Dynamic OWL Ontology Design Using UML and BPMN

J. I. Olszewska

, R. Simpson

and T. L. McCluskey

School of Computing and Technology, University of Gloucestershire, The Park, Cheltenham, GL50 2RH, U.K.

School of Computing and Engineering, University of Huddersﬁeld, Queensgate, Huddersﬁeld, HD1 3DH, U.K.

Keywords:

Ontology, OWL, Dynamic Design, Knowledge Engineering, Software Engineering, UML, BPMN.

Abstract:

Ontology design is a crucial task for the Semantic Web. In the literature, methodologies have been proposed to

develop ontologies, however the phase between knowledge gathering and knowledge coding remains challeng-

ing. In this paper, we propose a dynamic ontology design based on dynamic design notations for a systematic

identiﬁcation of the relations between domain concepts. For this purpose, we propose the use of the Uniﬁed

Modeling Language (UML) and the Business Process Modeling Notation (BPMN), and the mapping of the

related dynamic notations to the ontology domain. Our approach has been successfully validated in a study

case of an ontology with a publication repository domain.

1 INTRODUCTION

Ontologies are considered to be one of the pillars of

the Semantic Web. More speciﬁcally, an ontology is

a notion deﬁned by Gruber as an explicit speciﬁca-

tion of a conceptualization (Gruber, 1995). The term

(from the Greek, ontos: of being and logia: study)

is borrowed from Philosophy and it refers to the sub-

ject of existence. In Artiﬁcial Intelligence (AI), an

ontology is constituted by a speciﬁc vocabulary used

to describe a certain reality, plus a set of explicit as-

sumptions regarding the intended meaning of the vo-

cabulary (Green and Rosemann, 2005). Thus, the on-

tology describes a formal speciﬁcation of a certain do-

main, i.e. a shared understanding of a domain of in-

terest as well as a formal and machine understandable

model of this domain. Ontologies have been widely

used for capturing, sharing, and representing knowl-

edge (Fensel, 2001; Auer et al., 2006; Aljandal et al.,

2009; Corsar et al., 2009; Olszewska, 2011).

Despite many methodologies proposed to de-

velop ontologies (Fern

andez-L

opez, 1999; Corcho

et al., 2003; G

omez-P

erez et al., 2004; Seremeti and

Kameas, 2009), design of large-scale, interoperable

ontologies is still a challenge (Jimenez-Ruiz et al.,

2012).

Interoperability is the challenge of getting pro-

cesses to share and exchange information effectively.

Service orientation relates to creating self-contained,

self-describing, accessible, and open, computer ser-

vices.

Both these challenges relate to the representation

of the data being exchanged/manipulated. In our ap-

plication of research data management, there are var-

ious existing sources of research information in a

University, for example, its publications repository

‘ePrints’ (ePrints, 2014). Research information is

complex, structured data, and the future requirements

of it are only partially known. If we commit to one

encoding, or even one representation language, later

it may turn out to be inadequate or obsolete. Current

work (Jain and Pareek, 2010) on these issues points to

representing the data in an ontology.

In the e-Business context (Gessa et al., 2006), a

mechanism to improve system usability, maintenance,

efﬁciency, and interoperability could reside in the

formal description of the semantic of the document-

based framework for business collaborations. The

formal descriptions could be provided through the

deﬁnition of an ontology that represents the implicit

concepts and the relationships that underlie the busi-

ness vocabulary.

Hence, in this work, we propose to develop a

large-scale, interoperable ontology for research infor-

mation systems such as ePrints. For this purpose,

we have introduced dynamic design notations to sys-

tematically construct concept relations in addition to

capture the ontology scope with static design nota-

tions. Indeed, Uniﬁed Modeling Language (UML)

has been extensively applied in Software Engineer-

ing for requirements analysis and software design

(Marshall, 2000; Lunn, 2003). However, UML use

436

Olszewska J., Simpson R. and McCluskey T..

Dynamic OWL Ontology Design Using UML and BPMN.

DOI: 10.5220/0005159204360444

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2014), pages 436-444

ISBN: 978-989-758-049-9

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

in Knowledge Engineering (Baclwaski et al., 2001;

Kogut et al., 2002; Hermida et al., 2009) has been

limited to static design (De Nicola et al., 2009) for

class identiﬁcation.

Our dynamic ontology design involves Uniﬁed

Modeling Language (UML) and Business Process

Modeling Notation (BPMN) to capture the knowledge

and uses OWL-DL language to codify the ontology.

Thus, our approach allows not only the identiﬁcation

of the concepts, but also a systematic representation

of their relations.

The contributions of this work are:

• to combine BPMN with UML to dynamically cap-

ture the scope of the ontology;

• to propose an ontology development scheme ap-

propriate for interoperable research information

system such as ePrints;

• to develop representations of knowledge for re-

search information in an ontological form.

The paper is structured as follows. In Section

2, we brieﬂy introduce the state-of-the-art method-

ologies to build an ontology, while in Section 3,

we present our approach to design an ontology from

scratch, in particular using dynamic design notations

in the phase of ontology domain capture. The pro-

posed method has been successfully tested to develop

representations of knowledge for research informa-

tion system in an ontological form as reported and

discussed in Section 4. Conclusions are drawn up in

Section 5.

2 METHODOLOGIES

The creation of an ontology requires specialized skills

and involves various stakeholders. The ontology de-

velopment process depends on a variety of factors

like the choice of the software tool used to build and

edit the ontology, the language in which the ontology

is implemented, the methodology which will be fol-

lowed to develop it, the applications in which it will

be used, the type of the ontology under construction,

the available formal and informal existing knowledge

resources, such as lexicons, existing ontologies, etc,

and may include a large number of necessary activi-

ties.

There is no established and unique procedure

to develop ontologies despite several methodologies

that have been proposed over time such as Cyc

Methodology (Lenat and Guha, 1990), Enterprise

Ontology (EO) Methodology (Uschold and King,

1995), Toronto Virtual Enterprise (TOVE) Modelling

Methodology (Gruninger and Fox, 1995), KAC-

TUS Methodology (Bernaras et al., 1996), Skele-

tal Methodology (Uschold and Gruninger, 1996),

METHONTOLOGY (Fern

andez-L

opez et al., 1997),

SENSUS Methodology (Swartout et al., 1997), En-

hanced Methodology (Ohgren and Sandkuhl, 2005),

or Integrated Ontology Development Methodology

(Chaware and Rao, 2010).

However, four general tasks to build ontologies

have been identiﬁed:

• selection (includes selection of the available re-

sources such as related literature, existing ontolo-

gies, group of experts in the domain under de-

scription, selection of the appropriate tool and lan-

guage);

• analysis (includes analysis of selected resources,

of the classes and the properties of the selected

ontology);

• deﬁnition (includes deﬁnition of what is important

for the description of a speciﬁc domain through

the competency questions, deﬁnition of the pur-

pose and the domain of the ontology, the deﬁni-

tion of the classes, the class hierarchy, the proper-

ties and the instances of the ontology);

• evaluation (includes evaluation of the selected re-

sources, evaluation of the technical quality of the

ontology and evaluation of the overall quality of

the obtained results).

These tasks are distributed into different phases,

as follows:

• the speciﬁcation phase (answers why the ontology

is being built, what its intended uses are, who the

end-users are);

• the conceptualization phase (conceptualizes the

domain knowledge);

• the implementation phase (transforms the concep-

tual model into a formal computable model);

• the evaluation phase (assesses the resulting ontol-

ogy).

These phases correspond roughly to the main

steps of software engineering methodologies like pre-

sented in the IEEE Standard 1074-1995 for Develop-

ing Software Life Cycle Processes (Fern

andez-L

opez,

1999).

Moreover, the ontology development methodolo-

gies could be classiﬁed into two categories:

• methodologies focused on building a single ontol-

ogy for a speciﬁc ontology for a speciﬁc domain

of interest;

• methodologies focused on the construction of on-

tology networks.

DynamicOWLOntologyDesignUsingUMLandBPMN

437

Figure 1: UML Use Case Diagram describing the action of encoding an Item into our ontology.

The single ontologies could be further distin-

guished among those aiming at building ontologies:

• from scratch;

• by reusing pre-existing ontologies;

• by using non-ontological resources.

These single ontologies are also divided in collab-

orative and non-collaborative, according to the degree

of participation of the involved ontology engineers,

users, knowledge engineers and domain experts in the

ontology engineering process.

They are also described as application dependent,

semi-application dependent and application indepen-

dent, according to the degree of dependency of the

developed ontology on its ﬁnal application.

The single-ontology capture approach could vary

according to the adopted strategy for identifying con-

cepts, and could be bottom-up (from the most con-

crete to the most abstract), top-down (from the most

abstract to the most concrete), or middle-out (from the

most relevant to the most abstract and most concrete).

We can further distinguish manual, semi-

automatic and automatic ontology construction,

according to the degree of human involvement in the

building process (Seremeti and Kameas, 2009).

However, the above mentioned criteria are not

standards. Furthermore, none of the methodologies

proposed in the literature (Fern

andez-L

opez, 1999)

are fully mature and they need then to be adapted to

the project needs. Hence, we have followed our origi-

nal methodology based on the criteria previously enu-

merated, while developing the new ontology, and we

have carried out the actions as described below.

3 PROPOSED APPROACH

As noted above, we decided to investigate the ap-

plication of ontology using the publications reposi-

tory. Capturing this information within a standard

ontology language would make it universally acces-

sible throughout the Web, and allow it to be analysed,

queried and compared using powerful, open tools.

In the ﬁrst stage, we interviewed the Head of

Computing Library Services of the University of

Huddersﬁeld, UK, to understand the mechanism of

ePrints publication repository in order to capture

ePrints knowledge to build system interoperability

services. We thus identiﬁed the actors interacting

with the ePrints system as well as the procedures by

which the actors interact with this system. The result-

ing UseCase diagram (Fig.1) uses the standard Uni-

ﬁed Modeling Language (UML) (Ambler, 2005) and

shows the functionality of the system as well as its

dependencies at a high level viewpoint.

Next, we have modelled the business pro-

cess using Business Process Modeling Language

(BPML) (Dietz and Mallens, 2001) and generated

the ﬂowchart shown in Fig.2. The UseCase diagram

as well as the Business Process Modeling Notation

(BPMN) (Allweyer, 2005) have been designed with

Modelio Free Edition v1.2 (Modelio, 2014), mainly

because this software supports both UML and busi-

ness modelling while being a user-friendly and free

tool.

These steps have helped us not only in answering

to the competency questions to determine the domain

and the scope (“what we do with it”) of the ontology,

but also to systematically deﬁne the relations between

the ontology concepts.

In our approach, we have thus designed an on-

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Figure 2: BPMN Diagram describing the action of adding an Item in ePrints.

Table 1: Comparison between UML, BPMN, and OWL.

Modeling Categories UML UseCase BPMN OWL Ontology

Actor Actor Pool, Lane Class

Behaviour Use Case Process/Sub-process, Task Property, Sub-property

Decision Gateway (DL) Query

Event Event (start, intermediate, end) Open/Run/Close Ontology

Transition Relationship Sequence ﬂow Characteristic

Coaction Message Event Annotation

Object Data Object Individual

tology resulting from multi-dimensional information

combining data and processes. For this purpose, we

have proposed the translation of UML and BPMN

into OWL, and we have introduced the mapping as

shown in Table 1.

For a pilot implementation, we developed the on-

tology whose covered domain is ePrints (Univerity of

Huddersﬁeld ePrints, 2014) and its scope is to enable

interoperability/sharing knowledge, efﬁcient mainte-

nance, and also question queries. Moreover, our on-

tology is application independent, that means that it is

the same ontology, e.g. for maintenance as well as for

the query purpose.

Then, in order to build the domain ontology, we

have selected and analyzed the ePrints vocabulary.

The key related words have been identiﬁed when log-

ging into ePrints system and doing the task of adding

an Item to the repository.

To capture the ontology, we have chosen Prot

v4.3 (Prot

e, 2014), running on a Windows plat-

form. This choice is motivated by the fact that this

tool (Horridge, 2009) facilities the interoperability

with other knowledge-representation systems and has

a user-friendly, conﬁgurable interface.

The adopted language to express the ontology is

the Web Ontology Language (OWL) (Suwanmanee

et al., 2005), according to the World Wide Web Con-

sortium (W3C) recommendation (Bechhofer et al.,

2014). In particular, we have adopted OWL-DL

specie because, on one hand, it is more expressive

than OWL-Lite which is an OWL sub-language only

adapted for simple situations. On the other hand,

OWL-DL is based on Description Logics (DL) such

as in (Grimm et al., 2004). Thus, it is possible to per-

form automated reasoning on OWL-DL-based ontol-

ogy like in (Peim et al., 2002). Hence, OWL-DL en-

ables the use of a reasoner to compute the inferred on-

tology class hierarchy and to perform the consistency

check. Moreover, OWL-DL’s reasoners are tractable,

i.e. work in polynomial time, whereas in the case of

OWL-Full, which is the union of OWL syntax and

Resource Description Framework (RDF)s data repre-

sentation, automated reasoning is not tractable.

Table 2: Deﬁnition equivalence between Prot

e and OWL.

Prot

e OWL

Instances Individuals

Slots Properties

Classes Classes

A Prot

e ontology consists of classes, slots,

facets, and axioms as mentioned in Table 2.

Classes are concepts in the domain of discourse

and constitute a taxonomic hierarchy. Slots de-

scribe properties or attributes of classes and instances.

Facets describe properties of slots. Axioms specify

additional constraints. A Prot

e knowledge base

includes the ontology and individual instances of

classes with speciﬁc values for slots.

OWL ontology has similar components to

Prot

e-based one. However, the terminology used

to describe these components is slightly different (see

DynamicOWLOntologyDesignUsingUMLandBPMN

439

Table 2).

To develop our ontology, we have identiﬁed spe-

ciﬁc basic concepts used as cornerstones for the on-

tology design.

Next, we have mapped these concepts to a set of

OWL main classes, which represent the roots of a set

of corresponding subclasses together with their rela-

tionships with other classes. Each of these identiﬁed

concepts represents also a starting point for the brows-

ing of the ontology.

As expressed in Table 1, actors of UseCase dia-

grams (Fig. 1) could be modeled through pool and

lanes in BPMN (De Nicola et al., 2007) as presented

in Fig. 2, and could be mapped into class concepts of

the ontology as shown in Fig. 3.

Figure 3: Example of Class (Contributor) implemented into

our ontology.

An example of our ontology class is presented in

Fig. 3. The displayed class is called Contributor and

contains four subclasses, namely Contribution, Con-

tributor Email, Contributor Family Name, Contribu-

tor Given Name/ Initials).

OWL classes are interpreted as sets that contain

individuals. Individuals represent objects in the do-

main that we are interested in. Instances can be re-

ferred to as being “instances of classes”. In our exam-

ple, the subclass ItemID has individuals as partially

shown in Fig. 4.

Figure 4: Example of Individuals of the subclass ItemID of

the class Item implemented into our ontology.

Properties are binary relations on individuals, i.e.

properties that link two individuals together. They can

have inverses. Properties can be limited to having a

single value, i.e. to being functional. They can also

be either transitive or symmetric.

Properties are also used to create restrictions in

OWL. The latter ones could be of three categories,

namely, quantiﬁer restrictions, cardinality restric-

tions, and hasValue restrictions.

These quantiﬁer restrictions are composed of a

quantiﬁer, a property, and a ﬁller. The two quantiﬁers

that may be used are the existential quantiﬁer, read

as at least or some in OWL speak, and the universal

quantiﬁer, read as only in OWL speak.

For a set of individuals, an existential restriction

(∃) speciﬁes the existence of a (i.e. at least one) rela-

tionship along a given property to an individual that

is a member of a speciﬁc class. For example, ∃ has-

Contributor Contributor describes all of the individ-

uals that have at least one (some) relationship along

the hasContributor property to an individual that is

member of the class Contributor as in Fig. 5.

Figure 5: Example resulting from Check Item Details activ-

ity.

In fact, processes and sub-processes identiﬁed

in BPMN diagrams could be mapped into concepts

properties, as per our Table 1.

As an example, the activity group Add an Item in

the BPMN diagram (Fig. 2) could be mapped into the

data property hasAddedItem (Fig. 6), while the re-

lated BPMN tasks could be translated into OWL sub-

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440

Figure 6: Example of relations resulting from Add an Item

activity.

properties.

On the other hand, according to our Table 1, gate-

ways present in BPMN diagrams result in ontology

queries such as shown in Fig. 7.

Figure 7: Example of DL query resulting from “is Creator”

BPMN gateway.

For example, Fig. 7 illustrates ‘isCreator’ BPMN

gateway (Fig. 2) transformed into a isCreator data

property resulting in a DL query.

4 RESULTS AND DISCUSSION

The ontology for ePrints Information Management

we called ePrOnto could be characterized with re-

gards to the classiﬁcation presented in the methodol-

ogy section (Section 2).

Hence, our ontology, developed with Prot

OWL, could be considered as a semi-automatic sin-

gle ontology with multiple layers (different levels of

Figure 8: Example of DL query about data relation.

hierarchy). Some of the layers could be seen in left

part of Fig. 9 as well as the automatically generated

OWL code. An overview of the whole of our ontol-

ogy structure is demonstrated in the right part of Fig.

Moreover, our ontology was designed in a one-

step collaborative way from scratch and is application

independent. The adopted approach for the knowl-

edge capture is a middle-out strategy (Olszewska

et al., 2010).

As none of the ontology development methodolo-

gies described in the literature (Gruber, 1995; Dahlem

et al., 2009) were directly suitable, our ontology was

developed according to a unique scheme involving

dynamic design notations (see Section 3).

Some of the Items (publications) from ePrints

(Univerity of Huddersﬁeld ePrints, 2014) have been

encoded into our ePrOnto ontology to validate the in-

teroperable services of our designed and implemented

ontology. Hence, the proposed ontology is a ﬁrst on-

tology developed for ePrints repository management,

and which could be compatible with query process

such as illustrated in Figs. 5,7,8.

Recent works such as (Wei et al., 2010) have pro-

posed to automatically extract topics from text cor-

pus. Following this direction, our future work will

be the development of an innovative method to auto-

matically update the developed ontology in order to

provide a fully automatic, interoperable service.

DynamicOWLOntologyDesignUsingUMLandBPMN

441

Figure 9: Illustration of our ePronto ontology.

5 CONCLUSIONS

This paper is focused on the dynamic design of on-

tologies which could be of a large scale. Dynamic

design notations such as UML and BPMN have been

translated into OWL in order to systematically model

ontological concepts and their relations. The pro-

posed approach has led to the modelling and the

efﬁcient management of the University publication

repository (ePrints) system in an interoperable way.

Hence, the related complex data and information have

been transformed into structured knowledge through

the use of the ontological approach and dynamic de-

sign notations.

ACKNOWLEDGEMENTS

This work has been partly supported by the JISC

11/09 grant BRIM - Research Information Manage-

ment.

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