineering.

2.2 Knowledge Engineering

To make anything more understood, the only way is

to give it a meaning, which is called the semantics.

However, this is not an easy task to do given that

the semantics is expressed throughout the knowledge

term. Furthermore, Knowledge is tightly related to

the thoughts which are hard to extract. Literature pro-

vided several deﬁnitions of knowledge. We have cho-

sen to give deﬁnitions we think they are the most sig-

niﬁcant in our ﬁeld of research. For instance, knowl-

edge is deﬁned as ”true and justiﬁed beliefs” (Nonaka

and Takeuchi, 1995), ”a mix of experiences, values,

contextual information and expert insight that pro-

vides a framework to evaluate and interpret new ex-

periences” (Jablonski and Bussler, 1996), ”ability to

act in a given context” (Sveiby, 1997). In addition,

managing knowledge highly depends on its forms.

Knowledge has many forms; tacit, explicit, declar-

ative, procedural, conditional, individual and collec-

tive. The most popular ones are the tacit and explicit.

On the one hand, the explicit form is generally de-

scribed across a set of symbols -like words, forms . . . -

which formally express it (Evans et al., 2015). Its ac-

cess is not such a difﬁcult task because it can be artic-

ulated, codiﬁed and stored in some supports. On the

other hand, tacit knowledge, is deﬁned as the meta-

resources emanating from the thought, the reﬂection,

or the experience of the human mind (Van den Berg,

2013). Therefore, its access is as difﬁcult as the ex-

plicit form.

ENASE 2017 - 12th International Conference on Evaluation of Novel Approaches to Software Engineering

The process of capitalizing and constructing a

knowledge base is called Knowledge Engineering

(KE). KE deals with knowledge acquisition, repre-

sentation, validation, inferencing, explanation, and

maintenance. The business ﬁeld, like several research

ﬁelds, had beneﬁted from the KE. Indeed, to construct

a business knowledge base, the KE process should

pass by some major phases:

Figure 1: Principe of the construction a Business Knowl-

edge Base.

• Knowledge acquisition: based on heterogeneous

resources, this step enables us, on the one hand,

to identify the tacit knowledge and, on the other

hand, extract the business concepts which appear

to be useful to ﬁnd business knowledge. Today,

lots of theoretical and applied research studies are

still being conducted in this ﬁeld (Wagner et al.,

2002).

• Knowledge representation: this step aims at

preparing a knowledge map and encoding it in the

business knowledge base.

• Knowledge validation: there are two successive

ways to validate or verify knowledge. The ﬁrst

one consists in applying test cases. After that, the

obtained results are shown to the experts of the

domain to verify their conformity with the reality.

• Inferencing: this step consists in using the stored

knowledge to enable the system to give advice and

propositions to different types of users.

• Explanation and justiﬁcation: this step consists in

making knowledge available to be used in answer-

ing the queries.

Thereby, to support and exploit the semantics of in-

formation through structuring and encoding its mean-

ing in order to describe and characterize informa-

tion for the purpose of enhancing its processing, this

can be accomplished by the application of techniques

called semantic technologies (Sheth and Ramakrish-

nan, 2003). Ontology can be considered as the most

understood semantic model because it serves for cap-

turing and formalizing the meaning like a conceptual

schema (Antoniou et al., 2005). Basically, ontology is

deﬁned as a conceptualization of a domain of interest

(Gruber et al., 1993), (Daconta et al., 2003). Due to

the richness of its expressiveness and also its seman-

tic formalization high degree, the use of ontology is a

core element for knowledge engineering.

3 TOWARDS A SYSTEM THAT

HELPS DESIGN A BUSINESS

PROCESS

As previously mentioned, the business process mod-

eling is the most important ﬁeld in the BPM which

helps graphically visualize the activities and tasks of

the business process for a better understanding. To

give a complete model of the business process, it is

necessary to follow a cyclic methodology called a

business process lifecycle which consists of a set of

phases, starting with a diagnosis/requirements phase,

passing by a conﬁguration/ implementation phase, an

enactment/monitoring phase until arriving at an ad-

justment phase (Lodhi et al., 2011). This business

process lifecycle is recursive given that each phase

can have similar phases during its lifecycle. In what

follows, we brieﬂy describe the lifecycle based on the

modeling phase. In the diagnosis/requirement phase,

it is necessary to deﬁne which business processes are

the best to achieve the ﬁxed objectives. In this way,

these objectives and requirements must be described

in more detail. The output of these phases is a de-

tailed plan about the goals of a business process in

taking into account the important changes to be car-

ried out.

In the design phase, the main thing be considered is

the analysis of the business process key-perspectives

which are the process perspective, the organizational

perspective and the informational perspective. The

ﬁrst one aims at describing the activities involved in

the process, which operations are encapsulated in and

wherewith are they linked between them. The second

aims at structuring the business process actors and

authorizing them, in taking into account their skills,

to perform tasks making up the process. However,

the third aims at deﬁning the structure of the doc-

uments and data required and produced by the pro-

cess. Therefore, a good analysis of the business pro-

cess perspectives conducts the designers to deﬁne the

inputs, describe the procedure, extract the business

Knowledge Engineering for Business Process Modeling

Figure 2: Work related to the business process Lifecycle and our positioning.

rules, ensure the resource allocation, deﬁne the role

mapping and take into account the required changes.

In this phase, the designers must explicitly specify the

involved elements of the business process to give a de-

tailed design model to the implementation phase. In

this way, we distinguish a strong need for their knowl-

edge. Thereby, this makes knowledge an essential el-

ement to be used especially when the processes are

conceptualized and designed from scratch.

The conﬁguration/implementation phase is widely

dependent on the description of the language of the

models in such a way that business processes are sup-

ported with the Information Technology (IT). This

phase is considered as a mapping between the pro-

cess needs and requirements. On the one hand, and

the IT service, on the other hand, with the objective

to provide Business- IT support since an output of the

business processes is executed with the help of infor-

mation systems. Moreover, the information systems

are equally used on an evaluation and monitoring lev-

els. After implementing and conﬁguring the designed

business processes, it is necessary to enact and adjust

them when needed. The idea here is not to redesign

the processes or to create a new software, but to adapt

or reconﬁgure these processes to make them conform

to the reality and fulﬁll the requirements and the ob-

jectives of the organization.

On modeling business processes, several pieces of

information are needed during the business life cy-

cle from design to execution and monitoring. Indeed,

behind these pieces of information, there is a great

deal of knowledge which makes business processes

more understood and clearer. Knowledge is not usu-

ally explicit because it is closely related to the mind

and thoughts of the people in general, and to the de-

signers, in our context. In this way, we can say that

business process modeling is highly dependent on the

designers’ knowledge. In fact, due to different experi-

ences, intellectual levels and skills, the designers have

not the same knowledge given that they have differ-

ent abilities to understand, to describe the reality and

make decisions about the choice of the concepts and

details to be modeled. However, for one business pro-

cess, several models can be retrieved resulting in any

ambiguity. This has an impact on the quality of the

models and later, on their shareability and their reuse.

In addition, the designers’ knowledge is closely re-

lated to the business process perspectives. Therefore,

we can say that business knowledge is a set of differ-

ent pieces of knowledge related to the different parts

of the business process, like the activities, how their

are linked, who performs them and which skills are

required. In this way, we deﬁne business knowledge

as a set of dimensions and proposes of a multidimen-

sional model to link each dimension with the main

business process concepts. Consequently, our contri-

bution is to create a system based on a multidimen-

sional business knowledge to help the designers of the

business process to guaranty the reuse and the sher-

ability of knowledge. This contribution is considered

as a phase of pre-modeling. Figure 2 clariﬁes the main

directives of our contribution and its positioning in re-

lation to the business process lifecycle. Many efforts

have been made on the semantic level in the business

process context, with its different lifecycle phases

proposed in literature. In this context we elaborate

them by phases. For instance, concerning the phase

of design (modeling), there are (Lin and Krogstie,

ENASE 2017 - 12th International Conference on Evaluation of Novel Approaches to Software Engineering

2010), (Becker et al., 2010), (Koschmider et al., 2011)

and (Delfmann et al., 2011). While (Cherﬁ et al.,

2013) and (Fellmann, 2013) who treated not only the

design phase but also the analysis one. Concern-

ing the analysis phase, we mention (Di Francesco-

marino, 2011), (Missikoff et al., 2011), (Wang et al.,

2010) and (Hoang et al., 2014) and with the execu-

tion phase (Weissgerber, 2011), (Born et al., 2007)

and (Markovic, 2010). In short, we believe our solu-

tion is currently unique, not only in trying to be sub-

stituted at a pre-modeling phase but also to take into

account all the dimensions of business knowledge in

a coherent framework. In fact, this is made possible

thanks to the use of Ontology.

4 KNOWLEDGE ENGINEERING

FOR BUSINESS PROCESS

MODELING: AN

IMPLEMENTATION

OVERVIEW

Our contribution is to help business process designers

with their tasks by putting at their disposal a Multidi-

mensional Business Knowledge BASE (MBKBASE)

for the purpose of guaranteeing their shareability and

their reuse. For this reason, we have decomposed our

proposal solution into many bricks as it is demon-

strated in ﬁgure 3.

4.1 Business Concept Deﬁnition

The business process modeling requires an exhaustive

description of its components. It should be based on

a ﬁnite number of concepts. In this context, starting

with a set of heterogeneous inputs, such as graphi-

cal representations of the business process, like the

BPMN models, the activity diagrams (UML), the

EPC models and many others, we notice that there are

a lot of different used terms. This diversity leads to

many ambiguities and does not contribute to improve

the BP modeling. Actually, it requires a pre-treatment

to deﬁne the relevant information by eliminating the

redundancies and ambiguities. Furthermore, a deﬁni-

tion phase of the business concepts is necessary so as

to limit and regroup the similar expressed pieces of

information under only one signiﬁcant business con-

cept. Table 1 presents a glossary of concepts related

to the business domain.

Table 1: Business concepts.

Concept Description

Process A sequence needed to make

an output (product or ser-

vice) while using a set of

resources: physical and hu-

man

Activity part of the business process

with a well-deﬁned order.

There are two kinds of ac-

tivities: atomic activity and

composite activity

Operation Results of actions to perform

an activity

Coordination Pat-

tern

link between two or more

activities. It can be a condi-

tional pattern or an uncondi-

tional one.

Condition The fact triggers one or more

activities

Actor Someone who performs an

activity by applying some

techniques and has many

skills. It can be a person, a

machine or a software

Informational

Resource

Materials and tools used by

an actor to make his business

4.2 Business Knowledge Modeling

After the deﬁnition of the business concepts, the sec-

ond phase aims, as its name indicates, at model-

ing business knowledge. In fact, we deﬁne Business

Knowledge (BK) as a set of knowledge dimensions

that serve to better understand processes and espe-

cially to facilitate the modeling of tasks for design-

ers. Based on this deﬁnition, we identify seven di-

mensions of BK, namely, organizational knowledge

dimension, informational resource knowledge dimen-

sion, functional knowledge dimension, operational

knowledge dimension, conditional knowledge dimen-

sion and ﬁnally behavioural knowledge dimension.

Indeed, the aim of this phase is, on the one hand, to

segment the business knowledge since such knowl-

edge has a large meaning and, on the other hand, to

allocate the deﬁned business concepts to each BK di-

mension. Hence, this phase is decomposed into two

steps, the business knowledge classiﬁcation and the

business knowledge formalization. In the ﬁrst step,

we elaborate more than one dimension, like orga-

nizational knowledge, functional knowledge, opera-

tional knowledge, behavioral knowledge, skills, infor-

mational resource knowledge and contextual knowl-

edge. These dimensions had been well expressed by

Knowledge Engineering for Business Process Modeling

Figure 3: The proposed approach for representing the multidimensional Business Knowledge.

(Ouali et al., 2016). They have been presented around

the main-key perspectives of the BP. Moreover, we

have linked each dimension with the main related

business concepts. Concerning the second step, we

had elaborated an algorithm that permits to construct

a multidimensional business knowledge model with

the idea to automatically, generate the skeleton of the

business ontology, deﬁning the business knowledge

dimensions and, their related business concepts. In

this way, the used languages are, ﬁrst, the model to

model (M2M) transformation throughout the ATL in

order to build the multidimensional business knowl-

edge model. This model is later used to be trans-

formed into an owl code throughout a model to text

(code) (M2T) transformation using the Acceleo as a

code generator.

4.3 Business Ontology

As it is already known, ontology is increasingly used

for modeling knowledge. In addition, it provides a

theoretical and practical basis for robust modeling of

a domain (Andersson et al., 2006). It improves the ex-

change of operational concepts from one study to an-

other in the same domain of interest. In the literature,

there are several research studies that used ontology

to model a speciﬁc domain, such as (T

etreault, 2012)

and (Yessad and Labat, 2011). The skeleton of our

business ontology is the result of the business knowl-

edge modeling phase. In fact, the construction of our

business ontology is summarized in three principle di-

rectives which are:

• First, the deﬁnition of the business concepts re-

lated to the corresponding business dimensions.

• Second, the determination of the semantic rela-

tionships and their modeling.

• Third, the elaboration of the business rules.

The ﬁrst directive is automatically done as it has been

mentioned previously, that is the output of the busi-

ness knowledge modeling phase. The second one

consists in clearly and consistently describing the se-

mantic links between the business concepts. The ob-

jective of this modeling is to describe the behaviour

of a process in terms of activities, operations, infor-

mational resources, conditions, transitions and actors.

The process execution depends on the execution man-

ner of these business concepts. The semantic relation-

ships between these business concepts are described

by many rules. In this way, we have two kinds of re-

lationships: inter-perspective relationships (relations

that match concepts and are not from the same busi-

ness knowledge perspective) and intra-perspective re-

lationships (relations that match concepts and appear

in the same business knowledge perspective). In fact,

we present some of them which are expressed in the

Descriptive Logic (Baader et al., 2009).

As an example of the intra-perspective relationships,

we can mention:

• ”is composed”: this relationship models the com-

position of concepts. The concept process is com-

posed of more than one activity and each activity

is composed of more than an operation.

Process M >=1 is composed A c t i v i tie s

Activity >=1 is c o m p o sed O p e r a t ion s

And as an example of the inter-perspective rela-

tionships, we can mention:

• ”consume”: the activity consumes one or more in-

formational resources.

Activity M >=1 c o n s u m e I n fo r m a t i on a l

Resource

• ”realized by”: this relationship models the seman-

tic link between the activity and the actor: the ac-

tivities are realized by at least one actor.

Activity M > = 1 r e a l i z e d by A c t or

• ”trigger”: the operation triggers one or more con-

dition.

Oper a t i o n M > = 1 t r i g g e r Conditio n

ENASE 2017 - 12th International Conference on Evaluation of Novel Approaches to Software Engineering

Figure 4: Fragment of semantic relationships in the Business Ontology.

• ”provoke”: the condition concept provokes ex-

actly one activity

Cond i t i o n M > = 1 p r o v o k e Activity M

<= 1 p r o v o k e A c t i v i t y

These semantic relationships are expressed in a gen-

eral way to implicitly describe the semantic reference

of the manner by which business concepts are linked

to one another (Wache et al., 2001). Hence, ﬁgure 4

shows a fragment of the business knowledge dimen-

sions which are clearly represented with their related

business concepts and some semantic relationships.

In fact, these semantic relationships have a crucial

role by elaborating the business rules with the aim

of separating the application code from the business

knowledge (Omrane et al., 2011).

After constructing of our business ontology by en-

abling the designers to beneﬁt from a multidimen-

sional business knowledge base, the ﬁnal step is the

manipulation and the exploitation of the business

knowledge dimensions. For this reason, our objective

is to elaborate a Business Knowledge Deﬁnition Lan-

guage (BKDL) and a Business Knowledge Manipula-

tion and Querying Language (BKML). The objective

of these languages is to ensure simple operations with

an easy syntax to respond to complex queries.

5 ILLUSTRATIVE CASE STUDY

To illustrate the performance of our system, we have

opted for the healthcare domain to illustrate and eval-

uate the importance of the MBK

BASE with regard

to its applicability and capability to give the needed

knowledge dimension. In fact, we have chosen the

healthcare domain since it is one of the most complex

domains to model due to its delicateness. Since, in

one process, many activities ﬁgure in applying many

actors which work together to guarantee a high treat-

ment quality for the patients. Cancer is a part of

this complex domain. Moreover, its treatment needs

sharing knowledge because there is an interaction of

lots of healthcare professionals with many speciali-

ties who are located on different sites. On this side,

while a business process designer creates a PM for

one patient with breast cancer, he needs to have an-

swers to many questions (Q1 ··· Q10) since he is

not a healthcare specialist, as it is shown in ﬁgure

5. In fact, the performance of our MBK BASE is to

make the needed piece of knowledge available (that is

what we call the dimension). This will be made pos-

sible by consulting the MBK BASE throughout one

of the business knowledge dimension. For this rea-

son, our case study is conducted in a real clinical sce-

nario in the context of women with breast cancer. The

description of the process of making the good treat-

ment decisions is taken from an American Cancer

Society (ACS)

. To observe the practical applicabil-

ity of our proposed MBK Base, we illustrate in the

ﬁgure 6, a PM writing using the BPMN2 modeller,

enriched with a set of MBK. During our experimen-

tation, we have identiﬁed different dimensions related

to each business concept. For instance, we add, on the

one hand, an organizational knowledge dimension re-

lated to the actors of the ﬁrst activity ”Discuss about

to treatment options” and, on the other hand, a skill

dimension which is important in allocating the activ-

ity to the adequate actor. Furthermore, the third ac-

tivity in the process of the choice of the breast can-

cer treatment is related to a condition. Therefore, we

www.cancer.org

Knowledge Engineering for Business Process Modeling

Figure 5: Illustrative case study related to the PM of patients with breast cancer.

(a) PM of Breast Cancer Treatment Decision (b) Key

Figure 6: Some of used MBK in a PM of a patient with cancer.

integrated a behavioral knowledge dimension, to con-

duct the designer to make the good link between the

activities. The fourth activity is preceded by both a

condition ”if necessary”, which elicits a contextual

knowledge dimension, and an informational resource

knowledge dimension ”medical reports”. The activ-

ity ”Choose no treatment at all” requires contextual

knowledge and informational resource knowledge di-

mensions. The ﬁrst one is related to the knowledge of

the cancer stage (Cs) and the patient’s age (PATage).

ENASE 2017 - 12th International Conference on Evaluation of Novel Approaches to Software Engineering

Concerning the second one, it is related to the medi-

cal reports and the diagnostics’s results. Actually, if

cancer is at an early stage, the activity ”Choose LT

(Local Treatment)” is the appropriate choice. For this

reason, a contextual knowledge dimension is recom-

mended to explain this condition. Indeed, in this case,

the same activity applies a functional knowledge di-

mension to describe a set of composed activities of

getting surgery and/or radiation therapy. Moreover, it

is necessary to present an operational knowledge di-

mension to specify the main executed operations re-

lated to the surgery or the radiation therapy.

It can be concluded that, the aim of this illustrative

case study is to show how multidimensional business

knowledge can ﬁgure in the design of a PM. These

dimensions contribute to facilitate the design tasks.

6 CONCLUSION AND

PERSPECTIVES

On concluding this paper, we give an overview of

some preliminaries in which our research work is sub-

scribed. In addition, we presented our contribution in

relation to the research studies that focused on the se-

mantic expression of the business process modeling.

Thus, we organized them in three categories around

the business process modeling lifecycle phases. Fur-

thermore, we explained the different steps of our ap-

proach by presenting their principle directives and

some of their results. To validate our proposed solu-

tion, we elaborated an illustrative case study related

to one of the most complex domains, which is the

healthcare domain, especially the cancer one. In fact,

our choice is related to the process of treatment deci-

sion. For this purpose, we presented a set of business

knowledge dimensions that are necessary for a clear

understanding of the process.

Regarding the suggested future studies, ﬁrstly, we

plan to accomplish the implementation of the pro-

totype to support the business knowledge ontology.

Secondly, we plan to simplify the proposed language

of deﬁnition, manipulation and querying the ontology

because, such a language must be based on a clear

syntax without ambiguity to express complex queries

with simple operations.

ACKNOWLEDGMENT

This manuscript is in the memory of Mr LOTFI

BOUZGUENDA who provided insight and expertise

which, has greatly assisted the research for two years

but unfortunately, he suddenly passed away.

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