Data Integration and Visualization for Knowledge Mapping in

Strasbourg University

Amira Essaid

, Quynh Nguyen Thi

and Cecilia Zanni-Merk

INSA de Strasbourg, ICube laboratory, SDC team, Illkirch, France

Strasbourg University, Strasbourg, France

Keywords:

Ontology, Data Integration, Data Visualization, Intelligibility.

Abstract:

The work described in this paper is part of the IDEX (excellence initiative) project “Complex Identities”

launched by Strasbourg University in 2015. The main goal is to map available knowledge in Strasbourg uni-

versity in order to provide a comprehensive and structured view of its different components. Our approach

consists, ﬁrst, in building an ontology able to represent available knowledge in the university, making it un-

derstandable by users. Then, we are interested in visualizing the ontology to help users explore easily the

represented knowledge.

1 INTRODUCTION

Strasbourg University

is the second largest university

in France. It was founded on 1st January 2009 after

the fusion of the three former universities in the city:

Louis Pasteur, Marc Bloch and Robert Schuman. It

has more than 46000 students, almost 2800 lecturers

and lecturers-researchers, more than 2000 library, en-

gineering, administration, technical and health staff,

37 education and research departments (UFR), facul-

ties, schools and institutes, 6 libraries and 79 research

units. European by nature and international by design,

the University’s fundamental training and research

goals include forging partnerships with universities

on a European and international scale. It is a mem-

ber of several university networks in Europe such as

the Upper Rhine University (EUCOR)

, the League

of European Research Universities (LERU)

. . . . The

University’s strengths and assets stem from its active

involvement in virtually every discipline comprising

the current body of knowledge. This interdisciplinar-

ity, the constantly increasing number of students and

professors as well as the European exchanges make

Strasbourg University a complex institution.

Being part of the university, is being able to get ac-

cess to the university resources and knowledge. This

http://www.unistra.fr/index.php?id=accueil

http://www.eucor-uni.org/en/2016/01/18/

upper-rhine-cluster-sustainability-research

http://www.leru.org/index.php/public/home/

knowledge must be understandable for everyone. But

within the university, we are confronted daily to a

large number of logos and meaningless acronyms as

well as to structures that have each one their own data

repositories making the understanding of knowledge

difﬁcult. In addition to that, this data is stored in dif-

ferent spreadsheets, databases or other media where

each document adopts its own data representation.

Although this heterogeneity and this diversity

make the university a rich institution, they fail to facil-

itate the access to the knowledge on the one hand and

to make the university intelligible and understandable

in its signs, concepts and structures on the other.

For that reason, it is mandatory to ﬁnd a solution

where a uniﬁed vocabulary can be adopted to make

the different resources of the university understand-

able.

In order to ensure the readability and the intelligi-

bility of Strasbourg University, the IDEX (excellence

initiative) project “Complex identities” was launched

in January 2015. The main goal of the project is

to enhance the readability of Strasbourg university,

by proposing a uniﬁed representation of the available

knowledge.

This is a large scale project, for that reason it

has been scheduled in several phases. We intervened

during the second phase of the project to model the

knowledge map of the university. One of the outputs

of the ﬁrst phase is a “graphic-lexicon” which deﬁnes

and identiﬁes the different structures of the university.

After studying this “graphic-lexicon”, we noticed that

Essaid, A., Thi, Q. and Zanni-Merk, C.

Data Integration and Visualization for Knowledge Mapping in Strasbourg University.

DOI: 10.5220/0006069301630170

In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 2: KEOD, pages 163-170

ISBN: 978-989-758-203-5

163

this glossary does not establish links between the var-

ious structures of the university, because it just keeps

a ﬂat structure, not even hierarchical among compo-

nents. Therefore, we built an ontology to deﬁne all the

concepts associated to structures of the university as

well as all the relationships between these concepts.

Speciﬁc visualization tools were developed to help

surf over the individuals of the ontology, guided by

the ontology structure.

This article is organized as follows: In section 2,

we present the ontology we constructed. This on-

tology addressed the shortcomings discovered in the

“graphic-lexicon”. Section 3 is devoted to the visu-

alization of our ontology. In section 4 we give the

main tools that we used for implementation. Finally,

in Section 5 we conclude and give some perspectives

of future work.

2 ONTOLOGIES FOR DATA

INTEGRATION

As stated in the introduction, in the ﬁrst phase of the

project, a “graphic-lexicon” was developed. It is like

a glossary where the concepts related to Strasbourg

university were identiﬁed and deﬁned. This glossary

represents a reference for the users and guarantees a

better understanding of the university. It has been de-

veloped using the information system of the univer-

sity as a basis. Figure 1 shows a sample of the estab-

lished “graphic-lexicon”.

We have to note that as this project concerns a

french university the “graphic-lexicon” as well as the

screen shots of the visualization of the ontology are

in french. In section 2, we illustrate the constructed

ontology, the classes and relations are in english.

Although this glossary presents comprehensive in-

formation and lists all the structures composing the

university, such as the campuses, buildings, graduate

schools, university museums . . . , it fails to underline

the relations between the different structures and does

not emphasize the hierarchy or composition links that

can exist between two components. For example, the

relationship “one campus consists of buildings” does

not appear in the “graphic-lexicon”.

To overcome this problem, we propose to build an

ontology for describing Strasbourg University. Ac-

cording to the literature, an ontology is “an explicit

speciﬁcation of a conceptualization” (Gruber, 1993)

or “a shared understanding of some domain of inter-

est” (Uschold and Gruninger, 1996).

The advantage of using ontologies in this project

is twofold: on the one hand, it will permit to have an

integrated vocabulary, understandable and shared by

all users; on the other hand, strict logical formaliza-

tion associated with ontologies will permit to detect

inconsistencies, if any.

In addition to describing the different concepts re-

lated to Strasbourg university, this ontology will al-

low the speciﬁcation of the existing links that exist

between the different structures and will ease the nav-

igation over the individuals, in order to ﬁnd a certain

required information.

When constructing the ontology, we faced some

problems. In fact, we noticed that the “graphic-

lexicon” is incomplete; some information is missing

or some deﬁnitions are not given. Engineering pro-

cedures were implemented to recover the missing in-

formation (exhaustive research in the website of the

University, interviews with the ofﬁcials in charge of

the university information system, or queries to the

databases to which we had access). The methodology

described in (Noy and McGuinness, 2001) has been

used for the development of the ontology.

During the design phase, we opted to distinguish

between two parts. The ﬁrst concerns the campus life.

It models the different campuses and the facilities of-

fered by the university (libraries, students’ associa-

tions, . . . ). The second part concerns education and

research within the university. In fact, our ontology

models:

• the various study units (faculties, institutes, . . . )

as well as the research units (laboratories, . . . ).

• the degree programmes and courses offered for all

educational levels.

• all the aspects regarding doctoral training, and in

particular, the PhD theses that were defended with

the associated information (committees, labora-

tory, supervisors, . . . ).

• the different university services

As our ontology is of large number of concepts

and properties, we present for each part (campus

life, degrees awarded . . . ) its corresponding excerpt.

For this representation, we used OWLGrEd (Barzdins

et al., 2010) which extends UML class diagram nota-

tion with additional constructs for representing OWL

features. The yellow rectangles are used in this ex-

cerpt as UML classes. The hierarchical relation is

represented by thick lines. The datatype property is

represented as a label inside the class box and the ob-

ject property as an association. The tool OWLGrEd

helps to show very well the conceptual structure of

the ontology. Figure 2 presents an excerpt of our on-

tology. In this excerpt, we show only the concepts

of the highest level of Strasbourg University such as

campuses, buildings, branches, collegiums, . . . .

KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development

164

Figure 1: A graphic-lexicon for Strasbourg University.

Figure 2: An excerpt of the ontology of Strasbourg University.

Unistra is the class representing Strasbourg Uni-

versity. Unistra is composed of many structures

(campus, collegium, Doctoral school, Association,

Branch, . . . ). In this excerpt, we have many associ-

ations which describe the object properties. For ex-

ample, the association hasCampus relates Unistra to

Campus.

Ob jectProperty : < hasCampus >

Class : < Unistra >

Class : < Campus >

The current version of the ontology has 110 con-

cepts, 89 subsumption relationships, 37 composition

or association relationships and more than 3000 indi-

viduals. Next subsections present the most important

sub-ontologies in detail.

2.1 The Campus Life

The ﬁgure 3 represents a subontology related to the

campus life in Strasbourg University where these

campuses are composed of building and each build-

ing is composed of parts that can be a department, a

library, a museum or a research team.

In the following, we give a description of the hier-

archic relations in OWL.

Department v Part

Library v Part

Museum v Part

Research Team v Part

hasPart is an object property between the two classes

Data Integration and Visualization for Knowledge Mapping in Strasbourg University

165

Figure 3: An excerpt representing the campus life in Stras-

bourg University.

Building and Part. It is deﬁned as:

Ob jectProperty : < hasPart >

Class : < Building >

Class : < Part >

2.2 Study and Research Units

In this subsection, we present the research and study

units of Strasbourg University as represented in ﬁg-

ure 4. There are different types of research and study

units which are described as follows.

UMR v Research Units

USR v Research Units

UPR v Research Units

EA v Research Units

Faculty Team v Study Units

IUT Team v Study U nits

Institute Team v Study Units

School v Study Units

Each research unit is attached to a doctoral school

and is related to a collegium. These two associations

are described as follows:

Ob jectProperty : < attachedTo >

Class : < Research Units >

Class : < Doctoral School >

2.3 The Degrees Awarded

In this subsection, we present the sub-ontology re-

lated to the degree programmes and courses offered

for all educational levels. See ﬁgure 5 for an illustra-

tion.

Strasbourg University offers two kinds of stud-

ies: studies leading to a diploma and studies,like the

preparation for a certiﬁcate or a competition, not lead-

ing to a diploma. In the following we present in OWL

some hierarchic relation as well as some associations.

with Diploma v Study

without Diploma v Study

University Diploma v with Diploma

National Diploma v with Diploma

cycle 0 v National Diploma

cycle 1 v National Diploma

cycle 2 v National Diploma

cycle 3 v National Diploma

Ob jectProperty : < o f f ers >

Class : < Unistra >

Class : < Study >

Ob jectProperty : < ensures >

Class : < Study Units >

Class : < Study >

2.4 The Theses and Their Environment

In ﬁgure 6, we present the aspects regarding PhD the-

ses, and in particular, those that were defended with

the associated information (supervisor, PhD student,

. . . ) There are three kinds of lecturers: Research-

lecturer, non research lecturer and a part time lecturer.

Only the research lecturer has the possibility to su-

pervise a thesis. At Strasbourg university, there are

two types of theses: PhD thesis and a thesis defended

in the health domain (medicine, pharmacy, . . . ). We

represent only the PhD students who defend a thesis.

This thesis is done in a research unit and must be at-

tached to a doctoral school. In the following, we give

a description of the sub-ontology in OWL.

Researcher v Lecturer

Non Researcher v Lecturer

Part Time v Lecturer

Ob jectProperty : < attachedTo >

Class : < Student >

Class : < Research Unit >

3 ONTOLOGY VISUALIZATION

Providing users with visual representations and intu-

itive user interfaces can signiﬁcantly aid the under-

standing of the knowledge represented by ontologies.

KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development

166

Figure 4: An excerpt representing the study and research units in Strasbourg University.

Figure 5: Degrees in Strasbourg University.

Figure 6: Theses in Strasbourg University.

This is exactly the case of the our constructed ontol-

ogy.

Ontology visualization is not a new topic and a

number of approaches have become available in re-

Data Integration and Visualization for Knowledge Mapping in Strasbourg University

167

cent years particularly in the ﬁeld of ontology model-

ing. However, few of them provide a clear graphical

user interface with navigational aids or comprehen-

sive visualization techniques. For an exhaustive state

of the art, the reader may refer to (Dud

as et al., 2014;

Katifori et al., 2007; Lanzenberger et al., 2009).

For our speciﬁc needs (an easy tool for non ex-

perts to navigate over the individuals of the ontology)

we have chosen to visualize ontologies with VOWL

(Lohmann et al., 2014b) and to use especially We-

bVOWL which is a web implementation of VOWL

(Lohmann et al., 2015).

VOWL, the Visual Notation for OWL ontolo-

gies, is a visual language for representing ontolo-

gies. Based on graphical primitives and color scheme,

VOWL is able to visualize classes, properties and

datatypes. Classes are represented by circles where

the size of each circle depends on the number of the

individuals of the represented class. Lines are used

to represent properties. Property labels and datatypes

are shown in rectangles. To demonstrate its appli-

cability, VOWL was implemented in two different

tools: Prot

eVOWL (Lohmann et al., 2014a) and We-

bVOWL. The former is a VOWL plugin for the Prot

editor while the latter is a standalone web application.

As part of this project, we used WebVOWL as our aim

is to propose an application for university users.

Once the software is launched, only a circle rep-

resenting Strasbourg University is displayed as shown

in ﬁgure 7. By a simple click on this circle, we can see

the different structures composing Strasbourg Univer-

sity such as (campuses, collegiums . . . ) as described

in ﬁgure 8. A click on one of these structures allows

the user to have more details about the different com-

ponents of the structure itself as well as the individu-

als if there are any. The individuals are displayed on

the right side bar. Figures 9 and 10 show the compo-

nents of campuses and theses respectively.

In addition to visualizing all the classes and prop-

erties of our ontology, we are interested in visualizing

individuals through creating a graph able to highlight

different pieces of information related to a speciﬁc

individual. In ﬁgure 11, we display the information

related to a speciﬁc thesis (the different committee

members, the PhD student . . . )

4 IMPLEMENTATION

As university data is stored in different spreadsheets

and databases, a thorough study was handled to depict

the different classes, properties as well as datatypes.

We used Prot

e 5.0.0

, as the most popular and

widely used tool for ontology development. It is a

free open-source tool developed by Standford univer-

sity. It gains popularity because it offers to users a set

of packages for editing and visualizing ontologies.

As already mentioned, we used WebVOWL (ver-

sions 0.5.2) for visualization. As it is a standalone

application, the OWL ontology is converted into a

VOWL-JSON ﬁle proper to WebVOWL. At the time

being, WebVOWL is able to visualize classes, prop-

erties and datatypes. To visualize individuals, we

created a second VOWL-JSON ﬁle containing all the

information related to individuals. In order to get

the expected result visualization, we made some im-

provements on WebVOWL. These improvements will

be subject of a further paper.

5 CONCLUSION

The IDEX (excellence initiative) project “Complex

Identities” is a large scale project. Its main goal is

to ensure the intelligibility of the university through

providing a comprehensive and structured view of its

different components. In this paper, we presented our

solution of creating a knowledge mapping based on

ontologies. We described in detail this ontology and

how it has been constructed. To visualize the ontol-

ogy we used the WebVOWL tool which helps users to

explore easily the represented knowledge. Although

we have not been able to get access to all the existing

databases, we think that given the constructed ontol-

ogy and the visualization results we have been able to

achieve the objectives of this project. However, an ex-

haustive experimentation protocol is being setup dur-

ing the new academic year 2016-2017, for validation

of the correctness of the enriched ontology and evalu-

ation of the ergonomics of the proposed visualisation

tools.

As this is a large scale project, many other re-

search works will be launched in the future. On the

one hand, we will focus on reasoning tasks across the

ontology to respond to users’ queries. Discovering

new information through the navigation across the on-

tology is another research axis.

ACKNOWLEDGEMENTS

This work was supported by Strasbourg University.

It is carried out in collaboration with the Faculty of

Fine Arts and the Communication service. Special

http://protege.stanford.edu/

KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development

168

Figure 7: First knowledge map interface.

Figure 8: Main structures of Strasbourg University.

Figure 9: Campus life in Strasbourg University.

Figure 10: Aspects regarding doctoral training.

Figure 11: A thesis information.

thanks go to Pierre Litzler, Najman Faustine, Laurie

Chapotte and Olivier Kohtz.

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